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_tets99.90 299.90 299.90 499.96 499.79 3399.72 2699.88 1799.92 599.98 399.93 1399.94 299.98 799.77 30100.00 199.92 3
jajsoiax99.89 399.89 399.89 699.96 499.78 3599.70 3099.86 2199.89 1099.98 399.90 2299.94 299.98 799.75 31100.00 199.90 5
ANet_high99.88 499.87 499.91 299.99 199.91 299.65 55100.00 199.90 6100.00 199.97 999.61 1799.97 1699.75 31100.00 199.84 15
LTVRE_ROB99.19 199.88 499.87 499.88 1199.91 2099.90 399.96 199.92 699.90 699.97 699.87 3799.81 799.95 4199.54 4699.99 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 1499.90 399.83 799.91 1099.85 1899.94 2099.95 1199.73 1099.90 11199.65 3599.97 4699.69 58
v5299.85 799.84 799.89 699.96 499.89 599.87 499.81 5599.85 1899.96 899.90 2299.27 4199.95 4199.93 199.99 2099.82 23
V499.85 799.84 799.88 1199.96 499.89 599.87 499.81 5599.85 1899.96 899.90 2299.27 4199.95 4199.93 1100.00 199.82 23
PS-MVSNAJss99.84 999.82 999.89 699.96 499.77 3799.68 4299.85 2899.95 399.98 399.92 1699.28 3899.98 799.75 31100.00 199.94 2
test_djsdf99.84 999.81 1099.91 299.94 1499.84 1899.77 1499.80 5999.73 4399.97 699.92 1699.77 999.98 799.43 55100.00 199.90 5
v7n99.82 1199.80 1199.88 1199.96 499.84 1899.82 999.82 4799.84 2299.94 2099.91 1999.13 5699.96 3399.83 2099.99 2099.83 18
anonymousdsp99.80 1299.77 1399.90 499.96 499.88 799.73 2299.85 2899.70 5199.92 3099.93 1399.45 2299.97 1699.36 65100.00 199.85 14
pm-mvs199.79 1399.79 1299.78 3899.91 2099.83 2299.76 1799.87 1999.73 4399.89 3999.87 3799.63 1599.87 16199.54 4699.92 9199.63 99
UA-Net99.78 1499.76 1799.86 1799.72 13099.71 5399.91 399.95 599.96 299.71 11099.91 1999.15 5299.97 1699.50 50100.00 199.90 5
TransMVSNet (Re)99.78 1499.77 1399.81 2899.91 2099.85 1299.75 1899.86 2199.70 5199.91 3399.89 3199.60 1999.87 16199.59 3999.74 19499.71 51
v74899.76 1699.74 2099.84 2099.95 1299.83 2299.82 999.80 5999.82 2699.95 1699.87 3798.72 11299.93 6599.72 3499.98 3699.75 42
v1399.76 1699.77 1399.73 6399.86 3599.55 9999.77 1499.86 2199.79 3399.96 899.91 1998.90 8399.87 16199.91 5100.00 199.78 32
v1299.75 1899.77 1399.72 6999.85 3999.53 10299.75 1899.86 2199.78 3499.96 899.90 2298.88 8699.86 18199.91 5100.00 199.77 34
v1199.75 1899.76 1799.71 7399.85 3999.49 10599.73 2299.84 3699.75 3999.95 1699.90 2298.93 7899.86 18199.92 3100.00 199.77 34
OurMVSNet-221017-099.75 1899.71 2499.84 2099.96 499.83 2299.83 799.85 2899.80 3199.93 2599.93 1398.54 14199.93 6599.59 3999.98 3699.76 38
Vis-MVSNetpermissive99.75 1899.74 2099.79 3599.88 2899.66 7199.69 3999.92 699.67 6099.77 8899.75 9599.61 1799.98 799.35 6699.98 3699.72 48
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
V999.74 2299.75 1999.71 7399.84 4299.50 10399.74 2099.86 2199.76 3899.96 899.90 2298.83 8999.85 19999.91 5100.00 199.77 34
V1499.73 2399.74 2099.69 8099.83 4699.48 10899.72 2699.85 2899.74 4099.96 899.89 3198.79 9799.85 19999.91 5100.00 199.76 38
v1599.72 2499.73 2399.68 8399.82 5399.44 12099.70 3099.85 2899.72 4699.95 1699.88 3498.76 10599.84 21599.90 9100.00 199.75 42
TDRefinement99.72 2499.70 2799.77 4099.90 2599.85 1299.86 699.92 699.69 5599.78 8399.92 1699.37 2999.88 14198.93 12699.95 6799.60 124
XXY-MVS99.71 2699.67 3199.81 2899.89 2799.72 5299.59 6799.82 4799.39 11799.82 6699.84 5099.38 2799.91 9399.38 6299.93 8899.80 25
nrg03099.70 2799.66 3299.82 2599.76 10399.84 1899.61 6299.70 10999.93 499.78 8399.68 14099.10 5899.78 27599.45 5399.96 5999.83 18
FC-MVSNet-test99.70 2799.65 3399.86 1799.88 2899.86 1199.72 2699.78 6999.90 699.82 6699.83 5198.45 15599.87 16199.51 4999.97 4699.86 12
v1799.70 2799.71 2499.67 8699.81 6199.44 12099.70 3099.83 3999.69 5599.94 2099.87 3798.70 11399.84 21599.88 1499.99 2099.73 45
v1699.70 2799.71 2499.67 8699.81 6199.43 12699.70 3099.83 3999.70 5199.94 2099.87 3798.69 11599.84 21599.88 1499.99 2099.73 45
v1099.69 3199.69 2899.66 9499.81 6199.39 13899.66 5099.75 8599.60 8499.92 3099.87 3798.75 10899.86 18199.90 999.99 2099.73 45
v1899.68 3299.69 2899.65 9899.79 8299.40 13599.68 4299.83 3999.66 6599.93 2599.85 4598.65 12499.84 21599.87 1899.99 2099.71 51
v899.68 3299.69 2899.65 9899.80 6999.40 13599.66 5099.76 7999.64 7099.93 2599.85 4598.66 12299.84 21599.88 1499.99 2099.71 51
DTE-MVSNet99.68 3299.61 4199.88 1199.80 6999.87 899.67 4799.71 10699.72 4699.84 6199.78 8098.67 12099.97 1699.30 7599.95 6799.80 25
Anonymous2024052199.67 3599.62 3799.84 2099.91 2099.85 1299.81 1199.76 7999.72 4699.92 3099.83 5198.10 18299.90 11199.58 4299.97 4699.77 34
VPA-MVSNet99.66 3699.62 3799.79 3599.68 14999.75 4499.62 5899.69 11599.85 1899.80 7599.81 6298.81 9099.91 9399.47 5299.88 11599.70 55
PS-CasMVS99.66 3699.58 4599.89 699.80 6999.85 1299.66 5099.73 9399.62 7499.84 6199.71 11498.62 12999.96 3399.30 7599.96 5999.86 12
PEN-MVS99.66 3699.59 4399.89 699.83 4699.87 899.66 5099.73 9399.70 5199.84 6199.73 10198.56 13599.96 3399.29 7999.94 8099.83 18
FMVSNet199.66 3699.63 3699.73 6399.78 8899.77 3799.68 4299.70 10999.67 6099.82 6699.83 5198.98 7299.90 11199.24 8399.97 4699.53 158
MIMVSNet199.66 3699.62 3799.80 3099.94 1499.87 899.69 3999.77 7299.78 3499.93 2599.89 3197.94 19699.92 8399.65 3599.98 3699.62 113
FIs99.65 4199.58 4599.84 2099.84 4299.85 1299.66 5099.75 8599.86 1599.74 10299.79 7198.27 16899.85 19999.37 6499.93 8899.83 18
wuykxyi23d99.65 4199.64 3599.69 8099.92 1899.20 19098.89 22199.99 298.73 20599.95 1699.80 6499.84 499.99 499.64 3799.98 3699.89 9
Anonymous2023121199.62 4399.57 4899.76 4399.61 16999.60 8999.81 1199.73 9399.82 2699.90 3599.90 2297.97 19599.86 18199.42 5999.96 5999.80 25
DeepC-MVS98.90 499.62 4399.61 4199.67 8699.72 13099.44 12099.24 14499.71 10699.27 13099.93 2599.90 2299.70 1299.93 6598.99 11399.99 2099.64 95
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 4599.53 6299.87 1599.80 6999.83 2299.67 4799.75 8599.58 8799.85 5899.69 12798.18 17999.94 5499.28 8199.95 6799.83 18
ACMH98.42 699.59 4699.54 5499.72 6999.86 3599.62 8499.56 7299.79 6798.77 19799.80 7599.85 4599.64 1499.85 19998.70 14199.89 10999.70 55
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testing_299.58 4799.56 5299.62 11999.81 6199.44 12099.14 17899.43 23299.69 5599.82 6699.79 7199.14 5399.79 26799.31 7499.95 6799.63 99
v119299.57 4899.57 4899.57 14099.77 9899.22 18499.04 19899.60 16599.18 14799.87 5299.72 10799.08 6399.85 19999.89 1399.98 3699.66 81
EG-PatchMatch MVS99.57 4899.56 5299.62 11999.77 9899.33 15799.26 13999.76 7999.32 12599.80 7599.78 8099.29 3699.87 16199.15 9699.91 10199.66 81
Gipumacopyleft99.57 4899.59 4399.49 16399.98 399.71 5399.72 2699.84 3699.81 2899.94 2099.78 8098.91 8299.71 30398.41 15799.95 6799.05 283
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v192192099.56 5199.57 4899.55 14999.75 11199.11 19999.05 19699.61 15199.15 15499.88 4799.71 11499.08 6399.87 16199.90 999.97 4699.66 81
