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
MED-MVS99.12 198.97 499.56 999.77 298.86 2399.32 2299.24 2097.87 3199.30 5299.54 2097.61 699.92 4398.30 7599.80 2599.90 5
fmvsm_l_conf0.5_n_a99.09 299.08 199.11 6299.43 6397.48 9098.88 13099.30 1498.47 1899.85 1199.43 4596.71 1899.96 499.86 199.80 2599.89 8
SED-MVS99.09 298.91 599.63 599.71 2499.24 599.02 8598.87 8597.65 4199.73 2399.48 3597.53 899.94 1498.43 6799.81 1699.70 67
DVP-MVS++99.08 498.89 699.64 499.17 11199.23 799.69 198.88 7897.32 6599.53 3899.47 3797.81 399.94 1498.47 6399.72 6699.74 50
fmvsm_l_conf0.5_n99.07 599.05 299.14 5899.41 6697.54 8898.89 12399.31 1398.49 1799.86 899.42 4696.45 2899.96 499.86 199.74 5799.90 5
TestfortrainingZip a99.05 698.85 999.65 299.77 299.13 1199.32 2299.01 5297.87 3199.74 2199.54 2096.71 1899.92 4398.35 7299.33 13999.90 5
DVP-MVScopyleft99.03 798.83 1199.63 599.72 1799.25 298.97 9798.58 17697.62 4399.45 4099.46 4297.42 1099.94 1498.47 6399.81 1699.69 70
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
APDe-MVScopyleft99.02 898.84 1099.55 1199.57 3998.96 1899.39 1198.93 6597.38 6299.41 4499.54 2096.66 2099.84 8898.86 3999.85 699.87 12
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
lecture98.95 998.78 1499.45 1999.75 698.63 3199.43 1099.38 897.60 4699.58 3499.47 3795.36 6499.93 3498.87 3899.57 9899.78 33
reproduce_model98.94 1098.81 1299.34 3299.52 4598.26 5598.94 10798.84 9698.06 2599.35 4899.61 596.39 3199.94 1498.77 4299.82 1499.83 19
reproduce-ours98.93 1198.78 1499.38 2499.49 5298.38 4198.86 14198.83 9898.06 2599.29 5499.58 1696.40 2999.94 1498.68 4599.81 1699.81 25
our_new_method98.93 1198.78 1499.38 2499.49 5298.38 4198.86 14198.83 9898.06 2599.29 5499.58 1696.40 2999.94 1498.68 4599.81 1699.81 25
test_fmvsmconf_n98.92 1398.87 799.04 6898.88 14797.25 11298.82 15499.34 1198.75 1199.80 1499.61 595.16 7799.95 999.70 1799.80 2599.93 1
DPE-MVScopyleft98.92 1398.67 2099.65 299.58 3799.20 998.42 26698.91 7297.58 4799.54 3799.46 4297.10 1399.94 1497.64 12099.84 1199.83 19
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_l_conf0.5_n_998.90 1598.79 1399.24 4699.34 7197.83 7998.70 19599.26 1698.85 699.92 199.51 2893.91 10699.95 999.86 199.79 3499.92 2
fmvsm_l_conf0.5_n_398.90 1598.74 1899.37 2899.36 6898.25 5698.89 12399.24 2098.77 1099.89 399.59 1393.39 11299.96 499.78 1099.76 4799.89 8
SteuartSystems-ACMMP98.90 1598.75 1799.36 3099.22 10698.43 3999.10 6998.87 8597.38 6299.35 4899.40 4997.78 599.87 7997.77 10899.85 699.78 33
Skip Steuart: Steuart Systems R&D Blog.
test_fmvsm_n_192098.87 1899.01 398.45 12499.42 6496.43 15698.96 10399.36 1098.63 1399.86 899.51 2895.91 4699.97 199.72 1499.75 5398.94 233
ME-MVS98.83 1998.60 2499.52 1499.58 3798.86 2398.69 19898.93 6597.00 9199.17 6399.35 6296.62 2399.90 6498.30 7599.80 2599.79 29
TSAR-MVS + MP.98.78 2098.62 2299.24 4699.69 2998.28 5499.14 6098.66 15396.84 9899.56 3599.31 7196.34 3299.70 14398.32 7499.73 6199.73 55
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS98.78 2098.56 2899.45 1999.32 7798.87 2198.47 25398.81 10797.72 3698.76 9699.16 10897.05 1499.78 12498.06 8999.66 7799.69 70
MSP-MVS98.74 2298.55 2999.29 3999.75 698.23 5799.26 3398.88 7897.52 5099.41 4498.78 18996.00 4299.79 12197.79 10799.59 9499.85 16
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
fmvsm_s_conf0.5_n_898.73 2398.62 2299.05 6799.35 7097.27 10698.80 16399.23 2798.93 399.79 1599.59 1392.34 12999.95 999.82 699.71 6899.92 2
XVS98.70 2498.49 3699.34 3299.70 2798.35 5099.29 2898.88 7897.40 5998.46 11999.20 9395.90 4899.89 6897.85 10399.74 5799.78 33
fmvsm_s_conf0.5_n_1098.66 2598.54 3199.02 6999.36 6897.21 11598.86 14199.23 2798.90 599.83 1299.59 1391.57 16099.94 1499.79 999.74 5799.89 8
fmvsm_s_conf0.5_n_698.65 2698.55 2998.95 7898.50 18797.30 10298.79 17199.16 3998.14 2399.86 899.41 4893.71 10999.91 5699.71 1599.64 8599.65 83
MCST-MVS98.65 2698.37 4599.48 1799.60 3698.87 2198.41 26798.68 14597.04 8898.52 11798.80 18396.78 1799.83 9097.93 9699.61 9099.74 50
SD-MVS98.64 2898.68 1998.53 11399.33 7498.36 4998.90 11998.85 9597.28 6999.72 2699.39 5096.63 2297.60 44198.17 8499.85 699.64 86
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
fmvsm_s_conf0.5_n_998.63 2998.66 2198.54 11099.40 6795.83 20398.79 17199.17 3798.94 299.92 199.61 592.49 12499.93 3499.86 199.76 4799.86 13
HFP-MVS98.63 2998.40 4299.32 3899.72 1798.29 5399.23 3898.96 6096.10 14098.94 7899.17 10596.06 3999.92 4397.62 12199.78 3999.75 48
ACMMP_NAP98.61 3198.30 6099.55 1199.62 3598.95 1998.82 15498.81 10795.80 15599.16 6799.47 3795.37 6399.92 4397.89 10099.75 5399.79 29
region2R98.61 3198.38 4499.29 3999.74 1298.16 6399.23 3898.93 6596.15 13598.94 7899.17 10595.91 4699.94 1497.55 13399.79 3499.78 33
NCCC98.61 3198.35 4899.38 2499.28 9298.61 3298.45 25598.76 12597.82 3598.45 12298.93 16296.65 2199.83 9097.38 15599.41 12899.71 63
SF-MVS98.59 3498.32 5999.41 2399.54 4198.71 2799.04 7998.81 10795.12 20999.32 5199.39 5096.22 3399.84 8897.72 11199.73 6199.67 79
ACMMPR98.59 3498.36 4699.29 3999.74 1298.15 6499.23 3898.95 6196.10 14098.93 8299.19 10095.70 5299.94 1497.62 12199.79 3499.78 33
fmvsm_s_conf0.5_n_1198.58 3698.57 2698.62 10099.42 6497.16 11898.97 9798.86 9198.91 499.87 499.66 391.82 15299.95 999.82 699.82 1498.75 255
test_fmvsmconf0.1_n98.58 3698.44 4098.99 7197.73 30497.15 11998.84 15098.97 5798.75 1199.43 4299.54 2093.29 11499.93 3499.64 2099.79 3499.89 8
SMA-MVScopyleft98.58 3698.25 6399.56 999.51 4699.04 1798.95 10498.80 11493.67 30399.37 4799.52 2596.52 2699.89 6898.06 8999.81 1699.76 47
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
MTAPA98.58 3698.29 6199.46 1899.76 598.64 3098.90 11998.74 12997.27 7398.02 15099.39 5094.81 8799.96 497.91 9899.79 3499.77 40
HPM-MVS++copyleft98.58 3698.25 6399.55 1199.50 4899.08 1298.72 19098.66 15397.51 5198.15 13598.83 18095.70 5299.92 4397.53 13699.67 7499.66 82
SR-MVS98.57 4198.35 4899.24 4699.53 4298.18 6199.09 7098.82 10196.58 11499.10 6999.32 6995.39 6199.82 9797.70 11699.63 8799.72 59
CP-MVS98.57 4198.36 4699.19 5199.66 3197.86 7599.34 1798.87 8595.96 14698.60 11399.13 11596.05 4099.94 1497.77 10899.86 299.77 40
MSLP-MVS++98.56 4398.57 2698.55 10899.26 9596.80 13498.71 19199.05 4997.28 6998.84 8899.28 7696.47 2799.40 20798.52 6199.70 7099.47 116
DeepC-MVS_fast96.70 198.55 4498.34 5499.18 5399.25 9698.04 6998.50 24898.78 12197.72 3698.92 8499.28 7695.27 7099.82 9797.55 13399.77 4199.69 70
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post98.54 4598.35 4899.13 5999.49 5297.86 7599.11 6698.80 11496.49 11899.17 6399.35 6295.34 6699.82 9797.72 11199.65 8099.71 63
fmvsm_s_conf0.5_n_598.53 4698.35 4899.08 6499.07 12797.46 9498.68 20199.20 3397.50 5299.87 499.50 3191.96 14999.96 499.76 1199.65 8099.82 23
fmvsm_s_conf0.5_n_398.53 4698.45 3998.79 8699.23 10497.32 9998.80 16399.26 1698.82 799.87 499.60 1090.95 19399.93 3499.76 1199.73 6199.12 203
APD-MVS_3200maxsize98.53 4698.33 5899.15 5799.50 4897.92 7499.15 5798.81 10796.24 13199.20 6099.37 5695.30 6899.80 10997.73 11099.67 7499.72 59
MM98.51 4998.24 6599.33 3699.12 12198.14 6698.93 11397.02 42398.96 199.17 6399.47 3791.97 14899.94 1499.85 599.69 7199.91 4
mPP-MVS98.51 4998.26 6299.25 4599.75 698.04 6999.28 3098.81 10796.24 13198.35 13099.23 8695.46 5899.94 1497.42 15099.81 1699.77 40
ZNCC-MVS98.49 5198.20 7199.35 3199.73 1698.39 4099.19 5098.86 9195.77 15798.31 13499.10 12395.46 5899.93 3497.57 13299.81 1699.74 50
SPE-MVS-test98.49 5198.50 3498.46 12399.20 10997.05 12499.64 498.50 19997.45 5898.88 8599.14 11295.25 7299.15 25898.83 4099.56 10699.20 187
PGM-MVS98.49 5198.23 6799.27 4499.72 1798.08 6898.99 9399.49 595.43 18499.03 7099.32 6995.56 5599.94 1496.80 18899.77 4199.78 33
EI-MVSNet-Vis-set98.47 5498.39 4398.69 9499.46 5896.49 15398.30 28098.69 14297.21 7698.84 8899.36 6095.41 6099.78 12498.62 4999.65 8099.80 28
MVS_111021_HR98.47 5498.34 5498.88 8399.22 10697.32 9997.91 33999.58 397.20 7798.33 13299.00 15095.99 4399.64 15798.05 9199.76 4799.69 70
BridgeMVS98.45 5698.35 4898.74 9098.65 17697.55 8699.19 5098.60 16496.72 10899.35 4898.77 19295.06 8299.55 18198.95 3499.87 199.12 203
test_fmvsmvis_n_192098.44 5798.51 3298.23 14698.33 22096.15 17198.97 9799.15 4198.55 1698.45 12299.55 1894.26 10099.97 199.65 1899.66 7798.57 280
CS-MVS98.44 5798.49 3698.31 13799.08 12696.73 13899.67 398.47 20697.17 8098.94 7899.10 12395.73 5199.13 26398.71 4499.49 11799.09 211
GST-MVS98.43 5998.12 7599.34 3299.72 1798.38 4199.09 7098.82 10195.71 16198.73 9999.06 13995.27 7099.93 3497.07 16599.63 8799.72 59
fmvsm_s_conf0.5_n98.42 6098.51 3298.13 16399.30 8395.25 24198.85 14699.39 797.94 2999.74 2199.62 492.59 12399.91 5699.65 1899.52 11299.25 180
EI-MVSNet-UG-set98.41 6198.34 5498.61 10299.45 6196.32 16398.28 28398.68 14597.17 8098.74 9799.37 5695.25 7299.79 12198.57 5299.54 10999.73 55
DELS-MVS98.40 6298.20 7198.99 7199.00 13597.66 8197.75 36198.89 7597.71 3898.33 13298.97 15294.97 8499.88 7798.42 6999.76 4799.42 132
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
fmvsm_s_conf0.5_n_a98.38 6398.42 4198.27 13999.09 12595.41 22798.86 14199.37 997.69 4099.78 1799.61 592.38 12799.91 5699.58 2399.43 12699.49 112
TSAR-MVS + GP.98.38 6398.24 6598.81 8599.22 10697.25 11298.11 31498.29 27597.19 7898.99 7699.02 14496.22 3399.67 15098.52 6198.56 18499.51 104
HPM-MVS_fast98.38 6398.13 7499.12 6199.75 697.86 7599.44 998.82 10194.46 25798.94 7899.20 9395.16 7799.74 13497.58 12899.85 699.77 40
patch_mono-298.36 6698.87 796.82 28099.53 4290.68 40498.64 21199.29 1597.88 3099.19 6299.52 2596.80 1699.97 199.11 3099.86 299.82 23
HPM-MVScopyleft98.36 6698.10 7799.13 5999.74 1297.82 8099.53 698.80 11494.63 24598.61 11298.97 15295.13 7999.77 12997.65 11999.83 1399.79 29
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_498.35 6898.50 3497.90 19299.16 11595.08 25198.75 17699.24 2098.39 1999.81 1399.52 2592.35 12899.90 6499.74 1399.51 11498.71 261
APD-MVScopyleft98.35 6898.00 8399.42 2299.51 4698.72 2698.80 16398.82 10194.52 25299.23 5999.25 8595.54 5799.80 10996.52 19799.77 4199.74 50
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR98.34 7098.23 6798.67 9699.27 9396.90 13097.95 33299.58 397.14 8398.44 12499.01 14895.03 8399.62 16497.91 9899.75 5399.50 107
PHI-MVS98.34 7098.06 7899.18 5399.15 11898.12 6799.04 7999.09 4493.32 32198.83 9199.10 12396.54 2499.83 9097.70 11699.76 4799.59 94
MP-MVScopyleft98.33 7298.01 8299.28 4299.75 698.18 6199.22 4298.79 11996.13 13697.92 16499.23 8694.54 9099.94 1496.74 19199.78 3999.73 55
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVSMamba_PlusPlus98.31 7398.19 7398.67 9698.96 14197.36 9799.24 3698.57 17894.81 23398.99 7698.90 16895.22 7599.59 16799.15 2999.84 1199.07 219
MP-MVS-pluss98.31 7397.92 8599.49 1699.72 1798.88 2098.43 26398.78 12194.10 26897.69 18799.42 4695.25 7299.92 4398.09 8899.80 2599.67 79
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_s_conf0.5_n_298.30 7598.21 6998.57 10599.25 9697.11 12198.66 20899.20 3398.82 799.79 1599.60 1089.38 24099.92 4399.80 899.38 13398.69 263
fmvsm_s_conf0.5_n_798.23 7698.35 4897.89 19498.86 15194.99 25798.58 22499.00 5398.29 2099.73 2399.60 1091.70 15599.92 4399.63 2199.73 6198.76 254
