{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:35:14Z","timestamp":1742949314293,"version":"3.40.3"},"publisher-location":"Cham","reference-count":56,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030586065"},{"type":"electronic","value":"9783030586072"}],"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\/978-3-030-58607-2_27","type":"book-chapter","created":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T08:04:11Z","timestamp":1604649851000},"page":"455-472","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Mapping in a Cycle: Sinkhorn Regularized Unsupervised Learning for\u00a0Point Cloud Shapes"],"prefix":"10.1007","author":[{"given":"Lei","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiming","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nenglun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenping","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,7]]},"reference":[{"unstructured":"Adams, R.P., Zemel, R.S.: Ranking via Sinkhorn propagation. arXiv preprint arXiv:1106.1925 (2011)","key":"27_CR1"},{"doi-asserted-by":"crossref","unstructured":"Aoki, Y., Goforth, H., Srivatsan, R.A., Lucey, S.: PointNetLK: robust & efficient point cloud registration using PointNet. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7163\u20137172 (2019)","key":"27_CR2","DOI":"10.1109\/CVPR.2019.00733"},{"issue":"5","key":"27_CR3","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1109\/TPAMI.1987.4767965","volume":"9","author":"KS Arun","year":"1987","unstructured":"Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-D point sets. IEEE Trans. Pattern Anal. Mach. Intell. 9(5), 698\u2013700 (1987)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"27_CR4","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.cagd.2019.04.021","volume":"72","author":"M Chen","year":"2019","unstructured":"Chen, M., Zou, Q., Wang, C., Liu, L.: EdgeNet: deep metric learning for 3D shapes. Comput. Aided Geom. Des. 72, 19\u201333 (2019)","journal-title":"Comput. Aided Geom. Des."},{"unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709 (2020)","key":"27_CR5"},{"doi-asserted-by":"crossref","unstructured":"Choy, C., Park, J., Koltun, V.: Fully convolutional geometric features. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 8958\u20138966 (2019)","key":"27_CR6","DOI":"10.1109\/ICCV.2019.00905"},{"key":"27_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1007\/978-3-030-01228-1_37","volume-title":"Computer Vision \u2013 ECCV 2018","author":"H Deng","year":"2018","unstructured":"Deng, H., Birdal, T., Ilic, S.: PPF-FoldNet: unsupervised learning of rotation invariant 3D local descriptors. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 620\u2013638. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01228-1_37"},{"doi-asserted-by":"crossref","unstructured":"Deng, H., Birdal, T., Ilic, S.: PPFNet: global context aware local features for robust 3D point matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 195\u2013205 (2018)","key":"27_CR8","DOI":"10.1109\/CVPR.2018.00028"},{"unstructured":"Deprelle, T., Groueix, T., Fisher, M., Kim, V., Russell, B., Aubry, M.: Learning elementary structures for 3D shape generation and matching. In: Advances in Neural Information Processing Systems, pp. 7433\u20137443 (2019)","key":"27_CR9"},{"unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)","key":"27_CR10"},{"doi-asserted-by":"crossref","unstructured":"Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., Zisserman, A.: Temporal cycle-consistency learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1801\u20131810 (2019)","key":"27_CR11","DOI":"10.1109\/CVPR.2019.00190"},{"doi-asserted-by":"crossref","unstructured":"Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: AtlasNet: a papier-m\u00e2ch\u00e9 approach to learning 3D surface generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 216\u2013224 (2018)","key":"27_CR12","DOI":"10.1109\/CVPR.2018.00030"},{"doi-asserted-by":"crossref","unstructured":"Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: Unsupervised cycle-consistent deformation for shape matching. In: Computer Graphics Forum, vol. 38, pp. 123\u2013133. Wiley Online Library (2019)","key":"27_CR13","DOI":"10.1111\/cgf.13794"},{"doi-asserted-by":"crossref","unstructured":"Halimi, O., Litany, O., Rodola, E., Bronstein, A.M., Kimmel, R.: Unsupervised learning of dense shape correspondence. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4370\u20134379 (2019)","key":"27_CR14","DOI":"10.1109\/CVPR.2019.00450"},{"doi-asserted-by":"crossref","unstructured":"Han, Z., Wang, X., Liu, Y.S., Zwicker, M.: Multi-angle point cloud-VAE: unsupervised feature learning for 3D point clouds from multiple angles by joint self-reconstruction and half-to-half prediction. arXiv preprint arXiv:1907.12704 (2019)","key":"27_CR15","DOI":"10.1109\/ICCV.2019.01054"},{"doi-asserted-by":"crossref","unstructured":"Hassani, K., Haley, M.: Unsupervised multi-task feature learning on point clouds. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 8160\u20138171 (2019)","key":"27_CR16","DOI":"10.1109\/ICCV.2019.00825"},{"doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. arXiv preprint arXiv:1911.05722 (2019)","key":"27_CR17","DOI":"10.1109\/CVPR42600.2020.00975"},{"issue":"1","key":"27_CR18","first-page":"1","volume":"37","author":"H Huang","year":"2017","unstructured":"Huang, H., Kalogerakis, E., Chaudhuri, S., Ceylan, D., Kim, V.G., Yumer, E.: Learning local shape descriptors from part correspondences with multiview convolutional networks. ACM Trans. Graph. (TOG) 37(1), 1\u201314 (2017)","journal-title":"ACM Trans. Graph. (TOG)"},{"doi-asserted-by":"crossref","unstructured":"Huang, Q.X., Guibas, L.: Consistent shape maps via semidefinite programming. In: Computer Graphics Forum, vol. 32, pp. 177\u2013186. Wiley Online Library (2013)","key":"27_CR19","DOI":"10.1111\/cgf.12184"},{"issue":"6","key":"27_CR20","first-page":"1","volume":"32","author":"QX Huang","year":"2013","unstructured":"Huang, Q.X., Su, H., Guibas, L.: Fine-grained semi-supervised labeling of large shape collections. ACM Trans. Graph. (TOG) 32(6), 1\u201310 (2013)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"4","key":"27_CR21","first-page":"1","volume":"32","author":"VG Kim","year":"2013","unstructured":"Kim, V.G., Li, W., Mitra, N.J., Chaudhuri, S., DiVerdi, S., Funkhouser, T.: Learning part-based templates from large collections of 3D shapes. ACM Trans. Graph. (TOG) 32(4), 1\u201312 (2013)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"4","key":"27_CR22","first-page":"1","volume":"31","author":"VG Kim","year":"2012","unstructured":"Kim, V.G., Li, W., Mitra, N.J., DiVerdi, S., Funkhouser, T.: Exploring collections of 3D models using fuzzy correspondences. ACM Trans. Graph. (TOG) 31(4), 1\u201311 (2012)","journal-title":"ACM Trans. Graph. (TOG)"},{"unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)","key":"27_CR23"},{"issue":"1","key":"27_CR24","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1137\/060659624","volume":"30","author":"PA Knight","year":"2008","unstructured":"Knight, P.A.: The Sinkhorn-Knopp algorithm: convergence and applications. SIAM J. Matrix Anal. Appl. 30(1), 261\u2013275 (2008)","journal-title":"SIAM J. Matrix Anal. Appl."},{"unstructured":"Lee, J., Lee, Y., Kim, J., Kosiorek, A.R., Choi, S., Teh, Y.W.: Set transformer: A framework for attention-based permutation-invariant neural networks. arXiv preprint arXiv:1810.00825 (2018)","key":"27_CR25"},{"unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: PointCNN: convolution on X-transformed points. In: Advances in Neural Information Processing Systems, pp. 820\u2013830 (2018)","key":"27_CR26"},{"key":"27_CR27","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"unstructured":"Mena, G., Belanger, D., Linderman, S., Snoek, J.: Learning latent permutations with Gumbel-Sinkhorn networks. arXiv preprint arXiv:1802.08665 (2018)","key":"27_CR28"},{"doi-asserted-by":"crossref","unstructured":"Misra, I., van der Maaten, L.: Self-supervised learning of pretext-invariant representations. arXiv preprint arXiv:1912.01991 (2019)","key":"27_CR29","DOI":"10.1109\/CVPR42600.2020.00674"},{"doi-asserted-by":"crossref","unstructured":"Muralikrishnan, S., Kim, V.G., Fisher, M., Chaudhuri, S.: Shape unicode: a unified shape representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3790\u20133799 (2019)","key":"27_CR30","DOI":"10.1109\/CVPR.2019.00391"},{"unstructured":"van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)","key":"27_CR31"},{"unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652\u2013660 (2017)","key":"27_CR32"},{"unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems, pp. 5099\u20135108 (2017)","key":"27_CR33"},{"unstructured":"Reddi, S.J., Kale, S., Kumar, S.: On the convergence of Adam and beyond. arXiv preprint arXiv:1904.09237 (2019)","key":"27_CR34"},{"key":"27_CR35","doi-asserted-by":"publisher","first-page":"1705","DOI":"10.1007\/s00371-019-01760-0","volume":"36","author":"Y Sahillio\u011flu","year":"2019","unstructured":"Sahillio\u011flu, Y.: Recent advances in shape correspondence. Vis. Comput. 36, 1705\u20131721 (2019). https:\/\/doi.org\/10.1007\/s00371-019-01760-0","journal-title":"Vis. Comput."},{"unstructured":"Sauder, J., Sievers, B.: Self-supervised deep learning on point clouds by reconstructing space. In: Advances in Neural Information Processing Systems, pp. 12942\u201312952 (2019)","key":"27_CR36"},{"issue":"2","key":"27_CR37","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1214\/aoms\/1177703591","volume":"35","author":"R Sinkhorn","year":"1964","unstructured":"Sinkhorn, R.: A relationship between arbitrary positive matrices and doubly stochastic matrices. Ann. Math. Stat. 35(2), 876\u2013879 (1964)","journal-title":"Ann. Math. Stat."