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Learning Depth From Stereo

2004

Conference Paper

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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.

Author(s): Sinz, F. and Candela, JQ. and BakIr, G. and Rasmussen, CE. and Franz, M.
Book Title: 26th DAGM Symposium
Journal: Pattern Recognition: 26th DAGM Symposium
Pages: 245-252
Year: 2004
Month: September
Day: 0

Series: LNCS 3175
Editors: Rasmussen, C. E., H. H. B{\"u}lthoff, B. Sch{\"o}lkopf, M. A. Giese
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 26th DAGM Symposium
Event Place: Tübingen, Germany

Address: Berlin, Germany
Digital: 0
Institution: Deutsche Arbeitsgemeinschaft für Mustererkennung e.V.
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{2644,
  title = {Learning Depth From Stereo},
  author = {Sinz, F. and Candela, JQ. and BakIr, G. and Rasmussen, CE. and Franz, M.},
  journal = {Pattern Recognition: 26th DAGM Symposium},
  booktitle = {26th DAGM Symposium},
  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},
  month_numeric = {9}
}