{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T04:27:27Z","timestamp":1773635247057,"version":"3.50.1"},"reference-count":24,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772421"],"award-info":[{"award-number":["61772421"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Surface reconstruction from point clouds with open boundaries and incomplete geometry remains challenging for existing implicit learning methods, as signed distance field and occupancy\u2010based approaches inherently rely on closed\u2010surface assumptions and often produce false closures or boundary distortions, while existing unsigned distance field (UDF) methods suffer from unstable optimization, discontinuities near boundaries, and difficulties in reliable surface extraction. To address these issues, this paper proposes an asymptotically consistent surface reconstruction framework based on UDFs for non\u2010watertight point clouds. The method adopts a fully unsupervised learning strategy, in which a continuous UDF is optimized through a gradient\u2010guided point projection mechanism combined with geometric and gradient consistency constraints, enabling stable distance estimation without requiring ground\u2010truth distances or normals. A progressive learning strategy based on high\u2010confidence projected points is further introduced to alleviate early\u2010stage instability and improve robustness under sparse sampling and complex topology. In addition, an intersection\u2010detection\u2010based isosurface extraction scheme is designed to infer pseudo\u2010sign information from local gradient configurations, allowing reliable mesh generation using the Marching Cubes algorithm. Extensive experiments demonstrate that the proposed method achieves superior reconstruction accuracy and robustness compared with state\u2010of\u2010the\u2010art methods, particularly in preserving open boundaries, thin\u2010walled structures, and fractured regions without erroneous infilling.<\/jats:p>","DOI":"10.1002\/cpe.70608","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T04:20:42Z","timestamp":1770697242000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>ACL<\/scp>\n                    \u2010\n                    <scp>UDF<\/scp>\n                    : Asymptotically Consistent Learning of Unsigned Distance Fields for Surface Reconstruction"],"prefix":"10.1002","volume":"38","author":[{"given":"Siyu","family":"Jin","sequence":"first","affiliation":[{"name":"College of Computer Science Northwest University  Xi'an China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxiu","family":"Tuo","sequence":"additional","affiliation":[{"name":"College of Computer Science Northwest University  Xi'an China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4180-1341","authenticated-orcid":false,"given":"Shunli","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science Northwest University  Xi'an China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"M.Kazhdan M.Bolitho andH.Hoppe \u201cPoisson Surface Reconstruction. Proceedings of the Fourth Eurographics Symposium on Geometry Processing. 2006 7 (4)\u201d."},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cagd.2021.102062"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3554730"},{"key":"e_1_2_10_5_1","doi-asserted-by":"crossref","unstructured":"H.Hoppe T.DeRose T.Duchamp et al. \u201cSurface Reconstruction From Unorganized Points. Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH). 1992: 71\u201378\u201d.","DOI":"10.1145\/133994.134011"},{"key":"e_1_2_10_6_1","doi-asserted-by":"crossref","unstructured":"L.Mescheder M.Oechsle M.Niemeyer et al. \u201cOccupancy Networks: Learning 3D Reconstruction in Function Space. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2019: 4460\u20134470\u201d.","DOI":"10.1109\/CVPR.2019.00459"},{"key":"e_1_2_10_7_1","doi-asserted-by":"crossref","unstructured":"Z.ChenandH.Zhang \u201cLearning Implicit Fields for Generative Shape Modeling. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2019: 5939\u20135948\u201d.","DOI":"10.1109\/CVPR.2019.00609"},{"key":"e_1_2_10_8_1","doi-asserted-by":"crossref","unstructured":"J.Chibane T.Alldieck andG.Pons\u2010Moll \u201cImplicit Functions in Feature Space for 3d Shape Reconstruction and Completion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2020: 6970\u20136981\u201d.","DOI":"10.1109\/CVPR42600.2020.00700"},{"key":"e_1_2_10_9_1","doi-asserted-by":"crossref","unstructured":"S.Peng M.Niemeyer L.Mescheder et al. \u201cConvolutional Occupancy Networks. Proceedings of the European Conference on Computer Vision. 