Numerical-Precision-Optimized Volume Rendering Ingmar Bitter Neophytos Neophytou Klaus Mueller Arie Kaufman.

Numerical-Precision-Optimized Volume Rendering

Ingmar Bitter Neophytos Neophytou

Klaus Mueller Arie Kaufman

Numerical-Precision-Optimized Volume Rendering

Ingmar Bitter Neophytos Neophytou

Klaus Mueller Arie Kaufman

Outline

• Numerical precision - a rendering resource

Outline


• Fixed-point arithmetic

Outline



• Reverse order precision analysis

– Compositing, shading, gradients, classification, sampling/splatting, sample/splat location

Outline




– Compositing, shading, gradients, classification,sampling/splatting, sample/splat location

• Results

Outline




– Compositing, shading, gradients, classification, sampling/splatting, sample/splat location

• Results

• Conclusions

Numerical Precision: A Resource

• Double precision computation for all – ideal?



– slower then all other alternatives

– not possible on graphics cards (at least for now)

– expensive on custom chip implementations

– and most importantly:

not needed to create best possible images!!



– slower then all other alternatives

– not possible on graphics cards (at least for now)

– expensive on custom chip implementations

– and most importantly:

not needed to create best possible images!!

reasons: predominantly 8-bit displays (per channel)

limited range intervals throughout

Current Status• Stable volume rendering pipeline: both CPU and GPU

[LL94, Lev88, MJC02, Wes90, EKE01, RSEB00]

• Interpolation before classification, even for splatting [MMC99]

• Caching optimized for volume rendering[Kni00, LCCK02, PSL98]

• Precision-limited rendering systems: ATI, NVidia,VolumePro [PHK99], VizardII [MKW02], UltraVis [Kni00]

• Completely fixed: final output image display bit precision

– 8 bits per RGB color channel on CRTs and LCDs

– 8 bits max in DVI standard

– SGIs 12 bit color displays are nearly extinct

– Radiologists’ requirements are not mass market, same analysis applies

OpenGL Arithmetic: 12=1?

• Representation [0, 255] a = b = 255

• Computation = a[0, 255] × b[0, 255] >> 8;

= 254 wrong

1 mult, one shift

• Alternatively: tmp = a[0, 255] × b[0, 255] + 128; result = (tmp+(tmp >> 8)) >> 8;

= 255, correct [Bli95]

1 mult, 2 adds, 2 shifts

OpenGL Arithmetic: 12=1?

• Representation: fixed-point I.Fb

– I.Fb = I integer bits, F fraction bits

• 8 bits 1.7b fixed point number

then a = b = 11.7b = 128

• Computation = a1.7b × b1.7b >> 7

= 128 correct

1 mult, one shift

one fewer bit of resolution, but OK (we will see)

Reverse Order Precision Analysis

• Unified ray casting and splatting pipelines

• Composite creates the final image

Sample Location Splat Location

Sample Splat

Classify

Gradient

Shade

Composite

Ray Casting Splatting

Reverse Order Precision Analysis

• Unified ray casting and splatting pipelines

• Composite creates the final image

• Precision requirements propagate backwards

Sample Location Splat Location

Sample Splat

Classify

Gradient

Shade

Composite

Ray Casting Splatting

Compositing - Math

• Pre-(alpha)-multiplied colors:

– C = αC = αR, αG, αB

• Alpha correction (r samples per unit):

– Tcorrected = (1- α)r

Compositing - Math• Pre-(alpha)-multiplied colors:

– C = αC = αR, αG, αB

• Alpha correction:

– Tcorrected = (1- α)r

• With back-to-front compositing:

– CCompositingBuffer ×= Tcorrected += Cfront

– TCompositingBuffer ×= Tcorrected; αCompositingBuffer = 1-Tcorrected

– perform multiplication N times per pixel

correct solution needs N × F × r bits precision

T/CCompositingBuffer

Tcorrected, Cfront

T/CCompositingBuffer

Compositing – Precision Theory• 8-bit destination resolution

– therefore all partial results can be rounded

– drop all bits not contributing to the 8 most significant bits (MSB)

• Adding N = 2p samples – allows 8+p bits to influence the 8 MSB

• Conversion from αCompositingBufferC to C for display (division)– allows 8+p more bits to influence the 8 MSB

• Conversion from αcorrectedC to C for display– allows r times as many bits to influence the 8 MSB

• Sufficient resolution is: r × 2 × (8+p) for C, r × (8+p) for α– 32/16 bits for C/αCompositingBuffer for 2563 volumes and no super-sampling

