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

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

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

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

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




Method Infoallhigh-res
multi-view
indooroutdoorbotani.boulde.bridgedoorexhibi.lectur.living.loungeobserv.old co.statueterrac.
sort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
LCM-MVSNet99.86 199.86 199.87 199.99 199.77 199.77 199.80 199.97 199.97 199.95 199.74 199.98 199.56 1100.00 199.85 4
pmmvs699.07 399.24 398.56 4499.81 296.38 5698.87 999.30 899.01 1599.63 999.66 399.27 299.68 10397.75 4599.89 3299.62 32
OurMVSNet-221017-098.61 1898.61 2698.63 4099.77 396.35 5799.17 599.05 3898.05 4399.61 1199.52 493.72 16899.88 1898.72 2099.88 3399.65 25
Gipumacopyleft98.07 4298.31 3897.36 12999.76 496.28 6098.51 2299.10 2598.76 2196.79 18999.34 2096.61 6398.82 29796.38 8899.50 11496.98 296
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet198.51 2498.45 3298.67 3799.72 596.71 4598.76 1198.89 7998.49 2699.38 1899.14 4295.44 10799.84 2796.47 8699.80 4799.47 66
LTVRE_ROB96.88 199.18 299.34 298.72 3499.71 696.99 3999.69 299.57 399.02 1499.62 1099.36 1698.53 799.52 16598.58 2499.95 1399.66 24
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
mvs_tets98.90 498.94 798.75 2999.69 796.48 5498.54 2199.22 996.23 11599.71 499.48 698.77 699.93 298.89 1099.95 1399.84 6
PS-MVSNAJss98.53 2398.63 2298.21 6999.68 894.82 10598.10 4599.21 1096.91 9099.75 399.45 895.82 8999.92 498.80 1399.96 1199.89 1
jajsoiax98.77 1098.79 1498.74 3199.66 996.48 5498.45 2699.12 2295.83 13299.67 699.37 1498.25 1099.92 498.77 1499.94 1999.82 7
pcd1.5k->3k41.47 33944.19 34133.29 35299.65 100.00 3700.00 36199.07 340.00 3650.00 3670.00 36799.04 30.00 3670.00 36499.96 1199.87 2
v7n98.73 1298.99 697.95 8299.64 1194.20 12898.67 1399.14 2099.08 999.42 1699.23 2996.53 6699.91 1299.27 499.93 2199.73 15
test_djsdf98.73 1298.74 1898.69 3699.63 1296.30 5998.67 1399.02 5196.50 10399.32 2199.44 997.43 2999.92 498.73 1799.95 1399.86 3
anonymousdsp98.72 1598.63 2298.99 1099.62 1397.29 3498.65 1699.19 1395.62 13899.35 2099.37 1497.38 3199.90 1398.59 2399.91 2699.77 9
v74898.58 1998.89 997.67 10199.61 1493.53 15298.59 1798.90 7798.97 1799.43 1599.15 4196.53 6699.85 2398.88 1199.91 2699.64 28
v5298.85 799.01 498.37 5599.61 1495.53 8399.01 699.04 4598.48 2799.31 2299.41 1196.82 5699.87 2099.44 299.95 1399.70 19
V498.85 799.01 498.37 5599.61 1495.53 8399.01 699.04 4598.48 2799.31 2299.41 1196.81 5799.87 2099.44 299.95 1399.70 19
PEN-MVS98.75 1198.85 1298.44 5099.58 1795.67 7698.45 2699.15 1899.33 499.30 2499.00 4997.27 3699.92 497.64 4899.92 2399.75 13
Baseline_NR-MVSNet97.72 7597.79 6197.50 11499.56 1893.29 15795.44 19398.86 8598.20 3998.37 7999.24 2794.69 12799.55 15795.98 10299.79 4899.65 25
SixPastTwentyTwo97.49 9297.57 8297.26 13599.56 1892.33 17098.28 3296.97 26598.30 3599.45 1499.35 1888.43 26099.89 1698.01 3599.76 5199.54 46
PS-CasMVS98.73 1298.85 1298.39 5499.55 2095.47 8598.49 2399.13 2199.22 799.22 3098.96 5397.35 3299.92 497.79 4399.93 2199.79 8
DTE-MVSNet98.79 998.86 1098.59 4299.55 2096.12 6498.48 2599.10 2599.36 399.29 2599.06 4897.27 3699.93 297.71 4799.91 2699.70 19
HPM-MVS_fast98.32 3198.13 4598.88 2299.54 2297.48 2798.35 2999.03 5095.88 12897.88 13698.22 11598.15 1299.74 5896.50 8599.62 8099.42 88
TDRefinement98.90 498.86 1099.02 899.54 2298.06 699.34 499.44 698.85 1999.00 4299.20 3197.42 3099.59 14597.21 6699.76 5199.40 93
Anonymous2024052198.58 1998.65 2198.36 5899.52 2495.60 7898.96 898.95 7398.36 3199.25 2799.17 3995.28 11399.80 3798.46 2599.88 3399.68 23
pm-mvs198.47 2598.67 1997.86 8799.52 2494.58 11398.28 3299.00 6297.57 6699.27 2699.22 3098.32 999.50 17797.09 7399.75 5599.50 52
TransMVSNet (Re)98.38 2998.67 1997.51 11199.51 2693.39 15698.20 4098.87 8398.23 3799.48 1299.27 2598.47 899.55 15796.52 8399.53 10799.60 35
WR-MVS_H98.65 1798.62 2498.75 2999.51 2696.61 5098.55 2099.17 1499.05 1299.17 3398.79 6295.47 10599.89 1697.95 3699.91 2699.75 13
PMVScopyleft89.60 1796.71 14696.97 12395.95 21699.51 2697.81 1397.42 9097.49 24597.93 4795.95 22798.58 7896.88 5196.91 35189.59 26299.36 15593.12 350
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MP-MVS-pluss97.69 7897.36 9298.70 3599.50 2996.84 4295.38 20298.99 6592.45 24498.11 10598.31 10297.25 3899.77 4896.60 8099.62 8099.48 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FC-MVSNet-test98.16 3798.37 3497.56 10699.49 3093.10 16098.35 2999.21 1098.43 2998.89 4698.83 6194.30 14599.81 3297.87 3999.91 2699.77 9
VPNet97.26 10897.49 8896.59 17099.47 3190.58 20696.27 14598.53 14997.77 5198.46 7498.41 9194.59 13399.68 10394.61 15599.29 17299.52 49
CP-MVSNet98.42 2798.46 3098.30 6499.46 3295.22 9398.27 3498.84 8999.05 1299.01 4098.65 7595.37 10899.90 1397.57 5399.91 2699.77 9
XXY-MVS97.54 8997.70 6797.07 14399.46 3292.21 17497.22 9799.00 6294.93 17198.58 6498.92 5797.31 3499.41 21194.44 15999.43 14099.59 36
zzz-MVS98.01 4797.66 7199.06 599.44 3497.90 895.66 18498.73 11497.69 6097.90 13297.96 14695.81 9399.82 3096.13 9399.61 8599.45 73
MTAPA98.14 3897.84 5999.06 599.44 3497.90 897.25 9498.73 11497.69 6097.90 13297.96 14695.81 9399.82 3096.13 9399.61 8599.45 73
wuykxyi23d98.68 1698.53 2799.13 399.44 3497.97 796.85 12199.02 5195.81 13399.88 299.38 1398.14 1399.69 9798.32 2999.95 1399.73 15
SteuartSystems-ACMMP98.02 4597.76 6498.79 2799.43 3797.21 3697.15 9998.90 7796.58 10198.08 11197.87 15797.02 4699.76 4995.25 12999.59 9099.40 93
Skip Steuart: Steuart Systems R&D Blog.
