## 5 Discussion
### Real Dataset Evaluation
#### 5.1.2 Floor Map Evaluation  
We used the same dataset as [21] in order to evaluate the room segmentation method and compare the results to related works. The results and a comparison with the state of the art are shown in Table 3. The results of the [PERSON] method [35], [PERSON] et al. [40], and [PERSON] et al. [14] were referenced. The completeness and correctness metrics were calculated from the number of detected rooms in the reconstructed building against the ground truth. The calculated IoU metrics for each room and corridor are shown in Figure 17.  
As Table 3 shows, all of the indoor rooms and corridors were detected with the proposed method. Moreover, the mean IoU was more than 95%, so the detected rooms and ground truth were almost consistent. According to [21], the method of [PERSON] et al., which segments rooms by graph cut, tended to over-segment corridor areas in both datasets, although the results when detecting true walls were good. Labeling the partitioned cells in the corridor area was difficult because of the presence of implausible walls through energy minimization, which was resolved by a graph-cut operation. As Table 3 shows, the completeness and correctness of [PERSON] et al. were high, while the area of the rooms was more prone to errors, such as in Dataset-6, possibly because the method of [PERSON] et al. encodes the environment into six types of structural paths, while some cases in Dataset-6 challenged this approach.  
As Table 3 shows, the proposed method had better room-segmentation results when compared to the Voronoi Method [35], [PERSON] et al. [40], and [PERSON] et al. [14]. As Figure 17 shows, the IoUs of all the corridors were more than 94%, which indicates that under- or over-segmentation in long corridors was overcome by using the proposed method. This performance could be explained as  
Figure 17: Intersection over union (IoU) of rooms and corridors (red points represent corridors). (**a**) IoU of each room and corridor in Dataset-6. (**b**) IoU of each room and corridor in Dataset-7.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Real Data**} & \multicolumn{2}{c}{**Voronoi Method**[35]} & \multicolumn{2}{c}{**[PERSON] et al. [40]**} & \multicolumn{2}{c}{**[PERSON] et al. [14]**} & \multicolumn{3}{c}{**Our Method**} \\ \cline{2-13}  & **Com.** & **Cor.** & **IoU** & **Com.** & **Cor.** & **IoU** & **Com.** & **Cor.** & **IoU** & **Com.** & **Cor.** & **IoU** \\ \hline Dataset-6 & 1 & 0.9 & 0.71 & 0.8 & 0.8 & 0.74 & 0.9 & 1 & 0.75 & 1 & 1 & 0.955 \\ Dataset-7 & 0.75 & 1 & 0.77 & 0.6 & 1 & 0.7 & 1 & 1 & 0.9 & 1 & 1 & 0.955 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results and comparison with the state of the art.  
follows: (i) the projected offset space cut off the connection between rooms and improved the precision of room-space segmentation, and (ii) overlapping the room-space segmentation results with partitioned cells through wall lines ensured the precision of boundaries in each room. Furthermore, the methods of [PERSON] et al. and [PERSON] et al. relied on viewpoints, while our method did not require prior knowledge regarding viewpoints.