Quantitative Verification of Zhang’s Camera Calibration Method Using Synthetic Checkerboard Images with Known Parameters

Abstract

Camera calibration is a basic prerequisite in computer vision, and one of the A photogrammetry techniques has been developed to allow the accurate recovery of the geometric relationship The coordinates on the two-dimensional screen. The coordinates that are used to map between 3D scene coordinates and their 2D counterparts. image projections. Without knowledge of the intrinsic optical parameters and lens When analyzing an image, metric measurements taken from the distortion characteristics of the image Observations continue to be systematically biased and unreliable in downstream processes such as 3D reconstruction, self-guided navigation, robot manipulation, augmented reality and medical imaging. This report is a full implementation, verification and error analysis of a A camera calibration pipeline based on Zhang’s flexible method was analyzed. Using planar checkerboard method, in python and OpenCV. library. A series of 15 synthetic calibration images is produced that are precisely The parameters of the camera are known a priori, and consist of a focal length of 1000 pixels, one of the main points at the centre of the image, and a The five-parameter radial-tangential lens distortion model was used to correct the images. This synthetic evaluation approach provides a strong quantitative An approach of verification by direct comparison with known parameters. ground-truth values, giving a level of validation that is not Impossible to do using only physical calibration targets. The intrinsic matrix and distortion coefficients recovered is evaluated. Identify and understand the need for multiple complementary verification metrics, such as overall root mean square reprojection error, per-image error analysis, cumulative reprojection error. of error distribution, spatial corner coverage assessment and visual evaluation. reprojection overlay. The pipeline itself results in an overall RMS reprojection error < 0.5 pixels in all the calibration views, focal length recover the correct answer within 0.1less than 0.5 pixels principal point estimation error Seven different sources of calibration error are identified and quantifiable, such as in terms of corner detection accuracy, view diversity, The distortion model completeness, target planarity, image resolution, stability of the environment, and numbers. The identified are followed by corresponding improvement strategies. error source. This experimentation is done using Python, OpenCV and NumPy.

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