ACMH93.61 998.44 2698.76 1597.51 11199.43 3793.54 15198.23 3599.05 3897.40 8299.37 1999.08 4798.79 599.47 18597.74 4699.71 6499.50 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVScopyleft98.11 4197.83 6098.92 1999.42 3997.46 2898.57 1899.05 3895.43 14797.41 16097.50 18797.98 1699.79 3995.58 11999.57 9599.50 52
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
K. test v396.44 15996.28 16196.95 14999.41 4091.53 19297.65 7390.31 34298.89 1898.93 4599.36 1684.57 28699.92 497.81 4199.56 9899.39 96
VDDNet96.98 11896.84 13197.41 12699.40 4193.26 15897.94 5495.31 29499.26 698.39 7899.18 3587.85 26799.62 12895.13 13999.09 19299.35 105
ACMH+93.58 1098.23 3698.31 3897.98 8199.39 4295.22 9397.55 8399.20 1298.21 3899.25 2798.51 8598.21 1199.40 21494.79 15099.72 6099.32 107
TSAR-MVS + MP.97.42 9697.23 10598.00 8099.38 4395.00 9997.63 7598.20 19493.00 23098.16 10098.06 13795.89 8499.72 6995.67 11099.10 19199.28 118
FIs97.93 5698.07 4897.48 11899.38 4392.95 16298.03 5199.11 2398.04 4498.62 5998.66 7393.75 16799.78 4097.23 6599.84 4199.73 15
lessismore_v097.05 14499.36 4592.12 17884.07 36298.77 5398.98 5185.36 28099.74 5897.34 6399.37 15299.30 111
ACMMP_Plus97.89 6197.63 7698.67 3799.35 4696.84 4296.36 14198.79 10395.07 16797.88 13698.35 9797.24 3999.72 6996.05 9699.58 9299.45 73
Vis-MVSNetpermissive98.27 3398.34 3698.07 7499.33 4795.21 9598.04 4999.46 597.32 8597.82 14599.11 4496.75 5999.86 2297.84 4099.36 15599.15 135
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ANet_high98.31 3298.94 796.41 18299.33 4789.64 21997.92 5699.56 499.27 599.66 899.50 597.67 2499.83 2997.55 5499.98 399.77 9
MP-MVScopyleft97.64 8197.18 10899.00 999.32 4997.77 1497.49 8698.73 11496.27 11295.59 24097.75 16796.30 7799.78 4093.70 18899.48 12499.45 73
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PVSNet_Blended_VisFu95.95 17695.80 17896.42 18199.28 5090.62 20595.31 20899.08 3088.40 28696.97 18398.17 12292.11 21199.78 4093.64 18999.21 17998.86 186
tfpnnormal97.72 7597.97 5396.94 15099.26 5192.23 17397.83 6298.45 15698.25 3699.13 3498.66 7396.65 6199.69 9793.92 18299.62 8098.91 175
HSP-MVS97.37 10096.85 13098.92 1999.26 5197.70 1597.66 7298.23 19095.65 13698.51 6896.46 24892.15 20999.81 3295.14 13898.58 24199.26 122
testgi96.07 17296.50 15394.80 25699.26 5187.69 27595.96 16798.58 14695.08 16698.02 11896.25 25997.92 1797.60 34788.68 27798.74 22599.11 146
IS-MVSNet96.93 12396.68 14097.70 9799.25 5494.00 13498.57 1896.74 27398.36 3198.14 10397.98 14588.23 26199.71 8093.10 19999.72 6099.38 98
ACMMPcopyleft98.05 4397.75 6598.93 1899.23 5597.60 1998.09 4698.96 7195.75 13597.91 13198.06 13796.89 4999.76 4995.32 12799.57 9599.43 86
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
nrg03098.54 2298.62 2498.32 6199.22 5695.66 7797.90 5799.08 3098.31 3499.02 3998.74 6797.68 2399.61 13497.77 4499.85 4099.70 19
region2R97.92 5797.59 8098.92 1999.22 5697.55 2397.60 7998.84 8996.00 12397.22 16597.62 17796.87 5299.76 4995.48 12199.43 14099.46 68
mPP-MVS97.91 5997.53 8499.04 799.22 5697.87 1197.74 6998.78 10696.04 12197.10 17297.73 17096.53 6699.78 4095.16 13699.50 11499.46 68
COLMAP_ROBcopyleft94.48 698.25 3598.11 4698.64 3999.21 5997.35 3297.96 5399.16 1598.34 3398.78 5098.52 8497.32 3399.45 19494.08 17399.67 7499.13 138
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMPR97.95 5297.62 7898.94 1599.20 6097.56 2297.59 8098.83 9796.05 11997.46 15897.63 17696.77 5899.76 4995.61 11699.46 12899.49 60
PGM-MVS97.88 6297.52 8598.96 1399.20 6097.62 1897.09 10899.06 3695.45 14597.55 14997.94 15097.11 4199.78 4094.77 15299.46 12899.48 63
test_040297.84 6597.97 5397.47 11999.19 6294.07 13196.71 12898.73 11498.66 2398.56 6598.41 9196.84 5499.69 9794.82 14799.81 4498.64 204
EPP-MVSNet96.84 13296.58 14497.65 10299.18 6393.78 14398.68 1296.34 27697.91 4897.30 16398.06 13788.46 25999.85 2393.85 18499.40 15099.32 107
abl_698.42 2798.19 4299.09 499.16 6498.10 597.73 7199.11 2397.76 5298.62 5998.27 10997.88 2099.80 3795.67 11099.50 11499.38 98
XVG-ACMP-BASELINE97.58 8797.28 9898.49 4799.16 6496.90 4196.39 13698.98 6895.05 16898.06 11498.02 14195.86 8599.56 15394.37 16499.64 7899.00 159
CHOSEN 1792x268894.10 24393.41 24896.18 19999.16 6490.04 21292.15 31798.68 12779.90 34496.22 21897.83 15887.92 26699.42 20089.18 26899.65 7799.08 151
HFP-MVS97.94 5497.64 7498.83 2499.15 6797.50 2597.59 8098.84 8996.05 11997.49 15397.54 18297.07 4499.70 8895.61 11699.46 12899.30 111
#test#97.62 8397.22 10698.83 2499.15 6797.50 2596.81 12398.84 8994.25 19497.49 15397.54 18297.07 4499.70 8894.37 16499.46 12899.30 111
XVS97.96 4997.63 7698.94 1599.15 6797.66 1697.77 6498.83 9797.42 7496.32 21197.64 17596.49 7099.72 6995.66 11299.37 15299.45 73
X-MVStestdata92.86 26590.83 29998.94 1599.15 6797.66 1697.77 6498.83 9797.42 7496.32 21136.50 36296.49 7099.72 6995.66 11299.37 15299.45 73
LPG-MVS_test97.94 5497.67 7098.74 3199.15 6797.02 3797.09 10899.02 5195.15 16098.34 8398.23 11297.91 1899.70 8894.41 16199.73 5799.50 52
LGP-MVS_train98.74 3199.15 6797.02 3799.02 5195.15 16098.34 8398.23 11297.91 1899.70 8894.41 16199.73 5799.50 52
RPSCF97.87 6397.51 8698.95 1499.15 6798.43 397.56 8299.06 3696.19 11698.48 7198.70 7094.72 12599.24 25094.37 16499.33 16699.17 131
ACMM93.33 1198.05 4397.79 6198.85 2399.15 6797.55 2396.68 12998.83 9795.21 15598.36 8198.13 12798.13 1599.62 12896.04 9799.54 10499.39 96
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet197.95 5298.08 4797.56 10699.14 7593.67 14598.23 3598.66 13397.41 8199.00 4299.19 3295.47 10599.73 6395.83 10699.76 5199.30 111
Vis-MVSNet (Re-imp)95.11 20994.85 20695.87 22199.12 7689.17 23797.54 8594.92 29696.50 10396.58 19597.27 20183.64 28799.48 18388.42 28099.67 7498.97 163
OPM-MVS97.54 8997.25 9998.41 5299.11 7796.61 5095.24 21498.46 15594.58 18298.10 10898.07 13497.09 4399.39 22095.16 13699.44 13399.21 126
UA-Net98.88 698.76 1599.22 299.11 7797.89 1099.47 399.32 799.08 997.87 14199.67 296.47 7299.92 497.88 3899.98 399.85 4
AllTest97.20 11196.92 12798.06 7599.08 7996.16 6297.14 10199.16 1594.35 19197.78 14698.07 13495.84 8699.12 26091.41 22299.42 14398.91 175
TestCases98.06 7599.08 7996.16 6299.16 1594.35 19197.78 14698.07 13495.84 8699.12 26091.41 22299.42 14398.91 175
TranMVSNet+NR-MVSNet98.33 3098.30 4098.43 5199.07 8195.87 6996.73 12799.05 3898.67 2298.84 4798.45 8997.58 2699.88 1896.45 8799.86 3899.54 46
VPA-MVSNet98.27 3398.46 3097.70 9799.06 8293.80 14197.76 6699.00 6298.40 3099.07 3798.98 5196.89 4999.75 5397.19 6999.79 4899.55 45
114514_t93.96 24793.22 25296.19 19899.06 8290.97 19995.99 16198.94 7473.88 35893.43 30796.93 21992.38 20799.37 22889.09 26999.28 17398.25 242
EG-PatchMatch MVS97.69 7897.79 6197.40 12799.06 8293.52 15395.96 16798.97 7094.55 18398.82 4898.76 6597.31 3499.29 24297.20 6899.44 13399.38 98
ACMP92.54 1397.47 9497.10 11698.55 4599.04 8596.70 4696.24 14998.89 7993.71 21397.97 12397.75 16797.44 2899.63 12293.22 19699.70 6799.32 107
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_part299.03 8696.07 6598.08 111
v1.040.70 34054.26 3400.00 35599.03 860.00 3700.00 36198.84 8994.84 17298.08 11197.60 1790.00 3720.00 3670.00 3640.00 3650.00 365
