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 32598.84 6593.32 20696.74 22099.72 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 34798.36 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 27898.40 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 28392.06 31899.15 7199.94 1797.50 11199.94 9398.42 16896.22 9399.41 8741.37 53294.34 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 17699.76 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 18799.70 10194.40 8499.98 5197.00 19399.98 3299.99 26
MG-MVS98.91 2298.65 2799.68 1899.94 1799.07 2699.64 23599.44 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 26699.31 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 17498.80 12699.74 8892.98 133100.00 198.16 14399.76 8899.93 88
TEST999.92 3698.92 3199.96 5698.43 15693.90 18299.71 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 19499.50 1793.90 18299.37 9299.76 7393.24 126100.00 197.75 17299.96 4699.98 57
ACMMPcopyleft97.74 10397.44 10798.66 11399.92 3696.13 18099.18 31899.45 1894.84 13296.41 23999.71 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 16999.74 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 31398.47 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 23999.89 5091.92 33499.90 11799.07 3788.67 37195.26 27199.82 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 20999.78 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 21199.77 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 24297.78 27096.52 7898.61 14099.31 15792.73 14199.67 16896.77 20899.48 12199.06 250
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 32699.62 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 28798.28 20495.76 10697.18 20299.88 2992.74 140100.00 198.67 11199.88 7699.99 26
LS3D95.84 20895.11 22498.02 16799.85 6195.10 23198.74 37598.50 13787.22 39693.66 29399.86 3487.45 23999.95 8590.94 33099.81 8699.02 258
HPM-MVScopyleft97.96 8097.72 9098.68 11099.84 6396.39 16699.90 11798.17 22292.61 24798.62 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 28398.51 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 24399.92 10398.46 14293.93 17997.20 20099.27 16395.44 5599.97 6497.41 17899.51 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 17899.37 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 29398.50 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 18898.31 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 18898.31 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 28998.94 11999.54 13491.82 17299.65 17297.62 17599.99 2199.99 26
SF-MVS98.67 3398.40 3999.50 3599.77 7298.67 5499.90 11798.21 21793.53 19499.81 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 22999.76 7393.36 30199.65 23197.95 24996.03 9897.41 19399.70 10189.61 20699.51 17896.73 21098.25 18099.38 200
新几何199.42 4399.75 7698.27 7198.63 9792.69 24299.55 7199.82 5494.40 84100.00 191.21 32299.94 5899.99 26
MP-MVS-pluss98.07 7897.64 9699.38 4999.74 7798.41 6999.74 19898.18 22193.35 20496.45 23299.85 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 18298.38 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 17299.62 6299.85 3894.97 6999.96 7695.11 24299.95 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 20598.23 21297.02 5899.18 10599.90 2394.54 8199.99 3999.77 3799.90 7299.99 26
F-COLMAP96.93 14896.95 12996.87 25699.71 8391.74 34499.85 14797.95 24993.11 21995.72 26099.16 18292.35 15699.94 9495.32 23899.35 13698.92 266
reproduce-ours98.78 2798.67 2499.09 8099.70 8597.30 11999.74 19898.25 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 19898.25 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 29999.67 8886.91 43099.95 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 37299.63 9081.76 46599.96 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 19895.82 19096.72 26299.59 9296.99 13699.95 7599.10 3494.06 17298.27 15995.80 36789.00 21899.95 8599.12 7987.53 35693.24 430
PVSNet_Blended97.94 8297.64 9698.83 10099.59 9296.99 136100.00 199.10 3495.38 11798.27 15999.08 18789.00 21899.95 8599.12 7999.25 14099.57 162
PatchMatch-RL96.04 19995.40 20797.95 17099.59 9295.22 22599.52 26499.07 3793.96 17796.49 23098.35 27982.28 32199.82 14290.15 34699.22 14398.81 273
dcpmvs_297.42 12198.09 6395.42 30699.58 9687.24 42699.23 31496.95 40294.28 16298.93 12099.73 9294.39 8799.16 20799.89 2199.82 8499.86 102
test22299.55 9797.41 11799.34 29598.55 11991.86 28499.27 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 18696.55 22899.69 10592.28 15899.98 5197.13 18899.44 12899.93 88
API-MVS97.86 8897.66 9498.47 13599.52 9995.41 21099.47 27498.87 5891.68 29098.84 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 17484.48 29799.95 8594.92 24898.74 16499.58 160
114514_t97.41 12296.83 13599.14 7399.51 10197.83 9499.89 12798.27 20688.48 37699.06 11499.66 11690.30 19899.64 17396.32 22299.97 4299.96 75
cl2293.77 28893.25 28995.33 31099.49 10294.43 25599.61 24298.09 23490.38 33689.16 36595.61 37590.56 19397.34 34991.93 31384.45 37994.21 368
testdata98.42 14299.47 10395.33 21698.56 11393.78 18699.79 3799.85 3893.64 11499.94 9494.97 24699.94 58100.00 1
MAR-MVS97.43 11797.19 12098.15 15899.47 10394.79 24299.05 33698.76 7392.65 24598.66 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 25793.42 27997.91 17699.46 10594.04 27398.93 35497.48 30781.15 45190.04 33699.55 13287.02 24799.95 8588.97 36198.11 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 39499.42 2197.03 5799.02 11699.09 18699.35 298.21 31199.73 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 9397.65 311
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 27999.94 5899.98 57
TAPA-MVS92.12 894.42 26593.60 27196.90 25599.33 11091.78 34399.78 17798.00 24389.89 34894.52 27899.47 13891.97 16899.18 20469.90 47499.52 11499.73 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs95.38 22995.07 22696.32 27799.32 11296.60 15699.76 18898.85 6296.65 7487.83 39496.05 36499.52 198.11 31696.58 21481.07 40894.25 361
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 19499.28 11395.20 22699.98 2497.15 36295.53 11499.62 6299.79 6392.08 16698.38 29498.75 10799.28 13999.52 173
test_fmvsm_n_192098.44 4998.61 3097.92 17499.27 11595.18 227100.00 198.90 5098.05 2099.80 2899.73 9292.64 14599.99 3999.58 5799.51 11798.59 283
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 23599.27 2791.43 29997.88 17798.99 20495.84 4699.84 13898.82 10195.32 28399.79 112
DCV-MVSNet97.83 9297.37 11199.21 6099.18 11997.98 8699.64 23599.27 2791.43 29997.88 17798.99 20495.84 4699.84 13898.82 10195.32 28399.79 112
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 23098.06 23796.37 8994.37 28499.49 13783.29 31499.90 11397.63 17499.61 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 17092.58 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 22699.10 12594.42 25699.99 897.10 37595.07 12399.68 5299.75 8192.95 13498.34 29898.38 12899.14 14599.54 168
Anonymous20240521193.10 30691.99 31996.40 27399.10 12589.65 39598.88 36097.93 25183.71 43594.00 29098.75 24168.79 43499.88 12495.08 24391.71 31699.68 131
fmvsm_s_conf0.5_n97.80 9797.85 8597.67 19599.06 12894.41 25799.98 2498.97 4397.34 4299.63 5999.69 10587.27 24299.97 6499.62 5599.06 15198.62 282
HyFIR lowres test96.66 16696.43 15697.36 23499.05 12993.91 27999.70 22199.80 390.54 33296.26 24298.08 29192.15 16498.23 31096.84 20395.46 27899.93 88
LFMVS94.75 25193.56 27498.30 14899.03 13095.70 19698.74 37597.98 24687.81 38998.47 14899.39 14967.43 44399.53 17598.01 15295.20 28699.67 133
fmvsm_s_conf0.5_n_497.75 10297.86 8497.42 22799.01 13194.69 24699.97 4298.76 7397.91 2599.87 1499.76 7386.70 25399.93 10499.67 5299.12 14897.64 312
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 32799.94 9499.78 3598.79 16297.51 320
AllTest92.48 32391.64 32695.00 31999.01 13188.43 41398.94 35296.82 41686.50 40588.71 37098.47 27474.73 40899.88 12485.39 40796.18 25296.71 326
TestCases95.00 31999.01 13188.43 41396.82 41686.50 40588.71 37098.47 27474.73 40899.88 12485.39 40796.18 25296.71 326
COLMAP_ROBcopyleft90.47 1492.18 33091.49 33294.25 35399.00 13588.04 41998.42 40096.70 42382.30 44688.43 38299.01 19776.97 38399.85 13086.11 40396.50 24494.86 337
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 30199.97 6499.76 4099.50 11998.39 290
test_fmvs195.35 23095.68 19794.36 34998.99 13684.98 44199.96 5696.65 42597.60 3499.73 4798.96 21071.58 42499.93 10498.31 13499.37 13498.17 295
HY-MVS92.50 797.79 9997.17 12299.63 1998.98 13899.32 1097.49 42899.52 1495.69 10998.32 15797.41 31293.32 12199.77 15098.08 14995.75 26899.81 109
