变形物体的神经网络碰撞检测
Neural Collision Detection for Deformable Objects
摘要：
提出了一种基于神经网络的可变形物体碰撞检测方法。与以前基于包围体层次结构的方法不同，我们的神经方法不需要在对象变形时更新空间数据结构。我们的网络是在物体的减少的自由度上训练的，这样即使物体变形，我们也可以使用相同的网络来查询碰撞。我们的方法是简单的使用和实现，它可以很容易地在GPU上使用。我们展示了我们的方法与两个具体的例子：一个触觉应用程序与有限元网格，和布模拟皮肤的字符。
英文摘要：
We propose a neural network-based approach for collision detection with deformable objects. Unlike previous approaches based on bounding volume hierarchies, our neural approach does not require an update of the spatial data structure when the object deforms. Our network is trained on the reduced degrees of freedom of the object, so that we can use the same network to query for collisions even when the object deforms. Our approach is simple to use and implement, and it can readily be employed on the GPU. We demonstrate our approach with two concrete examples: a haptics application with a finite element mesh, and cloth simulation with a skinned character.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
