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 bysort bysort bysorted bysort bysort bysort by
MM89.16 789.23 988.97 490.79 10373.65 1092.66 2891.17 15486.57 187.39 5894.97 2571.70 6597.68 192.19 195.63 3195.57 2
MSC_two_6792asdad89.16 194.34 3175.53 292.99 5597.53 289.67 1596.44 994.41 61
No_MVS89.16 194.34 3175.53 292.99 5597.53 289.67 1596.44 994.41 61
DVP-MVS++90.23 191.01 187.89 2494.34 3171.25 6595.06 194.23 678.38 3992.78 495.74 882.45 397.49 489.42 1996.68 294.95 15
test_0728_SECOND87.71 3595.34 171.43 6193.49 1094.23 697.49 489.08 2296.41 1294.21 74
MGCNet87.69 2487.55 2988.12 1389.45 14071.76 5491.47 5789.54 21182.14 386.65 6794.28 4668.28 12397.46 690.81 695.31 3795.15 9
SED-MVS90.08 290.85 287.77 2895.30 270.98 7393.57 894.06 1477.24 6593.10 195.72 1082.99 197.44 789.07 2596.63 494.88 19
test_241102_TWO94.06 1477.24 6592.78 495.72 1081.26 997.44 789.07 2596.58 694.26 73
DVP-MVScopyleft89.60 490.35 487.33 4595.27 571.25 6593.49 1092.73 7177.33 6092.12 1195.78 680.98 1097.40 989.08 2296.41 1293.33 127
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 3992.12 1195.78 681.46 897.40 989.42 1996.57 794.67 42
MED-MVS test87.86 2794.57 1771.43 6193.28 1294.36 375.24 13192.25 995.03 2297.39 1188.15 3995.96 1994.75 35
MED-MVS89.75 390.37 387.89 2494.57 1771.43 6193.28 1294.36 377.30 6292.25 995.87 381.59 797.39 1188.15 3995.96 1994.85 24
DPE-MVScopyleft89.48 689.98 588.01 1694.80 1172.69 3191.59 5194.10 1275.90 11292.29 795.66 1281.67 697.38 1387.44 4896.34 1593.95 89
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_241102_ONE95.30 270.98 7394.06 1477.17 6893.10 195.39 1882.99 197.27 14
SMA-MVScopyleft89.08 989.23 988.61 694.25 3573.73 992.40 2993.63 2674.77 15192.29 795.97 274.28 3497.24 1588.58 3396.91 194.87 21
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
CANet86.45 4886.10 6187.51 4290.09 11670.94 7789.70 9492.59 8181.78 481.32 16291.43 14970.34 8297.23 1684.26 7593.36 7394.37 65
SteuartSystems-ACMMP88.72 1488.86 1488.32 992.14 7972.96 2593.73 593.67 2580.19 1388.10 4394.80 2773.76 3897.11 1787.51 4695.82 2494.90 18
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS79.81 287.08 4086.88 4587.69 3691.16 9272.32 4590.31 7993.94 1877.12 7182.82 13894.23 5072.13 5997.09 1884.83 6795.37 3493.65 110
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ME-MVS88.98 1189.39 887.75 3094.54 2071.43 6191.61 4994.25 576.30 10490.62 2195.03 2278.06 1597.07 1988.15 3995.96 1994.75 35
APDe-MVScopyleft89.15 889.63 787.73 3194.49 2271.69 5593.83 493.96 1775.70 11991.06 1996.03 176.84 1897.03 2089.09 2195.65 3094.47 60
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
NCCC88.06 1888.01 2288.24 1194.41 2673.62 1191.22 6292.83 6681.50 585.79 7393.47 8173.02 4697.00 2184.90 6494.94 4394.10 80
TestfortrainingZip a88.83 1389.21 1187.68 3794.57 1771.25 6593.28 1293.91 1977.30 6291.13 1895.87 377.62 1696.95 2286.12 5793.07 7594.85 24
ZNCC-MVS87.94 2287.85 2488.20 1294.39 2873.33 1993.03 1993.81 2276.81 8085.24 7894.32 4471.76 6396.93 2385.53 6195.79 2594.32 69
OPU-MVS89.06 394.62 1575.42 493.57 894.02 6182.45 396.87 2483.77 8296.48 894.88 19
CNVR-MVS88.93 1289.13 1388.33 894.77 1273.82 890.51 7093.00 5280.90 788.06 4494.06 5976.43 2096.84 2588.48 3695.99 1894.34 67
GST-MVS87.42 3187.26 3487.89 2494.12 4072.97 2492.39 3193.43 3376.89 7884.68 8893.99 6570.67 8096.82 2684.18 7995.01 4093.90 92
HPM-MVS++copyleft89.02 1089.15 1288.63 595.01 976.03 192.38 3292.85 6580.26 1287.78 4994.27 4775.89 2396.81 2787.45 4796.44 993.05 148
MSP-MVS89.51 589.91 688.30 1094.28 3473.46 1792.90 2194.11 1080.27 1191.35 1694.16 5478.35 1496.77 2889.59 1794.22 6594.67 42
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
DeepC-MVS_fast79.65 386.91 4186.62 5087.76 2993.52 5072.37 4391.26 5993.04 4776.62 8884.22 10393.36 8571.44 6996.76 2980.82 11495.33 3694.16 76
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator+77.84 485.48 7484.47 9488.51 791.08 9473.49 1693.18 1693.78 2380.79 876.66 26193.37 8460.40 24296.75 3077.20 16593.73 6995.29 7
ZD-MVS94.38 2972.22 4692.67 7470.98 24487.75 5194.07 5874.01 3796.70 3184.66 7094.84 47
region2R87.42 3187.20 3788.09 1494.63 1473.55 1393.03 1993.12 4676.73 8584.45 9694.52 3269.09 10896.70 3184.37 7494.83 4894.03 84
TestfortrainingZip87.28 4692.85 6872.05 5093.28 1293.32 3776.52 9088.91 3293.52 7777.30 1796.67 3391.98 9493.13 142
ACMMP_NAP88.05 2088.08 2187.94 1993.70 4573.05 2290.86 6593.59 2876.27 10588.14 4295.09 2171.06 7596.67 3387.67 4496.37 1494.09 81
ACMMPR87.44 2987.23 3688.08 1594.64 1373.59 1293.04 1793.20 4076.78 8284.66 9194.52 3268.81 11496.65 3584.53 7294.90 4494.00 86
PGM-MVS86.68 4586.27 5587.90 2294.22 3773.38 1890.22 8193.04 4775.53 12283.86 11194.42 4067.87 12896.64 3682.70 9994.57 5593.66 106
HFP-MVS87.58 2687.47 3187.94 1994.58 1673.54 1593.04 1793.24 3976.78 8284.91 8394.44 3970.78 7896.61 3784.53 7294.89 4593.66 106
XVS87.18 3786.91 4488.00 1794.42 2473.33 1992.78 2392.99 5579.14 2783.67 11694.17 5367.45 13196.60 3883.06 8794.50 5694.07 82
X-MVStestdata80.37 20477.83 24488.00 1794.42 2473.33 1992.78 2392.99 5579.14 2783.67 11612.47 51667.45 13196.60 3883.06 8794.50 5694.07 82
DeepPCF-MVS80.84 188.10 1688.56 1786.73 6092.24 7869.03 11189.57 9993.39 3577.53 5589.79 2594.12 5678.98 1396.58 4085.66 5895.72 2794.58 51
reproduce-ours87.47 2787.61 2787.07 5193.27 5471.60 5691.56 5493.19 4174.98 14288.96 3095.54 1471.20 7396.54 4186.28 5493.49 7093.06 146
our_new_method87.47 2787.61 2787.07 5193.27 5471.60 5691.56 5493.19 4174.98 14288.96 3095.54 1471.20 7396.54 4186.28 5493.49 7093.06 146
APD-MVScopyleft87.44 2987.52 3087.19 4894.24 3672.39 4191.86 4592.83 6673.01 20388.58 3594.52 3273.36 3996.49 4384.26 7595.01 4092.70 162
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
reproduce_model87.28 3587.39 3386.95 5593.10 6271.24 7091.60 5093.19 4174.69 15288.80 3495.61 1370.29 8496.44 4486.20 5693.08 7493.16 138
PHI-MVS86.43 4986.17 5987.24 4790.88 10070.96 7592.27 3794.07 1372.45 20985.22 7991.90 12569.47 9896.42 4583.28 8695.94 2294.35 66
MCST-MVS87.37 3487.25 3587.73 3194.53 2172.46 4089.82 8893.82 2173.07 20184.86 8692.89 9676.22 2196.33 4684.89 6695.13 3994.40 63
ACMMPcopyleft85.89 6685.39 7787.38 4493.59 4972.63 3392.74 2593.18 4576.78 8280.73 17793.82 7264.33 17196.29 4782.67 10090.69 11993.23 130
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
MP-MVScopyleft87.71 2387.64 2687.93 2194.36 3073.88 692.71 2792.65 7777.57 5183.84 11294.40 4172.24 5696.28 4885.65 5995.30 3893.62 113
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
lecture88.09 1788.59 1686.58 6393.26 5669.77 9793.70 694.16 877.13 7089.76 2695.52 1672.26 5596.27 4986.87 5094.65 5193.70 105
mPP-MVS86.67 4686.32 5387.72 3394.41 2673.55 1392.74 2592.22 10376.87 7982.81 13994.25 4966.44 14596.24 5082.88 9294.28 6393.38 123
MTAPA87.23 3687.00 3987.90 2294.18 3974.25 586.58 23292.02 11479.45 2385.88 7194.80 2768.07 12596.21 5186.69 5295.34 3593.23 130
SF-MVS88.46 1588.74 1587.64 3992.78 7171.95 5292.40 2994.74 275.71 11789.16 2995.10 2075.65 2596.19 5287.07 4996.01 1794.79 28
test1286.80 5992.63 7470.70 8291.79 12982.71 14171.67 6696.16 5394.50 5693.54 119
CDPH-MVS85.76 6985.29 8287.17 4993.49 5171.08 7188.58 14992.42 8768.32 31884.61 9393.48 7972.32 5496.15 5479.00 14395.43 3394.28 72
BridgeMVS86.78 4286.99 4086.15 7291.24 9167.61 16490.51 7092.90 6277.26 6487.44 5791.63 13971.27 7296.06 5585.62 6095.01 4094.78 29
MVSMamba_PlusPlus85.99 6085.96 6586.05 7591.09 9367.64 16389.63 9792.65 7772.89 20684.64 9291.71 13471.85 6196.03 5684.77 6994.45 5994.49 59
DP-MVS Recon83.11 13682.09 14786.15 7294.44 2370.92 7888.79 13692.20 10670.53 25779.17 20291.03 16564.12 17396.03 5668.39 27490.14 12991.50 213
DPM-MVS84.93 8884.29 9586.84 5790.20 11473.04 2387.12 20893.04 4769.80 27982.85 13791.22 15673.06 4596.02 5876.72 17794.63 5391.46 217
HPM-MVScopyleft87.11 3886.98 4187.50 4393.88 4372.16 4792.19 3893.33 3676.07 10983.81 11393.95 6869.77 9596.01 5985.15 6294.66 5094.32 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MP-MVS-pluss87.67 2587.72 2587.54 4093.64 4872.04 5189.80 9093.50 3075.17 13886.34 6995.29 1970.86 7796.00 6088.78 3196.04 1694.58 51
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.88.02 2188.11 2087.72 3393.68 4772.13 4891.41 5892.35 9074.62 15588.90 3393.85 7175.75 2496.00 6087.80 4394.63 5395.04 12
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PC_three_145268.21 31992.02 1494.00 6382.09 595.98 6284.58 7196.68 294.95 15
CP-MVS87.11 3886.92 4387.68 3794.20 3873.86 793.98 392.82 6976.62 8883.68 11594.46 3667.93 12695.95 6384.20 7894.39 6093.23 130
9.1488.26 1992.84 7091.52 5694.75 173.93 17488.57 3694.67 3075.57 2695.79 6486.77 5195.76 26
SR-MVS86.73 4386.67 4886.91 5694.11 4172.11 4992.37 3392.56 8274.50 15686.84 6594.65 3167.31 13395.77 6584.80 6892.85 7892.84 160
AdaColmapbinary80.58 19879.42 20484.06 16993.09 6368.91 11689.36 11188.97 24569.27 29275.70 28389.69 20457.20 26995.77 6563.06 31988.41 16487.50 359
DELS-MVS85.41 7785.30 8185.77 8188.49 18467.93 15485.52 27293.44 3278.70 3583.63 11889.03 22574.57 2895.71 6780.26 12394.04 6693.66 106
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
APD-MVS_3200maxsize85.97 6285.88 6686.22 6992.69 7369.53 10091.93 4292.99 5573.54 18585.94 7094.51 3565.80 15795.61 6883.04 8992.51 8393.53 120
SR-MVS-dyc-post85.77 6885.61 7386.23 6893.06 6470.63 8391.88 4392.27 9673.53 18685.69 7494.45 3765.00 16595.56 6982.75 9591.87 9692.50 173
EPNet83.72 11482.92 12986.14 7484.22 33669.48 10291.05 6485.27 33881.30 676.83 25691.65 13766.09 15295.56 6976.00 18493.85 6793.38 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HPM-MVS_fast85.35 8084.95 8786.57 6493.69 4670.58 8592.15 4091.62 13973.89 17582.67 14294.09 5762.60 19495.54 7180.93 11292.93 7793.57 116
NormalMVS86.29 5485.88 6687.52 4193.26 5672.47 3891.65 4792.19 10879.31 2584.39 9892.18 11664.64 16895.53 7280.70 11794.65 5194.56 55
SymmetryMVS85.38 7984.81 8887.07 5191.47 8872.47 3891.65 4788.06 27479.31 2584.39 9892.18 11664.64 16895.53 7280.70 11790.91 11693.21 133
h-mvs3383.15 13382.19 14486.02 7890.56 10670.85 8088.15 17089.16 23476.02 11084.67 8991.39 15061.54 21595.50 7482.71 9775.48 37491.72 207
