Dynamic Radial View Based Culling for Continuous Self-Collision Detection

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. The radial view-based culling (RVBC) method has been presented for continuous self-collision detection to efficiently cull away non-colliding regions. While this technique mainly relies on the seg-mented clusters of the reference pose and the associated fixed ob-server 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 de-formable 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 vol-ume 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 tech-niques