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 1287.78 4994.27 4775.89 2396.81 2787.45 4796.44 993.05 149
MSC_two_6792asdad89.16 194.34 3175.53 292.99 5597.53 289.67 1596.44 994.41 61
No_MVS89.16 194.34 3175.53 292.99 5597.53 289.67 1596.44 994.41 61
OPU-MVS89.06 394.62 1575.42 493.57 894.02 6182.45 396.87 2483.77 8296.48 894.88 19
MTAPA87.23 3687.00 3987.90 2294.18 3974.25 586.58 23292.02 11479.45 2385.88 7194.80 2768.07 12596.21 5186.69 5295.34 3593.23 131
MP-MVScopyleft87.71 2387.64 2687.93 2194.36 3073.88 692.71 2792.65 7777.57 5183.84 11294.40 4172.24 5696.28 4885.65 5995.30 3893.62 113
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS87.11 3886.92 4387.68 3794.20 3873.86 793.98 392.82 6976.62 8883.68 11594.46 3667.93 12695.95 6384.20 7894.39 6093.23 131
CNVR-MVS88.93 1289.13 1388.33 894.77 1273.82 890.51 7093.00 5280.90 788.06 4494.06 5976.43 2096.84 2588.48 3695.99 1894.34 67
SMA-MVScopyleft89.08 989.23 988.61 694.25 3573.73 992.40 2993.63 2674.77 15192.29 795.97 274.28 3497.24 1588.58 3396.91 194.87 21
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
MM89.16 789.23 988.97 490.79 10373.65 1092.66 2891.17 15486.57 187.39 5894.97 2571.70 6597.68 192.19 195.63 3195.57 2
NCCC88.06 1888.01 2288.24 1194.41 2673.62 1191.22 6292.83 6681.50 585.79 7393.47 8173.02 4697.00 2184.90 6494.94 4394.10 80
ACMMPR87.44 2987.23 3688.08 1594.64 1373.59 1293.04 1793.20 4076.78 8284.66 9194.52 3268.81 11496.65 3584.53 7294.90 4494.00 86
region2R87.42 3187.20 3788.09 1494.63 1473.55 1393.03 1993.12 4676.73 8584.45 9694.52 3269.09 10896.70 3184.37 7494.83 4894.03 84
mPP-MVS86.67 4686.32 5387.72 3394.41 2673.55 1392.74 2592.22 10376.87 7982.81 13994.25 4966.44 14596.24 5082.88 9294.28 6393.38 123
HFP-MVS87.58 2687.47 3187.94 1994.58 1673.54 1593.04 1793.24 3976.78 8284.91 8394.44 3970.78 7896.61 3784.53 7294.89 4593.66 106
3Dnovator+77.84 485.48 7484.47 9488.51 791.08 9473.49 1693.18 1693.78 2380.79 876.66 26293.37 8460.40 24396.75 3077.20 16693.73 6995.29 7
MSP-MVS89.51 589.91 688.30 1094.28 3473.46 1792.90 2194.11 1080.27 1191.35 1694.16 5478.35 1496.77 2889.59 1794.22 6594.67 42
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
PGM-MVS86.68 4586.27 5587.90 2294.22 3773.38 1890.22 8193.04 4775.53 12283.86 11194.42 4067.87 12896.64 3682.70 9994.57 5593.66 106
ZNCC-MVS87.94 2287.85 2488.20 1294.39 2873.33 1993.03 1993.81 2276.81 8085.24 7894.32 4471.76 6396.93 2385.53 6195.79 2594.32 69
XVS87.18 3786.91 4488.00 1794.42 2473.33 1992.78 2392.99 5579.14 2783.67 11694.17 5367.45 13196.60 3883.06 8794.50 5694.07 82
X-MVStestdata80.37 20577.83 24588.00 1794.42 2473.33 1992.78 2392.99 5579.14 2783.67 11612.47 51867.45 13196.60 3883.06 8794.50 5694.07 82
ACMMP_NAP88.05 2088.08 2187.94 1993.70 4573.05 2290.86 6593.59 2876.27 10588.14 4295.09 2171.06 7596.67 3387.67 4496.37 1494.09 81
DPM-MVS84.93 8884.29 9586.84 5790.20 11473.04 2387.12 20893.04 4769.80 28082.85 13791.22 15673.06 4596.02 5876.72 17894.63 5391.46 218
GST-MVS87.42 3187.26 3487.89 2494.12 4072.97 2492.39 3193.43 3376.89 7884.68 8893.99 6570.67 8096.82 2684.18 7995.01 4093.90 92
TEST993.26 5672.96 2588.75 13991.89 12268.44 31785.00 8193.10 8974.36 3395.41 81
train_agg86.43 4986.20 5687.13 5093.26 5672.96 2588.75 13991.89 12268.69 31285.00 8193.10 8974.43 3195.41 8184.97 6395.71 2893.02 151
SteuartSystems-ACMMP88.72 1488.86 1488.32 992.14 7972.96 2593.73 593.67 2580.19 1388.10 4394.80 2773.76 3897.11 1787.51 4695.82 2494.90 18
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator76.31 583.38 12782.31 14186.59 6287.94 21172.94 2890.64 6892.14 11377.21 6775.47 28892.83 9858.56 25594.72 11873.24 21792.71 8192.13 196
SD-MVS88.06 1888.50 1886.71 6192.60 7672.71 2991.81 4693.19 4177.87 4490.32 2394.00 6374.83 2793.78 16287.63 4594.27 6493.65 110
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
MVS_111021_HR85.14 8384.75 8986.32 6691.65 8672.70 3085.98 25490.33 18376.11 10882.08 14991.61 14271.36 7194.17 14281.02 11192.58 8292.08 197
DPE-MVScopyleft89.48 689.98 588.01 1694.80 1172.69 3191.59 5194.10 1275.90 11292.29 795.66 1281.67 697.38 1387.44 4896.34 1593.95 89
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part295.06 872.65 3291.80 15
ACMMPcopyleft85.89 6685.39 7787.38 4493.59 4972.63 3392.74 2593.18 4576.78 8280.73 17793.82 7264.33 17296.29 4782.67 10090.69 11993.23 131
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 3489.01 126
test_893.13 6072.57 3588.68 14591.84 12668.69 31284.87 8593.10 8974.43 3195.16 91
TSAR-MVS + GP.85.71 7085.33 7986.84 5791.34 8972.50 3689.07 12587.28 29776.41 9685.80 7290.22 19374.15 3695.37 8681.82 10491.88 9592.65 167
CSCG86.41 5186.19 5887.07 5192.91 6772.48 3790.81 6693.56 2973.95 17283.16 13091.07 16275.94 2295.19 9079.94 12794.38 6193.55 118
NormalMVS86.29 5485.88 6687.52 4193.26 5672.47 3891.65 4792.19 10879.31 2584.39 9892.18 11664.64 16995.53 7280.70 11794.65 5194.56 55
SymmetryMVS85.38 7984.81 8887.07 5191.47 8872.47 3891.65 4788.06 27579.31 2584.39 9892.18 11664.64 16995.53 7280.70 11790.91 11693.21 134
MCST-MVS87.37 3487.25 3587.73 3194.53 2172.46 4089.82 8893.82 2173.07 20184.86 8692.89 9676.22 2196.33 4684.89 6695.13 3994.40 63
FOURS195.00 1072.39 4195.06 193.84 2074.49 15791.30 17
APD-MVScopyleft87.44 2987.52 3087.19 4894.24 3672.39 4191.86 4592.83 6673.01 20388.58 3594.52 3273.36 3996.49 4384.26 7595.01 4092.70 163
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 8884.22 10393.36 8571.44 6996.76 2980.82 11495.33 3694.16 76
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
save fliter93.80 4472.35 4490.47 7491.17 15474.31 163
DeepC-MVS79.81 287.08 4086.88 4587.69 3691.16 9272.32 4590.31 7993.94 1877.12 7182.82 13894.23 5072.13 5997.09 1884.83 6795.37 3493.65 110
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZD-MVS94.38 2972.22 4692.67 7470.98 24587.75 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 10983.81 11393.95 6869.77 9596.01 5985.15 6294.66 5094.32 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + MP.88.02 2188.11 2087.72 3393.68 4772.13 4891.41 5892.35 9074.62 15588.90 3393.85 7175.75 2496.00 6087.80 4394.63 5395.04 12
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS86.73 4386.67 4886.91 5694.11 4172.11 4992.37 3392.56 8274.50 15686.84 6594.65 3167.31 13395.77 6584.80 6892.85 7892.84 161
TestfortrainingZip87.28 4692.85 6872.05 5093.28 1293.32 3776.52 9088.91 3293.52 7777.30 1796.67 3391.98 9493.13 143
MP-MVS-pluss87.67 2587.72 2587.54 4093.64 4872.04 5189.80 9093.50 3075.17 13886.34 6995.29 1970.86 7796.00 6088.78 3196.04 1694.58 51
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS88.46 1588.74 1587.64 3992.78 7171.95 5292.40 2994.74 275.71 11789.16 2995.10 2075.65 2596.19 5287.07 4996.01 1794.79 28
agg_prior92.85 6871.94 5391.78 13084.41 9794.93 103
MGCNet87.69 2487.55 2988.12 1389.45 14071.76 5491.47 5789.54 21182.14 386.65 6794.28 4668.28 12397.46 690.81 695.31 3795.15 9
APDe-MVScopyleft89.15 889.63 787.73 3194.49 2271.69 5593.83 493.96 1775.70 11991.06 1996.03 176.84 1897.03 2089.09 2195.65 3094.47 60
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
reproduce-ours87.47 2787.61 2787.07 5193.27 5471.60 5691.56 5493.19 4174.98 14288.96 3095.54 1471.20 7396.54 4186.28 5493.49 7093.06 147
our_new_method87.47 2787.61 2787.07 5193.27 5471.60 5691.56 5493.19 4174.98 14288.96 3095.54 1471.20 7396.54 4186.28 5493.49 7093.06 147
MVS_111021_LR82.61 14482.11 14584.11 15988.82 16871.58 5885.15 27886.16 32974.69 15280.47 18491.04 16362.29 20290.55 32980.33 12390.08 13190.20 265
MAR-MVS81.84 15780.70 16885.27 9791.32 9071.53 5989.82 8890.92 16169.77 28278.50 21686.21 31462.36 20194.52 12665.36 29992.05 9389.77 290
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 4292.05 1395.74 880.83 12
MED-MVS test87.86 2794.57 1771.43 6193.28 1294.36 375.24 13192.25 995.03 2297.39 1188.15 3995.96 1994.75 35
MED-MVS89.75 390.37 387.89 2494.57 1771.43 6193.28 1294.36 377.30 6292.25 995.87 381.59 797.39 1188.15 3995.96 1994.85 24
ME-MVS88.98 1189.39 887.75 3094.54 2071.43 6191.61 4994.25 576.30 10490.62 2195.03 2278.06 1597.07 1988.15 3995.96 1994.75 35
test_0728_SECOND87.71 3595.34 171.43 6193.49 1094.23 697.49 489.08 2296.41 1294.21 74
TestfortrainingZip a88.83 1389.21 1187.68 3794.57 1771.25 6593.28 1293.91 1977.30 6291.13 1895.87 377.62 1696.95 2286.12 5793.07 7594.85 24
DVP-MVS++90.23 191.01 187.89 2494.34 3171.25 6595.06 194.23 678.38 3992.78 495.74 882.45 397.49 489.42 1996.68 294.95 15
IU-MVS95.30 271.25 6592.95 6166.81 33392.39 688.94 2896.63 494.85 24
DVP-MVScopyleft89.60 490.35 487.33 4595.27 571.25 6593.49 1092.73 7177.33 6092.12 1195.78 680.98 1097.40 989.08 2296.41 1293.33 127
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test072695.27 571.25 6593.60 794.11 1077.33 6092.81 395.79 580.98 10
reproduce_model87.28 3587.39 3386.95 5593.10 6271.24 7091.60 5093.19 4174.69 15288.80 3495.61 1370.29 8496.44 4486.20 5693.08 7493.16 139
CDPH-MVS85.76 6985.29 8287.17 4993.49 5171.08 7188.58 14992.42 8768.32 31984.61 9393.48 7972.32 5496.15 5479.00 14495.43 3394.28 72
CNLPA78.08 26376.79 27481.97 25790.40 11071.07 7287.59 18884.55 34966.03 34972.38 34989.64 20857.56 26486.04 39859.61 36383.35 26988.79 323
SED-MVS90.08 290.85 287.77 2895.30 270.98 7393.57 894.06 1477.24 6593.10 195.72 1082.99 197.44 789.07 2596.63 494.88 19
test_241102_ONE95.30 270.98 7394.06 1477.17 6893.10 195.39 1882.99 197.27 14
PHI-MVS86.43 4986.17 5987.24 4790.88 10070.96 7592.27 3794.07 1372.45 21085.22 7991.90 12569.47 9896.42 4583.28 8695.94 2294.35 66
OPM-MVS83.50 12382.95 12885.14 10188.79 17470.95 7689.13 12291.52 14377.55 5480.96 17191.75 13260.71 23394.50 12779.67 13586.51 20589.97 282
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 8181.78 481.32 16291.43 14970.34 8297.23 1684.26 7593.36 7394.37 65
DP-MVS Recon83.11 13682.09 14786.15 7294.44 2370.92 7888.79 13692.20 10670.53 25879.17 20391.03 16564.12 17496.03 5668.39 27590.14 12991.50 214
CPTT-MVS83.73 11383.33 12184.92 11593.28 5370.86 7992.09 4190.38 17968.75 31179.57 19592.83 9860.60 23993.04 21880.92 11391.56 10390.86 236
h-mvs3383.15 13382.19 14486.02 7890.56 10670.85 8088.15 17089.16 23476.02 11084.67 8991.39 15061.54 21695.50 7482.71 9775.48 37591.72 208
新几何183.42 19793.13 6070.71 8185.48 33857.43 45181.80 15491.98 12363.28 18092.27 25164.60 30692.99 7687.27 370
test1286.80 5992.63 7470.70 8291.79 12982.71 14171.67 6696.16 5394.50 5693.54 119
SR-MVS-dyc-post85.77 6885.61 7386.23 6893.06 6470.63 8391.88 4392.27 9673.53 18685.69 7494.45 3765.00 16695.56 6982.75 9591.87 9692.50 174
RE-MVS-def85.48 7693.06 6470.63 8391.88 4392.27 9673.53 18685.69 7494.45 3763.87 17682.75 9591.87 9692.50 174
HPM-MVS_fast85.35 8084.95 8786.57 6493.69 4670.58 8592.15 4091.62 13973.89 17582.67 14294.09 5762.60 19595.54 7180.93 11292.93 7793.57 116
MSLP-MVS++85.43 7685.76 7084.45 13791.93 8270.24 8690.71 6792.86 6477.46 5784.22 10392.81 10067.16 13592.94 22080.36 12194.35 6290.16 266
MVSFormer82.85 14082.05 14885.24 9887.35 24570.21 8790.50 7290.38 17968.55 31481.32 16289.47 21461.68 21393.46 18978.98 14590.26 12792.05 198
lupinMVS81.39 17280.27 18184.76 12487.35 24570.21 8785.55 26886.41 32362.85 39681.32 16288.61 24161.68 21392.24 25378.41 15290.26 12791.83 201
xiu_mvs_v1_base_debu80.80 18779.72 19884.03 17487.35 24570.19 8985.56 26588.77 25269.06 30281.83 15188.16 25550.91 34192.85 22478.29 15487.56 18489.06 307
xiu_mvs_v1_base80.80 18779.72 19884.03 17487.35 24570.19 8985.56 26588.77 25269.06 30281.83 15188.16 25550.91 34192.85 22478.29 15487.56 18489.06 307
xiu_mvs_v1_base_debi80.80 18779.72 19884.03 17487.35 24570.19 8985.56 26588.77 25269.06 30281.83 15188.16 25550.91 34192.85 22478.29 15487.56 18489.06 307
API-MVS81.99 15481.23 15884.26 15590.94 9870.18 9291.10 6389.32 22371.51 23078.66 21288.28 25165.26 16095.10 9864.74 30591.23 10987.51 359
test_fmvsm_n_192085.29 8185.34 7885.13 10486.12 29169.93 9388.65 14690.78 16869.97 27688.27 3993.98 6671.39 7091.54 28488.49 3590.45 12493.91 90
OpenMVScopyleft72.83 1079.77 21778.33 23384.09 16485.17 31369.91 9490.57 6990.97 16066.70 33672.17 35291.91 12454.70 29293.96 14761.81 34390.95 11588.41 336
jason81.39 17280.29 18084.70 12686.63 27969.90 9585.95 25586.77 31563.24 38981.07 16889.47 21461.08 22992.15 25578.33 15390.07 13292.05 198
jason: jason.
MVP-Stereo76.12 30774.46 31781.13 28085.37 30969.79 9684.42 30487.95 28065.03 36767.46 41085.33 33553.28 30791.73 27358.01 38283.27 27181.85 457
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 7089.76 2695.52 1672.26 5596.27 4986.87 5094.65 5193.70 105
PVSNet_Blended_VisFu82.62 14381.83 15384.96 11190.80 10269.76 9888.74 14191.70 13569.39 28978.96 20588.46 24665.47 15994.87 11074.42 20388.57 15990.24 264
test_prior86.33 6592.61 7569.59 9992.97 6095.48 7593.91 90
APD-MVS_3200maxsize85.97 6285.88 6686.22 6992.69 7369.53 10091.93 4292.99 5573.54 18585.94 7094.51 3565.80 15795.61 6883.04 8992.51 8393.53 120
test_fmvsmconf_n85.92 6386.04 6385.57 8985.03 32069.51 10189.62 9890.58 17273.42 18987.75 5194.02 6172.85 4993.24 19990.37 890.75 11893.96 87
EPNet83.72 11482.92 12986.14 7484.22 33669.48 10291.05 6485.27 33981.30 676.83 25791.65 13766.09 15295.56 6976.00 18593.85 6793.38 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ET-MVSNet_ETH3D78.63 24976.63 28084.64 12786.73 27569.47 10385.01 28384.61 34869.54 28766.51 42786.59 30250.16 35291.75 27176.26 18084.24 25092.69 165
alignmvs85.48 7485.32 8085.96 7989.51 13669.47 10389.74 9292.47 8376.17 10787.73 5391.46 14870.32 8393.78 16281.51 10588.95 15194.63 48
DP-MVS76.78 29374.57 31383.42 19793.29 5269.46 10588.55 15183.70 36163.98 38370.20 37088.89 23354.01 30094.80 11446.66 45381.88 28986.01 402
sasdasda85.91 6485.87 6886.04 7689.84 12669.44 10690.45 7693.00 5276.70 8688.01 4691.23 15373.28 4193.91 15581.50 10688.80 15494.77 30
canonicalmvs85.91 6485.87 6886.04 7689.84 12669.44 10690.45 7693.00 5276.70 8688.01 4691.23 15373.28 4193.91 15581.50 10688.80 15494.77 30
test_fmvsmconf0.1_n85.61 7285.65 7285.50 9082.99 37569.39 10889.65 9590.29 18673.31 19387.77 5094.15 5571.72 6493.23 20090.31 990.67 12093.89 93
test_fmvsmvis_n_192084.02 10283.87 10484.49 13684.12 33869.37 10988.15 17087.96 27970.01 27483.95 11093.23 8768.80 11591.51 28788.61 3289.96 13392.57 168
nrg03083.88 10783.53 11684.96 11186.77 27469.28 11090.46 7592.67 7474.79 15082.95 13391.33 15272.70 5293.09 21380.79 11679.28 32492.50 174
test_fmvsmconf0.01_n84.73 9184.52 9385.34 9580.25 41969.03 11189.47 10289.65 20773.24 19786.98 6394.27 4766.62 14193.23 20090.26 1089.95 13493.78 102
DeepPCF-MVS80.84 188.10 1688.56 1786.73 6092.24 7869.03 11189.57 9993.39 3577.53 5589.79 2594.12 5678.98 1396.58 4085.66 5895.72 2794.58 51
XVG-OURS80.41 20179.23 21383.97 18085.64 30069.02 11383.03 34490.39 17871.09 24077.63 23991.49 14754.62 29491.35 29475.71 18883.47 26791.54 212
PCF-MVS73.52 780.38 20378.84 22285.01 10987.71 22768.99 11483.65 32191.46 14863.00 39377.77 23790.28 18966.10 15195.09 9961.40 34888.22 17090.94 234
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
QAPM80.88 18179.50 20485.03 10788.01 20968.97 11591.59 5192.00 11666.63 34275.15 30692.16 11857.70 26295.45 7663.52 31188.76 15690.66 245
AdaColmapbinary80.58 19979.42 20584.06 16993.09 6368.91 11689.36 11188.97 24569.27 29375.70 28489.69 20557.20 27095.77 6563.06 32088.41 16487.50 360
fmvsm_l_conf0.5_n84.47 9284.54 9184.27 15385.42 30768.81 11788.49 15387.26 30268.08 32188.03 4593.49 7872.04 6091.77 27088.90 2989.14 15092.24 188
原ACMM184.35 14493.01 6668.79 11892.44 8463.96 38481.09 16791.57 14366.06 15395.45 7667.19 28594.82 4988.81 322
XVG-OURS-SEG-HR80.81 18479.76 19583.96 18185.60 30268.78 11983.54 32890.50 17570.66 25676.71 26191.66 13660.69 23491.26 29776.94 17081.58 29291.83 201
LPG-MVS_test82.08 15181.27 15784.50 13489.23 15468.76 12090.22 8191.94 12075.37 12876.64 26391.51 14554.29 29594.91 10478.44 15083.78 25589.83 287
LGP-MVS_train84.50 13489.23 15468.76 12091.94 12075.37 12876.64 26391.51 14554.29 29594.91 10478.44 15083.78 25589.83 287
Effi-MVS+-dtu80.03 21478.57 22684.42 13985.13 31768.74 12288.77 13788.10 27274.99 14174.97 31283.49 38157.27 26893.36 19373.53 21180.88 30091.18 223
Vis-MVSNetpermissive83.46 12482.80 13185.43 9290.25 11368.74 12290.30 8090.13 19176.33 10380.87 17492.89 9661.00 23094.20 13972.45 23190.97 11393.35 126
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HQP_MVS83.64 11783.14 12285.14 10190.08 11768.71 12491.25 6092.44 8479.12 2978.92 20791.00 16660.42 24195.38 8378.71 14886.32 20891.33 219
plane_prior68.71 12490.38 7877.62 4986.16 213
plane_prior689.84 12668.70 12660.42 241
ACMP74.13 681.51 17080.57 17284.36 14389.42 14168.69 12789.97 8591.50 14774.46 15875.04 31090.41 18453.82 30194.54 12477.56 16282.91 27589.86 286
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ETV-MVS84.90 9084.67 9085.59 8889.39 14468.66 12888.74 14192.64 7979.97 1784.10 10685.71 32369.32 10195.38 8380.82 11491.37 10692.72 162
plane_prior368.60 12978.44 3778.92 207
CHOSEN 1792x268877.63 27975.69 29183.44 19689.98 12368.58 13078.70 41187.50 29256.38 45675.80 28386.84 29058.67 25491.40 29361.58 34685.75 22590.34 259
fmvsm_l_conf0.5_n_386.02 5886.32 5385.14 10187.20 25668.54 13189.57 9990.44 17775.31 13087.49 5594.39 4272.86 4892.72 23089.04 2790.56 12294.16 76
plane_prior790.08 11768.51 132
GDP-MVS83.52 12282.64 13486.16 7188.14 20068.45 13389.13 12292.69 7272.82 20783.71 11491.86 12855.69 28295.35 8780.03 12589.74 13894.69 37
fmvsm_l_conf0.5_n_a84.13 9984.16 9684.06 16985.38 30868.40 13488.34 16186.85 31467.48 32887.48 5693.40 8370.89 7691.61 27588.38 3789.22 14792.16 195
ACMM73.20 880.78 19179.84 19383.58 19289.31 14968.37 13589.99 8491.60 14170.28 26877.25 24689.66 20753.37 30693.53 17974.24 20682.85 27688.85 320
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs474.03 33671.91 34780.39 29681.96 39568.32 13681.45 36582.14 39059.32 43169.87 37985.13 34152.40 31388.13 37560.21 35874.74 39084.73 427
NP-MVS89.62 13168.32 13690.24 191
SSM_040481.91 15580.84 16785.13 10489.24 15368.26 13887.84 18389.25 22971.06 24280.62 17990.39 18659.57 24694.65 12272.45 23187.19 19292.47 177
test22291.50 8768.26 13884.16 31183.20 37354.63 46379.74 19291.63 13958.97 25191.42 10486.77 387
Elysia81.53 16680.16 18385.62 8685.51 30468.25 14088.84 13492.19 10871.31 23380.50 18289.83 19946.89 38494.82 11176.85 17189.57 14093.80 100
StellarMVS81.53 16680.16 18385.62 8685.51 30468.25 14088.84 13492.19 10871.31 23380.50 18289.83 19946.89 38494.82 11176.85 17189.57 14093.80 100
CDS-MVSNet79.07 23877.70 25283.17 20987.60 23468.23 14284.40 30586.20 32867.49 32776.36 27186.54 30661.54 21690.79 32161.86 34287.33 18990.49 253
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ81.69 16181.02 16383.70 18889.51 13668.21 14384.28 30790.09 19270.79 24981.26 16685.62 32863.15 18694.29 13275.62 19088.87 15388.59 331
fmvsm_s_conf0.5_n_a83.63 11883.41 11884.28 15186.14 29068.12 14489.43 10582.87 38070.27 26987.27 6093.80 7369.09 10891.58 27788.21 3883.65 26293.14 142
UGNet80.83 18379.59 20284.54 12988.04 20668.09 14589.42 10788.16 27076.95 7676.22 27489.46 21649.30 36793.94 15068.48 27390.31 12591.60 209
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 13082.99 12784.28 15183.79 34668.07 14689.34 11282.85 38169.80 28087.36 5994.06 5968.34 12291.56 28087.95 4283.46 26893.21 134
UA-Net85.08 8684.96 8685.45 9192.07 8068.07 14689.78 9190.86 16582.48 284.60 9493.20 8869.35 10095.22 8971.39 23990.88 11793.07 146
cashybrid286.09 5686.04 6386.24 6788.17 19768.05 14889.44 10492.79 7080.30 1084.71 8792.78 10372.83 5095.05 10082.81 9390.57 12195.62 1
xiu_mvs_v2_base81.69 16181.05 16283.60 19089.15 15768.03 14984.46 29990.02 19370.67 25381.30 16586.53 30763.17 18594.19 14175.60 19188.54 16088.57 332
LuminaMVS80.68 19279.62 20183.83 18485.07 31968.01 15086.99 21388.83 24970.36 26481.38 16187.99 26250.11 35392.51 24079.02 14286.89 19990.97 232
mamba_040879.37 23177.52 25784.93 11488.81 16967.96 15165.03 48888.66 26270.96 24679.48 19789.80 20158.69 25294.65 12270.35 25185.93 22092.18 191
SSM_0407277.67 27877.52 25778.12 35788.81 16967.96 15165.03 48888.66 26270.96 24679.48 19789.80 20158.69 25274.23 48170.35 25185.93 22092.18 191
SSM_040781.58 16580.48 17584.87 11888.81 16967.96 15187.37 20089.25 22971.06 24279.48 19790.39 18659.57 24694.48 12972.45 23185.93 22092.18 191
DELS-MVS85.41 7785.30 8185.77 8188.49 18467.93 15485.52 27293.44 3278.70 3583.63 11889.03 22674.57 2895.71 6780.26 12494.04 6693.66 106
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
fmvsm_s_conf0.5_n_987.39 3387.95 2385.70 8389.48 13967.88 15588.59 14889.05 23980.19 1390.70 2095.40 1774.56 2993.92 15491.54 292.07 9295.31 6
BP-MVS184.32 9383.71 11086.17 7087.84 21667.85 15689.38 11089.64 20877.73 4783.98 10992.12 12156.89 27395.43 7884.03 8091.75 9995.24 8
EI-MVSNet-Vis-set84.19 9883.81 10785.31 9688.18 19667.85 15687.66 18689.73 20580.05 1682.95 13389.59 21170.74 7994.82 11180.66 11984.72 23993.28 129
PLCcopyleft70.83 1178.05 26576.37 28683.08 21491.88 8467.80 15888.19 16789.46 21464.33 37769.87 37988.38 24853.66 30293.58 17158.86 37282.73 27887.86 349
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAMVS78.89 24477.51 25983.03 21787.80 21867.79 15984.72 28985.05 34467.63 32476.75 26087.70 26762.25 20390.82 32058.53 37687.13 19490.49 253
CLD-MVS82.31 14881.65 15484.29 15088.47 18567.73 16085.81 26292.35 9075.78 11578.33 22286.58 30464.01 17594.35 13176.05 18487.48 18790.79 238
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
viewdifsd2359ckpt0983.34 12882.55 13685.70 8387.64 23367.72 16188.43 15491.68 13671.91 22281.65 15890.68 17567.10 13694.75 11676.17 18187.70 18394.62 50
hse-mvs281.72 15980.94 16584.07 16688.72 17767.68 16285.87 25887.26 30276.02 11084.67 8988.22 25461.54 21693.48 18782.71 9773.44 40391.06 227
MVSMamba_PlusPlus85.99 6085.96 6586.05 7591.09 9367.64 16389.63 9792.65 7772.89 20684.64 9291.71 13471.85 6196.03 5684.77 6994.45 5994.49 59
BridgeMVS86.78 4286.99 4086.15 7291.24 9167.61 16490.51 7092.90 6277.26 6487.44 5791.63 13971.27 7296.06 5585.62 6095.01 4094.78 29
AUN-MVS79.21 23477.60 25584.05 17288.71 17867.61 16485.84 26087.26 30269.08 30177.23 24888.14 25953.20 30893.47 18875.50 19373.45 40291.06 227
