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
TDRefinement93.52 193.39 293.88 195.94 290.26 395.70 296.46 190.58 292.86 1196.29 488.16 1194.17 2186.07 598.48 797.22 1
LTVRE_ROB86.10 193.04 293.44 191.82 593.73 1285.72 996.79 195.51 388.86 595.63 296.99 284.81 1893.16 3391.10 197.53 1796.58 4
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
COLMAP_ROBcopyleft83.01 391.97 391.95 392.04 293.68 1386.15 793.37 595.10 490.28 392.11 1495.03 1389.75 894.93 1479.95 2098.27 1295.04 13
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMPcopyleft91.91 491.87 492.03 395.53 385.91 893.35 694.16 982.52 1492.39 1394.14 2189.15 995.62 687.35 398.24 1394.56 15
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
CP-MVS91.67 591.58 691.96 495.29 487.62 593.38 493.36 1583.16 1391.06 1794.00 2388.26 1095.71 587.28 498.39 992.55 30
LPG-MVS_test91.47 691.68 590.82 994.75 781.69 2190.00 1494.27 682.35 1593.67 894.82 1691.18 295.52 785.36 698.73 595.23 11
SteuartSystems-ACMMP91.04 791.18 890.60 1293.83 1181.72 2090.66 1193.61 1382.15 1792.63 1293.72 2687.29 1495.73 488.35 298.39 994.36 18
Skip Steuart: Steuart Systems R&D Blog.
ACMH+77.89 1190.73 891.50 788.44 2293.00 1576.26 3789.65 1795.55 287.72 993.89 694.94 1491.62 193.44 2878.35 2498.76 395.61 8
ACMM79.39 990.65 990.99 1089.63 1795.03 683.53 1789.62 1893.35 1679.20 2593.83 793.60 2790.81 592.96 3485.02 898.45 892.41 32
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D90.60 1090.34 1391.38 689.03 5184.23 1593.58 394.68 590.65 190.33 1993.95 2484.50 2095.37 980.87 1795.50 3794.53 17
ACMP79.16 1090.54 1190.60 1290.35 1494.36 980.98 2689.16 2294.05 1179.03 2792.87 1093.74 2590.60 795.21 1282.87 1398.76 394.87 14
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PMVScopyleft80.48 690.08 1290.66 1188.34 2496.71 192.97 290.31 1289.57 4888.51 790.11 2095.12 1290.98 488.92 6977.55 2997.07 2383.13 98
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
3Dnovator+83.92 289.97 1389.66 1490.92 891.27 3281.66 2491.25 994.13 1088.89 488.83 3194.26 2077.55 4295.86 284.88 995.87 3395.24 10
DNCC-MVS89.54 1489.63 1589.26 2092.57 1781.34 2590.19 1393.08 1880.87 2191.13 1693.19 2886.22 1695.97 182.23 1597.18 2190.45 51
CPTT-MVS89.39 1588.98 1990.63 1195.09 586.95 692.09 792.30 2779.74 2387.50 3592.38 4081.42 2993.28 3183.07 1297.24 2091.67 41
ACMH76.49 1489.34 1691.14 983.96 4992.50 1970.36 5589.55 1993.84 1281.89 1894.70 495.44 1090.69 688.31 7583.33 1198.30 1193.20 27
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DeepC-MVS82.31 489.15 1789.08 1789.37 1993.64 1479.07 3088.54 2694.20 873.53 4889.71 2294.82 1685.09 1795.77 384.17 1098.03 1493.26 25
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_040288.65 1889.58 1685.88 3692.55 1872.22 5284.01 5289.44 5088.63 694.38 595.77 686.38 1593.59 2779.84 2195.21 4291.82 38
DP-MVS88.60 1989.01 1887.36 3091.30 3077.50 3387.55 3092.97 2087.95 889.62 2592.87 3384.56 1993.89 2377.65 2896.62 2790.70 47
wuykxyi23d88.46 2088.80 2087.44 2990.96 3593.03 185.85 4481.96 8474.58 4398.58 197.29 187.73 1287.31 7882.84 1499.41 181.99 107
OMC-MVS88.19 2187.52 2190.19 1591.94 2581.68 2387.49 3193.17 1776.02 3988.64 3291.22 5084.24 2193.37 2977.97 2797.03 2495.52 9
RPSCF88.00 2286.93 2291.22 790.08 4489.30 489.68 1691.11 3479.26 2489.68 2394.81 1982.44 2587.74 7676.54 3388.74 8296.61 3
NCCC87.36 2386.87 2388.83 2192.32 2178.84 3186.58 4191.09 3578.77 2984.85 5290.89 5580.85 3095.29 1081.14 1695.32 4092.34 33
DeepPCF-MVS81.24 587.28 2486.21 2890.49 1391.48 2984.90 1383.41 5492.38 2670.25 6789.35 2990.68 5882.85 2494.57 1779.55 2295.95 3192.00 37
Vis-MVSNetpermissive86.86 2586.58 2587.72 2792.09 2377.43 3487.35 3292.09 2878.87 2884.27 5594.05 2278.35 3893.65 2580.54 1991.58 6992.08 36
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS-MVSNet86.66 2686.82 2486.17 3592.05 2466.87 6591.21 1088.64 5486.30 1089.60 2792.59 3669.22 6894.91 1573.89 3897.89 1696.72 2
PHI-MVS86.38 2785.81 2988.08 2588.44 5877.34 3589.35 2193.05 1973.15 5384.76 5387.70 8078.87 3694.18 2080.67 1896.29 2892.73 29
CSCG86.26 2886.47 2685.60 3990.87 3774.26 4387.98 2991.85 3180.35 2289.54 2888.01 7779.09 3492.13 4475.51 3595.06 4690.41 52
DeepC-MVS_fast80.27 886.23 2985.65 3187.96 2691.30 3076.92 3687.19 3391.99 2970.56 6584.96 4990.69 5780.01 3395.14 1378.37 2395.78 3491.82 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TAPA-MVS77.73 1285.71 3084.83 3588.37 2388.78 5479.72 2987.15 3593.50 1469.17 6885.80 4489.56 6580.76 3192.13 4473.21 4395.51 3693.25 26
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EPP-MVSNet85.47 3185.04 3386.77 3191.52 2869.37 5791.63 887.98 6181.51 2087.05 3691.83 4766.18 7595.29 1070.75 4696.89 2595.64 7
F-COLMAP84.97 3283.42 4289.63 1792.39 2083.40 1888.83 2391.92 3073.19 5280.18 7989.15 6877.04 4493.28 3165.82 6492.28 6692.21 35
3Dnovator80.37 784.80 3384.71 3685.06 4286.36 7174.71 4288.77 2490.00 4575.65 4084.96 4993.17 2974.06 5691.19 5278.28 2591.09 7189.29 59
IterMVS-LS84.73 3484.98 3483.96 4987.35 6163.66 7283.25 5689.88 4776.06 3889.62 2592.37 4373.40 5992.52 3978.16 2694.77 4795.69 6
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR84.63 3584.34 3885.49 4090.18 4375.86 3979.23 8287.13 6573.35 4985.56 4689.34 6683.60 2290.50 6376.64 3294.05 5290.09 55
FMVSNet184.55 3685.45 3281.85 5890.27 4261.05 8486.83 3788.27 5778.57 3089.66 2495.64 775.43 5090.68 5969.09 5395.33 3993.82 19
