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-MVSNet99.93 199.92 199.94 199.99 199.97 199.90 199.89 1399.98 199.99 199.96 199.77 2100.00 199.81 16100.00 199.85 30
Gipumacopyleft99.03 8599.16 6398.64 22399.94 298.51 11299.32 2699.75 4299.58 3998.60 27399.62 4098.22 10999.51 40797.70 19499.73 18597.89 440
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
OurMVSNet-221017-099.37 2999.31 4299.53 3999.91 398.98 7299.63 799.58 9499.44 5399.78 4099.76 1596.39 25499.92 6599.44 5599.92 6999.68 71
pmmvs699.67 399.70 399.60 1699.90 499.27 2899.53 999.76 3999.64 2799.84 3099.83 499.50 999.87 13499.36 5899.92 6999.64 84
PS-MVSNAJss99.46 1799.49 1699.35 8099.90 498.15 13999.20 4899.65 6999.48 4599.92 899.71 2298.07 12499.96 1499.53 48100.00 199.93 11
testf199.25 4199.16 6399.51 4999.89 699.63 498.71 10599.69 5498.90 13399.43 10699.35 10998.86 3499.67 33097.81 18199.81 13499.24 283
APD_test299.25 4199.16 6399.51 4999.89 699.63 498.71 10599.69 5498.90 13399.43 10699.35 10998.86 3499.67 33097.81 18199.81 13499.24 283
ANet_high99.57 1099.67 699.28 9699.89 698.09 14699.14 5799.93 599.82 899.93 699.81 899.17 2099.94 4299.31 62100.00 199.82 36
anonymousdsp99.51 1499.47 2199.62 1099.88 999.08 7099.34 2399.69 5498.93 12999.65 6499.72 2198.93 3299.95 2699.11 78100.00 199.82 36
v7n99.53 1299.57 1399.41 7099.88 998.54 11099.45 1499.61 8299.66 2499.68 5899.66 3298.44 8299.95 2699.73 2899.96 2899.75 60
mvs_tets99.63 699.67 699.49 5599.88 998.61 10299.34 2399.71 4799.27 7499.90 1499.74 1899.68 499.97 799.55 4399.99 599.88 20
test_fmvsmconf0.01_n99.57 1099.63 1099.36 7499.87 1298.13 14298.08 19299.95 199.45 5199.98 299.75 1699.80 199.97 799.82 1299.99 599.99 2
jajsoiax99.58 999.61 1199.48 5799.87 1298.61 10299.28 4099.66 6599.09 10899.89 1899.68 2599.53 799.97 799.50 5199.99 599.87 22
test_djsdf99.52 1399.51 1599.53 3999.86 1498.74 9299.39 2099.56 11099.11 9899.70 5299.73 2099.00 2799.97 799.26 6699.98 1299.89 16
MIMVSNet199.38 2899.32 4099.55 2999.86 1499.19 4399.41 1799.59 9199.59 3799.71 5099.57 4997.12 20999.90 8199.21 7199.87 9899.54 142
LTVRE_ROB98.40 199.67 399.71 299.56 2799.85 1699.11 6599.90 199.78 3699.63 2999.78 4099.67 3099.48 1099.81 22299.30 6399.97 2199.77 50
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
UniMVSNet_ETH3D99.69 299.69 499.69 399.84 1799.34 2099.69 599.58 9499.90 399.86 2499.78 1399.58 699.95 2699.00 8899.95 3899.78 47
SixPastTwentyTwo98.75 13598.62 15099.16 11899.83 1897.96 16699.28 4098.20 38799.37 6199.70 5299.65 3692.65 36099.93 5499.04 8599.84 11299.60 100
sc_t199.62 799.66 899.53 3999.82 1999.09 6999.50 1199.63 7399.88 499.86 2499.80 1199.03 2499.89 9799.48 5399.93 5699.60 100
Baseline_NR-MVSNet98.98 9398.86 11299.36 7499.82 1998.55 10797.47 30099.57 10199.37 6199.21 16499.61 4396.76 23699.83 19298.06 15899.83 12399.71 63
pm-mvs199.44 1999.48 1899.33 8999.80 2198.63 9999.29 3699.63 7399.30 7199.65 6499.60 4599.16 2299.82 20599.07 8199.83 12399.56 129
TransMVSNet (Re)99.44 1999.47 2199.36 7499.80 2198.58 10599.27 4299.57 10199.39 5999.75 4599.62 4099.17 2099.83 19299.06 8399.62 24499.66 78
K. test v398.00 25497.66 27999.03 14599.79 2397.56 20299.19 5292.47 47399.62 3399.52 8899.66 3289.61 39299.96 1499.25 6899.81 13499.56 129
test_fmvsmconf0.1_n99.49 1599.54 1499.34 8399.78 2498.11 14397.77 24999.90 1199.33 6699.97 399.66 3299.71 399.96 1499.79 1999.99 599.96 8
APD_test198.83 11998.66 14399.34 8399.78 2499.47 998.42 15099.45 15998.28 19298.98 20199.19 15497.76 15699.58 38196.57 29699.55 27198.97 342
test_vis3_rt99.14 6399.17 6199.07 13599.78 2498.38 11998.92 8299.94 297.80 23899.91 1299.67 3097.15 20898.91 46699.76 2399.56 26799.92 12
EGC-MVSNET85.24 45280.54 45599.34 8399.77 2799.20 4099.08 6199.29 23912.08 49120.84 49299.42 9097.55 17599.85 15697.08 24299.72 19398.96 344
Anonymous2024052198.69 14898.87 10898.16 30099.77 2795.11 34299.08 6199.44 16799.34 6599.33 13099.55 5794.10 33599.94 4299.25 6899.96 2899.42 212
FC-MVSNet-test99.27 3899.25 5399.34 8399.77 2798.37 12199.30 3599.57 10199.61 3599.40 11599.50 6997.12 20999.85 15699.02 8799.94 5099.80 42
test_vis1_n98.31 21898.50 17097.73 33899.76 3094.17 37398.68 10899.91 996.31 35499.79 3999.57 4992.85 35699.42 42799.79 1999.84 11299.60 100
test_fmvs399.12 7099.41 2698.25 28899.76 3095.07 34399.05 6799.94 297.78 24199.82 3499.84 398.56 7299.71 30199.96 199.96 2899.97 4
XXY-MVS99.14 6399.15 6899.10 12899.76 3097.74 19198.85 9299.62 7998.48 17599.37 12099.49 7598.75 4699.86 14398.20 14899.80 14599.71 63
TDRefinement99.42 2499.38 2999.55 2999.76 3099.33 2199.68 699.71 4799.38 6099.53 8399.61 4398.64 6099.80 23198.24 14399.84 11299.52 158
fmvsm_s_conf0.1_n_a99.17 5399.30 4598.80 18899.75 3496.59 27497.97 22299.86 1698.22 19599.88 2199.71 2298.59 6699.84 17499.73 2899.98 1299.98 3
tt080598.69 14898.62 15098.90 17199.75 3499.30 2399.15 5696.97 42498.86 13998.87 23497.62 39898.63 6298.96 46399.41 5798.29 41198.45 406
test_vis1_n_192098.40 20198.92 10096.81 40199.74 3690.76 45298.15 18099.91 998.33 18399.89 1899.55 5795.07 30699.88 11599.76 2399.93 5699.79 44
FOURS199.73 3799.67 399.43 1599.54 11999.43 5599.26 148
PEN-MVS99.41 2599.34 3699.62 1099.73 3799.14 5899.29 3699.54 11999.62 3399.56 7499.42 9098.16 11899.96 1498.78 10399.93 5699.77 50
lessismore_v098.97 15799.73 3797.53 20486.71 48899.37 12099.52 6889.93 38899.92 6598.99 8999.72 19399.44 203
SteuartSystems-ACMMP98.79 12898.54 16399.54 3299.73 3799.16 4998.23 17099.31 22397.92 22998.90 22398.90 24198.00 13099.88 11596.15 32899.72 19399.58 115
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended_VisFu98.17 23998.15 23198.22 29499.73 3795.15 33997.36 31499.68 6094.45 41498.99 20099.27 12996.87 22599.94 4297.13 23999.91 7899.57 123
Vis-MVSNetpermissive99.34 3099.36 3399.27 9999.73 3798.26 12899.17 5399.78 3699.11 9899.27 14499.48 7698.82 3799.95 2698.94 9299.93 5699.59 107
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
tt0320-xc99.64 599.68 599.50 5499.72 4398.98 7299.51 1099.85 1899.86 699.88 2199.82 599.02 2699.90 8199.54 4499.95 3899.61 98
SSC-MVS98.71 13998.74 12398.62 22999.72 4396.08 29998.74 9898.64 36499.74 1399.67 6099.24 14294.57 32199.95 2699.11 7899.24 33599.82 36
test_f98.67 15798.87 10898.05 31099.72 4395.59 31498.51 13499.81 3196.30 35699.78 4099.82 596.14 26598.63 47399.82 1299.93 5699.95 9
ACMH96.65 799.25 4199.24 5499.26 10199.72 4398.38 11999.07 6499.55 11498.30 18799.65 6499.45 8599.22 1799.76 26798.44 12999.77 16299.64 84
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt032099.61 899.65 999.48 5799.71 4798.94 7999.54 899.83 2599.87 599.89 1899.82 598.75 4699.90 8199.54 4499.95 3899.59 107
fmvsm_s_conf0.1_n99.16 5799.33 3898.64 22399.71 4796.10 29497.87 23599.85 1898.56 17199.90 1499.68 2598.69 5699.85 15699.72 3099.98 1299.97 4
PS-CasMVS99.40 2699.33 3899.62 1099.71 4799.10 6699.29 3699.53 12399.53 4299.46 10199.41 9498.23 10699.95 2698.89 9799.95 3899.81 40
DTE-MVSNet99.43 2399.35 3499.66 799.71 4799.30 2399.31 3099.51 12999.64 2799.56 7499.46 8198.23 10699.97 798.78 10399.93 5699.72 62
WR-MVS_H99.33 3199.22 5599.65 899.71 4799.24 3199.32 2699.55 11499.46 5099.50 9499.34 11397.30 19799.93 5498.90 9599.93 5699.77 50
HPM-MVS_fast99.01 8798.82 11699.57 2299.71 4799.35 1799.00 7299.50 13297.33 28798.94 21898.86 25198.75 4699.82 20597.53 20899.71 20299.56 129
ACMH+96.62 999.08 7799.00 9299.33 8999.71 4798.83 8798.60 12099.58 9499.11 9899.53 8399.18 15898.81 3899.67 33096.71 28099.77 16299.50 166
PMVScopyleft91.26 2097.86 26897.94 25597.65 34699.71 4797.94 16898.52 12998.68 36098.99 12197.52 36799.35 10997.41 19098.18 47991.59 44299.67 22396.82 468
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FE-MVSNET299.15 5899.22 5598.94 16199.70 5597.49 20598.62 11799.67 6498.85 14299.34 12799.54 6398.47 7699.81 22298.93 9399.91 7899.51 162
KinetiMVS99.03 8599.02 8899.03 14599.70 5597.48 20898.43 14799.29 23999.70 1699.60 7199.07 18796.13 26699.94 4299.42 5699.87 9899.68 71
FIs99.14 6399.09 8099.29 9599.70 5598.28 12799.13 5899.52 12899.48 4599.24 15899.41 9496.79 23399.82 20598.69 11399.88 9499.76 56
VPNet98.87 10998.83 11599.01 14999.70 5597.62 20098.43 14799.35 20499.47 4899.28 14299.05 19596.72 23999.82 20598.09 15599.36 31499.59 107
fmvsm_s_conf0.1_n_299.20 5199.38 2998.65 22199.69 5996.08 29997.49 29599.90 1199.53 4299.88 2199.64 3798.51 7599.90 8199.83 1099.98 1299.97 4
test_cas_vis1_n_192098.33 21598.68 13897.27 37799.69 5992.29 42698.03 20399.85 1897.62 25199.96 499.62 4093.98 33699.74 28399.52 5099.86 10599.79 44
MP-MVS-pluss98.57 17498.23 21999.60 1699.69 5999.35 1797.16 33699.38 19094.87 40498.97 20598.99 21798.01 12999.88 11597.29 22699.70 20999.58 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SDMVSNet99.23 4699.32 4098.96 15899.68 6297.35 21698.84 9499.48 14299.69 1899.63 6799.68 2599.03 2499.96 1497.97 16999.92 6999.57 123
sd_testset99.28 3799.31 4299.19 11299.68 6298.06 15599.41 1799.30 23199.69 1899.63 6799.68 2599.25 1699.96 1497.25 22999.92 6999.57 123
test_fmvs1_n98.09 24598.28 21097.52 36399.68 6293.47 40598.63 11599.93 595.41 39299.68 5899.64 3791.88 37199.48 41499.82 1299.87 9899.62 90
CHOSEN 1792x268897.49 29797.14 31298.54 25299.68 6296.09 29796.50 37299.62 7991.58 45298.84 23798.97 22492.36 36299.88 11596.76 27399.95 3899.67 76
tfpnnormal98.90 10498.90 10298.91 16899.67 6697.82 18399.00 7299.44 16799.45 5199.51 9399.24 14298.20 11399.86 14395.92 33799.69 21299.04 328