v124099.56 5199.58 4599.51 15999.80 6999.00 21099.00 20599.65 13699.15 15499.90 3599.75 9599.09 6099.88 14199.90 999.96 5999.67 71
v799.56 5199.54 5499.61 12299.80 6999.39 13899.30 12699.59 17099.14 15699.82 6699.72 10798.75 10899.84 21599.83 2099.94 8099.61 118
V4299.56 5199.54 5499.63 11099.79 8299.46 11399.39 9199.59 17099.24 13999.86 5799.70 12198.55 13999.82 24099.79 2699.95 6799.60 124
v14419299.55 5599.54 5499.58 13499.78 8899.20 19099.11 18699.62 14799.18 14799.89 3999.72 10798.66 12299.87 16199.88 1499.97 4699.66 81
v1neww99.55 5599.54 5499.61 12299.80 6999.39 13899.32 11699.61 15199.18 14799.87 5299.69 12798.64 12799.82 24099.79 2699.94 8099.60 124
v7new99.55 5599.54 5499.61 12299.80 6999.39 13899.32 11699.61 15199.18 14799.87 5299.69 12798.64 12799.82 24099.79 2699.94 8099.60 124
v699.55 5599.54 5499.61 12299.80 6999.39 13899.32 11699.60 16599.18 14799.87 5299.68 14098.65 12499.82 24099.79 2699.95 6799.61 118
test20.0399.55 5599.54 5499.58 13499.79 8299.37 14799.02 20199.89 1499.60 8499.82 6699.62 17498.81 9099.89 12699.43 5599.86 12999.47 188
v114499.54 6099.53 6299.59 13099.79 8299.28 16799.10 18799.61 15199.20 14599.84 6199.73 10198.67 12099.84 21599.86 1999.98 3699.64 95
v114199.54 6099.52 6499.57 14099.78 8899.27 17199.15 17399.61 15199.26 13499.89 3999.69 12798.56 13599.82 24099.82 2399.97 4699.63 99
divwei89l23v2f11299.54 6099.52 6499.57 14099.78 8899.27 17199.15 17399.61 15199.26 13499.89 3999.69 12798.56 13599.82 24099.82 2399.96 5999.63 99
v199.54 6099.52 6499.58 13499.77 9899.28 16799.15 17399.61 15199.26 13499.88 4799.68 14098.56 13599.82 24099.82 2399.97 4699.63 99
CP-MVSNet99.54 6099.43 7999.87 1599.76 10399.82 2799.57 7099.61 15199.54 8999.80 7599.64 15997.79 20899.95 4199.21 8499.94 8099.84 15
TranMVSNet+NR-MVSNet99.54 6099.47 7099.76 4399.58 17799.64 7899.30 12699.63 14499.61 7899.71 11099.56 20498.76 10599.96 3399.14 10299.92 9199.68 64
testmv99.53 6699.51 6799.59 13099.73 12099.31 16098.48 26799.92 699.57 8899.87 5299.79 7199.12 5799.91 9399.16 9599.99 2099.55 147
v2v48299.50 6799.47 7099.58 13499.78 8899.25 17799.14 17899.58 17899.25 13799.81 7299.62 17498.24 17099.84 21599.83 2099.97 4699.64 95
ACMH+98.40 899.50 6799.43 7999.71 7399.86 3599.76 4299.32 11699.77 7299.53 9199.77 8899.76 9199.26 4499.78 27597.77 20299.88 11599.60 124
Baseline_NR-MVSNet99.49 6999.37 8999.82 2599.91 2099.84 1898.83 23299.86 2199.68 5899.65 12999.88 3497.67 21799.87 16199.03 11099.86 12999.76 38
TAMVS99.49 6999.45 7499.63 11099.48 22899.42 13099.45 8499.57 18199.66 6599.78 8399.83 5197.85 20399.86 18199.44 5499.96 5999.61 118
pmmvs-eth3d99.48 7199.47 7099.51 15999.77 9899.41 13498.81 23699.66 12799.42 11499.75 9499.66 15399.20 4799.76 28398.98 11599.99 2099.36 223
EI-MVSNet-UG-set99.48 7199.50 6899.42 18399.57 18698.65 24399.24 14499.46 22499.68 5899.80 7599.66 15398.99 7199.89 12699.19 8799.90 10399.72 48
APDe-MVS99.48 7199.36 9299.85 1999.55 20099.81 2899.50 7799.69 11598.99 17199.75 9499.71 11498.79 9799.93 6598.46 15599.85 13299.80 25
PMMVS299.48 7199.45 7499.57 14099.76 10398.99 21198.09 30299.90 1398.95 17599.78 8399.58 19499.57 2099.93 6599.48 5199.95 6799.79 31
DSMNet-mixed99.48 7199.65 3398.95 25699.71 13397.27 30299.50 7799.82 4799.59 8699.41 19599.85 4599.62 16100.00 199.53 4899.89 10999.59 135
DP-MVS99.48 7199.39 8399.74 5799.57 18699.62 8499.29 13499.61 15199.87 1399.74 10299.76 9198.69 11599.87 16198.20 17499.80 16899.75 42
EI-MVSNet-Vis-set99.47 7799.49 6999.42 18399.57 18698.66 24199.24 14499.46 22499.67 6099.79 8099.65 15898.97 7499.89 12699.15 9699.89 10999.71 51
VPNet99.46 7899.37 8999.71 7399.82 5399.59 9199.48 8199.70 10999.81 2899.69 11499.58 19497.66 22199.86 18199.17 9299.44 25699.67 71
ACMM98.09 1199.46 7899.38 8599.72 6999.80 6999.69 6499.13 18399.65 13698.99 17199.64 13199.72 10799.39 2399.86 18198.23 17199.81 16399.60 124
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Regformer-499.45 8099.44 7699.50 16199.52 20798.94 21799.17 16599.53 19699.64 7099.76 9199.60 18698.96 7799.90 11198.91 12799.84 13699.67 71
COLMAP_ROBcopyleft98.06 1299.45 8099.37 8999.70 7999.83 4699.70 6099.38 9799.78 6999.53 9199.67 11999.78 8099.19 4899.86 18197.32 23099.87 12299.55 147
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tfpnnormal99.43 8299.38 8599.60 12899.87 3299.75 4499.59 6799.78 6999.71 4999.90 3599.69 12798.85 8899.90 11197.25 23799.78 17799.15 257
HPM-MVS_fast99.43 8299.30 10599.80 3099.83 4699.81 2899.52 7599.70 10998.35 24099.51 17599.50 22299.31 3499.88 14198.18 17899.84 13699.69 58
3Dnovator99.15 299.43 8299.36 9299.65 9899.39 25499.42 13099.70 3099.56 18499.23 14199.35 21199.80 6499.17 5099.95 4198.21 17399.84 13699.59 135
Anonymous2024052999.42 8599.34 9599.65 9899.53 20499.60 8999.63 5799.39 24499.47 10099.76 9199.78 8098.13 18199.86 18198.70 14199.68 21199.49 181
SixPastTwentyTwo99.42 8599.30 10599.76 4399.92 1899.67 6999.70 3099.14 28899.65 6899.89 3999.90 2296.20 26899.94 5499.42 5999.92 9199.67 71
GBi-Net99.42 8599.31 10099.73 6399.49 22299.77 3799.68 4299.70 10999.44 10799.62 14299.83 5197.21 24099.90 11198.96 12099.90 10399.53 158
test199.42 8599.31 10099.73 6399.49 22299.77 3799.68 4299.70 10999.44 10799.62 14299.83 5197.21 24099.90 11198.96 12099.90 10399.53 158
Regformer-399.41 8999.41 8199.40 19199.52 20798.70 23899.17 16599.44 22999.62 7499.75 9499.60 18698.90 8399.85 19998.89 12899.84 13699.65 89
MVSFormer99.41 8999.44 7699.31 21499.57 18698.40 25499.77 1499.80 5999.73 4399.63 13599.30 26298.02 19099.98 799.43 5599.69 20999.55 147
IterMVS-LS99.41 8999.47 7099.25 22899.81 6198.09 27998.85 22999.76 7999.62 7499.83 6599.64 15998.54 14199.97 1699.15 9699.99 2099.68 64
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v14899.40 9299.41 8199.39 19499.76 10398.94 21799.09 19199.59 17099.17 15299.81 7299.61 18398.41 15899.69 31099.32 7299.94 8099.53 158
casdiffmvs199.40 9299.38 8599.46 17199.51 21199.31 16099.53 7499.64 14199.74 4099.08 25299.77 8798.10 18299.73 29599.59 3999.47 25299.33 228
NR-MVSNet99.40 9299.31 10099.68 8399.43 24499.55 9999.73 2299.50 21299.46 10499.88 4799.36 24997.54 22499.87 16198.97 11999.87 12299.63 99
PVSNet_Blended_VisFu99.40 9299.38 8599.44 17899.90 2598.66 24198.94 21999.91 1097.97 26299.79 8099.73 10199.05 6899.97 1699.15 9699.99 2099.68 64
EU-MVSNet99.39 9699.62 3798.72 28199.88 2896.44 31399.56 7299.85 2899.90 699.90 3599.85 4598.09 18499.83 23299.58 4299.95 6799.90 5
CHOSEN 1792x268899.39 9699.30 10599.65 9899.88 2899.25 17798.78 24199.88 1798.66 20999.96 899.79 7197.45 22899.93 6599.34 6799.99 2099.78 32
EI-MVSNet99.38 9899.44 7699.21 23399.58 17798.09 27999.26 13999.46 22499.62 7499.75 9499.67 14798.54 14199.85 19999.15 9699.92 9199.68 64
UGNet99.38 9899.34 9599.49 16398.90 32298.90 22599.70 3099.35 25499.86 1598.57 30599.81 6298.50 15099.93 6599.38 6299.98 3699.66 81
