  Most point cloud compression methods operate in the voxel or octree domain
which is not the original representation of point clouds. Those representations
either remove the geometric information or require high computational power for
processing. In this paper, we propose a context-based lossless point cloud
geometry compression that directly processes the point representation.
Operating on a point representation allows us to preserve geometry correlation
between points and thus to obtain an accurate context model while significantly
reduce the computational cost. Specifically, our method uses a sparse
convolution neural network to estimate the voxel occupancy sequentially from
the x,y,z input data. Experimental results show that our method outperforms the
state-of-the-art geometry compression standard from MPEG with average rate
savings of 52% on a diverse set of point clouds from four different datasets.
