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A Safe and Fast Repulsion Method for GPU-based Cloth Self Collisions
ACM Transactions on Graphics  ( IF 6.2 ) Pub Date : 2020-12-25 , DOI: 10.1145/3430025
Longhua Wu 1 , Botao Wu 2 , Yin Yang 3 , Huamin Wang 1
Affiliation  

Cloth dynamics and collision handling are the two most challenging topics in cloth simulation. While researchers have substantially improved the performances of cloth dynamics solvers recently, their success in fast collision detection and handling is rather limited. In this article, we focus our research on the safety, efficiency, and realism of the repulsion-based collision handling approach, which has demonstrated its potential in existing GPU-based simulators. Our first discovery is the necessary vertex distance conditions for cloth to enter self intersections, the negations of which can be viewed as vertex distance constraints continuous in time for sufficiently avoiding self collisions. Continuous constraints, however, cannot be enforced with ease. Our solution is to convert continuous constraints into three types of constraints: discrete edge length constraints, discrete vertex distance constraints, and vertex displacement constraints. Based on this solution, we develop a fast and safe collision handling process for enforcing constraints, a novel splitting method for integrating collision handling with dynamics solvers, and static and adaptive remeshing schemes to further improve the runtime performance. In summary, our cloth simulator is efficient, safe, robust, and parallelizable on a GPU. The experiment shows that it runs at least one order of magnitude faster than existing simulators.

中文翻译:

基于 GPU 的布料自碰撞安全快速排斥方法

布料动力学和碰撞处理是布料模拟中最具挑战性的两个主题。虽然研究人员最近已经大大提高了布料动力学求解器的性能,但它们在快速碰撞检测和处理方面的成功相当有限。在本文中,我们将研究重点放在基于排斥的碰撞处理方法的安全性、效率和真实性上,这已经证明了它在现有的基于 GPU 的模拟器中的潜力。我们的第一个发现是布料进入自交点的必要顶点距离条件,其否定可以看作是在时间上连续的顶点距离约束,以充分避免自碰撞。然而,连续约束不能轻易实施。我们的解决方案是将连续约束转化为三种类型的约束:离散边长约束、离散顶点距离约束和顶点位移约束。在此解决方案的基础上,我们开发了一种用于强制约束的快速安全的碰撞处理过程,一种将碰撞处理与动力学求解器相结合的新型拆分方法,以及用于进一步提高运行时性能的静态和自适应网格划分方案。总之,我们的布料模拟器在 GPU 上是高效、安全、健壮且可并行的。实验表明,它的运行速度至少比现有模拟器快一个数量级。以及静态和自适应网格划分方案,以进一步提高运行时性能。总之,我们的布料模拟器在 GPU 上是高效、安全、健壮且可并行的。实验表明,它的运行速度至少比现有模拟器快一个数量级。以及静态和自适应网格划分方案,以进一步提高运行时性能。总之,我们的布料模拟器在 GPU 上是高效、安全、健壮且可并行的。实验表明,它的运行速度至少比现有模拟器快一个数量级。
更新日期:2020-12-25
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