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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
Anonymous2023121199.83 199.80 199.86 199.97 199.87 199.90 199.92 199.76 199.82 299.79 3799.98 199.63 1299.84 399.78 399.94 199.61 6
WR-MVS99.61 1099.44 1199.82 399.92 599.80 299.80 899.89 298.54 1999.66 1599.78 4099.16 8699.68 1099.70 699.63 699.94 199.49 16
SixPastTwentyTwo99.70 499.59 799.82 399.93 399.80 299.86 399.87 798.87 1499.79 599.85 2799.33 6399.74 799.85 299.82 199.74 2299.63 4
DTE-MVSNet99.52 1399.27 1999.82 399.93 399.77 499.79 1099.87 797.89 4599.70 1199.55 6299.21 7899.77 299.65 1099.43 2399.90 399.36 21
PEN-MVS99.54 1199.30 1899.83 299.92 599.76 599.80 899.88 497.60 6699.71 699.59 5599.52 4399.75 699.64 1299.51 1999.90 399.46 17
WR-MVS_H99.48 1599.23 2199.76 999.91 999.76 599.75 1599.88 497.27 8899.58 2099.56 5999.24 7299.56 1899.60 1599.60 1499.88 899.58 7
PS-CasMVS99.50 1499.23 2199.82 399.92 599.75 799.78 1199.89 297.30 8599.71 699.60 5399.23 7499.71 999.65 1099.55 1899.90 399.56 8
v7n99.68 599.61 499.76 999.89 1499.74 899.87 299.82 1499.20 699.71 699.96 199.73 1299.76 599.58 1799.59 1599.52 4399.46 17
LTVRE_ROB98.82 199.76 299.75 299.77 899.87 1799.71 999.77 1299.76 2299.52 399.80 399.79 3799.91 299.56 1899.83 499.75 499.86 999.75 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
CP-MVSNet99.39 2099.04 2999.80 799.91 999.70 1099.75 1599.88 496.82 10799.68 1299.32 7698.86 12099.68 1099.57 2199.47 2199.89 699.52 10
v5299.67 699.59 799.76 999.91 999.69 1199.85 499.79 1699.12 999.68 1299.95 299.72 1499.77 299.58 1799.61 1199.54 3899.50 13
V499.67 699.60 699.76 999.91 999.69 1199.85 499.79 1699.13 899.68 1299.95 299.72 1499.77 299.58 1799.61 1199.54 3899.50 13
v74899.67 699.61 499.75 1399.87 1799.68 1399.84 699.79 1699.14 799.64 1799.89 1299.88 599.72 899.58 1799.57 1799.62 3099.50 13
MIMVSNet199.46 1799.34 1399.60 1999.83 2399.68 1399.74 1899.71 3398.20 2799.41 3599.86 2299.66 2799.41 4299.50 2399.39 2599.50 4999.10 41
pmmvs699.74 399.75 299.73 1599.92 599.67 1599.76 1499.84 1199.59 299.52 2799.87 1899.91 299.43 3999.87 199.81 299.89 699.52 10
UA-Net99.30 2499.22 2399.39 4699.94 299.66 1698.91 12299.86 997.74 5598.74 12399.00 10299.60 3899.17 7199.50 2399.39 2599.70 2399.64 2
FC-MVSNet-test99.32 2399.33 1499.31 6599.87 1799.65 1799.63 2999.75 2597.76 5097.29 20299.87 1899.63 3399.52 2499.66 999.63 699.77 1999.12 37
TransMVSNet (Re)99.45 1899.32 1699.61 1799.88 1699.60 1899.75 1599.63 4799.11 1099.28 5799.83 3198.35 14099.27 6299.70 699.62 1099.84 1099.03 48
EPP-MVSNet98.61 9598.19 10499.11 8799.86 2299.60 1899.44 6399.53 7097.37 8396.85 21098.69 11293.75 18699.18 6899.22 3699.35 2999.82 1399.32 23
v1399.22 2998.99 3199.49 3199.68 6699.58 2099.67 2099.77 2198.10 2999.36 3899.88 1399.37 5799.54 2298.50 8298.51 8998.92 12199.03 48
FC-MVSNet-train99.13 3799.05 2899.21 7499.87 1799.57 2199.67 2099.60 5496.75 11498.28 15599.48 6799.52 4398.10 13599.47 2699.37 2799.76 2199.21 32
conf0.05thres100097.44 16095.93 18199.20 7799.82 2599.56 2299.41 6499.61 5297.42 7998.01 17094.34 20582.73 22098.68 10199.33 3299.42 2499.67 2798.74 84
anonymousdsp99.64 999.55 999.74 1499.87 1799.56 2299.82 799.73 2898.54 1999.71 699.92 699.84 799.61 1399.70 699.63 699.69 2699.64 2
v1299.19 3198.95 3299.48 3299.67 6999.56 2299.66 2299.76 2298.06 3199.33 4399.88 1399.34 6299.53 2398.42 8998.43 9498.91 12498.97 55
Baseline_NR-MVSNet99.18 3498.87 4199.54 2699.74 5099.56 2299.36 7199.62 5196.53 12899.29 5299.85 2798.64 13499.40 4399.03 5399.63 699.83 1198.86 69
tfpnnormal99.19 3198.90 3999.54 2699.81 2899.55 2699.60 3599.54 6698.53 2199.23 6198.40 12098.23 14399.40 4399.29 3399.36 2899.63 2998.95 61
Vis-MVSNetpermissive99.25 2699.32 1699.17 7999.65 7999.55 2699.63 2999.33 11998.16 2899.29 5299.65 4999.77 897.56 15399.44 2899.14 3999.58 3599.51 12
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1199.19 3198.95 3299.47 3399.66 7399.54 2899.65 2399.73 2898.06 3199.38 3799.92 699.40 5499.55 2098.29 10298.50 9098.88 12998.92 64
V999.16 3598.90 3999.46 3499.66 7399.54 2899.65 2399.75 2598.01 3499.31 4799.87 1899.31 6699.51 2598.34 9698.34 9798.90 12698.91 65
V1499.13 3798.85 4499.45 3599.65 7999.52 3099.63 2999.74 2797.97 3699.30 5099.87 1899.27 7099.49 2998.23 10898.24 10098.88 12998.83 70
v1599.09 4098.79 4799.43 3999.64 8799.50 3199.61 3399.73 2897.92 4099.28 5799.86 2299.24 7299.47 3198.12 11998.14 10598.87 13198.76 81
pm-mvs199.47 1699.38 1299.57 2299.82 2599.49 3299.63 2999.65 4398.88 1399.31 4799.85 2799.02 11299.23 6599.60 1599.58 1699.80 1599.22 31
FMVSNet198.90 6199.10 2798.67 13799.54 11099.48 3399.22 9099.66 4198.39 2597.50 19099.66 4599.04 11096.58 17399.05 4899.03 4999.52 4399.08 43
