Institutional Research Information Service
UCL Logo
Please report any queries concerning the funding data grouped in the sections named "Externally Awarded" or "Internally Disbursed" (shown on the profile page) to your Research Finance Administrator. Your can find your Research Finance Administrator at https://www.ucl.ac.uk/finance/research/rs-contacts.php by entering your department
Please report any queries concerning the student data shown on the profile page to:

Email: portico-services@ucl.ac.uk

Help Desk: http://www.ucl.ac.uk/ras/portico/helpdesk
Publication Detail
Online dense non-rigid 3D shape and camera motion recovery
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Conference Proceeding
  • Authors:
    Agudo A, Montiel JMM, Agapito L, Calvo B
  • Publication date:
  • Journal:
    BMVC 2014 - Proceedings of the British Machine Vision Conference 2014
  • Status:
© 2014. The copyright of this document resides with its authors. This paper describes a sequential solution to dense non-rigid structure from motion that recovers the camera motion and 3D shape of non-rigid objects by processing a monocular image sequence as the data arrives. We propose to model the time-varying shape with a probabilistic linear subspace of mode shapes obtained from continuum mechanics. To efficiently encode the deformations of dense 3D shapes that contain a large number of mesh vertexes, we propose to compute the deformation modes on a down sampled rest shape using finite element modal analysis at a low computational cost. This sparse shape basis is then grown back to dense exploiting the shape functions within a finite element. With this probabilistic low-rank constraint, we estimate camera pose and non-rigid shape in each frame using expectation maximization over a sliding window of frames. Since the time-varying weights are marginalized out, our approach only estimates a small number of parameters per frame, and hence can potentially run in real time. We evaluate our algorithm on both synthetic and real sequences with 3D ground truth data for different objects ranging from inextensible to extensible deformations and from sparse to dense shapes. We show the advantages of our approach with respect to competing sequential methods.
Publication data is maintained in RPS. Visit https://rps.ucl.ac.uk
 More search options
UCL Researchers
Dept of Computer Science
University College London - Gower Street - London - WC1E 6BT Tel:+44 (0)20 7679 2000

© UCL 1999–2011

Search by