This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.




Method Infoallhigh-res
multi-view
indooroutdoorbotani.boulde.bridgedoorexhibi.lectur.living.loungeobserv.old co.statueterrac.
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort by
OPU-MVS99.49 499.64 1798.51 499.77 1099.19 2895.12 899.97 2199.90 199.92 399.99 1
PC_three_145294.60 2799.41 299.12 4295.50 799.96 2899.84 299.92 399.97 7
SED-MVS98.18 298.10 498.41 1799.63 1895.24 2499.77 1097.72 7194.17 3399.30 699.54 393.32 1999.98 999.70 399.81 2399.99 1
test_241102_TWO97.72 7194.17 3399.23 899.54 393.14 2499.98 999.70 399.82 1999.99 1
IU-MVS99.63 1895.38 2197.73 7095.54 1999.54 199.69 599.81 2399.99 1
DVP-MVScopyleft98.07 798.00 698.29 1899.66 1295.20 2999.72 1697.47 12693.95 3899.07 1299.46 1093.18 2299.97 2199.64 699.82 1999.69 53
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND98.77 799.66 1296.37 1399.72 1697.68 8099.98 999.64 699.82 1999.96 10
patch_mono-297.10 2397.97 894.49 15899.21 6183.73 27499.62 3098.25 2995.28 2299.38 498.91 6792.28 2899.94 3499.61 899.22 7099.78 37
DPE-MVScopyleft98.11 698.00 698.44 1599.50 4295.39 2099.29 7297.72 7194.50 2898.64 2299.54 393.32 1999.97 2199.58 999.90 799.95 15
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSC_two_6792asdad99.51 299.61 2498.60 297.69 7899.98 999.55 1099.83 1599.96 10
No_MVS99.51 299.61 2498.60 297.69 7899.98 999.55 1099.83 1599.96 10
MVS_030497.53 1197.15 1998.67 1097.30 12396.52 1199.60 3198.88 1397.14 397.21 5798.94 6586.89 9499.91 4299.43 1298.91 8499.59 69
DeepPCF-MVS93.56 196.55 3697.84 1092.68 21098.71 8578.11 32999.70 1997.71 7598.18 197.36 5499.76 190.37 4599.94 3499.27 1399.54 5299.99 1
APDe-MVS97.53 1197.47 1397.70 3599.58 3093.63 6399.56 3697.52 11693.59 5398.01 4299.12 4290.80 3999.55 9699.26 1499.79 2799.93 20
DVP-MVS++98.18 298.09 598.44 1599.61 2495.38 2199.55 3797.68 8093.01 6099.23 899.45 1495.12 899.98 999.25 1599.92 399.97 7
test_0728_THIRD93.01 6099.07 1299.46 1094.66 1499.97 2199.25 1599.82 1999.95 15
dcpmvs_295.67 6396.18 3994.12 17598.82 8184.22 26797.37 23995.45 27390.70 10895.77 9298.63 9290.47 4298.68 15499.20 1799.22 7099.45 78
test_fmvsm_n_192097.08 2497.55 1295.67 12097.94 10489.61 15099.93 198.48 2397.08 499.08 1199.13 4088.17 6699.93 3799.11 1899.06 7497.47 187
TSAR-MVS + GP.96.95 2696.91 2397.07 5498.88 7991.62 9599.58 3496.54 19595.09 2496.84 6798.63 9291.16 3199.77 7599.04 1996.42 13499.81 32
MCST-MVS98.18 297.95 998.86 599.85 396.60 999.70 1997.98 4797.18 295.96 8599.33 1992.62 26100.00 198.99 2099.93 199.98 6
CNVR-MVS98.46 198.38 198.72 899.80 496.19 1499.80 997.99 4697.05 599.41 299.59 292.89 25100.00 198.99 2099.90 799.96 10
CANet97.00 2596.49 3298.55 1198.86 8096.10 1599.83 597.52 11695.90 1497.21 5798.90 6882.66 16699.93 3798.71 2298.80 8999.63 62
