Fractional Motion Estimation for Point Cloud Compression

Motivated by the success of fractional pixel motion in video coding, we explore the design of motion estimation with fractional-voxel resolution for compression of color attributes of dynamic 3D point clouds. Our proposed block-based fractional-voxel motion estimation scheme takes into account the fundamental differences between point clouds and videos, i.e., the irregularity of the distribution of voxels within a frame and across frames. We show that motion compensation can benefit from the higher resolution reference and more accurate displacements provided by fractional precision. Our proposed scheme significantly outperforms comparable methods that only use integer motion. The proposed scheme can be combined with and add sizeable gains to state-of-the-art systems that use transforms such as Region Adaptive Graph Fourier Transform and Region Adaptive Haar Transform.

1 Introduction

Recent progress in 3D acquisition and reconstruction technology makes the capture of 3D scenes ubiquitous. In dynamic point clouds, each frame consists of a list of data points with 3D coordinates and RGB color values. Since point clouds in raw format would require a huge amount of bandwidth for transmission there has been a significant interest in point cloud compression techniques, which has led to MPEG standardization efforts [1], considering both video-based point cloud compression (V-PCC) and geometry-based point cloud compression (G-PCC) [1], [2].