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Scan-to-BIM for ‘secondary’ building components
Affiliation:1. Faculty of Civil Engineering, Transportation Engineering and Architecture, University of Maribor, Slovenia;2. Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia;1. Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY, United States;2. Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States;3. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, United States;1. School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Drive, Atlanta, GA 30332-0355, USA;2. Department of Architectural Engineering, School of Engineering, Chung-Ang University, 221 Heuksuk-dong, Dongjak-gu, Seoul 156-756, Republic of Korea
Abstract:Works dealing with Scan-to-BIM have, to date, principally focused on 'structural' components such as floors, ceilings and walls (with doors and windows). But the control of new facilities and the production of their corresponding as-is BIM models requires the identification and inspection of numerous other building components and objects, e.g. MEP components, such as plugs, switches, ducts, and signs. In this paper, we present a new 6D-based (XYZ + RGB) approach that processes dense coloured 3D points provided by terrestrial laser scanners in order to recognize the aforementioned smaller objects that are commonly located on walls. This paper focuses on the recognition of objects such as sockets, switches, signs, extinguishers and others. After segmenting the point clouds corresponding to the walls of a building, a set of candidate objects are detected independently in the colour and geometric spaces, and an original consensus procedure integrates both results in order to infer recognition. Finally, the recognized object is positioned and inserted in the as-is semantically-rich 3D model, or BIM model. The assessment of the method has been carried out in simulated scenarios under virtual scanning providing high recognition rates and precise positioning results. Experimental tests in real indoors using our MoPAD (Mobile Platform for Autonomous Digitization) platform have also yielded promising results.
Keywords:Object recognition  Scan-to-BIM  Automatic BIM  3D data processing
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