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
APDe-MVS99.66 199.57 199.92 199.77 3899.89 199.75 3499.56 4699.02 1199.88 399.85 2699.18 599.96 1899.22 3099.92 999.90 1
Regformer-499.59 299.54 499.73 4599.76 4199.41 7099.58 8299.49 10299.02 1199.88 399.80 6499.00 1799.94 4099.45 1499.92 999.84 12
TSAR-MVS + MP.99.58 399.50 799.81 2799.91 199.66 3499.63 6799.39 17598.91 3099.78 2299.85 2699.36 299.94 4098.84 6699.88 3399.82 30
EI-MVSNet-UG-set99.58 399.57 199.64 6199.78 3499.14 9499.60 7799.45 14599.01 1499.90 199.83 3798.98 1899.93 5599.59 199.95 599.86 5
EI-MVSNet-Vis-set99.58 399.56 399.64 6199.78 3499.15 9399.61 7699.45 14599.01 1499.89 299.82 4499.01 1199.92 6399.56 499.95 599.85 8
Regformer-399.57 699.53 599.68 5099.76 4199.29 8099.58 8299.44 15399.01 1499.87 699.80 6498.97 1999.91 7299.44 1599.92 999.83 23
Regformer-299.54 799.47 899.75 3899.71 6399.52 5899.49 12299.49 10298.94 2699.83 1199.76 8599.01 1199.94 4099.15 3799.87 3799.80 39
SteuartSystems-ACMMP99.54 799.42 1199.87 699.82 2999.81 1199.59 7999.51 8498.62 4999.79 1899.83 3799.28 399.97 1098.48 10899.90 2299.84 12
Skip Steuart: Steuart Systems R&D Blog.
Regformer-199.53 999.47 899.72 4799.71 6399.44 6799.49 12299.46 13498.95 2599.83 1199.76 8599.01 1199.93 5599.17 3599.87 3799.80 39
XVS99.53 999.42 1199.87 699.85 2399.83 799.69 4499.68 1998.98 2099.37 9799.74 9498.81 3399.94 4098.79 7299.86 4799.84 12
MTAPA99.52 1199.39 1599.89 299.90 399.86 399.66 5499.47 12598.79 4099.68 3499.81 5398.43 6199.97 1098.88 5699.90 2299.83 23
HPM-MVS_fast99.51 1299.40 1499.85 1799.91 199.79 1699.76 2799.56 4697.72 12399.76 2699.75 9099.13 699.92 6399.07 4399.92 999.85 8
MPTG99.49 1399.36 1999.89 299.90 399.86 399.36 17399.47 12598.79 4099.68 3499.81 5398.43 6199.97 1098.88 5699.90 2299.83 23
HFP-MVS99.49 1399.37 1799.86 1299.87 1599.80 1299.66 5499.67 2298.15 8099.68 3499.69 11199.06 899.96 1898.69 8399.87 3799.84 12
ACMMPR99.49 1399.36 1999.86 1299.87 1599.79 1699.66 5499.67 2298.15 8099.67 4099.69 11198.95 2399.96 1898.69 8399.87 3799.84 12
DeepC-MVS_fast98.69 199.49 1399.39 1599.77 3599.63 8799.59 4699.36 17399.46 13499.07 1099.79 1899.82 4498.85 3099.92 6398.68 8599.87 3799.82 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
region2R99.48 1799.35 2299.87 699.88 1199.80 1299.65 6499.66 2598.13 8299.66 4599.68 11698.96 2099.96 1898.62 9199.87 3799.84 12
APD-MVS_3200maxsize99.48 1799.35 2299.85 1799.76 4199.83 799.63 6799.54 6198.36 6599.79 1899.82 4498.86 2999.95 3398.62 9199.81 6599.78 47
DELS-MVS99.48 1799.42 1199.65 5699.72 6199.40 7299.05 24599.66 2599.14 799.57 6299.80 6498.46 5999.94 4099.57 299.84 5699.60 101
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
ACMMP_Plus99.47 2099.34 2499.88 499.87 1599.86 399.47 13199.48 11198.05 9899.76 2699.86 2298.82 3299.93 5598.82 7199.91 1599.84 12
MSLP-MVS++99.46 2199.47 899.44 9699.60 9799.16 9199.41 15599.71 1398.98 2099.45 8099.78 7799.19 499.54 18899.28 2699.84 5699.63 96
PGM-MVS99.45 2299.31 3199.86 1299.87 1599.78 2099.58 8299.65 3097.84 11199.71 2999.80 6499.12 799.97 1098.33 12199.87 3799.83 23
CP-MVS99.45 2299.32 2699.85 1799.83 2899.75 2199.69 4499.52 7598.07 9399.53 6799.63 13898.93 2599.97 1098.74 7599.91 1599.83 23
ACMMPcopyleft99.45 2299.32 2699.82 2499.89 899.67 3299.62 7099.69 1898.12 8499.63 5099.84 3598.73 4699.96 1898.55 10399.83 6099.81 34
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
abl_699.44 2599.31 3199.83 2299.85 2399.75 2199.66 5499.59 3798.13 8299.82 1499.81 5398.60 5499.96 1898.46 11199.88 3399.79 43
mPP-MVS99.44 2599.30 3399.86 1299.88 1199.79 1699.69 4499.48 11198.12 8499.50 7299.75 9098.78 3699.97 1098.57 9899.89 3099.83 23
#test#99.43 2799.29 3699.86 1299.87 1599.80 1299.55 10099.67 2297.83 11299.68 3499.69 11199.06 899.96 1898.39 11499.87 3799.84 12
MCST-MVS99.43 2799.30 3399.82 2499.79 3399.74 2499.29 19099.40 17298.79 4099.52 6999.62 14398.91 2699.90 8498.64 8899.75 7799.82 30
UA-Net99.42 2999.29 3699.80 2999.62 9199.55 5199.50 11699.70 1598.79 4099.77 2399.96 197.45 9199.96 1898.92 5499.90 2299.89 2
HPM-MVS99.42 2999.28 3899.83 2299.90 399.72 2599.81 1599.54 6197.59 13199.68 3499.63 13898.91 2699.94 4098.58 9699.91 1599.84 12
CNVR-MVS99.42 2999.30 3399.78 3399.62 9199.71 2699.26 20599.52 7598.82 3699.39 9399.71 10298.96 2099.85 10398.59 9599.80 6799.77 49
HSP-MVS99.41 3299.26 4399.85 1799.89 899.80 1299.67 5199.37 18898.70 4599.77 2399.49 17998.21 7399.95 3398.46 11199.77 7499.81 34
SD-MVS99.41 3299.52 699.05 13699.74 5399.68 3099.46 13499.52 7599.11 899.88 399.91 599.43 197.70 30798.72 8099.93 899.77 49
MVS_111021_LR99.41 3299.33 2599.65 5699.77 3899.51 6098.94 27699.85 698.82 3699.65 4899.74 9498.51 5699.80 12798.83 6899.89 3099.64 93
MVS_111021_HR99.41 3299.32 2699.66 5399.72 6199.47 6498.95 27499.85 698.82 3699.54 6699.73 9798.51 5699.74 13998.91 5599.88 3399.77 49
HPM-MVS++99.39 3699.23 4599.87 699.75 4799.84 699.43 14499.51 8498.68 4799.27 12399.53 16798.64 5299.96 1898.44 11399.80 6799.79 43
MP-MVS-pluss99.37 3799.20 4699.88 499.90 399.87 299.30 18699.52 7597.18 16799.60 5699.79 7298.79 3599.95 3398.83 6899.91 1599.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.99.36 3899.36 1999.36 10399.67 7298.61 17299.07 23999.33 20999.00 1899.82 1499.81 5399.06 899.84 10899.09 4199.42 10699.65 88
PVSNet_Blended_VisFu99.36 3899.28 3899.61 6599.86 2099.07 10199.47 13199.93 297.66 12999.71 2999.86 2297.73 8699.96 1899.47 1299.82 6499.79 43
NCCC99.34 4099.19 4799.79 3299.61 9599.65 3799.30 18699.48 11198.86 3299.21 13799.63 13898.72 4799.90 8498.25 12599.63 10099.80 39
MP-MVScopyleft99.33 4199.15 5099.87 699.88 1199.82 1099.66 5499.46 13498.09 8999.48 7699.74 9498.29 7099.96 1897.93 14899.87 3799.82 30
PS-MVSNAJ99.32 4299.32 2699.30 11299.57 10298.94 12398.97 26899.46 13498.92 2999.71 2999.24 24599.01 1199.98 599.35 1799.66 9598.97 177
CSCG99.32 4299.32 2699.32 10899.85 2398.29 19199.71 4199.66 2598.11 8699.41 8899.80 6498.37 6799.96 1898.99 4999.96 499.72 69
PHI-MVS99.30 4499.17 4999.70 4999.56 10599.52 5899.58 8299.80 897.12 17399.62 5399.73 9798.58 5599.90 8498.61 9399.91 1599.68 81
DeepC-MVS98.35 299.30 4499.19 4799.64 6199.82 2999.23 8799.62 7099.55 5398.94 2699.63 5099.95 295.82 13699.94 4099.37 1699.97 299.73 63