MGCNet98.23 7697.91 8699.21 5098.06 26797.96 7398.58 22495.51 46398.58 1498.87 8699.26 8092.99 11899.95 999.62 2299.67 7499.73 55
ACMMPcopyleft98.23 7697.95 8499.09 6399.74 1297.62 8499.03 8299.41 695.98 14597.60 20099.36 6094.45 9599.93 3497.14 16298.85 16799.70 67
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
EC-MVSNet98.21 7998.11 7698.49 12098.34 21697.26 11199.61 598.43 22696.78 10198.87 8698.84 17693.72 10899.01 29198.91 3799.50 11599.19 191
fmvsm_s_conf0.1_n98.18 8098.21 6998.11 16898.54 18595.24 24298.87 13399.24 2097.50 5299.70 2799.67 191.33 17299.89 6899.47 2599.54 10999.21 186
fmvsm_s_conf0.1_n_298.14 8198.02 8198.53 11398.88 14797.07 12398.69 19898.82 10198.78 999.77 1899.61 588.83 26299.91 5699.71 1599.07 15098.61 273
fmvsm_s_conf0.1_n_a98.08 8298.04 8098.21 14797.66 31095.39 23298.89 12399.17 3797.24 7499.76 2099.67 191.13 18499.88 7799.39 2699.41 12899.35 147
dcpmvs_298.08 8298.59 2596.56 31099.57 3990.34 41699.15 5798.38 24796.82 10099.29 5499.49 3495.78 5099.57 17198.94 3599.86 299.77 40
NormalMVS98.07 8497.90 8798.59 10499.75 696.60 14498.94 10798.60 16497.86 3398.71 10299.08 13491.22 17999.80 10997.40 15299.57 9899.37 142
CANet98.05 8597.76 9098.90 8298.73 16197.27 10698.35 27098.78 12197.37 6497.72 18498.96 15791.53 16599.92 4398.79 4199.65 8099.51 104
train_agg97.97 8697.52 10399.33 3699.31 7998.50 3597.92 33798.73 13292.98 33797.74 18198.68 20596.20 3599.80 10996.59 19299.57 9899.68 75
ETV-MVS97.96 8797.81 8898.40 13298.42 19997.27 10698.73 18698.55 18496.84 9898.38 12797.44 32895.39 6199.35 21297.62 12198.89 16198.58 279
UA-Net97.96 8797.62 9498.98 7398.86 15197.47 9298.89 12399.08 4596.67 11198.72 10199.54 2093.15 11699.81 10294.87 25698.83 16899.65 83
CDPH-MVS97.94 8997.49 10599.28 4299.47 5698.44 3797.91 33998.67 15092.57 35398.77 9598.85 17595.93 4599.72 13795.56 23499.69 7199.68 75
DeepPCF-MVS96.37 297.93 9098.48 3896.30 33899.00 13589.54 43297.43 38698.87 8598.16 2299.26 5899.38 5596.12 3899.64 15798.30 7599.77 4199.72 59
DeepC-MVS95.98 397.88 9197.58 9698.77 8899.25 9696.93 12898.83 15298.75 12796.96 9396.89 23299.50 3190.46 20699.87 7997.84 10599.76 4799.52 101
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_fmvsmconf0.01_n97.86 9297.54 10298.83 8495.48 43596.83 13398.95 10498.60 16498.58 1498.93 8299.55 1888.57 26799.91 5699.54 2499.61 9099.77 40
DP-MVS Recon97.86 9297.46 10899.06 6699.53 4298.35 5098.33 27298.89 7592.62 35098.05 14598.94 16095.34 6699.65 15496.04 21399.42 12799.19 191
CSCG97.85 9497.74 9198.20 14999.67 3095.16 24699.22 4299.32 1293.04 33597.02 22598.92 16695.36 6499.91 5697.43 14899.64 8599.52 101
SymmetryMVS97.84 9597.58 9698.62 10099.01 13396.60 14498.94 10798.44 21597.86 3398.71 10299.08 13491.22 17999.80 10997.40 15297.53 25499.47 116
BP-MVS197.82 9697.51 10498.76 8998.25 23697.39 9699.15 5797.68 35496.69 10998.47 11899.10 12390.29 21499.51 18798.60 5099.35 13699.37 142
MG-MVS97.81 9797.60 9598.44 12699.12 12195.97 18497.75 36198.78 12196.89 9698.46 11999.22 8993.90 10799.68 14994.81 26099.52 11299.67 79
VNet97.79 9897.40 11398.96 7698.88 14797.55 8698.63 21498.93 6596.74 10599.02 7198.84 17690.33 21399.83 9098.53 5596.66 27799.50 107
EIA-MVS97.75 9997.58 9698.27 13998.38 20696.44 15599.01 8898.60 16495.88 15097.26 21197.53 32294.97 8499.33 21597.38 15599.20 14699.05 220
PS-MVSNAJ97.73 10097.77 8997.62 22598.68 17195.58 21697.34 39598.51 19497.29 6798.66 10997.88 28694.51 9199.90 6497.87 10299.17 14897.39 326
casdiffmvs_mvgpermissive97.72 10197.48 10798.44 12698.42 19996.59 14898.92 11698.44 21596.20 13397.76 17899.20 9391.66 15899.23 24398.27 8298.41 20599.49 112
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CPTT-MVS97.72 10197.32 12098.92 7999.64 3397.10 12299.12 6498.81 10792.34 36198.09 14099.08 13493.01 11799.92 4396.06 21299.77 4199.75 48
PVSNet_Blended_VisFu97.70 10397.46 10898.44 12699.27 9395.91 19298.63 21499.16 3994.48 25697.67 18998.88 17192.80 12099.91 5697.11 16399.12 14999.50 107
mvsany_test197.69 10497.70 9297.66 22198.24 23894.18 30097.53 37797.53 37595.52 17999.66 2999.51 2894.30 9899.56 17498.38 7098.62 17899.23 182
sasdasda97.67 10597.23 13098.98 7398.70 16698.38 4199.34 1798.39 24096.76 10397.67 18997.40 33292.26 13399.49 19198.28 7996.28 29599.08 215
canonicalmvs97.67 10597.23 13098.98 7398.70 16698.38 4199.34 1798.39 24096.76 10397.67 18997.40 33292.26 13399.49 19198.28 7996.28 29599.08 215
xiu_mvs_v2_base97.66 10797.70 9297.56 22998.61 18095.46 22597.44 38398.46 20797.15 8298.65 11098.15 26194.33 9799.80 10997.84 10598.66 17797.41 324
GDP-MVS97.64 10897.28 12398.71 9398.30 22597.33 9899.05 7598.52 19196.34 12898.80 9299.05 14189.74 22799.51 18796.86 18498.86 16599.28 170
baseline97.64 10897.44 11098.25 14398.35 21196.20 16899.00 9098.32 26296.33 13098.03 14899.17 10591.35 17199.16 25498.10 8798.29 21599.39 137
casdiffmvspermissive97.63 11097.41 11298.28 13898.33 22096.14 17298.82 15498.32 26296.38 12697.95 15999.21 9191.23 17899.23 24398.12 8698.37 20899.48 114
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MGCFI-Net97.62 11197.19 13398.92 7998.66 17398.20 5999.32 2298.38 24796.69 10997.58 20297.42 33192.10 14299.50 19098.28 7996.25 29899.08 215
xiu_mvs_v1_base_debu97.60 11297.56 9997.72 21098.35 21195.98 17997.86 34998.51 19497.13 8499.01 7398.40 23391.56 16199.80 10998.53 5598.68 17397.37 328
xiu_mvs_v1_base97.60 11297.56 9997.72 21098.35 21195.98 17997.86 34998.51 19497.13 8499.01 7398.40 23391.56 16199.80 10998.53 5598.68 17397.37 328
xiu_mvs_v1_base_debi97.60 11297.56 9997.72 21098.35 21195.98 17997.86 34998.51 19497.13 8499.01 7398.40 23391.56 16199.80 10998.53 5598.68 17397.37 328
diffmvs_AUTHOR97.59 11597.44 11098.01 18198.26 23495.47 22498.12 31098.36 25396.38 12698.84 8899.10 12391.13 18499.26 22898.24 8398.56 18499.30 161
diffmvspermissive97.58 11697.40 11398.13 16398.32 22395.81 20698.06 32098.37 24996.20 13398.74 9798.89 17091.31 17499.25 23298.16 8598.52 18899.34 149
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
guyue97.57 11797.37 11698.20 14998.50 18795.86 20098.89 12397.03 42097.29 6798.73 9998.90 16889.41 23999.32 21698.68 4598.86 16599.42 132
MVSFormer97.57 11797.49 10597.84 19798.07 26495.76 21099.47 798.40 23494.98 22298.79 9398.83 18092.34 12998.41 36596.91 17299.59 9499.34 149
alignmvs97.56 11997.07 14699.01 7098.66 17398.37 4898.83 15298.06 32796.74 10598.00 15497.65 30990.80 19599.48 19698.37 7196.56 28199.19 191
E3new97.55 12097.35 11898.16 15498.48 19295.85 20198.55 23798.41 23195.42 18698.06 14399.12 11892.23 13699.24 23997.43 14898.45 19499.39 137
DPM-MVS97.55 12096.99 15399.23 4999.04 12998.55 3397.17 41398.35 25494.85 23297.93 16398.58 21695.07 8199.71 14292.60 34699.34 13799.43 129
OMC-MVS97.55 12097.34 11998.20 14999.33 7495.92 19198.28 28398.59 17195.52 17997.97 15799.10 12393.28 11599.49 19195.09 25198.88 16299.19 191
balanced_ft_v197.54 12397.38 11598.02 17998.34 21695.58 21699.32 2298.40 23495.88 15098.43 12698.65 20988.95 25999.59 16798.94 3599.48 12098.90 237
viewcassd2359sk1197.53 12497.32 12098.16 15498.45 19595.83 20398.57 23398.42 23095.52 17998.07 14199.12 11891.81 15399.25 23297.46 14698.48 19399.41 135
hybridcas97.52 12597.29 12298.20 14998.44 19696.00 17799.02 8598.39 24096.12 13897.69 18799.23 8690.77 20099.17 25397.55 13398.42 20499.44 126
LuminaMVS97.49 12697.18 13498.42 13097.50 32597.15 11998.45 25597.68 35496.56 11798.68 10498.78 18989.84 22499.32 21698.60 5098.57 18398.79 246
E297.48 12797.25 12598.16 15498.40 20395.79 20798.58 22498.44 21595.58 16898.00 15499.14 11291.21 18399.24 23997.50 14198.43 19899.45 123
E397.48 12797.25 12598.16 15498.38 20695.79 20798.58 22498.44 21595.58 16898.00 15499.14 11291.25 17799.24 23997.50 14198.44 19599.45 123
KinetiMVS97.48 12797.05 14898.78 8798.37 20997.30 10298.99 9398.70 14097.18 7999.02 7199.01 14887.50 29899.67 15095.33 24199.33 13999.37 142
viewmanbaseed2359cas97.47 13097.25 12598.14 15898.41 20195.84 20298.57 23398.43 22695.55 17597.97 15799.12 11891.26 17699.15 25897.42 15098.53 18799.43 129
PAPM_NR97.46 13197.11 14398.50 11899.50 4896.41 15898.63 21498.60 16495.18 20297.06 22398.06 26794.26 10099.57 17193.80 30598.87 16499.52 101
EPP-MVSNet97.46 13197.28 12397.99 18398.64 17795.38 23399.33 2198.31 26693.61 30997.19 21599.07 13894.05 10399.23 24396.89 17698.43 19899.37 142
3Dnovator94.51 597.46 13196.93 15799.07 6597.78 29897.64 8299.35 1699.06 4797.02 8993.75 35299.16 10889.25 24499.92 4397.22 16199.75 5399.64 86
CNLPA97.45 13497.03 15098.73 9199.05 12897.44 9598.07 31998.53 18895.32 19596.80 23898.53 22193.32 11399.72 13794.31 28699.31 14199.02 224
lupinMVS97.44 13597.22 13298.12 16698.07 26495.76 21097.68 36697.76 35194.50 25598.79 9398.61 21192.34 12999.30 22197.58 12899.59 9499.31 157
3Dnovator+94.38 697.43 13696.78 16899.38 2497.83 29598.52 3499.37 1398.71 13797.09 8792.99 38299.13 11589.36 24199.89 6896.97 16899.57 9899.71 63
Vis-MVSNetpermissive97.42 13797.11 14398.34 13598.66 17396.23 16799.22 4299.00 5396.63 11398.04 14799.21 9188.05 28599.35 21296.01 21599.21 14599.45 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS97.41 13897.25 12597.91 19198.70 16696.80 13498.82 15498.69 14294.53 25098.11 13898.28 24894.50 9499.57 17194.12 29499.49 11797.37 328
sss97.39 13996.98 15598.61 10298.60 18196.61 14398.22 28998.93 6593.97 27898.01 15398.48 22691.98 14699.85 8496.45 19998.15 22499.39 137
test_cas_vis1_n_192097.38 14097.36 11797.45 23398.95 14293.25 34399.00 9098.53 18897.70 3999.77 1899.35 6284.71 35499.85 8498.57 5299.66 7799.26 178
PVSNet_Blended97.38 14097.12 14298.14 15899.25 9695.35 23697.28 40099.26 1693.13 33197.94 16198.21 25692.74 12199.81 10296.88 17899.40 13199.27 171
E5new97.37 14297.16 13697.98 18498.30 22595.41 22798.87 13398.45 21195.56 17097.84 17099.19 10090.39 20999.25 23297.61 12498.22 21999.29 164
E6new97.37 14297.16 13697.98 18498.28 23195.40 23098.87 13398.45 21195.55 17597.84 17099.20 9390.44 20799.25 23297.61 12498.22 21999.29 164
E697.37 14297.16 13697.98 18498.28 23195.40 23098.87 13398.45 21195.55 17597.84 17099.20 9390.44 20799.25 23297.61 12498.22 21999.29 164
E597.37 14297.16 13697.98 18498.30 22595.41 22798.87 13398.45 21195.56 17097.84 17099.19 10090.39 20999.25 23297.61 12498.22 21999.29 164
E497.37 14297.13 14198.12 16698.27 23395.70 21298.59 22098.44 21595.56 17097.80 17599.18 10390.57 20499.26 22897.45 14798.28 21799.40 136
WTY-MVS97.37 14296.92 15898.72 9298.86 15196.89 13298.31 27798.71 13795.26 19897.67 18998.56 22092.21 13899.78 12495.89 21796.85 27199.48 114
hybrid97.34 14897.16 13697.88 19598.25 23695.18 24598.18 30098.33 25995.36 19298.35 13099.06 13990.61 20299.18 25197.88 10198.40 20699.27 171
AstraMVS97.34 14897.24 12997.65 22298.13 25894.15 30198.94 10796.25 45397.47 5698.60 11399.28 7689.67 22999.41 20698.73 4398.07 22899.38 141
viewmacassd2359aftdt97.32 15097.07 14698.08 17198.30 22595.69 21398.62 21798.44 21595.56 17097.86 16999.22 8989.91 22299.14 26197.29 15898.43 19899.42 132
jason97.32 15097.08 14598.06 17597.45 33195.59 21597.87 34797.91 33894.79 23598.55 11698.83 18091.12 18699.23 24397.58 12899.60 9299.34 149
jason: jason.
