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
indooroutdoorbotani.boulde.bridgedoorexhibi.lectur.living.loungeobserv.old co.statueterrac.
sort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
LCM-MVSNet99.86 199.86 199.87 199.99 199.77 199.77 199.80 199.97 199.97 199.95 199.74 199.98 199.56 1100.00 199.85 4
Anonymous2023121199.29 299.41 298.91 2299.94 297.08 3799.47 399.51 599.56 299.83 399.80 299.13 399.90 1397.55 4999.93 2199.75 13
LTVRE_ROB96.88 199.18 399.34 398.72 3599.71 796.99 4099.69 299.57 399.02 1499.62 1099.36 1698.53 899.52 15598.58 2499.95 1399.66 23
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
pmmvs699.07 499.24 498.56 4499.81 396.38 5698.87 999.30 999.01 1599.63 999.66 499.27 299.68 9997.75 4199.89 3399.62 31
mvs_tets98.90 598.94 898.75 3099.69 896.48 5498.54 2099.22 1096.23 9999.71 599.48 798.77 799.93 298.89 1099.95 1399.84 6
TDRefinement98.90 598.86 1199.02 899.54 2398.06 699.34 599.44 798.85 1999.00 3999.20 3197.42 3199.59 13697.21 6299.76 4999.40 90
UA-Net98.88 798.76 1699.22 299.11 7697.89 1099.47 399.32 899.08 997.87 13399.67 396.47 7099.92 497.88 3499.98 399.85 4
v5298.85 899.01 598.37 5499.61 1595.53 8099.01 799.04 4598.48 2699.31 2299.41 1196.82 5699.87 2199.44 299.95 1399.70 19
V498.85 899.01 598.37 5499.61 1595.53 8099.01 799.04 4598.48 2699.31 2299.41 1196.81 5799.87 2199.44 299.95 1399.70 19
DTE-MVSNet98.79 1098.86 1198.59 4299.55 2196.12 6398.48 2499.10 2599.36 399.29 2599.06 4797.27 3799.93 297.71 4399.91 2799.70 19
jajsoiax98.77 1198.79 1598.74 3299.66 1096.48 5498.45 2599.12 2295.83 11499.67 799.37 1498.25 1199.92 498.77 1499.94 1999.82 7
PEN-MVS98.75 1298.85 1398.44 4999.58 1895.67 7498.45 2599.15 1999.33 499.30 2499.00 4897.27 3799.92 497.64 4499.92 2499.75 13
v7n98.73 1398.99 797.95 8099.64 1294.20 12398.67 1299.14 2099.08 999.42 1699.23 2996.53 6599.91 1299.27 499.93 2199.73 16
PS-CasMVS98.73 1398.85 1398.39 5399.55 2195.47 8298.49 2299.13 2199.22 799.22 2898.96 5297.35 3399.92 497.79 3999.93 2199.79 8
test_djsdf98.73 1398.74 1898.69 3799.63 1396.30 5998.67 1299.02 5096.50 9099.32 2199.44 1097.43 3099.92 498.73 1799.95 1399.86 3
anonymousdsp98.72 1698.63 2198.99 1099.62 1497.29 3498.65 1599.19 1495.62 12099.35 2099.37 1497.38 3299.90 1398.59 2399.91 2799.77 9
wuykxyi23d98.68 1798.53 2699.13 399.44 3497.97 796.85 10599.02 5095.81 11599.88 299.38 1398.14 1499.69 9498.32 2899.95 1399.73 16
WR-MVS_H98.65 1898.62 2398.75 3099.51 2696.61 5098.55 1999.17 1599.05 1299.17 3198.79 6095.47 10199.89 1797.95 3299.91 2799.75 13
OurMVSNet-221017-098.61 1998.61 2598.63 4199.77 496.35 5799.17 699.05 3898.05 4099.61 1199.52 593.72 16099.88 1998.72 2099.88 3499.65 24
v74898.58 2098.89 1097.67 9799.61 1593.53 14698.59 1698.90 7498.97 1799.43 1599.15 4096.53 6599.85 2498.88 1199.91 2799.64 27
nrg03098.54 2198.62 2398.32 5999.22 5595.66 7597.90 5699.08 3098.31 3299.02 3698.74 6597.68 2499.61 12997.77 4099.85 3999.70 19
PS-MVSNAJss98.53 2298.63 2198.21 6799.68 994.82 10298.10 4499.21 1196.91 8299.75 499.45 995.82 8799.92 498.80 1399.96 1199.89 1
MIMVSNet198.51 2398.45 3198.67 3899.72 696.71 4698.76 1098.89 7698.49 2599.38 1899.14 4195.44 10399.84 2896.47 8199.80 4599.47 64
pm-mvs198.47 2498.67 1997.86 8499.52 2594.58 11098.28 3199.00 6197.57 6299.27 2699.22 3098.32 1099.50 16097.09 6899.75 5399.50 50
ACMH93.61 998.44 2598.76 1697.51 10799.43 3793.54 14598.23 3499.05 3897.40 7499.37 1999.08 4698.79 699.47 16697.74 4299.71 6299.50 50
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet98.42 2698.46 2998.30 6299.46 3295.22 9098.27 3398.84 8699.05 1299.01 3798.65 7295.37 10499.90 1397.57 4899.91 2799.77 9
abl_698.42 2698.19 4199.09 499.16 6398.10 597.73 6899.11 2397.76 4998.62 5598.27 10297.88 2199.80 3795.67 10499.50 10999.38 95
TransMVSNet (Re)98.38 2898.67 1997.51 10799.51 2693.39 15098.20 3998.87 8098.23 3499.48 1299.27 2598.47 999.55 14796.52 7899.53 10299.60 34
TranMVSNet+NR-MVSNet98.33 2998.30 3998.43 5099.07 8095.87 6796.73 10999.05 3898.67 2198.84 4498.45 8597.58 2799.88 1996.45 8299.86 3899.54 45
HPM-MVS_fast98.32 3098.13 4498.88 2399.54 2397.48 2798.35 2899.03 4995.88 11197.88 12898.22 10798.15 1399.74 5796.50 8099.62 7999.42 85
ANet_high98.31 3198.94 896.41 17299.33 4789.64 20897.92 5599.56 499.27 599.66 899.50 697.67 2599.83 3097.55 4999.98 399.77 9
VPA-MVSNet98.27 3298.46 2997.70 9399.06 8193.80 13597.76 6399.00 6198.40 2999.07 3498.98 5096.89 5099.75 5297.19 6599.79 4699.55 44
Vis-MVSNetpermissive98.27 3298.34 3598.07 7299.33 4795.21 9298.04 4899.46 697.32 7797.82 13799.11 4396.75 5999.86 2397.84 3699.36 14999.15 131
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
COLMAP_ROBcopyleft94.48 698.25 3498.11 4598.64 4099.21 5897.35 3297.96 5299.16 1698.34 3198.78 4798.52 8097.32 3499.45 17494.08 16899.67 7299.13 134
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMH+93.58 1098.23 3598.31 3797.98 7999.39 4295.22 9097.55 8099.20 1398.21 3599.25 2798.51 8198.21 1299.40 18894.79 14299.72 5899.32 104
FC-MVSNet-test98.16 3698.37 3397.56 10299.49 3093.10 15498.35 2899.21 1198.43 2898.89 4398.83 5994.30 13999.81 3397.87 3599.91 2799.77 9
MTAPA98.14 3797.84 5699.06 599.44 3497.90 897.25 9198.73 10997.69 5697.90 12497.96 13695.81 9199.82 3196.13 8899.61 8399.45 71
APDe-MVS98.14 3798.03 5098.47 4898.72 10796.04 6498.07 4699.10 2595.96 10898.59 5998.69 6996.94 4899.81 3396.64 7499.58 8999.57 40
APD-MVS_3200maxsize98.13 3997.90 5398.79 2898.79 10097.31 3397.55 8098.92 7297.72 5498.25 8798.13 11997.10 4399.75 5295.44 11699.24 17099.32 104
HPM-MVS98.11 4097.83 5798.92 1999.42 3997.46 2898.57 1799.05 3895.43 12997.41 15297.50 17597.98 1799.79 3895.58 11399.57 9299.50 50
Gipumacopyleft98.07 4198.31 3797.36 12499.76 596.28 6098.51 2199.10 2598.76 2096.79 17599.34 2096.61 6298.82 26696.38 8399.50 10996.98 276
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ACMMPcopyleft98.05 4297.75 6298.93 1899.23 5497.60 1998.09 4598.96 6995.75 11797.91 12398.06 12896.89 5099.76 4895.32 12199.57 9299.43 83
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
ACMM93.33 1198.05 4297.79 5898.85 2499.15 6697.55 2396.68 11198.83 9295.21 13798.36 7598.13 11998.13 1699.62 12396.04 9299.54 10099.39 93
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1398.02 4498.52 2796.51 16699.02 8590.14 20098.07 4699.09 2998.10 3999.13 3299.35 1894.84 11899.74 5799.12 599.98 399.65 24
