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Propagated mesh normal filtering.

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Title: Propagated mesh normal filtering.
Authors: Liu, Bin1, Cao, Junjie1, Wang, Weiming1, Ma, Ning1, Li, Bo2, Liu, Ligang3, Liu, Xiuping1 xpliu@dlut.edu.cn
Source: Computers & Graphics. Aug2018, Vol. 74, p119-125. 7p.
Subjects: Computers in geometry, Geodesic equation, Kernel functions, Perturbation theory, Gaussian function
Abstract: Weighted average is one of the most common strategies used in various mesh filters, and its performance depends on the weight design. When computing the weight between the current face and one of its neighbours, existing methods consider only properties of the two faces, such as positions and normals. Although they generate some convincing results, they definitely tend to suffer from cross-region mixing. For example, assigning such a large weight between two nearby faces separated by some feature edges, even when their properties are close, will damage the local structure. In this paper, we present a novel mesh filter model, named as Propagated Mesh Normal Filtering. It estimates the weight between the current face and its neighbours based on the integral of two kinds of face normal differences along the geodesic path, connecting them. Therefore, prominent features are better preserved when removing noises or textures. Furthermore, in view of the sparseness of large normal difference for most of geometry shapes, the L 1 norm is employed when integrating to further improve the filter. Experiments illustrate the enhanced efficacy of our propagated filter comparing with state-of-the-art methods. [ABSTRACT FROM AUTHOR]
Copyright of Computers & Graphics is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 131031320
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: <searchLink fieldCode="JN" term="%22Computers+%26+Graphics%22">Computers & Graphics</searchLink>. Aug2018, Vol. 74, p119-125. 7p.
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  Data: Weighted average is one of the most common strategies used in various mesh filters, and its performance depends on the weight design. When computing the weight between the current face and one of its neighbours, existing methods consider only properties of the two faces, such as positions and normals. Although they generate some convincing results, they definitely tend to suffer from cross-region mixing. For example, assigning such a large weight between two nearby faces separated by some feature edges, even when their properties are close, will damage the local structure. In this paper, we present a novel mesh filter model, named as Propagated Mesh Normal Filtering. It estimates the weight between the current face and its neighbours based on the integral of two kinds of face normal differences along the geodesic path, connecting them. Therefore, prominent features are better preserved when removing noises or textures. Furthermore, in view of the sparseness of large normal difference for most of geometry shapes, the L 1 norm is employed when integrating to further improve the filter. Experiments illustrate the enhanced efficacy of our propagated filter comparing with state-of-the-art methods. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computers & Graphics is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.cag.2018.05.003
    Languages:
      – Code: eng
        Text: English
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        PageCount: 7
        StartPage: 119
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      – SubjectFull: Computers in geometry
        Type: general
      – SubjectFull: Geodesic equation
        Type: general
      – SubjectFull: Kernel functions
        Type: general
      – SubjectFull: Perturbation theory
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      – SubjectFull: Gaussian function
        Type: general
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      – TitleFull: Propagated mesh normal filtering.
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            NameFull: Liu, Bin
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            NameFull: Li, Bo
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            NameFull: Liu, Ligang
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            – D: 01
              M: 08
              Text: Aug2018
              Type: published
              Y: 2018
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