BSH for Collision Detection in Point Cloud models
This addresses computational and modeling issues for applications using 3D scanning and point cloud rendering, though it appears incremental as it builds on existing structures like R-trees.
The paper tackles the problem of collision detection in large point cloud models, which lack extensive literature, by proposing an algorithm using voxels, octrees, and bounding sphere hierarchies (BSH) to reduce bounding volume checks and updates, effectively finding intersections.
Point cloud 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 large point cloud models using voxels, octrees and bounding spheres hierarchies (BSH). The scene graph is divided in voxels. The objects of each voxel are organized into an octree. Due to the high number of points in the scene, each non-empty cell of the octree is organized in a bounding sphere hierarchy, based on an R-tree hierarchy like structure. The BSH hierarchies are used to group neighboring points and filter out very quickly parts of objects that do not interact with other models. 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 our 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.