IS_MVSNet98.20 12598.00 11698.44 15299.82 2599.48 3399.25 8699.56 5895.58 15593.93 23297.56 14996.52 17298.27 12799.08 4699.20 3699.80 1598.56 100
gm-plane-assit94.62 20891.39 21798.39 15699.90 1399.47 3599.40 6699.65 4397.44 7799.56 2399.68 4459.40 24194.23 20996.17 19694.77 20797.61 19192.79 216
TranMVSNet+NR-MVSNet99.23 2798.91 3899.61 1799.81 2899.45 3699.47 5899.68 3797.28 8799.39 3699.54 6399.08 10799.45 3499.09 4398.84 6199.83 1199.04 46
CSCG99.23 2799.15 2599.32 6499.83 2399.45 3698.97 11499.21 13998.83 1599.04 9399.43 7199.64 3199.26 6398.85 6598.20 10399.62 3099.62 5
ACMH97.81 699.44 1999.33 1499.56 2399.81 2899.42 3899.73 1999.58 5599.02 1199.10 8199.41 7399.69 1999.60 1499.45 2799.26 3599.55 3799.05 45
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1798.96 5198.63 5899.35 6099.54 11099.41 3999.55 4399.70 3497.40 8099.10 8199.79 3799.10 10199.40 4397.96 12697.99 11398.80 14598.77 80
v1698.95 5298.62 5999.34 6299.53 11799.41 3999.54 4799.70 3497.34 8499.07 8799.76 4199.10 10199.40 4397.96 12698.00 11298.79 14798.76 81
v898.94 5398.60 6099.35 6099.54 11099.39 4199.55 4399.67 4097.48 7399.13 7699.81 3299.10 10199.39 5397.86 13697.89 12198.81 14098.66 91
v1099.01 4598.66 5799.41 4299.52 12299.39 4199.57 3899.66 4197.59 6799.32 4599.88 1399.23 7499.50 2797.77 14797.98 11598.92 12198.78 79
IterMVS-LS98.23 12297.66 12998.90 11099.63 9299.38 4399.07 10599.48 8197.75 5398.81 11899.37 7594.57 18497.88 14596.54 19297.04 17398.53 16798.97 55
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1898.89 6398.54 6599.30 6699.50 12599.37 4499.51 5299.68 3797.25 9299.00 9699.76 4199.04 11099.36 5597.81 14397.86 12698.77 15098.68 90
TDRefinement99.54 1199.50 1099.60 1999.70 6299.35 4599.77 1299.58 5599.40 599.28 5799.66 4599.41 5199.55 2099.74 599.65 599.70 2399.25 27
UniMVSNet_NR-MVSNet98.97 4998.46 7399.56 2399.76 4399.34 4699.29 7999.61 5296.55 12699.55 2499.05 9697.96 15499.36 5598.84 6698.50 9099.81 1498.97 55
PVSNet_Blended_VisFu98.98 4898.79 4799.21 7499.76 4399.34 4699.35 7299.35 11697.12 9999.46 3299.56 5998.89 11898.08 13899.05 4898.58 8599.27 8798.98 54
tfpn94.97 20491.60 21698.90 11099.73 5599.33 4899.11 10399.51 7395.05 16397.19 20789.03 22162.62 23898.37 12098.53 8098.97 5399.48 5297.70 154
HSP-MVS98.50 10698.05 11399.03 9799.67 6999.33 4899.51 5299.26 13195.28 15998.51 13698.19 12999.74 1198.29 12597.69 15296.70 17998.96 11499.41 20
test20.0398.84 7398.74 5098.95 10799.77 3799.33 4899.21 9299.46 8597.29 8698.88 11499.65 4999.10 10197.07 16799.11 4098.76 6999.32 8097.98 146
UniMVSNet (Re)99.08 4198.69 5599.54 2699.75 4699.33 4899.29 7999.64 4696.75 11499.48 3199.30 7898.69 12899.26 6398.94 5998.76 6999.78 1899.02 51
tfpn100097.10 16995.97 17998.41 15499.64 8799.30 5298.89 12699.49 8096.49 12995.97 21795.31 19385.62 21396.92 16997.86 13699.13 4199.53 4298.11 137
Vis-MVSNet (Re-imp)98.46 11198.23 10298.73 12999.81 2899.29 5398.79 13599.50 7696.20 14196.03 21598.29 12596.98 16898.54 11299.11 4099.08 4399.70 2398.62 93
APDe-MVS99.15 3698.95 3299.39 4699.77 3799.28 5499.52 5199.54 6697.22 9399.06 8899.20 8699.64 3199.05 8299.14 3899.02 5299.39 6999.17 35
view80096.48 18094.42 19298.87 11499.70 6299.26 5599.05 10699.45 8994.77 17297.32 19988.21 22283.40 21898.28 12698.37 9299.33 3099.44 5697.58 159
UGNet98.52 10599.00 3097.96 18099.58 10099.26 5599.27 8399.40 9198.07 3098.28 15598.76 11099.71 1892.24 22398.94 5998.85 5999.00 11299.43 19
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
tfpnview1197.49 15796.22 17498.97 10599.63 9299.24 5799.12 10299.54 6696.76 11297.77 17794.60 20087.78 19998.25 13097.93 12999.14 3999.52 4398.08 140
Gipumacopyleft99.22 2998.86 4299.64 1699.70 6299.24 5799.17 9699.63 4799.52 399.89 196.54 17899.14 9299.93 199.42 2999.15 3899.52 4399.04 46
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testgi98.18 12898.44 8397.89 18199.78 3599.23 5998.78 13699.21 13997.26 9097.41 19297.39 15499.36 6192.85 21998.82 6898.66 8099.31 8198.35 113
DU-MVS99.04 4398.59 6199.56 2399.74 5099.23 5999.29 7999.63 4796.58 12299.55 2499.05 9698.68 13099.36 5599.03 5398.60 8399.77 1998.97 55
NR-MVSNet99.10 3998.68 5699.58 2199.89 1499.23 5999.35 7299.63 4796.58 12299.36 3899.05 9698.67 13299.46 3299.63 1398.73 7399.80 1598.88 68
ACMH+97.53 799.29 2599.20 2499.40 4599.81 2899.22 6299.59 3699.50 7698.64 1898.29 15499.21 8599.69 1999.57 1699.53 2299.33 3099.66 2898.81 74
ESAPD98.60 9798.41 8698.83 11999.56 10599.21 6398.66 14999.47 8295.22 16098.35 14998.48 11899.67 2697.84 14898.80 7098.57 8799.10 9798.93 63
