Neural Network Modeling of Probabilities for Coding the Octree Representation of Point Clouds

This paper describes a novel lossless point cloud
compression algorithm that uses a neural network for estimating
the coding probabilities for the occupancy status of voxels,
depending on wide three dimensional contexts around the voxel
to be encoded. The point cloud is represented as an octree, with
each resolution layer being sequentially encoded and decoded
using arithmetic coding, starting from the lowest resolution, until
the final resolution is reached. The occupancy probability of
each voxel of the splitting pattern at each node of the octree
is modeled by a neural network, having at its input the already
encoded occupancy status of several octree nodes (belonging to
the past and current resolutions), corresponding to a 3D context
surrounding the node to be encoded. The algorithm has a fast
and a slow version, the fast version selecting differently several
voxels of the context, which allows an increased parallelization by
sending larger batches of templates to be estimated by the neural
network, at both encoder and decoder. The proposed algorithms
yield state-of-the-art results on benchmark datasets. The imple-
mentation will be available at https://github.com/marmus12/nnct