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A Probabilistic Approach to Building Roof Reconstruction Using Semantic Labelling

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Pattern Recognition (DAGM 2002)

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

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Abstract

This paper investigates into model-based reconstruction of complex polyhedral building roofs. A roof is modelled as a structured ensemble of planar polygonal faces. The modelling is done in two different regimes. One focuses on geometry, whereas the other is ruled by semantics. Inside the geometric regime, 3D line segments are grouped into planes and further into faces using a Bayesian analysis. In the second regime, the preliminary geometric models are subject to a semantic interpretation. The knowledge gained in this step is used to infer missing parts of the roof model (by invoking the geometric regime once more) and to adjust the overall roof topology. Several successfully reconstructed complex roof structures corroborate the potential of the approach.

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Author information

Authors and Affiliations

  1. Computer Vision Laboratory (BIWI), ETH Zürich, Zürich, Switzerland

    Stephan Scholze & Luc Van Gool

  2. Center for Processing Speech and Images (ESAT/PSI), Katholieke Universiteit Leuven, Leuven, Belgium

    Theo Moons & Luc Van Gool

  3. Group of Exact Sciences, Katholieke Universiteit Brussel, Brussel, Belgium

    Theo Moons

Authors
  1. Stephan Scholze
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  2. Theo Moons
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  3. Luc Van Gool
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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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Scholze, S., Moons, T., Van Gool, L. (2002). A Probabilistic Approach to Building Roof Reconstruction Using Semantic Labelling. 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_31

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  • DOI: https://doi.org/10.1007/3-540-45783-6_31

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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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