Minimum Normal Cone for Continuous Collision Detection

Abstract

Continuous collision detection (CCD) algorithm is a vital step in virtual reality. This paper describes an efficient self-collision detection algorithm called the minimum normal cone algorithm. The algorithm calculates the parent-normal cone in a new way, which optimizes the two test conditions for the self-collision detection: In the first test condition, we analyze and calculate the relative position between the two child-normal cones, and then a bottom-up technology is used to build the parent-normal cone. Thus the two child normal cones can be surrounded by the parent-normal cone perfectly, and the normal vectors motion range of the parent-triangular mesh can also be closely restricted; In the second test condition, we calculate the normal vectors motion range of the contour projection line, and check whether the projection line is self-colliding or not, to avoid excessive tests between segments. The algorithm has been implemented and tested on two classical models, cloth-ball and N-body model respectively. Experimental results demonstrate that our algorithm can decrease the number of elementary tests by two orders of magnitudes, and significantly improve the performance of the overall CCD algorithm.