A fractal calibration pattern for improved camera calibration

Camera calibration, crucial for computer vision tasks, often relies on planar calibration targets to calibrate the camera parameters. This work explores a planar, fractal, self-identifying calibration pattern, which provides a high density of calibration points for a large range of magnification factors. An evaluation on ground truth data shows the target provides very high accuracy over a wide range of conditions.