A Framework for Compressive-Sensing of 3D Point Clouds

In this paper we propose a framework for analyzing 3D point cloud data compression using compressive-sensing. This framework uses real 3D point clouds and investigates different error sources that may affect performance of the system. Experimental results show that excluding edge points from error calculation gives us better criteria to decide the best compression ratio in the system.