canonicalmvs98.34 11697.92 12098.83 11999.45 13199.21 6398.37 17399.53 7097.06 10197.74 18196.95 16995.05 18298.36 12198.77 7298.85 5999.51 4899.53 9
GBi-Net97.69 14597.75 12697.62 18798.71 19999.21 6398.62 15299.33 11994.09 18995.60 22198.17 13195.97 17694.39 20599.05 4899.03 4999.08 10298.70 87
test197.69 14597.75 12697.62 18798.71 19999.21 6398.62 15299.33 11994.09 18995.60 22198.17 13195.97 17694.39 20599.05 4899.03 4999.08 10298.70 87
FMVSNet297.94 13498.08 11197.77 18698.71 19999.21 6398.62 15299.47 8296.62 11996.37 21499.20 8697.70 15894.39 20597.39 17297.75 13899.08 10298.70 87
tfpn_n40097.59 15296.36 17099.01 10199.66 7399.19 6899.21 9299.55 6097.62 6397.77 17794.60 20087.78 19998.27 12798.44 8598.72 7499.62 3098.21 128
tfpnconf97.59 15296.36 17099.01 10199.66 7399.19 6899.21 9299.55 6097.62 6397.77 17794.60 20087.78 19998.27 12798.44 8598.72 7499.62 3098.21 128
tfpn11196.48 18094.67 19198.59 14399.37 14099.18 7098.68 14299.39 9392.02 21497.21 20490.63 21886.34 20797.45 15598.15 11599.08 4399.43 5897.28 169
conf200view1196.16 19394.08 19798.59 14399.37 14099.18 7098.68 14299.39 9392.02 21497.21 20486.53 22886.34 20797.45 15598.15 11599.08 4399.43 5897.28 169
tfpn200view996.17 19194.08 19798.60 14299.37 14099.18 7098.68 14299.39 9392.02 21497.30 20086.53 22886.34 20797.45 15598.15 11599.08 4399.43 5897.28 169
thres600view796.35 18594.27 19498.79 12599.66 7399.18 7098.94 11799.38 10094.37 18597.21 20487.19 22584.10 21798.10 13598.16 11299.47 2199.42 6197.43 163
thres20096.23 18994.13 19598.69 13599.44 13499.18 7098.58 15999.38 10093.52 19897.35 19786.33 23285.83 21297.93 14398.16 11298.78 6799.42 6197.10 178
view60096.39 18494.30 19398.82 12299.65 7999.16 7598.98 11399.36 11194.46 17997.39 19587.28 22384.16 21698.16 13498.16 11299.48 2099.40 6697.42 164
zzz-MVS98.94 5398.57 6499.37 5399.77 3799.15 7699.24 8799.55 6097.38 8299.16 7196.64 17499.69 1999.15 7599.09 4398.92 5499.37 7199.11 38
ACMMPcopyleft98.82 8098.33 9499.39 4699.77 3799.14 7799.37 6999.54 6696.47 13299.03 9596.26 18399.52 4399.28 6198.92 6298.80 6699.37 7199.16 36
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
tfpn_ndepth96.69 17795.49 18898.09 17499.17 17399.13 7898.61 15599.38 10094.90 17195.85 21992.85 21388.19 19896.07 18497.28 17998.67 7899.49 5197.44 162
SteuartSystems-ACMMP98.94 5398.52 6999.43 3999.79 3399.13 7899.33 7699.55 6096.17 14299.04 9397.53 15099.65 3099.46 3299.04 5298.76 6999.44 5699.35 22
Skip Steuart: Steuart Systems R&D Blog.
ACMMPR99.05 4298.72 5199.44 3699.79 3399.12 8099.35 7299.56 5897.74 5599.21 6297.72 14499.55 4199.29 6098.90 6498.81 6399.41 6499.19 33
COLMAP_ROBcopyleft98.29 299.37 2199.25 2099.51 3099.74 5099.12 8099.56 4099.39 9398.96 1299.17 6899.44 7099.63 3399.58 1599.48 2599.27 3399.60 3498.81 74
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SMA-MVS98.94 5398.80 4699.11 8799.73 5599.09 8298.78 13699.18 14496.32 13798.89 11299.19 8899.72 1498.75 9799.09 4398.89 5699.31 8199.27 26
LS3D98.79 8298.52 6999.12 8599.64 8799.09 8299.24 8799.46 8597.75 5398.93 10697.47 15298.23 14397.98 14199.36 3099.30 3299.46 5398.42 109
ACMMP_Plus98.94 5398.72 5199.21 7499.67 6999.08 8499.26 8499.39 9396.84 10498.88 11498.22 12799.68 2298.82 9299.06 4798.90 5599.25 8999.25 27
XVS99.77 3799.07 8599.46 6098.95 10299.37 5799.33 77
X-MVStestdata99.77 3799.07 8599.46 6098.95 10299.37 5799.33 77
X-MVS98.59 9897.99 11799.30 6699.75 4699.07 8599.17 9699.50 7696.62 11998.95 10293.95 20699.37 5799.11 7898.94 5998.86 5799.35 7599.09 42
PGM-MVS98.69 8698.09 11099.39 4699.76 4399.07 8599.30 7899.51 7394.76 17399.18 6796.70 17299.51 4699.20 6698.79 7198.71 7699.39 6999.11 38
conf0.0194.53 21191.09 21998.53 15099.29 15799.05 8998.68 14299.35 11692.02 21497.04 20884.45 23468.52 23497.45 15597.79 14699.08 4399.41 6496.70 184
v124098.86 7098.41 8699.38 5199.59 9899.05 8999.65 2399.14 15097.68 6199.66 1599.93 598.72 12799.45 3497.38 17497.72 14298.79 14798.35 113
thres40096.22 19094.08 19798.72 13099.58 10099.05 8998.83 13099.22 13794.01 19297.40 19386.34 23184.91 21597.93 14397.85 13999.08 4399.37 7197.28 169
DeepC-MVS97.88 499.33 2299.15 2599.53 2999.73 5599.05 8999.49 5699.40 9198.42 2299.55 2499.71 4399.89 499.49 2999.14 3898.81 6399.54 3899.02 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v192192098.89 6398.46 7399.39 4699.58 10099.04 9399.64 2699.17 14697.91 4299.64 1799.92 698.99 11699.44 3797.44 17097.57 15498.84 13898.35 113
v114198.87 6698.45 7799.36 5599.65 7999.04 9399.56 4099.38 10097.83 4699.29 5299.86 2299.16 8699.40 4397.68 15397.78 13098.86 13497.82 150
divwei89l23v2f11298.87 6698.45 7799.36 5599.65 7999.04 9399.56 4099.38 10097.83 4699.29 5299.86 2299.15 9099.40 4397.68 15397.78 13098.86 13497.82 150
