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 8199.16 6398.64 21899.94 298.51 11399.32 2799.75 4399.58 3998.60 26799.62 4198.22 10499.51 39697.70 18999.73 18097.89 429
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
OurMVSNet-221017-099.37 3099.31 4399.53 3999.91 398.98 7299.63 799.58 8899.44 5499.78 4099.76 1596.39 24899.92 6599.44 5599.92 6999.68 71
FE-MVSNET199.51 1499.54 1499.43 6899.90 498.85 8699.33 2699.79 3699.47 4899.51 9399.75 1699.10 2499.84 17499.14 7699.91 7899.59 107
pmmvs699.67 399.70 399.60 1699.90 499.27 2899.53 999.76 4099.64 2799.84 3099.83 499.50 999.87 13499.36 5899.92 6999.64 84
PS-MVSNAJss99.46 1899.49 1799.35 8199.90 498.15 14099.20 4999.65 6999.48 4599.92 899.71 2398.07 11999.96 1499.53 48100.00 199.93 11
testf199.25 4299.16 6399.51 4999.89 799.63 498.71 10599.69 5598.90 13499.43 10799.35 10598.86 3599.67 32097.81 17699.81 12999.24 277
APD_test299.25 4299.16 6399.51 4999.89 799.63 498.71 10599.69 5598.90 13499.43 10799.35 10598.86 3599.67 32097.81 17699.81 12999.24 277
ANet_high99.57 1099.67 699.28 9799.89 798.09 14799.14 5899.93 599.82 899.93 699.81 899.17 2099.94 4299.31 62100.00 199.82 36
anonymousdsp99.51 1499.47 2299.62 1099.88 1099.08 7099.34 2399.69 5598.93 13099.65 6499.72 2298.93 3399.95 2699.11 79100.00 199.82 36
v7n99.53 1299.57 1399.41 7199.88 1098.54 11199.45 1499.61 7899.66 2499.68 5899.66 3398.44 7899.95 2699.73 2899.96 2899.75 60
mvs_tets99.63 699.67 699.49 5599.88 1098.61 10399.34 2399.71 4899.27 7599.90 1499.74 1999.68 499.97 799.55 4399.99 599.88 20
test_fmvsmconf0.01_n99.57 1099.63 1099.36 7599.87 1398.13 14398.08 18799.95 199.45 5299.98 299.75 1699.80 199.97 799.82 1299.99 599.99 2
jajsoiax99.58 999.61 1199.48 5799.87 1398.61 10399.28 4199.66 6599.09 10999.89 1899.68 2699.53 799.97 799.50 5199.99 599.87 22
test_djsdf99.52 1399.51 1699.53 3999.86 1598.74 9399.39 2099.56 10499.11 9999.70 5299.73 2199.00 2899.97 799.26 6699.98 1299.89 16
MIMVSNet199.38 2999.32 4199.55 2999.86 1599.19 4399.41 1799.59 8699.59 3799.71 5099.57 5097.12 20399.90 8199.21 7199.87 9899.54 143
LTVRE_ROB98.40 199.67 399.71 299.56 2799.85 1799.11 6599.90 199.78 3799.63 2999.78 4099.67 3199.48 1099.81 22399.30 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 1899.34 2099.69 599.58 8899.90 399.86 2499.78 1399.58 699.95 2699.00 8999.95 3899.78 47
SixPastTwentyTwo98.75 13098.62 14599.16 11999.83 1997.96 16799.28 4198.20 37699.37 6299.70 5299.65 3792.65 35499.93 5499.04 8699.84 11299.60 100
sc_t199.62 799.66 899.53 3999.82 2099.09 6999.50 1199.63 7399.88 499.86 2499.80 1199.03 2599.89 9799.48 5399.93 5699.60 100
Baseline_NR-MVSNet98.98 8998.86 10799.36 7599.82 2098.55 10897.47 29499.57 9599.37 6299.21 16099.61 4496.76 23099.83 19398.06 15499.83 11999.71 63
pm-mvs199.44 2099.48 1999.33 9099.80 2298.63 10099.29 3799.63 7399.30 7299.65 6499.60 4699.16 2299.82 20699.07 8299.83 11999.56 130
TransMVSNet (Re)99.44 2099.47 2299.36 7599.80 2298.58 10699.27 4399.57 9599.39 6099.75 4599.62 4199.17 2099.83 19399.06 8499.62 23999.66 78
K. test v398.00 24897.66 27399.03 14699.79 2497.56 20399.19 5392.47 46299.62 3399.52 8899.66 3389.61 38599.96 1499.25 6899.81 12999.56 130
test_fmvsmconf0.1_n99.49 1699.54 1499.34 8499.78 2598.11 14497.77 24499.90 1199.33 6799.97 399.66 3399.71 399.96 1499.79 1999.99 599.96 8
APD_test198.83 11498.66 13899.34 8499.78 2599.47 998.42 14999.45 15298.28 18698.98 19699.19 14997.76 15199.58 37096.57 28599.55 26698.97 331
test_vis3_rt99.14 6399.17 6199.07 13699.78 2598.38 12098.92 8399.94 297.80 23299.91 1299.67 3197.15 20298.91 45599.76 2399.56 26299.92 12
EGC-MVSNET85.24 44180.54 44499.34 8499.77 2899.20 4099.08 6299.29 23212.08 48020.84 48199.42 9097.55 17099.85 15697.08 23399.72 18898.96 333
Anonymous2024052198.69 14398.87 10398.16 29499.77 2895.11 33599.08 6299.44 16099.34 6699.33 13099.55 5894.10 32999.94 4299.25 6899.96 2899.42 207
FC-MVSNet-test99.27 3999.25 5499.34 8499.77 2898.37 12299.30 3699.57 9599.61 3599.40 11699.50 6997.12 20399.85 15699.02 8899.94 5099.80 42
test_vis1_n98.31 21298.50 16597.73 32899.76 3194.17 36398.68 10899.91 996.31 34699.79 3999.57 5092.85 35099.42 41699.79 1999.84 11299.60 100
test_fmvs399.12 7099.41 2798.25 28299.76 3195.07 33699.05 6899.94 297.78 23599.82 3499.84 398.56 6999.71 29699.96 199.96 2899.97 4
XXY-MVS99.14 6399.15 6899.10 12999.76 3197.74 19298.85 9399.62 7598.48 16999.37 12199.49 7598.75 4799.86 14398.20 14499.80 14099.71 63
TDRefinement99.42 2599.38 3099.55 2999.76 3199.33 2199.68 699.71 4899.38 6199.53 8399.61 4498.64 5799.80 23198.24 13999.84 11299.52 155
fmvsm_s_conf0.1_n_a99.17 5499.30 4698.80 18399.75 3596.59 26897.97 21799.86 1698.22 18999.88 2199.71 2398.59 6399.84 17499.73 2899.98 1299.98 3
tt080598.69 14398.62 14598.90 17199.75 3599.30 2399.15 5796.97 41398.86 13998.87 22997.62 39198.63 5998.96 45299.41 5798.29 40498.45 395
test_vis1_n_192098.40 19598.92 9696.81 39099.74 3790.76 44198.15 17599.91 998.33 17799.89 1899.55 5895.07 30099.88 11599.76 2399.93 5699.79 44
FOURS199.73 3899.67 399.43 1599.54 11399.43 5699.26 148
PEN-MVS99.41 2699.34 3799.62 1099.73 3899.14 5899.29 3799.54 11399.62 3399.56 7499.42 9098.16 11399.96 1498.78 10399.93 5699.77 50
lessismore_v098.97 15899.73 3897.53 20586.71 47799.37 12199.52 6889.93 38199.92 6598.99 9099.72 18899.44 198
SteuartSystems-ACMMP98.79 12398.54 15899.54 3299.73 3899.16 4998.23 16599.31 21697.92 22398.90 21898.90 23598.00 12599.88 11596.15 31799.72 18899.58 116
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended_VisFu98.17 23398.15 22598.22 28899.73 3895.15 33297.36 30899.68 6194.45 40398.99 19599.27 12496.87 21999.94 4297.13 23099.91 7899.57 124
Vis-MVSNetpermissive99.34 3199.36 3499.27 10099.73 3898.26 12999.17 5499.78 3799.11 9999.27 14499.48 7698.82 3899.95 2698.94 9399.93 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 4498.98 7299.51 1099.85 1899.86 699.88 2199.82 599.02 2799.90 8199.54 4499.95 3899.61 98
SSC-MVS98.71 13498.74 11898.62 22499.72 4496.08 29398.74 9898.64 35699.74 1399.67 6099.24 13794.57 31599.95 2699.11 7999.24 32899.82 36
test_f98.67 15298.87 10398.05 30399.72 4495.59 30898.51 13399.81 3196.30 34899.78 4099.82 596.14 25998.63 46299.82 1299.93 5699.95 9
ACMH96.65 799.25 4299.24 5599.26 10299.72 4498.38 12099.07 6599.55 10898.30 18199.65 6499.45 8599.22 1799.76 26798.44 12999.77 15799.64 84
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt032099.61 899.65 999.48 5799.71 4898.94 7999.54 899.83 2599.87 599.89 1899.82 598.75 4799.90 8199.54 4499.95 3899.59 107
fmvsm_s_conf0.1_n99.16 5899.33 3998.64 21899.71 4896.10 28897.87 23099.85 1898.56 16599.90 1499.68 2698.69 5399.85 15699.72 3099.98 1299.97 4
PS-CasMVS99.40 2799.33 3999.62 1099.71 4899.10 6699.29 3799.53 11799.53 4299.46 10299.41 9498.23 10199.95 2698.89 9799.95 3899.81 40
DTE-MVSNet99.43 2499.35 3599.66 799.71 4899.30 2399.31 3199.51 12399.64 2799.56 7499.46 8198.23 10199.97 798.78 10399.93 5699.72 62
WR-MVS_H99.33 3299.22 5699.65 899.71 4899.24 3199.32 2799.55 10899.46 5199.50 9599.34 10997.30 19299.93 5498.90 9599.93 5699.77 50
HPM-MVS_fast99.01 8398.82 11199.57 2299.71 4899.35 1799.00 7399.50 12697.33 28098.94 21398.86 24598.75 4799.82 20697.53 20199.71 19799.56 130
ACMH+96.62 999.08 7799.00 8899.33 9099.71 4898.83 8898.60 11999.58 8899.11 9999.53 8399.18 15398.81 3999.67 32096.71 27199.77 15799.50 161
PMVScopyleft91.26 2097.86 26297.94 24997.65 33599.71 4897.94 16998.52 12898.68 35298.99 12297.52 36099.35 10597.41 18598.18 46891.59 43199.67 21896.82 457
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
KinetiMVS99.03 8199.02 8499.03 14699.70 5697.48 20898.43 14699.29 23299.70 1699.60 7199.07 18296.13 26099.94 4299.42 5699.87 9899.68 71
FIs99.14 6399.09 7699.29 9699.70 5698.28 12899.13 5999.52 12299.48 4599.24 15499.41 9496.79 22799.82 20698.69 11399.88 9499.76 56
VPNet98.87 10598.83 11099.01 15099.70 5697.62 20198.43 14699.35 19799.47 4899.28 14299.05 19096.72 23399.82 20698.09 15199.36 30799.59 107
fmvsm_s_conf0.1_n_299.20 5299.38 3098.65 21699.69 5996.08 29397.49 28999.90 1199.53 4299.88 2199.64 3898.51 7299.90 8199.83 1099.98 1299.97 4
test_cas_vis1_n_192098.33 20998.68 13397.27 36699.69 5992.29 41598.03 19899.85 1897.62 24599.96 499.62 4193.98 33099.74 28099.52 5099.86 10599.79 44
MP-MVS-pluss98.57 16998.23 21399.60 1699.69 5999.35 1797.16 32999.38 18394.87 39398.97 20098.99 21298.01 12499.88 11597.29 21799.70 20499.58 116
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SDMVSNet99.23 4799.32 4198.96 15999.68 6297.35 21698.84 9599.48 13599.69 1899.63 6799.68 2699.03 2599.96 1497.97 16499.92 6999.57 124
sd_testset99.28 3899.31 4399.19 11399.68 6298.06 15699.41 1799.30 22499.69 1899.63 6799.68 2699.25 1699.96 1497.25 22099.92 6999.57 124
test_fmvs1_n98.09 23998.28 20497.52 35299.68 6293.47 39498.63 11599.93 595.41 38199.68 5899.64 3891.88 36499.48 40399.82 1299.87 9899.62 90
CHOSEN 1792x268897.49 29197.14 30698.54 24699.68 6296.09 29196.50 36599.62 7591.58 44198.84 23298.97 21992.36 35699.88 11596.76 26499.95 3899.67 76
tfpnnormal98.90 10098.90 9898.91 16899.67 6697.82 18499.00 7399.44 16099.45 5299.51 9399.24 13798.20 10899.86 14395.92 32699.69 20799.04 318
