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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
HPM-MVS++copyleft89.02 1089.15 1288.63 595.01 976.03 192.38 3292.85 6580.26 1187.78 4994.27 4775.89 2396.81 2787.45 4796.44 993.05 147
MSC_two_6792asdad89.16 194.34 3175.53 292.99 5597.53 289.67 1596.44 994.41 60
No_MVS89.16 194.34 3175.53 292.99 5597.53 289.67 1596.44 994.41 60
OPU-MVS89.06 394.62 1575.42 493.57 894.02 6182.45 396.87 2483.77 8296.48 894.88 18
MTAPA87.23 3687.00 3987.90 2294.18 3974.25 586.58 23192.02 11379.45 2285.88 7194.80 2768.07 12496.21 5186.69 5295.34 3593.23 129
MP-MVScopyleft87.71 2387.64 2687.93 2194.36 3073.88 692.71 2792.65 7677.57 5083.84 11194.40 4172.24 5596.28 4885.65 5995.30 3893.62 112
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS87.11 3886.92 4387.68 3794.20 3873.86 793.98 392.82 6976.62 8783.68 11494.46 3667.93 12595.95 6384.20 7894.39 6093.23 129
CNVR-MVS88.93 1289.13 1388.33 894.77 1273.82 890.51 7093.00 5280.90 788.06 4494.06 5976.43 2096.84 2588.48 3695.99 1894.34 66
SMA-MVScopyleft89.08 989.23 988.61 694.25 3573.73 992.40 2993.63 2674.77 15092.29 795.97 274.28 3497.24 1588.58 3396.91 194.87 20
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
MM89.16 789.23 988.97 490.79 10373.65 1092.66 2891.17 15386.57 187.39 5894.97 2571.70 6497.68 192.19 195.63 3195.57 1
NCCC88.06 1888.01 2288.24 1194.41 2673.62 1191.22 6292.83 6681.50 585.79 7393.47 8173.02 4697.00 2184.90 6494.94 4394.10 79
ACMMPR87.44 2987.23 3688.08 1594.64 1373.59 1293.04 1793.20 4076.78 8184.66 9094.52 3268.81 11396.65 3584.53 7294.90 4494.00 85
region2R87.42 3187.20 3788.09 1494.63 1473.55 1393.03 1993.12 4676.73 8484.45 9594.52 3269.09 10796.70 3184.37 7494.83 4894.03 83
mPP-MVS86.67 4686.32 5387.72 3394.41 2673.55 1392.74 2592.22 10276.87 7882.81 13894.25 4966.44 14496.24 5082.88 9294.28 6393.38 122
HFP-MVS87.58 2687.47 3187.94 1994.58 1673.54 1593.04 1793.24 3976.78 8184.91 8394.44 3970.78 7796.61 3784.53 7294.89 4593.66 105
3Dnovator+77.84 485.48 7384.47 9388.51 791.08 9473.49 1693.18 1693.78 2380.79 876.66 25993.37 8460.40 24096.75 3077.20 16493.73 6995.29 6
MSP-MVS89.51 589.91 688.30 1094.28 3473.46 1792.90 2194.11 1080.27 1091.35 1694.16 5478.35 1496.77 2889.59 1794.22 6594.67 41
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
PGM-MVS86.68 4586.27 5587.90 2294.22 3773.38 1890.22 8193.04 4775.53 12183.86 11094.42 4067.87 12796.64 3682.70 9894.57 5593.66 105
ZNCC-MVS87.94 2287.85 2488.20 1294.39 2873.33 1993.03 1993.81 2276.81 7985.24 7894.32 4471.76 6296.93 2385.53 6195.79 2594.32 68
XVS87.18 3786.91 4488.00 1794.42 2473.33 1992.78 2392.99 5579.14 2683.67 11594.17 5367.45 13096.60 3883.06 8794.50 5694.07 81
X-MVStestdata80.37 20377.83 24288.00 1794.42 2473.33 1992.78 2392.99 5579.14 2683.67 11512.47 51267.45 13096.60 3883.06 8794.50 5694.07 81
ACMMP_NAP88.05 2088.08 2187.94 1993.70 4573.05 2290.86 6593.59 2876.27 10488.14 4295.09 2171.06 7496.67 3387.67 4496.37 1494.09 80
DPM-MVS84.93 8784.29 9486.84 5790.20 11473.04 2387.12 20793.04 4769.80 27882.85 13691.22 15573.06 4596.02 5876.72 17694.63 5391.46 215
GST-MVS87.42 3187.26 3487.89 2494.12 4072.97 2492.39 3193.43 3376.89 7784.68 8793.99 6570.67 7996.82 2684.18 7995.01 4093.90 91
TEST993.26 5672.96 2588.75 13891.89 12168.44 31585.00 8193.10 8974.36 3395.41 81
train_agg86.43 4986.20 5687.13 5093.26 5672.96 2588.75 13891.89 12168.69 31085.00 8193.10 8974.43 3195.41 8184.97 6395.71 2893.02 149
SteuartSystems-ACMMP88.72 1488.86 1488.32 992.14 7972.96 2593.73 593.67 2580.19 1288.10 4394.80 2773.76 3897.11 1787.51 4695.82 2494.90 17
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator76.31 583.38 12682.31 14086.59 6287.94 21072.94 2890.64 6892.14 11277.21 6675.47 28592.83 9858.56 25294.72 11773.24 21592.71 8192.13 193
SD-MVS88.06 1888.50 1886.71 6192.60 7672.71 2991.81 4693.19 4177.87 4390.32 2394.00 6374.83 2793.78 16187.63 4594.27 6493.65 109
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
MVS_111021_HR85.14 8284.75 8886.32 6691.65 8672.70 3085.98 25390.33 18276.11 10782.08 14891.61 14171.36 7094.17 14181.02 11092.58 8292.08 194
DPE-MVScopyleft89.48 689.98 588.01 1694.80 1172.69 3191.59 5194.10 1275.90 11192.29 795.66 1281.67 697.38 1387.44 4896.34 1593.95 88
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part295.06 872.65 3291.80 15
ACMMPcopyleft85.89 6585.39 7687.38 4493.59 4972.63 3392.74 2593.18 4576.78 8180.73 17593.82 7264.33 16996.29 4782.67 9990.69 11993.23 129
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
test_prior472.60 3489.01 125
test_893.13 6072.57 3588.68 14491.84 12568.69 31084.87 8593.10 8974.43 3195.16 91
TSAR-MVS + GP.85.71 6985.33 7886.84 5791.34 8972.50 3689.07 12487.28 29476.41 9585.80 7290.22 19074.15 3695.37 8681.82 10391.88 9592.65 165
CSCG86.41 5186.19 5887.07 5192.91 6772.48 3790.81 6693.56 2973.95 17183.16 12991.07 16175.94 2295.19 9079.94 12594.38 6193.55 117
NormalMVS86.29 5485.88 6587.52 4193.26 5672.47 3891.65 4792.19 10779.31 2484.39 9792.18 11564.64 16695.53 7280.70 11694.65 5194.56 54
SymmetryMVS85.38 7884.81 8787.07 5191.47 8872.47 3891.65 4788.06 27379.31 2484.39 9792.18 11564.64 16695.53 7280.70 11690.91 11693.21 132
MCST-MVS87.37 3487.25 3587.73 3194.53 2172.46 4089.82 8893.82 2173.07 20084.86 8692.89 9676.22 2196.33 4684.89 6695.13 3994.40 62
FOURS195.00 1072.39 4195.06 193.84 2074.49 15691.30 17
APD-MVScopyleft87.44 2987.52 3087.19 4894.24 3672.39 4191.86 4592.83 6673.01 20288.58 3594.52 3273.36 3996.49 4384.26 7595.01 4092.70 161
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast79.65 386.91 4186.62 5087.76 2993.52 5072.37 4391.26 5993.04 4776.62 8784.22 10293.36 8571.44 6896.76 2980.82 11395.33 3694.16 75
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
save fliter93.80 4472.35 4490.47 7491.17 15374.31 162
DeepC-MVS79.81 287.08 4086.88 4587.69 3691.16 9272.32 4590.31 7993.94 1877.12 7082.82 13794.23 5072.13 5897.09 1884.83 6795.37 3493.65 109
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZD-MVS94.38 2972.22 4692.67 7370.98 24387.75 5194.07 5874.01 3796.70 3184.66 7094.84 47
HPM-MVScopyleft87.11 3886.98 4187.50 4393.88 4372.16 4792.19 3893.33 3676.07 10883.81 11293.95 6869.77 9496.01 5985.15 6294.66 5094.32 68
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + MP.88.02 2188.11 2087.72 3393.68 4772.13 4891.41 5892.35 8974.62 15488.90 3393.85 7175.75 2496.00 6087.80 4394.63 5395.04 11
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS86.73 4386.67 4886.91 5694.11 4172.11 4992.37 3392.56 8174.50 15586.84 6594.65 3167.31 13295.77 6584.80 6892.85 7892.84 159
TestfortrainingZip87.28 4692.85 6872.05 5093.28 1293.32 3776.52 8988.91 3293.52 7777.30 1796.67 3391.98 9493.13 141
MP-MVS-pluss87.67 2587.72 2587.54 4093.64 4872.04 5189.80 9093.50 3075.17 13786.34 6995.29 1970.86 7696.00 6088.78 3196.04 1694.58 50
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS88.46 1588.74 1587.64 3992.78 7171.95 5292.40 2994.74 275.71 11689.16 2995.10 2075.65 2596.19 5287.07 4996.01 1794.79 27
agg_prior92.85 6871.94 5391.78 12984.41 9694.93 102
MGCNet87.69 2487.55 2988.12 1389.45 14071.76 5491.47 5789.54 21082.14 386.65 6794.28 4668.28 12297.46 690.81 695.31 3795.15 8
APDe-MVScopyleft89.15 889.63 787.73 3194.49 2271.69 5593.83 493.96 1775.70 11891.06 1996.03 176.84 1897.03 2089.09 2195.65 3094.47 59
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
reproduce-ours87.47 2787.61 2787.07 5193.27 5471.60 5691.56 5493.19 4174.98 14188.96 3095.54 1471.20 7296.54 4186.28 5493.49 7093.06 145
our_new_method87.47 2787.61 2787.07 5193.27 5471.60 5691.56 5493.19 4174.98 14188.96 3095.54 1471.20 7296.54 4186.28 5493.49 7093.06 145
MVS_111021_LR82.61 14382.11 14484.11 15888.82 16871.58 5885.15 27786.16 32674.69 15180.47 18191.04 16262.29 19990.55 32680.33 12190.08 13090.20 262
MAR-MVS81.84 15680.70 16685.27 9691.32 9071.53 5989.82 8890.92 16069.77 28078.50 21386.21 31062.36 19894.52 12565.36 29692.05 9389.77 287
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
test_one_060195.07 771.46 6094.14 978.27 4192.05 1395.74 880.83 12
MED-MVS test87.86 2794.57 1771.43 6193.28 1294.36 375.24 13092.25 995.03 2297.39 1188.15 3995.96 1994.75 34
MED-MVS89.75 390.37 387.89 2494.57 1771.43 6193.28 1294.36 377.30 6192.25 995.87 381.59 797.39 1188.15 3995.96 1994.85 23
ME-MVS88.98 1189.39 887.75 3094.54 2071.43 6191.61 4994.25 576.30 10390.62 2195.03 2278.06 1597.07 1988.15 3995.96 1994.75 34
test_0728_SECOND87.71 3595.34 171.43 6193.49 1094.23 697.49 489.08 2296.41 1294.21 73
TestfortrainingZip a88.83 1389.21 1187.68 3794.57 1771.25 6593.28 1293.91 1977.30 6191.13 1895.87 377.62 1696.95 2286.12 5793.07 7594.85 23
DVP-MVS++90.23 191.01 187.89 2494.34 3171.25 6595.06 194.23 678.38 3892.78 495.74 882.45 397.49 489.42 1996.68 294.95 14
IU-MVS95.30 271.25 6592.95 6166.81 33092.39 688.94 2896.63 494.85 23
DVP-MVScopyleft89.60 490.35 487.33 4595.27 571.25 6593.49 1092.73 7077.33 5992.12 1195.78 680.98 1097.40 989.08 2296.41 1293.33 126
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
test072695.27 571.25 6593.60 794.11 1077.33 5992.81 395.79 580.98 10
reproduce_model87.28 3587.39 3386.95 5593.10 6271.24 7091.60 5093.19 4174.69 15188.80 3495.61 1370.29 8396.44 4486.20 5693.08 7493.16 137
CDPH-MVS85.76 6885.29 8187.17 4993.49 5171.08 7188.58 14892.42 8668.32 31784.61 9293.48 7972.32 5396.15 5479.00 14295.43 3394.28 71
CNLPA78.08 26076.79 27181.97 25590.40 11071.07 7287.59 18784.55 34666.03 34672.38 34689.64 20557.56 26186.04 39559.61 35983.35 26688.79 320
SED-MVS90.08 290.85 287.77 2895.30 270.98 7393.57 894.06 1477.24 6493.10 195.72 1082.99 197.44 789.07 2596.63 494.88 18
test_241102_ONE95.30 270.98 7394.06 1477.17 6793.10 195.39 1882.99 197.27 14
PHI-MVS86.43 4986.17 5987.24 4790.88 10070.96 7592.27 3794.07 1372.45 20885.22 7991.90 12469.47 9796.42 4583.28 8695.94 2294.35 65
OPM-MVS83.50 12282.95 12785.14 10088.79 17470.95 7689.13 12191.52 14277.55 5380.96 16991.75 13160.71 23094.50 12679.67 13386.51 20489.97 279
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CANet86.45 4886.10 6187.51 4290.09 11670.94 7789.70 9492.59 8081.78 481.32 16191.43 14870.34 8197.23 1684.26 7593.36 7394.37 64
DP-MVS Recon83.11 13582.09 14686.15 7194.44 2370.92 7888.79 13592.20 10570.53 25679.17 20091.03 16464.12 17196.03 5668.39 27290.14 12891.50 211
CPTT-MVS83.73 11283.33 12084.92 11493.28 5370.86 7992.09 4190.38 17868.75 30979.57 19292.83 9860.60 23693.04 21780.92 11291.56 10390.86 233
h-mvs3383.15 13282.19 14386.02 7790.56 10670.85 8088.15 16989.16 23376.02 10984.67 8891.39 14961.54 21395.50 7482.71 9675.48 37191.72 205
新几何183.42 19693.13 6070.71 8185.48 33557.43 44681.80 15391.98 12263.28 17792.27 25064.60 30392.99 7687.27 367
test1286.80 5992.63 7470.70 8291.79 12882.71 14071.67 6596.16 5394.50 5693.54 118
SR-MVS-dyc-post85.77 6785.61 7286.23 6793.06 6470.63 8391.88 4392.27 9573.53 18585.69 7494.45 3765.00 16495.56 6982.75 9491.87 9692.50 171
RE-MVS-def85.48 7593.06 6470.63 8391.88 4392.27 9573.53 18585.69 7494.45 3763.87 17382.75 9491.87 9692.50 171
HPM-MVS_fast85.35 7984.95 8686.57 6493.69 4670.58 8592.15 4091.62 13873.89 17482.67 14194.09 5762.60 19295.54 7180.93 11192.93 7793.57 115
MSLP-MVS++85.43 7585.76 6984.45 13691.93 8270.24 8690.71 6792.86 6477.46 5684.22 10292.81 10067.16 13492.94 21980.36 12094.35 6290.16 263
MVSFormer82.85 13982.05 14785.24 9787.35 24470.21 8790.50 7290.38 17868.55 31281.32 16189.47 21161.68 21093.46 18878.98 14390.26 12692.05 195
lupinMVS81.39 17080.27 17984.76 12387.35 24470.21 8785.55 26786.41 32062.85 39381.32 16188.61 23861.68 21092.24 25278.41 15090.26 12691.83 198
xiu_mvs_v1_base_debu80.80 18579.72 19684.03 17387.35 24470.19 8985.56 26488.77 25169.06 30081.83 15088.16 25250.91 33892.85 22378.29 15287.56 18389.06 304
xiu_mvs_v1_base80.80 18579.72 19684.03 17387.35 24470.19 8985.56 26488.77 25169.06 30081.83 15088.16 25250.91 33892.85 22378.29 15287.56 18389.06 304
xiu_mvs_v1_base_debi80.80 18579.72 19684.03 17387.35 24470.19 8985.56 26488.77 25169.06 30081.83 15088.16 25250.91 33892.85 22378.29 15287.56 18389.06 304
API-MVS81.99 15381.23 15784.26 15490.94 9870.18 9291.10 6389.32 22271.51 22878.66 20988.28 24865.26 15995.10 9864.74 30291.23 10987.51 356
test_fmvsm_n_192085.29 8085.34 7785.13 10386.12 29069.93 9388.65 14590.78 16769.97 27488.27 3993.98 6671.39 6991.54 28388.49 3590.45 12393.91 89
OpenMVScopyleft72.83 1079.77 21478.33 23084.09 16385.17 31269.91 9490.57 6990.97 15966.70 33372.17 34991.91 12354.70 28993.96 14661.81 34090.95 11588.41 333
jason81.39 17080.29 17884.70 12586.63 27869.90 9585.95 25486.77 31263.24 38681.07 16789.47 21161.08 22692.15 25478.33 15190.07 13192.05 195
jason: jason.
