Conference item icon

Conference item

Random forests versus neural networks - What's best for camera localization?

Abstract:

This work addresses the task of camera localization in a known 3D scene given a single input RGB image. State-of-the-art approaches accomplish this in two steps: firstly, regressing for every pixel in the image its 3D scene coordinate and subsequently, using these coordinates to estimate the final 6D camera pose via RANSAC. To solve the first step, Random Forests (RFs) are typically used. On the other hand, Neural Networks (NNs) reign in many dense regression tasks, but are not test-time effi...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

Actions


Access Document


Files:
Publisher copy:
10.1109/ICRA.2017.7989598

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Brachmann, E More by this author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Skye Foundation More from this funder
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Publication date:
2017-07-05
Acceptance date:
2017-02-23
DOI:
Pubs id:
pubs:810844
URN:
uri:64d0f9b0-ae7e-453a-810b-40d5896c550d
UUID:
uuid:64d0f9b0-ae7e-453a-810b-40d5896c550d
Local pid:
pubs:810844

Terms of use


Metrics



If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP