{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:33:15Z","timestamp":1780356795289,"version":"3.54.1"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585167","type":"print"},{"value":"9783030585174","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\/978-3-030-58517-4_37","type":"book-chapter","created":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T19:03:11Z","timestamp":1602270191000},"page":"632-648","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["GeoLayout: Geometry Driven Room Layout Estimation Based on Depth Maps of Planes"],"prefix":"10.1007","author":[{"given":"Weidong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinda","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,10,10]]},"reference":[{"key":"37_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1007\/978-3-540-88688-4_8","volume-title":"Computer Vision \u2013 ECCV 2008","author":"O Barinova","year":"2008","unstructured":"Barinova, O., Konushin, V., Yakubenko, A., Lee, K.C., Lim, H., Konushin, A.: Fast automatic single-view 3-D reconstruction of urban scenes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5303, pp. 100\u2013113. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-88688-4_8"},{"issue":"6","key":"37_CR2","doi-asserted-by":"publisher","first-page":"1560","DOI":"10.1109\/TCYB.2013.2271112","volume":"43","author":"M Camplani","year":"2013","unstructured":"Camplani, M., Mantecon, T., Salgado, L.: Depth-color fusion strategy for 3-D scene modeling with kinect. IEEE Trans. Cybern. 43(6), 1560\u20131571 (2013)","journal-title":"IEEE Trans. Cybern."},{"key":"37_CR3","doi-asserted-by":"crossref","unstructured":"Chang, A., et al.: Matterport3D: learning from RGB-D data in indoor environments. In: International Conference on 3D Vision (3DV) (2017)","DOI":"10.1109\/3DV.2017.00081"},{"key":"37_CR4","doi-asserted-by":"crossref","unstructured":"Choi, W., Chao, Y.W., Pantofaru, C., Savarese, S.: Understanding indoor scenes using 3D geometric phrases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 33\u201340 (2013)","DOI":"10.1109\/CVPR.2013.12"},{"key":"37_CR5","doi-asserted-by":"crossref","unstructured":"Dasgupta, S., Fang, K., Chen, K., Savarese, S.: Delay: robust spatial layout estimation for cluttered indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 616\u2013624 (2016)","DOI":"10.1109\/CVPR.2016.73"},{"key":"37_CR6","doi-asserted-by":"crossref","unstructured":"De Brabandere, B., Neven, D., Van Gool, L.: Semantic instance segmentation with a discriminative loss function. arXiv preprint arXiv:1708.02551 (2017)","DOI":"10.1109\/CVPRW.2017.66"},{"key":"37_CR7","doi-asserted-by":"crossref","unstructured":"Del Pero, L., Bowdish, J., Kermgard, B., Hartley, E., Barnard, K.: Understanding Bayesian rooms using composite 3D object models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 153\u2013160 (2013)","DOI":"10.1109\/CVPR.2013.27"},{"key":"37_CR8","series-title":"Springer Tracts in Advanced Robotics","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/978-3-540-48113-3_28","volume-title":"Robotics Research","author":"E Delage","year":"2007","unstructured":"Delage, E., Lee, H., Ng, A.Y.: Automatic single-image 3D reconstructions of indoor manhattan world scenes. In: Thrun, S., Brooks, R., Durrant-Whyte, H. (eds.) Robotics Research. STAR, vol. 28, pp. 305\u2013321. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-48113-3_28"},{"issue":"6","key":"37_CR9","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381\u2013395 (1981)","journal-title":"Commun. ACM"},{"key":"37_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1007\/978-3-319-10599-4_44","volume-title":"Computer Vision \u2013 ECCV 2014","author":"DF Fouhey","year":"2014","unstructured":"Fouhey, D.F., Gupta, A., Hebert, M.: Unfolding an indoor origami world. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 687\u2013702. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10599-4_44"},{"key":"37_CR11","unstructured":"Guo, R., Zou, C., Hoiem, D.: Predicting complete 3D models of indoor scenes. arXiv preprint arXiv:1504.02437 (2015)"},{"issue":"9","key":"37_CR12","doi-asserted-by":"publisher","first-page":"1849","DOI":"10.1109\/TPAMI.2014.2382097","volume":"37","author":"O Haines","year":"2014","unstructured":"Haines, O., Calway, A.: Recognising planes in a single image. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1849\u20131861 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"37_CR13","doi-asserted-by":"crossref","unstructured":"Hedau, V., Hoiem, D., Forsyth, D.: Recovering the spatial layout of cluttered rooms. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1849\u20131856. IEEE (2009)","DOI":"10.1109\/ICCV.2009.5459411"},{"key":"37_CR14","doi-asserted-by":"crossref","unstructured":"Hirzer, M., Lepetit, V., Roth, P.: Smart hypothesis generation for efficient and robust room layout estimation. In: The IEEE Winter Conference on Applications of Computer Vision (WACV), March 2020","DOI":"10.1109\/WACV45572.2020.9093451"},{"key":"37_CR15","unstructured":"Hsiao, C.W., Sun, C., Sun, M., Chen, H.T.: Flat2layout: flat representation for estimating layout of general room types. arXiv preprint arXiv:1905.12571 (2019)"},{"key":"37_CR16","doi-asserted-by":"crossref","unstructured":"Hu, J., Ozay, M., Zhang, Y., Okatani, T.: Revisiting single image depth estimation: toward higher resolution maps with accurate object boundaries. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1043\u20131051. IEEE (2019)","DOI":"10.1109\/WACV.2019.00116"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Izadinia, H., Shan, Q., Seitz, S.M.: Im2cad. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5134\u20135143 (2017)","DOI":"10.1109\/CVPR.2017.260"},{"key":"37_CR18","doi-asserted-by":"crossref","unstructured":"Karsch, K., Hedau, V., Forsyth, D., Hoiem, D.: Rendering synthetic objects into legacy photographs. In: ACM Transactions on Graphics (TOG), vol. 30, p. 157. ACM (2011)","DOI":"10.1145\/2070781.2024191"},{"key":"37_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1007\/978-3-030-41404-7_39","volume-title":"Pattern Recognition","author":"I Kruzhilov","year":"2020","unstructured":"Kruzhilov, I., Romanov, M., Babichev, D., Konushin, A.: Double refinement network for room layout estimation. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W.Q. (eds.) ACPR 2019. LNCS, vol. 12046, pp. 557\u2013568. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-41404-7_39"},{"key":"37_CR20","doi-asserted-by":"crossref","unstructured":"Lee, C.Y., Badrinarayanan, V., Malisiewicz, T., Rabinovich, A.: Roomnet: end-to-end room layout estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4865\u20134874 (2017)","DOI":"10.1109\/ICCV.2017.521"},{"key":"37_CR21","doi-asserted-by":"crossref","unstructured":"Lee, D.C., Hebert, M., Kanade, T.: Geometric reasoning for single image structure recovery. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2136\u20132143. IEEE (2009)","DOI":"10.1109\/CVPRW.2009.5206872"},{"key":"37_CR22","doi-asserted-by":"crossref","unstructured":"Liu, C., Kim, K., Gu, J., Furukawa, Y., Kautz, J.: PlaneRCNN: 3D plane detection and reconstruction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4450\u20134459 (2019)","DOI":"10.1109\/CVPR.2019.00458"},{"key":"37_CR23","doi-asserted-by":"crossref","unstructured":"Liu, C., Yang, J., Ceylan, D., Yumer, E., Furukawa, Y.: PlaneNet: piece-wise planar reconstruction from a single RGB image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2579\u20132588 (2018)","DOI":"10.1109\/CVPR.2018.00273"},{"key":"37_CR24","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. IEEE (2015)"},{"key":"37_CR25","doi-asserted-by":"crossref","unstructured":"Mallya, A., Lazebnik, S.: Learning informative edge maps for indoor scene layout prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 936\u2013944 (2015)","DOI":"10.1109\/ICCV.2015.113"},{"key":"37_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-10578-9_1","volume-title":"Computer Vision \u2013 ECCV 2014","author":"R Martin-Brualla","year":"2014","unstructured":"Martin-Brualla, R., He, Y., Russell, B.C., Seitz, S.M.: The 3D jigsaw puzzle: mapping large indoor spaces. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 1\u201316. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10578-9_1"},{"key":"37_CR27","doi-asserted-by":"crossref","unstructured":"Micusk, B., Wildenauer, H., Vincze, M.: Towards detection of orthogonal planes in monocular images of indoor environments. In: 2008 IEEE International Conference on Robotics and Automation, pp. 999\u20131004. IEEE (2008)","DOI":"10.1109\/ROBOT.2008.4543335"},{"key":"37_CR28","unstructured":"Paszke, A., et al.: Automatic differentiation in pytorch (2017)"},{"key":"37_CR29","doi-asserted-by":"crossref","unstructured":"Pero, L.D., Bowdish, J., Fried, D., Kermgard, B., Hartley, E., Barnard, K.: Bayesian geometric modeling of indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2719\u20132726. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6247994"},{"key":"37_CR30","doi-asserted-by":"crossref","unstructured":"Ramalingam, S., Pillai, J., Jain, A., Taguchi, Y.: Manhattan junction catalogue for spatial reasoning of indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3065\u20133072 (2013)","DOI":"10.1109\/CVPR.2013.394"},{"key":"37_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/978-3-319-54193-8_3","volume-title":"Computer Vision \u2013 ACCV 2016","author":"Y Ren","year":"2017","unstructured":"Ren, Y., Li, S., Chen, C., Kuo, C.-C.J.: A coarse-to-fine indoor layout estimation (CFILE) method. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10115, pp. 36\u201351. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-54193-8_3"},{"issue":"1\u20133","key":"37_CR32","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s11263-007-0090-8","volume":"77","author":"BC Russell","year":"2008","unstructured":"Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77(1\u20133), 157\u2013173 (2008). https:\/\/doi.org\/10.1007\/s11263-007-0090-8","journal-title":"Int. J. Comput. Vis."},{"issue":"4","key":"37_CR33","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640\u2013651 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"37_CR34","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1145\/2436256.2436276","volume":"56","author":"H Wang","year":"2013","unstructured":"Wang, H., Gould, S., Roller, D.: Discriminative learning with latent variables for cluttered indoor scene understanding. Commun. ACM 56(4), 92\u201399 (2013)","journal-title":"Commun. ACM"},{"issue":"3","key":"37_CR35","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s11263-014-0711-y","volume":"110","author":"J Xiao","year":"2014","unstructured":"Xiao, J., Furukawa, Y.: Reconstructing the world\u2019s museums. Int. J. Comput. Vis. 110(3), 243\u2013258 (2014). https:\/\/doi.org\/10.1007\/s11263-014-0711-y","journal-title":"Int. J. Comput. Vis."},{"key":"37_CR36","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-3-030-01249-6_6","volume-title":"Computer Vision \u2013 ECCV 2018","author":"F Yang","year":"2018","unstructured":"Yang, F., Zhou, Z.: Recovering 3D planes from a single image via convolutional neural networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 87\u2013103. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_6"},{"key":"37_CR37","doi-asserted-by":"crossref","unstructured":"Yu, Z., Zheng, J., Lian, D., Zhou, Z., Gao, S.: Single-image piece-wise planar 3D reconstruction via associative embedding. arXiv preprint arXiv:1902.09777 (2019)","DOI":"10.1109\/CVPR.2019.00112"},{"issue":"6","key":"37_CR38","doi-asserted-by":"publisher","first-page":"2730","DOI":"10.1109\/TCYB.2019.2895837","volume":"50","author":"W Zhang","year":"2019","unstructured":"Zhang, W., Zhang, W., Gu, J.: Edge-semantic learning strategy for layout estimation in indoor environment. IEEE Trans. Cybern. 50(6), 2730\u20132739 (2019)","journal-title":"IEEE Trans. Cybern."},{"key":"37_CR39","unstructured":"Zhang, Y., Yu, F., Song, S., Xu, P., Seff, A., Xiao, J.: Largescale scene understanding challenge: room layout estimation. http:\/\/lsun.cs.princeton.edu\/2016\/"},{"key":"37_CR40","doi-asserted-by":"crossref","unstructured":"Zhao, H., Lu, M., Yao, A., Guo, Y., Chen, Y., Zhang, L.: Physics inspired optimization on semantic transfer features: an alternative method for room layout estimation. arXiv preprint arXiv:1707.00383 (2017)","DOI":"10.1109\/CVPR.2017.99"},{"key":"37_CR41","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"37_CR42","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhu, S.C.: Scene parsing by integrating function, geometry and appearance models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3119\u20133126 (2013)","DOI":"10.1109\/CVPR.2013.401"},{"key":"37_CR43","doi-asserted-by":"crossref","unstructured":"Zou, C., Colburn, A., Shan, Q., Hoiem, D.: LayoutNet: reconstructing the 3D room layout from a single RGB image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2051\u20132059 (2018)","DOI":"10.1109\/CVPR.2018.00219"}],"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\/978-3-030-58517-4_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:16:54Z","timestamp":1728433014000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58517-4_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585167","9783030585174"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58517-4_37","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":"10 October 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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}