The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 747–752, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-747-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 747–752, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-747-2019

  05 Jun 2019

05 Jun 2019

ENERGY FUNCTION ALGORITHM FOR DETECTION OF OPENINGS IN INDOOR POINT CLOUDS

R. Assi, T. Landes, H. Macher, and P. Grussenmeyer R. Assi et al.
  • ICube Laboratory UMR 7357, Photogrammetry and Geomatics Group, National Institute of Applied Sciences (INSA), 24 Boulevard de la Victoire, 67084 Strasbourg Cedex, France

Keywords: Indoor point clouds, Building information model, Deep Learning, Segmentation, Registration

Abstract. As the use of building information model (BIM) for architectural heritage becomes more relevant, this paper explores different solutions to further automatize the modelling process. The scan-to-BIM process still requires manual intervention that is time consuming, subject to errors and user-dependent. In this paper, the main focus is the automated segmentation of windows. In the first part of our paper, we will review and compare several state-of-the-art methods for automatic detection and segmentation of openings in a point cloud. Based on the most pertinent aspects of those methods, a new algorithm focusing on indoor point clouds is proposed. After walls are already detected, they are converted in 2D binary images. Holes in those images correspond to openings. We submit each opening to an energy function with two terms: data and coherence. The data term depends on the shape of the opening. The coherence term considers the position of the opening in the scene. Those function let us determine if an opening in the point cloud is due to a window/door or an object obstructing the acquisition. In the third part we discuss the results obtained by applying the method to different datasets.