This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.




Method Infoallhigh-res
multi-view
indooroutdoorbotani.boulde.bridgedoorexhibi.lectur.living.loungeobserv.old co.statueterrac.
sort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
SteuartSystems-ACMMP94.94 195.02 194.69 697.22 1486.17 896.92 697.20 690.90 395.04 397.27 387.92 299.34 493.93 298.80 298.57 3
Skip Steuart: Steuart Systems R&D Blog.
NCCC94.81 294.69 395.17 297.83 887.46 295.66 1996.93 1292.34 293.94 496.58 1087.74 499.44 292.83 398.40 898.62 1
CP-MVS94.34 394.21 494.74 598.39 186.64 597.60 197.24 488.53 1692.73 797.23 485.20 1099.32 692.15 698.83 198.25 7
DNCC-MVS94.24 494.07 594.75 498.06 686.90 395.88 1696.94 1185.68 3095.05 297.18 587.31 599.07 1591.90 898.61 698.28 6
DeepPCF-MVS89.96 194.20 594.77 292.49 3196.52 2180.00 6794.00 4897.08 890.05 695.65 197.29 289.66 198.97 2193.95 198.71 498.50 4
DeepC-MVS_fast89.43 294.04 693.79 694.80 397.48 1186.78 495.65 2096.89 1389.40 992.81 696.97 785.37 999.24 990.87 1198.69 598.38 5
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS93.89 793.65 794.62 796.84 1886.43 696.69 1097.49 185.15 3493.56 596.28 1485.60 899.31 792.45 498.79 398.12 9
MVS_111021_HR93.45 893.31 893.84 1496.99 1684.84 1593.24 5797.24 488.76 1591.60 995.85 2086.07 798.66 2891.91 798.16 1198.03 13
DELS-MVS93.43 993.25 993.97 1295.42 3885.04 1493.06 6097.13 790.74 591.84 895.09 3386.32 699.21 1091.22 1098.45 797.65 17
DeepC-MVS88.79 393.31 1092.99 1194.26 1096.07 2885.83 1094.89 3096.99 1089.02 1289.56 1897.37 182.51 1299.38 392.20 598.30 997.57 20
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMPcopyleft93.24 1192.88 1294.30 998.09 585.33 1296.86 797.45 288.33 1790.15 1497.03 681.44 1799.51 190.85 1295.74 3098.04 12
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
CSCG93.23 1293.05 1093.76 1598.04 784.07 2296.22 1297.37 384.15 3790.05 1595.66 2487.77 399.15 1489.91 1398.27 1098.07 10
MVS_111021_LR92.47 1392.29 1392.98 2195.99 3084.43 2093.08 5996.09 2988.20 1891.12 1095.72 2381.33 1897.76 5291.74 997.37 1696.75 30
3Dnovator+87.14 492.42 1491.37 1795.55 195.63 3688.73 197.07 496.77 1690.84 484.02 7396.62 975.95 4299.34 487.77 1797.68 1498.59 2
CPTT-MVS91.99 1591.80 1492.55 2898.24 381.98 4396.76 996.49 2081.89 6290.24 1396.44 1378.59 3198.61 3089.68 1497.85 1397.06 24
DP-MVS Recon91.95 1691.28 1893.96 1398.33 285.92 994.66 3696.66 1882.69 5390.03 1695.82 2182.30 1399.03 1784.57 3396.48 2696.91 27
EPNet91.79 1791.02 2194.10 1190.10 10485.25 1396.03 1592.05 8892.83 187.39 3495.78 2279.39 2699.01 1988.13 1597.48 1598.05 11
MG-MVS91.77 1891.70 1592.00 4197.08 1580.03 6693.60 5395.18 4787.85 2090.89 1196.47 1282.06 1598.36 3685.07 3097.04 1797.62 18
