{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:42:06Z","timestamp":1742920926745,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031732287"},{"type":"electronic","value":"9783031732294"}],"license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"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\/978-3-031-73229-4_18","type":"book-chapter","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T15:03:09Z","timestamp":1729782189000},"page":"309-324","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Instant Uncertainty Calibration of\u00a0NeRFs Using a\u00a0Meta-calibrator"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8301-1010","authenticated-orcid":false,"given":"Niki","family":"Amini-Naieni","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomas","family":"Jakab","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1374-2858","authenticated-orcid":false,"given":"Andrea","family":"Vedaldi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronald","family":"Clark","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"18_CR1","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. In: ICCV, pp. 5835\u20135844 (2021)","DOI":"10.1109\/ICCV48922.2021.00580"},{"key":"18_CR2","unstructured":"Bhalgat, Y.: Hashnerf-pytorch (2022). https:\/\/github.com\/yashbhalgat\/HashNeRF-pytorch\/"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Gafni, G., Thies, J., Zollh\u00f6fer, M., Nie\u00dfner, M.: Dynamic neural radiance fields for monocular 4D facial avatar reconstruction. In: CVPR, pp. 8649\u20138658 (2021)","DOI":"10.1109\/CVPR46437.2021.00854"},{"key":"18_CR4","unstructured":"Ghoshal, B., Tucker, A.: On calibrated model uncertainty in deep learning. In: ECML (2022)"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Goli, L., Reading, C., Sell\u00e1n, S., Jacobson, A., Tagliasacchi, A.: Bayes\u2019 Rays: uncertainty quantification in neural radiance fields. ArXiv abs\/2309.03185 (2023)","DOI":"10.1109\/CVPR52733.2024.01896"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Jain, A., Tancik, M., Abbeel, P.: Putting nerf on a diet: semantically consistent few-shot view synthesis. In: ICCV, pp. 5865\u20135874 (2021)","DOI":"10.1109\/ICCV48922.2021.00583"},{"key":"18_CR7","unstructured":"Jang, T.J., Hyun, C.M.: Nerf solves undersampled mri reconstruction. ArXiv abs\/2402.13226 (2024)"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Jensen, R.R., Dahl, A., Vogiatzis, G., Tola, E., Aan\u00e6s, H.: Large scale multi-view stereopsis evaluation. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.59"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Jin, L., Chen, X., Ruckin, J., Popovi\u2019c, M.: Neu-nbv: next best view planning using uncertainty estimation in image-based neural rendering. In: IROS (2023)","DOI":"10.1109\/IROS55552.2023.10342226"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Kajiya, J.T., Von\u00a0Herzen, B.P.: Ray tracing volume densities. In: SIGGRAPH, pp. 165\u2013174 (1984)","DOI":"10.1145\/800031.808594"},{"key":"18_CR11","unstructured":"Kosiorek, A.R., et al.: Nerf-vae: a geometry aware 3D scene generative model. In: ICML (2021)"},{"key":"18_CR12","unstructured":"Kuleshov, V., Fenner, N., Ermon, S.: Accurate uncertainties for deep learning using calibrated regression. In: ICML, pp. 2796\u20132804 (2018)"},{"key":"18_CR13","unstructured":"Liu, L., Gu, J., Lin, K.Z., Chua, T.S., Theobalt, C.: Neural sparse voxel fields. In: NIPS (2020)"},{"key":"18_CR14","doi-asserted-by":"crossref","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: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00713"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Mildenhall, B., et al.: Local light field fusion: practical view synthesis with prescriptive sampling guidelines. In: TOG (2019)","DOI":"10.1145\/3306346.3322980"},{"key":"18_CR16","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: ECCV (2020)","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"M\u00fcller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. In: ACM Trans. Graph. (2022)","DOI":"10.1145\/3528223.3530127"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Neff, T., et al.: Donerf: towards real-time rendering of compact neural radiance fields using depth oracle networks. In: CGF, pp. 45\u201359 (2021)","DOI":"10.1111\/cgf.14340"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Barron, J.T., Mildenhall, B., Sajjadi, M.S.M., Geiger, A., Radwan, N.: Regnerf: regularizing neural radiance fields for view synthesis from sparse inputs. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00540"},{"key":"18_CR20","unstructured":"Oquab, M., et al.: Dinov2: learning robust visual features without supervision (2023)"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"Peng, S., et al.: Animatable neural radiance fields for modeling dynamic human bodies. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01405"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Ran, Y., et al.: Neurar: neural uncertainty for autonomous 3d reconstruction. In: RAL (2023)","DOI":"10.1109\/LRA.2023.3235686"},{"key":"18_CR23","doi-asserted-by":"crossref","unstructured":"Rebain, D., Jiang, W., Yazdani, S., Li, K., Yi, K.M., Tagliasacchi, A.: Derf: decomposed radiance fields. In: CVPR, pp. 14148\u201314156 (2020)","DOI":"10.1109\/CVPR46437.2021.01393"},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Seo, S., Chang, Y., Kwak, N.: Flipnerf: flipped reflection rays for few-shot novel view synthesis. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.02092"},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Seo, S., Han, D., Chang, Y., Kwak, N.: Mixnerf: modeling a ray with mixture density for novel view synthesis from sparse inputs. In: CVPR, pp. 20659\u201320668 (2023)","DOI":"10.1109\/CVPR52729.2023.01979"},{"key":"18_CR26","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1007\/978-3-031-20062-5_31","volume-title":"ECCV 2022","author":"J Shen","year":"2022","unstructured":"Shen, J., Agudo, A., Moreno-Noguer, F., Ruiz, A.: Conditional-flow nerf: accurate 3D modelling with reliable uncertainty quantification. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13663, pp. 540\u2013557. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20062-5_31"},{"key":"18_CR27","doi-asserted-by":"crossref","unstructured":"Shen, J., Ruiz, A., Agudo, A., Moreno-Noguer, F.: Stochastic neural radiance fields: quantifying uncertainty in implicit 3d representations. In: 3DV, pp. 972\u2013981 (2021)","DOI":"10.1109\/3DV53792.2021.00105"},{"key":"18_CR28","unstructured":"Sitzmann, V., Martel, J.N., Bergman, A.W., Lindell, D.B., Wetzstein, G.: Implicit neural representations with periodic activation functions. In: NIPS (2020)"},{"key":"18_CR29","doi-asserted-by":"crossref","unstructured":"S\u00fcnderhauf, N., Abou-Chakra, J., Miller, D.: Density-aware nerf ensembles: quantifying predictive uncertainty in neural radiance fields. In: ICRA (2023)","DOI":"10.1109\/ICRA48891.2023.10161012"},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Wang, P., et al.: F2-nerf: fast neural radiance field training with free camera trajectories. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00404"},{"key":"18_CR31","unstructured":"Zhang, K., Riegler, G., Snavely, N., Koltun, V.: Nerf++: analyzing and improving neural radiance fields. ArXiv abs\/2010.07492 (2020)"},{"key":"18_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00068"}],"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\/978-3-031-73229-4_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T15:08:20Z","timestamp":1729782500000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73229-4_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"ISBN":["9783031732287","9783031732294"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73229-4_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,25]]},"assertion":[{"value":"25 October 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"}}]}}