v1398.02 4598.52 2896.51 17599.02 8890.14 21098.07 4799.09 2998.10 4299.13 3499.35 1894.84 12399.74 5899.12 599.98 399.65 25
XVG-OURS-SEG-HR97.38 9997.07 11998.30 6499.01 8997.41 3194.66 24299.02 5195.20 15698.15 10297.52 18598.83 498.43 32594.87 14596.41 31999.07 153
XVG-OURS97.12 11296.74 13898.26 6698.99 9097.45 2993.82 27899.05 3895.19 15798.32 8697.70 17395.22 11598.41 32694.27 16998.13 25998.93 171
CP-MVS97.92 5797.56 8398.99 1098.99 9097.82 1297.93 5598.96 7196.11 11896.89 18797.45 19096.85 5399.78 4095.19 13299.63 7999.38 98
v1297.97 4898.47 2996.46 17998.98 9290.01 21497.97 5299.08 3098.00 4599.11 3699.34 2094.70 12699.73 6399.07 699.98 399.64 28
CSCG97.40 9897.30 9597.69 9998.95 9394.83 10497.28 9398.99 6596.35 11098.13 10495.95 27395.99 8299.66 11594.36 16799.73 5798.59 209
v1197.82 6998.36 3596.17 20098.93 9489.16 23897.79 6399.08 3097.64 6399.19 3199.32 2294.28 14699.72 6999.07 699.97 899.63 30
V997.90 6098.40 3396.40 18398.93 9489.86 21697.86 5999.07 3497.88 4999.05 3899.30 2394.53 13799.72 6999.01 899.98 399.63 30
HyFIR lowres test93.72 25192.65 26296.91 15398.93 9491.81 18991.23 33198.52 15082.69 33296.46 20296.52 24680.38 29799.90 1390.36 25398.79 22099.03 157
testing_297.43 9597.71 6696.60 16798.91 9790.85 20096.01 16098.54 14894.78 17598.78 5098.96 5396.35 7699.54 15997.25 6499.82 4399.40 93
PM-MVS97.36 10397.10 11698.14 7298.91 9796.77 4496.20 15198.63 14093.82 21098.54 6698.33 10093.98 15799.05 27095.99 10199.45 13298.61 208
CPTT-MVS96.69 14796.08 16798.49 4798.89 9996.64 4997.25 9498.77 10792.89 23796.01 22697.13 20692.23 20899.67 11092.24 20899.34 16199.17 131
V1497.83 6698.33 3796.35 18498.88 10089.72 21797.75 6799.05 3897.74 5399.01 4099.27 2594.35 14399.71 8098.95 999.97 899.62 32
ESAPD97.64 8197.35 9398.50 4698.85 10196.18 6195.21 21698.99 6595.84 13198.78 5098.08 13296.84 5499.81 3293.98 18099.57 9599.52 49
SMA-MVS97.48 9397.11 11598.60 4198.83 10296.67 4796.74 12598.73 11491.61 25798.48 7198.36 9696.53 6699.68 10395.17 13499.54 10499.45 73
v1597.77 7298.26 4196.30 18998.81 10389.59 22497.62 7699.04 4597.59 6598.97 4499.24 2794.19 15199.70 8898.88 1199.97 899.61 34
UniMVSNet (Re)97.83 6697.65 7298.35 6098.80 10495.86 7095.92 17299.04 4597.51 7198.22 9597.81 16294.68 12999.78 4097.14 7199.75 5599.41 90
Anonymous2023121198.55 2198.76 1597.94 8398.79 10594.37 12098.84 1099.15 1899.37 299.67 699.43 1095.61 10099.72 6998.12 3199.86 3899.73 15
APD-MVS_3200maxsize98.13 4097.90 5698.79 2798.79 10597.31 3397.55 8398.92 7597.72 5798.25 9398.13 12797.10 4299.75 5395.44 12399.24 17799.32 107
DeepC-MVS95.41 497.82 6997.70 6798.16 7098.78 10795.72 7396.23 15099.02 5193.92 20398.62 5998.99 5097.69 2299.62 12896.18 9299.87 3699.15 135
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v1797.70 7798.17 4396.28 19298.77 10889.59 22497.62 7699.01 6097.54 6898.72 5699.18 3594.06 15599.68 10398.74 1699.92 2399.58 37
v1697.69 7898.16 4496.29 19198.75 10989.60 22297.62 7699.01 6097.53 7098.69 5899.18 3594.05 15699.68 10398.73 1799.88 3399.58 37
MCST-MVS96.24 16595.80 17897.56 10698.75 10994.13 13094.66 24298.17 19990.17 27296.21 21996.10 26795.14 11699.43 19994.13 17298.85 21999.13 138
DU-MVS97.79 7197.60 7998.36 5898.73 11195.78 7195.65 18698.87 8397.57 6698.31 8897.83 15894.69 12799.85 2397.02 7599.71 6499.46 68
NR-MVSNet97.96 4997.86 5898.26 6698.73 11195.54 8198.14 4398.73 11497.79 5099.42 1697.83 15894.40 14299.78 4095.91 10599.76 5199.46 68
Anonymous2023120695.27 20595.06 19995.88 22098.72 11389.37 23395.70 18097.85 22188.00 29396.98 17897.62 17791.95 21699.34 23289.21 26799.53 10798.94 167
APDe-MVS98.14 3898.03 5298.47 4998.72 11396.04 6698.07 4799.10 2595.96 12598.59 6398.69 7196.94 4799.81 3296.64 7999.58 9299.57 41
UniMVSNet_NR-MVSNet97.83 6697.65 7298.37 5598.72 11395.78 7195.66 18499.02 5198.11 4198.31 8897.69 17494.65 13199.85 2397.02 7599.71 6499.48 63
v897.60 8598.06 4996.23 19398.71 11689.44 22997.43 8998.82 10197.29 8698.74 5499.10 4593.86 15999.68 10398.61 2299.94 1999.56 42
v696.97 11997.24 10196.15 20198.71 11689.44 22995.97 16398.33 17695.25 15297.89 13498.15 12393.86 15999.61 13497.51 5699.50 11499.42 88
v1neww96.97 11997.24 10196.15 20198.70 11889.44 22995.97 16398.33 17695.25 15297.88 13698.15 12393.83 16299.61 13497.50 5799.50 11499.41 90
v7new96.97 11997.24 10196.15 20198.70 11889.44 22995.97 16398.33 17695.25 15297.88 13698.15 12393.83 16299.61 13497.50 5799.50 11499.41 90
HQP_MVS96.66 14996.33 16097.68 10098.70 11894.29 12296.50 13398.75 11196.36 10896.16 22196.77 23091.91 22099.46 19092.59 20499.20 18099.28 118
plane_prior798.70 11894.67 111
Anonymous2024052997.96 4998.04 5197.71 9598.69 12294.28 12597.86 5998.31 18598.79 2099.23 2998.86 6095.76 9699.61 13495.49 12099.36 15599.23 124
VDD-MVS97.37 10097.25 9997.74 9498.69 12294.50 11697.04 11195.61 29198.59 2498.51 6898.72 6892.54 20199.58 14796.02 9999.49 12199.12 143
v1897.60 8598.06 4996.23 19398.68 12489.46 22897.48 8798.98 6897.33 8498.60 6299.13 4393.86 15999.67 11098.62 2199.87 3699.56 42
HPM-MVS++copyleft96.99 11596.38 15698.81 2698.64 12597.59 2095.97 16398.20 19495.51 14395.06 24896.53 24494.10 15499.70 8894.29 16899.15 18499.13 138
ab-mvs96.59 15196.59 14396.60 16798.64 12592.21 17498.35 2997.67 23394.45 18496.99 17798.79 6294.96 12199.49 18090.39 25299.07 19598.08 252
F-COLMAP95.30 20394.38 22798.05 7898.64 12596.04 6695.61 19098.66 13389.00 28093.22 31296.40 25492.90 18999.35 23187.45 29997.53 29598.77 196
ITE_SJBPF97.85 8898.64 12596.66 4898.51 15295.63 13797.22 16597.30 20095.52 10298.55 32090.97 23198.90 21098.34 233
v14896.58 15296.97 12395.42 23698.63 12987.57 27695.09 22197.90 21895.91 12798.24 9497.96 14693.42 17499.39 22096.04 9799.52 11199.29 117
UnsupCasMVSNet_bld94.72 22294.26 22996.08 20698.62 13090.54 20993.38 29498.05 21390.30 27097.02 17596.80 22889.54 24899.16 25988.44 27996.18 32298.56 212
DP-MVS97.87 6397.89 5797.81 9098.62 13094.82 10597.13 10298.79 10398.98 1698.74 5498.49 8695.80 9599.49 18095.04 14399.44 13399.11 146
v1097.55 8897.97 5396.31 18898.60 13289.64 21997.44 8899.02 5196.60 9998.72 5699.16 4093.48 17299.72 6998.76 1599.92 2399.58 37
Test_1112_low_res93.53 25792.86 25795.54 23398.60 13288.86 24792.75 30698.69 12582.66 33392.65 32296.92 22084.75 28499.56 15390.94 23297.76 27398.19 248
v796.93 12397.17 10996.23 19398.59 13489.64 21995.96 16798.66 13394.41 18797.87 14198.38 9493.47 17399.64 11997.93 3799.24 17799.43 86
V4297.04 11397.16 11096.68 16598.59 13491.05 19796.33 14398.36 17194.60 17997.99 11998.30 10593.32 17899.62 12897.40 6299.53 10799.38 98
1112_ss94.12 24293.42 24796.23 19398.59 13490.85 20094.24 25598.85 8685.49 31592.97 31594.94 29386.01 27799.64 11991.78 21697.92 26898.20 247
v2v48296.78 14097.06 12095.95 21698.57 13788.77 25195.36 20398.26 18895.18 15897.85 14398.23 11292.58 19899.63 12297.80 4299.69 6899.45 73
WR-MVS96.90 12796.81 13497.16 13798.56 13892.20 17694.33 25098.12 20597.34 8398.20 9797.33 19992.81 19099.75 5394.79 15099.81 4499.54 46
v114196.86 12997.14 11296.04 20898.55 13989.06 24195.44 19398.33 17695.14 16297.93 12998.19 11793.36 17699.62 12897.61 4999.69 6899.44 82
