# Implicit-Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
## 3 Experiments
### The Accuracy of 3D Mesh Modeling  
We tested the final mesh modeling results using PhotoScan as the baseline. We randomly sampled the triangular faces of the model and calculated the face centroids as sampling points. Similarly, we evaluated our final mesh modeling results by calculating their MD (mean Euclidean distance) and SD (standard deviation). Table 4 shows the superiority of our method, which leverages co-visibility constraints for large-scale scene representation, uses neural implicit modeling to compensate for missing views, and finally, accurately reconstructs the point cloud through dense SLAM. After texture enhancement and smoothing, we obtain high-quality mesh models. It is evident that our method achieves good accuracy in most scenes, and it ranks first in terms of stability across all scenarios. Finally, in Figure 5, we show the final modeling results of our method for all the scenes we considered.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**COLMAP**} & \multicolumn{2}{c}{**Context Capture**} & \multicolumn{2}{c}{**Pix4D**} & \multicolumn{2}{c}{**Ours**} \\ \cline{2-9}  & **MD** & **SD** & **MD** & **SD** & **MD** & **SD** & **MD** & **SD** \\ \hline Urban & **1.405** & 0.953 & 1.563 & 0.661 & 1.732 & 0.891 & 1.412 & **0.553** \\ Campus & 2.528 & 2.611 & 1.345 & 1.737 & 3.022 & 2.145 & **1.257** & **1.316** \\ Rural & 0.953 & 0.776 & 3.589 & 2.718 & 1.658 & 0.946 & **0.736** & **0.641** \\ Mountain & 8.631 & 13.253 & 6.763 & 5.707 & 3.713 & 2.017 & **2.828** & **1.716** \\ Countryside & 2.219 & 1.546 & 1.894 & 1.993 & **0.942** & 1.033 & 1.106 & **0.977** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The accuracy of 3D mesh modeling (highlighted with the best data for reference) based on PhotoScan as the benchmark.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Open MVS**} & \multicolumn{2}{c}{**COLMAP**} & \multicolumn{2}{c}{**Context Capture**} & \multicolumn{2}{c}{**Pix4D**} & \multicolumn{2}{c}{**Ours**} \\ \cline{2-9}  & **MD** & **SD** & **MD** & **SD** & **MD** & **SD** & **MD** & **SD** \\ \hline Urban & 0.385 & 0.291 & 0.119 & 0.112 & 0.187 & 0.166 & 0.192 & 0.152 & **0.111** & **0.107** \\ Rural & 0.853 & 0.21 & 0.141 & 0.176 & 0.306 & 0.229 & 0.278 & 0.261 & **0.121** & **0.116** \\ Countryside & **0.062** & 0.161 & 0.335 & 0.256 & 0.295 & 0.301 & 0.164 & 0.166 & 0.138 & **0.096** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy comparison of 3D dense point cloud reconstruction (measurement units of MD and SD are meters).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**RTSfM**} & \multicolumn{2}{c}{**Ours**} \\ \cline{2-5}  & **MD** & **SD** & **MD** & **SD** \\ \hline Mountain & 0.762 & 0.583 & **0.626** & **0.508** \\ Campus & 0.416 & 0.441 & **0.213** & **0.437** \\ \hline \hline \end{tabular}
\end{table}
Table 3: RTSfM [42] and our method are evaluated for accuracy using new data (scene 4 and scene 5 are large-scale scene data; ground truth is obtained from PhotoScan; and the units for MD and SD measurements are meters).