# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 2 Materials and Methods
### Network Design  
The optimal positioning of imaging stations requires the consideration of factors that determine the principles of network design and have significant effects on the outputs. The increase in geometric distortion towards the edges and corners of images, whether in fisheye or constructed panoramic images, magnifies the importance of feature placement in a less distorted camera field of view. Thus, determining the appropriate camera height and direction can minimize sequential reconstruction errors. As the height of the structure was about 6 m, the camera height was set to 2.7 m. Images were captured and positioned in two modes based on the geometry and corridor-shaped structure (Figure 4). In the first mode, the camera was directed towards the walls (sideways mode), the main features of the structure. In the second mode, the images were captured with the camera oriented towards the road of the underpass (stereo mode), and the space between each individual station was 1.5 m. In parallel, preparing for GNSS (global navigation satellite system) control point measurement is a crucial step to obtain the absolute positioning coordinates for the final reconstructed point cloud. Iron nails and control point plates were used to position the GPS points. This step was implemented directly with a total station without using the GNSS antenna. The points that indicated where to start and finish the capturing action were marked. Finally, eight targets were placed on the ground with adequate distribution to cover the entire ground section of the underpass. These targets provided control points later in the construction process. The image in Figure 5 displays one of the targets utilized.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Solutions** & \begin{tabular}{c} **Network Design** \\ **Solution** \\ \end{tabular} & \begin{tabular}{c} **Data Type** \\ **Solution** \\ \end{tabular} & \begin{tabular}{c} **Calibration** \\ **Solution** \\ \end{tabular} \\ \hline Solution 1 & Sideways mode & Fisheye images & Self-calibration \\ \hline Solution 2 & Sideways mode & Fisheye images & \begin{tabular}{c} Chessboard calibration \\ (splitting mode) \\ \end{tabular} \\ \hline Solution 3 & Sideways mode & Fisheye images & \begin{tabular}{c} Chessboard calibration \\ (without splitting mode) \\ \end{tabular} \\ \hline Solution 4 & Sideways mode & Panorama images & \multicolumn{1}{c}{—} \\ \hline Solution 5 & Stereo mode & Fisheye images & \begin{tabular}{c} Self-calibration \\ (splitting mode) \\ \end{tabular} \\ \hline Solution 6 & Stereo mode & Fisheye images & \
\begin{tabular}{c} Chessboard calibration \\ (splitting mode) \\ \end{tabular} \\ \hline Solution 7 & Stereo mode & Fisheye images &
\begin{tabular}{c} Chessboard calibration \\ (without splitting mode) \\ \end{tabular} \\ \hline Solution 8 & Stereo mode & Panorama images & \multicolumn{1}{c}{—} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different strategies of data acquisition and pre-processing implemented in this study. Panorama images used no calibration solution at this stage.