MVS_Test97.28 15297.00 15198.13 16398.33 22095.97 18498.74 18098.07 32294.27 26398.44 12498.07 26692.48 12599.26 22896.43 20098.19 22399.16 197
EPNet97.28 15296.87 16098.51 11594.98 44496.14 17298.90 11997.02 42398.28 2195.99 27399.11 12191.36 17099.89 6896.98 16799.19 14799.50 107
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SSM_040497.26 15497.00 15198.03 17798.46 19395.99 17898.62 21798.44 21594.77 23697.24 21298.93 16291.22 17999.28 22596.54 19498.74 17298.84 242
mvsmamba97.25 15596.99 15398.02 17998.34 21695.54 22199.18 5497.47 38195.04 21598.15 13598.57 21989.46 23699.31 22097.68 11899.01 15599.22 184
viewdifsd2359ckpt1397.24 15696.97 15698.06 17598.43 19795.77 20998.59 22098.34 25794.81 23397.60 20098.94 16090.78 19999.09 27396.93 17198.33 21199.32 156
test_yl97.22 15796.78 16898.54 11098.73 16196.60 14498.45 25598.31 26694.70 23998.02 15098.42 23190.80 19599.70 14396.81 18596.79 27399.34 149
DCV-MVSNet97.22 15796.78 16898.54 11098.73 16196.60 14498.45 25598.31 26694.70 23998.02 15098.42 23190.80 19599.70 14396.81 18596.79 27399.34 149
IS-MVSNet97.22 15796.88 15998.25 14398.85 15496.36 16199.19 5097.97 33295.39 18897.23 21398.99 15191.11 18798.93 30394.60 27498.59 18099.47 116
viewdifsd2359ckpt0797.20 16097.05 14897.65 22298.40 20394.33 29398.39 26898.43 22695.67 16397.66 19399.08 13490.04 21999.32 21697.47 14598.29 21599.31 157
PLCcopyleft95.07 497.20 16096.78 16898.44 12699.29 8896.31 16598.14 30798.76 12592.41 35996.39 26198.31 24694.92 8699.78 12494.06 29798.77 17199.23 182
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x42097.18 16297.18 13497.20 24698.81 15793.27 34095.78 46299.15 4195.25 19996.79 23998.11 26492.29 13299.07 27698.56 5499.85 699.25 180
SSM_040797.17 16396.87 16098.08 17198.19 24695.90 19398.52 24098.44 21594.77 23696.75 24098.93 16291.22 17999.22 24796.54 19498.43 19899.10 208
LS3D97.16 16496.66 17798.68 9598.53 18697.19 11698.93 11398.90 7392.83 34495.99 27399.37 5692.12 14199.87 7993.67 30999.57 9898.97 229
AdaColmapbinary97.15 16596.70 17398.48 12199.16 11596.69 14098.01 32698.89 7594.44 25896.83 23498.68 20590.69 20199.76 13094.36 28299.29 14298.98 228
viewdifsd2359ckpt0997.13 16696.79 16698.14 15898.43 19795.90 19398.52 24098.37 24994.32 26197.33 20798.86 17490.23 21799.16 25496.81 18598.25 21899.36 146
Effi-MVS+97.12 16796.69 17498.39 13398.19 24696.72 13997.37 39198.43 22693.71 29697.65 19498.02 27092.20 13999.25 23296.87 18197.79 23799.19 191
CHOSEN 1792x268897.12 16796.80 16498.08 17199.30 8394.56 28298.05 32199.71 193.57 31197.09 21998.91 16788.17 27999.89 6896.87 18199.56 10699.81 25
F-COLMAP97.09 16996.80 16497.97 18899.45 6194.95 26198.55 23798.62 16393.02 33696.17 26898.58 21694.01 10499.81 10293.95 29998.90 16099.14 201
RRT-MVS97.03 17096.78 16897.77 20697.90 29194.34 29199.12 6498.35 25495.87 15298.06 14398.70 20386.45 31799.63 16098.04 9298.54 18699.35 147
TAMVS97.02 17196.79 16697.70 21398.06 26795.31 23998.52 24098.31 26693.95 27997.05 22498.61 21193.49 11198.52 34795.33 24197.81 23699.29 164
viewmambaseed2359dif97.01 17296.84 16297.51 23198.19 24694.21 29998.16 30398.23 28793.61 30997.78 17699.13 11590.79 19899.18 25197.24 15998.40 20699.15 198
CDS-MVSNet96.99 17396.69 17497.90 19298.05 26995.98 17998.20 29298.33 25993.67 30396.95 22698.49 22593.54 11098.42 35895.24 24897.74 24199.31 157
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
casdiffseed41469214796.97 17496.55 18298.25 14398.26 23496.28 16698.93 11398.33 25994.99 22096.87 23399.09 13188.97 25799.07 27695.70 23097.77 23999.39 137
CANet_DTU96.96 17596.55 18298.21 14798.17 25596.07 17697.98 33098.21 28997.24 7497.13 21798.93 16286.88 30999.91 5695.00 25499.37 13598.66 269
114514_t96.93 17696.27 19698.92 7999.50 4897.63 8398.85 14698.90 7384.80 46997.77 17799.11 12192.84 11999.66 15394.85 25799.77 4199.47 116
MAR-MVS96.91 17796.40 19098.45 12498.69 16996.90 13098.66 20898.68 14592.40 36097.07 22297.96 27791.54 16499.75 13293.68 30798.92 15998.69 263
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
HyFIR lowres test96.90 17896.49 18798.14 15899.33 7495.56 21897.38 38999.65 292.34 36197.61 19798.20 25789.29 24399.10 27296.97 16897.60 24699.77 40
Vis-MVSNet (Re-imp)96.87 17996.55 18297.83 19898.73 16195.46 22599.20 4898.30 27394.96 22496.60 24998.87 17290.05 21898.59 34293.67 30998.60 17999.46 121
SDMVSNet96.85 18096.42 18898.14 15899.30 8396.38 15999.21 4599.23 2795.92 14795.96 27598.76 19785.88 32999.44 20397.93 9695.59 31098.60 274
PAPR96.84 18196.24 19898.65 9898.72 16596.92 12997.36 39398.57 17893.33 32096.67 24497.57 31894.30 9899.56 17491.05 38998.59 18099.47 116
HY-MVS93.96 896.82 18296.23 19998.57 10598.46 19397.00 12598.14 30798.21 28993.95 27996.72 24397.99 27491.58 15999.76 13094.51 27896.54 28298.95 232
mamba_040896.81 18396.38 19198.09 17098.19 24695.90 19395.69 46398.32 26294.51 25396.75 24098.73 19990.99 19199.27 22795.83 22098.43 19899.10 208
UGNet96.78 18496.30 19598.19 15398.24 23895.89 19898.88 13098.93 6597.39 6196.81 23797.84 29082.60 38399.90 6496.53 19699.49 11798.79 246
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
IMVS_040796.74 18596.64 17897.05 26197.99 27892.82 35798.45 25598.27 27695.16 20397.30 20898.79 18591.53 16599.06 27894.74 26297.54 25099.27 171
IMVS_040396.74 18596.61 17997.12 25597.99 27892.82 35798.47 25398.27 27695.16 20397.13 21798.79 18591.44 16899.26 22894.74 26297.54 25099.27 171
PVSNet_BlendedMVS96.73 18796.60 18097.12 25599.25 9695.35 23698.26 28699.26 1694.28 26297.94 16197.46 32592.74 12199.81 10296.88 17893.32 34896.20 430
SSM_0407296.71 18896.38 19197.68 21698.19 24695.90 19395.69 46398.32 26294.51 25396.75 24098.73 19990.99 19198.02 41495.83 22098.43 19899.10 208
test_vis1_n_192096.71 18896.84 16296.31 33799.11 12389.74 42599.05 7598.58 17698.08 2499.87 499.37 5678.48 42199.93 3499.29 2799.69 7199.27 171
mvs_anonymous96.70 19096.53 18597.18 24998.19 24693.78 31198.31 27798.19 29394.01 27594.47 30798.27 25192.08 14498.46 35397.39 15497.91 23299.31 157
Elysia96.64 19196.02 20898.51 11598.04 27197.30 10298.74 18098.60 16495.04 21597.91 16598.84 17683.59 37899.48 19694.20 29099.25 14398.75 255
StellarMVS96.64 19196.02 20898.51 11598.04 27197.30 10298.74 18098.60 16495.04 21597.91 16598.84 17683.59 37899.48 19694.20 29099.25 14398.75 255
1112_ss96.63 19396.00 21098.50 11898.56 18296.37 16098.18 30098.10 31592.92 34094.84 29598.43 22992.14 14099.58 17094.35 28396.51 28399.56 100
PMMVS96.60 19496.33 19497.41 23797.90 29193.93 30797.35 39498.41 23192.84 34397.76 17897.45 32791.10 18899.20 24896.26 20597.91 23299.11 206
DP-MVS96.59 19595.93 21398.57 10599.34 7196.19 17098.70 19598.39 24089.45 43194.52 30599.35 6291.85 15099.85 8492.89 33498.88 16299.68 75
PatchMatch-RL96.59 19596.03 20798.27 13999.31 7996.51 15297.91 33999.06 4793.72 29596.92 23098.06 26788.50 27299.65 15491.77 37199.00 15798.66 269
GeoE96.58 19796.07 20498.10 16998.35 21195.89 19899.34 1798.12 30993.12 33296.09 26998.87 17289.71 22898.97 29392.95 33098.08 22799.43 129
icg_test_0407_296.56 19896.50 18696.73 28697.99 27892.82 35797.18 41098.27 27695.16 20397.30 20898.79 18591.53 16598.10 39994.74 26297.54 25099.27 171
XVG-OURS96.55 19996.41 18996.99 26498.75 16093.76 31297.50 38098.52 19195.67 16396.83 23499.30 7488.95 25999.53 18395.88 21896.26 29797.69 317
FIs96.51 20096.12 20397.67 21897.13 35597.54 8899.36 1499.22 3295.89 14994.03 33698.35 23991.98 14698.44 35696.40 20192.76 35697.01 336
XVG-OURS-SEG-HR96.51 20096.34 19397.02 26398.77 15993.76 31297.79 35898.50 19995.45 18396.94 22799.09 13187.87 29099.55 18196.76 19095.83 30997.74 314
PS-MVSNAJss96.43 20296.26 19796.92 27595.84 42495.08 25199.16 5698.50 19995.87 15293.84 34798.34 24394.51 9198.61 33896.88 17893.45 34397.06 334
test_fmvs196.42 20396.67 17695.66 37498.82 15688.53 45298.80 16398.20 29196.39 12599.64 3199.20 9380.35 40899.67 15099.04 3299.57 9898.78 250
FC-MVSNet-test96.42 20396.05 20597.53 23096.95 36497.27 10699.36 1499.23 2795.83 15493.93 33998.37 23792.00 14598.32 37796.02 21492.72 35797.00 337
ab-mvs96.42 20395.71 22498.55 10898.63 17896.75 13797.88 34698.74 12993.84 28596.54 25498.18 25985.34 34099.75 13295.93 21696.35 28799.15 198
FA-MVS(test-final)96.41 20695.94 21297.82 20098.21 24295.20 24497.80 35697.58 36593.21 32697.36 20697.70 30289.47 23499.56 17494.12 29497.99 22998.71 261
PVSNet91.96 1896.35 20796.15 20096.96 27099.17 11192.05 37796.08 45598.68 14593.69 29997.75 18097.80 29688.86 26199.69 14894.26 28899.01 15599.15 198
Test_1112_low_res96.34 20895.66 22998.36 13498.56 18295.94 18797.71 36498.07 32292.10 37094.79 29997.29 34091.75 15499.56 17494.17 29296.50 28499.58 98
viewdifsd2359ckpt1196.30 20996.13 20196.81 28198.10 26192.10 37398.49 25198.40 23496.02 14297.61 19799.31 7186.37 31999.29 22397.52 13793.36 34799.04 221
viewmsd2359difaftdt96.30 20996.13 20196.81 28198.10 26192.10 37398.49 25198.40 23496.02 14297.61 19799.31 7186.37 31999.30 22197.52 13793.37 34699.04 221
Effi-MVS+-dtu96.29 21196.56 18195.51 37997.89 29390.22 41798.80 16398.10 31596.57 11696.45 25996.66 39790.81 19498.91 30695.72 22797.99 22997.40 325
QAPM96.29 21195.40 23598.96 7697.85 29497.60 8599.23 3898.93 6589.76 42593.11 37999.02 14489.11 24999.93 3491.99 36599.62 8999.34 149
Fast-Effi-MVS+96.28 21395.70 22698.03 17798.29 22995.97 18498.58 22498.25 28591.74 37895.29 28897.23 34591.03 19099.15 25892.90 33297.96 23198.97 229
nrg03096.28 21395.72 22197.96 19096.90 36998.15 6499.39 1198.31 26695.47 18294.42 31398.35 23992.09 14398.69 33097.50 14189.05 40997.04 335
131496.25 21595.73 22097.79 20297.13 35595.55 22098.19 29598.59 17193.47 31592.03 41597.82 29491.33 17299.49 19194.62 27298.44 19598.32 294
sd_testset96.17 21695.76 21997.42 23699.30 8394.34 29198.82 15499.08 4595.92 14795.96 27598.76 19782.83 38299.32 21695.56 23495.59 31098.60 274
