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-MVSNet99.95 199.95 199.95 199.99 199.99 199.95 299.97 399.99 1100.00 199.98 899.78 8100.00 199.92 3100.00 199.87 10
mvs_tets99.90 299.90 299.90 499.96 599.79 3399.72 2599.88 1899.92 599.98 399.93 1499.94 299.98 799.77 30100.00 199.92 3
jajsoiax99.89 399.89 399.89 699.96 599.78 3599.70 2999.86 2299.89 1099.98 399.90 2399.94 299.98 799.75 31100.00 199.90 5
ANet_high99.88 499.87 499.91 299.99 199.91 399.65 54100.00 199.90 6100.00 199.97 1099.61 1799.97 1699.75 31100.00 199.84 15
LTVRE_ROB99.19 199.88 499.87 499.88 1299.91 2199.90 499.96 199.92 799.90 699.97 699.87 3799.81 799.95 4099.54 4499.99 2099.80 25
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
pmmvs699.86 699.86 699.83 2499.94 1599.90 499.83 899.91 1199.85 1999.94 2099.95 1299.73 1099.90 10899.65 3599.97 4699.69 56
v5299.85 799.84 799.89 699.96 599.89 699.87 599.81 5699.85 1999.96 899.90 2399.27 4299.95 4099.93 199.99 2099.82 23
V499.85 799.84 799.88 1299.96 599.89 699.87 599.81 5699.85 1999.96 899.90 2399.27 4299.95 4099.93 1100.00 199.82 23
PS-MVSNAJss99.84 999.82 999.89 699.96 599.77 3799.68 4199.85 2999.95 399.98 399.92 1799.28 3999.98 799.75 31100.00 199.94 2
test_djsdf99.84 999.81 1099.91 299.94 1599.84 1899.77 1399.80 6099.73 4299.97 699.92 1799.77 999.98 799.43 53100.00 199.90 5
Anonymous2023121199.83 1199.81 1099.89 699.97 499.95 299.88 499.93 699.87 1399.94 2099.98 899.55 2199.95 4099.21 8099.98 3599.78 31
v7n99.82 1299.80 1299.88 1299.96 599.84 1899.82 1099.82 4899.84 2399.94 2099.91 2099.13 5799.96 3399.83 2099.99 2099.83 18
anonymousdsp99.80 1399.77 1499.90 499.96 599.88 899.73 2199.85 2999.70 4899.92 3199.93 1499.45 2399.97 1699.36 61100.00 199.85 14
pm-mvs199.79 1499.79 1399.78 3799.91 2199.83 2299.76 1699.87 2099.73 4299.89 3799.87 3799.63 1599.87 14899.54 4499.92 8699.63 97
UA-Net99.78 1599.76 1899.86 1899.72 12399.71 5099.91 399.95 599.96 299.71 10599.91 2099.15 5399.97 1699.50 48100.00 199.90 5
TransMVSNet (Re)99.78 1599.77 1499.81 2799.91 2199.85 1399.75 1799.86 2299.70 4899.91 3399.89 3199.60 1999.87 14899.59 3999.74 18899.71 49
v74899.76 1799.74 2199.84 2199.95 1399.83 2299.82 1099.80 6099.82 2799.95 1699.87 3798.72 11099.93 6499.72 3499.98 3599.75 40
v1399.76 1799.77 1499.73 6199.86 3499.55 9299.77 1399.86 2299.79 3399.96 899.91 2098.90 8399.87 14899.91 5100.00 199.78 31
v1299.75 1999.77 1499.72 6699.85 3899.53 9599.75 1799.86 2299.78 3499.96 899.90 2398.88 8699.86 16799.91 5100.00 199.77 34
v1199.75 1999.76 1899.71 7099.85 3899.49 9899.73 2199.84 3799.75 3999.95 1699.90 2398.93 7999.86 16799.92 3100.00 199.77 34
OurMVSNet-221017-099.75 1999.71 2599.84 2199.96 599.83 2299.83 899.85 2999.80 3199.93 2699.93 1498.54 13899.93 6499.59 3999.98 3599.76 37
Vis-MVSNetpermissive99.75 1999.74 2199.79 3499.88 2899.66 6799.69 3899.92 799.67 5799.77 8599.75 9299.61 1799.98 799.35 6299.98 3599.72 46
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
V999.74 2399.75 2099.71 7099.84 4199.50 9699.74 1999.86 2299.76 3899.96 899.90 2398.83 8899.85 18399.91 5100.00 199.77 34
V1499.73 2499.74 2199.69 7799.83 4599.48 10199.72 2599.85 2999.74 4099.96 899.89 3198.79 9699.85 18399.91 5100.00 199.76 37
v1599.72 2599.73 2499.68 8099.82 5299.44 11399.70 2999.85 2999.72 4599.95 1699.88 3498.76 10399.84 19999.90 9100.00 199.75 40
TDRefinement99.72 2599.70 2899.77 3999.90 2599.85 1399.86 799.92 799.69 5299.78 8199.92 1799.37 3099.88 13698.93 12199.95 6499.60 121
XXY-MVS99.71 2799.67 3299.81 2799.89 2799.72 4999.59 6599.82 4899.39 11199.82 6499.84 5099.38 2899.91 9199.38 5899.93 8399.80 25
nrg03099.70 2899.66 3399.82 2599.76 10299.84 1899.61 6099.70 10599.93 499.78 8199.68 13599.10 5999.78 25399.45 5199.96 5799.83 18
FC-MVSNet-test99.70 2899.65 3499.86 1899.88 2899.86 1299.72 2599.78 7099.90 699.82 6499.83 5198.45 15199.87 14899.51 4799.97 4699.86 12
v1799.70 2899.71 2599.67 8399.81 6099.44 11399.70 2999.83 4099.69 5299.94 2099.87 3798.70 11199.84 19999.88 1499.99 2099.73 43
v1699.70 2899.71 2599.67 8399.81 6099.43 11999.70 2999.83 4099.70 4899.94 2099.87 3798.69 11399.84 19999.88 1499.99 2099.73 43
v1099.69 3299.69 2999.66 9199.81 6099.39 13199.66 4999.75 8399.60 7999.92 3199.87 3798.75 10699.86 16799.90 999.99 2099.73 43
v1899.68 3399.69 2999.65 9599.79 8199.40 12899.68 4199.83 4099.66 6199.93 2699.85 4598.65 12299.84 19999.87 1899.99 2099.71 49
v899.68 3399.69 2999.65 9599.80 6899.40 12899.66 4999.76 7899.64 6699.93 2699.85 4598.66 12099.84 19999.88 1499.99 2099.71 49
DTE-MVSNet99.68 3399.61 4199.88 1299.80 6899.87 999.67 4699.71 10299.72 4599.84 5999.78 7998.67 11899.97 1699.30 7099.95 6499.80 25
VPA-MVSNet99.66 3699.62 3899.79 3499.68 13899.75 4399.62 5699.69 11199.85 1999.80 7399.81 6198.81 8999.91 9199.47 5099.88 11199.70 53
PS-CasMVS99.66 3699.58 4599.89 699.80 6899.85 1399.66 4999.73 9199.62 7099.84 5999.71 11198.62 12699.96 3399.30 7099.96 5799.86 12
PEN-MVS99.66 3699.59 4399.89 699.83 4599.87 999.66 4999.73 9199.70 4899.84 5999.73 9898.56 13299.96 3399.29 7399.94 7599.83 18
FMVSNet199.66 3699.63 3799.73 6199.78 8799.77 3799.68 4199.70 10599.67 5799.82 6499.83 5198.98 7399.90 10899.24 7799.97 4699.53 154
MIMVSNet199.66 3699.62 3899.80 2999.94 1599.87 999.69 3899.77 7299.78 3499.93 2699.89 3197.94 18699.92 8199.65 3599.98 3599.62 110
FIs99.65 4199.58 4599.84 2199.84 4199.85 1399.66 4999.75 8399.86 1699.74 9799.79 7098.27 16499.85 18399.37 6099.93 8399.83 18
wuykxyi23d99.65 4199.64 3699.69 7799.92 1999.20 18298.89 19399.99 298.73 18699.95 1699.80 6399.84 499.99 499.64 3799.98 3599.89 9
DeepC-MVS98.90 499.62 4399.61 4199.67 8399.72 12399.44 11399.24 12899.71 10299.27 12399.93 2699.90 2399.70 1299.93 6498.99 10899.99 2099.64 93
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WR-MVS_H99.61 4499.53 6199.87 1699.80 6899.83 2299.67 4699.75 8399.58 8399.85 5699.69 12498.18 17399.94 5399.28 7599.95 6499.83 18
ACMH98.42 699.59 4599.54 5399.72 6699.86 3499.62 7999.56 6999.79 6898.77 18299.80 7399.85 4599.64 1499.85 18398.70 13799.89 10599.70 53
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testing_299.58 4699.56 5199.62 11499.81 6099.44 11399.14 15599.43 22199.69 5299.82 6499.79 7099.14 5499.79 24999.31 6999.95 6499.63 97