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 10099.25 11899.72 6999.47 23399.56 9698.97 21499.61 15199.43 11299.67 11999.28 26697.85 20399.95 4199.17 9299.81 16399.65 89
UniMVSNet (Re)99.37 10099.26 11699.68 8399.51 21199.58 9398.98 21399.60 16599.43 11299.70 11299.36 24997.70 21299.88 14199.20 8699.87 12299.59 135
CSCG99.37 10099.29 11099.60 12899.71 13399.46 11399.43 8799.85 2898.79 19499.41 19599.60 18698.92 8099.92 8398.02 18799.92 9199.43 205
PM-MVS99.36 10399.29 11099.58 13499.83 4699.66 7198.95 21699.86 2198.85 18699.81 7299.73 10198.40 16099.92 8398.36 16199.83 14699.17 255
abl_699.36 10399.23 12199.75 5399.71 13399.74 4999.33 11399.76 7999.07 16699.65 12999.63 16799.09 6099.92 8397.13 24599.76 18399.58 139
new-patchmatchnet99.35 10599.57 4898.71 28299.82 5396.62 31198.55 25899.75 8599.50 9499.88 4799.87 3799.31 3499.88 14199.43 55100.00 199.62 113
Anonymous2023120699.35 10599.31 10099.47 16899.74 11799.06 20999.28 13599.74 9099.23 14199.72 10699.53 21397.63 22399.88 14199.11 10499.84 13699.48 183
MTAPA99.35 10599.20 12599.80 3099.81 6199.81 2899.33 11399.53 19699.27 13099.42 18999.63 16798.21 17499.95 4197.83 19999.79 17199.65 89
FMVSNet299.35 10599.28 11299.55 14999.49 22299.35 15499.45 8499.57 18199.44 10799.70 11299.74 9797.21 24099.87 16199.03 11099.94 8099.44 199
3Dnovator+98.92 399.35 10599.24 11999.67 8699.35 26299.47 10999.62 5899.50 21299.44 10799.12 24999.78 8098.77 10399.94 5497.87 19699.72 20599.62 113
TSAR-MVS + MP.99.34 11099.24 11999.63 11099.82 5399.37 14799.26 13999.35 25498.77 19799.57 15399.70 12199.27 4199.88 14197.71 20599.75 18699.65 89
Regformer-299.34 11099.27 11499.53 15499.41 25099.10 20298.99 20999.53 19699.47 10099.66 12399.52 21598.80 9499.89 12698.31 16699.74 19499.60 124
diffmvs199.34 11099.35 9499.32 21199.42 24898.94 21799.22 15199.77 7299.61 7898.78 28699.67 14798.77 10399.90 11199.30 7599.59 23099.13 264
DELS-MVS99.34 11099.30 10599.48 16699.51 21199.36 15098.12 29899.53 19699.36 12199.41 19599.61 18399.22 4699.87 16199.21 8499.68 21199.20 247
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 11499.21 12499.71 7399.43 24499.56 9698.83 23299.53 19699.38 11899.67 11999.36 24997.67 21799.95 4199.17 9299.81 16399.63 99
ab-mvs99.33 11499.28 11299.47 16899.57 18699.39 13899.78 1399.43 23298.87 18499.57 15399.82 5998.06 18799.87 16198.69 14399.73 20099.15 257
Regformer-199.32 11699.27 11499.47 16899.41 25098.95 21698.99 20999.48 21799.48 9699.66 12399.52 21598.78 10099.87 16198.36 16199.74 19499.60 124
APD-MVS_3200maxsize99.31 11799.16 12799.74 5799.53 20499.75 4499.27 13899.61 15199.19 14699.57 15399.64 15998.76 10599.90 11197.29 23299.62 22499.56 144
zzz-MVS99.30 11899.14 13099.80 3099.81 6199.81 2898.73 24599.53 19699.27 13099.42 18999.63 16798.21 17499.95 4197.83 19999.79 17199.65 89
SteuartSystems-ACMMP99.30 11899.14 13099.76 4399.87 3299.66 7199.18 15899.60 16598.55 21899.57 15399.67 14799.03 7099.94 5497.01 24999.80 16899.69 58
Skip Steuart: Steuart Systems R&D Blog.
testgi99.29 12099.26 11699.37 20099.75 11198.81 23498.84 23099.89 1498.38 23399.75 9499.04 30699.36 3299.86 18199.08 10799.25 28499.45 194
ACMMP_Plus99.28 12199.11 13999.79 3599.75 11199.81 2898.95 21699.53 19698.27 24999.53 17199.73 10198.75 10899.87 16197.70 20699.83 14699.68 64
LCM-MVSNet-Re99.28 12199.15 12999.67 8699.33 27799.76 4299.34 11199.97 398.93 17899.91 3399.79 7198.68 11799.93 6596.80 25999.56 23499.30 235
mvs_anonymous99.28 12199.39 8398.94 25799.19 29897.81 29099.02 20199.55 18799.78 3499.85 5899.80 6498.24 17099.86 18199.57 4499.50 24899.15 257
MVS_Test99.28 12199.31 10099.19 23699.35 26298.79 23699.36 10399.49 21699.17 15299.21 23899.67 14798.78 10099.66 32999.09 10699.66 21999.10 271
no-one99.28 12199.23 12199.45 17699.87 3299.08 20598.95 21699.52 20698.88 18399.77 8899.83 5197.78 20999.90 11198.46 15599.99 2099.38 216
XVS99.27 12699.11 13999.75 5399.71 13399.71 5399.37 10199.61 15199.29 12698.76 28899.47 22898.47 15299.88 14197.62 21399.73 20099.67 71
OPM-MVS99.26 12799.13 13399.63 11099.70 14099.61 8898.58 25399.48 21798.50 22399.52 17399.63 16799.14 5399.76 28397.89 19599.77 18199.51 169
HFP-MVS99.25 12899.08 15099.76 4399.73 12099.70 6099.31 12399.59 17098.36 23599.36 20999.37 24498.80 9499.91 9397.43 22599.75 18699.68 64
HPM-MVScopyleft99.25 12899.07 15499.78 3899.81 6199.75 4499.61 6299.67 12397.72 27599.35 21199.25 27299.23 4599.92 8397.21 24199.82 15599.67 71
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMPcopyleft99.25 12899.08 15099.74 5799.79 8299.68 6799.50 7799.65 13698.07 25699.52 17399.69 12798.57 13499.92 8397.18 24399.79 17199.63 99
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
casdiffmvs99.24 13199.23 12199.26 22499.42 24898.85 23299.48 8199.58 17899.67 6098.70 29399.67 14797.85 20399.72 29799.41 6199.28 27999.20 247
LS3D99.24 13199.11 13999.61 12298.38 35299.79 3399.57 7099.68 11899.61 7899.15 24699.71 11498.70 11399.91 9397.54 21999.68 21199.13 264
xiu_mvs_v1_base_debu99.23 13399.34 9598.91 26099.59 17498.23 26898.47 26899.66 12799.61 7899.68 11698.94 31999.39 2399.97 1699.18 8999.55 24098.51 312
xiu_mvs_v1_base99.23 13399.34 9598.91 26099.59 17498.23 26898.47 26899.66 12799.61 7899.68 11698.94 31999.39 2399.97 1699.18 8999.55 24098.51 312
xiu_mvs_v1_base_debi99.23 13399.34 9598.91 26099.59 17498.23 26898.47 26899.66 12799.61 7899.68 11698.94 31999.39 2399.97 1699.18 8999.55 24098.51 312
region2R99.23 13399.05 16099.77 4099.76 10399.70 6099.31 12399.59 17098.41 23099.32 21999.36 24998.73 11199.93 6597.29 23299.74 19499.67 71
ACMMPR99.23 13399.06 15699.76 4399.74 11799.69 6499.31 12399.59 17098.36 23599.35 21199.38 24398.61 13199.93 6597.43 22599.75 18699.67 71
XVG-ACMP-BASELINE99.23 13399.10 14699.63 11099.82 5399.58 9398.83 23299.72 10398.36 23599.60 14999.71 11498.92 8099.91 9397.08 24699.84 13699.40 210
CP-MVS99.23 13399.05 16099.75 5399.66 15499.66 7199.38 9799.62 14798.38 23399.06 25799.27 26898.79 9799.94 5497.51 22199.82 15599.66 81
DeepC-MVS_fast98.47 599.23 13399.12 13699.56 14699.28 28699.22 18498.99 20999.40 24199.08 16499.58 15199.64 15998.90 8399.83 23297.44 22499.75 18699.63 99
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 14199.05 16099.74 5799.82 5399.63 8299.16 17199.73 9397.56 28399.64 13199.69 12799.37 2999.89 12696.66 26799.87 12299.69 58
CDS-MVSNet99.22 14199.13 13399.50 16199.35 26299.11 19998.96 21599.54 19199.46 10499.61 14799.70 12196.31 26599.83 23299.34 6799.88 11599.55 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test_040299.22 14199.14 13099.45 17699.79 8299.43 12699.28 13599.68 11899.54 8999.40 19999.56 20499.07 6599.82 24096.01 29399.96 5999.11 267
AllTest99.21 14499.07 15499.63 11099.78 8899.64 7899.12 18599.83 3998.63 21299.63 13599.72 10798.68 11799.75 28996.38 27999.83 14699.51 169
XVG-OURS99.21 14499.06 15699.65 9899.82 5399.62 8497.87 32699.74 9098.36 23599.66 12399.68 14099.71 1199.90 11196.84 25799.88 11599.43 205
