Refining the bounding volumes for lossless compression of voxelized point clouds geometry

TL;DR

This paper introduces a lossless point cloud compression method that enhances partial geometry reconstruction from depthmaps with a novel encoding scheme, achieving state-of-the-art compression rates.

Contribution

It presents a new lossless compression scheme that combines partial geometry reconstruction from depthmaps with a novel 3D context coding method exploiting rotational invariances.

Findings

Achieves state-of-the-art bits-per-voxel compression on benchmark datasets.

Utilizes a list-based encoding with a novel arithmetic 3D context coding.

Effectively reconstructs full point clouds from partial depthmap data.

Abstract

This paper describes a novel lossless compression method for point cloud geometry, building on a recent lossy compression method that aimed at reconstructing only the bounding volume of a point cloud. The proposed scheme starts by partially reconstructing the geometry from the two depthmaps associated to a single projection direction. The partial reconstruction obtained from the depthmaps is completed to a full reconstruction of the point cloud by sweeping section by section along one direction and encoding the points which were not contained in the two depthmaps. The main ingredient is a list-based encoding of the inner points (situated inside the feasible regions) by a novel arithmetic three dimensional context coding procedure that efficiently utilizes rotational invariances present in the input data. State-of-the-art bits-per-voxel results are obtained on benchmark datasets.