{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T06:20:55Z","timestamp":1768803655426,"version":"3.49.0"},"reference-count":64,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1109\/tpami.2024.3430101","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T17:20:07Z","timestamp":1721236807000},"page":"9940-9956","source":"Crossref","is-referenced-by-count":3,"title":["Deep Learning on Object-Centric 3D Neural Fields"],"prefix":"10.1109","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7734-5064","authenticated-orcid":false,"given":"Pierluigi Zama","family":"Ramirez","sequence":"first","affiliation":[{"name":"University of Bologna, Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2654-7480","authenticated-orcid":false,"given":"Luca","family":"De Luigi","sequence":"additional","affiliation":[{"name":"University of Bologna, Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6464-9338","authenticated-orcid":false,"given":"Daniele","family":"Sirocchi","sequence":"additional","affiliation":[{"name":"University of Bologna, Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0584-3109","authenticated-orcid":false,"given":"Adriano","family":"Cardace","sequence":"additional","affiliation":[{"name":"University of Bologna, Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9748-2824","authenticated-orcid":false,"given":"Riccardo","family":"Spezialetti","sequence":"additional","affiliation":[{"name":"University of Bologna, Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8774-5532","authenticated-orcid":false,"given":"Francesco","family":"Ballerini","sequence":"additional","affiliation":[{"name":"University of Bologna, Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5609-426X","authenticated-orcid":false,"given":"Samuele","family":"Salti","sequence":"additional","affiliation":[{"name":"University of Bologna, Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6014-6421","authenticated-orcid":false,"given":"Luigi Di","family":"Stefano","sequence":"additional","affiliation":[{"name":"University of Bologna, Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"5099","article-title":"PointNet++: Deep hierarchical feature learning on point sets in a metric space","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Qi"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3326362"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3506694"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14505"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00025"},{"key":"ref6","first-page":"21 638","article-title":"Neural unsigned distance fields for implicit function learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Chibane"},{"key":"ref7","first-page":"3789","article-title":"Implicit geometric regularization for learning shapes","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gropp"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01120"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00459"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_31"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3528223.3530127"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459785"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3528233.3530713"},{"key":"ref15","first-page":"5694","article-title":"From data to functa: Your data point is a function and you can treat it like one","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Dupont"},{"key":"ref16","first-page":"7462","article-title":"Implicit neural representations with periodic activation functions","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Sitzmann"},{"key":"ref17","first-page":"10 136","article-title":"MetaSDF: Meta-learning signed distance functions","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Sitzmann"},{"key":"ref18","article-title":"COIN: Compression with implicit neural representations","author":"Dupont","year":"2021"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19809-0_5"},{"key":"ref20","article-title":"Implicit neural video compression","author":"Zhang","year":"2021"},{"key":"ref21","first-page":"7537","article-title":"Fourier features let networks learn high frequency functions in low dimensional domains","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Tancik"},{"key":"ref22","first-page":"1","article-title":"Deep learning on implicit neural representations of shapes","volume-title":"Proc. Int. Conf. Learn. Representations","author":"De Luigi"},{"key":"ref23","article-title":"Neural functional transformers","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhou"},{"key":"ref24","first-page":"25790","article-title":"Equivariant architectures for learning in deep weight spaces","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Navon"},{"key":"ref25","article-title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks","author":"Frankle","year":"2018"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09816-7"},{"key":"ref27","first-page":"1119","article-title":"Scene representation networks: Continuous 3D-structure-aware neural scene representations","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Sitzmann"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00604"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00264"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00609"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00239"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00463"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00356"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00548"},{"key":"ref35","article-title":"Predicting neural network accuracy from weights","author":"Unterthiner","year":"2020"},{"key":"ref36","first-page":"16481","article-title":"Self-supervised representation learning on neural network weights for model characteristic prediction","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Sch\u00fcrholt"},{"key":"ref37","first-page":"29433","article-title":"Parameter prediction for unseen deep architectures","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Knyazev"},{"key":"ref38","first-page":"1556","article-title":"Generating adversarial examples with graph neural networks","volume-title":"Proc. 37th Conf. Uncertainty Artif. Intell.","author":"Jaeckle"},{"key":"ref39","article-title":"Neural network branching for neural network verification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lu"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00092"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-444-88400-8.50019-4"},{"key":"ref42","article-title":"Permutation equivariant neural functionals","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhou"},{"key":"ref43","first-page":"1","article-title":"Neural processing of tri-plane hybrid neural fields","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Cardace"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01315"},{"key":"ref45","article-title":"ShapeNet: An information-rich 3D model repository","author":"Chang","year":"2015"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/37402.37422"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298801"},{"key":"ref48","first-page":"652","article-title":"PointNet: Deep learning on point sets for 3D classification and segmentation","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Qi"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.264"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3414685.3417806"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2015.7353481"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.261"},{"key":"ref53","article-title":"E-stitchup: Data augmentation for pre-trained embeddings","author":"Wolfe","year":"2019"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2980238"},{"key":"ref55","first-page":"40","article-title":"Learning representations and generative models for 3D point clouds","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Achlioptas"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3476576.3476732"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00842"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref60","article-title":"DISN: Deep implicit surface network for high-quality single-view 3D reconstruction","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Xu"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01699"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00352"},{"key":"ref63","article-title":"Git re-basin: Merging models modulo permutation symmetries","author":"Ainsworth","year":"2022"},{"key":"ref64","first-page":"1","article-title":"The role of permutation invariance in linear mode connectivity of neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Entezari"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/10746266\/10601345.pdf?arnumber=10601345","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T04:12:47Z","timestamp":1743826367000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10601345\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":64,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2024.3430101","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":[[2024,12]]}}}