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 bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort by
LCM-MVSNet99.86 199.86 199.87 199.99 199.77 199.77 199.80 199.97 199.97 199.95 199.74 199.98 199.56 1100.00 199.85 3
pmmvs699.07 499.24 498.56 4999.81 296.38 6598.87 999.30 1299.01 1699.63 999.66 399.27 299.68 12897.75 3099.89 2299.62 26
UniMVSNet_ETH3D99.12 399.28 398.65 4399.77 396.34 6799.18 599.20 1899.67 299.73 399.65 499.15 399.86 2297.22 4899.92 1499.77 8
XVG-OURS-SEG-HR97.38 10297.07 11798.30 6999.01 9797.41 3694.66 24599.02 5495.20 16698.15 10797.52 18898.83 498.43 34194.87 16196.41 33399.07 164
ACMH93.61 998.44 2298.76 1397.51 13199.43 3493.54 18298.23 4099.05 4597.40 7599.37 1899.08 3798.79 599.47 19797.74 3199.71 5899.50 47
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_tets98.90 598.94 698.75 3399.69 896.48 6398.54 2099.22 1596.23 11599.71 499.48 798.77 699.93 398.89 399.95 599.84 5
LTVRE_ROB96.88 199.18 299.34 298.72 3899.71 796.99 4699.69 299.57 699.02 1599.62 1099.36 1498.53 799.52 18498.58 1299.95 599.66 22
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
TransMVSNet (Re)98.38 2598.67 1797.51 13199.51 2493.39 18698.20 4598.87 8998.23 3699.48 1299.27 1998.47 899.55 17596.52 7099.53 10599.60 27
pm-mvs198.47 2198.67 1797.86 10599.52 2394.58 14198.28 3799.00 6297.57 6399.27 2499.22 2298.32 999.50 18997.09 5699.75 4899.50 47
jajsoiax98.77 998.79 1298.74 3599.66 1096.48 6398.45 2799.12 3095.83 14299.67 699.37 1298.25 1099.92 598.77 599.94 899.82 6
ACMH+93.58 1098.23 3298.31 2997.98 9799.39 3995.22 11997.55 8399.20 1898.21 3799.25 2598.51 7698.21 1199.40 22194.79 16599.72 5599.32 103
HPM-MVS_fast98.32 2798.13 3398.88 2499.54 2197.48 3298.35 3099.03 5295.88 13797.88 13998.22 11298.15 1299.74 7996.50 7299.62 7399.42 83
wuyk23d93.25 27895.20 19587.40 35496.07 32595.38 10697.04 11494.97 31895.33 16199.70 598.11 12398.14 1391.94 37277.76 36599.68 6474.89 372
ACMM93.33 1198.05 4297.79 5798.85 2599.15 7497.55 2796.68 13398.83 10895.21 16598.36 8098.13 11998.13 1499.62 15496.04 8899.54 10299.39 88
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVScopyleft98.11 3897.83 5598.92 2299.42 3697.46 3398.57 1799.05 4595.43 15997.41 16797.50 19097.98 1599.79 4395.58 11999.57 9099.50 47
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
testgi96.07 17296.50 15394.80 27499.26 5087.69 29095.96 16998.58 16295.08 17298.02 12596.25 27697.92 1697.60 36188.68 30798.74 25199.11 157
LPG-MVS_test97.94 5397.67 6898.74 3599.15 7497.02 4497.09 11199.02 5495.15 16998.34 8398.23 10997.91 1799.70 11394.41 18099.73 5299.50 47
LGP-MVS_train98.74 3599.15 7497.02 4499.02 5495.15 16998.34 8398.23 10997.91 1799.70 11394.41 18099.73 5299.50 47
abl_698.42 2398.19 3299.09 399.16 7198.10 697.73 7499.11 3197.76 5298.62 5398.27 10597.88 1999.80 4295.67 11099.50 11999.38 90
SED-MVS97.94 5397.90 4798.07 8999.22 5995.35 10996.79 12498.83 10896.11 12199.08 3298.24 10797.87 2099.72 9095.44 12899.51 11599.14 145
test_241102_ONE99.22 5995.35 10998.83 10896.04 12699.08 3298.13 11997.87 2099.33 242
SD-MVS97.37 10397.70 6496.35 20798.14 19595.13 12396.54 13698.92 7895.94 13399.19 2898.08 12597.74 2295.06 37095.24 14099.54 10298.87 200
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
DeepC-MVS95.41 497.82 7097.70 6498.16 8198.78 11695.72 8696.23 15399.02 5493.92 21498.62 5398.99 4297.69 2399.62 15496.18 8299.87 2499.15 142
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
nrg03098.54 1898.62 2198.32 6599.22 5995.66 9297.90 6199.08 3998.31 3399.02 3598.74 5997.68 2499.61 16097.77 2999.85 2899.70 18
ANet_high98.31 2898.94 696.41 20699.33 4589.64 25197.92 6099.56 799.27 699.66 899.50 697.67 2599.83 3297.55 3799.98 299.77 8
canonicalmvs97.23 11397.21 10997.30 15597.65 25794.39 14797.84 6499.05 4597.42 7196.68 20993.85 33397.63 2699.33 24296.29 7998.47 27098.18 270
GeoE97.75 7597.70 6497.89 10298.88 10794.53 14297.10 11098.98 6895.75 14697.62 15097.59 18297.61 2799.77 5796.34 7899.44 13799.36 98
TranMVSNet+NR-MVSNet98.33 2698.30 3198.43 5799.07 8995.87 8296.73 13199.05 4598.67 2498.84 4398.45 8097.58 2899.88 1996.45 7499.86 2599.54 40
cdsmvs_eth3d_5k24.22 34332.30 3460.00 3610.00 3840.00 3850.00 37298.10 2220.00 3790.00 38095.06 31397.54 290.00 3800.00 3780.00 3780.00 376
ACMP92.54 1397.47 9697.10 11498.55 5099.04 9596.70 5396.24 15298.89 8193.71 21897.97 13097.75 16897.44 3099.63 14693.22 22199.70 6199.32 103
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_djsdf98.73 1198.74 1698.69 4099.63 1296.30 6998.67 1399.02 5496.50 10399.32 2099.44 1097.43 3199.92 598.73 799.95 599.86 2
TDRefinement98.90 598.86 899.02 999.54 2198.06 899.34 499.44 1098.85 2099.00 3799.20 2397.42 3299.59 16297.21 5099.76 4499.40 86
anonymousdsp98.72 1498.63 1998.99 1399.62 1397.29 3998.65 1699.19 2095.62 15099.35 1999.37 1297.38 3399.90 1498.59 1199.91 1799.77 8
PS-CasMVS98.73 1198.85 1098.39 6099.55 1995.47 10398.49 2499.13 2999.22 899.22 2798.96 4597.35 3499.92 597.79 2899.93 1099.79 7
COLMAP_ROBcopyleft94.48 698.25 3198.11 3498.64 4499.21 6697.35 3797.96 5699.16 2298.34 3298.78 4698.52 7597.32 3599.45 20494.08 19599.67 6599.13 148
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EG-PatchMatch MVS97.69 7997.79 5797.40 14999.06 9093.52 18395.96 16998.97 7294.55 19398.82 4498.76 5897.31 3699.29 25397.20 5299.44 13799.38 90
XXY-MVS97.54 9097.70 6497.07 16799.46 3092.21 20997.22 10399.00 6294.93 18098.58 5998.92 4897.31 3699.41 21994.44 17899.43 14599.59 28
PEN-MVS98.75 1098.85 1098.44 5699.58 1595.67 9198.45 2799.15 2699.33 599.30 2199.00 4197.27 3899.92 597.64 3499.92 1499.75 13
DTE-MVSNet98.79 898.86 898.59 4799.55 1996.12 7498.48 2699.10 3399.36 499.29 2399.06 3997.27 3899.93 397.71 3299.91 1799.70 18
ZNCC-MVS97.92 5797.62 7898.83 2699.32 4797.24 4197.45 9098.84 10195.76 14496.93 19797.43 19697.26 4099.79 4396.06 8599.53 10599.45 71
MP-MVS-pluss97.69 7997.36 9798.70 3999.50 2796.84 4995.38 20298.99 6592.45 25498.11 11198.31 9297.25 4199.77 5796.60 6699.62 7399.48 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP97.89 6297.63 7698.67 4199.35 4396.84 4996.36 14498.79 11895.07 17397.88 13998.35 8897.24 4299.72 9096.05 8799.58 8799.45 71
Effi-MVS+96.19 16896.01 17196.71 18797.43 27492.19 21296.12 15899.10 3395.45 15793.33 32394.71 32097.23 4399.56 17193.21 22297.54 30798.37 247
PGM-MVS97.88 6397.52 8698.96 1699.20 6797.62 2297.09 11199.06 4395.45 15797.55 15297.94 14797.11 4499.78 4794.77 16899.46 13299.48 61
test_0728_THIRD96.62 9598.40 7498.28 10197.10 4599.71 10495.70 10699.62 7399.58 29
APD-MVS_3200maxsize98.13 3797.90 4798.79 3198.79 11497.31 3897.55 8398.92 7897.72 5698.25 9598.13 11997.10 4599.75 6995.44 12899.24 19399.32 103
OPM-MVS97.54 9097.25 10498.41 5899.11 8496.61 5795.24 21598.46 17294.58 19298.10 11498.07 12797.09 4799.39 22695.16 14699.44 13799.21 131
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HFP-MVS97.94 5397.64 7498.83 2699.15 7497.50 3097.59 8098.84 10196.05 12497.49 15897.54 18597.07 4899.70 11395.61 11699.46 13299.30 109
#test#97.62 8497.22 10898.83 2699.15 7497.50 3096.81 12398.84 10194.25 20297.49 15897.54 18597.07 4899.70 11394.37 18399.46 13299.30 109
DVP-MVScopyleft97.78 7397.65 7198.16 8199.24 5495.51 9896.74 12798.23 20295.92 13498.40 7498.28 10197.06 5099.71 10495.48 12499.52 11099.26 122
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
test072699.24 5495.51 9896.89 12098.89 8195.92 13498.64 5298.31 9297.06 50
casdiffmvs97.50 9397.81 5696.56 19798.51 15091.04 23195.83 17799.09 3897.23 8198.33 8798.30 9697.03 5299.37 23296.58 6899.38 15899.28 117
SteuartSystems-ACMMP98.02 4497.76 6198.79 3199.43 3497.21 4397.15 10698.90 8096.58 9998.08 11797.87 15697.02 5399.76 6295.25 13999.59 8599.40 86
Skip Steuart: Steuart Systems R&D Blog.
