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I-COLLIDE: an interactive and exact collision detection system for large-scale environments

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Cohen, Jonathan D., Lin, Ming C., Manocha, Dinesh, Ponamgi, Madhav·1995·Proceedings of the 1995 symposium on Interactive 3D graphics - SI3D '95

Abstract

we present an exact and interactive collision detection system, I-COLLIDE, for large-scale environments. Such environments are characterized by the number of objects undergoing rigid motion and the complexity of the models. The algorithm does not assume the objects' motions can be expressed as a closed form function of time. The collision detection system is general and can be easily interfaced with a variety of applications. The algorithm uses a two-level approach based on pruning multiple-object pairs using bounding boxes and performing exact collision detection between selected pairs of polyhedral models. We demonstrate the performance of the system in walkthrough and simulation environments consisting of a large number of moving objects. In particular, the system takes less than 1/20 of a second to determine all the collisions and contacts in an environment consisting of more than 1000 moving polytopes, each consisting of more than 50 faces on an HP-9000/750.

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Metadata

Authors
Cohen, J. D., Lin, M. C., Manocha, D., & Ponamgi, M.
Venue
Proceedings of the 1995 symposium on Interactive 3D graphics - SI3D '95
Publisher
ACM Press
Published
1995
Pages
189-ff.
OpenAlex
W2094539937
Cited by
628
References
0
FWCI
9.93
Type
conference paper
Language
en
Open access
bronze

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CoChat. (2026). Citation check: I-COLLIDE: an interactive and exact collision detection system for large-scale environments. https://app.cochat.ai/papers/10.1145/199404.199437
The paper
Cohen, J. D., Lin, M. C., Manocha, D., & Ponamgi, M. (1995). I-COLLIDE: an interactive and exact collision detection system for large-scale environments. Proceedings of the 1995 symposium on Interactive 3D graphics - SI3D '95. https://doi.org/10.1145/199404.199437

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.