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
APDe-MVScopyleft89.15 989.63 887.73 3194.49 2271.69 5493.83 493.96 1875.70 10991.06 1996.03 176.84 1797.03 2189.09 2195.65 3194.47 51
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVScopyleft89.08 1089.23 1088.61 694.25 3573.73 992.40 2993.63 2674.77 14192.29 795.97 274.28 3397.24 1688.58 3396.91 194.87 18
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
test072695.27 571.25 6493.60 794.11 1177.33 5892.81 395.79 380.98 11
DVP-MVScopyleft89.60 390.35 387.33 4595.27 571.25 6493.49 1092.73 6977.33 5892.12 1295.78 480.98 1197.40 989.08 2296.41 1293.33 117
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_THIRD78.38 3892.12 1295.78 481.46 997.40 989.42 1996.57 794.67 33
DVP-MVS++90.23 191.01 187.89 2494.34 3171.25 6495.06 194.23 778.38 3892.78 495.74 682.45 397.49 489.42 1996.68 294.95 12
test_one_060195.07 771.46 5994.14 1078.27 4192.05 1495.74 680.83 13
SED-MVS90.08 290.85 287.77 2895.30 270.98 7193.57 894.06 1577.24 6193.10 195.72 882.99 197.44 789.07 2596.63 494.88 16
test_241102_TWO94.06 1577.24 6192.78 495.72 881.26 1097.44 789.07 2596.58 694.26 63
DPE-MVScopyleft89.48 689.98 588.01 1694.80 1172.69 3191.59 5194.10 1375.90 10292.29 795.66 1081.67 697.38 1487.44 4896.34 1593.95 79
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
reproduce_model87.28 3587.39 3386.95 5493.10 6271.24 6891.60 5093.19 4074.69 14288.80 3395.61 1170.29 8196.44 4386.20 5693.08 7593.16 127
reproduce-ours87.47 2787.61 2787.07 5093.27 5471.60 5591.56 5493.19 4074.98 13288.96 3095.54 1271.20 7096.54 4086.28 5493.49 7193.06 134
our_new_method87.47 2787.61 2787.07 5093.27 5471.60 5591.56 5493.19 4074.98 13288.96 3095.54 1271.20 7096.54 4086.28 5493.49 7193.06 134
lecture88.09 1788.59 1686.58 6293.26 5669.77 9693.70 694.16 977.13 6689.76 2695.52 1472.26 5396.27 4886.87 5094.65 5293.70 95
fmvsm_s_conf0.5_n_987.39 3387.95 2385.70 8189.48 13767.88 15388.59 14689.05 22880.19 1290.70 2095.40 1574.56 2893.92 15191.54 292.07 9295.31 5
test_241102_ONE95.30 270.98 7194.06 1577.17 6493.10 195.39 1682.99 197.27 15
MP-MVS-pluss87.67 2587.72 2587.54 4093.64 4872.04 5089.80 9093.50 3075.17 12986.34 6895.29 1770.86 7496.00 5988.78 3196.04 1694.58 42
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS88.46 1588.74 1587.64 3892.78 7071.95 5192.40 2994.74 275.71 10789.16 2995.10 1875.65 2496.19 5187.07 4996.01 1794.79 23
ACMMP_NAP88.05 2088.08 2187.94 1993.70 4573.05 2290.86 6593.59 2876.27 9588.14 4195.09 1971.06 7296.67 3387.67 4496.37 1494.09 71
MED-MVS test87.86 2694.57 1771.43 6093.28 1294.36 375.24 12192.25 995.03 2097.39 1188.15 3995.96 1994.75 29
MED-MVS89.59 490.16 487.86 2694.57 1771.43 6093.28 1294.36 376.30 9392.25 995.03 2081.59 797.39 1188.15 3995.96 1994.75 29
TestfortrainingZip a89.27 789.82 787.60 3994.57 1770.90 7793.28 1294.36 375.24 12192.25 995.03 2081.59 797.39 1186.12 5795.96 1994.52 49
ME-MVS88.98 1289.39 987.75 3094.54 2071.43 6091.61 4994.25 676.30 9390.62 2195.03 2078.06 1697.07 2088.15 3995.96 1994.75 29
fmvsm_s_conf0.5_n_386.36 5387.46 3283.09 20187.08 25465.21 22189.09 12390.21 17779.67 1989.98 2495.02 2473.17 4291.71 26391.30 391.60 9992.34 167
MM89.16 889.23 1088.97 490.79 10273.65 1092.66 2891.17 14386.57 187.39 5794.97 2571.70 6297.68 192.19 195.63 3295.57 1
fmvsm_s_conf0.5_n_886.56 4787.17 3884.73 12087.76 22165.62 21289.20 11492.21 9779.94 1789.74 2794.86 2668.63 10894.20 13690.83 591.39 10494.38 55
MTAPA87.23 3687.00 3987.90 2294.18 3974.25 586.58 22492.02 10579.45 2285.88 7094.80 2768.07 11696.21 5086.69 5295.34 3693.23 120
SteuartSystems-ACMMP88.72 1488.86 1488.32 992.14 7872.96 2593.73 593.67 2580.19 1288.10 4294.80 2773.76 3797.11 1887.51 4695.82 2594.90 15
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_1086.38 5286.76 4685.24 9587.33 24067.30 17489.50 10190.98 14876.25 9690.56 2294.75 2968.38 11194.24 13590.80 792.32 8994.19 65
9.1488.26 1992.84 6991.52 5694.75 173.93 16388.57 3594.67 3075.57 2595.79 6386.77 5195.76 27
SR-MVS86.73 4386.67 4886.91 5594.11 4172.11 4992.37 3392.56 8074.50 14686.84 6494.65 3167.31 12595.77 6484.80 6892.85 7892.84 148
region2R87.42 3187.20 3788.09 1494.63 1473.55 1393.03 1993.12 4576.73 8084.45 9494.52 3269.09 9996.70 3184.37 7494.83 4994.03 74
ACMMPR87.44 2987.23 3688.08 1594.64 1373.59 1293.04 1793.20 3976.78 7784.66 8994.52 3268.81 10596.65 3484.53 7294.90 4594.00 76
APD-MVScopyleft87.44 2987.52 3087.19 4794.24 3672.39 4191.86 4592.83 6573.01 19288.58 3494.52 3273.36 3896.49 4284.26 7595.01 4192.70 150
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize85.97 6185.88 6586.22 6792.69 7269.53 9991.93 4292.99 5473.54 17485.94 6994.51 3565.80 14895.61 6783.04 8992.51 8393.53 110
CP-MVS87.11 3886.92 4387.68 3794.20 3873.86 793.98 392.82 6876.62 8383.68 11294.46 3667.93 11895.95 6284.20 7894.39 6193.23 120
SR-MVS-dyc-post85.77 6785.61 7286.23 6693.06 6470.63 8291.88 4392.27 8973.53 17585.69 7394.45 3765.00 15695.56 6882.75 9491.87 9592.50 160
RE-MVS-def85.48 7593.06 6470.63 8291.88 4392.27 8973.53 17585.69 7394.45 3763.87 16482.75 9491.87 9592.50 160
HFP-MVS87.58 2687.47 3187.94 1994.58 1673.54 1593.04 1793.24 3876.78 7784.91 8294.44 3970.78 7596.61 3684.53 7294.89 4693.66 96
PGM-MVS86.68 4586.27 5587.90 2294.22 3773.38 1890.22 8193.04 4675.53 11283.86 10894.42 4067.87 12096.64 3582.70 9894.57 5693.66 96
MP-MVScopyleft87.71 2387.64 2687.93 2194.36 3073.88 692.71 2792.65 7577.57 4983.84 10994.40 4172.24 5496.28 4785.65 5995.30 3993.62 103
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_l_conf0.5_n_386.02 5786.32 5385.14 9887.20 24568.54 13089.57 9990.44 16675.31 12087.49 5494.39 4272.86 4792.72 21989.04 2790.56 11894.16 66
fmvsm_s_conf0.1_n_283.80 10183.79 10183.83 17385.62 29164.94 23387.03 20386.62 29774.32 15187.97 4794.33 4360.67 22292.60 22289.72 1487.79 17093.96 77
fmvsm_l_conf0.5_n_985.84 6686.63 4983.46 18487.12 25366.01 19988.56 14889.43 20475.59 11189.32 2894.32 4472.89 4691.21 28890.11 1192.33 8793.16 127
ZNCC-MVS87.94 2287.85 2488.20 1294.39 2873.33 1993.03 1993.81 2276.81 7585.24 7794.32 4471.76 6096.93 2385.53 6195.79 2694.32 60
MGCNet87.69 2487.55 2988.12 1389.45 13871.76 5391.47 5789.54 20082.14 386.65 6694.28 4668.28 11497.46 690.81 695.31 3895.15 8
test_fmvsmconf0.01_n84.73 8984.52 9185.34 9280.25 40469.03 11089.47 10289.65 19673.24 18686.98 6294.27 4766.62 13293.23 18990.26 1089.95 13093.78 92
HPM-MVS++copyleft89.02 1189.15 1288.63 595.01 976.03 192.38 3292.85 6480.26 1187.78 4894.27 4775.89 2296.81 2787.45 4796.44 993.05 136
mPP-MVS86.67 4686.32 5387.72 3394.41 2673.55 1392.74 2592.22 9576.87 7482.81 13094.25 4966.44 13696.24 4982.88 9294.28 6493.38 113
fmvsm_s_conf0.5_n_284.04 9484.11 9583.81 17586.17 27865.00 22986.96 20687.28 27974.35 15088.25 3994.23 5061.82 19892.60 22289.85 1288.09 16493.84 86
DeepC-MVS79.81 287.08 4086.88 4587.69 3691.16 9172.32 4590.31 7993.94 1977.12 6782.82 12994.23 5072.13 5697.09 1984.83 6795.37 3593.65 100
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n_1186.06 5686.75 4784.00 16687.78 21866.09 19689.96 8690.80 15677.37 5786.72 6594.20 5272.51 5192.78 21889.08 2292.33 8793.13 131
XVS87.18 3786.91 4488.00 1794.42 2473.33 1992.78 2392.99 5479.14 2683.67 11394.17 5367.45 12396.60 3783.06 8794.50 5794.07 72
MSP-MVS89.51 589.91 688.30 1094.28 3473.46 1792.90 2194.11 1180.27 1091.35 1794.16 5478.35 1596.77 2889.59 1794.22 6694.67 33
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
test_fmvsmconf0.1_n85.61 7185.65 7185.50 8882.99 36269.39 10789.65 9590.29 17573.31 18287.77 4994.15 5571.72 6193.23 18990.31 990.67 11793.89 83
DeepPCF-MVS80.84 188.10 1688.56 1786.73 5992.24 7769.03 11089.57 9993.39 3577.53 5389.79 2594.12 5678.98 1496.58 3985.66 5895.72 2894.58 42
HPM-MVS_fast85.35 7984.95 8586.57 6393.69 4670.58 8492.15 4091.62 12873.89 16482.67 13294.09 5762.60 18295.54 7080.93 11192.93 7793.57 106
ZD-MVS94.38 2972.22 4692.67 7270.98 23387.75 5094.07 5874.01 3696.70 3184.66 7094.84 48
fmvsm_s_conf0.1_n_a83.32 12082.99 11784.28 14083.79 33668.07 14589.34 11182.85 35769.80 26787.36 5894.06 5968.34 11391.56 26987.95 4283.46 25493.21 123
CNVR-MVS88.93 1389.13 1388.33 894.77 1273.82 890.51 7093.00 5180.90 788.06 4394.06 5976.43 1996.84 2588.48 3695.99 1894.34 58
test_fmvsmconf_n85.92 6286.04 6385.57 8785.03 31069.51 10089.62 9890.58 16173.42 17887.75 5094.02 6172.85 4893.24 18890.37 890.75 11593.96 77
OPU-MVS89.06 394.62 1575.42 493.57 894.02 6182.45 396.87 2483.77 8296.48 894.88 16
PC_three_145268.21 30492.02 1594.00 6382.09 595.98 6184.58 7196.68 294.95 12
SD-MVS88.06 1888.50 1886.71 6092.60 7572.71 2991.81 4693.19 4077.87 4290.32 2394.00 6374.83 2693.78 15887.63 4594.27 6593.65 100
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
GST-MVS87.42 3187.26 3487.89 2494.12 4072.97 2492.39 3193.43 3376.89 7384.68 8693.99 6570.67 7796.82 2684.18 7995.01 4193.90 82
test_fmvsm_n_192085.29 8085.34 7785.13 10186.12 28069.93 9288.65 14490.78 15769.97 26388.27 3893.98 6671.39 6791.54 27388.49 3590.45 12093.91 80
fmvsm_s_conf0.1_n83.56 11183.38 11084.10 14984.86 31267.28 17589.40 10883.01 35270.67 24087.08 6093.96 6768.38 11191.45 27988.56 3484.50 22893.56 107
HPM-MVScopyleft87.11 3886.98 4187.50 4393.88 4372.16 4792.19 3893.33 3676.07 9983.81 11093.95 6869.77 9096.01 5885.15 6294.66 5194.32 60
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_783.34 11884.03 9681.28 26085.73 28865.13 22485.40 26389.90 18774.96 13482.13 13893.89 6966.65 13187.92 35086.56 5391.05 10990.80 223
