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 bysort bysort bysort bysorted by
MM89.16 689.23 788.97 490.79 9873.65 1092.66 2491.17 13386.57 187.39 5294.97 2171.70 5897.68 192.19 195.63 2895.57 1
casdiffmvs_mvgpermissive85.99 5486.09 5785.70 7787.65 22067.22 17388.69 13593.04 4279.64 2185.33 7092.54 9873.30 3694.50 11983.49 7791.14 10295.37 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
dcpmvs_285.63 6586.15 5584.06 14791.71 8064.94 22386.47 21791.87 10873.63 15986.60 6193.02 8776.57 1591.87 24483.36 7892.15 8495.35 3
casdiffmvspermissive85.11 7885.14 7785.01 9987.20 23565.77 20187.75 17292.83 6177.84 4384.36 9392.38 10072.15 5193.93 14481.27 10290.48 11395.33 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
fmvsm_s_conf0.5_n_987.39 3087.95 2085.70 7789.48 13367.88 14988.59 13989.05 21480.19 1290.70 1795.40 1574.56 2593.92 14591.54 292.07 8695.31 5
3Dnovator+77.84 485.48 6884.47 8788.51 791.08 8973.49 1693.18 1293.78 1980.79 876.66 23293.37 7760.40 21596.75 2677.20 14493.73 6695.29 6
BP-MVS184.32 8683.71 9586.17 6487.84 20967.85 15089.38 10289.64 18377.73 4583.98 10092.12 10756.89 24595.43 7384.03 7491.75 9295.24 7
MVS_030487.69 2187.55 2688.12 1389.45 13471.76 5391.47 5289.54 18682.14 386.65 6094.28 4168.28 10597.46 690.81 695.31 3495.15 8
CS-MVS86.69 4186.95 3985.90 7490.76 9967.57 15992.83 1893.30 3379.67 1984.57 8792.27 10171.47 6195.02 9684.24 7193.46 6995.13 9
TSAR-MVS + MP.88.02 1888.11 1787.72 3093.68 4372.13 4891.41 5392.35 8374.62 13488.90 2793.85 6575.75 2096.00 5587.80 3894.63 5095.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
baseline84.93 8184.98 7884.80 11187.30 23365.39 21087.30 18792.88 5877.62 4784.04 9992.26 10271.81 5593.96 13881.31 10090.30 11695.03 11
DVP-MVS++90.23 191.01 187.89 2494.34 2771.25 6195.06 194.23 378.38 3892.78 495.74 682.45 397.49 489.42 1896.68 294.95 12
PC_three_145268.21 28892.02 1294.00 5782.09 595.98 5784.58 6596.68 294.95 12
IS-MVSNet83.15 11382.81 11184.18 13789.94 11963.30 26691.59 4688.46 23879.04 3079.49 16692.16 10565.10 14094.28 12567.71 25091.86 9194.95 12
SteuartSystems-ACMMP88.72 1188.86 1188.32 992.14 7472.96 2593.73 593.67 2180.19 1288.10 3794.80 2373.76 3497.11 1587.51 4195.82 2194.90 15
Skip Steuart: Steuart Systems R&D Blog.
SED-MVS90.08 290.85 287.77 2695.30 270.98 6893.57 894.06 1177.24 6093.10 195.72 882.99 197.44 789.07 2396.63 494.88 16
OPU-MVS89.06 394.62 1575.42 493.57 894.02 5582.45 396.87 2083.77 7696.48 894.88 16
SMA-MVScopyleft89.08 889.23 788.61 694.25 3173.73 992.40 2593.63 2274.77 13092.29 795.97 274.28 3097.24 1388.58 3196.91 194.87 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
test250677.30 25776.49 25479.74 28190.08 11252.02 40287.86 17063.10 44574.88 12680.16 15992.79 9438.29 40992.35 22568.74 24392.50 8094.86 19
ECVR-MVScopyleft79.61 19179.26 18480.67 26190.08 11254.69 38587.89 16877.44 39874.88 12680.27 15692.79 9448.96 33592.45 21968.55 24492.50 8094.86 19
IU-MVS95.30 271.25 6192.95 5666.81 30092.39 688.94 2696.63 494.85 21
test111179.43 19879.18 18780.15 27389.99 11753.31 39887.33 18677.05 40275.04 11980.23 15892.77 9648.97 33492.33 22768.87 24192.40 8294.81 22
SF-MVS88.46 1288.74 1287.64 3592.78 6671.95 5192.40 2594.74 275.71 10089.16 2495.10 1875.65 2196.19 4787.07 4496.01 1794.79 23
balanced_conf0386.78 3986.99 3786.15 6691.24 8667.61 15790.51 6592.90 5777.26 5987.44 5191.63 12171.27 6596.06 5085.62 5495.01 3794.78 24
sasdasda85.91 5885.87 6286.04 7089.84 12169.44 10190.45 7193.00 4776.70 8088.01 4091.23 13373.28 3793.91 14681.50 9888.80 14394.77 25
SPE-MVS-test86.29 5086.48 4685.71 7691.02 9167.21 17492.36 3093.78 1978.97 3383.51 11091.20 13670.65 7495.15 8781.96 9594.89 4294.77 25
canonicalmvs85.91 5885.87 6286.04 7089.84 12169.44 10190.45 7193.00 4776.70 8088.01 4091.23 13373.28 3793.91 14681.50 9888.80 14394.77 25
GDP-MVS83.52 10382.64 11486.16 6588.14 19368.45 12889.13 11492.69 6672.82 18283.71 10591.86 11355.69 25295.35 8280.03 11589.74 12894.69 28
test_0728_THIRD78.38 3892.12 995.78 481.46 797.40 989.42 1896.57 794.67 29
MSP-MVS89.51 489.91 588.30 1094.28 3073.46 1792.90 1794.11 780.27 1091.35 1494.16 4878.35 1396.77 2489.59 1694.22 6294.67 29
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
RRT-MVS82.60 12482.10 12384.10 13987.98 20362.94 27787.45 18191.27 12977.42 5679.85 16190.28 16056.62 24894.70 11279.87 11888.15 15694.67 29
MGCFI-Net85.06 8085.51 6983.70 16589.42 13563.01 27289.43 9792.62 7476.43 8487.53 4891.34 13172.82 4693.42 17281.28 10188.74 14694.66 32
viewmanbaseed2359cas83.66 9783.55 9784.00 15586.81 25064.53 23086.65 21191.75 11574.89 12583.15 11491.68 11768.74 9992.83 20579.02 12189.24 13694.63 33
alignmvs85.48 6885.32 7485.96 7389.51 13069.47 9889.74 8692.47 7776.17 9387.73 4791.46 12870.32 7693.78 15281.51 9788.95 14094.63 33
MP-MVS-pluss87.67 2287.72 2287.54 3693.64 4472.04 5089.80 8493.50 2675.17 11886.34 6295.29 1770.86 7096.00 5588.78 2996.04 1694.58 35
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepPCF-MVS80.84 188.10 1388.56 1486.73 5592.24 7369.03 10689.57 9393.39 3177.53 5389.79 2094.12 5078.98 1296.58 3585.66 5295.72 2494.58 35
VDD-MVS83.01 11882.36 11984.96 10191.02 9166.40 18488.91 12188.11 24177.57 4984.39 9093.29 7952.19 28693.91 14677.05 14788.70 14794.57 37
NormalMVS86.29 5085.88 6087.52 3793.26 5272.47 3891.65 4392.19 9279.31 2484.39 9092.18 10364.64 14595.53 6780.70 10994.65 4894.56 38
KinetiMVS83.31 11182.61 11585.39 8687.08 24467.56 16088.06 16091.65 11777.80 4482.21 12591.79 11457.27 24094.07 13677.77 13889.89 12694.56 38
VDDNet81.52 14580.67 14584.05 15090.44 10464.13 24289.73 8785.91 29271.11 21183.18 11293.48 7250.54 31293.49 16673.40 18888.25 15494.54 40
MVSMamba_PlusPlus85.99 5485.96 5986.05 6991.09 8867.64 15689.63 9192.65 7172.89 18184.64 8491.71 11671.85 5496.03 5184.77 6394.45 5694.49 41
APDe-MVScopyleft89.15 789.63 687.73 2894.49 1871.69 5493.83 493.96 1475.70 10291.06 1696.03 176.84 1497.03 1789.09 2095.65 2794.47 42
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSC_two_6792asdad89.16 194.34 2775.53 292.99 5097.53 289.67 1496.44 994.41 43
No_MVS89.16 194.34 2775.53 292.99 5097.53 289.67 1496.44 994.41 43
MCST-MVS87.37 3187.25 3287.73 2894.53 1772.46 4089.82 8293.82 1773.07 17684.86 7992.89 8976.22 1796.33 4184.89 6095.13 3694.40 45
fmvsm_s_conf0.5_n_886.56 4487.17 3584.73 11387.76 21665.62 20489.20 10792.21 9079.94 1789.74 2294.86 2268.63 10094.20 13090.83 591.39 9894.38 46
CANet86.45 4586.10 5687.51 3890.09 11170.94 7289.70 8892.59 7581.78 481.32 13891.43 12970.34 7597.23 1484.26 6993.36 7094.37 47
PHI-MVS86.43 4686.17 5487.24 4290.88 9570.96 7092.27 3394.07 1072.45 18485.22 7291.90 11069.47 8696.42 4083.28 8095.94 1994.35 48
CNVR-MVS88.93 1089.13 1088.33 894.77 1273.82 890.51 6593.00 4780.90 788.06 3894.06 5376.43 1696.84 2188.48 3495.99 1894.34 49
ZNCC-MVS87.94 1987.85 2188.20 1294.39 2473.33 1993.03 1593.81 1876.81 7485.24 7194.32 3971.76 5696.93 1985.53 5595.79 2294.32 50
HPM-MVScopyleft87.11 3586.98 3887.50 3993.88 3972.16 4792.19 3493.33 3276.07 9583.81 10493.95 6269.77 8396.01 5485.15 5694.66 4794.32 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CDPH-MVS85.76 6385.29 7687.17 4493.49 4771.08 6688.58 14092.42 8168.32 28784.61 8593.48 7272.32 4896.15 4979.00 12395.43 3094.28 52
test_241102_TWO94.06 1177.24 6092.78 495.72 881.26 897.44 789.07 2396.58 694.26 53
test_0728_SECOND87.71 3295.34 171.43 6093.49 1094.23 397.49 489.08 2196.41 1294.21 54
fmvsm_l_conf0.5_n_386.02 5286.32 4885.14 9287.20 23568.54 12689.57 9390.44 15375.31 11287.49 4994.39 3772.86 4492.72 20789.04 2590.56 11294.16 55
DeepC-MVS_fast79.65 386.91 3886.62 4587.76 2793.52 4672.37 4391.26 5493.04 4276.62 8284.22 9493.36 7871.44 6296.76 2580.82 10695.33 3394.16 55
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet83.40 10783.02 10784.57 11690.13 11064.47 23592.32 3190.73 14574.45 13879.35 17191.10 13969.05 9495.12 8872.78 19587.22 16894.13 57
NCCC88.06 1588.01 1988.24 1194.41 2273.62 1191.22 5792.83 6181.50 585.79 6693.47 7473.02 4297.00 1884.90 5894.94 4094.10 58
