Refining the bounding volumes for lossless compression of voxelized point clouds geometry
This work addresses efficient storage and transmission of 3D data for applications like virtual reality or autonomous driving, but it is incremental as it builds on existing lossy compression techniques.
The paper tackles lossless compression of voxelized point cloud geometry by extending a prior lossy method, achieving state-of-the-art bits-per-voxel results on benchmark datasets.
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.