MTAPA98.88 10898.64 14699.61 1499.67 6699.36 1698.43 14799.20 26398.83 14498.89 22698.90 24196.98 21999.92 6597.16 23499.70 20999.56 129
test_fmvsmvis_n_192099.26 4099.49 1698.54 25299.66 6896.97 25398.00 21099.85 1899.24 7699.92 899.50 6999.39 1299.95 2699.89 399.98 1298.71 383
mvs5depth99.30 3499.59 1298.44 26699.65 6995.35 33199.82 399.94 299.83 799.42 11099.94 298.13 12199.96 1499.63 3699.96 28100.00 1
fmvsm_l_conf0.5_n_a99.19 5299.27 4898.94 16199.65 6997.05 24897.80 24499.76 3998.70 15399.78 4099.11 17798.79 4299.95 2699.85 699.96 2899.83 33
WB-MVS98.52 18898.55 16198.43 26799.65 6995.59 31498.52 12998.77 34999.65 2699.52 8899.00 21594.34 32799.93 5498.65 11598.83 38399.76 56
CP-MVSNet99.21 4899.09 8099.56 2799.65 6998.96 7899.13 5899.34 21099.42 5699.33 13099.26 13597.01 21799.94 4298.74 10899.93 5699.79 44
HPM-MVScopyleft98.79 12898.53 16599.59 2099.65 6999.29 2599.16 5499.43 17396.74 33498.61 27198.38 34298.62 6399.87 13496.47 30899.67 22399.59 107
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
RPSCF98.62 16698.36 19699.42 6899.65 6999.42 1198.55 12599.57 10197.72 24598.90 22399.26 13596.12 26899.52 40295.72 34899.71 20299.32 259
NormalMVS98.26 22597.97 25299.15 12199.64 7597.83 17898.28 16499.43 17399.24 7698.80 24598.85 25489.76 39099.94 4298.04 16099.67 22399.68 71
lecture99.25 4199.12 7199.62 1099.64 7599.40 1298.89 8799.51 12999.19 8899.37 12099.25 14098.36 8799.88 11598.23 14599.67 22399.59 107
fmvsm_l_conf0.5_n99.21 4899.28 4799.02 14899.64 7597.28 22797.82 24099.76 3998.73 14699.82 3499.09 18598.81 3899.95 2699.86 499.96 2899.83 33
test_fmvsmconf_n99.44 1999.48 1899.31 9499.64 7598.10 14597.68 26399.84 2299.29 7299.92 899.57 4999.60 599.96 1499.74 2799.98 1299.89 16
TSAR-MVS + MP.98.63 16398.49 17599.06 14199.64 7597.90 17298.51 13498.94 31496.96 31899.24 15898.89 24797.83 14899.81 22296.88 26399.49 29399.48 184
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PM-MVS98.82 12298.72 12799.12 12499.64 7598.54 11097.98 21899.68 6097.62 25199.34 12799.18 15897.54 17799.77 26197.79 18399.74 18299.04 328
Elysia99.15 5899.14 6999.18 11399.63 8197.92 16998.50 13699.43 17399.67 2199.70 5299.13 17396.66 24299.98 499.54 4499.96 2899.64 84
StellarMVS99.15 5899.14 6999.18 11399.63 8197.92 16998.50 13699.43 17399.67 2199.70 5299.13 17396.66 24299.98 499.54 4499.96 2899.64 84
KD-MVS_self_test99.25 4199.18 6099.44 6699.63 8199.06 7198.69 10799.54 11999.31 6999.62 7099.53 6597.36 19499.86 14399.24 7099.71 20299.39 225
EU-MVSNet97.66 28598.50 17095.13 44399.63 8185.84 47498.35 16098.21 38698.23 19499.54 7999.46 8195.02 30799.68 32698.24 14399.87 9899.87 22
HyFIR lowres test97.19 32496.60 34898.96 15899.62 8597.28 22795.17 43999.50 13294.21 41999.01 19598.32 35086.61 41199.99 297.10 24199.84 11299.60 100
E5new99.05 8099.11 7298.85 17599.60 8697.30 22198.42 15099.63 7398.73 14699.26 14899.39 10098.71 5099.70 30898.43 13199.84 11299.54 142
E6new99.05 8099.11 7298.85 17599.60 8697.30 22198.42 15099.63 7398.73 14699.26 14899.39 10098.71 5099.70 30898.43 13199.84 11299.54 142
E699.05 8099.11 7298.85 17599.60 8697.30 22198.42 15099.63 7398.73 14699.26 14899.39 10098.71 5099.70 30898.43 13199.84 11299.54 142
E599.05 8099.11 7298.85 17599.60 8697.30 22198.42 15099.63 7398.73 14699.26 14899.39 10098.71 5099.70 30898.43 13199.84 11299.54 142
fmvsm_l_conf0.5_n_999.32 3399.43 2498.98 15599.59 9097.18 23997.44 30499.83 2599.56 4099.91 1299.34 11399.36 1399.93 5499.83 1099.98 1299.85 30
fmvsm_l_conf0.5_n_399.45 1899.48 1899.34 8399.59 9098.21 13697.82 24099.84 2299.41 5899.92 899.41 9499.51 899.95 2699.84 999.97 2199.87 22
MED-MVS test99.45 6499.58 9298.93 8098.68 10899.60 8496.46 34799.53 8398.77 27499.83 19296.67 28599.64 23499.58 115
MED-MVS98.90 10498.72 12799.45 6499.58 9298.93 8098.68 10899.60 8498.14 21399.53 8398.77 27497.87 14599.83 19296.67 28599.64 23499.58 115
TestfortrainingZip a98.95 9798.72 12799.64 999.58 9299.32 2298.68 10899.60 8496.46 34799.53 8398.77 27497.87 14599.83 19298.39 13699.64 23499.77 50
FE-MVSNET98.59 17198.50 17098.87 17299.58 9297.30 22198.08 19299.74 4396.94 32098.97 20599.10 18096.94 22199.74 28397.33 22499.86 10599.55 136
mmtdpeth99.30 3499.42 2598.92 16799.58 9296.89 26199.48 1399.92 799.92 298.26 30999.80 1198.33 9399.91 7499.56 4199.95 3899.97 4
ACMMP_NAP98.75 13598.48 17699.57 2299.58 9299.29 2597.82 24099.25 25296.94 32098.78 24799.12 17698.02 12899.84 17497.13 23999.67 22399.59 107
nrg03099.40 2699.35 3499.54 3299.58 9299.13 6198.98 7599.48 14299.68 2099.46 10199.26 13598.62 6399.73 29099.17 7599.92 6999.76 56
VDDNet98.21 23297.95 25399.01 14999.58 9297.74 19199.01 7097.29 41599.67 2198.97 20599.50 6990.45 38599.80 23197.88 17699.20 34399.48 184
COLMAP_ROBcopyleft96.50 1098.99 9098.85 11499.41 7099.58 9299.10 6698.74 9899.56 11099.09 10899.33 13099.19 15498.40 8499.72 30095.98 33599.76 17799.42 212
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_fmvsm_n_192099.33 3199.45 2398.99 15199.57 10197.73 19397.93 22499.83 2599.22 7999.93 699.30 12399.42 1199.96 1499.85 699.99 599.29 269
ZNCC-MVS98.68 15498.40 18899.54 3299.57 10199.21 3498.46 14499.29 23997.28 29398.11 32198.39 34098.00 13099.87 13496.86 26699.64 23499.55 136
MSP-MVS98.40 20198.00 24799.61 1499.57 10199.25 3098.57 12399.35 20497.55 26299.31 13897.71 39194.61 32099.88 11596.14 32999.19 34699.70 68
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
testgi98.32 21698.39 19198.13 30199.57 10195.54 31797.78 24699.49 14097.37 28499.19 16697.65 39598.96 2999.49 41196.50 30798.99 37199.34 250
MP-MVScopyleft98.46 19498.09 23699.54 3299.57 10199.22 3398.50 13699.19 26797.61 25497.58 36198.66 30297.40 19199.88 11594.72 37499.60 25199.54 142
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LPG-MVS_test98.71 13998.46 18099.47 6199.57 10198.97 7498.23 17099.48 14296.60 33999.10 17799.06 18898.71 5099.83 19295.58 35599.78 15699.62 90
LGP-MVS_train99.47 6199.57 10198.97 7499.48 14296.60 33999.10 17799.06 18898.71 5099.83 19295.58 35599.78 15699.62 90
IS-MVSNet98.19 23597.90 26199.08 13399.57 10197.97 16399.31 3098.32 38299.01 12098.98 20199.03 19991.59 37399.79 24495.49 35799.80 14599.48 184
viewdifsd2359ckpt1198.84 11699.04 8598.24 29099.56 10995.51 31997.38 30999.70 5299.16 9399.57 7299.40 9798.26 10299.71 30198.55 12499.82 12899.50 166
viewmsd2359difaftdt98.84 11699.04 8598.24 29099.56 10995.51 31997.38 30999.70 5299.16 9399.57 7299.40 9798.26 10299.71 30198.55 12499.82 12899.50 166
dcpmvs_298.78 13099.11 7297.78 32899.56 10993.67 40099.06 6599.86 1699.50 4499.66 6199.26 13597.21 20599.99 298.00 16599.91 7899.68 71
test_040298.76 13498.71 13298.93 16499.56 10998.14 14198.45 14699.34 21099.28 7398.95 21198.91 23898.34 9299.79 24495.63 35299.91 7898.86 361
EPP-MVSNet98.30 21998.04 24399.07 13599.56 10997.83 17899.29 3698.07 39399.03 11898.59 27599.13 17392.16 36699.90 8196.87 26499.68 21799.49 173
ACMMPcopyleft98.75 13598.50 17099.52 4599.56 10999.16 4998.87 8899.37 19497.16 30898.82 24199.01 21197.71 15999.87 13496.29 32099.69 21299.54 142
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
fmvsm_s_conf0.5_n_a99.10 7299.20 5998.78 19599.55 11596.59 27497.79 24599.82 3098.21 19799.81 3799.53 6598.46 8099.84 17499.70 3399.97 2199.90 15
fmvsm_s_conf0.5_n99.09 7399.26 5198.61 23399.55 11596.09 29797.74 25699.81 3198.55 17299.85 2799.55 5798.60 6599.84 17499.69 3599.98 1299.89 16
FMVSNet199.17 5399.17 6199.17 11599.55 11598.24 13099.20 4899.44 16799.21 8199.43 10699.55 5797.82 15199.86 14398.42 13599.89 9299.41 215
Vis-MVSNet (Re-imp)97.46 29997.16 30998.34 27999.55 11596.10 29498.94 8098.44 37698.32 18598.16 31598.62 31188.76 39799.73 29093.88 40099.79 15199.18 304
ACMM96.08 1298.91 10298.73 12599.48 5799.55 11599.14 5898.07 19699.37 19497.62 25199.04 19198.96 22798.84 3699.79 24497.43 21899.65 23299.49 173
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_fmvs298.70 14498.97 9697.89 32099.54 12094.05 37798.55 12599.92 796.78 33299.72 4899.78 1396.60 24699.67 33099.91 299.90 8699.94 10
mPP-MVS98.64 16198.34 19999.54 3299.54 12099.17 4598.63 11599.24 25797.47 27098.09 32398.68 29797.62 16899.89 9796.22 32399.62 24499.57 123
XVG-ACMP-BASELINE98.56 17598.34 19999.22 10999.54 12098.59 10497.71 25999.46 15597.25 29698.98 20198.99 21797.54 17799.84 17495.88 33899.74 18299.23 285
viewmacassd2359aftdt98.86 11398.87 10898.83 18199.53 12397.32 22097.70 26199.64 7198.22 19599.25 15699.27 12998.40 8499.61 36797.98 16899.87 9899.55 136
region2R98.69 14898.40 18899.54 3299.53 12399.17 4598.52 12999.31 22397.46 27598.44 29498.51 32597.83 14899.88 11596.46 30999.58 26099.58 115
PGM-MVS98.66 15898.37 19599.55 2999.53 12399.18 4498.23 17099.49 14097.01 31798.69 25898.88 24898.00 13099.89 9795.87 34199.59 25599.58 115
E498.87 10998.88 10598.81 18599.52 12697.23 23097.62 27499.61 8298.58 16699.18 17099.33 11698.29 9699.69 31697.99 16799.83 12399.52 158
Patchmatch-RL test97.26 31797.02 31897.99 31499.52 12695.53 31896.13 39799.71 4797.47 27099.27 14499.16 16484.30 43499.62 36097.89 17399.77 16298.81 369
ACMMPR98.70 14498.42 18699.54 3299.52 12699.14 5898.52 12999.31 22397.47 27098.56 28198.54 32097.75 15799.88 11596.57 29699.59 25599.58 115
fmvsm_s_conf0.5_n_999.17 5399.38 2998.53 25499.51 12995.82 30997.62 27499.78 3699.72 1599.90 1499.48 7698.66 5899.89 9799.85 699.93 5699.89 16
AstraMVS98.16 24198.07 24198.41 26999.51 12995.86 30698.00 21095.14 45698.97 12499.43 10699.24 14293.25 34499.84 17499.21 7199.87 9899.54 142
