## 6 Conclusions and Future Work  
This paper presents methods to automatically reconstruct indoor navigation elements from unstructured 3D point cloud of buildings with occlusions and openings. To reiterate the value of the proposed methods, we summarized the process taking a practical work example as shown in Figure 18. This is a real indoor scene consisting of a long corridor and six rooms. The indoor scene contains a lot of occlusion. We accurately reconstructed the navigation network and the 3D model. This is difficult for other methods to successfully extract some elements such as connecting transition spaces and \"hidden doors\". The methods extract possible planes from the point cloud as building components. The walls \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Real Room Number** & **Detected Real Room Number** & **Detected Wall Surface Number** \\ \hline Synth & 3 & 3 & 25 \\ Office1 & 7 & 7 & 106 \\ Office2 & 6 & 6 & 36 \\ Room & 6 & 6 & 29 \\ \hline
**Data** & **Door Number** & **Detected Door Number** & **Correctnesson Door** & **Completenesson Door** \\ \hline Synth & 3 & 3 & 1 & 1 \\ Office1 & 18 & 18 & 0.88 & 0.88 \\ Office2 & 5 & 5 & 1 & 1 \\ Room & 4 & 5 & 0.80 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of some experiments without real ground data. Figure 17: Doors with different geometric features. (**a**) Thick door (**b**) Thin door. are restored using the point cloud horizontal projection information. Next, the indoor space is segmented by simulating the work of the laser scanner. Then, we used the statistical graph to fill the missing walls and set thresholds to obtain information about the doors. The reconstructed building models were also optimized using the relationship between doors and connecting transition space. Finally, the indoor navigation element model and the navigation network are generated. The proposed methods were tested on different building datasets with complex structures and occlusions. The experiments show that the method can detect and reconstruct indoor navigation elements and navigation networks without viewpoint information. The mean deviation in the reconstructed model is in the range of 0-5 cm, and the completeness and correctness are greater than 80%. These results demonstrate the robustness and effectiveness of the method. However, the proposed method also has some limitations, for example, for \"thin doors\" with a large amount of occlusion, the method in this paper is not successful in extracting them and needs to be combined with other point cloud information (e.g., density, intensity, color, etc.). Secondly, the reconstruction results will be different from the actual situation when facing building components with larger curvature or rooms with fewer planes. In the future, we intend to extend the existing approach to indoor environments containing building structures with large curvature. Facing the missing wall segments caused in cluttered environments, we will try to build a semantic model library to provide better restoration solutions for the missing wall segments. Finally, indoor path planning in cluttered environments is also the next direction we will investigate. **Author Contributions:** Conceptualization, G.L., S.W. and S.Z. ; methodology, G.L., S.W. and S.Z. ; software, G.L. and S.H. ; validation, G.L. and S.W. ; writing--original draft preparation, G.L. ; writing--review and editing, S.W., S.Z. and R.Z. ; visualization, G.L. and S.H. All authors have read and agreed to the published version of the manuscript. Figure 18: Practical work examples. (**a**) is a real scene. (**b**) is the real point cloud. (**c**) is the navigation network. (**d**) is the 3D semantic model. **Funding:** This research is partially supported by National Natural Science Foundation of China (Grant no. 72174031) and the Beijing Key Laboratory of Operation Safety of Gas, Heating and Underground Pipelines. **Institutional Review Board Statement:** Not applicable.