{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T18:15:57Z","timestamp":1762539357422,"version":"build-2065373602"},"reference-count":65,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","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":["62573343","62088102"],"award-info":[{"award-number":["62573343","62088102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Basic Strengthen Research Program of ReRAM","award":["2022-00-03"],"award-info":[{"award-number":["2022-00-03"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["xzy012024066"],"award-info":[{"award-number":["xzy012024066"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open-End Fund of Beijing Institute of Control Engineering","award":["OBCandETL-2024-04"],"award-info":[{"award-number":["OBCandETL-2024-04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Visual. Comput. Graphics"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1109\/tvcg.2025.3616156","type":"journal-article","created":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T17:39:17Z","timestamp":1759426757000},"page":"10854-10868","source":"Crossref","is-referenced-by-count":0,"title":["LeOp-GS: Learned Optimizer With Dynamic Gradient Update for Sparse-View 3DGS"],"prefix":"10.1109","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8464-1344","authenticated-orcid":false,"given":"Xinyu","family":"Lei","sequence":"first","affiliation":[{"name":"State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University, Xi’an, China"}]},{"given":"Xuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Ant Group, Hang Zhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7467-4994","authenticated-orcid":false,"given":"Longjun","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2135-3422","authenticated-orcid":false,"given":"Haoteng","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4239-6141","authenticated-orcid":false,"given":"Haonan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics, Control Engineering, Chang’an University, Xi’an, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2022.3230369"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2024.3413860"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3592433"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00798"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00832"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2024.3397828"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2024.3494046"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01963"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01254"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73232-4_19"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72933-1_9"},{"key":"ref13","first-page":"2936","article-title":"Optimization as a model for few-shot learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ravi","year":"2017"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3450508.3464573"},{"key":"ref15","first-page":"7462","article-title":"Implicit neural representations with periodic activation functions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Sitzmann","year":"2020"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14505"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01254"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3528223.3530127"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3323020"},{"key":"ref20","first-page":"15651","article-title":"Neural sparse voxel fields","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Liu","year":"2020"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00536"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01408"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02052"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02024"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01952"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.52202\/079017-4447"},{"article-title":"Turbo-GS: Accelerating 3D Gaussian fitting for high-quality radiance fields","year":"2024","author":"Lu","key":"ref27"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72986-7_4"},{"key":"ref29","article-title":"VistaDream: Sampling multiview consistent images for single-view scene reconstruction","volume-title":"Proc. Int. Conf. Comput. Vis.","author":"Wang","year":"2025"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2023.3327168"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.1997.609457"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_18"},{"article-title":"SparSegs: Real-time 360 sparse view synthesis using Gaussian splatting","year":"2023","author":"Xiong","key":"ref33"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01585"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00540"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i9.33070"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01840"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72664-4_21"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3641519.3657427"},{"article-title":"InstantSplat: Sparse-view Gaussian splatting in seconds","year":"2024","author":"Fan","key":"ref40"},{"key":"ref41","first-page":"2025","article-title":"No pose, no problem: Surprisingly simple 3D Gaussian splats from sparse unposed images","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ye"},{"key":"ref42","first-page":"4556","article-title":"Understanding and correcting pathologies in the training of learned optimizers","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Metz","year":"2019"},{"key":"ref43","first-page":"3988","article-title":"Learning to learn by gradient descent by gradient descent","volume-title":"Proc. 30th Int. Conf. Neural Inf. Process. Syst.","author":"Andrychowicz","year":"2016"},{"key":"ref44","first-page":"3758","article-title":"A closer look at learned optimization: Stability, robustness, and inductive biases","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Harrison","year":"2022"},{"key":"ref45","first-page":"142","article-title":"Practical tradeoffs between memory, compute, and performance in learned optimizers","volume-title":"Proc. Conf. Lifelong Learn. Agents","volume":"199","author":"Metz","year":"2022"},{"key":"ref46","first-page":"15018","article-title":"Learning to optimize in swarms","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Cao","year":"2019"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58598-3_6"},{"key":"ref48","article-title":"Evolutionary principles in self-referential learning","author":"Schmidhuber","year":"1987","journal-title":"Genet. Program."},{"key":"ref49","article-title":"Simple principles of metalearning","author":"Schmidhuber","year":"1996","journal-title":"Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale"},{"key":"ref50","first-page":"2247","article-title":"Learning gradient descent: Better generalization and longer horizons","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lyu","year":"2017"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.1991.155621"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6639349"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.1990.137898"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/72.750553"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2001.938471"},{"key":"ref56","first-page":"1568","article-title":"Bilevel programming for hyperparameter optimization and meta-learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Franceschi","year":"2018"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015187"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00580"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00583"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3322980"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.59"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"}],"container-title":["IEEE Transactions on Visualization and Computer Graphics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/2945\/11231102\/11189234.pdf?arnumber=11189234","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T18:10:18Z","timestamp":1762539018000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11189234\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":65,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tvcg.2025.3616156","relation":{},"ISSN":["1077-2626","1941-0506","2160-9306"],"issn-type":[{"type":"print","value":"1077-2626"},{"type":"electronic","value":"1941-0506"},{"type":"electronic","value":"2160-9306"}],"subject":[],"published":{"date-parts":[[2025,12]]}}}