SteuartSystems-ACMMP98.02 4497.76 6198.79 2899.43 3797.21 3697.15 9598.90 7496.58 8998.08 10597.87 14797.02 4799.76 4895.25 12399.59 8799.40 90
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MPTG98.01 4697.66 6899.06 599.44 3497.90 895.66 15798.73 10997.69 5697.90 12497.96 13695.81 9199.82 3196.13 8899.61 8399.45 71
v1297.97 4798.47 2896.46 16998.98 8990.01 20497.97 5199.08 3098.00 4299.11 3399.34 2094.70 12199.73 6299.07 699.98 399.64 27
XVS97.96 4897.63 7398.94 1599.15 6697.66 1697.77 6198.83 9297.42 7096.32 19297.64 16596.49 6899.72 6795.66 10699.37 14699.45 71
NR-MVSNet97.96 4897.86 5598.26 6498.73 10595.54 7898.14 4298.73 10997.79 4799.42 1697.83 14894.40 13699.78 3995.91 10099.76 4999.46 66
ACMMPR97.95 5097.62 7598.94 1599.20 5997.56 2297.59 7798.83 9296.05 10297.46 15097.63 16696.77 5899.76 4895.61 11099.46 12299.49 58
FMVSNet197.95 5098.08 4697.56 10299.14 7493.67 13998.23 3498.66 12697.41 7399.00 3999.19 3295.47 10199.73 6295.83 10199.76 4999.30 108
HFP-MVS97.94 5297.64 7198.83 2599.15 6697.50 2597.59 7798.84 8696.05 10297.49 14597.54 17097.07 4599.70 8495.61 11099.46 12299.30 108
LPG-MVS_test97.94 5297.67 6798.74 3299.15 6697.02 3897.09 9999.02 5095.15 14198.34 7798.23 10497.91 1999.70 8494.41 15499.73 5599.50 50
FIs97.93 5498.07 4797.48 11499.38 4392.95 15698.03 5099.11 2398.04 4198.62 5598.66 7193.75 15999.78 3997.23 6199.84 4099.73 16
region2R97.92 5597.59 7798.92 1999.22 5597.55 2397.60 7698.84 8696.00 10697.22 15797.62 16796.87 5399.76 4895.48 11499.43 13499.46 66
CP-MVS97.92 5597.56 8098.99 1098.99 8797.82 1297.93 5498.96 6996.11 10196.89 17397.45 17896.85 5499.78 3995.19 12699.63 7899.38 95
mPP-MVS97.91 5797.53 8199.04 799.22 5597.87 1197.74 6698.78 10196.04 10497.10 16397.73 16096.53 6599.78 3995.16 12999.50 10999.46 66
V997.90 5898.40 3296.40 17398.93 9189.86 20697.86 5899.07 3497.88 4699.05 3599.30 2394.53 13299.72 6799.01 899.98 399.63 29
ACMMP_Plus97.89 5997.63 7398.67 3899.35 4696.84 4396.36 11898.79 9895.07 14797.88 12898.35 9197.24 4099.72 6796.05 9199.58 8999.45 71
PGM-MVS97.88 6097.52 8298.96 1399.20 5997.62 1897.09 9999.06 3695.45 12797.55 14197.94 14097.11 4299.78 3994.77 14499.46 12299.48 61
DP-MVS97.87 6197.89 5497.81 8798.62 12394.82 10297.13 9898.79 9898.98 1698.74 5098.49 8295.80 9399.49 16295.04 13599.44 12799.11 142
RPSCF97.87 6197.51 8398.95 1499.15 6698.43 397.56 7999.06 3696.19 10098.48 6798.70 6894.72 12099.24 22094.37 15799.33 15999.17 127
test_040297.84 6397.97 5197.47 11599.19 6194.07 12696.71 11098.73 10998.66 2298.56 6198.41 8796.84 5599.69 9494.82 13999.81 4398.64 194
V1497.83 6498.33 3696.35 17498.88 9789.72 20797.75 6499.05 3897.74 5099.01 3799.27 2594.35 13799.71 7798.95 999.97 899.62 31
UniMVSNet_NR-MVSNet97.83 6497.65 6998.37 5498.72 10795.78 6995.66 15799.02 5098.11 3898.31 8297.69 16494.65 12699.85 2497.02 7099.71 6299.48 61
UniMVSNet (Re)97.83 6497.65 6998.35 5898.80 9995.86 6895.92 14799.04 4597.51 6798.22 8997.81 15294.68 12499.78 3997.14 6799.75 5399.41 87
v1197.82 6798.36 3496.17 19198.93 9189.16 22697.79 6099.08 3097.64 5999.19 2999.32 2294.28 14099.72 6799.07 699.97 899.63 29
DeepC-MVS95.41 497.82 6797.70 6498.16 6898.78 10195.72 7196.23 12699.02 5093.92 18198.62 5598.99 4997.69 2399.62 12396.18 8799.87 3699.15 131
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DU-MVS97.79 6997.60 7698.36 5798.73 10595.78 6995.65 15998.87 8097.57 6298.31 8297.83 14894.69 12299.85 2497.02 7099.71 6299.46 66
v1597.77 7098.26 4096.30 18098.81 9889.59 21397.62 7399.04 4597.59 6198.97 4199.24 2794.19 14499.70 8498.88 1199.97 899.61 33
LS3D97.77 7097.50 8498.57 4396.24 27597.58 2198.45 2598.85 8398.58 2497.51 14397.94 14095.74 9499.63 11795.19 12698.97 19698.51 205
3Dnovator+96.13 397.73 7297.59 7798.15 6998.11 17895.60 7698.04 4898.70 11898.13 3796.93 17198.45 8595.30 10899.62 12395.64 10898.96 19799.24 120
Baseline_NR-MVSNet97.72 7397.79 5897.50 11099.56 1993.29 15195.44 16698.86 8298.20 3698.37 7499.24 2794.69 12299.55 14795.98 9799.79 4699.65 24
v1797.70 7498.17 4296.28 18398.77 10289.59 21397.62 7399.01 5997.54 6498.72 5299.18 3594.06 14899.68 9998.74 1699.92 2499.58 36
MP-MVS-pluss97.69 7597.36 8998.70 3699.50 2996.84 4395.38 17598.99 6492.45 21498.11 9998.31 9597.25 3999.77 4796.60 7599.62 7999.48 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
v1697.69 7598.16 4396.29 18298.75 10389.60 21197.62 7399.01 5997.53 6698.69 5499.18 3594.05 14999.68 9998.73 1799.88 3499.58 36
EG-PatchMatch MVS97.69 7597.79 5897.40 12299.06 8193.52 14795.96 14398.97 6894.55 16298.82 4598.76 6397.31 3599.29 21497.20 6499.44 12799.38 95
MP-MVScopyleft97.64 7897.18 10499.00 999.32 4997.77 1497.49 8398.73 10996.27 9895.59 21897.75 15796.30 7599.78 3993.70 17899.48 11999.45 71
#test#97.62 7997.22 10298.83 2599.15 6697.50 2596.81 10798.84 8694.25 17297.49 14597.54 17097.07 4599.70 8494.37 15799.46 12299.30 108
3Dnovator96.53 297.61 8097.64 7197.50 11097.74 21793.65 14398.49 2298.88 7896.86 8397.11 16298.55 7895.82 8799.73 6295.94 9899.42 13799.13 134
v1897.60 8198.06 4896.23 18498.68 11789.46 21697.48 8498.98 6697.33 7698.60 5899.13 4293.86 15299.67 10598.62 2199.87 3699.56 41
v897.60 8198.06 4896.23 18498.71 11089.44 21797.43 8698.82 9697.29 7898.74 5099.10 4493.86 15299.68 9998.61 2299.94 1999.56 41
XVG-ACMP-BASELINE97.58 8397.28 9498.49 4699.16 6396.90 4296.39 11698.98 6695.05 14898.06 10698.02 13195.86 8399.56 14394.37 15799.64 7799.00 155
v1097.55 8497.97 5196.31 17898.60 12589.64 20897.44 8599.02 5096.60 8798.72 5299.16 3993.48 16499.72 6798.76 1599.92 2499.58 36
OPM-MVS97.54 8597.25 9598.41 5199.11 7696.61 5095.24 18798.46 14894.58 16198.10 10298.07 12597.09 4499.39 19495.16 12999.44 12799.21 122
XXY-MVS97.54 8597.70 6497.07 13899.46 3292.21 16697.22 9499.00 6194.93 15198.58 6098.92 5697.31 3599.41 18594.44 15299.43 13499.59 35
Regformer-497.53 8797.47 8697.71 9297.35 24393.91 13195.26 18598.14 19397.97 4398.34 7797.89 14595.49 9999.71 7797.41 5799.42 13799.51 49
SixPastTwentyTwo97.49 8897.57 7997.26 13099.56 1992.33 16398.28 3196.97 25298.30 3399.45 1499.35 1888.43 24899.89 1798.01 3199.76 4999.54 45
ACMP92.54 1397.47 8997.10 11198.55 4599.04 8496.70 4796.24 12598.89 7693.71 18597.97 11597.75 15797.44 2999.63 11793.22 18599.70 6599.32 104
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testing_297.43 9097.71 6396.60 15998.91 9490.85 19096.01 13698.54 14094.78 15498.78 4798.96 5296.35 7499.54 14997.25 6099.82 4299.40 90
