{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:13:54Z","timestamp":1766067234327,"version":"3.40.3"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030584511"},{"type":"electronic","value":"9783030584528"}],"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\/978-3-030-58452-8_39","type":"book-chapter","created":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:34:03Z","timestamp":1604363643000},"page":"670-686","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Multi-view Optimization of Local Feature Geometry"],"prefix":"10.1007","author":[{"given":"Mihai","family":"Dusmanu","sequence":"first","affiliation":[]},{"given":"Johannes L.","family":"Sch\u00f6nberger","sequence":"additional","affiliation":[]},{"given":"Marc","family":"Pollefeys","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"39_CR1","unstructured":"Agarwal, S., Mierle, K., et al.: Ceres solver. http:\/\/ceres-solver.org"},{"key":"39_CR2","doi-asserted-by":"crossref","unstructured":"Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: NetVLAD: CNN architecture for weakly supervised place recognition. In: Proceedings CVPR (2016)","DOI":"10.1109\/CVPR.2016.572"},{"key":"39_CR3","doi-asserted-by":"crossref","unstructured":"Arandjelovic, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: Proceedings of CVPR (2012)","DOI":"10.1109\/CVPR.2012.6248018"},{"key":"39_CR4","doi-asserted-by":"crossref","unstructured":"Balntas, V., Lenc, K., Vedaldi, A., Mikolajczyk, K.: HPatches: a benchmark and evaluation of handcrafted and learned local descriptors. In: Proceedings of CVPR (2017)","DOI":"10.1109\/CVPR.2017.410"},{"key":"39_CR5","doi-asserted-by":"crossref","unstructured":"Balntas, V., Riba, E., Ponsa, D., Mikolajczyk, K.: Learning local feature descriptors with triplets and shallow convolutional neural networks. In: Proceedings of BMVC (2016)","DOI":"10.5244\/C.30.119"},{"key":"39_CR6","doi-asserted-by":"crossref","unstructured":"Barroso-Laguna, A., Riba, E., Ponsa, D., Mikolajczyk, K.: Key. Net: Keypoint detection by handcrafted and learned CNN filters. In: Proceedings ICCV (2019)","DOI":"10.1109\/ICCV.2019.00593"},{"key":"39_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/11744023_32","volume-title":"Computer Vision \u2013 ECCV 2006","author":"H Bay","year":"2006","unstructured":"Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404\u2013417. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744023_32"},{"key":"39_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1007\/978-3-642-15561-1_56","volume-title":"Computer Vision \u2013 ECCV 2010","author":"M Calonder","year":"2010","unstructured":"Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 778\u2013792. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15561-1_56"},{"key":"39_CR9","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of CVPR (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"39_CR10","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., Rabinovich, A.: SuperPoint: self-supervised interest point detection and description. In: CVPR Workshops (2018)","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"39_CR11","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks. In: Proceedings of ICCV (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"39_CR12","doi-asserted-by":"crossref","unstructured":"Dusmanu, M., et al.: D2-Net: a trainable CNN for joint detection and description of local features. In: Proceedings of CVPR (2019)","DOI":"10.1109\/CVPR.2019.00828"},{"key":"39_CR13","unstructured":"Eichhardt, I., Barath, D.: Optimal multi-view correction of local affine frames. In: Proceedings of BMVC (2019)"},{"key":"39_CR14","unstructured":"Goesele, M., Curless, B., Seitz, S.M.: Multi-view stereo revisited. In: Proceedings of CVPR (2006)"},{"key":"39_CR15","unstructured":"Han, X., Leung, T., Jia, Y., Sukthankar, R., Berg, A.C.: MatchNet: unifying feature and metric learning for patch-based matching. In: Proceedings of CVPR (2015)"},{"key":"39_CR16","doi-asserted-by":"crossref","unstructured":"Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of Alvey Vision