learning models of shape from 3d range data - Stanford AI Lab

203 Pages·2005·17.99 MB·English

LEARNING MODELS OF SHAPE FROM 3D RANGE
DATA
A DISSERTATION
SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
DragomirAnguelov
December2005
(cid:176)c Copyright by Dragomir Anguelov 2006
All Rights Reserved
ii
IcertifythatIhavereadthisdissertationandthat,inmyopin-
ion,itisfullyadequateinscopeandqualityasadissertation
forthedegreeofDoctorofPhilosophy.
DaphneKoller
(PrincipalAdvisor)
IcertifythatIhavereadthisdissertationandthat,inmyopin-
ion,itisfullyadequateinscopeandqualityasadissertation
forthedegreeofDoctorofPhilosophy.
AndrewNg
IcertifythatIhavereadthisdissertationandthat,inmyopin-
ion,itisfullyadequateinscopeandqualityasadissertation
forthedegreeofDoctorofPhilosophy.
MarcLevoy
Approved for the University Committee on Graduate Stud-
ies:
iii
iv
TomyfamilyandOlya.

Abstract
Constructingshapemodelsofcomplexarticulatedanddeformableobjectsisafundamental
capability that enables a variety of applications in computer graphics, biomechanics, arts
andentertainment. Currentapproachesrequireasignificantamountofmanualintervention
inthemodelconstructionprocess.
In this thesis, we present algorithms for learning models of shape that reduce the need
for human input. First, we describe an unsupervised algorithm for registering 3D surface
scans of an object undergoing significant deformations. Our algorithm does not use mark-
ers, nor does it assume prior knowledge about object shape, the dynamics of its deforma-
tion,orscanalignment. Itisbasedonaprobabilisticmodel,whichminimizesdeformation
andattemptstopreservegeodesicdistancesandlocalmeshgeometry. Second,wedescribe
analgorithmwhoseinputisasetofmeshescorrespondingtodifferentconfigurationsofan
articulatedobject. Thealgorithmautomaticallyrecoversadecompositionoftheobjectinto
approximatelyrigidparts,thelocationofthepartsinthedifferentobjectinstances,andthe
articulatedobjectskeletonlinkingtheparts.
We also address the problem of learning the space of human body deformations from
3D scans. Unlike existing example-based approaches, our model spans variation in both
subject shape and pose. We learn a model of surface deformation as a function of the joint
angles of the articulated human skeleton. We also learn a separate model of the variation
between different body shapes. We show how to combine these two models to produce
realistic deformation for different people in different poses. Finally, we show how our
framework can be used for shape completion – generating a complete surface mesh given
a limited set of markers specifying the target shape. We use this capability to complete
partialmeshgeometryandtoanimatemarkermotioncapturesequences.
vii
viii
Acknowledgements
I am profoundly grateful to my adviser Daphne Koller for many years of guidance and
support. She has been the most influential person in my academic career, responsible for
kindlingmy interest in research when I wasa wide-eyedundergraduate, and for givingme
the opportunity to develop this interest. From my years as her advisee, I gleaned insights
into what kind of problems to tackle, how to go about addressing them and how to present
my results in a compelling manner. She set an example for me with her brilliance, high
standards and drive for excellence. In short, she has been a terrific adviser, and is the
primaryculpritformegettingtothepointofwritingtheseacknowledgements.
IfeelprivilegedforhavingSebastianThrunbemyguidetotheexcitingworldofproba-
bilisticrobotics. Iamforeverindebtedtohisgenerosity—inpracticehehasbeenasecond
advisertome,spendingcountlesshourstohelpdeveloptheideasinthisthesisandprovid-
ing valuable help and advice. His never-ending energy, flair and quick wit have truly been
an inspiration. It has been a joy to be around Sebastian; his research enthusiasm is truly
contagious.
Myfirstgraduateadviser,CarloTomasi,helpedmewithmyfirststepsingraduatelife,
and exposed me to the subject of computer vision. His kindness, generosity, and clarity of
thoughthavebeenagreatexampleforme.
I would like to acknowledge the kind help of Marc Levoy, who on many occasions
shared his computer graphics expertise with this humble artificial intelligence student. He
providedvaluableadvice,whichhelpedmetoshapemyresearchideas,andpresentthemin
a compelling manner to the computer graphics community. I would like to thank Andrew
Ng and Ron Fedkiw for valuable discussions and research suggestions, as well as Tom
Andriacchi for chairing my thesis defense committee and for generously providing me
ix
accesstothewonderfulfacilitiesoftheStanfordbiomechanicslab.
I am grateful to James Davis, a 3D sensor guru and multimedia wizard, who was in-
strumental in providing the sensor data, which is the input to the algorithms in this thesis.
He is also responsible for taking the output of the algorithms and turning it into what I
personally think is a work of art. The work on SCAPE would not have been possible if
James Davis had not introduced me to two great Europeans in the Stanford biomechanics
lab – Lars Mu¨ndermann and Stefano Corazza, who spent countless hours helping me with
the data acquisition process. Their passion for markerless motion capture is contagious,
whichresultedinagreatcollaboration,whichIhopewillcontinue.
I feel privileged to be a member of our storied DAGS research group. I am greatly
thankful to have had a chance to work, hang around, travel with, spend time in interest-
ing discussions with, being motivated by the great work of: Pieter Abbeel, Alexis Battle,
Luke Biewald, Rahul Biswas, Vassil Chatalbashev, Gal Chechik, Gal Elidan, Lise Getoor,
Carlos Guestrin, Geremy Heitz, Uri Lerner, Uri Nodelman, Dirk Ormoneit, Jimmy Pang,
Evan Parker, Ron Parr, Jim Rodgers, Suchi Saria, Eran Segal, Christian Shelton, Praveen
Srinivasan,SimonTong,DavidVickrey,HaidongWangandMingFaiWong.
I would like to say cheers mate! to my officemate Ben Taskar, friend in work and play,
with whom we faced early morning deadlines and grizzly bears, shared research ideas,
music,booksandlifeexperiences. Iwouldliketosaythankyou! toPraveenSrinivasan,the
czarofmorphing,whosesteadfastness,dedicationandinsightsalwayskeptusontrackfor
thedeadlines(meaningwewouldsubmitatthelastmomentafteramadscramble). Iwould
like to thank Pieter Abbeel, a remarkable researcher, and a serious disciple of the game of
tennis, for numerous interesting discussions that have enriched my understanding of many
a concept. I would like to thank Uri Lerner, chess and Diplomacy master, for taking an
inexperienced undergrad and showing him a thing or two in the ways of research. I would
liketothankJimmyPang,andJimRodgers—greatguys,whomIenjoyedworkingwith. I
wouldalsoliketothankGalElidan,anavidhikerandbackpacker,aguywhoknowshowto
do great research and enjoy life, for many interesting conversations and for useful advice.
I thank Geremy Heitz for multiple discussions about various computer vision methods. I
wouldliketothankMarkPaskin,BrianGerkeyandMichaelMontemerlofromSebastian’s
group,whomIenjoyedtalkingrobots,andnotsimplyrobots,with.
x