3D Sensing
Post on 08-Feb-2016
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3D Sensingbull 3D Shape from X
bull Perspective Geometry
bull Camera Model
bull Camera Calibration
bull General Stereo Triangulation
bull 3D Reconstruction
3D Shape from X
bull shadingbull silhouettebull texture
bull stereo bull light stripingbull motion
mainly research
used in practice
Perspective Imaging Model 1D
xi
xf
f
This is the axis of the real image plane
O O is the center of projection
This is the axis of the frontimage plane which we usezc
xc
xi xc
f zc
=
camera lens
3D objectpoint
B
D
E
image of pointB in front image
real imagepoint
Perspective in 2D(Simplified)
P=(xcyczc) =(xwywzw)
3D object point
xc
yc
zw=zc
yi
Yc
Xc
Zc
xi
F f
cameraPacute=(xiyif)
xi xc
f zc
yi yc
f zc
=
=
xi = (fzc)xc
yi = (fzc)ycHere camera coordinatesequal world coordinates
opticalaxis
ray
3D from Stereo
left image right image
3D point
disparity the difference in image location of the same 3Dpoint when projected under perspective to two different cameras
d = xleft - xright
Depth Perception from StereoSimple Model Parallel Optic Axes
f
f
L
R
camera
baselinecamera
b
P=(xz)
Z
X
image plane
xl
xr
z
z xf xl
=
x-b
z x-bf xr
= z y yf yl yr
= =y-axis is
perpendicularto the page
Resultant Depth Calculation
For stereo cameras with parallel optical axes focal length fbaseline b corresponding image points (xlyl) and (xryr)with disparity d
z = fb (xl - xr) = fbd
x = xlzf or b + xrzf
y = ylzf or yrzf
This method ofdetermining depthfrom disparity is called triangulation
Finding Correspondences
bull If the correspondence is correct triangulation works VERY well
bull But correspondence finding is not perfectly solved (What methods have we studied)
bull For some very specific applications it can be solved for those specific kind of images eg windshield of a car
deg deg
3 Main Matching Methods
1 Cross correlation using small windows
2 Symbolic feature matching usually using segmentscorners
3 Use the newer interest operators ie SIFT
dense
sparse
sparse
Epipolar Geometry Constraint1 Normal Pair of Images
x
y1
y2
z1 z2
C1 C2b
P
P1 P2
epipolarplane
The epipolar plane cuts through the image plane(s)forming 2 epipolar lines
The match for P1 (or P2) in the other image must lie on the same epipolar line
Epipolar GeometryGeneral Case
P
P1P2
y1
y2
x1
x2
e1
e2
C1
C2
Constraints
P
e1
e2
C1
C2
1 Epipolar Constraint Matching points lie on corresponding epipolar lines
2 Ordering Constraint Usually in the same order across the linesQ
Structured Light
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
camera
light stripe
Structured Light3D Computation
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
x axisf
(xacuteyacutef)
3D point(x y z)
b b[x y z] = --------------- [xacute yacute f] f cot - xacute
(000)
3D image
Depth from Multiple Light Stripes
What are these objects
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
3D Shape from X
bull shadingbull silhouettebull texture
bull stereo bull light stripingbull motion
mainly research
used in practice
Perspective Imaging Model 1D
xi
xf
f
This is the axis of the real image plane
O O is the center of projection
This is the axis of the frontimage plane which we usezc
xc
xi xc
f zc
=
camera lens
3D objectpoint
B
D
E
image of pointB in front image
real imagepoint
Perspective in 2D(Simplified)
P=(xcyczc) =(xwywzw)
3D object point
xc
yc
zw=zc
yi
Yc
Xc
Zc
xi
F f
cameraPacute=(xiyif)
xi xc
f zc
yi yc
f zc
=
=
xi = (fzc)xc
yi = (fzc)ycHere camera coordinatesequal world coordinates
opticalaxis
ray
3D from Stereo
left image right image
3D point
disparity the difference in image location of the same 3Dpoint when projected under perspective to two different cameras
d = xleft - xright
Depth Perception from StereoSimple Model Parallel Optic Axes
f
f
L
R
camera
baselinecamera
b
P=(xz)
Z
X
image plane
xl
xr
z
z xf xl
=
x-b
z x-bf xr
= z y yf yl yr
= =y-axis is
perpendicularto the page
Resultant Depth Calculation
For stereo cameras with parallel optical axes focal length fbaseline b corresponding image points (xlyl) and (xryr)with disparity d
z = fb (xl - xr) = fbd
x = xlzf or b + xrzf
y = ylzf or yrzf