fmvsm_s_conf0.5_n_899.13 6799.26 5198.74 20899.51 12996.44 28697.65 26999.65 6999.66 2499.78 4099.48 7697.92 13899.93 5499.72 3099.95 3899.87 22
GST-MVS98.61 16798.30 20799.52 4599.51 12999.20 4098.26 16899.25 25297.44 27898.67 26198.39 34097.68 16099.85 15696.00 33399.51 28399.52 158
Anonymous2023120698.21 23298.21 22098.20 29599.51 12995.43 32898.13 18299.32 21896.16 36298.93 21998.82 26496.00 27399.83 19297.32 22599.73 18599.36 243
ACMP95.32 1598.41 19898.09 23699.36 7499.51 12998.79 9097.68 26399.38 19095.76 37998.81 24398.82 26498.36 8799.82 20594.75 37199.77 16299.48 184
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LuminaMVS98.39 20798.20 22198.98 15599.50 13597.49 20597.78 24697.69 40298.75 14599.49 9599.25 14092.30 36499.94 4299.14 7699.88 9499.50 166
DVP-MVScopyleft98.77 13398.52 16699.52 4599.50 13599.21 3498.02 20698.84 33897.97 22399.08 17999.02 20097.61 17099.88 11596.99 25099.63 24199.48 184
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND99.60 1699.50 13599.23 3298.02 20699.32 21899.88 11596.99 25099.63 24199.68 71
test072699.50 13599.21 3498.17 17899.35 20497.97 22399.26 14899.06 18897.61 170
AllTest98.44 19698.20 22199.16 11899.50 13598.55 10798.25 16999.58 9496.80 33098.88 23099.06 18897.65 16399.57 38394.45 38199.61 24999.37 236
TestCases99.16 11899.50 13598.55 10799.58 9496.80 33098.88 23099.06 18897.65 16399.57 38394.45 38199.61 24999.37 236
XVG-OURS98.53 18498.34 19999.11 12699.50 13598.82 8995.97 40399.50 13297.30 29199.05 18998.98 22299.35 1499.32 44195.72 34899.68 21799.18 304
EG-PatchMatch MVS98.99 9099.01 9098.94 16199.50 13597.47 20998.04 20199.59 9198.15 21299.40 11599.36 10898.58 7199.76 26798.78 10399.68 21799.59 107
fmvsm_s_conf0.5_n_299.14 6399.31 4298.63 22799.49 14396.08 29997.38 30999.81 3199.48 4599.84 3099.57 4998.46 8099.89 9799.82 1299.97 2199.91 13
SED-MVS98.91 10298.72 12799.49 5599.49 14399.17 4598.10 18999.31 22398.03 21999.66 6199.02 20098.36 8799.88 11596.91 25699.62 24499.41 215
IU-MVS99.49 14399.15 5398.87 32992.97 43799.41 11296.76 27399.62 24499.66 78
test_241102_ONE99.49 14399.17 4599.31 22397.98 22299.66 6198.90 24198.36 8799.48 414
UA-Net99.47 1699.40 2799.70 299.49 14399.29 2599.80 499.72 4599.82 899.04 19199.81 898.05 12799.96 1498.85 9999.99 599.86 28
HFP-MVS98.71 13998.44 18399.51 4999.49 14399.16 4998.52 12999.31 22397.47 27098.58 27798.50 32997.97 13499.85 15696.57 29699.59 25599.53 155
VPA-MVSNet99.30 3499.30 4599.28 9699.49 14398.36 12499.00 7299.45 15999.63 2999.52 8899.44 8698.25 10499.88 11599.09 8099.84 11299.62 90
XVG-OURS-SEG-HR98.49 19198.28 21099.14 12299.49 14398.83 8796.54 36899.48 14297.32 28999.11 17498.61 31399.33 1599.30 44496.23 32298.38 40799.28 272
fmvsm_s_conf0.5_n_1199.21 4899.34 3698.80 18899.48 15196.56 27997.97 22299.69 5499.63 2999.84 3099.54 6398.21 11199.94 4299.76 2399.95 3899.88 20
114514_t96.50 35795.77 36698.69 21699.48 15197.43 21397.84 23999.55 11481.42 48496.51 42398.58 31795.53 29399.67 33093.41 41399.58 26098.98 338
IterMVS-LS98.55 17998.70 13598.09 30399.48 15194.73 35697.22 33099.39 18898.97 12499.38 11899.31 12296.00 27399.93 5498.58 11899.97 2199.60 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
fmvsm_s_conf0.5_n_1099.15 5899.27 4898.78 19599.47 15496.56 27997.75 25599.71 4799.60 3699.74 4799.44 8697.96 13599.95 2699.86 499.94 5099.82 36
fmvsm_s_conf0.5_n_599.07 7999.10 7898.99 15199.47 15497.22 23397.40 30699.83 2597.61 25499.85 2799.30 12398.80 4099.95 2699.71 3299.90 8699.78 47
v899.01 8799.16 6398.57 24099.47 15496.31 29198.90 8399.47 15199.03 11899.52 8899.57 4996.93 22299.81 22299.60 3799.98 1299.60 100
SSC-MVS3.298.53 18498.79 11997.74 33599.46 15793.62 40396.45 37499.34 21099.33 6698.93 21998.70 29397.90 13999.90 8199.12 7799.92 6999.69 70
fmvsm_s_conf0.5_n_399.22 4799.37 3298.78 19599.46 15796.58 27797.65 26999.72 4599.47 4899.86 2499.50 6998.94 3099.89 9799.75 2699.97 2199.86 28
XVS98.72 13898.45 18199.53 3999.46 15799.21 3498.65 11399.34 21098.62 16097.54 36598.63 30997.50 18399.83 19296.79 26999.53 27799.56 129
X-MVStestdata94.32 40992.59 42899.53 3999.46 15799.21 3498.65 11399.34 21098.62 16097.54 36545.85 48997.50 18399.83 19296.79 26999.53 27799.56 129
test20.0398.78 13098.77 12298.78 19599.46 15797.20 23697.78 24699.24 25799.04 11799.41 11298.90 24197.65 16399.76 26797.70 19499.79 15199.39 225
guyue98.01 25397.93 25798.26 28699.45 16295.48 32398.08 19296.24 43998.89 13599.34 12799.14 17191.32 37799.82 20599.07 8199.83 12399.48 184
CSCG98.68 15498.50 17099.20 11099.45 16298.63 9998.56 12499.57 10197.87 23398.85 23598.04 37197.66 16299.84 17496.72 27899.81 13499.13 317
GeoE99.05 8098.99 9499.25 10499.44 16498.35 12598.73 10299.56 11098.42 17898.91 22298.81 26798.94 3099.91 7498.35 13899.73 18599.49 173
v14898.45 19598.60 15598.00 31399.44 16494.98 34597.44 30499.06 29398.30 18799.32 13698.97 22496.65 24499.62 36098.37 13799.85 10799.39 225
v1098.97 9499.11 7298.55 24799.44 16496.21 29398.90 8399.55 11498.73 14699.48 9699.60 4596.63 24599.83 19299.70 3399.99 599.61 98
V4298.78 13098.78 12198.76 20299.44 16497.04 24998.27 16799.19 26797.87 23399.25 15699.16 16496.84 22699.78 25599.21 7199.84 11299.46 194
MDA-MVSNet-bldmvs97.94 25997.91 26098.06 30899.44 16494.96 34696.63 36499.15 28398.35 18198.83 23899.11 17794.31 32899.85 15696.60 29398.72 38999.37 236
viewdifsd2359ckpt0798.71 13998.86 11298.26 28699.43 16995.65 31397.20 33199.66 6599.20 8399.29 14099.01 21198.29 9699.73 29097.92 17299.75 18199.39 225
casdiffmvs_mvgpermissive99.12 7099.16 6398.99 15199.43 16997.73 19398.00 21099.62 7999.22 7999.55 7799.22 14898.93 3299.75 27798.66 11499.81 13499.50 166
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SSM_040498.90 10499.01 9098.57 24099.42 17196.59 27498.13 18299.66 6599.09 10899.30 13999.02 20098.79 4299.89 9797.87 17899.80 14599.23 285
test111196.49 35896.82 33295.52 43699.42 17187.08 47199.22 4587.14 48799.11 9899.46 10199.58 4788.69 39899.86 14398.80 10199.95 3899.62 90
v2v48298.56 17598.62 15098.37 27699.42 17195.81 31097.58 28399.16 27897.90 23199.28 14299.01 21195.98 27899.79 24499.33 6099.90 8699.51 162
OPM-MVS98.56 17598.32 20599.25 10499.41 17498.73 9597.13 33899.18 27197.10 31198.75 25398.92 23598.18 11499.65 35096.68 28499.56 26799.37 236
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PMMVS298.07 24798.08 23998.04 31199.41 17494.59 36294.59 45799.40 18697.50 26798.82 24198.83 26196.83 22899.84 17497.50 21199.81 13499.71 63
E298.70 14498.68 13898.73 21099.40 17697.10 24697.48 29699.57 10198.09 21699.00 19699.20 15197.90 13999.67 33097.73 19299.77 16299.43 207
E398.69 14898.68 13898.73 21099.40 17697.10 24697.48 29699.57 10198.09 21699.00 19699.20 15197.90 13999.67 33097.73 19299.77 16299.43 207
test_one_060199.39 17899.20 4099.31 22398.49 17498.66 26399.02 20097.64 166
mvsany_test398.87 10998.92 10098.74 20899.38 17996.94 25798.58 12299.10 28896.49 34499.96 499.81 898.18 11499.45 42298.97 9099.79 15199.83 33
patch_mono-298.51 18998.63 14898.17 29899.38 17994.78 35397.36 31499.69 5498.16 20798.49 29099.29 12697.06 21299.97 798.29 14299.91 7899.76 56
test250692.39 44091.89 44293.89 45799.38 17982.28 48899.32 2666.03 49599.08 11298.77 25099.57 4966.26 48199.84 17498.71 11199.95 3899.54 142
ECVR-MVScopyleft96.42 36096.61 34695.85 42899.38 17988.18 46699.22 4586.00 48999.08 11299.36 12399.57 4988.47 40399.82 20598.52 12699.95 3899.54 142
casdiffmvspermissive98.95 9799.00 9298.81 18599.38 17997.33 21897.82 24099.57 10199.17 9299.35 12599.17 16298.35 9199.69 31698.46 12899.73 18599.41 215
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline98.96 9699.02 8898.76 20299.38 17997.26 22998.49 13999.50 13298.86 13999.19 16699.06 18898.23 10699.69 31698.71 11199.76 17799.33 256
TranMVSNet+NR-MVSNet99.17 5399.07 8399.46 6399.37 18598.87 8598.39 15699.42 17999.42 5699.36 12399.06 18898.38 8699.95 2698.34 13999.90 8699.57 123
fmvsm_s_conf0.5_n_699.08 7799.21 5898.69 21699.36 18696.51 28197.62 27499.68 6098.43 17799.85 2799.10 18099.12 2399.88 11599.77 2299.92 6999.67 76
tttt051795.64 38794.98 39797.64 34999.36 18693.81 39598.72 10390.47 48198.08 21898.67 26198.34 34773.88 46799.92 6597.77 18599.51 28399.20 295
test_part299.36 18699.10 6699.05 189
v114498.60 16998.66 14398.41 26999.36 18695.90 30497.58 28399.34 21097.51 26699.27 14499.15 16896.34 25999.80 23199.47 5499.93 5699.51 162
CP-MVS98.70 14498.42 18699.52 4599.36 18699.12 6398.72 10399.36 19897.54 26498.30 30398.40 33997.86 14799.89 9796.53 30599.72 19399.56 129
diffmvs_AUTHOR98.50 19098.59 15798.23 29399.35 19195.48 32396.61 36599.60 8498.37 17998.90 22399.00 21597.37 19399.76 26798.22 14699.85 10799.46 194
Test_1112_low_res96.99 33996.55 35098.31 28299.35 19195.47 32695.84 41599.53 12391.51 45496.80 40898.48 33291.36 37699.83 19296.58 29499.53 27799.62 90
DeepC-MVS97.60 498.97 9498.93 9999.10 12899.35 19197.98 16298.01 20999.46 15597.56 26099.54 7999.50 6998.97 2899.84 17498.06 15899.92 6999.49 173
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
1112_ss97.29 31696.86 32898.58 23799.34 19496.32 29096.75 35799.58 9493.14 43596.89 40397.48 40592.11 36899.86 14396.91 25699.54 27399.57 123
reproduce_model99.15 5898.97 9699.67 499.33 19599.44 1098.15 18099.47 15199.12 9799.52 8899.32 12198.31 9499.90 8197.78 18499.73 18599.66 78