MTAPA98.88 10498.64 14199.61 1499.67 6699.36 1698.43 14699.20 25698.83 14398.89 22198.90 23596.98 21399.92 6597.16 22599.70 20499.56 130
test_fmvsmvis_n_192099.26 4199.49 1798.54 24699.66 6896.97 24898.00 20599.85 1899.24 7799.92 899.50 6999.39 1299.95 2699.89 399.98 1298.71 372
mvs5depth99.30 3599.59 1298.44 26099.65 6995.35 32499.82 399.94 299.83 799.42 11199.94 298.13 11699.96 1499.63 3699.96 28100.00 1
fmvsm_l_conf0.5_n_a99.19 5399.27 4998.94 16299.65 6997.05 24397.80 23999.76 4098.70 14899.78 4099.11 17298.79 4399.95 2699.85 699.96 2899.83 33
WB-MVS98.52 18398.55 15698.43 26199.65 6995.59 30898.52 12898.77 34199.65 2699.52 8899.00 21094.34 32199.93 5498.65 11598.83 37699.76 56
CP-MVSNet99.21 4999.09 7699.56 2799.65 6998.96 7899.13 5999.34 20399.42 5799.33 13099.26 13097.01 21199.94 4298.74 10899.93 5699.79 44
HPM-MVScopyleft98.79 12398.53 16099.59 2099.65 6999.29 2599.16 5599.43 16696.74 32698.61 26598.38 33698.62 6099.87 13496.47 29799.67 21899.59 107
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
RPSCF98.62 16198.36 19199.42 6999.65 6999.42 1198.55 12499.57 9597.72 23998.90 21899.26 13096.12 26299.52 39195.72 33799.71 19799.32 254
NormalMVS98.26 21997.97 24699.15 12299.64 7597.83 17998.28 15999.43 16699.24 7798.80 24098.85 24889.76 38399.94 4298.04 15699.67 21899.68 71
lecture99.25 4299.12 7199.62 1099.64 7599.40 1298.89 8899.51 12399.19 8999.37 12199.25 13598.36 8399.88 11598.23 14199.67 21899.59 107
fmvsm_l_conf0.5_n99.21 4999.28 4899.02 14999.64 7597.28 22397.82 23599.76 4098.73 14599.82 3499.09 18098.81 3999.95 2699.86 499.96 2899.83 33
test_fmvsmconf_n99.44 2099.48 1999.31 9599.64 7598.10 14697.68 25899.84 2299.29 7399.92 899.57 5099.60 599.96 1499.74 2799.98 1299.89 16
TSAR-MVS + MP.98.63 15898.49 17099.06 14299.64 7597.90 17398.51 13398.94 30696.96 31199.24 15498.89 24197.83 14399.81 22396.88 25499.49 28799.48 179
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 11798.72 12299.12 12599.64 7598.54 11197.98 21399.68 6197.62 24599.34 12899.18 15397.54 17299.77 26197.79 17899.74 17799.04 318
Elysia99.15 5999.14 6999.18 11499.63 8197.92 17098.50 13599.43 16699.67 2199.70 5299.13 16896.66 23699.98 499.54 4499.96 2899.64 84
StellarMVS99.15 5999.14 6999.18 11499.63 8197.92 17098.50 13599.43 16699.67 2199.70 5299.13 16896.66 23699.98 499.54 4499.96 2899.64 84
KD-MVS_self_test99.25 4299.18 6099.44 6699.63 8199.06 7198.69 10799.54 11399.31 7099.62 7099.53 6597.36 18999.86 14399.24 7099.71 19799.39 220
EU-MVSNet97.66 27998.50 16595.13 43299.63 8185.84 46398.35 15598.21 37598.23 18899.54 7999.46 8195.02 30199.68 31698.24 13999.87 9899.87 22
HyFIR lowres test97.19 31796.60 34198.96 15999.62 8597.28 22395.17 43099.50 12694.21 40899.01 19098.32 34486.61 40399.99 297.10 23299.84 11299.60 100
fmvsm_l_conf0.5_n_999.32 3499.43 2598.98 15699.59 8697.18 23497.44 29899.83 2599.56 4099.91 1299.34 10999.36 1399.93 5499.83 1099.98 1299.85 30
fmvsm_l_conf0.5_n_399.45 1999.48 1999.34 8499.59 8698.21 13797.82 23599.84 2299.41 5999.92 899.41 9499.51 899.95 2699.84 999.97 2199.87 22
MED-MVS test99.45 6499.58 8898.93 8098.68 10899.60 7996.46 33999.53 8398.77 26899.83 19396.67 27499.64 22999.58 116
MED-MVS98.90 10098.72 12299.45 6499.58 8898.93 8098.68 10899.60 7998.14 20799.53 8398.77 26897.87 14099.83 19396.67 27499.64 22999.58 116
TestfortrainingZip a98.95 9398.72 12299.64 999.58 8899.32 2298.68 10899.60 7996.46 33999.53 8398.77 26897.87 14099.83 19398.39 13299.64 22999.77 50
FE-MVSNET98.59 16698.50 16598.87 17299.58 8897.30 22198.08 18799.74 4496.94 31398.97 20099.10 17596.94 21599.74 28097.33 21599.86 10599.55 137
mmtdpeth99.30 3599.42 2698.92 16799.58 8896.89 25599.48 1399.92 799.92 298.26 30399.80 1198.33 8999.91 7499.56 4199.95 3899.97 4
ACMMP_NAP98.75 13098.48 17199.57 2299.58 8899.29 2597.82 23599.25 24596.94 31398.78 24299.12 17198.02 12399.84 17497.13 23099.67 21899.59 107
nrg03099.40 2799.35 3599.54 3299.58 8899.13 6198.98 7699.48 13599.68 2099.46 10299.26 13098.62 6099.73 28799.17 7599.92 6999.76 56
VDDNet98.21 22697.95 24799.01 15099.58 8897.74 19299.01 7197.29 40499.67 2198.97 20099.50 6990.45 37899.80 23197.88 17199.20 33699.48 179
COLMAP_ROBcopyleft96.50 1098.99 8698.85 10999.41 7199.58 8899.10 6698.74 9899.56 10499.09 10999.33 13099.19 14998.40 8099.72 29595.98 32499.76 17299.42 207
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 3299.45 2498.99 15299.57 9797.73 19497.93 21999.83 2599.22 8099.93 699.30 11899.42 1199.96 1499.85 699.99 599.29 263
ZNCC-MVS98.68 14998.40 18399.54 3299.57 9799.21 3498.46 14399.29 23297.28 28698.11 31598.39 33498.00 12599.87 13496.86 25799.64 22999.55 137
MSP-MVS98.40 19598.00 24199.61 1499.57 9799.25 3098.57 12299.35 19797.55 25699.31 13897.71 38494.61 31499.88 11596.14 31899.19 33999.70 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 21098.39 18698.13 29599.57 9795.54 31197.78 24199.49 13397.37 27799.19 16297.65 38898.96 3099.49 40096.50 29698.99 36499.34 245
MP-MVScopyleft98.46 18998.09 23099.54 3299.57 9799.22 3398.50 13599.19 26097.61 24897.58 35498.66 29697.40 18699.88 11594.72 36399.60 24699.54 143
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LPG-MVS_test98.71 13498.46 17599.47 6199.57 9798.97 7498.23 16599.48 13596.60 33199.10 17299.06 18398.71 5199.83 19395.58 34499.78 15199.62 90
LGP-MVS_train99.47 6199.57 9798.97 7499.48 13596.60 33199.10 17299.06 18398.71 5199.83 19395.58 34499.78 15199.62 90
IS-MVSNet98.19 22997.90 25599.08 13499.57 9797.97 16499.31 3198.32 37199.01 12198.98 19699.03 19491.59 36699.79 24495.49 34699.80 14099.48 179
viewdifsd2359ckpt1198.84 11199.04 8198.24 28499.56 10595.51 31397.38 30399.70 5399.16 9499.57 7299.40 9798.26 9799.71 29698.55 12499.82 12399.50 161
viewmsd2359difaftdt98.84 11199.04 8198.24 28499.56 10595.51 31397.38 30399.70 5399.16 9499.57 7299.40 9798.26 9799.71 29698.55 12499.82 12399.50 161
dcpmvs_298.78 12599.11 7297.78 31899.56 10593.67 38999.06 6699.86 1699.50 4499.66 6199.26 13097.21 20099.99 298.00 16199.91 7899.68 71
test_040298.76 12998.71 12798.93 16499.56 10598.14 14298.45 14599.34 20399.28 7498.95 20698.91 23298.34 8899.79 24495.63 34199.91 7898.86 350
EPP-MVSNet98.30 21398.04 23799.07 13699.56 10597.83 17999.29 3798.07 38299.03 11998.59 26999.13 16892.16 36099.90 8196.87 25599.68 21299.49 168
ACMMPcopyleft98.75 13098.50 16599.52 4599.56 10599.16 4998.87 8999.37 18797.16 30198.82 23699.01 20697.71 15499.87 13496.29 30999.69 20799.54 143
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 19099.55 11196.59 26897.79 24099.82 3098.21 19199.81 3799.53 6598.46 7699.84 17499.70 3399.97 2199.90 15
fmvsm_s_conf0.5_n99.09 7399.26 5298.61 22799.55 11196.09 29197.74 25199.81 3198.55 16699.85 2799.55 5898.60 6299.84 17499.69 3599.98 1299.89 16
FMVSNet199.17 5499.17 6199.17 11699.55 11198.24 13199.20 4999.44 16099.21 8299.43 10799.55 5897.82 14699.86 14398.42 13199.89 9299.41 210
Vis-MVSNet (Re-imp)97.46 29397.16 30398.34 27399.55 11196.10 28898.94 8198.44 36598.32 17998.16 30998.62 30588.76 39099.73 28793.88 38999.79 14699.18 297
ACMM96.08 1298.91 9898.73 12099.48 5799.55 11199.14 5898.07 19199.37 18797.62 24599.04 18698.96 22298.84 3799.79 24497.43 21099.65 22799.49 168
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_fmvs298.70 13998.97 9297.89 31199.54 11694.05 36698.55 12499.92 796.78 32499.72 4899.78 1396.60 24099.67 32099.91 299.90 8699.94 10
mPP-MVS98.64 15698.34 19499.54 3299.54 11699.17 4598.63 11599.24 25097.47 26498.09 31798.68 29197.62 16399.89 9796.22 31299.62 23999.57 124
XVG-ACMP-BASELINE98.56 17098.34 19499.22 11099.54 11698.59 10597.71 25499.46 14897.25 28998.98 19698.99 21297.54 17299.84 17495.88 32799.74 17799.23 279
viewmacassd2359aftdt98.86 10898.87 10398.83 17799.53 11997.32 22097.70 25699.64 7198.22 18999.25 15299.27 12498.40 8099.61 35697.98 16399.87 9899.55 137
region2R98.69 14398.40 18399.54 3299.53 11999.17 4598.52 12899.31 21697.46 26998.44 28898.51 31997.83 14399.88 11596.46 29899.58 25599.58 116
PGM-MVS98.66 15398.37 19099.55 2999.53 11999.18 4498.23 16599.49 13397.01 31098.69 25398.88 24298.00 12599.89 9795.87 33099.59 25099.58 116
Patchmatch-RL test97.26 31097.02 31197.99 30799.52 12295.53 31296.13 39099.71 4897.47 26499.27 14499.16 15984.30 42499.62 34997.89 16899.77 15798.81 358
ACMMPR98.70 13998.42 18199.54 3299.52 12299.14 5898.52 12899.31 21697.47 26498.56 27598.54 31497.75 15299.88 11596.57 28599.59 25099.58 116
fmvsm_s_conf0.5_n_999.17 5499.38 3098.53 24899.51 12495.82 30397.62 26999.78 3799.72 1599.90 1499.48 7698.66 5599.89 9799.85 699.93 5699.89 16
AstraMVS98.16 23598.07 23598.41 26399.51 12495.86 30098.00 20595.14 44598.97 12599.43 10799.24 13793.25 33899.84 17499.21 7199.87 9899.54 143
fmvsm_s_conf0.5_n_899.13 6799.26 5298.74 20399.51 12496.44 28097.65 26499.65 6999.66 2499.78 4099.48 7697.92 13399.93 5499.72 3099.95 3899.87 22
GST-MVS98.61 16298.30 20199.52 4599.51 12499.20 4098.26 16399.25 24597.44 27298.67 25698.39 33497.68 15599.85 15696.00 32299.51 27799.52 155
Anonymous2023120698.21 22698.21 21498.20 28999.51 12495.43 32298.13 17799.32 21196.16 35298.93 21498.82 25896.00 26799.83 19397.32 21699.73 18099.36 238