v198.87 6698.45 7799.36 5599.65 7999.04 9399.55 4399.38 10097.83 4699.30 5099.86 2299.17 8399.40 4397.68 15397.77 13798.86 13497.82 150
DELS-MVS98.63 9398.70 5398.55 14899.24 16699.04 9398.96 11598.52 19196.83 10698.38 14799.58 5799.68 2297.06 16898.74 7398.44 9399.10 9798.59 94
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
v14419298.88 6598.46 7399.37 5399.56 10599.03 9899.61 3399.26 13197.79 4999.58 2099.88 1399.11 10099.43 3997.38 17497.61 15098.80 14598.43 108
v119298.91 5998.48 7299.41 4299.61 9799.03 9899.64 2699.25 13497.91 4299.58 2099.92 699.07 10999.45 3497.55 16397.68 14498.93 11898.23 125
v114498.94 5398.53 6799.42 4199.62 9499.03 9899.58 3799.36 11197.99 3599.49 3099.91 1199.20 8099.51 2597.61 15997.85 12798.95 11698.10 138
conf0.00293.97 21690.06 22398.52 15199.26 16199.02 10198.68 14299.33 11992.02 21497.01 20983.82 23563.41 23797.45 15597.73 15097.98 11599.40 6696.47 186
Anonymous2023120698.50 10698.03 11499.05 9599.50 12599.01 10299.15 9899.26 13196.38 13499.12 7899.50 6699.12 9698.60 10597.68 15397.24 16898.66 15697.30 168
FMVSNet396.85 17396.67 15997.06 19997.56 23099.01 10297.99 19199.33 11994.09 18995.60 22198.17 13195.97 17693.26 21794.76 21696.22 18898.59 16398.46 104
v798.91 5998.53 6799.36 5599.53 11798.99 10499.57 3899.36 11197.58 6999.32 4599.88 1399.23 7499.50 2797.77 14797.98 11598.91 12498.26 122
HFP-MVS98.97 4998.70 5399.29 6999.67 6998.98 10599.13 10099.53 7097.76 5098.90 10998.07 13499.50 4899.14 7798.64 7798.78 6799.37 7199.18 34
v1neww98.84 7398.45 7799.29 6999.54 11098.98 10599.54 4799.37 10897.48 7399.10 8199.80 3599.12 9699.40 4397.85 13997.89 12198.81 14098.04 141
v7new98.84 7398.45 7799.29 6999.54 11098.98 10599.54 4799.37 10897.48 7399.10 8199.80 3599.12 9699.40 4397.85 13997.89 12198.81 14098.04 141
v698.84 7398.46 7399.30 6699.54 11098.98 10599.54 4799.37 10897.49 7299.11 8099.81 3299.13 9599.40 4397.86 13697.89 12198.81 14098.04 141
v2v48298.85 7298.40 8899.38 5199.65 7998.98 10599.55 4399.39 9397.92 4099.35 4199.85 2799.14 9299.39 5397.50 16597.78 13098.98 11397.60 157
LGP-MVS_train98.84 7398.33 9499.44 3699.78 3598.98 10599.39 6799.55 6095.41 15798.90 10997.51 15199.68 2299.44 3799.03 5398.81 6399.57 3698.91 65
PMVScopyleft92.51 1798.66 8998.86 4298.43 15399.26 16198.98 10598.60 15698.59 18897.73 5799.45 3399.38 7498.54 13795.24 19499.62 1499.61 1199.42 6198.17 134
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs598.37 11497.81 12399.03 9799.46 13098.97 11299.03 10798.96 16895.85 15199.05 9099.45 6998.66 13398.79 9496.02 19997.52 15698.87 13198.21 128
CP-MVS98.86 7098.43 8599.36 5599.68 6698.97 11299.19 9599.46 8596.60 12199.20 6397.11 16299.51 4699.15 7598.92 6298.82 6299.45 5499.08 43
MP-MVScopyleft98.78 8398.30 9699.34 6299.75 4698.95 11499.26 8499.46 8595.78 15399.17 6896.98 16799.72 1499.06 8198.84 6698.74 7299.33 7799.11 38
MVSTER95.38 20193.99 20197.01 20398.83 19598.95 11496.62 22699.14 15092.17 21197.44 19197.29 15577.88 22791.63 22797.45 16896.18 19198.41 17397.99 144
RPSCF98.84 7398.81 4598.89 11299.37 14098.95 11498.51 16398.85 17297.73 5798.33 15198.97 10499.14 9298.95 8599.18 3798.68 7799.31 8198.99 53
ACMM96.66 1198.90 6198.44 8399.44 3699.74 5098.95 11499.47 5899.55 6097.66 6299.09 8596.43 17999.41 5199.35 5898.95 5898.67 7899.45 5499.03 48
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS_030498.57 10098.36 9198.82 12299.72 5898.94 11898.92 12099.14 15096.76 11299.33 4398.30 12499.73 1296.74 17098.05 12297.79 12999.08 10298.97 55
v14898.77 8498.45 7799.15 8199.68 6698.94 11899.49 5699.31 12697.95 3898.91 10899.65 4999.62 3599.18 6897.99 12597.64 14898.33 17597.38 166
V4298.81 8198.49 7199.18 7899.52 12298.92 12099.50 5599.29 12797.43 7898.97 9899.81 3299.00 11599.30 5997.93 12998.01 11198.51 17098.34 117
APD-MVScopyleft98.47 10997.97 11899.05 9599.64 8798.91 12198.94 11799.45 8994.40 18398.77 11997.26 15699.41 5198.21 13298.67 7598.57 8799.31 8198.57 97
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HyFIR lowres test98.08 13097.16 14999.14 8499.72 5898.91 12199.41 6499.58 5597.93 3998.82 11799.24 8095.81 17998.73 9995.16 21195.13 20498.60 16297.94 147
PHI-MVS98.57 10098.20 10399.00 10399.48 12998.91 12198.68 14299.17 14694.97 16899.27 6098.33 12299.33 6398.05 13998.82 6898.62 8299.34 7698.38 111
EG-PatchMatch MVS99.01 4598.77 4999.28 7399.64 8798.90 12498.81 13499.27 13096.55 12699.71 699.31 7799.66 2799.17 7199.28 3599.11 4299.10 9798.57 97
ACMP96.54 1398.87 6698.40 8899.41 4299.74 5098.88 12599.29 7999.50 7696.85 10398.96 10097.05 16399.66 2799.43 3998.98 5798.60 8399.52 4398.81 74
