True orthophoto generation from unmanned aerial vehicle imagery using neural radiance fields with multi-resolution hash encoding

True orthophotos have become one of the most important spatial foundational geographic information due to their accurate geometric information and realistic texture features. The traditional method of producing true orthophotos is limited by the accuracy of digital elevation model (DEM)/digital surface model (DSM)/three-dimensional (3D) models. It inevitably has non-orthophoto issues, such as facade distortions and edge deformations, especially in areas with buildings. To address these challenges, we propose an innovative method that leverages a neural radiance field integrated with multi-resolution hash encoding. This method generates orthophotos directly from multi-view images and does not require additional data such as DEM, DSM, or 3D models. In comparison with the existing methods, our experimental results have achieved high-quality orthophotos, addressing various issues such as building facade distortions, building edge deformations, tree area deformations, and the presence of moving objects in orthophotos. In addition, evaluating the orthophoto generation method requires a ground truth dataset. We offer an open-source dataset containing multi-view images of unmanned aerial vehicle scenes and corresponding ground truth orthophotos of the area. This dataset comprises real-world and synthetic data, encompassing diverse scenes such as cities, trees, lakes, and more. It can be used for a comprehensive assessment of the quality of orthophotos produced by different methods. Our proposed method has been thoroughly evaluated using a wide range of challenging datasets. The experimental results demonstrate that our method outperforms traditional algorithms and the relatively latest method.