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Learning Depth from Stereo

  • Fabian H. Sinz
  • Joaquin Quiñonero Candela
  • Gökhan H. Bakır
  • Carl Edward Rasmussen
  • Matthias O. Franz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3175)

Abstract

We compare two approaches to the problem of estimating the depth of a point in space from observing its image position in two different cameras: 1. The classical photogrammetric approach explicitly models the two cameras and estimates their intrinsic and extrinsic parameters using a tedious calibration procedure; 2. A generic machine learning approach where the mapping from image to spatial coordinates is directly approximated by a Gaussian Process regression. Our results show that the generic learning approach, in addition to simplifying the procedure of calibration, can lead to higher depth accuracies than classical calibration although no specific domain knowledge is used.

Keywords

Predictive Distribution Camera Parameter Camera Model Image Position Bundle Adjustment 
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 2004

Authors and Affiliations

  • Fabian H. Sinz
    • 1
  • Joaquin Quiñonero Candela
    • 2
  • Gökhan H. Bakır
    • 1
  • Carl Edward Rasmussen
    • 1
  • Matthias O. Franz
    • 1
  1. 1.Max Planck Institute for Biological CyberneticsTübingen
  2. 2.Informatics and Mathematical ModellingTechnical University of DenmarkKongens LyngbyDenmark

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