MVP-Stereo76.12 30474.46 31481.13 27785.37 30869.79 9684.42 30387.95 27765.03 36467.46 40685.33 33153.28 30491.73 27258.01 37883.27 26881.85 453
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lecture88.09 1788.59 1686.58 6393.26 5669.77 9793.70 694.16 877.13 6989.76 2695.52 1672.26 5496.27 4986.87 5094.65 5193.70 104
PVSNet_Blended_VisFu82.62 14281.83 15284.96 11090.80 10269.76 9888.74 14091.70 13469.39 28778.96 20288.46 24365.47 15894.87 10974.42 20188.57 15890.24 261
test_prior86.33 6592.61 7569.59 9992.97 6095.48 7593.91 89
APD-MVS_3200maxsize85.97 6185.88 6586.22 6892.69 7369.53 10091.93 4292.99 5573.54 18485.94 7094.51 3565.80 15695.61 6883.04 8992.51 8393.53 119
test_fmvsmconf_n85.92 6286.04 6385.57 8885.03 31969.51 10189.62 9890.58 17173.42 18887.75 5194.02 6172.85 4993.24 19890.37 890.75 11893.96 86
EPNet83.72 11382.92 12886.14 7384.22 33569.48 10291.05 6485.27 33681.30 676.83 25491.65 13666.09 15195.56 6976.00 18393.85 6793.38 122
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ET-MVSNet_ETH3D78.63 24676.63 27784.64 12686.73 27469.47 10385.01 28284.61 34569.54 28566.51 42386.59 29950.16 34891.75 27076.26 17884.24 24792.69 163
alignmvs85.48 7385.32 7985.96 7889.51 13669.47 10389.74 9292.47 8276.17 10687.73 5391.46 14770.32 8293.78 16181.51 10488.95 15094.63 47
DP-MVS76.78 29074.57 31083.42 19693.29 5269.46 10588.55 15083.70 35863.98 38070.20 36788.89 23054.01 29794.80 11346.66 44881.88 28686.01 399
sasdasda85.91 6385.87 6786.04 7589.84 12669.44 10690.45 7693.00 5276.70 8588.01 4691.23 15273.28 4193.91 15481.50 10588.80 15394.77 29
canonicalmvs85.91 6385.87 6786.04 7589.84 12669.44 10690.45 7693.00 5276.70 8588.01 4691.23 15273.28 4193.91 15481.50 10588.80 15394.77 29
test_fmvsmconf0.1_n85.61 7185.65 7185.50 8982.99 37369.39 10889.65 9590.29 18573.31 19287.77 5094.15 5571.72 6393.23 19990.31 990.67 12093.89 92
test_fmvsmvis_n_192084.02 10183.87 10384.49 13584.12 33769.37 10988.15 16987.96 27670.01 27283.95 10993.23 8768.80 11491.51 28688.61 3289.96 13292.57 166
nrg03083.88 10683.53 11584.96 11086.77 27369.28 11090.46 7592.67 7374.79 14982.95 13291.33 15172.70 5193.09 21280.79 11579.28 32092.50 171
test_fmvsmconf0.01_n84.73 9084.52 9285.34 9480.25 41669.03 11189.47 10289.65 20673.24 19686.98 6394.27 4766.62 14093.23 19990.26 1089.95 13393.78 101
DeepPCF-MVS80.84 188.10 1688.56 1786.73 6092.24 7869.03 11189.57 9993.39 3577.53 5489.79 2594.12 5678.98 1396.58 4085.66 5895.72 2794.58 50
XVG-OURS80.41 19979.23 21083.97 17985.64 29969.02 11383.03 34190.39 17771.09 23877.63 23691.49 14654.62 29191.35 29375.71 18683.47 26491.54 209
PCF-MVS73.52 780.38 20178.84 21985.01 10887.71 22668.99 11483.65 32091.46 14763.00 39077.77 23490.28 18666.10 15095.09 9961.40 34488.22 16990.94 231
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
QAPM80.88 17979.50 20285.03 10688.01 20868.97 11591.59 5192.00 11566.63 33975.15 30392.16 11757.70 25995.45 7663.52 30888.76 15590.66 242
AdaColmapbinary80.58 19779.42 20384.06 16893.09 6368.91 11689.36 11088.97 24469.27 29175.70 28189.69 20257.20 26795.77 6563.06 31788.41 16387.50 357
fmvsm_l_conf0.5_n84.47 9184.54 9084.27 15285.42 30668.81 11788.49 15287.26 29968.08 31988.03 4593.49 7872.04 5991.77 26988.90 2989.14 14992.24 185
原ACMM184.35 14393.01 6668.79 11892.44 8363.96 38181.09 16691.57 14266.06 15295.45 7667.19 28294.82 4988.81 319
XVG-OURS-SEG-HR80.81 18279.76 19383.96 18085.60 30168.78 11983.54 32690.50 17470.66 25476.71 25891.66 13560.69 23191.26 29676.94 16881.58 28891.83 198
LPG-MVS_test82.08 15081.27 15684.50 13389.23 15468.76 12090.22 8191.94 11975.37 12776.64 26091.51 14454.29 29294.91 10378.44 14883.78 25289.83 284
LGP-MVS_train84.50 13389.23 15468.76 12091.94 11975.37 12776.64 26091.51 14454.29 29294.91 10378.44 14883.78 25289.83 284
Effi-MVS+-dtu80.03 21178.57 22384.42 13885.13 31668.74 12288.77 13688.10 27074.99 14074.97 30983.49 37757.27 26593.36 19273.53 20980.88 29691.18 220
Vis-MVSNetpermissive83.46 12382.80 13085.43 9190.25 11368.74 12290.30 8090.13 19076.33 10280.87 17292.89 9661.00 22794.20 13872.45 22990.97 11393.35 125
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HQP_MVS83.64 11683.14 12185.14 10090.08 11768.71 12491.25 6092.44 8379.12 2878.92 20491.00 16560.42 23895.38 8378.71 14686.32 20691.33 216
plane_prior68.71 12490.38 7877.62 4886.16 211
plane_prior689.84 12668.70 12660.42 238
ACMP74.13 681.51 16980.57 17084.36 14289.42 14168.69 12789.97 8591.50 14674.46 15775.04 30790.41 18153.82 29894.54 12377.56 16082.91 27289.86 283
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ETV-MVS84.90 8984.67 8985.59 8789.39 14468.66 12888.74 14092.64 7879.97 1684.10 10585.71 31969.32 10095.38 8380.82 11391.37 10692.72 160
plane_prior368.60 12978.44 3678.92 204
CHOSEN 1792x268877.63 27675.69 28883.44 19589.98 12368.58 13078.70 40787.50 28956.38 45175.80 28086.84 28758.67 25191.40 29261.58 34385.75 22390.34 256
fmvsm_l_conf0.5_n_386.02 5786.32 5385.14 10087.20 25568.54 13189.57 9990.44 17675.31 12987.49 5594.39 4272.86 4892.72 22989.04 2790.56 12194.16 75
plane_prior790.08 11768.51 132
GDP-MVS83.52 12182.64 13386.16 7088.14 19968.45 13389.13 12192.69 7172.82 20683.71 11391.86 12755.69 27995.35 8780.03 12389.74 13794.69 36
fmvsm_l_conf0.5_n_a84.13 9884.16 9584.06 16885.38 30768.40 13488.34 16086.85 31167.48 32687.48 5693.40 8370.89 7591.61 27488.38 3789.22 14692.16 192
ACMM73.20 880.78 18979.84 19183.58 19189.31 14968.37 13589.99 8491.60 14070.28 26677.25 24389.66 20453.37 30393.53 17874.24 20482.85 27388.85 317
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs474.03 33371.91 34480.39 29381.96 39268.32 13681.45 36282.14 38759.32 42769.87 37685.13 33752.40 31088.13 37260.21 35474.74 38684.73 423
NP-MVS89.62 13168.32 13690.24 188
SSM_040481.91 15480.84 16585.13 10389.24 15368.26 13887.84 18289.25 22871.06 24080.62 17690.39 18359.57 24394.65 12172.45 22987.19 19192.47 174
test22291.50 8768.26 13884.16 31083.20 37054.63 45879.74 18991.63 13858.97 24891.42 10486.77 384
Elysia81.53 16580.16 18185.62 8585.51 30368.25 14088.84 13392.19 10771.31 23180.50 17989.83 19646.89 37994.82 11076.85 16989.57 13993.80 99
StellarMVS81.53 16580.16 18185.62 8585.51 30368.25 14088.84 13392.19 10771.31 23180.50 17989.83 19646.89 37994.82 11076.85 16989.57 13993.80 99
CDS-MVSNet79.07 23577.70 24983.17 20887.60 23368.23 14284.40 30486.20 32567.49 32576.36 26886.54 30361.54 21390.79 32061.86 33987.33 18890.49 250
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ81.69 16081.02 16183.70 18789.51 13668.21 14384.28 30690.09 19170.79 24781.26 16585.62 32463.15 18394.29 13175.62 18888.87 15288.59 328
fmvsm_s_conf0.5_n_a83.63 11783.41 11784.28 15086.14 28968.12 14489.43 10482.87 37770.27 26787.27 6093.80 7369.09 10791.58 27688.21 3883.65 25993.14 140
UGNet80.83 18179.59 20084.54 12888.04 20568.09 14589.42 10688.16 26876.95 7576.22 27189.46 21349.30 36293.94 14968.48 27090.31 12491.60 206
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
fmvsm_s_conf0.1_n_a83.32 12982.99 12684.28 15083.79 34568.07 14689.34 11182.85 37869.80 27887.36 5994.06 5968.34 12191.56 27987.95 4283.46 26593.21 132
UA-Net85.08 8584.96 8585.45 9092.07 8068.07 14689.78 9190.86 16482.48 284.60 9393.20 8869.35 9995.22 8971.39 23790.88 11793.07 144
xiu_mvs_v2_base81.69 16081.05 16083.60 18989.15 15768.03 14884.46 29890.02 19270.67 25181.30 16486.53 30463.17 18294.19 14075.60 18988.54 15988.57 329
LuminaMVS80.68 19079.62 19983.83 18385.07 31868.01 14986.99 21288.83 24870.36 26281.38 16087.99 25950.11 34992.51 23979.02 14086.89 19890.97 229
mamba_040879.37 22877.52 25484.93 11388.81 16967.96 15065.03 48388.66 26070.96 24479.48 19489.80 19858.69 24994.65 12170.35 24885.93 21892.18 188
SSM_0407277.67 27577.52 25478.12 35488.81 16967.96 15065.03 48388.66 26070.96 24479.48 19489.80 19858.69 24974.23 47770.35 24885.93 21892.18 188
SSM_040781.58 16480.48 17384.87 11788.81 16967.96 15087.37 19989.25 22871.06 24079.48 19490.39 18359.57 24394.48 12872.45 22985.93 21892.18 188
DELS-MVS85.41 7685.30 8085.77 8088.49 18467.93 15385.52 27193.44 3278.70 3483.63 11789.03 22374.57 2895.71 6780.26 12294.04 6693.66 105
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
fmvsm_s_conf0.5_n_987.39 3387.95 2385.70 8289.48 13967.88 15488.59 14789.05 23880.19 1290.70 2095.40 1774.56 2993.92 15391.54 292.07 9295.31 5
BP-MVS184.32 9283.71 10986.17 6987.84 21567.85 15589.38 10989.64 20777.73 4683.98 10892.12 12056.89 27095.43 7884.03 8091.75 9995.24 7
EI-MVSNet-Vis-set84.19 9783.81 10685.31 9588.18 19667.85 15587.66 18589.73 20480.05 1582.95 13289.59 20870.74 7894.82 11080.66 11884.72 23693.28 128
PLCcopyleft70.83 1178.05 26276.37 28383.08 21391.88 8467.80 15788.19 16689.46 21364.33 37469.87 37688.38 24553.66 29993.58 17058.86 36882.73 27587.86 346
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAMVS78.89 24177.51 25683.03 21687.80 21767.79 15884.72 28885.05 34167.63 32276.75 25787.70 26462.25 20090.82 31958.53 37287.13 19390.49 250
CLD-MVS82.31 14781.65 15384.29 14988.47 18567.73 15985.81 26192.35 8975.78 11478.33 21986.58 30164.01 17294.35 13076.05 18287.48 18690.79 235
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
viewdifsd2359ckpt0983.34 12782.55 13585.70 8287.64 23267.72 16088.43 15391.68 13571.91 22081.65 15790.68 17367.10 13594.75 11576.17 17987.70 18294.62 49
hse-mvs281.72 15880.94 16384.07 16588.72 17767.68 16185.87 25787.26 29976.02 10984.67 8888.22 25161.54 21393.48 18682.71 9673.44 39991.06 224
MVSMamba_PlusPlus85.99 5985.96 6486.05 7491.09 9367.64 16289.63 9792.65 7672.89 20584.64 9191.71 13371.85 6096.03 5684.77 6994.45 5994.49 58
BridgeMVS86.78 4286.99 4086.15 7191.24 9167.61 16390.51 7092.90 6277.26 6387.44 5791.63 13871.27 7196.06 5585.62 6095.01 4094.78 28
