| Summary: | 碩士 === 國立交通大學 === 多媒體工程研究所 === 101 === In this thesis, we propose a new method to accelerate self-collision detection with closed objects. Our method includes object decomposition and detector-based collision detection. In detector-based collision detection, it utilizes a point that inside a region to check all triangles?稛!!!@brvbar; orientation of this region. And this is a method that performs self-collision detection based on triangles?稛!!!@brvbar; orientation of this region. On the part of object decomposition,
we analyze the physical property of an object by computing its charge distribution in the preprocessing phase. The charge distribution of an object could present strength of structure of the object. A region with less charge means that the region is concave part at the local area. We regard the region as lower structural strength at the local area, and it is easily deformed region of an object. On the other hand, a region with more charge indicates that the region is convex part at the local area. We regard the region as higher structural strength at the local area. We segment an object into several not easily deformed regions based on structural intensity of the object. In the simulation phase, we perform view-based self-collision detection on these segmented regions and inter-collision detection between each region. We use some different experiments to compare our method with K-means decomposition method and our implementation on ICCD. The experiment results show that our approach is more stable than K-means decomposition method. Compared to ICCD, our method improves self-collision detection by a factor of 1.88X ∼ 2.19X.
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