ACMMP_NAP88.05 1788.08 1887.94 1993.70 4173.05 2290.86 6093.59 2476.27 9288.14 3695.09 1971.06 6896.67 2987.67 3996.37 1494.09 59
XVS87.18 3486.91 4188.00 1794.42 2073.33 1992.78 1992.99 5079.14 2683.67 10794.17 4767.45 11396.60 3383.06 8194.50 5394.07 60
X-MVStestdata80.37 18077.83 21888.00 1794.42 2073.33 1992.78 1992.99 5079.14 2683.67 10712.47 45967.45 11396.60 3383.06 8194.50 5394.07 60
region2R87.42 2887.20 3488.09 1494.63 1473.55 1393.03 1593.12 4176.73 7984.45 8894.52 2769.09 9196.70 2784.37 6894.83 4594.03 62
fmvsm_s_conf0.5_n_485.39 7285.75 6584.30 12886.70 25465.83 19788.77 12989.78 17675.46 10788.35 3193.73 6869.19 9093.06 19491.30 388.44 15294.02 63
ACMMPR87.44 2687.23 3388.08 1594.64 1373.59 1293.04 1393.20 3576.78 7684.66 8394.52 2768.81 9796.65 3084.53 6694.90 4194.00 64
fmvsm_s_conf0.1_n_283.80 9383.79 9483.83 16185.62 27864.94 22387.03 19486.62 28174.32 14087.97 4294.33 3860.67 20792.60 21089.72 1387.79 15993.96 65
test_fmvsmconf_n85.92 5786.04 5885.57 8285.03 29769.51 9689.62 9290.58 14873.42 16787.75 4594.02 5572.85 4593.24 17890.37 790.75 10993.96 65
DPE-MVScopyleft89.48 589.98 488.01 1694.80 1172.69 3191.59 4694.10 975.90 9892.29 795.66 1081.67 697.38 1187.44 4396.34 1593.95 67
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_fmvsm_n_192085.29 7585.34 7285.13 9586.12 26769.93 8888.65 13790.78 14469.97 24788.27 3393.98 6071.39 6391.54 25888.49 3390.45 11493.91 68
test_prior86.33 6092.61 7069.59 9492.97 5595.48 7093.91 68
GST-MVS87.42 2887.26 3187.89 2494.12 3672.97 2492.39 2793.43 2976.89 7284.68 8093.99 5970.67 7396.82 2284.18 7395.01 3793.90 70
test_fmvsmconf0.1_n85.61 6685.65 6685.50 8382.99 34669.39 10389.65 8990.29 16273.31 17087.77 4494.15 4971.72 5793.23 17990.31 890.67 11193.89 71
Anonymous20240521178.25 22977.01 24081.99 22791.03 9060.67 30784.77 26483.90 31970.65 22880.00 16091.20 13641.08 39491.43 26565.21 27285.26 20593.85 72
LFMVS81.82 13581.23 13583.57 17091.89 7863.43 26489.84 8181.85 35277.04 6983.21 11193.10 8252.26 28593.43 17171.98 20789.95 12493.85 72
fmvsm_s_conf0.5_n_284.04 8984.11 9083.81 16386.17 26565.00 22186.96 19787.28 26574.35 13988.25 3494.23 4561.82 18392.60 21089.85 1188.09 15793.84 74
Effi-MVS+83.62 10183.08 10585.24 9088.38 18467.45 16288.89 12289.15 21075.50 10682.27 12388.28 22269.61 8594.45 12277.81 13787.84 15893.84 74
Anonymous2024052980.19 18478.89 19384.10 13990.60 10064.75 22888.95 12090.90 14065.97 31780.59 15291.17 13849.97 31993.73 15869.16 23882.70 25293.81 76
MVS_Test83.15 11383.06 10683.41 17686.86 24763.21 26886.11 22992.00 10074.31 14182.87 11789.44 19070.03 7993.21 18177.39 14388.50 15193.81 76
Elysia81.53 14380.16 15885.62 7985.51 28168.25 13588.84 12692.19 9271.31 20580.50 15389.83 17046.89 34694.82 10476.85 14989.57 13093.80 78
StellarMVS81.53 14380.16 15885.62 7985.51 28168.25 13588.84 12692.19 9271.31 20580.50 15389.83 17046.89 34694.82 10476.85 14989.57 13093.80 78
test_fmvsmconf0.01_n84.73 8484.52 8685.34 8780.25 38869.03 10689.47 9589.65 18273.24 17486.98 5794.27 4266.62 12093.23 17990.26 989.95 12493.78 80
GeoE81.71 13781.01 14083.80 16489.51 13064.45 23688.97 11988.73 23171.27 20878.63 18389.76 17566.32 12693.20 18469.89 23086.02 19093.74 81
diffmvspermissive82.10 12781.88 12982.76 21283.00 34463.78 25083.68 29189.76 17872.94 17982.02 12889.85 16965.96 13490.79 28582.38 9387.30 16793.71 82
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
lecture88.09 1488.59 1386.58 5893.26 5269.77 9293.70 694.16 577.13 6589.76 2195.52 1472.26 4996.27 4486.87 4594.65 4893.70 83
HFP-MVS87.58 2387.47 2887.94 1994.58 1673.54 1593.04 1393.24 3476.78 7684.91 7694.44 3470.78 7196.61 3284.53 6694.89 4293.66 84
VNet82.21 12682.41 11781.62 23390.82 9660.93 30284.47 27389.78 17676.36 9084.07 9891.88 11164.71 14490.26 29270.68 21988.89 14193.66 84
PGM-MVS86.68 4286.27 5087.90 2294.22 3373.38 1890.22 7693.04 4275.53 10583.86 10294.42 3567.87 11096.64 3182.70 9194.57 5293.66 84
DELS-MVS85.41 7185.30 7585.77 7588.49 17867.93 14885.52 24993.44 2878.70 3483.63 10989.03 19774.57 2495.71 6280.26 11494.04 6393.66 84
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
SD-MVS88.06 1588.50 1586.71 5692.60 7172.71 2991.81 4293.19 3677.87 4290.32 1894.00 5774.83 2393.78 15287.63 4094.27 6193.65 88
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
DeepC-MVS79.81 287.08 3786.88 4287.69 3391.16 8772.32 4590.31 7493.94 1577.12 6682.82 11994.23 4572.13 5297.09 1684.83 6195.37 3193.65 88
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
patch_mono-283.65 9884.54 8480.99 25390.06 11665.83 19784.21 28288.74 23071.60 20085.01 7392.44 9974.51 2683.50 37982.15 9492.15 8493.64 90
EIA-MVS83.31 11182.80 11284.82 10989.59 12665.59 20588.21 15492.68 6774.66 13378.96 17586.42 28069.06 9395.26 8375.54 16690.09 12093.62 91
MP-MVScopyleft87.71 2087.64 2387.93 2194.36 2673.88 692.71 2392.65 7177.57 4983.84 10394.40 3672.24 5096.28 4385.65 5395.30 3593.62 91
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS_fast85.35 7484.95 8086.57 5993.69 4270.58 8092.15 3691.62 11973.89 15382.67 12294.09 5162.60 16795.54 6680.93 10492.93 7393.57 93
fmvsm_s_conf0.1_n83.56 10283.38 10184.10 13984.86 29967.28 16989.40 10183.01 33670.67 22487.08 5593.96 6168.38 10391.45 26488.56 3284.50 21493.56 94
CSCG86.41 4886.19 5387.07 4692.91 6372.48 3790.81 6193.56 2573.95 15083.16 11391.07 14175.94 1895.19 8579.94 11794.38 5893.55 95
test1286.80 5492.63 6970.70 7791.79 11282.71 12171.67 5996.16 4894.50 5393.54 96
APD-MVS_3200maxsize85.97 5685.88 6086.22 6392.69 6869.53 9591.93 3892.99 5073.54 16385.94 6394.51 3065.80 13595.61 6383.04 8392.51 7993.53 97
mvs_anonymous79.42 19979.11 18880.34 26884.45 31057.97 33782.59 31387.62 25867.40 29776.17 24888.56 21568.47 10289.59 30570.65 22086.05 18993.47 98
fmvsm_s_conf0.5_n83.80 9383.71 9584.07 14586.69 25567.31 16889.46 9683.07 33571.09 21286.96 5893.70 6969.02 9691.47 26388.79 2884.62 21393.44 99
fmvsm_s_conf0.5_n_585.22 7685.55 6884.25 13586.26 26167.40 16589.18 10889.31 19972.50 18388.31 3293.86 6469.66 8491.96 23889.81 1291.05 10393.38 100
mPP-MVS86.67 4386.32 4887.72 3094.41 2273.55 1392.74 2192.22 8976.87 7382.81 12094.25 4466.44 12496.24 4582.88 8694.28 6093.38 100
EPNet83.72 9682.92 11086.14 6884.22 31369.48 9791.05 5985.27 29981.30 676.83 22791.65 11966.09 13095.56 6476.00 16093.85 6493.38 100
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNetpermissive83.46 10582.80 11285.43 8590.25 10868.74 11790.30 7590.13 16776.33 9180.87 14792.89 8961.00 20294.20 13072.45 20490.97 10593.35 103
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DVP-MVScopyleft89.60 390.35 387.33 4195.27 571.25 6193.49 1092.73 6577.33 5792.12 995.78 480.98 997.40 989.08 2196.41 1293.33 104
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
UniMVSNet_ETH3D79.10 20978.24 20781.70 23286.85 24860.24 31487.28 18888.79 22574.25 14476.84 22690.53 15549.48 32591.56 25667.98 24882.15 25693.29 105
EI-MVSNet-Vis-set84.19 8783.81 9385.31 8888.18 19067.85 15087.66 17489.73 18080.05 1582.95 11589.59 18270.74 7294.82 10480.66 11184.72 21193.28 106
MTAPA87.23 3387.00 3687.90 2294.18 3574.25 586.58 21492.02 9879.45 2285.88 6494.80 2368.07 10696.21 4686.69 4795.34 3293.23 107
CP-MVS87.11 3586.92 4087.68 3494.20 3473.86 793.98 392.82 6476.62 8283.68 10694.46 3167.93 10895.95 5884.20 7294.39 5793.23 107
ACMMPcopyleft85.89 6085.39 7187.38 4093.59 4572.63 3392.74 2193.18 4076.78 7680.73 15093.82 6664.33 14796.29 4282.67 9290.69 11093.23 107
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
SymmetryMVS85.38 7384.81 8187.07 4691.47 8372.47 3891.65 4388.06 24579.31 2484.39 9092.18 10364.64 14595.53 6780.70 10990.91 10793.21 110
fmvsm_s_conf0.1_n_a83.32 11082.99 10884.28 13083.79 32368.07 14189.34 10482.85 34169.80 25187.36 5394.06 5368.34 10491.56 25687.95 3783.46 24093.21 110
fmvsm_s_conf0.5_n_685.55 6786.20 5183.60 16787.32 23265.13 21688.86 12391.63 11875.41 10888.23 3593.45 7568.56 10192.47 21889.52 1792.78 7593.20 112
PAPM_NR83.02 11782.41 11784.82 10992.47 7266.37 18587.93 16691.80 11173.82 15477.32 21590.66 15167.90 10994.90 10070.37 22289.48 13393.19 113