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xiu_mvs_v1_base_debu99.29 4699.27 4099.34 10499.63 8798.97 11599.12 22899.51 8498.86 3299.84 899.47 18998.18 7499.99 199.50 799.31 11399.08 163
xiu_mvs_v1_base99.29 4699.27 4099.34 10499.63 8798.97 11599.12 22899.51 8498.86 3299.84 899.47 18998.18 7499.99 199.50 799.31 11399.08 163
xiu_mvs_v1_base_debi99.29 4699.27 4099.34 10499.63 8798.97 11599.12 22899.51 8498.86 3299.84 899.47 18998.18 7499.99 199.50 799.31 11399.08 163
APD-MVScopyleft99.27 4999.08 5699.84 2199.75 4799.79 1699.50 11699.50 9797.16 16999.77 2399.82 4498.78 3699.94 4097.56 18199.86 4799.80 39
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
LS3D99.27 4999.12 5399.74 4399.18 17799.75 2199.56 9599.57 4398.45 5999.49 7599.85 2697.77 8599.94 4098.33 12199.84 5699.52 116
xiu_mvs_v2_base99.26 5199.25 4499.29 11699.53 10798.91 12899.02 25499.45 14598.80 3999.71 2999.26 24398.94 2499.98 599.34 2199.23 11798.98 176
MVS_030599.24 5299.13 5199.57 7099.44 12699.12 9699.29 19099.55 5398.93 2899.52 6999.61 14696.36 12099.97 1099.57 299.92 999.63 96
3Dnovator97.25 999.24 5299.05 5899.81 2799.12 19099.66 3499.84 999.74 1099.09 998.92 18499.90 795.94 13199.98 598.95 5299.92 999.79 43
test_prior399.21 5499.05 5899.68 5099.67 7299.48 6298.96 27099.56 4698.34 6699.01 16899.52 17198.68 4999.83 11597.96 14599.74 7999.74 58
CHOSEN 1792x268899.19 5599.10 5599.45 9399.89 898.52 17999.39 16299.94 198.73 4499.11 15199.89 1095.50 14399.94 4099.50 799.97 299.89 2
F-COLMAP99.19 5599.04 6199.64 6199.78 3499.27 8399.42 15199.54 6197.29 15899.41 8899.59 15198.42 6499.93 5598.19 12799.69 9099.73 63
3Dnovator+97.12 1399.18 5798.97 7199.82 2499.17 18299.68 3099.81 1599.51 8499.20 698.72 20699.89 1095.68 14099.97 1098.86 6399.86 4799.81 34
MVSFormer99.17 5899.12 5399.29 11699.51 11098.94 12399.88 199.46 13497.55 13699.80 1699.65 12897.39 9299.28 22999.03 4599.85 5199.65 88
sss99.17 5899.05 5899.53 7899.62 9198.97 11599.36 17399.62 3197.83 11299.67 4099.65 12897.37 9599.95 3399.19 3299.19 12099.68 81
DP-MVS99.16 6098.95 7599.78 3399.77 3899.53 5599.41 15599.50 9797.03 18299.04 16599.88 1497.39 9299.92 6398.66 8699.90 2299.87 4
CNLPA99.14 6198.99 6899.59 6799.58 10099.41 7099.16 22199.44 15398.45 5999.19 14399.49 17998.08 7799.89 9297.73 16699.75 7799.48 125
CDPH-MVS99.13 6298.91 7999.80 2999.75 4799.71 2699.15 22499.41 16596.60 20699.60 5699.55 16398.83 3199.90 8497.48 18999.83 6099.78 47
jason99.13 6299.03 6399.45 9399.46 12198.87 13199.12 22899.26 23598.03 10199.79 1899.65 12897.02 10299.85 10399.02 4799.90 2299.65 88
jason: jason.
lupinMVS99.13 6299.01 6799.46 9299.51 11098.94 12399.05 24599.16 24697.86 10799.80 1699.56 16097.39 9299.86 10098.94 5399.85 5199.58 107
EPP-MVSNet99.13 6298.99 6899.53 7899.65 8499.06 10299.81 1599.33 20997.43 14699.60 5699.88 1497.14 9999.84 10899.13 3898.94 13999.69 77
MG-MVS99.13 6299.02 6699.45 9399.57 10298.63 16799.07 23999.34 20198.99 1999.61 5599.82 4497.98 8099.87 9797.00 21799.80 6799.85 8
CHOSEN 280x42099.12 6799.13 5199.08 13299.66 8297.89 20398.43 30499.71 1398.88 3199.62 5399.76 8596.63 11499.70 16399.46 1399.99 199.66 85
DP-MVS Recon99.12 6798.95 7599.65 5699.74 5399.70 2899.27 19799.57 4396.40 22499.42 8699.68 11698.75 4499.80 12797.98 14499.72 8399.44 137
Vis-MVSNetpermissive99.12 6798.97 7199.56 7399.78 3499.10 9899.68 4999.66 2598.49 5699.86 799.87 1994.77 18299.84 10899.19 3299.41 10799.74 58
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS99.12 6799.08 5699.24 12199.46 12198.55 17499.51 11199.46 13498.09 8999.45 8099.82 4498.34 6899.51 18998.70 8198.93 14099.67 84
VNet99.11 7198.90 8099.73 4599.52 10899.56 4999.41 15599.39 17599.01 1499.74 2899.78 7795.56 14199.92 6399.52 698.18 17499.72 69
CPTT-MVS99.11 7198.90 8099.74 4399.80 3299.46 6599.59 7999.49 10297.03 18299.63 5099.69 11197.27 9799.96 1897.82 15699.84 5699.81 34
HyFIR lowres test99.11 7198.92 7799.65 5699.90 399.37 7399.02 25499.91 397.67 12899.59 5999.75 9095.90 13399.73 14799.53 599.02 13299.86 5
MVS_Test99.10 7498.97 7199.48 8799.49 11699.14 9499.67 5199.34 20197.31 15699.58 6099.76 8597.65 8899.82 12098.87 6099.07 12999.46 132
112199.09 7598.87 8499.75 3899.74 5399.60 4499.27 19799.48 11196.82 19399.25 12799.65 12898.38 6599.93 5597.53 18499.67 9499.73 63
CDS-MVSNet99.09 7599.03 6399.25 12099.42 12898.73 15899.45 13599.46 13498.11 8699.46 7999.77 8298.01 7999.37 20698.70 8198.92 14299.66 85
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet_Blended99.08 7798.97 7199.42 10099.76 4198.79 15498.78 28699.91 396.74 19599.67 4099.49 17997.53 8999.88 9598.98 5099.85 5199.60 101
OMC-MVS99.08 7799.04 6199.20 12599.67 7298.22 19499.28 19499.52 7598.07 9399.66 4599.81 5397.79 8499.78 13297.79 15899.81 6599.60 101
WTY-MVS99.06 7998.88 8399.61 6599.62 9199.16 9199.37 16999.56 4698.04 9999.53 6799.62 14396.84 10699.94 4098.85 6598.49 16599.72 69
IS-MVSNet99.05 8098.87 8499.57 7099.73 5899.32 7699.75 3499.20 24298.02 10299.56 6399.86 2296.54 11699.67 16898.09 13499.13 12399.73 63
PAPM_NR99.04 8198.84 8899.66 5399.74 5399.44 6799.39 16299.38 18197.70 12699.28 11999.28 24098.34 6899.85 10396.96 22199.45 10499.69 77
API-MVS99.04 8199.03 6399.06 13499.40 13599.31 7999.55 10099.56 4698.54 5399.33 10899.39 21198.76 4199.78 13296.98 21999.78 7298.07 283
mvs_anonymous99.03 8398.99 6899.16 12799.38 13898.52 17999.51 11199.38 18197.79 11599.38 9599.81 5397.30 9699.45 19399.35 1798.99 13499.51 119
train_agg99.02 8498.77 9599.77 3599.67 7299.65 3799.05 24599.41 16596.28 22998.95 17999.49 17998.76 4199.91 7297.63 17599.72 8399.75 53
canonicalmvs99.02 8498.86 8699.51 8499.42 12899.32 7699.80 1999.48 11198.63 4899.31 11098.81 27697.09 10099.75 13899.27 2897.90 18499.47 129
PLCcopyleft97.94 499.02 8498.85 8799.53 7899.66 8299.01 10899.24 20899.52 7596.85 19199.27 12399.48 18598.25 7299.91 7297.76 16299.62 10199.65 88
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
agg_prior199.01 8798.76 9799.76 3799.67 7299.62 4098.99 26199.40 17296.26 23298.87 19099.49 17998.77 3999.91 7297.69 17299.72 8399.75 53
AdaColmapbinary99.01 8798.80 9299.66 5399.56 10599.54 5299.18 21999.70 1598.18 7999.35 10499.63 13896.32 12199.90 8497.48 18999.77 7499.55 109
