RingMatch improves wide-area cross-view camera localization accuracy
Beyond Local Refinement: Wide-Area Precise Cross-View Localization
Computer Vision and Pattern Recognition
Summary
Finding the exact location of a camera on the ground using aerial images is tough when looking at large areas because many places look alike from above and the views are very different. The authors created a method called RingMatch that narrows down possible locations step-by-step and uses a special ring-shaped map of the area’s depth to tell subtle differences between places. They also made a new dataset to test this method on bigger areas compared to before. Their approach works better than previous methods, even as the area gets much larger.
What this means in practice
- •For autonomous vehicle teams: Improve vehicle positioning accuracy over large areas by matching ground camera views to aerial maps using the RingMatch method.
- •For urban planning teams: Accurately verify and locate street-level images within large urban aerial datasets to support mapping and city analysis tasks.
Authors
Qingguo Hu, Xinyu Ma, Yujie Lin, Yaoxiang Wang, Qi Shi, Zonghao Guo, Chi Chen, Jinsong Su
Abstract
Fine-grained cross-view localization aims to precisely localize a ground camera against geo-referenced aerial imagery, but current methods typically assume a narrow geographic prior, substantially limiting their applicability when upstream localization remains coarse. To bridge this gap, we study this task over much larger aerial regions. However, this seemingly simple extension fundamentally changes the localization problem: the growing disparity in spatial extent between ground and aerial views makes reliable cross-view matching difficult to establish directly, while the increasing number of look-alike locations in larger regions introduces substantial visual ambiguity. To address these challenges, we propose RingMatch, a search-and-verification framework for wide-area cross-view localization. RingMatch first leverages predictive uncertainty to uncover plausible locations and progressively zooms into their surroundings to obtain increasingly fine-grained evidence, yielding a compact set of promising candidates. We then introduce the depth ring, a novel geometric representation that reveals subtle variations in the spatial layout around a location and serves as a view-shared spatial signature for an LVLM verifier to resolve residual ambiguity. To benchmark this setting, we construct VIGOR-Pro by composing VIGOR aerial tiles into contiguous regions with spatial extents of up to 400 m. Experiments show that RingMatch maintains strong localization performance as the aerial region expands, consistently outperforming representative end-to-end and retrieval-based methods.