PC_three_145287.24 31098.37 7797.44 19597.00 5496.78 36792.01 23599.25 19099.21 131
DROMVSNet97.90 6197.94 4697.79 10998.66 13195.14 12298.31 3499.66 397.57 6395.95 24697.01 23296.99 5599.82 3397.66 3399.64 7098.39 245
DVP-MVS++97.96 4797.90 4798.12 8697.75 24695.40 10499.03 798.89 8196.62 9598.62 5398.30 9696.97 5699.75 6995.70 10699.25 19099.21 131
OPU-MVS97.64 12298.01 20695.27 11496.79 12497.35 20796.97 5698.51 33891.21 25599.25 19099.14 145
RE-MVS-def97.88 5198.81 11198.05 997.55 8398.86 9297.77 4998.20 10098.07 12796.94 5895.49 12199.20 19599.26 122
APDe-MVS98.14 3498.03 4098.47 5598.72 12296.04 7798.07 5299.10 3395.96 13198.59 5898.69 6396.94 5899.81 3696.64 6499.58 8799.57 34
test_one_060199.05 9495.50 10198.87 8997.21 8298.03 12398.30 9696.93 60
GST-MVS97.82 7097.49 9098.81 2999.23 5697.25 4097.16 10598.79 11895.96 13197.53 15397.40 19896.93 6099.77 5795.04 15599.35 16799.42 83
test_241102_TWO98.83 10896.11 12198.62 5398.24 10796.92 6299.72 9095.44 12899.49 12399.49 55
LCM-MVSNet-Re97.33 10697.33 9997.32 15498.13 19893.79 17296.99 11799.65 496.74 9399.47 1398.93 4796.91 6399.84 2990.11 28599.06 21998.32 254
VPA-MVSNet98.27 2998.46 2497.70 11799.06 9093.80 17197.76 6999.00 6298.40 3099.07 3498.98 4396.89 6499.75 6997.19 5399.79 3899.55 39
ACMMPcopyleft98.05 4297.75 6398.93 2199.23 5697.60 2398.09 5198.96 7395.75 14697.91 13598.06 13296.89 6499.76 6295.32 13599.57 9099.43 82
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
CS-MVS98.08 3998.01 4198.29 7198.46 16196.58 6098.53 2299.69 298.07 4296.04 24297.18 21896.88 6699.86 2297.48 4199.74 5098.43 242
PMVScopyleft89.60 1796.71 14596.97 12295.95 22699.51 2497.81 1797.42 9497.49 26397.93 4695.95 24698.58 6996.88 6696.91 36489.59 29399.36 16293.12 364
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
region2R97.92 5797.59 8198.92 2299.22 5997.55 2797.60 7998.84 10196.00 12997.22 17197.62 18096.87 6899.76 6295.48 12499.43 14599.46 66
CP-MVS97.92 5797.56 8498.99 1398.99 9897.82 1697.93 5898.96 7396.11 12196.89 20097.45 19496.85 6999.78 4795.19 14299.63 7299.38 90
DPE-MVScopyleft97.64 8297.35 9898.50 5298.85 10996.18 7195.21 21798.99 6595.84 14198.78 4698.08 12596.84 7099.81 3693.98 20299.57 9099.52 44
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_040297.84 6797.97 4397.47 13999.19 6994.07 16096.71 13298.73 13198.66 2598.56 6098.41 8396.84 7099.69 12194.82 16399.81 3398.64 223
ACMMPR97.95 5197.62 7898.94 1899.20 6797.56 2697.59 8098.83 10896.05 12497.46 16497.63 17996.77 7299.76 6295.61 11699.46 13299.49 55
Vis-MVSNetpermissive98.27 2998.34 2898.07 8999.33 4595.21 12198.04 5399.46 997.32 7897.82 14799.11 3496.75 7399.86 2297.84 2599.36 16299.15 142
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Fast-Effi-MVS+95.49 19395.07 20196.75 18597.67 25692.82 19794.22 26298.60 15991.61 26593.42 32192.90 34396.73 7499.70 11392.60 22897.89 29197.74 297
baseline97.44 9897.78 6096.43 20398.52 14990.75 23896.84 12199.03 5296.51 10297.86 14398.02 13696.67 7599.36 23497.09 5699.47 12999.19 135
SR-MVS98.00 4697.66 6999.01 1198.77 11897.93 1197.38 9698.83 10897.32 7898.06 11997.85 15796.65 7699.77 5795.00 15899.11 21099.32 103
tfpnnormal97.72 7797.97 4396.94 17399.26 5092.23 20897.83 6598.45 17398.25 3599.13 3198.66 6596.65 7699.69 12193.92 20499.62 7398.91 190
DeepPCF-MVS94.58 596.90 12896.43 15598.31 6797.48 26897.23 4292.56 31398.60 15992.84 24998.54 6197.40 19896.64 7898.78 31294.40 18299.41 15498.93 185
MVS_111021_LR96.82 13596.55 14697.62 12398.27 17695.34 11193.81 28398.33 19394.59 19196.56 21696.63 25696.61 7998.73 31794.80 16499.34 17098.78 209
Gipumacopyleft98.07 4198.31 2997.36 15299.76 596.28 7098.51 2399.10 3398.76 2396.79 20299.34 1796.61 7998.82 30896.38 7699.50 11996.98 318
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test117298.08 3997.76 6199.05 698.78 11698.07 797.41 9598.85 9697.57 6398.15 10797.96 14296.60 8199.76 6295.30 13699.18 19999.33 102
SR-MVS-dyc-post98.14 3497.84 5399.02 998.81 11198.05 997.55 8398.86 9297.77 4998.20 10098.07 12796.60 8199.76 6295.49 12199.20 19599.26 122
MVS_111021_HR96.73 14296.54 14897.27 15698.35 16993.66 17993.42 29398.36 18894.74 18496.58 21496.76 24996.54 8398.99 29394.87 16199.27 18899.15 142
SMA-MVScopyleft97.48 9597.11 11398.60 4698.83 11096.67 5496.74 12798.73 13191.61 26598.48 6798.36 8796.53 8499.68 12895.17 14499.54 10299.45 71
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
v7n98.73 1198.99 597.95 9899.64 1194.20 15798.67 1399.14 2899.08 1099.42 1599.23 2196.53 8499.91 1399.27 299.93 1099.73 15
mPP-MVS97.91 6097.53 8599.04 799.22 5997.87 1597.74 7298.78 12296.04 12697.10 18197.73 17196.53 8499.78 4795.16 14699.50 11999.46 66
XVS97.96 4797.63 7698.94 1899.15 7497.66 2097.77 6798.83 10897.42 7196.32 22797.64 17896.49 8799.72 9095.66 11299.37 15999.45 71
X-MVStestdata92.86 28290.83 30798.94 1899.15 7497.66 2097.77 6798.83 10897.42 7196.32 22736.50 37496.49 8799.72 9095.66 11299.37 15999.45 71
9.1496.69 13898.53 14896.02 16498.98 6893.23 23197.18 17597.46 19396.47 8999.62 15492.99 22599.32 179
UA-Net98.88 798.76 1399.22 299.11 8497.89 1499.47 399.32 1199.08 1097.87 14299.67 296.47 8999.92 597.88 2399.98 299.85 3
xxxxxxxxxxxxxcwj97.24 11297.03 12097.89 10298.48 15694.71 13594.53 25099.07 4295.02 17697.83 14597.88 15496.44 9199.72 9094.59 17599.39 15699.25 126
SF-MVS97.60 8697.39 9598.22 7798.93 10395.69 8897.05 11399.10 3395.32 16297.83 14597.88 15496.44 9199.72 9094.59 17599.39 15699.25 126
xiu_mvs_v1_base_debu95.62 18895.96 17594.60 28198.01 20688.42 27193.99 27498.21 20392.98 24395.91 24894.53 32396.39 9399.72 9095.43 13198.19 27895.64 348
xiu_mvs_v1_base95.62 18895.96 17594.60 28198.01 20688.42 27193.99 27498.21 20392.98 24395.91 24894.53 32396.39 9399.72 9095.43 13198.19 27895.64 348
xiu_mvs_v1_base_debi95.62 18895.96 17594.60 28198.01 20688.42 27193.99 27498.21 20392.98 24395.91 24894.53 32396.39 9399.72 9095.43 13198.19 27895.64 348
ETV-MVS96.13 17195.90 17896.82 18197.76 24493.89 16695.40 20098.95 7595.87 13895.58 26291.00 36496.36 9699.72 9093.36 21698.83 24396.85 325
MP-MVScopyleft97.64 8297.18 11099.00 1299.32 4797.77 1897.49 8998.73 13196.27 11295.59 26197.75 16896.30 9799.78 4793.70 21299.48 12799.45 71
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TinyColmap96.00 17796.34 15894.96 26597.90 21887.91 28394.13 26998.49 17094.41 19598.16 10597.76 16596.29 9898.68 32490.52 27899.42 14898.30 258
Fast-Effi-MVS+-dtu96.44 15996.12 16697.39 15097.18 29194.39 14795.46 19498.73 13196.03 12894.72 27894.92 31796.28 9999.69 12193.81 20797.98 28698.09 272
OMC-MVS96.48 15796.00 17297.91 10198.30 17196.01 8094.86 23798.60 15991.88 26297.18 17597.21 21796.11 10099.04 28790.49 28199.34 17098.69 220
xiu_mvs_v2_base94.22 25094.63 22592.99 31997.32 28584.84 33092.12 32197.84 24191.96 26094.17 29293.43 33496.07 10199.71 10491.27 25297.48 31094.42 357
CSCG97.40 10197.30 10097.69 11998.95 10094.83 13097.28 9998.99 6596.35 11198.13 11095.95 29395.99 10299.66 13994.36 18699.73 5298.59 229
PHI-MVS96.96 12496.53 14998.25 7597.48 26896.50 6296.76 12698.85 9693.52 22196.19 23696.85 24095.94 10399.42 21093.79 20899.43 14598.83 203
