Affiliation:
1. i3mainz, Institute for Spatial Information and Surveying Technology, School of Technology, Mainz University of Applied Sciences, D-55118 Mainz, Germany
Abstract
Three-dimensional point cloud evaluation is used in photogrammetry to validate and assess the accuracy of data acquisition in order to generate various three-dimensional products. This paper determines the optimal accuracy and correctness of a 3D point cloud produced by a low-cost spherical camera in comparison to the 3D point cloud produced by laser scanner. The fisheye images were captured from a chessboard using a spherical camera, which was calibrated using the commercial Agisoft Metashape software (version 2.1). For this purpose, the results of different calibration methods are compared. In order to achieve data acquisition, multiple images were captured from the inside area of our case study structure (an underpass in Wiesbaden, Germany) in different configurations with the aim of optimal network design for camera location and orientation. The relative orientation was generated from multiple images obtained by removing the point cloud noise. For assessment purposes, the same scene was captured with a laser scanner to generate a metric comparison between the correspondence point cloud and the spherical one. The geometric features of both point clouds were analyzed for a complete geometric quality assessment. In conclusion, this study highlights the promising capabilities of low-cost spherical cameras for capturing and generating high-quality 3D point clouds by conducting a thorough analysis of the geometric features and accuracy assessments of the absolute and relative orientations of the generated clouds. This research demonstrated the applicability of spherical camera-based photogrammetry to challenging structures, such as underpasses with limited space for data acquisition, and achieved a 0.34 RMS re-projection error in the relative orientation step and a ground control point accuracy of nearly 1 mm. Compared to the laser scanner point cloud, the spherical point cloud reached an average distance of 0.05 m and acceptable geometric consistency.
Reference36 articles.
1. Intelligent City 3D Modeling Model Based on Multisource Data Point Cloud Algorithm;Wu;J. Funct. Spaces,2022
2. Evaluating the quality of photogrammetric point-clouds in challenging geo-environments—A case study in an alpine valley;Karantanellis;ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.,2020
3. Rapid and Accurate Production of 3D Point Cloud via Latest-Generation Sensors in the Field of Cultural Heritage: A Comparison between SLAM and Spherical Videogrammetry;Pepe;Heritage,2022
4. Zwęgliński, T., and Smolarkiewicz, M. (2022). Three-Dimensional model and orthophotomap’s quality evaluation towards facilitating aerial reconnaissance of flood response needs. Innovation in Crisis Management, Routledge.
5. Mendonca, P.R.S., and Cipolla, R. (1999, January 23–25). A simple technique for self-calibration. Proceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, USA.