MVSMamba_PlusPlus98.83 11998.98 9598.36 27799.32 19696.58 27798.90 8399.41 18399.75 1198.72 25699.50 6996.17 26499.94 4299.27 6599.78 15698.57 399
fmvsm_s_conf0.5_n_499.01 8799.22 5598.38 27399.31 19795.48 32397.56 28599.73 4498.87 13799.75 4599.27 12998.80 4099.86 14399.80 1799.90 8699.81 40
SF-MVS98.53 18498.27 21399.32 9199.31 19798.75 9198.19 17499.41 18396.77 33398.83 23898.90 24197.80 15399.82 20595.68 35199.52 28099.38 234
CPTT-MVS97.84 27497.36 29899.27 9999.31 19798.46 11598.29 16399.27 24694.90 40397.83 34598.37 34394.90 30999.84 17493.85 40299.54 27399.51 162
UnsupCasMVSNet_eth97.89 26397.60 28498.75 20499.31 19797.17 24197.62 27499.35 20498.72 15298.76 25298.68 29792.57 36199.74 28397.76 18995.60 47299.34 250
fmvsm_s_conf0.5_n_798.83 11999.04 8598.20 29599.30 20194.83 35197.23 32699.36 19898.64 15599.84 3099.43 8998.10 12399.91 7499.56 4199.96 2899.87 22
pmmvs-eth3d98.47 19398.34 19998.86 17499.30 20197.76 18997.16 33699.28 24395.54 38599.42 11099.19 15497.27 20099.63 35797.89 17399.97 2199.20 295
mamv499.44 1999.39 2899.58 2199.30 20199.74 299.04 6899.81 3199.77 1099.82 3499.57 4997.82 15199.98 499.53 4899.89 9299.01 332
viewcassd2359sk1198.55 17998.51 16798.67 21999.29 20496.99 25297.39 30799.54 11997.73 24398.81 24399.08 18697.55 17599.66 34397.52 21099.67 22399.36 243
SymmetryMVS98.05 24997.71 27499.09 13299.29 20497.83 17898.28 16497.64 40799.24 7698.80 24598.85 25489.76 39099.94 4298.04 16099.50 29199.49 173
Anonymous2023121199.27 3899.27 4899.26 10199.29 20498.18 13799.49 1299.51 12999.70 1699.80 3899.68 2596.84 22699.83 19299.21 7199.91 7899.77 50
viewmanbaseed2359cas98.58 17398.54 16398.70 21499.28 20797.13 24597.47 30099.55 11497.55 26298.96 21098.92 23597.77 15599.59 37497.59 20399.77 16299.39 225
UnsupCasMVSNet_bld97.30 31496.92 32498.45 26499.28 20796.78 26896.20 39199.27 24695.42 38998.28 30798.30 35193.16 34799.71 30194.99 36597.37 44598.87 360
EC-MVSNet99.09 7399.05 8499.20 11099.28 20798.93 8099.24 4499.84 2299.08 11298.12 32098.37 34398.72 4999.90 8199.05 8499.77 16298.77 377
mamba_040898.80 12698.88 10598.55 24799.27 21096.50 28298.00 21099.60 8498.93 12999.22 16198.84 25998.59 6699.89 9797.74 19099.72 19399.27 273
SSM_0407298.80 12698.88 10598.56 24599.27 21096.50 28298.00 21099.60 8498.93 12999.22 16198.84 25998.59 6699.90 8197.74 19099.72 19399.27 273
SSM_040798.86 11398.96 9898.55 24799.27 21096.50 28298.04 20199.66 6599.09 10899.22 16199.02 20098.79 4299.87 13497.87 17899.72 19399.27 273
reproduce-ours99.09 7398.90 10299.67 499.27 21099.49 698.00 21099.42 17999.05 11599.48 9699.27 12998.29 9699.89 9797.61 20099.71 20299.62 90
our_new_method99.09 7398.90 10299.67 499.27 21099.49 698.00 21099.42 17999.05 11599.48 9699.27 12998.29 9699.89 9797.61 20099.71 20299.62 90
DPE-MVScopyleft98.59 17198.26 21499.57 2299.27 21099.15 5397.01 34199.39 18897.67 24799.44 10598.99 21797.53 17999.89 9795.40 35999.68 21799.66 78
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
IterMVS-SCA-FT97.85 27398.18 22696.87 39799.27 21091.16 44695.53 42599.25 25299.10 10599.41 11299.35 10993.10 34999.96 1498.65 11599.94 5099.49 173
v119298.60 16998.66 14398.41 26999.27 21095.88 30597.52 29099.36 19897.41 27999.33 13099.20 15196.37 25799.82 20599.57 3999.92 6999.55 136
N_pmnet97.63 28797.17 30898.99 15199.27 21097.86 17595.98 40293.41 47095.25 39499.47 10098.90 24195.63 29099.85 15696.91 25699.73 18599.27 273
viewdifsd2359ckpt1398.39 20798.29 20998.70 21499.26 21997.19 23797.51 29299.48 14296.94 32098.58 27798.82 26497.47 18899.55 39097.21 23199.33 31999.34 250
FPMVS93.44 42692.23 43397.08 38599.25 22097.86 17595.61 42297.16 41992.90 43993.76 47298.65 30475.94 46595.66 48679.30 48497.49 43897.73 450
ME-MVS98.61 16798.33 20499.44 6699.24 22198.93 8097.45 30299.06 29398.14 21399.06 18198.77 27496.97 22099.82 20596.67 28599.64 23499.58 115
new-patchmatchnet98.35 21098.74 12397.18 38099.24 22192.23 42896.42 37899.48 14298.30 18799.69 5699.53 6597.44 18999.82 20598.84 10099.77 16299.49 173
MCST-MVS98.00 25497.63 28299.10 12899.24 22198.17 13896.89 35098.73 35795.66 38097.92 33697.70 39397.17 20799.66 34396.18 32799.23 33899.47 192
UniMVSNet (Re)98.87 10998.71 13299.35 8099.24 22198.73 9597.73 25899.38 19098.93 12999.12 17398.73 28396.77 23499.86 14398.63 11799.80 14599.46 194
jason97.45 30197.35 29997.76 33299.24 22193.93 38995.86 41298.42 37894.24 41898.50 28998.13 36194.82 31399.91 7497.22 23099.73 18599.43 207
jason: jason.
IterMVS97.73 27998.11 23596.57 40799.24 22190.28 45595.52 42799.21 26198.86 13999.33 13099.33 11693.11 34899.94 4298.49 12799.94 5099.48 184
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v124098.55 17998.62 15098.32 28099.22 22795.58 31697.51 29299.45 15997.16 30899.45 10499.24 14296.12 26899.85 15699.60 3799.88 9499.55 136
ITE_SJBPF98.87 17299.22 22798.48 11499.35 20497.50 26798.28 30798.60 31597.64 16699.35 43793.86 40199.27 33098.79 375
h-mvs3397.77 27797.33 30199.10 12899.21 22997.84 17798.35 16098.57 36999.11 9898.58 27799.02 20088.65 40199.96 1498.11 15396.34 46199.49 173
v14419298.54 18298.57 15998.45 26499.21 22995.98 30297.63 27399.36 19897.15 31099.32 13699.18 15895.84 28599.84 17499.50 5199.91 7899.54 142
APDe-MVScopyleft98.99 9098.79 11999.60 1699.21 22999.15 5398.87 8899.48 14297.57 25899.35 12599.24 14297.83 14899.89 9797.88 17699.70 20999.75 60
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DP-MVS98.93 10098.81 11899.28 9699.21 22998.45 11698.46 14499.33 21699.63 2999.48 9699.15 16897.23 20399.75 27797.17 23399.66 23199.63 89
SR-MVS-dyc-post98.81 12498.55 16199.57 2299.20 23399.38 1398.48 14299.30 23198.64 15598.95 21198.96 22797.49 18699.86 14396.56 30099.39 31099.45 199
RE-MVS-def98.58 15899.20 23399.38 1398.48 14299.30 23198.64 15598.95 21198.96 22797.75 15796.56 30099.39 31099.45 199
v192192098.54 18298.60 15598.38 27399.20 23395.76 31297.56 28599.36 19897.23 30299.38 11899.17 16296.02 27199.84 17499.57 3999.90 8699.54 142
E3new98.41 19898.34 19998.62 22999.19 23696.90 26097.32 31799.50 13297.40 28198.63 26698.92 23597.21 20599.65 35097.34 22299.52 28099.31 263
thisisatest053095.27 39594.45 40697.74 33599.19 23694.37 36697.86 23690.20 48297.17 30798.22 31097.65 39573.53 46899.90 8196.90 26199.35 31698.95 345
Anonymous2024052998.93 10098.87 10899.12 12499.19 23698.22 13599.01 7098.99 31199.25 7599.54 7999.37 10497.04 21399.80 23197.89 17399.52 28099.35 248
APD-MVS_3200maxsize98.84 11698.61 15499.53 3999.19 23699.27 2898.49 13999.33 21698.64 15599.03 19498.98 22297.89 14399.85 15696.54 30499.42 30799.46 194
HQP_MVS97.99 25797.67 27698.93 16499.19 23697.65 19797.77 24999.27 24698.20 20197.79 34897.98 37594.90 30999.70 30894.42 38399.51 28399.45 199
plane_prior799.19 23697.87 174
ab-mvs98.41 19898.36 19698.59 23699.19 23697.23 23099.32 2698.81 34397.66 24898.62 26999.40 9796.82 22999.80 23195.88 33899.51 28398.75 380
F-COLMAP97.30 31496.68 34199.14 12299.19 23698.39 11897.27 32599.30 23192.93 43896.62 41698.00 37395.73 28899.68 32692.62 42998.46 40699.35 248
viewdifsd2359ckpt0998.13 24297.92 25898.77 20099.18 24497.35 21697.29 32199.53 12395.81 37798.09 32398.47 33396.34 25999.66 34397.02 24699.51 28399.29 269
SR-MVS98.71 13998.43 18499.57 2299.18 24499.35 1798.36 15999.29 23998.29 19098.88 23098.85 25497.53 17999.87 13496.14 32999.31 32399.48 184
UniMVSNet_NR-MVSNet98.86 11398.68 13899.40 7299.17 24698.74 9297.68 26399.40 18699.14 9699.06 18198.59 31696.71 24099.93 5498.57 12099.77 16299.53 155
LF4IMVS97.90 26197.69 27598.52 25599.17 24697.66 19697.19 33599.47 15196.31 35497.85 34498.20 35896.71 24099.52 40294.62 37599.72 19398.38 416
SMA-MVScopyleft98.40 20198.03 24499.51 4999.16 24899.21 3498.05 19999.22 26094.16 42098.98 20199.10 18097.52 18199.79 24496.45 31099.64 23499.53 155
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
DU-MVS98.82 12298.63 14899.39 7399.16 24898.74 9297.54 28899.25 25298.84 14399.06 18198.76 28096.76 23699.93 5498.57 12099.77 16299.50 166
NR-MVSNet98.95 9798.82 11699.36 7499.16 24898.72 9799.22 4599.20 26399.10 10599.72 4898.76 28096.38 25699.86 14398.00 16599.82 12899.50 166
MVS_111021_LR98.30 21998.12 23498.83 18199.16 24898.03 15796.09 39999.30 23197.58 25798.10 32298.24 35498.25 10499.34 43896.69 28399.65 23299.12 318
DSMNet-mixed97.42 30497.60 28496.87 39799.15 25291.46 43598.54 12799.12 28592.87 44097.58 36199.63 3996.21 26399.90 8195.74 34799.54 27399.27 273
D2MVS97.84 27497.84 26597.83 32499.14 25394.74 35596.94 34598.88 32795.84 37598.89 22698.96 22794.40 32599.69 31697.55 20599.95 3899.05 324
pmmvs597.64 28697.49 29098.08 30699.14 25395.12 34196.70 36099.05 29793.77 42798.62 26998.83 26193.23 34599.75 27798.33 14199.76 17799.36 243
SPE-MVS-test99.13 6799.09 8099.26 10199.13 25598.97 7499.31 3099.88 1499.44 5398.16 31598.51 32598.64 6099.93 5498.91 9499.85 10798.88 359
VDD-MVS98.56 17598.39 19199.07 13599.13 25598.07 15298.59 12197.01 42299.59 3799.11 17499.27 12994.82 31399.79 24498.34 13999.63 24199.34 250
save fliter99.11 25797.97 16396.53 37099.02 30598.24 193
APD-MVScopyleft98.10 24397.67 27699.42 6899.11 25798.93 8097.76 25299.28 24394.97 40198.72 25698.77 27497.04 21399.85 15693.79 40399.54 27399.49 173
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EI-MVSNet-UG-set98.69 14898.71 13298.62 22999.10 25996.37 28897.23 32698.87 32999.20 8399.19 16698.99 21797.30 19799.85 15698.77 10699.79 15199.65 83
EI-MVSNet98.40 20198.51 16798.04 31199.10 25994.73 35697.20 33198.87 32998.97 12499.06 18199.02 20096.00 27399.80 23198.58 11899.82 12899.60 100
