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German Conference on Pattern Recognition

DAGM 2015: Pattern Recognition pp 222–233Cite as

Graph-Based Deformable 3D Object Matching

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

Abstract

We present a method for efficient detection of deformed 3D objects in 3D point clouds that can handle large amounts of clutter, noise, and occlusion. The method generalizes well to different object classes and does not require an explicit deformation model. Instead, deformations are learned based on a few registered deformed object instances. The approach builds upon graph matching to find correspondences between scene and model points. The robustness is increased through a parametrization where each graph vertex represents a full rigid transformation. We speed up the matching through greedy multi-step graph pruning and a constant-time feature matching. Quantitative and qualitative experiments demonstrate that our method is robust, efficient, able to detect rigid and non-rigid objects and exceeds state of the art.

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Author information

Authors and Affiliations

  1. MVTec Software GmbH, Munich, Germany

    Bertram Drost

  2. Siemens AG, Munich, Germany

    Slobodan Ilic

Authors
  1. Bertram Drost
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  2. Slobodan Ilic
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Corresponding author

Correspondence to Bertram Drost .

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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© 2015 Springer International Publishing Switzerland

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Drost, B., Ilic, S. (2015). Graph-Based Deformable 3D Object 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_18

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  • DOI: https://doi.org/10.1007/978-3-319-24947-6_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24946-9

  • Online ISBN: 978-3-319-24947-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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