  In this paper, we propose a two-stage deep learning framework called
VoxelContext-Net for both static and dynamic point cloud compression. Taking
advantages of both octree based methods and voxel based schemes, our approach
employs the voxel context to compress the octree structured data. Specifically,
we first extract the local voxel representation that encodes the spatial
neighbouring context information for each node in the constructed octree. Then,
in the entropy coding stage, we propose a voxel context based deep entropy
model to compress the symbols of non-leaf nodes in a lossless way. Furthermore,
for dynamic point cloud compression, we additionally introduce the local voxel
representations from the temporal neighbouring point clouds to exploit temporal
dependency. More importantly, to alleviate the distortion from the octree
construction procedure, we propose a voxel context based 3D coordinate
refinement method to produce more accurate reconstructed point cloud at the
decoder side, which is applicable to both static and dynamic point cloud
compression. The comprehensive experiments on both static and dynamic point
cloud benchmark datasets(e.g., ScanNet and Semantic KITTI) clearly demonstrate
the effectiveness of our newly proposed method VoxelContext-Net for 3D point
cloud geometry compression.