9.1496.87 2499.34 5099.50 4397.49 12389.41 14798.59 2499.43 1689.78 5099.69 8198.69 2399.62 44
SD-MVS97.51 1397.40 1697.81 3399.01 7293.79 6299.33 6997.38 13993.73 4998.83 2099.02 5390.87 3899.88 4998.69 2399.74 2999.77 42
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
test9_res98.60 2599.87 999.90 22
PS-MVSNAJ96.87 2896.40 3498.29 1897.35 12197.29 599.03 10497.11 16395.83 1598.97 1599.14 3882.48 16999.60 9398.60 2599.08 7398.00 174
xiu_mvs_v2_base96.66 3296.17 4298.11 2697.11 13496.96 699.01 10797.04 17095.51 2098.86 1899.11 4682.19 17699.36 12098.59 2798.14 10598.00 174
train_agg97.20 2097.08 2097.57 4199.57 3393.17 7299.38 6297.66 8390.18 12498.39 2899.18 3190.94 3599.66 8498.58 2899.85 1399.88 26
TSAR-MVS + MP.97.44 1597.46 1497.39 4599.12 6593.49 6898.52 15697.50 12194.46 2998.99 1498.64 9091.58 3099.08 13898.49 2999.83 1599.60 65
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SF-MVS97.22 1996.92 2298.12 2599.11 6694.88 3499.44 5397.45 12989.60 14098.70 2199.42 1790.42 4499.72 7998.47 3099.65 3899.77 42
PHI-MVS96.65 3396.46 3397.21 5199.34 5091.77 9299.70 1998.05 4286.48 22898.05 3999.20 2789.33 5399.96 2898.38 3199.62 4499.90 22
test_fmvsmvis_n_192095.47 6595.40 6395.70 11894.33 23490.22 13099.70 1996.98 17796.80 692.75 13698.89 7082.46 17299.92 3998.36 3298.33 10296.97 202
ZD-MVS99.67 1093.28 7097.61 9687.78 19897.41 5299.16 3490.15 4799.56 9598.35 3399.70 35
test_prior299.57 3591.43 9598.12 3698.97 5690.43 4398.33 3499.81 23
SMA-MVScopyleft97.24 1796.99 2198.00 2899.30 5494.20 5499.16 8297.65 8889.55 14499.22 1099.52 890.34 4699.99 598.32 3599.83 1599.82 31
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
CHOSEN 280x42096.80 3096.85 2596.66 8197.85 10794.42 5094.76 30898.36 2692.50 7195.62 9697.52 13897.92 197.38 22398.31 3698.80 8998.20 170
NCCC98.12 598.11 398.13 2399.76 694.46 4799.81 797.88 4996.54 998.84 1999.46 1092.55 2799.98 998.25 3799.93 199.94 18
MSP-MVS97.77 998.18 296.53 8899.54 3690.14 13299.41 5997.70 7695.46 2198.60 2399.19 2895.71 499.49 10298.15 3899.85 1399.95 15
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
ETV-MVS96.00 4896.00 4796.00 10896.56 15191.05 11199.63 2996.61 18793.26 5897.39 5398.30 10986.62 10198.13 17298.07 3997.57 11598.82 134
MSLP-MVS++97.50 1497.45 1597.63 3799.65 1693.21 7199.70 1998.13 3994.61 2697.78 4799.46 1089.85 4999.81 7097.97 4099.91 699.88 26
APD-MVScopyleft96.95 2696.72 2897.63 3799.51 4193.58 6499.16 8297.44 13290.08 12998.59 2499.07 4789.06 5599.42 11397.92 4199.66 3799.88 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SteuartSystems-ACMMP97.25 1697.34 1797.01 5797.38 12091.46 9999.75 1497.66 8394.14 3798.13 3499.26 2192.16 2999.66 8497.91 4299.64 4099.90 22
Skip Steuart: Steuart Systems R&D Blog.
