{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T03:04:49Z","timestamp":1763348689791,"version":"3.40.3"},"publisher-location":"Cham","reference-count":55,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031727634"},{"type":"electronic","value":"9783031727641"}],"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\/978-3-031-72764-1_9","type":"book-chapter","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T14:03:10Z","timestamp":1729778590000},"page":"145-162","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Optimization Framework to\u00a0Enforce Multi-view Consistency for\u00a0Texturing 3D Meshes"],"prefix":"10.1007","author":[{"given":"Zhengyi","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihao","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liefeng","family":"Bo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zilong","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qixing","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Cao, T., Kreis, K., Fidler, S., Sharp, N., Yin, K.: Texfusion: synthesizing 3d textures with text-guided image diffusion models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4169\u20134181 (2023)","DOI":"10.1109\/ICCV51070.2023.00385"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Chen, D.Z., Siddiqui, Y., Lee, H.Y., Tulyakov, S., Nie\u00dfner, M.: Text2tex: text-driven texture synthesis via diffusion models. arXiv preprint arXiv:2303.11396 (2023)","DOI":"10.1109\/ICCV51070.2023.01701"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Chen, R., Chen, Y., Jiao, N., Jia, K.: Fantasia3d: disentangling geometry and appearance for high-quality text-to-3d content creation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2023)","DOI":"10.1109\/ICCV51070.2023.02033"},{"key":"9_CR4","unstructured":"Chen, Y., Chen, R., Lei, J., Zhang, Y., Jia, K.: Tango: text-driven photorealistic and robust 3D stylization via lighting decomposition (2022)"},{"key":"9_CR5","doi-asserted-by":"publisher","unstructured":"Christie, M., Olivier, P., Normand, J.: Camera control in computer graphics. Comput. Graph. Forum 27(8), 2197\u20132218 (2008). https:\/\/doi.org\/10.1111\/j.1467-8659.2008.01181.x","DOI":"10.1111\/j.1467-8659.2008.01181.x"},{"key":"9_CR6","doi-asserted-by":"publisher","unstructured":"Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603\u2013619 (2002). https:\/\/doi.org\/10.1109\/34.1000236","DOI":"10.1109\/34.1000236"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Deitke, M., et al.: Objaverse: a universe of annotated 3D objects (2022)","DOI":"10.1109\/CVPR52729.2023.01263"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Deng, K., et al.: Flashtex: fast relightable mesh texturing with lightcontrolnet (2024)","DOI":"10.1007\/978-3-031-73383-3_6"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Gpld3d: latent diffusion of 3d shape generative models by enforcing geometric and physical priors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 56\u201366 (2024)","DOI":"10.1109\/CVPR52733.2024.00014"},{"key":"9_CR10","doi-asserted-by":"publisher","unstructured":"Dutagaci, H., Cheung, C.P., Godil, A.: A benchmark for best view selection of 3d objects. In: Proceedings of the ACM Workshop on 3D Object Retrieval (3DOR 2010), pp. 45\u201350. Association for Computing Machinery, New York (2010). https:\/\/doi.org\/10.1145\/1877808.1877819","DOI":"10.1145\/1877808.1877819"},{"key":"9_CR11","doi-asserted-by":"publisher","unstructured":"Efros, A.A., Freeman, W.T.: Image Quilting for Texture Synthesis and Transfer, 1st edn. Association for Computing Machinery, New York (2023). https:\/\/doi.org\/10.1145\/3596711.3596771","DOI":"10.1145\/3596711.3596771"},{"key":"9_CR12","unstructured":"Guo, Y., et al.: Decorate3d: text-driven high-quality texture generation for mesh decoration in the wild. In: Thirty-Seventh Conference on Neural Information Processing Systems (NeurIPS) (2023)"},{"key":"9_CR13","doi-asserted-by":"publisher","unstructured":"Hamdi, A., Giancola, S., Ghanem, B.: MVTN: multi-view transformation network for 3d shape recognition. In: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, 10\u201317 October 2021, pp. 1\u201311. IEEE (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00007","DOI":"10.1109\/ICCV48922.2021.00007"},{"key":"9_CR14","unstructured":"Hamdi, A., Giancola, S., Ghanem, B.: Voint cloud: multi-view point cloud representation for 3d understanding. In: The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, 1\u20135 May 2023. OpenReview.net (2023). https:\/\/openreview.net\/pdf?id=IpGgfpMucHj"},{"key":"9_CR15","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local Nash equilibrium. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"9_CR16","doi-asserted-by":"publisher","unstructured":"Kanezaki, A., Matsushita, Y., Nishida, Y.: Rotationnet: joint object categorization and pose estimation using multiviews from unsupervised viewpoints. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, 18\u201322 June 2018, pp. 5010\u20135019. Computer Vision Foundation\/IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00526","DOI":"10.1109\/CVPR.2018.00526"},{"key":"9_CR17","doi-asserted-by":"publisher","unstructured":"Kappes, J.H., et al.: A comparative study of modern inference techniques for structured discrete energy minimization problems. Int. J. Comput. Vision 1\u201330 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0809-x","DOI":"10.1007\/s11263-015-0809-x"},{"key":"9_CR18","doi-asserted-by":"publisher","unstructured":"Kim, S., Tai, Y., Lee, J., Park, J., Kweon, I.S.: Category-specific salient view selection via deep convolutional neural networks. Comput. Graph. Forum 36(8), 313\u2013328 (2017). https:\/\/doi.org\/10.1111\/cgf.13082","DOI":"10.1111\/cgf.13082"},{"key":"9_CR19","unstructured":"Knodt, J., Gao, X.: Consistent latent diffusion for mesh texturing (2023)"},{"key":"9_CR20","doi-asserted-by":"publisher","unstructured":"Kolmogorov, V.: Convergent tree-reweighted message passing for energy minimization. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1568\u20131583 (2006). https:\/\/doi.org\/10.1109\/TPAMI.2006.200","DOI":"10.1109\/TPAMI.2006.200"},{"key":"9_CR21","doi-asserted-by":"publisher","unstructured":"Kundu, A., et al.: Virtual multi-view fusion for 3d semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J. (eds.) ECCV 2020, Part XXIV. LNCS, vol. 12369, pp. 518\u2013535. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58586-0_31","DOI":"10.1007\/978-3-030-58586-0_31"},{"key":"9_CR22","doi-asserted-by":"publisher","unstructured":"Lee, C.H., Varshney, A., Jacobs, D.W.: Mesh saliency. In: ACM SIGGRAPH 2005 Papers (SIGGRAPH 2005), pp. 659\u2013666. Association for Computing Machinery, New York (2005). https:\/\/doi.org\/10.1145\/1186822.1073244","DOI":"10.1145\/1186822.1073244"},{"key":"9_CR23","doi-asserted-by":"publisher","unstructured":"Leifman, G., Shtrom, E., Tal, A.: Surface regions of interest for viewpoint selection. IEEE Trans. Pattern Anal. Mach. Intell. 38(12), 2544\u20132556 (2016). https:\/\/doi.org\/10.1109\/TPAMI.2016.2522437","DOI":"10.1109\/TPAMI.2016.2522437"},{"key":"9_CR24","doi-asserted-by":"publisher","unstructured":"Liu, C., Yuen, J., Torralba, A.: SIFT flow: dense correspondence across scenes and its applications. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 978\u2013994 (2011). https:\/\/doi.org\/10.1109\/TPAMI.2010.147","DOI":"10.1109\/TPAMI.2010.147"},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Liu, R., Wu, R., Hoorick, B.V., Tokmakov, P., Zakharov, S., Vondrick, C.: Zero-1-to-3: zero-shot one image to 3d object. arXiv preprint arXiv:2303.11328 (2023)","DOI":"10.1109\/ICCV51070.2023.00853"},{"key":"9_CR26","unstructured":"Liu, Y., et al.: Syncdreamer: generating multiview-consistent images from a single-view image. arXiv preprint arXiv:2309.03453 (2023)"},{"key":"9_CR27","unstructured":"Liu, Y., Xie, M., Liu, H., Wong, T.T.: Text-guided texturing by synchronized multi-view diffusion. arXiv preprint arXiv:2311.12891 (2023)"},{"key":"9_CR28","doi-asserted-by":"publisher","unstructured":"Metzer, G., Richardson, E., Patashnik, O., Giryes, R., Cohen-Or, D.: Latent-nerf for shape-guided generation of 3d shapes and textures. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, 17\u201324 June 2023, pp. 12663\u201312673. IEEE (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.01218","DOI":"10.1109\/CVPR52729.2023.01218"},{"key":"9_CR29","doi-asserted-by":"publisher","unstructured":"Michel, O., Bar-On, R., Liu, R., Benaim, S., Hanocka, R.: Text2mesh: text-driven neural stylization for meshes. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022). https:\/\/doi.org\/10.1109\/cvpr52688.2022.01313","DOI":"10.1109\/cvpr52688.2022.01313"},{"key":"9_CR30","doi-asserted-by":"publisher","unstructured":"Mohammad\u00a0Khalid, N., Xie, T., Belilovsky, E., Popa, T.: Clip-mesh: generating textured meshes from text using pretrained image-text models. In: SIGGRAPH Asia 2022 Conference Papers (2022). https:\/\/doi.org\/10.1145\/3550469.3555392","DOI":"10.1145\/3550469.3555392"},{"key":"9_CR31","unstructured":"Poole, B., Jain, A., Barron, J.T., Mildenhall, B.: Dreamfusion: text-to-3d using 2d diffusion. In: The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, 1\u20135 May 2023. OpenReview.net (2023). https:\/\/openreview.net\/pdf?id=FjNys5c7VyY"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Qiu, L., et al.: Richdreamer: a generalizable normal-depth diffusion model for detail richness in text-to-3d. arXiv preprint arXiv:2311.16918 (2023)","DOI":"10.1109\/CVPR52733.2024.00946"},{"key":"9_CR33","doi-asserted-by":"publisher","unstructured":"Richardson, E., Metzer, G., Alaluf, Y., Giryes, R., Cohen-Or, D.: Texture: text-guided texturing of 3d shapes. In: ACM SIGGRAPH 2023 Conference Proceedings (SIGGRAPH 2023). Association for Computing Machinery, New York (2023). https:\/\/doi.org\/10.1145\/3588432.3591503","DOI":"10.1145\/3588432.3591503"},{"key":"9_CR34","doi-asserted-by":"publisher","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022). https:\/\/doi.org\/10.1109\/cvpr52688.2022.01042","DOI":"10.1109\/cvpr52688.2022.01042"},{"key":"9_CR35","unstructured":"Schuhmann, C., et al.: LAION-5B: an open large-scale dataset for training next generation image-text models. In: NeurIPS (2022). http:\/\/papers.nips.cc\/paper_files\/paper\/2022\/hash\/a1859debfb3b59d094f3504d5ebb6c25-Abstract-Datasets_and_Benchmarks.html"},{"key":"9_CR36","doi-asserted-by":"publisher","unstructured":"Secord, A., Lu, J., Finkelstein, A., Singh, M., Nealen, A.: Perceptual models of viewpoint preference. ACM Trans. Graph. 30(5), 1\u201312 (2011). https:\/\/doi.org\/10.1145\/2019627.2019628","DOI":"10.1145\/2019627.2019628"},{"key":"9_CR37","doi-asserted-by":"publisher","unstructured":". Sederberg, T.W., Parry, S.R.: Free-form deformation of solid geometric models. SIGGRAPH Comput. Graph. 20(4), 151\u2013160 (1986). https:\/\/doi.org\/10.1145\/15886.15903","DOI":"10.1145\/15886.15903"},{"key":"9_CR38","unstructured":"Shi, R., et al.: Zero123++: a single image to consistent multi-view diffusion base model"},{"key":"9_CR39","unstructured":"Shi, Y., Wang, P., Ye, J., Long, M., Li, K., Yang, X.: Mvdream: multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512 (2023)"},{"key":"9_CR40","doi-asserted-by":"publisher","unstructured":"Soltani, A.A., Huang, H., Wu, J., Kulkarni, T.D., Tenenbaum, J.B.: Synthesizing 3D shapes via modeling multi-view depth maps and silhouettes with deep generative networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, 21\u201326 July 2017, pp. 2511\u20132519. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.269","DOI":"10.1109\/CVPR.2017.269"},{"key":"9_CR41","doi-asserted-by":"publisher","unstructured":"Song, R., Zhang, W., Zhao, Y., Liu, Y.: Unsupervised multi-view CNN for salient view selection and 3d interest point detection. Int. J. Comput. Vis. 130(5), 1210\u20131227 (2022). https:\/\/doi.org\/10.1007\/s11263-022-01592-x","DOI":"10.1007\/s11263-022-01592-x"},{"key":"9_CR42","doi-asserted-by":"publisher","unstructured":"Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.G.: Multi-view convolutional neural networks for 3d shape recognition. In: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, 7\u201313 