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Zero-shot category-level object pose estimation

Abstract:

Object pose estimation is an important component of most vision pipelines for embodied agents, as well as in 3D vision more generally. In this paper we tackle the problem of estimating the pose of novel object categories in a zero-shot manner. This extends much of the existing literature by removing the need for pose-labelled datasets or category-specific CAD models for training or inference. Specifically, we make the following contributions. First, we formalise the zero-shot, category-level ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-031-19842-7_30

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-4371-4623
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Pembroke College
Role:
Author
ORCID:
0000-0001-6270-700X
Publisher:
Springer
Series:
Lecture Notes in Computer Science
Issue:
13699
Pages:
516-532
Publication date:
2022-10-23
Event title:
17th European Conference on Computer Vision (ECCV 2022)
Event location:
Tel Aviv, Israel
Event website:
https://eccv2022.ecva.net/
Event start date:
2022-10-23
Event end date:
2022-10-27
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
ISBN:
9783031198410
Language:
English
Keywords:
Pubs id:
1314595
Local pid:
pubs:1314595
Deposit date:
2023-01-23

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