## 1 Introduction  
Low-altitude economy (LAE) [1], as a new economic form, has rapidly developed in the past two years. The unmanned aerial vehicle (UAV), as an important aspect of LAE, has played an increasingly important role. UAV images are not constrained by orbital and temporal resolution limitations, offering advantages such as rapid imaging, immediate data retrieval, and a reduced yet highly accurate measurement range for terrestrial objects. These benefits provide an efficient data source for 3D reconstruction and applications in aerial scenes. They also exhibit excellent potential in various application domains, such as agricultural management [2], logistics delivery [3], urban planning [4], and emergency response [5]. As an important research topic, multiview UAV image-based 3D surface reconstruction [6] has achieved promising progress in the past few decades, and many commercial software programs have emerged. However, in traditional photogrammetry-based methods, radiance information (RGB values) is mostly used to extract features for image matching and is then discarded. Subsequently, the whole 3D reconstruction is geometrically performed, focusing only on the relative and absolute orientation of the stereo image pairs to obtain the exterior orientation parameters of each image. Following this, a final digital surface model (DSM) is generated using the forward intersection method. Thus, the reconstructed 3D model lacks the radiometric attributes of ground objects, and another texture reprojection process such as digital differential correction is needed for visual display purposes. The neural radiance field (NeRF) [7] offers a promising solution. With the NeRF, the whole scene is treated as a radiation field composed of several rays. A neural network is employed to estimate the volume density of all sampled points in the 3D space, and the RGB values generated by each ray in an image are calculated according to the ray-marching algorithm [8]. The NeRF training process is self-supervised with the images and corresponding camera poses. Unlike traditional 3D reconstruction methods, NeRF can adjust the transmittance of a single ray rather than its direction, thus having more stable convergence with the help of radiance. Recently, scholars have presented several NeRF-based methods to deal with DSM generation based on remote sensing images. For example, S-NeRF [9] introduces shadow information to decouple rays and constructs neural radiation fields for satellite images. In Sat-NeRF [10], the concept of a \"transient object\" is introduced to address inconsistent transient objects in multitemporal remote sensing images. However, the former NeRF variants for close-range and satellite views are not suitable for UAV images due to the different imaging model used, and the original point sampling strategy causes large computational redundancy due to the distance of the camera from the ground objects. In addition, we found that NeRF completely relies on radiance features for decoding, but the changeable geometry in the imaging process affects the acquisition of radiance. The most obvious factor is the field of view (FOV). As shown in Figure 1a, a larger FOV corresponds to a larger ground area, thus bringing larger radiance energy received by the corresponding pixels of the sensor. This can lead to biases in the process of calculating the colors of the sampled points. For UAV photogrammetry, the strip flight line and the changeable flight attitude cause frequent changes in the incident angle; as such, it is necessary to use the geometric conditions in traditional photogrammetry to correct the radiance acquisition. To solve these issues, we propose an end-to-end 3D reconstruction and radiance acquisition method, UAV-NeRF, for UAV image scenes based on Sat-NeRF [10]. First, to make NeRF suitable for UAV images, we sampled field points according to the UAV geometric imaging model.