AUN-MVS79.21 23177.60 25284.05 17188.71 17867.61 16385.84 25987.26 29969.08 29977.23 24588.14 25653.20 30593.47 18775.50 19173.45 39891.06 224
CS-MVS86.69 4486.95 4285.90 7990.76 10467.57 16592.83 2293.30 3879.67 1984.57 9492.27 10971.47 6795.02 10184.24 7793.46 7295.13 10
KinetiMVS83.31 13082.61 13485.39 9387.08 26467.56 16688.06 17191.65 13677.80 4582.21 14691.79 12857.27 26594.07 14477.77 15789.89 13594.56 54
EI-MVSNet-UG-set83.81 10783.38 11885.09 10587.87 21367.53 16787.44 19889.66 20579.74 1882.23 14589.41 21770.24 8494.74 11679.95 12483.92 25192.99 152
casdiffseed41469214783.62 11883.02 12485.40 9287.31 25267.50 16888.70 14291.72 13276.97 7482.77 13991.72 13266.85 13793.71 16873.06 21788.12 17194.98 13
Effi-MVS+83.62 11883.08 12285.24 9788.38 19067.45 16988.89 12989.15 23475.50 12282.27 14488.28 24869.61 9694.45 12977.81 15687.84 17893.84 95
EG-PatchMatch MVS74.04 33171.82 34580.71 28784.92 32067.42 17085.86 25888.08 27166.04 34564.22 44183.85 36535.10 45992.56 23557.44 38280.83 29782.16 451
OMC-MVS82.69 14181.97 15084.85 11888.75 17667.42 17087.98 17390.87 16374.92 14479.72 19091.65 13662.19 20293.96 14675.26 19486.42 20593.16 137
fmvsm_s_conf0.5_n_585.22 8185.55 7384.25 15586.26 28467.40 17289.18 11589.31 22372.50 20788.31 3893.86 7069.66 9591.96 26189.81 1391.05 11193.38 122
PatchMatch-RL72.38 36070.90 36176.80 37788.60 18167.38 17379.53 39376.17 44962.75 39669.36 38182.00 40345.51 39884.89 40953.62 40880.58 30178.12 467
LS3D76.95 28874.82 30783.37 19990.45 10867.36 17489.15 12086.94 30861.87 40869.52 37990.61 17751.71 32894.53 12446.38 45186.71 20188.21 339
fmvsm_s_conf0.5_n83.80 10883.71 10984.07 16586.69 27667.31 17589.46 10383.07 37271.09 23886.96 6493.70 7569.02 11291.47 28988.79 3084.62 23893.44 121
fmvsm_s_conf0.5_n_1086.38 5286.76 4685.24 9787.33 24967.30 17689.50 10190.98 15876.25 10590.56 2294.75 2968.38 11994.24 13790.80 792.32 8994.19 74
fmvsm_s_conf0.1_n83.56 12083.38 11884.10 15984.86 32167.28 17789.40 10883.01 37370.67 25187.08 6193.96 6768.38 11991.45 29088.56 3484.50 23993.56 116
PS-MVSNAJss82.07 15181.31 15584.34 14486.51 28167.27 17889.27 11291.51 14371.75 22179.37 19790.22 19063.15 18394.27 13377.69 15982.36 28091.49 212
114514_t80.68 19079.51 20184.20 15694.09 4267.27 17889.64 9691.11 15658.75 43574.08 32290.72 17158.10 25595.04 10069.70 25789.42 14390.30 259
mvsmamba80.60 19479.38 20484.27 15289.74 13067.24 18087.47 19086.95 30770.02 27175.38 29188.93 22851.24 33592.56 23575.47 19289.22 14693.00 151
casdiffmvs_mvgpermissive85.99 5986.09 6285.70 8287.65 23167.22 18188.69 14393.04 4779.64 2185.33 7792.54 10573.30 4094.50 12683.49 8391.14 11095.37 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
SPE-MVS-test86.29 5486.48 5185.71 8191.02 9667.21 18292.36 3493.78 2378.97 3383.51 12291.20 15670.65 8095.15 9281.96 10294.89 4594.77 29
anonymousdsp78.60 24777.15 26282.98 22080.51 41467.08 18387.24 20589.53 21165.66 35175.16 30287.19 28152.52 30792.25 25177.17 16579.34 31989.61 291
MVS78.19 25876.99 26681.78 25785.66 29866.99 18484.66 29090.47 17555.08 45772.02 35185.27 33263.83 17494.11 14366.10 29089.80 13684.24 427
HQP5-MVS66.98 185
HQP-MVS82.61 14382.02 14884.37 14189.33 14666.98 18589.17 11692.19 10776.41 9577.23 24590.23 18960.17 24195.11 9577.47 16185.99 21691.03 226
Fast-Effi-MVS+-dtu78.02 26376.49 27882.62 23983.16 36566.96 18786.94 21587.45 29172.45 20871.49 35784.17 36154.79 28891.58 27667.61 27680.31 30589.30 300
F-COLMAP76.38 30274.33 31682.50 24289.28 15166.95 18888.41 15589.03 23964.05 37866.83 41588.61 23846.78 38192.89 22157.48 38178.55 32487.67 349
viewdifsd2359ckpt1382.91 13882.29 14184.77 12286.96 26766.90 18987.47 19091.62 13872.19 21381.68 15690.71 17266.92 13693.28 19475.90 18487.15 19294.12 78
HyFIR lowres test77.53 27775.40 29683.94 18189.59 13266.62 19080.36 38288.64 26356.29 45276.45 26585.17 33657.64 26093.28 19461.34 34683.10 27191.91 197
ACMH67.68 1675.89 30873.93 32081.77 25888.71 17866.61 19188.62 14689.01 24169.81 27766.78 41686.70 29541.95 42491.51 28655.64 39778.14 33387.17 371
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax79.29 22977.96 23683.27 20284.68 32666.57 19289.25 11390.16 18969.20 29675.46 28789.49 21045.75 39693.13 21076.84 17180.80 29890.11 267
VDD-MVS83.01 13782.36 13984.96 11091.02 9666.40 19388.91 12888.11 26977.57 5084.39 9793.29 8652.19 31393.91 15477.05 16788.70 15794.57 52
mvs_tets79.13 23377.77 24683.22 20684.70 32566.37 19489.17 11690.19 18869.38 28875.40 29089.46 21344.17 40893.15 20876.78 17580.70 30090.14 264
PAPM_NR83.02 13682.41 13784.82 11992.47 7766.37 19487.93 17791.80 12773.82 17577.32 24290.66 17467.90 12694.90 10570.37 24789.48 14293.19 135
EC-MVSNet86.01 5886.38 5284.91 11589.31 14966.27 19692.32 3593.63 2679.37 2384.17 10491.88 12569.04 11195.43 7883.93 8193.77 6893.01 150
pmmvs-eth3d70.50 38267.83 39678.52 34777.37 45166.18 19781.82 35381.51 39458.90 43263.90 44580.42 41642.69 41786.28 39258.56 37165.30 44983.11 440
fmvsm_s_conf0.5_n_1186.06 5686.75 4784.00 17687.78 22066.09 19889.96 8690.80 16677.37 5886.72 6694.20 5272.51 5292.78 22889.08 2292.33 8793.13 141
IB-MVS68.01 1575.85 30973.36 32983.31 20084.76 32466.03 19983.38 32985.06 34070.21 26969.40 38081.05 40845.76 39594.66 12065.10 29975.49 37089.25 301
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
MS-PatchMatch73.83 33472.67 33677.30 37283.87 34466.02 20081.82 35384.66 34461.37 41268.61 38982.82 39047.29 37488.21 37059.27 36284.32 24677.68 468
fmvsm_l_conf0.5_n_985.84 6686.63 4983.46 19487.12 26366.01 20188.56 14989.43 21475.59 12089.32 2894.32 4472.89 4791.21 30190.11 1192.33 8793.16 137
FE-MVS77.78 26975.68 28984.08 16488.09 20366.00 20283.13 33587.79 28268.42 31678.01 22785.23 33445.50 39995.12 9359.11 36585.83 22291.11 222
test_040272.79 35870.44 36979.84 31288.13 20065.99 20385.93 25584.29 35065.57 35267.40 40985.49 32746.92 37892.61 23135.88 47874.38 38980.94 458
BH-RMVSNet79.61 21678.44 22683.14 20989.38 14565.93 20484.95 28487.15 30273.56 18378.19 22289.79 20056.67 27293.36 19259.53 36086.74 20090.13 265
BH-untuned79.47 22178.60 22282.05 25289.19 15665.91 20586.07 25288.52 26572.18 21475.42 28987.69 26561.15 22493.54 17760.38 35286.83 19986.70 386
cascas76.72 29174.64 30982.99 21885.78 29665.88 20682.33 34789.21 23160.85 41472.74 33981.02 40947.28 37593.75 16567.48 27885.02 23189.34 299
balanced_ft_v183.98 10483.64 11285.03 10689.76 12965.86 20788.31 16291.71 13374.41 15980.41 18290.82 17062.90 19094.90 10583.04 8991.37 10694.32 68
fmvsm_s_conf0.5_n_485.39 7785.75 7084.30 14886.70 27565.83 20888.77 13689.78 19975.46 12488.35 3793.73 7469.19 10693.06 21491.30 388.44 16294.02 84
patch_mono-283.65 11584.54 9080.99 28090.06 12165.83 20884.21 30788.74 25771.60 22685.01 8092.44 10774.51 3083.50 42182.15 10192.15 9093.64 111
MSDG73.36 34470.99 35980.49 29284.51 33165.80 21080.71 37686.13 32765.70 35065.46 43183.74 36944.60 40390.91 31651.13 42276.89 34684.74 422
旧先验191.96 8165.79 21186.37 32293.08 9369.31 10192.74 8088.74 324
casdiffmvspermissive85.11 8385.14 8385.01 10887.20 25565.77 21287.75 18392.83 6677.84 4484.36 10092.38 10872.15 5793.93 15281.27 10990.48 12295.33 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
hybridcas85.11 8385.18 8284.90 11687.47 24365.68 21388.53 15192.38 8777.91 4284.27 10192.48 10672.19 5693.88 15880.37 11990.97 11395.15 8
COLMAP_ROBcopyleft66.92 1773.01 35270.41 37080.81 28587.13 25865.63 21488.30 16384.19 35362.96 39163.80 44687.69 26538.04 44892.56 23546.66 44874.91 38484.24 427
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_886.56 4787.17 3884.73 12487.76 22365.62 21589.20 11492.21 10479.94 1789.74 2794.86 2668.63 11694.20 13890.83 591.39 10594.38 63
EIA-MVS83.31 13082.80 13084.82 11989.59 13265.59 21688.21 16592.68 7274.66 15378.96 20286.42 30669.06 10995.26 8875.54 19090.09 12993.62 112
v7n78.97 23877.58 25383.14 20983.45 35565.51 21788.32 16191.21 15173.69 17972.41 34586.32 30957.93 25693.81 16069.18 26275.65 36790.11 267
V4279.38 22778.24 23282.83 22681.10 40865.50 21885.55 26789.82 19871.57 22778.21 22186.12 31360.66 23393.18 20775.64 18775.46 37389.81 286
PVSNet_BlendedMVS80.60 19480.02 18582.36 24588.85 16565.40 21986.16 25092.00 11569.34 28978.11 22486.09 31466.02 15394.27 13371.52 23482.06 28387.39 359
PVSNet_Blended80.98 17780.34 17682.90 22388.85 16565.40 21984.43 30192.00 11567.62 32378.11 22485.05 34066.02 15394.27 13371.52 23489.50 14189.01 309
baseline84.93 8784.98 8484.80 12187.30 25365.39 22187.30 20392.88 6377.62 4884.04 10792.26 11071.81 6193.96 14681.31 10790.30 12595.03 12
test_djsdf80.30 20679.32 20783.27 20283.98 34165.37 22290.50 7290.38 17868.55 31276.19 27288.70 23456.44 27493.46 18878.98 14380.14 30890.97 229
E5new84.22 9384.12 9684.51 13187.60 23365.36 22387.45 19392.31 9176.51 9083.53 11892.26 11069.25 10493.50 18179.88 12688.26 16494.69 36
E6new84.22 9384.12 9684.52 12987.60 23365.36 22387.45 19392.30 9376.51 9083.53 11892.26 11069.26 10293.49 18379.88 12688.26 16494.69 36
E684.22 9384.12 9684.52 12987.60 23365.36 22387.45 19392.30 9376.51 9083.53 11892.26 11069.26 10293.49 18379.88 12688.26 16494.69 36
E584.22 9384.12 9684.51 13187.60 23365.36 22387.45 19392.31 9176.51 9083.53 11892.26 11069.25 10493.50 18179.88 12688.26 16494.69 36
ACMH+68.96 1476.01 30774.01 31882.03 25388.60 18165.31 22788.86 13087.55 28770.25 26867.75 40187.47 27341.27 42793.19 20658.37 37475.94 36487.60 351
fmvsm_s_conf0.5_n_386.36 5387.46 3283.09 21187.08 26465.21 22889.09 12390.21 18779.67 1989.98 2495.02 2473.17 4391.71 27391.30 391.60 10092.34 178
CR-MVSNet73.37 34271.27 35479.67 32281.32 40665.19 22975.92 43480.30 41359.92 42272.73 34081.19 40652.50 30886.69 38659.84 35677.71 33687.11 375
