GNF-Net: An Adaptive Mesh Denoising Method with GCN-Based Guided Normal Filtering

Mesh Denoising has become a popular area of research, many traditional and learning-based methods have been proposed to remove noise from meshes. However, most approaches only focus on denoising meshes with low levels of noise. When the noise is high-frequency, it can be difficult to recover the original shape. In this paper, we present a mesh denoising approach that utilizes graph convolution representations to enhance the understanding of the mesh characteristics. It integrates precisely designed graphs to explore the inherent compositional structure of the mesh. When analyzing meshes under the influence of various noises, we extract information about the original features of the mesh by capturing spatial geometric features through graph convolution calculations. Our method is based on Guided Normal Filtering (GNF) to design a Graphical Representation Module (GRM) and a GCN-Based Normal Prediction Module (NPM). It can adaptively obtain the optimal guided normal vectors for noisy meshes. We have compared and analyzed the various methods to produce state-of-the-art results.