TSAR-MVS + MP.97.42 9197.23 10198.00 7899.38 4395.00 9697.63 7298.20 18493.00 20098.16 9498.06 12895.89 8299.72 6795.67 10499.10 18399.28 115
Regformer-297.41 9297.24 9797.93 8197.21 25294.72 10594.85 20898.27 17697.74 5098.11 9997.50 17595.58 9799.69 9496.57 7799.31 16199.37 100
CSCG97.40 9397.30 9197.69 9598.95 9094.83 10197.28 9098.99 6496.35 9798.13 9895.95 25995.99 8099.66 11094.36 16099.73 5598.59 199
XVG-OURS-SEG-HR97.38 9497.07 11498.30 6299.01 8697.41 3194.66 21399.02 5095.20 13898.15 9697.52 17398.83 598.43 29494.87 13796.41 28899.07 149
HSP-MVS97.37 9596.85 12498.92 1999.26 5197.70 1597.66 6998.23 18095.65 11898.51 6496.46 23592.15 19999.81 3395.14 13198.58 23299.26 119
VDD-MVS97.37 9597.25 9597.74 9198.69 11694.50 11397.04 10195.61 27998.59 2398.51 6498.72 6692.54 19199.58 13896.02 9499.49 11699.12 139
SD-MVS97.37 9597.70 6496.35 17498.14 17495.13 9396.54 11298.92 7295.94 10999.19 2998.08 12497.74 2295.06 32695.24 12499.54 10098.87 176
PM-MVS97.36 9897.10 11198.14 7098.91 9496.77 4596.20 12798.63 13393.82 18298.54 6298.33 9393.98 15099.05 23895.99 9699.45 12698.61 197
LCM-MVSNet-Re97.33 9997.33 9097.32 12698.13 17793.79 13696.99 10399.65 296.74 8599.47 1398.93 5596.91 4999.84 2890.11 24399.06 18998.32 222
EI-MVSNet-UG-set97.32 10097.40 8797.09 13797.34 24692.01 17495.33 17997.65 22597.74 5098.30 8498.14 11895.04 11499.69 9497.55 4999.52 10699.58 36
EI-MVSNet-Vis-set97.32 10097.39 8897.11 13597.36 24292.08 17295.34 17897.65 22597.74 5098.29 8598.11 12295.05 11299.68 9997.50 5399.50 10999.56 41
Regformer-197.27 10297.16 10697.61 10097.21 25293.86 13394.85 20898.04 20497.62 6098.03 10997.50 17595.34 10599.63 11796.52 7899.31 16199.35 102
VPNet97.26 10397.49 8596.59 16199.47 3190.58 19596.27 12298.53 14197.77 4898.46 6998.41 8794.59 12899.68 9994.61 14899.29 16599.52 48
Regformer-397.25 10497.29 9297.11 13597.35 24392.32 16495.26 18597.62 23097.67 5898.17 9397.89 14595.05 11299.56 14397.16 6699.42 13799.46 66
canonicalmvs97.23 10597.21 10397.30 12797.65 22594.39 11597.84 5999.05 3897.42 7096.68 17893.85 29697.63 2699.33 20896.29 8598.47 23798.18 238
AllTest97.20 10696.92 12298.06 7399.08 7896.16 6197.14 9799.16 1694.35 16997.78 13898.07 12595.84 8499.12 22991.41 21199.42 13798.91 168
XVG-OURS97.12 10796.74 13198.26 6498.99 8797.45 2993.82 24799.05 3895.19 13998.32 8097.70 16395.22 11098.41 29594.27 16298.13 24498.93 165
V4297.04 10897.16 10696.68 15798.59 12791.05 18796.33 12098.36 16194.60 15897.99 11198.30 9893.32 17099.62 12397.40 5899.53 10299.38 95
APD-MVScopyleft97.00 10996.53 14398.41 5198.55 13296.31 5896.32 12198.77 10292.96 20697.44 15197.58 16995.84 8499.74 5791.96 19999.35 15299.19 124
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++96.99 11096.38 14898.81 2798.64 11897.59 2095.97 13998.20 18495.51 12595.06 22596.53 23194.10 14799.70 8494.29 16199.15 17699.13 134
GBi-Net96.99 11096.80 12897.56 10297.96 18993.67 13998.23 3498.66 12695.59 12297.99 11199.19 3289.51 24099.73 6294.60 14999.44 12799.30 108
test196.99 11096.80 12897.56 10297.96 18993.67 13998.23 3498.66 12695.59 12297.99 11199.19 3289.51 24099.73 6294.60 14999.44 12799.30 108
VDDNet96.98 11396.84 12597.41 12199.40 4193.26 15297.94 5395.31 28199.26 698.39 7399.18 3587.85 25599.62 12395.13 13299.09 18499.35 102
v1neww96.97 11497.24 9796.15 19298.70 11289.44 21795.97 13998.33 16695.25 13497.88 12898.15 11593.83 15599.61 12997.50 5399.50 10999.41 87
v7new96.97 11497.24 9796.15 19298.70 11289.44 21795.97 13998.33 16695.25 13497.88 12898.15 11593.83 15599.61 12997.50 5399.50 10999.41 87
v696.97 11497.24 9796.15 19298.71 11089.44 21795.97 13998.33 16695.25 13497.89 12698.15 11593.86 15299.61 12997.51 5299.50 10999.42 85
PHI-MVS96.96 11796.53 14398.25 6697.48 23496.50 5396.76 10898.85 8393.52 18896.19 20196.85 21095.94 8199.42 17993.79 17699.43 13498.83 179
v796.93 11897.17 10596.23 18498.59 12789.64 20895.96 14398.66 12694.41 16697.87 13398.38 9093.47 16599.64 11497.93 3399.24 17099.43 83
IS-MVSNet96.93 11896.68 13397.70 9399.25 5394.00 12998.57 1796.74 26098.36 3098.14 9797.98 13588.23 24999.71 7793.10 18899.72 5899.38 95
CNVR-MVS96.92 12096.55 14098.03 7798.00 18695.54 7894.87 20698.17 18994.60 15896.38 19097.05 19995.67 9599.36 20395.12 13399.08 18599.19 124
IterMVS-LS96.92 12097.29 9295.79 21498.51 13988.13 25095.10 19198.66 12696.99 7998.46 6998.68 7092.55 18999.74 5796.91 7299.79 4699.50 50
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WR-MVS96.90 12296.81 12797.16 13298.56 13192.20 16894.33 22098.12 19597.34 7598.20 9197.33 18892.81 18199.75 5294.79 14299.81 4399.54 45
DeepPCF-MVS94.58 596.90 12296.43 14798.31 6197.48 23497.23 3592.56 28198.60 13692.84 20898.54 6297.40 18196.64 6198.78 27094.40 15699.41 14398.93 165
v114196.86 12497.14 10896.04 19998.55 13289.06 22995.44 16698.33 16695.14 14397.93 12198.19 10993.36 16899.62 12397.61 4599.69 6699.44 79
divwei89l23v2f11296.86 12497.14 10896.04 19998.54 13589.06 22995.44 16698.33 16695.14 14397.93 12198.19 10993.36 16899.61 12997.61 4599.68 7099.44 79
v196.86 12497.14 10896.04 19998.55 13289.06 22995.44 16698.33 16695.14 14397.94 11898.18 11393.39 16799.61 12997.61 4599.69 6699.44 79
v114496.84 12797.08 11396.13 19698.42 14589.28 22495.41 17398.67 12494.21 17497.97 11598.31 9593.06 17599.65 11198.06 3099.62 7999.45 71
VNet96.84 12796.83 12696.88 14798.06 18092.02 17396.35 11997.57 23297.70 5597.88 12897.80 15392.40 19699.54 14994.73 14798.96 19799.08 147
EPP-MVSNet96.84 12796.58 13797.65 9899.18 6293.78 13798.68 1196.34 26397.91 4597.30 15598.06 12888.46 24799.85 2493.85 17499.40 14499.32 104
v119296.83 13097.06 11596.15 19298.28 15589.29 22395.36 17698.77 10293.73 18498.11 9998.34 9293.02 17999.67 10598.35 2699.58 8999.50 50
MVS_111021_LR96.82 13196.55 14097.62 9998.27 15795.34 8593.81 24898.33 16694.59 16096.56 18296.63 22696.61 6298.73 27494.80 14199.34 15498.78 185
Effi-MVS+-dtu96.81 13296.09 15798.99 1096.90 26398.69 296.42 11598.09 19795.86 11295.15 22495.54 26994.26 14199.81 3394.06 16998.51 23598.47 207
UGNet96.81 13296.56 13997.58 10196.64 26693.84 13497.75 6497.12 24796.47 9393.62 26498.88 5893.22 17399.53 15195.61 11099.69 6699.36 101
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
v2v48296.78 13497.06 11595.95 20698.57 13088.77 23995.36 17698.26 17895.18 14097.85 13598.23 10492.58 18899.63 11797.80 3899.69 6699.45 71
v124096.74 13597.02 11795.91 20998.18 16788.52 24195.39 17498.88 7893.15 19798.46 6998.40 8992.80 18299.71 7798.45 2599.49 11699.49 58
