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-ACMMP97.53 197.44 197.77 598.54 1494.08 698.20 1398.21 1195.12 597.97 298.48 394.19 199.68 1596.64 299.64 299.57 1
Skip Steuart: Steuart Systems R&D Blog.
NCCC97.30 297.03 398.11 298.77 995.06 297.34 3298.04 2295.96 297.09 597.88 1693.18 399.71 1095.84 799.17 1499.56 2
CP-MVS97.02 396.81 497.64 799.33 193.54 898.80 198.28 992.99 1996.45 798.30 891.90 899.85 195.61 899.68 199.54 3
DNCC-MVS96.95 496.60 698.01 399.03 794.93 397.72 1898.10 1891.50 2698.01 198.32 792.33 699.58 2094.85 1199.51 599.53 4
DeepC-MVS_fast93.89 296.93 596.64 597.78 498.64 1194.30 497.41 2898.04 2294.81 696.59 698.37 591.24 1099.64 1795.16 1099.52 499.42 7
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS96.77 696.46 897.71 698.40 1894.07 798.21 1298.45 589.86 4297.11 498.01 1492.52 599.69 1396.03 499.53 399.36 8
MVS_111021_HR96.68 796.58 796.99 1498.46 1592.31 1996.20 6398.90 194.30 1095.86 997.74 2292.33 699.38 3296.04 399.42 899.28 10
DELS-MVS96.61 896.38 997.30 1097.79 3293.19 1095.96 6898.18 1295.23 495.87 897.65 2491.45 999.70 1295.87 599.44 799.00 19
DeepPCF-MVS93.97 196.61 897.09 295.15 5198.09 2786.63 8196.00 6798.15 1395.43 397.95 398.56 293.40 299.36 3396.77 199.48 699.45 5
ACMMPcopyleft96.27 1095.93 1197.28 1299.24 392.62 1498.25 1198.81 292.99 1994.56 1898.39 488.96 1799.85 194.57 1397.63 3699.36 8
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
MVS_111021_LR96.24 1196.19 1096.39 3098.23 2491.35 3496.24 6298.79 393.99 1295.80 1097.65 2489.92 1299.24 3895.87 599.20 1398.58 28
DeepC-MVS93.07 396.06 1295.66 1397.29 1197.96 2993.17 1197.30 3398.06 2093.92 1393.38 3198.66 186.83 2999.73 795.60 999.22 1298.96 21
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CSCG96.05 1395.91 1296.46 2899.24 390.47 4998.30 898.57 489.01 5393.97 2697.57 2892.62 499.76 594.66 1299.27 1199.15 12
DP-MVS Recon95.68 1495.12 1697.37 999.19 594.19 597.03 3998.08 1988.35 6595.09 1397.65 2489.97 1199.48 2892.08 2898.59 2398.44 37
MG-MVS95.61 1595.38 1496.31 3398.42 1790.53 4896.04 6697.48 3493.47 1695.67 1198.10 1089.17 1599.25 3791.27 3598.77 1899.13 14
CPTT-MVS95.57 1695.19 1596.70 1799.27 291.48 3198.33 798.11 1787.79 7295.17 1298.03 1287.09 2799.61 1893.51 1699.42 899.02 16
3Dnovator+91.43 495.40 1794.48 2698.16 196.90 5095.34 198.48 497.87 2594.65 788.53 8898.02 1383.69 4799.71 1093.18 2298.96 1699.44 6
PVSNet_Blended_VisFu95.27 1894.91 1896.38 3198.20 2590.86 4497.27 3498.25 1090.21 3694.18 2497.27 3187.48 2599.73 793.53 1597.77 3598.55 29
Vis-MVSNetpermissive95.23 1994.81 1996.51 2497.18 4291.58 3098.26 1098.12 1494.38 994.90 1498.15 982.28 5798.92 5091.45 3398.58 2499.01 18