VNet97.21 13196.57 14999.13 7798.97 13997.82 9599.03 33999.21 3294.31 15999.18 10598.88 22286.26 26099.89 11898.93 9294.32 29699.69 130
thres20096.96 14596.21 16699.22 5998.97 13998.84 3899.85 14799.71 793.17 21396.26 24298.88 22289.87 20399.51 17894.26 26794.91 28899.31 217
tfpn200view996.79 15495.99 17499.19 6298.94 14198.82 3999.78 17799.71 792.86 22996.02 25198.87 22989.33 21099.50 18093.84 27694.57 29299.27 226
thres40096.78 15695.99 17499.16 6998.94 14198.82 3999.78 17799.71 792.86 22996.02 25198.87 22989.33 21099.50 18093.84 27694.57 29299.16 238
sasdasda97.09 13896.32 16099.39 4698.93 14398.95 2999.72 20997.35 32194.45 14797.88 17799.42 14286.71 25199.52 17698.48 12393.97 30299.72 122
Anonymous2023121189.86 38188.44 38994.13 36198.93 14390.68 37398.54 39198.26 20776.28 46986.73 40895.54 37970.60 43097.56 34290.82 33380.27 41794.15 377
canonicalmvs97.09 13896.32 16099.39 4698.93 14398.95 2999.72 20997.35 32194.45 14797.88 17799.42 14286.71 25199.52 17698.48 12393.97 30299.72 122
SDMVSNet94.80 24693.96 26197.33 23798.92 14695.42 20999.59 24798.99 4092.41 26292.55 30897.85 30375.81 39898.93 22197.90 16191.62 31797.64 312
sd_testset93.55 29592.83 29995.74 29798.92 14690.89 36998.24 40798.85 6292.41 26292.55 30897.85 30371.07 42998.68 25893.93 27391.62 31797.64 312
EPNet_dtu95.71 21895.39 20896.66 26498.92 14693.41 29799.57 25398.90 5096.19 9597.52 18798.56 26492.65 14497.36 34777.89 45598.33 17599.20 235
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 27799.78 115
CHOSEN 1792x268896.81 15396.53 15097.64 19998.91 15093.07 30399.65 23199.80 395.64 11095.39 26798.86 23184.35 29999.90 11396.98 19599.16 14499.95 83
thres100view90096.74 16195.92 18699.18 6398.90 15198.77 4799.74 19899.71 792.59 24995.84 25498.86 23189.25 21299.50 18093.84 27694.57 29299.27 226
thres600view796.69 16495.87 18999.14 7398.90 15198.78 4699.74 19899.71 792.59 24995.84 25498.86 23189.25 21299.50 18093.44 28994.50 29599.16 238
MSDG94.37 26793.36 28697.40 23098.88 15393.95 27899.37 29197.38 31685.75 41690.80 32799.17 17984.11 30399.88 12486.35 39998.43 17398.36 292
MGCFI-Net97.00 14396.22 16599.34 5198.86 15498.80 4199.67 22997.30 33394.31 15997.77 18399.41 14686.36 25899.50 18098.38 12893.90 30499.72 122
h-mvs3394.92 24394.36 24696.59 26698.85 15591.29 36198.93 35498.94 4495.90 10198.77 12998.42 27790.89 18899.77 15097.80 16570.76 46098.72 279
Anonymous2024052992.10 33190.65 34396.47 26898.82 15690.61 37598.72 37798.67 8775.54 47393.90 29298.58 26266.23 44799.90 11394.70 25790.67 32098.90 269
PVSNet_Blended_VisFu97.27 12796.81 13798.66 11398.81 15796.67 15299.92 10398.64 9194.51 14496.38 24098.49 27089.05 21699.88 12497.10 19098.34 17499.43 194
PS-MVSNAJ98.44 4998.20 5499.16 6998.80 15898.92 3199.54 26298.17 22297.34 4299.85 2099.85 3891.20 17799.89 11899.41 6899.67 9498.69 280
CANet_DTU96.76 15796.15 16898.60 11898.78 15997.53 10899.84 15297.63 28497.25 5099.20 10299.64 11981.36 33399.98 5192.77 30198.89 15698.28 294
mvsany_test197.82 9597.90 8097.55 21098.77 16093.04 30699.80 17297.93 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 29899.67 133
SymmetryMVS97.64 11097.46 10498.17 15498.74 16295.39 21299.61 24299.26 2996.52 7898.61 14099.31 15792.73 14199.67 16896.77 20895.63 27599.45 190
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 26498.08 23697.05 5699.86 1699.86 3490.65 19099.71 16099.39 7098.63 16698.69 280
miper_enhance_ethall94.36 26993.98 26095.49 30098.68 16595.24 22399.73 20597.29 34193.28 20889.86 34195.97 36594.37 8897.05 37092.20 30584.45 37994.19 369
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 31298.17 16698.59 25993.86 10898.19 31295.64 23595.24 28599.28 224
test250697.53 11497.19 12098.58 12298.66 16896.90 14098.81 36999.77 594.93 12697.95 17298.96 21092.51 15199.20 20294.93 24798.15 18399.64 139
ECVR-MVScopyleft95.66 22195.05 22797.51 21598.66 16893.71 28398.85 36698.45 14394.93 12696.86 21498.96 21075.22 40499.20 20295.34 23798.15 18399.64 139
BridgeMVS98.27 6397.99 7099.11 7898.64 17098.43 6899.47 27497.79 26694.56 14299.74 4598.35 27994.33 9199.25 19699.12 7999.96 4699.64 139
fmvsm_s_conf0.5_n_a97.73 10597.72 9097.77 18898.63 17194.26 26499.96 5698.92 4997.18 5299.75 4299.69 10587.00 24899.97 6499.46 6498.89 15699.08 248
MVSMamba_PlusPlus97.83 9297.45 10698.99 9098.60 17298.15 7299.58 24997.74 27590.34 33999.26 10198.32 28294.29 9399.23 19799.03 8899.89 7399.58 160
balanced_ft_v196.88 15096.52 15197.96 16998.60 17294.94 23599.41 28297.56 29693.53 19499.42 8697.89 30283.33 31399.31 19399.29 7399.62 9999.64 139
testing22297.08 14196.75 14098.06 16498.56 17496.82 14299.85 14798.61 9992.53 25798.84 12398.84 23593.36 11898.30 30295.84 23194.30 29799.05 252
test111195.57 22494.98 23097.37 23298.56 17493.37 30098.86 36498.45 14394.95 12596.63 22298.95 21575.21 40599.11 20895.02 24498.14 18599.64 139
MVSTER95.53 22595.22 21996.45 27198.56 17497.72 9999.91 11197.67 28092.38 26591.39 31897.14 31997.24 2097.30 35494.80 25387.85 34994.34 356
testing3-297.72 10697.43 10998.60 11898.55 17797.11 131100.00 199.23 3193.78 18697.90 17498.73 24395.50 5399.69 16498.53 12194.63 29098.99 260
VDD-MVS93.77 28892.94 29796.27 27898.55 17790.22 38498.77 37497.79 26690.85 31896.82 21799.42 14261.18 46799.77 15098.95 9094.13 29998.82 272
tpmvs94.28 27193.57 27396.40 27398.55 17791.50 35995.70 46798.55 11987.47 39192.15 31194.26 43291.42 17398.95 22088.15 37895.85 26398.76 275
UGNet95.33 23194.57 24297.62 20398.55 17794.85 23798.67 38399.32 2695.75 10796.80 21996.27 35472.18 42199.96 7694.58 26099.05 15298.04 300
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 23494.10 25498.43 14098.55 17795.99 18497.91 42197.31 33290.35 33889.48 35499.22 17185.19 28199.89 11890.40 34398.47 17299.41 198
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-2895.95 20296.49 15294.34 35098.51 18289.99 38999.39 28798.57 10793.14 21697.33 19698.31 28493.44 11694.68 45893.69 28695.98 25798.34 293
UWE-MVS96.79 15496.72 14297.00 25098.51 18293.70 28499.71 21498.60 10192.96 22497.09 20498.34 28196.67 3398.85 22792.11 31196.50 24498.44 288
myMVS_eth3d2897.86 8897.59 10098.68 11098.50 18497.26 12199.92 10398.55 11993.79 18598.26 16198.75 24195.20 5899.48 18698.93 9296.40 24799.29 222
test_vis1_n_192095.44 22795.31 21595.82 29498.50 18488.74 40799.98 2497.30 33397.84 2899.85 2099.19 17766.82 44599.97 6498.82 10199.46 12698.76 275
BH-w/o95.71 21895.38 21396.68 26398.49 18692.28 32599.84 15297.50 30592.12 27592.06 31498.79 23984.69 29398.67 26095.29 23999.66 9599.09 246
baseline195.78 21494.86 23398.54 12898.47 18798.07 8099.06 33297.99 24492.68 24394.13 28998.62 25693.28 12498.69 25793.79 28185.76 36698.84 271
fmvsm_s_conf0.5_n_797.70 10897.74 8997.59 20898.44 18895.16 22999.97 4298.65 8897.95 2499.62 6299.78 6786.09 26299.94 9499.69 5099.50 11997.66 310
EPMVS96.53 17396.01 17398.09 16298.43 18996.12 18296.36 45499.43 2093.53 19497.64 18595.04 40794.41 8398.38 29491.13 32498.11 18699.75 118
kuosan93.17 30392.60 30594.86 32698.40 19089.54 39798.44 39698.53 12684.46 43088.49 37797.92 29990.57 19297.05 37083.10 42493.49 30797.99 301
WBMVS94.52 26094.03 25895.98 28498.38 19196.68 15199.92 10397.63 28490.75 32789.64 34995.25 40096.77 2796.90 38394.35 26583.57 38694.35 354
UBG97.84 9197.69 9398.29 14998.38 19196.59 15899.90 11798.53 12693.91 18198.52 14498.42 27796.77 2799.17 20598.54 11996.20 25199.11 245
sss97.57 11397.03 12799.18 6398.37 19398.04 8399.73 20599.38 2293.46 19998.76 13299.06 19191.21 17699.89 11896.33 22197.01 23099.62 147
testing1197.48 11697.27 11698.10 16198.36 19496.02 18399.92 10398.45 14393.45 20198.15 16798.70 24695.48 5499.22 19897.85 16395.05 28799.07 249
BH-untuned95.18 23494.83 23496.22 27998.36 19491.22 36299.80 17297.32 33190.91 31691.08 32198.67 24883.51 30698.54 27694.23 26899.61 10498.92 266
testing9197.16 13396.90 13197.97 16898.35 19695.67 19999.91 11198.42 16892.91 22797.33 19698.72 24494.81 7299.21 19996.98 19594.63 29099.03 257
testing9997.17 13296.91 13097.95 17098.35 19695.70 19699.91 11198.43 15692.94 22597.36 19498.72 24494.83 7199.21 19997.00 19394.64 28998.95 262
ET-MVSNet_ETH3D94.37 26793.28 28897.64 19998.30 19897.99 8599.99 897.61 29094.35 15671.57 48199.45 14196.23 3995.34 44896.91 20185.14 37399.59 154
AUN-MVS93.28 30092.60 30595.34 30998.29 19990.09 38799.31 30198.56 11391.80 28896.35 24198.00 29489.38 20998.28 30592.46 30269.22 46697.64 312
FMVSNet392.69 31891.58 32895.99 28398.29 19997.42 11699.26 31297.62 28789.80 34989.68 34595.32 39481.62 33196.27 42487.01 39585.65 36794.29 358
PMMVS96.76 15796.76 13996.76 26098.28 20192.10 32999.91 11197.98 24694.12 16799.53 7499.39 14986.93 24998.73 24996.95 19897.73 19499.45 190
