FASTCD

Abstract

Simulating realistic fractures in deforming models is one of the main challenges in computer animation, sculpting in CAD, virtual surgery, etc. Simulating such complex phenomena requires collision detection methods to avoid any inter-collisions among deforming models and self-collisions within each deforming model. Moreover, fractures change the mesh connectivity , in addition to changing the geometry. Also, at a fracture or merge event, multiple parts of the same object can appear in a close proximity, causing a higher computation time for collision detection. As a result, collision detection including self-collision detection is typically the main computational bottleneck of simulating these complex phenomena. In this thesis, we present a collision detection 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 collision detection, 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 collision detection 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 coll...