# Fast 3D Reconstruction of UAV Images Based on Neural Radiance Field
## 4 Conclusions  
In summary, our approach has demonstrated remarkable advancements in enhancing the efficiency of 3D reconstruction tasks using UAV imagery, surpassing the capabilities of conventional methods. Moreover, it has effectively minimized reconstruction time, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Dataset 1** & **Dataset 2** & **Dataset 3** \\ \hline PSNR & 26.6 & 22.7 & 23.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PSNR. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Number**} & \multicolumn{3}{c}{**Reconstruction Time/min**} \\ \cline{3-4}  & & **Classic Method** & **Neural Radiance Field Method** \\ \hline Dataset 1 & 34 & 47 & 11 \\ \hline Dataset 2 & 55 & 58 & 18 \\ \hline Dataset 3 & 100 & 136 & 22.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Reconstruction timetable. resulting in a highly efficient workflow. The demonstrated robustness of our approach positions it as a reliable choice for expedited 3D reconstruction across diverse scenarios involving UAV imagery. Highlighting our contributions, we have introduced a novel neural network architecture that specifically addresses the challenges of UAV-based 3D reconstruction. By leveraging this architecture, we have successfully achieved substantial improvements in both the reconstruction effect and processing speed. These contributions collectively contribute to the evolution of 3D reconstruction techniques in the context of UAV imagery. The implications of our work are significant. The efficiency gains and accuracy improvements realized by our approach hold great promise for a wide range of applications. From urban planning to disaster assessment, our method's capabilities open new avenues for leveraging UAV data in critical decision-making processes. The ability to rapidly generate detailed 3D models empowers professionals across various domains to make more informed choices. Moreover, our method boasts two key innovative features. Firstly, by seamlessly integrating a neural network into our framework, we demonstrate the immense potential of synergistic learning approaches in tackling intricate tasks like 3D reconstruction. Secondly, our dedicated efforts to minimize reconstruction time underscore our unwavering commitment to real-world usability and practicality. These groundbreaking advancements collectively enrich the realm of UAV-based 3D reconstruction and deep learning applications. Looking ahead, our research trajectory is clear. We intend to refine our neural network structure by leveraging the latest advancements in deep learning architectures. Additionally, we will explore the integration of diverse neural networks, aiming to harness their complementary strengths for enhanced performance. Concurrently, we are dedicated to further optimizing reconstruction time using algorithmic enhancements and hardware acceleration. Expanding the scope of our work, we aspire to extend the application of deep learning-based techniques to encompass large-scale 3D reconstruction tasks spanning entire cities. This ambitious direction not only pushes the boundaries of technology but also holds immense potential for transforming urban planning, architecture, and infrastructure development. By producing comprehensive 3D models of urban environments, we hope to facilitate smarter city design, resource allocation, and sustainable development. In conclusion, our research marks a significant step forward in the realm of UAV-based 3D reconstruction. The method's efficiency gains, accuracy improvements, and innovative elements contribute to its relevance and applicability. As we move forward, our commitment to refining the method and expanding its scope promises to bring about transformative impacts in various domains. [PERSON] contributed to searching the literature, describing the model, analyzing the data and model performances, providing the figures and maps, and writing the manuscript. [PERSON] contributed to designing the research.