fmvsm_s_conf0.5_n_585.22 8185.55 7384.25 14586.26 27467.40 17089.18 11589.31 21372.50 19788.31 3793.86 7069.66 9191.96 25189.81 1391.05 10993.38 113
TSAR-MVS + MP.88.02 2188.11 2087.72 3393.68 4772.13 4891.41 5892.35 8774.62 14588.90 3293.85 7175.75 2396.00 5987.80 4394.63 5495.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMPcopyleft85.89 6585.39 7687.38 4493.59 4972.63 3392.74 2593.18 4476.78 7780.73 16693.82 7264.33 16096.29 4682.67 9990.69 11693.23 120
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
fmvsm_s_conf0.5_n_a83.63 10983.41 10984.28 14086.14 27968.12 14389.43 10482.87 35670.27 25687.27 5993.80 7369.09 9991.58 26688.21 3883.65 24893.14 130
fmvsm_s_conf0.5_n_485.39 7785.75 7084.30 13886.70 26565.83 20588.77 13689.78 18975.46 11588.35 3693.73 7469.19 9893.06 20491.30 388.44 15994.02 75
fmvsm_s_conf0.5_n83.80 10183.71 10384.07 15586.69 26667.31 17389.46 10383.07 35171.09 22886.96 6393.70 7569.02 10491.47 27888.79 3084.62 22793.44 112
test_prior288.85 13275.41 11684.91 8293.54 7674.28 3383.31 8595.86 24
fmvsm_l_conf0.5_n84.47 9084.54 8984.27 14285.42 29768.81 11688.49 15087.26 28168.08 30588.03 4493.49 7772.04 5791.77 25988.90 2989.14 14692.24 174
VDDNet81.52 15880.67 15884.05 16190.44 10864.13 25489.73 9385.91 30871.11 22783.18 12193.48 7850.54 32793.49 17573.40 20488.25 16194.54 48
CDPH-MVS85.76 6885.29 8187.17 4893.49 5171.08 6988.58 14792.42 8568.32 30384.61 9193.48 7872.32 5296.15 5379.00 13495.43 3494.28 62
NCCC88.06 1888.01 2288.24 1194.41 2673.62 1191.22 6292.83 6581.50 585.79 7293.47 8073.02 4597.00 2284.90 6494.94 4494.10 70
fmvsm_s_conf0.5_n_685.55 7286.20 5683.60 17987.32 24265.13 22488.86 13091.63 12775.41 11688.23 4093.45 8168.56 10992.47 23089.52 1892.78 7993.20 125
fmvsm_l_conf0.5_n_a84.13 9384.16 9484.06 15885.38 29868.40 13388.34 15886.85 29167.48 31287.48 5593.40 8270.89 7391.61 26488.38 3789.22 14392.16 181
3Dnovator+77.84 485.48 7384.47 9288.51 791.08 9373.49 1693.18 1693.78 2380.79 876.66 24893.37 8360.40 23096.75 3077.20 15693.73 7095.29 6
DeepC-MVS_fast79.65 386.91 4186.62 5087.76 2993.52 5072.37 4391.26 5993.04 4676.62 8384.22 10093.36 8471.44 6696.76 2980.82 11395.33 3794.16 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS83.01 12882.36 13084.96 10791.02 9566.40 19188.91 12888.11 25577.57 4984.39 9693.29 8552.19 30193.91 15277.05 15988.70 15494.57 44
test_fmvsmvis_n_192084.02 9583.87 9784.49 12784.12 32869.37 10888.15 16687.96 26270.01 26183.95 10793.23 8668.80 10691.51 27688.61 3289.96 12992.57 155
UA-Net85.08 8484.96 8485.45 8992.07 7968.07 14589.78 9190.86 15482.48 284.60 9293.20 8769.35 9595.22 8871.39 22890.88 11493.07 133
TEST993.26 5672.96 2588.75 13891.89 11368.44 30185.00 8093.10 8874.36 3295.41 80
train_agg86.43 4986.20 5687.13 4993.26 5672.96 2588.75 13891.89 11368.69 29685.00 8093.10 8874.43 3095.41 8084.97 6395.71 2993.02 138
test_893.13 6072.57 3588.68 14391.84 11768.69 29684.87 8493.10 8874.43 3095.16 90
LFMVS81.82 14881.23 14883.57 18291.89 8263.43 27789.84 8781.85 36877.04 7083.21 11893.10 8852.26 30093.43 18071.98 22389.95 13093.85 84
旧先验191.96 8065.79 20886.37 30193.08 9269.31 9792.74 8088.74 313
dcpmvs_285.63 7086.15 6084.06 15891.71 8464.94 23386.47 22791.87 11573.63 17086.60 6793.02 9376.57 1891.87 25783.36 8492.15 9095.35 3
testdata79.97 29290.90 9864.21 25284.71 32259.27 40585.40 7592.91 9462.02 19589.08 33268.95 25691.37 10586.63 365
MCST-MVS87.37 3487.25 3587.73 3194.53 2172.46 4089.82 8893.82 2173.07 19084.86 8592.89 9576.22 2096.33 4584.89 6695.13 4094.40 54
Vis-MVSNetpermissive83.46 11482.80 12185.43 9090.25 11268.74 12190.30 8090.13 18076.33 9280.87 16392.89 9561.00 21794.20 13672.45 22090.97 11193.35 116
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS83.73 10483.33 11284.92 11193.28 5370.86 7892.09 4190.38 16868.75 29579.57 18192.83 9760.60 22693.04 20780.92 11291.56 10290.86 222
3Dnovator76.31 583.38 11782.31 13186.59 6187.94 20872.94 2890.64 6892.14 10477.21 6375.47 27492.83 9758.56 24294.72 11573.24 20792.71 8192.13 182
MSLP-MVS++85.43 7585.76 6984.45 12891.93 8170.24 8590.71 6792.86 6377.46 5584.22 10092.81 9967.16 12792.94 20980.36 11994.35 6390.16 252
test250677.30 27276.49 26979.74 29790.08 11652.02 41987.86 17863.10 46274.88 13780.16 17592.79 10038.29 42592.35 23768.74 25992.50 8494.86 19
ECVR-MVScopyleft79.61 20679.26 19980.67 27790.08 11654.69 40187.89 17677.44 41574.88 13780.27 17292.79 10048.96 35092.45 23168.55 26092.50 8494.86 19
test111179.43 21379.18 20280.15 28989.99 12153.31 41487.33 19577.05 41975.04 13080.23 17492.77 10248.97 34992.33 23968.87 25792.40 8694.81 22
MG-MVS83.41 11583.45 10883.28 19192.74 7162.28 30288.17 16489.50 20275.22 12381.49 15092.74 10366.75 13095.11 9472.85 21091.58 10192.45 164
casdiffmvs_mvgpermissive85.99 5986.09 6285.70 8187.65 22867.22 17988.69 14293.04 4679.64 2185.33 7692.54 10473.30 3994.50 12483.49 8391.14 10895.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
patch_mono-283.65 10784.54 8980.99 26990.06 12065.83 20584.21 29688.74 24471.60 21685.01 7992.44 10574.51 2983.50 39682.15 10192.15 9093.64 102
casdiffmvspermissive85.11 8385.14 8285.01 10587.20 24565.77 20987.75 18092.83 6577.84 4384.36 9992.38 10672.15 5593.93 15081.27 10990.48 11995.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CS-MVS86.69 4486.95 4285.90 7890.76 10367.57 16492.83 2293.30 3779.67 1984.57 9392.27 10771.47 6595.02 10084.24 7793.46 7395.13 9
baseline84.93 8684.98 8384.80 11787.30 24365.39 21887.30 19692.88 6277.62 4784.04 10592.26 10871.81 5993.96 14481.31 10790.30 12295.03 11
NormalMVS86.29 5485.88 6587.52 4193.26 5672.47 3891.65 4792.19 9979.31 2484.39 9692.18 10964.64 15895.53 7180.70 11694.65 5294.56 46
SymmetryMVS85.38 7884.81 8687.07 5091.47 8772.47 3891.65 4788.06 25979.31 2484.39 9692.18 10964.64 15895.53 7180.70 11690.91 11393.21 123
QAPM80.88 16979.50 19285.03 10488.01 20668.97 11491.59 5192.00 10766.63 32575.15 29292.16 11157.70 24995.45 7563.52 29988.76 15290.66 231
IS-MVSNet83.15 12382.81 12084.18 14789.94 12363.30 27991.59 5188.46 25279.04 3079.49 18292.16 11165.10 15394.28 13067.71 26691.86 9794.95 12
viewmacassd2359aftdt83.76 10383.66 10584.07 15586.59 26964.56 24186.88 21191.82 11875.72 10683.34 11792.15 11368.24 11592.88 21279.05 13189.15 14594.77 25
BP-MVS184.32 9183.71 10386.17 6887.84 21367.85 15489.38 10989.64 19777.73 4583.98 10692.12 11456.89 26095.43 7784.03 8091.75 9895.24 7
新几何183.42 18693.13 6070.71 8085.48 31457.43 42381.80 14491.98 11563.28 16892.27 24064.60 29492.99 7687.27 347
OpenMVScopyleft72.83 1079.77 20478.33 22084.09 15385.17 30369.91 9390.57 6990.97 14966.70 31972.17 33791.91 11654.70 27793.96 14461.81 32090.95 11288.41 322
PHI-MVS86.43 4986.17 5987.24 4690.88 9970.96 7392.27 3794.07 1472.45 19885.22 7891.90 11769.47 9396.42 4483.28 8695.94 2394.35 57
VNet82.21 13982.41 12881.62 24990.82 10060.93 31884.47 28789.78 18976.36 9184.07 10491.88 11864.71 15790.26 30870.68 23588.89 14893.66 96
EC-MVSNet86.01 5886.38 5284.91 11289.31 14766.27 19492.32 3593.63 2679.37 2384.17 10291.88 11869.04 10395.43 7783.93 8193.77 6993.01 139
GDP-MVS83.52 11282.64 12486.16 6988.14 19768.45 13289.13 12192.69 7072.82 19683.71 11191.86 12055.69 26795.35 8680.03 12289.74 13494.69 32
KinetiMVS83.31 12182.61 12585.39 9187.08 25467.56 16588.06 16891.65 12677.80 4482.21 13791.79 12157.27 25594.07 14277.77 14989.89 13294.56 46
E284.00 9683.87 9784.39 13087.70 22564.95 23086.40 23292.23 9275.85 10383.21 11891.78 12270.09 8493.55 17079.52 12888.05 16594.66 36
E384.00 9683.87 9784.39 13087.70 22564.95 23086.40 23292.23 9275.85 10383.21 11891.78 12270.09 8493.55 17079.52 12888.05 16594.66 36
OPM-MVS83.50 11382.95 11885.14 9888.79 17270.95 7489.13 12191.52 13277.55 5280.96 16091.75 12460.71 22094.50 12479.67 12786.51 19489.97 268
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVSMamba_PlusPlus85.99 5985.96 6486.05 7391.09 9267.64 16189.63 9792.65 7572.89 19584.64 9091.71 12571.85 5896.03 5584.77 6994.45 6094.49 50
viewmanbaseed2359cas83.66 10683.55 10684.00 16686.81 26164.53 24286.65 22191.75 12374.89 13683.15 12391.68 12668.74 10792.83 21679.02 13289.24 14294.63 39
XVG-OURS-SEG-HR80.81 17279.76 18383.96 17085.60 29268.78 11883.54 31590.50 16470.66 24376.71 24791.66 12760.69 22191.26 28576.94 16081.58 27791.83 187
EPNet83.72 10582.92 11986.14 7284.22 32669.48 10191.05 6485.27 31581.30 676.83 24391.65 12866.09 14395.56 6876.00 17593.85 6893.38 113
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS82.69 13281.97 14184.85 11488.75 17467.42 16887.98 17090.87 15374.92 13579.72 17991.65 12862.19 19293.96 14475.26 18686.42 19593.16 127
viewdifsd2359ckpt0782.83 13182.78 12382.99 20886.51 27162.58 29385.09 27190.83 15575.22 12382.28 13491.63 13069.43 9492.03 24777.71 15086.32 19694.34 58
balanced_conf0386.78 4286.99 4086.15 7091.24 9067.61 16290.51 7092.90 6177.26 6087.44 5691.63 13071.27 6996.06 5485.62 6095.01 4194.78 24
test22291.50 8668.26 13784.16 29983.20 34954.63 43479.74 17891.63 13058.97 23891.42 10386.77 361
MVS_111021_HR85.14 8284.75 8786.32 6591.65 8572.70 3085.98 24490.33 17276.11 9882.08 13991.61 13371.36 6894.17 13981.02 11092.58 8292.08 183
原ACMM184.35 13493.01 6668.79 11792.44 8263.96 36181.09 15791.57 13466.06 14495.45 7567.19 27394.82 5088.81 308
viewcassd2359sk1183.89 9883.74 10284.34 13587.76 22164.91 23686.30 23692.22 9575.47 11483.04 12491.52 13570.15 8393.53 17379.26 13087.96 16794.57 44
LPG-MVS_test82.08 14181.27 14784.50 12589.23 15268.76 11990.22 8191.94 11175.37 11876.64 24991.51 13654.29 28094.91 10278.44 14083.78 24189.83 273