fmvsm_l_conf0.5_n_985.84 6186.63 4483.46 17287.12 24366.01 19188.56 14189.43 19075.59 10489.32 2394.32 3972.89 4391.21 27390.11 1092.33 8393.16 114
reproduce_model87.28 3287.39 3086.95 5093.10 5871.24 6591.60 4593.19 3674.69 13188.80 2895.61 1170.29 7796.44 3986.20 5193.08 7193.16 114
OMC-MVS82.69 12081.97 12884.85 10888.75 17067.42 16387.98 16290.87 14274.92 12479.72 16391.65 11962.19 17793.96 13875.26 17086.42 18293.16 114
fmvsm_s_conf0.5_n_a83.63 10083.41 10084.28 13086.14 26668.12 13989.43 9782.87 34070.27 24087.27 5493.80 6769.09 9191.58 25388.21 3683.65 23493.14 117
PAPR81.66 14080.89 14283.99 15690.27 10764.00 24386.76 20891.77 11468.84 27877.13 22589.50 18367.63 11194.88 10267.55 25288.52 15093.09 118
UA-Net85.08 7984.96 7985.45 8492.07 7568.07 14189.78 8590.86 14382.48 284.60 8693.20 8169.35 8795.22 8471.39 21290.88 10893.07 119
reproduce-ours87.47 2487.61 2487.07 4693.27 5071.60 5591.56 4993.19 3674.98 12188.96 2595.54 1271.20 6696.54 3686.28 4993.49 6793.06 120
our_new_method87.47 2487.61 2487.07 4693.27 5071.60 5591.56 4993.19 3674.98 12188.96 2595.54 1271.20 6696.54 3686.28 4993.49 6793.06 120
HPM-MVS++copyleft89.02 989.15 988.63 595.01 976.03 192.38 2892.85 6080.26 1187.78 4394.27 4275.89 1996.81 2387.45 4296.44 993.05 122
thisisatest053079.40 20077.76 22384.31 12787.69 21965.10 21987.36 18484.26 31570.04 24377.42 21288.26 22449.94 32094.79 10870.20 22584.70 21293.03 123
train_agg86.43 4686.20 5187.13 4593.26 5272.96 2588.75 13191.89 10668.69 28085.00 7493.10 8274.43 2795.41 7684.97 5795.71 2593.02 124
EC-MVSNet86.01 5386.38 4784.91 10689.31 14366.27 18792.32 3193.63 2279.37 2384.17 9691.88 11169.04 9595.43 7383.93 7593.77 6593.01 125
mvsmamba80.60 17179.38 17984.27 13289.74 12467.24 17287.47 17986.95 27370.02 24475.38 26488.93 20251.24 30392.56 21375.47 16889.22 13793.00 126
EI-MVSNet-UG-set83.81 9283.38 10185.09 9787.87 20767.53 16187.44 18289.66 18179.74 1882.23 12489.41 19170.24 7894.74 10979.95 11683.92 22692.99 127
tttt051779.40 20077.91 21483.90 16088.10 19663.84 24888.37 14984.05 31771.45 20376.78 22989.12 19449.93 32294.89 10170.18 22683.18 24592.96 128
test9_res84.90 5895.70 2692.87 129
viewmambaseed2359dif80.41 17679.84 16882.12 22282.95 34862.50 28283.39 29988.06 24567.11 29880.98 14590.31 15966.20 12891.01 28174.62 17484.90 20892.86 130
AstraMVS80.81 15980.14 16082.80 20686.05 27063.96 24486.46 21885.90 29373.71 15780.85 14890.56 15354.06 26991.57 25579.72 11983.97 22592.86 130
SR-MVS86.73 4086.67 4386.91 5194.11 3772.11 4992.37 2992.56 7674.50 13586.84 5994.65 2667.31 11595.77 6084.80 6292.85 7492.84 132
ETV-MVS84.90 8384.67 8385.59 8189.39 13868.66 12388.74 13392.64 7379.97 1684.10 9785.71 29369.32 8895.38 7880.82 10691.37 9992.72 133
agg_prior282.91 8595.45 2992.70 134
APD-MVScopyleft87.44 2687.52 2787.19 4394.24 3272.39 4191.86 4192.83 6173.01 17888.58 2994.52 2773.36 3596.49 3884.26 6995.01 3792.70 134
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ET-MVSNet_ETH3D78.63 22176.63 25384.64 11586.73 25369.47 9885.01 25984.61 30869.54 25766.51 38786.59 27350.16 31691.75 24776.26 15684.24 22292.69 136
Vis-MVSNet (Re-imp)78.36 22878.45 20078.07 31788.64 17451.78 40886.70 20979.63 38074.14 14775.11 27790.83 14961.29 19689.75 30258.10 33991.60 9392.69 136
TSAR-MVS + GP.85.71 6485.33 7386.84 5291.34 8472.50 3689.07 11787.28 26576.41 8585.80 6590.22 16474.15 3295.37 8181.82 9691.88 8892.65 138
test_fmvsmvis_n_192084.02 9083.87 9284.49 12084.12 31569.37 10488.15 15887.96 24870.01 24583.95 10193.23 8068.80 9891.51 26188.61 3089.96 12392.57 139
FA-MVS(test-final)80.96 15579.91 16584.10 13988.30 18765.01 22084.55 27290.01 17073.25 17379.61 16487.57 24258.35 22994.72 11071.29 21386.25 18592.56 140
guyue81.13 15280.64 14682.60 21686.52 25863.92 24786.69 21087.73 25673.97 14980.83 14989.69 17656.70 24691.33 26978.26 13685.40 20492.54 141
test_yl81.17 15080.47 15183.24 18289.13 15263.62 25186.21 22689.95 17272.43 18781.78 13389.61 18057.50 23793.58 16070.75 21786.90 17392.52 142
DCV-MVSNet81.17 15080.47 15183.24 18289.13 15263.62 25186.21 22689.95 17272.43 18781.78 13389.61 18057.50 23793.58 16070.75 21786.90 17392.52 142
SR-MVS-dyc-post85.77 6285.61 6786.23 6293.06 6070.63 7891.88 3992.27 8573.53 16485.69 6794.45 3265.00 14395.56 6482.75 8791.87 8992.50 144
RE-MVS-def85.48 7093.06 6070.63 7891.88 3992.27 8573.53 16485.69 6794.45 3263.87 15182.75 8791.87 8992.50 144
nrg03083.88 9183.53 9884.96 10186.77 25269.28 10590.46 7092.67 6874.79 12982.95 11591.33 13272.70 4793.09 19280.79 10879.28 29392.50 144
mamba_040481.91 13280.84 14385.13 9589.24 14768.26 13387.84 17189.25 20471.06 21480.62 15190.39 15759.57 21894.65 11472.45 20487.19 16992.47 147
MG-MVS83.41 10683.45 9983.28 17992.74 6762.28 28688.17 15689.50 18875.22 11381.49 13692.74 9766.75 11895.11 9072.85 19491.58 9592.45 148
FIs82.07 12982.42 11681.04 25288.80 16758.34 33188.26 15393.49 2776.93 7178.47 18991.04 14269.92 8192.34 22669.87 23184.97 20792.44 149
testing3-275.12 29575.19 27774.91 35690.40 10545.09 43880.29 34678.42 39078.37 4076.54 23787.75 23644.36 37287.28 34357.04 34983.49 23892.37 150
fmvsm_s_conf0.5_n_386.36 4987.46 2983.09 18987.08 24465.21 21389.09 11690.21 16479.67 1989.98 1995.02 2073.17 3991.71 25091.30 391.60 9392.34 151
FC-MVSNet-test81.52 14582.02 12680.03 27588.42 18355.97 37087.95 16493.42 3077.10 6777.38 21390.98 14869.96 8091.79 24568.46 24684.50 21492.33 152
Fast-Effi-MVS+80.81 15979.92 16483.47 17188.85 15964.51 23285.53 24789.39 19270.79 22178.49 18785.06 31367.54 11293.58 16067.03 26086.58 17992.32 153
TranMVSNet+NR-MVSNet80.84 15780.31 15482.42 21987.85 20862.33 28487.74 17391.33 12880.55 977.99 20189.86 16865.23 13992.62 20867.05 25975.24 35392.30 154
ab-mvs79.51 19478.97 19181.14 24988.46 18060.91 30383.84 28789.24 20670.36 23579.03 17488.87 20563.23 15990.21 29465.12 27382.57 25392.28 155
CANet_DTU80.61 16979.87 16782.83 20385.60 27963.17 27187.36 18488.65 23476.37 8975.88 25188.44 21853.51 27493.07 19373.30 18989.74 12892.25 156
UniMVSNet_NR-MVSNet81.88 13381.54 13282.92 19988.46 18063.46 26287.13 19092.37 8280.19 1278.38 19089.14 19371.66 6093.05 19570.05 22776.46 32692.25 156
fmvsm_l_conf0.5_n84.47 8584.54 8484.27 13285.42 28468.81 11288.49 14387.26 26768.08 28988.03 3993.49 7172.04 5391.77 24688.90 2789.14 13992.24 158
DU-MVS81.12 15380.52 14982.90 20087.80 21163.46 26287.02 19591.87 10879.01 3178.38 19089.07 19565.02 14193.05 19570.05 22776.46 32692.20 159
NR-MVSNet80.23 18279.38 17982.78 21087.80 21163.34 26586.31 22391.09 13779.01 3172.17 32189.07 19567.20 11692.81 20666.08 26675.65 33992.20 159
mamba_040879.37 20377.52 23084.93 10488.81 16367.96 14565.03 44288.66 23270.96 21879.48 16789.80 17258.69 22494.65 11470.35 22385.93 19392.18 161
mamba_test_0407_277.67 25077.52 23078.12 31588.81 16367.96 14565.03 44288.66 23270.96 21879.48 16789.80 17258.69 22474.23 43570.35 22385.93 19392.18 161
mamba_test_040781.58 14280.48 15084.87 10788.81 16367.96 14587.37 18389.25 20471.06 21479.48 16790.39 15759.57 21894.48 12172.45 20485.93 19392.18 161
TAPA-MVS73.13 979.15 20777.94 21382.79 20989.59 12662.99 27688.16 15791.51 12365.77 31877.14 22491.09 14060.91 20393.21 18150.26 39187.05 17192.17 164
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
fmvsm_l_conf0.5_n_a84.13 8884.16 8984.06 14785.38 28568.40 12988.34 15086.85 27767.48 29687.48 5093.40 7670.89 6991.61 25188.38 3589.22 13792.16 165
3Dnovator76.31 583.38 10882.31 12086.59 5787.94 20472.94 2890.64 6392.14 9777.21 6275.47 25892.83 9158.56 22794.72 11073.24 19192.71 7792.13 166
MVS_111021_HR85.14 7784.75 8286.32 6191.65 8172.70 3085.98 23190.33 15976.11 9482.08 12791.61 12371.36 6494.17 13381.02 10392.58 7892.08 167
MVSFormer82.85 11982.05 12585.24 9087.35 22670.21 8290.50 6790.38 15568.55 28281.32 13889.47 18561.68 18593.46 16978.98 12490.26 11792.05 168
jason81.39 14880.29 15584.70 11486.63 25769.90 9085.95 23286.77 27863.24 34881.07 14489.47 18561.08 20192.15 23278.33 13290.07 12292.05 168
jason: jason.
