The pcl_octree library provides efficient methods for creating a hierarchical tree data structure from point cloud data. This enables spatial partitioning, downsampling and search operations on the point data set. Each octree node the has either eight children or no children. The root node describes a cubic bounding box which encapsulates all points. At every tree level, this space becomes subdivided by a factor of 2 which results in an increased voxel resolution.
The pcl_octree implementation provides efficient nearest neighbor search routines, such as "Neighbors within Voxel Search”, “K Nearest Neighbor Search” and “Neighbors within Radius Search”. It automatically adjusts its dimension to the point data set. A set of leaf node classes provide additional functionality, such as spacial "occupancy" and "point density per voxel" checks. Functions for serialization and deserialization enable to efficiently encode the octree structure into a binary format. Furthermore, a memory pool implementation reduces expensive memory allocation and deallocation operations in scenarios where octrees needs to be created at high rate.
The following figure illustrates the voxel bounding boxes of an octree nodes at lowest tree level. The octree voxels are surrounding every 3D point from the bunny's surface. The red dots represent the point data. This image is create with the octree_viewer (visualization/tools/octree_viewer).
For examples how to use the pcl_octree library, please visit the pcl tutorial page.