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The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Efficient Collision Detection of Complex Deformable Models using AABB Trees

Bergen, Gino van den·1997·Journal of Graphics Tools

Abstract

We present a scheme for exact collision detection between complex models undergoing rigid motion and deformation. The scheme relies on a hierarchical model representation using axis-aligned bounding boxes (AABBs). Recent work has shown that AABB trees are slower than oriented bounding box (OBB) trees for performing overlap tests. In this paper, we describe a way to speed up overlap tests between AABBs, such that for collision detection of rigid models, the difference in performance between the two representations is greatly reduced. Furthermore, we show how to update an AABB tree quickly as a model is deformed. We thus find AABB trees to be the method of choice for collision detection of complex models undergoing deformation. In fact, because they are not much slower to test, are faster to build, and use less storage than OBB trees, AABB trees might be a reasonable choice for rigid models as well.

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Crossref · 10.1080/10867651.1997.10487480

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Metadata

Authors
Bergen, G. V. D.
Venue
Journal of Graphics Tools
Published
1997-01
Volume
2
Issue
4
Pages
1-13
ISSN
1086-7651 (ISSN-L)
OpenAlex
W2123703543
Cited by
801
References
8
FWCI
2.31
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Efficient Collision Detection of Complex Deformable Models using AABB Trees. https://app.cochat.ai/papers/10.1080/10867651.1997.10487480
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Bergen, G. V. D. (1997). Efficient Collision Detection of Complex Deformable Models using AABB Trees. Journal of Graphics Tools. https://doi.org/10.1080/10867651.1997.10487480

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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