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Joint Viewpoint and Keypoint Estimation with Real and Synthetic Data

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Pattern Recognition (DAGM GCPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11824))

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Abstract

The estimation of viewpoints and keypoints effectively enhance object detection methods by extracting valuable traits of the object instances. While the output of both processes differ, i.e., angles vs. list of characteristic points, they indeed share the same focus on how the object is placed in the scene, inducing that there is a certain level of correlation between them. Therefore, we propose a convolutional neural network that jointly computes the viewpoint and keypoints for different object categories. By training both tasks together, each task improves the accuracy of the other. Since the labelling of object keypoints is very time consuming for human annotators, we also introduce a new synthetic dataset with automatically generated viewpoint and keypoints annotations. Our proposed network can also be trained on datasets that contain viewpoint and keypoints annotations or only one of them. The experiments show that the proposed approach successfully exploits this implicit correlation between the tasks and outperforms previous techniques that are trained independently .

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Acknowledgement

The work has been supported by the ERC Starting Grant ARCA (677650).

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Authors and Affiliations

  1. Computer Vision Group, University of Bonn, Bonn, Germany

    Pau Panareda Busto & Juergen Gall

Authors
  1. Pau Panareda Busto
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  2. Juergen Gall
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Corresponding author

Correspondence to Pau Panareda Busto .

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Editors and Affiliations

  1. TU Dortmund University, Dortmund, Germany

    Gernot A. Fink

  2. University of Hamburg, Hamburg, Germany

    Simone Frintrop

  3. University of Münster, Münster, Germany

    Xiaoyi Jiang

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Panareda Busto, P., Gall, J. (2019). Joint Viewpoint and Keypoint Estimation with Real and Synthetic Data. In: Fink, G., Frintrop, S., Jiang, X. (eds) Pattern Recognition. DAGM GCPR 2019. Lecture Notes in Computer Science(), vol 11824. Springer, Cham. https://doi.org/10.1007/978-3-030-33676-9_8

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  • DOI: https://doi.org/10.1007/978-3-030-33676-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33675-2

  • Online ISBN: 978-3-030-33676-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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