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Stereo Matching
• Information Permeability For Stereo Matching
– Cevahir Cigla and A.Aydın Alatan
– Signal Processing: Image Communication, 2013
• Radiometric Invariant Stereo Matching Based On
Relative Gradients
– Xiaozhou Zhou and Pierre Boulanger
– International Conference on Image Processing (ICIP), IEEE 2012
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Outline
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Introduction
Related Works
Methods
Conclusion
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Introduction
• Goal
– Get accurate disaprity maps effectively.
– Find more robust algorithm, especially refinement
technique.
• Foucus : Refinement step and Comparison
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Related Works
• Stereo Matching
– The same object, the same disparity
• Segmentation
• Calculate correspond pixels similarity
(color and geographic distance)
– Occlusion handling
• Refinement
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Related Works
• Global Methods
– Energy minimization
process
(GC,BP,DP,Cooperative)
– Per-processing
– Accurate but slow
• Local Methods
– A local support region
with winner take all
– Fast but inaccurate.
– Adaptive Support Weight
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Related Works
• Local methods algorithm
Matching Cost Computation
Cost Aggregation
Disparity Optimization
Disparity Refinement
[1] D. Scharstein and R. Szeliski.
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms.
International Journal of Computer Vision (IJCV), 47:7–42, 2002.
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Related Works
• Edge Preserving filter : Remove noise and preserve
structure/edge, like object consideration.
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Adaptive Support Weight [3]
Bilateral filter(BF) [34]
Guided filter(GF) [5]
Geodesic diffusion [33]
Arbitrary Support Region [39]
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Reference Papers
[3] Kuk-JinYoon, InSoKweon, Adaptive support weight approach for
correspondence search, IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2006.
[5] C. Rhemann, A. Hosni, M. Bleyer, C. Rother, M. Gelautz, Fast cost-volume
filtering for visual correspondence and beyond, CVPR 2011.
[33] L. De-Maetzu, A. Villanueva nad, R. Cabeza, Near real-time stereo
matching using geodesic diffusion, IEEE Transactions on Pattern Analysis and
Machine Intelligence, 2012.
[34] A. Ansar, A. Castano, L. Matthies, Enhanced real time stereo using
bilateral filtering, in: Proceedings of the International Symposium on 3D Data
Processing Visualization and Transmission, 2004.
[39] X. Mei, X Sun, M Zhou, S. Jiao, H. Wang, Z. Zhang, On building an
accurate stereo matching system on graphics hardware, in: Proceed- ings of
GPUCV 2011.
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Information Permeability For
Stereo Matching
Method A.
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Methods A.
• Goal : Get high quality but low complexity
Save memory
Real-time application
• Successive Weighted Summation (SWS)
– Constant time filtering + Weighted aggregation
◎Qingqing Yang, Dongxiao Li, Lianghao Wang, and Ming Zhang,
“Full-Image Guided Filtering for Fast Stereo Matching”,
Signal Processing Letters, IEEE March 2013
http://www.camdemy.com/media/7110
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Methods A.
• Cost Computation
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Census Transform
121 130
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Census transform window :
1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1
Census Hamming Distance
• Left image
1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1
• Right image
XOR
1 1 1 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 1 1 1 1 1 1
Hamming Distance = 3
0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Methods A.
• Cost Computation
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Methods A.
• Cost Aggregation
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Methods A.
• Cost Aggregation
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Methods A.
(b)Horizontal effective weights (c)Vertical effective weights (d)2D effective weights
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Comparison
With
Other
Methods
(a) AW
[3]
(b) Geodesic
support
[12]
(c) Arbitrary
(d) Proposed
support region
[4]
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Methods A.
• Refinement
– Using cross-check to detect reliable and occluded
region detection
ф is a constant
(set to 0.1 throughout experiments)
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Methods A.
