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 bysorted bysort bysort bysort bysort bysort by
LCM-MVSNet99.43 199.49 199.24 199.95 198.13 199.37 199.57 199.82 199.86 199.85 199.52 199.73 197.58 199.94 199.85 1
UniMVSNet_ETH3D97.13 697.72 395.35 8799.51 287.38 13497.70 897.54 11098.16 298.94 299.33 297.84 499.08 9990.73 12899.73 1499.59 12
pmmvs696.80 1397.36 995.15 9999.12 887.82 12996.68 2597.86 8396.10 2698.14 2499.28 397.94 398.21 21691.38 11999.69 1599.42 19
UA-Net97.35 497.24 1197.69 598.22 7093.87 3198.42 698.19 3596.95 1495.46 13099.23 493.45 7599.57 1395.34 1299.89 299.63 9
OurMVSNet-221017-096.80 1396.75 1896.96 3699.03 1191.85 5997.98 798.01 6994.15 5098.93 399.07 588.07 18499.57 1395.86 999.69 1599.46 18
gg-mvs-nofinetune82.10 32081.02 32285.34 33087.46 36271.04 34194.74 10567.56 37496.44 2279.43 36498.99 645.24 37396.15 31467.18 35992.17 34188.85 356
Anonymous2023121196.60 2597.13 1295.00 10397.46 12386.35 16397.11 1698.24 3097.58 898.72 898.97 793.15 8699.15 8793.18 6899.74 1399.50 16
ANet_high94.83 9596.28 3790.47 26196.65 15873.16 32994.33 12298.74 896.39 2398.09 2598.93 893.37 7998.70 16790.38 13599.68 1899.53 14
mvs_tets96.83 996.71 1997.17 2798.83 2392.51 5096.58 2997.61 10587.57 20598.80 798.90 996.50 1099.59 1296.15 799.47 3999.40 21
PS-MVSNAJss96.01 5196.04 5295.89 6698.82 2488.51 11595.57 7597.88 8288.72 17898.81 698.86 1090.77 14599.60 895.43 1199.53 3599.57 13
test_djsdf96.62 2396.49 2897.01 3398.55 4191.77 6197.15 1397.37 12088.98 17298.26 2298.86 1093.35 8099.60 896.41 499.45 4399.66 6
K. test v393.37 13793.27 14893.66 15798.05 8182.62 21394.35 12186.62 33596.05 2897.51 4098.85 1276.59 29499.65 393.21 6798.20 20098.73 90
Gipumacopyleft95.31 7795.80 6493.81 15597.99 9190.91 7196.42 3897.95 7896.69 1791.78 25198.85 1291.77 12095.49 32791.72 10999.08 9795.02 290
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LTVRE_ROB93.87 197.93 298.16 297.26 2698.81 2593.86 3299.07 298.98 497.01 1398.92 498.78 1495.22 3798.61 17996.85 299.77 1099.31 27
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
anonymousdsp96.74 1796.42 2997.68 798.00 8894.03 2696.97 1797.61 10587.68 20298.45 1898.77 1594.20 6799.50 1996.70 399.40 5399.53 14
SixPastTwentyTwo94.91 8895.21 8393.98 14498.52 4683.19 20595.93 6094.84 24394.86 3998.49 1598.74 1681.45 25599.60 894.69 1699.39 5499.15 37
jajsoiax96.59 2796.42 2997.12 2998.76 2892.49 5196.44 3797.42 11886.96 21498.71 1098.72 1795.36 3199.56 1695.92 899.45 4399.32 26
VDDNet94.03 12594.27 11993.31 17098.87 2082.36 21595.51 7891.78 30497.19 1296.32 8698.60 1884.24 23098.75 15687.09 21398.83 13098.81 79
TransMVSNet (Re)95.27 8096.04 5292.97 17898.37 6181.92 21995.07 9496.76 17393.97 5497.77 2898.57 1995.72 1897.90 24088.89 17999.23 7999.08 45
Baseline_NR-MVSNet94.47 10995.09 8892.60 19698.50 5480.82 23592.08 19496.68 17693.82 5896.29 8998.56 2090.10 16397.75 25890.10 15299.66 2199.24 31
GBi-Net93.21 14692.96 15193.97 14595.40 24184.29 18895.99 5696.56 18388.63 18095.10 14798.53 2181.31 25798.98 11586.74 21698.38 17498.65 97
test193.21 14692.96 15193.97 14595.40 24184.29 18895.99 5696.56 18388.63 18095.10 14798.53 2181.31 25798.98 11586.74 21698.38 17498.65 97
FMVSNet194.84 9495.13 8693.97 14597.60 11484.29 18895.99 5696.56 18392.38 7997.03 5798.53 2190.12 16098.98 11588.78 18199.16 9098.65 97
MIMVSNet195.52 6695.45 7395.72 7599.14 589.02 10196.23 5096.87 16493.73 5997.87 2798.49 2490.73 14999.05 10486.43 22599.60 2599.10 44
pm-mvs195.43 7095.94 5593.93 14898.38 5985.08 18295.46 7997.12 14591.84 10197.28 4898.46 2595.30 3497.71 26090.17 14899.42 4798.99 53
TDRefinement97.68 397.60 497.93 299.02 1295.95 598.61 398.81 697.41 1097.28 4898.46 2594.62 5898.84 13794.64 1799.53 3598.99 53
v7n96.82 1097.31 1095.33 8998.54 4386.81 14896.83 2098.07 5696.59 2098.46 1798.43 2792.91 9499.52 1796.25 699.76 1199.65 8
test_part194.39 11094.55 10793.92 14996.14 19982.86 21195.54 7698.09 5295.36 3698.27 2098.36 2875.91 29699.44 2493.41 5899.84 399.47 17
DTE-MVSNet96.74 1797.43 594.67 11699.13 684.68 18596.51 3197.94 8198.14 398.67 1298.32 2995.04 4599.69 293.27 6599.82 899.62 10
ACMH88.36 1296.59 2797.43 594.07 14298.56 3885.33 17996.33 4398.30 2394.66 4098.72 898.30 3097.51 598.00 23494.87 1499.59 2798.86 73
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EGC-MVSNET80.97 32775.73 33996.67 4498.85 2294.55 1596.83 2096.60 1802.44 3745.32 37598.25 3192.24 10898.02 23291.85 10599.21 8297.45 200
PEN-MVS96.69 2097.39 894.61 11899.16 484.50 18696.54 3098.05 6098.06 498.64 1398.25 3195.01 4899.65 392.95 7899.83 699.68 4
test111190.39 21690.61 21089.74 27998.04 8471.50 34095.59 7279.72 36989.41 16095.94 10998.14 3370.79 31398.81 14488.52 18799.32 6298.90 69
PS-CasMVS96.69 2097.43 594.49 12999.13 684.09 19596.61 2797.97 7597.91 598.64 1398.13 3495.24 3699.65 393.39 5999.84 399.72 2
test250685.42 29884.57 30087.96 30897.81 9766.53 36096.14 5156.35 37789.04 17093.55 19898.10 3542.88 37998.68 17188.09 19599.18 8798.67 95
ECVR-MVScopyleft90.12 22690.16 21890.00 27697.81 9772.68 33495.76 6778.54 37089.04 17095.36 13498.10 3570.51 31498.64 17787.10 21299.18 8798.67 95
Vis-MVSNetpermissive95.50 6795.48 7295.56 8298.11 7689.40 9695.35 8098.22 3292.36 8194.11 17798.07 3792.02 11399.44 2493.38 6097.67 23497.85 172
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Anonymous2024052995.50 6795.83 6294.50 12797.33 12985.93 17195.19 9096.77 17296.64 1997.61 3598.05 3893.23 8398.79 14788.60 18699.04 10698.78 82
VPA-MVSNet95.14 8295.67 6893.58 16097.76 10083.15 20694.58 11297.58 10793.39 6697.05 5698.04 3993.25 8298.51 19289.75 16099.59 2799.08 45
LCM-MVSNet-Re94.20 12194.58 10693.04 17595.91 21783.13 20793.79 13999.19 292.00 9198.84 598.04 3993.64 7299.02 11081.28 27798.54 15896.96 223
v1094.68 10195.27 8292.90 18396.57 16480.15 23994.65 10997.57 10890.68 13697.43 4398.00 4188.18 18199.15 8794.84 1599.55 3499.41 20
DeepC-MVS91.39 495.43 7095.33 7895.71 7697.67 11090.17 8093.86 13898.02 6787.35 20796.22 9597.99 4294.48 6299.05 10492.73 8399.68 1897.93 162
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
JIA-IIPM85.08 30183.04 31091.19 24087.56 35986.14 16889.40 27984.44 35688.98 17282.20 35397.95 4356.82 36196.15 31476.55 32083.45 36391.30 348
v894.65 10295.29 8092.74 18896.65 15879.77 25394.59 11097.17 14191.86 9797.47 4297.93 4488.16 18299.08 9994.32 2299.47 3999.38 22
APDe-MVS96.46 3296.64 2295.93 6197.68 10989.38 9796.90 1998.41 1692.52 7797.43 4397.92 4595.11 4299.50 1994.45 1999.30 6598.92 67
nrg03096.32 4196.55 2695.62 7897.83 9688.55 11395.77 6698.29 2692.68 7398.03 2697.91 4695.13 4098.95 12293.85 3699.49 3899.36 24
lessismore_v093.87 15398.05 8183.77 19980.32 36797.13 5297.91 4677.49 28299.11 9592.62 8698.08 21198.74 88
