two algorithms for progressive computation of accurate global illumination

126 Pages·2002·0.94 MB·English

TWO ALGORITHMS FOR PROGRESSIVE
COMPUTATION OF ACCURATE GLOBAL
ILLUMINATION
A Dissertation
Presented to the Faculty of the Graduate School
of Cornell University
in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
by
Parag Prabhakar Tole
January 2003
c 2003 Parag Prabhakar Tole
(cid:0)
ALL RIGHTSRESERVED
TWO ALGORITHMSFOR PROGRESSIVE COMPUTATION OFACCURATE
GLOBAL ILLUMINATION
Parag Prabhakar Tole,Ph.D.
CornellUniversity2003
In this thesis, we present two new algorithms for computing accurate global illumina-
tion. These algorithmsare general and progressivelikepath tracing, but providesignif-
icant efficiencygainsby exploitingcoherence intheillumination.
The first algorithm reduces the variance of path tracing by filtering the contribution
ofindividuallightpathsintheimageplane. Pathspacecoherenceisexploitedbyspread-
ing the contribution of each path over several pixels. This redistribution is performed
using an energy-preserving filter that is constructed based on the local geometry and
reflectivity of the environment. The filter size is selected adaptivelyfor each path so as
to minimize the noise in the image. A major advantage of our algorithm is its progres-
sive convergence property: the user does not have to specify any parameters that affect
the accuracy of the final image because the algorithm can generate and filter additional
light paths until the displayed image is sufficiently accurate. The filtering approach is
predictive,progressive,generalandfullyautomatic. Therefore,webelievethatitcanbe
successfullyemployedinsystemsforhighqualityrendering.
The second algorithm is better suited for interactive computation of global illumi-
nation in fully dynamic scenes. This algorithm uses a novel scheme for caching the
results of a path tracer. The Shading Cache is an object-space hierarchical subdivision
mesh with lazily computed shading values at its vertices. A high frame rate display is
generatedfromtheShadingCacheusinghardware-basedinterpolationandtexturemap-
ping. An image space sampling scheme refines the Shading Cache in regions that have
themostestimatederrororthosethatare mostlikelyto beaffected by objectorcamera
motion. Our system can efficiently handle dynamic scenes and moving lights, provid-
ing useful feedback within a few seconds and high quality images within a few tens
of seconds. For the models tested, the Shading Cache significantly outperforms other
interactiveglobal illuminationsystems, especially in dynamic scenes. Based on our re-
sults,webelievethattheShadingCachecanbeaninvaluabletoolinlightingdesignand
modellingwhilerendering.
BIOGRAPHICALSKETCH
The author was born on February 8, 1975. He studied Computer Science at the Indian
InstituteofTechnologyinMumbaifrom1993to1997andthenjoinedthePh.D.program
at Cornell Universityin Ithaca, New York in thefall of 1997. He received his doctorate
from Cornellin January 2003.
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ACKNOWLEDGEMENTS
IamverygratefultoDonGreenbergforgivingmetheopportunitytostudyatoneofthe
bestresearchlabsintheworld,theProgramofComputerGraphicsatCornellUniversity.
Thank you Don for your vision and your leadership, for giving me the chance to work
withso manysmart peopleand ofcourse, fortheresearch grants.
Some of the research presented in this dissertation was performed in collaboration
withFabioPellaciniandthisdissertationwouldnothavetakenitspresentshapewithout
his insights. Thanks also to Prof. Stephen Vavasis and Prof. Michael Nussbaum for
serving on my special committee. Special thanks to Philip Dutre´ and Bruce Walter for
answering every graphics question I could ever think of and many that I did not think
of. Thanks also to Bruce, Sebastian Fernandez and the rest of the Real Time Global
Illumination group at PCG for developing the jkar, skar, fileformat and rtgi code bases
that I used extensively in all of my projects, to Ryan Ismert, Ben Trumbore, David
Hart and others who worked on the 3dStudio Max plugin for exporting models to our
homegrownmdlformatand toMahesh Ramasubramanianfortheglimageviewer.
The 3D models used in this dissertation were produced by Fabio Pellacini, Jeremy
Selan, Reynald Dumont and Hector Yee using Discreet’s 3DStudio MAX software.
Some portions of these models and the textures used in them were acquired from the
3dStudioMAXdistribution,3dcafe.com,the3dstudio.comandPaulBourke. Additional
textures were painted in Adobe Photoshop or photographed by Fabio Pellacini, Jeremy
SelanandReynaldDumont. Thankstoeleganza.comforallowingustousetwooftheir
imagesas texturesinsomeofourrelated ACM publications.
