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Vision-based Registration for AR

Vision-based Registration for AR. Presented by Diem Vu Nov 20, 2003. Markerless Tracking using Planar Structure in the Scene . G. Simon, A.W. Fitzgibbon and A. Zisserman, 2000. Calibration-Free Augmented Reality . K.N Kutulakos and J.R. Vallino , 1998. Planar-surface tracking.

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Vision-based Registration for AR

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  1. Vision-based Registration for AR Presented by Diem Vu Nov 20, 2003

  2. Markerless Tracking using Planar Structure in the Scene. G. Simon, A.W. Fitzgibbon and A. Zisserman, 2000. • Calibration-Free Augmented Reality. K.N Kutulakos and J.R. Vallino, 1998.

  3. Planar-surface tracking. • Camera position can be recovered from planar homography. • Planar structure is common in almost all scenarios.

  4. World to image homography y Image to image homography Hw x z

  5. y Hw x z World to image homography • Consider our tracking plane is the plane Z=0

  6. Projection matrix

  7. y P x z Projection matrix

  8. y P x z Projection matrix

  9. If K and Hw are known, then r1, r2 and t can be recovered, thus P. • Question: How to compute Hw? • Direct. • Indirect.

  10. (0,1) (1,1) (0,0) (1,0) Direct measurement of Hw • Select 4 points {xk} on a rectangle in the scene. • Compute H which maps the unit square to {xk}.

  11. (0,s) (1,s) (0,0) (1,0) Direct measurement of Hw • Select 4 points {xk} on a rectangle in the scene. • Compute H which maps the unit square to {xk}. • Compute Hw=Hdiag(1,1/s,1)

  12. y x z Indirect measurement of Hw

  13. y x z Indirect measurement of Hw

  14. Algorithm summary • Compute (direct measure). • For each frame i, compute frame to frame homography (RANSAC) • Compute by:

  15. Other … • Using only 2 points in direct method ?? • Matching the frame i with frame 0 in order to reduce error. • Estimate intrinsic parameters K • Hand-off mechanism.

  16. Possible problems? • Homography is only up-to-scale? • Plain surface (no texture) or moving objects in the foreground ? • Depth order, occlusion ? • Speed ?

  17. Affine virtual object representation • Represent virtual objects so that their projection can be computed as a linear combination of the projection of the fiducial points.

  18. Project a point from its affine coordinates

  19. Compute affine coordinates from projection along two viewing direction

  20. Algorithm • Setup the affine basis

  21. Algorithm • Setup the affine basis • Locate the object in 2 frames.

  22. Algorithm • Setup the affine basis • Locate the object in 2 frames. • Compute the affine coordinates for each point.

  23. Algorithm • Setup the affine basis • Locate the object in 2 frames. • Compute the affine coordinates for each point. • Compute projection of the object and render the object in each frame.

  24. Camera viewing direction • and are the first and second row of 2x3. • The camera viewing direction expressed in the coordinate frame of the affine basis points:  =   

  25. Depth order • w is the z-value of point p (x,y,z).

  26. Advantages • No need any metric information. • Able to use with the existing hardware to accelerate graphics operations. • Can be used to improve tracking.

  27. Limitation • Affine constraints. • Lost of metric information.

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