test_prior86.33 6592.61 7569.59 9992.97 6095.48 7593.91 90
原ACMM184.35 14493.01 6668.79 11892.44 8463.96 38381.09 16791.57 14366.06 15395.45 7667.19 28494.82 4988.81 321
QAPM80.88 18079.50 20385.03 10788.01 20968.97 11591.59 5192.00 11666.63 34175.15 30592.16 11857.70 26195.45 7663.52 31088.76 15690.66 244
BP-MVS184.32 9383.71 11086.17 7087.84 21667.85 15689.38 11089.64 20877.73 4783.98 10992.12 12156.89 27295.43 7884.03 8091.75 9995.24 8
RPMNet73.51 34070.49 37082.58 24281.32 40865.19 23075.92 43792.27 9657.60 44772.73 34276.45 45452.30 31395.43 7848.14 44777.71 33987.11 377
EC-MVSNet86.01 5986.38 5284.91 11689.31 14966.27 19792.32 3593.63 2679.37 2484.17 10591.88 12669.04 11295.43 7883.93 8193.77 6893.01 151
TEST993.26 5672.96 2588.75 13991.89 12268.44 31685.00 8193.10 8974.36 3395.41 81
train_agg86.43 4986.20 5687.13 5093.26 5672.96 2588.75 13991.89 12268.69 31185.00 8193.10 8974.43 3195.41 8184.97 6395.71 2893.02 150
ETV-MVS84.90 9084.67 9085.59 8889.39 14468.66 12888.74 14192.64 7979.97 1784.10 10685.71 32269.32 10195.38 8380.82 11491.37 10692.72 161
HQP_MVS83.64 11783.14 12285.14 10190.08 11768.71 12491.25 6092.44 8479.12 2978.92 20691.00 16660.42 24095.38 8378.71 14786.32 20791.33 218
plane_prior592.44 8495.38 8378.71 14786.32 20791.33 218
TSAR-MVS + GP.85.71 7085.33 7986.84 5791.34 8972.50 3689.07 12587.28 29676.41 9685.80 7290.22 19274.15 3695.37 8681.82 10491.88 9592.65 166
GDP-MVS83.52 12282.64 13486.16 7188.14 20068.45 13389.13 12292.69 7272.82 20783.71 11491.86 12855.69 28195.35 8780.03 12489.74 13894.69 37
EIA-MVS83.31 13182.80 13184.82 12089.59 13265.59 21788.21 16692.68 7374.66 15478.96 20486.42 30869.06 11095.26 8875.54 19190.09 13093.62 113
UA-Net85.08 8684.96 8685.45 9192.07 8068.07 14689.78 9190.86 16582.48 284.60 9493.20 8869.35 10095.22 8971.39 23890.88 11793.07 145
CSCG86.41 5186.19 5887.07 5192.91 6772.48 3790.81 6693.56 2973.95 17283.16 13091.07 16275.94 2295.19 9079.94 12694.38 6193.55 118
test_893.13 6072.57 3588.68 14591.84 12668.69 31184.87 8593.10 8974.43 3195.16 91
SPE-MVS-test86.29 5486.48 5185.71 8291.02 9667.21 18392.36 3493.78 2378.97 3483.51 12391.20 15770.65 8195.15 9281.96 10394.89 4594.77 30
FE-MVS77.78 27175.68 29184.08 16588.09 20466.00 20383.13 33787.79 28468.42 31778.01 22985.23 33745.50 40395.12 9359.11 36885.83 22391.11 224
EPP-MVSNet83.40 12683.02 12584.57 12890.13 11564.47 25792.32 3590.73 16974.45 15979.35 20091.10 16069.05 11195.12 9372.78 22187.22 19194.13 78
HQP4-MVS77.24 24695.11 9591.03 228
HQP-MVS82.61 14482.02 14984.37 14289.33 14666.98 18689.17 11792.19 10876.41 9677.23 24790.23 19160.17 24395.11 9577.47 16285.99 21791.03 228
MG-MVS83.41 12583.45 11783.28 20292.74 7262.28 31488.17 16889.50 21375.22 13281.49 16092.74 10566.75 13995.11 9572.85 22091.58 10292.45 177
API-MVS81.99 15481.23 15884.26 15590.94 9870.18 9291.10 6389.32 22371.51 22978.66 21188.28 25065.26 16095.10 9864.74 30491.23 10987.51 358
PCF-MVS73.52 780.38 20278.84 22185.01 10987.71 22768.99 11483.65 32191.46 14863.00 39277.77 23690.28 18866.10 15195.09 9961.40 34788.22 17090.94 233
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
cashybrid286.09 5686.04 6386.24 6788.17 19768.05 14889.44 10492.79 7080.30 1084.71 8792.78 10372.83 5095.05 10082.81 9390.57 12195.62 1
114514_t80.68 19179.51 20284.20 15794.09 4267.27 17989.64 9691.11 15758.75 43874.08 32490.72 17258.10 25795.04 10169.70 25989.42 14490.30 261
CS-MVS86.69 4486.95 4285.90 8090.76 10467.57 16692.83 2293.30 3879.67 2084.57 9592.27 11071.47 6895.02 10284.24 7793.46 7295.13 11
agg_prior92.85 6871.94 5391.78 13084.41 9794.93 103
LPG-MVS_test82.08 15181.27 15784.50 13489.23 15468.76 12090.22 8191.94 12075.37 12876.64 26291.51 14554.29 29494.91 10478.44 14983.78 25489.83 286
LGP-MVS_train84.50 13489.23 15468.76 12091.94 12075.37 12876.64 26291.51 14554.29 29494.91 10478.44 14983.78 25489.83 286
balanced_ft_v183.98 10583.64 11385.03 10789.76 12965.86 20888.31 16391.71 13474.41 16080.41 18490.82 17162.90 19294.90 10683.04 8991.37 10694.32 69
PAPM_NR83.02 13782.41 13884.82 12092.47 7766.37 19587.93 17891.80 12873.82 17677.32 24490.66 17567.90 12794.90 10670.37 24989.48 14393.19 136
tttt051779.40 22777.91 24083.90 18388.10 20363.84 27088.37 16084.05 35671.45 23076.78 25889.12 22249.93 35794.89 10870.18 25383.18 27292.96 154
PAPR81.66 16380.89 16583.99 17990.27 11264.00 26586.76 22691.77 13168.84 30977.13 25489.50 21167.63 12994.88 10967.55 27988.52 16193.09 144
PVSNet_Blended_VisFu82.62 14381.83 15384.96 11190.80 10269.76 9888.74 14191.70 13569.39 28878.96 20488.46 24565.47 15994.87 11074.42 20288.57 15990.24 263
Elysia81.53 16680.16 18285.62 8685.51 30468.25 14088.84 13492.19 10871.31 23280.50 18189.83 19846.89 38394.82 11176.85 17089.57 14093.80 100
StellarMVS81.53 16680.16 18285.62 8685.51 30468.25 14088.84 13492.19 10871.31 23280.50 18189.83 19846.89 38394.82 11176.85 17089.57 14093.80 100
EI-MVSNet-Vis-set84.19 9883.81 10785.31 9688.18 19667.85 15687.66 18689.73 20580.05 1682.95 13389.59 21070.74 7994.82 11180.66 11984.72 23893.28 129
DP-MVS76.78 29274.57 31283.42 19793.29 5269.46 10588.55 15183.70 36063.98 38270.20 36988.89 23254.01 29994.80 11446.66 45281.88 28886.01 401
thisisatest053079.40 22777.76 24984.31 14787.69 23165.10 23587.36 20184.26 35470.04 27177.42 24188.26 25249.94 35594.79 11570.20 25284.70 23993.03 149
viewdifsd2359ckpt0983.34 12882.55 13685.70 8387.64 23367.72 16188.43 15491.68 13671.91 22181.65 15890.68 17467.10 13694.75 11676.17 18087.70 18394.62 50
EI-MVSNet-UG-set83.81 10883.38 11985.09 10687.87 21467.53 16887.44 19989.66 20679.74 1982.23 14689.41 21970.24 8594.74 11779.95 12583.92 25392.99 153
FA-MVS(test-final)80.96 17979.91 18984.10 16088.30 19365.01 23684.55 29690.01 19473.25 19679.61 19387.57 27058.35 25694.72 11871.29 23986.25 21092.56 168
3Dnovator76.31 583.38 12782.31 14186.59 6287.94 21172.94 2890.64 6892.14 11377.21 6775.47 28792.83 9858.56 25494.72 11873.24 21692.71 8192.13 195
RRT-MVS82.60 14682.10 14684.10 16087.98 21062.94 30187.45 19491.27 15077.42 5879.85 19090.28 18856.62 27594.70 12079.87 13188.15 17194.67 42
IB-MVS68.01 1575.85 31173.36 33183.31 20184.76 32566.03 20083.38 33185.06 34270.21 27069.40 38281.05 41145.76 39994.66 12165.10 30175.49 37389.25 303
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
mamba_040879.37 23077.52 25684.93 11488.81 16967.96 15165.03 48788.66 26170.96 24579.48 19689.80 20058.69 25194.65 12270.35 25085.93 21992.18 190
SSM_040481.91 15580.84 16685.13 10489.24 15368.26 13887.84 18389.25 22971.06 24180.62 17890.39 18559.57 24594.65 12272.45 23087.19 19292.47 176
ACMP74.13 681.51 17080.57 17184.36 14389.42 14168.69 12789.97 8591.50 14774.46 15875.04 30990.41 18353.82 30094.54 12477.56 16182.91 27489.86 285
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LS3D76.95 29074.82 30983.37 20090.45 10867.36 17589.15 12186.94 31061.87 41069.52 38190.61 17851.71 33094.53 12546.38 45586.71 20288.21 341
MAR-MVS81.84 15780.70 16785.27 9791.32 9071.53 5989.82 8890.92 16169.77 28178.50 21586.21 31362.36 20094.52 12665.36 29892.05 9389.77 289
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
OPM-MVS83.50 12382.95 12885.14 10188.79 17470.95 7689.13 12291.52 14377.55 5480.96 17191.75 13260.71 23294.50 12779.67 13486.51 20589.97 281
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
casdiffmvs_mvgpermissive85.99 6086.09 6285.70 8387.65 23267.22 18288.69 14493.04 4779.64 2285.33 7792.54 10673.30 4094.50 12783.49 8391.14 11095.37 3
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SSM_040781.58 16580.48 17484.87 11888.81 16967.96 15187.37 20089.25 22971.06 24179.48 19690.39 18559.57 24594.48 12972.45 23085.93 21992.18 190
Effi-MVS+83.62 11983.08 12385.24 9888.38 19067.45 17088.89 13089.15 23575.50 12382.27 14588.28 25069.61 9794.45 13077.81 15787.84 17993.84 96
CLD-MVS82.31 14881.65 15484.29 15088.47 18567.73 16085.81 26292.35 9075.78 11578.33 22186.58 30364.01 17494.35 13176.05 18387.48 18790.79 237
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PS-MVSNAJ81.69 16181.02 16283.70 18889.51 13668.21 14384.28 30790.09 19270.79 24881.26 16685.62 32763.15 18594.29 13275.62 18988.87 15388.59 330
IS-MVSNet83.15 13382.81 13084.18 15889.94 12463.30 28991.59 5188.46 26779.04 3179.49 19592.16 11865.10 16294.28 13367.71 27791.86 9894.95 15
thisisatest051577.33 28375.38 29983.18 20885.27 31263.80 27182.11 35383.27 36865.06 36575.91 27983.84 36949.54 36094.27 13467.24 28386.19 21191.48 215
PS-MVSNAJss82.07 15281.31 15684.34 14586.51 28267.27 17989.27 11391.51 14471.75 22279.37 19990.22 19263.15 18594.27 13477.69 16082.36 28291.49 214
PVSNet_BlendedMVS80.60 19580.02 18682.36 24688.85 16565.40 22086.16 25192.00 11669.34 29078.11 22686.09 31766.02 15494.27 13471.52 23582.06 28587.39 361
PVSNet_Blended80.98 17880.34 17782.90 22488.85 16565.40 22084.43 30292.00 11667.62 32478.11 22685.05 34366.02 15494.27 13471.52 23589.50 14289.01 311
fmvsm_s_conf0.5_n_1086.38 5286.76 4685.24 9887.33 25067.30 17789.50 10190.98 15976.25 10690.56 2294.75 2968.38 12094.24 13890.80 792.32 8994.19 75
fmvsm_s_conf0.5_n_886.56 4787.17 3884.73 12587.76 22465.62 21689.20 11592.21 10579.94 1889.74 2794.86 2668.63 11794.20 13990.83 591.39 10594.38 64
Vis-MVSNetpermissive83.46 12482.80 13185.43 9290.25 11368.74 12290.30 8090.13 19176.33 10380.87 17492.89 9661.00 22994.20 13972.45 23090.97 11393.35 126
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
xiu_mvs_v2_base81.69 16181.05 16183.60 19089.15 15768.03 14984.46 29990.02 19370.67 25281.30 16586.53 30663.17 18494.19 14175.60 19088.54 16088.57 331
MVS_111021_HR85.14 8384.75 8986.32 6691.65 8672.70 3085.98 25490.33 18376.11 10882.08 14991.61 14271.36 7194.17 14281.02 11192.58 8292.08 196
无先验87.48 19088.98 24360.00 42494.12 14367.28 28288.97 314
MVS78.19 26076.99 26881.78 25985.66 29966.99 18584.66 29190.47 17655.08 46172.02 35385.27 33563.83 17694.11 14466.10 29289.80 13784.24 430
KinetiMVS83.31 13182.61 13585.39 9487.08 26567.56 16788.06 17291.65 13777.80 4682.21 14791.79 12957.27 26794.07 14577.77 15889.89 13694.56 55
v1079.74 21778.67 22282.97 22284.06 34064.95 23987.88 18190.62 17173.11 20075.11 30686.56 30461.46 21894.05 14673.68 20875.55 37289.90 283