Anonymous2024052192.86 15993.57 13790.74 25496.57 16475.50 31394.15 12795.60 21989.38 16195.90 11297.90 4880.39 26497.96 23892.60 8799.68 1898.75 85
WR-MVS_H96.60 2597.05 1495.24 9599.02 1286.44 15996.78 2498.08 5397.42 998.48 1697.86 4991.76 12199.63 694.23 2699.84 399.66 6
VDD-MVS94.37 11194.37 11394.40 13497.49 12086.07 16993.97 13593.28 27494.49 4496.24 9397.78 5087.99 18798.79 14788.92 17799.14 9298.34 124
RPSCF95.58 6594.89 9297.62 897.58 11596.30 495.97 5997.53 11292.42 7893.41 20097.78 5091.21 13797.77 25591.06 12197.06 25198.80 80
test_040295.73 6096.22 4094.26 13798.19 7285.77 17493.24 15297.24 13796.88 1697.69 3097.77 5294.12 6899.13 9191.54 11699.29 6897.88 168
tfpnnormal94.27 11794.87 9392.48 20197.71 10580.88 23494.55 11695.41 23093.70 6096.67 7397.72 5391.40 12998.18 22087.45 20699.18 8798.36 123
XXY-MVS92.58 16893.16 15090.84 25297.75 10179.84 24991.87 20996.22 20185.94 22895.53 12797.68 5492.69 10094.48 34083.21 25897.51 23998.21 135
UGNet93.08 14992.50 16694.79 11193.87 28687.99 12595.07 9494.26 25990.64 13787.33 32397.67 5586.89 20898.49 19388.10 19498.71 14297.91 165
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
KD-MVS_self_test94.10 12394.73 9992.19 20797.66 11179.49 25894.86 10197.12 14589.59 15896.87 6497.65 5690.40 15798.34 20689.08 17599.35 5798.75 85
wuyk23d87.83 26990.79 20678.96 35090.46 33988.63 10992.72 16390.67 31291.65 11398.68 1197.64 5796.06 1677.53 37159.84 36699.41 5270.73 369
EG-PatchMatch MVS94.54 10794.67 10394.14 14097.87 9586.50 15592.00 19996.74 17488.16 19096.93 6297.61 5893.04 9197.90 24091.60 11398.12 20798.03 150
DSMNet-mixed82.21 31781.56 31684.16 33889.57 34870.00 34990.65 24177.66 37254.99 37083.30 34797.57 5977.89 28190.50 36366.86 36095.54 28891.97 343
FC-MVSNet-test95.32 7595.88 5893.62 15898.49 5581.77 22095.90 6298.32 2093.93 5597.53 3997.56 6088.48 17799.40 4392.91 7999.83 699.68 4
ab-mvs92.40 17292.62 16291.74 22197.02 14181.65 22295.84 6495.50 22886.95 21592.95 22097.56 6090.70 15097.50 26879.63 29597.43 24296.06 257
COLMAP_ROBcopyleft91.06 596.75 1696.62 2397.13 2898.38 5994.31 1796.79 2398.32 2096.69 1796.86 6597.56 6095.48 2598.77 15590.11 15099.44 4598.31 127
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CP-MVSNet96.19 4696.80 1794.38 13598.99 1483.82 19896.31 4597.53 11297.60 798.34 1997.52 6391.98 11699.63 693.08 7499.81 999.70 3
ACMH+88.43 1196.48 3096.82 1695.47 8498.54 4389.06 10095.65 7198.61 996.10 2698.16 2397.52 6396.90 798.62 17890.30 14199.60 2598.72 91
SMA-MVScopyleft95.77 5995.54 7096.47 5198.27 6691.19 6795.09 9297.79 9486.48 21897.42 4597.51 6594.47 6399.29 7193.55 4699.29 6898.93 63
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
ambc92.98 17796.88 14983.01 21095.92 6196.38 19396.41 8097.48 6688.26 18097.80 25189.96 15598.93 11798.12 142
PMVScopyleft87.21 1494.97 8695.33 7893.91 15098.97 1597.16 295.54 7695.85 21396.47 2193.40 20297.46 6795.31 3395.47 32886.18 22998.78 13789.11 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
abl_697.31 597.12 1397.86 398.54 4395.32 796.61 2798.35 1995.81 3197.55 3697.44 6896.51 999.40 4394.06 3099.23 7998.85 76
3Dnovator92.54 394.80 9794.90 9194.47 13095.47 23987.06 14196.63 2697.28 13591.82 10494.34 17597.41 6990.60 15298.65 17692.47 8998.11 20897.70 184
mvs_anonymous90.37 21891.30 19487.58 31392.17 31468.00 35389.84 26994.73 24883.82 25793.22 21197.40 7087.54 19397.40 27687.94 19995.05 30097.34 210
MP-MVS-pluss96.08 4995.92 5796.57 4699.06 1091.21 6693.25 15198.32 2087.89 19596.86 6597.38 7195.55 2499.39 4895.47 1099.47 3999.11 41
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test072698.51 4786.69 15195.34 8198.18 3691.85 9897.63 3297.37 7295.58 22
EU-MVSNet87.39 28086.71 28389.44 28393.40 29176.11 30694.93 10090.00 31557.17 36895.71 12097.37 7264.77 33897.68 26292.67 8594.37 31394.52 302
FMVSNet292.78 16192.73 16092.95 18095.40 24181.98 21894.18 12695.53 22788.63 18096.05 10497.37 7281.31 25798.81 14487.38 20998.67 14898.06 144
DVP-MVS++95.93 5396.34 3494.70 11596.54 16786.66 15398.45 498.22 3293.26 6897.54 3797.36 7593.12 8799.38 5493.88 3498.68 14698.04 147
test_one_060198.26 6787.14 13998.18 3694.25 4896.99 6097.36 7595.13 40
HPM-MVS_fast97.01 796.89 1597.39 2299.12 893.92 2997.16 1298.17 4093.11 7096.48 7997.36 7596.92 699.34 6294.31 2399.38 5598.92 67
DVP-MVScopyleft95.82 5896.18 4294.72 11498.51 4786.69 15195.20 8897.00 15191.85 9897.40 4697.35 7895.58 2299.34 6293.44 5599.31 6398.13 141
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
test_0728_THIRD93.26 6897.40 4697.35 7894.69 5599.34 6293.88 3499.42 4798.89 70
ACMMP_NAP96.21 4596.12 4796.49 5098.90 1891.42 6494.57 11398.03 6590.42 14396.37 8297.35 7895.68 1999.25 7794.44 2099.34 5898.80 80
DP-MVS95.62 6395.84 6194.97 10497.16 13688.62 11094.54 11897.64 10196.94 1596.58 7797.32 8193.07 9098.72 16190.45 13298.84 12597.57 192
MVS-HIRNet78.83 33680.60 32773.51 35393.07 29747.37 37687.10 31578.00 37168.94 35077.53 36697.26 8271.45 31194.62 33863.28 36588.74 35478.55 368
SED-MVS96.00 5296.41 3294.76 11298.51 4786.97 14495.21 8698.10 4991.95 9297.63 3297.25 8396.48 1199.35 5993.29 6399.29 6897.95 160
test_241102_TWO98.10 4991.95 9297.54 3797.25 8395.37 2899.35 5993.29 6399.25 7698.49 115
3Dnovator+92.74 295.86 5795.77 6596.13 5396.81 15490.79 7496.30 4797.82 8996.13 2594.74 16497.23 8591.33 13199.16 8693.25 6698.30 18698.46 118
LPG-MVS_test96.38 4096.23 3996.84 4098.36 6292.13 5495.33 8298.25 2791.78 10597.07 5397.22 8696.38 1399.28 7392.07 9799.59 2799.11 41
LGP-MVS_train96.84 4098.36 6292.13 5498.25 2791.78 10597.07 5397.22 8696.38 1399.28 7392.07 9799.59 2799.11 41
FIs94.90 8995.35 7693.55 16198.28 6581.76 22195.33 8298.14 4493.05 7197.07 5397.18 8887.65 19199.29 7191.72 10999.69 1599.61 11
PatchT87.51 27788.17 25885.55 32790.64 33466.91 35592.02 19886.09 33992.20 8789.05 29797.16 8964.15 34096.37 31089.21 17392.98 33393.37 328
TranMVSNet+NR-MVSNet96.07 5096.26 3895.50 8398.26 6787.69 13093.75 14097.86 8395.96 3097.48 4197.14 9095.33 3299.44 2490.79 12799.76 1199.38 22
TSAR-MVS + MP.94.96 8794.75 9795.57 8198.86 2188.69 10796.37 4096.81 16885.23 23894.75 16397.12 9191.85 11899.40 4393.45 5398.33 18198.62 105
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
VPNet93.08 14993.76 12991.03 24398.60 3575.83 31191.51 22195.62 21891.84 10195.74 11897.10 9289.31 17198.32 20785.07 24299.06 9898.93 63
IterMVS-LS93.78 12994.28 11792.27 20496.27 18879.21 26591.87 20996.78 17091.77 10796.57 7897.07 9387.15 20098.74 15991.99 9999.03 10798.86 73
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
LFMVS91.33 19791.16 19991.82 21896.27 18879.36 26095.01 9785.61 34696.04 2994.82 16097.06 9472.03 31098.46 19984.96 24398.70 14497.65 188
