# Implicit-Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
## 1 Introduction
In summary, it is evident that existing professional large-scale indoor modeling [25, 26, 27] requires extensive division, block-level computation, data merging, heightened computational complexity, and reduced fault tolerance. The entire workflow process encounters numerous challenges, emphasizing the necessity for operators to possess extensive experience in both indoor and outdoor operations to adeptly handle various situations that may arise. For the majority of non-professionally captured UAV imagery, the accuracy of utilizing existing reconstruction methods is significantly compromised and may even lead to failure. The entire data collection and reconstruction process is tailored for professional operators, rendering it complex, time-consuming, and costly. Consequently, extending building scene modeling with UAVs to ordinary users and achieving consumer-level applications proves challenging. Recently, the advent of NeRF technology [28] has facilitated the modeling of non-professional UAV-captured data [29]. Through the calibration of UAV image parameters, precise implicit model reconstruction is attainable, enabling the reconstructed implicit model to generate high-definition images from specified viewpoints. Addressing unbounded scenes, NeRF++ [30] partitions the space into foreground and background regions, employing separate MLP models for each region. These models independently conduct ray rendering before their final combination. Furthermore, there are implicit modeling methods specifically designed for large scenes, such as CityNeRF [31], which utilizes multiscale data inputs, including LiDAR, to achieve large-scale modeling. In contrast, BlockNeRF [32] and Mega-NeRF [33] spatially decompose the scene, aligning with our approach in the implicit modeling stage. However, the spatial decomposition algorithms in these methods are relatively straightforward, resulting in mediocre performance. In this paper, our objective is to develop a more flexible and straightforward large-scale UAV scene modeling method that strikes a balance between high accuracy and efficiency. This method harnesses the mutually beneficial aspects of implicit and explicit models at different stages of modeling, employing a novel neural radiation field to synthesize images with spatiotemporal coherence between frames. Subsequently, it engages in dense depth estimation unit-by-unit using co-visible clusters and integrates the implicit scene model to recover missing spatial information, resulting in a more comprehensive and high-quality dense point cloud. In the modeling phase, it reverses the neural radiation field to recover keyframe poses that were initially omitted during reconstruction initialization. Furthermore, it integrates more texture images into the signed distance field (SDF) [34] to generate a more realistic and high-quality scene mesh model. The comparative results can be observed in Figure 1. The innovations in this paper are as follows:  
**(1) A novel implicit-explicit coupling framework is designed for improving UAV large-scale scene modeling performance. ** The dense reconstruction module (explicit) supervises the neural radiation field (implicit) for the precise implicit modeling of large-scale UAV scenes. Expanding on this foundation, the neural radiation field employs neural rendering to synthesize high-precision scene images. This compensates for occlusion and depth missing data caused by obstacles and environmental noise during the dense reconstruction process, thereby further enhancing the accuracy of the dense point cloud reconstruction. Ultimately, in the mesh modeling stage, the texture-rich images overlooked in the dense reconstruction are recovered by reversing the neural radiation field, thereby improving the details of the mesh modeling. The experiments show that our method achieves SOTA performance compared with the related mainstream methods and commercial software. **(2) An implicit synthetic co-visibility-cluster-guided dense point cloud reconstruction is proposed.