@inproceedings{2644,
  title = {Learning Depth From Stereo},
  journal = {Pattern Recognition: 26th DAGM Symposium},
  booktitle = {26th DAGM Symposium},
  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.},
  pages = {245-252},
  series = {LNCS 3175},
  editors = {Rasmussen, C. E., H. H. B{\"u}lthoff, B. Sch{\"o}lkopf, M. A. Giese},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  institution = {Deutsche Arbeitsgemeinschaft für Mustererkennung e.V.},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2004},
  slug = {2644},
  author = {Sinz, F. and Candela, JQ. and BakIr, G. and Rasmussen, CE. and Franz, M.},
  month_numeric = {9}
}