K-IOS: Intersection of spheres for efficient proximity query

Abstract

We present a new bounding volume structure, k-IOS that is an intersection of k spheres, for accelerating proximity query including collision detection and Euclidean distance computation between arbitrary polygon-soup models that undergo rigid motion. Our new bounding volume is easy to implement and highly efficient both for its construction and runtime query. In our experiments, we have observed up to 4.0 times performance improvement of proximity query compared to an existing well-known algorithm based on swept sphere volume (SSV) [1]. Moreover, k-IOS is strictly convex that can guarantee a continuous gradient of distance function with respect to object's configuration parameter.