v196.86 12997.14 11296.04 20898.55 13989.06 24195.44 19398.33 17695.14 16297.94 12698.18 12193.39 17599.61 13497.61 4999.69 6899.44 82
APD-MVScopyleft97.00 11496.53 15098.41 5298.55 13996.31 5896.32 14498.77 10792.96 23697.44 15997.58 18195.84 8699.74 5891.96 21099.35 15999.19 128
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Patchmatch-RL test94.66 22494.49 22195.19 24298.54 14288.91 24592.57 31098.74 11391.46 26098.32 8697.75 16777.31 31198.81 29996.06 9599.61 8597.85 268
divwei89l23v2f11296.86 12997.14 11296.04 20898.54 14289.06 24195.44 19398.33 17695.14 16297.93 12998.19 11793.36 17699.61 13497.61 4999.68 7299.44 82
111188.78 31989.39 31286.96 34498.53 14462.84 36391.49 32697.48 24794.45 18496.56 19796.45 24943.83 36898.87 29386.33 30699.40 15099.18 130
.test124573.49 33779.27 33856.15 35098.53 14462.84 36391.49 32697.48 24794.45 18496.56 19796.45 24943.83 36898.87 29386.33 3068.32 3636.75 363
IterMVS-LS96.92 12597.29 9695.79 22398.51 14688.13 26195.10 21998.66 13396.99 8798.46 7498.68 7292.55 19999.74 5896.91 7799.79 4899.50 52
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon95.55 18795.13 19596.80 15698.51 14693.99 13594.60 24498.69 12590.20 27195.78 23496.21 26292.73 19398.98 28090.58 24698.86 21797.42 283
test20.0396.58 15296.61 14296.48 17898.49 14891.72 19095.68 18397.69 23296.81 9598.27 9297.92 15394.18 15298.71 30790.78 23899.66 7699.00 159
plane_prior198.49 148
MDA-MVSNet-bldmvs95.69 18195.67 18195.74 22498.48 15088.76 25292.84 30397.25 25396.00 12397.59 14897.95 14991.38 22899.46 19093.16 19896.35 32098.99 162
UnsupCasMVSNet_eth95.91 17795.73 18096.44 18098.48 15091.52 19395.31 20898.45 15695.76 13497.48 15697.54 18289.53 25098.69 30994.43 16094.61 33699.13 138
view60092.56 26992.11 26993.91 28298.45 15284.76 31197.10 10490.23 34397.42 7496.98 17894.48 30273.62 32799.60 14182.49 33198.28 25097.36 284
view80092.56 26992.11 26993.91 28298.45 15284.76 31197.10 10490.23 34397.42 7496.98 17894.48 30273.62 32799.60 14182.49 33198.28 25097.36 284
conf0.05thres100092.56 26992.11 26993.91 28298.45 15284.76 31197.10 10490.23 34397.42 7496.98 17894.48 30273.62 32799.60 14182.49 33198.28 25097.36 284
tfpn92.56 26992.11 26993.91 28298.45 15284.76 31197.10 10490.23 34397.42 7496.98 17894.48 30273.62 32799.60 14182.49 33198.28 25097.36 284
v114496.84 13297.08 11896.13 20598.42 15689.28 23695.41 20098.67 13094.21 19697.97 12398.31 10293.06 18399.65 11698.06 3499.62 8099.45 73
plane_prior698.38 15794.37 12091.91 220
FPMVS89.92 31388.63 32093.82 28798.37 15896.94 4091.58 32593.34 31388.00 29390.32 34297.10 20970.87 34291.13 36071.91 35696.16 32393.39 349
PAPM_NR94.61 22794.17 23495.96 21498.36 15991.23 19595.93 17197.95 21592.98 23193.42 30894.43 30790.53 23698.38 33087.60 29696.29 32198.27 240
MVS_111021_HR96.73 14396.54 14997.27 13398.35 16093.66 14893.42 29298.36 17194.74 17696.58 19596.76 23296.54 6598.99 27894.87 14599.27 17599.15 135
TAMVS95.49 19094.94 20197.16 13798.31 16193.41 15595.07 22496.82 27091.09 26397.51 15197.82 16189.96 24599.42 20088.42 28099.44 13398.64 204
OMC-MVS96.48 15796.00 17097.91 8598.30 16296.01 6894.86 23598.60 14391.88 25597.18 16797.21 20396.11 8099.04 27190.49 25099.34 16198.69 202
新几何197.25 13698.29 16394.70 11097.73 22977.98 35194.83 25696.67 23792.08 21399.45 19488.17 28498.65 23497.61 277
jason94.39 23394.04 23795.41 23898.29 16387.85 27192.74 30896.75 27285.38 32095.29 24496.15 26388.21 26299.65 11694.24 17099.34 16198.74 198
jason: jason.
no-one94.84 21794.76 21095.09 24798.29 16387.49 27891.82 32397.49 24588.21 28997.84 14498.75 6691.51 22599.27 24688.96 27299.99 298.52 215
v119296.83 13597.06 12096.15 20198.28 16689.29 23595.36 20398.77 10793.73 21298.11 10598.34 9893.02 18899.67 11098.35 2799.58 9299.50 52
CDPH-MVS95.45 19694.65 21397.84 8998.28 16694.96 10193.73 28198.33 17685.03 32295.44 24196.60 24095.31 11199.44 19890.01 25799.13 18799.11 146
conf0.0191.90 28590.98 29094.67 26098.27 16888.03 26396.98 11488.58 35193.90 20494.64 26091.45 33869.62 34699.52 16587.62 29097.74 27496.46 315
conf0.00291.90 28590.98 29094.67 26098.27 16888.03 26396.98 11488.58 35193.90 20494.64 26091.45 33869.62 34699.52 16587.62 29097.74 27496.46 315
thresconf0.0291.72 29290.98 29093.97 27898.27 16888.03 26396.98 11488.58 35193.90 20494.64 26091.45 33869.62 34699.52 16587.62 29097.74 27494.35 341
tfpn_n40091.72 29290.98 29093.97 27898.27 16888.03 26396.98 11488.58 35193.90 20494.64 26091.45 33869.62 34699.52 16587.62 29097.74 27494.35 341
tfpnconf91.72 29290.98 29093.97 27898.27 16888.03 26396.98 11488.58 35193.90 20494.64 26091.45 33869.62 34699.52 16587.62 29097.74 27494.35 341
tfpnview1191.72 29290.98 29093.97 27898.27 16888.03 26396.98 11488.58 35193.90 20494.64 26091.45 33869.62 34699.52 16587.62 29097.74 27494.35 341
MVS_111021_LR96.82 13696.55 14797.62 10398.27 16895.34 8893.81 27998.33 17694.59 18196.56 19796.63 23996.61 6398.73 30594.80 14999.34 16198.78 195
CLD-MVS95.47 19395.07 19796.69 16498.27 16892.53 16791.36 32998.67 13091.22 26295.78 23494.12 31195.65 9998.98 28090.81 23699.72 6098.57 211
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
112194.26 23493.26 25097.27 13398.26 17694.73 10795.86 17497.71 23177.96 35294.53 26896.71 23491.93 21899.40 21487.71 28698.64 23597.69 274
Anonymous20240521196.34 16395.98 17297.43 12498.25 17793.85 13996.74 12594.41 30197.72 5798.37 7998.03 14087.15 27299.53 16194.06 17499.07 19598.92 174
pmmvs-eth3d96.49 15696.18 16397.42 12598.25 17794.29 12294.77 24098.07 21189.81 27597.97 12398.33 10093.11 18299.08 26795.46 12299.84 4198.89 179
v14419296.69 14796.90 12896.03 21198.25 17788.92 24495.49 19198.77 10793.05 22998.09 10998.29 10692.51 20399.70 8898.11 3299.56 9899.47 66
ambc96.56 17498.23 18091.68 19197.88 5898.13 20498.42 7798.56 8194.22 15099.04 27194.05 17799.35 15998.95 165
testmv95.51 18895.33 19096.05 20798.23 18089.51 22793.50 29098.63 14094.25 19498.22 9597.73 17092.51 20399.47 18585.22 31699.72 6099.17 131
tfpn11191.92 28491.39 27993.49 29498.21 18284.50 31696.39 13690.39 33896.87 9196.33 20793.08 31973.44 33399.51 17579.87 33997.94 26796.46 315
conf200view1191.81 28991.26 28493.46 29598.21 18284.50 31696.39 13690.39 33896.87 9196.33 20793.08 31973.44 33399.42 20078.85 34497.74 27496.46 315
thres100view90091.76 29191.26 28493.26 29998.21 18284.50 31696.39 13690.39 33896.87 9196.33 20793.08 31973.44 33399.42 20078.85 34497.74 27495.85 325
v192192096.72 14496.96 12595.99 21298.21 18288.79 25095.42 19898.79 10393.22 22198.19 9898.26 11092.68 19499.70 8898.34 2899.55 10299.49 60
thres600view792.03 28291.43 27893.82 28798.19 18684.61 31596.27 14590.39 33896.81 9596.37 20693.11 31773.44 33399.49 18080.32 33897.95 26497.36 284
PatchMatch-RL94.61 22793.81 24297.02 14898.19 18695.72 7393.66 28397.23 25488.17 29094.94 25295.62 28191.43 22798.57 31787.36 30097.68 28796.76 306
LF4IMVS96.07 17295.63 18397.36 12998.19 18695.55 8095.44 19398.82 10192.29 24795.70 23896.55 24292.63 19798.69 30991.75 21999.33 16697.85 268
v124096.74 14197.02 12295.91 21998.18 18988.52 25395.39 20198.88 8193.15 22798.46 7498.40 9392.80 19199.71 8098.45 2699.49 12199.49 60
