## 4 Experiment
### Ablation Study and Analysis
**Comparison with Different Number of Input Point Features in AE and Segmentation Network:** We demonstrate the effects of involving more features of input in our model. The codeword dimensions for all four experiments were taken as 512. Input point size was 2048 both in the AE training stage and the segmentation network training stage. Results are described in Table 8.  
1. The input point feature only contains coordinates \((x,\,y,\,z)\);
2. The input point feature contains coordinates and radiometric information \((x,\,y,\,z,\,r,\,g,\,b)\);
3. The input point size contains coordinates, radiometric information and geometric information \((x,\,y,\,z,\,r,\,g,\,b,\,normal)\); and
4. The input point size contains coordinates, normalized coordinates' radiometric information and geometric information \((x,\,y,\,z,\,x^{\prime},\,y^{\prime},\,z^{\prime},\,h,\,s,\,v,\,normal)\), where the normalized coordinate is following the setting in DGCNN_Mod [5]. From the data in Table 8, it is apparent that increasing the number of features per point is beneficial for training a good network in most cases. For example, by adding color features, our performance on \"Scene_B\" slightly goes up, and when adding color and normal features at each point, performance is optimal on both test scenes (0.513 and 0.464 of mIoU on \"Scene_A\" and \"Scene_B\", respectively). It is worth noting that adding geometric information (normal) results has a more significant improvement than adding radiometric information (RGB values). However, the performance of our model will decrease rather than increase when involving too many input features (12 features per point). This is probably due to the fact that our training dataset is very small, and too many features will result in over-fitting our training process. Compared to the fully supervised DGCNN-Mod [5], our results are slightly lower than theirs when employing more features in our model. Our mIoU is 0.513 on \"Scene_A\" compared to 0.535 obtained from DGCNN-Mod, while the results of \"Scene_B\" are very close (0.464 vs. 0.470). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Seg\_n\_Points** & **AE\_n\_Points** & **Codeword\_Dims** & **OA\_Scene\_A** \\ \hline
2048 & 2048 & 512 & 0.747 \\
4096 & 2048 & 512 & 0.773 \\
2048 & 2048 & 1024 & 0.722 \\
4096 & 2048 & 1024 & 0.694 \\
2048 & 4096 & 512 & 0.497 \\
4096 & 4096 & 512 & 0.494 \\
2048 & 4096 & 1024 & 0.520 \\
4096 & 4096 & 1024 & 0.502 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ablation analysis of the varying number of input point size in the AE training stage and segmentation network training stage. **Comparison with Different Training Data Size:** To evaluate the impact of the training data size (both labeled and unlabeled) we further provided more solid experiments on four aspects:  
1. To add one scene (\"4_CA_church\", as shown in Figure 9) as unlabeled training data in the AE training stage;
2. To add one scene (\"4_CA_church\") as labeled training data in the segmentation network training stage;
3. To add one scene (\"4_CA_church\") both in the AE and the segmentation network training stage; and
4. To decrease the labeled training data size whilst keeping one scene (\"7_SMV_chapel_24\") in the segmentation network training stage. The segmentation results of \"Scene_A\" are shown in Table 9. These achievements suggest that, if we add labeled training data in the segmentation network training stage, our performance could further improve. For instance, when we add one labeled scene in the segmentation network training stage, our performance on the AE pre-trained network increases by 1% and 3% on three scenes and four scenes, respectively. Furthermore, no increase was detected when we tried to add the unlabeled training data, which infers that training the AE from three scenes provides a good representation to learn. More importantly, we can further prove that our network is label efficient.