{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T20:44:47Z","timestamp":1781556287743,"version":"3.54.5"},"publisher-location":"Cham","reference-count":57,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198236","type":"print"},{"value":"9783031198243","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19824-3_2","type":"book-chapter","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T21:14:32Z","timestamp":1668114872000},"page":"20-36","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":229,"title":["ASpanFormer: Detector-Free Image Matching with\u00a0Adaptive Span Transformer"],"prefix":"10.1007","author":[{"given":"Hongkai","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixin","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yurun","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingmin","family":"Zhen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"McKinnon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanghai","family":"Tsin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Quan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,11]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.445"},{"issue":"1","key":"2_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41074-018-0042-y","volume":"10","author":"AR Widya","year":"2018","unstructured":"Widya, A.R., Torii, A., Okutomi, M.: Structure from motion using dense CNN features with keypoint relocalization. IPSJ Trans. Comput. Vis. Appl. 10(1), 1\u20137 (2018). https:\/\/doi.org\/10.1186\/s41074-018-0042-y","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Sattler, T., Weyand, T., Leibe, B., Kobbelt, L.: Image retrieval for image-based localization revisited. In: BMVC (2012)","DOI":"10.5244\/C.26.76"},{"issue":"5","key":"2_CR4","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1109\/TRO.2015.2463671","volume":"31","author":"R Mur-Artal","year":"2015","unstructured":"Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular slam system. IEEE Trans. Robot. 31(5), 1147\u20131163 (2015)","journal-title":"IEEE Trans. Robot."},{"issue":"5","key":"2_CR5","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1109\/TRO.2017.2705103","volume":"33","author":"R Mur-Artal","year":"2016","unstructured":"Mur-Artal, R., Tardos, J.: ORB-SLAM2: an open-source slam system for monocular, stereo and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255\u20131262 (2016)","journal-title":"IEEE Trans. Robot."},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. In: IJCV (2004)","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., Bradski, G.R.: ORB: an efficient alternative to sift or surf. In: ICCV (2011)","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"2_CR8","unstructured":"Revaud, J., et al.: R2D2: repeatable and reliable detector and descriptor. In: NeurIPS (2019)"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., Rabinovich, A.: SuperPoint: self-supervised interest point detection and description. In: CVPRW (2018)","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Luo, Z., et al.: ASLFeat: learning local features of accurate shape and localization. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00662"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Dusmanu, M., et al.: D2-net: a trainable CNN for joint description and detection of local features. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00828"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Luo, Z., et al.: ContextDesc: local descriptor augmentation with cross-modality context. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00263"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Sun, J., Shen, Z., Wang, Y., Bao, H., Zhou, X.: LoFTR: detector-free local feature matching with transformers. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00881"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Jiang, W., Trulls, E., Hosang, J., Tagliasacchi, A., Yi, K.M.: COTR: correspondence transformer for matching across images. In: CVPR (2021)","DOI":"10.1109\/ICCV48922.2021.00615"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Truong, P., Danelljan, M., Gool, L.V., Timofte, R.: Learning accurate dense correspondences and when to trust them. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00566"},{"key":"2_CR16","unstructured":"Rocco, I., Cimpoi, M., Arandjelovi, R., Torii, A., Pajdla, T., Sivic, J.: Neighbourhood consensus networks. In: NeurIPS (2018)"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Rocco, I., Arandjelovi\u0107, R., Sivic, J.: Efficient neighbourhood consensus networks via submanifold sparse convolutions. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58545-7_35"},{"key":"2_CR18","unstructured":"Li, X., Han, K., Li, S., Prisacariu, V.: Dual-resolution correspondence networks. In: NeurIPS (2020)"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Truong, P., Danelljan, M., Timofte, R.: GLU-Net: global-local universal network for dense flow and correspondences. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00629"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Min, J., Cho, M.: Convolutional hough matching networks. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00296"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Shen, X., Darmon, F., Efros, A., Aubry, M.: Ransac-flow: generic two-stage image alignment. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58548-8_36"},{"key":"2_CR22","unstructured":"Tang, S., Zhang, J., Zhu, S., Tan, P.: Quadtree attention for vision transformers. In: ICLR (2021)"},{"key":"2_CR23","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NeurIPS (2017)"},{"key":"2_CR24","unstructured":"Dosovitskiy, A., et al.: An image is worth 16 x 16 words: transformers for image recognition at scale. In: ICLR (2020)"},{"key":"2_CR25","unstructured":"Katharopoulos, A., Vyas, A., Pappas, N., Fleuret, F.: Transformers are RNNs: fast autoregressive transformers with linear attention. In: ICML (2020)"},{"key":"2_CR26","unstructured":"Mishchuk, A., Mishkin, D., Radenovi\u0107, F., Matas, J.: Working hard to know your neighbor\u2019s margins: local descriptor learning loss. In: NeurIPS (2017)"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Tian, Y., Fan, B., Wu, F.: L2-net: deep learning of discriminative patch descriptor in Euclidean space. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.649"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Luo, Z., et al.: GeoDesc: learning local descriptors by integrating geometry constraints. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01240-3_11"},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhou, X., Hariharan, B., Snavely, N.: Learning feature descriptors using camera pose supervision. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58452-8_44"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Sarlin, P.E., DeTone, D., Malisiewicz, T., Rabinovich, A.: Superglue: learning feature matching with graph neural networks. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"2_CR31","doi-asserted-by":"crossref","unstructured":"Chen, H., et al.: Learning to match features with seeded graph matching network. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00624"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Learning two-view correspondences and geometry using order-aware network. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00594"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Yi*, K.M., Trulls*, E., Ono, Y., Lepetit, V., Salzmann, M., Fua, P.: Learning to find good correspondences. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00282"},{"key":"2_CR34","doi-asserted-by":"crossref","unstructured":"Sun, W., Jiang, W., Tagliasacchi, A., Trulls, E., Yi, K.M.: Attentive context normalization for robust permutation-equivariant learning. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01130"},{"key":"2_CR35","doi-asserted-by":"crossref","unstructured":"Cavalli, L., Larsson, V., Oswald, M.R., Sattler, T., Pollefeys, M.: Handcrafted outlier detection revisited. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58529-7_45"},{"key":"2_CR36","doi-asserted-by":"crossref","unstructured":"Bian, J., et al.: GMS: grid-based motion statistics for fast, ultra-robust feature correspondence. In: IJCV (2020)","DOI":"10.1007\/s11263-019-01280-3"},{"key":"2_CR37","unstructured":"Truong, P., Danelljan, M., Gool, L., Timofte, R.: Gocor: bringing globally optimized correspondence volumes into your neural network. In: NeurIPS (2020)"},{"key":"2_CR38","doi-asserted-by":"crossref","unstructured":"Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: evolution of optical flow estimation with deep networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.179"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Teed, Z., Deng, J.: Raft: recurrent all-pairs field transforms for optical flow. In: ECCV (2020)","DOI":"10.24963\/ijcai.2021\/662"},{"key":"2_CR40","unstructured":"Fischer, P., et al.: FlowNet: learning optical flow with convolutional networks. In: ICCV (2015)"},{"key":"2_CR41","doi-asserted-by":"crossref","unstructured":"Yin, Z., Shi, J.: Geonet: unsupervised learning of dense depth, optical flow and camera pose. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00212"},{"key":"2_CR42","doi-asserted-by":"crossref","unstructured":"Zhou, L., et al.: Kfnet: learning temporal camera relocalization using kalman filtering. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00497"},{"key":"2_CR43","doi-asserted-by":"crossref","unstructured":"Gast, J., Roth, S.: Lightweight probabilistic deep networks. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00355"},{"key":"2_CR44","doi-asserted-by":"crossref","unstructured":"Ilg, E., et al.: Uncertainty estimates and multi-hypotheses networks for optical flow. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01234-2_40"},{"key":"2_CR45","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Gool, L., Timofte, R.: Probabilistic regression for visual tracking. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00721"},{"key":"2_CR46","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2_CR47","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nie\u00dfner, M.: ScanNet: richly-annotated 3d reconstructions of indoor scenes. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.261"},{"key":"2_CR48","doi-asserted-by":"crossref","unstructured":"Li, Z., Snavely, N.: MegaDepth: learning single-view depth prediction from internet photos. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00218"},{"issue":"2","key":"2_CR49","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1145\/2812802","volume":"59","author":"B Thomee","year":"2016","unstructured":"Thomee, B., et al.: YFCC100M: the new data in multimedia research. Commun. ACM 59(2), 64\u201373 (2016)","journal-title":"Commun. ACM"},{"key":"2_CR50","unstructured":"Truong, P., Danelljan, M., Timofte, R., Van Gool, L.: PDC-Net+: enhanced probabilistic dense correspondence network (2021)"},{"key":"2_CR51","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhang, J., Yang, K., Peng, K., Stiefelhagen, R.: Matchformer: interleaving attention in transformers for feature matching (2022)","DOI":"10.1007\/978-3-031-26313-2_16"},{"key":"2_CR52","unstructured":"Edstedt, J., Wadenb\u00e4ck, M., Felsberg, M.: Deep kernelized dense geometric matching (2022)"},{"key":"2_CR53","doi-asserted-by":"crossref","unstructured":"Taira, H., et al.: InLoc: indoor visual localization with dense matching and view synthesis. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00752"},{"key":"2_CR54","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Sattler, T., Scaramuzza, D.: Reference pose generation for long-term visual localization via learned features and view synthesis. In: IJCV (2021)","DOI":"10.1007\/s11263-020-01399-8"},{"key":"2_CR55","doi-asserted-by":"crossref","unstructured":"Sattler, T., et al.: Benchmarking 6DOF outdoor visual localization in changing conditions. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00897"},{"key":"2_CR56","unstructured":"Toft, C., et al.: Long-term visual localization revisited. In: TPAMI (2020)"},{"key":"2_CR57","doi-asserted-by":"crossref","unstructured":"Sarlin, P.E., Cadena, C., Siegwart, R., Dymczyk, M.: From coarse to fine: robust hierarchical localization at large scale. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01300"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19824-3_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T22:54:56Z","timestamp":1728341696000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19824-3_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198236","9783031198243"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19824-3_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 November 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}