v119299.57 4799.57 4899.57 13499.77 9799.22 17699.04 17399.60 15999.18 13999.87 5099.72 10499.08 6499.85 18399.89 1399.98 3599.66 79
EG-PatchMatch MVS99.57 4799.56 5199.62 11499.77 9799.33 15099.26 12299.76 7899.32 11999.80 7399.78 7999.29 3799.87 14899.15 9299.91 9699.66 79
Gipumacopyleft99.57 4799.59 4399.49 15799.98 399.71 5099.72 2599.84 3799.81 2899.94 2099.78 7998.91 8299.71 27798.41 15199.95 6499.05 268
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v192192099.56 5099.57 4899.55 14399.75 11099.11 19199.05 17199.61 14599.15 14699.88 4599.71 11199.08 6499.87 14899.90 999.97 4699.66 79
v124099.56 5099.58 4599.51 15399.80 6899.00 20299.00 18099.65 13099.15 14699.90 3599.75 9299.09 6199.88 13699.90 999.96 5799.67 69
v799.56 5099.54 5399.61 11799.80 6899.39 13199.30 10999.59 16399.14 14899.82 6499.72 10498.75 10699.84 19999.83 2099.94 7599.61 115
V4299.56 5099.54 5399.63 10699.79 8199.46 10699.39 8499.59 16399.24 13199.86 5599.70 11898.55 13699.82 22299.79 2699.95 6499.60 121
v14419299.55 5499.54 5399.58 12899.78 8799.20 18299.11 16299.62 14199.18 13999.89 3799.72 10498.66 12099.87 14899.88 1499.97 4699.66 79
v1neww99.55 5499.54 5399.61 11799.80 6899.39 13199.32 9999.61 14599.18 13999.87 5099.69 12498.64 12499.82 22299.79 2699.94 7599.60 121
v7new99.55 5499.54 5399.61 11799.80 6899.39 13199.32 9999.61 14599.18 13999.87 5099.69 12498.64 12499.82 22299.79 2699.94 7599.60 121
v699.55 5499.54 5399.61 11799.80 6899.39 13199.32 9999.60 15999.18 13999.87 5099.68 13598.65 12299.82 22299.79 2699.95 6499.61 115
test20.0399.55 5499.54 5399.58 12899.79 8199.37 14099.02 17699.89 1599.60 7999.82 6499.62 16598.81 8999.89 12199.43 5399.86 12699.47 182
v114499.54 5999.53 6199.59 12499.79 8199.28 15999.10 16399.61 14599.20 13799.84 5999.73 9898.67 11899.84 19999.86 1999.98 3599.64 93
v114199.54 5999.52 6399.57 13499.78 8799.27 16399.15 15099.61 14599.26 12799.89 3799.69 12498.56 13299.82 22299.82 2399.97 4699.63 97
divwei89l23v2f11299.54 5999.52 6399.57 13499.78 8799.27 16399.15 15099.61 14599.26 12799.89 3799.69 12498.56 13299.82 22299.82 2399.96 5799.63 97
v199.54 5999.52 6399.58 12899.77 9799.28 15999.15 15099.61 14599.26 12799.88 4599.68 13598.56 13299.82 22299.82 2399.97 4699.63 97
CP-MVSNet99.54 5999.43 7899.87 1699.76 10299.82 2799.57 6799.61 14599.54 8599.80 7399.64 15197.79 19899.95 4099.21 8099.94 7599.84 15
TranMVSNet+NR-MVSNet99.54 5999.47 6999.76 4299.58 15599.64 7399.30 10999.63 13899.61 7499.71 10599.56 19398.76 10399.96 3399.14 9899.92 8699.68 62
testmv99.53 6599.51 6699.59 12499.73 11999.31 15398.48 23799.92 799.57 8499.87 5099.79 7099.12 5899.91 9199.16 9199.99 2099.55 144
v2v48299.50 6699.47 6999.58 12899.78 8799.25 16999.14 15599.58 17199.25 13099.81 7099.62 16598.24 16699.84 19999.83 2099.97 4699.64 93
ACMH+98.40 899.50 6699.43 7899.71 7099.86 3499.76 4199.32 9999.77 7299.53 8899.77 8599.76 8899.26 4599.78 25397.77 19499.88 11199.60 121
Baseline_NR-MVSNet99.49 6899.37 8699.82 2599.91 2199.84 1898.83 20499.86 2299.68 5599.65 12499.88 3497.67 20799.87 14899.03 10599.86 12699.76 37
TAMVS99.49 6899.45 7399.63 10699.48 20199.42 12399.45 7899.57 17299.66 6199.78 8199.83 5197.85 19499.86 16799.44 5299.96 5799.61 115
pmmvs-eth3d99.48 7099.47 6999.51 15399.77 9799.41 12798.81 20899.66 12199.42 10899.75 8999.66 14599.20 4899.76 26198.98 11099.99 2099.36 216
EI-MVSNet-UG-set99.48 7099.50 6799.42 17699.57 16298.65 23399.24 12899.46 21399.68 5599.80 7399.66 14598.99 7299.89 12199.19 8499.90 9999.72 46
APDe-MVS99.48 7099.36 8999.85 2099.55 17599.81 2899.50 7399.69 11198.99 15799.75 8999.71 11198.79 9699.93 6498.46 14999.85 12999.80 25
PMMVS299.48 7099.45 7399.57 13499.76 10298.99 20398.09 27199.90 1498.95 16099.78 8199.58 18399.57 2099.93 6499.48 4999.95 6499.79 30
DSMNet-mixed99.48 7099.65 3498.95 24499.71 12697.27 28299.50 7399.82 4899.59 8199.41 18499.85 4599.62 16100.00 199.53 4699.89 10599.59 132
DP-MVS99.48 7099.39 8299.74 5599.57 16299.62 7999.29 11799.61 14599.87 1399.74 9799.76 8898.69 11399.87 14898.20 16799.80 16599.75 40
EI-MVSNet-Vis-set99.47 7699.49 6899.42 17699.57 16298.66 23199.24 12899.46 21399.67 5799.79 7899.65 15098.97 7599.89 12199.15 9299.89 10599.71 49
VPNet99.46 7799.37 8699.71 7099.82 5299.59 8499.48 7799.70 10599.81 2899.69 10999.58 18397.66 21199.86 16799.17 8899.44 24699.67 69
ACMM98.09 1199.46 7799.38 8499.72 6699.80 6899.69 6199.13 16099.65 13098.99 15799.64 12699.72 10499.39 2499.86 16798.23 16499.81 16099.60 121
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Regformer-499.45 7999.44 7599.50 15599.52 18198.94 20999.17 14299.53 18899.64 6699.76 8899.60 17598.96 7899.90 10898.91 12299.84 13399.67 69
COLMAP_ROBcopyleft98.06 1299.45 7999.37 8699.70 7699.83 4599.70 5799.38 8699.78 7099.53 8899.67 11499.78 7999.19 4999.86 16797.32 22199.87 11899.55 144
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
HPM-MVS_fast99.43 8199.30 10099.80 2999.83 4599.81 2899.52 7199.70 10598.35 21899.51 16599.50 21099.31 3599.88 13698.18 17199.84 13399.69 56
3Dnovator99.15 299.43 8199.36 8999.65 9599.39 22399.42 12399.70 2999.56 17699.23 13399.35 19499.80 6399.17 5199.95 4098.21 16699.84 13399.59 132
SixPastTwentyTwo99.42 8399.30 10099.76 4299.92 1999.67 6699.70 2999.14 27499.65 6499.89 3799.90 2396.20 25699.94 5399.42 5799.92 8699.67 69
GBi-Net99.42 8399.31 9599.73 6199.49 19599.77 3799.68 4199.70 10599.44 10099.62 13699.83 5197.21 23099.90 10898.96 11599.90 9999.53 154
test199.42 8399.31 9599.73 6199.49 19599.77 3799.68 4199.70 10599.44 10099.62 13699.83 5197.21 23099.90 10898.96 11599.90 9999.53 154
Regformer-399.41 8699.41 8099.40 18499.52 18198.70 22899.17 14299.44 21899.62 7099.75 8999.60 17598.90 8399.85 18398.89 12399.84 13399.65 87
MVSFormer99.41 8699.44 7599.31 20599.57 16298.40 24299.77 1399.80 6099.73 4299.63 12999.30 25198.02 18199.98 799.43 5399.69 20399.55 144
IterMVS-LS99.41 8699.47 6999.25 21799.81 6098.09 26098.85 20199.76 7899.62 7099.83 6399.64 15198.54 13899.97 1699.15 9299.99 2099.68 62
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v14899.40 8999.41 8099.39 18799.76 10298.94 20999.09 16799.59 16399.17 14499.81 7099.61 17298.41 15499.69 28499.32 6799.94 7599.53 154
NR-MVSNet99.40 8999.31 9599.68 8099.43 21599.55 9299.73 2199.50 20199.46 9899.88 4599.36 23897.54 21399.87 14898.97 11499.87 11899.63 97
