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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort by
9.1488.26 1892.84 6891.52 5594.75 173.93 16188.57 3494.67 2975.57 2495.79 6286.77 4995.76 26
SF-MVS88.46 1488.74 1487.64 3792.78 6971.95 5192.40 2894.74 275.71 10589.16 2895.10 1875.65 2396.19 5087.07 4796.01 1794.79 23
MED-MVS test87.86 2694.57 1771.43 6093.28 1294.36 375.24 11992.25 995.03 2097.39 1188.15 3895.96 1994.75 29
TestfortrainingZip a89.27 689.82 687.60 3894.57 1770.90 7693.28 1294.36 375.24 11992.25 995.03 2081.59 797.39 1186.12 5595.96 1994.52 48
ME-MVS88.98 1189.39 887.75 2994.54 1971.43 6091.61 4894.25 576.30 9290.62 2095.03 2078.06 1597.07 1988.15 3895.96 1994.75 29
DVP-MVS++90.23 191.01 187.89 2494.34 3071.25 6395.06 194.23 678.38 3892.78 495.74 682.45 397.49 489.42 1996.68 294.95 12
test_0728_SECOND87.71 3495.34 171.43 6093.49 1094.23 697.49 489.08 2296.41 1294.21 63
lecture88.09 1688.59 1586.58 6193.26 5569.77 9593.70 694.16 877.13 6589.76 2595.52 1472.26 5196.27 4786.87 4894.65 5193.70 94
test_one_060195.07 771.46 5994.14 978.27 4192.05 1395.74 680.83 12
test072695.27 571.25 6393.60 794.11 1077.33 5792.81 395.79 380.98 10
MSP-MVS89.51 489.91 588.30 1094.28 3373.46 1792.90 2094.11 1080.27 1091.35 1694.16 5278.35 1496.77 2789.59 1794.22 6594.67 32
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
DPE-MVScopyleft89.48 589.98 488.01 1694.80 1172.69 3191.59 5094.10 1275.90 10092.29 795.66 1081.67 697.38 1387.44 4696.34 1593.95 78
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
PHI-MVS86.43 4886.17 5787.24 4590.88 9870.96 7292.27 3694.07 1372.45 19685.22 7691.90 11569.47 9196.42 4383.28 8495.94 2294.35 56
SED-MVS90.08 290.85 287.77 2795.30 270.98 7093.57 894.06 1477.24 6093.10 195.72 882.99 197.44 789.07 2496.63 494.88 16
test_241102_TWO94.06 1477.24 6092.78 495.72 881.26 997.44 789.07 2496.58 694.26 62
test_241102_ONE95.30 270.98 7094.06 1477.17 6393.10 195.39 1682.99 197.27 14
APDe-MVScopyleft89.15 889.63 787.73 3094.49 2171.69 5493.83 493.96 1775.70 10791.06 1896.03 176.84 1697.03 2089.09 2195.65 3094.47 50
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DeepC-MVS79.81 287.08 3986.88 4487.69 3591.16 9072.32 4590.31 7893.94 1877.12 6682.82 12794.23 4972.13 5497.09 1884.83 6595.37 3493.65 99
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FOURS195.00 1072.39 4195.06 193.84 1974.49 14591.30 17
MCST-MVS87.37 3387.25 3487.73 3094.53 2072.46 4089.82 8693.82 2073.07 18884.86 8392.89 9376.22 1996.33 4484.89 6495.13 3994.40 53
ZNCC-MVS87.94 2187.85 2388.20 1294.39 2773.33 1993.03 1893.81 2176.81 7485.24 7594.32 4371.76 5896.93 2285.53 5995.79 2594.32 59
SPE-MVS-test86.29 5386.48 4985.71 7991.02 9467.21 17992.36 3393.78 2278.97 3383.51 11491.20 14570.65 7695.15 9081.96 10094.89 4594.77 25
3Dnovator+77.84 485.48 7184.47 9088.51 791.08 9273.49 1693.18 1593.78 2280.79 876.66 24693.37 8160.40 22896.75 2977.20 15493.73 6995.29 6
SteuartSystems-ACMMP88.72 1388.86 1388.32 992.14 7772.96 2593.73 593.67 2480.19 1288.10 4194.80 2673.76 3697.11 1787.51 4495.82 2494.90 15
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVScopyleft89.08 989.23 988.61 694.25 3473.73 992.40 2893.63 2574.77 13992.29 795.97 274.28 3297.24 1588.58 3296.91 194.87 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
EC-MVSNet86.01 5686.38 5084.91 11189.31 14666.27 19392.32 3493.63 2579.37 2384.17 10091.88 11669.04 10195.43 7683.93 7993.77 6893.01 137
ACMMP_NAP88.05 1988.08 2087.94 1993.70 4473.05 2290.86 6493.59 2776.27 9388.14 4095.09 1971.06 7096.67 3287.67 4296.37 1494.09 70
CSCG86.41 5086.19 5687.07 4992.91 6672.48 3790.81 6593.56 2873.95 15983.16 12091.07 15075.94 2095.19 8879.94 12294.38 6193.55 107
MP-MVS-pluss87.67 2487.72 2487.54 3993.64 4772.04 5089.80 8893.50 2975.17 12786.34 6695.29 1770.86 7296.00 5888.78 3096.04 1694.58 41
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FIs82.07 14082.42 12581.04 26688.80 17058.34 34588.26 15993.49 3076.93 7178.47 20391.04 15169.92 8692.34 23669.87 24584.97 21992.44 163
DELS-MVS85.41 7485.30 7885.77 7888.49 18167.93 15185.52 26093.44 3178.70 3483.63 11389.03 21174.57 2695.71 6580.26 11994.04 6693.66 95
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
GST-MVS87.42 3087.26 3387.89 2494.12 3972.97 2492.39 3093.43 3276.89 7284.68 8493.99 6370.67 7596.82 2584.18 7795.01 4093.90 81
FC-MVSNet-test81.52 15682.02 13780.03 28988.42 18655.97 38487.95 17093.42 3377.10 6777.38 22790.98 15769.96 8591.79 25668.46 26084.50 22692.33 166
DeepPCF-MVS80.84 188.10 1588.56 1686.73 5892.24 7669.03 10989.57 9793.39 3477.53 5389.79 2494.12 5478.98 1396.58 3885.66 5695.72 2794.58 41
HPM-MVScopyleft87.11 3786.98 4087.50 4293.88 4272.16 4792.19 3793.33 3576.07 9783.81 10893.95 6669.77 8896.01 5785.15 6094.66 5094.32 59
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CS-MVS86.69 4386.95 4185.90 7790.76 10267.57 16392.83 2193.30 3679.67 1984.57 9192.27 10571.47 6395.02 9984.24 7593.46 7295.13 9
HFP-MVS87.58 2587.47 3087.94 1994.58 1673.54 1593.04 1693.24 3776.78 7684.91 8094.44 3870.78 7396.61 3584.53 7094.89 4593.66 95
ACMMPR87.44 2887.23 3588.08 1594.64 1373.59 1293.04 1693.20 3876.78 7684.66 8794.52 3168.81 10396.65 3384.53 7094.90 4494.00 75
reproduce_model87.28 3487.39 3286.95 5393.10 6171.24 6791.60 4993.19 3974.69 14088.80 3295.61 1170.29 7996.44 4286.20 5493.08 7493.16 126
reproduce-ours87.47 2687.61 2687.07 4993.27 5371.60 5591.56 5393.19 3974.98 13088.96 2995.54 1271.20 6896.54 3986.28 5293.49 7093.06 132
our_new_method87.47 2687.61 2687.07 4993.27 5371.60 5591.56 5393.19 3974.98 13088.96 2995.54 1271.20 6896.54 3986.28 5293.49 7093.06 132
SD-MVS88.06 1788.50 1786.71 5992.60 7472.71 2991.81 4593.19 3977.87 4290.32 2294.00 6174.83 2593.78 15787.63 4394.27 6493.65 99
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
ACMMPcopyleft85.89 6385.39 7487.38 4393.59 4872.63 3392.74 2493.18 4376.78 7680.73 16493.82 7064.33 15896.29 4582.67 9790.69 11493.23 119
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
region2R87.42 3087.20 3688.09 1494.63 1473.55 1393.03 1893.12 4476.73 7984.45 9294.52 3169.09 9796.70 3084.37 7294.83 4894.03 73
DPM-MVS84.93 8484.29 9186.84 5590.20 11273.04 2387.12 19893.04 4569.80 26582.85 12691.22 14473.06 4396.02 5676.72 16694.63 5391.46 202
PGM-MVS86.68 4486.27 5387.90 2294.22 3673.38 1890.22 8093.04 4575.53 11083.86 10694.42 3967.87 11896.64 3482.70 9694.57 5593.66 95
casdiffmvs_mvgpermissive85.99 5786.09 6085.70 8087.65 22667.22 17888.69 14093.04 4579.64 2185.33 7492.54 10273.30 3894.50 12383.49 8191.14 10695.37 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
DeepC-MVS_fast79.65 386.91 4086.62 4887.76 2893.52 4972.37 4391.26 5893.04 4576.62 8284.22 9893.36 8271.44 6496.76 2880.82 11195.33 3694.16 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet (Re)81.60 15281.11 14883.09 19988.38 18764.41 24787.60 18193.02 4978.42 3778.56 19988.16 24069.78 8793.26 18669.58 24876.49 33991.60 193
sasdasda85.91 6185.87 6586.04 7389.84 12469.44 10490.45 7593.00 5076.70 8088.01 4491.23 14273.28 3993.91 15181.50 10388.80 14894.77 25
canonicalmvs85.91 6185.87 6586.04 7389.84 12469.44 10490.45 7593.00 5076.70 8088.01 4491.23 14273.28 3993.91 15181.50 10388.80 14894.77 25
CNVR-MVS88.93 1289.13 1288.33 894.77 1273.82 890.51 6993.00 5080.90 788.06 4294.06 5776.43 1896.84 2488.48 3595.99 1894.34 57
MSC_two_6792asdad89.16 194.34 3075.53 292.99 5397.53 289.67 1596.44 994.41 51
No_MVS89.16 194.34 3075.53 292.99 5397.53 289.67 1596.44 994.41 51
XVS87.18 3686.91 4388.00 1794.42 2373.33 1992.78 2292.99 5379.14 2683.67 11194.17 5167.45 12196.60 3683.06 8594.50 5694.07 71
X-MVStestdata80.37 19177.83 23188.00 1794.42 2373.33 1992.78 2292.99 5379.14 2683.67 11112.47 47467.45 12196.60 3683.06 8594.50 5694.07 71
APD-MVS_3200maxsize85.97 5985.88 6386.22 6692.69 7169.53 9891.93 4192.99 5373.54 17285.94 6794.51 3465.80 14695.61 6683.04 8792.51 8293.53 109
test_prior86.33 6392.61 7369.59 9792.97 5895.48 7393.91 79
IU-MVS95.30 271.25 6392.95 5966.81 31492.39 688.94 2796.63 494.85 21
balanced_conf0386.78 4186.99 3986.15 6991.24 8967.61 16190.51 6992.90 6077.26 5987.44 5591.63 12871.27 6796.06 5385.62 5895.01 4094.78 24
baseline84.93 8484.98 8184.80 11687.30 24165.39 21687.30 19492.88 6177.62 4784.04 10392.26 10671.81 5793.96 14381.31 10590.30 12095.03 11
MSLP-MVS++85.43 7385.76 6784.45 12791.93 8070.24 8490.71 6692.86 6277.46 5584.22 9892.81 9767.16 12592.94 20880.36 11794.35 6290.16 250
HPM-MVS++copyleft89.02 1089.15 1188.63 595.01 976.03 192.38 3192.85 6380.26 1187.78 4794.27 4675.89 2196.81 2687.45 4596.44 993.05 134
casdiffmvspermissive85.11 8185.14 8085.01 10487.20 24365.77 20787.75 17892.83 6477.84 4384.36 9792.38 10472.15 5393.93 14981.27 10790.48 11795.33 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
APD-MVScopyleft87.44 2887.52 2987.19 4694.24 3572.39 4191.86 4492.83 6473.01 19088.58 3394.52 3173.36 3796.49 4184.26 7395.01 4092.70 148
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC88.06 1788.01 2188.24 1194.41 2573.62 1191.22 6192.83 6481.50 585.79 7093.47 7873.02 4497.00 2184.90 6294.94 4394.10 69