CVMVSNet96.25 36697.21 30793.38 46499.10 25980.56 49297.20 33198.19 38996.94 32099.00 19699.02 20089.50 39499.80 23196.36 31699.59 25599.78 47
EI-MVSNet-Vis-set98.68 15498.70 13598.63 22799.09 26296.40 28797.23 32698.86 33499.20 8399.18 17098.97 22497.29 19999.85 15698.72 11099.78 15699.64 84
HPM-MVS++copyleft98.10 24397.64 28199.48 5799.09 26299.13 6197.52 29098.75 35497.46 27596.90 40297.83 38596.01 27299.84 17495.82 34599.35 31699.46 194
DP-MVS Recon97.33 31296.92 32498.57 24099.09 26297.99 15996.79 35399.35 20493.18 43497.71 35298.07 36995.00 30899.31 44293.97 39699.13 35498.42 413
MVS_111021_HR98.25 22898.08 23998.75 20499.09 26297.46 21095.97 40399.27 24697.60 25697.99 33398.25 35398.15 12099.38 43396.87 26499.57 26499.42 212
BP-MVS197.40 30696.97 32098.71 21399.07 26696.81 26498.34 16297.18 41798.58 16698.17 31298.61 31384.01 43699.94 4298.97 9099.78 15699.37 236
9.1497.78 26799.07 26697.53 28999.32 21895.53 38698.54 28598.70 29397.58 17299.76 26794.32 38899.46 296
PAPM_NR96.82 34696.32 35798.30 28399.07 26696.69 27297.48 29698.76 35195.81 37796.61 41796.47 43194.12 33499.17 45590.82 45697.78 43299.06 323
TAMVS98.24 22998.05 24298.80 18899.07 26697.18 23997.88 23298.81 34396.66 33899.17 17299.21 14994.81 31599.77 26196.96 25499.88 9499.44 203
CLD-MVS97.49 29797.16 30998.48 26199.07 26697.03 25094.71 45099.21 26194.46 41298.06 32697.16 41797.57 17399.48 41494.46 38099.78 15698.95 345
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CS-MVS99.13 6799.10 7899.24 10699.06 27199.15 5399.36 2299.88 1499.36 6498.21 31198.46 33498.68 5799.93 5499.03 8699.85 10798.64 392
thres100view90094.19 41293.67 41795.75 43199.06 27191.35 43998.03 20394.24 46598.33 18397.40 37794.98 46179.84 45299.62 36083.05 47798.08 42396.29 472
thres600view794.45 40793.83 41496.29 41599.06 27191.53 43497.99 21794.24 46598.34 18297.44 37595.01 45979.84 45299.67 33084.33 47598.23 41297.66 453
plane_prior199.05 274
YYNet197.60 28897.67 27697.39 37399.04 27593.04 41295.27 43598.38 38197.25 29698.92 22198.95 23195.48 29799.73 29096.99 25098.74 38799.41 215
MDA-MVSNet_test_wron97.60 28897.66 27997.41 37299.04 27593.09 40895.27 43598.42 37897.26 29598.88 23098.95 23195.43 29899.73 29097.02 24698.72 38999.41 215
MIMVSNet96.62 35396.25 36197.71 33999.04 27594.66 35999.16 5496.92 42897.23 30297.87 34199.10 18086.11 41799.65 35091.65 44099.21 34298.82 364
FE-MVSNET397.37 30897.13 31398.11 30299.03 27895.40 32994.47 46098.99 31196.87 32697.97 33497.81 38692.12 36799.75 27797.49 21699.43 30699.16 313
icg_test_0407_298.20 23498.38 19397.65 34699.03 27894.03 38095.78 41799.45 15998.16 20799.06 18198.71 28698.27 10099.68 32697.50 21199.45 29899.22 290
IMVS_040798.39 20798.64 14697.66 34499.03 27894.03 38098.10 18999.45 15998.16 20799.06 18198.71 28698.27 10099.71 30197.50 21199.45 29899.22 290
IMVS_040498.07 24798.20 22197.69 34099.03 27894.03 38096.67 36199.45 15998.16 20798.03 33098.71 28696.80 23299.82 20597.50 21199.45 29899.22 290
IMVS_040398.34 21198.56 16097.66 34499.03 27894.03 38097.98 21899.45 15998.16 20798.89 22698.71 28697.90 13999.74 28397.50 21199.45 29899.22 290
PatchMatch-RL97.24 32096.78 33598.61 23399.03 27897.83 17896.36 38199.06 29393.49 43297.36 38197.78 38795.75 28799.49 41193.44 41298.77 38698.52 401
viewmambaseed2359dif98.19 23598.26 21497.99 31499.02 28495.03 34496.59 36799.53 12396.21 35899.00 19698.99 21797.62 16899.61 36797.62 19999.72 19399.33 256
GDP-MVS97.50 29497.11 31498.67 21999.02 28496.85 26298.16 17999.71 4798.32 18598.52 28898.54 32083.39 44099.95 2698.79 10299.56 26799.19 300
ZD-MVS99.01 28698.84 8699.07 29294.10 42298.05 32898.12 36396.36 25899.86 14392.70 42899.19 346
CDPH-MVS97.26 31796.66 34499.07 13599.00 28798.15 13996.03 40199.01 30891.21 45897.79 34897.85 38496.89 22499.69 31692.75 42699.38 31399.39 225
diffmvspermissive98.22 23098.24 21898.17 29899.00 28795.44 32796.38 38099.58 9497.79 24098.53 28698.50 32996.76 23699.74 28397.95 17199.64 23499.34 250
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
WR-MVS98.40 20198.19 22599.03 14599.00 28797.65 19796.85 35198.94 31498.57 16898.89 22698.50 32995.60 29199.85 15697.54 20799.85 10799.59 107
plane_prior698.99 29097.70 19594.90 309
xiu_mvs_v1_base_debu97.86 26898.17 22796.92 39498.98 29193.91 39096.45 37499.17 27597.85 23598.41 29797.14 41998.47 7699.92 6598.02 16299.05 36096.92 465
xiu_mvs_v1_base97.86 26898.17 22796.92 39498.98 29193.91 39096.45 37499.17 27597.85 23598.41 29797.14 41998.47 7699.92 6598.02 16299.05 36096.92 465
xiu_mvs_v1_base_debi97.86 26898.17 22796.92 39498.98 29193.91 39096.45 37499.17 27597.85 23598.41 29797.14 41998.47 7699.92 6598.02 16299.05 36096.92 465
MVP-Stereo98.08 24697.92 25898.57 24098.96 29496.79 26597.90 23099.18 27196.41 35098.46 29298.95 23195.93 28299.60 37096.51 30698.98 37499.31 263
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS98.40 20198.68 13897.54 36198.96 29497.99 15997.88 23299.36 19898.20 20199.63 6799.04 19798.76 4595.33 48896.56 30099.74 18299.31 263
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
新几何198.91 16898.94 29697.76 18998.76 35187.58 47596.75 41098.10 36594.80 31699.78 25592.73 42799.00 36999.20 295
USDC97.41 30597.40 29497.44 37098.94 29693.67 40095.17 43999.53 12394.03 42498.97 20599.10 18095.29 30099.34 43895.84 34499.73 18599.30 267
tfpn200view994.03 41693.44 41995.78 43098.93 29891.44 43797.60 28094.29 46397.94 22797.10 38794.31 46879.67 45499.62 36083.05 47798.08 42396.29 472
testdata98.09 30398.93 29895.40 32998.80 34590.08 46697.45 37498.37 34395.26 30199.70 30893.58 40898.95 37799.17 308
thres40094.14 41493.44 41996.24 41898.93 29891.44 43797.60 28094.29 46397.94 22797.10 38794.31 46879.67 45499.62 36083.05 47798.08 42397.66 453
TAPA-MVS96.21 1196.63 35295.95 36398.65 22198.93 29898.09 14696.93 34799.28 24383.58 48198.13 31997.78 38796.13 26699.40 42993.52 40999.29 32898.45 406
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test22298.92 30296.93 25895.54 42498.78 34885.72 47896.86 40598.11 36494.43 32399.10 35999.23 285
PVSNet_BlendedMVS97.55 29397.53 28797.60 35398.92 30293.77 39796.64 36399.43 17394.49 41097.62 35799.18 15896.82 22999.67 33094.73 37299.93 5699.36 243
PVSNet_Blended96.88 34296.68 34197.47 36898.92 30293.77 39794.71 45099.43 17390.98 46097.62 35797.36 41396.82 22999.67 33094.73 37299.56 26798.98 338
MSDG97.71 28197.52 28898.28 28598.91 30596.82 26394.42 46199.37 19497.65 24998.37 30298.29 35297.40 19199.33 44094.09 39499.22 33998.68 390
Anonymous20240521197.90 26197.50 28999.08 13398.90 30698.25 12998.53 12896.16 44098.87 13799.11 17498.86 25190.40 38699.78 25597.36 22199.31 32399.19 300
原ACMM198.35 27898.90 30696.25 29298.83 34292.48 44496.07 43498.10 36595.39 29999.71 30192.61 43098.99 37199.08 320
GBi-Net98.65 15998.47 17899.17 11598.90 30698.24 13099.20 4899.44 16798.59 16398.95 21199.55 5794.14 33199.86 14397.77 18599.69 21299.41 215
test198.65 15998.47 17899.17 11598.90 30698.24 13099.20 4899.44 16798.59 16398.95 21199.55 5794.14 33199.86 14397.77 18599.69 21299.41 215
FMVSNet298.49 19198.40 18898.75 20498.90 30697.14 24498.61 11999.13 28498.59 16399.19 16699.28 12794.14 33199.82 20597.97 16999.80 14599.29 269
OMC-MVS97.88 26597.49 29099.04 14498.89 31198.63 9996.94 34599.25 25295.02 39998.53 28698.51 32597.27 20099.47 41793.50 41199.51 28399.01 332
VortexMVS97.98 25898.31 20697.02 38898.88 31291.45 43698.03 20399.47 15198.65 15499.55 7799.47 7991.49 37599.81 22299.32 6199.91 7899.80 42
MVSFormer98.26 22598.43 18497.77 32998.88 31293.89 39399.39 2099.56 11099.11 9898.16 31598.13 36193.81 33999.97 799.26 6699.57 26499.43 207
lupinMVS97.06 33296.86 32897.65 34698.88 31293.89 39395.48 42897.97 39593.53 43098.16 31597.58 39993.81 33999.91 7496.77 27299.57 26499.17 308
dmvs_re95.98 37695.39 38597.74 33598.86 31597.45 21198.37 15895.69 45297.95 22596.56 41895.95 44090.70 38397.68 48288.32 46596.13 46598.11 428
DELS-MVS98.27 22398.20 22198.48 26198.86 31596.70 27195.60 42399.20 26397.73 24398.45 29398.71 28697.50 18399.82 20598.21 14799.59 25598.93 350
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
TinyColmap97.89 26397.98 24997.60 35398.86 31594.35 36796.21 39099.44 16797.45 27799.06 18198.88 24897.99 13399.28 44894.38 38799.58 26099.18 304
LCM-MVSNet-Re98.64 16198.48 17699.11 12698.85 31898.51 11298.49 13999.83 2598.37 17999.69 5699.46 8198.21 11199.92 6594.13 39399.30 32698.91 354
pmmvs497.58 29197.28 30298.51 25698.84 31996.93 25895.40 43298.52 37393.60 42998.61 27198.65 30495.10 30599.60 37096.97 25399.79 15198.99 337
NP-MVS98.84 31997.39 21596.84 422
sss97.21 32296.93 32298.06 30898.83 32195.22 33796.75 35798.48 37594.49 41097.27 38397.90 38192.77 35799.80 23196.57 29699.32 32199.16 313
PVSNet93.40 1795.67 38595.70 36995.57 43598.83 32188.57 46292.50 47997.72 40092.69 44296.49 42696.44 43293.72 34299.43 42593.61 40699.28 32998.71 383
MVEpermissive83.40 2292.50 43991.92 44194.25 45198.83 32191.64 43392.71 47883.52 49195.92 37386.46 48995.46 45395.20 30295.40 48780.51 48298.64 39895.73 480
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testing3-293.78 42093.91 41293.39 46398.82 32481.72 49097.76 25295.28 45498.60 16296.54 41996.66 42665.85 48499.62 36096.65 28998.99 37198.82 364
ambc98.24 29098.82 32495.97 30398.62 11799.00 31099.27 14499.21 14996.99 21899.50 40896.55 30399.50 29199.26 279
