{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T02:50:48Z","timestamp":1772679048027,"version":"3.50.1"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031919886","type":"print"},{"value":"9783031919893","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-91989-3_7","type":"book-chapter","created":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T17:33:18Z","timestamp":1748194398000},"page":"103-119","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["NeRF-Supervised Feature Point Detection and\u00a0Description"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8347-4829","authenticated-orcid":false,"given":"Ali","family":"Youssef","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4609-1177","authenticated-orcid":false,"given":"Francisco","family":"Vasconcelos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"7_CR1","doi-asserted-by":"publisher","unstructured":"Abaspur Kazerouni, I., Fitzgerald, L., Dooly, G., Toal, D.: A survey of state-of-the-art on visual slam. Expert Syst. Appl. 205, 117734 (2022). https:\/\/doi.org\/10.1016\/j.eswa.2022.117734, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417422010156","DOI":"10.1016\/j.eswa.2022.117734"},{"key":"7_CR2","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: CVPR (2017)","DOI":"10.1109\/CVPR.2017.410"},{"key":"7_CR3","doi-asserted-by":"publisher","unstructured":"Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Zip-nerf: anti-aliased grid-based neural radiance fields. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 19640\u201319648. IEEE Computer Society, Los Alamitos, CA, USA (2023). https:\/\/doi.org\/10.1109\/ICCV51070.2023.01804, https:\/\/doi.ieeecomputersociety.org\/10.1109\/ICCV51070.2023.01804","DOI":"10.1109\/ICCV51070.2023.01804"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., Srinivasan, P.P.: Mip-nerf: a multiscale representation for anti-aliasing neural radiance fields. CoRR arXiv:2103.13415 (2021)","DOI":"10.1109\/ICCV48922.2021.00580"},{"key":"7_CR5","doi-asserted-by":"publisher","unstructured":"Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-nerf 360: unbounded anti-aliased neural radiance fields. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5460\u20135469 (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00539","DOI":"10.1109\/CVPR52688.2022.00539"},{"key":"7_CR6","unstructured":"Bitterli, B.: Rendering resources (2016). https:\/\/benedikt-bitterli.me\/resources\/"},{"key":"7_CR7","doi-asserted-by":"publisher","unstructured":"Bruno, H., Colombini, E.L.: Lift-slam: a deep-learning feature-based monocular visual slam method. Neurocomputing 455, 97\u2013110 (2021). https:\/\/doi.org\/10.1016\/j.neucom.2021.05.027","DOI":"10.1016\/j.neucom.2021.05.027"},{"key":"7_CR8","doi-asserted-by":"publisher","unstructured":"Choy, C., Dong, W., Koltun, V.: Deep global registration. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2511\u20132520 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00259","DOI":"10.1109\/CVPR42600.2020.00259"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T.A., Nie\u00dfner, M.: Scannet: richly-annotated 3D reconstructions of indoor scenes. CoRR arXiv:1702.04405 (2017)","DOI":"10.1109\/CVPR.2017.261"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., Rabinovich, A.: Superpoint: self-supervised interest point detection and description. CoRR arXiv:1712.07629 (2017)","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"7_CR11","doi-asserted-by":"publisher","unstructured":"Dusmanu, M., et al.: D2-net: a trainable CNN for joint description and detection of local features. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8084\u20138093 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00828","DOI":"10.1109\/CVPR.2019.00828"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"El\u00a0Banani, M., Gao, L., Johnson, J.: UnsupervisedR &R: unsupervised point cloud registration via differentiable rendering. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00705"},{"key":"7_CR13","unstructured":"Feldmann, C., et al.: Nerfmentation: nerf-based augmentation for monocular depth estimation (2024)"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Gleize, P., Wang, W., Feiszli, M.: Silk: simple learned keypoints. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 22499\u201322508 (2023)","DOI":"10.1109\/ICCV51070.2023.02056"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, Second edn. Cambridge University Press (2004)","DOI":"10.1017\/CBO9780511811685"},{"key":"7_CR16","doi-asserted-by":"publisher","unstructured":"Jiang, W., Trulls, E., Hosang, J., Tagliasacchi, A., Yi, K.M.: Cotr: correspondence transformer for matching across images. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 6187\u20136197 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00615","DOI":"10.1109\/ICCV48922.2021.00615"},{"key":"7_CR17","doi-asserted-by":"publisher","unstructured":"Kabsch, W.: A solution for the best rotation to relate two sets of vectors. Acta Crystallogr. A 32(5), 922\u2013923 (1976) . https:\/\/doi.org\/10.1107\/S0567739476001873","DOI":"10.1107\/S0567739476001873"},{"key":"7_CR18","doi-asserted-by":"publisher","unstructured":"Li, G., Yu, L., Fei, S.: A deep-learning real-time visual slam system based on multi-task feature extraction network and self-supervised feature points. Measurement 168, 108403 (2021).https:\/\/doi.org\/10.1016\/j.measurement.2020.108403, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0263224120309374","DOI":"10.1016\/j.measurement.2020.108403"},{"key":"7_CR19","doi-asserted-by":"publisher","unstructured":"Li, Z., Snavely, N.: Megadepth: learning single-view depth prediction from internet photos. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2041\u20132050 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00218","DOI":"10.1109\/CVPR.2018.00218"},{"key":"7_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Ling, H.: Adfactory: an effective framework for generalizing optical flow with nerf (2023)","DOI":"10.1109\/CVPR52733.2024.01946"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91\u2013110 (2004). http:\/\/link.springer.com\/10.1023\/B:VISI.0000029664.99615.94","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"issue":"1","key":"7_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TRO.2015.2496823","volume":"32","author":"S Lowry","year":"2016","unstructured":"Lowry, S., et al.: Visual place recognition: a survey. IEEE Trans. Rob. 32(1), 1\u201319 (2016). https:\/\/doi.org\/10.1109\/TRO.2015.2496823","journal-title":"IEEE Trans. Rob."},{"key":"7_CR24","doi-asserted-by":"publisher","unstructured":"Martin-Brualla, R., Radwan, N., Sajjadi, M.S.M., Barron, J.T., Dosovitskiy, A., Duckworth, D.: Nerf in the wild: neural radiance fields for unconstrained photo collections. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7206\u20137215 (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.00713","DOI":"10.1109\/CVPR46437.2021.00713"},{"key":"7_CR25","doi-asserted-by":"publisher","unstructured":"M\u00fcller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph. 41(4), 102:1\u2013102:15 (2022). https:\/\/doi.org\/10.1145\/3528223.3530127","DOI":"10.1145\/3528223.3530127"},{"key":"7_CR26","doi-asserted-by":"crossref","unstructured":"Ozyesil, O., Voroninski, V., Basri, R., Singer, A.: A survey of structure from motion (2017)","DOI":"10.1017\/S096249291700006X"},{"key":"7_CR27","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. CoRR arXiv:1912.01703 (2019)"},{"key":"7_CR28","unstructured":"Pautrat*, R., Su\u00e1rez*, I., Lindenberger, P., Sarlin, P.E.: Glue factory. https:\/\/github.com\/cvg\/glue-factory"},{"key":"7_CR29","unstructured":"Revaud, J., et al.: R2d2: repeatable and reliable detector and descriptor. arXiv preprint arXiv:1906.06195 (2019)"},{"key":"7_CR30","doi-asserted-by":"publisher","unstructured":"Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: an efficient alternative to sift or surf. In: 2011 International Conference on Computer Vision, pp. 2564\u20132571 (2011). https:\/\/doi.org\/10.1109\/ICCV.2011.6126544","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"7_CR31","doi-asserted-by":"publisher","unstructured":"Sarlin, P.E., DeTone, D., Malisiewicz, T., Rabinovich, A.: Superglue: learning feature matching with graph neural networks. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4937\u20134946 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00499","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"7_CR32","doi-asserted-by":"publisher","unstructured":"Sattler, T., et al.: Benchmarking 6dof outdoor visual localization in changing conditions. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8601\u20138610 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00897","DOI":"10.1109\/CVPR.2018.00897"},{"key":"7_CR33","doi-asserted-by":"crossref","unstructured":"Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.445"},{"key":"7_CR34","doi-asserted-by":"publisher","unstructured":"Sun, J., Shen, Z., Wang, Y., Bao, H., Zhou, X.: Loftr: detector-free local feature matching with transformers. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8918\u20138927 (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.00881","DOI":"10.1109\/CVPR46437.2021.00881"},{"key":"7_CR35","doi-asserted-by":"publisher","unstructured":"Taketomi, T., Uchiyama, H., Ikeda, S.: Visual slam algorithms: a survey from 2010 to 2016. IPSJ Trans. Comput. Vis. Appl. 9(1), 16 (2017). https:\/\/doi.org\/10.1186\/s41074-017-0027-2","DOI":"10.1186\/s41074-017-0027-2"},{"key":"7_CR36","doi-asserted-by":"publisher","unstructured":"Tancik, M., et al.: Nerfstudio: a modular framework for neural radiance field development. In: ACM SIGGRAPH 2023 Conference Proceedings. SIGGRAPH \u201923, Association for Computing Machinery, New York, NY, USA (2023). https:\/\/doi.org\/10.1145\/3588432.3591516","DOI":"10.1145\/3588432.3591516"},{"issue":"4","key":"7_CR37","doi-asserted-by":"publisher","first-page":"3505","DOI":"10.1109\/LRA.2019.2927954","volume":"4","author":"J Tang","year":"2019","unstructured":"Tang, J., Ericson, L., Folkesson, J., Jensfelt, P.: GCNV2: efficient correspondence prediction for real-time slam. IEEE Robot. Autom. Lett. 4(4), 3505\u20133512 (2019). https:\/\/doi.org\/10.1109\/LRA.2019.2927954","journal-title":"IEEE Robot. Autom. Lett."},{"key":"7_CR38","unstructured":"Thomee, B., et al.: The new data and new challenges in multimedia research. CoRR arXiv:1503.01817 (2015)"},{"key":"7_CR39","doi-asserted-by":"publisher","unstructured":"Tosi, F., Tonioni, A., Gregorio, D.D., Poggi, M.: Nerf-supervised deep stereo. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 855\u2013866. IEEE Computer Society, Los Alamitos, CA, USA (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.00089, https:\/\/doi.ieeecomputersociety.org\/10.1109\/CVPR52729.2023.00089","DOI":"10.1109\/CVPR52729.2023.00089"},{"key":"7_CR40","unstructured":"Tyszkiewicz, M.J., Fua, P., Trulls, E.: DISK: learning local features with policy gradient. CoRR arXiv:2006.13566 (2020)"},{"key":"7_CR41","unstructured":"Wang, Z., Wu, S., Xie, W., Chen, M., Prisacariu, V.A.: NeRF$$--$$: neural radiance fields without known camera parameters. arXiv preprint arXiv:2102.07064 (2021)"},{"key":"7_CR42","doi-asserted-by":"crossref","unstructured":"Widya, A.R., Torii, A., Okutomi, M.: Structure-from-motion using dense CNN features with keypoint relocalization (2018)","DOI":"10.1186\/s41074-018-0042-y"},{"key":"7_CR43","doi-asserted-by":"publisher","unstructured":"Yen-Chen, L., Florence, P., Barron, J.T., Lin, T.Y., Rodriguez, A., Isola, P.: Nerf-supervision: learning dense object descriptors from neural radiance fields. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 6496\u20136503. IEEE Press (2022). https:\/\/doi.org\/10.1109\/ICRA46639.2022.9812291","DOI":"10.1109\/ICRA46639.2022.9812291"},{"key":"7_CR44","doi-asserted-by":"publisher","unstructured":"Yi, K.M., Trulls, E., Ono, Y., Lepetit, V., Salzmann, M., Fua, P.: Learning to find good correspondences. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2666\u20132674 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00282","DOI":"10.1109\/CVPR.2018.00282"},{"key":"7_CR45","doi-asserted-by":"publisher","unstructured":"Zhang, X., Wang, L., Su, Y.: Visual place recognition: a survey from deep learning perspective. Pattern Recogn. 113, 107760 (2021). https:\/\/doi.org\/10.1016\/j.patcog.2020.107760, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S003132032030563X","DOI":"10.1016\/j.patcog.2020.107760"},{"key":"7_CR46","doi-asserted-by":"publisher","first-page":"3101","DOI":"10.1109\/TMM.2022.3155927","volume":"25","author":"X Zhao","year":"2023","unstructured":"Zhao, X., Wu, X., Miao, J., Chen, W., Chen, P., Li, Z.: Alike: accurate and lightweight keypoint detection and descriptor extraction. IEEE Trans. Multimedia 25, 3101\u20133112 (2023). https:\/\/doi.org\/10.1109\/TMM.2022.3155927","journal-title":"IEEE Trans. Multimedia"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-91989-3_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T17:33:25Z","timestamp":1748194405000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-91989-3_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031919886","9783031919893"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-91989-3_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","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":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}