BSH for Collision Detection in Point Cloud models

TL;DR

This paper introduces a novel collision detection algorithm for large point cloud models that leverages voxel, octree, and bounding sphere hierarchies to efficiently identify intersections, addressing challenges posed by unsegmented laser scan data.

Contribution

The paper presents a new collision detection method for point clouds using a hierarchical BSH approach, improving efficiency over existing techniques.

Findings

Reduces the number of bounding volume checks needed

Effectively finds intersections in large point cloud models

Addresses computational issues with unsegmented laser scan data

Abstract

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.