LGP-MVS_train84.50 12589.23 15268.76 11991.94 11175.37 11876.64 24991.51 13654.29 28094.91 10278.44 14083.78 24189.83 273
XVG-OURS80.41 18979.23 20083.97 16985.64 29069.02 11283.03 32890.39 16771.09 22877.63 22591.49 13854.62 27991.35 28275.71 17883.47 25391.54 198
alignmvs85.48 7385.32 7985.96 7789.51 13469.47 10289.74 9292.47 8176.17 9787.73 5291.46 13970.32 8093.78 15881.51 10488.95 14794.63 39
CANet86.45 4886.10 6187.51 4290.09 11570.94 7589.70 9492.59 7981.78 481.32 15291.43 14070.34 7997.23 1784.26 7593.36 7494.37 56
h-mvs3383.15 12382.19 13486.02 7690.56 10570.85 7988.15 16689.16 22376.02 10084.67 8791.39 14161.54 20395.50 7382.71 9675.48 35991.72 194
MGCFI-Net85.06 8585.51 7483.70 17789.42 13963.01 28589.43 10492.62 7876.43 8587.53 5391.34 14272.82 4993.42 18181.28 10888.74 15394.66 36
nrg03083.88 9983.53 10784.96 10786.77 26369.28 10990.46 7592.67 7274.79 14082.95 12591.33 14372.70 5093.09 20280.79 11579.28 30792.50 160
sasdasda85.91 6385.87 6786.04 7489.84 12569.44 10590.45 7693.00 5176.70 8188.01 4591.23 14473.28 4093.91 15281.50 10588.80 15094.77 25
canonicalmvs85.91 6385.87 6786.04 7489.84 12569.44 10590.45 7693.00 5176.70 8188.01 4591.23 14473.28 4093.91 15281.50 10588.80 15094.77 25
DPM-MVS84.93 8684.29 9386.84 5690.20 11373.04 2387.12 20093.04 4669.80 26782.85 12891.22 14673.06 4496.02 5776.72 16894.63 5491.46 204
Anonymous20240521178.25 24477.01 25581.99 24391.03 9460.67 32384.77 27883.90 33570.65 24480.00 17691.20 14741.08 41091.43 28065.21 28885.26 21993.85 84
SPE-MVS-test86.29 5486.48 5185.71 8091.02 9567.21 18092.36 3493.78 2378.97 3383.51 11691.20 14770.65 7895.15 9181.96 10294.89 4694.77 25
Anonymous2024052980.19 19978.89 20884.10 14990.60 10464.75 23988.95 12790.90 15165.97 33380.59 16891.17 14949.97 33493.73 16469.16 25482.70 26693.81 88
EPP-MVSNet83.40 11683.02 11684.57 12390.13 11464.47 24792.32 3590.73 15874.45 14979.35 18791.10 15069.05 10295.12 9272.78 21187.22 18094.13 68
TAPA-MVS73.13 979.15 22277.94 22882.79 22289.59 13062.99 28988.16 16591.51 13365.77 33477.14 24091.09 15160.91 21893.21 19150.26 40787.05 18492.17 180
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG86.41 5186.19 5887.07 5092.91 6772.48 3790.81 6693.56 2973.95 16183.16 12291.07 15275.94 2195.19 8979.94 12494.38 6293.55 108
FIs82.07 14282.42 12781.04 26888.80 17158.34 34788.26 16193.49 3176.93 7278.47 20591.04 15369.92 8892.34 23869.87 24784.97 22192.44 165
MVS_111021_LR82.61 13482.11 13584.11 14888.82 16671.58 5785.15 26886.16 30574.69 14280.47 17191.04 15362.29 18990.55 30680.33 12090.08 12790.20 251
DP-MVS Recon83.11 12682.09 13786.15 7094.44 2370.92 7688.79 13592.20 9870.53 24579.17 18991.03 15564.12 16296.03 5568.39 26390.14 12591.50 200
mamv476.81 28078.23 22472.54 39986.12 28065.75 21078.76 38582.07 36564.12 35572.97 32591.02 15667.97 11768.08 46483.04 8978.02 32183.80 409
HQP_MVS83.64 10883.14 11385.14 9890.08 11668.71 12391.25 6092.44 8279.12 2878.92 19391.00 15760.42 22895.38 8278.71 13886.32 19691.33 205
plane_prior491.00 157
FC-MVSNet-test81.52 15882.02 13980.03 29188.42 18755.97 38687.95 17293.42 3477.10 6877.38 22990.98 15969.96 8791.79 25868.46 26284.50 22892.33 168
diffmvs_AUTHOR82.38 13782.27 13382.73 22783.26 35063.80 26183.89 30389.76 19173.35 18182.37 13390.84 16066.25 13990.79 30082.77 9387.93 16893.59 105
Vis-MVSNet (Re-imp)78.36 24378.45 21578.07 33388.64 17851.78 42586.70 21979.63 39774.14 15875.11 29390.83 16161.29 21189.75 31858.10 35591.60 9992.69 152
114514_t80.68 18079.51 19184.20 14694.09 4267.27 17689.64 9691.11 14658.75 41274.08 31190.72 16258.10 24595.04 9969.70 24889.42 14090.30 248
viewdifsd2359ckpt1382.91 12982.29 13284.77 11886.96 25766.90 18787.47 18791.62 12872.19 20381.68 14790.71 16366.92 12993.28 18475.90 17687.15 18294.12 69
viewdifsd2359ckpt0983.34 11882.55 12685.70 8187.64 22967.72 15988.43 15191.68 12571.91 21081.65 14890.68 16467.10 12894.75 11376.17 17187.70 17294.62 41
PAPM_NR83.02 12782.41 12884.82 11592.47 7666.37 19287.93 17491.80 11973.82 16577.32 23190.66 16567.90 11994.90 10470.37 23889.48 13993.19 126
viewdifsd2359ckpt1180.37 19379.73 18482.30 23683.70 34062.39 29784.20 29786.67 29373.22 18780.90 16190.62 16663.00 17991.56 26976.81 16578.44 31492.95 143
viewmsd2359difaftdt80.37 19379.73 18482.30 23683.70 34062.39 29784.20 29786.67 29373.22 18780.90 16190.62 16663.00 17991.56 26976.81 16578.44 31492.95 143
LS3D76.95 27874.82 29683.37 18990.45 10767.36 17289.15 12086.94 28861.87 38569.52 36790.61 16851.71 31494.53 12246.38 42986.71 19188.21 326
AstraMVS80.81 17280.14 17382.80 21986.05 28363.96 25686.46 22885.90 30973.71 16880.85 16490.56 16954.06 28491.57 26879.72 12683.97 23992.86 146
VPNet78.69 23578.66 21178.76 31688.31 19055.72 39084.45 29086.63 29676.79 7678.26 20990.55 17059.30 23689.70 32066.63 27777.05 33290.88 221
UniMVSNet_ETH3D79.10 22478.24 22281.70 24886.85 25960.24 33087.28 19788.79 23974.25 15576.84 24290.53 17149.48 34091.56 26967.98 26482.15 27093.29 118
ACMP74.13 681.51 16080.57 16084.36 13389.42 13968.69 12689.97 8591.50 13674.46 14875.04 29690.41 17253.82 28694.54 12177.56 15282.91 26189.86 272
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SSM_040781.58 15580.48 16384.87 11388.81 16767.96 14987.37 19289.25 21871.06 23079.48 18390.39 17359.57 23394.48 12672.45 22085.93 20792.18 177
SSM_040481.91 14580.84 15685.13 10189.24 15168.26 13787.84 17989.25 21871.06 23080.62 16790.39 17359.57 23394.65 11972.45 22087.19 18192.47 163
viewmambaseed2359dif80.41 18979.84 18182.12 23882.95 36462.50 29683.39 31688.06 25967.11 31480.98 15990.31 17566.20 14191.01 29674.62 19084.90 22292.86 146
RRT-MVS82.60 13682.10 13684.10 14987.98 20762.94 29087.45 19091.27 13977.42 5679.85 17790.28 17656.62 26394.70 11779.87 12588.15 16394.67 33
PCF-MVS73.52 780.38 19178.84 20985.01 10587.71 22368.99 11383.65 30991.46 13763.00 36977.77 22390.28 17666.10 14295.09 9861.40 32388.22 16290.94 220
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS89.62 12968.32 13590.24 178
HQP-MVS82.61 13482.02 13984.37 13289.33 14466.98 18389.17 11692.19 9976.41 8677.23 23490.23 17960.17 23195.11 9477.47 15385.99 20591.03 215
PS-MVSNAJss82.07 14281.31 14684.34 13586.51 27167.27 17689.27 11291.51 13371.75 21179.37 18690.22 18063.15 17494.27 13177.69 15182.36 26991.49 201
TSAR-MVS + GP.85.71 6985.33 7886.84 5691.34 8872.50 3689.07 12487.28 27976.41 8685.80 7190.22 18074.15 3595.37 8581.82 10391.88 9492.65 154
SDMVSNet80.38 19180.18 17080.99 26989.03 16164.94 23380.45 36089.40 20575.19 12776.61 25189.98 18260.61 22587.69 35476.83 16483.55 25090.33 246
sd_testset77.70 26377.40 24878.60 31989.03 16160.02 33279.00 38185.83 31075.19 12776.61 25189.98 18254.81 27285.46 37962.63 31083.55 25090.33 246
TranMVSNet+NR-MVSNet80.84 17080.31 16782.42 23387.85 21262.33 30087.74 18191.33 13880.55 977.99 21789.86 18465.23 15292.62 22067.05 27575.24 36992.30 170
diffmvspermissive82.10 14081.88 14282.76 22583.00 36063.78 26383.68 30889.76 19172.94 19382.02 14089.85 18565.96 14790.79 30082.38 10087.30 17993.71 94
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Elysia81.53 15680.16 17185.62 8485.51 29468.25 13988.84 13392.19 9971.31 22180.50 16989.83 18646.89 36194.82 10876.85 16189.57 13693.80 90
StellarMVS81.53 15680.16 17185.62 8485.51 29468.25 13988.84 13392.19 9971.31 22180.50 16989.83 18646.89 36194.82 10876.85 16189.57 13693.80 90
mamba_040879.37 21877.52 24584.93 11088.81 16767.96 14965.03 46088.66 24670.96 23479.48 18389.80 18858.69 23994.65 11970.35 23985.93 20792.18 177
SSM_0407277.67 26577.52 24578.12 33188.81 16767.96 14965.03 46088.66 24670.96 23479.48 18389.80 18858.69 23974.23 45270.35 23985.93 20792.18 177
BH-RMVSNet79.61 20678.44 21683.14 19989.38 14365.93 20284.95 27587.15 28473.56 17378.19 21189.79 19056.67 26293.36 18259.53 33986.74 19090.13 254
GeoE81.71 15081.01 15383.80 17689.51 13464.45 24888.97 12688.73 24571.27 22478.63 19989.76 19166.32 13893.20 19469.89 24686.02 20493.74 93
guyue81.13 16580.64 15982.60 23086.52 27063.92 25986.69 22087.73 27073.97 16080.83 16589.69 19256.70 26191.33 28478.26 14785.40 21892.54 157
AdaColmapbinary80.58 18779.42 19384.06 15893.09 6368.91 11589.36 11088.97 23469.27 27975.70 27089.69 19257.20 25795.77 6463.06 30488.41 16087.50 341
ACMM73.20 880.78 17979.84 18183.58 18189.31 14768.37 13489.99 8491.60 13070.28 25577.25 23289.66 19453.37 29193.53 17374.24 19682.85 26288.85 306
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA78.08 25076.79 26281.97 24490.40 10971.07 7087.59 18484.55 32566.03 33272.38 33489.64 19557.56 25186.04 37159.61 33883.35 25588.79 309
test_yl81.17 16380.47 16483.24 19489.13 15663.62 26486.21 23989.95 18572.43 20181.78 14589.61 19657.50 25293.58 16670.75 23386.90 18692.52 158
DCV-MVSNet81.17 16380.47 16483.24 19489.13 15663.62 26486.21 23989.95 18572.43 20181.78 14589.61 19657.50 25293.58 16670.75 23386.90 18692.52 158
EI-MVSNet-Vis-set84.19 9283.81 10085.31 9388.18 19467.85 15487.66 18289.73 19480.05 1582.95 12589.59 19870.74 7694.82 10880.66 11884.72 22593.28 119
PAPR81.66 15380.89 15583.99 16890.27 11164.00 25586.76 21891.77 12268.84 29477.13 24189.50 19967.63 12194.88 10667.55 26888.52 15793.09 132
jajsoiax79.29 21977.96 22783.27 19284.68 31766.57 19089.25 11390.16 17969.20 28475.46 27689.49 20045.75 37793.13 20076.84 16380.80 28790.11 256
MVSFormer82.85 13082.05 13885.24 9587.35 23570.21 8690.50 7290.38 16868.55 29881.32 15289.47 20161.68 20093.46 17878.98 13590.26 12392.05 184
jason81.39 16180.29 16884.70 12186.63 26869.90 9485.95 24586.77 29263.24 36581.07 15889.47 20161.08 21692.15 24478.33 14390.07 12892.05 184
jason: jason.
