We present a new hybrid CPU/GPU collision detection technique for rigid and deformable objects based on spatial subdivision. Our approach efficiently exploits the massive computational capabilities of modern CPUs and GPUs commonly found in off-the-shelf computer systems. The algorithm is specifically tailored to be highly scalable on both the CPU and the GPU sides. We can compute discrete and continuous external and self-collisions of non-penetrating rigid and deformable objects consisting of many tens of thousands of triangles in few milliseconds on a modern PC. Our approach is orders of magnitude faster than earlier CPU-based approaches and up to twice as fast as the most recent GPU-based techniques.
@article{PabstKS10,
author = {Simon Pabst and Artur Koch and Wolfgang Stra{\ss}er},
title = {Fast and Scalable {CPU}/{GPU} Collision Detection for Rigid and Deformable
Surfaces},
journal = {Computer Graphics Forum},
year = {2010},
volume = {29},
pages = {1605-1612},
number = {5},
abstract = {We present a new hybrid CPU/GPU collision detection technique for
rigid and deformable objects based on spatial subdivision. Our approach
efficiently exploits the massive computational capabilities of modern
CPUs and GPUs commonly found in off-the-shelf computer systems. The
algorithm is specifically tailored to be highly scalable on both
the CPU and the GPU sides. We can compute discrete and continuous
external and self-collisions of non-penetrating rigid and deformable
objects consisting of many tens of thousands of triangles in few
milliseconds on a modern PC. Our approach is orders of magnitude
faster than earlier CPU-based approaches and up to twice as fast
as the most recent GPU-based techniques.},
doi = {10.1111/j.1467-8659.2010.01769.x},
pdf = {http://www.gris.uni-tuebingen.de/people/staff/spabst/pdf/hashing-SGP10.pdf},
}