PVSNet_Blended_VisFu99.40 8999.38 8499.44 17199.90 2598.66 23198.94 19199.91 1197.97 23999.79 7899.73 9899.05 6999.97 1699.15 9299.99 2099.68 62
EU-MVSNet99.39 9299.62 3898.72 26699.88 2896.44 29499.56 6999.85 2999.90 699.90 3599.85 4598.09 17599.83 21599.58 4199.95 6499.90 5
CHOSEN 1792x268899.39 9299.30 10099.65 9599.88 2899.25 16998.78 21399.88 1898.66 19099.96 899.79 7097.45 21799.93 6499.34 6399.99 2099.78 31
EI-MVSNet99.38 9499.44 7599.21 22399.58 15598.09 26099.26 12299.46 21399.62 7099.75 8999.67 14198.54 13899.85 18399.15 9299.92 8699.68 62
UGNet99.38 9499.34 9199.49 15798.90 28898.90 21699.70 2999.35 24199.86 1698.57 27499.81 6198.50 14799.93 6499.38 5899.98 3599.66 79
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
UniMVSNet_NR-MVSNet99.37 9699.25 11399.72 6699.47 20599.56 8998.97 18699.61 14599.43 10599.67 11499.28 25497.85 19499.95 4099.17 8899.81 16099.65 87
UniMVSNet (Re)99.37 9699.26 11199.68 8099.51 18599.58 8698.98 18599.60 15999.43 10599.70 10799.36 23897.70 20299.88 13699.20 8399.87 11899.59 132
CSCG99.37 9699.29 10599.60 12399.71 12699.46 10699.43 8199.85 2998.79 17999.41 18499.60 17598.92 8099.92 8198.02 18099.92 8699.43 199
PM-MVS99.36 9999.29 10599.58 12899.83 4599.66 6798.95 18899.86 2298.85 17199.81 7099.73 9898.40 15699.92 8198.36 15499.83 14399.17 243
abl_699.36 9999.23 11699.75 5199.71 12699.74 4799.33 9699.76 7899.07 15399.65 12499.63 15899.09 6199.92 8197.13 23499.76 17899.58 136
new-patchmatchnet99.35 10199.57 4898.71 26799.82 5296.62 29298.55 22999.75 8399.50 9099.88 4599.87 3799.31 3599.88 13699.43 53100.00 199.62 110
Anonymous2023120699.35 10199.31 9599.47 16299.74 11699.06 20199.28 11899.74 8899.23 13399.72 10199.53 20297.63 21299.88 13699.11 10199.84 13399.48 178
MTAPA99.35 10199.20 11999.80 2999.81 6099.81 2899.33 9699.53 18899.27 12399.42 17899.63 15898.21 16999.95 4097.83 19199.79 16899.65 87
FMVSNet299.35 10199.28 10799.55 14399.49 19599.35 14799.45 7899.57 17299.44 10099.70 10799.74 9497.21 23099.87 14899.03 10599.94 7599.44 193
3Dnovator+98.92 399.35 10199.24 11499.67 8399.35 23199.47 10299.62 5699.50 20199.44 10099.12 22999.78 7998.77 10299.94 5397.87 18899.72 19999.62 110
TSAR-MVS + MP.99.34 10699.24 11499.63 10699.82 5299.37 14099.26 12299.35 24198.77 18299.57 14799.70 11899.27 4299.88 13697.71 19799.75 18199.65 87
Regformer-299.34 10699.27 10999.53 14899.41 21999.10 19498.99 18199.53 18899.47 9599.66 11899.52 20498.80 9399.89 12198.31 15999.74 18899.60 121
DELS-MVS99.34 10699.30 10099.48 16099.51 18599.36 14398.12 26799.53 18899.36 11599.41 18499.61 17299.22 4799.87 14899.21 8099.68 20599.20 237
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
DU-MVS99.33 10999.21 11899.71 7099.43 21599.56 8998.83 20499.53 18899.38 11299.67 11499.36 23897.67 20799.95 4099.17 8899.81 16099.63 97
ab-mvs99.33 10999.28 10799.47 16299.57 16299.39 13199.78 1299.43 22198.87 16999.57 14799.82 5898.06 17899.87 14898.69 13899.73 19499.15 245
Regformer-199.32 11199.27 10999.47 16299.41 21998.95 20898.99 18199.48 20699.48 9299.66 11899.52 20498.78 9999.87 14898.36 15499.74 18899.60 121
APD-MVS_3200maxsize99.31 11299.16 12099.74 5599.53 17999.75 4399.27 12199.61 14599.19 13899.57 14799.64 15198.76 10399.90 10897.29 22399.62 21799.56 141
MPTG99.30 11399.14 12399.80 2999.81 6099.81 2898.73 21699.53 18899.27 12399.42 17899.63 15898.21 16999.95 4097.83 19199.79 16899.65 87
SteuartSystems-ACMMP99.30 11399.14 12399.76 4299.87 3299.66 6799.18 13999.60 15998.55 19999.57 14799.67 14199.03 7199.94 5397.01 23899.80 16599.69 56
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testgi99.29 11599.26 11199.37 19299.75 11098.81 22498.84 20299.89 1598.38 21199.75 8999.04 28799.36 3399.86 16799.08 10399.25 27399.45 188
ACMMP_Plus99.28 11699.11 13199.79 3499.75 11099.81 2898.95 18899.53 18898.27 22799.53 16199.73 9898.75 10699.87 14897.70 19899.83 14399.68 62
LCM-MVSNet-Re99.28 11699.15 12299.67 8399.33 24599.76 4199.34 9499.97 398.93 16399.91 3399.79 7098.68 11599.93 6496.80 24899.56 22599.30 225
mvs_anonymous99.28 11699.39 8298.94 24599.19 26697.81 27199.02 17699.55 17999.78 3499.85 5699.80 6398.24 16699.86 16799.57 4299.50 23999.15 245
MVS_Test99.28 11699.31 9599.19 22699.35 23198.79 22699.36 9299.49 20599.17 14499.21 21799.67 14198.78 9999.66 29899.09 10299.66 21299.10 255
no-one99.28 11699.23 11699.45 16999.87 3299.08 19798.95 18899.52 19698.88 16899.77 8599.83 5197.78 19999.90 10898.46 14999.99 2099.38 209
XVS99.27 12199.11 13199.75 5199.71 12699.71 5099.37 9099.61 14599.29 12098.76 26299.47 21598.47 14899.88 13697.62 20599.73 19499.67 69
OPM-MVS99.26 12299.13 12699.63 10699.70 13399.61 8398.58 22499.48 20698.50 20399.52 16399.63 15899.14 5499.76 26197.89 18799.77 17699.51 165
HFP-MVS99.25 12399.08 14199.76 4299.73 11999.70 5799.31 10699.59 16398.36 21399.36 19299.37 23298.80 9399.91 9197.43 21699.75 18199.68 62
HPM-MVS99.25 12399.07 14599.78 3799.81 6099.75 4399.61 6099.67 11797.72 25299.35 19499.25 26099.23 4699.92 8197.21 23099.82 15299.67 69
ACMMPcopyleft99.25 12399.08 14199.74 5599.79 8199.68 6499.50 7399.65 13098.07 23499.52 16399.69 12498.57 13199.92 8197.18 23299.79 16899.63 97
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
LS3D99.24 12699.11 13199.61 11798.38 31799.79 3399.57 6799.68 11499.61 7499.15 22699.71 11198.70 11199.91 9197.54 21099.68 20599.13 249
xiu_mvs_v1_base_debu99.23 12799.34 9198.91 24899.59 15298.23 25098.47 23899.66 12199.61 7499.68 11198.94 29999.39 2499.97 1699.18 8599.55 23198.51 298
xiu_mvs_v1_base99.23 12799.34 9198.91 24899.59 15298.23 25098.47 23899.66 12199.61 7499.68 11198.94 29999.39 2499.97 1699.18 8599.55 23198.51 298
xiu_mvs_v1_base_debi99.23 12799.34 9198.91 24899.59 15298.23 25098.47 23899.66 12199.61 7499.68 11198.94 29999.39 2499.97 1699.18 8599.55 23198.51 298
region2R99.23 12799.05 15199.77 3999.76 10299.70 5799.31 10699.59 16398.41 20899.32 20199.36 23898.73 10999.93 6497.29 22399.74 18899.67 69
ACMMPR99.23 12799.06 14799.76 4299.74 11699.69 6199.31 10699.59 16398.36 21399.35 19499.38 23198.61 12899.93 6497.43 21699.75 18199.67 69
XVG-ACMP-BASELINE99.23 12799.10 13899.63 10699.82 5299.58 8698.83 20499.72 9998.36 21399.60 14399.71 11198.92 8099.91 9197.08 23599.84 13399.40 204
