Multi-Scale end-to-End Learning for Point Cloud Geometry Compression

As 3D scanning devices and depth sensors advance, point clouds have attracted increasing attention as a format for 3D representation. Nevertheless, the tremendous amount of data in point clouds significantly burden transmission and storage. To address these problems, we propose a multi-scale end-to-end framework for point cloud geometry compression. Firstly, point transformer is used to extract the global feature of geometry information, embedding the geometry information and the relation among points. Secondly, the multi-scale neighbor embedding strategy is used to extract the level of details within multi-scale and multi-resolution feature of point clouds. Finally, to reconstruct the point cloud with high quality in the decoder, the local spatial information is restored via graph spatial extension based on local down-sampled features and global features. Experimental results show that we achieve around 34% bit rate reduction on average over competitive point cloud geometry compression methods.