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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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References

  1. Open CASCADE Technology OCCT. https://www.opencascade.com/, Accessed 05 March 2005

  2. PrecisionFloorplan. http://precisionfloorplan.com, Accessed 05 March 2020

  3. Bessmeltsev, M., Solomon, J.: Vectorization of line drawings via polyvector fields. ACM Trans. Graph. (TOG) 38(1), 9 (2019)

    Article  Google Scholar 

  4. Chai, D., Forstner, W., Lafarge, F.: Recovering line-networks in images by junction-point processes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1894–1901 (2013)

    Google Scholar 

  5. Chen, J., Du, M., Qin, X., Miao, Y.: An improved topology extraction approach for vectorization of sketchy line drawings. The Visual Comput. 34(12), 1633–1644 (2018). https://doi.org/10.1007/s00371-018-1549-z

    Article  Google Scholar 

  6. Chen, J.Z., Lei, Q., Miao, Y.W., Peng, Q.S.: Vectorization of line drawing image based on junction analysis. Sci. China Inf. Sci. 58(7), 1–14 (2015). https://doi.org/10.1007/s11432-014-5246-x

    Article  MathSciNet  Google Scholar 

  7. Chu, H., Wang, S., Urtasun, R., Fidler, S.: HouseCraft: building houses from rental ads and street views. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 500–516. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_30

    Chapter  Google Scholar 

  8. Delalandre, M., Valveny, E., Pridmore, T., Karatzas, D.: Generation of synthetic documents for performance evaluation of symbol recognition & spotting systems. Int. J. Document Anal. Recogn. (IJDAR) 13(3), 187–207 (2010)

    Article  Google Scholar 

  9. Donati, L., Cesano, S., Prati, A.: A complete hand-drawn sketch vectorization framework. Multimed. Tools Appl. 78(14), 19083–19113 (2019). https://doi.org/10.1007/s11042-019-7311-3

    Article  Google Scholar 

  10. Ellis, K., Ritchie, D., Solar-Lezama, A., Tenenbaum, J.: Learning to infer graphics programs from hand-drawn images. In: Advances in Neural Information Processing Systems. pp. 6059–6068 (2018)

    Google Scholar 

  11. Favreau, J.D., Lafarge, F., Bousseau, A.: Fidelity vs. simplicity: a global approach to line drawing vectorization. ACM Trans. Graph. (TOG) 35(4), 120 (2016)

    Article  Google Scholar 

  12. Gao, J., Tang, C., Ganapathi-Subramanian, V., Huang, J., Su, H., Guibas, L.J.: Deepspline: Data-driven reconstruction of parametric curves and surfaces. arXiv preprint arXiv:1901.03781 (2019)

  13. Guo, Y., Zhang, Z., Han, C., Hu, W.B., Li, C., Wong, T.T.: Deep line drawing vectorization via line subdivision and topology reconstruction. Comput. Graph. Forum 38, 81–90 (2019)

    Article  Google Scholar 

  14. Ha, D., Eck, D.: A neural representation of sketch drawings. arXiv preprint arXiv:1704.03477 (2018)

  15. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778 (2016)

    Google Scholar 

  16. de las Heras, L.-P., Terrades, O.R., Robles, S., Sánchez, G.: CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool. Int. J. Document Anal. Recogn (IJDAR) 18(1), 15–30 (2015). https://doi.org/10.1007/s10032-014-0236-5

    Article  Google Scholar 

  17. Hilaire, X., Tombre, K.: Robust and accurate vectorization of line drawings. IEEE Trans. Pattern Anal. Mach. Intell. 6, 890–904 (2006)

    Article  Google Scholar 

  18. Kaiyrbekov, K., Sezgin, M.: Stroke-based sketched symbol reconstruction and segmentation. arXiv preprint arXiv:1901.03427 (2019)

  19. Kansal, R., Kumar, S.: A vectorization framework for constant and linear gradient filled regions. The Visual Comput. 31(5), 717–732 (2014). https://doi.org/10.1007/s00371-014-0997-3