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CANet98.47 10998.30 9698.67 13799.65 7998.87 12698.82 13399.01 16496.14 14399.29 5298.86 10799.01 11396.54 17498.36 9498.08 10898.72 15398.80 78
MVS_111021_HR98.58 9998.26 9998.96 10699.32 15198.81 12798.48 16498.99 16696.81 10999.16 7198.07 13499.23 7498.89 8998.43 8898.27 9998.90 12698.24 124
QAPM98.62 9498.40 8898.89 11299.57 10498.80 12898.63 15099.35 11696.82 10798.60 12898.85 10999.08 10798.09 13798.31 10098.21 10199.08 10298.72 85
TSAR-MVS + MP.99.02 4498.95 3299.11 8799.23 16798.79 12999.51 5298.73 17997.50 7198.56 13199.03 9999.59 3999.16 7399.29 3399.17 3799.50 4999.24 30
CPTT-MVS98.28 11897.51 13699.16 8099.54 11098.78 13098.96 11599.36 11196.30 13898.89 11293.10 21199.30 6799.20 6698.35 9597.96 11899.03 11098.82 72
DI_MVS_plusplus_trai97.57 15596.55 16598.77 12699.55 10798.76 13199.22 9099.00 16597.08 10097.95 17397.78 14391.35 19398.02 14096.20 19596.81 17898.87 13197.87 149
SD-MVS98.73 8598.54 6598.95 10799.14 17698.76 13198.46 16699.14 15097.71 5998.56 13198.06 13699.61 3698.85 9198.56 7997.74 13999.54 3899.32 23
111194.22 21592.26 21296.51 21699.71 6098.75 13399.03 10799.83 1295.01 16593.39 23499.54 6360.23 23989.58 22997.90 13297.62 14997.50 19496.75 182
.test124574.10 23168.09 23381.11 23299.71 6098.75 13399.03 10799.83 1295.01 16593.39 23499.54 6360.23 23989.58 22997.90 13210.38 2355.14 23914.81 235
EU-MVSNet98.68 8798.94 3698.37 15899.14 17698.74 13599.64 2698.20 20498.21 2699.17 6899.66 4599.18 8299.08 7999.11 4098.86 5795.00 21398.83 70
FMVSNet594.57 21092.77 20996.67 21297.88 22598.72 13697.54 21298.70 18288.64 23595.11 22786.90 22681.77 22393.27 21697.92 13198.07 10997.50 19497.34 167
thresconf0.0295.49 19992.74 21098.70 13399.32 15198.70 13798.87 12899.21 13995.95 14897.57 18790.63 21873.55 23297.86 14796.09 19897.03 17499.40 6697.22 174
3Dnovator98.16 398.65 9098.35 9299.00 10399.59 9898.70 13798.90 12599.36 11197.97 3699.09 8596.55 17799.09 10597.97 14298.70 7498.65 8199.12 9698.81 74
PVSNet_BlendedMVS97.93 13597.66 12998.25 16399.30 15498.67 13998.31 17897.95 20994.30 18698.75 12197.63 14698.76 12496.30 18198.29 10297.78 13098.93 11898.18 132
PVSNet_Blended97.93 13597.66 12998.25 16399.30 15498.67 13998.31 17897.95 20994.30 18698.75 12197.63 14698.76 12496.30 18198.29 10297.78 13098.93 11898.18 132
Fast-Effi-MVS+98.42 11297.79 12499.15 8199.69 6598.66 14198.94 11799.68 3794.49 17799.05 9098.06 13698.86 12098.48 11598.18 11197.78 13099.05 10898.54 101
IterMVS97.40 16196.67 15998.25 16399.45 13198.66 14198.87 12898.73 17996.40 13398.94 10599.56 5995.26 18197.58 15295.38 20794.70 20895.90 21196.72 183
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
new-patchmatchnet97.26 16496.12 17698.58 14599.55 10798.63 14399.14 9997.04 22298.80 1699.19 6599.92 699.19 8198.92 8695.51 20687.04 22297.66 19093.73 211
TSAR-MVS + ACMM98.64 9298.58 6398.72 13099.17 17398.63 14398.69 14199.10 15797.69 6098.30 15399.12 9299.38 5698.70 10098.45 8497.51 15798.35 17499.25 27
HPM-MVS++copyleft98.56 10398.08 11199.11 8799.53 11798.61 14599.02 11199.32 12496.29 13999.06 8897.23 15799.50 4898.77 9598.15 11597.90 11998.96 11498.90 67
thres100view90095.74 19793.66 20598.17 16999.37 14098.59 14698.10 18798.33 19892.02 21497.30 20086.53 22886.34 20796.69 17196.77 18898.47 9299.24 9196.89 181
PM-MVS98.57 10098.24 10198.95 10799.26 16198.59 14699.03 10798.74 17896.84 10499.44 3499.13 9098.31 14298.75 9798.03 12398.21 10198.48 17198.58 95
TSAR-MVS + GP.98.54 10498.29 9898.82 12299.28 15998.59 14697.73 20299.24 13695.93 14998.59 12999.07 9599.17 8398.86 9098.44 8598.10 10799.26 8898.72 85
CHOSEN 1792x268898.31 11798.02 11598.66 13999.55 10798.57 14999.38 6899.25 13498.42 2298.48 14299.58 5799.85 698.31 12495.75 20295.71 19596.96 20298.27 121
Effi-MVS+98.11 12997.29 14199.06 9299.62 9498.55 15098.16 18699.80 1594.64 17499.15 7496.59 17597.43 16198.44 11697.46 16797.90 11999.17 9498.45 106
no-one99.01 4598.94 3699.09 9198.97 19098.55 15099.37 6999.04 16197.59 6799.36 3899.66 4599.75 999.57 1698.47 8399.27 3398.21 18199.30 25
OpenMVScopyleft97.26 997.88 13797.17 14898.70 13399.50 12598.55 15098.34 17799.11 15593.92 19398.90 10995.04 19698.23 14397.38 16198.11 12098.12 10698.95 11698.23 125
MVS_111021_LR98.39 11398.11 10898.71 13299.08 18398.54 15398.23 18498.56 19096.57 12499.13 7698.41 11998.86 12098.65 10398.23 10897.87 12598.65 15898.28 119
MIMVSNet97.24 16597.15 15097.36 19399.03 18698.52 15498.55 16199.73 2894.94 17094.94 22997.98 13997.37 16393.66 21497.60 16097.34 16398.23 18096.29 188
Effi-MVS+-dtu97.78 14097.37 13998.26 16299.25 16498.50 15597.89 19699.19 14394.51 17698.16 16095.93 18898.80 12395.97 18598.27 10797.38 16199.10 9798.23 125
