# A 3D Motion Vector Database for Dynamic Point Clouds

arXiv: https://arxiv.org/abs/2008.08438 · HTML: https://mlsanity.com/paper/2008.08438/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [Fractional Motion Estimation for Point Cloud Compression](https://mlsanity.com/paper/2202.00172/) (2022)
- [Motion estimation and filtered prediction for dynamic point cloud attribute compression](https://mlsanity.com/paper/2210.08262/) (2022)
- [Motion Estimation Techniques for Volumetric Video Attribute Compression](https://mlsanity.com/paper/2607.03576/) (2026)

## Abstract (arXiv)

Due to the large amount of data that point clouds represent and the differences in geometry of successive frames, the generation of motion vectors for an entire point cloud dataset may require a significant amount of time and computational resources. With that in mind, we provide a 3D motion vector database for all frames of two popular dynamic point cloud datasets. The motion vectors were obtained through translational motion estimation procedure that partitions the point clouds into blocks of dimensions M x M x M , and for each block, a motion vector is estimated. Our database contains motion vectors for M = 8 and M = 16. The goal of this work is to describe this publicly available 3D motion vector database that can be used for different purposes, such as compression of dynamic point clouds.