# Fast 3D Reconstruction of UAV Images Based on Neural Radiance Field
## 1 Introduction  
The reconstruction of 3D geometry from multiple images has been a long-standing problem in the fields of oblique photogrammetry and computer vision [1]. In the era before the advent of deep learning, traditional methods for multi-view 3D reconstruction could be broadly categorized into two groups: point- and surface-based reconstruction [2, 3, 4] and volumetric reconstruction [5, 6, 7, 8, 9]. Point- and surface-based reconstruction methods extract and match image features across different views by exploiting the photometric consistency [10] between images to estimate the depth value of each pixel [11]. They then fuse the depth maps into a global dense point cloud [12]. To obtain the surface, an additional meshing step such as Poisson surface reconstruction [13] is applied. The quality of reconstruction heavily relies on the accuracy of correspondence matching. Difficulties in matching correspondences for objects without rich textures often result in severe artifacts and missing parts in the reconstructed output. Furthermore, these methods with complex pipelines may accumulate errors at each stage, often resulting in incomplete 3D models. This is especially true for non-Lambertian surfaces, as they cannot handle view-dependent colors. Alternatively, volumetric reconstruction methods overcome the challenge of explicitcorrespondence matching by estimating occupancy and color in a voxel grid from multi-view images [14]. They then evaluate the color consistency of each voxel. Voxel-based approaches are often limited to low resolution due to their high memory consumption [15]. In contrast, neural radiance fields [16] for 3D reconstruction do not suffer from discretization artifacts, as they represent surfaces based on the level set for a neural network with continuous outputs. By combining neural radiance fields with the classic 3D reconstruction method, it is possible to reconstruct the smooth surface of a 3D object from a 2D image, showcasing its effectiveness beyond the classical 3D reconstruction method. This novel approach opens up new possibilities for research and applications in the fields of oblique photogrammetry [17] and computer vision. Despite the significant potential demonstrated by deep learning-based methods in 3D reconstruction, limited research has been conducted on their application in UAV image reconstruction [18]. Hence, it is crucial to conduct further studies and explore the potential of employing these methods in this field. However, the quality of classical multi-view 3D reconstruction heavily depends on the accuracy of feature matching. In regions with limited texture information, feature matching becomes challenging, leading to incomplete model generation. Moreover, this traditional approach involves complex procedures and is associated with significant time costs. In recent times, the field of multi-view 3D reconstruction has witnessed remarkable advancements due to the rapid development of deep learning methods. State-of-the-art results have been achieved by research projects such as Colmap [19] and MVSnet [20]. Nevertheless, even with these advanced methods, reconstructing scenes in areas with weak textures remains a persistent challenge, and the computational time required remains high. Although neural radiance fields, represented by models like Nerf [16], have shown promise in generating complete 3D scenes, their applicability is currently limited to smaller indoor objects and scenes. Further research is needed to extend their capabilities for more diverse and complex scenes. To address challenges like model incompleteness, discontinuity, and high time costs in UAV oblique photogrammetry 3D reconstruction, we present a fast 3D reconstruction method for UAV oblique photogrammetry based on neural radiance fields. By integrating UAV oblique photogrammetry techniques with neural radiance fields, we have developed a method that generates continuous 3D surfaces.