{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:03:51Z","timestamp":1775325831579,"version":"3.50.1"},"reference-count":113,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&#x0026;D Program of China","award":["2022YFC3800600"],"award-info":[{"award-number":["2022YFC3800600"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272263"],"award-info":[{"award-number":["62272263"]}],"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":["62072268"],"award-info":[{"award-number":["62072268"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1109\/tpami.2024.3476349","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T17:44:18Z","timestamp":1728323058000},"page":"565-582","source":"Crossref","is-referenced-by-count":8,"title":["NeuralTPS: Learning Signed Distance Functions Without Priors From Single Sparse Point Clouds"],"prefix":"10.1109","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9916-662X","authenticated-orcid":false,"given":"Chao","family":"Chen","sequence":"first","affiliation":[{"name":"School of Software, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7305-1915","authenticated-orcid":false,"given":"Yu-Shen","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9540-9973","authenticated-orcid":false,"given":"Zhizhong","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Wayne State University, Detroit, MI, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19824-3_9"},{"key":"ref2","article-title":"Neural poisson: Indicator functions for neural fields","author":"Dai","year":"2022"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01996"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00536"},{"key":"ref5","first-page":"3789","article-title":"Implicit geometric regularization for learning shapes","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gropp"},{"key":"ref6","article-title":"RangeUDF: Semantic surface reconstruction from 3 D point clouds","author":"Bing","year":"2022"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr42600.2020.00264"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00620"},{"key":"ref9","first-page":"7246","article-title":"Neural-Pull: Learning signed distance function from point clouds by learning to pull space onto surface","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ma"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00604"},{"key":"ref11","first-page":"13032","article-title":"Shape as points: A differentiable poisson solver","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Peng"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr46437.2021.01012"},{"key":"ref13","article-title":"SALD: Sign agnostic learning with derivatives","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Atzmon"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01872"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00044"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20062-5_19"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00105"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00725"},{"key":"ref19","article-title":"Learning occupancy function from point clouds for surface reconstruction","author":"Jia","year":"2010"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00183"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00644"},{"key":"ref22","first-page":"523","article-title":"Convolutional occupancy networks","volume-title":"Proc. Eur. Conf. Comput. Vis.","author":"Songyou"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58558-7_7"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i1.25224"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01700"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01306"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02093"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i4.28102"},{"key":"ref29","first-page":"66006","article-title":"NeuralGF: Unsupervised point normal estimation by learning neural gradient function","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3610548.3618253"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i3.28005"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i4.28100"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02053"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01680"},{"key":"ref35","article-title":"Deep level sets: Implicit surface representations for 3D shape inference","author":"Michalkiewicz","year":"1901"},{"key":"ref36","first-page":"165","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Park"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3550454.3555457"},{"key":"ref38","first-page":"18060","article-title":"VisCo grids: Surface reconstruction with viscosity and coarea grids","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Pumarola"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19824-3_33"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01250"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2024.3431221"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01037"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_18"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58526-6_36"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00621"},{"key":"ref46","first-page":"7462","article-title":"Implicit neural representations with periodic activation functions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sitzmann"},{"key":"ref47","first-page":"21638","article-title":"Neural unsigned distance fields for implicit function learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chibane"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/34.24792"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01699"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00554"},{"key":"ref52","first-page":"3994","article-title":"DRWR: A differentiable renderer without rendering for unsupervised 3D structure learning from silhouette images","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Han"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58586-0_36"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01224"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00545"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3159003"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01120"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459785"},{"key":"ref59","first-page":"8948","article-title":"ShaRF: Shape-conditioned radiance fields from a single view","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Rematas"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413889"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475172"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3217161"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72967-6_22"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73195-2_18"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00459"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00609"},{"key":"ref67","first-page":"1121","article-title":"Scene representation networks: Continuous 3D-structure-aware neural scene representations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sitzmann"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00209"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00133"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01224"},{"key":"ref71","first-page":"8295","article-title":"Learning to infer implicit surfaces without 3D supervision","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14082"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00356"},{"key":"ref74","first-page":"11453","article-title":"SDF-SRN: Learning signed distance 3D object reconstruction from static images","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref75","first-page":"2492","article-title":"Multiview neural surface reconstruction by disentangling geometry and appearance","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yariv"},{"key":"ref76","first-page":"4805","article-title":"Volume rendering of neural implicit surfaces","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yariv"},{"key":"ref77","first-page":"3403","article-title":"Geo-Neus: Geometry-consistent neural implicit surfaces learning for multi-view reconstruction","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Fu"},{"key":"ref78","first-page":"27171","article-title":"NeuS: Learning neural implicit surfaces by volume rendering for multi-view reconstruction","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref79","first-page":"25018","article-title":"MonoSDF: Exploring monocular geometric cues for neural implicit surface reconstruction","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yu"},{"key":"ref80","first-page":"1966","article-title":"HF-NeuS: Improved surface reconstruction using high-frequency details","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1145\/3528223.3530139"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3681668"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58598-3_5"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00426"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01795"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00425"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1145\/37402.37422"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00622"},{"key":"ref89","first-page":"23338","article-title":"Learning signed distance functions from noisy 3D point clouds via noise to noise mapping","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ma"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01700"},{"key":"ref91","first-page":"16481","article-title":"Learning consistency-aware unsigned distance functions progressively from raw point clouds","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhou"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2024.3392364"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2024.3416068"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02031"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00295"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/3DV53792.2021.00102"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3322994"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00364"},{"key":"ref99","first-page":"7509","article-title":"Neural spline flows","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Durkan"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00982"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00029"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00030"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2018.00088"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1145\/571647.571650"},{"key":"ref105","article-title":"ShapeNet: An information-rich 3D model repository","author":"Chang","year":"2015"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.591"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00552"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_22"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1145\/2487228.2487237"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1145\/2461912.2461919"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref112","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01808"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/10777928\/10707197.pdf?arnumber=10707197","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T06:30:38Z","timestamp":1733380238000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10707197\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":113,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2024.3476349","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":[[2025,1]]}}}