Collision-streams: fast GPU-based collision detection for deformable models
Collision-streams: fast GPU-based collision detection for deformable models
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DOI：
10.1145/1944745.1944756
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发表时间：
2011-02
期刊：
影响因子：
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通讯作者：
Min Tang;Dinesh Manocha;Jiang Lin;Ruofeng Tong
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文献类型：
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作者：
Min Tang;Dinesh Manocha;Jiang Lin;Ruofeng Tong
我们提出了一种基于GPU的快速流算法，用于在可变形模型之间执行碰撞查询。我们的方法基于层次剔除，并将计算简化为生成不同的流。我们提出了一种新颖的流注册方法来压缩流并高效地计算可能碰撞的图元对。我们还使用了一种延迟前沿跟踪方法来降低内存开销。整个算法已在不同的GPU上实现，并且我们已经在非刚性和可变形模拟中评估了其性能。我们强调了我们相对于先前基于CPU和基于GPU的算法的加速。在实际应用中，我们的算法能够在几十毫秒内对由数十万个三角形组成的模型执行对象间和对象内的计算。
We present a fast GPU-based streaming algorithm to perform collision queries between deformable models. Our approach is based on hierarchical culling and reduces the computation to generating different streams. We present a novel stream registration method to compact the streams and efficiently compute the potentially colliding pairs of primitives. We also use a deferred front tracking method to lower the memory overhead. The overall algorithm has been implemented on different GPUs and we have evaluated its performance on non-rigid and deformable simulations. We highlight our speedups over prior CPU-based and GPU-based algorithms. In practice, our algorithm can perform inter-object and intra-object computations on models composed of hundreds of thousands of triangles in tens of milliseconds.