IS = { zkontrolovano 29 Jan 2010 },
  UPDATE  = { 2009-10-15 },
  author =      {Jancosek, Michal and Shekhovtsov, Alexander and Pajdla, Tomas},
  title =       {Scalable Multi-View Stereo},
  year =        {2009},
  pages =       {1-8},
  book_pages =  {2235},
  booktitle =   {3DIM '09: The 2009 IEEE International Workshop on 3-D Digital Imaging and Modeling},
  publisher =   {IEEE Computer Society Press},
  address =     {Los Alamitos, USA},
  month =	{October},
  day =		{3-4},
  venue =	{Kyoto, Japan},
  annote = {This paper presents a scalable multi-view stereo
    reconstruction method which can deal with a large number of large
    unorganized images in affordable time and effort. The
    computational effort of our technique is a linear function of the
    surface area of the observed scene which is conveniently
    discretized to represent sufficient but not excessive detail.  Our
    technique works as a filter on a limited number of images at a
    time and can thus process arbitrarily large data sets using
    limited memory. By building reconstructions gradually, we avoid
    unnecessary processing of data which bring little improvement. In
    experiments with Middlebury and Strecha's databases, we
    demonstrate that we achieve results comparable to the state of the
    art with considerably smaller effort than used by previous
    methods. We present a large scale experiments in which we
    processed 294 unorganized images of an outdoor scene and
    reconstruct its 3D model and 1000 images from the Google Street
    View Pittsburgh Experimental Data Set.},
  keywords =	 {computer vision, surface reconstruction},
  project =	 {FP7-SPACE-218814 PRoVisG, MSM6840770038, CTU0908713},
  isbn = {978-1-4244-4441-0},
  note      = { CD-ROM },