The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 667–674, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-667-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 667–674, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-667-2016

  16 Jun 2016

16 Jun 2016

VALIDATION OF POINT CLOUDS SEGMENTATION ALGORITHMS THROUGH THEIR APPLICATION TO SEVERAL CASE STUDIES FOR INDOOR BUILDING MODELLING

H. Macher, T. Landes, and P. Grussenmeyer H. Macher et al.
  • Photogrammetry and Geomatics Group, ICube Laboratory UMR 7357, INSA Strasbourg, France

Keywords: 3D modelling, laser scanning, indoor point clouds, space segmentation, building elements, datasets

Abstract. Laser scanners are widely used for the modelling of existing buildings and particularly in the creation process of as-built BIM (Building Information Modelling). However, the generation of as-built BIM from point clouds involves mainly manual steps and it is consequently time consuming and error-prone. Along the path to automation, a three steps segmentation approach has been developed. This approach is composed of two phases: a segmentation into sub-spaces namely floors and rooms and a plane segmentation combined with the identification of building elements.

In order to assess and validate the developed approach, different case studies are considered. Indeed, it is essential to apply algorithms to several datasets and not to develop algorithms with a unique dataset which could influence the development with its particularities. Indoor point clouds of different types of buildings will be used as input for the developed algorithms, going from an individual house of almost one hundred square meters to larger buildings of several thousand square meters. Datasets provide various space configurations and present numerous different occluding objects as for example desks, computer equipments, home furnishings and even wine barrels. For each dataset, the results will be illustrated. The analysis of the results will provide an insight into the transferability of the developed approach for the indoor modelling of several types of buildings.