{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:38:39Z","timestamp":1767339519207,"version":"3.40.3"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031728471"},{"type":"electronic","value":"9783031728488"}],"license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"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\/978-3-031-72848-8_8","type":"book-chapter","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T13:36:56Z","timestamp":1732801016000},"page":"125-142","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Few-Shot NeRF by\u00a0Adaptive Rendering Loss Regularization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0405-3962","authenticated-orcid":false,"given":"Qingshan","family":"Xu","sequence":"first","affiliation":[]},{"given":"Xuanyu","family":"Yi","sequence":"additional","affiliation":[]},{"given":"Jianyao","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3284-864X","authenticated-orcid":false,"given":"Wenbing","family":"Tao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4480-169X","authenticated-orcid":false,"given":"Yew-Soon","family":"Ong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7374-8739","authenticated-orcid":false,"given":"Hanwang","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"8_CR1","unstructured":"Bai, J., Huang, L., Gong, W., Guo, J., Guo, Y.: Self-nerf: a self-training pipeline for few-shot neural radiance fields. arXiv preprint arXiv:2303.05775 (2023)"},{"key":"8_CR2","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: Proceedings of the IEEE International Conference on Computer Vision, pp. 5855\u20135864 (2021)","DOI":"10.1109\/ICCV48922.2021.00580"},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-NeRF 360: unbounded anti-aliased neural radiance fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5470\u20135479 (2022)","DOI":"10.1109\/CVPR52688.2022.00539"},{"key":"8_CR4","first-page":"14","volume":"5","author":"J Bradbury","year":"2018","unstructured":"Bradbury, J., et al.: Jax: composable transformations of python+ NumPy programs. Version 0.2 5, 14\u201324 (2018)","journal-title":"Version 0.2"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Chan, E.R., Monteiro, M., Kellnhofer, P., Wu, J., Wetzstein, G.: pi-GAN: periodic implicit generative adversarial networks for 3D-aware image synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5799\u20135809 (2021)","DOI":"10.1109\/CVPR46437.2021.00574"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Chen, A., et al.: MVSNeRF: fast generalizable radiance field reconstruction from multi-view stereo. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 14124\u201314133 (2021)","DOI":"10.1109\/ICCV48922.2021.01386"},{"key":"8_CR7","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1007\/978-3-031-19790-1_20","volume-title":"ECCV 2022","author":"D Chen","year":"2022","unstructured":"Chen, D., Liu, Y., Huang, L., Wang, B., Pan, P.: GeoAug: data augmentation for few-shot nerf with geometry constraints. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13677, pp. 322\u2013337. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19790-1_20"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Chibane, J., Bansal, A., Lazova, V., Pons-Moll, G.: Stereo radiance fields (SRF): learning view synthesis for sparse views of novel scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7911\u20137920 (2021)","DOI":"10.1109\/CVPR46437.2021.00782"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Deng, K., Liu, A., Zhu, J.Y., Ramanan, D.: Depth-supervised NeRF: fewer views and faster training for free. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12882\u201312891 (2022)","DOI":"10.1109\/CVPR52688.2022.01254"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Esposito, S., et al.: GeoGen: geometry-aware generative modeling via signed distance functions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7479\u20137488 (2024)","DOI":"10.1109\/CVPRW63382.2024.00743"},{"key":"8_CR11","unstructured":"Fu, Q., Xu, Q., Ong, Y.S., Tao, W.: Geo-Neus: geometry-consistent neural implicit surfaces learning for multi-view reconstruction. In: Advances in Neural Information Processing Systems, vol. 35, pp. 3403\u20133416 (2022)"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Jain, A., Tancik, M., Abbeel, P.: Putting NeRF on a diet: semantically consistent few-shot view synthesis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5885\u20135894 (2021)","DOI":"10.1109\/ICCV48922.2021.00583"},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Jensen, R., Dahl, A., Vogiatzis, G., Tola, E., Aan\u00e6s, H.: Large scale multi-view stereopsis evaluation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 406\u2013413 (2014)","DOI":"10.1109\/CVPR.2014.59"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Johari, M.M., Lepoittevin, Y., Fleuret, F.: GeoNeRF: generalizing nerf with geometry priors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 18365\u201318375 (2022)","DOI":"10.1109\/CVPR52688.2022.01782"},{"key":"8_CR15","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Kim, M., Seo, S., Han, B.: InfoNeRF: ray entropy minimization for few-shot neural volume rendering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12912\u201312921 (2022)","DOI":"10.1109\/CVPR52688.2022.01257"},{"key":"8_CR17","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"8_CR18","unstructured":"Kwak, M., Song, J., Kim, S.: GecoNeRF: few-shot neural radiance fields via geometric consistency. arXiv preprint arXiv:2301.10941 (2023)"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Lin, C.H., et al.: Magic3D: high-resolution text-to-3D content creation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 300\u2013309 (2023)","DOI":"10.1109\/CVPR52729.2023.00037"},{"key":"8_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 Conference on Computer Vision and Pattern Recognition, pp. 7210\u20137219 (2021)","DOI":"10.1109\/CVPR46437.2021.00713"},{"issue":"2","key":"8_CR21","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1109\/2945.468400","volume":"1","author":"N Max","year":"1995","unstructured":"Max, N.: Optical models for direct volume rendering. IEEE Trans. Visual Comput. Graph. 1(2), 99\u2013108 (1995)","journal-title":"IEEE Trans. Visual Comput. Graph."},{"issue":"4","key":"8_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3306346.3322980","volume":"38","author":"B Mildenhall","year":"2019","unstructured":"Mildenhall, B., et al.: Local light field fusion: practical view synthesis with prescriptive sampling guidelines. ACM Trans. Graph. (TOG) 38(4), 1\u201314 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"1","key":"8_CR23","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1145\/3503250","volume":"65","author":"B Mildenhall","year":"2021","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. Commun. ACM 65(1), 99\u2013106 (2021)","journal-title":"Commun. ACM"},{"key":"8_CR24","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Barron, J.T., Mildenhall, B., Sajjadi, M.S., Geiger, A., Radwan, N.: RegNeRF: regularizing neural radiance fields for view synthesis from sparse inputs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5480\u20135490 (2022)","DOI":"10.1109\/CVPR52688.2022.00540"},{"key":"8_CR25","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1007\/978-3-031-19827-4_14","volume-title":"ECCV 2022","author":"X Pan","year":"2022","unstructured":"Pan, X., Lai, Z., Song, S., Huang, G.: ActiveNeRF: learning where to see with uncertainty estimation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13693, pp. 230\u2013246. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19827-4_14"},{"key":"8_CR26","unstructured":"Poole, B., Jain, A., Barron, J.T., Mildenhall, B.: DreamFusion: text-to-3D using 2D diffusion. arXiv preprint arXiv:2209.14988 (2022)"},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"Ren, C., Xu, Q., Zhang, S., Yang, J.: Hierarchical prior mining for non-local multi-view stereo. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3611\u20133620 (2023)","DOI":"10.1109\/ICCV51070.2023.00334"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Roessle, B., Barron, J.T., Mildenhall, B., Srinivasan, P.P., Nie\u00dfner, M.: Dense depth priors for neural radiance fields from sparse input views. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12892\u201312901 (2022)","DOI":"10.1109\/CVPR52688.2022.01255"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Seo, S., Chang, Y., Kwak, N.: FlipNeRF: flipped reflection rays for few-shot novel view synthesis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 22883\u201322893 (2023)","DOI":"10.1109\/ICCV51070.2023.02092"},{"key":"8_CR30","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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 20659\u201320668 (2023)","DOI":"10.1109\/CVPR52729.2023.01979"},{"key":"8_CR31","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":"8_CR32","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: Proceedings of the International Conference on 3D Vision, pp. 972\u2013981. IEEE (2021)","DOI":"10.1109\/3DV53792.2021.00105"},{"key":"8_CR33","doi-asserted-by":"crossref","unstructured":"Su, W., Zhang, C., Xu, Q., Tao, W.: PSDF: prior-driven neural implicit surface learning for multi-view reconstruction. arXiv preprint arXiv:2401.12751 (2024)","DOI":"10.1109\/TVCG.2024.3444035"},{"key":"8_CR34","doi-asserted-by":"crossref","unstructured":"Sun, X., Xu, Q., Yang, X., Zang, Y., Wang, C.: Global and hierarchical geometry consistency priors for few-shot nerfs in indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 20530\u201320539 (2024)","DOI":"10.1109\/CVPR52733.2024.01940"},{"key":"8_CR35","unstructured":"Tancik, M., et al.: Fourier features let networks learn high frequency functions in low dimensional domains. In: Advances in Neural Information Processing Systems, vol. 33, pp. 7537\u20137547 (2020)"},{"key":"8_CR36","doi-asserted-by":"crossref","unstructured":"Truong, P., Rakotosaona, M.J., Manhardt, F., Tombari, F.: SPARF: neural radiance fields from sparse and noisy poses. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4190\u20134200 (2023)","DOI":"10.1109\/CVPR52729.2023.00408"},{"key":"8_CR37","doi-asserted-by":"crossref","unstructured":"Uy, M.A., Martin-Brualla, R., Guibas, L., Li, K.: SCADE: NeRFs from space carving with ambiguity-aware depth estimates. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 16518\u201316527 (2023)","DOI":"10.1109\/CVPR52729.2023.01585"},{"key":"8_CR38","doi-asserted-by":"crossref","unstructured":"Verbin, D., Hedman, P., Mildenhall, B., Zickler, T., Barron, J.T., Srinivasan, P.P.: Ref-NeRF: structured view-dependent appearance for neural radiance fields. In: 2022 IEEE Conference on Computer Vision and Pattern Recognition, pp. 5481\u20135490. IEEE (2022)","DOI":"10.1109\/CVPR52688.2022.00541"},{"key":"8_CR39","doi-asserted-by":"crossref","unstructured":"Wang, G., Chen, Z., Loy, C.C., Liu, Z.: SparseNeRF: distilling depth ranking for few-shot novel view synthesis. arXiv preprint arXiv:2303.16196 (2023)","DOI":"10.1109\/ICCV51070.2023.00832"},{"key":"8_CR40","unstructured":"Wang, H., Xu, Q., Chen, H., Ma, R.: PGAHum: prior-guided geometry and appearance learning for high-fidelity animatable human reconstruction. arXiv preprint arXiv:2404.13862 (2024)"},{"key":"8_CR41","doi-asserted-by":"crossref","unstructured":"Wang, H., Du, X., Li, J., Yeh, R.A., Shakhnarovich, G.: Score Jacobian chaining: lifting pretrained 2D diffusion models for 3d generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12619\u201312629 (2023)","DOI":"10.1109\/CVPR52729.2023.01214"},{"key":"8_CR42","unstructured":"Wang, P., Liu, L., Liu, Y., Theobalt, C., Komura, T., Wang, W.: NeuS: learning neural implicit surfaces by volume rendering for multi-view reconstruction. In: Advances in Neural Information Processing Systems (2021)"},{"issue":"4","key":"8_CR43","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"8_CR44","doi-asserted-by":"crossref","unstructured":"Wu, R., et\u00a0al.: Reconfusion: 3D reconstruction with diffusion priors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 21551\u201321561 (2024)","DOI":"10.1109\/CVPR52733.2024.02036"},{"key":"8_CR45","doi-asserted-by":"crossref","unstructured":"Wynn, J., Turmukhambetov, D.: DiffusioNeRF: regularizing neural radiance fields with denoising diffusion models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4180\u20134189 (2023)","DOI":"10.1109\/CVPR52729.2023.00407"},{"key":"8_CR46","unstructured":"Xu, J., et al.: PR-Neus: a prior-based residual learning paradigm for fast multi-view neural surface reconstruction. arXiv preprint arXiv:2312.11577 (2023)"},{"issue":"4","key":"8_CR47","first-page":"4945","volume":"45","author":"Q Xu","year":"2022","unstructured":"Xu, Q., Kong, W., Tao, W., Pollefeys, M.: Multi-scale geometric consistency guided and planar prior assisted multi-view stereo. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4945\u20134963 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR48","doi-asserted-by":"crossref","unstructured":"Xu, Q., Tao, W.: Multi-scale geometric consistency guided multi-view stereo. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5483\u20135492 (2019)","DOI":"10.1109\/CVPR.2019.00563"},{"key":"8_CR49","doi-asserted-by":"crossref","unstructured":"Yang, J., Pavone, M., Wang, Y.: FreeNeRF: improving few-shot neural rendering with free frequency regularization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8254\u20138263 (2023)","DOI":"10.1109\/CVPR52729.2023.00798"},{"key":"8_CR50","unstructured":"Yariv, L., Gu, J., Kasten, Y., Lipman, Y.: Volume rendering of neural implicit surfaces. In: Advances in Neural Information Processing Systems, vol. 34, pp. 4805\u20134815 (2021)"},{"key":"8_CR51","doi-asserted-by":"crossref","unstructured":"Yi, X., Wu, Z., Xu, Q., Zhou, P., Lim, J.H., Zhang, H.: Diffusion time-step curriculum for one image to 3D generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9948\u20139958 (2024)","DOI":"10.1109\/CVPR52733.2024.00949"},{"key":"8_CR52","doi-asserted-by":"crossref","unstructured":"Yu, A., Ye, V., Tancik, M., Kanazawa, A.: pixelNeRF: neural radiance fields from one or few images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4578\u20134587 (2021)","DOI":"10.1109\/CVPR46437.2021.00455"},{"key":"8_CR53","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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (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\/978-3-031-72848-8_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T14:06:08Z","timestamp":1732802768000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72848-8_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"ISBN":["9783031728471","9783031728488"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72848-8_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,29]]},"assertion":[{"value":"29 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"}}]}}