Graph Dictionary Learning for 3-D Point Cloud Compression

Xin Li, Wenrui Dai, Shaohui Li, Chenglin Li, Junni Zou, Hongkai Xiong

Abstract: 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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> -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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> -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.