# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 1 Introduction  
The significance of 3D point cloud quality assessment extends across various domains, including urban planning [1], environmental monitoring [2], archeology, cultural heritage documentation [3], and disaster management [4]. This detailed spatial information facilitates accurate terrain analysis, volumetric assessments, and object localization, facilitating informed processes [5]. Recent advancements in machine learning and computer vision techniques have further amplified the utility of 3D point cloud data, enabling advanced semantic segmentation, object detection, and classification tasks [6, 7] based on the importance and diverse applications of 3D point clouds for their transformative impact on localization analysis [8, 9]. This kind of dataset facilitates precise and comprehensive representations of three-dimensional (3D) spaces. Their importance stems from their ability to capture spatial information with high fidelity. In the past, the features and significance of 3D point clouds were restricted by only being provided by some sensors, such as LiDAR (Light Detectionand Ranging), which is generally known for its remarkable detail and accuracy [10; 11]. More recently, it has become possible to derive the clouds from optical cameras, e.g., DSLR (Digital Single-Lens Reflex) cameras and spherical cameras. Spherical cameras, as a tool for generating 3D point cloud data, have revolutionized spatial data acquisition due to their expanded field of view. These cameras mostly capture using an omnidirectional imagery system, enabling comprehensive scene representation and detailed 3D reconstructions. Recent advances in spherical camera technology have improved their accuracy, resolution, and efficiency. This has led to their increased use in a variety of applications [1]. Spherical cameras are used to produce 3D point cloud data for a wide range of applications, including virtual reality, urban planning, indoor mapping, and the preservation of cultural heritage [12]. Their ability to capture immersive visual data enables precise spatial analysis and visualization [1]. Additionally, point cloud datasets frequently contain supplementary attributes beyond spatial coordinates, such as color values, intensity, or classification information [13]. The richness of a dataset can be enhanced with these advanced analyses and interpretations. Incorporating color information can be used for texture mapping, while intensity data can provide information about surface or material properties [14]. Researchers have employed low-cost spherical cameras, commonly used in projects such as virtual reality and street view mapping, for three-dimensional reconstruction. The primary data of these cameras consist of fisheye images stored separately for each lens. These cameras adhere to dioptric geometry imagery systems [15]. Panoramic images can be generated by stitching together images from each lens of a camera to create overlapping images. This facilitates the implementation of three-dimensional reconstruction based on geometric principles. The volume of data acquisition is significantly reduced compared to other optical cameras, such as DSLR cameras. Primary strategies for photogrammetric 3D reconstruction and data collection in confined spaces such as tunnels [16; 17] and underpasses, or with restrictions on network design and camera placement [18], create critical challenges for proper data collection. On the other hand, generating robust 3D point clouds from image-based data requires a consistent and smooth level of lighting in the scene [19]. Generally, in the field of 3D point clouds, terrestrial laser scanning sensors or LSs (laser scanners) can provide data with acceptable accuracy [20] compared with other optical cameras. In addition, LSs have greater costs and more complex customization [21; 22] compared to other optical cameras.