{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T14:51:47Z","timestamp":1784904707895,"version":"3.55.0"},"reference-count":67,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021ZD0111900"],"award-info":[{"award-number":["2021ZD0111900"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172022"],"award-info":[{"award-number":["62172022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21B2038"],"award-info":[{"award-number":["U21B2038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U19B2039"],"award-info":[{"award-number":["U19B2039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876012"],"award-info":[{"award-number":["61876012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["4202003"],"award-info":[{"award-number":["4202003"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Multimedia"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tmm.2022.3189247","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T19:24:43Z","timestamp":1657221883000},"page":"5248-5262","source":"Crossref","is-referenced-by-count":8,"title":["DASI: Learning Domain Adaptive Shape Impression for 3D Object Reconstruction"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0804-8688","authenticated-orcid":false,"given":"Junna","family":"Gao","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7722-7172","authenticated-orcid":false,"given":"Dehui","family":"Kong","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3045-624X","authenticated-orcid":false,"given":"Shaofan","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinghua","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3121-1823","authenticated-orcid":false,"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","article-title":"Parallelizing linear recurrent neural nets over sequence length","author":"martin","year":"2017","journal-title":"arXiv 1709 04057"},{"key":"ref57","first-page":"1936","article-title":"Matryoshka networks: Predicting 3D geometry via nested shape layers","author":"richter","year":"0","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref12","article-title":"Gradient flow in recurrent nets: The difficulty of learning long-term dependencies","author":"hochreiter","year":"2001","journal-title":"A Field Guide to Dynamical Recurrent Neural Networks"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1176"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00314"},{"key":"ref59","first-page":"52","article-title":"Pixel2mesh: Generating 3D mesh models from single RGB images","author":"wang","year":"0","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref14","first-page":"1912","article-title":"3D shapenets: A deep representation for volumetric shapes","author":"wu","year":"0","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref58","first-page":"216","article-title":"A papier-mâché approach to learning 3D surface generation","author":"groueix","year":"0","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref53","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","author":"long","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref52","first-page":"513","article-title":"A kernel method for the two-sample-problem","volume":"19","author":"gretton","year":"2006","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"ref55","article-title":"Adaptive input representations for neural language modeling","author":"baevski","year":"2018","journal-title":"arXiv 1809 10853"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01347-6"},{"key":"ref54","article-title":"An image is worth $16\\times 16$ words: Transformers for image recognition at scale","author":"dosovitskiy","year":"2020","journal-title":"arXiv 2010 11929"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/34.273735"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_6"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/344779.344951"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2001.937544"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-13560-1_76"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3123266.3123340"},{"key":"ref46","first-page":"4996","article-title":"Unsupervised learning of 3D structure from images","author":"rezende","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2017.00018"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00298"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.253"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.106"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2016.2612761"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_21"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00410"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01217-w"},{"key":"ref7","first-page":"365","article-title":"Learning a multi-view stereo machine","author":"kar","year":"0","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00278"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2018.2870081"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2993962"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_38"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2933338"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1017\/S096249291700006X"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00061"},{"key":"ref34","article-title":"3D-LMNet: Latent embedding matching for accurate and diverse 3D point cloud reconstruction from a single image","author":"mandikal","year":"2018","journal-title":"arXiv 1807 07796"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.252"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.10.097"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00306"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46478-7_20"},{"key":"ref33","first-page":"540","article-title":"Marrnet: 3D shape reconstruction via 2.5D sketches","author":"wu","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref32","first-page":"82","article-title":"Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling","author":"wu","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2707341"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2016.2523421"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073608"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.230"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298716"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.584"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.30"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2845697"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(81)90019-9"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539790"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073599"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00567"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-012-9365-8"},{"key":"ref66","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref21","first-page":"926","article-title":"The structure-from-motion reconstruction pipeline–A survey with focus on short image sequences","volume":"46","author":"h\u00e4ming","year":"2010","journal-title":"Kybernetika"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00352"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2017.00038"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.264"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2017.86"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00459"},{"key":"ref62","article-title":"Legoformer: Transformers for block-by-block multi-view 3D reconstruction","author":"yagubbayli","year":"2021","journal-title":"arXiv 2106 12102"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00609"}],"container-title":["IEEE Transactions on Multimedia"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6046\/10016790\/09817616.pdf?arnumber=9817616","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T20:04:10Z","timestamp":1701115450000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9817616\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":67,"URL":"https:\/\/doi.org\/10.1109\/tmm.2022.3189247","relation":{},"ISSN":["1520-9210","1941-0077"],"issn-type":[{"value":"1520-9210","type":"print"},{"value":"1941-0077","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}