## 5 Discussion
### Real Dataset Evaluation
#### 5.1.1 General Evaluation  
Evaluating the reconstructed results with real data might be difficult without ground-truth data. Thus, the evaluation of real data is shown in Table 2. In these tests, a corridor is considered to be one room, and only doors that are connected to rooms are considered.  
As Table 2 shows, all of the indoor rooms and corridors in a real dataset were detected in both the raw data and reconstructed models, except for Dataset-5 and -6. A false room and under-segmented room both occurred in the second floor of Dataset-5, as shown in the red and purple boxes in Figure 16. The room in the purple box exhibited extremely high levels of clutter, and heavy occlusions from walls and other structures prevented the doorway from closing. Moreover, the size of this room was very small (2.5 m\({}^{2}\)), while its connected room was the largest room (more than 50 m\({}^{2}\)). These two reasons caused the under-segmentation in the room in the purple box. The error in the red box mainly occurred because of missing data in the connected area between the corridor and this room. Two rooms were merged in Dataset-6 because two adjacent regions that are separated by an opening in the wall (not a door) are they considered to be one room, as shown in the purple region in row 6 in Figure 15c.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Real Data** & **Floor** & \begin{tabular}{c} **Room \&** \\ **Corridor Number** \\ \end{tabular} & \begin{tabular}{c} **Detected Room \&** \\ **Corridor Number** \\ \end{tabular} & \
\begin{tabular}{c} **Door** \\ **Number** \\ \end{tabular} &
\begin{tabular}{c} **Detected Door** \\ **Number** \\ \end{tabular} \\ \hline Dataset-1 & Overall & 5 & 5 & 4 & 4 \\ \hline \multirow{3}{*}{Dataset-2} & First Floor & 15 & 15 & 14 & 14 \\  & Second & 9 & 9 & 8 & 8 \\  & Floor & Overall & 24 & 24 & 22 & 22 \\ \hline \multirow{3}{*}{Dataset-3} & Overall & 7 & 7 & 6 & 6 \\ \cline{2-6}  & First Floor & 8 & 8 & 6 & 6 \\ \cline{2-6}  & Second & 12 & 12 & 10 & 10 \\  & Overall & 20 & 20 & 16 & 16 \\ \hline \multirow{3}{*}{Dataset-5} & First Floor & 9 & 9 & 8 & 11 \\  & Second & 9 & 8 & 7 & 10 \\ \cline{1-1}  & Third & 14 & 14 & 13 & 13 \\ \cline{1-1}  & Overall & 31 & 31 & 28 & 34 \\ \hline Dataset-6 & Overall & 9 & 8 & 8 & 7 \\ \hline Dataset-7 & Overall & 6 & 6 & 5 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation metrics.  
Figure 16: Comparison between a top view of original data and the room segmentation results of the second floor for Dataset-5. (**a**) Top view of the original data for the second floor in Dataset-5. (**b**) Room segmentation results for the second floor in Dataset-5.  
The number of reconstructed doors was the same as that in the real model, except for Dataset-5 and -6. The false-room segmentation results lead to the wrong number of detected doors; if two adjacent regions are merged, then the door between two rooms cannot be detected. Furthermore, a door between two adjacent regions may be added because of heavy noise in the wall, as shown in the green box in Figure 16.  
These results showed that the proposed method was robust for indoor interior reconstructions of real-world datasets. However, the test on Dataset-5 showed that room segmentation may fail in rooms with very small size and high levels of clutter. The test on Dataset-6 indicated that the door-detection method encountered difficulties when the doors were located in the under-segmented regions.