-
In archaeology, measurement and docu-mentation are both
important, not only torecord endangered archaeological sites, but
also torecord the excavation process itself. Annotation andprecise
documentation are important because evidenceis actually destroyed
during archaeological work. Onmost sites, archaeologists spend a
large amount of time
drawing plans, making notes, andtaking photographs. Because of
thepublicity that accompanied somerecent archaeological research
pro-jects, such as Stanfords DigitalMichelangelo project1 or
IBMsPieta project,2 archaeologists arebecoming aware of the
advantagesof using 3D visualization tools.
Archaeologists can now use thedata recorded during excavations
togenerate virtual 3D models suitedfor project report
presentation,restoration planning, or even digitalarchiving,
although many issuesremain unresolved. Until recently,the cost in
time and money to gener-ate virtual reconstructions
remainedprohibitive for most archaeologicalprojects. At a more
modest level,some archaeologists use commer-cially available
software, such asPhotoModeler (http://www.photo-modeler.com), to
build simple virtu-al models. These models can sufcefor some types
of presentations, but
typically lack the detail and accuracy needed for mostscientic
applications.
Clearly, archaeologists need more exible measure-ment
techniques, especially for eldwork. Archaeolo-gists should be able
to acquire their own measurementssimply and easily. Our image-based
3D recordingapproach offers several possibilities.3-8 To acquire a
3Dreconstruction, our system lets archaeologists take sev-eral
pictures from different viewpoints using a standard
photo or video camera. In principle, using our systemmeans that
archaeologists need not take additional mea-surements of the scene
to obtain a 3D model. However,a reference length can help in
obtaining the recon-structions global scale. Archaeologists can use
theresulting 3D model for measurement and visualizationpurposes.
Figure 1 shows an example of the types of pic-tures possible with a
standard camera.
In developing our system, we regularly visitedSagalassos, a site
that is one of the largest archaeologi-cal projects in the
Mediterranean. The site consists ofelements from a Greco-Roman
period spanning morethan a thousand years from the 4th century BC
to the7th century AD. Sagalassos, one of the three great citiesof
ancient Pisidia, lies a few miles north of the villageAglassun in
the province of Burdur, Turkey. The ruins ofthe city lie on the
southern ank of the Aglassun moun-tain ridge (a part of the Taurus
mountains) at an eleva-tion of several thousand feet. Figure 2
shows Sagalassosagainst the mountains. A team from the University
ofLeuven, under the supervision of Marc Waelkens, hasbeen
excavating the area since 1990. Because of the dif-ferent
measurement problems, Sagalassos has been anideal test eld for our
algorithms.
Image-based 3D recordingThe rst step in our 3D recording system
recovers the
relative motion between images taken consecutively.This process
involves nding corresponding featuresbetween these imagesimage
points that originate fromthe same 3D features. The process happens
in two phas-es. First, the reconstruction algorithm generates a
recon-struction containing a projective skew so that
initiallyparallel lines are not parallel, angles are not correct,
anddistances are too long or too short. Next, using a
self-cal-ibration algorithm,3,9 our system removes these
distor-tions, yielding a reconstruction equivalent to the
original.
The reconstruction only contains a sparse set of 3Dpoints.
Although interpolation might be one solution,it yields models with
poor visual quality. Therefore, thenext step attempts to match all
of an images pixels withthose from neighboring images so that the
system can
Feature Article
Until recently, archaeologists
have had limited 3D
recording options because
of the complexity and
expense of the necessary
recording equipment. We
outline a system that helps
archaeologists acquire 3D
models without using
equipment more complex or
delicate than a standard
digital camera.
Marc PollefeysUniversity of North Carolina, Chapel Hill
Luc Van GoolKatholieke Universiteit Leuven and ETH Zrich
Maarten Vergauwen, Kurt Cornelis, FrankVerbiest, and Jan
TopsKatholieke Universiteit Leuven
3D Recording forArchaeologicalFieldwork
2 May/June 2003 Published by the IEEE Computer Society
0272-1716/03/$17.00 2003 IEEE
-
reconstruct these points. A pixel inthe image corresponds to a
ray inspace. Because we can predict theprojection of this ray in
other imagesfrom the cameras recovered poseand calibration, we can
restrict thesearch for a corresponding pixel inother images to a
single line. We alsoemploy additional constraints, suchas the
assumption of a piecewisecontinuous 3D surface, to constrainthe
search even further.