RPMNet73.51 33870.49 36882.58 24181.32 40665.19 22975.92 43492.27 9557.60 44472.73 34076.45 45152.30 31195.43 7848.14 44377.71 33687.11 375
fmvsm_s_conf0.5_n_783.34 12784.03 10181.28 27185.73 29765.13 23185.40 27289.90 19774.96 14382.13 14793.89 6966.65 13987.92 37486.56 5391.05 11190.80 234
fmvsm_s_conf0.5_n_685.55 7286.20 5683.60 18987.32 25165.13 23188.86 13091.63 13775.41 12588.23 4193.45 8268.56 11792.47 24089.52 1892.78 7993.20 134
BH-w/o78.21 25677.33 26080.84 28488.81 16965.13 23184.87 28587.85 28169.75 28174.52 31784.74 34661.34 21993.11 21158.24 37685.84 22184.27 426
thisisatest053079.40 22577.76 24784.31 14687.69 23065.10 23487.36 20084.26 35270.04 27077.42 23988.26 25049.94 35294.79 11470.20 25084.70 23793.03 148
FA-MVS(test-final)80.96 17879.91 18884.10 15988.30 19365.01 23584.55 29590.01 19373.25 19579.61 19187.57 26858.35 25494.72 11771.29 23886.25 20992.56 167
fmvsm_s_conf0.5_n_284.04 10084.11 10083.81 18586.17 28865.00 23686.96 21387.28 29474.35 16088.25 4094.23 5061.82 20892.60 23289.85 1288.09 17293.84 95
E484.10 9983.99 10284.45 13687.58 24164.99 23786.54 23392.25 9876.38 9983.37 12392.09 12169.88 9293.58 17079.78 13188.03 17594.77 29
E284.00 10283.87 10384.39 13987.70 22864.95 23886.40 24092.23 9975.85 11283.21 12591.78 12970.09 8793.55 17579.52 13588.05 17394.66 44
E384.00 10283.87 10384.39 13987.70 22864.95 23886.40 24092.23 9975.85 11283.21 12591.78 12970.09 8793.55 17579.52 13588.05 17394.66 44
v1079.74 21578.67 22082.97 22184.06 33964.95 23887.88 18090.62 17073.11 19975.11 30486.56 30261.46 21694.05 14573.68 20775.55 36989.90 281
fmvsm_s_conf0.1_n_283.80 10883.79 10783.83 18385.62 30064.94 24187.03 21086.62 31874.32 16187.97 4894.33 4360.67 23292.60 23289.72 1487.79 17993.96 86
SDMVSNet80.38 20180.18 18080.99 28089.03 16364.94 24180.45 38189.40 21575.19 13576.61 26289.98 19260.61 23587.69 37876.83 17283.55 26190.33 257
dcpmvs_285.63 7086.15 6084.06 16891.71 8564.94 24186.47 23591.87 12373.63 18086.60 6893.02 9476.57 1991.87 26783.36 8492.15 9095.35 3
viewcassd2359sk1183.89 10583.74 10884.34 14487.76 22364.91 24486.30 24492.22 10275.47 12383.04 13191.52 14370.15 8593.53 17879.26 13787.96 17694.57 52
E3new83.78 11083.60 11384.31 14687.76 22364.89 24586.24 24792.20 10575.15 13882.87 13491.23 15270.11 8693.52 18079.05 13887.79 17994.51 57
IterMVS-SCA-FT75.43 31573.87 32280.11 30482.69 38064.85 24681.57 36083.47 36369.16 29770.49 36484.15 36251.95 32088.15 37169.23 26172.14 40987.34 364
MVSTER79.01 23677.88 24182.38 24483.07 36664.80 24784.08 31388.95 24569.01 30378.69 20787.17 28254.70 28992.43 24274.69 19780.57 30289.89 282
Anonymous2024052980.19 20978.89 21884.10 15990.60 10564.75 24888.95 12790.90 16165.97 34880.59 17791.17 15849.97 35193.73 16769.16 26382.70 27793.81 97
XVG-ACMP-BASELINE76.11 30574.27 31781.62 26083.20 36264.67 24983.60 32389.75 20369.75 28171.85 35287.09 28432.78 46392.11 25569.99 25480.43 30488.09 341
viewmacassd2359aftdt83.76 11183.66 11184.07 16586.59 27964.56 25086.88 21891.82 12675.72 11583.34 12492.15 11968.24 12392.88 22279.05 13889.15 14894.77 29
viewmanbaseed2359cas83.66 11483.55 11484.00 17686.81 27164.53 25186.65 22891.75 13174.89 14583.15 13091.68 13468.74 11592.83 22679.02 14089.24 14594.63 47
v119279.59 21878.43 22783.07 21483.55 35364.52 25286.93 21690.58 17170.83 24677.78 23385.90 31559.15 24793.94 14973.96 20677.19 34390.76 237
Fast-Effi-MVS+80.81 18279.92 18783.47 19388.85 16564.51 25385.53 26989.39 21670.79 24778.49 21485.06 33967.54 12993.58 17067.03 28586.58 20292.32 180
v114480.03 21179.03 21483.01 21783.78 34664.51 25387.11 20890.57 17371.96 21978.08 22686.20 31161.41 21793.94 14974.93 19677.23 34190.60 245
v879.97 21379.02 21582.80 22984.09 33864.50 25587.96 17490.29 18574.13 16975.24 30086.81 28862.88 19193.89 15774.39 20275.40 37690.00 275
EPP-MVSNet83.40 12583.02 12484.57 12790.13 11564.47 25692.32 3590.73 16874.45 15879.35 19891.10 15969.05 11095.12 9372.78 22087.22 19094.13 77
GeoE81.71 15981.01 16283.80 18689.51 13664.45 25788.97 12688.73 25871.27 23478.63 21089.76 20166.32 14693.20 20469.89 25586.02 21593.74 102
UniMVSNet (Re)81.60 16381.11 15983.09 21188.38 19064.41 25887.60 18693.02 5178.42 3778.56 21288.16 25269.78 9393.26 19769.58 25976.49 35391.60 206
LTVRE_ROB69.57 1376.25 30374.54 31281.41 26688.60 18164.38 25979.24 39789.12 23770.76 24969.79 37887.86 26149.09 36593.20 20456.21 39680.16 30686.65 388
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
Anonymous2023121178.97 23877.69 25082.81 22890.54 10764.29 26090.11 8391.51 14365.01 36576.16 27688.13 25750.56 34393.03 21869.68 25877.56 34091.11 222
testdata79.97 30890.90 9964.21 26184.71 34359.27 42885.40 7692.91 9562.02 20589.08 35568.95 26591.37 10686.63 389
v2v48280.23 20779.29 20883.05 21583.62 35164.14 26287.04 20989.97 19473.61 18178.18 22387.22 27961.10 22593.82 15976.11 18076.78 35091.18 220
VDDNet81.52 16780.67 16784.05 17190.44 10964.13 26389.73 9385.91 32971.11 23783.18 12893.48 7950.54 34493.49 18373.40 21288.25 16894.54 56
PAPR81.66 16280.89 16483.99 17890.27 11264.00 26486.76 22591.77 13068.84 30877.13 25289.50 20967.63 12894.88 10867.55 27788.52 16093.09 143
AstraMVS80.81 18280.14 18382.80 22986.05 29263.96 26586.46 23685.90 33073.71 17880.85 17390.56 17854.06 29691.57 27879.72 13283.97 25092.86 157
v14419279.47 22178.37 22882.78 23383.35 35663.96 26586.96 21390.36 18169.99 27377.50 23785.67 32260.66 23393.77 16374.27 20376.58 35190.62 243
v192192079.22 23078.03 23582.80 22983.30 35863.94 26786.80 22190.33 18269.91 27677.48 23885.53 32658.44 25393.75 16573.60 20876.85 34890.71 241
guyue81.13 17480.64 16982.60 24086.52 28063.92 26886.69 22787.73 28473.97 17080.83 17489.69 20256.70 27191.33 29578.26 15585.40 22992.54 168
tttt051779.40 22577.91 23883.90 18288.10 20263.84 26988.37 15984.05 35471.45 22976.78 25689.12 22049.93 35494.89 10770.18 25183.18 27092.96 153
diffmvs_AUTHOR82.38 14682.27 14282.73 23783.26 35963.80 27083.89 31489.76 20173.35 19182.37 14290.84 16866.25 14790.79 32082.77 9387.93 17793.59 114
thisisatest051577.33 28175.38 29783.18 20785.27 31163.80 27082.11 35183.27 36665.06 36375.91 27783.84 36649.54 35794.27 13367.24 28186.19 21091.48 213
diffmvspermissive82.10 14981.88 15182.76 23583.00 36963.78 27283.68 31989.76 20172.94 20382.02 14989.85 19565.96 15590.79 32082.38 10087.30 18993.71 103
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_yl81.17 17280.47 17483.24 20489.13 15863.62 27386.21 24889.95 19572.43 21181.78 15489.61 20657.50 26293.58 17070.75 24286.90 19692.52 169
DCV-MVSNet81.17 17280.47 17483.24 20489.13 15863.62 27386.21 24889.95 19572.43 21181.78 15489.61 20657.50 26293.58 17070.75 24286.90 19692.52 169
AllTest70.96 37468.09 38979.58 32485.15 31463.62 27384.58 29479.83 41862.31 40260.32 45986.73 28932.02 46488.96 35950.28 42771.57 41386.15 395
TestCases79.58 32485.15 31463.62 27379.83 41862.31 40260.32 45986.73 28932.02 46488.96 35950.28 42771.57 41386.15 395
icg_test_0407_278.92 24078.93 21778.90 33787.13 25863.59 27776.58 43089.33 21870.51 25777.82 23089.03 22361.84 20681.38 43672.56 22585.56 22591.74 201
IMVS_040780.61 19279.90 18982.75 23687.13 25863.59 27785.33 27389.33 21870.51 25777.82 23089.03 22361.84 20692.91 22072.56 22585.56 22591.74 201
IMVS_040477.16 28476.42 28179.37 32887.13 25863.59 27777.12 42789.33 21870.51 25766.22 42689.03 22350.36 34682.78 42672.56 22585.56 22591.74 201
IMVS_040380.80 18580.12 18482.87 22587.13 25863.59 27785.19 27489.33 21870.51 25778.49 21489.03 22363.26 17993.27 19672.56 22585.56 22591.74 201
v124078.99 23777.78 24582.64 23883.21 36163.54 28186.62 23090.30 18469.74 28377.33 24185.68 32157.04 26893.76 16473.13 21676.92 34590.62 243
CHOSEN 280x42066.51 41864.71 42071.90 42681.45 40163.52 28257.98 49068.95 47353.57 46062.59 45176.70 44946.22 38975.29 47355.25 39879.68 31176.88 470
IterMVS74.29 32672.94 33478.35 35081.53 40063.49 28381.58 35982.49 38168.06 32069.99 37383.69 37251.66 32985.54 40165.85 29371.64 41286.01 399
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet81.88 15581.54 15482.92 22288.46 18663.46 28487.13 20692.37 8880.19 1278.38 21789.14 21971.66 6693.05 21570.05 25276.46 35492.25 183
DU-MVS81.12 17580.52 17282.90 22387.80 21763.46 28487.02 21191.87 12379.01 3178.38 21789.07 22165.02 16293.05 21570.05 25276.46 35492.20 186
LFMVS81.82 15781.23 15783.57 19291.89 8363.43 28689.84 8781.85 39177.04 7383.21 12593.10 8952.26 31293.43 19071.98 23289.95 13393.85 93
NR-MVSNet80.23 20779.38 20482.78 23387.80 21763.34 28786.31 24391.09 15779.01 3172.17 34989.07 22167.20 13392.81 22766.08 29175.65 36792.20 186
IS-MVSNet83.15 13282.81 12984.18 15789.94 12463.30 28891.59 5188.46 26679.04 3079.49 19392.16 11765.10 16194.28 13267.71 27591.86 9894.95 14
TR-MVS77.44 27876.18 28481.20 27488.24 19463.24 28984.61 29386.40 32167.55 32477.81 23286.48 30554.10 29493.15 20857.75 38082.72 27687.20 369
MVS_Test83.15 13283.06 12383.41 19886.86 26863.21 29086.11 25192.00 11574.31 16282.87 13489.44 21670.03 8993.21 20177.39 16388.50 16193.81 97
IterMVS-LS80.06 21079.38 20482.11 25185.89 29363.20 29186.79 22289.34 21774.19 16675.45 28886.72 29166.62 14092.39 24472.58 22276.86 34790.75 238
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet80.52 19879.98 18682.12 24984.28 33363.19 29286.41 23788.95 24574.18 16778.69 20787.54 27166.62 14092.43 24272.57 22380.57 30290.74 239
CANet_DTU80.61 19279.87 19082.83 22685.60 30163.17 29387.36 20088.65 26276.37 10075.88 27888.44 24453.51 30193.07 21373.30 21389.74 13792.25 183
MGCFI-Net85.06 8685.51 7483.70 18789.42 14163.01 29489.43 10492.62 7976.43 9487.53 5491.34 15072.82 5093.42 19181.28 10888.74 15694.66 44
