{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T18:39:00Z","timestamp":1790015940368,"version":"4.0.1"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"HKU Start-up Fund"},{"name":"Seed Fund for Basic Research"},{"name":"ERC","award":["ERC-2012-AdG321162-HELIOS"],"award-info":[{"award-number":["ERC-2012-AdG321162-HELIOS"]}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["Seebibyte EP\/M013774\/1"],"award-info":[{"award-number":["Seebibyte EP\/M013774\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"name":"EPSRC\/MURI","award":["EP\/N019474\/1"],"award-info":[{"award-number":["EP\/N019474\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2022,2,1]]},"DOI":"10.1109\/tpami.2020.3020800","type":"journal-article","created":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T16:56:26Z","timestamp":1598979386000},"page":"969-984","source":"Crossref","is-referenced-by-count":66,"title":["GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4285-1626","authenticated-orcid":false,"given":"Xiaojuan","family":"Qi","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengzhe","family":"Liu","sequence":"additional","affiliation":[{"name":"DJI corporation, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renjie","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Uber ATG, University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philip H. S.","family":"Torr","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, University of Oxford, Oxford, U.K"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raquel","family":"Urtasun","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Uber ATG, University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1246-553X","authenticated-orcid":false,"given":"Jiaya","family":"Jia","sequence":"additional","affiliation":[{"name":"Tencent X-Lab, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913491297"},{"key":"ref3","first-page":"1161","article-title":"Learning depth from single monocular images","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Saxena"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539823"},{"key":"ref5","first-page":"2366","article-title":"Depth map prediction from a single image using a multi-scale deep network","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Eigen"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.304"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.97"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298897"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.594"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.32"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2505283"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.25"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298715"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298652"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.642"},{"key":"ref16","article-title":"PixelNet: Representation of the pixels, by the pixels, and for the pixels","author":"Bansal","year":"2017","journal-title":"arXiv:1702.06506"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2377712"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00214"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00037"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1033214"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-006-0031-y"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.51"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.19"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818136"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.43"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2015.39"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_13"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_45"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.699"},{"key":"ref30","first-page":"172","article-title":"SURGE: Surface regularized geometry estimation from a single image","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00077"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_15"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00026"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12257"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.492"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2010324.1964964"},{"key":"ref37","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Simonyan"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1412.7062"},{"key":"ref39","article-title":"ParseNet: Looking wider to see better","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.421"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10599-4_44"},{"key":"ref43","first-page":"468","article-title":"Discriminatively trained dense surface normal estimation","volume-title":"Proc. 13th Eur. Conf. Comput. Vis.","author":"Zeisl"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2017.00012"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/1015706.1015780"},{"key":"ref46","first-page":"265","article-title":"TensorFlow: A system for large-scale machine learning","volume-title":"Proc. 12th Symp. Operating Syst. Des. Implementation","author":"Abadi"},{"key":"ref47","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma"},{"key":"ref48","first-page":"2658","article-title":"Depth from a single image by harmonizing overcomplete local network predictions","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Chakrabarti"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33715-4_56"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298660"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2016.7477699"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.132"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.238"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2839602"},{"key":"ref55","first-page":"468","article-title":"Discriminatively trained dense surface normal estimation","volume-title":"Proc. 13th Eur. Conf. Comput. Vis.","author":"Zeisl"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/9674188\/09184024.pdf?arnumber=9184024","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T15:40:37Z","timestamp":1743781237000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9184024\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,1]]},"references-count":55,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2020.3020800","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,1]]}}}