# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
###### Abstract
In addressing the fundamental need for a robust geometric understanding of these visual elements, particularly their template structures, we propose utilizing deep-learning techniques to learn reliable keypoints that overcome the limitations of traditional feature detection in feature-poor templates. The manual annotation of keypoints in extensive datasets  
Figure 1: Example of real-world visual cues: (**a**) an underground parking area with marking signs on the floor and walls at Chaoyang University of Technology, Taichung, Taiwan. These markings can mimic those used in factory settings to guide and automate machinery in transportation and other processes, and (**b**) a road-marking arrow on a road surface in Taichung City, Taiwan. Both types of markings are essential for guidance and safety but are subject to environmental degradation and variations in appearance, posing challenges for robust visual analysis and alignment. is often unfeasible. Consequently, focusing on road-marking templates as representative structured elements, we introduce GeoTemplateKPNet. This innovative framework utilizes self-supervised learning to accurately predict robust, geometrically consistent keypoints tailored to the distinct geometries or structures of specific visual element classes, using only synthetic template data for training and experimentation. This eliminates the need for manual keypoint labels and avoids reliance on real-world instance data during experimental training and evaluation. Our core idea is that meaningful keypoints or features can be learned by enforcing equivariance and consistency constraints under various relevant transformations applied to input template data. Uniquely, GeoTemplateKPNet is trained and experimentally evaluated exclusively on synthetic template images, enabling the model to learn features that are robust to geometric transformations of the template itself. We employ a multi-head architecture where a shared backbone extracts common visual features, and class-specific heads learn to predict \(K\) keypoints tailored to the distinct geometry or structure of each visual element class, focusing on five arrow-based road-marking template types in this work. Despite advancements in image alignment, a persistent issue remains: aligning structured, feature-poor templates accurately to their transformed versions. This challenge is critical in areas like road-marking analysis, where templates (e.g., arrow shapes and crosswalks) lack distinctive textures and are geometrically simple and and symmetrical. Traditional feature-based methods, such as SIFT or ORB, fail due to the unreliability of their features. Moreover, creating comprehensive datasets with manually labeled ground-truth keypoints for all road-marking variants is impractical and often undefined. This gap--lacking a reliable method for self-supervised keypoint learning on these templates--hinders applications needing precise geometric analysis, such as automated condition assessment and maintenance planning. This paper proposes a novel framework to learn geometrically consistent keypoints in a self-supervised manner on these challenging template structures. The keypoints learned by GeoTemplateKPNet on the template serve as a foundation for robust geometric analysis. By detecting corresponding keypoints on a template and its transformed version (using the class ID to select the appropriate class head) and establishing correspondences, we can estimate the transformation that aligns the template. Achieving such robust and accurate alignment on templates, where traditional methods often fail due to insufficient features, demonstrates the significant potential of our method.