ACMP95.32 1598.41 19398.09 23099.36 7599.51 12498.79 9197.68 25899.38 18395.76 36898.81 23898.82 25898.36 8399.82 20694.75 36099.77 15799.48 179
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LuminaMVS98.39 20198.20 21598.98 15699.50 13097.49 20697.78 24197.69 39198.75 14499.49 9699.25 13592.30 35899.94 4299.14 7699.88 9499.50 161
DVP-MVScopyleft98.77 12898.52 16199.52 4599.50 13099.21 3498.02 20198.84 33097.97 21799.08 17499.02 19597.61 16599.88 11596.99 24199.63 23699.48 179
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 13099.23 3298.02 20199.32 21199.88 11596.99 24199.63 23699.68 71
test072699.50 13099.21 3498.17 17399.35 19797.97 21799.26 14899.06 18397.61 165
AllTest98.44 19198.20 21599.16 11999.50 13098.55 10898.25 16499.58 8896.80 32298.88 22599.06 18397.65 15899.57 37294.45 37099.61 24499.37 231
TestCases99.16 11999.50 13098.55 10899.58 8896.80 32298.88 22599.06 18397.65 15899.57 37294.45 37099.61 24499.37 231
XVG-OURS98.53 17998.34 19499.11 12799.50 13098.82 9095.97 39699.50 12697.30 28499.05 18498.98 21799.35 1499.32 43095.72 33799.68 21299.18 297
EG-PatchMatch MVS98.99 8699.01 8698.94 16299.50 13097.47 20998.04 19699.59 8698.15 20699.40 11699.36 10498.58 6899.76 26798.78 10399.68 21299.59 107
fmvsm_s_conf0.5_n_299.14 6399.31 4398.63 22299.49 13896.08 29397.38 30399.81 3199.48 4599.84 3099.57 5098.46 7699.89 9799.82 1299.97 2199.91 13
SED-MVS98.91 9898.72 12299.49 5599.49 13899.17 4598.10 18499.31 21698.03 21399.66 6199.02 19598.36 8399.88 11596.91 24799.62 23999.41 210
IU-MVS99.49 13899.15 5398.87 32192.97 42699.41 11396.76 26499.62 23999.66 78
test_241102_ONE99.49 13899.17 4599.31 21697.98 21699.66 6198.90 23598.36 8399.48 403
UA-Net99.47 1799.40 2899.70 299.49 13899.29 2599.80 499.72 4699.82 899.04 18699.81 898.05 12299.96 1498.85 9999.99 599.86 28
HFP-MVS98.71 13498.44 17899.51 4999.49 13899.16 4998.52 12899.31 21697.47 26498.58 27198.50 32397.97 12999.85 15696.57 28599.59 25099.53 152
VPA-MVSNet99.30 3599.30 4699.28 9799.49 13898.36 12599.00 7399.45 15299.63 2999.52 8899.44 8698.25 9999.88 11599.09 8199.84 11299.62 90
XVG-OURS-SEG-HR98.49 18698.28 20499.14 12399.49 13898.83 8896.54 36199.48 13597.32 28299.11 16998.61 30799.33 1599.30 43396.23 31198.38 40099.28 266
fmvsm_s_conf0.5_n_1199.21 4999.34 3798.80 18399.48 14696.56 27397.97 21799.69 5599.63 2999.84 3099.54 6498.21 10699.94 4299.76 2399.95 3899.88 20
114514_t96.50 35095.77 35998.69 21199.48 14697.43 21397.84 23499.55 10881.42 47396.51 41398.58 31195.53 28799.67 32093.41 40299.58 25598.98 328
IterMVS-LS98.55 17498.70 13098.09 29699.48 14694.73 34697.22 32399.39 18198.97 12599.38 11999.31 11796.00 26799.93 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 5999.27 4998.78 19099.47 14996.56 27397.75 25099.71 4899.60 3699.74 4799.44 8697.96 13099.95 2699.86 499.94 5099.82 36
fmvsm_s_conf0.5_n_599.07 7999.10 7498.99 15299.47 14997.22 22897.40 30099.83 2597.61 24899.85 2799.30 11898.80 4199.95 2699.71 3299.90 8699.78 47
v899.01 8399.16 6398.57 23499.47 14996.31 28598.90 8499.47 14499.03 11999.52 8899.57 5096.93 21699.81 22399.60 3799.98 1299.60 100
SSC-MVS3.298.53 17998.79 11497.74 32599.46 15293.62 39296.45 36799.34 20399.33 6798.93 21498.70 28797.90 13499.90 8199.12 7899.92 6999.69 70
fmvsm_s_conf0.5_n_399.22 4899.37 3398.78 19099.46 15296.58 27197.65 26499.72 4699.47 4899.86 2499.50 6998.94 3199.89 9799.75 2699.97 2199.86 28
XVS98.72 13398.45 17699.53 3999.46 15299.21 3498.65 11399.34 20398.62 15597.54 35898.63 30397.50 17899.83 19396.79 26099.53 27299.56 130
X-MVStestdata94.32 39992.59 41899.53 3999.46 15299.21 3498.65 11399.34 20398.62 15597.54 35845.85 47897.50 17899.83 19396.79 26099.53 27299.56 130
test20.0398.78 12598.77 11798.78 19099.46 15297.20 23197.78 24199.24 25099.04 11899.41 11398.90 23597.65 15899.76 26797.70 18999.79 14699.39 220
guyue98.01 24797.93 25198.26 28099.45 15795.48 31798.08 18796.24 42898.89 13699.34 12899.14 16691.32 37099.82 20699.07 8299.83 11999.48 179
CSCG98.68 14998.50 16599.20 11199.45 15798.63 10098.56 12399.57 9597.87 22798.85 23098.04 36597.66 15799.84 17496.72 26999.81 12999.13 307
GeoE99.05 8098.99 9099.25 10599.44 15998.35 12698.73 10299.56 10498.42 17298.91 21798.81 26198.94 3199.91 7498.35 13499.73 18099.49 168
v14898.45 19098.60 15098.00 30699.44 15994.98 33897.44 29899.06 28698.30 18199.32 13698.97 21996.65 23899.62 34998.37 13399.85 10799.39 220
v1098.97 9099.11 7298.55 24199.44 15996.21 28798.90 8499.55 10898.73 14599.48 9799.60 4696.63 23999.83 19399.70 3399.99 599.61 98
V4298.78 12598.78 11698.76 19799.44 15997.04 24498.27 16299.19 26097.87 22799.25 15299.16 15996.84 22099.78 25599.21 7199.84 11299.46 189
MDA-MVSNet-bldmvs97.94 25397.91 25498.06 30199.44 15994.96 33996.63 35799.15 27698.35 17598.83 23399.11 17294.31 32299.85 15696.60 28298.72 38299.37 231
viewdifsd2359ckpt0798.71 13498.86 10798.26 28099.43 16495.65 30797.20 32499.66 6599.20 8499.29 14099.01 20698.29 9299.73 28797.92 16799.75 17699.39 220
casdiffmvs_mvgpermissive99.12 7099.16 6398.99 15299.43 16497.73 19498.00 20599.62 7599.22 8099.55 7799.22 14398.93 3399.75 27598.66 11499.81 12999.50 161
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 10099.01 8698.57 23499.42 16696.59 26898.13 17799.66 6599.09 10999.30 13999.02 19598.79 4399.89 9797.87 17399.80 14099.23 279
test111196.49 35196.82 32595.52 42599.42 16687.08 46099.22 4687.14 47699.11 9999.46 10299.58 4888.69 39199.86 14398.80 10199.95 3899.62 90
v2v48298.56 17098.62 14598.37 27099.42 16695.81 30497.58 27799.16 27197.90 22599.28 14299.01 20695.98 27299.79 24499.33 6099.90 8699.51 158
OPM-MVS98.56 17098.32 19999.25 10599.41 16998.73 9697.13 33199.18 26497.10 30498.75 24898.92 23098.18 10999.65 34096.68 27399.56 26299.37 231
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PMMVS298.07 24198.08 23398.04 30499.41 16994.59 35294.59 44899.40 17997.50 26198.82 23698.83 25596.83 22299.84 17497.50 20499.81 12999.71 63
E298.70 13998.68 13398.73 20599.40 17197.10 24197.48 29099.57 9598.09 21099.00 19199.20 14697.90 13499.67 32097.73 18799.77 15799.43 202
E398.69 14398.68 13398.73 20599.40 17197.10 24197.48 29099.57 9598.09 21099.00 19199.20 14697.90 13499.67 32097.73 18799.77 15799.43 202
test_one_060199.39 17399.20 4099.31 21698.49 16898.66 25899.02 19597.64 161
mvsany_test398.87 10598.92 9698.74 20399.38 17496.94 25298.58 12199.10 28196.49 33699.96 499.81 898.18 10999.45 41198.97 9199.79 14699.83 33
patch_mono-298.51 18498.63 14398.17 29299.38 17494.78 34397.36 30899.69 5598.16 20198.49 28499.29 12197.06 20699.97 798.29 13899.91 7899.76 56
test250692.39 43091.89 43293.89 44699.38 17482.28 47799.32 2766.03 48499.08 11398.77 24599.57 5066.26 47199.84 17498.71 11199.95 3899.54 143
ECVR-MVScopyleft96.42 35396.61 33995.85 41799.38 17488.18 45599.22 4686.00 47899.08 11399.36 12499.57 5088.47 39699.82 20698.52 12699.95 3899.54 143
casdiffmvspermissive98.95 9399.00 8898.81 18199.38 17497.33 21897.82 23599.57 9599.17 9399.35 12699.17 15798.35 8799.69 30798.46 12899.73 18099.41 210
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 9299.02 8498.76 19799.38 17497.26 22598.49 13899.50 12698.86 13999.19 16299.06 18398.23 10199.69 30798.71 11199.76 17299.33 251
TranMVSNet+NR-MVSNet99.17 5499.07 7999.46 6399.37 18098.87 8598.39 15199.42 17299.42 5799.36 12499.06 18398.38 8299.95 2698.34 13599.90 8699.57 124
fmvsm_s_conf0.5_n_699.08 7799.21 5898.69 21199.36 18196.51 27597.62 26999.68 6198.43 17199.85 2799.10 17599.12 2399.88 11599.77 2299.92 6999.67 76
tttt051795.64 37894.98 38897.64 33899.36 18193.81 38498.72 10390.47 47098.08 21298.67 25698.34 34173.88 45799.92 6597.77 18099.51 27799.20 289
test_part299.36 18199.10 6699.05 184
v114498.60 16498.66 13898.41 26399.36 18195.90 29897.58 27799.34 20397.51 26099.27 14499.15 16396.34 25399.80 23199.47 5499.93 5699.51 158
CP-MVS98.70 13998.42 18199.52 4599.36 18199.12 6398.72 10399.36 19197.54 25898.30 29798.40 33397.86 14299.89 9796.53 29499.72 18899.56 130
diffmvs_AUTHOR98.50 18598.59 15298.23 28799.35 18695.48 31796.61 35899.60 7998.37 17398.90 21899.00 21097.37 18899.76 26798.22 14299.85 10799.46 189
Test_1112_low_res96.99 33296.55 34398.31 27699.35 18695.47 32095.84 40899.53 11791.51 44396.80 40098.48 32691.36 36999.83 19396.58 28399.53 27299.62 90
DeepC-MVS97.60 498.97 9098.93 9599.10 12999.35 18697.98 16398.01 20499.46 14897.56 25499.54 7999.50 6998.97 2999.84 17498.06 15499.92 6999.49 168
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 30996.86 32198.58 23199.34 18996.32 28496.75 35099.58 8893.14 42496.89 39597.48 39892.11 36199.86 14396.91 24799.54 26899.57 124
reproduce_model99.15 5998.97 9299.67 499.33 19099.44 1098.15 17599.47 14499.12 9899.52 8899.32 11698.31 9099.90 8197.78 17999.73 18099.66 78
MVSMamba_PlusPlus98.83 11498.98 9198.36 27199.32 19196.58 27198.90 8499.41 17699.75 1198.72 25199.50 6996.17 25899.94 4299.27 6599.78 15198.57 388
fmvsm_s_conf0.5_n_499.01 8399.22 5698.38 26799.31 19295.48 31797.56 27999.73 4598.87 13799.75 4599.27 12498.80 4199.86 14399.80 1799.90 8699.81 40
SF-MVS98.53 17998.27 20799.32 9299.31 19298.75 9298.19 16999.41 17696.77 32598.83 23398.90 23597.80 14899.82 20695.68 34099.52 27599.38 229
