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 …

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 …

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 …

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

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 …

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 present a framework for 3D model reconstruction, which has potential applications to a
spectrum of engineering problems with impacts on rapid design and prototy**, shape …