h-mvs3396.17 21695.62 23097.81 20199.03 13094.45 28498.64 21198.75 12797.48 5498.67 10598.72 20289.76 22599.86 8397.95 9481.59 45999.11 206
HQP_MVS96.14 21895.90 21496.85 27897.42 33394.60 28098.80 16398.56 18297.28 6995.34 28498.28 24887.09 30499.03 28596.07 20994.27 31896.92 344
tttt051796.07 21995.51 23397.78 20398.41 20194.84 26599.28 3094.33 47994.26 26497.64 19598.64 21084.05 36999.47 20095.34 24097.60 24699.03 223
MVSTER96.06 22095.72 22197.08 25998.23 24095.93 19098.73 18698.27 27694.86 23095.07 29098.09 26588.21 27898.54 34596.59 19293.46 34196.79 363
thisisatest053096.01 22195.36 24097.97 18898.38 20695.52 22298.88 13094.19 48294.04 27097.64 19598.31 24683.82 37699.46 20195.29 24597.70 24398.93 234
test_djsdf96.00 22295.69 22796.93 27295.72 42695.49 22399.47 798.40 23494.98 22294.58 30397.86 28789.16 24798.41 36596.91 17294.12 32696.88 353
EI-MVSNet95.96 22395.83 21696.36 33397.93 28993.70 31898.12 31098.27 27693.70 29895.07 29099.02 14492.23 13698.54 34594.68 26793.46 34196.84 359
VortexMVS95.95 22495.79 21796.42 32898.29 22993.96 30698.68 20198.31 26696.02 14294.29 32197.57 31889.47 23498.37 37297.51 14091.93 36696.94 342
ECVR-MVScopyleft95.95 22495.71 22496.65 29599.02 13190.86 39999.03 8291.80 49596.96 9398.10 13999.26 8081.31 39499.51 18796.90 17599.04 15299.59 94
BH-untuned95.95 22495.72 22196.65 29598.55 18492.26 36898.23 28897.79 35093.73 29394.62 30298.01 27288.97 25799.00 29293.04 32798.51 18998.68 265
test111195.94 22795.78 21896.41 32998.99 13890.12 41899.04 7992.45 49496.99 9298.03 14899.27 7981.40 39399.48 19696.87 18199.04 15299.63 88
MSDG95.93 22895.30 24797.83 19898.90 14595.36 23496.83 44298.37 24991.32 39494.43 31298.73 19990.27 21599.60 16690.05 40398.82 16998.52 282
BH-RMVSNet95.92 22995.32 24597.69 21498.32 22394.64 27498.19 29597.45 38694.56 24896.03 27198.61 21185.02 34599.12 26690.68 39499.06 15199.30 161
test_fmvs1_n95.90 23095.99 21195.63 37598.67 17288.32 45699.26 3398.22 28896.40 12499.67 2899.26 8073.91 46399.70 14399.02 3399.50 11598.87 239
Fast-Effi-MVS+-dtu95.87 23195.85 21595.91 35997.74 30391.74 38398.69 19898.15 30595.56 17094.92 29397.68 30788.98 25698.79 32493.19 32197.78 23897.20 332
LFMVS95.86 23294.98 26298.47 12298.87 15096.32 16398.84 15096.02 45493.40 31898.62 11199.20 9374.99 45599.63 16097.72 11197.20 25999.46 121
baseline195.84 23395.12 25598.01 18198.49 19195.98 17998.73 18697.03 42095.37 19196.22 26498.19 25889.96 22199.16 25494.60 27487.48 42598.90 237
OpenMVScopyleft93.04 1395.83 23495.00 26098.32 13697.18 35297.32 9999.21 4598.97 5789.96 42191.14 42499.05 14186.64 31299.92 4393.38 31599.47 12197.73 315
IMVS_040495.82 23595.52 23196.73 28697.99 27892.82 35797.23 40298.27 27695.16 20394.31 31998.79 18585.63 33398.10 39994.74 26297.54 25099.27 171
VDD-MVS95.82 23595.23 24997.61 22698.84 15593.98 30598.68 20197.40 39095.02 21997.95 15999.34 6874.37 46199.78 12498.64 4896.80 27299.08 215
UniMVSNet (Re)95.78 23795.19 25197.58 22796.99 36297.47 9298.79 17199.18 3695.60 16693.92 34097.04 36791.68 15698.48 34995.80 22487.66 42496.79 363
VPA-MVSNet95.75 23895.11 25697.69 21497.24 34497.27 10698.94 10799.23 2795.13 20895.51 28297.32 33885.73 33198.91 30697.33 15789.55 40096.89 352
HQP-MVS95.72 23995.40 23596.69 29297.20 34894.25 29798.05 32198.46 20796.43 12094.45 30897.73 29986.75 31098.96 29795.30 24394.18 32296.86 358
hse-mvs295.71 24095.30 24796.93 27298.50 18793.53 32398.36 26998.10 31597.48 5498.67 10597.99 27489.76 22599.02 28997.95 9480.91 46598.22 297
UniMVSNet_NR-MVSNet95.71 24095.15 25297.40 23996.84 37296.97 12698.74 18099.24 2095.16 20393.88 34297.72 30191.68 15698.31 37995.81 22287.25 43096.92 344
PatchmatchNetpermissive95.71 24095.52 23196.29 33997.58 31690.72 40396.84 44197.52 37694.06 26997.08 22096.96 37789.24 24598.90 30992.03 36498.37 20899.26 178
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS95.69 24395.33 24496.76 28596.16 40894.63 27598.43 26398.39 24096.64 11295.02 29298.78 18985.15 34499.05 27995.21 25094.20 32196.60 389
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMM93.85 995.69 24395.38 23996.61 30397.61 31393.84 31098.91 11898.44 21595.25 19994.28 32298.47 22786.04 32899.12 26695.50 23793.95 33196.87 356
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst95.63 24595.69 22795.44 38397.54 32188.54 45196.97 42597.56 36893.50 31397.52 20496.93 38189.49 23299.16 25495.25 24796.42 28698.64 271
FE-MVS95.62 24694.90 26697.78 20398.37 20994.92 26297.17 41397.38 39290.95 40597.73 18397.70 30285.32 34299.63 16091.18 38198.33 21198.79 246
LPG-MVS_test95.62 24695.34 24196.47 32297.46 32893.54 32198.99 9398.54 18694.67 24394.36 31698.77 19285.39 33799.11 26895.71 22894.15 32496.76 366
CLD-MVS95.62 24695.34 24196.46 32597.52 32493.75 31497.27 40198.46 20795.53 17894.42 31398.00 27386.21 32398.97 29396.25 20794.37 31696.66 381
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest051595.61 24994.89 26797.76 20798.15 25795.15 24896.77 44394.41 47792.95 33997.18 21697.43 32984.78 35199.45 20294.63 27097.73 24298.68 265
MonoMVSNet95.51 25095.45 23495.68 37295.54 43190.87 39898.92 11697.37 39395.79 15695.53 28197.38 33489.58 23197.68 43796.40 20192.59 35898.49 284
thres600view795.49 25194.77 27097.67 21898.98 13995.02 25398.85 14696.90 43095.38 18996.63 24696.90 38384.29 36199.59 16788.65 42796.33 28898.40 288
test_vis1_n95.47 25295.13 25396.49 31997.77 29990.41 41399.27 3298.11 31296.58 11499.66 2999.18 10367.00 47899.62 16499.21 2899.40 13199.44 126
SCA95.46 25395.13 25396.46 32597.67 30891.29 39197.33 39697.60 36494.68 24296.92 23097.10 35283.97 37198.89 31092.59 34898.32 21499.20 187
IterMVS-LS95.46 25395.21 25096.22 34198.12 25993.72 31798.32 27698.13 30893.71 29694.26 32397.31 33992.24 13598.10 39994.63 27090.12 39196.84 359
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing3-295.45 25595.34 24195.77 37098.69 16988.75 44798.87 13397.21 40796.13 13697.22 21497.68 30777.95 42999.65 15497.58 12896.77 27598.91 236
jajsoiax95.45 25595.03 25996.73 28695.42 43994.63 27599.14 6098.52 19195.74 15893.22 37298.36 23883.87 37498.65 33596.95 17094.04 32796.91 349
CVMVSNet95.43 25796.04 20693.57 43697.93 28983.62 47698.12 31098.59 17195.68 16296.56 25099.02 14487.51 29697.51 44693.56 31397.44 25599.60 92
anonymousdsp95.42 25894.91 26596.94 27195.10 44395.90 19399.14 6098.41 23193.75 29093.16 37597.46 32587.50 29898.41 36595.63 23394.03 32896.50 414
DU-MVS95.42 25894.76 27197.40 23996.53 38996.97 12698.66 20898.99 5695.43 18493.88 34297.69 30488.57 26798.31 37995.81 22287.25 43096.92 344
mvs_tets95.41 26095.00 26096.65 29595.58 43094.42 28699.00 9098.55 18495.73 16093.21 37398.38 23683.45 38098.63 33697.09 16494.00 32996.91 349
thres100view90095.38 26194.70 27597.41 23798.98 13994.92 26298.87 13396.90 43095.38 18996.61 24896.88 38484.29 36199.56 17488.11 43096.29 29297.76 312
thres40095.38 26194.62 27997.65 22298.94 14394.98 25898.68 20196.93 42895.33 19396.55 25296.53 40384.23 36599.56 17488.11 43096.29 29298.40 288
BH-w/o95.38 26195.08 25796.26 34098.34 21691.79 38097.70 36597.43 38892.87 34294.24 32597.22 34688.66 26598.84 31691.55 37797.70 24398.16 301
VDDNet95.36 26494.53 28497.86 19698.10 26195.13 24998.85 14697.75 35290.46 41298.36 12899.39 5073.27 46599.64 15797.98 9396.58 28098.81 245
TAPA-MVS93.98 795.35 26594.56 28397.74 20999.13 11994.83 26798.33 27298.64 15886.62 45796.29 26398.61 21194.00 10599.29 22380.00 47499.41 12899.09 211
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP93.49 1095.34 26694.98 26296.43 32797.67 30893.48 32598.73 18698.44 21594.94 22892.53 39698.53 22184.50 36099.14 26195.48 23894.00 32996.66 381
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROBcopyleft93.27 1295.33 26794.87 26896.71 28999.29 8893.24 34498.58 22498.11 31289.92 42293.57 35799.10 12386.37 31999.79 12190.78 39298.10 22697.09 333
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UBG95.32 26894.72 27497.13 25398.05 26993.26 34197.87 34797.20 40894.96 22496.18 26795.66 43980.97 40099.35 21294.47 28097.08 26298.78 250
tfpn200view995.32 26894.62 27997.43 23598.94 14394.98 25898.68 20196.93 42895.33 19396.55 25296.53 40384.23 36599.56 17488.11 43096.29 29297.76 312
Anonymous20240521195.28 27094.49 28697.67 21899.00 13593.75 31498.70 19597.04 41990.66 40896.49 25698.80 18378.13 42599.83 9096.21 20895.36 31499.44 126
thres20095.25 27194.57 28297.28 24398.81 15794.92 26298.20 29297.11 41295.24 20196.54 25496.22 41684.58 35899.53 18387.93 43596.50 28497.39 326
AllTest95.24 27294.65 27896.99 26499.25 9693.21 34598.59 22098.18 29691.36 39093.52 35998.77 19284.67 35599.72 13789.70 41097.87 23498.02 306
LCM-MVSNet-Re95.22 27395.32 24594.91 40098.18 25287.85 46298.75 17695.66 46195.11 21088.96 44796.85 38790.26 21697.65 43895.65 23298.44 19599.22 184
EPNet_dtu95.21 27494.95 26495.99 35296.17 40690.45 41198.16 30397.27 40296.77 10293.14 37898.33 24490.34 21298.42 35885.57 44998.81 17099.09 211
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS95.20 27594.45 29297.46 23296.75 37996.56 15098.86 14198.65 15793.30 32393.27 37198.27 25184.85 34998.87 31394.82 25991.26 37796.96 339
D2MVS95.18 27695.08 25795.48 38097.10 35792.07 37698.30 28099.13 4394.02 27292.90 38396.73 39389.48 23398.73 32894.48 27993.60 34095.65 445
WR-MVS95.15 27794.46 28997.22 24596.67 38496.45 15498.21 29098.81 10794.15 26693.16 37597.69 30487.51 29698.30 38195.29 24588.62 41596.90 351
TranMVSNet+NR-MVSNet95.14 27894.48 28797.11 25796.45 39596.36 16199.03 8299.03 5095.04 21593.58 35697.93 28088.27 27798.03 41394.13 29386.90 43596.95 341
myMVS_eth3d2895.12 27994.62 27996.64 29998.17 25592.17 36998.02 32597.32 39695.41 18796.22 26496.05 42278.01 42799.13 26395.22 24997.16 26098.60 274
