Volume XLII-2/W15
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W15, 821–827, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W15-821-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W15, 821–827, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W15-821-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Aug 2019

23 Aug 2019

AUTOMATIC HERITAGE BUILDING POINT CLOUD SEGMENTATION AND CLASSIFICATION USING GEOMETRICAL RULES

A. Murtiyoso and P. Grussenmeyer A. Murtiyoso and P. Grussenmeyer
  • Photogrammetry and Geomatics Group, ICube Laboratory UMR 7357, INSA Strasbourg, France

Keywords: Point Cloud, Segmentation, Heritage, Automation, Classification

Abstract. The segmentation of a point cloud presents an important step in the 3D modelling process of heritage structures. This is true in many scale levels, including the segmentation, identification, and classification of architectural elements from the point cloud of a building. In this regard, historical buildings often present complex elements which render the 3D modelling process longer when performed manually. The aim of this paper is to explore approaches based on certain common geometric rules in order to segment, identify, and classify point clouds into architectural elements. In particular, the detection of attics and structural supports (i.e. columns and piers) will be addressed. Results show that the developed algorithm manages to detect supports in three separate data sets representing three different types of architecture. The algorithm also managed to identify the type of support and divide them into two groups: columns and piers. Overall, the developed method provides a fast and simple approach to classify point clouds automatically into several classes, with a mean success rate of 81.61 % and median success rate of 85.61&thinsp% for three tested data sets.