Topological and Node Noise Filtering on 3D Meshes Using Graph Neural Networks

Abstract

Topological and node noise filtration are typically considered separately. Graph Neural Networks (GNN) are commonly used for node noise filtration, as they offer high efficiency and low exploitation costs. This paper explores the solution of joint node and topological noise filtration through the use of graph neural networks. Since treating a 3D mesh as a graph is challenging, an indicator function grid representation is employed as input for GNNs to perform the joint filtering. The resulting machine learning model is inspired by point cloud to mesh reconstruction algorithms and demonstrates low computational requirements during inference, producing successful results for smooth, watertight 3D models.