Gipumacopyleft84.44 3786.33 2778.78 7684.20 8473.57 4489.55 1990.44 4084.24 1184.38 5494.89 1576.35 4880.40 10076.14 3496.80 2682.36 105
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVS_111021_LR84.28 3883.76 4085.83 3889.23 4983.07 1980.99 7283.56 7972.71 5586.07 4089.07 6981.75 2886.19 8477.11 3193.36 5688.24 64
EG-PatchMatch MVS84.08 3984.11 3983.98 4892.22 2272.61 4882.20 6687.02 6772.63 5688.86 3091.02 5378.52 3791.11 5473.41 4291.09 7188.21 65
DP-MVS Recon84.05 4083.22 4386.52 3291.73 2675.27 4083.23 5792.40 2572.04 6082.04 6388.33 7477.91 4093.95 2266.17 6095.12 4490.34 53
AdaColmapbinary83.66 4183.69 4183.57 5190.05 4572.26 5186.29 4290.00 4578.19 3281.65 6687.16 8483.40 2394.24 1961.69 7794.76 4884.21 90
MIMVSNet183.63 4284.59 3780.74 6794.06 1062.77 7882.72 5884.53 7877.57 3590.34 1895.92 576.88 4685.83 8861.88 7597.42 1893.62 22
CNLPA83.55 4383.10 4584.90 4389.34 4883.87 1684.54 5088.77 5279.09 2683.54 5888.66 7274.87 5481.73 9866.84 5992.29 6589.11 60
PAPM_NR83.23 4483.19 4483.33 5290.90 3665.98 6888.19 2890.78 3678.13 3380.87 7387.92 7873.49 5892.42 4070.07 4988.40 8391.60 42
CLD-MVS83.18 4582.64 4684.79 4489.05 5067.82 6377.93 8792.52 2368.33 7185.07 4881.54 10982.06 2792.96 3469.35 5297.91 1593.57 23
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high83.17 4685.68 3075.65 9481.24 9245.26 11979.94 7792.91 2183.83 1291.33 1596.88 380.25 3285.92 8668.89 5595.89 3295.76 5
114514_t83.10 4782.54 4984.77 4592.90 1669.10 5986.65 4090.62 3854.66 10381.46 6890.81 5676.98 4594.38 1872.62 4496.18 2990.82 46
UGNet82.78 4881.64 5586.21 3486.20 7376.24 3886.86 3685.68 7577.07 3673.76 9892.82 3469.64 6691.82 4769.04 5493.69 5590.56 49
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
LF4IMVS82.75 4981.93 5485.19 4182.08 8980.15 2885.53 4688.76 5368.01 7385.58 4587.75 7971.80 6586.85 8074.02 3793.87 5488.58 63
QAPM82.59 5082.59 4882.58 5586.44 6666.69 6689.94 1590.36 4367.97 7484.94 5192.58 3872.71 6292.18 4370.63 4887.73 8788.85 62
API-MVS82.28 5182.61 4781.30 6386.29 7269.79 5688.71 2587.67 6378.42 3182.15 6284.15 9977.98 3991.59 4965.39 6592.75 6282.51 104
PCF-MVS74.62 1582.15 5280.92 6085.84 3789.43 4772.30 5080.53 7491.82 3257.36 9787.81 3389.92 6377.67 4193.63 2658.69 8595.08 4591.58 43
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLCcopyleft73.85 1682.09 5380.31 6387.45 2890.86 3880.29 2785.88 4390.65 3768.17 7276.32 8986.33 8973.12 6192.61 3861.40 7990.02 7889.44 57
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test182.02 5482.07 5181.85 5886.38 6861.05 8486.83 3788.27 5772.43 5786.00 4195.64 763.78 8090.68 5965.95 6193.34 5793.82 19
GBi-Net82.02 5482.07 5181.85 5886.38 6861.05 8486.83 3788.27 5772.43 5786.00 4195.64 763.78 8090.68 5965.95 6193.34 5793.82 19
