• DocumentCode
    3402534
  • Title

    High-resolution modeling of moving and deforming objects using sparse geometric and dense photometric measurements

  • Author

    Xu, Yi ; Aliaga, Daniel G.

  • Author_Institution
    Dept. of Comput. Sci., Purdue Univ., West Lafayette, IN, USA
  • fYear
    2010
  • fDate
    13-18 June 2010
  • Firstpage
    1237
  • Lastpage
    1244
  • Abstract
    Modeling moving and deforming objects requires capturing as much information as possible during a very short time. When using off-the-shelf hardware, this often hinders the resolution and accuracy of the acquired model. Our key observation is that in as little as four frames both sparse surface-positional measurements and dense surface-orientation measurements can be acquired using a combination of structured light and photometric stereo, resulting in high-resolution models of moving and deforming objects. Our system projects alternating geometric and photometric patterns onto the object using a set of three projectors and captures the object using a synchronized camera. Small motion among temporally close frames is compensated by estimating the optical flow of images captured under the uniform illumination of the photometric light. Then spatial-temporal photogeometric reconstructions are performed to obtain dense and accurate point samples with a sampling resolution equal to that of the camera. Temporal coherence is also enforced. We demonstrate our system by successfully modeling several moving and deforming real-world objects.
  • Keywords
    computational geometry; image motion analysis; image reconstruction; image resolution; stereo image processing; dense photometric measurement; dense surface-orientation measurement; high-resolution modeling; optical flow; photometric stereo; sparse geometric measurement; sparse surface-positional measurement; spatial-temporal photogeometric reconstruction; structured light; temporal coherence; Cameras; Deformable models; Geometrical optics; Hardware; Image motion analysis; Lighting; Motion estimation; Optical variables control; Photometry; Solid modeling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on
  • Conference_Location
    San Francisco, CA
  • ISSN
    1063-6919
  • Print_ISBN
    978-1-4244-6984-0
  • Type

    conf

  • DOI
    10.1109/CVPR.2010.5539825
  • Filename
    5539825