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.
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SteuartSystems-ACMMP98.90 198.75 199.36 499.22 2498.43 499.10 1698.87 2197.38 799.35 199.40 297.78 199.87 1397.77 699.85 299.78 2
Skip Steuart: Steuart Systems R&D Blog.
NCCC98.61 298.35 499.38 299.28 1798.61 298.45 5298.76 3097.82 398.45 1398.93 3396.65 299.83 1897.38 1599.41 2999.71 8
CP-MVS98.57 398.36 399.19 599.66 597.86 899.34 798.87 2195.96 1998.60 1099.13 1596.05 599.94 197.77 699.86 199.77 3
DeepC-MVS_fast96.70 198.55 498.34 599.18 699.25 2098.04 798.50 5098.78 2897.72 498.92 599.28 895.27 1099.82 2097.55 1399.77 599.69 11
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_HR98.47 598.34 598.88 1799.22 2497.32 1797.91 8299.58 197.20 898.33 1599.00 2895.99 699.64 4198.05 399.76 1099.69 11
DELS-MVS98.40 698.20 898.99 1399.00 3297.66 1197.75 8898.89 1897.71 598.33 1598.97 3094.97 1499.88 1298.42 199.76 1099.42 32
HPM-MVS_fast98.38 798.13 999.12 999.75 197.86 899.44 498.82 2494.46 4698.94 499.20 1395.16 1199.74 3497.58 1099.85 299.77 3
HPM-MVS98.36 898.10 1099.13 899.74 297.82 1099.53 198.80 2794.63 4098.61 998.97 3095.13 1299.77 2997.65 999.83 499.79 1
APD-MVS98.35 998.00 1299.42 199.51 898.72 198.80 3498.82 2494.52 4399.23 399.25 1095.54 799.80 2596.52 2999.77 599.74 6
PHI-MVS98.34 1098.06 1199.18 699.15 2898.12 699.04 1899.09 893.32 7598.83 699.10 1996.54 399.83 1897.70 899.76 1099.59 16
MVS_111021_LR98.34 1098.23 798.67 2299.27 1896.90 2497.95 8199.58 197.14 998.44 1499.01 2795.03 1399.62 4397.91 499.75 1499.50 22
ACMMPcopyleft98.23 1297.95 1399.09 1099.74 297.62 1499.03 1999.41 395.98 1897.60 3199.36 594.45 1899.93 297.14 1798.85 4699.70 10
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
DeepPCF-MVS96.37 297.93 1398.48 296.30 9099.00 3289.54 11297.43 9998.87 2198.16 299.26 299.38 396.12 499.64 4198.30 299.77 599.72 7
DeepC-MVS95.98 397.88 1497.58 1698.77 1899.25 2096.93 2298.83 2898.75 3296.96 1296.89 4899.50 190.46 5199.87 1397.84 599.76 1099.52 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DP-MVS Recon97.86 1597.46 1899.06 1299.53 798.35 598.33 5598.89 1892.62 8798.05 1998.94 3295.34 999.65 3996.04 3499.42 2899.19 45
CSCG97.85 1697.74 1498.20 4299.67 495.16 6199.22 1099.32 493.04 7997.02 4298.92 3495.36 899.91 797.43 1499.64 1699.52 19
