## 4 Experimental Results
### Performance Comparison  
We compared vanilla NeRF [7], S-NeRF [9], Sat-NeRF [10], and UAV-NeRF on the DengFeng-City dataset proposed in this paper. In particular, we added a comparison with the traditional photogrammetric scheme CC [23] for DSM reconstruction. The comparison indicators included the PSNR, SSIM (used to evaluate the accuracy of the rendered image from a viewing angle), and MAE. Since the traditional CC photogrammetric reconstruction method does not contain radiance information, only the MAE calculated between the model prediction and the LiDAR DSM ground truth value was used.  
Table 1 shows that our method outperformed the NeRF series methods in terms of perspective rendering and DSM reconstruction. At navigation altitudes of 300-500 m, the average reconstruction errors of vanilla NeRF, S-NeRF, and Sat-NeRF were 2.16 m, 1.20 m, and 0.91 m, respectively, while our method achieved a reconstruction accuracy of 0.82 m. On the XinMi dataset, the corresponding performances were 2.15 m, 1.32 m, 0.89 m, and 0.82 m. Notably, although the geometric accuracy of our method could not reach that of the traditional CC photogrammetry method (with an MAE of 0.63 m), the latter reliedsolely on a geometric perspective and largely disregarded the radiation information in the reconstruction process; thus, the results could not be optimized according to the image color. In addition, our method does not require complex image matching, adjustment, or other operations, and it is an end-to-end reconstruction method. In terms of the reconstruction effect, the accuracy gaps of 0.19 m (DengFeng) and 0.07 m (XinMi) were within the acceptable range; thus, our method is a better alternative to the CC method.  
As shown in Figure 4, we found that CC was more effective in flat areas but less effective in terms of building edge effects (or with areas that had abrupt elevation changes), and it was far less effective than UAV-NeRF for details such as trees. In addition, since UAV-NeRF introduces the processing of transient objects, potentially dynamic objects could be detected (such as vehicles in parking lots), whereas the CC-reconstructed DSM dodides not differentiate between vehicles.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Area** & \multicolumn{3}{c}{**DengFeng Area\#1**} & \multicolumn{3}{c}{**XinMi Area\#1**} \\ \hline Method & PSNR \(\uparrow\) & SSIM \(\uparrow\) & Alt MAE[m] \(\downarrow\) & PSNR \(\uparrow\) & SSIM \(\uparrow\) & Alt MAE[m] \(\downarrow\) \\ NeRF [7] & 18.381 & 0.798 & 2.02 & 18.639 & 0.802 & 2.15 \\ Sat-NeRF [10] & 24.936 & 0.887 & 0.86 & 24.935 & 0.875 & 1.32 \\ UAV-NeRF & 25.684 & 0.903 & 0.74 & 26.341 & 0.987 & 0.89 \\ \hline CC [24] & - & & 0.62 & & - & 0.82 \\ \hline \hline \end{tabular}
\end{table}
Table 1: **Performance comparison among the NeRF series and traditional methods.** All the results were acquired using 50 images for a single scene, except for the CCs in the XinMi area, which utilized 40 images.  
Figure 4: **Intuitive performance comparison of different methods.** These experiments were conducted on the DengFeng and XinMi areas, and they demonstrate the improvement in 3D surface construction achieved using our proposed method. From (**a**) to (**d**), the images are as follows: (**a**) the original imagery captured by the drone, (**b**) the corresponding ground DSM truth obtained from LIDAR, (**c**) the DSM predicted by the method proposed in this paper, and (**d**) the DSM obtained using the CC method. Additionally, another representation of the results, showcasing additional details, is provided in Table 1.