Deep Vectorization of Technical Drawings
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.
Cite
Text
Egiazarian et al. "Deep Vectorization of Technical Drawings." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58601-0_35Markdown
[Egiazarian et al. "Deep Vectorization of Technical Drawings." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/egiazarian2020eccv-deep/) doi:10.1007/978-3-030-58601-0_35BibTeX
@inproceedings{egiazarian2020eccv-deep,
title = {{Deep Vectorization of Technical Drawings}},
author = {Egiazarian, Vage and Voynov, Oleg and Artemov, Alexey and Volkhonskiy, Denis and Safin, Aleksandr and Taktasheva, Maria and Zorin, Denis and Burnaev, Evgeny},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58601-0_35},
url = {https://mlanthology.org/eccv/2020/egiazarian2020eccv-deep/}
}