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gProximity: Hierarchical GPU-based Operations for Collision and Distance Queries

C. Lauterbach, Q. Mo, D. Manocha
University of North Carolina at Chapel Hill
Computer Graphics Forum, Vol. 29, No. 2., pp. 419-428
@conference{lauterbach2010gproximity,

   title={gProximity: Hierarchical GPU-based Operations for Collision and Distance Queries},

   author={Lauterbach, C. and Mo, Q. and Manocha, D.},

   booktitle={Computer Graphics Forum},

   volume={29},

   number={2},

   pages={419–428},

   issn={1467-8659},

   year={2010},

   organization={Wiley Online Library}

}

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.
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