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Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12365))

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Abstract

A novel algorithm to detect semantic lines is proposed in this paper. We develop three networks: detection network with mirror attention (D-Net) and comparative ranking and matching networks (R-Net and M-Net). D-Net extracts semantic lines by exploiting rich contextual information. To this end, we design the mirror attention module. Then, through pairwise comparisons of extracted semantic lines, we iteratively select the most semantic line and remove redundant ones overlapping with the selected one. For the pairwise comparisons, we develop R-Net and M-Net in the Siamese architecture. Experiments demonstrate that the proposed algorithm outperforms the conventional semantic line detector significantly. Moreover, we apply the proposed algorithm to detect two important kinds of semantic lines successfully: dominant parallel lines and reflection symmetry axes. Our codes are available at https://github.com/dongkwonjin/Semantic-Line-DRM.

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Notes

  1. 1.

    SEL_Hard is available at https://github.com/dongkwonjin/Semantic-Line-DRM.

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Acknowledgement

This work was supported in part by the Agency for Defense Development (ADD) and Defense Acquisition Program Administration (DAPA) of Korea under grant UC160016FD and in part by the National Research Foundation of Korea (NRF) through the Korea Government (MSIP) under grant NRF-2018R1A2B3003896.

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Authors and Affiliations

  1. School of Electrical Engineering, Korea University, Seoul, Korea

    Dongkwon Jin, Jun-Tae Lee & Chang-Su Kim

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  1. Dongkwon Jin
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  2. Jun-Tae Lee
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  3. Chang-Su Kim
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Correspondence to Chang-Su Kim .

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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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Jin, D., Lee, JT., Kim, CS. (2020). Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12365. Springer, Cham. https://doi.org/10.1007/978-3-030-58565-5_8

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  • DOI: https://doi.org/10.1007/978-3-030-58565-5_8

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