Graph representations of 3D data for machine learning

TL;DR

This paper reviews combinatorial graph and mesh representations of 3D data, discussing their advantages, disadvantages, and conversion methods, with applications in life sciences and industry, aimed at improving machine learning analysis.

Contribution

It provides a comprehensive overview of 3D data representations for machine learning, highlighting practical considerations and real-world applications.

Findings

Graphs and meshes have distinct pros and cons for 3D data analysis.

Methods for converting between different 3D representations are discussed.

Applications demonstrate the practical relevance of these representations.

Abstract

We give an overview of combinatorial methods to represent 3D data, such as graphs and meshes, from the viewpoint of their amenability to analysis using machine learning algorithms. We highlight pros and cons of various representations and we discuss some methods of generating/switching between the representations. We finally present two concrete applications in life science and industry. Despite its theoretical nature, our discussion is in general motivated by, and biased towards real-world challenges.