{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T22:40:05Z","timestamp":1749422405890,"version":"3.41.0"},"publisher-location":"Cham","reference-count":60,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030779764"},{"type":"electronic","value":"9783030779771"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-77977-1_3","type":"book-chapter","created":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T07:07:25Z","timestamp":1623222445000},"page":"31-44","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["State-of-the-Art in 3D Face Reconstruction from a Single RGB Image"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5417-0791","authenticated-orcid":false,"given":"Haibin","family":"Fu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1108-744X","authenticated-orcid":false,"given":"Shaojun","family":"Bian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6797-3614","authenticated-orcid":false,"given":"Ehtzaz","family":"Chaudhry","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5672-8274","authenticated-orcid":false,"given":"Andres","family":"Iglesias","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9738-4378","authenticated-orcid":false,"given":"Lihua","family":"You","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6403-4486","authenticated-orcid":false,"given":"Jian Jun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,9]]},"reference":[{"issue":"1","key":"3_CR1","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1023\/A:1012369907247","volume":"45","author":"W-Y Zhao","year":"2001","unstructured":"Zhao, W.-Y., Chellappa, R.: Symmetric shape-from-shading using self-ratio image. Int. J. Comput. Vis. 45(1), 55\u201375 (2001)","journal-title":"Int. J. Comput. Vis."},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Zhang, R., Tsai, P., Cryer, J.-E., Shah, M.: Shape-from-shading: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 21(8), 690\u2013706 (1999)","DOI":"10.1109\/34.784284"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lei, Z., Yan, J., Yi, D., Li, S.-Z.: High-fidelity pose and expression normalization for face recognition in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 787\u2013796 (2015)","DOI":"10.1109\/CVPR.2015.7298679"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Li, Y., Ma, L., Fan, H., Mitchell, K.: Feature-preserving detailed 3D face reconstruction from a single image. In: Proceedings of the 15th ACM SIGGRAPH European Conference on Visual Media Production, pp. 1\u20139 (2018)","DOI":"10.1145\/3278471.3278473"},{"issue":"3","key":"3_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2890493","volume":"35","author":"P Garrido","year":"2016","unstructured":"Garrido, P., et al.: Reconstruction of personalized 3D face rigs from monocular video. ACM Trans. Graph. (TOG) 35(3), 1\u201315 (2016)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Sengupta, S., Kanazawa, A., Castillo, C.-D., Jacobs, D.-W.: SfSNet: Learning shape, reflectance\nand illuminance of faces in the wild. In: Proceedings of the IEEE Conference on\nComputer Vision and Pattern Recognition, pp. 6296\u20136305 (2018)","DOI":"10.1109\/CVPR.2018.00659"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques (1999)","DOI":"10.1145\/311535.311556"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Tran, L., Liu, X.: Nonlinear 3D face morphable model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7346\u20137355 (2018)","DOI":"10.1109\/CVPR.2018.00767"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lei, Z., Liu, X.: Face alignment across large poses: a 3D solution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 146\u2013155 (2016)","DOI":"10.1109\/CVPR.2016.23"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Huber, P.: Real-time 3D morphable shape model fitting to monocular in-the-wild videos. University of Surrey (2017)","DOI":"10.1145\/2945078.2945145"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Paysan, P., Knothe, R., Amberg, B., Romdhani, S., Vetter, T.: A 3D face model for pose and illumination invariant face recognition. In: 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 296\u2013301. IEEE (2009)","DOI":"10.1109\/AVSS.2009.58"},{"issue":"2","key":"3_CR12","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1007\/s11071-019-05170-8","volume":"98","author":"P Liu","year":"2019","unstructured":"Liu, P., Yu, H., Cang, S.: Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances. Nonlinear Dyn. 98(2), 1447\u20131464 (2019)","journal-title":"Nonlinear Dyn."},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Gerig, T., et al.: Morphable face models-an open framework. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 75\u201382. IEEE (2018)","DOI":"10.1109\/FG.2018.00021"},{"issue":"5","key":"3_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3395208","volume":"39","author":"B Egger","year":"2020","unstructured":"Egger, B., Smith, W., Tewari, A., Wuhrer, A., Zollhoefer, M.: 3D morphable face models\u2014past, present, and future. ACM Trans. Graph. (TOG) 39(5), 1\u201338 (2020)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"2","key":"3_CR15","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1111\/cgf.13382","volume":"37","author":"M Zollh\u00f6fer","year":"2018","unstructured":"Zollh\u00f6fer, M., Thies, J., Garrido, P.: State of the art on monocular 3D face reconstruction, tracking, and applications. Comput. Graph. Forum 37(2), 523\u2013550 (2018)","journal-title":"Comput. Graph. Forum"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Sun, L., Zhao, C., Yan, Z., Liu, P., Duckett, T., Stolkin, R.: A novel weakly-supervised approach for RGB-D-based nuclear waste object detection. IEEE Sens. J. (2018)","DOI":"10.1109\/JSEN.2018.2888815"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Chen, A., Chen, Z., Zhang, G., Mitchell, K., Yu, J.: Photo-realistic facial details synthesis from single image. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9429\u20139439 (2019)","DOI":"10.1109\/ICCV.2019.00952"},{"issue":"3\u20134","key":"3_CR18","first-page":"59","volume":"13","author":"A Kasinski","year":"2008","unstructured":"Kasinski, A., Florek, A., Schmidt, A.: The PUT face database. Image Process. Commun. 13(3\u20134), 59\u201364 (2008)","journal-title":"Image Process. Commun."},{"issue":"5","key":"3_CR19","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1016\/j.imavis.2009.08.002","volume":"28","author":"R Gross","year":"2010","unstructured":"Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-pie. Image Vis. Comput. 28(5), 807\u2013813 (2010)","journal-title":"Image Vis. Comput."},{"key":"3_CR20","unstructured":"Milborrow, S., Morkel, J., Nicolls, F.: The MUCT landmarked face database. Pattern Recognit. Assoc. S. Afr. 201(0) (2010)"},{"issue":"3","key":"3_CR21","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1007\/s11042-009-0417-2","volume":"51","author":"M Grgic","year":"2011","unstructured":"Grgic, M., Delac, K., Grgic, S.: SCface\u2013surveillance cameras face database. Multimedia Tools Appl. 51(3), 863\u2013879 (2011)","journal-title":"Multimedia Tools Appl."},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Koestinger, M., Wohlhart, P., Roth, P., Bischof, H.: Annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization. In: 2011 IEEE International Conference on Computer Vision Workshops, pp. 2144\u20132151. IEEE (2011)","DOI":"10.1109\/ICCVW.2011.6130513"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Lui, Y.-M., Bolme, D., Phillips, P.-J., Beveridge, J.-R.: Preliminary studies on the good, the bad, and the ugly face recognition challenge problem. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 9\u201316. IEEE (2012)","DOI":"10.1109\/CVPRW.2012.6239209"},{"key":"3_CR24","unstructured":"Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: Conference on Computer Vision and Pattern Recognition. IEEE (2012)"},{"key":"3_CR25","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.imavis.2016.01.002","volume":"47","author":"C Sagonas","year":"2016","unstructured":"Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: 300 faces in-the-wild challenge: database and results. Image Vis. Comput. 47, 3\u201318 (2016)","journal-title":"Image Vis. Comput."},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Ng, H.-W., Winkler, S.: A data-driven approach to cleaning large face datasets. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 343\u2013347. IEEE (2014)","DOI":"10.1109\/ICIP.2014.7025068"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Parkhi, O.-M., Vedaldi, A., Zisserman, A.: Deep face recognition (2015)","DOI":"10.5244\/C.29.41"},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang. X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3730\u20133738 (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"3_CR29","doi-asserted-by":"crossref","unstructured":"Bansal, A., Nanduri, A., Castillo, C.-D., Ranjan, R., Chellappa, R.: UMDfaces: an annotated face dataset for training deep networks. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 464\u2013473. IEEE (2017)","DOI":"10.1109\/BTAS.2017.8272731"},{"key":"3_CR30","doi-asserted-by":"crossref","unstructured":"Shlizerman, I.-K., Seitz, S.-M., Miller, D., Brossard, E.: The MegaFace benchmark: 1 million faces for recognition at scale. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4873\u20134882 (2016)","DOI":"10.1109\/CVPR.2016.527"},{"key":"3_CR31","doi-asserted-by":"crossref","unstructured":"Bulat, A., Tzimiropoulos, G.: How far are we from solving the 2D & 3D face alignment problem. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1021\u20131030 (2017)","DOI":"10.1109\/ICCV.2017.116"},{"key":"3_CR32","unstructured":"Huang, G.-B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments (2008)"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"3_CR34","doi-asserted-by":"crossref","unstructured":"Panetta, K., et al.: A comprehensive database for benchmarking imaging systems. IEEE Trans. Pattern Anal. Mach. Intell. 42(3), 509\u2013520 (2020)","DOI":"10.1109\/TPAMI.2018.2884458"},{"key":"3_CR35","doi-asserted-by":"crossref","unstructured":"Faltemier, T.-C., Bowyer, K.-W., Flynn, P.: Using a multi-instance enrollment representation to improve 3D face recognition. In: 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp. 1\u20136. IEEE (2007)","DOI":"10.1109\/BTAS.2007.4401928"},{"key":"3_CR36","unstructured":"Yin, L., Wei, X., Sun, Y., Wang, J., Rosato, M.-J.: A 3D facial expression database for facial behavior research. In: 7th International Conference on Automatic Face and Gesture Recognition, pp. 211\u2013216. IEEE (2006)"},{"key":"3_CR37","doi-asserted-by":"crossref","unstructured":"Yin, L., Chen, X, Sun, Y., Worm, T., Reale, M.: 3D dynamic facial expression database. In: 8th\nInternational Conference on Automatic Face & Gesture Recognition, pp. 1\u20136. IEEE (2008)","DOI":"10.1109\/AFGR.2008.4813324"},{"issue":"3","key":"3_CR38","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.imavis.2006.12.008","volume":"26","author":"T Heseltine","year":"2008","unstructured":"Heseltine, T., Pears, N.: Three-dimensional face recognition using combinations of surface feature map subspace components. Image Vis. Comput. 26(3), 382\u2013396 (2008)","journal-title":"Image Vis. Comput."},{"key":"3_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/978-3-540-89991-4_6","volume-title":"Biometrics and Identity Management","author":"A Savran","year":"2008","unstructured":"Savran, A., et al.: Bosphorus database for 3D face analysis. In: Schouten, B., Juul, N.C., Drygajlo, A., Tistarelli, M. (eds.) BioID 2008. LNCS, vol. 5372, pp. 47\u201356. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-89991-4_6"},{"issue":"6","key":"3_CR40","first-page":"1009","volume":"46","author":"B Yin","year":"2009","unstructured":"Yin, B., Sun, Y., Wang, C., Ge, Y.: BJUT-3D large scale 3D face database and information processing. J. Comput. Res. Dev. 46(6), 1009 (2009)","journal-title":"J. Comput. Res. Dev."},{"key":"3_CR41","doi-asserted-by":"crossref","unstructured":"Cosker, D., Krumhuber, E., Hilton, A.: A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling. In: 2011 International Conference on Computer Vision, pp. 2296\u20132303. IEEE (2011)","DOI":"10.1109\/ICCV.2011.6126510"},{"key":"3_CR42","doi-asserted-by":"crossref","unstructured":"Bagdanov, A.-D., Bimbo, A.-D.: The florence 2D\/3D hybrid face dataset. In: Proceedings of ACM Workshop on Human Gesture and Behavior Understanding, pp. 79\u201380 (2011)","DOI":"10.1145\/2072572.2072597"},{"key":"3_CR43","doi-asserted-by":"crossref","unstructured":"Cao, C., Weng, Y., Zhou, S., Tong, Y., Zhou., K.: FaceWarehouse: a 3D facial expression database for visual computing. IEEE Trans. Vis. Comput. Graph. 20(3), 413\u2013425 (2013)","DOI":"10.1109\/TVCG.2013.249"},{"issue":"10","key":"3_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.imavis.2014.06.002","volume":"32","author":"X Zhang","year":"2014","unstructured":"Zhang, X., Yin, L., Cohn, J.-F.: BP4D-spontaneous: a high-resolution spontaneous 3D dynamic facial expression database. Image Vis. Comput. 32(10), 1\u20136 (2014)","journal-title":"Image Vis. Comput."},{"key":"3_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, Z., et al.: Multimodal spontaneous emotion corpus for human behavior analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3438\u20133446 (2016)","DOI":"10.1109\/CVPR.2016.374"},{"key":"3_CR46","doi-asserted-by":"crossref","unstructured":"Le, H.-A., Kakadiaris, I.-A.: UHDB31: a dataset for better understanding face recognition across pose and illumination variation. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 2555\u20132563 (2017)","DOI":"10.1109\/ICCVW.2017.300"},{"key":"3_CR47","doi-asserted-by":"crossref","unstructured":"Cheng, S., Kotsia, I., Pantic, M., Zafeiriou, S.: 4DFAB: a large scale 4D database for facial expression analysis and biometric applications. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5117\u20135126 (2018)","DOI":"10.1109\/CVPR.2018.00537"},{"key":"3_CR48","doi-asserted-by":"crossref","unstructured":"Ertugrul, I.-O., Cohn, J.-F., Jeni, L.-A., Zhang, A., Yin, L. Ji, Q.: Cross-domain au detection: domains, learning approaches, and measures. In: 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/FG.2019.8756543"},{"key":"3_CR49","doi-asserted-by":"crossref","unstructured":"Yang, H., Zhu, H., Wang, Y., Huang, M., Shen, Q.: FaceScape: a large-scale high quality 3D face dataset and detailed riggable 3D face prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 601\u2013610 (2020)","DOI":"10.1109\/CVPR42600.2020.00068"},{"issue":"2\u20134","key":"3_CR50","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s11263-017-1009-7","volume":"126","author":"J Booth","year":"2018","unstructured":"Booth, J., Roussos, A., Ponniah, A., Dunaway, D., Zafeiriou, S.: Large scale 3D morphable models. Int. J. Comput. Vis. 126(2\u20134), 233\u2013254 (2018)","journal-title":"Int. J. Comput. Vis."},{"key":"3_CR51","doi-asserted-by":"crossref","unstructured":"Dai, H., Pears, N., Smith, W.-A., Duncan, C.: A 3D morphable model of craniofacial shape and texture variation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3085\u20133093 (2017)","DOI":"10.1109\/ICCV.2017.335"},{"key":"3_CR52","doi-asserted-by":"crossref","unstructured":"Chang, F., Tran, A.-T., Hassner, T., Masi, I., Nevatia, R., Medioni, G.: ExpNet: landmark-free, deep, 3D facial expressions. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 122\u2013129. IEEE (2018)","DOI":"10.1109\/FG.2018.00027"},{"issue":"11","key":"3_CR53","doi-asserted-by":"publisher","first-page":"2638","DOI":"10.1109\/TPAMI.2018.2832138","volume":"40","author":"J Booth","year":"2018","unstructured":"Booth, J., Roussos, A., Ververas, E., Antonakos, E., Ploumpis, S., Panagakis, Y.: 3D reconstruction of \u201cin-the-wild\u201d faces in images and videos. IEEE Trans. Pattern Anal. Mach. Intell. 40(11), 2638\u20132652 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR54","doi-asserted-by":"crossref","unstructured":"Shang, J., Shen, T., Li, S., Zhou, L., Zhen, M.: Self-supervised monocular 3D face reconstruction by occlusion-aware multi-view geometry consistency (2020)","DOI":"10.1007\/978-3-030-58555-6_4"},{"key":"3_CR55","doi-asserted-by":"crossref","unstructured":"Feng, Y., Wu, F., Shao, X., Wang, Y., Zhou, X.: Joint 3D face reconstruction and dense alignment with position map regression network. In: Proceedings of the European Conference on Computer Vision, pp. 534\u2013551 (2018)","DOI":"10.1007\/978-3-030-01264-9_33"},{"key":"3_CR56","doi-asserted-by":"crossref","unstructured":"Zeng, Xi., Peng, X., Qiao, Y.: DF2Net: a dense-fine-finer network for detailed 3D face reconstruction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2315\u20132324 (2019)","DOI":"10.1109\/ICCV.2019.00240"},{"key":"3_CR57","unstructured":"Zhang, J., Cai, H., Guo, Y., Peng, Z.: Landmark detection and 3D face reconstruction for caricature using a nonlinear parametric model (2020)"},{"key":"3_CR58","doi-asserted-by":"crossref","unstructured":"Browatzki, B., Wallraven, C.: 3FabRec: fast few-shot face alignment by reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6110\u20136120 (2020)","DOI":"10.1109\/CVPR42600.2020.00615"},{"key":"3_CR59","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1\u201310 (2020)","DOI":"10.1109\/CVPR42600.2020.00008"},{"key":"3_CR60","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"MICCAI 2015. LNCS","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., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-77977-1_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T22:03:13Z","timestamp":1749420193000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-77977-1_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030779764","9783030779771"],"references-count":60,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-77977-1_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Krakow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"156","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":"48","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":"14","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":"31% - 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":"2.8","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":"3.9","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":"212 full and 43 short papers were selected from 479 submissions to the workshops\/ thematic tracks. The conference was held virtually.","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)"}}]}}