hse-mvs294.38 26694.08 25795.31 31198.27 20290.02 38899.29 30898.56 11395.90 10198.77 12998.00 29490.89 18898.26 30997.80 16569.20 46797.64 312
PVSNet_088.03 1991.80 33890.27 35296.38 27598.27 20290.46 37999.94 9399.61 1393.99 17586.26 41897.39 31471.13 42899.89 11898.77 10567.05 47398.79 274
UA-Net96.54 17295.96 18098.27 15098.23 20495.71 19598.00 41998.45 14393.72 19098.41 15299.27 16388.71 22399.66 17191.19 32397.69 19599.44 193
test_cas_vis1_n_192096.59 16996.23 16397.65 19898.22 20594.23 26699.99 897.25 34697.77 2999.58 7099.08 18777.10 37899.97 6497.64 17399.45 12798.74 277
FE-MVS95.70 22095.01 22997.79 18498.21 20694.57 24895.03 46898.69 8288.90 36597.50 18996.19 35692.60 14799.49 18589.99 34897.94 19299.31 217
GG-mvs-BLEND98.54 12898.21 20698.01 8493.87 47398.52 12897.92 17397.92 29999.02 397.94 32998.17 14299.58 10999.67 133
mvs_anonymous95.65 22295.03 22897.53 21298.19 20895.74 19399.33 29697.49 30690.87 31790.47 33097.10 32188.23 22697.16 36195.92 22997.66 19899.68 131
MVS_Test96.46 17695.74 19398.61 11798.18 20997.23 12399.31 30197.15 36291.07 31398.84 12397.05 32588.17 22798.97 21794.39 26297.50 20099.61 151
BH-RMVSNet95.18 23494.31 24997.80 18298.17 21095.23 22499.76 18897.53 30192.52 25894.27 28799.25 16976.84 38598.80 23990.89 33299.54 11199.35 208
dongtai91.55 34491.13 33792.82 40298.16 21186.35 43199.47 27498.51 13183.24 43885.07 42897.56 30890.33 19794.94 45476.09 46391.73 31597.18 323
RPSCF91.80 33892.79 30188.83 44598.15 21269.87 48598.11 41596.60 42783.93 43394.33 28599.27 16379.60 35699.46 18991.99 31293.16 31297.18 323
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 23199.02 8998.54 17099.46 185
IS-MVSNet96.29 18995.90 18797.45 22298.13 21494.80 24199.08 32797.61 29092.02 28095.54 26598.96 21090.64 19198.08 31893.73 28497.41 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 34099.93 10499.59 5698.17 18197.29 321
ab-mvs94.69 25293.42 27998.51 13398.07 21796.26 17096.49 45298.68 8490.31 34094.54 27797.00 32876.30 39399.71 16095.98 22893.38 31099.56 163
XVG-OURS-SEG-HR94.79 24794.70 24195.08 31698.05 21889.19 39999.08 32797.54 29993.66 19194.87 27499.58 12878.78 36499.79 14597.31 18193.40 30996.25 330
EIA-MVS97.53 11497.46 10497.76 19098.04 21994.84 23899.98 2497.61 29094.41 15497.90 17499.59 12592.40 15598.87 22598.04 15199.13 14699.59 154
XVG-OURS94.82 24494.74 24095.06 31798.00 22089.19 39999.08 32797.55 29794.10 16894.71 27599.62 12380.51 34799.74 15696.04 22793.06 31496.25 330
mvsmamba96.94 14696.73 14197.55 21097.99 22194.37 26199.62 23897.70 27793.13 21798.42 15197.92 29988.02 22898.75 24798.78 10499.01 15399.52 173
dp95.05 23894.43 24496.91 25397.99 22192.73 31496.29 45797.98 24689.70 35095.93 25394.67 42293.83 11098.45 28286.91 39896.53 24399.54 168
tpmrst96.27 19195.98 17697.13 24597.96 22393.15 30296.34 45598.17 22292.07 27698.71 13595.12 40493.91 10598.73 24994.91 25096.62 24199.50 179
TR-MVS94.54 25793.56 27497.49 22097.96 22394.34 26298.71 37897.51 30490.30 34194.51 27998.69 24775.56 39998.77 24392.82 30095.99 25699.35 208
Vis-MVSNet (Re-imp)96.32 18695.98 17697.35 23697.93 22594.82 24099.47 27498.15 23091.83 28595.09 27299.11 18591.37 17597.47 34593.47 28897.43 20199.74 119
MDTV_nov1_ep1395.69 19597.90 22694.15 27095.98 46398.44 14893.12 21897.98 17195.74 36995.10 6198.58 26990.02 34796.92 232
Fast-Effi-MVS+95.02 24094.19 25297.52 21497.88 22794.55 24999.97 4297.08 37988.85 36794.47 28097.96 29884.59 29498.41 28689.84 35097.10 22299.59 154
ADS-MVSNet293.80 28793.88 26493.55 38597.87 22885.94 43594.24 46996.84 41390.07 34496.43 23794.48 42790.29 19995.37 44787.44 38597.23 21299.36 204
ADS-MVSNet94.79 24794.02 25997.11 24797.87 22893.79 28094.24 46998.16 22790.07 34496.43 23794.48 42790.29 19998.19 31287.44 38597.23 21299.36 204
Effi-MVS+96.30 18895.69 19598.16 15597.85 23096.26 17097.41 43197.21 35390.37 33798.65 13898.58 26286.61 25598.70 25597.11 18997.37 20699.52 173
PatchmatchNetpermissive95.94 20395.45 20497.39 23197.83 23194.41 25796.05 46198.40 17792.86 22997.09 20495.28 39994.21 9798.07 32089.26 35998.11 18699.70 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas94.64 25593.61 26997.74 19297.82 23296.26 17099.96 5697.78 27085.76 41494.00 29097.54 30976.95 38499.21 19997.23 18695.43 28097.76 309
1112_ss96.01 20095.20 22098.42 14297.80 23396.41 16399.65 23196.66 42492.71 24092.88 30499.40 14792.16 16399.30 19491.92 31493.66 30599.55 164
E3new96.75 15996.43 15697.71 19397.79 23494.83 23999.80 17297.33 32593.52 19797.49 19099.31 15787.73 23198.83 22897.52 17697.40 20599.48 182
Test_1112_low_res95.72 21694.83 23498.42 14297.79 23496.41 16399.65 23196.65 42592.70 24192.86 30596.13 36092.15 16499.30 19491.88 31593.64 30699.55 164
Effi-MVS+-dtu94.53 25995.30 21692.22 41097.77 23682.54 45899.59 24797.06 38894.92 12895.29 26995.37 39285.81 26697.89 33094.80 25397.07 22396.23 332
tpm cat193.51 29692.52 31196.47 26897.77 23691.47 36096.13 45998.06 23780.98 45292.91 30393.78 43689.66 20498.87 22587.03 39496.39 24899.09 246
FA-MVS(test-final)95.86 20695.09 22598.15 15897.74 23895.62 20196.31 45698.17 22291.42 30196.26 24296.13 36090.56 19399.47 18892.18 30697.07 22399.35 208
xiu_mvs_v1_base_debu97.43 11797.06 12398.55 12497.74 23898.14 7499.31 30197.86 26096.43 8399.62 6299.69 10585.56 27399.68 16599.05 8298.31 17697.83 305
xiu_mvs_v1_base97.43 11797.06 12398.55 12497.74 23898.14 7499.31 30197.86 26096.43 8399.62 6299.69 10585.56 27399.68 16599.05 8298.31 17697.83 305
xiu_mvs_v1_base_debi97.43 11797.06 12398.55 12497.74 23898.14 7499.31 30197.86 26096.43 8399.62 6299.69 10585.56 27399.68 16599.05 8298.31 17697.83 305
EPP-MVSNet96.69 16496.60 14796.96 25297.74 23893.05 30599.37 29198.56 11388.75 36995.83 25699.01 19796.01 4098.56 27296.92 19997.20 21499.25 230
gg-mvs-nofinetune93.51 29691.86 32398.47 13597.72 24397.96 8992.62 48398.51 13174.70 47697.33 19669.59 50598.91 497.79 33397.77 17099.56 11099.67 133
IB-MVS92.85 694.99 24193.94 26298.16 15597.72 24395.69 19899.99 898.81 6794.28 16292.70 30696.90 33295.08 6299.17 20596.07 22673.88 44999.60 153
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 28597.45 19199.04 19397.50 1099.10 20994.75 25596.37 24999.16 238
VortexMVS94.11 27593.50 27695.94 28697.70 24696.61 15599.35 29497.18 35693.52 19789.57 35295.74 36987.55 23696.97 37895.76 23485.13 37494.23 363
viewdifsd2359ckpt0996.21 19395.77 19197.53 21297.69 24794.50 25299.78 17797.23 35192.88 22896.58 22599.26 16784.85 28698.66 26396.61 21297.02 22999.43 194
Syy-MVS90.00 37990.63 34488.11 45297.68 24874.66 48299.71 21498.35 19090.79 32492.10 31298.67 24879.10 36293.09 47463.35 48995.95 26096.59 328
myMVS_eth3d94.46 26494.76 23993.55 38597.68 24890.97 36499.71 21498.35 19090.79 32492.10 31298.67 24892.46 15493.09 47487.13 39195.95 26096.59 328
test_fmvs1_n94.25 27294.36 24693.92 37297.68 24883.70 44899.90 11796.57 42897.40 4099.67 5398.88 22261.82 46499.92 11098.23 14099.13 14698.14 298
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 19295.68 19797.94 17397.65 25294.92 23699.27 31197.10 37592.79 23597.43 19297.99 29681.85 32699.37 19298.46 12598.57 16799.53 172
diffmvspermissive97.00 14396.64 14598.09 16297.64 25396.17 17999.81 16797.19 35494.67 14098.95 11899.28 16086.43 25698.76 24598.37 13097.42 20399.33 211
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 19797.63 25494.70 24499.77 18297.33 32593.41 20297.34 19599.17 17986.72 25098.83 22897.40 17997.32 20999.46 185
viewdifsd2359ckpt1396.19 19495.77 19197.45 22297.62 25594.40 25999.70 22197.23 35192.76 23796.63 22299.05 19284.96 28598.64 26596.65 21197.35 20799.31 217
Vis-MVSNetpermissive95.72 21695.15 22397.45 22297.62 25594.28 26399.28 30998.24 21094.27 16496.84 21598.94 21779.39 35798.76 24593.25 29198.49 17199.30 220
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 31197.07 20698.97 20897.47 1399.03 21293.73 28496.09 25498.92 266
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 24598.17 18199.37 202
miper_ehance_all_eth93.16 30492.60 30594.82 32797.57 25993.56 29299.50 26897.07 38788.75 36988.85 36995.52 38190.97 18496.74 39490.77 33484.45 37994.17 371
guyue97.15 13496.82 13698.15 15897.56 26096.25 17499.71 21497.84 26395.75 10798.13 16898.65 25187.58 23598.82 23198.29 13697.91 19399.36 204
viewmanbaseed2359cas96.45 17796.07 17097.59 20897.55 26194.59 24799.70 22197.33 32593.62 19397.00 21099.32 15485.57 27298.71 25297.26 18597.33 20899.47 183
testing393.92 28194.23 25192.99 39997.54 26290.23 38399.99 899.16 3390.57 33191.33 32098.63 25592.99 13292.52 47882.46 42895.39 28196.22 333