This method ofdetermining depthfrom disparity is called triangulation
Finding Correspondences
bull If the correspondence is correct triangulation works VERY well
bull But correspondence finding is not perfectly solved (What methods have we studied)
bull For some very specific applications it can be solved for those specific kind of images eg windshield of a car
deg deg
3 Main Matching Methods
1 Cross correlation using small windows
2 Symbolic feature matching usually using segmentscorners
3 Use the newer interest operators ie SIFT
dense
sparse
sparse
Epipolar Geometry Constraint1 Normal Pair of Images
x
y1
y2
z1 z2
C1 C2b
P
P1 P2
epipolarplane
The epipolar plane cuts through the image plane(s)forming 2 epipolar lines
The match for P1 (or P2) in the other image must lie on the same epipolar line
Epipolar GeometryGeneral Case
P
P1P2
y1
y2
x1
x2
e1
e2
C1
C2
Constraints
P
e1
e2
C1
C2
1 Epipolar Constraint Matching points lie on corresponding epipolar lines
2 Ordering Constraint Usually in the same order across the linesQ
Structured Light
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
camera
light stripe
Structured Light3D Computation
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
x axisf
(xacuteyacutef)
3D point(x y z)
b b[x y z] = --------------- [xacute yacute f] f cot - xacute
(000)
3D image
Depth from Multiple Light Stripes
What are these objects
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Perspective Imaging Model 1D
xi
xf
f
This is the axis of the real image plane
O O is the center of projection
This is the axis of the frontimage plane which we usezc
xc
xi xc
f zc
=
camera lens
3D objectpoint
B
D
E
image of pointB in front image
real imagepoint
Perspective in 2D(Simplified)
P=(xcyczc) =(xwywzw)
3D object point
xc
yc
zw=zc
yi
Yc
Xc
Zc
xi
F f
cameraPacute=(xiyif)
xi xc
f zc
yi yc
f zc
=
=
xi = (fzc)xc
yi = (fzc)ycHere camera coordinatesequal world coordinates
opticalaxis
ray
3D from Stereo
left image right image
3D point
disparity the difference in image location of the same 3Dpoint when projected under perspective to two different cameras
d = xleft - xright
Depth Perception from StereoSimple Model Parallel Optic Axes
f
f
L
R
camera
baselinecamera
b
P=(xz)
Z
X
image plane
xl
xr
z
z xf xl
=
x-b
z x-bf xr
= z y yf yl yr
= =y-axis is
perpendicularto the page
Resultant Depth Calculation
For stereo cameras with parallel optical axes focal length fbaseline b corresponding image points (xlyl) and (xryr)with disparity d
z = fb (xl - xr) = fbd
x = xlzf or b + xrzf
y = ylzf or yrzf
This method ofdetermining depthfrom disparity is called triangulation
Finding Correspondences
bull If the correspondence is correct triangulation works VERY well
bull But correspondence finding is not perfectly solved (What methods have we studied)
bull For some very specific applications it can be solved for those specific kind of images eg windshield of a car
deg deg
3 Main Matching Methods
1 Cross correlation using small windows
2 Symbolic feature matching usually using segmentscorners
3 Use the newer interest operators ie SIFT
dense
sparse
sparse
Epipolar Geometry Constraint1 Normal Pair of Images
x
y1
y2
z1 z2
C1 C2b
P
P1 P2
epipolarplane
The epipolar plane cuts through the image plane(s)forming 2 epipolar lines
The match for P1 (or P2) in the other image must lie on the same epipolar line
Epipolar GeometryGeneral Case
P
P1P2
y1
y2
x1
x2
e1
e2
C1
C2
Constraints
P
e1
e2
C1
C2
1 Epipolar Constraint Matching points lie on corresponding epipolar lines
2 Ordering Constraint Usually in the same order across the linesQ
Structured Light
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
camera
light stripe
Structured Light3D Computation
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
x axisf
(xacuteyacutef)
3D point(x y z)
b b[x y z] = --------------- [xacute yacute f] f cot - xacute
(000)
3D image
Depth from Multiple Light Stripes
What are these objects
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Perspective in 2D(Simplified)
P=(xcyczc) =(xwywzw)
3D object point
xc
yc
zw=zc
yi
Yc
Xc
Zc
xi
F f
cameraPacute=(xiyif)
xi xc
f zc
yi yc
f zc
=
=
xi = (fzc)xc
