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Line3D: Efficient 3D Scene Abstraction for the Built Environment

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Pattern Recognition (DAGM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9358))

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Abstract

Extracting 3D information from a moving camera is traditionally based on interest point detection and matching. This is especially challenging in the built environment, where the number of distinctive interest points is naturally limited. While common Structure-from-Motion (SfM) approaches usually manage to obtain the correct camera poses, the number of accurate 3D points is very small due to the low number of matchable features. Subsequent Multi-view Stereo approaches may help to overcome this problem, but suffer from a high computational complexity. We propose a novel approach for the task of 3D scene abstraction, which uses straight line segments as underlying features. We use purely geometric constraints to match 2D line segments from different images, and formulate the reconstruction procedure as a graph-clustering problem. We show that our method generates accurate 3D models, with a low computational overhead compared to SfM alone.

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Acknowledgements

This work has been supported by the Austrian Research Promotion Agency (FFG) project FreeLine (Bridge1/843450) and OMICRON electronics GmbH.

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Authors and Affiliations

  1. Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria

    Manuel Hofer, Michael Maurer & Horst Bischof

Authors
  1. Manuel Hofer
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  2. Michael Maurer
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  3. Horst Bischof
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Corresponding author

Correspondence to Manuel Hofer .

Editor information

Editors and Affiliations

  1. Institute of Computer Science III, University of Bonn, Bonn, Germany

    Juergen Gall

  2. MPI for Intelligent Systems, University of Tübingen, Tübingen, Germany

    Peter Gehler

  3. Computer Vision Group, Visual Computing Institute, RWTH Aachen, Aachen, Germany

    Bastian Leibe

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Hofer, M., Maurer, M., Bischof, H. (2015). Line3D: Efficient 3D Scene Abstraction for the Built Environment. In: Gall, J., Gehler, P., Leibe, B. (eds) Pattern Recognition. DAGM 2015. Lecture Notes in Computer Science(), vol 9358. Springer, Cham. https://doi.org/10.1007/978-3-319-24947-6_19

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  • DOI: https://doi.org/10.1007/978-3-319-24947-6_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24946-9

  • Online ISBN: 978-3-319-24947-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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