{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T12:29:00Z","timestamp":1780057740235,"version":"3.54.0"},"publisher-location":"Cham","reference-count":54,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030660956","type":"print"},{"value":"9783030660963","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\/978-3-030-66096-3_39","type":"book-chapter","created":{"date-parts":[[2021,1,2]],"date-time":"2021-01-02T07:03:14Z","timestamp":1609570994000},"page":"577-594","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":177,"title":["BOP Challenge 2020 on 6D Object Localization"],"prefix":"10.1007","author":[{"given":"Tom\u00e1\u0161","family":"Hoda\u0148","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Sundermeyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bertram","family":"Drost","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yann","family":"Labb\u00e9","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Brachmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Michel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carsten","family":"Rother","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji\u0159\u00ed","family":"Matas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,3]]},"reference":[{"key":"39_CR1","unstructured":"Intel Open Image Denoise (2020). https:\/\/www.openimagedenoise.org\/"},{"key":"39_CR2","unstructured":"MVTec HALCON (2020). https:\/\/www.mvtec.com\/halcon\/"},{"key":"39_CR3","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":"39_CR4","unstructured":"Blender Online Community: Blender - a 3D modelling and rendering package (2018). http:\/\/www.blender.org"},{"key":"39_CR5","unstructured":"Demes, L.: CC0 textures (2020). https:\/\/cc0textures.com\/"},{"key":"39_CR6","unstructured":"Denninger, M., et al.: BlenderProc: reducing the reality gap with photorealistic rendering. In: Robotics: Science and Systems (RSS) Workshops (2020)"},{"key":"39_CR7","unstructured":"Denninger, M., et al.: BlenderProc. arXiv preprint arXiv:1911.01911 (2019)"},{"key":"39_CR8","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":"39_CR9","doi-asserted-by":"crossref","unstructured":"Drost, B., Ulrich, M., Bergmann, P., Hartinger, P., Steger, C.: Introducing MVTec ITODD - a dataset for 3D object recognition in industry. In: ICCVW (2017)","DOI":"10.1109\/ICCVW.2017.257"},{"key":"39_CR10","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":"39_CR11","doi-asserted-by":"crossref","unstructured":"Dwibedi, D., Misra, I., Hebert, M.: Cut, paste and learn: surprisingly easy synthesis for instance detection. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.146"},{"key":"39_CR12","unstructured":"Fu, C.Y., Shvets, M., Berg, A.C.: RetinaMask: learning to predict masks improves state-of-the-art single-shot detection for free. arXiv preprint arXiv:1901.03353 (2019)"},{"key":"39_CR13","unstructured":"Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv preprint arXiv:1811.12231 (2018)"},{"key":"39_CR14","doi-asserted-by":"crossref","unstructured":"Godard, C., Hedman, P., Li, W., Brostow, G.J.: Multi-view reconstruction of highly specular surfaces in uncontrolled environments. In: 3DV (2015)","DOI":"10.1109\/3DV.2015.10"},{"key":"39_CR15","doi-asserted-by":"crossref","unstructured":"Hagelskj\u00e6r, F., Buch, A.G.: PointPoseNet: accurate object detection and 6 DOF pose estimation in point clouds. arXiv preprint arXiv:1912.09057 (2019)","DOI":"10.1109\/ICIP40778.2020.9191119"},{"key":"39_CR16","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":"39_CR17","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 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"39_CR18","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":"39_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1007\/978-3-030-11009-3_42","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"S Hinterstoisser","year":"2019","unstructured":"Hinterstoisser, S., Lepetit, V., Wohlhart, P., Konolige, K.: On pre-trained image features and synthetic images for deep learning. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11129, pp. 682\u2013697. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11009-3_42"},{"key":"39_CR20","doi-asserted-by":"crossref","unstructured":"Hinterstoisser, S., Pauly, O., Heibel, H., Martina, M., Bokeloh, M.: An annotation saved is an annotation earned: using fully synthetic training for object detection. In: ICCVW (2019)","DOI":"10.1109\/ICCVW.2019.00340"},{"key":"39_CR21","doi-asserted-by":"crossref","unstructured":"Hoda\u0148, T., Bar\u00e1th, D., Matas, J.: EPOS: estimating 6D pose of objects with symmetries. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01172"},{"key":"39_CR22","unstructured":"Hoda\u0148, T., et al.: BOP challenge 2019 (2019). https:\/\/bop.felk.cvut.cz\/media\/bop_challenge_2019_results.pdf"},{"key":"39_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: IEEE Winter Conference on Applications of Computer Vision (WACV) (2017)","DOI":"10.1109\/WACV.2017.103"},{"key":"39_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":"39_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":"39_CR26","unstructured":"Hoda\u0148, T., Michel, F., Sahin, C., Kim, T.K., Matas, J., Rother, C.: SIXD challenge 2017 (2017). http:\/\/cmp.felk.cvut.cz\/sixd\/challenge_2017\/"},{"key":"39_CR27","unstructured":"Hoda\u0148, T., Sundermeyer, M.: BOP toolkit (2020). https:\/\/github.com\/thodan\/bop_toolkit"},{"key":"39_CR28","unstructured":"Hoda\u0148, T., et al.: 6th International Workshop on Recovering 6D Object Pose (2020). http:\/\/cmp.felk.cvut.cz\/sixd\/workshop_2020\/"},{"key":"39_CR29","doi-asserted-by":"crossref","unstructured":"Hoda\u0148, T., et al.: Photorealistic image synthesis for object instance detection. In: IEEE International Conference on Image Processing (ICIP) (2019)","DOI":"10.1109\/ICIP.2019.8803821"},{"key":"39_CR30","doi-asserted-by":"crossref","unstructured":"Kaskman, R., Zakharov, S., Shugurov, I., Ilic, S.: HomebrewedDB: RGB-D dataset for 6D pose estimation of 3D objects. In: ICCVW (2019)","DOI":"10.1109\/ICCVW.2019.00338"},{"key":"39_CR31","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":"39_CR32","doi-asserted-by":"crossref","unstructured":"Koenig, R., Drost, B.: A hybrid approach for 6DoF pose estimation. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020 Workshops. LNCS, vol. 12536, pp. 700\u2013706. Springer, Cham (2020)","DOI":"10.1007\/978-3-030-66096-3_46"},{"key":"39_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1007\/978-3-030-58520-4_34","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Labb\u00e9","year":"2020","unstructured":"Labb\u00e9, Y., Carpentier, J., Aubry, M., Sivic, J.: CosyPose: consistent multi-view multi-object 6D pose estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) ECCV 2020. LNCS, vol. 12362, pp. 574\u2013591. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58520-4_34"},{"key":"39_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1007\/978-3-030-01231-1_42","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Li","year":"2018","unstructured":"Li, Y., Wang, G., Ji, X., Xiang, Yu., Fox, D.: DeepIM: deep iterative matching for 6D pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 695\u2013711. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_42"},{"key":"39_CR35","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, G., Ji, X.: CDPN: coordinates-based disentangled pose network for real-time RGB-based 6-DoF object pose estimation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00777"},{"key":"39_CR36","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"39_CR37","doi-asserted-by":"crossref","unstructured":"Liu, J., Zou, Z., Ye, X., Tan, X., Ding, E., Xu, F., Yu, X.: Leaping from 2D detection to efficient 6DoF object pose estimation. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12536, pp. 1\u201311. Springer, Cham (2020)","DOI":"10.1007\/978-3-030-66096-3_47"},{"key":"39_CR38","volume-title":"Fundamentals of Computer Graphics","author":"S Marschner","year":"2015","unstructured":"Marschner, S., Shirley, P.: Fundamentals of Computer Graphics. CRC Press, Boca Raton (2015)"},{"key":"39_CR39","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., et al.: KinectFusion: real-time dense surface mapping and tracking. In: ISMAR (2011)","DOI":"10.1109\/ISMAR.2011.6162880"},{"key":"39_CR40","doi-asserted-by":"crossref","unstructured":"Park, K., Patten, T., Vincze, M.: Pix2Pose: pixel-wise coordinate regression of objects for 6D pose estimation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00776"},{"key":"39_CR41","volume-title":"Physically Based Rendering: From Theory to Implementation","author":"M Pharr","year":"2016","unstructured":"Pharr, M., Jakob, W., Humphreys, G.: Physically Based Rendering: From Theory to Implementation. Morgan Kaufmann, Burlington (2016)"},{"key":"39_CR42","doi-asserted-by":"crossref","unstructured":"Qian, Y., Gong, M., Hong Yang, Y.: 3D reconstruction of transparent objects with position-normal consistency. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.473"},{"key":"39_CR43","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":"39_CR44","doi-asserted-by":"crossref","unstructured":"Raposo, C., Barreto, J.P.: Using 2 point+normal sets for fast registration of point clouds with small overlap. In: ICRA (2017)","DOI":"10.1109\/ICRA.2017.7989664"},{"issue":"2","key":"39_CR45","first-page":"1179","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. RA-L 1(2), 1179\u20131185 (2016)","journal-title":"RA-L"},{"issue":"6","key":"39_CR46","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1049\/htl.2019.0078","volume":"6","author":"P Rodrigues","year":"2019","unstructured":"Rodrigues, P., Antunes, M., Raposo, C., Marques, P., Fonseca, F., Barreto, J.: Deep segmentation leverages geometric pose estimation in computer-aided total knee arthroplasty. Healthc. Technol. Lett. 6(6), 226\u2013230 (2019)","journal-title":"Healthc. Technol. Lett."},{"key":"39_CR47","doi-asserted-by":"crossref","unstructured":"Sundermeyer, M., et al.: Multi-path learning for object pose estimation across domains. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01393"},{"key":"39_CR48","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1007\/978-3-030-01231-1_43","volume-title":"Computer Vision \u2013 ECCV 2018","author":"M Sundermeyer","year":"2018","unstructured":"Sundermeyer, M., Marton, Z.-C., Durner, M., Brucker, M., Triebel, R.: Implicit 3D orientation learning for 6D object detection from RGB images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 712\u2013729. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_43"},{"key":"39_CR49","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1007\/s11263-019-01243-8","volume":"128","author":"M Sundermeyer","year":"2019","unstructured":"Sundermeyer, M., Marton, Z.C., Durner, M., Triebel, R.: Augmented autoencoders: implicit 3D orientation learning for 6D object detection. IJCV 128, 714\u2013729 (2019). https:\/\/doi.org\/10.1007\/s11263-019-01243-8","journal-title":"IJCV"},{"key":"39_CR50","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":"39_CR51","doi-asserted-by":"publisher","first-page":"2678","DOI":"10.3390\/s18082678","volume":"18","author":"J Vidal","year":"2018","unstructured":"Vidal, J., Lin, C.Y., Llad\u00f3, X., Mart\u00ed, R.: A method for 6D pose estimation of free-form rigid objects using point pair features on range data. Sensors 18, 2678 (2018)","journal-title":"Sensors"},{"key":"39_CR52","doi-asserted-by":"crossref","unstructured":"Wu, B., Zhou, Y., Qian, Y., Cong, M., Huang, H.: Full 3D reconstruction of transparent objects. ACM TOG 37, 1\u201311 (2018)","DOI":"10.1145\/3197517.3201286"},{"key":"39_CR53","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":"39_CR54","doi-asserted-by":"crossref","unstructured":"Zakharov, S., Shugurov, I., Ilic, S.: DPOD: 6D pose object detector and refiner. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00203"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-66096-3_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T00:14:57Z","timestamp":1735776897000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-66096-3_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030660956","9783030660963"],"references-count":54,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-66096-3_39","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":"3 January 2021","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. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","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)"}}]}}