  In this paper, we propose a novel variable rate deep compression architecture
that operates on raw 3D point cloud data. The majority of learning-based point
cloud compression methods work on a downsampled representation of the data.
Moreover, many existing techniques require training multiple networks for
different compression rates to generate consolidated point clouds of varying
quality. In contrast, our network is capable of explicitly processing point
clouds and generating a compressed description at a comprehensive range of
bitrates. Furthermore, our approach ensures that there is no loss of
information as a result of the voxelization process and the density of the
point cloud does not affect the encoder/decoder performance. An extensive
experimental evaluation shows that our model obtains state-of-the-art results,
it is computationally efficient, and it can work directly with point cloud data
thus avoiding an expensive voxelized representation.