CP-MVS87.11 3786.92 4287.68 3694.20 3773.86 793.98 392.82 6776.62 8283.68 11094.46 3567.93 11695.95 6184.20 7694.39 6093.23 119
DVP-MVScopyleft89.60 390.35 387.33 4495.27 571.25 6393.49 1092.73 6877.33 5792.12 1195.78 480.98 1097.40 989.08 2296.41 1293.33 116
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
GDP-MVS83.52 11082.64 12286.16 6888.14 19668.45 13189.13 11992.69 6972.82 19483.71 10991.86 11855.69 26595.35 8580.03 12089.74 13294.69 31
EIA-MVS83.31 11982.80 11984.82 11489.59 12965.59 21188.21 16092.68 7074.66 14278.96 18986.42 29469.06 9995.26 8675.54 18090.09 12493.62 102
ZD-MVS94.38 2872.22 4692.67 7170.98 23187.75 4994.07 5674.01 3596.70 3084.66 6894.84 47
nrg03083.88 9783.53 10584.96 10686.77 26169.28 10890.46 7492.67 7174.79 13882.95 12391.33 14172.70 4993.09 20180.79 11379.28 30592.50 158
WR-MVS_H78.51 23878.49 21278.56 31988.02 20356.38 37888.43 14992.67 7177.14 6473.89 31187.55 25866.25 13789.24 32658.92 34373.55 38390.06 260
MVSMamba_PlusPlus85.99 5785.96 6286.05 7291.09 9167.64 16089.63 9592.65 7472.89 19384.64 8891.71 12371.85 5696.03 5484.77 6794.45 5994.49 49
MP-MVScopyleft87.71 2287.64 2587.93 2194.36 2973.88 692.71 2692.65 7477.57 4983.84 10794.40 4072.24 5296.28 4685.65 5795.30 3893.62 102
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ETV-MVS84.90 8684.67 8685.59 8589.39 14168.66 12688.74 13892.64 7679.97 1684.10 10185.71 30769.32 9495.38 8180.82 11191.37 10392.72 147
MGCFI-Net85.06 8385.51 7283.70 17589.42 13863.01 28389.43 10292.62 7776.43 8487.53 5291.34 14072.82 4893.42 18081.28 10688.74 15194.66 35
CANet86.45 4786.10 5987.51 4190.09 11470.94 7489.70 9292.59 7881.78 481.32 15091.43 13870.34 7797.23 1684.26 7393.36 7394.37 55
SR-MVS86.73 4286.67 4686.91 5494.11 4072.11 4992.37 3292.56 7974.50 14486.84 6394.65 3067.31 12395.77 6384.80 6692.85 7792.84 146
alignmvs85.48 7185.32 7785.96 7689.51 13369.47 10189.74 9092.47 8076.17 9587.73 5191.46 13770.32 7893.78 15781.51 10288.95 14594.63 38
原ACMM184.35 13393.01 6568.79 11692.44 8163.96 35981.09 15591.57 13266.06 14295.45 7467.19 27194.82 4988.81 306
HQP_MVS83.64 10683.14 11185.14 9790.08 11568.71 12291.25 5992.44 8179.12 2878.92 19191.00 15560.42 22695.38 8178.71 13686.32 19491.33 203
plane_prior592.44 8195.38 8178.71 13686.32 19491.33 203
CDPH-MVS85.76 6685.29 7987.17 4793.49 5071.08 6888.58 14592.42 8468.32 30184.61 8993.48 7672.32 5096.15 5279.00 13295.43 3394.28 61
UniMVSNet_NR-MVSNet81.88 14481.54 14382.92 21088.46 18363.46 27387.13 19792.37 8580.19 1278.38 20489.14 20771.66 6293.05 20470.05 24176.46 34092.25 170
TSAR-MVS + MP.88.02 2088.11 1987.72 3293.68 4672.13 4891.41 5792.35 8674.62 14388.90 3193.85 6975.75 2296.00 5887.80 4194.63 5395.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CLD-MVS82.31 13681.65 14284.29 13888.47 18267.73 15785.81 25092.35 8675.78 10378.33 20686.58 28964.01 16194.35 12776.05 17287.48 17490.79 222
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
SR-MVS-dyc-post85.77 6585.61 7086.23 6593.06 6370.63 8191.88 4292.27 8873.53 17385.69 7194.45 3665.00 15495.56 6782.75 9291.87 9392.50 158
RE-MVS-def85.48 7393.06 6370.63 8191.88 4292.27 8873.53 17385.69 7194.45 3663.87 16282.75 9291.87 9392.50 158
RPMNet73.51 32570.49 34882.58 22981.32 39265.19 22075.92 40992.27 8857.60 41972.73 32676.45 43452.30 29795.43 7648.14 41977.71 32287.11 351
E284.00 9483.87 9584.39 12987.70 22364.95 22886.40 23092.23 9175.85 10183.21 11691.78 12070.09 8293.55 16979.52 12688.05 16394.66 35
E384.00 9483.87 9584.39 12987.70 22364.95 22886.40 23092.23 9175.85 10183.21 11691.78 12070.09 8293.55 16979.52 12688.05 16394.66 35
test1192.23 91
viewcassd2359sk1183.89 9683.74 10084.34 13487.76 21964.91 23486.30 23492.22 9475.47 11283.04 12291.52 13370.15 8193.53 17279.26 12887.96 16594.57 43
mPP-MVS86.67 4586.32 5187.72 3294.41 2573.55 1392.74 2492.22 9476.87 7382.81 12894.25 4866.44 13496.24 4882.88 9094.28 6393.38 112
fmvsm_s_conf0.5_n_886.56 4687.17 3784.73 11987.76 21965.62 21089.20 11292.21 9679.94 1789.74 2694.86 2568.63 10694.20 13590.83 591.39 10294.38 54
DP-MVS Recon83.11 12482.09 13586.15 6994.44 2270.92 7588.79 13392.20 9770.53 24379.17 18791.03 15364.12 16096.03 5468.39 26190.14 12391.50 198
NormalMVS86.29 5385.88 6387.52 4093.26 5572.47 3891.65 4692.19 9879.31 2484.39 9492.18 10764.64 15695.53 7080.70 11494.65 5194.56 45
Elysia81.53 15480.16 16985.62 8385.51 29268.25 13888.84 13192.19 9871.31 21980.50 16789.83 18446.89 35994.82 10776.85 15989.57 13493.80 89
StellarMVS81.53 15480.16 16985.62 8385.51 29268.25 13888.84 13192.19 9871.31 21980.50 16789.83 18446.89 35994.82 10776.85 15989.57 13493.80 89
HQP3-MVS92.19 9885.99 203
HQP-MVS82.61 13282.02 13784.37 13189.33 14366.98 18289.17 11492.19 9876.41 8577.23 23290.23 17760.17 22995.11 9377.47 15185.99 20391.03 213
3Dnovator76.31 583.38 11582.31 12986.59 6087.94 20772.94 2890.64 6792.14 10377.21 6275.47 27292.83 9558.56 24094.72 11473.24 20592.71 8092.13 180
MTGPAbinary92.02 104
MTAPA87.23 3587.00 3887.90 2294.18 3874.25 586.58 22292.02 10479.45 2285.88 6894.80 2668.07 11496.21 4986.69 5095.34 3593.23 119
MVS_Test83.15 12183.06 11383.41 18686.86 25663.21 27986.11 24092.00 10674.31 15082.87 12589.44 20470.03 8493.21 19077.39 15388.50 15693.81 87
PVSNet_BlendedMVS80.60 18280.02 17382.36 23388.85 16265.40 21486.16 23992.00 10669.34 27578.11 21186.09 30266.02 14394.27 13071.52 22382.06 27087.39 340
PVSNet_Blended80.98 16580.34 16482.90 21188.85 16265.40 21484.43 28992.00 10667.62 30778.11 21185.05 32866.02 14394.27 13071.52 22389.50 13689.01 296
QAPM80.88 16779.50 19085.03 10388.01 20568.97 11391.59 5092.00 10666.63 32375.15 29092.16 10957.70 24795.45 7463.52 29788.76 15090.66 229
LPG-MVS_test82.08 13981.27 14584.50 12489.23 15168.76 11890.22 8091.94 11075.37 11676.64 24791.51 13454.29 27894.91 10178.44 13883.78 23989.83 271
LGP-MVS_train84.50 12489.23 15168.76 11891.94 11075.37 11676.64 24791.51 13454.29 27894.91 10178.44 13883.78 23989.83 271
TEST993.26 5572.96 2588.75 13691.89 11268.44 29985.00 7893.10 8674.36 3195.41 79
train_agg86.43 4886.20 5487.13 4893.26 5572.96 2588.75 13691.89 11268.69 29485.00 7893.10 8674.43 2995.41 7984.97 6195.71 2893.02 136
dcpmvs_285.63 6886.15 5884.06 15791.71 8364.94 23186.47 22591.87 11473.63 16886.60 6593.02 9176.57 1791.87 25583.36 8292.15 8895.35 3
DU-MVS81.12 16480.52 16082.90 21187.80 21463.46 27387.02 20291.87 11479.01 3178.38 20489.07 20965.02 15293.05 20470.05 24176.46 34092.20 173
test_893.13 5972.57 3588.68 14191.84 11668.69 29484.87 8293.10 8674.43 2995.16 89
viewmacassd2359aftdt83.76 10183.66 10384.07 15486.59 26764.56 23986.88 20991.82 11775.72 10483.34 11592.15 11168.24 11392.88 21179.05 12989.15 14394.77 25
PAPM_NR83.02 12582.41 12684.82 11492.47 7566.37 19187.93 17291.80 11873.82 16377.32 22990.66 16367.90 11794.90 10370.37 23689.48 13793.19 125
test1286.80 5792.63 7270.70 8091.79 11982.71 12971.67 6196.16 5194.50 5693.54 108
agg_prior92.85 6771.94 5291.78 12084.41 9394.93 100
PAPR81.66 15180.89 15383.99 16690.27 11064.00 25386.76 21691.77 12168.84 29277.13 23989.50 19767.63 11994.88 10567.55 26688.52 15593.09 130
viewmanbaseed2359cas83.66 10483.55 10484.00 16586.81 25964.53 24086.65 21991.75 12274.89 13483.15 12191.68 12468.74 10592.83 21579.02 13089.24 14094.63 38
PVSNet_Blended_VisFu82.62 13181.83 14184.96 10690.80 10069.76 9688.74 13891.70 12369.39 27378.96 18988.46 23165.47 14894.87 10674.42 19188.57 15390.24 248
viewdifsd2359ckpt0983.34 11682.55 12485.70 8087.64 22767.72 15888.43 14991.68 12471.91 20881.65 14690.68 16267.10 12694.75 11276.17 16987.70 17094.62 40
KinetiMVS83.31 11982.61 12385.39 9087.08 25267.56 16488.06 16691.65 12577.80 4482.21 13591.79 11957.27 25394.07 14177.77 14789.89 13094.56 45
fmvsm_s_conf0.5_n_685.55 7086.20 5483.60 17787.32 24065.13 22288.86 12891.63 12675.41 11488.23 3993.45 7968.56 10792.47 22889.52 1892.78 7893.20 124
viewdifsd2359ckpt1382.91 12782.29 13084.77 11786.96 25566.90 18687.47 18591.62 12772.19 20181.68 14590.71 16166.92 12793.28 18375.90 17487.15 18094.12 68
HPM-MVS_fast85.35 7784.95 8386.57 6293.69 4570.58 8392.15 3991.62 12773.89 16282.67 13094.09 5562.60 18095.54 6980.93 10992.93 7693.57 105
ACMM73.20 880.78 17779.84 17983.58 17989.31 14668.37 13389.99 8391.60 12970.28 25377.25 23089.66 19253.37 28993.53 17274.24 19482.85 26088.85 304
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPA-MVSNet80.60 18280.55 15980.76 27388.07 20160.80 31986.86 21091.58 13075.67 10880.24 17189.45 20363.34 16590.25 30770.51 23579.22 30691.23 206
OPM-MVS83.50 11182.95 11685.14 9788.79 17170.95 7389.13 11991.52 13177.55 5280.96 15891.75 12260.71 21894.50 12379.67 12586.51 19289.97 266
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Anonymous2023121178.97 22677.69 23982.81 21690.54 10564.29 24990.11 8291.51 13265.01 34376.16 26388.13 24550.56 32493.03 20769.68 24777.56 32691.11 209
PS-MVSNAJss82.07 14081.31 14484.34 13486.51 26967.27 17589.27 11091.51 13271.75 20979.37 18490.22 17863.15 17294.27 13077.69 14982.36 26791.49 199
TAPA-MVS73.13 979.15 22077.94 22682.79 22089.59 12962.99 28788.16 16391.51 13265.77 33277.14 23891.09 14960.91 21693.21 19050.26 40587.05 18292.17 178
