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
AdaColmapbinary97.23 13096.80 13898.51 13399.99 195.60 20299.09 33098.84 6593.32 20896.74 22299.72 9586.04 263100.00 198.01 15299.43 12999.94 87
CNVR-MVS99.40 199.26 199.84 799.98 299.51 799.98 2498.69 8298.20 999.93 399.98 296.82 26100.00 199.75 41100.00 199.99 26
TestfortrainingZip99.90 599.97 399.70 599.97 4298.89 5296.02 9999.99 199.96 397.97 5100.00 199.65 96100.00 1
MCST-MVS99.32 399.14 499.86 699.97 399.59 699.97 4298.64 9198.47 399.13 10799.92 1696.38 36100.00 199.74 43100.00 1100.00 1
mPP-MVS98.39 5698.20 5498.97 9399.97 396.92 13999.95 7598.38 18495.04 12498.61 14099.80 5993.39 117100.00 198.64 114100.00 199.98 57
CPTT-MVS97.64 11097.32 11498.58 12299.97 395.77 19199.96 5698.35 19089.90 35298.36 15599.79 6391.18 18099.99 3998.37 13099.99 2199.99 26
DP-MVS Recon98.41 5398.02 6899.56 3099.97 398.70 5399.92 10398.44 14892.06 28098.40 15499.84 4995.68 48100.00 198.19 14199.71 9199.97 67
PAPR98.52 4398.16 5899.58 2999.97 398.77 4799.95 7598.43 15695.35 11898.03 17099.75 8194.03 10299.98 5198.11 14699.83 8099.99 26
MED-MVS test99.60 2499.96 998.79 4299.97 4298.88 5596.36 9099.07 11299.93 12100.00 199.98 999.96 4699.99 26
MED-MVS99.24 899.11 799.60 2499.96 998.79 4299.97 4298.88 5596.91 6299.07 11299.92 1697.36 18100.00 199.98 999.96 46100.00 1
TestfortrainingZip a99.01 1698.78 2199.69 1799.96 999.09 2599.97 4298.74 7696.91 6299.86 1699.92 1696.29 3799.99 3998.32 13399.09 149100.00 1
HFP-MVS98.56 3998.37 4399.14 7399.96 997.43 11599.95 7598.61 9994.77 13499.31 9599.85 3894.22 95100.00 198.70 10999.98 3299.98 57
region2R98.54 4198.37 4399.05 8399.96 997.18 12599.96 5698.55 11994.87 13199.45 8199.85 3894.07 101100.00 198.67 111100.00 199.98 57
ACMMPR98.50 4498.32 4799.05 8399.96 997.18 12599.95 7598.60 10194.77 13499.31 9599.84 4993.73 111100.00 198.70 10999.98 3299.98 57
NCCC99.37 299.25 299.71 1699.96 999.15 2399.97 4298.62 9898.02 2299.90 799.95 497.33 19100.00 199.54 58100.00 1100.00 1
CP-MVS98.45 4898.32 4798.87 9899.96 996.62 15499.97 4298.39 18094.43 15198.90 12199.87 3294.30 92100.00 199.04 8599.99 2199.99 26
test_one_060199.94 1799.30 1398.41 17396.63 7599.75 4299.93 1297.49 11
test_0728_SECOND99.82 899.94 1799.47 899.95 7598.43 156100.00 199.99 5100.00 1100.00 1
XVS98.70 3298.55 3199.15 7199.94 1797.50 11199.94 9398.42 16896.22 9399.41 8799.78 6794.34 8999.96 7698.92 9499.95 5399.99 26
X-MVStestdata93.83 28792.06 32299.15 7199.94 1797.50 11199.94 9398.42 16896.22 9399.41 8741.37 53994.34 8999.96 7698.92 9499.95 5399.99 26
test_prior99.43 4199.94 1798.49 6698.65 8899.80 14399.99 26
MSLP-MVS++99.13 999.01 1299.49 3799.94 1798.46 6799.98 2498.86 5997.10 5399.80 2899.94 595.92 44100.00 199.51 59100.00 1100.00 1
APDe-MVScopyleft99.06 1398.91 1599.51 3499.94 1798.76 5099.91 11198.39 18097.20 5199.46 8099.85 3895.53 5299.79 14599.86 27100.00 199.99 26
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MP-MVScopyleft98.23 7197.97 7299.03 8599.94 1797.17 12899.95 7598.39 18094.70 13898.26 16199.81 5891.84 171100.00 198.85 10099.97 4299.93 88
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS98.65 3598.36 4599.49 3799.94 1798.73 5199.87 13398.33 19593.97 17799.76 4199.87 3294.99 6899.75 15498.55 118100.00 199.98 57
PAPM_NR98.12 7597.93 7898.70 10999.94 1796.13 18099.82 16598.43 15694.56 14297.52 18899.70 10194.40 8499.98 5197.00 19599.98 3299.99 26
MG-MVS98.91 2298.65 2799.68 1899.94 1799.07 2699.64 23899.44 1997.33 4499.00 11799.72 9594.03 10299.98 5198.73 108100.00 1100.00 1
ME-MVS99.07 1198.89 1799.59 2799.93 2898.79 4299.95 7598.80 7195.89 10399.28 9999.93 1296.28 3899.98 5199.98 999.96 4699.99 26
SED-MVS99.28 599.11 799.77 999.93 2899.30 1399.96 5698.43 15697.27 4799.80 2899.94 596.71 29100.00 1100.00 1100.00 1100.00 1
IU-MVS99.93 2899.31 1198.41 17397.71 3199.84 23100.00 1100.00 1100.00 1
test_241102_ONE99.93 2899.30 1398.43 15697.26 4999.80 2899.88 2996.71 29100.00 1
DVP-MVScopyleft99.30 499.16 399.73 1399.93 2899.29 1699.95 7598.32 19797.28 4599.83 2499.91 1997.22 21100.00 199.99 5100.00 199.89 97
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
test072699.93 2899.29 1699.96 5698.42 16897.28 4599.86 1699.94 597.22 21
MSP-MVS99.09 1099.12 598.98 9299.93 2897.24 12299.95 7598.42 16897.50 3899.52 7699.88 2997.43 1799.71 16099.50 6199.98 32100.00 1
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
agg_prior99.93 2898.77 4798.43 15699.63 5999.85 130
FOURS199.92 3697.66 10599.95 7598.36 18895.58 11299.52 76
ZD-MVS99.92 3698.57 6198.52 12892.34 26899.31 9599.83 5195.06 6399.80 14399.70 4999.97 42
GST-MVS98.27 6397.97 7299.17 6699.92 3697.57 10799.93 10098.39 18094.04 17598.80 12699.74 8892.98 133100.00 198.16 14399.76 8899.93 88
TEST999.92 3698.92 3199.96 5698.43 15693.90 18399.71 4999.86 3495.88 4599.85 130
train_agg98.88 2398.65 2799.59 2799.92 3698.92 3199.96 5698.43 15694.35 15699.71 4999.86 3495.94 4299.85 13099.69 5099.98 3299.99 26
test_899.92 3698.88 3499.96 5698.43 15694.35 15699.69 5199.85 3895.94 4299.85 130
PGM-MVS98.34 5898.13 6098.99 9099.92 3697.00 13599.75 19799.50 1793.90 18399.37 9299.76 7393.24 126100.00 197.75 17399.96 4699.98 57
ACMMPcopyleft97.74 10397.44 10798.66 11399.92 3696.13 18099.18 32299.45 1894.84 13296.41 24199.71 9891.40 17499.99 3997.99 15498.03 19099.87 100
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
DVP-MVS++99.26 699.09 1099.77 999.91 4499.31 1199.95 7598.43 15696.48 8099.80 2899.93 1297.44 15100.00 199.92 1699.98 32100.00 1
MSC_two_6792asdad99.93 299.91 4499.80 298.41 173100.00 199.96 12100.00 1100.00 1
No_MVS99.93 299.91 4499.80 298.41 173100.00 199.96 12100.00 1100.00 1
HPM-MVS++copyleft99.07 1198.88 1899.63 1999.90 4799.02 2799.95 7598.56 11397.56 3799.44 8299.85 3895.38 56100.00 199.31 7199.99 2199.87 100
APD-MVScopyleft98.62 3698.35 4699.41 4499.90 4798.51 6499.87 13398.36 18894.08 17099.74 4599.73 9294.08 10099.74 15699.42 6799.99 2199.99 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast96.59 198.81 2698.54 3299.62 2299.90 4798.85 3799.24 31798.47 14098.14 1699.08 11099.91 1993.09 130100.00 199.04 8599.99 21100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OPU-MVS99.93 299.89 5099.80 299.96 5699.80 5997.44 15100.00 1100.00 199.98 32100.00 1
DPE-MVScopyleft99.26 699.10 999.74 1299.89 5099.24 2099.87 13398.44 14897.48 3999.64 5899.94 596.68 3199.99 3999.99 5100.00 199.99 26
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part299.89 5099.25 1999.49 79
CSCG97.10 13697.04 12697.27 24199.89 5091.92 33799.90 11799.07 3788.67 37695.26 27499.82 5493.17 12999.98 5198.15 14499.47 12499.90 96
ZNCC-MVS98.31 6098.03 6799.17 6699.88 5497.59 10699.94 9398.44 14894.31 15998.50 14799.82 5493.06 13199.99 3998.30 13599.99 2199.93 88
SR-MVS98.46 4798.30 5098.93 9699.88 5497.04 13499.84 15298.35 19094.92 12899.32 9499.80 5993.35 11999.78 14799.30 7299.95 5399.96 75
9.1498.38 4199.87 5699.91 11198.33 19593.22 21199.78 3999.89 2794.57 8099.85 13099.84 2999.97 42
SMA-MVScopyleft98.76 2998.48 3599.62 2299.87 5698.87 3599.86 14498.38 18493.19 21399.77 4099.94 595.54 50100.00 199.74 4399.99 21100.00 1
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
NormalMVS97.90 8597.85 8598.04 16699.86 5895.39 21299.61 24597.78 27096.52 7898.61 14099.31 15792.73 14199.67 16896.77 21199.48 12199.06 252
lecture98.67 3398.46 3699.28 5399.86 5897.88 9299.97 4299.25 3096.07 9799.79 3799.70 10192.53 15099.98 5199.51 5999.48 12199.97 67
PHI-MVS98.41 5398.21 5399.03 8599.86 5897.10 13299.98 2498.80 7190.78 33099.62 6299.78 6795.30 57100.00 199.80 3299.93 6499.99 26
MTAPA98.29 6297.96 7599.30 5299.85 6197.93 9099.39 29098.28 20495.76 10697.18 20399.88 2992.74 140100.00 198.67 11199.88 7699.99 26
LS3D95.84 21095.11 22798.02 16799.85 6195.10 23298.74 38098.50 13787.22 40193.66 29799.86 3487.45 23999.95 8590.94 33399.81 8699.02 260
HPM-MVScopyleft97.96 8097.72 9098.68 11099.84 6396.39 16699.90 11798.17 22292.61 24998.62 13999.57 13191.87 17099.67 16898.87 9999.99 2199.99 26
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EI-MVSNet-Vis-set98.27 6398.11 6298.75 10699.83 6496.59 15899.40 28698.51 13195.29 12098.51 14699.76 7393.60 11599.71 16098.53 12199.52 11499.95 83
save fliter99.82 6598.79 4299.96 5698.40 17797.66 33
PLCcopyleft95.54 397.93 8397.89 8298.05 16599.82 6594.77 24499.92 10398.46 14293.93 18097.20 20199.27 16595.44 5599.97 6497.41 17999.51 11799.41 198
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
APD-MVS_3200maxsize98.25 6898.08 6498.78 10399.81 6796.60 15699.82 16598.30 20293.95 17999.37 9299.77 7192.84 13799.76 15398.95 9099.92 6799.97 67
EI-MVSNet-UG-set98.14 7497.99 7098.60 11899.80 6896.27 16999.36 29698.50 13795.21 12298.30 15899.75 8193.29 12399.73 15998.37 13099.30 13899.81 109
SR-MVS-dyc-post98.31 6098.17 5798.71 10899.79 6996.37 16799.76 19198.31 19994.43 15199.40 8999.75 8193.28 12499.78 14798.90 9799.92 6799.97 67
RE-MVS-def98.13 6099.79 6996.37 16799.76 19198.31 19994.43 15199.40 8999.75 8192.95 13498.90 9799.92 6799.97 67
HPM-MVS_fast97.80 9797.50 10398.68 11099.79 6996.42 16299.88 13098.16 22791.75 29298.94 11999.54 13491.82 17299.65 17297.62 17699.99 2199.99 26
SF-MVS98.67 3398.40 3999.50 3599.77 7298.67 5499.90 11798.21 21793.53 19599.81 2699.89 2794.70 7699.86 12999.84 2999.93 6499.96 75
MGCNet99.06 1398.84 1999.72 1499.76 7399.21 2299.99 899.34 2598.70 299.44 8299.75 8193.24 12699.99 3999.94 1499.41 13199.95 83
旧先验199.76 7397.52 10998.64 9199.85 3895.63 4999.94 5899.99 26
OMC-MVS97.28 12697.23 11897.41 23099.76 7393.36 30399.65 23497.95 24996.03 9897.41 19499.70 10189.61 20699.51 17896.73 21398.25 18099.38 201
新几何199.42 4399.75 7698.27 7198.63 9792.69 24499.55 7199.82 5494.40 84100.00 191.21 32599.94 5899.99 26
MP-MVS-pluss98.07 7897.64 9699.38 4999.74 7798.41 6999.74 20198.18 22193.35 20696.45 23499.85 3892.64 14599.97 6498.91 9699.89 7399.77 116
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.98.93 2098.77 2299.41 4499.74 7798.67 5499.77 18598.38 18496.73 7199.88 1399.74 8894.89 7099.59 17499.80 3299.98 3299.97 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test1299.43 4199.74 7798.56 6298.40 17799.65 5594.76 7399.75 15499.98 3299.99 26
原ACMM198.96 9499.73 8096.99 13698.51 13194.06 17399.62 6299.85 3894.97 6999.96 7695.11 24599.95 5399.92 93
TSAR-MVS + GP.98.60 3798.51 3498.86 9999.73 8096.63 15399.97 4297.92 25498.07 1998.76 13299.55 13295.00 6799.94 9499.91 1997.68 19799.99 26
CANet98.27 6397.82 8799.63 1999.72 8299.10 2499.98 2498.51 13197.00 5998.52 14499.71 9887.80 23099.95 8599.75 4199.38 13399.83 105