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet95.22 2095.04 1795.76 4597.49 3989.56 5598.67 297.00 5690.69 3294.24 2397.62 2789.79 1398.81 5293.39 2196.49 4898.92 24
EPNet95.20 2194.56 2297.14 1392.80 11092.68 1397.85 1694.87 10496.64 192.46 4297.80 1986.23 3399.65 1693.72 1498.62 2299.10 15
3Dnovator91.36 595.19 2294.44 2797.44 896.56 5593.36 998.65 398.36 694.12 1189.25 8198.06 1182.20 5899.77 493.41 2099.32 1099.18 11
OMC-MVS95.09 2394.70 2096.25 3798.46 1591.28 3596.43 5497.57 3392.04 2394.77 1697.96 1587.01 2899.09 4591.31 3496.77 4598.36 41
PAPM_NR95.01 2494.59 2196.26 3698.89 890.68 4697.24 3597.73 2791.80 2492.93 3996.62 4989.13 1699.14 4189.21 4797.78 3498.97 20
IS-MVSNet94.90 2594.52 2496.05 4097.67 3590.56 4798.44 596.22 7893.21 1793.99 2597.74 2285.55 3798.45 6389.98 4197.86 3299.14 13
PVSNet_Blended94.87 2694.56 2295.81 4498.27 2289.46 5795.47 7798.36 688.84 5794.36 2096.09 6088.02 2199.58 2093.44 1898.18 2898.40 39
API-MVS94.84 2794.49 2595.90 4297.90 3192.00 2497.80 1797.48 3489.19 5094.81 1596.71 4388.84 1899.17 4088.91 5098.76 1996.53 76
WTY-MVS94.71 2894.02 2896.79 1697.71 3492.05 2396.59 5097.35 4690.61 3594.64 1796.93 4086.41 3299.39 3191.20 3794.71 6098.94 22
sss94.51 2993.80 3196.64 1897.07 4691.97 2596.32 5898.06 2088.94 5694.50 1996.78 4284.60 4299.27 3691.90 2996.02 5198.68 27
AdaColmapbinary94.34 3093.68 3296.31 3398.59 1291.68 2796.59 5097.81 2689.87 4192.15 4897.06 3983.62 4899.54 2489.34 4598.07 3097.70 59
CNLPA94.28 3193.53 3496.52 2298.38 2092.55 1696.59 5096.88 6490.13 3891.91 5197.24 3285.21 3899.09 4587.64 5697.83 3397.92 51
MAR-MVS94.22 3293.46 3596.51 2498.00 2892.19 2297.67 1997.47 3688.13 6893.00 3495.84 6584.86 4199.51 2687.99 5498.17 2997.83 55
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
PAPR94.18 3393.42 3896.48 2697.64 3691.42 3395.55 7497.71 2988.99 5492.34 4795.82 6689.19 1499.11 4386.14 6697.38 4098.90 25
Vis-MVSNet (Re-imp)94.15 3493.88 3094.95 5697.61 3787.92 6998.10 1495.80 8492.22 2293.02 3397.45 3084.53 4497.91 8888.24 5297.97 3199.02 16
CDS-MVSNet94.14 3593.54 3395.93 4196.18 6791.46 3296.33 5797.04 5588.97 5593.56 2796.51 5187.55 2497.89 8989.80 4395.95 5298.44 37
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLCcopyleft91.00 694.11 3693.43 3796.13 3998.58 1391.15 4096.69 4697.39 4287.29 7791.37 5896.71 4388.39 2099.52 2587.33 5897.13 4397.73 57
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet_BlendedMVS94.06 3793.92 2994.47 6398.27 2289.46 5796.73 4498.36 690.17 3794.36 2095.24 7388.02 2199.58 2093.44 1890.72 8894.36 103
UGNet94.04 3893.28 3996.31 3396.85 5191.19 3697.88 1597.68 3094.40 893.00 3496.18 5873.39 9999.61 1891.72 3198.46 2598.13 45
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
