This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.




Method Infoallhigh-res
multi-view
indooroutdoorcourty.delive.electrofacadekickermeadowofficepipesplaygr.reliefrelief.terraceterrai.
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort by
TDRefinement97.59 298.32 396.73 495.90 6198.10 299.08 293.92 3298.24 496.44 1598.12 2797.86 6896.06 299.24 198.93 199.00 297.77 6
WR-MVS97.53 398.20 496.76 396.93 2798.17 198.60 1196.67 696.39 1394.46 4499.14 198.92 1394.57 1899.06 398.80 299.32 196.92 27
PMVScopyleft87.16 1695.88 3696.47 3095.19 3897.00 2696.02 5596.70 6791.57 7794.43 3395.33 2397.16 6395.37 12392.39 5098.89 1098.72 398.17 3394.71 69
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
LTVRE_ROB95.06 197.73 198.39 296.95 196.33 4896.94 3298.30 2294.90 1598.61 297.73 397.97 3298.57 2895.74 799.24 198.70 498.72 798.70 1
Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
Anonymous2023121196.59 1298.43 194.44 5195.89 6396.12 5295.23 11695.91 899.42 192.75 8598.87 599.94 188.19 12298.64 1398.50 598.66 1097.49 9
PS-CasMVS97.22 597.84 796.50 597.08 2397.92 698.17 2897.02 294.71 2795.32 2498.52 1598.97 1292.91 4299.04 498.47 698.49 1997.24 12
WR-MVS_H97.06 997.78 896.23 1496.74 3698.04 398.25 2597.32 194.40 3493.71 6598.55 1498.89 1492.97 3998.91 998.45 798.38 2897.19 14
PEN-MVS97.16 697.87 696.33 1297.20 2097.97 498.25 2596.86 595.09 2594.93 3698.66 1199.16 892.27 5398.98 698.39 898.49 1996.83 31
CP-MVSNet96.97 1097.42 1396.44 797.06 2497.82 898.12 3096.98 393.50 4695.21 2697.98 3198.44 3292.83 4598.93 898.37 998.46 2296.91 28
DTE-MVSNet97.16 697.75 996.47 697.40 1297.95 598.20 2796.89 495.30 2095.15 2898.66 1198.80 1892.77 4698.97 798.27 1098.44 2396.28 41
COLMAP_ROBcopyleft93.74 297.09 897.98 596.05 1895.97 5897.78 998.56 1291.72 7397.53 796.01 1798.14 2698.76 1995.28 898.76 1198.23 1198.77 596.67 36
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UA-Net96.56 1396.73 2396.36 1098.99 197.90 797.79 4195.64 1092.78 6192.54 9096.23 7995.02 13494.31 2198.43 1598.12 1298.89 398.58 2
LS3D95.83 3996.35 3395.22 3796.47 4597.49 1597.99 3192.35 5494.92 2694.58 4394.88 10795.11 13291.52 6798.48 1498.05 1398.42 2595.49 55
TSAR-MVS + MP.95.99 3196.57 2895.31 3396.87 2896.50 4398.71 591.58 7693.25 5292.71 8696.86 6996.57 9693.92 2498.09 2197.91 1498.08 3696.81 32
ACMH+89.90 1096.27 2097.52 1294.81 4795.19 7797.18 2997.97 3392.52 4996.72 990.50 13197.31 5999.11 994.10 2398.67 1297.90 1598.56 1695.79 50
zzz-MVS96.18 2396.01 4496.38 898.30 296.18 5098.51 1494.48 2294.56 2994.81 4291.73 14396.96 8394.30 2298.09 2197.83 1697.91 4296.73 33
SixPastTwentyTwo97.36 497.73 1096.92 297.36 1396.15 5198.29 2394.43 2396.50 1196.96 898.74 898.74 2096.04 399.03 597.74 1798.44 2397.22 13
RPSCF95.46 4496.95 2193.73 8095.72 6895.94 5895.58 11188.08 15395.31 1991.34 11596.26 7698.04 5793.63 3098.28 1797.67 1898.01 4097.13 16
APDe-MVS96.23 2197.22 1795.08 4296.66 4097.56 1498.63 993.69 3994.62 2889.80 14097.73 4398.13 5293.84 2797.79 3697.63 1997.87 4397.08 19
CP-MVS96.21 2296.16 4296.27 1397.56 897.13 3198.43 1694.70 1792.62 6394.13 5392.71 13498.03 5894.54 1998.00 2797.60 2098.23 3097.05 20
X-MVS95.33 4995.13 6295.57 2697.35 1497.48 1698.43 1694.28 2492.30 7193.28 7386.89 18996.82 8991.87 5897.85 3297.59 2198.19 3196.95 25
ACMMPcopyleft96.12 2696.27 3895.93 1997.20 2097.60 1298.64 893.74 3892.47 6593.13 8093.23 12898.06 5594.51 2097.99 2897.57 2298.39 2796.99 21
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
ACMMPR96.54 1496.71 2496.35 1197.55 997.63 1198.62 1094.54 1894.45 3194.19 5095.04 10497.35 7594.92 1397.85 3297.50 2398.26 2997.17 15
HFP-MVS96.18 2396.53 2995.77 2197.34 1697.26 2598.16 2994.54 1894.45 3192.52 9195.05 10296.95 8493.89 2697.28 4497.46 2498.19 3197.25 10
ACMM90.06 996.31 1996.42 3196.19 1597.21 1997.16 3098.71 593.79 3694.35 3593.81 6192.80 13398.23 4495.11 998.07 2397.45 2598.51 1896.86 30
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMMP_Plus95.86 3796.18 3995.47 3097.11 2297.26 2598.37 2193.48 4293.49 4793.99 5695.61 8694.11 14492.49 4797.87 3197.44 2697.40 5397.52 8
MP-MVScopyleft96.13 2595.93 4796.37 998.19 497.31 2398.49 1594.53 2191.39 9794.38 4794.32 11696.43 10094.59 1797.75 3897.44 2698.04 3996.88 29
SteuartSystems-ACMMP95.96 3296.13 4395.76 2297.06 2497.36 2198.40 2094.24 2691.49 8991.91 10594.50 11296.89 8694.99 1198.01 2697.44 2697.97 4197.25 10
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anonymousdsp95.45 4696.70 2593.99 6688.43 20892.05 16199.18 185.42 18994.29 3696.10 1698.63 1399.08 1196.11 197.77 3797.41 2998.70 897.69 7
EPP-MVSNet93.63 8693.95 8993.26 9495.15 7896.54 4196.18 9791.97 6691.74 8385.76 16394.95 10684.27 18791.60 6697.61 4197.38 3098.87 495.18 62