Vis-MVSNetpermissive91.75 1991.23 1993.29 1795.32 3983.78 2496.14 1395.98 3289.89 790.45 1296.58 1075.09 4698.31 3984.75 3296.90 1897.78 16
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator86.66 591.73 2090.82 2394.44 894.59 5386.37 797.18 397.02 989.20 1184.31 7296.66 873.74 5399.17 1286.74 2597.96 1297.79 15
EPP-MVSNet91.70 2191.56 1692.13 4095.88 3180.50 6397.33 295.25 4386.15 2889.76 1795.60 2583.42 1198.32 3887.37 2193.25 4797.56 21
IS-MVSNet91.43 2291.09 2092.46 3295.87 3381.38 4996.95 593.69 7189.72 889.50 2095.98 1878.57 3297.77 5183.02 3696.50 2598.22 8
PVSNet_Blended_VisFu91.38 2390.91 2292.80 2496.39 2283.17 3094.87 3196.66 1883.29 4489.27 2194.46 3980.29 2299.17 1287.57 2095.37 3296.05 41
OMC-MVS91.23 2490.62 2593.08 1896.27 2584.07 2293.52 5495.93 3386.95 2289.51 1996.13 1678.50 3398.35 3785.84 2892.90 5196.83 29
PAPM_NR91.22 2590.78 2492.52 3097.60 1081.46 4894.37 4296.24 2386.39 2787.41 3394.80 3682.06 1598.48 3282.80 3795.37 3297.61 19
PVSNet_Blended90.73 2690.32 2691.98 4296.12 2681.25 5192.55 6796.83 1482.04 5989.10 2392.56 6681.04 1998.85 2386.72 2695.91 2995.84 45
API-MVS90.66 2790.07 2792.45 3396.36 2384.57 1896.06 1495.22 4682.39 5489.13 2294.27 4480.32 2198.46 3380.16 4996.71 2194.33 69
MAR-MVS90.30 2889.37 3093.07 2096.61 2084.48 1995.68 1895.67 3882.36 5587.85 2892.85 6176.63 4198.80 2580.01 5096.68 2295.91 42
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
PAPR90.02 2989.27 3192.29 3695.78 3480.95 5692.68 6696.22 2481.91 6186.66 3893.75 5482.23 1498.44 3579.40 5494.79 3697.48 22
PVSNet_BlendedMVS89.98 3089.70 2890.82 6296.12 2681.25 5193.92 4996.83 1483.49 4389.10 2392.26 7181.04 1998.85 2386.72 2687.86 7992.35 96
UGNet89.95 3188.95 3392.95 2294.51 5483.31 2895.70 1795.23 4489.37 1087.58 3293.94 4864.00 9498.78 2683.92 3496.31 2796.74 31
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
AdaColmapbinary89.89 3289.07 3292.37 3497.41 1283.03 3494.42 3895.92 3482.81 5286.34 4294.65 3773.89 5199.02 1880.69 4795.51 3195.05 58
WTY-MVS89.60 3388.92 3491.67 4595.47 3781.15 5492.38 7094.78 5983.11 4789.06 2594.32 4278.67 3096.61 7681.57 4490.89 5697.24 23
Vis-MVSNet (Re-imp)89.59 3489.44 2990.03 7295.74 3575.85 8995.61 2190.80 10287.66 2187.83 2995.40 2776.79 3896.46 8078.37 5696.73 2097.80 14
QAPM89.51 3588.15 4593.59 1694.92 4584.58 1796.82 896.70 1778.43 8183.41 8196.19 1573.18 5599.30 877.11 6896.54 2496.89 28
114514_t89.51 3588.50 3892.54 2998.11 481.99 4295.16 2596.36 2270.19 11485.81 4695.25 2876.70 3998.63 2982.07 4296.86 1997.00 25
CLD-MVS89.47 3788.90 3591.18 5194.22 6082.07 4192.13 7496.09 2987.90 1985.37 5692.45 6774.38 4897.56 5687.15 2290.43 5893.93 75
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CDS-MVSNet89.45 3888.51 3792.29 3693.62 7283.61 2793.01 6194.68 6181.95 6087.82 3093.24 5878.69 2996.99 6680.34 4893.23 4896.28 35