GBi-Net78.40 25177.40 25781.40 26787.60 23363.01 29488.39 15689.28 22471.63 22375.34 29387.28 27554.80 28591.11 30262.72 32279.57 31290.09 269
test178.40 25177.40 25781.40 26787.60 23363.01 29488.39 15689.28 22471.63 22375.34 29387.28 27554.80 28591.11 30262.72 32279.57 31290.09 269
FMVSNet177.44 27876.12 28581.40 26786.81 27163.01 29488.39 15689.28 22470.49 26174.39 31987.28 27549.06 36691.11 30260.91 34878.52 32590.09 269
hybrid81.05 17680.66 16882.22 24881.97 39162.99 29883.42 32788.68 25970.76 24980.56 17890.40 18264.49 16890.48 32779.57 13486.06 21393.19 135
TAPA-MVS73.13 979.15 23277.94 23782.79 23289.59 13262.99 29888.16 16891.51 14365.77 34977.14 25191.09 16060.91 22893.21 20150.26 42987.05 19492.17 191
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RRT-MVS82.60 14582.10 14584.10 15987.98 20962.94 30087.45 19391.27 14977.42 5779.85 18890.28 18656.62 27394.70 11979.87 13088.15 17094.67 41
FMVSNet278.20 25777.21 26181.20 27487.60 23362.89 30187.47 19089.02 24071.63 22375.29 29987.28 27554.80 28591.10 30562.38 33079.38 31889.61 291
VortexMVS78.57 24977.89 24080.59 28985.89 29362.76 30285.61 26289.62 20872.06 21774.99 30885.38 33055.94 27890.77 32374.99 19576.58 35188.23 337
viewdifsd2359ckpt0782.83 14082.78 13282.99 21886.51 28162.58 30385.09 28090.83 16575.22 13182.28 14391.63 13869.43 9892.03 25777.71 15886.32 20694.34 66
GA-MVS76.87 28975.17 30481.97 25582.75 37862.58 30381.44 36386.35 32372.16 21674.74 31282.89 38846.20 39092.02 25968.85 26781.09 29391.30 218
D2MVS74.82 32273.21 33079.64 32379.81 42362.56 30580.34 38387.35 29364.37 37368.86 38682.66 39246.37 38690.10 33467.91 27481.24 29186.25 392
viewmambaseed2359dif80.41 19979.84 19182.12 24982.95 37562.50 30683.39 32888.06 27367.11 32880.98 16890.31 18566.20 14991.01 31074.62 19884.90 23392.86 157
viewdifsd2359ckpt1180.37 20379.73 19482.30 24683.70 34962.39 30784.20 30886.67 31473.22 19780.90 17090.62 17563.00 18891.56 27976.81 17378.44 32792.95 154
viewmsd2359difaftdt80.37 20379.73 19482.30 24683.70 34962.39 30784.20 30886.67 31473.22 19780.90 17090.62 17563.00 18891.56 27976.81 17378.44 32792.95 154
FMVSNet377.88 26776.85 26980.97 28286.84 27062.36 30986.52 23488.77 25171.13 23675.34 29386.66 29754.07 29591.10 30562.72 32279.57 31289.45 295
TranMVSNet+NR-MVSNet80.84 18080.31 17782.42 24387.85 21462.33 31087.74 18491.33 14880.55 977.99 22889.86 19465.23 16092.62 23067.05 28475.24 38192.30 181
131476.53 29375.30 30280.21 30183.93 34262.32 31184.66 29088.81 24960.23 41970.16 37084.07 36355.30 28290.73 32467.37 27983.21 26987.59 353
MG-MVS83.41 12483.45 11683.28 20192.74 7262.28 31288.17 16789.50 21275.22 13181.49 15992.74 10466.75 13895.11 9572.85 21991.58 10292.45 175
SCA74.22 32872.33 34179.91 30984.05 34062.17 31379.96 39079.29 42566.30 34272.38 34680.13 42151.95 32088.60 36559.25 36377.67 33988.96 313
usedtu_blend_shiyan573.29 34670.96 36080.25 29977.80 44562.16 31484.44 30087.38 29264.41 37168.09 39676.28 45451.32 33191.23 29863.21 31565.76 44287.35 361
blend_shiyan472.29 36369.65 37580.21 30178.24 44162.16 31482.29 34887.27 29765.41 35668.43 39576.42 45339.91 43691.23 29863.21 31565.66 44787.22 368
PMMVS69.34 39668.67 38271.35 43275.67 45862.03 31675.17 44073.46 45950.00 47068.68 38779.05 43152.07 31878.13 44961.16 34782.77 27473.90 474
eth_miper_zixun_eth77.92 26676.69 27581.61 26283.00 36961.98 31783.15 33489.20 23269.52 28674.86 31184.35 35361.76 20992.56 23571.50 23672.89 40390.28 260
v14878.72 24477.80 24481.47 26482.73 37961.96 31886.30 24488.08 27173.26 19476.18 27385.47 32862.46 19692.36 24671.92 23373.82 39590.09 269
PAPM77.68 27476.40 28281.51 26387.29 25461.85 31983.78 31689.59 20964.74 36771.23 35988.70 23462.59 19393.66 16952.66 41387.03 19589.01 309
cl2278.07 26177.01 26481.23 27382.37 38861.83 32083.55 32487.98 27568.96 30675.06 30683.87 36461.40 21891.88 26673.53 20976.39 35689.98 278
baseline275.70 31073.83 32381.30 27083.26 35961.79 32182.57 34480.65 40466.81 33066.88 41483.42 37857.86 25892.19 25363.47 30979.57 31289.91 280
JIA-IIPM66.32 42062.82 43276.82 37677.09 45261.72 32265.34 48175.38 45058.04 44164.51 43962.32 48242.05 42386.51 38951.45 42069.22 42482.21 449
gbinet_0.2-2-1-0.0273.24 34870.86 36380.39 29378.03 44361.62 32383.10 33686.69 31365.98 34769.29 38376.15 45749.77 35591.51 28662.75 32166.00 44088.03 342
miper_ehance_all_eth78.59 24877.76 24781.08 27882.66 38161.56 32483.65 32089.15 23468.87 30775.55 28483.79 36866.49 14392.03 25773.25 21476.39 35689.64 290
c3_l78.75 24277.91 23881.26 27282.89 37661.56 32484.09 31289.13 23669.97 27475.56 28384.29 35466.36 14592.09 25673.47 21175.48 37190.12 266
blended_shiyan873.38 34071.17 35680.02 30678.36 43861.51 32682.43 34587.28 29465.40 35768.61 38977.53 44651.91 32391.00 31363.28 31365.76 44287.53 355
blended_shiyan673.38 34071.17 35680.01 30778.36 43861.48 32782.43 34587.27 29765.40 35768.56 39177.55 44551.94 32291.01 31063.27 31465.76 44287.55 354
miper_enhance_ethall77.87 26876.86 26880.92 28381.65 39661.38 32882.68 34288.98 24265.52 35375.47 28582.30 39765.76 15792.00 26072.95 21876.39 35689.39 297
0.4-1-1-0.170.93 37567.94 39379.91 30979.35 43161.27 32978.95 40482.19 38663.36 38567.50 40469.40 47639.83 43791.04 30962.44 32768.40 42987.40 358
mmtdpeth74.16 32973.01 33377.60 36883.72 34861.13 33085.10 27985.10 33972.06 21777.21 24980.33 41843.84 41085.75 39777.14 16652.61 47885.91 402
ppachtmachnet_test70.04 38867.34 40678.14 35379.80 42461.13 33079.19 39980.59 40559.16 42965.27 43379.29 43046.75 38287.29 38249.33 43466.72 43586.00 401
sc_t172.19 36569.51 37680.23 30084.81 32261.09 33284.68 28980.22 41560.70 41571.27 35883.58 37536.59 45489.24 35160.41 35163.31 45490.37 255
0.3-1-1-0.01570.03 38966.80 41179.72 31978.18 44261.07 33377.63 42282.32 38562.65 39865.50 43067.29 47737.62 45190.91 31661.99 33768.04 43187.19 370
TDRefinement67.49 40964.34 42176.92 37573.47 47161.07 33384.86 28682.98 37559.77 42358.30 46685.13 33726.06 47587.89 37547.92 44560.59 46481.81 454
wanda-best-256-51272.94 35470.66 36479.79 31477.80 44561.03 33581.31 36587.15 30265.18 36068.09 39676.28 45451.32 33190.97 31463.06 31765.76 44287.35 361
FE-blended-shiyan772.94 35470.66 36479.79 31477.80 44561.03 33581.31 36587.15 30265.18 36068.09 39676.28 45451.32 33190.97 31463.06 31765.76 44287.35 361
VNet82.21 14882.41 13781.62 26090.82 10160.93 33784.47 29689.78 19976.36 10184.07 10691.88 12564.71 16590.26 33170.68 24488.89 15193.66 105
ab-mvs79.51 21978.97 21681.14 27688.46 18660.91 33883.84 31589.24 23070.36 26279.03 20188.87 23163.23 18190.21 33365.12 29882.57 27892.28 182
PatchmatchNetpermissive73.12 35071.33 35278.49 34883.18 36360.85 33979.63 39278.57 43064.13 37571.73 35379.81 42651.20 33685.97 39657.40 38376.36 36188.66 325
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet80.60 19480.55 17180.76 28688.07 20460.80 34086.86 21991.58 14175.67 11980.24 18489.45 21563.34 17690.25 33270.51 24679.22 32191.23 219
usedtu_dtu_shiyan176.43 29875.32 30079.76 31683.00 36960.72 34181.74 35588.76 25568.99 30472.98 33684.19 35956.41 27590.27 32962.39 32879.40 31688.31 334
FE-MVSNET376.43 29875.32 30079.76 31683.00 36960.72 34181.74 35588.76 25568.99 30472.98 33684.19 35956.41 27590.27 32962.39 32879.40 31688.31 334
EGC-MVSNET52.07 45147.05 45567.14 45383.51 35460.71 34380.50 38067.75 4750.07 5340.43 53575.85 46124.26 48081.54 43428.82 48562.25 45859.16 486
Anonymous20240521178.25 25477.01 26481.99 25491.03 9560.67 34484.77 28783.90 35670.65 25580.00 18791.20 15641.08 42991.43 29165.21 29785.26 23093.85 93
0.4-1-1-0.270.01 39066.86 41079.44 32777.61 44860.64 34576.77 42982.34 38462.40 40165.91 42866.65 47840.05 43490.83 31861.77 34168.24 43086.86 381
ITE_SJBPF78.22 35181.77 39560.57 34683.30 36569.25 29367.54 40387.20 28036.33 45687.28 38354.34 40474.62 38786.80 383
MDA-MVSNet-bldmvs66.68 41663.66 42675.75 38379.28 43260.56 34773.92 45078.35 43264.43 37050.13 48279.87 42544.02 40983.67 41746.10 45356.86 46883.03 442
cl____77.72 27176.76 27280.58 29082.49 38560.48 34883.09 33787.87 27969.22 29474.38 32085.22 33562.10 20391.53 28471.09 23975.41 37589.73 289
DIV-MVS_self_test77.72 27176.76 27280.58 29082.48 38660.48 34883.09 33787.86 28069.22 29474.38 32085.24 33362.10 20391.53 28471.09 23975.40 37689.74 288
1112_ss77.40 28076.43 28080.32 29789.11 16260.41 35083.65 32087.72 28562.13 40573.05 33586.72 29162.58 19489.97 33762.11 33680.80 29890.59 246
tt080578.73 24377.83 24281.43 26585.17 31260.30 35189.41 10790.90 16171.21 23577.17 25088.73 23346.38 38593.21 20172.57 22378.96 32290.79 235
UniMVSNet_ETH3D79.10 23478.24 23281.70 25986.85 26960.24 35287.28 20488.79 25074.25 16576.84 25390.53 18049.48 35891.56 27967.98 27382.15 28193.29 127
HY-MVS69.67 1277.95 26577.15 26280.36 29587.57 24260.21 35383.37 33087.78 28366.11 34375.37 29287.06 28663.27 17890.48 32761.38 34582.43 27990.40 254
sd_testset77.70 27377.40 25778.60 34289.03 16360.02 35479.00 40285.83 33175.19 13576.61 26289.98 19254.81 28485.46 40362.63 32683.55 26190.33 257
RPSCF73.23 34971.46 34978.54 34582.50 38459.85 35582.18 35082.84 37958.96 43171.15 36189.41 21745.48 40084.77 41058.82 36971.83 41191.02 228
test_cas_vis1_n_192073.76 33573.74 32473.81 41175.90 45559.77 35680.51 37982.40 38258.30 43781.62 15885.69 32044.35 40776.41 46176.29 17778.61 32385.23 413
dmvs_re71.14 37270.58 36672.80 42181.96 39259.68 35775.60 43879.34 42468.55 31269.27 38480.72 41449.42 35976.54 45852.56 41477.79 33582.19 450
miper_lstm_enhance74.11 33073.11 33277.13 37480.11 41859.62 35872.23 45486.92 31066.76 33270.40 36582.92 38756.93 26982.92 42569.06 26472.63 40488.87 316
OurMVSNet-221017-074.26 32772.42 34079.80 31383.76 34759.59 35985.92 25686.64 31666.39 34166.96 41387.58 26739.46 43891.60 27565.76 29469.27 42388.22 338