TAPA-MVS93.32 1294.93 21594.23 23097.04 14598.18 18994.51 11495.22 21598.73 11481.22 33996.25 21795.95 27393.80 16698.98 28089.89 25898.87 21597.62 276
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test22298.17 19193.24 15992.74 30897.61 24375.17 35694.65 25996.69 23690.96 23398.66 23397.66 275
MIMVSNet93.42 25892.86 25795.10 24698.17 19188.19 25898.13 4493.69 30692.07 24895.04 25098.21 11680.95 29599.03 27481.42 33698.06 26198.07 254
原ACMM196.58 17198.16 19392.12 17898.15 20285.90 31293.49 30396.43 25192.47 20599.38 22587.66 28998.62 23698.23 244
testdata95.70 22898.16 19390.58 20697.72 23080.38 34295.62 23997.02 21392.06 21498.98 28089.06 27198.52 24297.54 280
MVP-Stereo95.69 18195.28 19196.92 15198.15 19593.03 16195.64 18898.20 19490.39 26996.63 19497.73 17091.63 22399.10 26591.84 21597.31 30498.63 206
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS97.37 10097.70 6796.35 18498.14 19695.13 9696.54 13198.92 7595.94 12699.19 3198.08 13297.74 2195.06 35795.24 13099.54 10498.87 185
EU-MVSNet94.25 23594.47 22293.60 29198.14 19682.60 32797.24 9692.72 32185.08 32198.48 7198.94 5582.59 29098.76 30397.47 6099.53 10799.44 82
NP-MVS98.14 19693.72 14495.08 289
LCM-MVSNet-Re97.33 10497.33 9497.32 13198.13 19993.79 14296.99 11399.65 296.74 9799.47 1398.93 5696.91 4899.84 2790.11 25599.06 19898.32 234
3Dnovator+96.13 397.73 7497.59 8098.15 7198.11 20095.60 7898.04 4998.70 12498.13 4096.93 18598.45 8995.30 11299.62 12895.64 11498.96 20499.24 123
Test495.39 19895.24 19295.82 22298.07 20189.60 22294.40 24898.49 15391.39 26197.40 16196.32 25787.32 27199.41 21195.09 14298.71 23198.44 221
VNet96.84 13296.83 13396.88 15498.06 20292.02 18196.35 14297.57 24497.70 5997.88 13697.80 16392.40 20699.54 15994.73 15498.96 20499.08 151
DI_MVS_plusplus_test95.46 19495.43 18895.55 23298.05 20388.84 24894.18 26095.75 28791.92 25497.32 16296.94 21791.44 22699.39 22094.81 14898.48 24598.43 222
LFMVS95.32 20294.88 20596.62 16698.03 20491.47 19497.65 7390.72 33799.11 897.89 13498.31 10279.20 30099.48 18393.91 18399.12 19098.93 171
tfpn200view991.55 29791.00 28893.21 30198.02 20584.35 32095.70 18090.79 33596.26 11395.90 23192.13 33473.62 32799.42 20078.85 34497.74 27495.85 325
thres40091.68 29691.00 28893.71 28998.02 20584.35 32095.70 18090.79 33596.26 11395.90 23192.13 33473.62 32799.42 20078.85 34497.74 27497.36 284
xiu_mvs_v1_base_debu95.62 18495.96 17394.60 26498.01 20788.42 25493.99 27098.21 19192.98 23195.91 22894.53 29996.39 7399.72 6995.43 12498.19 25695.64 329
xiu_mvs_v1_base95.62 18495.96 17394.60 26498.01 20788.42 25493.99 27098.21 19192.98 23195.91 22894.53 29996.39 7399.72 6995.43 12498.19 25695.64 329
xiu_mvs_v1_base_debi95.62 18495.96 17394.60 26498.01 20788.42 25493.99 27098.21 19192.98 23195.91 22894.53 29996.39 7399.72 6995.43 12498.19 25695.64 329
casdiffmvs196.82 13696.84 13196.77 15898.01 20792.02 18197.20 9898.67 13092.30 24696.09 22398.64 7693.81 16499.50 17798.22 3098.62 23698.79 192
CNVR-MVS96.92 12596.55 14798.03 7998.00 21195.54 8194.87 23498.17 19994.60 17996.38 20597.05 21195.67 9899.36 22995.12 14099.08 19399.19 128
PLCcopyleft91.02 1694.05 24692.90 25697.51 11198.00 21195.12 9794.25 25498.25 18986.17 30891.48 33395.25 28791.01 23199.19 25485.02 31896.69 31598.22 245
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_normal95.51 18895.46 18795.68 22997.97 21389.12 24093.73 28195.86 28591.98 25197.17 16896.94 21791.55 22499.42 20095.21 13198.73 22898.51 216
tfpn100091.88 28891.20 28693.89 28697.96 21487.13 28697.13 10288.16 35894.41 18794.87 25592.77 32668.34 35399.47 18589.24 26697.95 26495.06 335
GBi-Net96.99 11596.80 13597.56 10697.96 21493.67 14598.23 3598.66 13395.59 14097.99 11999.19 3289.51 25199.73 6394.60 15699.44 13399.30 111
test196.99 11596.80 13597.56 10697.96 21493.67 14598.23 3598.66 13395.59 14097.99 11999.19 3289.51 25199.73 6394.60 15699.44 13399.30 111
FMVSNet296.72 14496.67 14196.87 15597.96 21491.88 18697.15 9998.06 21295.59 14098.50 7098.62 7789.51 25199.65 11694.99 14499.60 8899.07 153
BH-untuned94.69 22394.75 21194.52 26997.95 21887.53 27794.07 26797.01 26393.99 20197.10 17295.65 27992.65 19698.95 28587.60 29696.74 31497.09 292
QAPM95.88 17995.57 18496.80 15697.90 21991.84 18898.18 4298.73 11488.41 28596.42 20398.13 12794.73 12499.75 5388.72 27598.94 20898.81 190
TinyColmap96.00 17596.34 15994.96 25097.90 21987.91 26994.13 26598.49 15394.41 18798.16 10097.76 16496.29 7898.68 31290.52 24799.42 14398.30 237
HQP-NCC97.85 22194.26 25193.18 22392.86 317
ACMP_Plane97.85 22194.26 25193.18 22392.86 317
N_pmnet95.18 20794.23 23098.06 7597.85 22196.55 5292.49 31291.63 32989.34 27798.09 10997.41 19190.33 23999.06 26991.58 22199.31 16898.56 212
HQP-MVS95.17 20894.58 21896.92 15197.85 22192.47 16894.26 25198.43 16093.18 22392.86 31795.08 28990.33 23999.23 25290.51 24898.74 22599.05 156
TEST997.84 22595.23 9093.62 28598.39 16786.81 30393.78 29095.99 26894.68 12999.52 165
train_agg95.46 19494.66 21297.88 8697.84 22595.23 9093.62 28598.39 16787.04 30193.78 29095.99 26894.58 13499.52 16591.76 21798.90 21098.89 179
MSLP-MVS++96.42 16296.71 13995.57 23197.82 22790.56 20895.71 17998.84 8994.72 17796.71 19297.39 19594.91 12298.10 34195.28 12899.02 20098.05 258
test_897.81 22895.07 9893.54 28898.38 16987.04 30193.71 29495.96 27294.58 13499.52 165
NCCC96.52 15595.99 17198.10 7397.81 22895.68 7595.00 23098.20 19495.39 14895.40 24396.36 25593.81 16499.45 19493.55 19198.42 24799.17 131
WTY-MVS93.55 25693.00 25595.19 24297.81 22887.86 27093.89 27596.00 28089.02 27994.07 28195.44 28586.27 27599.33 23587.69 28896.82 31198.39 225
CNLPA95.04 21294.47 22296.75 16097.81 22895.25 8994.12 26697.89 21994.41 18794.57 26695.69 27790.30 24298.35 33386.72 30598.76 22396.64 310
agg_prior395.30 20394.46 22597.80 9197.80 23295.00 9993.63 28498.34 17586.33 30793.40 31095.84 27594.15 15399.50 17791.76 21798.90 21098.89 179
agg_prior195.39 19894.60 21697.75 9397.80 23294.96 10193.39 29398.36 17187.20 29993.49 30395.97 27194.65 13199.53 16191.69 22098.86 21798.77 196
agg_prior97.80 23294.96 10198.36 17193.49 30399.53 161
旧先验197.80 23293.87 13797.75 22797.04 21293.57 17198.68 23298.72 200
PCF-MVS89.43 1892.12 28190.64 30296.57 17397.80 23293.48 15489.88 34598.45 15674.46 35796.04 22595.68 27890.71 23599.31 23773.73 35299.01 20296.91 300
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_prior395.91 17795.39 18997.46 12097.79 23794.26 12693.33 29698.42 16394.21 19694.02 28396.25 25993.64 16999.34 23291.90 21198.96 20498.79 192
test_prior97.46 12097.79 23794.26 12698.42 16399.34 23298.79 192
PVSNet_BlendedMVS95.02 21494.93 20395.27 24097.79 23787.40 28194.14 26498.68 12788.94 28194.51 26998.01 14293.04 18499.30 23989.77 26099.49 12199.11 146
PVSNet_Blended93.96 24793.65 24494.91 25197.79 23787.40 28191.43 32898.68 12784.50 32694.51 26994.48 30293.04 18499.30 23989.77 26098.61 23898.02 263
USDC94.56 22994.57 22094.55 26897.78 24186.43 29492.75 30698.65 13985.96 31096.91 18697.93 15290.82 23498.74 30490.71 24299.59 9098.47 218
