Robust Radial Distortion from a Single Image

Many computer vision algorithms rely on the assumption of the pinhole camera model, but lens distortion with off-the-shelf cameras is significant enough to violate this assumption. Many methods for radial distortion estimation have been proposed, but they all have limitations. Robust automatic radial distortion estimation from a single natural image would be extremely useful for some applications. We propose a new method for radial distortion estimation based on the plumb-line approach. The method works from a single image and does not require a special calibration pattern. It is based on Fitzgibbon’s division model, robust estimation of circular arcs, and robust estimation of distortion parameters. In a series of experiments on synthetic and real images, we demonstrate the method’s ability to accurately identify distortion parameters and remove radial distortion from images.