baseline295.11 28094.52 28596.87 27796.65 38593.56 32098.27 28594.10 48493.45 31692.02 41697.43 32987.45 30199.19 24993.88 30297.41 25797.87 310
miper_enhance_ethall95.10 28194.75 27296.12 34597.53 32393.73 31696.61 44998.08 32092.20 36993.89 34196.65 39992.44 12698.30 38194.21 28991.16 37896.34 423
Anonymous2024052995.10 28194.22 30497.75 20899.01 13394.26 29698.87 13398.83 9885.79 46596.64 24598.97 15278.73 41899.85 8496.27 20494.89 31599.12 203
test-LLR95.10 28194.87 26895.80 36796.77 37689.70 42796.91 43095.21 46695.11 21094.83 29795.72 43587.71 29298.97 29393.06 32598.50 19098.72 258
WR-MVS_H95.05 28494.46 28996.81 28196.86 37195.82 20599.24 3699.24 2093.87 28492.53 39696.84 38890.37 21198.24 38793.24 31987.93 42196.38 422
miper_ehance_all_eth95.01 28594.69 27695.97 35697.70 30693.31 33797.02 42398.07 32292.23 36693.51 36196.96 37791.85 15098.15 39493.68 30791.16 37896.44 420
testing1195.00 28694.28 29997.16 25197.96 28693.36 33498.09 31797.06 41894.94 22895.33 28796.15 41876.89 44299.40 20795.77 22696.30 29198.72 258
ADS-MVSNet95.00 28694.45 29296.63 30098.00 27691.91 37996.04 45697.74 35390.15 41896.47 25796.64 40087.89 28898.96 29790.08 40197.06 26399.02 224
VPNet94.99 28894.19 30697.40 23997.16 35396.57 14998.71 19198.97 5795.67 16394.84 29598.24 25580.36 40798.67 33496.46 19887.32 42996.96 339
EPMVS94.99 28894.48 28796.52 31697.22 34691.75 38297.23 40291.66 49694.11 26797.28 21096.81 39085.70 33298.84 31693.04 32797.28 25898.97 229
testing9194.98 29094.25 30397.20 24697.94 28793.41 32898.00 32897.58 36594.99 22095.45 28396.04 42377.20 43799.42 20594.97 25596.02 30598.78 250
NR-MVSNet94.98 29094.16 30997.44 23496.53 38997.22 11498.74 18098.95 6194.96 22489.25 44597.69 30489.32 24298.18 39194.59 27687.40 42796.92 344
FMVSNet394.97 29294.26 30297.11 25798.18 25296.62 14198.56 23698.26 28493.67 30394.09 33297.10 35284.25 36398.01 41592.08 36092.14 36396.70 375
usedtu_dtu_shiyan194.96 29394.28 29996.98 26795.93 41996.11 17497.08 41998.39 24093.62 30793.86 34496.40 40888.28 27598.21 38892.61 34392.36 36196.63 383
FE-MVSNET394.96 29394.28 29996.98 26795.93 41996.11 17497.08 41998.39 24093.62 30793.86 34496.40 40888.28 27598.21 38892.61 34392.36 36196.63 383
CostFormer94.95 29594.73 27395.60 37797.28 34289.06 44097.53 37796.89 43289.66 42796.82 23696.72 39486.05 32698.95 30295.53 23696.13 30398.79 246
PAPM94.95 29594.00 32297.78 20397.04 35995.65 21496.03 45898.25 28591.23 39994.19 32897.80 29691.27 17598.86 31582.61 46697.61 24598.84 242
CP-MVSNet94.94 29794.30 29896.83 27996.72 38195.56 21899.11 6698.95 6193.89 28292.42 40297.90 28387.19 30398.12 39894.32 28588.21 41896.82 362
TR-MVS94.94 29794.20 30597.17 25097.75 30094.14 30297.59 37497.02 42392.28 36595.75 27997.64 31283.88 37398.96 29789.77 40796.15 30298.40 288
RPSCF94.87 29995.40 23593.26 44298.89 14682.06 48298.33 27298.06 32790.30 41796.56 25099.26 8087.09 30499.49 19193.82 30496.32 28998.24 295
testing9994.83 30094.08 31497.07 26097.94 28793.13 34798.10 31697.17 41094.86 23095.34 28496.00 42776.31 44599.40 20795.08 25295.90 30698.68 265
GA-MVS94.81 30194.03 31897.14 25297.15 35493.86 30996.76 44497.58 36594.00 27694.76 30197.04 36780.91 40198.48 34991.79 37096.25 29899.09 211
c3_l94.79 30294.43 29495.89 36197.75 30093.12 34997.16 41598.03 32992.23 36693.46 36597.05 36691.39 16998.01 41593.58 31289.21 40796.53 405
V4294.78 30394.14 31196.70 29196.33 40095.22 24398.97 9798.09 31992.32 36394.31 31997.06 36388.39 27398.55 34492.90 33288.87 41396.34 423
reproduce_monomvs94.77 30494.67 27795.08 39598.40 20389.48 43398.80 16398.64 15897.57 4893.21 37397.65 30980.57 40698.83 31997.72 11189.47 40396.93 343
CR-MVSNet94.76 30594.15 31096.59 30697.00 36093.43 32694.96 47597.56 36892.46 35496.93 22896.24 41288.15 28097.88 42987.38 43896.65 27898.46 286
v2v48294.69 30694.03 31896.65 29596.17 40694.79 27098.67 20698.08 32092.72 34694.00 33797.16 34987.69 29598.45 35492.91 33188.87 41396.72 371
pmmvs494.69 30693.99 32496.81 28195.74 42595.94 18797.40 38797.67 35790.42 41493.37 36897.59 31689.08 25098.20 39092.97 32991.67 37196.30 426
cl2294.68 30894.19 30696.13 34498.11 26093.60 31996.94 42798.31 26692.43 35893.32 37096.87 38686.51 31398.28 38594.10 29691.16 37896.51 412
eth_miper_zixun_eth94.68 30894.41 29595.47 38197.64 31191.71 38496.73 44698.07 32292.71 34793.64 35397.21 34790.54 20598.17 39293.38 31589.76 39596.54 403
PCF-MVS93.45 1194.68 30893.43 36098.42 13098.62 17996.77 13695.48 46998.20 29184.63 47093.34 36998.32 24588.55 27099.81 10284.80 45898.96 15898.68 265
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS94.67 31193.54 35598.08 17196.88 37096.56 15098.19 29598.50 19978.05 48792.69 39098.02 27091.07 18999.63 16090.09 40098.36 21098.04 305
PS-CasMVS94.67 31193.99 32496.71 28996.68 38395.26 24099.13 6399.03 5093.68 30192.33 40697.95 27885.35 33998.10 39993.59 31188.16 42096.79 363
cascas94.63 31393.86 33496.93 27296.91 36894.27 29596.00 45998.51 19485.55 46694.54 30496.23 41484.20 36798.87 31395.80 22496.98 26897.66 318
tpmvs94.60 31494.36 29795.33 38797.46 32888.60 45096.88 43897.68 35491.29 39693.80 34996.42 40788.58 26699.24 23991.06 38796.04 30498.17 300
LTVRE_ROB92.95 1594.60 31493.90 33096.68 29397.41 33694.42 28698.52 24098.59 17191.69 38191.21 42398.35 23984.87 34899.04 28291.06 38793.44 34496.60 389
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
v114494.59 31693.92 32796.60 30596.21 40294.78 27198.59 22098.14 30791.86 37794.21 32797.02 37087.97 28698.41 36591.72 37289.57 39896.61 387
ADS-MVSNet294.58 31794.40 29695.11 39398.00 27688.74 44896.04 45697.30 39890.15 41896.47 25796.64 40087.89 28897.56 44490.08 40197.06 26399.02 224
WBMVS94.56 31894.04 31696.10 34698.03 27393.08 35197.82 35598.18 29694.02 27293.77 35196.82 38981.28 39598.34 37495.47 23991.00 38196.88 353
ACMH92.88 1694.55 31993.95 32696.34 33597.63 31293.26 34198.81 16298.49 20493.43 31789.74 43998.53 22181.91 38799.08 27593.69 30693.30 34996.70 375
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt080594.54 32093.85 33596.63 30097.98 28493.06 35298.77 17597.84 34193.67 30393.80 34998.04 26976.88 44398.96 29794.79 26192.86 35497.86 311
XVG-ACMP-BASELINE94.54 32094.14 31195.75 37196.55 38891.65 38598.11 31498.44 21594.96 22494.22 32697.90 28379.18 41699.11 26894.05 29893.85 33396.48 417
AUN-MVS94.53 32293.73 34596.92 27598.50 18793.52 32498.34 27198.10 31593.83 28795.94 27797.98 27685.59 33599.03 28594.35 28380.94 46498.22 297
DIV-MVS_self_test94.52 32394.03 31895.99 35297.57 32093.38 33297.05 42197.94 33591.74 37892.81 38597.10 35289.12 24898.07 40792.60 34690.30 38896.53 405
cl____94.51 32494.01 32196.02 34897.58 31693.40 33197.05 42197.96 33491.73 38092.76 38797.08 35889.06 25198.13 39692.61 34390.29 38996.52 408
ETVMVS94.50 32593.44 35997.68 21698.18 25295.35 23698.19 29597.11 41293.73 29396.40 26095.39 44274.53 45898.84 31691.10 38396.31 29098.84 242
GBi-Net94.49 32693.80 33896.56 31098.21 24295.00 25498.82 15498.18 29692.46 35494.09 33297.07 35981.16 39697.95 42092.08 36092.14 36396.72 371
test194.49 32693.80 33896.56 31098.21 24295.00 25498.82 15498.18 29692.46 35494.09 33297.07 35981.16 39697.95 42092.08 36092.14 36396.72 371
dmvs_re94.48 32894.18 30895.37 38597.68 30790.11 41998.54 23997.08 41494.56 24894.42 31397.24 34484.25 36397.76 43591.02 39092.83 35598.24 295
v894.47 32993.77 34196.57 30996.36 39894.83 26799.05 7598.19 29391.92 37493.16 37596.97 37588.82 26498.48 34991.69 37387.79 42296.39 421
FMVSNet294.47 32993.61 35197.04 26298.21 24296.43 15698.79 17198.27 27692.46 35493.50 36297.09 35681.16 39698.00 41791.09 38491.93 36696.70 375
test250694.44 33193.91 32996.04 34799.02 13188.99 44399.06 7379.47 51096.96 9398.36 12899.26 8077.21 43699.52 18696.78 18999.04 15299.59 94
Patchmatch-test94.42 33293.68 34996.63 30097.60 31491.76 38194.83 47997.49 38089.45 43194.14 33097.10 35288.99 25398.83 31985.37 45298.13 22599.29 164
PEN-MVS94.42 33293.73 34596.49 31996.28 40194.84 26599.17 5599.00 5393.51 31292.23 40897.83 29386.10 32597.90 42492.55 35186.92 43496.74 368
v14419294.39 33493.70 34796.48 32196.06 41294.35 29098.58 22498.16 30491.45 38794.33 31897.02 37087.50 29898.45 35491.08 38689.11 40896.63 383
Baseline_NR-MVSNet94.35 33593.81 33795.96 35796.20 40394.05 30498.61 21996.67 44291.44 38893.85 34697.60 31588.57 26798.14 39594.39 28186.93 43395.68 444
miper_lstm_enhance94.33 33694.07 31595.11 39397.75 30090.97 39597.22 40498.03 32991.67 38292.76 38796.97 37590.03 22097.78 43492.51 35389.64 39796.56 400
v119294.32 33793.58 35296.53 31596.10 41094.45 28498.50 24898.17 30291.54 38594.19 32897.06 36386.95 30898.43 35790.14 39989.57 39896.70 375
UWE-MVS94.30 33893.89 33295.53 37897.83 29588.95 44497.52 37993.25 48794.44 25896.63 24697.07 35978.70 41999.28 22591.99 36597.56 24998.36 291
ACMH+92.99 1494.30 33893.77 34195.88 36297.81 29792.04 37898.71 19198.37 24993.99 27790.60 43098.47 22780.86 40399.05 27992.75 33992.40 36096.55 402
v14894.29 34093.76 34395.91 35996.10 41092.93 35598.58 22497.97 33292.59 35293.47 36496.95 37988.53 27198.32 37792.56 35087.06 43296.49 415
v1094.29 34093.55 35496.51 31796.39 39794.80 26998.99 9398.19 29391.35 39293.02 38196.99 37388.09 28298.41 36590.50 39688.41 41796.33 425
SD_040394.28 34294.46 28993.73 43398.02 27485.32 47298.31 27798.40 23494.75 23893.59 35498.16 26089.01 25296.54 46582.32 46797.58 24899.34 149
MVP-Stereo94.28 34293.92 32795.35 38694.95 44592.60 36397.97 33197.65 35891.61 38390.68 42997.09 35686.32 32298.42 35889.70 41099.34 13795.02 458
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UniMVSNet_ETH3D94.24 34493.33 36296.97 26997.19 35193.38 33298.74 18098.57 17891.21 40193.81 34898.58 21672.85 46798.77 32695.05 25393.93 33298.77 253