SSM_040495.75 21595.16 22297.50 21797.53 26395.39 21299.11 32397.25 34690.81 32095.27 27098.83 23684.74 29098.67 26095.24 24097.69 19598.45 287
LCM-MVSNet-Re92.31 32792.60 30591.43 41997.53 26379.27 47599.02 34191.83 49092.07 27680.31 45294.38 43083.50 30795.48 44497.22 18797.58 19999.54 168
GBi-Net90.88 35589.82 36194.08 36397.53 26391.97 33098.43 39796.95 40287.05 39789.68 34594.72 41871.34 42596.11 43087.01 39585.65 36794.17 371
test190.88 35589.82 36194.08 36397.53 26391.97 33098.43 39796.95 40287.05 39789.68 34594.72 41871.34 42596.11 43087.01 39585.65 36794.17 371
FMVSNet291.02 35289.56 36695.41 30797.53 26395.74 19398.98 34497.41 31487.05 39788.43 38295.00 41271.34 42596.24 42685.12 41085.21 37294.25 361
tttt051796.85 15196.49 15297.92 17497.48 26895.89 18799.85 14798.54 12390.72 32896.63 22298.93 22097.47 1399.02 21393.03 29895.76 26798.85 270
BP-MVS198.33 5998.18 5698.81 10197.44 26997.98 8699.96 5698.17 22294.88 13098.77 12999.59 12597.59 899.08 21098.24 13998.93 15599.36 204
casdiffmvs_mvgpermissive96.43 17895.94 18497.89 17897.44 26995.47 20599.86 14497.29 34193.35 20496.03 24999.19 17785.39 27798.72 25197.89 16297.04 22699.49 181
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 18395.95 18297.60 20597.41 27194.52 25099.71 21497.33 32593.20 21097.02 20799.07 18985.37 27898.82 23197.27 18297.14 21999.46 185
EC-MVSNet97.38 12497.24 11797.80 18297.41 27195.64 20099.99 897.06 38894.59 14199.63 5999.32 15489.20 21598.14 31498.76 10699.23 14299.62 147
viewdifsd2359ckpt0795.83 20995.42 20697.07 24897.40 27393.04 30699.60 24597.24 34992.39 26496.09 24899.14 18483.07 31798.93 22197.02 19296.87 23399.23 233
c3_l92.53 32291.87 32294.52 33997.40 27392.99 30899.40 28396.93 40787.86 38788.69 37295.44 38689.95 20296.44 41290.45 34080.69 41394.14 381
hybrid96.53 17396.15 16897.67 19597.39 27595.12 23099.80 17297.15 36293.38 20398.23 16499.16 18285.20 28098.70 25597.92 15897.15 21899.20 235
viewmambaseed2359dif95.92 20595.55 20297.04 24997.38 27693.41 29799.78 17796.97 40091.14 31096.58 22599.27 16384.85 28698.75 24796.87 20297.12 22198.97 261
fmvsm_s_conf0.1_n97.30 12597.21 11997.60 20597.38 27694.40 25999.90 11798.64 9196.47 8299.51 7899.65 11884.99 28499.93 10499.22 7699.09 14998.46 286
hybridcas96.09 19795.62 19997.50 21797.37 27894.44 25399.84 15297.16 36093.16 21496.03 24999.21 17484.19 30098.65 26496.53 21697.07 22399.42 197
E396.36 18395.95 18297.60 20597.37 27894.52 25099.71 21497.33 32593.18 21297.02 20799.07 18985.45 27698.82 23197.27 18297.14 21999.46 185
CDS-MVSNet96.34 18596.07 17097.13 24597.37 27894.96 23399.53 26397.91 25591.55 29395.37 26898.32 28295.05 6497.13 36493.80 28095.75 26899.30 220
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,196.74 16196.26 16298.16 15597.36 28196.48 16099.96 5698.29 20391.93 28195.77 25798.07 29295.54 5098.29 30390.55 33898.89 15699.70 125
miper_lstm_enhance91.81 33591.39 33493.06 39897.34 28289.18 40199.38 28996.79 41886.70 40487.47 40095.22 40190.00 20195.86 43988.26 37481.37 40294.15 377
baseline96.43 17895.98 17697.76 19097.34 28295.17 22899.51 26697.17 35893.92 18096.90 21399.28 16085.37 27898.64 26597.50 17796.86 23599.46 185
cl____92.31 32791.58 32894.52 33997.33 28492.77 31099.57 25396.78 41986.97 40187.56 39895.51 38289.43 20896.62 40188.60 36482.44 39494.16 376
SD_040392.63 32193.38 28390.40 43397.32 28577.91 47797.75 42698.03 24291.89 28290.83 32698.29 28682.00 32393.79 46788.51 36995.75 26899.52 173
DIV-MVS_self_test92.32 32691.60 32794.47 34397.31 28692.74 31299.58 24996.75 42086.99 40087.64 39695.54 37989.55 20796.50 40788.58 36582.44 39494.17 371
casdiffmvspermissive96.42 18095.97 17997.77 18897.30 28794.98 23299.84 15297.09 37893.75 18996.58 22599.26 16785.07 28298.78 24297.77 17097.04 22699.54 168
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 26993.48 27796.99 25197.29 28893.54 29399.96 5696.72 42288.35 38093.43 29498.94 21782.05 32298.05 32188.12 38096.48 24699.37 202
eth_miper_zixun_eth92.41 32591.93 32093.84 37697.28 28990.68 37398.83 36796.97 40088.57 37489.19 36495.73 37289.24 21496.69 39989.97 34981.55 40094.15 377
MVSFormer96.94 14696.60 14797.95 17097.28 28997.70 10299.55 26097.27 34391.17 30799.43 8499.54 13490.92 18596.89 38494.67 25899.62 9999.25 230
lupinMVS97.85 9097.60 9898.62 11697.28 28997.70 10299.99 897.55 29795.50 11699.43 8499.67 11490.92 18598.71 25298.40 12799.62 9999.45 190
diffmvs_AUTHOR96.75 15996.41 15897.79 18497.20 29295.46 20699.69 22497.15 36294.46 14698.78 12799.21 17485.64 27098.77 24398.27 13797.31 21099.13 242
mamba_040894.98 24294.09 25597.64 19997.14 29395.31 21793.48 47997.08 37990.48 33394.40 28198.62 25684.49 29598.67 26093.99 27197.18 21598.93 263
SSM_0407294.77 24994.09 25596.82 25797.14 29395.31 21793.48 47997.08 37990.48 33394.40 28198.62 25684.49 29596.21 42793.99 27197.18 21598.93 263
SSM_040795.62 22394.95 23197.61 20497.14 29395.31 21799.00 34297.25 34690.81 32094.40 28198.83 23684.74 29098.58 26995.24 24097.18 21598.93 263
SCA94.69 25293.81 26697.33 23797.10 29694.44 25398.86 36498.32 19793.30 20796.17 24795.59 37776.48 39197.95 32791.06 32697.43 20199.59 154
viewmacassd2359aftdt95.93 20495.45 20497.36 23497.09 29794.12 27299.57 25397.26 34593.05 22296.50 22999.17 17982.76 31898.68 25896.61 21297.04 22699.28 224
KinetiMVS96.10 19595.29 21798.53 13097.08 29897.12 12999.56 25798.12 23394.78 13398.44 14998.94 21780.30 35199.39 19191.56 31998.79 16299.06 250
TAMVS95.85 20795.58 20096.65 26597.07 29993.50 29499.17 31997.82 26591.39 30395.02 27398.01 29392.20 16297.30 35493.75 28395.83 26499.14 241
Fast-Effi-MVS+-dtu93.72 29193.86 26593.29 39097.06 30086.16 43299.80 17296.83 41492.66 24492.58 30797.83 30581.39 33297.67 33889.75 35196.87 23396.05 335
E496.01 20095.53 20397.44 22597.05 30194.23 26699.57 25397.30 33392.72 23896.47 23199.03 19483.98 30498.83 22896.92 19996.77 23699.27 226
E5new95.83 20995.39 20897.15 24197.03 30293.59 28799.32 29997.30 33392.58 25196.45 23299.00 20183.37 31098.81 23596.81 20496.65 23999.04 253
E595.83 20995.39 20897.15 24197.03 30293.59 28799.32 29997.30 33392.58 25196.45 23299.00 20183.37 31098.81 23596.81 20496.65 23999.04 253
CostFormer96.10 19595.88 18896.78 25997.03 30292.55 32097.08 44097.83 26490.04 34698.72 13494.89 41695.01 6698.29 30396.54 21595.77 26699.50 179
test_fmvsmvis_n_192097.67 10997.59 10097.91 17697.02 30595.34 21599.95 7598.45 14397.87 2697.02 20799.59 12589.64 20599.98 5199.41 6899.34 13798.42 289
test-LLR96.47 17596.04 17297.78 18697.02 30595.44 20799.96 5698.21 21794.07 17095.55 26396.38 34993.90 10698.27 30790.42 34198.83 16099.64 139
test-mter96.39 18195.93 18597.78 18697.02 30595.44 20799.96 5698.21 21791.81 28795.55 26396.38 34995.17 5998.27 30790.42 34198.83 16099.64 139
casdiffseed41469214795.07 23794.26 25097.50 21797.01 30894.70 24499.58 24997.02 39291.27 30594.66 27698.82 23880.79 34298.55 27593.39 29095.79 26599.27 226
E6new95.83 20995.39 20897.14 24397.00 30993.58 28999.31 30197.30 33392.57 25396.45 23299.01 19783.44 30898.81 23596.80 20696.66 23799.04 253
E695.83 20995.39 20897.14 24397.00 30993.58 28999.31 30197.30 33392.57 25396.45 23299.01 19783.44 30898.81 23596.80 20696.66 23799.04 253
icg_test_0407_295.04 23994.78 23895.84 29396.97 31191.64 35198.63 38697.12 36892.33 26795.60 26198.88 22285.65 26896.56 40492.12 30795.70 27199.32 213
IMVS_040795.21 23394.80 23796.46 27096.97 31191.64 35198.81 36997.12 36892.33 26795.60 26198.88 22285.65 26898.42 28492.12 30795.70 27199.32 213
IMVS_040493.83 28393.17 29095.80 29596.97 31191.64 35197.78 42597.12 36892.33 26790.87 32598.88 22276.78 38696.43 41392.12 30795.70 27199.32 213
IMVS_040395.25 23294.81 23696.58 26796.97 31191.64 35198.97 34997.12 36892.33 26795.43 26698.88 22285.78 26798.79 24092.12 30795.70 27199.32 213
gm-plane-assit96.97 31193.76 28291.47 29798.96 21098.79 24094.92 248
WB-MVSnew92.90 31092.77 30293.26 39296.95 31693.63 28699.71 21498.16 22791.49 29494.28 28698.14 28981.33 33496.48 41079.47 44595.46 27889.68 475
QAPM95.40 22894.17 25399.10 7996.92 31797.71 10099.40 28398.68 8489.31 35388.94 36898.89 22182.48 32099.96 7693.12 29799.83 8099.62 147
KD-MVS_2432*160088.00 40186.10 40593.70 38196.91 31894.04 27397.17 43797.12 36884.93 42581.96 44292.41 45292.48 15294.51 46079.23 44652.68 50392.56 442
miper_refine_blended88.00 40186.10 40593.70 38196.91 31894.04 27397.17 43797.12 36884.93 42581.96 44292.41 45292.48 15294.51 46079.23 44652.68 50392.56 442
tpm295.47 22695.18 22196.35 27696.91 31891.70 34996.96 44397.93 25188.04 38598.44 14995.40 38893.32 12197.97 32494.00 27095.61 27699.38 200
FMVSNet588.32 39787.47 39990.88 42296.90 32188.39 41597.28 43495.68 45082.60 44584.67 43092.40 45479.83 35491.16 48376.39 46281.51 40193.09 433