CS-MVS86.69 4486.95 4285.90 8090.76 10467.57 16692.83 2293.30 3879.67 2084.57 9592.27 11071.47 6895.02 10284.24 7793.46 7295.13 11
KinetiMVS83.31 13182.61 13585.39 9487.08 26567.56 16788.06 17291.65 13777.80 4682.21 14791.79 12957.27 26894.07 14577.77 15989.89 13694.56 55
EI-MVSNet-UG-set83.81 10883.38 11985.09 10687.87 21467.53 16887.44 19989.66 20679.74 1982.23 14689.41 22070.24 8594.74 11779.95 12683.92 25492.99 154
casdiffseed41469214783.62 11983.02 12585.40 9387.31 25367.50 16988.70 14391.72 13376.97 7582.77 14091.72 13366.85 13893.71 16973.06 21988.12 17294.98 14
Effi-MVS+83.62 11983.08 12385.24 9888.38 19067.45 17088.89 13089.15 23575.50 12382.27 14588.28 25169.61 9794.45 13077.81 15887.84 17993.84 96
EG-PatchMatch MVS74.04 33471.82 34880.71 29084.92 32167.42 17185.86 25988.08 27366.04 34864.22 44583.85 36935.10 46492.56 23657.44 38680.83 30182.16 455
OMC-MVS82.69 14281.97 15184.85 11988.75 17667.42 17187.98 17490.87 16474.92 14579.72 19391.65 13762.19 20593.96 14775.26 19686.42 20693.16 139
fmvsm_s_conf0.5_n_585.22 8285.55 7484.25 15686.26 28567.40 17389.18 11689.31 22472.50 20988.31 3893.86 7069.66 9691.96 26289.81 1391.05 11193.38 123
PatchMatch-RL72.38 36370.90 36476.80 38088.60 18167.38 17479.53 39776.17 45462.75 39969.36 38482.00 40745.51 40384.89 41253.62 41380.58 30578.12 472
LS3D76.95 29174.82 31083.37 20090.45 10867.36 17589.15 12186.94 31161.87 41169.52 38290.61 17951.71 33194.53 12546.38 45686.71 20288.21 342
fmvsm_s_conf0.5_n83.80 10983.71 11084.07 16686.69 27767.31 17689.46 10383.07 37571.09 24086.96 6493.70 7569.02 11391.47 29088.79 3084.62 24193.44 122
fmvsm_s_conf0.5_n_1086.38 5286.76 4685.24 9887.33 25067.30 17789.50 10190.98 15976.25 10690.56 2294.75 2968.38 12094.24 13890.80 792.32 8994.19 75
fmvsm_s_conf0.1_n83.56 12183.38 11984.10 16084.86 32267.28 17889.40 10983.01 37670.67 25387.08 6193.96 6768.38 12091.45 29188.56 3484.50 24293.56 117
PS-MVSNAJss82.07 15281.31 15684.34 14586.51 28267.27 17989.27 11391.51 14471.75 22379.37 20090.22 19363.15 18694.27 13477.69 16182.36 28391.49 215
114514_t80.68 19279.51 20384.20 15794.09 4267.27 17989.64 9691.11 15758.75 43974.08 32590.72 17358.10 25895.04 10169.70 26089.42 14490.30 262
mvsmamba80.60 19679.38 20784.27 15389.74 13067.24 18187.47 19186.95 31070.02 27375.38 29488.93 23151.24 33892.56 23675.47 19489.22 14793.00 153
casdiffmvs_mvgpermissive85.99 6086.09 6285.70 8387.65 23267.22 18288.69 14493.04 4779.64 2285.33 7792.54 10673.30 4094.50 12783.49 8391.14 11095.37 3
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SPE-MVS-test86.29 5486.48 5185.71 8291.02 9667.21 18392.36 3493.78 2378.97 3483.51 12391.20 15770.65 8195.15 9281.96 10394.89 4594.77 30
anonymousdsp78.60 25077.15 26582.98 22180.51 41767.08 18487.24 20689.53 21265.66 35475.16 30587.19 28452.52 31092.25 25277.17 16779.34 32389.61 294
MVS78.19 26176.99 26981.78 26085.66 29966.99 18584.66 29190.47 17655.08 46272.02 35485.27 33663.83 17794.11 14466.10 29389.80 13784.24 431
HQP5-MVS66.98 186
HQP-MVS82.61 14482.02 14984.37 14289.33 14666.98 18689.17 11792.19 10876.41 9677.23 24890.23 19260.17 24495.11 9577.47 16385.99 21891.03 229
Fast-Effi-MVS+-dtu78.02 26676.49 28182.62 24083.16 36666.96 18886.94 21687.45 29472.45 21071.49 36084.17 36554.79 29191.58 27767.61 27980.31 30989.30 303
F-COLMAP76.38 30574.33 31982.50 24389.28 15166.95 18988.41 15689.03 24064.05 38166.83 41988.61 24146.78 38692.89 22257.48 38578.55 32887.67 352
viewdifsd2359ckpt1382.91 13982.29 14284.77 12386.96 26866.90 19087.47 19191.62 13972.19 21581.68 15790.71 17466.92 13793.28 19575.90 18687.15 19394.12 79
HyFIR lowres test77.53 28075.40 29983.94 18289.59 13266.62 19180.36 38588.64 26556.29 45776.45 26885.17 34057.64 26393.28 19561.34 35083.10 27491.91 200
ACMH67.68 1675.89 31173.93 32381.77 26188.71 17866.61 19288.62 14789.01 24269.81 27966.78 42086.70 29841.95 42991.51 28755.64 40178.14 33787.17 374
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax79.29 23277.96 23983.27 20384.68 32766.57 19389.25 11490.16 19069.20 29875.46 29089.49 21345.75 40193.13 21176.84 17380.80 30290.11 270
VDD-MVS83.01 13882.36 14084.96 11191.02 9666.40 19488.91 12988.11 27177.57 5184.39 9893.29 8652.19 31693.91 15577.05 16988.70 15894.57 53
mvs_tets79.13 23677.77 24983.22 20784.70 32666.37 19589.17 11790.19 18969.38 29075.40 29389.46 21644.17 41393.15 20976.78 17780.70 30490.14 267
PAPM_NR83.02 13782.41 13884.82 12092.47 7766.37 19587.93 17891.80 12873.82 17677.32 24590.66 17667.90 12794.90 10670.37 25089.48 14393.19 137
EC-MVSNet86.01 5986.38 5284.91 11689.31 14966.27 19792.32 3593.63 2679.37 2484.17 10591.88 12669.04 11295.43 7883.93 8193.77 6893.01 152
pmmvs-eth3d70.50 38567.83 40078.52 35077.37 45566.18 19881.82 35681.51 39758.90 43663.90 44980.42 42042.69 42286.28 39558.56 37565.30 45383.11 444
fmvsm_s_conf0.5_n_1186.06 5786.75 4784.00 17787.78 22166.09 19989.96 8690.80 16777.37 5986.72 6694.20 5272.51 5392.78 22989.08 2292.33 8793.13 143
IB-MVS68.01 1575.85 31273.36 33283.31 20184.76 32566.03 20083.38 33285.06 34370.21 27169.40 38381.05 41245.76 40094.66 12165.10 30275.49 37489.25 304
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 33772.67 33977.30 37583.87 34566.02 20181.82 35684.66 34761.37 41568.61 39282.82 39447.29 37988.21 37359.27 36684.32 24977.68 473
fmvsm_l_conf0.5_n_985.84 6786.63 4983.46 19587.12 26466.01 20288.56 15089.43 21575.59 12189.32 2894.32 4472.89 4791.21 30290.11 1192.33 8793.16 139
FE-MVS77.78 27275.68 29284.08 16588.09 20466.00 20383.13 33887.79 28568.42 31878.01 23085.23 33845.50 40495.12 9359.11 36985.83 22491.11 225
test_040272.79 36170.44 37279.84 31588.13 20165.99 20485.93 25684.29 35365.57 35567.40 41385.49 33146.92 38392.61 23235.88 48374.38 39380.94 462
BH-RMVSNet79.61 21978.44 22983.14 21089.38 14565.93 20584.95 28587.15 30573.56 18478.19 22589.79 20356.67 27593.36 19359.53 36486.74 20190.13 268
BH-untuned79.47 22478.60 22582.05 25489.19 15665.91 20686.07 25388.52 26772.18 21675.42 29287.69 26861.15 22793.54 17860.38 35686.83 20086.70 389
cascas76.72 29474.64 31282.99 21985.78 29765.88 20782.33 35089.21 23260.85 41772.74 34281.02 41347.28 38093.75 16667.48 28185.02 23389.34 302
balanced_ft_v183.98 10583.64 11385.03 10789.76 12965.86 20888.31 16391.71 13474.41 16080.41 18590.82 17162.90 19394.90 10683.04 8991.37 10694.32 69
fmvsm_s_conf0.5_n_485.39 7885.75 7184.30 14986.70 27665.83 20988.77 13789.78 20075.46 12588.35 3793.73 7469.19 10793.06 21591.30 388.44 16394.02 85
patch_mono-283.65 11684.54 9180.99 28390.06 12165.83 20984.21 30888.74 25871.60 22885.01 8092.44 10874.51 3083.50 42482.15 10292.15 9093.64 112
MSDG73.36 34770.99 36280.49 29584.51 33265.80 21180.71 37986.13 33065.70 35365.46 43583.74 37344.60 40890.91 31751.13 42776.89 35084.74 426
旧先验191.96 8165.79 21286.37 32593.08 9369.31 10292.74 8088.74 327
casdiffmvspermissive85.11 8485.14 8485.01 10987.20 25665.77 21387.75 18492.83 6677.84 4584.36 10192.38 10972.15 5893.93 15381.27 11090.48 12395.33 5
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
hybridcas85.11 8485.18 8384.90 11787.47 24465.68 21488.53 15292.38 8877.91 4384.27 10292.48 10772.19 5793.88 15980.37 12090.97 11395.15 9
COLMAP_ROBcopyleft66.92 1773.01 35570.41 37380.81 28887.13 25965.63 21588.30 16484.19 35662.96 39463.80 45087.69 26838.04 45392.56 23646.66 45374.91 38884.24 431
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 12587.76 22465.62 21689.20 11592.21 10579.94 1889.74 2794.86 2668.63 11794.20 13990.83 591.39 10594.38 64
EIA-MVS83.31 13182.80 13184.82 12089.59 13265.59 21788.21 16692.68 7374.66 15478.96 20586.42 30969.06 11095.26 8875.54 19290.09 13093.62 113
v7n78.97 24177.58 25683.14 21083.45 35665.51 21888.32 16291.21 15273.69 18072.41 34886.32 31257.93 25993.81 16169.18 26575.65 37190.11 270
V4279.38 23078.24 23582.83 22781.10 41165.50 21985.55 26889.82 19971.57 22978.21 22486.12 31760.66 23693.18 20875.64 18975.46 37789.81 289
PVSNet_BlendedMVS80.60 19680.02 18782.36 24788.85 16565.40 22086.16 25192.00 11669.34 29178.11 22786.09 31866.02 15494.27 13471.52 23682.06 28687.39 362
PVSNet_Blended80.98 17980.34 17882.90 22488.85 16565.40 22084.43 30292.00 11667.62 32578.11 22785.05 34466.02 15494.27 13471.52 23689.50 14289.01 312
baseline84.93 8884.98 8584.80 12287.30 25465.39 22287.30 20492.88 6377.62 4984.04 10892.26 11171.81 6293.96 14781.31 10890.30 12695.03 13
test_djsdf80.30 20879.32 21083.27 20383.98 34265.37 22390.50 7290.38 17968.55 31476.19 27588.70 23756.44 27793.46 18978.98 14580.14 31290.97 232
E5new84.22 9484.12 9784.51 13287.60 23465.36 22487.45 19492.31 9276.51 9183.53 11992.26 11169.25 10593.50 18279.88 12888.26 16594.69 37
E6new84.22 9484.12 9784.52 13087.60 23465.36 22487.45 19492.30 9476.51 9183.53 11992.26 11169.26 10393.49 18479.88 12888.26 16594.69 37
E684.22 9484.12 9784.52 13087.60 23465.36 22487.45 19492.30 9476.51 9183.53 11992.26 11169.26 10393.49 18479.88 12888.26 16594.69 37
E584.22 9484.12 9784.51 13287.60 23465.36 22487.45 19492.31 9276.51 9183.53 11992.26 11169.25 10593.50 18279.88 12888.26 16594.69 37
ACMH+68.96 1476.01 31074.01 32182.03 25588.60 18165.31 22888.86 13187.55 29070.25 27067.75 40587.47 27641.27 43293.19 20758.37 37875.94 36887.60 354
fmvsm_s_conf0.5_n_386.36 5387.46 3283.09 21287.08 26565.21 22989.09 12490.21 18879.67 2089.98 2495.02 2473.17 4391.71 27491.30 391.60 10092.34 181
CR-MVSNet73.37 34571.27 35779.67 32581.32 40965.19 23075.92 43880.30 41759.92 42672.73 34381.19 41052.50 31186.69 38959.84 36077.71 34087.11 378
RPMNet73.51 34170.49 37182.58 24281.32 40965.19 23075.92 43892.27 9657.60 44872.73 34376.45 45552.30 31495.43 7848.14 44877.71 34087.11 378
fmvsm_s_conf0.5_n_783.34 12884.03 10281.28 27485.73 29865.13 23285.40 27389.90 19874.96 14482.13 14893.89 6966.65 14087.92 37786.56 5391.05 11190.80 237
