## 4 Experimental Test  
### Input Data  
The proposed method was tested on seven real and eight synthetic datasets of indoor scenes. The algorithm was implemented by the Computational Geometry Algorithms Library, Cloud Compare, and MATLAB. All of the experiments were performed on a 3.60 Hz Intel Core i7-4790 processor with 12 GB of RAM. Real dataset: Figure 14a illustrates the seven real building model datasets, and their statistics are shown in Table 1. Dataset-1 was captured by an MLS device from [24]. Dataset-2 and -3 were obtained by [23]. Dataset-2 was captured by an MLS device in a Zeb-Revo sensor. Dataset-3 was acquired by a Zeb-1 sensor. Dataset-4 and -5 were provided by [20; 22] and were captured by RGB-D sensors. Dataset-6 and -7 were acquired by RGB-D sensors from [21]. Clutter and occlusion were present in these datasets. Dataset-1, -2, and -3 were captured by an MLS device. The density of these point clouds was moderate, and the accuracy was much better than that of Dataset-4 to -7. Dataset-4 to -7 were obtained by RGB-D sensors; their accuracy was not as good as in Dataset-1, -2, and -3. Dataset-3, -4, and -5 were acquired in a multistory building. The clutter in Dataset-3 was low, while the clutter and occlusion were moderate in  
designed to evaluate the performance on a multistory building with ring-shaped corridor. Synthetic Data-4 was tested for reconstructing indoor interiors with round rooms and curving walls. Synthetic Data-5 was a large-scale indoor environment with more than fifty rooms that shared different ceiling and floor heights. Moreover, the thickness of the wall in certain rooms varied in Synthetic Data-5, which inhibited wall-line extraction and room segmentation. Synthetic Data-6, -7, and -8 shared the same indoor structures, but their scanning conditions were different. Synthetic Data-6 was tested for reconstructing a building with arbitrary orientations along the z-axis. Neither of its wall lines was restricted along the x-axis or y-axis. In Synthetic Data-7, abundant furniture was present in the building, especially at the corners of walls. Many missing regions were present in corners, which inhibited indoor wall-line extraction. In Synthetic Data-8, many areas were removed from the scanning data to test the effect of missing data, as shown in row 8 in Figure 15b. The removed areas in the first floor in Synthetic Data-8 were located inside a room, and the removed areas in the second floor in Synthetic Data-8 were located in the area that was sandwiched between rooms. Quantitative evaluations on the reconstruction results were conducted by using five metrics: IoU (intersection over union), DDP (Euclidean distance deviation between corner points), ADR (area deviation between rooms), completeness and correctness. The IoU metric is defined as the ratio between the area of intersection, which is overlaid on the segmented map with the ground truth of the area of union. The DDP metric represents deviations between the selected corner points in the created floor map and reference data. This measure indicates the robustness against over- or under-segmentation [36]. The ADR metric represents deviations in rooms in the same location between the created floor map and ground-truth data. \[IoU=\frac{Area\ of\ Intersection}{Area\ of\ Union} \tag{4}\]  
\[DDP=dis(P_{m}-P_{a}) \tag{5}\]  
\[ADR=Area_{m}-Area_{a} \tag{6}\]  
\[Completeness=\frac{TP}{TP+FN} \tag{7}\]  
\[Correctness=\frac{TP}{TP+FP} \tag{8}\]  
where \(P_{m}\) is the selected corner points for ground truthing; \(P_{a}\) represents the same points from the proposed method; \(Area_{m}\) is the area of the room in the reference map; \(Area_{m}\) is the area of the same room in the reconstructed floor map; TP represents true positives, which refer to the number of rooms,