An Efficient Collision Detection Algorithm for Point Cloud Models

Point clouds models are a common shape representation for several reasons. Three-dimensional scanning devices are widely used nowadays and points are an attractive primitive for rendering complex geometry. Nevertheless, there is not much literature on collision detection for point cloud models. This paper presents a novel collision detection algorithm for point cloud models. The scene graph is divided in voxels. The objects of each voxel are organized in R-trees hierarchies of Axis-Aligned Bounding Boxes to group neighboring points and filter out very quickly parts of objects that do not interact with other models. The proposed algorithm also uses Overlapping Axis-Aligned Bounding Boxes to improve the performance of the collision detection process. Points derived from laser scanned data typically are not segmented and can have arbitrary spatial resolution thus introducing computational and modeling issues. We address these issues and results show that the proposed collision detection algorithm effectively finds intersections between point cloud models since it is able to reduce the number of bounding volume checks and updates.