ray tracing dynamic scenes using selective restructuring
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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Ray Tracing Dynamic Scenes using
Selective Restructuring
Sung-eui Yoon
Sean Curtis
Dinesh Manocha
Univ. of North Carolina atChapel Hill
Lawrence Livermore National Lab
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Motivations
Dynamic scenes are widely used Movies, VR applications, and games
Complex and large dynamic scenes E.g, high-resolution explosion, tears, and
fractures
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An Example of Exploding Dragon (252K triangles)
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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
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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
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BVHs
Object partitioning hierarchies Uses axis-aligned bounding boxes Considers surface-area heuristic (SAH)
[Goldsmith and Salmon 87]
A BVHA BVH
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Two BVH Update Methods
Frame 1Frame 1
Frame 2Frame 2
BV refittingBV refitting
BV reconstructionBV reconstruction• O(n log n)O(n log n)• Good-quality BVsGood-quality BVs
• O(n)O(n)• Poor-quality BVsPoor-quality BVs
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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
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Our Contributions
Proposes a novel algorithm to selectively restructure BVHs Selective restructuring operations Two probabilistic metrics: culling efficiency
and restructuring benefit
BVH
RestructureRestructureRefitRefit
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Example of Exploding Dragon Model
BV refitting
Complete reconstruction
# of intersections Ray tracing time (sec):construction + traversal
Selective restructuring
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Runtime Captured Video – BART Model (65K triangles)
Compared with the BV refitting method
Enabled primary Enabled primary & shadow rays& shadow rays
Single threadSingle thread
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Prior Hybrid BVH Update Methods
Based on simple heuristics RT-Deform [Lauterbach et al. 06] LM method [Larsson and Akenine-Möller
06]
Hard to see what dynamic scenarios they work with or not
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Runtime Captured Video –BART Model
Compared with RT-Deform
Single threadSingle thread
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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
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Major Observation
Restructuring two nodes with BV overlaps can improve the culling efficiency Assumes that restructuring operation will
remove all the BV overlaps
A BVHA BVH
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Selective Restructuring Operations
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Overall Framework
At a new frame Refits BVs with deformed triangles Performs our selective restructuring
algorithm Runs BVH-based ray tracing
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Detecting BV Overlaps
Brute-force method Requires O(m2) where m is # of BVs
Hierarchical traversal and culling Inspired by efficient collision detection
methods
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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
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Evaluating Our Algorithm
Implement BVH-based ray tracer [Lauterbach et al. 06] Tests with four dynamic scenes having
different characteristics
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Dynamic Scenes
Cloth simulation (92K)
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Dynamic Scenes
N-body simulation (146K)
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Dynamic Scenes
Exploding dragon (252K)
BART (65K)
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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-Möller
06]
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Performance Improvement Ratio
Complete re-construction
Exploding dragon 8.5
N-body simulation 1.8
BART 1.1
Cloth simulation 4.7
Refitting only
11
> 80
28
0.96
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Image Shots from Cloth Simulation
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Performance Improvement Ratio
Complete const.
Refitting only
Exploding dragon 8.5 11
N-body simulation 1.8 > 80
BART 1.1 28
Cloth simulation 4.7 0.96
Robust performance improvement Robust performance improvement across our benchmarksacross our benchmarks
RT-Deform
LM method
1.65 2.16
1.25 1.36
2.5 1.11
1.03 1.29
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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
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Acknowledgements
Naga Govindaraju Christian Lauterbach Ingo Wald Ming Jang Hanan Samet Peter Lindstrom Other members of data analysis group Anonymous reviewers Our funding agencies