OpenMVScopyleft76.72 1381.98 5682.00 5381.93 5784.42 8168.22 6188.50 2789.48 4966.92 7681.80 6591.86 4572.59 6390.16 6671.19 4591.25 7087.40 73
PVSNet_Blended_VisFu81.55 5780.49 6284.70 4791.58 2773.24 4684.21 5191.67 3362.86 8680.94 7187.16 8467.27 7292.87 3669.82 5088.94 8187.99 68
DELS-MVS81.44 5881.25 5782.03 5684.27 8362.87 7776.47 9892.49 2470.97 6481.64 6783.83 10075.03 5292.70 3774.29 3692.22 6890.51 50
FMVSNet281.31 5981.61 5680.41 6986.38 6858.75 9183.93 5386.58 6972.43 5787.65 3492.98 3163.78 8090.22 6566.86 5893.92 5392.27 34
TinyColmap81.25 6082.34 5077.99 8385.33 7560.68 8782.32 6388.33 5671.26 6386.97 3792.22 4477.10 4386.98 7962.37 7395.17 4386.31 79
BH-untuned80.96 6180.99 5880.84 6688.55 5668.23 6080.33 7588.46 5572.79 5486.55 3886.76 8874.72 5591.77 4861.79 7688.99 8082.52 103
BH-RMVSNet80.53 6280.22 6481.49 6287.19 6366.21 6777.79 8986.23 7174.21 4483.69 5688.50 7373.25 6090.75 5663.18 7187.90 8687.52 71
EPNet80.37 6378.41 6986.23 3376.75 11373.28 4587.18 3477.45 9076.24 3768.14 11388.93 7065.41 7793.85 2469.47 5196.12 3091.55 44
MG-MVS80.32 6480.94 5978.47 7988.18 5952.62 10482.29 6485.01 7772.01 6179.24 8192.54 3969.36 6793.36 3070.65 4789.19 7989.45 56
MAR-MVS80.24 6578.74 6784.73 4686.87 6578.18 3285.75 4587.81 6265.67 7977.84 8378.50 11973.79 5790.53 6261.59 7890.87 7585.49 83
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
MSDG80.06 6679.99 6580.25 7083.91 8568.04 6277.51 9289.19 5177.65 3481.94 6483.45 10376.37 4786.31 8363.31 7086.59 9286.41 77
ab-mvs79.67 6780.56 6176.99 8788.48 5756.93 9384.70 4986.06 7368.95 7080.78 7493.08 3075.30 5184.62 9056.78 9190.90 7489.43 58
PAPR78.84 6878.10 7081.07 6585.17 7660.22 8982.21 6590.57 3962.51 8775.32 9584.61 9774.99 5392.30 4259.48 8388.04 8590.68 48
FMVSNet378.80 6978.55 6879.57 7582.89 8856.89 9481.76 6985.77 7469.04 6986.00 4190.44 5951.75 10490.09 6765.95 6193.34 5791.72 40
PVSNet_BlendedMVS78.80 6977.84 7181.65 6184.43 7963.41 7379.49 7990.44 4061.70 9075.43 9387.07 8669.11 6991.44 5060.68 8192.24 6790.11 54
TAMVS78.08 7176.36 7683.23 5390.62 4072.87 4779.08 8380.01 8761.72 8981.35 7086.92 8763.96 7988.78 7050.61 10593.01 6088.04 67
Vis-MVSNet (Re-imp)77.82 7277.79 7277.92 8488.82 5351.29 10883.28 5571.97 10974.04 4582.23 6189.78 6457.38 9589.41 6857.22 8995.41 3893.05 28
OpenMVS_ROBcopyleft70.19 1777.77 7377.46 7378.71 7784.39 8261.15 8281.18 7182.52 8162.45 8883.34 5987.37 8366.20 7488.66 7364.69 6785.02 9786.32 78
CDS-MVSNet77.32 7475.40 8383.06 5489.00 5272.48 4977.90 8882.17 8360.81 9178.94 8283.49 10259.30 8988.76 7154.64 9992.37 6487.93 70
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER77.09 7575.70 8281.25 6475.27 11961.08 8377.49 9385.07 7660.78 9286.55 3888.68 7143.14 12090.25 6473.69 3990.67 7692.42 31