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP74.13 681.51 15880.57 15884.36 13289.42 13868.69 12589.97 8491.50 13574.46 14675.04 29490.41 17053.82 28494.54 12077.56 15082.91 25989.86 270
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PCF-MVS73.52 780.38 18978.84 20785.01 10487.71 22168.99 11283.65 30791.46 13663.00 36777.77 22190.28 17466.10 14095.09 9761.40 32188.22 16090.94 218
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TranMVSNet+NR-MVSNet80.84 16880.31 16582.42 23187.85 21162.33 29887.74 17991.33 13780.55 977.99 21589.86 18265.23 15092.62 21867.05 27375.24 36792.30 168
RRT-MVS82.60 13482.10 13484.10 14887.98 20662.94 28887.45 18891.27 13877.42 5679.85 17590.28 17456.62 26194.70 11679.87 12388.15 16194.67 32
PS-CasMVS78.01 25278.09 22377.77 33787.71 22154.39 40388.02 16791.22 13977.50 5473.26 31988.64 22560.73 21788.41 34361.88 31673.88 38090.53 235
v7n78.97 22677.58 24283.14 19783.45 34465.51 21288.32 15791.21 14073.69 16772.41 33186.32 29757.93 24493.81 15669.18 25175.65 35390.11 254
PEN-MVS77.73 25877.69 23977.84 33587.07 25453.91 40687.91 17391.18 14177.56 5173.14 32188.82 22061.23 21089.17 32859.95 33272.37 39190.43 239
MM89.16 789.23 988.97 490.79 10173.65 1092.66 2791.17 14286.57 187.39 5694.97 2471.70 6097.68 192.19 195.63 3195.57 1
save fliter93.80 4372.35 4490.47 7391.17 14274.31 150
CP-MVSNet78.22 24378.34 21777.84 33587.83 21354.54 40187.94 17191.17 14277.65 4673.48 31788.49 23062.24 18988.43 34262.19 31274.07 37690.55 234
114514_t80.68 17879.51 18984.20 14594.09 4167.27 17589.64 9491.11 14558.75 41074.08 30990.72 16058.10 24395.04 9869.70 24689.42 13890.30 246
NR-MVSNet80.23 19579.38 19282.78 22187.80 21463.34 27686.31 23391.09 14679.01 3172.17 33589.07 20967.20 12492.81 21666.08 28075.65 35392.20 173
fmvsm_s_conf0.5_n_1086.38 5186.76 4585.24 9487.33 23867.30 17389.50 9990.98 14776.25 9490.56 2194.75 2868.38 10994.24 13490.80 792.32 8794.19 64
OpenMVScopyleft72.83 1079.77 20278.33 21884.09 15285.17 30169.91 9290.57 6890.97 14866.70 31772.17 33591.91 11454.70 27593.96 14361.81 31890.95 11088.41 320
MAR-MVS81.84 14580.70 15585.27 9391.32 8871.53 5889.82 8690.92 14969.77 26778.50 20086.21 29862.36 18694.52 12265.36 28592.05 9189.77 274
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
tt080578.73 23177.83 23181.43 25285.17 30160.30 32789.41 10590.90 15071.21 22377.17 23788.73 22146.38 36493.21 19072.57 21278.96 30790.79 222
Anonymous2024052980.19 19778.89 20684.10 14890.60 10364.75 23788.95 12590.90 15065.97 33180.59 16691.17 14749.97 33293.73 16369.16 25282.70 26493.81 87
OMC-MVS82.69 13081.97 13984.85 11388.75 17367.42 16787.98 16890.87 15274.92 13379.72 17791.65 12662.19 19093.96 14375.26 18486.42 19393.16 126
UA-Net85.08 8284.96 8285.45 8892.07 7868.07 14489.78 8990.86 15382.48 284.60 9093.20 8569.35 9395.22 8771.39 22690.88 11293.07 131
viewdifsd2359ckpt0782.83 12982.78 12182.99 20686.51 26962.58 29185.09 26990.83 15475.22 12182.28 13291.63 12869.43 9292.03 24577.71 14886.32 19494.34 57
test_fmvsm_n_192085.29 7885.34 7585.13 10086.12 27869.93 9188.65 14290.78 15569.97 26188.27 3793.98 6471.39 6591.54 27188.49 3490.45 11893.91 79
EPP-MVSNet83.40 11483.02 11484.57 12290.13 11364.47 24592.32 3490.73 15674.45 14779.35 18591.10 14869.05 10095.12 9172.78 20987.22 17894.13 67
DTE-MVSNet76.99 27476.80 25977.54 34386.24 27353.06 41587.52 18390.66 15777.08 6872.50 32988.67 22460.48 22589.52 32057.33 36070.74 40390.05 261
v1079.74 20378.67 20882.97 20984.06 32864.95 22887.88 17590.62 15873.11 18775.11 29186.56 29061.46 20494.05 14273.68 19775.55 35589.90 268
test_fmvsmconf_n85.92 6086.04 6185.57 8685.03 30869.51 9989.62 9690.58 15973.42 17687.75 4994.02 5972.85 4793.24 18790.37 890.75 11393.96 76
v119279.59 20678.43 21583.07 20283.55 34264.52 24186.93 20790.58 15970.83 23477.78 22085.90 30359.15 23593.94 14673.96 19677.19 32990.76 224
v114480.03 19979.03 20283.01 20583.78 33564.51 24287.11 19990.57 16171.96 20778.08 21386.20 29961.41 20593.94 14674.93 18677.23 32790.60 232
XVG-OURS-SEG-HR80.81 17079.76 18183.96 16885.60 29068.78 11783.54 31390.50 16270.66 24176.71 24591.66 12560.69 21991.26 28376.94 15881.58 27591.83 185
MVS78.19 24676.99 25581.78 24485.66 28766.99 18184.66 27990.47 16355.08 43172.02 33785.27 32063.83 16394.11 14066.10 27989.80 13184.24 400
fmvsm_l_conf0.5_n_386.02 5586.32 5185.14 9787.20 24368.54 12989.57 9790.44 16475.31 11887.49 5394.39 4172.86 4692.72 21789.04 2690.56 11694.16 65
XVG-OURS80.41 18779.23 19883.97 16785.64 28869.02 11183.03 32690.39 16571.09 22677.63 22391.49 13654.62 27791.35 28075.71 17683.47 25191.54 196
MVSFormer82.85 12882.05 13685.24 9487.35 23370.21 8590.50 7190.38 16668.55 29681.32 15089.47 19961.68 19893.46 17778.98 13390.26 12192.05 182
test_djsdf80.30 19479.32 19583.27 19083.98 33065.37 21790.50 7190.38 16668.55 29676.19 25988.70 22256.44 26293.46 17778.98 13380.14 29590.97 216
CPTT-MVS83.73 10283.33 11084.92 11093.28 5270.86 7792.09 4090.38 16668.75 29379.57 17992.83 9560.60 22493.04 20680.92 11091.56 10090.86 220
v14419279.47 20978.37 21682.78 22183.35 34563.96 25486.96 20490.36 16969.99 26077.50 22485.67 31060.66 22193.77 15974.27 19376.58 33790.62 230
v192192079.22 21878.03 22482.80 21783.30 34763.94 25686.80 21290.33 17069.91 26377.48 22585.53 31458.44 24193.75 16173.60 19876.85 33490.71 228
MVS_111021_HR85.14 8084.75 8586.32 6491.65 8472.70 3085.98 24290.33 17076.11 9682.08 13791.61 13171.36 6694.17 13881.02 10892.58 8192.08 181
v124078.99 22577.78 23482.64 22683.21 35063.54 27086.62 22190.30 17269.74 27077.33 22885.68 30957.04 25693.76 16073.13 20676.92 33190.62 230
test_fmvsmconf0.1_n85.61 6985.65 6985.50 8782.99 36069.39 10689.65 9390.29 17373.31 18087.77 4894.15 5371.72 5993.23 18890.31 990.67 11593.89 82
v879.97 20179.02 20382.80 21784.09 32764.50 24487.96 16990.29 17374.13 15775.24 28786.81 27662.88 17993.89 15474.39 19275.40 36290.00 262
fmvsm_s_conf0.5_n_386.36 5287.46 3183.09 19987.08 25265.21 21989.09 12190.21 17579.67 1989.98 2395.02 2373.17 4191.71 26191.30 391.60 9792.34 165
mvs_tets79.13 22177.77 23583.22 19484.70 31466.37 19189.17 11490.19 17669.38 27475.40 27789.46 20144.17 38793.15 19776.78 16580.70 28790.14 251
jajsoiax79.29 21777.96 22583.27 19084.68 31566.57 18989.25 11190.16 17769.20 28275.46 27489.49 19845.75 37593.13 19976.84 16180.80 28590.11 254
Vis-MVSNetpermissive83.46 11282.80 11985.43 8990.25 11168.74 12090.30 7990.13 17876.33 9180.87 16192.89 9361.00 21594.20 13572.45 21890.97 10993.35 115
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PS-MVSNAJ81.69 14981.02 15083.70 17589.51 13368.21 14184.28 29390.09 17970.79 23581.26 15485.62 31263.15 17294.29 12875.62 17888.87 14788.59 315
xiu_mvs_v2_base81.69 14981.05 14983.60 17789.15 15468.03 14684.46 28790.02 18070.67 23881.30 15386.53 29263.17 17194.19 13775.60 17988.54 15488.57 316
FA-MVS(test-final)80.96 16679.91 17684.10 14888.30 19065.01 22684.55 28490.01 18173.25 18379.61 17887.57 25658.35 24294.72 11471.29 22786.25 19792.56 154
v2v48280.23 19579.29 19683.05 20383.62 34064.14 25187.04 20089.97 18273.61 16978.18 21087.22 26761.10 21393.82 15576.11 17076.78 33691.18 207
test_yl81.17 16180.47 16283.24 19289.13 15563.62 26286.21 23789.95 18372.43 19981.78 14389.61 19457.50 25093.58 16570.75 23186.90 18492.52 156
DCV-MVSNet81.17 16180.47 16283.24 19289.13 15563.62 26286.21 23789.95 18372.43 19981.78 14389.61 19457.50 25093.58 16570.75 23186.90 18492.52 156
fmvsm_s_conf0.5_n_783.34 11684.03 9481.28 25885.73 28665.13 22285.40 26189.90 18574.96 13282.13 13693.89 6766.65 12987.92 34886.56 5191.05 10790.80 221
V4279.38 21578.24 22082.83 21481.10 39465.50 21385.55 25689.82 18671.57 21578.21 20886.12 30160.66 22193.18 19675.64 17775.46 35989.81 273
fmvsm_s_conf0.5_n_485.39 7585.75 6884.30 13786.70 26365.83 20388.77 13489.78 18775.46 11388.35 3593.73 7269.19 9693.06 20391.30 388.44 15794.02 74
VNet82.21 13782.41 12681.62 24790.82 9960.93 31684.47 28589.78 18776.36 9084.07 10291.88 11664.71 15590.26 30670.68 23388.89 14693.66 95
diffmvs_AUTHOR82.38 13582.27 13182.73 22583.26 34863.80 25983.89 30189.76 18973.35 17982.37 13190.84 15866.25 13790.79 29882.77 9187.93 16693.59 104
diffmvspermissive82.10 13881.88 14082.76 22383.00 35863.78 26183.68 30689.76 18972.94 19182.02 13889.85 18365.96 14590.79 29882.38 9887.30 17793.71 93
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
XVG-ACMP-BASELINE76.11 29274.27 30481.62 24783.20 35164.67 23883.60 31089.75 19169.75 26871.85 33887.09 27232.78 43892.11 24369.99 24380.43 29188.09 326
EI-MVSNet-Vis-set84.19 9083.81 9885.31 9288.18 19367.85 15387.66 18089.73 19280.05 1582.95 12389.59 19670.74 7494.82 10780.66 11684.72 22393.28 118
EI-MVSNet-UG-set83.81 9883.38 10885.09 10287.87 21067.53 16587.44 18989.66 19379.74 1882.23 13489.41 20570.24 8094.74 11379.95 12183.92 23892.99 139
test_fmvsmconf0.01_n84.73 8784.52 8985.34 9180.25 40269.03 10989.47 10089.65 19473.24 18486.98 6194.27 4666.62 13093.23 18890.26 1089.95 12893.78 91
BP-MVS184.32 8983.71 10186.17 6787.84 21267.85 15389.38 10789.64 19577.73 4583.98 10492.12 11256.89 25895.43 7684.03 7891.75 9695.24 7