APD-MVS_3200maxsize96.82 1096.65 2197.32 2597.95 9293.82 3496.31 4598.25 2795.51 3596.99 6097.05 9595.63 2199.39 4893.31 6298.88 12098.75 85
SR-MVS-dyc-post96.84 896.60 2597.56 1098.07 7995.27 896.37 4098.12 4695.66 3397.00 5897.03 9694.85 5299.42 2993.49 4898.84 12598.00 152
RE-MVS-def96.66 2098.07 7995.27 896.37 4098.12 4695.66 3397.00 5897.03 9695.40 2793.49 4898.84 12598.00 152
test_241102_ONE98.51 4786.97 14498.10 4991.85 9897.63 3297.03 9696.48 1198.95 122
DPE-MVScopyleft95.89 5495.88 5895.92 6397.93 9389.83 8693.46 14798.30 2392.37 8097.75 2996.95 9995.14 3999.51 1891.74 10899.28 7398.41 122
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
zzz-MVS96.47 3196.14 4597.47 1598.95 1694.05 2393.69 14297.62 10294.46 4596.29 8996.94 10093.56 7399.37 5694.29 2499.42 4798.99 53
MTAPA96.65 2296.38 3397.47 1598.95 1694.05 2395.88 6397.62 10294.46 4596.29 8996.94 10093.56 7399.37 5694.29 2499.42 4798.99 53
CR-MVSNet87.89 26787.12 27690.22 26991.01 33178.93 26792.52 17092.81 28173.08 33289.10 29596.93 10267.11 32297.64 26388.80 18092.70 33594.08 309
Patchmtry90.11 22789.92 22590.66 25690.35 34077.00 29592.96 15792.81 28190.25 14694.74 16496.93 10267.11 32297.52 26785.17 23598.98 10997.46 199
FMVSNet587.82 27086.56 28591.62 22592.31 30979.81 25293.49 14694.81 24683.26 25991.36 25596.93 10252.77 36897.49 27076.07 32298.03 21597.55 195
RPMNet90.31 22290.14 22290.81 25391.01 33178.93 26792.52 17098.12 4691.91 9589.10 29596.89 10568.84 31799.41 3690.17 14892.70 33594.08 309
PGM-MVS96.32 4195.94 5597.43 1998.59 3793.84 3395.33 8298.30 2391.40 11895.76 11696.87 10695.26 3599.45 2392.77 8099.21 8299.00 51
test117296.79 1596.52 2797.60 998.03 8594.87 1096.07 5598.06 5995.76 3296.89 6396.85 10794.85 5299.42 2993.35 6198.81 13398.53 112
OPM-MVS95.61 6495.45 7396.08 5498.49 5591.00 6992.65 16797.33 12990.05 14896.77 7096.85 10795.04 4598.56 18792.77 8099.06 9898.70 94
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMM88.83 996.30 4396.07 5096.97 3598.39 5892.95 4694.74 10598.03 6590.82 13297.15 5196.85 10796.25 1599.00 11493.10 7299.33 6098.95 61
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMMPcopyleft96.61 2496.34 3497.43 1998.61 3493.88 3096.95 1898.18 3692.26 8596.33 8596.84 11095.10 4399.40 4393.47 5299.33 6099.02 50
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
casdiffmvs94.32 11594.80 9592.85 18596.05 20681.44 22692.35 18398.05 6091.53 11695.75 11796.80 11193.35 8098.49 19391.01 12498.32 18398.64 101
QAPM92.88 15792.77 15693.22 17395.82 22083.31 20296.45 3597.35 12783.91 25693.75 19196.77 11289.25 17298.88 12984.56 24897.02 25397.49 198
LS3D96.11 4895.83 6296.95 3794.75 25994.20 1997.34 1197.98 7297.31 1195.32 13696.77 11293.08 8999.20 8391.79 10698.16 20297.44 202
XVG-ACMP-BASELINE95.68 6295.34 7796.69 4398.40 5793.04 4394.54 11898.05 6090.45 14296.31 8796.76 11492.91 9498.72 16191.19 12099.42 4798.32 125
MIMVSNet87.13 28886.54 28688.89 29396.05 20676.11 30694.39 12088.51 32081.37 27888.27 31296.75 11572.38 30795.52 32565.71 36295.47 29095.03 289
AllTest94.88 9194.51 11096.00 5698.02 8692.17 5295.26 8598.43 1390.48 14095.04 15296.74 11692.54 10497.86 24685.11 24098.98 10997.98 156
TestCases96.00 5698.02 8692.17 5298.43 1390.48 14095.04 15296.74 11692.54 10497.86 24685.11 24098.98 10997.98 156
SR-MVS96.70 1996.42 2997.54 1198.05 8194.69 1196.13 5298.07 5695.17 3796.82 6796.73 11895.09 4499.43 2892.99 7798.71 14298.50 114
MP-MVScopyleft96.14 4795.68 6797.51 1398.81 2594.06 2196.10 5397.78 9592.73 7293.48 19996.72 11994.23 6699.42 2991.99 9999.29 6899.05 48
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVS_Test92.57 17093.29 14590.40 26493.53 29075.85 30992.52 17096.96 15488.73 17792.35 23896.70 12090.77 14598.37 20592.53 8895.49 28996.99 222
xxxxxxxxxxxxxcwj95.03 8394.93 9095.33 8997.46 12388.05 12392.04 19698.42 1587.63 20396.36 8396.68 12194.37 6499.32 6892.41 9199.05 10198.64 101
SF-MVS95.88 5695.88 5895.87 6798.12 7589.65 8995.58 7498.56 1191.84 10196.36 8396.68 12194.37 6499.32 6892.41 9199.05 10198.64 101
mPP-MVS96.46 3296.05 5197.69 598.62 3294.65 1396.45 3597.74 9692.59 7695.47 12896.68 12194.50 6199.42 2993.10 7299.26 7598.99 53
Anonymous20240521192.58 16892.50 16692.83 18696.55 16683.22 20492.43 17791.64 30594.10 5195.59 12496.64 12481.88 25497.50 26885.12 23998.52 16097.77 179
IterMVS-SCA-FT91.65 18891.55 18591.94 21693.89 28579.22 26487.56 30693.51 27191.53 11695.37 13396.62 12578.65 27398.90 12691.89 10494.95 30197.70 184
ACMMPR96.46 3296.14 4597.41 2198.60 3593.82 3496.30 4797.96 7692.35 8295.57 12596.61 12694.93 5199.41 3693.78 3899.15 9199.00 51
PM-MVS93.33 13892.67 16195.33 8996.58 16394.06 2192.26 18892.18 29585.92 22996.22 9596.61 12685.64 22495.99 32090.35 13798.23 19595.93 262
region2R96.41 3796.09 4897.38 2398.62 3293.81 3696.32 4497.96 7692.26 8595.28 13996.57 12895.02 4799.41 3693.63 4299.11 9698.94 62
SteuartSystems-ACMMP96.40 3896.30 3696.71 4298.63 3191.96 5795.70 6898.01 6993.34 6796.64 7496.57 12894.99 4999.36 5893.48 5199.34 5898.82 78
Skip Steuart: Steuart Systems R&D Blog.
XVS96.49 2996.18 4297.44 1798.56 3893.99 2796.50 3297.95 7894.58 4194.38 17396.49 13094.56 5999.39 4893.57 4499.05 10198.93 63
HFP-MVS96.39 3996.17 4497.04 3198.51 4793.37 4096.30 4797.98 7292.35 8295.63 12296.47 13195.37 2899.27 7593.78 3899.14 9298.48 116
#test#95.89 5495.51 7197.04 3198.51 4793.37 4095.14 9197.98 7289.34 16395.63 12296.47 13195.37 2899.27 7591.99 9999.14 9298.48 116
XVG-OURS94.72 9994.12 12296.50 4998.00 8894.23 1891.48 22298.17 4090.72 13495.30 13796.47 13187.94 18896.98 28991.41 11897.61 23798.30 128
ACMP88.15 1395.71 6195.43 7596.54 4798.17 7391.73 6294.24 12498.08 5389.46 15996.61 7696.47 13195.85 1799.12 9390.45 13299.56 3398.77 84
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OpenMVScopyleft89.45 892.27 17792.13 17292.68 19194.53 27184.10 19495.70 6897.03 14982.44 27291.14 26196.42 13588.47 17898.38 20285.95 23097.47 24195.55 280
HPM-MVScopyleft96.81 1296.62 2397.36 2498.89 1993.53 3997.51 998.44 1292.35 8295.95 10796.41 13696.71 899.42 2993.99 3399.36 5699.13 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
v124093.29 14093.71 13192.06 21496.01 21177.89 28391.81 21597.37 12085.12 24396.69 7296.40 13786.67 21199.07 10394.51 1898.76 13999.22 32
SD-MVS95.19 8195.73 6693.55 16196.62 16188.88 10694.67 10798.05 6091.26 12197.25 5096.40 13795.42 2694.36 34492.72 8499.19 8597.40 206
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
test20.0390.80 20490.85 20490.63 25795.63 23479.24 26389.81 27092.87 28089.90 15194.39 17296.40 13785.77 22095.27 33573.86 33399.05 10197.39 207
IterMVS90.18 22490.16 21890.21 27093.15 29675.98 30887.56 30692.97 27986.43 22094.09 17896.40 13778.32 27797.43 27387.87 20094.69 30897.23 214
