# Robust and High Fidelity Mesh Denoising

arXiv: https://arxiv.org/abs/1711.05341 · HTML: https://mlsanity.com/paper/1711.05341/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Voxel Structure-based Mesh Reconstruction from a 3D Point Cloud](https://mlsanity.com/paper/2104.10622/) (2021)
- [Geometric and Learning-based Mesh Denoising: A Comprehensive Survey](https://mlsanity.com/paper/2209.00841/) (2022)
- [PRS: Sharp Feature Priors for Resolution-Free Surface Remeshing](https://mlsanity.com/paper/2311.18494/) (2023)
- [DMD-Net: Deep Mesh Denoising Network](https://mlsanity.com/paper/2506.22850/) (2025)

## Abstract (arXiv)

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.