HyFIR lowres test77.53 25275.40 27283.94 15989.59 12666.62 18180.36 34488.64 23556.29 41276.45 23885.17 31057.64 23593.28 17561.34 30983.10 24691.91 170
XVG-OURS-SEG-HR80.81 15979.76 17083.96 15885.60 27968.78 11483.54 29890.50 15170.66 22776.71 23191.66 11860.69 20691.26 27076.94 14881.58 26391.83 171
lupinMVS81.39 14880.27 15684.76 11287.35 22670.21 8285.55 24586.41 28362.85 35581.32 13888.61 21261.68 18592.24 23078.41 13190.26 11791.83 171
WR-MVS79.49 19579.22 18680.27 27088.79 16858.35 33085.06 25888.61 23678.56 3577.65 20888.34 22063.81 15390.66 28964.98 27577.22 31491.80 173
icg_test_0407_278.92 21578.93 19278.90 29887.13 23863.59 25576.58 38989.33 19470.51 23077.82 20389.03 19761.84 18181.38 39472.56 20085.56 20091.74 174
icg_test_040780.61 16979.90 16682.75 21387.13 23863.59 25585.33 25189.33 19470.51 23077.82 20389.03 19761.84 18192.91 20072.56 20085.56 20091.74 174
ICG_test_040477.16 25976.42 25779.37 28987.13 23863.59 25577.12 38789.33 19470.51 23066.22 39089.03 19750.36 31482.78 38472.56 20085.56 20091.74 174
icg_test_040380.80 16280.12 16182.87 20287.13 23863.59 25585.19 25289.33 19470.51 23078.49 18789.03 19763.26 15793.27 17672.56 20085.56 20091.74 174
h-mvs3383.15 11382.19 12186.02 7290.56 10170.85 7588.15 15889.16 20976.02 9684.67 8191.39 13061.54 18895.50 6982.71 8975.48 34391.72 178
UniMVSNet (Re)81.60 14181.11 13783.09 18988.38 18464.41 23787.60 17593.02 4678.42 3778.56 18588.16 22669.78 8293.26 17769.58 23476.49 32591.60 179
UGNet80.83 15879.59 17584.54 11788.04 19968.09 14089.42 9988.16 24076.95 7076.22 24489.46 18749.30 32993.94 14168.48 24590.31 11591.60 179
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
testing9176.54 26875.66 26779.18 29488.43 18255.89 37181.08 33083.00 33773.76 15675.34 26684.29 32846.20 35690.07 29664.33 27984.50 21491.58 181
XVG-OURS80.41 17679.23 18583.97 15785.64 27769.02 10883.03 31190.39 15471.09 21277.63 20991.49 12754.62 26491.35 26775.71 16283.47 23991.54 182
LCM-MVSNet-Re77.05 26076.94 24377.36 33087.20 23551.60 40980.06 34880.46 36875.20 11567.69 36786.72 26562.48 17088.98 31863.44 28589.25 13591.51 183
DP-MVS Recon83.11 11682.09 12486.15 6694.44 1970.92 7388.79 12892.20 9170.53 22979.17 17391.03 14464.12 14996.03 5168.39 24790.14 11991.50 184
PS-MVSNAJss82.07 12981.31 13384.34 12686.51 25967.27 17089.27 10591.51 12371.75 19579.37 17090.22 16463.15 16194.27 12677.69 13982.36 25591.49 185
testing9976.09 28075.12 27979.00 29588.16 19155.50 37780.79 33481.40 35773.30 17175.17 27484.27 33144.48 37190.02 29764.28 28084.22 22391.48 186
thisisatest051577.33 25675.38 27383.18 18585.27 28963.80 24982.11 31883.27 32965.06 32775.91 25083.84 33849.54 32494.27 12667.24 25686.19 18691.48 186
DPM-MVS84.93 8184.29 8886.84 5290.20 10973.04 2387.12 19193.04 4269.80 25182.85 11891.22 13573.06 4196.02 5376.72 15494.63 5091.46 188
HQP_MVS83.64 9983.14 10485.14 9290.08 11268.71 11991.25 5592.44 7879.12 2878.92 17791.00 14660.42 21395.38 7878.71 12786.32 18391.33 189
plane_prior592.44 7895.38 7878.71 12786.32 18391.33 189
GA-MVS76.87 26475.17 27881.97 22882.75 35162.58 28081.44 32786.35 28672.16 19174.74 28582.89 36046.20 35692.02 23668.85 24281.09 26891.30 191
VPA-MVSNet80.60 17180.55 14880.76 25988.07 19860.80 30586.86 20291.58 12175.67 10380.24 15789.45 18963.34 15490.25 29370.51 22179.22 29491.23 192
Effi-MVS+-dtu80.03 18678.57 19884.42 12285.13 29468.74 11788.77 12988.10 24274.99 12074.97 28283.49 34957.27 24093.36 17373.53 18580.88 27191.18 193
v2v48280.23 18279.29 18383.05 19383.62 32764.14 24187.04 19389.97 17173.61 16078.18 19687.22 25361.10 20093.82 15076.11 15776.78 32291.18 193
FE-MVS77.78 24475.68 26584.08 14488.09 19766.00 19283.13 30687.79 25468.42 28678.01 20085.23 30845.50 36595.12 8859.11 32785.83 19791.11 195
Anonymous2023121178.97 21377.69 22682.81 20590.54 10264.29 23990.11 7891.51 12365.01 32976.16 24988.13 23150.56 31193.03 19869.68 23377.56 31291.11 195
hse-mvs281.72 13680.94 14184.07 14588.72 17167.68 15585.87 23587.26 26776.02 9684.67 8188.22 22561.54 18893.48 16782.71 8973.44 37191.06 197
AUN-MVS79.21 20677.60 22884.05 15088.71 17267.61 15785.84 23787.26 26769.08 27177.23 21888.14 23053.20 27893.47 16875.50 16773.45 37091.06 197
HQP4-MVS77.24 21795.11 9091.03 199
HQP-MVS82.61 12282.02 12684.37 12389.33 14066.98 17789.17 10992.19 9276.41 8577.23 21890.23 16360.17 21695.11 9077.47 14185.99 19191.03 199
RPSCF73.23 31971.46 32378.54 30682.50 35759.85 31782.18 31782.84 34258.96 39171.15 33389.41 19145.48 36684.77 37058.82 33171.83 38391.02 201
LuminaMVS80.68 16779.62 17483.83 16185.07 29668.01 14486.99 19688.83 22370.36 23581.38 13787.99 23350.11 31792.51 21779.02 12186.89 17590.97 202
test_djsdf80.30 18179.32 18283.27 18083.98 31965.37 21190.50 6790.38 15568.55 28276.19 24588.70 20856.44 24993.46 16978.98 12480.14 28390.97 202
PCF-MVS73.52 780.38 17878.84 19485.01 9987.71 21768.99 10983.65 29291.46 12763.00 35277.77 20790.28 16066.10 12995.09 9461.40 30788.22 15590.94 204
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VPNet78.69 22078.66 19678.76 30088.31 18655.72 37484.45 27686.63 28076.79 7578.26 19390.55 15459.30 22189.70 30466.63 26177.05 31690.88 205
CPTT-MVS83.73 9583.33 10384.92 10593.28 4970.86 7492.09 3790.38 15568.75 27979.57 16592.83 9160.60 21193.04 19780.92 10591.56 9690.86 206
fmvsm_s_conf0.5_n_783.34 10984.03 9181.28 24485.73 27565.13 21685.40 25089.90 17474.96 12382.13 12693.89 6366.65 11987.92 33486.56 4891.05 10390.80 207
tt080578.73 21877.83 21881.43 23885.17 29060.30 31389.41 10090.90 14071.21 20977.17 22388.73 20746.38 35193.21 18172.57 19878.96 29590.79 208
CLD-MVS82.31 12581.65 13184.29 12988.47 17967.73 15485.81 23992.35 8375.78 9978.33 19286.58 27564.01 15094.35 12376.05 15987.48 16490.79 208
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v119279.59 19378.43 20283.07 19283.55 32964.52 23186.93 20090.58 14870.83 22077.78 20685.90 28959.15 22293.94 14173.96 18277.19 31590.76 210
IterMVS-LS80.06 18579.38 17982.11 22485.89 27163.20 26986.79 20589.34 19374.19 14575.45 26186.72 26566.62 12092.39 22272.58 19776.86 31990.75 211
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d2873.62 31073.53 30073.90 36988.20 18947.41 42878.06 37879.37 38274.29 14373.98 29684.29 32844.67 36883.54 37851.47 38187.39 16590.74 212
EI-MVSNet80.52 17579.98 16382.12 22284.28 31163.19 27086.41 21988.95 22174.18 14678.69 18087.54 24566.62 12092.43 22072.57 19880.57 27790.74 212
v192192079.22 20578.03 21182.80 20683.30 33463.94 24686.80 20490.33 15969.91 24977.48 21185.53 30058.44 22893.75 15673.60 18476.85 32090.71 214
QAPM80.88 15679.50 17785.03 9888.01 20268.97 11091.59 4692.00 10066.63 30975.15 27692.16 10557.70 23495.45 7163.52 28388.76 14590.66 215
v14419279.47 19678.37 20382.78 21083.35 33263.96 24486.96 19790.36 15869.99 24677.50 21085.67 29660.66 20893.77 15474.27 17976.58 32390.62 216
v124078.99 21277.78 22182.64 21483.21 33663.54 25986.62 21390.30 16169.74 25677.33 21485.68 29557.04 24393.76 15573.13 19276.92 31790.62 216
v114480.03 18679.03 18983.01 19583.78 32464.51 23287.11 19290.57 15071.96 19478.08 19986.20 28561.41 19293.94 14174.93 17277.23 31390.60 218
1112_ss77.40 25576.43 25680.32 26989.11 15660.41 31283.65 29287.72 25762.13 36573.05 30886.72 26562.58 16989.97 29862.11 30180.80 27390.59 219
CP-MVSNet78.22 23078.34 20477.84 32187.83 21054.54 38787.94 16591.17 13377.65 4673.48 30388.49 21662.24 17688.43 32862.19 29874.07 36290.55 220
testing22274.04 30572.66 31178.19 31387.89 20655.36 37881.06 33179.20 38571.30 20774.65 28883.57 34839.11 40488.67 32551.43 38385.75 19890.53 221
PS-CasMVS78.01 23978.09 21077.77 32387.71 21754.39 38988.02 16191.22 13077.50 5473.26 30588.64 21160.73 20488.41 32961.88 30273.88 36690.53 221
CDS-MVSNet79.07 21077.70 22583.17 18687.60 22168.23 13784.40 27986.20 28867.49 29576.36 24186.54 27761.54 18890.79 28561.86 30387.33 16690.49 223
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS78.89 21677.51 23283.03 19487.80 21167.79 15384.72 26585.05 30467.63 29276.75 23087.70 23862.25 17590.82 28458.53 33487.13 17090.49 223
PEN-MVS77.73 24577.69 22677.84 32187.07 24653.91 39287.91 16791.18 13277.56 5173.14 30788.82 20661.23 19789.17 31459.95 31872.37 37790.43 225
Test_1112_low_res76.40 27575.44 27079.27 29189.28 14558.09 33381.69 32287.07 27159.53 38672.48 31686.67 27061.30 19589.33 30960.81 31380.15 28290.41 226
HY-MVS69.67 1277.95 24077.15 23880.36 26787.57 22560.21 31583.37 30187.78 25566.11 31375.37 26587.06 26063.27 15690.48 29161.38 30882.43 25490.40 227
sc_t172.19 33169.51 34280.23 27184.81 30061.09 30084.68 26680.22 37460.70 37571.27 33083.58 34736.59 41589.24 31260.41 31463.31 41590.37 228
CHOSEN 1792x268877.63 25175.69 26483.44 17389.98 11868.58 12578.70 36887.50 26156.38 41175.80 25386.84 26158.67 22691.40 26661.58 30685.75 19890.34 229
SDMVSNet80.38 17880.18 15780.99 25389.03 15764.94 22380.45 34389.40 19175.19 11676.61 23589.98 16660.61 21087.69 33876.83 15283.55 23690.33 230
sd_testset77.70 24877.40 23378.60 30389.03 15760.02 31679.00 36385.83 29475.19 11676.61 23589.98 16654.81 25785.46 36362.63 29483.55 23690.33 230
114514_t80.68 16779.51 17684.20 13694.09 3867.27 17089.64 9091.11 13658.75 39574.08 29590.72 15058.10 23095.04 9569.70 23289.42 13490.30 232
eth_miper_zixun_eth77.92 24176.69 25181.61 23583.00 34461.98 28983.15 30589.20 20869.52 25874.86 28484.35 32761.76 18492.56 21371.50 21172.89 37590.28 233
