{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T16:06:17Z","timestamp":1778515577670,"version":"3.51.4"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030586003","type":"print"},{"value":"9783030586010","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58601-0_35","type":"book-chapter","created":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T19:02:52Z","timestamp":1606503772000},"page":"582-598","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Deep Vectorization of Technical Drawings"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4444-9769","authenticated-orcid":false,"given":"Vage","family":"Egiazarian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3666-9166","authenticated-orcid":false,"given":"Oleg","family":"Voynov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5451-7492","authenticated-orcid":false,"given":"Alexey","family":"Artemov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9463-7883","authenticated-orcid":false,"given":"Denis","family":"Volkhonskiy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5453-1101","authenticated-orcid":false,"given":"Aleksandr","family":"Safin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6907-5922","authenticated-orcid":false,"given":"Maria","family":"Taktasheva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Denis","family":"Zorin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8424-0690","authenticated-orcid":false,"given":"Evgeny","family":"Burnaev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,28]]},"reference":[{"key":"35_CR1","unstructured":"Open CASCADE Technology OCCT. https:\/\/www.opencascade.com\/, Accessed 05 March 2005"},{"key":"35_CR2","unstructured":"PrecisionFloorplan. http:\/\/precisionfloorplan.com, Accessed 05 March 2020"},{"issue":"1","key":"35_CR3","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1145\/3202661","volume":"38","author":"M Bessmeltsev","year":"2019","unstructured":"Bessmeltsev, M., Solomon, J.: Vectorization of line drawings via polyvector fields. ACM Trans. Graph. (TOG) 38(1), 9 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"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\u20131901 (2013)","DOI":"10.1109\/CVPR.2013.247"},{"issue":"12","key":"35_CR5","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.1007\/s00371-018-1549-z","volume":"34","author":"J Chen","year":"2018","unstructured":"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\u20131644 (2018). https:\/\/doi.org\/10.1007\/s00371-018-1549-z","journal-title":"The Visual Comput."},{"issue":"7","key":"35_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-014-5246-x","volume":"58","author":"JZ Chen","year":"2015","unstructured":"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\u201314 (2015). https:\/\/doi.org\/10.1007\/s11432-014-5246-x","journal-title":"Sci. China Inf. Sci."},{"key":"35_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1007\/978-3-319-46466-4_30","volume-title":"Computer Vision \u2013 ECCV 2016","author":"H Chu","year":"2016","unstructured":"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\u2013516. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_30"},{"issue":"3","key":"35_CR8","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s10032-010-0120-x","volume":"13","author":"M Delalandre","year":"2010","unstructured":"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\u2013207 (2010)","journal-title":"Int. J. Document Anal. Recogn. (IJDAR)"},{"issue":"14","key":"35_CR9","doi-asserted-by":"publisher","first-page":"19083","DOI":"10.1007\/s11042-019-7311-3","volume":"78","author":"L Donati","year":"2019","unstructured":"Donati, L., Cesano, S., Prati, A.: A complete hand-drawn sketch vectorization framework. Multimed. Tools Appl. 78(14), 19083\u201319113 (2019). https:\/\/doi.org\/10.1007\/s11042-019-7311-3","journal-title":"Multimed. Tools Appl."},{"key":"35_CR10","unstructured":"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\u20136068 (2018)"},{"issue":"4","key":"35_CR11","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1145\/2897824.2925946","volume":"35","author":"JD Favreau","year":"2016","unstructured":"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)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"35_CR12","unstructured":"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)"},{"key":"35_CR13","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1111\/cgf.13818","volume":"38","author":"Y Guo","year":"2019","unstructured":"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\u201390 (2019)","journal-title":"Comput. Graph. Forum"},{"key":"35_CR14","unstructured":"Ha, D., Eck, D.: A neural representation of sketch drawings. arXiv preprint arXiv:1704.03477 (2018)"},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"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\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"35_CR16","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s10032-014-0236-5","volume":"18","author":"L-P de las Heras","year":"2015","unstructured":"de las Heras, L.-P., Terrades, O.R., Robles, S., S\u00e1nchez, 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\u201330 (2015). https:\/\/doi.org\/10.1007\/s10032-014-0236-5","journal-title":"Int. J. Document Anal. Recogn (IJDAR)"},{"key":"35_CR17","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1109\/TPAMI.2006.127","volume":"6","author":"X Hilaire","year":"2006","unstructured":"Hilaire, X., Tombre, K.: Robust and accurate vectorization of line drawings. IEEE Trans. Pattern Anal. Mach. Intell. 