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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)

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.

Keywords

Focal Length Single Image Importance Sampling Camera Calibration Principal Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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