LGP-MVS_train96.10 2896.29 3695.87 2096.72 3797.35 2298.43 1693.83 3590.81 11292.67 8995.05 10298.86 1695.01 1098.11 2097.37 3198.52 1796.50 38
PGM-MVS95.90 3595.72 5096.10 1697.53 1097.45 1998.55 1394.12 2990.25 11593.71 6593.20 12997.18 7994.63 1697.68 4097.34 3298.08 3696.97 22
SMA-MVS96.11 2796.61 2695.53 2897.49 1197.41 2097.62 4693.78 3794.14 4094.18 5197.16 6394.67 13792.42 4997.74 3997.33 3397.70 4797.79 4
ESAPD95.63 4296.35 3394.80 4996.76 3597.29 2497.74 4294.15 2891.69 8490.01 13796.65 7197.29 7692.45 4897.41 4297.18 3497.67 5096.95 25
APD-MVScopyleft95.38 4795.68 5195.03 4397.30 1796.90 3497.83 3893.92 3289.40 13190.35 13295.41 9297.69 7092.97 3997.24 4697.17 3597.83 4495.96 47
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMH90.17 896.61 1197.69 1195.35 3195.29 7596.94 3298.43 1692.05 6598.04 595.38 2298.07 2999.25 793.23 3698.35 1697.16 3697.72 4596.00 46
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP89.62 1195.96 3296.28 3795.59 2496.58 4297.23 2798.26 2493.22 4492.33 7092.31 9794.29 11798.73 2194.68 1598.04 2497.14 3798.47 2196.17 44
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs694.58 6097.30 1691.40 12494.84 8494.61 10293.40 14892.43 5398.51 385.61 16698.73 1099.53 384.40 14397.88 3097.03 3897.72 4594.79 67
IS_MVSNet92.76 11593.25 12092.19 11594.91 8395.56 6795.86 10192.12 6288.10 14682.71 18393.15 13088.30 17688.86 11497.29 4396.95 3998.66 1093.38 91
TranMVSNet+NR-MVSNet95.72 4196.42 3194.91 4696.21 5196.77 3696.90 5994.99 1392.62 6391.92 10498.51 1698.63 2590.82 9397.27 4596.83 4098.63 1394.31 76
UniMVSNet (Re)95.46 4495.86 4895.00 4496.09 5396.60 3796.68 7194.99 1390.36 11492.13 10097.64 5198.13 5291.38 7096.90 5496.74 4198.73 694.63 72
FC-MVSNet-train92.75 11695.40 5889.66 14995.21 7694.82 9597.00 5389.40 12791.13 10381.71 18997.72 4496.43 10077.57 19696.89 5596.72 4297.05 6794.09 80
PHI-MVS94.65 5994.84 6894.44 5194.95 8296.55 3896.46 8091.10 8688.96 13596.00 1894.55 11195.32 12690.67 9696.97 5196.69 4397.44 5294.84 65
DU-MVS95.51 4395.68 5195.33 3296.45 4696.44 4596.61 7495.32 1189.97 12193.78 6297.46 5698.07 5491.19 7797.03 4996.53 4498.61 1494.22 77
3Dnovator+92.82 395.22 5095.16 6195.29 3596.17 5296.55 3897.64 4494.02 3194.16 3994.29 4992.09 14093.71 14991.90 5696.68 6296.51 4597.70 4796.40 39
DeepC-MVS92.47 496.44 1696.75 2296.08 1797.57 797.19 2897.96 3494.28 2495.29 2194.92 3798.31 2296.92 8593.69 2996.81 5996.50 4698.06 3896.27 42
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NR-MVSNet94.55 6295.66 5393.25 9694.26 9896.44 4596.69 6995.32 1189.97 12191.79 10997.46 5698.39 3782.85 15296.87 5796.48 4798.57 1593.98 82
MSLP-MVS++93.91 7694.30 8593.45 8495.51 7295.83 6193.12 15891.93 6991.45 9491.40 11487.42 18496.12 11093.27 3496.57 6696.40 4895.49 12096.29 40
OPM-MVS95.96 3296.59 2795.23 3696.67 3996.52 4297.86 3793.28 4395.27 2393.46 7096.26 7698.85 1792.89 4397.09 4896.37 4997.22 6195.78 51
v5296.35 1797.40 1495.12 4093.83 11995.54 6897.82 3988.95 13996.27 1497.22 599.11 299.40 595.80 598.16 1996.37 4997.10 6496.96 23
V496.35 1797.40 1495.12 4093.83 11995.54 6897.82 3988.95 13996.27 1497.21 699.10 399.40 595.79 698.17 1896.37 4997.10 6496.96 23
UniMVSNet_NR-MVSNet95.34 4895.51 5595.14 3995.80 6696.55 3896.61 7494.79 1690.04 12093.78 6297.51 5497.25 7791.19 7796.68 6296.31 5298.65 1294.22 77
Baseline_NR-MVSNet94.85 5595.35 5994.26 5696.45 4693.86 12696.70 6794.54 1890.07 11990.17 13698.77 797.89 6390.64 9897.03 4996.16 5397.04 6893.67 85
v7n96.49 1597.20 1895.65 2395.57 7196.04 5497.93 3592.49 5196.40 1297.13 798.99 499.41 493.79 2897.84 3496.15 5497.00 6995.60 54
TransMVSNet (Re)93.55 9196.32 3590.32 13694.38 9494.05 11893.30 15589.53 12297.15 885.12 16898.83 697.89 6382.21 15896.75 6196.14 5597.35 5593.46 90
FC-MVSNet-test91.49 13594.43 8088.07 17494.97 8190.53 17695.42 11391.18 8493.24 5372.94 21998.37 1893.86 14778.78 18397.82 3596.13 5695.13 13091.05 134
CPTT-MVS95.00 5494.52 7795.57 2696.84 3296.78 3597.88 3693.67 4092.20 7392.35 9685.87 19697.56 7294.98 1296.96 5296.07 5797.70 4796.18 43
v74896.05 3097.00 2094.95 4594.41 9394.77 9796.72 6691.03 8896.12 1696.71 1198.74 899.59 293.55 3197.97 2995.96 5897.28 5795.84 49
Vis-MVSNetpermissive94.39 6595.85 4992.68 10890.91 19095.88 5997.62 4691.41 8091.95 7989.20 14297.29 6096.26 10390.60 10296.95 5395.91 5996.32 9196.71 34
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thres600view789.14 15388.83 16389.51 15293.71 12793.55 13593.93 14088.02 15487.30 15782.40 18481.18 21480.63 20082.69 15694.27 11295.90 6096.27 9588.94 157
SD-MVS95.77 4096.17 4095.30 3496.72 3796.19 4997.01 5293.04 4594.03 4192.71 8696.45 7496.78 9393.91 2596.79 6095.89 6198.42 2597.09 18
