Master Thesis - Computer Graphics and Visualization - TU Delft

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List of Figures List of Figures x 7.7 TPPT time partition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 7.8 SSPT time partition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 7.9 TPPT performance vs. stream size . . . . . . . . . . . . . . . . . . . . . . . . 64 7.10 SSPT performance vs. stream size . . . . . . . . . . . . . . . . . . . . . . . . 64 7.11 GPU PT conference room rendering . . . . . . . . . . . . . . . . . . . . . . . 65 8.1 MIS denominator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 8.2 SBDPT sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 8.3 SBDPT flowchart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 8.4 Collapsed SBDPT flowchart . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 8.5 SBDPT vertex memory access . . . . . . . . . . . . . . . . . . . . . . . . . . 83 8.6 SBDPT performance with stream compaction . . . . . . . . . . . . . . . . . . 88 8.7 SBDPT time partition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 8.8 SSPT vs SBDPT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 8.9 Contribution of bidirectional strategies . . . . . . . . . . . . . . . . . . . . . . 91 9.1 Feature shapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 9.2 High correlation in feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 9.3 Local feature optimum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 9.4 Low energy feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 9.5 Improved mutation strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 9.6 ERPT flowchart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 9.7 ERPT path vertex access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 9.8 ERPT progress estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 9.9 ERPT performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 9.10 ERPT time partition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 9.11 Complex caustic with ERPT and SBDPT . . . . . . . . . . . . . . . . . . . . 117 9.12 ERPT and SSPT comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 9.13 Invisible date with ERPT, SBDPT and SSPT . . . . . . . . . . . . . . . . . . . 119 9.14 Stanford scene with ERPT and SBDPT . . . . . . . . . . . . . . . . . . . . . . 120 10.1 Iteration performance comparison . . . . . . . . . . . . . . . . . . . . . . . . 124 10.2 Traversal performance comparison . . . . . . . . . . . . . . . . . . . . . . . . 125 10.3 Sponza with ERPT, SBDPT and SSPT . . . . . . . . . . . . . . . . . . . . . . 126 10.4 Glass Egg with ERPT, SBDPT and SSPT . . . . . . . . . . . . . . . . . . . . 127 10.5 Invisible Date with ERPT, SBDPT and SSPT . . . . . . . . . . . . . . . . . . 128 B.1 Conversion between unit projected solid angle and unit area . . . . . . . . . . . 139 B.2 Vertex sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 B.3 Bidirectional Sampled Path . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 C.1 Camera model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 D.1 Implicit path contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 D.2 Explicit path contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
List of Figures List of Figures E.1 One eye subpath contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 E.2 Bidirectional path contribition . . . . . . . . . . . . . . . . . . . . . . . . . . 151 F.1 Acceptance probability for partial lens mutation . . . . . . . . . . . . . . . . . 157 F.2 Acceptance probability for full lens mutation . . . . . . . . . . . . . . . . . . 157 F.3 Acceptance probability for partial caustic mutation . . . . . . . . . . . . . . . 157 F.4 Acceptance probability for full caustic mutation . . . . . . . . . . . . . . . . . 158 xi
Page 1: Unbiased physically based rendering
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Page 9 and 10: Contents Preface iii Contents v Lis
Page 11: CONTENTS CONTENTS Bibliography 131
Page 17: List of Tables 5.1 Test scenes . .
Page 21 and 22: Chapter 1 Introduction Since the in
Page 23 and 24: Introduction 1.1 Summary of Contrib
Page 25: Part I Preliminaries 5
Page 28 and 29: 2.1 Rendering equation Unbiased Ren
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Page 32 and 33: 2.5 Importance sampling Unbiased Re
Page 34 and 35: 2.7 BiDirectional Path Tracing Unbi
Page 36 and 37: 2.7 BiDirectional Path Tracing Unbi
Page 38 and 39: 2.9 Metropolis Light Transport Unbi
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Page 42 and 43: 2.10 Energy Redistribution Path Tra
Page 45 and 46: Chapter 3 3.1 Introduction GPGPU Mo
Page 47 and 48: GPGPU 3.3 Memory Hierarchy divergin
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Page 54 and 55: 4.1 GPU ray tracing Related Work 34
Page 56 and 57: 4.2 Unbiased rendering Related Work
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Hybrid Tracer 6.2 Hybrid architectu
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Hybrid Tracer 6.3 Results 6.3.2 Hyb
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Hybrid Tracer 6.3 Results 6.3.3 Con
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7.2 Two-Phase PT Path Tracer (PT) 5
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7.4 Stream compaction Path Tracer (
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7.4 Stream compaction Path Tracer (
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7.5 Results Path Tracer (PT) 58 sam
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7.5 Results Path Tracer (PT) most g
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7.5 Results Path Tracer (PT) 62 Mil
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7.5 Results Path Tracer (PT) 64 Mil
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Chapter 8 Streaming BiDirectional P
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Streaming BiDirectional Path Tracer
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9.2 ERPT mutation Energy Redistribu
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9.3 GPU ERPT Energy Redistribution
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Chapter 10 Comparison In this secti
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Comparison 10.1 Performance Million
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Comparison 10.2 Convergence (a) (b)
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Chapter 11 Conclusions and Future W
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Bibliography [1] Timo Aila and Samu
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BIBLIOGRAPHY BIBLIOGRAPHY [25] Jare
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BIBLIOGRAPHY BIBLIOGRAPHY [49] Yinl
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Appendix B Sample probability When
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Sample probability B.2 Subpath samp
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Appendix C Camera Model Usually, th
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Appendix D PT Algorithm This append
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PT Algorithm D.3 Algorithm are used
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Appendix E BDPT Algorithm This appe
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BDPT Algorithm E.2 Algorithm Figure
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BDPT Algorithm E.2 Algorithm Algori
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Appendix F MLT Mutations This appen
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MLT Mutations F.1 Acceptance probab
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MLT Mutations F.2 Algorithm Algorit
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Master Thesis - Computer Graphics and Visualization - TU Delft

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