{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:21:08Z","timestamp":1743132068663,"version":"3.40.3"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030110086"},{"type":"electronic","value":"9783030110093"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-11009-3_36","type":"book-chapter","created":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T06:24:44Z","timestamp":1548311084000},"page":"589-600","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Summary of the 4th International Workshop on\u00a0Recovering 6D Object Pose"],"prefix":"10.1007","author":[{"given":"Tom\u00e1\u0161","family":"Hoda\u0148","sequence":"first","affiliation":[]},{"given":"Rigas","family":"Kouskouridas","sequence":"additional","affiliation":[]},{"given":"Tae-Kyun","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Federico","family":"Tombari","sequence":"additional","affiliation":[]},{"given":"Kostas","family":"Bekris","sequence":"additional","affiliation":[]},{"given":"Bertram","family":"Drost","sequence":"additional","affiliation":[]},{"given":"Thibault","family":"Groueix","sequence":"additional","affiliation":[]},{"given":"Krzysztof","family":"Walas","sequence":"additional","affiliation":[]},{"given":"Vincent","family":"Lepetit","sequence":"additional","affiliation":[]},{"given":"Ales","family":"Leonardis","sequence":"additional","affiliation":[]},{"given":"Carsten","family":"Steger","sequence":"additional","affiliation":[]},{"given":"Frank","family":"Michel","sequence":"additional","affiliation":[]},{"given":"Caner","family":"Sahin","sequence":"additional","affiliation":[]},{"given":"Carsten","family":"Rother","sequence":"additional","affiliation":[]},{"given":"Ji\u0159\u00ed","family":"Matas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,1,23]]},"reference":[{"key":"36_CR1","unstructured":"1st International Workshop on Recovering 6D Object Pose, ICCV 2015, Santiago. https:\/\/labicvl.github.io\/3DPose-2015.html"},{"key":"36_CR2","unstructured":"2nd International Workshop on Recovering 6D Object Pose, ECCV 2016, Amsterdam. https:\/\/labicvl.github.io\/R6D"},{"key":"36_CR3","unstructured":"3rd International Workshop on Recovering 6D Object Pose, ICCV 2017, Venice. http:\/\/cmp.felk.cvut.cz\/sixd\/workshop_2017\/"},{"key":"36_CR4","unstructured":"4th International Workshop on Recovering 6D Object Pose, ECCV 2018, Munich. http:\/\/cmp.felk.cvut.cz\/sixd\/workshop_2018\/"},{"key":"36_CR5","unstructured":"SIXD challenge (2017). http:\/\/cmp.felk.cvut.cz\/sixd\/challenge_2017\/"},{"key":"36_CR6","unstructured":"Besl, P.J., McKay, N.D.: Method for registration of 3D shapes. In: International Society for Optics and Photonics (1992)"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Bogo, F., Romero, J., Loper, M., Black, M.J.: FAUST: dataset and evaluation for 3D mesh registration. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.491"},{"key":"36_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1007\/978-3-319-10605-2_35","volume-title":"Computer Vision \u2013 ECCV 2014","author":"E Brachmann","year":"2014","unstructured":"Brachmann, E., Krull, A., Michel, F., Gumhold, S., Shotton, J., Rother, C.: Learning 6D object pose estimation using 3D object coordinates. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 536\u2013551. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10605-2_35"},{"key":"36_CR9","doi-asserted-by":"crossref","unstructured":"Brachmann, E., Michel, F., Krull, A., Yang, M.Y., Gumhold, S., Rother, C.: Uncertainty-driven 6D pose estimation of objects and scenes from a single RGB image. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.366"},{"key":"36_CR10","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1186\/s40064-016-1906-1","volume":"5","author":"AG Buch","year":"2016","unstructured":"Buch, A.G., Petersen, H.G., Kr\u00fcger, N.: Local shape feature fusion for improved matching, pose estimation and 3D object recognition. SpringerPlus 5, 297 (2016)","journal-title":"SpringerPlus"},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Buch, A.G., Kiforenko, L., Kraft, D.: Rotational subgroup voting and pose clustering for robust 3D object recognition. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.443"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Chen, X., Kundu, K., Zhang, Z., Ma, H., Fidler, S., Urtasun, R.: Monocular 3D object detection for autonomous driving. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.236"},{"key":"36_CR13","unstructured":"Chen, X., et al.: 3D object proposals for accurate object class detection. In: Advances in Neural Information Processing Systems. pp. 424\u2013432 (2015)"},{"key":"36_CR14","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TASE.2016.2600527","volume":"15","author":"N Correll","year":"2016","unstructured":"Correll, N., et al.: Analysis and observations from the first Amazon picking challenge. IEEE Trans. Autom. Sci. Eng. (T-ASE) 15, 172\u2013188 (2016)","journal-title":"IEEE Trans. Autom. Sci. Eng. (T-ASE)"},{"key":"36_CR15","unstructured":"Do, T.T., Cai, M., Pham, T., Reid, I.: Deep-6Dpose: recovering 6D object pose from a single RGB image. arXiv preprint arXiv:1802.10367 (2018)"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Doumanoglou, A., Kouskouridas, R., Malassiotis, S., Kim, T.K.: Recovering 6D object pose and predicting next-best-view in the crowd. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.390"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Drost, B., Ulrich, M., Bergmann, P., H\u00e4rtinger, P., Steger, C.: Introducing MVTec ITODD - a dataset for 3D object recognition in industry. In: CVPR (2017)","DOI":"10.1109\/ICCVW.2017.257"},{"key":"36_CR18","doi-asserted-by":"crossref","unstructured":"Drost, B., Ulrich, M., Navab, N., Ilic, S.: Model globally, match locally: efficient and robust 3D object recognition. In: CVPR (2010)","DOI":"10.1109\/CVPR.2010.5540108"},{"key":"36_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/978-3-030-01216-8_15","volume-title":"Computer Vision \u2013 ECCV 2018","author":"T Groueix","year":"2018","unstructured":"Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: 3D-CODED: 3D correspondences by deep deformation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 235\u2013251. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01216-8_15"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: AtlasNet: a Papier-Mache approach to learning 3D surface generation. arXiv preprint arXiv:1802.05384 (2018)","DOI":"10.1109\/CVPR.2018.00030"},{"key":"36_CR21","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/978-3-319-68792-6_51","volume-title":"RoboCup 2016: Robot World Cup XX","author":"C Hernandez","year":"2017","unstructured":"Hernandez, C., et al.: Team Delft\u2019s robot winner of the Amazon picking challenge 2016. In: Behnke, S., Sheh, R., Sar\u0131el, S., Lee, D.D. (eds.) RoboCup 2016. LNCS (LNAI), vol. 9776, pp. 613\u2013624. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68792-6_51"},{"key":"36_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1007\/978-3-642-37331-2_42","volume-title":"Computer Vision \u2013 ACCV 2012","author":"S Hinterstoisser","year":"2013","unstructured":"Hinterstoisser, S., et al.: Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 548\u2013562. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-37331-2_42"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Hoda\u0148, T., Haluza, P., Obdr\u017e\u00e1lek, \u0160., Matas, J., Lourakis, M., Zabulis, X.: T-LESS: an RGB-D dataset for 6D pose estimation of texture-less objects. In: WACV (2017)","DOI":"10.1109\/WACV.2017.103"},{"key":"36_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/978-3-319-49409-8_52","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"T Hoda\u0148","year":"2016","unstructured":"Hoda\u0148, T., Matas, J., Obdr\u017e\u00e1lek, \u0160.: On evaluation of 6D object pose estimation. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 606\u2013619. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_52"},{"key":"36_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-030-01249-6_2","volume-title":"Computer Vision \u2013 ECCV 2018","author":"T Hoda\u0148","year":"2018","unstructured":"Hoda\u0148, T., et al.: BOP: benchmark for 6D object pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 19\u201335. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_2"},{"key":"36_CR26","doi-asserted-by":"crossref","unstructured":"Hoda\u0148, T., Zabulis, X., Lourakis, M., Obdr\u017e\u00e1lek, \u0160., Matas, J.: Detection and fine 3D pose estimation of texture-less objects in RGB-D images. In: IROS (2015)","DOI":"10.1109\/IROS.2015.7354005"},{"key":"36_CR27","doi-asserted-by":"crossref","unstructured":"Kehl, W., Manhardt, F., Tombari, F., Ilic, S., Navab, N.: SSD-6D: making RGB-based 3D detection and 6D pose estimation great again. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.169"},{"key":"36_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/978-3-319-46487-9_13","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Kehl","year":"2016","unstructured":"Kehl, W., Milletari, F., Tombari, F., Ilic, S., Navab, N.: Deep learning of local RGB-D patches for 3D\u00a0object detection and 6D pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 205\u2013220. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_13"},{"key":"36_CR29","doi-asserted-by":"crossref","unstructured":"Khoury, M., Zhou, Q.Y., Koltun, V.: Learning compact geometric features. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.26"},{"key":"36_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01264-9_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"F Manhardt","year":"2018","unstructured":"Manhardt, F., Kehl, W., Navab, N., Tombari, F.: Deep model-based 6D pose refinement in RGB. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11218, pp. 833\u2013849. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_49"},{"key":"36_CR31","doi-asserted-by":"crossref","unstructured":"Mellado, N., Aiger, D., Mitra, N.J.: Super4PCS fast global pointcloud registration via smart indexing. In: Computer Graphics Forum, vol. 33, pp. 205\u2013215. Wiley Online Library (2014)","DOI":"10.1111\/cgf.12446"},{"key":"36_CR32","unstructured":"Mitash, C., Boularias, A., Bekris, K.E.: Robust 6D object pose estimation with stochastic congruent sets. In: British Machine Vision Conference (BMVC) (2018)"},{"key":"36_CR33","doi-asserted-by":"crossref","unstructured":"Mitash, C., Bekris, K.E., Boularias, A.: A self-supervised learning system for object detection using physics simulation and multi-view pose estimation. In: 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 545\u2013551. IEEE (2017)","DOI":"10.1109\/IROS.2017.8202206"},{"key":"36_CR34","doi-asserted-by":"crossref","unstructured":"Mitash, C., Boularias, A., Bekris, K.E.: Improving 6D pose estimation of objects in clutter via physics-aware Monte Carlo tree search. In: IEEE International Conference on Robotics and Automation (ICRA) (2018)","DOI":"10.1109\/ICRA.2018.8461163"},{"key":"36_CR35","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: CVPR (2017)"},{"key":"36_CR36","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 (2017)","DOI":"10.1109\/ICCV.2017.413"},{"key":"36_CR37","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"36_CR38","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91\u201399 (2015)"},{"key":"36_CR39","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1109\/LRA.2016.2532924","volume":"1","author":"C Rennie","year":"2016","unstructured":"Rennie, C., Shome, R., Bekris, K.E., De Souza, A.F.: A dataset for improved RGBD-based object detection and pose estimation for warehouse pick-and-place. Robot. Autom. Lett. 1, 1179\u20131185 (2016)","journal-title":"Robot. Autom. Lett."},{"key":"36_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1007\/978-3-030-01225-0_37","volume-title":"Computer Vision \u2013 ECCV 2018","author":"D Rethage","year":"2018","unstructured":"Rethage, D., Wald, J., Sturm, J., Navab, N., Tombari, F.: Fully-convolutional point networks for large-scale point clouds. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 625\u2013640. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_37"},{"key":"36_CR41","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: ICRA (2009)","DOI":"10.1109\/ROBOT.2009.5152473"},{"key":"36_CR42","first-page":"1","volume":"1","author":"DJ Tan","year":"2017","unstructured":"Tan, D.J., Navab, N., Tombari, F.: Looking beyond the simple scenarios: combining learners and optimizers in 3D temporal tracking. IEEE Trans. Vis. Comput. Graph. 1, 1 (2017)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"36_CR43","doi-asserted-by":"crossref","unstructured":"Tateno, K., Tombari, F., Laina, I., Navab, N.: CNN-SLAM: real-time dense monocular slam with learned depth prediction. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.695"},{"key":"36_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1007\/978-3-319-10599-4_30","volume-title":"Computer Vision \u2013 ECCV 2014","author":"A Tejani","year":"2014","unstructured":"Tejani, A., Tang, D., Kouskouridas, R., Kim, T.-K.: Latent-class hough forests for 3D object detection and pose estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 462\u2013477. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10599-4_30"},{"key":"36_CR45","doi-asserted-by":"crossref","unstructured":"Tekin, B., Sinha, S.N., Fua, P.: Real-time seamless single shot 6D object pose prediction. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00038"},{"key":"36_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1007\/978-3-642-15558-1_26","volume-title":"Computer Vision \u2013 ECCV 2010","author":"F Tombari","year":"2010","unstructured":"Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6313, pp. 356\u2013369. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15558-1_26"},{"key":"36_CR47","doi-asserted-by":"crossref","unstructured":"Vidal, J., Lin, C.Y., Mart\u00ed, R.: 6D pose estimation using an improved method based on point pair features. In: ICCAR (2018)","DOI":"10.1109\/ICCAR.2018.8384709"},{"key":"36_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. arXiv preprint arXiv:1711.00199 (2017)","DOI":"10.15607\/RSS.2018.XIV.019"},{"key":"36_CR49","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: RSS (2018)","DOI":"10.15607\/RSS.2018.XIV.019"},{"key":"36_CR50","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. arXiv preprint arXiv:1801.07829 (2018)","DOI":"10.1145\/3326362"},{"key":"36_CR51","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: IEEE International Conference on Robotics and Automation (ICRA) (2017)","DOI":"10.1109\/ICRA.2017.7989165"},{"key":"36_CR52","doi-asserted-by":"crossref","unstructured":"Zeng, A., Song, S., Nie\u00dfner, M., Fisher, M., Xiao, J., Funkhouser, T.: 3DMatch: learning local geometric descriptors from RGB-D reconstructions. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.29"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-11009-3_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T01:13:27Z","timestamp":1674350007000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-11009-3_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030110086","9783030110093"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-11009-3_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 January 2019","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":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}