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
LPG-MVS_test89.45 3888.90 3591.12 5294.47 5581.49 4695.30 2396.14 2686.73 2385.45 5195.16 3069.89 7198.10 4287.70 1889.23 6693.77 82
ab-mvs89.41 4088.35 4092.60 2695.15 4382.65 3992.20 7395.60 3983.97 3888.55 2793.70 5574.16 5098.21 4182.46 4089.37 6296.94 26
TAMVS89.21 4188.29 4291.96 4393.71 7182.62 4093.30 5594.19 6682.22 5687.78 3193.94 4878.83 2796.95 6877.70 6292.98 5096.32 34
ACMM84.12 989.14 4288.48 3991.12 5294.65 5281.22 5395.31 2296.12 2885.31 3385.92 4594.34 4070.19 7098.06 4585.65 2988.86 7194.08 72
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA89.07 4387.98 4892.34 3596.87 1784.78 1694.08 4793.24 7581.41 6784.46 6595.13 3275.57 4596.62 7477.21 6793.84 4195.61 50
PLCcopyleft84.53 789.06 4488.03 4792.15 3997.27 1382.69 3894.29 4395.44 4179.71 7384.01 7494.18 4576.68 4098.75 2777.28 6693.41 4695.02 59
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
HY-MVS83.01 1289.03 4587.94 4992.29 3694.86 4882.77 3692.08 7594.49 6381.52 6686.93 3692.79 6378.32 3598.23 4079.93 5190.55 5795.88 43
ACMP84.23 889.01 4688.35 4090.99 6094.73 4981.27 5095.07 2995.89 3686.48 2683.67 7894.30 4369.33 7797.99 4687.10 2488.55 7393.72 85
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
sss88.93 4788.26 4490.94 6194.05 6380.78 5991.71 7895.38 4281.55 6588.63 2693.91 5075.04 4795.47 10182.47 3991.61 5396.57 32
MVSTER88.84 4888.29 4290.51 6592.95 7580.44 6493.73 5195.01 4984.66 3687.15 3593.12 6072.79 5797.21 6487.86 1687.36 8193.87 77
OpenMVScopyleft83.78 1188.74 4987.29 5393.08 1892.70 7885.39 1196.57 1196.43 2178.74 7980.85 9496.07 1769.64 7499.01 1978.01 6096.65 2394.83 63
BH-untuned88.60 5088.13 4690.01 7495.24 4278.50 7793.29 5694.15 6784.75 3584.46 6593.40 5675.76 4397.40 6077.59 6394.52 3894.12 70
1112_ss88.42 5187.33 5291.72 4494.92 4580.98 5592.97 6394.54 6278.16 8483.82 7693.88 5178.78 2897.91 4879.45 5389.41 6196.26 36
BH-RMVSNet88.37 5287.48 5191.02 5995.28 4079.45 7092.89 6493.07 7785.45 3286.91 3794.84 3570.35 6897.76 5273.97 8094.59 3795.85 44
IterMVS-LS88.36 5387.91 5089.70 7893.80 6878.29 8093.73 5195.08 4885.73 2984.75 6191.90 7579.88 2496.92 6983.83 3582.51 10693.89 76
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAPA-MVS84.62 688.16 5487.01 5591.62 4696.64 1980.65 6094.39 3996.21 2576.38 8986.19 4395.44 2679.75 2598.08 4462.75 11195.29 3496.13 38
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
F-COLMAP87.95 5586.80 5791.40 4796.35 2480.88 5894.73 3495.45 4079.65 7482.04 8694.61 3871.13 6398.50 3176.24 7191.05 5594.80 65
LS3D87.89 5686.32 5992.59 2796.07 2882.92 3595.23 2494.92 5475.66 9582.89 8295.98 1872.48 5999.21 1068.43 10195.23 3595.64 49
