Attribute compression of 3D point clouds using Laplacian sparsity optimized graph transform
3D sensing and content capturing have made significant progress in recent years and the MPEG standardization organization is launching a new project on immersive media with point cloud compression (PCC) as one key corner stone. In this work, we introduce a new binary tree based point cloud partition and explore the graph signal processing tools, especially the graph transform with optimized Laplacian sparsity, to achieve better energy compaction and compression efficiency. The resulting rate-distortion operating points are convex-hull optimized over the existing Lagrangian solutions. Simulation results on the latest high quality point cloud content from the MPEG PCC demonstrate the transform efficiency and rate-distortion (R-D) optimal potential of the proposed solutions.