## 4 Experiment
### Results
The closed-loop polyhedron was created using the constrained Delaunay triangulation [51], which the detected closed polygons are as boundary rings. Figure 18c,d illustrate that the reconstructed models and point clouds are well matched. For the Xiamen University corridor, a high accuracy indoor structured model was obtained. Figure 19a,b show the structural models and the wall models, which are presented with detailed regularization information and accurate room representation with uneven ceiling heights. The doors (green) and windows (red) were correctly detected and completely embedded within the walls. The point clouds and the reconstructed model are well-matched, as presented in Figure 19c,d. Figure 18: Structural model results of the corridor at the Shenzhen University. (**a**) The structural model. (**b**) Vector model of walls and pillars. (**c**) Matching between point clouds and the structural model. (**d**) Matching between point clouds and vector model of walls and pillars. For the parking lot in Xiamen University, the point clouds showed an excessively high level of noise. However, our proposed framework is still well-built even with incomplete data caused by severe occlusion (see Figure 20a,b). Our approach can auto-complete and generate closed-loop polyhedrons, and also correctly reconstruct the slant ceiling, floor, vertical walls, and regularized pillars. However, some curved walls are represented by many small polygons. The reconstructed models matched well with the original point clouds, as shown in Figure 20c,d. Figure 19: Structural model results of the corridor at the Xiamen University. (**a**) The structural model. (**b**) Vector model of walls. (**c**) Matching between point clouds and structured model. (**d**) Matching between. More details of the results are displayed in Figure 21, despite the presence of noises and incomplete in the point clouds, our reconstructed models are of high correctness and well fit to the original point clouds. For 5G signal intensity simulation, we tested our method on the structural model reconstructed from the benchmark data, as shown in Figure 22. The signal intensity is shown to drastically decrease from the base stations when three were mounted on the first floor, as illustrated by the changing colors of intensity (red to blue) in Figure 22a. In order to visualize the trend of signal intensity loss, a horizontal intensity profile was generated using the IDW interpolation method and is shown in Figure 22b. Similarly, the results from the multipath signal propagation and horizontal profile from the second floor are shown in Figure 22c,d. In Figure 22e, the received energy value is shown to significantly decrease with increasing distance under the path loss model. Figure 20: Structural model results of the parking lot at the Xiamen university (**a**) The structural model with slant floor and ceiling. (**b**) Vector model of walls and pillars. (**c**) Matching between point clouds and structured model. (**d**) Matching between point clouds and the vector model of walls and pillars. Figure 21: Close-up views of selected details. (**a**) Benchmark point clouds and reconstructed model. (**b**) The point clouds and reconstructed model of the corridor at the Shenzhen University. (**c**) The point clouds and reconstructed model of the corridor at Xiamen University. (**d**) The point clouds and reconstructed model of the parking lot at Xiamen University. Figure 22: 5G signal intensity simulation based on the structural model by benchmark data. (**a**) The multipath signal propagation on the first floor. (**b**) Horizontal profile of signal intensity on the first floor. (**c**) The multipath signal propagation on the second floor. (**d**) Horizontal profile of signal intensity on the second floor. (**e**) Received energy value.