Local Texture and Geometry Descriptors for Fast Block-Based Motion Estimation of Dynamic Voxelized Point Clouds

• Published in: 2019 IEEE International Conference on Image Processing (ICIP) pp. 3721 - 3725
• Main Authors: Dorea, Camilo, Hung, Edson M., de Queiroz, Ricardo L.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-09-2019
• Subjects: Distortion; Geometry; Germanium; Image color analysis; Measurement; motion estimation; Point clouds; Proposals; Three-dimensional displays; volumetric media
• ISSN: 2381-8549
• DOI: 10.1109/ICIP.2019.8803690

Motion estimation in dynamic point cloud analysis or compression is a computationally intensive procedure generally involving a large search space and often complex voxel matching functions. We present an extension and improvement on prior work to speed up block-based motion estimation between temporally adjacent point clouds. We introduce local, or block-based, texture descriptors as a complement to voxel geometry description. Descriptors are organized in an occupancy map which may be efficiently computed and stored. By consulting the map, a point cloud motion estimator may significantly reduce its search space while maintaining prediction distortion at similar quality levels. The proposed texture-based occupancy maps provide significant speedup, an average of 26.9% for the tested data set, with respect to prior work.