Efficient and Reliable Self-Collision Culling Using Unprojected Normal Cones

Tongtong Wang, Zhihua Liu, Min Tang, Ruofeng Tong, Dinesh Manocha

Research output: Contribution to journalArticle

  • 2 Citations

Abstract

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.

LanguageEnglish (US)
Pages487-498
Number of pages12
JournalComputer Graphics Forum
Volume36
Issue number8
DOIs
StatePublished - Dec 1 2017

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Keywords

  • animation
  • collision detection

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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Efficient and Reliable Self-Collision Culling Using Unprojected Normal Cones. / Wang, Tongtong; Liu, Zhihua; Tang, Min; Tong, Ruofeng; Manocha, Dinesh.

In: Computer Graphics Forum, Vol. 36, No. 8, 01.12.2017, p. 487-498.

Research output: Contribution to journalArticle

Wang, Tongtong ; Liu, Zhihua ; Tang, Min ; Tong, Ruofeng ; Manocha, Dinesh. / Efficient and Reliable Self-Collision Culling Using Unprojected Normal Cones. In: Computer Graphics Forum. 2017 ; Vol. 36, No. 8. pp. 487-498.
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