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVS96.44 3596.08 4997.54 1198.29 6494.62 1496.80 2298.08 5392.67 7595.08 15096.39 14194.77 5499.42 2993.17 6999.44 4598.58 110
v119293.49 13493.78 12892.62 19596.16 19779.62 25591.83 21497.22 13986.07 22696.10 10396.38 14287.22 19899.02 11094.14 2998.88 12099.22 32
V4293.43 13693.58 13692.97 17895.34 24581.22 22992.67 16696.49 18887.25 20996.20 9796.37 14387.32 19798.85 13692.39 9398.21 19898.85 76
ZNCC-MVS96.42 3696.20 4197.07 3098.80 2792.79 4896.08 5498.16 4391.74 10995.34 13596.36 14495.68 1999.44 2494.41 2199.28 7398.97 59
IS-MVSNet94.49 10894.35 11494.92 10598.25 6986.46 15897.13 1594.31 25796.24 2496.28 9296.36 14482.88 23999.35 5988.19 19199.52 3798.96 60
v114493.50 13393.81 12692.57 19796.28 18779.61 25691.86 21396.96 15486.95 21595.91 11196.32 14687.65 19198.96 12093.51 4798.88 12099.13 39
baseline94.26 11894.80 9592.64 19296.08 20480.99 23293.69 14298.04 6490.80 13394.89 15896.32 14693.19 8498.48 19791.68 11198.51 16298.43 120
TinyColmap92.00 18392.76 15789.71 28095.62 23577.02 29490.72 23996.17 20487.70 20195.26 14096.29 14892.54 10496.45 30681.77 27298.77 13895.66 276
GST-MVS96.24 4495.99 5497.00 3498.65 3092.71 4995.69 7098.01 6992.08 9095.74 11896.28 14995.22 3799.42 2993.17 6999.06 9898.88 72
USDC89.02 24789.08 23788.84 29495.07 25074.50 32088.97 28896.39 19273.21 33193.27 20796.28 14982.16 24996.39 30877.55 31198.80 13595.62 279
v2v48293.29 14093.63 13492.29 20396.35 18178.82 27091.77 21796.28 19588.45 18495.70 12196.26 15186.02 21998.90 12693.02 7598.81 13399.14 38
XVG-OURS-SEG-HR95.38 7295.00 8996.51 4898.10 7794.07 2092.46 17598.13 4590.69 13593.75 19196.25 15298.03 297.02 28892.08 9695.55 28798.45 119
pmmvs-eth3d91.54 19190.73 20893.99 14395.76 22587.86 12890.83 23693.98 26678.23 30694.02 18496.22 15382.62 24596.83 29586.57 22198.33 18197.29 213
h-mvs3392.89 15691.99 17595.58 8096.97 14390.55 7693.94 13694.01 26589.23 16693.95 18596.19 15476.88 29199.14 8991.02 12295.71 28497.04 220
v192192093.26 14393.61 13592.19 20796.04 21078.31 27691.88 20897.24 13785.17 24096.19 9996.19 15486.76 21099.05 10494.18 2898.84 12599.22 32
EPP-MVSNet93.91 12793.68 13394.59 12398.08 7885.55 17797.44 1094.03 26294.22 4994.94 15596.19 15482.07 25099.57 1387.28 21098.89 11898.65 97
APD-MVScopyleft95.00 8594.69 10095.93 6197.38 12690.88 7294.59 11097.81 9089.22 16895.46 13096.17 15793.42 7899.34 6289.30 16698.87 12397.56 194
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
v14419293.20 14893.54 13992.16 21196.05 20678.26 27791.95 20197.14 14284.98 24795.96 10696.11 15887.08 20299.04 10793.79 3798.84 12599.17 35
VNet92.67 16592.96 15191.79 21996.27 18880.15 23991.95 20194.98 23892.19 8894.52 17096.07 15987.43 19597.39 27784.83 24498.38 17497.83 173
v14892.87 15893.29 14591.62 22596.25 19177.72 28691.28 22795.05 23689.69 15495.93 11096.04 16087.34 19698.38 20290.05 15397.99 21898.78 82
9.1494.81 9497.49 12094.11 12998.37 1787.56 20695.38 13296.03 16194.66 5699.08 9990.70 12998.97 113
FMVSNet390.78 20590.32 21792.16 21193.03 30079.92 24892.54 16994.95 23986.17 22595.10 14796.01 16269.97 31698.75 15686.74 21698.38 17497.82 175
MG-MVS89.54 24089.80 22788.76 29594.88 25272.47 33689.60 27392.44 29285.82 23089.48 29195.98 16382.85 24097.74 25981.87 27195.27 29696.08 256
UniMVSNet (Re)95.32 7595.15 8595.80 7097.79 9988.91 10392.91 15998.07 5693.46 6596.31 8795.97 16490.14 15999.34 6292.11 9499.64 2399.16 36
DU-MVS95.28 7895.12 8795.75 7497.75 10188.59 11192.58 16897.81 9093.99 5296.80 6895.90 16590.10 16399.41 3691.60 11399.58 3199.26 29
NR-MVSNet95.28 7895.28 8195.26 9497.75 10187.21 13895.08 9397.37 12093.92 5797.65 3195.90 16590.10 16399.33 6790.11 15099.66 2199.26 29
ETH3D-3000-0.194.86 9294.55 10795.81 6897.61 11389.72 8794.05 13198.37 1788.09 19195.06 15195.85 16792.58 10299.10 9790.33 14098.99 10898.62 105
EI-MVSNet92.99 15393.26 14992.19 20792.12 31579.21 26592.32 18594.67 25291.77 10795.24 14395.85 16787.14 20198.49 19391.99 9998.26 18998.86 73
CVMVSNet85.16 30084.72 29886.48 32092.12 31570.19 34592.32 18588.17 32556.15 36990.64 26895.85 16767.97 32096.69 29988.78 18190.52 35092.56 339
EI-MVSNet-UG-set94.35 11394.27 11994.59 12392.46 30885.87 17292.42 17894.69 25093.67 6496.13 10195.84 17091.20 13898.86 13493.78 3898.23 19599.03 49
EI-MVSNet-Vis-set94.36 11294.28 11794.61 11892.55 30785.98 17092.44 17694.69 25093.70 6096.12 10295.81 17191.24 13598.86 13493.76 4198.22 19798.98 58
ZD-MVS97.23 13190.32 7997.54 11084.40 25394.78 16295.79 17292.76 9999.39 4888.72 18498.40 169
MDA-MVSNet-bldmvs91.04 20090.88 20291.55 22794.68 26680.16 23885.49 33492.14 29890.41 14494.93 15695.79 17285.10 22596.93 29285.15 23794.19 31897.57 192
MVSTER89.32 24388.75 24591.03 24390.10 34276.62 30190.85 23594.67 25282.27 27395.24 14395.79 17261.09 35498.49 19390.49 13198.26 18997.97 159
UniMVSNet_NR-MVSNet95.35 7395.21 8395.76 7397.69 10888.59 11192.26 18897.84 8794.91 3896.80 6895.78 17590.42 15499.41 3691.60 11399.58 3199.29 28
PC_three_145275.31 32195.87 11395.75 17692.93 9396.34 31387.18 21198.68 14698.04 147
new-patchmatchnet88.97 25090.79 20683.50 34194.28 27655.83 37585.34 33593.56 27086.18 22495.47 12895.73 17783.10 23796.51 30485.40 23498.06 21298.16 137
UnsupCasMVSNet_eth90.33 22090.34 21690.28 26694.64 26880.24 23789.69 27295.88 21185.77 23193.94 18795.69 17881.99 25192.98 35584.21 25191.30 34697.62 190
RRT_MVS91.36 19690.05 22395.29 9389.21 35288.15 12092.51 17494.89 24186.73 21795.54 12695.68 17961.82 35199.30 7094.91 1399.13 9598.43 120
OPU-MVS95.15 9996.84 15189.43 9495.21 8695.66 18093.12 8798.06 22786.28 22898.61 15197.95 160
testtj94.81 9694.42 11196.01 5597.23 13190.51 7894.77 10497.85 8691.29 12094.92 15795.66 18091.71 12299.40 4388.07 19698.25 19298.11 143
MVP-Stereo90.07 23088.92 24193.54 16396.31 18586.49 15690.93 23495.59 22379.80 28691.48 25395.59 18280.79 26197.39 27778.57 30591.19 34796.76 232
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HQP_MVS94.26 11893.93 12495.23 9697.71 10588.12 12194.56 11497.81 9091.74 10993.31 20395.59 18286.93 20598.95 12289.26 17098.51 16298.60 108
plane_prior495.59 182
Anonymous2023120688.77 25588.29 25390.20 27196.31 18578.81 27189.56 27593.49 27274.26 32592.38 23695.58 18582.21 24795.43 33072.07 34298.75 14196.34 246
旧先验196.20 19384.17 19394.82 24495.57 18689.57 16997.89 22396.32 247
Regformer-394.28 11694.23 12194.46 13192.78 30586.28 16592.39 18094.70 24993.69 6395.97 10595.56 18791.34 13098.48 19793.45 5398.14 20498.62 105
Regformer-494.90 8994.67 10395.59 7992.78 30589.02 10192.39 18095.91 21094.50 4396.41 8095.56 18792.10 11299.01 11294.23 2698.14 20498.74 88
ETH3D cwj APD-0.1693.99 12693.38 14495.80 7096.82 15289.92 8392.72 16398.02 6784.73 25193.65 19595.54 18991.68 12399.22 8188.78 18198.49 16598.26 131