Fast-Effi-MVS+-dtu99.20 14699.12 13699.43 18199.25 28999.69 6499.05 19699.82 4799.50 9498.97 26299.05 30398.98 7299.98 798.20 17499.24 28698.62 305
VDD-MVS99.20 14699.11 13999.44 17899.43 24498.98 21299.50 7798.32 32499.80 3199.56 16199.69 12796.99 25099.85 19998.99 11399.73 20099.50 175
PGM-MVS99.20 14699.01 17099.77 4099.75 11199.71 5399.16 17199.72 10397.99 26099.42 18999.60 18698.81 9099.93 6596.91 25399.74 19499.66 81
SMA-MVS99.19 14999.00 17299.73 6399.46 23799.73 5099.13 18399.52 20697.40 29199.57 15399.64 15998.93 7899.83 23297.61 21599.79 17199.63 99
pmmvs599.19 14999.11 13999.42 18399.76 10398.88 22798.55 25899.73 9398.82 19099.72 10699.62 17496.56 25799.82 24099.32 7299.95 6799.56 144
mPP-MVS99.19 14999.00 17299.76 4399.76 10399.68 6799.38 9799.54 19198.34 24499.01 25999.50 22298.53 14599.93 6597.18 24399.78 17799.66 81
VNet99.18 15299.06 15699.56 14699.24 29199.36 15099.33 11399.31 26399.67 6099.47 18099.57 20096.48 26099.84 21599.15 9699.30 27799.47 188
RPSCF99.18 15299.02 16799.64 10699.83 4699.85 1299.44 8699.82 4798.33 24599.50 17799.78 8097.90 19899.65 33696.78 26099.83 14699.44 199
DeepPCF-MVS98.42 699.18 15299.02 16799.67 8699.22 29399.75 4497.25 34699.47 22198.72 20699.66 12399.70 12199.29 3699.63 33998.07 18699.81 16399.62 113
MVS_030499.17 15599.10 14699.38 19699.08 31398.86 23098.46 27299.73 9399.53 9199.35 21199.30 26297.11 24699.96 3399.33 6999.99 2099.33 228
diffmvs99.17 15599.19 12699.10 24399.36 26198.41 25399.24 14499.68 11899.46 10498.30 31699.68 14098.49 15199.91 9399.10 10599.43 26298.98 288
EPP-MVSNet99.17 15599.00 17299.66 9499.80 6999.43 12699.70 3099.24 27999.48 9699.56 16199.77 8794.89 28199.93 6598.72 14099.89 10999.63 99
MVP-Stereo99.16 15899.08 15099.43 18199.48 22899.07 20799.08 19399.55 18798.63 21299.31 22199.68 14098.19 17799.78 27598.18 17899.58 23299.45 194
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-OURS-SEG-HR99.16 15898.99 17699.66 9499.84 4299.64 7898.25 28799.73 9398.39 23299.63 13599.43 23499.70 1299.90 11197.34 22998.64 32099.44 199
jason99.16 15899.11 13999.32 21199.75 11198.44 25098.26 28699.39 24498.70 20799.74 10299.30 26298.54 14199.97 1698.48 15499.82 15599.55 147
jason: jason.
ESAPD99.14 16198.92 18699.82 2599.57 18699.77 3798.74 24399.60 16598.55 21899.76 9199.69 12798.23 17399.92 8396.39 27899.75 18699.76 38
MP-MVS-pluss99.14 16198.92 18699.80 3099.83 4699.83 2298.61 24999.63 14496.84 30699.44 18399.58 19498.81 9099.91 9397.70 20699.82 15599.67 71
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pmmvs499.13 16399.06 15699.36 20399.57 18699.10 20298.01 31099.25 27698.78 19699.58 15199.44 23398.24 17099.76 28398.74 13999.93 8899.22 243
MVS_111021_LR99.13 16399.03 16699.42 18399.58 17799.32 15997.91 32599.73 9398.68 20899.31 22199.48 22599.09 6099.66 32997.70 20699.77 18199.29 238
#test#99.12 16598.90 19099.76 4399.73 12099.70 6099.10 18799.59 17097.60 28199.36 20999.37 24498.80 9499.91 9396.84 25799.75 18699.68 64
TSAR-MVS + GP.99.12 16599.04 16599.38 19699.34 27299.16 19498.15 29499.29 26798.18 25399.63 13599.62 17499.18 4999.68 31998.20 17499.74 19499.30 235
MVS_111021_HR99.12 16599.02 16799.40 19199.50 21799.11 19997.92 32399.71 10698.76 20099.08 25299.47 22899.17 5099.54 34897.85 19899.76 18399.54 155
CANet99.11 16899.05 16099.28 21798.83 33298.56 24498.71 24799.41 23599.25 13799.23 23399.22 28197.66 22199.94 5499.19 8799.97 4699.33 228
WR-MVS99.11 16898.93 18399.66 9499.30 28399.42 13098.42 27699.37 25199.04 16899.57 15399.20 28396.89 25299.86 18198.66 14699.87 12299.70 55
PHI-MVS99.11 16898.95 18299.59 13099.13 30599.59 9199.17 16599.65 13697.88 26699.25 22999.46 23198.97 7499.80 26497.26 23599.82 15599.37 220
MSDG99.08 17198.98 17999.37 20099.60 17199.13 19797.54 33699.74 9098.84 18999.53 17199.55 20999.10 5899.79 26797.07 24799.86 12999.18 253
Effi-MVS+-dtu99.07 17298.92 18699.52 15698.89 32699.78 3599.15 17399.66 12799.34 12298.92 27299.24 27797.69 21499.98 798.11 18399.28 27998.81 300
Effi-MVS+99.06 17398.97 18099.34 20599.31 27998.98 21298.31 28499.91 1098.81 19198.79 28498.94 31999.14 5399.84 21598.79 13498.74 31599.20 247
MP-MVScopyleft99.06 17398.83 20099.76 4399.76 10399.71 5399.32 11699.50 21298.35 24098.97 26299.48 22598.37 16199.92 8395.95 29999.75 18699.63 99
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MDA-MVSNet-bldmvs99.06 17399.05 16099.07 24899.80 6997.83 28998.89 22199.72 10399.29 12699.63 13599.70 12196.47 26199.89 12698.17 18099.82 15599.50 175
MSLP-MVS++99.05 17699.09 14898.91 26099.21 29498.36 25898.82 23599.47 22198.85 18698.90 27599.56 20498.78 10099.09 35898.57 14999.68 21199.26 239
1112_ss99.05 17698.84 19799.67 8699.66 15499.29 16598.52 26399.82 4797.65 27999.43 18799.16 28596.42 26399.91 9399.07 10899.84 13699.80 25
ACMP97.51 1499.05 17698.84 19799.67 8699.78 8899.55 9998.88 22399.66 12797.11 30299.47 18099.60 18699.07 6599.89 12696.18 28599.85 13299.58 139
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet_BlendedMVS99.03 17999.01 17099.09 24499.54 20197.99 28398.58 25399.82 4797.62 28099.34 21599.71 11498.52 14799.77 28197.98 19199.97 4699.52 166
IS-MVSNet99.03 17998.85 19599.55 14999.80 6999.25 17799.73 2299.15 28799.37 11999.61 14799.71 11494.73 28399.81 25997.70 20699.88 11599.58 139
xiu_mvs_v2_base99.02 18199.11 13998.77 27499.37 25998.09 27998.13 29799.51 20999.47 10099.42 18998.54 33999.38 2799.97 1698.83 13199.33 27498.24 324
Fast-Effi-MVS+99.02 18198.87 19399.46 17199.38 25799.50 10399.04 19899.79 6797.17 29798.62 30098.74 33299.34 3399.95 4198.32 16599.41 26598.92 293
canonicalmvs99.02 18199.00 17299.09 24499.10 31298.70 23899.61 6299.66 12799.63 7398.64 29997.65 35699.04 6999.54 34898.79 13498.92 30099.04 284
MCST-MVS99.02 18198.81 20299.65 9899.58 17799.49 10598.58 25399.07 29198.40 23199.04 25899.25 27298.51 14999.80 26497.31 23199.51 24799.65 89
HSP-MVS99.01 18598.76 20699.76 4399.78 8899.73 5099.35 10499.31 26398.54 22099.54 16898.99 30896.81 25399.93 6596.97 25199.53 24599.61 118
SD-MVS99.01 18599.30 10598.15 30399.50 21799.40 13598.94 21999.61 15199.22 14499.75 9499.82 5999.54 2195.51 36497.48 22299.87 12299.54 155
LF4IMVS99.01 18598.92 18699.27 21999.71 13399.28 16798.59 25299.77 7298.32 24699.39 20099.41 23898.62 12999.84 21596.62 27099.84 13698.69 304
MS-PatchMatch99.00 18898.97 18099.09 24499.11 31098.19 27198.76 24299.33 25798.49 22499.44 18399.58 19498.21 17499.69 31098.20 17499.62 22499.39 213
PS-MVSNAJ99.00 18899.08 15098.76 27599.37 25998.10 27898.00 31299.51 20999.47 10099.41 19598.50 34199.28 3899.97 1698.83 13199.34 27298.20 328
CNVR-MVS98.99 19098.80 20499.56 14699.25 28999.43 12698.54 26199.27 27198.58 21698.80 28399.43 23498.53 14599.70 30497.22 23999.59 23099.54 155
VDDNet98.97 19198.82 20199.42 18399.71 13398.81 23499.62 5898.68 30999.81 2899.38 20799.80 6494.25 28799.85 19998.79 13499.32 27599.59 135
