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Articles | Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 241–250, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-241-2018
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 241–250, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-241-2018

  30 May 2018

30 May 2018

SEMANTIC SEGMENTATION OF BUILDING ELEMENTS USING POINT CLOUD HASHING

M. Chizhova1,2,3,4, A. Gurianov5, M. Hess1, T. Luhmann2, A. Brunn3, and U. Stilla4 M. Chizhova et al.
  • 1Otto-Friedrich University of Bamberg, Bamberg, Germany
  • 2Jade University of Applied Sciences, Oldenburg, Germany
  • 3University of Applied Sciences Würzburg-Schweinfurt, Würzburg, Germany
  • 4Technical University of Munich, Munich, Germany
  • 5Ivanovo State University, Ivanovo, Russia

Keywords: semantic segmentation, hashing, Orthodox church, point clouds

Abstract. For the interpretation of point clouds, the semantic definition of extracted segments from point clouds or images is a common problem. Usually, the semantic of geometrical pre-segmented point cloud elements are determined using probabilistic networks and scene databases. The proposed semantic segmentation method is based on the psychological human interpretation of geometric objects, especially on fundamental rules of primary comprehension. Starting from these rules the buildings could be quite well and simply classified by a human operator (e.g. architect) into different building types and structural elements (dome, nave, transept etc.), including particular building parts which are visually detected. The key part of the procedure is a novel method based on hashing where point cloud projections are transformed into binary pixel representations. A segmentation approach released on the example of classical Orthodox churches is suitable for other buildings and objects characterized through a particular typology in its construction (e.g. industrial objects in standardized enviroments with strict component design allowing clear semantic modelling).