OurMVSNet-221017-094.21 34594.00 32294.85 40595.60 42989.22 43898.89 12397.43 38895.29 19692.18 41198.52 22482.86 38198.59 34293.46 31491.76 36996.74 368
v192192094.20 34693.47 35896.40 33195.98 41694.08 30398.52 24098.15 30591.33 39394.25 32497.20 34886.41 31898.42 35890.04 40489.39 40596.69 380
WB-MVSnew94.19 34794.04 31694.66 41396.82 37492.14 37097.86 34995.96 45793.50 31395.64 28096.77 39288.06 28497.99 41884.87 45596.86 26993.85 479
v7n94.19 34793.43 36096.47 32295.90 42194.38 28999.26 3398.34 25791.99 37292.76 38797.13 35188.31 27498.52 34789.48 41587.70 42396.52 408
tpm294.19 34793.76 34395.46 38297.23 34589.04 44197.31 39896.85 43687.08 45096.21 26696.79 39183.75 37798.74 32792.43 35696.23 30098.59 277
TESTMET0.1,194.18 35093.69 34895.63 37596.92 36689.12 43996.91 43094.78 47493.17 32894.88 29496.45 40678.52 42098.92 30493.09 32498.50 19098.85 240
dp94.15 35193.90 33094.90 40197.31 34186.82 46796.97 42597.19 40991.22 40096.02 27296.61 40285.51 33699.02 28990.00 40594.30 31798.85 240
ET-MVSNet_ETH3D94.13 35292.98 37097.58 22798.22 24196.20 16897.31 39895.37 46594.53 25079.56 48797.63 31486.51 31397.53 44596.91 17290.74 38399.02 224
tpm94.13 35293.80 33895.12 39296.50 39187.91 46197.44 38395.89 46092.62 35096.37 26296.30 41184.13 36898.30 38193.24 31991.66 37299.14 201
testing22294.12 35493.03 36997.37 24298.02 27494.66 27297.94 33596.65 44494.63 24595.78 27895.76 43071.49 46898.92 30491.17 38295.88 30798.52 282
IterMVS-SCA-FT94.11 35593.87 33394.85 40597.98 28490.56 41097.18 41098.11 31293.75 29092.58 39397.48 32483.97 37197.41 44892.48 35591.30 37596.58 396
Anonymous2023121194.10 35693.26 36596.61 30399.11 12394.28 29499.01 8898.88 7886.43 45992.81 38597.57 31881.66 39298.68 33394.83 25889.02 41196.88 353
IterMVS94.09 35793.85 33594.80 40997.99 27890.35 41597.18 41098.12 30993.68 30192.46 40097.34 33584.05 36997.41 44892.51 35391.33 37496.62 386
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter94.08 35893.51 35695.80 36796.77 37689.70 42796.91 43095.21 46692.89 34194.83 29795.72 43577.69 43198.97 29393.06 32598.50 19098.72 258
test0.0.03 194.08 35893.51 35695.80 36795.53 43392.89 35697.38 38995.97 45695.11 21092.51 39896.66 39787.71 29296.94 45587.03 44093.67 33697.57 322
v124094.06 36093.29 36496.34 33596.03 41493.90 30898.44 26198.17 30291.18 40294.13 33197.01 37286.05 32698.42 35889.13 42189.50 40296.70 375
X-MVStestdata94.06 36092.30 38699.34 3299.70 2798.35 5099.29 2898.88 7897.40 5998.46 11943.50 53195.90 4899.89 6897.85 10399.74 5799.78 33
DTE-MVSNet93.98 36293.26 36596.14 34396.06 41294.39 28899.20 4898.86 9193.06 33491.78 41797.81 29585.87 33097.58 44390.53 39586.17 43996.46 419
pm-mvs193.94 36393.06 36896.59 30696.49 39295.16 24698.95 10498.03 32992.32 36391.08 42597.84 29084.54 35998.41 36592.16 35886.13 44296.19 431
MS-PatchMatch93.84 36493.63 35094.46 42396.18 40589.45 43497.76 36098.27 27692.23 36692.13 41397.49 32379.50 41398.69 33089.75 40899.38 13395.25 450
tfpnnormal93.66 36592.70 37696.55 31496.94 36595.94 18798.97 9799.19 3591.04 40391.38 42297.34 33584.94 34798.61 33885.45 45189.02 41195.11 454
EU-MVSNet93.66 36594.14 31192.25 45495.96 41883.38 47898.52 24098.12 30994.69 24192.61 39298.13 26387.36 30296.39 47091.82 36990.00 39396.98 338
our_test_393.65 36793.30 36394.69 41195.45 43789.68 42996.91 43097.65 35891.97 37391.66 42096.88 38489.67 22997.93 42388.02 43391.49 37396.48 417
pmmvs593.65 36792.97 37195.68 37295.49 43492.37 36598.20 29297.28 40189.66 42792.58 39397.26 34182.14 38698.09 40393.18 32290.95 38296.58 396
SSC-MVS3.293.59 36993.13 36794.97 39896.81 37589.71 42697.95 33298.49 20494.59 24793.50 36296.91 38277.74 43098.37 37291.69 37390.47 38696.83 361
test_fmvs293.43 37093.58 35292.95 44796.97 36383.91 47599.19 5097.24 40495.74 15895.20 28998.27 25169.65 47098.72 32996.26 20593.73 33596.24 428
tpm cat193.36 37192.80 37395.07 39697.58 31687.97 46096.76 44497.86 34082.17 47793.53 35896.04 42386.13 32499.13 26389.24 41995.87 30898.10 303
JIA-IIPM93.35 37292.49 38295.92 35896.48 39390.65 40595.01 47496.96 42685.93 46396.08 27087.33 49687.70 29498.78 32591.35 37995.58 31298.34 292
SixPastTwentyTwo93.34 37392.86 37294.75 41095.67 42789.41 43698.75 17696.67 44293.89 28290.15 43698.25 25480.87 40298.27 38690.90 39190.64 38496.57 398
USDC93.33 37492.71 37595.21 38996.83 37390.83 40196.91 43097.50 37893.84 28590.72 42898.14 26277.69 43198.82 32189.51 41493.21 35195.97 437
IB-MVS91.98 1793.27 37591.97 39097.19 24897.47 32793.41 32897.09 41895.99 45593.32 32192.47 39995.73 43378.06 42699.53 18394.59 27682.98 45398.62 272
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
MIMVSNet93.26 37692.21 38796.41 32997.73 30493.13 34795.65 46597.03 42091.27 39894.04 33596.06 42175.33 45197.19 45186.56 44296.23 30098.92 235
ppachtmachnet_test93.22 37792.63 37794.97 39895.45 43790.84 40096.88 43897.88 33990.60 40992.08 41497.26 34188.08 28397.86 43085.12 45490.33 38796.22 429
Patchmtry93.22 37792.35 38595.84 36696.77 37693.09 35094.66 48297.56 36887.37 44992.90 38396.24 41288.15 28097.90 42487.37 43990.10 39296.53 405
testing393.19 37992.48 38395.30 38898.07 26492.27 36698.64 21197.17 41093.94 28193.98 33897.04 36767.97 47596.01 47488.40 42897.14 26197.63 319
FMVSNet193.19 37992.07 38896.56 31097.54 32195.00 25498.82 15498.18 29690.38 41592.27 40797.07 35973.68 46497.95 42089.36 41791.30 37596.72 371
LF4IMVS93.14 38192.79 37494.20 42895.88 42288.67 44997.66 36897.07 41693.81 28891.71 41897.65 30977.96 42898.81 32291.47 37891.92 36895.12 453
mmtdpeth93.12 38292.61 37894.63 41597.60 31489.68 42999.21 4597.32 39694.02 27297.72 18494.42 45377.01 44199.44 20399.05 3177.18 47794.78 463
testgi93.06 38392.45 38494.88 40396.43 39689.90 42198.75 17697.54 37495.60 16691.63 42197.91 28274.46 46097.02 45386.10 44593.67 33697.72 316
PatchT93.06 38391.97 39096.35 33496.69 38292.67 36294.48 48697.08 41486.62 45797.08 22092.23 47987.94 28797.90 42478.89 48096.69 27698.49 284
RPMNet92.81 38591.34 39697.24 24497.00 36093.43 32694.96 47598.80 11482.27 47696.93 22892.12 48086.98 30799.82 9776.32 48796.65 27898.46 286
UWE-MVS-2892.79 38692.51 38193.62 43596.46 39486.28 46897.93 33692.71 49294.17 26594.78 30097.16 34981.05 39996.43 46881.45 47096.86 26998.14 302
myMVS_eth3d92.73 38792.01 38994.89 40297.39 33790.94 39697.91 33997.46 38293.16 32993.42 36695.37 44368.09 47496.12 47288.34 42996.99 26597.60 320
TransMVSNet (Re)92.67 38891.51 39596.15 34296.58 38794.65 27398.90 11996.73 43890.86 40689.46 44497.86 28785.62 33498.09 40386.45 44381.12 46295.71 443
ttmdpeth92.61 38991.96 39294.55 41794.10 45690.60 40998.52 24097.29 39992.67 34890.18 43497.92 28179.75 41297.79 43291.09 38486.15 44195.26 449
Syy-MVS92.55 39092.61 37892.38 45097.39 33783.41 47797.91 33997.46 38293.16 32993.42 36695.37 44384.75 35296.12 47277.00 48696.99 26597.60 320
K. test v392.55 39091.91 39394.48 42195.64 42889.24 43799.07 7294.88 47394.04 27086.78 46297.59 31677.64 43497.64 43992.08 36089.43 40496.57 398
DSMNet-mixed92.52 39292.58 38092.33 45194.15 45482.65 48098.30 28094.26 48189.08 43792.65 39195.73 43385.01 34695.76 47686.24 44497.76 24098.59 277
TinyColmap92.31 39391.53 39494.65 41496.92 36689.75 42496.92 42896.68 44190.45 41389.62 44197.85 28976.06 44898.81 32286.74 44192.51 35995.41 447
gg-mvs-nofinetune92.21 39490.58 40297.13 25396.75 37995.09 25095.85 46089.40 50285.43 46794.50 30681.98 50280.80 40498.40 37192.16 35898.33 21197.88 309
FMVSNet591.81 39590.92 39894.49 42097.21 34792.09 37598.00 32897.55 37389.31 43490.86 42795.61 44074.48 45995.32 48085.57 44989.70 39696.07 435
pmmvs691.77 39690.63 40195.17 39194.69 45191.24 39298.67 20697.92 33786.14 46189.62 44197.56 32175.79 44998.34 37490.75 39384.56 44695.94 438
Anonymous2023120691.66 39791.10 39793.33 44094.02 46087.35 46498.58 22497.26 40390.48 41190.16 43596.31 41083.83 37596.53 46679.36 47789.90 39496.12 433
Patchmatch-RL test91.49 39890.85 39993.41 43891.37 48484.40 47392.81 49295.93 45991.87 37687.25 45894.87 44988.99 25396.53 46692.54 35282.00 45699.30 161
blended_shiyan891.42 39989.89 41096.01 34991.50 48193.30 33897.48 38197.83 34286.93 45292.57 39592.37 47782.46 38498.13 39692.86 33774.99 48496.61 387
blended_shiyan691.37 40089.84 41195.98 35591.49 48293.28 33997.48 38197.83 34286.93 45292.43 40192.36 47882.44 38598.06 40892.74 34274.82 48796.59 392
test_040291.32 40190.27 40594.48 42196.60 38691.12 39398.50 24897.22 40586.10 46288.30 45496.98 37477.65 43397.99 41878.13 48292.94 35394.34 465
test_vis1_rt91.29 40290.65 40093.19 44497.45 33186.25 46998.57 23390.90 50093.30 32386.94 46193.59 46362.07 48699.11 26897.48 14495.58 31294.22 469
PVSNet_088.72 1991.28 40390.03 40895.00 39797.99 27887.29 46594.84 47898.50 19992.06 37189.86 43895.19 44579.81 41199.39 21092.27 35769.79 50298.33 293
mvs5depth91.23 40490.17 40694.41 42592.09 47689.79 42395.26 47296.50 44790.73 40791.69 41997.06 36376.12 44798.62 33788.02 43384.11 44994.82 460
Anonymous2024052191.18 40590.44 40393.42 43793.70 46188.47 45398.94 10797.56 36888.46 44389.56 44395.08 44877.15 43996.97 45483.92 46189.55 40094.82 460
wanda-best-256-51291.17 40689.60 41495.88 36291.33 48592.99 35396.89 43597.82 34586.89 45592.36 40391.75 48381.83 38898.06 40892.75 33974.82 48796.59 392
FE-blended-shiyan791.17 40689.60 41495.88 36291.33 48592.99 35396.89 43597.82 34586.89 45592.36 40391.75 48381.83 38898.06 40892.75 33974.82 48796.59 392
EG-PatchMatch MVS91.13 40890.12 40794.17 43094.73 45089.00 44298.13 30997.81 34989.22 43585.32 47296.46 40567.71 47698.42 35887.89 43793.82 33495.08 455
TDRefinement91.06 40989.68 41295.21 38985.35 51091.49 38898.51 24797.07 41691.47 38688.83 45197.84 29077.31 43599.09 27392.79 33877.98 47595.04 457
gbinet_0.2-2-1-0.0291.03 41089.37 42096.01 34991.39 48393.41 32897.19 40897.82 34587.00 45192.18 41191.87 48278.97 41798.04 41293.13 32374.75 49196.60 389