alignmvs96.01 17495.52 18597.50 11497.77 24294.71 10996.07 15696.84 26897.48 7296.78 19194.28 31085.50 27999.40 21496.22 9198.73 22898.40 223
TSAR-MVS + GP.96.47 15896.12 16497.49 11797.74 24395.23 9094.15 26396.90 26793.26 22098.04 11696.70 23594.41 14198.89 28994.77 15299.14 18598.37 227
3Dnovator96.53 297.61 8497.64 7497.50 11497.74 24393.65 14998.49 2398.88 8196.86 9497.11 17198.55 8295.82 8999.73 6395.94 10399.42 14399.13 138
tfpn_ndepth90.98 30390.24 30893.20 30397.72 24587.18 28596.52 13288.20 35792.63 24093.69 29690.70 35168.22 35499.42 20086.98 30297.47 29993.00 351
sss94.22 23693.72 24395.74 22497.71 24689.95 21593.84 27796.98 26488.38 28893.75 29295.74 27687.94 26398.89 28991.02 22998.10 26098.37 227
DeepC-MVS_fast94.34 796.74 14196.51 15297.44 12397.69 24794.15 12996.02 15998.43 16093.17 22697.30 16397.38 19795.48 10499.28 24493.74 18799.34 16198.88 183
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
semantic-postprocess94.85 25597.68 24885.53 29897.63 24196.99 8798.36 8198.54 8387.44 26999.75 5397.07 7499.08 19399.27 121
MVSFormer96.14 17096.36 15895.49 23597.68 24887.81 27398.67 1399.02 5196.50 10394.48 27196.15 26386.90 27399.92 498.73 1799.13 18798.74 198
lupinMVS93.77 24993.28 24995.24 24197.68 24887.81 27392.12 31896.05 27984.52 32594.48 27195.06 29186.90 27399.63 12293.62 19099.13 18798.27 240
Fast-Effi-MVS+95.49 19095.07 19796.75 16097.67 25192.82 16394.22 25798.60 14391.61 25793.42 30892.90 32496.73 6099.70 8892.60 20397.89 27197.74 273
canonicalmvs97.23 11097.21 10797.30 13297.65 25294.39 11897.84 6199.05 3897.42 7496.68 19393.85 31397.63 2599.33 23596.29 9098.47 24698.18 250
CDS-MVSNet94.88 21694.12 23597.14 13997.64 25393.57 15093.96 27397.06 26290.05 27396.30 21496.55 24286.10 27699.47 18590.10 25699.31 16898.40 223
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pmmvs594.63 22694.34 22895.50 23497.63 25488.34 25794.02 26897.13 25987.15 30095.22 24697.15 20587.50 26899.27 24693.99 17999.26 17698.88 183
test1297.46 12097.61 25594.07 13197.78 22693.57 30193.31 17999.42 20098.78 22198.89 179
PMMVS293.66 25394.07 23692.45 31697.57 25680.67 33586.46 35296.00 28093.99 20197.10 17297.38 19789.90 24697.82 34488.76 27499.47 12698.86 186
BH-RMVSNet94.56 22994.44 22694.91 25197.57 25687.44 28093.78 28096.26 27793.69 21496.41 20496.50 24792.10 21299.00 27785.96 30897.71 28498.31 235
PVSNet86.72 1991.10 30090.97 29691.49 32397.56 25878.04 34487.17 35094.60 29984.65 32492.34 32692.20 33387.37 27098.47 32385.17 31797.69 28697.96 265
DELS-MVS96.17 16996.23 16295.99 21297.55 25990.04 21292.38 31598.52 15094.13 19996.55 20097.06 21094.99 12099.58 14795.62 11599.28 17398.37 227
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
IterMVS95.42 19795.83 17794.20 27697.52 26083.78 32492.41 31497.47 25095.49 14498.06 11498.49 8687.94 26399.58 14796.02 9999.02 20099.23 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
casdiffmvs96.43 16196.38 15696.60 16797.51 26191.95 18597.08 11098.41 16593.69 21493.95 28798.34 9893.03 18699.45 19498.09 3397.30 30598.39 225
new-patchmatchnet95.67 18396.58 14492.94 30997.48 26280.21 33692.96 30298.19 19894.83 17398.82 4898.79 6293.31 17999.51 17595.83 10699.04 19999.12 143
MDA-MVSNet_test_wron94.73 22094.83 20994.42 27097.48 26285.15 30490.28 34095.87 28492.52 24197.48 15697.76 16491.92 21999.17 25893.32 19296.80 31398.94 167
PHI-MVS96.96 12296.53 15098.25 6897.48 26296.50 5396.76 12498.85 8693.52 21796.19 22096.85 22395.94 8399.42 20093.79 18699.43 14098.83 189
DeepPCF-MVS94.58 596.90 12796.43 15498.31 6397.48 26297.23 3592.56 31198.60 14392.84 23898.54 6697.40 19296.64 6298.78 30194.40 16399.41 14998.93 171
thres20091.00 30290.42 30692.77 31197.47 26683.98 32394.01 26991.18 33395.12 16595.44 24191.21 34673.93 32399.31 23777.76 34897.63 29295.01 336
YYNet194.73 22094.84 20794.41 27197.47 26685.09 30690.29 33995.85 28692.52 24197.53 15097.76 16491.97 21599.18 25593.31 19396.86 31098.95 165
Effi-MVS+96.19 16896.01 16996.71 16297.43 26892.19 17796.12 15599.10 2595.45 14593.33 31194.71 29797.23 4099.56 15393.21 19797.54 29498.37 227
pmmvs494.82 21994.19 23396.70 16397.42 26992.75 16592.09 32096.76 27186.80 30495.73 23797.22 20289.28 25498.89 28993.28 19499.14 18598.46 220
MSDG95.33 20195.13 19595.94 21897.40 27091.85 18791.02 33298.37 17095.30 15096.31 21395.99 26894.51 13998.38 33089.59 26297.65 29097.60 278
EI-MVSNet-Vis-set97.32 10597.39 9197.11 14097.36 27192.08 18095.34 20597.65 23797.74 5398.29 9198.11 13095.05 11799.68 10397.50 5799.50 11499.56 42
PS-MVSNAJ94.10 24394.47 22293.00 30797.35 27284.88 30891.86 32297.84 22291.96 25294.17 27692.50 33195.82 8999.71 8091.27 22597.48 29794.40 340
Regformer-397.25 10997.29 9697.11 14097.35 27292.32 17195.26 21297.62 24297.67 6298.17 9997.89 15595.05 11799.56 15397.16 7099.42 14399.46 68
Regformer-497.53 9197.47 8997.71 9597.35 27293.91 13695.26 21298.14 20397.97 4698.34 8397.89 15595.49 10399.71 8097.41 6199.42 14399.51 51
EI-MVSNet-UG-set97.32 10597.40 9097.09 14297.34 27592.01 18395.33 20697.65 23797.74 5398.30 9098.14 12695.04 11999.69 9797.55 5499.52 11199.58 37
AdaColmapbinary95.11 20994.62 21596.58 17197.33 27694.45 11794.92 23298.08 20993.15 22793.98 28695.53 28494.34 14499.10 26585.69 31198.61 23896.20 322
xiu_mvs_v2_base94.22 23694.63 21492.99 30897.32 27784.84 30992.12 31897.84 22291.96 25294.17 27693.43 31496.07 8199.71 8091.27 22597.48 29794.42 339
OpenMVS_ROBcopyleft91.80 1493.64 25493.05 25395.42 23697.31 27891.21 19695.08 22396.68 27581.56 33696.88 18896.41 25290.44 23899.25 24985.39 31597.67 28895.80 327
EI-MVSNet96.63 15096.93 12695.74 22497.26 27988.13 26195.29 21097.65 23796.99 8797.94 12698.19 11792.55 19999.58 14796.91 7799.56 9899.50 52
CVMVSNet92.33 27792.79 25990.95 32997.26 27975.84 35195.29 21092.33 32481.86 33496.27 21598.19 11781.44 29298.46 32494.23 17198.29 24998.55 214
Regformer-197.27 10797.16 11097.61 10497.21 28193.86 13894.85 23698.04 21497.62 6498.03 11797.50 18795.34 10999.63 12296.52 8399.31 16899.35 105
Regformer-297.41 9797.24 10197.93 8497.21 28194.72 10894.85 23698.27 18697.74 5398.11 10597.50 18795.58 10199.69 9796.57 8299.31 16899.37 103
Fast-Effi-MVS+-dtu96.44 15996.12 16497.39 12897.18 28394.39 11895.46 19298.73 11496.03 12294.72 25794.92 29596.28 7999.69 9793.81 18597.98 26398.09 251
OpenMVScopyleft94.22 895.48 19295.20 19396.32 18797.16 28491.96 18497.74 6998.84 8987.26 29794.36 27398.01 14293.95 15899.67 11090.70 24398.75 22497.35 290
BH-w/o92.14 28091.94 27392.73 31297.13 28585.30 30192.46 31395.64 29089.33 27894.21 27592.74 32889.60 24798.24 33681.68 33594.66 33594.66 338
MG-MVS94.08 24594.00 23994.32 27397.09 28685.89 29593.19 30095.96 28292.52 24194.93 25397.51 18689.54 24898.77 30287.52 29897.71 28498.31 235
MVS_030496.22 16695.94 17697.04 14597.07 28792.54 16694.19 25999.04 4595.17 15993.74 29396.92 22091.77 22299.73 6395.76 10899.81 4498.85 188
MVS-HIRNet88.40 32390.20 30982.99 34797.01 28860.04 36593.11 30185.61 36084.45 32788.72 35099.09 4684.72 28598.23 33782.52 33096.59 31790.69 357
GA-MVS92.83 26692.15 26894.87 25496.97 28987.27 28490.03 34196.12 27891.83 25694.05 28294.57 29876.01 31898.97 28492.46 20697.34 30398.36 232