baseline84.93 8884.98 8584.80 12287.30 25465.39 22287.30 20492.88 6377.62 4984.04 10892.26 11171.81 6293.96 14781.31 10890.30 12695.03 13
OMC-MVS82.69 14281.97 15184.85 11988.75 17667.42 17187.98 17490.87 16474.92 14579.72 19291.65 13762.19 20493.96 14775.26 19586.42 20693.16 138
OpenMVScopyleft72.83 1079.77 21678.33 23284.09 16485.17 31369.91 9490.57 6990.97 16066.70 33572.17 35191.91 12454.70 29193.96 14761.81 34290.95 11588.41 335
v119279.59 22078.43 22983.07 21583.55 35464.52 25386.93 21790.58 17270.83 24777.78 23585.90 31859.15 24993.94 15073.96 20777.19 34690.76 239
v114480.03 21379.03 21683.01 21883.78 34764.51 25487.11 20990.57 17471.96 22078.08 22886.20 31461.41 21993.94 15074.93 19777.23 34490.60 247
UGNet80.83 18279.59 20184.54 12988.04 20668.09 14589.42 10788.16 26976.95 7676.22 27389.46 21549.30 36693.94 15068.48 27290.31 12591.60 208
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
casdiffmvspermissive85.11 8485.14 8485.01 10987.20 25665.77 21387.75 18492.83 6677.84 4584.36 10192.38 10972.15 5893.93 15381.27 11090.48 12395.33 5
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_987.39 3387.95 2385.70 8389.48 13967.88 15588.59 14889.05 23980.19 1390.70 2095.40 1774.56 2993.92 15491.54 292.07 9295.31 6
sasdasda85.91 6485.87 6886.04 7689.84 12669.44 10690.45 7693.00 5276.70 8688.01 4691.23 15373.28 4193.91 15581.50 10688.80 15494.77 30
canonicalmvs85.91 6485.87 6886.04 7689.84 12669.44 10690.45 7693.00 5276.70 8688.01 4691.23 15373.28 4193.91 15581.50 10688.80 15494.77 30
VDD-MVS83.01 13882.36 14084.96 11191.02 9666.40 19488.91 12988.11 27077.57 5184.39 9893.29 8652.19 31593.91 15577.05 16888.70 15894.57 53
v879.97 21579.02 21782.80 23084.09 33964.50 25687.96 17590.29 18674.13 17075.24 30286.81 29062.88 19393.89 15874.39 20375.40 37990.00 277
hybridcas85.11 8485.18 8384.90 11787.47 24465.68 21488.53 15292.38 8877.91 4384.27 10292.48 10772.19 5793.88 15980.37 12090.97 11395.15 9
v2v48280.23 20879.29 21083.05 21683.62 35264.14 26387.04 21089.97 19573.61 18278.18 22587.22 28161.10 22793.82 16076.11 18176.78 35391.18 222
v7n78.97 24077.58 25583.14 21083.45 35665.51 21888.32 16291.21 15273.69 18072.41 34786.32 31157.93 25893.81 16169.18 26475.65 37090.11 269
alignmvs85.48 7485.32 8085.96 7989.51 13669.47 10389.74 9292.47 8376.17 10787.73 5391.46 14870.32 8393.78 16281.51 10588.95 15194.63 48
SD-MVS88.06 1888.50 1886.71 6192.60 7672.71 2991.81 4693.19 4177.87 4490.32 2394.00 6374.83 2793.78 16287.63 4594.27 6493.65 110
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
v14419279.47 22378.37 23082.78 23483.35 35763.96 26686.96 21490.36 18269.99 27477.50 23985.67 32560.66 23593.77 16474.27 20476.58 35490.62 245
v124078.99 23977.78 24782.64 23983.21 36263.54 28286.62 23190.30 18569.74 28477.33 24385.68 32457.04 27093.76 16573.13 21776.92 34890.62 245
v192192079.22 23278.03 23782.80 23083.30 35963.94 26886.80 22290.33 18369.91 27777.48 24085.53 32958.44 25593.75 16673.60 20976.85 35190.71 243
cascas76.72 29374.64 31182.99 21985.78 29765.88 20782.33 34989.21 23260.85 41672.74 34181.02 41247.28 37993.75 16667.48 28085.02 23289.34 301
Anonymous2024052980.19 21078.89 22084.10 16090.60 10564.75 24988.95 12890.90 16265.97 35080.59 17991.17 15949.97 35493.73 16869.16 26582.70 27993.81 98
casdiffseed41469214783.62 11983.02 12585.40 9387.31 25367.50 16988.70 14391.72 13376.97 7582.77 14091.72 13366.85 13893.71 16973.06 21888.12 17294.98 14
PAPM77.68 27676.40 28481.51 26587.29 25561.85 32183.78 31789.59 21064.74 36971.23 36188.70 23662.59 19593.66 17052.66 41787.03 19689.01 311
E484.10 10083.99 10384.45 13787.58 24264.99 23886.54 23492.25 9976.38 10083.37 12492.09 12269.88 9393.58 17179.78 13288.03 17694.77 30
test_yl81.17 17380.47 17583.24 20589.13 15863.62 27486.21 24989.95 19672.43 21281.78 15589.61 20857.50 26493.58 17170.75 24486.90 19792.52 171
DCV-MVSNet81.17 17380.47 17583.24 20589.13 15863.62 27486.21 24989.95 19672.43 21281.78 15589.61 20857.50 26493.58 17170.75 24486.90 19792.52 171
Fast-Effi-MVS+80.81 18379.92 18883.47 19488.85 16564.51 25485.53 27089.39 21770.79 24878.49 21685.06 34267.54 13093.58 17167.03 28786.58 20392.32 182
PLCcopyleft70.83 1178.05 26476.37 28583.08 21491.88 8467.80 15888.19 16789.46 21464.33 37669.87 37888.38 24753.66 30193.58 17158.86 37182.73 27787.86 348
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
E284.00 10383.87 10484.39 14087.70 22964.95 23986.40 24192.23 10075.85 11383.21 12691.78 13070.09 8893.55 17679.52 13688.05 17494.66 45
E384.00 10383.87 10484.39 14087.70 22964.95 23986.40 24192.23 10075.85 11383.21 12691.78 13070.09 8893.55 17679.52 13688.05 17494.66 45
BH-untuned79.47 22378.60 22482.05 25389.19 15665.91 20686.07 25388.52 26672.18 21575.42 29187.69 26761.15 22693.54 17860.38 35586.83 20086.70 388
viewcassd2359sk1183.89 10683.74 10984.34 14587.76 22464.91 24586.30 24592.22 10375.47 12483.04 13291.52 14470.15 8693.53 17979.26 13887.96 17794.57 53
ACMM73.20 880.78 19079.84 19283.58 19289.31 14968.37 13589.99 8491.60 14170.28 26777.25 24589.66 20653.37 30593.53 17974.24 20582.85 27588.85 319
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
E3new83.78 11183.60 11484.31 14787.76 22464.89 24686.24 24892.20 10675.15 13982.87 13591.23 15370.11 8793.52 18179.05 13987.79 18094.51 58
E5new84.22 9484.12 9784.51 13287.60 23465.36 22487.45 19492.31 9276.51 9183.53 11992.26 11169.25 10593.50 18279.88 12788.26 16594.69 37
E584.22 9484.12 9784.51 13287.60 23465.36 22487.45 19492.31 9276.51 9183.53 11992.26 11169.25 10593.50 18279.88 12788.26 16594.69 37
E6new84.22 9484.12 9784.52 13087.60 23465.36 22487.45 19492.30 9476.51 9183.53 11992.26 11169.26 10393.49 18479.88 12788.26 16594.69 37
E684.22 9484.12 9784.52 13087.60 23465.36 22487.45 19492.30 9476.51 9183.53 11992.26 11169.26 10393.49 18479.88 12788.26 16594.69 37
VDDNet81.52 16880.67 16884.05 17290.44 10964.13 26489.73 9385.91 33171.11 23883.18 12993.48 7950.54 34793.49 18473.40 21388.25 16994.54 57
hse-mvs281.72 15980.94 16484.07 16688.72 17767.68 16285.87 25887.26 30176.02 11084.67 8988.22 25361.54 21593.48 18782.71 9773.44 40291.06 226
AUN-MVS79.21 23377.60 25484.05 17288.71 17867.61 16485.84 26087.26 30169.08 30077.23 24788.14 25853.20 30793.47 18875.50 19273.45 40191.06 226
MVSFormer82.85 14082.05 14885.24 9887.35 24570.21 8790.50 7290.38 17968.55 31381.32 16289.47 21361.68 21293.46 18978.98 14490.26 12792.05 197
test_djsdf80.30 20779.32 20983.27 20383.98 34265.37 22390.50 7290.38 17968.55 31376.19 27488.70 23656.44 27693.46 18978.98 14480.14 31190.97 231
LFMVS81.82 15881.23 15883.57 19391.89 8363.43 28789.84 8781.85 39377.04 7483.21 12693.10 8952.26 31493.43 19171.98 23389.95 13493.85 94
MGCFI-Net85.06 8785.51 7583.70 18889.42 14163.01 29589.43 10592.62 8076.43 9587.53 5491.34 15172.82 5193.42 19281.28 10988.74 15794.66 45
Effi-MVS+-dtu80.03 21378.57 22584.42 13985.13 31768.74 12288.77 13788.10 27174.99 14174.97 31183.49 38057.27 26793.36 19373.53 21080.88 29991.18 222
BH-RMVSNet79.61 21878.44 22883.14 21089.38 14565.93 20584.95 28587.15 30473.56 18478.19 22489.79 20256.67 27493.36 19359.53 36386.74 20190.13 267
viewdifsd2359ckpt1382.91 13982.29 14284.77 12386.96 26866.90 19087.47 19191.62 13972.19 21481.68 15790.71 17366.92 13793.28 19575.90 18587.15 19394.12 79
HyFIR lowres test77.53 27975.40 29883.94 18289.59 13266.62 19180.36 38488.64 26456.29 45676.45 26785.17 33957.64 26293.28 19561.34 34983.10 27391.91 199
IMVS_040380.80 18680.12 18582.87 22687.13 25963.59 27885.19 27589.33 21970.51 25878.49 21689.03 22563.26 18193.27 19772.56 22685.56 22691.74 203
UniMVSNet (Re)81.60 16481.11 16083.09 21288.38 19064.41 25987.60 18793.02 5178.42 3878.56 21488.16 25469.78 9493.26 19869.58 26176.49 35691.60 208
test_fmvsmconf_n85.92 6386.04 6385.57 8985.03 32069.51 10189.62 9890.58 17273.42 18987.75 5194.02 6172.85 4993.24 19990.37 890.75 11893.96 87
test_fmvsmconf0.1_n85.61 7285.65 7285.50 9082.99 37569.39 10889.65 9590.29 18673.31 19387.77 5094.15 5571.72 6493.23 20090.31 990.67 12093.89 93
test_fmvsmconf0.01_n84.73 9184.52 9385.34 9580.25 41869.03 11189.47 10289.65 20773.24 19786.98 6394.27 4766.62 14193.23 20090.26 1089.95 13493.78 102
tt080578.73 24577.83 24481.43 26785.17 31360.30 35389.41 10890.90 16271.21 23677.17 25288.73 23546.38 38993.21 20272.57 22478.96 32590.79 237
MVS_Test83.15 13383.06 12483.41 19986.86 26963.21 29186.11 25292.00 11674.31 16382.87 13589.44 21870.03 9093.21 20277.39 16488.50 16293.81 98
TAPA-MVS73.13 979.15 23477.94 23982.79 23389.59 13262.99 29988.16 16991.51 14465.77 35177.14 25391.09 16160.91 23093.21 20250.26 43387.05 19592.17 193
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
GeoE81.71 16081.01 16383.80 18789.51 13664.45 25888.97 12788.73 25971.27 23578.63 21289.76 20366.32 14793.20 20569.89 25786.02 21693.74 103
LTVRE_ROB69.57 1376.25 30574.54 31481.41 26888.60 18164.38 26079.24 40089.12 23870.76 25069.79 38087.86 26349.09 36993.20 20556.21 39980.16 30986.65 390
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
ACMH+68.96 1476.01 30974.01 32082.03 25488.60 18165.31 22888.86 13187.55 28970.25 26967.75 40487.47 27541.27 43193.19 20758.37 37775.94 36787.60 353
V4279.38 22978.24 23482.83 22781.10 41065.50 21985.55 26889.82 19971.57 22878.21 22386.12 31660.66 23593.18 20875.64 18875.46 37689.81 288
mvs_tets79.13 23577.77 24883.22 20784.70 32666.37 19589.17 11790.19 18969.38 28975.40 29289.46 21544.17 41293.15 20976.78 17680.70 30390.14 266
TR-MVS77.44 28076.18 28681.20 27688.24 19463.24 29084.61 29486.40 32367.55 32577.81 23486.48 30754.10 29693.15 20957.75 38382.72 27887.20 371
jajsoiax79.29 23177.96 23883.27 20384.68 32766.57 19389.25 11490.16 19069.20 29775.46 28989.49 21245.75 40093.13 21176.84 17280.80 30190.11 269
BH-w/o78.21 25877.33 26280.84 28688.81 16965.13 23284.87 28687.85 28369.75 28274.52 31984.74 34961.34 22193.11 21258.24 37985.84 22284.27 429
nrg03083.88 10783.53 11684.96 11186.77 27469.28 11090.46 7592.67 7474.79 15082.95 13391.33 15272.70 5293.09 21380.79 11679.28 32392.50 173
CANet_DTU80.61 19379.87 19182.83 22785.60 30263.17 29487.36 20188.65 26376.37 10175.88 28088.44 24653.51 30393.07 21473.30 21489.74 13892.25 185
fmvsm_s_conf0.5_n_485.39 7885.75 7184.30 14986.70 27665.83 20988.77 13789.78 20075.46 12588.35 3793.73 7469.19 10793.06 21591.30 388.44 16394.02 85
