Fractional Motion Estimation for Point Cloud Compression
This work addresses compression efficiency for dynamic 3D point clouds, which is incremental as it adapts fractional motion from video coding to point clouds.
The paper tackles the problem of compressing color attributes in dynamic 3D point clouds by introducing a fractional-voxel motion estimation scheme, which significantly outperforms integer-based methods and adds sizeable gains to state-of-the-art systems.
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.