TAMVS94.01 3993.46 3595.64 4696.16 6990.45 5096.71 4596.89 6389.27 4993.46 3096.92 4187.29 2697.94 8588.70 5195.74 5498.53 30
114514_t93.95 4093.06 4096.63 2099.07 691.61 2897.46 2797.96 2477.99 11493.00 3497.57 2886.14 3499.33 3489.22 4699.15 1598.94 22
HY-MVS89.66 993.87 4192.95 4296.63 2097.10 4592.49 1895.64 7296.64 7089.05 5293.00 3495.79 6885.77 3699.45 3089.16 4994.35 6197.96 50
F-COLMAP93.58 4292.98 4195.37 4998.40 1888.98 6297.18 3797.29 4787.75 7390.49 6297.10 3885.21 3899.50 2786.70 6396.72 4697.63 60
ab-mvs93.57 4392.55 4796.64 1897.28 4091.96 2695.40 7897.45 4089.81 4493.22 3296.28 5579.62 7299.46 2990.74 3893.11 7098.50 33
LS3D93.57 4392.61 4596.47 2797.59 3891.61 2897.67 1997.72 2885.17 8590.29 6398.34 684.60 4299.73 783.85 8398.27 2798.06 49
QAPM93.45 4592.27 5296.98 1596.77 5392.62 1498.39 698.12 1484.50 9188.27 9097.77 2082.39 5699.81 385.40 7298.81 1798.51 32
1112_ss93.37 4692.42 5096.21 3897.05 4790.99 4196.31 5996.72 6786.87 7989.83 7196.69 4586.51 3199.14 4188.12 5393.67 6598.50 33
MVSTER93.20 4792.81 4394.37 6596.56 5589.59 5497.06 3897.12 5191.24 3191.30 5995.96 6182.02 6098.05 8293.48 1790.55 8995.47 84
CLD-MVS92.98 4892.53 4894.32 6796.12 7289.20 6095.28 8097.47 3692.66 2189.90 7095.62 7080.58 6598.40 6492.73 2392.40 7395.38 86
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ACMM89.79 892.96 4992.50 4994.35 6696.30 6488.71 6497.58 2297.36 4591.40 2890.53 6196.65 4779.77 7098.75 5491.24 3691.64 8295.59 83
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test92.94 5092.56 4694.10 7096.16 6988.26 6597.65 2197.46 3891.29 2990.12 6797.16 3579.05 7598.73 5592.25 2491.89 7995.31 89
BH-untuned92.94 5092.62 4493.92 7597.22 4186.16 8396.40 5596.25 7790.06 3989.79 7296.17 5983.19 5098.35 6787.19 6097.27 4297.24 68
PatchMatch-RL92.90 5292.02 5495.56 4798.19 2690.80 4595.27 8197.18 5087.96 6991.86 5395.68 6980.44 6698.99 4884.01 8197.54 3896.89 71
PMMVS92.86 5392.34 5194.42 6494.92 9186.73 7994.53 8496.38 7484.78 8994.27 2295.12 7583.13 5198.40 6491.47 3296.49 4898.12 46
OpenMVScopyleft89.19 1292.86 5391.68 6096.40 2995.34 8092.73 1298.27 998.12 1484.86 8885.78 10197.75 2178.89 7799.74 687.50 5798.65 2196.73 73
Test_1112_low_res92.84 5591.84 5795.85 4397.04 4889.97 5195.53 7596.64 7085.38 8489.65 7395.18 7485.86 3599.10 4487.70 5593.58 6998.49 35
DP-MVS92.76 5691.51 6196.52 2298.77 990.99 4197.38 3096.08 8082.38 10189.29 7997.87 1783.77 4699.69 1381.37 9596.69 4798.89 26
BH-RMVSNet92.72 5791.97 5594.97 5497.16 4387.99 6896.15 6495.60 8990.62 3491.87 5297.15 3778.41 8198.57 5983.16 8497.60 3798.36 41
ACMP89.59 1092.62 5892.14 5394.05 7296.40 6288.20 6797.36 3197.25 4991.52 2588.30 8996.64 4878.46 8098.72 5791.86 3091.48 8495.23 95