},{"doi-asserted-by":"crossref","unstructured":"Thewlis, J., Albanie, S., Bilen, H., Vedaldi, A.: Unsupervised learning of landmarks by descriptor vector exchange. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6361\u20136371 (2019)","key":"27_CR38","DOI":"10.1109\/ICCV.2019.00646"},{"doi-asserted-by":"crossref","unstructured":"Thewlis, J., Bilen, H., Vedaldi, A.: Unsupervised learning of object landmarks by factorized spatial embeddings. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5916\u20135925 (2017)","key":"27_CR39","DOI":"10.1109\/ICCV.2017.348"},{"doi-asserted-by":"crossref","unstructured":"Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. arXiv preprint arXiv:1906.05849 (2019)","key":"27_CR40","DOI":"10.1007\/978-3-030-58621-8_45"},{"unstructured":"Tschannen, M., Djolonga, J., Rubenstein, P.K., Gelly, S., Lucic, M.: On mutual information maximization for representation learning. arXiv preprint arXiv:1907.13625 (2019)","key":"27_CR41"},{"doi-asserted-by":"crossref","unstructured":"Van Kaick, O., Zhang, H., Hamarneh, G., Cohen-Or, D.: A survey on shape correspondence. In: Computer Graphics Forum, vol. 30, pp. 1681\u20131707. Wiley Online Library (2011)","key":"27_CR42","DOI":"10.1111\/j.1467-8659.2011.01884.x"},{"unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)","key":"27_CR43"},{"doi-asserted-by":"crossref","unstructured":"Wang, X., Jabri, A., Efros, A.A.: Learning correspondence from the cycle-consistency of time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2566\u20132576 (2019)","key":"27_CR44","DOI":"10.1109\/CVPR.2019.00267"},{"doi-asserted-by":"crossref","unstructured":"Wang, Y., Solomon, J.M.: Deep closest point: learning representations for point cloud registration. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3523\u20133532 (2019)","key":"27_CR45","DOI":"10.1109\/ICCV.2019.00362"},{"unstructured":"Wang, Y., Solomon, J.M.: PRNet: self-supervised learning for partial-to-partial registration. In: Advances in Neural Information Processing Systems, pp. 8814\u20138826 (2019)","key":"27_CR46"},{"issue":"5","key":"27_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. (TOG) 38(5), 1\u201312 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., Fuxin, L.: PointConv: deep convolutional networks on 3D point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9621\u20139630 (2019)","key":"27_CR48","DOI":"10.1109\/CVPR.2019.00985"},{"unstructured":"Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912\u20131920 (2015)","key":"27_CR49"},{"doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733\u20133742 (2018)","key":"27_CR50","DOI":"10.1109\/CVPR.2018.00393"},{"doi-asserted-by":"crossref","unstructured":"Yang, Y., Feng, C., Shen, Y., Tian, D.: FoldingNet: point cloud auto-encoder via deep grid deformation, pp. 206\u2013215 (2018)","key":"27_CR51","DOI":"10.1109\/CVPR.2018.00029"},{"issue":"6","key":"27_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2980179.2980238","volume":"35","author":"L Yi","year":"2016","unstructured":"Yi, L., et al.: A scalable active framework for region annotation in 3D shape collections. ACM Trans. Graph. (TOG) 35(6), 1\u201312 (2016)","journal-title":"ACM Trans. Graph. (TOG)"},{"doi-asserted-by":"crossref","unstructured":"Zeng, A., Song, S., Nie\u00dfner, M., Fisher, M., Xiao, J., Funkhouser, T.: 3DMatch: learning local geometric descriptors from RGB-D reconstructions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1802\u20131811 (2017)","key":"27_CR53","DOI":"10.1109\/CVPR.2017.29"},{"doi-asserted-by":"crossref","unstructured":"Zhao, Y., Birdal, T., Deng, H., Tombari, F.: 3D point capsule networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1009\u20131018 (2019)","key":"27_CR54","DOI":"10.1109\/CVPR.2019.00110"},{"doi-asserted-by":"crossref","unstructured":"Zhou, T., Krahenbuhl, P., Aubry, M., Huang, Q., Efros, A.A.: Learning dense correspondence via 3D-guided cycle consistency. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 117\u2013126 (2016)","key":"27_CR55","DOI":"10.1109\/CVPR.2016.20"},{"doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (2017)","key":"27_CR56","DOI":"10.1109\/ICCV.2017.244"}],"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\/978-3-030-58607-2_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T00:47:37Z","timestamp":1730854057000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58607-2_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030586065","9783030586072"],"references-count":56,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58607-2_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"7 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.","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)"}}]}}