OPM-MVS98.84 7398.59 6199.12 8599.52 12298.50 15599.13 10099.22 13797.76 5098.76 12098.70 11199.61 3698.90 8798.67 7598.37 9699.19 9398.57 97
MSLP-MVS++97.99 13197.64 13298.40 15598.91 19298.47 15797.12 22198.78 17696.49 12998.48 14293.57 20999.12 9698.51 11498.31 10098.58 8598.58 16498.95 61
pmmvs-eth3d98.68 8798.14 10699.29 6999.49 12898.45 15899.45 6299.38 10097.21 9499.50 2999.65 4999.21 7899.16 7397.11 18297.56 15598.79 14797.82 150
MDA-MVSNet-bldmvs97.75 14197.26 14298.33 15999.35 14798.45 15899.32 7797.21 22097.90 4499.05 9099.01 10196.86 17099.08 7999.36 3092.97 21495.97 21096.25 189
MVS_Test97.69 14597.15 15098.33 15999.27 16098.43 16098.25 18299.29 12795.00 16797.39 19598.86 10798.00 15297.14 16595.38 20796.22 18898.62 16098.15 136
3Dnovator+97.85 598.61 9598.14 10699.15 8199.62 9498.37 16199.10 10499.51 7398.04 3398.98 9796.07 18798.75 12698.55 11098.51 8198.40 9599.17 9498.82 72
CLD-MVS98.48 10898.15 10598.86 11799.53 11798.35 16298.55 16197.83 21496.02 14798.97 9899.08 9399.75 999.03 8398.10 12197.33 16499.28 8698.44 107
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
GA-MVS96.84 17495.86 18397.98 17899.16 17598.29 16397.91 19498.64 18695.14 16297.71 18398.04 13888.90 19696.50 17596.41 19396.61 18397.97 18797.60 157
CDPH-MVS97.99 13197.23 14598.87 11499.58 10098.29 16398.83 13099.20 14293.76 19598.11 16396.11 18599.16 8698.23 13197.80 14497.22 16999.29 8598.28 119
DeepC-MVS_fast97.38 898.65 9098.34 9399.02 10099.33 14898.29 16398.99 11298.71 18197.40 8099.31 4798.20 12899.40 5498.54 11298.33 9998.18 10499.23 9298.58 95
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NCCC97.84 13996.96 15598.87 11499.39 13998.27 16698.46 16699.02 16396.78 11098.73 12491.12 21798.91 11798.57 10897.83 14297.49 15899.04 10998.33 118
CNVR-MVS98.22 12497.76 12598.76 12799.33 14898.26 16798.48 16498.88 17196.22 14098.47 14495.79 18999.33 6398.35 12298.37 9297.99 11399.03 11098.38 111
IB-MVS95.85 1495.87 19594.88 19097.02 20299.09 18198.25 16897.16 21997.38 21891.97 22197.77 17783.61 23697.29 16492.03 22697.16 18197.66 14598.66 15698.20 131
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
MCST-MVS98.25 12197.57 13499.06 9299.53 11798.24 16998.63 15099.17 14695.88 15098.58 13096.11 18599.09 10599.18 6897.58 16297.31 16599.25 8998.75 83
PCF-MVS95.58 1697.60 15096.67 15998.69 13599.44 13498.23 17098.37 17398.81 17593.01 20598.22 15797.97 14099.59 3998.20 13395.72 20495.08 20599.08 10297.09 180
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
train_agg97.99 13197.26 14298.83 11999.43 13698.22 17198.91 12299.07 15894.43 18197.96 17296.42 18099.30 6798.81 9397.39 17296.62 18298.82 13998.47 103
CDS-MVSNet97.75 14197.68 12897.83 18499.08 18398.20 17298.68 14298.61 18795.63 15497.80 17699.24 8096.93 16994.09 21097.96 12697.82 12898.71 15497.99 144
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAPA-MVS96.65 1298.23 12297.96 11998.55 14898.81 19698.16 17398.40 17097.94 21196.68 11798.49 14098.61 11598.89 11898.57 10897.45 16897.59 15299.09 10198.35 113
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test0.0.03 195.81 19695.77 18595.85 22499.20 16898.15 17497.49 21698.50 19292.24 20992.74 23796.82 17192.70 19088.60 23297.31 17897.01 17698.57 16596.19 191
DeepPCF-MVS96.68 1098.20 12598.26 9998.12 17297.03 23498.11 17598.44 16897.70 21596.77 11198.52 13598.91 10599.17 8398.58 10798.41 9098.02 11098.46 17298.46 104
CANet_DTU97.65 14897.50 13797.82 18599.19 17198.08 17698.41 16998.67 18394.40 18399.16 7198.32 12398.69 12893.96 21297.87 13597.61 15097.51 19397.56 160
MDTV_nov1_ep13_2view97.12 16796.19 17598.22 16899.13 17898.05 17799.24 8799.47 8297.61 6599.15 7499.59 5599.01 11398.40 11994.87 21390.14 21793.91 21894.04 210
abl_698.38 15799.03 18698.04 17898.08 18998.65 18493.23 20198.56 13194.58 20398.57 13697.17 16498.81 14097.42 164
AdaColmapbinary97.57 15596.57 16498.74 12899.25 16498.01 17998.36 17698.98 16794.44 18098.47 14492.44 21597.91 15598.62 10498.19 11097.74 13998.73 15297.28 169
N_pmnet96.68 17895.70 18697.84 18399.42 13798.00 18099.35 7298.21 20298.40 2498.13 16299.42 7299.30 6797.44 16094.00 22188.79 21994.47 21791.96 219
HQP-MVS97.58 15496.65 16398.66 13999.30 15497.99 18197.88 19798.65 18494.58 17598.66 12594.65 19999.15 9098.59 10696.10 19795.59 19798.90 12698.50 102
OMC-MVS98.35 11598.10 10998.64 14198.85 19497.99 18198.56 16098.21 20297.26 9098.87 11698.54 11799.27 7098.43 11798.34 9697.66 14598.92 12197.65 156
USDC98.26 12097.57 13499.06 9299.42 13797.98 18398.83 13098.85 17297.57 7099.59 1999.15 8998.59 13598.99 8497.42 17196.08 19498.69 15596.23 190
