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Efficient and Reliable Self‐Collision Culling Using Unprojected Normal Cones
Author(s) -
Wang Tongtong,
Liu Zhihua,
Tang Min,
Tong Ruofeng,
Manocha Dinesh
Publication year - 2017
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13095
Subject(s) - collision detection , tree traversal , computer science , algorithm , sequence (biology) , collision , polygon mesh , computer graphics (images) , computer security , biology , genetics
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 hundreds of thousands of triangles. We observe considerable performance improvement over prior continuous collision detection algorithms.