HPM-MVS++copyleft95.21 5194.89 6695.59 2497.79 695.39 7697.68 4394.05 3091.91 8194.35 4893.38 12795.07 13392.94 4196.01 7395.88 6296.73 7296.61 37
pm-mvs193.27 10095.94 4690.16 13794.13 10293.66 12992.61 16889.91 11095.73 1884.28 17498.51 1698.29 4082.80 15396.44 6795.76 6397.25 5993.21 95
canonicalmvs93.38 9794.36 8292.24 11493.94 11396.41 4794.18 13690.47 9993.07 5688.47 14988.66 17093.78 14888.80 11595.74 7895.75 6497.57 5197.13 16
view60089.09 15488.78 16689.46 15393.59 13093.33 14193.92 14187.76 15987.40 15482.79 18081.29 21380.71 19982.59 15794.28 11195.72 6596.12 10588.70 160
FPMVS90.81 13891.60 13989.88 14392.52 16088.18 18293.31 15483.62 19991.59 8888.45 15088.96 16889.73 17186.96 12896.42 6895.69 6694.43 16190.65 136
TSAR-MVS + GP.94.25 6894.81 6993.60 8196.52 4495.80 6294.37 13092.47 5290.89 10988.92 14395.34 9494.38 14192.85 4496.36 6995.62 6796.47 7895.28 60
TSAR-MVS + ACMM95.17 5295.95 4594.26 5696.07 5596.46 4495.67 10894.21 2793.84 4390.99 12397.18 6295.24 13193.55 3196.60 6595.61 6895.06 13496.69 35
conf0.05thres100091.24 13691.85 13590.53 13294.59 9194.56 10694.33 13389.52 12393.67 4583.77 17691.04 14679.10 20483.98 14496.66 6495.56 6996.98 7092.36 116
CNVR-MVS94.24 6994.47 7993.96 6996.56 4395.67 6596.43 8291.95 6792.08 7691.28 11790.51 15595.35 12491.20 7696.34 7095.50 7096.34 8895.88 48
view80089.42 15089.11 15989.78 14494.00 10493.71 12893.96 13988.47 14888.10 14682.91 17982.61 21179.85 20283.10 15194.92 9895.38 7196.26 9789.19 153
CSCG96.07 2997.15 1994.81 4796.06 5697.58 1396.52 7790.98 8996.51 1093.60 6897.13 6598.55 3093.01 3897.17 4795.36 7298.68 997.78 5
Vis-MVSNet (Re-imp)90.68 13992.18 13188.92 15894.63 8792.75 14792.91 16291.20 8389.21 13475.01 21493.96 12489.07 17482.72 15595.88 7695.30 7397.08 6689.08 156
DeepC-MVS_fast91.38 694.73 5894.98 6394.44 5196.83 3496.12 5296.69 6992.17 6092.98 5793.72 6494.14 11895.45 12190.49 10395.73 7995.30 7396.71 7395.13 63
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
no-one92.05 13294.57 7689.12 15585.55 22087.65 19094.21 13577.34 21793.43 4989.64 14195.11 10199.11 995.86 495.38 8395.24 7592.08 18496.11 45
NCCC93.87 8193.42 11394.40 5496.84 3295.42 7396.47 7992.62 4892.36 6992.05 10183.83 20595.55 11791.84 6095.89 7595.23 7696.56 7695.63 53
EG-PatchMatch MVS94.81 5695.53 5493.97 6795.89 6394.62 10195.55 11288.18 14992.77 6294.88 3997.04 6798.61 2693.31 3396.89 5595.19 7795.99 10893.56 89
MIMVSNet192.52 12094.88 6789.77 14596.09 5391.99 16296.92 5689.68 11595.92 1784.55 17196.64 7298.21 4778.44 18996.08 7295.10 7892.91 18090.22 140
AdaColmapbinary92.41 12391.49 14093.48 8395.96 5995.02 8995.37 11491.73 7287.97 15191.28 11782.82 21091.04 16490.62 10095.82 7795.07 7995.95 10992.67 107
OMC-MVS94.74 5795.46 5693.91 7294.62 8896.26 4896.64 7389.36 12994.20 3794.15 5294.02 12297.73 6991.34 7296.15 7195.04 8097.37 5494.80 66
GBi-Net89.35 15190.58 14487.91 17591.22 18594.05 11892.88 16390.05 10379.40 20578.60 20590.58 15287.05 17978.54 18695.32 8694.98 8196.17 10292.67 107
test189.35 15190.58 14487.91 17591.22 18594.05 11892.88 16390.05 10379.40 20578.60 20590.58 15287.05 17978.54 18695.32 8694.98 8196.17 10292.67 107
FMVSNet192.86 11395.26 6090.06 13992.40 16395.16 8394.37 13092.22 5693.18 5582.16 18896.76 7097.48 7381.85 16395.32 8694.98 8197.34 5693.93 83
DeepPCF-MVS90.68 794.56 6194.92 6594.15 5894.11 10395.71 6497.03 5190.65 9593.39 5194.08 5495.29 9694.15 14393.21 3795.22 9194.92 8495.82 11595.75 52
3Dnovator91.81 593.36 9894.27 8692.29 11392.99 15195.03 8795.76 10387.79 15893.82 4492.38 9592.19 13993.37 15388.14 12395.26 9094.85 8596.69 7495.40 56
CDPH-MVS93.96 7393.86 9294.08 6196.31 4995.84 6096.92 5691.85 7087.21 16091.25 11992.83 13196.06 11191.05 8695.57 8094.81 8697.12 6294.72 68
Gipumacopyleft95.86 3796.17 4095.50 2995.92 6094.59 10494.77 12392.50 5097.82 697.90 295.56 8897.88 6694.71 1498.02 2594.81 8697.23 6094.48 75
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UGNet92.31 12894.70 7289.53 15190.99 18995.53 7096.19 9692.10 6491.35 9885.76 16395.31 9595.48 12076.84 20095.22 9194.79 8895.32 12295.19 61
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
MVS_111021_HR93.82 8394.26 8793.31 9095.01 8093.97 12395.73 10589.75 11392.06 7792.49 9294.01 12396.05 11290.61 10195.95 7494.78 8996.28 9393.04 99
tfpn11187.59 17886.89 18188.41 16692.28 16793.64 13193.36 14988.12 15080.90 19380.71 19373.93 22882.25 18979.65 17894.27 11294.76 9096.36 8388.48 163
conf0.0185.72 18683.49 19788.32 16992.11 17493.35 14093.36 14988.02 15480.90 19380.51 19674.83 22659.86 23179.65 17893.80 12294.76 9096.29 9286.94 176