December 2015, pp. 945\u2013953. IEEE Computer Society (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.114","DOI":"10.1109\/ICCV.2015.114"},{"key":"9_CR43","doi-asserted-by":"publisher","unstructured":"Sun, Y., Huang, Q., Hsiao, D., Guan, L., Hua, G.: Learning view selection for 3d scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, Virtual, 19\u201325 June 2021, pp. 14464\u201314473. Computer Vision Foundation\/IEEE (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.01423","DOI":"10.1109\/CVPR46437.2021.01423"},{"key":"9_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1007\/978-3-319-46478-7_20","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Tatarchenko","year":"2016","unstructured":"Tatarchenko, M., Dosovitskiy, A., Brox, T.: Multi-view 3D models from single images with a convolutional network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 322\u2013337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_20"},{"key":"9_CR45","doi-asserted-by":"crossref","unstructured":"Tsalicoglou, C., Manhardt, F., Tonioni, A., Niemeyer, M., Tombari, F.: Textmesh: generation of realistic 3D meshes from text prompts (2023)","DOI":"10.1109\/3DV62453.2024.00154"},{"key":"9_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1007\/978-3-319-10602-1_54","volume-title":"Computer Vision \u2013 ECCV 2014","author":"M Waechter","year":"2014","unstructured":"Waechter, M., Moehrle, N., Goesele, M.: Let there be color! Large-scale texturing of 3D reconstructions. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 836\u2013850. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_54"},{"key":"9_CR47","doi-asserted-by":"publisher","unstructured":"Wei, X., Yu, R., Sun, J.: View-GCN: view-based graph convolutional network for 3d shape analysis. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, 13\u201319 June 2020, pp. 1847\u20131856. Computer Vision Foundation\/IEEE (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00192","DOI":"10.1109\/CVPR42600.2020.00192"},{"key":"9_CR48","unstructured":"Weng, H., et al.: Consistent123: improve consistency for one image to 3d object synthesis. arXiv preprint arXiv:2310.08092 (2023)"},{"key":"9_CR49","unstructured":"Xu, Y., et al.: DMV3D: denoising multi-view diffusion using 3d large reconstruction model (2023)"},{"key":"9_CR50","doi-asserted-by":"crossref","unstructured":"Ye, J., Wang, P., Li, K., Shi, Y., Wang, H.: Consistent-1-to-3: consistent image to 3D view synthesis via geometry-aware diffusion models (2023)","DOI":"10.1109\/3DV62453.2024.00027"},{"key":"9_CR51","doi-asserted-by":"crossref","unstructured":"Youwang, K., Oh, T.H., Pons-Moll, G.: Paint-it: text-to-texture synthesis via deep convolutional texture map optimization and physically-based rendering. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2024)","DOI":"10.1109\/CVPR52733.2024.00416"},{"key":"9_CR52","doi-asserted-by":"crossref","unstructured":"Yu, X., Dai, P., Li, W., Ma, L., Liu, Z., Qi, X.: Texture generation on 3d meshes with point-UV diffusion. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4206\u20134216 (2023)","DOI":"10.1109\/ICCV51070.2023.00388"},{"key":"9_CR53","doi-asserted-by":"crossref","unstructured":"Zeng, X., et al.: Paint3d: paint anything 3d with lighting-less texture diffusion models (2023)","DOI":"10.1109\/CVPR52733.2024.00407"},{"key":"9_CR54","doi-asserted-by":"crossref","unstructured":"Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3836\u20133847 (2023)","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"9_CR55","unstructured":"Zuo, Q., et al.: Videomv: consistent multi-view generation based on large video generative model (2024)"}],"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\/978-3-031-72764-1_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T06:27:24Z","timestamp":1732948044000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72764-1_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"ISBN":["9783031727634","9783031727641"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72764-1_9","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"}}]}}