{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:45:34Z","timestamp":1774629934509,"version":"3.50.1"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585884","type":"print"},{"value":"9783030585891","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58589-1_32","type":"book-chapter","created":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T06:18:04Z","timestamp":1605075484000},"page":"530-546","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":174,"title":["Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9937-8975","authenticated-orcid":false,"given":"Meng","family":"Tian","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8277-6408","authenticated-orcid":false,"suffix":"Jr.","given":"Marcelo H.","family":"Ang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1583-0475","authenticated-orcid":false,"given":"Gim Hee","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"key":"32_CR1","unstructured":"Abdulla, W.: Mask r-cnn for object detection and instance segmentation on keras and tensorflow (2017). https:\/\/github.com\/matterport\/Mask_RCNN"},{"key":"32_CR2","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":"32_CR3","unstructured":"Burchfiel, B., Konidaris, G.: Probabilistic category-level pose estimation via segmentation and predicted-shape priors. arXiv preprint arXiv:1905.12079 (2019)"},{"key":"32_CR4","doi-asserted-by":"crossref","unstructured":"Chen, D., Li, J., Wang, Z., Xu, K.: Learning canonical shape space for category-level 6d object pose and size estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01199"},{"issue":"6","key":"32_CR5","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381\u2013395 (1981)","journal-title":"Commun. ACM"},{"key":"32_CR6","doi-asserted-by":"crossref","unstructured":"Gupta, S., Arbel\u00e1ez, P., Girshick, R., Malik, J.: Aligning 3d models to rgb-d images of cluttered scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4731\u20134740 (2015)","DOI":"10.1109\/CVPR.2015.7299105"},{"key":"32_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"32_CR9","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":"32_CR10","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: Proceedings of the IEEE International Conference on Computer Vision, pp. 1521\u20131529 (2017)","DOI":"10.1109\/ICCV.2017.169"},{"key":"32_CR11","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":"32_CR12","doi-asserted-by":"crossref","unstructured":"Krull, A., Brachmann, E., Michel, F., Ying Yang, M., Gumhold, S., Rother, C.: Learning analysis-by-synthesis for 6d pose estimation in rgb-d images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 954\u2013962 (2015)","DOI":"10.1109\/ICCV.2015.115"},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"Kurenkov, A., et al.: Deformnet: Free-form deformation network for 3d shape reconstruction from a single image. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 858\u2013866. IEEE (2018)","DOI":"10.1109\/WACV.2018.00099"},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Lahoud, J., Ghanem, B.: 2d-driven 3d object detection in rgb-d images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4622\u20134630 (2017)","DOI":"10.1109\/ICCV.2017.495"},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Li, C., Bai, J., Hager, G.D.: A unified framework for multi-view multi-class object pose estimation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 254\u2013269 (2018)","DOI":"10.1007\/978-3-030-01270-0_16"},{"key":"32_CR16","first-page":"2579","volume":"9","author":"LVD Maaten","year":"2008","unstructured":"Maaten, L.V.D.: Visualizing data using t-sne. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"32_CR17","doi-asserted-by":"crossref","unstructured":"Michel, F., et al.: Global hypothesis generation for 6d object pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 462\u2013471 (2017)","DOI":"10.1109\/CVPR.2017.20"},{"key":"32_CR18","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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4561\u20134570 (2019)","DOI":"10.1109\/CVPR.2019.00469"},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"Pitteri, G., Ramamonjisoa, M., Ilic, S., Lepetit, V.: On object symmetries and 6d pose estimation from images. In: 2019 International Conference on 3D Vision (3DV), pp. 614\u2013622. IEEE (2019)","DOI":"10.1109\/3DV.2019.00073"},{"key":"32_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/978-3-030-20887-5_23","volume-title":"Computer Vision \u2013 ACCV 2018","author":"JK Pontes","year":"2019","unstructured":"Pontes, J.K., Kong, C., Sridharan, S., Lucey, S., Eriksson, A., Fookes, C.: Image2mesh: a learning framework for single image 3d reconstruction. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11361, pp. 365\u2013381. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20887-5_23"},{"key":"32_CR21","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum pointnets for 3d object detection from rgb-d data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 918\u2013927 (2018)","DOI":"10.1109\/CVPR.2018.00102"},{"key":"32_CR22","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652\u2013660 (2017)"},{"key":"32_CR23","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: Proceedings of the IEEE International Conference on Computer Vision, pp. 3828\u20133836 (2017)","DOI":"10.1109\/ICCV.2017.413"},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Sahin, C., Kim, T.K.: Category-level 6d object pose recovery in depth images. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)","DOI":"10.1007\/978-3-030-11009-3_41"},{"key":"32_CR25","doi-asserted-by":"crossref","unstructured":"Song, S., Xiao, J.: Deep sliding shapes for amodal 3d object detection in rgb-d images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 808\u2013816 (2016)","DOI":"10.1109\/CVPR.2016.94"},{"key":"32_CR26","doi-asserted-by":"crossref","unstructured":"Sundermeyer, M., Marton, Z.C., Durner, M., Brucker, M., Triebel, R.: Implicit 3d orientation learning for 6d object detection from rgb images. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 699\u2013715 (2018)","DOI":"10.1007\/978-3-030-01231-1_43"},{"key":"32_CR27","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":"32_CR28","doi-asserted-by":"crossref","unstructured":"Tekin, B., Sinha, S.N., Fua, P.: Real-time seamless single shot 6d object pose prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 292\u2013301 (2018)","DOI":"10.1109\/CVPR.2018.00038"},{"key":"32_CR29","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1109\/34.88573","volume":"4","author":"S Umeyama","year":"1991","unstructured":"Umeyama, S.: Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. Mach. Intell. 4, 376\u2013380 (1991)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"32_CR30","doi-asserted-by":"crossref","unstructured":"Wallace, B., Hariharan, B.: Few-shot generalization for single-image 3d reconstruction via priors. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3818\u20133827 (2019)","DOI":"10.1109\/ICCV.2019.00392"},{"key":"32_CR31","doi-asserted-by":"crossref","unstructured":"Wang, C., et al.: 6-pack: Category-level 6d pose tracker with anchor-based keypoints. In: International Conference on Robotics and Automation (ICRA) (2020)","DOI":"10.1109\/ICRA40945.2020.9196679"},{"key":"32_CR32","doi-asserted-by":"crossref","unstructured":"Wang, C., et al.: Densefusion: 6d object pose estimation by iterative dense fusion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3343\u20133352 (2019)","DOI":"10.1109\/CVPR.2019.00346"},{"key":"32_CR33","doi-asserted-by":"crossref","unstructured":"Wang, H., Sridhar, S., Huang, J., Valentin, J., Song, S., Guibas, L.J.: Normalized object coordinate space for category-level 6d object pose and size estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2642\u20132651 (2019)","DOI":"10.1109\/CVPR.2019.00275"},{"key":"32_CR34","doi-asserted-by":"crossref","unstructured":"Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., Jiang, Y.G.: Pixel2mesh: Generating 3d mesh models from single rgb images. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 52\u201367 (2018)","DOI":"10.1007\/978-3-030-01252-6_4"},{"key":"32_CR35","doi-asserted-by":"crossref","unstructured":"Wang, W., Ceylan, D., Mech, R., Neumann, U.: 3dn: 3d deformation network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1038\u20131046 (2019)","DOI":"10.1109\/CVPR.2019.00113"},{"key":"32_CR36","doi-asserted-by":"crossref","unstructured":"Wen, C., Zhang, Y., Li, Z., Fu, Y.: Pixel2mesh++: Multi-view 3d mesh generation via deformation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1042\u20131051 (2019)","DOI":"10.1109\/ICCV.2019.00113"},{"key":"32_CR37","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. Robotics: Science and Systems (RSS) (2018)","DOI":"10.15607\/RSS.2018.XIV.019"},{"key":"32_CR38","doi-asserted-by":"crossref","unstructured":"Yang, B., Luo, W., Urtasun, R.: Pixor: Real-time 3d object detection from point clouds. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 7652\u20137660 (2018)","DOI":"10.1109\/CVPR.2018.00798"},{"key":"32_CR39","doi-asserted-by":"crossref","unstructured":"Yang, Y., Feng, C., Shen, Y., Tian, D.: Foldingnet: Point cloud auto-encoder via deep grid deformation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 206\u2013215 (2018)","DOI":"10.1109\/CVPR.2018.00029"},{"key":"32_CR40","doi-asserted-by":"crossref","unstructured":"Yuan, W., Khot, T., Held, D., Mertz, C., Hebert, M.: Pcn: Point completion network. In: 2018 International Conference on 3D Vision (3DV), pp. 728\u2013737. IEEE (2018)","DOI":"10.1109\/3DV.2018.00088"},{"key":"32_CR41","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/978-3-319-46466-4_18","volume-title":"Computer Vision \u2013 ECCV 2016","author":"ME Yumer","year":"2016","unstructured":"Yumer, M.E., Mitra, N.J.: Learning semantic deformation flows with 3d convolutional networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 294\u2013311. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_18"},{"key":"32_CR42","doi-asserted-by":"crossref","unstructured":"Zakharov, S., Shugurov, I., Ilic, S.: Dpod: 6d pose object detector and refiner. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1941\u20131950 (2019)","DOI":"10.1109\/ICCV.2019.00203"},{"key":"32_CR43","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"32_CR44","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Tuzel, O.: Voxelnet: End-to-end learning for point cloud based 3d object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4490\u20134499 (2018)","DOI":"10.1109\/CVPR.2018.00472"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58589-1_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:22:45Z","timestamp":1731284565000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58589-1_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585884","9783030585891"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58589-1_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"12 November 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1360","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}