{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T14:52:44Z","timestamp":1786114364705,"version":"3.56.0"},"publisher-location":"Cham","reference-count":69,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200731","type":"print"},{"value":"9783031200748","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\/978-3-031-20074-8_11","type":"book-chapter","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T20:23:11Z","timestamp":1668198191000},"page":"181-200","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Facial Depth and\u00a0Normal Estimation Using Single Dual-Pixel Camera"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3102-7591","authenticated-orcid":false,"given":"Minjun","family":"Kang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8978-6702","authenticated-orcid":false,"given":"Jaesung","family":"Choe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2824-917X","authenticated-orcid":false,"given":"Hyowon","family":"Ha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1105-1666","authenticated-orcid":false,"given":"Hae-Gon","family":"Jeon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9776-8101","authenticated-orcid":false,"given":"Sunghoon","family":"Im","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9626-5983","authenticated-orcid":false,"given":"In So","family":"Kweon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1634-2756","authenticated-orcid":false,"given":"Kuk-Jin","family":"Yoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,12]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Aan\u00e6s, H., Jensen, R.R., Vogiatzis, G., Tola, E., Dahl, A.B.: Large-scale data for multiple-view stereopsis. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1\u201316 (2016)","DOI":"10.1007\/s11263-016-0902-9"},{"key":"11_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/978-3-030-58607-2_7","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Abuolaim","year":"2020","unstructured":"Abuolaim, A., Brown, M.S.: Defocus deblurring using dual-pixel data. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12355, pp. 111\u2013126. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58607-2_7"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Abuolaim, A., Delbracio, M., Kelly, D., Brown, M.S., Milanfar, P.: Learning to reduce defocus blur by realistically modeling dual-pixel data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 2289\u20132298 (2021)","DOI":"10.1109\/ICCV48922.2021.00229"},{"key":"11_CR4","unstructured":"Apple: Apple iphone 11 pro (2019). https:\/\/www.apple.com\/iphone-11-pro\/, Accessed 20 Sept 2019"},{"key":"11_CR5","unstructured":"ARCore: Augmented faces. https:\/\/developers.google.com\/ar\/develop\/java\/augmented-faces (2019), accessed: 2019\u201312-18"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Bai, Z., Cui, Z., Rahim, J.A., Liu, X., Tan, P.: Deep facial non-rigid multi-view stereo. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5850\u20135860 (2020)","DOI":"10.1109\/CVPR42600.2020.00589"},{"key":"11_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, pp. 187\u2013194 (1999)","DOI":"10.1145\/311535.311556"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Boss, M., Jampani, V., Kim, K., Lensch, H., Kautz, J.: Two-shot spatially-varying brdf and shape estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3982\u20133991 (2020)","DOI":"10.1109\/CVPR42600.2020.00404"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Chang, J.R., Chen, Y.S.: Pyramid stereo matching network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00567"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Chen, C.H., Zhou, H., Ahonen, T.: Blur-aware disparity estimation from defocus stereo images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 855\u2013863 (2015)","DOI":"10.1109\/ICCV.2015.104"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV) (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Chen, W., Mirdehghan, P., Fidler, S., Kutulakos, K.N.: Auto-tuning structured light by optical stochastic gradient descent. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00601"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Deng, Y., Yang, J., Xu, S., Chen, D., Jia, Y., Tong, X.: Accurate 3D face reconstruction with weakly-supervised learning: From single image to image set. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00038"},{"key":"11_CR14","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 (ECCV) (2018)","DOI":"10.1007\/978-3-030-01264-9_33"},{"key":"11_CR15","unstructured":"Galaxy: Samsung galaxy s10 (2019). https:\/\/www.samsung.com\/us\/mobile\/galaxy-s10\/, Accessed 08 Mar 2019"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Garg, R., Wadhwa, N., Ansari, S., Barron, J.T.: Learning single camera depth estimation using dual-pixels. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00772"},{"issue":"11","key":"11_CR17","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: The kitti dataset. Int. J. Rob. Res. 32(11), 1231\u20131237 (2013)","journal-title":"Int. J. Rob. Res."},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354\u20133361. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"11_CR19","unstructured":"Google: Google photos: One year, 200 million users, and a whole lot of selfies (2016). https:\/\/blog.google\/products\/photos\/google-photos-one-year-200-million\/, Accessed 27 May 2016"},{"key":"11_CR20","unstructured":"Google: More controls and transparency for your selfies (2020). https:\/\/blog.google\/outreach-initiatives\/digital-wellbeing\/more-controls-selfie-filters\/, Accessed 01 Oct 2020"},{"key":"11_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1007\/978-3-030-58529-7_10","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Guo","year":"2020","unstructured":"Guo, J., Zhu, X., Yang, Y., Yang, F., Lei, Z., Li, S.Z.: Towards fast, accurate and stable 3D dense face alignment. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12364, pp. 152\u2013168. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58529-7_10"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Ha, H., Oh, T.H., Kweon, I.S.: A multi-view structured-light system for highly accurate 3D modeling. In: International Conference on 3D Vision (3DV) (2015)","DOI":"10.1109\/3DV.2015.21"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Ha, H., Park, J., Kweon, I.S.: Dense depth and albedo from a single-shot structured light. In: International Conference on 3D Vision (3DV), pp. 127\u2013134 (2015)","DOI":"10.1109\/3DV.2015.22"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Han, Y., Lee, J.Y., So Kweon, I.: High quality shape from a single rgb-d image under uncalibrated natural illumination. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2013)","DOI":"10.1109\/ICCV.2013.204"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Hu, P., Ramanan, D.: Finding tiny faces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 951\u2013959 (2017)","DOI":"10.1109\/CVPR.2017.166"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Im, S., Ha, H., Choe, G., Jeon, H.G., Joo, K., Kweon, I.S.: High quality structure from small motion for rolling shutter cameras. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.102"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Jensen, R., Dahl, A., Vogiatzis, G., Tola, E., Aan\u00e6s, H.: Large scale multi-view stereopsis evaluation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2014)","DOI":"10.1109\/CVPR.2014.59"},{"issue":"2","key":"11_CR28","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1109\/TPAMI.2018.2794979","volume":"41","author":"HG Jeon","year":"2018","unstructured":"Jeon, H.G., Park, J., Choe, G., Park, J., Bok, Y., Tai, Y.W., Kweon, I.S.: Depth from a light field image with learning-based matching costs. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 297\u2013310 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Jeon, H.G., Pet al.: Accurate depth map estimation from a lenslet light field camera. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298762"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Keselman, L., Iselin Woodfill, J., Grunnet-Jepsen, A., Bhowmik, A.: Intel realsense stereoscopic depth cameras. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2017)","DOI":"10.1109\/CVPRW.2017.167"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Khamis, S., Fanello, S., Rhemann, C., Kowdle, A., Valentin, J., Izadi, S.: Stereonet: guided hierarchical refinement for real-time edge-aware depth prediction. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 573\u2013590 (2018)","DOI":"10.1007\/978-3-030-01267-0_35"},{"key":"11_CR32","unstructured":"Kinect2: Kinect for windows sdk 2.0 (2014). https:\/\/developer.microsoft.com\/en-us\/windows\/kinect\/, Accessed 21 Oct 2014"},{"key":"11_CR33","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Kusupati, U., Cheng, S., Chen, R., Su, H.: Normal assisted stereo depth estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00226"},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Lattas, A., et al.: Avatarme: Realistically renderable 3D facial reconstruction \u201c in-the-wild\u201d. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 760\u2013769 (2020)","DOI":"10.1109\/CVPR42600.2020.00084"},{"key":"11_CR36","unstructured":"Lee, J.H., Han, M.K., Ko, D.W., Suh, I.H.: From big to small: multi-scale local planar guidance for monocular depth estimation. arXiv preprint arXiv:1907.10326 (2019)"},{"key":"11_CR37","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.neucom.2020.05.076","volume":"410","author":"J Liang","year":"2020","unstructured":"Liang, J., Tu, H., Liu, F., Zhao, Q., Jain, A.K.: 3D face reconstruction from mugshots: application to arbitrary view face recognition. Neurocomputing 410, 12\u201327 (2020)","journal-title":"Neurocomputing"},{"key":"11_CR38","doi-asserted-by":"crossref","unstructured":"Lichy, D., Wu, J., Sengupta, S., Jacobs, D.W.: Shape and material capture at home. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6123\u20136133 (2021)","DOI":"10.1109\/CVPR46437.2021.00606"},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.106"},{"issue":"3","key":"11_CR40","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1109\/TPAMI.2018.2885995","volume":"42","author":"F Liu","year":"2018","unstructured":"Liu, F., Zhao, Q., Liu, X., Zeng, D.: Joint face alignment and 3D face reconstruction with application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 42(3), 664\u2013678 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR41","doi-asserted-by":"crossref","unstructured":"Long, X., et al.: Adaptive surface normal constraint for depth estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01261"},{"key":"11_CR42","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1007\/978-3-030-58545-7_37","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Long","year":"2020","unstructured":"Long, X., Liu, L., Theobalt, C., Wang, W.: Occlusion-aware depth estimation with adaptive normal constraints. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 640\u2013657. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58545-7_37"},{"key":"11_CR43","doi-asserted-by":"crossref","unstructured":"Luo, H., et al.: Normalized avatar synthesis using stylegan and perceptual refinement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11662\u201311672 (2021)","DOI":"10.1109\/CVPR46437.2021.01149"},{"issue":"3","key":"11_CR44","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1145\/1073204.1073226","volume":"24","author":"D Nehab","year":"2005","unstructured":"Nehab, D., Rusinkiewicz, S., Davis, J., Ramamoorthi, R.: Efficiently combining positions and normals for precise 3D geometry. ACM Trans. Graph. (ToG) 24(3), 536\u2013543 (2005)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"11_CR45","doi-asserted-by":"crossref","unstructured":"Pan, L., Chowdhury, S., Hartley, R., Liu, M., Zhang, H., Li, H.: Dual pixel exploration: simultaneous depth estimation and image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4340\u20134349 (2021)","DOI":"10.1109\/CVPR46437.2021.00432"},{"key":"11_CR46","doi-asserted-by":"crossref","unstructured":"Punnappurath, A., Abuolaim, A., Afifi, M., Brown, M.S.: Modeling defocus-disparity in dual-pixel sensors. In: 2020 IEEE International Conference on Computational Photography (ICCP) (2020)","DOI":"10.1109\/ICCP48838.2020.9105278"},{"key":"11_CR47","doi-asserted-by":"crossref","unstructured":"Qi, X., Liao, R., Liu, Z., Urtasun, R., Jia, J.: Geonet: geometric neural network for joint depth and surface normal estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 283\u2013291 (2018)","DOI":"10.1109\/CVPR.2018.00037"},{"key":"11_CR48","doi-asserted-by":"crossref","unstructured":"Qiu, J., et al.: Deeplidar: deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00343"},{"key":"11_CR49","doi-asserted-by":"crossref","unstructured":"Richardson, E., Sela, M., Or-El, R., Kimmel, R.: Learning detailed face reconstruction from a single image. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1259\u20131268 (2017)","DOI":"10.1109\/CVPR.2017.589"},{"key":"11_CR50","unstructured":"Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1 (2003)"},{"key":"11_CR51","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/978-3-319-46487-9_31","volume-title":"Computer Vision \u2013 ECCV 2016","author":"JL Sch\u00f6nberger","year":"2016","unstructured":"Sch\u00f6nberger, J.L., Zheng, E., Frahm, J.-M., Pollefeys, M.: Pixelwise view selection for unstructured multi-view stereo. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 501\u2013518. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_31"},{"key":"11_CR52","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-030-58555-6_4","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Shang","year":"2020","unstructured":"Shang, J., et al.: Self-supervised monocular 3D face reconstruction by occlusion-aware multi-view geometry consistency. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12360, pp. 53\u201370. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58555-6_4"},{"key":"11_CR53","doi-asserted-by":"crossref","unstructured":"Shi, B., Wu, Z., Mo, Z., Duan, D., Yeung, S.K., Tan, P.: A benchmark dataset and evaluation for non-lambertian and uncalibrated photometric stereo. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.403"},{"key":"11_CR54","doi-asserted-by":"crossref","unstructured":"Silberman, N., Fergus, R.: Indoor scene segmentation using a structured light sensor. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) - Workshop on 3D Representation and Recognition (2011)","DOI":"10.1109\/ICCVW.2011.6130298"},{"key":"11_CR55","doi-asserted-by":"publisher","first-page":"103897","DOI":"10.1016\/j.imavis.2020.103897","volume":"96","author":"G Song","year":"2020","unstructured":"Song, G., Zheng, J., Cai, J., Cham, T.J.: Recovering facial reflectance and geometry from multi-view images. Image Vision Comput. 96, 103897 (2020)","journal-title":"Image Vision Comput."},{"key":"11_CR56","doi-asserted-by":"crossref","unstructured":"Tran, L., Liu, X.: Nonlinear 3D face morphable model. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7346\u20137355 (2018)","DOI":"10.1109\/CVPR.2018.00767"},{"issue":"4","key":"11_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3197517.3201329","volume":"37","author":"N Wadhwa","year":"2018","unstructured":"Wadhwa, N.: Synthetic depth-of-field with a single-camera mobile phone. ACM Trans. Graph. (ToG) 37(4), 1\u201313 (2018)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"11_CR58","doi-asserted-by":"crossref","unstructured":"Wu, F., et al.: Mvf-net: Multi-view 3D face morphable model regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 959\u2013968 (2019)","DOI":"10.1109\/CVPR.2019.00105"},{"key":"11_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 (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00008"},{"key":"11_CR60","doi-asserted-by":"publisher","first-page":"1440","DOI":"10.1109\/TIFS.2020.3035879","volume":"16","author":"X Wu","year":"2020","unstructured":"Wu, X., Zhou, J., Liu, J., Ni, F., Fan, H.: Single-shot face anti-spoofing for dual pixel camera. IEEE Trans. Inf. Forensics Secur. 16, 1440\u20131451 (2020)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"11_CR61","doi-asserted-by":"crossref","unstructured":"Xin, S., et al.: Defocus map estimation and deblurring from a single dual-pixel image. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00223"},{"key":"11_CR62","doi-asserted-by":"crossref","unstructured":"Xu, H., Zhang, J.: Aanet: adaptive aggregation network for efficient stereo matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1959\u20131968 (2020)","DOI":"10.1109\/CVPR42600.2020.00203"},{"key":"11_CR63","doi-asserted-by":"crossref","unstructured":"Yang, F., Wang, J., Shechtman, E., Bourdev, L., Metaxas, D.: Expression flow for 3D-aware face component transfer. In: ACM SIGGRAPH 2011 papers, pp. 1\u201310 (2011)","DOI":"10.1145\/2010324.1964955"},{"key":"11_CR64","doi-asserted-by":"publisher","first-page":"1500","DOI":"10.1109\/LSP.2020.3013518","volume":"27","author":"X Ying","year":"2020","unstructured":"Ying, X., Wang, L., Wang, Y., Sheng, W., An, W., Guo, Y.: Deformable 3D convolution for video super-resolution. IEEE Signal Process. Lett. 27, 1500\u20131504 (2020)","journal-title":"IEEE Signal Process. Lett."},{"key":"11_CR65","doi-asserted-by":"crossref","unstructured":"Yu, Z., Qin, Y., Li, X., Zhao, C., Lei, Z., Zhao, G.: Deep learning for face anti-spoofing: a survey. arXiv preprint arXiv:2106.14948 (2021)","DOI":"10.1109\/TPAMI.2022.3215850"},{"key":"11_CR66","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\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 185\u2013194 (2019)","DOI":"10.1109\/CVPR.2019.00027"},{"key":"11_CR67","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1007\/978-3-030-58452-8_34","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Wadhwa, N., Orts-Escolano, S., H\u00e4ne, C., Fanello, S., Garg, R.: Du2Net: learning depth estimation from dual-cameras and dual-pixels. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 582\u2013598. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_34"},{"key":"11_CR68","doi-asserted-by":"crossref","unstructured":"Zhou, H., Hadap, S., Sunkavalli, K., Jacobs, D.W.: Deep single-image portrait relighting. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00729"},{"key":"11_CR69","doi-asserted-by":"crossref","unstructured":"Zollh\u00f6fer, M., et al.: State of the art on monocular 3D face reconstruction, tracking, and applications. In: Computer Graphics Forum, vol. 37, pp. 523\u2013550. Wiley Online Library (2018)","DOI":"10.1111\/cgf.13382"}],"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\/978-3-031-20074-8_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T20:26:11Z","timestamp":1668198371000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20074-8_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200731","9783031200748"],"references-count":69,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20074-8_11","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":"12 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)"}}]}}