UniMVSNet_NR-MVSNet81.88 15681.54 15582.92 22388.46 18663.46 28587.13 20792.37 8980.19 1378.38 21989.14 22171.66 6793.05 21670.05 25476.46 35792.25 185
DU-MVS81.12 17680.52 17382.90 22487.80 21863.46 28587.02 21291.87 12479.01 3278.38 21989.07 22365.02 16393.05 21670.05 25476.46 35792.20 188
CPTT-MVS83.73 11383.33 12184.92 11593.28 5370.86 7992.09 4190.38 17968.75 31079.57 19492.83 9860.60 23893.04 21880.92 11391.56 10390.86 235
Anonymous2023121178.97 24077.69 25282.81 22990.54 10764.29 26190.11 8391.51 14465.01 36776.16 27888.13 25950.56 34693.03 21969.68 26077.56 34391.11 224
MSLP-MVS++85.43 7685.76 7084.45 13791.93 8270.24 8690.71 6792.86 6477.46 5784.22 10392.81 10067.16 13592.94 22080.36 12194.35 6290.16 265
IMVS_040780.61 19379.90 19082.75 23787.13 25963.59 27885.33 27489.33 21970.51 25877.82 23289.03 22561.84 20892.91 22172.56 22685.56 22691.74 203
F-COLMAP76.38 30474.33 31882.50 24389.28 15166.95 18988.41 15689.03 24064.05 38066.83 41888.61 24046.78 38592.89 22257.48 38478.55 32787.67 351
viewmacassd2359aftdt83.76 11283.66 11284.07 16686.59 28064.56 25186.88 21991.82 12775.72 11683.34 12592.15 12068.24 12492.88 22379.05 13989.15 14994.77 30
xiu_mvs_v1_base_debu80.80 18679.72 19784.03 17487.35 24570.19 8985.56 26588.77 25269.06 30181.83 15188.16 25450.91 34092.85 22478.29 15387.56 18489.06 306
xiu_mvs_v1_base80.80 18679.72 19784.03 17487.35 24570.19 8985.56 26588.77 25269.06 30181.83 15188.16 25450.91 34092.85 22478.29 15387.56 18489.06 306
xiu_mvs_v1_base_debi80.80 18679.72 19784.03 17487.35 24570.19 8985.56 26588.77 25269.06 30181.83 15188.16 25450.91 34092.85 22478.29 15387.56 18489.06 306
viewmanbaseed2359cas83.66 11583.55 11584.00 17786.81 27264.53 25286.65 22991.75 13274.89 14683.15 13191.68 13568.74 11692.83 22779.02 14189.24 14694.63 48
NR-MVSNet80.23 20879.38 20682.78 23487.80 21863.34 28886.31 24491.09 15879.01 3272.17 35189.07 22367.20 13492.81 22866.08 29375.65 37092.20 188
fmvsm_s_conf0.5_n_1186.06 5786.75 4784.00 17787.78 22166.09 19989.96 8690.80 16777.37 5986.72 6694.20 5272.51 5392.78 22989.08 2292.33 8793.13 142
fmvsm_l_conf0.5_n_386.02 5886.32 5385.14 10187.20 25668.54 13189.57 9990.44 17775.31 13087.49 5594.39 4272.86 4892.72 23089.04 2790.56 12294.16 76
TranMVSNet+NR-MVSNet80.84 18180.31 17882.42 24487.85 21562.33 31287.74 18591.33 14980.55 977.99 23089.86 19665.23 16192.62 23167.05 28675.24 38492.30 183
test_040272.79 36070.44 37179.84 31488.13 20165.99 20485.93 25684.29 35265.57 35467.40 41285.49 33046.92 38292.61 23235.88 48274.38 39280.94 461
fmvsm_s_conf0.5_n_284.04 10184.11 10183.81 18686.17 28965.00 23786.96 21487.28 29674.35 16188.25 4094.23 5061.82 21092.60 23389.85 1288.09 17393.84 96
fmvsm_s_conf0.1_n_283.80 10983.79 10883.83 18485.62 30164.94 24287.03 21186.62 32074.32 16287.97 4894.33 4360.67 23492.60 23389.72 1487.79 18093.96 87
SixPastTwentyTwo73.37 34471.26 35779.70 32285.08 31857.89 37985.57 26483.56 36371.03 24365.66 43285.88 31942.10 42692.57 23559.11 36863.34 45688.65 328
eth_miper_zixun_eth77.92 26876.69 27781.61 26483.00 37161.98 31983.15 33689.20 23369.52 28774.86 31384.35 35661.76 21192.56 23671.50 23772.89 40690.28 262
mvsmamba80.60 19579.38 20684.27 15389.74 13067.24 18187.47 19186.95 30970.02 27275.38 29388.93 23051.24 33792.56 23675.47 19389.22 14793.00 152
EG-PatchMatch MVS74.04 33371.82 34780.71 28984.92 32167.42 17185.86 25988.08 27266.04 34764.22 44483.85 36835.10 46392.56 23657.44 38580.83 30082.16 454
COLMAP_ROBcopyleft66.92 1773.01 35470.41 37280.81 28787.13 25965.63 21588.30 16484.19 35562.96 39363.80 44987.69 26738.04 45292.56 23646.66 45274.91 38784.24 430
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LuminaMVS80.68 19179.62 20083.83 18485.07 31968.01 15086.99 21388.83 24970.36 26381.38 16187.99 26150.11 35292.51 24079.02 14186.89 19990.97 231
fmvsm_s_conf0.5_n_685.55 7386.20 5683.60 19087.32 25265.13 23288.86 13191.63 13875.41 12688.23 4193.45 8268.56 11892.47 24189.52 1892.78 7993.20 135
ECVR-MVScopyleft79.61 21879.26 21180.67 29090.08 11754.69 42787.89 18077.44 44274.88 14780.27 18592.79 10148.96 37292.45 24268.55 27192.50 8494.86 22
EI-MVSNet80.52 19979.98 18782.12 25084.28 33463.19 29386.41 23888.95 24674.18 16878.69 20987.54 27366.62 14192.43 24372.57 22480.57 30590.74 241
MVSTER79.01 23877.88 24382.38 24583.07 36864.80 24884.08 31488.95 24669.01 30478.69 20987.17 28454.70 29192.43 24374.69 19880.57 30589.89 284
gm-plane-assit81.40 40453.83 43562.72 39980.94 41492.39 24563.40 313
IterMVS-LS80.06 21179.38 20682.11 25285.89 29463.20 29286.79 22389.34 21874.19 16775.45 29086.72 29366.62 14192.39 24572.58 22376.86 35090.75 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v14878.72 24677.80 24681.47 26682.73 38161.96 32086.30 24588.08 27273.26 19576.18 27585.47 33162.46 19892.36 24771.92 23473.82 39890.09 271
test250677.30 28476.49 28079.74 32090.08 11752.02 44687.86 18263.10 49074.88 14780.16 18892.79 10138.29 45192.35 24868.74 27092.50 8494.86 22
FIs82.07 15282.42 13781.04 28188.80 17358.34 37188.26 16593.49 3176.93 7778.47 21891.04 16369.92 9292.34 24969.87 25884.97 23392.44 178
test111179.43 22579.18 21480.15 30589.99 12253.31 44087.33 20377.05 44675.04 14080.23 18792.77 10448.97 37192.33 25068.87 26892.40 8694.81 27
新几何183.42 19793.13 6070.71 8185.48 33757.43 45081.80 15491.98 12363.28 17992.27 25164.60 30592.99 7687.27 369
anonymousdsp78.60 24977.15 26482.98 22180.51 41667.08 18487.24 20689.53 21265.66 35375.16 30487.19 28352.52 30992.25 25277.17 16679.34 32289.61 293
lupinMVS81.39 17180.27 18084.76 12487.35 24570.21 8785.55 26886.41 32262.85 39581.32 16288.61 24061.68 21292.24 25378.41 15190.26 12791.83 200
baseline275.70 31273.83 32581.30 27283.26 36061.79 32382.57 34680.65 40666.81 33266.88 41783.42 38157.86 26092.19 25463.47 31179.57 31589.91 282
jason81.39 17180.29 17984.70 12686.63 27969.90 9585.95 25586.77 31463.24 38881.07 16889.47 21361.08 22892.15 25578.33 15290.07 13292.05 197
jason: jason.
XVG-ACMP-BASELINE76.11 30774.27 31981.62 26283.20 36364.67 25083.60 32589.75 20469.75 28271.85 35487.09 28632.78 46792.11 25669.99 25680.43 30788.09 343
c3_l78.75 24477.91 24081.26 27482.89 37861.56 32684.09 31389.13 23769.97 27575.56 28584.29 35766.36 14692.09 25773.47 21275.48 37490.12 268
viewdifsd2359ckpt0782.83 14182.78 13382.99 21986.51 28262.58 30585.09 28190.83 16675.22 13282.28 14491.63 13969.43 9992.03 25877.71 15986.32 20794.34 67
miper_ehance_all_eth78.59 25077.76 24981.08 28082.66 38361.56 32683.65 32189.15 23568.87 30875.55 28683.79 37166.49 14492.03 25873.25 21576.39 35989.64 292
GA-MVS76.87 29175.17 30681.97 25682.75 38062.58 30581.44 36586.35 32572.16 21774.74 31482.89 39146.20 39492.02 26068.85 26981.09 29691.30 220
miper_enhance_ethall77.87 27076.86 27080.92 28581.65 39861.38 33082.68 34488.98 24365.52 35575.47 28782.30 40065.76 15892.00 26172.95 21976.39 35989.39 299
fmvsm_s_conf0.5_n_585.22 8285.55 7484.25 15686.26 28567.40 17389.18 11689.31 22472.50 20888.31 3893.86 7069.66 9691.96 26289.81 1391.05 11193.38 123
thres100view90076.50 29675.55 29579.33 33189.52 13556.99 39485.83 26183.23 36973.94 17376.32 27187.12 28551.89 32691.95 26348.33 44383.75 25789.07 304
tfpn200view976.42 30275.37 30079.55 32889.13 15857.65 38585.17 27683.60 36173.41 19076.45 26786.39 30952.12 31691.95 26348.33 44383.75 25789.07 304
thres40076.50 29675.37 30079.86 31389.13 15857.65 38585.17 27683.60 36173.41 19076.45 26786.39 30952.12 31691.95 26348.33 44383.75 25790.00 277
thres600view776.50 29675.44 29679.68 32389.40 14357.16 39185.53 27083.23 36973.79 17776.26 27287.09 28651.89 32691.89 26648.05 44883.72 26090.00 277
cl2278.07 26377.01 26681.23 27582.37 39061.83 32283.55 32687.98 27668.96 30775.06 30883.87 36761.40 22091.88 26773.53 21076.39 35989.98 280
dcpmvs_285.63 7186.15 6084.06 16991.71 8564.94 24286.47 23691.87 12473.63 18186.60 6893.02 9476.57 1991.87 26883.36 8492.15 9095.35 4
FC-MVSNet-test81.52 16882.02 14980.03 30788.42 18955.97 41187.95 17693.42 3477.10 7277.38 24290.98 16869.96 9191.79 26968.46 27384.50 24192.33 181
fmvsm_l_conf0.5_n84.47 9284.54 9184.27 15385.42 30768.81 11788.49 15387.26 30168.08 32088.03 4593.49 7872.04 6091.77 27088.90 2989.14 15092.24 187
ET-MVSNet_ETH3D78.63 24876.63 27984.64 12786.73 27569.47 10385.01 28384.61 34769.54 28666.51 42686.59 30150.16 35191.75 27176.26 17984.24 24992.69 164
thres20075.55 31474.47 31578.82 34087.78 22157.85 38083.07 34183.51 36472.44 21175.84 28184.42 35252.08 31991.75 27147.41 45083.64 26286.86 383
MVP-Stereo76.12 30674.46 31681.13 27985.37 30969.79 9684.42 30487.95 27965.03 36667.46 40985.33 33453.28 30691.73 27358.01 38183.27 27081.85 456
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
fmvsm_s_conf0.5_n_386.36 5387.46 3283.09 21287.08 26565.21 22989.09 12490.21 18879.67 2089.98 2495.02 2473.17 4391.71 27491.30 391.60 10092.34 180
fmvsm_l_conf0.5_n_a84.13 9984.16 9684.06 16985.38 30868.40 13488.34 16186.85 31367.48 32787.48 5693.40 8370.89 7691.61 27588.38 3789.22 14792.16 194
OurMVSNet-221017-074.26 32972.42 34279.80 31583.76 34859.59 36185.92 25786.64 31866.39 34366.96 41687.58 26939.46 44291.60 27665.76 29669.27 42688.22 340
fmvsm_s_conf0.5_n_a83.63 11883.41 11884.28 15186.14 29068.12 14489.43 10582.87 37970.27 26887.27 6093.80 7369.09 10891.58 27788.21 3883.65 26193.14 141
Fast-Effi-MVS+-dtu78.02 26576.49 28082.62 24083.16 36666.96 18886.94 21687.45 29372.45 20971.49 35984.17 36454.79 29091.58 27767.61 27880.31 30889.30 302
AstraMVS80.81 18380.14 18482.80 23086.05 29363.96 26686.46 23785.90 33273.71 17980.85 17590.56 17954.06 29891.57 27979.72 13383.97 25292.86 158
viewdifsd2359ckpt1180.37 20479.73 19582.30 24783.70 35062.39 30984.20 30986.67 31673.22 19880.90 17290.62 17663.00 19091.56 28076.81 17478.44 33092.95 155
viewmsd2359difaftdt80.37 20479.73 19582.30 24783.70 35062.39 30984.20 30986.67 31673.22 19880.90 17290.62 17663.00 19091.56 28076.81 17478.44 33092.95 155
fmvsm_s_conf0.1_n_a83.32 13082.99 12784.28 15183.79 34668.07 14689.34 11282.85 38069.80 27987.36 5994.06 5968.34 12291.56 28087.95 4283.46 26793.21 133
UniMVSNet_ETH3D79.10 23678.24 23481.70 26186.85 27060.24 35487.28 20588.79 25174.25 16676.84 25590.53 18149.48 36191.56 28067.98 27582.15 28393.29 128