TSAR-MVS + MP.97.42 9997.23 10798.00 9699.38 4095.00 12697.63 7898.20 20693.00 24298.16 10598.06 13295.89 10499.72 9095.67 11099.10 21299.28 117
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
XVG-ACMP-BASELINE97.58 8897.28 10398.49 5399.16 7196.90 4896.39 14198.98 6895.05 17498.06 11998.02 13695.86 10599.56 17194.37 18399.64 7099.00 173
AllTest97.20 11496.92 12798.06 9199.08 8796.16 7297.14 10899.16 2294.35 19897.78 14898.07 12795.84 10699.12 27691.41 24999.42 14898.91 190
TestCases98.06 9199.08 8796.16 7299.16 2294.35 19897.78 14898.07 12795.84 10699.12 27691.41 24999.42 14898.91 190
APD-MVScopyleft97.00 11996.53 14998.41 5898.55 14696.31 6896.32 14798.77 12392.96 24797.44 16697.58 18495.84 10699.74 7991.96 23699.35 16799.19 135
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
pcd_1.5k_mvsjas7.98 34610.65 3490.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.00 37995.82 1090.00 3800.00 3780.00 3780.00 376
PS-MVSNAJss98.53 1998.63 1998.21 8099.68 994.82 13198.10 5099.21 1696.91 8899.75 299.45 995.82 10999.92 598.80 499.96 499.89 1
PS-MVSNAJ94.10 25694.47 23593.00 31897.35 27884.88 32991.86 32597.84 24191.96 26094.17 29292.50 35095.82 10999.71 10491.27 25297.48 31094.40 358
3Dnovator96.53 297.61 8597.64 7497.50 13497.74 24993.65 18098.49 2498.88 8796.86 9097.11 18098.55 7395.82 10999.73 8595.94 9699.42 14899.13 148
zzz-MVS98.01 4597.66 6999.06 499.44 3297.90 1295.66 18598.73 13197.69 5997.90 13697.96 14295.81 11399.82 3396.13 8399.61 7999.45 71
MTAPA98.14 3497.84 5399.06 499.44 3297.90 1297.25 10098.73 13197.69 5997.90 13697.96 14295.81 11399.82 3396.13 8399.61 7999.45 71
DP-MVS97.87 6497.89 5097.81 10898.62 13794.82 13197.13 10998.79 11898.98 1798.74 4998.49 7795.80 11599.49 19195.04 15599.44 13799.11 157
Anonymous2024052997.96 4798.04 3997.71 11598.69 12994.28 15497.86 6398.31 19698.79 2299.23 2698.86 5395.76 11699.61 16095.49 12199.36 16299.23 129
LS3D97.77 7497.50 8998.57 4896.24 31597.58 2598.45 2798.85 9698.58 2797.51 15597.94 14795.74 11799.63 14695.19 14298.97 22498.51 235
EIA-MVS96.04 17495.77 18296.85 17997.80 23292.98 19496.12 15899.16 2294.65 18793.77 30591.69 35895.68 11899.67 13394.18 19198.85 24197.91 290
CNVR-MVS96.92 12696.55 14698.03 9598.00 21095.54 9694.87 23698.17 21294.60 18996.38 22497.05 22895.67 11999.36 23495.12 15299.08 21499.19 135
CLD-MVS95.47 19695.07 20196.69 18998.27 17692.53 20291.36 33198.67 14991.22 27395.78 25594.12 33195.65 12098.98 29590.81 26499.72 5598.57 230
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous2023121198.55 1798.76 1397.94 9998.79 11494.37 14998.84 1099.15 2699.37 399.67 699.43 1195.61 12199.72 9098.12 1699.86 2599.73 15
EGC-MVSNET83.08 34077.93 34398.53 5199.57 1697.55 2798.33 3398.57 1634.71 37610.38 37798.90 5095.60 12299.50 18995.69 10899.61 7998.55 233
Regformer-297.41 10097.24 10697.93 10097.21 28994.72 13494.85 23898.27 19797.74 5398.11 11197.50 19095.58 12399.69 12196.57 6999.31 18199.37 97
ITE_SJBPF97.85 10698.64 13296.66 5598.51 16995.63 14997.22 17197.30 21295.52 12498.55 33590.97 25998.90 23398.34 253
CS-MVS-test97.69 7997.49 9098.31 6798.48 15696.61 5797.21 10499.53 898.10 4196.05 24195.33 30895.49 12599.86 2297.49 4099.74 5098.45 241
Regformer-497.53 9297.47 9397.71 11597.35 27893.91 16595.26 21298.14 21897.97 4598.34 8397.89 15295.49 12599.71 10497.41 4399.42 14899.51 46
DeepC-MVS_fast94.34 796.74 14096.51 15297.44 14597.69 25294.15 15896.02 16498.43 17693.17 23797.30 16997.38 20495.48 12799.28 25593.74 20999.34 17098.88 198
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WR-MVS_H98.65 1598.62 2198.75 3399.51 2496.61 5798.55 1999.17 2199.05 1399.17 2998.79 5595.47 12899.89 1797.95 2199.91 1799.75 13
FMVSNet197.95 5198.08 3597.56 12699.14 8293.67 17698.23 4098.66 15197.41 7499.00 3799.19 2495.47 12899.73 8595.83 10399.76 4499.30 109
MIMVSNet198.51 2098.45 2698.67 4199.72 696.71 5298.76 1198.89 8198.49 2899.38 1799.14 3395.44 13099.84 2996.47 7399.80 3699.47 64
CP-MVSNet98.42 2398.46 2498.30 6999.46 3095.22 11998.27 3998.84 10199.05 1399.01 3698.65 6795.37 13199.90 1497.57 3699.91 1799.77 8
Regformer-197.27 10997.16 11197.61 12497.21 28993.86 16894.85 23898.04 23297.62 6298.03 12397.50 19095.34 13299.63 14696.52 7099.31 18199.35 100
segment_acmp95.34 132
CDPH-MVS95.45 19894.65 22297.84 10798.28 17494.96 12793.73 28598.33 19385.03 33595.44 26396.60 25795.31 13499.44 20790.01 28799.13 20699.11 157
3Dnovator+96.13 397.73 7697.59 8198.15 8498.11 20095.60 9498.04 5398.70 14198.13 3996.93 19798.45 8095.30 13599.62 15495.64 11498.96 22599.24 128
MVS_Test96.27 16496.79 13594.73 27796.94 30086.63 30796.18 15598.33 19394.94 17896.07 24098.28 10195.25 13699.26 25897.21 5097.90 29098.30 258
XVG-OURS97.12 11596.74 13698.26 7298.99 9897.45 3493.82 28199.05 4595.19 16798.32 8897.70 17495.22 13798.41 34294.27 18898.13 28198.93 185
dcpmvs_297.12 11597.99 4294.51 28799.11 8484.00 33897.75 7099.65 497.38 7699.14 3098.42 8295.16 13899.96 295.52 12099.78 4199.58 29
MCST-MVS96.24 16595.80 18097.56 12698.75 11994.13 15994.66 24598.17 21290.17 28396.21 23596.10 28695.14 13999.43 20994.13 19498.85 24199.13 148
EI-MVSNet-Vis-set97.32 10797.39 9597.11 16497.36 27792.08 21595.34 20597.65 25597.74 5398.29 9398.11 12395.05 14099.68 12897.50 3999.50 11999.56 37
Regformer-397.25 11197.29 10197.11 16497.35 27892.32 20695.26 21297.62 26097.67 6198.17 10497.89 15295.05 14099.56 17197.16 5499.42 14899.46 66
EI-MVSNet-UG-set97.32 10797.40 9497.09 16697.34 28292.01 21795.33 20697.65 25597.74 5398.30 9298.14 11895.04 14299.69 12197.55 3799.52 11099.58 29
KD-MVS_self_test97.86 6698.07 3697.25 15999.22 5992.81 19897.55 8398.94 7697.10 8498.85 4298.88 5195.03 14399.67 13397.39 4599.65 6899.26 122
ZD-MVS98.43 16395.94 8198.56 16490.72 27796.66 21097.07 22695.02 14499.74 7991.08 25698.93 231
DELS-MVS96.17 16996.23 16195.99 22297.55 26590.04 24592.38 31898.52 16794.13 20696.55 21897.06 22794.99 14599.58 16495.62 11599.28 18698.37 247
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
patch_mono-296.59 15196.93 12595.55 24498.88 10787.12 30094.47 25299.30 1294.12 20796.65 21298.41 8394.98 14699.87 2195.81 10599.78 4199.66 22
ab-mvs96.59 15196.59 14296.60 19298.64 13292.21 20998.35 3097.67 25194.45 19496.99 19298.79 5594.96 14799.49 19190.39 28299.07 21698.08 273
ETH3D-3000-0.196.89 13096.46 15498.16 8198.62 13795.69 8895.96 16998.98 6893.36 22697.04 18897.31 21194.93 14899.63 14692.60 22899.34 17099.17 138
MSLP-MVS++96.42 16196.71 13795.57 24197.82 22790.56 24295.71 18098.84 10194.72 18596.71 20897.39 20294.91 14998.10 35695.28 13799.02 22198.05 282
QAPM95.88 18195.57 18996.80 18297.90 21891.84 22198.18 4798.73 13188.41 29996.42 22298.13 11994.73 15099.75 6988.72 30598.94 22998.81 205
RPSCF97.87 6497.51 8798.95 1799.15 7498.43 397.56 8299.06 4396.19 11898.48 6798.70 6294.72 15199.24 26194.37 18399.33 17799.17 138
DU-MVS97.79 7297.60 8098.36 6298.73 12095.78 8495.65 18898.87 8997.57 6398.31 9097.83 15994.69 15299.85 2697.02 5999.71 5899.46 66
Baseline_NR-MVSNet97.72 7797.79 5797.50 13499.56 1793.29 18795.44 19598.86 9298.20 3898.37 7799.24 2094.69 15299.55 17595.98 9499.79 3899.65 24