MG-MVS97.81 1797.60 1598.44 3399.12 3095.97 4497.75 8898.78 2896.89 1398.46 1299.22 1193.90 2499.68 3794.81 5399.52 2399.67 14
CPTT-MVS97.72 1897.32 2198.92 1599.64 697.10 2099.12 1598.81 2692.34 9598.09 1899.08 2393.01 2999.92 496.06 3399.77 599.75 5
PVSNet_Blended_VisFu97.70 1997.46 1898.44 3399.27 1895.91 4898.63 4299.16 794.48 4597.67 2998.88 3592.80 3199.91 797.11 1899.12 3699.50 22
MVSFormer97.57 2097.49 1797.84 5298.07 6495.76 5099.47 298.40 6294.98 3198.79 798.83 3792.34 3498.41 10796.91 2199.59 1999.34 35
OMC-MVS97.55 2197.34 2098.20 4299.33 1395.92 4798.28 6198.59 4495.52 2297.97 2299.10 1993.28 2899.49 5395.09 5098.88 4399.19 45
PAPM_NR97.46 2297.11 2698.50 2999.50 996.41 3698.63 4298.60 4395.18 2997.06 4098.06 7194.26 2099.57 4693.80 7198.87 4599.52 19
EPP-MVSNet97.46 2297.28 2297.99 4898.64 4595.38 5699.33 898.31 7093.61 7097.19 3699.07 2494.05 2199.23 5996.89 2398.43 5599.37 34
3Dnovator94.51 597.46 2296.93 3199.07 1197.78 7497.64 1299.35 699.06 997.02 1193.75 9899.16 1489.25 6099.92 497.22 1699.75 1499.64 15
CNLPA97.45 2597.03 2998.73 1999.05 3197.44 1698.07 7598.53 5095.32 2596.80 5398.53 4993.32 2799.72 3594.31 6299.31 3299.02 55
lupinMVS97.44 2697.22 2498.12 4698.07 6495.76 5097.68 9297.76 9494.50 4498.79 798.61 4392.34 3499.30 5797.58 1099.59 1999.31 38
3Dnovator+94.38 697.43 2796.78 3799.38 297.83 7298.52 399.37 598.71 3497.09 1092.99 10899.13 1589.36 5999.89 1096.97 2099.57 2199.71 8
Vis-MVSNetpermissive97.42 2897.11 2698.34 3898.66 4496.23 4099.22 1099.00 1196.63 1598.04 2099.21 1288.05 7499.35 5696.01 3599.21 3499.45 30
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS97.41 2997.25 2397.91 5098.70 4296.80 2798.82 2998.69 3694.53 4298.11 1798.28 6494.50 1799.57 4694.12 6599.49 2497.37 93
sss97.39 3096.98 3098.61 2498.60 4896.61 3298.22 6498.93 1393.97 5498.01 2198.48 5491.98 4099.85 1696.45 3098.15 5799.39 33
PVSNet_Blended97.38 3197.12 2598.14 4599.25 2095.35 5897.28 10499.26 593.13 7897.94 2398.21 6792.74 3299.81 2196.88 2499.40 3199.27 42
WTY-MVS97.37 3296.92 3298.72 2098.86 3696.89 2698.31 5998.71 3495.26 2797.67 2998.56 4892.21 3799.78 2795.89 3796.85 6899.48 25
jason97.32 3397.08 2898.06 4797.45 9295.59 5397.87 8697.91 9094.79 3798.55 1198.83 3791.12 4799.23 5997.58 1099.60 1899.34 35
jason: jason.
