Show simple item record

dc.contributor.author
Probst, Thomas
dc.contributor.author
Paudel, Danda Pani
dc.contributor.author
Chhatkuli, Ajad
dc.contributor.author
Van Gool, Luc
dc.date.accessioned
2020-03-30T06:22:27Z
dc.date.available
2020-03-30T06:22:27Z
dc.date.issued
2019
dc.identifier.isbn
978-1-7281-4803-8
en_US
dc.identifier.isbn
978-1-7281-4804-5
en_US
dc.identifier.other
10.1109/ICCV.2019.01033
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/407171
dc.identifier.doi
10.3929/ethz-b-000391493
dc.description.abstract
In this paper, we formulate a generic non-minimal solver using the existing tools of Polynomials Optimization Problems (POP) from computational algebraic geometry. The proposed method exploits the well known Shor’s or Lasserre’s relaxations, whose theoretical aspects are also discussed. Notably, we further exploit the POP formulation of non-minimal solver also for the generic consensus maximization problems in 3D vision. Our framework is simple and straightforward to implement, which is also supported by three diverse applications in 3D vision, namely rigid body transformation estimation, Non-Rigid Structure-fromMotion (NRSfM), and camera autocalibration. In all three cases, both non-minimal and consensus maximization are tested, which are also compared against the state-of-the-art methods. Our results are competitive to the compared methods, and are also coherent with our theoretical analysis. The main contribution of this paper is the claim that a good approximate solution for many polynomial problems involved in 3D vision can be obtained using the existing theory of numerical computational algebra. This claim leads us to reason about why many relaxed methods in 3D vision behave so well? And also allows us to offer a generic relaxed solver in a rather straightforward way. We further show that the convex relaxation of these polynomials can easily be used for maximizing consensus in a deterministic manner. We support our claim using several experiments for aforementioned three diverse problems in 3D vision.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.title
Convex Relaxations for Consensus and Non-Minimal Problems in 3D Vision
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2020-02-27
ethz.book.title
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
en_US
ethz.pages.start
10232
en_US
ethz.pages.end
10241
en_US
ethz.size
10 p. accepted version
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
International Conference on Computer Vision (ICCV 2019)
en_US
ethz.event.location
Seoul, South Korea
en_US
ethz.event.date
October 27 - November 2, 2019
en_US
ethz.identifier.scopus
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::03514 - Van Gool, Luc / Van Gool, Luc
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::03514 - Van Gool, Luc / Van Gool, Luc
en_US
ethz.date.deposited
2020-01-17T12:41:53Z
ethz.source
FORM
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.exportRequired
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/391493
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/406631
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Convex%20Relaxations%20for%20Consensus%20and%20Non-Minimal%20Problems%20in%203D%20Vision&rft.date=2019&rft.spage=10232&rft.epage=10241&rft.au=Probst,%20Thomas&Paudel,%20Danda%20Pani&Chhatkuli,%20Ajad&Van%20Gool,%20Luc&rft.isbn=978-1-7281-4803-8&978-1-7281-4804-5&rft.genre=proceeding&rft_id=info:doi/978-1-7281-4803-8&info:doi/978-1-7281-4804-5&rft.btitle=2019%20IEEE/CVF%20International%20Conference%20on%20Computer%20Vision%20(ICCV)
 Search via SFX

Files in this item

Thumbnail

Publication type

Show simple item record