Maximum likelihood autocalibration

  • Authors:

  • Stuart B. Heinrich;Wesley E. Snyder;Jan-Michael Frahm

  • Affiliations:

  • Department of Electrical and Computer Engineering, NC State University, Box 7911, Raleigh, NC 27695-7911, United States;Department of Electrical and Computer Engineering, NC State University, Box 7911, Raleigh, NC 27695-7911, United States;Department of Computer Science, Campus Box 3175, Brooks Computer Science Building, 201 South Columbia Street, UNC-Chapel Hill, Chapel Hill, NC 27599-3175, United States

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2011

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Abstract

This paper addresses the problem of autocalibration, which is a critical step in existing uncalibrated structure from motion algorithms that utilize an initialization to avoid the local minima in metric bundle adjustment. Currently, all known direct (not non-linear) solutions to the uncalibrated structure from motion problem solve for a projective reconstruction that is related to metric by some unknown homography, and hence a necessary step in obtaining a metric reconstruction is the subsequent estimation of the rectifying homography, known as autocalibration. Although autocalibration is a well-studied problem, previous approaches have relied upon heuristic objective functions, and have a reputation for instability. We propose a maximum likelihood objective and show that it can be implemented robustly and efficiently and often provides substantially greater accuracy, especially when there are fewer views or greater noise.