1112_ss98.98 8998.77 9599.59 6799.68 7199.02 10699.25 20699.48 11197.23 16499.13 14799.58 15496.93 10599.90 8498.87 6098.78 15299.84 12
MSDG98.98 8998.80 9299.53 7899.76 4199.19 8898.75 28999.55 5397.25 16199.47 7799.77 8297.82 8399.87 9796.93 22499.90 2299.54 111
agg_prior398.97 9198.71 10199.75 3899.67 7299.60 4499.04 25099.41 16595.93 25198.87 19099.48 18598.61 5399.91 7297.63 17599.72 8399.75 53
114514_t98.93 9298.67 10599.72 4799.85 2399.53 5599.62 7099.59 3792.65 29799.71 2999.78 7798.06 7899.90 8498.84 6699.91 1599.74 58
PS-MVSNAJss98.92 9398.92 7798.90 16298.78 25898.53 17699.78 2299.54 6198.07 9399.00 17599.76 8599.01 1199.37 20699.13 3897.23 21698.81 184
Test_1112_low_res98.89 9498.66 10899.57 7099.69 7098.95 12099.03 25199.47 12596.98 18499.15 14699.23 24696.77 11099.89 9298.83 6898.78 15299.86 5
AllTest98.87 9598.72 9999.31 10999.86 2098.48 18499.56 9599.61 3297.85 10999.36 10199.85 2695.95 12999.85 10396.66 23799.83 6099.59 105
UGNet98.87 9598.69 10399.40 10199.22 17098.72 15999.44 13999.68 1999.24 499.18 14599.42 20092.74 23999.96 1899.34 2199.94 799.53 115
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
Vis-MVSNet (Re-imp)98.87 9598.72 9999.31 10999.71 6398.88 13099.80 1999.44 15397.91 10599.36 10199.78 7795.49 14499.43 20197.91 14999.11 12499.62 99
mvs-test198.86 9898.84 8898.89 16499.33 14797.77 20799.44 13999.30 21898.47 5799.10 15499.43 19896.78 10899.95 3398.73 7899.02 13298.96 179
EPNet98.86 9898.71 10199.30 11297.20 30498.18 19599.62 7098.91 27799.28 298.63 22599.81 5395.96 12899.99 199.24 2999.72 8399.73 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS98.86 9898.80 9299.03 13799.76 4198.79 15499.28 19499.91 397.42 14899.67 4099.37 21597.53 8999.88 9598.98 5097.29 21598.42 274
ab-mvs98.86 9898.63 11099.54 7499.64 8599.19 8899.44 13999.54 6197.77 11799.30 11199.81 5394.20 20699.93 5599.17 3598.82 14999.49 123
MAR-MVS98.86 9898.63 11099.54 7499.37 14099.66 3499.45 13599.54 6196.61 20499.01 16899.40 20797.09 10099.86 10097.68 17499.53 10399.10 158
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
COLMAP_ROBcopyleft97.56 698.86 9898.75 9899.17 12699.88 1198.53 17699.34 17999.59 3797.55 13698.70 21399.89 1095.83 13599.90 8498.10 13399.90 2299.08 163
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
HY-MVS97.30 798.85 10498.64 10999.47 9099.42 12899.08 10099.62 7099.36 18997.39 15199.28 11999.68 11696.44 11799.92 6398.37 11798.22 17299.40 142
PVSNet96.02 1798.85 10498.84 8898.89 16499.73 5897.28 21498.32 30899.60 3497.86 10799.50 7299.57 15896.75 11199.86 10098.56 10199.70 8999.54 111
PatchMatch-RL98.84 10698.62 11399.52 8299.71 6399.28 8199.06 24399.77 997.74 12199.50 7299.53 16795.41 14599.84 10897.17 20999.64 9899.44 137
Effi-MVS+98.81 10798.59 12099.48 8799.46 12199.12 9698.08 31499.50 9797.50 14199.38 9599.41 20396.37 11999.81 12499.11 4098.54 16299.51 119
alignmvs98.81 10798.56 12299.58 6999.43 12799.42 6999.51 11198.96 27098.61 5099.35 10498.92 27194.78 17899.77 13499.35 1798.11 17899.54 111
DeepPCF-MVS98.18 398.81 10799.37 1797.12 27999.60 9791.75 30698.61 29799.44 15399.35 199.83 1199.85 2698.70 4899.81 12499.02 4799.91 1599.81 34
PMMVS98.80 11098.62 11399.34 10499.27 16398.70 16098.76 28899.31 21697.34 15399.21 13799.07 25897.20 9899.82 12098.56 10198.87 14699.52 116
MVS_test032698.79 11198.62 11399.28 11899.00 21098.41 18999.01 25899.09 25499.23 598.67 21699.68 11694.31 20399.95 3398.74 7599.89 3099.46 132
Effi-MVS+-dtu98.78 11298.89 8298.47 20999.33 14796.91 23999.57 8899.30 21898.47 5799.41 8898.99 26596.78 10899.74 13998.73 7899.38 10898.74 196
FIs98.78 11298.63 11099.23 12399.18 17799.54 5299.83 1299.59 3798.28 7098.79 20099.81 5396.75 11199.37 20699.08 4296.38 23198.78 187
Fast-Effi-MVS+-dtu98.77 11498.83 9198.60 19599.41 13196.99 23399.52 10799.49 10298.11 8699.24 12899.34 22996.96 10499.79 13097.95 14799.45 10499.02 172
MVS_dtu98.77 11498.60 11999.30 11298.95 22498.47 18699.08 23899.27 23399.26 398.94 18199.71 10293.54 22699.96 1898.86 6399.79 7199.45 136
FC-MVSNet-test98.75 11698.62 11399.15 12999.08 19899.45 6699.86 899.60 3498.23 7598.70 21399.82 4496.80 10799.22 24499.07 4396.38 23198.79 186
XVG-OURS98.73 11798.68 10498.88 16799.70 6897.73 20998.92 27799.55 5398.52 5599.45 8099.84 3595.27 14999.91 7298.08 13898.84 14899.00 173
diffmvs98.72 11898.49 12499.43 9999.48 11999.19 8899.62 7099.42 16295.58 25799.37 9799.67 12196.14 12699.74 13998.14 13198.96 13799.37 144
Fast-Effi-MVS+98.70 11998.43 12699.51 8499.51 11099.28 8199.52 10799.47 12596.11 24699.01 16899.34 22996.20 12599.84 10897.88 15198.82 14999.39 143
XVG-OURS-SEG-HR98.69 12098.62 11398.89 16499.71 6397.74 20899.12 22899.54 6198.44 6299.42 8699.71 10294.20 20699.92 6398.54 10598.90 14499.00 173
131498.68 12198.54 12399.11 13198.89 24198.65 16599.27 19799.49 10296.89 18997.99 25699.56 16097.72 8799.83 11597.74 16599.27 11698.84 183
EI-MVSNet98.67 12298.67 10598.68 19199.35 14397.97 20299.50 11699.38 18196.93 18899.20 14099.83 3797.87 8199.36 21098.38 11697.56 19698.71 200
test_djsdf98.67 12298.57 12198.98 14398.70 26998.91 12899.88 199.46 13497.55 13699.22 13599.88 1495.73 13999.28 22999.03 4597.62 19198.75 193
QAPM98.67 12298.30 13599.80 2999.20 17399.67 3299.77 2499.72 1194.74 26698.73 20599.90 795.78 13799.98 596.96 22199.88 3399.76 52
nrg03098.64 12598.42 12799.28 11899.05 20499.69 2999.81 1599.46 13498.04 9999.01 16899.82 4496.69 11399.38 20399.34 2194.59 26898.78 187
PAPR98.63 12698.34 13199.51 8499.40 13599.03 10598.80 28599.36 18996.33 22699.00 17599.12 25698.46 5999.84 10895.23 26699.37 11299.66 85
CVMVSNet98.57 12798.67 10598.30 22499.35 14395.59 26899.50 11699.55 5398.60 5199.39 9399.83 3794.48 19699.45 19398.75 7498.56 16199.85 8
MVSTER98.49 12898.32 13399.00 14199.35 14399.02 10699.54 10399.38 18197.41 14999.20 14099.73 9793.86 22099.36 21098.87 6097.56 19698.62 249
OpenMVScopyleft96.50 1698.47 12998.12 14399.52 8299.04 20599.53 5599.82 1399.72 1194.56 27298.08 25199.88 1494.73 18599.98 597.47 19199.76 7699.06 168
IterMVS-LS98.46 13098.42 12798.58 19799.59 9998.00 20099.37 16999.43 16196.94 18799.07 15999.59 15197.87 8199.03 26598.32 12395.62 24498.71 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp98.44 13198.28 13698.94 14898.50 28398.96 11999.77 2499.50 9797.07 17898.87 19099.77 8294.76 18399.28 22998.66 8697.60 19298.57 265
