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Joint Pattern Recognition Symposium

DAGM 2002: Pattern Recognition pp 34–41Cite as

Quality Enhancement of Reconstructed 3D Models Using Coplanarity and Constraints

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2449))

Abstract

We present a process to improve the structural quality of automatically acquired architectural 3D models. Common architectural features like orientations of walls are exploited. The location of these features is extracted by using a probabilistic technique (RANSAC). The relationships among the features are automatically obtained by labelling them using a semantic net of an architectural scene. An evolutionary algorithm is used to optimise the orientations of the planes. Small irregularities in the planes are removed by projecting the triangulation vertices onto the planes. Planes in the resulting model are aligned to each other. The technique produces models with improved appearance. It is validated on synthetic and real data.

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Author information

Authors and Affiliations

  1. Division of Informatics, University of Edinburgh, Edinburgh, EH1 2QL, UK

    H. Cantzler & R. B. Fisher

  2. LAAS-CNRS, 31077, Toulouse, France

    M. Devy

Authors
  1. H. Cantzler
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  2. R. B. Fisher
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  3. M. Devy
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Editor information

Editors and Affiliations

  1. Computer Vision Laboratory, ETH Zürich, Gloriastrasse 35, 8092, Zürich, Switzerland

    Luc Van Gool

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© 2002 Springer-Verlag Berlin Heidelberg

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Cantzler, H., Fisher, R.B., Devy, M. (2002). Quality Enhancement of Reconstructed 3D Models Using Coplanarity and Constraints. In: Van Gool, L. (eds) Pattern Recognition. DAGM 2002. Lecture Notes in Computer Science, vol 2449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45783-6_5

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  • DOI: https://doi.org/10.1007/3-540-45783-6_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44209-7

  • Online ISBN: 978-3-540-45783-1

  • eBook Packages: Springer Book Archive

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