PCF-MVS84.11 1087.74 5786.08 6192.70 2594.02 6484.43 2089.27 9195.87 3773.62 10284.43 6794.33 4178.48 3498.86 2270.27 9394.45 3994.81 64
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
XXY-MVS87.65 5886.85 5690.03 7292.14 7980.60 6293.76 5095.23 4482.94 5084.60 6394.02 4674.27 4995.49 10081.04 4583.68 10194.01 74
Test_1112_low_res87.65 5886.51 5891.08 5594.94 4479.28 7691.77 7694.30 6576.04 9483.51 8092.37 6977.86 3697.73 5478.69 5589.13 6896.22 37
BH-w/o87.57 6087.05 5489.12 8094.90 4777.90 8392.41 6993.51 7282.89 5183.70 7791.34 8275.75 4497.07 6575.49 7293.49 4392.39 94
FMVSNet387.40 6186.11 6091.30 4893.79 7083.64 2694.20 4594.81 5883.89 3984.37 6891.87 7668.45 8196.56 7778.23 5785.36 9193.70 86
GBi-Net87.26 6285.98 6291.08 5594.01 6583.10 3195.14 2694.94 5083.57 4084.37 6891.64 7766.59 8596.34 8478.23 5785.36 9193.79 79
test187.26 6285.98 6291.08 5594.01 6583.10 3195.14 2694.94 5083.57 4084.37 6891.64 7766.59 8596.34 8478.23 5785.36 9193.79 79
DP-MVS87.25 6485.36 6992.90 2397.65 983.24 2994.81 3292.00 9074.99 9981.92 8895.00 3472.66 5899.05 1666.92 10592.33 5296.40 33
FMVSNet287.19 6585.82 6591.30 4894.01 6583.67 2594.79 3394.94 5083.57 4083.88 7592.05 7366.59 8596.51 7977.56 6485.01 9493.73 84
TR-MVS86.78 6685.76 6689.82 7594.37 5978.41 7892.47 6892.83 7981.11 6986.36 4192.40 6868.73 7997.48 5873.75 8389.85 5993.57 87
PatchMatch-RL86.77 6785.54 6790.47 6695.88 3182.71 3790.54 8292.31 8479.82 7284.32 7191.57 8168.77 7896.39 8273.16 8593.48 4592.32 97
PAPM86.68 6885.39 6890.53 6493.05 7479.33 7589.79 8794.77 6078.82 7781.95 8793.24 5876.81 3797.30 6166.94 10493.16 4994.95 60
EPNet_dtu86.49 6985.94 6488.14 8790.24 10272.82 9994.11 4692.20 8786.66 2579.42 10092.36 7073.52 5495.81 9671.26 9093.66 4295.80 46
cascas86.43 7084.98 7190.80 6392.10 8180.92 5790.24 8395.91 3573.10 10583.57 7988.39 10165.15 9297.46 5984.90 3191.43 5494.03 73
LTVRE_ROB82.13 1386.26 7184.90 7490.34 6894.44 5881.50 4592.31 7294.89 5583.03 4879.63 9992.67 6469.69 7397.79 5071.20 9186.26 8791.72 101
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
FMVSNet185.85 7284.11 7891.08 5592.81 7783.10 3195.14 2694.94 5081.64 6382.68 8391.64 7759.01 10396.34 8475.37 7383.78 10093.79 79
PatchmatchNetpermissive85.85 7284.70 7589.29 7991.76 8475.54 9088.49 9991.30 9881.63 6485.05 5788.70 9871.71 6196.24 8774.61 7889.05 6996.08 39
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CostFormer85.77 7484.94 7388.26 8491.16 9472.58 10389.47 8991.04 10176.26 9286.45 4089.97 9170.74 6796.86 7082.35 4187.07 8595.34 56
PMMVS85.71 7584.96 7287.95 8988.90 10977.09 8488.68 9790.06 11172.32 10986.47 3990.76 8472.15 6094.40 10781.78 4393.49 4392.36 95
PVSNet78.82 1885.55 7684.65 7688.23 8694.72 5071.93 10487.12 10692.75 8078.80 7884.95 5990.53 8564.43 9396.71 7274.74 7693.86 4096.06 40
