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.

Abstract We present a novel hierarchical grid based method for fast collision detection (CD) for deformable models on GPU architecture. A two‐level grid is employed to accommodate the non‐uniform distribution of practical scene geometry. A bottom‐to‐top method is implemented to assign the triangles into the hierarchical grid without any iteration while a deferred scheme is introduced to efficiently update the data structure. To address the issue of load balancing, which greatly influences the performance in SIMD parallelism, a propagation scheme which utilizes a parallel scan and a segmented scan is presented, distributing workloads evenly across all concurrent threads. The proposed method supports both discrete collision detection (DCD) and continuous collision detection (CCD) with self‐collision. Some typical benchmarks are tested to verify the effectiveness of our method. The results highlight our speedups over prior algorithms on different commodity GPUs.