This article presents a deep normal filtering network, called DNF-Net, for mesh denoising. To
better capture local geometry, our network processes the mesh in terms of local patches …

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 …

Geometrical models of the airways offer a comprehensive perspective on the complex
interplay between lung structure and function. Originating from mathematical frameworks …

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

The new generation 3D scanner devices have revolutionized the way information from 3D
objects is acquired, making the process of scene capturing and digitization straightforward …

This track of the SHREC 2018 originally aimed at recognizing relief patterns over a set of
triangle meshes from laser scan acquisitions of archaeological fragments. This track …

Distinguishing between geometric features and noise is of paramount importance for mesh
denoising. In this paper, a graph-based feature-preserving mesh normal filtering scheme is …

This paper presents a fast and accurate method using the Allen–Cahn (AC) equation with a
fidelity term for curves smoothing of 2 D shapes and volume smoothing of 3 D shapes. The …