Kinetic Bounding Volume Hierarchies for Collision Detection of Deformable Objects

We present novel algorithms for updating bounding volume hierarchies of objects undergoing arbitrary deformations. Therefore, we introduce two new data structures, the kinetic AABB tree and the kinetic BoxTree. The event-based approach of the kinetic data structures framework enables us to show that our algorithms are optimal in the number of updates. Moreover, we show a lower bound for the total number of BV updates, which is independent of the number of frames. Furthermore, we present a kinetic data structures which uses the kinetic AABB tree for collision detection and show that this structure can be easily extended for continuous collision detection of deformable objects. We performed a comparison of our kinetic approaches with the classical bottom-up update method. The results show that our algorithms perform up to ten times faster in practically relevant scenarios.