GPCD : Grid-based predictive collision detection for large-scale environments in computer games

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publication2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings
Pages1025-1028
Volume2006
Publication statusPublished - 2006

Publication series

Name
Volume2006

Conference

Title2006 IEEE International Conference on Multimedia and Expo, ICME 2006
PlaceCanada
CityToronto, ON
Period9 - 12 July 2006

Abstract

Given a time horizon parameter h and an object set O, predictive collision detection finds all the object pairs 〈 oi, oj, t i 〉 which will collide in the future time interval [t, t + h] (where 1 ≤ i, j ≤ n, ti ∈ [t, t + h]). Although there are a number of state-of-the-art approaches to solve collision detection problems, predictive collision detection is addressed for the first time. In this paper, we propose a grid-based predictive collision detection algorithm (GPCD), which is a general technique for the efficient detection of the collision of object pairs in a future time interval. GPCD first determines a candidate list which stores the object pairs having a non-zero probability to collide in a future time. Then, GPCD achieves low running time based on two pruning strategies: (i) space intersection test and (ii) time intersection test. These two pruning strategies eliminate most of the false collision cases in an initial filtering phase. In the refinement phase, a bounding-volume tree is applied to refine the detection results. Our experiments show that GPCD works well for the purpose of predictive collision detection. © 2006 IEEE.

Citation Format(s)

GPCD : Grid-based predictive collision detection for large-scale environments in computer games. / Yu, Zhiwen; Wong, Hau-San.

2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings. Vol. 2006 2006. p. 1025-1028 4036777.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review