## 6 Conclusions and Outlook  
The current bottleneck in 3D indoor reconstruction is the low level of automation and accuracy in the reconstruction of the complex indoor environment. To address this problem, we proposed a novel method that combines the rich structures of lines and 3D geometric information of surfaces to automatically build a three-dimensional structured model from MLS point clouds. First, a fully automatic room segmentation is performed on the unstructured point clouds via multi-label graph cuts to overcome over-segmentation problems. The floorplan lines are then extracted and regularized from the image to obtain detail structural information. Finally, the segmented room, line, and surface elements are used as semantic information, and the 3D structured models are reconstructed by multi-label graph cuts. We showed how our proposed approach is able to accurately reconstructreal-world datasets without requiring manual operation. Also, the signal intensity simulation for 5G small base station was conducted using the results of our 3D model, which showed how our proposed technique can be very useful in such an application.  
We tested our method on four real-world datasets acquired using the MLS. In analyzing the results, we included the assessment of the geometric elements, time-efficiency, and geometric errors in the evaluation. Experimental results show that the reconstructed structured models, including ceilings, floors, walls, doors, windows, and pillars, etc. The Combination of linear structures with 3D geometric surfaces to reconstruct structured models, which improve the computational efficiency and structural accuracy. The resulting models show that the geometric error of is within 0.1m for different indoor scenes. The detection of geometric elements is highly dependent on the geometric quality of point clouds. For our future endeavors, we will try to combine image and point clouds to further enrich the model results, which could help improve opening detection and compensate for poor point cloud quality. We will also reconstruct the full volumetric models using the extracted geometric elements, and further close to the requirements for Building Information Modeling. Finally, we will be investigating further the use of our approach in optimal location for 5G small base stations and other similar technologies, as well as considering other applications that may benefit from our approach.  
Conceptualization, Y.C., Q.L.; methodology, Y.C., Q.L. and Z.D.; software, Y.C. and Z.D.; validation, Y.C. and Q.L.; formal analysis, Y.C. and Z.D.; investigation, Y.C. and Z.D., resources, Q.L.; writing--original draft preparation, Y.C., Q.L. and Z.D.; writing--review and editing, Y.C., Q.L. and Z.D.; visualization, Y.C. and Z.D.; supervision, Q.L. and Z.D.; project administration, Q.L. and Z.D.  
This research was funded by the Key Program of the National Natural Science Foundation of China (No. 41531177), the National Science Fund for Distinguished Young Scholars of China (No. 41725005), the Technical Cooperation Agreement between Wuhan University and Huawei Space Information Technology Innovation Laboratory (No. YBN2018095106), the National Natural Science Foundation of China (No. 41901403), the National Key Research and Development Program of China (No. 2016 YFB0502203).  
We would like to thank the ISPRS Commission WG IV/5 for provision of the data. In addition, we also thank to Key Laboratory of Sensing and Computing for Smart City and the School of Information Science and Engineering, Xiamen University for providing the corridor and parking lot datasets, and special thanks to the professional English editing service from EditX to improve the language.  
The authors declare no conflict of interest.