旧先验198.82 32497.45 21198.76 35198.34 34795.50 29699.01 36899.23 285
test_vis1_rt97.75 27897.72 27397.83 32498.81 32796.35 28997.30 32099.69 5494.61 40897.87 34198.05 37096.26 26298.32 47698.74 10898.18 41598.82 364
WTY-MVS96.67 35096.27 36097.87 32298.81 32794.61 36196.77 35597.92 39794.94 40297.12 38697.74 39091.11 37999.82 20593.89 39998.15 41999.18 304
3Dnovator+97.89 398.69 14898.51 16799.24 10698.81 32798.40 11799.02 6999.19 26798.99 12198.07 32599.28 12797.11 21199.84 17496.84 26799.32 32199.47 192
QAPM97.31 31396.81 33498.82 18398.80 33097.49 20599.06 6599.19 26790.22 46497.69 35499.16 16496.91 22399.90 8190.89 45599.41 30899.07 322
VNet98.42 19798.30 20798.79 19298.79 33197.29 22698.23 17098.66 36199.31 6998.85 23598.80 26894.80 31699.78 25598.13 15299.13 35499.31 263
DPM-MVS96.32 36295.59 37698.51 25698.76 33297.21 23594.54 45998.26 38491.94 44996.37 42797.25 41593.06 35199.43 42591.42 44598.74 38798.89 356
3Dnovator98.27 298.81 12498.73 12599.05 14298.76 33297.81 18699.25 4399.30 23198.57 16898.55 28399.33 11697.95 13699.90 8197.16 23499.67 22399.44 203
PLCcopyleft94.65 1696.51 35595.73 36898.85 17598.75 33497.91 17196.42 37899.06 29390.94 46195.59 44197.38 41194.41 32499.59 37490.93 45398.04 42899.05 324
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-untuned96.83 34496.75 33797.08 38598.74 33593.33 40696.71 35998.26 38496.72 33598.44 29497.37 41295.20 30299.47 41791.89 43597.43 44298.44 409
hse-mvs297.46 29997.07 31598.64 22398.73 33697.33 21897.45 30297.64 40799.11 9898.58 27797.98 37588.65 40199.79 24498.11 15397.39 44498.81 369
CDS-MVSNet97.69 28297.35 29998.69 21698.73 33697.02 25196.92 34998.75 35495.89 37498.59 27598.67 29992.08 36999.74 28396.72 27899.81 13499.32 259
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SD_040396.28 36495.83 36597.64 34998.72 33894.30 36898.87 8898.77 34997.80 23896.53 42098.02 37297.34 19599.47 41776.93 48699.48 29499.16 313
EIA-MVS98.00 25497.74 27098.80 18898.72 33898.09 14698.05 19999.60 8497.39 28296.63 41595.55 44897.68 16099.80 23196.73 27799.27 33098.52 401
LFMVS97.20 32396.72 33898.64 22398.72 33896.95 25698.93 8194.14 46799.74 1398.78 24799.01 21184.45 43199.73 29097.44 21799.27 33099.25 280
new_pmnet96.99 33996.76 33697.67 34298.72 33894.89 34995.95 40798.20 38792.62 44398.55 28398.54 32094.88 31299.52 40293.96 39799.44 30598.59 398
Fast-Effi-MVS+97.67 28497.38 29698.57 24098.71 34297.43 21397.23 32699.45 15994.82 40596.13 43196.51 42898.52 7499.91 7496.19 32598.83 38398.37 418
TEST998.71 34298.08 15095.96 40599.03 30291.40 45595.85 43897.53 40196.52 24999.76 267
train_agg97.10 32996.45 35499.07 13598.71 34298.08 15095.96 40599.03 30291.64 45095.85 43897.53 40196.47 25199.76 26793.67 40599.16 34999.36 243
TSAR-MVS + GP.98.18 23797.98 24998.77 20098.71 34297.88 17396.32 38498.66 36196.33 35299.23 16098.51 32597.48 18799.40 42997.16 23499.46 29699.02 331
FA-MVS(test-final)96.99 33996.82 33297.50 36598.70 34694.78 35399.34 2396.99 42395.07 39898.48 29199.33 11688.41 40499.65 35096.13 33198.92 38098.07 431
AUN-MVS96.24 36895.45 38198.60 23598.70 34697.22 23397.38 30997.65 40595.95 37295.53 44897.96 37982.11 44899.79 24496.31 31897.44 44198.80 374
our_test_397.39 30797.73 27296.34 41398.70 34689.78 45894.61 45698.97 31396.50 34399.04 19198.85 25495.98 27899.84 17497.26 22899.67 22399.41 215
ppachtmachnet_test97.50 29497.74 27096.78 40398.70 34691.23 44594.55 45899.05 29796.36 35199.21 16498.79 27096.39 25499.78 25596.74 27599.82 12899.34 250
PCF-MVS92.86 1894.36 40893.00 42698.42 26898.70 34697.56 20293.16 47799.11 28779.59 48597.55 36497.43 40892.19 36599.73 29079.85 48399.45 29897.97 437
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ttmdpeth97.91 26098.02 24597.58 35598.69 35194.10 37698.13 18298.90 32397.95 22597.32 38299.58 4795.95 28198.75 47196.41 31299.22 33999.87 22
ETV-MVS98.03 25097.86 26498.56 24598.69 35198.07 15297.51 29299.50 13298.10 21597.50 36995.51 44998.41 8399.88 11596.27 32199.24 33597.71 452
test_prior98.95 16098.69 35197.95 16799.03 30299.59 37499.30 267
mvsmamba97.57 29297.26 30398.51 25698.69 35196.73 27098.74 9897.25 41697.03 31697.88 34099.23 14790.95 38099.87 13496.61 29299.00 36998.91 354
agg_prior98.68 35597.99 15999.01 30895.59 44199.77 261
test_898.67 35698.01 15895.91 41199.02 30591.64 45095.79 44097.50 40496.47 25199.76 267
HQP-NCC98.67 35696.29 38696.05 36595.55 444
ACMP_Plane98.67 35696.29 38696.05 36595.55 444
CNVR-MVS98.17 23997.87 26399.07 13598.67 35698.24 13097.01 34198.93 31797.25 29697.62 35798.34 34797.27 20099.57 38396.42 31199.33 31999.39 225
HQP-MVS97.00 33896.49 35398.55 24798.67 35696.79 26596.29 38699.04 30096.05 36595.55 44496.84 42293.84 33799.54 39692.82 42399.26 33399.32 259
MM98.22 23097.99 24898.91 16898.66 36196.97 25397.89 23194.44 46199.54 4198.95 21199.14 17193.50 34399.92 6599.80 1799.96 2899.85 30
test_fmvs197.72 28097.94 25597.07 38798.66 36192.39 42397.68 26399.81 3195.20 39799.54 7999.44 8691.56 37499.41 42899.78 2199.77 16299.40 224
balanced_conf0398.63 16398.72 12798.38 27398.66 36196.68 27398.90 8399.42 17998.99 12198.97 20599.19 15495.81 28699.85 15698.77 10699.77 16298.60 395
thres20093.72 42293.14 42495.46 43998.66 36191.29 44196.61 36594.63 46097.39 28296.83 40693.71 47179.88 45199.56 38682.40 48098.13 42095.54 481
wuyk23d96.06 37197.62 28391.38 46898.65 36598.57 10698.85 9296.95 42696.86 32899.90 1499.16 16499.18 1998.40 47589.23 46399.77 16277.18 488
NCCC97.86 26897.47 29399.05 14298.61 36698.07 15296.98 34398.90 32397.63 25097.04 39297.93 38095.99 27799.66 34395.31 36098.82 38599.43 207
DeepC-MVS_fast96.85 698.30 21998.15 23198.75 20498.61 36697.23 23097.76 25299.09 29097.31 29098.75 25398.66 30297.56 17499.64 35496.10 33299.55 27199.39 225
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testing393.51 42492.09 43597.75 33398.60 36894.40 36597.32 31795.26 45597.56 26096.79 40995.50 45053.57 49399.77 26195.26 36198.97 37599.08 320
thisisatest051594.12 41593.16 42396.97 39298.60 36892.90 41393.77 47390.61 48094.10 42296.91 39995.87 44374.99 46699.80 23194.52 37899.12 35798.20 424
GA-MVS95.86 37995.32 38997.49 36698.60 36894.15 37493.83 47297.93 39695.49 38796.68 41397.42 40983.21 44199.30 44496.22 32398.55 40499.01 332
dmvs_testset92.94 43492.21 43495.13 44398.59 37190.99 44897.65 26992.09 47696.95 31994.00 46893.55 47292.34 36396.97 48572.20 48792.52 48297.43 460
OPU-MVS98.82 18398.59 37198.30 12698.10 18998.52 32498.18 11498.75 47194.62 37599.48 29499.41 215
MSLP-MVS++98.02 25198.14 23397.64 34998.58 37395.19 33897.48 29699.23 25997.47 27097.90 33898.62 31197.04 21398.81 46997.55 20599.41 30898.94 349
test1298.93 16498.58 37397.83 17898.66 36196.53 42095.51 29599.69 31699.13 35499.27 273
CL-MVSNet_self_test97.44 30297.22 30698.08 30698.57 37595.78 31194.30 46498.79 34696.58 34198.60 27398.19 35994.74 31999.64 35496.41 31298.84 38298.82 364
PS-MVSNAJ97.08 33197.39 29596.16 42498.56 37692.46 42195.24 43798.85 33797.25 29697.49 37095.99 43998.07 12499.90 8196.37 31498.67 39796.12 477
CNLPA97.17 32696.71 33998.55 24798.56 37698.05 15696.33 38398.93 31796.91 32497.06 39097.39 41094.38 32699.45 42291.66 43999.18 34898.14 427
xiu_mvs_v2_base97.16 32797.49 29096.17 42298.54 37892.46 42195.45 42998.84 33897.25 29697.48 37196.49 42998.31 9499.90 8196.34 31798.68 39696.15 476
alignmvs97.35 31096.88 32798.78 19598.54 37898.09 14697.71 25997.69 40299.20 8397.59 36095.90 44288.12 40699.55 39098.18 14998.96 37698.70 386
FE-MVS95.66 38694.95 39997.77 32998.53 38095.28 33499.40 1996.09 44393.11 43697.96 33599.26 13579.10 45899.77 26192.40 43298.71 39198.27 422
Effi-MVS+98.02 25197.82 26698.62 22998.53 38097.19 23797.33 31699.68 6097.30 29196.68 41397.46 40798.56 7299.80 23196.63 29098.20 41498.86 361
baseline195.96 37795.44 38297.52 36398.51 38293.99 38798.39 15696.09 44398.21 19798.40 30197.76 38986.88 40999.63 35795.42 35889.27 48598.95 345
MVS_Test98.18 23798.36 19697.67 34298.48 38394.73 35698.18 17599.02 30597.69 24698.04 32999.11 17797.22 20499.56 38698.57 12098.90 38198.71 383
MGCFI-Net98.34 21198.28 21098.51 25698.47 38497.59 20198.96 7799.48 14299.18 9197.40 37795.50 45098.66 5899.50 40898.18 14998.71 39198.44 409
BH-RMVSNet96.83 34496.58 34997.58 35598.47 38494.05 37796.67 36197.36 41196.70 33797.87 34197.98 37595.14 30499.44 42490.47 45898.58 40399.25 280
sasdasda98.34 21198.26 21498.58 23798.46 38697.82 18398.96 7799.46 15599.19 8897.46 37295.46 45398.59 6699.46 42098.08 15698.71 39198.46 403
canonicalmvs98.34 21198.26 21498.58 23798.46 38697.82 18398.96 7799.46 15599.19 8897.46 37295.46 45398.59 6699.46 42098.08 15698.71 39198.46 403
MVS-HIRNet94.32 40995.62 37290.42 46998.46 38675.36 49396.29 38689.13 48495.25 39495.38 45099.75 1692.88 35499.19 45494.07 39599.39 31096.72 470
PHI-MVS98.29 22297.95 25399.34 8398.44 38999.16 4998.12 18699.38 19096.01 36998.06 32698.43 33797.80 15399.67 33095.69 35099.58 26099.20 295
DVP-MVS++98.90 10498.70 13599.51 4998.43 39099.15 5399.43 1599.32 21898.17 20499.26 14899.02 20098.18 11499.88 11597.07 24399.45 29899.49 173
MSC_two_6792asdad99.32 9198.43 39098.37 12198.86 33499.89 9797.14 23799.60 25199.71 63
No_MVS99.32 9198.43 39098.37 12198.86 33499.89 9797.14 23799.60 25199.71 63
Fast-Effi-MVS+-dtu98.27 22398.09 23698.81 18598.43 39098.11 14397.61 27999.50 13298.64 15597.39 37997.52 40398.12 12299.95 2696.90 26198.71 39198.38 416