DeepC-MVS_fast94.34 796.74 13596.51 14597.44 11997.69 22094.15 12496.02 13598.43 15293.17 19697.30 15597.38 18695.48 10099.28 21593.74 17799.34 15498.88 174
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_HR96.73 13796.54 14297.27 12898.35 14993.66 14293.42 26298.36 16194.74 15596.58 18096.76 21996.54 6498.99 24694.87 13799.27 16899.15 131
v192192096.72 13896.96 12095.99 20398.21 16488.79 23895.42 17198.79 9893.22 19298.19 9298.26 10392.68 18599.70 8498.34 2799.55 9899.49 58
FMVSNet296.72 13896.67 13496.87 14897.96 18991.88 17697.15 9598.06 20295.59 12298.50 6698.62 7389.51 24099.65 11194.99 13699.60 8699.07 149
PMVScopyleft89.60 1796.71 14096.97 11895.95 20699.51 2697.81 1397.42 8797.49 23397.93 4495.95 20798.58 7496.88 5296.91 32089.59 25099.36 14993.12 318
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v14419296.69 14196.90 12396.03 20298.25 16088.92 23295.49 16498.77 10293.05 19998.09 10398.29 9992.51 19399.70 8498.11 2999.56 9499.47 64
CPTT-MVS96.69 14196.08 15898.49 4698.89 9696.64 4997.25 9198.77 10292.89 20796.01 20697.13 19592.23 19899.67 10592.24 19799.34 15499.17 127
HQP_MVS96.66 14396.33 15197.68 9698.70 11294.29 11896.50 11398.75 10696.36 9596.16 20296.77 21791.91 21199.46 17092.59 19399.20 17399.28 115
EI-MVSNet96.63 14496.93 12195.74 21597.26 25088.13 25095.29 18397.65 22596.99 7997.94 11898.19 10992.55 18999.58 13896.91 7299.56 9499.50 50
ab-mvs96.59 14596.59 13696.60 15998.64 11892.21 16698.35 2897.67 22194.45 16396.99 16798.79 6094.96 11699.49 16290.39 24099.07 18798.08 241
v14896.58 14696.97 11895.42 22598.63 12287.57 25995.09 19397.90 20795.91 11098.24 8897.96 13693.42 16699.39 19496.04 9299.52 10699.29 114
test20.0396.58 14696.61 13596.48 16898.49 14191.72 18095.68 15697.69 22096.81 8498.27 8697.92 14394.18 14598.71 27690.78 22799.66 7499.00 155
NCCC96.52 14895.99 16298.10 7197.81 20295.68 7395.00 20298.20 18495.39 13095.40 22096.36 24293.81 15799.45 17493.55 18198.42 23899.17 127
pmmvs-eth3d96.49 14996.18 15497.42 12098.25 16094.29 11894.77 21298.07 20189.81 24397.97 11598.33 9393.11 17499.08 23595.46 11599.84 4098.89 170
OMC-MVS96.48 15096.00 16197.91 8298.30 15196.01 6694.86 20798.60 13691.88 22597.18 15997.21 19296.11 7899.04 23990.49 23899.34 15498.69 192
TSAR-MVS + GP.96.47 15196.12 15597.49 11397.74 21795.23 8794.15 23396.90 25493.26 19198.04 10896.70 22194.41 13598.89 25894.77 14499.14 17798.37 215
Fast-Effi-MVS+-dtu96.44 15296.12 15597.39 12397.18 25494.39 11595.46 16598.73 10996.03 10594.72 23294.92 28296.28 7799.69 9493.81 17597.98 24898.09 240
K. test v396.44 15296.28 15296.95 14399.41 4091.53 18297.65 7090.31 32198.89 1898.93 4299.36 1684.57 27599.92 497.81 3799.56 9499.39 93
MSLP-MVS++96.42 15496.71 13295.57 22097.82 20190.56 19795.71 15498.84 8694.72 15696.71 17797.39 18494.91 11798.10 31095.28 12299.02 19198.05 244
MVS_Test96.27 15596.79 13094.73 24996.94 26186.63 27396.18 12898.33 16694.94 15096.07 20498.28 10095.25 10999.26 21897.21 6297.90 25298.30 225
MCST-MVS96.24 15695.80 16797.56 10298.75 10394.13 12594.66 21398.17 18990.17 24096.21 20096.10 25395.14 11199.43 17894.13 16698.85 21299.13 134
mvs-test196.20 15795.50 17498.32 5996.90 26398.16 495.07 19698.09 19795.86 11293.63 26394.32 29294.26 14199.71 7794.06 16997.27 27597.07 273
Effi-MVS+96.19 15896.01 16096.71 15497.43 23992.19 16996.12 13199.10 2595.45 12793.33 27794.71 28497.23 4199.56 14393.21 18697.54 26498.37 215
DELS-MVS96.17 15996.23 15395.99 20397.55 23290.04 20292.38 28598.52 14294.13 17796.55 18597.06 19894.99 11599.58 13895.62 10999.28 16698.37 215
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
MVSFormer96.14 16096.36 14995.49 22497.68 22187.81 25698.67 1299.02 5096.50 9094.48 24096.15 25086.90 26099.92 498.73 1799.13 17998.74 188
testgi96.07 16196.50 14694.80 24699.26 5187.69 25895.96 14398.58 13995.08 14698.02 11096.25 24697.92 1897.60 31688.68 26498.74 21899.11 142
LF4IMVS96.07 16195.63 17197.36 12498.19 16595.55 7795.44 16698.82 9692.29 21795.70 21696.55 22992.63 18798.69 27891.75 20899.33 15997.85 254
alignmvs96.01 16395.52 17397.50 11097.77 21694.71 10696.07 13296.84 25597.48 6896.78 17694.28 29385.50 26699.40 18896.22 8698.73 22198.40 212
TinyColmap96.00 16496.34 15094.96 24097.90 19387.91 25394.13 23598.49 14694.41 16698.16 9497.76 15496.29 7698.68 28190.52 23599.42 13798.30 225
PVSNet_Blended_VisFu95.95 16595.80 16796.42 17199.28 5090.62 19495.31 18199.08 3088.40 25496.97 16998.17 11492.11 20199.78 3993.64 17999.21 17298.86 177
test_prior395.91 16695.39 17897.46 11697.79 21194.26 12193.33 26698.42 15594.21 17494.02 25296.25 24693.64 16199.34 20591.90 20098.96 19798.79 183
UnsupCasMVSNet_eth95.91 16695.73 16996.44 17098.48 14391.52 18395.31 18198.45 14995.76 11697.48 14897.54 17089.53 23998.69 27894.43 15394.61 30599.13 134
QAPM95.88 16895.57 17296.80 14997.90 19391.84 17898.18 4198.73 10988.41 25396.42 18898.13 11994.73 11999.75 5288.72 26298.94 20198.81 180
MVP-Stereo95.69 16995.28 18096.92 14498.15 17393.03 15595.64 16198.20 18490.39 23796.63 17997.73 16091.63 21499.10 23391.84 20497.31 27398.63 196
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet-bldmvs95.69 16995.67 17095.74 21598.48 14388.76 24092.84 27397.25 24096.00 10697.59 14097.95 13991.38 21999.46 17093.16 18796.35 28998.99 158
new-patchmatchnet95.67 17196.58 13792.94 27997.48 23480.21 30492.96 27298.19 18894.83 15298.82 4598.79 6093.31 17199.51 15995.83 10199.04 19099.12 139
MVS_030595.66 17295.44 17696.31 17896.60 26890.43 19994.23 22698.52 14295.01 14991.76 29896.65 22591.75 21399.70 8494.75 14699.65 7598.81 180
xiu_mvs_v1_base_debu95.62 17395.96 16394.60 25298.01 18388.42 24293.99 23998.21 18192.98 20195.91 20894.53 28696.39 7199.72 6795.43 11898.19 24195.64 303
xiu_mvs_v1_base95.62 17395.96 16394.60 25298.01 18388.42 24293.99 23998.21 18192.98 20195.91 20894.53 28696.39 7199.72 6795.43 11898.19 24195.64 303
xiu_mvs_v1_base_debi95.62 17395.96 16394.60 25298.01 18388.42 24293.99 23998.21 18192.98 20195.91 20894.53 28696.39 7199.72 6795.43 11898.19 24195.64 303
DP-MVS Recon95.55 17695.13 18496.80 14998.51 13993.99 13094.60 21598.69 11990.20 23995.78 21296.21 24992.73 18498.98 24890.58 23498.86 21097.42 269
test_normal95.51 17795.46 17595.68 21997.97 18889.12 22893.73 25095.86 27391.98 22197.17 16096.94 20591.55 21599.42 17995.21 12598.73 22198.51 205
testmv95.51 17795.33 17996.05 19898.23 16289.51 21593.50 25998.63 13394.25 17298.22 8997.73 16092.51 19399.47 16685.22 29699.72 5899.17 127