TEST997.84 22495.23 11693.62 28798.39 18486.81 31593.78 30395.99 28894.68 15499.52 184
UniMVSNet (Re)97.83 6897.65 7198.35 6498.80 11395.86 8395.92 17399.04 5197.51 6898.22 9997.81 16394.68 15499.78 4797.14 5599.75 4899.41 85
agg_prior195.39 20094.60 22797.75 11297.80 23294.96 12793.39 29598.36 18887.20 31193.49 31695.97 29194.65 15699.53 18091.69 24698.86 23998.77 212
UniMVSNet_NR-MVSNet97.83 6897.65 7198.37 6198.72 12295.78 8495.66 18599.02 5498.11 4098.31 9097.69 17694.65 15699.85 2697.02 5999.71 5899.48 61
VPNet97.26 11097.49 9096.59 19399.47 2990.58 24096.27 14898.53 16697.77 4998.46 7098.41 8394.59 15899.68 12894.61 17199.29 18599.52 44
train_agg95.46 19794.66 22197.88 10497.84 22495.23 11693.62 28798.39 18487.04 31393.78 30395.99 28894.58 15999.52 18491.76 24498.90 23398.89 194
test_897.81 22895.07 12593.54 29098.38 18687.04 31393.71 30795.96 29294.58 15999.52 184
API-MVS95.09 21395.01 20595.31 25396.61 30694.02 16296.83 12297.18 27395.60 15195.79 25394.33 32894.54 16198.37 34785.70 33498.52 26793.52 361
Test By Simon94.51 162
MSDG95.33 20295.13 19895.94 22897.40 27691.85 22091.02 34298.37 18795.30 16396.31 22995.99 28894.51 16298.38 34589.59 29397.65 30497.60 304
TSAR-MVS + GP.96.47 15896.12 16697.49 13797.74 24995.23 11694.15 26696.90 28493.26 23098.04 12296.70 25294.41 16498.89 30394.77 16899.14 20298.37 247
NR-MVSNet97.96 4797.86 5298.26 7298.73 12095.54 9698.14 4898.73 13197.79 4899.42 1597.83 15994.40 16599.78 4795.91 9899.76 4499.46 66
AdaColmapbinary95.11 21194.62 22696.58 19497.33 28494.45 14694.92 23498.08 22593.15 23893.98 30195.53 30594.34 16699.10 28185.69 33598.61 26396.20 342
FC-MVSNet-test98.16 3398.37 2797.56 12699.49 2893.10 19298.35 3099.21 1698.43 2998.89 4098.83 5494.30 16799.81 3697.87 2499.91 1799.77 8
Effi-MVS+-dtu96.81 13696.09 16898.99 1396.90 30298.69 296.42 14098.09 22395.86 13995.15 26995.54 30494.26 16899.81 3694.06 19698.51 26998.47 238
mvs-test196.20 16795.50 19198.32 6596.90 30298.16 595.07 22598.09 22395.86 13993.63 31094.32 32994.26 16899.71 10494.06 19697.27 31897.07 315
ambc96.56 19798.23 18291.68 22497.88 6298.13 22098.42 7398.56 7294.22 17099.04 28794.05 19999.35 16798.95 179
test20.0396.58 15396.61 14196.48 20198.49 15491.72 22395.68 18497.69 25096.81 9198.27 9497.92 15094.18 17198.71 31990.78 26699.66 6799.00 173
HPM-MVS++copyleft96.99 12096.38 15698.81 2998.64 13297.59 2495.97 16898.20 20695.51 15595.06 27096.53 26194.10 17299.70 11394.29 18799.15 20199.13 148
testtj96.69 14696.13 16598.36 6298.46 16196.02 7996.44 13998.70 14194.26 20196.79 20297.13 22094.07 17399.75 6990.53 27798.80 24599.31 108
ETH3D cwj APD-0.1696.23 16695.61 18898.09 8897.91 21695.65 9394.94 23398.74 12991.31 27196.02 24497.08 22594.05 17499.69 12191.51 24898.94 22998.93 185
PM-MVS97.36 10597.10 11498.14 8598.91 10596.77 5196.20 15498.63 15793.82 21598.54 6198.33 9093.98 17599.05 28695.99 9399.45 13698.61 228
OpenMVScopyleft94.22 895.48 19595.20 19596.32 20997.16 29291.96 21897.74 7298.84 10187.26 30994.36 28998.01 13893.95 17699.67 13390.70 27298.75 25097.35 312
v897.60 8698.06 3896.23 21398.71 12589.44 25597.43 9398.82 11697.29 8098.74 4999.10 3593.86 17799.68 12898.61 1099.94 899.56 37
diffmvs96.04 17496.23 16195.46 24997.35 27888.03 28293.42 29399.08 3994.09 20996.66 21096.93 23693.85 17899.29 25396.01 9298.67 25699.06 166
NCCC96.52 15595.99 17398.10 8797.81 22895.68 9095.00 23198.20 20695.39 16095.40 26596.36 27293.81 17999.45 20493.55 21598.42 27199.17 138
TAPA-MVS93.32 1294.93 21894.23 24297.04 16998.18 18894.51 14395.22 21698.73 13181.22 35296.25 23395.95 29393.80 18098.98 29589.89 28998.87 23797.62 302
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FIs97.93 5698.07 3697.48 13899.38 4092.95 19598.03 5599.11 3198.04 4498.62 5398.66 6593.75 18199.78 4797.23 4799.84 2999.73 15
OurMVSNet-221017-098.61 1698.61 2398.63 4599.77 396.35 6699.17 699.05 4598.05 4399.61 1199.52 593.72 18299.88 1998.72 999.88 2399.65 24
test_prior395.91 17995.39 19297.46 14297.79 23894.26 15593.33 29898.42 17994.21 20394.02 29896.25 27693.64 18399.34 23991.90 23898.96 22598.79 207
test_prior293.33 29894.21 20394.02 29896.25 27693.64 18391.90 23898.96 225
旧先验197.80 23293.87 16797.75 24697.04 22993.57 18598.68 25598.72 217
v1097.55 8997.97 4396.31 21098.60 14089.64 25197.44 9199.02 5496.60 9798.72 5199.16 3093.48 18699.72 9098.76 699.92 1499.58 29
v14896.58 15396.97 12295.42 25098.63 13687.57 29195.09 22297.90 23695.91 13698.24 9797.96 14293.42 18799.39 22696.04 8899.52 11099.29 116
V4297.04 11897.16 11196.68 19098.59 14291.05 23096.33 14698.36 18894.60 18997.99 12698.30 9693.32 18899.62 15497.40 4499.53 10599.38 90
new-patchmatchnet95.67 18796.58 14392.94 32197.48 26880.21 35692.96 30498.19 21194.83 18298.82 4498.79 5593.31 18999.51 18895.83 10399.04 22099.12 153
test1297.46 14297.61 26094.07 16097.78 24593.57 31493.31 18999.42 21098.78 24798.89 194
UGNet96.81 13696.56 14597.58 12596.64 30593.84 17097.75 7097.12 27696.47 10693.62 31198.88 5193.22 19199.53 18095.61 11699.69 6299.36 98
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
pmmvs-eth3d96.49 15696.18 16497.42 14798.25 17994.29 15194.77 24298.07 22989.81 28697.97 13098.33 9093.11 19299.08 28395.46 12799.84 2998.89 194
v114496.84 13197.08 11696.13 21998.42 16489.28 25895.41 19998.67 14994.21 20397.97 13098.31 9293.06 19399.65 14198.06 1999.62 7399.45 71
PVSNet_BlendedMVS95.02 21794.93 20895.27 25497.79 23887.40 29594.14 26898.68 14688.94 29494.51 28598.01 13893.04 19499.30 24989.77 29199.49 12399.11 157
PVSNet_Blended93.96 26093.65 25994.91 26697.79 23887.40 29591.43 33098.68 14684.50 34094.51 28594.48 32693.04 19499.30 24989.77 29198.61 26398.02 285
mvs_anonymous95.36 20196.07 17093.21 31396.29 31381.56 35194.60 24797.66 25393.30 22996.95 19698.91 4993.03 19699.38 22996.60 6697.30 31798.69 220
v119296.83 13497.06 11896.15 21898.28 17489.29 25795.36 20398.77 12393.73 21798.11 11198.34 8993.02 19799.67 13398.35 1499.58 8799.50 47
F-COLMAP95.30 20494.38 23998.05 9498.64 13296.04 7795.61 19198.66 15189.00 29393.22 32496.40 26992.90 19899.35 23787.45 32497.53 30898.77 212
WR-MVS96.90 12896.81 13297.16 16198.56 14592.20 21194.33 25598.12 22197.34 7798.20 10097.33 20992.81 19999.75 6994.79 16599.81 3399.54 40
v124096.74 14097.02 12195.91 22998.18 18888.52 27095.39 20198.88 8793.15 23898.46 7098.40 8692.80 20099.71 10498.45 1399.49 12399.49 55
MVEpermissive73.61 2286.48 33885.92 34088.18 35296.23 31785.28 32381.78 37075.79 37686.01 32082.53 37291.88 35592.74 20187.47 37571.42 37294.86 35091.78 366
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DP-MVS Recon95.55 19195.13 19896.80 18298.51 15093.99 16494.60 24798.69 14490.20 28295.78 25596.21 27992.73 20298.98 29590.58 27698.86 23997.42 309
CANet95.86 18295.65 18596.49 20096.41 31190.82 23594.36 25498.41 18194.94 17892.62 33796.73 25092.68 20399.71 10495.12 15299.60 8398.94 181
v192192096.72 14396.96 12495.99 22298.21 18388.79 26795.42 19798.79 11893.22 23298.19 10398.26 10692.68 20399.70 11398.34 1599.55 9999.49 55
