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ISPRS Int. J. Geo-Inf. 2019, 8(1), 9; https://doi.org/10.3390/ijgi8010009

Semantic Geometric Modelling of Unstructured Indoor Point Cloud

Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
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Received: 24 October 2018 / Revised: 24 November 2018 / Accepted: 17 December 2018 / Published: 27 December 2018
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Abstract

A method capable of automatically reconstructing 3D building models with semantic information from the unstructured 3D point cloud of indoor scenes is presented in this paper. This method has three main steps: 3D segmentation using a new hybrid algorithm, room layout reconstruction, and wall-surface object reconstruction by using an enriched approach. Unlike existing methods, this method aims to detect, cluster, and model complex structures without having prior scanner or trajectory information. In addition, this method enables the accurate detection of wall-surface “defacements”, such as windows, doors, and virtual openings. In addition to the detection of wall-surface apertures, the detection of closed objects, such as doors, is also possible. Hence, for the first time, the whole 3D modelling process of the indoor scene from a backpack laser scanner (BLS) dataset was achieved and is recorded for the first time. This novel method was validated using both synthetic data and real data acquired by a developed BLS system for indoor scenes. Evaluating our approach on synthetic datasets achieved a precision of around 94% and a recall of around 97%, while for BLS datasets our approach achieved a precision of around 95% and a recall of around 89%. The results reveal this novel method to be robust and accurate for 3D indoor modelling. View Full-Text
Keywords: backpack laser scanner; 3D modelling; indoor scene; 3D segmentation; graph cut backpack laser scanner; 3D modelling; indoor scene; 3D segmentation; graph cut
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Shi, W.; Ahmed, W.; Li, N.; Fan, W.; Xiang, H.; Wang, M. Semantic Geometric Modelling of Unstructured Indoor Point Cloud. ISPRS Int. J. Geo-Inf. 2019, 8, 9.

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