3D structure refinement of nonrigid surfaces through efficient image alignment

  • Authors:

  • Yinqiang Zheng;Shigeki Sugimoto;Masatoshi Okutomi

  • Affiliations:

  • Department of Mechanical and Control Engineering, Tokyo Institute of Technology;Department of Mechanical and Control Engineering, Tokyo Institute of Technology;Department of Mechanical and Control Engineering, Tokyo Institute of Technology

  • Venue:
  • ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
  • Year:
  • 2010

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Abstract

Given a template image with known 3D structure, we show how to refine the rough reconstruction of nonrigid surfaces from existing feature-based methods through efficient direct image alignment. Under the mild assumption that the barycentric coordinates of each 3D point on the surface keep constant, we prove that the template and the input image are correlated by piecewise homography, based on which a direct Lucas-Kanade image alignment method is proposed to iteratively recover an inextensible surface even with poor texture and sharp creases. To accelerate the direct Lucas-Kanade method, an equivalent but much more efficient method is proposed as well, in which the most time-consuming part of the Hessian can be pre-computed as a result of combining additive and inverse compositional expressions. Sufficient experiments on both synthetic and real images demonstrate the accuracy and efficiency of our proposed methods.