mvs_tets79.13 22377.77 23783.22 19684.70 31666.37 19289.17 11690.19 17869.38 27675.40 27989.46 20344.17 38993.15 19876.78 16780.70 28990.14 253
UGNet80.83 17179.59 19084.54 12488.04 20368.09 14489.42 10688.16 25476.95 7176.22 26089.46 20349.30 34493.94 14768.48 26190.31 12191.60 195
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
VPA-MVSNet80.60 18480.55 16180.76 27588.07 20260.80 32186.86 21291.58 13175.67 11080.24 17389.45 20563.34 16790.25 30970.51 23779.22 30891.23 208
MVS_Test83.15 12383.06 11583.41 18886.86 25863.21 28186.11 24292.00 10774.31 15282.87 12789.44 20670.03 8693.21 19177.39 15588.50 15893.81 88
EI-MVSNet-UG-set83.81 10083.38 11085.09 10387.87 21167.53 16687.44 19189.66 19579.74 1882.23 13689.41 20770.24 8294.74 11479.95 12383.92 24092.99 141
RPSCF73.23 33471.46 33878.54 32282.50 37359.85 33382.18 33482.84 35858.96 40871.15 34989.41 20745.48 38184.77 38658.82 34771.83 39991.02 217
UniMVSNet_NR-MVSNet81.88 14681.54 14582.92 21288.46 18463.46 27587.13 19992.37 8680.19 1278.38 20689.14 20971.66 6493.05 20570.05 24376.46 34292.25 172
tttt051779.40 21577.91 22983.90 17288.10 20063.84 26088.37 15784.05 33371.45 21976.78 24589.12 21049.93 33794.89 10570.18 24283.18 25992.96 142
DU-MVS81.12 16680.52 16282.90 21387.80 21563.46 27587.02 20491.87 11579.01 3178.38 20689.07 21165.02 15493.05 20570.05 24376.46 34292.20 175
NR-MVSNet80.23 19779.38 19482.78 22387.80 21563.34 27886.31 23591.09 14779.01 3172.17 33789.07 21167.20 12692.81 21766.08 28275.65 35592.20 175
icg_test_0407_278.92 23078.93 20778.90 31487.13 24863.59 26876.58 40789.33 20870.51 24677.82 21989.03 21361.84 19681.38 41172.56 21685.56 21491.74 190
IMVS_040780.61 18279.90 17982.75 22687.13 24863.59 26885.33 26489.33 20870.51 24677.82 21989.03 21361.84 19692.91 21072.56 21685.56 21491.74 190
IMVS_040477.16 27476.42 27279.37 30587.13 24863.59 26877.12 40589.33 20870.51 24666.22 40689.03 21350.36 32982.78 40172.56 21685.56 21491.74 190
IMVS_040380.80 17580.12 17482.87 21587.13 24863.59 26885.19 26589.33 20870.51 24678.49 20389.03 21363.26 17093.27 18672.56 21685.56 21491.74 190
DELS-MVS85.41 7685.30 8085.77 7988.49 18267.93 15285.52 26293.44 3278.70 3483.63 11589.03 21374.57 2795.71 6680.26 12194.04 6793.66 96
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
mvsmamba80.60 18479.38 19484.27 14289.74 12867.24 17887.47 18786.95 28770.02 26075.38 28088.93 21851.24 31892.56 22575.47 18489.22 14393.00 140
baseline176.98 27776.75 26577.66 34088.13 19855.66 39185.12 26981.89 36673.04 19176.79 24488.90 21962.43 18787.78 35363.30 30371.18 40389.55 282
DP-MVS76.78 28174.57 29983.42 18693.29 5269.46 10488.55 14983.70 33763.98 36070.20 35588.89 22054.01 28594.80 11146.66 42681.88 27586.01 375
ab-mvs79.51 20978.97 20681.14 26588.46 18460.91 31983.84 30489.24 22070.36 25179.03 19088.87 22163.23 17290.21 31065.12 28982.57 26792.28 171
PEN-MVS77.73 26077.69 24177.84 33787.07 25653.91 40887.91 17591.18 14277.56 5173.14 32388.82 22261.23 21289.17 33059.95 33472.37 39390.43 241
tt080578.73 23377.83 23381.43 25485.17 30360.30 32989.41 10790.90 15171.21 22577.17 23988.73 22346.38 36693.21 19172.57 21478.96 30990.79 224
test_djsdf80.30 19679.32 19783.27 19283.98 33265.37 21990.50 7290.38 16868.55 29876.19 26188.70 22456.44 26493.46 17878.98 13580.14 29790.97 218
PAPM77.68 26476.40 27381.51 25287.29 24461.85 30783.78 30589.59 19964.74 34771.23 34788.70 22462.59 18393.66 16552.66 39187.03 18589.01 298
DTE-MVSNet76.99 27676.80 26177.54 34586.24 27553.06 41787.52 18590.66 15977.08 6972.50 33188.67 22660.48 22789.52 32257.33 36270.74 40590.05 263
PS-CasMVS78.01 25478.09 22577.77 33987.71 22354.39 40588.02 16991.22 14077.50 5473.26 32188.64 22760.73 21988.41 34561.88 31873.88 38290.53 237
cdsmvs_eth3d_5k19.96 44326.61 4450.00 4640.00 4870.00 4890.00 47689.26 2170.00 4820.00 48388.61 22861.62 2020.00 4830.00 4820.00 4810.00 479
lupinMVS81.39 16180.27 16984.76 11987.35 23570.21 8685.55 25886.41 29962.85 37281.32 15288.61 22861.68 20092.24 24278.41 14290.26 12391.83 187
F-COLMAP76.38 29174.33 30582.50 23289.28 14966.95 18688.41 15389.03 22964.05 35866.83 39588.61 22846.78 36392.89 21157.48 35978.55 31187.67 335
mvs_anonymous79.42 21479.11 20380.34 28484.45 32357.97 35382.59 33087.62 27267.40 31376.17 26488.56 23168.47 11089.59 32170.65 23686.05 20393.47 111
CP-MVSNet78.22 24578.34 21977.84 33787.83 21454.54 40387.94 17391.17 14377.65 4673.48 31988.49 23262.24 19188.43 34462.19 31474.07 37890.55 236
PVSNet_Blended_VisFu82.62 13381.83 14384.96 10790.80 10169.76 9788.74 14091.70 12469.39 27578.96 19188.46 23365.47 15094.87 10774.42 19388.57 15590.24 250
CANet_DTU80.61 18279.87 18082.83 21685.60 29263.17 28487.36 19388.65 24876.37 9075.88 26788.44 23453.51 28993.07 20373.30 20589.74 13492.25 172
PLCcopyleft70.83 1178.05 25276.37 27483.08 20391.88 8367.80 15688.19 16389.46 20364.33 35369.87 36488.38 23553.66 28793.58 16658.86 34682.73 26487.86 332
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS79.49 21079.22 20180.27 28688.79 17258.35 34685.06 27288.61 25078.56 3577.65 22488.34 23663.81 16690.66 30564.98 29177.22 33091.80 189
XXY-MVS75.41 30575.56 28374.96 37183.59 34357.82 35780.59 35783.87 33666.54 32674.93 29988.31 23763.24 17180.09 41762.16 31576.85 33686.97 357
Effi-MVS+83.62 11083.08 11485.24 9588.38 18867.45 16788.89 12989.15 22475.50 11382.27 13588.28 23869.61 9294.45 12777.81 14887.84 16993.84 86
API-MVS81.99 14481.23 14884.26 14490.94 9770.18 9191.10 6389.32 21271.51 21878.66 19888.28 23865.26 15195.10 9764.74 29391.23 10787.51 340
thisisatest053079.40 21577.76 23884.31 13787.69 22765.10 22787.36 19384.26 33170.04 25977.42 22888.26 24049.94 33594.79 11270.20 24184.70 22693.03 137
hse-mvs281.72 14980.94 15484.07 15588.72 17567.68 16085.87 24887.26 28176.02 10084.67 8788.22 24161.54 20393.48 17682.71 9673.44 38791.06 213
xiu_mvs_v1_base_debu80.80 17579.72 18684.03 16387.35 23570.19 8885.56 25588.77 24069.06 28881.83 14188.16 24250.91 32192.85 21378.29 14487.56 17389.06 293
xiu_mvs_v1_base80.80 17579.72 18684.03 16387.35 23570.19 8885.56 25588.77 24069.06 28881.83 14188.16 24250.91 32192.85 21378.29 14487.56 17389.06 293
xiu_mvs_v1_base_debi80.80 17579.72 18684.03 16387.35 23570.19 8885.56 25588.77 24069.06 28881.83 14188.16 24250.91 32192.85 21378.29 14487.56 17389.06 293
UniMVSNet (Re)81.60 15481.11 15083.09 20188.38 18864.41 24987.60 18393.02 5078.42 3778.56 20188.16 24269.78 8993.26 18769.58 25076.49 34191.60 195
AUN-MVS79.21 22177.60 24384.05 16188.71 17667.61 16285.84 25087.26 28169.08 28777.23 23488.14 24653.20 29393.47 17775.50 18373.45 38691.06 213
Anonymous2023121178.97 22877.69 24182.81 21890.54 10664.29 25190.11 8391.51 13365.01 34576.16 26588.13 24750.56 32693.03 20869.68 24977.56 32891.11 211
pm-mvs177.25 27376.68 26778.93 31384.22 32658.62 34486.41 22988.36 25371.37 22073.31 32088.01 24861.22 21389.15 33164.24 29773.01 39089.03 297
LuminaMVS80.68 18079.62 18983.83 17385.07 30968.01 14886.99 20588.83 23770.36 25181.38 15187.99 24950.11 33292.51 22979.02 13286.89 18890.97 218
SD_040374.65 31374.77 29774.29 38086.20 27747.42 44483.71 30785.12 31769.30 27868.50 37887.95 25059.40 23586.05 37049.38 41183.35 25589.40 285
LTVRE_ROB69.57 1376.25 29274.54 30181.41 25588.60 17964.38 25079.24 37689.12 22770.76 23969.79 36687.86 25149.09 34793.20 19456.21 37480.16 29586.65 364
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
testing3-275.12 31075.19 29274.91 37290.40 10945.09 45580.29 36378.42 40778.37 4076.54 25387.75 25244.36 38787.28 35957.04 36583.49 25292.37 166
WTY-MVS75.65 30075.68 28075.57 36286.40 27356.82 37177.92 39982.40 36165.10 34276.18 26287.72 25363.13 17780.90 41460.31 33281.96 27389.00 300
TAMVS78.89 23177.51 24783.03 20687.80 21567.79 15784.72 27985.05 32067.63 30876.75 24687.70 25462.25 19090.82 29958.53 35087.13 18390.49 239
BH-untuned79.47 21178.60 21282.05 24189.19 15465.91 20386.07 24388.52 25172.18 20475.42 27887.69 25561.15 21493.54 17260.38 33186.83 18986.70 363
COLMAP_ROBcopyleft66.92 1773.01 33770.41 35280.81 27487.13 24865.63 21188.30 16084.19 33262.96 37063.80 42387.69 25538.04 42692.56 22546.66 42674.91 37284.24 402
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-074.26 31672.42 32979.80 29683.76 33859.59 33785.92 24786.64 29566.39 32766.96 39387.58 25739.46 41691.60 26565.76 28569.27 41188.22 325
FA-MVS(test-final)80.96 16879.91 17884.10 14988.30 19165.01 22884.55 28690.01 18373.25 18579.61 18087.57 25858.35 24494.72 11571.29 22986.25 19992.56 156
Baseline_NR-MVSNet78.15 24978.33 22077.61 34285.79 28656.21 38486.78 21685.76 31173.60 17277.93 21887.57 25865.02 15488.99 33367.14 27475.33 36687.63 336
WR-MVS_H78.51 24078.49 21478.56 32188.02 20456.38 38088.43 15192.67 7277.14 6573.89 31387.55 26066.25 13989.24 32858.92 34573.55 38590.06 262
