Efficient Collision Detection Using a Dual K-DOP-Sphere Bounding Volume Hierarchy

Collision detection is of paramount importance for many applications in computer graphics and visualization. In this research, we present an efficient algorithm for collision detection using a dual bounding hierarchy which consists of an discrete orientation polytopes (k-DOPs) tree enhanced with bounding sphere. The algorithm combines the compactness of the k-DOP and the efficient overlap test for spheres. The more efficient sphere test is applied firstly to eliminate distant objects. The remaining objects are in close proximity are tested using separation axis, where some separation axis are more effective and should be chosen first. We apply the efficient approach to the virtual acupuncture medical treatment systems, and the experimental results show that the new algorithm effectively reduces the query time and enhances the reality character with respect to the existing collection detection algorithms.