  This paper proposes a lossless point cloud (PC) geometry compression method
that uses neural networks to estimate the probability distribution of voxel
occupancy. First, to take into account the PC sparsity, our method adaptively
partitions a point cloud into multiple voxel block sizes. This partitioning is
signalled via an octree. Second, we employ a deep auto-regressive generative
model to estimate the occupancy probability of each voxel given the previously
encoded ones. We then employ the estimated probabilities to code efficiently a
block using a context-based arithmetic coder. Our context has variable size and
can expand beyond the current block to learn more accurate probabilities. We
also consider using data augmentation techniques to increase the generalization
capability of the learned probability models, in particular in the presence of
noise and lower-density point clouds. Experimental evaluation, performed on a
variety of point clouds from four different datasets and with diverse
characteristics, demonstrates that our method reduces significantly (by up to
30%) the rate for lossless coding compared to the state-of-the-art MPEG codec.