0601test94.40 23294.00 23995.59 23096.95 29089.52 22694.75 24195.55 29396.18 11796.79 18996.14 26581.09 29499.18 25590.75 23997.77 27298.07 254
test123567892.95 26492.40 26494.61 26396.95 29086.87 28990.75 33497.75 22791.00 26596.33 20795.38 28685.21 28198.92 28679.00 34299.20 18098.03 261
MVS_Test96.27 16496.79 13794.73 25996.94 29286.63 29296.18 15298.33 17694.94 16996.07 22498.28 10795.25 11499.26 24897.21 6697.90 27098.30 237
MAR-MVS94.21 23993.03 25497.76 9296.94 29297.44 3096.97 12097.15 25887.89 29592.00 32992.73 32992.14 21099.12 26083.92 32497.51 29696.73 307
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
Effi-MVS+-dtu96.81 13896.09 16698.99 1096.90 29498.69 296.42 13598.09 20795.86 12995.15 24795.54 28394.26 14899.81 3294.06 17498.51 24498.47 218
mvs-test196.20 16795.50 18698.32 6196.90 29498.16 495.07 22498.09 20795.86 12993.63 29794.32 30994.26 14899.71 8094.06 17497.27 30797.07 293
MS-PatchMatch94.83 21894.91 20494.57 26796.81 29687.10 28794.23 25697.34 25188.74 28397.14 16997.11 20891.94 21798.23 33792.99 20197.92 26898.37 227
diffmvs196.57 15496.86 12995.72 22796.74 29789.30 23495.90 17398.58 14696.33 11194.93 25398.37 9594.52 13899.29 24297.60 5298.73 22898.58 210
UGNet96.81 13896.56 14697.58 10596.64 29893.84 14097.75 6797.12 26096.47 10693.62 29898.88 5993.22 18199.53 16195.61 11699.69 6899.36 104
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
API-MVS95.09 21195.01 20095.31 23996.61 29994.02 13396.83 12297.18 25795.60 13995.79 23394.33 30894.54 13698.37 33285.70 31098.52 24293.52 347
PAPM87.64 33085.84 33393.04 30596.54 30084.99 30788.42 34995.57 29279.52 34583.82 35993.05 32380.57 29698.41 32662.29 36092.79 34295.71 328
FMVSNet395.26 20694.94 20196.22 19796.53 30190.06 21195.99 16197.66 23594.11 20097.99 11997.91 15480.22 29899.63 12294.60 15699.44 13398.96 164
diffmvs96.10 17196.43 15495.12 24496.52 30287.85 27195.95 17097.91 21696.52 10293.02 31498.25 11194.28 14699.28 24497.11 7298.26 25498.24 243
HY-MVS91.43 1592.58 26891.81 27694.90 25396.49 30388.87 24697.31 9194.62 29885.92 31190.50 34196.84 22485.05 28299.40 21483.77 32795.78 32796.43 319
TR-MVS92.54 27392.20 26793.57 29296.49 30386.66 29193.51 28994.73 29789.96 27494.95 25193.87 31290.24 24498.61 31581.18 33794.88 33395.45 333
CANet95.86 18095.65 18296.49 17796.41 30590.82 20294.36 24998.41 16594.94 16992.62 32496.73 23392.68 19499.71 8095.12 14099.60 8898.94 167
mvs_anonymous95.36 20096.07 16893.21 30196.29 30681.56 33094.60 24497.66 23593.30 21996.95 18498.91 5893.03 18699.38 22596.60 8097.30 30598.69 202
Patchmatch-test193.38 26093.59 24592.73 31296.24 30781.40 33193.24 29894.00 30491.58 25994.57 26696.67 23787.94 26399.03 27490.42 25197.66 28997.77 272
LS3D97.77 7297.50 8798.57 4396.24 30797.58 2198.45 2698.85 8698.58 2597.51 15197.94 15095.74 9799.63 12295.19 13298.97 20398.51 216
new_pmnet92.34 27691.69 27794.32 27396.23 30989.16 23892.27 31692.88 31884.39 32895.29 24496.35 25685.66 27896.74 35484.53 32197.56 29397.05 294
MVEpermissive73.61 2286.48 33285.92 33288.18 34196.23 30985.28 30281.78 36075.79 36486.01 30982.53 36191.88 33692.74 19287.47 36271.42 35794.86 33491.78 353
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DSMNet-mixed92.19 27991.83 27593.25 30096.18 31183.68 32596.27 14593.68 30876.97 35592.54 32599.18 3589.20 25698.55 32083.88 32598.60 24097.51 281
our_test_394.20 24194.58 21893.07 30496.16 31281.20 33290.42 33896.84 26890.72 26797.14 16997.13 20690.47 23799.11 26394.04 17898.25 25598.91 175
ppachtmachnet_test94.49 23194.84 20793.46 29596.16 31282.10 32990.59 33697.48 24790.53 26897.01 17697.59 18091.01 23199.36 22993.97 18199.18 18398.94 167
Patchmatch-test93.60 25593.25 25194.63 26296.14 31487.47 27996.04 15894.50 30093.57 21696.47 20196.97 21576.50 31498.61 31590.67 24498.41 24897.81 271
testus90.90 30590.51 30492.06 32096.07 31579.45 33888.99 34698.44 15985.46 31794.15 27890.77 34889.12 25798.01 34373.66 35397.95 26498.71 201
PNet_i23d83.82 33583.39 33585.10 34696.07 31565.16 36181.87 35994.37 30290.87 26693.92 28892.89 32552.80 36696.44 35677.52 35070.22 36093.70 346
wuyk23d93.25 26295.20 19387.40 34396.07 31595.38 8697.04 11194.97 29595.33 14999.70 598.11 13098.14 1391.94 35977.76 34899.68 7274.89 359
CANet_DTU94.65 22594.21 23295.96 21495.90 31889.68 21893.92 27497.83 22493.19 22290.12 34495.64 28088.52 25899.57 15293.27 19599.47 12698.62 207
test1235687.98 32788.41 32286.69 34595.84 31963.49 36287.15 35197.32 25287.21 29891.78 33293.36 31570.66 34498.39 32874.70 35197.64 29198.19 248
MVSTER94.21 23993.93 24195.05 24995.83 32086.46 29395.18 21797.65 23792.41 24597.94 12698.00 14472.39 33799.58 14796.36 8999.56 9899.12 143
FMVSNet593.39 25992.35 26596.50 17695.83 32090.81 20497.31 9198.27 18692.74 23996.27 21598.28 10762.23 35899.67 11090.86 23499.36 15599.03 157
PVSNet_081.89 2184.49 33483.21 33788.34 34095.76 32274.97 35483.49 35692.70 32278.47 35087.94 35386.90 35983.38 28896.63 35573.44 35466.86 36193.40 348
PAPR92.22 27891.27 28395.07 24895.73 32388.81 24991.97 32197.87 22085.80 31390.91 33592.73 32991.16 22998.33 33479.48 34095.76 32898.08 252
CHOSEN 280x42089.98 31189.19 31792.37 31795.60 32481.13 33386.22 35397.09 26181.44 33887.44 35593.15 31673.99 32299.47 18588.69 27699.07 19596.52 314
ADS-MVSNet291.47 29890.51 30494.36 27295.51 32585.63 29695.05 22795.70 28883.46 33092.69 32096.84 22479.15 30199.41 21185.66 31290.52 34698.04 259
ADS-MVSNet90.95 30490.26 30793.04 30595.51 32582.37 32895.05 22793.41 31283.46 33092.69 32096.84 22479.15 30198.70 30885.66 31290.52 34698.04 259
CR-MVSNet93.29 26192.79 25994.78 25795.44 32788.15 25996.18 15297.20 25584.94 32394.10 27998.57 7977.67 30699.39 22095.17 13495.81 32496.81 304
RPMNet94.22 23694.03 23894.78 25795.44 32788.15 25996.18 15293.73 30597.43 7394.10 27998.49 8679.40 29999.39 22095.69 10995.81 32496.81 304
131492.38 27592.30 26692.64 31495.42 32985.15 30495.86 17496.97 26585.40 31990.62 33793.06 32291.12 23097.80 34586.74 30495.49 33294.97 337
tpm91.08 30190.85 29891.75 32295.33 33078.09 34295.03 22991.27 33288.75 28293.53 30297.40 19271.24 34099.30 23991.25 22793.87 33897.87 267
IB-MVS85.98 2088.63 32086.95 32993.68 29095.12 33184.82 31090.85 33390.17 34787.55 29688.48 35191.34 34558.01 36099.59 14587.24 30193.80 33996.63 312
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
PatchT93.75 25093.57 24694.29 27595.05 33287.32 28396.05 15792.98 31697.54 6894.25 27498.72 6875.79 31999.24 25095.92 10495.81 32496.32 320
tpm288.47 32187.69 32490.79 33094.98 33377.34 34795.09 22191.83 32777.51 35489.40 34796.41 25267.83 35598.73 30583.58 32992.60 34496.29 321
Patchmtry95.03 21394.59 21796.33 18694.83 33490.82 20296.38 14097.20 25596.59 10097.49 15398.57 7977.67 30699.38 22592.95 20299.62 8098.80 191
MVS90.02 30989.20 31692.47 31594.71 33586.90 28895.86 17496.74 27364.72 36090.62 33792.77 32692.54 20198.39 32879.30 34195.56 33192.12 352
CostFormer89.75 31489.25 31391.26 32694.69 33678.00 34595.32 20791.98 32681.50 33790.55 33996.96 21671.06 34198.89 28988.59 27892.63 34396.87 301