yi = (fzc)ycHere camera coordinatesequal world coordinates
opticalaxis
ray
3D from Stereo
left image right image
3D point
disparity the difference in image location of the same 3Dpoint when projected under perspective to two different cameras
d = xleft - xright
Depth Perception from StereoSimple Model Parallel Optic Axes
f
f
L
R
camera
baselinecamera
b
P=(xz)
Z
X
image plane
xl
xr
z
z xf xl
=
x-b
z x-bf xr
= z y yf yl yr
= =y-axis is
perpendicularto the page
Resultant Depth Calculation
For stereo cameras with parallel optical axes focal length fbaseline b corresponding image points (xlyl) and (xryr)with disparity d
z = fb (xl - xr) = fbd
x = xlzf or b + xrzf
y = ylzf or yrzf
This method ofdetermining depthfrom disparity is called triangulation
Finding Correspondences
bull If the correspondence is correct triangulation works VERY well
bull But correspondence finding is not perfectly solved (What methods have we studied)
bull For some very specific applications it can be solved for those specific kind of images eg windshield of a car
deg deg
3 Main Matching Methods
1 Cross correlation using small windows
2 Symbolic feature matching usually using segmentscorners
3 Use the newer interest operators ie SIFT
dense
sparse
sparse
Epipolar Geometry Constraint1 Normal Pair of Images
x
y1
y2
z1 z2
C1 C2b
P
P1 P2
epipolarplane
The epipolar plane cuts through the image plane(s)forming 2 epipolar lines
The match for P1 (or P2) in the other image must lie on the same epipolar line
Epipolar GeometryGeneral Case
P
P1P2
y1
y2
x1
x2
e1
e2
C1
C2
Constraints
P
e1
e2
C1
C2
1 Epipolar Constraint Matching points lie on corresponding epipolar lines
2 Ordering Constraint Usually in the same order across the linesQ
Structured Light
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
camera
light stripe
Structured Light3D Computation
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
x axisf
(xacuteyacutef)
3D point(x y z)
b b[x y z] = --------------- [xacute yacute f] f cot - xacute
(000)
3D image
Depth from Multiple Light Stripes
What are these objects
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
3D from Stereo
left image right image
3D point
disparity the difference in image location of the same 3Dpoint when projected under perspective to two different cameras
d = xleft - xright
Depth Perception from StereoSimple Model Parallel Optic Axes
f
f
L
R
camera
baselinecamera
b
P=(xz)
Z
X
image plane
xl
xr
z
z xf xl
=
x-b
z x-bf xr
= z y yf yl yr
= =y-axis is
perpendicularto the page
Resultant Depth Calculation
For stereo cameras with parallel optical axes focal length fbaseline b corresponding image points (xlyl) and (xryr)with disparity d
z = fb (xl - xr) = fbd
x = xlzf or b + xrzf
y = ylzf or yrzf
This method ofdetermining depthfrom disparity is called triangulation
Finding Correspondences
bull If the correspondence is correct triangulation works VERY well
bull But correspondence finding is not perfectly solved (What methods have we studied)
bull For some very specific applications it can be solved for those specific kind of images eg windshield of a car
deg deg
3 Main Matching Methods
1 Cross correlation using small windows
2 Symbolic feature matching usually using segmentscorners
3 Use the newer interest operators ie SIFT
dense
sparse
sparse
Epipolar Geometry Constraint1 Normal Pair of Images
x
y1
y2
z1 z2
C1 C2b
P
P1 P2
epipolarplane
The epipolar plane cuts through the image plane(s)forming 2 epipolar lines
The match for P1 (or P2) in the other image must lie on the same epipolar line
Epipolar GeometryGeneral Case
P
P1P2
y1
y2
x1
x2
e1
e2
C1
C2
Constraints
P
e1
e2
C1
C2
1 Epipolar Constraint Matching points lie on corresponding epipolar lines
2 Ordering Constraint Usually in the same order across the linesQ
Structured Light
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
camera
light stripe
Structured Light3D Computation
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
x axisf
(xacuteyacutef)
3D point(x y z)
b b[x y z] = --------------- [xacute yacute f] f cot - xacute
(000)
3D image
Depth from Multiple Light Stripes
What are these objects