PVSNet_Blended_VisFu82.62 12181.83 13084.96 10190.80 9769.76 9388.74 13391.70 11669.39 25978.96 17588.46 21765.47 13794.87 10374.42 17788.57 14890.24 234
MVS_111021_LR82.61 12282.11 12284.11 13888.82 16271.58 5785.15 25586.16 28974.69 13180.47 15591.04 14262.29 17490.55 29080.33 11390.08 12190.20 235
MSLP-MVS++85.43 7085.76 6484.45 12191.93 7770.24 8190.71 6292.86 5977.46 5584.22 9492.81 9367.16 11792.94 19980.36 11294.35 5990.16 236
mvs_tets79.13 20877.77 22283.22 18484.70 30366.37 18589.17 10990.19 16569.38 26075.40 26389.46 18744.17 37493.15 18876.78 15380.70 27590.14 237
BH-RMVSNet79.61 19178.44 20183.14 18789.38 13965.93 19484.95 26187.15 27073.56 16278.19 19589.79 17456.67 24793.36 17359.53 32386.74 17790.13 238
c3_l78.75 21777.91 21481.26 24582.89 34961.56 29584.09 28589.13 21269.97 24775.56 25684.29 32866.36 12592.09 23473.47 18775.48 34390.12 239
v7n78.97 21377.58 22983.14 18783.45 33165.51 20688.32 15191.21 13173.69 15872.41 31786.32 28357.93 23193.81 15169.18 23775.65 33990.11 240
jajsoiax79.29 20477.96 21283.27 18084.68 30466.57 18389.25 10690.16 16669.20 26875.46 26089.49 18445.75 36293.13 19076.84 15180.80 27390.11 240
v14878.72 21977.80 22081.47 23782.73 35261.96 29086.30 22488.08 24373.26 17276.18 24685.47 30262.46 17192.36 22471.92 20873.82 36790.09 242
GBi-Net78.40 22677.40 23381.40 24087.60 22163.01 27288.39 14689.28 20071.63 19775.34 26687.28 24954.80 25891.11 27462.72 29079.57 28790.09 242
test178.40 22677.40 23381.40 24087.60 22163.01 27288.39 14689.28 20071.63 19775.34 26687.28 24954.80 25891.11 27462.72 29079.57 28790.09 242
FMVSNet177.44 25376.12 26181.40 24086.81 25063.01 27288.39 14689.28 20070.49 23474.39 29287.28 24949.06 33391.11 27460.91 31178.52 29890.09 242
WR-MVS_H78.51 22578.49 19978.56 30588.02 20056.38 36488.43 14492.67 6877.14 6473.89 29787.55 24466.25 12789.24 31258.92 32973.55 36990.06 246
DTE-MVSNet76.99 26176.80 24677.54 32986.24 26253.06 40187.52 17790.66 14677.08 6872.50 31588.67 21060.48 21289.52 30657.33 34670.74 38990.05 247
v879.97 18879.02 19082.80 20684.09 31664.50 23487.96 16390.29 16274.13 14875.24 27386.81 26262.88 16693.89 14974.39 17875.40 34890.00 248
thres600view776.50 27075.44 27079.68 28389.40 13757.16 35085.53 24783.23 33073.79 15576.26 24387.09 25851.89 29591.89 24248.05 40683.72 23390.00 248
thres40076.50 27075.37 27479.86 27889.13 15257.65 34485.17 25383.60 32273.41 16876.45 23886.39 28152.12 28791.95 23948.33 40183.75 23090.00 248
cl2278.07 23677.01 24081.23 24682.37 36161.83 29283.55 29687.98 24768.96 27675.06 27983.87 33661.40 19391.88 24373.53 18576.39 32889.98 251
OPM-MVS83.50 10482.95 10985.14 9288.79 16870.95 7189.13 11491.52 12277.55 5280.96 14691.75 11560.71 20594.50 11979.67 12086.51 18189.97 252
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline275.70 28473.83 29781.30 24383.26 33561.79 29382.57 31480.65 36466.81 30066.88 37883.42 35057.86 23392.19 23163.47 28479.57 28789.91 253
v1079.74 19078.67 19582.97 19884.06 31764.95 22287.88 16990.62 14773.11 17575.11 27786.56 27661.46 19194.05 13773.68 18375.55 34189.90 254
MVSTER79.01 21177.88 21782.38 22083.07 34164.80 22784.08 28688.95 22169.01 27578.69 18087.17 25654.70 26292.43 22074.69 17380.57 27789.89 255
ACMP74.13 681.51 14780.57 14784.36 12489.42 13568.69 12289.97 8091.50 12674.46 13775.04 28090.41 15653.82 27194.54 11677.56 14082.91 24789.86 256
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test82.08 12881.27 13484.50 11889.23 14868.76 11590.22 7691.94 10475.37 11076.64 23391.51 12554.29 26594.91 9878.44 12983.78 22789.83 257
LGP-MVS_train84.50 11889.23 14868.76 11591.94 10475.37 11076.64 23391.51 12554.29 26594.91 9878.44 12983.78 22789.83 257
V4279.38 20278.24 20782.83 20381.10 38065.50 20785.55 24589.82 17571.57 20178.21 19486.12 28760.66 20893.18 18775.64 16375.46 34589.81 259
MAR-MVS81.84 13480.70 14485.27 8991.32 8571.53 5889.82 8290.92 13969.77 25378.50 18686.21 28462.36 17394.52 11865.36 27192.05 8789.77 260
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
DIV-MVS_self_test77.72 24676.76 24880.58 26382.48 35960.48 31083.09 30787.86 25269.22 26674.38 29385.24 30762.10 17891.53 25971.09 21475.40 34889.74 261
cl____77.72 24676.76 24880.58 26382.49 35860.48 31083.09 30787.87 25169.22 26674.38 29385.22 30962.10 17891.53 25971.09 21475.41 34789.73 262
miper_ehance_all_eth78.59 22377.76 22381.08 25182.66 35461.56 29583.65 29289.15 21068.87 27775.55 25783.79 34066.49 12392.03 23573.25 19076.39 32889.64 263
anonymousdsp78.60 22277.15 23882.98 19780.51 38667.08 17587.24 18989.53 18765.66 32075.16 27587.19 25552.52 28092.25 22977.17 14579.34 29289.61 264
FMVSNet278.20 23277.21 23781.20 24787.60 22162.89 27887.47 17989.02 21671.63 19775.29 27287.28 24954.80 25891.10 27762.38 29579.38 29189.61 264
baseline176.98 26276.75 25077.66 32488.13 19455.66 37585.12 25681.89 35073.04 17776.79 22888.90 20362.43 17287.78 33763.30 28771.18 38789.55 266
ETVMVS72.25 33071.05 32975.84 34287.77 21551.91 40579.39 35674.98 41169.26 26473.71 29982.95 35840.82 39686.14 35346.17 41484.43 21989.47 267
FMVSNet377.88 24276.85 24580.97 25586.84 24962.36 28386.52 21688.77 22671.13 21075.34 26686.66 27154.07 26891.10 27762.72 29079.57 28789.45 268
SD_040374.65 29874.77 28274.29 36486.20 26447.42 42783.71 29085.12 30169.30 26268.50 36287.95 23459.40 22086.05 35449.38 39583.35 24189.40 269
miper_enhance_ethall77.87 24376.86 24480.92 25681.65 36861.38 29782.68 31288.98 21865.52 32275.47 25882.30 36965.76 13692.00 23772.95 19376.39 32889.39 270
testing1175.14 29474.01 29278.53 30788.16 19156.38 36480.74 33780.42 37070.67 22472.69 31483.72 34343.61 37889.86 29962.29 29783.76 22989.36 271
cascas76.72 26774.64 28382.99 19685.78 27465.88 19682.33 31589.21 20760.85 37472.74 31181.02 38047.28 34293.75 15667.48 25385.02 20689.34 272
Fast-Effi-MVS+-dtu78.02 23876.49 25482.62 21583.16 34066.96 17986.94 19987.45 26372.45 18471.49 32984.17 33354.79 26191.58 25367.61 25180.31 28089.30 273
IB-MVS68.01 1575.85 28373.36 30383.31 17884.76 30266.03 18983.38 30085.06 30370.21 24269.40 35281.05 37945.76 36194.66 11365.10 27475.49 34289.25 274
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
thres100view90076.50 27075.55 26979.33 29089.52 12956.99 35385.83 23883.23 33073.94 15176.32 24287.12 25751.89 29591.95 23948.33 40183.75 23089.07 275
tfpn200view976.42 27475.37 27479.55 28889.13 15257.65 34485.17 25383.60 32273.41 16876.45 23886.39 28152.12 28791.95 23948.33 40183.75 23089.07 275
xiu_mvs_v1_base_debu80.80 16279.72 17184.03 15287.35 22670.19 8485.56 24288.77 22669.06 27281.83 12988.16 22650.91 30692.85 20278.29 13387.56 16189.06 277
xiu_mvs_v1_base80.80 16279.72 17184.03 15287.35 22670.19 8485.56 24288.77 22669.06 27281.83 12988.16 22650.91 30692.85 20278.29 13387.56 16189.06 277
xiu_mvs_v1_base_debi80.80 16279.72 17184.03 15287.35 22670.19 8485.56 24288.77 22669.06 27281.83 12988.16 22650.91 30692.85 20278.29 13387.56 16189.06 277
EPNet_dtu75.46 28874.86 28077.23 33382.57 35654.60 38686.89 20183.09 33471.64 19666.25 38985.86 29155.99 25088.04 33354.92 36386.55 18089.05 280
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pm-mvs177.25 25876.68 25278.93 29784.22 31358.62 32886.41 21988.36 23971.37 20473.31 30488.01 23261.22 19889.15 31564.24 28173.01 37489.03 281
PVSNet_Blended80.98 15480.34 15382.90 20088.85 15965.40 20884.43 27792.00 10067.62 29378.11 19785.05 31466.02 13294.27 12671.52 20989.50 13289.01 282
PAPM77.68 24976.40 25881.51 23687.29 23461.85 29183.78 28889.59 18564.74 33171.23 33188.70 20862.59 16893.66 15952.66 37587.03 17289.01 282
WTY-MVS75.65 28575.68 26575.57 34686.40 26056.82 35577.92 38182.40 34565.10 32676.18 24687.72 23763.13 16480.90 39760.31 31681.96 25989.00 284
无先验87.48 17888.98 21860.00 38194.12 13467.28 25588.97 285
GSMVS88.96 286
sam_mvs151.32 30288.96 286
SCA74.22 30272.33 31579.91 27784.05 31862.17 28779.96 35179.29 38466.30 31272.38 31880.13 39251.95 29388.60 32659.25 32577.67 31188.96 286
miper_lstm_enhance74.11 30473.11 30677.13 33480.11 39059.62 32072.23 41386.92 27666.76 30270.40 33782.92 35956.93 24482.92 38369.06 23972.63 37688.87 289
ACMM73.20 880.78 16679.84 16883.58 16989.31 14368.37 13089.99 7991.60 12070.28 23977.25 21689.66 17853.37 27693.53 16574.24 18082.85 24888.85 290
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs674.69 29773.39 30178.61 30281.38 37557.48 34786.64 21287.95 24964.99 33070.18 34086.61 27250.43 31389.52 30662.12 30070.18 39288.83 291
原ACMM184.35 12593.01 6268.79 11392.44 7863.96 34581.09 14391.57 12466.06 13195.45 7167.19 25794.82 4688.81 292
CNLPA78.08 23576.79 24781.97 22890.40 10571.07 6787.59 17684.55 30966.03 31672.38 31889.64 17957.56 23686.04 35559.61 32283.35 24188.79 293
UWE-MVS72.13 33271.49 32274.03 36786.66 25647.70 42581.40 32876.89 40463.60 34775.59 25584.22 33239.94 39985.62 36048.98 39886.13 18888.77 294
UBG73.08 32172.27 31675.51 34888.02 20051.29 41378.35 37577.38 39965.52 32273.87 29882.36 36745.55 36386.48 35055.02 36284.39 22088.75 295
K. test v371.19 33768.51 34979.21 29383.04 34357.78 34384.35 28076.91 40372.90 18062.99 41082.86 36139.27 40191.09 27961.65 30552.66 43688.75 295
旧先验191.96 7665.79 20086.37 28593.08 8669.31 8992.74 7688.74 297
PatchmatchNetpermissive73.12 32071.33 32678.49 30983.18 33860.85 30479.63 35378.57 38964.13 33871.73 32579.81 39751.20 30485.97 35657.40 34576.36 33388.66 298
