• DocumentCode
    2917272
  • Title

    Multicore bundle adjustment

  • Author

    Wu, Changchang ; Agarwal, Sameer ; Curless, Brian ; Seitz, Steven M.

  • Author_Institution
    Univ. of Washington, Seattle, WA, USA
  • fYear
    2011
  • fDate
    20-25 June 2011
  • Firstpage
    3057
  • Lastpage
    3064
  • Abstract
    We present the design and implementation of new inexact Newton type Bundle Adjustment algorithms that exploit hardware parallelism for efficiently solving large scale 3D scene reconstruction problems. We explore the use of multicore CPU as well as multicore GPUs for this purpose. We show that overcoming the severe memory and bandwidth limitations of current generation GPUs not only leads to more space efficient algorithms, but also to surprising savings in runtime. Our CPU based system is up to ten times and our GPU based system is up to thirty times faster than the current state of the art methods, while maintaining comparable convergence behavior. The code and additional results are available at http://grail.cs.washington.edu/projects/mcba.
  • Keywords
    computer graphic equipment; computer vision; coprocessors; image reconstruction; multiprocessing systems; natural scenes; 3D scene reconstruction; GPU; bundle adjustment algorithm; inexact Newton algorithm; multicore CPU; space efficient algorithms; Cameras; Graphics processing unit; Instruction sets; Jacobian matrices; Multicore processing; Random access memory; Sparse matrices;
  • 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.2011.5995552
  • Filename
    5995552