• DocumentCode
    2917543
  • Title

    Multiview registration via graph diffusion of dual quaternions

  • Author

    Torsello, Andrea ; Rodolà, Emanuele ; Albarelli, Andrea

  • Author_Institution
    Dipt. di Sci. Ambientali, Inf. e Statistica, Univ. Ca´´ Foscari Venezia, Venice, Italy
  • fYear
    2011
  • fDate
    20-25 June 2011
  • Firstpage
    2441
  • Lastpage
    2448
  • Abstract
    Surface registration is a fundamental step in the reconstruction of three-dimensional objects. While there are several fast and reliable methods to align two surfaces, the tools available to align multiple surfaces are relatively limited. In this paper we propose a novel multiview registration algorithm that projects several pairwise alignments onto a common reference frame. The projection is performed by representing the motions as dual quaternions, an algebraic structure that is related to the group of 3D rigid transformations, and by performing a diffusion along the graph of adjacent (i.e., pairwise alignable) views. The approach allows for a completely generic topology with which the pair-wise motions are diffused. An extensive set of experiments shows that the proposed approach is both orders of magnitude faster than the state of the art, and more robust to extreme positional noise and outliers. The dramatic speedup of the approach allows it to be alternated with pairwise alignment resulting in a smoother energy profile, reducing the risk of getting stuck at local minima.
  • Keywords
    graph theory; image reconstruction; image registration; image representation; solid modelling; 3D rigid transformation; algebraic structure; dual quaternions; generic topology; graph diffusion; multiview registration algorithm; pairwise motion representation; positional noise; surface registration; three-dimensional object reconstruction; Fasteners; Interpolation; Noise; Noise level; Quaternions; Robustness; Three dimensional displays;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
  • Conference_Location
    Providence, RI
  • ISSN
    1063-6919
  • Print_ISBN
    978-1-4577-0394-2
  • Type

    conf

  • DOI
    10.1109/CVPR.2011.5995565
  • Filename
    5995565