# UV3D: Underwater Video Stream 3D Reconstruction Based on Efficient Global SFM
## 6 Experiments
### Pool Experiment  
The experiment was carried out in a pool measuring 3 m \(\times\) 4 m \(\times\) 1 m. The camera was equipped with a waterproof case and lighting device, and can be seen in Figure 7. In order to simulate the AUV's shooting effect, the camera was fixed to the rod to shoot around the target. As shown in Figure 6, the experimental equipment includes a camera with a lighting device, which is shown on the left, while the experimental environment is shown on the right, where red lines represent the motion trajectory of the camera. We respectively experiment with lights on and off in the same route. Additionally, the parameter list for the experiment is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Method**} \\ \cline{2-4}  & **LIRA-IRLS** & **LTS-LIRA** & **Comparison** \\ \hline Running time (s) & 106.712 & 85.400 & \(-\)19.97\% \\ Mean error(degree) & 3.394 & 3.432 & \(+\)1.12\% \\ RMS error (degree) & 7.274 & 7.161 & \(-\)1.55\% \\ Processing rate (Amount of data/s) & 606.099 & 757.354 & \(+\)24.96\% \\ Mean error/processing rate & \(5.6\times 10^{-3}\) & \(3.7\times 10^{-3}\) & \(-\)19.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Efficiency verification test.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Name**} & **Configuration** \\ \hline Pool & 3 m \(\times\) 4 m \(\times\) 1 m \\ Medium & Water with suspended matter \\ Calibration board & GP100 \\ Camera & With waterproof case and lighting device \\ Frame Width & 1920 pixels \\ Frame Height & 1080 pixels \\ Frame Rate & 60 FPS (frames per second) \\ Environment & Python 3.5, Ubuntu 16.04 \\ Visualization tool & MeshLab \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment parameters.  
Figure 7: Experimental equipment and environment.  
We firstly obtain the image sequence by using the key frame extraction algorithm proposed in Section 3.2, and the 3D reconstruction results based on the image sequence are shown in Figure 8.  
In Figure 8, image (a), (b) are original underwater images with natural light and external light device, and image (c), (d) are the point cloud calculated based on original image sequence. The point cloud based on the image sequence enhanced by the image enhancement method described in Section 3.3 are shown in image (e) and (d). We can see that after image enhancement the red channel recession has been greatly reduced and there are more point clouds in image (e). Also, we can see that there is much noise caused by suspended matter scattering in image (d), it is shown in image (f) that after enhancement there are much less noise and better color performance.  
In Figure 9, we show an independent experiment performed in the air. The result shows better color information; in other words, our image enhancement algorithm is still insufficient. In Table 3, we compare the number of bytes from the video streams and the corresponding point cloud data. We can see our method reduces the transmission costs by 74.21% and 64.22% in the underwater experiment above, meaning it can greatly save on transmission costs when data are transferred through wireless acoustic communication.  
Figure 8: (a) Underwater image with natural light. (b) Underwater image with a lighting device. (c) Point cloud based on original image sequence (natural light). (d) Point cloud based on original image sequence (external lighting device). (e) Point cloud based on enhanced image sequence (natural light). (f) Point cloud based on enhanced image sequence (external lighting device).