ACMH80.38 1785.36 7783.68 8390.39 6794.45 5780.63 6194.73 3494.85 5782.09 5877.24 10492.65 6560.01 10197.58 5572.25 8784.87 9592.96 90
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst85.35 7884.99 7086.43 10490.88 9667.88 11388.71 9691.43 9780.13 7186.08 4488.80 9673.05 5696.02 9282.48 3883.40 10395.40 53
CR-MVSNet85.35 7883.76 8290.12 7090.58 9879.34 7385.24 11091.96 9178.27 8285.55 4987.87 10771.03 6495.61 9773.96 8189.36 6395.40 53
IB-MVS80.51 1585.24 8083.26 8691.19 5092.13 8079.86 6891.75 7791.29 9983.28 4580.66 9688.49 10061.28 9898.46 3380.99 4679.46 11495.25 57
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
RPSCF85.07 8184.27 7787.48 9392.91 7670.62 10991.69 7992.46 8276.20 9382.67 8495.22 2963.94 9597.29 6277.51 6585.80 8994.53 67
ACMH+81.04 1485.05 8283.46 8589.82 7594.66 5179.37 7194.44 3794.12 6882.19 5778.04 10292.82 6258.23 10597.54 5773.77 8282.90 10592.54 91
IterMVS84.88 8383.98 8087.60 9191.44 9076.03 8890.18 8492.41 8383.24 4681.06 9290.42 8666.60 8494.28 10879.46 5280.98 11092.48 92
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG84.86 8483.09 8790.14 6993.80 6880.05 6589.18 9493.09 7678.89 7678.19 10191.91 7465.86 9197.27 6368.47 10088.45 7593.11 89
HyFIR84.73 8583.91 8187.19 9991.64 8773.54 9584.44 11393.89 6974.63 10180.91 9385.24 11171.67 6294.46 10576.78 6995.98 2893.57 87
tpm84.73 8584.02 7986.87 10390.33 10068.90 11289.06 9589.94 11280.85 7085.75 4789.86 9268.54 8095.97 9377.76 6184.05 9995.75 47
tpm284.08 8782.94 8887.48 9391.39 9171.27 10589.23 9390.37 10571.95 11184.64 6289.33 9367.30 8396.55 7875.17 7587.09 8494.63 66
COLMAP_ROBcopyleft80.39 1683.96 8882.04 9189.74 7795.28 4079.75 6994.25 4492.28 8575.17 9778.02 10393.77 5358.60 10497.84 4965.06 10985.92 8891.63 102
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EPMVS83.90 8982.70 8987.51 9290.23 10372.67 10188.62 9881.96 12381.37 6885.01 5888.34 10266.31 8894.45 10675.30 7487.12 8395.43 52
tpmp4_e2383.87 9082.33 9088.48 8291.46 8972.82 9989.82 8691.57 9473.02 10781.86 8989.05 9466.20 8996.97 6771.57 8986.39 8695.66 48
RPMNet83.18 9180.87 9590.12 7090.58 9879.34 7385.24 11090.78 10371.44 11385.55 4982.97 11570.87 6695.61 9761.01 11289.36 6395.40 53
USDC82.76 9281.26 9387.26 9691.17 9274.55 9289.27 9193.39 7478.26 8375.30 10892.08 7254.43 11196.63 7371.64 8885.79 9090.61 107
Patchmtry82.71 9380.93 9488.06 8890.05 10676.37 8684.74 11291.96 9172.28 11081.32 9087.87 10771.03 6495.50 9968.97 9880.15 11292.32 97
PatchT82.68 9481.27 9286.89 10290.09 10570.94 10884.06 11590.15 10874.91 10085.63 4883.57 11369.37 7694.87 10465.19 10888.50 7494.84 62
MIMVSNet82.59 9580.53 9688.76 8191.51 8878.32 7986.57 10890.13 10979.32 7580.70 9588.69 9952.98 11293.07 11366.03 10788.86 7194.90 61
