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

arXiv: https://arxiv.org/abs/2106.00828 · HTML: https://mlsanity.com/paper/2106.00828/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [Comments on "Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform"](https://mlsanity.com/paper/1805.09146/) (2018)

## Built on by (most cited first, 2 of 2)

- [Neural Network Modeling of Probabilities for Coding the Octree Representation of Point Clouds](https://mlsanity.com/paper/2106.06482/) (2021)
- [Lossless Compression of Point Cloud Sequences Using Sequence Optimized CNN Models](https://mlsanity.com/paper/2206.01297/) (2022)

## Abstract (arXiv)

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.