A Survey of Deep Learning-Based Mesh Processing

Abstract

In the past ten years, deep learning technology has achieved a great success in many fields, like computer vision and speech recognition. Recently, large-scale geometry data become more and more available, and the learned geometry priors have been successfully applied to 3D computer vision and computer graphics fields. Different from the regular representation of images, surface meshes have irregular structures with different vertex numbers and topologies. Therefore, the traditional convolution neural networks used for images cannot be directly used to handle surface meshes, and thus, many methods have been proposed to solve this problem. In this paper, we provide a comprehensive survey of existing geometric deep learning methods for mesh processing. We first introduce the relevant knowledge and theoretical background of geometric deep learning and some basic mesh data knowledge, including some commonly used mesh datasets. Then, we review various deep learning models for mesh data with two different types: graph-based methods and mesh structure-based methods. We also review the deep learning-based applications for mesh data. In the final, we give some potential research directions in this field.

Keywords

Geometric deep learning / Non-Euclidean space / Mesh / Convolution / Spectral domain / Spatial domain