jajsoiax98.43 13298.28 13698.88 16798.60 27898.43 18799.82 1399.53 7198.19 7698.63 22599.80 6493.22 22999.44 19899.22 3097.50 20198.77 190
BH-untuned98.42 13398.36 12998.59 19699.49 11696.70 24599.27 19799.13 25097.24 16398.80 19999.38 21295.75 13899.74 13997.07 21499.16 12199.33 147
BH-RMVSNet98.41 13498.08 14799.40 10199.41 13198.83 13899.30 18698.77 28797.70 12698.94 18199.65 12892.91 23599.74 13996.52 24199.55 10299.64 93
mvs_tets98.40 13598.23 13898.91 15898.67 27398.51 18199.66 5499.53 7198.19 7698.65 22399.81 5392.75 23799.44 19899.31 2497.48 20598.77 190
XXY-MVS98.38 13698.09 14699.24 12199.26 16599.32 7699.56 9599.55 5397.45 14598.71 20799.83 3793.23 22899.63 17998.88 5696.32 23398.76 192
ACMM97.58 598.37 13798.34 13198.48 20799.41 13197.10 22299.56 9599.45 14598.53 5499.04 16599.85 2693.00 23199.71 15798.74 7597.45 20698.64 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst98.33 13898.48 12597.90 25799.16 18494.78 28499.31 18499.11 25197.27 15999.45 8099.59 15195.33 14699.84 10898.48 10898.61 15599.09 162
PatchmatchNetpermissive98.31 13998.36 12998.19 24099.16 18495.32 27699.27 19798.92 27497.37 15299.37 9799.58 15494.90 17099.70 16397.43 19599.21 11899.54 111
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet98.29 14097.95 15799.30 11299.16 18499.54 5299.50 11699.58 4298.27 7199.35 10499.37 21592.53 24899.65 17299.35 1794.46 26998.72 198
UniMVSNet (Re)98.29 14098.00 15399.13 13099.00 21099.36 7499.49 12299.51 8497.95 10498.97 17899.13 25396.30 12299.38 20398.36 11993.34 28598.66 236
HQP_MVS98.27 14298.22 13998.44 21499.29 15896.97 23599.39 16299.47 12598.97 2399.11 15199.61 14692.71 24199.69 16697.78 15997.63 18998.67 225
UniMVSNet_NR-MVSNet98.22 14397.97 15598.96 14598.92 23598.98 11299.48 12799.53 7197.76 11898.71 20799.46 19396.43 11899.22 24498.57 9892.87 29198.69 209
LPG-MVS_test98.22 14398.13 14298.49 20599.33 14797.05 22899.58 8299.55 5397.46 14299.24 12899.83 3792.58 24699.72 15198.09 13497.51 19998.68 214
RPSCF98.22 14398.62 11396.99 28099.82 2991.58 30799.72 3999.44 15396.61 20499.66 4599.89 1095.92 13299.82 12097.46 19299.10 12699.57 108
ADS-MVSNet98.20 14698.08 14798.56 20099.33 14796.48 25299.23 20999.15 24796.24 23499.10 15499.67 12194.11 21199.71 15796.81 22899.05 13099.48 125
OPM-MVS98.19 14798.10 14498.45 21198.88 24297.07 22699.28 19499.38 18198.57 5299.22 13599.81 5392.12 25699.66 17098.08 13897.54 19898.61 258
CR-MVSNet98.17 14897.93 15998.87 17199.18 17798.49 18299.22 21399.33 20996.96 18599.56 6399.38 21294.33 20199.00 26894.83 27298.58 15899.14 155
Patchmatch-test198.16 14998.14 14198.22 23799.30 15595.55 26999.07 23998.97 26897.57 13499.43 8499.60 14992.72 24099.60 18297.38 19799.20 11999.50 122
CLD-MVS98.16 14998.10 14498.33 22199.29 15896.82 24298.75 28999.44 15397.83 11299.13 14799.55 16392.92 23399.67 16898.32 12397.69 18898.48 270
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
pmmvs498.13 15197.90 16098.81 18298.61 27798.87 13198.99 26199.21 24196.44 21999.06 16399.58 15495.90 13399.11 25797.18 20896.11 23698.46 273
WR-MVS_H98.13 15197.87 16598.90 16299.02 20898.84 13599.70 4299.59 3797.27 15998.40 23799.19 24995.53 14299.23 24198.34 12093.78 28298.61 258
v1neww98.12 15397.84 16698.93 15198.97 21998.81 14799.66 5499.35 19396.49 21199.29 11599.37 21595.02 16099.32 22097.73 16694.73 26098.67 225
v7new98.12 15397.84 16698.93 15198.97 21998.81 14799.66 5499.35 19396.49 21199.29 11599.37 21595.02 16099.32 22097.73 16694.73 26098.67 225
v698.12 15397.84 16698.94 14898.94 22898.83 13899.66 5499.34 20196.49 21199.30 11199.37 21594.95 16499.34 21697.77 16194.74 25998.67 225
ACMH97.28 898.10 15697.99 15498.44 21499.41 13196.96 23799.60 7799.56 4698.09 8998.15 24899.91 590.87 26899.70 16398.88 5697.45 20698.67 225
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet98.09 15797.78 17299.01 13998.97 21999.24 8699.67 5199.46 13497.25 16198.48 23499.64 13493.79 22199.06 26198.63 8994.10 27698.74 196
DU-MVS98.08 15897.79 17098.96 14598.87 24598.98 11299.41 15599.45 14597.87 10698.71 20799.50 17694.82 17599.22 24498.57 9892.87 29198.68 214
divwei89l23v2f11298.06 15997.78 17298.91 15898.90 23898.77 15799.57 8899.35 19396.45 21899.24 12899.37 21594.92 16899.27 23297.50 18794.71 26498.68 214
v2v48298.06 15997.77 17698.92 15698.90 23898.82 14599.57 8899.36 18996.65 20199.19 14399.35 22694.20 20699.25 23897.72 17094.97 25698.69 209
V4298.06 15997.79 17098.86 17598.98 21698.84 13599.69 4499.34 20196.53 21099.30 11199.37 21594.67 18899.32 22097.57 18094.66 26598.42 274
test-LLR98.06 15997.90 16098.55 20298.79 25497.10 22298.67 29397.75 31697.34 15398.61 22898.85 27294.45 19799.45 19397.25 20299.38 10899.10 158
WR-MVS98.06 15997.73 18399.06 13498.86 24899.25 8599.19 21899.35 19397.30 15798.66 21799.43 19893.94 21699.21 24898.58 9694.28 27298.71 200
ACMP97.20 1198.06 15997.94 15898.45 21199.37 14097.01 23199.44 13999.49 10297.54 13998.45 23599.79 7291.95 25799.72 15197.91 14997.49 20498.62 249
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v114198.05 16597.76 17998.91 15898.91 23798.78 15699.57 8899.35 19396.41 22399.23 13399.36 22294.93 16799.27 23297.38 19794.72 26298.68 214
v798.05 16597.78 17298.87 17198.99 21298.67 16299.64 6699.34 20196.31 22899.29 11599.51 17494.78 17899.27 23297.03 21595.15 25298.66 236
v198.05 16597.76 17998.93 15198.92 23598.80 15299.57 8899.35 19396.39 22599.28 11999.36 22294.86 17399.32 22097.38 19794.72 26298.68 214
EPNet_dtu98.03 16897.96 15698.23 23598.27 28895.54 27199.23 20998.75 28899.02 1197.82 26199.71 10296.11 12799.48 19093.04 29299.65 9799.69 77
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet398.03 16897.76 17998.84 17999.39 13798.98 11299.40 16199.38 18196.67 20099.07 15999.28 24092.93 23298.98 27097.10 21196.65 22498.56 266
ADS-MVSNet298.02 17098.07 14997.87 25899.33 14795.19 27999.23 20999.08 25596.24 23499.10 15499.67 12194.11 21198.93 27896.81 22899.05 13099.48 125
HQP-MVS98.02 17097.90 16098.37 21999.19 17496.83 24098.98 26599.39 17598.24 7298.66 21799.40 20792.47 25099.64 17497.19 20697.58 19498.64 241
LTVRE_ROB97.16 1298.02 17097.90 16098.40 21799.23 16896.80 24399.70 4299.60 3497.12 17398.18 24799.70 10691.73 25999.72 15198.39 11497.45 20698.68 214