reproduce_model98.75 3098.66 2699.03 8599.71 8397.10 13299.73 20898.23 21297.02 5899.18 10599.90 2394.54 8199.99 3999.77 3799.90 7299.99 26
F-COLMAP96.93 14896.95 12996.87 25999.71 8391.74 34799.85 14797.95 24993.11 22195.72 26399.16 18492.35 15699.94 9495.32 24199.35 13698.92 268
reproduce-ours98.78 2798.67 2499.09 8099.70 8597.30 11999.74 20198.25 20897.10 5399.10 10899.90 2394.59 7799.99 3999.77 3799.91 7099.99 26
our_new_method98.78 2798.67 2499.09 8099.70 8597.30 11999.74 20198.25 20897.10 5399.10 10899.90 2394.59 7799.99 3999.77 3799.91 7099.99 26
SD-MVS98.92 2198.70 2399.56 3099.70 8598.73 5199.94 9398.34 19496.38 8699.81 2699.76 7394.59 7799.98 5199.84 2999.96 4699.97 67
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
patch_mono-298.24 6999.12 595.59 30399.67 8886.91 43499.95 7598.89 5297.60 3499.90 799.76 7396.54 3499.98 5199.94 1499.82 8499.88 98
ACMMP_NAP98.49 4598.14 5999.54 3299.66 8998.62 6099.85 14798.37 18794.68 13999.53 7499.83 5192.87 136100.00 198.66 11399.84 7999.99 26
DeepPCF-MVS95.94 297.71 10798.98 1393.92 37699.63 9081.76 46999.96 5698.56 11399.47 199.19 10499.99 194.16 99100.00 199.92 1699.93 64100.00 1
EPNet98.49 4598.40 3998.77 10599.62 9196.80 14799.90 11799.51 1697.60 3499.20 10299.36 15293.71 11299.91 11197.99 15498.71 16599.61 151
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MM98.83 2498.53 3399.76 1199.59 9299.33 999.99 899.76 698.39 499.39 9199.80 5990.49 19599.96 7699.89 2199.43 12999.98 57
PVSNet_BlendedMVS96.05 20095.82 19296.72 26599.59 9296.99 13699.95 7599.10 3494.06 17398.27 15995.80 37189.00 21899.95 8599.12 7987.53 36093.24 434
PVSNet_Blended97.94 8297.64 9698.83 10099.59 9296.99 136100.00 199.10 3495.38 11798.27 15999.08 18989.00 21899.95 8599.12 7999.25 14099.57 162
PatchMatch-RL96.04 20195.40 21097.95 17099.59 9295.22 22699.52 26799.07 3793.96 17896.49 23298.35 28382.28 32499.82 14290.15 34999.22 14398.81 276
dcpmvs_297.42 12198.09 6395.42 31099.58 9687.24 43099.23 31896.95 40494.28 16298.93 12099.73 9294.39 8799.16 20799.89 2199.82 8499.86 102
test22299.55 9797.41 11799.34 29898.55 11991.86 28699.27 10099.83 5193.84 10999.95 5399.99 26
CNLPA97.76 10197.38 11098.92 9799.53 9896.84 14199.87 13398.14 23193.78 18796.55 23099.69 10592.28 15899.98 5197.13 19099.44 12899.93 88
API-MVS97.86 8897.66 9498.47 13599.52 9995.41 21099.47 27798.87 5891.68 29498.84 12399.85 3892.34 15799.99 3998.44 12699.96 46100.00 1
PVSNet91.05 1397.13 13596.69 14498.45 13899.52 9995.81 18999.95 7599.65 1294.73 13699.04 11599.21 17684.48 29999.95 8594.92 25198.74 16499.58 160
114514_t97.41 12296.83 13599.14 7399.51 10197.83 9499.89 12798.27 20688.48 38199.06 11499.66 11690.30 19899.64 17396.32 22599.97 4299.96 75
cl2293.77 29293.25 29295.33 31499.49 10294.43 25699.61 24598.09 23490.38 34189.16 36995.61 38090.56 19397.34 35391.93 31684.45 38394.21 372
testdata98.42 14299.47 10395.33 21698.56 11393.78 18799.79 3799.85 3893.64 11499.94 9494.97 24999.94 58100.00 1
MAR-MVS97.43 11797.19 12098.15 15899.47 10394.79 24399.05 34198.76 7392.65 24798.66 13799.82 5488.52 22499.98 5198.12 14599.63 9899.67 133
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
DP-MVS94.54 26093.42 28297.91 17699.46 10594.04 27498.93 35997.48 30781.15 45890.04 34099.55 13287.02 24799.95 8588.97 36498.11 18699.73 120
MVS_111021_LR98.42 5298.38 4198.53 13099.39 10695.79 19099.87 13399.86 296.70 7298.78 12799.79 6392.03 16799.90 11399.17 7899.86 7899.88 98
CHOSEN 280x42099.01 1699.03 1198.95 9599.38 10798.87 3598.46 39999.42 2197.03 5799.02 11699.09 18899.35 298.21 31599.73 4599.78 8799.77 116
MVS_111021_HR98.72 3198.62 2999.01 8999.36 10897.18 12599.93 10099.90 196.81 6998.67 13699.77 7193.92 10499.89 11899.27 7499.94 5899.96 75
fmvsm_s_conf0.5_n_1198.03 7997.89 8298.46 13799.35 10997.76 9899.99 898.04 24098.20 999.90 799.78 6786.21 26199.95 8599.89 2199.68 9397.65 315
DPM-MVS98.83 2498.46 3699.97 199.33 11099.92 199.96 5698.44 14897.96 2399.55 7199.94 597.18 23100.00 193.81 28299.94 5899.98 57
TAPA-MVS92.12 894.42 26893.60 27496.90 25899.33 11091.78 34699.78 17998.00 24389.89 35394.52 28299.47 13891.97 16899.18 20469.90 48199.52 11499.73 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs95.38 23295.07 22996.32 28099.32 11296.60 15699.76 19198.85 6296.65 7487.83 39896.05 36899.52 198.11 32096.58 21781.07 41294.25 365
fmvsm_s_conf0.5_n_998.15 7398.02 6898.55 12499.28 11395.84 18899.99 898.57 10798.17 1399.93 399.74 8887.04 24699.97 6499.86 2799.59 10899.83 105
SPE-MVS-test97.88 8697.94 7797.70 19599.28 11395.20 22799.98 2497.15 36395.53 11499.62 6299.79 6392.08 16698.38 29798.75 10799.28 13999.52 173
test_fmvsm_n_192098.44 4998.61 3097.92 17499.27 11595.18 228100.00 198.90 5098.05 2099.80 2899.73 9292.64 14599.99 3999.58 5799.51 11798.59 286
fmvsm_s_conf0.5_n_1098.24 6997.90 8099.26 5599.24 11697.88 9299.99 898.76 7398.20 999.92 599.74 8885.97 26599.94 9499.72 4699.53 11399.96 75
fmvsm_l_conf0.5_n_a99.00 1898.91 1599.28 5399.21 11797.91 9199.98 2498.85 6298.25 599.92 599.75 8194.72 7499.97 6499.87 2599.64 9799.95 83
fmvsm_s_conf0.5_n_898.38 5798.05 6699.35 5099.20 11898.12 7799.98 2498.81 6798.22 799.80 2899.71 9887.37 24199.97 6499.91 1999.48 12199.97 67
test_yl97.83 9297.37 11199.21 6099.18 11997.98 8699.64 23899.27 2791.43 30397.88 17898.99 20695.84 4699.84 13898.82 10195.32 28799.79 112
DCV-MVSNet97.83 9297.37 11199.21 6099.18 11997.98 8699.64 23899.27 2791.43 30397.88 17898.99 20695.84 4699.84 13898.82 10195.32 28799.79 112
fmvsm_l_conf0.5_n98.94 1998.84 1999.25 5699.17 12197.81 9699.98 2498.86 5998.25 599.90 799.76 7394.21 9799.97 6499.87 2599.52 11499.98 57
DeepC-MVS94.51 496.92 14996.40 15998.45 13899.16 12295.90 18699.66 23398.06 23796.37 8994.37 28899.49 13783.29 31799.90 11397.63 17599.61 10499.55 164
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS98.54 4198.22 5299.50 3599.15 12398.65 58100.00 198.58 10597.70 3298.21 16599.24 17292.58 14899.94 9498.63 11699.94 5899.92 93
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_l_conf0.5_n_398.41 5398.08 6499.39 4699.12 12498.29 7099.98 2498.64 9198.14 1699.86 1699.76 7387.99 22999.97 6499.72 4699.54 11199.91 95
fmvsm_l_conf0.5_n_998.55 4098.23 5199.49 3799.10 12598.50 6599.99 898.70 8098.14 1699.94 299.68 11289.02 21799.98 5199.89 2199.61 10499.99 26
CS-MVS97.79 9997.91 7997.43 22799.10 12594.42 25799.99 897.10 37795.07 12399.68 5299.75 8192.95 13498.34 30198.38 12899.14 14599.54 168
Anonymous20240521193.10 31091.99 32396.40 27699.10 12589.65 39998.88 36597.93 25183.71 44194.00 29498.75 24468.79 43899.88 12495.08 24691.71 32099.68 131
fmvsm_s_conf0.5_n97.80 9797.85 8597.67 19699.06 12894.41 25899.98 2498.97 4397.34 4299.63 5999.69 10587.27 24299.97 6499.62 5599.06 15198.62 285
HyFIR lowres test96.66 16696.43 15697.36 23599.05 12993.91 28099.70 22499.80 390.54 33696.26 24498.08 29592.15 16498.23 31496.84 20595.46 28299.93 88
LFMVS94.75 25493.56 27798.30 14899.03 13095.70 19698.74 38097.98 24687.81 39498.47 14899.39 14967.43 44799.53 17598.01 15295.20 29099.67 133
fmvsm_s_conf0.5_n_497.75 10297.86 8497.42 22899.01 13194.69 24799.97 4298.76 7397.91 2599.87 1499.76 7386.70 25399.93 10499.67 5299.12 14897.64 316
fmvsm_s_conf0.5_n_297.59 11297.28 11598.53 13099.01 13198.15 7299.98 2498.59 10398.17 1399.75 4299.63 12281.83 33099.94 9499.78 3598.79 16297.51 324
AllTest92.48 32791.64 33095.00 32399.01 13188.43 41798.94 35796.82 41986.50 41188.71 37498.47 27874.73 41299.88 12485.39 41196.18 25696.71 330
TestCases95.00 32399.01 13188.43 41796.82 41986.50 41188.71 37498.47 27874.73 41299.88 12485.39 41196.18 25696.71 330
COLMAP_ROBcopyleft90.47 1492.18 33491.49 33694.25 35799.00 13588.04 42398.42 40596.70 42682.30 45388.43 38699.01 19976.97 38799.85 13086.11 40796.50 24794.86 341
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_397.95 8197.66 9498.81 10198.99 13698.07 8099.98 2498.81 6798.18 1299.89 1199.70 10184.15 30399.97 6499.76 4099.50 11998.39 293
test_fmvs195.35 23395.68 19994.36 35398.99 13684.98 44599.96 5696.65 42897.60 3499.73 4798.96 21271.58 42899.93 10498.31 13499.37 13498.17 299
HY-MVS92.50 797.79 9997.17 12299.63 1998.98 13899.32 1097.49 43499.52 1495.69 10998.32 15797.41 31693.32 12199.77 15098.08 14995.75 27299.81 109
VNet97.21 13196.57 14999.13 7798.97 13997.82 9599.03 34499.21 3294.31 15999.18 10598.88 22586.26 26099.89 11898.93 9294.32 30099.69 130
thres20096.96 14596.21 16699.22 5998.97 13998.84 3899.85 14799.71 793.17 21596.26 24498.88 22589.87 20399.51 17894.26 27094.91 29299.31 219
tfpn200view996.79 15495.99 17599.19 6298.94 14198.82 3999.78 17999.71 792.86 23196.02 25498.87 23289.33 21099.50 18093.84 27994.57 29699.27 228
thres40096.78 15695.99 17599.16 6998.94 14198.82 3999.78 17999.71 792.86 23196.02 25498.87 23289.33 21099.50 18093.84 27994.57 29699.16 240
sasdasda97.09 13896.32 16099.39 4698.93 14398.95 2999.72 21297.35 32194.45 14797.88 17899.42 14286.71 25199.52 17698.48 12393.97 30699.72 122
Anonymous2023121189.86 38588.44 39394.13 36598.93 14390.68 37798.54 39698.26 20776.28 47686.73 41295.54 38470.60 43497.56 34690.82 33680.27 42194.15 381
canonicalmvs97.09 13896.32 16099.39 4698.93 14398.95 2999.72 21297.35 32194.45 14797.88 17899.42 14286.71 25199.52 17698.48 12393.97 30699.72 122
SDMVSNet94.80 24993.96 26497.33 23898.92 14695.42 20999.59 25098.99 4092.41 26492.55 31297.85 30775.81 40298.93 22197.90 16191.62 32197.64 316
sd_testset93.55 29992.83 30395.74 30198.92 14690.89 37398.24 41298.85 6292.41 26492.55 31297.85 30771.07 43398.68 26193.93 27691.62 32197.64 316
EPNet_dtu95.71 22195.39 21196.66 26798.92 14693.41 29999.57 25698.90 5096.19 9597.52 18898.56 26892.65 14497.36 35177.89 46298.33 17599.20 237
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS98.10 7697.60 9899.60 2498.92 14699.28 1899.89 12799.52 1495.58 11298.24 16399.39 14993.33 12099.74 15697.98 15695.58 28199.78 115
CHOSEN 1792x268896.81 15396.53 15097.64 20098.91 15093.07 30699.65 23499.80 395.64 11095.39 27098.86 23484.35 30199.90 11396.98 19799.16 14499.95 83
thres100view90096.74 16195.92 18799.18 6398.90 15198.77 4799.74 20199.71 792.59 25195.84 25798.86 23489.25 21299.50 18093.84 27994.57 29699.27 228
thres600view796.69 16495.87 19199.14 7398.90 15198.78 4699.74 20199.71 792.59 25195.84 25798.86 23489.25 21299.50 18093.44 29294.50 29999.16 240
MSDG94.37 27093.36 28997.40 23198.88 15393.95 27999.37 29497.38 31685.75 42290.80 33199.17 18184.11 30599.88 12486.35 40398.43 17398.36 295