BH-untuned94.69 23194.75 21994.52 28697.95 21587.53 29294.07 27197.01 28093.99 21197.10 18195.65 30092.65 20598.95 30087.60 32096.74 32797.09 314
LF4IMVS96.07 17295.63 18697.36 15298.19 18595.55 9595.44 19598.82 11692.29 25695.70 25996.55 25992.63 20698.69 32191.75 24599.33 17797.85 292
v2v48296.78 13897.06 11895.95 22698.57 14488.77 26895.36 20398.26 19995.18 16897.85 14498.23 10992.58 20799.63 14697.80 2799.69 6299.45 71
EI-MVSNet96.63 15096.93 12595.74 23597.26 28788.13 28095.29 21097.65 25596.99 8597.94 13398.19 11492.55 20899.58 16496.91 6299.56 9399.50 47
IterMVS-LS96.92 12697.29 10195.79 23398.51 15088.13 28095.10 22098.66 15196.99 8598.46 7098.68 6492.55 20899.74 7996.91 6299.79 3899.50 47
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VDD-MVS97.37 10397.25 10497.74 11398.69 12994.50 14597.04 11495.61 31198.59 2698.51 6398.72 6092.54 21099.58 16496.02 9099.49 12399.12 153
MVS90.02 31589.20 32292.47 32894.71 34886.90 30495.86 17496.74 29164.72 37290.62 34892.77 34592.54 21098.39 34479.30 36095.56 34592.12 365
v14419296.69 14696.90 12996.03 22198.25 17988.92 26295.49 19398.77 12393.05 24098.09 11598.29 10092.51 21299.70 11398.11 1799.56 9399.47 64
原ACMM196.58 19498.16 19292.12 21398.15 21785.90 32393.49 31696.43 26692.47 21399.38 22987.66 31998.62 26298.23 265
VNet96.84 13196.83 13196.88 17798.06 20192.02 21696.35 14597.57 26297.70 5897.88 13997.80 16492.40 21499.54 17894.73 17098.96 22599.08 162
114514_t93.96 26093.22 26796.19 21699.06 9090.97 23395.99 16698.94 7673.88 37093.43 32096.93 23692.38 21599.37 23289.09 30099.28 18698.25 264
CPTT-MVS96.69 14696.08 16998.49 5398.89 10696.64 5697.25 10098.77 12392.89 24896.01 24597.13 22092.23 21699.67 13392.24 23399.34 17099.17 138
MSP-MVS97.45 9796.92 12799.03 899.26 5097.70 1997.66 7598.89 8195.65 14898.51 6396.46 26592.15 21799.81 3695.14 14998.58 26699.58 29
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
MAR-MVS94.21 25293.03 26997.76 11196.94 30097.44 3596.97 11897.15 27487.89 30792.00 34292.73 34792.14 21899.12 27683.92 34897.51 30996.73 332
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
PVSNet_Blended_VisFu95.95 17895.80 18096.42 20499.28 4990.62 23995.31 20899.08 3988.40 30096.97 19598.17 11792.11 21999.78 4793.64 21399.21 19498.86 201
BH-RMVSNet94.56 23994.44 23894.91 26697.57 26187.44 29493.78 28496.26 29693.69 21996.41 22396.50 26492.10 22099.00 29185.96 33297.71 29898.31 256
新几何197.25 15998.29 17294.70 13897.73 24777.98 36394.83 27796.67 25492.08 22199.45 20488.17 31498.65 26097.61 303
testdata95.70 23898.16 19290.58 24097.72 24880.38 35595.62 26097.02 23092.06 22298.98 29589.06 30298.52 26797.54 305
YYNet194.73 22694.84 21394.41 29097.47 27285.09 32790.29 34895.85 30692.52 25197.53 15397.76 16591.97 22399.18 26793.31 21896.86 32398.95 179
Anonymous2023120695.27 20595.06 20395.88 23098.72 12289.37 25695.70 18197.85 23988.00 30596.98 19497.62 18091.95 22499.34 23989.21 29899.53 10598.94 181
MS-PatchMatch94.83 22294.91 21094.57 28496.81 30487.10 30194.23 26197.34 26888.74 29797.14 17797.11 22391.94 22598.23 35292.99 22597.92 28898.37 247
112194.26 24893.26 26597.27 15698.26 17894.73 13395.86 17497.71 24977.96 36494.53 28496.71 25191.93 22699.40 22187.71 31698.64 26197.69 300
MDA-MVSNet_test_wron94.73 22694.83 21594.42 28997.48 26885.15 32590.28 34995.87 30592.52 25197.48 16197.76 16591.92 22799.17 27193.32 21796.80 32698.94 181
HQP_MVS96.66 14996.33 15997.68 12098.70 12794.29 15196.50 13798.75 12796.36 10996.16 23796.77 24791.91 22899.46 20092.59 23099.20 19599.28 117
plane_prior698.38 16694.37 14991.91 228
ETH3 D test640094.77 22593.87 25697.47 13998.12 19993.73 17494.56 24998.70 14185.45 33094.70 28095.93 29591.77 23099.63 14686.45 33099.14 20299.05 168
MVP-Stereo95.69 18595.28 19496.92 17498.15 19493.03 19395.64 19098.20 20690.39 28096.63 21397.73 17191.63 23199.10 28191.84 24297.31 31698.63 225
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchMatch-RL94.61 23793.81 25797.02 17198.19 18595.72 8693.66 28697.23 27088.17 30394.94 27595.62 30291.43 23298.57 33287.36 32597.68 30196.76 331
MDA-MVSNet-bldmvs95.69 18595.67 18495.74 23598.48 15688.76 26992.84 30597.25 26996.00 12997.59 15197.95 14691.38 23399.46 20093.16 22396.35 33498.99 176
PAPR92.22 29391.27 29995.07 26195.73 33488.81 26691.97 32497.87 23885.80 32490.91 34792.73 34791.16 23498.33 34979.48 35995.76 34398.08 273
131492.38 29092.30 28692.64 32595.42 34185.15 32595.86 17496.97 28285.40 33190.62 34893.06 34191.12 23597.80 35986.74 32895.49 34694.97 355
ppachtmachnet_test94.49 24394.84 21393.46 30796.16 32182.10 34790.59 34597.48 26590.53 27997.01 19197.59 18291.01 23699.36 23493.97 20399.18 19998.94 181
PLCcopyleft91.02 1694.05 25992.90 27197.51 13198.00 21095.12 12494.25 25998.25 20086.17 31991.48 34595.25 30991.01 23699.19 26685.02 34396.69 32898.22 266
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test22298.17 19093.24 18992.74 31097.61 26175.17 36894.65 28196.69 25390.96 23898.66 25897.66 301
CL-MVSNet_self_test95.04 21494.79 21895.82 23297.51 26789.79 24991.14 33996.82 28793.05 24096.72 20796.40 26990.82 23999.16 27291.95 23798.66 25898.50 236
USDC94.56 23994.57 23294.55 28597.78 24286.43 31092.75 30898.65 15685.96 32196.91 19997.93 14990.82 23998.74 31690.71 27199.59 8598.47 238
PCF-MVS89.43 1892.12 29690.64 31096.57 19697.80 23293.48 18489.88 35598.45 17374.46 36996.04 24295.68 29990.71 24199.31 24673.73 36899.01 22396.91 322
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PAPM_NR94.61 23794.17 24695.96 22498.36 16891.23 22895.93 17297.95 23392.98 24393.42 32194.43 32790.53 24298.38 34587.60 32096.29 33598.27 262
our_test_394.20 25494.58 23093.07 31596.16 32181.20 35390.42 34796.84 28590.72 27797.14 17797.13 22090.47 24399.11 27994.04 20098.25 27798.91 190
OpenMVS_ROBcopyleft91.80 1493.64 26993.05 26895.42 25097.31 28691.21 22995.08 22496.68 29381.56 34996.88 20196.41 26790.44 24499.25 26085.39 33997.67 30295.80 346
HQP2-MVS90.33 245
N_pmnet95.18 20894.23 24298.06 9197.85 22096.55 6192.49 31491.63 34889.34 28998.09 11597.41 19790.33 24599.06 28591.58 24799.31 18198.56 231
HQP-MVS95.17 21094.58 23096.92 17497.85 22092.47 20394.26 25698.43 17693.18 23492.86 32995.08 31190.33 24599.23 26390.51 27998.74 25199.05 168
CNLPA95.04 21494.47 23596.75 18597.81 22895.25 11594.12 27097.89 23794.41 19594.57 28295.69 29890.30 24898.35 34886.72 32998.76 24996.64 334
PMMVS92.39 28991.08 30196.30 21193.12 36792.81 19890.58 34695.96 30379.17 36091.85 34492.27 35190.29 24998.66 32689.85 29096.68 32997.43 308
TR-MVS92.54 28792.20 28793.57 30596.49 30986.66 30693.51 29194.73 32089.96 28594.95 27493.87 33290.24 25098.61 32981.18 35794.88 34995.45 352
MVS_030495.50 19295.05 20496.84 18096.28 31493.12 19197.00 11696.16 29795.03 17589.22 35997.70 17490.16 25199.48 19494.51 17799.34 17097.93 289
TAMVS95.49 19394.94 20697.16 16198.31 17093.41 18595.07 22596.82 28791.09 27497.51 15597.82 16289.96 25299.42 21088.42 31099.44 13798.64 223
DPM-MVS93.68 26792.77 27896.42 20497.91 21692.54 20191.17 33897.47 26684.99 33693.08 32694.74 31989.90 25399.00 29187.54 32298.09 28397.72 298
PMMVS293.66 26894.07 24892.45 32997.57 26180.67 35586.46 36496.00 30193.99 21197.10 18197.38 20489.90 25397.82 35888.76 30499.47 12998.86 201