3Dnovator+91.53 1196.31 18795.24 21899.52 3396.88 32298.64 5999.72 20998.24 21095.27 12188.42 38498.98 20682.76 31899.94 9497.10 19099.83 8099.96 75
Patchmatch-test92.65 32091.50 33196.10 28296.85 32390.49 37891.50 48897.19 35482.76 44490.23 33195.59 37795.02 6598.00 32377.41 45796.98 23199.82 107
MVS96.60 16895.56 20199.72 1496.85 32399.22 2198.31 40398.94 4491.57 29290.90 32499.61 12486.66 25499.96 7697.36 18099.88 7699.99 26
3Dnovator91.47 1296.28 19095.34 21499.08 8296.82 32597.47 11499.45 27998.81 6795.52 11589.39 35599.00 20181.97 32499.95 8597.27 18299.83 8099.84 104
EI-MVSNet93.73 29093.40 28294.74 32896.80 32692.69 31599.06 33297.67 28088.96 36291.39 31899.02 19588.75 22297.30 35491.07 32587.85 34994.22 366
CVMVSNet94.68 25494.94 23293.89 37596.80 32686.92 42999.06 33298.98 4194.45 14794.23 28899.02 19585.60 27195.31 44990.91 33195.39 28199.43 194
IterMVS-LS92.69 31892.11 31694.43 34796.80 32692.74 31299.45 27996.89 41088.98 36089.65 34895.38 39188.77 22196.34 42090.98 32982.04 39794.22 366
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS96.57 17196.46 15596.91 25396.79 32992.50 32199.90 11797.38 31696.02 9997.79 18299.32 15486.36 25898.99 21498.26 13896.33 25099.23 233
IterMVS90.91 35490.17 35693.12 39596.78 33090.42 38198.89 35897.05 39189.03 35786.49 41395.42 38776.59 38995.02 45187.22 39084.09 38293.93 404
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
131496.84 15295.96 18099.48 4096.74 33198.52 6398.31 40398.86 5995.82 10489.91 33998.98 20687.49 23899.96 7697.80 16599.73 9099.96 75
IterMVS-SCA-FT90.85 35790.16 35792.93 40096.72 33289.96 39098.89 35896.99 39688.95 36386.63 41095.67 37376.48 39195.00 45287.04 39384.04 38593.84 411
MVS-HIRNet86.22 41483.19 42795.31 31196.71 33390.29 38292.12 48597.33 32562.85 49286.82 40770.37 50369.37 43397.49 34475.12 46597.99 19198.15 296
viewdifsd2359ckpt1194.09 27793.63 26895.46 30496.68 33488.92 40499.62 23897.12 36893.07 22095.73 25899.22 17177.05 37998.88 22496.52 21787.69 35498.58 284
viewmsd2359difaftdt94.09 27793.64 26795.46 30496.68 33488.92 40499.62 23897.13 36793.07 22095.73 25899.22 17177.05 37998.89 22396.52 21787.70 35398.58 284
VDDNet93.12 30591.91 32196.76 26096.67 33692.65 31898.69 38198.21 21782.81 44397.75 18499.28 16061.57 46599.48 18698.09 14894.09 30098.15 296
dmvs_re93.20 30293.15 29193.34 38896.54 33783.81 44798.71 37898.51 13191.39 30392.37 31098.56 26478.66 36697.83 33293.89 27489.74 32198.38 291
Elysia94.50 26193.38 28397.85 18096.49 33896.70 14898.98 34497.78 27090.81 32096.19 24598.55 26673.63 41698.98 21589.41 35298.56 16897.88 303
StellarMVS94.50 26193.38 28397.85 18096.49 33896.70 14898.98 34497.78 27090.81 32096.19 24598.55 26673.63 41698.98 21589.41 35298.56 16897.88 303
MIMVSNet90.30 37088.67 38595.17 31596.45 34091.64 35192.39 48497.15 36285.99 41190.50 32993.19 44566.95 44494.86 45682.01 43293.43 30899.01 259
CR-MVSNet93.45 29992.62 30495.94 28696.29 34192.66 31692.01 48696.23 43692.62 24696.94 21193.31 44291.04 18296.03 43579.23 44695.96 25899.13 242
RPMNet89.76 38387.28 40097.19 24096.29 34192.66 31692.01 48698.31 19970.19 48396.94 21185.87 49087.25 24399.78 14762.69 49095.96 25899.13 242
tt080591.28 34790.18 35594.60 33496.26 34387.55 42298.39 40198.72 7889.00 35989.22 36198.47 27462.98 46098.96 21990.57 33788.00 34897.28 322
Patchmtry89.70 38488.49 38893.33 38996.24 34489.94 39391.37 48996.23 43678.22 46687.69 39593.31 44291.04 18296.03 43580.18 44482.10 39694.02 394
test_vis1_rt86.87 41186.05 40889.34 44196.12 34578.07 47699.87 13383.54 50692.03 27978.21 46389.51 47245.80 48699.91 11196.25 22393.11 31390.03 471
JIA-IIPM91.76 34190.70 34294.94 32196.11 34687.51 42393.16 48198.13 23275.79 47297.58 18677.68 49892.84 13797.97 32488.47 37096.54 24299.33 211
OpenMVScopyleft90.15 1594.77 24993.59 27298.33 14696.07 34797.48 11399.56 25798.57 10790.46 33586.51 41298.95 21578.57 36799.94 9493.86 27599.74 8997.57 317
PAPM98.60 3798.42 3899.14 7396.05 34898.96 2899.90 11799.35 2496.68 7398.35 15699.66 11696.45 3598.51 27799.45 6599.89 7399.96 75
CLD-MVS94.06 28093.90 26394.55 33896.02 34990.69 37299.98 2497.72 27696.62 7791.05 32398.85 23477.21 37798.47 27898.11 14689.51 32794.48 342
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT90.38 36788.75 38495.25 31395.99 35090.16 38591.22 49097.54 29976.80 46897.26 19986.01 48991.88 16996.07 43466.16 48395.91 26299.51 177
ACMH+89.98 1690.35 36889.54 36792.78 40495.99 35086.12 43398.81 36997.18 35689.38 35283.14 43897.76 30668.42 43898.43 28389.11 36086.05 36593.78 414
DeepMVS_CXcopyleft82.92 46695.98 35258.66 49996.01 44292.72 23878.34 46295.51 38258.29 47298.08 31882.57 42785.29 37092.03 451
ACMP92.05 992.74 31692.42 31393.73 37795.91 35388.72 40899.81 16797.53 30194.13 16687.00 40698.23 28774.07 41298.47 27896.22 22488.86 33493.99 399
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n93.61 29493.03 29495.35 30895.86 35486.94 42899.87 13396.36 43496.85 6499.54 7398.79 23952.41 48099.83 14098.64 11498.97 15499.29 222
HQP-NCC95.78 35599.87 13396.82 6693.37 295
ACMP_Plane95.78 35599.87 13396.82 6693.37 295
HQP-MVS94.61 25694.50 24394.92 32295.78 35591.85 33799.87 13397.89 25696.82 6693.37 29598.65 25180.65 34598.39 29097.92 15889.60 32294.53 338
NP-MVS95.77 35891.79 34198.65 251
test_fmvsmconf0.1_n97.74 10397.44 10798.64 11595.76 35996.20 17699.94 9398.05 23998.17 1398.89 12299.42 14287.65 23399.90 11399.50 6199.60 10799.82 107
plane_prior695.76 35991.72 34880.47 349
ACMM91.95 1092.88 31192.52 31193.98 37195.75 36189.08 40399.77 18297.52 30393.00 22389.95 33897.99 29676.17 39598.46 28193.63 28788.87 33394.39 350
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS93.83 28392.84 29896.80 25895.73 36293.57 29199.88 13097.24 34992.57 25392.92 30296.66 34178.73 36597.67 33887.75 38394.06 30199.17 237
plane_prior195.73 362
jason97.24 12996.86 13398.38 14595.73 36297.32 11899.97 4297.40 31595.34 11998.60 14399.54 13487.70 23298.56 27297.94 15799.47 12499.25 230
jason: jason.
mmtdpeth88.52 39587.75 39790.85 42495.71 36583.47 45398.94 35294.85 46688.78 36897.19 20189.58 47163.29 45898.97 21798.54 11962.86 48290.10 470
HQP_MVS94.49 26394.36 24694.87 32395.71 36591.74 34499.84 15297.87 25896.38 8693.01 30098.59 25980.47 34998.37 29697.79 16889.55 32594.52 340
plane_prior795.71 36591.59 357
ITE_SJBPF92.38 40795.69 36885.14 43995.71 44992.81 23289.33 35898.11 29070.23 43198.42 28485.91 40588.16 34693.59 422
fmvsm_s_conf0.1_n_a97.09 13896.90 13197.63 20295.65 36994.21 26899.83 16098.50 13796.27 9299.65 5599.64 11984.72 29299.93 10499.04 8598.84 15998.74 277
ACMH89.72 1790.64 36189.63 36493.66 38395.64 37088.64 41198.55 38997.45 30889.03 35781.62 44597.61 30769.75 43298.41 28689.37 35487.62 35593.92 405
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline296.71 16396.49 15297.37 23295.63 37195.96 18599.74 19898.88 5592.94 22591.61 31698.97 20897.72 798.62 26794.83 25298.08 18997.53 319
FMVSNet188.50 39686.64 40394.08 36395.62 37291.97 33098.43 39796.95 40283.00 44186.08 42094.72 41859.09 47196.11 43081.82 43484.07 38394.17 371
LuminaMVS96.63 16796.21 16697.87 17995.58 37396.82 14299.12 32197.67 28094.47 14597.88 17798.31 28487.50 23798.71 25298.07 15097.29 21198.10 299
0.3-1-1-0.01594.22 27393.13 29397.49 22095.50 37494.17 269100.00 198.22 21388.44 37897.14 20397.04 32792.73 14198.59 26896.45 21972.65 45499.70 125
0.4-1-1-0.294.14 27493.02 29597.51 21595.45 37594.25 265100.00 198.22 21388.53 37596.83 21696.95 33092.25 16098.57 27196.34 22072.65 45499.70 125
LPG-MVS_test92.96 30892.71 30393.71 37995.43 37688.67 40999.75 19497.62 28792.81 23290.05 33498.49 27075.24 40298.40 28895.84 23189.12 32994.07 390
LGP-MVS_train93.71 37995.43 37688.67 40997.62 28792.81 23290.05 33498.49 27075.24 40298.40 28895.84 23189.12 32994.07 390
tpm93.70 29293.41 28194.58 33695.36 37887.41 42497.01 44196.90 40990.85 31896.72 22194.14 43390.40 19696.84 38890.75 33588.54 34199.51 177
0.4-1-1-0.194.07 27992.95 29697.42 22795.24 37994.00 276100.00 198.22 21388.27 38296.81 21896.93 33192.27 15998.56 27296.21 22572.63 45699.70 125
D2MVS92.76 31592.59 30993.27 39195.13 38089.54 39799.69 22499.38 2292.26 27287.59 39794.61 42485.05 28397.79 33391.59 31888.01 34792.47 446
VPA-MVSNet92.70 31791.55 33096.16 28095.09 38196.20 17698.88 36099.00 3991.02 31591.82 31595.29 39876.05 39797.96 32695.62 23681.19 40394.30 357
LTVRE_ROB88.28 1890.29 37189.05 37894.02 36695.08 38290.15 38697.19 43697.43 31084.91 42783.99 43497.06 32474.00 41398.28 30584.08 41687.71 35193.62 421