IterMVS98.97 19199.16 12798.42 29199.74 11795.64 33198.06 30799.83 3999.83 2599.85 5899.74 9796.10 27199.99 499.27 82100.00 199.63 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TinyColmap98.97 19198.93 18399.07 24899.46 23798.19 27197.75 32999.75 8598.79 19499.54 16899.70 12198.97 7499.62 34096.63 26999.83 14699.41 209
HPM-MVS++copyleft98.96 19498.70 20999.74 5799.52 20799.71 5398.86 22699.19 28398.47 22698.59 30399.06 30298.08 18699.91 9396.94 25299.60 22999.60 124
lupinMVS98.96 19498.87 19399.24 23099.57 18698.40 25498.12 29899.18 28498.28 24899.63 13599.13 28798.02 19099.97 1698.22 17299.69 20999.35 225
USDC98.96 19498.93 18399.05 25099.54 20197.99 28397.07 34899.80 5998.21 25199.75 9499.77 8798.43 15699.64 33897.90 19499.88 11599.51 169
YYNet198.95 19798.99 17698.84 26899.64 16097.14 30598.22 28999.32 25998.92 18099.59 15099.66 15397.40 23099.83 23298.27 17099.90 10399.55 147
MDA-MVSNet_test_wron98.95 19798.99 17698.85 26699.64 16097.16 30498.23 28899.33 25798.93 17899.56 16199.66 15397.39 23299.83 23298.29 16899.88 11599.55 147
Test_1112_low_res98.95 19798.73 20799.63 11099.68 14999.15 19698.09 30299.80 5997.14 29999.46 18299.40 23996.11 27099.89 12699.01 11299.84 13699.84 15
test123567898.93 20098.84 19799.19 23699.46 23798.55 24597.53 33899.77 7298.76 20099.69 11499.48 22596.69 25499.90 11198.30 16799.91 10199.11 267
CANet_DTU98.91 20198.85 19599.09 24498.79 33798.13 27498.18 29199.31 26399.48 9698.86 27899.51 21996.56 25799.95 4199.05 10999.95 6799.19 250
HyFIR lowres test98.91 20198.64 21499.73 6399.85 3999.47 10998.07 30699.83 3998.64 21199.89 3999.60 18692.57 301100.00 199.33 6999.97 4699.72 48
HQP_MVS98.90 20398.68 21199.55 14999.58 17799.24 18098.80 23799.54 19198.94 17699.14 24799.25 27297.24 23899.82 24095.84 30299.78 17799.60 124
sss98.90 20398.77 20599.27 21999.48 22898.44 25098.72 24699.32 25997.94 26499.37 20899.35 25496.31 26599.91 9398.85 13099.63 22399.47 188
OMC-MVS98.90 20398.72 20899.44 17899.39 25499.42 13098.58 25399.64 14197.31 29599.44 18399.62 17498.59 13399.69 31096.17 28699.79 17199.22 243
ppachtmachnet_test98.89 20699.12 13698.20 30199.66 15495.24 33797.63 33299.68 11899.08 16499.78 8399.62 17498.65 12499.88 14198.02 18799.96 5999.48 183
new_pmnet98.88 20798.89 19198.84 26899.70 14097.62 29698.15 29499.50 21297.98 26199.62 14299.54 21198.15 18099.94 5497.55 21899.84 13698.95 290
K. test v398.87 20898.60 21699.69 8099.93 1799.46 11399.74 2094.97 36399.78 3499.88 4799.88 3493.66 29199.97 1699.61 3899.95 6799.64 95
APD-MVScopyleft98.87 20898.59 21799.71 7399.50 21799.62 8499.01 20399.57 18196.80 30899.54 16899.63 16798.29 16699.91 9395.24 32399.71 20699.61 118
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
our_test_398.85 21099.09 14898.13 30499.66 15494.90 34097.72 33099.58 17899.07 16699.64 13199.62 17498.19 17799.93 6598.41 15799.95 6799.55 147
mvs-test198.83 21198.70 20999.22 23298.89 32699.65 7698.88 22399.66 12799.34 12298.29 31798.94 31997.69 21499.96 3398.11 18398.54 33198.04 332
UnsupCasMVSNet_eth98.83 21198.57 22099.59 13099.68 14999.45 11898.99 20999.67 12399.48 9699.55 16599.36 24994.92 28099.86 18198.95 12496.57 35699.45 194
test_normal98.82 21398.67 21299.27 21999.56 19898.83 23398.22 28998.01 32899.03 16999.49 17999.24 27796.21 26799.76 28398.69 14399.56 23499.22 243
NCCC98.82 21398.57 22099.58 13499.21 29499.31 16098.61 24999.25 27698.65 21098.43 31399.26 27097.86 20299.81 25996.55 27299.27 28399.61 118
PMVScopyleft92.94 2198.82 21398.81 20298.85 26699.84 4297.99 28399.20 15699.47 22199.71 4999.42 18999.82 5998.09 18499.47 35293.88 33899.85 13299.07 281
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DI_MVS_plusplus_test98.80 21698.65 21399.27 21999.57 18698.90 22598.44 27497.95 33199.02 17099.51 17599.23 28096.18 26999.76 28398.52 15399.42 26399.14 261
FMVSNet398.80 21698.63 21599.32 21199.13 30598.72 23799.10 18799.48 21799.23 14199.62 14299.64 15992.57 30199.86 18198.96 12099.90 10399.39 213
Patchmtry98.78 21898.54 22399.49 16398.89 32699.19 19299.32 11699.67 12399.65 6899.72 10699.79 7191.87 30799.95 4198.00 19099.97 4699.33 228
Vis-MVSNet (Re-imp)98.77 21998.58 21999.34 20599.78 8898.88 22799.61 6299.56 18499.11 15999.24 23299.56 20493.00 29999.78 27597.43 22599.89 10999.35 225
CLD-MVS98.76 22098.57 22099.33 20799.57 18698.97 21497.53 33899.55 18796.41 31799.27 22699.13 28799.07 6599.78 27596.73 26499.89 10999.23 242
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous20240521198.75 22198.46 22699.63 11099.34 27299.66 7199.47 8397.65 33999.28 12999.56 16199.50 22293.15 29599.84 21598.62 14799.58 23299.40 210
CPTT-MVS98.74 22298.44 22899.64 10699.61 16999.38 14499.18 15899.55 18796.49 31699.27 22699.37 24497.11 24699.92 8395.74 30699.67 21699.62 113
F-COLMAP98.74 22298.45 22799.62 11999.57 18699.47 10998.84 23099.65 13696.31 31898.93 27099.19 28497.68 21699.87 16196.52 27399.37 27099.53 158
N_pmnet98.73 22498.53 22499.35 20499.72 13098.67 24098.34 28194.65 36498.35 24099.79 8099.68 14098.03 18899.93 6598.28 16999.92 9199.44 199
PVSNet_Blended98.70 22598.59 21799.02 25399.54 20197.99 28397.58 33599.82 4795.70 32999.34 21598.98 31198.52 14799.77 28197.98 19199.83 14699.30 235
PatchMatch-RL98.68 22698.47 22599.30 21699.44 24299.28 16798.14 29699.54 19197.12 30199.11 25099.25 27297.80 20799.70 30496.51 27499.30 27798.93 292
Test498.65 22798.44 22899.27 21999.57 18698.86 23098.43 27599.41 23598.85 18699.57 15398.95 31893.05 29799.75 28998.57 14999.56 23499.19 250
test_prior398.62 22898.34 24199.46 17199.35 26299.22 18497.95 31999.39 24497.87 26798.05 33099.05 30397.90 19899.69 31095.99 29599.49 25099.48 183
CVMVSNet98.61 22998.88 19297.80 31799.58 17793.60 34599.26 13999.64 14199.66 6599.72 10699.67 14793.26 29499.93 6599.30 7599.81 16399.87 10
Patchmatch-RL test98.60 23098.36 23999.33 20799.77 9899.07 20798.27 28599.87 1998.91 18199.74 10299.72 10790.57 32299.79 26798.55 15199.85 13299.11 267
AdaColmapbinary98.60 23098.35 24099.38 19699.12 30799.22 18498.67 24899.42 23497.84 27198.81 28199.27 26897.32 23699.81 25995.14 32499.53 24599.10 271
WTY-MVS98.59 23298.37 23899.26 22499.43 24498.40 25498.74 24399.13 29098.10 25599.21 23899.24 27794.82 28299.90 11197.86 19798.77 31199.49 181
CNLPA98.57 23398.34 24199.28 21799.18 30099.10 20298.34 28199.41 23598.48 22598.52 30798.98 31197.05 24899.78 27595.59 31499.50 24898.96 289
112198.56 23498.24 24699.52 15699.49 22299.24 18099.30 12699.22 28195.77 32798.52 30799.29 26597.39 23299.85 19995.79 30499.34 27299.46 192
CDPH-MVS98.56 23498.20 25099.61 12299.50 21799.46 11398.32 28399.41 23595.22 33599.21 23899.10 29398.34 16399.82 24095.09 32699.66 21999.56 144
UnsupCasMVSNet_bld98.55 23698.27 24599.40 19199.56 19899.37 14797.97 31899.68 11897.49 28799.08 25299.35 25495.41 27999.82 24097.70 20698.19 34299.01 287