OpenMVS_ROBcopyleft95.38 1495.84 38195.18 39497.81 32698.41 39497.15 24397.37 31398.62 36583.86 48098.65 26498.37 34394.29 32999.68 32688.41 46498.62 40196.60 471
DeepPCF-MVS96.93 598.32 21698.01 24699.23 10898.39 39598.97 7495.03 44399.18 27196.88 32599.33 13098.78 27298.16 11899.28 44896.74 27599.62 24499.44 203
Patchmatch-test96.55 35496.34 35697.17 38298.35 39693.06 40998.40 15597.79 39897.33 28798.41 29798.67 29983.68 43999.69 31695.16 36399.31 32398.77 377
AdaColmapbinary97.14 32896.71 33998.46 26398.34 39797.80 18796.95 34498.93 31795.58 38496.92 39797.66 39495.87 28499.53 39890.97 45299.14 35298.04 432
OpenMVScopyleft96.65 797.09 33096.68 34198.32 28098.32 39897.16 24298.86 9199.37 19489.48 46896.29 42999.15 16896.56 24799.90 8192.90 42099.20 34397.89 440
MG-MVS96.77 34796.61 34697.26 37898.31 39993.06 40995.93 40898.12 39296.45 34997.92 33698.73 28393.77 34199.39 43191.19 45099.04 36399.33 256
test_yl96.69 34896.29 35897.90 31898.28 40095.24 33597.29 32197.36 41198.21 19798.17 31297.86 38286.27 41399.55 39094.87 36998.32 40898.89 356
DCV-MVSNet96.69 34896.29 35897.90 31898.28 40095.24 33597.29 32197.36 41198.21 19798.17 31297.86 38286.27 41399.55 39094.87 36998.32 40898.89 356
CHOSEN 280x42095.51 39195.47 37995.65 43498.25 40288.27 46593.25 47698.88 32793.53 43094.65 45997.15 41886.17 41599.93 5497.41 21999.93 5698.73 382
SCA96.41 36196.66 34495.67 43298.24 40388.35 46495.85 41496.88 42996.11 36397.67 35598.67 29993.10 34999.85 15694.16 38999.22 33998.81 369
DeepMVS_CXcopyleft93.44 46298.24 40394.21 37194.34 46264.28 48891.34 48294.87 46589.45 39592.77 48977.54 48593.14 48193.35 484
MS-PatchMatch97.68 28397.75 26997.45 36998.23 40593.78 39697.29 32198.84 33896.10 36498.64 26598.65 30496.04 27099.36 43496.84 26799.14 35299.20 295
BH-w/o95.13 39894.89 40195.86 42798.20 40691.31 44095.65 42197.37 41093.64 42896.52 42295.70 44693.04 35299.02 46088.10 46695.82 47197.24 463
mvs_anonymous97.83 27698.16 23096.87 39798.18 40791.89 43097.31 31998.90 32397.37 28498.83 23899.46 8196.28 26199.79 24498.90 9598.16 41898.95 345
miper_lstm_enhance97.18 32597.16 30997.25 37998.16 40892.85 41495.15 44199.31 22397.25 29698.74 25598.78 27290.07 38799.78 25597.19 23299.80 14599.11 319
RRT-MVS97.88 26597.98 24997.61 35298.15 40993.77 39798.97 7699.64 7199.16 9398.69 25899.42 9091.60 37299.89 9797.63 19898.52 40599.16 313
ET-MVSNet_ETH3D94.30 41193.21 42297.58 35598.14 41094.47 36494.78 44993.24 47294.72 40689.56 48495.87 44378.57 46199.81 22296.91 25697.11 45398.46 403
ADS-MVSNet295.43 39394.98 39796.76 40498.14 41091.74 43197.92 22797.76 39990.23 46296.51 42398.91 23885.61 42299.85 15692.88 42196.90 45498.69 387
ADS-MVSNet95.24 39694.93 40096.18 42198.14 41090.10 45797.92 22797.32 41490.23 46296.51 42398.91 23885.61 42299.74 28392.88 42196.90 45498.69 387
c3_l97.36 30997.37 29797.31 37498.09 41393.25 40795.01 44499.16 27897.05 31398.77 25098.72 28592.88 35499.64 35496.93 25599.76 17799.05 324
FMVSNet397.50 29497.24 30598.29 28498.08 41495.83 30897.86 23698.91 32297.89 23298.95 21198.95 23187.06 40899.81 22297.77 18599.69 21299.23 285
PAPM91.88 44990.34 45196.51 40898.06 41592.56 41992.44 48097.17 41886.35 47690.38 48396.01 43886.61 41199.21 45370.65 48995.43 47397.75 449
Effi-MVS+-dtu98.26 22597.90 26199.35 8098.02 41699.49 698.02 20699.16 27898.29 19097.64 35697.99 37496.44 25399.95 2696.66 28898.93 37998.60 395
eth_miper_zixun_eth97.23 32197.25 30497.17 38298.00 41792.77 41694.71 45099.18 27197.27 29498.56 28198.74 28291.89 37099.69 31697.06 24599.81 13499.05 324
HY-MVS95.94 1395.90 37895.35 38797.55 36097.95 41894.79 35298.81 9796.94 42792.28 44795.17 45298.57 31889.90 38999.75 27791.20 44997.33 44998.10 429
UGNet98.53 18498.45 18198.79 19297.94 41996.96 25599.08 6198.54 37199.10 10596.82 40799.47 7996.55 24899.84 17498.56 12399.94 5099.55 136
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
MAR-MVS96.47 35995.70 36998.79 19297.92 42099.12 6398.28 16498.60 36692.16 44895.54 44796.17 43694.77 31899.52 40289.62 46198.23 41297.72 451
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
MVSTER96.86 34396.55 35097.79 32797.91 42194.21 37197.56 28598.87 32997.49 26999.06 18199.05 19580.72 44999.80 23198.44 12999.82 12899.37 236
API-MVS97.04 33496.91 32697.42 37197.88 42298.23 13498.18 17598.50 37497.57 25897.39 37996.75 42496.77 23499.15 45790.16 45999.02 36794.88 482
myMVS_eth3d2892.92 43592.31 43194.77 44697.84 42387.59 46996.19 39296.11 44297.08 31294.27 46293.49 47466.07 48398.78 47091.78 43797.93 43197.92 439
miper_ehance_all_eth97.06 33297.03 31797.16 38497.83 42493.06 40994.66 45399.09 29095.99 37098.69 25898.45 33592.73 35999.61 36796.79 26999.03 36498.82 364
cl____97.02 33596.83 33197.58 35597.82 42594.04 37994.66 45399.16 27897.04 31498.63 26698.71 28688.68 40099.69 31697.00 24899.81 13499.00 336
DIV-MVS_self_test97.02 33596.84 33097.58 35597.82 42594.03 38094.66 45399.16 27897.04 31498.63 26698.71 28688.69 39899.69 31697.00 24899.81 13499.01 332
CANet97.87 26797.76 26898.19 29797.75 42795.51 31996.76 35699.05 29797.74 24296.93 39698.21 35795.59 29299.89 9797.86 18099.93 5699.19 300
UBG93.25 42992.32 43096.04 42697.72 42890.16 45695.92 41095.91 44796.03 36893.95 47093.04 47769.60 47399.52 40290.72 45797.98 42998.45 406
mvsany_test197.60 28897.54 28697.77 32997.72 42895.35 33195.36 43397.13 42094.13 42199.71 5099.33 11697.93 13799.30 44497.60 20298.94 37898.67 391
PVSNet_089.98 2191.15 45090.30 45293.70 45997.72 42884.34 48390.24 48397.42 40990.20 46593.79 47193.09 47690.90 38298.89 46886.57 47272.76 48997.87 442
CR-MVSNet96.28 36495.95 36397.28 37697.71 43194.22 36998.11 18798.92 32092.31 44696.91 39999.37 10485.44 42599.81 22297.39 22097.36 44797.81 445
RPMNet97.02 33596.93 32297.30 37597.71 43194.22 36998.11 18799.30 23199.37 6196.91 39999.34 11386.72 41099.87 13497.53 20897.36 44797.81 445
ETVMVS92.60 43891.08 44797.18 38097.70 43393.65 40296.54 36895.70 45096.51 34294.68 45892.39 48161.80 49099.50 40886.97 46997.41 44398.40 414
pmmvs395.03 40094.40 40796.93 39397.70 43392.53 42095.08 44297.71 40188.57 47297.71 35298.08 36879.39 45699.82 20596.19 32599.11 35898.43 411
baseline293.73 42192.83 42796.42 41197.70 43391.28 44296.84 35289.77 48393.96 42692.44 47895.93 44179.14 45799.77 26192.94 41996.76 45898.21 423
WBMVS95.18 39794.78 40296.37 41297.68 43689.74 45995.80 41698.73 35797.54 26498.30 30398.44 33670.06 47199.82 20596.62 29199.87 9899.54 142
tpm94.67 40594.34 40995.66 43397.68 43688.42 46397.88 23294.90 45794.46 41296.03 43798.56 31978.66 45999.79 24495.88 33895.01 47598.78 376
CANet_DTU97.26 31797.06 31697.84 32397.57 43894.65 36096.19 39298.79 34697.23 30295.14 45398.24 35493.22 34699.84 17497.34 22299.84 11299.04 328
testing1193.08 43292.02 43796.26 41797.56 43990.83 45196.32 38495.70 45096.47 34692.66 47793.73 47064.36 48799.59 37493.77 40497.57 43698.37 418
tpm293.09 43192.58 42994.62 44897.56 43986.53 47297.66 26795.79 44986.15 47794.07 46798.23 35675.95 46499.53 39890.91 45496.86 45797.81 445
testing9193.32 42792.27 43296.47 41097.54 44191.25 44396.17 39696.76 43197.18 30693.65 47393.50 47365.11 48699.63 35793.04 41897.45 44098.53 400
TR-MVS95.55 38995.12 39596.86 40097.54 44193.94 38896.49 37396.53 43694.36 41797.03 39496.61 42794.26 33099.16 45686.91 47196.31 46297.47 459
testing9993.04 43391.98 44096.23 41997.53 44390.70 45396.35 38295.94 44696.87 32693.41 47493.43 47563.84 48899.59 37493.24 41697.19 45098.40 414
131495.74 38395.60 37496.17 42297.53 44392.75 41798.07 19698.31 38391.22 45794.25 46396.68 42595.53 29399.03 45991.64 44197.18 45196.74 469
CostFormer93.97 41793.78 41594.51 44997.53 44385.83 47597.98 21895.96 44589.29 47094.99 45598.63 30978.63 46099.62 36094.54 37796.50 45998.09 430
FMVSNet596.01 37395.20 39398.41 26997.53 44396.10 29498.74 9899.50 13297.22 30598.03 33099.04 19769.80 47299.88 11597.27 22799.71 20299.25 280
PMMVS96.51 35595.98 36298.09 30397.53 44395.84 30794.92 44698.84 33891.58 45296.05 43695.58 44795.68 28999.66 34395.59 35498.09 42298.76 379
reproduce_monomvs95.00 40295.25 39094.22 45297.51 44883.34 48497.86 23698.44 37698.51 17399.29 14099.30 12367.68 47799.56 38698.89 9799.81 13499.77 50
PAPR95.29 39494.47 40597.75 33397.50 44995.14 34094.89 44798.71 35991.39 45695.35 45195.48 45294.57 32199.14 45884.95 47497.37 44598.97 342
testing22291.96 44790.37 45096.72 40597.47 45092.59 41896.11 39894.76 45896.83 32992.90 47692.87 47857.92 49199.55 39086.93 47097.52 43798.00 436
PatchT96.65 35196.35 35597.54 36197.40 45195.32 33397.98 21896.64 43399.33 6696.89 40399.42 9084.32 43399.81 22297.69 19697.49 43897.48 458
tpm cat193.29 42893.13 42593.75 45897.39 45284.74 47897.39 30797.65 40583.39 48294.16 46498.41 33882.86 44499.39 43191.56 44395.35 47497.14 464
PatchmatchNetpermissive95.58 38895.67 37195.30 44297.34 45387.32 47097.65 26996.65 43295.30 39397.07 38998.69 29584.77 42899.75 27794.97 36798.64 39898.83 363
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmtry97.35 31096.97 32098.50 26097.31 45496.47 28598.18 17598.92 32098.95 12898.78 24799.37 10485.44 42599.85 15695.96 33699.83 12399.17 308
LS3D98.63 16398.38 19399.36 7497.25 45599.38 1399.12 6099.32 21899.21 8198.44 29498.88 24897.31 19699.80 23196.58 29499.34 31898.92 351
IB-MVS91.63 1992.24 44490.90 44896.27 41697.22 45691.24 44494.36 46393.33 47192.37 44592.24 48094.58 46766.20 48299.89 9793.16 41794.63 47797.66 453
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
