Lightweight super resolution network for point cloud geometry
  compression
Wei Zhang Dingquan Li Ge Li Wen Gao
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.