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TAPA-MVS90.10 792.30 5991.22 6695.56 4798.33 2189.60 5396.79 4397.65 3181.83 10391.52 5597.23 3387.94 2398.91 5171.31 11598.37 2698.17 44
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IterMVS-LS92.29 6091.94 5693.34 8296.25 6586.97 7896.57 5397.05 5490.67 3389.50 7494.80 8086.59 3097.64 9389.91 4286.11 11195.40 85
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet86.66 1892.24 6191.74 5993.73 7897.77 3383.69 9792.88 10496.72 6787.91 7093.00 3494.86 7878.51 7999.05 4786.53 6497.45 3998.47 36
XXY-MVS92.16 6291.23 6594.95 5694.75 9790.94 4397.47 2697.43 4189.14 5188.90 8396.43 5379.71 7198.24 7289.56 4487.68 10595.67 82
BH-w/o92.14 6391.75 5893.31 8396.99 4985.73 8495.67 7095.69 8688.73 6289.26 8094.82 7982.97 5298.07 8185.26 7396.32 5096.13 80
PatchmatchNetpermissive91.91 6491.35 6293.59 8095.38 7884.11 9393.15 10195.39 9189.54 4692.10 4993.68 9282.82 5398.13 7784.81 7795.32 5698.52 31
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
FMVSNet391.78 6590.69 7095.03 5296.53 5792.27 2097.02 4096.93 5989.79 4589.35 7694.65 8477.01 8597.47 9686.12 6788.82 9795.35 87
EPNet_dtu91.71 6691.28 6492.99 8693.76 10683.71 9696.69 4695.28 9693.15 1887.02 9895.95 6283.37 4997.38 10079.46 10096.84 4497.88 54
PCF-MVS89.48 1191.56 6789.95 7996.36 3296.60 5492.52 1792.51 10797.26 4879.41 11088.90 8396.56 5084.04 4599.55 2377.01 10797.30 4197.01 69
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PAPM91.52 6890.30 7395.20 5095.30 8289.83 5293.38 10096.85 6586.26 8288.59 8795.80 6784.88 4098.15 7675.67 10995.93 5397.63 60
TR-MVS91.48 6990.59 7194.16 6996.40 6287.33 7395.67 7095.34 9587.68 7491.46 5695.52 7276.77 8698.35 6782.85 8793.61 6896.79 72
tpmrst91.44 7091.32 6391.79 9795.15 8779.20 11393.42 9995.37 9288.55 6393.49 2993.67 9382.49 5498.27 7190.41 4089.34 9597.90 52
MSDG91.42 7190.24 7694.96 5597.15 4488.91 6393.69 9596.32 7685.72 8386.93 9996.47 5280.24 6798.98 4980.57 9795.05 5896.98 70
GBi-Net91.35 7290.27 7494.59 5996.51 5891.18 3797.50 2396.93 5988.82 5989.35 7694.51 8573.87 9597.29 10186.12 6788.82 9795.31 89
test191.35 7290.27 7494.59 5996.51 5891.18 3797.50 2396.93 5988.82 5989.35 7694.51 8573.87 9597.29 10186.12 6788.82 9795.31 89
FMVSNet291.31 7490.08 7794.99 5396.51 5892.21 2197.41 2896.95 5788.82 5988.62 8594.75 8173.87 9597.42 9885.20 7488.55 10295.35 87
cascas91.20 7590.08 7794.58 6294.97 9089.16 6193.65 9797.59 3279.90 10989.40 7592.92 10075.36 9198.36 6692.14 2694.75 5996.23 78
CostFormer91.18 7690.70 6992.62 8994.84 9481.76 10394.09 9094.43 10784.15 9292.72 4193.77 9179.43 7398.20 7390.70 3992.18 7597.90 52
HyFIR91.07 7790.50 7292.77 8896.08 7383.37 9891.52 10997.09 5284.77 9089.13 8290.06 11282.20 5896.76 10984.95 7598.67 2098.18 43
