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Sphere-tree construction using dynamic medial axis approximation

Published: 21 July 2002 Publication History

Abstract

Collision handling is very computationally expensive, especially in large scale interactive animations. Hierarchical object representations play an important role in performing efficient collision handling. Many different geometric primitives have been used to construct these representations, which allow areas of interaction to be localised quickly. For time-critical algorithms, such as interruptible collision detection, there are distinct advantages to using hierarchies of spheres, known as sphere-trees. This paper presents a novel algorithm for the construction of sphere-trees. The algorithm presented approximates objects, both convex and non-convex, with a higher degree of fit than existing algorithms. In the lower levels of the representations, there is almost an order of magnitude decrease in the number of spheres required to represent the objects to a given accuracy.

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cover image ACM Conferences
SCA '02: Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation
July 2002
203 pages
ISBN:1581135734
DOI:10.1145/545261
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 21 July 2002

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Author Tags

  1. construction
  2. level-of-detail collision detection
  3. medial axis
  4. object approximation
  5. sphere-tree

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SCA02
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SCA02: Symposium on Computer Animation
July 21 - 22, 2002
Texas, San Antonio

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SCA '02 Paper Acceptance Rate 22 of 53 submissions, 42%;
Overall Acceptance Rate 183 of 487 submissions, 38%

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