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Pose Estimation and Shape Retrieval with Hough Voting in a Continuous Voting Space

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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

In this paper we present a method for 3D shape classification and pose estimation. Our approach is related to the recently popular adaptations of Implicit Shape Models to 3D data, but differs in some key aspects. We propose to omit the quantization of feature descriptors in favor of a better descriptiveness of training data. Additionally, a continuous voting space, in contrast to discrete Hough spaces in state of the art approaches, allows for more stable classification results under parameter variations. We evaluate and compare the performance of our approach with recently presented methods. The proposed algorithm achieves best results on three challenging datasets for 3D shape retrieval.

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Authors and Affiliations

  1. Active Vision Group (AGAS), University of Koblenz-Landau, Universitaetsstr. 1, 56070, Koblenz, Germany

    Viktor Seib, Norman Link & Dietrich Paulus

Authors
  1. Viktor Seib
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  2. Dietrich Paulus
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Correspondence to Viktor Seib .

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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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Seib, V., Link, N., Paulus, D. (2015). Pose Estimation and Shape Retrieval with Hough Voting in a Continuous Voting Space. 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_38

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  • DOI: https://doi.org/10.1007/978-3-319-24947-6_38

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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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