2020: 523\u2013540\u201d.","DOI":"10.1007\/978-3-030-58580-8_31"},{"key":"e_1_2_10_10_1","doi-asserted-by":"crossref","unstructured":"A.BoulchandR.Marlet \u201cPOCO: Point Convolution for Surface Reconstruction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2022: 6302\u20136314\u201d.","DOI":"10.1109\/CVPR52688.2022.00620"},{"key":"e_1_2_10_11_1","doi-asserted-by":"crossref","unstructured":"M.AtzmonandY.Lipman \u201cSal: Sign Agnostic Learning of Shapes From Raw Data. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2020: 2565\u20132574\u201d.","DOI":"10.1109\/CVPR42600.2020.00264"},{"key":"e_1_2_10_12_1","unstructured":"A.Gropp L.Yariv N.Haim et al. \u201cImplicit Geometric Regularization for Learning Shapes. arXiv Preprint arXiv:2002.10099 2020\u201d."},{"key":"e_1_2_10_13_1","doi-asserted-by":"crossref","unstructured":"J. J.Park P.Florence J.Straub et al. \u201cDeepsdf: Learning Continuous Signed Distance Functions for Shape Representation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2019: 165\u2013174\u201d.","DOI":"10.1109\/CVPR.2019.00025"},{"key":"e_1_2_10_14_1","unstructured":"B.Ma Z.Han Y. S.Liu et al. \u201cNeural\u2010Pull: Learning Signed Distance Functions From Point Clouds by Learning to Pull Space Onto Surfaces. arXiv Preprint arXiv:2011.13495 2020\u201d."},{"key":"e_1_2_10_15_1","doi-asserted-by":"crossref","unstructured":"B.Ma Y. S.Liu andZ.Han \u201cReconstructing Surfaces for Sparse Point Clouds With On\u2010Surface Priors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2022: 6315\u20136325\u201d.","DOI":"10.1109\/CVPR52688.2022.00621"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/280811.281026"},{"key":"e_1_2_10_17_1","first-page":"21638","article-title":"Neural Unsigned Distance Fields for Implicit Function Learning","volume":"33","author":"Chibane J.","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3392364"},{"key":"e_1_2_10_19_1","doi-asserted-by":"crossref","unstructured":"S.Ren J.Hou X.Chen et al. \u201cGeoUDF: Surface Reconstruction From 3d Point Clouds via Geometry\u2010Guided Distance Representation. Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2023: 14214\u201314224\u201d.","DOI":"10.1109\/ICCV51070.2023.01307"},{"key":"e_1_2_10_20_1","doi-asserted-by":"crossref","unstructured":"M.Fainstein V.Siless andE.Iarussi \u201cDUDF: Differentiable Unsigned Distance Fields With Hyperbolic Scaling. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2024: 4484\u20134493\u201d.","DOI":"10.1109\/CVPR52733.2024.00429"},{"key":"e_1_2_10_21_1","doi-asserted-by":"crossref","unstructured":"B.Guillard F.Stella andP.Fua \u201cMeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networks. European Conference on Computer Vision. Cham: Springer Nature Switzerland 2022: 576\u2013592\u201d.","DOI":"10.1007\/978-3-031-20062-5_33"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/2945.817351"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-8659.00236"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.232073"},{"key":"e_1_2_10_25_1","unstructured":"A. X.Chang T.Funkhouser L.Guibas et al. \u201cShapenet: An Information\u2010Rich 3d Model Repository. arXiv Preprint arXiv:1512.03012 2015\u201d."}],"container-title":["Concurrency and Computation: Practice and Experience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpe.70608","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/cpe.70608","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpe.70608","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T03:22:11Z","timestamp":1773631331000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cpe.70608"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":24,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["10.1002\/cpe.70608"],"URL":"https:\/\/doi.org\/10.1002\/cpe.70608","archive":["Portico"],"relation":{},"ISSN":["1532-0626","1532-0634"],"issn-type":[{"value":"1532-0626","type":"print"},{"value":"1532-0634","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"2025-12-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-30","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70608"}}