arXiv:2510.22736v2 Announce Type: replace 
Abstract: Cross-view localization and synthesis are two fundamental tasks in cross-view visual understanding, which deals with cross-view datasets: overhead (satellite or aerial) and ground-level imagery. These tasks have gained increasing attention due to their broad applications in autonomous navigation, urban planning, and augmented reality. Cross-view localization aims to estimate the geographic position of ground-level images based on information provided by overhead imagery while cross-view synthesis seeks to generate ground-level images based on information from the overhead imagery. Both tasks remain challenging due to significant differences in viewing perspective, resolution, and occlusion, which are widely embedded in cross-view datasets. Recent years have witnessed rapid progress driven by the availability of large-scale datasets and novel approaches. Typically, cross-view localization is formulated as an image retrieval problem where ground-level features are matched with tiled overhead images feature, extracted by convolutional neural networks (CNNs) or vision transformers (ViTs) for cross-view feature embedding. Cross-view synthesis, on the other hand, seeks to generate ground-level views based on information from overhead imagery, generally using generative adversarial networks (GANs) or diffusion models. This paper presents a comprehensive survey of advances in cross-view localization and synthesis, reviewing widely used datasets, highlighting key challenges, and providing an organized overview of state-of-the-art techniques. Furthermore, it discusses current limitations, offers comparative analyses, and outlines promising directions for future research. We also include the project page via https://github.com/GDAOSU/Awesome-Cross-View-Methods.

تسلط التطورات الأخيرة في تحديد المواقع المتقاطعة والتوليف الضوء على أهميتها في مجالات مثل الملاحة الذاتية والتخطيط الحضري. من خلال تقدير المواقع الجغرافية بفعالية من الصور على مستوى الأرض، تمهد هذه التقنيات الطريق لتطبيقات محسّنة في الواقع المعزز. لا يُظهر هذا التقدم فقط إمكانيات مجموعات البيانات المتقاطعة، بل يفتح أيضًا فرصًا جديدة للابتكار في الفهم البصري.

Los recientes avances en localización y síntesis de vistas cruzadas destacan su importancia en campos como la navegación autónoma y la planificación urbana. Al estimar de manera efectiva las posiciones geográficas a partir de imágenes a nivel del suelo, estas técnicas están allanando el camino para aplicaciones mejoradas en realidad aumentada. Este progreso no solo muestra el potencial de los conjuntos de datos de vistas cruzadas, sino que también abre nuevas oportunidades para la innovación en la comprensión visual.

Les récentes avancées en matière de localisation et de synthèse croisées soulignent leur importance dans divers domaines tels que la navigation autonome et l'urbanisme. En estimant efficacement les positions géographiques à partir d'images au niveau du sol, ces techniques ouvrent la voie à des applications améliorées en réalité augmentée. Ce progrès met en lumière le potentiel des ensembles de données croisées et ouvre de nouvelles opportunités d'innovation dans la compréhension visuelle.

The recent advancements in cross-view localization and synthesis highlight their significance in various fields like autonomous navigation and urban planning. By effectively estimating geographic positions from ground-level images, these techniques are paving the way for enhanced applications in augmented reality. This progress not only showcases the potential of cross-view datasets but also opens up new opportunities for innovation in visual understanding.

Cross-view Localization and Synthesis -- Datasets, Challenges and Opportunities

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Cross-view Localization and Synthesis -- Datasets, Challenges and Opportunities

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