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 40386.51 40491.94 41395.05 38385.57 43797.65 42794.08 47684.40 43181.82 44496.85 33662.14 46398.33 29980.25 44386.37 36291.91 453
test0.0.03 193.86 28293.61 26994.64 33295.02 38492.18 32899.93 10098.58 10594.07 17087.96 39298.50 26993.90 10694.96 45381.33 43593.17 31196.78 325
UniMVSNet (Re)93.07 30792.13 31595.88 29094.84 38596.24 17599.88 13098.98 4192.49 26089.25 35995.40 38887.09 24597.14 36393.13 29678.16 42794.26 359
USDC90.00 37988.96 37993.10 39794.81 38688.16 41798.71 37895.54 45493.66 19183.75 43697.20 31865.58 44998.31 30183.96 41987.49 35792.85 439
VPNet91.81 33590.46 34695.85 29294.74 38795.54 20498.98 34498.59 10392.14 27490.77 32897.44 31168.73 43697.54 34394.89 25177.89 42994.46 343
FIs94.10 27693.43 27896.11 28194.70 38896.82 14299.58 24998.93 4892.54 25689.34 35797.31 31587.62 23497.10 36794.22 26986.58 36094.40 349
UniMVSNet_ETH3D90.06 37888.58 38794.49 34294.67 38988.09 41897.81 42497.57 29583.91 43488.44 37997.41 31257.44 47397.62 34091.41 32088.59 34097.77 308
UniMVSNet_NR-MVSNet92.95 30992.11 31695.49 30094.61 39095.28 22199.83 16099.08 3691.49 29489.21 36296.86 33587.14 24496.73 39593.20 29277.52 43294.46 343
test_fmvs289.47 38889.70 36388.77 44894.54 39175.74 47899.83 16094.70 47294.71 13791.08 32196.82 34054.46 47697.78 33592.87 29988.27 34492.80 440
MonoMVSNet94.82 24494.43 24495.98 28494.54 39190.73 37199.03 33997.06 38893.16 21493.15 29995.47 38588.29 22597.57 34197.85 16391.33 31999.62 147
WR-MVS92.31 32791.25 33595.48 30394.45 39395.29 22099.60 24598.68 8490.10 34388.07 39196.89 33380.68 34496.80 39293.14 29579.67 42094.36 351
nrg03093.51 29692.53 31096.45 27194.36 39497.20 12499.81 16797.16 36091.60 29189.86 34197.46 31086.37 25797.68 33795.88 23080.31 41694.46 343
tfpnnormal89.29 39187.61 39894.34 35094.35 39594.13 27198.95 35198.94 4483.94 43284.47 43195.51 38274.84 40797.39 34677.05 46080.41 41491.48 456
FC-MVSNet-test93.81 28693.15 29195.80 29594.30 39696.20 17699.42 28198.89 5292.33 26789.03 36797.27 31787.39 24096.83 39093.20 29286.48 36194.36 351
SSC-MVS3.289.59 38688.66 38692.38 40794.29 39786.12 43399.49 27097.66 28390.28 34288.63 37595.18 40264.46 45496.88 38685.30 40982.66 39194.14 381
MS-PatchMatch90.65 36090.30 35191.71 41894.22 39885.50 43898.24 40797.70 27788.67 37186.42 41596.37 35167.82 44198.03 32283.62 42199.62 9991.60 454
WR-MVS_H91.30 34590.35 34994.15 35794.17 39992.62 31999.17 31998.94 4488.87 36686.48 41494.46 42984.36 29896.61 40288.19 37678.51 42593.21 431
DU-MVS92.46 32491.45 33395.49 30094.05 40095.28 22199.81 16798.74 7692.25 27389.21 36296.64 34381.66 32996.73 39593.20 29277.52 43294.46 343
NR-MVSNet91.56 34390.22 35395.60 29894.05 40095.76 19298.25 40698.70 8091.16 30980.78 45196.64 34383.23 31596.57 40391.41 32077.73 43194.46 343
CP-MVSNet91.23 34990.22 35394.26 35293.96 40292.39 32499.09 32598.57 10788.95 36386.42 41596.57 34679.19 36096.37 41890.29 34478.95 42294.02 394
XXY-MVS91.82 33490.46 34695.88 29093.91 40395.40 21198.87 36397.69 27988.63 37387.87 39397.08 32274.38 41197.89 33091.66 31784.07 38394.35 354
PS-CasMVS90.63 36289.51 36993.99 36993.83 40491.70 34998.98 34498.52 12888.48 37686.15 41996.53 34875.46 40096.31 42388.83 36278.86 42493.95 402
test_040285.58 41683.94 42190.50 43093.81 40585.04 44098.55 38995.20 46376.01 47079.72 45795.13 40364.15 45696.26 42566.04 48586.88 35990.21 467
XVG-ACMP-BASELINE91.22 35090.75 34192.63 40693.73 40685.61 43698.52 39397.44 30992.77 23689.90 34096.85 33666.64 44698.39 29092.29 30488.61 33893.89 407
TranMVSNet+NR-MVSNet91.68 34290.61 34594.87 32393.69 40793.98 27799.69 22498.65 8891.03 31488.44 37996.83 33980.05 35396.18 42890.26 34576.89 44094.45 348
TransMVSNet (Re)87.25 40985.28 41693.16 39493.56 40891.03 36398.54 39194.05 47883.69 43681.09 44996.16 35775.32 40196.40 41776.69 46168.41 46992.06 450
v1090.25 37288.82 38194.57 33793.53 40993.43 29699.08 32796.87 41285.00 42487.34 40494.51 42580.93 33997.02 37782.85 42679.23 42193.26 429
testgi89.01 39388.04 39491.90 41493.49 41084.89 44299.73 20595.66 45193.89 18485.14 42698.17 28859.68 46994.66 45977.73 45688.88 33296.16 334
v890.54 36489.17 37494.66 33193.43 41193.40 29999.20 31696.94 40685.76 41487.56 39894.51 42581.96 32597.19 36084.94 41278.25 42693.38 427
V4291.28 34790.12 35894.74 32893.42 41293.46 29599.68 22797.02 39287.36 39389.85 34395.05 40681.31 33597.34 34987.34 38880.07 41893.40 425
pm-mvs189.36 39087.81 39694.01 36793.40 41391.93 33398.62 38796.48 43286.25 40983.86 43596.14 35973.68 41597.04 37386.16 40275.73 44593.04 435
v114491.09 35189.83 36094.87 32393.25 41493.69 28599.62 23896.98 39886.83 40389.64 34994.99 41380.94 33897.05 37085.08 41181.16 40493.87 409
v119290.62 36389.25 37394.72 33093.13 41593.07 30399.50 26897.02 39286.33 40889.56 35395.01 41079.22 35997.09 36982.34 43081.16 40494.01 396
v2v48291.30 34590.07 35995.01 31893.13 41593.79 28099.77 18297.02 39288.05 38489.25 35995.37 39280.73 34397.15 36287.28 38980.04 41994.09 389
OPM-MVS93.21 30192.80 30094.44 34593.12 41790.85 37099.77 18297.61 29096.19 9591.56 31798.65 25175.16 40698.47 27893.78 28289.39 32893.99 399
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v14419290.79 35889.52 36894.59 33593.11 41892.77 31099.56 25796.99 39686.38 40789.82 34494.95 41580.50 34897.10 36783.98 41880.41 41493.90 406
PEN-MVS90.19 37489.06 37793.57 38493.06 41990.90 36899.06 33298.47 14088.11 38385.91 42196.30 35376.67 38795.94 43887.07 39276.91 43993.89 407
v124090.20 37388.79 38294.44 34593.05 42092.27 32699.38 28996.92 40885.89 41289.36 35694.87 41777.89 37497.03 37580.66 43981.08 40794.01 396
usedtu_dtu_shiyan192.78 31391.73 32495.92 28893.03 42196.82 14299.83 16097.79 26690.58 32990.09 33295.04 40784.75 28896.72 39788.19 37686.23 36394.23 363
FE-MVSNET392.78 31391.73 32495.92 28893.03 42196.82 14299.83 16097.79 26690.58 32990.09 33295.04 40784.75 28896.72 39788.20 37586.23 36394.23 363
v14890.70 35989.63 36493.92 37292.97 42390.97 36499.75 19496.89 41087.51 39088.27 38895.01 41081.67 32897.04 37387.40 38777.17 43793.75 415
v192192090.46 36589.12 37594.50 34192.96 42492.46 32299.49 27096.98 39886.10 41089.61 35195.30 39578.55 36897.03 37582.17 43180.89 41294.01 396
MVStest185.03 42282.76 43191.83 41592.95 42589.16 40298.57 38894.82 46771.68 48168.54 48695.11 40583.17 31695.66 44274.69 46665.32 47690.65 463
tt0320-xc82.94 43780.35 44490.72 42892.90 42683.54 45196.85 44694.73 47063.12 49179.85 45693.77 43749.43 48495.46 44580.98 43871.54 45893.16 432
Baseline_NR-MVSNet90.33 36989.51 36992.81 40392.84 42789.95 39199.77 18293.94 47984.69 42989.04 36695.66 37481.66 32996.52 40690.99 32876.98 43891.97 452
test_method80.79 44379.70 44684.08 46292.83 42867.06 48999.51 26695.42 45654.34 49981.07 45093.53 43944.48 48792.22 48078.90 45177.23 43692.94 437
pmmvs492.10 33191.07 33995.18 31492.82 42994.96 23399.48 27396.83 41487.45 39288.66 37496.56 34783.78 30596.83 39089.29 35784.77 37793.75 415
LF4IMVS89.25 39288.85 38090.45 43292.81 43081.19 46898.12 41494.79 46891.44 29886.29 41797.11 32065.30 45298.11 31688.53 36785.25 37192.07 449
tt032083.56 43681.15 43990.77 42692.77 43183.58 45096.83 44795.52 45563.26 49081.36 44792.54 44953.26 47895.77 44080.45 44074.38 44892.96 436
DTE-MVSNet89.40 38988.24 39292.88 40192.66 43289.95 39199.10 32498.22 21387.29 39485.12 42796.22 35576.27 39495.30 45083.56 42275.74 44493.41 424
EU-MVSNet90.14 37690.34 35089.54 44092.55 43381.06 46998.69 38198.04 24091.41 30286.59 41196.84 33880.83 34193.31 47286.20 40181.91 39894.26 359
APD_test181.15 44180.92 44181.86 46792.45 43459.76 49896.04 46293.61 48373.29 47977.06 46696.64 34344.28 48896.16 42972.35 47082.52 39289.67 476
sc_t185.01 42382.46 43392.67 40592.44 43583.09 45497.39 43295.72 44865.06 48885.64 42496.16 35749.50 48397.34 34984.86 41375.39 44697.57 317
our_test_390.39 36689.48 37193.12 39592.40 43689.57 39699.33 29696.35 43587.84 38885.30 42594.99 41384.14 30296.09 43380.38 44184.56 37893.71 420
ppachtmachnet_test89.58 38788.35 39093.25 39392.40 43690.44 38099.33 29696.73 42185.49 41985.90 42295.77 36881.09 33796.00 43776.00 46482.49 39393.30 428
v7n89.65 38588.29 39193.72 37892.22 43890.56 37799.07 33197.10 37585.42 42186.73 40894.72 41880.06 35297.13 36481.14 43678.12 42893.49 423
dmvs_testset83.79 43286.07 40776.94 47392.14 43948.60 51096.75 44890.27 49489.48 35178.65 46098.55 26679.25 35886.65 49666.85 48182.69 39095.57 336
PS-MVSNAJss93.64 29393.31 28794.61 33392.11 44092.19 32799.12 32197.38 31692.51 25988.45 37896.99 32991.20 17797.29 35794.36 26387.71 35194.36 351
pmmvs590.17 37589.09 37693.40 38792.10 44189.77 39499.74 19895.58 45385.88 41387.24 40595.74 36973.41 41896.48 41088.54 36683.56 38793.95 402