CPTT-MVS97.84 26897.36 29299.27 10099.31 19298.46 11698.29 15899.27 23994.90 39297.83 33898.37 33794.90 30399.84 17493.85 39199.54 26899.51 158
UnsupCasMVSNet_eth97.89 25797.60 27898.75 19999.31 19297.17 23697.62 26999.35 19798.72 14798.76 24798.68 29192.57 35599.74 28097.76 18495.60 46299.34 245
fmvsm_s_conf0.5_n_798.83 11499.04 8198.20 28999.30 19694.83 34197.23 31999.36 19198.64 15099.84 3099.43 8998.10 11899.91 7499.56 4199.96 2899.87 22
pmmvs-eth3d98.47 18898.34 19498.86 17499.30 19697.76 19097.16 32999.28 23695.54 37499.42 11199.19 14997.27 19599.63 34697.89 16899.97 2199.20 289
mamv499.44 2099.39 2999.58 2199.30 19699.74 299.04 6999.81 3199.77 1099.82 3499.57 5097.82 14699.98 499.53 4899.89 9299.01 322
viewcassd2359sk1198.55 17498.51 16298.67 21499.29 19996.99 24797.39 30199.54 11397.73 23798.81 23899.08 18197.55 17099.66 33397.52 20399.67 21899.36 238
SymmetryMVS98.05 24397.71 26899.09 13399.29 19997.83 17998.28 15997.64 39699.24 7798.80 24098.85 24889.76 38399.94 4298.04 15699.50 28599.49 168
Anonymous2023121199.27 3999.27 4999.26 10299.29 19998.18 13899.49 1299.51 12399.70 1699.80 3899.68 2696.84 22099.83 19399.21 7199.91 7899.77 50
viewmanbaseed2359cas98.58 16898.54 15898.70 20999.28 20297.13 24097.47 29499.55 10897.55 25698.96 20598.92 23097.77 15099.59 36397.59 19799.77 15799.39 220
UnsupCasMVSNet_bld97.30 30796.92 31798.45 25899.28 20296.78 26296.20 38499.27 23995.42 37898.28 30198.30 34593.16 34199.71 29694.99 35497.37 43898.87 349
EC-MVSNet99.09 7399.05 8099.20 11199.28 20298.93 8099.24 4599.84 2299.08 11398.12 31498.37 33798.72 5099.90 8199.05 8599.77 15798.77 366
mamba_040898.80 12198.88 10198.55 24199.27 20596.50 27698.00 20599.60 7998.93 13099.22 15798.84 25398.59 6399.89 9797.74 18599.72 18899.27 267
SSM_0407298.80 12198.88 10198.56 23999.27 20596.50 27698.00 20599.60 7998.93 13099.22 15798.84 25398.59 6399.90 8197.74 18599.72 18899.27 267
SSM_040798.86 10898.96 9498.55 24199.27 20596.50 27698.04 19699.66 6599.09 10999.22 15799.02 19598.79 4399.87 13497.87 17399.72 18899.27 267
reproduce-ours99.09 7398.90 9899.67 499.27 20599.49 698.00 20599.42 17299.05 11699.48 9799.27 12498.29 9299.89 9797.61 19499.71 19799.62 90
our_new_method99.09 7398.90 9899.67 499.27 20599.49 698.00 20599.42 17299.05 11699.48 9799.27 12498.29 9299.89 9797.61 19499.71 19799.62 90
DPE-MVScopyleft98.59 16698.26 20899.57 2299.27 20599.15 5397.01 33499.39 18197.67 24199.44 10698.99 21297.53 17499.89 9795.40 34899.68 21299.66 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 26798.18 22096.87 38699.27 20591.16 43595.53 41899.25 24599.10 10699.41 11399.35 10593.10 34399.96 1498.65 11599.94 5099.49 168
v119298.60 16498.66 13898.41 26399.27 20595.88 29997.52 28499.36 19197.41 27399.33 13099.20 14696.37 25199.82 20699.57 3999.92 6999.55 137
N_pmnet97.63 28197.17 30298.99 15299.27 20597.86 17695.98 39593.41 45995.25 38399.47 10198.90 23595.63 28499.85 15696.91 24799.73 18099.27 267
viewdifsd2359ckpt1398.39 20198.29 20398.70 20999.26 21497.19 23297.51 28699.48 13596.94 31398.58 27198.82 25897.47 18399.55 37997.21 22299.33 31299.34 245
FPMVS93.44 41692.23 42397.08 37499.25 21597.86 17695.61 41597.16 40892.90 42893.76 46198.65 29875.94 45595.66 47579.30 47397.49 43197.73 439
ME-MVS98.61 16298.33 19899.44 6699.24 21698.93 8097.45 29699.06 28698.14 20799.06 17698.77 26896.97 21499.82 20696.67 27499.64 22999.58 116
new-patchmatchnet98.35 20498.74 11897.18 36999.24 21692.23 41796.42 37199.48 13598.30 18199.69 5699.53 6597.44 18499.82 20698.84 10099.77 15799.49 168
MCST-MVS98.00 24897.63 27699.10 12999.24 21698.17 13996.89 34398.73 34995.66 36997.92 32997.70 38697.17 20199.66 33396.18 31699.23 33199.47 187
UniMVSNet (Re)98.87 10598.71 12799.35 8199.24 21698.73 9697.73 25399.38 18398.93 13099.12 16898.73 27796.77 22899.86 14398.63 11799.80 14099.46 189
jason97.45 29597.35 29397.76 32299.24 21693.93 37895.86 40598.42 36794.24 40798.50 28398.13 35594.82 30799.91 7497.22 22199.73 18099.43 202
jason: jason.
IterMVS97.73 27398.11 22996.57 39699.24 21690.28 44495.52 42099.21 25498.86 13999.33 13099.33 11293.11 34299.94 4298.49 12799.94 5099.48 179
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v124098.55 17498.62 14598.32 27499.22 22295.58 31097.51 28699.45 15297.16 30199.45 10599.24 13796.12 26299.85 15699.60 3799.88 9499.55 137
ITE_SJBPF98.87 17299.22 22298.48 11599.35 19797.50 26198.28 30198.60 30997.64 16199.35 42693.86 39099.27 32398.79 364
h-mvs3397.77 27197.33 29599.10 12999.21 22497.84 17898.35 15598.57 35999.11 9998.58 27199.02 19588.65 39499.96 1498.11 14996.34 45499.49 168
v14419298.54 17798.57 15498.45 25899.21 22495.98 29697.63 26899.36 19197.15 30399.32 13699.18 15395.84 27999.84 17499.50 5199.91 7899.54 143
APDe-MVScopyleft98.99 8698.79 11499.60 1699.21 22499.15 5398.87 8999.48 13597.57 25299.35 12699.24 13797.83 14399.89 9797.88 17199.70 20499.75 60
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DP-MVS98.93 9698.81 11399.28 9799.21 22498.45 11798.46 14399.33 20999.63 2999.48 9799.15 16397.23 19899.75 27597.17 22499.66 22699.63 89
SR-MVS-dyc-post98.81 11998.55 15699.57 2299.20 22899.38 1398.48 14199.30 22498.64 15098.95 20698.96 22297.49 18199.86 14396.56 28999.39 30399.45 194
RE-MVS-def98.58 15399.20 22899.38 1398.48 14199.30 22498.64 15098.95 20698.96 22297.75 15296.56 28999.39 30399.45 194
v192192098.54 17798.60 15098.38 26799.20 22895.76 30697.56 27999.36 19197.23 29599.38 11999.17 15796.02 26599.84 17499.57 3999.90 8699.54 143
thisisatest053095.27 38594.45 39697.74 32599.19 23194.37 35697.86 23190.20 47197.17 30098.22 30497.65 38873.53 45899.90 8196.90 25299.35 30998.95 334
Anonymous2024052998.93 9698.87 10399.12 12599.19 23198.22 13699.01 7198.99 30499.25 7699.54 7999.37 10097.04 20799.80 23197.89 16899.52 27599.35 243
APD-MVS_3200maxsize98.84 11198.61 14999.53 3999.19 23199.27 2898.49 13899.33 20998.64 15099.03 18998.98 21797.89 13899.85 15696.54 29399.42 30099.46 189
HQP_MVS97.99 25197.67 27098.93 16499.19 23197.65 19897.77 24499.27 23998.20 19597.79 34197.98 36994.90 30399.70 30394.42 37299.51 27799.45 194
plane_prior799.19 23197.87 175
ab-mvs98.41 19398.36 19198.59 23099.19 23197.23 22699.32 2798.81 33597.66 24298.62 26399.40 9796.82 22399.80 23195.88 32799.51 27798.75 369
F-COLMAP97.30 30796.68 33499.14 12399.19 23198.39 11997.27 31899.30 22492.93 42796.62 40698.00 36795.73 28299.68 31692.62 41898.46 39999.35 243
viewdifsd2359ckpt0998.13 23697.92 25298.77 19599.18 23897.35 21697.29 31499.53 11795.81 36698.09 31798.47 32796.34 25399.66 33397.02 23799.51 27799.29 263
SR-MVS98.71 13498.43 17999.57 2299.18 23899.35 1798.36 15499.29 23298.29 18498.88 22598.85 24897.53 17499.87 13496.14 31899.31 31699.48 179
UniMVSNet_NR-MVSNet98.86 10898.68 13399.40 7399.17 24098.74 9397.68 25899.40 17999.14 9799.06 17698.59 31096.71 23499.93 5498.57 12099.77 15799.53 152
LF4IMVS97.90 25597.69 26998.52 24999.17 24097.66 19797.19 32899.47 14496.31 34697.85 33798.20 35296.71 23499.52 39194.62 36499.72 18898.38 405
SMA-MVScopyleft98.40 19598.03 23899.51 4999.16 24299.21 3498.05 19499.22 25394.16 40998.98 19699.10 17597.52 17699.79 24496.45 29999.64 22999.53 152
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 11798.63 14399.39 7499.16 24298.74 9397.54 28299.25 24598.84 14299.06 17698.76 27496.76 23099.93 5498.57 12099.77 15799.50 161
NR-MVSNet98.95 9398.82 11199.36 7599.16 24298.72 9899.22 4699.20 25699.10 10699.72 4898.76 27496.38 25099.86 14398.00 16199.82 12399.50 161
MVS_111021_LR98.30 21398.12 22898.83 17799.16 24298.03 15896.09 39299.30 22497.58 25198.10 31698.24 34898.25 9999.34 42796.69 27299.65 22799.12 308
DSMNet-mixed97.42 29897.60 27896.87 38699.15 24691.46 42498.54 12699.12 27892.87 42997.58 35499.63 4096.21 25799.90 8195.74 33699.54 26899.27 267
D2MVS97.84 26897.84 25997.83 31499.14 24794.74 34596.94 33898.88 31995.84 36598.89 22198.96 22294.40 31999.69 30797.55 19899.95 3899.05 314
pmmvs597.64 28097.49 28498.08 29999.14 24795.12 33496.70 35399.05 29093.77 41698.62 26398.83 25593.23 33999.75 27598.33 13799.76 17299.36 238
SPE-MVS-test99.13 6799.09 7699.26 10299.13 24998.97 7499.31 3199.88 1499.44 5498.16 30998.51 31998.64 5799.93 5498.91 9499.85 10798.88 348
VDD-MVS98.56 17098.39 18699.07 13699.13 24998.07 15398.59 12097.01 41199.59 3799.11 16999.27 12494.82 30799.79 24498.34 13599.63 23699.34 245
save fliter99.11 25197.97 16496.53 36399.02 29898.24 187
APD-MVScopyleft98.10 23797.67 27099.42 6999.11 25198.93 8097.76 24799.28 23694.97 39098.72 25198.77 26897.04 20799.85 15693.79 39299.54 26899.49 168
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EI-MVSNet-UG-set98.69 14398.71 12798.62 22499.10 25396.37 28297.23 31998.87 32199.20 8499.19 16298.99 21297.30 19299.85 15698.77 10699.79 14699.65 83
EI-MVSNet98.40 19598.51 16298.04 30499.10 25394.73 34697.20 32498.87 32198.97 12599.06 17699.02 19596.00 26799.80 23198.58 11899.82 12399.60 100
CVMVSNet96.25 35997.21 30193.38 45399.10 25380.56 48197.20 32498.19 37896.94 31399.00 19199.02 19589.50 38799.80 23196.36 30599.59 25099.78 47