EI-MVSNet80.52 18879.98 17682.12 23884.28 32463.19 28386.41 22988.95 23574.18 15778.69 19687.54 26166.62 13292.43 23272.57 21480.57 29190.74 228
CVMVSNet72.99 33872.58 32774.25 38184.28 32450.85 43386.41 22983.45 34344.56 45373.23 32287.54 26149.38 34285.70 37465.90 28378.44 31486.19 370
ACMH+68.96 1476.01 29674.01 30782.03 24288.60 17965.31 22088.86 13087.55 27370.25 25767.75 38287.47 26341.27 40893.19 19658.37 35275.94 35287.60 337
TransMVSNet (Re)75.39 30774.56 30077.86 33685.50 29657.10 36886.78 21686.09 30772.17 20571.53 34487.34 26463.01 17889.31 32656.84 36861.83 43587.17 349
GBi-Net78.40 24177.40 24881.40 25687.60 23063.01 28588.39 15489.28 21471.63 21375.34 28287.28 26554.80 27391.11 28962.72 30679.57 30190.09 258
test178.40 24177.40 24881.40 25687.60 23063.01 28588.39 15489.28 21471.63 21375.34 28287.28 26554.80 27391.11 28962.72 30679.57 30190.09 258
FMVSNet278.20 24777.21 25281.20 26387.60 23062.89 29187.47 18789.02 23071.63 21375.29 28887.28 26554.80 27391.10 29262.38 31179.38 30589.61 280
FMVSNet177.44 26876.12 27681.40 25686.81 26163.01 28588.39 15489.28 21470.49 25074.39 30887.28 26549.06 34891.11 28960.91 32778.52 31290.09 258
v2v48280.23 19779.29 19883.05 20583.62 34264.14 25387.04 20289.97 18473.61 17178.18 21287.22 26961.10 21593.82 15676.11 17276.78 33891.18 209
ITE_SJBPF78.22 32881.77 38360.57 32483.30 34469.25 28167.54 38487.20 27036.33 43387.28 35954.34 38274.62 37586.80 360
anonymousdsp78.60 23777.15 25382.98 21080.51 40267.08 18187.24 19889.53 20165.66 33675.16 29187.19 27152.52 29592.25 24177.17 15779.34 30689.61 280
MVSTER79.01 22677.88 23282.38 23483.07 35764.80 23884.08 30288.95 23569.01 29178.69 19687.17 27254.70 27792.43 23274.69 18980.57 29189.89 271
thres100view90076.50 28575.55 28479.33 30689.52 13356.99 36985.83 25183.23 34673.94 16276.32 25887.12 27351.89 31091.95 25248.33 41783.75 24489.07 291
thres600view776.50 28575.44 28579.68 29989.40 14157.16 36685.53 26083.23 34673.79 16676.26 25987.09 27451.89 31091.89 25548.05 42283.72 24790.00 264
XVG-ACMP-BASELINE76.11 29474.27 30681.62 24983.20 35364.67 24083.60 31289.75 19369.75 27071.85 34087.09 27432.78 44092.11 24569.99 24580.43 29388.09 328
HY-MVS69.67 1277.95 25577.15 25380.36 28387.57 23460.21 33183.37 31887.78 26966.11 32975.37 28187.06 27663.27 16990.48 30761.38 32482.43 26890.40 243
CHOSEN 1792x268877.63 26675.69 27983.44 18589.98 12268.58 12978.70 38687.50 27556.38 42875.80 26986.84 27758.67 24191.40 28161.58 32285.75 21290.34 245
v879.97 20379.02 20582.80 21984.09 32964.50 24687.96 17190.29 17574.13 15975.24 28986.81 27862.88 18193.89 15574.39 19475.40 36490.00 264
AllTest70.96 35568.09 37079.58 30285.15 30563.62 26484.58 28579.83 39462.31 37960.32 43686.73 27932.02 44188.96 33650.28 40571.57 40186.15 371
TestCases79.58 30285.15 30563.62 26479.83 39462.31 37960.32 43686.73 27932.02 44188.96 33650.28 40571.57 40186.15 371
LCM-MVSNet-Re77.05 27576.94 25877.36 34687.20 24551.60 42680.06 36680.46 38575.20 12667.69 38386.72 28162.48 18588.98 33463.44 30189.25 14191.51 199
1112_ss77.40 27076.43 27180.32 28589.11 16060.41 32883.65 30987.72 27162.13 38273.05 32486.72 28162.58 18489.97 31462.11 31780.80 28790.59 235
ab-mvs-re7.23 4469.64 4490.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 48386.72 2810.00 4860.00 4830.00 4820.00 4810.00 479
IterMVS-LS80.06 20079.38 19482.11 24085.89 28463.20 28286.79 21589.34 20774.19 15675.45 27786.72 28166.62 13292.39 23472.58 21376.86 33590.75 227
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH67.68 1675.89 29773.93 30981.77 24788.71 17666.61 18988.62 14589.01 23169.81 26666.78 39686.70 28541.95 40591.51 27655.64 37578.14 32087.17 349
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res76.40 29075.44 28579.27 30789.28 14958.09 34981.69 33987.07 28559.53 40372.48 33286.67 28661.30 21089.33 32560.81 32980.15 29690.41 242
FMVSNet377.88 25776.85 26080.97 27186.84 26062.36 29986.52 22688.77 24071.13 22675.34 28286.66 28754.07 28391.10 29262.72 30679.57 30189.45 284
pmmvs674.69 31273.39 31678.61 31881.38 39157.48 36386.64 22287.95 26364.99 34670.18 35686.61 28850.43 32889.52 32262.12 31670.18 40888.83 307
ET-MVSNet_ETH3D78.63 23676.63 26884.64 12286.73 26469.47 10285.01 27384.61 32469.54 27366.51 40386.59 28950.16 33191.75 26076.26 17084.24 23692.69 152
testgi66.67 39566.53 39167.08 43075.62 43641.69 46575.93 41076.50 42266.11 32965.20 41486.59 28935.72 43574.71 44943.71 43873.38 38884.84 396
CLD-MVS82.31 13881.65 14484.29 13988.47 18367.73 15885.81 25292.35 8775.78 10578.33 20886.58 29164.01 16394.35 12876.05 17487.48 17690.79 224
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v1079.74 20578.67 21082.97 21184.06 33064.95 23087.88 17790.62 16073.11 18975.11 29386.56 29261.46 20694.05 14373.68 19975.55 35789.90 270
CDS-MVSNet79.07 22577.70 24083.17 19887.60 23068.23 14184.40 29386.20 30467.49 31176.36 25786.54 29361.54 20390.79 30061.86 31987.33 17890.49 239
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base81.69 15181.05 15183.60 17989.15 15568.03 14784.46 28990.02 18270.67 24081.30 15586.53 29463.17 17394.19 13875.60 18188.54 15688.57 318
TR-MVS77.44 26876.18 27581.20 26388.24 19263.24 28084.61 28486.40 30067.55 31077.81 22186.48 29554.10 28293.15 19857.75 35882.72 26587.20 348
EIA-MVS83.31 12182.80 12184.82 11589.59 13065.59 21388.21 16292.68 7174.66 14478.96 19186.42 29669.06 10195.26 8775.54 18290.09 12693.62 103
tfpn200view976.42 28975.37 28979.55 30489.13 15657.65 36085.17 26683.60 33873.41 17976.45 25486.39 29752.12 30291.95 25248.33 41783.75 24489.07 291
thres40076.50 28575.37 28979.86 29489.13 15657.65 36085.17 26683.60 33873.41 17976.45 25486.39 29752.12 30291.95 25248.33 41783.75 24490.00 264
v7n78.97 22877.58 24483.14 19983.45 34665.51 21488.32 15991.21 14173.69 16972.41 33386.32 29957.93 24693.81 15769.18 25375.65 35590.11 256
MAR-MVS81.84 14780.70 15785.27 9491.32 8971.53 5889.82 8890.92 15069.77 26978.50 20286.21 30062.36 18894.52 12365.36 28792.05 9389.77 276
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
v114480.03 20179.03 20483.01 20783.78 33764.51 24487.11 20190.57 16371.96 20978.08 21586.20 30161.41 20793.94 14774.93 18877.23 32990.60 234
test_vis1_n_192075.52 30275.78 27874.75 37679.84 41057.44 36483.26 32085.52 31362.83 37379.34 18886.17 30245.10 38279.71 41878.75 13781.21 28187.10 355
V4279.38 21778.24 22282.83 21681.10 39665.50 21585.55 25889.82 18871.57 21778.21 21086.12 30360.66 22393.18 19775.64 17975.46 36189.81 275
PVSNet_BlendedMVS80.60 18480.02 17582.36 23588.85 16365.40 21686.16 24192.00 10769.34 27778.11 21386.09 30466.02 14594.27 13171.52 22582.06 27287.39 342
v119279.59 20878.43 21783.07 20483.55 34464.52 24386.93 20990.58 16170.83 23677.78 22285.90 30559.15 23793.94 14773.96 19877.19 33190.76 226
SixPastTwentyTwo73.37 32971.26 34379.70 29885.08 30857.89 35585.57 25483.56 34071.03 23265.66 40885.88 30642.10 40392.57 22459.11 34363.34 43088.65 315
EPNet_dtu75.46 30374.86 29577.23 34982.57 37254.60 40286.89 21083.09 35071.64 21266.25 40585.86 30755.99 26588.04 34954.92 37986.55 19389.05 296
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss73.60 32673.64 31473.51 38882.80 36655.01 39976.12 40981.69 36962.47 37874.68 30385.85 30857.32 25478.11 42560.86 32880.93 28387.39 342
ETV-MVS84.90 8884.67 8885.59 8689.39 14268.66 12788.74 14092.64 7779.97 1684.10 10385.71 30969.32 9695.38 8280.82 11391.37 10592.72 149
test_cas_vis1_n_192073.76 32473.74 31373.81 38675.90 43259.77 33480.51 35882.40 36158.30 41481.62 14985.69 31044.35 38876.41 43676.29 16978.61 31085.23 388
v124078.99 22777.78 23682.64 22883.21 35263.54 27286.62 22390.30 17469.74 27277.33 23085.68 31157.04 25893.76 16173.13 20876.92 33390.62 232
v14419279.47 21178.37 21882.78 22383.35 34763.96 25686.96 20690.36 17169.99 26277.50 22685.67 31260.66 22393.77 16074.27 19576.58 33990.62 232
tfpnnormal74.39 31473.16 32078.08 33286.10 28258.05 35084.65 28387.53 27470.32 25471.22 34885.63 31354.97 27189.86 31543.03 44175.02 37186.32 367
PS-MVSNAJ81.69 15181.02 15283.70 17789.51 13468.21 14284.28 29590.09 18170.79 23781.26 15685.62 31463.15 17494.29 12975.62 18088.87 14988.59 317
SSC-MVS3.273.35 33273.39 31673.23 38985.30 30149.01 44074.58 42481.57 37075.21 12573.68 31685.58 31552.53 29482.05 40654.33 38377.69 32688.63 316
v192192079.22 22078.03 22682.80 21983.30 34963.94 25886.80 21490.33 17269.91 26577.48 22785.53 31658.44 24393.75 16273.60 20076.85 33690.71 230
test_040272.79 34070.44 35179.84 29588.13 19865.99 20185.93 24684.29 32965.57 33767.40 38985.49 31746.92 36092.61 22135.88 45574.38 37780.94 434
v14878.72 23477.80 23581.47 25382.73 36861.96 30686.30 23688.08 25773.26 18476.18 26285.47 31862.46 18692.36 23671.92 22473.82 38390.09 258
USDC70.33 36468.37 36576.21 35680.60 40056.23 38379.19 37886.49 29860.89 39061.29 43185.47 31831.78 44389.47 32453.37 38876.21 35082.94 420
