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Personalized Bare Hand Modeling Based on Stereo Vision and Geometry Deformation

Personalized Bare Hand Modeling Based on Stereo Vision and Geometry Deformation. Quan Yu State Key Lab of CAD&CG Zhejiang University. Outline. Introduction System Overview Algorithm Input Features Alignment & Deformation Result & Feature Work Conclusion. Introduction.

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Personalized Bare Hand Modeling Based on Stereo Vision and Geometry Deformation

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  1. Personalized Bare Hand Modeling Based on Stereo Vision and Geometry Deformation Quan Yu State Key Lab of CAD&CG Zhejiang University

  2. Outline • Introduction • System Overview • Algorithm • Input • Features • Alignment & Deformation • Result & Feature Work • Conclusion

  3. Introduction • Personalized Hand Modeling(NSFC,No.60970078) • No markers nor gloves • Low-end devices (web cameras) • Challenge • Lack of strong features • Lack of solution

  4. Introduction(Cont.) • Idea • Corse features come from stereo vision • Fine features come from a template • Solution • Deform a template under constrains of vision data step by step

  5. System Overview Fig. 1 System overview. (a)extract convexity defects of contours and build a local coordinate; (b)align the template with defects and refine alignment with ICP algorithm; (c)laplacian deformation under constrains of defects (point level); (d)generate contour points with a single image; (e)laplacian deformation under constrains of contours (line level); (f)extract surface features and construct a point cloud; (g)laplacian deformation under constrains of surface points (surface level).

  6. Input • Two pairs of stereo images • front and back faces of a hand • A generic template • Denote contours and defects • Camera parameters • Web cameras • No markers

  7. Features: defect points • Convexity defects of contours • Stable • Strong • Used to determine: • Size • Position • Alignment with the template

  8. Features: contour points • Generate contour points from a single image • Contours of left and right image are different. • Assume the depths of contour points are constant • A non-linear interpolation between defects Disparities are unknown.

  9. Features: contour points(Cont.) • Approximate contours as • Correspondences: arc length matching mean of contours arc length matching

  10. Features: surface points • Image enhancement • Contrast-Limited Adaptive Histogram Equalization • Hard to extract robust features of hand skin. • SIFT • SURF • GLOH ? • DAISY ? • Efficient Large-Scale Stereo Matching(ACCV 2010) • Sobel responses on a regular grid

  11. Features: surface points(Cont.) • Find correspondences • Estimate normals (MLS) • Project 3D points onto the template • Split the template at the projection point

  12. Features: surface points(Cont.) • Iterative deformation to eliminate outliers • Reject Threshold correspondences deformation iter=1 iter=2 iter=3 Details or Outliers?

  13. Alignment • Extract defects • Local coordinate • Refine: ICP • Efficient Variants of the ICP Algorithm[S. R. 2001]

  14. Laplacian Deformation • Laplacian Mesh Processing • Ogla Sorkine, 2005 • Laplacian Mesh Optimization • Andrew Nealen, 2006

  15. Result & Feature Work • Result • Demo • Feature Work • Resampling • Geometry Optimization • Texture

  16. Conclusion • A novel approach to construct personalized hand model with low-end equipments; • Generate 3D contour points from a single image; • Eliminate outliers with an iterative deformation.

  17. Thank you! Question & Suggestion ?

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