EI-MVSNet-Vis-set98.68 14998.70 13098.63 22299.09 25696.40 28197.23 31998.86 32699.20 8499.18 16698.97 21997.29 19499.85 15698.72 11099.78 15199.64 84
HPM-MVS++copyleft98.10 23797.64 27599.48 5799.09 25699.13 6197.52 28498.75 34697.46 26996.90 39497.83 37996.01 26699.84 17495.82 33499.35 30999.46 189
DP-MVS Recon97.33 30596.92 31798.57 23499.09 25697.99 16096.79 34699.35 19793.18 42397.71 34598.07 36395.00 30299.31 43193.97 38599.13 34798.42 402
MVS_111021_HR98.25 22298.08 23398.75 19999.09 25697.46 21095.97 39699.27 23997.60 25097.99 32798.25 34798.15 11599.38 42296.87 25599.57 25999.42 207
BP-MVS197.40 30096.97 31398.71 20899.07 26096.81 25898.34 15797.18 40698.58 16198.17 30698.61 30784.01 42699.94 4298.97 9199.78 15199.37 231
9.1497.78 26199.07 26097.53 28399.32 21195.53 37598.54 27998.70 28797.58 16799.76 26794.32 37799.46 290
PAPM_NR96.82 33996.32 35098.30 27799.07 26096.69 26697.48 29098.76 34395.81 36696.61 40796.47 42494.12 32899.17 44490.82 44597.78 42599.06 313
TAMVS98.24 22398.05 23698.80 18399.07 26097.18 23497.88 22798.81 33596.66 33099.17 16799.21 14494.81 30999.77 26196.96 24599.88 9499.44 198
CLD-MVS97.49 29197.16 30398.48 25599.07 26097.03 24594.71 44199.21 25494.46 40198.06 32097.16 41097.57 16899.48 40394.46 36999.78 15198.95 334
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 7499.24 10799.06 26599.15 5399.36 2299.88 1499.36 6598.21 30598.46 32898.68 5499.93 5499.03 8799.85 10798.64 381
thres100view90094.19 40293.67 40795.75 42099.06 26591.35 42898.03 19894.24 45498.33 17797.40 37094.98 45479.84 44299.62 34983.05 46698.08 41696.29 461
thres600view794.45 39793.83 40496.29 40499.06 26591.53 42397.99 21294.24 45498.34 17697.44 36895.01 45279.84 44299.67 32084.33 46498.23 40597.66 442
plane_prior199.05 268
YYNet197.60 28297.67 27097.39 36299.04 26993.04 40195.27 42798.38 37097.25 28998.92 21698.95 22695.48 29199.73 28796.99 24198.74 38099.41 210
MDA-MVSNet_test_wron97.60 28297.66 27397.41 36199.04 26993.09 39795.27 42798.42 36797.26 28898.88 22598.95 22695.43 29299.73 28797.02 23798.72 38299.41 210
MIMVSNet96.62 34696.25 35497.71 32999.04 26994.66 34999.16 5596.92 41797.23 29597.87 33499.10 17586.11 40999.65 34091.65 42999.21 33598.82 353
icg_test_0407_298.20 22898.38 18897.65 33599.03 27294.03 36995.78 41099.45 15298.16 20199.06 17698.71 28098.27 9599.68 31697.50 20499.45 29299.22 284
IMVS_040798.39 20198.64 14197.66 33399.03 27294.03 36998.10 18499.45 15298.16 20199.06 17698.71 28098.27 9599.71 29697.50 20499.45 29299.22 284
IMVS_040498.07 24198.20 21597.69 33099.03 27294.03 36996.67 35499.45 15298.16 20198.03 32498.71 28096.80 22699.82 20697.50 20499.45 29299.22 284
IMVS_040398.34 20598.56 15597.66 33399.03 27294.03 36997.98 21399.45 15298.16 20198.89 22198.71 28097.90 13499.74 28097.50 20499.45 29299.22 284
PatchMatch-RL97.24 31396.78 32898.61 22799.03 27297.83 17996.36 37499.06 28693.49 42197.36 37497.78 38095.75 28199.49 40093.44 40198.77 37998.52 390
viewmambaseed2359dif98.19 22998.26 20897.99 30799.02 27795.03 33796.59 36099.53 11796.21 34999.00 19198.99 21297.62 16399.61 35697.62 19399.72 18899.33 251
GDP-MVS97.50 28897.11 30798.67 21499.02 27796.85 25698.16 17499.71 4898.32 17998.52 28298.54 31483.39 43099.95 2698.79 10299.56 26299.19 294
ZD-MVS99.01 27998.84 8799.07 28594.10 41198.05 32298.12 35796.36 25299.86 14392.70 41799.19 339
CDPH-MVS97.26 31096.66 33799.07 13699.00 28098.15 14096.03 39499.01 30191.21 44797.79 34197.85 37896.89 21899.69 30792.75 41599.38 30699.39 220
diffmvspermissive98.22 22498.24 21298.17 29299.00 28095.44 32196.38 37399.58 8897.79 23498.53 28098.50 32396.76 23099.74 28097.95 16699.64 22999.34 245
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 19598.19 21999.03 14699.00 28097.65 19896.85 34498.94 30698.57 16298.89 22198.50 32395.60 28599.85 15697.54 20099.85 10799.59 107
plane_prior698.99 28397.70 19694.90 303
xiu_mvs_v1_base_debu97.86 26298.17 22196.92 38398.98 28493.91 37996.45 36799.17 26897.85 22998.41 29197.14 41298.47 7399.92 6598.02 15899.05 35396.92 454
xiu_mvs_v1_base97.86 26298.17 22196.92 38398.98 28493.91 37996.45 36799.17 26897.85 22998.41 29197.14 41298.47 7399.92 6598.02 15899.05 35396.92 454
xiu_mvs_v1_base_debi97.86 26298.17 22196.92 38398.98 28493.91 37996.45 36799.17 26897.85 22998.41 29197.14 41298.47 7399.92 6598.02 15899.05 35396.92 454
MVP-Stereo98.08 24097.92 25298.57 23498.96 28796.79 25997.90 22599.18 26496.41 34298.46 28698.95 22695.93 27699.60 35996.51 29598.98 36799.31 258
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS98.40 19598.68 13397.54 35098.96 28797.99 16097.88 22799.36 19198.20 19599.63 6799.04 19298.76 4695.33 47796.56 28999.74 17799.31 258
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 28997.76 19098.76 34387.58 46496.75 40298.10 35994.80 31099.78 25592.73 41699.00 36299.20 289
USDC97.41 29997.40 28897.44 35998.94 28993.67 38995.17 43099.53 11794.03 41398.97 20099.10 17595.29 29499.34 42795.84 33399.73 18099.30 261
tfpn200view994.03 40693.44 40995.78 41998.93 29191.44 42697.60 27494.29 45297.94 22197.10 38094.31 46179.67 44499.62 34983.05 46698.08 41696.29 461
testdata98.09 29698.93 29195.40 32398.80 33790.08 45597.45 36798.37 33795.26 29599.70 30393.58 39798.95 37099.17 301
thres40094.14 40493.44 40996.24 40798.93 29191.44 42697.60 27494.29 45297.94 22197.10 38094.31 46179.67 44499.62 34983.05 46698.08 41697.66 442
TAPA-MVS96.21 1196.63 34595.95 35698.65 21698.93 29198.09 14796.93 34099.28 23683.58 47098.13 31397.78 38096.13 26099.40 41893.52 39899.29 32198.45 395
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test22298.92 29596.93 25395.54 41798.78 34085.72 46796.86 39798.11 35894.43 31799.10 35299.23 279
PVSNet_BlendedMVS97.55 28797.53 28197.60 34298.92 29593.77 38696.64 35699.43 16694.49 39997.62 35099.18 15396.82 22399.67 32094.73 36199.93 5699.36 238
PVSNet_Blended96.88 33596.68 33497.47 35798.92 29593.77 38694.71 44199.43 16690.98 44997.62 35097.36 40696.82 22399.67 32094.73 36199.56 26298.98 328
MSDG97.71 27597.52 28298.28 27998.91 29896.82 25794.42 45199.37 18797.65 24398.37 29698.29 34697.40 18699.33 42994.09 38399.22 33298.68 379
Anonymous20240521197.90 25597.50 28399.08 13498.90 29998.25 13098.53 12796.16 42998.87 13799.11 16998.86 24590.40 37999.78 25597.36 21399.31 31699.19 294
原ACMM198.35 27298.90 29996.25 28698.83 33492.48 43396.07 42498.10 35995.39 29399.71 29692.61 41998.99 36499.08 310
GBi-Net98.65 15498.47 17399.17 11698.90 29998.24 13199.20 4999.44 16098.59 15898.95 20699.55 5894.14 32599.86 14397.77 18099.69 20799.41 210
test198.65 15498.47 17399.17 11698.90 29998.24 13199.20 4999.44 16098.59 15898.95 20699.55 5894.14 32599.86 14397.77 18099.69 20799.41 210
FMVSNet298.49 18698.40 18398.75 19998.90 29997.14 23998.61 11899.13 27798.59 15899.19 16299.28 12294.14 32599.82 20697.97 16499.80 14099.29 263
OMC-MVS97.88 25997.49 28499.04 14598.89 30498.63 10096.94 33899.25 24595.02 38898.53 28098.51 31997.27 19599.47 40693.50 40099.51 27799.01 322
VortexMVS97.98 25298.31 20097.02 37798.88 30591.45 42598.03 19899.47 14498.65 14999.55 7799.47 7991.49 36899.81 22399.32 6199.91 7899.80 42
MVSFormer98.26 21998.43 17997.77 31998.88 30593.89 38299.39 2099.56 10499.11 9998.16 30998.13 35593.81 33399.97 799.26 6699.57 25999.43 202
lupinMVS97.06 32596.86 32197.65 33598.88 30593.89 38295.48 42197.97 38493.53 41998.16 30997.58 39293.81 33399.91 7496.77 26399.57 25999.17 301
dmvs_re95.98 36795.39 37797.74 32598.86 30897.45 21198.37 15395.69 44197.95 21996.56 40895.95 43390.70 37697.68 47188.32 45496.13 45898.11 417
DELS-MVS98.27 21798.20 21598.48 25598.86 30896.70 26595.60 41699.20 25697.73 23798.45 28798.71 28097.50 17899.82 20698.21 14399.59 25098.93 339
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 25797.98 24397.60 34298.86 30894.35 35796.21 38399.44 16097.45 27199.06 17698.88 24297.99 12899.28 43794.38 37699.58 25599.18 297
LCM-MVSNet-Re98.64 15698.48 17199.11 12798.85 31198.51 11398.49 13899.83 2598.37 17399.69 5699.46 8198.21 10699.92 6594.13 38299.30 31998.91 343
pmmvs497.58 28597.28 29698.51 25098.84 31296.93 25395.40 42598.52 36293.60 41898.61 26598.65 29895.10 29999.60 35996.97 24499.79 14698.99 327
NP-MVS98.84 31297.39 21596.84 415
sss97.21 31596.93 31598.06 30198.83 31495.22 33096.75 35098.48 36494.49 39997.27 37697.90 37592.77 35199.80 23196.57 28599.32 31499.16 304
PVSNet93.40 1795.67 37695.70 36295.57 42498.83 31488.57 45192.50 46897.72 38992.69 43196.49 41696.44 42593.72 33699.43 41493.61 39599.28 32298.71 372
MVEpermissive83.40 2292.50 42991.92 43194.25 44098.83 31491.64 42292.71 46783.52 48095.92 36386.46 47895.46 44695.20 29695.40 47680.51 47198.64 39195.73 469
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testing3-293.78 41093.91 40293.39 45298.82 31781.72 47997.76 24795.28 44398.60 15796.54 40996.66 41965.85 47499.62 34996.65 27898.99 36498.82 353
ambc98.24 28498.82 31795.97 29798.62 11799.00 30399.27 14499.21 14496.99 21299.50 39796.55 29299.50 28599.26 273
旧先验198.82 31797.45 21198.76 34398.34 34195.50 29099.01 36199.23 279
test_vis1_rt97.75 27297.72 26797.83 31498.81 32096.35 28397.30 31399.69 5594.61 39797.87 33498.05 36496.26 25698.32 46598.74 10898.18 40898.82 353
