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Advanced AR Vision-Based Registration Techniques

Explore markerless tracking using planar structures for AR applications, addressing direct and indirect measurement methods, homography computations, and affine virtual object representation. Learn about advantages and limitations of these cutting-edge techniques. Discover how to leverage existing hardware for efficient graphics operations.

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Advanced AR Vision-Based Registration Techniques

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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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