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
PatchFormer-LS_test98.01 17398.05 15097.87 25899.15 18794.76 28599.42 15198.93 27297.12 17398.84 19698.59 28493.74 22599.80 12798.55 10398.17 17599.06 168
BH-w/o98.00 17497.89 16498.32 22299.35 14396.20 26199.01 25898.90 27996.42 22198.38 23899.00 26495.26 15099.72 15196.06 24998.61 15599.03 170
v114497.98 17597.69 18698.85 17898.87 24598.66 16499.54 10399.35 19396.27 23199.23 13399.35 22694.67 18899.23 24196.73 23295.16 25198.68 214
EU-MVSNet97.98 17598.03 15197.81 26498.72 26696.65 24899.66 5499.66 2598.09 8998.35 24199.82 4495.25 15198.01 29997.41 19695.30 24898.78 187
tpmvs97.98 17598.02 15297.84 26199.04 20594.73 28699.31 18499.20 24296.10 24998.76 20399.42 20094.94 16599.81 12496.97 22098.45 16698.97 177
NR-MVSNet97.97 17897.61 19199.02 13898.87 24599.26 8499.47 13199.42 16297.63 13097.08 27299.50 17695.07 15899.13 25497.86 15393.59 28398.68 214
v897.95 17997.63 19098.93 15198.95 22498.81 14799.80 1999.41 16596.03 25099.10 15499.42 20094.92 16899.30 22696.94 22394.08 27798.66 236
Patchmatch-test97.93 18097.65 18898.77 18799.18 17797.07 22699.03 25199.14 24996.16 24198.74 20499.57 15894.56 19299.72 15193.36 29199.11 12499.52 116
PS-CasMVS97.93 18097.59 19398.95 14798.99 21299.06 10299.68 4999.52 7597.13 17198.31 24399.68 11692.44 25499.05 26298.51 10694.08 27798.75 193
TranMVSNet+NR-MVSNet97.93 18097.66 18798.76 18898.78 25898.62 16999.65 6499.49 10297.76 11898.49 23399.60 14994.23 20598.97 27798.00 14392.90 28998.70 204
v14419297.92 18397.60 19298.87 17198.83 25198.65 16599.55 10099.34 20196.20 23799.32 10999.40 20794.36 20099.26 23796.37 24695.03 25598.70 204
ACMH+97.24 1097.92 18397.78 17298.32 22299.46 12196.68 24799.56 9599.54 6198.41 6397.79 26399.87 1990.18 27599.66 17098.05 14297.18 21998.62 249
LFMVS97.90 18597.35 21899.54 7499.52 10899.01 10899.39 16298.24 30897.10 17799.65 4899.79 7284.79 31199.91 7299.28 2698.38 16999.69 77
OurMVSNet-221017-097.88 18697.77 17698.19 24098.71 26896.53 25099.88 199.00 26597.79 11598.78 20199.94 391.68 26099.35 21397.21 20496.99 22298.69 209
v7n97.87 18797.52 19698.92 15698.76 26298.58 17399.84 999.46 13496.20 23798.91 18599.70 10694.89 17199.44 19896.03 25093.89 28198.75 193
v1097.85 18897.52 19698.86 17598.99 21298.67 16299.75 3499.41 16595.70 25598.98 17799.41 20394.75 18499.23 24196.01 25194.63 26798.67 225
GA-MVS97.85 18897.47 20399.00 14199.38 13897.99 20198.57 29999.15 24797.04 18198.90 18799.30 23789.83 27799.38 20396.70 23498.33 17099.62 99
VPNet97.84 19097.44 20899.01 13999.21 17198.94 12399.48 12799.57 4398.38 6499.28 11999.73 9788.89 28599.39 20299.19 3293.27 28698.71 200
LCM-MVSNet-Re97.83 19198.15 14096.87 28499.30 15592.25 30599.59 7998.26 30797.43 14696.20 28199.13 25396.27 12398.73 28398.17 12998.99 13499.64 93
XVG-ACMP-BASELINE97.83 19197.71 18598.20 23999.11 19296.33 25799.41 15599.52 7598.06 9799.05 16499.50 17689.64 27999.73 14797.73 16697.38 21298.53 267
IterMVS97.83 19197.77 17698.02 24899.58 10096.27 25999.02 25499.48 11197.22 16598.71 20799.70 10692.75 23799.13 25497.46 19296.00 23898.67 225
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPMVS97.82 19497.65 18898.35 22098.88 24295.98 26399.49 12294.71 32797.57 13499.26 12699.48 18592.46 25399.71 15797.87 15299.08 12899.35 145
MVP-Stereo97.81 19597.75 18297.99 25197.53 29796.60 24998.96 27098.85 28397.22 16597.23 26999.36 22295.28 14899.46 19295.51 26099.78 7297.92 292
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v119297.81 19597.44 20898.91 15898.88 24298.68 16199.51 11199.34 20196.18 23999.20 14099.34 22994.03 21499.36 21095.32 26595.18 25098.69 209
v192192097.80 19797.45 20598.84 17998.80 25298.53 17699.52 10799.34 20196.15 24399.24 12899.47 18993.98 21599.29 22895.40 26395.13 25398.69 209
V497.80 19797.51 19898.67 19398.79 25498.63 16799.87 499.44 15395.87 25299.01 16899.46 19394.52 19599.33 21796.64 24093.97 27998.05 284
v14897.79 19997.55 19498.50 20498.74 26397.72 21099.54 10399.33 20996.26 23298.90 18799.51 17494.68 18799.14 25197.83 15593.15 28898.63 247
v5297.79 19997.50 19998.66 19498.80 25298.62 16999.87 499.44 15395.87 25299.01 16899.46 19394.44 19999.33 21796.65 23993.96 28098.05 284
PEN-MVS97.76 20197.44 20898.72 19098.77 26198.54 17599.78 2299.51 8497.06 18098.29 24599.64 13492.63 24598.89 27998.09 13493.16 28798.72 198
Baseline_NR-MVSNet97.76 20197.45 20598.68 19199.09 19798.29 19199.41 15598.85 28395.65 25698.63 22599.67 12194.82 17599.10 25998.07 14092.89 29098.64 241
TR-MVS97.76 20197.41 21398.82 18199.06 20197.87 20498.87 28298.56 30296.63 20398.68 21599.22 24792.49 24999.65 17295.40 26397.79 18698.95 180
Patchmtry97.75 20497.40 21498.81 18299.10 19598.87 13199.11 23499.33 20994.83 26498.81 19899.38 21294.33 20199.02 26696.10 24895.57 24598.53 267
dp97.75 20497.80 16997.59 27199.10 19593.71 29699.32 18198.88 28196.48 21799.08 15899.55 16392.67 24499.82 12096.52 24198.58 15899.24 152
TAPA-MVS97.07 1597.74 20697.34 22198.94 14899.70 6897.53 21199.25 20699.51 8491.90 30199.30 11199.63 13898.78 3699.64 17488.09 30899.87 3799.65 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VDD-MVS97.73 20797.35 21898.88 16799.47 12097.12 22199.34 17998.85 28398.19 7699.67 4099.85 2682.98 31599.92 6399.49 1198.32 17199.60 101
MIMVSNet97.73 20797.45 20598.57 19899.45 12597.50 21299.02 25498.98 26796.11 24699.41 8899.14 25290.28 27198.74 28295.74 25598.93 14099.47 129
CostFormer97.72 20997.73 18397.71 26999.15 18794.02 29299.54 10399.02 26494.67 26799.04 16599.35 22692.35 25599.77 13498.50 10797.94 18399.34 146
FMVSNet297.72 20997.36 21698.80 18499.51 11098.84 13599.45 13599.42 16296.49 21198.86 19599.29 23990.26 27298.98 27096.44 24396.56 22798.58 264
test0.0.03 197.71 21197.42 21298.56 20098.41 28697.82 20598.78 28698.63 29897.34 15398.05 25598.98 26894.45 19798.98 27095.04 26997.15 22098.89 181
v124097.69 21297.32 22498.79 18598.85 24998.43 18799.48 12799.36 18996.11 24699.27 12399.36 22293.76 22399.24 24094.46 27895.23 24998.70 204
cascas97.69 21297.43 21198.48 20798.60 27897.30 21398.18 31399.39 17592.96 29498.41 23698.78 27993.77 22299.27 23298.16 13098.61 15598.86 182
pm-mvs197.68 21497.28 22898.88 16799.06 20198.62 16999.50 11699.45 14596.32 22797.87 25999.79 7292.47 25099.35 21397.54 18393.54 28498.67 225
