{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T16:24:58Z","timestamp":1778171098718,"version":"3.51.4"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198267","type":"print"},{"value":"9783031198274","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\/978-3-031-19827-4_12","type":"book-chapter","created":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T14:42:19Z","timestamp":1667313739000},"page":"197-213","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["MODE: Multi-view Omnidirectional Depth Estimation with\u00a0360$$^\\circ $$\u00a0Cameras"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1341-5585","authenticated-orcid":false,"given":"Ming","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8255-3068","authenticated-orcid":false,"given":"Xueqian","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3291-2157","authenticated-orcid":false,"given":"Xuejiao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1790-5570","authenticated-orcid":false,"given":"Jingzhao","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6432-3704","authenticated-orcid":false,"given":"Sidan","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6769-4076","authenticated-orcid":false,"given":"Yang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","unstructured":"Armeni, I., Sax, S., Zamir, A., Savarese, S.: Joint 2D\u20133D-semantic data for indoor scene understanding. https:\/\/doi.org\/10.48550\/arXiv.1702.01105 (2017)","DOI":"10.48550\/arXiv.1702.01105"},{"key":"12_CR2","unstructured":"Cassini projection: Cassini projection \u2013 Wikipedia, the free encyclopedia (2022). https:\/\/en.wikipedia.org\/wiki\/Cassini_projection"},{"key":"12_CR3","doi-asserted-by":"publisher","unstructured":"Chang, A., et al.: Matterport3d: Learning from RGB-D data in indoor environments. In: 2017 International Conference on 3D Vision (3DV), pp. 667\u2013676 (2017). https:\/\/doi.org\/10.1109\/3DV.2017.00081","DOI":"10.1109\/3DV.2017.00081"},{"key":"12_CR4","doi-asserted-by":"publisher","unstructured":"Chang, J., Chen, Y.: Pyramid stereo matching network. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5410\u20135418 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00567","DOI":"10.1109\/CVPR.2018.00567"},{"key":"12_CR5","doi-asserted-by":"publisher","unstructured":"Chen, R., Han, S., Xu, J., Su, H.: Point-based multi-view stereo network. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1538\u20131547 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00162","DOI":"10.1109\/ICCV.2019.00162"},{"key":"12_CR6","doi-asserted-by":"publisher","unstructured":"Cheng, X., Wang, P., Zhou, Y., Guan, C., Yang, R.: Omnidirectional depth extension networks. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). pp. 589\u2013595 (2020). https:\/\/doi.org\/10.1109\/ICRA40945.2020.9197123","DOI":"10.1109\/ICRA40945.2020.9197123"},{"key":"12_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-030-01240-3_32","volume-title":"Computer Vision \u2013 ECCV 2018","author":"B Coors","year":"2018","unstructured":"Coors, B., Condurache, A.P., Geiger, A.: SphereNet: learning spherical representations for detection and classification in omnidirectional images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 525\u2013541. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01240-3_32"},{"key":"12_CR8","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: Proceedings of the 1st Annual Conference on Robot Learning, pp. 1\u201316 (2017)"},{"key":"12_CR9","unstructured":"Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. In: 27th Proceedings of the Conference on Advances in Neural Information Processing Systems (2014)"},{"key":"12_CR10","doi-asserted-by":"publisher","unstructured":"Gu, X., Fan, Z., Zhu, S., Dai, Z., Tan, F., Tan, P.: Cascade cost volume for high-resolution multi-view stereo and stereo matching. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2492\u20132501 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00257","DOI":"10.1109\/CVPR42600.2020.00257"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Handa, A., P\u0103tr\u0103ucean, V., Stent, S., Cipolla, R.: SceneNet: an annotated model generator for indoor scene understanding. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 5737\u20135743. IEEE (2016)","DOI":"10.1109\/ICRA.2016.7487797"},{"key":"12_CR12","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"12_CR13","unstructured":"Jiang, C.M., Huang, J., Kashinath, K., Prabhat, M.P., Niessner, M.: Spherical CNNs on unstructured grids. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=Bkl-43C9FQ"},{"issue":"2","key":"12_CR14","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1109\/LRA.2021.3058957","volume":"6","author":"H Jiang","year":"2021","unstructured":"Jiang, H., Sheng, Z., Zhu, S., Dong, Z., Huang, R.: UniFuse: unidirectional fusion for 360$$^{\\circ }$$ panorama depth estimation. IEEE Rob. Autom. Lett. 6(2), 1519\u20131526 (2021). https:\/\/doi.org\/10.1109\/LRA.2021.3058957","journal-title":"IEEE Rob. Autom. Lett."},{"key":"12_CR15","doi-asserted-by":"publisher","unstructured":"Kendall, A., et al.: End-to-end learning of geometry and context for deep stereo regression. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 66\u201375 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.17","DOI":"10.1109\/ICCV.2017.17"},{"key":"12_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1007\/978-3-030-01261-8_48","volume-title":"Computer Vision \u2013 ECCV 2018","author":"G Payen de La Garanderie","year":"2018","unstructured":"Payen de La Garanderie, G., Atapour Abarghouei, A., Breckon, T.P.: Eliminating the blind spot: adapting 3D object detection and monocular depth estimation to 360$$^\\circ $$\u00a0panoramic imagery. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 812\u2013830. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_48"},{"key":"12_CR17","doi-asserted-by":"publisher","unstructured":"Ladick\u00fd, L., Shi, J., Pollefeys, M.: Pulling things out of perspective. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 89\u201396 (2014). https:\/\/doi.org\/10.1109\/CVPR.2014.19","DOI":"10.1109\/CVPR.2014.19"},{"key":"12_CR18","doi-asserted-by":"publisher","unstructured":"Li, M., Hu, X., Dai, J., Li, Y., Du, S.: Omnidirectional stereo depth estimation based on spherical deep network. Image Vis. Compu. 114, 104264 (2021).https:\/\/doi.org\/10.1016\/j.imavis.2021.104264, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0262885621001694","DOI":"10.1016\/j.imavis.2021.104264"},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Lipson, L., Teed, Z., Deng, J.: Raft-stereo: Multilevel recurrent field transforms for stereo matching. In: International Conference on 3D Vision (3DV) (2021)","DOI":"10.1109\/3DV53792.2021.00032"},{"key":"12_CR20","doi-asserted-by":"publisher","unstructured":"Mayer, N., et al.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4040\u20134048 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.438","DOI":"10.1109\/CVPR.2016.438"},{"key":"12_CR21","doi-asserted-by":"crossref","unstructured":"Menze, M., Heipke, C., Geiger, A.: Joint 3d estimation of vehicles and scene flow. In: ISPRS Workshop on Image Sequence Analysis (ISA) (2015)","DOI":"10.5194\/isprsannals-II-3-W5-427-2015"},{"key":"12_CR22","doi-asserted-by":"publisher","unstructured":"Pang, J., Sun, W., Ren, J.S., Yang, C., Yan, Q.: Cascade residual learning: a two-stage convolutional neural network for stereo matching. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). pp. 878\u2013886 (2017), https:\/\/doi.org\/10.1109\/ICCVW.2017.108","DOI":"10.1109\/ICCVW.2017.108"},{"key":"12_CR23","doi-asserted-by":"publisher","unstructured":"Poggi, M., et al.: On the confidence of stereo matching in a deep-learning era: a quantitative evaluation. IEEE Trans. Pattern Anal. Mach. Intell. pp. 1\u20131 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2021.3069706","DOI":"10.1109\/TPAMI.2021.3069706"},{"key":"12_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"12_CR25","doi-asserted-by":"crossref","unstructured":"Shen, Z., Dai, Y., Rao, Z.: CfNet: cascade and fused cost volume for robust stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13906\u201313915, June 2021","DOI":"10.1109\/CVPR46437.2021.01369"},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"Song, S., Yu, F., Zeng, A., Chang, A.X., Savva, M., Funkhouser, T.: Semantic scene completion from a single depth image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1746\u20131754 (2017)","DOI":"10.1109\/CVPR.2017.28"},{"key":"12_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-030-20873-8_4","volume-title":"Computer Vision \u2013 ACCV 2018","author":"F-E Wang","year":"2019","unstructured":"Wang, F.-E., Hu, H.-N., Cheng, H.-T., Lin, J.-T., Yang, S.-T., Shih, M.-L., Chu, H.-K., Sun, M.: Self-supervised learning of depth and camera motion from 360$$^\\circ $$ Videos. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11365, pp. 53\u201368. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20873-8_4"},{"key":"12_CR28","doi-asserted-by":"publisher","unstructured":"Wang, F.E., Yeh, Y.H., Sun, M., Chiu, W.C., Tsai, Y.H.: BiFuse: monocular 360 depth estimation via bi-projection fusion. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 459\u2013468 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00054","DOI":"10.1109\/CVPR42600.2020.00054"},{"key":"12_CR29","doi-asserted-by":"publisher","unstructured":"Wang, N.H., Solarte, B., Tsai, Y.H., Chiu, W.C., Sun, M.: 360sd-net: 360$$^\\circ $$ stereo depth estimation with learnable cost volume. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 582\u2013588 (2020). https:\/\/doi.org\/10.1109\/ICRA40945.2020.9196975","DOI":"10.1109\/ICRA40945.2020.9196975"},{"key":"12_CR30","doi-asserted-by":"publisher","unstructured":"Won, C., Ryu, J., Lim, J.: SweepNet: wide-baseline omnidirectional depth estimation. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 6073\u20136079 (2019). https:\/\/doi.org\/10.1109\/ICRA.2019.8793823","DOI":"10.1109\/ICRA.2019.8793823"},{"key":"12_CR31","doi-asserted-by":"crossref","unstructured":"Won, C., Ryu, J., Lim, J.: OmniMVS: end-to-end learning for omnidirectional stereo matching. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8987\u20138996 (2019)","DOI":"10.1109\/ICCV.2019.00908"},{"key":"12_CR32","doi-asserted-by":"crossref","unstructured":"Won, C., Ryu, J., Lim, J.: End-to-end learning for omnidirectional stereo matching with uncertainty prior. IEEE Trans. Pattern Anal. Mach. Intell. 43(11), 3850\u20133862 (2020)","DOI":"10.1109\/TPAMI.2020.2992497"},{"key":"12_CR33","doi-asserted-by":"publisher","unstructured":"Xu, H., Zhang, J.: AaNet: Adaptive aggregation network for efficient stereo matching. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1956\u20131965 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00203","DOI":"10.1109\/CVPR42600.2020.00203"},{"key":"12_CR34","doi-asserted-by":"publisher","unstructured":"Yang, J., Mao, W., Alvarez, J., Liu, M.: Cost volume pyramid based depth inference for multi-view stereo. IEEE Trans. Pattern Anal. Mach. Intell. 44, 4748\u20134760 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2021.3082562","DOI":"10.1109\/TPAMI.2021.3082562"},{"key":"12_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/978-3-030-01237-3_4","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y-T Hu","year":"2018","unstructured":"Hu, Y.-T., Huang, J.-B., Schwing, A.G.: VideoMatch: matching based video object segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 56\u201373. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01237-3_4"},{"key":"12_CR36","unstructured":"\u017dbontar, J., LeCun, Y.: Stereo matching by training a convolutional neural network to compare image patches. J. Mach. Learn. Res. 17(65), 1\u201332 (2016). http:\/\/jmlr.org\/papers\/v17\/15-535.html"},{"key":"12_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, F., Prisacariu, V., Yang, R., Torr, P.H.: GA-Net: guided aggregation net for end-to-end stereo matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 185\u2013194 (2019)","DOI":"10.1109\/CVPR.2019.00027"},{"key":"12_CR38","doi-asserted-by":"publisher","unstructured":"Zioulis, N., Karakottas, A., Zarpalas, D., Alvarez, F., Daras, P.: Spherical view synthesis for self-supervised 360$$^{\\circ }$$ depth estimation. In: 2019 International Conference on 3D Vision (3DV), pp. 690\u2013699 (2019). https:\/\/doi.org\/10.1109\/3DV.2019.00081","DOI":"10.1109\/3DV.2019.00081"},{"key":"12_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/978-3-030-01231-1_28","volume-title":"Computer Vision \u2013 ECCV 2018","author":"N Zioulis","year":"2018","unstructured":"Zioulis, N., Karakottas, A., Zarpalas, D., Daras, P.: OmniDepth: dense depth estimation for\u00a0indoors spherical panoramas. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 453\u2013471. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_28"}],"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\/978-3-031-19827-4_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T14:44:43Z","timestamp":1667313883000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19827-4_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198267","9783031198274"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19827-4_12","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":"2 November 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)"}}]}}