test_fmvsm_n_192085.29 8185.34 7885.13 10486.12 29169.93 9388.65 14690.78 16869.97 27588.27 3993.98 6671.39 7091.54 28488.49 3590.45 12493.91 90
cl____77.72 27376.76 27480.58 29282.49 38760.48 35083.09 33987.87 28169.22 29574.38 32285.22 33862.10 20591.53 28571.09 24175.41 37889.73 291
DIV-MVS_self_test77.72 27376.76 27480.58 29282.48 38860.48 35083.09 33987.86 28269.22 29574.38 32285.24 33662.10 20591.53 28571.09 24175.40 37989.74 290
gbinet_0.2-2-1-0.0273.24 35070.86 36580.39 29578.03 44661.62 32583.10 33886.69 31565.98 34969.29 38576.15 46149.77 35891.51 28762.75 32366.00 44388.03 344
test_fmvsmvis_n_192084.02 10283.87 10484.49 13684.12 33869.37 10988.15 17087.96 27870.01 27383.95 11093.23 8768.80 11591.51 28788.61 3289.96 13392.57 167
ACMH67.68 1675.89 31073.93 32281.77 26088.71 17866.61 19288.62 14789.01 24269.81 27866.78 41986.70 29741.95 42891.51 28755.64 40078.14 33687.17 373
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n83.80 10983.71 11084.07 16686.69 27767.31 17689.46 10383.07 37471.09 23986.96 6493.70 7569.02 11391.47 29088.79 3084.62 24093.44 122
fmvsm_s_conf0.1_n83.56 12183.38 11984.10 16084.86 32267.28 17889.40 10983.01 37570.67 25287.08 6193.96 6768.38 12091.45 29188.56 3484.50 24193.56 117
Anonymous20240521178.25 25677.01 26681.99 25591.03 9560.67 34684.77 28883.90 35870.65 25680.00 18991.20 15741.08 43391.43 29265.21 29985.26 23193.85 94
CHOSEN 1792x268877.63 27875.69 29083.44 19689.98 12368.58 13078.70 41087.50 29156.38 45575.80 28286.84 28958.67 25391.40 29361.58 34585.75 22490.34 258
XVG-OURS80.41 20079.23 21283.97 18085.64 30069.02 11383.03 34390.39 17871.09 23977.63 23891.49 14754.62 29391.35 29475.71 18783.47 26691.54 211
lessismore_v078.97 33781.01 41157.15 39265.99 48361.16 45982.82 39339.12 44591.34 29559.67 36146.92 48888.43 334
guyue81.13 17580.64 17082.60 24186.52 28163.92 26986.69 22887.73 28673.97 17180.83 17689.69 20456.70 27391.33 29678.26 15685.40 23092.54 169
XVG-OURS-SEG-HR80.81 18379.76 19483.96 18185.60 30268.78 11983.54 32890.50 17570.66 25576.71 26091.66 13660.69 23391.26 29776.94 16981.58 29191.83 200
tpm273.26 34971.46 35178.63 34283.34 35856.71 39980.65 37980.40 41456.63 45473.55 33182.02 40551.80 32891.24 29856.35 39878.42 33387.95 345
usedtu_blend_shiyan573.29 34870.96 36280.25 30177.80 44862.16 31684.44 30187.38 29464.41 37368.09 39876.28 45851.32 33391.23 29963.21 31765.76 44587.35 363
blend_shiyan472.29 36569.65 37880.21 30378.24 44462.16 31682.29 35087.27 29965.41 35868.43 39776.42 45739.91 44091.23 29963.21 31765.66 45087.22 370
OpenMVS_ROBcopyleft64.09 1970.56 38368.19 38977.65 36780.26 41759.41 36485.01 28382.96 37858.76 43765.43 43582.33 39937.63 45491.23 29945.34 46276.03 36682.32 451
fmvsm_l_conf0.5_n_985.84 6786.63 4983.46 19587.12 26466.01 20288.56 15089.43 21575.59 12189.32 2894.32 4472.89 4791.21 30290.11 1192.33 8793.16 138
GBi-Net78.40 25377.40 25981.40 26987.60 23463.01 29588.39 15789.28 22571.63 22475.34 29587.28 27754.80 28791.11 30362.72 32479.57 31590.09 271
test178.40 25377.40 25981.40 26987.60 23463.01 29588.39 15789.28 22571.63 22475.34 29587.28 27754.80 28791.11 30362.72 32479.57 31590.09 271
FMVSNet177.44 28076.12 28781.40 26986.81 27263.01 29588.39 15789.28 22570.49 26274.39 32187.28 27749.06 37091.11 30360.91 35178.52 32890.09 271
FMVSNet377.88 26976.85 27180.97 28486.84 27162.36 31186.52 23588.77 25271.13 23775.34 29586.66 29954.07 29791.10 30662.72 32479.57 31589.45 297
FMVSNet278.20 25977.21 26381.20 27687.60 23462.89 30287.47 19189.02 24171.63 22475.29 30187.28 27754.80 28791.10 30662.38 33279.38 32189.61 293
K. test v371.19 37368.51 38679.21 33483.04 37057.78 38384.35 30676.91 44772.90 20562.99 45282.86 39239.27 44391.09 30861.65 34452.66 48188.75 324
CostFormer75.24 32173.90 32379.27 33282.65 38458.27 37280.80 37382.73 38261.57 41175.33 29983.13 38655.52 28291.07 30964.98 30278.34 33588.45 333
0.4-1-1-0.170.93 37767.94 39679.91 31179.35 43461.27 33178.95 40782.19 38863.36 38767.50 40769.40 48039.83 44191.04 31062.44 32968.40 43287.40 360
blended_shiyan673.38 34271.17 35880.01 30978.36 44161.48 32982.43 34787.27 29965.40 35968.56 39377.55 44851.94 32491.01 31163.27 31665.76 44587.55 356
viewmambaseed2359dif80.41 20079.84 19282.12 25082.95 37762.50 30883.39 33088.06 27467.11 33080.98 17090.31 18766.20 15091.01 31174.62 19984.90 23492.86 158
testdata291.01 31162.37 333
blended_shiyan873.38 34271.17 35880.02 30878.36 44161.51 32882.43 34787.28 29665.40 35968.61 39177.53 44951.91 32591.00 31463.28 31565.76 44587.53 357
wanda-best-256-51272.94 35670.66 36679.79 31677.80 44861.03 33781.31 36787.15 30465.18 36268.09 39876.28 45851.32 33390.97 31563.06 31965.76 44587.35 363
FE-blended-shiyan772.94 35670.66 36679.79 31677.80 44861.03 33781.31 36787.15 30465.18 36268.09 39876.28 45851.32 33390.97 31563.06 31965.76 44587.35 363
0.3-1-1-0.01570.03 39166.80 41579.72 32178.18 44561.07 33577.63 42582.32 38762.65 40065.50 43367.29 48137.62 45590.91 31761.99 33968.04 43487.19 372
MSDG73.36 34670.99 36180.49 29484.51 33265.80 21180.71 37886.13 32965.70 35265.46 43483.74 37244.60 40790.91 31751.13 42676.89 34984.74 425
0.4-1-1-0.270.01 39266.86 41479.44 32977.61 45160.64 34776.77 43282.34 38662.40 40365.91 43166.65 48240.05 43890.83 31961.77 34368.24 43386.86 383
TAMVS78.89 24377.51 25883.03 21787.80 21867.79 15984.72 28985.05 34367.63 32376.75 25987.70 26662.25 20290.82 32058.53 37587.13 19490.49 252
diffmvs_AUTHOR82.38 14782.27 14382.73 23883.26 36063.80 27183.89 31589.76 20273.35 19282.37 14390.84 16966.25 14890.79 32182.77 9487.93 17893.59 115
diffmvspermissive82.10 15081.88 15282.76 23683.00 37163.78 27383.68 32089.76 20272.94 20482.02 15089.85 19765.96 15690.79 32182.38 10187.30 19093.71 104
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CDS-MVSNet79.07 23777.70 25183.17 20987.60 23468.23 14284.40 30586.20 32767.49 32676.36 27086.54 30561.54 21590.79 32161.86 34187.33 18990.49 252
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
dtuplus80.04 21279.40 20581.97 25683.08 36762.61 30483.63 32487.98 27667.47 32881.02 16990.50 18264.86 16690.77 32471.28 24084.76 23792.53 170
VortexMVS78.57 25177.89 24280.59 29185.89 29462.76 30385.61 26389.62 20972.06 21874.99 31085.38 33355.94 28090.77 32474.99 19676.58 35488.23 339
131476.53 29575.30 30480.21 30383.93 34362.32 31384.66 29188.81 25060.23 42170.16 37284.07 36655.30 28490.73 32667.37 28183.21 27187.59 355
WR-MVS79.49 22279.22 21380.27 30088.79 17458.35 37085.06 28288.61 26578.56 3677.65 23788.34 24863.81 17790.66 32764.98 30277.22 34591.80 202
MVS_111021_LR82.61 14482.11 14584.11 15988.82 16871.58 5885.15 27886.16 32874.69 15280.47 18391.04 16362.29 20190.55 32880.33 12290.08 13190.20 264
hybrid81.05 17780.66 16982.22 24981.97 39362.99 29983.42 32988.68 26070.76 25080.56 18090.40 18464.49 17090.48 32979.57 13586.06 21493.19 136
HY-MVS69.67 1277.95 26777.15 26480.36 29787.57 24360.21 35583.37 33287.78 28566.11 34575.37 29487.06 28863.27 18090.48 32961.38 34882.43 28190.40 256
usedtu_dtu_shiyan176.43 30075.32 30279.76 31883.00 37160.72 34381.74 35788.76 25668.99 30572.98 33884.19 36256.41 27790.27 33162.39 33079.40 31988.31 336
FE-MVSNET376.43 30075.32 30279.76 31883.00 37160.72 34381.74 35788.76 25668.99 30572.98 33884.19 36256.41 27790.27 33162.39 33079.40 31988.31 336
VNet82.21 14982.41 13881.62 26290.82 10160.93 33984.47 29789.78 20076.36 10284.07 10791.88 12664.71 16790.26 33370.68 24688.89 15293.66 106
VPA-MVSNet80.60 19580.55 17280.76 28888.07 20560.80 34286.86 22091.58 14275.67 12080.24 18689.45 21763.34 17890.25 33470.51 24879.22 32491.23 221
ab-mvs79.51 22178.97 21881.14 27888.46 18660.91 34083.84 31689.24 23170.36 26379.03 20388.87 23363.23 18390.21 33565.12 30082.57 28092.28 184
D2MVS74.82 32473.21 33279.64 32579.81 42662.56 30780.34 38587.35 29564.37 37568.86 38882.66 39546.37 39090.10 33667.91 27681.24 29486.25 394
testing9176.54 29475.66 29379.18 33588.43 18855.89 41281.08 37083.00 37673.76 17875.34 29584.29 35746.20 39490.07 33764.33 30684.50 24191.58 210
testing9976.09 30875.12 30779.00 33688.16 19855.50 41880.79 37481.40 39873.30 19475.17 30384.27 36044.48 40990.02 33864.28 30784.22 25091.48 215
1112_ss77.40 28276.43 28280.32 29989.11 16260.41 35283.65 32187.72 28762.13 40773.05 33786.72 29362.58 19689.97 33962.11 33880.80 30190.59 248
testing1175.14 32274.01 32078.53 34888.16 19856.38 40580.74 37780.42 41370.67 25272.69 34483.72 37443.61 41689.86 34062.29 33483.76 25689.36 300
tfpnnormal74.39 32773.16 33378.08 35786.10 29258.05 37484.65 29387.53 29070.32 26671.22 36285.63 32654.97 28589.86 34043.03 46775.02 38686.32 393
tpmvs71.09 37569.29 38176.49 38082.04 39256.04 41078.92 40881.37 39964.05 38067.18 41478.28 44249.74 35989.77 34249.67 43672.37 40883.67 437
Vis-MVSNet (Re-imp)78.36 25578.45 22778.07 35888.64 18051.78 45286.70 22779.63 42474.14 16975.11 30690.83 17061.29 22389.75 34358.10 38091.60 10092.69 164
ambc75.24 39573.16 47650.51 46263.05 49287.47 29264.28 44377.81 44617.80 49389.73 34457.88 38260.64 46785.49 411
VPNet78.69 24778.66 22378.76 34188.31 19255.72 41584.45 30086.63 31976.79 8178.26 22290.55 18059.30 24889.70 34566.63 28877.05 34790.88 234
mvs_anonymous79.42 22679.11 21580.34 29884.45 33357.97 37782.59 34587.62 28867.40 32976.17 27788.56 24368.47 11989.59 34670.65 24786.05 21593.47 121
pmmvs674.69 32573.39 32978.61 34381.38 40557.48 38886.64 23087.95 27964.99 36870.18 37086.61 30050.43 34889.52 34762.12 33770.18 42388.83 320
DTE-MVSNet76.99 28876.80 27277.54 37186.24 28653.06 44487.52 18990.66 17077.08 7372.50 34588.67 23860.48 23989.52 34757.33 38770.74 42090.05 276
USDC70.33 38668.37 38776.21 38280.60 41456.23 40879.19 40286.49 32160.89 41561.29 45885.47 33131.78 47089.47 34953.37 41476.21 36582.94 447
Test_1112_low_res76.40 30375.44 29679.27 33289.28 15158.09 37381.69 36087.07 30759.53 42972.48 34686.67 29861.30 22289.33 35060.81 35380.15 31090.41 255
TransMVSNet (Re)75.39 32074.56 31377.86 36185.50 30657.10 39386.78 22486.09 33072.17 21671.53 35887.34 27663.01 18989.31 35156.84 39361.83 46287.17 373
reproduce_monomvs75.40 31974.38 31778.46 35183.92 34457.80 38283.78 31786.94 31073.47 18872.25 35084.47 35138.74 44789.27 35275.32 19470.53 42188.31 336
sc_t172.19 36769.51 37980.23 30284.81 32361.09 33484.68 29080.22 41860.70 41771.27 36083.58 37836.59 45889.24 35360.41 35463.31 45790.37 257
WR-MVS_H78.51 25278.49 22678.56 34688.02 20756.38 40588.43 15492.67 7477.14 6973.89 32687.55 27266.25 14889.24 35358.92 37073.55 40090.06 275
PEN-MVS77.73 27277.69 25277.84 36287.07 26753.91 43487.91 17991.18 15377.56 5373.14 33688.82 23461.23 22489.17 35559.95 35872.37 40890.43 254
pm-mvs177.25 28576.68 27878.93 33884.22 33658.62 36886.41 23888.36 26871.37 23173.31 33388.01 26061.22 22589.15 35664.24 30873.01 40589.03 310
testdata79.97 31090.90 9964.21 26284.71 34559.27 43185.40 7692.91 9562.02 20789.08 35768.95 26791.37 10686.63 391
Baseline_NR-MVSNet78.15 26178.33 23277.61 36885.79 29656.21 40986.78 22485.76 33473.60 18377.93 23187.57 27065.02 16388.99 35867.14 28575.33 38187.63 352
旧先验286.56 23358.10 44387.04 6288.98 35974.07 206
LCM-MVSNet-Re77.05 28776.94 26977.36 37287.20 25651.60 45380.06 38980.46 41175.20 13567.69 40586.72 29362.48 19788.98 35963.44 31289.25 14591.51 212
AllTest70.96 37668.09 39279.58 32685.15 31563.62 27484.58 29579.83 42162.31 40460.32 46386.73 29132.02 46888.96 36150.28 43171.57 41686.15 397
TestCases79.58 32685.15 31563.62 27479.83 42162.31 40460.32 46386.73 29132.02 46888.96 36150.28 43171.57 41686.15 397
GG-mvs-BLEND75.38 39381.59 40055.80 41479.32 39969.63 47367.19 41373.67 47143.24 41788.90 36350.41 42884.50 24181.45 458
MonoMVSNet76.49 29975.80 28878.58 34581.55 40158.45 36986.36 24386.22 32674.87 14974.73 31583.73 37351.79 32988.73 36470.78 24372.15 41188.55 332
gg-mvs-nofinetune69.95 39367.96 39475.94 38383.07 36854.51 43077.23 42970.29 47163.11 39070.32 36862.33 48543.62 41588.69 36553.88 41187.76 18284.62 427
testing22274.04 33372.66 33978.19 35487.89 21355.36 41981.06 37179.20 42971.30 23474.65 31783.57 37939.11 44688.67 36651.43 42585.75 22490.53 250
patchmatchnet-post74.00 47051.12 33988.60 367
SCA74.22 33072.33 34379.91 31184.05 34162.17 31579.96 39279.29 42866.30 34472.38 34880.13 42451.95 32288.60 36759.25 36677.67 34288.96 315
FE-MVSNET272.88 35971.28 35577.67 36578.30 44357.78 38384.43 30288.92 24869.56 28564.61 44181.67 40746.73 38788.54 36959.33 36467.99 43586.69 389
CP-MVSNet78.22 25778.34 23177.84 36287.83 21754.54 42987.94 17791.17 15477.65 4873.48 33288.49 24462.24 20388.43 37062.19 33574.07 39390.55 249
PS-CasMVS78.01 26678.09 23677.77 36487.71 22754.39 43188.02 17391.22 15177.50 5673.26 33488.64 23960.73 23188.41 37161.88 34073.88 39790.53 250
MS-PatchMatch73.83 33672.67 33877.30 37483.87 34566.02 20181.82 35584.66 34661.37 41468.61 39182.82 39347.29 37888.21 37259.27 36584.32 24877.68 472
IterMVS-SCA-FT75.43 31773.87 32480.11 30682.69 38264.85 24781.57 36283.47 36569.16 29870.49 36684.15 36551.95 32288.15 37369.23 26372.14 41287.34 366
pmmvs474.03 33571.91 34680.39 29581.96 39468.32 13681.45 36482.14 38959.32 43069.87 37885.13 34052.40 31288.13 37460.21 35774.74 38984.73 426
EPNet_dtu75.46 31674.86 30877.23 37582.57 38554.60 42886.89 21883.09 37371.64 22366.25 42885.86 32055.99 27988.04 37554.92 40586.55 20489.05 309
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n_783.34 12884.03 10281.28 27385.73 29865.13 23285.40 27389.90 19874.96 14482.13 14893.89 6966.65 14087.92 37686.56 5391.05 11190.80 236
TDRefinement67.49 41364.34 42576.92 37773.47 47461.07 33584.86 28782.98 37759.77 42658.30 47085.13 34026.06 47987.89 37747.92 44960.59 46881.81 457
tpm cat170.57 38268.31 38877.35 37382.41 38957.95 37878.08 41980.22 41852.04 46868.54 39477.66 44752.00 32187.84 37851.77 42072.07 41386.25 394
baseline176.98 28976.75 27677.66 36688.13 20155.66 41685.12 27981.89 39173.04 20276.79 25788.90 23162.43 19987.78 37963.30 31471.18 41889.55 295
SDMVSNet80.38 20280.18 18180.99 28289.03 16364.94 24280.45 38389.40 21675.19 13676.61 26489.98 19460.61 23787.69 38076.83 17383.55 26390.33 259
TinyColmap67.30 41664.81 42374.76 40181.92 39656.68 40080.29 38681.49 39760.33 41956.27 47883.22 38324.77 48387.66 38145.52 46069.47 42579.95 466
tt032070.49 38568.03 39377.89 36084.78 32459.12 36583.55 32680.44 41258.13 44267.43 41180.41 42039.26 44487.54 38255.12 40263.18 45886.99 380
tt0320-xc70.11 38967.45 40778.07 35885.33 31059.51 36383.28 33378.96 43158.77 43667.10 41580.28 42236.73 45787.42 38356.83 39459.77 47087.29 368
ppachtmachnet_test70.04 39067.34 40978.14 35579.80 42761.13 33279.19 40280.59 40759.16 43265.27 43679.29 43346.75 38687.29 38449.33 43866.72 43886.00 403
testing3-275.12 32375.19 30574.91 39890.40 11045.09 48380.29 38678.42 43478.37 4176.54 26687.75 26444.36 41087.28 38557.04 39083.49 26592.37 179
ITE_SJBPF78.22 35381.77 39760.57 34883.30 36769.25 29467.54 40687.20 28236.33 46087.28 38554.34 40874.62 39086.80 385
MDTV_nov1_ep1369.97 37783.18 36453.48 43777.10 43180.18 42060.45 41869.33 38480.44 41848.89 37386.90 38751.60 42278.51 329
CR-MVSNet73.37 34471.27 35679.67 32481.32 40865.19 23075.92 43780.30 41659.92 42572.73 34281.19 40952.50 31086.69 38859.84 35977.71 33987.11 377
WBMVS73.43 34172.81 33775.28 39487.91 21250.99 45978.59 41381.31 40065.51 35774.47 32084.83 34646.39 38886.68 38958.41 37677.86 33788.17 342
Patchmtry70.74 38069.16 38375.49 39180.72 41254.07 43374.94 44880.30 41658.34 43970.01 37381.19 40952.50 31086.54 39053.37 41471.09 41985.87 406
JIA-IIPM66.32 42462.82 43676.82 37877.09 45561.72 32465.34 48575.38 45458.04 44464.51 44262.32 48642.05 42786.51 39151.45 42469.22 42782.21 452
UBG73.08 35372.27 34475.51 39088.02 20751.29 45778.35 41777.38 44365.52 35573.87 32782.36 39845.55 40186.48 39255.02 40484.39 24788.75 324
CMPMVSbinary51.72 2170.19 38868.16 39076.28 38173.15 47757.55 38779.47 39783.92 35748.02 47756.48 47684.81 34743.13 41886.42 39362.67 32781.81 28984.89 423
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs-eth3d70.50 38467.83 39978.52 34977.37 45466.18 19881.82 35581.51 39658.90 43563.90 44880.42 41942.69 42186.28 39458.56 37465.30 45283.11 443
ETVMVS72.25 36671.05 36075.84 38487.77 22351.91 44979.39 39874.98 45669.26 29373.71 32882.95 38940.82 43586.14 39546.17 45684.43 24689.47 296
SD_040374.65 32674.77 31074.29 40686.20 28847.42 47283.71 31985.12 34069.30 29168.50 39587.95 26259.40 24786.05 39649.38 43783.35 26889.40 298
CNLPA78.08 26276.79 27381.97 25690.40 11071.07 7287.59 18884.55 34866.03 34872.38 34889.64 20757.56 26386.04 39759.61 36283.35 26888.79 322
PatchmatchNetpermissive73.12 35271.33 35478.49 35083.18 36460.85 34179.63 39578.57 43364.13 37771.73 35579.81 42951.20 33885.97 39857.40 38676.36 36488.66 327
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
mmtdpeth74.16 33173.01 33577.60 37083.72 34961.13 33285.10 28085.10 34172.06 21877.21 25180.33 42143.84 41485.75 39977.14 16752.61 48285.91 404
CVMVSNet72.99 35572.58 34074.25 40784.28 33450.85 46086.41 23883.45 36644.56 48173.23 33587.54 27349.38 36385.70 40065.90 29478.44 33086.19 396
testing368.56 40667.67 40371.22 43787.33 25042.87 48883.06 34271.54 46870.36 26369.08 38784.38 35430.33 47485.69 40137.50 48075.45 37785.09 421
UWE-MVS72.13 36871.49 35074.03 41086.66 27847.70 47081.40 36676.89 44863.60 38675.59 28484.22 36139.94 43985.62 40248.98 44086.13 21388.77 323
IterMVS74.29 32872.94 33678.35 35281.53 40263.49 28481.58 36182.49 38368.06 32169.99 37583.69 37551.66 33185.54 40365.85 29571.64 41586.01 401
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-RL test70.24 38767.78 40177.61 36877.43 45359.57 36271.16 46270.33 47062.94 39468.65 39072.77 47350.62 34585.49 40469.58 26166.58 44087.77 350
sd_testset77.70 27577.40 25978.60 34489.03 16360.02 35679.00 40585.83 33375.19 13676.61 26489.98 19454.81 28685.46 40562.63 32883.55 26390.33 259
test_post178.90 4095.43 52448.81 37485.44 40659.25 366
pmmvs571.55 37170.20 37575.61 38777.83 44756.39 40481.74 35780.89 40257.76 44567.46 40984.49 35049.26 36785.32 40757.08 38975.29 38285.11 420
mvs5depth69.45 39867.45 40775.46 39273.93 46855.83 41379.19 40283.23 36966.89 33171.63 35783.32 38233.69 46685.09 40859.81 36055.34 47885.46 412
KD-MVS_2432*160066.22 42563.89 42873.21 41775.47 46453.42 43870.76 46584.35 35064.10 37866.52 42478.52 44034.55 46484.98 40950.40 42950.33 48581.23 459
miper_refine_blended66.22 42563.89 42873.21 41775.47 46453.42 43870.76 46584.35 35064.10 37866.52 42478.52 44034.55 46484.98 40950.40 42950.33 48581.23 459
PatchMatch-RL72.38 36270.90 36376.80 37988.60 18167.38 17479.53 39676.17 45362.75 39869.36 38382.00 40645.51 40284.89 41153.62 41280.58 30478.12 471
KD-MVS_self_test68.81 40267.59 40572.46 42674.29 46745.45 47877.93 42287.00 30863.12 38963.99 44778.99 43842.32 42384.77 41256.55 39764.09 45587.16 375
RPSCF73.23 35171.46 35178.54 34782.50 38659.85 35782.18 35282.84 38158.96 43471.15 36389.41 21945.48 40484.77 41258.82 37271.83 41491.02 230
FE-MVSNET67.25 41765.33 42173.02 42175.86 45952.54 44580.26 38880.56 40863.80 38560.39 46179.70 43041.41 43084.66 41443.34 46662.62 46081.86 455
test_post5.46 52350.36 34984.24 415
CL-MVSNet_self_test72.37 36371.46 35175.09 39679.49 43253.53 43680.76 37685.01 34469.12 29970.51 36582.05 40457.92 25984.13 41652.27 41966.00 44387.60 353
our_test_369.14 40067.00 41275.57 38879.80 42758.80 36677.96 42177.81 43759.55 42862.90 45378.25 44347.43 37783.97 41751.71 42167.58 43783.93 435
EU-MVSNet68.53 40767.61 40471.31 43678.51 44047.01 47584.47 29784.27 35342.27 48466.44 42784.79 34840.44 43683.76 41858.76 37368.54 43183.17 441
MDA-MVSNet-bldmvs66.68 42063.66 43075.75 38579.28 43560.56 34973.92 45478.35 43564.43 37250.13 48679.87 42844.02 41383.67 41946.10 45756.86 47283.03 445
MIMVSNet168.58 40566.78 41673.98 41180.07 42251.82 45180.77 37584.37 34964.40 37459.75 46682.16 40336.47 45983.63 42042.73 46870.33 42286.48 392
usedtu_dtu_shiyan264.75 43261.63 44074.10 40970.64 48453.18 44382.10 35481.27 40156.22 45756.39 47774.67 46827.94 47783.56 42142.71 46962.73 45985.57 410
myMVS_eth3d2873.62 33873.53 32873.90 41288.20 19547.41 47378.06 42079.37 42674.29 16573.98 32584.29 35744.67 40683.54 42251.47 42387.39 18890.74 241
patch_mono-283.65 11684.54 9180.99 28290.06 12165.83 20984.21 30888.74 25871.60 22785.01 8092.44 10874.51 3083.50 42382.15 10292.15 9093.64 112