EG-PatchMatch MVS82.37 9680.34 9788.46 8390.27 10179.35 7292.80 6594.33 6477.14 8673.26 11390.18 8947.47 11996.72 7170.25 9487.32 8289.30 111
tpm cat181.96 9780.27 9887.01 10091.09 9571.02 10787.38 10591.53 9666.25 11880.17 9786.35 11068.22 8296.15 9069.16 9782.29 10793.86 78
FMVSNet581.52 9879.60 10087.27 9591.17 9277.95 8291.49 8192.26 8676.87 8776.16 10587.91 10651.67 11392.34 11467.74 10381.16 10891.52 103
dp81.47 9980.23 9985.17 10989.92 10765.49 11786.74 10790.10 11076.30 9181.10 9187.12 10962.81 9695.92 9468.13 10279.88 11394.09 71
test_040281.30 10079.17 10287.67 9093.19 7378.17 8192.98 6291.71 9375.25 9676.02 10690.31 8759.23 10296.37 8350.22 12183.63 10288.47 115
JIA-IIPM81.04 10178.98 10487.25 9788.64 11073.48 9681.75 11989.61 11473.19 10482.05 8573.71 12266.07 9095.87 9571.18 9284.60 9792.41 93
CMPMVSbinary59.16 2180.52 10279.20 10184.48 11183.98 11867.63 11489.95 8593.84 7064.79 11966.81 12191.14 8357.93 10795.17 10276.25 7088.10 7790.65 106
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LF4IMVS80.37 10379.07 10384.27 11386.64 11469.87 11189.39 9091.05 10076.38 8974.97 11090.00 9047.85 11894.25 10974.55 7980.82 11188.69 113
UnsupCasMVSNet_eth80.07 10478.27 10585.46 10885.24 11772.63 10288.45 10094.87 5682.99 4971.64 11788.07 10556.34 10991.75 11673.48 8463.36 12392.01 100
TDRefinement79.81 10577.34 10787.22 9879.24 12275.48 9193.12 5892.03 8976.45 8875.01 10991.58 8049.19 11696.44 8170.22 9669.18 12189.75 109
TinyColmap79.76 10677.69 10685.97 10591.71 8573.12 9789.55 8890.36 10675.03 9872.03 11590.19 8846.22 12096.19 8963.11 11081.03 10988.59 114
OpenMVS_ROBcopyleft74.94 1979.51 10777.03 10986.93 10187.00 11376.23 8792.33 7190.74 10468.93 11674.52 11188.23 10349.58 11596.62 7457.64 11784.29 9887.94 117
MIMVSNet179.38 10877.28 10885.69 10686.35 11673.67 9491.61 8092.75 8078.11 8572.64 11488.12 10448.16 11791.97 11560.32 11377.49 11591.43 105
PVSNet_073.20 2077.22 10974.83 11184.37 11290.70 9771.10 10683.09 11789.67 11372.81 10873.93 11283.13 11460.79 10093.70 11068.54 9950.84 12688.30 116
DSMNet-mixed76.94 11076.29 11078.89 11483.10 12056.11 12387.78 10279.77 12560.65 12275.64 10788.71 9761.56 9788.34 12060.07 11589.29 6592.21 99
UnsupCasMVSNet_bld76.23 11173.27 11285.09 11083.79 11972.92 9885.65 10993.47 7371.52 11268.84 12079.08 11949.77 11493.21 11166.81 10660.52 12489.13 112
LP75.51 11272.15 11485.61 10787.86 11273.93 9380.20 12188.43 11567.39 11770.05 11880.56 11758.18 10693.18 11246.28 12370.36 12089.71 110
MVS-HIRNet73.70 11372.20 11378.18 11691.81 8356.42 12282.94 11882.58 12155.24 12468.88 11966.48 12355.32 11095.13 10358.12 11688.42 7683.01 118
new_pmnet72.15 11470.13 11578.20 11582.95 12165.68 11583.91 11682.40 12262.94 12164.47 12279.82 11842.85 12186.26 12157.41 11874.44 11882.65 119
