Camera Calibration With Distortion Models And Accuracy Evaluation
Camera calibration is the process of estimating the internal parameters of a camera so that the 3D world can be correctly mapped onto the 2D image plane. Distortion models are used to account for lens imperfections, which create geometric distortions in the image. The accuracy of the calibration process is an important factor that affects the quality of the resulting images.
What is camera calibration?
Camera calibration is the process of estimating the intrinsic and extrinsic parameters of a camera. Intrinsic parameters refer to the internal characteristics of the camera, such as the focal length, principal point, and aspect ratio. Extrinsic parameters refer to the position and orientation of the camera in 3D space.
The calibration process involves capturing several images of a calibration target, which is a known 3D object. The image coordinates of the target points are then used to estimate the camera parameters using mathematical models.
What are distortion models?
Distortion models are mathematical functions that describe the geometric distortions caused by lens imperfections. These distortions include radial distortion, tangential distortion, and lens distortion. Radial distortion refers to the curvature of straight lines on the image plane, tangential distortion refers to the skewness of the image plane, and lens distortion refers to the asymmetrical distortion caused by the lens.
Distortion models are used to correct the geometric distortions in the image, which improves the accuracy of the resulting images. The most commonly used distortion model is the Brown-Conrady model, which is a polynomial model that approximates the distortion as a function of the distance from the principal point.
How is accuracy evaluated?
The accuracy of the calibration process can be evaluated using different metrics, such as the reprojection error, the pixel error, and the root mean square error. The reprojection error measures the distance between the observed image point and the reprojected image point in 3D space. The pixel error measures the difference between the observed image point and the ideal image point. The root mean square error measures the overall accuracy of the calibration process.
The accuracy of the calibration process depends on several factors, such as the quality of the calibration target, the number of images captured, and the noise level in the images. To ensure the accuracy of the calibration process, it is important to use a high-quality calibration target, capture a sufficient number of images, and minimize the noise level in the images.
Conclusion
Camera calibration with distortion models is an important process that enables accurate mapping of 3D world onto 2D image plane. Distortion models account for lens imperfections and improve the accuracy of the resulting images. Accuracy evaluation measures the quality of the calibration process and helps to identify potential sources of error. To ensure the accuracy of the calibration process, it is important to use a high-quality calibration target, capture a sufficient number of images, and minimize the noise level in the images.