conf200view1187.93 17487.51 17888.41 16692.28 16793.64 13193.36 14988.12 15080.90 19380.71 19378.25 21982.25 18979.65 17894.27 11294.76 9096.36 8388.48 163
tfpn200view987.94 17387.51 17888.44 16592.28 16793.63 13393.35 15388.11 15280.90 19380.89 19178.25 21982.25 18979.65 17894.27 11294.76 9096.36 8388.48 163
thres40088.54 16288.15 17388.98 15693.17 14292.84 14593.56 14586.93 17286.45 16582.37 18579.96 21681.46 19781.83 16493.21 13294.76 9096.04 10688.39 166
PLCcopyleft87.27 1593.08 10792.92 12393.26 9494.67 8595.03 8794.38 12990.10 10191.69 8492.14 9987.24 18593.91 14691.61 6595.05 9594.73 9596.67 7592.80 103
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tfpn100088.13 17288.68 16887.49 18093.94 11392.64 15091.50 18588.70 14590.12 11774.35 21686.74 19175.27 21280.14 17494.16 11894.66 9696.33 9087.16 175
tfpnview1188.74 16088.95 16188.50 16493.91 11592.43 15391.70 18388.90 14190.93 10878.90 20184.93 19978.24 20681.71 16894.32 11094.60 9795.86 11187.23 174
CLD-MVS92.81 11494.32 8391.05 12695.39 7395.31 7895.82 10281.44 21189.40 13191.94 10395.86 8397.36 7485.83 13695.35 8494.59 9895.85 11392.34 118
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tfpn87.65 17785.66 18989.96 14294.36 9593.94 12493.85 14289.02 13488.71 14182.78 18183.79 20653.79 23383.43 14995.35 8494.54 9996.35 8789.51 151
tfpnnormal92.45 12194.77 7189.74 14693.95 11193.44 13993.25 15688.49 14795.27 2383.20 17896.51 7396.23 10583.17 15095.47 8194.52 10096.38 8291.97 124
MAR-MVS91.86 13391.14 14392.71 10794.29 9694.24 11194.91 12091.82 7181.66 19093.32 7284.51 20393.42 15286.86 13095.16 9394.44 10195.05 13594.53 74
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
QAPM92.57 11993.51 10991.47 12292.91 15494.82 9593.01 16087.51 16291.49 8991.21 12092.24 13791.70 16088.74 11694.54 10594.39 10295.41 12195.37 59
Effi-MVS+92.93 11092.16 13393.83 7494.29 9693.53 13795.04 11892.98 4685.27 17394.46 4490.24 15895.34 12589.99 10793.72 12394.23 10396.22 9992.79 104
MVS_030493.92 7593.81 9794.05 6296.06 5696.00 5696.43 8292.76 4785.99 16994.43 4694.04 12197.08 8088.12 12494.65 10494.20 10496.47 7894.71 69
TAPA-MVS88.94 1393.78 8494.31 8493.18 9994.14 10195.99 5795.74 10486.98 17193.43 4993.88 6090.16 15996.88 8791.05 8694.33 10893.95 10597.28 5795.40 56
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CANet93.07 10893.05 12293.10 10395.90 6195.41 7495.88 10091.94 6884.77 17693.36 7194.05 12095.25 13086.25 13494.33 10893.94 10695.30 12393.58 88
Fast-Effi-MVS+92.93 11092.64 12793.27 9393.81 12193.88 12595.90 9990.61 9683.98 18192.71 8692.81 13296.22 10690.67 9694.90 10093.92 10795.92 11092.77 105
DELS-MVS92.33 12693.61 10490.83 12992.84 15695.13 8594.76 12487.22 16987.78 15288.42 15195.78 8595.28 12885.71 13794.44 10693.91 10896.01 10792.97 101
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
CNLPA93.14 10693.67 10192.53 11094.62 8894.73 9895.00 11986.57 17792.85 6092.43 9390.94 14894.67 13790.35 10595.41 8293.70 10996.23 9893.37 92
tfpn_n40089.03 15589.39 15688.61 16293.98 10892.33 15491.83 17788.97 13692.97 5878.90 20184.93 19978.24 20681.77 16695.00 9693.67 11096.22 9988.59 161
tfpnconf89.03 15589.39 15688.61 16293.98 10892.33 15491.83 17788.97 13692.97 5878.90 20184.93 19978.24 20681.77 16695.00 9693.67 11096.22 9988.59 161
thres20088.29 16687.88 17588.76 15992.50 16193.55 13592.47 17188.02 15484.80 17481.44 19079.28 21882.20 19381.83 16494.27 11293.67 11096.27 9587.40 172
Effi-MVS+-dtu92.32 12791.66 13893.09 10495.13 7994.73 9894.57 12892.14 6181.74 18990.33 13388.13 17695.91 11489.24 11194.23 11793.65 11397.12 6293.23 94
train_agg93.89 7893.46 11294.40 5497.35 1493.78 12797.63 4592.19 5988.12 14590.52 13093.57 12695.78 11592.31 5294.78 10193.46 11496.36 8394.70 71
MVS_111021_LR93.15 10593.65 10292.56 10993.89 11792.28 15795.09 11786.92 17391.26 10192.99 8394.46 11496.22 10690.64 9895.11 9493.45 11595.85 11392.74 106
tfpn_ndepth85.89 18586.40 18585.30 19591.31 18392.47 15290.78 19287.75 16084.79 17571.04 22376.95 22378.80 20574.52 20892.72 13793.43 11696.39 8185.65 183
PVSNet_Blended_VisFu93.60 8793.41 11493.83 7496.31 4995.65 6695.71 10690.58 9888.08 14893.17 7895.29 9692.20 15890.72 9594.69 10393.41 11796.51 7794.54 73
OpenMVScopyleft89.22 1291.09 13791.42 14190.71 13092.79 15793.61 13492.74 16785.47 18786.10 16890.73 12485.71 19793.07 15686.69 13194.07 12093.34 11895.86 11194.02 81
MSDG92.09 13192.84 12491.22 12592.55 15992.97 14393.42 14785.43 18890.24 11691.83 10794.70 10894.59 13988.48 11994.91 9993.31 11995.59 11989.15 154
MCST-MVS93.60 8793.40 11693.83 7495.30 7495.40 7596.49 7890.87 9090.08 11891.72 11090.28 15795.99 11391.69 6393.94 12192.99 12096.93 7195.13 63