CP-MVS99.23 12799.05 15199.75 5199.66 14299.66 6799.38 8699.62 14198.38 21199.06 23699.27 25698.79 9699.94 5397.51 21299.82 15299.66 79
DeepC-MVS_fast98.47 599.23 12799.12 12999.56 14099.28 25499.22 17698.99 18199.40 22999.08 15299.58 14599.64 15198.90 8399.83 21597.44 21599.75 18199.63 97
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LPG-MVS_test99.22 13599.05 15199.74 5599.82 5299.63 7799.16 14899.73 9197.56 25999.64 12699.69 12499.37 3099.89 12196.66 25599.87 11899.69 56
CDS-MVSNet99.22 13599.13 12699.50 15599.35 23199.11 19198.96 18799.54 18399.46 9899.61 14199.70 11896.31 25399.83 21599.34 6399.88 11199.55 144
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test_040299.22 13599.14 12399.45 16999.79 8199.43 11999.28 11899.68 11499.54 8599.40 18899.56 19399.07 6699.82 22296.01 27999.96 5799.11 251
AllTest99.21 13899.07 14599.63 10699.78 8799.64 7399.12 16199.83 4098.63 19399.63 12999.72 10498.68 11599.75 26796.38 26699.83 14399.51 165
XVG-OURS99.21 13899.06 14799.65 9599.82 5299.62 7997.87 29699.74 8898.36 21399.66 11899.68 13599.71 1199.90 10896.84 24699.88 11199.43 199
Fast-Effi-MVS+-dtu99.20 14099.12 12999.43 17499.25 25799.69 6199.05 17199.82 4899.50 9098.97 24299.05 28498.98 7399.98 798.20 16799.24 27598.62 291
VDD-MVS99.20 14099.11 13199.44 17199.43 21598.98 20499.50 7398.32 31099.80 3199.56 15499.69 12496.99 23999.85 18398.99 10899.73 19499.50 171
PGM-MVS99.20 14099.01 16099.77 3999.75 11099.71 5099.16 14899.72 9997.99 23799.42 17899.60 17598.81 8999.93 6496.91 24299.74 18899.66 79
pmmvs599.19 14399.11 13199.42 17699.76 10298.88 21998.55 22999.73 9198.82 17599.72 10199.62 16596.56 24699.82 22299.32 6799.95 6499.56 141
mPP-MVS99.19 14399.00 16299.76 4299.76 10299.68 6499.38 8699.54 18398.34 22299.01 23999.50 21098.53 14299.93 6497.18 23299.78 17399.66 79
VNet99.18 14599.06 14799.56 14099.24 25999.36 14399.33 9699.31 25099.67 5799.47 17099.57 19096.48 24899.84 19999.15 9299.30 26799.47 182
RPSCF99.18 14599.02 15799.64 10299.83 4599.85 1399.44 8099.82 4898.33 22399.50 16799.78 7997.90 18899.65 30596.78 24999.83 14399.44 193
DeepPCF-MVS98.42 699.18 14599.02 15799.67 8399.22 26199.75 4397.25 31499.47 21098.72 18799.66 11899.70 11899.29 3799.63 30998.07 17999.81 16099.62 110
EPP-MVSNet99.17 14899.00 16299.66 9199.80 6899.43 11999.70 2999.24 26599.48 9299.56 15499.77 8594.89 27199.93 6498.72 13699.89 10599.63 97
MVP-Stereo99.16 14999.08 14199.43 17499.48 20199.07 19999.08 16899.55 17998.63 19399.31 20399.68 13598.19 17299.78 25398.18 17199.58 22499.45 188
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-OURS-SEG-HR99.16 14998.99 16599.66 9199.84 4199.64 7398.25 25699.73 9198.39 21099.63 12999.43 22199.70 1299.90 10897.34 22098.64 30399.44 193
jason99.16 14999.11 13199.32 20399.75 11098.44 23898.26 25599.39 23298.70 18899.74 9799.30 25198.54 13899.97 1698.48 14899.82 15299.55 144
jason: jason.
MP-MVS-pluss99.14 15298.92 17599.80 2999.83 4599.83 2298.61 21999.63 13896.84 28199.44 17399.58 18398.81 8999.91 9197.70 19899.82 15299.67 69
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MVS_030599.13 15399.10 13899.22 22198.89 29298.44 23898.59 22299.57 17299.43 10599.19 22399.22 26997.88 19199.94 5399.14 9899.96 5799.26 229
pmmvs499.13 15399.06 14799.36 19599.57 16299.10 19498.01 28099.25 26298.78 18199.58 14599.44 22098.24 16699.76 26198.74 13499.93 8399.22 233
MVS_111021_LR99.13 15399.03 15699.42 17699.58 15599.32 15297.91 29599.73 9198.68 18999.31 20399.48 21299.09 6199.66 29897.70 19899.77 17699.29 228
#test#99.12 15698.90 17899.76 4299.73 11999.70 5799.10 16399.59 16397.60 25799.36 19299.37 23298.80 9399.91 9196.84 24699.75 18199.68 62
TSAR-MVS + GP.99.12 15699.04 15599.38 18999.34 24199.16 18698.15 26399.29 25398.18 23199.63 12999.62 16599.18 5099.68 29298.20 16799.74 18899.30 225
MVS_111021_HR99.12 15699.02 15799.40 18499.50 19099.11 19197.92 29399.71 10298.76 18499.08 23299.47 21599.17 5199.54 31797.85 19099.76 17899.54 151
WR-MVS99.11 15998.93 17299.66 9199.30 25199.42 12398.42 24599.37 23899.04 15499.57 14799.20 27196.89 24199.86 16798.66 14199.87 11899.70 53
PHI-MVS99.11 15998.95 17199.59 12499.13 27299.59 8499.17 14299.65 13097.88 24399.25 21099.46 21898.97 7599.80 24697.26 22699.82 15299.37 213
MSDG99.08 16198.98 16899.37 19299.60 15099.13 18997.54 30499.74 8898.84 17499.53 16199.55 19899.10 5999.79 24997.07 23699.86 12699.18 241
Effi-MVS+-dtu99.07 16298.92 17599.52 15098.89 29299.78 3599.15 15099.66 12199.34 11698.92 24899.24 26597.69 20499.98 798.11 17699.28 26998.81 286
Effi-MVS+99.06 16398.97 16999.34 19799.31 24798.98 20498.31 25399.91 1198.81 17698.79 25998.94 29999.14 5499.84 19998.79 12998.74 29999.20 237
MP-MVScopyleft99.06 16398.83 18899.76 4299.76 10299.71 5099.32 9999.50 20198.35 21898.97 24299.48 21298.37 15799.92 8195.95 28599.75 18199.63 97
MDA-MVSNet-bldmvs99.06 16399.05 15199.07 23699.80 6897.83 27098.89 19399.72 9999.29 12099.63 12999.70 11896.47 24999.89 12198.17 17399.82 15299.50 171
MSLP-MVS++99.05 16699.09 14098.91 24899.21 26298.36 24698.82 20799.47 21098.85 17198.90 25199.56 19398.78 9999.09 32798.57 14399.68 20599.26 229
1112_ss99.05 16698.84 18599.67 8399.66 14299.29 15798.52 23399.82 4897.65 25599.43 17799.16 27396.42 25199.91 9199.07 10499.84 13399.80 25
ACMP97.51 1499.05 16698.84 18599.67 8399.78 8799.55 9298.88 19599.66 12197.11 27799.47 17099.60 17599.07 6699.89 12196.18 27299.85 12999.58 136
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet_BlendedMVS99.03 16999.01 16099.09 23399.54 17697.99 26498.58 22499.82 4897.62 25699.34 19799.71 11198.52 14499.77 25997.98 18399.97 4699.52 162
IS-MVSNet99.03 16998.85 18499.55 14399.80 6899.25 16999.73 2199.15 27399.37 11399.61 14199.71 11194.73 27399.81 24197.70 19899.88 11199.58 136
xiu_mvs_v2_base99.02 17199.11 13198.77 26399.37 22898.09 26098.13 26699.51 19899.47 9599.42 17898.54 31899.38 2899.97 1698.83 12699.33 26498.24 309
Fast-Effi-MVS+99.02 17198.87 18199.46 16599.38 22699.50 9699.04 17399.79 6897.17 27398.62 26998.74 31199.34 3499.95 4098.32 15899.41 25598.92 278
canonicalmvs99.02 17199.00 16299.09 23399.10 27998.70 22899.61 6099.66 12199.63 6998.64 26897.65 33199.04 7099.54 31798.79 12998.92 28899.04 269
MCST-MVS99.02 17198.81 19099.65 9599.58 15599.49 9898.58 22499.07 27898.40 20999.04 23799.25 26098.51 14699.80 24697.31 22299.51 23899.65 87