HyFIR lowres test70.96 11569.05 11676.67 11786.42 11564.06 11877.75 12382.95 12055.25 12362.90 12378.84 12033.89 12390.95 11860.14 11483.30 10475.01 123
N_pmnet68.89 11668.44 11770.23 12189.07 10828.79 13188.06 10119.50 13069.47 11571.86 11684.93 11261.24 9991.75 11654.70 11977.15 11690.15 108
FPMVS64.63 11762.55 11870.88 12070.80 12456.71 12084.42 11484.42 11951.78 12549.57 12481.61 11623.49 12681.48 12440.61 12676.25 11774.46 124
no-one61.56 11856.58 11976.49 11867.80 12762.76 11978.13 12286.11 11663.16 12043.24 12664.70 12526.12 12588.95 11950.84 12029.15 12877.77 121
ANet_high58.88 11954.22 12172.86 11956.50 13056.67 12180.75 12086.00 11773.09 10637.39 12864.63 12622.17 12779.49 12643.51 12423.96 13182.43 120
Gipumacopyleft57.99 12054.91 12067.24 12388.51 11165.59 11652.21 12890.33 10743.58 12842.84 12751.18 12920.29 12885.07 12234.77 12870.45 11951.05 129
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVScopyleft47.18 2252.22 12148.46 12263.48 12445.72 13146.20 12873.41 12478.31 12641.03 12930.06 13065.68 1246.05 13083.43 12330.04 12965.86 12260.80 126
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d50.55 12244.13 12469.81 12256.77 12854.58 12573.22 12580.78 12439.79 13022.08 13246.69 1304.03 13279.71 12547.65 12226.13 13075.14 122
PNet_i23d50.48 12347.18 12360.36 12568.59 12544.56 12972.75 12672.61 12743.92 12733.91 12960.19 1276.16 12973.52 12738.50 12728.04 12963.01 125
MVEpermissive39.65 2343.39 12438.59 12557.77 12656.52 12948.77 12755.38 12758.64 12829.33 13128.96 13152.65 1284.68 13164.62 12828.11 13033.07 12759.93 127
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d21.27 12520.48 12623.63 12868.59 12536.41 13049.57 1296.85 1319.37 1327.89 1334.46 1314.03 13231.37 13017.47 13116.07 1323.12 130
ab-mvs-re7.82 12610.43 1270.00 1290.00 1320.00 1320.00 1300.00 1320.00 1330.00 13493.88 510.00 1340.00 1310.00 1320.00 1330.00 131
door85.33 118
HQP-MVS37.31 122
ACMP_Plane94.17 6194.39 3988.81 1385.43 53
HQP5-MVS81.56 44
HQP-NCC94.17 6194.39 3988.81 1385.43 53
MDTV_nov1_ep1383.56 8491.69 8669.93 11087.75 10391.54 9578.60 8084.86 6088.90 9569.54 7596.03 9170.25 9488.93 70
ACMMP++88.01 78
BP-MVS87.11 23
HQP4-MVS85.43 5397.96 4794.51 68
HQP3-MVS96.04 3189.77 60
HQP2-MVS73.83 52
LGP-MVS_train91.12 5294.47 5581.49 4696.14 2686.73 2385.45 5195.16 3069.89 7198.10 4287.70 1889.23 6693.77 82
NP-MVS93.98 47
DeepMVS_CXcopyleft56.31 12774.23 12351.81 12656.67 12944.85 12648.54 12575.16 12127.87 12458.74 12940.92 12552.22 12558.39 128
MDTV_nov1_ep13_2view55.91 12487.62 10473.32 10384.59 6470.33 6974.65 7795.50 51
ACMMP++_ref87.47 80
Test By Simon80.02 23
ITE_SJBPF88.24 8591.88 8277.05 8592.92 7885.54 3180.13 9893.30 5757.29 10896.20 8872.46 8684.71 9691.49 104