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Deep Learning Methods for Extracting Object-Oriented Models of Building Interiors from Images

In: iCity. Transformative Research for the Livable, Intelligent, and Sustainable City

Author

Listed:
  • Lars Obrock

    (Hochschule für Technik Stuttgart)

  • Eberhard Gülch

    (Hochschule für Technik Stuttgart)

Abstract

In this chapter, we present an approach of enriching photogrammetric point clouds with semantic information extracted from images of digital cameras or smartphones to enable a later automation of BIM modelling with object-oriented models. Based on the DeepLabv3+ architecture, we extract building components and objects of interiors in full 3D. During the photogrammetric reconstruction, we project the segmented categories derived from the images into the point cloud. Based on the semantic information, we align the point cloud, correct the scale and extract further information. The combined extraction of geometric and semantic information yields a high potential for automated BIM model reconstruction.

Suggested Citation

  • Lars Obrock & Eberhard Gülch, 2022. "Deep Learning Methods for Extracting Object-Oriented Models of Building Interiors from Images," Springer Books, in: Volker Coors & Dirk Pietruschka & Berndt Zeitler (ed.), iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, chapter 17, pages 267-279, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-92096-8_17
    DOI: 10.1007/978-3-030-92096-8_17
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