test_vis1_n_192093.08 13293.42 10592.04 22296.31 16279.36 31799.83 596.06 22796.72 798.53 2698.10 11758.57 32299.91 4297.86 4398.79 9196.85 204
agg_prior297.84 4499.87 999.91 21
mvsany_test194.57 9195.09 7192.98 20195.84 18182.07 29598.76 13195.24 28692.87 6796.45 7898.71 8684.81 13299.15 13197.68 4595.49 15297.73 179
HPM-MVS++copyleft97.72 1097.59 1198.14 2299.53 4094.76 4199.19 7697.75 6695.66 1798.21 3299.29 2091.10 3399.99 597.68 4599.87 999.68 54
test_vis1_n90.40 18290.27 17390.79 25191.55 29076.48 33399.12 9494.44 30994.31 3197.34 5596.95 16543.60 36199.42 11397.57 4797.60 11496.47 212
SR-MVS96.13 4596.16 4496.07 10599.42 4789.04 15698.59 15197.33 14390.44 11896.84 6799.12 4286.75 9799.41 11697.47 4899.44 5899.76 44
PVSNet_BlendedMVS93.36 12293.20 11193.84 18698.77 8391.61 9699.47 4698.04 4391.44 9494.21 11692.63 25883.50 14599.87 5297.41 4983.37 25490.05 318
PVSNet_Blended95.94 5395.66 5896.75 7398.77 8391.61 9699.88 298.04 4393.64 5294.21 11697.76 12583.50 14599.87 5297.41 4997.75 11398.79 137
test_fmvs192.35 14592.94 12090.57 25697.19 12775.43 33799.55 3794.97 29395.20 2396.82 7097.57 13759.59 32099.84 6197.30 5198.29 10496.46 213
EC-MVSNet95.09 7495.17 6894.84 14695.42 19588.17 17699.48 4495.92 23991.47 9397.34 5598.36 10682.77 16297.41 22297.24 5298.58 9698.94 122
MVS_111021_HR96.69 3196.69 2996.72 7798.58 8891.00 11399.14 9099.45 193.86 4495.15 10398.73 8188.48 6299.76 7697.23 5399.56 5099.40 81
test_fmvs1_n91.07 17091.41 15190.06 27094.10 23974.31 34199.18 7894.84 29794.81 2596.37 8097.46 14150.86 35099.82 6797.14 5497.90 10796.04 220
xiu_mvs_v1_base_debu94.73 8393.98 9196.99 5995.19 20495.24 2498.62 14596.50 19792.99 6297.52 4998.83 7472.37 24599.15 13197.03 5596.74 12996.58 208
xiu_mvs_v1_base94.73 8393.98 9196.99 5995.19 20495.24 2498.62 14596.50 19792.99 6297.52 4998.83 7472.37 24599.15 13197.03 5596.74 12996.58 208
xiu_mvs_v1_base_debi94.73 8393.98 9196.99 5995.19 20495.24 2498.62 14596.50 19792.99 6297.52 4998.83 7472.37 24599.15 13197.03 5596.74 12996.58 208
lupinMVS96.32 4195.94 4897.44 4395.05 21694.87 3599.86 396.50 19793.82 4798.04 4098.77 7785.52 11998.09 17596.98 5898.97 7999.37 83
CS-MVS-test95.98 5096.34 3694.90 14398.06 10187.66 18899.69 2696.10 22393.66 5098.35 3199.05 5086.28 11097.66 20596.96 5998.90 8599.37 83
MVS_111021_LR95.78 5895.94 4895.28 13298.19 9787.69 18598.80 12599.26 793.39 5595.04 10598.69 8884.09 13999.76 7696.96 5999.06 7498.38 159
VNet95.08 7594.26 8197.55 4298.07 10093.88 6098.68 13798.73 1790.33 12197.16 6197.43 14379.19 19999.53 9996.91 6191.85 19199.24 95
test_cas_vis1_n_192093.86 10693.74 10094.22 17195.39 19886.08 23199.73 1596.07 22696.38 1297.19 6097.78 12465.46 29999.86 5796.71 6298.92 8396.73 205
CS-MVS95.75 6196.19 3894.40 16297.88 10686.22 22599.66 2796.12 22292.69 6898.07 3898.89 7087.09 8897.59 21196.71 6298.62 9599.39 82
APD-MVS_3200maxsize95.64 6495.65 6095.62 12299.24 5887.80 18498.42 16997.22 15088.93 16196.64 7798.98 5585.49 12299.36 12096.68 6499.27 6899.70 51
SR-MVS-dyc-post95.75 6195.86 5195.41 12899.22 5987.26 20498.40 17497.21 15189.63 13896.67 7598.97 5686.73 9999.36 12096.62 6599.31 6599.60 65
RE-MVS-def95.70 5799.22 5987.26 20498.40 17497.21 15189.63 13896.67 7598.97 5685.24 12796.62 6599.31 6599.60 65
