Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks

dc.contributor.author	Geiger, Andreas
dc.date.accessioned	2022-01-27T16:27:33Z
dc.date.available	2022-01-27T16:27:33Z
dc.date.issued	2021-11-13
dc.identifier.isbn	978-1-6654-4509-2
dc.identifier.uri	http://hdl.handle.net/10900/123610
dc.language.iso	en	de_DE
dc.publisher	IEEE Computer Society	de_DE
dc.relation.uri	http://dx.doi.org/10.1109/CVPR46437.2021.00322	de_DE
dc.subject.ddc	004	de_DE
dc.title	Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks	de_DE
dc.type	Article	de_DE
utue.publikation.seiten	3203-3214	de_DE
utue.personen.roh	Paschalidou, Despoina
utue.personen.roh	Katharopoulos, Angelos
utue.personen.roh	Geiger, Andreas
utue.personen.roh	Fidler, Sanja
dcterms.isPartOf.ZSTitelID	2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)	de_DE