EPNet97.28 3496.87 3498.51 2894.98 13196.14 4298.90 2397.02 11998.28 195.99 6899.11 1791.36 4599.89 1096.98 1999.19 3599.50 22
IS-MVSNet97.22 3596.88 3398.25 4198.85 3796.36 3899.19 1497.97 8995.39 2497.23 3598.99 2991.11 4898.93 8594.60 5598.59 5299.47 26
PLCcopyleft95.07 497.20 3696.78 3798.44 3399.29 1596.31 3998.14 6998.76 3092.41 9396.39 5998.31 6394.92 1599.78 2794.06 6698.77 4999.23 44
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D97.16 3796.66 4198.68 2198.53 5297.19 1998.93 2198.90 1692.83 8695.99 6899.37 492.12 3999.87 1393.67 7499.57 2198.97 57
AdaColmapbinary97.15 3896.70 3998.48 3199.16 2796.69 3098.01 7898.89 1894.44 4796.83 4998.68 4290.69 5099.76 3094.36 6099.29 3398.98 56
F-COLMAP97.09 3996.80 3597.97 4999.45 1194.95 6598.55 4698.62 4293.02 8096.17 6398.58 4794.01 2299.81 2193.95 6898.90 4299.14 50
TAMVS97.02 4096.79 3697.70 5798.06 6695.31 6098.52 4798.31 7093.95 5597.05 4198.61 4393.49 2698.52 10295.33 4597.81 6099.29 41
CDS-MVSNet96.99 4196.69 4097.90 5198.05 6795.98 4398.20 6598.33 6993.67 6896.95 4398.49 5393.54 2598.42 10395.24 4997.74 6299.31 38
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
114514_t96.93 4296.27 4998.92 1599.50 997.63 1398.85 2798.90 1684.80 13197.77 2699.11 1792.84 3099.66 3894.85 5199.77 599.47 26
MAR-MVS96.91 4396.40 4598.45 3298.69 4396.90 2498.66 4198.68 3792.40 9497.07 3997.96 7691.54 4499.75 3293.68 7398.92 4198.69 67
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
Vis-MVSNet (Re-imp)96.87 4496.55 4397.83 5398.73 4095.46 5599.20 1398.30 7294.96 3396.60 5698.87 3690.05 5598.59 9993.67 7498.60 5199.46 29
PAPR96.84 4596.24 5098.65 2398.72 4196.92 2397.36 10198.57 4693.33 7496.67 5597.57 9094.30 1999.56 4891.05 10198.59 5299.47 26
HY-MVS93.96 896.82 4696.23 5198.57 2598.46 5397.00 2198.14 6998.21 7893.95 5596.72 5497.99 7591.58 4399.76 3094.51 5996.54 7298.95 59
UGNet96.78 4796.30 4898.19 4498.24 5695.89 4998.88 2598.93 1397.39 696.81 5297.84 8082.60 10899.90 996.53 2899.49 2498.79 63
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
PVSNet_BlendedMVS96.73 4896.60 4297.12 6399.25 2095.35 5898.26 6299.26 594.28 4897.94 2397.46 9192.74 3299.81 2196.88 2493.32 10696.20 121
1112_ss96.63 4996.00 5498.50 2998.56 4996.37 3798.18 6798.10 8692.92 8194.84 7598.43 5792.14 3899.58 4594.35 6196.51 7399.56 18
PMMVS96.60 5096.33 4797.41 6097.90 7093.93 7297.35 10298.41 6192.84 8597.76 2797.45 9291.10 4999.20 6196.26 3197.91 5999.11 52
PatchMatch-RL96.59 5196.03 5398.27 4099.31 1496.51 3497.91 8299.06 993.72 6396.92 4798.06 7188.50 7099.65 3991.77 9599.00 3998.66 70
DP-MVS96.59 5195.93 5598.57 2599.34 1296.19 4198.70 4098.39 6489.45 11494.52 8099.35 691.85 4199.85 1692.89 8198.88 4399.68 13
XVG-OURS96.55 5396.41 4496.99 6898.75 3993.76 7497.50 9798.52 5195.67 2096.83 4999.30 788.95 6699.53 5195.88 3896.26 7997.69 89
XVG-OURS-SEG-HR96.51 5496.34 4697.02 6798.77 3893.76 7497.79 8798.50 5395.45 2396.94 4499.09 2287.87 7699.55 5096.76 2695.83 8697.74 87
ab-mvs96.42 5595.71 5898.55 2798.63 4696.75 2997.88 8598.74 3393.84 5896.54 5898.18 6885.34 9299.75 3295.93 3696.35 7799.15 48
PVSNet91.96 1896.35 5696.15 5296.96 6999.17 2692.05 9196.08 12298.68 3793.69 6597.75 2897.80 8288.86 6799.69 3694.26 6399.01 3899.15 48
Test_1112_low_res96.34 5795.66 6198.36 3798.56 4995.94 4597.71 9098.07 8792.10 9794.79 7697.29 9591.75 4299.56 4894.17 6496.50 7499.58 17
QAPM96.29 5895.40 6398.96 1497.85 7197.60 1599.23 998.93 1389.76 11293.11 10799.02 2689.11 6499.93 291.99 9299.62 1799.34 35
MVSTER96.06 5995.72 5697.08 6598.23 5795.93 4698.73 3798.27 7494.86 3695.07 7398.09 7088.21 7198.54 10196.59 2793.46 10396.79 100
test_djsdf96.00 6095.69 5996.93 7095.72 12595.49 5499.47 298.40 6294.98 3194.58 7897.86 7889.16 6398.41 10796.91 2194.12 9696.88 96
BH-untuned95.95 6195.72 5696.65 7498.55 5192.26 8998.23 6397.79 9393.73 6294.62 7798.01 7388.97 6599.00 8093.04 7998.51 5498.68 68
MSDG95.93 6295.30 7097.83 5398.90 3495.36 5796.83 11898.37 6691.32 10294.43 8498.73 4190.27 5399.60 4490.05 10898.82 4798.52 75
BH-RMVSNet95.92 6395.32 6997.69 5898.32 5594.64 6798.19 6697.45 10294.56 4196.03 6698.61 4385.02 9499.12 6590.68 10499.06 3799.30 40
OpenMVScopyleft93.04 1395.83 6495.00 7498.32 3997.18 10497.32 1799.21 1298.97 1289.96 11091.14 11899.05 2586.64 8299.92 493.38 7799.47 2697.73 88
PatchmatchNetpermissive95.71 6595.52 6296.29 9197.58 8290.72 10596.84 11797.52 9994.06 5097.08 3796.96 10589.24 6198.90 8692.03 9198.37 5699.26 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMM93.85 995.69 6695.38 6696.61 7597.61 8093.84 7398.91 2298.44 5895.25 2894.28 8898.47 5586.04 8799.12 6595.50 4393.95 9896.87 97
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst95.63 6795.69 5995.44 10497.54 8588.54 12496.97 11197.56 9593.50 7197.52 3296.93 10689.49 5899.16 6295.25 4896.42 7698.64 71
CLD-MVS95.62 6895.34 6796.46 8497.52 8793.75 7697.27 10598.46 5695.53 2194.42 8598.00 7486.21 8498.97 8196.25 3294.37 9196.66 111
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test95.62 6895.34 6796.47 8297.46 9093.54 7898.99 2098.54 4894.67 3894.36 8698.77 3985.39 9099.11 7095.71 4094.15 9496.76 102
PatchFormer-LS_test95.47 7095.27 7196.08 9697.59 8190.66 10698.10 7497.34 10893.98 5396.08 6496.15 12087.65 7899.12 6595.27 4795.24 9098.44 80
IterMVS-LS95.46 7195.21 7296.22 9398.12 6393.72 7798.32 5898.13 8393.71 6494.26 8997.31 9492.24 3698.10 11494.63 5490.12 12996.84 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
BH-w/o95.38 7295.08 7396.26 9298.34 5491.79 9497.70 9197.43 10492.87 8494.24 9097.22 9788.66 6898.84 9191.55 9697.70 6398.16 85
TAPA-MVS93.98 795.35 7394.56 8097.74 5699.13 2994.83 6698.33 5598.64 4186.62 12296.29 6198.61 4394.00 2399.29 5880.00 13399.41 2999.09 53
