This table lists the benchmark results for the low-res two-view scenario.
This benchmark evaluates the Middlebury stereo metrics (for all metrics, smaller is better):
bad 0.5, 1.0, 2.0, 4.0 [%]: Fraction of pixels with errors larger than the given number of disparities.Average error [px]: The per-pixel average disparity error.Root mean square error [px]: The per-pixel root mean square disparity error.50%, 90%, 95%, 99% error quantile [px]: The highest disparity error within the given percentage of best pixels (for 50%, this is the median error).Time [s]: The runtime of the method.
The mask determines whether the metric is evaluated for all pixels with ground truth, or only for pixels which are visible in both images (non-occluded).
Methods with suffix _ROB may participate in the Robust Vision Challenge .
training datasets . The visualizations may not work with mobile browsers.
Coverage : dense
Set : Test
Metric : bad 1.0 [%]
Mask : non-occluded
Method
Info all
DN-CSS_ROB 2.69 1 1.40 22 5.34 12 2.31 22 0.75 10 3.14 2 0.06 1 6.11 1 3.87 1 5.34 2 12.18 11 2.34 1 1.22 1 7.84 2 1.48 1 0.03 17 0.00 1 0.00 1 0.00 1 0.35 26 0.03 4 iResNetv2_ROB 4.28 8 1.43 23 7.17 24 2.91 27 1.26 20 4.36 3 1.62 11 13.64 18 10.25 14 9.83 11 11.41 9 7.68 8 4.00 8 7.75 1 1.85 2 0.00 1 0.00 1 0.00 1 0.00 1 0.37 28 0.09 5 NOSS_ROB 3.30 2 0.46 3 2.62 1 2.08 18 1.01 17 5.60 6 0.74 7 10.37 7 11.48 16 5.15 1 8.43 3 5.67 4 1.73 2 7.97 4 2.34 3 0.02 10 0.06 22 0.00 1 0.00 1 0.07 7 0.14 13 HSM 4.00 5 0.79 8 3.16 4 1.59 8 2.17 28 6.77 10 1.11 8 12.28 12 6.35 2 6.75 5 8.11 2 13.90 17 5.37 16 8.85 14 2.71 4 0.00 1 0.00 1 0.00 1 0.00 1 0.02 1 0.02 1 MLCV 3.44 3 0.88 10 5.60 14 1.39 5 0.25 1 4.36 3 0.33 3 7.25 2 7.28 4 9.17 9 12.24 12 5.09 3 2.47 3 9.15 16 3.23 5 0.00 1 0.00 1 0.00 1 0.00 1 0.10 9 0.02 1 iResNet 3.68 4 0.91 11 7.94 27 2.97 29 0.34 3 4.44 5 0.48 6 7.70 3 9.74 11 7.72 6 12.74 14 4.03 2 2.87 4 8.05 6 3.37 6 0.02 10 0.01 11 0.00 1 0.00 1 0.10 9 0.09 5 iResNet_ROB 4.23 7 1.02 15 4.90 11 2.18 19 0.93 15 2.92 1 0.37 5 15.10 23 16.91 29 7.89 7 10.51 7 7.03 5 3.07 5 8.16 9 3.46 7 0.01 7 0.00 1 0.00 1 0.00 1 0.10 9 0.02 1 CBMV_ROB 4.14 6 0.52 4 3.14 3 1.30 3 0.77 11 6.92 11 1.97 12 10.11 6 9.58 9 8.92 8 14.20 20 7.12 7 5.90 19 8.65 11 3.50 8 0.01 7 0.05 19 0.00 1 0.00 1 0.04 4 0.09 5 SGM-Forest 4.96 11 0.32 1 2.84 2 1.21 1 0.64 7 10.23 20 6.64 26 11.55 8 10.98 15 10.94 14 13.59 17 11.65 14 4.30 10 8.94 15 4.63 9 0.11 26 0.04 17 0.00 1 0.00 1 0.05 5 0.46 29 Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching . ECCV 2018 CBMV 5.35 13 0.91 11 3.67 6 1.62 9 0.44 6 10.09 19 7.19 30 12.49 13 12.33 18 12.22 17 14.69 21 10.93 11 6.48 21 8.51 10 4.96 10 0.02 10 0.15 31 0.00 1 0.00 1 0.17 17 0.17 16 Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation . Computer Vision and Pattern Recognition (CVPR) 2018 DLCB_ROB 4.51 10 0.91 11 3.78 7 2.19 20 1.07 18 6.28 7 3.09 13 9.78 4 7.72 5 10.65 13 12.97 15 13.91 18 3.71 6 8.72 12 5.30 11 0.00 1 0.00 1 0.00 1 0.00 1 0.03 3 0.10 9 MDST_ROB 8.37 26 0.32 1 9.03 29 4.18 33 2.42 29 26.86 40 6.14 21 19.36 32 13.52 19 27.09 35 22.75 27 9.47 10 4.74 12 15.06 31 6.34 12 0.02 10 0.02 14 0.00 1 0.00 1 0.02 1 0.13 12 NaN_ROB 6.00 18 1.24 19 6.29 20 1.34 4 1.68 24 9.60 17 10.31 35 15.09 22 15.79 25 12.62 20 8.95 5 11.67 15 5.83 17 11.78 21 6.41 13 0.05 21 0.13 30 0.08 30 0.20 31 0.22 22 0.79 32 ETE_ROB 5.80 15 1.77 29 6.33 21 1.44 6 0.78 12 6.43 9 6.90 27 12.53 14 8.08 6 12.93 23 14.89 22 21.13 28 5.87 18 9.83 18 6.57 14 0.04 18 0.01 11 0.00 1 0.02 21 0.08 8 0.33 27 MSMD_ROB 9.28 29 1.09 17 4.65 10 1.58 7 0.39 4 16.52 31 4.41 18 13.60 17 14.87 23 22.34 31 39.89 39 25.67 31 20.71 34 12.42 23 6.98 15 0.34 29 0.03 16 0.00 1 0.00 1 0.05 5 0.09 5 NCCL2 5.88 17 1.59 25 5.44 13 1.87 13 0.92 14 9.55 15 11.55 38 12.11 10 9.94 12 9.67 10 8.85 4 22.28 29 7.41 23 8.78 13 7.17 16 0.01 7 0.00 1 0.03 25 0.00 1 0.13 15 0.23 19 LALA_ROB 6.58 21 1.80 30 6.25 19 1.26 2 0.94 16 10.08 18 9.02 31 16.00 25 11.51 17 12.74 22 13.02 16 24.77 30 5.25 14 10.56 20 8.02 17 0.04 18 0.05 19 0.00 1 0.02 21 0.10 9 0.25 22 StereoDRNet-Refined 4.46 9 0.62 7 3.80 8 1.92 16 0.40 5 9.35 13 0.15 2 10.02 5 8.83 8 12.69 21 11.62 10 9.34 9 3.87 7 8.06 7 8.02 17 0.00 1 0.00 1 0.01 21 0.05 28 0.20 19 0.26 24 StereoDRNet 5.59 14 1.75 28 6.80 23 3.12 30 4.45 34 10.61 22 4.35 17 18.80 28 9.73 10 12.22 17 6.87 1 11.44 13 4.65 11 8.09 8 8.26 19 0.02 10 0.11 29 0.00 1 0.03 24 0.20 19 0.28 26 CSAN 7.62 22 1.60 26 6.56 22 1.83 11 0.66 8 12.40 25 10.52 37 14.45 20 21.32 32 14.19 24 15.98 25 17.84 22 13.02 28 12.32 22 8.38 20 0.09 25 0.07 25 0.03 25 0.04 26 0.33 24 0.67 31 pmcnn 7.72 23 1.27 20 9.42 31 2.91 27 3.14 30 9.44 14 6.23 22 12.56 15 16.51 28 14.53 25 24.08 28 27.44 34 8.49 24 9.32 17 8.44 21 0.06 23 0.08 27 0.00 1 0.00 1 0.30 23 0.15 14 XPNet_ROB 6.03 19 1.22 18 5.61 15 2.56 25 0.90 13 6.32 8 7.07 28 12.92 16 8.30 7 14.76 26 15.13 24 19.84 25 6.66 22 10.36 19 8.58 22 0.02 10 0.04 17 0.00 1 0.03 24 0.11 13 0.24 21 PSMNet_ROB 5.02 12 1.63 27 6.03 18 1.90 15 1.83 26 9.57 16 6.35 25 15.58 24 7.23 3 6.15 3 10.48 6 12.22 16 4.16 9 8.02 5 8.71 23 0.02 10 0.01 11 0.01 21 0.10 30 0.20 19 0.12 11 PWCDC_ROB 7.92 24 3.17 37 7.48 26 5.73 36 4.40 32 10.45 21 0.35 4 14.52 21 28.19 36 10.36 12 31.27 34 7.04 6 9.14 25 13.22 26 8.78 24 2.74 37 0.02 14 0.00 1 0.00 1 1.31 34 0.17 16 DRN-Test 5.87 16 0.98 14 5.89 17 2.69 26 3.65 31 12.37 24 3.35 14 20.07 35 10.20 13 11.93 16 12.31 13 11.06 12 5.31 15 