Graph representations of 3D data for machine learning
This is an incremental review paper that addresses representation challenges for 3D data in machine learning applications.
The paper provides an overview of combinatorial methods for representing 3D data, such as graphs and meshes, focusing on their suitability for machine learning analysis, and presents two concrete applications in life science and industry.
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.