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Published byAndrew George Modified over 9 years ago
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Quan Yu State Key Lab of CAD&CG Zhejiang University
Personalized Bare Hand Modeling Based on Stereo Vision and Geometry Deformation Quan Yu State Key Lab of CAD&CG Zhejiang University
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Outline Introduction System Overview Algorithm Result & Feature Work
Input Features Alignment & Deformation Result & Feature Work Conclusion
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Introduction Personalized Hand Modeling(NSFC, No.60970078) Challenge
No markers nor gloves Low-end devices (web cameras) Challenge Lack of strong features Lack of solution
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Introduction(Cont.) Idea Solution
Corse features come from stereo vision Fine features come from a template Solution Deform a template under constrains of vision data step by step
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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).
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Input Two pairs of stereo images A generic template Camera parameters
front and back faces of a hand A generic template Denote contours and defects Camera parameters Web cameras No markers
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Features: defect points
Convexity defects of contours Stable Strong Used to determine: Size Position Alignment with the template
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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.
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Features: contour points(Cont.)
Approximate contours as Correspondences: arc length matching mean of contours arc length matching
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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
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Features: surface points(Cont.)
Find correspondences Estimate normals (MLS) Project 3D points onto the template Split the template at the projection point
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Features: surface points(Cont.)
Iterative deformation to eliminate outliers Reject Threshold correspondences deformation iter=1 iter=2 iter=3 Details or Outliers?
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Alignment Extract defects Local coordinate Refine: ICP
Efficient Variants of the ICP Algorithm[S. R. 2001]
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Laplacian Deformation
Laplacian Mesh Processing Ogla Sorkine, 2005 Laplacian Mesh Optimization Andrew Nealen, 2006
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Result & Feature Work Result Feature Work Demo Resampling
Geometry Optimization Texture
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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.
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Thank you! Question & Suggestion ?
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