7.89 3 9.05 25 0.04 18 0.05 19 0.04 28 0.04 26 0.18 18 0.25 22 PDISCO_ROB 9.62 30 1.99 33 11.51 34 9.88 40 9.61 40 21.48 36 3.83 16 19.33 31 28.49 37 11.27 15 14.17 19 19.92 26 5.02 13 16.35 32 9.18 26 5.28 39 0.41 34 0.14 33 0.09 29 2.05 37 2.36 37 MFMNet_re 13.29 35 8.60 41 18.29 40 9.75 39 7.25 38 19.65 34 14.84 39 20.71 37 30.72 39 23.03 32 28.77 32 18.85 23 26.09 40 13.55 27 9.82 27 2.44 36 1.35 38 0.34 35 0.23 33 4.78 39 6.69 39 FBW_ROB 8.50 27 1.03 16 7.98 28 1.93 17 1.28 21 13.10 27 6.23 22 22.50 38 18.98 30 18.82 28 14.91 23 19.06 24 10.04 26 18.41 33 9.83 28 0.62 31 0.22 32 1.82 38 0.82 37 0.99 32 1.36 34 WCMA_ROB 9.21 28 0.87 9 7.37 25 2.54 24 2.13 27 13.59 28 5.80 20 11.64 9 14.01 20 24.43 33 32.99 35 27.09 33 18.02 29 12.51 24 9.85 29 0.81 33 0.07 25 0.01 21 0.01 20 0.16 16 0.23 19 SANet 10.64 33 1.86 31 10.91 33 1.76 10 0.71 9 14.62 30 9.23 33 19.18 30 37.14 41 19.22 29 27.96 30 25.86 32 19.11 33 13.02 25 10.63 30 0.08 24 0.06 22 0.03 25 0.02 21 0.62 31 0.81 33 DISCO 6.28 20 0.57 5 5.78 16 3.43 31 1.17 19 11.22 23 3.39 15 12.14 11 16.16 27 6.52 4 11.22 8 16.96 21 6.32 20 19.51 35 10.74 31 0.00 1 0.00 1 0.00 1 0.00 1 0.35 26 0.11 10 DispFullNet 17.47 42 26.01 42 33.98 42 22.58 42 20.86 43 13.84 29 1.28 10 16.50 26 26.27 34 19.97 30 17.17 26 20.52 27 18.49 31 22.86 38 10.76 32 5.13 38 2.83 39 30.72 43 7.72 42 20.86 42 11.01 43 MeshStereo 11.52 34 1.52 24 4.55 9 1.89 14 1.46 22 19.87 35 5.11 19 20.66 36 15.91 26 32.67 39 34.51 37 39.34 42 21.15 35 18.74 34 12.10 33 0.11 26 0.06 22 0.01 21 0.00 1 0.45 29 0.22 18 C. Zhang, Z. Li, Y. Cheng, R. Cai, H. Chao, Y. Rui: MeshStereo: A Global Stereo Model with Mesh Alignment Regularization for View Interpolation . ICCV 2015 DPSNet 10.14 32 1.88 32 16.82 39 1.85 12 1.73 25 24.84 38 17.20 42 19.92 34 27.41 35 12.23 19 13.62 18 16.52 20 18.35 30 14.42 30 12.50 34 0.78 32 0.54 35 0.08 30 0.25 35 1.18 33 0.59 30 SGM_ROB 10.08 31 0.60 6 3.42 5 2.30 21 0.32 2 19.41 33 6.33 24 18.95 29 14.64 21 25.14 34 24.32 29 33.34 37 18.79 32 19.86 36 12.55 35 0.25 28 0.26 33 0.22 34 0.24 34 0.34 25 0.40 28 Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information . TPAMI 2008, Volume 30(2), pp. 328-341 PWC_ROB 8.24 25 3.13 36 12.74 36 2.43 23 4.43 33 7.51 12 1.22 9 16.63 27 19.24 31 16.08 27 28.29 31 13.99 19 10.16 27 13.63 28 14.06 36 0.42 30 0.00 1 0.05 29 0.00 1 0.59 30 0.27 25 SGM+DAISY 15.62 37 7.26 40 19.28 41 8.94 38 10.11 41 26.25 39 10.49 36 19.36 32 14.65 22 30.64 37 33.59 36 33.00 36 22.32 37 24.96 40 16.42 37 7.90 42 6.25 43 4.51 40 3.37 40 5.86 40 7.20 40 SPS-STEREO 15.04 36 6.23 38 13.21 38 11.34 41 11.65 42 23.30 37 7.15 29 24.16 40 15.65 24 31.78 38 29.19 33 31.62 35 21.32 36 24.62 39 19.50 38 7.59 41 4.19 42 3.22 39 