Beyond Local Refinement: Wide-Area Precise Cross-View Localization

TL;DR

This paper introduces RingMatch, a search-and-verification framework for wide-area cross-view localization that overcomes challenges from large spatial disparities and visual ambiguity by using predictive uncertainty to narrow candidate regions and a depth ring representation to capture subtle spatial layouts. The method enables precise localization across aerial regions up to 400m in extent, significantly expanding the scope beyond prior narrow-prior assumptions.

Why It Matters

Wide-area cross-view localization is critical for real-world applications like autonomous navigation and geospatial AI, where initial localization is often coarse. By enabling accurate matching over large regions without relying on tight geographic priors, this work enhances robustness and scalability in practical deployment scenarios.

Key Findings

• RingMatch effectively reduces search space using predictive uncertainty, enabling scalable localization over large aerial regions.
• The depth ring provides a view-shared geometric signature that captures fine-grained spatial layout differences, improving ambiguity resolution.
• VIGOR-Pro benchmark demonstrates that RingMatch maintains high accuracy as aerial region size increases, outperforming end-to-end and retrieval-based methods.