MGCFI-Net97.00 14396.22 16599.34 5198.86 15498.80 4199.67 23297.30 33394.31 15997.77 18499.41 14686.36 25899.50 18098.38 12893.90 30899.72 122
h-mvs3394.92 24694.36 24996.59 26998.85 15591.29 36598.93 35998.94 4495.90 10198.77 12998.42 28190.89 18899.77 15097.80 16670.76 46698.72 282
Anonymous2024052992.10 33590.65 34796.47 27198.82 15690.61 37998.72 38298.67 8775.54 48093.90 29698.58 26666.23 45299.90 11394.70 26090.67 32498.90 271
PVSNet_Blended_VisFu97.27 12796.81 13798.66 11398.81 15796.67 15299.92 10398.64 9194.51 14496.38 24298.49 27489.05 21699.88 12497.10 19298.34 17499.43 194
PS-MVSNAJ98.44 4998.20 5499.16 6998.80 15898.92 3199.54 26598.17 22297.34 4299.85 2099.85 3891.20 17799.89 11899.41 6899.67 9498.69 283
CANet_DTU96.76 15796.15 16998.60 11898.78 15997.53 10899.84 15297.63 28497.25 5099.20 10299.64 11981.36 33699.98 5192.77 30498.89 15698.28 297
mvsany_test197.82 9597.90 8097.55 21198.77 16093.04 30999.80 17397.93 25196.95 6199.61 6999.68 11290.92 18599.83 14099.18 7798.29 17999.80 111
alignmvs97.81 9697.33 11399.25 5698.77 16098.66 5699.99 898.44 14894.40 15598.41 15299.47 13893.65 11399.42 19098.57 11794.26 30299.67 133
SymmetryMVS97.64 11097.46 10498.17 15498.74 16295.39 21299.61 24599.26 2996.52 7898.61 14099.31 15792.73 14199.67 16896.77 21195.63 27999.45 190
SteuartSystems-ACMMP99.02 1598.97 1499.18 6398.72 16397.71 10099.98 2498.44 14896.85 6499.80 2899.91 1997.57 999.85 13099.44 6699.99 2199.99 26
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base98.23 7197.97 7299.02 8898.69 16498.66 5699.52 26798.08 23697.05 5699.86 1699.86 3490.65 19099.71 16099.39 7098.63 16698.69 283
miper_enhance_ethall94.36 27293.98 26395.49 30498.68 16595.24 22499.73 20897.29 34193.28 21089.86 34595.97 36994.37 8897.05 37492.20 30884.45 38394.19 373
fmvsm_s_conf0.5_n_598.08 7797.71 9299.17 6698.67 16697.69 10499.99 898.57 10797.40 4099.89 1199.69 10585.99 26499.96 7699.80 3299.40 13299.85 103
ETVMVS97.03 14296.64 14598.20 15398.67 16697.12 12999.89 12798.57 10791.10 31698.17 16698.59 26393.86 10898.19 31695.64 23895.24 28999.28 226
test250697.53 11497.19 12098.58 12298.66 16896.90 14098.81 37499.77 594.93 12697.95 17398.96 21292.51 15199.20 20294.93 25098.15 18399.64 139
ECVR-MVScopyleft95.66 22495.05 23097.51 21698.66 16893.71 28498.85 37198.45 14394.93 12696.86 21598.96 21275.22 40899.20 20295.34 24098.15 18399.64 139
BridgeMVS98.27 6397.99 7099.11 7898.64 17098.43 6899.47 27797.79 26694.56 14299.74 4598.35 28394.33 9199.25 19699.12 7999.96 4699.64 139
fmvsm_s_conf0.5_n_a97.73 10597.72 9097.77 18998.63 17194.26 26599.96 5698.92 4997.18 5299.75 4299.69 10587.00 24899.97 6499.46 6498.89 15699.08 250
MVSMamba_PlusPlus97.83 9297.45 10698.99 9098.60 17298.15 7299.58 25297.74 27590.34 34499.26 10198.32 28694.29 9399.23 19799.03 8899.89 7399.58 160
balanced_ft_v196.88 15096.52 15197.96 16998.60 17294.94 23699.41 28597.56 29693.53 19599.42 8697.89 30683.33 31699.31 19399.29 7399.62 9999.64 139
testing22297.08 14196.75 14098.06 16498.56 17496.82 14299.85 14798.61 9992.53 25998.84 12398.84 23893.36 11898.30 30695.84 23494.30 30199.05 254
test111195.57 22794.98 23397.37 23398.56 17493.37 30298.86 36998.45 14394.95 12596.63 22498.95 21775.21 40999.11 20895.02 24798.14 18599.64 139
MVSTER95.53 22895.22 22296.45 27498.56 17497.72 9999.91 11197.67 28092.38 26791.39 32297.14 32397.24 2097.30 35894.80 25687.85 35394.34 360
testing3-297.72 10697.43 10998.60 11898.55 17797.11 131100.00 199.23 3193.78 18797.90 17598.73 24695.50 5399.69 16498.53 12194.63 29498.99 262
VDD-MVS93.77 29292.94 30196.27 28198.55 17790.22 38898.77 37997.79 26690.85 32296.82 21999.42 14261.18 47299.77 15098.95 9094.13 30398.82 275
tpmvs94.28 27493.57 27696.40 27698.55 17791.50 36395.70 47398.55 11987.47 39692.15 31594.26 43891.42 17398.95 22088.15 38195.85 26798.76 278
UGNet95.33 23494.57 24597.62 20498.55 17794.85 23898.67 38899.32 2695.75 10796.80 22196.27 35872.18 42599.96 7694.58 26399.05 15298.04 304
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
PCF-MVS94.20 595.18 23794.10 25798.43 14098.55 17795.99 18497.91 42797.31 33290.35 34389.48 35899.22 17385.19 28199.89 11890.40 34698.47 17299.41 198
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-2895.95 20496.49 15294.34 35498.51 18289.99 39399.39 29098.57 10793.14 21897.33 19798.31 28893.44 11694.68 46393.69 28995.98 26198.34 296
UWE-MVS96.79 15496.72 14297.00 25298.51 18293.70 28599.71 21798.60 10192.96 22697.09 20598.34 28596.67 3398.85 22892.11 31496.50 24798.44 291
myMVS_eth3d2897.86 8897.59 10098.68 11098.50 18497.26 12199.92 10398.55 11993.79 18698.26 16198.75 24495.20 5899.48 18698.93 9296.40 25099.29 224
test_vis1_n_192095.44 23095.31 21895.82 29898.50 18488.74 41199.98 2497.30 33397.84 2899.85 2099.19 17966.82 45099.97 6498.82 10199.46 12698.76 278
BH-w/o95.71 22195.38 21696.68 26698.49 18692.28 32899.84 15297.50 30592.12 27792.06 31898.79 24284.69 29498.67 26395.29 24299.66 9599.09 248
baseline195.78 21794.86 23698.54 12898.47 18798.07 8099.06 33797.99 24492.68 24594.13 29398.62 26093.28 12498.69 26093.79 28485.76 37098.84 274
fmvsm_s_conf0.5_n_797.70 10897.74 8997.59 20998.44 18895.16 23099.97 4298.65 8897.95 2499.62 6299.78 6786.09 26299.94 9499.69 5099.50 11997.66 314
EPMVS96.53 17496.01 17498.09 16298.43 18996.12 18296.36 46099.43 2093.53 19597.64 18695.04 41294.41 8398.38 29791.13 32798.11 18699.75 118
kuosan93.17 30792.60 30994.86 33098.40 19089.54 40198.44 40198.53 12684.46 43688.49 38197.92 30390.57 19297.05 37483.10 42893.49 31197.99 305
WBMVS94.52 26394.03 26195.98 28898.38 19196.68 15199.92 10397.63 28490.75 33189.64 35395.25 40596.77 2796.90 38794.35 26883.57 39094.35 358
UBG97.84 9197.69 9398.29 14998.38 19196.59 15899.90 11798.53 12693.91 18298.52 14498.42 28196.77 2799.17 20598.54 11996.20 25599.11 247
sss97.57 11397.03 12799.18 6398.37 19398.04 8399.73 20899.38 2293.46 20098.76 13299.06 19391.21 17699.89 11896.33 22497.01 23399.62 147
testing1197.48 11697.27 11698.10 16198.36 19496.02 18399.92 10398.45 14393.45 20298.15 16798.70 25095.48 5499.22 19897.85 16395.05 29199.07 251
BH-untuned95.18 23794.83 23796.22 28298.36 19491.22 36699.80 17397.32 33190.91 32091.08 32598.67 25283.51 30998.54 27994.23 27199.61 10498.92 268
testing9197.16 13396.90 13197.97 16898.35 19695.67 19999.91 11198.42 16892.91 22997.33 19798.72 24794.81 7299.21 19996.98 19794.63 29499.03 259
testing9997.17 13296.91 13097.95 17098.35 19695.70 19699.91 11198.43 15692.94 22797.36 19598.72 24794.83 7199.21 19997.00 19594.64 29398.95 264
ET-MVSNet_ETH3D94.37 27093.28 29197.64 20098.30 19897.99 8599.99 897.61 29094.35 15671.57 48899.45 14196.23 3995.34 45396.91 20385.14 37799.59 154
AUN-MVS93.28 30492.60 30995.34 31398.29 19990.09 39199.31 30498.56 11391.80 29096.35 24398.00 29889.38 20998.28 30992.46 30569.22 47397.64 316
FMVSNet392.69 32291.58 33295.99 28798.29 19997.42 11699.26 31697.62 28789.80 35489.68 34995.32 39981.62 33496.27 42887.01 39985.65 37194.29 362
PMMVS96.76 15796.76 13996.76 26398.28 20192.10 33299.91 11197.98 24694.12 16899.53 7499.39 14986.93 24998.73 25196.95 20097.73 19499.45 190
hse-mvs294.38 26994.08 26095.31 31598.27 20290.02 39299.29 31198.56 11395.90 10198.77 12998.00 29890.89 18898.26 31397.80 16669.20 47497.64 316
PVSNet_088.03 1991.80 34290.27 35696.38 27898.27 20290.46 38399.94 9399.61 1393.99 17686.26 42297.39 31871.13 43299.89 11898.77 10567.05 48098.79 277
UA-Net96.54 17395.96 18198.27 15098.23 20495.71 19598.00 42498.45 14393.72 19198.41 15299.27 16588.71 22399.66 17191.19 32697.69 19599.44 193
test_cas_vis1_n_192096.59 16996.23 16397.65 19998.22 20594.23 26799.99 897.25 34697.77 2999.58 7099.08 18977.10 38299.97 6497.64 17499.45 12798.74 280
FE-MVS95.70 22395.01 23297.79 18598.21 20694.57 24995.03 47498.69 8288.90 37097.50 19096.19 36092.60 14799.49 18589.99 35197.94 19299.31 219
GG-mvs-BLEND98.54 12898.21 20698.01 8493.87 47998.52 12897.92 17497.92 30399.02 397.94 33398.17 14299.58 10999.67 133
mvs_anonymous95.65 22595.03 23197.53 21398.19 20895.74 19399.33 29997.49 30690.87 32190.47 33497.10 32588.23 22697.16 36595.92 23297.66 19899.68 131
MVS_Test96.46 17795.74 19598.61 11798.18 20997.23 12399.31 30497.15 36391.07 31798.84 12397.05 32988.17 22798.97 21794.39 26597.50 20099.61 151
BH-RMVSNet95.18 23794.31 25297.80 18398.17 21095.23 22599.76 19197.53 30192.52 26094.27 29199.25 17176.84 38998.80 24090.89 33599.54 11199.35 209
dongtai91.55 34891.13 34192.82 40698.16 21186.35 43599.47 27798.51 13183.24 44485.07 43397.56 31290.33 19794.94 45976.09 47091.73 31997.18 327
RPSCF91.80 34292.79 30588.83 44998.15 21269.87 49198.11 42096.60 43083.93 43994.33 28999.27 16579.60 36099.46 18991.99 31593.16 31697.18 327
ETV-MVS97.92 8497.80 8898.25 15198.14 21396.48 16099.98 2497.63 28495.61 11199.29 9899.46 14092.55 14998.82 23299.02 8998.54 17099.46 185
IS-MVSNet96.29 19095.90 18897.45 22398.13 21494.80 24299.08 33297.61 29092.02 28295.54 26898.96 21290.64 19198.08 32293.73 28797.41 20499.47 183
test_fmvsmconf_n98.43 5198.32 4798.78 10398.12 21596.41 16399.99 898.83 6698.22 799.67 5399.64 11991.11 18199.94 9499.67 5299.62 9999.98 57
fmvsm_s_conf0.1_n_297.25 12896.85 13498.43 14098.08 21698.08 7999.92 10397.76 27498.05 2099.65 5599.58 12880.88 34499.93 10499.59 5698.17 18197.29 325
ab-mvs94.69 25593.42 28298.51 13398.07 21796.26 17096.49 45898.68 8490.31 34594.54 28197.00 33276.30 39799.71 16095.98 23193.38 31499.56 163
XVG-OURS-SEG-HR94.79 25094.70 24495.08 32098.05 21889.19 40399.08 33297.54 29993.66 19294.87 27799.58 12878.78 36899.79 14597.31 18293.40 31396.25 334
EIA-MVS97.53 11497.46 10497.76 19198.04 21994.84 23999.98 2497.61 29094.41 15497.90 17599.59 12592.40 15598.87 22598.04 15199.13 14699.59 154
XVG-OURS94.82 24794.74 24395.06 32198.00 22089.19 40399.08 33297.55 29794.10 16994.71 27999.62 12380.51 35199.74 15696.04 23093.06 31896.25 334
mvsmamba96.94 14696.73 14197.55 21197.99 22194.37 26299.62 24197.70 27793.13 21998.42 15197.92 30388.02 22898.75 24998.78 10499.01 15399.52 173
dp95.05 24194.43 24796.91 25697.99 22192.73 31796.29 46397.98 24689.70 35595.93 25694.67 42793.83 11098.45 28586.91 40296.53 24699.54 168
tpmrst96.27 19295.98 17797.13 24797.96 22393.15 30596.34 46198.17 22292.07 27898.71 13595.12 40993.91 10598.73 25194.91 25396.62 24499.50 179
TR-MVS94.54 26093.56 27797.49 22197.96 22394.34 26398.71 38397.51 30490.30 34694.51 28398.69 25175.56 40398.77 24592.82 30395.99 26099.35 209