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Depth Perception from StereoSimple Model Parallel Optic Axes
f
f
L
R
camera
baselinecamera
b
P=(xz)
Z
X
image plane
xl
xr
z
z xf xl
=
x-b
z x-bf xr
= z y yf yl yr
= =y-axis is
perpendicularto the page
Resultant Depth Calculation
For stereo cameras with parallel optical axes focal length fbaseline b corresponding image points (xlyl) and (xryr)with disparity d
z = fb (xl - xr) = fbd
x = xlzf or b + xrzf
y = ylzf or yrzf
This method ofdetermining depthfrom disparity is called triangulation
Finding Correspondences
bull If the correspondence is correct triangulation works VERY well
bull But correspondence finding is not perfectly solved (What methods have we studied)
bull For some very specific applications it can be solved for those specific kind of images eg windshield of a car
deg deg
3 Main Matching Methods
1 Cross correlation using small windows
2 Symbolic feature matching usually using segmentscorners
3 Use the newer interest operators ie SIFT
dense
sparse
sparse
Epipolar Geometry Constraint1 Normal Pair of Images
x
y1
y2
z1 z2
C1 C2b
P
P1 P2
epipolarplane
The epipolar plane cuts through the image plane(s)forming 2 epipolar lines
The match for P1 (or P2) in the other image must lie on the same epipolar line
Epipolar GeometryGeneral Case
P
P1P2
y1
y2
x1
x2
e1
e2
C1
C2
Constraints
P
e1
e2
C1
C2
1 Epipolar Constraint Matching points lie on corresponding epipolar lines
2 Ordering Constraint Usually in the same order across the linesQ
Structured Light
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
camera
light stripe
Structured Light3D Computation
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
x axisf
(xacuteyacutef)
3D point(x y z)
b b[x y z] = --------------- [xacute yacute f] f cot - xacute
(000)
3D image
Depth from Multiple Light Stripes
What are these objects
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Resultant Depth Calculation
For stereo cameras with parallel optical axes focal length fbaseline b corresponding image points (xlyl) and (xryr)with disparity d
z = fb (xl - xr) = fbd
x = xlzf or b + xrzf
y = ylzf or yrzf
This method ofdetermining depthfrom disparity is called triangulation
Finding Correspondences
bull If the correspondence is correct triangulation works VERY well
bull But correspondence finding is not perfectly solved (What methods have we studied)
bull For some very specific applications it can be solved for those specific kind of images eg windshield of a car
deg deg
3 Main Matching Methods
1 Cross correlation using small windows
2 Symbolic feature matching usually using segmentscorners
3 Use the newer interest operators ie SIFT
dense
sparse
sparse
Epipolar Geometry Constraint1 Normal Pair of Images
x
y1
y2
z1 z2
C1 C2b
P
P1 P2
epipolarplane
The epipolar plane cuts through the image plane(s)forming 2 epipolar lines
The match for P1 (or P2) in the other image must lie on the same epipolar line
Epipolar GeometryGeneral Case
P
P1P2
y1
y2
x1
x2
e1
e2
C1
C2
Constraints
P
e1
e2
C1
C2
1 Epipolar Constraint Matching points lie on corresponding epipolar lines
2 Ordering Constraint Usually in the same order across the linesQ
Structured Light
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
camera
light stripe
Structured Light3D Computation
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
x axisf
(xacuteyacutef)
3D point(x y z)
b b[x y z] = --------------- [xacute yacute f] f cot - xacute
(000)
3D image
Depth from Multiple Light Stripes
What are these objects
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Finding Correspondences
bull If the correspondence is correct triangulation works VERY well
bull But correspondence finding is not perfectly solved (What methods have we studied)
bull For some very specific applications it can be solved for those specific kind of images eg windshield of a car
deg deg
3 Main Matching Methods
1 Cross correlation using small windows
2 Symbolic feature matching usually using segmentscorners
3 Use the newer interest operators ie SIFT
dense
sparse
sparse
Epipolar Geometry Constraint1 Normal Pair of Images
x
y1
y2
z1 z2
C1 C2b
P
P1 P2
epipolarplane
The epipolar plane cuts through the image plane(s)forming 2 epipolar lines
