Efficient and Reliable Self-Collision Culling using Unprojected Normal Cones

Tongtong Wang, Zhihua Liu, Min Tang, Roufeng Tong, and Dinesh Manocha

We present an efficient and accurate algorithm for self-collision detection in deformable models. Our approach can perform discrete and continuous collision queries on triangulated meshes. We present a simple and linear time algorithm to perform the normal cone test using the unprojected 3D vertices, which reduces to a sequence point-plane classification tests. Moreover, we present a hierarchical traversal scheme that can significantly reduce the number of normal cone tests and the memory overhead using front-based normal cone culling. The overall algorithm can reliably detect all (self) collisions in models composed of hundred of thousands of triangles. We observe considerable performance improvement over prior CCD algorithms.

Efficient and Reliable Self-Collision Culling using Unprojected Normal Cones

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