HSP-MVS95.04 5395.45 5794.57 5096.87 2897.77 1098.71 593.88 3491.21 10291.48 11395.36 9398.37 3890.73 9494.37 10792.98 12195.77 11698.08 3
PM-MVS92.65 11893.20 12192.00 11792.11 17490.16 17795.99 9884.81 19391.31 9992.41 9495.87 8296.64 9592.35 5193.65 12692.91 12294.34 16391.85 126
thres100view90086.46 18386.00 18886.99 18492.28 16791.03 16991.09 18884.49 19580.90 19380.89 19178.25 21982.25 18977.57 19690.17 17692.84 12395.63 11786.57 179
conf0.00284.82 18981.84 20388.30 17092.05 17693.28 14293.36 14988.00 15780.90 19380.48 19773.43 23052.48 23679.65 17893.72 12392.82 12496.28 9386.22 180
CDS-MVSNet88.41 16389.79 15186.79 18694.55 9290.82 17292.50 17089.85 11183.26 18580.52 19591.05 14589.93 16969.11 21493.17 13392.71 12594.21 16587.63 170
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EPNet90.17 14389.07 16091.45 12397.25 1890.62 17594.84 12193.54 4180.96 19291.85 10686.98 18885.88 18377.79 19392.30 15292.58 12693.41 17394.20 79
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + COLMAP93.06 10993.65 10292.36 11194.62 8894.28 11095.36 11589.46 12692.18 7491.64 11195.55 8995.27 12988.60 11893.24 13092.50 12794.46 16092.55 112
IB-MVS86.01 1788.24 16887.63 17788.94 15792.03 17791.77 16392.40 17285.58 18678.24 21584.85 16971.99 23193.45 15183.96 14693.48 12892.33 12894.84 14492.15 120
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
PatchMatch-RL89.59 14888.80 16590.51 13392.20 17288.00 18691.72 18186.64 17484.75 17788.25 15287.10 18790.66 16789.85 10993.23 13192.28 12994.41 16285.60 184
v1394.54 6394.93 6494.09 5993.81 12195.44 7296.99 5591.67 7492.43 6795.20 2798.33 1998.73 2191.87 5893.67 12592.26 13095.00 13693.63 87
FMVSNet290.28 14192.04 13488.23 17291.22 18594.05 11892.88 16390.69 9486.53 16479.89 19994.38 11592.73 15778.54 18691.64 16592.26 13096.17 10292.67 107
v1194.32 6794.62 7493.97 6793.95 11195.31 7896.83 6291.30 8291.95 7995.51 2098.32 2198.61 2691.44 6992.83 13692.23 13294.77 14593.08 98
v793.65 8593.73 9993.57 8293.38 13794.60 10396.83 6289.92 10989.69 12895.02 3197.89 3598.24 4391.27 7392.38 14692.18 13394.99 13791.12 133
v1093.96 7394.12 8893.77 7893.37 13895.45 7196.83 6291.13 8589.70 12795.02 3197.88 3798.23 4491.27 7392.39 14592.18 13394.99 13793.00 100
v1294.44 6494.79 7094.02 6393.75 12495.37 7796.92 5691.61 7592.21 7295.10 2998.27 2398.69 2391.73 6293.49 12792.15 13594.97 14093.37 92
V4292.67 11793.50 11091.71 12191.41 18192.96 14495.71 10685.00 19089.67 12993.22 7697.67 4998.01 5991.02 8892.65 13992.12 13693.86 16891.42 132
v119293.98 7293.94 9094.01 6493.91 11594.63 10097.00 5389.75 11391.01 10696.50 1297.93 3398.26 4291.74 6192.06 15592.05 13795.18 12991.66 131
V994.33 6694.66 7393.94 7093.69 12895.31 7896.84 6191.53 7892.04 7895.00 3398.22 2498.64 2491.62 6493.29 12992.05 13794.93 14193.10 97
v114493.83 8293.87 9193.78 7793.72 12694.57 10596.85 6089.98 10691.31 9995.90 1997.89 3598.40 3691.13 8192.01 15892.01 13995.10 13290.94 135
ambc94.61 7598.09 595.14 8491.71 18294.18 3896.46 1496.26 7696.30 10291.26 7594.70 10292.00 14093.45 17293.67 85
V1494.21 7094.52 7793.85 7393.62 12995.25 8196.76 6591.42 7991.83 8294.91 3898.15 2598.57 2891.49 6893.06 13491.93 14194.90 14292.82 102
HQP-MVS92.87 11292.49 12893.31 9095.75 6795.01 9095.64 10991.06 8788.54 14291.62 11288.16 17596.25 10489.47 11092.26 15391.81 14296.34 8895.40 56
v1594.09 7194.37 8193.77 7893.56 13195.18 8296.68 7191.34 8191.64 8694.83 4198.09 2898.51 3191.37 7192.84 13591.80 14394.85 14392.53 113
v2v48293.42 9693.49 11193.32 8993.44 13694.05 11896.36 9489.76 11291.41 9695.24 2597.63 5298.34 3990.44 10491.65 16491.76 14494.69 14989.62 150
v14419293.89 7893.85 9393.94 7093.50 13294.33 10797.12 4889.49 12490.89 10996.49 1397.78 4298.27 4191.89 5792.17 15491.70 14595.19 12891.78 129
v1693.53 9293.80 9893.20 9793.10 14994.98 9296.43 8290.81 9189.39 13393.12 8197.63 5298.01 5991.19 7792.60 14291.65 14694.58 15892.36 116
v1793.60 8793.85 9393.30 9293.15 14394.99 9196.46 8090.81 9189.58 13093.61 6797.66 5098.15 5191.19 7792.60 14291.61 14794.61 15692.37 115
v124093.89 7893.72 10094.09 5993.98 10894.31 10897.12 4889.37 12890.74 11396.92 998.05 3097.89 6392.15 5491.53 16691.60 14894.99 13791.93 125
TinyColmap93.17 10493.33 11993.00 10693.84 11892.76 14694.75 12588.90 14193.97 4297.48 495.28 9895.29 12788.37 12095.31 8991.58 14994.65 15189.10 155
v192192093.90 7793.82 9594.00 6593.74 12594.31 10897.12 4889.33 13091.13 10396.77 1097.90 3498.06 5591.95 5591.93 16291.54 15095.10 13291.85 126
v1neww93.27 10093.40 11693.12 10093.13 14494.20 11296.39 8889.56 11989.87 12593.95 5797.71 4698.21 4791.09 8392.36 14791.49 15194.62 15489.96 142
v7new93.27 10093.40 11693.12 10093.13 14494.20 11296.39 8889.56 11989.87 12593.95 5797.71 4698.21 4791.09 8392.36 14791.49 15194.62 15489.96 142
