# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
## 4 Experiments
### Comparisons  
Our comparison against the state-of-the-art methods is presented in Table 1. Compared to the best performance (from SV-Seg [19]), our approach achieves HmIoU improvements of 0.7%, 1.3%, and 3.2% on the ScanNet, S3 DIS, and SemanticKITTI datasets, respectively. The mIoU improvements for unseen classes are 0.6%, 0.7%, and 2.4% for the three datasets, demonstrating that our approach effectively mitigates semantic bias and enhances generalization to unseen classes. In addition, the improvements for seen classes are 0.4%, 9.2%, and 3.1%. This indicates that fewer unseen classes are wrongly classified as seen ones, leading to an mIoU increase for seen classes. We conduct a qualitative comparison with SV-Seg [19], which performs bias calibration by subtracting a pre-defined constant value from their predicted probability scores, on the three benchmark datasets, as shown in Figure 4. Consequently, for the unseen classes, such as \"desk\" in ScanNet, \"beam\" in S3 DIS, and \"motorcycle\" in SemanticKITTI, our model outperforms SV-Seg [19], demonstrating its effectiveness in addressing the bias toward seen classes. Regarding the seen classes, such as \"sink\" of ScanNet, \"chair\" of S3 DIS, and \"poly\" of SemanticKITTI, the baseline incorrectly classifies them as unseen classes: \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Training Set**} & \multicolumn{3}{c}{**ScanNet**} & \multicolumn{3}{c}{**S3 DIS**} & \multicolumn{3}{c}{**SemanticKITTI**} \\ \cline{2-13} \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Encoder**} & \multirow{2}{*}{**Segmentor**} & \multicolumn{2}{c}{**mIoU**} & \multicolumn{2}{c}{**mIoU**} & \multicolumn{2}{c}{**mIoU**} & \multicolumn{2}{c}{**mIoU**} & \multicolumn{2}{c}{**mIoU**} & \multicolumn{2}{c}{**mIoU**} & \multicolumn{2}{c}{**mIoU**} & \multicolumn{2}{c}{**mIoU**} \\ \cline{3-13} \cline{6-13}  & & & \(\mathbf{C^{s}}\) & \(\mathbf{C^{u}}\) & \(\mathbf{All}\) & & \(\mathbf{C^{s}}\) & \(\mathbf{C^{u}}\) & \(\mathbf{All}\) & & \(\mathbf{C^{s}}\) & \(\mathbf{C^{u}}\) & \(\mathbf{All}\) & & \\ \hline Full supervision & \(\mathbf{C^{s}}\cup\mathbf{C^{u}}\) & \(\mathbf{C^{s}}\cup\mathbf{C^{u}}\) & 43.3 & 51.9 & 45.1 & 47.2 & 74.0 & 50.0 & 66.6 & 59.6 & 59.4 & 50.3 & 57.5 & 54.5 \\ Unseen classes only for segmentor & \(\mathbf{C^{s}}\) & \(\mathbf{C^{s}}\cup\mathbf{C^{u}}\) & 41.5 & 39.2 & 40.3 & 40.3 & 60.9 & 21.5 & 48.7 & 31.8 & 52.9 & 13.2 & 42.3 & 21.2 \\ Supervision with seen classes & \(\mathbf{C^{s}}\) & \(\mathbf{C^{s}}\) & 39.2 & 0.0 & 31.3 & 0.0 & 70.2 & 0.0 & 48.6 & 0.0 & 55.8 & 0.0 & 44.0 & 0.0 \\ \hline ZSLPC-Seg [32] & \(\mathbf{C^{s}}\) & \(\mathbf{C^{u}}\) & 16.4 & 4.2 & 13.9 & 6.7 & 5.2 & 1.3 & 4.0 & 2.1 & 26.4 & 10.2 & 21.8 & 14.7 \\ DeViS-3 DSeg [42] & \(\mathbf{C^{s}}\) & \(\mathbf{C^{u}}\) & 12.8 & 3.0 & 10.9 & 4.8 & 3.6 & 1.4 & 3.0 & 2.0 & 42.9 & 4.2 & 27.6 & 7.5 \\ S3 DGenZ [18] & \(\mathbf{C^{s}}\) & \(\mathbf{C^{s}}\cup\mathbf{C^{u}}\) & 32.8 & 7.7 & 27.8 & 12.5 & 53.1 & 7.3 & 39 & 12.9 & 41.4 & 10.8 & 35 & 17.1 \\ SVSeg [19] & \(\mathbf{C^{s}}\) & \(\mathbf{C^{s}}\cup\mathbf{C^{u}}\) & 34.5 & 14.3 & 30.4 & 20.2 & 58.9 & 9.7 & 43.8 & 16.7 & 46.4 & 12.8 & 39.4 & 20.1 \\ \hline Ours & \(\mathbf{C^{s}}\) & \(\mathbf{C^{s}}\cup\mathbf{C^{u}}\) & **34.9** & **14.9** & **30.9** & **20.9** & **68.1** & **10.4** & **50.3** & **18.0** & **49.5** & **15.2** & **42.3** & **23.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results are presented on three benchmark datasets. The first part introduces the upper limits of various supervision levels, while the second part demonstrates the performance of the baseline and our method on generalized 3D zero-shot semantic segmentation. In this context, \(\mathbf{C^{s}}\) represents visible classes, and \(\mathbf{C^{U}}\) represents unseen classes. \(\mathbf{C^{U}}\) represents the synthesized unseen data. \"toilet\", \"beam/column\", and \"truck\". In contrast, our model exhibits better accuracy in recognizing these seen class samples.