GeoE94.55 10594.68 10294.15 13997.23 13185.11 18194.14 12897.34 12888.71 17995.26 14095.50 19094.65 5799.12 9390.94 12598.40 16998.23 132
MVS_030490.96 20290.15 22193.37 16793.17 29587.06 14193.62 14492.43 29389.60 15782.25 35295.50 19082.56 24697.83 24984.41 25097.83 22695.22 284
CPTT-MVS94.74 9894.12 12296.60 4598.15 7493.01 4495.84 6497.66 10089.21 16993.28 20695.46 19288.89 17498.98 11589.80 15798.82 13197.80 177
DeepC-MVS_fast89.96 793.73 13093.44 14294.60 12296.14 19987.90 12693.36 15097.14 14285.53 23593.90 18895.45 19391.30 13398.59 18389.51 16398.62 15097.31 212
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CNVR-MVS94.58 10494.29 11695.46 8596.94 14589.35 9891.81 21596.80 16989.66 15593.90 18895.44 19492.80 9898.72 16192.74 8298.52 16098.32 125
testdata91.03 24396.87 15082.01 21794.28 25871.55 33892.46 23295.42 19585.65 22397.38 27982.64 26397.27 24693.70 322
DeepPCF-MVS90.46 694.20 12193.56 13896.14 5295.96 21392.96 4589.48 27697.46 11685.14 24196.23 9495.42 19593.19 8498.08 22690.37 13698.76 13997.38 209
OMC-MVS94.22 12093.69 13295.81 6897.25 13091.27 6592.27 18797.40 11987.10 21394.56 16895.42 19593.74 7198.11 22586.62 22098.85 12498.06 144
WR-MVS93.49 13493.72 13092.80 18797.57 11680.03 24590.14 25895.68 21793.70 6096.62 7595.39 19887.21 19999.04 10787.50 20599.64 2399.33 25
ITE_SJBPF95.95 5897.34 12893.36 4296.55 18691.93 9494.82 16095.39 19891.99 11597.08 28685.53 23397.96 21997.41 203
RRT_test8_iter0588.21 26388.17 25888.33 30491.62 32466.82 35991.73 21896.60 18086.34 22194.14 17695.38 20047.72 37299.11 9591.78 10798.26 18999.06 47
MSLP-MVS++93.25 14593.88 12591.37 23196.34 18282.81 21293.11 15397.74 9689.37 16294.08 17995.29 20190.40 15796.35 31190.35 13798.25 19294.96 291
HPM-MVS++copyleft95.02 8494.39 11296.91 3897.88 9493.58 3894.09 13096.99 15391.05 12692.40 23595.22 20291.03 14399.25 7792.11 9498.69 14597.90 166
MSP-MVS95.34 7494.63 10597.48 1498.67 2994.05 2396.41 3998.18 3691.26 12195.12 14695.15 20386.60 21399.50 1993.43 5796.81 26198.89 70
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
MDA-MVSNet_test_wron88.16 26588.23 25687.93 30992.22 31173.71 32580.71 35988.84 31782.52 27094.88 15995.14 20482.70 24393.61 35083.28 25793.80 32196.46 242
Vis-MVSNet (Re-imp)90.42 21490.16 21891.20 23997.66 11177.32 29194.33 12287.66 32891.20 12392.99 21895.13 20575.40 29898.28 20977.86 30799.19 8597.99 155
YYNet188.17 26488.24 25587.93 30992.21 31273.62 32680.75 35888.77 31882.51 27194.99 15495.11 20682.70 24393.70 34983.33 25693.83 32096.48 241
D2MVS89.93 23489.60 23290.92 24894.03 28278.40 27588.69 29594.85 24278.96 30093.08 21495.09 20774.57 29996.94 29088.19 19198.96 11597.41 203
CDPH-MVS92.67 16591.83 17995.18 9896.94 14588.46 11690.70 24097.07 14877.38 30992.34 24095.08 20892.67 10198.88 12985.74 23198.57 15498.20 136
PVSNet_BlendedMVS90.35 21989.96 22491.54 22894.81 25678.80 27290.14 25896.93 15679.43 29288.68 30795.06 20986.27 21698.15 22380.27 28598.04 21497.68 186
Regformer-194.55 10594.33 11595.19 9792.83 30388.54 11491.87 20995.84 21493.99 5295.95 10795.04 21092.00 11498.79 14793.14 7198.31 18498.23 132
Regformer-294.86 9294.55 10795.77 7292.83 30389.98 8291.87 20996.40 19194.38 4796.19 9995.04 21092.47 10799.04 10793.49 4898.31 18498.28 129
tpm84.38 30584.08 30485.30 33190.47 33863.43 37089.34 28085.63 34577.24 31287.62 31995.03 21261.00 35597.30 28079.26 30091.09 34995.16 285
PVSNet_Blended_VisFu91.63 18991.20 19692.94 18197.73 10483.95 19792.14 19297.46 11678.85 30292.35 23894.98 21384.16 23199.08 9986.36 22696.77 26395.79 270
miper_lstm_enhance89.90 23589.80 22790.19 27291.37 32877.50 28883.82 35095.00 23784.84 24993.05 21694.96 21476.53 29595.20 33689.96 15598.67 14897.86 170
新几何193.17 17497.16 13687.29 13594.43 25467.95 35391.29 25694.94 21586.97 20498.23 21581.06 28297.75 22793.98 315
112190.26 22389.23 23393.34 16897.15 13887.40 13391.94 20394.39 25567.88 35491.02 26294.91 21686.91 20798.59 18381.17 28097.71 23194.02 314
cl____90.65 20990.56 21290.91 25091.85 31976.98 29786.75 32395.36 23385.53 23594.06 18194.89 21777.36 28697.98 23790.27 14398.98 10997.76 180
DIV-MVS_self_test90.65 20990.56 21290.91 25091.85 31976.99 29686.75 32395.36 23385.52 23794.06 18194.89 21777.37 28597.99 23690.28 14298.97 11397.76 180
test22296.95 14485.27 18088.83 29193.61 26865.09 36190.74 26694.85 21984.62 22997.36 24493.91 316
test_prior393.29 14092.85 15494.61 11895.95 21487.23 13690.21 25497.36 12589.33 16490.77 26494.81 22090.41 15598.68 17188.21 18998.55 15597.93 162
test_prior290.21 25489.33 16490.77 26494.81 22090.41 15588.21 18998.55 155
CHOSEN 1792x268887.19 28685.92 29491.00 24697.13 13979.41 25984.51 34395.60 21964.14 36290.07 27994.81 22078.26 27897.14 28573.34 33595.38 29496.46 242
114514_t90.51 21189.80 22792.63 19498.00 8882.24 21693.40 14997.29 13365.84 35989.40 29294.80 22386.99 20398.75 15683.88 25398.61 15196.89 226
tttt051789.81 23788.90 24392.55 19897.00 14279.73 25495.03 9683.65 35889.88 15295.30 13794.79 22453.64 36699.39 4891.99 9998.79 13698.54 111
EPNet89.80 23888.25 25494.45 13283.91 37386.18 16793.87 13787.07 33391.16 12580.64 36194.72 22578.83 27198.89 12885.17 23598.89 11898.28 129
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PMMVS281.31 32383.44 30774.92 35290.52 33746.49 37769.19 36685.23 35284.30 25487.95 31694.71 22676.95 29084.36 37064.07 36398.09 21093.89 317
testgi90.38 21791.34 19387.50 31497.49 12071.54 33989.43 27795.16 23588.38 18694.54 16994.68 22792.88 9693.09 35471.60 34697.85 22597.88 168
NCCC94.08 12493.54 13995.70 7796.49 17289.90 8592.39 18096.91 16090.64 13792.33 24194.60 22890.58 15398.96 12090.21 14797.70 23298.23 132
MVS_111021_HR93.63 13293.42 14394.26 13796.65 15886.96 14689.30 28296.23 19988.36 18793.57 19794.60 22893.45 7597.77 25590.23 14598.38 17498.03 150
TAMVS90.16 22589.05 23893.49 16696.49 17286.37 16190.34 25192.55 29080.84 28292.99 21894.57 23081.94 25398.20 21773.51 33498.21 19895.90 265
DROMVSNet95.44 6995.62 6994.89 10696.93 14787.69 13096.48 3499.14 393.93 5592.77 22494.52 23193.95 7099.49 2293.62 4399.22 8197.51 197
原ACMM192.87 18496.91 14884.22 19197.01 15076.84 31489.64 29094.46 23288.00 18698.70 16781.53 27598.01 21795.70 274
agg_prior192.60 16791.76 18295.10 10196.20 19388.89 10490.37 24996.88 16279.67 29090.21 27494.41 23391.30 13398.78 15188.46 18898.37 17997.64 189
MVS_111021_LR93.66 13193.28 14794.80 11096.25 19190.95 7090.21 25495.43 22987.91 19393.74 19394.40 23492.88 9696.38 30990.39 13498.28 18797.07 217
TEST996.45 17489.46 9290.60 24296.92 15879.09 29890.49 26994.39 23591.31 13298.88 129
train_agg92.71 16491.83 17995.35 8796.45 17489.46 9290.60 24296.92 15879.37 29390.49 26994.39 23591.20 13898.88 12988.66 18598.43 16797.72 183