VortexMVS78.57 23777.89 22980.59 27685.89 28262.76 29085.61 25189.62 19672.06 20574.99 29585.38 31855.94 26490.77 30174.99 18576.58 33788.23 322
PAPM77.68 26276.40 27181.51 25087.29 24261.85 30583.78 30389.59 19764.74 34571.23 34588.70 22262.59 18193.66 16452.66 38987.03 18389.01 296
MGCNet87.69 2387.55 2888.12 1389.45 13771.76 5391.47 5689.54 19882.14 386.65 6494.28 4568.28 11297.46 690.81 695.31 3795.15 8
anonymousdsp78.60 23577.15 25182.98 20880.51 40067.08 18087.24 19689.53 19965.66 33475.16 28987.19 26952.52 29392.25 23977.17 15579.34 30489.61 278
MG-MVS83.41 11383.45 10683.28 18992.74 7062.28 30088.17 16289.50 20075.22 12181.49 14892.74 10166.75 12895.11 9372.85 20891.58 9992.45 162
PLCcopyleft70.83 1178.05 25076.37 27283.08 20191.88 8267.80 15588.19 16189.46 20164.33 35169.87 36288.38 23353.66 28593.58 16558.86 34482.73 26287.86 330
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n_985.84 6486.63 4783.46 18287.12 25166.01 19788.56 14689.43 20275.59 10989.32 2794.32 4372.89 4591.21 28690.11 1192.33 8693.16 126
SDMVSNet80.38 18980.18 16880.99 26789.03 16064.94 23180.45 35889.40 20375.19 12576.61 24989.98 18060.61 22387.69 35276.83 16283.55 24890.33 244
Fast-Effi-MVS+80.81 17079.92 17583.47 18188.85 16264.51 24285.53 25889.39 20470.79 23578.49 20185.06 32767.54 12093.58 16567.03 27486.58 19092.32 167
IterMVS-LS80.06 19879.38 19282.11 23885.89 28263.20 28086.79 21389.34 20574.19 15475.45 27586.72 27966.62 13092.39 23272.58 21176.86 33390.75 225
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
icg_test_0407_278.92 22878.93 20578.90 31287.13 24663.59 26676.58 40589.33 20670.51 24477.82 21789.03 21161.84 19481.38 40972.56 21485.56 21291.74 188
IMVS_040780.61 18079.90 17782.75 22487.13 24663.59 26685.33 26289.33 20670.51 24477.82 21789.03 21161.84 19492.91 20972.56 21485.56 21291.74 188
IMVS_040477.16 27276.42 27079.37 30387.13 24663.59 26677.12 40389.33 20670.51 24466.22 40489.03 21150.36 32782.78 39972.56 21485.56 21291.74 188
IMVS_040380.80 17380.12 17282.87 21387.13 24663.59 26685.19 26389.33 20670.51 24478.49 20189.03 21163.26 16893.27 18572.56 21485.56 21291.74 188
API-MVS81.99 14281.23 14684.26 14390.94 9670.18 9091.10 6289.32 21071.51 21678.66 19688.28 23665.26 14995.10 9664.74 29191.23 10587.51 338
fmvsm_s_conf0.5_n_585.22 7985.55 7184.25 14486.26 27267.40 16989.18 11389.31 21172.50 19588.31 3693.86 6869.66 8991.96 24989.81 1391.05 10793.38 112
GBi-Net78.40 23977.40 24681.40 25487.60 22863.01 28388.39 15289.28 21271.63 21175.34 28087.28 26354.80 27191.11 28762.72 30479.57 29990.09 256
test178.40 23977.40 24681.40 25487.60 22863.01 28388.39 15289.28 21271.63 21175.34 28087.28 26354.80 27191.11 28762.72 30479.57 29990.09 256
FMVSNet177.44 26676.12 27481.40 25486.81 25963.01 28388.39 15289.28 21270.49 24874.39 30687.28 26349.06 34691.11 28760.91 32578.52 31090.09 256
cdsmvs_eth3d_5k19.96 44126.61 4430.00 4620.00 4850.00 4870.00 47489.26 2150.00 4800.00 48188.61 22661.62 2000.00 4810.00 4800.00 4790.00 477
SSM_040781.58 15380.48 16184.87 11288.81 16667.96 14887.37 19089.25 21671.06 22879.48 18190.39 17159.57 23194.48 12572.45 21885.93 20592.18 175
SSM_040481.91 14380.84 15485.13 10089.24 15068.26 13687.84 17789.25 21671.06 22880.62 16590.39 17159.57 23194.65 11872.45 21887.19 17992.47 161
ab-mvs79.51 20778.97 20481.14 26388.46 18360.91 31783.84 30289.24 21870.36 24979.03 18888.87 21963.23 17090.21 30865.12 28782.57 26592.28 169
cascas76.72 28074.64 29682.99 20685.78 28565.88 20282.33 33089.21 21960.85 38972.74 32581.02 39447.28 35593.75 16167.48 26785.02 21889.34 286
eth_miper_zixun_eth77.92 25476.69 26481.61 24983.00 35861.98 30383.15 32089.20 22069.52 27274.86 29884.35 34161.76 19792.56 22371.50 22572.89 38990.28 247
h-mvs3383.15 12182.19 13286.02 7590.56 10470.85 7888.15 16489.16 22176.02 9884.67 8591.39 13961.54 20195.50 7282.71 9475.48 35791.72 192
miper_ehance_all_eth78.59 23677.76 23681.08 26582.66 36861.56 30983.65 30789.15 22268.87 29175.55 27183.79 35466.49 13392.03 24573.25 20476.39 34289.64 277
Effi-MVS+83.62 10883.08 11285.24 9488.38 18767.45 16688.89 12789.15 22275.50 11182.27 13388.28 23669.61 9094.45 12677.81 14687.84 16793.84 85
c3_l78.75 23077.91 22781.26 25982.89 36361.56 30984.09 29989.13 22469.97 26175.56 27084.29 34266.36 13592.09 24473.47 20175.48 35790.12 253
LTVRE_ROB69.57 1376.25 29074.54 29981.41 25388.60 17864.38 24879.24 37489.12 22570.76 23769.79 36487.86 24949.09 34593.20 19356.21 37280.16 29386.65 362
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
fmvsm_s_conf0.5_n_987.39 3287.95 2285.70 8089.48 13667.88 15288.59 14489.05 22680.19 1290.70 1995.40 1574.56 2793.92 15091.54 292.07 9095.31 5
F-COLMAP76.38 28974.33 30382.50 23089.28 14866.95 18588.41 15189.03 22764.05 35666.83 39388.61 22646.78 36192.89 21057.48 35778.55 30987.67 333
FMVSNet278.20 24577.21 25081.20 26187.60 22862.89 28987.47 18589.02 22871.63 21175.29 28687.28 26354.80 27191.10 29062.38 30979.38 30389.61 278
ACMH67.68 1675.89 29573.93 30781.77 24588.71 17566.61 18888.62 14389.01 22969.81 26466.78 39486.70 28341.95 40391.51 27455.64 37378.14 31887.17 347
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
miper_enhance_ethall77.87 25676.86 25780.92 27081.65 38261.38 31182.68 32788.98 23065.52 33675.47 27282.30 38365.76 14792.00 24872.95 20776.39 34289.39 284
无先验87.48 18488.98 23060.00 39694.12 13967.28 26988.97 299
AdaColmapbinary80.58 18579.42 19184.06 15793.09 6268.91 11489.36 10888.97 23269.27 27775.70 26889.69 19057.20 25595.77 6363.06 30288.41 15887.50 339
EI-MVSNet80.52 18679.98 17482.12 23684.28 32263.19 28186.41 22788.95 23374.18 15578.69 19487.54 25966.62 13092.43 23072.57 21280.57 28990.74 226
MVSTER79.01 22477.88 23082.38 23283.07 35564.80 23684.08 30088.95 23369.01 28978.69 19487.17 27054.70 27592.43 23074.69 18780.57 28989.89 269
LuminaMVS80.68 17879.62 18783.83 17185.07 30768.01 14786.99 20388.83 23570.36 24981.38 14987.99 24750.11 33092.51 22779.02 13086.89 18690.97 216
131476.53 28275.30 28980.21 28683.93 33162.32 29984.66 27988.81 23660.23 39470.16 35684.07 34955.30 26890.73 30267.37 26883.21 25687.59 337
UniMVSNet_ETH3D79.10 22278.24 22081.70 24686.85 25760.24 32887.28 19588.79 23774.25 15376.84 24090.53 16949.48 33891.56 26767.98 26282.15 26893.29 117
xiu_mvs_v1_base_debu80.80 17379.72 18484.03 16287.35 23370.19 8785.56 25388.77 23869.06 28681.83 13988.16 24050.91 31992.85 21278.29 14287.56 17189.06 291
xiu_mvs_v1_base80.80 17379.72 18484.03 16287.35 23370.19 8785.56 25388.77 23869.06 28681.83 13988.16 24050.91 31992.85 21278.29 14287.56 17189.06 291
xiu_mvs_v1_base_debi80.80 17379.72 18484.03 16287.35 23370.19 8785.56 25388.77 23869.06 28681.83 13988.16 24050.91 31992.85 21278.29 14287.56 17189.06 291
FMVSNet377.88 25576.85 25880.97 26986.84 25862.36 29786.52 22488.77 23871.13 22475.34 28086.66 28554.07 28191.10 29062.72 30479.57 29989.45 282
patch_mono-283.65 10584.54 8780.99 26790.06 11965.83 20384.21 29488.74 24271.60 21485.01 7792.44 10374.51 2883.50 39482.15 9992.15 8893.64 101
GeoE81.71 14881.01 15183.80 17489.51 13364.45 24688.97 12488.73 24371.27 22278.63 19789.76 18966.32 13693.20 19369.89 24486.02 20293.74 92
mamba_040879.37 21677.52 24384.93 10988.81 16667.96 14865.03 45888.66 24470.96 23279.48 18189.80 18658.69 23794.65 11870.35 23785.93 20592.18 175
SSM_0407277.67 26377.52 24378.12 32988.81 16667.96 14865.03 45888.66 24470.96 23279.48 18189.80 18658.69 23774.23 45070.35 23785.93 20592.18 175
CANet_DTU80.61 18079.87 17882.83 21485.60 29063.17 28287.36 19188.65 24676.37 8975.88 26588.44 23253.51 28793.07 20273.30 20389.74 13292.25 170
HyFIR lowres test77.53 26575.40 28583.94 16989.59 12966.62 18780.36 35988.64 24756.29 42776.45 25285.17 32457.64 24893.28 18361.34 32383.10 25891.91 184
WR-MVS79.49 20879.22 19980.27 28488.79 17158.35 34485.06 27088.61 24878.56 3577.65 22288.34 23463.81 16490.66 30364.98 28977.22 32891.80 187
BH-untuned79.47 20978.60 21082.05 23989.19 15365.91 20186.07 24188.52 24972.18 20275.42 27687.69 25361.15 21293.54 17160.38 32986.83 18786.70 361
IS-MVSNet83.15 12182.81 11884.18 14689.94 12263.30 27791.59 5088.46 25079.04 3079.49 18092.16 10965.10 15194.28 12967.71 26491.86 9594.95 12
pm-mvs177.25 27176.68 26578.93 31184.22 32458.62 34286.41 22788.36 25171.37 21873.31 31888.01 24661.22 21189.15 32964.24 29573.01 38889.03 295
UGNet80.83 16979.59 18884.54 12388.04 20268.09 14389.42 10488.16 25276.95 7076.22 25889.46 20149.30 34293.94 14668.48 25990.31 11991.60 193
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
VDD-MVS83.01 12682.36 12884.96 10691.02 9466.40 19088.91 12688.11 25377.57 4984.39 9493.29 8352.19 29993.91 15177.05 15788.70 15294.57 43
Effi-MVS+-dtu80.03 19978.57 21184.42 12885.13 30568.74 12088.77 13488.10 25474.99 12974.97 29683.49 36357.27 25393.36 18173.53 19980.88 28391.18 207
v14878.72 23277.80 23381.47 25182.73 36661.96 30486.30 23488.08 25573.26 18276.18 26085.47 31662.46 18492.36 23471.92 22273.82 38190.09 256
EG-PatchMatch MVS74.04 31871.82 33280.71 27484.92 30967.42 16785.86 24788.08 25566.04 32964.22 41683.85 35135.10 43492.56 22357.44 35880.83 28482.16 425
viewmambaseed2359dif80.41 18779.84 17982.12 23682.95 36262.50 29483.39 31488.06 25767.11 31280.98 15790.31 17366.20 13991.01 29474.62 18884.90 22092.86 144
