# Implicit-Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
## 2 Implicit-Explicit Coupling-Enhancement-Based 3D Reconstruction
### Implicit Synthetic Co-Visibility-Cluster-Guided Dense 3D Reconstruction
#### 2.1.1 Implicit Representation of UAV Large-Scale Scenes  
In this work, we commence with an input sequence of UAV-captured frames for a large-scale scene, denoted as \(K=\{i=1,2,3,\ldots,n\}\), without prior trajectory planning or preprocessing. Our approach initially employs the visual odometry provided by a dense reconstruction module [35] to generate accurate depth and pose information. Similar to the RAFT [36] framework, we iteratively and globally optimize camera pose and depth information with finer updates to the optical flow field. This iterative process is particularly well suited for addressing issues such as drift in long trajectories and loop closures that commonly arise in large-scale UAV scene reconstruction. Upon determining the 3D point positions (D) and camera parameters (T) that minimize the reprojection error, this data is employed as supervised training data for the subsequent training of the large-scale implicit model for UAV-based scene reconstruction. This approach effectively leverages the accurate depth and pose information obtained from visual odometry to generate supervised data for training the implicit model. This step is crucial for achieving high-quality large-scale scene reconstructions using UAVs without the need for prior trajectory planning and preprocessing. In contrast to conventional large-scale implicit modeling, this work primarily focuses on non-professional data acquisition with UAVs. Unplanned flights can introduce more variation in pitch and roll angles, resulting in irregular alignment of non-professional UAV images. This irregularity is mainly evident in significant variations in heading and cross-track overlap, leading to the possibility of some parts of the scene being missed in the images, sparse image overlap, and significant changes in yaw angles between adjacent image frames. However, utilizing a single multi-layer perceptron (MLP) in traditional neural radiance field (NeRF) models has limitations in terms of capacity. If a specific area in the scene cannot capture a substantial number of images and complex scene details, it can lead to underfitting in the neural radiance field, resulting in significant blurring of synthesized new perspective images. This can be detrimental for the subsequent training of implicit models for large-scale scenes and can negatively impact modeling accuracy. Based on the characteristics of UAV-captured data, it is observed that the images in the sequence are arranged in chronological order, representing the progression of time. Consequently, adjacent images along the time axis are likely to exhibit high spatial similarity. However, within short time intervals, the number of images may be insufficient to supervise the construction of implicit models. Regardless of whether the aerial photography is professional or non-professional, the nature of UAV flights often involves multiple repeated scans and loops in a given region. This implies that images taken at different times might depict the same scene. Recognizing this pattern, the approach adopted in this work is to divide the entire large-scale scene into regions based on the UAV's trajectory. For each region, a spatiotemporal consistency co-visible cluster is constructed, ensuring that each region of the large scene has a sufficient number of images with high overlap. This method provides each region of the large scene with good training data for implicit modeling. Specifically, the approach commences with the first frame \(I_{1}\) of the aerial image sequence as the starting point. It then utilizes the LoFTR [37] method, which can perform keypoint detection for full-image coverage and is effective in handling low-texture or repetitive pattern areas, to conduct similarity matching with subsequent images. For similarity calculation, any image frame with a similarity exceeding a predefined threshold \(\xi_{covisibility}\) (typically around 20%) is considered as forming a spatial co-visible relationship.