fmvsm_s_conf0.5_n_685.55 7386.20 5683.60 19087.32 25265.13 23288.86 13191.63 13875.41 12688.23 4193.45 8268.56 11892.47 24189.52 1892.78 7993.20 136
BH-w/o78.21 25977.33 26380.84 28788.81 16965.13 23284.87 28687.85 28469.75 28374.52 32084.74 35061.34 22293.11 21258.24 38085.84 22384.27 430
thisisatest053079.40 22877.76 25084.31 14787.69 23165.10 23587.36 20184.26 35570.04 27277.42 24288.26 25349.94 35694.79 11570.20 25384.70 24093.03 150
FA-MVS(test-final)80.96 18079.91 19084.10 16088.30 19365.01 23684.55 29690.01 19473.25 19679.61 19487.57 27158.35 25794.72 11871.29 24086.25 21192.56 169
fmvsm_s_conf0.5_n_284.04 10184.11 10183.81 18686.17 28965.00 23786.96 21487.28 29774.35 16188.25 4094.23 5061.82 21192.60 23389.85 1288.09 17393.84 96
E484.10 10083.99 10384.45 13787.58 24264.99 23886.54 23492.25 9976.38 10083.37 12492.09 12269.88 9393.58 17179.78 13388.03 17694.77 30
E284.00 10383.87 10484.39 14087.70 22964.95 23986.40 24192.23 10075.85 11383.21 12691.78 13070.09 8893.55 17679.52 13788.05 17494.66 45
E384.00 10383.87 10484.39 14087.70 22964.95 23986.40 24192.23 10075.85 11383.21 12691.78 13070.09 8893.55 17679.52 13788.05 17494.66 45
v1079.74 21878.67 22382.97 22284.06 34064.95 23987.88 18190.62 17173.11 20075.11 30786.56 30561.46 21994.05 14673.68 20975.55 37389.90 284
fmvsm_s_conf0.1_n_283.80 10983.79 10883.83 18485.62 30164.94 24287.03 21186.62 32174.32 16287.97 4894.33 4360.67 23592.60 23389.72 1487.79 18093.96 87
SDMVSNet80.38 20380.18 18280.99 28389.03 16364.94 24280.45 38489.40 21675.19 13676.61 26589.98 19560.61 23887.69 38176.83 17483.55 26490.33 260
dcpmvs_285.63 7186.15 6084.06 16991.71 8564.94 24286.47 23691.87 12473.63 18186.60 6893.02 9476.57 1991.87 26883.36 8492.15 9095.35 4
viewcassd2359sk1183.89 10683.74 10984.34 14587.76 22464.91 24586.30 24592.22 10375.47 12483.04 13291.52 14470.15 8693.53 17979.26 13987.96 17794.57 53
E3new83.78 11183.60 11484.31 14787.76 22464.89 24686.24 24892.20 10675.15 13982.87 13591.23 15370.11 8793.52 18179.05 14087.79 18094.51 58
IterMVS-SCA-FT75.43 31873.87 32580.11 30782.69 38264.85 24781.57 36383.47 36669.16 29970.49 36784.15 36651.95 32388.15 37469.23 26472.14 41387.34 367
MVSTER79.01 23977.88 24482.38 24583.07 36864.80 24884.08 31488.95 24669.01 30578.69 21087.17 28554.70 29292.43 24374.69 19980.57 30689.89 285
Anonymous2024052980.19 21178.89 22184.10 16090.60 10564.75 24988.95 12890.90 16265.97 35180.59 18091.17 15949.97 35593.73 16869.16 26682.70 28093.81 98
XVG-ACMP-BASELINE76.11 30874.27 32081.62 26383.20 36364.67 25083.60 32589.75 20469.75 28371.85 35587.09 28732.78 46892.11 25669.99 25780.43 30888.09 344
viewmacassd2359aftdt83.76 11283.66 11284.07 16686.59 28064.56 25186.88 21991.82 12775.72 11683.34 12592.15 12068.24 12492.88 22379.05 14089.15 14994.77 30
viewmanbaseed2359cas83.66 11583.55 11584.00 17786.81 27264.53 25286.65 22991.75 13274.89 14683.15 13191.68 13568.74 11692.83 22779.02 14289.24 14694.63 48
v119279.59 22178.43 23083.07 21583.55 35464.52 25386.93 21790.58 17270.83 24877.78 23685.90 31959.15 25093.94 15073.96 20877.19 34790.76 240
Fast-Effi-MVS+80.81 18479.92 18983.47 19488.85 16564.51 25485.53 27089.39 21770.79 24978.49 21785.06 34367.54 13093.58 17167.03 28886.58 20392.32 183
v114480.03 21479.03 21783.01 21883.78 34764.51 25487.11 20990.57 17471.96 22178.08 22986.20 31561.41 22093.94 15074.93 19877.23 34590.60 248
v879.97 21679.02 21882.80 23084.09 33964.50 25687.96 17590.29 18674.13 17075.24 30386.81 29162.88 19493.89 15874.39 20475.40 38090.00 278
EPP-MVSNet83.40 12683.02 12584.57 12890.13 11564.47 25792.32 3590.73 16974.45 15979.35 20191.10 16069.05 11195.12 9372.78 22287.22 19194.13 78
GeoE81.71 16081.01 16483.80 18789.51 13664.45 25888.97 12788.73 26071.27 23678.63 21389.76 20466.32 14793.20 20569.89 25886.02 21793.74 103
UniMVSNet (Re)81.60 16481.11 16183.09 21288.38 19064.41 25987.60 18793.02 5178.42 3878.56 21588.16 25569.78 9493.26 19869.58 26276.49 35791.60 209
LTVRE_ROB69.57 1376.25 30674.54 31581.41 26988.60 18164.38 26079.24 40189.12 23870.76 25169.79 38187.86 26449.09 37093.20 20556.21 40080.16 31086.65 391
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 24177.69 25382.81 22990.54 10764.29 26190.11 8391.51 14465.01 36876.16 27988.13 26050.56 34793.03 21969.68 26177.56 34491.11 225
testdata79.97 31190.90 9964.21 26284.71 34659.27 43285.40 7692.91 9562.02 20889.08 35868.95 26891.37 10686.63 392
v2v48280.23 20979.29 21183.05 21683.62 35264.14 26387.04 21089.97 19573.61 18278.18 22687.22 28261.10 22893.82 16076.11 18276.78 35491.18 223
VDDNet81.52 16880.67 16984.05 17290.44 10964.13 26489.73 9385.91 33271.11 23983.18 12993.48 7950.54 34893.49 18473.40 21488.25 16994.54 57
PAPR81.66 16380.89 16683.99 17990.27 11264.00 26586.76 22691.77 13168.84 31077.13 25589.50 21267.63 12994.88 10967.55 28088.52 16193.09 145
AstraMVS80.81 18480.14 18582.80 23086.05 29363.96 26686.46 23785.90 33373.71 17980.85 17590.56 18054.06 29991.57 27979.72 13483.97 25392.86 159
v14419279.47 22478.37 23182.78 23483.35 35763.96 26686.96 21490.36 18269.99 27577.50 24085.67 32660.66 23693.77 16474.27 20576.58 35590.62 246
v192192079.22 23378.03 23882.80 23083.30 35963.94 26886.80 22290.33 18369.91 27877.48 24185.53 33058.44 25693.75 16673.60 21076.85 35290.71 244
guyue81.13 17680.64 17182.60 24186.52 28163.92 26986.69 22887.73 28773.97 17180.83 17689.69 20556.70 27491.33 29678.26 15785.40 23192.54 170
tttt051779.40 22877.91 24183.90 18388.10 20363.84 27088.37 16084.05 35771.45 23176.78 25989.12 22349.93 35894.89 10870.18 25483.18 27392.96 155
diffmvs_AUTHOR82.38 14782.27 14382.73 23883.26 36063.80 27183.89 31589.76 20273.35 19282.37 14390.84 16966.25 14890.79 32182.77 9487.93 17893.59 115
thisisatest051577.33 28475.38 30083.18 20885.27 31263.80 27182.11 35483.27 36965.06 36675.91 28083.84 37049.54 36194.27 13467.24 28486.19 21291.48 216
diffmvspermissive82.10 15081.88 15282.76 23683.00 37163.78 27383.68 32089.76 20272.94 20482.02 15089.85 19865.96 15690.79 32182.38 10187.30 19093.71 104
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_yl81.17 17480.47 17683.24 20589.13 15863.62 27486.21 24989.95 19672.43 21381.78 15589.61 20957.50 26593.58 17170.75 24586.90 19792.52 172
DCV-MVSNet81.17 17480.47 17683.24 20589.13 15863.62 27486.21 24989.95 19672.43 21381.78 15589.61 20957.50 26593.58 17170.75 24586.90 19792.52 172
AllTest70.96 37768.09 39379.58 32785.15 31563.62 27484.58 29579.83 42262.31 40560.32 46486.73 29232.02 46988.96 36250.28 43271.57 41786.15 398
TestCases79.58 32785.15 31563.62 27479.83 42262.31 40560.32 46486.73 29232.02 46988.96 36250.28 43271.57 41786.15 398
icg_test_0407_278.92 24378.93 22078.90 34087.13 25963.59 27876.58 43489.33 21970.51 25977.82 23389.03 22661.84 20981.38 44072.56 22785.56 22791.74 204
IMVS_040780.61 19479.90 19182.75 23787.13 25963.59 27885.33 27489.33 21970.51 25977.82 23389.03 22661.84 20992.91 22172.56 22785.56 22791.74 204
IMVS_040477.16 28776.42 28479.37 33187.13 25963.59 27877.12 43189.33 21970.51 25966.22 43089.03 22650.36 35082.78 42972.56 22785.56 22791.74 204
IMVS_040380.80 18780.12 18682.87 22687.13 25963.59 27885.19 27589.33 21970.51 25978.49 21789.03 22663.26 18293.27 19772.56 22785.56 22791.74 204
v124078.99 24077.78 24882.64 23983.21 36263.54 28286.62 23190.30 18569.74 28577.33 24485.68 32557.04 27193.76 16573.13 21876.92 34990.62 246
CHOSEN 280x42066.51 42364.71 42571.90 43081.45 40463.52 28357.98 49568.95 47853.57 46562.59 45576.70 45346.22 39475.29 47755.25 40279.68 31576.88 475
IterMVS74.29 32972.94 33778.35 35381.53 40363.49 28481.58 36282.49 38468.06 32269.99 37683.69 37651.66 33285.54 40465.85 29671.64 41686.01 402
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet81.88 15681.54 15582.92 22388.46 18663.46 28587.13 20792.37 8980.19 1378.38 22089.14 22271.66 6793.05 21670.05 25576.46 35892.25 186
DU-MVS81.12 17780.52 17482.90 22487.80 21863.46 28587.02 21291.87 12479.01 3278.38 22089.07 22465.02 16493.05 21670.05 25576.46 35892.20 189
LFMVS81.82 15881.23 15883.57 19391.89 8363.43 28789.84 8781.85 39477.04 7483.21 12693.10 8952.26 31593.43 19171.98 23489.95 13493.85 94
NR-MVSNet80.23 20979.38 20782.78 23487.80 21863.34 28886.31 24491.09 15879.01 3272.17 35289.07 22467.20 13492.81 22866.08 29475.65 37192.20 189
IS-MVSNet83.15 13382.81 13084.18 15889.94 12463.30 28991.59 5188.46 26879.04 3179.49 19692.16 11865.10 16394.28 13367.71 27891.86 9894.95 15
TR-MVS77.44 28176.18 28781.20 27788.24 19463.24 29084.61 29486.40 32467.55 32677.81 23586.48 30854.10 29793.15 20957.75 38482.72 27987.20 372
MVS_Test83.15 13383.06 12483.41 19986.86 26963.21 29186.11 25292.00 11674.31 16382.87 13589.44 21970.03 9093.21 20277.39 16588.50 16293.81 98
IterMVS-LS80.06 21279.38 20782.11 25385.89 29463.20 29286.79 22389.34 21874.19 16775.45 29186.72 29466.62 14192.39 24572.58 22476.86 35190.75 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet80.52 20079.98 18882.12 25184.28 33463.19 29386.41 23888.95 24674.18 16878.69 21087.54 27466.62 14192.43 24372.57 22580.57 30690.74 242
CANet_DTU80.61 19479.87 19282.83 22785.60 30263.17 29487.36 20188.65 26476.37 10175.88 28188.44 24753.51 30493.07 21473.30 21589.74 13892.25 186
hybridnocas0781.44 17181.13 16082.37 24682.13 39263.11 29583.45 32988.74 25872.54 20880.71 17890.73 17265.14 16290.74 32680.35 12286.41 20793.27 130
MGCFI-Net85.06 8785.51 7583.70 18889.42 14163.01 29689.43 10592.62 8076.43 9587.53 5491.34 15172.82 5193.42 19281.28 10988.74 15794.66 45
GBi-Net78.40 25477.40 26081.40 27087.60 23463.01 29688.39 15789.28 22571.63 22575.34 29687.28 27854.80 28891.11 30362.72 32579.57 31690.09 272
test178.40 25477.40 26081.40 27087.60 23463.01 29688.39 15789.28 22571.63 22575.34 29687.28 27854.80 28891.11 30362.72 32579.57 31690.09 272
FMVSNet177.44 28176.12 28881.40 27086.81 27263.01 29688.39 15789.28 22570.49 26374.39 32287.28 27849.06 37191.11 30360.91 35278.52 32990.09 272