Patchmatch-RL test70.24 38567.78 39877.61 36677.43 45059.57 36071.16 45870.33 46662.94 39268.65 38872.77 46950.62 34285.49 40269.58 25966.58 43787.77 348
tt0320-xc70.11 38767.45 40478.07 35685.33 30959.51 36183.28 33178.96 42858.77 43367.10 41280.28 41936.73 45387.42 38156.83 39159.77 46687.29 366
OpenMVS_ROBcopyleft64.09 1970.56 38168.19 38677.65 36580.26 41559.41 36285.01 28282.96 37658.76 43465.43 43282.33 39637.63 45091.23 29845.34 45876.03 36382.32 448
tt032070.49 38368.03 39077.89 35884.78 32359.12 36383.55 32480.44 41058.13 43967.43 40880.41 41739.26 44087.54 38055.12 39963.18 45586.99 378
our_test_369.14 39767.00 40875.57 38679.80 42458.80 36477.96 41877.81 43459.55 42562.90 45078.25 44047.43 37383.97 41551.71 41767.58 43483.93 432
ADS-MVSNet266.20 42363.33 42774.82 39879.92 42058.75 36567.55 47375.19 45153.37 46165.25 43475.86 45942.32 41980.53 44141.57 46868.91 42585.18 414
pm-mvs177.25 28376.68 27678.93 33684.22 33558.62 36686.41 23788.36 26771.37 23073.31 33188.01 25861.22 22389.15 35464.24 30673.01 40289.03 308
MonoMVSNet76.49 29775.80 28678.58 34381.55 39958.45 36786.36 24286.22 32474.87 14874.73 31383.73 37051.79 32788.73 36270.78 24172.15 40888.55 330
WR-MVS79.49 22079.22 21180.27 29888.79 17458.35 36885.06 28188.61 26478.56 3577.65 23588.34 24663.81 17590.66 32564.98 30077.22 34291.80 200
FIs82.07 15182.42 13681.04 27988.80 17358.34 36988.26 16493.49 3176.93 7678.47 21691.04 16269.92 9192.34 24869.87 25684.97 23292.44 176
CostFormer75.24 31973.90 32179.27 33082.65 38258.27 37080.80 37182.73 38061.57 40975.33 29783.13 38355.52 28091.07 30864.98 30078.34 33288.45 331
Test_1112_low_res76.40 30175.44 29479.27 33089.28 15158.09 37181.69 35887.07 30559.53 42672.48 34486.67 29661.30 22089.33 34860.81 35080.15 30790.41 253
tfpnnormal74.39 32573.16 33178.08 35586.10 29158.05 37284.65 29287.53 28870.32 26571.22 36085.63 32354.97 28389.86 33843.03 46375.02 38386.32 391
test-LLR72.94 35472.43 33974.48 40181.35 40458.04 37378.38 41177.46 43766.66 33469.95 37479.00 43348.06 37179.24 44466.13 28884.83 23486.15 395
test-mter71.41 37070.39 37174.48 40181.35 40458.04 37378.38 41177.46 43760.32 41869.95 37479.00 43336.08 45779.24 44466.13 28884.83 23486.15 395
mvs_anonymous79.42 22479.11 21380.34 29684.45 33257.97 37582.59 34387.62 28667.40 32776.17 27588.56 24168.47 11889.59 34470.65 24586.05 21493.47 120
tpm cat170.57 38068.31 38577.35 37182.41 38757.95 37678.08 41680.22 41552.04 46468.54 39277.66 44452.00 31987.84 37651.77 41672.07 41086.25 392
SixPastTwentyTwo73.37 34271.26 35579.70 32085.08 31757.89 37785.57 26383.56 36171.03 24265.66 42985.88 31642.10 42292.57 23459.11 36563.34 45388.65 326
thres20075.55 31274.47 31378.82 33887.78 22057.85 37883.07 33983.51 36272.44 21075.84 27984.42 34952.08 31791.75 27047.41 44683.64 26086.86 381
XXY-MVS75.41 31675.56 29274.96 39583.59 35257.82 37980.59 37883.87 35766.54 34074.93 31088.31 24763.24 18080.09 44262.16 33476.85 34886.97 379
reproduce_monomvs75.40 31774.38 31578.46 34983.92 34357.80 38083.78 31686.94 30873.47 18772.25 34884.47 34838.74 44389.27 35075.32 19370.53 41888.31 334
FE-MVSNET272.88 35771.28 35377.67 36378.30 44057.78 38184.43 30188.92 24769.56 28464.61 43881.67 40446.73 38388.54 36759.33 36167.99 43286.69 387
K. test v371.19 37168.51 38379.21 33283.04 36857.78 38184.35 30576.91 44472.90 20462.99 44982.86 38939.27 43991.09 30761.65 34252.66 47788.75 322
tfpn200view976.42 30075.37 29879.55 32689.13 15857.65 38385.17 27583.60 35973.41 18976.45 26586.39 30752.12 31491.95 26248.33 43983.75 25589.07 302
thres40076.50 29475.37 29879.86 31189.13 15857.65 38385.17 27583.60 35973.41 18976.45 26586.39 30752.12 31491.95 26248.33 43983.75 25590.00 275
CMPMVSbinary51.72 2170.19 38668.16 38776.28 37973.15 47457.55 38579.47 39483.92 35548.02 47356.48 47284.81 34443.13 41486.42 39162.67 32581.81 28784.89 420
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs674.69 32373.39 32778.61 34181.38 40357.48 38686.64 22987.95 27764.99 36670.18 36886.61 29850.43 34589.52 34562.12 33570.18 42088.83 318
test_vis1_n_192075.52 31375.78 28774.75 40079.84 42257.44 38783.26 33285.52 33462.83 39479.34 19986.17 31245.10 40179.71 44378.75 14581.21 29287.10 377
PVSNet_057.27 2061.67 43659.27 43968.85 44579.61 42757.44 38768.01 47173.44 46055.93 45458.54 46570.41 47444.58 40477.55 45347.01 44735.91 48971.55 477
thres600view776.50 29475.44 29479.68 32189.40 14357.16 38985.53 26983.23 36773.79 17676.26 27087.09 28451.89 32491.89 26548.05 44483.72 25890.00 275
lessismore_v078.97 33581.01 40957.15 39065.99 47961.16 45582.82 39039.12 44191.34 29459.67 35846.92 48488.43 332
TransMVSNet (Re)75.39 31874.56 31177.86 35985.50 30557.10 39186.78 22386.09 32872.17 21571.53 35687.34 27463.01 18789.31 34956.84 39061.83 45987.17 371
thres100view90076.50 29475.55 29379.33 32989.52 13556.99 39285.83 26083.23 36773.94 17276.32 26987.12 28351.89 32491.95 26248.33 43983.75 25589.07 302
TESTMET0.1,169.89 39269.00 38172.55 42379.27 43356.85 39378.38 41174.71 45657.64 44368.09 39677.19 44837.75 44976.70 45763.92 30784.09 24984.10 430
WTY-MVS75.65 31175.68 28975.57 38686.40 28356.82 39477.92 42082.40 38265.10 36276.18 27387.72 26363.13 18680.90 43960.31 35381.96 28489.00 311
MDA-MVSNet_test_wron65.03 42562.92 42971.37 43075.93 45456.73 39569.09 47074.73 45557.28 44754.03 47777.89 44145.88 39274.39 47649.89 43161.55 46082.99 443
pmmvs357.79 44054.26 44568.37 44864.02 49056.72 39675.12 44365.17 48140.20 48252.93 47869.86 47520.36 48675.48 47045.45 45755.25 47572.90 476
tpm273.26 34771.46 34978.63 34083.34 35756.71 39780.65 37780.40 41256.63 45073.55 32982.02 40251.80 32691.24 29756.35 39578.42 33087.95 343
TinyColmap67.30 41264.81 41974.76 39981.92 39456.68 39880.29 38481.49 39560.33 41756.27 47483.22 38024.77 47987.66 37945.52 45669.47 42279.95 463
YYNet165.03 42562.91 43071.38 42975.85 45756.60 39969.12 46974.66 45757.28 44754.12 47677.87 44245.85 39374.48 47549.95 43061.52 46183.05 441
PM-MVS66.41 41964.14 42273.20 41773.92 46656.45 40078.97 40364.96 48363.88 38264.72 43780.24 42019.84 48783.44 42266.24 28764.52 45179.71 464
PVSNet64.34 1872.08 36770.87 36275.69 38486.21 28656.44 40174.37 44880.73 40362.06 40670.17 36982.23 39942.86 41683.31 42354.77 40284.45 24387.32 365
pmmvs571.55 36970.20 37375.61 38577.83 44456.39 40281.74 35580.89 40057.76 44267.46 40684.49 34749.26 36385.32 40557.08 38675.29 37985.11 417
testing1175.14 32074.01 31878.53 34688.16 19756.38 40380.74 37580.42 41170.67 25172.69 34283.72 37143.61 41289.86 33862.29 33283.76 25489.36 298
WR-MVS_H78.51 25078.49 22478.56 34488.02 20656.38 40388.43 15392.67 7377.14 6873.89 32487.55 27066.25 14789.24 35158.92 36773.55 39790.06 273
MIMVSNet70.69 37969.30 37774.88 39784.52 33056.35 40575.87 43679.42 42264.59 36867.76 40082.41 39441.10 42881.54 43446.64 45081.34 28986.75 385
USDC70.33 38468.37 38476.21 38080.60 41256.23 40679.19 39986.49 31960.89 41361.29 45485.47 32831.78 46689.47 34753.37 41076.21 36282.94 444
Baseline_NR-MVSNet78.15 25978.33 23077.61 36685.79 29556.21 40786.78 22385.76 33273.60 18277.93 22987.57 26865.02 16288.99 35667.14 28375.33 37887.63 350
tpmvs71.09 37369.29 37876.49 37882.04 39056.04 40878.92 40581.37 39764.05 37867.18 41178.28 43949.74 35689.77 34049.67 43272.37 40583.67 434
FC-MVSNet-test81.52 16782.02 14880.03 30588.42 18955.97 40987.95 17593.42 3477.10 7177.38 24090.98 16769.96 9091.79 26868.46 27184.50 23992.33 179
testing9176.54 29275.66 29179.18 33388.43 18855.89 41081.08 36883.00 37473.76 17775.34 29384.29 35446.20 39090.07 33564.33 30484.50 23991.58 208
mvs5depth69.45 39567.45 40475.46 39073.93 46555.83 41179.19 39983.23 36766.89 32971.63 35583.32 37933.69 46285.09 40659.81 35755.34 47485.46 409
GG-mvs-BLEND75.38 39181.59 39855.80 41279.32 39669.63 46967.19 41073.67 46743.24 41388.90 36150.41 42484.50 23981.45 455
VPNet78.69 24578.66 22178.76 33988.31 19255.72 41384.45 29986.63 31776.79 8078.26 22090.55 17959.30 24689.70 34366.63 28677.05 34490.88 232
baseline176.98 28776.75 27477.66 36488.13 20055.66 41485.12 27881.89 38973.04 20176.79 25588.90 22962.43 19787.78 37763.30 31271.18 41589.55 293
test_vis1_rt60.28 43758.42 44065.84 45667.25 48555.60 41570.44 46360.94 48944.33 47859.00 46366.64 47924.91 47868.67 48762.80 32069.48 42173.25 475
testing9976.09 30675.12 30579.00 33488.16 19755.50 41680.79 37281.40 39673.30 19375.17 30184.27 35744.48 40590.02 33664.28 30584.22 24891.48 213
testing22274.04 33172.66 33778.19 35287.89 21255.36 41781.06 36979.20 42671.30 23374.65 31583.57 37639.11 44288.67 36451.43 42185.75 22390.53 248
FMVSNet569.50 39467.96 39174.15 40682.97 37455.35 41880.01 38982.12 38862.56 39963.02 44781.53 40536.92 45281.92 43248.42 43874.06 39185.17 416
test_fmvs1_n70.86 37770.24 37272.73 42272.51 47855.28 41981.27 36779.71 42051.49 46878.73 20684.87 34227.54 47477.02 45576.06 18179.97 31085.88 403
test_vis1_n69.85 39369.21 37971.77 42772.66 47755.27 42081.48 36176.21 44852.03 46575.30 29883.20 38228.97 47176.22 46374.60 19978.41 33183.81 433
test_fmvs170.93 37570.52 36772.16 42573.71 46755.05 42180.82 37078.77 42951.21 46978.58 21184.41 35031.20 46876.94 45675.88 18580.12 30984.47 425
sss73.60 33773.64 32573.51 41382.80 37755.01 42276.12 43281.69 39262.47 40074.68 31485.85 31857.32 26478.11 45060.86 34980.93 29487.39 359
mvsany_test162.30 43461.26 43865.41 45769.52 48154.86 42366.86 47549.78 49746.65 47468.50 39383.21 38149.15 36466.28 48956.93 38960.77 46275.11 473
ECVR-MVScopyleft79.61 21679.26 20980.67 28890.08 11754.69 42487.89 17977.44 43974.88 14680.27 18392.79 10148.96 36892.45 24168.55 26992.50 8494.86 21