BH-w/o92.14 29591.94 28992.73 32497.13 29385.30 32192.46 31595.64 30889.33 29094.21 29192.74 34689.60 25598.24 35181.68 35594.66 35194.66 356
Anonymous2024052197.07 11797.51 8795.76 23499.35 4388.18 27797.78 6698.40 18397.11 8398.34 8399.04 4089.58 25699.79 4398.09 1899.93 1099.30 109
UnsupCasMVSNet_bld94.72 23094.26 24196.08 22098.62 13790.54 24393.38 29698.05 23190.30 28197.02 19096.80 24689.54 25799.16 27288.44 30996.18 33698.56 231
MG-MVS94.08 25894.00 25194.32 29297.09 29485.89 31593.19 30295.96 30392.52 25194.93 27697.51 18989.54 25798.77 31387.52 32397.71 29898.31 256
UnsupCasMVSNet_eth95.91 17995.73 18396.44 20298.48 15691.52 22695.31 20898.45 17395.76 14497.48 16197.54 18589.53 25998.69 32194.43 17994.61 35299.13 148
GBi-Net96.99 12096.80 13397.56 12697.96 21293.67 17698.23 4098.66 15195.59 15297.99 12699.19 2489.51 26099.73 8594.60 17299.44 13799.30 109
test196.99 12096.80 13397.56 12697.96 21293.67 17698.23 4098.66 15195.59 15297.99 12699.19 2489.51 26099.73 8594.60 17299.44 13799.30 109
FMVSNet296.72 14396.67 14096.87 17897.96 21291.88 21997.15 10698.06 23095.59 15298.50 6598.62 6889.51 26099.65 14194.99 15999.60 8399.07 164
pmmvs494.82 22394.19 24596.70 18897.42 27592.75 20092.09 32396.76 28986.80 31695.73 25897.22 21689.28 26398.89 30393.28 21999.14 20298.46 240
cascas91.89 29991.35 29793.51 30694.27 35485.60 31788.86 36098.61 15879.32 35992.16 34191.44 36089.22 26498.12 35590.80 26597.47 31296.82 328
DSMNet-mixed92.19 29491.83 29193.25 31196.18 32083.68 34196.27 14893.68 32976.97 36792.54 33899.18 2789.20 26598.55 33583.88 34998.60 26597.51 306
c3_l95.20 20795.32 19394.83 27396.19 31986.43 31091.83 32698.35 19293.47 22397.36 16897.26 21488.69 26699.28 25595.41 13499.36 16298.78 209
CANet_DTU94.65 23594.21 24495.96 22495.90 32789.68 25093.92 27897.83 24393.19 23390.12 35495.64 30188.52 26799.57 17093.27 22099.47 12998.62 226
EPP-MVSNet96.84 13196.58 14397.65 12199.18 7093.78 17398.68 1296.34 29597.91 4797.30 16998.06 13288.46 26899.85 2693.85 20699.40 15599.32 103
SixPastTwentyTwo97.49 9497.57 8397.26 15899.56 1792.33 20598.28 3796.97 28298.30 3499.45 1499.35 1688.43 26999.89 1798.01 2099.76 4499.54 40
miper_ehance_all_eth94.69 23194.70 22094.64 27895.77 33286.22 31291.32 33598.24 20191.67 26497.05 18796.65 25588.39 27099.22 26594.88 16098.34 27398.49 237
IS-MVSNet96.93 12596.68 13997.70 11799.25 5394.00 16398.57 1796.74 29198.36 3198.14 10997.98 14188.23 27199.71 10493.10 22499.72 5599.38 90
jason94.39 24694.04 25095.41 25298.29 17287.85 28692.74 31096.75 29085.38 33295.29 26696.15 28188.21 27299.65 14194.24 18999.34 17098.74 214
jason: jason.
IterMVS-SCA-FT95.86 18296.19 16394.85 27197.68 25385.53 31892.42 31697.63 25996.99 8598.36 8098.54 7487.94 27399.75 6997.07 5899.08 21499.27 121
SCA93.38 27593.52 26192.96 32096.24 31581.40 35293.24 30094.00 32691.58 26794.57 28296.97 23387.94 27399.42 21089.47 29597.66 30398.06 279
sss94.22 25093.72 25895.74 23597.71 25189.95 24793.84 28096.98 28188.38 30193.75 30695.74 29787.94 27398.89 30391.02 25898.10 28298.37 247
IterMVS95.42 19995.83 17994.20 29597.52 26683.78 34092.41 31797.47 26695.49 15698.06 11998.49 7787.94 27399.58 16496.02 9099.02 22199.23 129
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 1792x268894.10 25693.41 26396.18 21799.16 7190.04 24592.15 32098.68 14679.90 35796.22 23497.83 15987.92 27799.42 21089.18 29999.65 6899.08 162
VDDNet96.98 12396.84 13097.41 14899.40 3893.26 18897.94 5795.31 31799.26 798.39 7699.18 2787.85 27899.62 15495.13 15199.09 21399.35 100
pmmvs594.63 23694.34 24095.50 24697.63 25988.34 27494.02 27297.13 27587.15 31295.22 26897.15 21987.50 27999.27 25793.99 20199.26 18998.88 198
D2MVS95.18 20895.17 19795.21 25697.76 24487.76 28994.15 26697.94 23489.77 28796.99 19297.68 17787.45 28099.14 27495.03 15799.81 3398.74 214
PVSNet86.72 1991.10 30790.97 30491.49 33697.56 26378.04 36187.17 36394.60 32284.65 33892.34 33992.20 35287.37 28198.47 33985.17 34297.69 30097.96 287
Anonymous20240521196.34 16295.98 17497.43 14698.25 17993.85 16996.74 12794.41 32497.72 5698.37 7798.03 13587.15 28299.53 18094.06 19699.07 21698.92 189
MVSFormer96.14 17096.36 15795.49 24797.68 25387.81 28798.67 1399.02 5496.50 10394.48 28796.15 28186.90 28399.92 598.73 799.13 20698.74 214
lupinMVS93.77 26393.28 26495.24 25597.68 25387.81 28792.12 32196.05 29984.52 33994.48 28795.06 31386.90 28399.63 14693.62 21499.13 20698.27 262
eth_miper_zixun_eth94.89 22094.93 20894.75 27695.99 32686.12 31391.35 33298.49 17093.40 22497.12 17997.25 21586.87 28599.35 23795.08 15498.82 24498.78 209
WTY-MVS93.55 27193.00 27095.19 25797.81 22887.86 28493.89 27996.00 30189.02 29294.07 29695.44 30786.27 28699.33 24287.69 31896.82 32498.39 245
CDS-MVSNet94.88 22194.12 24797.14 16397.64 25893.57 18193.96 27797.06 27990.05 28496.30 23096.55 25986.10 28799.47 19790.10 28699.31 18198.40 243
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
1112_ss94.12 25593.42 26296.23 21398.59 14290.85 23494.24 26098.85 9685.49 32792.97 32794.94 31586.01 28899.64 14491.78 24397.92 28898.20 268
miper_enhance_ethall93.14 28092.78 27794.20 29593.65 36185.29 32289.97 35197.85 23985.05 33496.15 23994.56 32285.74 28999.14 27493.74 20998.34 27398.17 271
new_pmnet92.34 29191.69 29494.32 29296.23 31789.16 26092.27 31992.88 33784.39 34295.29 26696.35 27385.66 29096.74 36884.53 34697.56 30697.05 316
alignmvs96.01 17695.52 19097.50 13497.77 24394.71 13596.07 16096.84 28597.48 6996.78 20694.28 33085.50 29199.40 22196.22 8098.73 25498.40 243
lessismore_v097.05 16899.36 4292.12 21384.07 37298.77 4898.98 4385.36 29299.74 7997.34 4699.37 15999.30 109
HY-MVS91.43 1592.58 28691.81 29294.90 26896.49 30988.87 26497.31 9794.62 32185.92 32290.50 35196.84 24185.05 29399.40 22183.77 35195.78 34296.43 339
EPNet93.72 26592.62 28297.03 17087.61 37992.25 20796.27 14891.28 35196.74 9387.65 36597.39 20285.00 29499.64 14492.14 23499.48 12799.20 134
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_lstm_enhance94.81 22494.80 21794.85 27196.16 32186.45 30991.14 33998.20 20693.49 22297.03 18997.37 20684.97 29599.26 25895.28 13799.56 9398.83 203
Test_1112_low_res93.53 27292.86 27295.54 24598.60 14088.86 26592.75 30898.69 14482.66 34692.65 33496.92 23884.75 29699.56 17190.94 26097.76 29498.19 269
MVS-HIRNet88.40 33090.20 31582.99 35597.01 29660.04 37993.11 30385.61 37184.45 34188.72 36199.09 3684.72 29798.23 35282.52 35496.59 33190.69 370
K. test v396.44 15996.28 16096.95 17299.41 3791.53 22597.65 7690.31 36098.89 1998.93 3999.36 1484.57 29899.92 597.81 2699.56 9399.39 88
h-mvs3396.29 16395.63 18698.26 7298.50 15396.11 7596.90 11997.09 27796.58 9997.21 17398.19 11484.14 29999.78 4795.89 9996.17 33798.89 194
hse-mvs295.77 18495.09 20097.79 10997.84 22495.51 9895.66 18595.43 31696.58 9997.21 17396.16 28084.14 29999.54 17895.89 9996.92 32098.32 254
DIV-MVS_self_test94.73 22694.64 22395.01 26395.86 32887.00 30291.33 33398.08 22593.34 22797.10 18197.34 20884.02 30199.31 24695.15 14899.55 9998.72 217
cl____94.73 22694.64 22395.01 26395.85 32987.00 30291.33 33398.08 22593.34 22797.10 18197.33 20984.01 30299.30 24995.14 14999.56 9398.71 219