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP93.49 1095.34 7494.98 7596.43 8597.67 7793.48 8098.73 3798.44 5894.94 3592.53 11198.53 4984.50 9899.14 6395.48 4494.00 9796.66 111
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROBcopyleft93.27 1295.33 7594.87 7796.71 7299.29 1593.24 8598.58 4498.11 8589.92 11193.57 10099.10 1986.37 8399.79 2690.78 10398.10 5897.09 94
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EPNet_dtu95.21 7694.95 7695.99 9796.17 12290.45 10898.16 6897.27 11196.77 1493.14 10698.33 6190.34 5298.42 10385.57 12598.81 4899.09 53
HyFIR95.20 7794.82 7896.37 8798.22 5891.97 9396.35 12198.55 4791.40 10194.99 7494.05 13490.14 5497.71 12790.18 10799.46 2799.44 31
XXY-MVS95.20 7794.45 8297.46 5996.75 11396.56 3398.86 2698.65 4093.30 7793.27 10598.27 6584.85 9798.87 8894.82 5291.26 12596.96 95
EPMVS94.99 7994.48 8196.52 8197.22 10091.75 9597.23 10691.66 14394.11 4997.28 3496.81 10785.70 8898.84 9193.04 7997.28 6698.97 57
FMVSNet394.97 8094.26 8697.11 6498.18 6296.62 3198.56 4598.26 7693.67 6894.09 9497.10 9884.25 10098.01 11592.08 8792.14 11596.70 108
CostFormer94.95 8194.73 7995.60 10297.28 9889.06 11797.53 9596.89 12289.66 11396.82 5196.72 11086.05 8698.95 8495.53 4296.13 8398.79 63
PAPM94.95 8194.00 9097.78 5597.04 10695.65 5296.03 12398.25 7791.23 10494.19 9297.80 8291.27 4698.86 9082.61 12997.61 6498.84 62
TR-MVS94.94 8394.20 8797.17 6297.75 7594.14 7197.59 9497.02 11992.28 9695.75 7097.64 8783.88 10498.96 8289.77 11196.15 8298.40 81
RPSCF94.87 8495.40 6393.26 12998.89 3582.06 13798.33 5598.06 8890.30 10996.56 5799.26 987.09 7999.49 5393.82 7096.32 7898.24 84
Test494.85 8594.40 8396.20 9497.35 9590.79 10298.11 7296.57 12892.91 8295.33 7197.00 10382.00 10999.12 6594.52 5795.78 8798.70 65
PatchFormer_test94.82 8694.36 8496.20 9497.35 9590.79 10298.34 5396.57 12892.91 8295.33 7196.44 11382.00 10999.12 6594.52 5795.78 8798.70 65
CR-MVSNet94.76 8794.15 8896.59 7697.00 10793.43 8194.96 12997.56 9592.46 8996.93 4596.24 11688.15 7297.88 12487.38 11996.65 7098.46 78
PCF-MVS93.45 1194.68 8893.43 10398.42 3698.62 4796.77 2895.48 12798.20 7984.63 13293.34 10498.32 6288.55 6999.81 2184.80 12798.96 4098.68 68
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
cascas94.63 8993.86 9596.93 7096.91 11094.27 6996.00 12498.51 5285.55 12894.54 7996.23 11884.20 10198.87 8895.80 3996.98 6797.66 90
LTVRE_ROB92.95 1594.60 9093.90 9396.68 7397.41 9494.42 6898.52 4798.59 4491.69 9991.21 11798.35 5984.87 9699.04 7891.06 10093.44 10496.60 114
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
ACMH92.88 1694.55 9193.95 9296.34 8997.63 7893.26 8498.81 3398.49 5593.43 7389.74 12798.53 4981.91 11199.08 7493.69 7293.30 10796.70 108
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PT_06_test94.55 9194.32 8595.25 10796.26 12190.75 10498.34 5398.39 6493.45 7293.41 10396.44 11389.97 5698.24 11391.88 9490.75 12696.77 101