LTVRE_ROB88.41 1390.99 7889.92 8094.19 6896.18 6789.55 5696.31 5997.09 5287.88 7185.67 10295.91 6378.79 7898.57 5981.50 9489.98 9194.44 102
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
CR-MVSNet90.82 7989.77 8293.95 7394.45 10187.19 7590.23 11495.68 8786.89 7892.40 4392.36 10580.91 6397.05 10581.09 9693.95 6397.60 62
RPSCF90.75 8090.86 6790.42 11096.84 5276.29 11595.61 7396.34 7583.89 9591.38 5797.87 1776.45 8798.78 5387.16 6192.23 7496.20 79
EPMVS90.70 8189.81 8193.37 8194.73 9884.21 9293.67 9688.02 12389.50 4792.38 4593.49 9477.82 8397.78 9086.03 7092.68 7298.11 48
ACMH87.59 1690.53 8289.42 8593.87 7696.21 6687.92 6997.24 3596.94 5888.45 6483.91 10796.27 5671.92 10198.62 5884.43 7989.43 9495.05 96
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROBcopyleft87.81 1590.40 8389.28 8693.79 7797.95 3087.13 7796.92 4295.89 8382.83 10086.88 10097.18 3473.77 9899.29 3578.44 10493.62 6794.95 97
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tpm90.25 8489.74 8391.76 9993.92 10579.73 11193.98 9193.54 11588.28 6691.99 5093.25 9877.51 8497.44 9787.30 5987.94 10498.12 46
ACMH+87.92 1490.20 8589.18 8893.25 8496.48 6186.45 8296.99 4196.68 6988.83 5884.79 10496.22 5770.16 10598.53 6184.42 8088.04 10394.77 100
IterMVS90.15 8689.67 8491.61 10195.48 7683.72 9594.33 8696.12 7989.99 4087.31 9694.15 8875.78 9096.27 11286.97 6286.89 11094.83 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm289.96 8789.21 8792.23 9394.91 9281.25 10693.78 9494.42 10880.62 10891.56 5493.44 9676.44 8897.94 8585.60 7192.08 7797.49 66
IB-MVS87.33 1789.91 8888.28 9194.79 5895.26 8487.70 7295.12 8293.95 11489.35 4887.03 9792.49 10370.74 10499.19 3989.18 4881.37 11997.49 66
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
FMVSNet189.88 8988.31 9094.59 5995.41 7791.18 3797.50 2396.93 5986.62 8087.41 9594.51 8565.94 11097.29 10183.04 8587.43 10795.31 89
tpmp4_e2389.58 9088.59 8992.54 9095.16 8681.53 10494.11 8995.09 10081.66 10488.60 8693.44 9675.11 9298.33 7082.45 9191.72 8197.75 56
USDC88.94 9187.83 9392.27 9294.66 9984.96 8993.86 9395.90 8287.34 7683.40 10995.56 7167.43 10898.19 7482.64 9089.67 9393.66 107
dp88.90 9288.26 9290.81 10794.58 10076.62 11492.85 10594.93 10285.12 8690.07 6993.07 9975.81 8998.12 7980.53 9887.42 10897.71 58
PatchT88.87 9387.42 9493.22 8594.08 10485.10 8789.51 11794.64 10581.92 10292.36 4688.15 11780.05 6997.01 10872.43 11293.65 6697.54 65
Patchmtry88.64 9487.25 9592.78 8794.09 10386.64 8089.82 11695.68 8780.81 10787.63 9392.36 10580.91 6397.03 10778.86 10285.12 11394.67 101
RPMNet88.52 9586.72 10093.95 7394.45 10187.19 7590.23 11494.99 10177.87 11692.40 4387.55 11980.17 6897.05 10568.84 11793.95 6397.60 62