v693.27 10093.41 11493.12 10093.13 14494.20 11296.39 8889.55 12189.89 12493.93 5997.72 4498.22 4691.10 8292.36 14791.49 15194.63 15289.95 144
v1893.33 9993.59 10593.04 10592.94 15294.87 9496.31 9590.59 9788.96 13592.89 8497.51 5497.90 6291.01 8992.33 15191.48 15494.50 15992.05 122
v893.60 8793.82 9593.34 8893.13 14495.06 8696.39 8890.75 9389.90 12394.03 5597.70 4898.21 4791.08 8592.36 14791.47 15594.63 15292.07 121
pmmvs-eth3d92.34 12592.33 12992.34 11292.67 15890.67 17396.37 9289.06 13390.98 10793.60 6897.13 6597.02 8288.29 12190.20 17591.42 15694.07 16688.89 158
v114193.47 9493.56 10793.36 8793.48 13394.17 11696.42 8589.62 11691.44 9594.99 3597.81 4098.42 3490.94 9192.00 15991.38 15794.74 14889.69 149
divwei89l23v2f11293.47 9493.56 10793.37 8593.48 13394.17 11696.42 8589.62 11691.46 9295.00 3397.81 4098.42 3490.94 9192.00 15991.38 15794.75 14689.70 147
v193.48 9393.57 10693.37 8593.48 13394.18 11596.41 8789.61 11891.46 9295.03 3097.82 3998.43 3390.95 9092.00 15991.37 15994.75 14689.70 147
test20.0388.20 17091.26 14284.63 20096.64 4189.39 17990.73 19489.97 10791.07 10572.02 22194.98 10595.45 12169.35 21392.70 13891.19 16089.06 19384.02 185
CANet_DTU88.95 15889.51 15588.29 17193.12 14891.22 16893.61 14483.47 20280.07 20490.71 12889.19 16793.68 15076.27 20491.44 16791.17 16192.59 18189.83 145
Fast-Effi-MVS+-dtu89.57 14988.42 17190.92 12893.35 13991.57 16593.01 16095.71 978.94 21387.65 15484.68 20293.14 15582.00 16090.84 17191.01 16293.78 17088.77 159
IterMVS-LS92.10 13092.33 12991.82 12093.18 14193.66 12992.80 16692.27 5590.82 11190.59 12997.19 6190.97 16587.76 12589.60 18190.94 16394.34 16393.16 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PCF-MVS87.46 1492.44 12291.80 13693.19 9894.66 8695.80 6296.37 9290.19 10087.57 15392.23 9889.26 16693.97 14589.24 11191.32 16890.82 16496.46 8093.86 84
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FMVSNet579.08 21678.83 21379.38 21487.52 21586.78 19187.64 21278.15 21469.54 23370.64 22465.97 23565.44 22563.87 22090.17 17690.46 16588.48 19583.45 189
DI_MVS_plusplus_trai90.68 13990.40 14791.00 12792.43 16292.61 15194.17 13788.98 13588.32 14488.76 14793.67 12587.58 17886.44 13389.74 17990.33 16695.24 12790.56 139
thresconf0.0284.34 19382.02 20287.06 18292.23 17190.93 17091.05 18986.43 17988.83 14077.65 21173.93 22855.81 23279.68 17790.62 17390.28 16795.30 12383.73 188
PVSNet_BlendedMVS90.09 14490.12 14990.05 14092.40 16392.74 14891.74 17985.89 18280.54 19990.30 13488.54 17195.51 11884.69 14192.64 14090.25 16895.28 12590.61 137
PVSNet_Blended90.09 14490.12 14990.05 14092.40 16392.74 14891.74 17985.89 18280.54 19990.30 13488.54 17195.51 11884.69 14192.64 14090.25 16895.28 12590.61 137
testgi86.49 18290.31 14882.03 20595.63 7088.18 18293.47 14684.89 19293.23 5469.54 22887.16 18697.96 6160.66 22291.90 16389.90 17087.99 19683.84 186
FMVSNet387.90 17588.63 16987.04 18389.78 19993.46 13891.62 18490.05 10379.40 20578.60 20590.58 15287.05 17977.07 19988.03 19589.86 17195.12 13192.04 123
v14892.38 12492.78 12591.91 11892.86 15592.13 16094.84 12187.03 17091.47 9193.07 8296.92 6898.89 1490.10 10692.05 15689.69 17293.56 17188.27 168
CVMVSNet88.97 15789.73 15288.10 17387.33 21685.22 19694.68 12678.68 21388.94 13786.98 15995.55 8985.71 18489.87 10891.19 16989.69 17291.05 18591.78 129
CMPMVSbinary66.55 1885.55 18787.46 18083.32 20384.99 22181.97 21079.19 23275.93 21979.32 20888.82 14585.09 19891.07 16382.12 15992.56 14489.63 17488.84 19492.56 111
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs489.95 14689.32 15890.69 13191.60 18089.17 18194.37 13087.63 16188.07 14991.02 12294.50 11290.50 16886.13 13586.33 20389.40 17593.39 17487.29 173
MVS_Test90.19 14290.58 14489.74 14692.12 17391.74 16492.51 16988.54 14682.80 18687.50 15594.62 10995.02 13483.97 14588.69 18889.32 17693.79 16991.85 126
GA-MVS88.76 15988.04 17489.59 15092.32 16691.46 16692.28 17386.62 17583.82 18389.84 13992.51 13681.94 19483.53 14889.41 18389.27 17792.95 17987.90 169
USDC92.17 12992.17 13292.18 11692.93 15392.22 15893.66 14387.41 16493.49 4797.99 194.10 11996.68 9486.46 13292.04 15789.18 17894.61 15687.47 171
MS-PatchMatch87.72 17688.62 17086.66 18890.81 19288.18 18290.92 19082.25 20585.86 17080.40 19890.14 16089.29 17384.93 13889.39 18489.12 17990.67 18688.34 167
gg-mvs-nofinetune88.32 16488.81 16487.75 17793.07 15089.37 18089.06 20795.94 795.29 2187.15 15697.38 5876.38 21068.05 21791.04 17089.10 18093.24 17683.10 192
pmmvs588.63 16189.70 15387.39 18189.24 20290.64 17491.87 17682.13 20683.34 18487.86 15394.58 11096.15 10979.87 17587.33 19989.07 18193.39 17486.76 177
