@InProceedings{JancosekCVWW09,
  IS = { zkontrolovano 29 Jan 2010 },
  UPDATE  = { 2009-10-15 },
  author =      {Jancosek, Michal and Pajdla, Tomas},
  title =       {Segmentation Based Multi-View Stereo},
  year =        {2009},
  pages =       {37-42},
  book_pages =  {},
  booktitle =   {CVWW '09: Computer Vision Winter Workshop 2009},
  editor =	 {Ion, Adrian and Kropatsch, Walter G.},
  publisher =	 {PRIP TU Wien},
  address =	 {Wien, Austria},
  month =	 {February},
  day =		 {4-6},
  venue =	 {Eibiswald, Austria},
  annote = {This paper presents a segmentation based multiview stereo
    reconstruction method. We address (i) dealing with uninformative
    texture in very homogeneous image areas and (ii) processing of
    large images in affordable time.  To avoid searching for optimal
    surface position and orientation based on uninformative texture,
    we (over)segment images into segments of low variation of color
    and intensity and use each segment to generate a candidate 3D
    planar patch explaining the underlying 3D surface. Every point of
    the surface is explained by multiple candidate patches generated
    from image segments from different images. Observing that the
    correctly reconstructed surface is consistently generated from
    different images, the candidates that do not have consistent
    support by other candidates from other images are rejected. This
    approach leads to stable and good results since (i) we use larger
    3D patches in homogeneous image areas where small patches covered
    by uninformative texture would lead to ambiguous results, and (ii)
    we accept only candidates that are consistent across several
    images.  Since the image segmentation used is very fast and it
    considerably reduces the number of candidates per image on typical
    scenes, we typically generate and test relatively small number of
    3D hypotheses per image and thus can process large images in
    affordable time. We demonstrate the performance of our algorithm
    on large images from Strecha's dataset.},
  keywords =	 {computer vision, surface reconstruction},
  authorship =	 {50-50},
  project =	 {specific research},
  isbn = {978-3-200-01390-2},
}