Vis-MVSNet (Re-imp)96.32 18795.98 17797.35 23797.93 22594.82 24199.47 27798.15 23091.83 28795.09 27599.11 18791.37 17597.47 34993.47 29197.43 20199.74 119
MDTV_nov1_ep1395.69 19797.90 22694.15 27195.98 46998.44 14893.12 22097.98 17295.74 37395.10 6198.58 27290.02 35096.92 235
Fast-Effi-MVS+95.02 24394.19 25597.52 21597.88 22794.55 25099.97 4297.08 38188.85 37294.47 28497.96 30284.59 29698.41 28989.84 35397.10 22499.59 154
ADS-MVSNet293.80 29193.88 26793.55 38997.87 22885.94 43994.24 47596.84 41690.07 34996.43 23994.48 43290.29 19995.37 45287.44 38897.23 21299.36 205
ADS-MVSNet94.79 25094.02 26297.11 24997.87 22893.79 28194.24 47598.16 22790.07 34996.43 23994.48 43290.29 19998.19 31687.44 38897.23 21299.36 205
Effi-MVS+96.30 18995.69 19798.16 15597.85 23096.26 17097.41 43797.21 35490.37 34298.65 13898.58 26686.61 25598.70 25897.11 19197.37 20699.52 173
PatchmatchNetpermissive95.94 20595.45 20697.39 23297.83 23194.41 25896.05 46798.40 17792.86 23197.09 20595.28 40494.21 9798.07 32489.26 36298.11 18699.70 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas94.64 25893.61 27297.74 19397.82 23296.26 17099.96 5697.78 27085.76 42094.00 29497.54 31376.95 38899.21 19997.23 18795.43 28497.76 313
1112_ss96.01 20295.20 22398.42 14297.80 23396.41 16399.65 23496.66 42792.71 24292.88 30899.40 14792.16 16399.30 19491.92 31793.66 30999.55 164
E3new96.75 15996.43 15697.71 19497.79 23494.83 24099.80 17397.33 32593.52 19897.49 19199.31 15787.73 23198.83 22997.52 17797.40 20599.48 182
Test_1112_low_res95.72 21994.83 23798.42 14297.79 23496.41 16399.65 23496.65 42892.70 24392.86 30996.13 36492.15 16499.30 19491.88 31893.64 31099.55 164
Effi-MVS+-dtu94.53 26295.30 21992.22 41497.77 23682.54 46299.59 25097.06 39094.92 12895.29 27295.37 39785.81 26697.89 33494.80 25697.07 22596.23 336
tpm cat193.51 30092.52 31596.47 27197.77 23691.47 36496.13 46598.06 23780.98 45992.91 30793.78 44389.66 20498.87 22587.03 39896.39 25199.09 248
FA-MVS(test-final)95.86 20895.09 22898.15 15897.74 23895.62 20196.31 46298.17 22291.42 30596.26 24496.13 36490.56 19399.47 18892.18 30997.07 22599.35 209
xiu_mvs_v1_base_debu97.43 11797.06 12398.55 12497.74 23898.14 7499.31 30497.86 26096.43 8399.62 6299.69 10585.56 27399.68 16599.05 8298.31 17697.83 309
xiu_mvs_v1_base97.43 11797.06 12398.55 12497.74 23898.14 7499.31 30497.86 26096.43 8399.62 6299.69 10585.56 27399.68 16599.05 8298.31 17697.83 309
xiu_mvs_v1_base_debi97.43 11797.06 12398.55 12497.74 23898.14 7499.31 30497.86 26096.43 8399.62 6299.69 10585.56 27399.68 16599.05 8298.31 17697.83 309
EPP-MVSNet96.69 16496.60 14796.96 25497.74 23893.05 30899.37 29498.56 11388.75 37495.83 25999.01 19996.01 4098.56 27596.92 20197.20 21499.25 232
gg-mvs-nofinetune93.51 30091.86 32798.47 13597.72 24397.96 8992.62 48998.51 13174.70 48397.33 19769.59 51298.91 497.79 33797.77 17199.56 11099.67 133
IB-MVS92.85 694.99 24493.94 26598.16 15597.72 24395.69 19899.99 898.81 6794.28 16292.70 31096.90 33695.08 6299.17 20596.07 22973.88 45499.60 153
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
thisisatest051597.41 12297.02 12898.59 12197.71 24597.52 10999.97 4298.54 12391.83 28797.45 19299.04 19597.50 1099.10 20994.75 25896.37 25299.16 240
VortexMVS94.11 27893.50 27995.94 29097.70 24696.61 15599.35 29797.18 35793.52 19889.57 35695.74 37387.55 23696.97 38295.76 23785.13 37894.23 367
viewdifsd2359ckpt0996.21 19595.77 19397.53 21397.69 24794.50 25399.78 17997.23 35192.88 23096.58 22799.26 16984.85 28798.66 26696.61 21597.02 23199.43 194
Syy-MVS90.00 38390.63 34888.11 45797.68 24874.66 48899.71 21798.35 19090.79 32892.10 31698.67 25279.10 36693.09 47963.35 49695.95 26496.59 332
myMVS_eth3d94.46 26794.76 24293.55 38997.68 24890.97 36899.71 21798.35 19090.79 32892.10 31698.67 25292.46 15493.09 47987.13 39595.95 26496.59 332
test_fmvs1_n94.25 27594.36 24993.92 37697.68 24883.70 45299.90 11796.57 43197.40 4099.67 5398.88 22561.82 46999.92 11098.23 14099.13 14698.14 302
fmvsm_s_conf0.5_n_698.27 6397.96 7599.23 5897.66 25198.11 7899.98 2498.64 9197.85 2799.87 1499.72 9588.86 22099.93 10499.64 5499.36 13599.63 146
RRT-MVS96.24 19495.68 19997.94 17397.65 25294.92 23799.27 31497.10 37792.79 23797.43 19397.99 30081.85 32999.37 19298.46 12598.57 16799.53 172
diffmvspermissive97.00 14396.64 14598.09 16297.64 25396.17 17999.81 16797.19 35594.67 14098.95 11899.28 16186.43 25698.76 24798.37 13097.42 20399.33 212
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewcassd2359sk1196.59 16996.23 16397.66 19897.63 25494.70 24599.77 18597.33 32593.41 20397.34 19699.17 18186.72 25098.83 22997.40 18097.32 20999.46 185
viewdifsd2359ckpt1396.19 19695.77 19397.45 22397.62 25594.40 26099.70 22497.23 35192.76 23996.63 22499.05 19484.96 28698.64 26896.65 21497.35 20799.31 219
Vis-MVSNetpermissive95.72 21995.15 22697.45 22397.62 25594.28 26499.28 31298.24 21094.27 16496.84 21798.94 21979.39 36198.76 24793.25 29498.49 17199.30 222
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest053097.10 13696.72 14298.22 15297.60 25796.70 14899.92 10398.54 12391.11 31597.07 20798.97 21097.47 1399.03 21293.73 28796.09 25898.92 268
GDP-MVS97.88 8697.59 10098.75 10697.59 25897.81 9699.95 7597.37 31994.44 15099.08 11099.58 12897.13 2599.08 21094.99 24898.17 18199.37 203
miper_ehance_all_eth93.16 30892.60 30994.82 33197.57 25993.56 29499.50 27197.07 38988.75 37488.85 37395.52 38690.97 18496.74 39890.77 33784.45 38394.17 375
guyue97.15 13496.82 13698.15 15897.56 26096.25 17499.71 21797.84 26395.75 10798.13 16898.65 25587.58 23598.82 23298.29 13697.91 19399.36 205
viewmanbaseed2359cas96.45 17896.07 17197.59 20997.55 26194.59 24899.70 22497.33 32593.62 19497.00 21199.32 15485.57 27298.71 25597.26 18697.33 20899.47 183
testing393.92 28494.23 25492.99 40397.54 26290.23 38799.99 899.16 3390.57 33591.33 32498.63 25992.99 13292.52 48382.46 43295.39 28596.22 337
SSM_040495.75 21895.16 22597.50 21897.53 26395.39 21299.11 32897.25 34690.81 32495.27 27398.83 23984.74 29198.67 26395.24 24397.69 19598.45 290
LCM-MVSNet-Re92.31 33192.60 30991.43 42397.53 26379.27 48099.02 34691.83 49792.07 27880.31 45894.38 43683.50 31095.48 44997.22 18897.58 19999.54 168
GBi-Net90.88 35989.82 36594.08 36797.53 26391.97 33398.43 40296.95 40487.05 40289.68 34994.72 42371.34 42996.11 43487.01 39985.65 37194.17 375
test190.88 35989.82 36594.08 36797.53 26391.97 33398.43 40296.95 40487.05 40289.68 34994.72 42371.34 42996.11 43487.01 39985.65 37194.17 375
FMVSNet291.02 35689.56 37095.41 31197.53 26395.74 19398.98 34997.41 31487.05 40288.43 38695.00 41771.34 42996.24 43085.12 41485.21 37694.25 365
tttt051796.85 15196.49 15297.92 17497.48 26895.89 18799.85 14798.54 12390.72 33296.63 22498.93 22297.47 1399.02 21393.03 30195.76 27198.85 273
cashybrid296.25 19395.89 18997.32 24097.45 26993.68 28799.80 17397.22 35393.38 20496.86 21599.28 16184.64 29598.87 22597.18 18997.19 21599.41 198
BP-MVS198.33 5998.18 5698.81 10197.44 27097.98 8699.96 5698.17 22294.88 13098.77 12999.59 12597.59 899.08 21098.24 13998.93 15599.36 205
casdiffmvs_mvgpermissive96.43 17995.94 18597.89 17897.44 27095.47 20599.86 14497.29 34193.35 20696.03 25299.19 17985.39 27798.72 25497.89 16297.04 22899.49 181
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
E296.36 18495.95 18397.60 20697.41 27294.52 25199.71 21797.33 32593.20 21297.02 20899.07 19185.37 27898.82 23297.27 18397.14 22199.46 185
EC-MVSNet97.38 12497.24 11797.80 18397.41 27295.64 20099.99 897.06 39094.59 14199.63 5999.32 15489.20 21598.14 31898.76 10699.23 14299.62 147
viewdifsd2359ckpt0795.83 21195.42 20897.07 25097.40 27493.04 30999.60 24897.24 34992.39 26696.09 25199.14 18683.07 32098.93 22197.02 19496.87 23699.23 235
c3_l92.53 32691.87 32694.52 34397.40 27492.99 31199.40 28696.93 40987.86 39288.69 37695.44 39189.95 20296.44 41690.45 34380.69 41794.14 385
hybrid96.53 17496.15 16997.67 19697.39 27695.12 23199.80 17397.15 36393.38 20498.23 16499.16 18485.20 28098.70 25897.92 15897.15 22099.20 237
viewmambaseed2359dif95.92 20795.55 20497.04 25197.38 27793.41 29999.78 17996.97 40291.14 31496.58 22799.27 16584.85 28798.75 24996.87 20497.12 22398.97 263
fmvsm_s_conf0.1_n97.30 12597.21 11997.60 20697.38 27794.40 26099.90 11798.64 9196.47 8299.51 7899.65 11884.99 28599.93 10499.22 7699.09 14998.46 289
hybridcas96.09 19995.62 20197.50 21897.37 27994.44 25499.84 15297.16 36193.16 21696.03 25299.21 17684.19 30298.65 26796.53 21997.07 22599.42 197
E396.36 18495.95 18397.60 20697.37 27994.52 25199.71 21797.33 32593.18 21497.02 20899.07 19185.45 27698.82 23297.27 18397.14 22199.46 185
CDS-MVSNet96.34 18696.07 17197.13 24797.37 27994.96 23499.53 26697.91 25591.55 29795.37 27198.32 28695.05 6497.13 36893.80 28395.75 27299.30 222
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
hybridnocas0796.57 17196.16 16897.81 18297.36 28295.32 21799.81 16797.12 36994.17 16698.02 17198.90 22385.05 28398.80 24097.85 16397.18 21699.32 214
TESTMET0.1,196.74 16196.26 16298.16 15597.36 28296.48 16099.96 5698.29 20391.93 28395.77 26098.07 29695.54 5098.29 30790.55 34198.89 15699.70 125
miper_lstm_enhance91.81 33991.39 33893.06 40297.34 28489.18 40599.38 29296.79 42186.70 41087.47 40495.22 40690.00 20195.86 44388.26 37781.37 40694.15 381
baseline96.43 17995.98 17797.76 19197.34 28495.17 22999.51 26997.17 35993.92 18196.90 21499.28 16185.37 27898.64 26897.50 17896.86 23899.46 185
cl____92.31 33191.58 33294.52 34397.33 28692.77 31399.57 25696.78 42286.97 40687.56 40295.51 38789.43 20896.62 40588.60 36782.44 39894.16 380
SD_040392.63 32593.38 28690.40 43797.32 28777.91 48297.75 43298.03 24291.89 28490.83 33098.29 29082.00 32693.79 47288.51 37295.75 27299.52 173
DIV-MVS_self_test92.32 33091.60 33194.47 34797.31 28892.74 31599.58 25296.75 42386.99 40587.64 40095.54 38489.55 20796.50 41188.58 36882.44 39894.17 375
casdiffmvspermissive96.42 18195.97 18097.77 18997.30 28994.98 23399.84 15297.09 38093.75 19096.58 22799.26 16985.07 28298.78 24497.77 17197.04 22899.54 168
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GeoE94.36 27293.48 28096.99 25397.29 29093.54 29599.96 5696.72 42588.35 38593.43 29898.94 21982.05 32598.05 32588.12 38396.48 24999.37 203
eth_miper_zixun_eth92.41 32991.93 32493.84 38097.28 29190.68 37798.83 37296.97 40288.57 37989.19 36895.73 37689.24 21496.69 40389.97 35281.55 40494.15 381
MVSFormer96.94 14696.60 14797.95 17097.28 29197.70 10299.55 26397.27 34391.17 31199.43 8499.54 13490.92 18596.89 38894.67 26199.62 9999.25 232
lupinMVS97.85 9097.60 9898.62 11697.28 29197.70 10299.99 897.55 29795.50 11699.43 8499.67 11490.92 18598.71 25598.40 12799.62 9999.45 190
