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Creating Photorealistic Virtual Model with Polarization-based Vision System

  • Takushi Shibata
  • Toru Takahashi
  • Daisuke Miyazaki
  • Yoichi Sato
  • Katsushi Ikeuchi

Recently, 3D models are used in many fields such as education, medical services, entertainment, art, digital archive, etc., because of the progress of computational time and demand for creating photorealistic virtual model is increasing for higher reality. In the field of computer vision, a number of techniques have been developed for creating the virtual model by observing the real object in computer vision field. In this chapter, we propose the method for creating photorealistic virtual model by using laser range sensor and polarization based image capture system. We capture the range and color images of the object which is rotated on the rotary table. In geometrical aspects, an object surface shape is reconstructed by merging multiple range images of the object. In optical aspects, color images are captured under fixed point light source. By using reconstructed object shape and sequence of color images of the object, parameter of a reflection model are estimated in a robust manner. As a result, we can make photorealistic 3D model in consideration of surface reflection. The key point of the proposed method is that, first, the diffuse and specular reflection components are separated from the color image sequence, and then, reflectance parameters of each reflection component are estimated separately. In separation of reflection components, we use polarization filter. This approach enables estimation of reflectance properties of real objects whose surfaces show specularity as well as diffusely reflected lights. The recovered object shape and reflectance properties are then used for synthesizing object images with realistic shading effects under arbitrary illumination conditions.

Keywords

Color Image Linear Polarization Geometrical Optic Range Image Object Surface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Takushi Shibata
  • Toru Takahashi
  • Daisuke Miyazaki
  • Yoichi Sato
  • Katsushi Ikeuchi
    • 1
  1. 1.Institute of Industrial ScienceThe University of TokyoMeguro-kuJapan

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