RPMNet98.53 23798.44 22898.83 27099.05 31698.12 27599.30 12698.78 30499.86 1599.16 24499.74 9792.53 30399.91 9398.75 13898.77 31198.44 315
MG-MVS98.52 23898.39 23598.94 25799.15 30297.39 30198.18 29199.21 28298.89 18299.23 23399.63 16797.37 23499.74 29394.22 33499.61 22899.69 58
DP-MVS Recon98.50 23998.23 24799.31 21499.49 22299.46 11398.56 25799.63 14494.86 34198.85 27999.37 24497.81 20699.59 34596.08 28899.44 25698.88 295
CMPMVSbinary77.52 2398.50 23998.19 25399.41 19098.33 35399.56 9699.01 20399.59 17095.44 33299.57 15399.80 6495.64 27599.46 35596.47 27799.92 9199.21 246
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
114514_t98.49 24198.11 25699.64 10699.73 12099.58 9399.24 14499.76 7989.94 35699.42 18999.56 20497.76 21099.86 18197.74 20499.82 15599.47 188
PMMVS98.49 24198.29 24499.11 24198.96 31998.42 25297.54 33699.32 25997.53 28698.47 31298.15 34697.88 20199.82 24097.46 22399.24 28699.09 274
MVSTER98.47 24398.22 24899.24 23099.06 31598.35 25999.08 19399.46 22499.27 13099.75 9499.66 15388.61 33299.85 19999.14 10299.92 9199.52 166
LFMVS98.46 24498.19 25399.26 22499.24 29198.52 24799.62 5896.94 34899.87 1399.31 22199.58 19491.04 31399.81 25998.68 14599.42 26399.45 194
PatchT98.45 24598.32 24398.83 27098.94 32098.29 26699.24 14498.82 30299.84 2299.08 25299.76 9191.37 31099.94 5498.82 13399.00 29898.26 322
test1235698.43 24698.39 23598.55 28599.46 23796.36 31497.32 34599.81 5597.60 28199.62 14299.37 24494.57 28499.89 12697.80 20199.92 9199.40 210
MIMVSNet98.43 24698.20 25099.11 24199.53 20498.38 25799.58 6998.61 31298.96 17499.33 21799.76 9190.92 31599.81 25997.38 22899.76 18399.15 257
PVSNet97.47 1598.42 24898.44 22898.35 29599.46 23796.26 31596.70 35399.34 25697.68 27899.00 26099.13 28797.40 23099.72 29797.59 21799.68 21199.08 277
CHOSEN 280x42098.41 24998.41 23398.40 29399.34 27295.89 32596.94 34999.44 22998.80 19399.25 22999.52 21593.51 29299.98 798.94 12599.98 3699.32 233
BH-RMVSNet98.41 24998.14 25599.21 23399.21 29498.47 24898.60 25198.26 32598.35 24098.93 27099.31 25997.20 24399.66 32994.32 33299.10 29299.51 169
QAPM98.40 25197.99 26299.65 9899.39 25499.47 10999.67 4799.52 20691.70 35398.78 28699.80 6498.55 13999.95 4194.71 33099.75 18699.53 158
API-MVS98.38 25298.39 23598.35 29598.83 33299.26 17399.14 17899.18 28498.59 21598.66 29898.78 32998.61 13199.57 34794.14 33599.56 23496.21 357
HQP-MVS98.36 25398.02 26199.39 19499.31 27998.94 21797.98 31599.37 25197.45 28898.15 32498.83 32596.67 25599.70 30494.73 32899.67 21699.53 158
PAPM_NR98.36 25398.04 26099.33 20799.48 22898.93 22298.79 24099.28 27097.54 28598.56 30698.57 33797.12 24599.69 31094.09 33698.90 30299.38 216
PLCcopyleft97.35 1698.36 25397.99 26299.48 16699.32 27899.24 18098.50 26599.51 20995.19 33798.58 30498.96 31696.95 25199.83 23295.63 31399.25 28499.37 220
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
train_agg98.35 25697.95 26699.57 14099.35 26299.35 15498.11 30099.41 23594.90 33997.92 33598.99 30898.02 19099.85 19995.38 32199.44 25699.50 175
CR-MVSNet98.35 25698.20 25098.83 27099.05 31698.12 27599.30 12699.67 12397.39 29299.16 24499.79 7191.87 30799.91 9398.78 13798.77 31198.44 315
LP98.34 25898.44 22898.05 30698.88 32995.31 33699.28 13598.74 30699.12 15898.98 26199.79 7193.40 29399.93 6598.38 15999.41 26598.90 294
agg_prior198.33 25997.92 26999.57 14099.35 26299.36 15097.99 31499.39 24494.85 34297.76 34598.98 31198.03 18899.85 19995.49 31699.44 25699.51 169
alignmvs98.28 26097.96 26599.25 22899.12 30798.93 22299.03 20098.42 32199.64 7098.72 29197.85 34990.86 31899.62 34098.88 12999.13 29099.19 250
0601test98.25 26197.95 26699.13 24099.17 30198.47 24899.00 20598.67 31198.97 17399.22 23799.02 30791.31 31199.69 31097.26 23598.93 29999.24 241
agg_prior398.24 26297.81 27599.53 15499.34 27299.26 17398.09 30299.39 24494.21 34797.77 34498.96 31697.74 21199.84 21595.38 32199.44 25699.50 175
MAR-MVS98.24 26297.92 26999.19 23698.78 33999.65 7699.17 16599.14 28895.36 33398.04 33298.81 32797.47 22799.72 29795.47 31899.06 29398.21 326
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 26497.89 27399.26 22499.19 29899.26 17399.65 5599.69 11591.33 35498.14 32899.77 8798.28 16799.96 3395.41 32099.55 24098.58 309
BH-untuned98.22 26598.09 25798.58 28499.38 25797.24 30398.55 25898.98 29797.81 27399.20 24398.76 33097.01 24999.65 33694.83 32798.33 33798.86 297
HY-MVS98.23 998.21 26697.95 26698.99 25499.03 31898.24 26799.61 6298.72 30796.81 30798.73 29099.51 21994.06 28899.86 18196.91 25398.20 34098.86 297
testus98.15 26798.06 25998.40 29399.11 31095.95 32096.77 35199.89 1495.83 32599.23 23398.47 34297.50 22699.84 21596.58 27199.20 28999.39 213
Patchmatch-test198.13 26898.40 23497.31 33199.20 29792.99 34798.17 29398.49 31898.24 25099.10 25199.52 21596.01 27299.83 23297.22 23999.62 22499.12 266
EPNet98.13 26897.77 27999.18 23994.57 36697.99 28399.24 14497.96 32999.74 4097.29 35199.62 17493.13 29699.97 1698.59 14899.83 14699.58 139
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test98.10 27097.98 26498.48 29099.27 28896.48 31299.40 9099.07 29198.81 19199.23 23399.57 20090.11 32699.87 16196.69 26599.64 22299.09 274
pmmvs398.08 27197.80 27698.91 26099.41 25097.69 29497.87 32699.66 12795.87 32499.50 17799.51 21990.35 32499.97 1698.55 15199.47 25299.08 277
JIA-IIPM98.06 27297.92 26998.50 28998.59 34797.02 30698.80 23798.51 31699.88 1297.89 33799.87 3791.89 30699.90 11198.16 18197.68 35298.59 307
TAPA-MVS97.92 1398.03 27397.55 28599.46 17199.47 23399.44 12098.50 26599.62 14786.79 35799.07 25699.26 27098.26 16999.62 34097.28 23499.73 20099.31 234
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
131498.00 27497.90 27298.27 30098.90 32297.45 30099.30 12699.06 29394.98 33897.21 35299.12 29198.43 15699.67 32495.58 31598.56 33097.71 344
GA-MVS97.99 27597.68 28298.93 25999.52 20798.04 28297.19 34799.05 29498.32 24698.81 28198.97 31489.89 32999.41 35698.33 16499.05 29499.34 227
MVS-HIRNet97.86 27698.22 24896.76 33599.28 28691.53 35798.38 27892.60 36599.13 15799.31 22199.96 1097.18 24499.68 31998.34 16399.83 14699.07 281
FMVSNet597.80 27797.25 28899.42 18398.83 33298.97 21499.38 9799.80 5998.87 18499.25 22999.69 12780.60 36499.91 9398.96 12099.90 10399.38 216
ADS-MVSNet297.78 27897.66 28498.12 30599.14 30395.36 33499.22 15198.75 30596.97 30398.25 32099.64 15990.90 31699.94 5496.51 27499.56 23499.08 277
tpmrst97.73 27998.07 25896.73 33798.71 34492.00 35199.10 18798.86 29998.52 22198.92 27299.54 21191.90 30599.82 24098.02 18799.03 29698.37 317
ADS-MVSNet97.72 28097.67 28397.86 31599.14 30394.65 34199.22 15198.86 29996.97 30398.25 32099.64 15990.90 31699.84 21596.51 27499.56 23499.08 277
PatchmatchNetpermissive97.65 28197.80 27697.18 33298.82 33592.49 34999.17 16598.39 32298.12 25498.79 28499.58 19490.71 32099.89 12697.23 23899.41 26599.16 256