IterMVS76.91 7676.34 7778.64 7880.91 9364.03 7176.30 9979.03 8864.88 8183.11 6089.16 6759.90 8784.46 9168.61 5785.15 9687.42 72
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TR-MVS76.77 7775.79 8079.72 7386.10 7465.79 7077.14 9483.02 8065.20 8081.40 6982.10 10766.30 7390.73 5855.57 9585.27 9482.65 100
USDC76.63 7876.73 7576.34 9283.46 8657.20 9280.02 7688.04 6052.14 11183.65 5791.25 4963.24 8386.65 8254.66 9894.11 5185.17 84
BH-w/o76.57 7976.07 7978.10 8186.88 6465.92 6977.63 9086.33 7065.69 7880.89 7279.95 11568.97 7190.74 5753.01 10385.25 9577.62 116
Patchmtry76.56 8077.46 7373.83 9979.37 10146.60 11882.41 6176.90 9373.81 4685.56 4692.38 4048.07 11283.98 9463.36 6995.31 4190.92 45
PVSNet_Blended76.49 8175.40 8379.76 7284.43 7963.41 7375.14 10390.44 4057.36 9775.43 9378.30 12069.11 6991.44 5060.68 8187.70 8884.42 88
cascas76.29 8274.81 8580.72 6884.47 7862.94 7673.89 10987.34 6455.94 10075.16 9776.53 12363.97 7891.16 5365.00 6690.97 7388.06 66
RPMNet76.06 8375.79 8076.85 8979.58 9762.64 7982.58 5971.75 11274.80 4275.72 9192.59 3648.69 11084.07 9273.48 4182.91 10383.85 91
wuyk23d75.13 8479.30 6662.63 11875.56 11675.18 4180.89 7373.10 10475.06 4194.76 395.32 1187.73 1252.85 12734.16 12997.11 2259.85 124
CMPMVSbinary59.41 2075.12 8573.57 8779.77 7175.84 11567.22 6481.21 7082.18 8250.78 11776.50 8787.66 8155.20 9982.99 9662.17 7490.64 7789.09 61
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
1112_ss74.82 8673.74 8678.04 8289.57 4660.04 9076.49 9787.09 6654.31 10473.66 9979.80 11660.25 8586.76 8158.37 8684.15 9987.32 74
PatchMatch-RL74.48 8773.22 8878.27 8087.70 6085.26 1075.92 10070.09 11564.34 8276.09 9081.25 11065.87 7678.07 10453.86 10083.82 10071.48 118
XXY-MVS74.44 8876.19 7869.21 10884.61 7752.43 10571.70 11377.18 9160.73 9380.60 7590.96 5475.44 4969.35 11656.13 9388.33 8485.86 81
CR-MVSNet74.00 8973.04 9076.85 8979.58 9762.64 7982.58 5976.90 9350.50 11975.72 9192.38 4048.07 11284.07 9268.72 5682.91 10383.85 91
Test_1112_low_res73.90 9073.08 8976.35 9190.35 4155.95 9673.40 11186.17 7250.70 11873.14 10085.94 9158.31 9285.90 8756.51 9283.22 10187.20 75
HY-MVS64.64 1873.03 9172.47 9274.71 9683.36 8754.19 9782.14 6781.96 8456.76 9969.57 11086.21 9060.03 8684.83 8949.58 11082.65 10585.11 85
EPNet_dtu72.87 9271.33 9777.49 8677.72 10860.55 8882.35 6275.79 9866.49 7758.39 12981.06 11153.68 10385.98 8553.55 10192.97 6185.95 80
FPMVS72.29 9372.00 9473.14 10288.63 5585.00 1174.65 10667.39 11771.94 6277.80 8487.66 8150.48 10775.83 10949.95 10779.51 11358.58 126
FMVSNet572.10 9471.69 9573.32 10081.57 9153.02 10176.77 9578.37 8963.31 8476.37 8891.85 4636.68 12778.98 10347.87 11692.45 6387.95 69
PAPM71.77 9570.06 9976.92 8886.39 6753.97 9876.62 9686.62 6853.44 10863.97 12184.73 9657.79 9492.34 4139.65 12381.33 10884.45 87