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SixPastTwentyTwo73.37 31471.26 32879.70 28285.08 29557.89 33985.57 24183.56 32471.03 21665.66 39285.88 29042.10 38892.57 21259.11 32763.34 41488.65 299
SSC-MVS3.273.35 31773.39 30173.23 37385.30 28849.01 42374.58 40681.57 35475.21 11473.68 30085.58 29952.53 27982.05 38954.33 36777.69 31088.63 300
PS-MVSNAJ81.69 13881.02 13983.70 16589.51 13068.21 13884.28 28190.09 16870.79 22181.26 14285.62 29863.15 16194.29 12475.62 16488.87 14288.59 301
xiu_mvs_v2_base81.69 13881.05 13883.60 16789.15 15168.03 14384.46 27590.02 16970.67 22481.30 14186.53 27863.17 16094.19 13275.60 16588.54 14988.57 302
MonoMVSNet76.49 27375.80 26278.58 30481.55 37158.45 32986.36 22286.22 28774.87 12874.73 28683.73 34251.79 29888.73 32370.78 21672.15 38088.55 303
CostFormer75.24 29373.90 29579.27 29182.65 35558.27 33280.80 33382.73 34361.57 36975.33 27083.13 35555.52 25391.07 28064.98 27578.34 30388.45 304
lessismore_v078.97 29681.01 38157.15 35165.99 43861.16 41682.82 36239.12 40391.34 26859.67 32146.92 44388.43 305
OpenMVScopyleft72.83 1079.77 18978.33 20584.09 14385.17 29069.91 8990.57 6490.97 13866.70 30372.17 32191.91 10954.70 26293.96 13861.81 30490.95 10688.41 306
reproduce_monomvs75.40 29174.38 28978.46 31083.92 32157.80 34283.78 28886.94 27473.47 16672.25 32084.47 32238.74 40589.27 31175.32 16970.53 39088.31 307
VortexMVS78.57 22477.89 21680.59 26285.89 27162.76 27985.61 24089.62 18472.06 19274.99 28185.38 30455.94 25190.77 28774.99 17176.58 32388.23 308
OurMVSNet-221017-074.26 30172.42 31479.80 28083.76 32559.59 32185.92 23486.64 27966.39 31166.96 37787.58 24139.46 40091.60 25265.76 26969.27 39588.22 309
LS3D76.95 26374.82 28183.37 17790.45 10367.36 16789.15 11386.94 27461.87 36869.52 35190.61 15251.71 29994.53 11746.38 41386.71 17888.21 310
WBMVS73.43 31372.81 30975.28 35287.91 20550.99 41578.59 37181.31 35965.51 32474.47 29184.83 31746.39 35086.68 34758.41 33577.86 30688.17 311
XVG-ACMP-BASELINE76.11 27974.27 29181.62 23383.20 33764.67 22983.60 29589.75 17969.75 25471.85 32487.09 25832.78 42492.11 23369.99 22980.43 27988.09 312
tpm273.26 31871.46 32378.63 30183.34 33356.71 35880.65 33980.40 37156.63 41073.55 30282.02 37451.80 29791.24 27156.35 35778.42 30187.95 313
MDTV_nov1_ep13_2view37.79 45275.16 40055.10 41566.53 38449.34 32853.98 36887.94 314
Patchmatch-test64.82 38963.24 39069.57 39979.42 40249.82 42163.49 44669.05 43151.98 42559.95 42180.13 39250.91 30670.98 44040.66 43073.57 36887.90 315
PLCcopyleft70.83 1178.05 23776.37 25983.08 19191.88 7967.80 15288.19 15589.46 18964.33 33769.87 34888.38 21953.66 27293.58 16058.86 33082.73 25087.86 316
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm72.37 32871.71 32074.35 36382.19 36252.00 40379.22 35977.29 40064.56 33372.95 31083.68 34551.35 30183.26 38258.33 33775.80 33787.81 317
Patchmatch-RL test70.24 35067.78 36377.61 32677.43 41159.57 32271.16 41770.33 42562.94 35468.65 35972.77 43150.62 31085.49 36269.58 23466.58 40587.77 318
F-COLMAP76.38 27674.33 29082.50 21889.28 14566.95 18088.41 14589.03 21564.05 34266.83 37988.61 21246.78 34892.89 20157.48 34378.55 29787.67 319
Baseline_NR-MVSNet78.15 23478.33 20577.61 32685.79 27356.21 36886.78 20685.76 29573.60 16177.93 20287.57 24265.02 14188.99 31767.14 25875.33 35087.63 320
CL-MVSNet_self_test72.37 32871.46 32375.09 35479.49 40153.53 39480.76 33685.01 30569.12 27070.51 33582.05 37357.92 23284.13 37352.27 37766.00 40887.60 321
ACMH+68.96 1476.01 28174.01 29282.03 22688.60 17565.31 21288.86 12387.55 25970.25 24167.75 36687.47 24741.27 39293.19 18658.37 33675.94 33687.60 321
131476.53 26975.30 27680.21 27283.93 32062.32 28584.66 26788.81 22460.23 37970.16 34284.07 33555.30 25590.73 28867.37 25483.21 24487.59 323
API-MVS81.99 13181.23 13584.26 13490.94 9370.18 8791.10 5889.32 19871.51 20278.66 18288.28 22265.26 13895.10 9364.74 27791.23 10187.51 324
AdaColmapbinary80.58 17479.42 17884.06 14793.09 5968.91 11189.36 10388.97 22069.27 26375.70 25489.69 17657.20 24295.77 6063.06 28888.41 15387.50 325
PVSNet_BlendedMVS80.60 17180.02 16282.36 22188.85 15965.40 20886.16 22892.00 10069.34 26178.11 19786.09 28866.02 13294.27 12671.52 20982.06 25887.39 326
sss73.60 31173.64 29973.51 37282.80 35055.01 38376.12 39181.69 35362.47 36174.68 28785.85 29257.32 23978.11 40860.86 31280.93 26987.39 326
IterMVS-SCA-FT75.43 28973.87 29680.11 27482.69 35364.85 22681.57 32483.47 32669.16 26970.49 33684.15 33451.95 29388.15 33169.23 23672.14 38187.34 328
PVSNet64.34 1872.08 33370.87 33275.69 34486.21 26356.44 36274.37 40780.73 36362.06 36670.17 34182.23 37142.86 38283.31 38154.77 36484.45 21887.32 329
tt0320-xc70.11 35267.45 36978.07 31785.33 28759.51 32383.28 30278.96 38758.77 39367.10 37680.28 39036.73 41487.42 34156.83 35359.77 42587.29 330
新几何183.42 17493.13 5670.71 7685.48 29857.43 40681.80 13291.98 10863.28 15592.27 22864.60 27892.99 7287.27 331
TR-MVS77.44 25376.18 26081.20 24788.24 18863.24 26784.61 27086.40 28467.55 29477.81 20586.48 27954.10 26793.15 18857.75 34282.72 25187.20 332
TransMVSNet (Re)75.39 29274.56 28577.86 32085.50 28357.10 35286.78 20686.09 29172.17 19071.53 32887.34 24863.01 16589.31 31056.84 35261.83 41887.17 333
ACMH67.68 1675.89 28273.93 29481.77 23188.71 17266.61 18288.62 13889.01 21769.81 25066.78 38086.70 26941.95 39091.51 26155.64 35978.14 30487.17 333
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
KD-MVS_self_test68.81 36267.59 36772.46 38374.29 42445.45 43377.93 38087.00 27263.12 34963.99 40578.99 40542.32 38584.77 37056.55 35664.09 41387.16 335
EPMVS69.02 36168.16 35371.59 38779.61 39949.80 42277.40 38466.93 43662.82 35770.01 34379.05 40145.79 36077.86 41056.58 35575.26 35287.13 336
CR-MVSNet73.37 31471.27 32779.67 28481.32 37865.19 21475.92 39380.30 37259.92 38272.73 31281.19 37752.50 28186.69 34659.84 31977.71 30887.11 337
RPMNet73.51 31270.49 33582.58 21781.32 37865.19 21475.92 39392.27 8557.60 40472.73 31276.45 41952.30 28495.43 7348.14 40577.71 30887.11 337
test_vis1_n_192075.52 28775.78 26374.75 36079.84 39457.44 34883.26 30385.52 29762.83 35679.34 17286.17 28645.10 36779.71 40178.75 12681.21 26787.10 339
tt032070.49 34868.03 35677.89 31984.78 30159.12 32583.55 29680.44 36958.13 39967.43 37280.41 38839.26 40287.54 34055.12 36163.18 41686.99 340
XXY-MVS75.41 29075.56 26874.96 35583.59 32857.82 34180.59 34083.87 32066.54 31074.93 28388.31 22163.24 15880.09 40062.16 29976.85 32086.97 341
tpmrst72.39 32672.13 31773.18 37780.54 38549.91 42079.91 35279.08 38663.11 35071.69 32679.95 39455.32 25482.77 38565.66 27073.89 36586.87 342
thres20075.55 28674.47 28778.82 29987.78 21457.85 34083.07 30983.51 32572.44 18675.84 25284.42 32352.08 29091.75 24747.41 40883.64 23586.86 343
ITE_SJBPF78.22 31281.77 36760.57 30883.30 32869.25 26567.54 36887.20 25436.33 41787.28 34354.34 36674.62 35986.80 344
test22291.50 8268.26 13384.16 28383.20 33354.63 41779.74 16291.63 12158.97 22391.42 9786.77 345
MIMVSNet70.69 34469.30 34374.88 35784.52 30856.35 36675.87 39579.42 38164.59 33267.76 36582.41 36641.10 39381.54 39246.64 41281.34 26486.75 346
BH-untuned79.47 19678.60 19782.05 22589.19 15065.91 19586.07 23088.52 23772.18 18975.42 26287.69 23961.15 19993.54 16460.38 31586.83 17686.70 347
LTVRE_ROB69.57 1376.25 27774.54 28681.41 23988.60 17564.38 23879.24 35889.12 21370.76 22369.79 35087.86 23549.09 33293.20 18456.21 35880.16 28186.65 348
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
testdata79.97 27690.90 9464.21 24084.71 30659.27 38885.40 6992.91 8862.02 18089.08 31668.95 24091.37 9986.63 349
MIMVSNet168.58 36566.78 37573.98 36880.07 39151.82 40780.77 33584.37 31064.40 33559.75 42282.16 37236.47 41683.63 37742.73 42570.33 39186.48 350
tfpnnormal74.39 29973.16 30578.08 31686.10 26958.05 33484.65 26987.53 26070.32 23871.22 33285.63 29754.97 25689.86 29943.03 42475.02 35586.32 351
D2MVS74.82 29673.21 30479.64 28579.81 39562.56 28180.34 34587.35 26464.37 33668.86 35782.66 36446.37 35290.10 29567.91 24981.24 26686.25 352
tpm cat170.57 34568.31 35177.35 33182.41 36057.95 33878.08 37780.22 37452.04 42368.54 36177.66 41452.00 29287.84 33651.77 37872.07 38286.25 352
CVMVSNet72.99 32372.58 31274.25 36584.28 31150.85 41686.41 21983.45 32744.56 43673.23 30687.54 24549.38 32785.70 35865.90 26778.44 30086.19 354
AllTest70.96 34068.09 35579.58 28685.15 29263.62 25184.58 27179.83 37762.31 36260.32 41986.73 26332.02 42588.96 32050.28 38971.57 38586.15 355
TestCases79.58 28685.15 29263.62 25179.83 37762.31 36260.32 41986.73 26332.02 42588.96 32050.28 38971.57 38586.15 355
test-LLR72.94 32472.43 31374.48 36181.35 37658.04 33578.38 37277.46 39666.66 30469.95 34679.00 40348.06 33879.24 40266.13 26384.83 20986.15 355
test-mter71.41 33670.39 33874.48 36181.35 37658.04 33578.38 37277.46 39660.32 37869.95 34679.00 40336.08 41879.24 40266.13 26384.83 20986.15 355
IterMVS74.29 30072.94 30878.35 31181.53 37263.49 26181.58 32382.49 34468.06 29069.99 34583.69 34451.66 30085.54 36165.85 26871.64 38486.01 359
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS76.78 26674.57 28483.42 17493.29 4869.46 10088.55 14283.70 32163.98 34470.20 33988.89 20454.01 27094.80 10746.66 41081.88 26186.01 359
ppachtmachnet_test70.04 35367.34 37178.14 31479.80 39661.13 29879.19 36080.59 36559.16 38965.27 39579.29 40046.75 34987.29 34249.33 39666.72 40386.00 361