MIMVSNet88.50 9686.76 9993.72 7994.84 9487.77 7191.39 11094.05 11186.41 8187.99 9192.59 10263.27 11495.82 11577.44 10592.84 7197.57 64
tpm cat188.36 9787.21 9791.81 9695.13 8880.55 10792.58 10695.70 8574.97 11987.45 9491.96 10878.01 8298.17 7580.39 9988.74 10096.72 74
JIA-IIPM88.26 9887.04 9891.91 9493.52 10781.42 10589.38 11894.38 10980.84 10690.93 6080.74 12279.22 7497.92 8782.76 8891.62 8396.38 77
LF4IMVS87.94 9987.25 9589.98 11292.38 11480.05 11094.38 8595.25 9987.59 7584.34 10594.74 8264.31 11397.66 9284.83 7687.45 10692.23 114
FMVSNet587.29 10085.79 10291.78 9894.80 9687.28 7495.49 7695.28 9684.09 9383.85 10891.82 10962.95 11594.17 11878.48 10385.34 11293.91 106
EG-PatchMatch MVS87.02 10185.44 10391.76 9992.67 11285.00 8896.08 6596.45 7383.41 9979.52 11893.49 9457.10 12097.72 9179.34 10190.87 8792.56 111
TinyColmap86.82 10285.35 10491.21 10394.91 9282.99 9993.94 9294.02 11383.58 9881.56 11394.68 8362.34 11698.13 7775.78 10887.35 10992.52 112
TDRefinement86.53 10384.76 10791.85 9582.23 12384.25 9196.38 5695.35 9384.97 8784.09 10694.94 7665.76 11198.34 6984.60 7874.52 12192.97 110
test_040286.46 10484.79 10691.45 10295.02 8985.55 8596.29 6194.89 10380.90 10582.21 11193.97 8968.21 10797.29 10162.98 12088.68 10191.51 117
DSMNet-mixed86.34 10586.12 10187.00 11589.88 11870.43 11994.93 8390.08 12177.97 11585.42 10392.78 10174.44 9493.96 11974.43 11095.14 5796.62 75
UnsupCasMVSNet_eth85.99 10684.45 10890.62 10889.97 11782.40 10193.62 9897.37 4489.86 4278.59 11992.37 10465.25 11295.35 11682.27 9370.75 12494.10 104
CMPMVSbinary62.92 2185.62 10784.92 10587.74 11489.14 12173.12 11894.17 8796.80 6673.98 12073.65 12394.93 7766.36 10997.61 9583.95 8291.28 8592.48 113
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_082.17 1985.46 10883.64 10990.92 10595.27 8379.49 11290.55 11395.60 8983.76 9783.00 11089.95 11371.09 10297.97 8382.75 8960.79 12695.31 89
MIMVSNet184.93 10983.05 11090.56 10989.56 12084.84 9095.40 7895.35 9383.91 9480.38 11692.21 10757.23 11993.34 12070.69 11682.75 11793.50 108
OpenMVS_ROBcopyleft81.14 2084.42 11082.28 11190.83 10690.06 11684.05 9495.73 6994.04 11273.89 12180.17 11791.53 11159.15 11897.64 9366.92 11989.05 9690.80 118
LP84.13 11181.85 11290.97 10493.20 10882.12 10287.68 12094.27 11076.80 11781.93 11288.52 11472.97 10095.95 11459.53 12381.73 11894.84 98
new_pmnet82.89 11281.12 11488.18 11389.63 11980.18 10991.77 10892.57 11776.79 11875.56 12288.23 11661.22 11794.48 11771.43 11482.92 11589.87 119
MVS-HIRNet82.47 11381.21 11386.26 11695.38 7869.21 12288.96 11989.49 12266.28 12480.79 11574.08 12468.48 10697.39 9971.93 11395.47 5592.18 115
UnsupCasMVSNet_bld82.13 11479.46 11590.14 11188.00 12282.47 10090.89 11296.62 7278.94 11175.61 12184.40 12156.63 12196.31 11177.30 10666.77 12591.63 116