SymmetryMVS85.38 7684.81 8487.07 4991.47 8672.47 3891.65 4688.06 25779.31 2484.39 9492.18 10764.64 15695.53 7080.70 11490.91 11193.21 122
cl2278.07 24977.01 25381.23 26082.37 37561.83 30683.55 31187.98 25968.96 29075.06 29383.87 35061.40 20691.88 25473.53 19976.39 34289.98 265
test_fmvsmvis_n_192084.02 9383.87 9584.49 12684.12 32669.37 10788.15 16487.96 26070.01 25983.95 10593.23 8468.80 10491.51 27488.61 3189.96 12792.57 153
pmmvs674.69 31073.39 31478.61 31681.38 38957.48 36186.64 22087.95 26164.99 34470.18 35486.61 28650.43 32689.52 32062.12 31470.18 40688.83 305
MVP-Stereo76.12 29174.46 30181.13 26485.37 29769.79 9484.42 29087.95 26165.03 34267.46 38485.33 31953.28 29091.73 26058.01 35483.27 25581.85 427
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
cl____77.72 25976.76 26180.58 27782.49 37260.48 32483.09 32287.87 26369.22 28074.38 30785.22 32362.10 19191.53 27271.09 22875.41 36189.73 276
DIV-MVS_self_test77.72 25976.76 26180.58 27782.48 37360.48 32483.09 32287.86 26469.22 28074.38 30785.24 32162.10 19191.53 27271.09 22875.40 36289.74 275
BH-w/o78.21 24477.33 24980.84 27188.81 16665.13 22284.87 27487.85 26569.75 26874.52 30484.74 33461.34 20793.11 20058.24 35285.84 20884.27 399
FE-MVS77.78 25775.68 27884.08 15388.09 20066.00 19883.13 32187.79 26668.42 30078.01 21485.23 32245.50 37895.12 9159.11 34185.83 20991.11 209
HY-MVS69.67 1277.95 25377.15 25180.36 28187.57 23260.21 32983.37 31687.78 26766.11 32775.37 27987.06 27463.27 16790.48 30561.38 32282.43 26690.40 241
guyue81.13 16380.64 15782.60 22886.52 26863.92 25786.69 21887.73 26873.97 15880.83 16389.69 19056.70 25991.33 28278.26 14585.40 21692.54 155
1112_ss77.40 26876.43 26980.32 28389.11 15960.41 32683.65 30787.72 26962.13 38073.05 32286.72 27962.58 18289.97 31262.11 31580.80 28590.59 233
mvs_anonymous79.42 21279.11 20180.34 28284.45 32157.97 35182.59 32887.62 27067.40 31176.17 26288.56 22968.47 10889.59 31970.65 23486.05 20193.47 110
ACMH+68.96 1476.01 29474.01 30582.03 24088.60 17865.31 21888.86 12887.55 27170.25 25567.75 38087.47 26141.27 40693.19 19558.37 35075.94 35087.60 335
tfpnnormal74.39 31273.16 31878.08 33086.10 28058.05 34884.65 28187.53 27270.32 25271.22 34685.63 31154.97 26989.86 31343.03 43975.02 36986.32 365
CHOSEN 1792x268877.63 26475.69 27783.44 18389.98 12168.58 12878.70 38487.50 27356.38 42675.80 26786.84 27558.67 23991.40 27961.58 32085.75 21090.34 243
ambc75.24 36773.16 44850.51 43363.05 46387.47 27464.28 41577.81 42817.80 46389.73 31757.88 35560.64 43785.49 381
Fast-Effi-MVS+-dtu78.02 25176.49 26782.62 22783.16 35466.96 18486.94 20687.45 27572.45 19671.49 34384.17 34754.79 27491.58 26467.61 26580.31 29289.30 287
D2MVS74.82 30973.21 31779.64 29979.81 40962.56 29380.34 36087.35 27664.37 35068.86 37182.66 37846.37 36590.10 30967.91 26381.24 27886.25 366
fmvsm_s_conf0.5_n_284.04 9284.11 9383.81 17386.17 27665.00 22786.96 20487.28 27774.35 14888.25 3894.23 4961.82 19692.60 22089.85 1288.09 16293.84 85
TSAR-MVS + GP.85.71 6785.33 7686.84 5591.34 8772.50 3689.07 12287.28 27776.41 8585.80 6990.22 17874.15 3495.37 8481.82 10191.88 9292.65 152
fmvsm_l_conf0.5_n84.47 8884.54 8784.27 14185.42 29568.81 11588.49 14887.26 27968.08 30388.03 4393.49 7572.04 5591.77 25788.90 2889.14 14492.24 172
hse-mvs281.72 14780.94 15284.07 15488.72 17467.68 15985.87 24687.26 27976.02 9884.67 8588.22 23961.54 20193.48 17582.71 9473.44 38591.06 211
AUN-MVS79.21 21977.60 24184.05 16088.71 17567.61 16185.84 24887.26 27969.08 28577.23 23288.14 24453.20 29193.47 17675.50 18173.45 38491.06 211
BH-RMVSNet79.61 20478.44 21483.14 19789.38 14265.93 20084.95 27387.15 28273.56 17178.19 20989.79 18856.67 26093.36 18159.53 33786.74 18890.13 252
Test_1112_low_res76.40 28875.44 28379.27 30589.28 14858.09 34781.69 33787.07 28359.53 40172.48 33086.67 28461.30 20889.33 32360.81 32780.15 29490.41 240
KD-MVS_self_test68.81 37567.59 38072.46 39874.29 43945.45 44877.93 39687.00 28463.12 36463.99 41978.99 42042.32 39884.77 38456.55 37064.09 42787.16 349
mvsmamba80.60 18279.38 19284.27 14189.74 12767.24 17787.47 18586.95 28570.02 25875.38 27888.93 21651.24 31692.56 22375.47 18289.22 14193.00 138
reproduce_monomvs75.40 30474.38 30278.46 32483.92 33257.80 35683.78 30386.94 28673.47 17572.25 33484.47 33638.74 41989.27 32575.32 18370.53 40488.31 321
LS3D76.95 27674.82 29483.37 18790.45 10667.36 17189.15 11886.94 28661.87 38369.52 36590.61 16651.71 31294.53 12146.38 42786.71 18988.21 324
miper_lstm_enhance74.11 31773.11 31977.13 34880.11 40459.62 33472.23 42986.92 28866.76 31670.40 35182.92 37356.93 25782.92 39869.06 25372.63 39088.87 303
fmvsm_l_conf0.5_n_a84.13 9184.16 9284.06 15785.38 29668.40 13288.34 15686.85 28967.48 31087.48 5493.40 8070.89 7191.61 26288.38 3689.22 14192.16 179
jason81.39 15980.29 16684.70 12086.63 26669.90 9385.95 24386.77 29063.24 36381.07 15689.47 19961.08 21492.15 24278.33 14190.07 12692.05 182
jason: jason.
viewdifsd2359ckpt1180.37 19179.73 18282.30 23483.70 33862.39 29584.20 29586.67 29173.22 18580.90 15990.62 16463.00 17791.56 26776.81 16378.44 31292.95 141
viewmsd2359difaftdt80.37 19179.73 18282.30 23483.70 33862.39 29584.20 29586.67 29173.22 18580.90 15990.62 16463.00 17791.56 26776.81 16378.44 31292.95 141
OurMVSNet-221017-074.26 31472.42 32779.80 29483.76 33659.59 33585.92 24586.64 29366.39 32566.96 39187.58 25539.46 41491.60 26365.76 28369.27 40988.22 323
VPNet78.69 23378.66 20978.76 31488.31 18955.72 38884.45 28886.63 29476.79 7578.26 20790.55 16859.30 23489.70 31866.63 27577.05 33090.88 219
fmvsm_s_conf0.1_n_283.80 9983.79 9983.83 17185.62 28964.94 23187.03 20186.62 29574.32 14987.97 4694.33 4260.67 22092.60 22089.72 1487.79 16893.96 76
USDC70.33 36268.37 36376.21 35480.60 39856.23 38179.19 37686.49 29660.89 38861.29 42985.47 31631.78 44189.47 32253.37 38676.21 34882.94 418
lupinMVS81.39 15980.27 16784.76 11887.35 23370.21 8585.55 25686.41 29762.85 37081.32 15088.61 22661.68 19892.24 24078.41 14090.26 12191.83 185
TR-MVS77.44 26676.18 27381.20 26188.24 19163.24 27884.61 28286.40 29867.55 30877.81 21986.48 29354.10 28093.15 19757.75 35682.72 26387.20 346
旧先验191.96 7965.79 20686.37 29993.08 9069.31 9592.74 7988.74 311
GA-MVS76.87 27775.17 29181.97 24282.75 36562.58 29181.44 34286.35 30072.16 20474.74 29982.89 37446.20 36992.02 24768.85 25681.09 28091.30 205
MonoMVSNet76.49 28675.80 27578.58 31881.55 38558.45 34386.36 23286.22 30174.87 13774.73 30083.73 35651.79 31188.73 33770.78 23072.15 39488.55 317
CDS-MVSNet79.07 22377.70 23883.17 19687.60 22868.23 14084.40 29186.20 30267.49 30976.36 25586.54 29161.54 20190.79 29861.86 31787.33 17690.49 237
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_111021_LR82.61 13282.11 13384.11 14788.82 16571.58 5785.15 26686.16 30374.69 14080.47 16991.04 15162.29 18790.55 30480.33 11890.08 12590.20 249
MSDG73.36 32970.99 34380.49 27984.51 32065.80 20580.71 35386.13 30465.70 33365.46 40783.74 35544.60 38290.91 29651.13 39876.89 33284.74 395
TransMVSNet (Re)75.39 30574.56 29877.86 33485.50 29457.10 36686.78 21486.09 30572.17 20371.53 34287.34 26263.01 17689.31 32456.84 36661.83 43387.17 347
VDDNet81.52 15680.67 15684.05 16090.44 10764.13 25289.73 9185.91 30671.11 22583.18 11993.48 7650.54 32593.49 17473.40 20288.25 15994.54 47
AstraMVS80.81 17080.14 17182.80 21786.05 28163.96 25486.46 22685.90 30773.71 16680.85 16290.56 16754.06 28291.57 26679.72 12483.97 23792.86 144
sd_testset77.70 26177.40 24678.60 31789.03 16060.02 33079.00 37985.83 30875.19 12576.61 24989.98 18054.81 27085.46 37762.63 30883.55 24890.33 244
Baseline_NR-MVSNet78.15 24778.33 21877.61 34085.79 28456.21 38286.78 21485.76 30973.60 17077.93 21687.57 25665.02 15288.99 33167.14 27275.33 36487.63 334
Anonymous2024052168.80 37667.22 38573.55 38574.33 43854.11 40483.18 31985.61 31058.15 41361.68 42880.94 39630.71 44481.27 41057.00 36473.34 38785.28 385
test_vis1_n_192075.52 30075.78 27674.75 37479.84 40857.44 36283.26 31885.52 31162.83 37179.34 18686.17 30045.10 38079.71 41678.75 13581.21 27987.10 353
新几何183.42 18493.13 5970.71 7985.48 31257.43 42181.80 14291.98 11363.28 16692.27 23864.60 29292.99 7587.27 345
EPNet83.72 10382.92 11786.14 7184.22 32469.48 10091.05 6385.27 31381.30 676.83 24191.65 12666.09 14195.56 6776.00 17393.85 6793.38 112
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UnsupCasMVSNet_eth67.33 38765.99 39171.37 40473.48 44551.47 42675.16 41685.19 31465.20 33960.78 43180.93 39842.35 39777.20 42757.12 36153.69 45085.44 383
SD_040374.65 31174.77 29574.29 37886.20 27547.42 44283.71 30585.12 31569.30 27668.50 37687.95 24859.40 23386.05 36849.38 40983.35 25389.40 283
mmtdpeth74.16 31673.01 32077.60 34283.72 33761.13 31285.10 26885.10 31672.06 20577.21 23680.33 40343.84 38985.75 37177.14 15652.61 45285.91 376
IB-MVS68.01 1575.85 29673.36 31683.31 18884.76 31366.03 19583.38 31585.06 31770.21 25669.40 36681.05 39345.76 37494.66 11765.10 28875.49 35689.25 288
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
TAMVS78.89 22977.51 24583.03 20487.80 21467.79 15684.72 27785.05 31867.63 30676.75 24487.70 25262.25 18890.82 29758.53 34887.13 18190.49 237
