Adaptive Bounding Volume Hierarchies For Deformable Surface Models

Abstract

This master’s thesis explores a new mechanism for maintaining Bounding-Volume Hierarchies (BVH) of deformable surface models. Typical algorithms found in the literature are based on refitting only a portion of the BVH, leaving sometimes a large portion of the Bounding Volumes (BVs) inaccurately representing the parts of the object they should enclose. The algorithm proposed in this thesis allows the BVH’s quality to degrade as the model it represents deforms, while guaranteeing that every point in the model is contained within the BVH at all times, and thus maintaining the accuracy of any collision detection or distance measurement queries performed on the model. Through a tunable asynchronous refitting of the individual bounding volumes, the algorithm offers a computationally efficient, low memory cost solution to the accurate simulation of deformable surface models in real environments. The decision criteria for the refitting of the BVs along with the parameters of these criteria are optimized through a Genetic Algorithm search. The resulting algorithm is shown to outperform the most commonly referred to BVH-based algorithm referred to in the literature.