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gProximity: Hierarchical GPU‐based Operations for Collision and Distance Queries
Author(s) -
Lauterbach C.,
Mo Q.,
Manocha D.
Publication year - 2010
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2009.01611.x
Subject(s) - computer science , bounding volume , collision detection , tree traversal , cuda , parallel computing , bounding overwatch , computation , collision , algorithm , computational science , titan (rocket family) , rendering (computer graphics) , computer graphics (images) , artificial intelligence , computer security , engineering , aerospace engineering
We present novel parallel algorithms for collision detection and separation distance computation for rigid and deformable models that exploit the computational capabilities of many‐core GPUs. Our approach uses thread and data parallelism to perform fast hierarchy construction, updating, and traversal using tight‐fitting bounding volumes such as oriented bounding boxes (OBB) and rectangular swept spheres (RSS). We also describe efficient algorithms to compute a linear bounding volume hierarchy (LBVH) and update them using refitting methods. Moreover, we show that tight‐fitting bounding volume hierarchies offer improved performance on GPU‐like throughput architectures. We use our algorithms to perform discrete and continuous collision detection including self‐collisions, as well as separation distance computation between non‐overlapping models. In practice, our approach (gProximity) can perform these queries in a few milliseconds on a PC with NVIDIA GTX 285 card on models composed of tens or hundreds of thousands of triangles used in cloth simulation, surgical simulation, virtual prototyping and N‐body simulation. Moreover, we observe more than an order of magnitude performance improvement over prior GPU‐based algorithms.