Method and system for compressing and decompressing digital three-dimensional point cloud data

摘要

Compressing (and/or decompressing) 3D image data (e.g. real world scene) comprising a point cloud (600), each point having one or more attributes, comprises encoding (620) the position data using an octree decomposition (610). Encoding the attribute data involves constructing (630) a relative neighbourhood graph (an undirected graph) for the points in each sub-octree partitioned from the octree, wherein the attributes of each point are signals on the graph. A combinatorial Laplacian matrix of each graph is calculated, as is an eigenvector decomposition for each matrix. A graph Fourier Transform of each attribute for each graph based on the corresponding eigenvector decomposition is then calculated (640), with a portion (e.g. at least 50%, 70% or 90%) of each graph Fourier Transform then being discarded. Each reduced graph Fourier Transform is compressed (650) (e.g. entropy encoded) to provide compressed point cloud attribute data, and a bit stream is generated corresponding to octree voxels occupancy. The bit stream is compressed (e.g. entropy encoded), providing (660) compressed point cloud position and attribute data. The octree may have a depth corresponding to a point cloud resolution level such that each voxel in the point cloud contains no more than one point.