LindaStephenson,PeggyAndersonand JonathanCorson-Rikerttookcareofallthe
administrativeworkduringmyfouryearsinthelabandIamextremelygratefultothem
forshieldingmefrombureaucracyasmuchaspossible. ThankstoHurfSheldon,Martin
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Berggren,MitchCollinsworthandtheirstudentassistantsforkeepingthecomputersys-
tems up and running and for giving meaccess to the latest and greatest hardware in the
lab. ThankstoMartin,Jeremy andRandy Fernando forhelpingout withtheproduction
of the SIGGRAPH videos. Finally, thanks also to Hongsong Li, SuAnne Fu, Sumant
Pattanaik,EricLafortune,SteveWestin,JackTumblin,JimFerwerda, KavitaBala,Eric
Haines,DanGelb,EricWong,DanKartch,JohnMollis,VikashGoel,BryanVandrovec,
Corey Toler, Sherwin Tam, Michael Malone, Moreno Piccolotto, Steve Berman and
RichLevyandtherestofmycolleaguesatPCGforparticipatinginmylearningexperi-
enceand formakingmystay at Cornellamemorableone.
A portion of the work in Chapter 4 of this dissertation has been published in ACM
SIGGRAPH 2002 and ACM Transactions on Graphics. ACM retains the copyright for
those publications. The presentation of parts of that published material in this disserta-
tionisin accordance withtheACM TransferofCopyrightAgreement.
Thiswork was supportedbytheNSF Scienceand TechnologyCenterfor Computer
Graphics and Scientific Visualization (ASC-8920219) and MRA parallel global illumi-
nation project (ASC-9523483), and performed using equipment generously donated by
theIntelCorporation andNVIDIA Corporation.
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TABLE OFCONTENTS
Biographical Sketch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
TableofContents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
ListofTables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
ListofFigures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
1 Introduction 1
1.1 Predictiveimagesynthesisusingglobalillumination. . . . . . . . . . . 2
1.2 Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 ThesisOutline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Previous Work 11
2.1 Ray-tracing Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 Finiteelementalgorithms . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 Hybridglobalilluminationalgorithms . . . . . . . . . . . . . . . . . . 15
2.4 InteractiveGlobalIllumination . . . . . . . . . . . . . . . . . . . . . . 17
2.4.1 Visualizationofpre-computedglobalillumination . . . . . . . 18
2.4.2 Interactivelyupdatedglobalillumination . . . . . . . . . . . . 19
2.4.3 Brute-force techniques . . . . . . . . . . . . . . . . . . . . . . 21
2.5 Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3 ProgressiveGlobalIlluminationusing Energy Preserving Filters 27
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Pixel-independentestimatorforglobalillumination . . . . . . . . . . . 32
3.2.1 Variance estimationand adaptivesampling . . . . . . . . . . . 34
3.3 Filteringforvariancereduction . . . . . . . . . . . . . . . . . . . . . . 36
3.3.1 Filtering thepixel-independentestimator . . . . . . . . . . . . 36
3.3.2 BRDF-based EnergyPreserving Filter . . . . . . . . . . . . . . 38
3.3.3 Adaptivefiltersizeselection . . . . . . . . . . . . . . . . . . . 42
3.4 ImplementationDetails . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.4.1 DataStructure . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.4.2 Efficient ImagePlaneSampling . . . . . . . . . . . . . . . . . 49
3.4.3 Direct IlluminationComputation . . . . . . . . . . . . . . . . . 50
3.5 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4 The Shading Cache: AnArchitecture forInteractive GlobalIllumination 59
4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.2 SystemOverview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.2.1 UpdatingtheShadingCache . . . . . . . . . . . . . . . . . . . 65
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4.2.2 SampleRenderer . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.2.3 Efficient display . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.3 SampleSelection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.3.1 Estimationofinterpolationerror . . . . . . . . . . . . . . . . . 69
4.3.2 Samplingtheprioritymap . . . . . . . . . . . . . . . . . . . . 69
4.3.3 Avoidingbias inthesampling . . . . . . . . . . . . . . . . . . 71
4.3.4 Reconstructing shadingdiscontinuities . . . . . . . . . . . . . 72
4.4 View-dependenteffects and object motion . . . . . . . . . . . . . . . . 73
4.4.1 View-dependentillumination . . . . . . . . . . . . . . . . . . . 73
4.4.2 Motion-dependentshading . . . . . . . . . . . . . . . . . . . . 75
4.4.3 Providingfast updates . . . . . . . . . . . . . . . . . . . . . . 76
4.5 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.6 Comparisonwithsimilarsystems . . . . . . . . . . . . . . . . . . . . . 80
4.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5 Accurate GlobalIlluminationusing theShading Cache 90
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5.2 Progressivemesh refinement andnoisereduction . . . . . . . . . . . . 92
5.3 Progressiverefinement fortheShading Cache . . . . . . . . . . . . . . 98
5.3.1 Computingtheerror estimates . . . . . . . . . . . . . . . . . . 98
5.3.2 Priority assignment . . . . . . . . . . . . . . . . . . . . . . . . 98
5.3.3 Load balancing . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.4 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
6 Conclusion 104
Bibliography 108
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