Vis-MVSNet (Re-imp)95.11 21194.85 21295.87 23199.12 8389.17 25997.54 8894.92 31996.50 10396.58 21497.27 21383.64 30399.48 19488.42 31099.67 6598.97 177
PVSNet_081.89 2184.49 33983.21 34288.34 35195.76 33374.97 37383.49 36792.70 34178.47 36287.94 36486.90 37183.38 30496.63 36973.44 36966.86 37593.40 362
CMPMVSbinary73.10 2392.74 28491.39 29696.77 18493.57 36394.67 13994.21 26397.67 25180.36 35693.61 31296.60 25782.85 30597.35 36284.86 34498.78 24798.29 261
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet94.25 24994.47 23593.60 30498.14 19582.60 34597.24 10292.72 34085.08 33398.48 6798.94 4682.59 30698.76 31597.47 4299.53 10599.44 81
bset_n11_16_dypcd94.53 24193.95 25496.25 21297.56 26389.85 24888.52 36191.32 35094.90 18197.51 15596.38 27182.34 30799.78 4797.22 4899.80 3699.12 153
baseline193.14 28092.64 28194.62 28097.34 28287.20 29996.67 13493.02 33594.71 18696.51 21995.83 29681.64 30898.60 33190.00 28888.06 36798.07 275
test111194.53 24194.81 21693.72 30199.06 9081.94 35098.31 3483.87 37396.37 10898.49 6699.17 2981.49 30999.73 8596.64 6499.86 2599.49 55
CVMVSNet92.33 29292.79 27590.95 34097.26 28775.84 37095.29 21092.33 34381.86 34796.27 23198.19 11481.44 31098.46 34094.23 19098.29 27698.55 233
EPNet_dtu91.39 30590.75 30893.31 30990.48 37682.61 34494.80 24092.88 33793.39 22581.74 37394.90 31881.36 31199.11 27988.28 31298.87 23798.21 267
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ECVR-MVScopyleft94.37 24794.48 23494.05 29898.95 10083.10 34298.31 3482.48 37496.20 11698.23 9899.16 3081.18 31299.66 13995.95 9599.83 3199.38 90
test_yl94.40 24494.00 25195.59 23996.95 29889.52 25394.75 24395.55 31396.18 11996.79 20296.14 28381.09 31399.18 26790.75 26797.77 29298.07 275
DCV-MVSNet94.40 24494.00 25195.59 23996.95 29889.52 25394.75 24395.55 31396.18 11996.79 20296.14 28381.09 31399.18 26790.75 26797.77 29298.07 275
MIMVSNet93.42 27392.86 27295.10 26098.17 19088.19 27698.13 4993.69 32792.07 25795.04 27398.21 11380.95 31599.03 29081.42 35698.06 28498.07 275
PAPM87.64 33685.84 34193.04 31696.54 30784.99 32888.42 36295.57 31279.52 35883.82 37093.05 34280.57 31698.41 34262.29 37492.79 35895.71 347
HyFIR lowres test93.72 26592.65 28096.91 17698.93 10391.81 22291.23 33798.52 16782.69 34596.46 22196.52 26380.38 31799.90 1490.36 28398.79 24699.03 170
FMVSNet395.26 20694.94 20696.22 21596.53 30890.06 24495.99 16697.66 25394.11 20897.99 12697.91 15180.22 31899.63 14694.60 17299.44 13798.96 178
RPMNet94.68 23394.60 22794.90 26895.44 33988.15 27896.18 15598.86 9297.43 7094.10 29498.49 7779.40 31999.76 6295.69 10895.81 33996.81 329
test_part196.77 13996.53 14997.47 13998.04 20292.92 19697.93 5898.85 9698.83 2199.30 2199.07 3879.25 32099.79 4397.59 3599.93 1099.69 20
LFMVS95.32 20394.88 21196.62 19198.03 20391.47 22797.65 7690.72 35799.11 997.89 13898.31 9279.20 32199.48 19493.91 20599.12 20998.93 185
ADS-MVSNet291.47 30490.51 31294.36 29195.51 33785.63 31695.05 22895.70 30783.46 34392.69 33296.84 24179.15 32299.41 21985.66 33690.52 36298.04 283
ADS-MVSNet90.95 31090.26 31493.04 31695.51 33782.37 34695.05 22893.41 33283.46 34392.69 33296.84 24179.15 32298.70 32085.66 33690.52 36298.04 283
MDTV_nov1_ep13_2view57.28 38094.89 23580.59 35494.02 29878.66 32485.50 33897.82 294
cl2293.25 27892.84 27494.46 28894.30 35386.00 31491.09 34196.64 29490.74 27695.79 25396.31 27478.24 32598.77 31394.15 19398.34 27398.62 226
PatchmatchNetpermissive91.98 29891.87 29092.30 33194.60 35079.71 35795.12 21993.59 33189.52 28893.61 31297.02 23077.94 32699.18 26790.84 26394.57 35498.01 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sam_mvs177.80 32798.06 279
CR-MVSNet93.29 27792.79 27594.78 27595.44 33988.15 27896.18 15597.20 27184.94 33794.10 29498.57 7077.67 32899.39 22695.17 14495.81 33996.81 329
Patchmtry95.03 21694.59 22996.33 20894.83 34790.82 23596.38 14397.20 27196.59 9897.49 15898.57 7077.67 32899.38 22992.95 22799.62 7398.80 206
tpmrst90.31 31390.61 31189.41 34794.06 35872.37 37695.06 22793.69 32788.01 30492.32 34096.86 23977.45 33098.82 30891.04 25787.01 36997.04 317
sam_mvs77.38 331
patchmatchnet-post96.84 24177.36 33299.42 210
Patchmatch-RL test94.66 23494.49 23395.19 25798.54 14788.91 26392.57 31298.74 12991.46 26898.32 8897.75 16877.31 33398.81 31096.06 8599.61 7997.85 292
tpmvs90.79 31190.87 30590.57 34392.75 37176.30 36895.79 17893.64 33091.04 27591.91 34396.26 27577.19 33498.86 30789.38 29789.85 36596.56 337
test_post10.87 37776.83 33599.07 284
Patchmatch-test93.60 27093.25 26694.63 27996.14 32487.47 29396.04 16294.50 32393.57 22096.47 22096.97 23376.50 33698.61 32990.67 27398.41 27297.81 296
MDTV_nov1_ep1391.28 29894.31 35273.51 37494.80 24093.16 33486.75 31793.45 31997.40 19876.37 33798.55 33588.85 30396.43 332
EMVS89.06 32589.22 32088.61 35093.00 36877.34 36582.91 36990.92 35494.64 18892.63 33691.81 35676.30 33897.02 36383.83 35096.90 32291.48 368
test_post194.98 23210.37 37876.21 33999.04 28789.47 295
GA-MVS92.83 28392.15 28894.87 27096.97 29787.27 29890.03 35096.12 29891.83 26394.05 29794.57 32176.01 34098.97 29992.46 23297.34 31598.36 252
PatchT93.75 26493.57 26094.29 29495.05 34587.32 29796.05 16192.98 33697.54 6794.25 29098.72 6075.79 34199.24 26195.92 9795.81 33996.32 340
E-PMN89.52 32389.78 31788.73 34993.14 36677.61 36383.26 36892.02 34494.82 18393.71 30793.11 33675.31 34296.81 36585.81 33396.81 32591.77 367
DeepMVS_CXcopyleft77.17 35690.94 37585.28 32374.08 37952.51 37380.87 37488.03 37075.25 34370.63 37659.23 37584.94 37175.62 371
AUN-MVS93.95 26292.69 27997.74 11397.80 23295.38 10695.57 19295.46 31591.26 27292.64 33596.10 28674.67 34499.55 17593.72 21196.97 31998.30 258
CHOSEN 280x42089.98 31789.19 32392.37 33095.60 33681.13 35486.22 36597.09 27781.44 35187.44 36693.15 33573.99 34599.47 19788.69 30699.07 21696.52 338
thres20091.00 30990.42 31392.77 32397.47 27283.98 33994.01 27391.18 35395.12 17195.44 26391.21 36273.93 34699.31 24677.76 36597.63 30595.01 354
test-LLR89.97 31889.90 31690.16 34494.24 35574.98 37189.89 35289.06 36392.02 25889.97 35590.77 36573.92 34798.57 33291.88 24097.36 31396.92 320
test0.0.03 190.11 31489.21 32192.83 32293.89 35986.87 30591.74 32788.74 36592.02 25894.71 27991.14 36373.92 34794.48 37183.75 35292.94 35797.16 313
tpm cat188.01 33387.33 33490.05 34694.48 35176.28 36994.47 25294.35 32573.84 37189.26 35895.61 30373.64 34998.30 35084.13 34786.20 37095.57 351
tfpn200view991.55 30391.00 30293.21 31398.02 20484.35 33595.70 18190.79 35596.26 11395.90 25192.13 35373.62 35099.42 21078.85 36297.74 29595.85 344
thres40091.68 30291.00 30293.71 30298.02 20484.35 33595.70 18190.79 35596.26 11395.90 25192.13 35373.62 35099.42 21078.85 36297.74 29597.36 310
test_method66.88 34166.13 34469.11 35762.68 38025.73 38249.76 37196.04 30014.32 37564.27 37691.69 35873.45 35288.05 37476.06 36766.94 37493.54 360
thres100view90091.76 30191.26 30093.26 31098.21 18384.50 33396.39 14190.39 35896.87 8996.33 22693.08 34073.44 35399.42 21078.85 36297.74 29595.85 344
thres600view792.03 29791.43 29593.82 29998.19 18584.61 33296.27 14890.39 35896.81 9196.37 22593.11 33673.44 35399.49 19180.32 35897.95 28797.36 310
