Parallel Spatial Hashing for Collision Detection of Deformable Surfaces

We present a fast collision detection method for deformable surfaces with parallel spatial hashing on GPU architecture. 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 workload into irregular segments and design an efficient evaluation algorithm, which employs parallel scan and stream compaction, to build the stream data in parallel. The load balancing is a key aspect that needs to be considered in the SIMD parallelism. We break the heavy and irregular collision computation down into lightweight part and heavyweight part, ensuring the later perfectly run in load balancing manner with each concurrent thread processes just a single collision. In practice, our approach can perform collision detection in tens of milliseconds on a PC with NVIDIAGTX 260 graphics card on benchmarks composed of millions of triangles. The results highlight our speedups over prior CPU-based and GPU-based algorithms.