PM-MVS66.41 42364.14 42673.20 41973.92 46956.45 40278.97 40664.96 48763.88 38464.72 44080.24 42319.84 49183.44 42466.24 28964.52 45479.71 467
PVSNet64.34 1872.08 36970.87 36475.69 38686.21 28756.44 40374.37 45280.73 40562.06 40870.17 37182.23 40242.86 42083.31 42554.77 40684.45 24587.32 367
tpm72.37 36371.71 34874.35 40582.19 39152.00 44779.22 40177.29 44464.56 37172.95 34083.68 37651.35 33283.26 42658.33 37875.80 36887.81 349
miper_lstm_enhance74.11 33273.11 33477.13 37680.11 42159.62 36072.23 45886.92 31266.76 33470.40 36782.92 39056.93 27182.92 42769.06 26672.63 40788.87 318
IMVS_040477.16 28676.42 28379.37 33087.13 25963.59 27877.12 43089.33 21970.51 25866.22 42989.03 22550.36 34982.78 42872.56 22685.56 22691.74 203
tpmrst72.39 36172.13 34573.18 42080.54 41549.91 46479.91 39379.08 43063.11 39071.69 35679.95 42655.32 28382.77 42965.66 29773.89 39686.87 382
MVS-HIRNet59.14 44357.67 44563.57 46381.65 39843.50 48771.73 45965.06 48639.59 48851.43 48357.73 49238.34 45082.58 43039.53 47573.95 39564.62 487
dtuonlycased68.45 40967.29 41071.92 42880.18 42054.90 42579.76 39480.38 41560.11 42362.57 45576.44 45649.34 36482.31 43155.05 40361.77 46378.53 470
Syy-MVS68.05 41167.85 39768.67 45184.68 32740.97 49478.62 41173.08 46566.65 33966.74 42079.46 43152.11 31882.30 43232.89 48576.38 36282.75 448
myMVS_eth3d67.02 41866.29 41869.21 44684.68 32742.58 48978.62 41173.08 46566.65 33966.74 42079.46 43131.53 47182.30 43239.43 47776.38 36282.75 448
SSC-MVS3.273.35 34773.39 32973.23 41685.30 31149.01 46874.58 45081.57 39575.21 13473.68 32985.58 32852.53 30882.05 43454.33 40977.69 34188.63 329
FMVSNet569.50 39767.96 39474.15 40882.97 37655.35 42080.01 39182.12 39062.56 40163.02 45081.53 40836.92 45681.92 43548.42 44274.06 39485.17 419
PatchT68.46 40867.85 39770.29 44180.70 41343.93 48672.47 45774.88 45760.15 42270.55 36476.57 45349.94 35581.59 43650.58 42774.83 38885.34 414
EGC-MVSNET52.07 45547.05 45967.14 45783.51 35560.71 34580.50 38267.75 4790.07 5380.43 53975.85 46524.26 48481.54 43728.82 48962.25 46159.16 490
MIMVSNet70.69 38169.30 38074.88 39984.52 33156.35 40775.87 43979.42 42564.59 37067.76 40382.41 39741.10 43281.54 43746.64 45481.34 29286.75 387
icg_test_0407_278.92 24278.93 21978.90 33987.13 25963.59 27876.58 43389.33 21970.51 25877.82 23289.03 22561.84 20881.38 43972.56 22685.56 22691.74 203
Anonymous2024052168.80 40367.22 41173.55 41474.33 46654.11 43283.18 33585.61 33558.15 44161.68 45780.94 41430.71 47381.27 44057.00 39173.34 40485.28 415
WB-MVSnew71.96 37071.65 34972.89 42284.67 33051.88 45082.29 35077.57 43962.31 40473.67 33083.00 38853.49 30481.10 44145.75 45982.13 28485.70 408
WTY-MVS75.65 31375.68 29175.57 38886.40 28456.82 39677.92 42382.40 38465.10 36476.18 27587.72 26563.13 18880.90 44260.31 35681.96 28689.00 313
dp66.80 41965.43 42070.90 44079.74 42948.82 46975.12 44674.77 45859.61 42764.08 44677.23 45042.89 41980.72 44348.86 44166.58 44083.16 442
ADS-MVSNet266.20 42763.33 43174.82 40079.92 42358.75 36767.55 47775.19 45553.37 46565.25 43775.86 46342.32 42380.53 44441.57 47268.91 42885.18 417
XXY-MVS75.41 31875.56 29474.96 39783.59 35357.82 38180.59 38083.87 35966.54 34274.93 31288.31 24963.24 18280.09 44562.16 33676.85 35186.97 381
test_vis1_n_192075.52 31575.78 28974.75 40279.84 42557.44 38983.26 33485.52 33662.83 39679.34 20186.17 31545.10 40579.71 44678.75 14681.21 29587.10 379
test-LLR72.94 35672.43 34174.48 40381.35 40658.04 37578.38 41477.46 44066.66 33669.95 37679.00 43648.06 37579.24 44766.13 29084.83 23586.15 397
test-mter71.41 37270.39 37374.48 40381.35 40658.04 37578.38 41477.46 44060.32 42069.95 37679.00 43636.08 46179.24 44766.13 29084.83 23586.15 397
Anonymous2023120668.60 40467.80 40071.02 43880.23 41950.75 46178.30 41880.47 41056.79 45366.11 43082.63 39646.35 39178.95 44943.62 46575.70 36983.36 440
UnsupCasMVSNet_bld63.70 43561.53 44170.21 44273.69 47151.39 45672.82 45681.89 39155.63 45957.81 47271.80 47538.67 44878.61 45049.26 43952.21 48380.63 463
test20.0367.45 41466.95 41368.94 44775.48 46344.84 48477.50 42677.67 43866.66 33663.01 45183.80 37047.02 38178.40 45142.53 47168.86 43083.58 438
PMMVS69.34 39968.67 38571.35 43575.67 46162.03 31875.17 44373.46 46350.00 47468.68 38979.05 43452.07 32078.13 45261.16 35082.77 27673.90 478
sss73.60 33973.64 32773.51 41582.80 37955.01 42476.12 43581.69 39462.47 40274.68 31685.85 32157.32 26678.11 45360.86 35280.93 29787.39 361
LCM-MVSNet54.25 44849.68 45867.97 45653.73 50445.28 48166.85 48080.78 40435.96 49339.45 49462.23 4878.70 50378.06 45448.24 44651.20 48480.57 464
EPMVS69.02 40168.16 39071.59 43179.61 43049.80 46677.40 42766.93 48162.82 39770.01 37379.05 43445.79 39877.86 45556.58 39675.26 38387.13 376
PVSNet_057.27 2061.67 44059.27 44368.85 44979.61 43057.44 38968.01 47573.44 46455.93 45858.54 46970.41 47844.58 40877.55 45647.01 45135.91 49371.55 481
UnsupCasMVSNet_eth67.33 41565.99 41971.37 43373.48 47351.47 45575.16 44485.19 33965.20 36160.78 46080.93 41642.35 42277.20 45757.12 38853.69 48085.44 413
test_fmvs1_n70.86 37970.24 37472.73 42472.51 48255.28 42181.27 36979.71 42351.49 47278.73 20884.87 34527.54 47877.02 45876.06 18279.97 31385.88 405
test_fmvs170.93 37770.52 36972.16 42773.71 47055.05 42380.82 37278.77 43251.21 47378.58 21384.41 35331.20 47276.94 45975.88 18680.12 31284.47 428
TESTMET0.1,169.89 39569.00 38472.55 42579.27 43656.85 39578.38 41474.71 46057.64 44668.09 39877.19 45137.75 45376.70 46063.92 30984.09 25184.10 433
dmvs_re71.14 37470.58 36872.80 42381.96 39459.68 35975.60 44179.34 42768.55 31369.27 38680.72 41749.42 36276.54 46152.56 41877.79 33882.19 453
LF4IMVS64.02 43462.19 43769.50 44570.90 48353.29 44176.13 43477.18 44552.65 46758.59 46880.98 41323.55 48676.52 46253.06 41666.66 43978.68 469
new-patchmatchnet61.73 43961.73 43961.70 46572.74 48024.50 50869.16 47278.03 43661.40 41256.72 47575.53 46638.42 44976.48 46345.95 45857.67 47184.13 432
test_cas_vis1_n_192073.76 33773.74 32673.81 41375.90 45859.77 35880.51 38182.40 38458.30 44081.62 15985.69 32344.35 41176.41 46476.29 17878.61 32685.23 416
APD_test153.31 45249.93 45763.42 46465.68 49150.13 46371.59 46166.90 48234.43 49440.58 49371.56 4768.65 50476.27 46534.64 48455.36 47763.86 488
test_vis1_n69.85 39669.21 38271.77 43072.66 48155.27 42281.48 36376.21 45252.03 46975.30 30083.20 38528.97 47576.22 46674.60 20078.41 33483.81 436
PMVScopyleft37.38 2244.16 46340.28 46755.82 47440.82 50942.54 49165.12 48663.99 48934.43 49424.48 50157.12 4943.92 50976.17 46717.10 50355.52 47648.75 497
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UWE-MVS-2865.32 42864.93 42266.49 45978.70 43838.55 49677.86 42464.39 48862.00 40964.13 44583.60 37741.44 42976.00 46831.39 48780.89 29884.92 422
ttmdpeth59.91 44257.10 44668.34 45367.13 49046.65 47774.64 44967.41 48048.30 47662.52 45685.04 34420.40 48975.93 46942.55 47045.90 49182.44 450
test0.0.03 168.00 41267.69 40268.90 44877.55 45247.43 47175.70 44072.95 46766.66 33666.56 42282.29 40148.06 37575.87 47044.97 46374.51 39183.41 439
WB-MVS54.94 44754.72 44855.60 47573.50 47220.90 51074.27 45361.19 49259.16 43250.61 48474.15 46947.19 38075.78 47117.31 50235.07 49470.12 482
Gipumacopyleft45.18 46241.86 46555.16 47677.03 45651.52 45432.50 50380.52 40932.46 49627.12 49935.02 5069.52 50275.50 47222.31 49960.21 46938.45 503
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs357.79 44454.26 44968.37 45264.02 49456.72 39875.12 44665.17 48540.20 48652.93 48269.86 47920.36 49075.48 47345.45 46155.25 47972.90 480
SSC-MVS53.88 45053.59 45054.75 47772.87 47919.59 51173.84 45560.53 49457.58 44849.18 48873.45 47246.34 39275.47 47416.20 50532.28 49669.20 483
test_fmvs268.35 41067.48 40670.98 43969.50 48651.95 44880.05 39076.38 45149.33 47574.65 31784.38 35423.30 48775.40 47574.51 20175.17 38585.60 409
CHOSEN 280x42066.51 42264.71 42471.90 42981.45 40363.52 28357.98 49468.95 47753.57 46462.59 45476.70 45246.22 39375.29 47655.25 40179.68 31476.88 474
testgi66.67 42166.53 41767.08 45875.62 46241.69 49375.93 43676.50 44966.11 34565.20 43986.59 30135.72 46274.71 47743.71 46473.38 40384.84 424
YYNet165.03 42962.91 43471.38 43275.85 46056.60 40169.12 47374.66 46157.28 45154.12 48077.87 44545.85 39774.48 47849.95 43461.52 46583.05 444
MDA-MVSNet_test_wron65.03 42962.92 43371.37 43375.93 45756.73 39769.09 47474.73 45957.28 45154.03 48177.89 44445.88 39674.39 47949.89 43561.55 46482.99 446
dtuonly69.95 39369.98 37669.85 44373.09 47849.46 46774.55 45176.40 45057.56 44967.82 40286.31 31250.89 34474.23 48061.46 34681.71 29085.86 407
SSM_0407277.67 27777.52 25678.12 35688.81 16967.96 15165.03 48788.66 26170.96 24579.48 19689.80 20058.69 25174.23 48070.35 25085.93 21992.18 190
ADS-MVSNet64.36 43362.88 43568.78 45079.92 42347.17 47467.55 47771.18 46953.37 46565.25 43775.86 46342.32 42373.99 48241.57 47268.91 42885.18 417
dmvs_testset62.63 43764.11 42758.19 46978.55 43924.76 50775.28 44265.94 48467.91 32260.34 46276.01 46253.56 30273.94 48331.79 48667.65 43675.88 476
ANet_high50.57 45746.10 46163.99 46248.67 50739.13 49570.99 46480.85 40361.39 41331.18 49657.70 49317.02 49473.65 48431.22 48815.89 50679.18 468
test_fmvs363.36 43661.82 43867.98 45562.51 49546.96 47677.37 42874.03 46245.24 48067.50 40778.79 43912.16 49972.98 48572.77 22266.02 44283.99 434
Patchmatch-test64.82 43163.24 43269.57 44479.42 43349.82 46563.49 49169.05 47651.98 47059.95 46580.13 42450.91 34070.98 48640.66 47473.57 39987.90 347
MVStest156.63 44652.76 45268.25 45461.67 49653.25 44271.67 46068.90 47838.59 48950.59 48583.05 38725.08 48170.66 48736.76 48138.56 49280.83 462
testf145.72 45941.96 46357.00 47056.90 49845.32 47966.14 48259.26 49526.19 49830.89 49760.96 4894.14 50770.64 48826.39 49546.73 48955.04 493
APD_test245.72 45941.96 46357.00 47056.90 49845.32 47966.14 48259.26 49526.19 49830.89 49760.96 4894.14 50770.64 48826.39 49546.73 48955.04 493
FPMVS53.68 45151.64 45359.81 46865.08 49251.03 45869.48 47069.58 47441.46 48540.67 49272.32 47416.46 49570.00 49024.24 49765.42 45158.40 492
test_vis1_rt60.28 44158.42 44465.84 46067.25 48955.60 41770.44 46760.94 49344.33 48259.00 46766.64 48324.91 48268.67 49162.80 32269.48 42473.25 479
DSMNet-mixed57.77 44556.90 44760.38 46767.70 48835.61 49869.18 47153.97 49932.30 49757.49 47379.88 42740.39 43768.57 49238.78 47872.37 40876.97 473