IB-MVS62.13 1971.64 9668.97 10179.66 7480.80 9662.26 8173.94 10876.90 9363.27 8568.63 11276.79 12233.83 12991.84 4659.28 8487.26 8984.88 86
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
UnsupCasMVSNet_eth71.63 9772.30 9369.62 10676.47 11452.70 10370.03 11680.97 8659.18 9579.36 8088.21 7560.50 8469.12 11758.33 8777.62 11887.04 76
no-one71.52 9870.43 9874.81 9578.45 10663.41 7357.73 12677.03 9251.46 11477.17 8690.33 6054.96 10180.35 10147.41 11799.29 280.68 113
MIMVSNet71.09 9971.59 9669.57 10787.23 6250.07 11378.91 8471.83 11060.20 9471.26 10491.76 4855.08 10076.09 10741.06 12287.02 9182.54 102
PatchT70.52 10072.76 9163.79 11779.38 10033.53 12877.63 9065.37 12073.61 4771.77 10392.79 3544.38 11975.65 11164.53 6885.37 9382.18 106
N_pmnet70.20 10168.80 10374.38 9780.91 9384.81 1459.12 12576.45 9755.06 10175.31 9682.36 10655.74 9754.82 12647.02 11987.24 9083.52 94
CostFormer69.98 10268.68 10473.87 9877.14 11150.72 11179.26 8174.51 10251.94 11370.97 10684.75 9545.16 11887.49 7755.16 9779.23 11683.40 95
PatchmatchNetpermissive69.71 10368.83 10272.33 10477.66 10953.60 9979.29 8069.99 11657.66 9672.53 10182.93 10546.45 11480.08 10260.91 8072.09 12083.31 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmp4_e2369.43 10467.33 10975.72 9378.53 10552.75 10282.13 6874.91 10049.23 12066.37 11884.17 9841.28 12388.67 7249.73 10979.63 11285.75 82
LP69.42 10568.30 10672.77 10371.48 12656.84 9573.66 11074.84 10163.52 8370.95 10783.35 10449.55 10977.15 10557.13 9070.21 12384.33 89
JIA-IIPM69.41 10666.64 11477.70 8573.19 12171.24 5375.67 10165.56 11970.42 6665.18 12092.97 3233.64 13083.06 9553.52 10269.61 12578.79 115
UnsupCasMVSNet_bld69.21 10769.68 10067.82 11079.42 9951.15 10967.82 11875.79 9854.15 10577.47 8585.36 9459.26 9070.64 11548.46 11479.35 11581.66 108
tpm268.45 10866.83 11273.30 10178.93 10348.50 11679.76 7871.76 11147.50 12269.92 10983.60 10142.07 12288.40 7448.44 11579.51 11383.01 99
tpm67.95 10968.08 10867.55 11178.74 10443.53 12375.60 10267.10 11854.92 10272.23 10288.10 7642.87 12175.97 10852.21 10480.95 11183.15 97
WTY-MVS67.91 11068.35 10566.58 11280.82 9548.12 11765.96 11972.60 10553.67 10771.20 10581.68 10858.97 9169.06 11848.57 11381.67 10682.55 101
sss66.92 11167.26 11065.90 11377.23 11051.10 11064.79 12071.72 11352.12 11270.13 10880.18 11357.96 9365.36 12250.21 10681.01 11081.25 110
tpm cat166.76 11265.21 11671.42 10577.09 11250.62 11278.01 8673.68 10344.89 12568.64 11179.00 11845.51 11582.42 9749.91 10870.15 12481.23 111
HyFIR66.60 11367.24 11164.68 11672.26 12451.46 10762.34 12276.80 9649.02 12163.68 12285.65 9251.09 10575.77 11062.95 7281.37 10766.82 122
