{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:08:16Z","timestamp":1743113296252,"version":"3.40.3"},"publisher-location":"Cham","reference-count":54,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031198113"},{"type":"electronic","value":"9783031198120"}],"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\/978-3-031-19812-0_37","type":"book-chapter","created":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T14:03:42Z","timestamp":1667052222000},"page":"641-658","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Initialization and\u00a0Alignment for\u00a0Adversarial Texture Optimization"],"prefix":"10.1007","author":[{"given":"Xiaoming","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Zhizhen","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Alexander G.","family":"Schwing","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,30]]},"reference":[{"key":"37_CR1","unstructured":"Augmented reality - Apple developer. https:\/\/developer.apple.com\/augmented-reality\/ (2021). Accessed 14 Nov 2021"},{"key":"37_CR2","unstructured":"Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: OSDI (2016)"},{"key":"37_CR3","unstructured":"Abdelhafiz, A., Mostafa, Y.G.: Automatic texture mapping mega-projects. J. Spat. Sci. (2020)"},{"key":"37_CR4","doi-asserted-by":"crossref","unstructured":"Aganj, E., Monasse, P., Keriven, R.: Multi-view texturing of imprecise mesh. In: ACCV (2009)","DOI":"10.1007\/978-3-642-12304-7_44"},{"key":"37_CR5","doi-asserted-by":"crossref","unstructured":"Aliev, K.A., Ulyanov, D., Lempitsky, V.S.: Neural point-based graphics. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58542-6_42"},{"key":"37_CR6","doi-asserted-by":"crossref","unstructured":"Anuta, P.E.: Spatial registration of multispectral and multitemporal digital imagery using FastFourierTransform techniques. Trans. Geosci. Electron. (1970)","DOI":"10.1109\/TGE.1970.271435"},{"key":"37_CR7","doi-asserted-by":"crossref","unstructured":"Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: PatchMatch: a randomized correspondence algorithm for structural image editing. In: SIGGRAPH (2009)","DOI":"10.1145\/1576246.1531330"},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"Baumberg, A.: Blending images for texturing 3D models. In: BMVC (2002)","DOI":"10.5244\/C.16.38"},{"key":"37_CR9","doi-asserted-by":"crossref","unstructured":"Bernardini, F., Martin, I., Rushmeier, H.: High quality texture reconstruction from multiple scans. In: TVCG (2001)","DOI":"10.1109\/2945.965346"},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Bi, S., Kalantari, N.K., Ramamoorthi, R.: Patch-based optimization for image-based texture mapping. In: TOG (2017)","DOI":"10.1145\/3072959.3073610"},{"key":"37_CR11","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T.A., Nie\u00dfner, M.: Scannet: richly-annotated 3D reconstructions of indoor scenes. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.261"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Debevec, P., Taylor, C., Malik, J.: Modeling and rendering architecture from photographs: a hybrid geometry and image-based approach. In: SIGGRAPH (1996)","DOI":"10.1145\/237170.237191"},{"key":"37_CR13","unstructured":"Duan, Y.: Topology adaptive deformable models for visual computing. Ph.D. thesis, State University of New York (2003)"},{"key":"37_CR14","unstructured":"El-Hakim, S., Gonzo, L., Picard, M., Girardi, S., Simoni, A.: Visualization of frescoed surfaces: buonconsiglio castle - aquila tower, \u201cCycle of the Months\". In: IAPRS (2003)"},{"key":"37_CR15","unstructured":"Fr\u00fch, C., Sammon, R., Zakhor, A.: Automated texture mapping of 3D city models with oblique aerial imagery. In: 3DPVT (2004)"},{"key":"37_CR16","doi-asserted-by":"crossref","unstructured":"Fu, Y., Yan, Q., Yang, L., Liao, J., Xiao, C.: Texture mapping for 3D reconstruction with RGB-D sensor. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00488"},{"key":"37_CR17","unstructured":"Globerson, A., Jaakkola, T.: Fixing Max-Product: convergent message passing algorithms for MAP LP-Relaxations. In: NIPS (2007)"},{"key":"37_CR18","doi-asserted-by":"crossref","unstructured":"Goel, S., Kanazawa, A., Malik, J.: Shape and viewpoint without keypoints. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58555-6_6"},{"key":"37_CR19","unstructured":"Grammatikopoulos, L., Kalisperakis, I., Karras, G., Petsa, E.: Automatic multi-view texture mapping of 3D surface projections. In: International Workshop 3D-ARCH (2007)"},{"key":"37_CR20","doi-asserted-by":"crossref","unstructured":"Groueix, T., Fisher, M., Kim, V.G., Russell, B.C., Aubry, M.: AtlasNet: a Papier-M\u00e2ch\u00e9 approach to learning 3D surface generation. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00030"},{"key":"37_CR21","doi-asserted-by":"crossref","unstructured":"Henzler, P., Mitra, N.J., Ritschel, T.: Learning a neural 3D Texture space from 2D exemplars. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00838"},{"key":"37_CR22","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez-Esteban, C.: Stereo and Silhouette fusion for 3D object modeling from uncalibrated images under circular motion. Ph.D. thesis, \u00c9cole Nationale Sup\u00e9rieure des T\u00e9l\u00e9Communications (2004)","DOI":"10.1016\/j.cviu.2004.03.016"},{"key":"37_CR23","doi-asserted-by":"crossref","unstructured":"Huang, J., Dai, A., Guibas, L., Nie\u00dfner, M.: 3DLite: towards commodity 3D scanning for content creation. In: ACM TOG (2017)","DOI":"10.1145\/3130800.3130824"},{"key":"37_CR24","doi-asserted-by":"crossref","unstructured":"Huang, J., et al.: Adversarial texture optimization from RGB-D scans. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00163"},{"key":"37_CR25","doi-asserted-by":"crossref","unstructured":"Huynh, D.: Metrics for 3D rotations: comparison and analysis. J. Math. Imag. Vis. (2009)","DOI":"10.1007\/s10851-009-0161-2"},{"key":"37_CR26","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. ArXiv (2015)"},{"key":"37_CR27","doi-asserted-by":"crossref","unstructured":"Lempitsky, V., Ivanov, D.: Seamless mosaicing of image-based texture maps. In: CVPR (2007)","DOI":"10.1109\/CVPR.2007.383078"},{"key":"37_CR28","unstructured":"Lensch, H., Heidrich, W., Seidel, H.P.: Automated texture registration and stitching for real world models. In: Graphical Models (2001)"},{"key":"37_CR29","unstructured":"Marshner, S.R.: Inverse rendering for computer graphics. Ph.D. thesis, Cornell University (1998)"},{"key":"37_CR30","first-page":"245","volume":"18","author":"PJ Neugebauer","year":"1999","unstructured":"Neugebauer, P.J., Klein, K.: Texturing 3D models of real world objects from multiple unregistered photographic views. Eurographics 18, 245\u2013256 (1999)","journal-title":"Eurographics"},{"key":"37_CR31","doi-asserted-by":"crossref","unstructured":"Niem, W., Wingberm\u00fchle, J.: Automatic reconstruction of 3D objects using a mobile camera. In: IVC (1999)","DOI":"10.1016\/S0262-8856(98)00116-4"},{"key":"37_CR32","doi-asserted-by":"crossref","unstructured":"Oechsle, M., Mescheder, L.M., Niemeyer, M., Strauss, T., Geiger, A.: Texture fields: learning texture representations in function space. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00463"},{"key":"37_CR33","doi-asserted-by":"crossref","unstructured":"Ofek, E., Shilat, E., Rappoport, A., Werman, M.: Multiresolution Textures from image sequences. Comput. Graph. Appl. (1997)","DOI":"10.1109\/38.574667"},{"key":"37_CR34","doi-asserted-by":"crossref","unstructured":"Pan, R., Taubin, G.: Color adjustment in image-based texture maps. Graph. Models (2015)","DOI":"10.1016\/j.gmod.2015.04.002"},{"key":"37_CR35","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. ArXiv abs\/1912.01703 (2019)"},{"key":"37_CR36","doi-asserted-by":"crossref","unstructured":"Pighin, F., Hecker, J., Lischinski, D., Szeliski, R., Salesin, D.H.: Synthesizing realistic facial expressions from photographs. In: CGIT (1998)","DOI":"10.1145\/280814.280825"},{"key":"37_CR37","unstructured":"Ravi, N., Reizenstein, J., Novotny, D., Gordon, T., Lo, W.Y., Johnson, J., Gkioxari, G.: Accelerating 3D Deep Learning with PyTorch3D. arXiv:2007.08501 (2020)"},{"key":"37_CR38","doi-asserted-by":"crossref","unstructured":"Rocchini, C., Cignoni, P., Montani, C., Scopigno, R.: Multiple textures stitching