These figures show two complex and large-scale fracturing benchmarks that have topological
changes. (Left) Three frames of a breaking dragon benchmark that
consists of 252K triangles throughout the simulation. (Right) Three frames of a
breaking-wall benchmark that starts with 42K triangles and ends
with 140K triangles. Our method spends 252ms and 97ms for discrete collision
detection including self-collision detections of these dragon and
wall benchmarks respectively by using a single CPU-thread. Moreover, we show
a more stable performance by achieving up to two orders of magnitude
performance improvement at fracturing events where deforming meshes change their
topologies.
We release our fracturing benchmarks (Click figure)
Abstract
We present a collision detection (CD) method for complex and large-scale
fracturing models that have geometric and topological changes. We first
propose a novel dual-cone culling method to improve the performance of CD,
especially self-collision detection among fracturing models. Our dual-cone
culling method has a small computational overhead and a conservative algorithm.
Combined with bounding volume hierarchies (BVHs), our dual-cone culling method
becomes approximate. However, we found that our method does not miss any
collisions in the tested benchmarks.
We also propose a novel, selective restructuring
method that improves the overall performance of CD and reduces
performance degradations at fracturing events. Our restructuring method is
based on a culling efficiency metric that measures the expected number of
overlap tests of a BVH. To further reduce the performance degradations at
fracturing events, we also propose a novel, fast BVH construction method that builds
multiple levels of the hierarchy in one iteration using a grid and
hashing.
We test our method with four different large-scale deforming benchmarks. Compared to
the state-of-the-art methods, our method shows a more stable performance for CD
by improving the performance by a factor of up to two orders of magnitude at
frames when deforming models change their mesh topologies.
@inproceedings{JP10,
author = {Jae-Pil Heo, Joon-Kyung Seong, DukSu Kim, Miguel A. Otaduy, Jeong-Mo Hong, Min Tang, Sung-Eui Yoon},
title = {{FASTCD}: Fracturing-Aware Stable Collision Detection},
booktitle = {SCA '10: Proceedings of the 2010 ACM SIGGRAPH / Eurographics Symposium on Computer Animation},
year = {2010}
}
