Satellite-Free Training for Drone-View Geo-Localization
Drone-view geo-localization (DVGL) typically assumes access to satellite imagery during task-specific development. We study a late-arriving-gallery setting, where model development relies only on grouped source UAV observations and the target satellite gallery remains unavailable until the model is frozen. We propose \ours, Orbit Completion Learning, which uses source-side multi-view geometry as privileged training information while preserving image-only deployment. OCL reconstructs each source scene using structure-from-motion and 3D Gaussian Splatting, and renders canonical complementary views to complete its observation orbit. An exponential-moving-average teacher aggregates the comple...
| Authors | Tao Liu, Yingzhi Zhang, Xingrun Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 April 2026 |
| Last updated at source | 13 October 2026 |
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