# Motion estimation and filtered prediction for dynamic point cloud attribute compression

arXiv: https://arxiv.org/abs/2210.08262 · HTML: https://mlsanity.com/paper/2210.08262/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 15 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [A comprehensive survey on point cloud registration](https://mlsanity.com/paper/2103.02690/) (2021)
- [Graph Spectral Image Processing](https://mlsanity.com/paper/1801.04749/) (2018)
- [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)
- [Irregularity-Aware Graph Fourier Transforms](https://mlsanity.com/paper/1802.10220/) (2018)
- [Region adaptive graph fourier transform for 3d point clouds](https://mlsanity.com/paper/2003.01866/) (2020)
- [Dynamic Polygon Clouds: Representation and Compression for VR/AR](https://mlsanity.com/paper/1610.00402/) (2016)
- [A 3D Motion Vector Database for Dynamic Point Clouds](https://mlsanity.com/paper/2008.08438/) (2020)
- [Fractional Motion Estimation for Point Cloud Compression](https://mlsanity.com/paper/2202.00172/) (2022)

## Built on by (most cited first, 1 of 1)

- [High Efficiency Wiener Filter-based Point Cloud Quality Enhancement for MPEG G-PCC](https://mlsanity.com/paper/2503.17467/) (2025)

## Abstract (arXiv)

In point cloud compression, exploiting temporal redundancy for inter predictive coding is challenging because of the irregular geometry. This paper proposes an efficient block-based inter-coding scheme for color attribute compression. The scheme includes integer-precision motion estimation and an adaptive graph based in-loop filtering scheme for improved attribute prediction. The proposed block-based motion estimation scheme consists of an initial motion search that exploits geometric and color attributes, followed by a motion refinement that only minimizes color prediction error. To further improve color prediction, we propose a vertex-domain low-pass graph filtering scheme that can adaptively remove noise from predictors computed from motion estimation with different accuracy. Our experiments demonstrate significant coding gain over state-of-the-art coding methods.