Graph Dictionary Learning for 3-D Point Cloud Compression

3-D point clouds rendering solid representations of scenes or objects often carry a tremendous amount of points, compulsorily requesting high-efficiency compression for storage and transmission. In this paper, we propose a novel $p$ -Laplacian embedding graph dictionary learning algorithm for 3-D point cloud attribute compression. The proposed method integrates the underlying graph topology to the learned graph dictionary capitalizing on $p$ -Laplacian eigenfunctions and leads to parsimonious representations of 3-D point clouds. We further devise alternating optimization with the help of ADMM to efficiently solve the resulting non-convex minimization problem.