VortexMVS78.57 23977.89 23180.59 27885.89 28462.76 29285.61 25389.62 19872.06 20774.99 29785.38 32055.94 26690.77 30374.99 18776.58 33988.23 324
MVP-Stereo76.12 29374.46 30381.13 26685.37 29969.79 9584.42 29287.95 26365.03 34467.46 38685.33 32153.28 29291.73 26258.01 35683.27 25781.85 429
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS78.19 24876.99 25781.78 24685.66 28966.99 18284.66 28190.47 16555.08 43372.02 33985.27 32263.83 16594.11 14166.10 28189.80 13384.24 402
DIV-MVS_self_test77.72 26176.76 26380.58 27982.48 37560.48 32683.09 32487.86 26669.22 28274.38 30985.24 32362.10 19391.53 27471.09 23075.40 36489.74 277
FE-MVS77.78 25975.68 28084.08 15488.09 20166.00 20083.13 32387.79 26868.42 30278.01 21685.23 32445.50 38095.12 9259.11 34385.83 21191.11 211
cl____77.72 26176.76 26380.58 27982.49 37460.48 32683.09 32487.87 26569.22 28274.38 30985.22 32562.10 19391.53 27471.09 23075.41 36389.73 278
HyFIR lowres test77.53 26775.40 28783.94 17189.59 13066.62 18880.36 36188.64 24956.29 42976.45 25485.17 32657.64 25093.28 18461.34 32583.10 26091.91 186
pmmvs474.03 32271.91 33380.39 28281.96 38068.32 13581.45 34382.14 36359.32 40469.87 36485.13 32752.40 29888.13 34860.21 33374.74 37484.73 398
TDRefinement67.49 38764.34 39976.92 35173.47 44861.07 31784.86 27782.98 35459.77 40058.30 44385.13 32726.06 45187.89 35147.92 42360.59 44081.81 430
Fast-Effi-MVS+80.81 17279.92 17783.47 18388.85 16364.51 24485.53 26089.39 20670.79 23778.49 20385.06 32967.54 12293.58 16667.03 27686.58 19292.32 169
PVSNet_Blended80.98 16780.34 16682.90 21388.85 16365.40 21684.43 29192.00 10767.62 30978.11 21385.05 33066.02 14594.27 13171.52 22589.50 13889.01 298
ttmdpeth59.91 41557.10 41968.34 42567.13 46246.65 44974.64 42367.41 45248.30 44862.52 42985.04 33120.40 46175.93 44142.55 44345.90 46382.44 423
test_fmvs1_n70.86 35770.24 35472.73 39772.51 45555.28 39681.27 34679.71 39651.49 44478.73 19584.87 33227.54 45077.02 43076.06 17379.97 29985.88 379
WBMVS73.43 32872.81 32475.28 36887.91 20950.99 43278.59 38981.31 37565.51 34074.47 30784.83 33346.39 36586.68 36358.41 35177.86 32288.17 327
CMPMVSbinary51.72 2170.19 36668.16 36876.28 35573.15 45157.55 36279.47 37383.92 33448.02 44956.48 44984.81 33443.13 39586.42 36762.67 30981.81 27684.89 395
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet68.53 38267.61 38171.31 40978.51 42447.01 44784.47 28784.27 33042.27 45666.44 40484.79 33540.44 41383.76 39258.76 34868.54 41683.17 414
BH-w/o78.21 24677.33 25180.84 27388.81 16765.13 22484.87 27687.85 26769.75 27074.52 30684.74 33661.34 20993.11 20158.24 35485.84 21084.27 401
pmmvs571.55 35070.20 35575.61 36177.83 42556.39 37981.74 33880.89 37657.76 41967.46 38684.49 33749.26 34585.32 38157.08 36475.29 36785.11 392
reproduce_monomvs75.40 30674.38 30478.46 32683.92 33457.80 35883.78 30586.94 28873.47 17772.25 33684.47 33838.74 42189.27 32775.32 18570.53 40688.31 323
thres20075.55 30174.47 30278.82 31587.78 21857.85 35683.07 32683.51 34172.44 20075.84 26884.42 33952.08 30591.75 26047.41 42483.64 24986.86 359
test_fmvs170.93 35670.52 34972.16 40173.71 44455.05 39880.82 34978.77 40551.21 44578.58 20084.41 34031.20 44576.94 43175.88 17780.12 29884.47 400
testing368.56 38167.67 38071.22 41087.33 24042.87 46083.06 32771.54 44070.36 25169.08 37284.38 34130.33 44785.69 37537.50 45375.45 36285.09 393
test_fmvs268.35 38467.48 38370.98 41269.50 45851.95 42180.05 36776.38 42349.33 44774.65 30484.38 34123.30 45975.40 44774.51 19275.17 37085.60 382
eth_miper_zixun_eth77.92 25676.69 26681.61 25183.00 36061.98 30583.15 32289.20 22269.52 27474.86 30084.35 34361.76 19992.56 22571.50 22772.89 39190.28 249
myMVS_eth3d2873.62 32573.53 31573.90 38588.20 19347.41 44578.06 39679.37 39974.29 15473.98 31284.29 34444.67 38383.54 39551.47 39787.39 17790.74 228
testing9176.54 28375.66 28279.18 31088.43 18655.89 38781.08 34783.00 35373.76 16775.34 28284.29 34446.20 37190.07 31264.33 29584.50 22891.58 197
c3_l78.75 23277.91 22981.26 26182.89 36561.56 31184.09 30189.13 22669.97 26375.56 27284.29 34466.36 13792.09 24673.47 20375.48 35990.12 255
testing9976.09 29575.12 29479.00 31188.16 19555.50 39380.79 35181.40 37373.30 18375.17 29084.27 34744.48 38690.02 31364.28 29684.22 23791.48 202
UWE-MVS72.13 34771.49 33774.03 38386.66 26747.70 44281.40 34576.89 42163.60 36475.59 27184.22 34839.94 41585.62 37648.98 41486.13 20288.77 310
Fast-Effi-MVS+-dtu78.02 25376.49 26982.62 22983.16 35666.96 18586.94 20887.45 27772.45 19871.49 34584.17 34954.79 27691.58 26667.61 26780.31 29489.30 289
IterMVS-SCA-FT75.43 30473.87 31180.11 29082.69 36964.85 23781.57 34183.47 34269.16 28570.49 35284.15 35051.95 30888.15 34769.23 25272.14 39787.34 344
131476.53 28475.30 29180.21 28883.93 33362.32 30184.66 28188.81 23860.23 39670.16 35884.07 35155.30 27090.73 30467.37 27083.21 25887.59 339
cl2278.07 25177.01 25581.23 26282.37 37761.83 30883.55 31387.98 26168.96 29275.06 29583.87 35261.40 20891.88 25673.53 20176.39 34489.98 267
EG-PatchMatch MVS74.04 32071.82 33480.71 27684.92 31167.42 16885.86 24988.08 25766.04 33164.22 41883.85 35335.10 43692.56 22557.44 36080.83 28682.16 427
thisisatest051577.33 27175.38 28883.18 19785.27 30263.80 26182.11 33583.27 34565.06 34375.91 26683.84 35449.54 33994.27 13167.24 27286.19 20091.48 202
test20.0367.45 38866.95 38968.94 41975.48 43744.84 45677.50 40177.67 41166.66 32063.01 42583.80 35547.02 35978.40 42342.53 44468.86 41583.58 411
miper_ehance_all_eth78.59 23877.76 23881.08 26782.66 37061.56 31183.65 30989.15 22468.87 29375.55 27383.79 35666.49 13592.03 24773.25 20676.39 34489.64 279
MSDG73.36 33170.99 34580.49 28184.51 32265.80 20780.71 35586.13 30665.70 33565.46 40983.74 35744.60 38490.91 29851.13 40076.89 33484.74 397
MonoMVSNet76.49 28875.80 27778.58 32081.55 38758.45 34586.36 23486.22 30374.87 13974.73 30283.73 35851.79 31388.73 33970.78 23272.15 39688.55 319
testing1175.14 30974.01 30778.53 32388.16 19556.38 38080.74 35480.42 38770.67 24072.69 33083.72 35943.61 39389.86 31562.29 31383.76 24389.36 287
IterMVS74.29 31572.94 32378.35 32781.53 38863.49 27481.58 34082.49 36068.06 30669.99 36183.69 36051.66 31585.54 37765.85 28471.64 40086.01 375
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm72.37 34371.71 33574.35 37982.19 37852.00 42079.22 37777.29 41764.56 34972.95 32683.68 36151.35 31683.26 39958.33 35375.80 35387.81 333
UWE-MVS-2865.32 40264.93 39666.49 43178.70 42238.55 46877.86 40064.39 46062.00 38464.13 41983.60 36241.44 40676.00 44031.39 46080.89 28484.92 394
sc_t172.19 34669.51 35780.23 28784.81 31361.09 31684.68 28080.22 39160.70 39271.27 34683.58 36336.59 43189.24 32860.41 33063.31 43190.37 244
testing22274.04 32072.66 32678.19 32987.89 21055.36 39481.06 34879.20 40271.30 22374.65 30483.57 36439.11 42088.67 34151.43 39985.75 21290.53 237
Effi-MVS+-dtu80.03 20178.57 21384.42 12985.13 30768.74 12188.77 13688.10 25674.99 13174.97 29883.49 36557.27 25593.36 18273.53 20180.88 28591.18 209
baseline275.70 29973.83 31281.30 25983.26 35061.79 30982.57 33180.65 38066.81 31666.88 39483.42 36657.86 24892.19 24363.47 30079.57 30189.91 269
mvs5depth69.45 37367.45 38475.46 36673.93 44255.83 38879.19 37883.23 34666.89 31571.63 34383.32 36733.69 43985.09 38259.81 33655.34 45085.46 384
TinyColmap67.30 39064.81 39774.76 37581.92 38256.68 37580.29 36381.49 37260.33 39456.27 45083.22 36824.77 45587.66 35545.52 43469.47 41079.95 439
mvsany_test162.30 41161.26 41565.41 43369.52 45754.86 40066.86 45249.78 47346.65 45068.50 37883.21 36949.15 34666.28 46556.93 36760.77 43875.11 449
test_vis1_n69.85 37169.21 36071.77 40372.66 45455.27 39781.48 34276.21 42452.03 44175.30 28783.20 37028.97 44876.22 43874.60 19178.41 31883.81 408
CostFormer75.24 30873.90 31079.27 30782.65 37158.27 34880.80 35082.73 35961.57 38675.33 28683.13 37155.52 26891.07 29564.98 29178.34 31988.45 320
MVStest156.63 41952.76 42568.25 42661.67 46853.25 41671.67 43368.90 45038.59 46150.59 45783.05 37225.08 45370.66 45836.76 45438.56 46480.83 435
WB-MVSnew71.96 34971.65 33672.89 39584.67 32051.88 42382.29 33377.57 41262.31 37973.67 31783.00 37353.49 29081.10 41345.75 43382.13 27185.70 381
ETVMVS72.25 34571.05 34475.84 35887.77 22051.91 42279.39 37474.98 42869.26 28073.71 31582.95 37440.82 41286.14 36946.17 43084.43 23389.47 283
miper_lstm_enhance74.11 31973.11 32177.13 35080.11 40659.62 33672.23 43186.92 29066.76 31870.40 35382.92 37556.93 25982.92 40069.06 25572.63 39288.87 305
GA-MVS76.87 27975.17 29381.97 24482.75 36762.58 29381.44 34486.35 30272.16 20674.74 30182.89 37646.20 37192.02 24968.85 25881.09 28291.30 207
K. test v371.19 35268.51 36479.21 30983.04 35957.78 35984.35 29476.91 42072.90 19462.99 42682.86 37739.27 41791.09 29461.65 32152.66 45388.75 311
