Graph-based motion estimation and compensation for dynamic 3D point cloud compression
This paper addresses the problem of motion estimation in 3D point cloud sequences that are characterized by moving 3D positions and color attributes. Motion estimation is key to effective compression of these sequences, but it remains a challenging problem as the temporally successive frames have varying sizes without explicit correspondence information. We represent the time-varying geometry of these sequences with a set of graphs, and consider 3D positions and color attributes of the points clouds as signals on the vertices of the graph. We then cast motion estimation as a feature matching problem between successive graphs. The motion is estimated on a sparse set of representative vertices using new spectral graph wavelet descriptors. A dense motion field is eventually interpolated by solving a graph-based regularization problem. The estimated motion is finally used for color compensation in the compression of 3D point cloud sequences. Experimental results demonstrate that our method is able to accurately estimate the motion and to bring significant improvement in terms of color compression performance.