CL-MVSNet_self_test72.37 34171.46 33675.09 36879.49 41553.53 40880.76 35185.01 31969.12 28470.51 34982.05 38757.92 24584.13 38852.27 39166.00 42287.60 335
testdata79.97 29090.90 9764.21 25084.71 32059.27 40385.40 7392.91 9262.02 19389.08 33068.95 25491.37 10386.63 363
MS-PatchMatch73.83 32172.67 32377.30 34683.87 33366.02 19681.82 33484.66 32161.37 38768.61 37482.82 37647.29 35488.21 34459.27 33884.32 23377.68 442
ET-MVSNet_ETH3D78.63 23476.63 26684.64 12186.73 26269.47 10185.01 27184.61 32269.54 27166.51 40186.59 28750.16 32991.75 25876.26 16884.24 23492.69 150
CNLPA78.08 24876.79 26081.97 24290.40 10871.07 6987.59 18284.55 32366.03 33072.38 33289.64 19357.56 24986.04 36959.61 33683.35 25388.79 307
MIMVSNet168.58 37866.78 38873.98 38280.07 40551.82 42280.77 35084.37 32464.40 34959.75 43782.16 38636.47 43083.63 39242.73 44070.33 40586.48 364
KD-MVS_2432*160066.22 39763.89 40073.21 38875.47 43653.42 41070.76 43684.35 32564.10 35466.52 39978.52 42234.55 43584.98 38150.40 40150.33 45581.23 430
miper_refine_blended66.22 39763.89 40073.21 38875.47 43653.42 41070.76 43684.35 32564.10 35466.52 39978.52 42234.55 43584.98 38150.40 40150.33 45581.23 430
test_040272.79 33870.44 34979.84 29388.13 19765.99 19985.93 24484.29 32765.57 33567.40 38785.49 31546.92 35892.61 21935.88 45374.38 37580.94 432
EU-MVSNet68.53 38067.61 37971.31 40778.51 42247.01 44584.47 28584.27 32842.27 45466.44 40284.79 33340.44 41183.76 39058.76 34668.54 41483.17 412
thisisatest053079.40 21377.76 23684.31 13687.69 22565.10 22587.36 19184.26 32970.04 25777.42 22688.26 23849.94 33394.79 11170.20 23984.70 22493.03 135
COLMAP_ROBcopyleft66.92 1773.01 33570.41 35080.81 27287.13 24665.63 20988.30 15884.19 33062.96 36863.80 42187.69 25338.04 42492.56 22346.66 42474.91 37084.24 400
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tttt051779.40 21377.91 22783.90 17088.10 19963.84 25888.37 15584.05 33171.45 21776.78 24389.12 20849.93 33594.89 10470.18 24083.18 25792.96 140
CMPMVSbinary51.72 2170.19 36468.16 36676.28 35373.15 44957.55 36079.47 37183.92 33248.02 44756.48 44784.81 33243.13 39386.42 36562.67 30781.81 27484.89 393
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20240521178.25 24277.01 25381.99 24191.03 9360.67 32184.77 27683.90 33370.65 24280.00 17491.20 14541.08 40891.43 27865.21 28685.26 21793.85 83
XXY-MVS75.41 30375.56 28174.96 36983.59 34157.82 35580.59 35583.87 33466.54 32474.93 29788.31 23563.24 16980.09 41562.16 31376.85 33486.97 355
DP-MVS76.78 27974.57 29783.42 18493.29 5169.46 10388.55 14783.70 33563.98 35870.20 35388.89 21854.01 28394.80 11046.66 42481.88 27386.01 373
tfpn200view976.42 28775.37 28779.55 30289.13 15557.65 35885.17 26483.60 33673.41 17776.45 25286.39 29552.12 30091.95 25048.33 41583.75 24289.07 289
thres40076.50 28375.37 28779.86 29289.13 15557.65 35885.17 26483.60 33673.41 17776.45 25286.39 29552.12 30091.95 25048.33 41583.75 24290.00 262
SixPastTwentyTwo73.37 32771.26 34179.70 29685.08 30657.89 35385.57 25283.56 33871.03 23065.66 40685.88 30442.10 40192.57 22259.11 34163.34 42888.65 313
thres20075.55 29974.47 30078.82 31387.78 21757.85 35483.07 32483.51 33972.44 19875.84 26684.42 33752.08 30391.75 25847.41 42283.64 24786.86 357
IterMVS-SCA-FT75.43 30273.87 30980.11 28882.69 36764.85 23581.57 33983.47 34069.16 28370.49 35084.15 34851.95 30688.15 34569.23 25072.14 39587.34 342
CVMVSNet72.99 33672.58 32574.25 37984.28 32250.85 43186.41 22783.45 34144.56 45173.23 32087.54 25949.38 34085.70 37265.90 28178.44 31286.19 368
ITE_SJBPF78.22 32681.77 38160.57 32283.30 34269.25 27967.54 38287.20 26836.33 43187.28 35754.34 38074.62 37386.80 358
thisisatest051577.33 26975.38 28683.18 19585.27 30063.80 25982.11 33383.27 34365.06 34175.91 26483.84 35249.54 33794.27 13067.24 27086.19 19891.48 200
mvs5depth69.45 37167.45 38275.46 36473.93 44055.83 38679.19 37683.23 34466.89 31371.63 34183.32 36533.69 43785.09 38059.81 33455.34 44885.46 382
thres100view90076.50 28375.55 28279.33 30489.52 13256.99 36785.83 24983.23 34473.94 16076.32 25687.12 27151.89 30891.95 25048.33 41583.75 24289.07 289
thres600view776.50 28375.44 28379.68 29789.40 14057.16 36485.53 25883.23 34473.79 16476.26 25787.09 27251.89 30891.89 25348.05 42083.72 24590.00 262
test22291.50 8568.26 13684.16 29783.20 34754.63 43279.74 17691.63 12858.97 23691.42 10186.77 359
EPNet_dtu75.46 30174.86 29377.23 34782.57 37054.60 40086.89 20883.09 34871.64 21066.25 40385.86 30555.99 26388.04 34754.92 37786.55 19189.05 294
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n83.80 9983.71 10184.07 15486.69 26467.31 17289.46 10183.07 34971.09 22686.96 6293.70 7369.02 10291.47 27688.79 2984.62 22593.44 111
fmvsm_s_conf0.1_n83.56 10983.38 10884.10 14884.86 31067.28 17489.40 10683.01 35070.67 23887.08 5993.96 6568.38 10991.45 27788.56 3384.50 22693.56 106
testing9176.54 28175.66 28079.18 30888.43 18555.89 38581.08 34583.00 35173.76 16575.34 28084.29 34246.20 36990.07 31064.33 29384.50 22691.58 195
TDRefinement67.49 38564.34 39776.92 34973.47 44661.07 31584.86 27582.98 35259.77 39858.30 44185.13 32526.06 44987.89 34947.92 42160.59 43881.81 428
OpenMVS_ROBcopyleft64.09 1970.56 35968.19 36577.65 33980.26 40159.41 33885.01 27182.96 35358.76 40965.43 40882.33 38237.63 42691.23 28545.34 43476.03 34982.32 422
fmvsm_s_conf0.5_n_a83.63 10783.41 10784.28 13986.14 27768.12 14289.43 10282.87 35470.27 25487.27 5893.80 7169.09 9791.58 26488.21 3783.65 24693.14 129
fmvsm_s_conf0.1_n_a83.32 11882.99 11584.28 13983.79 33468.07 14489.34 10982.85 35569.80 26587.36 5794.06 5768.34 11191.56 26787.95 4083.46 25293.21 122
RPSCF73.23 33271.46 33678.54 32082.50 37159.85 33182.18 33282.84 35658.96 40671.15 34789.41 20545.48 37984.77 38458.82 34571.83 39791.02 215
CostFormer75.24 30673.90 30879.27 30582.65 36958.27 34680.80 34882.73 35761.57 38475.33 28483.13 36955.52 26691.07 29364.98 28978.34 31788.45 318
IterMVS74.29 31372.94 32178.35 32581.53 38663.49 27281.58 33882.49 35868.06 30469.99 35983.69 35851.66 31385.54 37565.85 28271.64 39886.01 373
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_cas_vis1_n_192073.76 32273.74 31173.81 38475.90 43059.77 33280.51 35682.40 35958.30 41281.62 14785.69 30844.35 38676.41 43476.29 16778.61 30885.23 386
WTY-MVS75.65 29875.68 27875.57 36086.40 27156.82 36977.92 39782.40 35965.10 34076.18 26087.72 25163.13 17580.90 41260.31 33081.96 27189.00 298
pmmvs474.03 32071.91 33180.39 28081.96 37868.32 13481.45 34182.14 36159.32 40269.87 36285.13 32552.40 29688.13 34660.21 33174.74 37284.73 396
FMVSNet569.50 37067.96 37074.15 38082.97 36155.35 39380.01 36682.12 36262.56 37563.02 42281.53 39036.92 42781.92 40548.42 41474.06 37785.17 389
mamv476.81 27878.23 22272.54 39786.12 27865.75 20878.76 38382.07 36364.12 35372.97 32391.02 15467.97 11568.08 46283.04 8778.02 31983.80 407
baseline176.98 27576.75 26377.66 33888.13 19755.66 38985.12 26781.89 36473.04 18976.79 24288.90 21762.43 18587.78 35163.30 30171.18 40189.55 280
UnsupCasMVSNet_bld63.70 40661.53 41270.21 41373.69 44351.39 42772.82 42781.89 36455.63 42957.81 44371.80 44838.67 42078.61 42049.26 41152.21 45380.63 434
LFMVS81.82 14681.23 14683.57 18091.89 8163.43 27589.84 8581.85 36677.04 6983.21 11693.10 8652.26 29893.43 17971.98 22189.95 12893.85 83
sss73.60 32473.64 31273.51 38682.80 36455.01 39776.12 40781.69 36762.47 37674.68 30185.85 30657.32 25278.11 42360.86 32680.93 28187.39 340
SSC-MVS3.273.35 33073.39 31473.23 38785.30 29949.01 43874.58 42281.57 36875.21 12373.68 31485.58 31352.53 29282.05 40454.33 38177.69 32488.63 314
pmmvs-eth3d70.50 36067.83 37478.52 32277.37 42666.18 19481.82 33481.51 36958.90 40763.90 42080.42 40142.69 39686.28 36658.56 34765.30 42483.11 414
TinyColmap67.30 38864.81 39574.76 37381.92 38056.68 37380.29 36181.49 37060.33 39256.27 44883.22 36624.77 45387.66 35345.52 43269.47 40879.95 437
testing9976.09 29375.12 29279.00 30988.16 19455.50 39180.79 34981.40 37173.30 18175.17 28884.27 34544.48 38490.02 31164.28 29484.22 23591.48 200
tpmvs71.09 35269.29 35776.49 35282.04 37756.04 38378.92 38181.37 37264.05 35667.18 38978.28 42449.74 33689.77 31549.67 40872.37 39183.67 408
WBMVS73.43 32672.81 32275.28 36687.91 20850.99 43078.59 38781.31 37365.51 33874.47 30584.83 33146.39 36386.68 36158.41 34977.86 32088.17 325
pmmvs571.55 34870.20 35375.61 35977.83 42356.39 37781.74 33680.89 37457.76 41767.46 38484.49 33549.26 34385.32 37957.08 36275.29 36585.11 390
ANet_high50.57 42846.10 43263.99 43248.67 47739.13 46570.99 43580.85 37561.39 38631.18 46657.70 46217.02 46473.65 45331.22 45915.89 47479.18 439
LCM-MVSNet54.25 41949.68 42967.97 42653.73 47445.28 45166.85 45180.78 37635.96 46339.45 46462.23 4578.70 47378.06 42448.24 41851.20 45480.57 435
PVSNet64.34 1872.08 34670.87 34575.69 35886.21 27456.44 37674.37 42380.73 37762.06 38170.17 35582.23 38542.86 39583.31 39654.77 37884.45 23087.32 343
baseline275.70 29773.83 31081.30 25783.26 34861.79 30782.57 32980.65 37866.81 31466.88 39283.42 36457.86 24692.19 24163.47 29879.57 29989.91 267
ppachtmachnet_test70.04 36667.34 38478.14 32879.80 41061.13 31279.19 37680.59 37959.16 40465.27 40979.29 41546.75 36287.29 35649.33 41066.72 41786.00 375
