# Implicit-Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
## 2 Implicit-Explicit Coupling-Enhancement-Based 3D Reconstruction
### Detail-Implicit Texture Mapping Enhancement for High-Accuracy Mesh Modeling
#### 2.2.1 Implicit Pose-Recovery-Based Texture Refinement  
In order to achieve higher modeling accuracy, it is necessary to collect information at multiple heights during aerial data acquisition. In addition to capturing data from scenes at higher altitudes, it is common to include close-range information such as details of buildings. However, traditional reconstruction methods often discard images containing fine details, as they typically lack a sufficient number of co-observable scene images and are, thus, excluded during scene modeling. These images, containing rich texture information, have the potential to significantly improve the accuracy of scene modeling.  
To effectively utilize this information, it is essential to remap multi-view images containing potential texture details back into three-dimensional space. Nevertheless, the images filtered out during the reconstruction process have lost their original camera poses, making texture mapping challenging. Inspired by the implicit modeling of the entire UAV scene,drawing inspiration from inerf [39], we can establish a differential relationship between the camera pose T and the rendered image I through an end-to-end implicit neural rendering method. Given the camera pose \(T\), the rendered image I can be represented as \(I=F_{\theta}(T)\), where \(\theta\) represents the learnable parameters of the neural network. Thus, if the scene image for the pose to be estimated is denoted as \(I_{tar}\), it is necessary to solve for its true pose \(T_{tar}\). Based on the differential relationship between images and poses established by NeRF, the problem of missing image poses can be addressed by approximating the pose \(T_{est}\) to approach \(T_{tar}\) using the following equation:  
\[T_{est}=argmin_{T_{est}}||F_{\theta}(T_{est})-I_{tar}|| \tag{7}\]  
However, usually, after the optimization process mentioned above, precise pose estimation results can be gradually obtained. Nevertheless, due to the lack of depth information supervision, direct utilization of Formula (7) can easily lead to local minima in complex scenes, making it challenging to achieve optimal results. On the other hand, NeRF relies on pose initialization for iterative fitting, resulting in slow convergence and susceptibility to error accumulation. To address this, we integrate the pose calculation method of NeRF depth estimation and feature point detection. By rapidly obtaining a better initial pose for NeRF, we enhance the accuracy and efficiency of pose recovery. Initially, for scene images \(I_{tar}\) and \(I_{est}\), we extract feature points and calculate matched feature point pairs \(S_{tar,est}\). For \(I_{est}\), NeRF provides its corresponding depth \(D_{est}\). Thus, combining \(D_{est}\) and \(S_{tar,est}\), the pose estimation problem transforms into a PnP problem. The EPnP algorithm is employed to rapidly solve for an appropriate initial pose. Finally, based on more accurate pose initialization information, we iteratively fit the image to better approximate \(T_{tar}\), obtaining a higher-precision camera pose \(P_{\text{C}}\). The corresponding dense depth map is generated through the neural radiance field. Following the reconstruction method in Section 2.1.3, the texture details of the image are projected onto the reconstructed scene surface.