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.

翻译：运动图像专家组开发了基于几何的点云压缩（G-PCC）标准，用于压缩点云数据。在其有损模式下，由于简单的几何量化（即网格下采样），G-PCC重建的点云常出现明显失真。本文提出了一种基于层次先验的超分辨率方法，用于点云几何压缩。编码端构建了内容相关的层次先验，使解码端能够对点云几何进行从粗到细的超分辨率重建。更准确的先验通常能提升重建性能，但需要消耗更多比特数来编码这一辅助信息。通过合理平衡先验精度与比特消耗，该方法在MPEG Cat1A数据集上实现了显著的Bjontegaard-delta码率节省，性能优于基于八叉树和三角面片的G-PCC v14版本。我们提供了可复现研究的实现代码：https://github.com/lidq92/mpeg-pcc-tmc13。