dtuplus95.79 21695.42 20896.93 25597.24 29493.16 30499.78 17996.93 40991.69 29396.18 24999.29 16083.80 30798.73 25196.83 20697.02 23198.89 272
diffmvs_AUTHOR96.75 15996.41 15897.79 18597.20 29595.46 20699.69 22797.15 36394.46 14698.78 12799.21 17685.64 27098.77 24598.27 13797.31 21099.13 244
mamba_040894.98 24594.09 25897.64 20097.14 29695.31 21893.48 48597.08 38190.48 33894.40 28598.62 26084.49 29798.67 26393.99 27497.18 21698.93 265
SSM_0407294.77 25294.09 25896.82 26097.14 29695.31 21893.48 48597.08 38190.48 33894.40 28598.62 26084.49 29796.21 43193.99 27497.18 21698.93 265
SSM_040795.62 22694.95 23497.61 20597.14 29695.31 21899.00 34797.25 34690.81 32494.40 28598.83 23984.74 29198.58 27295.24 24397.18 21698.93 265
SCA94.69 25593.81 26997.33 23897.10 29994.44 25498.86 36998.32 19793.30 20996.17 25095.59 38276.48 39597.95 33191.06 32997.43 20199.59 154
viewmacassd2359aftdt95.93 20695.45 20697.36 23597.09 30094.12 27399.57 25697.26 34593.05 22496.50 23199.17 18182.76 32198.68 26196.61 21597.04 22899.28 226
KinetiMVS96.10 19795.29 22098.53 13097.08 30197.12 12999.56 26098.12 23394.78 13398.44 14998.94 21980.30 35599.39 19191.56 32298.79 16299.06 252
TAMVS95.85 20995.58 20296.65 26897.07 30293.50 29699.17 32397.82 26591.39 30795.02 27698.01 29792.20 16297.30 35893.75 28695.83 26899.14 243
Fast-Effi-MVS+-dtu93.72 29593.86 26893.29 39497.06 30386.16 43699.80 17396.83 41792.66 24692.58 31197.83 30981.39 33597.67 34289.75 35496.87 23696.05 339
E496.01 20295.53 20597.44 22697.05 30494.23 26799.57 25697.30 33392.72 24096.47 23399.03 19683.98 30698.83 22996.92 20196.77 23999.27 228
E5new95.83 21195.39 21197.15 24397.03 30593.59 28999.32 30297.30 33392.58 25396.45 23499.00 20383.37 31398.81 23696.81 20796.65 24299.04 255
E595.83 21195.39 21197.15 24397.03 30593.59 28999.32 30297.30 33392.58 25396.45 23499.00 20383.37 31398.81 23696.81 20796.65 24299.04 255
CostFormer96.10 19795.88 19096.78 26297.03 30592.55 32397.08 44697.83 26490.04 35198.72 13494.89 42195.01 6698.29 30796.54 21895.77 27099.50 179
test_fmvsmvis_n_192097.67 10997.59 10097.91 17697.02 30895.34 21599.95 7598.45 14397.87 2697.02 20899.59 12589.64 20599.98 5199.41 6899.34 13798.42 292
test-LLR96.47 17696.04 17397.78 18797.02 30895.44 20799.96 5698.21 21794.07 17195.55 26696.38 35393.90 10698.27 31190.42 34498.83 16099.64 139
test-mter96.39 18295.93 18697.78 18797.02 30895.44 20799.96 5698.21 21791.81 28995.55 26696.38 35395.17 5998.27 31190.42 34498.83 16099.64 139
casdiffseed41469214795.07 24094.26 25397.50 21897.01 31194.70 24599.58 25297.02 39491.27 30994.66 28098.82 24180.79 34698.55 27893.39 29395.79 26999.27 228
E6new95.83 21195.39 21197.14 24597.00 31293.58 29199.31 30497.30 33392.57 25596.45 23499.01 19983.44 31198.81 23696.80 20996.66 24099.04 255
E695.83 21195.39 21197.14 24597.00 31293.58 29199.31 30497.30 33392.57 25596.45 23499.01 19983.44 31198.81 23696.80 20996.66 24099.04 255
icg_test_0407_295.04 24294.78 24195.84 29796.97 31491.64 35598.63 39197.12 36992.33 26995.60 26498.88 22585.65 26896.56 40892.12 31095.70 27599.32 214
IMVS_040795.21 23694.80 24096.46 27396.97 31491.64 35598.81 37497.12 36992.33 26995.60 26498.88 22585.65 26898.42 28792.12 31095.70 27599.32 214
IMVS_040493.83 28793.17 29395.80 29996.97 31491.64 35597.78 43197.12 36992.33 26990.87 32998.88 22576.78 39096.43 41792.12 31095.70 27599.32 214
IMVS_040395.25 23594.81 23996.58 27096.97 31491.64 35598.97 35497.12 36992.33 26995.43 26998.88 22585.78 26798.79 24292.12 31095.70 27599.32 214
gm-plane-assit96.97 31493.76 28391.47 30198.96 21298.79 24294.92 251
WB-MVSnew92.90 31492.77 30693.26 39696.95 31993.63 28899.71 21798.16 22791.49 29894.28 29098.14 29381.33 33796.48 41479.47 45195.46 28289.68 481
QAPM95.40 23194.17 25699.10 7996.92 32097.71 10099.40 28698.68 8489.31 35888.94 37298.89 22482.48 32399.96 7693.12 30099.83 8099.62 147
KD-MVS_2432*160088.00 40586.10 40993.70 38596.91 32194.04 27497.17 44397.12 36984.93 43181.96 44792.41 45992.48 15294.51 46579.23 45352.68 51092.56 446
miper_refine_blended88.00 40586.10 40993.70 38596.91 32194.04 27497.17 44397.12 36984.93 43181.96 44792.41 45992.48 15294.51 46579.23 45352.68 51092.56 446
tpm295.47 22995.18 22496.35 27996.91 32191.70 35296.96 44997.93 25188.04 39098.44 14995.40 39393.32 12197.97 32894.00 27395.61 28099.38 201
FMVSNet588.32 40187.47 40390.88 42696.90 32488.39 41997.28 44095.68 45382.60 45284.67 43592.40 46179.83 35891.16 48976.39 46981.51 40593.09 437
3Dnovator+91.53 1196.31 18895.24 22199.52 3396.88 32598.64 5999.72 21298.24 21095.27 12188.42 38898.98 20882.76 32199.94 9497.10 19299.83 8099.96 75
Patchmatch-test92.65 32491.50 33596.10 28596.85 32690.49 38291.50 49597.19 35582.76 45190.23 33595.59 38295.02 6598.00 32777.41 46496.98 23499.82 107
MVS96.60 16895.56 20399.72 1496.85 32699.22 2198.31 40898.94 4491.57 29690.90 32899.61 12486.66 25499.96 7697.36 18199.88 7699.99 26
3Dnovator91.47 1296.28 19195.34 21799.08 8296.82 32897.47 11499.45 28298.81 6795.52 11589.39 35999.00 20381.97 32799.95 8597.27 18399.83 8099.84 104
EI-MVSNet93.73 29493.40 28594.74 33296.80 32992.69 31899.06 33797.67 28088.96 36791.39 32299.02 19788.75 22297.30 35891.07 32887.85 35394.22 370
CVMVSNet94.68 25794.94 23593.89 37996.80 32986.92 43399.06 33798.98 4194.45 14794.23 29299.02 19785.60 27195.31 45490.91 33495.39 28599.43 194
IterMVS-LS92.69 32292.11 32094.43 35196.80 32992.74 31599.45 28296.89 41388.98 36589.65 35295.38 39688.77 22196.34 42490.98 33282.04 40194.22 370
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS96.57 17196.46 15596.91 25696.79 33292.50 32499.90 11797.38 31696.02 9997.79 18399.32 15486.36 25898.99 21498.26 13896.33 25399.23 235
IterMVS90.91 35890.17 36093.12 39996.78 33390.42 38598.89 36397.05 39389.03 36286.49 41795.42 39276.59 39395.02 45687.22 39484.09 38693.93 408
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
131496.84 15295.96 18199.48 4096.74 33498.52 6398.31 40898.86 5995.82 10489.91 34398.98 20887.49 23899.96 7697.80 16699.73 9099.96 75
IterMVS-SCA-FT90.85 36190.16 36192.93 40496.72 33589.96 39498.89 36396.99 39888.95 36886.63 41495.67 37776.48 39595.00 45787.04 39784.04 38993.84 415
MVS-HIRNet86.22 41883.19 43395.31 31596.71 33690.29 38692.12 49197.33 32562.85 49986.82 41170.37 51069.37 43797.49 34875.12 47297.99 19198.15 300
viewdifsd2359ckpt1194.09 28093.63 27195.46 30896.68 33788.92 40899.62 24197.12 36993.07 22295.73 26199.22 17377.05 38398.88 22496.52 22087.69 35898.58 287
viewmsd2359difaftdt94.09 28093.64 27095.46 30896.68 33788.92 40899.62 24197.13 36893.07 22295.73 26199.22 17377.05 38398.89 22396.52 22087.70 35798.58 287
VDDNet93.12 30991.91 32596.76 26396.67 33992.65 32198.69 38698.21 21782.81 45097.75 18599.28 16161.57 47099.48 18698.09 14894.09 30498.15 300
dmvs_re93.20 30693.15 29593.34 39296.54 34083.81 45198.71 38398.51 13191.39 30792.37 31498.56 26878.66 37097.83 33693.89 27789.74 32598.38 294
Elysia94.50 26493.38 28697.85 18096.49 34196.70 14898.98 34997.78 27090.81 32496.19 24798.55 27073.63 42098.98 21589.41 35598.56 16897.88 307
StellarMVS94.50 26493.38 28697.85 18096.49 34196.70 14898.98 34997.78 27090.81 32496.19 24798.55 27073.63 42098.98 21589.41 35598.56 16897.88 307
MIMVSNet90.30 37488.67 38995.17 31996.45 34391.64 35592.39 49097.15 36385.99 41790.50 33393.19 45266.95 44894.86 46182.01 43693.43 31299.01 261
CR-MVSNet93.45 30392.62 30895.94 29096.29 34492.66 31992.01 49296.23 43992.62 24896.94 21293.31 44991.04 18296.03 43979.23 45395.96 26299.13 244
RPMNet89.76 38787.28 40497.19 24296.29 34492.66 31992.01 49298.31 19970.19 49096.94 21285.87 49787.25 24399.78 14762.69 49795.96 26299.13 244
tt080591.28 35190.18 35994.60 33896.26 34687.55 42698.39 40698.72 7889.00 36489.22 36598.47 27862.98 46598.96 21990.57 34088.00 35297.28 326
Patchmtry89.70 38888.49 39293.33 39396.24 34789.94 39791.37 49696.23 43978.22 47387.69 39993.31 44991.04 18296.03 43980.18 45082.10 40094.02 398
test_vis1_rt86.87 41586.05 41289.34 44596.12 34878.07 48199.87 13383.54 51392.03 28178.21 47089.51 47945.80 49299.91 11196.25 22693.11 31790.03 477
JIA-IIPM91.76 34590.70 34694.94 32596.11 34987.51 42793.16 48798.13 23275.79 47997.58 18777.68 50592.84 13797.97 32888.47 37396.54 24599.33 212
OpenMVScopyleft90.15 1594.77 25293.59 27598.33 14696.07 35097.48 11399.56 26098.57 10790.46 34086.51 41698.95 21778.57 37199.94 9493.86 27899.74 8997.57 321
PAPM98.60 3798.42 3899.14 7396.05 35198.96 2899.90 11799.35 2496.68 7398.35 15699.66 11696.45 3598.51 28099.45 6599.89 7399.96 75
CLD-MVS94.06 28393.90 26694.55 34296.02 35290.69 37699.98 2497.72 27696.62 7791.05 32798.85 23777.21 38198.47 28198.11 14689.51 33194.48 346
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT90.38 37188.75 38895.25 31795.99 35390.16 38991.22 49797.54 29976.80 47597.26 20086.01 49691.88 16996.07 43866.16 49095.91 26699.51 177
ACMH+89.98 1690.35 37289.54 37192.78 40895.99 35386.12 43798.81 37497.18 35789.38 35783.14 44397.76 31068.42 44298.43 28689.11 36386.05 36993.78 418
DeepMVS_CXcopyleft82.92 47295.98 35558.66 50596.01 44592.72 24078.34 46995.51 38758.29 47798.08 32282.57 43185.29 37492.03 456
ACMP92.05 992.74 32092.42 31793.73 38195.91 35688.72 41299.81 16797.53 30194.13 16787.00 41098.23 29174.07 41698.47 28196.22 22788.86 33893.99 403
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n93.61 29893.03 29895.35 31295.86 35786.94 43299.87 13396.36 43796.85 6499.54 7398.79 24252.41 48599.83 14098.64 11498.97 15499.29 224
HQP-NCC95.78 35899.87 13396.82 6693.37 299
ACMP_Plane95.78 35899.87 13396.82 6693.37 299
HQP-MVS94.61 25994.50 24694.92 32695.78 35891.85 34099.87 13397.89 25696.82 6693.37 29998.65 25580.65 34998.39 29397.92 15889.60 32694.53 342
NP-MVS95.77 36191.79 34498.65 255
test_fmvsmconf0.1_n97.74 10397.44 10798.64 11595.76 36296.20 17699.94 9398.05 23998.17 1398.89 12299.42 14287.65 23399.90 11399.50 6199.60 10799.82 107
plane_prior695.76 36291.72 35180.47 353
ACMM91.95 1092.88 31592.52 31593.98 37595.75 36489.08 40799.77 18597.52 30393.00 22589.95 34297.99 30076.17 39998.46 28493.63 29088.87 33794.39 354
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS93.83 28792.84 30296.80 26195.73 36593.57 29399.88 13097.24 34992.57 25592.92 30696.66 34578.73 36997.67 34287.75 38694.06 30599.17 239
plane_prior195.73 365
jason97.24 12996.86 13398.38 14595.73 36597.32 11899.97 4297.40 31595.34 11998.60 14399.54 13487.70 23298.56 27597.94 15799.47 12499.25 232
jason: jason.