GBi-Net97.68 21497.48 20198.29 22599.51 11097.26 21699.43 14499.48 11196.49 21199.07 15999.32 23490.26 27298.98 27097.10 21196.65 22498.62 249
test197.68 21497.48 20198.29 22599.51 11097.26 21699.43 14499.48 11196.49 21199.07 15999.32 23490.26 27298.98 27097.10 21196.65 22498.62 249
tpm97.67 21797.55 19498.03 24699.02 20895.01 28299.43 14498.54 30396.44 21999.12 14999.34 22991.83 25899.60 18297.75 16496.46 22999.48 125
PCF-MVS97.08 1497.66 21897.06 23599.47 9099.61 9599.09 9998.04 31599.25 23791.24 30498.51 23199.70 10694.55 19399.91 7292.76 29499.85 5199.42 140
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
testgi97.65 21997.50 19998.13 24399.36 14296.45 25399.42 15199.48 11197.76 11897.87 25999.45 19691.09 26598.81 28194.53 27698.52 16399.13 157
PAPM97.59 22097.09 23499.07 13399.06 20198.26 19398.30 30999.10 25294.88 26398.08 25199.34 22996.27 12399.64 17489.87 30298.92 14299.31 148
VDDNet97.55 22197.02 23699.16 12799.49 11698.12 19999.38 16799.30 21895.35 25999.68 3499.90 782.62 31799.93 5599.31 2498.13 17799.42 140
TESTMET0.1,197.55 22197.27 22998.40 21798.93 23396.53 25098.67 29397.61 32096.96 18598.64 22499.28 24088.63 29199.45 19397.30 20199.38 10899.21 153
DWT-MVSNet_test97.53 22397.40 21497.93 25499.03 20794.86 28399.57 8898.63 29896.59 20898.36 24098.79 27789.32 28199.74 13998.14 13198.16 17699.20 154
pmmvs597.52 22497.30 22698.16 24298.57 28096.73 24499.27 19798.90 27996.14 24498.37 23999.53 16791.54 26299.14 25197.51 18695.87 23998.63 247
v74897.52 22497.23 23098.41 21698.69 27097.23 21999.87 499.45 14595.72 25498.51 23199.53 16794.13 21099.30 22696.78 23092.39 29598.70 204
LF4IMVS97.52 22497.46 20497.70 27098.98 21695.55 26999.29 19098.82 28698.07 9398.66 21799.64 13489.97 27699.61 18197.01 21696.68 22397.94 290
DTE-MVSNet97.51 22797.19 23298.46 21098.63 27698.13 19899.84 999.48 11196.68 19997.97 25799.67 12192.92 23398.56 28596.88 22792.60 29498.70 204
SixPastTwentyTwo97.50 22897.33 22398.03 24698.65 27496.23 26099.77 2498.68 29697.14 17097.90 25899.93 490.45 27099.18 25097.00 21796.43 23098.67 225
JIA-IIPM97.50 22897.02 23698.93 15198.73 26497.80 20699.30 18698.97 26891.73 30298.91 18594.86 31895.10 15799.71 15797.58 17897.98 18299.28 150
test-mter97.49 23097.13 23398.55 20298.79 25497.10 22298.67 29397.75 31696.65 20198.61 22898.85 27288.23 29699.45 19397.25 20299.38 10899.10 158
DI_MVS_plusplus_test97.45 23196.79 24099.44 9697.76 29599.04 10499.21 21598.61 30097.74 12194.01 29798.83 27487.38 30299.83 11598.63 8998.90 14499.44 137
test_normal97.44 23296.77 24299.44 9697.75 29699.00 11099.10 23698.64 29797.71 12493.93 30098.82 27587.39 30199.83 11598.61 9398.97 13699.49 123
tpm297.44 23297.34 22197.74 26899.15 18794.36 28999.45 13598.94 27193.45 29298.90 18799.44 19791.35 26399.59 18497.31 20098.07 17999.29 149
tpm cat197.39 23497.36 21697.50 27499.17 18293.73 29499.43 14499.31 21691.27 30398.71 20799.08 25794.31 20399.77 13496.41 24598.50 16499.00 173
tpmp4_e2397.34 23597.29 22797.52 27299.25 16793.73 29499.58 8299.19 24594.00 28398.20 24699.41 20390.74 26999.74 13997.13 21098.07 17999.07 167
USDC97.34 23597.20 23197.75 26799.07 19995.20 27898.51 30299.04 26297.99 10398.31 24399.86 2289.02 28399.55 18795.67 25897.36 21398.49 269
MVS97.28 23796.55 24499.48 8798.78 25898.95 12099.27 19799.39 17583.53 31798.08 25199.54 16696.97 10399.87 9794.23 28599.16 12199.63 96
DSMNet-mixed97.25 23897.35 21896.95 28297.84 29393.61 29899.57 8896.63 32396.13 24598.87 19098.61 28394.59 19197.70 30795.08 26898.86 14799.55 109
MS-PatchMatch97.24 23997.32 22496.99 28098.45 28593.51 29998.82 28499.32 21597.41 14998.13 24999.30 23788.99 28499.56 18595.68 25799.80 6797.90 293
TransMVSNet (Re)97.15 24096.58 24398.86 17599.12 19098.85 13499.49 12298.91 27795.48 25897.16 27199.80 6493.38 22799.11 25794.16 28791.73 29698.62 249
TinyColmap97.12 24196.89 23897.83 26299.07 19995.52 27298.57 29998.74 29197.58 13397.81 26299.79 7288.16 29799.56 18595.10 26797.21 21798.39 277
K. test v397.10 24296.79 24098.01 24998.72 26696.33 25799.87 497.05 32297.59 13196.16 28299.80 6488.71 28799.04 26396.69 23596.55 22898.65 239
LP97.04 24396.80 23997.77 26698.90 23895.23 27798.97 26899.06 26094.02 28298.09 25099.41 20393.88 21898.82 28090.46 30098.42 16899.26 151
PatchT97.03 24496.44 24598.79 18598.99 21298.34 19099.16 22199.07 25892.13 29899.52 6997.31 31194.54 19498.98 27088.54 30698.73 15499.03 170
FMVSNet196.84 24596.36 24698.29 22599.32 15397.26 21699.43 14499.48 11195.11 26198.55 23099.32 23483.95 31498.98 27095.81 25496.26 23498.62 249
test_040296.64 24696.24 24797.85 26098.85 24996.43 25499.44 13999.26 23593.52 28996.98 27599.52 17188.52 29299.20 24992.58 29697.50 20197.93 291
RPMNet96.61 24795.85 25598.87 17199.18 17798.49 18299.22 21399.08 25588.72 31399.56 6397.38 30994.08 21399.00 26886.87 31398.58 15899.14 155
X-MVStestdata96.55 24895.45 26999.87 699.85 2399.83 799.69 4499.68 1998.98 2099.37 9764.01 33398.81 3399.94 4098.79 7299.86 4799.84 12
pmmvs696.53 24996.09 25097.82 26398.69 27095.47 27399.37 16999.47 12593.46 29197.41 26699.78 7787.06 30399.33 21796.92 22592.70 29398.65 239
UnsupCasMVSNet_eth96.44 25096.12 24997.40 27698.65 27495.65 26699.36 17399.51 8497.13 17196.04 28598.99 26588.40 29498.17 28896.71 23390.27 29998.40 276
FMVSNet596.43 25196.19 24897.15 27799.11 19295.89 26599.32 18199.52 7594.47 27698.34 24299.07 25887.54 30097.07 31092.61 29595.72 24298.47 271
v1896.42 25295.80 25998.26 22898.95 22498.82 14599.76 2799.28 22894.58 26994.12 29297.70 29695.22 15398.16 28994.83 27287.80 30697.79 301
v1796.42 25295.81 25798.25 23298.94 22898.80 15299.76 2799.28 22894.57 27094.18 29197.71 29595.23 15298.16 28994.86 27087.73 30897.80 296
v1696.39 25495.76 26098.26 22898.96 22298.81 14799.76 2799.28 22894.57 27094.10 29397.70 29695.04 15998.16 28994.70 27487.77 30797.80 296
new_pmnet96.38 25596.03 25197.41 27598.13 29195.16 28199.05 24599.20 24293.94 28497.39 26798.79 27791.61 26199.04 26390.43 30195.77 24198.05 284
v1596.28 25695.62 26298.25 23298.94 22898.83 13899.76 2799.29 22194.52 27494.02 29697.61 30395.02 16098.13 29394.53 27686.92 31197.80 296