hybrid81.05 17880.66 17082.22 25081.97 39462.99 30083.42 33088.68 26170.76 25180.56 18190.40 18564.49 17190.48 33079.57 13686.06 21593.19 137
TAPA-MVS73.13 979.15 23577.94 24082.79 23389.59 13262.99 30088.16 16991.51 14465.77 35277.14 25491.09 16160.91 23193.21 20250.26 43487.05 19592.17 194
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RRT-MVS82.60 14682.10 14684.10 16087.98 21062.94 30287.45 19491.27 15077.42 5879.85 19190.28 18956.62 27694.70 12079.87 13288.15 17194.67 42
FMVSNet278.20 26077.21 26481.20 27787.60 23462.89 30387.47 19189.02 24171.63 22575.29 30287.28 27854.80 28891.10 30662.38 33379.38 32289.61 294
VortexMVS78.57 25277.89 24380.59 29285.89 29462.76 30485.61 26389.62 20972.06 21974.99 31185.38 33455.94 28190.77 32474.99 19776.58 35588.23 340
dtuplus80.04 21379.40 20681.97 25783.08 36762.61 30583.63 32487.98 27767.47 32981.02 16990.50 18364.86 16790.77 32471.28 24184.76 23892.53 171
viewdifsd2359ckpt0782.83 14182.78 13382.99 21986.51 28262.58 30685.09 28190.83 16675.22 13282.28 14491.63 13969.43 9992.03 25877.71 16086.32 20894.34 67
GA-MVS76.87 29275.17 30781.97 25782.75 38062.58 30681.44 36686.35 32672.16 21874.74 31582.89 39246.20 39592.02 26068.85 27081.09 29791.30 221
D2MVS74.82 32573.21 33379.64 32679.81 42762.56 30880.34 38687.35 29664.37 37668.86 38982.66 39646.37 39190.10 33767.91 27781.24 29586.25 395
viewmambaseed2359dif80.41 20179.84 19382.12 25182.95 37762.50 30983.39 33188.06 27567.11 33180.98 17090.31 18866.20 15091.01 31174.62 20084.90 23592.86 159
viewdifsd2359ckpt1180.37 20579.73 19682.30 24883.70 35062.39 31084.20 30986.67 31773.22 19880.90 17290.62 17763.00 19191.56 28076.81 17578.44 33192.95 156
viewmsd2359difaftdt80.37 20579.73 19682.30 24883.70 35062.39 31084.20 30986.67 31773.22 19880.90 17290.62 17763.00 19191.56 28076.81 17578.44 33192.95 156
FMVSNet377.88 27076.85 27280.97 28586.84 27162.36 31286.52 23588.77 25271.13 23875.34 29686.66 30054.07 29891.10 30662.72 32579.57 31689.45 298
TranMVSNet+NR-MVSNet80.84 18280.31 17982.42 24487.85 21562.33 31387.74 18591.33 14980.55 977.99 23189.86 19765.23 16192.62 23167.05 28775.24 38592.30 184
131476.53 29675.30 30580.21 30483.93 34362.32 31484.66 29188.81 25060.23 42270.16 37384.07 36755.30 28590.73 32767.37 28283.21 27287.59 356
MG-MVS83.41 12583.45 11783.28 20292.74 7262.28 31588.17 16889.50 21375.22 13281.49 16092.74 10566.75 13995.11 9572.85 22191.58 10292.45 178
SCA74.22 33172.33 34479.91 31284.05 34162.17 31679.96 39379.29 42966.30 34572.38 34980.13 42551.95 32388.60 36859.25 36777.67 34388.96 316
usedtu_blend_shiyan573.29 34970.96 36380.25 30277.80 44962.16 31784.44 30187.38 29564.41 37468.09 39976.28 45951.32 33491.23 29963.21 31865.76 44687.35 364
blend_shiyan472.29 36669.65 37980.21 30478.24 44562.16 31782.29 35187.27 30065.41 35968.43 39876.42 45839.91 44191.23 29963.21 31865.66 45187.22 371
PMMVS69.34 40068.67 38671.35 43675.67 46262.03 31975.17 44473.46 46450.00 47568.68 39079.05 43552.07 32178.13 45361.16 35182.77 27773.90 479
eth_miper_zixun_eth77.92 26976.69 27881.61 26583.00 37161.98 32083.15 33789.20 23369.52 28874.86 31484.35 35761.76 21292.56 23671.50 23872.89 40790.28 263
v14878.72 24777.80 24781.47 26782.73 38161.96 32186.30 24588.08 27373.26 19576.18 27685.47 33262.46 19992.36 24771.92 23573.82 39990.09 272
PAPM77.68 27776.40 28581.51 26687.29 25561.85 32283.78 31789.59 21064.74 37071.23 36288.70 23762.59 19693.66 17052.66 41887.03 19689.01 312
cl2278.07 26477.01 26781.23 27682.37 39061.83 32383.55 32687.98 27768.96 30875.06 30983.87 36861.40 22191.88 26773.53 21176.39 36089.98 281
baseline275.70 31373.83 32681.30 27383.26 36061.79 32482.57 34780.65 40766.81 33366.88 41883.42 38257.86 26192.19 25463.47 31279.57 31689.91 283
JIA-IIPM66.32 42562.82 43776.82 37977.09 45661.72 32565.34 48675.38 45558.04 44564.51 44362.32 48742.05 42886.51 39251.45 42569.22 42882.21 453
gbinet_0.2-2-1-0.0273.24 35170.86 36680.39 29678.03 44761.62 32683.10 33986.69 31665.98 35069.29 38676.15 46249.77 35991.51 28762.75 32466.00 44488.03 345
miper_ehance_all_eth78.59 25177.76 25081.08 28182.66 38361.56 32783.65 32189.15 23568.87 30975.55 28783.79 37266.49 14492.03 25873.25 21676.39 36089.64 293
c3_l78.75 24577.91 24181.26 27582.89 37861.56 32784.09 31389.13 23769.97 27675.56 28684.29 35866.36 14692.09 25773.47 21375.48 37590.12 269
blended_shiyan873.38 34371.17 35980.02 30978.36 44261.51 32982.43 34887.28 29765.40 36068.61 39277.53 45051.91 32691.00 31463.28 31665.76 44687.53 358
blended_shiyan673.38 34371.17 35980.01 31078.36 44261.48 33082.43 34887.27 30065.40 36068.56 39477.55 44951.94 32591.01 31163.27 31765.76 44687.55 357
miper_enhance_ethall77.87 27176.86 27180.92 28681.65 39961.38 33182.68 34588.98 24365.52 35675.47 28882.30 40165.76 15892.00 26172.95 22076.39 36089.39 300
0.4-1-1-0.170.93 37867.94 39779.91 31279.35 43561.27 33278.95 40882.19 38963.36 38867.50 40869.40 48139.83 44291.04 31062.44 33068.40 43387.40 361
mmtdpeth74.16 33273.01 33677.60 37183.72 34961.13 33385.10 28085.10 34272.06 21977.21 25280.33 42243.84 41585.75 40077.14 16852.61 48385.91 405
ppachtmachnet_test70.04 39167.34 41078.14 35679.80 42861.13 33379.19 40380.59 40859.16 43365.27 43779.29 43446.75 38787.29 38549.33 43966.72 43986.00 404
sc_t172.19 36869.51 38080.23 30384.81 32361.09 33584.68 29080.22 41960.70 41871.27 36183.58 37936.59 45989.24 35460.41 35563.31 45890.37 258
0.3-1-1-0.01570.03 39266.80 41679.72 32278.18 44661.07 33677.63 42682.32 38862.65 40165.50 43467.29 48237.62 45690.91 31761.99 34068.04 43587.19 373
TDRefinement67.49 41464.34 42676.92 37873.47 47561.07 33684.86 28782.98 37859.77 42758.30 47185.13 34126.06 48087.89 37847.92 45060.59 46981.81 458
wanda-best-256-51272.94 35770.66 36779.79 31777.80 44961.03 33881.31 36887.15 30565.18 36368.09 39976.28 45951.32 33490.97 31563.06 32065.76 44687.35 364
FE-blended-shiyan772.94 35770.66 36779.79 31777.80 44961.03 33881.31 36887.15 30565.18 36368.09 39976.28 45951.32 33490.97 31563.06 32065.76 44687.35 364
VNet82.21 14982.41 13881.62 26390.82 10160.93 34084.47 29789.78 20076.36 10284.07 10791.88 12664.71 16890.26 33470.68 24788.89 15293.66 106
ab-mvs79.51 22278.97 21981.14 27988.46 18660.91 34183.84 31689.24 23170.36 26479.03 20488.87 23463.23 18490.21 33665.12 30182.57 28192.28 185
PatchmatchNetpermissive73.12 35371.33 35578.49 35183.18 36460.85 34279.63 39678.57 43464.13 37871.73 35679.81 43051.20 33985.97 39957.40 38776.36 36588.66 328
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet80.60 19680.55 17380.76 28988.07 20560.80 34386.86 22091.58 14275.67 12080.24 18789.45 21863.34 17990.25 33570.51 24979.22 32591.23 222
usedtu_dtu_shiyan176.43 30175.32 30379.76 31983.00 37160.72 34481.74 35888.76 25668.99 30672.98 33984.19 36356.41 27890.27 33262.39 33179.40 32088.31 337
FE-MVSNET376.43 30175.32 30379.76 31983.00 37160.72 34481.74 35888.76 25668.99 30672.98 33984.19 36356.41 27890.27 33262.39 33179.40 32088.31 337
EGC-MVSNET52.07 45647.05 46067.14 45883.51 35560.71 34680.50 38367.75 4800.07 5400.43 54175.85 46624.26 48581.54 43828.82 49062.25 46259.16 491
Anonymous20240521178.25 25777.01 26781.99 25691.03 9560.67 34784.77 28883.90 35970.65 25780.00 19091.20 15741.08 43491.43 29265.21 30085.26 23293.85 94
0.4-1-1-0.270.01 39366.86 41579.44 33077.61 45260.64 34876.77 43382.34 38762.40 40465.91 43266.65 48340.05 43990.83 31961.77 34468.24 43486.86 384
ITE_SJBPF78.22 35481.77 39860.57 34983.30 36869.25 29567.54 40787.20 28336.33 46187.28 38654.34 40974.62 39186.80 386
MDA-MVSNet-bldmvs66.68 42163.66 43175.75 38679.28 43660.56 35073.92 45578.35 43664.43 37350.13 48779.87 42944.02 41483.67 42046.10 45856.86 47383.03 446
cl____77.72 27476.76 27580.58 29382.49 38760.48 35183.09 34087.87 28269.22 29674.38 32385.22 33962.10 20691.53 28571.09 24275.41 37989.73 292
DIV-MVS_self_test77.72 27476.76 27580.58 29382.48 38860.48 35183.09 34087.86 28369.22 29674.38 32385.24 33762.10 20691.53 28571.09 24275.40 38089.74 291
1112_ss77.40 28376.43 28380.32 30089.11 16260.41 35383.65 32187.72 28862.13 40873.05 33886.72 29462.58 19789.97 34062.11 33980.80 30290.59 249
tt080578.73 24677.83 24581.43 26885.17 31360.30 35489.41 10890.90 16271.21 23777.17 25388.73 23646.38 39093.21 20272.57 22578.96 32690.79 238
UniMVSNet_ETH3D79.10 23778.24 23581.70 26286.85 27060.24 35587.28 20588.79 25174.25 16676.84 25690.53 18249.48 36291.56 28067.98 27682.15 28493.29 128
HY-MVS69.67 1277.95 26877.15 26580.36 29887.57 24360.21 35683.37 33387.78 28666.11 34675.37 29587.06 28963.27 18190.48 33061.38 34982.43 28290.40 257
sd_testset77.70 27677.40 26078.60 34589.03 16360.02 35779.00 40685.83 33475.19 13676.61 26589.98 19554.81 28785.46 40662.63 32983.55 26490.33 260
RPSCF73.23 35271.46 35278.54 34882.50 38659.85 35882.18 35382.84 38258.96 43571.15 36489.41 22045.48 40584.77 41358.82 37371.83 41591.02 231
test_cas_vis1_n_192073.76 33873.74 32773.81 41475.90 45959.77 35980.51 38282.40 38558.30 44181.62 15985.69 32444.35 41276.41 46576.29 17978.61 32785.23 417
dmvs_re71.14 37570.58 36972.80 42481.96 39559.68 36075.60 44279.34 42868.55 31469.27 38780.72 41849.42 36376.54 46252.56 41977.79 33982.19 454
miper_lstm_enhance74.11 33373.11 33577.13 37780.11 42259.62 36172.23 45986.92 31366.76 33570.40 36882.92 39156.93 27282.92 42869.06 26772.63 40888.87 319
OurMVSNet-221017-074.26 33072.42 34379.80 31683.76 34859.59 36285.92 25786.64 31966.39 34466.96 41787.58 27039.46 44391.60 27665.76 29769.27 42788.22 341
Patchmatch-RL test70.24 38867.78 40277.61 36977.43 45459.57 36371.16 46370.33 47162.94 39568.65 39172.77 47450.62 34685.49 40569.58 26266.58 44187.77 351
tt0320-xc70.11 39067.45 40878.07 35985.33 31059.51 36483.28 33478.96 43258.77 43767.10 41680.28 42336.73 45887.42 38456.83 39559.77 47187.29 369
OpenMVS_ROBcopyleft64.09 1970.56 38468.19 39077.65 36880.26 41859.41 36585.01 28382.96 37958.76 43865.43 43682.33 40037.63 45591.23 29945.34 46376.03 36782.32 452
