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Joint Epipolar Tracking (JET): Simultaneous optimization of epipolar geometry and feature correspondences
Goethe University, Germany.
Goethe University, Germany.
Goethe University, Germany.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Goethe University, Germany.
2017 (English)In: 2017 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2017), IEEE , 2017, 445-453 p.Conference paper, Published paper (Refereed)
Abstract [en]

Traditionally, pose estimation is considered as a two step problem. First, feature correspondences are determined by direct comparison of image patches, or by associating feature descriptors. In a second step, the relative pose and the coordinates of corresponding points are estimated, most often by minimizing the reprojection error (RPE). RPE optimization is based on a loss function that is merely aware of the feature pixel positions but not of the underlying image intensities. In this paper, we propose a sparse direct method which introduces a loss function that allows to simultaneously optimize the unscaled relative pose, as well as the set of feature correspondences directly considering the image intensity values. Furthermore, we show how to integrate statistical prior information on the motion into the optimization process. This constructive inclusion of a Bayesian bias term is particularly efficient in application cases with a strongly predictable (short term) dynamic, e.g. in a driving scenario. In our experiments, we demonstrate that the JET` algorithm we propose outperforms the classical reprojection error optimization on two synthetic datasets and on the KITTI dataset. The JET algorithm runs in real-time on a single CPU thread.

Place, publisher, year, edition, pages
IEEE , 2017. 445-453 p.
Series
IEEE Winter Conference on Applications of Computer Vision, ISSN 2472-6737
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-139430DOI: 10.1109/WACV.2017.56ISI: 000404165800049ISBN: 978-1-5090-4822-9 OAI: oai:DiVA.org:liu-139430DiVA: diva2:1129770
Conference
17th IEEE Winter Conference on Applications of Computer Vision (WACV)
Available from: 2017-08-07 Created: 2017-08-07 Last updated: 2017-08-07

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Mester, Rudolf
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Total: 27 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf