Attribute Compression of 3D Point Clouds Using Laplacian Sparsity Optimized Graph Transform

TL;DR

This paper presents a novel graph transform method with Laplacian sparsity optimization for efficient compression of 3D point clouds, demonstrating improved energy compaction and rate-distortion performance.

Contribution

It introduces a new binary tree based partition and an optimized graph transform to enhance point cloud compression efficiency.

Findings

Demonstrates improved energy compaction over existing methods.

Achieves near-optimal rate-distortion performance.

Validates effectiveness on high-quality MPEG PCC point clouds.

Abstract

3D sensing and content capture 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 content 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 with the latest high quality point cloud content captured from the MPEG PCC demonstrated the transform efficiency and rate-distortion (R-D) optimal potential of the proposed solutions.