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Table 3 Quantitative evaluation on Middlebury stereo 2001 and 2003 datasets [2] and comparison with state-of-the-art global dense stereo methods in terms of bad matching pixels over entire image as well as non occluded regions with δ = 1

From: A learned sparseness and IGMRF-based regularization framework for dense disparity estimation using unsupervised feature learning

Method

Venus

Teddy

Cones

 

All

Nonocc

All

Nonocc

All

Nonocc

Initial

3.47

2.00

19.65

5.61

16.43

7.15

Proposed

0.20

0.10

9.76

3.44

8.46

2.36

AdaptBP [16]

0.21

0.10

7.06

4.22

7.92

2.48

DoubleBP [38]

0.45

0.13

8.30

3.53

8.78

2.90

GCP [52]

0.53

0.16

11.5

6.44

9.49

3.59

TwoStep [17]

0.45

0.27

12.6

7.42

10.1

4.09

SemiGlob [18]

1.57

1.00

12.2

6.02

9.75

3.06

2OP [39]

0.49

0.24

15.4

10.9

10.8

5.42

CompSens [42]

0.68

0.31

13.30

7.88

9.79

3.97

MultiGC [37]

3.13

2.79

17.6

12.0

11.8

4.89

Mumford [51]

0.76

0.28

14.3

9.34

9.91

4.14

GC [36]

3.44

1.79

25.0

16.5

18.2

7.70

CRF [53]

1.3

–

11.1

–

10.8

–

Sparse [46]

–

–

11.98

–

8.14

–

  1. Here, en dash indicates the result not reported. First row shows the results using initial estimate