mvsany_test162.30 43861.26 44265.41 46169.52 48554.86 42666.86 47949.78 50146.65 47868.50 39583.21 38449.15 36866.28 49356.93 39260.77 46675.11 477
N_pmnet52.79 45353.26 45151.40 47978.99 4377.68 52269.52 4693.89 52151.63 47157.01 47474.98 46740.83 43465.96 49437.78 47964.67 45380.56 465
test_vis3_rt49.26 45847.02 46056.00 47254.30 50145.27 48266.76 48148.08 50236.83 49144.38 49053.20 4987.17 50664.07 49556.77 39555.66 47558.65 491
mvsany_test353.99 44951.45 45461.61 46655.51 50044.74 48563.52 49045.41 50543.69 48358.11 47176.45 45417.99 49263.76 49654.77 40647.59 48776.34 475
dongtai45.42 46145.38 46245.55 48173.36 47526.85 50567.72 47634.19 50754.15 46349.65 48756.41 49625.43 48062.94 49719.45 50028.09 49846.86 500
new_pmnet50.91 45650.29 45652.78 47868.58 48734.94 50063.71 48956.63 49839.73 48744.95 48965.47 48421.93 48858.48 49834.98 48356.62 47364.92 486
test_f52.09 45450.82 45555.90 47353.82 50342.31 49259.42 49358.31 49736.45 49256.12 47970.96 47712.18 49857.79 49953.51 41356.57 47467.60 484
PMMVS240.82 46438.86 46846.69 48053.84 50216.45 51548.61 49749.92 50037.49 49031.67 49560.97 4888.14 50556.42 50028.42 49030.72 49767.19 485
E-PMN31.77 46630.64 46935.15 48752.87 50527.67 50257.09 49547.86 50324.64 50016.40 51033.05 50711.23 50054.90 50114.46 50618.15 50422.87 508
EMVS30.81 46829.65 47034.27 48850.96 50625.95 50656.58 49646.80 50424.01 50115.53 51130.68 50912.47 49754.43 50212.81 50817.05 50522.43 509
test_method31.52 46729.28 47138.23 48427.03 5166.50 52420.94 50762.21 4914.05 51222.35 50552.50 49913.33 49647.58 50327.04 49234.04 49560.62 489
MVEpermissive26.22 2330.37 46925.89 47343.81 48244.55 50835.46 49928.87 50639.07 50618.20 50518.58 50840.18 5042.68 51047.37 50417.07 50423.78 50148.60 498
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
kuosan39.70 46540.40 46637.58 48564.52 49326.98 50365.62 48433.02 50846.12 47942.79 49148.99 50124.10 48546.56 50512.16 50926.30 49939.20 502
RoMa-SfM28.67 47025.38 47438.54 48332.61 51322.48 50940.24 4987.23 51721.81 50226.66 50060.46 4910.96 51341.72 50626.47 49411.95 50951.40 496
LoFTR27.52 47124.27 47537.29 48634.75 51219.27 51233.78 50221.60 51212.42 50721.61 50656.59 4950.91 51440.37 50713.94 50722.80 50252.22 495
DKM25.67 47223.01 47633.64 48932.08 51419.25 51337.50 5005.52 51818.67 50323.58 50455.44 4970.64 51734.02 50823.95 4989.73 51047.66 499
MatchFormer22.13 47419.86 47928.93 49128.66 51515.74 51631.91 50517.10 5137.75 50818.87 50747.50 5030.62 51933.92 5097.49 51218.87 50337.14 504
PDCNetPlus24.75 47322.46 47731.64 49035.53 51117.00 51432.00 5049.46 51418.43 50418.56 50951.31 5001.65 51133.00 51026.51 4938.70 51244.91 501
DeepMVS_CXcopyleft27.40 49240.17 51026.90 50424.59 51117.44 50623.95 50248.61 5029.77 50126.48 51118.06 50124.47 50028.83 507
ELoFTR14.23 47811.56 48122.24 49311.02 5216.56 52313.59 5107.57 5165.55 51011.96 51339.09 5050.21 52824.93 5129.43 5115.66 51735.22 505
wuyk23d16.82 47715.94 48019.46 49558.74 49731.45 50139.22 4993.74 5236.84 5096.04 5142.70 5381.27 51224.29 51310.54 51014.40 5082.63 521
GLUNet-SfM12.90 47910.00 48221.62 49413.58 5208.30 52010.19 5129.30 5154.31 51112.18 51230.90 5080.50 52322.76 5144.89 5134.14 52333.79 506
tmp_tt18.61 47621.40 47810.23 4964.82 54010.11 51734.70 50130.74 5101.48 51623.91 50326.07 51028.42 47613.41 51527.12 49115.35 5077.17 516
ALIKED-LG8.61 4808.70 4848.33 49720.63 5178.70 51915.50 5084.61 5192.19 5135.84 51518.70 5110.80 5158.06 5161.03 5218.97 5118.25 510
ALIKED-MNN7.86 4817.83 4877.97 49819.40 5188.86 51814.48 5093.90 5201.59 5144.74 52016.49 5120.59 5207.65 5170.91 5228.34 5147.39 513
ALIKED-NN7.51 4827.61 4887.21 49918.26 5198.10 52113.45 5113.88 5221.50 5154.87 51816.47 5130.64 5177.00 5180.88 5238.50 5136.52 518
XFeat-MNN4.39 4874.49 4904.10 5002.88 5421.91 5375.86 5182.57 5241.06 5185.04 51613.99 5140.43 5264.47 5192.00 5156.55 5155.92 519
XFeat-NN3.78 4933.96 4963.23 5062.65 5431.53 5424.99 5191.92 5290.81 5234.77 51912.37 5170.38 5273.39 5201.64 5166.13 5164.77 520
SP-MNN4.14 4914.24 4943.82 50210.32 5241.83 5388.11 5151.99 5280.82 5222.23 5238.27 5200.47 5252.14 5211.20 5194.77 5217.49 511
SP-LightGlue4.27 4894.41 4923.86 50110.99 5221.99 5348.19 5132.06 5270.98 5202.37 5228.29 5180.56 5212.10 5221.27 5174.99 5197.48 512
SP-NN4.00 4924.12 4953.63 5059.92 5251.81 5397.94 5161.90 5300.86 5212.15 5248.00 5210.50 5232.09 5231.20 5194.63 5226.98 517
SP-SuperGlue4.24 4904.38 4933.81 50310.75 5232.00 5338.18 5142.09 5261.00 5192.41 5218.29 5180.56 5212.05 5241.27 5174.91 5207.39 513
SP-DiffGlue4.29 4884.46 4913.77 5043.68 5412.12 5315.97 5172.22 5251.10 5174.89 51713.93 5150.66 5161.95 5252.47 5145.24 5187.22 515
SIFT-NN2.77 4942.92 4972.34 5078.70 5263.08 5254.46 5201.01 5320.68 5241.46 5255.49 5220.16 5291.65 5260.26 5244.04 5242.27 522
SIFT-MNN2.63 4952.75 4982.25 5088.10 5272.84 5264.08 5211.02 5310.68 5241.28 5265.34 5250.15 5301.64 5270.26 5243.88 5262.27 522
SIFT-NCM-Cal2.40 4972.52 5002.05 5107.74 5282.54 5283.75 5240.84 5340.65 5270.89 5334.78 5310.13 5341.60 5280.19 5353.71 5272.01 528
SIFT-NN-NCMNet2.52 4962.64 4992.14 5097.53 5292.74 5274.00 5220.98 5330.65 5271.24 5285.08 5280.14 5311.60 5280.23 5273.94 5252.07 526
SIFT-NN-UMatch2.26 4992.39 5021.89 5136.21 5352.08 5323.76 5230.83 5350.66 5261.04 5305.09 5260.14 5311.52 5300.23 5273.51 5282.07 526
SIFT-NN-CMatch2.31 4982.41 5012.00 5116.59 5332.34 5303.48 5250.83 5350.65 5271.28 5265.09 5260.14 5311.52 5300.23 5273.41 5292.14 524
SIFT-ConvMatch2.25 5002.37 5031.90 5127.29 5302.37 5293.21 5280.75 5370.65 5271.03 5314.91 5290.12 5371.51 5320.22 5303.13 5311.81 529
SIFT-UMatch2.16 5012.30 5041.72 5156.99 5311.97 5363.32 5260.70 5390.64 5310.91 5324.86 5300.12 5371.49 5330.22 5302.97 5321.72 531
SIFT-NN-PointCN2.07 5022.18 5051.74 5145.75 5361.65 5413.27 5270.73 5380.60 5341.07 5294.62 5320.13 5341.43 5340.21 5323.22 5302.12 525
SIFT-CM-Cal2.02 5032.13 5061.67 5166.79 5321.99 5342.79 5300.64 5400.63 5320.87 5344.48 5340.13 5341.41 5350.19 5352.70 5331.61 533
SIFT-UM-Cal1.97 5042.12 5071.52 5176.57 5341.67 5402.93 5290.57 5420.62 5330.83 5354.55 5330.11 5391.37 5360.20 5342.69 5341.53 534
SIFT-PointCN1.72 5051.83 5081.36 5195.55 5381.22 5432.59 5310.59 5410.55 5360.71 5373.77 5360.08 5411.24 5370.17 5372.48 5351.63 532
SIFT-PCN-Cal1.72 5051.82 5091.39 5185.64 5371.19 5442.39 5320.53 5430.55 5360.72 5363.90 5350.09 5401.22 5380.17 5372.42 5361.76 530
SIFT-NCMNet1.44 5071.56 5101.08 5205.14 5391.07 5451.97 5330.32 5440.56 5350.64 5383.23 5370.07 5421.01 5390.14 5391.95 5371.15 535
testmvs6.04 4858.02 4860.10 5220.08 5440.03 54769.74 4680.04 5450.05 5390.31 5401.68 5390.02 5440.04 5400.24 5260.02 5380.25 537
test1236.12 4848.11 4850.14 5210.06 5450.09 54671.05 4630.03 5460.04 5400.25 5411.30 5400.05 5430.03 5410.21 5320.01 5390.29 536
mmdepth0.00 5080.00 5110.00 5230.00 5460.00 5480.00 5340.00 5470.00 5410.00 5420.00 5410.00 5450.00 5420.00 5400.00 5400.00 538
monomultidepth0.00 5080.00 5110.00 5230.00 5460.00 5480.00 5340.00 5470.00 5410.00 5420.00 5410.00 5450.00 5420.00 5400.00 5400.00 538
test_blank0.00 5080.00 5110.00 5230.00 5460.00 5480.00 5340.00 5470.00 5410.00 5420.00 5410.00 5450.00 5420.00 5400.00 5400.00 538
uanet_test0.00 5080.00 5110.00 5230.00 5460.00 5480.00 5340.00 5470.00 5410.00 5420.00 5410.00 5450.00 5420.00 5400.00 5400.00 538
DCPMVS0.00 5080.00 5110.00 5230.00 5460.00 5480.00 5340.00 5470.00 5410.00 5420.00 5410.00 5450.00 5420.00 5400.00 5400.00 538
cdsmvs_eth3d_5k19.96 47526.61 4720.00 5230.00 5460.00 5480.00 53489.26 2280.00 5410.00 54288.61 24061.62 2140.00 5420.00 5400.00 5400.00 538
pcd_1.5k_mvsjas5.26 4867.02 4890.00 5230.00 5460.00 5480.00 5340.00 5470.00 5410.00 5420.00 54163.15 1850.00 5420.00 5400.00 5400.00 538
sosnet-low-res0.00 5080.00 5110.00 5230.00 5460.00 5480.00 5340.00 5470.00 5410.00 5420.00 5410.00 5450.00 5420.00 5400.00 5400.00 538
sosnet0.00 5080.00 5110.00 5230.00 5460.00 5480.00 5340.00 5470.00 5410.00 5420.00 5410.00 5450.00 5420.00 5400.00 5400.00 538
uncertanet0.00 5080.00 5110.00 5230.00 5460.00 5480.00 5340.00 5470.00 5410.00 5420.00 5410.00 5450.00 5420.00 5400.00 5400.00 538
Regformer0.00 5080.00 5110.00 5230.00 5460.00 5480.00 5340.00 5470.00 5410.00 5420.00 5410.00 5450.00 5420.00 5400.00 5400.00 538
ab-mvs-re7.23 4839.64 4830.00 5230.00 5460.00 5480.00 5340.00 5470.00 5410.00 54286.72 2930.00 5450.00 5420.00 5400.00 5400.00 538
uanet0.00 5080.00 5110.00 5230.00 5460.00 5480.00 5340.00 5470.00 5410.00 5420.00 5410.00 5450.00 5420.00 5400.00 5400.00 538
WAC-MVS42.58 48939.46 476
FOURS195.00 1072.39 4195.06 193.84 2074.49 15791.30 17
test_one_060195.07 771.46 6094.14 978.27 4292.05 1395.74 880.83 12
eth-test20.00 546
eth-test0.00 546
RE-MVS-def85.48 7693.06 6470.63 8391.88 4392.27 9673.53 18685.69 7494.45 3763.87 17582.75 9591.87 9692.50 173
IU-MVS95.30 271.25 6592.95 6166.81 33292.39 688.94 2896.63 494.85 24
save fliter93.80 4472.35 4490.47 7491.17 15474.31 163
test072695.27 571.25 6593.60 794.11 1077.33 6092.81 395.79 580.98 10
GSMVS88.96 315
test_part295.06 872.65 3291.80 15
sam_mvs151.32 33388.96 315
sam_mvs50.01 353
MTGPAbinary92.02 114
MTMP92.18 3932.83 509
test9_res84.90 6495.70 2992.87 157
agg_prior282.91 9195.45 3292.70 162
test_prior472.60 3489.01 126
test_prior288.85 13375.41 12684.91 8393.54 7674.28 3483.31 8595.86 23
新几何286.29 247
旧先验191.96 8165.79 21286.37 32493.08 9369.31 10292.74 8088.74 326
原ACMM286.86 220
test22291.50 8768.26 13884.16 31183.20 37254.63 46279.74 19191.63 13958.97 25091.42 10486.77 386
segment_acmp73.08 44
testdata184.14 31275.71 117
plane_prior790.08 11768.51 132
plane_prior689.84 12668.70 12660.42 240
plane_prior491.00 166
plane_prior368.60 12978.44 3778.92 206
plane_prior291.25 6079.12 29
plane_prior189.90 125
plane_prior68.71 12490.38 7877.62 4986.16 212
n20.00 547
nn0.00 547
door-mid69.98 472
test1192.23 100
door69.44 475
HQP5-MVS66.98 186
HQP-NCC89.33 14689.17 11776.41 9677.23 247
ACMP_Plane89.33 14689.17 11776.41 9677.23 247
BP-MVS77.47 162
HQP3-MVS92.19 10885.99 217
HQP2-MVS60.17 243
NP-MVS89.62 13168.32 13690.24 190
MDTV_nov1_ep13_2view37.79 49775.16 44455.10 46066.53 42349.34 36453.98 41087.94 346
ACMMP++_ref81.95 287
ACMMP++81.25 293
Test By Simon64.33 171