mmtdpeth74.16 30373.01 30777.60 32883.72 32661.13 29885.10 25785.10 30272.06 19277.21 22280.33 38943.84 37685.75 35777.14 14652.61 43785.91 362
test_fmvs1_n70.86 34270.24 33972.73 38072.51 43855.28 38081.27 32979.71 37951.49 42778.73 17984.87 31627.54 43477.02 41376.06 15879.97 28585.88 363
Patchmtry70.74 34369.16 34675.49 34980.72 38254.07 39174.94 40480.30 37258.34 39670.01 34381.19 37752.50 28186.54 34853.37 37271.09 38885.87 364
WB-MVSnew71.96 33471.65 32172.89 37884.67 30751.88 40682.29 31677.57 39562.31 36273.67 30183.00 35753.49 27581.10 39645.75 41782.13 25785.70 365
test_fmvs268.35 36967.48 36870.98 39569.50 44151.95 40480.05 34976.38 40649.33 43074.65 28884.38 32523.30 44375.40 43074.51 17675.17 35485.60 366
ambc75.24 35373.16 43350.51 41863.05 44787.47 26264.28 40177.81 41317.80 44989.73 30357.88 34160.64 42285.49 367
mvs5depth69.45 35867.45 36975.46 35073.93 42555.83 37279.19 36083.23 33066.89 29971.63 32783.32 35133.69 42385.09 36659.81 32055.34 43385.46 368
UnsupCasMVSNet_eth67.33 37465.99 37871.37 38973.48 43051.47 41175.16 40085.19 30065.20 32560.78 41780.93 38442.35 38477.20 41257.12 34753.69 43585.44 369
PatchT68.46 36867.85 35970.29 39780.70 38343.93 44172.47 41274.88 41260.15 38070.55 33476.57 41849.94 32081.59 39150.58 38574.83 35785.34 370
Anonymous2024052168.80 36367.22 37273.55 37174.33 42354.11 39083.18 30485.61 29658.15 39861.68 41480.94 38230.71 43081.27 39557.00 35073.34 37385.28 371
test_cas_vis1_n_192073.76 30973.74 29873.81 37075.90 41659.77 31880.51 34182.40 34558.30 39781.62 13585.69 29444.35 37376.41 41976.29 15578.61 29685.23 372
ADS-MVSNet266.20 38563.33 38974.82 35879.92 39258.75 32767.55 43275.19 41053.37 42065.25 39675.86 42242.32 38580.53 39941.57 42868.91 39785.18 373
ADS-MVSNet64.36 39062.88 39368.78 40579.92 39247.17 42967.55 43271.18 42453.37 42065.25 39675.86 42242.32 38573.99 43641.57 42868.91 39785.18 373
FMVSNet569.50 35767.96 35774.15 36682.97 34755.35 37980.01 35082.12 34862.56 36063.02 40881.53 37636.92 41381.92 39048.42 40074.06 36385.17 375
pmmvs571.55 33570.20 34075.61 34577.83 40956.39 36381.74 32180.89 36057.76 40267.46 37084.49 32149.26 33085.32 36557.08 34875.29 35185.11 376
testing368.56 36667.67 36571.22 39387.33 23142.87 44383.06 31071.54 42370.36 23569.08 35684.38 32530.33 43185.69 35937.50 43675.45 34685.09 377
UWE-MVS-2865.32 38664.93 38066.49 41478.70 40638.55 45177.86 38264.39 44362.00 36764.13 40383.60 34641.44 39176.00 42331.39 44380.89 27084.92 378
CMPMVSbinary51.72 2170.19 35168.16 35376.28 33973.15 43457.55 34679.47 35583.92 31848.02 43256.48 43284.81 31843.13 38086.42 35162.67 29381.81 26284.89 379
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testgi66.67 37966.53 37667.08 41375.62 41941.69 44875.93 39276.50 40566.11 31365.20 39886.59 27335.72 41974.71 43243.71 42273.38 37284.84 380
MSDG73.36 31670.99 33080.49 26584.51 30965.80 19980.71 33886.13 29065.70 31965.46 39383.74 34144.60 36990.91 28351.13 38476.89 31884.74 381
pmmvs474.03 30771.91 31880.39 26681.96 36468.32 13181.45 32682.14 34759.32 38769.87 34885.13 31152.40 28388.13 33260.21 31774.74 35884.73 382
gg-mvs-nofinetune69.95 35467.96 35775.94 34183.07 34154.51 38877.23 38670.29 42663.11 35070.32 33862.33 44043.62 37788.69 32453.88 36987.76 16084.62 383
test_fmvs170.93 34170.52 33472.16 38473.71 42755.05 38280.82 33278.77 38851.21 42878.58 18484.41 32431.20 42976.94 41475.88 16180.12 28484.47 384
BH-w/o78.21 23177.33 23680.84 25788.81 16365.13 21684.87 26287.85 25369.75 25474.52 29084.74 32061.34 19493.11 19158.24 33885.84 19684.27 385
MVS78.19 23376.99 24281.78 23085.66 27666.99 17684.66 26790.47 15255.08 41672.02 32385.27 30663.83 15294.11 13566.10 26589.80 12784.24 386
COLMAP_ROBcopyleft66.92 1773.01 32270.41 33780.81 25887.13 23865.63 20388.30 15284.19 31662.96 35363.80 40787.69 23938.04 41092.56 21346.66 41074.91 35684.24 386
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet61.73 39661.73 39761.70 42072.74 43624.50 46369.16 42778.03 39261.40 37056.72 43175.53 42538.42 40776.48 41845.95 41657.67 42684.13 388
TESTMET0.1,169.89 35569.00 34772.55 38179.27 40456.85 35478.38 37274.71 41557.64 40368.09 36477.19 41637.75 41176.70 41563.92 28284.09 22484.10 389
test_fmvs363.36 39361.82 39667.98 41062.51 45046.96 43177.37 38574.03 41745.24 43567.50 36978.79 40612.16 45572.98 43972.77 19666.02 40783.99 390
our_test_369.14 36067.00 37375.57 34679.80 39658.80 32677.96 37977.81 39359.55 38562.90 41178.25 41047.43 34083.97 37451.71 37967.58 40283.93 391
test_vis1_n69.85 35669.21 34571.77 38672.66 43755.27 38181.48 32576.21 40752.03 42475.30 27183.20 35428.97 43276.22 42174.60 17578.41 30283.81 392
mamv476.81 26578.23 20972.54 38286.12 26765.75 20278.76 36782.07 34964.12 33972.97 30991.02 14567.97 10768.08 44783.04 8378.02 30583.80 393
tpmvs71.09 33969.29 34476.49 33882.04 36356.04 36978.92 36581.37 35864.05 34267.18 37578.28 40949.74 32389.77 30149.67 39472.37 37783.67 394
test20.0367.45 37366.95 37468.94 40275.48 42044.84 43977.50 38377.67 39466.66 30463.01 40983.80 33947.02 34478.40 40642.53 42768.86 39983.58 395
test0.0.03 168.00 37167.69 36468.90 40377.55 41047.43 42675.70 39672.95 42266.66 30466.56 38382.29 37048.06 33875.87 42544.97 42174.51 36083.41 396
Anonymous2023120668.60 36467.80 36271.02 39480.23 38950.75 41778.30 37680.47 36756.79 40966.11 39182.63 36546.35 35378.95 40443.62 42375.70 33883.36 397
EU-MVSNet68.53 36767.61 36671.31 39278.51 40847.01 43084.47 27384.27 31442.27 43966.44 38884.79 31940.44 39783.76 37558.76 33268.54 40083.17 398
dp66.80 37765.43 37970.90 39679.74 39848.82 42475.12 40274.77 41359.61 38464.08 40477.23 41542.89 38180.72 39848.86 39966.58 40583.16 399
pmmvs-eth3d70.50 34767.83 36178.52 30877.37 41266.18 18881.82 31981.51 35558.90 39263.90 40680.42 38742.69 38386.28 35258.56 33365.30 41083.11 400
YYNet165.03 38762.91 39271.38 38875.85 41756.60 36069.12 42874.66 41657.28 40754.12 43577.87 41245.85 35974.48 43349.95 39261.52 42083.05 401
MDA-MVSNet-bldmvs66.68 37863.66 38875.75 34379.28 40360.56 30973.92 40978.35 39164.43 33450.13 44179.87 39644.02 37583.67 37646.10 41556.86 42783.03 402
MDA-MVSNet_test_wron65.03 38762.92 39171.37 38975.93 41556.73 35669.09 42974.73 41457.28 40754.03 43677.89 41145.88 35874.39 43449.89 39361.55 41982.99 403
USDC70.33 34968.37 35076.21 34080.60 38456.23 36779.19 36086.49 28260.89 37361.29 41585.47 30231.78 42789.47 30853.37 37276.21 33482.94 404
Syy-MVS68.05 37067.85 35968.67 40684.68 30440.97 44978.62 36973.08 42066.65 30766.74 38179.46 39852.11 28982.30 38732.89 44176.38 33182.75 405
myMVS_eth3d67.02 37666.29 37769.21 40184.68 30442.58 44478.62 36973.08 42066.65 30766.74 38179.46 39831.53 42882.30 38739.43 43376.38 33182.75 405
ttmdpeth59.91 39957.10 40368.34 40867.13 44546.65 43274.64 40567.41 43548.30 43162.52 41385.04 31520.40 44575.93 42442.55 42645.90 44682.44 407
OpenMVS_ROBcopyleft64.09 1970.56 34668.19 35277.65 32580.26 38759.41 32485.01 25982.96 33958.76 39465.43 39482.33 36837.63 41291.23 27245.34 42076.03 33582.32 408
JIA-IIPM66.32 38262.82 39476.82 33677.09 41361.72 29465.34 44075.38 40958.04 40164.51 40062.32 44142.05 38986.51 34951.45 38269.22 39682.21 409
dmvs_re71.14 33870.58 33372.80 37981.96 36459.68 31975.60 39779.34 38368.55 28269.27 35580.72 38549.42 32676.54 41652.56 37677.79 30782.19 410
EG-PatchMatch MVS74.04 30571.82 31980.71 26084.92 29867.42 16385.86 23688.08 24366.04 31564.22 40283.85 33735.10 42092.56 21357.44 34480.83 27282.16 411
MVP-Stereo76.12 27874.46 28881.13 25085.37 28669.79 9184.42 27887.95 24965.03 32867.46 37085.33 30553.28 27791.73 24958.01 34083.27 24381.85 412
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TDRefinement67.49 37264.34 38376.92 33573.47 43161.07 30184.86 26382.98 33859.77 38358.30 42685.13 31126.06 43587.89 33547.92 40760.59 42381.81 413
GG-mvs-BLEND75.38 35181.59 37055.80 37379.32 35769.63 42867.19 37473.67 42943.24 37988.90 32250.41 38684.50 21481.45 414
KD-MVS_2432*160066.22 38363.89 38673.21 37475.47 42153.42 39670.76 42084.35 31164.10 34066.52 38578.52 40734.55 42184.98 36750.40 38750.33 44081.23 415
miper_refine_blended66.22 38363.89 38673.21 37475.47 42153.42 39670.76 42084.35 31164.10 34066.52 38578.52 40734.55 42184.98 36750.40 38750.33 44081.23 415
test_040272.79 32570.44 33679.84 27988.13 19465.99 19385.93 23384.29 31365.57 32167.40 37385.49 30146.92 34592.61 20935.88 43874.38 36180.94 417
MVStest156.63 40352.76 40968.25 40961.67 45153.25 40071.67 41568.90 43338.59 44450.59 44083.05 35625.08 43770.66 44136.76 43738.56 44780.83 418
UnsupCasMVSNet_bld63.70 39261.53 39870.21 39873.69 42851.39 41272.82 41181.89 35055.63 41457.81 42871.80 43338.67 40678.61 40549.26 39752.21 43880.63 419
LCM-MVSNet54.25 40549.68 41567.97 41153.73 45945.28 43666.85 43580.78 36235.96 44839.45 44962.23 4428.70 45978.06 40948.24 40451.20 43980.57 420
N_pmnet52.79 41053.26 40851.40 43478.99 4057.68 46869.52 4243.89 46751.63 42657.01 43074.98 42640.83 39565.96 44937.78 43564.67 41180.56 421
TinyColmap67.30 37564.81 38174.76 35981.92 36656.68 35980.29 34681.49 35660.33 37756.27 43383.22 35224.77 43987.66 33945.52 41869.47 39479.95 422
PM-MVS66.41 38164.14 38473.20 37673.92 42656.45 36178.97 36464.96 44263.88 34664.72 39980.24 39119.84 44783.44 38066.24 26264.52 41279.71 423
