1 research outputs found
Point Cloud Attribute Compression via Successive Subspace Graph Transform
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.Comment: Accepted by VCIP 202