article

Non-iterative, feature-preserving mesh smoothing

Authors:

Thouis R. Jones,

Mathieu DesbrunAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 22, Issue 3

Pages 943 - 949

https://doi.org/10.1145/882262.882367

Published: 01 July 2003 Publication History

Abstract

With the increasing use of geometry scanners to create 3D models, there is a rising need for fast and robust mesh smoothing to remove inevitable noise in the measurements. While most previous work has favored diffusion-based iterative techniques for feature-preserving smoothing, we propose a radically different approach, based on robust statistics and local first-order predictors of the surface. The robustness of our local estimates allows us to derive a non-iterative feature-preserving filtering technique applicable to arbitrary "triangle soups". We demonstrate its simplicity of implementation and its efficiency, which make it an excellent solution for smoothing large, noisy, and non-manifold meshes.

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Chen S(2024)An improved bilateral filtering based on curvature feature for point cloud denoising in road surface detectionMIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications10.1117/12.3000895(11)Online publication date: 7-Mar-2024
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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 22, Issue 3

July 2003

683 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/882262

Issue’s Table of Contents

Copyright © 2003 ACM.

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Publication History

Published: 01 July 2003

Published in TOG Volume 22, Issue 3

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Cited By

Chen S(2024)An improved bilateral filtering based on curvature feature for point cloud denoising in road surface detectionMIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications10.1117/12.3000895(11)Online publication date: 7-Mar-2024
https://doi.org/10.1117/12.3000895
Fletcher JMiskovic D(2024)Computer-Generated modelling in surgerySeminars in Colon and Rectal Surgery10.1016/j.scrs.2024.10100335:1(101003)Online publication date: Mar-2024
https://doi.org/10.1016/j.scrs.2024.101003
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
Jaiswal MCorpuz AHsu M(2024)Mesh-driven resampling and regularization for robust point cloud-based flow analysis directly on scanned objectsComputer Methods in Applied Mechanics and Engineering10.1016/j.cma.2024.117426432(117426)Online publication date: Dec-2024
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Wang MFeng YLyu BShen LYuan C(2024)An attention enhanced dual graph neural network for mesh denoisingComputer Aided Geometric Design10.1016/j.cagd.2024.102307111:COnline publication date: 1-Jun-2024
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Hurtado JGattass MRaposo ALopez C(2024)Sharp feature-preserving mesh denoisingMultimedia Tools and Applications10.1007/s11042-024-18390-x83:27(69555-69580)Online publication date: 1-Feb-2024
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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
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Ozendi MAkca DTopan H(2023)A point cloud filtering method based on anisotropic error modelThe Photogrammetric Record10.1111/phor.1246038:184(460-497)Online publication date: Sep-2023
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Zhao TYu MAlliez PLafarge F(2023)Sharp feature consolidation from raw 3D point clouds via displacement learningComputer Aided Geometric Design10.1016/j.cagd.2023.102204103:COnline publication date: 1-Jun-2023
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