论文标题
可变形物体的神经碰撞检测
Neural Collision Detection for Deformable Objects
论文作者
论文摘要
我们提出了一种基于神经网络的方法，用于与可变形对象的碰撞检测。与以前的基于边界量层次结构的方法不同，当对象变形时，我们的神经方法不需要更新空间数据结构。我们的网络受到对象自由度降低的程度的培训，因此即使对象变形，我们也可以使用相同的网络查询碰撞。我们的方法易于使用和实施，并且可以轻松地在GPU上使用。我们通过两个具体的例子来演示我们的方法：具有有限元网格的触觉应用，以及带有皮肤特征的布模拟。
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.
