We present a data-driven approach for mesh denoising. Our key idea is to formulate the
denoising process with cascaded non-linear regression functions and learn them from a set …

Point cloud is a collection of 3D coordinates that are discrete geometric samples of an
object's 2D surfaces. Imperfection in the acquisition process means that point clouds are …

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

Feature lines are important geometric cues in characterizing the structure of a CAD model.
Despite great progress in both explicit reconstruction and implicit reconstruction, it remains a …

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 …

Recently, the emergence of diffusion models has opened up new opportunities for single-
view reconstruction. However, all the existing methods represent the target object as a …

Decades of digital image processing research has culminated in a wealth of complex filters
and effects. These filters are not only important as pre-and post-processes to other …

We propose a robust normal estimation method for both point clouds and meshes using a
low rank matrix approximation algorithm. First, we compute a local isotropic structure for …

Mesh denoising is a classical, yet not well-solved problem in digital geometry processing.
The challenge arises from noise removal with the minimal disturbance of surface intrinsic …

Point cloud is the primary source from 3D scanners and depth cameras. It usually contains
more raw geometric features, as well as higher levels of noise than the reconstructed mesh …