{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:59:40Z","timestamp":1777568380713,"version":"3.51.4"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031732010","type":"print"},{"value":"9783031732027","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"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\/978-3-031-73202-7_9","type":"book-chapter","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T14:16:54Z","timestamp":1732112214000},"page":"144-161","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DecentNeRFs: Decentralized Neural Radiance Fields from\u00a0Crowdsourced Images"],"prefix":"10.1007","author":[{"given":"Zaid","family":"Tasneem","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akshat","family":"Dave","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhishek","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kushagra","family":"Tiwary","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Praneeth","family":"Vepakomma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashok","family":"Veeraraghavan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramesh","family":"Raskar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Asadi, M., Zareinia, K., Saeedi, S.: Di-nerf: distributed nerf for collaborative learning with unknown relative poses. arXiv preprint arXiv:2402.01485 (2024)","DOI":"10.1109\/LRA.2024.3474551"},{"key":"9_CR2","unstructured":"Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., Shmatikov, V.: How to backdoor federated learning. In: International Conference on Artificial Intelligence and Statistics, pp. 2938\u20132948. PMLR (2020)"},{"key":"9_CR3","unstructured":"Beutel, D.J., et\u00a0al.: Flower: a friendly federated learning research framework. arXiv preprint arXiv:2007.14390 (2020)"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. In: proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175\u20131191 (2017)","DOI":"10.1145\/3133956.3133982"},{"key":"9_CR5","unstructured":"Burkhart, M., Strasser, M., Many, D., Dimitropoulos, X.: $$\\{$$SEPIA$$\\}$$:$$\\{$$Privacy-Preserving$$\\}$$ aggregation of $$\\{$$Multi-Domain$$\\}$$ network events and statistics. In: 19th USENIX Security Symposium (USENIX Security 10) (2010)"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Cao, J., et al.: Real-time neural light field on mobile devices. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8328\u20138337 (2023)","DOI":"10.1109\/CVPR52729.2023.00805"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Chen, X., et al.: Hallucinated neural radiance fields in the wild. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12943\u201312952 (2022)","DOI":"10.1109\/CVPR52688.2022.01260"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Z., Funkhouser, T., Hedman, P., Tagliasacchi, A.: Mobilenerf: exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In: The Conference on Computer Vision and Pattern Recognition (CVPR) (2023)","DOI":"10.1109\/CVPR52729.2023.01590"},{"key":"9_CR9","unstructured":"Choi, B., Sohn, J.Y., Han, D.J., Moon, J.: Communication-computation efficient secure aggregation for federated learning. arXiv preprint arXiv:2012.05433 (2020)"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Dusmanu, M., Schonberger, J.L., Sinha, S.N., Pollefeys, M.: Privacy-preserving image features via adversarial affine subspace embeddings. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14267\u201314277 (2021)","DOI":"10.1109\/CVPR46437.2021.01404"},{"key":"9_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/11681878_14","volume-title":"Theory of Cryptography","author":"C Dwork","year":"2006","unstructured":"Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265\u2013284. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11681878_14"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"9_CR13","unstructured":"Holden, L., Dayoub, F., Harvey, D., Chin, T.J.: Federated neural radiance fields. arXiv preprint arXiv:2305.01163 (2023)"},{"key":"9_CR14","first-page":"29898","volume":"34","author":"J Jeon","year":"2021","unstructured":"Jeon, J., Lee, K., Oh, S., Ok, J., et al.: Gradient inversion with generative image prior. Adv. Neural. Inf. Process. Syst. 34, 29898\u201329908 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"9_CR15","unstructured":"Kairouz, P., et\u00a0al.: Advances and open problems in federated learning. Found. Trends\u00ae Mach. Learn. 14(1\u20132), 1\u2013210 (2021)"},{"key":"9_CR16","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H.B., Yu, F.X., Richt\u00e1rik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)"},{"key":"9_CR17","unstructured":"kwea123: nerf_pl implementation of nerf (neural radiance fields) in pytorch lightning (2023). https:\/\/github.com\/kwea123\/nerf_pl\/tree\/nerfw. Accessed 17 Nov 2023"},{"key":"9_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1007\/978-3-030-58452-8_11","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z Li","year":"2020","unstructured":"Li, Z., Xian, W., Davis, A., Snavely, N.: Crowdsampling the plenoptic function. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 178\u2013196. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_11"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Lindell, Y.: Secure multiparty computation (MPC). Cryptology ePrint Archive (2020)","DOI":"10.1145\/3387108"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Martin-Brualla, R., Radwan, N., Sajjadi, M.S., Barron, J.T., Dosovitskiy, A., Duckworth, D.: Nerf in the wild: neural radiance fields for unconstrained photo collections. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7210\u20137219 (2021)","DOI":"10.1109\/CVPR46437.2021.00713"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: The European Conference on Computer Vision (ECCV) (2020)","DOI":"10.1007\/978-3-030-58452-8_24"},{"issue":"4","key":"9_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3528223.3530127","volume":"41","author":"T M\u00fcller","year":"2022","unstructured":"M\u00fcller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph. (ToG) 41(4), 1\u201315 (2022)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Ng, T., et al.: Ninjadesc: content-concealing visual descriptors via adversarial learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12797\u201312807 (2022)","DOI":"10.1109\/CVPR52688.2022.01246"},{"key":"9_CR24","unstructured":"Nguyen, J., et al.: Federated learning with buffered asynchronous aggregation. In: International Conference on Artificial Intelligence and Statistics, pp. 3581\u20133607. PMLR (2022)"},{"key":"9_CR25","unstructured":"Photutorial: how many photos are there? (statistics & trends in 2023) (2023). https:\/\/photutorial.com\/photos-statistics\/"},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"Pittaluga, F., Koppal, S.J., Kang, S.B., Sinha, S.N.: Revealing scenes by inverting structure from motion reconstructions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 145\u2013154 (2019)","DOI":"10.1109\/CVPR.2019.00023"},{"key":"9_CR27","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, pp. 4104\u20134113 (2016)","DOI":"10.1109\/CVPR.2016.445"},{"issue":"3","key":"9_CR28","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1145\/1141911.1141964","volume":"25","author":"N Snavely","year":"2006","unstructured":"Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3D. ACM Trans. Graph. 25(3), 835\u2013846 (2006). https:\/\/doi.org\/10.1145\/1141911.1141964","journal-title":"ACM Trans. Graph."},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3D. In: ACM Siggraph 2006 Papers, pp. 835\u2013846 (2006)","DOI":"10.1145\/1141911.1141964"},{"key":"9_CR30","first-page":"694","volume":"4","author":"J So","year":"2022","unstructured":"So, J., et al.: LightSecAgg: a lightweight and versatile design for secure aggregation in federated learning. Proc. Mach. Learn. Syst. 4, 694\u2013720 (2022)","journal-title":"Proc. Mach. Learn. Syst."},{"key":"9_CR31","unstructured":"Suzuki, T.: Federated learning for large-scale scene modeling with neural radiance fields. arXiv preprint arXiv:2309.06030 (2023)"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Tan, A.Z., Yu, H., Cui, L., Yang, Q.: Towards personalized federated learning. IEEE Trans. Neural Netw. Learn. Syst. (2022)","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"9_CR33","series-title":"LNCS","first-page":"504","volume-title":"ECCV","author":"Z Tasneem","year":"2022","unstructured":"Tasneem, Z., et al.: Learning phase mask for privacy-preserving passive depth estimation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV. LNCS, vol. 13667, pp. 504\u2013521. Springer, Cham (2022)"},{"key":"9_CR34","doi-asserted-by":"crossref","unstructured":"Weder, S., et al.: Removing objects from neural radiance fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16528\u201316538 (2023)","DOI":"10.1109\/CVPR52729.2023.01586"},{"key":"9_CR35","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zhang, S., Huang, Z., Zhang, Y., Tan, M.: Cross-ray neural radiance fields for novel-view synthesis from unconstrained image collections. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15901\u201315911 (2023)","DOI":"10.1109\/ICCV51070.2023.01457"},{"key":"9_CR36","doi-asserted-by":"crossref","unstructured":"Yin, H., Mallya, A., Vahdat, A., Alvarez, J.M., Kautz, J., Molchanov, P.: See through gradients: image batch recovery via gradinversion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16337\u201316346 (2021)","DOI":"10.1109\/CVPR46437.2021.01607"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73202-7_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T15:09:13Z","timestamp":1732115353000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73202-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,21]]},"ISBN":["9783031732010","9783031732027"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73202-7_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,21]]},"assertion":[{"value":"21 November 2024","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"}}]}}