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

• Published in: IEEE transactions on circuits and systems for video technology Vol. 33; no. 8; pp. 4337 - 4348
• Main Authors: Nguyen, Dat Thanh, Kaup, Andre
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Autoregressive models; CNeT; Color; Conditional probability; Context modeling; deep learning; G-PCC; generative probability model; Geometry; Image coding; Machine learning; Mathematical analysis; Neural networks; Octrees; Point cloud attribute coding; Point cloud compression; sparse convolution; Tensors; Three-dimensional displays; Transforms
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2023.3239321

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.