Block-Based Motion Estimation Speedup for Dynamic Voxelized Point Clouds

Motion estimation is a key component in dynamic point cloud analysis and compression. We present a method for reducing motion estimation computation when processing block-based partitions of temporally adjacent point clouds. We propose the use of an occupancy map containing information regarding size or other higher-order local statistics of the partitions. By consulting the map, the estimator may significantly reduce its search space, avoiding expensive block-matching evaluations. To form the maps we use 3D moment descriptors efficiently computed with one-pass update formulas and stored as scalar-values for multiple, subsequent references. Results show that a speedup of 2 produces a maximum distortion dropoff of less than 2% for the adopted PSNR-based metrics, relative to distortion of predictions attained from full search. Speedups of 5 and 10 are achievable with small average distortion dropoffs, less than 3% and 5%, respectively, for the tested data set.