Volume XLII-5/W2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5/W2, 41–47, 2019
https://doi.org/10.5194/isprs-archives-XLII-5-W2-41-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5/W2, 41–47, 2019
https://doi.org/10.5194/isprs-archives-XLII-5-W2-41-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  20 Sep 2019

20 Sep 2019

EXTRACTION OF MAIN LEVELS OF A BUILDING FROM A LARGE POINT CLOUD

C. Leoni1, S. Ferrarese1, W. Wahbeh2, and C. Nardinocchi1 C. Leoni et al.
  • 1DICEA, Sapienza University of Rome, Area Geodesia e Geomatica - Via Eudossiana 18, 00184 Roma, Italy
  • 2University of Applied Sciences and Arts Northwestern Switzerland School of Architecture, Civil Engineering and Geomatics Institut Digitales Bauen - Hofackerstrasse 30, 4132 Muttenz, Switzerland

Keywords: 3D Scanning Data, Scan-to-BIM, Subsample, Outliers, Plane Extraction, Robust Estimator, Positioning Levels

Abstract. Horizontal levels are references entities, the base of man-made environments. Their creation is the first step for various applications including the BIM (Building Information Modelling). BIM is an emerging methodology, widely used for new constructions, and increasingly applied to existing buildings (scan-to-BIM). The as-built BIM process is still mainly manual or semi-automatic and therefore is highly time-consuming. The automation of the as-built BIM is a challenging topic among the research community. This study is part of an ongoing research into the scan-to-BIM process regarding the extraction of the principal structure of a building. More specifically, here we present a strategy to automatically detect the building levels from a large point cloud obtained with a terrestrial laser scanner survey. The identification of the horizontal planes is the first indispensable step to produce an as-built BIM model. Our algorithm, developed in C++, is based on plane extraction by means of the RANSAC algorithm followed by the minimization of the quadrate sum of points-plane distance. Moreover, this paper will take an in-depth look at the influence of data resolution in the accuracy of plane extraction and at the necessary accuracy for the construction of a BIM model. A laser scanner survey of a three floors building composed by 36 scan stations has produced a point cloud of about 550 million points. The estimated plane parameters at different data resolution are analysed in terms of distance from the full points cloud resolution.