DeepC-MVS_fast93.52 297.16 2196.84 2698.13 2399.61 2494.45 4898.85 12097.64 8996.51 1195.88 8899.39 1887.35 8599.99 596.61 6799.69 3699.96 10
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS91.24 16890.18 17494.45 16197.08 13585.84 24098.40 17496.10 22386.99 21393.36 12998.16 11554.27 33999.20 12896.59 6890.63 20798.31 165
MP-MVS-pluss95.80 5795.30 6497.29 4798.95 7692.66 8298.59 15197.14 15988.95 15993.12 13299.25 2285.62 11899.94 3496.56 6999.48 5499.28 92
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
diffmvspermissive94.59 9094.19 8495.81 11495.54 19190.69 12098.70 13595.68 26091.61 8995.96 8597.81 12180.11 19198.06 17796.52 7095.76 14798.67 146
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ACMMP_NAP96.59 3496.18 3997.81 3398.82 8193.55 6598.88 11997.59 10290.66 10997.98 4399.14 3886.59 102100.00 196.47 7199.46 5599.89 25
PAPM96.35 3995.94 4897.58 3994.10 23995.25 2398.93 11498.17 3494.26 3293.94 12198.72 8389.68 5197.88 18796.36 7299.29 6799.62 64
MTAPA96.09 4695.80 5596.96 6499.29 5591.19 10397.23 24797.45 12992.58 6994.39 11499.24 2486.43 10899.99 596.22 7399.40 6299.71 50
alignmvs95.77 5995.00 7398.06 2797.35 12195.68 1899.71 1897.50 12191.50 9296.16 8398.61 9486.28 11099.00 14096.19 7491.74 19399.51 74
canonicalmvs95.02 7693.96 9498.20 2097.53 11895.92 1698.71 13396.19 21791.78 8795.86 9098.49 10179.53 19699.03 13996.12 7591.42 19999.66 58
DELS-MVS97.12 2296.60 3198.68 998.03 10296.57 1099.84 497.84 5296.36 1395.20 10298.24 11188.17 6699.83 6496.11 7699.60 4899.64 60
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
jason95.40 6994.86 7497.03 5692.91 27194.23 5399.70 1996.30 20893.56 5496.73 7398.52 9781.46 18597.91 18496.08 7798.47 10098.96 117
jason: jason.
CP-MVS96.22 4496.15 4596.42 9399.67 1089.62 14999.70 1997.61 9690.07 13096.00 8499.16 3487.43 7999.92 3996.03 7899.72 3199.70 51
MP-MVScopyleft96.00 4895.82 5296.54 8799.47 4690.13 13499.36 6697.41 13690.64 11295.49 9798.95 6285.51 12199.98 996.00 7999.59 4999.52 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
h-mvs3392.47 14491.95 14094.05 17997.13 13285.01 25798.36 18098.08 4093.85 4596.27 8196.73 17583.19 15499.43 11295.81 8068.09 34197.70 180
hse-mvs291.67 15991.51 14992.15 21996.22 16682.61 29197.74 22697.53 11393.85 4596.27 8196.15 18983.19 15497.44 22095.81 8066.86 34896.40 215
HFP-MVS96.42 3896.26 3796.90 6699.69 890.96 11499.47 4697.81 5890.54 11596.88 6499.05 5087.57 7699.96 2895.65 8299.72 3199.78 37
XVS96.47 3796.37 3596.77 7199.62 2290.66 12299.43 5697.58 10492.41 7596.86 6598.96 6087.37 8199.87 5295.65 8299.43 5999.78 37
X-MVStestdata90.69 17988.66 20196.77 7199.62 2290.66 12299.43 5697.58 10492.41 7596.86 6529.59 38587.37 8199.87 5295.65 8299.43 5999.78 37
ACMMPR96.28 4396.14 4696.73 7599.68 990.47 12599.47 4697.80 6090.54 11596.83 6999.03 5286.51 10699.95 3195.65 8299.72 3199.75 45
HPM-MVScopyleft95.41 6895.22 6795.99 10999.29 5589.14 15499.17 8197.09 16787.28 21195.40 9898.48 10284.93 12999.38 11895.64 8699.65 3899.47 77
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_yl95.27 7194.60 7797.28 4898.53 8992.98 7899.05 10198.70 1886.76 22294.65 11197.74 12787.78 7399.44 10995.57 8792.61 17799.44 79
DCV-MVSNet95.27 7194.60 7797.28 4898.53 8992.98 7899.05 10198.70 1886.76 22294.65 11197.74 12787.78 7399.44 10995.57 8792.61 17799.44 79