HSP-MVS99.01 17598.76 19499.76 4299.78 8799.73 4899.35 9399.31 25098.54 20099.54 15898.99 28896.81 24299.93 6496.97 24099.53 23699.61 115
SD-MVS99.01 17599.30 10098.15 28499.50 19099.40 12898.94 19199.61 14599.22 13699.75 8999.82 5899.54 2295.51 33397.48 21399.87 11899.54 151
LF4IMVS99.01 17598.92 17599.27 20999.71 12699.28 15998.59 22299.77 7298.32 22499.39 18999.41 22598.62 12699.84 19996.62 25899.84 13398.69 290
MS-PatchMatch99.00 17898.97 16999.09 23399.11 27798.19 25398.76 21499.33 24498.49 20499.44 17399.58 18398.21 16999.69 28498.20 16799.62 21799.39 206
PS-MVSNAJ99.00 17899.08 14198.76 26499.37 22898.10 25998.00 28299.51 19899.47 9599.41 18498.50 32099.28 3999.97 1698.83 12699.34 26298.20 313
CNVR-MVS98.99 18098.80 19299.56 14099.25 25799.43 11998.54 23299.27 25798.58 19798.80 25899.43 22198.53 14299.70 27897.22 22899.59 22399.54 151
VDDNet98.97 18198.82 18999.42 17699.71 12698.81 22499.62 5698.68 29699.81 2899.38 19099.80 6394.25 27799.85 18398.79 12999.32 26599.59 132
IterMVS98.97 18199.16 12098.42 27499.74 11695.64 30198.06 27699.83 4099.83 2699.85 5699.74 9496.10 25999.99 499.27 76100.00 199.63 97
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TinyColmap98.97 18198.93 17299.07 23699.46 20998.19 25397.75 29999.75 8398.79 17999.54 15899.70 11898.97 7599.62 31096.63 25799.83 14399.41 203
HPM-MVS++98.96 18498.70 19799.74 5599.52 18199.71 5098.86 19899.19 26998.47 20698.59 27299.06 28398.08 17799.91 9196.94 24199.60 22299.60 121
lupinMVS98.96 18498.87 18199.24 21999.57 16298.40 24298.12 26799.18 27098.28 22699.63 12999.13 27598.02 18199.97 1698.22 16599.69 20399.35 218
USDC98.96 18498.93 17299.05 23899.54 17697.99 26497.07 31699.80 6098.21 22999.75 8999.77 8598.43 15299.64 30797.90 18699.88 11199.51 165
YYNet198.95 18798.99 16598.84 25799.64 14697.14 28598.22 25899.32 24698.92 16599.59 14499.66 14597.40 21999.83 21598.27 16399.90 9999.55 144
MDA-MVSNet_test_wron98.95 18798.99 16598.85 25599.64 14697.16 28498.23 25799.33 24498.93 16399.56 15499.66 14597.39 22199.83 21598.29 16199.88 11199.55 144
Test_1112_low_res98.95 18798.73 19599.63 10699.68 13899.15 18898.09 27199.80 6097.14 27599.46 17299.40 22796.11 25899.89 12199.01 10799.84 13399.84 15
diffmvs98.94 19098.87 18199.13 23099.37 22898.90 21699.25 12699.64 13597.55 26199.04 23799.58 18397.23 22999.64 30798.73 13599.44 24698.86 282
test123567898.93 19198.84 18599.19 22699.46 20998.55 23497.53 30699.77 7298.76 18499.69 10999.48 21296.69 24399.90 10898.30 16099.91 9699.11 251
HyFIR lowres test98.91 19298.64 20299.73 6199.85 3899.47 10298.07 27599.83 4098.64 19299.89 3799.60 17592.57 290100.00 199.33 6599.97 4699.72 46
HQP_MVS98.90 19398.68 19999.55 14399.58 15599.24 17298.80 20999.54 18398.94 16199.14 22799.25 26097.24 22799.82 22295.84 28899.78 17399.60 121
sss98.90 19398.77 19399.27 20999.48 20198.44 23898.72 21799.32 24697.94 24199.37 19199.35 24396.31 25399.91 9198.85 12599.63 21699.47 182
OMC-MVS98.90 19398.72 19699.44 17199.39 22399.42 12398.58 22499.64 13597.31 27199.44 17399.62 16598.59 13099.69 28496.17 27399.79 16899.22 233
new_pmnet98.88 19698.89 17998.84 25799.70 13397.62 27698.15 26399.50 20197.98 23899.62 13699.54 20098.15 17499.94 5397.55 20999.84 13398.95 275
K. test v398.87 19798.60 20499.69 7799.93 1899.46 10699.74 1994.97 33099.78 3499.88 4599.88 3493.66 28199.97 1699.61 3899.95 6499.64 93
APD-MVScopyleft98.87 19798.59 20599.71 7099.50 19099.62 7999.01 17899.57 17296.80 28399.54 15899.63 15898.29 16299.91 9195.24 30399.71 20099.61 115
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mvs-test198.83 19998.70 19799.22 22198.89 29299.65 7198.88 19599.66 12199.34 11698.29 28298.94 29997.69 20499.96 3398.11 17698.54 30598.04 317
UnsupCasMVSNet_eth98.83 19998.57 20899.59 12499.68 13899.45 11198.99 18199.67 11799.48 9299.55 15799.36 23894.92 27099.86 16798.95 11996.57 32599.45 188
test_normal98.82 20198.67 20099.27 20999.56 17398.83 22398.22 25898.01 31599.03 15599.49 16999.24 26596.21 25599.76 26198.69 13899.56 22599.22 233
NCCC98.82 20198.57 20899.58 12899.21 26299.31 15398.61 21999.25 26298.65 19198.43 28099.26 25897.86 19399.81 24196.55 26099.27 27299.61 115
PMVScopyleft92.94 2198.82 20198.81 19098.85 25599.84 4197.99 26499.20 13799.47 21099.71 4799.42 17899.82 5898.09 17599.47 32193.88 31899.85 12999.07 265
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DI_MVS_plusplus_test98.80 20498.65 20199.27 20999.57 16298.90 21698.44 24397.95 31899.02 15699.51 16599.23 26896.18 25799.76 26198.52 14799.42 25399.14 248
FMVSNet398.80 20498.63 20399.32 20399.13 27298.72 22799.10 16399.48 20699.23 13399.62 13699.64 15192.57 29099.86 16798.96 11599.90 9999.39 206
Patchmtry98.78 20698.54 21199.49 15798.89 29299.19 18499.32 9999.67 11799.65 6499.72 10199.79 7091.87 29699.95 4098.00 18299.97 4699.33 221
Vis-MVSNet (Re-imp)98.77 20798.58 20799.34 19799.78 8798.88 21999.61 6099.56 17699.11 15199.24 21399.56 19393.00 28899.78 25397.43 21699.89 10599.35 218
CLD-MVS98.76 20898.57 20899.33 19999.57 16298.97 20697.53 30699.55 17996.41 28699.27 20899.13 27599.07 6699.78 25396.73 25299.89 10599.23 232
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CPTT-MVS98.74 20998.44 21699.64 10299.61 14999.38 13799.18 13999.55 17996.49 28599.27 20899.37 23297.11 23699.92 8195.74 29299.67 20999.62 110
F-COLMAP98.74 20998.45 21599.62 11499.57 16299.47 10298.84 20299.65 13096.31 28798.93 24699.19 27297.68 20699.87 14896.52 26199.37 26099.53 154
N_pmnet98.73 21198.53 21299.35 19699.72 12398.67 23098.34 25094.65 33198.35 21899.79 7899.68 13598.03 17999.93 6498.28 16299.92 8699.44 193
PVSNet_Blended98.70 21298.59 20599.02 24199.54 17697.99 26497.58 30399.82 4895.70 29799.34 19798.98 29198.52 14499.77 25997.98 18399.83 14399.30 225
PatchMatch-RL98.68 21398.47 21499.30 20799.44 21399.28 15998.14 26599.54 18397.12 27699.11 23099.25 26097.80 19799.70 27896.51 26299.30 26798.93 277
Test498.65 21498.44 21699.27 20999.57 16298.86 22298.43 24499.41 22498.85 17199.57 14798.95 29893.05 28699.75 26798.57 14399.56 22599.19 239
MVS_dtu98.62 21598.53 21298.90 25397.96 32597.70 27498.18 26099.14 27499.59 8197.90 30299.41 22595.40 26999.92 8199.24 7799.91 9699.07 265
