Parallel Spatial Hashing for Collision Detection of Deformable Surfaces
We present a fast collision detection method for de- formable surfaces with parallel spatial hashing on GPU context. The efficient update and access of the uniform grid are exploited to accelerate the performance in our method. To deal with the inflexible memory system, which makes the building of stream data a challenging task on GPU, we propose to subdivide the whole work into irregular segments and design an efficient evaluation followed by parallel scan and stream compaction to build the stream data in parallel. The load balancing is a key aspect that needs to be considered on the SIMD parallelism. We break the heavy and irregular collision computation down into lightweight and heavyweight parts, ensuring the later perfectly run in load balancing manner with each concurrent thread processing just a single collision. In practice, our approach can perform collision detection in tens of milliseconds on a PC with NVIDIA GTX 260 card on benchmarks composed of millions of triangles. We highlight our speedups over prior CPU- based and GPU-based algorithms.
国际会议
2011国际计算机辅助设计与图形学学术会议(CAD/Graphics 2011)
济南
英文
1-8
2011-09-15（万方平台首次上网日期，不代表论文的发表时间）