Its possible to warp the images sothat the search range
coincides withthe horizontal scan-lines, letting ususe an efcient
stereo algorithm tocompute an optimal match for thewhole scan-line
at once.6 Using thisalgorithm, we can obtain a depthestimatethe
distance from the camera to the objectsurfacefor almost every pixel
of an image. Fusing allthe images results gives us a complete
surface model.To achieve a photorealistic result, we can apply
theimages used in the reconstruction as texture maps. Fig-ure 3
(next page) illustrates the four steps of the process.The following
sections describe the steps in more detail.
Relating imagesStarting from a collection of images or a
video
sequence, our systems first step relates the differentimages to
each other. A restricted number of corre-
sponding points helps determine the images
geometricrelationships. Our system selects the feature points
suit-ed for matching or tracking. Depending on the type ofimage
datasuch as video or still picturesour systemtracks the feature
points to obtain several potential cor-respondences. From these
correspondences, we com-pute the multiview constraints.
However, the set of corresponding points can beand almost
certainly will becontaminated with sev-eral wrong matches. In light
of this potential trouble, atraditional least-squares approach will
fail; we there-fore use a more robust method. The system uses the
mul-
IEEE Computer Graphics and Applications 3
1 Reconstruc-tion of a cornerof the Romanbaths at
theSagalassosarchaeologysite. (a) Oursystem used thesix images (b)
and auto-matically creat-ed the model.
(a)
(b)
2 Overview ofthe Sagalassossite.
-
tiview constraints to guide the search for additional
cor-respondences, which it can in turn employ to refineresults for
the multiview constraints.
Structure and motion recoveryOur system uses the relation
between the views and
the correspondences between the features to retrievethe scenes
structure and the cameras motion. Our
approach doesnt depend on the initialization becausewe carry out
all measurements in the images usingreprojection errors instead of
3D errors. The systemselects two images to set up an initial
projective recon-struction frame and then reconstructs matching
featurepoints through triangulation. The system then renesthe
initial reconstruction and extends it. By sequential-ly applying
the same procedure, the system can com-pute the structure and
motion of the whole sequence.Figure 4 illustrates the
pose-estimation procedure.
The system can rene these results through a globalleast-squares
minimization of all reprojection errors.Efcient bundle-adjustment
techniques work well forthis process. The ambiguity is then
restricted furtherthrough self-calibration. Finally, the system
carries outa second bundle adjustment, taking the
self-calibrationinto account to obtain an optimal estimation of
theimages structure and motion.
Dense surface estimationTo obtain a more detailed model of the
observed sur-
face, we use a dense-matching technique. The systemcan use the
structure and motion obtained previouslyto constrain the
correspondence search. Because wecompute the calibration between
successive image pairs,
we can exploit the epipolar con-straint that restricts the
correspon-dence search to a one-dimensionalsearch range. The system
warpsimage pairs so that epipolar linescoincide with the image
scan-lines.For this purpose, we use a rectica-tion scheme5 that
deals with arbi-trary relative camera motion. Wethen reduce the
correspondencesearch to a matching of the imagepoints along each
image scan-line,which increases the algorithmscomputational
efciency.
Figure 5 shows an example of arectied stereo pair. The system
haslocated all corresponding points onthe same horizontal scan-line
inboth images. In addition to theepipolar geometry, we can
exploitother constraints, such as the neigh-boring pixels order and
the matchsbidirectional uniqueness. We usethese constraints to
guide the corre-spondence search toward the mostprobable scan-line
match usingdynamic programming.6 Thematcher searches at each pixel
inone image for maximum normal-
ized cross-correlation in the other image by shifting asmall
measurement window along the correspondingscan-line. The algorithms
pyramidal estimation schemedeals with large disparity ranges, but
the system limitsthe disparity search range according to observed
fea-ture disparities from the previous reconstruction stage.