test_prior398.62 21598.34 23099.46 16599.35 23199.22 17697.95 28999.39 23297.87 24498.05 29599.05 28497.90 18899.69 28495.99 28199.49 24199.48 178
CVMVSNet98.61 21798.88 18097.80 29199.58 15593.60 31399.26 12299.64 13599.66 6199.72 10199.67 14193.26 28499.93 6499.30 7099.81 16099.87 10
Patchmatch-RL test98.60 21898.36 22799.33 19999.77 9799.07 19998.27 25499.87 2098.91 16699.74 9799.72 10490.57 31099.79 24998.55 14599.85 12999.11 251
AdaColmapbinary98.60 21898.35 22999.38 18999.12 27499.22 17698.67 21899.42 22397.84 24898.81 25699.27 25697.32 22599.81 24195.14 30499.53 23699.10 255
WTY-MVS98.59 22098.37 22699.26 21499.43 21598.40 24298.74 21599.13 27798.10 23399.21 21799.24 26594.82 27299.90 10897.86 18998.77 29599.49 177
CNLPA98.57 22198.34 23099.28 20899.18 26899.10 19498.34 25099.41 22498.48 20598.52 27698.98 29197.05 23799.78 25395.59 29499.50 23998.96 274
112198.56 22298.24 23599.52 15099.49 19599.24 17299.30 10999.22 26795.77 29598.52 27699.29 25397.39 22199.85 18395.79 29099.34 26299.46 186
CDPH-MVS98.56 22298.20 23999.61 11799.50 19099.46 10698.32 25299.41 22495.22 30399.21 21799.10 28198.34 15999.82 22295.09 30699.66 21299.56 141
UnsupCasMVSNet_bld98.55 22498.27 23499.40 18499.56 17399.37 14097.97 28899.68 11497.49 26499.08 23299.35 24395.41 26899.82 22297.70 19898.19 31199.01 273
RPMNet98.53 22598.44 21698.83 25999.05 28298.12 25699.30 10998.78 29199.86 1699.16 22499.74 9492.53 29299.91 9198.75 13398.77 29598.44 301
MG-MVS98.52 22698.39 22398.94 24599.15 26997.39 28198.18 26099.21 26898.89 16799.23 21499.63 15897.37 22399.74 27194.22 31499.61 22199.69 56
DP-MVS Recon98.50 22798.23 23699.31 20599.49 19599.46 10698.56 22899.63 13894.86 30998.85 25499.37 23297.81 19699.59 31496.08 27599.44 24698.88 280
CMPMVSbinary77.52 2398.50 22798.19 24299.41 18398.33 31899.56 8999.01 17899.59 16395.44 30099.57 14799.80 6395.64 26399.46 32496.47 26599.92 8699.21 236
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
114514_t98.49 22998.11 24599.64 10299.73 11999.58 8699.24 12899.76 7889.94 32499.42 17899.56 19397.76 20099.86 16797.74 19699.82 15299.47 182
PMMVS98.49 22998.29 23399.11 23198.96 28598.42 24197.54 30499.32 24697.53 26398.47 27998.15 32597.88 19199.82 22297.46 21499.24 27599.09 258
MVSTER98.47 23198.22 23799.24 21999.06 28198.35 24799.08 16899.46 21399.27 12399.75 8999.66 14588.61 32099.85 18399.14 9899.92 8699.52 162
LFMVS98.46 23298.19 24299.26 21499.24 25998.52 23699.62 5696.94 32399.87 1399.31 20399.58 18391.04 30199.81 24198.68 14099.42 25399.45 188
PatchT98.45 23398.32 23298.83 25998.94 28698.29 24899.24 12898.82 28999.84 2399.08 23299.76 8891.37 29999.94 5398.82 12899.00 28798.26 307
MVS_test032698.43 23498.36 22798.65 26898.03 32497.02 28698.04 27798.32 31099.54 8597.38 31599.37 23295.45 26799.90 10899.22 7999.87 11899.02 271
test1235698.43 23498.39 22398.55 27099.46 20996.36 29597.32 31399.81 5697.60 25799.62 13699.37 23294.57 27499.89 12197.80 19399.92 8699.40 204
MIMVSNet98.43 23498.20 23999.11 23199.53 17998.38 24599.58 6698.61 29898.96 15999.33 19999.76 8890.92 30399.81 24197.38 21999.76 17899.15 245
PVSNet97.47 1598.42 23798.44 21698.35 27799.46 20996.26 29696.70 32199.34 24397.68 25499.00 24099.13 27597.40 21999.72 27497.59 20899.68 20599.08 261
CHOSEN 280x42098.41 23898.41 22198.40 27599.34 24195.89 29996.94 31799.44 21898.80 17899.25 21099.52 20493.51 28299.98 798.94 12099.98 3599.32 223
BH-RMVSNet98.41 23898.14 24499.21 22399.21 26298.47 23798.60 22198.26 31298.35 21898.93 24699.31 24897.20 23399.66 29894.32 31299.10 28199.51 165
QAPM98.40 24097.99 25199.65 9599.39 22399.47 10299.67 4699.52 19691.70 32198.78 26199.80 6398.55 13699.95 4094.71 31099.75 18199.53 154
API-MVS98.38 24198.39 22398.35 27798.83 29999.26 16599.14 15599.18 27098.59 19698.66 26798.78 30898.61 12899.57 31694.14 31599.56 22596.21 325
HQP-MVS98.36 24298.02 25099.39 18799.31 24798.94 20997.98 28599.37 23897.45 26598.15 28998.83 30596.67 24499.70 27894.73 30899.67 20999.53 154
PAPM_NR98.36 24298.04 24999.33 19999.48 20198.93 21398.79 21299.28 25697.54 26298.56 27598.57 31697.12 23599.69 28494.09 31698.90 29099.38 209
PLCcopyleft97.35 1698.36 24297.99 25199.48 16099.32 24699.24 17298.50 23599.51 19895.19 30598.58 27398.96 29696.95 24099.83 21595.63 29399.25 27399.37 213
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
train_agg98.35 24597.95 25599.57 13499.35 23199.35 14798.11 26999.41 22494.90 30797.92 30098.99 28898.02 18199.85 18395.38 30199.44 24699.50 171
CR-MVSNet98.35 24598.20 23998.83 25999.05 28298.12 25699.30 10999.67 11797.39 26899.16 22499.79 7091.87 29699.91 9198.78 13298.77 29598.44 301
LP98.34 24798.44 21698.05 28698.88 29695.31 30699.28 11898.74 29399.12 15098.98 24199.79 7093.40 28399.93 6498.38 15299.41 25598.90 279
agg_prior198.33 24897.92 25799.57 13499.35 23199.36 14397.99 28499.39 23294.85 31097.76 31198.98 29198.03 17999.85 18395.49 29699.44 24699.51 165
alignmvs98.28 24997.96 25499.25 21799.12 27498.93 21399.03 17598.42 30799.64 6698.72 26597.85 32890.86 30699.62 31098.88 12499.13 27999.19 239
agg_prior398.24 25097.81 26399.53 14899.34 24199.26 16598.09 27199.39 23294.21 31597.77 31098.96 29697.74 20199.84 19995.38 30199.44 24699.50 171
MAR-MVS98.24 25097.92 25799.19 22698.78 30499.65 7199.17 14299.14 27495.36 30198.04 29798.81 30797.47 21699.72 27495.47 29899.06 28298.21 311
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
OpenMVScopyleft98.12 1098.23 25297.89 26199.26 21499.19 26699.26 16599.65 5499.69 11191.33 32298.14 29399.77 8598.28 16399.96 3395.41 30099.55 23198.58 295
BH-untuned98.22 25398.09 24698.58 26999.38 22697.24 28398.55 22998.98 28497.81 25099.20 22298.76 30997.01 23899.65 30594.83 30798.33 30698.86 282
HY-MVS98.23 998.21 25497.95 25598.99 24299.03 28498.24 24999.61 6098.72 29496.81 28298.73 26499.51 20894.06 27899.86 16796.91 24298.20 30998.86 282
testus98.15 25598.06 24898.40 27599.11 27795.95 29796.77 31999.89 1595.83 29399.23 21498.47 32197.50 21599.84 19996.58 25999.20 27899.39 206
Patchmatch-test198.13 25698.40 22297.31 30099.20 26592.99 31598.17 26298.49 30498.24 22899.10 23199.52 20496.01 26099.83 21597.22 22899.62 21799.12 250
EPNet98.13 25697.77 26799.18 22994.57 33397.99 26499.24 12897.96 31699.74 4097.29 31899.62 16593.13 28599.97 1698.59 14299.83 14399.58 136
