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1
UDA-COPE: Unsupervised Domain Adaptation for Category-level..:
, In:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
,
Lee, Taeyeop
;
Lee, Byeong-Uk
;
Shin, Inkyu
... - p. 14871-14880 , 2022
Link:
https://doi.org/10.1109/CVPR52688.2022.01447
RT T1 <cTitle>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</cTitle>: T1 <Title>UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-9878728&Exemplar=1&LAN=DE A1 Lee, Taeyeop A1 Lee, Byeong-Uk A1 Shin, Inkyu A1 Choe, Jaesung A1 Shin, Ukcheol A1 Kweon, In So A1 Yoon, Kuk-Jin YR 2022 SN 2575-7075 K1 Training K1 Point cloud compression K1 Solid modeling K1 Filtering K1 Pose estimation K1 Pipelines K1 Self-supervised learning K1 Robot vision; 3D from single images; Pose estimation and tracking; RGBD sensors and analytics; Vision applications and systems SP 14871 OP 14880 LK http://dx.doi.org/https://doi.org/10.1109/CVPR52688.2022.01447 DO https://doi.org/10.1109/CVPR52688.2022.01447 SF ELIB - SuUB Bremen
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