The match for P1 (or P2) in the other image must lie on the same epipolar line
Epipolar GeometryGeneral Case
P
P1P2
y1
y2
x1
x2
e1
e2
C1
C2
Constraints
P
e1
e2
C1
C2
1 Epipolar Constraint Matching points lie on corresponding epipolar lines
2 Ordering Constraint Usually in the same order across the linesQ
Structured Light
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
camera
light stripe
Structured Light3D Computation
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
x axisf
(xacuteyacutef)
3D point(x y z)
b b[x y z] = --------------- [xacute yacute f] f cot - xacute
(000)
3D image
Depth from Multiple Light Stripes
What are these objects
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
3 Main Matching Methods
1 Cross correlation using small windows
2 Symbolic feature matching usually using segmentscorners
3 Use the newer interest operators ie SIFT
dense
sparse
sparse
Epipolar Geometry Constraint1 Normal Pair of Images
x
y1
y2
z1 z2
C1 C2b
P
P1 P2
epipolarplane
The epipolar plane cuts through the image plane(s)forming 2 epipolar lines
The match for P1 (or P2) in the other image must lie on the same epipolar line
Epipolar GeometryGeneral Case
P
P1P2
y1
y2
x1
x2
e1
e2
C1
C2
Constraints
P
e1
e2
C1
C2
1 Epipolar Constraint Matching points lie on corresponding epipolar lines
2 Ordering Constraint Usually in the same order across the linesQ
Structured Light
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
camera
light stripe
Structured Light3D Computation
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
x axisf
(xacuteyacutef)
3D point(x y z)
b b[x y z] = --------------- [xacute yacute f] f cot - xacute
(000)
3D image
Depth from Multiple Light Stripes
What are these objects
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Epipolar Geometry Constraint1 Normal Pair of Images
x
y1
y2
z1 z2
C1 C2b
P
P1 P2
epipolarplane
The epipolar plane cuts through the image plane(s)forming 2 epipolar lines
The match for P1 (or P2) in the other image must lie on the same epipolar line
Epipolar GeometryGeneral Case
P
P1P2
y1
y2
x1
x2
e1
e2
C1
C2
Constraints
P
e1
e2
C1
C2
1 Epipolar Constraint Matching points lie on corresponding epipolar lines
2 Ordering Constraint Usually in the same order across the linesQ
Structured Light
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
camera
light stripe
Structured Light3D Computation
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
x axisf
(xacuteyacutef)
3D point(x y z)
b b[x y z] = --------------- [xacute yacute f] f cot - xacute
(000)
3D image
Depth from Multiple Light Stripes
What are these objects
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Epipolar GeometryGeneral Case
P
P1P2
y1
y2
x1
x2
e1
e2
C1
C2
Constraints
P
e1
e2
C1
C2
1 Epipolar Constraint Matching points lie on corresponding epipolar lines
2 Ordering Constraint Usually in the same order across the linesQ
Structured Light
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
camera
light stripe
Structured Light3D Computation
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
x axisf
(xacuteyacutef)
3D point(x y z)
b b[x y z] = --------------- [xacute yacute f] f cot - xacute
(000)
3D image
Depth from Multiple Light Stripes
What are these objects
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Constraints
P
e1
e2
C1
C2
1 Epipolar Constraint Matching points lie on corresponding epipolar lines
2 Ordering Constraint Usually in the same order across the linesQ
Structured Light
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
camera
light stripe
Structured Light3D Computation
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
x axisf
(xacuteyacutef)
3D point(x y z)
b b[x y z] = --------------- [xacute yacute f] f cot - xacute
(000)
3D image
Depth from Multiple Light Stripes
What are these objects
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Structured Light
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
camera
light stripe
Structured Light3D Computation
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
x axisf
(xacuteyacutef)
3D point(x y z)
b b[x y z] = --------------- [xacute yacute f] f cot - xacute
