Efficient BVH-based Collision Detection
Scheme with Ordering and Restructuring
1 - Zhejiang University, China
2 - University of North Carolina at Chapel
Hill, USA
3 - Alibaba-Zhejiang University Joint
Institute of Frontier Technologies, China
Abstract
We present a fast and robust
BVH-based collision detection scheme on GPU. By efficiently ordering and
restructuring BVHs and BVTT fronts, our approach addresses the problem of inefficient
caching on GPU and performance drop due to model deformations. Our techniques
are based on the use of histogram sort and an auxiliary structure BVTT front
log, through which we analyze the dynamic status of BVTT front and BVH quality.
Our approach efficiently handles inter- and intra-object collisions and
performs especially well in simulations where there is considerable spatio-temporal coherence. The benchmark results
demonstrate that our approach is significantly faster than the previous
BVH-based method, and also outperforms other
state-of-the-art spatial subdivision schemes in terms of speed.
Xinlei Wang, Min Tang, Dinesh Manocha, Ruofeng Tong, Efficient
BVH-based Collision Detection Scheme with Ordering and Restructuring, Computer Graphics Forum, 37(2): 227-237, (Proceedings of Eurographics), 2018.
@article{BVHCD18,
author = {Wang, Xinlei and Tang, Min and Manocha, Dinesh and Tong, Ruofeng},
title = {Efficient {BVH}-based Collision
Detection Scheme with Ordering and Restructuring},
journal = {Computer Graphics Forum
(Proceedings of Eurographics 2018)},
volume = {37},
number = {2},
pages = {227--237},
year = {2018},
}
I-Cloth: Incremental Collision Handling for GPU-Based Interactive Cloth Simulation
I-Cloth: API for fast and reliable cloth simulation with CUDA
PSCC: Parallel Self-Collision Culling with Spatial Hashing on GPUs
Accurate Self-Collision Detection Using Enhanced Dual-Cone Method
CAMA: Contact-Aware Matrix Assembly with
Unified Collision Handling for GPU-based Cloth Simulation
A GPU-based Streaming Algorithm
for High-Resolution Cloth Simulation
UNC dynamic model benchmark repository
Collision-Streams: Fast GPU-based
Collision Detection for Deformable Models
Fast Continuous Collision Detection using Deforming
Non-Penetration Filters
MCCD: Multi-Core Collision Detection
between Deformable Models using Front-Based Decomposition
Fast Collision Detection for Deformable
Models using Representative-Triangles
DeformCD:
Collision Detection between Deforming Objects
Self-CCD: Continuous Collision Detection
for Deforming Objects
Interactive Collision Detection between
Deformable Models using Chromatic Decomposition
Fast Proximity Computation Among Deformable
Models using Discrete Voronoi Diagrams
CULLIDE: Interactive Collision
Detection between Complex Models using Graphics Hardware
RCULLIDE: Fast and Reliable Collision
Culling using Graphics Processors
Quick-CULLIDE: Efficient Inter- and
Intra-Object Collision Culling using Graphics Hardware
All benchmarks are from the
UNC Dynamic Scene Benchmarks collection. The project is supported in part by
the National Key Research and Development Program (2017YFB1002700), NSFC
(61572423, 61572424, 61732015), the Science and Technology Project of Zhejiang
Province (2018C01080), and Zhejiang Provincial NSFC (LZ16F020003).