{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T02:07:09Z","timestamp":1768442829133,"version":"3.49.0"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"German Federal Ministry for Economic Affairs and Energy"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Robot. Autom. Lett."],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1109\/lra.2022.3214791","type":"journal-article","created":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T20:07:22Z","timestamp":1665778042000},"page":"392-399","source":"Crossref","is-referenced-by-count":12,"title":["Lidar Upsampling With Sliced Wasserstein Distance"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4767-7153","authenticated-orcid":false,"given":"Artem","family":"Savkin","sequence":"first","affiliation":[{"name":"School of Computation, Information and Technology, Department of Computer Science, Technical University of Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4519-9108","authenticated-orcid":false,"given":"Yida","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computation, Information and Technology, Department of Computer Science, Technical University of Munich, Germany"}]},{"given":"Sebastian","family":"Wirkert","sequence":"additional","affiliation":[{"name":"BMW Group, Germany"}]},{"given":"Nassir","family":"Navab","sequence":"additional","affiliation":[{"name":"School of Computation, Information and Technology, Department of Computer Science, Technical University of Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5598-5212","authenticated-orcid":false,"given":"Federico","family":"Tombari","sequence":"additional","affiliation":[{"name":"School of Computation, Information and Technology, Department of Computer Science, Technical University of Munich, Germany"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref38","article-title":"ShapeNet: An information-rich 3D model repository","author":"chang","year":"2015"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2018.00088"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00041"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01151"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00611"},{"key":"ref37","first-page":"8139","article-title":"Cascaded refinement network for point cloud completion with self-supervision","volume":"44","author":"wang","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2994483"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6827"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00047"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.264"},{"key":"ref40","first-page":"1","article-title":"Dynamic graph CNN for learning on point clouds","volume":"38","author":"wang","year":"2019","journal-title":"Trans Graph"},{"key":"ref11","first-page":"40","article-title":"Learning representations and generative models for 3D point clouds","author":"achlioptas","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00464"},{"key":"ref13","article-title":"Point cloud GAN","author":"li","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref14","article-title":"Sliced-wasserstein auto-encoders","author":"kolouri","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00383"},{"key":"ref16","article-title":"Distributional sliced-wasserstein and applications to generative modeling","author":"nguyen","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2020.103647"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00730"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/IROS40897.2019.8968535"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093430"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01161"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_22"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00086"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3147326"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00295"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2019.00022"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_24"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9780262017091.001.0001"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00651"},{"key":"ref1","first-page":"5105","article-title":"PointNet++: Deep hierarchical feature learning on point sets in a metric space","author":"qi","year":"0","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cad.2019.02.006","article-title":"Data-driven upsampling of point clouds","volume":"112","author":"zhang","year":"2018","journal-title":"Comput -Aided Des"},{"key":"ref46","first-page":"2292","article-title":"Sinkhorn distances: Lightspeed computation of optimal transportation distances","author":"cuturi","year":"0","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9811992"},{"key":"ref45","first-page":"652","article-title":"Pointnet: Deep learning on point sets for 3D classification and segmentation","author":"qi","year":"0","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref22","first-page":"216","article-title":"Atlasnet: A papier-m&#x00E2;ch&#x00E9; approach to learning 3D surface generation","author":"groueix","year":"0","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref47","first-page":"14753","article-title":"Sliced gromov-wasserstein","author":"vayer","year":"0","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9811914"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01090"},{"key":"ref24","article-title":"Learning localized generative models for 3D point clouds via graph convolution","author":"valsesia","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01031"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00029"},{"key":"ref44","first-page":"2446","article-title":"Scalability in perception for autonomous driving: Waymo open dataset","author":"pie","year":"0","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00286"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00939"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00396"}],"container-title":["IEEE Robotics and Automation Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7083369\/9963768\/09919373.pdf?arnumber=9919373","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T19:15:21Z","timestamp":1673896521000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9919373\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":47,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/lra.2022.3214791","relation":{},"ISSN":["2377-3766","2377-3774"],"issn-type":[{"value":"2377-3766","type":"electronic"},{"value":"2377-3774","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]}}}