Abstract
Camera distortion is a critical factor affecting the accuracy of camera calibration. A conventional calibration approach cannot satisfy the requirement of a measurement system demanding high calibration accuracy due to the inaccurate distortion compensation. This paper presents a novel camera calibration method with an iterative distortion compensation algorithm. The initial parameters of the camera are calibrated by full-field camera pixels and the corresponding points on a phase target. An iterative algorithm is proposed to compensate for the distortion. A 2D fitting and interpolation method is also developed to enhance the accuracy of the phase target. Compared to the conventional calibration method, the proposed method does not rely on a distortion mathematical model, and is stable and effective in terms of complex distortion conditions. Both the simulation work and experimental results show that the proposed calibration method is more than 100% more accurate than the conventional calibration method.
Highlights
Camera calibration is the first and most essential step in optical 3D measurement such as stereo vision [1,2], fringe projection techniques [3,4,5] and deflectometry [6,7,8]
Camera lens distortion can be classified as radial distortion, eccentric distortion and thin prism distortion
Conventional calibration approaches use infinite high order polynomials with distortion parameters to express the distortion caused from different sources [1,9,10]
Summary
Camera calibration is the first and most essential step in optical 3D measurement such as stereo vision [1,2], fringe projection techniques [3,4,5] and deflectometry [6,7,8]. There are generally two aspects affecting the accuracy of camera calibration. One is the accuracy of the chosen camera model. The pinhole model is popularly adopted for the description of a general imaging process, which is a linear projection between a 3D point in the world and the corresponding 2D image in a camera. The real imaging process is a nonlinear projection due to camera distortion. The parameter-based approaches are popularly researched to compensate lens distortion [1,9,10]. Since the accuracy of the input plays an important role in the constringency of the iterative optimization, the accuracy of camera calibration is highly dependent on the accuracy of the initial distortion parameters. The real distortion is the result of the cumulative effects of a complex lens system, the camera geometry error, and the imperfect shape of the image
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