Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80675
Title: Semantic geometric modelling of unstructured indoor point cloud
Authors: Shi, W 
Ahmed, W 
Li, N 
Fan, W 
Xiang, H 
Wang, M 
Keywords: 3D modelling
3D segmentation
Backpack laser scanner
Graph cut
Indoor scene
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: ISPRS international journal of geo-information, 2019, v. 8, no. 1, 9 How to cite?
Journal: ISPRS international journal of geo-information 
Abstract: A method capable of automatically reconstructing 3D building models with semantic information fromthe unstructured 3Dpoint cloud of indoor scenes is presented in this paper. Thismethod 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.
URI: http://hdl.handle.net/10397/80675
EISSN: 2220-9964
DOI: 10.3390/ijgi8010009
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