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.

翻译：我们建议采用神经网络方法来探测与变形物体的碰撞。 与以往基于捆绑体积等级的方法不同, 我们的神经方法不需要在物体变形时更新空间数据结构。 我们的网络受过关于物体自由度降低的培训, 这样我们就可以使用同样的网络来查询碰撞, 即使物体变形时也是如此。 我们的方法简单易用, 并且可以在GPU上使用。 我们用两个具体的例子来展示我们的方法: 带有有限元素网格的随机应用, 以及带有皮肤特征的布料模拟 。