Master Thesis - Computer Graphics and Visualization - TU Delft

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10.1 Performance Comparison 124 Because SBDPT does not benefit from any kind of sampler coherence, the performance is very stable and scene-independent. The only exception is the INVISIBLE DATE scene where SBDPT reaches significantly higher performance because the scene’s triangle data completely fits into the GPU’s L2 cache and most connection rays fail. Million per second 300 250 200 150 100 50 0 Sponza Sibenik Iteration performance Stanford Fairy Forest Glass Egg Invisible Date Conference ERPT SBDPT SSPT Figure 10.1: Iteration performance comparison in iterations per second. Ray traversal times are not included in these figures. Even though there are significant differences in sampler performance between the three GPU tracers, the maximum difference is no more than 150% and on average about 70%. Furthermore, for reasonably sized scenes, the performance hardly depends on scene size. Hence, the differences in sampler performance are not that large (significantly less than one order of magnitude) so the overall performance and scalability of these algorithms will be largely determined by their ray traversal performance. Figure 10.2 shows the ray traversal performance in rays per second, excluding sampler iteration times. We used the same ray traversal kernel in all tracers [1]. Using a different traversal algorithm, for example one using a kd-tree as its spatial structure, would obviously give significantly different results. We believe however that many of the observations drawn from these results will be similar for most traversal algorithms because traversal algorithms usually benefit from ray coherence. Although the same ray traversal kernel is used, the traversal performance for the different tracers differs by up to 70%. The SSPT and ERPT algorithms achieve roughly the same ray traversal performance, somewhat dependent on the particular scene. Again, the SBDPT algorithm performs worst on all scenes. The reasons for this difference in ray traversal performance are similar to those causing the difference in sampler iteration performance. The
Comparison 10.1 Performance Million per second 120 100 80 60 40 20 0 Sponza Sibenik Ray traversal performance Stanford Fairy Forest Glass Egg Invisible Date Conference ERPT SBDPT SSPT Figure 10.2: Ray traversal performance comparison in rays per second. Sampler iteration times are not included in these figures. SSPT algorithm benefits heavily from primary ray coherence, achieving high traversal performance. Similar, the ERPT algorithm achieves some ray coherence because all mutation chains in a warp start with the same initial mutation. Therefore mutations in a warp will often contain a reasonable amount of coherence, resulting in high traversal performance. SBDPT does not benefit from such coherence. Furthermore, while explicit connection rays in path tracing all end in the same light sources, causing significant ray coherence, connection rays in BDPT connect eye and light vertices from all over the scene. Therefore, the average ray traversal performance for bidirectional connection rays is lower than for ordinary connection rays in path tracing. This further reduces the ray traversal performance of SBDPT. Although ray traversal performance differs significantly from one tracer to another, the differences remain limited. Figure 10.2 shows that ray traversal performance decreases somewhat for larger scenes, but although CONFERENCE is several orders of magnitude larger than INVISIBLE DATE, the ray traversal speed decreases by less than a factor of 3. As sampler performance barely depends on scene size, this indicates that our GPU tracers scale very well with scene size. Lastly, a word on memory consumption. SSPT has the smallest memory footprint, requiring storage for only a single path vertex per sampler. SBDPT also has a relatively small memory footprint of two path vertices per sampler, independent of path length. ERPT on the other hand requires storage for all path vertices, making its memory footprint dependent on the maximum path length. This restricts the scalability of our ERPT implementation. 125
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Unbiased physically based rendering
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c○ 2010 Dietger van Antwerpen. Co
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ii memory-footprint independent of
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Contents Preface iii Contents v Lis
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CONTENTS CONTENTS Bibliography 131
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List of Figures List of Figures x 7
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List of Tables 5.1 Test scenes . .
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Chapter 1 Introduction Since the in
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Introduction 1.1 Summary of Contrib
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Part I Preliminaries 5
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2.1 Rendering equation Unbiased Ren
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2.3 Unbiased Monte Carlo estimate U
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2.5 Importance sampling Unbiased Re
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2.7 BiDirectional Path Tracing Unbi
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2.7 BiDirectional Path Tracing Unbi
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2.9 Metropolis Light Transport Unbi
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2.9 Metropolis Light Transport Unbi
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2.10 Energy Redistribution Path Tra
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Chapter 3 3.1 Introduction GPGPU Mo
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GPGPU 3.3 Memory Hierarchy divergin
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GPGPU 3.3 Memory Hierarchy (1 trans
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GPGPU 3.5 Parallel scan Algorithm 3
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4.1 GPU ray tracing Related Work 34
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4.2 Unbiased rendering Related Work
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Chapter 5 The Problem Statement In
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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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Streaming BiDirectional Path Tracer
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Page 114 and 115: 9.2 ERPT mutation Energy Redistribu
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Page 159 and 160: Appendix B Sample probability When
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Page 169 and 170: Appendix E BDPT Algorithm This appe
Page 171 and 172: BDPT Algorithm E.2 Algorithm Figure
Page 173 and 174: BDPT Algorithm E.2 Algorithm Algori
Page 175 and 176: Appendix F MLT Mutations This appen
Page 177 and 178: MLT Mutations F.1 Acceptance probab
Page 179 and 180: MLT Mutations F.2 Algorithm Algorit
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Master Thesis - Computer Graphics and Visualization - TU Delft

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