# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 3 Results  
In the pre-processing step, the following settings were implemented on the final output of the laser scanner point cloud in Cyclone (Figure 6) and for that of the commercial spherical cameras. Insta360 Studio stitched two fisheye images into a single panorama image with 6080 \(\times\) 3040 resolution but an unknown camera geometric model (Figure 4). The spatial accuracy of the measured control points was 0.002 m.  
The purpose of the described method in Section 2.2 for network design is to find out the most optimized solution for camera location and orientation. Otherwise, the final generated point cloud cannot cover the desired sections with sufficient accuracy and correctness. Twenty spherical images were captured two times in 2 min using both sideways mode and stereo mode. The optimized results of the network design can be derived from the relative orientation accuracy, data collection speed, and number of images. Since the data collection specifications were almost the same in both network design strategies, only relative orientation was evaluated. Also, as a result of Section 2.4, the stitching image processing of panoramic images and the camera calibration solutions were performed using three different strategies. Table 9 depicts the quantities of each accuracy value achieved by the process mentioned in Section 2.5. The relative orientation accuracy is described using re-projection accuracy, which refers to the difference between the observed tie points and the calculated corresponding ones defined by the root mean square error. According to Tables 7 and 9, the sideways-mode capturing strategy showed more reliable tie points and final point clouds compared to the stereo-mode strategy. Panorama images were better with respect to the RMS re-projection error compared to fisheye images. However, for the X2 camera, the best results were obtained in the sideways-capture mode without chessboard calibration, and slightly better results were shown for the tie points and point clouds. Among all three solutions for camera calibration, self-calibration during the alignment achieved more accurate RMS re-projection and calibration residuals. The DSLR camera was also used in order to compare these sequential results. The accuracy of calibration for the DSLR camera (0.158 RMS residual pixel error) was close to that of the X2 camera (0.427 RMS residual pixel error), but still better since the calibration method was genuinely equal. As shown in Figure 10, X2 camera distortion was even better than that of the DSLR fisheye lens in some parts around the border and basically all over the image plane. The X3 camera showed much more distortion (0.835  
The absolute orientation for geo-referencing the 3D model was also measured by the ground control points and check points. The average accuracy of the GCPs was 0.02 m, and that of the check points, which indicates the correctness of the geo-referenced 3D point cloud, was 0.1 m (Table 10). If there was any insufficiently overlapping area, this shows that inaccurate tie points were generated [13]. Therefore, a higher number of DSLR images should be captured in comparison to a spherical camera dataset with a full field of view. The final 3D point cloud of the DSLR camera covers a small part of the underpass only for calibration and geometric accuracy assessments. The distance measurements between the reference point cloud and each spherical point cloud, and the mean distance and standard deviation between two point clouds, are shown in the table below (Table 11 and Figure 11). The M3C2 mean distance for the X3 camera was nearly 5 cm compared to 3 cm for the X2 camera. The C2C values were 22 cm and 18 cm for X3 and X2. Since the LS point cloud was four times denser than the spherical ones (about 262 million points in the LS cloud compared to 52 million points in the X3 camera cloud), this indicates that there are some occluded areas which created a significant gap in the generation of spherical 3D point clouds.