XVG-ACMP-BASELINE94.54 9394.14 8995.75 10196.55 11791.65 9698.11 7298.44 5894.96 3394.22 9197.90 7779.18 12099.11 7094.05 6793.85 9996.48 120
test194.49 9493.80 9796.56 7898.21 5995.00 6298.82 2998.18 8092.46 8994.09 9497.07 10081.16 11397.95 11892.08 8792.14 11596.72 105
GBi-Net94.49 9493.80 9796.56 7898.21 5995.00 6298.82 2998.18 8092.46 8994.09 9497.07 10081.16 11397.95 11892.08 8792.14 11596.72 105
FMVSNet294.47 9693.61 10297.04 6698.21 5996.43 3598.79 3598.27 7492.46 8993.50 10297.09 9981.16 11398.00 11691.09 9991.93 11896.70 108
ACMH+92.99 1494.30 9793.77 10095.88 9997.81 7392.04 9298.71 3998.37 6693.99 5290.60 12398.47 5580.86 11799.05 7592.75 8392.40 11496.55 117
OurMVSNet-221017-094.21 9894.00 9094.85 11495.60 12889.22 11698.89 2497.43 10495.29 2692.18 11598.52 5282.86 10798.59 9993.46 7691.76 12196.74 104
tpm294.19 9993.76 10195.46 10397.23 9989.04 11897.31 10396.85 12587.08 12196.21 6296.79 10883.75 10598.74 9792.43 8596.23 8098.59 73
dp94.15 10093.90 9394.90 11397.31 9786.82 13196.97 11197.19 11491.22 10596.02 6796.61 11185.51 8999.02 7990.00 10994.30 9298.85 61
tpm94.13 10193.80 9795.12 11096.50 11887.91 12997.44 9895.89 13392.62 8796.37 6096.30 11584.13 10298.30 11093.24 7891.66 12299.14 50
IterMVS94.09 10293.85 9694.80 11597.99 6890.35 10997.18 10798.12 8493.68 6792.46 11397.34 9384.05 10397.41 13092.51 8491.33 12396.62 113
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpmp4_e2393.91 10393.42 10495.38 10697.62 7988.59 12397.52 9697.34 10887.94 11994.17 9396.79 10882.91 10699.05 7590.62 10595.91 8498.50 76
tpm cat193.36 10492.80 10695.07 11197.58 8287.97 12896.76 11997.86 9182.17 13693.53 10196.04 12286.13 8599.13 6489.24 11695.87 8598.10 86
JIA-IIPM93.35 10592.49 11095.92 9896.48 11990.65 10795.01 12896.96 12185.93 12696.08 6487.33 14087.70 7798.78 9691.35 9895.58 8998.34 82
SixPastTwentyTwo93.34 10692.86 10594.75 11695.67 12689.41 11498.75 3696.67 12793.89 5790.15 12598.25 6680.87 11698.27 11290.90 10290.64 12796.57 115
USDC93.33 10792.71 10895.21 10896.83 11190.83 10196.91 11497.50 10093.84 5890.72 12298.14 6977.69 12398.82 9389.51 11493.21 11095.97 125
IB-MVS91.98 1793.27 10891.97 11497.19 6197.47 8993.41 8397.09 10995.99 13193.32 7592.47 11295.73 12578.06 12199.53 5194.59 5682.98 13998.62 72
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
MIMVSNet93.26 10992.21 11296.41 8697.73 7693.13 8695.65 12697.03 11891.27 10394.04 9796.06 12175.33 13097.19 13286.56 12296.23 8098.92 60
Patchmtry93.22 11092.35 11195.84 10096.77 11293.09 8794.66 13497.56 9587.37 12092.90 10996.24 11688.15 7297.90 12287.37 12090.10 13096.53 118
FMVSNet193.19 11192.07 11396.56 7897.54 8595.00 6298.82 2998.18 8090.38 10892.27 11497.07 10073.68 13597.95 11889.36 11591.30 12496.72 105
LF4IMVS93.14 11292.79 10794.20 12395.88 12388.67 12297.66 9397.07 11693.81 6091.71 11697.65 8677.96 12298.81 9491.47 9791.92 11995.12 130