Fast-Effi-MVS+-dtu96.99 17096.46 16797.61 18998.98 18997.89 18497.54 21299.76 2293.43 19996.55 21394.93 19798.06 14994.32 20896.93 18596.50 18598.53 16797.47 161
ambc97.89 12199.45 13197.88 18597.78 19997.27 8899.80 398.99 10398.48 13898.55 11097.80 14496.68 18098.54 16698.10 138
CNLPA97.75 14197.26 14298.32 16198.58 20897.86 18697.80 19898.09 20696.49 12998.49 14096.15 18498.08 14898.35 12298.00 12497.03 17498.61 16197.21 175
gg-mvs-nofinetune96.77 17696.52 16697.06 19999.66 7397.82 18797.54 21299.86 998.69 1798.61 12799.94 489.62 19488.37 23397.55 16396.67 18198.30 17695.35 199
diffmvs97.29 16396.67 15998.01 17799.00 18897.82 18798.37 17399.18 14496.73 11697.74 18199.08 9394.26 18596.50 17594.86 21595.67 19697.29 19698.25 123
PLCcopyleft95.63 1597.73 14497.01 15498.57 14699.10 18097.80 18997.72 20398.77 17796.34 13598.38 14793.46 21098.06 14998.66 10297.90 13297.65 14798.77 15097.90 148
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
testmv97.48 15996.83 15898.24 16699.37 14097.79 19098.59 15799.07 15892.40 20897.59 18599.24 8098.11 14797.66 15097.64 15797.11 17197.17 19895.54 198
test123567897.49 15796.84 15798.24 16699.37 14097.79 19098.59 15799.07 15892.41 20797.59 18599.24 8098.15 14697.66 15097.64 15797.12 17097.17 19895.55 197
TinyColmap98.27 11997.62 13399.03 9799.29 15797.79 19098.92 12098.95 16997.48 7399.52 2798.65 11497.86 15698.90 8798.34 9697.27 16698.64 15995.97 193
MSDG98.20 12597.88 12298.56 14799.33 14897.74 19398.27 18198.10 20597.20 9698.06 16598.59 11699.16 8698.76 9698.39 9197.71 14398.86 13496.38 187
CVMVSNet97.38 16297.39 13897.37 19298.58 20897.72 19498.70 14097.42 21797.21 9495.95 21899.46 6893.31 18997.38 16197.60 16097.78 13096.18 20798.66 91
pmmvs497.87 13897.02 15398.86 11799.20 16897.68 19598.89 12699.03 16296.57 12499.12 7899.03 9997.26 16598.42 11895.16 21196.34 18698.53 16797.10 178
TAMVS96.95 17296.94 15696.97 20599.07 18597.67 19697.98 19297.12 22195.04 16495.41 22499.27 7995.57 18094.09 21097.32 17697.11 17198.16 18396.59 185
PatchMatch-RL97.24 16596.45 16898.17 16998.70 20297.57 19797.31 21798.48 19494.42 18298.39 14695.74 19096.35 17597.88 14597.75 14997.48 15998.24 17995.87 194
LP95.33 20393.45 20697.54 19098.68 20397.40 19898.73 13998.41 19696.33 13698.92 10797.84 14288.30 19795.92 18692.98 22289.38 21894.56 21691.90 220
TSAR-MVS + COLMAP97.62 14997.31 14097.98 17898.47 21497.39 19998.29 18098.25 20096.68 11797.54 18998.87 10698.04 15197.08 16696.78 18796.26 18798.26 17897.12 177
MAR-MVS97.12 16796.28 17398.11 17398.94 19197.22 20097.65 20799.38 10090.93 22998.15 16195.17 19497.13 16696.48 17797.71 15197.40 16098.06 18498.40 110
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
MS-PatchMatch97.60 15097.22 14698.04 17698.67 20497.18 20197.91 19498.28 19995.82 15298.34 15097.66 14598.38 13997.77 14997.10 18397.25 16797.27 19797.18 176
EPNet96.44 18396.08 17796.86 20699.32 15197.15 20297.69 20699.32 12493.67 19698.11 16395.64 19193.44 18889.07 23196.86 18696.83 17797.67 18998.97 55
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testus96.13 19495.13 18997.28 19499.13 17897.00 20396.84 22597.89 21390.48 23097.40 19393.60 20896.47 17395.39 19296.21 19496.19 19097.05 20095.99 192
test-mter94.62 20894.02 20095.32 22697.72 22896.75 20496.23 22995.67 22589.83 23493.23 23696.99 16685.94 21192.66 22197.32 17696.11 19396.44 20495.22 201
ADS-MVSNet94.41 21492.13 21497.07 19898.86 19396.60 20598.38 17298.47 19596.13 14598.02 16796.98 16787.50 20395.87 18789.89 22687.58 22192.79 22690.27 225
CHOSEN 280x42096.80 17596.30 17297.39 19199.09 18196.52 20698.76 13899.29 12793.88 19497.65 18498.34 12193.66 18796.29 18398.28 10597.73 14193.27 22295.70 195
test1235695.71 19895.55 18795.89 22398.27 22296.48 20796.90 22497.35 21992.13 21295.64 22099.13 9097.97 15392.34 22296.94 18496.55 18494.87 21589.61 228
pmmvs396.30 18795.87 18296.80 20997.66 22996.48 20797.93 19393.80 23193.40 20098.54 13498.27 12697.50 16097.37 16397.49 16693.11 21395.52 21294.85 204
MDTV_nov1_ep1394.47 21292.15 21397.17 19698.54 21296.42 20998.10 18798.89 17094.49 17798.02 16797.41 15386.49 20495.56 19090.85 22587.95 22093.91 21891.45 223
EPMVS93.67 21990.82 22196.99 20498.62 20796.39 21098.40 17099.11 15595.54 15697.87 17597.14 16081.27 22594.97 19888.54 23086.80 22392.95 22490.06 227
PatchT95.49 19993.29 20798.06 17598.65 20596.20 21198.91 12299.73 2892.00 22098.50 13796.67 17383.25 21996.34 17994.40 21795.50 19896.21 20695.04 202
test-LLR94.79 20693.71 20396.06 22099.20 16896.16 21296.31 22798.50 19289.98 23194.08 23097.01 16486.43 20592.20 22496.76 18995.31 20096.05 20894.31 207