sc_t191.01 41189.39 41695.85 36595.99 41590.39 41498.43 26397.64 36078.79 48492.20 41097.94 27966.00 48098.60 34191.59 37685.94 44398.57 280
UnsupCasMVSNet_eth90.99 41289.92 40994.19 42994.08 45789.83 42297.13 41798.67 15093.69 29985.83 46896.19 41775.15 45496.74 45989.14 42079.41 46996.00 436
0.4-1-1-0.190.89 41388.97 42696.67 29494.15 45492.76 36195.28 47195.03 47189.11 43690.43 43289.57 49175.41 45099.04 28294.70 26677.06 47898.20 299
test20.0390.89 41390.38 40492.43 44993.48 46488.14 45998.33 27297.56 36893.40 31887.96 45596.71 39580.69 40594.13 48779.15 47886.17 43995.01 459
usedtu_blend_shiyan590.87 41589.15 42196.01 34991.33 48593.35 33598.12 31097.36 39481.93 47892.36 40391.75 48381.83 38898.09 40392.88 33574.82 48796.59 392
blend_shiyan490.76 41689.01 42495.99 35291.69 48093.35 33597.44 38397.83 34286.93 45292.23 40891.98 48175.19 45398.09 40392.88 33574.96 48596.52 408
MDA-MVSNet_test_wron90.71 41789.38 41894.68 41294.83 44790.78 40297.19 40897.46 38287.60 44772.41 49595.72 43586.51 31396.71 46285.92 44786.80 43696.56 400
YYNet190.70 41889.39 41694.62 41694.79 44990.65 40597.20 40697.46 38287.54 44872.54 49495.74 43186.51 31396.66 46386.00 44686.76 43796.54 403
0.4-1-1-0.290.43 41988.45 43096.38 33293.34 46692.12 37193.88 49195.04 47088.62 44290.00 43788.31 49475.31 45299.03 28594.61 27376.91 48098.01 308
KD-MVS_self_test90.38 42089.38 41893.40 43992.85 47188.94 44597.95 33297.94 33590.35 41690.25 43393.96 46079.82 41095.94 47584.62 46076.69 48295.33 448
pmmvs-eth3d90.36 42189.05 42394.32 42791.10 48992.12 37197.63 37396.95 42788.86 43984.91 47393.13 46978.32 42296.74 45988.70 42581.81 45894.09 472
0.3-1-1-0.01590.29 42288.21 43496.51 31793.56 46392.44 36494.41 48795.03 47188.71 44089.20 44688.50 49373.12 46699.04 28294.67 26976.70 48198.05 304
FE-MVSNET290.29 42288.94 42794.36 42690.48 49492.27 36698.45 25597.82 34591.59 38484.90 47493.10 47073.92 46296.42 46987.92 43682.26 45494.39 464
tt032090.26 42488.73 42994.86 40496.12 40990.62 40798.17 30297.63 36177.46 48889.68 44096.04 42369.19 47297.79 43288.98 42285.29 44596.16 432
CL-MVSNet_self_test90.11 42589.14 42293.02 44591.86 47888.23 45896.51 45298.07 32290.49 41090.49 43194.41 45484.75 35295.34 47980.79 47274.95 48695.50 446
new_pmnet90.06 42689.00 42593.22 44394.18 45388.32 45696.42 45496.89 43286.19 46085.67 46993.62 46277.18 43897.10 45281.61 46989.29 40694.23 468
MDA-MVSNet-bldmvs89.97 42788.35 43294.83 40895.21 44191.34 38997.64 37097.51 37788.36 44571.17 49696.13 41979.22 41596.63 46483.65 46286.27 43896.52 408
tt0320-xc89.79 42888.11 43594.84 40796.19 40490.61 40898.16 30397.22 40577.35 48988.75 45296.70 39665.94 48197.63 44089.31 41883.39 45196.28 427
CMPMVSbinary66.06 2189.70 42989.67 41389.78 46093.19 46976.56 48997.00 42498.35 25480.97 47981.57 48197.75 29874.75 45798.61 33889.85 40693.63 33894.17 470
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet189.67 43088.28 43393.82 43292.81 47291.08 39498.01 32697.45 38687.95 44687.90 45695.87 42967.63 47794.56 48678.73 48188.18 41995.83 441
KD-MVS_2432*160089.61 43187.96 43994.54 41894.06 45891.59 38695.59 46697.63 36189.87 42388.95 44894.38 45678.28 42396.82 45784.83 45668.05 50395.21 451
miper_refine_blended89.61 43187.96 43994.54 41894.06 45891.59 38695.59 46697.63 36189.87 42388.95 44894.38 45678.28 42396.82 45784.83 45668.05 50395.21 451
MVStest189.53 43387.99 43894.14 43194.39 45290.42 41298.25 28796.84 43782.81 47381.18 48397.33 33777.09 44096.94 45585.27 45378.79 47095.06 456
MVS-HIRNet89.46 43488.40 43192.64 44897.58 31682.15 48194.16 49093.05 49175.73 49290.90 42682.52 50079.42 41498.33 37683.53 46398.68 17397.43 323
OpenMVS_ROBcopyleft86.42 2089.00 43587.43 44393.69 43493.08 47089.42 43597.91 33996.89 43278.58 48585.86 46794.69 45069.48 47198.29 38477.13 48593.29 35093.36 481
mvsany_test388.80 43688.04 43691.09 45889.78 49881.57 48397.83 35495.49 46493.81 28887.53 45793.95 46156.14 48997.43 44794.68 26783.13 45294.26 466
FE-MVSNET88.56 43787.09 44492.99 44689.93 49789.99 42098.15 30695.59 46288.42 44484.87 47592.90 47274.82 45694.99 48477.88 48381.21 46193.99 475
new-patchmatchnet88.50 43887.45 44291.67 45690.31 49685.89 47097.16 41597.33 39589.47 43083.63 47892.77 47476.38 44495.06 48382.70 46577.29 47694.06 474
APD_test188.22 43988.01 43788.86 46495.98 41674.66 49797.21 40596.44 44983.96 47286.66 46497.90 28360.95 48797.84 43182.73 46490.23 39094.09 472
PM-MVS87.77 44086.55 44691.40 45791.03 49183.36 47996.92 42895.18 46891.28 39786.48 46693.42 46553.27 49196.74 45989.43 41681.97 45794.11 471
dmvs_testset87.64 44188.93 42883.79 47595.25 44063.36 50897.20 40691.17 49793.07 33385.64 47095.98 42885.30 34391.52 49769.42 49587.33 42896.49 415
test_fmvs387.17 44287.06 44587.50 46791.21 48875.66 49299.05 7596.61 44592.79 34588.85 45092.78 47343.72 49693.49 48993.95 29984.56 44693.34 482
UnsupCasMVSNet_bld87.17 44285.12 44993.31 44191.94 47788.77 44694.92 47798.30 27384.30 47182.30 47990.04 48963.96 48497.25 45085.85 44874.47 49493.93 477
N_pmnet87.12 44487.77 44185.17 47195.46 43661.92 51197.37 39170.66 52285.83 46488.73 45396.04 42385.33 34197.76 43580.02 47390.48 38595.84 440
pmmvs386.67 44584.86 45092.11 45588.16 50287.19 46696.63 44894.75 47579.88 48187.22 45992.75 47566.56 47995.20 48281.24 47176.56 48393.96 476
test_f86.07 44685.39 44788.10 46589.28 50075.57 49397.73 36396.33 45189.41 43385.35 47191.56 48643.31 49895.53 47791.32 38084.23 44893.21 483
WB-MVS84.86 44785.33 44883.46 47689.48 49969.56 50298.19 29596.42 45089.55 42981.79 48094.67 45184.80 35090.12 49852.44 50380.64 46690.69 489
usedtu_dtu_shiyan284.80 44882.31 45292.27 45386.38 50785.55 47197.77 35996.56 44678.34 48683.90 47793.50 46454.16 49095.32 48077.55 48472.62 49595.92 439
SSC-MVS84.27 44984.71 45182.96 48089.19 50168.83 50398.08 31896.30 45289.04 43881.37 48294.47 45284.60 35789.89 49949.80 50679.52 46890.15 490
RoMa-SfM83.81 45082.08 45389.00 46393.33 46779.94 48695.51 46892.48 49379.75 48279.89 48695.69 43846.23 49393.20 49278.90 47976.93 47993.87 478
LoFTR83.16 45180.62 45590.80 45992.28 47580.01 48595.35 47094.33 47980.44 48070.79 49792.93 47146.38 49298.17 39275.01 48978.03 47494.24 467
dongtai82.47 45281.88 45484.22 47495.19 44276.03 49094.59 48574.14 51482.63 47487.19 46096.09 42064.10 48387.85 50258.91 50184.11 44988.78 496
DKM81.60 45379.57 45687.68 46692.65 47478.36 48794.65 48391.17 49779.69 48376.11 48993.98 45937.88 50791.54 49679.64 47670.38 49993.15 484
MatchFormer80.21 45477.20 46289.24 46291.79 47977.21 48895.16 47393.59 48672.46 49667.08 50089.93 49043.14 49997.90 42467.07 49774.55 49392.61 486
test_vis3_rt79.22 45577.40 46184.67 47286.44 50674.85 49697.66 36881.43 50884.98 46867.12 49981.91 50328.09 51997.60 44188.96 42380.04 46781.55 506
test_method79.03 45678.17 45781.63 48186.06 50854.40 52282.75 51096.89 43239.54 51580.98 48495.57 44158.37 48894.73 48584.74 45978.61 47195.75 442
testf179.02 45777.70 45882.99 47888.10 50366.90 50594.67 48093.11 48871.08 49774.02 49193.41 46634.15 51293.25 49072.25 49278.50 47288.82 494
APD_test279.02 45777.70 45882.99 47888.10 50366.90 50594.67 48093.11 48871.08 49774.02 49193.41 46634.15 51293.25 49072.25 49278.50 47288.82 494
LCM-MVSNet78.70 45976.24 46586.08 46977.26 52471.99 49994.34 48896.72 43961.62 50176.53 48889.33 49233.91 51592.78 49481.85 46874.60 49293.46 480
kuosan78.45 46077.69 46080.72 48292.73 47375.32 49494.63 48474.51 51375.96 49080.87 48593.19 46863.23 48579.99 51142.56 51381.56 46086.85 503
Gipumacopyleft78.40 46176.75 46483.38 47795.54 43180.43 48479.42 51197.40 39064.67 50073.46 49380.82 50445.65 49593.14 49366.32 49887.43 42676.56 509
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS277.95 46275.44 46685.46 47082.54 51374.95 49594.23 48993.08 49072.80 49474.68 49087.38 49536.36 51091.56 49573.95 49063.94 50589.87 491
FPMVS77.62 46377.14 46379.05 48579.25 51960.97 51395.79 46195.94 45865.96 49967.93 49894.40 45537.73 50888.88 50168.83 49688.46 41687.29 500
ELoFTR75.37 46472.33 46784.51 47384.48 51168.41 50491.57 49688.78 50373.84 49362.84 50390.14 48827.38 52094.11 48871.45 49460.46 50891.00 487
EGC-MVSNET75.22 46569.54 46892.28 45294.81 44889.58 43197.64 37096.50 4471.82 5365.57 53795.74 43168.21 47396.26 47173.80 49191.71 37090.99 488
PDCNetPlus71.79 46669.26 46979.39 48485.67 50969.92 50190.34 50162.32 52472.62 49565.36 50290.26 48739.20 50486.38 50375.32 48842.24 51781.88 505
SP-DiffGlue70.13 46769.16 47073.04 49477.73 52257.48 51788.44 50574.91 51250.96 50766.64 50185.99 49741.44 50073.46 51764.21 49972.15 49688.19 499
ANet_high69.08 46865.37 47580.22 48365.99 53771.96 50090.91 50090.09 50182.62 47549.93 51778.39 51129.36 51881.75 50862.49 50038.52 52186.95 502
tmp_tt68.90 46966.97 47174.68 48750.78 53959.95 51487.13 50783.47 50738.80 51662.21 50496.23 41464.70 48276.91 51388.91 42430.49 52587.19 501
SP-LightGlue68.17 47066.54 47373.06 49391.08 49055.79 51891.09 49872.78 51648.55 51160.77 50679.95 50838.55 50574.10 51545.47 50870.64 49889.28 492
SP-SuperGlue68.14 47166.58 47272.81 49590.65 49355.53 51991.37 49773.04 51549.07 51061.03 50580.24 50738.13 50674.06 51645.46 50970.26 50088.84 493
ALIKED-LG67.40 47265.16 47674.11 48993.21 46862.30 50988.98 50371.99 51755.04 50259.47 50982.33 50139.27 50385.49 50532.61 51963.58 50774.55 510
SP-NN67.39 47365.69 47472.49 49790.68 49255.34 52090.33 50271.01 52046.77 51359.09 51079.83 50937.26 50973.38 51844.68 51071.51 49788.74 497
ALIKED-NN66.93 47464.81 47773.32 49193.41 46562.03 51087.55 50671.25 51850.21 50859.98 50882.57 49939.72 50284.03 50734.94 51763.64 50673.90 511
SP-MNN66.66 47564.70 47872.53 49690.32 49555.08 52191.01 49971.05 51944.81 51456.48 51379.62 51035.87 51174.11 51443.13 51269.98 50188.39 498