(000)
3D image
Depth from Multiple Light Stripes
What are these objects
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Structured Light3D Computation
3D data can also be derived using
bull a single camera
bull a light source that can produce stripe(s) on the 3D object
lightsource
x axisf
(xacuteyacutef)
3D point(x y z)
b b[x y z] = --------------- [xacute yacute f] f cot - xacute
(000)
3D image
Depth from Multiple Light Stripes
What are these objects
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Depth from Multiple Light Stripes
What are these objects
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Our (former) System4-camera light-striping stereo
projector
rotationtable
cameras
3Dobject
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Camera Model Recall there are 5 Different Frames of Reference
bull Object
bull World
bull Camera
bull Real Image
bull Pixel Image
yc
xc
zc
zwC
Wyw
xw
A
axf
yf
xp
yp
zppyramidobject
image
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
The Camera Model
How do we get an image point IP from a world point P
c11 c12 c13 c14
c21 c22 c23 c24
c31 c32 c33 1
s IPr
s IPc
s
Px
Py
Pz
1
=
imagepoint
camera matrix C worldpoint
Whatrsquos in C
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
1 CP = T R WP2 FP = (f) CP
s FPx
s FPy
s FPz
s
1 0 0 00 1 0 00 0 1 00 0 1f 0
CPx
CPy
CPz
1
=
perspectivetransformation
imagepoint
3D point incamera
coordinates
Why is there not a scale factor here
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Camera Calibration
bull In order work in 3D we need to know the parameters of the particular camera setup
bull Solving for the camera parameters is called calibration
yw
xwzw
W
yc
xc
zc
C
bull intrinsic parameters are of the camera device
bull extrinsic parameters are where the camera sits in the world
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Intrinsic Parameters
bull principal point (u0v0)
bull scale factors (dxdy)
bull aspect ratio distortion factor
bull focal length f
bull lens distortion factor (models radial lens distortion)
C
(u0v0)
f
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Extrinsic Parameters
bull translation parameters t = [tx ty tz]
bull rotation matrix
r11 r12 r13 0r21 r22 r23 0r31 r32 r33 00 0 0 1
R = Are there reallynine parameters
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Calibration Object
The idea is to snapimages at differentdepths and get alot of 2D-3D pointcorrespondences
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
The Tsai Procedure
bull The Tsai procedure was developed by Roger Tsai at IBM Research and is most widely used
bull Several images are taken of the calibration object yielding point correspondences at different distances
bull Tsairsquos algorithm requires n gt 5 correspondences
(xi yi zi) (ui vi)) | i = 1hellipn
between (real) image points and 3D points
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
In this version of Tsairsquos algorithm
bull The real-valued (uv) are computed from their pixel positions (rc)
u = dx (c-u0) v = -dy (r - v0)
where
- (u0v0) is the center of the image
- dx and dy are the center-to-center (real) distances between pixels and come from the camerarsquos specs
- is a scale factor learned from previous trials
This version is for single-plane calibration
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Tsairsquos Procedure
1 Given the n point correspondences ((xiyizi) (uivi))
Compute matrix A with rows ai
ai = (vixi viyi -uixi -uivi vi)
These are known quantities which will be used to solve for intermediate values which will then beused to solve for the parameters sought
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Intermediate Unknowns
2 The vector of unknowns is = (1 2 3 4 5)
1=r11ty 2=r12ty 3=r21ty 4=r22ty 5=txty
where the rrsquos and trsquos are unknown rotation and translation parameters
3 Let vector b = (u1u2hellipun) contain the u image coordinates
4 Solve the system of linear equations
A = b
for unknown parameter vector
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Use to solve for ty tx and 4 rotation parameters
5 Let U = 12 + 2
2 + 32 + 4
2 Use U to calculate ty