HyFIR lowres test81.01 11579.31 11686.13 11793.19 10974.03 11686.45 12293.06 11668.57 12376.44 12087.03 12043.72 12496.27 11273.27 11192.05 7888.09 120
N_pmnet78.73 11678.71 11778.79 11992.80 11046.50 13094.14 8843.71 13078.61 11280.83 11491.66 11074.94 9396.36 11067.24 11884.45 11493.50 108
FPMVS71.27 11769.85 11875.50 12174.64 12459.03 12691.30 11191.50 11858.80 12657.92 12588.28 11529.98 12785.53 12453.43 12582.84 11681.95 122
no-one68.12 11863.78 12081.13 11874.01 12570.22 12187.61 12190.71 12072.63 12253.13 12771.89 12530.29 12691.45 12161.53 12232.21 13081.72 123
Gipumacopyleft67.86 11965.41 11975.18 12292.66 11373.45 11766.50 12894.52 10653.33 12757.80 12666.07 12830.81 12589.20 12248.15 12878.88 12062.90 129
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high63.94 12059.58 12177.02 12061.24 13066.06 12385.66 12387.93 12478.53 11342.94 12871.04 12625.42 12880.71 12652.60 12630.83 13184.28 121
PNet_i23d59.01 12155.87 12268.44 12573.98 12651.37 12881.36 12482.41 12752.37 12842.49 12970.39 12711.39 12979.99 12849.77 12738.71 12873.97 126
PMVScopyleft53.92 2258.58 12255.40 12368.12 12651.00 13148.64 12978.86 12687.10 12546.77 13035.84 13174.28 1238.76 13086.34 12342.07 12973.91 12269.38 127
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d56.92 12351.11 12474.38 12462.30 12961.47 12580.09 12584.87 12649.62 12930.80 13257.20 1307.03 13182.94 12555.69 12432.36 12978.72 125
MVEpermissive50.73 2353.25 12448.81 12566.58 12765.34 12857.50 12772.49 12770.94 12940.15 13139.28 13063.51 1296.89 13373.48 12938.29 13042.38 12768.76 128
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d25.11 12524.57 12626.74 12873.98 12639.89 13157.88 1299.80 13112.27 13210.39 1336.97 1317.03 13136.44 13025.43 13117.39 1323.89 130
ab-mvs-re8.06 12610.74 1270.00 1290.00 1320.00 1320.00 1300.00 1320.00 1330.00 13496.69 450.00 1340.00 1310.00 1320.00 1330.00 131
DeepMVS_CXcopyleft74.68 12390.84 11564.34 12481.61 12865.34 12567.47 12488.01 11848.60 12380.13 12762.33 12173.68 12379.58 124
HQP-MVS51.25 122
door91.13 119
HQP5-MVS89.33 59
HQP-NCC95.86 7496.65 4893.55 1490.14 64
ITE_SJBPF92.43 9195.34 8085.37 8695.92 8191.47 2787.75 9296.39 5471.00 10397.96 8482.36 9289.86 9293.97 105
ACMP_Plane95.86 7496.65 4893.55 1490.14 64
BP-MVS92.13 27
ACMMP++91.02 86
HQP4-MVS90.14 6498.50 6295.78 81
HQP3-MVS97.39 4292.10 76
HQP2-MVS80.95 62
ACMMP++_ref90.30 90
Test By Simon88.73 19
MDTV_nov1_ep1390.76 6895.22 8580.33 10893.03 10395.28 9688.14 6792.84 4093.83 9081.34 6198.08 8082.86 8694.34 62
NP-MVS95.87 64
MDTV_nov1_ep13_2view70.35 12093.10 10283.88 9693.55 2882.47 5586.25 6598.38 40
LGP-MVS_train94.10 7096.16 6988.26 6597.46 3891.29 2990.12 6797.16 3579.05 7598.73 5592.25 2491.89 7995.31 89