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TightCCD: Efficient and Robust Continuous Collision Detection using Tight Error Bounds

机译:TightCCD:使用紧密误差范围进行高效,鲁棒的连续碰撞检测

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摘要

We present a realtime and reliable continuous collision detection (CCD) algorithm between triangulated models that exploits the floating point hardware capability of current CPUs and GPUs. Our formulation is based on Bernstein Sign Classification that takes advantage of the geometry properties of Bernstein basis and Bezier curves to perform Boolean collision queries. We derive tight numerical error bounds on the computations and employ those bounds to design an accurate algorithm using finite-precision arithmetic. Compared with prior floatingpoint CCD algorithms, our approach eliminates all the false negatives and 90-95% of the false positives. We integrated our algorithm (TightCCD) with physically-based simulation system and observe speedups in collision queries of 5-15X compared with prior reliable CCD algorithms. Furthermore, we demonstrate its benefits in terms of improving the performance or robustness of cloth simulation systems.
机译:我们在三角模型之间提出了实时,可靠的连续碰撞检测(CCD)算法,该算法利用了当前CPU和GPU的浮点硬件功能。我们的公式基于伯恩斯坦符号分类法,该分类法利用伯恩斯坦基数和贝塞尔曲线的几何特性来执行布尔碰撞查询。我们在计算中得出严格的数值误差范围,并使用这些范围使用有限精度算法设计精确的算法。与以前的浮点CCD算法相比,我们的方法消除了所有的假阴性和90-95%的假阳性。我们将算法(TightCCD)与基于物理的仿真系统集成在一起,与以前的可靠CCD算法相比,在碰撞查询中观察到了5-15倍的加速。此外,我们在改善布料模拟系统的性能或耐用性方面展示了其优势。

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