# Fast 3D Reconstruction of UAV Images Based on Neural Radiance Field
## 3 Experiments and Results  
### Data and Implementation Details  
We perform data collection using the DJI Phantom 4 UAV. Figure 3 shows the UAV platform used in the experiments. The DJI UAV platform consists of two integral components: the aircraft itself and the integrated camera systems it carries. Detailed information is shown in Table 1.  
Figure 3: The system hardware of DJI Phantom 4. For the acquisition of UAV imagery, we employ the publicly available dataset provided by Pix4 Dmapper. Pix4 Dmapper is one of the most widely used commercial software. Pix4 Dmapper is a three-dimensional modeling software specifically designed for automated UAV aerial surveying. It combines the features of full automation, high-speed processing, and professional-grade precision, allowing effortless handling of UAV data and aerial imagery to achieve an automated three-dimensional modeling process. The dataset is divided into three subsets, among which two are customized datasets. These datasets are highly representative and encompass scenes crucial for reconstruction in production operations, such as significant landmarks, large-scale apartment buildings, and small-scale villas. Table 2 is the details of the dataset. The acquired UAV imagery dataset is highly representative, encompassing scenes crucial for reconstruction in production operations, such as significant landmarks, large-scale apartment buildings, and small-scale villas. Figure 4 shows the types of scenarios. We conducted experiments employing classical 3D reconstruction and neural radiance field methods, respectively. Classical 3D reconstruction methods generally use Colmap software. Colmap is a widely recognized and extensively employed software in the field of 3D reconstruction. Colmap serves as a comprehensive Structure from Motion (SFM) and Multiview Stereo (MVS) pipeline, adeptly catering to both graphical and command-line \begin{table}
\begin{tabular}{c c c} \hline \hline
**Datasets** & **Number** & **Scene** \\ \hline Dataset 1 & 34 & Small Villa \\ \hline Dataset 2 & 55 & Unconventional Landmark Architecture \\ \hline Dataset 3 & 100 & Large Apartment Building \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of datasets and types of scenarios represented. Figure 4: Real images captured by UAV. \begin{table}
\begin{tabular}{c c} \hline \hline
**Equipment** & **Specifications** \\ \hline \multirow{4}{*}{camera} & Image sensor: 1/2 3 inch CMOS; 12.4 million effective pixels \\  & Lens: FOV 94\({}^{\circ}\) 20 mm (35 mm format equivalent); f/2.8 focus to infinity \\  & Image size: 4000 \(\times\) 3000 \\  & Image format: JPEG, DNG(RAW) \\ \hline \multirow{4}{*}{aircraft} & Weight (including battery and paddles): 1380 g \\  & Maximum ascent speed: 6 m/s \\  & Maximum descent speed: 4 m/s \\ \cline{1-1}  & Maximum horizontal flight speed: 72 km/h \\ \cline{1-1}  & Maximum wind speed: 10 m/s \\ \cline{1-1}  & Flight time: about 28 min \\ \cline{1-1}  & Working temperature: 0 \({}^{\circ}\)C to 40 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information on DJI Phantom 4 UAV. interfaces. Within its framework lies an extensive spectrum of functionalities meticulously tailored to facilitate the meticulous reconstruction of both meticulously curated and apparently disordered image assemblages. To enhance the utilization of the interactive interface, we utilized the Instant-ngp [22] software, which offers visual reconstruction results. The input of the neural radiation field requires the pose parameters of the camera; however, the camera-captured images frequently lack these parameters. In this study, we employ the incremental SFM method to estimate the camera's pose. The incremental pose estimation method exhibits robustness and can accurately estimate the camera's pose across diverse environments. We estimated the camera pose using Colmap software and conducted sparse reconstruction. Refer to Figure 5 for the visualization of the depicted camera pose and sparse reconstruction.