The pairwise disparity estimation lets us computeimage-to-image
correspondence between adjacent rec-
Feature Article
4 May/June 2003
Input sequence
Feature matches
3D features and cameras
Dense depth maps
3D surface model
Structure andmotion recovery
Dense matching
Relating images
3D model building
3 Overview ofour image-based 3Drecordingapproach.
M
m
m
mi3
mi2
i1
i
4 Estimation ofa new viewusing inferredstructure-to-image
matches.
5 Example of arectified stereopair.
-
tied image pairs and independent depth estimates foreach camera
viewpoint. We obtain an optimal joint esti-mate by fusing all
independent estimates into a com-mon 3D model using a Kalman
filter. The system canperform the fusion economically through
controlled cor-respondence linking,4 which combines the
advantagesof small- and wide-baseline stereo and provides a
densedepth map by avoiding most occlusions. Multiple view-points
increase the depth resolution while small localbaselines simplify
the matching.
Building virtual modelsOur dense structure and motion recovery
approach
yields all the necessary information to build textured3D models.
We approximate the 3D surface with a tri-angular mesh to reduce
geometric complexity and tai-lor the model to the computer graphics
visualizationsystem requirements. A simple approach consists
ofoverlaying a 2D triangular mesh on one of the images,then
building a corresponding 3D mesh by placing thetriangle vertices in
3D space according to the valuesfound in the corresponding depth
map. We use theimage itself as the texture map. If no depth value
is avail-able or the confidence is too low, our system
doesntreconstruct the corresponding triangles. This approachworks
well on dense depth maps obtained from multi-ple stereo pairs.
A multiview linking scheme can enhance the textureitself. The
system computes a median or robust meanof the corresponding texture
values to discard imag-ing artifacts such as sensor noise, specular
reections,and highlights. To reconstruct more complex shapes,the
system must combine multiple depth maps.Because all depth maps
reside in a single metric frame,registration is not an issue. To
integrate the multipledepth maps into a single surface
representation, weuse a volumetric technique.10 Alternatively, to
rendernew views from similar viewpoints, we use image-based
approaches11 that avoid the difcult problem ofobtaining a
consistent 3D model by using view-depen-dent texture and geometry.
Doing so also helps us takeinto account more complex visual
effects, such asreections and highlights.
Applications to archaeological fieldworkThe techniques described
here have many applica-
tions in the eld of archaeology. The on-site
acquisitionprocedure consists of recording an image sequence ofthe
scene. So the algorithms can yield good results,although the
viewpoint changes between consecutiveimages should not exceed 5 to
10 degrees. An exampleis the Dionysus statues found in Sagalassos
on the uppermarket square. The statue is now located in the
gardenof the museum in Burdur.
It was simple to record a one-minute video of the stat-ue.
Bringing in more advanced equipment, such as alaser range scanner,
would not only be logistically morecomplicated but would also
require special authoriza-tion. Figure 6 illustrates different
steps of the recon-struction process. We obtained the 3D model from
asingle depth map. We could have obtained a more com-plete and
accurate model by combining multiple depthmaps. And we could have
obtained a smoother look forthe shaded model by ltering the 3D mesh
in accordancewith the standard deviations obtained as a byproductof
the depth computation. This type of result is not soimportant when
the model is texture mapped.
Figure 7 shows a second example, a Medusa headlocated on the
entablature of a fountain. We obtainedthe 3D model from a short
video sequence and used asingle depth map to reconstruct the 3D
model. Errorson the camera motion and calibration computations
canresult in a global bias on the reconstruction. From theresults
of the bundle adjustment, we estimate this errorto be of the order
of just a few millimeters for points onthe reconstruction. The
depth computations indicatethat 90 percent of the reconstructed
points have a rela-tive error of less than 1 mm. The stereo
correlation usesa window that corresponds to the object and
thereforethe measured depth will typically correspond to
thedominant visual feature within that patch.
An important advantage of our approach compared tomore
interactive techniques12 is that it can deal withmore complex
objects. Compared to non-image-basedtechniques, we can extract
surface texture directly fromthe images, resulting in a much higher
degree of real-ism and contributing to the authenticity of the
recon-
IEEE Computer Graphics and Applications 5
6 3D reconstruction of Dionysus showing (a) one of the original
video frames, (b) the corresponding depth map, (c) a shaded view
ofthe 3D reconstruction, and (d) a view of the textured 3D model
with the original images.