PatchT93.06 11391.97 11496.35 8896.69 11492.67 8894.48 13597.08 11586.62 12297.08 3792.23 13787.94 7597.90 12278.89 13596.69 6998.49 77
K. test v392.55 11491.91 11694.48 12095.64 12789.24 11599.07 1794.88 13594.04 5186.78 13497.59 8977.64 12597.64 12892.08 8789.43 13296.57 115
DSMNet-mixed92.52 11592.58 10992.33 13394.15 13582.65 13598.30 6094.26 13889.08 11792.65 11095.73 12585.01 9595.76 13686.24 12397.76 6198.59 73
RPMNet92.52 11591.17 11896.59 7697.00 10793.43 8194.96 12997.26 11282.27 13596.93 4592.12 13886.98 8097.88 12476.32 13996.65 7098.46 78
TinyColmap92.31 11791.53 11794.65 11796.92 10989.75 11196.92 11396.68 12690.45 10789.62 12897.85 7976.06 12998.81 9486.74 12192.51 11395.41 129
FMVSNet591.81 11890.92 11994.49 11997.21 10192.09 9098.00 8097.55 9889.31 11590.86 12195.61 12774.48 13495.32 13885.57 12589.70 13196.07 123
test_040291.32 11990.27 12094.48 12096.60 11591.12 9998.50 5097.22 11386.10 12588.30 13196.98 10477.65 12497.99 11778.13 13792.94 11294.34 133
PVSNet_088.72 1991.28 12090.03 12295.00 11297.99 6887.29 13094.84 13298.50 5392.06 9889.86 12695.19 12879.81 11899.39 5592.27 8669.79 14498.33 83
EG-PatchMatch MVS91.13 12190.12 12194.17 12594.73 13389.00 11998.13 7197.81 9289.22 11685.32 13996.46 11267.71 13898.42 10387.89 11893.82 10095.08 131
LP91.12 12289.99 12394.53 11896.35 12088.70 12193.86 13797.35 10784.88 13090.98 11994.77 13084.40 9997.43 12975.41 14091.89 12097.47 91
TDRefinement91.06 12389.68 12595.21 10885.35 14191.49 9798.51 4997.07 11691.47 10088.83 12997.84 8077.31 12699.09 7392.79 8277.98 14295.04 132
UnsupCasMVSNet_eth90.99 12489.92 12494.19 12494.08 13689.83 11097.13 10898.67 3993.69 6585.83 13796.19 11975.15 13196.74 13489.14 11779.41 14196.00 124
new_pmnet90.06 12589.00 12893.22 13094.18 13488.32 12796.42 12096.89 12286.19 12485.67 13893.62 13577.18 12797.10 13381.61 13189.29 13394.23 134
CMPMVSbinary66.06 2189.70 12689.67 12689.78 13493.19 13776.56 14097.00 11098.35 6880.97 13781.57 14297.75 8474.75 13398.61 9889.85 11093.63 10194.17 135
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet189.67 12788.28 13093.82 12692.81 13991.08 10098.01 7897.45 10287.95 11887.90 13395.87 12467.63 13994.56 13978.73 13688.18 13695.83 128
MVS-HIRNet89.46 12888.40 12992.64 13197.58 8282.15 13694.16 13693.05 14275.73 14290.90 12082.52 14279.42 11998.33 10983.53 12898.68 5097.43 92
OpenMVS_ROBcopyleft86.42 2089.00 12987.43 13293.69 12793.08 13889.42 11397.91 8296.89 12278.58 14085.86 13694.69 13169.48 13798.29 11177.13 13893.29 10893.36 137
testpf88.74 13089.09 12787.69 13595.78 12483.16 13484.05 14494.13 14085.22 12990.30 12494.39 13374.92 13295.80 13589.77 11193.28 10984.10 142
HyFIR lowres test87.96 13186.43 13392.55 13296.56 11683.82 13392.80 13896.03 13079.62 13988.30 13192.88 13652.77 14297.95 11882.45 13096.43 7595.89 126
