{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:52:41Z","timestamp":1777567961728,"version":"3.51.4"},"publisher-location":"Cham","reference-count":61,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031250552","type":"print"},{"value":"9783031250569","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-25056-9_34","type":"book-chapter","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T12:09:56Z","timestamp":1676376596000},"page":"537-553","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["4D-StOP: Panoptic Segmentation of\u00a04D LiDAR Using Spatio-Temporal Object Proposal Generation and\u00a0Aggregation"],"prefix":"10.1007","author":[{"given":"Lars","family":"Kreuzberg","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Idil Esen","family":"Zulfikar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sabarinath","family":"Mahadevan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francis","family":"Engelmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bastian","family":"Leibe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"key":"34_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1007\/978-3-030-58621-8_10","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Athar","year":"2020","unstructured":"Athar, A., Mahadevan, S., Os\u0306ep, A., Leal-Taix\u00e9, L., Leibe, B.: STEm-Seg: spatio-temporal embeddings for instance segmentation in videos. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 158\u2013177. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58621-8_10"},{"key":"34_CR2","doi-asserted-by":"crossref","unstructured":"Ayg\u00fcn, M., et al.: 4D panoptic segmentation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00548"},{"key":"34_CR3","doi-asserted-by":"crossref","unstructured":"Bai, X., et al.: TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers. arXiv preprint arXiv:2203.11496 (2022)","DOI":"10.1109\/CVPR52688.2022.00116"},{"key":"34_CR4","doi-asserted-by":"crossref","unstructured":"Behley, J., et al.: SemanticKITTI: a dataset for semantic scene understanding of LiDAR sequences. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00939"},{"key":"34_CR5","doi-asserted-by":"crossref","unstructured":"Behley, J., Milioto, A., Stachniss, C.: A benchmark for LiDAR-based panoptic segmentation based on KITTI. In: ICRA (2021)","DOI":"10.1109\/ICRA48506.2021.9561476"},{"key":"34_CR6","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Meinhardt, T., Leal-Taix\u00e9, L.: Tracking without bells and whistles. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00103"},{"key":"34_CR7","doi-asserted-by":"crossref","unstructured":"Braso, G., Leal-Taix\u00e9, L.: Learning a neural solver for multiple object tracking. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00628"},{"key":"34_CR8","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"34_CR9","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. In: NIPS (2020)"},{"key":"34_CR10","unstructured":"Chen, T., Kornblith, S., 0002, M.N., Hinton, G.E.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning (ICML) (2020)"},{"key":"34_CR11","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/978-3-031-19821-2_39","volume-title":"Computer Vision","author":"J Chibane","year":"2022","unstructured":"Chibane, J., Engelmann, F., Tran, T.A., Pons-Moll, G.: Box2Mask: weakly supervised 3D semantic instance segmentation using bounding boxes. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, pp. 681\u2013699. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19821-2_39"},{"key":"34_CR12","doi-asserted-by":"crossref","unstructured":"Chiu, H.K., Prioletti, A., Li, J., Bohg, J.: Probabilistic 3D Multi-Object Tracking for Autonomous Driving. In: arXiv preprint arXiv:2001.05673 (2020)","DOI":"10.1109\/ICRA48506.2021.9561754"},{"key":"34_CR13","doi-asserted-by":"crossref","unstructured":"Cortinhal, T., Tzelepis, G., Erdal Aksoy, E.: SalsaNext: fast, uncertainty-aware semantic segmentation of LiDAR point clouds. In: International Symposium on Visual Computing (2020)","DOI":"10.1007\/978-3-030-64559-5_16"},{"key":"34_CR14","doi-asserted-by":"crossref","unstructured":"Elich, C., Engelmann, F., Schult, J., Kontogianni, T., Leibe, B.: 3D-BEVIS: birds-eye-view instance segmentation. In: German Conference on Pattern Recognition (GCPR) (2019)","DOI":"10.1007\/978-3-030-33676-9_4"},{"key":"34_CR15","doi-asserted-by":"crossref","unstructured":"Engelmann, F., Bokeloh, M., Fathi, A., Leibe, B., Nie\u00dfner, M.: 3D-MPA: multi-proposal aggregation for 3D semantic instance segmentation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00905"},{"key":"34_CR16","doi-asserted-by":"crossref","unstructured":"Engelmann, F., Kontogianni, T., Hermans, A., Leibe, B.: Exploring spatial context for 3D semantic segmentation of point clouds. In: ICCV Workshops (2017)","DOI":"10.1109\/ICCVW.2017.90"},{"key":"34_CR17","doi-asserted-by":"crossref","unstructured":"Engelmann, F., Kontogianni, T., Schult, J., Leibe, B.: Know what your neighbors do: 3D semantic segmentation of point clouds. In: ECCV Workshops (2018)","DOI":"10.1109\/ICCVW.2017.90"},{"key":"34_CR18","doi-asserted-by":"crossref","unstructured":"Fong, W.K., et al.: Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking. In: arXiv preprint arXiv:2109.03805 (2021)","DOI":"10.1109\/LRA.2022.3148457"},{"key":"34_CR19","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: CVPR (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"34_CR20","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"34_CR21","doi-asserted-by":"crossref","unstructured":"Hong, F., Zhou, H., Zhu, X., Li, H., Liu, Z.: LiDAR-based panoptic segmentation via dynamic shifting network. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01289"},{"key":"34_CR22","doi-asserted-by":"crossref","unstructured":"Hou, J., Dai, A., Nie\u00dfner, M.: 3D-SIS: 3D semantic instance segmentation of RGB-D scans. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00455"},{"key":"34_CR23","unstructured":"Hurtado, J.V., Mohan, R., Burgard, W., Valada, A.: MOPT: multi-object panoptic tracking. In: CVPR Workshops (2020)"},{"key":"34_CR24","doi-asserted-by":"crossref","unstructured":"Kim, D., Woo, S., Lee, J.Y., Kweon, I.S.: Video panoptic segmentation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00988"},{"key":"34_CR25","doi-asserted-by":"crossref","unstructured":"Kim, Aleksandr, O.A., Leal-Taix\u00e9, L.: EagerMOT: 3D multi-object tracking via sensor fusion. In: ICRA (2021)","DOI":"10.1109\/ICRA48506.2021.9562072"},{"key":"34_CR26","doi-asserted-by":"crossref","unstructured":"Lahoud, J., Ghanem, B., Pollefeys, M., Oswald, M.R.: 3D instance segmentation via multi-task metric learning. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00935"},{"key":"34_CR27","doi-asserted-by":"crossref","unstructured":"Landrieu, L., Simonovsky, M.: Large-scale point cloud semantic segmentation with superpoint graphs. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00479"},{"key":"34_CR28","doi-asserted-by":"crossref","unstructured":"Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: PointPillars: fast encoders for object detection from point clouds. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01298"},{"key":"34_CR29","doi-asserted-by":"crossref","unstructured":"Leal-Taix\u00e9, L., Fenzi, M., Kuznetsova, A., Rosenhahn, B., Savarese, S.: Learning an image-based motion context for multiple people tracking. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.453"},{"key":"34_CR30","doi-asserted-by":"crossref","unstructured":"Marcuzzi, R., Nunes, L., Wiesmann, L., Vizzo, I., Behley, J., Stachniss, C.: Contrastive instance association for 4D panoptic segmentatio using sequences of 3D LiDAR scans. In: IEEE Robotics and Automation Society (2022)","DOI":"10.1109\/LRA.2022.3140439"},{"key":"34_CR31","doi-asserted-by":"crossref","unstructured":"Meinhardt, T., Kirillov, A., Leal-Taix\u00e9, L., Feichtenhofer, C.: TrackFormer: multi-object tracking with transformers. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00864"},{"key":"34_CR32","doi-asserted-by":"crossref","unstructured":"Milan, A., Leal-Taix\u00e9, L., Schindler, K., Reid, I.D.: Joint tracking and segmentation of multiple targets. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7299178"},{"key":"34_CR33","doi-asserted-by":"crossref","unstructured":"Milioto, A., Vizzo, I., Behley, J., Stachniss, C.: RangeNet++: fast and accurate LiDAR semantic segmentation. In: IROS (2019)","DOI":"10.1109\/IROS40897.2019.8967762"},{"key":"34_CR34","doi-asserted-by":"crossref","unstructured":"Milioto, A., Behley, J., McCool, C., Stachniss, C.: LiDAR panoptic segmentation for autonomous driving. In: IROS (2020)","DOI":"10.1109\/IROS45743.2020.9340837"},{"key":"34_CR35","doi-asserted-by":"crossref","unstructured":"Misra, I., Girdhar, R., Joulin, A.: An end-to-end transformer model for 3D object detection. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00290"},{"key":"34_CR36","doi-asserted-by":"crossref","unstructured":"Mittal, H., Okorn, B., Held, D.: Just go with the flow: self-supervised scene flow estimation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01119"},{"key":"34_CR37","doi-asserted-by":"crossref","unstructured":"Neven, D., Brabandere, B.D., Proesmans, M., Gool, L.V.: Instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00904"},{"key":"34_CR38","unstructured":"Oord, A.V.D., Li, Y., Vinyals, O.: Representation Learning with Contrastive Predictive Coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"34_CR39","doi-asserted-by":"crossref","unstructured":"Pang, J., et al.: Quasi-dense similarity learning for multiple object tracking. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00023"},{"key":"34_CR40","unstructured":"Qi, C., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: CVPR (2017)"},{"key":"34_CR41","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Litany, O., He, K., Guibas, L.J.: Deep hough voting for 3D object detection in point clouds. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00937"},{"key":"34_CR42","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)"},{"key":"34_CR43","doi-asserted-by":"crossref","unstructured":"Shi, S., et al.: PV-RCNN: point-voxel feature set abstraction for 3D object detection. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01054"},{"key":"34_CR44","doi-asserted-by":"crossref","unstructured":"Shi, S., Wang, X., Li, H.: PointRCNN: 3D object proposal generation and detection from point cloud. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00086"},{"key":"34_CR45","doi-asserted-by":"crossref","unstructured":"Sun, P., et al.: Scalability in perception for autonomous driving: waymo open dataset. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"34_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/978-3-030-58604-1_41","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Tang","year":"2020","unstructured":"Tang, H., et al.: Searching efficient 3D architectures with sparse point-voxel convolution. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12373, pp. 685\u2013702. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58604-1_41"},{"key":"34_CR47","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: KPConv: flexible and deformable convolution for point clouds. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00651"},{"key":"34_CR48","doi-asserted-by":"crossref","unstructured":"Voigtlaender, P., et al.: MOTS: multi-object tracking and segmentation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00813"},{"key":"34_CR49","doi-asserted-by":"crossref","unstructured":"Wang, Y., Kitani, K., Weng, X.: Joint object detection and multi-object tracking with graph neural networks. In: ICRA (2021)","DOI":"10.1109\/ICRA48506.2021.9561110"},{"key":"34_CR50","doi-asserted-by":"crossref","unstructured":"Weng, X., Wang, J., Held, D., Kitani, K.: 3D multi-object tracking: a baseline and new evaluation metrics. In: IROS (2020)","DOI":"10.1109\/IROS45743.2020.9341164"},{"key":"34_CR51","doi-asserted-by":"crossref","unstructured":"Weng, X., Wang, J., Held, D., Kitani, K.: AB3DMOT: a baseline for 3D multi-object tracking and new evaluation metrics. In: ECCV Workshops (2020)","DOI":"10.1109\/IROS45743.2020.9341164"},{"key":"34_CR52","doi-asserted-by":"crossref","unstructured":"Wu, B., Wan, A., Yue, X., Keutzer, K.: SqueezeSeg: convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3D LiDAR point cloud. In: ICRA (2018)","DOI":"10.1109\/ICRA.2018.8462926"},{"issue":"10","key":"34_CR53","doi-asserted-by":"publisher","first-page":"3337","DOI":"10.3390\/s18103337","volume":"18","author":"Y Yan","year":"2018","unstructured":"Yan, Y., Mao, Y., Li, B.: SECOND: sparsely embedded convolutional detection. Sensors 18(10), 3337 (2018)","journal-title":"Sensors"},{"key":"34_CR54","unstructured":"Yang, B., et al.: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds. arXiv preprint arXiv:1906.01140 (2019)"},{"key":"34_CR55","doi-asserted-by":"crossref","unstructured":"Yang, Z., Sun, Y., Liu, S., Jia, J.: 3DSSD: point-based 3D single stage object detector. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01105"},{"key":"34_CR56","doi-asserted-by":"crossref","unstructured":"Yin, T., Zhou, X., Kr\u00e4henb\u00fchl, P.: Center-based 3D object detection and tracking. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01161"},{"key":"34_CR57","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhou, Z., David, P., Yue, X., Xi, Z., Foroosh, H.: PolarNet: an improved grid representation for online LiDAR point clouds semantic segmentation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00962"},{"key":"34_CR58","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1007\/978-3-030-58548-8_28","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Koltun, V., Kr\u00e4henb\u00fchl, P.: Tracking objects as points. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 474\u2013490. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58548-8_28"},{"key":"34_CR59","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Tuzel, O.: VoxelNet: end-to-end learning for point cloud based 3D object detection. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00472"},{"key":"34_CR60","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Zhang, Y., Foroosh, H.: Panoptic-PolarNet: proposal-free LiDAR point cloud panoptic segmentation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01299"},{"key":"34_CR61","doi-asserted-by":"crossref","unstructured":"Zhu, X., et al.: Cylindrical and asymmetrical 3D convolution networks for LiDAR segmentation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00981"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25056-9_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:36:00Z","timestamp":1710268560000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25056-9_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250552","9783031250569"],"references-count":61,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25056-9_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 February 2023","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)"}},{"value":"From the workshops, 367 reviewed full papers have been selected for publication","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)"}}]}}