# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
## 4 Results
### Keypoint-Matching Comparison vs. SIFT/ORB
#### 4.2.3 Qualitative Results  
This subsection presents selected visual examples to qualitatively illustrate the alignment performance of our proposed GeoTemplateKPNet method (focusing on the K = 3 configuration for both backbones) and to provide a direct visual comparison with traditional feature-based methods (SIFT+RANSAC and ORB+RANSAC) in the synthetic template test set. These examples complement the quantitative metrics by showing the visual accuracy of the resulting geometric alignments and the typical behavior of each method.  
Figure 5 presents a visual comparison of the alignment results obtained from GeoTemplateKPNet (ResNet-50, K = 3), GeoTemplateKPNet (VGG-16, K = 3), SIFT+RANSAC, and ORB+RANSAC for several example image pairs from the test set. Each row in the figure corresponds to a single test example, showing the source template, the target-transformed template, and the reconstructed template obtained by warping the source template using the transformation estimated using each method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**K** & **Class 0** & **Class 1** & **Class 2** & **Class 3** & **Class 4** \\ \hline
3 & 0.094 & 0.058 & NaN & 0.150 & 0.309 \\ \hline
4 & 0.569 & NaN & 0.215 & NaN & NaN \\ \hline
5 & NaN & NaN & 0.3058 & NaN & NaN \\ \hline \hline \end{tabular}
\end{table}
Table 6: Per-class AREs in the synthetic test set for GeoTemplateKPNet with the ResNet-50 backbone.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**K** & **Class 0** & **Class 1** & **Class 2** & **Class 3** & **Class 4** \\ \hline
3 & 0.108 & 0.070 & 0.112 & 0.160 & 0.311 \\ \hline
4 & 0.303 & 0.270 & NaN & 0.364 & NaN \\ \hline
5 & 0.267 & 0.604 & NaN & NaN & 0.501 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Per-class AREs in the synthetic test set for GeoTemplateKPNet with the VGG-16 backbone.  
The visual comparisons in Figure 5 highlight the differences in alignment performances. GeoTemplateKPNet, when successfully predicting keypoints (which vary by class and configuration, as discussed in the quantitative results), produces accurately reconstructed templates that closely resemble the original, demonstrating the geometric reliability of the learned keypoints for alignments. While SIFT+RANSAC can also achieve good visual alignment when it finds sufficient reliable matches, it often struggles to consistently provide enough correspondences across the dataset (as shown in Table 2). ORB+RANSAC, despite sometimes reporting a high count of sufficient matches (Table 2), frequently results in visually poor and unrecognizable reconstructed templates, indicating that the matches deemed as inliers by RANSAC are not geometrically accurate for this type of template data (supporting the higher ARE values in Table 5). These visual examples reinforce the quantitative findings, demonstrating that compared to traditional feature-based methods, our self-supervised approach learns keypoints that enable more consistent and accurate alignment on template data.  
Figure 5: Qualitative comparison of template-to-transformed-template alignment results. Each row shows the source template, the target-transformed template, and the reconstructed template after alignment using ORB+RANSAC, SIFT+RANSAC, and GeoTemplateKPNet (ResNet-50, K = 3), highlighting the differences in the reconstruction accuracies and method reliabilities.