Hierarchical Prior-based Super Resolution for Point Cloud Geometry
  Compression
Dingquan Li Kede Ma Jing Wang Ge Li
The Geometry-based Point Cloud Compression (G-PCC) has been developed by the
Moving Picture Experts Group to compress point clouds. In its lossy mode, the
reconstructed point cloud by G-PCC often suffers from noticeable distortions
due to the na\"{i}ve geometry quantization (i.e., grid downsampling). This
paper proposes a hierarchical prior-based super resolution method for point
cloud geometry compression. The content-dependent hierarchical prior is
constructed at the encoder side, which enables coarse-to-fine super resolution
of the point cloud geometry at the decoder side. A more accurate prior
generally yields improved reconstruction performance, at the cost of increased
bits required to encode this side information. With a proper balance between
prior accuracy and bit consumption, the proposed method demonstrates
substantial Bjontegaard-delta bitrate savings on the MPEG Cat1A dataset,
surpassing the octree-based and trisoup-based G-PCC v14. We provide our
implementations for reproducible research at
https://github.com/lidq92/mpeg-pcc-tmc13.