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test98.10 25897.98 25398.48 27399.27 25696.48 29399.40 8399.07 27898.81 17699.23 21499.57 19090.11 31499.87 14896.69 25399.64 21599.09 258
pmmvs398.08 25997.80 26498.91 24899.41 21997.69 27597.87 29699.66 12195.87 29299.50 16799.51 20890.35 31299.97 1698.55 14599.47 24399.08 261
JIA-IIPM98.06 26097.92 25798.50 27298.59 31297.02 28698.80 20998.51 30299.88 1297.89 30399.87 3791.89 29599.90 10898.16 17497.68 32198.59 293
TAPA-MVS97.92 1398.03 26197.55 27399.46 16599.47 20599.44 11398.50 23599.62 14186.79 32599.07 23599.26 25898.26 16599.62 31097.28 22599.73 19499.31 224
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
131498.00 26297.90 26098.27 28298.90 28897.45 28099.30 10999.06 28094.98 30697.21 31999.12 27998.43 15299.67 29795.58 29598.56 30497.71 319
GA-MVS97.99 26397.68 27098.93 24799.52 18198.04 26397.19 31599.05 28198.32 22498.81 25698.97 29489.89 31799.41 32598.33 15799.05 28399.34 220
MVS-HIRNet97.86 26498.22 23796.76 30499.28 25491.53 32598.38 24792.60 33299.13 14999.31 20399.96 1197.18 23499.68 29298.34 15699.83 14399.07 265
FMVSNet597.80 26597.25 27699.42 17698.83 29998.97 20699.38 8699.80 6098.87 16999.25 21099.69 12480.60 33399.91 9198.96 11599.90 9999.38 209
ADS-MVSNet297.78 26697.66 27298.12 28599.14 27095.36 30499.22 13498.75 29296.97 27898.25 28599.64 15190.90 30499.94 5396.51 26299.56 22599.08 261
tpmrst97.73 26798.07 24796.73 30698.71 30992.00 31999.10 16398.86 28698.52 20198.92 24899.54 20091.90 29499.82 22298.02 18099.03 28598.37 303
ADS-MVSNet97.72 26897.67 27197.86 28999.14 27094.65 30999.22 13498.86 28696.97 27898.25 28599.64 15190.90 30499.84 19996.51 26299.56 22599.08 261
PatchmatchNetpermissive97.65 26997.80 26497.18 30198.82 30192.49 31799.17 14298.39 30898.12 23298.79 25999.58 18390.71 30899.89 12197.23 22799.41 25599.16 244
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu97.62 27097.79 26697.11 30396.67 33292.31 31898.51 23498.04 31399.24 13195.77 32799.47 21593.78 28099.66 29898.98 11099.62 21799.37 213
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d97.58 27199.13 12692.93 31899.69 13599.49 9899.52 7199.77 7297.97 23999.96 899.79 7099.84 499.94 5395.85 28799.82 15279.36 328
PAPR97.56 27297.07 27899.04 23998.80 30298.11 25897.63 30199.25 26294.56 31398.02 29898.25 32497.43 21899.68 29290.90 32598.74 29999.33 221
TR-MVS97.44 27397.15 27798.32 27998.53 31497.46 27998.47 23897.91 31996.85 28098.21 28898.51 31996.42 25199.51 31992.16 32197.29 32297.98 318
tpmvs97.39 27497.69 26996.52 31098.41 31691.76 32299.30 10998.94 28597.74 25197.85 30699.55 19892.40 29399.73 27396.25 27198.73 30198.06 316
test0.0.03 197.37 27596.91 28598.74 26597.72 32697.57 27797.60 30297.36 32298.00 23599.21 21798.02 32690.04 31599.79 24998.37 15395.89 32898.86 282
OpenMVS_ROBcopyleft97.31 1797.36 27696.84 28698.89 25499.29 25299.45 11198.87 19799.48 20686.54 32799.44 17399.74 9497.34 22499.86 16791.61 32299.28 26997.37 323
111197.29 27796.71 28999.04 23999.65 14497.72 27298.35 24899.80 6099.40 10999.66 11899.43 22175.10 33799.87 14898.98 11099.98 3599.52 162
BH-w/o97.20 27897.01 28197.76 29299.08 28095.69 30098.03 27998.52 30195.76 29697.96 29998.02 32695.62 26499.47 32192.82 32097.25 32398.12 315
test-LLR97.15 27996.95 28397.74 29498.18 32195.02 30797.38 30996.10 32598.00 23597.81 30798.58 31490.04 31599.91 9197.69 20398.78 29398.31 305
tpm97.15 27996.95 28397.75 29398.91 28794.24 31199.32 9997.96 31697.71 25398.29 28299.32 24686.72 32299.92 8198.10 17896.24 32799.09 258
E-PMN97.14 28197.43 27496.27 31298.79 30391.62 32495.54 32599.01 28399.44 10098.88 25299.12 27992.78 28999.68 29294.30 31399.03 28597.50 320
PNet_i23d97.02 28297.87 26294.49 31799.69 13584.81 33595.18 32899.85 2997.83 24999.32 20199.57 19095.53 26699.47 32196.09 27497.74 32099.18 241
cascas96.99 28396.82 28797.48 29697.57 32995.64 30196.43 32399.56 17691.75 32097.13 32097.61 33295.58 26598.63 33096.68 25499.11 28098.18 314
EMVS96.96 28497.28 27595.99 31698.76 30691.03 32795.26 32798.61 29899.34 11698.92 24898.88 30493.79 27999.66 29892.87 31999.05 28397.30 324
PatchFormer-LS_test96.95 28597.07 27896.62 30998.76 30691.85 32199.18 13998.45 30697.29 27297.73 31397.22 33688.77 31999.76 26198.13 17598.04 31598.25 308
dp96.86 28697.07 27896.24 31498.68 31190.30 33299.19 13898.38 30997.35 27098.23 28799.59 18187.23 32199.82 22296.27 27098.73 30198.59 293
tpm cat196.78 28796.98 28296.16 31598.85 29890.59 33199.08 16899.32 24692.37 31997.73 31399.46 21891.15 30099.69 28496.07 27698.80 29298.21 311
PCF-MVS96.03 1896.73 28895.86 29499.33 19999.44 21399.16 18696.87 31899.44 21886.58 32698.95 24499.40 22794.38 27699.88 13687.93 32899.80 16598.95 275
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CostFormer96.71 28996.79 28896.46 31198.90 28890.71 32999.41 8298.68 29694.69 31298.14 29399.34 24586.32 32499.80 24697.60 20798.07 31498.88 280
MVEpermissive92.54 2296.66 29096.11 29298.31 28099.68 13897.55 27897.94 29195.60 32999.37 11390.68 33198.70 31296.56 24698.61 33186.94 32999.55 23198.77 288
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EPMVS96.53 29196.32 29097.17 30298.18 32192.97 31699.39 8489.95 33498.21 22998.61 27099.59 18186.69 32399.72 27496.99 23999.23 27798.81 286
tpm296.35 29296.22 29196.73 30698.88 29691.75 32399.21 13698.51 30293.27 31897.89 30399.21 27084.83 32599.70 27896.04 27898.18 31298.75 289
FPMVS96.32 29395.50 29898.79 26299.60 15098.17 25598.46 24298.80 29097.16 27496.28 32399.63 15882.19 32899.09 32788.45 32798.89 29199.10 255
TESTMET0.1,196.24 29495.84 29597.41 29898.24 31993.84 31297.38 30995.84 32898.43 20797.81 30798.56 31779.77 33499.89 12197.77 19498.77 29598.52 297
test-mter96.23 29595.73 29797.74 29498.18 32195.02 30797.38 30996.10 32597.90 24297.81 30798.58 31479.12 33599.91 9197.69 20398.78 29398.31 305
tpmp4_e2396.11 29696.06 29396.27 31298.90 28890.70 33099.34 9499.03 28293.72 31696.56 32299.31 24883.63 32699.75 26796.06 27798.02 31698.35 304
X-MVStestdata96.09 29794.87 30499.75 5199.71 12699.71 5099.37 9099.61 14599.29 12098.76 26261.30 33798.47 14899.88 13697.62 20599.73 19499.67 69
DWT-MVSNet_test96.03 29895.80 29696.71 30898.50 31591.93 32099.25 12697.87 32095.99 29196.81 32197.61 33281.02 33099.66 29897.20 23197.98 31798.54 296
