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MLS2LoD3: Refining Low LoDs Building Models with MLS Point Clouds to Reconstruct Semantic LoD3 Building Models

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Recent Advances in 3D Geoinformation Science (3DGeoInfo 2023)

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

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Abstract

Although highly-detailed LoD3 building models reveal great potential in various applications, they have yet to be available. The primary challenges in creating such models concern not only automatic detection and reconstruction but also standard-consistent modeling. In this paper, we introduce a novel refinement strategy enabling LoD3 reconstruction by leveraging the ubiquity of lower LoD building models and the accuracy of MLS point clouds. Such a strategy promises at-scale LoD3 reconstruction and unlocks LoD3 applications, which we also describe and illustrate in this paper. Additionally, we present guidelines for reconstructing LoD3 facade elements and their embedding into the CityGML standard model, disseminating gained knowledge to academics and professionals. We believe that our method can foster development of LoD3 reconstruction algorithms and subsequently enable their wider adoption.

This article was selected based on the results of a double-blind review of an extended abstract.

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Notes

  1. 1.

    https://github.com/OloOcki/awesome-citygml.

  2. 2.

    https://github.com/OloOcki/awesome-citygml.

  3. 3.

    https://github.com/tum-gis/tum2twin.

  4. 4.

    https://github.com/OloOcki/scan2lod3.

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Acknowledgements

This work was supported by the Bavarian State Ministry for Economic Affairs, Regional Development and Energy within the framework of the IuK Bayern project MoFa3D - Mobile Erfassung von Fassaden mittels 3D Punktwolken, Grant No. IUK643/001. Moreover, the work was conducted within the framework of the Leonhard Obermeyer Center at the Technical University of Munich (TUM). We gratefully acknowledge the Geoinformatics team at the TUM for the valuable insights and for providing the CityGML datasets.

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Authors and Affiliations

  1. Photogrammetry and Remote Sensing, TUM School of Engineering and Design, Technical University of Munich (TUM), Munich, Germany

    Olaf Wysocki, Ludwig Hoegner & Uwe Stilla

  2. Department of Geoinformatics, University of Applied Science (HM), Munich, Germany

    Ludwig Hoegner

Authors
  1. Olaf Wysocki
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  2. Ludwig Hoegner
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  3. Uwe Stilla
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Corresponding author

Correspondence to Olaf Wysocki .

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Editors and Affiliations

  1. Chair of Geoinformatics, Department of Aerospace and Geodesy, Technical University of Munich, Munich, Germany

    Thomas H. Kolbe

  2. Chair of Geoinformatics, Department of Aerospace and Geodesy, Technical University of Munich, Munich, Germany

    Andreas Donaubauer

  3. Chair of Geoinformatics, Department of Aerospace and Geodesy, Technical University of Munich, Munich, Germany

    Christof Beil

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Wysocki, O., Hoegner, L., Stilla, U. (2024). MLS2LoD3: Refining Low LoDs Building Models with MLS Point Clouds to Reconstruct Semantic LoD3 Building Models. In: Kolbe, T.H., Donaubauer, A., Beil, C. (eds) Recent Advances in 3D Geoinformation Science. 3DGeoInfo 2023. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-031-43699-4_23

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