{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T15:38:11Z","timestamp":1782833891022,"version":"3.54.5"},"publisher-location":"Cham","reference-count":66,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031197680","type":"print"},{"value":"9783031197697","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19769-7_17","type":"book-chapter","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T11:40:06Z","timestamp":1666438806000},"page":"285-303","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Share with\u00a0Thy Neighbors: Single-View Reconstruction by\u00a0Cross-Instance Consistency"],"prefix":"10.1007","author":[{"given":"Tom","family":"Monnier","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Fisher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexei A.","family":"Efros","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathieu","family":"Aubry","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"17_CR1","unstructured":"Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks. In: NIPS (2015)"},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: ICML (2009)","DOI":"10.1145\/1553374.1553380"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Besl, P., McKay, N.D.: A method for registration of 3-D shapes. TPAMI 14(2) (1992)","DOI":"10.1109\/34.121791"},{"key":"17_CR4","unstructured":"Chang, A.X., et al.: ShapeNet: an information-rich 3d model repository. arXiv:1512.03012 [cs] (2015)"},{"key":"17_CR5","unstructured":"Chen, W., et al.: Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer. In: NeurIPS (2019)"},{"key":"17_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1007\/978-3-319-46484-8_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"CB Choy","year":"2016","unstructured":"Choy, C.B., Xu, D., Gwak, J.Y., Chen, K., Savarese, S.: 3D-R2N2: a unified approach for single and multi-view 3d object reconstruction. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 628\u2013644. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_38"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Desbrun, M., Meyer, M., Schr\u00f6der, P., Barr, A.H.: Implicit fairing of irregular meshes using diffusion and curvature flow. In: SIGGRAPH (1999)","DOI":"10.1145\/311535.311576"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Duggal, S., Pathak, D.: Topologically-aware deformation fields for single-view 3D reconstruction. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00159"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Elman, J.L.: Learning and development in neural networks: The importance of starting small. Cognition (1993)","DOI":"10.1016\/0010-0277(93)90058-4"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Finger, S.: Origins of neuroscience: a history of explorations into brain function. Oxford University Press (1994)","DOI":"10.1093\/oso\/9780195065039.001.0001"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Gadelha, M., Maji, S., Wang, R.: 3D shape induction from 2D views of multiple objects. In: 3DV (2017)","DOI":"10.1109\/3DV.2017.00053"},{"key":"17_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1007\/978-3-030-58555-6_6","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Goel","year":"2020","unstructured":"Goel, S., Kanazawa, A., Malik, J.: Shape and viewpoint without keypoints. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12360, pp. 88\u2013104. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58555-6_6"},{"key":"17_CR13","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: NIPS (2014)"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: AtlasNet: a papier-M\u00e2ch\u00e9 approach to learning 3D surface generation. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00030"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Henderson, P., Ferrari, V.: Learning single-image 3D reconstruction by generative modelling of shape, pose and shading. IJCV (2019)","DOI":"10.1007\/s11263-019-01219-8"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Henderson, P., Tsiminaki, V., Lampert, C.H.: Leveraging 2D data to learn textured 3D mesh generation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00752"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Henzler, P., Mitra, N., Ritschel, T.: Escaping Plato\u2019s Cave: 3D shape from adversarial rendering. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.01008"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Hoiem, D., Efros, A.A., Hebert, M.: Geometric context from a single image. In: ICCV (2005)","DOI":"10.1109\/ICCV.2005.107"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Hoiem, D., Efros, A.A., Hebert, M.: Putting objects in perspective. IJCV (2008)","DOI":"10.1007\/s11263-008-0137-5"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Hu, T., Wang, L., Xu, X., Liu, S., Jia, J.: Self-supervised 3D mesh reconstruction from single images. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00594"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: FlowNet 2.0: evolution of optical flow estimation with deep networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.179"},{"key":"17_CR23","unstructured":"Insafutdinov, E., Dosovitskiy, A.: Unsupervised learning of shape and pose with differentiable point clouds. In: NIPS (2018)"},{"key":"17_CR24","unstructured":"Jojic, N., Frey, B.J.: Learning Flexible Sprites in Video Layers. In: CVPR (2001)"},{"key":"17_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1007\/978-3-030-01267-0_23","volume-title":"Computer Vision \u2013 ECCV 2018","author":"A Kanazawa","year":"2018","unstructured":"Kanazawa, A., Tulsiani, S., Efros, A.A., Malik, J.: Learning category-specific mesh reconstruction from image collections. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 386\u2013402. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01267-0_23"},{"key":"17_CR26","doi-asserted-by":"crossref","unstructured":"Kar, A., Tulsiani, S., Carreira, J., Malik, J.: Category-specific object reconstruction from a single image. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298807"},{"key":"17_CR27","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"17_CR28","unstructured":"Kato, H., et al.: Differentiable rendering: a survey. arXiv:2006.12057 [cs] (2020)"},{"key":"17_CR29","doi-asserted-by":"crossref","unstructured":"Kato, H., Harada, T.: Learning view priors for single-view 3D reconstruction. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01001"},{"key":"17_CR30","doi-asserted-by":"crossref","unstructured":"Kato, H., Ushiku, Y., Harada, T.: Neural 3D mesh renderer. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00411"},{"key":"17_CR31","doi-asserted-by":"crossref","unstructured":"Kulkarni, N., Gupta, A., Tulsiani, S.: Canonical surface mapping via geometric cycle consistency. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00229"},{"key":"17_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/978-3-030-58568-6_40","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Li","year":"2020","unstructured":"Li, X., Liu, S., Kim, K., De Mello, S., Jampani, V., Yang, M.-H., Kautz, J.: Self-supervised single-view 3D reconstruction via semantic consistency. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 677\u2013693. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_40"},{"key":"17_CR33","unstructured":"Lin, C.H., Wang, C., Lucey, S.: SDF-SRN: learning signed distance 3D object reconstruction from static images. In: NeurIPS (2020)"},{"key":"17_CR34","doi-asserted-by":"crossref","unstructured":"Liu, S., Li, T., Chen, W., Li, H.: Soft rasterizer: a differentiable renderer for image-based 3D reasoning. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00780"},{"key":"17_CR35","doi-asserted-by":"crossref","unstructured":"Loper, M.M., Black, M.J.: OpenDR: An Approximate Differentiable Renderer. In: ECCV 2014, vol. 8695 (2014)","DOI":"10.1007\/978-3-319-10584-0_11"},{"key":"17_CR36","doi-asserted-by":"crossref","unstructured":"Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3D reconstruction in function space. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00459"},{"key":"17_CR37","unstructured":"Monnier, T., Groueix, T., Aubry, M.: Deep transformation-invariant clustering. In: NeurIPS (2020)"},{"key":"17_CR38","doi-asserted-by":"crossref","unstructured":"Monnier, T., Vincent, E., Ponce, J., Aubry, M.: Unsupervised layered image decomposition into object prototypes. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00852"},{"key":"17_CR39","doi-asserted-by":"crossref","unstructured":"Navaneet, K.L., Mathew, A., Kashyap, S., Hung, W.C., Jampani, V., Babu, R.V.: From image collections to point clouds with self-supervised shape and pose networks. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00121"},{"key":"17_CR40","doi-asserted-by":"crossref","unstructured":"Nealen, A., Igarashi, T., Sorkine, O., Alexa, M.: Laplacian mesh optimization. In: GRAPHITE (2006)","DOI":"10.1145\/1174429.1174494"},{"key":"17_CR41","doi-asserted-by":"crossref","unstructured":"Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., Yang, Y.L.: HoloGAN: unsupervised learning of 3D representations from natural images. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00768"},{"key":"17_CR42","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Geiger, A.: GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01129"},{"key":"17_CR43","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: learning implicit 3D representations without 3D supervision. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00356"},{"key":"17_CR44","unstructured":"Pavllo, D., Spinks, G., Hofmann, T., Moens, M.F., Lucchi, A.: Convolutional generation of textured 3D meshes. In: NeurIPS (2020)"},{"key":"17_CR45","unstructured":"Ravi, N., et al.: Accelerating 3D deep learning with PyTorch3D. arXiv:2007.08501 [cs] (2020)"},{"key":"17_CR46","doi-asserted-by":"crossref","unstructured":"Saxena, A., Min Sun, Ng, A.: Make3D: learning 3D scene structure from a single still image. TPAMI (2009)","DOI":"10.1109\/TPAMI.2008.132"},{"key":"17_CR47","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"17_CR48","unstructured":"Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. In: ICLR (2015)"},{"key":"17_CR49","doi-asserted-by":"crossref","unstructured":"Tulsiani, S., Efros, A.A., Malik, J.: Multi-view consistency as supervisory signal for learning shape and pose prediction. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00306"},{"key":"17_CR50","unstructured":"Tulsiani, S., Kulkarni, N., Gupta, A.: Implicit mesh reconstruction from unannotated image collections. arXiv:2007.08504 [cs] (2020)"},{"key":"17_CR51","doi-asserted-by":"crossref","unstructured":"Tulsiani, S., Zhou, T., Efros, A.A., Malik, J.: Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.30"},{"key":"17_CR52","doi-asserted-by":"crossref","unstructured":"Vicente, S., Carreira, J., Agapito, L., Batista, J.: Reconstructing PASCAL VOC. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.13"},{"key":"17_CR53","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/978-3-030-01252-6_4","volume-title":"Computer Vision \u2013 ECCV 2018","author":"N Wang","year":"2018","unstructured":"Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., Jiang, Y.-G.: Pixel2Mesh: generating 3D mesh models from single RGB images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 55\u201371. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01252-6_4"},{"key":"17_CR54","volume-title":"Caltech-UCSD Birds 200","author":"P Welinder","year":"2010","unstructured":"Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., Perona, P.: Caltech-UCSD Birds 200. Technical report, California Institute of Technology (2010)"},{"key":"17_CR55","doi-asserted-by":"crossref","unstructured":"Wu, S., Makadia, A., Wu, J., Snavely, N., Tucker, R., Kanazawa, A.: De-rendering the world\u2019s revolutionary artefacts. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00627"},{"key":"17_CR56","doi-asserted-by":"crossref","unstructured":"Wu, S., Rupprecht, C., Vedaldi, A.: Unsupervised learning of probably symmetric deformable 3D objects from images in the wild. In: CVPR (2020)","DOI":"10.24963\/ijcai.2021\/665"},{"key":"17_CR57","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Mottaghi, R., Savarese, S.: Beyond PASCAL: a benchmark for 3D object detection in the wild. In: WACV (2014)","DOI":"10.1109\/WACV.2014.6836101"},{"key":"17_CR58","unstructured":"Xu, Q., Wang, W., Ceylan, D., Mech, R., Neumann, U.: DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction. In: NeurIPS (2019)"},{"key":"17_CR59","unstructured":"Yan, X., Yang, J., Yumer, E., Guo, Y., Lee, H.: Perspective transformer nets: learning single-view 3D object reconstruction without 3D supervision. In: NeurIPS (2016)"},{"key":"17_CR60","doi-asserted-by":"crossref","unstructured":"Yang, L., Luo, P., Loy, C.C., Tang, X.: A large-scale car dataset for fine-grained categorization and verification. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7299023"},{"key":"17_CR61","doi-asserted-by":"crossref","unstructured":"Yao, C.H., Hung, W.C., Jampani, V., Yang, M.H.: Discovering 3D parts from image collections. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01274"},{"key":"17_CR62","doi-asserted-by":"crossref","unstructured":"Ye, Y., Tulsiani, S., Gupta, A.: Shelf-supervised mesh prediction in the wild. arXiv:2102.06195 [cs] (2021)","DOI":"10.1109\/CVPR46437.2021.00873"},{"key":"17_CR63","unstructured":"Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., Xiao, J.: LSUN: construction of a large-scale image dataset using deep learning with humans in the loop. arXiv:1506.03365 [cs] (2016)"},{"key":"17_CR64","unstructured":"Zhang, J.Y., Yang, G., Tulsiani, S., Ramanan, D.: NeRS: neural reflectance surfaces for sparse-view 3D reconstruction in the wild. In: NeurIPS (2021)"},{"key":"17_CR65","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"17_CR66","unstructured":"Zhang, Y., et al.: Image GANs meet differentiable rendering for inverse graphics and interpretable 3D neural rendering. In: ICLR (2021)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19769-7_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T14:03:52Z","timestamp":1710338632000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19769-7_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197680","9783031197697"],"references-count":66,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19769-7_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"23 October 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}