tt032070.49 38668.03 39477.89 36184.78 32459.12 36683.55 32680.44 41358.13 44367.43 41280.41 42139.26 44587.54 38355.12 40363.18 45986.99 381
our_test_369.14 40167.00 41375.57 38979.80 42858.80 36777.96 42277.81 43859.55 42962.90 45478.25 44447.43 37883.97 41851.71 42267.58 43883.93 436
ADS-MVSNet266.20 42863.33 43274.82 40179.92 42458.75 36867.55 47875.19 45653.37 46665.25 43875.86 46442.32 42480.53 44541.57 47368.91 42985.18 418
pm-mvs177.25 28676.68 27978.93 33984.22 33658.62 36986.41 23888.36 26971.37 23273.31 33488.01 26161.22 22689.15 35764.24 30973.01 40689.03 311
MonoMVSNet76.49 30075.80 28978.58 34681.55 40258.45 37086.36 24386.22 32774.87 14974.73 31683.73 37451.79 33088.73 36570.78 24472.15 41288.55 333
WR-MVS79.49 22379.22 21480.27 30188.79 17458.35 37185.06 28288.61 26678.56 3677.65 23888.34 24963.81 17890.66 32864.98 30377.22 34691.80 203
FIs82.07 15282.42 13781.04 28288.80 17358.34 37288.26 16593.49 3176.93 7778.47 21991.04 16369.92 9292.34 24969.87 25984.97 23492.44 179
CostFormer75.24 32273.90 32479.27 33382.65 38458.27 37380.80 37482.73 38361.57 41275.33 30083.13 38755.52 28391.07 30964.98 30378.34 33688.45 334
Test_1112_low_res76.40 30475.44 29779.27 33389.28 15158.09 37481.69 36187.07 30859.53 43072.48 34786.67 29961.30 22389.33 35160.81 35480.15 31190.41 256
tfpnnormal74.39 32873.16 33478.08 35886.10 29258.05 37584.65 29387.53 29170.32 26771.22 36385.63 32754.97 28689.86 34143.03 46875.02 38786.32 394
test-LLR72.94 35772.43 34274.48 40481.35 40758.04 37678.38 41577.46 44166.66 33769.95 37779.00 43748.06 37679.24 44866.13 29184.83 23686.15 398
test-mter71.41 37370.39 37474.48 40481.35 40758.04 37678.38 41577.46 44160.32 42169.95 37779.00 43736.08 46279.24 44866.13 29184.83 23686.15 398
mvs_anonymous79.42 22779.11 21680.34 29984.45 33357.97 37882.59 34687.62 28967.40 33076.17 27888.56 24468.47 11989.59 34770.65 24886.05 21693.47 121
tpm cat170.57 38368.31 38977.35 37482.41 38957.95 37978.08 42080.22 41952.04 46968.54 39577.66 44852.00 32287.84 37951.77 42172.07 41486.25 395
SixPastTwentyTwo73.37 34571.26 35879.70 32385.08 31857.89 38085.57 26483.56 36471.03 24465.66 43385.88 32042.10 42792.57 23559.11 36963.34 45788.65 329
thres20075.55 31574.47 31678.82 34187.78 22157.85 38183.07 34283.51 36572.44 21275.84 28284.42 35352.08 32091.75 27147.41 45183.64 26386.86 384
XXY-MVS75.41 31975.56 29574.96 39883.59 35357.82 38280.59 38183.87 36066.54 34374.93 31388.31 25063.24 18380.09 44662.16 33776.85 35286.97 382
reproduce_monomvs75.40 32074.38 31878.46 35283.92 34457.80 38383.78 31786.94 31173.47 18872.25 35184.47 35238.74 44889.27 35375.32 19570.53 42288.31 337
FE-MVSNET272.88 36071.28 35677.67 36678.30 44457.78 38484.43 30288.92 24869.56 28664.61 44281.67 40846.73 38888.54 37059.33 36567.99 43686.69 390
K. test v371.19 37468.51 38779.21 33583.04 37057.78 38484.35 30676.91 44872.90 20562.99 45382.86 39339.27 44491.09 30861.65 34552.66 48288.75 325
tfpn200view976.42 30375.37 30179.55 32989.13 15857.65 38685.17 27683.60 36273.41 19076.45 26886.39 31052.12 31791.95 26348.33 44483.75 25889.07 305
thres40076.50 29775.37 30179.86 31489.13 15857.65 38685.17 27683.60 36273.41 19076.45 26886.39 31052.12 31791.95 26348.33 44483.75 25890.00 278
CMPMVSbinary51.72 2170.19 38968.16 39176.28 38273.15 47857.55 38879.47 39883.92 35848.02 47856.48 47784.81 34843.13 41986.42 39462.67 32881.81 29084.89 424
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs674.69 32673.39 33078.61 34481.38 40657.48 38986.64 23087.95 28064.99 36970.18 37186.61 30150.43 34989.52 34862.12 33870.18 42488.83 321
test_vis1_n_192075.52 31675.78 29074.75 40379.84 42657.44 39083.26 33585.52 33762.83 39779.34 20286.17 31645.10 40679.71 44778.75 14781.21 29687.10 380
PVSNet_057.27 2061.67 44159.27 44468.85 45079.61 43157.44 39068.01 47673.44 46555.93 45958.54 47070.41 47944.58 40977.55 45747.01 45235.91 49471.55 482
thres600view776.50 29775.44 29779.68 32489.40 14357.16 39285.53 27083.23 37073.79 17776.26 27387.09 28751.89 32791.89 26648.05 44983.72 26190.00 278
lessismore_v078.97 33881.01 41257.15 39365.99 48461.16 46082.82 39439.12 44691.34 29559.67 36246.92 48988.43 335
TransMVSNet (Re)75.39 32174.56 31477.86 36285.50 30657.10 39486.78 22486.09 33172.17 21771.53 35987.34 27763.01 19089.31 35256.84 39461.83 46387.17 374
thres100view90076.50 29775.55 29679.33 33289.52 13556.99 39585.83 26183.23 37073.94 17376.32 27287.12 28651.89 32791.95 26348.33 44483.75 25889.07 305
TESTMET0.1,169.89 39669.00 38572.55 42679.27 43756.85 39678.38 41574.71 46157.64 44768.09 39977.19 45237.75 45476.70 46163.92 31084.09 25284.10 434
WTY-MVS75.65 31475.68 29275.57 38986.40 28456.82 39777.92 42482.40 38565.10 36576.18 27687.72 26663.13 18980.90 44360.31 35781.96 28789.00 314
MDA-MVSNet_test_wron65.03 43062.92 43471.37 43475.93 45856.73 39869.09 47574.73 46057.28 45254.03 48277.89 44545.88 39774.39 48049.89 43661.55 46582.99 447
pmmvs357.79 44554.26 45068.37 45364.02 49556.72 39975.12 44765.17 48640.20 48752.93 48369.86 48020.36 49175.48 47445.45 46255.25 48072.90 481
tpm273.26 35071.46 35278.63 34383.34 35856.71 40080.65 38080.40 41556.63 45573.55 33282.02 40651.80 32991.24 29856.35 39978.42 33487.95 346
TinyColmap67.30 41764.81 42474.76 40281.92 39756.68 40180.29 38781.49 39860.33 42056.27 47983.22 38424.77 48487.66 38245.52 46169.47 42679.95 467
YYNet165.03 43062.91 43571.38 43375.85 46156.60 40269.12 47474.66 46257.28 45254.12 48177.87 44645.85 39874.48 47949.95 43561.52 46683.05 445
PM-MVS66.41 42464.14 42773.20 42073.92 47056.45 40378.97 40764.96 48863.88 38564.72 44180.24 42419.84 49283.44 42566.24 29064.52 45579.71 468
PVSNet64.34 1872.08 37070.87 36575.69 38786.21 28756.44 40474.37 45380.73 40662.06 40970.17 37282.23 40342.86 42183.31 42654.77 40784.45 24687.32 368
pmmvs571.55 37270.20 37675.61 38877.83 44856.39 40581.74 35880.89 40357.76 44667.46 41084.49 35149.26 36885.32 40857.08 39075.29 38385.11 421
testing1175.14 32374.01 32178.53 34988.16 19856.38 40680.74 37880.42 41470.67 25372.69 34583.72 37543.61 41789.86 34162.29 33583.76 25789.36 301
WR-MVS_H78.51 25378.49 22778.56 34788.02 20756.38 40688.43 15492.67 7477.14 6973.89 32787.55 27366.25 14889.24 35458.92 37173.55 40190.06 276
MIMVSNet70.69 38269.30 38174.88 40084.52 33156.35 40875.87 44079.42 42664.59 37167.76 40482.41 39841.10 43381.54 43846.64 45581.34 29386.75 388
USDC70.33 38768.37 38876.21 38380.60 41556.23 40979.19 40386.49 32260.89 41661.29 45985.47 33231.78 47189.47 35053.37 41576.21 36682.94 448
Baseline_NR-MVSNet78.15 26278.33 23377.61 36985.79 29656.21 41086.78 22485.76 33573.60 18377.93 23287.57 27165.02 16488.99 35967.14 28675.33 38287.63 353
tpmvs71.09 37669.29 38276.49 38182.04 39356.04 41178.92 40981.37 40064.05 38167.18 41578.28 44349.74 36089.77 34349.67 43772.37 40983.67 438
FC-MVSNet-test81.52 16882.02 14980.03 30888.42 18955.97 41287.95 17693.42 3477.10 7277.38 24390.98 16869.96 9191.79 26968.46 27484.50 24292.33 182
testing9176.54 29575.66 29479.18 33688.43 18855.89 41381.08 37183.00 37773.76 17875.34 29684.29 35846.20 39590.07 33864.33 30784.50 24291.58 211
mvs5depth69.45 39967.45 40875.46 39373.93 46955.83 41479.19 40383.23 37066.89 33271.63 35883.32 38333.69 46785.09 40959.81 36155.34 47985.46 413
GG-mvs-BLEND75.38 39481.59 40155.80 41579.32 40069.63 47467.19 41473.67 47243.24 41888.90 36450.41 42984.50 24281.45 459
VPNet78.69 24878.66 22478.76 34288.31 19255.72 41684.45 30086.63 32076.79 8178.26 22390.55 18159.30 24989.70 34666.63 28977.05 34890.88 235
baseline176.98 29076.75 27777.66 36788.13 20155.66 41785.12 27981.89 39273.04 20276.79 25888.90 23262.43 20087.78 38063.30 31571.18 41989.55 296
test_vis1_rt60.28 44258.42 44565.84 46167.25 49055.60 41870.44 46860.94 49444.33 48359.00 46866.64 48424.91 48368.67 49262.80 32369.48 42573.25 480
testing9976.09 30975.12 30879.00 33788.16 19855.50 41980.79 37581.40 39973.30 19475.17 30484.27 36144.48 41090.02 33964.28 30884.22 25191.48 216
testing22274.04 33472.66 34078.19 35587.89 21355.36 42081.06 37279.20 43071.30 23574.65 31883.57 38039.11 44788.67 36751.43 42685.75 22590.53 251
FMVSNet569.50 39867.96 39574.15 40982.97 37655.35 42180.01 39282.12 39162.56 40263.02 45181.53 40936.92 45781.92 43648.42 44374.06 39585.17 420
test_fmvs1_n70.86 38070.24 37572.73 42572.51 48355.28 42281.27 37079.71 42451.49 47378.73 20984.87 34627.54 47977.02 45976.06 18379.97 31485.88 406
test_vis1_n69.85 39769.21 38371.77 43172.66 48255.27 42381.48 36476.21 45352.03 47075.30 30183.20 38628.97 47676.22 46774.60 20178.41 33583.81 437
test_fmvs170.93 37870.52 37072.16 42873.71 47155.05 42480.82 37378.77 43351.21 47478.58 21484.41 35431.20 47376.94 46075.88 18780.12 31384.47 429
sss73.60 34073.64 32873.51 41682.80 37955.01 42576.12 43681.69 39562.47 40374.68 31785.85 32257.32 26778.11 45460.86 35380.93 29887.39 362
dtuonlycased68.45 41067.29 41171.92 42980.18 42154.90 42679.76 39580.38 41660.11 42462.57 45676.44 45749.34 36582.31 43255.05 40461.77 46478.53 471
mvsany_test162.30 43961.26 44365.41 46269.52 48654.86 42766.86 48049.78 50246.65 47968.50 39683.21 38549.15 36966.28 49456.93 39360.77 46775.11 478
ECVR-MVScopyleft79.61 21979.26 21280.67 29190.08 11754.69 42887.89 18077.44 44374.88 14780.27 18692.79 10148.96 37392.45 24268.55 27292.50 8494.86 22
EPNet_dtu75.46 31774.86 30977.23 37682.57 38554.60 42986.89 21883.09 37471.64 22466.25 42985.86 32155.99 28088.04 37654.92 40686.55 20489.05 310
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVSNet78.22 25878.34 23277.84 36387.83 21754.54 43087.94 17791.17 15477.65 4873.48 33388.49 24562.24 20488.43 37162.19 33674.07 39490.55 250
gg-mvs-nofinetune69.95 39467.96 39575.94 38483.07 36854.51 43177.23 43070.29 47263.11 39170.32 36962.33 48643.62 41688.69 36653.88 41287.76 18284.62 428
PS-CasMVS78.01 26778.09 23777.77 36587.71 22754.39 43288.02 17391.22 15177.50 5673.26 33588.64 24060.73 23288.41 37261.88 34173.88 39890.53 251