Fast-Effi-MVS+95.49 17995.07 18696.75 15297.67 22492.82 15794.22 22898.60 13691.61 22793.42 27492.90 30396.73 6099.70 8492.60 19297.89 25397.74 259
TAMVS95.49 17994.94 19197.16 13298.31 15093.41 14995.07 19696.82 25691.09 23297.51 14397.82 15189.96 23499.42 17988.42 26799.44 12798.64 194
OpenMVScopyleft94.22 895.48 18195.20 18296.32 17797.16 25591.96 17597.74 6698.84 8687.26 26594.36 24298.01 13293.95 15199.67 10590.70 23198.75 21797.35 270
CLD-MVS95.47 18295.07 18696.69 15698.27 15792.53 16091.36 29998.67 12491.22 23195.78 21294.12 29495.65 9698.98 24890.81 22599.72 5898.57 200
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
train_agg95.46 18394.66 20197.88 8397.84 19995.23 8793.62 25498.39 15787.04 26993.78 25895.99 25494.58 12999.52 15591.76 20698.90 20398.89 170
DI_MVS_plusplus_test95.46 18395.43 17795.55 22198.05 18188.84 23694.18 23095.75 27591.92 22497.32 15496.94 20591.44 21799.39 19494.81 14098.48 23698.43 211
CDPH-MVS95.45 18594.65 20297.84 8698.28 15594.96 9893.73 25098.33 16685.03 29095.44 21996.60 22795.31 10799.44 17790.01 24599.13 17999.11 142
IterMVS95.42 18695.83 16694.20 26497.52 23383.78 29492.41 28497.47 23795.49 12698.06 10698.49 8287.94 25199.58 13896.02 9499.02 19199.23 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
agg_prior195.39 18794.60 20597.75 9097.80 20694.96 9893.39 26398.36 16187.20 26793.49 26995.97 25794.65 12699.53 15191.69 20998.86 21098.77 186
Test495.39 18795.24 18195.82 21398.07 17989.60 21194.40 21998.49 14691.39 23097.40 15396.32 24487.32 25999.41 18595.09 13498.71 22398.44 210
mvs_anonymous95.36 18996.07 15993.21 27496.29 27481.56 29994.60 21597.66 22393.30 19096.95 17098.91 5793.03 17899.38 19996.60 7597.30 27498.69 192
MSDG95.33 19095.13 18495.94 20897.40 24191.85 17791.02 30298.37 16095.30 13296.31 19495.99 25494.51 13398.38 29989.59 25097.65 26197.60 264
LFMVS95.32 19194.88 19596.62 15898.03 18291.47 18497.65 7090.72 32099.11 897.89 12698.31 9579.20 28899.48 16493.91 17399.12 18298.93 165
agg_prior395.30 19294.46 21397.80 8897.80 20695.00 9693.63 25398.34 16586.33 27593.40 27695.84 26194.15 14699.50 16091.76 20698.90 20398.89 170
F-COLMAP95.30 19294.38 21598.05 7698.64 11896.04 6495.61 16398.66 12689.00 24893.22 27896.40 24192.90 18099.35 20487.45 28097.53 26598.77 186
Anonymous2023120695.27 19495.06 18895.88 21098.72 10789.37 22295.70 15597.85 21088.00 26196.98 16897.62 16791.95 20799.34 20589.21 25499.53 10298.94 163
FMVSNet395.26 19594.94 19196.22 18896.53 27090.06 20195.99 13797.66 22394.11 17897.99 11197.91 14480.22 28699.63 11794.60 14999.44 12798.96 160
N_pmnet95.18 19694.23 21898.06 7397.85 19596.55 5292.49 28291.63 31589.34 24598.09 10397.41 18090.33 22899.06 23791.58 21099.31 16198.56 201
HQP-MVS95.17 19794.58 20796.92 14497.85 19592.47 16194.26 22198.43 15293.18 19392.86 28295.08 27690.33 22899.23 22290.51 23698.74 21899.05 152
Vis-MVSNet (Re-imp)95.11 19894.85 19695.87 21199.12 7589.17 22597.54 8294.92 28396.50 9096.58 18097.27 19083.64 27699.48 16488.42 26799.67 7298.97 159
AdaColmapbinary95.11 19894.62 20496.58 16297.33 24794.45 11494.92 20498.08 19993.15 19793.98 25595.53 27094.34 13899.10 23385.69 29198.61 22996.20 298
API-MVS95.09 20095.01 18995.31 22996.61 26794.02 12896.83 10697.18 24495.60 12195.79 21194.33 29194.54 13198.37 30185.70 29098.52 23393.52 315
CNLPA95.04 20194.47 21096.75 15297.81 20295.25 8694.12 23697.89 20894.41 16694.57 23595.69 26390.30 23198.35 30286.72 28598.76 21696.64 290
Patchmtry95.03 20294.59 20696.33 17694.83 29990.82 19296.38 11797.20 24296.59 8897.49 14598.57 7577.67 29499.38 19992.95 19199.62 7998.80 182
PVSNet_BlendedMVS95.02 20394.93 19395.27 23097.79 21187.40 26494.14 23498.68 12188.94 24994.51 23898.01 13293.04 17699.30 21189.77 24899.49 11699.11 142
diffmvs95.00 20495.00 19095.01 23996.53 27087.96 25295.73 15298.32 17590.67 23691.89 29597.43 17992.07 20498.90 25595.44 11696.88 27898.16 239
TAPA-MVS93.32 1294.93 20594.23 21897.04 14098.18 16794.51 11195.22 18898.73 10981.22 30796.25 19895.95 25993.80 15898.98 24889.89 24698.87 20897.62 262
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CDS-MVSNet94.88 20694.12 22297.14 13497.64 22693.57 14493.96 24297.06 24990.05 24196.30 19596.55 22986.10 26399.47 16690.10 24499.31 16198.40 212
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
no-one94.84 20794.76 19995.09 23698.29 15287.49 26191.82 29397.49 23388.21 25797.84 13698.75 6491.51 21699.27 21688.96 25999.99 298.52 204
MS-PatchMatch94.83 20894.91 19494.57 25596.81 26587.10 26894.23 22697.34 23888.74 25197.14 16197.11 19691.94 20898.23 30692.99 19097.92 25098.37 215
pmmvs494.82 20994.19 22096.70 15597.42 24092.75 15992.09 29096.76 25886.80 27295.73 21597.22 19189.28 24398.89 25893.28 18499.14 17798.46 209
YYNet194.73 21094.84 19794.41 25997.47 23885.09 28790.29 30795.85 27492.52 21197.53 14297.76 15491.97 20699.18 22593.31 18396.86 27998.95 161
MDA-MVSNet_test_wron94.73 21094.83 19894.42 25897.48 23485.15 28590.28 30895.87 27292.52 21197.48 14897.76 15491.92 21099.17 22793.32 18296.80 28298.94 163
UnsupCasMVSNet_bld94.72 21294.26 21796.08 19798.62 12390.54 19893.38 26498.05 20390.30 23897.02 16696.80 21589.54 23799.16 22888.44 26696.18 29198.56 201
BH-untuned94.69 21394.75 20094.52 25797.95 19287.53 26094.07 23797.01 25093.99 17997.10 16395.65 26692.65 18698.95 25387.60 27796.74 28397.09 272
Patchmatch-RL test94.66 21494.49 20995.19 23298.54 13588.91 23392.57 28098.74 10891.46 22998.32 8097.75 15777.31 29998.81 26896.06 9099.61 8397.85 254
pmmvs594.63 21594.34 21695.50 22397.63 22788.34 24594.02 23897.13 24687.15 26895.22 22397.15 19487.50 25699.27 21693.99 17299.26 16998.88 174
PAPM_NR94.61 21694.17 22195.96 20598.36 14891.23 18595.93 14697.95 20592.98 20193.42 27494.43 29090.53 22698.38 29987.60 27796.29 29098.27 228
PatchMatch-RL94.61 21693.81 22897.02 14298.19 16595.72 7193.66 25297.23 24188.17 25894.94 22995.62 26791.43 21898.57 28687.36 28197.68 25896.76 286
BH-RMVSNet94.56 21894.44 21494.91 24197.57 22987.44 26393.78 24996.26 26493.69 18696.41 18996.50 23492.10 20299.00 24585.96 28897.71 25598.31 223
USDC94.56 21894.57 20894.55 25697.78 21586.43 27592.75 27698.65 13285.96 27896.91 17297.93 14290.82 22498.74 27390.71 23099.59 8798.47 207
jason94.39 22094.04 22495.41 22798.29 15287.85 25592.74 27896.75 25985.38 28895.29 22196.15 25088.21 25099.65 11194.24 16499.34 15498.74 188
jason: jason.
