# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
## 5 Discussion  
This paper presents GeoTemplateKPNet, a novel self-supervised deep-learning framework that leverages the strengths of CNNs for learning robust, geometrically consistent keypoints specifically tailored for structured template images, as demonstrated on road-marking templates. The experimental evaluation, conducted in a synthetic template test set with applied affine transformations, aimed to demonstrate the potential of this approach to overcome the limitations of traditional methods on feature-poor template data and serve as a foundational step toward template-to-real-world instance alignment. The quantitative results from the keypoint-matching comparison (Section 4.2), ARE (Section 4.2.2), and IMR (Section 4.2.1), supported by the qualitative results (Section 4.2.3), provide insights into the performance of GeoTemplateKPNet compared to those of traditional feature-based methods and the impacts of different configurations. The keypoint-matching comparison (Section 4.2) revealed that while traditional methods, like SIFT+RANSAC and ORB+RANSAC, can achieve high success rates in finding the minimum count of matches, particularly ORB at lower \(N_{min}\) thresholds, they still fail to consistently provide sufficient correspondences across all the test cases, especially at higher \(N_{min}\) values for SIFT. However, a crucial finding highlighted by the ARE analysis (Section 4.2.2) and visually demonstrated in the qualitative results (Section 4.2.3) is that a high match count from traditional methods does not necessarily guarantee geometric accuracy. ORB+RANSAC, despite its relatively high success rate in finding matches, often yields visually inaccurate and unreliable alignments on the template data, as evidenced by its higher ARE values compared to those of SIFT and GeoTemplateKPNet and the poor visual reconstruction quality. SIFT+RANSAC tends to produce better visual alignment when it finds sufficient matches, but its ability to consistently find enough reliable matches is limited compared to those of ORB or the number of heatmaps predicted using our model. Our proposed GeoTemplateKPNet, by learning to predict a fixed set of keypoints (varying by K), consistently provides a defined number of correspondences for alignment in the template test set when it successfully localizes keypoints. More importantly, the ARE results (Section 4.2.2) demonstrate that when GeoTemplateKPNet successfully predicts keypoints, the resulting template-to-transformed-template alignment is generally more accurate than that achieved using traditional methods. As shown in Table 5, the GeoTemplateKPNet configurations, particularly VGG-16 with K = 3, achieve the lowest overall average AREs, indicating superior alignment accuracy compared to those of both SIFT+RANSAC and ORB+RANSAC. This indicates that the self-supervised learning process effectively guides the model to learn keypoints that are not only localizable but also geometrically reliable for estimating accurate transformations on template structures. The IMR results (Section 4.2.1) demonstrate that the learned keypoints adhere to the template boundaries, with the adherence generally varying with the K values and backbones. The per-class breakdowns of IMRs and AREs (Tables 4, 6, and 7) highlight significant performance variations across the different template geometries and configurations. Classes with simpler, more prominent features (like Classes 0 and 1) tend to have higher IMRs and lower AREs, indicating more successful keypoint localization and accurate alignment. The ablation studies (Section 4.2.5) on the effect of K on the performance further demonstrate that the number of keypoints influences the performance. While increasing K might improve the IMR for some classes, such as Classes 3 and 4, which benefit from higher K values, the overall ARE generally increases with higher K values, supporting the observation that K = 3 often provides the most suitable balance for the overall alignment accuracy.