Anonymous2024052168.80 40467.22 41273.55 41574.33 46754.11 43383.18 33685.61 33658.15 44261.68 45880.94 41530.71 47481.27 44157.00 39273.34 40585.28 416
Patchmtry70.74 38169.16 38475.49 39280.72 41354.07 43474.94 44980.30 41758.34 44070.01 37481.19 41052.50 31186.54 39153.37 41571.09 42085.87 407
PEN-MVS77.73 27377.69 25377.84 36387.07 26753.91 43587.91 17991.18 15377.56 5373.14 33788.82 23561.23 22589.17 35659.95 35972.37 40990.43 255
gm-plane-assit81.40 40553.83 43662.72 40080.94 41592.39 24563.40 314
CL-MVSNet_self_test72.37 36471.46 35275.09 39779.49 43353.53 43780.76 37785.01 34569.12 30070.51 36682.05 40557.92 26084.13 41752.27 42066.00 44487.60 354
MDTV_nov1_ep1369.97 37883.18 36453.48 43877.10 43280.18 42160.45 41969.33 38580.44 41948.89 37486.90 38851.60 42378.51 330
KD-MVS_2432*160066.22 42663.89 42973.21 41875.47 46553.42 43970.76 46684.35 35164.10 37966.52 42578.52 44134.55 46584.98 41050.40 43050.33 48681.23 460
miper_refine_blended66.22 42663.89 42973.21 41875.47 46553.42 43970.76 46684.35 35164.10 37966.52 42578.52 44134.55 46584.98 41050.40 43050.33 48681.23 460
test111179.43 22679.18 21580.15 30689.99 12253.31 44187.33 20377.05 44775.04 14080.23 18892.77 10448.97 37292.33 25068.87 26992.40 8694.81 27
LF4IMVS64.02 43562.19 43869.50 44670.90 48453.29 44276.13 43577.18 44652.65 46858.59 46980.98 41423.55 48776.52 46353.06 41766.66 44078.68 470
MVStest156.63 44752.76 45368.25 45561.67 49753.25 44371.67 46168.90 47938.59 49050.59 48683.05 38825.08 48270.66 48836.76 48238.56 49380.83 463
usedtu_dtu_shiyan264.75 43361.63 44174.10 41070.64 48553.18 44482.10 35581.27 40256.22 45856.39 47874.67 46927.94 47883.56 42242.71 47062.73 46085.57 411
DTE-MVSNet76.99 28976.80 27377.54 37286.24 28653.06 44587.52 18990.66 17077.08 7372.50 34688.67 23960.48 24089.52 34857.33 38870.74 42190.05 277
FE-MVSNET67.25 41865.33 42273.02 42275.86 46052.54 44680.26 38980.56 40963.80 38660.39 46279.70 43141.41 43184.66 41543.34 46762.62 46181.86 456
test250677.30 28576.49 28179.74 32190.08 11752.02 44787.86 18263.10 49174.88 14780.16 18992.79 10138.29 45292.35 24868.74 27192.50 8494.86 22
tpm72.37 36471.71 34974.35 40682.19 39152.00 44879.22 40277.29 44564.56 37272.95 34183.68 37751.35 33383.26 42758.33 37975.80 36987.81 350
test_fmvs268.35 41167.48 40770.98 44069.50 48751.95 44980.05 39176.38 45249.33 47674.65 31884.38 35523.30 48875.40 47674.51 20275.17 38685.60 410
ETVMVS72.25 36771.05 36175.84 38587.77 22351.91 45079.39 39974.98 45769.26 29473.71 32982.95 39040.82 43686.14 39646.17 45784.43 24789.47 297
WB-MVSnew71.96 37171.65 35072.89 42384.67 33051.88 45182.29 35177.57 44062.31 40573.67 33183.00 38953.49 30581.10 44245.75 46082.13 28585.70 409
MIMVSNet168.58 40666.78 41773.98 41280.07 42351.82 45280.77 37684.37 35064.40 37559.75 46782.16 40436.47 46083.63 42142.73 46970.33 42386.48 393
Vis-MVSNet (Re-imp)78.36 25678.45 22878.07 35988.64 18051.78 45386.70 22779.63 42574.14 16975.11 30790.83 17061.29 22489.75 34458.10 38191.60 10092.69 165
LCM-MVSNet-Re77.05 28876.94 27077.36 37387.20 25651.60 45480.06 39080.46 41275.20 13567.69 40686.72 29462.48 19888.98 36063.44 31389.25 14591.51 213
Gipumacopyleft45.18 46341.86 46655.16 47777.03 45751.52 45532.50 50480.52 41032.46 49727.12 50035.02 5089.52 50375.50 47322.31 50060.21 47038.45 504
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth67.33 41665.99 42071.37 43473.48 47451.47 45675.16 44585.19 34065.20 36260.78 46180.93 41742.35 42377.20 45857.12 38953.69 48185.44 414
UnsupCasMVSNet_bld63.70 43661.53 44270.21 44373.69 47251.39 45772.82 45781.89 39255.63 46057.81 47371.80 47638.67 44978.61 45149.26 44052.21 48480.63 464
UBG73.08 35472.27 34575.51 39188.02 20751.29 45878.35 41877.38 44465.52 35673.87 32882.36 39945.55 40286.48 39355.02 40584.39 24888.75 325
FPMVS53.68 45251.64 45459.81 46965.08 49351.03 45969.48 47169.58 47541.46 48640.67 49372.32 47516.46 49670.00 49124.24 49865.42 45258.40 493
WBMVS73.43 34272.81 33875.28 39587.91 21250.99 46078.59 41481.31 40165.51 35874.47 32184.83 34746.39 38986.68 39058.41 37777.86 33888.17 343
CVMVSNet72.99 35672.58 34174.25 40884.28 33450.85 46186.41 23883.45 36744.56 48273.23 33687.54 27449.38 36485.70 40165.90 29578.44 33186.19 397
Anonymous2023120668.60 40567.80 40171.02 43980.23 42050.75 46278.30 41980.47 41156.79 45466.11 43182.63 39746.35 39278.95 45043.62 46675.70 37083.36 441
ambc75.24 39673.16 47750.51 46363.05 49387.47 29364.28 44477.81 44717.80 49489.73 34557.88 38360.64 46885.49 412
APD_test153.31 45349.93 45863.42 46565.68 49250.13 46471.59 46266.90 48334.43 49540.58 49471.56 4778.65 50576.27 46634.64 48555.36 47863.86 489
tpmrst72.39 36272.13 34673.18 42180.54 41649.91 46579.91 39479.08 43163.11 39171.69 35779.95 42755.32 28482.77 43065.66 29873.89 39786.87 383
Patchmatch-test64.82 43263.24 43369.57 44579.42 43449.82 46663.49 49269.05 47751.98 47159.95 46680.13 42550.91 34170.98 48740.66 47573.57 40087.90 348
EPMVS69.02 40268.16 39171.59 43279.61 43149.80 46777.40 42866.93 48262.82 39870.01 37479.05 43545.79 39977.86 45656.58 39775.26 38487.13 377
dtuonly69.95 39469.98 37769.85 44473.09 47949.46 46874.55 45276.40 45157.56 45067.82 40386.31 31350.89 34574.23 48161.46 34781.71 29185.86 408
SSC-MVS3.273.35 34873.39 33073.23 41785.30 31149.01 46974.58 45181.57 39675.21 13473.68 33085.58 32952.53 30982.05 43554.33 41077.69 34288.63 330
dp66.80 42065.43 42170.90 44179.74 43048.82 47075.12 44774.77 45959.61 42864.08 44777.23 45142.89 42080.72 44448.86 44266.58 44183.16 443
UWE-MVS72.13 36971.49 35174.03 41186.66 27847.70 47181.40 36776.89 44963.60 38775.59 28584.22 36239.94 44085.62 40348.98 44186.13 21488.77 324
test0.0.03 168.00 41367.69 40368.90 44977.55 45347.43 47275.70 44172.95 46866.66 33766.56 42382.29 40248.06 37675.87 47144.97 46474.51 39283.41 440
SD_040374.65 32774.77 31174.29 40786.20 28847.42 47383.71 31985.12 34169.30 29268.50 39687.95 26359.40 24886.05 39749.38 43883.35 26989.40 299
myMVS_eth3d2873.62 33973.53 32973.90 41388.20 19547.41 47478.06 42179.37 42774.29 16573.98 32684.29 35844.67 40783.54 42351.47 42487.39 18890.74 242
ADS-MVSNet64.36 43462.88 43668.78 45179.92 42447.17 47567.55 47871.18 47053.37 46665.25 43875.86 46442.32 42473.99 48341.57 47368.91 42985.18 418
EU-MVSNet68.53 40867.61 40571.31 43778.51 44147.01 47684.47 29784.27 35442.27 48566.44 42884.79 34940.44 43783.76 41958.76 37468.54 43283.17 442
test_fmvs363.36 43761.82 43967.98 45662.51 49646.96 47777.37 42974.03 46345.24 48167.50 40878.79 44012.16 50072.98 48672.77 22366.02 44383.99 435
ttmdpeth59.91 44357.10 44768.34 45467.13 49146.65 47874.64 45067.41 48148.30 47762.52 45785.04 34520.40 49075.93 47042.55 47145.90 49282.44 451
KD-MVS_self_test68.81 40367.59 40672.46 42774.29 46845.45 47977.93 42387.00 30963.12 39063.99 44878.99 43942.32 42484.77 41356.55 39864.09 45687.16 376
testf145.72 46041.96 46457.00 47156.90 49945.32 48066.14 48359.26 49626.19 49930.89 49860.96 4904.14 50870.64 48926.39 49646.73 49055.04 494
APD_test245.72 46041.96 46457.00 47156.90 49945.32 48066.14 48359.26 49626.19 49930.89 49860.96 4904.14 50870.64 48926.39 49646.73 49055.04 494
LCM-MVSNet54.25 44949.68 45967.97 45753.73 50545.28 48266.85 48180.78 40535.96 49439.45 49562.23 4888.70 50478.06 45548.24 44751.20 48580.57 465
test_vis3_rt49.26 45947.02 46156.00 47354.30 50245.27 48366.76 48248.08 50336.83 49244.38 49153.20 4997.17 50764.07 49656.77 39655.66 47658.65 492
testing3-275.12 32475.19 30674.91 39990.40 11045.09 48480.29 38778.42 43578.37 4176.54 26787.75 26544.36 41187.28 38657.04 39183.49 26692.37 180
test20.0367.45 41566.95 41468.94 44875.48 46444.84 48577.50 42777.67 43966.66 33763.01 45283.80 37147.02 38278.40 45242.53 47268.86 43183.58 439
mvsany_test353.99 45051.45 45561.61 46755.51 50144.74 48663.52 49145.41 50643.69 48458.11 47276.45 45517.99 49363.76 49754.77 40747.59 48876.34 476
PatchT68.46 40967.85 39870.29 44280.70 41443.93 48772.47 45874.88 45860.15 42370.55 36576.57 45449.94 35681.59 43750.58 42874.83 38985.34 415
MVS-HIRNet59.14 44457.67 44663.57 46481.65 39943.50 48871.73 46065.06 48739.59 48951.43 48457.73 49338.34 45182.58 43139.53 47673.95 39664.62 488
testing368.56 40767.67 40471.22 43887.33 25042.87 48983.06 34371.54 46970.36 26469.08 38884.38 35530.33 47585.69 40237.50 48175.45 37885.09 422
WAC-MVS42.58 49039.46 477
myMVS_eth3d67.02 41966.29 41969.21 44784.68 32742.58 49078.62 41273.08 46666.65 34066.74 42179.46 43231.53 47282.30 43339.43 47876.38 36382.75 449
PMVScopyleft37.38 2244.16 46440.28 46855.82 47540.82 51042.54 49265.12 48763.99 49034.43 49524.48 50257.12 4953.92 51076.17 46817.10 50455.52 47748.75 498
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_f52.09 45550.82 45655.90 47453.82 50442.31 49359.42 49458.31 49836.45 49356.12 48070.96 47812.18 49957.79 50053.51 41456.57 47567.60 485
testgi66.67 42266.53 41867.08 45975.62 46341.69 49475.93 43776.50 45066.11 34665.20 44086.59 30235.72 46374.71 47843.71 46573.38 40484.84 425
Syy-MVS68.05 41267.85 39868.67 45284.68 32740.97 49578.62 41273.08 46666.65 34066.74 42179.46 43252.11 31982.30 43332.89 48676.38 36382.75 449
ANet_high50.57 45846.10 46263.99 46348.67 50839.13 49670.99 46580.85 40461.39 41431.18 49757.70 49417.02 49573.65 48531.22 48915.89 50779.18 469
UWE-MVS-2865.32 42964.93 42366.49 46078.70 43938.55 49777.86 42564.39 48962.00 41064.13 44683.60 37841.44 43076.00 46931.39 48880.89 29984.92 423
MDTV_nov1_ep13_2view37.79 49875.16 44555.10 46166.53 42449.34 36553.98 41187.94 347
DSMNet-mixed57.77 44656.90 44860.38 46867.70 48935.61 49969.18 47253.97 50032.30 49857.49 47479.88 42840.39 43868.57 49338.78 47972.37 40976.97 474
MVEpermissive26.22 2330.37 47025.89 47443.81 48344.55 50935.46 50028.87 50739.07 50718.20 50618.58 50940.18 5062.68 51147.37 50517.07 50523.78 50248.60 499
