{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T15:08:21Z","timestamp":1769267301255,"version":"3.49.0"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031726699","type":"print"},{"value":"9783031726705","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"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\/978-3-031-72670-5_9","type":"book-chapter","created":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T07:01:50Z","timestamp":1727593310000},"page":"148-165","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["NGP-RT: Fusing Multi-level Hash Features with\u00a0Lightweight Attention for\u00a0Real-Time Novel View Synthesis"],"prefix":"10.1007","author":[{"given":"Yubin","family":"Hu","sequence":"first","affiliation":[]},{"given":"Xiaoyang","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Jingwei","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Yong-Jin","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","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: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, 10\u201317 October 2021, pp. 5835\u20135844. IEEE (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00580","DOI":"10.1109\/ICCV48922.2021.00580"},{"key":"9_CR2","doi-asserted-by":"publisher","unstructured":"Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-Nerf 360: unbounded anti-aliased neural radiance fields. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, 18\u201324 June 2022, pp. 5460\u20135469. IEEE (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00539","DOI":"10.1109\/CVPR52688.2022.00539"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Zip-Nerf: anti-aliased grid-based neural radiance fields. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01804"},{"key":"9_CR4","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_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/978-3-031-19824-3_20","volume-title":"Computer Vision - ECCV 2022, Part XXXII","author":"A Chen","year":"2022","unstructured":"Chen, A., Xu, Z., Geiger, A., Yu, J., Su, H.: Tensorf: tensorial radiance fields. In: Avidan, S., Brostow, G.J., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part XXXII. LNCS, vol. 13692, pp. 333\u2013350. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19824-3_20"},{"key":"9_CR6","doi-asserted-by":"publisher","unstructured":"Chen, Z., Funkhouser, T.A., Hedman, P., Tagliasacchi, A.: MobileNerf: exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, 17\u201324 June 2023, pp. 16569\u201316578. IEEE (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.01590","DOI":"10.1109\/CVPR52729.2023.01590"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Deng, C.L., Tartaglione, E.: Compressing explicit voxel grid representations: fast nerfs become also small. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1236\u20131245 (2023)","DOI":"10.1109\/WACV56688.2023.00129"},{"key":"9_CR8","doi-asserted-by":"publisher","unstructured":"Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., Kanazawa, A.: Plenoxels: radiance fields without neural networks. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, 18\u201324 June 2022, pp. 5491\u20135500. IEEE (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00542","DOI":"10.1109\/CVPR52688.2022.00542"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Gao, Q., Xu, Q., Su, H., Neumann, U., Xu, Z.: Strivec: sparse tri-vector radiance fields. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 17569\u201317579 (2023)","DOI":"10.1109\/ICCV51070.2023.01611"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Garbin, S.J., Kowalski, M., Johnson, M., Shotton, J., Valentin, J.: Fastnerf: high-fidelity neural rendering at 200fps. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 14346\u201314355 (2021)","DOI":"10.1109\/ICCV48922.2021.01408"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"He, Y., et al.: MMPI: a flexible radiance field representation by multiple multi-plane images blending. arXiv preprint arXiv:2310.00249 (2023)","DOI":"10.1109\/ICRA57147.2024.10611248"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Hedman, P., Srinivasan, P.P., Mildenhall, B., Barron, J.T., Debevec, P.: Baking neural radiance fields for real-time view synthesis. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5875\u20135884 (2021)","DOI":"10.1109\/ICCV48922.2021.00582"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Hu, D., Zhang, Z., Hou, T., Liu, T., Fu, H., Gong, M.: Multiscale representation for real-time anti-aliasing neural rendering. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 17772\u201317783 (2023)","DOI":"10.1109\/ICCV51070.2023.01629"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Hu, T., Liu, S., Chen, Y., Shen, T., Jia, J.: Efficientnerf efficient neural radiance fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12902\u201312911 (2022)","DOI":"10.1109\/CVPR52688.2022.01256"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Hu, W., et al.: Tri-miprf: tri-mip representation for efficient anti-aliasing neural radiance fields. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 19774\u201319783 (2023)","DOI":"10.1109\/ICCV51070.2023.01811"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Kaneko, T.: Mimo-nerf: fast neural rendering with multi-input multi-output neural radiance fields. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3273\u20133283 (2023)","DOI":"10.1109\/ICCV51070.2023.00303"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Karnewar, A., Ritschel, T., Wang, O., Mitra, N.: Relu fields: the little non-linearity that could. In: ACM SIGGRAPH 2022 Conference Proceedings, pp.\u00a01\u20139 (2022)","DOI":"10.1145\/3528233.3530707"},{"key":"9_CR18","doi-asserted-by":"publisher","unstructured":"Kerbl, B., Kopanas, G., Leimk\u00fchler, T., Drettakis, G.: 3D gaussian splatting for real-time radiance field rendering. ACM Trans. Graph. 42(4), 139:1\u2013139:14 (2023). https:\/\/doi.org\/10.1145\/3592433","DOI":"10.1145\/3592433"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Kocabas, M., Chang, J.H.R., Gabriel, J., Tuzel, O., Ranjan, A.: Hugs: human gaussian splats. arXiv preprint arXiv:2311.17910 (2023)","DOI":"10.1109\/CVPR52733.2024.00055"},{"key":"9_CR20","unstructured":"Kohler, J., et al.: fMPI: fast novel view synthesis in the wild with layered scene representations. arXiv preprint arXiv:2312.16109 (2023)"},{"key":"9_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1007\/978-3-031-19790-1_16","volume-title":"Computer Vision - ECCV 2022","author":"A Kurz","year":"2022","unstructured":"Kurz, A., Neff, T., Lv, Z., Zollh\u00f6fer, M., Steinberger, M.: Adanerf: adaptive sampling for real-time rendering of neural radiance fields. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13677, pp. 254\u2013270. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19790-1_16"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Li, S., Li, H., Wang, Y., Liao, Y., Yu, L.: Steernerf: accelerating nerf rendering via smooth viewpoint trajectory. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20701\u201320711 (2023)","DOI":"10.1109\/CVPR52729.2023.01983"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Lin, Z.H., Ma, W.C., Hsu, H.Y., Wang, Y.C.F., Wang, S.: Neurmips: neural mixture of planar experts for view synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15702\u201315712 (2022)","DOI":"10.1109\/CVPR52688.2022.01525"},{"issue":"2","key":"9_CR24","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. Vis. Comput. Graph. 1(2), 99\u2013108 (1995)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"9_CR25","series-title":"Lecture Notes in Computer Science()","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/978-3-030-58452-8_24","volume-title":"Computer Vision - ECCV 2020","author":"B Mildenhall","year":"2020","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: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 405\u2013421. Springer, Cham (2020)"},{"issue":"4","key":"9_CR26","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_CR27","doi-asserted-by":"crossref","unstructured":"Neff, T., et al.: Donerf: towards real-time rendering of compact neural radiance fields using depth oracle networks. In: Computer Graphics Forum, vol.\u00a040, pp. 45\u201359. Wiley Online Library (2021)","DOI":"10.1111\/cgf.14340"},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Piala, M., Clark, R.: Terminerf: ray termination prediction for efficient neural rendering. In: 2021 International Conference on 3D Vision (3DV), pp. 1106\u20131114. IEEE (2021)","DOI":"10.1109\/3DV53792.2021.00118"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Rakhimov, R., Ardelean, A.T., Lempitsky, V., Burnaev, E.: Npbg++: accelerating neural point-based graphics. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15969\u201315979 (2022)","DOI":"10.1109\/CVPR52688.2022.01550"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Reiser, C., Peng, S., Liao, Y., Geiger, A.: Kilonerf: speeding up neural radiance fields with thousands of tiny MLPs. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 14335\u201314345 (2021)","DOI":"10.1109\/ICCV48922.2021.01407"},{"issue":"4","key":"9_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3592426","volume":"42","author":"C Reiser","year":"2023","unstructured":"Reiser, C., et al.: Merf: memory-efficient radiance fields for real-time view synthesis in unbounded scenes. ACM Trans. Graph. (TOG) 42(4), 1\u201312 (2023)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Riegler, G., Koltun, V.: Stable view synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12216\u201312225 (2021)","DOI":"10.1109\/CVPR46437.2021.01204"},{"key":"9_CR33","doi-asserted-by":"crossref","unstructured":"Rojas, S., et al.: Re-rend: real-time rendering of nerfs across devices. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3632\u20133641 (2023)","DOI":"10.1109\/ICCV51070.2023.00336"},{"key":"9_CR34","doi-asserted-by":"publisher","unstructured":"Sun, C., Sun, M., Chen, H.: Direct voxel grid optimization: super-fast convergence for radiance fields reconstruction. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, 18\u201324 June 2022, pp. 5449\u20135459. IEEE (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00538","DOI":"10.1109\/CVPR52688.2022.00538"},{"key":"9_CR35","unstructured":"Tang, J., Chen, X., Wang, J., Zeng, G.: Compressible-composable nerf via rank-residual decomposition. In: Advances in Neural Information Processing Systems, vol. 35, pp. 14798\u201314809 (2022)"},{"key":"9_CR36","doi-asserted-by":"crossref","unstructured":"Tang, J., et al.: Delicate textured mesh recovery from nerf via adaptive surface refinement. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 17739\u201317749 (2023)","DOI":"10.1109\/ICCV51070.2023.01626"},{"key":"9_CR37","doi-asserted-by":"crossref","unstructured":"Wadhwani, K., Kojima, T.: Squeezenerf: further factorized fastnerf for memory-efficient inference. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2717\u20132725 (2022)","DOI":"10.1109\/CVPRW56347.2022.00307"},{"key":"9_CR38","doi-asserted-by":"crossref","unstructured":"Wan, Z., et\u00a0al.: Learning neural duplex radiance fields for real-time view synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8307\u20138316 (2023)","DOI":"10.1109\/CVPR52729.2023.00803"},{"key":"9_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1007\/978-3-031-19821-2_35","volume-title":"Computer Vision - ECCV 2022","author":"H Wang","year":"2022","unstructured":"Wang, H., et al.: R2L: distilling neural radiance field to neural light field for efficient novel view synthesis. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13691, pp. 612\u2013629. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19821-2_35"},{"key":"9_CR40","doi-asserted-by":"crossref","unstructured":"Wang, P., et al.: F2-nerf: fast neural radiance field training with free camera trajectories. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4150\u20134159 (2023)","DOI":"10.1109\/CVPR52729.2023.00404"},{"key":"9_CR41","unstructured":"Wang, Z., Li, L., Shen, Z., Shen, L., Bo, L.: 4k-nerf: high fidelity neural radiance fields at ultra high resolutions. arXiv preprint arXiv:2212.04701 (2022)"},{"key":"9_CR42","doi-asserted-by":"crossref","unstructured":"Wu, X., et al.: Scalable neural indoor scene rendering. ACM Trans. Graph. 41(4) (2022)","DOI":"10.1145\/3528223.3530153"},{"key":"9_CR43","doi-asserted-by":"crossref","unstructured":"Xie, X., Gherardi, R., Pan, Z., Huang, S.: Hollownerf: pruning hashgrid-based nerfs with trainable collision mitigation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3480\u20133490 (2023)","DOI":"10.1109\/ICCV51070.2023.00322"},{"key":"9_CR44","doi-asserted-by":"publisher","unstructured":"Yan, H., Liu, C., Ma, C., Mei, X.: Plen-vdb: memory efficient VDB-based radiance fields for fast training and rendering. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, 17\u201324 June 2023, pp. 88\u201396. IEEE (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.00017","DOI":"10.1109\/CVPR52729.2023.00017"},{"key":"9_CR45","doi-asserted-by":"publisher","unstructured":"Yariv, L., et al.: BakedSDF: meshing neural sdfs for real-time view synthesis. In: Brunvand, E., Sheffer, A., Wimmer, M. (eds.) ACM SIGGRAPH 2023 Conference Proceedings, SIGGRAPH 2023, Los Angeles, CA, USA, 6\u201310 August 2023, pp. 46:1\u201346:9. ACM (2023). https:\/\/doi.org\/10.1145\/3588432.3591536","DOI":"10.1145\/3588432.3591536"},{"key":"9_CR46","doi-asserted-by":"crossref","unstructured":"Yu, A., Li, R., Tancik, M., Li, H., Ng, R., Kanazawa, A.: Plenoctrees for real-time rendering of neural radiance fields. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5752\u20135761 (2021)","DOI":"10.1109\/ICCV48922.2021.00570"},{"key":"9_CR47","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1007\/978-3-031-19784-0_42","volume-title":"Computer Vision - ECCV 2022","author":"J Zhang","year":"2022","unstructured":"Zhang, J., et al.: Digging into radiance grid for real-time view synthesis with detail preservation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13675, pp. 724\u2013740. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19784-0_42"},{"key":"9_CR48","unstructured":"Zhang, K., Riegler, G., Snavely, N., Koltun, V.: Nerf++: analyzing and improving neural radiance fields. CoRR abs\/2010.07492 (2020). https:\/\/arxiv.org\/abs\/2010.07492"},{"key":"9_CR49","doi-asserted-by":"crossref","unstructured":"Zou, Z.X., et al.: Triplane meets gaussian splatting: fast and generalizable single-view 3d reconstruction with transformers. arXiv preprint arXiv:2312.09147 (2023)","DOI":"10.1109\/CVPR52733.2024.00983"}],"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\/978-3-031-72670-5_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T07:16:21Z","timestamp":1727594181000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72670-5_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"ISBN":["9783031726699","9783031726705"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72670-5_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,30]]},"assertion":[{"value":"30 September 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"}}]}}