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu97.62 28297.79 27897.11 33496.67 36592.31 35098.51 26498.04 32699.24 13995.77 36099.47 22893.78 29099.66 32998.98 11599.62 22499.37 220
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d97.58 28399.13 13392.93 34999.69 14299.49 10599.52 7599.77 7297.97 26299.96 899.79 7199.84 499.94 5495.85 30199.82 15579.36 361
PAPR97.56 28497.07 29099.04 25198.80 33698.11 27797.63 33299.25 27694.56 34598.02 33398.25 34597.43 22999.68 31990.90 34598.74 31599.33 228
TR-MVS97.44 28597.15 28998.32 29798.53 34997.46 29998.47 26897.91 33296.85 30598.21 32398.51 34096.42 26399.51 35092.16 34197.29 35397.98 337
tpmvs97.39 28697.69 28196.52 34198.41 35191.76 35499.30 12698.94 29897.74 27497.85 34099.55 20992.40 30499.73 29596.25 28498.73 31798.06 331
test0.0.03 197.37 28796.91 29798.74 28097.72 35997.57 29797.60 33497.36 34798.00 25899.21 23898.02 34790.04 32799.79 26798.37 16095.89 35998.86 297
OpenMVS_ROBcopyleft97.31 1797.36 28896.84 29898.89 26599.29 28499.45 11898.87 22599.48 21786.54 35999.44 18399.74 9797.34 23599.86 18191.61 34299.28 27997.37 349
111197.29 28996.71 30899.04 25199.65 15897.72 29198.35 27999.80 5999.40 11599.66 12399.43 23475.10 36899.87 16198.98 11599.98 3699.52 166
tfpn100097.28 29096.83 29998.64 28399.67 15397.68 29599.41 8895.47 36197.14 29999.43 18799.07 30185.87 35499.88 14196.78 26098.67 31998.34 319
thresconf0.0297.25 29196.74 30298.75 27699.73 12098.35 25999.35 10495.78 35496.54 31099.39 20099.08 29486.57 34799.88 14195.69 30798.57 32398.02 333
tfpn_n40097.25 29196.74 30298.75 27699.73 12098.35 25999.35 10495.78 35496.54 31099.39 20099.08 29486.57 34799.88 14195.69 30798.57 32398.02 333
tfpnconf97.25 29196.74 30298.75 27699.73 12098.35 25999.35 10495.78 35496.54 31099.39 20099.08 29486.57 34799.88 14195.69 30798.57 32398.02 333
tfpnview1197.25 29196.74 30298.75 27699.73 12098.35 25999.35 10495.78 35496.54 31099.39 20099.08 29486.57 34799.88 14195.69 30798.57 32398.02 333
BH-w/o97.20 29597.01 29397.76 31899.08 31395.69 33098.03 30998.52 31595.76 32897.96 33498.02 34795.62 27699.47 35292.82 34097.25 35498.12 330
conf0.0197.19 29696.74 30298.51 28699.73 12098.35 25999.35 10495.78 35496.54 31099.39 20099.08 29486.57 34799.88 14195.69 30798.57 32397.30 350
conf0.00297.19 29696.74 30298.51 28699.73 12098.35 25999.35 10495.78 35496.54 31099.39 20099.08 29486.57 34799.88 14195.69 30798.57 32397.30 350
test-LLR97.15 29896.95 29597.74 32098.18 35695.02 33897.38 34196.10 35098.00 25897.81 34198.58 33590.04 32799.91 9397.69 21198.78 30998.31 320
tpm97.15 29896.95 29597.75 31998.91 32194.24 34399.32 11697.96 32997.71 27698.29 31799.32 25786.72 34599.92 8398.10 18596.24 35899.09 274
E-PMN97.14 30097.43 28696.27 34398.79 33791.62 35695.54 35799.01 29699.44 10798.88 27699.12 29192.78 30099.68 31994.30 33399.03 29697.50 346
PNet_i23d97.02 30197.87 27494.49 34899.69 14284.81 36795.18 36099.85 2897.83 27299.32 21999.57 20095.53 27899.47 35296.09 28797.74 35199.18 253
cascas96.99 30296.82 30097.48 32597.57 36295.64 33196.43 35599.56 18491.75 35297.13 35397.61 35795.58 27798.63 36196.68 26699.11 29198.18 329
EMVS96.96 30397.28 28795.99 34798.76 34191.03 35995.26 35998.61 31299.34 12298.92 27298.88 32493.79 28999.66 32992.87 33999.05 29497.30 350
PatchFormer-LS_test96.95 30497.07 29096.62 34098.76 34191.85 35399.18 15898.45 32097.29 29697.73 34797.22 36588.77 33199.76 28398.13 18298.04 34698.25 323
tfpn_ndepth96.93 30596.43 31398.42 29199.60 17197.72 29199.22 15195.16 36295.91 32399.26 22898.79 32885.56 35599.87 16196.03 29298.35 33697.68 345
view60096.86 30696.52 30997.88 31199.69 14295.87 32699.39 9197.68 33599.11 15998.96 26497.82 35187.40 33399.79 26789.78 34698.83 30497.98 337
view80096.86 30696.52 30997.88 31199.69 14295.87 32699.39 9197.68 33599.11 15998.96 26497.82 35187.40 33399.79 26789.78 34698.83 30497.98 337
conf0.05thres100096.86 30696.52 30997.88 31199.69 14295.87 32699.39 9197.68 33599.11 15998.96 26497.82 35187.40 33399.79 26789.78 34698.83 30497.98 337
tfpn96.86 30696.52 30997.88 31199.69 14295.87 32699.39 9197.68 33599.11 15998.96 26497.82 35187.40 33399.79 26789.78 34698.83 30497.98 337
dp96.86 30697.07 29096.24 34598.68 34690.30 36499.19 15798.38 32397.35 29498.23 32299.59 19287.23 33799.82 24096.27 28398.73 31798.59 307
tpm cat196.78 31196.98 29496.16 34698.85 33190.59 36399.08 19399.32 25992.37 35197.73 34799.46 23191.15 31299.69 31096.07 28998.80 30898.21 326
PCF-MVS96.03 1896.73 31295.86 32499.33 20799.44 24299.16 19496.87 35099.44 22986.58 35898.95 26899.40 23994.38 28699.88 14187.93 35499.80 16898.95 290
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CostFormer96.71 31396.79 30196.46 34298.90 32290.71 36199.41 8898.68 30994.69 34498.14 32899.34 25686.32 35399.80 26497.60 21698.07 34598.88 295
MVEpermissive92.54 2296.66 31496.11 31898.31 29899.68 14997.55 29897.94 32195.60 36099.37 11990.68 36498.70 33396.56 25798.61 36286.94 36099.55 24098.77 302
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres600view796.60 31596.16 31697.93 30999.63 16296.09 31999.18 15897.57 34098.77 19798.72 29197.32 36087.04 33899.72 29788.57 35198.62 32197.98 337
EPMVS96.53 31696.32 31497.17 33398.18 35692.97 34899.39 9189.95 36798.21 25198.61 30199.59 19286.69 34699.72 29796.99 25099.23 28898.81 300
tfpn11196.50 31796.12 31797.65 32299.63 16295.93 32199.18 15897.57 34098.75 20298.70 29397.31 36187.04 33899.72 29788.27 35398.61 32297.30 350
conf200view1196.43 31896.03 32097.63 32399.63 16295.93 32199.18 15897.57 34098.75 20298.70 29397.31 36187.04 33899.67 32487.62 35598.51 33297.30 350
thres40096.40 31995.89 32297.92 31099.58 17796.11 31799.00 20597.54 34598.43 22798.52 30796.98 36686.85 34299.67 32487.62 35598.51 33297.98 337
thres100view90096.39 32096.03 32097.47 32699.63 16295.93 32199.18 15897.57 34098.75 20298.70 29397.31 36187.04 33899.67 32487.62 35598.51 33296.81 355
tpm296.35 32196.22 31596.73 33798.88 32991.75 35599.21 15598.51 31693.27 35097.89 33799.21 28284.83 35699.70 30496.04 29198.18 34398.75 303
FPMVS96.32 32295.50 32998.79 27399.60 17198.17 27398.46 27298.80 30397.16 29896.28 35699.63 16782.19 35999.09 35888.45 35298.89 30399.10 271
tfpn200view996.30 32395.89 32297.53 32499.58 17796.11 31799.00 20597.54 34598.43 22798.52 30796.98 36686.85 34299.67 32487.62 35598.51 33296.81 355
TESTMET0.1,196.24 32495.84 32597.41 32898.24 35493.84 34497.38 34195.84 35398.43 22797.81 34198.56 33879.77 36599.89 12697.77 20298.77 31198.52 311
test-mter96.23 32595.73 32797.74 32098.18 35695.02 33897.38 34196.10 35097.90 26597.81 34198.58 33579.12 36699.91 9397.69 21198.78 30998.31 320
tpmp4_e2396.11 32696.06 31996.27 34398.90 32290.70 36299.34 11199.03 29593.72 34896.56 35599.31 25983.63 35799.75 28996.06 29098.02 34798.35 318
X-MVStestdata96.09 32794.87 33599.75 5399.71 13399.71 5399.37 10199.61 15199.29 12698.76 28861.30 36998.47 15299.88 14197.62 21399.73 20099.67 71