WTY-MVS96.67 34396.27 35397.87 31298.81 32094.61 35196.77 34897.92 38694.94 39197.12 37997.74 38391.11 37299.82 20693.89 38898.15 41299.18 297
3Dnovator+97.89 398.69 14398.51 16299.24 10798.81 32098.40 11899.02 7099.19 26098.99 12298.07 31999.28 12297.11 20599.84 17496.84 25899.32 31499.47 187
QAPM97.31 30696.81 32798.82 17998.80 32397.49 20699.06 6699.19 26090.22 45397.69 34799.16 15996.91 21799.90 8190.89 44499.41 30199.07 312
VNet98.42 19298.30 20198.79 18798.79 32497.29 22298.23 16598.66 35399.31 7098.85 23098.80 26294.80 31099.78 25598.13 14899.13 34799.31 258
DPM-MVS96.32 35595.59 36898.51 25098.76 32597.21 23094.54 45098.26 37391.94 43896.37 41797.25 40893.06 34599.43 41491.42 43498.74 38098.89 345
3Dnovator98.27 298.81 11998.73 12099.05 14398.76 32597.81 18799.25 4499.30 22498.57 16298.55 27799.33 11297.95 13199.90 8197.16 22599.67 21899.44 198
PLCcopyleft94.65 1696.51 34895.73 36198.85 17598.75 32797.91 17296.42 37199.06 28690.94 45095.59 43097.38 40494.41 31899.59 36390.93 44298.04 42199.05 314
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-untuned96.83 33796.75 33097.08 37498.74 32893.33 39596.71 35298.26 37396.72 32798.44 28897.37 40595.20 29699.47 40691.89 42497.43 43598.44 398
hse-mvs297.46 29397.07 30898.64 21898.73 32997.33 21897.45 29697.64 39699.11 9998.58 27197.98 36988.65 39499.79 24498.11 14997.39 43798.81 358
CDS-MVSNet97.69 27697.35 29398.69 21198.73 32997.02 24696.92 34298.75 34695.89 36498.59 26998.67 29392.08 36299.74 28096.72 26999.81 12999.32 254
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SD_040396.28 35795.83 35897.64 33898.72 33194.30 35898.87 8998.77 34197.80 23296.53 41098.02 36697.34 19099.47 40676.93 47599.48 28899.16 304
EIA-MVS98.00 24897.74 26498.80 18398.72 33198.09 14798.05 19499.60 7997.39 27596.63 40595.55 44197.68 15599.80 23196.73 26899.27 32398.52 390
LFMVS97.20 31696.72 33198.64 21898.72 33196.95 25198.93 8294.14 45699.74 1398.78 24299.01 20684.45 42199.73 28797.44 20999.27 32399.25 274
new_pmnet96.99 33296.76 32997.67 33198.72 33194.89 34095.95 40098.20 37692.62 43298.55 27798.54 31494.88 30699.52 39193.96 38699.44 29998.59 387
Fast-Effi-MVS+97.67 27897.38 29098.57 23498.71 33597.43 21397.23 31999.45 15294.82 39496.13 42196.51 42198.52 7199.91 7496.19 31498.83 37698.37 407
TEST998.71 33598.08 15195.96 39899.03 29591.40 44495.85 42797.53 39496.52 24399.76 267
train_agg97.10 32296.45 34799.07 13698.71 33598.08 15195.96 39899.03 29591.64 43995.85 42797.53 39496.47 24599.76 26793.67 39499.16 34299.36 238
TSAR-MVS + GP.98.18 23197.98 24398.77 19598.71 33597.88 17496.32 37798.66 35396.33 34499.23 15698.51 31997.48 18299.40 41897.16 22599.46 29099.02 321
FA-MVS(test-final)96.99 33296.82 32597.50 35498.70 33994.78 34399.34 2396.99 41295.07 38798.48 28599.33 11288.41 39799.65 34096.13 32098.92 37398.07 420
AUN-MVS96.24 36195.45 37398.60 22998.70 33997.22 22897.38 30397.65 39495.95 36295.53 43797.96 37382.11 43899.79 24496.31 30797.44 43498.80 363
our_test_397.39 30197.73 26696.34 40298.70 33989.78 44794.61 44798.97 30596.50 33599.04 18698.85 24895.98 27299.84 17497.26 21999.67 21899.41 210
ppachtmachnet_test97.50 28897.74 26496.78 39298.70 33991.23 43494.55 44999.05 29096.36 34399.21 16098.79 26496.39 24899.78 25596.74 26699.82 12399.34 245
PCF-MVS92.86 1894.36 39893.00 41698.42 26298.70 33997.56 20393.16 46699.11 28079.59 47497.55 35797.43 40192.19 35999.73 28779.85 47299.45 29297.97 426
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ttmdpeth97.91 25498.02 23997.58 34498.69 34494.10 36598.13 17798.90 31597.95 21997.32 37599.58 4895.95 27598.75 46096.41 30199.22 33299.87 22
ETV-MVS98.03 24497.86 25898.56 23998.69 34498.07 15397.51 28699.50 12698.10 20997.50 36295.51 44298.41 7999.88 11596.27 31099.24 32897.71 441
test_prior98.95 16198.69 34497.95 16899.03 29599.59 36399.30 261
mvsmamba97.57 28697.26 29798.51 25098.69 34496.73 26498.74 9897.25 40597.03 30997.88 33399.23 14290.95 37399.87 13496.61 28199.00 36298.91 343
agg_prior98.68 34897.99 16099.01 30195.59 43099.77 261
test_898.67 34998.01 15995.91 40499.02 29891.64 43995.79 42997.50 39796.47 24599.76 267
HQP-NCC98.67 34996.29 37996.05 35595.55 433
ACMP_Plane98.67 34996.29 37996.05 35595.55 433
CNVR-MVS98.17 23397.87 25799.07 13698.67 34998.24 13197.01 33498.93 30997.25 28997.62 35098.34 34197.27 19599.57 37296.42 30099.33 31299.39 220
HQP-MVS97.00 33196.49 34698.55 24198.67 34996.79 25996.29 37999.04 29396.05 35595.55 43396.84 41593.84 33199.54 38592.82 41299.26 32699.32 254
MM98.22 22497.99 24298.91 16898.66 35496.97 24897.89 22694.44 45099.54 4198.95 20699.14 16693.50 33799.92 6599.80 1799.96 2899.85 30
test_fmvs197.72 27497.94 24997.07 37698.66 35492.39 41297.68 25899.81 3195.20 38699.54 7999.44 8691.56 36799.41 41799.78 2199.77 15799.40 219
balanced_conf0398.63 15898.72 12298.38 26798.66 35496.68 26798.90 8499.42 17298.99 12298.97 20099.19 14995.81 28099.85 15698.77 10699.77 15798.60 384
thres20093.72 41293.14 41495.46 42898.66 35491.29 43096.61 35894.63 44997.39 27596.83 39893.71 46479.88 44199.56 37582.40 46998.13 41395.54 470
wuyk23d96.06 36397.62 27791.38 45798.65 35898.57 10798.85 9396.95 41596.86 32099.90 1499.16 15999.18 1998.40 46489.23 45299.77 15777.18 477
NCCC97.86 26297.47 28799.05 14398.61 35998.07 15396.98 33698.90 31597.63 24497.04 38497.93 37495.99 27199.66 33395.31 34998.82 37899.43 202
DeepC-MVS_fast96.85 698.30 21398.15 22598.75 19998.61 35997.23 22697.76 24799.09 28397.31 28398.75 24898.66 29697.56 16999.64 34396.10 32199.55 26699.39 220
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testing393.51 41492.09 42597.75 32398.60 36194.40 35597.32 31195.26 44497.56 25496.79 40195.50 44353.57 48299.77 26195.26 35098.97 36899.08 310
thisisatest051594.12 40593.16 41396.97 38198.60 36192.90 40293.77 46290.61 46994.10 41196.91 39195.87 43674.99 45699.80 23194.52 36799.12 35098.20 413
GA-MVS95.86 37095.32 38097.49 35598.60 36194.15 36493.83 46197.93 38595.49 37696.68 40397.42 40283.21 43199.30 43396.22 31298.55 39799.01 322
dmvs_testset92.94 42492.21 42495.13 43298.59 36490.99 43797.65 26492.09 46596.95 31294.00 45793.55 46592.34 35796.97 47472.20 47692.52 47297.43 449
OPU-MVS98.82 17998.59 36498.30 12798.10 18498.52 31898.18 10998.75 46094.62 36499.48 28899.41 210
MSLP-MVS++98.02 24598.14 22797.64 33898.58 36695.19 33197.48 29099.23 25297.47 26497.90 33198.62 30597.04 20798.81 45897.55 19899.41 30198.94 338
test1298.93 16498.58 36697.83 17998.66 35396.53 41095.51 28999.69 30799.13 34799.27 267
CL-MVSNet_self_test97.44 29697.22 30098.08 29998.57 36895.78 30594.30 45498.79 33896.58 33398.60 26798.19 35394.74 31399.64 34396.41 30198.84 37598.82 353
PS-MVSNAJ97.08 32497.39 28996.16 41398.56 36992.46 41095.24 42998.85 32997.25 28997.49 36395.99 43298.07 11999.90 8196.37 30398.67 39096.12 466
CNLPA97.17 31996.71 33298.55 24198.56 36998.05 15796.33 37698.93 30996.91 31797.06 38397.39 40394.38 32099.45 41191.66 42899.18 34198.14 416
xiu_mvs_v2_base97.16 32097.49 28496.17 41198.54 37192.46 41095.45 42298.84 33097.25 28997.48 36496.49 42298.31 9099.90 8196.34 30698.68 38996.15 465
alignmvs97.35 30396.88 32098.78 19098.54 37198.09 14797.71 25497.69 39199.20 8497.59 35395.90 43588.12 39999.55 37998.18 14598.96 36998.70 375
FE-MVS95.66 37794.95 39097.77 31998.53 37395.28 32799.40 1996.09 43293.11 42597.96 32899.26 13079.10 44899.77 26192.40 42198.71 38498.27 411
Effi-MVS+98.02 24597.82 26098.62 22498.53 37397.19 23297.33 31099.68 6197.30 28496.68 40397.46 40098.56 6999.80 23196.63 27998.20 40798.86 350
baseline195.96 36895.44 37497.52 35298.51 37593.99 37698.39 15196.09 43298.21 19198.40 29597.76 38286.88 40199.63 34695.42 34789.27 47598.95 334
MVS_Test98.18 23198.36 19197.67 33198.48 37694.73 34698.18 17099.02 29897.69 24098.04 32399.11 17297.22 19999.56 37598.57 12098.90 37498.71 372
MGCFI-Net98.34 20598.28 20498.51 25098.47 37797.59 20298.96 7899.48 13599.18 9297.40 37095.50 44398.66 5599.50 39798.18 14598.71 38498.44 398
BH-RMVSNet96.83 33796.58 34297.58 34498.47 37794.05 36696.67 35497.36 40096.70 32997.87 33497.98 36995.14 29899.44 41390.47 44798.58 39699.25 274
sasdasda98.34 20598.26 20898.58 23198.46 37997.82 18498.96 7899.46 14899.19 8997.46 36595.46 44698.59 6399.46 40998.08 15298.71 38498.46 392
canonicalmvs98.34 20598.26 20898.58 23198.46 37997.82 18498.96 7899.46 14899.19 8997.46 36595.46 44698.59 6399.46 40998.08 15298.71 38498.46 392
MVS-HIRNet94.32 39995.62 36590.42 45898.46 37975.36 48296.29 37989.13 47395.25 38395.38 43999.75 1692.88 34899.19 44394.07 38499.39 30396.72 459
PHI-MVS98.29 21697.95 24799.34 8498.44 38299.16 4998.12 18199.38 18396.01 35998.06 32098.43 33197.80 14899.67 32095.69 33999.58 25599.20 289
DVP-MVS++98.90 10098.70 13099.51 4998.43 38399.15 5399.43 1599.32 21198.17 19899.26 14899.02 19598.18 10999.88 11597.07 23499.45 29299.49 168
MSC_two_6792asdad99.32 9298.43 38398.37 12298.86 32699.89 9797.14 22899.60 24699.71 63
No_MVS99.32 9298.43 38398.37 12298.86 32699.89 9797.14 22899.60 24699.71 63
Fast-Effi-MVS+-dtu98.27 21798.09 23098.81 18198.43 38398.11 14497.61 27399.50 12698.64 15097.39 37297.52 39698.12 11799.95 2696.90 25298.71 38498.38 405
