# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 1 Introduction
Furthermore, the application of image-based algorithms and relative orientation under the cover of SFM-MVS (Structure From Motion--Multi-View Stereo) workflow has been increasingly used according to the capabilities and fast 3D modeling [23; 24; 25]. The workflow of an image-based algorithm begins with feature extraction from multiple images to establish their correspondence within the entire dataset. Camera position estimation is then performed before triangulation processing. This ultimately leads to the calculation of the 3D position of each image feature point (known as connection points) in a local system. This system is implemented through a series of computer vision algorithms [23]. These algorithms also solve calibration parameters through a self-calibration process and include various geometric image models, such as fisheye and equirectangular [17]. The results of the self-calibration process are compared to two separately performed calibrations to show the differences in accuracy. This allows low-cost optical camera consumers to use non-calibrated or calibrated images for 3D reconstruction and 3D point cloud generation. The precision and advantages of the relative and absolute orientation of 3D point clouds clarify the optimal network design and data capturing strategies for producing the final 3D point clouds according to the corresponding criteria. After this thorough analysis, the best 3D point cloud from the spherical camera can be compared with other types of sensors. This paper evaluates the quality of a point cloud obtained from a graffiti underpass using fisheye and panoramic images from spherical cameras by using the principles of close-range photogrammetry in three-dimensional reconstruction. The goal is to analyze the 3D point cloud resulting from the optimized and most effective workflow among the proposedmethods for data acquisition using various strategies. This optimization is based on relative orientation accuracy, the impact of image calibration methods, and network design. This sequential analysis of spherical camera use in point cloud generation can be compared with other sensors, both absolutely and uniquely. In terms of analysis, the evaluation of the geometric features of 3D point clouds is one of the most important analytical solutions for camera and fisheye lens images in order to achieve 3D reconstruction [16; 23; 26]. Therefore, this comparative analysis continues by measuring the distance between pairs of point clouds. This is calculated by two individual sensors and the geometric features of each point cloud are analyzed. This text describes the network design of camera stations and their orientations, data acquisition, and 3D reconstruction based on photogrammetry principles. This includes camera calibration and relative and absolute orientation, which result in the generation of 3D point clouds. Geometric analysis is also performed, including a mean and standard deviation assessment of spherical camera, laser scanner, and DSLR camera point clouds. The final section includes the conclusion, a discussion of the results, and statistical evaluations.