Conference (1988)","DOI":"10.5244\/C.2.23"},{"key":"39_CR17","doi-asserted-by":"crossref","unstructured":"Hartmann, W., Galliani, S., Havlena, M., Van Gool, L., Schindler, K.: Learned multi-patch similarity. In: Proceedings of ICCV (2017)","DOI":"10.1109\/ICCV.2017.176"},{"key":"39_CR18","doi-asserted-by":"crossref","unstructured":"Heinly, J., Sch\u00f6nberger, J.L., Dunn, E., Frahm, J.M.: Reconstructing the world* in six days *(as captured by the Yahoo 100 million image dataset). In: Proceedings of CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298949"},{"key":"39_CR19","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of ICLR (2015)"},{"issue":"1","key":"39_CR20","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1090\/S0002-9939-1956-0078686-7","volume":"7","author":"JB Kruskal","year":"1956","unstructured":"Kruskal, J.B.: On the shortest spanning subtree of a graph and the traveling salesman problem. Proc. Am. Math. Soc. 7(1), 48\u201350 (1956)","journal-title":"Proc. Am. Math. Soc."},{"key":"39_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/978-3-642-33718-5_2","volume-title":"Computer Vision \u2013 ECCV 2012","author":"Y Li","year":"2012","unstructured":"Li, Y., Snavely, N., Huttenlocher, D., Fua, P.: Worldwide pose estimation using 3D point clouds. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 15\u201329. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33718-5_2"},{"key":"39_CR22","doi-asserted-by":"crossref","unstructured":"Li, Z., Snavely, N.: MegaDepth: learning single-view depth prediction from internet photos. In: Proceedings of CVPR (2018)","DOI":"10.1109\/CVPR.2018.00218"},{"key":"39_CR23","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91\u2013110 (2004)","journal-title":"Int. J. Comput. Vis."},{"key":"39_CR24","doi-asserted-by":"crossref","unstructured":"Luo, W., Schwing, A.G., Urtasun, R.: Efficient deep learning for stereo matching. In: Proceedings of CVPR (2016)","DOI":"10.1109\/CVPR.2016.614"},{"key":"39_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1007\/978-3-030-01240-3_11","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Z Luo","year":"2018","unstructured":"Luo, Z., et al.: GeoDesc: learning local descriptors by integrating geometry constraints. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 170\u2013185. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01240-3_11"},{"key":"39_CR26","unstructured":"Mishchuk, A., Mishkin, D., Radenovic, F., Matas, J.: Working hard to know your neighbor\u2019s margins: local descriptor learning loss. In: Advances in NeurIPS (2017)"},{"key":"39_CR27","doi-asserted-by":"crossref","unstructured":"Noh, H., Araujo, A., Sim, J., Weyand, T., Han, B.: Largescale image retrieval with attentive deep local features. In: Proceedings of ICCV (2017)","DOI":"10.1109\/ICCV.2017.374"},{"key":"39_CR28","unstructured":"Olson, E., Leonard, J., Teller, S.: Fast iterative optimization of pose graphs with poor initial estimates. In: Proceedings of ICRA (2006)"},{"key":"39_CR29","unstructured":"Ono, Y., Trulls, E., Fua, P., Yi, K.M.: LF-Net: learning local features from images. In: Advances in NeurIPS (2019)"},{"key":"39_CR30","unstructured":"Revaud, J., Weinzaepfel, P., de Souza, C.R., Humenberger, M.: R2D2: repeatable and reliable detector and descriptor. In: Advances in NeurIPS (2019)"},{"key":"39_CR31","doi-asserted-by":"crossref","unstructured":"Rocco, I., Arandjelovi\u0107, R., Sivic, J.: Convolutional neural network architecture for geometric matching. In: Proceedings of CVPR (2017)","DOI":"10.1109\/CVPR.2017.12"},{"key":"39_CR32","doi-asserted-by":"crossref","unstructured":"Rocco, I., Arandjelovi\u0107, R., Sivic, J.: End-to-end weakly-supervised semantic alignment. In: Proceedings of CVPR (2018)","DOI":"10.1109\/CVPR.2018.00723"},{"key":"39_CR33","unstructured":"Rocco, I., Cimpoi, M., Arandjelovi\u0107, R., Torii, A., Pajdla, T., Sivic, J.: Neighbourhood consensus networks. In: Advances in NeurIPS (2018)"},{"key":"39_CR34","doi-asserted-by":"crossref","unstructured":"Sattler, T., Leibe, B., Kobbelt, L.: Fast image-based localization using