V1496.26 25795.60 26398.26 22898.94 22898.83 13899.76 2799.29 22194.49 27593.96 29897.66 29994.99 16398.13 29394.41 27986.90 31297.80 296
V996.25 25895.58 26498.26 22898.94 22898.83 13899.75 3499.29 22194.45 27793.96 29897.62 30294.94 16598.14 29294.40 28086.87 31397.81 294
v1396.24 25995.58 26498.25 23298.98 21698.83 13899.75 3499.29 22194.35 27993.89 30197.60 30495.17 15598.11 29594.27 28486.86 31497.81 294
v1296.24 25995.58 26498.23 23598.96 22298.81 14799.76 2799.29 22194.42 27893.85 30297.60 30495.12 15698.09 29694.32 28186.85 31597.80 296
v1196.23 26195.57 26798.21 23898.93 23398.83 13899.72 3999.29 22194.29 28094.05 29597.64 30194.88 17298.04 29792.89 29388.43 30497.77 302
Anonymous2023120696.22 26296.03 25196.79 28697.31 30294.14 29199.63 6799.08 25596.17 24097.04 27399.06 26093.94 21697.76 30686.96 31295.06 25498.47 271
IB-MVS95.67 1896.22 26295.44 27098.57 19899.21 17196.70 24598.65 29697.74 31896.71 19797.27 26898.54 28686.03 30599.92 6398.47 11086.30 31699.10 158
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
gg-mvs-nofinetune96.17 26495.32 27198.73 18998.79 25498.14 19799.38 16794.09 32891.07 30698.07 25491.04 32489.62 28099.35 21396.75 23199.09 12798.68 214
test20.0396.12 26595.96 25496.63 28797.44 29895.45 27499.51 11199.38 18196.55 20996.16 28299.25 24493.76 22396.17 31587.35 31194.22 27498.27 279
PVSNet_094.43 1996.09 26695.47 26897.94 25399.31 15494.34 29097.81 31699.70 1597.12 17397.46 26598.75 28089.71 27899.79 13097.69 17281.69 32099.68 81
EG-PatchMatch MVS95.97 26795.69 26196.81 28597.78 29492.79 30299.16 22198.93 27296.16 24194.08 29499.22 24782.72 31699.47 19195.67 25897.50 20198.17 281
Patchmatch-RL test95.84 26895.81 25795.95 29195.61 30790.57 30898.24 31098.39 30495.10 26295.20 28798.67 28294.78 17897.77 30596.28 24790.02 30099.51 119
MVS-HIRNet95.75 26995.16 27397.51 27399.30 15593.69 29798.88 28195.78 32485.09 31698.78 20192.65 32091.29 26499.37 20694.85 27199.85 5199.46 132
testpf95.66 27096.02 25394.58 29498.35 28792.32 30497.25 32197.91 31592.83 29597.03 27498.99 26588.69 28898.61 28495.72 25697.40 21092.80 318
MIMVSNet195.51 27195.04 27496.92 28397.38 29995.60 26799.52 10799.50 9793.65 28796.97 27699.17 25085.28 30996.56 31488.36 30795.55 24698.60 260
MDA-MVSNet_test_wron95.45 27294.60 27798.01 24998.16 29097.21 22099.11 23499.24 23893.49 29080.73 32298.98 26893.02 23098.18 28794.22 28694.45 27098.64 241
TDRefinement95.42 27394.57 27897.97 25289.83 32396.11 26299.48 12798.75 28896.74 19596.68 27799.88 1488.65 29099.71 15798.37 11782.74 31998.09 282
YYNet195.36 27494.51 27997.92 25597.89 29297.10 22299.10 23699.23 23993.26 29380.77 32199.04 26292.81 23698.02 29894.30 28294.18 27598.64 241
pmmvs-eth3d95.34 27594.73 27697.15 27795.53 30995.94 26499.35 17799.10 25295.13 26093.55 30397.54 30788.15 29897.91 30194.58 27589.69 30297.61 305
Test495.05 27693.67 28499.22 12496.07 30698.94 12399.20 21799.27 23397.71 12489.96 31597.59 30666.18 32399.25 23898.06 14198.96 13799.47 129
MDA-MVSNet-bldmvs94.96 27793.98 28297.92 25598.24 28997.27 21599.15 22499.33 20993.80 28680.09 32399.03 26388.31 29597.86 30393.49 29094.36 27198.62 249
N_pmnet94.95 27895.83 25692.31 30298.47 28479.33 32499.12 22892.81 33393.87 28597.68 26499.13 25393.87 21999.01 26791.38 29896.19 23598.59 261
testus94.61 27995.30 27292.54 30196.44 30584.18 31698.36 30599.03 26394.18 28196.49 27898.57 28588.74 28695.09 31987.41 31098.45 16698.36 278
new-patchmatchnet94.48 28094.08 28195.67 29295.08 31192.41 30399.18 21999.28 22894.55 27393.49 30497.37 31087.86 29997.01 31191.57 29788.36 30597.61 305
testing_294.44 28192.93 28798.98 14394.16 31499.00 11099.42 15199.28 22896.60 20684.86 31796.84 31270.91 32099.27 23298.23 12696.08 23798.68 214
OpenMVS_ROBcopyleft92.34 2094.38 28293.70 28396.41 29097.38 29993.17 30099.06 24398.75 28886.58 31494.84 29098.26 28981.53 31899.32 22089.01 30597.87 18596.76 309
CMPMVSbinary69.68 2394.13 28394.90 27591.84 30397.24 30380.01 32398.52 30199.48 11189.01 31191.99 30999.67 12185.67 30799.13 25495.44 26197.03 22196.39 311
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs394.09 28493.25 28696.60 28894.76 31294.49 28798.92 27798.18 31189.66 30896.48 27998.06 29086.28 30497.33 30989.68 30387.20 31097.97 289
test235694.07 28594.46 28092.89 29995.18 31086.13 31497.60 31999.06 26093.61 28896.15 28498.28 28885.60 30893.95 32186.68 31498.00 18198.59 261
UnsupCasMVSNet_bld93.53 28692.51 28896.58 28997.38 29993.82 29398.24 31099.48 11191.10 30593.10 30596.66 31374.89 31998.37 28694.03 28887.71 30997.56 307
PM-MVS92.96 28792.23 28995.14 29395.61 30789.98 31099.37 16998.21 30994.80 26595.04 28997.69 29865.06 32497.90 30294.30 28289.98 30197.54 308
test123567892.91 28893.30 28591.71 30593.14 31783.01 31898.75 28998.58 30192.80 29692.45 30797.91 29288.51 29393.54 32282.26 31895.35 24798.59 261
111192.30 28992.21 29092.55 30093.30 31586.27 31299.15 22498.74 29191.94 29990.85 31297.82 29384.18 31295.21 31779.65 32094.27 27396.19 312
test1235691.74 29092.19 29190.37 30891.22 31982.41 31998.61 29798.28 30690.66 30791.82 31097.92 29184.90 31092.61 32381.64 31994.66 26596.09 313
Gipumacopyleft90.99 29190.15 29293.51 29698.73 26490.12 30993.98 32599.45 14579.32 32092.28 30894.91 31769.61 32197.98 30087.42 30995.67 24392.45 320
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous2023121190.69 29289.39 29394.58 29494.25 31388.18 31199.29 19099.07 25882.45 31992.95 30697.65 30063.96 32697.79 30489.27 30485.63 31797.77 302
testmv87.91 29387.80 29488.24 30987.68 32677.50 32699.07 23997.66 31989.27 30986.47 31696.22 31568.35 32292.49 32576.63 32488.82 30394.72 316
PMMVS286.87 29485.37 29791.35 30790.21 32283.80 31798.89 28097.45 32183.13 31891.67 31195.03 31648.49 33094.70 32085.86 31577.62 32195.54 314
LCM-MVSNet86.80 29585.22 29891.53 30687.81 32580.96 32298.23 31298.99 26671.05 32390.13 31496.51 31448.45 33196.88 31290.51 29985.30 31896.76 309
FPMVS84.93 29685.65 29682.75 31686.77 32763.39 33398.35 30798.92 27474.11 32283.39 31998.98 26850.85 32992.40 32684.54 31694.97 25692.46 319
.test124583.42 29786.17 29575.15 31993.30 31586.27 31299.15 22498.74 29191.94 29990.85 31297.82 29384.18 31295.21 31779.65 32039.90 33043.98 329
no-one83.04 29880.12 30091.79 30489.44 32485.65 31599.32 18198.32 30589.06 31079.79 32589.16 32644.86 33296.67 31384.33 31746.78 32893.05 317