UWE-MVS92.38 44191.76 44494.21 45397.16 45784.65 47995.42 43188.45 48595.96 37196.17 43095.84 44566.36 48099.71 30191.87 43698.64 39898.28 421
tpmrst95.07 39995.46 38093.91 45697.11 45884.36 48297.62 27496.96 42594.98 40096.35 42898.80 26885.46 42499.59 37495.60 35396.23 46397.79 448
Syy-MVS96.04 37295.56 37897.49 36697.10 45994.48 36396.18 39496.58 43495.65 38194.77 45692.29 48291.27 37899.36 43498.17 15198.05 42698.63 393
myMVS_eth3d91.92 44890.45 44996.30 41497.10 45990.90 44996.18 39496.58 43495.65 38194.77 45692.29 48253.88 49299.36 43489.59 46298.05 42698.63 393
blended_shiyan695.99 37595.33 38897.95 31697.06 46194.89 34995.34 43498.58 36796.17 35997.06 39092.41 48087.64 40799.76 26797.64 19796.09 46699.19 300
MDTV_nov1_ep1395.22 39297.06 46183.20 48597.74 25696.16 44094.37 41696.99 39598.83 26183.95 43799.53 39893.90 39897.95 430
MVS93.19 43092.09 43596.50 40996.91 46394.03 38098.07 19698.06 39468.01 48794.56 46196.48 43095.96 28099.30 44483.84 47696.89 45696.17 474
E-PMN94.17 41394.37 40893.58 46096.86 46485.71 47690.11 48597.07 42198.17 20497.82 34797.19 41684.62 43098.94 46489.77 46097.68 43596.09 478
JIA-IIPM95.52 39095.03 39697.00 38996.85 46594.03 38096.93 34795.82 44899.20 8394.63 46099.71 2283.09 44299.60 37094.42 38394.64 47697.36 462
EMVS93.83 41994.02 41193.23 46596.83 46684.96 47789.77 48696.32 43897.92 22997.43 37696.36 43586.17 41598.93 46587.68 46797.73 43495.81 479
blend_shiyan492.09 44690.16 45397.88 32196.78 46794.93 34795.24 43798.58 36796.22 35796.07 43491.42 48463.46 48999.73 29096.70 28176.98 48898.98 338
cl2295.79 38295.39 38596.98 39196.77 46892.79 41594.40 46298.53 37294.59 40997.89 33998.17 36082.82 44599.24 45096.37 31499.03 36498.92 351
WB-MVSnew95.73 38495.57 37796.23 41996.70 46990.70 45396.07 40093.86 46895.60 38397.04 39295.45 45696.00 27399.55 39091.04 45198.31 41098.43 411
dp93.47 42593.59 41893.13 46696.64 47081.62 49197.66 26796.42 43792.80 44196.11 43298.64 30778.55 46299.59 37493.31 41492.18 48498.16 426
MonoMVSNet96.25 36696.53 35295.39 44096.57 47191.01 44798.82 9697.68 40498.57 16898.03 33099.37 10490.92 38197.78 48194.99 36593.88 48097.38 461
FE-blended-shiyan795.48 39294.74 40497.68 34196.53 47294.12 37594.17 46698.57 36995.84 37596.71 41191.16 48586.05 41899.76 26797.57 20496.09 46699.17 308
usedtu_blend_shiyan596.20 36995.62 37297.94 31796.53 47294.93 34798.83 9599.59 9198.89 13596.71 41191.16 48586.05 41899.73 29096.70 28196.09 46699.17 308
test-LLR93.90 41893.85 41394.04 45496.53 47284.62 48094.05 46992.39 47496.17 35994.12 46595.07 45782.30 44699.67 33095.87 34198.18 41597.82 443
test-mter92.33 44391.76 44494.04 45496.53 47284.62 48094.05 46992.39 47494.00 42594.12 46595.07 45765.63 48599.67 33095.87 34198.18 41597.82 443
TESTMET0.1,192.19 44591.77 44393.46 46196.48 47682.80 48794.05 46991.52 47994.45 41494.00 46894.88 46366.65 47999.56 38695.78 34698.11 42198.02 433
MGCNet97.44 30297.01 31998.72 21296.42 47796.74 26997.20 33191.97 47798.46 17698.30 30398.79 27092.74 35899.91 7499.30 6399.94 5099.52 158
miper_enhance_ethall96.01 37395.74 36796.81 40196.41 47892.27 42793.69 47498.89 32691.14 45998.30 30397.35 41490.58 38499.58 38196.31 31899.03 36498.60 395
tpmvs95.02 40195.25 39094.33 45096.39 47985.87 47398.08 19296.83 43095.46 38895.51 44998.69 29585.91 42099.53 39894.16 38996.23 46397.58 456
CMPMVSbinary75.91 2396.29 36395.44 38298.84 18096.25 48098.69 9897.02 34099.12 28588.90 47197.83 34598.86 25189.51 39398.90 46791.92 43499.51 28398.92 351
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test0.0.03 194.51 40693.69 41696.99 39096.05 48193.61 40494.97 44593.49 46996.17 35997.57 36394.88 46382.30 44699.01 46293.60 40794.17 47998.37 418
EPMVS93.72 42293.27 42195.09 44596.04 48287.76 46798.13 18285.01 49094.69 40796.92 39798.64 30778.47 46399.31 44295.04 36496.46 46098.20 424
cascas94.79 40494.33 41096.15 42596.02 48392.36 42592.34 48199.26 25185.34 47995.08 45494.96 46292.96 35398.53 47494.41 38698.59 40297.56 457
MVStest195.86 37995.60 37496.63 40695.87 48491.70 43297.93 22498.94 31498.03 21999.56 7499.66 3271.83 46998.26 47799.35 5999.24 33599.91 13
gg-mvs-nofinetune92.37 44291.20 44695.85 42895.80 48592.38 42499.31 3081.84 49299.75 1191.83 48199.74 1868.29 47499.02 46087.15 46897.12 45296.16 475
gm-plane-assit94.83 48681.97 48988.07 47494.99 46099.60 37091.76 438
GG-mvs-BLEND94.76 44794.54 48792.13 42999.31 3080.47 49388.73 48791.01 48767.59 47898.16 48082.30 48194.53 47893.98 483
UWE-MVS-2890.22 45189.28 45493.02 46794.50 48882.87 48696.52 37187.51 48695.21 39692.36 47996.04 43771.57 47098.25 47872.04 48897.77 43397.94 438
EPNet_dtu94.93 40394.78 40295.38 44193.58 48987.68 46896.78 35495.69 45297.35 28689.14 48698.09 36788.15 40599.49 41194.95 36899.30 32698.98 338
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
dongtai76.24 45575.95 45877.12 47292.39 49067.91 49690.16 48459.44 49782.04 48389.42 48594.67 46649.68 49481.74 49048.06 49077.66 48781.72 486
KD-MVS_2432*160092.87 43691.99 43895.51 43791.37 49189.27 46094.07 46798.14 39095.42 38997.25 38496.44 43267.86 47599.24 45091.28 44796.08 46998.02 433
miper_refine_blended92.87 43691.99 43895.51 43791.37 49189.27 46094.07 46798.14 39095.42 38997.25 38496.44 43267.86 47599.24 45091.28 44796.08 46998.02 433
EPNet96.14 37095.44 38298.25 28890.76 49395.50 32297.92 22794.65 45998.97 12492.98 47598.85 25489.12 39699.87 13495.99 33499.68 21799.39 225
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
kuosan69.30 45668.95 45970.34 47387.68 49465.00 49791.11 48259.90 49669.02 48674.46 49188.89 48848.58 49568.03 49228.61 49172.33 49077.99 487
test_method79.78 45379.50 45680.62 47080.21 49545.76 49870.82 48798.41 38031.08 49080.89 49097.71 39184.85 42797.37 48391.51 44480.03 48698.75 380
tmp_tt78.77 45478.73 45778.90 47158.45 49674.76 49594.20 46578.26 49439.16 48986.71 48892.82 47980.50 45075.19 49186.16 47392.29 48386.74 485
testmvs17.12 45820.53 4616.87 47512.05 4974.20 50093.62 4756.73 4984.62 49310.41 49324.33 4908.28 4973.56 4949.69 49315.07 49112.86 490
test12317.04 45920.11 4627.82 47410.25 4984.91 49994.80 4484.47 4994.93 49210.00 49424.28 4919.69 4963.64 49310.14 49212.43 49214.92 489
mmdepth0.00 4620.00 4650.00 4760.00 4990.00 5010.00 4880.00 5000.00 4940.00 4950.00 4940.00 4980.00 4950.00 4940.00 4930.00 491
monomultidepth0.00 4620.00 4650.00 4760.00 4990.00 5010.00 4880.00 5000.00 4940.00 4950.00 4940.00 4980.00 4950.00 4940.00 4930.00 491
test_blank0.00 4620.00 4650.00 4760.00 4990.00 5010.00 4880.00 5000.00 4940.00 4950.00 4940.00 4980.00 4950.00 4940.00 4930.00 491
eth-test20.00 499
eth-test0.00 499
uanet_test0.00 4620.00 4650.00 4760.00 4990.00 5010.00 4880.00 5000.00 4940.00 4950.00 4940.00 4980.00 4950.00 4940.00 4930.00 491
DCPMVS0.00 4620.00 4650.00 4760.00 4990.00 5010.00 4880.00 5000.00 4940.00 4950.00 4940.00 4980.00 4950.00 4940.00 4930.00 491
cdsmvs_eth3d_5k24.66 45732.88 4600.00 4760.00 4990.00 5010.00 48899.10 2880.00 4940.00 49597.58 39999.21 180.00 4950.00 4940.00 4930.00 491
pcd_1.5k_mvsjas8.17 46010.90 4630.00 4760.00 4990.00 5010.00 4880.00 5000.00 4940.00 4950.00 49498.07 1240.00 4950.00 4940.00 4930.00 491
sosnet-low-res0.00 4620.00 4650.00 4760.00 4990.00 5010.00 4880.00 5000.00 4940.00 4950.00 4940.00 4980.00 4950.00 4940.00 4930.00 491
sosnet0.00 4620.00 4650.00 4760.00 4990.00 5010.00 4880.00 5000.00 4940.00 4950.00 4940.00 4980.00 4950.00 4940.00 4930.00 491
uncertanet0.00 4620.00 4650.00 4760.00 4990.00 5010.00 4880.00 5000.00 4940.00 4950.00 4940.00 4980.00 4950.00 4940.00 4930.00 491
Regformer0.00 4620.00 4650.00 4760.00 4990.00 5010.00 4880.00 5000.00 4940.00 4950.00 4940.00 4980.00 4950.00 4940.00 4930.00 491
ab-mvs-re8.12 46110.83 4640.00 4760.00 4990.00 5010.00 4880.00 5000.00 4940.00 49597.48 4050.00 4980.00 4950.00 4940.00 4930.00 491
uanet0.00 4620.00 4650.00 4760.00 4990.00 5010.00 4880.00 5000.00 4940.00 4950.00 4940.00 4980.00 4950.00 4940.00 4930.00 491
TestfortrainingZip98.68 108
WAC-MVS90.90 44991.37 446
PC_three_145293.27 43399.40 11598.54 32098.22 10997.00 48495.17 36299.45 29899.49 173
test_241102_TWO99.30 23198.03 21999.26 14899.02 20097.51 18299.88 11596.91 25699.60 25199.66 78
test_0728_THIRD98.17 20499.08 17999.02 20097.89 14399.88 11597.07 24399.71 20299.70 68
GSMVS98.81 369
sam_mvs184.74 42998.81 369
sam_mvs84.29 435
MTGPAbinary99.20 263
test_post197.59 28220.48 49383.07 44399.66 34394.16 389
test_post21.25 49283.86 43899.70 308
patchmatchnet-post98.77 27484.37 43299.85 156
MTMP97.93 22491.91 478
test9_res93.28 41599.15 35199.38 234
agg_prior292.50 43199.16 34999.37 236
test_prior497.97 16395.86 412
test_prior295.74 41996.48 34596.11 43297.63 39795.92 28394.16 38999.20 343
旧先验295.76 41888.56 47397.52 36799.66 34394.48 379
新几何295.93 408
无先验95.74 41998.74 35689.38 46999.73 29092.38 43399.22 290
原ACMM295.53 425
testdata299.79 24492.80 425
segment_acmp97.02 216
testdata195.44 43096.32 353
plane_prior599.27 24699.70 30894.42 38399.51 28399.45 199
plane_prior497.98 375
plane_prior397.78 18897.41 27997.79 348
plane_prior297.77 24998.20 201
plane_prior97.65 19797.07 33996.72 33599.36 314
n20.00 500
nn0.00 500
door-mid99.57 101
test1198.87 329
door99.41 183
HQP5-MVS96.79 265
BP-MVS92.82 423
HQP4-MVS95.56 44399.54 39699.32 259
HQP3-MVS99.04 30099.26 333
HQP2-MVS93.84 337
MDTV_nov1_ep13_2view74.92 49497.69 26290.06 46797.75 35185.78 42193.52 40998.69 387
ACMMP++_ref99.77 162
ACMMP++99.68 217
Test By Simon96.52 249