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Curvature Minimization for Surface Reconstruction with Features

  • Conference paper
Scale Space and Variational Methods in Computer Vision (SSVM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6667))

Abstract

A new surface reconstruction method is proposed based on graph cuts and local swap. We novelly integrate a curvature based variational model and Delaunay based tetrahedral mesh framework. The minimization task is performed by graph cuts and local swap sequentially. The proposed method could reconstruct surfaces with important features such as sharp edges and corners. Various numerical examples indicate the robustness and effectiveness of the method.

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Author information

Authors and Affiliations

  1. Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, 637371

    Juan Shi, Min Wan, Xue-Cheng Tai & Desheng Wang

  2. Department of Mathematics, University of Bergen, Johannes Brunsgate 12, 5007, Bergen, Norway

    Xue-Cheng Tai

Authors
  1. Juan Shi
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  2. Min Wan
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  3. Xue-Cheng Tai
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  4. Desheng Wang
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Editor information

Editors and Affiliations

  1. Computer Science Department, Technion — Israel Institute of Technology, 718 Taub Building Technion, 32000, Haifa, Israel

    Alfred M. Bruckstein

  2. Department of Biomedical Engineering, Biomedical Image Analysis and Interpretation, Eindhoven University of Technology, Den Dolech 2 – WH 2.101, 5600 MB, Eindhoven, The Netherlands

    Bart M. ter Haar Romeny

  3. Faculaty of Engineering, School of Electrical Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Alexander M. Bronstein

  4. Institute of Computationa Science, Faculty of Informatics SI-109, Università della Svizzera Italiana, Via Giuseppe Buffi 13, 6904, Lugano, Switzerland

    Michael M. Bronstein

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© 2012 Springer-Verlag Berlin Heidelberg

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Shi, J., Wan, M., Tai, XC., Wang, D. (2012). Curvature Minimization for Surface Reconstruction with Features. In: Bruckstein, A.M., ter Haar Romeny, B.M., Bronstein, A.M., Bronstein, M.M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2011. Lecture Notes in Computer Science, vol 6667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24785-9_42

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  • DOI: https://doi.org/10.1007/978-3-642-24785-9_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24784-2

  • Online ISBN: 978-3-642-24785-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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