EPNet_dtu75.46 31474.86 30677.23 37382.57 38354.60 42586.89 21783.09 37171.64 22266.25 42585.86 31755.99 27788.04 37354.92 40186.55 20389.05 307
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVSNet78.22 25578.34 22977.84 36087.83 21654.54 42687.94 17691.17 15377.65 4773.48 33088.49 24262.24 20188.43 36862.19 33374.07 39090.55 247
gg-mvs-nofinetune69.95 39167.96 39175.94 38183.07 36654.51 42777.23 42670.29 46763.11 38870.32 36662.33 48143.62 41188.69 36353.88 40787.76 18184.62 424
PS-CasMVS78.01 26478.09 23477.77 36287.71 22654.39 42888.02 17291.22 15077.50 5573.26 33288.64 23760.73 22988.41 36961.88 33873.88 39490.53 248
Anonymous2024052168.80 40067.22 40773.55 41274.33 46354.11 42983.18 33385.61 33358.15 43861.68 45380.94 41130.71 46981.27 43757.00 38873.34 40185.28 412
Patchmtry70.74 37869.16 38075.49 38980.72 41054.07 43074.94 44580.30 41358.34 43670.01 37181.19 40652.50 30886.54 38853.37 41071.09 41685.87 404
PEN-MVS77.73 27077.69 25077.84 36087.07 26653.91 43187.91 17891.18 15277.56 5273.14 33488.82 23261.23 22289.17 35359.95 35572.37 40590.43 252
gm-plane-assit81.40 40253.83 43262.72 39780.94 41192.39 24463.40 311
CL-MVSNet_self_test72.37 36171.46 34975.09 39479.49 42953.53 43380.76 37485.01 34269.12 29870.51 36382.05 40157.92 25784.13 41452.27 41566.00 44087.60 351
MDTV_nov1_ep1369.97 37483.18 36353.48 43477.10 42880.18 41760.45 41669.33 38280.44 41548.89 36986.90 38551.60 41878.51 326
KD-MVS_2432*160066.22 42163.89 42473.21 41575.47 46153.42 43570.76 46184.35 34864.10 37666.52 42178.52 43734.55 46084.98 40750.40 42550.33 48181.23 456
miper_refine_blended66.22 42163.89 42473.21 41575.47 46153.42 43570.76 46184.35 34864.10 37666.52 42178.52 43734.55 46084.98 40750.40 42550.33 48181.23 456
test111179.43 22379.18 21280.15 30389.99 12253.31 43787.33 20277.05 44375.04 13980.23 18592.77 10348.97 36792.33 24968.87 26692.40 8694.81 26
LF4IMVS64.02 43062.19 43369.50 44170.90 47953.29 43876.13 43177.18 44252.65 46358.59 46480.98 41023.55 48276.52 45953.06 41266.66 43678.68 466
MVStest156.63 44252.76 44868.25 45061.67 49253.25 43971.67 45668.90 47438.59 48550.59 48183.05 38425.08 47770.66 48336.76 47738.56 48880.83 459
usedtu_dtu_shiyan264.75 42861.63 43674.10 40770.64 48053.18 44082.10 35281.27 39956.22 45356.39 47374.67 46427.94 47383.56 41942.71 46562.73 45685.57 407
DTE-MVSNet76.99 28676.80 27077.54 36986.24 28553.06 44187.52 18890.66 16977.08 7272.50 34388.67 23660.48 23789.52 34557.33 38470.74 41790.05 274
FE-MVSNET67.25 41365.33 41773.02 41975.86 45652.54 44280.26 38680.56 40663.80 38360.39 45779.70 42741.41 42684.66 41243.34 46262.62 45781.86 452
test250677.30 28276.49 27879.74 31890.08 11752.02 44387.86 18163.10 48674.88 14680.16 18692.79 10138.29 44792.35 24768.74 26892.50 8494.86 21
tpm72.37 36171.71 34674.35 40382.19 38952.00 44479.22 39877.29 44164.56 36972.95 33883.68 37351.35 33083.26 42458.33 37575.80 36587.81 347
test_fmvs268.35 40667.48 40370.98 43669.50 48251.95 44580.05 38876.38 44749.33 47174.65 31584.38 35123.30 48375.40 47274.51 20075.17 38285.60 406
ETVMVS72.25 36471.05 35875.84 38287.77 22251.91 44679.39 39574.98 45269.26 29273.71 32682.95 38640.82 43186.14 39346.17 45284.43 24489.47 294
WB-MVSnew71.96 36871.65 34772.89 42084.67 32951.88 44782.29 34877.57 43662.31 40273.67 32883.00 38553.49 30281.10 43845.75 45582.13 28285.70 405
MIMVSNet168.58 40266.78 41273.98 40980.07 41951.82 44880.77 37384.37 34764.40 37259.75 46282.16 40036.47 45583.63 41842.73 46470.33 41986.48 390
Vis-MVSNet (Re-imp)78.36 25378.45 22578.07 35688.64 18051.78 44986.70 22679.63 42174.14 16875.11 30490.83 16961.29 22189.75 34158.10 37791.60 10092.69 163
LCM-MVSNet-Re77.05 28576.94 26777.36 37087.20 25551.60 45080.06 38780.46 40975.20 13467.69 40286.72 29162.48 19588.98 35763.44 31089.25 14491.51 210
Gipumacopyleft45.18 45841.86 46155.16 47277.03 45351.52 45132.50 49980.52 40732.46 49227.12 49535.02 5029.52 49875.50 46922.31 49560.21 46538.45 499
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth67.33 41165.99 41571.37 43073.48 47051.47 45275.16 44185.19 33765.20 35960.78 45680.93 41342.35 41877.20 45457.12 38553.69 47685.44 410
UnsupCasMVSNet_bld63.70 43161.53 43770.21 43973.69 46851.39 45372.82 45281.89 38955.63 45557.81 46871.80 47138.67 44478.61 44749.26 43552.21 47980.63 460
UBG73.08 35172.27 34275.51 38888.02 20651.29 45478.35 41477.38 44065.52 35373.87 32582.36 39545.55 39786.48 39055.02 40084.39 24588.75 322
FPMVS53.68 44751.64 44959.81 46465.08 48851.03 45569.48 46669.58 47041.46 48140.67 48872.32 47016.46 49170.00 48624.24 49365.42 44858.40 488
WBMVS73.43 33972.81 33575.28 39287.91 21150.99 45678.59 41081.31 39865.51 35574.47 31884.83 34346.39 38486.68 38758.41 37377.86 33488.17 340
CVMVSNet72.99 35372.58 33874.25 40584.28 33350.85 45786.41 23783.45 36444.56 47773.23 33387.54 27149.38 36085.70 39865.90 29278.44 32786.19 394
Anonymous2023120668.60 40167.80 39771.02 43580.23 41750.75 45878.30 41580.47 40856.79 44966.11 42782.63 39346.35 38778.95 44643.62 46175.70 36683.36 437
ambc75.24 39373.16 47350.51 45963.05 48887.47 29064.28 44077.81 44317.80 48989.73 34257.88 37960.64 46385.49 408
APD_test153.31 44849.93 45363.42 46065.68 48750.13 46071.59 45766.90 47834.43 49040.58 48971.56 4728.65 50076.27 46234.64 48055.36 47363.86 484
tpmrst72.39 35972.13 34373.18 41880.54 41349.91 46179.91 39179.08 42763.11 38871.69 35479.95 42355.32 28182.77 42765.66 29573.89 39386.87 380
Patchmatch-test64.82 42763.24 42869.57 44079.42 43049.82 46263.49 48769.05 47251.98 46659.95 46180.13 42150.91 33870.98 48240.66 47073.57 39687.90 345
EPMVS69.02 39868.16 38771.59 42879.61 42749.80 46377.40 42466.93 47762.82 39570.01 37179.05 43145.79 39477.86 45256.58 39375.26 38087.13 374
SSC-MVS3.273.35 34573.39 32773.23 41485.30 31049.01 46474.58 44781.57 39375.21 13373.68 32785.58 32552.53 30682.05 43154.33 40577.69 33888.63 327
dp66.80 41565.43 41670.90 43779.74 42648.82 46575.12 44374.77 45459.61 42464.08 44377.23 44742.89 41580.72 44048.86 43766.58 43783.16 439
UWE-MVS72.13 36671.49 34874.03 40886.66 27747.70 46681.40 36476.89 44563.60 38475.59 28284.22 35839.94 43585.62 40048.98 43686.13 21288.77 321
test0.0.03 168.00 40867.69 39968.90 44477.55 44947.43 46775.70 43772.95 46366.66 33466.56 41982.29 39848.06 37175.87 46744.97 45974.51 38883.41 436
SD_040374.65 32474.77 30874.29 40486.20 28747.42 46883.71 31885.12 33869.30 29068.50 39387.95 26059.40 24586.05 39449.38 43383.35 26689.40 296
myMVS_eth3d2873.62 33673.53 32673.90 41088.20 19547.41 46978.06 41779.37 42374.29 16473.98 32384.29 35444.67 40283.54 42051.47 41987.39 18790.74 239
ADS-MVSNet64.36 42962.88 43168.78 44679.92 42047.17 47067.55 47371.18 46553.37 46165.25 43475.86 45942.32 41973.99 47841.57 46868.91 42585.18 414
EU-MVSNet68.53 40467.61 40171.31 43378.51 43747.01 47184.47 29684.27 35142.27 48066.44 42484.79 34540.44 43283.76 41658.76 37068.54 42883.17 438
test_fmvs363.36 43261.82 43467.98 45162.51 49146.96 47277.37 42574.03 45845.24 47667.50 40478.79 43612.16 49572.98 48172.77 22166.02 43983.99 431
ttmdpeth59.91 43857.10 44268.34 44967.13 48646.65 47374.64 44667.41 47648.30 47262.52 45285.04 34120.40 48575.93 46642.55 46645.90 48782.44 447
KD-MVS_self_test68.81 39967.59 40272.46 42474.29 46445.45 47477.93 41987.00 30663.12 38763.99 44478.99 43542.32 41984.77 41056.55 39464.09 45287.16 373
testf145.72 45541.96 45957.00 46656.90 49445.32 47566.14 47859.26 49126.19 49430.89 49360.96 4854.14 50370.64 48426.39 49146.73 48555.04 489
APD_test245.72 45541.96 45957.00 46656.90 49445.32 47566.14 47859.26 49126.19 49430.89 49360.96 4854.14 50370.64 48426.39 49146.73 48555.04 489
LCM-MVSNet54.25 44449.68 45467.97 45253.73 50045.28 47766.85 47680.78 40235.96 48939.45 49062.23 4838.70 49978.06 45148.24 44251.20 48080.57 461
test_vis3_rt49.26 45447.02 45656.00 46854.30 49745.27 47866.76 47748.08 49836.83 48744.38 48653.20 4947.17 50264.07 49156.77 39255.66 47158.65 487
testing3-275.12 32175.19 30374.91 39690.40 11045.09 47980.29 38478.42 43178.37 4076.54 26487.75 26244.36 40687.28 38357.04 38783.49 26392.37 177
test20.0367.45 41066.95 40968.94 44375.48 46044.84 48077.50 42377.67 43566.66 33463.01 44883.80 36747.02 37778.40 44842.53 46768.86 42783.58 435
mvsany_test353.99 44551.45 45061.61 46255.51 49644.74 48163.52 48645.41 50143.69 47958.11 46776.45 45117.99 48863.76 49254.77 40247.59 48376.34 471
PatchT68.46 40567.85 39470.29 43880.70 41143.93 48272.47 45374.88 45360.15 42070.55 36276.57 45049.94 35281.59 43350.58 42374.83 38585.34 411
MVS-HIRNet59.14 43957.67 44163.57 45981.65 39643.50 48371.73 45565.06 48239.59 48451.43 47957.73 48838.34 44682.58 42839.53 47173.95 39264.62 483
testing368.56 40367.67 40071.22 43487.33 24942.87 48483.06 34071.54 46470.36 26269.08 38584.38 35130.33 47085.69 39937.50 47675.45 37485.09 418
WAC-MVS42.58 48539.46 472
myMVS_eth3d67.02 41466.29 41469.21 44284.68 32642.58 48578.62 40873.08 46166.65 33766.74 41779.46 42831.53 46782.30 42939.43 47376.38 35982.75 445
PMVScopyleft37.38 2244.16 45940.28 46355.82 47040.82 50542.54 48765.12 48263.99 48534.43 49024.48 49757.12 4903.92 50576.17 46417.10 49955.52 47248.75 493
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_f52.09 45050.82 45155.90 46953.82 49942.31 48859.42 48958.31 49336.45 48856.12 47570.96 47312.18 49457.79 49553.51 40956.57 47067.60 480
testgi66.67 41766.53 41367.08 45475.62 45941.69 48975.93 43376.50 44666.11 34365.20 43686.59 29935.72 45874.71 47443.71 46073.38 40084.84 421
Syy-MVS68.05 40767.85 39468.67 44784.68 32640.97 49078.62 40873.08 46166.65 33766.74 41779.46 42852.11 31682.30 42932.89 48176.38 35982.75 445
ANet_high50.57 45346.10 45763.99 45848.67 50339.13 49170.99 46080.85 40161.39 41131.18 49257.70 48917.02 49073.65 48031.22 48415.89 50279.18 465
