{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T10:57:52Z","timestamp":1765018672005,"version":"3.46.0"},"publisher-location":"Cham","reference-count":57,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031736353"},{"type":"electronic","value":"9783031736360"}],"license":[{"start":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T00:00:00Z","timestamp":1730764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T00:00:00Z","timestamp":1730764800000},"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\/978-3-031-73636-0_26","type":"book-chapter","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T15:06:16Z","timestamp":1730732776000},"page":"445-463","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Improving Neural Surface Reconstruction with\u00a0Feature Priors from\u00a0Multi-view Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8175-7392","authenticated-orcid":false,"given":"Xinlin","family":"Ren","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3916-2843","authenticated-orcid":false,"given":"Chenjie","family":"Cao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6595-6893","authenticated-orcid":false,"given":"Yanwei","family":"Fu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4897-9209","authenticated-orcid":false,"given":"Xiangyang","family":"Xue","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,5]]},"reference":[{"issue":"3","key":"26_CR1","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1145\/1531326.1531330","volume":"28","author":"C Barnes","year":"2009","unstructured":"Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. 28(3), 24 (2009)","journal-title":"ACM Trans. Graph."},{"key":"26_CR2","unstructured":"Cao, C., Ren, X., Fu, Y.: MVSFormer: multi-view stereo by learning robust image features and temperature-based depth. Transactions of Machine Learning Research (2022)"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Chen, D., Zhang, P., Feldmann, I., Schreer, O., Eisert, P.: Recovering fine details for neural implicit surface reconstruction. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 4330\u20134339 (2023)","DOI":"10.1109\/WACV56688.2023.00431"},{"key":"26_CR4","first-page":"9355","volume":"34","author":"X Chu","year":"2021","unstructured":"Chu, X., et al.: Twins: revisiting the design of spatial attention in vision transformers. Adv. Neural. Inf. Process. Syst. 34, 9355\u20139366 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Dai, Y., Zhu, Z., Rao, Z., Li, B.: Mvs2: Deep unsupervised multi-view stereo with multi-view symmetry. In: 2019 International Conference on 3D Vision (3DV), pp.\u00a01\u20138. IEEE (2019)","DOI":"10.1109\/3DV.2019.00010"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Darmon, F., Bascle, B., Devaux, J.C., Monasse, P., Aubry, M.: Improving neural implicit surfaces geometrym fan with patch warping. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6260\u20136269 (2022)","DOI":"10.1109\/CVPR52688.2022.00616"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognitio, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Drebin, R.A., Carpenter, L., Hanrahan, P.: Volume rendering. ACM Siggraph Comput. Graph. 22(4), 65\u201374 (1988)","DOI":"10.1145\/378456.378484"},{"key":"26_CR9","unstructured":"Fu, Q., Xu, Q., Ong, Y.S., Tao, W.: Geo-neus: geometry-consistent neural implicit surfaces learning for multi-view reconstruction. arXiv preprint arXiv:2205.15848 (2022)"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Galliani, S., Lasinger, K., Schindler, K.: Massively parallel multiview stereopsis by surface normal diffusion. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 873\u2013881 (2015)","DOI":"10.1109\/ICCV.2015.106"},{"key":"26_CR11","unstructured":"Gao, P., Ma, T., Li, H., Dai, J., Qiao, Y.: Convmae: masked convolution meets masked autoencoders. arXiv preprint arXiv:2205.03892 (2022)"},{"key":"26_CR12","unstructured":"Gropp, A., Yariv, L., Haim, N., Atzmon, M., Lipman, Y.: Implicit geometric regularization for learning shapes. arXiv preprint arXiv:2002.10099 (2020)"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Gu, X., Fan, Z., Zhu, S., Dai, Z., Tan, F., Tan, P.: Cascade cost volume for high-resolution multi-view stereo and stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2495\u20132504 (2020)","DOI":"10.1109\/CVPR42600.2020.00257"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Guo, H., et al.: Neural 3d scene reconstruction with the manhattan-world assumption. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5511\u20135520 (2022)","DOI":"10.1109\/CVPR52688.2022.00543"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"26_CR16","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"},{"issue":"3","key":"26_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2487228.2487237","volume":"32","author":"M Kazhdan","year":"2013","unstructured":"Kazhdan, M., Hoppe, H.: Screened poisson surface reconstruction. ACM Trans. Graph. (ToG) 32(3), 1\u201313 (2013)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"26_CR18","unstructured":"Khot, T., Agrawal, S., Tulsiani, S., Mertz, C., Lucey, S., Hebert, M.: Learning unsupervised multi-view stereopsis via robust photometric consistency. arXiv preprint arXiv:1905.02706 (2019)"},{"key":"26_CR19","unstructured":"Kim, D., Ka, W., Ahn, P., Joo, D., Chun, S., Kim, J.: Global-local path networks for monocular depth estimation with vertical cutdepth. arXiv preprint arXiv:2201.07436 (2022)"},{"key":"26_CR20","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Labatut, P., Pons, J.P., Keriven, R.: Efficient multi-view reconstruction of large-scale scenes using interest points, delaunay triangulation and graph cuts. In: 2007 IEEE 11th International Conference on Computer Vision, pp.\u00a01\u20138. IEEE (2007)","DOI":"10.1109\/ICCV.2007.4408892"},{"key":"26_CR22","doi-asserted-by":"crossref","unstructured":"Li, Z., et al: Neuralangelo: High-fidelity neural surface reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8456\u20138465 (2023)","DOI":"10.1109\/CVPR52729.2023.00817"},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Li, Z., Snavely, N.: Megadepth: learning single-view depth prediction from internet photos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2041\u20132050 (2018)","DOI":"10.1109\/CVPR.2018.00218"},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"Lipson, L., Teed, Z., Deng, J.: Raft-stereo: multilevel recurrent field transforms for stereo matching. In: 2021 International Conference on 3D Vision (3DV), pp. 218\u2013227. IEEE (2021)","DOI":"10.1109\/3DV53792.2021.00032"},{"key":"26_CR25","doi-asserted-by":"crossref","unstructured":"Liu, S., Zhang, Y., Peng, S., Shi, B., Pollefeys, M., Cui, Z.: Dist: rendering deep implicit signed distance function with differentiable sphere tracing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2019\u20132028 (2020)","DOI":"10.1109\/CVPR42600.2020.00209"},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"26_CR27","doi-asserted-by":"crossref","unstructured":"Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3d reconstruction in function space. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and pattern recognition, pp. 4460\u20134470 (2019)","DOI":"10.1109\/CVPR.2019.00459"},{"issue":"1","key":"26_CR28","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"},{"issue":"4","key":"26_CR29","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)"},{"issue":"5","key":"26_CR30","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1109\/TRO.2017.2705103","volume":"33","author":"R Mur-Artal","year":"2017","unstructured":"Mur-Artal, R., Tard\u00f3s, J.D.: Orb-slam2: an open-source slam system for monocular, stereo, and RGB -d cameras. IEEE Trans. Rob. 33(5), 1255\u20131262 (2017)","journal-title":"IEEE Trans. Rob."},{"key":"26_CR31","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3504\u20133515 (2020)","DOI":"10.1109\/CVPR42600.2020.00356"},{"key":"26_CR32","doi-asserted-by":"crossref","unstructured":"Oechsle, M., Peng, S., Geiger, A.: Unisurf: unifying neural implicit surfaces and radiance fields for multi-view reconstruction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5589\u20135599 (2021)","DOI":"10.1109\/ICCV48922.2021.00554"},{"issue":"3","key":"26_CR33","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1109\/TPAMI.2020.3019967","volume":"44","author":"R Ranftl","year":"2020","unstructured":"Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., Koltun, V.: Towards robust monocular depth estimation: mixing datasets for zero-shot cross-dataset transfer. IEEE Trans. Pattern Anal. Mach. Intell. 44(3), 1623\u20131637 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"26_CR34","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"},{"key":"26_CR35","doi-asserted-by":"publisher","unstructured":"Sch\u00f6nberger, J.L., Zheng, E., Frahm, JM., Pollefeys, M.: Pixelwise view selection for unstructured multi-view stereo. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision - ECCV 2016, ECCV 2016, Part III, LNCS, vol. 9907, pp. 501\u2013518. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_31","DOI":"10.1007\/978-3-319-46487-9_31"},{"key":"26_CR36","doi-asserted-by":"crossref","unstructured":"Strecha, C., Von\u00a0Hansen, W., Van\u00a0Gool, L., Fua, P., Thoennessen, U.: On benchmarking camera calibration and multi-view stereo for high resolution imagery. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.