N_pmnet80.06 44680.78 44277.89 47291.94 44245.28 51498.80 37256.82 51778.10 46780.08 45493.33 44077.03 38195.76 44168.14 47882.81 38992.64 441
test_djsdf92.83 31292.29 31494.47 34391.90 44392.46 32299.55 26097.27 34391.17 30789.96 33796.07 36381.10 33696.89 38494.67 25888.91 33194.05 393
SixPastTwentyTwo88.73 39488.01 39590.88 42291.85 44482.24 46098.22 41195.18 46488.97 36182.26 44196.89 33371.75 42396.67 40084.00 41782.98 38893.72 419
K. test v388.05 40087.24 40190.47 43191.82 44582.23 46198.96 35097.42 31289.05 35676.93 46895.60 37668.49 43795.42 44685.87 40681.01 41093.75 415
OurMVSNet-221017-089.81 38289.48 37190.83 42591.64 44681.21 46798.17 41395.38 45891.48 29685.65 42397.31 31572.66 41997.29 35788.15 37884.83 37693.97 401
mvs_tets91.81 33591.08 33894.00 36891.63 44790.58 37698.67 38397.43 31092.43 26187.37 40397.05 32571.76 42297.32 35294.75 25588.68 33794.11 388
Gipumacopyleft66.95 46465.00 46472.79 47991.52 44867.96 48666.16 51495.15 46547.89 50158.54 49467.99 51029.74 49587.54 49550.20 50177.83 43062.87 509
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n96.39 18195.74 19398.32 14791.47 44995.56 20399.84 15297.30 33397.74 3097.89 17699.35 15379.62 35599.85 13099.25 7599.24 14199.55 164
jajsoiax91.92 33391.18 33694.15 35791.35 45090.95 36799.00 34297.42 31292.61 24787.38 40297.08 32272.46 42097.36 34794.53 26188.77 33594.13 386
MDA-MVSNet-bldmvs84.09 43081.52 43791.81 41691.32 45188.00 42098.67 38395.92 44480.22 45555.60 49793.32 44168.29 43993.60 47073.76 46776.61 44193.82 413
MVP-Stereo90.93 35390.45 34892.37 40991.25 45288.76 40698.05 41896.17 43887.27 39584.04 43295.30 39578.46 36997.27 35983.78 42099.70 9291.09 457
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron85.51 41883.32 42692.10 41190.96 45388.58 41299.20 31696.52 43079.70 45757.12 49692.69 44879.11 36193.86 46677.10 45977.46 43493.86 410
YYNet185.50 41983.33 42592.00 41290.89 45488.38 41699.22 31596.55 42979.60 45857.26 49592.72 44779.09 36393.78 46877.25 45877.37 43593.84 411
ALIKED-NN54.48 47352.67 47559.89 49590.79 45545.45 51281.25 50755.75 52134.99 51044.87 50771.98 50125.50 50374.36 50921.88 51947.04 50559.85 511
anonymousdsp91.79 34090.92 34094.41 34890.76 45692.93 30998.93 35497.17 35889.08 35587.46 40195.30 39578.43 37096.92 38192.38 30388.73 33693.39 426
lessismore_v090.53 42990.58 45780.90 47095.80 44577.01 46795.84 36666.15 44896.95 37983.03 42575.05 44793.74 418
EG-PatchMatch MVS85.35 42083.81 42389.99 43890.39 45881.89 46398.21 41296.09 44081.78 44874.73 47493.72 43851.56 48297.12 36679.16 44988.61 33890.96 460
EGC-MVSNET69.38 45763.76 46786.26 45890.32 45981.66 46696.24 45893.85 4800.99 5363.22 53792.33 45952.44 47992.92 47659.53 49584.90 37584.21 491
CMPMVSbinary61.59 2184.75 42685.14 41783.57 46390.32 45962.54 49496.98 44297.59 29474.33 47769.95 48396.66 34164.17 45598.32 30087.88 38288.41 34389.84 473
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ALIKED-MNN52.51 47750.15 48259.60 49790.05 46144.33 51681.60 50654.93 52232.36 51340.96 51468.77 50720.90 51275.30 50720.00 52041.78 50959.18 512
new_pmnet84.49 42982.92 42989.21 44290.03 46282.60 45796.89 44595.62 45280.59 45375.77 47389.17 47365.04 45394.79 45772.12 47181.02 40990.23 466
pmmvs685.69 41583.84 42291.26 42190.00 46384.41 44597.82 42396.15 43975.86 47181.29 44895.39 39061.21 46696.87 38783.52 42373.29 45092.50 445
ttmdpeth88.23 39987.06 40291.75 41789.91 46487.35 42598.92 35795.73 44787.92 38684.02 43396.31 35268.23 44096.84 38886.33 40076.12 44291.06 458
DSMNet-mixed88.28 39888.24 39288.42 45089.64 46575.38 48198.06 41789.86 49585.59 41888.20 39092.14 46176.15 39691.95 48178.46 45396.05 25597.92 302
UnsupCasMVSNet_eth85.52 41783.99 41990.10 43689.36 46683.51 45296.65 44997.99 24489.14 35475.89 47293.83 43563.25 45993.92 46481.92 43367.90 47292.88 438
Anonymous2023120686.32 41385.42 41589.02 44489.11 46780.53 47399.05 33695.28 45985.43 42082.82 43993.92 43474.40 41093.44 47166.99 48081.83 39993.08 434
ALIKED-LG54.29 47452.28 47660.32 49188.90 46845.51 51181.66 50556.33 51838.60 50342.62 51270.81 50225.00 50575.20 50819.87 52146.76 50760.24 510
Anonymous2024052185.15 42183.81 42389.16 44388.32 46982.69 45698.80 37295.74 44679.72 45681.53 44690.99 46465.38 45194.16 46272.69 46981.11 40690.63 464
OpenMVS_ROBcopyleft79.82 2083.77 43381.68 43690.03 43788.30 47082.82 45598.46 39495.22 46273.92 47876.00 47191.29 46355.00 47596.94 38068.40 47788.51 34290.34 465
test20.0384.72 42783.99 41986.91 45588.19 47180.62 47298.88 36095.94 44388.36 37978.87 45894.62 42368.75 43589.11 49066.52 48275.82 44391.00 459
RoMa-SfM74.91 45372.77 45581.35 46888.00 47267.35 48893.55 47886.23 50468.27 48666.79 48792.92 44630.40 49387.68 49266.14 48462.62 48389.02 482
gbinet_0.2-2-1-0.0287.63 40885.51 41493.99 36987.22 47391.56 35899.81 16797.36 32079.54 45988.60 37693.29 44473.76 41496.34 42089.27 35860.78 49194.06 392
blend_shiyan490.13 37788.79 38294.17 35487.12 47491.83 33999.75 19497.08 37979.27 46488.69 37292.53 45092.25 16096.50 40789.35 35573.04 45294.18 370
KD-MVS_self_test83.59 43482.06 43488.20 45186.93 47580.70 47197.21 43596.38 43382.87 44282.49 44088.97 47467.63 44292.32 47973.75 46862.30 48591.58 455
DKM72.18 45569.80 45879.34 47186.79 47665.15 49092.70 48284.00 50567.67 48761.97 49089.63 47023.69 50885.17 49867.39 47954.35 50187.70 486
MIMVSNet182.58 43880.51 44388.78 44686.68 47784.20 44696.65 44995.41 45778.75 46578.59 46192.44 45151.88 48189.76 48865.26 48678.95 42292.38 448
wanda-best-256-51287.82 40485.71 41094.15 35786.66 47891.88 33599.76 18897.08 37979.46 46088.37 38592.36 45578.01 37196.43 41388.39 37161.26 48794.14 381
FE-blended-shiyan787.82 40485.71 41094.15 35786.66 47891.88 33599.76 18897.08 37979.46 46088.37 38592.36 45578.01 37196.43 41388.39 37161.26 48794.14 381
usedtu_blend_shiyan586.75 41284.29 41894.16 35586.66 47891.83 33997.42 42995.23 46169.94 48488.37 38592.36 45578.01 37196.50 40789.35 35561.26 48794.14 381
SP-NN55.28 47253.59 47460.34 49086.63 48139.01 52186.70 49956.31 51931.08 51543.77 51068.45 50823.39 50960.24 51429.19 51456.76 49881.77 496
LoFTR74.41 45470.88 45784.99 46186.56 48267.85 48793.74 47489.63 49769.46 48554.95 49887.39 48430.76 49296.92 38161.37 49264.06 47990.19 468
blended_shiyan887.82 40485.71 41094.16 35586.54 48391.79 34199.72 20997.08 37979.32 46288.44 37992.35 45877.88 37596.56 40488.53 36761.51 48694.15 377
blended_shiyan687.74 40785.62 41394.09 36286.53 48491.73 34799.72 20997.08 37979.32 46288.22 38992.31 46077.82 37696.43 41388.31 37361.26 48794.13 386
CL-MVSNet_self_test84.50 42883.15 42888.53 44986.00 48581.79 46498.82 36897.35 32185.12 42383.62 43790.91 46676.66 38891.40 48269.53 47560.36 49292.40 447
MatchFormer70.84 45666.72 46283.19 46585.99 48664.61 49193.58 47788.62 50059.32 49550.64 50182.31 49528.00 49896.79 39352.52 50059.50 49488.18 484
UnsupCasMVSNet_bld79.97 44877.03 45388.78 44685.62 48781.98 46293.66 47597.35 32175.51 47470.79 48283.05 49248.70 48594.91 45578.31 45460.29 49389.46 479
mvs5depth84.87 42482.90 43090.77 42685.59 48884.84 44391.10 49193.29 48583.14 43985.07 42894.33 43162.17 46297.32 35278.83 45272.59 45790.14 469
SP-LightGlue55.29 47053.65 47360.20 49285.58 48939.12 52086.36 50257.52 51632.34 51444.34 50967.75 51124.36 50659.32 51729.62 51254.98 49982.17 494
SP-SuperGlue55.29 47053.71 47260.00 49485.11 49038.86 52286.96 49857.95 51532.77 51244.54 50868.00 50923.90 50759.51 51629.61 51354.59 50081.63 497
SP-MNN53.97 47552.04 47959.73 49684.72 49138.63 52386.51 50055.94 52029.25 51640.20 51567.48 51222.18 51159.59 51527.79 51554.33 50280.98 498
Patchmatch-RL test86.90 41085.98 40989.67 43984.45 49275.59 47989.71 49492.43 48786.89 40277.83 46590.94 46594.22 9593.63 46987.75 38369.61 46399.79 112
pmmvs-eth3d84.03 43181.97 43590.20 43484.15 49387.09 42798.10 41694.73 47083.05 44074.10 47887.77 48165.56 45094.01 46381.08 43769.24 46589.49 478
test_fmvs379.99 44780.17 44579.45 47084.02 49462.83 49299.05 33693.49 48488.29 38180.06 45586.65 48728.09 49788.00 49188.63 36373.27 45187.54 488
PM-MVS80.47 44478.88 44885.26 45983.79 49572.22 48395.89 46591.08 49285.71 41776.56 47088.30 47736.64 49193.90 46582.39 42969.57 46489.66 477
new-patchmatchnet81.19 44079.34 44786.76 45682.86 49680.36 47497.92 42095.27 46082.09 44772.02 48086.87 48662.81 46190.74 48671.10 47263.08 48189.19 481
FE-MVSNET283.57 43581.36 43890.20 43482.83 49787.59 42198.28 40596.04 44185.33 42274.13 47787.45 48259.16 47093.26 47379.12 45069.91 46189.77 474
FE-MVSNET81.05 44278.81 44987.79 45381.98 49883.70 44898.23 40991.78 49181.27 45074.29 47687.44 48360.92 46890.67 48764.92 48768.43 46889.01 483
