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In recent years, we have witnessed the presence of point cloud data in manyaspects of our life, from immersive media, autonomous driving to healthcare,although at the cost of a tremendous amount of data. In this paper, we presentan efficient lossless point cloud compression method that uses sparsetensor-based deep neural networks to learn point cloud geometry and colorprobability distributions. Our method represents a point cloud with bothoccupancy feature and three attribute features at different bit depths in aunified sparse representation. This allows us to efficiently exploitfeature-wise and point-wise dependencies within point clouds using a sparsetensor-based neural network and thus build an accurate auto-regressive contextmodel for an arithmetic coder. To the best of our knowledge, this is the firstlearning-based lossless point cloud geometry and attribute compressionapproach. Compared with the-state-of-the-art lossless point cloud compressionmethod from Moving Picture Experts Group (MPEG), our method achieves 22.6%reduction in total bitrate on a diverse set of test point clouds while having49.0% and 18.3% rate reduction on geometry and color attribute component,respectively.

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