A Synthetic Benchmarking Pipeline to Compare Camera Calibration Algorithms (2307.01013v2)
Abstract: Accurate camera calibration is crucial for various computer vision applications. However, measuring calibration accuracy in the real world is challenging due to the lack of datasets with ground truth to evaluate them. In this paper, we present SynthCal, a synthetic camera calibration benchmarking pipeline that generates images of calibration patterns to measure and enable accurate quantification of calibration algorithm performance in camera parameter estimation. We present a SynthCal generated calibration dataset with four common patterns, two camera types, and two environments with varying view, distortion, lighting, and noise levels for both monocular and multi-camera systems. The dataset evaluates both single and multi-view calibration algorithms by measuring re-projection and root-mean-square errors for identical patterns and camera settings. Additionally, we analyze the significance of different patterns using different calibration configurations. The experimental results demonstrate the effectiveness of SynthCal in evaluating various calibration algorithms and patterns.
- “Handcrafted and deep trackers: Recent visual object tracking approaches and trends,” ACM Computing Surveys (CSUR), vol. 52, no. 2, pp. 1–44, 2019.
- “A review of techniques for 3d reconstruction of indoor environments,” ISPRS International Journal of Geo-Information, vol. 9, no. 5, pp. 330, 2020.
- “Social interaction in augmented reality,” PloS one, vol. 14, no. 5, pp. e0216290, 2019.
- “Smartphone-based photogrammetric 3d modelling assessment by comparison with radiological medical imaging for cranial deformation analysis,” Measurement, vol. 131, pp. 372–379, 2019.
- “Can we trust you? on calibration of a probabilistic object detector for autonomous driving,” arXiv preprint arXiv:1909.12358, 2019.
- “Geometric camera calibration.,” Wiley encyclopedia of computer science and engineering, vol. 13, no. 6, pp. 1–20, 2008.
- Zhengyou Zhang, “A flexible new technique for camera calibration,” IEEE Transactions on pattern analysis and machine intelligence, vol. 22, no. 11, pp. 1330–1334, 2000.
- Roger Tsai, “A versatile camera calibration technique for high-accuracy 3d machine vision metrology using off-the-shelf tv cameras and lenses,” IEEE Journal on Robotics and Automation, vol. 3, no. 4, pp. 323–344, 1987.
- J-Y Bouguet, “Camera calibration toolbox for matlab,” http://www. vision. caltech. edu/bouguetj/calib_doc/index. html, 2004.
- Comparison of methods for geometric camera calibration using planar calibration targets, na, 2004.
- “Comparing two new camera calibration methods with traditional pinhole calibrations,” Optics Express, vol. 15, no. 6, pp. 3012–3022, 2007.
- “A comparison of three geometric self-calibration methods for range cameras,” Remote Sensing, vol. 3, no. 5, pp. 1014–1028, 2011.
- Jan Hieronymus, “Comparison of methods for geometric camera calibration,” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 39, pp. 595–599, 2012.
- “Sports camera calibration via synthetic data,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, 2019, pp. 0–0.
- “Geometry-based camera calibration using closed-form solution of principal line,” IEEE Transactions on Image Processing, vol. 30, pp. 2599–2610, 2021.
- Gary Bradski, “The opencv library.,” Dr. Dobb’s Journal: Software Tools for the Professional Programmer, vol. 25, no. 11, pp. 120–123, 2000.
- Blender Online Community, Blender - a 3D modelling and rendering package, Blender Foundation, Stichting Blender Foundation, Amsterdam, 2023.
- “A dataset and evaluation methodology for depth estimation on 4d light fields,” in Computer Vision–ACCV 2016: 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III 13. Springer, 2017, pp. 19–34.
- “Charuco board-based omnidirectional camera calibration method,” Electronics, vol. 7, no. 12, pp. 421, 2018.
- “The minmax k-means clustering algorithm,” Pattern recognition, vol. 47, no. 7, pp. 2505–2516, 2014.