Spatial context improves cross view geo localization accuracy
Learning Holistic Scene Layout against View Shifts in Cross-View Geo-Localization
Computer Vision and Pattern Recognition
Summary
Finding locations from images taken at very different angles is hard because pictures can look very different. The authors noticed that people recognize places by looking at the arrangement of multiple objects rather than just single details. They created a new method that looks at the layout of scenes by analyzing features along four directions to get a fuller picture. They also added a way to focus on stable edges and shapes to avoid confusion from changes in color or lighting. Their method works well in tests, even when the images are from unusual angles or limited views.
What this means in practice
- •For mapping engineers: Improve map matching accuracy by integrating layout-aware scene features under varying viewpoints.
- •For drone navigation teams: Enable drones to localize themselves accurately from aerial images taken at unusual angles or partial views.
Authors
Chao Yang, Songlian Li, Zhigang Tu
Abstract
Cross-view geo-localization remains challenging due to drastic appearance variations and geometric distortions caused by viewpoint changes. Existing methods primarily rely on discriminative feature learning or explicit geometric alignment, yet often overlook an important observation: humans tend to match scenes by integrating the spatial context of multiple landmarks rather than relying solely on isolated local visual cues. Inspired by this, we propose a Spatial Context Modeling (SCM) module, which traverses feature maps along four orthogonal directions and models interactions among the resulting directional representations at each spatial position. By aggregating complementary directional observations, SCM constructs richer scene layout representations. Since reliable spatial context depends on robust visual features, we introduce a High-Frequency Information Guidance (HFIG) module that extracts stable structural cues from the frequency domain. By emphasizing edges and contours that are less sensitive to appearance variations, HFIG encourages the model to learn more robust representations. Extensive experiments demonstrate that our method achieves competitive performance on standard cross-view geo-localization benchmarks while exhibiting strong robustness under more challenging scenarios with unknown orientations and limited fields of view (FoVs).