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arXiv · 2202.02309

Neural Collision Detection for Deformable Objects

Ryan S. Zesch·Bethany R. Witemeyer·Ziyan Xiong·David I. W. Levin·Shinjiro Sueda
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Abstract

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.

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Ryan S. Zesch, Bethany R. Witemeyer, Ziyan Xiong, David I. W. Levin, Shinjiro Sueda. 2022-02-04. Neural Collision Detection for Deformable Objects. https://arxiv.org/abs/2202.02309

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