112194.26 22193.26 23797.27 12898.26 15994.73 10495.86 14897.71 21977.96 32094.53 23796.71 22091.93 20999.40 18887.71 27398.64 22797.69 260
EU-MVSNet94.25 22294.47 21093.60 26898.14 17482.60 29797.24 9392.72 30785.08 28998.48 6798.94 5482.59 27998.76 27297.47 5699.53 10299.44 79
xiu_mvs_v2_base94.22 22394.63 20392.99 27897.32 24884.84 29092.12 28897.84 21191.96 22294.17 24593.43 29796.07 7999.71 7791.27 21497.48 26794.42 311
RPMNet94.22 22394.03 22594.78 24795.44 29288.15 24896.18 12893.73 29197.43 6994.10 24898.49 8279.40 28799.39 19495.69 10395.81 29396.81 284
sss94.22 22393.72 22995.74 21597.71 21989.95 20593.84 24696.98 25188.38 25693.75 26095.74 26287.94 25198.89 25891.02 21898.10 24598.37 215
MVSTER94.21 22693.93 22795.05 23895.83 28586.46 27495.18 18997.65 22592.41 21697.94 11898.00 13472.39 31499.58 13896.36 8499.56 9499.12 139
MAR-MVS94.21 22693.03 24197.76 8996.94 26197.44 3096.97 10497.15 24587.89 26392.00 29392.73 30792.14 20099.12 22983.92 30497.51 26696.73 287
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
1112_ss94.12 22893.42 23396.23 18498.59 12790.85 19094.24 22598.85 8385.49 28392.97 28094.94 28086.01 26499.64 11491.78 20597.92 25098.20 235
PS-MVSNAJ94.10 22994.47 21093.00 27797.35 24384.88 28991.86 29297.84 21191.96 22294.17 24592.50 30995.82 8799.71 7791.27 21497.48 26794.40 312
CHOSEN 1792x268894.10 22993.41 23496.18 19099.16 6390.04 20292.15 28798.68 12179.90 31296.22 19997.83 14887.92 25499.42 17989.18 25599.65 7599.08 147
MG-MVS94.08 23194.00 22694.32 26197.09 25785.89 27693.19 27095.96 27092.52 21194.93 23097.51 17489.54 23798.77 27187.52 27997.71 25598.31 223
PLCcopyleft91.02 1694.05 23292.90 24497.51 10798.00 18695.12 9494.25 22498.25 17986.17 27691.48 29995.25 27491.01 22299.19 22485.02 29896.69 28498.22 233
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
114514_t93.96 23393.22 23996.19 18999.06 8190.97 18995.99 13798.94 7173.88 32693.43 27396.93 20792.38 19799.37 20289.09 25699.28 16698.25 230
PVSNet_Blended93.96 23393.65 23094.91 24197.79 21187.40 26491.43 29898.68 12184.50 29494.51 23894.48 28993.04 17699.30 21189.77 24898.61 22998.02 249
MVS_dtu93.94 23593.30 23595.86 21294.60 30289.39 22193.96 24296.81 25792.57 21087.83 31995.66 26584.98 27299.60 13594.25 16399.02 19198.60 198
lupinMVS93.77 23693.28 23695.24 23197.68 22187.81 25692.12 28896.05 26684.52 29394.48 24095.06 27886.90 26099.63 11793.62 18099.13 17998.27 228
PatchT93.75 23793.57 23294.29 26395.05 29787.32 26696.05 13392.98 30297.54 6494.25 24398.72 6675.79 30799.24 22095.92 9995.81 29396.32 296
EPNet93.72 23892.62 25197.03 14187.61 33392.25 16596.27 12291.28 31796.74 8587.65 32097.39 18485.00 27199.64 11492.14 19899.48 11999.20 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HyFIR lowres test93.72 23892.65 25096.91 14698.93 9191.81 17991.23 30198.52 14282.69 30096.46 18796.52 23380.38 28599.90 1390.36 24198.79 21399.03 153
PMMVS293.66 24094.07 22392.45 28597.57 22980.67 30386.46 32096.00 26793.99 17997.10 16397.38 18689.90 23597.82 31388.76 26199.47 12198.86 177
OpenMVS_ROBcopyleft91.80 1493.64 24193.05 24095.42 22597.31 24991.21 18695.08 19596.68 26281.56 30496.88 17496.41 23990.44 22799.25 21985.39 29597.67 25995.80 301
Patchmatch-test93.60 24293.25 23894.63 25096.14 28087.47 26296.04 13494.50 28793.57 18796.47 18696.97 20376.50 30298.61 28490.67 23298.41 23997.81 257
WTY-MVS93.55 24393.00 24295.19 23297.81 20287.86 25493.89 24496.00 26789.02 24794.07 25095.44 27186.27 26299.33 20887.69 27596.82 28098.39 214
MVS_test032693.54 24492.94 24395.35 22894.17 31288.28 24693.48 26095.99 26992.45 21486.60 32395.26 27385.37 26799.53 15194.11 16798.99 19598.23 231
Test_1112_low_res93.53 24592.86 24595.54 22298.60 12588.86 23592.75 27698.69 11982.66 30192.65 28796.92 20884.75 27399.56 14390.94 22197.76 25498.19 236
MIMVSNet93.42 24692.86 24595.10 23598.17 16988.19 24798.13 4393.69 29292.07 21895.04 22798.21 10880.95 28399.03 24281.42 31298.06 24698.07 243
FMVSNet593.39 24792.35 25396.50 16795.83 28590.81 19397.31 8898.27 17692.74 20996.27 19698.28 10062.23 32799.67 10590.86 22399.36 14999.03 153
Patchmatch-test193.38 24893.59 23192.73 28196.24 27581.40 30093.24 26894.00 29091.58 22894.57 23596.67 22387.94 25199.03 24290.42 23997.66 26097.77 258
CR-MVSNet93.29 24992.79 24794.78 24795.44 29288.15 24896.18 12897.20 24284.94 29194.10 24898.57 7577.67 29499.39 19495.17 12895.81 29396.81 284
wuyk23d93.25 25095.20 18287.40 31296.07 28195.38 8397.04 10194.97 28295.33 13199.70 698.11 12298.14 1491.94 32877.76 31899.68 7074.89 326
LP93.12 25192.78 24994.14 26594.50 30585.48 28095.73 15295.68 27792.97 20595.05 22697.17 19381.93 28099.40 18893.06 18988.96 32097.55 265
test123567892.95 25292.40 25294.61 25196.95 26086.87 27090.75 30497.75 21591.00 23496.33 19195.38 27285.21 26998.92 25479.00 31699.20 17398.03 247
X-MVStestdata92.86 25390.83 27098.94 1599.15 6697.66 1697.77 6198.83 9297.42 7096.32 19236.50 33096.49 6899.72 6795.66 10699.37 14699.45 71
GA-MVS92.83 25492.15 25694.87 24496.97 25987.27 26790.03 30996.12 26591.83 22694.05 25194.57 28576.01 30698.97 25292.46 19597.34 27298.36 220
CMPMVSbinary73.10 2392.74 25591.39 26296.77 15193.57 31894.67 10894.21 22997.67 22180.36 31193.61 26596.60 22782.85 27897.35 31784.86 29998.78 21498.29 227
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
HY-MVS91.43 1592.58 25691.81 26094.90 24396.49 27288.87 23497.31 8894.62 28585.92 27990.50 30796.84 21185.05 27099.40 18883.77 30795.78 29696.43 295
TR-MVS92.54 25792.20 25593.57 26996.49 27286.66 27293.51 25894.73 28489.96 24294.95 22893.87 29590.24 23398.61 28481.18 31394.88 30295.45 307
PMMVS92.39 25891.08 26696.30 18093.12 32192.81 15890.58 30695.96 27079.17 31591.85 29692.27 31090.29 23298.66 28389.85 24796.68 28597.43 268
131492.38 25992.30 25492.64 28395.42 29485.15 28595.86 14896.97 25285.40 28790.62 30393.06 30191.12 22197.80 31486.74 28495.49 30194.97 309
new_pmnet92.34 26091.69 26194.32 26196.23 27789.16 22692.27 28692.88 30484.39 29695.29 22196.35 24385.66 26596.74 32384.53 30197.56 26397.05 274