PVSNet58.17 2166.41 11465.63 11568.75 10981.96 9049.88 11462.19 12372.51 10751.03 11668.04 11475.34 12550.84 10674.77 11245.82 12082.96 10281.60 109
tpmrst66.28 11566.69 11365.05 11572.82 12339.33 12478.20 8570.69 11453.16 10967.88 11580.36 11248.18 11174.75 11358.13 8870.79 12281.08 112
EPMVS62.47 11662.63 11862.01 11970.63 12738.74 12574.76 10452.86 12553.91 10667.71 11680.01 11439.40 12566.60 11955.54 9668.81 12680.68 113
PMMVS61.65 11760.38 12065.47 11465.40 12869.26 5863.97 12161.73 12336.80 12960.11 12668.43 12759.42 8866.35 12048.97 11278.57 11760.81 123
MVS-HIRNet61.16 11862.92 11755.87 12379.09 10235.34 12771.83 11257.98 12446.56 12359.05 12891.14 5249.95 10876.43 10638.74 12471.92 12155.84 127
DSMNet-mixed60.98 11961.61 11959.09 12272.88 12245.05 12174.70 10546.61 12926.20 13165.34 11990.32 6155.46 9863.12 12541.72 12181.30 10969.09 121
dp60.70 12060.29 12161.92 12072.04 12538.67 12670.83 11464.08 12151.28 11560.75 12577.28 12136.59 12871.58 11447.41 11762.34 12975.52 117
PVSNet_051.08 2256.10 12154.97 12359.48 12175.12 12053.28 10055.16 12761.89 12244.30 12659.16 12762.48 13054.22 10265.91 12135.40 12847.01 13059.25 125
new_pmnet55.69 12257.66 12249.76 12775.47 11830.59 12959.56 12451.45 12743.62 12762.49 12375.48 12440.96 12449.15 12837.39 12572.52 11969.55 120
PNet_i23d52.13 12351.24 12554.79 12575.56 11645.26 11954.54 12852.55 12666.95 7557.19 13065.82 12913.15 13363.40 12436.39 12739.04 13155.71 128
MVEpermissive40.22 2351.82 12450.47 12655.87 12362.66 13051.91 10631.61 12939.28 13040.65 12850.76 13274.98 12656.24 9644.67 12933.94 13064.11 12871.04 119
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
HyFIR lowres test51.64 12551.77 12451.24 12664.21 12949.39 11568.80 11746.62 12828.12 13052.64 13165.93 12838.34 12664.88 12337.16 12664.13 12739.44 129
ab-mvs-re6.65 1268.87 1270.00 1290.00 1320.00 1320.00 1300.00 1320.00 1330.00 13479.80 1160.00 1340.00 1310.00 1320.00 1330.00 131
door72.57 106
NP-MVS90.17 62
HQP4-MVS80.56 7694.61 1693.56 24
ACMMP++97.35 19
BP-MVS77.30 30
HQP5-MVS70.66 54
HQP-NCC91.19 3384.77 4773.30 5080.55 77
DeepMVS_CXcopyleft24.13 12832.95 13129.49 13021.63 13112.07 13237.95 13345.07 13130.84 13119.21 13017.94 13133.06 13223.69 130
ITE_SJBPF90.11 1690.72 3984.97 1290.30 4481.56 1990.02 2191.20 5182.40 2690.81 5573.58 4094.66 4994.56 15
HQP-MVS30.83 132
ACMP_Plane91.19 3384.77 4773.30 5080.55 77
Test By Simon79.09 34
MDTV_nov1_ep13_2view27.60 13170.76 11546.47 12461.27 12445.20 11749.18 11183.75 93
MDTV_nov1_ep1368.29 10778.03 10743.87 12274.12 10772.22 10852.17 11067.02 11785.54 9345.36 11680.85 9955.73 9484.42 98
ACMMP++_ref95.74 35
LGP-MVS_train90.82 994.75 781.69 2194.27 682.35 1593.67 894.82 1691.18 295.52 785.36 698.73 595.23 11
HQP2-MVS72.10 64
HQP3-MVS92.68 2294.47 50