# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 4 Discussion and Conclusions
In the Cloud Comparison process, the corresponding values of the Elite Sphere 3D point cloud can be read using colored areas on the scale during C2C measurements. The goal is to highlight the accuracy of the point cloud. Achieving as many blue areas as possible is crucial, as these areas show virtually no deviation from the reference point cloud. However, the areas with very large deviations are shown in red. For further analysis, the same parameters were adopted for the M3C2 algorithm. Taking the LS and X2 sphere cameras as an example, the mean distance and resulting standard deviation are shown in the figures below (Figures 13 and 14). As mentioned in Section 2.6.3, each 3D model used standard deviation and mean distance as measurement criteria. Therefore, they were used in Gaussian statistical representation to detect the deviation. Figure 12: Quality of colorized 3D point clouds generated by spherical cameras X2 (**a**) and X3 (**b**) in comparison with LS (**c**) and DSLR (**d**). The distance measurements show the higher accuracy of the X2 camera in terms of correctness and lower noise based on standard deviation. The higher average distance of the C2C method is essentially due to some significant gaps in the spherical point cloud compared to the LS method. This difference has a direct impact on the average distance value for this distance calculation. On the other hand, by considering the normal vectors of the M3C2 algorithm, more accurate mean and deviation measurements can be achieved. According to the geometric features, all the mentioned features (Table 12) of the spherical models are slightly weaker than those of the LS model, except for the density, due to the noisy point cloud. The main reason for this can be attributed to the challenging part of the spherical point cloud, where the middle part of the ceiling and floor is generated. Due to the lack of sufficient overlapping images and the smaller number of connection points, a large gap is seen, and in the edges, the inconsistency of geometric features is clearly visible. Regardless of these gaps, only the noise level of the spherical clouds made a difference, and at a certain point, e.g., in terms of density, spherical point clouds achieved a better or denser result compared to the DSLR model. Geometrically, it is also important that the structure of the spherical model is correct, but due to the noise and gap area in the ceiling and floor, they play the main role in the differences between the spherical and LS models. In conclusion, the 3D point cloud analysis workflow has demonstrated that spherical models are reliable in terms of accepted accuracy and correctness at short range by applying the optimal data acquisition strategies such as network design and leveraging the SFM-MVS photogrammetry method. Due to the fast data acquisition, small data volume and low  
Figure 14: Mean and standard deviation of distance using normal vectors (with M3C2 solution) between X2 and LS (**a**), X3 and LS (**b**), and X2 and X3 (**c**) cameras in the following Gaussian histogram plots. The values and corresponding colors of plots are mentioned in Table 11.  
Figure 13: Mean and standard deviation of distance per point (with C2C solution) between X2 and X3 (**a**), X2 and LS (**b**), and X3 and LS (**c**) cameras in the following Gaussian histogram plots. The values and corresponding colors of plots are mentioned in Table 11.  
initial cost of the instruments, two of the best low-cost spherical cameras (Table 1) were considered to be acceptable optical cameras for 3D point cloud generation. The Insta360 One X2 had a slightly more stable geometric image model compared to the Insta360 One X3. In contrast to the X2 camera, this model offers extensive functions and diverse applications. These conclusions are the result of a comparison between the results, using the DSLR and LS models as reference models.