test235695.99 29995.26 30298.18 28396.93 33195.53 30395.31 32698.71 29595.67 29898.48 27897.83 32980.72 33199.88 13695.47 29898.21 30899.11 251
gg-mvs-nofinetune95.87 30095.17 30397.97 28898.19 32096.95 28899.69 3889.23 33599.89 1096.24 32599.94 1381.19 32999.51 31993.99 31798.20 30997.44 321
PVSNet_095.53 1995.85 30195.31 30097.47 29798.78 30493.48 31495.72 32499.40 22996.18 28997.37 31697.73 33095.73 26299.58 31595.49 29681.40 33099.36 216
tmp_tt95.75 30295.42 29996.76 30489.90 33494.42 31098.86 19897.87 32078.01 32899.30 20799.69 12497.70 20295.89 33299.29 7398.14 31399.95 1
MVS95.72 30394.63 30698.99 24298.56 31397.98 26999.30 10998.86 28672.71 33097.30 31799.08 28298.34 15999.74 27189.21 32698.33 30699.26 229
PAPM95.61 30494.71 30598.31 28099.12 27496.63 29196.66 32298.46 30590.77 32396.25 32498.68 31393.01 28799.69 28481.60 33097.86 31998.62 291
IB-MVS95.41 2095.30 30594.46 30797.84 29098.76 30695.33 30597.33 31296.07 32796.02 29095.37 32997.41 33476.17 33699.96 3397.54 21095.44 32998.22 310
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
testpf94.48 30695.31 30091.99 31997.22 33089.64 33398.86 19896.52 32494.36 31496.09 32698.76 30982.21 32798.73 32997.05 23796.74 32487.60 327
.test124585.84 30789.27 30875.54 32099.65 14497.72 27298.35 24899.80 6099.40 10999.66 11899.43 22175.10 33799.87 14898.98 11033.07 33129.03 330
pcd1.5k->3k49.97 30855.52 30933.31 32199.95 130.00 3380.00 32999.81 560.00 3330.00 334100.00 199.96 10.00 3360.00 333100.00 199.92 3
test12329.31 30933.05 31218.08 32225.93 33612.24 33697.53 30610.93 33811.78 33124.21 33250.08 34121.04 3398.60 33423.51 33132.43 33333.39 329
testmvs28.94 31033.33 31015.79 32326.03 3359.81 33796.77 31915.67 33711.55 33223.87 33350.74 34019.03 3408.53 33523.21 33233.07 33129.03 330
cdsmvs_eth3d_5k24.88 31133.17 3110.00 3240.00 3370.00 3380.00 32999.62 1410.00 3330.00 33499.13 27599.82 60.00 3360.00 3330.00 3340.00 332
pcd_1.5k_mvsjas16.61 31222.14 3130.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 334100.00 199.28 390.00 3360.00 3330.00 3340.00 332
sosnet-low-res8.33 31311.11 3140.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 334100.00 10.00 3410.00 3360.00 3330.00 3340.00 332
sosnet8.33 31311.11 3140.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 334100.00 10.00 3410.00 3360.00 3330.00 3340.00 332
uncertanet8.33 31311.11 3140.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 334100.00 10.00 3410.00 3360.00 3330.00 3340.00 332
Regformer8.33 31311.11 3140.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 334100.00 10.00 3410.00 3360.00 3330.00 3340.00 332
uanet8.33 31311.11 3140.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 334100.00 10.00 3410.00 3360.00 3330.00 3340.00 332
ab-mvs-re8.26 31811.02 3190.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 33499.16 2730.00 3410.00 3360.00 3330.00 3340.00 332
sam_mvs190.81 307
sam_mvs90.52 311
semantic-postprocess98.51 27199.75 11095.90 29899.84 3799.84 2399.89 3799.73 9895.96 26199.99 499.33 65100.00 199.63 97
ambc99.20 22599.35 23198.53 23599.17 14299.46 21399.67 11499.80 6398.46 15099.70 27897.92 18599.70 20299.38 209
MTGPAbinary99.53 188
test_post199.14 15551.63 33989.54 31899.82 22296.86 245
test_post52.41 33890.25 31399.86 167
patchmatchnet-post99.62 16590.58 30999.94 53
GG-mvs-BLEND97.36 29997.59 32796.87 29099.70 2988.49 33694.64 33097.26 33580.66 33299.12 32691.50 32396.50 32696.08 326
MTMP98.59 300
gm-plane-assit97.59 32789.02 33493.47 31798.30 32299.84 19996.38 266
test9_res95.10 30599.44 24699.50 171
TEST999.35 23199.35 14798.11 26999.41 22494.83 31197.92 30098.99 28898.02 18199.85 183
test_899.34 24199.31 15398.08 27499.40 22994.90 30797.87 30598.97 29498.02 18199.84 199
agg_prior294.58 31199.46 24599.50 171
agg_prior99.35 23199.36 14399.39 23297.76 31199.85 183
TestCases99.63 10699.78 8799.64 7399.83 4098.63 19399.63 12999.72 10498.68 11599.75 26796.38 26699.83 14399.51 165
test_prior499.19 18498.00 282
test_prior297.95 28997.87 24498.05 29599.05 28497.90 18895.99 28199.49 241
test_prior99.46 16599.35 23199.22 17699.39 23299.69 28499.48 178
旧先验297.94 29195.33 30298.94 24599.88 13696.75 250
新几何298.04 277
新几何199.52 15099.50 19099.22 17699.26 25995.66 29998.60 27199.28 25497.67 20799.89 12195.95 28599.32 26599.45 188
旧先验199.49 19599.29 15799.26 25999.39 23097.67 20799.36 26199.46 186
无先验98.01 28099.23 26695.83 29399.85 18395.79 29099.44 193
原ACMM297.92 293
原ACMM199.37 19299.47 20598.87 22199.27 25796.74 28498.26 28499.32 24697.93 18799.82 22295.96 28499.38 25899.43 199
test22299.51 18599.08 19797.83 29899.29 25395.21 30498.68 26699.31 24897.28 22699.38 25899.43 199
testdata299.89 12195.99 281
segment_acmp98.37 157
testdata99.42 17699.51 18598.93 21399.30 25296.20 28898.87 25399.40 22798.33 16199.89 12196.29 26999.28 26999.44 193
testdata197.72 30097.86 247
test1299.54 14799.29 25299.33 15099.16 27298.43 28097.54 21399.82 22299.47 24399.48 178
plane_prior799.58 15599.38 137
plane_prior699.47 20599.26 16597.24 227
plane_prior599.54 18399.82 22295.84 28899.78 17399.60 121
plane_prior499.25 260
plane_prior399.31 15398.36 21399.14 227
plane_prior298.80 20998.94 161
plane_prior199.51 185
plane_prior99.24 17298.42 24597.87 24499.71 200
n20.00 339
nn0.00 339
door-mid99.83 40
lessismore_v099.64 10299.86 3499.38 13790.66 33399.89 3799.83 5194.56 27599.97 1699.56 4399.92 8699.57 140
LGP-MVS_train99.74 5599.82 5299.63 7799.73 9197.56 25999.64 12699.69 12499.37 3099.89 12196.66 25599.87 11899.69 56
test1199.29 253
door99.77 72
HQP5-MVS98.94 209
HQP-NCC99.31 24797.98 28597.45 26598.15 289
ACMP_Plane99.31 24797.98 28597.45 26598.15 289
BP-MVS94.73 308
HQP4-MVS98.15 28999.70 27899.53 154
HQP3-MVS99.37 23899.67 209
HQP2-MVS96.67 244
NP-MVS99.40 22299.13 18998.83 305
MDTV_nov1_ep13_2view91.44 32699.14 15597.37 26999.21 21791.78 29896.75 25099.03 270
MDTV_nov1_ep1397.73 26898.70 31090.83 32899.15 15098.02 31498.51 20298.82 25599.61 17290.98 30299.66 29896.89 24498.92 288
ACMMP++_ref99.94 75
ACMMP++99.79 168
Test By Simon98.41 154
ITE_SJBPF99.38 18999.63 14899.44 11399.73 9198.56 19899.33 19999.53 20298.88 8699.68 29296.01 27999.65 21499.02 271
DeepMVS_CXcopyleft97.98 28799.69 13596.95 28899.26 25975.51 32995.74 32898.28 32396.47 24999.62 31091.23 32497.89 31897.38 322