Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model

TL;DR

This paper introduces a novel lossless point cloud compression method using deep neural networks to learn probability distributions, achieving significant bitrate reductions compared to existing standards.

Contribution

It presents the first learning-based approach for lossless point cloud geometry and attribute compression utilizing sparse tensor neural networks.

Findings

Achieves 22.6% bitrate reduction over MPEG lossless methods.

Reduces geometry bitrate by 49.0%.

Reduces color attribute bitrate by 18.3%.

Abstract

In recent years, we have witnessed the presence of point cloud data in many aspects of our life, from immersive media, autonomous driving to healthcare, although at the cost of a tremendous amount of data. In this paper, we present an efficient lossless point cloud compression method that uses sparse tensor-based deep neural networks to learn point cloud geometry and color probability distributions. Our method represents a point cloud with both occupancy feature and three attribute features at different bit depths in a unified sparse representation. This allows us to efficiently exploit feature-wise and point-wise dependencies within point clouds using a sparse tensor-based neural network and thus build an accurate auto-regressive context model for an arithmetic coder. To the best of our knowledge, this is the first learning-based lossless point cloud geometry and attribute compression approach. Compared with the-state-of-the-art lossless point cloud compression method from Moving Picture Experts Group (MPEG), our method achieves 22.6% reduction in total bitrate on a diverse set of test point clouds while having 49.0% and 18.3% rate reduction on geometry and color attribute component, respectively.