# Implicit-Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
## 1 Introduction  
The 3D point cloud reconstruction and mesh modeling of UAV large-scale scenes are key technologies in various domains such as environmental quality monitoring, geographic resource surveys, and urban planning [1]. Typically, they capture a series of images to reconstruct the 3D geometric information of scanned scenes. Consequently, a significant challenge in modern large-scale scene modeling lies in efficiently utilizing the multi-view images acquired from UAV platforms to reconstruct three-dimensional models of large-scale scenes. However, prevailing methods [2, 3, 4] for modeling extensive 3D maps from UAV aerial images often encounter challenges such as incomplete scene modeling and blurred surface geometry and textures. These issues are frequently attributed to limitations in existing visual 3D reconstruction methods and suboptimal UAV aerial image acquisition techniques. Current mainstream methods for large-scale multi-view 3D reconstruction, such as structure from motion (SfM) [5, 6, 7, 8], Multi-view stereo (MVS) [9, 10, 11], and simultaneous localization and mapping (SLAM) [12, 13], have been successfully integrated into various commercial software, producing satisfactory high-quality scene models. However, challenges arise when applying these methods to UAV video data, particularly in complex large-scale geographic environments, such as areas with diverse terrain like plateaus, plains, hills, and mountains. Various factors contribute to these challenges, including numerous blind spots in the field of view, unstable signal propagation, and thin air at high altitudes,which collectively limit the operational radii of UAVs. These limitations can impact the redundancy and effectiveness of UAVs' multi-view image collection. Even in scenarios where elements in the scene, such as buildings, may appear in multiple frames, certain aspects of the target or scene may be overlooked due to geographical interference or insufficient coverage in some images. This directly affects the accuracy of camera pose estimation and loop closure detection, resulting in reduced completeness and accuracy in subsequent 3D map modeling and leading to deformations in the modeling process. On the other hand, large-scale aerial surveys are impacted by climatic conditions and the material properties of target surfaces. The complexities of large-scale environments, with variations in lighting at different times, contribute to inconsistent data collection. Many previous methods rely on multi-view stereo (MVS) [14, 15, 16] for scene reconstruction, predicting dense depth maps for each frame and fusing them to create a comprehensive model. However, depth-based methods, while adept at accurately reconstructing local geometric shapes, face challenges in enhancing the accuracy of depth information due to inconsistencies between different viewpoints. Additionally, in complex scenes, numerous textureless, repetitive, or reflective objects (e.g., fields, water surfaces, glass walls, etc.) can directly lead to depth estimation errors or missing data, posing difficulties in effective fusion of these depth maps. Finally, existing reconstruction methods [17, 18, 19, 20, 21, 22, 23, 24], frequently omit images unsuitable for geometric modeling to mitigate the influence of redundant frames and images with excessively wide or narrow baselines on reconstruction accuracy. However, this curation process may lead to the exclusion of images containing vital texture information, thereby diminishing the quality of subsequent modeling. In scenarios with relatively sparse imagery data of building surfaces, particularly in areas lacking texture, this selective approach can substantially affect the accuracy of measurements (depth estimation), lighting consistency, the reconstruction of dense point clouds, and the realism of geometric modeling. Ultimately, for areas posing reconstruction challenges or exhibiting modeling gaps, manual filling and repair by professionals may be essential to establish a complete and detailed geometric model structure.