GeoRefGS: Towards Georeferenced 3D Gaussian Splatting from Unmanned Aerial Vehicle Platforms

Three-dimensional reconstruction using unmanned aerial vehicle (UAV) platforms has been extensively utilized in various fields. While conventional techniques such as oblique photogrammetry can produce mesh models with geographical references, they often require substantial computational resources. Although recent studies have attempted to incorporate camera pose parameters into the emerging 3D Gaussian Splatting (3DGS), these methods often treat georeferencing as a post-processing step or rely on global bundle adjustment, which may propagate systematic errors and compromise final accuracy. This work integrates georeferencing as an intrinsic constraint during 3DGS training, enabling simultaneous optimization of geographic and photometric accuracy. The core of our approach lies in introducing a similarity transformation matrix T connecting the local model space with the global geographic coordinate system, along with a dedicated geographic loss function. Geographic coordinates are transformed via T before reprojection to compute the loss function. It was demonstrated that GeoRefGS presents a viable solution for efficiently integrating georeferenced information into 3DGS. Indeed, the proposed framework achieves an improvement of approximately 3.31 dB in peak signal-to-noise ratio while maintaining distance errors below 0.054 m, enabling reliable geographically referenced 3D reconstruction in substantially less time compared to conventional photogrammetric approaches.