Continuous collision detection for deformable objects using permissible clusters

Sai-Keung Wong*, George Baciu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

In this paper, we propose a new data structure to perform continuous collision detection (CCD) for deformable triangular meshes. The critical component of this data structure is permissible clusters. At the preprocessing phase, the triangular meshes are divided into permissible clusters. Then, the features of the triangular meshes are assigned to the permissible clusters. At the runtime phase, the potentially colliding feature pairs are collected and they are processed only once in the elementary processing. Our method has been integrated with a normal cone-based method and compared with other CCD methods. Experimental results show that our method improves the overall performance of CCD for deformable objects.

Original languageEnglish
Pages (from-to)377-389
Number of pages13
JournalVisual Computer
Volume31
Issue number4
DOIs
StatePublished - 1 Jan 2015

Keywords

  • Continuous collision detection
  • Deformable objects
  • Triangle clusters
  • Virtual reality

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