We present a simple and fast mesh denoising method, which can remove noise effectively
while preserving mesh features such as sharp edges and corners. The method consists of …

We present an algorithm for denoising triangulated models based on L 0 minimization. Our
method maximizes the flat regions of the model and gradually removes noise while …

Decoupling local geometric features from the spatial location of a mesh is crucial for feature-
preserving mesh denoising. This paper focuses on first order features, ie, facet normals, and …

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 …

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

This paper introduces a robust normal vector estimator for point cloud data. It can handle
sharp features as well as smooth areas. Our method is based on the inclusion of a robust …

Mesh surface denoising is a fundamental problem in geometry processing. The main
challenge is to remove noise while preserving sharp features (such as edges and corners) …

The peer group of an image pixel is a pixel similarity-based concept which has been
successfully used to devise image denoising methods. However, since it is difficult to define …

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 …