• DocumentCode
    2503174
  • Title

    Super-Resolution Texture Mapping from Multiple View Images

  • Author

    Iiyama, Masaaki ; Kakusho, Koh ; Minoh, Michihiko

  • Author_Institution
    Kyoto Univ., Kyoto, Japan
  • fYear
    2010
  • fDate
    23-26 Aug. 2010
  • Firstpage
    1820
  • Lastpage
    1823
  • Abstract
    This paper presents an artifact-free super resolution texture mapping from multiple-view images. The multiple-view images are upscaled with a learning-based super resolution technique and are mapped onto a 3D mesh model. However, mapping multiple-view images onto a 3D model is not an easy task, because artifacts may appear when different upscaled images are mapped onto neighboring meshes. We define a cost function that becomes large when artifacts appear on neighboring meshes, and our method seeks the image-and mesh assignment that minimizes the cost function. Experimental results with real images demonstrate the effectiveness of our method.
  • Keywords
    image resolution; image texture; learning (artificial intelligence); mesh generation; realistic images; solid modelling; 3D mesh model; artifact-free super resolution texture mapping; cost function; image-and mesh assignment; learning-based super resolution technique; multiple view images; neighboring meshes; real images; super-resolution texture mapping; upscaled images; Computational modeling; Cost function; Image resolution; Minimization; Signal resolution; Solid modeling; Three dimensional displays; graph cut; super resolution; texture mapping;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition (ICPR), 2010 20th International Conference on
  • Conference_Location
    Istanbul
  • ISSN
    1051-4651
  • Print_ISBN
    978-1-4244-7542-1
  • Type

    conf

  • DOI
    10.1109/ICPR.2010.449
  • Filename
    5597208