MS-PatchMatch73.83 32372.67 32577.30 34883.87 33566.02 19881.82 33684.66 32361.37 38968.61 37682.82 37847.29 35688.21 34659.27 34084.32 23577.68 444
lessismore_v078.97 31281.01 39757.15 36765.99 45561.16 43282.82 37839.12 41991.34 28359.67 33746.92 46088.43 321
D2MVS74.82 31173.21 31979.64 30179.81 41162.56 29580.34 36287.35 27864.37 35268.86 37382.66 38046.37 36790.10 31167.91 26581.24 28086.25 368
Anonymous2023120668.60 37967.80 37771.02 41180.23 40550.75 43478.30 39480.47 38456.79 42666.11 40782.63 38146.35 36878.95 42143.62 43975.70 35483.36 413
MIMVSNet70.69 35969.30 35874.88 37384.52 32156.35 38275.87 41379.42 39864.59 34867.76 38182.41 38241.10 40981.54 40946.64 42881.34 27886.75 362
UBG73.08 33672.27 33175.51 36488.02 20451.29 43078.35 39377.38 41665.52 33873.87 31482.36 38345.55 37886.48 36655.02 37884.39 23488.75 311
OpenMVS_ROBcopyleft64.09 1970.56 36168.19 36777.65 34180.26 40359.41 34085.01 27382.96 35558.76 41165.43 41082.33 38437.63 42891.23 28745.34 43676.03 35182.32 424
miper_enhance_ethall77.87 25876.86 25980.92 27281.65 38461.38 31382.68 32988.98 23265.52 33875.47 27482.30 38565.76 14992.00 25072.95 20976.39 34489.39 286
test0.0.03 168.00 38667.69 37968.90 42077.55 42647.43 44375.70 41472.95 43966.66 32066.56 39982.29 38648.06 35375.87 44244.97 43774.51 37683.41 412
PVSNet64.34 1872.08 34870.87 34775.69 36086.21 27656.44 37874.37 42580.73 37962.06 38370.17 35782.23 38742.86 39783.31 39854.77 38084.45 23287.32 345
MIMVSNet168.58 38066.78 39073.98 38480.07 40751.82 42480.77 35284.37 32664.40 35159.75 43982.16 38836.47 43283.63 39442.73 44270.33 40786.48 366
CL-MVSNet_self_test72.37 34371.46 33875.09 37079.49 41753.53 41080.76 35385.01 32169.12 28670.51 35182.05 38957.92 24784.13 39052.27 39366.00 42487.60 337
tpm273.26 33371.46 33878.63 31783.34 34856.71 37480.65 35680.40 38856.63 42773.55 31882.02 39051.80 31291.24 28656.35 37378.42 31787.95 329
PatchMatch-RL72.38 34270.90 34676.80 35388.60 17967.38 17179.53 37276.17 42562.75 37569.36 36982.00 39145.51 37984.89 38553.62 38680.58 29078.12 443
FMVSNet569.50 37267.96 37274.15 38282.97 36355.35 39580.01 36882.12 36462.56 37763.02 42481.53 39236.92 42981.92 40748.42 41674.06 37985.17 391
CR-MVSNet73.37 32971.27 34279.67 30081.32 39465.19 22275.92 41180.30 38959.92 39972.73 32881.19 39352.50 29686.69 36259.84 33577.71 32487.11 353
Patchmtry70.74 35869.16 36175.49 36580.72 39854.07 40774.94 42280.30 38958.34 41370.01 35981.19 39352.50 29686.54 36453.37 38871.09 40485.87 380
IB-MVS68.01 1575.85 29873.36 31883.31 19084.76 31566.03 19783.38 31785.06 31970.21 25869.40 36881.05 39545.76 37694.66 11865.10 29075.49 35889.25 290
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
cascas76.72 28274.64 29882.99 20885.78 28765.88 20482.33 33289.21 22160.85 39172.74 32781.02 39647.28 35793.75 16267.48 26985.02 22089.34 288
LF4IMVS64.02 40762.19 41169.50 41770.90 45653.29 41576.13 40877.18 41852.65 43958.59 44180.98 39723.55 45876.52 43453.06 39066.66 42078.68 442
Anonymous2024052168.80 37867.22 38773.55 38774.33 44054.11 40683.18 32185.61 31258.15 41561.68 43080.94 39830.71 44681.27 41257.00 36673.34 38985.28 387
gm-plane-assit81.40 39053.83 40962.72 37680.94 39892.39 23463.40 302
UnsupCasMVSNet_eth67.33 38965.99 39371.37 40673.48 44751.47 42875.16 41885.19 31665.20 34160.78 43380.93 40042.35 39977.20 42957.12 36353.69 45285.44 385
dmvs_re71.14 35370.58 34872.80 39681.96 38059.68 33575.60 41579.34 40068.55 29869.27 37180.72 40149.42 34176.54 43352.56 39277.79 32382.19 426
MDTV_nov1_ep1369.97 35683.18 35453.48 41177.10 40680.18 39360.45 39369.33 37080.44 40248.89 35186.90 36151.60 39678.51 313
pmmvs-eth3d70.50 36267.83 37678.52 32477.37 42866.18 19581.82 33681.51 37158.90 40963.90 42280.42 40342.69 39886.28 36858.56 34965.30 42683.11 416
tt032070.49 36368.03 37177.89 33584.78 31459.12 34183.55 31380.44 38658.13 41667.43 38880.41 40439.26 41887.54 35655.12 37763.18 43286.99 356
mmtdpeth74.16 31873.01 32277.60 34483.72 33961.13 31485.10 27085.10 31872.06 20777.21 23880.33 40543.84 39185.75 37377.14 15852.61 45485.91 378
tt0320-xc70.11 36767.45 38478.07 33385.33 30059.51 33983.28 31978.96 40458.77 41067.10 39280.28 40636.73 43087.42 35756.83 36959.77 44287.29 346
PM-MVS66.41 39764.14 40073.20 39273.92 44356.45 37778.97 38264.96 45963.88 36264.72 41580.24 40719.84 46383.44 39766.24 27864.52 42879.71 440
SCA74.22 31772.33 33079.91 29384.05 33162.17 30379.96 36979.29 40166.30 32872.38 33480.13 40851.95 30888.60 34259.25 34177.67 32788.96 302
Patchmatch-test64.82 40563.24 40669.57 41679.42 41849.82 43863.49 46469.05 44851.98 44259.95 43880.13 40850.91 32170.98 45740.66 44773.57 38487.90 331
tpmrst72.39 34172.13 33273.18 39380.54 40149.91 43779.91 37079.08 40363.11 36771.69 34279.95 41055.32 26982.77 40265.66 28673.89 38186.87 358
DSMNet-mixed57.77 41856.90 42060.38 43967.70 46035.61 47069.18 44453.97 47132.30 46957.49 44679.88 41140.39 41468.57 46338.78 45172.37 39376.97 445
MDA-MVSNet-bldmvs66.68 39463.66 40475.75 35979.28 41960.56 32573.92 42778.35 40864.43 35050.13 45879.87 41244.02 39083.67 39346.10 43156.86 44483.03 418
PatchmatchNetpermissive73.12 33571.33 34178.49 32583.18 35460.85 32079.63 37178.57 40664.13 35471.73 34179.81 41351.20 31985.97 37257.40 36176.36 34988.66 314
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
FE-MVSNET67.25 39165.33 39573.02 39475.86 43352.54 41880.26 36580.56 38263.80 36360.39 43479.70 41441.41 40784.66 38843.34 44062.62 43381.86 428
Syy-MVS68.05 38567.85 37468.67 42384.68 31740.97 46678.62 38773.08 43766.65 32366.74 39779.46 41552.11 30482.30 40432.89 45876.38 34782.75 421
myMVS_eth3d67.02 39266.29 39269.21 41884.68 31742.58 46178.62 38773.08 43766.65 32366.74 39779.46 41531.53 44482.30 40439.43 45076.38 34782.75 421
ppachtmachnet_test70.04 36867.34 38678.14 33079.80 41261.13 31479.19 37880.59 38159.16 40665.27 41179.29 41746.75 36487.29 35849.33 41266.72 41986.00 377
EPMVS69.02 37668.16 36871.59 40479.61 41549.80 43977.40 40266.93 45362.82 37470.01 35979.05 41845.79 37577.86 42756.58 37175.26 36887.13 352
PMMVS69.34 37468.67 36371.35 40875.67 43562.03 30475.17 41773.46 43550.00 44668.68 37479.05 41852.07 30678.13 42461.16 32682.77 26373.90 450
test-LLR72.94 33972.43 32874.48 37781.35 39258.04 35178.38 39077.46 41366.66 32069.95 36279.00 42048.06 35379.24 41966.13 27984.83 22386.15 371
test-mter71.41 35170.39 35374.48 37781.35 39258.04 35178.38 39077.46 41360.32 39569.95 36279.00 42036.08 43479.24 41966.13 27984.83 22386.15 371
KD-MVS_self_test68.81 37767.59 38272.46 40074.29 44145.45 45077.93 39887.00 28663.12 36663.99 42178.99 42242.32 40084.77 38656.55 37264.09 42987.16 351
test_fmvs363.36 40961.82 41267.98 42762.51 46746.96 44877.37 40374.03 43445.24 45267.50 38578.79 42312.16 47172.98 45672.77 21266.02 42383.99 406
KD-MVS_2432*160066.22 39963.89 40273.21 39075.47 43853.42 41270.76 43884.35 32764.10 35666.52 40178.52 42434.55 43784.98 38350.40 40350.33 45781.23 432
miper_refine_blended66.22 39963.89 40273.21 39075.47 43853.42 41270.76 43884.35 32764.10 35666.52 40178.52 42434.55 43784.98 38350.40 40350.33 45781.23 432
tpmvs71.09 35469.29 35976.49 35482.04 37956.04 38578.92 38381.37 37464.05 35867.18 39178.28 42649.74 33889.77 31749.67 41072.37 39383.67 410
our_test_369.14 37567.00 38875.57 36279.80 41258.80 34277.96 39777.81 41059.55 40262.90 42778.25 42747.43 35583.97 39151.71 39567.58 41883.93 407
MDA-MVSNet_test_wron65.03 40362.92 40771.37 40675.93 43156.73 37269.09 44774.73 43157.28 42454.03 45377.89 42845.88 37374.39 45149.89 40961.55 43682.99 419
YYNet165.03 40362.91 40871.38 40575.85 43456.60 37669.12 44674.66 43357.28 42454.12 45277.87 42945.85 37474.48 45049.95 40861.52 43783.05 417
ambc75.24 36973.16 45050.51 43563.05 46587.47 27664.28 41777.81 43017.80 46589.73 31957.88 35760.64 43985.49 383
tpm cat170.57 36068.31 36677.35 34782.41 37657.95 35478.08 39580.22 39152.04 44068.54 37777.66 43152.00 30787.84 35251.77 39472.07 39886.25 368
dp66.80 39365.43 39470.90 41379.74 41448.82 44175.12 42074.77 43059.61 40164.08 42077.23 43242.89 39680.72 41548.86 41566.58 42183.16 415
TESTMET0.1,169.89 37069.00 36272.55 39879.27 42056.85 37078.38 39074.71 43257.64 42068.09 38077.19 43337.75 42776.70 43263.92 29884.09 23884.10 405
CHOSEN 280x42066.51 39664.71 39871.90 40281.45 38963.52 27357.98 46768.95 44953.57 43662.59 42876.70 43446.22 37075.29 44855.25 37679.68 30076.88 446
PatchT68.46 38367.85 37470.29 41480.70 39943.93 45872.47 43074.88 42960.15 39770.55 35076.57 43549.94 33581.59 40850.58 40174.83 37385.34 386
mvsany_test353.99 42251.45 42761.61 43855.51 47244.74 45763.52 46345.41 47743.69 45558.11 44476.45 43617.99 46463.76 46854.77 38047.59 45976.34 447
RPMNet73.51 32770.49 35082.58 23181.32 39465.19 22275.92 41192.27 8957.60 42172.73 32876.45 43652.30 29995.43 7748.14 42177.71 32487.11 353