UWE-MVS-2865.32 42464.93 41866.49 45578.70 43538.55 49277.86 42164.39 48462.00 40764.13 44283.60 37441.44 42576.00 46531.39 48380.89 29584.92 419
MDTV_nov1_ep13_2view37.79 49375.16 44155.10 45666.53 42049.34 36153.98 40687.94 344
DSMNet-mixed57.77 44156.90 44360.38 46367.70 48435.61 49469.18 46753.97 49532.30 49357.49 46979.88 42440.39 43368.57 48838.78 47472.37 40576.97 469
MVEpermissive26.22 2330.37 46525.89 46943.81 47844.55 50435.46 49528.87 50239.07 50218.20 50118.58 50440.18 5002.68 50647.37 50017.07 50023.78 49748.60 494
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet50.91 45250.29 45252.78 47468.58 48334.94 49663.71 48556.63 49439.73 48344.95 48565.47 48021.93 48458.48 49434.98 47956.62 46964.92 482
wuyk23d16.82 47315.94 47619.46 49158.74 49331.45 49739.22 4953.74 5196.84 5056.04 5102.70 5341.27 50824.29 50910.54 50614.40 5042.63 517
E-PMN31.77 46230.64 46535.15 48352.87 50127.67 49857.09 49147.86 49924.64 49616.40 50633.05 50311.23 49654.90 49714.46 50218.15 50022.87 504
kuosan39.70 46140.40 46237.58 48164.52 48926.98 49965.62 48033.02 50446.12 47542.79 48748.99 49724.10 48146.56 50112.16 50526.30 49539.20 498
DeepMVS_CXcopyleft27.40 48840.17 50626.90 50024.59 50717.44 50223.95 49848.61 4989.77 49726.48 50718.06 49724.47 49628.83 503
dongtai45.42 45745.38 45845.55 47773.36 47226.85 50167.72 47234.19 50354.15 45949.65 48356.41 49225.43 47662.94 49319.45 49628.09 49446.86 496
EMVS30.81 46429.65 46634.27 48450.96 50225.95 50256.58 49246.80 50024.01 49715.53 50730.68 50512.47 49354.43 49812.81 50417.05 50122.43 505
dmvs_testset62.63 43364.11 42358.19 46578.55 43624.76 50375.28 43965.94 48067.91 32160.34 45876.01 45853.56 30073.94 47931.79 48267.65 43375.88 472
new-patchmatchnet61.73 43561.73 43561.70 46172.74 47624.50 50469.16 46878.03 43361.40 41056.72 47175.53 46238.42 44576.48 46045.95 45457.67 46784.13 429
RoMa-SfM28.67 46625.38 47038.54 47932.61 50922.48 50540.24 4947.23 51321.81 49826.66 49660.46 4870.96 50941.72 50226.47 49011.95 50551.40 492
WB-MVS54.94 44354.72 44455.60 47173.50 46920.90 50674.27 44961.19 48859.16 42950.61 48074.15 46547.19 37675.78 46817.31 49835.07 49070.12 478
SSC-MVS53.88 44653.59 44654.75 47372.87 47519.59 50773.84 45160.53 49057.58 44549.18 48473.45 46846.34 38875.47 47116.20 50132.28 49269.20 479
LoFTR27.52 46724.27 47137.29 48234.75 50819.27 50833.78 49821.60 50812.42 50321.61 50256.59 4910.91 51040.37 50313.94 50322.80 49852.22 491
DKM25.67 46823.01 47233.64 48532.08 51019.25 50937.50 4965.52 51418.67 49923.58 50055.44 4930.64 51334.02 50423.95 4949.73 50647.66 495
PDCNetPlus24.75 46922.46 47331.64 48635.53 50717.00 51032.00 5009.46 51018.43 50018.56 50551.31 4961.65 50733.00 50626.51 4898.70 50844.91 497
PMMVS240.82 46038.86 46446.69 47653.84 49816.45 51148.61 49349.92 49637.49 48631.67 49160.97 4848.14 50156.42 49628.42 48630.72 49367.19 481
MatchFormer22.13 47019.86 47528.93 48728.66 51115.74 51231.91 50117.10 5097.75 50418.87 50347.50 4990.62 51533.92 5057.49 50818.87 49937.14 500
tmp_tt18.61 47221.40 47410.23 4924.82 53610.11 51334.70 49730.74 5061.48 51223.91 49926.07 50628.42 47213.41 51127.12 48715.35 5037.17 512
ALIKED-MNN7.86 4777.83 4837.97 49419.40 5148.86 51414.48 5053.90 5161.59 5104.74 51616.49 5080.59 5167.65 5130.91 5188.34 5107.39 509
ALIKED-LG8.61 4768.70 4808.33 49320.63 5138.70 51515.50 5044.61 5152.19 5095.84 51118.70 5070.80 5118.06 5121.03 5178.97 5078.25 506
GLUNet-SfM12.90 47510.00 47821.62 49013.58 5168.30 51610.19 5089.30 5114.31 50712.18 50830.90 5040.50 51922.76 5104.89 5094.14 51933.79 502
ALIKED-NN7.51 4787.61 4847.21 49518.26 5158.10 51713.45 5073.88 5181.50 5114.87 51416.47 5090.64 5137.00 5140.88 5198.50 5096.52 514
N_pmnet52.79 44953.26 44751.40 47578.99 4347.68 51869.52 4653.89 51751.63 46757.01 47074.98 46340.83 43065.96 49037.78 47564.67 45080.56 462
ELoFTR14.23 47411.56 47722.24 48911.02 5176.56 51913.59 5067.57 5125.55 50611.96 50939.09 5010.21 52424.93 5089.43 5075.66 51335.22 501
test_method31.52 46329.28 46738.23 48027.03 5126.50 52020.94 50362.21 4874.05 50822.35 50152.50 49513.33 49247.58 49927.04 48834.04 49160.62 485
SIFT-NN2.77 4902.92 4932.34 5038.70 5223.08 5214.46 5161.01 5280.68 5201.46 5215.49 5180.16 5251.65 5220.26 5204.04 5202.27 518
SIFT-MNN2.63 4912.75 4942.25 5048.10 5232.84 5224.08 5171.02 5270.68 5201.28 5225.34 5210.15 5261.64 5230.26 5203.88 5222.27 518
SIFT-NN-NCMNet2.52 4922.64 4952.14 5057.53 5252.74 5234.00 5180.98 5290.65 5231.24 5245.08 5240.14 5271.60 5240.23 5233.94 5212.07 522
SIFT-NCM-Cal2.40 4932.52 4962.05 5067.74 5242.54 5243.75 5200.84 5300.65 5230.89 5294.78 5270.13 5301.60 5240.19 5313.71 5232.01 524
SIFT-ConvMatch2.25 4962.37 4991.90 5087.29 5262.37 5253.21 5240.75 5330.65 5231.03 5274.91 5250.12 5331.51 5280.22 5263.13 5271.81 525
SIFT-NN-CMatch2.31 4942.41 4972.00 5076.59 5292.34 5263.48 5210.83 5310.65 5231.28 5225.09 5220.14 5271.52 5260.23 5233.41 5252.14 520
SP-DiffGlue4.29 4844.46 4873.77 5003.68 5372.12 5275.97 5132.22 5211.10 5134.89 51313.93 5110.66 5121.95 5212.47 5105.24 5147.22 511
SIFT-NN-UMatch2.26 4952.39 4981.89 5096.21 5312.08 5283.76 5190.83 5310.66 5221.04 5265.09 5220.14 5271.52 5260.23 5233.51 5242.07 522
SP-SuperGlue4.24 4864.38 4893.81 49910.75 5192.00 5298.18 5102.09 5221.00 5152.41 5178.29 5140.56 5172.05 5201.27 5134.91 5167.39 509
SIFT-CM-Cal2.02 4992.13 5021.67 5126.79 5281.99 5302.79 5260.64 5360.63 5280.87 5304.48 5300.13 5301.41 5310.19 5312.70 5291.61 529
SP-LightGlue4.27 4854.41 4883.86 49710.99 5181.99 5308.19 5092.06 5230.98 5162.37 5188.29 5140.56 5172.10 5181.27 5134.99 5157.48 508
SIFT-UMatch2.16 4972.30 5001.72 5116.99 5271.97 5323.32 5220.70 5350.64 5270.91 5284.86 5260.12 5331.49 5290.22 5262.97 5281.72 527
XFeat-MNN4.39 4834.49 4864.10 4962.88 5381.91 5335.86 5142.57 5201.06 5145.04 51213.99 5100.43 5224.47 5152.00 5116.55 5115.92 515
SP-MNN4.14 4874.24 4903.82 49810.32 5201.83 5348.11 5111.99 5240.82 5182.23 5198.27 5160.47 5212.14 5171.20 5154.77 5177.49 507
SP-NN4.00 4884.12 4913.63 5019.92 5211.81 5357.94 5121.90 5260.86 5172.15 5208.00 5170.50 5192.09 5191.20 5154.63 5186.98 513
SIFT-UM-Cal1.97 5002.12 5031.52 5136.57 5301.67 5362.93 5250.57 5380.62 5290.83 5314.55 5290.11 5351.37 5320.20 5302.69 5301.53 530
SIFT-NN-PointCN2.07 4982.18 5011.74 5105.75 5321.65 5373.27 5230.73 5340.60 5301.07 5254.62 5280.13 5301.43 5300.21 5283.22 5262.12 521
XFeat-NN3.78 4893.96 4923.23 5022.65 5391.53 5384.99 5151.92 5250.81 5194.77 51512.37 5130.38 5233.39 5161.64 5126.13 5124.77 516
SIFT-PointCN1.72 5011.83 5041.36 5155.55 5341.22 5392.59 5270.59 5370.55 5320.71 5333.77 5320.08 5371.24 5330.17 5332.48 5311.63 528
SIFT-PCN-Cal1.72 5011.82 5051.39 5145.64 5331.19 5402.39 5280.53 5390.55 5320.72 5323.90 5310.09 5361.22 5340.17 5332.42 5321.76 526
SIFT-NCMNet1.44 5031.56 5061.08 5165.14 5351.07 5411.97 5290.32 5400.56 5310.64 5343.23 5330.07 5381.01 5350.14 5351.95 5331.15 531
test1236.12 4808.11 4810.14 5170.06 5410.09 54271.05 4590.03 5420.04 5360.25 5371.30 5360.05 5390.03 5370.21 5280.01 5350.29 532
testmvs6.04 4818.02 4820.10 5180.08 5400.03 54369.74 4640.04 5410.05 5350.31 5361.68 5350.02 5400.04 5360.24 5220.02 5340.25 533
mmdepth0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
monomultidepth0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
test_blank0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
uanet_test0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
DCPMVS0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
cdsmvs_eth3d_5k19.96 47126.61 4680.00 5190.00 5420.00 5440.00 53089.26 2270.00 5370.00 53888.61 23861.62 2120.00 5380.00 5360.00 5360.00 534
pcd_1.5k_mvsjas5.26 4827.02 4850.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 53763.15 1830.00 5380.00 5360.00 5360.00 534
sosnet-low-res0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
sosnet0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
uncertanet0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
Regformer0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
ab-mvs-re7.23 4799.64 4790.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 53886.72 2910.00 5410.00 5380.00 5360.00 5360.00 534
uanet0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
PC_three_145268.21 31892.02 1494.00 6382.09 595.98 6284.58 7196.68 294.95 14
eth-test20.00 542
eth-test0.00 542
test_241102_TWO94.06 1477.24 6492.78 495.72 1081.26 997.44 789.07 2596.58 694.26 72
9.1488.26 1992.84 7091.52 5694.75 173.93 17388.57 3694.67 3075.57 2695.79 6486.77 5195.76 26
test_0728_THIRD78.38 3892.12 1195.78 681.46 897.40 989.42 1996.57 794.67 41
GSMVS88.96 313
sam_mvs151.32 33188.96 313
sam_mvs50.01 350
MTGPAbinary92.02 113
test_post178.90 4065.43 52048.81 37085.44 40459.25 363
test_post5.46 51950.36 34684.24 413
patchmatchnet-post74.00 46651.12 33788.60 365
MTMP92.18 3932.83 505
test9_res84.90 6495.70 2992.87 156
agg_prior282.91 9195.45 3292.70 161
test_prior288.85 13275.41 12584.91 8393.54 7674.28 3483.31 8595.86 23
旧先验286.56 23258.10 44087.04 6288.98 35774.07 205
新几何286.29 246
无先验87.48 18988.98 24260.00 42194.12 14267.28 28088.97 312
原ACMM286.86 219
testdata291.01 31062.37 331
segment_acmp73.08 44
testdata184.14 31175.71 116
plane_prior592.44 8395.38 8378.71 14686.32 20691.33 216
plane_prior491.00 165
plane_prior291.25 6079.12 28
plane_prior189.90 125
n20.00 543
nn0.00 543
door-mid69.98 468
test1192.23 99
door69.44 471
HQP-NCC89.33 14689.17 11676.41 9577.23 245
ACMP_Plane89.33 14689.17 11676.41 9577.23 245
BP-MVS77.47 161
HQP4-MVS77.24 24495.11 9591.03 226
HQP3-MVS92.19 10785.99 216
HQP2-MVS60.17 241
ACMMP++_ref81.95 285
ACMMP++81.25 290
Test By Simon64.33 169