{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T19:38:45Z","timestamp":1768073925970,"version":"3.49.0"},"publisher-location":"Cham","reference-count":59,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031915680","type":"print"},{"value":"9783031915697","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-3-031-91569-7_4","type":"book-chapter","created":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T12:50:03Z","timestamp":1748091003000},"page":"43-59","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["LanPose: Language-Instructed 6D Object Pose Estimation for\u00a0Robotic Assembly"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0002-1165","authenticated-orcid":false,"given":"Bowen","family":"Fu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7114-3445","authenticated-orcid":false,"given":"Sek Kun","family":"Leong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0671-8323","authenticated-orcid":false,"given":"Yan","family":"Di","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0759-0782","authenticated-orcid":false,"given":"Gu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0134-3809","authenticated-orcid":false,"given":"Jiwen","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5598-5212","authenticated-orcid":false,"given":"Federico","family":"Tombari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7333-9975","authenticated-orcid":false,"given":"Xiangyang","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"issue":"4","key":"4_CR1","doi-asserted-by":"publisher","first-page":"3308","DOI":"10.1109\/LRA.2018.2852786","volume":"3","author":"H Ahn","year":"2018","unstructured":"Ahn, H., Choi, S., Kim, N., Cha, G., Oh, S.: Interactive text2pickup networks for natural language-based human-robot collaboration. IEEE Rob. Autom. Lett. 3(4), 3308\u20133315 (2018)","journal-title":"IEEE Rob. Autom. Lett."},{"key":"4_CR2","doi-asserted-by":"crossref","unstructured":"Ahn, H., et al.: Visually grounding language instruction for history-dependent manipulation. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 675\u2013682. IEEE (2022)","DOI":"10.1109\/ICRA46639.2022.9812279"},{"key":"4_CR3","unstructured":"Ahn, M., et\u00a0al.: Do as i can, not as i say: grounding language in robotic affordances. arXiv preprint arXiv:2204.01691 (2022)"},{"key":"4_CR4","doi-asserted-by":"publisher","unstructured":"Cheang, C., Lin, H., Fu, Y., Xue, X.: Learning 6-DOF object poses to grasp category-level objects by language instructions. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 8476\u20138482 (2022). https:\/\/doi.org\/10.1109\/ICRA46639.2022.9811367","DOI":"10.1109\/ICRA46639.2022.9811367"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Chen, K., et al.: Sim-to-real 6D object pose estimation via iterative self-training for robotic bin-picking. In: ECCV (2022)","DOI":"10.1007\/978-3-031-19842-7_31"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Y., Xu, R., Lin, Y., Vela, P.A.: A joint network for grasp detection conditioned on natural language commands. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 4576\u20134582. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9561994"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Deng, X., Xiang, Y., Mousavian, A., Eppner, C., Bretl, T., Fox, D.: Self-supervised 6D object pose estimation for robot manipulation. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 3665\u20133671. IEEE (2020)","DOI":"10.1109\/ICRA40945.2020.9196714"},{"key":"4_CR8","unstructured":"Denninger, M., et al.: Blenderproc: reducing the reality gap with photorealistic rendering. In: International Conference on Robotics: Science and Systems, RSS 2020 (2020)"},{"key":"4_CR9","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Di, Y., Manhardt, F., Wang, G., Ji, X., Navab, N., Tombari, F.: So-pose: exploiting self-occlusion for direct 6D pose estimation. In: ICCV, pp. 12396\u201312405 (2021)","DOI":"10.1109\/ICCV48922.2021.01217"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Di, Y., Zhang, R., Lou, Z., Manhardt, F., Ji, X., Navab, N., Tombari, F.: GPV-Pose: category-level object pose estimation via geometry-guided point-wise voting. In: CVPR, pp. 6781\u20136791 (2022)","DOI":"10.1109\/CVPR52688.2022.00666"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Fu, B., Leong, S.K., Lian, X., Ji, X.: 6D robotic assembly based on RGB-only object pose estimation. In: 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4736\u20134742. IEEE (2022)","DOI":"10.1109\/IROS47612.2022.9982262"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Goodwin, W., Vaze, S., Havoutis, I., Posner, I.: Semantically grounded object matching for robust robotic scene rearrangement. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 11138\u201311144. IEEE (2022)","DOI":"10.1109\/ICRA46639.2022.9811817"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Goyal, A., Mousavian, A., Paxton, C., Chao, Y.W., Okorn, B., Deng, J., Fox, D.: IFOR: iterative flow minimization for robotic object rearrangement. In: CVPR, pp. 14787\u201314797 (2022)","DOI":"10.1109\/CVPR52688.2022.01437"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Hatori, J., et al.: Interactively picking real-world objects with unconstrained spoken language instructions. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 3774\u20133781. IEEE (2018)","DOI":"10.1109\/ICRA.2018.8460699"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Haugaard, R.L., Buch, A.G.: SurfEmb: dense and continuous correspondence distributions for object pose estimation with learnt surface embeddings. In: CVPR, pp. 6749\u20136758 (2022)","DOI":"10.1109\/CVPR52688.2022.00663"},{"issue":"2","key":"4_CR17","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1109\/TMI.2016.2620723","volume":"36","author":"C Hennersperger","year":"2017","unstructured":"Hennersperger, C., et al.: Towards MRI-based autonomous robotic us acquisitions: a first feasibility study. IEEE Trans. Med. Imaging 36(2), 538\u2013548 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR18","doi-asserted-by":"publisher","unstructured":"Hinterstoisser, S., et al.: Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In: ACCV, pp. 548\u2013562. Springer (2012). https:\/\/doi.org\/10.1007\/978-3-642-37331-2_42","DOI":"10.1007\/978-3-642-37331-2_42"},{"key":"4_CR19","doi-asserted-by":"publisher","unstructured":"Hoda\u0148, T., Matas, J., Obdr\u017e\u00e1lek, \u0160.: On evaluation of 6D object pose estimation. In: ECCV, pp. 606\u2013619. Springer (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_52","DOI":"10.1007\/978-3-319-49409-8_52"},{"key":"4_CR20","unstructured":"Hu, Y., Lin, F., Zhang, T., Yi, L., Gao, Y.: Look before you leap: unveiling the power of GPT-4V in robotic vision-language planning. arXiv preprint arXiv:2311.17842 (2023)"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Hu, Y., Fua, P., Wang, W., Salzmann, M.: Single-stage 6D object pose estimation. In: CVPR, pp. 2930\u20132939 (2020)","DOI":"10.1109\/CVPR46437.2021.01561"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Johnson, J., Hariharan, B., Van Der\u00a0Maaten, L., Fei-Fei, L., Lawrence\u00a0Zitnick, C., Girshick, R.: CLEVR: a diagnostic dataset for compositional language and elementary visual reasoning. In: CVPR, pp. 2901\u20132910 (2017)","DOI":"10.1109\/CVPR.2017.215"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Kamath, A., Singh, M., LeCun, Y., Synnaeve, G., Misra, I., Carion, N.: Mdetr-modulated detection for end-to-end multi-modal understanding. In: ICCV, pp. 1780\u20131790 (2021)","DOI":"10.1109\/ICCV48922.2021.00180"},{"key":"4_CR24","doi-asserted-by":"crossref","unstructured":"Kapelyukh, I., Ren, Y., Alzugaray, I., Johns, E.: Dream2Real: zero-shot 3D object rearrangement with vision-language models. In: First Workshop on Vision-Language Models for Navigation and Manipulation at ICRA 2024 (2024)","DOI":"10.1109\/ICRA57147.2024.10611220"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Kazemzadeh, S., Ordonez, V., Matten, M., Berg, T.: ReferitGame: referring to objects in photographs of natural scenes. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 787\u2013798 (2014)","DOI":"10.3115\/v1\/D14-1086"},{"key":"4_CR26","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"4_CR27","doi-asserted-by":"crossref","unstructured":"Labb\u00e9, Y., Carpentier, J., Aubry, M., Sivic, J.: CosyPOSE: consistent multi-view multi-object 6D pose estimation. In: ECCV, pp. 574\u2013591. Springer (2020)","DOI":"10.1007\/978-3-030-58520-4_34"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, G., Ji, X., Xiang, Y., Fox, D.: DeepIM: deep iterative matching for 6d pose estimation. In: ECCV, pp. 683\u2013698 (2018)","DOI":"10.1007\/978-3-030-01231-1_42"},{"key":"4_CR29","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, G., Ji, X.: CDPN: coordinates-based disentangled pose network for real-time RGB-based 6-DOF object pose estimation. In: ICCV, pp. 7678\u20137687 (2019)","DOI":"10.1109\/ICCV.2019.00777"},{"key":"4_CR30","doi-asserted-by":"crossref","unstructured":"Litvak, Y., Biess, A., Bar-Hillel, A.: Learning pose estimation for high-precision robotic assembly using simulated depth images. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 3521\u20133527. IEEE (2019)","DOI":"10.1109\/ICRA.2019.8794226"},{"key":"4_CR31","unstructured":"Liu, L., et al.: On the variance of the adaptive learning rate and beyond. In: ICLR (2020)"},{"key":"4_CR32","doi-asserted-by":"crossref","unstructured":"Liu, R., Liu, C., Bai, Y., Yuille, A.L.: CLEVR-Ref+: diagnosing visual reasoning with referring expressions. In: CVPR, pp. 4185\u20134194 (2019)","DOI":"10.1109\/CVPR.2019.00431"},{"key":"4_CR33","doi-asserted-by":"crossref","unstructured":"Liu, W., Paxton, C., Hermans, T., Fox, D.: StructFormer: learning spatial structure for language-guided semantic rearrangement of novel objects. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 6322\u20136329. IEEE (2022)","DOI":"10.1109\/ICRA46639.2022.9811931"},{"key":"4_CR34","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"4_CR35","doi-asserted-by":"crossref","unstructured":"Mao, J., Huang, J., Toshev, A., Camburu, O., Yuille, A.L., Murphy, K.: Generation and comprehension of unambiguous object descriptions. In: CVPR, pp. 11\u201320 (2016)","DOI":"10.1109\/CVPR.2016.9"},{"key":"4_CR36","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2021.XVII.070","volume-title":"Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks","author":"AS Morgan","year":"2021","unstructured":"Morgan, A.S., Wen, B., Liang, J., Boularias, A., Dollar, A.M., Bekris, K.: Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks. Science and Systems XVII, Robotics (2021)"},{"key":"4_CR37","doi-asserted-by":"crossref","unstructured":"Peng, S., Liu, Y., Huang, Q., Zhou, X., Bao, H.: PVNet: pixel-wise voting network for 6dof pose estimation. In: CVPR, pp. 4561\u20134570 (2019)","DOI":"10.1109\/CVPR.2019.00469"},{"key":"4_CR38","doi-asserted-by":"crossref","unstructured":"Rad, M., Lepetit, V.: BB8: a scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth. In: ICCV, pp. 3828\u20133836 (2017)","DOI":"10.1109\/ICCV.2017.413"},{"key":"4_CR39","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2018.XIV.028","volume-title":"Interactive visual grounding of referring expressions for human-robot interaction","author":"M Shridhar","year":"2018","unstructured":"Shridhar, M., Hsu, D.: Interactive visual grounding of referring expressions for human-robot interaction. Science and Systems (RSS), Robotics (2018)"},{"key":"4_CR40","unstructured":"Shridhar, M., Manuelli, L., Fox, D.: CLIPort: what and where pathways for robotic manipulation. In: Conference on Robot Learning, pp. 894\u2013906. PMLR (2022)"},{"issue":"2","key":"4_CR41","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1109\/LRA.2020.2967325","volume":"5","author":"S Stev\u0161i\u0107","year":"2020","unstructured":"Stev\u0161i\u0107, S., Christen, S., Hilliges, O.: Learning to assemble: estimating 6D poses for robotic object-object manipulation. IEEE Rob. Autom. Lett. 5(2), 1159\u20131166 (2020)","journal-title":"IEEE Rob. Autom. Lett."},{"key":"4_CR42","unstructured":"Tremblay, J., To, T., Sundaralingam, B., Xiang, Y., Fox, D., Birchfield, S.: Deep object pose estimation for semantic robotic grasping of household objects. In: Conference on Robot Learning (CoRL) (2018)"},{"key":"4_CR43","unstructured":"Vaswani, A., et al.: Attention is all you need. NeurIPS 30 (2017)"},{"key":"4_CR44","doi-asserted-by":"crossref","unstructured":"Wang, G., Manhardt, F., Tombari, F., Ji, X.: GDR-Net: geometry-guided direct regression network for monocular 6D object pose estimation. In: CVPR, pp. 16611\u201316621 (2021)","DOI":"10.1109\/CVPR46437.2021.01634"},{"key":"4_CR45","doi-asserted-by":"crossref","unstructured":"Wei, Q.A., et al.: LEGO-Net: learning regular rearrangements of objects in rooms. In: CVPR, pp. 19037\u201319047 (2023)","DOI":"10.1109\/CVPR52729.2023.01825"},{"key":"4_CR46","doi-asserted-by":"crossref","unstructured":"Wen, B., Lian, W., Bekris, K., Schaal, S.: CaTGrasp: learning category-level task-relevant grasping in clutter from simulation. In: 2022 International Conference on Robotics and Automation (ICRA), pp. 6401\u20136408. IEEE (2022)","DOI":"10.1109\/ICRA46639.2022.9811568"},{"key":"4_CR47","first-page":"31986","volume":"35","author":"M Wu","year":"2022","unstructured":"Wu, M., Zhong, F., Xia, Y., Dong, H.: TarGF: learning target gradient field to rearrange objects without explicit goal specification. NeurIPS 35, 31986\u201331999 (2022)","journal-title":"NeurIPS"},{"key":"4_CR48","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Schmidt, T., Narayanan, V., Fox, D.: PoseCNN: a convolutional neural network for 6D object pose estimation in cluttered scenes. In: Robotics: Science and Systems (RSS) (2018)","DOI":"10.15607\/RSS.2018.XIV.019"},{"key":"4_CR49","doi-asserted-by":"crossref","unstructured":"Yong, H., Huang, J., Hua, X., Zhang, L.: Gradient centralization: a new optimization technique for deep neural networks. In: ECCV, pp. 635\u2013652. Springer (2020)","DOI":"10.1007\/978-3-030-58452-8_37"},{"key":"4_CR50","doi-asserted-by":"crossref","unstructured":"Yu, L., et al.: MattNet: modular attention network for referring expression comprehension. In: CVPR, pp. 1307\u20131315 (2018)","DOI":"10.1109\/CVPR.2018.00142"},{"key":"4_CR51","doi-asserted-by":"publisher","unstructured":"Yu, L., Poirson, P., Yang, S., Berg, A.C., Berg, T.L.: Modeling context in referring expressions. In: ECCV, pp. 69\u201385. Springer (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_5","DOI":"10.1007\/978-3-319-46475-6_5"},{"issue":"5","key":"4_CR52","doi-asserted-by":"publisher","first-page":"2510","DOI":"10.1109\/LRA.2023.3254463","volume":"8","author":"M Zaccaria","year":"2023","unstructured":"Zaccaria, M., Manhardt, F., Di, Y., Tombari, F., Aleotti, J., Giorgini, M.: Self-supervised category-level 6D object pose estimation with optical flow consistency. IEEE Rob. Autom. Lett. 8(5), 2510\u20132517 (2023)","journal-title":"IEEE Rob. Autom. Lett."},{"key":"4_CR53","doi-asserted-by":"crossref","unstructured":"Zeng, A., et al.: Multi-view self-supervised deep learning for 6D pose estimation in the amazon picking challenge. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 1386\u20131383. IEEE (2017)","DOI":"10.1109\/ICRA.2017.7989165"},{"key":"4_CR54","doi-asserted-by":"crossref","unstructured":"Zhai, G., et al.: SG-Bot: object rearrangement via coarse-to-fine robotic imagination on scene graphs. 2024 IEEE International Conference on Robotics and Automation (ICRA) (2024)","DOI":"10.1109\/ICRA57147.2024.10610792"},{"key":"4_CR55","doi-asserted-by":"crossref","unstructured":"Zhai, G., et al.: MonoGraspNet: 6-DOF grasping with a single RGB image. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 1708\u20131714. IEEE (2023)","DOI":"10.1109\/ICRA48891.2023.10160779"},{"key":"4_CR56","doi-asserted-by":"crossref","unstructured":"Zhang, H., Lu, Y., Yu, C., Hsu, D., La, X., Zheng, N.: Invigorate: interactive visual grounding and grasping in clutter. Science and Systems (RSS), Robotics (2021)","DOI":"10.15607\/RSS.2021.XVII.020"},{"key":"4_CR57","unstructured":"Zhang, M., Lucas, J., Ba, J., Hinton, G.E.: Lookahead optimizer: k steps forward, 1 step back. NeurIPS 32 (2019)"},{"key":"4_CR58","doi-asserted-by":"crossref","unstructured":"Zhang, R., Di, Y., Lou, Z., Manhardt, F., Tombari, F., Ji, X.: RBP-POSE: residual bounding box projection for category-level pose estimation. In: ECCV, pp. 655\u2013672. Springer (2022)","DOI":"10.1007\/978-3-031-19769-7_38"},{"key":"4_CR59","doi-asserted-by":"crossref","unstructured":"Zhang, R., Di, Y., Manhardt, F., Tombari, F., Ji, X.: Ssp-pose: Symmetry-aware shape prior deformation for direct category-level object pose estimation. In: 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 7452\u20137459. IEEE (2022)","DOI":"10.1109\/IROS47612.2022.9981506"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-91569-7_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T12:50:20Z","timestamp":1748091020000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-91569-7_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031915680","9783031915697"],"references-count":59,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-91569-7_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","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"}}]}}