(a) (b) (c) (d)
-
struction. Archaeologists can use reconstructionsobtained with
this system as scale models on which theycan carry out measurements
or plan restorations.
A disadvantage of our technique is that it cant direct-ly
capture the photometric properties of an object. Itstherefore not
immediately possible to rerender the 3Dmodel under different
lighting. We could possibly com-bine recent work13 on recovering
the radiometry of mul-tiple images with our approach so that we
coulddecouple reectance and illumination. However, doingso would
require us to record the scene under differentilluminations or
lighting conditions.
Recording 3D stratigraphyAn important aspect of archaeological
annotation
and documentation is stratigraphy, a process thatreflects the
different layers of soil that correspond todifferent time periods
in an excavated sector. Becauseof practical limitations,
stratigraphy is often onlyrecorded for certain soil slices, not for
the whole sec-tor. Our technique allows a more optimal approach
tothis documentation problem. We can generate a com-plete 3D model
of the excavated sector for every layer.
Because the technique only involves taking a series ofpictures,
it does not slow down the progress of thearchaeological work.
In addition, our system enables modeling all foundartifacts
separately and including the models in the nal3D stratigraphy,
which makes it possible to use the 3Drecord as a visual database.
For example, we recordedthe excavations of an ancient Roman villa
at Sagalassosusing our technique. Figure 8 shows several layers of
theexcavations 3D model. It took about one minute perlayer to
acquire the images at the site. From the resultsof the bundle
adjustment, we can estimate the globalerror to be of the order of 1
cm for points on the recon-struction. Similarly, the depth
computations indicatethat the depth error of most of the
reconstructed pointsshould be within 1 cm. The correlation window
corre-sponds to an area of approximately five square cen-timeters
in the scene. This means that some small detailsmight not appear in
the reconstruction, but this accu-racy level is more than sufcient
to satisfy the require-ments of the archaeologists. To obtain a
single 3Drepresentation for each stratigraphic layer, we used
avolumetric integration approach.
Feature Article
6 May/June 2003
(a) (b) (c) (d)
7 3D reconstruction of a Medusa head showing (a) one of the
original video frames, (b) the corresponding depth map, (c) a
shadedview of the 3D model, and (d) a textured view of the 3D
model.
8 3D stratigra-phy showingthe excavationof a Roman villaat three
differ-ent moments.The left imageshows a frontview of
threestratigraphiclayers. The rightimage shows atop view of
thefirst two layers.
-
Construction and reconstructionOur technique also offers many
advantages in terms
of generating and testing construction hypotheses. Easeof
acquisition and the level of detail we can obtain makeit possible
to reconstruct every building block separate-ly. Archaeologists
could then interactively verify differ-ent construction hypotheses
on a virtual reconstructionsite. We could even use registration
algorithms14,15 toautomate this process. Figure 9 shows two
segments ofa broken column. The whole monument contains 16columns
that were all broken in several pieces by anearthquake. Because
each piece can weigh several hun-dreds kilograms, trying to t the
pieces together is verydifcult. Traditional drawings also do not
offer a prop-er solution.
Our approachs exibility lets us use existing photo orvideo
archives to reconstruct a virtual site. This appli-cation is suited
for monuments or sites destroyedthrough war or natural disaster. We
illustrated the fea-sibility of this type of approach with a
reconstruction ofthe ancient theater of Sagalassos based on a
videosequence lmed by Belgian TV as part of a documen-tary on
Sagalassos. From the 30-second helicopter shot,we extracted about
one hundred images. Because of the
motion in the images, we could only use fields, notframes,
restricting the vertical resolution to 288 pixels.Figure 10 shows
three images from the sequence. Fig-ure 11 shows the reconstruction
of the feature pointstogether with the recovered camera poses.
Obtaining a virtual reality model for a whole site con-sists of
taking a few overview photographs from a dis-tance. Because our
technique is independent of scale, itcan yield an overview model of
the whole site. The onlydifference is the distance needed between
two cameraposes. Figure 12 shows an example of the resultsobtained
for Sagalassos. We created the model fromnine images taken from a
hillside near the excavationsite. Its a relatively straightforward
process to extract adigital terrain map from the global site
reconstruction.We could achieve absolute localization by localizing
asfew as three reference points in the 3D reconstruction.