ANet_high50.57 41446.10 41863.99 41748.67 46239.13 45070.99 41980.85 36161.39 37131.18 45157.70 44717.02 45073.65 43831.22 44415.89 45979.18 424
LF4IMVS64.02 39162.19 39569.50 40070.90 43953.29 39976.13 39077.18 40152.65 42258.59 42480.98 38123.55 44276.52 41753.06 37466.66 40478.68 425
PatchMatch-RL72.38 32770.90 33176.80 33788.60 17567.38 16679.53 35476.17 40862.75 35869.36 35382.00 37545.51 36484.89 36953.62 37080.58 27678.12 426
MS-PatchMatch73.83 30872.67 31077.30 33283.87 32266.02 19081.82 31984.66 30761.37 37268.61 36082.82 36247.29 34188.21 33059.27 32484.32 22177.68 427
DSMNet-mixed57.77 40256.90 40460.38 42267.70 44335.61 45369.18 42653.97 45432.30 45257.49 42979.88 39540.39 39868.57 44638.78 43472.37 37776.97 428
CHOSEN 280x42066.51 38064.71 38271.90 38581.45 37363.52 26057.98 44968.95 43253.57 41962.59 41276.70 41746.22 35575.29 43155.25 36079.68 28676.88 429
mvsany_test353.99 40651.45 41161.61 42155.51 45544.74 44063.52 44545.41 46043.69 43858.11 42776.45 41917.99 44863.76 45154.77 36447.59 44276.34 430
dmvs_testset62.63 39464.11 38558.19 42478.55 40724.76 46275.28 39865.94 43967.91 29160.34 41876.01 42153.56 27373.94 43731.79 44267.65 40175.88 431
mvsany_test162.30 39561.26 39965.41 41669.52 44054.86 38466.86 43449.78 45646.65 43368.50 36283.21 35349.15 33166.28 44856.93 35160.77 42175.11 432
PMMVS69.34 35968.67 34871.35 39175.67 41862.03 28875.17 39973.46 41850.00 42968.68 35879.05 40152.07 29178.13 40761.16 31082.77 24973.90 433
test_vis1_rt60.28 39858.42 40165.84 41567.25 44455.60 37670.44 42260.94 44844.33 43759.00 42366.64 43824.91 43868.67 44562.80 28969.48 39373.25 434
pmmvs357.79 40154.26 40668.37 40764.02 44956.72 35775.12 40265.17 44040.20 44152.93 43769.86 43720.36 44675.48 42845.45 41955.25 43472.90 435
PVSNet_057.27 2061.67 39759.27 40068.85 40479.61 39957.44 34868.01 43073.44 41955.93 41358.54 42570.41 43644.58 37077.55 41147.01 40935.91 44871.55 436
WB-MVS54.94 40454.72 40555.60 43073.50 42920.90 46474.27 40861.19 44759.16 38950.61 43974.15 42747.19 34375.78 42617.31 45535.07 44970.12 437
SSC-MVS53.88 40753.59 40754.75 43272.87 43519.59 46573.84 41060.53 44957.58 40549.18 44373.45 43046.34 35475.47 42916.20 45832.28 45169.20 438
test_f52.09 41150.82 41255.90 42853.82 45842.31 44759.42 44858.31 45236.45 44756.12 43470.96 43512.18 45457.79 45453.51 37156.57 42967.60 439
PMMVS240.82 42138.86 42546.69 43553.84 45716.45 46648.61 45249.92 45537.49 44531.67 45060.97 4438.14 46156.42 45528.42 44630.72 45267.19 440
new_pmnet50.91 41350.29 41352.78 43368.58 44234.94 45563.71 44456.63 45339.73 44244.95 44465.47 43921.93 44458.48 45334.98 43956.62 42864.92 441
MVS-HIRNet59.14 40057.67 40263.57 41881.65 36843.50 44271.73 41465.06 44139.59 44351.43 43857.73 44638.34 40882.58 38639.53 43173.95 36464.62 442
APD_test153.31 40949.93 41463.42 41965.68 44650.13 41971.59 41666.90 43734.43 44940.58 44871.56 4348.65 46076.27 42034.64 44055.36 43263.86 443
test_method31.52 42429.28 42838.23 43827.03 4666.50 46920.94 45762.21 4464.05 46022.35 45852.50 45113.33 45247.58 45827.04 44834.04 45060.62 444
EGC-MVSNET52.07 41247.05 41667.14 41283.51 33060.71 30680.50 34267.75 4340.07 4620.43 46375.85 42424.26 44081.54 39228.82 44562.25 41759.16 445
test_vis3_rt49.26 41547.02 41756.00 42754.30 45645.27 43766.76 43648.08 45736.83 44644.38 44553.20 4507.17 46264.07 45056.77 35455.66 43058.65 446
FPMVS53.68 40851.64 41059.81 42365.08 44751.03 41469.48 42569.58 42941.46 44040.67 44772.32 43216.46 45170.00 44424.24 45165.42 40958.40 447
testf145.72 41641.96 42057.00 42556.90 45345.32 43466.14 43759.26 45026.19 45330.89 45260.96 4444.14 46370.64 44226.39 44946.73 44455.04 448
APD_test245.72 41641.96 42057.00 42556.90 45345.32 43466.14 43759.26 45026.19 45330.89 45260.96 4444.14 46370.64 44226.39 44946.73 44455.04 448
PMVScopyleft37.38 2244.16 42040.28 42455.82 42940.82 46442.54 44665.12 44163.99 44434.43 44924.48 45557.12 4483.92 46576.17 42217.10 45655.52 43148.75 450
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVEpermissive26.22 2330.37 42625.89 43043.81 43744.55 46335.46 45428.87 45639.07 46118.20 45718.58 45940.18 4542.68 46647.37 45917.07 45723.78 45648.60 451
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai45.42 41845.38 41945.55 43673.36 43226.85 46067.72 43134.19 46254.15 41849.65 44256.41 44925.43 43662.94 45219.45 45328.09 45346.86 452
kuosan39.70 42240.40 42337.58 43964.52 44826.98 45865.62 43933.02 46346.12 43442.79 44648.99 45224.10 44146.56 46012.16 46126.30 45439.20 453
Gipumacopyleft45.18 41941.86 42255.16 43177.03 41451.52 41032.50 45580.52 36632.46 45127.12 45435.02 4559.52 45875.50 42722.31 45260.21 42438.45 454
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CXcopyleft27.40 44240.17 46526.90 45924.59 46617.44 45823.95 45648.61 4539.77 45726.48 46118.06 45424.47 45528.83 455
E-PMN31.77 42330.64 42635.15 44052.87 46027.67 45757.09 45047.86 45824.64 45516.40 46033.05 45611.23 45654.90 45614.46 45918.15 45722.87 456
EMVS30.81 42529.65 42734.27 44150.96 46125.95 46156.58 45146.80 45924.01 45615.53 46130.68 45712.47 45354.43 45712.81 46017.05 45822.43 457
tmp_tt18.61 42821.40 43110.23 4444.82 46710.11 46734.70 45430.74 4651.48 46123.91 45726.07 45828.42 43313.41 46327.12 44715.35 4607.17 458
wuyk23d16.82 42915.94 43219.46 44358.74 45231.45 45639.22 4533.74 4686.84 4596.04 4622.70 4621.27 46724.29 46210.54 46214.40 4612.63 459
test1236.12 4318.11 4340.14 4450.06 4690.09 47071.05 4180.03 4700.04 4640.25 4651.30 4640.05 4680.03 4650.21 4640.01 4630.29 460
testmvs6.04 4328.02 4350.10 4460.08 4680.03 47169.74 4230.04 4690.05 4630.31 4641.68 4630.02 4690.04 4640.24 4630.02 4620.25 461
mmdepth0.00 4340.00 4370.00 4470.00 4700.00 4720.00 4580.00 4710.00 4650.00 4660.00 4650.00 4700.00 4660.00 4650.00 4640.00 462
monomultidepth0.00 4340.00 4370.00 4470.00 4700.00 4720.00 4580.00 4710.00 4650.00 4660.00 4650.00 4700.00 4660.00 4650.00 4640.00 462
test_blank0.00 4340.00 4370.00 4470.00 4700.00 4720.00 4580.00 4710.00 4650.00 4660.00 4650.00 4700.00 4660.00 4650.00 4640.00 462
uanet_test0.00 4340.00 4370.00 4470.00 4700.00 4720.00 4580.00 4710.00 4650.00 4660.00 4650.00 4700.00 4660.00 4650.00 4640.00 462
DCPMVS0.00 4340.00 4370.00 4470.00 4700.00 4720.00 4580.00 4710.00 4650.00 4660.00 4650.00 4700.00 4660.00 4650.00 4640.00 462
cdsmvs_eth3d_5k19.96 42726.61 4290.00 4470.00 4700.00 4720.00 45889.26 2030.00 4650.00 46688.61 21261.62 1870.00 4660.00 4650.00 4640.00 462
pcd_1.5k_mvsjas5.26 4337.02 4360.00 4470.00 4700.00 4720.00 4580.00 4710.00 4650.00 4660.00 46563.15 1610.00 4660.00 4650.00 4640.00 462
sosnet-low-res0.00 4340.00 4370.00 4470.00 4700.00 4720.00 4580.00 4710.00 4650.00 4660.00 4650.00 4700.00 4660.00 4650.00 4640.00 462
sosnet0.00 4340.00 4370.00 4470.00 4700.00 4720.00 4580.00 4710.00 4650.00 4660.00 4650.00 4700.00 4660.00 4650.00 4640.00 462
uncertanet0.00 4340.00 4370.00 4470.00 4700.00 4720.00 4580.00 4710.00 4650.00 4660.00 4650.00 4700.00 4660.00 4650.00 4640.00 462
Regformer0.00 4340.00 4370.00 4470.00 4700.00 4720.00 4580.00 4710.00 4650.00 4660.00 4650.00 4700.00 4660.00 4650.00 4640.00 462
ab-mvs-re7.23 4309.64 4330.00 4470.00 4700.00 4720.00 4580.00 4710.00 4650.00 46686.72 2650.00 4700.00 4660.00 4650.00 4640.00 462
uanet0.00 4340.00 4370.00 4470.00 4700.00 4720.00 4580.00 4710.00 4650.00 4660.00 4650.00 4700.00 4660.00 4650.00 4640.00 462
WAC-MVS42.58 44439.46 432
FOURS195.00 1072.39 4195.06 193.84 1674.49 13691.30 15
test_one_060195.07 771.46 5994.14 678.27 4192.05 1195.74 680.83 11
eth-test20.00 470
eth-test0.00 470
ZD-MVS94.38 2572.22 4692.67 6870.98 21787.75 4594.07 5274.01 3396.70 2784.66 6494.84 44
test_241102_ONE95.30 270.98 6894.06 1177.17 6393.10 195.39 1682.99 197.27 12
9.1488.26 1692.84 6591.52 5194.75 173.93 15288.57 3094.67 2575.57 2295.79 5986.77 4695.76 23
save fliter93.80 4072.35 4490.47 6991.17 13374.31 141
test072695.27 571.25 6193.60 794.11 777.33 5792.81 395.79 380.98 9
test_part295.06 872.65 3291.80 13
sam_mvs50.01 318
MTGPAbinary92.02 98
test_post178.90 3665.43 46148.81 33785.44 36459.25 325
test_post5.46 46050.36 31484.24 372
patchmatchnet-post74.00 42851.12 30588.60 326
MTMP92.18 3532.83 464
gm-plane-assit81.40 37453.83 39362.72 35980.94 38292.39 22263.40 286
TEST993.26 5272.96 2588.75 13191.89 10668.44 28585.00 7493.10 8274.36 2995.41 76
test_893.13 5672.57 3588.68 13691.84 11068.69 28084.87 7893.10 8274.43 2795.16 86
agg_prior92.85 6471.94 5291.78 11384.41 8994.93 97
test_prior472.60 3489.01 118
test_prior288.85 12575.41 10884.91 7693.54 7074.28 3083.31 7995.86 20
旧先验286.56 21558.10 40087.04 5688.98 31874.07 181
新几何286.29 225
原ACMM286.86 202
testdata291.01 28162.37 296
segment_acmp73.08 40
testdata184.14 28475.71 100
plane_prior790.08 11268.51 127
plane_prior689.84 12168.70 12160.42 213
plane_prior491.00 146
plane_prior368.60 12478.44 3678.92 177
plane_prior291.25 5579.12 28
plane_prior189.90 120
plane_prior68.71 11990.38 7377.62 4786.16 187
n20.00 471
nn0.00 471
door-mid69.98 427
test1192.23 88
door69.44 430
HQP5-MVS66.98 177
HQP-NCC89.33 14089.17 10976.41 8577.23 218
ACMP_Plane89.33 14089.17 10976.41 8577.23 218
BP-MVS77.47 141
HQP3-MVS92.19 9285.99 191
HQP2-MVS60.17 216
NP-MVS89.62 12568.32 13190.24 162
MDTV_nov1_ep1369.97 34183.18 33853.48 39577.10 38880.18 37660.45 37669.33 35480.44 38648.89 33686.90 34551.60 38078.51 299
ACMMP++_ref81.95 260
ACMMP++81.25 265
Test By Simon64.33 147