## 4 Experiment
### Ablation Study and Analysis  
The following part of this paper describes in greater detail the design choices of our label-efficient model by a set of experiments. We also show the effects of our chosen hyper-parameters. **Effectiveness of DGCNN Encoder:** In Table 3, we show the positive effects of our proposed DGCNN-based encoder. In order to compare the performance of FoldingNet's encoder and our improved encoder on the semantic segmentation task, we set up two sets of controlled experiments. In the first set of experiments, we used one scene as training data, and in the second set, we used three scenes that we commonly used in this study as training data to compare the segmentation results. As shown in Table 3, compared to FoldingNet, the performance of the DGCNN-based encoder has a 7% boost while using one scene in the training stage and a 14% improvement while using three scenes in the training stage, testing on \"Scene_B\". **Effectiveness of Data Augmentation:** In Table 4, we demonstrate the positive effects of data augmentation. Compared to excluding translation (but using random rotation and jitter data augmentations), there is an 11% performance boost when all three augmentations are used together on \"Scene_A\". In the same case, a 4% improvement is obtained when testing on \"Scene_B\". Since \"Scene_B\" contains both indoor and outdoor elements, while our three training scenes contain only outdoor elements, it is more challenging to perform semantic segmentation on \"Scene_B\" than on \"Scene_A\", and it is also harder to improve the performance. The following experiment was conducted with the data augmentation strategies. **Effectiveness of Reconstruction Loss:** Table 5 demonstrates the positive effects of using original Chamfer Distance (CD) loss [57] as our reconstruction loss. Compared to using modified CD loss in FoldingNet [22], our performance on \"Scene_A\" of the ArCH dataset has a 6% boost while using a 512-dimensional codeword in the AE training stage and a 1% improvement while using a 1024-dimensional codeword and original CD loss in the AE training stage. The following experiment utilized the original CD loss as reconstruction loss. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Augment** & **OA\_Scene\_A** & **OA\_Scene\_B** \\ \hline wo\_translation & 0.631 & 0.649 \\ w\_translation & 0.747 & 0.681 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation analysis of using or not data augmentation. Semantic segmentation results for the “Scene_A” and “Scene_B” of ArCH dataset. The names “wo_translation” and “w_translation” denote without using translation and using translation in the training process. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Encoder** & **Training Scene** & **OA 1** \\ \hline FoldingNet & 1 scene & 0.425 \\ FoldingNet & 3 scenes & 0.42 \\ DGCNN-based & 1 scene & 0.493 \\ DGCNN-based & 3 scenes & 0.561 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of semantic segmentation results for “Scene_B” of ArCH dataset using different encoders. “1 scene” (“SMV_24”) and “3 scenes” (“SMV_1”, “SMV_24”, “SMV_28”) in “Training Scenes” denote that we used it only for one scene or three scenes in both AE and segmentation network training stage. **Comparison with Different Dimensions of Codeword:** We used a 512-dimensional codeword and a 1024-dimensional codeword as controlled experiments to compare the segmentation result of buildings' point clouds. In the AE training phase, the input point-cloud size was fixed at 2048, and we set up two control groups to compare the segmentation results for different codeword dimensions:  
1. The input point size was 2048 in the segmentation training stage; and
2. The input point size was 4096 in the segmentation training stage. The semantic segmentation result of \"Scene_A\" is shown in Table 6. The model performs better when the dimension of the codeword is 512 than when the dimension of the codeword is 1024, regardless of whether the input point size is 2048 or 4096 in the semantic segmentation network.