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet50.91 45750.29 45752.78 47968.58 48834.94 50163.71 49056.63 49939.73 48844.95 49065.47 48521.93 48958.48 49934.98 48456.62 47464.92 487
wuyk23d16.82 47815.94 48119.46 49658.74 49831.45 50239.22 5003.74 5256.84 5106.04 5162.70 5401.27 51324.29 51410.54 51214.40 5092.63 523
E-PMN31.77 46730.64 47035.15 48852.87 50627.67 50357.09 49647.86 50424.64 50116.40 51233.05 50911.23 50154.90 50214.46 50718.15 50522.87 509
kuosan39.70 46640.40 46737.58 48664.52 49426.98 50465.62 48533.02 50946.12 48042.79 49248.99 50224.10 48646.56 50612.16 51026.30 50039.20 503
DeepMVS_CXcopyleft27.40 49340.17 51126.90 50524.59 51217.44 50723.95 50348.61 5049.77 50226.48 51218.06 50224.47 50128.83 508
dongtai45.42 46245.38 46345.55 48273.36 47626.85 50667.72 47734.19 50854.15 46449.65 48856.41 49725.43 48162.94 49819.45 50128.09 49946.86 501
EMVS30.81 46929.65 47134.27 48950.96 50725.95 50756.58 49746.80 50524.01 50215.53 51330.68 51112.47 49854.43 50312.81 50917.05 50622.43 510
dmvs_testset62.63 43864.11 42858.19 47078.55 44024.76 50875.28 44365.94 48567.91 32360.34 46376.01 46353.56 30373.94 48431.79 48767.65 43775.88 477
new-patchmatchnet61.73 44061.73 44061.70 46672.74 48124.50 50969.16 47378.03 43761.40 41356.72 47675.53 46738.42 45076.48 46445.95 45957.67 47284.13 433
RoMa-SfM28.67 47125.38 47538.54 48432.61 51422.48 51040.24 4997.23 51821.81 50326.66 50160.46 4920.96 51441.72 50726.47 49511.95 51051.40 497
WB-MVS54.94 44854.72 44955.60 47673.50 47320.90 51174.27 45461.19 49359.16 43350.61 48574.15 47047.19 38175.78 47217.31 50335.07 49570.12 483
SSC-MVS53.88 45153.59 45154.75 47872.87 48019.59 51273.84 45660.53 49557.58 44949.18 48973.45 47346.34 39375.47 47516.20 50632.28 49769.20 484
LoFTR27.52 47224.27 47637.29 48734.75 51319.27 51333.78 50321.60 51312.42 50821.61 50756.59 4960.91 51540.37 50813.94 50822.80 50352.22 496
DKM25.67 47323.01 47733.64 49032.08 51519.25 51437.50 5015.52 52018.67 50423.58 50555.44 4980.64 51934.02 50923.95 4999.73 51247.66 500
PDCNetPlus24.75 47422.46 47831.64 49135.53 51217.00 51532.00 5059.46 51518.43 50518.56 51051.31 5011.65 51233.00 51126.51 4948.70 51444.91 502
PMMVS240.82 46538.86 46946.69 48153.84 50316.45 51648.61 49849.92 50137.49 49131.67 49660.97 4898.14 50656.42 50128.42 49130.72 49867.19 486
MatchFormer22.13 47519.86 48028.93 49228.66 51615.74 51731.91 50617.10 5147.75 50918.87 50847.50 5050.62 52133.92 5107.49 51418.87 50437.14 505
tmp_tt18.61 47721.40 47910.23 4984.82 54210.11 51834.70 50230.74 5111.48 51823.91 50426.07 51228.42 47713.41 51627.12 49215.35 5087.17 518
ALIKED-MNN7.86 4837.83 4897.97 50019.40 5198.86 51914.48 5113.90 5221.59 5164.74 52216.49 5140.59 5227.65 5190.91 5248.34 5167.39 515
ALIKED-LG8.61 4828.70 4868.33 49920.63 5188.70 52015.50 5104.61 5212.19 5155.84 51718.70 5130.80 5168.06 5181.03 5238.97 5138.25 512
GLUNet-SfM12.90 48110.00 48421.62 49513.58 5218.30 52110.19 5149.30 5164.31 51312.18 51430.90 5100.50 52522.76 5154.89 5154.14 52533.79 507
ALIKED-NN7.51 4847.61 4907.21 50118.26 5208.10 52213.45 5133.88 5241.50 5174.87 52016.47 5150.64 5197.00 5200.88 5258.50 5156.52 520
N_pmnet52.79 45453.26 45251.40 48078.99 4387.68 52369.52 4703.89 52351.63 47257.01 47574.98 46840.83 43565.96 49537.78 48064.67 45480.56 466
ELoFTR14.23 47911.56 48322.24 49411.02 5226.56 52413.59 5127.57 5175.55 51111.96 51539.09 5070.21 53024.93 5139.43 5135.66 51935.22 506
test_method31.52 46829.28 47238.23 48527.03 5176.50 52520.94 50862.21 4924.05 51422.35 50652.50 50013.33 49747.58 50427.04 49334.04 49660.62 490
MASt3R-SfM13.55 48013.93 48212.41 49710.54 5255.97 52616.61 5096.07 5194.50 51216.53 51148.67 5030.73 5179.44 51711.56 51110.18 51121.81 511
SIFT-NN2.77 4962.92 4992.34 5098.70 5283.08 5274.46 5221.01 5340.68 5261.46 5275.49 5240.16 5311.65 5280.26 5264.04 5262.27 524
SIFT-MNN2.63 4972.75 5002.25 5108.10 5292.84 5284.08 5231.02 5330.68 5261.28 5285.34 5270.15 5321.64 5290.26 5263.88 5282.27 524
SIFT-NN-NCMNet2.52 4982.64 5012.14 5117.53 5312.74 5294.00 5240.98 5350.65 5291.24 5305.08 5300.14 5331.60 5300.23 5293.94 5272.07 528
SIFT-NCM-Cal2.40 4992.52 5022.05 5127.74 5302.54 5303.75 5260.84 5360.65 5290.89 5354.78 5330.13 5361.60 5300.19 5373.71 5292.01 530
SIFT-ConvMatch2.25 5022.37 5051.90 5147.29 5322.37 5313.21 5300.75 5390.65 5291.03 5334.91 5310.12 5391.51 5340.22 5323.13 5331.81 531
SIFT-NN-CMatch2.31 5002.41 5032.00 5136.59 5352.34 5323.48 5270.83 5370.65 5291.28 5285.09 5280.14 5331.52 5320.23 5293.41 5312.14 526
SP-DiffGlue4.29 4904.46 4933.77 5063.68 5432.12 5335.97 5192.22 5271.10 5194.89 51913.93 5170.66 5181.95 5272.47 5165.24 5207.22 517
SIFT-NN-UMatch2.26 5012.39 5041.89 5156.21 5372.08 5343.76 5250.83 5370.66 5281.04 5325.09 5280.14 5331.52 5320.23 5293.51 5302.07 528
SP-SuperGlue4.24 4924.38 4953.81 50510.75 5242.00 5358.18 5162.09 5281.00 5212.41 5238.29 5200.56 5232.05 5261.27 5194.91 5227.39 515
SIFT-CM-Cal2.02 5052.13 5081.67 5186.79 5341.99 5362.79 5320.64 5420.63 5340.87 5364.48 5360.13 5361.41 5370.19 5372.70 5351.61 535
SP-LightGlue4.27 4914.41 4943.86 50310.99 5231.99 5368.19 5152.06 5290.98 5222.37 5248.29 5200.56 5232.10 5241.27 5194.99 5217.48 514
SIFT-UMatch2.16 5032.30 5061.72 5176.99 5331.97 5383.32 5280.70 5410.64 5330.91 5344.86 5320.12 5391.49 5350.22 5322.97 5341.72 533
XFeat-MNN4.39 4894.49 4924.10 5022.88 5441.91 5395.86 5202.57 5261.06 5205.04 51813.99 5160.43 5284.47 5212.00 5176.55 5175.92 521
SP-MNN4.14 4934.24 4963.82 50410.32 5261.83 5408.11 5171.99 5300.82 5242.23 5258.27 5220.47 5272.14 5231.20 5214.77 5237.49 513
SP-NN4.00 4944.12 4973.63 5079.92 5271.81 5417.94 5181.90 5320.86 5232.15 5268.00 5230.50 5252.09 5251.20 5214.63 5246.98 519
SIFT-UM-Cal1.97 5062.12 5091.52 5196.57 5361.67 5422.93 5310.57 5440.62 5350.83 5374.55 5350.11 5411.37 5380.20 5362.69 5361.53 536
SIFT-NN-PointCN2.07 5042.18 5071.74 5165.75 5381.65 5433.27 5290.73 5400.60 5361.07 5314.62 5340.13 5361.43 5360.21 5343.22 5322.12 527
XFeat-NN3.78 4953.96 4983.23 5082.65 5451.53 5444.99 5211.92 5310.81 5254.77 52112.37 5190.38 5293.39 5221.64 5186.13 5184.77 522
SIFT-PointCN1.72 5071.83 5101.36 5215.55 5401.22 5452.59 5330.59 5430.55 5380.71 5393.77 5380.08 5431.24 5390.17 5392.48 5371.63 534
SIFT-PCN-Cal1.72 5071.82 5111.39 5205.64 5391.19 5462.39 5340.53 5450.55 5380.72 5383.90 5370.09 5421.22 5400.17 5392.42 5381.76 532
SIFT-NCMNet1.44 5091.56 5121.08 5225.14 5411.07 5471.97 5350.32 5460.56 5370.64 5403.23 5390.07 5441.01 5410.14 5411.95 5391.15 537
test1236.12 4868.11 4870.14 5230.06 5470.09 54871.05 4640.03 5480.04 5420.25 5431.30 5420.05 5450.03 5430.21 5340.01 5410.29 538
testmvs6.04 4878.02 4880.10 5240.08 5460.03 54969.74 4690.04 5470.05 5410.31 5421.68 5410.02 5460.04 5420.24 5280.02 5400.25 539
mmdepth0.00 5100.00 5130.00 5250.00 5480.00 5500.00 5360.00 5490.00 5430.00 5440.00 5430.00 5470.00 5440.00 5420.00 5420.00 540
monomultidepth0.00 5100.00 5130.00 5250.00 5480.00 5500.00 5360.00 5490.00 5430.00 5440.00 5430.00 5470.00 5440.00 5420.00 5420.00 540
test_blank0.00 5100.00 5130.00 5250.00 5480.00 5500.00 5360.00 5490.00 5430.00 5440.00 5430.00 5470.00 5440.00 5420.00 5420.00 540
uanet_test0.00 5100.00 5130.00 5250.00 5480.00 5500.00 5360.00 5490.00 5430.00 5440.00 5430.00 5470.00 5440.00 5420.00 5420.00 540
DCPMVS0.00 5100.00 5130.00 5250.00 5480.00 5500.00 5360.00 5490.00 5430.00 5440.00 5430.00 5470.00 5440.00 5420.00 5420.00 540
cdsmvs_eth3d_5k19.96 47626.61 4730.00 5250.00 5480.00 5500.00 53689.26 2280.00 5430.00 54488.61 24161.62 2150.00 5440.00 5420.00 5420.00 540
pcd_1.5k_mvsjas5.26 4887.02 4910.00 5250.00 5480.00 5500.00 5360.00 5490.00 5430.00 5440.00 54363.15 1860.00 5440.00 5420.00 5420.00 540
sosnet-low-res0.00 5100.00 5130.00 5250.00 5480.00 5500.00 5360.00 5490.00 5430.00 5440.00 5430.00 5470.00 5440.00 5420.00 5420.00 540
sosnet0.00 5100.00 5130.00 5250.00 5480.00 5500.00 5360.00 5490.00 5430.00 5440.00 5430.00 5470.00 5440.00 5420.00 5420.00 540
uncertanet0.00 5100.00 5130.00 5250.00 5480.00 5500.00 5360.00 5490.00 5430.00 5440.00 5430.00 5470.00 5440.00 5420.00 5420.00 540
Regformer0.00 5100.00 5130.00 5250.00 5480.00 5500.00 5360.00 5490.00 5430.00 5440.00 5430.00 5470.00 5440.00 5420.00 5420.00 540
ab-mvs-re7.23 4859.64 4850.00 5250.00 5480.00 5500.00 5360.00 5490.00 5430.00 54486.72 2940.00 5470.00 5440.00 5420.00 5420.00 540
uanet0.00 5100.00 5130.00 5250.00 5480.00 5500.00 5360.00 5490.00 5430.00 5440.00 5430.00 5470.00 5440.00 5420.00 5420.00 540
PC_three_145268.21 32092.02 1494.00 6382.09 595.98 6284.58 7196.68 294.95 15
eth-test20.00 548
eth-test0.00 548
test_241102_TWO94.06 1477.24 6592.78 495.72 1081.26 997.44 789.07 2596.58 694.26 73
9.1488.26 1992.84 7091.52 5694.75 173.93 17488.57 3694.67 3075.57 2695.79 6486.77 5195.76 26
test_0728_THIRD78.38 3992.12 1195.78 681.46 897.40 989.42 1996.57 794.67 42
GSMVS88.96 316
sam_mvs151.32 33488.96 316
sam_mvs50.01 354
MTGPAbinary92.02 114
test_post178.90 4105.43 52648.81 37585.44 40759.25 367
test_post5.46 52550.36 35084.24 416
patchmatchnet-post74.00 47151.12 34088.60 368
MTMP92.18 3932.83 510
test9_res84.90 6495.70 2992.87 158
agg_prior282.91 9195.45 3292.70 163
test_prior288.85 13375.41 12684.91 8393.54 7674.28 3483.31 8595.86 23
旧先验286.56 23358.10 44487.04 6288.98 36074.07 207
新几何286.29 247
无先验87.48 19088.98 24360.00 42594.12 14367.28 28388.97 315
原ACMM286.86 220
testdata291.01 31162.37 334
segment_acmp73.08 44
testdata184.14 31275.71 117
plane_prior592.44 8495.38 8378.71 14886.32 20891.33 219
plane_prior491.00 166
plane_prior291.25 6079.12 29
plane_prior189.90 125
n20.00 549
nn0.00 549
door-mid69.98 473
test1192.23 100
door69.44 476
HQP-NCC89.33 14689.17 11776.41 9677.23 248
ACMP_Plane89.33 14689.17 11776.41 9677.23 248
BP-MVS77.47 163
HQP4-MVS77.24 24795.11 9591.03 229
HQP3-MVS92.19 10885.99 218
HQP2-MVS60.17 244
ACMMP++_ref81.95 288
ACMMP++81.25 294
Test By Simon64.33 172