Distance-Based Probability Model for Octree Coding

Abstract

We present a context-driven method to encode nodes of an octree, which is typically used to encode the point-cloud geometry. Instead of using one bit per node of the tree, the context allows for deriving probabilities for that node based on distances of the actual voxel to the voxels in a reference point cloud. Accurate probabilities of the node state allow for the use of an arithmetic coder to reduce the bit rate. Results point to potentially large reductions in rate if there is a good model from which to derive the context, i.e., one can get large reductions if the reference-cloud geometry is close enough to the one being encoded.