A Sampling based 3D Point Cloud Compression Algorithm for Immersive Communication
3D point cloud is one of the most common and basic 3D object representation model that is widely used in virtual/augmented reality applications, e.g. immersive communication. Compression of 3D point cloud is a big challenge because of its huge data volume and irregular data structure. In this paper, we propose a sampling based compression algorithm for 3D point clouds. First, a 3D point cloud was resampled by a graph filter to obtain a subset of representative 3D points. Then, the representative points were compressed by the G PCC (geometry based point cloud compression) encoder software that was released by MPEG. Finally, the decoded representative points were used to reconstruct the original 3D point clouds by a CNN based up sampling approach. Experimental results demonstrate that a significant (73.15% bit rate reduction can be achieved by the proposed 3D point cloud compression algorithm with minimal quality degradation of the reconstructed 3D point clouds.
Mob. Networks Appl.