RRT_MVS94.90 21994.07 24897.39 15093.18 36493.21 19095.26 21297.49 26393.94 21398.25 9597.85 15772.96 35599.84 2997.90 2299.78 4199.14 145
MVSTER94.21 25293.93 25595.05 26295.83 33086.46 30895.18 21897.65 25592.41 25597.94 13398.00 14072.39 35699.58 16496.36 7799.56 9399.12 153
JIA-IIPM91.79 30090.69 30995.11 25993.80 36090.98 23294.16 26591.78 34796.38 10790.30 35399.30 1872.02 35798.90 30188.28 31290.17 36495.45 352
tpm91.08 30890.85 30691.75 33595.33 34278.09 36095.03 23091.27 35288.75 29693.53 31597.40 19871.24 35899.30 24991.25 25493.87 35597.87 291
baseline289.65 32288.44 32993.25 31195.62 33582.71 34393.82 28185.94 37088.89 29587.35 36792.54 34971.23 35999.33 24286.01 33194.60 35397.72 298
CostFormer89.75 32189.25 31991.26 33994.69 34978.00 36295.32 20791.98 34581.50 35090.55 35096.96 23571.06 36098.89 30388.59 30892.63 35996.87 323
FPMVS89.92 31988.63 32793.82 29998.37 16796.94 4791.58 32893.34 33388.00 30590.32 35297.10 22470.87 36191.13 37371.91 37196.16 33893.39 363
EPMVS89.26 32488.55 32891.39 33792.36 37279.11 35895.65 18879.86 37588.60 29893.12 32596.53 26170.73 36298.10 35690.75 26789.32 36696.98 318
tmp_tt57.23 34262.50 34541.44 35834.77 38149.21 38183.93 36660.22 38215.31 37471.11 37579.37 37370.09 36344.86 37764.76 37382.93 37330.25 373
ET-MVSNet_ETH3D91.12 30689.67 31895.47 24896.41 31189.15 26191.54 32990.23 36189.07 29186.78 36992.84 34469.39 36499.44 20794.16 19296.61 33097.82 294
dp88.08 33288.05 33088.16 35392.85 36968.81 37894.17 26492.88 33785.47 32891.38 34696.14 28368.87 36598.81 31086.88 32783.80 37296.87 323
tpm288.47 32987.69 33390.79 34194.98 34677.34 36595.09 22291.83 34677.51 36689.40 35796.41 26767.83 36698.73 31783.58 35392.60 36096.29 341
pmmvs390.00 31688.90 32693.32 30894.20 35785.34 32091.25 33692.56 34278.59 36193.82 30295.17 31067.36 36798.69 32189.08 30198.03 28595.92 343
thisisatest051590.43 31289.18 32494.17 29797.07 29585.44 31989.75 35687.58 36688.28 30293.69 30991.72 35765.27 36899.58 16490.59 27598.67 25697.50 307
tttt051793.31 27692.56 28395.57 24198.71 12587.86 28497.44 9187.17 36895.79 14397.47 16396.84 24164.12 36999.81 3696.20 8199.32 17999.02 172
thisisatest053092.71 28591.76 29395.56 24398.42 16488.23 27596.03 16387.35 36794.04 21096.56 21695.47 30664.03 37099.77 5794.78 16799.11 21098.68 222
FMVSNet593.39 27492.35 28596.50 19995.83 33090.81 23797.31 9798.27 19792.74 25096.27 23198.28 10162.23 37199.67 13390.86 26299.36 16299.03 170
DWT-MVSNet_test87.92 33486.77 33891.39 33793.18 36478.62 35995.10 22091.42 34985.58 32688.00 36388.73 36960.60 37298.90 30190.60 27487.70 36896.65 333
IB-MVS85.98 2088.63 32886.95 33793.68 30395.12 34484.82 33190.85 34390.17 36287.55 30888.48 36291.34 36158.01 37399.59 16287.24 32693.80 35696.63 336
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
RRT_test8_iter0592.46 28892.52 28492.29 33295.33 34277.43 36495.73 17998.55 16594.41 19597.46 16497.72 17357.44 37499.74 7996.92 6199.14 20299.69 20
gg-mvs-nofinetune88.28 33186.96 33692.23 33392.84 37084.44 33498.19 4674.60 37799.08 1087.01 36899.47 856.93 37598.23 35278.91 36195.61 34494.01 359
KD-MVS_2432*160088.93 32687.74 33192.49 32688.04 37781.99 34889.63 35795.62 30991.35 26995.06 27093.11 33656.58 37698.63 32785.19 34095.07 34796.85 325
miper_refine_blended88.93 32687.74 33192.49 32688.04 37781.99 34889.63 35795.62 30991.35 26995.06 27093.11 33656.58 37698.63 32785.19 34095.07 34796.85 325
GG-mvs-BLEND90.60 34291.00 37484.21 33798.23 4072.63 38082.76 37184.11 37256.14 37896.79 36672.20 37092.09 36190.78 369
TESTMET0.1,187.20 33786.57 33989.07 34893.62 36272.84 37589.89 35287.01 36985.46 32989.12 36090.20 36756.00 37997.72 36090.91 26196.92 32096.64 334
test250689.86 32089.16 32591.97 33498.95 10076.83 36798.54 2061.07 38196.20 11697.07 18699.16 3055.19 38099.69 12196.43 7599.83 3199.38 90
test-mter87.92 33487.17 33590.16 34494.24 35574.98 37189.89 35289.06 36386.44 31889.97 35590.77 36554.96 38198.57 33291.88 24097.36 31396.92 320
test12312.59 34415.49 3473.87 3596.07 3822.55 38390.75 3442.59 3842.52 3775.20 37913.02 3764.96 3821.85 3795.20 3769.09 3767.23 374
testmvs12.33 34515.23 3483.64 3605.77 3832.23 38488.99 3593.62 3832.30 3785.29 37813.09 3754.52 3831.95 3785.16 3778.32 3776.75 375
test_blank0.00 3480.00 3510.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.00 3790.00 3840.00 3800.00 3780.00 3780.00 376
uanet_test0.00 3480.00 3510.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.00 3790.00 3840.00 3800.00 3780.00 3780.00 376
DCPMVS0.00 3480.00 3510.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.00 3790.00 3840.00 3800.00 3780.00 3780.00 376
sosnet-low-res0.00 3480.00 3510.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.00 3790.00 3840.00 3800.00 3780.00 3780.00 376
sosnet0.00 3480.00 3510.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.00 3790.00 3840.00 3800.00 3780.00 3780.00 376
uncertanet0.00 3480.00 3510.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.00 3790.00 3840.00 3800.00 3780.00 3780.00 376
Regformer0.00 3480.00 3510.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.00 3790.00 3840.00 3800.00 3780.00 3780.00 376
ab-mvs-re7.91 34710.55 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 38094.94 3150.00 3840.00 3800.00 3780.00 3780.00 376
uanet0.00 3480.00 3510.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.00 3790.00 3840.00 3800.00 3780.00 3780.00 376
FOURS199.59 1498.20 499.03 799.25 1498.96 1898.87 41
MSC_two_6792asdad98.22 7797.75 24695.34 11198.16 21599.75 6995.87 10199.51 11599.57 34
No_MVS98.22 7797.75 24695.34 11198.16 21599.75 6995.87 10199.51 11599.57 34
eth-test20.00 384
eth-test0.00 384
IU-MVS99.22 5995.40 10498.14 21885.77 32598.36 8095.23 14199.51 11599.49 55
save fliter98.48 15694.71 13594.53 25098.41 18195.02 176
test_0728_SECOND98.25 7599.23 5695.49 10296.74 12798.89 8199.75 6995.48 12499.52 11099.53 43
GSMVS98.06 279
test_part299.03 9696.07 7698.08 117
MTGPAbinary98.73 131
MTMP96.55 13574.60 377
gm-plane-assit91.79 37371.40 37781.67 34890.11 36898.99 29384.86 344
test9_res91.29 25198.89 23699.00 173
agg_prior290.34 28498.90 23399.10 161
agg_prior97.80 23294.96 12798.36 18893.49 31699.53 180
test_prior495.38 10693.61 289
test_prior97.46 14297.79 23894.26 15598.42 17999.34 23998.79 207
旧先验293.35 29777.95 36595.77 25798.67 32590.74 270
新几何293.43 292
无先验93.20 30197.91 23580.78 35399.40 22187.71 31697.94 288
原ACMM292.82 306
testdata299.46 20087.84 315
testdata192.77 30793.78 216
plane_prior798.70 12794.67 139
plane_prior598.75 12799.46 20092.59 23099.20 19599.28 117
plane_prior496.77 247
plane_prior394.51 14395.29 16496.16 237
plane_prior296.50 13796.36 109
plane_prior198.49 154
plane_prior94.29 15195.42 19794.31 20098.93 231
n20.00 385
nn0.00 385
door-mid98.17 212
test1198.08 225
door97.81 244
HQP5-MVS92.47 203
HQP-NCC97.85 22094.26 25693.18 23492.86 329
ACMP_Plane97.85 22094.26 25693.18 23492.86 329
BP-MVS90.51 279
HQP4-MVS92.87 32899.23 26399.06 166
HQP3-MVS98.43 17698.74 251
NP-MVS98.14 19593.72 17595.08 311
ACMMP++_ref99.52 110
ACMMP++99.55 99