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Hierarchical Prior-Based Super Resolution for Point Cloud Geometry Compression
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DOI：10.1109/TIP.2024.3372464.png)
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The Geometry-based Point Cloud Compression (G-PCC) has been developed by the Moving Picture Experts Group to compress point clouds efficiently. Nevertheless, in its lossy mode, the reconstructed point cloud by G-PCC often suffers from noticeable distortions due to naive 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, albeit at the cost of increased bits required to encode this piece of side information. Our experiments on the MPEG Cat1A dataset demonstrate substantial Bjontegaard-delta bitrate savings, surpassing the performance of the octree-based and trisoup-based G-PCC v14. We provide our implementations for reproducible research at https://github.com/lidq92/mpeg-pcc-tmc13.
Keyword:
Point cloud compression
Geometry
Superresolution
Three-dimensional displays
Image coding
Image reconstruction
Decoding
Point cloud geometry compression
hierarchical prior
coarse-to-fine super resolution
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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