# Geometric and Learning-based Mesh Denoising: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2209.00841 · HTML: https://mlsanity.com/paper/2209.00841/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 126 references that are in the map.
- First posted on arXiv 2 Sep 2022.

## Builds on (most cited first, 17 of 17)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PU-Net: Point Cloud Upsampling Network](https://mlsanity.com/paper/1801.06761/) (2018)
- [PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds](https://mlsanity.com/paper/1901.01060/) (2019)
- [EC-Net: an Edge-aware Point set Consolidation Network](https://mlsanity.com/paper/1807.06010/) (2018)
- [Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning](https://mlsanity.com/paper/1904.07615/) (2019)
- [DNF-Net: a Deep Normal Filtering Network for Mesh Denoising](https://mlsanity.com/paper/2006.15510/) (2020)
- [Zero-order Reverse Filtering](https://mlsanity.com/paper/1704.04037/) (2017)
- [Static/Dynamic Filtering for Mesh Geometry](https://mlsanity.com/paper/1712.03574/) (2017)
- [Robust and High Fidelity Mesh Denoising](https://mlsanity.com/paper/1711.05341/) (2017)
- [GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks](https://mlsanity.com/paper/2108.05128/) (2021)
- [Triangulated Surface Denoising using High Order Regularization with Dynamic Weights](https://mlsanity.com/paper/1711.08137/) (2017)
- [HLO: Half-kernel Laplacian Operator for Surface Smoothing](https://mlsanity.com/paper/1905.04678/) (2019)
- [Mesh Total Generalized Variation for Denoising](https://mlsanity.com/paper/2101.02322/) (2021)
- [A General Framework for Bilateral and Mean Shift Filtering](https://mlsanity.com/paper/1405.4734/) (2014)
- [Mesh Denoising based on Normal Voting Tensor and Binary Optimization](https://mlsanity.com/paper/1607.07427/) (2016)
- [Fast mesh denoising with data driven normal filtering using deep variational autoencoders](https://mlsanity.com/paper/2111.12782/) (2021)
- [Low Rank Matrix Approximation for Geometry Filtering](https://mlsanity.com/paper/1803.06783/) (2018)

## Built on by (most cited first, 2 of 2)

- [PRS: Sharp Feature Priors for Resolution-Free Surface Remeshing](https://mlsanity.com/paper/2311.18494/) (2023)
- [Geometry Denoising with Preferred Normal Vectors](https://mlsanity.com/paper/2511.04848/) (2025)

## Abstract (arXiv)

Mesh denoising is a fundamental problem in digital geometry processing. It seeks to remove surface noise, while preserving surface intrinsic signals as accurately as possible. While the traditional wisdom has been built upon specialized priors to smooth surfaces, learning-based approaches are making their debut with great success in generalization and automation. In this work, we provide a comprehensive review of the advances in mesh denoising, containing both traditional geometric approaches and recent learning-based methods. First, to familiarize readers with the denoising tasks, we summarize four common issues in mesh denoising. We then provide two categorizations of the existing denoising methods. Furthermore, three important categories, including optimization-, filter-, and data-driven-based techniques, are introduced and analyzed in detail, respectively. Both qualitative and quantitative comparisons are illustrated, to demonstrate the effectiveness of the state-of-the-art denoising methods. Finally, potential directions of future work are pointed out to solve the common problems of these approaches. A mesh denoising benchmark is also built in this work, and future researchers will easily and conveniently evaluate their methods with the state-of-the-art approaches.