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.

该论文介绍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，通过将点云分解为基点云和插值模式，用于重构原始点云。通过训练超分辨率网络来获取插值模式的信息，然后将网络参数传输到解码器以帮助点云重构。实验证明了该方法在 MPEG Cat1 和 Cat2 数据集上具有出色的压缩性能。