# Implicit-Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
## 2 Implicit-Explicit Coupling-Enhancement-Based 3D Reconstruction  
This article presents an implicit-explicit coupling framework for UAV large-scale scene modeling. This framework eliminates the necessity for professional UAV trajectory planning and data preprocessing, enabling high-quality large-scale 3D reconstruction with non-professional aerial photography. The resultant model surfaces also exhibit finer textures. The overall framework is illustrated in Figure 2.  
Specifically, the proposed framework addresses the challenge of processing non-trajectory-planned and non-preprocessed UAV cityscape aerial images. The framework is meticulously structured into two distinct stages, each contributing to the overall efficacy of the reconstruction process.  
In the initial stage, a cutting-edge multi-view 3D reconstruction method is strategically employed. This method serves the crucial purpose of rapidly estimating intricate camera parameters and depth information within tightly knit clusters of co-visible images. In this way, the framework gains access to highly accurate poses and depth information, which play a pivotal role in supervising the neural radiation field. This supervision is paramount for establishing the implicit representation of the large-scale scene, ensuring the faithful reconstruction of the intricate details within the cityscape.  
Furthermore, to enhance the completeness and accuracy of the reconstructed scene, a sophisticated neural radiance field (NeRF) model is brought into play. This well-trained NeRF model is specifically tailored to the characteristics of the large scene under consideration. Leveraging this model, the framework synthesizes images and corresponding dense depth data for regions where information may be missing or overlap is limited due to unplanned flight paths. This integration of the NeRF model not only addresses potential  
Figure 2: Overall framework of our method. This work employs the NeRF model to supplement low-overlap co-visibility clusters, which are then processed by the explicit reconstruction module for the dense reconstruction. Meanwhile, the reverse radiation field is utilized for missing camera pose recovery and achieve high-precision texture mapping through the illumination model. Ultimately, large-scale scene modeling of UAVs is achieved through dense reconstruction with texture details.  
data gaps but also significantly contributes to the overall robustness of the reconstruction process.  
In summary, the framework's comprehensive approach begins with the rapid acquisition of camera parameters and depth information, followed by the meticulous supervision of the neural radiation field for implicit scene representation. The incorporation of a specialized NeRF model further enhances the reconstruction accuracy by synthesizing data for areas with limited information, ultimately culminating in an explicit high-accuracy mesh model of the UAV cityscape aerial images.  
Subsequently, the synthesized data from the NeRF model is utilized to optimize the co-visible relationships within the multi-view 3D dense reconstruction. This optimization specifically addresses feature mismatch problems arising from low overlap, occlusion, and environmental noise caused by unplanned flight paths and pose planning. Concurrently, depth information is employed to rectify point cloud data in textureless or repetitive texture areas, generating a dense, high-precision point cloud model. Finally, the image and depth information are fused into a signed distance field (SDF), and the reversal of the neural radiation field is applied to recover the excluded near-range image poses. This precision texturing of building exteriors results in a high-quality 3D model of large-scale scenes. Each key step of the algorithm will be elaborated upon in the subsequent sections.