test_896.37 17689.14 9990.51 24596.89 16179.37 29390.42 27194.36 23791.20 13898.82 139
FPMVS84.50 30483.28 30888.16 30696.32 18494.49 1685.76 33285.47 34783.09 26385.20 33394.26 23863.79 34386.58 36863.72 36491.88 34583.40 363
MCST-MVS92.91 15592.51 16594.10 14197.52 11885.72 17591.36 22697.13 14480.33 28492.91 22194.24 23991.23 13698.72 16189.99 15497.93 22197.86 170
BH-RMVSNet90.47 21390.44 21490.56 26095.21 24878.65 27489.15 28693.94 26788.21 18892.74 22594.22 24086.38 21497.88 24278.67 30495.39 29395.14 287
pmmvs488.95 25187.70 26692.70 19094.30 27585.60 17687.22 31292.16 29774.62 32389.75 28994.19 24177.97 28096.41 30782.71 26296.36 27296.09 255
Patchmatch-RL test88.81 25488.52 24789.69 28195.33 24679.94 24786.22 33192.71 28578.46 30495.80 11594.18 24266.25 33095.33 33389.22 17298.53 15993.78 319
PHI-MVS94.34 11493.80 12795.95 5895.65 23291.67 6394.82 10297.86 8387.86 19693.04 21794.16 24391.58 12598.78 15190.27 14398.96 11597.41 203
TAPA-MVS88.58 1092.49 17191.75 18394.73 11396.50 17189.69 8892.91 15997.68 9978.02 30792.79 22394.10 24490.85 14497.96 23884.76 24698.16 20296.54 235
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DP-MVS Recon92.31 17591.88 17893.60 15997.18 13586.87 14791.10 23197.37 12084.92 24892.08 24694.08 24588.59 17698.20 21783.50 25598.14 20495.73 272
CANet92.38 17391.99 17593.52 16593.82 28883.46 20191.14 22997.00 15189.81 15386.47 32794.04 24687.90 18999.21 8289.50 16498.27 18897.90 166
F-COLMAP92.28 17691.06 20095.95 5897.52 11891.90 5893.53 14597.18 14083.98 25588.70 30694.04 24688.41 17998.55 18980.17 28895.99 27897.39 207
UnsupCasMVSNet_bld88.50 25988.03 26189.90 27795.52 23878.88 26987.39 31094.02 26479.32 29693.06 21594.02 24880.72 26294.27 34575.16 32793.08 33196.54 235
MDTV_nov1_ep1383.88 30689.42 35061.52 37188.74 29487.41 33073.99 32784.96 33694.01 24965.25 33595.53 32478.02 30693.16 328
OpenMVS_ROBcopyleft85.12 1689.52 24189.05 23890.92 24894.58 26981.21 23091.10 23193.41 27377.03 31393.41 20093.99 25083.23 23697.80 25179.93 29294.80 30593.74 321
diffmvs91.74 18691.93 17791.15 24193.06 29878.17 27888.77 29397.51 11586.28 22292.42 23493.96 25188.04 18597.46 27190.69 13096.67 26697.82 175
CL-MVSNet_self_test90.04 23289.90 22690.47 26195.24 24777.81 28486.60 32992.62 28885.64 23493.25 21093.92 25283.84 23296.06 31879.93 29298.03 21597.53 196
eth_miper_zixun_eth90.72 20690.61 21091.05 24292.04 31776.84 29986.91 31896.67 17785.21 23994.41 17193.92 25279.53 26898.26 21389.76 15997.02 25398.06 144
c3_l91.32 19891.42 19091.00 24692.29 31076.79 30087.52 30996.42 19085.76 23294.72 16693.89 25482.73 24298.16 22290.93 12698.55 15598.04 147
pmmvs587.87 26887.14 27590.07 27393.26 29476.97 29888.89 29092.18 29573.71 32988.36 31093.89 25476.86 29396.73 29880.32 28496.81 26196.51 237
PCF-MVS84.52 1789.12 24687.71 26593.34 16896.06 20585.84 17386.58 33097.31 13068.46 35293.61 19693.89 25487.51 19498.52 19167.85 35798.11 20895.66 276
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TSAR-MVS + GP.93.07 15192.41 16895.06 10295.82 22090.87 7390.97 23392.61 28988.04 19294.61 16793.79 25788.08 18397.81 25089.41 16598.39 17296.50 240
ETH3 D test640091.91 18491.25 19593.89 15196.59 16284.41 18792.10 19397.72 9878.52 30391.82 25093.78 25888.70 17599.13 9183.61 25498.39 17298.14 139
HY-MVS82.50 1886.81 29285.93 29389.47 28293.63 28977.93 28194.02 13291.58 30675.68 31683.64 34493.64 25977.40 28397.42 27471.70 34592.07 34293.05 333
LF4IMVS92.72 16392.02 17494.84 10995.65 23291.99 5692.92 15896.60 18085.08 24592.44 23393.62 26086.80 20996.35 31186.81 21598.25 19296.18 253
Test_1112_low_res87.50 27886.58 28490.25 26896.80 15577.75 28587.53 30896.25 19769.73 34886.47 32793.61 26175.67 29797.88 24279.95 29093.20 32795.11 288
MS-PatchMatch88.05 26687.75 26488.95 29193.28 29277.93 28187.88 30292.49 29175.42 31992.57 23093.59 26280.44 26394.24 34781.28 27792.75 33494.69 299
CNLPA91.72 18791.20 19693.26 17296.17 19691.02 6891.14 22995.55 22690.16 14790.87 26393.56 26386.31 21594.40 34379.92 29497.12 25094.37 305
ppachtmachnet_test88.61 25888.64 24688.50 30091.76 32170.99 34384.59 34292.98 27879.30 29792.38 23693.53 26479.57 26797.45 27286.50 22497.17 24997.07 217
CSCG94.69 10094.75 9794.52 12697.55 11787.87 12795.01 9797.57 10892.68 7396.20 9793.44 26591.92 11798.78 15189.11 17499.24 7896.92 224
NP-MVS96.82 15287.10 14093.40 266
HQP-MVS92.09 18191.49 18993.88 15296.36 17884.89 18391.37 22397.31 13087.16 21088.81 30093.40 26684.76 22798.60 18186.55 22297.73 22898.14 139
test_yl90.11 22789.73 23091.26 23594.09 28079.82 25090.44 24692.65 28690.90 12893.19 21293.30 26873.90 30198.03 22982.23 26896.87 25995.93 262
DCV-MVSNet90.11 22789.73 23091.26 23594.09 28079.82 25090.44 24692.65 28690.90 12893.19 21293.30 26873.90 30198.03 22982.23 26896.87 25995.93 262
CMPMVSbinary68.83 2287.28 28285.67 29592.09 21388.77 35685.42 17890.31 25294.38 25670.02 34788.00 31593.30 26873.78 30394.03 34875.96 32496.54 26896.83 228
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CostFormer83.09 31182.21 31485.73 32689.27 35167.01 35490.35 25086.47 33670.42 34583.52 34693.23 27161.18 35396.85 29477.21 31588.26 35693.34 329
DELS-MVS92.05 18292.16 17091.72 22294.44 27280.13 24187.62 30397.25 13687.34 20892.22 24393.18 27289.54 17098.73 16089.67 16198.20 20096.30 248
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
baseline187.62 27587.31 27088.54 29994.71 26574.27 32393.10 15488.20 32486.20 22392.18 24493.04 27373.21 30495.52 32579.32 29985.82 35995.83 267
BH-untuned90.68 20890.90 20190.05 27595.98 21279.57 25790.04 26194.94 24087.91 19394.07 18093.00 27487.76 19097.78 25479.19 30195.17 29892.80 336
hse-mvs292.24 17891.20 19695.38 8696.16 19790.65 7592.52 17092.01 30289.23 16693.95 18592.99 27576.88 29198.69 16991.02 12296.03 27696.81 229
HyFIR lowres test87.19 28685.51 29692.24 20597.12 14080.51 23685.03 33796.06 20666.11 35891.66 25292.98 27670.12 31599.14 8975.29 32695.23 29797.07 217
AUN-MVS90.05 23188.30 25295.32 9296.09 20390.52 7792.42 17892.05 30182.08 27588.45 30992.86 27765.76 33298.69 16988.91 17896.07 27596.75 233
SCA87.43 27987.21 27388.10 30792.01 31871.98 33889.43 27788.11 32682.26 27488.71 30592.83 27878.65 27397.59 26479.61 29693.30 32694.75 296
Patchmatch-test86.10 29586.01 29286.38 32490.63 33574.22 32489.57 27486.69 33485.73 23389.81 28692.83 27865.24 33691.04 36177.82 31095.78 28393.88 318
MVSFormer92.18 17992.23 16992.04 21594.74 26180.06 24397.15 1397.37 12088.98 17288.83 29892.79 28077.02 28899.60 896.41 496.75 26496.46 242
jason89.17 24588.32 25191.70 22395.73 22780.07 24288.10 30093.22 27571.98 33790.09 27692.79 28078.53 27698.56 18787.43 20797.06 25196.46 242
jason: jason.