dmvs_testset62.63 41064.11 40158.19 44178.55 42324.76 47975.28 41665.94 45667.91 30760.34 43576.01 43853.56 28873.94 45431.79 45967.65 41775.88 448
ADS-MVSNet266.20 40163.33 40574.82 37479.92 40858.75 34367.55 45075.19 42753.37 43765.25 41275.86 43942.32 40080.53 41641.57 44568.91 41385.18 389
ADS-MVSNet64.36 40662.88 40968.78 42279.92 40847.17 44667.55 45071.18 44153.37 43765.25 41275.86 43942.32 40073.99 45341.57 44568.91 41385.18 389
EGC-MVSNET52.07 42847.05 43267.14 42983.51 34560.71 32280.50 35967.75 4510.07 4790.43 48075.85 44124.26 45681.54 40928.82 46262.25 43459.16 462
new-patchmatchnet61.73 41261.73 41361.70 43772.74 45324.50 48069.16 44578.03 40961.40 38756.72 44875.53 44238.42 42376.48 43545.95 43257.67 44384.13 404
N_pmnet52.79 42653.26 42451.40 45178.99 4217.68 48569.52 4423.89 48451.63 44357.01 44774.98 44340.83 41165.96 46637.78 45264.67 42780.56 438
WB-MVS54.94 42054.72 42155.60 44773.50 44620.90 48174.27 42661.19 46459.16 40650.61 45674.15 44447.19 35875.78 44317.31 47235.07 46670.12 454
patchmatchnet-post74.00 44551.12 32088.60 342
GG-mvs-BLEND75.38 36781.59 38655.80 38979.32 37569.63 44567.19 39073.67 44643.24 39488.90 33850.41 40284.50 22881.45 431
SSC-MVS53.88 42353.59 42354.75 44972.87 45219.59 48273.84 42860.53 46657.58 42249.18 46073.45 44746.34 36975.47 44616.20 47532.28 46869.20 455
Patchmatch-RL test70.24 36567.78 37877.61 34277.43 42759.57 33871.16 43570.33 44262.94 37168.65 37572.77 44850.62 32585.49 37869.58 25066.58 42187.77 334
FPMVS53.68 42451.64 42659.81 44065.08 46451.03 43169.48 44369.58 44641.46 45740.67 46472.32 44916.46 46770.00 46124.24 46865.42 42558.40 464
UnsupCasMVSNet_bld63.70 40861.53 41470.21 41573.69 44551.39 42972.82 42981.89 36655.63 43157.81 44571.80 45038.67 42278.61 42249.26 41352.21 45580.63 436
APD_test153.31 42549.93 43063.42 43665.68 46350.13 43671.59 43466.90 45434.43 46640.58 46571.56 4518.65 47676.27 43734.64 45755.36 44963.86 460
test_f52.09 42750.82 42855.90 44553.82 47542.31 46459.42 46658.31 46936.45 46456.12 45170.96 45212.18 47057.79 47153.51 38756.57 44667.60 456
PVSNet_057.27 2061.67 41359.27 41668.85 42179.61 41557.44 36468.01 44873.44 43655.93 43058.54 44270.41 45344.58 38577.55 42847.01 42535.91 46571.55 453
pmmvs357.79 41754.26 42268.37 42464.02 46656.72 37375.12 42065.17 45740.20 45852.93 45469.86 45420.36 46275.48 44545.45 43555.25 45172.90 452
test_vis1_rt60.28 41458.42 41765.84 43267.25 46155.60 39270.44 44060.94 46544.33 45459.00 44066.64 45524.91 45468.67 46262.80 30569.48 40973.25 451
new_pmnet50.91 42950.29 42952.78 45068.58 45934.94 47263.71 46256.63 47039.73 45944.95 46165.47 45621.93 46058.48 47034.98 45656.62 44564.92 458
gg-mvs-nofinetune69.95 36967.96 37275.94 35783.07 35754.51 40477.23 40470.29 44363.11 36770.32 35462.33 45743.62 39288.69 34053.88 38587.76 17184.62 399
JIA-IIPM66.32 39862.82 41076.82 35277.09 42961.72 31065.34 45875.38 42658.04 41864.51 41662.32 45842.05 40486.51 36551.45 39869.22 41282.21 425
LCM-MVSNet54.25 42149.68 43167.97 42853.73 47645.28 45366.85 45380.78 37835.96 46539.45 46662.23 4598.70 47578.06 42648.24 42051.20 45680.57 437
PMMVS240.82 43738.86 44146.69 45253.84 47416.45 48348.61 47049.92 47237.49 46231.67 46760.97 4608.14 47756.42 47228.42 46330.72 46967.19 457
testf145.72 43241.96 43657.00 44256.90 47045.32 45166.14 45559.26 46726.19 47030.89 46960.96 4614.14 47970.64 45926.39 46646.73 46155.04 465
APD_test245.72 43241.96 43657.00 44256.90 47045.32 45166.14 45559.26 46726.19 47030.89 46960.96 4614.14 47970.64 45926.39 46646.73 46155.04 465
MVS-HIRNet59.14 41657.67 41863.57 43581.65 38443.50 45971.73 43265.06 45839.59 46051.43 45557.73 46338.34 42482.58 40339.53 44873.95 38064.62 459
ANet_high50.57 43046.10 43463.99 43448.67 47939.13 46770.99 43780.85 37761.39 38831.18 46857.70 46417.02 46673.65 45531.22 46115.89 47679.18 441
PMVScopyleft37.38 2244.16 43640.28 44055.82 44640.82 48142.54 46365.12 45963.99 46134.43 46624.48 47257.12 4653.92 48176.17 43917.10 47355.52 44848.75 467
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai45.42 43445.38 43545.55 45373.36 44926.85 47767.72 44934.19 47954.15 43549.65 45956.41 46625.43 45262.94 46919.45 47028.09 47046.86 469
test_vis3_rt49.26 43147.02 43356.00 44454.30 47345.27 45466.76 45448.08 47436.83 46344.38 46253.20 4677.17 47864.07 46756.77 37055.66 44758.65 463
test_method31.52 44029.28 44438.23 45527.03 4836.50 48620.94 47562.21 4634.05 47722.35 47552.50 46813.33 46847.58 47527.04 46534.04 46760.62 461
kuosan39.70 43840.40 43937.58 45664.52 46526.98 47565.62 45733.02 48046.12 45142.79 46348.99 46924.10 45746.56 47712.16 47826.30 47139.20 470
DeepMVS_CXcopyleft27.40 45940.17 48226.90 47624.59 48317.44 47523.95 47348.61 4709.77 47326.48 47818.06 47124.47 47228.83 472
MVEpermissive26.22 2330.37 44225.89 44643.81 45444.55 48035.46 47128.87 47439.07 47818.20 47418.58 47640.18 4712.68 48247.37 47617.07 47423.78 47348.60 468
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipumacopyleft45.18 43541.86 43855.16 44877.03 43051.52 42732.50 47380.52 38332.46 46827.12 47135.02 4729.52 47475.50 44422.31 46960.21 44138.45 471
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN31.77 43930.64 44235.15 45752.87 47727.67 47457.09 46847.86 47524.64 47216.40 47733.05 47311.23 47254.90 47314.46 47618.15 47422.87 473
EMVS30.81 44129.65 44334.27 45850.96 47825.95 47856.58 46946.80 47624.01 47315.53 47830.68 47412.47 46954.43 47412.81 47717.05 47522.43 474
tmp_tt18.61 44421.40 44710.23 4614.82 48410.11 48434.70 47230.74 4821.48 47823.91 47426.07 47528.42 44913.41 48027.12 46415.35 4777.17 475
X-MVStestdata80.37 19377.83 23388.00 1794.42 2473.33 1992.78 2392.99 5479.14 2683.67 11312.47 47667.45 12396.60 3783.06 8794.50 5794.07 72
test_post5.46 47750.36 32984.24 389
test_post178.90 3845.43 47848.81 35285.44 38059.25 341
wuyk23d16.82 44515.94 44819.46 46058.74 46931.45 47339.22 4713.74 4856.84 4766.04 4792.70 4791.27 48324.29 47910.54 47914.40 4782.63 476
testmvs6.04 4488.02 4510.10 4630.08 4850.03 48869.74 4410.04 4860.05 4800.31 4811.68 4800.02 4850.04 4810.24 4800.02 4790.25 478
test1236.12 4478.11 4500.14 4620.06 4860.09 48771.05 4360.03 4870.04 4810.25 4821.30 4810.05 4840.03 4820.21 4810.01 4800.29 477
mmdepth0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
monomultidepth0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
test_blank0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
uanet_test0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
DCPMVS0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
pcd_1.5k_mvsjas5.26 4497.02 4520.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 48263.15 1740.00 4830.00 4820.00 4810.00 479
sosnet-low-res0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
sosnet0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
uncertanet0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
Regformer0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
uanet0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
TestfortrainingZip93.28 12
WAC-MVS42.58 46139.46 449
FOURS195.00 1072.39 4195.06 193.84 2074.49 14791.30 18
MSC_two_6792asdad89.16 194.34 3175.53 292.99 5497.53 289.67 1596.44 994.41 52
No_MVS89.16 194.34 3175.53 292.99 5497.53 289.67 1596.44 994.41 52
eth-test20.00 487
eth-test0.00 487
IU-MVS95.30 271.25 6492.95 6066.81 31692.39 688.94 2896.63 494.85 21
save fliter93.80 4472.35 4490.47 7491.17 14374.31 152
test_0728_SECOND87.71 3595.34 171.43 6093.49 1094.23 797.49 489.08 2296.41 1294.21 64
GSMVS88.96 302
test_part295.06 872.65 3291.80 16
sam_mvs151.32 31788.96 302
sam_mvs50.01 333
MTGPAbinary92.02 105
MTMP92.18 3932.83 481
test9_res84.90 6495.70 3092.87 145
agg_prior282.91 9195.45 3392.70 150
agg_prior92.85 6871.94 5291.78 12184.41 9594.93 101
test_prior472.60 3489.01 125
test_prior86.33 6492.61 7469.59 9892.97 5995.48 7493.91 80
旧先验286.56 22558.10 41787.04 6188.98 33474.07 197
新几何286.29 238
无先验87.48 18688.98 23260.00 39894.12 14067.28 27188.97 301
原ACMM286.86 212
testdata291.01 29662.37 312
segment_acmp73.08 43
testdata184.14 30075.71 107
test1286.80 5892.63 7370.70 8191.79 12082.71 13171.67 6396.16 5294.50 5793.54 109
plane_prior790.08 11668.51 131
plane_prior689.84 12568.70 12560.42 228
plane_prior592.44 8295.38 8278.71 13886.32 19691.33 205
plane_prior368.60 12878.44 3678.92 193
plane_prior291.25 6079.12 28
plane_prior189.90 124
plane_prior68.71 12390.38 7877.62 4786.16 201
n20.00 488
nn0.00 488
door-mid69.98 444
test1192.23 92
door69.44 447
HQP5-MVS66.98 183
HQP-NCC89.33 14489.17 11676.41 8677.23 234
ACMP_Plane89.33 14489.17 11676.41 8677.23 234
BP-MVS77.47 153
HQP4-MVS77.24 23395.11 9491.03 215
HQP3-MVS92.19 9985.99 205
HQP2-MVS60.17 231
MDTV_nov1_ep13_2view37.79 46975.16 41855.10 43266.53 40049.34 34353.98 38487.94 330
ACMMP++_ref81.95 274
ACMMP++81.25 279
Test By Simon64.33 160