Continuous collision detection for deformable objects using permissible clusters

Sai Keung Wong, George Baciu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)


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
Issue number4
Publication statusPublished - 1 Jan 2015


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

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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