{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T11:28:00Z","timestamp":1783078080821,"version":"3.54.6"},"reference-count":31,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:00:00Z","timestamp":1780099200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007567","name":"City University of Hong Kong","doi-asserted-by":"publisher","award":["11207422"],"award-info":[{"award-number":["11207422"]}],"id":[{"id":"10.13039\/100007567","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100001641","name":"Glaucoma Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001641","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LQN26F020068"],"award-info":[{"award-number":["LQN26F020068"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LGEZ26F030002"],"award-info":[{"award-number":["LGEZ26F030002"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer-Aided Design"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.cad.2026.104109","type":"journal-article","created":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T15:21:10Z","timestamp":1780759270000},"page":"104109","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Weakly supervised learning for 3D mesh segmentation via pixel-level labeling"],"prefix":"10.1016","volume":"199","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0919-3948","authenticated-orcid":false,"given":"Wen","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9760-7789","authenticated-orcid":false,"given":"Weiyin","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xian-Tao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Diao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.cad.2026.104109_b1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2835487","article-title":"3D mesh labeling via deep convolutional neural networks","volume":"35","author":"Guo","year":"2015","journal-title":"ACM Trans Graph"},{"key":"10.1016\/j.cad.2026.104109_b2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.gmod.2018.01.001","article-title":"3D mesh segmentation via multi-branch 1D convolutional neural networks","volume":"96","author":"George","year":"2018","journal-title":"Graph Model"},{"issue":"4","key":"10.1016\/j.cad.2026.104109_b3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3306346.3322959","article-title":"MeshCNN: A network with an edge","volume":"38","author":"Hanocka","year":"2019","journal-title":"ACM Trans Graph"},{"key":"10.1016\/j.cad.2026.104109_b4","first-page":"952","article-title":"Primal-dual mesh convolutional neural networks","volume":"33","author":"Milano","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.cad.2026.104109_b5","unstructured":"He W, Jiang Z, Zhang C, Sainju AM. CurvaNet: Geometric deep learning based on directional curvature for 3D shape analysis. In: Proceedings of the ACM SIGKDD. 2020, p. 2214\u201324."},{"issue":"6","key":"10.1016\/j.cad.2026.104109_b6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3414685.3417806","article-title":"MeshWalker: Deep mesh understanding by random walks","volume":"39","author":"Lahav","year":"2020","journal-title":"ACM Trans Graph"},{"issue":"3","key":"10.1016\/j.cad.2026.104109_b7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3506694","article-title":"Subdivision-based mesh convolution networks","volume":"41","author":"Hu","year":"2022","journal-title":"ACM Trans Graph"},{"issue":"3","key":"10.1016\/j.cad.2026.104109_b8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3507905","article-title":"DiffusionNet: Discretization agnostic learning on surfaces","volume":"41","author":"Sharp","year":"2022","journal-title":"ACM Trans Graph"},{"issue":"7","key":"10.1016\/j.cad.2026.104109_b9","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.1109\/TVCG.2023.3259044","article-title":"Laplacian2Mesh: Laplacian-based mesh understanding","volume":"30","author":"Dong","year":"2024","journal-title":"IEEE Trans Vis Comput Graphics"},{"key":"10.1016\/j.cad.2026.104109_b10","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1109\/TMM.2024.3521674","article-title":"3D shape segmentation with potential consistency mining and enhancement","volume":"27","author":"Shu","year":"2024","journal-title":"IEEE Trans Multimed"},{"key":"10.1016\/j.cad.2026.104109_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.cagd.2025.102440","article-title":"MTSegNet: Manifold transformer for 3D shape segmentation","volume":"119","author":"Shu","year":"2025","journal-title":"Comput Aided Geom Design"},{"key":"10.1016\/j.cad.2026.104109_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.cad.2023.103512","article-title":"SCMS-Net: Self-supervised clustering-based 3D meshes segmentation network","volume":"160","author":"Jiao","year":"2023","journal-title":"Computer-Aided Des"},{"key":"10.1016\/j.cad.2026.104109_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.cag.2024.104100","article-title":"ADA-SCMS Net: A self-supervised clustering-based 3D mesh segmentation network with aggregation dual autoencoder","volume":"124","author":"Jiao","year":"2024","journal-title":"Comput Graph"},{"key":"10.1016\/j.cad.2026.104109_b14","doi-asserted-by":"crossref","unstructured":"Zhong Z, Xu Y, Li J, Xu J, Li Z, Yu C, Gao S. MeshSegmenter: Zero-shot mesh semantic segmentation via texture synthesis. In: Proceedings of the European conference on computer vision. 