Point Cloud Attribute Compression via Successive Subspace Graph Transform
This work addresses compression for point cloud data, which is incremental as it builds on existing SSL principles to improve upon prior methods.
The paper tackled point cloud attribute compression by developing a successive subspace graph transform (SSGT) based on octree partitioning, and it showed better rate-distortion performance than the previous RAHT method in experiments.
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.