Point Cloud Attribute Compression via Successive Subspace Graph Transform

TL;DR

This paper introduces a novel successive subspace graph transform (SSGT) for point cloud attribute compression, leveraging octree structures and graph Fourier transforms to improve rate-distortion performance over existing methods.

Contribution

The paper proposes a new SSGT method based on SSL principles and graph Fourier transforms, enhancing point cloud attribute compression efficiency.

Findings

SSGT outperforms RAHT in rate-distortion performance

Utilizes octree structure for hierarchical subspace processing

Employs graph Fourier transform for attribute compression

Abstract

Inspired by the recently proposed successive subspace learning (SSL) principles, we develop a successive subspace graph transform (SSGT) to address point cloud attribute compression in this work. The octree geometry structure is utilized to partition the point cloud, where every node of the octree represents a point cloud subspace with a certain spatial size. We design a weighted graph with self-loop to describe the subspace and define a graph Fourier transform based on the normalized graph Laplacian. The transforms are applied to large point clouds from the leaf nodes to the root node of the octree recursively, while the represented subspace is expanded from the smallest one to the whole point cloud successively. It is shown by experimental results that the proposed SSGT method offers better R-D performances than the previous Region Adaptive Haar Transform (RAHT) method.