Hierarchical Prior-based Super Resolution for Point Cloud Geometry Compression
<|reference_start|>Hierarchical Prior-based Super Resolution for Point Cloud Geometry Compression: 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.<|reference_end|>
@article{li2024hierarchical,
    title={Hierarchical Prior-based Super Resolution for Point Cloud Geometry
  Compression},
    author={Dingquan Li and Kede Ma and Jing Wang and Ge Li},
    journal={arXiv preprint arXiv:2402.11250},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11250},
    primaryClass={eess.IV cs.CV cs.MM}
}