Robust and High Fidelity Mesh Denoising

11/14/2017
by   Sunil Kumar Yadav, et al.
0

This paper presents a simple and effective two-stage mesh denoising algorithm, where in the first stage, the face normal filtering is done by using the bilateral normal filtering in the robust statistics framework. Tukey's bi-weight function is used as similarity function in the bilateral weighting, which is a robust estimator and stops the diffusion at sharp edges to retain features and removes noise from flat regions effectively. In the second stage, an edge weighted Laplace operator is introduced to compute a differential coordinate. This differential coordinate helps the algorithm to produce a high-quality mesh without any face normal flips and makes the method robust against high-intensity noise.

READ FULL TEXT

page 4

page 5

page 6

research
03/10/2019

NormalNet: Learning based Guided Normal Filtering for Mesh Denoising

Mesh denoising is a critical technology in geometry processing, which ai...
research
07/20/2016

Mesh Denoising based on Normal Voting Tensor and Binary Optimization

This paper presents a tensor multiplication based smoothing algorithm th...
research
07/02/2020

Surface Denoising based on Normal Filtering in a Robust Statistics Framework

During a surface acquisition process using 3D scanners, noise is inevita...
research
09/23/2010

3D-Mesh denoising using an improved vertex based anisotropic diffusion

This paper deals with an improvement of vertex based nonlinear diffusion...
research
06/28/2020

DNF-Net: a Deep Normal Filtering Network for Mesh Denoising

This paper presents a deep normal filtering network, called DNF-Net, for...
research
08/04/2020

Segmentation Based Mesh Denoising

Feature-preserving mesh denoising has received noticeable attention rece...
research
11/24/2021

Fast mesh denoising with data driven normal filtering using deep variational autoencoders

Recent advances in 3D scanning technology have enabled the deployment of...

Please sign up or login with your details

Forgot password? Click here to reset