TESTMET0.1,194.44 21393.71 20395.30 22797.84 22696.16 21296.31 22795.32 22889.98 23194.08 23097.01 16486.43 20592.20 22496.76 18995.31 20096.05 20894.31 207
PMMVS296.29 18897.05 15295.40 22598.32 22096.16 21298.18 18597.46 21697.20 9684.51 23999.60 5398.68 13096.37 17898.59 7897.38 16197.58 19291.76 221
FPMVS96.97 17197.20 14796.70 21197.75 22796.11 21597.72 20395.47 22697.13 9898.02 16797.57 14896.67 17192.97 21899.00 5698.34 9798.28 17795.58 196
tpm93.89 21791.21 21897.03 20198.36 21896.07 21697.53 21599.65 4392.24 20998.64 12697.23 15774.67 23194.64 20392.68 22390.73 21693.37 22194.82 205
Patchmtry96.05 21797.64 20899.78 1998.50 137
PatchmatchNetpermissive93.88 21891.08 22097.14 19798.75 19896.01 21898.25 18299.39 9394.95 16998.96 10096.32 18185.35 21495.50 19188.89 22885.89 22691.99 23090.15 226
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PMMVS96.47 18295.81 18497.23 19597.38 23295.96 21997.31 21796.91 22393.21 20297.93 17497.14 16097.64 15995.70 18895.24 20996.18 19198.17 18295.33 200
dps92.35 22588.78 22896.52 21598.21 22495.94 22097.78 19998.38 19789.88 23396.81 21195.07 19575.31 22994.70 20288.62 22986.21 22593.21 22390.41 224
CR-MVSNet95.38 20193.01 20898.16 17198.63 20695.85 22197.64 20899.78 1991.27 22598.50 13796.84 17082.16 22196.34 17994.40 21795.50 19898.05 18595.04 202
RPMNet94.72 20792.01 21597.88 18298.56 21095.85 22197.78 19999.70 3491.27 22598.33 15193.69 20781.88 22294.91 19992.60 22494.34 21098.01 18694.46 206
CostFormer92.75 22189.49 22496.55 21498.78 19795.83 22397.55 21198.59 18891.83 22297.34 19896.31 18278.53 22694.50 20486.14 23184.92 22792.54 22792.84 215
DWT-MVSNet_training91.07 22986.55 23196.35 21798.28 22195.82 22498.00 19095.03 22991.24 22797.99 17190.35 22063.43 23695.25 19386.06 23286.62 22493.55 22092.30 218
new_pmnet96.59 17996.40 16996.81 20898.24 22395.46 22597.71 20594.75 23096.92 10296.80 21299.23 8497.81 15796.69 17196.58 19195.16 20396.69 20393.64 212
EPNet_dtu96.31 18695.96 18096.72 21099.18 17295.39 22697.03 22399.13 15493.02 20499.35 4197.23 15797.07 16790.70 22895.74 20395.08 20594.94 21498.16 135
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test235692.46 22288.72 22996.82 20798.48 21395.34 22796.22 23098.09 20687.46 23696.01 21692.82 21464.42 23595.10 19694.08 21994.05 21197.02 20192.87 214
tpmp4_e2392.43 22488.82 22796.64 21398.46 21595.17 22897.61 21098.85 17292.42 20698.18 15893.03 21274.92 23093.80 21388.91 22784.60 22892.95 22492.66 217
tpmrst92.45 22389.48 22595.92 22298.43 21795.03 22997.14 22097.92 21294.16 18897.56 18897.86 14181.63 22493.56 21585.89 23382.86 22990.91 23488.95 232
tpm cat191.52 22887.70 23095.97 22198.33 21994.98 23097.06 22298.03 20892.11 21398.03 16694.77 19877.19 22892.71 22083.56 23482.24 23191.67 23189.04 231
CMPMVSbinary74.71 1996.17 19196.06 17896.30 21897.41 23194.52 23194.83 23395.46 22791.57 22397.26 20394.45 20498.33 14194.98 19798.28 10597.59 15297.86 18897.68 155
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MVS-HIRNet94.86 20593.83 20296.07 21997.07 23394.00 23294.31 23499.17 14691.23 22898.17 15998.69 11297.43 16195.66 18994.05 22091.92 21592.04 22989.46 229
GG-mvs-BLEND65.66 23292.62 21134.20 2341.45 24093.75 23385.40 2381.64 23891.37 22417.21 24187.25 22494.78 1833.25 23895.64 20593.80 21296.27 20591.74 222
E-PMN92.28 22690.12 22294.79 22898.56 21090.90 23495.16 23293.68 23295.36 15895.10 22896.56 17689.05 19595.24 19495.21 21081.84 23290.98 23281.94 233
EMVS91.84 22789.39 22694.70 22998.44 21690.84 23595.27 23193.53 23395.18 16195.26 22695.62 19287.59 20294.77 20194.87 21380.72 23390.95 23380.88 234
testpf87.81 23083.90 23292.37 23096.76 23588.65 23693.04 23698.24 20185.20 23795.28 22586.82 22772.43 23382.35 23482.62 23582.30 23088.55 23589.29 230
DeepMVS_CXcopyleft87.86 23792.27 23761.98 23493.64 19793.62 23391.17 21691.67 19294.90 20095.99 20092.48 22894.18 209
MVEpermissive82.47 1893.12 22094.09 19691.99 23190.79 23682.50 23893.93 23596.30 22496.06 14688.81 23898.19 12996.38 17497.56 15397.24 18095.18 20284.58 23693.07 213
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt65.28 23382.24 23771.50 23970.81 23923.21 23596.14 14381.70 24085.98 23392.44 19149.84 23595.81 20194.36 20983.86 237
testmvs9.73 23313.38 2345.48 2363.62 2384.12 2406.40 2413.19 23714.92 2387.68 24322.10 23713.89 2436.83 23613.47 23610.38 2355.14 23914.81 235
test1239.37 23412.26 2356.00 2353.32 2394.06 2416.39 2423.41 23613.20 23910.48 24216.43 23816.22 2426.76 23711.37 23710.40 2345.62 23814.10 237
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
MTAPA99.19 6599.68 22
MTMP99.20 6399.54 42
Patchmatch-RL test32.47 240
mPP-MVS99.75 4699.49 50
NP-MVS93.07 203