region2R96.30 4296.17 4296.70 7899.70 790.31 12799.46 5097.66 8390.55 11497.07 6299.07 4786.85 9599.97 2195.43 8999.74 2999.81 32
EI-MVSNet-Vis-set95.76 6095.63 6296.17 10299.14 6490.33 12698.49 16297.82 5591.92 8594.75 10898.88 7287.06 9099.48 10695.40 9097.17 12698.70 144
EPNet96.82 2996.68 3097.25 5098.65 8693.10 7499.48 4498.76 1496.54 997.84 4698.22 11287.49 7899.66 8495.35 9197.78 11299.00 113
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MG-MVS97.24 1796.83 2798.47 1499.79 595.71 1799.07 9899.06 994.45 3096.42 7998.70 8788.81 5999.74 7895.35 9199.86 1299.97 7
HY-MVS88.56 795.29 7094.23 8298.48 1397.72 10996.41 1294.03 31698.74 1592.42 7495.65 9594.76 21786.52 10599.49 10295.29 9392.97 17299.53 71
mPP-MVS95.90 5495.75 5696.38 9599.58 3089.41 15399.26 7397.41 13690.66 10994.82 10798.95 6286.15 11399.98 995.24 9499.64 4099.74 46
ZNCC-MVS96.09 4695.81 5496.95 6599.42 4791.19 10399.55 3797.53 11389.72 13595.86 9098.94 6586.59 10299.97 2195.13 9599.56 5099.68 54
GG-mvs-BLEND96.98 6296.53 15294.81 4087.20 35497.74 6793.91 12296.40 18396.56 296.94 23795.08 9698.95 8299.20 99
EIA-MVS95.11 7395.27 6694.64 15596.34 16186.51 21399.59 3396.62 18692.51 7094.08 11998.64 9086.05 11498.24 16995.07 9798.50 9999.18 100
DeepC-MVS91.02 494.56 9293.92 9696.46 9097.16 12990.76 11898.39 17897.11 16393.92 4088.66 19098.33 10778.14 20799.85 6095.02 9898.57 9798.78 139
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffmvs_mvgpermissive94.00 9993.33 10796.03 10695.22 20290.90 11699.09 9695.99 22990.58 11391.55 15397.37 14579.91 19298.06 17795.01 9995.22 15499.13 104
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
WTY-MVS95.97 5195.11 7098.54 1297.62 11396.65 899.44 5398.74 1592.25 7995.21 10198.46 10586.56 10499.46 10895.00 10092.69 17699.50 75
CSCG94.87 7894.71 7595.36 12999.54 3686.49 21499.34 6898.15 3782.71 28990.15 17799.25 2289.48 5299.86 5794.97 10198.82 8899.72 49
EI-MVSNet-UG-set95.43 6695.29 6595.86 11399.07 7089.87 14398.43 16897.80 6091.78 8794.11 11898.77 7786.25 11299.48 10694.95 10296.45 13398.22 168
CPTT-MVS94.60 8994.43 7995.09 13699.66 1286.85 20999.44 5397.47 12683.22 27894.34 11598.96 6082.50 16799.55 9694.81 10399.50 5398.88 127
PVSNet_083.28 1687.31 24085.16 25593.74 19094.78 22684.59 26298.91 11798.69 2089.81 13478.59 30493.23 24861.95 31199.34 12494.75 10455.72 36897.30 191
CLD-MVS91.06 17190.71 16792.10 22094.05 24386.10 23099.55 3796.29 21194.16 3584.70 22597.17 15669.62 26597.82 19194.74 10586.08 22992.39 242
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
casdiffmvspermissive93.98 10193.43 10495.61 12395.07 21589.86 14498.80 12595.84 25290.98 10392.74 13797.66 13279.71 19398.10 17494.72 10695.37 15398.87 129
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
VDDNet90.08 19288.54 20794.69 15294.41 23387.68 18698.21 19296.40 20276.21 33693.33 13097.75 12654.93 33798.77 14794.71 10790.96 20297.61 185
iter_conf0593.48 11693.18 11294.39 16597.15 13094.17 5699.30 7192.97 33392.38 7886.70 21195.42 20495.67 596.59 24994.67 10884.32 24392.39 242
CDPH-MVS96.56 3596.18 3997.70 3599.59 2893.92 5999.13 9397.44 13289.02 15697.90 4599.22 2588.90 5899.49 10294.63 10999.79 2799.68 54