1.48 39 6.99 41 6.54 38 K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation . ECCV 2014 ELAS_ROB 16.54 39 2.26 35 10.09 32 5.50 35 4.46 35 28.28 41 16.72 41 25.55 41 33.54 40 40.19 40 40.30 40 36.68 40 30.03 41 29.40 42 20.61 39 0.98 35 1.21 36 0.86 37 0.70 36 1.39 35 2.16 35 A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching . ACCV 2010 NVStereoNet_ROB 16.04 38 6.75 39 12.90 37 6.37 37 7.42 39 12.89 26 9.74 34 22.78 39 25.12 33 30.32 36 46.19 43 34.37 38 25.38 38 21.48 37 21.38 40 5.94 40 3.10 40 6.07 41 10.09 43 4.01 38 8.54 42 Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach . Arxiv ELAS 16.72 40 2.14 34 9.23 30 4.92 34 4.53 36 32.66 42 15.11 40 27.40 42 28.68 38 40.27 41 44.90 42 38.33 41 30.50 42 26.44 41 21.94 41 0.88 34 1.23 37 0.67 36 0.89 38 1.49 36 2.18 36 A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching . ACCV 2010 LE_ROB 16.73 41 1.28 21 11.61 35 3.72 32 1.65 23 16.67 32 9.17 32 14.39 19 55.91 43 63.81 43 40.86 41 35.94 39 37.73 43 14.24 29 26.87 42 0.05 21 0.10 28 0.13 32 0.22 32 0.12 14 0.15 14 PWCK 30.53 43 44.32 43 47.25 44 29.76 43 7.23 37 40.78 43 27.10 43 44.73 43 44.32 42 47.31 42 36.37 38 47.16 43 26.05 39 41.26 43 31.87 43 21.83 43 4.03 41 29.50 42 4.67 41 27.17 43 7.80 41 DPSimNet_ROB 53.45 44 64.73 44 44.39 43 53.97 44 45.39 44 53.66 44 54.83 44 55.15 44 57.87 44 64.16 44 50.83 44 63.40 44 53.34 44 46.45 44 65.81 44 63.13 44 26.54 44 57.94 44 51.11 44 45.52 44 50.69 44 MEDIAN_ROB 98.41 45 99.70 45 99.30 46 97.09 45 97.02 45 96.89 45 95.77 46 97.66 45 97.28 45 98.79 47 98.94 45 99.18 45 98.14 45 96.89 45 96.88 45 99.96 47 99.16 45 100.00 45 99.99 45 99.69 45 99.88 45 AVERAGE_ROB 99.62 46 99.95 46 98.81 45 100.00 50 100.00 46 98.08 46 95.47 45 100.00 48 100.00 46 100.00 48 100.00 46 100.00 48 100.00 46 100.00 48 99.99 46 100.00 49 100.00 46 100.00 45 100.00 46 100.00 48 100.00 48 DGTPSM_ROB 99.90 47 100.00 47 99.99 47 99.99 48 100.00 46 100.00 47 100.00 47 99.97 46 100.00 46 98.35 45 100.00 46 99.84 46 100.00 46 99.98 46 99.99 46 99.99 48 100.00 46 100.00 45 100.00 46 100.00 48 100.00 48 DPSMNet_ROB 99.91 48 100.00 47 99.99 47 99.99 48 100.00 46 100.00 47 100.00 47 99.98 47 100.00 46 98.35 45 100.00 46 99.84 46 100.00 46 99.98 46 99.99 46 100.00 49 100.00 46 100.00 45 100.00 46 100.00 48 100.00 48 DPSM 99.95 49 100.00 47 100.00 49 99.76 46 100.00 46 100.00 47 100.00 47 100.00 48 100.00 46 100.00 48 100.00 46 100.00 48 100.00 46 100.00 48 100.00 49 99.21 45 100.00 46 100.00 45 100.00 46 99.99 46 99.95 46 DPSM_ROB 99.95 49 100.00 47 100.00 49 99.76 46 100.00 46 100.00 47 100.00 47 100.00 48 100.00 46 100.00 48 100.00 46 100.00 48 100.00 46 100.00 48 100.00 49 99.21 45 100.00 46 100.00 45 100.00 46 99.99 46 99.95 46
