stereo matching using loopy belief propagation
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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
Left image Right image
Matching Costs
Birchfield-Tomasi matching cost
}Piece-wise linear segment
Left image Right image
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.
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
Tsukuba image
Tsukuba image—MAP result
Iteration 1
Tsukuba image—MAP result
Iteration 2
Tsukuba image—MAP result
Iteration 3
Tsukuba image—MAP result
Iteration 5
Tsukuba image—MMSE result
Iteration 1
Tsukuba image—MMSE result
Iteration 3
Tsukuba image—MMSE result
Iteration 10
Tsukuba image—MMSE result
Iteration 20
Tsukuba image—MMSE result
Iteration 30
Tsukuba image—MMSE result
Iteration 40
Tsukuba image—MMSE result
Iteration 50
Tsukuba image—MAP vs. MMSE
Algorithm
Iterations before converge
Time (sec)
MAP 5 43
MMSE 50 328
Tsukuba image—Parameters
S = 50Best result
S = 500Over-smoothed
S = 5Under-smoothed
Change the separation cost parameter (S) in the Potts model
Sawtooth image—MAP result
Map Image—MAP result
Venus Image—MAP result
Structure Light Stereo
Richer texture Larger disparity range ~[0-
100]
Bust—MAP result
Iteration 1
Bust—MAP result
Iteration 2
Bust—MAP result
Iteration 3
Bust—MAP result
Iteration 5
Bust—MAP result
Iteration 10
Bust—MAP result
Iteration 20
Bust—MAP result
Iteration 30
Bust—MAP result
Iteration 40
Bust—MAP result
Iteration 50
Bust—BP vs. DP
Belief Propagation Dynamic Programming
320x240, 60 labels, 30 sec per iteration
640x480, 120 labels, ~30 sec one pass
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