Ortho-3DGS: True Digital Orthophoto Generation From Unmanned Aerial Vehicle Imagery Using the Depth-Regulated 3D Gaussian Splatting

True digital orthophoto maps (DOMs) are vital spatial data sources due to their high precision, detail, and accessibility. However, traditional generation methods using image differential correction with DEM/DSM often produce significant distortions from inaccurate surface data and missing building information. Conventional geometric stitching and radiometric correction methods struggle to improve quality. While neural radiance field (NeRF)-based view synthesis offers progress, issues remain in training efficiency, rendering quality, and scene editability. To address these limitations, we propose Ortho-3DGS, a novel method for orthophoto generation from UAV imagery using 3D Gaussian Splatting (3DGS). Unlike NeRF, our approach models scenes via 3D Gaussian ellipsoids, optimized with depth supervision and gradient-based refinement for explicit and accurate reconstruction. We design a dedicated orthophoto rendering pipeline to generate high-quality, distortion-free DOMs efficiently. Experiments show Ortho-3DGS surpasses traditional tools (ContextCapture, Pix4Dmapper, Metashape) and NeRF-based methods (Ortho-NeRF, Ortho-NGP) in both radiometric and geometric performance. Ortho-3DGS offers commercial-grade accuracy while effectively mitigating distortions and artifacts, especially in complex environments. These results demonstrate its value for fast, precise orthophoto generation in diverse geospatial applications.