and blending on 3D objects. Eurograph. Workshop Render. (1999)","DOI":"10.1007\/978-3-7091-6809-7_12"},{"key":"37_CR39","doi-asserted-by":"crossref","unstructured":"Rocchini, C., Cignoni, P., Montani, C., Scopigno, R.: Aquiring, stitching and blending diffuse appearance attributes on 3D models. Vis. Comput. (2002)","DOI":"10.1007\/s003710100146"},{"key":"37_CR40","doi-asserted-by":"crossref","unstructured":"Saito, S., Wei, L., Hu, L., Nagano, K., Li, H.: Photorealistic facial texture inference using deep neural networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.250"},{"key":"37_CR41","doi-asserted-by":"crossref","unstructured":"Sawhney, R., Crane, K.: Boundary first flattening. In: ACM TOG (2018)","DOI":"10.1145\/3132705"},{"key":"37_CR42","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-981-10-2260-9_9","volume-title":"Advances in Image and Graphics Technologies","author":"J Shu","year":"2016","unstructured":"Shu, J., Liu, Y., Li, J., Xu, Z., Du, S.: Rich and seamless texture mapping to 3D mesh models. In: Tan, T., et al. (eds.) IGTA 2016. CCIS, vol. 634, pp. 69\u201376. Springer, Singapore (2016). https:\/\/doi.org\/10.1007\/978-981-10-2260-9_9"},{"key":"37_CR43","doi-asserted-by":"crossref","unstructured":"Sinha, S.N., Steedly, D., Szeliski, R., Agrawala, M., Pollefeys, M.: Interactive 3D architectural modeling from unordered photo collections. In: SIGGRAPH 2008 (2008)","DOI":"10.1145\/1457515.1409112"},{"key":"37_CR44","doi-asserted-by":"crossref","unstructured":"Sitzmann, V., et al.: DeepVoxels: learning persistent 3D feature embeddings. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00254"},{"key":"37_CR45","doi-asserted-by":"crossref","unstructured":"Thierry, M., David, F., Gorria, P., Salvi, J.: Automatic texture mapping on real 3D model. In: CVPR (2007)","DOI":"10.1109\/CVPR.2007.383482"},{"key":"37_CR46","doi-asserted-by":"crossref","unstructured":"Thies, J., Zollh\u00f6fer, M., Nie\u00dfner, M.: Deferred Neural Rendering. In: ACM TOG (2019)","DOI":"10.1145\/3306346.3323035"},{"key":"37_CR47","doi-asserted-by":"crossref","unstructured":"Vu, C.T., Phan, T.D., Chandler, D.M.: $$S_3$$: A spectral and spatial measure of local perceived sharpness in natural images. IEEE Trans. Image Process. 21, 934\u2013945 (2012)","DOI":"10.1109\/TIP.2011.2169974"},{"key":"37_CR48","doi-asserted-by":"crossref","unstructured":"Waechter, M., Moehrle, N., Goesele, M.: Let there be color! large-scale texturing of 3D Reconstructions. In: ECCV (2014)","DOI":"10.1007\/978-3-319-10602-1_54"},{"key":"37_CR49","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600\u20136112 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"37_CR50","unstructured":"Wexler, Y., Shechtman, E., Irani, M.: Space-time video completion. In: CVPR (2004)"},{"key":"37_CR51","unstructured":"Wuhrer, S., Atanassov, R., Shu, C.: Fully automatic texture mapping for image-based modeling. Technical Report, Institute for Information Technology (2006)"},{"key":"37_CR52","doi-asserted-by":"crossref","unstructured":"Xiang, F., Xu, Z., Havsan, M., Hold-Geoffroy, Y., Sunkavalli, K., Su, H.: NeuTex: neural texture mapping for volumetric neural rendering. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00704"},{"key":"37_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"37_CR54","doi-asserted-by":"crossref","unstructured":"Zhou, Q.Y., Koltun, V.: Color map optimization for 3D reconstruction with consumer depth cameras. In: ACM TOG (2014)","DOI":"10.1145\/2601097.2601134"}],"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\/978-3-031-19812-0_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T20:51:58Z","timestamp":1728247918000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19812-0_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198113","9783031198120"],"references-count":54,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19812-0_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"30 October 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)"}},{"value":"From the workshops, 367 reviewed full papers have been selected for publication","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}