## 4 Experiment
### Results  
The algorithm was implemented in C++, edited by Microsoft Visual Studio 2017. All experiments were performed with a Window 10, 64-bit operating system with an Alienware Intel (R) Core (TM) i7-7700 HQ CPU @ 2.80 GHz and a 16 GB RAM. Preliminary visual results of the structured model showed correctness and completeness of the model. For the benchmark data (shown in Figure 17a), the width and length of the extracted doors \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Benchmark** & **Corridor (Shenzhen University)** & **Corridor (Xiamen University)** & **Parking lot (Xiamen University)** \\ \hline Number of points & 21.560.263 & 1.980.911 & 2.098.634 & 7.683.766 \\ \hline Clutter & Low & High & Low & High \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specifications of point clouds. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Values** & **Descriptions** \\ \hline Extracting Openings & & \\ \(C_{2D}\) & 0.05 \(m\) & The size of the pixel (point clouds transform into image) \\ \(w_{d}/h_{d}\) & 0.7 \(m\leq w_{d}\leq 1.5\)\(m\) & The width and height of regularized door \\ \(w_{w}/h_{w}\) & 0.5 \(m\leq w_{w}\leq 1.5\)\(m\) & The width and height of the regularized window \\  & 0.5 \(m\leq h_{w}\leq 1.5\)\(m\) & The width and height of the regularized window \\ \hline Segmentation of Rooms & & \\ \(C_{3D}\) & 0.1 \(m\) & The size of the 3D grid (point clouds transform into 3D grid) \\ \(a_{i}\gamma\) & 1.0/0.5 & Parameters of data term and smooth term of the energy function \\ \hline Line Global Optimization & & \\ \(\theta_{\text{max}}\) & 0\({}^{\circ}\leq\theta_{\text{max}}\leq 45^{\circ}\) & Angle correction of lines \\ \(d_{\text{max}}\) & 0 \(\leq d_{\text{max}}\leq 0.1\)\(m\) & Distance correction of lines \\ \(K\) & 50 & k-nearest of lines \\ \(\lambda\) & 0.9 & The weight parameter of line global optimization \\ \hline Cluster Similar Lines & & \\ \(a_{thread}\) & 5\({}^{\circ}\) & Angle threshold of merging similar lines \\ \(d_{thread}\) & 0.1 \(m\) & Distance threshold of merging similar lines \\ \hline
5G Signal Intensity Simulation & & \\ \(d_{p}\) & 100 \(m\) & The signal propagation distance \\ \(f\) & 100 \(GHz\) & The frequency of the electromagnetic wave \\ \(m\) & 1 & The power exponent by IDW interpolation \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of the proposed indoor structural model method. (green) and windows (yellow) were close to the true value. The room segmentation results of the first and second floors (as shown in Figure 17b) showed that the unstructured point clouds were correctly partitioned based on the multi-label graph cuts. In order to ensure the model accuracy, the structural model was reconstructed using visible point clouds, which eliminated the error from wall thickness estimation. Figure 17c,d show the reconstructed models to have detailed wall information. The doors (yellow) and windows (red) were correctly positioned and completely embedded within the walls, and the adjacent rooms had different heights. The structured model and original point clouds were well-matched, as shown in Figure 17e. Figure 17: Opening extraction, room segmentation, structural model and wireframe model results with the benchmark point clouds. (**a**) Doors (green) and windows (yellow) of the first and the second floors. (**b**) Segmented rooms of the first and second floors. (**c**) The structural models with doors and windows of the first and the second floors. (**d**) The wireframe models with doors and windows of the first and second floors. (**e**) Matching between the point clouds and the structured models on the first and second floors. For the corridor data of Shenzhen University, the acquired point clouds suffered from multiple reflections and refraction due to the glass walls, which resulted in the dramatic challenges during model reconstruction. Figure 18a shows the structured models, while Figure 18b shows that the detailed vector models of walls, pillars, the doors (green).