EU-MVSNet91.63 13492.73 12690.35 13588.36 20987.89 18796.53 7681.51 21092.45 6691.82 10896.44 7597.05 8193.26 3594.10 11988.94 18290.61 18792.24 119
MVSTER84.79 19083.79 19585.96 19189.14 20389.80 17889.39 20582.99 20474.16 22782.78 18185.97 19566.81 22376.84 20090.77 17288.83 18394.66 15090.19 141
Anonymous2023120687.45 17989.66 15484.87 19794.00 10487.73 18991.36 18686.41 18088.89 13875.03 21392.59 13596.82 8972.48 21189.72 18088.06 18489.93 19083.81 187
EPNet_dtu87.40 18086.27 18688.72 16095.68 6983.37 20592.09 17490.08 10278.11 21891.29 11686.33 19289.74 17075.39 20589.07 18587.89 18587.81 19889.38 152
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MIMVSNet84.76 19186.75 18282.44 20491.71 17985.95 19489.74 20389.49 12485.28 17269.69 22787.93 17890.88 16664.85 21988.26 19387.74 18689.18 19281.24 196
HyFIR lowres test88.19 17186.56 18490.09 13891.24 18492.17 15994.30 13488.79 14384.06 17985.45 16789.52 16485.64 18588.64 11785.40 21187.28 18792.14 18381.87 195
test0.0.03 181.51 20583.30 19979.42 21393.99 10686.50 19385.93 22487.32 16678.16 21661.62 23180.78 21581.78 19559.87 22388.40 19287.27 18887.78 20080.19 202
TAMVS82.96 19786.15 18779.24 21590.57 19383.12 20887.29 21375.12 22284.06 17965.81 23092.22 13888.27 17769.11 21488.72 18687.26 18987.56 20179.38 208
PMMVS81.93 20283.48 19880.12 21072.35 23575.05 22888.54 20964.01 22877.02 22082.22 18787.51 18391.12 16279.70 17686.59 20186.64 19093.88 16780.41 200
diffmvs88.28 16788.88 16287.58 17889.51 20088.07 18591.88 17585.83 18587.31 15586.34 16196.01 8188.90 17581.90 16185.49 21086.61 19190.04 18889.77 146
MDA-MVSNet-bldmvs89.75 14791.67 13787.50 17974.25 23490.88 17194.68 12685.89 18291.64 8691.03 12195.86 8394.35 14289.10 11396.87 5786.37 19290.04 18885.72 182
gm-plane-assit86.15 18482.51 20090.40 13495.81 6592.29 15697.99 3184.66 19492.15 7593.15 7997.84 3844.65 23878.60 18588.02 19685.95 19392.20 18276.69 214
CR-MVSNet85.32 18881.58 20489.69 14890.36 19484.79 19986.72 21892.22 5675.38 22390.73 12490.41 15667.88 22184.86 13983.76 21385.74 19493.24 17683.14 190
PatchT83.44 19581.10 20686.18 19077.92 23182.58 20989.87 20187.39 16575.88 22290.73 12489.86 16166.71 22484.86 13983.76 21385.74 19486.33 20583.14 190
CHOSEN 1792x268886.64 18186.62 18386.65 18990.33 19587.86 18893.19 15783.30 20383.95 18282.32 18687.93 17889.34 17286.92 12985.64 20984.95 19683.85 21586.68 178
test123567881.50 20684.78 19077.67 22287.67 21280.27 21390.12 19877.62 21580.36 20169.71 22590.93 14996.51 9756.57 22788.60 19084.93 19784.34 21171.87 227
testmv81.49 20784.76 19177.67 22287.67 21280.25 21490.12 19877.62 21580.34 20269.71 22590.92 15096.47 9856.57 22788.58 19184.92 19884.33 21271.86 228
IterMVS88.32 16488.25 17288.41 16690.83 19191.24 16793.07 15981.69 20886.77 16288.55 14895.61 8686.91 18287.01 12787.38 19883.77 19989.29 19186.06 181
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVEpermissive60.41 1973.21 22780.84 20864.30 23056.34 23657.24 23775.28 23672.76 22387.14 16141.39 23886.31 19385.30 18680.66 17186.17 20583.36 20059.35 23580.38 201
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet76.65 22583.52 19668.63 22982.60 22472.08 23176.76 23464.17 22784.41 17849.73 23791.77 14191.53 16156.16 22986.59 20183.26 20182.37 22075.02 216
testus78.20 22081.50 20574.36 22685.59 21979.36 21686.99 21665.76 22676.01 22173.00 21877.98 22293.35 15451.30 23386.33 20382.79 20283.50 21774.68 219
pmmvs381.69 20383.83 19479.19 21678.33 23078.57 21889.53 20458.71 23278.88 21484.34 17388.36 17391.96 15977.69 19587.48 19782.42 20386.54 20479.18 209
RPMNet83.42 19678.40 21589.28 15489.79 19884.79 19990.64 19592.11 6375.38 22387.10 15779.80 21761.99 23082.79 15481.88 21982.07 20493.23 17882.87 193
LP84.09 19484.31 19283.85 20279.40 22984.34 20290.26 19784.02 19687.99 15084.66 17091.61 14479.13 20380.58 17285.90 20881.59 20584.16 21479.59 206
CHOSEN 280x42079.24 21378.26 21780.38 20979.60 22868.80 23589.32 20675.38 22077.25 21978.02 21075.57 22576.17 21181.19 17088.61 18981.39 20678.79 22480.03 204
111176.85 22478.03 22075.46 22494.16 9978.29 21986.40 22089.12 13187.23 15861.26 23295.15 9944.14 23951.46 23186.04 20681.00 20770.40 23474.37 220
test-mter78.71 21878.35 21679.12 21784.03 22376.58 22288.51 21059.06 23171.06 22978.87 20483.73 20771.83 21476.44 20383.41 21680.61 20887.79 19981.24 196
test1235675.40 22680.89 20769.01 22877.43 23275.75 22383.03 22761.48 22978.13 21759.08 23487.69 18194.95 13657.37 22688.18 19480.59 20975.65 22960.93 232
MDTV_nov1_ep13_2view88.22 16987.85 17688.65 16191.40 18286.75 19294.07 13884.97 19188.86 13993.20 7796.11 8096.21 10883.70 14787.29 20080.29 21084.56 20979.46 207
test235672.95 22871.24 23174.95 22584.89 22275.49 22582.67 22875.38 22068.02 23468.65 22974.40 22752.81 23555.61 23081.50 22079.80 21182.50 21966.70 231