2
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
6 Try the positive square root ty = (t 2 ) and use it to compute translation and rotation parameters
12
r11 = 1 ty
r12 = 2 ty
r21 = 3 ty
r22 = 4 ty
tx = 5 ty
Now we know 2 translation parameters and4 rotation parameters
excepthellip
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Determine true sign of ty and computeremaining rotation parameters
7 Select an object point P whose image coordinates (uv) are far from the image center
8 Use Prsquos coordinates and the translation and rotation parameters so far to estimate the image point that corresponds to P
If its coordinates have the same signs as (uv) then keep ty else negate it
9 Use the first 4 rotation parameters to calculate the remaining 5
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Calculating the remaining 5 rotation parameters
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Solve another linear system
10 We have tx and ty and the 9 rotation parameters Next step is to find tz and f
Form a matrix Aacute whose rows are
aiacute = (r21xi + r22yi + ty vi)
and a vector bacute whose rows are
biacute = (r31xi + r32yi) vi
11 Solve Aacutev = bacute for v = (f tz)
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Almost there
12 If f is negative change signs (see text)
13 Compute the lens distortion factor and improve the estimates for f and tz by solving a nonlinear system of equations by a nonlinear regression
14 All parameters have been computed Use them in 3D data acquisition systems
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
We use them for general stereo
P
P1=(r1c1)P2=(r2c2)
y1
y2
x1
x2
e1
e2
B
C
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
For a correspondence (r1c1) inimage 1 to (r2c2) in image 2
1 Both cameras were calibrated Both camera matrices are then known From the two camera equations B and C we get 4 linear equations in 3 unknowns
r1 = (b11 - b31r1)x + (b12 - b32r1)y + (b13-b33r1)zc1 = (b21 - b31c1)x + (b22 - b32c1)y + (b23-b33c1)z
r2 = (c11 - c31r2)x + (c12 - c32r2)y + (c13 - c33r2)zc2 = (c21 - c31c2)x + (c22 - c32c2)y + (c23 - c33c2)z
Direct solution uses 3 equations wonrsquot give reliable results
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
Solve by computing the closestapproach of the two skew rays
V
If the rays intersected perfectly in 3D the intersection would be PInstead we solve for the shortest line segment connecting the two rays and let P be its midpoint
P1
Q1
Psolve forshortest
V = (P1 + a1u1) ndash (Q1 + a2u2)
(P1 + a1u1) ndash (Q1 + a2u2) u1 = 0(P1 + a1u1) ndash (Q1 + a2u2) u2 = 0
u1
u2
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
- 3D Sensing
- 3D Shape from X
- Perspective Imaging Model 1D
- Perspective in 2D (Simplified)
- 3D from Stereo
- Depth Perception from Stereo Simple Model Parallel Optic Axes
- Resultant Depth Calculation
- Finding Correspondences
- 3 Main Matching Methods
- Epipolar Geometry Constraint 1 Normal Pair of Images
- Epipolar Geometry General Case
- Constraints
- Structured Light
- Structured Light 3D Computation
- Depth from Multiple Light Stripes
- Our (former) System 4-camera light-striping stereo
- Camera Model Recall there are 5 Different Frames of Reference
- The Camera Model
- The camera model handles the rigid body transformation from world coordinates to camera coordinates plus the perspective transformation to image coordinates
- Camera Calibration
- Intrinsic Parameters
- Extrinsic Parameters
- Calibration Object
- The Tsai Procedure
- In this version of Tsairsquos algorithm
- Tsairsquos Procedure
- Intermediate Unknowns
- Use to solve for ty tx and 4 rotation parameters
- Determine true sign of ty and compute remaining rotation parameters
- Calculating the remaining 5 rotation parameters
- Solve another linear system
- Almost there
- We use them for general stereo
- For a correspondence (r1c1) in image 1 to (r2c2) in image 2
- Solve by computing the closest approach of the two skew rays
- Slide 37
- Slide 38
- Slide 39
- Slide 40
- Slide 41
- Slide 42
- Slide 43
- Slide 44
- Slide 45
- Slide 46
- Slide 47
- Slide 48
- Slide 49
top related