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Kinetic Sweep and Prune for Collision Detection
Workshop On Virtual Reality Interaction and Physical Simulation (2005)
F. Ganovelli and C. Mendoza (Editors)
Kinetic Sweep and Prune for Collision Detection
Daniel S. Coming and Oliver G. Staadt
Institute for Data Analysis and Visualization
Department of Computer Science
University of California, Davis
Abstract
We propose an acceleration scheme for real-time many-body dynamic collision detection. We kinetize the sweep
and prune method for many-body collision pruning, extending its application to dynamic collision detection via
kinetic data structures. In doing so, we modify the method from sample-rate driven to event-driven, with no more
events than the original method processed, also removing the per-frame overhead, allowing our method to scale
well in terms of frame-rates. Unlike many schemes for many-body collision pruning, ours performs well in both
sparse and dense environments, with few or many collisions.
Categories and Subject Descriptors (according to ACM CCS): I.3.6 [Computer Graphics]: Methodology and
Techniques—Graphics data structures and data types; I.3.7 [Computer Graphics]: Three-Dimensional Graphics
and Realism—Virtual reality
General Terms: Algorithms, Performance, Reliability
Additional Key Words and Phrases: dynamic collision detection and kinetic data structures
1. Introduction
Collision detection is a common and often critical require-
ment for virtual and augmented reality, haptics, and physical
simulations. The work of collision detection is to determine
if/when objects intersect, or interact, which is useful in pro-
viding realistic environments, accurate simulations, and both
appropriate and robust user interactions. The level of accu-
racy in collision detection directly affects each of the afore-
mentioned properties. While some situations involve rela-
tively few objects, it is common to find scenarios with hun-
dreds or more objects. Scenarios requiring many-body colli-
sion detection include: large or complex environments, col-
laborat
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