research-article

A Robust Scheme for Feature-Preserving Mesh Denoising

Authors:

Wenzhi ChenAuthors Info & Claims

IEEE Transactions on Visualization and Computer Graphics, Volume 22, Issue 3

Pages 1181 - 1194

https://doi.org/10.1109/TVCG.2015.2500222

Published: 01 March 2016 Publication History

Abstract

In recent years researchers have made noticeable progresses in mesh denoising, that is, recovering high-quality 3D models from meshes corrupted with noise (raw or synthetic). Nevertheless, these state of the art approaches still fall short for robustly handling various noisy 3D models. The main technical challenge of robust mesh denoising is to remove noise while maximally preserving geometric features. In particular, this issue becomes more difficult for models with considerable amount of noise. In this paper we present a novel scheme for robust feature-preserving mesh denoising. Given a noisy mesh input, our method first estimates an initial mesh, then performs feature detection, identification and connection, and finally, iteratively updates vertex positions based on the constructed feature edges. Through many experiments, we show that our approach can robustly and effectively denoise various input mesh models with synthetic noise or raw scanned noise. The qualitative and quantitative comparisons between our method and the selected state of the art methods also show that our approach can noticeably outperform them in terms of both quality and robustness.

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Cited By

Edirimuni DLu XLi GRobles-Kelly A(2024)Contrastive Learning for Joint Normal Estimation and Point Cloud FilteringIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.326386630:8(4527-4541)Online publication date: 1-Aug-2024
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Ho TTran MCui XLin CBaek SKim WLee CJin GChae KChoi S(2024)Human-airway surface mesh smoothing based on graph convolutional neural networksComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2024.108061246:COnline publication date: 1-Apr-2024
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Wang WPan WDai CDazeley RWei LRolfe BLu X(2024)Segmentation-driven feature-preserving mesh denoisingThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-03161-w40:9(6201-6217)Online publication date: 1-Sep-2024
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Index Terms

A Robust Scheme for Feature-Preserving Mesh Denoising
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Shape modeling
      1. Parametric curve and surface models
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Computational geometry

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Visualization and Computer Graphics

IEEE Transactions on Visualization and Computer Graphics Volume 22, Issue 3

March 2016

147 pages

ISSN:1077-2626

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Copyright © 2015.

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IEEE Educational Activities Department

United States

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Published: 01 March 2016

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Cited By

Edirimuni DLu XLi GRobles-Kelly A(2024)Contrastive Learning for Joint Normal Estimation and Point Cloud FilteringIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.326386630:8(4527-4541)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3263866
Ho TTran MCui XLin CBaek SKim WLee CJin GChae KChoi S(2024)Human-airway surface mesh smoothing based on graph convolutional neural networksComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2024.108061246:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.cmpb.2024.108061
Wang WPan WDai CDazeley RWei LRolfe BLu X(2024)Segmentation-driven feature-preserving mesh denoisingThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-03161-w40:9(6201-6217)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s00371-023-03161-w
Chen HLi ZWei MWang J(2023)Geometric and Learning-Based Mesh Denoising: A Comprehensive SurveyACM Transactions on Multimedia Computing, Communications, and Applications10.1145/362509820:3(1-28)Online publication date: 10-Nov-2023
https://dl.acm.org/doi/10.1145/3625098
Wang CLiu ZLiu L(2022)Feature-preserving Mumford–Shah mesh processing via nonsmooth nonconvex regularizationComputers and Graphics10.1016/j.cag.2022.06.006106:C(222-236)Online publication date: 1-Aug-2022
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Hattori SYatagawa TOhtake YSuzuki H(2022)Learning Self-prior for Mesh Denoising Using Dual Graph Convolutional NetworksComputer Vision – ECCV 202210.1007/978-3-031-20062-5_21(363-379)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-20062-5_21
Zhong SSong ZLiu ZXie ZChen JLiu LChen R(2021)Shape-aware Mesh Normal FilteringComputer-Aided Design10.1016/j.cad.2021.103088140:COnline publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1016/j.cad.2021.103088
Pan WLu XGong YTang WLiu JHe YQiu G(2020)HLOComputer-Aided Design10.1016/j.cad.2019.102807121:COnline publication date: 1-Apr-2020
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Wang JLi YChoi YLee CKim J(2020)Fast and Accurate Smoothing Method Using A Modified Allen–Cahn EquationComputer-Aided Design10.1016/j.cad.2019.102804120:COnline publication date: 1-Mar-2020
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Haque SGovindu V(2020)A Face Fairness Framework for 3D MeshesInternational Journal of Computer Vision10.1007/s11263-019-01268-z128:6(1565-1579)Online publication date: 1-Jun-2020
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