  The ever-increasing 3D application makes the point cloud compression
unprecedentedly important and needed. In this paper, we propose a patch-based
compression process using deep learning, focusing on the lossy point cloud
geometry compression. Unlike existing point cloud compression networks, which
apply feature extraction and reconstruction on the entire point cloud, we
divide the point cloud into patches and compress each patch independently. In
the decoding process, we finally assemble the decompressed patches into a
complete point cloud. In addition, we train our network by a patch-to-patch
criterion, i.e., use the local reconstruction loss for optimization, to
approximate the global reconstruction optimality. Our method outperforms the
state-of-the-art in terms of rate-distortion performance, especially at low
bitrates. Moreover, the compression process we proposed can guarantee to
generate the same number of points as the input. The network model of this
method can be easily applied to other point cloud reconstruction problems, such
as upsampling.
