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Deep Hough-Transform Line Priors

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

Classical work on line segment detection is knowledge-based; it uses carefully designed geometric priors using either image gradients, pixel groupings, or Hough transform variants. Instead, current deep learning methods do away with all prior knowledge and replace priors by training deep networks on large manually annotated datasets. Here, we reduce the dependency on labeled data by building on the classic knowledge-based priors while using deep networks to learn features. We add line priors through a trainable Hough transform block into a deep network. Hough transform provides the prior knowledge about global line parameterizations, while the convolutional layers can learn the local gradient-like line features. On the Wireframe (ShanghaiTech) and York Urban datasets we show that adding prior knowledge improves data efficiency as line priors no longer need to be learned from data.

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Notes

  1. 1.

    https://github.com/yanconglin/Deep-Hough-Transform-Line-Priors.

  2. 2.

    https://github.com/svip-lab/PPGNet.

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Authors and Affiliations

  1. Computer Vision Lab, Delft University of Technology, Delft, The Netherlands

    Yancong Lin, Silvia L. Pintea & Jan C. van Gemert

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  1. Yancong Lin
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  2. Silvia L. Pintea
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  3. Jan C. van Gemert
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Correspondence to Silvia L. Pintea .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Lin, Y., Pintea, S.L., van Gemert, J.C. (2020). Deep Hough-Transform Line Priors. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12367. Springer, Cham. https://doi.org/10.1007/978-3-030-58542-6_20

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  • DOI: https://doi.org/10.1007/978-3-030-58542-6_20

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