This paper addresses the problem of calibrating a pinhole camera from images of a surface of revolution. Camera calibration is the process of determining the intrinsic or internal parameters (i.e., aspect ratio, focal length and principal point) of a camera, and it is important for both motion estimation and metric reconstruction of 3D models. In this paper a novel and simple calibration technique is introduced, which is based on exploiting the symmetry of images of surfaces of revolution. Traditional techniques for camera calibration involve taking images of some precisely machined calibration pattern (such as a calibration grid). The use of surfaces of revolution, which are commonly found in daily life (e.g., bowls and vases), makes the process easier as a result of the reduced cost and increased accessibility of the calibration objects. In this paper, it is shown that two images of a surface of revolution will provide enough information for determining the aspect ratio, focal length and principal point of a camera with fixed intrinsic parameters. The algorithms presented in this paper have been implemented and tested with both synthetic and real data. Experimental results show that the camera calibration method presented here is both practical and accurate.
In REF it is shown that a single SOR view can provide two constraints to partially calibrate a pinhole camera.
Camera Calibration from Surfaces of Revolution
IEEE TRANS. ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Computer Science