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Automatic Camera Calibration from a Single Manhattan Image
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Automatic Camera Calibration from a Single Manhattan Image
J. Deutscher7, M. Isard7 and J. MacCormick7
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Systems Research Center, Compaq Computer Corporation, 130 Lytton Avenue, Palo Alto, CA, 94301 |
Abstract
We present a completely automatic method for obtaining the approximate calibration of a camera (alignment to a world frame
and focal length) from a single image of an unknown scene, provided only that the scene satisfies a Manhattan world assumption. This assumption states that the imaged scene contains three orthogonal, dominant directions, and is often satisfied
by outdoor or indoor views of man-made structures and environments.
The proposed method combines the calibration likelihood introduced in [5] with a stochastic search algorithm to obtain a MAP estimate of the camera’s focal length and alignment. Results on real
images of indoor scenes are presented. The calibrations obtained are less accurate than those from standard methods employing
a calibration pattern or multiple images. However, the outputs are certainly good enough for common vision tasks such as tracking.
Moreover, the results are obtained without any user intervention, from a single image, and without use of a calibration pattern.
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