The problem is that this kind of overview model is toocoarse for
use in realistic walkthroughs or for close-upviews at monuments.
For these purposes, archaeologistswould need to integrate more
detailed models into theoverview model by taking additional image
sequencesfor all the interesting areas on the site. The system
woulduse these additional images to generate reconstructions
IEEE Computer Graphics and Applications 7
9 (a) Two images of a broken pillar. (b) The ortho-graphic views
of the matching surfaces generated fromthe obtained 3D models. The
surface on the right isobserved from the inside of the column.
(a)
(b)
10 Three images of the helicopter shot of the ancient theater of
Sagalassos.
11 The reconstructed feature points and camera posesrecovered
from the helicopter shot.
12 Overview model of Sagalassos.
-
of the site at different scales, going from a global
recon-struction of the whole site to a detailed reconstructionfor
every monument. These reconstructions thus natu-rally ll in the
different detail levels. Seamlessly merg-ing reconstructions
obtained at different scales remainsan issue for further
research.
Fusing real and virtualAnother potentially interesting
application is com-
bining recorded 3D models with other model types. Inthe case of
Sagalassos, we translated some reconstruc-tion drawings to CAD
models16 and integrated themwith our Sagalassos models. This
reconstruction is avail-able at
http://www.esat.kuleuven.ac.be/sagalassos/ asan interactive virtual
reality application that lets userstake a virtual visit to
Sagalassos.17
Another challenging application consists of seamless-ly
integrating virtual objects in video. In this case, the ulti-mate
goal is to make it impossible to differentiate betweenreal and
virtual objects. But to do this, we need to over-come several
problems rst. Among them are the rigidregistration of virtual
objects into the real environment,the mutual occlusion of real and
virtual objects, and theextraction of the real environments
illumination distri-bution to render virtual objects with the
illuminationmodel. Accurate registration of virtual objects into a
realenvironment, as shown in Figure 13, is a challengingproblem.
Systems that fail to do so will also fail to givethe user a
real-life impression of the augmented outcome.
Because our approach does not use markers or a prioriknowledge
of the scene or the camera, it lets us deal withvideo footage of
unprepared environments or archivedvideo footage. More details on
our approach can be foundelsewhere.18 To successfully insert a
large virtual objectin an image sequence, the 3D structure should
not be dis-torted. For this purpose, its important to use a
cameramodel that takes nonperspective effects into account andto
perform a global least-squares minimization of thereprojection
error through a bundle adjustment.
ConclusionsOur approach uses several different components
that
gradually retrieve all information necessary to constructvirtual
models from images. There are multiple advan-tages to using our 3D
modeling technique: The on-siteacquisition time is brief, the
construction of the modelsis automatic, and the generated models
are realistic. Ourtechnique supports some promising applications,
suchas recording 3D stratigraphy, generating and
verifyingconstruction hypotheses, reconstructing 3D scenesbased on
archive photographs or video footage, andintegrating virtual
reconstructions with archaeologicalremains in video footage.
Our future research plans consist of increasing thereliability
and exibility of our approach. One impor-tant topic is the
development of wide-baseline match-ing techniques so that pictures
can be taken furtherapart. Another aspect consists of being able to
takeadvantage of auto-exposure modes without degradingthe visual
quality of the models. In terms of applications,we are exploring
possibilities in different elds, includ-ing architectural
conservation, geology, forensics, moviespecial effects, and
planetary exploration.
AcknowledgmentsWe thank Marc Waelkens and his team for making
it
possible for us to do experiments at the Sagalassos(Turkey)
site. We also thank the FWO project G.0223.01,the IST-1999-20273
project 3DMURALE and the NSFproject IIS 0237533 for their nancial
support.
References1. M. Levoy et al., The Digital Michelangelo Project:
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Scanning of Large Statues, Proc. Siggraph, ACM, 2000,pp. 131-144
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2. H. Rushmeier et al., Acquiring Input for Rendering
atAppropriate Levels of Detail: Digitizing a Pieta, Proc.
9thEurographics Rendering Workshop, Springer-Verlag, 1998,pp.
