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<p><b>Abstract</b>—In computer vision and especially for 3D reconstruction, one of the key issues is the retrieval of the calibration parameters of the camera. These are needed to obtain metric information about the scene from the camera. Often these parameters are obtained through cumbersome calibration procedures. There is a way to avoid explicit calibration of the camera. Self-calibration is based on finding the set of calibration parameters which satisfy some constraints (e.g., constant calibration parameters). Several techniques have been proposed but it often proved difficult to reach a metric calibration at once. Therefore, in this paper, a stratified approach is proposed, which goes from projective through affine to metric. The key concept to achieve this is the modulus constraint. It allows retrieval of the affine calibration for constant intrinsic parameters. It is also suited for use in conjunction with scene knowledge. In addition, if the affine calibration is known, it can also be used to cope with a changing focal length.</p>
Self-calibration, affine calibration, metric calibration, metric 3D reconstruction, 3D modeling, uncalibrated image sequences.
Luc Van Gool, Marc Pollefeys, "Stratified Self-Calibration with the Modulus Constraint", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 21, no. , pp. 707-724, August 1999, doi:10.1109/34.784285
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