## 4 Experiment
### Results and Analysis  
If the features obtained by the proposed AE are already distinctive, the required number of labeled data in the semantic segmentation network training process should be small. In this section, to demonstrate this intuitive statement, we report our experiment's results for the ArCH dataset. We evaluated our model on two unseen scenes (\"Scene_A\" and \"Scene_B\") for testing. In Table 1, the overall performances are reported and compared with respect to the state-of-the-art (SOTA) methods, i.e., PointNet [35], PointNet++ [64], PCNN [65] and DGCNN [21], which are trained with 10 scenes and retrieved from Pierdicca [5]. For a fair comparison, we maintain consistency of input features for all methods (including only \(x,y,\ z\) coordinates such as most SOTA methods). The DGCNN was improved in [5] by adding additional input features, which we will compare and analyze in Section 4.4. Overall, with only about 10% of training data of the SOTA methods in both AE and segmentation network training stages, our model achieves the best results for the ArCH dataset with the same training strategy (only input \(x,\ y,\ z\) coordinates), as shown in Table 1. In particular, the test mIoU of \"Scene_A\" is 0.463, which overcomes the previous SOTA result (0.376). The mIoU of \"Scene_B\" is 0.408, which also outperforms the 0.353 of SOTA. As mIoU takes the false alarms and the unbalance of different categories into consideration, our mIoU is higher than SOTA methods, while OA is lower. Furthermore, we compared our results for \"Scene_A\" and \"Scene_B\" with the SOTA when training on the same subset (three scenes), as shown in Table 1. The results of \"Ours\" also outperform the ones from the SOTA methods. The results of per-class quantitative evaluations for \"Scene_B\" are provided in Table 2. We can see that the proposed model performs better than other competitive methods in many classes. In particular, we achieve considerable gains in the arch, column, wall and vault categories. In the ArCH dataset, the vault is pasted onto the roof and difficult to \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Training Scenes** & **Test Scene** & **mIoU** & **OA** \\ \hline PointNet [35] & 10 scenes & Scene\_B & 0.114 & 0.35 \\ PointNet++ [64] & 10 scenes & Scene\_B & 0.121 & 0.528 \\ PCNN [65] & 10 scenes & Scene\_B & 0.26 & 0.629 \\ DGCNN [21] & 10 scenes & Scene B & 0.29 & 0.74 \\ DGCNN [21] & 15 scenes & Scene A & 0.376 & 0.784 \\ DGCNN [21] & 15 scenes & Scene B & 0.353 & 0.752 \\ DGCNN [21] & 3 scenes & Scene A & 0.243 & 0.499 \\ DGCNN [21] & 3 scenes & Scene B & 0.163 & 0.362 \\ Ours & 3 scenes & Scene\_A & 0.463 & 0.773 \\ Ours & 3 scenes & Scene\_B & 0.408 & 0.666 \\ \hline \hline \end{tabular} Note: The OA and mIoU of SOTA methods (PointNet, PointNet++, PCNN and DGCNN) that use 10 scenes (9 for training and 1 for validation) are retrieved from Tables 5 and 6 of [PERSON] [5], respectively. Performances of DGCNN that use 15 scenes are updated in ArCH dataset website (archdataset.polito.it/. Accessed on 24 September 2021).  
\end{table} 
Table 1: Our results vs. prior work on ArCH dataset. OA and mIoU denote Overall Accuracy and mean Intersection over Union, respectively. Our method performs best on mIoU with only 3 scenes (about 10% of total number of points in 10 scenes). delineate geometrically, but it is apparent from Figure 4 that our model can still segment them out. Figures 5 and 6 describe the confusion matrices of the segmentation results when using nine features on \"Scene_A\" and \"Scene_B\", respectively. Each row of the confusion matrix represents the instances in a predicted class, while each column represents the instances in an actual class. It is easy to see whether our model is confusing two categories by a confusion matrix. For example, in Figure 5, the column and molding classes are often confused with the wall, possibly due to the columns and moldings having a similar geometrical shape/appearance to walls.