A 3D Motion Vector Database for Dynamic Point Clouds

TL;DR

This paper introduces a publicly available 3D motion vector database for dynamic point clouds, generated through translational motion estimation, to facilitate research in compression and analysis.

Contribution

It provides a comprehensive 3D motion vector database for dynamic point clouds, enabling efficient processing and research applications.

Findings

Database covers two popular datasets with different block sizes.

Motion vectors are estimated using translational motion estimation.

The database supports potential applications like point cloud compression.

Abstract

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.