OpenMVS_ROBcopyleft95.38 1495.84 37295.18 38597.81 31698.41 38797.15 23897.37 30798.62 35783.86 46998.65 25998.37 33794.29 32399.68 31688.41 45398.62 39496.60 460
DeepPCF-MVS96.93 598.32 21098.01 24099.23 10998.39 38898.97 7495.03 43499.18 26496.88 31899.33 13098.78 26698.16 11399.28 43796.74 26699.62 23999.44 198
Patchmatch-test96.55 34796.34 34997.17 37198.35 38993.06 39898.40 15097.79 38797.33 28098.41 29198.67 29383.68 42999.69 30795.16 35299.31 31698.77 366
AdaColmapbinary97.14 32196.71 33298.46 25798.34 39097.80 18896.95 33798.93 30995.58 37396.92 38997.66 38795.87 27899.53 38790.97 44199.14 34598.04 421
OpenMVScopyleft96.65 797.09 32396.68 33498.32 27498.32 39197.16 23798.86 9299.37 18789.48 45796.29 41999.15 16396.56 24199.90 8192.90 40999.20 33697.89 429
MG-MVS96.77 34096.61 33997.26 36798.31 39293.06 39895.93 40198.12 38196.45 34197.92 32998.73 27793.77 33599.39 42091.19 43999.04 35699.33 251
test_yl96.69 34196.29 35197.90 30998.28 39395.24 32897.29 31497.36 40098.21 19198.17 30697.86 37686.27 40599.55 37994.87 35898.32 40198.89 345
DCV-MVSNet96.69 34196.29 35197.90 30998.28 39395.24 32897.29 31497.36 40098.21 19198.17 30697.86 37686.27 40599.55 37994.87 35898.32 40198.89 345
CHOSEN 280x42095.51 38295.47 37195.65 42398.25 39588.27 45493.25 46598.88 31993.53 41994.65 44897.15 41186.17 40799.93 5497.41 21199.93 5698.73 371
SCA96.41 35496.66 33795.67 42198.24 39688.35 45395.85 40796.88 41896.11 35397.67 34898.67 29393.10 34399.85 15694.16 37899.22 33298.81 358
DeepMVS_CXcopyleft93.44 45198.24 39694.21 36194.34 45164.28 47791.34 47194.87 45889.45 38892.77 47877.54 47493.14 47193.35 473
MS-PatchMatch97.68 27797.75 26397.45 35898.23 39893.78 38597.29 31498.84 33096.10 35498.64 26098.65 29896.04 26499.36 42396.84 25899.14 34599.20 289
BH-w/o95.13 38894.89 39295.86 41698.20 39991.31 42995.65 41497.37 39993.64 41796.52 41295.70 43993.04 34699.02 44988.10 45595.82 46197.24 452
mvs_anonymous97.83 27098.16 22496.87 38698.18 40091.89 41997.31 31298.90 31597.37 27798.83 23399.46 8196.28 25599.79 24498.90 9598.16 41198.95 334
miper_lstm_enhance97.18 31897.16 30397.25 36898.16 40192.85 40395.15 43299.31 21697.25 28998.74 25098.78 26690.07 38099.78 25597.19 22399.80 14099.11 309
RRT-MVS97.88 25997.98 24397.61 34198.15 40293.77 38698.97 7799.64 7199.16 9498.69 25399.42 9091.60 36599.89 9797.63 19298.52 39899.16 304
ET-MVSNet_ETH3D94.30 40193.21 41297.58 34498.14 40394.47 35494.78 44093.24 46194.72 39589.56 47395.87 43678.57 45199.81 22396.91 24797.11 44698.46 392
ADS-MVSNet295.43 38394.98 38896.76 39398.14 40391.74 42097.92 22297.76 38890.23 45196.51 41398.91 23285.61 41299.85 15692.88 41096.90 44798.69 376
ADS-MVSNet95.24 38694.93 39196.18 41098.14 40390.10 44697.92 22297.32 40390.23 45196.51 41398.91 23285.61 41299.74 28092.88 41096.90 44798.69 376
c3_l97.36 30297.37 29197.31 36398.09 40693.25 39695.01 43599.16 27197.05 30698.77 24598.72 27992.88 34899.64 34396.93 24699.76 17299.05 314
FMVSNet397.50 28897.24 29998.29 27898.08 40795.83 30297.86 23198.91 31497.89 22698.95 20698.95 22687.06 40099.81 22397.77 18099.69 20799.23 279
PAPM91.88 43890.34 44196.51 39798.06 40892.56 40892.44 46997.17 40786.35 46590.38 47296.01 43186.61 40399.21 44270.65 47895.43 46397.75 438
Effi-MVS+-dtu98.26 21997.90 25599.35 8198.02 40999.49 698.02 20199.16 27198.29 18497.64 34997.99 36896.44 24799.95 2696.66 27798.93 37298.60 384
eth_miper_zixun_eth97.23 31497.25 29897.17 37198.00 41092.77 40594.71 44199.18 26497.27 28798.56 27598.74 27691.89 36399.69 30797.06 23699.81 12999.05 314
HY-MVS95.94 1395.90 36995.35 37997.55 34997.95 41194.79 34298.81 9796.94 41692.28 43695.17 44198.57 31289.90 38299.75 27591.20 43897.33 44298.10 418
UGNet98.53 17998.45 17698.79 18797.94 41296.96 25099.08 6298.54 36099.10 10696.82 39999.47 7996.55 24299.84 17498.56 12399.94 5099.55 137
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 35295.70 36298.79 18797.92 41399.12 6398.28 15998.60 35892.16 43795.54 43696.17 42994.77 31299.52 39189.62 45098.23 40597.72 440
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 33696.55 34397.79 31797.91 41494.21 36197.56 27998.87 32197.49 26399.06 17699.05 19080.72 43999.80 23198.44 12999.82 12399.37 231
API-MVS97.04 32796.91 31997.42 36097.88 41598.23 13598.18 17098.50 36397.57 25297.39 37296.75 41796.77 22899.15 44690.16 44899.02 36094.88 471
myMVS_eth3d2892.92 42592.31 42194.77 43597.84 41687.59 45896.19 38596.11 43197.08 30594.27 45193.49 46766.07 47398.78 45991.78 42697.93 42497.92 428
miper_ehance_all_eth97.06 32597.03 31097.16 37397.83 41793.06 39894.66 44499.09 28395.99 36098.69 25398.45 32992.73 35399.61 35696.79 26099.03 35798.82 353
cl____97.02 32896.83 32497.58 34497.82 41894.04 36894.66 44499.16 27197.04 30798.63 26198.71 28088.68 39399.69 30797.00 23999.81 12999.00 326
DIV-MVS_self_test97.02 32896.84 32397.58 34497.82 41894.03 36994.66 44499.16 27197.04 30798.63 26198.71 28088.69 39199.69 30797.00 23999.81 12999.01 322
CANet97.87 26197.76 26298.19 29197.75 42095.51 31396.76 34999.05 29097.74 23696.93 38898.21 35195.59 28699.89 9797.86 17599.93 5699.19 294
UBG93.25 41992.32 42096.04 41597.72 42190.16 44595.92 40395.91 43696.03 35893.95 45993.04 47069.60 46399.52 39190.72 44697.98 42298.45 395
mvsany_test197.60 28297.54 28097.77 31997.72 42195.35 32495.36 42697.13 40994.13 41099.71 5099.33 11297.93 13299.30 43397.60 19698.94 37198.67 380
PVSNet_089.98 2191.15 43990.30 44293.70 44897.72 42184.34 47290.24 47297.42 39890.20 45493.79 46093.09 46990.90 37598.89 45786.57 46172.76 47897.87 431
CR-MVSNet96.28 35795.95 35697.28 36597.71 42494.22 35998.11 18298.92 31292.31 43596.91 39199.37 10085.44 41599.81 22397.39 21297.36 44097.81 434
RPMNet97.02 32896.93 31597.30 36497.71 42494.22 35998.11 18299.30 22499.37 6296.91 39199.34 10986.72 40299.87 13497.53 20197.36 44097.81 434
ETVMVS92.60 42891.08 43797.18 36997.70 42693.65 39196.54 36195.70 43996.51 33494.68 44792.39 47361.80 47999.50 39786.97 45897.41 43698.40 403
pmmvs395.03 39094.40 39796.93 38297.70 42692.53 40995.08 43397.71 39088.57 46197.71 34598.08 36279.39 44699.82 20696.19 31499.11 35198.43 400
baseline293.73 41192.83 41796.42 40097.70 42691.28 43196.84 34589.77 47293.96 41592.44 46795.93 43479.14 44799.77 26192.94 40896.76 45198.21 412
WBMVS95.18 38794.78 39396.37 40197.68 42989.74 44895.80 40998.73 34997.54 25898.30 29798.44 33070.06 46199.82 20696.62 28099.87 9899.54 143
tpm94.67 39594.34 39995.66 42297.68 42988.42 45297.88 22794.90 44694.46 40196.03 42698.56 31378.66 44999.79 24495.88 32795.01 46598.78 365
CANet_DTU97.26 31097.06 30997.84 31397.57 43194.65 35096.19 38598.79 33897.23 29595.14 44298.24 34893.22 34099.84 17497.34 21499.84 11299.04 318
testing1193.08 42292.02 42796.26 40697.56 43290.83 44096.32 37795.70 43996.47 33892.66 46693.73 46364.36 47799.59 36393.77 39397.57 42998.37 407
tpm293.09 42192.58 41994.62 43797.56 43286.53 46197.66 26295.79 43886.15 46694.07 45698.23 35075.95 45499.53 38790.91 44396.86 45097.81 434
testing9193.32 41792.27 42296.47 39997.54 43491.25 43296.17 38996.76 42097.18 29993.65 46293.50 46665.11 47699.63 34693.04 40797.45 43398.53 389
TR-MVS95.55 38095.12 38696.86 38997.54 43493.94 37796.49 36696.53 42594.36 40697.03 38696.61 42094.26 32499.16 44586.91 46096.31 45597.47 448
testing9993.04 42391.98 43096.23 40897.53 43690.70 44296.35 37595.94 43596.87 31993.41 46393.43 46863.84 47899.59 36393.24 40597.19 44398.40 403
131495.74 37495.60 36696.17 41197.53 43692.75 40698.07 19198.31 37291.22 44694.25 45296.68 41895.53 28799.03 44891.64 43097.18 44496.74 458
CostFormer93.97 40793.78 40594.51 43897.53 43685.83 46497.98 21395.96 43489.29 45994.99 44498.63 30378.63 45099.62 34994.54 36696.50 45298.09 419
FMVSNet596.01 36595.20 38498.41 26397.53 43696.10 28898.74 9899.50 12697.22 29898.03 32499.04 19269.80 46299.88 11597.27 21899.71 19799.25 274
PMMVS96.51 34895.98 35598.09 29697.53 43695.84 30194.92 43798.84 33091.58 44196.05 42595.58 44095.68 28399.66 33395.59 34398.09 41598.76 368
reproduce_monomvs95.00 39295.25 38194.22 44197.51 44183.34 47397.86 23198.44 36598.51 16799.29 14099.30 11867.68 46799.56 37598.89 9799.81 12999.77 50
PAPR95.29 38494.47 39597.75 32397.50 44295.14 33394.89 43898.71 35191.39 44595.35 44095.48 44594.57 31599.14 44784.95 46397.37 43898.97 331
testing22291.96 43690.37 44096.72 39497.47 44392.59 40796.11 39194.76 44796.83 32192.90 46592.87 47157.92 48099.55 37986.93 45997.52 43098.00 425
PatchT96.65 34496.35 34897.54 35097.40 44495.32 32697.98 21396.64 42299.33 6796.89 39599.42 9084.32 42399.81 22397.69 19197.49 43197.48 447
tpm cat193.29 41893.13 41593.75 44797.39 44584.74 46797.39 30197.65 39483.39 47194.16 45398.41 33282.86 43499.39 42091.56 43295.35 46497.14 453
PatchmatchNetpermissive95.58 37995.67 36495.30 43197.34 44687.32 45997.65 26496.65 42195.30 38297.07 38298.69 28984.77 41899.75 27594.97 35698.64 39198.83 352
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmtry97.35 30396.97 31398.50 25497.31 44796.47 27998.18 17098.92 31298.95 12998.78 24299.37 10085.44 41599.85 15695.96 32599.83 11999.17 301
