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Stereo Matching Using Loopy Belief Propagation

Stereo Matching Using Loopy Belief Propagation. Li Zhang and Lin Liao April 23, 2004. Outline. Problem setup Matching on benchmark stereo images Matching on structure light stereo images Conclusion. Matching Costs. Birchfield-Tomasi matching cost. Right image. Left image. Piece-wise

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Stereo Matching Using Loopy Belief Propagation

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  1. Stereo Matching Using Loopy Belief Propagation Li Zhang and Lin Liao April 23, 2004

  2. Outline • Problem setup • Matching on benchmark stereo images • Matching on structure light stereo images • Conclusion

  3. Matching Costs • Birchfield-Tomasi matching cost Right image Left image

  4. Piece-wise linear segment } Matching Costs • Birchfield-Tomasi matching cost Right image Left image

  5. Separation Cost (Potts model) Let xi, xj be the labels of two adjacent nodes i, j. The separation cost V(xi,xj) is and where ∆I is the image gradient between i and j; T, s, and P are the parameters.

  6. Accelerated Belief Propagation • Propagate message in one direction and update each node immediately • Advantages: • Messages propagate much faster • Do not need to buffer the messages from previous iteration; it’s easier to implement • We implemented both the MAP estimator and the MMSE estimator

  7. Tsukuba image

  8. Tsukuba image—MAP result Iteration 1

  9. Tsukuba image—MAP result Iteration 2

  10. Tsukuba image—MAP result Iteration 3

  11. Tsukuba image—MAP result Iteration 5

  12. Tsukuba image—MMSE result Iteration 1

  13. Tsukuba image—MMSE result Iteration 3

  14. Tsukuba image—MMSE result Iteration 10

  15. Tsukuba image—MMSE result Iteration 20

  16. Tsukuba image—MMSE result Iteration 30

  17. Tsukuba image—MMSE result Iteration 40

  18. Tsukuba image—MMSE result Iteration 50

  19. Tsukuba image—MAP vs. MMSE

  20. Tsukuba image—Parameters Change the separation cost parameter (S) in the Potts model S = 50 Best result S = 500 Over-smoothed S = 5 Under-smoothed

  21. Sawtooth image—MAP result

  22. Map Image—MAP result

  23. Venus Image—MAP result

  24. Structure Light Stereo • Richer texture • Larger disparity range ~[0-100]

  25. Bust—MAP result Iteration 1

  26. Bust—MAP result Iteration 2

  27. Bust—MAP result Iteration 3

  28. Bust—MAP result Iteration 5

  29. Bust—MAP result Iteration 10

  30. Bust—MAP result Iteration 20

  31. Bust—MAP result Iteration 30

  32. Bust—MAP result Iteration 40

  33. Bust—MAP result Iteration 50

  34. Bust—BP vs. DP Belief Propagation Dynamic Programming 320x240, 60 labels, 30 sec per iteration 640x480, 120 labels, ~30 sec one pass

  35. Conclusion • BP-MAP works pretty well • BP-MMSE doesn’t work great • BP-MAP doesn’t show dramatic improvement over DP on stereo with dense texture

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