mmtdpeth88.52 39987.75 40190.85 42895.71 36883.47 45798.94 35794.85 47188.78 37397.19 20289.58 47863.29 46398.97 21798.54 11962.86 48990.10 476
HQP_MVS94.49 26694.36 24994.87 32795.71 36891.74 34799.84 15297.87 25896.38 8693.01 30498.59 26380.47 35398.37 29997.79 16989.55 32994.52 344
plane_prior795.71 36891.59 361
ITE_SJBPF92.38 41195.69 37185.14 44395.71 45292.81 23489.33 36298.11 29470.23 43598.42 28785.91 40988.16 35093.59 426
fmvsm_s_conf0.1_n_a97.09 13896.90 13197.63 20395.65 37294.21 26999.83 16098.50 13796.27 9299.65 5599.64 11984.72 29399.93 10499.04 8598.84 15998.74 280
ACMH89.72 1790.64 36589.63 36893.66 38795.64 37388.64 41598.55 39497.45 30889.03 36281.62 45097.61 31169.75 43698.41 28989.37 35787.62 35993.92 409
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline296.71 16396.49 15297.37 23395.63 37495.96 18599.74 20198.88 5592.94 22791.61 32098.97 21097.72 798.62 27094.83 25598.08 18997.53 323
FMVSNet188.50 40086.64 40794.08 36795.62 37591.97 33398.43 40296.95 40483.00 44886.08 42494.72 42359.09 47696.11 43481.82 43884.07 38794.17 375
LuminaMVS96.63 16796.21 16697.87 17995.58 37696.82 14299.12 32697.67 28094.47 14597.88 17898.31 28887.50 23798.71 25598.07 15097.29 21198.10 303
0.3-1-1-0.01594.22 27693.13 29797.49 22195.50 37794.17 270100.00 198.22 21388.44 38397.14 20497.04 33192.73 14198.59 27196.45 22272.65 46099.70 125
0.4-1-1-0.294.14 27793.02 29997.51 21695.45 37894.25 266100.00 198.22 21388.53 38096.83 21896.95 33492.25 16098.57 27496.34 22372.65 46099.70 125
LPG-MVS_test92.96 31292.71 30793.71 38395.43 37988.67 41399.75 19797.62 28792.81 23490.05 33898.49 27475.24 40698.40 29195.84 23489.12 33394.07 394
LGP-MVS_train93.71 38395.43 37988.67 41397.62 28792.81 23490.05 33898.49 27475.24 40698.40 29195.84 23489.12 33394.07 394
tpm93.70 29693.41 28494.58 34095.36 38187.41 42897.01 44796.90 41290.85 32296.72 22394.14 44090.40 19696.84 39290.75 33888.54 34599.51 177
0.4-1-1-0.194.07 28292.95 30097.42 22895.24 38294.00 277100.00 198.22 21388.27 38796.81 22096.93 33592.27 15998.56 27596.21 22872.63 46299.70 125
D2MVS92.76 31992.59 31393.27 39595.13 38389.54 40199.69 22799.38 2292.26 27487.59 40194.61 42985.05 28397.79 33791.59 32188.01 35192.47 450
VPA-MVSNet92.70 32191.55 33496.16 28395.09 38496.20 17698.88 36599.00 3991.02 31991.82 31995.29 40376.05 40197.96 33095.62 23981.19 40794.30 361
LTVRE_ROB88.28 1890.29 37589.05 38294.02 37095.08 38590.15 39097.19 44297.43 31084.91 43383.99 43997.06 32874.00 41798.28 30984.08 42087.71 35593.62 425
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
TinyColmap87.87 40786.51 40891.94 41795.05 38685.57 44197.65 43394.08 48284.40 43781.82 44996.85 34062.14 46898.33 30280.25 44986.37 36691.91 458
test0.0.03 193.86 28693.61 27294.64 33695.02 38792.18 33199.93 10098.58 10594.07 17187.96 39698.50 27393.90 10694.96 45881.33 43993.17 31596.78 329
UniMVSNet (Re)93.07 31192.13 31995.88 29494.84 38896.24 17599.88 13098.98 4192.49 26289.25 36395.40 39387.09 24597.14 36793.13 29978.16 43294.26 363
USDC90.00 38388.96 38393.10 40194.81 38988.16 42198.71 38395.54 45793.66 19283.75 44197.20 32265.58 45498.31 30483.96 42387.49 36192.85 443
VPNet91.81 33990.46 35095.85 29694.74 39095.54 20498.98 34998.59 10392.14 27690.77 33297.44 31568.73 44097.54 34794.89 25477.89 43494.46 347
FIs94.10 27993.43 28196.11 28494.70 39196.82 14299.58 25298.93 4892.54 25889.34 36197.31 31987.62 23497.10 37194.22 27286.58 36494.40 353
UniMVSNet_ETH3D90.06 38288.58 39194.49 34694.67 39288.09 42297.81 43097.57 29583.91 44088.44 38397.41 31657.44 47897.62 34491.41 32388.59 34497.77 312
UniMVSNet_NR-MVSNet92.95 31392.11 32095.49 30494.61 39395.28 22299.83 16099.08 3691.49 29889.21 36696.86 33987.14 24496.73 39993.20 29577.52 43794.46 347
test_fmvs289.47 39289.70 36788.77 45294.54 39475.74 48499.83 16094.70 47794.71 13791.08 32596.82 34454.46 48197.78 33992.87 30288.27 34892.80 444
MonoMVSNet94.82 24794.43 24795.98 28894.54 39490.73 37599.03 34497.06 39093.16 21693.15 30395.47 39088.29 22597.57 34597.85 16391.33 32399.62 147
WR-MVS92.31 33191.25 33995.48 30794.45 39695.29 22199.60 24898.68 8490.10 34888.07 39596.89 33780.68 34896.80 39693.14 29879.67 42494.36 355
dtuonly93.89 28593.16 29496.08 28694.37 39791.67 35499.15 32595.04 46991.79 29194.74 27898.72 24781.01 34198.31 30487.29 39296.33 25398.27 298
nrg03093.51 30092.53 31496.45 27494.36 39897.20 12499.81 16797.16 36191.60 29589.86 34597.46 31486.37 25797.68 34195.88 23380.31 42094.46 347
tfpnnormal89.29 39587.61 40294.34 35494.35 39994.13 27298.95 35698.94 4483.94 43884.47 43695.51 38774.84 41197.39 35077.05 46780.41 41891.48 461
FC-MVSNet-test93.81 29093.15 29595.80 29994.30 40096.20 17699.42 28498.89 5292.33 26989.03 37197.27 32187.39 24096.83 39493.20 29586.48 36594.36 355
SSC-MVS3.289.59 39088.66 39092.38 41194.29 40186.12 43799.49 27397.66 28390.28 34788.63 37995.18 40764.46 45996.88 39085.30 41382.66 39594.14 385
MS-PatchMatch90.65 36490.30 35591.71 42294.22 40285.50 44298.24 41297.70 27788.67 37686.42 41996.37 35567.82 44598.03 32683.62 42599.62 9991.60 459
WR-MVS_H91.30 34990.35 35394.15 36194.17 40392.62 32299.17 32398.94 4488.87 37186.48 41894.46 43484.36 30096.61 40688.19 37978.51 42993.21 435
DU-MVS92.46 32891.45 33795.49 30494.05 40495.28 22299.81 16798.74 7692.25 27589.21 36696.64 34781.66 33296.73 39993.20 29577.52 43794.46 347
NR-MVSNet91.56 34790.22 35795.60 30294.05 40495.76 19298.25 41198.70 8091.16 31380.78 45796.64 34783.23 31896.57 40791.41 32377.73 43694.46 347
CP-MVSNet91.23 35390.22 35794.26 35693.96 40692.39 32799.09 33098.57 10788.95 36886.42 41996.57 35079.19 36496.37 42290.29 34778.95 42694.02 398
XXY-MVS91.82 33890.46 35095.88 29493.91 40795.40 21198.87 36897.69 27988.63 37887.87 39797.08 32674.38 41597.89 33491.66 32084.07 38794.35 358
PS-CasMVS90.63 36689.51 37393.99 37393.83 40891.70 35298.98 34998.52 12888.48 38186.15 42396.53 35275.46 40496.31 42788.83 36578.86 42893.95 406
test_040285.58 42183.94 42790.50 43493.81 40985.04 44498.55 39495.20 46676.01 47779.72 46395.13 40864.15 46196.26 42966.04 49286.88 36390.21 473
XVG-ACMP-BASELINE91.22 35490.75 34592.63 41093.73 41085.61 44098.52 39897.44 30992.77 23889.90 34496.85 34066.64 45198.39 29392.29 30788.61 34293.89 411
TranMVSNet+NR-MVSNet91.68 34690.61 34994.87 32793.69 41193.98 27899.69 22798.65 8891.03 31888.44 38396.83 34380.05 35796.18 43290.26 34876.89 44594.45 352
TransMVSNet (Re)87.25 41385.28 42193.16 39893.56 41291.03 36798.54 39694.05 48483.69 44281.09 45496.16 36175.32 40596.40 42176.69 46868.41 47692.06 455
v1090.25 37688.82 38594.57 34193.53 41393.43 29899.08 33296.87 41585.00 43087.34 40894.51 43080.93 34397.02 38182.85 43079.23 42593.26 433
testgi89.01 39788.04 39891.90 41893.49 41484.89 44699.73 20895.66 45493.89 18585.14 43098.17 29259.68 47494.66 46477.73 46388.88 33696.16 338
v890.54 36889.17 37894.66 33593.43 41593.40 30199.20 32096.94 40885.76 42087.56 40294.51 43081.96 32897.19 36484.94 41678.25 43193.38 431
V4291.28 35190.12 36294.74 33293.42 41693.46 29799.68 23097.02 39487.36 39889.85 34795.05 41181.31 33897.34 35387.34 39180.07 42293.40 429
pm-mvs189.36 39487.81 40094.01 37193.40 41791.93 33698.62 39296.48 43586.25 41583.86 44096.14 36373.68 41997.04 37786.16 40675.73 45093.04 439
v114491.09 35589.83 36494.87 32793.25 41893.69 28699.62 24196.98 40086.83 40889.64 35394.99 41880.94 34297.05 37485.08 41581.16 40893.87 413
v119290.62 36789.25 37794.72 33493.13 41993.07 30699.50 27197.02 39486.33 41489.56 35795.01 41579.22 36397.09 37382.34 43481.16 40894.01 400
v2v48291.30 34990.07 36395.01 32293.13 41993.79 28199.77 18597.02 39488.05 38989.25 36395.37 39780.73 34797.15 36687.28 39380.04 42394.09 393
OPM-MVS93.21 30592.80 30494.44 34993.12 42190.85 37499.77 18597.61 29096.19 9591.56 32198.65 25575.16 41098.47 28193.78 28589.39 33293.99 403
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v14419290.79 36289.52 37294.59 33993.11 42292.77 31399.56 26096.99 39886.38 41389.82 34894.95 42080.50 35297.10 37183.98 42280.41 41893.90 410
PEN-MVS90.19 37889.06 38193.57 38893.06 42390.90 37299.06 33798.47 14088.11 38885.91 42596.30 35776.67 39195.94 44287.07 39676.91 44493.89 411
v124090.20 37788.79 38694.44 34993.05 42492.27 32999.38 29296.92 41185.89 41889.36 36094.87 42277.89 37897.03 37980.66 44481.08 41194.01 400
usedtu_dtu_shiyan192.78 31791.73 32895.92 29293.03 42596.82 14299.83 16097.79 26690.58 33390.09 33695.04 41284.75 28996.72 40188.19 37986.23 36794.23 367
FE-MVSNET392.78 31791.73 32895.92 29293.03 42596.82 14299.83 16097.79 26690.58 33390.09 33695.04 41284.75 28996.72 40188.20 37886.23 36794.23 367
ArgMatch-SfM85.25 42684.17 42488.48 45492.99 42777.23 48397.92 42594.24 48090.50 33785.08 43295.65 37949.84 48895.83 44481.06 44270.22 46792.39 452
v14890.70 36389.63 36893.92 37692.97 42890.97 36899.75 19796.89 41387.51 39588.27 39295.01 41581.67 33197.04 37787.40 39077.17 44293.75 419
v192192090.46 36989.12 37994.50 34592.96 42992.46 32599.49 27396.98 40086.10 41689.61 35595.30 40078.55 37297.03 37982.17 43580.89 41694.01 400
MVStest185.03 42882.76 43791.83 41992.95 43089.16 40698.57 39394.82 47271.68 48868.54 49395.11 41083.17 31995.66 44774.69 47365.32 48390.65 468
tt0320-xc82.94 44380.35 45090.72 43292.90 43183.54 45596.85 45294.73 47563.12 49879.85 46293.77 44449.43 49095.46 45080.98 44371.54 46493.16 436
Baseline_NR-MVSNet90.33 37389.51 37392.81 40792.84 43289.95 39599.77 18593.94 48584.69 43589.04 37095.66 37881.66 33296.52 41090.99 33176.98 44391.97 457
test_method80.79 44979.70 45284.08 46892.83 43367.06 49599.51 26995.42 45954.34 50681.07 45593.53 44644.48 49392.22 48678.90 45877.23 44192.94 441
pmmvs492.10 33591.07 34395.18 31892.82 43494.96 23499.48 27696.83 41787.45 39788.66 37896.56 35183.78 30896.83 39489.29 36084.77 38193.75 419
LF4IMVS89.25 39688.85 38490.45 43692.81 43581.19 47298.12 41994.79 47391.44 30286.29 42197.11 32465.30 45798.11 32088.53 37085.25 37592.07 454
