# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Camera Orientation** & **GCP Accuracy (m)** & **Check Point Accuracy (m)** \\ \hline \multicolumn{4}{c}{without calibration (being refined during relative orientation)} \\ \hline X2 & stereo & 0.003592 & 0.047857 \\ \hline X3 & stereo & 0.027332 & 0.100306 \\ \hline X2 & sideways & 0.102562 & 0.7241114 \\ \hline X3 & sideways & 0.001714 & 0.010127 \\ \hline \multicolumn{4}{c}{calibration without splitting groups (in two separate chunks)} \\ \hline X2 & stereo & 0.068918 & 0.368897 \\ \hline X3 & stereo & 0.056098 & 0.504710 \\ \hline X2 & sideways & 0.066433 & 0.453532 \\ \hline X3 & sideways & 0.045880 & 0.079698 \\ \hline \multicolumn{4}{c}{calibration with splitting groups (in one chunk)} \\ \hline X2 & stereo & 0.01 & 0.06 \\ \hline X3 & stereo & 0.02 & 0.13 \\ \hline X2 & sideways & 0.01 & 0.07 \\ \hline X3 & sideways & 0.04 & 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparing Insta360 spherical cameras’ (X2 and X3) 3D point clouds in absolute orientation accuracy using the below criteria. The optimal results underlined. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compared** & \multicolumn{2}{c}{**C2C Distance Measurement**} & \multicolumn{2}{c}{**M3C2 Distance Measurement**} \\ \cline{2-5}
**Clouds** & **Std. Deviation** & **Mean Distance** & **Std. Deviation** & **Mean Distance** \\ \hline X2 and X3 & 0.101 & 0.096 & 0.176 & 0.038 \\ \hline X2 and LS & 0.560 & 0.228 & 0.111 & 0.037 \\ \hline X3 and LS & 0.323 & 0.181 & 0.167 & 0.051 \\ \hline X2 and DSLR & 0.342 & 0.152 & 0.077 & 0.015 \\ \hline X3 and DSLR & 1.171 & 0.91 & 0.083 & 0.007 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Distance measurement between each pair point clouds for LS and the X2 and X3 spherical cameras. Figure 11: _Cont._  
The geometric rendered outputs are depicted according to the following features in Table 12. In order to have the possibility of comparison between two individual models with the same geometric feature, a consistent radius of 0.02 is assumed for the filter window size to calculate and include the neighboring points. According to relative orientation accuracy, this attitude yielded the best results. Therefore, some geometric features were identified in comparison only for these elite point clouds of X2 and X3 spherical cameras (based on Table 11). Figure 11: C2C distance models between each pair of point clouds: LS compared with X2 (**a**); X3 compared with X2 (**b**); X3 compared with LS (**c**); X2 compared with DSLR (**d**); X3 compared with DSLR (**e**). \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Geometric Features** & **Insta360 One X3** & **Insta360 One X2** & **Laser Scanner** & **DSLR Camera** \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Geometric features of the X2, X3, DSLR camera, and laser scanner 3D point clouds. The radius was 0.02 for all the data types. Due to limited data acquisition with the DSLR camera, the geometric model of the 3D point cloud only depicts one side of the underpass wall. Based on the roughness function, which indicates the noise level of the point clouds, the wall camera model in the X3 model had more noise points compared to the X2 model. Generally, when the lens was positioned towards the walls, it was expected that the density of these areas would be much higher with respect to the overhead ceiling and ground. Nevertheless, the X2 camera model achieved a much denser point cloud than the X3 camera (Table 11, b). Among the features of a structure, surfaces are essential elements. The geometric features of surface curvature, in terms of other geometric features such as verticality, are more prominent in the X2 model compared to the X3 one. Some geometric features, such as verticality, are independent of network design and only vary based on the type of sensor which creates the point cloud. According to the results, there is a significant connection among geometric features in terms of accuracy.