(a) Linear mapping function for reliable pixels
based on disparities
(b)The resultant map for the left image
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Disparity Variation
BeforeAfter
0 <=> 1.15
1 <=> 1.30
2 <=> 1.45
3 <=> 1.60
4 <=> 1.75
5 <=> 1.90
6 <=> 2.05
7 <=> 2.20
8 <=> 2.35
9 <=> 2.50
10 <=> 2.65
11 <=> 2.80
12 <=> 2.95
13 <=> 3.10
14 <=> 3.25
15 <=> 3.40
16 <=> 3.55
17 <=> 3.70
18 <=> 3.85
19 <=> 4
20 <=> 4.15
21 <=> 4.30
22 <=> 4.45
23 <=> 4.60
24 <=> 4.75
25 <=> 4.90
26 <=> 5.05
27 <=> 5.20
28 <=> 5.35
29 <=> 5.50
30 <=> 5.65
31 <=> 5.80
32 <=> 5.95
33 <=> 6.10
34 <=> 6.25
35 <=> 6.40
36 <=> 6.55
37 <=> 6.70
38 <=> 6.85
39 <=> 7
40 <=> 7.15
41 <=> 7.30
42 <=> 7.45
43 <=> 7.60
44 <=> 7.75
45 <=> 7.90
46 <=> 8.05
47 <=> 8.20
48 <=> 8.35
49 <=> 8.50
50 <=> 8.65
51 <=> 8.80
52 <=> 8.95
53 <=> 9.10
54 <=> 9.25
55 <=> 9.40
56 <=> 9.55
57 <=> 9.70
58 <=> 9.85
59 <=> 10
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•
(b) Without occlusion handling,
bright regions correspond to small disparities
(c) Detection of occluded and un-reliable regions
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Methods A.
(b) occlusion handling with no background favoring (c) the proposed occlusion
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Experimental Results A.
• Device : Core Duo 1.80 GHz 2G Ram CPU
• Implemented in C++
• Parameter : (T, α, 𝜎 )=(15, 0.2, 8)
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Parameter of Method A.
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Experimental Results A.
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Experimental Results A.
6D + 4D *
V.S.
129D + 21D *
10~15X
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Experimental Results A.
• Proposed method is the fastest method without any
special hardware implementation among Top-10 local
methods of the Middlebury test bench, as of February
2013.
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O(1) AW
Guided filter
Geodesic support
Arbitrary shaped
cross filter
Proposed
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Experimental Results A.
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Computational times A.
≈5%
≈20~25%
≈45%
≈70~75%
≈44%
Cost Initialization
Cost Aggregation
Refinement
Minimization
≈84%
Others
Refinement
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Error Analysis A.
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Comparison with Full-Image◎
Full-Image
Initialization
Proposed
AD + Gradient
SAD + Census
1.Cross checking
(lowest disparity)
1. Cross checking
(normalized disparity)
2. Median filter
(background handling)
Aggregation
Refinement
2.Weighted median filter
◎Qingqing Yang, Dongxiao Li, Lianghao Wang, and Ming Zhang,
“Full-Image Guided Filtering for Fast Stereo Matching”, Signal Processing Letters, IEEE
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Comparison with Full-Image
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Full-Image Results
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Ground Truth
Proposed Results
Full-Image Results
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Comparison with Full-Image
• My Experimental Results (SAD+Gradient)
• Lowest V.S. Normalized disparity
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Radiometric Invariant Stereo
Matching Based On Relative
Gradients
Method B.
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Methods B.
• Goal : Adapt different environmental
factors.(Illumination condition)
Effective and robust algorithm
• Relative gradient algorithm + Gaussian
weighted function
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Background
• Lighting Model :
– View independent, body reflection
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Background
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ANCC
• Lighting Model :
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Method B.
• Cost Computation
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(i,j)
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Method B.
• Cost Aggregation
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• Refinement
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– Avoid White and black noises
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Experimental Results B.
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Experimental Results B.
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Experimental Results B.
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Experimental Results B.
• My Experimental Results (SAD+Gradient)
• Original V.S.Rerange disparity
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Experimental Results B.
• Using related gradient intialization
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Conclusion
Initialization
Aggregation
Refinement
ADc/SADc
Weighted-Window
Lowest Neighbor
ADg
Permeability
Normalizes
C-Census
Cost-Filter
Re-Range
G-Census
Arbitrary Support
Region
Scan-line
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