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Subspace Self-Collision Culling
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DOI：10.1145/1778765.1778818.png)
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摘要
En 中文
We show how to greatly accelerate self-collision detection (SCD) for reduced deformable models. Given a triangle mesh and a set of deformation modes, our method precomputes Subspace Self-Collision Culling (SSCC) certificates which, if satisfied, prove the absence of self-collisions for large parts of the model. At run-time, bounding volume hierarchies augmented with our certificates can aggressively cull overlap tests and reduce hierarchy updates. Our method supports both discrete and continuous SCD, can handle complex geometry, and makes no assumptions about geometric smoothness or normal bounds. It is particularly effective for simulations with modest subspace deformations, where it can often verify the absence of self-collisions in constant time. Our certificates enable low amortized costs, in time and across many objects in multibody dynamics simulations. Finally, SSCC is effective enough to support self-collision tests at audio rates, which we demonstrate by producing the first sound simulations of clattering objects.
Keyword:
self-collision detection
model reduction
kinetic data structures
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9.5
论文数:
4.7K
被引数:
3.6W
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