Embedded binary tree for dynamic point cloud geometry compression with graph signal resampling and prediction

Birendra Kathariya, Ainala Karthik, Zhu Li, Rajan Joshi

Abstract: This paper proposes a temporal prediction scheme for the compression of point cloud sequences with dynamic 3D geometry information. Since the point cloud sequences are non-uniformly sampled signals on a graph, it is difficult to apply traditional prediction schemes in video coding such as motion compensation. Instead, we propose a binary tree with embedded octree coding scheme that allows for motion compensation and residual coding at the level of binary tree leaf node. Graph signal processing based reference block re-sampling is employed to eliminate the need to signal the reference points, and a bipartite graph pruning algorithm is used for residual prediction. Simulation results demonstrate the effectiveness of the proposed scheme in a rate distortion sense.