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A Framework for 3D Modeling of Construction Sites Using Aerial Imagery and Semantic NeRFs

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MultiMedia Modeling (MMM 2024)

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Abstract

The rapid evolution of drone technology has revolutionized data acquisition in the construction industry, offering a cost-effective and efficient method to monitor and map engineering structures. However, a significant challenge remains in transforming the drone-collected data into semantically meaningful 3D models. 3D reconstruction techniques usually lead to raw point clouds that are typically unstructured and lack the semantic and geometric information of objects needed for civil engineering tools. Our solution applies semantic segmentation algorithms to the data produced by NeRF (Neural Radiance Fields), effectively transforming drone-captured 3D volumetric representations into semantically rich 3D models. This approach offers a cost-effective and automated way to digitalize physical objects of construction sites into semantically annotated digital counterparts facilitating the development of digital twins or XR applications in the construction sector.

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Acknowledgments

This work was supported by the EC-funded research and innovation programme H2020 ASHVIN: “Digitising and transforming the European construction industry” under the grant agreement No.958161.

EU disclaimer: This publication reflects only author’s view and the European Commission is not responsible for any uses that may be made of the information it contains.

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Authors and Affiliations

  1. Information Technologies Institute - CERTH, Thessaloniki, Greece

    Panagiotis Vrachnos, Marios Krestenitis, Ilias Koulalis, Konstantinos Ioannidis & Stefanos Vrochidis

Authors
  1. Panagiotis Vrachnos
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  2. Marios Krestenitis
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  3. Ilias Koulalis
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  4. Konstantinos Ioannidis
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  5. Stefanos Vrochidis
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Corresponding author

Correspondence to Panagiotis Vrachnos .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  2. Delft University of Technology, Delft, The Netherlands

    Alan Hanjalic

  3. Delft University of Technology, Delft, The Netherlands

    Cynthia Liem

  4. University of Amsterdam, Amsterdam, The Netherlands

    Marcel Worring

  5. Reykjavik University, Reykjavik, Iceland

    Björn Þór Jónsson

  6. Microsoft Research Lab – Asia, Beijing, China

    Bei Liu

  7. The University of Tokyo, Tokyo, Japan

    Yoko Yamakata

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Vrachnos, P., Krestenitis, M., Koulalis, I., Ioannidis, K., Vrochidis, S. (2024). A Framework for 3D Modeling of Construction Sites Using Aerial Imagery and Semantic NeRFs. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14557. Springer, Cham. https://doi.org/10.1007/978-3-031-53302-0_13

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  • DOI: https://doi.org/10.1007/978-3-031-53302-0_13

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