We present a novel radial-view-based culling method for continuous self-collision detection (CSCD) of skeletal models. Our method targets closed triangular meshes used to represent the surface of a model. It can be easily integrated with bounding volume hierarchies (BVHs) and used as the first stage for culling non-colliding triangle pairs. A mesh is decomposed into clusters with respect to a set of observer primitives (i.e., observer points and line segments) on the skeleton of the mesh so that each cluster is associated with an observer primitive. One BVH is then built for each cluster. At the runtime stage, a radial view test is performed from the observer primitive of each cluster to check its collision state. Every pair of clusters is also checked for collisions. We evaluated our method on various models and compared its performance with prior methods. Experimental results show that our method reduces the number of the bounding volume overlapping tests and the number of potentially colliding triangle pairs, thereby improving the overall process of CSCD.

Figure 1: (a) (b) The clusters are fixed in the radial view based culling (RVBC) method at runtime. (c) (d) Our method dynamically merges the atomic clusters to improve the overall performance of continuous self-collision detection over RVBC. The speedup of our method compared to RVBC is 5.2× in this octopus example. AbstractThe radial view-based culling (RVBC) method has been presented for continuous self-collision detection to efficiently cull away noncolliding regions. While this technique mainly relies on the segmented clusters of the reference pose and the associated fixed observer points, it has several drawbacks during the animation and the reduced cost of executing collision detection is limited. We thus present a modified framework to improve the culling efficiency of RVBC. At the preprocessing stage, we segment the closed deformable mesh according to not only the attached skeleton but also the triangle orientations, in order to minimize the collision checks of triangles in a cluster. At the runtime stage, we dynamically merge adjacent clusters and update the positions of observer points if the merged shape is nearly convex. This strategy minimizes the number of triangles in different clusters that required collision check. Our framework can be easily integrated with bounding volume hierarchies to boost the culling efficiency. Experimental results show that our framework achieves up to 5.2 times speedup over the original RVBC method and even more times over the recent techniques.

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