PatchmatchNetpermissive85.22 29984.64 29986.98 31889.51 34969.83 35090.52 24487.34 33178.87 30187.22 32492.74 28266.91 32496.53 30281.77 27286.88 35894.58 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
AdaColmapbinary91.63 18991.36 19292.47 20295.56 23786.36 16292.24 19096.27 19688.88 17689.90 28392.69 28391.65 12498.32 20777.38 31497.64 23592.72 338
thisisatest053088.69 25787.52 26892.20 20696.33 18379.36 26092.81 16184.01 35786.44 21993.67 19492.68 28453.62 36799.25 7789.65 16298.45 16698.00 152
miper_ehance_all_eth90.48 21290.42 21590.69 25591.62 32476.57 30286.83 32196.18 20383.38 25894.06 18192.66 28582.20 24898.04 22889.79 15897.02 25397.45 200
cl2289.02 24788.50 24890.59 25989.76 34476.45 30386.62 32894.03 26282.98 26692.65 22792.49 28672.05 30997.53 26688.93 17697.02 25397.78 178
bset_n11_16_dypcd89.99 23389.15 23692.53 19994.75 25981.34 22784.19 34687.56 32985.13 24293.77 19092.46 28772.82 30599.01 11292.46 9099.21 8297.23 214
ADS-MVSNet284.01 30782.20 31589.41 28489.04 35376.37 30587.57 30490.98 30972.71 33584.46 33892.45 28868.08 31896.48 30570.58 35283.97 36195.38 282
ADS-MVSNet82.25 31681.55 31784.34 33789.04 35365.30 36287.57 30485.13 35372.71 33584.46 33892.45 28868.08 31892.33 35770.58 35283.97 36195.38 282
tpm281.46 32280.35 32984.80 33389.90 34365.14 36490.44 24685.36 34865.82 36082.05 35592.44 29057.94 35896.69 29970.71 35188.49 35592.56 339
N_pmnet88.90 25287.25 27293.83 15494.40 27493.81 3684.73 33987.09 33279.36 29593.26 20892.43 29179.29 26991.68 35977.50 31397.22 24896.00 259
alignmvs93.26 14392.85 15494.50 12795.70 22887.45 13293.45 14895.76 21591.58 11495.25 14292.42 29281.96 25298.72 16191.61 11297.87 22497.33 211
CDS-MVSNet89.55 23988.22 25793.53 16495.37 24486.49 15689.26 28393.59 26979.76 28891.15 26092.31 29377.12 28798.38 20277.51 31297.92 22295.71 273
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLCcopyleft85.34 1590.40 21588.92 24194.85 10896.53 17090.02 8191.58 22096.48 18980.16 28586.14 32992.18 29485.73 22198.25 21476.87 31794.61 31096.30 248
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
our_test_387.55 27687.59 26787.44 31591.76 32170.48 34483.83 34990.55 31379.79 28792.06 24792.17 29578.63 27595.63 32384.77 24594.73 30696.22 251
Effi-MVS+-dtu93.90 12892.60 16497.77 494.74 26196.67 394.00 13395.41 23089.94 14991.93 24992.13 29690.12 16098.97 11987.68 20397.48 24097.67 187
PAPM_NR91.03 20190.81 20591.68 22496.73 15681.10 23193.72 14196.35 19488.19 18988.77 30492.12 29785.09 22697.25 28182.40 26793.90 31996.68 234
canonicalmvs94.59 10394.69 10094.30 13695.60 23687.03 14395.59 7298.24 3091.56 11595.21 14592.04 29894.95 5098.66 17491.45 11797.57 23897.20 216
CS-MVS-test93.33 13893.53 14192.71 18995.74 22683.08 20894.55 11698.85 591.02 12789.30 29491.91 29991.79 11999.23 8090.23 14598.41 16895.82 268
MSDG90.82 20390.67 20991.26 23594.16 27783.08 20886.63 32796.19 20290.60 13991.94 24891.89 30089.16 17395.75 32280.96 28394.51 31194.95 292
CS-MVS92.12 18092.62 16290.60 25894.57 27078.12 27992.00 19998.58 1087.75 19990.08 27791.88 30189.79 16799.10 9790.35 13798.60 15394.58 300
sss87.23 28386.82 28088.46 30293.96 28377.94 28086.84 32092.78 28477.59 30887.61 32091.83 30278.75 27291.92 35877.84 30894.20 31795.52 281
CANet_DTU89.85 23689.17 23591.87 21792.20 31380.02 24690.79 23795.87 21286.02 22782.53 35191.77 30380.01 26598.57 18685.66 23297.70 23297.01 221
patchmatchnet-post91.71 30466.22 33197.59 264
PatchMatch-RL89.18 24488.02 26292.64 19295.90 21892.87 4788.67 29791.06 30880.34 28390.03 28091.67 30583.34 23494.42 34276.35 32194.84 30490.64 352
tpmrst82.85 31482.93 31282.64 34387.65 35858.99 37390.14 25887.90 32775.54 31883.93 34291.63 30666.79 32795.36 33181.21 27981.54 36793.57 327
WTY-MVS86.93 29186.50 28988.24 30594.96 25174.64 31687.19 31392.07 30078.29 30588.32 31191.59 30778.06 27994.27 34574.88 32893.15 32995.80 269
DPM-MVS89.35 24288.40 25092.18 21096.13 20284.20 19286.96 31796.15 20575.40 32087.36 32291.55 30883.30 23598.01 23382.17 27096.62 26794.32 307
EPMVS81.17 32680.37 32883.58 34085.58 36965.08 36590.31 25271.34 37377.31 31185.80 33191.30 30959.38 35692.70 35679.99 28982.34 36692.96 334
Fast-Effi-MVS+-dtu92.77 16292.16 17094.58 12594.66 26788.25 11892.05 19596.65 17889.62 15690.08 27791.23 31092.56 10398.60 18186.30 22796.27 27396.90 225
cdsmvs_eth3d_5k23.35 34131.13 3440.00 3590.00 3820.00 3830.00 37095.58 2250.00 3770.00 37891.15 31193.43 770.00 3780.00 3760.00 3760.00 374
lupinMVS88.34 26287.31 27091.45 22994.74 26180.06 24387.23 31192.27 29471.10 34188.83 29891.15 31177.02 28898.53 19086.67 21996.75 26495.76 271
API-MVS91.52 19291.61 18491.26 23594.16 27786.26 16694.66 10894.82 24491.17 12492.13 24591.08 31390.03 16697.06 28779.09 30297.35 24590.45 353
thres600view787.66 27387.10 27789.36 28696.05 20673.17 32892.72 16385.31 34991.89 9693.29 20590.97 31463.42 34498.39 20073.23 33696.99 25896.51 237
thres100view90087.35 28186.89 27988.72 29696.14 19973.09 33093.00 15685.31 34992.13 8993.26 20890.96 31563.42 34498.28 20971.27 34896.54 26894.79 294
tpmvs84.22 30683.97 30584.94 33287.09 36465.18 36391.21 22888.35 32182.87 26785.21 33290.96 31565.24 33696.75 29779.60 29885.25 36092.90 335
xiu_mvs_v1_base_debu91.47 19391.52 18691.33 23295.69 22981.56 22389.92 26596.05 20783.22 26091.26 25790.74 31791.55 12698.82 13989.29 16795.91 27993.62 324
xiu_mvs_v1_base91.47 19391.52 18691.33 23295.69 22981.56 22389.92 26596.05 20783.22 26091.26 25790.74 31791.55 12698.82 13989.29 16795.91 27993.62 324
xiu_mvs_v1_base_debi91.47 19391.52 18691.33 23295.69 22981.56 22389.92 26596.05 20783.22 26091.26 25790.74 31791.55 12698.82 13989.29 16795.91 27993.62 324
1112_ss88.42 26087.41 26991.45 22996.69 15780.99 23289.72 27196.72 17573.37 33087.00 32590.69 32077.38 28498.20 21781.38 27693.72 32295.15 286
ab-mvs-re7.56 34410.08 3480.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 37890.69 3200.00 3820.00 3780.00 3760.00 3760.00 374
Effi-MVS+92.79 16092.74 15892.94 18195.10 24983.30 20394.00 13397.53 11291.36 11989.35 29390.65 32294.01 6998.66 17487.40 20895.30 29596.88 227
mvs-test193.07 15191.80 18196.89 3994.74 26195.83 692.17 19195.41 23089.94 14989.85 28490.59 32390.12 16098.88 12987.68 20395.66 28595.97 260
GA-MVS87.70 27186.82 28090.31 26593.27 29377.22 29384.72 34192.79 28385.11 24489.82 28590.07 32466.80 32597.76 25784.56 24894.27 31695.96 261
EPNet_dtu85.63 29784.37 30189.40 28586.30 36774.33 32291.64 21988.26 32284.84 24972.96 37089.85 32571.27 31297.69 26176.60 31997.62 23696.18 253
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PAPM81.91 32180.11 33187.31 31693.87 28672.32 33784.02 34893.22 27569.47 34976.13 36889.84 32672.15 30897.23 28253.27 37089.02 35392.37 341
tfpn200view987.05 28986.52 28788.67 29795.77 22372.94 33191.89 20686.00 34190.84 13092.61 22889.80 32763.93 34198.28 20971.27 34896.54 26894.79 294
thres40087.20 28586.52 28789.24 29095.77 22372.94 33191.89 20686.00 34190.84 13092.61 22889.80 32763.93 34198.28 20971.27 34896.54 26896.51 237
TR-MVS87.70 27187.17 27489.27 28894.11 27979.26 26288.69 29591.86 30381.94 27690.69 26789.79 32982.82 24197.42 27472.65 34091.98 34391.14 349
new_pmnet81.22 32481.01 32381.86 34590.92 33370.15 34684.03 34780.25 36870.83 34385.97 33089.78 33067.93 32184.65 36967.44 35891.90 34490.78 351
PAPR87.65 27486.77 28290.27 26792.85 30277.38 29088.56 29896.23 19976.82 31584.98 33589.75 33186.08 21897.16 28472.33 34193.35 32596.26 250
CLD-MVS91.82 18591.41 19193.04 17596.37 17683.65 20086.82 32297.29 13384.65 25292.27 24289.67 33292.20 11097.85 24883.95 25299.47 3997.62 190
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tpm cat180.61 33179.46 33384.07 33988.78 35565.06 36689.26 28388.23 32362.27 36581.90 35789.66 33362.70 34995.29 33471.72 34480.60 36891.86 346
pmmvs380.83 32878.96 33586.45 32187.23 36377.48 28984.87 33882.31 36163.83 36385.03 33489.50 33449.66 36993.10 35373.12 33895.10 29988.78 358
miper_enhance_ethall88.42 26087.87 26390.07 27388.67 35775.52 31285.10 33695.59 22375.68 31692.49 23189.45 33578.96 27097.88 24287.86 20197.02 25396.81 229
KD-MVS_2432*160082.17 31880.75 32586.42 32282.04 37570.09 34781.75 35690.80 31082.56 26890.37 27289.30 33642.90 37796.11 31674.47 32992.55 33793.06 331
miper_refine_blended82.17 31880.75 32586.42 32282.04 37570.09 34781.75 35690.80 31082.56 26890.37 27289.30 33642.90 37796.11 31674.47 32992.55 33793.06 331