PMVScopyleft61.03 2365.95 47663.57 48073.09 49257.90 53851.22 52485.05 50993.93 48554.45 50344.32 51983.57 49813.22 53389.15 50058.68 50281.00 46378.91 508
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ALIKED-MNN65.35 47762.68 48273.35 49093.70 46161.07 51288.63 50470.76 52147.76 51257.06 51280.59 50534.03 51485.39 50632.73 51858.87 50973.59 512
E-PMN64.94 47864.25 47967.02 49882.28 51459.36 51591.83 49585.63 50552.69 50460.22 50777.28 51241.06 50180.12 51046.15 50741.14 51861.57 515
EMVS64.07 47963.26 48166.53 49981.73 51558.81 51691.85 49484.75 50651.93 50659.09 51075.13 51543.32 49779.09 51242.03 51439.47 51961.69 514
MVEpermissive62.14 2263.28 48059.38 48374.99 48674.33 52965.47 50785.55 50880.50 50952.02 50551.10 51575.00 51610.91 53880.50 50951.60 50553.40 51178.99 507
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GLUNet-SfM61.12 48156.63 48474.58 48869.78 53453.99 52378.71 51276.81 51149.09 50949.42 51880.47 50624.43 52185.82 50451.80 50429.17 52683.92 504
XFeat-NN56.16 48256.10 48556.36 50172.10 53142.54 53476.45 51461.18 52538.16 51753.08 51476.48 51332.95 51665.67 52044.15 51150.31 51460.87 516
XFeat-MNN55.84 48355.19 48657.82 50069.33 53543.25 52978.25 51362.64 52337.53 51850.90 51676.32 51432.43 51768.13 51942.00 51547.26 51662.07 513
SIFT-NN49.27 48449.25 48749.32 50283.88 51245.20 52574.57 51553.44 52632.44 51942.88 52064.93 51720.60 52261.35 52116.59 52153.96 51041.40 517
SIFT-MNN47.78 48547.47 48848.69 50381.04 51644.17 52673.46 51653.36 52731.82 52038.54 52163.76 51818.11 52661.27 52215.96 52351.17 51240.64 520
SIFT-NN-NCMNet47.55 48647.18 48948.67 50479.60 51844.09 52773.43 51752.90 52831.82 52038.38 52263.56 52118.47 52361.19 52315.91 52450.50 51340.74 519
SIFT-NN-CMatch45.31 48744.49 49047.75 50576.46 52542.98 53270.17 52149.20 53131.63 52337.94 52363.68 52018.19 52559.32 52615.91 52437.27 52240.95 518
SIFT-NCM-Cal44.98 48844.20 49147.33 50679.81 51743.05 53072.12 51849.31 53030.81 52525.90 52961.87 52615.80 52860.28 52414.09 53248.07 51538.66 523
SIFT-NN-UMatch44.69 48943.84 49247.24 50774.56 52842.59 53371.89 51949.78 52931.80 52229.27 52663.70 51918.26 52459.43 52515.86 52639.43 52039.71 521
SIFT-ConvMatch43.26 49042.18 49446.50 50878.34 52143.05 53068.67 52347.17 53231.06 52430.28 52562.56 52315.43 52958.95 52814.92 52831.22 52437.51 525
SIFT-NN-PointCN43.09 49142.61 49344.51 51172.48 53037.95 53870.10 52246.55 53330.16 52934.48 52461.93 52518.02 52755.90 53115.40 52734.41 52339.69 522
SIFT-UMatch42.35 49241.04 49546.29 50976.09 52641.80 53570.21 52045.21 53430.75 52627.33 52862.62 52215.13 53059.11 52714.72 52927.30 52737.95 524
SIFT-CM-Cal41.25 49340.03 49644.88 51077.37 52341.08 53665.71 52741.18 53630.42 52828.83 52761.42 52714.88 53156.40 52914.13 53126.37 52937.16 526
SIFT-UM-Cal39.93 49438.61 49743.88 51276.08 52739.30 53768.10 52437.89 53730.49 52722.74 53162.27 52413.89 53256.16 53014.17 53021.90 53036.17 527
SIFT-PointCN37.89 49537.50 49839.07 51371.45 53231.31 53966.27 52641.69 53527.82 53022.63 53256.73 52912.00 53650.56 53312.18 53426.71 52835.34 528
SIFT-PCN-Cal36.85 49636.40 49938.19 51471.43 53330.42 54064.34 52837.72 53827.48 53122.98 53057.03 52812.99 53451.22 53212.51 53321.13 53132.92 529
SIFT-NCMNet32.45 49731.84 50134.30 51568.74 53628.10 54157.85 52924.54 53927.25 53219.31 53352.59 5309.75 53945.69 53410.92 53515.56 53329.13 530
wuyk23d30.17 49830.18 50230.16 51678.61 52043.29 52866.79 52514.21 54017.31 53314.82 53611.93 53611.55 53741.43 53537.08 51619.30 5325.76 533
cdsmvs_eth3d_5k23.98 49931.98 5000.00 5190.00 5420.00 5440.00 53098.59 1710.00 5370.00 53898.61 21190.60 2030.00 5380.00 5360.00 5360.00 534
testmvs21.48 50024.95 50311.09 51814.89 5406.47 54396.56 4509.87 5417.55 53417.93 53439.02 5329.43 5405.90 53716.56 52212.72 53420.91 532
test12320.95 50123.72 50412.64 51713.54 5418.19 54296.55 4516.13 5427.48 53516.74 53537.98 53312.97 5356.05 53616.69 5205.43 53523.68 531
ab-mvs-re8.20 50210.94 5050.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 53898.43 2290.00 5410.00 5380.00 5360.00 5360.00 534
pcd_1.5k_mvsjas7.88 50310.50 5060.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 53794.51 910.00 5380.00 5360.00 5360.00 534
mmdepth0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
monomultidepth0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
test_blank0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
uanet_test0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
DCPMVS0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
sosnet-low-res0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
sosnet0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
uncertanet0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
Regformer0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
uanet0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
MED-MVS test99.52 1499.77 298.86 2399.32 2299.24 2096.41 12399.30 5299.35 6299.92 4398.30 7599.80 2599.79 29
TestfortrainingZip99.43 2199.13 11999.06 1599.32 2298.57 17896.88 9799.42 4399.05 14196.54 2499.73 13698.59 18099.51 104
WAC-MVS90.94 39688.66 426
FOURS199.82 198.66 2999.69 198.95 6197.46 5799.39 46
MSC_two_6792asdad99.62 799.17 11199.08 1298.63 16199.94 1498.53 5599.80 2599.86 13
PC_three_145295.08 21499.60 3399.16 10897.86 298.47 35297.52 13799.72 6699.74 50
No_MVS99.62 799.17 11199.08 1298.63 16199.94 1498.53 5599.80 2599.86 13
test_one_060199.66 3199.25 298.86 9197.55 4999.20 6099.47 3797.57 7
eth-test20.00 542
eth-test0.00 542
ZD-MVS99.46 5898.70 2898.79 11993.21 32698.67 10598.97 15295.70 5299.83 9096.07 20999.58 97
RE-MVS-def98.34 5499.49 5297.86 7599.11 6698.80 11496.49 11899.17 6399.35 6295.29 6997.72 11199.65 8099.71 63
IU-MVS99.71 2499.23 798.64 15895.28 19799.63 3298.35 7299.81 1699.83 19
OPU-MVS99.37 2899.24 10399.05 1699.02 8599.16 10897.81 399.37 21197.24 15999.73 6199.70 67
test_241102_TWO98.87 8597.65 4199.53 3899.48 3597.34 1299.94 1498.43 6799.80 2599.83 19
test_241102_ONE99.71 2499.24 598.87 8597.62 4399.73 2399.39 5097.53 899.74 134
9.1498.06 7899.47 5698.71 19198.82 10194.36 26099.16 6799.29 7596.05 4099.81 10297.00 16699.71 68
save fliter99.46 5898.38 4198.21 29098.71 13797.95 28
test_0728_THIRD97.32 6599.45 4099.46 4297.88 199.94 1498.47 6399.86 299.85 16
test_0728_SECOND99.71 199.72 1799.35 198.97 9798.88 7899.94 1498.47 6399.81 1699.84 18
test072699.72 1799.25 299.06 7398.88 7897.62 4399.56 3599.50 3197.42 10
GSMVS99.20 187
test_part299.63 3499.18 1099.27 57
sam_mvs189.45 23799.20 187
sam_mvs88.99 253
ambc89.49 46186.66 50575.78 49192.66 49396.72 43986.55 46592.50 47646.01 49497.90 42490.32 39782.09 45594.80 462
MTGPAbinary98.74 129
test_post196.68 44730.43 53587.85 29198.69 33092.59 348
test_post31.83 53488.83 26298.91 306
patchmatchnet-post95.10 44789.42 23898.89 310
GG-mvs-BLEND96.59 30696.34 39994.98 25896.51 45288.58 50493.10 38094.34 45880.34 40998.05 41189.53 41396.99 26596.74 368
MTMP98.89 12394.14 483
gm-plane-assit95.88 42287.47 46389.74 42696.94 38099.19 24993.32 318
test9_res96.39 20399.57 9899.69 70
TEST999.31 7998.50 3597.92 33798.73 13292.63 34997.74 18198.68 20596.20 3599.80 109
test_899.29 8898.44 3797.89 34598.72 13492.98 33797.70 18698.66 20896.20 3599.80 109
agg_prior295.87 21999.57 9899.68 75
agg_prior99.30 8398.38 4198.72 13497.57 20399.81 102
TestCases96.99 26499.25 9693.21 34598.18 29691.36 39093.52 35998.77 19284.67 35599.72 13789.70 41097.87 23498.02 306
test_prior498.01 7197.86 349
test_prior297.80 35696.12 13897.89 16898.69 20495.96 4496.89 17699.60 92
test_prior99.19 5199.31 7998.22 5898.84 9699.70 14399.65 83
旧先验297.57 37691.30 39598.67 10599.80 10995.70 230
新几何297.64 370
新几何199.16 5699.34 7198.01 7198.69 14290.06 42098.13 13798.95 15994.60 8999.89 6891.97 36799.47 12199.59 94
旧先验199.29 8897.48 9098.70 14099.09 13195.56 5599.47 12199.61 90
无先验97.58 37598.72 13491.38 38999.87 7993.36 31799.60 92
原ACMM297.67 367
原ACMM198.65 9899.32 7796.62 14198.67 15093.27 32597.81 17498.97 15295.18 7699.83 9093.84 30399.46 12499.50 107
test22299.23 10497.17 11797.40 38798.66 15388.68 44198.05 14598.96 15794.14 10299.53 11199.61 90
testdata299.89 6891.65 375
segment_acmp96.85 15
testdata98.26 14299.20 10995.36 23498.68 14591.89 37598.60 11399.10 12394.44 9699.82 9794.27 28799.44 12599.58 98
testdata197.32 39796.34 128
test1299.18 5399.16 11598.19 6098.53 18898.07 14195.13 7999.72 13799.56 10699.63 88
plane_prior797.42 33394.63 275
plane_prior697.35 34094.61 27887.09 304
plane_prior598.56 18299.03 28596.07 20994.27 31896.92 344
plane_prior498.28 248
plane_prior394.61 27897.02 8995.34 284
plane_prior298.80 16397.28 69
plane_prior197.37 339
plane_prior94.60 28098.44 26196.74 10594.22 320
n20.00 543
nn0.00 543
door-mid94.37 478
lessismore_v094.45 42494.93 44688.44 45491.03 49986.77 46397.64 31276.23 44698.42 35890.31 39885.64 44496.51 412
LGP-MVS_train96.47 32297.46 32893.54 32198.54 18694.67 24394.36 31698.77 19285.39 33799.11 26895.71 22894.15 32496.76 366
test1198.66 153
door94.64 476
HQP5-MVS94.25 297
HQP-NCC97.20 34898.05 32196.43 12094.45 308
ACMP_Plane97.20 34898.05 32196.43 12094.45 308
BP-MVS95.30 243
HQP4-MVS94.45 30898.96 29796.87 356
HQP3-MVS98.46 20794.18 322
HQP2-MVS86.75 310
NP-MVS97.28 34294.51 28397.73 299
MDTV_nov1_ep13_2view84.26 47496.89 43590.97 40497.90 16789.89 22393.91 30199.18 196
MDTV_nov1_ep1395.40 23597.48 32688.34 45596.85 44097.29 39993.74 29297.48 20597.26 34189.18 24699.05 27991.92 36897.43 256
ACMMP++_ref92.97 352
ACMMP++93.61 339
Test By Simon94.64 88
ITE_SJBPF95.44 38397.42 33391.32 39097.50 37895.09 21393.59 35498.35 23981.70 39198.88 31289.71 40993.39 34596.12 433
DeepMVS_CXcopyleft86.78 46897.09 35872.30 49895.17 46975.92 49184.34 47695.19 44570.58 46995.35 47879.98 47589.04 41092.68 485