The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 421–426, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-421-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 421–426, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-421-2020

  12 Aug 2020

12 Aug 2020

AUTOMATED SEMANTIC MODELLING OF BUILDING INTERIORS FROM IMAGES AND DERIVED POINT CLOUDS BASED ON DEEP LEARNING METHODS

E. Gülch and L. Obrock E. Gülch and L. Obrock
  • University of Applied Sciences Stuttgart (HFT), Schellingstr. 24, D-70174 Stuttgart, Germany

Keywords: Semantic Interior Modelling, Point clouds, Deep Learning, BIM

Abstract. In this paper, we present an improved approach of enriching photogrammetric point clouds with semantic information extracted from images to enable a later automation of BIM modelling. Based on the DeepLabv3+ architecture, we use Semantic Segmentation of images to extract building components and objects of interiors. During the photogrammetric reconstruction, we project the segmented categories into the point cloud. Any interpolations that occur during this process are corrected automatically and we achieve a mIoU of 51.9 % in the classified point cloud. Based on the semantic information, we align the point cloud, correct the scale and extract further information. Our investigation confirms that utilizing photogrammetry and Deep Learning to generate a semantically enriched point cloud of interiors achieves good results. The combined extraction of geometric and semantic information yields a high potential for automated BIM model reconstruction.