LS3D98.63 15898.38 18899.36 7597.25 44899.38 1399.12 6199.32 21199.21 8298.44 28898.88 24297.31 19199.80 23196.58 28399.34 31198.92 340
IB-MVS91.63 1992.24 43490.90 43896.27 40597.22 44991.24 43394.36 45393.33 46092.37 43492.24 46994.58 46066.20 47299.89 9793.16 40694.63 46797.66 442
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 43191.76 43494.21 44297.16 45084.65 46895.42 42488.45 47495.96 36196.17 42095.84 43866.36 47099.71 29691.87 42598.64 39198.28 410
tpmrst95.07 38995.46 37293.91 44597.11 45184.36 47197.62 26996.96 41494.98 38996.35 41898.80 26285.46 41499.59 36395.60 34296.23 45697.79 437
Syy-MVS96.04 36495.56 37097.49 35597.10 45294.48 35396.18 38796.58 42395.65 37094.77 44592.29 47491.27 37199.36 42398.17 14798.05 41998.63 382
myMVS_eth3d91.92 43790.45 43996.30 40397.10 45290.90 43896.18 38796.58 42395.65 37094.77 44592.29 47453.88 48199.36 42389.59 45198.05 41998.63 382
MDTV_nov1_ep1395.22 38397.06 45483.20 47497.74 25196.16 42994.37 40596.99 38798.83 25583.95 42799.53 38793.90 38797.95 423
MVS93.19 42092.09 42596.50 39896.91 45594.03 36998.07 19198.06 38368.01 47694.56 45096.48 42395.96 27499.30 43383.84 46596.89 44996.17 463
E-PMN94.17 40394.37 39893.58 44996.86 45685.71 46590.11 47497.07 41098.17 19897.82 34097.19 40984.62 42098.94 45389.77 44997.68 42896.09 467
JIA-IIPM95.52 38195.03 38797.00 37896.85 45794.03 36996.93 34095.82 43799.20 8494.63 44999.71 2383.09 43299.60 35994.42 37294.64 46697.36 451
EMVS93.83 40994.02 40193.23 45496.83 45884.96 46689.77 47596.32 42797.92 22397.43 36996.36 42886.17 40798.93 45487.68 45697.73 42795.81 468
cl2295.79 37395.39 37796.98 38096.77 45992.79 40494.40 45298.53 36194.59 39897.89 33298.17 35482.82 43599.24 43996.37 30399.03 35798.92 340
WB-MVSnew95.73 37595.57 36996.23 40896.70 46090.70 44296.07 39393.86 45795.60 37297.04 38495.45 44996.00 26799.55 37991.04 44098.31 40398.43 400
dp93.47 41593.59 40893.13 45596.64 46181.62 48097.66 26296.42 42692.80 43096.11 42298.64 30178.55 45299.59 36393.31 40392.18 47498.16 415
MonoMVSNet96.25 35996.53 34595.39 42996.57 46291.01 43698.82 9697.68 39398.57 16298.03 32499.37 10090.92 37497.78 47094.99 35493.88 47097.38 450
test-LLR93.90 40893.85 40394.04 44396.53 46384.62 46994.05 45892.39 46396.17 35094.12 45495.07 45082.30 43699.67 32095.87 33098.18 40897.82 432
test-mter92.33 43391.76 43494.04 44396.53 46384.62 46994.05 45892.39 46394.00 41494.12 45495.07 45065.63 47599.67 32095.87 33098.18 40897.82 432
TESTMET0.1,192.19 43591.77 43393.46 45096.48 46582.80 47694.05 45891.52 46894.45 40394.00 45794.88 45666.65 46999.56 37595.78 33598.11 41498.02 422
MGCNet97.44 29697.01 31298.72 20796.42 46696.74 26397.20 32491.97 46698.46 17098.30 29798.79 26492.74 35299.91 7499.30 6399.94 5099.52 155
miper_enhance_ethall96.01 36595.74 36096.81 39096.41 46792.27 41693.69 46398.89 31891.14 44898.30 29797.35 40790.58 37799.58 37096.31 30799.03 35798.60 384
tpmvs95.02 39195.25 38194.33 43996.39 46885.87 46298.08 18796.83 41995.46 37795.51 43898.69 28985.91 41099.53 38794.16 37896.23 45697.58 445
CMPMVSbinary75.91 2396.29 35695.44 37498.84 17696.25 46998.69 9997.02 33399.12 27888.90 46097.83 33898.86 24589.51 38698.90 45691.92 42399.51 27798.92 340
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test0.0.03 194.51 39693.69 40696.99 37996.05 47093.61 39394.97 43693.49 45896.17 35097.57 35694.88 45682.30 43699.01 45193.60 39694.17 46998.37 407
EPMVS93.72 41293.27 41195.09 43496.04 47187.76 45698.13 17785.01 47994.69 39696.92 38998.64 30178.47 45399.31 43195.04 35396.46 45398.20 413
cascas94.79 39494.33 40096.15 41496.02 47292.36 41492.34 47099.26 24485.34 46895.08 44394.96 45592.96 34798.53 46394.41 37598.59 39597.56 446
MVStest195.86 37095.60 36696.63 39595.87 47391.70 42197.93 21998.94 30698.03 21399.56 7499.66 3371.83 45998.26 46699.35 5999.24 32899.91 13
gg-mvs-nofinetune92.37 43291.20 43695.85 41795.80 47492.38 41399.31 3181.84 48199.75 1191.83 47099.74 1968.29 46499.02 44987.15 45797.12 44596.16 464
gm-plane-assit94.83 47581.97 47888.07 46394.99 45399.60 35991.76 427
GG-mvs-BLEND94.76 43694.54 47692.13 41899.31 3180.47 48288.73 47691.01 47667.59 46898.16 46982.30 47094.53 46893.98 472
UWE-MVS-2890.22 44089.28 44393.02 45694.50 47782.87 47596.52 36487.51 47595.21 38592.36 46896.04 43071.57 46098.25 46772.04 47797.77 42697.94 427
EPNet_dtu94.93 39394.78 39395.38 43093.58 47887.68 45796.78 34795.69 44197.35 27989.14 47598.09 36188.15 39899.49 40094.95 35799.30 31998.98 328
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
dongtai76.24 44475.95 44777.12 46192.39 47967.91 48590.16 47359.44 48682.04 47289.42 47494.67 45949.68 48381.74 47948.06 47977.66 47781.72 475
KD-MVS_2432*160092.87 42691.99 42895.51 42691.37 48089.27 44994.07 45698.14 37995.42 37897.25 37796.44 42567.86 46599.24 43991.28 43696.08 45998.02 422
miper_refine_blended92.87 42691.99 42895.51 42691.37 48089.27 44994.07 45698.14 37995.42 37897.25 37796.44 42567.86 46599.24 43991.28 43696.08 45998.02 422
EPNet96.14 36295.44 37498.25 28290.76 48295.50 31697.92 22294.65 44898.97 12592.98 46498.85 24889.12 38999.87 13495.99 32399.68 21299.39 220
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
kuosan69.30 44568.95 44870.34 46287.68 48365.00 48691.11 47159.90 48569.02 47574.46 48088.89 47748.58 48468.03 48128.61 48072.33 47977.99 476
test_method79.78 44279.50 44580.62 45980.21 48445.76 48770.82 47698.41 36931.08 47980.89 47997.71 38484.85 41797.37 47291.51 43380.03 47698.75 369
tmp_tt78.77 44378.73 44678.90 46058.45 48574.76 48494.20 45578.26 48339.16 47886.71 47792.82 47280.50 44075.19 48086.16 46292.29 47386.74 474
testmvs17.12 44720.53 4506.87 46412.05 4864.20 48993.62 4646.73 4874.62 48210.41 48224.33 4798.28 4863.56 4839.69 48215.07 48012.86 479
test12317.04 44820.11 4517.82 46310.25 4874.91 48894.80 4394.47 4884.93 48110.00 48324.28 4809.69 4853.64 48210.14 48112.43 48114.92 478
mmdepth0.00 4510.00 4540.00 4650.00 4880.00 4900.00 4770.00 4890.00 4830.00 4840.00 4830.00 4870.00 4840.00 4830.00 4820.00 480
monomultidepth0.00 4510.00 4540.00 4650.00 4880.00 4900.00 4770.00 4890.00 4830.00 4840.00 4830.00 4870.00 4840.00 4830.00 4820.00 480
test_blank0.00 4510.00 4540.00 4650.00 4880.00 4900.00 4770.00 4890.00 4830.00 4840.00 4830.00 4870.00 4840.00 4830.00 4820.00 480
eth-test20.00 488
eth-test0.00 488
uanet_test0.00 4510.00 4540.00 4650.00 4880.00 4900.00 4770.00 4890.00 4830.00 4840.00 4830.00 4870.00 4840.00 4830.00 4820.00 480
DCPMVS0.00 4510.00 4540.00 4650.00 4880.00 4900.00 4770.00 4890.00 4830.00 4840.00 4830.00 4870.00 4840.00 4830.00 4820.00 480
cdsmvs_eth3d_5k24.66 44632.88 4490.00 4650.00 4880.00 4900.00 47799.10 2810.00 4830.00 48497.58 39299.21 180.00 4840.00 4830.00 4820.00 480
pcd_1.5k_mvsjas8.17 44910.90 4520.00 4650.00 4880.00 4900.00 4770.00 4890.00 4830.00 4840.00 48398.07 1190.00 4840.00 4830.00 4820.00 480
sosnet-low-res0.00 4510.00 4540.00 4650.00 4880.00 4900.00 4770.00 4890.00 4830.00 4840.00 4830.00 4870.00 4840.00 4830.00 4820.00 480
sosnet0.00 4510.00 4540.00 4650.00 4880.00 4900.00 4770.00 4890.00 4830.00 4840.00 4830.00 4870.00 4840.00 4830.00 4820.00 480
uncertanet0.00 4510.00 4540.00 4650.00 4880.00 4900.00 4770.00 4890.00 4830.00 4840.00 4830.00 4870.00 4840.00 4830.00 4820.00 480
Regformer0.00 4510.00 4540.00 4650.00 4880.00 4900.00 4770.00 4890.00 4830.00 4840.00 4830.00 4870.00 4840.00 4830.00 4820.00 480
ab-mvs-re8.12 45010.83 4530.00 4650.00 4880.00 4900.00 4770.00 4890.00 4830.00 48497.48 3980.00 4870.00 4840.00 4830.00 4820.00 480
uanet0.00 4510.00 4540.00 4650.00 4880.00 4900.00 4770.00 4890.00 4830.00 4840.00 4830.00 4870.00 4840.00 4830.00 4820.00 480
TestfortrainingZip98.68 108
WAC-MVS90.90 43891.37 435
PC_three_145293.27 42299.40 11698.54 31498.22 10497.00 47395.17 35199.45 29299.49 168
test_241102_TWO99.30 22498.03 21399.26 14899.02 19597.51 17799.88 11596.91 24799.60 24699.66 78
test_0728_THIRD98.17 19899.08 17499.02 19597.89 13899.88 11597.07 23499.71 19799.70 68
GSMVS98.81 358
sam_mvs184.74 41998.81 358
sam_mvs84.29 425
MTGPAbinary99.20 256
test_post197.59 27620.48 48283.07 43399.66 33394.16 378
test_post21.25 48183.86 42899.70 303
patchmatchnet-post98.77 26884.37 42299.85 156
MTMP97.93 21991.91 467
test9_res93.28 40499.15 34499.38 229
agg_prior292.50 42099.16 34299.37 231
test_prior497.97 16495.86 405
test_prior295.74 41296.48 33796.11 42297.63 39095.92 27794.16 37899.20 336
旧先验295.76 41188.56 46297.52 36099.66 33394.48 368
新几何295.93 401
无先验95.74 41298.74 34889.38 45899.73 28792.38 42299.22 284
原ACMM295.53 418
testdata299.79 24492.80 414
segment_acmp97.02 210
testdata195.44 42396.32 345
plane_prior599.27 23999.70 30394.42 37299.51 27799.45 194
plane_prior497.98 369
plane_prior397.78 18997.41 27397.79 341
plane_prior297.77 24498.20 195
plane_prior97.65 19897.07 33296.72 32799.36 307
n20.00 489
nn0.00 489
door-mid99.57 95
test1198.87 321
door99.41 176
HQP5-MVS96.79 259
BP-MVS92.82 412
HQP4-MVS95.56 43299.54 38599.32 254
HQP3-MVS99.04 29399.26 326
HQP2-MVS93.84 331
MDTV_nov1_ep13_2view74.92 48397.69 25790.06 45697.75 34485.78 41193.52 39898.69 376
ACMMP++_ref99.77 157
ACMMP++99.68 212
Test By Simon96.52 243