PMMVS269.86 23082.14 20155.52 23275.19 23363.08 23675.52 23560.97 23088.50 14325.11 24091.77 14196.44 9925.43 23488.70 18779.34 21270.93 23367.17 229
test-LLR80.62 20977.20 22584.62 20193.99 10675.11 22687.04 21487.32 16670.11 23178.59 20883.17 20871.60 21573.88 20982.32 21779.20 21386.91 20278.87 210
TESTMET0.1,177.47 22377.20 22577.78 22081.94 22575.11 22687.04 21458.33 23370.11 23178.59 20883.17 20871.60 21573.88 20982.32 21779.20 21386.91 20278.87 210
GG-mvs-BLEND54.28 23277.89 22126.72 2340.37 24083.31 20670.04 2370.39 23874.71 2255.36 24168.78 23383.06 1880.62 23883.73 21578.99 21583.55 21672.68 226
dps81.42 20877.88 22285.56 19287.67 21285.17 19788.37 21187.46 16374.37 22684.55 17186.80 19062.18 22980.20 17381.13 22177.52 21685.10 20677.98 212
new-patchmatchnet84.45 19288.75 16779.43 21293.28 14081.87 21181.68 22983.48 20194.47 3071.53 22298.33 1997.88 6658.61 22590.35 17477.33 21787.99 19681.05 198
DWT-MVSNet_training79.22 21473.99 23085.33 19488.57 20684.41 20190.56 19680.96 21273.90 22885.72 16575.62 22450.09 23781.30 16976.91 23077.02 21884.88 20779.97 205
tpm81.58 20478.84 21284.79 19991.11 18879.50 21589.79 20283.75 19779.30 20992.05 10190.98 14764.78 22674.54 20680.50 22276.67 21977.49 22680.15 203
MDTV_nov1_ep1382.33 19979.66 20985.45 19388.83 20583.88 20390.09 20081.98 20779.07 21288.82 14588.70 16973.77 21378.41 19080.29 22376.08 22084.56 20975.83 215
ADS-MVSNet79.11 21579.38 21178.80 21881.90 22675.59 22484.36 22583.69 19887.31 15576.76 21287.58 18276.90 20968.55 21678.70 22575.56 22177.53 22574.07 222
CostFormer82.15 20179.54 21085.20 19688.92 20485.70 19590.87 19186.26 18179.19 21183.87 17587.89 18069.20 21976.62 20277.50 22975.28 22284.69 20882.02 194
EPMVS79.26 21278.20 21980.49 20887.04 21778.86 21786.08 22383.51 20082.63 18773.94 21789.59 16268.67 22072.03 21278.17 22775.08 22380.37 22374.37 220
tpmp4_e2382.16 20078.26 21786.70 18789.92 19684.82 19891.17 18789.95 10881.21 19187.10 15781.91 21264.01 22777.88 19279.89 22474.99 22484.18 21381.00 199
N_pmnet79.33 21184.22 19373.62 22791.72 17873.72 23086.11 22276.36 21892.38 6853.38 23595.54 9195.62 11659.14 22484.23 21274.84 22575.03 23173.25 224
PatchmatchNetpermissive82.44 19878.69 21486.83 18589.81 19781.55 21290.78 19287.27 16882.39 18888.85 14488.31 17470.96 21781.90 16178.58 22674.33 22682.35 22174.69 218
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVS-HIRNet78.28 21975.28 22981.79 20780.33 22769.38 23476.83 23386.59 17670.76 23086.66 16089.57 16381.04 19877.74 19477.81 22871.65 22782.62 21866.73 230
tpmrst78.81 21776.18 22781.87 20688.56 20777.45 22186.74 21781.52 20980.08 20383.48 17790.84 15166.88 22274.54 20673.04 23271.02 22876.38 22873.95 223
tpm cat180.03 21075.93 22884.81 19889.31 20183.26 20788.86 20886.55 17879.24 21086.10 16284.22 20463.62 22877.37 19873.43 23170.88 22980.67 22276.87 213
E-PMN77.81 22177.88 22277.73 22188.26 21070.48 23380.19 23171.20 22486.66 16372.89 22088.09 17781.74 19678.75 18490.02 17868.30 23075.10 23059.85 233
EMVS77.65 22277.49 22477.83 21987.75 21171.02 23281.13 23070.54 22586.38 16674.52 21589.38 16580.19 20178.22 19189.48 18267.13 23174.83 23258.84 234
testpf72.68 22966.81 23279.53 21186.52 21873.89 22983.56 22688.74 14458.70 23679.68 20071.31 23253.64 23462.23 22168.68 23366.64 23276.46 22774.82 217
tmp_tt28.44 23336.05 23715.86 23921.29 2396.40 23554.52 23751.96 23650.37 23638.68 2419.55 23561.75 23559.66 23345.36 237
test1232.16 2342.82 2351.41 2350.62 2391.18 2401.53 2410.82 2372.78 2392.27 2424.18 2381.98 2431.64 2362.58 2373.01 2341.56 2384.00 235
.test124560.07 23156.75 23363.93 23194.16 9978.29 21986.40 22089.12 13187.23 15861.26 23295.15 9944.14 23951.46 23186.04 2062.51 2351.21 2393.92 236
testmvs2.38 2333.35 2341.26 2360.83 2380.96 2411.53 2410.83 2363.59 2381.63 2436.03 2372.93 2421.55 2373.49 2362.51 2351.21 2393.92 236
sosnet-low-res0.00 2350.00 2360.00 2370.00 2410.00 2420.00 2430.00 2390.00 2400.00 2440.00 2390.00 2440.00 2390.00 2380.00 2370.00 2410.00 238
sosnet0.00 2350.00 2360.00 2370.00 2410.00 2420.00 2430.00 2390.00 2400.00 2440.00 2390.00 2440.00 2390.00 2380.00 2370.00 2410.00 238
MTAPA94.88 3996.88 87
MTMP95.43 2197.25 77
Patchmatch-RL test8.96 240
XVS96.86 3097.48 1698.73 393.28 7396.82 8998.17 33
X-MVStestdata96.86 3097.48 1698.73 393.28 7396.82 8998.17 33
abl_691.88 11993.76 12394.98 9295.64 10988.97 13686.20 16790.00 13886.31 19394.50 14087.31 12695.60 11892.48 114
mPP-MVS98.24 397.65 71
NP-MVS85.48 171
Patchmtry83.74 20486.72 21892.22 5690.73 124
DeepMVS_CXcopyleft47.68 23853.20 23819.21 23463.24 23526.96 23966.50 23469.82 21866.91 21864.27 23454.91 23672.72 225