\u00a01\u20138. IEEE (2008)","DOI":"10.1109\/CVPR.2008.4587706"},{"key":"26_CR37","doi-asserted-by":"crossref","unstructured":"Sun, C., Sun, M., Chen, H.T.: Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5459\u20135469 (2022)","DOI":"10.1109\/CVPR52688.2022.00538"},{"key":"26_CR38","unstructured":"Tang, S., Zhang, J., Zhu, S., Tan, P.: Quadtree attention for vision transformers. In: ICLR (2022)"},{"key":"26_CR39","unstructured":"Teed, Z., Deng, J.: Deepv2d: video to depth with differentiable structure from motion. arXiv preprint arXiv:1812.04605 (2018)"},{"key":"26_CR40","unstructured":"Vijayanarasimhan, S., Ricco, S., Schmid, C., Sukthankar, R., Fragkiadaki, K.: Sfm-net: learning of structure and motion from video. arXiv preprint arXiv:1704.07804 (2017)"},{"key":"26_CR41","doi-asserted-by":"crossref","unstructured":"Wang, J., Bleja, T., Agapito, L.: Go-surf: neural feature grid optimization for fast, high-fidelity RGB-d surface reconstruction. arXiv preprint arXiv:2206.14735 (2022)","DOI":"10.1109\/3DV57658.2022.00055"},{"key":"26_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. arXiv preprint arXiv:2106.10689 (2021)"},{"key":"26_CR43","first-page":"1966","volume":"35","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Skorokhodov, I., Wonka, P.: HF-NeuS: improved surface reconstruction using high-frequency details. Adv. Neural. Inf. Process. Syst. 35, 1966\u20131978 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"26_CR44","doi-asserted-by":"crossref","unstructured":"Wang, Y., Skorokhodov, I., Wonka, P.: Pet-neus: positional encoding tri-planes for neural surfaces. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12598\u201312607 (2023)","DOI":"10.1109\/CVPR52729.2023.01212"},{"issue":"4","key":"26_CR45","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":"26_CR46","unstructured":"Weinzaepfel, P., et al.: Croco: self-supervised pre-training for 3d vision tasks by cross-view completion. arXiv preprint arXiv:2210.10716 (2022)"},{"key":"26_CR47","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. Adv. Neural. Inf. Process. Syst. 34, 12077\u201312090 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"26_CR48","doi-asserted-by":"crossref","unstructured":"Xu, G., Wang, X., Ding, X., Yang, X.: Iterative geometry encoding volume for stereo matching. arXiv preprint arXiv:2303.06615 (2023)","DOI":"10.1109\/CVPR52729.2023.02099"},{"key":"26_CR49","unstructured":"Xu, Q., Wang, W., Ceylan, D., Mech, R., Neumann, U.: Disn: deep implicit surface network for high-quality single-view 3d reconstruction. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"26_CR50","doi-asserted-by":"crossref","unstructured":"Yang, Z., Wang, P., Xu, W., Zhao, L., Nevatia, R.: Unsupervised learning of geometry from videos with edge-aware depth-normal consistency. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a032 (2018)","DOI":"10.1609\/aaai.v32i1.12257"},{"key":"26_CR51","first-page":"4805","volume":"34","author":"L Yariv","year":"2021","unstructured":"Yariv, L., Gu, J., Kasten, Y., Lipman, Y.: Volume rendering of neural implicit surfaces. Adv. Neural. Inf. Process. Syst. 34, 4805\u20134815 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"26_CR52","first-page":"2492","volume":"33","author":"L Yariv","year":"2020","unstructured":"Yariv, L., et al.: Multiview neural surface reconstruction by disentangling geometry and appearance. Adv. Neural. Inf. Process. Syst. 33, 2492\u20132502 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"26_CR53","doi-asserted-by":"crossref","unstructured":"Yi, X., Zhou, Y., Habermann, M., Golyanik, V., Pan, S., Theobalt, C., Xu, F.: Egolocate: real-time motion capture, localization, and mapping with sparse body-mounted sensors. arXiv preprint arXiv:2305.01599 (2023)","DOI":"10.1145\/3592099"},{"key":"26_CR54","unstructured":"Yu, Z., Peng, S., Niemeyer, M., Sattler, T., Geiger, A.: Monosdf: exploring monocular geometric cues for neural implicit surface reconstruction. arXiv preprint arXiv:2206.00665 (2022)"},{"key":"26_CR55","doi-asserted-by":"crossref","unstructured":"Zhang, J., Yao, Y., Li, S., Luo, Z., Fang, T.: Visibility-aware multi-view stereo network. arXiv preprint arXiv:2008.07928 (2020)","DOI":"10.5244\/C.34.109"},{"key":"26_CR56","doi-asserted-by":"crossref","unstructured":"Zhang, J., Yao, Y., Quan, L.: Learning signed distance field for multi-view surface reconstruction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6525\u20136534 (2021)","DOI":"10.1109\/ICCV48922.2021.00646"},{"key":"26_CR57","doi-asserted-by":"crossref","unstructured":"Zhang, K., Luan, F., Wang, Q., Bala, K., Snavely, N.: PhySG: inverse rendering with spherical gaussians for physics-based material editing and relighting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5453\u20135462 (2021)","DOI":"10.1109\/CVPR46437.2021.00541"}],"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\/978-3-031-73636-0_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T10:53:33Z","timestamp":1765018413000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73636-0_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,5]]},"ISBN":["9783031736353","9783031736360"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73636-0_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,5]]},"assertion":[{"value":"5 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"}}]}}