PVSNet_Blended88.74 25688.16 26090.46 26394.81 25678.80 27286.64 32696.93 15674.67 32288.68 30789.18 33886.27 21698.15 22380.27 28596.00 27794.44 304
dp79.28 33478.62 33681.24 34685.97 36856.45 37486.91 31885.26 35172.97 33381.45 35989.17 33956.01 36395.45 32973.19 33776.68 36991.82 347
ET-MVSNet_ETH3D86.15 29484.27 30391.79 21993.04 29981.28 22887.17 31486.14 33879.57 29183.65 34388.66 34057.10 35998.18 22087.74 20295.40 29295.90 265
xiu_mvs_v2_base89.00 24989.19 23488.46 30294.86 25474.63 31786.97 31695.60 21980.88 28087.83 31788.62 34191.04 14298.81 14482.51 26694.38 31291.93 344
Fast-Effi-MVS+91.28 19990.86 20392.53 19995.45 24082.53 21489.25 28596.52 18785.00 24689.91 28288.55 34292.94 9298.84 13784.72 24795.44 29196.22 251
thres20085.85 29685.18 29787.88 31194.44 27272.52 33589.08 28786.21 33788.57 18391.44 25488.40 34364.22 33998.00 23468.35 35695.88 28293.12 330
BH-w/o87.21 28487.02 27887.79 31294.77 25877.27 29287.90 30193.21 27781.74 27789.99 28188.39 34483.47 23396.93 29271.29 34792.43 33989.15 354
MAR-MVS90.32 22188.87 24494.66 11794.82 25591.85 5994.22 12594.75 24780.91 27987.52 32188.07 34586.63 21297.87 24576.67 31896.21 27494.25 308
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
EIA-MVS92.35 17492.03 17393.30 17195.81 22283.97 19692.80 16298.17 4087.71 20089.79 28787.56 34691.17 14199.18 8587.97 19897.27 24696.77 231
baseline283.38 30981.54 31888.90 29291.38 32772.84 33388.78 29281.22 36478.97 29979.82 36387.56 34661.73 35297.80 25174.30 33190.05 35296.05 258
MVS84.98 30284.30 30287.01 31791.03 33077.69 28791.94 20394.16 26059.36 36784.23 34187.50 34885.66 22296.80 29671.79 34393.05 33286.54 360
PS-MVSNAJ88.86 25388.99 24088.48 30194.88 25274.71 31586.69 32595.60 21980.88 28087.83 31787.37 34990.77 14598.82 13982.52 26594.37 31391.93 344
131486.46 29386.33 29086.87 31991.65 32374.54 31891.94 20394.10 26174.28 32484.78 33787.33 35083.03 23895.00 33778.72 30391.16 34891.06 350
thisisatest051584.72 30382.99 31189.90 27792.96 30175.33 31484.36 34483.42 35977.37 31088.27 31286.65 35153.94 36598.72 16182.56 26497.40 24395.67 275
test0.0.03 182.48 31581.47 31985.48 32889.70 34573.57 32784.73 33981.64 36383.07 26488.13 31486.61 35262.86 34789.10 36766.24 36190.29 35193.77 320
IB-MVS77.21 1983.11 31081.05 32189.29 28791.15 32975.85 30985.66 33386.00 34179.70 28982.02 35686.61 35248.26 37198.39 20077.84 30892.22 34093.63 323
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
MVEpermissive59.87 2373.86 33872.65 34177.47 35187.00 36674.35 32161.37 36860.93 37667.27 35569.69 37186.49 35481.24 26072.33 37256.45 36983.45 36385.74 361
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PVSNet76.22 2082.89 31382.37 31384.48 33693.96 28364.38 36878.60 36188.61 31971.50 33984.43 34086.36 35574.27 30094.60 33969.87 35493.69 32394.46 303
ETV-MVS92.99 15392.74 15893.72 15695.86 21986.30 16492.33 18497.84 8791.70 11292.81 22286.17 35692.22 10999.19 8488.03 19797.73 22895.66 276
cascas87.02 29086.28 29189.25 28991.56 32676.45 30384.33 34596.78 17071.01 34286.89 32685.91 35781.35 25696.94 29083.09 25995.60 28694.35 306
PMMVS83.00 31281.11 32088.66 29883.81 37486.44 15982.24 35585.65 34461.75 36682.07 35485.64 35879.75 26691.59 36075.99 32393.09 33087.94 359
CHOSEN 280x42080.04 33377.97 33886.23 32590.13 34174.53 31972.87 36489.59 31666.38 35776.29 36785.32 35956.96 36095.36 33169.49 35594.72 30788.79 357
test-LLR83.58 30883.17 30984.79 33489.68 34666.86 35783.08 35184.52 35483.07 26482.85 34984.78 36062.86 34793.49 35182.85 26094.86 30294.03 312
test-mter81.21 32580.01 33284.79 33489.68 34666.86 35783.08 35184.52 35473.85 32882.85 34984.78 36043.66 37693.49 35182.85 26094.86 30294.03 312
gm-plane-assit87.08 36559.33 37271.22 34083.58 36297.20 28373.95 332
TESTMET0.1,179.09 33578.04 33782.25 34487.52 36064.03 36983.08 35180.62 36670.28 34680.16 36283.22 36344.13 37590.56 36279.95 29093.36 32492.15 342
E-PMN80.72 33080.86 32480.29 34885.11 37068.77 35272.96 36381.97 36287.76 19883.25 34883.01 36462.22 35089.17 36677.15 31694.31 31582.93 364
EMVS80.35 33280.28 33080.54 34784.73 37269.07 35172.54 36580.73 36587.80 19781.66 35881.73 36562.89 34689.84 36475.79 32594.65 30982.71 365
DWT-MVSNet_test80.74 32979.18 33485.43 32987.51 36166.87 35689.87 26886.01 34074.20 32680.86 36080.62 36648.84 37096.68 30181.54 27483.14 36592.75 337
test_method50.44 33948.94 34254.93 35439.68 37812.38 38028.59 36990.09 3146.82 37241.10 37478.41 36754.41 36470.69 37350.12 37151.26 37381.72 367
PVSNet_070.34 2174.58 33772.96 34079.47 34990.63 33566.24 36173.26 36283.40 36063.67 36478.02 36578.35 36872.53 30689.59 36556.68 36860.05 37282.57 366
GG-mvs-BLEND83.24 34285.06 37171.03 34294.99 9965.55 37574.09 36975.51 36944.57 37494.46 34159.57 36787.54 35784.24 362
DeepMVS_CXcopyleft53.83 35570.38 37764.56 36748.52 37933.01 37165.50 37274.21 37056.19 36246.64 37438.45 37370.07 37050.30 370
tmp_tt37.97 34044.33 34318.88 35611.80 37921.54 37963.51 36745.66 3804.23 37351.34 37350.48 37159.08 35722.11 37544.50 37268.35 37113.00 371
X-MVStestdata90.70 20788.45 24997.44 1798.56 3893.99 2796.50 3297.95 7894.58 4194.38 17326.89 37294.56 5999.39 4893.57 4499.05 10198.93 63
testmvs9.02 34311.42 3461.81 3582.77 3811.13 38279.44 3601.90 3811.18 3762.65 3776.80 3731.95 3810.87 3772.62 3753.45 3753.44 373
test1239.49 34212.01 3451.91 3572.87 3801.30 38182.38 3541.34 3821.36 3752.84 3766.56 3742.45 3800.97 3762.73 3745.56 3743.47 372
test_post6.07 37565.74 33395.84 321
test_post190.21 2545.85 37665.36 33496.00 31979.61 296
test_blank0.00 3460.00 3490.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 3770.00 3820.00 3780.00 3760.00 3760.00 374
uanet_test0.00 3460.00 3490.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 3770.00 3820.00 3780.00 3760.00 3760.00 374
pcd_1.5k_mvsjas7.56 34410.09 3470.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 37790.77 1450.00 3780.00 3760.00 3760.00 374
sosnet-low-res0.00 3460.00 3490.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 3770.00 3820.00 3780.00 3760.00 3760.00 374
sosnet0.00 3460.00 3490.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 3770.00 3820.00 3780.00 3760.00 3760.00 374
uncertanet0.00 3460.00 3490.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 3770.00 3820.00 3780.00 3760.00 3760.00 374
Regformer0.00 3460.00 3490.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 3770.00 3820.00 3780.00 3760.00 3760.00 374
uanet0.00 3460.00 3490.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 3770.00 3820.00 3780.00 3760.00 3760.00 374
FOURS199.21 394.68 1298.45 498.81 697.73 698.27 20
MSC_two_6792asdad95.90 6496.54 16789.57 9096.87 16499.41 3694.06 3099.30 6598.72 91
No_MVS95.90 6496.54 16789.57 9096.87 16499.41 3694.06 3099.30 6598.72 91
eth-test20.00 382
eth-test0.00 382
IU-MVS98.51 4786.66 15396.83 16772.74 33495.83 11493.00 7699.29 6898.64 101
save fliter97.46 12388.05 12392.04 19697.08 14787.63 203
test_0728_SECOND94.88 10798.55 4186.72 15095.20 8898.22 3299.38 5493.44 5599.31 6398.53 112
GSMVS94.75 296
test_part298.21 7189.41 9596.72 71
sam_mvs166.64 32894.75 296
sam_mvs66.41 329
MTGPAbinary97.62 102
MTMP94.82 10254.62 378
test9_res88.16 19398.40 16997.83 173
agg_prior287.06 21498.36 18097.98 156
agg_prior96.20 19388.89 10496.88 16290.21 27498.78 151
test_prior489.91 8490.74 238
test_prior94.61 11895.95 21487.23 13697.36 12598.68 17197.93 162
旧先验290.00 26368.65 35192.71 22696.52 30385.15 237
新几何290.02 262
无先验89.94 26495.75 21670.81 34498.59 18381.17 28094.81 293
原ACMM289.34 280
testdata298.03 22980.24 287
segment_acmp92.14 111
testdata188.96 28988.44 185
test1294.43 13395.95 21486.75 14996.24 19889.76 28889.79 16798.79 14797.95 22097.75 182
plane_prior797.71 10588.68 108
plane_prior697.21 13488.23 11986.93 205
plane_prior597.81 9098.95 12289.26 17098.51 16298.60 108
plane_prior388.43 11790.35 14593.31 203
plane_prior294.56 11491.74 109
plane_prior197.38 126
plane_prior88.12 12193.01 15588.98 17298.06 212
n20.00 383
nn0.00 383
door-mid92.13 299
test1196.65 178
door91.26 307
HQP5-MVS84.89 183
HQP-NCC96.36 17891.37 22387.16 21088.81 300
ACMP_Plane96.36 17891.37 22387.16 21088.81 300
BP-MVS86.55 222
HQP4-MVS88.81 30098.61 17998.15 138
HQP3-MVS97.31 13097.73 228
HQP2-MVS84.76 227
MDTV_nov1_ep13_2view42.48 37888.45 29967.22 35683.56 34566.80 32572.86 33994.06 311
ACMMP++_ref98.82 131
ACMMP++99.25 76
Test By Simon90.61 151