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Continuous Global Optimization in Multiview 3D Reconstruction

  • Conference paper
Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4679))

Abstract

In this work, we introduce a robust energy model for multiview 3D reconstruction that fuses silhouette- and stereo-based image information. It allows to cope with significant amounts of noise without manual pre-segmentation of the input images. Moreover, we suggest a method that can globally optimize this energy up to the visibility constraint. While similar global optimization has been presented in the discrete context in form of the maxflow-mincut framework, we suggest the use of a continuous counterpart. In contrast to graph cut methods, discretizations of the continuous optimization technique are consistent and independent of the choice of the grid connectivity. Our experiments demonstrate that this leads to visible improvements. Moreover, memory requirements are reduced, allowing for global reconstructions at higher resolutions.

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Authors and Affiliations

  1. Department of Computer Science, University of Bonn, Germany

    Kalin Kolev, Maria Klodt, Thomas Brox & Daniel Cremers

  2. Department of Mathematics, University of Michigan, USA

    Selim Esedoglu

Authors
  1. Kalin Kolev
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  2. Maria Klodt
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  3. Thomas Brox
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  4. Selim Esedoglu
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  5. Daniel Cremers
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Editor information

Alan L. Yuille Song-Chun Zhu Daniel Cremers Yongtian Wang

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© 2007 Springer-Verlag Berlin Heidelberg

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Kolev, K., Klodt, M., Brox, T., Esedoglu, S., Cremers, D. (2007). Continuous Global Optimization in Multiview 3D Reconstruction. In: Yuille, A.L., Zhu, SC., Cremers, D., Wang, Y. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2007. Lecture Notes in Computer Science, vol 4679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74198-5_34

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  • DOI: https://doi.org/10.1007/978-3-540-74198-5_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74195-4

  • Online ISBN: 978-3-540-74198-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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