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Refinement for absolute pose regression with neural feature synthesis

Abstract:

Absolute Pose Regression (APR) methods use deep neural networks to directly regress camera poses from RGB images. Despite their advantages in inference speed and simplicity, these methods still fall short of the accuracy achieved by geometry-based techniques. To address this issue, we propose a new model called the Neural Feature Synthesizer (NeFeS). Our approach encodes 3D geometric features during training and renders dense novel view features at test time to refine estimated camera poses f...

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Publication status:
Accepted
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Publisher:
IEEE
Acceptance date:
2023-02-27
Event title:
IEEE/CVF Computer Vision and Pattern Recognition (CVPR 2023)
Event location:
Vancouver, Canada
Event website:
https://cvpr2023.thecvf.com/
Event start date:
2023-06-18
Event end date:
2023-06-22
Language:
English
Keywords:
Pubs id:
1335413
Local pid:
pubs:1335413
Deposit date:
2023-04-03

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