CVMVSNet92.33 26192.79 24790.95 29897.26 25075.84 31995.29 18392.33 31081.86 30296.27 19698.19 10981.44 28198.46 29394.23 16598.29 24098.55 203
PAPR92.22 26291.27 26595.07 23795.73 28888.81 23791.97 29197.87 20985.80 28190.91 30192.73 30791.16 22098.33 30379.48 31495.76 29798.08 241
DSMNet-mixed92.19 26391.83 25993.25 27396.18 27983.68 29596.27 12293.68 29476.97 32392.54 28999.18 3589.20 24598.55 28983.88 30598.60 23197.51 267
BH-w/o92.14 26491.94 25792.73 28197.13 25685.30 28292.46 28395.64 27889.33 24694.21 24492.74 30689.60 23698.24 30581.68 31194.66 30494.66 310
PCF-MVS89.43 1892.12 26590.64 27396.57 16497.80 20693.48 14889.88 31398.45 14974.46 32596.04 20595.68 26490.71 22599.31 21073.73 32199.01 19496.91 280
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchmatchNetpermissive91.98 26691.87 25892.30 28794.60 30279.71 30595.12 19093.59 29789.52 24493.61 26597.02 20177.94 29299.18 22590.84 22494.57 30698.01 250
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas91.89 26791.35 26393.51 27094.27 30885.60 27888.86 31698.61 13579.32 31492.16 29291.44 31489.22 24498.12 30990.80 22697.47 26996.82 283
JIA-IIPM91.79 26890.69 27295.11 23493.80 31590.98 18894.16 23291.78 31496.38 9490.30 30999.30 2372.02 31698.90 25588.28 26990.17 31795.45 307
ADS-MVSNet291.47 26990.51 27594.36 26095.51 29085.63 27795.05 19995.70 27683.46 29892.69 28596.84 21179.15 28999.41 18585.66 29290.52 31598.04 245
EPNet_dtu91.39 27090.75 27193.31 27290.48 33182.61 29694.80 21092.88 30493.39 18981.74 32994.90 28381.36 28299.11 23288.28 26998.87 20898.21 234
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet86.72 1991.10 27190.97 26791.49 29297.56 23178.04 31287.17 31894.60 28684.65 29292.34 29092.20 31187.37 25898.47 29285.17 29797.69 25797.96 251
tpm91.08 27290.85 26991.75 29195.33 29578.09 31095.03 20191.27 31888.75 25093.53 26897.40 18171.24 31799.30 21191.25 21693.87 30797.87 253
ADS-MVSNet90.95 27390.26 27793.04 27595.51 29082.37 29895.05 19993.41 29883.46 29892.69 28596.84 21179.15 28998.70 27785.66 29290.52 31598.04 245
testus90.90 27490.51 27592.06 28996.07 28179.45 30688.99 31498.44 15185.46 28594.15 24790.77 31789.12 24698.01 31273.66 32297.95 24998.71 191
tpmvs90.79 27590.87 26890.57 30192.75 32576.30 31795.79 15193.64 29591.04 23391.91 29496.26 24577.19 30098.86 26489.38 25389.85 31896.56 293
tpmrst90.31 27690.61 27489.41 30594.06 31372.37 32695.06 19893.69 29288.01 26092.32 29196.86 20977.45 29698.82 26691.04 21787.01 32397.04 275
test0.0.03 190.11 27789.21 28492.83 28093.89 31486.87 27091.74 29488.74 32592.02 21994.71 23391.14 31673.92 31194.48 32783.75 30892.94 30997.16 271
MVS90.02 27889.20 28592.47 28494.71 30086.90 26995.86 14896.74 26064.72 32890.62 30392.77 30592.54 19198.39 29779.30 31595.56 30092.12 319
pmmvs390.00 27988.90 28893.32 27194.20 31185.34 28191.25 30092.56 30978.59 31793.82 25795.17 27567.36 32598.69 27889.08 25798.03 24795.92 299
CHOSEN 280x42089.98 28089.19 28692.37 28695.60 28981.13 30186.22 32197.09 24881.44 30687.44 32193.15 29973.99 31099.47 16688.69 26399.07 18796.52 294
test-LLR89.97 28189.90 27990.16 30294.24 30974.98 32089.89 31089.06 32392.02 21989.97 31090.77 31773.92 31198.57 28691.88 20297.36 27096.92 278
FPMVS89.92 28288.63 28993.82 26698.37 14796.94 4191.58 29593.34 29988.00 26190.32 30897.10 19770.87 31991.13 32971.91 32596.16 29293.39 317
CostFormer89.75 28389.25 28291.26 29594.69 30178.00 31395.32 18091.98 31281.50 30590.55 30596.96 20471.06 31898.89 25888.59 26592.63 31296.87 281
PatchFormer-LS_test89.62 28489.12 28791.11 29793.62 31678.42 30994.57 21793.62 29688.39 25590.54 30688.40 32472.33 31599.03 24292.41 19688.20 32195.89 300
E-PMN89.52 28589.78 28088.73 30793.14 32077.61 31483.26 32592.02 31194.82 15393.71 26193.11 30075.31 30896.81 32185.81 28996.81 28191.77 321
EPMVS89.26 28688.55 29091.39 29392.36 32679.11 30795.65 15979.86 33088.60 25293.12 27996.53 23170.73 32098.10 31090.75 22889.32 31996.98 276
EMVS89.06 28789.22 28388.61 30893.00 32277.34 31582.91 32690.92 31994.64 15792.63 28891.81 31376.30 30497.02 31983.83 30696.90 27791.48 322
111188.78 28889.39 28186.96 31398.53 13762.84 33191.49 29697.48 23594.45 16396.56 18296.45 23643.83 33798.87 26286.33 28699.40 14499.18 126
IB-MVS85.98 2088.63 28986.95 29893.68 26795.12 29684.82 29190.85 30390.17 32287.55 26488.48 31691.34 31558.01 32999.59 13687.24 28293.80 30896.63 292
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
tpm288.47 29087.69 29390.79 29994.98 29877.34 31595.09 19391.83 31377.51 32289.40 31296.41 23967.83 32498.73 27483.58 30992.60 31396.29 297
tpmp4_e2388.46 29187.54 29491.22 29694.56 30478.08 31195.63 16293.17 30079.08 31685.85 32496.80 21565.86 32698.85 26584.10 30392.85 31096.72 288
MVS-HIRNet88.40 29290.20 27882.99 31697.01 25860.04 33393.11 27185.61 32784.45 29588.72 31599.09 4584.72 27498.23 30682.52 31096.59 28690.69 324
gg-mvs-nofinetune88.28 29386.96 29792.23 28892.84 32484.44 29298.19 4074.60 33299.08 987.01 32299.47 856.93 33098.23 30678.91 31795.61 29994.01 313
dp88.08 29488.05 29288.16 31192.85 32368.81 32894.17 23192.88 30485.47 28491.38 30096.14 25268.87 32398.81 26886.88 28383.80 32796.87 281
tpm cat188.01 29587.33 29590.05 30494.48 30676.28 31894.47 21894.35 28973.84 32789.26 31395.61 26873.64 31398.30 30484.13 30286.20 32495.57 306
test1235687.98 29688.41 29186.69 31495.84 28463.49 33087.15 31997.32 23987.21 26691.78 29793.36 29870.66 32198.39 29774.70 32097.64 26298.19 236
test-mter87.92 29787.17 29690.16 30294.24 30974.98 32089.89 31089.06 32386.44 27489.97 31090.77 31754.96 33398.57 28691.88 20297.36 27096.92 278
DWT-MVSNet_test87.92 29786.77 29991.39 29393.18 31978.62 30895.10 19191.42 31685.58 28288.00 31788.73 32360.60 32898.90 25590.60 23387.70 32296.65 289
PAPM87.64 29985.84 30293.04 27596.54 26984.99 28888.42 31795.57 28079.52 31383.82 32693.05 30280.57 28498.41 29562.29 32992.79 31195.71 302