FE-MVSNET67.25 38965.33 39373.02 39275.86 43152.54 41680.26 36380.56 38063.80 36160.39 43279.70 41241.41 40584.66 38643.34 43862.62 43181.86 426
Gipumacopyleft45.18 43341.86 43655.16 44677.03 42851.52 42532.50 47180.52 38132.46 46627.12 46935.02 4709.52 47275.50 44222.31 46760.21 43938.45 469
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous2023120668.60 37767.80 37571.02 40980.23 40350.75 43278.30 39280.47 38256.79 42466.11 40582.63 37946.35 36678.95 41943.62 43775.70 35283.36 411
LCM-MVSNet-Re77.05 27376.94 25677.36 34487.20 24351.60 42480.06 36480.46 38375.20 12467.69 38186.72 27962.48 18388.98 33263.44 29989.25 13991.51 197
tt032070.49 36168.03 36977.89 33384.78 31259.12 33983.55 31180.44 38458.13 41467.43 38680.41 40239.26 41687.54 35455.12 37563.18 43086.99 354
testing1175.14 30774.01 30578.53 32188.16 19456.38 37880.74 35280.42 38570.67 23872.69 32883.72 35743.61 39189.86 31362.29 31183.76 24189.36 285
tpm273.26 33171.46 33678.63 31583.34 34656.71 37280.65 35480.40 38656.63 42573.55 31682.02 38851.80 31091.24 28456.35 37178.42 31587.95 327
CR-MVSNet73.37 32771.27 34079.67 29881.32 39265.19 22075.92 40980.30 38759.92 39772.73 32681.19 39152.50 29486.69 36059.84 33377.71 32287.11 351
Patchmtry70.74 35669.16 35975.49 36380.72 39654.07 40574.94 42080.30 38758.34 41170.01 35781.19 39152.50 29486.54 36253.37 38671.09 40285.87 378
sc_t172.19 34469.51 35580.23 28584.81 31161.09 31484.68 27880.22 38960.70 39071.27 34483.58 36136.59 42989.24 32660.41 32863.31 42990.37 242
tpm cat170.57 35868.31 36477.35 34582.41 37457.95 35278.08 39380.22 38952.04 43868.54 37577.66 42952.00 30587.84 35051.77 39272.07 39686.25 366
MDTV_nov1_ep1369.97 35483.18 35253.48 40977.10 40480.18 39160.45 39169.33 36880.44 40048.89 34986.90 35951.60 39478.51 311
AllTest70.96 35368.09 36879.58 30085.15 30363.62 26284.58 28379.83 39262.31 37760.32 43486.73 27732.02 43988.96 33450.28 40371.57 39986.15 369
TestCases79.58 30085.15 30363.62 26279.83 39262.31 37760.32 43486.73 27732.02 43988.96 33450.28 40371.57 39986.15 369
test_fmvs1_n70.86 35570.24 35272.73 39572.51 45355.28 39481.27 34479.71 39451.49 44278.73 19384.87 33027.54 44877.02 42876.06 17179.97 29785.88 377
Vis-MVSNet (Re-imp)78.36 24178.45 21378.07 33188.64 17751.78 42386.70 21779.63 39574.14 15675.11 29190.83 15961.29 20989.75 31658.10 35391.60 9792.69 150
MIMVSNet70.69 35769.30 35674.88 37184.52 31956.35 38075.87 41179.42 39664.59 34667.76 37982.41 38041.10 40781.54 40746.64 42681.34 27686.75 360
myMVS_eth3d2873.62 32373.53 31373.90 38388.20 19247.41 44378.06 39479.37 39774.29 15273.98 31084.29 34244.67 38183.54 39351.47 39587.39 17590.74 226
dmvs_re71.14 35170.58 34672.80 39481.96 37859.68 33375.60 41379.34 39868.55 29669.27 36980.72 39949.42 33976.54 43152.56 39077.79 32182.19 424
SCA74.22 31572.33 32879.91 29184.05 32962.17 30179.96 36779.29 39966.30 32672.38 33280.13 40651.95 30688.60 34059.25 33977.67 32588.96 300
testing22274.04 31872.66 32478.19 32787.89 20955.36 39281.06 34679.20 40071.30 22174.65 30283.57 36239.11 41888.67 33951.43 39785.75 21090.53 235
tpmrst72.39 33972.13 33073.18 39180.54 39949.91 43579.91 36879.08 40163.11 36571.69 34079.95 40855.32 26782.77 40065.66 28473.89 37986.87 356
tt0320-xc70.11 36567.45 38278.07 33185.33 29859.51 33783.28 31778.96 40258.77 40867.10 39080.28 40436.73 42887.42 35556.83 36759.77 44087.29 344
test_fmvs170.93 35470.52 34772.16 39973.71 44255.05 39680.82 34778.77 40351.21 44378.58 19884.41 33831.20 44376.94 42975.88 17580.12 29684.47 398
PatchmatchNetpermissive73.12 33371.33 33978.49 32383.18 35260.85 31879.63 36978.57 40464.13 35271.73 33979.81 41151.20 31785.97 37057.40 35976.36 34788.66 312
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing3-275.12 30875.19 29074.91 37090.40 10845.09 45380.29 36178.42 40578.37 4076.54 25187.75 25044.36 38587.28 35757.04 36383.49 25092.37 164
MDA-MVSNet-bldmvs66.68 39263.66 40275.75 35779.28 41760.56 32373.92 42578.35 40664.43 34850.13 45679.87 41044.02 38883.67 39146.10 42956.86 44283.03 416
new-patchmatchnet61.73 41061.73 41161.70 43572.74 45124.50 47869.16 44378.03 40761.40 38556.72 44675.53 44038.42 42176.48 43345.95 43057.67 44184.13 402
our_test_369.14 37367.00 38675.57 36079.80 41058.80 34077.96 39577.81 40859.55 40062.90 42578.25 42547.43 35383.97 38951.71 39367.58 41683.93 405
test20.0367.45 38666.95 38768.94 41775.48 43544.84 45477.50 39977.67 40966.66 31863.01 42383.80 35347.02 35778.40 42142.53 44268.86 41383.58 409
WB-MVSnew71.96 34771.65 33472.89 39384.67 31851.88 42182.29 33177.57 41062.31 37773.67 31583.00 37153.49 28881.10 41145.75 43182.13 26985.70 379
test-LLR72.94 33772.43 32674.48 37581.35 39058.04 34978.38 38877.46 41166.66 31869.95 36079.00 41848.06 35179.24 41766.13 27784.83 22186.15 369
test-mter71.41 34970.39 35174.48 37581.35 39058.04 34978.38 38877.46 41160.32 39369.95 36079.00 41836.08 43279.24 41766.13 27784.83 22186.15 369
ECVR-MVScopyleft79.61 20479.26 19780.67 27590.08 11554.69 39987.89 17477.44 41374.88 13580.27 17092.79 9848.96 34892.45 22968.55 25892.50 8394.86 19
UBG73.08 33472.27 32975.51 36288.02 20351.29 42878.35 39177.38 41465.52 33673.87 31282.36 38145.55 37686.48 36455.02 37684.39 23288.75 309
tpm72.37 34171.71 33374.35 37782.19 37652.00 41879.22 37577.29 41564.56 34772.95 32483.68 35951.35 31483.26 39758.33 35175.80 35187.81 331
LF4IMVS64.02 40562.19 40969.50 41570.90 45453.29 41376.13 40677.18 41652.65 43758.59 43980.98 39523.55 45676.52 43253.06 38866.66 41878.68 440
test111179.43 21179.18 20080.15 28789.99 12053.31 41287.33 19377.05 41775.04 12880.23 17292.77 10048.97 34792.33 23768.87 25592.40 8594.81 22
K. test v371.19 35068.51 36279.21 30783.04 35757.78 35784.35 29276.91 41872.90 19262.99 42482.86 37539.27 41591.09 29261.65 31952.66 45188.75 309
UWE-MVS72.13 34571.49 33574.03 38186.66 26547.70 44081.40 34376.89 41963.60 36275.59 26984.22 34639.94 41385.62 37448.98 41286.13 20088.77 308
testgi66.67 39366.53 38967.08 42875.62 43441.69 46375.93 40876.50 42066.11 32765.20 41286.59 28735.72 43374.71 44743.71 43673.38 38684.84 394
test_fmvs268.35 38267.48 38170.98 41069.50 45651.95 41980.05 36576.38 42149.33 44574.65 30284.38 33923.30 45775.40 44574.51 19075.17 36885.60 380
test_vis1_n69.85 36969.21 35871.77 40172.66 45255.27 39581.48 34076.21 42252.03 43975.30 28583.20 36828.97 44676.22 43674.60 18978.41 31683.81 406
PatchMatch-RL72.38 34070.90 34476.80 35188.60 17867.38 17079.53 37076.17 42362.75 37369.36 36782.00 38945.51 37784.89 38353.62 38480.58 28878.12 441
JIA-IIPM66.32 39662.82 40876.82 35077.09 42761.72 30865.34 45675.38 42458.04 41664.51 41462.32 45642.05 40286.51 36351.45 39669.22 41082.21 423
ADS-MVSNet266.20 39963.33 40374.82 37279.92 40658.75 34167.55 44875.19 42553.37 43565.25 41075.86 43742.32 39880.53 41441.57 44368.91 41185.18 387
ETVMVS72.25 34371.05 34275.84 35687.77 21851.91 42079.39 37274.98 42669.26 27873.71 31382.95 37240.82 41086.14 36746.17 42884.43 23189.47 281
PatchT68.46 38167.85 37270.29 41280.70 39743.93 45672.47 42874.88 42760.15 39570.55 34876.57 43349.94 33381.59 40650.58 39974.83 37185.34 384
dp66.80 39165.43 39270.90 41179.74 41248.82 43975.12 41874.77 42859.61 39964.08 41877.23 43042.89 39480.72 41348.86 41366.58 41983.16 413
MDA-MVSNet_test_wron65.03 40162.92 40571.37 40475.93 42956.73 37069.09 44574.73 42957.28 42254.03 45177.89 42645.88 37174.39 44949.89 40761.55 43482.99 417
TESTMET0.1,169.89 36869.00 36072.55 39679.27 41856.85 36878.38 38874.71 43057.64 41868.09 37877.19 43137.75 42576.70 43063.92 29684.09 23684.10 403
YYNet165.03 40162.91 40671.38 40375.85 43256.60 37469.12 44474.66 43157.28 42254.12 45077.87 42745.85 37274.48 44849.95 40661.52 43583.05 415
test_fmvs363.36 40761.82 41067.98 42562.51 46546.96 44677.37 40174.03 43245.24 45067.50 38378.79 42112.16 46972.98 45472.77 21066.02 42183.99 404
PMMVS69.34 37268.67 36171.35 40675.67 43362.03 30275.17 41573.46 43350.00 44468.68 37279.05 41652.07 30478.13 42261.16 32482.77 26173.90 448
PVSNet_057.27 2061.67 41159.27 41468.85 41979.61 41357.44 36268.01 44673.44 43455.93 42858.54 44070.41 45144.58 38377.55 42647.01 42335.91 46371.55 451
Syy-MVS68.05 38367.85 37268.67 42184.68 31540.97 46478.62 38573.08 43566.65 32166.74 39579.46 41352.11 30282.30 40232.89 45676.38 34582.75 419
myMVS_eth3d67.02 39066.29 39069.21 41684.68 31542.58 45978.62 38573.08 43566.65 32166.74 39579.46 41331.53 44282.30 40239.43 44876.38 34582.75 419
test0.0.03 168.00 38467.69 37768.90 41877.55 42447.43 44175.70 41272.95 43766.66 31866.56 39782.29 38448.06 35175.87 44044.97 43574.51 37483.41 410
testing368.56 37967.67 37871.22 40887.33 23842.87 45883.06 32571.54 43870.36 24969.08 37084.38 33930.33 44585.69 37337.50 45175.45 36085.09 391
ADS-MVSNet64.36 40462.88 40768.78 42079.92 40647.17 44467.55 44871.18 43953.37 43565.25 41075.86 43742.32 39873.99 45141.57 44368.91 41185.18 387
Patchmatch-RL test70.24 36367.78 37677.61 34077.43 42559.57 33671.16 43370.33 44062.94 36968.65 37372.77 44650.62 32385.49 37669.58 24866.58 41987.77 332
gg-mvs-nofinetune69.95 36767.96 37075.94 35583.07 35554.51 40277.23 40270.29 44163.11 36570.32 35262.33 45543.62 39088.69 33853.88 38387.76 16984.62 397
door-mid69.98 442
