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

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.

1. INTRODUCTION

Among the novel 3D representations for imaging systems, point clouds (PCs) constitute a geometrically simple yet versatile alternative, offering relative freedom to acquisition and rendering procedures. They are a set of points (x, y, z) in 3D space with associated data, such as color. In voxelized clouds, the points assume integer coordinate values on a regular 3D grid. Points within such a grid are called voxels and may be occupied or not. A temporal sequence of clouds, organized in frames, may be used to depict movement of dynamic objects or scenes.