tt032083.56 44281.15 44590.77 43092.77 43683.58 45496.83 45395.52 45863.26 49781.36 45292.54 45653.26 48395.77 44580.45 44574.38 45392.96 440
DTE-MVSNet89.40 39388.24 39692.88 40592.66 43789.95 39599.10 32998.22 21387.29 39985.12 43196.22 35976.27 39895.30 45583.56 42675.74 44993.41 428
EU-MVSNet90.14 38090.34 35489.54 44492.55 43881.06 47398.69 38698.04 24091.41 30686.59 41596.84 34280.83 34593.31 47786.20 40581.91 40294.26 363
APD_test181.15 44780.92 44781.86 47392.45 43959.76 50496.04 46893.61 48973.29 48677.06 47396.64 34744.28 49496.16 43372.35 47782.52 39689.67 482
sc_t185.01 42982.46 43992.67 40992.44 44083.09 45897.39 43895.72 45165.06 49585.64 42896.16 36149.50 48997.34 35384.86 41775.39 45197.57 321
our_test_390.39 37089.48 37593.12 39992.40 44189.57 40099.33 29996.35 43887.84 39385.30 42994.99 41884.14 30496.09 43780.38 44784.56 38293.71 424
ppachtmachnet_test89.58 39188.35 39493.25 39792.40 44190.44 38499.33 29996.73 42485.49 42585.90 42695.77 37281.09 34096.00 44176.00 47182.49 39793.30 432
v7n89.65 38988.29 39593.72 38292.22 44390.56 38199.07 33697.10 37785.42 42786.73 41294.72 42380.06 35697.13 36881.14 44078.12 43393.49 427
dmvs_testset83.79 43886.07 41176.94 48092.14 44448.60 51796.75 45490.27 50189.48 35678.65 46798.55 27079.25 36286.65 50366.85 48882.69 39495.57 340
PS-MVSNAJss93.64 29793.31 29094.61 33792.11 44592.19 33099.12 32697.38 31692.51 26188.45 38296.99 33391.20 17797.29 36194.36 26687.71 35594.36 355
pmmvs590.17 37989.09 38093.40 39192.10 44689.77 39899.74 20195.58 45685.88 41987.24 40995.74 37373.41 42296.48 41488.54 36983.56 39193.95 406
N_pmnet80.06 45280.78 44877.89 47891.94 44745.28 52198.80 37756.82 52478.10 47480.08 46093.33 44777.03 38595.76 44668.14 48582.81 39392.64 445
test_djsdf92.83 31692.29 31894.47 34791.90 44892.46 32599.55 26397.27 34391.17 31189.96 34196.07 36781.10 33996.89 38894.67 26188.91 33594.05 397
SixPastTwentyTwo88.73 39888.01 39990.88 42691.85 44982.24 46498.22 41695.18 46788.97 36682.26 44696.89 33771.75 42796.67 40484.00 42182.98 39293.72 423
dtuonlycased86.10 41985.82 41486.95 46091.84 45079.57 47999.27 31494.89 47086.79 40979.46 46494.46 43466.85 44990.93 49280.41 44678.44 43090.34 470
K. test v388.05 40487.24 40590.47 43591.82 45182.23 46598.96 35597.42 31289.05 36176.93 47595.60 38168.49 44195.42 45185.87 41081.01 41493.75 419
OurMVSNet-221017-089.81 38689.48 37590.83 42991.64 45281.21 47198.17 41895.38 46191.48 30085.65 42797.31 31972.66 42397.29 36188.15 38184.83 38093.97 405
mvs_tets91.81 33991.08 34294.00 37291.63 45390.58 38098.67 38897.43 31092.43 26387.37 40797.05 32971.76 42697.32 35694.75 25888.68 34194.11 392
Gipumacopyleft66.95 47165.00 47172.79 48691.52 45467.96 49266.16 52195.15 46847.89 50858.54 50167.99 51729.74 50287.54 50250.20 50877.83 43562.87 516
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n96.39 18295.74 19598.32 14791.47 45595.56 20399.84 15297.30 33397.74 3097.89 17799.35 15379.62 35999.85 13099.25 7599.24 14199.55 164
jajsoiax91.92 33791.18 34094.15 36191.35 45690.95 37199.00 34797.42 31292.61 24987.38 40697.08 32672.46 42497.36 35194.53 26488.77 33994.13 390
MDA-MVSNet-bldmvs84.09 43681.52 44391.81 42091.32 45788.00 42498.67 38895.92 44780.22 46255.60 50493.32 44868.29 44393.60 47573.76 47476.61 44693.82 417
MVP-Stereo90.93 35790.45 35292.37 41391.25 45888.76 41098.05 42396.17 44187.27 40084.04 43795.30 40078.46 37397.27 36383.78 42499.70 9291.09 462
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron85.51 42383.32 43292.10 41590.96 45988.58 41699.20 32096.52 43379.70 46457.12 50392.69 45579.11 36593.86 47177.10 46677.46 43993.86 414
YYNet185.50 42483.33 43192.00 41690.89 46088.38 42099.22 31996.55 43279.60 46557.26 50292.72 45479.09 36793.78 47377.25 46577.37 44093.84 415
ALIKED-NN54.48 48052.67 48259.89 50290.79 46145.45 51981.25 51455.75 52834.99 51744.87 51471.98 50825.50 51074.36 51621.88 52647.04 51259.85 518
anonymousdsp91.79 34490.92 34494.41 35290.76 46292.93 31298.93 35997.17 35989.08 36087.46 40595.30 40078.43 37496.92 38592.38 30688.73 34093.39 430
lessismore_v090.53 43390.58 46380.90 47495.80 44877.01 47495.84 37066.15 45396.95 38383.03 42975.05 45293.74 422
EG-PatchMatch MVS85.35 42583.81 42989.99 44290.39 46481.89 46798.21 41796.09 44381.78 45574.73 48193.72 44551.56 48797.12 37079.16 45688.61 34290.96 465
EGC-MVSNET69.38 46463.76 47486.26 46490.32 46581.66 47096.24 46493.85 4860.99 5433.22 54492.33 46652.44 48492.92 48159.53 50284.90 37984.21 498
CMPMVSbinary61.59 2184.75 43285.14 42283.57 46990.32 46562.54 50096.98 44897.59 29474.33 48469.95 49096.66 34564.17 46098.32 30387.88 38588.41 34789.84 479
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ALIKED-MNN52.51 48450.15 48959.60 50490.05 46744.33 52381.60 51354.93 52932.36 52040.96 52168.77 51420.90 51975.30 51420.00 52741.78 51659.18 519
new_pmnet84.49 43582.92 43589.21 44690.03 46882.60 46196.89 45195.62 45580.59 46075.77 48089.17 48065.04 45894.79 46272.12 47881.02 41390.23 472
pmmvs685.69 42083.84 42891.26 42590.00 46984.41 44997.82 42996.15 44275.86 47881.29 45395.39 39561.21 47196.87 39183.52 42773.29 45692.50 449
ttmdpeth88.23 40387.06 40691.75 42189.91 47087.35 42998.92 36295.73 45087.92 39184.02 43896.31 35668.23 44496.84 39286.33 40476.12 44791.06 463
DSMNet-mixed88.28 40288.24 39688.42 45589.64 47175.38 48798.06 42289.86 50285.59 42488.20 39492.14 46876.15 40091.95 48778.46 46096.05 25997.92 306
UnsupCasMVSNet_eth85.52 42283.99 42590.10 44089.36 47283.51 45696.65 45597.99 24489.14 35975.89 47993.83 44263.25 46493.92 46981.92 43767.90 47992.88 442
Anonymous2023120686.32 41785.42 42089.02 44889.11 47380.53 47799.05 34195.28 46285.43 42682.82 44493.92 44174.40 41493.44 47666.99 48781.83 40393.08 438
ALIKED-LG54.29 48152.28 48360.32 49888.90 47445.51 51881.66 51256.33 52538.60 51042.62 51970.81 50925.00 51275.20 51519.87 52846.76 51460.24 517
Anonymous2024052185.15 42783.81 42989.16 44788.32 47582.69 46098.80 37795.74 44979.72 46381.53 45190.99 47165.38 45694.16 46772.69 47681.11 41090.63 469
OpenMVS_ROBcopyleft79.82 2083.77 43981.68 44290.03 44188.30 47682.82 45998.46 39995.22 46573.92 48576.00 47891.29 47055.00 48096.94 38468.40 48488.51 34690.34 470
test20.0384.72 43383.99 42586.91 46188.19 47780.62 47698.88 36595.94 44688.36 38478.87 46594.62 42868.75 43989.11 49766.52 48975.82 44891.00 464
RoMa-SfM74.91 46072.77 46281.35 47488.00 47867.35 49493.55 48486.23 51168.27 49366.79 49492.92 45330.40 50087.68 49966.14 49162.62 49089.02 488
gbinet_0.2-2-1-0.0287.63 41285.51 41993.99 37387.22 47991.56 36299.81 16797.36 32079.54 46688.60 38093.29 45173.76 41896.34 42489.27 36160.78 49894.06 396
blend_shiyan490.13 38188.79 38694.17 35887.12 48091.83 34299.75 19797.08 38179.27 47188.69 37692.53 45792.25 16096.50 41189.35 35873.04 45894.18 374
KD-MVS_self_test83.59 44082.06 44088.20 45686.93 48180.70 47597.21 44196.38 43682.87 44982.49 44588.97 48167.63 44692.32 48473.75 47562.30 49291.58 460
DKM72.18 46269.80 46579.34 47786.79 48265.15 49692.70 48884.00 51267.67 49461.97 49789.63 47723.69 51585.17 50567.39 48654.35 50887.70 493
MIMVSNet182.58 44480.51 44988.78 45086.68 48384.20 45096.65 45595.41 46078.75 47278.59 46892.44 45851.88 48689.76 49565.26 49378.95 42692.38 453
wanda-best-256-51287.82 40885.71 41594.15 36186.66 48491.88 33899.76 19197.08 38179.46 46788.37 38992.36 46278.01 37596.43 41788.39 37461.26 49494.14 385
FE-blended-shiyan787.82 40885.71 41594.15 36186.66 48491.88 33899.76 19197.08 38179.46 46788.37 38992.36 46278.01 37596.43 41788.39 37461.26 49494.14 385
usedtu_blend_shiyan586.75 41684.29 42394.16 35986.66 48491.83 34297.42 43595.23 46469.94 49188.37 38992.36 46278.01 37596.50 41189.35 35861.26 49494.14 385
SP-NN55.28 47953.59 48160.34 49786.63 48739.01 52886.70 50656.31 52631.08 52243.77 51768.45 51523.39 51660.24 52129.19 52156.76 50581.77 503
LoFTR74.41 46170.88 46484.99 46786.56 48867.85 49393.74 48089.63 50469.46 49254.95 50587.39 49130.76 49996.92 38561.37 49964.06 48690.19 474
blended_shiyan887.82 40885.71 41594.16 35986.54 48991.79 34499.72 21297.08 38179.32 46988.44 38392.35 46577.88 37996.56 40888.53 37061.51 49394.15 381
blended_shiyan687.74 41185.62 41894.09 36686.53 49091.73 35099.72 21297.08 38179.32 46988.22 39392.31 46777.82 38096.43 41788.31 37661.26 49494.13 390
CL-MVSNet_self_test84.50 43483.15 43488.53 45386.00 49181.79 46898.82 37397.35 32185.12 42983.62 44290.91 47376.66 39291.40 48869.53 48260.36 49992.40 451
MatchFormer70.84 46366.72 46983.19 47185.99 49264.61 49793.58 48388.62 50759.32 50250.64 50882.31 50228.00 50596.79 39752.52 50759.50 50188.18 490
UnsupCasMVSNet_bld79.97 45477.03 46088.78 45085.62 49381.98 46693.66 48197.35 32175.51 48170.79 48983.05 49948.70 49194.91 46078.31 46160.29 50089.46 485
mvs5depth84.87 43082.90 43690.77 43085.59 49484.84 44791.10 49893.29 49183.14 44685.07 43394.33 43762.17 46797.32 35678.83 45972.59 46390.14 475
SP-LightGlue55.29 47753.65 48060.20 49985.58 49539.12 52786.36 50957.52 52332.34 52144.34 51667.75 51824.36 51359.32 52429.62 51954.98 50682.17 501
SP-SuperGlue55.29 47753.71 47960.00 50185.11 49638.86 52986.96 50557.95 52232.77 51944.54 51568.00 51623.90 51459.51 52329.61 52054.59 50781.63 504
SP-MNN53.97 48252.04 48659.73 50384.72 49738.63 53086.51 50755.94 52729.25 52340.20 52267.48 51922.18 51859.59 52227.79 52254.33 50980.98 505
Patchmatch-RL test86.90 41485.98 41389.67 44384.45 49875.59 48589.71 50192.43 49386.89 40777.83 47290.94 47294.22 9593.63 47487.75 38669.61 47099.79 112
MASt3R-SfM78.94 45579.57 45377.07 47984.15 49950.74 51391.56 49492.34 49483.22 44580.84 45694.16 43936.67 49792.30 48579.45 45273.71 45588.16 491
pmmvs-eth3d84.03 43781.97 44190.20 43884.15 49987.09 43198.10 42194.73 47583.05 44774.10 48587.77 48865.56 45594.01 46881.08 44169.24 47289.49 484
test_fmvs379.99 45380.17 45179.45 47684.02 50162.83 49899.05 34193.49 49088.29 38680.06 46186.65 49428.09 50488.00 49888.63 36673.27 45787.54 495
PM-MVS80.47 45078.88 45585.26 46583.79 50272.22 48995.89 47191.08 49985.71 42376.56 47788.30 48436.64 49893.90 47082.39 43369.57 47189.66 483
