{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T07:37:31Z","timestamp":1774078651784,"version":"3.50.1"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Doctoral Dissertation Innovation Fund of Xi&#x0027;an University of Technology","award":["252072401"],"award-info":[{"award-number":["252072401"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Robot. Autom. Lett."],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1109\/lra.2026.3673899","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T19:55:25Z","timestamp":1773431725000},"page":"5454-5461","source":"Crossref","is-referenced-by-count":0,"title":["Bridging the Depth Gap: Adaptive Scene-Instance Alignment for Training-Free Depth Refinement in Robotic Manipulation Scenes"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9657-3036","authenticated-orcid":false,"given":"Pihong","family":"Hou","sequence":"first","affiliation":[{"name":"School of Mechanical and Precision Instrument Engineering, Xi&#x0027;an University of Technology, Xi&#x0027;an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4257-2919","authenticated-orcid":false,"given":"Yongfang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Precision Instrument Engineering, Xi&#x0027;an University of Technology, Xi&#x0027;an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3104-2788","authenticated-orcid":false,"given":"Boxuan","family":"Ye","sequence":"additional","affiliation":[{"name":"Dyson School of Design Engineering, Imperial College London, London, U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7978-6678","authenticated-orcid":false,"given":"Yi","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Printing, Packaging Engineering and Digital Media Technology, Xi&#x0027;an University of Technology, Xi&#x0027;an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913491297"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01692"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3455849"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00987"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.52202\/079017-0688"},{"key":"ref6","first-page":"27469","article-title":"Category-level 6D object pose estimation in the wild: A semi-supervised learning approach and a new dataset","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Fu","year":"2022"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10605-2_35"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20074-8_22"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01994"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20086-1_3"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.02073"},{"key":"ref12","article-title":"Depth anything with any prior","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wang","year":"2026"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3019967"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.02126"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01956"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73220-1_5"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.00499"},{"key":"ref18","article-title":"ZoeDepth: Zero-shot transfer by combining relative and metric depth","author":"Bhat","year":"2023"},{"key":"ref19","first-page":"75602","article-title":"Depth pro: Sharp monocular metric depth in less than a second","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Bochkovskiy","year":"2025"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.00496"},{"key":"ref21","article-title":"MoGe-2: Accurate monocular geometry with metric scale and sharp details","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang","year":"2025"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00830"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3444912"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00963"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3628473"},{"key":"ref26","article-title":"Depth anything 3: Recovering the visual space from any views","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Lin","year":"2026"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2025.3560860"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00932"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3427549"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72646-0_5"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.00591"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73039-9_16"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA57147.2024.10610929"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.01591"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.15607\/rss.2018.xiv.019"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/IROS47612.2022.9981838"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3183256"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01073"},{"key":"ref39","first-page":"28085","article-title":"SAM 2: Segment anything in images and videos","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Ravi","year":"2025"},{"key":"ref40","article-title":"Ultralytics YOLO","author":"Jocher","year":"2023"}],"container-title":["IEEE Robotics and Automation Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7083369\/11435997\/11434512.pdf?arnumber=11434512","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T04:47:35Z","timestamp":1774068455000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11434512\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":40,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/lra.2026.3673899","relation":{},"ISSN":["2377-3766","2377-3774"],"issn-type":[{"value":"2377-3766","type":"electronic"},{"value":"2377-3774","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5]]}}}