UnsupCasMVSNet_bld87.17 13285.12 13493.31 12891.94 14088.77 12094.92 13198.30 7284.30 13382.30 14190.04 13963.96 14197.25 13185.85 12474.47 14393.93 136
N_pmnet87.12 13387.77 13185.17 13895.46 13061.92 14597.37 10070.66 14985.83 12788.73 13096.04 12285.33 9397.76 12680.02 13290.48 12895.84 127
Gipumacopyleft78.40 13476.75 13683.38 13995.54 12980.43 13879.42 14597.40 10664.67 14473.46 14380.82 14445.65 14393.14 14166.32 14387.43 13776.56 147
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS77.62 13577.14 13579.05 14179.25 14360.97 14695.79 12595.94 13265.96 14367.93 14494.40 13237.73 14488.88 14368.83 14288.46 13587.29 139
no-one74.41 13670.76 13785.35 13779.88 14276.83 13994.68 13394.22 13980.33 13863.81 14579.73 14535.45 14593.36 14071.78 14136.99 14985.86 141
ANet_high69.08 13765.37 13880.22 14065.99 14871.96 14290.91 13990.09 14582.62 13449.93 14878.39 14629.36 14681.75 14662.49 14638.52 14886.95 140
PNet_i23d67.70 13865.07 13975.60 14378.61 14459.61 14789.14 14088.24 14661.83 14552.37 14680.89 14318.91 14784.91 14562.70 14552.93 14682.28 143
PMVScopyleft61.03 2365.95 13963.57 14073.09 14557.90 14951.22 14885.05 14393.93 14154.45 14744.32 14983.57 14113.22 14889.15 14258.68 14781.00 14078.91 146
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d63.73 14058.86 14278.35 14267.62 14767.90 14386.56 14187.81 14758.26 14642.49 15070.28 14811.55 14985.05 14463.66 14441.50 14782.11 144
MVEpermissive62.14 2263.28 14159.38 14174.99 14474.33 14665.47 14485.55 14280.50 14852.02 14851.10 14775.00 14710.91 15180.50 14751.60 14853.40 14578.99 145
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d30.17 14230.18 14330.16 14678.61 14443.29 14966.79 14614.21 15017.31 14914.82 15111.93 14911.55 14941.43 14837.08 14919.30 1505.76 148
ab-mvs-re8.20 14310.94 1440.00 1470.00 1500.00 1500.00 1470.00 1510.00 1500.00 15298.43 570.00 1520.00 1490.00 1500.00 1510.00 149
DeepMVS_CXcopyleft86.78 13697.09 10572.30 14195.17 13475.92 14184.34 14095.19 12870.58 13695.35 13779.98 13489.04 13492.68 138
HQP4-MVS94.45 8198.96 8296.87 97
lessismore_v094.45 12294.93 13288.44 12591.03 14486.77 13597.64 8776.23 12898.42 10390.31 10685.64 13896.51 119
LGP-MVS_train96.47 8297.46 9093.54 7898.54 4894.67 3894.36 8698.77 3985.39 9099.11 7095.71 4094.15 9496.76 102
door94.64 136
HQP5-MVS94.25 70
HQP-NCC97.20 10298.05 7696.43 1694.45 81
ACMP_Plane97.20 10298.05 7696.43 1694.45 81
BP-MVS95.30 46
HQP3-MVS98.46 5694.18 93
ITE_SJBPF95.44 10497.42 9391.32 9897.50 10095.09 3093.59 9998.35 5981.70 11298.88 8789.71 11393.39 10596.12 122
HQP2-MVS86.75 81
NP-MVS97.73 85
MDTV_nov1_ep13_2view84.26 13296.89 11590.97 10697.90 2589.89 5793.91 6999.18 47
MDTV_nov1_ep1395.40 6397.48 8888.34 12696.85 11697.29 11093.74 6197.48 3397.26 9689.18 6299.05 7591.92 9397.43 65
ACMMP++_ref92.97 111
ACMMP++93.61 102
HQP-MVS66.68 140
Test By Simon94.64 16
door-mid94.37 137