thres20096.09 32795.68 32897.33 33099.48 22896.22 31698.53 26297.57 34098.06 25798.37 31596.73 36886.84 34499.61 34486.99 35998.57 32396.16 358
DWT-MVSNet_test96.03 32995.80 32696.71 33998.50 35091.93 35299.25 14397.87 33395.99 32296.81 35497.61 35781.02 36199.66 32997.20 24297.98 34898.54 310
test235695.99 33095.26 33398.18 30296.93 36495.53 33395.31 35898.71 30895.67 33098.48 31197.83 35080.72 36299.88 14195.47 31898.21 33999.11 267
gg-mvs-nofinetune95.87 33195.17 33497.97 30898.19 35596.95 30799.69 3989.23 36899.89 1096.24 35899.94 1281.19 36099.51 35093.99 33798.20 34097.44 347
PVSNet_095.53 1995.85 33295.31 33197.47 32698.78 33993.48 34695.72 35699.40 24196.18 32097.37 34997.73 35595.73 27499.58 34695.49 31681.40 36199.36 223
tmp_tt95.75 33395.42 33096.76 33589.90 36794.42 34298.86 22697.87 33378.01 36099.30 22599.69 12797.70 21295.89 36399.29 7998.14 34499.95 1
MVS95.72 33494.63 33798.99 25498.56 34897.98 28899.30 12698.86 29972.71 36297.30 35099.08 29498.34 16399.74 29389.21 35098.33 33799.26 239
PAPM95.61 33594.71 33698.31 29899.12 30796.63 31096.66 35498.46 31990.77 35596.25 35798.68 33493.01 29899.69 31081.60 36197.86 35098.62 305
IB-MVS95.41 2095.30 33694.46 33897.84 31698.76 34195.33 33597.33 34496.07 35296.02 32195.37 36297.41 35976.17 36799.96 3397.54 21995.44 36098.22 325
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 33795.31 33191.99 35097.22 36389.64 36598.86 22696.52 34994.36 34696.09 35998.76 33082.21 35898.73 36097.05 24896.74 35587.60 360
.test124585.84 33889.27 33975.54 35199.65 15897.72 29198.35 27999.80 5999.40 11599.66 12399.43 23475.10 36899.87 16198.98 11533.07 36229.03 363
pcd1.5k->3k49.97 33955.52 34033.31 35299.95 120.00 3700.00 36199.81 550.00 3650.00 367100.00 199.96 10.00 3670.00 364100.00 199.92 3
v1.041.33 34055.11 3410.00 35599.62 1670.00 3700.00 36199.53 19697.71 27699.55 16599.57 2000.00 3720.00 3670.00 3640.00 3650.00 365
test12329.31 34133.05 34418.08 35325.93 36912.24 36897.53 33810.93 37111.78 36324.21 36550.08 37321.04 3708.60 36523.51 36232.43 36433.39 362
testmvs28.94 34233.33 34215.79 35426.03 3689.81 36996.77 35115.67 37011.55 36423.87 36650.74 37219.03 3718.53 36623.21 36333.07 36229.03 363
cdsmvs_eth3d_5k24.88 34333.17 3430.00 3550.00 3700.00 3700.00 36199.62 1470.00 3650.00 36799.13 28799.82 60.00 3670.00 3640.00 3650.00 365
pcd_1.5k_mvsjas16.61 34422.14 3450.00 3550.00 3700.00 3700.00 3610.00 3720.00 3650.00 367100.00 199.28 380.00 3670.00 3640.00 3650.00 365
sosnet-low-res8.33 34511.11 3460.00 3550.00 3700.00 3700.00 3610.00 3720.00 3650.00 367100.00 10.00 3720.00 3670.00 3640.00 3650.00 365
sosnet8.33 34511.11 3460.00 3550.00 3700.00 3700.00 3610.00 3720.00 3650.00 367100.00 10.00 3720.00 3670.00 3640.00 3650.00 365
uncertanet8.33 34511.11 3460.00 3550.00 3700.00 3700.00 3610.00 3720.00 3650.00 367100.00 10.00 3720.00 3670.00 3640.00 3650.00 365
Regformer8.33 34511.11 3460.00 3550.00 3700.00 3700.00 3610.00 3720.00 3650.00 367100.00 10.00 3720.00 3670.00 3640.00 3650.00 365
uanet8.33 34511.11 3460.00 3550.00 3700.00 3700.00 3610.00 3720.00 3650.00 367100.00 10.00 3720.00 3670.00 3640.00 3650.00 365
ab-mvs-re8.26 35011.02 3510.00 3550.00 3700.00 3700.00 3610.00 3720.00 3650.00 36799.16 2850.00 3720.00 3670.00 3640.00 3650.00 365
GSMVS99.14 261
test_part299.62 16799.67 6999.55 165
test_part10.00 3550.00 3700.00 36199.53 1960.00 3720.00 3670.00 3640.00 3650.00 365
sam_mvs190.81 31999.14 261
sam_mvs90.52 323
semantic-postprocess98.51 28699.75 11195.90 32499.84 3699.84 2299.89 3999.73 10195.96 27399.99 499.33 69100.00 199.63 99
ambc99.20 23599.35 26298.53 24699.17 16599.46 22499.67 11999.80 6498.46 15499.70 30497.92 19399.70 20899.38 216
MTGPAbinary99.53 196
test_post199.14 17851.63 37189.54 33099.82 24096.86 256
test_post52.41 37090.25 32599.86 181
patchmatchnet-post99.62 17490.58 32199.94 54
GG-mvs-BLEND97.36 32997.59 36096.87 30999.70 3088.49 36994.64 36397.26 36480.66 36399.12 35791.50 34396.50 35796.08 359
MTMP99.09 19198.59 314
gm-plane-assit97.59 36089.02 36693.47 34998.30 34399.84 21596.38 279
test9_res95.10 32599.44 25699.50 175
TEST999.35 26299.35 15498.11 30099.41 23594.83 34397.92 33598.99 30898.02 19099.85 199
test_899.34 27299.31 16098.08 30599.40 24194.90 33997.87 33998.97 31498.02 19099.84 215
agg_prior294.58 33199.46 25599.50 175
agg_prior99.35 26299.36 15099.39 24497.76 34599.85 199
TestCases99.63 11099.78 8899.64 7899.83 3998.63 21299.63 13599.72 10798.68 11799.75 28996.38 27999.83 14699.51 169
test_prior499.19 19298.00 312
test_prior297.95 31997.87 26798.05 33099.05 30397.90 19895.99 29599.49 250
test_prior99.46 17199.35 26299.22 18499.39 24499.69 31099.48 183
旧先验297.94 32195.33 33498.94 26999.88 14196.75 262
新几何298.04 308
新几何199.52 15699.50 21799.22 18499.26 27395.66 33198.60 30299.28 26697.67 21799.89 12695.95 29999.32 27599.45 194
旧先验199.49 22299.29 16599.26 27399.39 24297.67 21799.36 27199.46 192
无先验98.01 31099.23 28095.83 32599.85 19995.79 30499.44 199
原ACMM297.92 323
原ACMM199.37 20099.47 23398.87 22999.27 27196.74 30998.26 31999.32 25797.93 19799.82 24095.96 29899.38 26899.43 205
test22299.51 21199.08 20597.83 32899.29 26795.21 33698.68 29799.31 25997.28 23799.38 26899.43 205
testdata299.89 12695.99 295
segment_acmp98.37 161
testdata99.42 18399.51 21198.93 22299.30 26696.20 31998.87 27799.40 23998.33 16599.89 12696.29 28299.28 27999.44 199
testdata197.72 33097.86 270
test1299.54 15399.29 28499.33 15799.16 28698.43 31397.54 22499.82 24099.47 25299.48 183
plane_prior799.58 17799.38 144
plane_prior699.47 23399.26 17397.24 238
plane_prior599.54 19199.82 24095.84 30299.78 17799.60 124
plane_prior499.25 272
plane_prior399.31 16098.36 23599.14 247
plane_prior298.80 23798.94 176
plane_prior199.51 211
plane_prior99.24 18098.42 27697.87 26799.71 206
n20.00 372
nn0.00 372
door-mid99.83 39
lessismore_v099.64 10699.86 3599.38 14490.66 36699.89 3999.83 5194.56 28599.97 1699.56 4599.92 9199.57 143
LGP-MVS_train99.74 5799.82 5399.63 8299.73 9397.56 28399.64 13199.69 12799.37 2999.89 12696.66 26799.87 12299.69 58
test1199.29 267
door99.77 72
HQP5-MVS98.94 217
HQP-NCC99.31 27997.98 31597.45 28898.15 324
ACMP_Plane99.31 27997.98 31597.45 28898.15 324
BP-MVS94.73 328
HQP4-MVS98.15 32499.70 30499.53 158
HQP3-MVS99.37 25199.67 216
HQP2-MVS96.67 255
NP-MVS99.40 25399.13 19798.83 325
MDTV_nov1_ep13_2view91.44 35899.14 17897.37 29399.21 23891.78 30996.75 26299.03 285
MDTV_nov1_ep1397.73 28098.70 34590.83 36099.15 17398.02 32798.51 22298.82 28099.61 18390.98 31499.66 32996.89 25598.92 300
ACMMP++_ref99.94 80
ACMMP++99.79 171
Test By Simon98.41 158
ITE_SJBPF99.38 19699.63 16299.44 12099.73 9398.56 21799.33 21799.53 21398.88 8699.68 31996.01 29399.65 22199.02 286
DeepMVS_CXcopyleft97.98 30799.69 14296.95 30799.26 27375.51 36195.74 36198.28 34496.47 26199.62 34091.23 34497.89 34997.38 348