Hierarchical Prior-based Super Resolution for Point Cloud Geometry Compression

• URL: http://arxiv.org/abs/2402.11250v1
• Date: Sat, 17 Feb 2024 11:15:38 GMT
• Title: Hierarchical Prior-based Super Resolution for Point Cloud Geometry Compression
• Authors: Dingquan Li and Kede Ma and Jing Wang and Ge Li
• Abstract summary: The Geometry-based Point Cloud Compression (G-PCC) has been developed by the Moving Picture Experts Group to compress point clouds. This paper proposes a hierarchical prior-based super resolution method for point cloud geometry compression.
• Score: 39.052583172727324
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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