    Article  Google Scholar 

  20. Kanungo, T., Haralick, R.M., Baird, H.S., Stuezle, W., Madigan, D.: A statistical, nonparametric methodology for document degradation model validation. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1209–1223 (2000)

    Article  Google Scholar 

  21. Kim, B., Wang, O., Öztireli, A.C., Gross, M.: Semantic segmentation for line drawing vectorization using neural networks. In: Computer Graphics Forum. vol. 37, pp. 329–338. Wiley Online Library (2018)

    Google Scholar 

  22. Koch, S., et al.: Abc: A big cad model dataset for geometric deep learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 9601–9611 (2019)

    Google Scholar 

  23. Li, C., Liu, X., Wong, T.T.: Deep extraction of manga structural lines. ACM Trans. Graph. (TOG) 36(4), 117 (2017)

    Google Scholar 

  24. Liu, C., Wu, J., Kohli, P., Furukawa, Y.: Raster-to-vector: revisiting floorplan transformation. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2195–2203 (2017)

    Google Scholar 

  25. Liu, C., Schwing, A.G., Kundu, K., Urtasun, R., Fidler, S.: Rent3d: Floor-plan priors for monocular layout estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3413–3421 (2015)

    Google Scholar 

  26. Máttyus, G., Luo, W., Urtasun, R.: Deeproadmapper: Extracting road topology from aerial images. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 3438–3446 (2017)

    Google Scholar 

  27. Munusamy Kabilan, V., Morris, B., Nguyen, A.: Vectordefense: Vectorization as a defense to adversarial examples. arXiv preprint arXiv:1804.08529 (2018)

  28. Najgebauer, P., Scherer, R.: Inertia-based fast vectorization of line drawings. Comput. Graph. Forum 38, 203–213 (2019)

    Article  Google Scholar 

  29. Noris, G., Hornung, A., Sumner, R.W., Simmons, M., Gross, M.: Topology-driven vectorization of clean line drawings. ACM Trans. Graph. (TOG) 32(1), 4 (2013)

    Article  Google Scholar 

  30. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  31. Rusiñol, M., Borràs, A., Lladós, J.: Relational indexing of vectorial primitives for symbol spotting in line-drawing images. Pattern Recogn. Lett. 31(3), 188–201 (2010)

    Article  Google Scholar 

  32. Sasaki, K., Iizuka, S., SimoSerra, E., Ishikawa, H.: Learning to restore deteriorated line drawing. The Visual Comput. 34(6–8), 1077–1085 (2018)

    Article  Google Scholar 

  33. Selinger, P.: Potrace: a polygon-based tracing algorithm. Potrace. http://potrace.sourceforge.net/potrace.pdf (2003)

  34. Sharma, D., Gupta, N., Chattopadhyay, C., Mehta, S.: Daniel: A deep architecture for automatic analysis and retrieval of building floor plans. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). vol. 1, pp. 420–425. IEEE (2017)

    Google Scholar 

  35. Simo-Serra, E., Iizuka, S., Ishikawa, H.: Mastering sketching: adversarial augmentation for structured prediction. ACM Trans. Graph. (TOG) 37(1), 11 (2018)

    Article  Google Scholar 

  36. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems. pp. 5998–6008 (2017)

    Google Scholar 

  37. Zhao, J., Feng, J., Zhou, B.: Image vectorization using blue-noise sampling. In: Imaging and Printing in a Web 2.0 World IV International Society for Optics and Photonics. vol. 8664, p. 86640H (2013)

    Google Scholar 

  38. Zheng, N., Jiang, Y., Huang, D.: Strokenet: Aneural painting environment. In: International Conference on Learning Representations (2018)

    Google Scholar 

  39. Zhou, T., et al.: Learning to doodle with stroke demonstrations and deep q-networks. In: BMVC. p. 13 (2018)

    Google Scholar 

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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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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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