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3. M. Pollefeys, R. Koch, and L. Van Gool, Self-Calibrationand
Metric Reconstruction in Spite of Varying andUnknown Internal
Camera Parameters, Intl J. ComputerVision, vol. 32, no. 1, 1999,
pp. 7-25.
4. R. Koch, M. Pollefeys, and L. Van Gool, Multi-ViewpointStereo
from Uncalibrated Video Sequences, Proc. EuropeanConf. Computer
Vision, Springer-Verlag, 1998, pp.55-71.
5. M. Pollefeys, R. Koch, and L. Van Gool, A Simple and Ef-cient
Rectication Method for General Motion, Proc. ICCV,IEEE Computer
Society Press, 1999, pp. 496- 501.
6. G. Van Meerbergen et al., A Hierarchical Symmetric
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7. M. Pollefeys et al., Virtual Models from Video and
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Springer-Verlag, 2001, pp.11-22.
8. M. Pollefeys, Obtaining 3D Models with a Handheld Cam-era,
Siggraph Course, ACM, 2001, Course notes
CD-ROM;http://www.cs.unc.edu/~marc/tutorial/.
9. M. Pollefeys, F. Verbiest, and L. Van Gool, Surviving
Dom-inant Planes in Uncalibrated Structure and Motion Recov-
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ery, Proc. European Conf. Computer Vision, Springer-Ver-lag,
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11. R. Koch, B. Heigl, and M. Pollefeys, Image-Based Render-ing
from Uncalibrated Light-elds with Scalable Geometry,LNCS 2032,
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12. P. Debevec, C. Taylor, and J. Malik, Modeling and Ren-dering
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Approach, Proc. SIGGRAPH 96, ACM,1996, pp. 11-20.
13. Q.-T. Luong, P. Fua, and Y. Leclerc, The Radiometry
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14. Y. Chen and G. Medioni, Object Modeling by Registrationof
Multiple Range Images, Proc. IEEE Intl Conf. on Robot-ics and
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15. J. Wyngaerd et al., Invariant-Based Registration of Sur-face
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Marc Pollefeys is an assistant pro-fessor of computer vision in
thedepartment of computer science atthe University of North
Carolina,Chapel Hill. His research interestsinclude developing
exible approach-es to capture visual representations
of real-world objects, scenes, and events. He received a PhDin
electrical engineering from the Katholieke UniversiteitLeuven.
Luc Van Gool is professor of com-puter vision at the Katholieke
Uni-versiteit Leuven and the SwissFederal Institute of Technology.
Hisresearch interests include 3D recon-struction, animation,
texture syn-thesis, object recognition, robot
vision, and motion capture. He received a PhD in Electri-cal
Engineering from the Katholieke Universiteit Leuven.
Maarten Vergauwen is PhD stu-dent in the electrical
engineeringdepartment at the Katholieke Uni-versiteit Leuven. His
research inter-ests include 3D reconstruction fromimages, camera
calibration, andvision in space applications. He
received an MS in electrical engineering from theKatholieke
Universiteit Leuven.
Kurt Cornelis is a PhD student inthe electrical engineering
departmentat the Katholieke Universiteit Leuven.His research
interests include 3Dreconstruction, recognition, and theuse of
computer vision in augmentedreality. He received an MS in
electri-
cal engineering from the Katholieke Universiteit Leuven.
Frank Verbiest is a PhD studentin the department of electrical
engi-neering at the Katholieke Universis-teit Leuven. His research
interestsinclude computer vision, especially3D reconstruction from
images. Hereceived an MS in electrical engi-
neering and an MS in artificial intelligence from theKatholieke
Universiteit Leuven.
Jan Tops is a research assistant inthe department of electrical
engi-neering at the Katholieke Universis-teit Leuven. His research
interestsinclude computer vision, 3D model-ing, and visualization.
He receivedan MS in computer science from the
Katholieke Universiteit Leuven.
Readers may contact Marc Pollefeys at the Univ. of NorthCarolina
at Chapel Hill, Dept. of Computer Science, CB#3175, 205 Sitterson
Hall, Chapel Hill, NC 27599-3175;[email protected].
IEEE Computer Graphics and Applications 9