GG-mvs-BLEND75.38 36581.59 38455.80 38779.32 37369.63 44367.19 38873.67 44443.24 39288.90 33650.41 40084.50 22681.45 429
FPMVS53.68 42251.64 42459.81 43865.08 46251.03 42969.48 44169.58 44441.46 45540.67 46272.32 44716.46 46570.00 45924.24 46665.42 42358.40 462
door69.44 445
Patchmatch-test64.82 40363.24 40469.57 41479.42 41649.82 43663.49 46269.05 44651.98 44059.95 43680.13 40650.91 31970.98 45540.66 44573.57 38287.90 329
CHOSEN 280x42066.51 39464.71 39671.90 40081.45 38763.52 27157.98 46568.95 44753.57 43462.59 42676.70 43246.22 36875.29 44655.25 37479.68 29876.88 444
MVStest156.63 41752.76 42368.25 42461.67 46653.25 41471.67 43168.90 44838.59 45950.59 45583.05 37025.08 45170.66 45636.76 45238.56 46280.83 433
EGC-MVSNET52.07 42647.05 43067.14 42783.51 34360.71 32080.50 35767.75 4490.07 4770.43 47875.85 43924.26 45481.54 40728.82 46062.25 43259.16 460
ttmdpeth59.91 41357.10 41768.34 42367.13 46046.65 44774.64 42167.41 45048.30 44662.52 42785.04 32920.40 45975.93 43942.55 44145.90 46182.44 421
EPMVS69.02 37468.16 36671.59 40279.61 41349.80 43777.40 40066.93 45162.82 37270.01 35779.05 41645.79 37377.86 42556.58 36975.26 36687.13 350
APD_test153.31 42349.93 42863.42 43465.68 46150.13 43471.59 43266.90 45234.43 46440.58 46371.56 4498.65 47476.27 43534.64 45555.36 44763.86 458
lessismore_v078.97 31081.01 39557.15 36565.99 45361.16 43082.82 37639.12 41791.34 28159.67 33546.92 45888.43 319
dmvs_testset62.63 40864.11 39958.19 43978.55 42124.76 47775.28 41465.94 45467.91 30560.34 43376.01 43653.56 28673.94 45231.79 45767.65 41575.88 446
pmmvs357.79 41554.26 42068.37 42264.02 46456.72 37175.12 41865.17 45540.20 45652.93 45269.86 45220.36 46075.48 44345.45 43355.25 44972.90 450
MVS-HIRNet59.14 41457.67 41663.57 43381.65 38243.50 45771.73 43065.06 45639.59 45851.43 45357.73 46138.34 42282.58 40139.53 44673.95 37864.62 457
PM-MVS66.41 39564.14 39873.20 39073.92 44156.45 37578.97 38064.96 45763.88 36064.72 41380.24 40519.84 46183.44 39566.24 27664.52 42679.71 438
UWE-MVS-2865.32 40064.93 39466.49 42978.70 42038.55 46677.86 39864.39 45862.00 38264.13 41783.60 36041.44 40476.00 43831.39 45880.89 28284.92 392
PMVScopyleft37.38 2244.16 43440.28 43855.82 44440.82 47942.54 46165.12 45763.99 45934.43 46424.48 47057.12 4633.92 47976.17 43717.10 47155.52 44648.75 465
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test250677.30 27076.49 26779.74 29590.08 11552.02 41787.86 17663.10 46074.88 13580.16 17392.79 9838.29 42392.35 23568.74 25792.50 8394.86 19
test_method31.52 43829.28 44238.23 45327.03 4816.50 48420.94 47362.21 4614.05 47522.35 47352.50 46613.33 46647.58 47327.04 46334.04 46560.62 459
WB-MVS54.94 41854.72 41955.60 44573.50 44420.90 47974.27 42461.19 46259.16 40450.61 45474.15 44247.19 35675.78 44117.31 47035.07 46470.12 452
test_vis1_rt60.28 41258.42 41565.84 43067.25 45955.60 39070.44 43860.94 46344.33 45259.00 43866.64 45324.91 45268.67 46062.80 30369.48 40773.25 449
SSC-MVS53.88 42153.59 42154.75 44772.87 45019.59 48073.84 42660.53 46457.58 42049.18 45873.45 44546.34 36775.47 44416.20 47332.28 46669.20 453
testf145.72 43041.96 43457.00 44056.90 46845.32 44966.14 45359.26 46526.19 46830.89 46760.96 4594.14 47770.64 45726.39 46446.73 45955.04 463
APD_test245.72 43041.96 43457.00 44056.90 46845.32 44966.14 45359.26 46526.19 46830.89 46760.96 4594.14 47770.64 45726.39 46446.73 45955.04 463
test_f52.09 42550.82 42655.90 44353.82 47342.31 46259.42 46458.31 46736.45 46256.12 44970.96 45012.18 46857.79 46953.51 38556.57 44467.60 454
new_pmnet50.91 42750.29 42752.78 44868.58 45734.94 47063.71 46056.63 46839.73 45744.95 45965.47 45421.93 45858.48 46834.98 45456.62 44364.92 456
DSMNet-mixed57.77 41656.90 41860.38 43767.70 45835.61 46869.18 44253.97 46932.30 46757.49 44479.88 40940.39 41268.57 46138.78 44972.37 39176.97 443
PMMVS240.82 43538.86 43946.69 45053.84 47216.45 48148.61 46849.92 47037.49 46031.67 46560.97 4588.14 47556.42 47028.42 46130.72 46767.19 455
mvsany_test162.30 40961.26 41365.41 43169.52 45554.86 39866.86 45049.78 47146.65 44868.50 37683.21 36749.15 34466.28 46356.93 36560.77 43675.11 447
test_vis3_rt49.26 42947.02 43156.00 44254.30 47145.27 45266.76 45248.08 47236.83 46144.38 46053.20 4657.17 47664.07 46556.77 36855.66 44558.65 461
E-PMN31.77 43730.64 44035.15 45552.87 47527.67 47257.09 46647.86 47324.64 47016.40 47533.05 47111.23 47054.90 47114.46 47418.15 47222.87 471
EMVS30.81 43929.65 44134.27 45650.96 47625.95 47656.58 46746.80 47424.01 47115.53 47630.68 47212.47 46754.43 47212.81 47517.05 47322.43 472
mvsany_test353.99 42051.45 42561.61 43655.51 47044.74 45563.52 46145.41 47543.69 45358.11 44276.45 43417.99 46263.76 46654.77 37847.59 45776.34 445
MVEpermissive26.22 2330.37 44025.89 44443.81 45244.55 47835.46 46928.87 47239.07 47618.20 47218.58 47440.18 4692.68 48047.37 47417.07 47223.78 47148.60 466
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai45.42 43245.38 43345.55 45173.36 44726.85 47567.72 44734.19 47754.15 43349.65 45756.41 46425.43 45062.94 46719.45 46828.09 46846.86 467
kuosan39.70 43640.40 43737.58 45464.52 46326.98 47365.62 45533.02 47846.12 44942.79 46148.99 46724.10 45546.56 47512.16 47626.30 46939.20 468
MTMP92.18 3832.83 479
tmp_tt18.61 44221.40 44510.23 4594.82 48210.11 48234.70 47030.74 4801.48 47623.91 47226.07 47328.42 44713.41 47827.12 46215.35 4757.17 473
DeepMVS_CXcopyleft27.40 45740.17 48026.90 47424.59 48117.44 47323.95 47148.61 4689.77 47126.48 47618.06 46924.47 47028.83 470
N_pmnet52.79 42453.26 42251.40 44978.99 4197.68 48369.52 4403.89 48251.63 44157.01 44574.98 44140.83 40965.96 46437.78 45064.67 42580.56 436
wuyk23d16.82 44315.94 44619.46 45858.74 46731.45 47139.22 4693.74 4836.84 4746.04 4772.70 4771.27 48124.29 47710.54 47714.40 4762.63 474
testmvs6.04 4468.02 4490.10 4610.08 4830.03 48669.74 4390.04 4840.05 4780.31 4791.68 4780.02 4830.04 4790.24 4780.02 4770.25 476
test1236.12 4458.11 4480.14 4600.06 4840.09 48571.05 4340.03 4850.04 4790.25 4801.30 4790.05 4820.03 4800.21 4790.01 4780.29 475
mmdepth0.00 4480.00 4510.00 4620.00 4850.00 4870.00 4740.00 4860.00 4800.00 4810.00 4800.00 4840.00 4810.00 4800.00 4790.00 477
monomultidepth0.00 4480.00 4510.00 4620.00 4850.00 4870.00 4740.00 4860.00 4800.00 4810.00 4800.00 4840.00 4810.00 4800.00 4790.00 477
test_blank0.00 4480.00 4510.00 4620.00 4850.00 4870.00 4740.00 4860.00 4800.00 4810.00 4800.00 4840.00 4810.00 4800.00 4790.00 477
uanet_test0.00 4480.00 4510.00 4620.00 4850.00 4870.00 4740.00 4860.00 4800.00 4810.00 4800.00 4840.00 4810.00 4800.00 4790.00 477
DCPMVS0.00 4480.00 4510.00 4620.00 4850.00 4870.00 4740.00 4860.00 4800.00 4810.00 4800.00 4840.00 4810.00 4800.00 4790.00 477
pcd_1.5k_mvsjas5.26 4477.02 4500.00 4620.00 4850.00 4870.00 4740.00 4860.00 4800.00 4810.00 48063.15 1720.00 4810.00 4800.00 4790.00 477
sosnet-low-res0.00 4480.00 4510.00 4620.00 4850.00 4870.00 4740.00 4860.00 4800.00 4810.00 4800.00 4840.00 4810.00 4800.00 4790.00 477
sosnet0.00 4480.00 4510.00 4620.00 4850.00 4870.00 4740.00 4860.00 4800.00 4810.00 4800.00 4840.00 4810.00 4800.00 4790.00 477
uncertanet0.00 4480.00 4510.00 4620.00 4850.00 4870.00 4740.00 4860.00 4800.00 4810.00 4800.00 4840.00 4810.00 4800.00 4790.00 477
Regformer0.00 4480.00 4510.00 4620.00 4850.00 4870.00 4740.00 4860.00 4800.00 4810.00 4800.00 4840.00 4810.00 4800.00 4790.00 477
n20.00 486
nn0.00 486
ab-mvs-re7.23 4449.64 4470.00 4620.00 4850.00 4870.00 4740.00 4860.00 4800.00 48186.72 2790.00 4840.00 4810.00 4800.00 4790.00 477
uanet0.00 4480.00 4510.00 4620.00 4850.00 4870.00 4740.00 4860.00 4800.00 4810.00 4800.00 4840.00 4810.00 4800.00 4790.00 477
TestfortrainingZip93.28 12
WAC-MVS42.58 45939.46 447
PC_three_145268.21 30292.02 1494.00 6182.09 595.98 6084.58 6996.68 294.95 12
eth-test20.00 485
eth-test0.00 485
OPU-MVS89.06 394.62 1575.42 493.57 894.02 5982.45 396.87 2383.77 8096.48 894.88 16
test_0728_THIRD78.38 3892.12 1195.78 481.46 897.40 989.42 1996.57 794.67 32
GSMVS88.96 300
test_part295.06 872.65 3291.80 15
sam_mvs151.32 31588.96 300
sam_mvs50.01 331
test_post178.90 3825.43 47648.81 35085.44 37859.25 339
test_post5.46 47550.36 32784.24 387
patchmatchnet-post74.00 44351.12 31888.60 340
gm-plane-assit81.40 38853.83 40762.72 37480.94 39692.39 23263.40 300
test9_res84.90 6295.70 2992.87 143
agg_prior282.91 8995.45 3292.70 148
test_prior472.60 3489.01 123
test_prior288.85 13075.41 11484.91 8093.54 7474.28 3283.31 8395.86 23
旧先验286.56 22358.10 41587.04 6088.98 33274.07 195
新几何286.29 236
原ACMM286.86 210
testdata291.01 29462.37 310
segment_acmp73.08 42
testdata184.14 29875.71 105
plane_prior790.08 11568.51 130
plane_prior689.84 12468.70 12460.42 226
plane_prior491.00 155
plane_prior368.60 12778.44 3678.92 191
plane_prior291.25 5979.12 28
plane_prior189.90 123
plane_prior68.71 12290.38 7777.62 4786.16 199
HQP5-MVS66.98 182
HQP-NCC89.33 14389.17 11476.41 8577.23 232
ACMP_Plane89.33 14389.17 11476.41 8577.23 232
BP-MVS77.47 151
HQP4-MVS77.24 23195.11 9391.03 213
HQP2-MVS60.17 229
NP-MVS89.62 12868.32 13490.24 176
MDTV_nov1_ep13_2view37.79 46775.16 41655.10 43066.53 39849.34 34153.98 38287.94 328
ACMMP++_ref81.95 272
ACMMP++81.25 277
Test By Simon64.33 158