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Inspired by the recently proposed successive subspace learning (SSL)principles, we develop a successive subspace graph transform (SSGT) to addresspoint cloud attribute compression in this work. The octree geometry structureis utilized to partition the point cloud, where every node of the octreerepresents a point cloud subspace with a certain spatial size. We design aweighted graph with self-loop to describe the subspace and define a graphFourier transform based on the normalized graph Laplacian. The transforms areapplied to large point clouds from the leaf nodes to the root node of theoctree recursively, while the represented subspace is expanded from thesmallest one to the whole point cloud successively. It is shown by experimentalresults that the proposed SSGT method offers better R-D performances than theprevious Region Adaptive Haar Transform (RAHT) method.

Point Cloud Attribute Compression via Successive Subspace Graph Transform