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Depth Map Based Facade Abstraction from Noisy Multi-View Stereo Point Clouds

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Pattern Recognition (GCPR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9796))

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Abstract

Multi-View Stereo offers an affordable and flexible method for the acquisition of 3D point clouds. However, these point clouds are prone to errors and missing regions. In addition, an abstraction in the form of a simple mesh capturing the essence of the surface is usually preferred over the raw point cloud measurement. We present a fully automatic pipeline that computes such a mesh from the noisy point cloud of a building facade. We leverage prior work on casting the computation of a 2.5D depth map as a labeling problem and show that this formulation has great potential as an intermediate representation in the context of building facade reconstruction.

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Acknowledgements

This paper was supported by a grant (HE 2459/21-1) from the Deutsche Forschungsgemeinschaft (DFG).

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Authors and Affiliations

  1. Computer Vision and Remote Sensing Group, TU Berlin, Berlin, Germany

    Andreas Ley & Olaf Hellwich

Authors
  1. Andreas Ley
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  2. Olaf Hellwich
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Corresponding author

Correspondence to Andreas Ley .

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Editors and Affiliations

  1. University of Hannover, Hannover, Germany

    Bodo Rosenhahn

  2. Max Planck Institute for Informatics, Saarbrücken, Germany

    Bjoern Andres

1 Electronic supplementary material

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Supplementary material 1 (pdf 1141 KB)

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Ley, A., Hellwich, O. (2016). Depth Map Based Facade Abstraction from Noisy Multi-View Stereo Point Clouds. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-45886-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45885-4

  • Online ISBN: 978-3-319-45886-1

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