Metadata-free Georegistration of Ground and Airborne Imagery
To address the misalignment of cross-platform 3D models caused by the absence of geospatial metadata in ground- and airborne imagery, this paper proposes a fully metadata-free georegistration method. Our approach leverages only publicly available satellite imagery and a digital surface model (DSM), integrating neural radiance fields (NeRF) modeling with differentiable rendering to achieve robust geolocalization of airborne imagery via multi-view geometric optimization, and subsequently enables precise alignment of ground-level imagery to the airborne 3D model. We introduce, for the first time, a novel “satellite + DSM + NeRF” collaborative registration paradigm, supporting unified georegistration of non-overlapping and disconnected 3D models. Evaluated across multiple real-world scenes, our method achieves an average geolocation error of under 5 meters—without requiring any raw sensor metadata such as GPS or IMU readings.