– 608 bits for 5122×2048 volumes and 16 samples per voxel

































Compositing – Precision Practice• No alpha correction (r = 1): 2 × (8+p) bits

• Iso-surface rendering using “old fashioned” OpenGL:

– store not αC but C in frame buffer: (8+p)

– bright colors: 5+p

– at most 8 non-zero samples per ray (p=3): 5+3=8 bits

standard 24 bit RGBA frame buffer is adequate

• Fog visualization

– what matters is the ability to see objects though volumetric fog (substance with low opacity)

– visual experiments show 15 fractional bits are sufficient














































Compositing – Conclusion

• Preferred bit-aware back-to-front compositing equations:

• αC1.15b ×= T1.15bsample += C1.15b

sample

• T1.15b ×= T1.15bsample

8 10 12 14 15 16

Least-significant-bit-fog at various bit precisions

5123 datasetr = 2

Shading - Math

• PhongCcolor = kambient OobjectColor IlightIntensity

+ kdiffuse O Σi{ Ii (N•Li) }

+ kspecular Σi{ Ii (R•Li)r }

• k є [0,1] kambient + kdiffuse + kspecular =1

• OobjectColor (8 bit) and IlightIntensity є [0,1]

• N•Li and R•Li є [-1,1], but є [0,1] after clamping

• PhongCcolor = є [0,1] (possibly clamping Σi)

Shading - Analysis

• PhongCcolor needs to be as precise as 1.15b

• Use 16.16b for all multiplications [0,1)× [0,1]

– sufficient precision and no overflow

Shading – New Computation• Replace specular

exponentiation with recursive multiplies

– repeatedly multiply number with itself

– works for all exponents r=2n

– when r=26 (16 bit precision), then max error < 0.005%

– better results than Knittel’s parabola approximation

Shading – New Computation• Replace specular

exponentiation with recursive multiplies

– repeatedly multiply number with itself

– works for all exponents r=2n

– when r=26 (16 bit precision), then max error < 0.005%

– better results than Knittel’s parabola approximation

pow

r=2n

Knittel’sparabola

Shading - Conclusion

• Preferred bit-aware Phong shading equation:

C16.16b = k16.16bambient O0.8b

objectColor I16.16blight

+ k16.16bdiffuseO0.8b Σi{ I16.16b

i (N16.16b•L16.16bi) }

+ k16.16bspecular Σi{ I16.16b

i (R16.16b•L16.16bi)2^n }

Gradients - Math

• Gx = 0.5 sample(x+1,y,z) - 0.5 sample(x-1,y,z)

• Gy = 0.5 sample(x,y+1,z) - 0.5 sample(x,y-1,z)

• Gy = 0.5 sample(x,y,z+1) - 0.5 sample(x,y,z-1)

Gradients - Analysis

• G = G1.Fb

• Discrete nearest gradient vector neighbors

– sin φ = 1/2F, sin φ ≈ φ → φ ≈ 1/2F

• Maximum error for specular intensity, large r

– r = 64, 164 != 1, but 164 = (1- 1/2F)64

– error of 22%, 6.1%, 1.6%, 0.4%for F of 8, 10, 12, 14

φ

Gradients - Analysis

• 5123-sized spheres with Phong highlights

• 4, 6, 8, 10, 12, 14 bit gradients

• Diffuse artifacts for 4 and 6 bits

• Specular artifacts up to 10 bits

1210 14

64 8

1210 14

Gradients - Conclusion• Thus, 12 bits dynamic range is needed

• Now consider normalization:

– reduces I.Fb to 1.Fb

– up to I bits will be added to the fractional part

• Volume samples often have 12 bits

• Gx,y,z with 12.12b minimum representation

• Gx,y,z with 16.16b preferred representation

– leaves room for interpolation bits in normalization

Classification – Prelims and Recaps

• Use of T instead of α is more efficient in compositing operation

• Largest visual precision/quantization error occurs at high transparencies (low opacities)

– need more bits for T than for C, just to be sure

• Want transfer function lookup table to be cache-friendly

– power-of-2 RGBA-tuple alignment

• Would like to use pre-integrated classification for color and opacity transfer functions [EKE01, MGS02]






















Classification - Math

• Desired lookup table entries:

R1.8bG1.8bB1.8bT1.16b 5.5 bytes

• Common lookup table entries:

R0.8bG0.8bB0.8bα0.8b 4 bytes

Classification - Math• Desired lookup table entries:

R1.8bG1.8bB1.8bT1.16b 5.5 bytes

• Common lookup table entries:

R0.8bG0.8bB0.8bα0.8b 4 bytes

• Better lookup table entries:

R0.8bG0.8bB0.8bsqrt(α)0.8b spreads low α

• Computed lookup after T = 1-(sqrt(α)2):

R0.8bG0.8bB0.8bT1.16b squaring doubles precision

Classification - Conclusion

• Preferred bit-aware lookup table entries: R0.8bG0.8bB0.8bsqrt(α)0.8b

Foot with least-significant-thin-tissue-fog

α0.8b sqrt(α)0.8b α0.16b

Sample Interpolation - Math

• sample = voxel0 × (1-w) + voxel1 × w

• sample = w × (voxel1 - voxel0) + voxel0

• Requirements:

– Gx,y,z, derived from samples, need 12 bit dynamic range

– samples need 12 bit values for transfer function lookup

– cover both low and high dynamic range neighborhoods

• Therefore, sample12.12b is a minimum requirement

– integer part comes from voxels voxel12.0b

– fractional part comes from interpolation w1.12b

Sample Interpolation - Conclusion

• Preferred bit-aware sample interpolation:

sample12.12b = w1.12b × (voxel112.0b - voxel012.0b) + voxel012.0b

• Splats start on voxels, need no interpolation:

splat12.0b = voxel12.0b

Sample Location - Math

• k-th sample location = startPos + Σk Vinc

• Perspective rays need to differ enough to allow 1024 rays across 60 degrees, or 0.05◦

– sin φ = (k 1/2F) / k, sin φ ≈ φ → φ ≈ 1/2F

– F = 6, 12, 16 → φ = 0.9◦, 0.05◦, 0.0009◦

• Also, need to address 2048 slices (integer positions) → 11bits

• Thus, need overall 11.12b

φ

k

Sample Location - Conclusion

• Preferred bit-aware sample location:

– perspective projection:

sampleLocation11.12b = startPos11.12b + Σ Vinc1.12b

– parallel projection: sampleLocation11.6b (0.9◦ OK)

Splat Scan Conversion - Math

• Splats project onto image grid → reverse rays

• Allow as many as 2048 splat rays across 60 degrees, or 0.025◦

• Hence, twice the ray casting precision

– one extra fractional bit F=13

• Also address 2048 slices (11bits)

• Thus, need overall 11.13b

φ

Splat Scan Conversion - Conclusion

• Preferred bit-aware splat scan conversion:splatLocation11.13b = startVoxelPos11.13b + Σ Vinc

1.13b

• Splats are usually pre-transformed and stored in bucket lists (one per sheet-buffer)

• Preferred voxel location sheet buffer formatx11.13b u8.0b y11.13b v8.0b (64 bits total)

– x, y: location on splat plane

– u: index into pre-integrated splat table

– v: voxel value (x, y) y)

u

Results• Summary of minimum precision requirements

Rendering Stage Input Output

Sample locations N/A 11.12b

Sample interpolation 12.00b 12.12b

Classification 12.00b 4× 0.8b

Gradients 12.12b 1.12b

Shading 1.12b 1.15b

Compositing 1.15b 1.15b

Results• Restricted iso-surface rendering:

– texture map volume rendering can be done using plain OpenGL or Direct X and 8 bit frame buffers

• General volume rendering, all pipeline stages:

– 32 bit single precision floating point format

– 16.16b fixed point format (up to 4x faster in our tests)

Pentium allows 2 simple 32-bit integer ops per clock cycle

Conclusions• 8 bits per RGB channel on final display

• Analysis of requirements by back propagation

• Sufficient precision computations using

– either 32 bits single precision floating point format

– or 16.16b fixed point format

• Voxel location sheet buffer x11.13bu8.0by11.13bv8.0b

• Transfer functions stored as R0.8bG0.8bB0.8bsqrt(α)0.8b

• Compositing/fragment buffer R1.15bG1.15bB1.15bT1.15b

Acknowledgements• Hewlett Packard Laboratories

• ONR grant N000140110034

• NSF CAREER grant ACI-0093157

• DOE grant MO-068

• Thanks to Tom Malzbender and Michael Meissner for technical discussions.

• Thanks to Ronald Summers for resources.

Numerical-Precision-Optimized Volume Rendering Ingmar Bitter Neophytos Neophytou Klaus Mueller Arie Kaufman.

Documents

p bits

compositingbuffer c

outline numerical precision

rendering resource slide

compositing precision

voxel slide

bits max

integer bits