6, 890\u2013904 (2006)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Kaiyrbekov, K., Sezgin, M.: Stroke-based sketched symbol reconstruction and segmentation. arXiv preprint arXiv:1901.03427 (2019)","DOI":"10.1109\/MCG.2019.2943333"},{"issue":"5","key":"35_CR19","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/s00371-014-0997-3","volume":"31","author":"R Kansal","year":"2014","unstructured":"Kansal, R., Kumar, S.: A vectorization framework for constant and linear gradient filled regions. The Visual Comput. 31(5), 717\u2013732 (2014). https:\/\/doi.org\/10.1007\/s00371-014-0997-3","journal-title":"The Visual Comput."},{"issue":"11","key":"35_CR20","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1109\/34.888707","volume":"22","author":"T Kanungo","year":"2000","unstructured":"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\u20131223 (2000)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"35_CR21","doi-asserted-by":"crossref","unstructured":"Kim, B., Wang, O., \u00d6ztireli, A.C., Gross, M.: Semantic segmentation for line drawing vectorization using neural networks. In: Computer Graphics Forum. vol. 37, pp. 329\u2013338. Wiley Online Library (2018)","DOI":"10.1111\/cgf.13365"},{"key":"35_CR22","doi-asserted-by":"crossref","unstructured":"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\u20139611 (2019)","DOI":"10.1109\/CVPR.2019.00983"},{"issue":"4","key":"35_CR23","first-page":"117","volume":"36","author":"C Li","year":"2017","unstructured":"Li, C., Liu, X., Wong, T.T.: Deep extraction of manga structural lines. ACM Trans. Graph. (TOG) 36(4), 117 (2017)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"35_CR24","doi-asserted-by":"crossref","unstructured":"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\u20132203 (2017)","DOI":"10.1109\/ICCV.2017.241"},{"key":"35_CR25","unstructured":"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\u20133421 (2015)"},{"key":"35_CR26","doi-asserted-by":"crossref","unstructured":"M\u00e1ttyus, G., Luo, W., Urtasun, R.: Deeproadmapper: Extracting road topology from aerial images. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 3438\u20133446 (2017)","DOI":"10.1109\/ICCV.2017.372"},{"key":"35_CR27","unstructured":"Munusamy Kabilan, V., Morris, B., Nguyen, A.: Vectordefense: Vectorization as a defense to adversarial examples. arXiv preprint arXiv:1804.08529 (2018)"},{"key":"35_CR28","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1111\/cgf.13829","volume":"38","author":"P Najgebauer","year":"2019","unstructured":"Najgebauer, P., Scherer, R.: Inertia-based fast vectorization of line drawings. Comput. Graph. Forum 38, 203\u2013213 (2019)","journal-title":"Comput. Graph. Forum"},{"issue":"1","key":"35_CR29","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1145\/2421636.2421640","volume":"32","author":"G Noris","year":"2013","unstructured":"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)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"35_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"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\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"3","key":"35_CR31","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.patrec.2009.10.002","volume":"31","author":"M Rusi\u00f1ol","year":"2010","unstructured":"Rusi\u00f1ol, M., Borr\u00e0s, A., Llad\u00f3s, J.: Relational indexing of vectorial primitives for symbol spotting in line-drawing images. Pattern Recogn. Lett. 31(3), 188\u2013201 (2010)","journal-title":"Pattern Recogn. Lett."},{"issue":"6\u20138","key":"35_CR32","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1007\/s00371-018-1528-4","volume":"34","author":"K Sasaki","year":"2018","unstructured":"Sasaki, K., Iizuka, S., SimoSerra, E., Ishikawa, H.: Learning to restore deteriorated line drawing. The Visual Comput. 34(6\u20138), 1077\u20131085 (2018)","journal-title":"The Visual Comput."},{"key":"35_CR33","unstructured":"Selinger, P.: Potrace: a polygon-based tracing algorithm. Potrace. http:\/\/potrace.sourceforge.net\/potrace.pdf (2003)"},{"key":"35_CR34","doi-asserted-by":"crossref","unstructured":"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\u2013425. IEEE (2017)","DOI":"10.1109\/ICDAR.2017.76"},{"issue":"1","key":"35_CR35","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1145\/3132703","volume":"37","author":"E Simo-Serra","year":"2018","unstructured":"Simo-Serra, E., Iizuka, S., Ishikawa, H.: Mastering sketching: adversarial augmentation for structured prediction. ACM Trans. Graph. (TOG) 37(1), 11 (2018)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"35_CR36","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems. pp. 5998\u20136008 (2017)"},{"key":"35_CR37","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1117\/12.2009412"},{"key":"35_CR38","unstructured":"Zheng, N., Jiang, Y., Huang, D.: Strokenet: Aneural painting environment. In: International Conference on Learning Representations (2018)"},{"key":"35_CR39","unstructured":"Zhou, T., et al.: Learning to doodle with stroke demonstrations and deep q-networks. In: BMVC. p. 13 (2018)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58601-0_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:16:51Z","timestamp":1732666611000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58601-0_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030586003","9783030586010"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58601-0_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"28 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1360","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}