mvsany_test382.12 43981.14 44085.06 46081.87 49970.41 48497.09 43992.14 48891.27 30577.84 46488.73 47539.31 48995.49 44390.75 33571.24 45989.29 480
WB-MVS76.28 45077.28 45273.29 47881.18 50054.68 50397.87 42294.19 47581.30 44969.43 48490.70 46777.02 38282.06 50135.71 50868.11 47183.13 492
test_f78.40 44977.59 45180.81 46980.82 50162.48 49596.96 44393.08 48683.44 43774.57 47584.57 49127.95 49992.63 47784.15 41572.79 45387.32 489
SSC-MVS75.42 45276.40 45472.49 48280.68 50253.62 50497.42 42994.06 47780.42 45468.75 48590.14 46976.54 39081.66 50233.25 50966.34 47582.19 493
pmmvs380.27 44577.77 45087.76 45480.32 50382.43 45998.23 40991.97 48972.74 48078.75 45987.97 48057.30 47490.99 48570.31 47362.37 48489.87 472
testf168.38 46066.92 46072.78 48078.80 50450.36 50790.95 49287.35 50255.47 49758.95 49288.14 47820.64 51487.60 49357.28 49664.69 47780.39 500
APD_test268.38 46066.92 46072.78 48078.80 50450.36 50790.95 49287.35 50255.47 49758.95 49288.14 47820.64 51487.60 49357.28 49664.69 47780.39 500
ambc83.23 46477.17 50662.61 49387.38 49694.55 47476.72 46986.65 48730.16 49496.36 41984.85 41469.86 46290.73 462
test_vis3_rt68.82 45866.69 46375.21 47776.24 50760.41 49796.44 45368.71 51275.13 47550.54 50269.52 50616.42 52096.32 42280.27 44266.92 47468.89 506
PDCNetPlus59.83 46757.26 47067.55 48676.18 50856.71 50187.01 49745.27 52559.54 49448.80 50483.01 49326.63 50176.54 50662.12 49126.78 51769.40 505
usedtu_dtu_shiyan275.87 45172.37 45686.39 45776.18 50875.49 48096.53 45193.82 48164.74 48972.53 47988.48 47637.67 49091.12 48464.13 48857.22 49692.56 442
TDRefinement84.76 42582.56 43291.38 42074.58 51084.80 44497.36 43394.56 47384.73 42880.21 45396.12 36263.56 45798.39 29087.92 38163.97 48090.95 461
SIFT-NN35.94 48636.54 48934.16 50273.93 51129.52 52562.74 51537.28 52619.65 52027.91 52249.19 52111.66 52346.35 5219.19 52237.30 51026.61 519
ELoFTR64.32 46660.56 46975.60 47673.46 51253.20 50586.50 50180.09 50860.74 49345.95 50682.48 49416.05 52189.20 48956.48 49943.34 50884.38 490
E-PMN52.30 47852.18 47852.67 49871.51 51345.40 51393.62 47676.60 51036.01 50743.50 51164.13 51527.11 50067.31 51231.06 51026.06 51845.30 518
EMVS51.44 48051.22 48152.11 49970.71 51444.97 51594.04 47175.66 51135.34 50942.40 51361.56 51928.93 49665.87 51327.64 51624.73 51945.49 516
PMMVS267.15 46364.15 46676.14 47570.56 51562.07 49693.89 47287.52 50158.09 49660.02 49178.32 49722.38 51084.54 49959.56 49447.03 50681.80 495
SIFT-MNN34.10 48734.41 49033.17 50468.99 51628.51 52660.22 51736.81 52719.08 52324.04 52447.28 52410.06 52745.04 5228.72 52334.47 51225.97 522
SIFT-NCM-Cal31.73 48931.67 49231.91 50767.18 51727.55 53258.36 51933.09 53118.38 52614.93 53145.16 5308.60 53043.82 5247.62 53231.68 51524.36 525
SIFT-NN-NCMNet33.88 48834.14 49133.10 50566.88 51828.42 52760.42 51636.72 52819.15 52124.06 52347.14 52510.24 52544.77 5238.72 52333.94 51426.10 521
FPMVS68.72 45968.72 45968.71 48465.95 51944.27 51795.97 46494.74 46951.13 50053.26 49990.50 46825.11 50483.00 50060.80 49380.97 41178.87 502
SP-DiffGlue56.84 46855.72 47160.19 49365.70 52040.86 51881.89 50460.28 51434.62 51150.39 50376.88 49926.61 50258.81 51848.21 50256.94 49780.90 499
wuyk23d20.37 50120.84 50418.99 51865.34 52127.73 53050.43 5277.67 5429.50 5358.01 5366.34 5366.13 53826.24 53523.40 51810.69 5332.99 533
SIFT-ConvMatch30.09 49229.76 49631.09 50965.16 52227.56 53154.13 52331.17 53218.55 52517.88 52745.89 5278.40 53142.26 5288.11 52818.51 52423.46 527
SIFT-CM-Cal28.34 49527.90 49929.63 51163.75 52325.98 53650.66 52626.18 53618.12 52916.88 52944.64 5318.08 53339.70 5297.65 53115.19 52923.22 528
LCM-MVSNet67.77 46264.73 46576.87 47462.95 52456.25 50289.37 49593.74 48244.53 50261.99 48980.74 49620.42 51686.53 49769.37 47659.50 49487.84 485
SIFT-NN-CMatch31.71 49031.56 49332.16 50662.58 52527.53 53356.45 52033.28 53019.00 52423.65 52547.34 52210.05 52842.72 5268.71 52522.96 52226.24 520
SIFT-UM-Cal27.47 49627.02 50028.83 51462.12 52624.58 53853.60 52423.46 53718.14 52812.85 53345.56 5287.49 53439.45 5307.68 53012.30 53022.45 529
SIFT-UMatch29.40 49428.87 49830.98 51062.08 52726.57 53556.09 52129.45 53418.31 52715.86 53046.00 5268.23 53242.54 5277.99 52915.81 52723.85 526
GLUNet-SfM51.10 48146.61 48464.56 48761.54 52839.88 51979.38 51065.13 51336.09 50633.36 51969.94 50414.50 52278.76 50442.46 50617.10 52675.02 504
SIFT-NN-UMatch31.23 49131.05 49531.79 50860.08 52927.23 53458.49 51833.65 52919.14 52217.30 52847.31 52310.12 52642.88 5258.67 52624.67 52025.27 523
XFeat-NN42.54 48242.87 48641.54 50159.73 53027.86 52969.53 51245.34 52424.36 51737.16 51664.79 51320.84 51351.40 52030.01 51134.12 51345.36 517
MVEpermissive53.74 2251.54 47947.86 48362.60 48859.56 53150.93 50679.41 50977.69 50935.69 50836.27 51761.76 5185.79 53969.63 51037.97 50736.61 51167.24 507
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SIFT-NN-PointCN29.63 49329.72 49729.36 51257.55 53223.55 53956.07 52230.57 53317.99 53020.99 52645.21 5299.94 52939.33 5318.40 52720.81 52325.20 524
SIFT-PointCN25.49 49725.71 50124.84 51556.17 53318.65 54051.37 52526.53 53516.31 53112.78 53439.87 5346.41 53734.09 5336.51 53415.42 52821.77 530
SIFT-PCN-Cal24.67 49824.81 50224.24 51656.13 53418.04 54149.05 52823.39 53816.07 53212.99 53240.17 5336.97 53634.68 5326.71 53311.81 53119.99 531
XFeat-MNN41.51 48341.24 48742.32 50055.40 53528.19 52869.39 51346.53 52323.57 51834.47 51863.21 51720.04 51752.41 51927.43 51731.08 51646.37 515
SIFT-NCMNet21.21 50021.22 50321.17 51752.99 53616.41 54242.12 52914.05 54015.89 53310.70 53535.85 5355.14 54029.82 5345.80 5358.44 53417.28 532
ANet_high56.10 46952.24 47767.66 48549.27 53756.82 50083.94 50382.02 50770.47 48233.28 52064.54 51417.23 51969.16 51145.59 50423.85 52177.02 503
tmp_tt65.23 46562.94 46872.13 48344.90 53850.03 50981.05 50889.42 49938.45 50448.51 50599.90 2354.09 47778.70 50591.84 31618.26 52587.64 487
PMVScopyleft49.05 2353.75 47651.34 48060.97 48940.80 53934.68 52474.82 51189.62 49837.55 50528.67 52172.12 5007.09 53581.63 50343.17 50568.21 47066.59 508
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12337.68 48539.14 48833.31 50319.94 54024.83 53798.36 4029.75 54115.53 53451.31 50087.14 48519.62 51817.74 53647.10 5033.47 53557.36 513
testmvs40.60 48444.45 48529.05 51319.49 54114.11 54399.68 22718.47 53920.74 51964.59 48898.48 27310.95 52417.09 53756.66 49811.01 53255.94 514
mmdepth0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5380.00 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 5380.00 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 5380.02 5370.00 5410.00 5380.00 5360.00 5360.00 534
eth-test20.00 542
eth-test0.00 542
uanet_test0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5380.00 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 5380.00 5410.00 5380.00 5360.00 5360.00 534
cdsmvs_eth3d_5k23.43 49931.24 4940.00 5190.00 5420.00 5440.00 53098.09 2340.00 5370.00 53899.67 11483.37 3100.00 5380.00 5360.00 5360.00 534
pcd_1.5k_mvsjas7.60 50310.13 5060.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 53891.20 1770.00 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 5380.00 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 5380.00 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 5380.00 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 5380.00 5410.00 5380.00 5360.00 5360.00 534
ab-mvs-re8.28 50211.04 5050.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 53899.40 1470.00 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 5380.00 5410.00 5380.00 5360.00 5360.00 534
WAC-MVS90.97 36486.10 404
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 46659.23 52093.20 12897.74 33691.06 326
test_post63.35 51694.43 8298.13 315
patchmatchnet-post91.70 46295.12 6097.95 327
MTMP99.87 13396.49 431
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 27894.21 16599.85 2099.95 8596.96 197
新几何299.40 283
无先验99.49 27098.71 7993.46 199100.00 194.36 26399.99 26
原ACMM299.90 117
testdata299.99 3990.54 339
segment_acmp96.68 31
testdata199.28 30996.35 91
plane_prior597.87 25898.37 29697.79 16889.55 32594.52 340
plane_prior498.59 259
plane_prior391.64 35196.63 7593.01 300
plane_prior299.84 15296.38 86
plane_prior91.74 34499.86 14496.76 7089.59 324
n20.00 543
nn0.00 543
door-mid89.69 496
test1198.44 148
door90.31 493
HQP5-MVS91.85 337
BP-MVS97.92 158
HQP4-MVS93.37 29598.39 29094.53 338
HQP3-MVS97.89 25689.60 322
HQP2-MVS80.65 345
MDTV_nov1_ep13_2view96.26 17096.11 46091.89 28298.06 16994.40 8494.30 26699.67 133
ACMMP++_ref87.04 358
ACMMP++88.23 345
Test By Simon92.82 139