PatchmatchNetpermissive91.98 28391.87 27492.30 31894.60 33779.71 33795.12 21893.59 31189.52 27693.61 29997.02 21377.94 30499.18 25590.84 23594.57 33798.01 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmp4_e2388.46 32287.54 32591.22 32794.56 33878.08 34395.63 18993.17 31479.08 34885.85 35796.80 22865.86 35798.85 29684.10 32392.85 34196.72 308
LP93.12 26392.78 26194.14 27794.50 33985.48 29995.73 17895.68 28992.97 23595.05 24997.17 20481.93 29199.40 21493.06 20088.96 35197.55 279
tpm cat188.01 32687.33 32690.05 33594.48 34076.28 35094.47 24794.35 30373.84 35989.26 34895.61 28273.64 32698.30 33584.13 32286.20 35595.57 332
MDTV_nov1_ep1391.28 28294.31 34173.51 35594.80 23893.16 31586.75 30593.45 30697.40 19276.37 31598.55 32088.85 27396.43 318
cascas91.89 28791.35 28193.51 29394.27 34285.60 29788.86 34898.61 14279.32 34692.16 32891.44 34489.22 25598.12 34090.80 23797.47 29996.82 303
test-LLR89.97 31289.90 31090.16 33394.24 34374.98 35289.89 34289.06 34892.02 24989.97 34590.77 34873.92 32498.57 31791.88 21397.36 30196.92 298
test-mter87.92 32887.17 32790.16 33394.24 34374.98 35289.89 34289.06 34886.44 30689.97 34590.77 34854.96 36498.57 31791.88 21397.36 30196.92 298
pmmvs390.00 31088.90 31993.32 29794.20 34585.34 30091.25 33092.56 32378.59 34993.82 28995.17 28867.36 35698.69 30989.08 27098.03 26295.92 323
tpmrst90.31 30790.61 30389.41 33694.06 34672.37 35895.06 22693.69 30688.01 29292.32 32796.86 22277.45 30898.82 29791.04 22887.01 35497.04 295
test0.0.03 190.11 30889.21 31592.83 31093.89 34786.87 28991.74 32488.74 35092.02 24994.71 25891.14 34773.92 32494.48 35883.75 32892.94 34097.16 291
JIA-IIPM91.79 29090.69 30195.11 24593.80 34890.98 19894.16 26291.78 32896.38 10790.30 34399.30 2372.02 33998.90 28788.28 28290.17 34895.45 333
TESTMET0.1,187.20 33186.57 33189.07 33793.62 34972.84 35789.89 34287.01 35985.46 31789.12 34990.20 35356.00 36397.72 34690.91 23396.92 30896.64 310
PatchFormer-LS_test89.62 31589.12 31891.11 32893.62 34978.42 34194.57 24693.62 31088.39 28790.54 34088.40 35672.33 33899.03 27492.41 20788.20 35295.89 324
CMPMVSbinary73.10 2392.74 26791.39 27996.77 15893.57 35194.67 11194.21 25897.67 23380.36 34393.61 29996.60 24082.85 28997.35 34884.86 31998.78 22198.29 239
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DWT-MVSNet_test87.92 32886.77 33091.39 32493.18 35278.62 34095.10 21991.42 33085.58 31488.00 35288.73 35560.60 35998.90 28790.60 24587.70 35396.65 309
E-PMN89.52 31689.78 31188.73 33893.14 35377.61 34683.26 35792.02 32594.82 17493.71 29493.11 31775.31 32096.81 35285.81 30996.81 31291.77 354
PMMVS92.39 27491.08 28796.30 18993.12 35492.81 16490.58 33795.96 28279.17 34791.85 33192.27 33290.29 24398.66 31489.85 25996.68 31697.43 282
EMVS89.06 31889.22 31488.61 33993.00 35577.34 34782.91 35890.92 33494.64 17892.63 32391.81 33776.30 31697.02 35083.83 32696.90 30991.48 355
dp88.08 32588.05 32388.16 34292.85 35668.81 36094.17 26192.88 31885.47 31691.38 33496.14 26568.87 35298.81 29986.88 30383.80 35896.87 301
gg-mvs-nofinetune88.28 32486.96 32892.23 31992.84 35784.44 31998.19 4174.60 36599.08 987.01 35699.47 756.93 36198.23 33778.91 34395.61 33094.01 345
tpmvs90.79 30690.87 29790.57 33292.75 35876.30 34995.79 17793.64 30991.04 26491.91 33096.26 25877.19 31298.86 29589.38 26589.85 34996.56 313
EPMVS89.26 31788.55 32191.39 32492.36 35979.11 33995.65 18679.86 36388.60 28493.12 31396.53 24470.73 34398.10 34190.75 23989.32 35096.98 296
gm-plane-assit91.79 36071.40 35981.67 33590.11 35498.99 27884.86 319
test235685.45 33383.26 33692.01 32191.12 36180.76 33485.16 35492.90 31783.90 32990.63 33687.71 35853.10 36597.24 34969.20 35895.65 32998.03 261
GG-mvs-BLEND90.60 33191.00 36284.21 32298.23 3572.63 36882.76 36084.11 36056.14 36296.79 35372.20 35592.09 34590.78 356
DeepMVS_CXcopyleft77.17 34990.94 36385.28 30274.08 36752.51 36180.87 36388.03 35775.25 32170.63 36359.23 36184.94 35675.62 358
EPNet_dtu91.39 29990.75 30093.31 29890.48 36482.61 32694.80 23892.88 31893.39 21881.74 36294.90 29681.36 29399.11 26388.28 28298.87 21598.21 246
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testpf82.70 33684.35 33477.74 34888.97 36573.23 35693.85 27684.33 36188.10 29185.06 35890.42 35252.62 36791.05 36191.00 23084.82 35768.93 360
EPNet93.72 25192.62 26397.03 14787.61 36692.25 17296.27 14591.28 33196.74 9787.65 35497.39 19585.00 28399.64 11992.14 20999.48 12499.20 127
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tmp_tt57.23 33862.50 33941.44 35134.77 36749.21 36783.93 35560.22 36915.31 36271.11 36479.37 36170.09 34544.86 36464.76 35982.93 35930.25 361
test12312.59 34215.49 3433.87 3536.07 3682.55 36890.75 3342.59 3712.52 3635.20 36613.02 3644.96 3701.85 3665.20 3629.09 3627.23 362
testmvs12.33 34315.23 3443.64 3545.77 3692.23 36988.99 3463.62 3702.30 3645.29 36513.09 3634.52 3711.95 3655.16 3638.32 3636.75 363
cdsmvs_eth3d_5k24.22 34132.30 3420.00 3550.00 3700.00 3700.00 36198.10 2060.00 3650.00 36795.06 29197.54 270.00 3670.00 3640.00 3650.00 365
pcd_1.5k_mvsjas7.98 34410.65 3450.00 3550.00 3700.00 3700.00 3610.00 3720.00 3650.00 3670.00 36795.82 890.00 3670.00 3640.00 3650.00 365
sosnet-low-res0.00 3460.00 3470.00 3550.00 3700.00 3700.00 3610.00 3720.00 3650.00 3670.00 3670.00 3720.00 3670.00 3640.00 3650.00 365
sosnet0.00 3460.00 3470.00 3550.00 3700.00 3700.00 3610.00 3720.00 3650.00 3670.00 3670.00 3720.00 3670.00 3640.00 3650.00 365
uncertanet0.00 3460.00 3470.00 3550.00 3700.00 3700.00 3610.00 3720.00 3650.00 3670.00 3670.00 3720.00 3670.00 3640.00 3650.00 365
Regformer0.00 3460.00 3470.00 3550.00 3700.00 3700.00 3610.00 3720.00 3650.00 3670.00 3670.00 3720.00 3670.00 3640.00 3650.00 365
ab-mvs-re7.91 34510.55 3460.00 3550.00 3700.00 3700.00 3610.00 3720.00 3650.00 36794.94 2930.00 3720.00 3670.00 3640.00 3650.00 365
uanet0.00 3460.00 3470.00 3550.00 3700.00 3700.00 3610.00 3720.00 3650.00 3670.00 3670.00 3720.00 3670.00 3640.00 3650.00 365
GSMVS98.06 256
test_part10.00 3550.00 3700.00 36198.84 890.00 3720.00 3670.00 3640.00 3650.00 365
sam_mvs177.80 30598.06 256
sam_mvs77.38 309
MTGPAbinary98.73 114
test_post194.98 23110.37 36676.21 31799.04 27189.47 264
test_post10.87 36576.83 31399.07 268
patchmatchnet-post96.84 22477.36 31099.42 200
MTMP96.55 13074.60 365
test9_res91.29 22498.89 21499.00 159
agg_prior290.34 25498.90 21099.10 150
test_prior495.38 8693.61 287
test_prior293.33 29694.21 19694.02 28396.25 25993.64 16991.90 21198.96 204
旧先验293.35 29577.95 35395.77 23698.67 31390.74 241
新几何293.43 291
无先验93.20 29997.91 21680.78 34099.40 21487.71 28697.94 266
原ACMM292.82 304
testdata299.46 19087.84 285
segment_acmp95.34 109
testdata192.77 30593.78 211
plane_prior598.75 11199.46 19092.59 20499.20 18099.28 118
plane_prior496.77 230
plane_prior394.51 11495.29 15196.16 221
plane_prior296.50 13396.36 108
plane_prior94.29 12295.42 19894.31 19398.93 209
n20.00 372
nn0.00 372
door-mid98.17 199
test1198.08 209
door97.81 225
HQP5-MVS92.47 168
BP-MVS90.51 248
HQP4-MVS92.87 31699.23 25299.06 155
HQP3-MVS98.43 16098.74 225
HQP2-MVS90.33 239
MDTV_nov1_ep13_2view57.28 36694.89 23380.59 34194.02 28378.66 30385.50 31497.82 270
ACMMP++_ref99.52 111
ACMMP++99.55 102
Test By Simon94.51 139