## 6 Conclusions  
The aim of this article was to present a processing chain conceived for the 3D semi-automatic reconstruction of indoors of existing buildings from point clouds for their integration in BIM software. The developed approach is composed of two parts. Based on indoor point clouds, the first part consists in several segmentations into spaces and planes and in the classification of points into several categories. The second part of the approach deals with the reconstruction of walls and slabs of buildings from the element point clouds extracted in the first part. At the end of the approach, a file in a BIM format is generated and reconstructed walls and slabs can be opened in BIM software.  
The approach was assessed based on two datasets which were not used for the development of the approach. The same thresholds were used for both datasets. The results obtained for the first part of the approach are promising in a geometric and a semantic point of view. Indeed, sub-spaces are almost all segmented and points are well classified into the different categories. The second part of the assessment was looked to the reconstruction of building elements. The results are also very satisfying for both datasets since, for individual house and the office building, more than 90% of objects located in the input point clouds were reconstructed automatically. Additionally, percentages of respectively 93% and 88% of walls and portions of walls detected automatically are correctly positioned and deviations from one millimetre to several centimetres are observed in relation to deliverables created manually.  
The assessment of the approach demonstrates that the same default thresholds can be applied to several datasets of quite different buildings, in other words that the approach is transferable. Moreover, the high percentages of elements correctly extracted shows that thresholds determined automatically are well determined. Finally, low deviations indicate that the reconstructed elements accurately described the as-is states of the buildings. Thus, the developed approach responds positively to the three items considered namely the transferability, the degree of automation and the geometric quality.  
Tools for the inspection of results were also proposed. Standard deviations associated to reconstructed walls and points-to-model distances were proposed as quality indexes. These tools for the quality assessment are essential since a complete automation of reconstruction seems utopian and reconstruction errors can potentially occur. Despite many scan-to-BIM approaches, currently there are very few tools on the market addressing the quality of generated Building Information Models from point clouds.  
The authors want to thank for its financial support the French College of Chartered Surveyors (Ordre des Geometres-Experts). They also want to thank the French Land Surveyors Offices [PERSON] (Paris), [PERSON] (Mandelieu) and SCHALLER-ROTH-SIMLER (Selestat) and the French company FUTURMAP (Lyon) who provided datasets for the assessment of the approach.  
Algorithms were developed by [PERSON] with the help of [PERSON] and [PERSON]. All the authors contributed to the writing of the paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BIM & Building Information Modelling \\ AEC & Architecture, Engineering and Construction \\ TLS & Terrestrial Laser Scanning \\ MEP & Mechanical, Electrical and Plumbing \\ RANSAC & RANdom SAmple Consensus \\ MW & Manhattan-World \\ PCA & Principal Component Analysis \\ MLESAC & Maximum Likelihood Estimation SAmple Consensus \\ RMSE & Root Mean Square Error \\ IFC & Industry Foundation Classes \\ \end{tabular}