TESTMET0.1,187.20 30086.57 30089.07 30693.62 31672.84 32589.89 31087.01 32685.46 28589.12 31490.20 32156.00 33297.72 31590.91 22296.92 27696.64 290
MVEpermissive73.61 2286.48 30185.92 30188.18 31096.23 27785.28 28381.78 32875.79 33186.01 27782.53 32891.88 31292.74 18387.47 33171.42 32694.86 30391.78 320
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test235685.45 30283.26 30592.01 29091.12 32880.76 30285.16 32292.90 30383.90 29790.63 30287.71 32653.10 33497.24 31869.20 32795.65 29898.03 247
PVSNet_081.89 2184.49 30383.21 30688.34 30995.76 28774.97 32283.49 32492.70 30878.47 31887.94 31886.90 32783.38 27796.63 32473.44 32366.86 33093.40 316
PNet_i23d83.82 30483.39 30485.10 31596.07 28165.16 32981.87 32794.37 28890.87 23593.92 25692.89 30452.80 33596.44 32577.52 31970.22 32993.70 314
testpf82.70 30584.35 30377.74 31788.97 33273.23 32493.85 24584.33 32888.10 25985.06 32590.42 32052.62 33691.05 33091.00 21984.82 32668.93 327
.test124573.49 30679.27 30756.15 31998.53 13762.84 33191.49 29697.48 23594.45 16396.56 18296.45 23643.83 33798.87 26286.33 2868.32 3326.75 330
tmp_tt57.23 30762.50 30841.44 32034.77 33449.21 33583.93 32360.22 33615.31 33071.11 33179.37 32970.09 32244.86 33364.76 32882.93 32830.25 328
pcd1.5k->3k41.47 30844.19 30933.29 32199.65 110.00 3380.00 32999.07 340.00 3330.00 3340.00 33599.04 40.00 3360.00 33399.96 1199.87 2
cdsmvs_eth3d_5k24.22 30932.30 3100.00 3240.00 3370.00 3380.00 32998.10 1960.00 3330.00 33495.06 27897.54 280.00 3360.00 3330.00 3340.00 332
test12312.59 31015.49 3113.87 3226.07 3352.55 33690.75 3042.59 3382.52 3315.20 33313.02 3324.96 3391.85 3355.20 3319.09 3317.23 329
testmvs12.33 31115.23 3123.64 3235.77 3362.23 33788.99 3143.62 3372.30 3325.29 33213.09 3314.52 3401.95 3345.16 3328.32 3326.75 330
pcd_1.5k_mvsjas7.98 31210.65 3130.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.00 33595.82 870.00 3360.00 3330.00 3340.00 332
ab-mvs-re7.91 31310.55 3140.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 33494.94 2800.00 3410.00 3360.00 3330.00 3340.00 332
sosnet-low-res0.00 3140.00 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.00 3350.00 3410.00 3360.00 3330.00 3340.00 332
sosnet0.00 3140.00 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.00 3350.00 3410.00 3360.00 3330.00 3340.00 332
uncertanet0.00 3140.00 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.00 3350.00 3410.00 3360.00 3330.00 3340.00 332
Regformer0.00 3140.00 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.00 3350.00 3410.00 3360.00 3330.00 3340.00 332
uanet0.00 3140.00 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.00 3350.00 3410.00 3360.00 3330.00 3340.00 332
sam_mvs177.80 293
sam_mvs77.38 297
semantic-postprocess94.85 24597.68 22185.53 27997.63 22996.99 7998.36 7598.54 7987.44 25799.75 5297.07 6999.08 18599.27 118
ambc96.56 16598.23 16291.68 18197.88 5798.13 19498.42 7298.56 7794.22 14399.04 23994.05 17199.35 15298.95 161
MTGPAbinary98.73 109
test_post194.98 20310.37 33476.21 30599.04 23989.47 252
test_post10.87 33376.83 30199.07 236
patchmatchnet-post96.84 21177.36 29899.42 179
GG-mvs-BLEND90.60 30091.00 32984.21 29398.23 3472.63 33582.76 32784.11 32856.14 33196.79 32272.20 32492.09 31490.78 323
MTMP74.60 332
gm-plane-assit91.79 32771.40 32781.67 30390.11 32298.99 24684.86 299
test9_res91.29 21398.89 20799.00 155
TEST997.84 19995.23 8793.62 25498.39 15786.81 27193.78 25895.99 25494.68 12499.52 155
test_897.81 20295.07 9593.54 25798.38 15987.04 26993.71 26195.96 25894.58 12999.52 155
agg_prior290.34 24298.90 20399.10 146
agg_prior97.80 20694.96 9898.36 16193.49 26999.53 151
TestCases98.06 7399.08 7896.16 6199.16 1694.35 16997.78 13898.07 12595.84 8499.12 22991.41 21199.42 13798.91 168
test_prior495.38 8393.61 256
test_prior293.33 26694.21 17494.02 25296.25 24693.64 16191.90 20098.96 197
test_prior97.46 11697.79 21194.26 12198.42 15599.34 20598.79 183
旧先验293.35 26577.95 32195.77 21498.67 28290.74 229
新几何293.43 261
新几何197.25 13198.29 15294.70 10797.73 21777.98 31994.83 23196.67 22392.08 20399.45 17488.17 27198.65 22697.61 263
旧先验197.80 20693.87 13297.75 21597.04 20093.57 16398.68 22498.72 190
无先验93.20 26997.91 20680.78 30899.40 18887.71 27397.94 252
原ACMM292.82 274
原ACMM196.58 16298.16 17192.12 17098.15 19285.90 28093.49 26996.43 23892.47 19599.38 19987.66 27698.62 22898.23 231
test22298.17 16993.24 15392.74 27897.61 23175.17 32494.65 23496.69 22290.96 22398.66 22597.66 261
testdata299.46 17087.84 272
segment_acmp95.34 105
testdata95.70 21898.16 17190.58 19597.72 21880.38 31095.62 21797.02 20192.06 20598.98 24889.06 25898.52 23397.54 266
testdata192.77 27593.78 183
test1297.46 11697.61 22894.07 12697.78 21493.57 26793.31 17199.42 17998.78 21498.89 170
plane_prior798.70 11294.67 108
plane_prior698.38 14694.37 11791.91 211
plane_prior598.75 10699.46 17092.59 19399.20 17399.28 115
plane_prior496.77 217
plane_prior394.51 11195.29 13396.16 202
plane_prior296.50 11396.36 95
plane_prior198.49 141
plane_prior94.29 11895.42 17194.31 17198.93 202
n20.00 339
nn0.00 339
door-mid98.17 189
lessismore_v097.05 13999.36 4592.12 17084.07 32998.77 4998.98 5085.36 26899.74 5797.34 5999.37 14699.30 108
LGP-MVS_train98.74 3299.15 6697.02 3899.02 5095.15 14198.34 7798.23 10497.91 1999.70 8494.41 15499.73 5599.50 50
test1198.08 199
door97.81 213
HQP5-MVS92.47 161
HQP-NCC97.85 19594.26 22193.18 19392.86 282
ACMP_Plane97.85 19594.26 22193.18 19392.86 282
BP-MVS90.51 236
HQP4-MVS92.87 28199.23 22299.06 151
HQP3-MVS98.43 15298.74 218
HQP2-MVS90.33 228
NP-MVS98.14 17493.72 13895.08 276
MDTV_nov1_ep13_2view57.28 33494.89 20580.59 30994.02 25278.66 29185.50 29497.82 256
MDTV_nov1_ep1391.28 26494.31 30773.51 32394.80 21093.16 30186.75 27393.45 27297.40 18176.37 30398.55 28988.85 26096.43 287
ACMMP++_ref99.52 106
ACMMP++99.55 98
Test By Simon94.51 133
ITE_SJBPF97.85 8598.64 11896.66 4898.51 14595.63 11997.22 15797.30 18995.52 9898.55 28990.97 22098.90 20398.34 221
DeepMVS_CXcopyleft77.17 31890.94 33085.28 28374.08 33452.51 32980.87 33088.03 32575.25 30970.63 33259.23 33084.94 32575.62 325