Automatic Camera Calibration from a Single Manhattan Image

  • J. Deutscher
  • M. Isard
  • J. MacCormick
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2353)


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.


Focal Length Single Image Importance Sampling Camera Calibration Principal Point 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • J. Deutscher
    • 1
  • M. Isard
    • 1
  • J. MacCormick
    • 1
  1. 1.Systems Research CenterCompaq Computer CorporationPalo Alto

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