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Title: A collision detection framework for deformable objects
Authors: Lau, R
Chan, O
Luk, MO
Li, F
Issue Date: 2002
Source: VRST '02 Proceedings of the ACM Symposium on Virtual Reality Software and Technology, Hong Kong, 11-13 November, 2002, p. 113-120
Abstract: Many collision detection methods have been proposed. Most of them can only be applied to rigid objects. In general, these methods precompute some geometric information of each object, such as bounding boxes, to be used for run-time collision detection. However, if the object deforms, the precomputed information may not be valid anymore and hence needs to be recomputed in every frame while the object is deforming. In this paper, we presents an efficient collision detection framework for deformable objects, which considers both inter-collisions and self-collisions of deformable objects modeled by NURBS surfaces. Towards the end of the paper, we show some experimental results to demonstrate the performance of the new method.
Keywords: NURBS surfaces
Collision detection
Deformable objects
Interference detection
ISBN: 1-58113-530-0
DOI: 10.1145/585740.585760
Appears in Collections:Conference Paper

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