Master Thesis - Computer Graphics and Visualization - TU Delft

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CONTENTS CONTENTS vi 3.4 Synchronization <strong>and</strong> Communication . . . . . . . . . . . . . . . . . . . . . 30 3.5 Parallel scan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4 Related Work 33 4.1 GPU ray tracing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2 Unbiased rendering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 II GPU Tracers 37 5 The Problem Statement 39 5.1 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 5.2 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 6 Hybrid Tracer 43 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 6.2 Hybrid architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 7 Path Tracer (PT) 51 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 7.2 Two-Phase PT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 7.3 GPU PT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 7.4 Stream compaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 7.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 8 Streaming BiDirectional Path Tracer (SBDPT) 67 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 8.2 Recursive Multiple Importance Sampling . . . . . . . . . . . . . . . . . . 69 8.3 SBDPT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 8.4 GPU SBDPT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 9 Energy Redistribution Path Tracer (ERPT) 93 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 9.2 ERPT mutation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 9.3 GPU ERPT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 III Results 121 10 Comparison 123 10.1 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 10.2 Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 11 Conclusions <strong>and</strong> Future Work 129
CONTENTS CONTENTS Bibliography 131 A Glossary 137 B Sample probability 139 B.1 Vertex sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 B.2 Subpath sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 B.3 Bidirectional sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 C Camera Model 143 D PT Algorithm 145 D.1 Path contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 D.2 MIS weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 D.3 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 E BDPT Algorithm 149 E.1 Path contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 E.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 F MLT Mutations 155 F.1 Acceptance probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 F.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 vii
Page 1: Unbiased physically based rendering
Page 4 and 5: c○ 2010 Dietger van Antwerpen. Co
Page 6 and 7: ii memory-footprint independent of
Page 9: Contents Preface iii Contents v Lis
Page 14 and 15: List of Figures List of Figures x 7
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
Page 30 and 31: 2.3 Unbiased Monte Carlo estimate U
Page 32 and 33: 2.5 Importance sampling Unbiased Re
Page 34 and 35: 2.7 BiDirectional Path Tracing Unbi
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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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The Problem Statement 5.1 Related w
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Chapter 6 6.1 Introduction Hybrid T
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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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