  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.