2024, p. 182\u201399.","DOI":"10.1007\/978-3-031-72980-5_11"},{"issue":"6","key":"10.1016\/j.cad.2026.104109_b15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2366145.2366184","article-title":"Active co-analysis of a set of shapes","volume":"31","author":"Wang","year":"2012","journal-title":"ACM Trans Graph"},{"issue":"4","key":"10.1016\/j.cad.2026.104109_b16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3073616","article-title":"Convolutional neural networks on surfaces via seamless toric covers","volume":"36","author":"Maron","year":"2017","journal-title":"ACM Trans Graph"},{"key":"10.1016\/j.cad.2026.104109_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102858","article-title":"A novel hybrid model combining vision transformers and graph convolutional networks for monkeypox disease effective diagnosis","volume":"117","author":"Das","year":"2025","journal-title":"Inf Fusion"},{"key":"10.1016\/j.cad.2026.104109_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113045","article-title":"AGMS-GCN: Attention-guided multi-scale graph convolutional networks for skeleton-based action recognition","author":"Kilic","year":"2025","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"10.1016\/j.cad.2026.104109_b19","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1007\/s11263-024-02187-4","article-title":"SplitNet: Learnable clean-noisy label splitting for learning with noisy labels","volume":"133","author":"Kim","year":"2025","journal-title":"Int J Comput Vis"},{"issue":"1","key":"10.1016\/j.cad.2026.104109_b20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3709657","article-title":"Agree to disagree: Robust anomaly detection with noisy labels","volume":"3","author":"Hofmann","year":"2025","journal-title":"Proc the ACM Manag Data"},{"key":"10.1016\/j.cad.2026.104109_b21","doi-asserted-by":"crossref","unstructured":"Li S, Gao Z, He X. Superpixel-guided iterative learning from noisy labels for medical image segmentation. In: Proceedings of the international conference on medical image computing and computer assisted intervention. 2021, p. 525\u201335.","DOI":"10.1007\/978-3-030-87193-2_50"},{"issue":"3","key":"10.1016\/j.cad.2026.104109_b22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1531326.1531379","article-title":"A benchmark for 3D mesh segmentation","volume":"28","author":"Chen","year":"2009","journal-title":"ACM Trans Graph"},{"key":"10.1016\/j.cad.2026.104109_b23","doi-asserted-by":"crossref","unstructured":"Anguelov D, Srinivasan P, Koller D, Thrun S, Rodgers J, Davis J. SCAPE: shape completion and animation of people. In: Proceedings of the ACM SIGGRAPH. 2005, p. 408\u201316.","DOI":"10.1145\/1186822.1073207"},{"key":"10.1016\/j.cad.2026.104109_b24","doi-asserted-by":"crossref","unstructured":"Bogo F, Romero J, Loper M, Black MJ. FAUST: Dataset and evaluation for 3D mesh registration. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2014, p. 3794\u2013801.","DOI":"10.1109\/CVPR.2014.491"},{"key":"10.1016\/j.cad.2026.104109_b25","doi-asserted-by":"crossref","unstructured":"Vlasic D, Baran I, Matusik W, Popovi\u0107 J. Articulated mesh animation from multi-view silhouettes. In: Proceedings of the ACM SIGGRAPH. 2008, p. 1\u20139.","DOI":"10.1145\/1399504.1360696"},{"key":"10.1016\/j.cad.2026.104109_b26","series-title":"Adobe Fuse 3D Characters","author":"Adobe","year":"2006"},{"issue":"7","key":"10.1016\/j.cad.2026.104109_b27","first-page":"7","article-title":"Shape retrieval contest 2007: Watertight models track","volume":"8","author":"Giorgi","year":"2007","journal-title":"SHREC Comp\u00e9t"},{"issue":"4","key":"10.1016\/j.cad.2026.104109_b28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3450626.3459797","article-title":"HodgeNet: Learning spectral geometry on triangle meshes","volume":"40","author":"Smirnov","year":"2021","journal-title":"ACM Trans Graph"},{"key":"10.1016\/j.cad.2026.104109_b29","doi-asserted-by":"crossref","unstructured":"Alexa M. Super-Fibonacci Spirals: Fast, low-discrepancy sampling of SO(3). In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2022, p. 8291\u2013300.","DOI":"10.1109\/CVPR52688.2022.00811"},{"issue":"11","key":"10.1016\/j.cad.2026.104109_b30","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/34.969114","article-title":"Fast approximate energy minimization via graph cuts","volume":"23","author":"Boykov","year":"2002","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10.1016\/j.cad.2026.104109_b31","doi-asserted-by":"crossref","unstructured":"Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo W-Y, et al. Segment anything. In: Proceedings of the IEEE\/CVF international conference on computer vision. 2023, p. 4015\u201326.","DOI":"10.1109\/ICCV51070.2023.00371"}],"container-title":["Computer-Aided Design"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010448526000795?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010448526000795?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T11:08:52Z","timestamp":1783076932000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010448526000795"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":31,"alternative-id":["S0010448526000795"],"URL":"https:\/\/doi.org\/10.1016\/j.cad.2026.104109","relation":{},"ISSN":["0010-4485"],"issn-type":[{"value":"0010-4485","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Weakly supervised learning for 3D mesh segmentation via pixel-level labeling","name":"articletitle","label":"Article Title"},{"value":"Computer-Aided Design","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cad.2026.104109","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"104109"}}