## 5 Discussion  
As mentioned in the literature review, previous DL-based approaches for 3D point-cloud analysis are strongly supervised and rely on a massive amount of labeled 3D building data. An initial objective of this paper was to identify if it is possible to reduce our need for a large amount of fine labels in point clouds of the building segmentation task. In response to the research purpose, in this study, the results indicate that nowadays it is possible to provide good results for semantic segmentation, while 10 times fewer labeled training data are provided. The most important relevant finding was, as reported in Table 1, that under the same training strategy, our method may achieve SOTA performance but with only approximately 10% of training data with respect to fully supervised methods in both AE and segmentation network training stages. An implication of this is the possibility that the proposed AE architecture may learn good representations from unlabeled data, and this learned representation may be further used in downstream tasks such as semantic segmentation. For more detailed analysis, we found: (1) considerable gains in the arch, column, wall and vault categories; (2) the worst performing classes--door-window, moldings and stairs--were also the categories with fewer labels (see Figure 10): the unbalances of the number of labels between these categories result in the lower performance. In addition, as described in Section 4.4, we noticed that our results benefit from our network design choices, such as the use of a DGCNN-based encoder, data augmentation and reconstruction loss. It is worth noting that our results demonstrate (referring to Table 3) that the DGCNN-based encoder enables AE unsupervised learning to be exploited for downstream semantic segmentation tasks. Our improvement over the FoldingNet-based encoder (14% on \"Scene_B\") demonstrates, on the other hand, the effective improvement on  
Figure 10: Comparison of the number of labels in different categories in selected training scenes from ArCH dataset, Scene_A and Scene_B. \begin{table}
\begin{tabular}{c c c} \hline \hline AE\_Training\_Scene & Seg\_Training\_Scene & OA\_Scene\_A \\ \hline
3\_scene & 1\_scene & 0.695 \\
3\_scene & 3\_scene & 0.747 \\
3\_scene & 4\_scene & 0.76 \\
4\_scene & 3\_scene & 0.743 \\
4\_scene & 4\_scene & 0.772 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Ablation analysis of the varying size of training data in the AE training stage and segmentation network training stage. Columns “AE_training_scene” and “Seg_training_scene“ denote the number of scenes of ArCH dataset used in our AE and segmentation training phases, respectively. the AE architecture. Moreover, we further demonstrated the successful choice of our hyperparameters, such as various dimension size of the codeword (referring to Table 6), different input point numbers in the AE and the segmentation training stage (referring to Table 7). We found that the representation size (dimension of the codeword) strongly influences the representation quality. Increasing the representation size might hurt representation usefulness in the few training data cases, which is in contrast with the observation in relatively large datasets. From this design and hyper-parameter choices, we achieved SOTA performances. We further investigated the impact of the input feature number. With more features as input, our performances are comparable to the fully supervised method (referring to Table 8). For instance, by adding RGB and normal features at each point, the performance was optimal on both test scenes: 0.513 and 0.464 of mIoU on \"Scene_A\" and \"Scene_B\", respectively. Intuitively, we believe that the segmentation performances should be better with more features involved in the training phase. What is surprising is that our experiments showed that our performances were degraded when we followed the SOTA setup in the supervised learning approach [5], which uses 12 features (coordinates, normalized coordinates, HSV and normal).