Tortho-Gaussian: splatting true digital orthophoto maps

True Digital Orthophoto Maps (TDOMs) are essential products for digital twins and Geographic Information Systems (GIS). Traditionally, TDOM generation involves a complex photogrammetric pipeline, which may deteriorate due to various challenges, including inaccurate Digital Surface Model (DSM), unreliable occlusion detections, and visual artifacts in weakly textured regions and reflective surfaces, etc. To address these challenges, we introduce Tortho-Gaussian, a novel method inspired by 3D Gaussian Splatting (3DGS) that generates TDOMs through orthogonal splatting of optimized anisotropic Gaussian kernels. More specifically, we first simplify the orthophoto generation by orthogonally splatting the Gaussian kernels onto 2D image planes, formulating a geometrically elegant solution that eliminates the need for an explicit DSM and occlusion detection. Second, to produce TDOM of large-scale area, a divide-and-conquer strategy is adopted to optimize memory usage and time efficiency of both training and rendering for 3DGS. Lastly, we design a fully anisotropic Gaussian kernel that adapts to the varying characteristics of different regions, particularly improving the rendering quality of reflective surfaces and slender structures. Extensive experimental evaluations demonstrate that our method outperforms existing commercial software in several aspects, including the accuracy of building boundaries and visual quality in low-texture regions and on building facades. These results underscore the potential of our approach for large-scale urban scene reconstruction, offering a robust alternative for enhancing TDOM quality and scalability. Project Web: https://github.com/xwangSGG/Tortho_Gaussian.