tmp_tt82.80 29981.52 29986.66 31066.61 33468.44 33292.79 32797.92 31368.96 32580.04 32499.85 2685.77 30696.15 31697.86 15343.89 32995.39 315
E-PMN80.61 30079.88 30182.81 31590.75 32176.38 32897.69 31795.76 32566.44 32783.52 31892.25 32162.54 32787.16 33068.53 32861.40 32484.89 327
EMVS80.02 30179.22 30282.43 31791.19 32076.40 32797.55 32092.49 33566.36 32883.01 32091.27 32264.63 32585.79 33165.82 32960.65 32585.08 326
PNet_i23d79.43 30277.68 30384.67 31286.18 32871.69 33196.50 32393.68 32975.17 32171.33 32691.18 32332.18 33590.62 32778.57 32374.34 32291.71 322
ANet_high77.30 30374.86 30584.62 31375.88 33277.61 32597.63 31893.15 33288.81 31264.27 32889.29 32536.51 33383.93 33275.89 32552.31 32792.33 321
MVEpermissive76.82 2176.91 30474.31 30684.70 31185.38 33076.05 32996.88 32293.17 33167.39 32671.28 32789.01 32721.66 34087.69 32971.74 32772.29 32390.35 323
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVScopyleft70.75 2275.98 30574.97 30479.01 31870.98 33355.18 33493.37 32698.21 30965.08 32961.78 33093.83 31921.74 33992.53 32478.59 32291.12 29889.34 324
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d74.42 30671.19 30784.14 31476.16 33174.29 33096.00 32492.57 33469.57 32463.84 32987.49 32821.98 33788.86 32875.56 32657.50 32689.26 325
pcd1.5k->3k40.85 30743.49 30932.93 32198.95 2240.00 3380.00 32999.53 710.00 3330.00 3340.27 33595.32 1470.00 3360.00 33397.30 21498.80 185
wuyk23d40.18 30841.29 31136.84 32086.18 32849.12 33579.73 32822.81 33727.64 33025.46 33328.45 33421.98 33748.89 33355.80 33023.56 33312.51 331
testmvs39.17 30943.78 30825.37 32336.04 33616.84 33798.36 30526.56 33620.06 33138.51 33267.32 32929.64 33615.30 33537.59 33139.90 33043.98 329
test12339.01 31042.50 31028.53 32239.17 33520.91 33698.75 28919.17 33819.83 33238.57 33166.67 33033.16 33415.42 33437.50 33229.66 33249.26 328
cdsmvs_eth3d_5k24.64 31132.85 3120.00 3240.00 3370.00 3380.00 32999.51 840.00 3330.00 33499.56 16096.58 1150.00 3360.00 3330.00 3340.00 332
ab-mvs-re8.30 31211.06 3130.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 33499.58 1540.00 3410.00 3360.00 3330.00 3340.00 332
pcd_1.5k_mvsjas8.27 31311.03 3140.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 33599.01 110.00 3360.00 3330.00 3340.00 332
sosnet-low-res0.02 3140.03 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 3350.00 3410.00 3360.00 3330.00 3340.00 332
sosnet0.02 3140.03 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 3350.00 3410.00 3360.00 3330.00 3340.00 332
uncertanet0.02 3140.03 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 3350.00 3410.00 3360.00 3330.00 3340.00 332
Regformer0.02 3140.03 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 3350.00 3410.00 3360.00 3330.00 3340.00 332
uanet0.02 3140.03 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 3350.00 3410.00 3360.00 3330.00 3340.00 332
sam_mvs194.86 173
sam_mvs94.72 186
semantic-postprocess98.06 24599.57 10296.36 25699.49 10297.18 16798.71 20799.72 10192.70 24399.14 25197.44 19495.86 24098.67 225
ambc93.06 29892.68 31882.36 32098.47 30398.73 29595.09 28897.41 30855.55 32899.10 25996.42 24491.32 29797.71 304
MTGPAbinary99.47 125
test_post199.23 20965.14 33294.18 20999.71 15797.58 178
test_post65.99 33194.65 19099.73 147
patchmatchnet-post98.70 28194.79 17799.74 139
GG-mvs-BLEND98.45 21198.55 28198.16 19699.43 14493.68 32997.23 26998.46 28789.30 28299.22 24495.43 26298.22 17297.98 288
MTMP98.88 281
gm-plane-assit98.54 28292.96 30194.65 26899.15 25199.64 17497.56 181
test9_res97.49 18899.72 8399.75 53
TEST999.67 7299.65 3799.05 24599.41 16596.22 23698.95 17999.49 17998.77 3999.91 72
test_899.67 7299.61 4299.03 25199.41 16596.28 22998.93 18399.48 18598.76 4199.91 72
agg_prior297.21 20499.73 8299.75 53
agg_prior99.67 7299.62 4099.40 17298.87 19099.91 72
TestCases99.31 10999.86 2098.48 18499.61 3297.85 10999.36 10199.85 2695.95 12999.85 10396.66 23799.83 6099.59 105
test_prior499.56 4998.99 261
test_prior298.96 27098.34 6699.01 16899.52 17198.68 4997.96 14599.74 79
test_prior99.68 5099.67 7299.48 6299.56 4699.83 11599.74 58
旧先验298.96 27096.70 19899.47 7799.94 4098.19 127
新几何299.01 258
新几何199.75 3899.75 4799.59 4699.54 6196.76 19499.29 11599.64 13498.43 6199.94 4096.92 22599.66 9599.72 69
旧先验199.74 5399.59 4699.54 6199.69 11198.47 5899.68 9399.73 63
无先验98.99 26199.51 8496.89 18999.93 5597.53 18499.72 69
原ACMM298.95 274
原ACMM199.65 5699.73 5899.33 7599.47 12597.46 14299.12 14999.66 12798.67 5199.91 7297.70 17199.69 9099.71 76
test22299.75 4799.49 6198.91 27999.49 10296.42 22199.34 10799.65 12898.28 7199.69 9099.72 69
testdata299.95 3396.67 236
segment_acmp98.96 20
testdata99.54 7499.75 4798.95 12099.51 8497.07 17899.43 8499.70 10698.87 2899.94 4097.76 16299.64 9899.72 69
testdata198.85 28398.32 69
test1299.75 3899.64 8599.61 4299.29 22199.21 13798.38 6599.89 9299.74 7999.74 58
plane_prior799.29 15897.03 230
plane_prior699.27 16396.98 23492.71 241
plane_prior599.47 12599.69 16697.78 15997.63 18998.67 225
plane_prior499.61 146
plane_prior397.00 23298.69 4699.11 151
plane_prior299.39 16298.97 23
plane_prior199.26 165
plane_prior96.97 23599.21 21598.45 5997.60 192
n20.00 339
nn0.00 339
door-mid98.05 312
lessismore_v097.79 26598.69 27095.44 27594.75 32695.71 28699.87 1988.69 28899.32 22095.89 25294.93 25898.62 249
LGP-MVS_train98.49 20599.33 14797.05 22899.55 5397.46 14299.24 12899.83 3792.58 24699.72 15198.09 13497.51 19998.68 214
test1199.35 193
door97.92 313
HQP5-MVS96.83 240
HQP-NCC99.19 17498.98 26598.24 7298.66 217
ACMP_Plane99.19 17498.98 26598.24 7298.66 217
BP-MVS97.19 206
HQP4-MVS98.66 21799.64 17498.64 241
HQP3-MVS99.39 17597.58 194
HQP2-MVS92.47 250
NP-MVS99.23 16896.92 23899.40 207
MDTV_nov1_ep13_2view95.18 28099.35 17796.84 19299.58 6095.19 15497.82 15699.46 132
MDTV_nov1_ep1398.32 13399.11 19294.44 28899.27 19798.74 29197.51 14099.40 9299.62 14394.78 17899.76 13797.59 17798.81 151
ACMMP++_ref97.19 218
ACMMP++97.43 209
Test By Simon98.75 44
ITE_SJBPF98.08 24499.29 15896.37 25598.92 27498.34 6698.83 19799.75 9091.09 26599.62 18095.82 25397.40 21098.25 280
DeepMVS_CXcopyleft93.34 29799.29 15882.27 32199.22 24085.15 31596.33 28099.05 26190.97 26799.73 14793.57 28997.77 18798.01 287