This paper presents an approach for compressing point cloud geometry by leveraging a lightweight super-resolution network. The proposed method involves decomposing a point cloud into a base point cloud and the interpolation patterns for reconstructing the original point cloud. While the base point cloud can be efficiently compressed using any lossless codec, such as Geometry-based Point Cloud Compression, a distinct strategy is employed for handling the interpolation patterns. Rather than directly compressing the interpolation patterns, a lightweight super-resolution network is utilized to learn this information through overfitting. Subsequently, the network parameter is transmitted to assist in point cloud reconstruction at the decoder side. Notably, our approach differentiates itself from lookup table-based methods, allowing us to obtain more accurate interpolation patterns by accessing a broader range of neighboring voxels at an acceptable computational cost. Experiments on MPEG Cat1 (Solid) and Cat2 datasets demonstrate the remarkable compression performance achieved by our method.

翻译：本文提出了一种利用轻量级超分辨率网络对点云几何进行压缩的方法。该方法通过将原始点云分解为基点点云及用于重建原始点云的插值模式来实现压缩。其中，基点点云可使用任意无损编解码器（如基于几何的点云压缩）进行高效压缩，而针对插值模式则采用专门策略处理。不同于直接压缩插值模式，该方法利用轻量级超分辨率网络通过过拟合方式学习插值信息，并将网络参数传输至解码端辅助点云重建。值得注意的是，本方法与基于查找表的方法存在本质区别，能够以可接受的计算成本访问更广泛的邻近体素，从而获得更精确的插值模式。在MPEG Cat1（实体）与Cat2数据集上的实验表明，所提方法取得了显著的压缩性能。