{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T08:15:48Z","timestamp":1774685748403,"version":"3.50.1"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031198267","type":"print"},{"value":"9783031198274","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19827-4_18","type":"book-chapter","created":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T14:42:19Z","timestamp":1667313739000},"page":"300-316","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Towards Learning Neural Representations from\u00a0Shadows"],"prefix":"10.1007","author":[{"given":"Kushagra","family":"Tiwary","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tzofi","family":"Klinghoffer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramesh","family":"Raskar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"issue":"2","key":"18_CR1","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/34.121791","volume":"14","author":"P Besl","year":"1992","unstructured":"Besl, P., McKay, N.D.: A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239\u2013256 (1992). https:\/\/doi.org\/10.1109\/34.121791","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Bobrow, D.G.: Comment on \u201cNumerical shape from shading and occluding boundaries\", pp. 89\u201394. The MIT Press (1994)","DOI":"10.1016\/0004-3702(93)90174-A"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Boss, M., Braun, R., Jampani, V., Barron, J.T., Liu, C., Lensch, H.P.: Nerd: neural reflectance decomposition from image collections. In: IEEE International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01245"},{"key":"18_CR4","unstructured":"Chang, A.X., et al.: ShapeNet: an Information-Rich 3D Model Repository. Technical report arXiv:1512.03012 [cs.GR], Stanford University \u2013 Princeton University \u2013 Toyota Technological Institute at Chicago (2015)"},{"key":"18_CR5","unstructured":"Falcon, W., et al.: Pytorch lightning. GitHub. Note (2019): https:\/\/github.com\/PyTorchLightning\/pytorch-lightning 3"},{"key":"18_CR6","unstructured":"Guo, Y., Kang, D., Bao, L., He, Y., Zhang, S.: Nerfren: neural radiance fields with reflections. CoRR abs\/2111.15234 (2021). https:\/\/arxiv.org\/abs\/2111.15234"},{"key":"18_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/978-3-030-58526-6_34","volume-title":"Computer Vision \u2013 ECCV 2020","author":"C Henley","year":"2020","unstructured":"Henley, C., Maeda, T., Swedish, T., Raskar, R.: Imaging behind occluders using two-bounce light. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 573\u2013588. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58526-6_34"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Kato, H., Ushiku, Y., Harada, T.: Neural 3d mesh renderer. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00411"},{"key":"18_CR9","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.cviu.2008.02.006","volume":"112","author":"JL Landabaso","year":"2008","unstructured":"Landabaso, J.L., Pard\u00e0s, M., Casas, J.R.: Shape from inconsistent silhouette. Comput. Vis. Image Underst. 112, 210\u2013224 (2008)","journal-title":"Comput. Vis. Image Underst."},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Li, T.M., Aittala, M., Durand, F., Lehtinen, J.: Differentiable monte carlo ray tracing through edge sampling. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 37(6), 222:1\u2013222:11 (2018)","DOI":"10.1145\/3272127.3275109"},{"key":"18_CR11","unstructured":"Liu, R., Menon, S., Mao, C., Park, D., Stent, S., Vondrick, C.: Shadows shed light on 3d objects. arXiv e-prints pp. arXiv-2206 (2022)"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Liu, S., Li, T., Chen, W., Li, H.: Soft rasterizer: a differentiable renderer for image-based 3d reasoning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7708\u20137717 (2019)","DOI":"10.1109\/ICCV.2019.00780"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Lombardi, S., Simon, T., Saragih, J., Schwartz, G., Lehrmann, A., Sheikh, Y.: Neural volumes: learning dynamic renderable volumes from images. ACM Trans. Graph. 38(4), 65:1\u201365:14 (2019)","DOI":"10.1145\/3306346.3323020"},{"key":"18_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/978-3-319-10584-0_11","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MM Loper","year":"2014","unstructured":"Loper, M.M., Black, M.J.: OpenDR: an approximate differentiable renderer. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 154\u2013169. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10584-0_11"},{"issue":"4","key":"18_CR15","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1145\/37402.37422","volume":"21","author":"WE Lorensen","year":"1987","unstructured":"Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3d surface construction algorithm. ACM Siggraph Comput. Graph. 21(4), 163\u2013169 (1987)","journal-title":"ACM Siggraph Comput. Graph."},{"key":"18_CR16","doi-asserted-by":"publisher","unstructured":"Martin, W.N., Aggarwal, J.K.: Volumetric descriptions of objects from multiple views. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-5(2), 150\u2013158 (1983). https:\/\/doi.org\/10.1109\/TPAMI.1983.4767367","DOI":"10.1109\/TPAMI.1983.4767367"},{"key":"18_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/978-3-030-58452-8_24","volume-title":"Computer Vision \u2013 ECCV 2020","author":"B Mildenhall","year":"2020","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 405\u2013421. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_24"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Geiger, A.: GIRAFFE: representing scenes as compositional generative neural feature fields (2020). https:\/\/arxiv.org\/abs\/2011.12100","DOI":"10.1109\/CVPR46437.2021.01129"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: Learning implicit 3D representations without 3D supervision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR42600.2020.00356"},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00356"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"Nimier-David, M., Vicini, D., Zeltner, T., Jakob, W.: Mitsuba 2: a retargetable forward and inverse renderer. ACM Trans. Graph. (TOG) 38(6), 1\u201317 (2019)","DOI":"10.1145\/3355089.3356498"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: Learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 165\u2013174 (2019)","DOI":"10.1109\/CVPR.2019.00025"},{"key":"18_CR23","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Adv. Neural Inf. Process. Syst. 32, pp. 8024\u20138035. Curran Associates, Inc. (2019). https:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"18_CR24","unstructured":"Quei-An, C.: Nerf_pl: a pytorch-lightning implementation of nerf (2020). https:\/\/github.com\/kwea123\/nerf_pl\/"},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"18_CR26","doi-asserted-by":"crossref","unstructured":"Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., Kanazawa, A.: Plenoxels: radiance fields without neural networks. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00542"},{"key":"18_CR27","doi-asserted-by":"crossref","unstructured":"Savarese, S., Rushmeier, H., Bernardini, F., Perona, P.: Shadow carving. In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, vol. 1, pp. 190\u2013197. IEEE (2001)","DOI":"10.1109\/ICCV.2001.937517"},{"key":"18_CR28","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nberger, J.L., Frahm, J.-M.: Structure-from-Motion Revisited. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.445"},{"key":"18_CR29","doi-asserted-by":"crossref","unstructured":"Sitzmann, V., Thies, J., Heide, F., Nie\u00dfner, M., Wetzstein, G., Zollhofer, M.: Deepvoxels: learning persistent 3d feature embeddings. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2437\u20132446 (2019)","DOI":"10.1109\/CVPR.2019.00254"},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Srinivasan, P.P., Deng, B., Zhang, X., Tancik, M., Mildenhall, B., Barron, J.T.: Nerv: neural reflectance and visibility fields for relighting and view synthesis (2020)","DOI":"10.1109\/CVPR46437.2021.00741"},{"key":"18_CR31","unstructured":"Tancik, M., et al.: Fourier features let networks learn high frequency functions in low dimensional domains (2020)"},{"key":"18_CR32","doi-asserted-by":"crossref","unstructured":"Tulsiani, S., Efros, A.A., Malik, J.: Multi-view consistency as supervisory signal for learning shape and pose prediction. In: Computer Vision and Pattern Regognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00306"},{"key":"18_CR33","doi-asserted-by":"crossref","unstructured":"Velten, A., Willwacher, T., Gupta, O., Veeraraghavan, A., Bawendi, M.G., Raskar, R.: Recovering threedimensional shape around a corner using ultrafast time-of-flight imaging. Nature, p. 745 (2012)","DOI":"10.1038\/ncomms1747"},{"key":"18_CR34","doi-asserted-by":"crossref","unstructured":"Verbin, D., Hedman, P., Mildenhall, B., Zickler, T., Barron, J.T., Srinivasan, P.P.: Ref-NeRF: structured view-dependent appearance for neural radiance fields. arXiv (2021)","DOI":"10.1109\/CVPR52688.2022.00541"},{"key":"18_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/978-3-642-03798-6_20","volume-title":"Pattern Recognition","author":"O Vogel","year":"2009","unstructured":"Vogel, O., Valgaerts, L., Breu\u00df, M., Weickert, J.: Making shape from shading work for real-world images. In: Denzler, J., Notni, G., S\u00fc\u00dfe, H. (eds.) DAGM 2009. LNCS, vol. 5748, pp. 191\u2013200. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-03798-6_20"},{"key":"18_CR36","doi-asserted-by":"crossref","unstructured":"Williams, L.: Casting curved shadows on curved surfaces. In: Proceedings of the 5th Annual Conference on Computer Graphics and Interactive Techniques, pp. 270\u2013274 (1978)","DOI":"10.1145\/965139.807402"},{"key":"18_CR37","doi-asserted-by":"publisher","unstructured":"Yamazaki, S., Srinivasa Narasimhan, G., Baker, S., Kanade, T.: The theory and practice of coplanar shadowgram imaging for acquiring visual hulls of intricate objects. Int. J. Comput. Vis. 81, March 2009. https:\/\/doi.org\/10.1007\/s11263-008-0170-4","DOI":"10.1007\/s11263-008-0170-4"},{"key":"18_CR38","doi-asserted-by":"crossref","unstructured":"Ye, Y., Tulsiani, S., Gupta, A.: Shelf-supervised mesh prediction in the wild. In: Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00873"},{"key":"18_CR39","doi-asserted-by":"crossref","unstructured":"Yu, A., Li, R., Tancik, M., Li, H., Ng, R., Kanazawa, A.: PlenOctrees for real-time rendering of neural radiance fields. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00570"},{"key":"18_CR40","unstructured":"Zhang, J.Y., Yang, G., Tulsiani, S., Ramanan, D.: NeRS: neural reflectance surfaces for sparse-view 3d reconstruction in the wild. In: Conference on Neural Information Processing Systems (2021)"},{"issue":"8","key":"18_CR41","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1109\/34.784284","volume":"21","author":"R Zhang","year":"1999","unstructured":"Zhang, R., Tsai, P.S., Cryer, J., Shah, M.: Shape-from-shading: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 21(8), 690\u2013706 (1999). https:\/\/doi.org\/10.1109\/34.784284","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"18_CR42","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1109\/34.85658","volume":"13","author":"Q Zheng","year":"1991","unstructured":"Zheng, Q., Chellappa, R.: Estimation of illuminant direction, albedo, and shape from shading. IEEE Trans. Pattern Anal. Mach. Intell. 13(7), 680\u2013702 (1991). https:\/\/doi.org\/10.1109\/34.85658","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19827-4_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T01:00:51Z","timestamp":1728262851000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19827-4_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198267","9783031198274"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19827-4_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}