# Implicit-Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
## 3 Experiments
### The 3D Point Cloud Reconstruction Accuracy  
Our GPU-accelerated offline modeling framework, based on the coupling of implicit-explicit representations for large-scale UAV aerial scenes, strikes a balance between reconstruction accuracy and efficiency in complex aerial environments. It is suitable for rapid presentation and analysis in practical applications. In order to demonstrate the effectiveness of our method based on implicit co-visibility clusters, we conducted ablation experiments by subsampling video frames. We simulated scenarios for non-professional UAV aerial scenes (overlap was only 50%). The results generated using traditional methods such as COLMAP for low-overlap images are often poor. Figure 4 presents the comparative results of point cloud reconstruction for low-overlap scenes.  
Our method achieves competitive quality in sparse and dense reconstruction compared to software like Context Capture 2023, Pix4D 4.5.6, and COLMAP 3.9. To assess the accuracy, we randomly selected several ROIs from the point cloud, search for their nearest points in the reference literature, and computed their mean Euclidean distance (MD) and standard deviation (SD). The results are provided in Table 2 (and compared to PhotoScan).  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Scene** & \multicolumn{2}{c}{**Urban**} & \multicolumn{3}{c}{**Rural**} & \multicolumn{3}{c}{**Countryside**} \\ \hline Metric & PSNR\(\uparrow\) & SSIM\(\uparrow\) & LPIPS\(\downarrow\) & PSNR\(\uparrow\) & SSIM\(\uparrow\) & LPIPS\(\downarrow\) & PSNR\(\uparrow\) & SSIM\(\uparrow\) & LPIPS\(\downarrow\) \\ \hline NeRF & 19.531 & 0.417 & 0.622 & 20.105 & 0.509 & 0.562 & 21.651 & 0.535 & 0.541 \\ TensoRF & 20.912 & 0.452 & 0.531 & 20.678 & 0.577 & 0.491 & 23.8 & 0.67 & 0.478 \\ Mega-NeRF & 23.096 & 0.466 & 0.51 & 22.625 & 0.545 & 0.477 & 23.508 & 0.642 & 0.431 \\ Ours & **24.887** & **0.712** & **0.373** & **25.307** & **0.757** & **0.358** & **25.463** & **0.811** & **0.208** \\ \hline \hline \end{tabular}
\end{table}
Table 1: A quantitative comparison of the datasets from the three scenes, where the urban scene features numerous buildings with rich textures, the countryside scene contains many textureless water surfaces, and the rural scene has many repetitive textures in the form of plants.  
Figure 4: The impact of overlap on point cloud reconstruction (compared with COLMAP in 50% overlap scene). The orange area provides a more detailed display of the red area. At low overlap, the model typically contains many gaps. After supplementing with implicit co-visibility cluster, denser point cloud results can be obtained (ablation experiment).  
Figure 3: Qualitative comparison between the baseline methods (**(A)**: [28], **(B)**: [41], **(C)**[33]) and our approach (**D**), in comparison to the Ground Truth (**E**). In large-scale scenes, all three methods suffer from significant blur, artifacts, and noticeable noise. Our method, on the other hand, performs well in this test.  
To further demonstrate the accuracy and quality of our method in dense point cloud reconstruction of natural scenes, we compare our approach to the state-of-the-art method RTSfM [42], used for 3D dense point cloud reconstruction. We propose an efficient and high-precision solution for large-baseline high-resolution aerial imagery. Compared to the most advanced 3D reconstruction methods, our system can provide real-time generation of large-scale high-quality dense point cloud models.  
Additionally, to illustrate our model's performance, we use very large aerial images (rural and mountain scenarios) as test data. These datasets have a wide range of capture times, different textures, and occlusions, which pose significant challenges for dense 3D point cloud reconstruction. As shown in Table 3, the point cloud results generated by our method are on a par with the state-of-the-art RTSfM. This indicates that our reconstruction accuracy is highly competitive.