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Practical Autocalibration

  • Riccardo Gherardi
  • Andrea Fusiello
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)

Abstract

As it has been noted several times in literature, the difficult part of autocalibration efforts resides in the structural non-linearity of the search for the plane at infinity. In this paper we present a robust and versatile autocalibration method based on the enumeration of the inherently bounded space of the intrinsic parameters of two cameras in order to find the collineation of space that upgrades a given projective reconstruction to Euclidean. Each sample of the search space (which reduces to a finite subset of ℝ2 under mild assumptions) defines a consistent plane at infinity. This in turn produces a tentative, approximate Euclidean upgrade of the whole reconstruction which is then scored according to the expected intrinsic parameters of a Euclidean camera. This approach has been compared with several other algorithms on both synthetic and concrete cases, obtaining favourable results.

Keywords

Focal Length Intrinsic Parameter Principal Point Projective Reconstruction Aggregate Cost 
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 2010

Authors and Affiliations

  • Riccardo Gherardi
    • 1
  • Andrea Fusiello
    • 1
  1. 1.Dipartimento di InformaticaUniversità di VeronaVeronaItaly

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