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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