Nguyen TD, Kaup A (2023)
Publication Type: Journal article
Publication year: 2023
Pages Range: 1-1
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.
Nguyen, T.D., & Kaup, A. (2023). Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model. IEEE Transactions on Circuits and Systems For Video Technology, 1-1. https://dx.doi.org/10.1109/TCSVT.2023.3239321
Nguyen, Thanh Dat, and André Kaup. "Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model." IEEE Transactions on Circuits and Systems For Video Technology (2023): 1-1.