# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 4 Discussion and Conclusions  
This study presents a comprehensive workflow for 3D point cloud quality analysis using low-cost spherical cameras. It covers the entire process from initial data acquisition to the generation of 3D point results through a photogrammetric framework. The strategies are divided into four solutions for network design and three for camera calibration. In network design, for both models of spherical cameras, the sideways mode was the best solution based on the relative orientation accuracy and completeness of the point cloud. In this orientation, the color of the point cloud was more realistic and precise according to the LS reference point clouds (Figure 12). Vividly, the colorized 3D point clouds generated by the spherical cameras were less precise and realistic than that generated by the LS cameras. Despite this comparison, LS also generated better results than DSLR. Due to the expansion of lighting and rebalancing the exposure through the entire field of view, spherical camera images generally cannot capture the true color values in restricted areas with limited lighting such as underpasses. In camera calibration, split mode calibration, where the relative orientation of the image from both lenses is generated by a single part of the processing software, produces the best result in absolute orientation. However, for overall relative orientation accuracy, self-calibration during the SFM-MVS algorithms based on the Brown fisheye calibration model (Section 2.5.2) achieved the best result. The respected Elite 3D spherical point cloud also used this calibration. In contrast, the number of tie points in the dense point cloud dramatically increased with this solution. The use of checkerboard calibration \begin{table}
\begin{tabular}{p{42.7 pt} 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 Linearity & & & & & \\ \hline Curvature & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: _Cont._accounts for the fact that there are different distortions for the front and back lenses in each spherical camera, although this fact was considered as an assumption before starting the data collection. In camera calibration, distortion dramatically increases in the borders of the fisheye images. All 11 parameters of the [PERSON] calibration model were used to model distortions for the corresponding fisheye lenses in the spherical cameras. Strikingly, the fisheye lens of the DSLR camera showed less distortion with respect to spherical camera lenses, which had an intense impact on the generated 3D point cloud noise level. Since chessboard calibration in this framework and toolbox did not refine the distortions during image alignment despite the use of calibration parameters, the final 3D point cloud inevitably contained significant noise. After the noise reduction, considerable numbers of points were interactively removed from the initial sparse point cloud. So far, the best relative orientation was achieved with a 0.1263 RMS re-projection error and 9601 reliable tie points as the best result. For control points with control point implementation, the accuracy and correctness were simultaneously achieved in return for the absolute orientation of the local 3D point cloud in the ETRS89/UTM zone 32N coordinate system. The best 3D point cloud of each spherical camera was compared to the two reference point clouds of the LS and DSLR cameras. This was based on geometric features and distance evaluation. For the X3 camera, the compared values when calibrating by splitting groups into two chunks were 0.05 m each for the GCP and check point accuracy. The calibration method with just one chunk achieved values of 0.02 and 0.04 m. This shows that this calibration method of the side-captured images provides the more accurate results. The quality of the check points was also better than the method in which images were taken sideways.