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Model-Based Pose Estimation for Rigid Objects

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Computer Vision Systems (ICVS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7963))

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Abstract

Determining the pose of objects appearing in images is a problem encountered often in several practical applications. The most effective strategy for dealing with this challenge is to proceed according to the model-based paradigm, which involves building 3D models of objects and then determining object poses by fitting their models to new images with the aid of detected features. This paper proposes a model-based approach for estimating the full pose of known objects from natural point features. The method employs a projective imaging model and incorporates reliable automatic mechanisms for pose initialization and convergence. Furthermore, it is extendable to multiple cameras without the need to perform multi-view matching and relies on sparse structure from motion techniques for the construction of object models offline. Experimental results demonstrate its accuracy and robustness.

This work has received funding from the EC FP7 programme under grant no. 270138 DARWIN.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Foundation for Research and Technology, Hellas Vassilika Vouton, P.O. Box 1385, GR 711 10, Heraklion, Crete, Greece

    Manolis Lourakis & Xenophon Zabulis

Authors
  1. Manolis Lourakis
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  2. Xenophon Zabulis
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Editor information

Editors and Affiliations

  1. Intel Science and Technology Center on Embedded Computing, Pittsburgh, PA, USA

    Mei Chen

  2. UMIC Research Centre, RWTH Aachen University, Aachen, Germany

    Bastian Leibe

  3. FB Informatik, Universität Hamburg, Germany

    Bernd Neumann

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Lourakis, M., Zabulis, X. (2013). Model-Based Pose Estimation for Rigid Objects. In: Chen, M., Leibe, B., Neumann, B. (eds) Computer Vision Systems. ICVS 2013. Lecture Notes in Computer Science, vol 7963. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39402-7_9

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  • DOI: https://doi.org/10.1007/978-3-642-39402-7_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39401-0

  • Online ISBN: 978-3-642-39402-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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