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GraphFlow – 6D Large Displacement Scene Flow via Graph Matching

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Pattern Recognition (DAGM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9358))

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Abstract

We present an approach for computing dense scene flow from two large displacement RGB-D images. When dealing with large displacements the crucial step is to estimate the overall motion correctly. While state-of-the-art approaches focus on RGB information to establish guiding correspondences, we explore the power of depth edges. To achieve this, we present a new graph matching technique that brings sparse depth edges into correspondence. An additional contribution is the formulation of a continuous-label energy which is used to densify the sparse graph matching output. We present results on challenging Kinect images, for which we outperform state-of-the-art techniques.

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Notes

  1. 1.

    Available on our web page

    http://cvlab-dresden.de/research/image-matching/graphflow/.

  2. 2.

    Adjusting the weighting parameters of [15] did not improve the results.

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Author information

Authors and Affiliations

  1. TU Dresden, Dresden, Germany

    Hassan Abu Alhaija, Anita Sellent & Carsten Rother

  2. Heidelberg University, Heidelberg, Germany

    Hassan Abu Alhaija & Daniel Kondermann

  3. TU Darmstadt, Darmstadt, Germany

    Anita Sellent

Authors
  1. Hassan Abu Alhaija
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  2. Anita Sellent
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  3. Daniel Kondermann
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  4. Carsten Rother
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Corresponding author

Correspondence to Hassan Abu Alhaija .

Editor information

Editors and Affiliations

  1. Institute of Computer Science III, University of Bonn, Bonn, Germany

    Juergen Gall

  2. MPI for Intelligent Systems, University of Tübingen, Tübingen, Germany

    Peter Gehler

  3. Computer Vision Group, Visual Computing Institute, RWTH Aachen, Aachen, Germany

    Bastian Leibe

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Abu Alhaija, H., Sellent, A., Kondermann, D., Rother, C. (2015). GraphFlow – 6D Large Displacement Scene Flow via Graph Matching. In: Gall, J., Gehler, P., Leibe, B. (eds) Pattern Recognition. DAGM 2015. Lecture Notes in Computer Science(), vol 9358. Springer, Cham. https://doi.org/10.1007/978-3-319-24947-6_23

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  • DOI: https://doi.org/10.1007/978-3-319-24947-6_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24946-9

  • Online ISBN: 978-3-319-24947-6

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