# Implicit-Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
## 4 Conclusions  
Due to challenges such as the trajectories of UAV data acquisition and environmental noise, UAV large-scale scene modeling often yields low model accuracy, incompleteness, and blurry geometric structures and textures. This paper proposes a novel implicit-explicit coupling framework for a high-accuracy UAV large-scale scene 3D modeling framework. It comprises an implicit co-visibility-cluster-guided large-scale scene dense 3D reconstruction and an implicit 3D pose-recovery-based 3D modeling and surface texture enhancement. Initially, the framework utilizes implicit rendering to synthesize new views, ensuring spatiotemporal consistency in co-visible relationships. Subsequently, within the co-visibility clusters, it employs implicit scene models to restore missing depth information from explicit multi-view pixel matching. Finally, the framework jointly optimizes image and depth information fused into the signed distance function (SDF) and estimates spatially varying lighting for building surface texture images and geometric shapes, resulting in high-quality 3D models of buildings. In comparison to existing methods, our approach achieves significant improvements in accuracy and reconstruction details, demonstrating strong competitiveness against commercial software. Nevertheless, limitations exist, such as vulnerability to the influence of dynamic objects and restricted generalization to new scenes, necessitating substantial training data for different scenarios. On the other hand, due to the extensive use of implicit modeling in this paper to compensate for perspective and depth information loss caused by occlusion or blurriness, our approach inherits limitations from the neural radiance field (NeRF) method when dealing with large-scale, complex, or dynamic three-dimensional scenes involving UAVs. These limitations include:  
(1) **Memory and Computational Efficiency Issues**: NeRF typically demands significant memory for storing and optimizing neural network parameters. Especially, as the scene size increases, the required parameter count sharply rises. Training time and computational costs significantly increase with the complexity and scale of the scene, impacting the practical applicability of our method in large-scale UAV scene modeling. (2) **Resolution Limitation Issues**: The modeling resolution produced by our method is constrained by the number of points sampled by NeRF and the learning capacity of the network. In large scenes, maintaining overall and local accuracy may necessitate higher-resolution sampling, greatly escalating computational complexity. Therefore, the model  
Figure 5: The results of the three-dimensional mesh reconstruction. may struggle to accurately capture intricate geometric and textural details in distant or extensive areas. (3) **Scalability and Updatability Issues in Scene Reconstruction**: Original NeRF methods face challenges in achieving incremental updates and expansions for continuously expanding large or dynamically changing scenes. In other words, efficient learning and model updates focusing on newly added or altered parts are difficult. To address this, new algorithmic frameworks need to be designed to support modular, chunk-based, or adaptive updates, better meeting the modeling requirements of large-scale and dynamic environments. Our current method solely focuses on dense 3D modeling and model surface texture accuracy in large-scale UAV scenarios. However, the resulting 3D scene models lack interactivity, editability, necessary semantic information for users, and generalization across different dynamic and static scenes, making them challenging for direct practical applications. Hence, future enhancements in this work will consider:  
(1) **Integration of Semantic and Instance Segmentation**: Developing a 3D semantic reconstruction framework for large UAV scenes that integrates semantic information, not only for geometric modeling but also for semantic understanding of the scene and object-level decoupling mapping.