# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 2 Materials and Methods
### Case Study and Sensor Selection
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Camera Model** & **Brand** & \begin{tabular}{c} **Resolution** \\ **(Megapixel)** \\ \end{tabular} & \begin{tabular}{c} **Image Resolution** \\ **(pix)** \\ \end{tabular} & \begin{tabular}{c} **Weight** \\ **(kg)** \\ \end{tabular} & \begin{tabular}{c} **Raw File** \\ **Accessibility** \\ \end{tabular} & \begin{tabular}{c} **Cost** \\ **(USD)** \\ \end{tabular} & \begin{tabular}{c} **Monitoring** \\ **Software** \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} Gear360 \\ \end{tabular} & Samsung & 15 & 4096 \(\times\) 2048 & 0.13 & ✓ & 369 & ✓ \\ \hline \begin{tabular}{c} Theta Z1 \\ \end{tabular} & Ricoh & 23 & 6720 \(\times\) 3360 & 0.18 & \(\times\) & 559 & \(\times\) \\ \hline \begin{tabular}{c} Theta X \\ \end{tabular} & Ricoh & 60 & 11,008 \(\times\) 5504 & 0.17 & ✓ & 796 & \(\times\) \\ \hline \begin{tabular}{c} Max360 \\ \end{tabular} & GoPro & 16 & 5760 \(\times\) 2880 & 0.16 & ✓ & 399 & ✓ \\ \hline \begin{tabular}{c} One X \\ \end{tabular} & Insta360 & 18 & 5760 \(\times\) 2880 & 0.11 & \(\times\) & 259 & ✓ \\ \hline \
\begin{tabular}{c} One X2 \\ \end{tabular} & Insta360 & 48 & 6080 \(\times\) 3040 & 0.14 & ✓ & 290 & ✓ \\ \hline
\begin{tabular}{c} One X3 \\ \end{tabular} & Insta360 & 18.72 & 11,968 \(\times\) 5984 & 0.18 & ✓ & 399 & ✓ \\ \hline \end{tabular}
\end{table} 
Table 1: The specification of well-known spherical cameras. Noticeably, the technical features of Insta360 spherical cameras are more suitable for data acquisition process [1; 9; 10; 11]. Figure 1: Location of the Theodor Heuss graffiti underpass **(a)**; The underpass is placed in the German federal state of Hessen. The entrance **(b)** and the indoor environment **(c)** of the underpass. The following section (Figure 3) illustrates the proposed workflow and detailed structure of the spherical camera quality investigation. As it indicates, the main steps include network design, data acquisition, pre-processing, 3D reconstruction, and quality analysis, which are described in more detail in the following sub-sections. In addition, several strategies were defined to find the best possible solutions for network design, appropriate data type, and calibration method when using spherical cameras (Table 2). The results in Section 3 will indicate the best solution in terms of relative and absolute orientation accuracy and also the accuracy of each calibration method. Figure 3: The flowchart of the present study in graffiti underpass point cloud analysis. Figure 2: All sensors used in this study. (**a**) Leica RTC 360 laser scanner used as the reference sensor for point cloud quality assessment. (**b**) Nikon D800, 16 mm equivalent focal length for point cloud quality assessment. (**c**) Insta360 One X3, one of the best available low-cost spherical cameras (based on Table 1). (**d**) Insta360 One X2, one of the most optimized low-cost spherical cameras. The network design options varied between stereo-mode and sideways-mode capture. There are two types of data in this framework, including fisheye and panorama images. In addition, there are three solutions for the calibration process, including chessboard calibration (calibration without splitting groups or in two separate chunks, and calibration with splitting groups or in one chunk) and the self-calibration method. The 8 stages of the process are all of critical importance for the generation of a point cloud. The resulting data also show impressive quality and accuracy. The further details of these stages will be explained in the corresponding sections.