direct 2D-to-3D matching. In: Proceedings of ICCV (2011)","DOI":"10.1109\/ICCV.2011.6126302"},{"key":"39_CR35","doi-asserted-by":"crossref","unstructured":"Sattler, T., et al.: Benchmarking 6DoF outdoor visual localization in changing conditions. In: Proceedings of CVPR (2018)","DOI":"10.1109\/CVPR.2018.00897"},{"key":"39_CR36","doi-asserted-by":"crossref","unstructured":"Savinov, N., Seki, A., Ladicky, L., Sattler, T., Pollefeys, M.: Quad-networks: unsupervised learning to rank for interest point detection. In: Proceedings of CVPR (2017)","DOI":"10.1109\/CVPR.2017.418"},{"key":"39_CR37","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nberger, J.L., Hardmeier, H., Sattler, T., Pollefeys, M.: Comparative evaluation of hand-crafted and learned local features. In: Proceedings of CVPR (2017)","DOI":"10.1109\/CVPR.2017.736"},{"key":"39_CR38","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Proceedings of CVPR (2016)","DOI":"10.1109\/CVPR.2016.445"},{"key":"39_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/978-3-319-46487-9_31","volume-title":"Computer Vision \u2013 ECCV 2016","author":"JL Sch\u00f6nberger","year":"2016","unstructured":"Sch\u00f6nberger, J.L., Zheng, E., Frahm, J.-M., Pollefeys, M.: Pixelwise view selection for unstructured multi-view stereo. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 501\u2013518. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_31"},{"key":"39_CR40","doi-asserted-by":"crossref","unstructured":"Sch\u00f6ps, T., et al.: A multi-view stereo benchmark with high-resolution images and multi-camera videos. In: Proceedings of CVPR (2017)","DOI":"10.1109\/CVPR.2017.272"},{"key":"39_CR41","doi-asserted-by":"crossref","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of ICLR (2015)","DOI":"10.1109\/ICCV.2015.314"},{"issue":"5","key":"39_CR42","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1109\/TPAMI.2009.77","volume":"32","author":"E Tola","year":"2009","unstructured":"Tola, E., Lepetit, V., Fua, P.: Daisy: an efficient dense descriptor applied to wide-baseline stereo. IEEE PAMI 32(5), 815\u2013830 (2009)","journal-title":"IEEE PAMI"},{"key":"39_CR43","doi-asserted-by":"crossref","unstructured":"Verdie, Y., Yi, K., Fua, P., Lepetit, V.: TILDE: a temporally invariant learned detector. In: Proceedings of CVPR (2015)","DOI":"10.1109\/CVPR.2015.7299165"},{"key":"39_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1007\/978-3-030-01237-3_47","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Yao","year":"2018","unstructured":"Yao, Y., Luo, Z., Li, S., Fang, T., Quan, L.: MVSNet: depth inference for unstructured multi-view stereo. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 785\u2013801. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01237-3_47"},{"key":"39_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/978-3-319-46466-4_28","volume-title":"Computer Vision \u2013 ECCV 2016","author":"KM Yi","year":"2016","unstructured":"Yi, K.M., Trulls, E., Lepetit, V., Fua, P.: LIFT: learned invariant feature transform. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 467\u2013483. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_28"},{"key":"39_CR46","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks. In: Proceedings of CVPR (2015)","DOI":"10.1109\/CVPR.2015.7299064"},{"issue":"1","key":"39_CR47","first-page":"2287","volume":"17","author":"J Zbontar","year":"2016","unstructured":"Zbontar, J., LeCun, Y.: Stereo matching by training a convolutional neural network to compare image patches. J. Mach. Learn. Res. 17(1), 2287\u20132318 (2016)","journal-title":"J. Mach. Learn. Res."}],"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\/978-3-030-58452-8_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:19:00Z","timestamp":1730593140000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58452-8_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030584511","9783030584528"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58452-8_39","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":"3 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)"}}]}}