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Ray Tracing Dynamic Scenes using Selective Restructuring

Ray Tracing Dynamic Scenes using Selective Restructuring. Sung-eui Yoon Sean Curtis Dinesh Manocha. Lawrence Livermore National Lab. Univ. of North Carolina at Chapel Hill. Motivations. Dynamic scenes are widely used Movies, VR applications, and games Complex and large dynamic scenes

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Ray Tracing Dynamic Scenes using Selective Restructuring

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  1. Ray Tracing Dynamic Scenes using Selective Restructuring Sung-eui Yoon Sean Curtis Dinesh Manocha Lawrence Livermore National Lab Univ. of North Carolina at Chapel Hill

  2. Motivations • Dynamic scenes are widely used • Movies, VR applications, and games • Complex and large dynamic scenes • E.g, high-resolution explosion, tears, and fractures

  3. An Example of Exploding Dragon (252K triangles)

  4. Ray Tracing Dynamic Scenes • Acceleration hierarchy construction • e.g., kd-trees, bounding volume hierarchies, grids, etc • Hierarchy traversal • Perform ray-triangle intersection tests • Key issue • Update the hierarchy as triangles deform

  5. Bounding Volume Hierarchies (BVH) based Ray Tracing • Employed early in [Whitted 80] • kd-trees and grids became popular for static models in 90’s • Recently get renewed interest in ray tracing dynamic scenes [Wald et al. 07, Lauterbach et al. 07, Larsson et al. 03] • Simple, but efficient BVH update method is available • Can have better performance

  6. BVHs • Object partitioning hierarchies • Uses axis-aligned bounding boxes • Considers surface-area heuristic (SAH) [Goldsmith and Salmon 87] A BVH

  7. BV refitting Frame 1 BV reconstruction Two BVH Update Methods • O(n) • Poor-quality BVs Frame 2 • O(n log n) • Good-quality BVs

  8. Our Goal • Existing BVH update methods • Work at particular classes of dynamic scenes • Design a robust BVH update method • Works well with wide classes of dynamic scenes • Improves the performance of ray tracing

  9. Our Contributions • Proposes a novel algorithm to selectively restructure BVHs • Selective restructuring operations • Two probabilistic metrics: culling efficiency and restructuring benefit Refit Restructure BVH

  10. Example of Exploding Dragon Model Ray tracing time (sec): construction + traversal # of intersections BV refitting Complete reconstruction Selective restructuring

  11. Runtime Captured Video – BART Model (65K triangles) • Compared with the BV refitting method Enabled primary & shadow rays Single thread

  12. Prior Hybrid BVH Update Methods • Based on simple heuristics • RT-Deform [Lauterbach et al. 06] • LM method [Larsson and Akenine-Möller06] • Hard to see what dynamic scenarios they work with or not

  13. Runtime Captured Video –BART Model • Compared with RT-Deform Single thread

  14. Probabilistic BVH Metrics for Ray Tracing • Culling efficiency • Quantifies the quality of any sub-BVHs • Measures the expected # of intersection tests for a ray • Restructuring benefit • Predicts the performance improvement • Measures improved culling efficiency when restructuring sub-BVHs

  15. Major Observation • Restructuring two nodes with BV overlaps can improve the culling efficiency • Assumes that restructuring operation will remove all the BV overlaps A BVH

  16. Selective Restructuring Operations

  17. Overall Framework • At a new frame • Refits BVs with deformed triangles • Performs our selective restructuring algorithm • Runs BVH-based ray tracing

  18. Detecting BV Overlaps • Brute-force method • Requires O(m2) where m is # of BVs • Hierarchical traversal and culling • Inspired by efficient collision detection methods

  19. Overview of Selective Restructuring Algorithm • Computes restructuring candidates • Detects nodes with BV overlaps during hierarchy traversal • Restructure node pairs with higher restructuring benefits greedily • Improves the performance of ray tracing

  20. Evaluating Our Algorithm • Implement BVH-based ray tracer [Lauterbach et al. 06] • Tests with four dynamic scenes having different characteristics

  21. Dynamic Scenes • Cloth simulation (92K)

  22. Dynamic Scenes • N-body simulation (146K)

  23. Dynamic Scenes • Exploding dragon (252K) • BART (65K)

  24. Prior Works • BV Refitting [Wald et al. 07, Bergen 97] • Complete re-construction from scratch • RT-Deform [Lauterbach et al. 06] • LM method [Larsson and Akenine-Möller06]

  25. Performance Improvement Ratio

  26. Image Shots from Cloth Simulation

  27. Performance Improvement Ratio Robust performance improvement across our benchmarks

  28. Conclusions • Novel algorithm to selectively restructure BVHs • Based on selective restructuring operations and two BVH metrics • Has more robustness and deals with bigger scene complexity • Can be used in other applications • Dynamic scenes are available

  29. Acknowledgements • Naga Govindaraju • Christian Lauterbach • Ingo Wald • Ming Jang • Hanan Samet • Peter Lindstrom • Other members of data analysis group • Anonymous reviewers • Our funding agencies

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