new-patchmatchnet81.19 44679.34 45486.76 46282.86 50380.36 47897.92 42595.27 46382.09 45472.02 48786.87 49362.81 46690.74 49371.10 47963.08 48889.19 487
FE-MVSNET283.57 44181.36 44490.20 43882.83 50487.59 42598.28 41096.04 44485.33 42874.13 48487.45 48959.16 47593.26 47879.12 45769.91 46889.77 480
FE-MVSNET81.05 44878.81 45687.79 45881.98 50583.70 45298.23 41491.78 49881.27 45774.29 48387.44 49060.92 47390.67 49464.92 49468.43 47589.01 489
mvsany_test382.12 44581.14 44685.06 46681.87 50670.41 49097.09 44592.14 49591.27 30977.84 47188.73 48239.31 49595.49 44890.75 33871.24 46589.29 486
WB-MVS76.28 45777.28 45973.29 48581.18 50754.68 50997.87 42894.19 48181.30 45669.43 49190.70 47477.02 38682.06 50835.71 51568.11 47883.13 499
test_f78.40 45677.59 45880.81 47580.82 50862.48 50196.96 44993.08 49283.44 44374.57 48284.57 49827.95 50692.63 48284.15 41972.79 45987.32 496
SSC-MVS75.42 45976.40 46172.49 48980.68 50953.62 51097.42 43594.06 48380.42 46168.75 49290.14 47676.54 39481.66 50933.25 51666.34 48282.19 500
pmmvs380.27 45177.77 45787.76 45980.32 51082.43 46398.23 41491.97 49672.74 48778.75 46687.97 48757.30 47990.99 49170.31 48062.37 49189.87 478
testf168.38 46766.92 46772.78 48778.80 51150.36 51490.95 49987.35 50955.47 50458.95 49988.14 48520.64 52187.60 50057.28 50364.69 48480.39 507
APD_test268.38 46766.92 46772.78 48778.80 51150.36 51490.95 49987.35 50955.47 50458.95 49988.14 48520.64 52187.60 50057.28 50364.69 48480.39 507
ambc83.23 47077.17 51362.61 49987.38 50394.55 47976.72 47686.65 49430.16 50196.36 42384.85 41869.86 46990.73 467
test_vis3_rt68.82 46566.69 47075.21 48476.24 51460.41 50396.44 45968.71 51975.13 48250.54 50969.52 51316.42 52796.32 42680.27 44866.92 48168.89 513
PDCNetPlus59.83 47457.26 47767.55 49376.18 51556.71 50787.01 50445.27 53259.54 50148.80 51183.01 50026.63 50876.54 51362.12 49826.78 52469.40 512
usedtu_dtu_shiyan275.87 45872.37 46386.39 46376.18 51575.49 48696.53 45793.82 48764.74 49672.53 48688.48 48337.67 49691.12 49064.13 49557.22 50392.56 446
TDRefinement84.76 43182.56 43891.38 42474.58 51784.80 44897.36 43994.56 47884.73 43480.21 45996.12 36663.56 46298.39 29387.92 38463.97 48790.95 466
SIFT-NN35.94 49336.54 49634.16 50973.93 51829.52 53262.74 52237.28 53319.65 52727.91 52949.19 52811.66 53046.35 5289.19 52937.30 51726.61 526
ELoFTR64.32 47360.56 47675.60 48373.46 51953.20 51186.50 50880.09 51560.74 50045.95 51382.48 50116.05 52889.20 49656.48 50643.34 51584.38 497
E-PMN52.30 48552.18 48552.67 50571.51 52045.40 52093.62 48276.60 51736.01 51443.50 51864.13 52227.11 50767.31 51931.06 51726.06 52545.30 525
EMVS51.44 48751.22 48852.11 50670.71 52144.97 52294.04 47775.66 51835.34 51642.40 52061.56 52628.93 50365.87 52027.64 52324.73 52645.49 523
PMMVS267.15 47064.15 47376.14 48270.56 52262.07 50293.89 47887.52 50858.09 50360.02 49878.32 50422.38 51784.54 50659.56 50147.03 51381.80 502
SIFT-MNN34.10 49434.41 49733.17 51168.99 52328.51 53360.22 52436.81 53419.08 53024.04 53147.28 53110.06 53445.04 5298.72 53034.47 51925.97 529
SIFT-NCM-Cal31.73 49631.67 49931.91 51467.18 52427.55 53958.36 52633.09 53818.38 53314.93 53845.16 5378.60 53743.82 5317.62 53931.68 52224.36 532
SIFT-NN-NCMNet33.88 49534.14 49833.10 51266.88 52528.42 53460.42 52336.72 53519.15 52824.06 53047.14 53210.24 53244.77 5308.72 53033.94 52126.10 528
FPMVS68.72 46668.72 46668.71 49165.95 52644.27 52495.97 47094.74 47451.13 50753.26 50690.50 47525.11 51183.00 50760.80 50080.97 41578.87 509
SP-DiffGlue56.84 47555.72 47860.19 50065.70 52740.86 52581.89 51160.28 52134.62 51850.39 51076.88 50626.61 50958.81 52548.21 50956.94 50480.90 506
wuyk23d20.37 50820.84 51118.99 52565.34 52827.73 53750.43 5347.67 5499.50 5428.01 5436.34 5436.13 54526.24 54223.40 52510.69 5402.99 540
SIFT-ConvMatch30.09 49929.76 50331.09 51665.16 52927.56 53854.13 53031.17 53918.55 53217.88 53445.89 5348.40 53842.26 5358.11 53518.51 53123.46 534
SIFT-CM-Cal28.34 50227.90 50629.63 51863.75 53025.98 54350.66 53326.18 54318.12 53616.88 53644.64 5388.08 54039.70 5367.65 53815.19 53623.22 535
LCM-MVSNet67.77 46964.73 47276.87 48162.95 53156.25 50889.37 50293.74 48844.53 50961.99 49680.74 50320.42 52386.53 50469.37 48359.50 50187.84 492
SIFT-NN-CMatch31.71 49731.56 50032.16 51362.58 53227.53 54056.45 52733.28 53719.00 53123.65 53247.34 52910.05 53542.72 5338.71 53222.96 52926.24 527
SIFT-UM-Cal27.47 50327.02 50728.83 52162.12 53324.58 54553.60 53123.46 54418.14 53512.85 54045.56 5357.49 54139.45 5377.68 53712.30 53722.45 536
SIFT-UMatch29.40 50128.87 50530.98 51762.08 53426.57 54256.09 52829.45 54118.31 53415.86 53746.00 5338.23 53942.54 5347.99 53615.81 53423.85 533
GLUNet-SfM51.10 48846.61 49164.56 49461.54 53539.88 52679.38 51765.13 52036.09 51333.36 52669.94 51114.50 52978.76 51142.46 51317.10 53375.02 511
SIFT-NN-UMatch31.23 49831.05 50231.79 51560.08 53627.23 54158.49 52533.65 53619.14 52917.30 53547.31 53010.12 53342.88 5328.67 53324.67 52725.27 530
XFeat-NN42.54 48942.87 49341.54 50859.73 53727.86 53669.53 51945.34 53124.36 52437.16 52364.79 52020.84 52051.40 52730.01 51834.12 52045.36 524
MVEpermissive53.74 2251.54 48647.86 49062.60 49559.56 53850.93 51279.41 51677.69 51635.69 51536.27 52461.76 5255.79 54669.63 51737.97 51436.61 51867.24 514
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SIFT-NN-PointCN29.63 50029.72 50429.36 51957.55 53923.55 54656.07 52930.57 54017.99 53720.99 53345.21 5369.94 53639.33 5388.40 53420.81 53025.20 531
SIFT-PointCN25.49 50425.71 50824.84 52256.17 54018.65 54751.37 53226.53 54216.31 53812.78 54139.87 5416.41 54434.09 5406.51 54115.42 53521.77 537
SIFT-PCN-Cal24.67 50524.81 50924.24 52356.13 54118.04 54849.05 53523.39 54516.07 53912.99 53940.17 5406.97 54334.68 5396.71 54011.81 53819.99 538
XFeat-MNN41.51 49041.24 49442.32 50755.40 54228.19 53569.39 52046.53 53023.57 52534.47 52563.21 52420.04 52452.41 52627.43 52431.08 52346.37 522
SIFT-NCMNet21.21 50721.22 51021.17 52452.99 54316.41 54942.12 53614.05 54715.89 54010.70 54235.85 5425.14 54729.82 5415.80 5428.44 54117.28 539
ANet_high56.10 47652.24 48467.66 49249.27 54456.82 50683.94 51082.02 51470.47 48933.28 52764.54 52117.23 52669.16 51845.59 51123.85 52877.02 510
tmp_tt65.23 47262.94 47572.13 49044.90 54550.03 51681.05 51589.42 50638.45 51148.51 51299.90 2354.09 48278.70 51291.84 31918.26 53287.64 494
PMVScopyleft49.05 2353.75 48351.34 48760.97 49640.80 54634.68 53174.82 51889.62 50537.55 51228.67 52872.12 5077.09 54281.63 51043.17 51268.21 47766.59 515
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12337.68 49239.14 49533.31 51019.94 54724.83 54498.36 4079.75 54815.53 54151.31 50787.14 49219.62 52517.74 54347.10 5103.47 54257.36 520
testmvs40.60 49144.45 49229.05 52019.49 54814.11 55099.68 23018.47 54620.74 52664.59 49598.48 27710.95 53117.09 54456.66 50511.01 53955.94 521
mmdepth0.00 5110.00 5140.00 5260.00 5490.00 5510.00 5370.00 5500.00 5440.00 5450.00 5450.00 5480.00 5450.00 5430.00 5430.00 541
monomultidepth0.00 5110.00 5140.00 5260.00 5490.00 5510.00 5370.00 5500.00 5440.00 5450.00 5450.00 5480.00 5450.00 5430.00 5430.00 541
test_blank0.00 5110.00 5140.00 5260.00 5490.00 5510.00 5370.00 5500.00 5440.00 5450.02 5440.00 5480.00 5450.00 5430.00 5430.00 541
eth-test20.00 549
eth-test0.00 549
uanet_test0.00 5110.00 5140.00 5260.00 5490.00 5510.00 5370.00 5500.00 5440.00 5450.00 5450.00 5480.00 5450.00 5430.00 5430.00 541
DCPMVS0.00 5110.00 5140.00 5260.00 5490.00 5510.00 5370.00 5500.00 5440.00 5450.00 5450.00 5480.00 5450.00 5430.00 5430.00 541
cdsmvs_eth3d_5k23.43 50631.24 5010.00 5260.00 5490.00 5510.00 53798.09 2340.00 5440.00 54599.67 11483.37 3130.00 5450.00 5430.00 5430.00 541
pcd_1.5k_mvsjas7.60 51010.13 5130.00 5260.00 5490.00 5510.00 5370.00 5500.00 5440.00 5450.00 54591.20 1770.00 5450.00 5430.00 5430.00 541
sosnet-low-res0.00 5110.00 5140.00 5260.00 5490.00 5510.00 5370.00 5500.00 5440.00 5450.00 5450.00 5480.00 5450.00 5430.00 5430.00 541
sosnet0.00 5110.00 5140.00 5260.00 5490.00 5510.00 5370.00 5500.00 5440.00 5450.00 5450.00 5480.00 5450.00 5430.00 5430.00 541
uncertanet0.00 5110.00 5140.00 5260.00 5490.00 5510.00 5370.00 5500.00 5440.00 5450.00 5450.00 5480.00 5450.00 5430.00 5430.00 541
Regformer0.00 5110.00 5140.00 5260.00 5490.00 5510.00 5370.00 5500.00 5440.00 5450.00 5450.00 5480.00 5450.00 5430.00 5430.00 541
ab-mvs-re8.28 50911.04 5120.00 5260.00 5490.00 5510.00 5370.00 5500.00 5440.00 54599.40 1470.00 5480.00 5450.00 5430.00 5430.00 541
uanet0.00 5110.00 5140.00 5260.00 5490.00 5510.00 5370.00 5500.00 5440.00 5450.00 5450.00 5480.00 5450.00 5430.00 5430.00 541
WAC-MVS90.97 36886.10 408
PC_three_145296.96 6099.80 2899.79 6397.49 11100.00 199.99 599.98 32100.00 1
test_241102_TWO98.43 15697.27 4799.80 2899.94 597.18 23100.00 1100.00 1100.00 1100.00 1
test_0728_THIRD96.48 8099.83 2499.91 1997.87 6100.00 199.92 16100.00 1100.00 1
GSMVS99.59 154
sam_mvs194.72 7499.59 154
sam_mvs94.25 94
MTGPAbinary98.28 204
test_post195.78 47259.23 52793.20 12897.74 34091.06 329
test_post63.35 52394.43 8298.13 319
patchmatchnet-post91.70 46995.12 6097.95 331
MTMP99.87 13396.49 434
test9_res99.71 4899.99 21100.00 1
agg_prior299.48 63100.00 1100.00 1
test_prior498.05 8299.94 93
test_prior299.95 7595.78 10599.73 4799.76 7396.00 4199.78 35100.00 1
旧先验299.46 28194.21 16599.85 2099.95 8596.96 199
新几何299.40 286
无先验99.49 27398.71 7993.46 200100.00 194.36 26699.99 26
原ACMM299.90 117
testdata299.99 3990.54 342
segment_acmp96.68 31
testdata199.28 31296.35 91
plane_prior597.87 25898.37 29997.79 16989.55 32994.52 344
plane_prior498.59 263
plane_prior391.64 35596.63 7593.01 304
plane_prior299.84 15296.38 86
plane_prior91.74 34799.86 14496.76 7089.59 328
n20.00 550
nn0.00 550
door-mid89.69 503
test1198.44 148
door90.31 500
HQP5-MVS91.85 340
BP-MVS97.92 158
HQP4-MVS93.37 29998.39 29394.53 342
HQP3-MVS97.89 25689.60 326
HQP2-MVS80.65 349
MDTV_nov1_ep13_2view96.26 17096.11 46691.89 28498.06 16994.40 8494.30 26999.67 133
ACMMP++_ref87.04 362
ACMMP++88.23 349
Test By Simon92.82 139