# Fractional Motion Estimation for Point Cloud Compression

arXiv: https://arxiv.org/abs/2202.00172 · HTML: https://mlsanity.com/paper/2202.00172/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 1 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Graph-based compression of dynamic 3D point cloud sequences](https://mlsanity.com/paper/1506.06096/) (2015)
- [Predictive Generalized Graph Fourier Transform for Attribute Compression of Dynamic Point Clouds](https://mlsanity.com/paper/1908.01970/) (2019)
- [Comments on "Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform"](https://mlsanity.com/paper/1805.09146/) (2018)
- [Region adaptive graph fourier transform for 3d point clouds](https://mlsanity.com/paper/2003.01866/) (2020)
- [A 3D Motion Vector Database for Dynamic Point Clouds](https://mlsanity.com/paper/2008.08438/) (2020)

## Built on by (most cited first, 2 of 2)

- [Learning Dynamic Point Cloud Compression via Hierarchical Inter-frame Block Matching](https://mlsanity.com/paper/2305.05356/) (2023)
- [Motion estimation and filtered prediction for dynamic point cloud attribute compression](https://mlsanity.com/paper/2210.08262/) (2022)

## Abstract (arXiv)

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.