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Deep Hough Transform for Semantic Line Detection

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12354))

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Abstract

In this paper, we put forward a simple yet effective method to detect meaningful straight lines, a.k.a. semantic lines, in given scenes. Prior methods take line detection as a special case of object detection, while neglect the inherent characteristics of lines, leading to less efficient and suboptimal results. We propose a one-shot end-to-end framework by incorporating the classical Hough transform into deeply learned representations. By parameterizing lines with slopes and biases, we perform Hough transform to translate deep representations to the parametric space and then directly detect lines in the parametric space. More concretely, we aggregate features along candidate lines on the feature map plane and then assign the aggregated features to corresponding locations in the parametric domain. Consequently, the problem of detecting semantic lines in the spatial domain is transformed to spotting individual points in the parametric domain, making the post-processing steps, i.e. non-maximal suppression, more efficient. Furthermore, our method makes it easy to extract contextual line features, that are critical to accurate line detection. Experimental results on a public dataset demonstrate the advantages of our method over state-of-the-arts. Codes are available at https://mmcheng.net/dhtline/.

Q. Han and K. Zhao—Equal contribution.

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Acknowledgements

This research was supported by Major Project for New Generation of AI under Grant No. 2018AAA0100400, NSFC (61922046), Tianjin Natural Science Foundation (18ZXZNGX00110), and the Fundamental Research Funds for the Central Universities (Nankai University: 63201169).

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Authors and Affiliations

  1. TKLNDST, CS, Nankai University, Tianjin, China

    Qi Han, Kai Zhao, Jun Xu & Ming-Ming Cheng

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  1. Qi Han
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  2. Kai Zhao
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  3. Jun Xu
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  4. Ming-Ming Cheng
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Correspondence to Ming-Ming Cheng .

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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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Han, Q., Zhao, K., Xu, J., Cheng, MM. (2020). Deep Hough Transform for Semantic Line Detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12354. Springer, Cham. https://doi.org/10.1007/978-3-030-58545-7_15

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  • DOI: https://doi.org/10.1007/978-3-030-58545-7_15

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