## 6 Conclusions  
Current methods of reconstructing 3D indoor interiors focus on room-space in each individual story and show obvious defects in terms of reconstructing long corridors and connected spaces across floors. To eliminate such deficiencies, this paper presented a comprehensive segmentation method for the reconstruction of 3D indoor interiors that includes multiple stories, long corridors, and connected areas across floors. The proposed approach overcomes the over-segmentation of graph-cut operations when reconstructing long corridors and reconstructs connected areas across multiple floors by removing shared surfaces.  
The proposed method was tested with different datasets, including seven real building models and eight synthetic models. The experiments on the real models showed that the proposed method reconstructed indoor interiors without viewpoint information, which is essential for other methods. The experiments on the synthetic models with ground-truth data showed that the proposed method output accurate 3D models, with the overall IoUs reaching 95% and almost all IoUs of corridors above 95%. These findings show that the proposed method is appropriate for reconstructing long corridors. The experiments showed the robustness and availability of the proposed method.  
However, this method can only address indoor interiors with vertical walls and horizontal floors, and experiences limitations when reconstructing 3D indoor rooms of very small size. The reconstruction of lofts and attics will be considered in future work. We will also further improve our method to reconstruct volumetric solid models, rather than surfaces.  
The following are available online at [[http://www.mdpi.com/2072-4292/10/8/1281/s1](http://www.mdpi.com/2072-4292/10/8/1281/s1)]([http://www.mdpi.com/2072-4292/10/8/1281/s1](http://www.mdpi.com/2072-4292/10/8/1281/s1)). The input parameters in this paper are shown in Table S1. The parameters for the real datasets are shown in Table S2. The parameters for the synthetic datasets are shown in Table S3.  
Conceptualization, L.L., F.S., F.Y. and S.Y.; Data curation, F.L.; Investigation, X.Z.; Methodology, L.L., F.S., F.Y., H.Z., D.L., X.Z., F.L., Y.L. and S.Y.; Resources, Y.L.; Validation, F.S. and F.Y.; Writing--original draft, F.S.; Writing--review & editing, L.L.  
This research was funded by the National Natural Science Fund of China (41471325, 41671381, 41531177), Scientific and Technological Leading Talent Fund of National Administration of Surveying, Mapping and Geo-information (2014), The National Key R&D Program of China (2016 YFF0201300, 2017 YFB0503500), Hubei Provincial Natural Science Fund (2017 CFA050) and Wuhan \({}^{\prime}\)Yellow Crane Excellence (Science and Technology) program (2014).  
**Acknowledgments:** The authors acknowledge the ISPRS WG IV/5 for the acquisition of the 3D point clouds. The authors would like to gratefully acknowledge [PERSON] [21] for their help. We would like to thank [PERSON] [22] for their help in accessing and processing the data. We would like to thank [PERSON] [34] for their help in this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.