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Deep Vectorization of Technical Drawings

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images. Our method includes (1) a deep learning-based cleaning stage to eliminate the background and imperfections in the image and fill in missing parts, (2) a transformer-based network to estimate vector primitives, and (3) optimization procedure to obtain the final primitive configurations. We train the networks on synthetic data, renderings of vector line drawings, and manually vectorized scans of line drawings. Our method quantitatively and qualitatively outperforms a number of existing techniques on a collection of representative technical drawings.

V. Egiazarian and O. Voynov—Equal contribution.

A. Artemov—Technical lead.

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Acknowledgements

We thank Milena Gazdieva and Natalia Soboleva for their valuable contributions in preparing real-world raster and vector datasets, as well as Maria Kolos and Alexey Bokhovkin for contributing parts of shared codebase used throughout this project. We acknowledge the usage of Skoltech CDISE HPC cluster Zhores for obtaining the presented results. The work was partially supported by Russian Science Foundation under Grant 19–41-04109.

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Authors and Affiliations

  1. Skolkovo Institute of Science and Technology, 3 Nobel Street, Skolkovo, 143026, Russian Federation

    Vage Egiazarian, Oleg Voynov, Alexey Artemov, Denis Volkhonskiy, Aleksandr Safin, Maria Taktasheva, Denis Zorin & Evgeny Burnaev

  2. New York University, 70 Washington Square South, New York, NY, 10012, USA

    Denis Zorin

Authors
  1. Vage Egiazarian
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  2. Oleg Voynov
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  3. Alexey Artemov
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  4. Denis Volkhonskiy
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  8. Evgeny Burnaev
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Correspondence to Vage Egiazarian .

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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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Egiazarian, V. et al. (2020). Deep Vectorization of Technical Drawings. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12358. Springer, Cham. https://doi.org/10.1007/978-3-030-58601-0_35

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  • DOI: https://doi.org/10.1007/978-3-030-58601-0_35

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