Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required
to be consolidated. In this paper, we present the first deep learning based {em edge-aware}�…

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�…

The joint bilateral filter is a variant of the standard bilateral filter, where the range kernel is
evaluated using a guidance signal instead of the original signal. It has been successfully�…

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�…

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�…

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�…