Convolution on 3D point clouds is widely researched yet far from perfect in geometric deep
learning. The traditional wisdom of convolution characterises feature correspondences …

Point normal, as an intrinsic geometric property of 3D objects, not only serves conventional
geometric tasks such as surface consolidation and reconstruction, but also facilitates cutting …

In this article, we present GCN-Denoiser, a novel feature-preserving mesh denoising
method based on graph convolutional networks (GCNs). Unlike previous learning-based …

Graph neural networks (GNNs) are ideally suited for mesh denoising. However, existing
solutions such as those based on graph convolutional networks (GCNs) are built for a fixed …

This study proposes a deep-learning framework for mesh denoising from a single noisy
input, where two graph convolutional networks are trained jointly to filter vertex positions and …

Mesh denoising is a fundamental problem in digital geometry processing. It seeks to remove
surface noise while preserving surface intrinsic signals as accurately as possible. While …

Recent studies have shown that the Total Generalized Variation (TGV) is highly effective in
preserving sharp features as well as smooth transition variations for image processing tasks …

Mesh denoising is an essential geometric processing step for raw meshes generated by 3D
scanners and depth cameras. It is intended to remove noise while preserving surface …

We examine the problem of mesh denoising, which consists of removing noise from
corrupted 3D meshes while preserving existing geometric features. Most mesh denoising …

3D meshes are getting popular in both research and industry. However, the meshes
obtained via the 3D scanning equipment frequently contain a high level of noise. In this …