GST-MVS95.97 5195.66 5896.90 6699.49 4591.22 10199.45 5297.48 12489.69 13695.89 8798.72 8386.37 10999.95 3194.62 11099.22 7099.52 72
Effi-MVS+93.87 10593.15 11396.02 10795.79 18290.76 11896.70 26995.78 25386.98 21695.71 9397.17 15679.58 19498.01 18294.57 11196.09 14299.31 89
LFMVS92.23 15090.84 16396.42 9398.24 9491.08 11098.24 18996.22 21483.39 27694.74 10998.31 10861.12 31598.85 14494.45 11292.82 17399.32 88
ET-MVSNet_ETH3D92.56 14291.45 15095.88 11296.39 15994.13 5799.46 5096.97 17892.18 8166.94 35698.29 11094.65 1594.28 33294.34 11383.82 25099.24 95
baseline93.91 10393.30 10895.72 11795.10 21390.07 13697.48 23595.91 24491.03 10193.54 12797.68 13079.58 19498.02 18194.27 11495.14 15599.08 109
SDMVSNet91.09 16989.91 17794.65 15396.80 14490.54 12497.78 22197.81 5888.34 18085.73 21595.26 20866.44 29098.26 16794.25 11586.75 22195.14 224
PAPR96.35 3995.82 5297.94 3099.63 1894.19 5599.42 5897.55 10992.43 7293.82 12599.12 4287.30 8699.91 4294.02 11699.06 7499.74 46
iter_conf_final93.22 12893.04 11693.76 18897.03 13892.22 8999.05 10193.31 33092.11 8386.93 20695.42 20495.01 1096.59 24993.98 11784.48 24092.46 241
PGM-MVS95.85 5595.65 6096.45 9199.50 4289.77 14698.22 19098.90 1289.19 15196.74 7298.95 6285.91 11799.92 3993.94 11899.46 5599.66 58
gg-mvs-nofinetune90.00 19387.71 21796.89 7096.15 17194.69 4485.15 36097.74 6768.32 36092.97 13560.16 37396.10 396.84 24093.89 11998.87 8699.14 102
MVS93.92 10292.28 13198.83 695.69 18696.82 796.22 28498.17 3484.89 25384.34 23098.61 9479.32 19899.83 6493.88 12099.43 5999.86 29
旧先验298.67 13985.75 23898.96 1698.97 14293.84 121
ACMMPcopyleft94.67 8794.30 8095.79 11599.25 5788.13 17898.41 17198.67 2190.38 12091.43 15598.72 8382.22 17599.95 3193.83 12295.76 14799.29 91
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
BP-MVS93.82 123
HQP-MVS91.50 16091.23 15492.29 21493.95 24486.39 21899.16 8296.37 20493.92 4087.57 19796.67 17773.34 23597.77 19593.82 12386.29 22492.72 236
DP-MVS Recon95.85 5595.15 6997.95 2999.87 294.38 5199.60 3197.48 12486.58 22594.42 11399.13 4087.36 8499.98 993.64 12598.33 10299.48 76
CHOSEN 1792x268894.35 9493.82 9895.95 11197.40 11988.74 16898.41 17198.27 2892.18 8191.43 15596.40 18378.88 20099.81 7093.59 12697.81 10999.30 90
cascas90.93 17489.33 18895.76 11695.69 18693.03 7798.99 10996.59 18980.49 31686.79 21094.45 22165.23 30098.60 15793.52 12792.18 18695.66 223
HQP_MVS91.26 16590.95 16092.16 21893.84 25186.07 23399.02 10596.30 20893.38 5686.99 20496.52 17972.92 24097.75 20193.46 12886.17 22792.67 238
plane_prior596.30 20897.75 20193.46 12886.17 22792.67 238
PVSNet_Blended_VisFu94.67 8794.11 8796.34 9797.14 13191.10 10899.32 7097.43 13492.10 8491.53 15496.38 18683.29 15199.68 8293.42 13096.37 13598.25 166
AdaColmapbinary93.82 10793.06 11496.10 10499.88 189.07 15598.33 18297.55 10986.81 22190.39 17498.65 8975.09 22099.98 993.32 13197.53 11899.26 94
HyFIR lowres test93.68 11293.29 10994.87 14497.57 11788.04 18098.18 19498.47 2487.57 20691.24 16095.05 21185.49 12297.46 21893.22 13292.82 17399.10 107
HPM-MVS_fast94.89 7794.62 7695.70 11899.11 6688.44 17499.14 9097.11 16385.82 23595.69 9498.47 10383.46 14799.32 12593.16 13399.63 4399.35 85
PMMVS93.62 11593.90 9792.79 20596.79 14681.40 30298.85 12096.81 18191.25 9996.82 7098.15 11677.02 21398.13 17293.15 13496.30 13898.83 133
LCM-MVSNet-Re88.59 22188.61 20288.51 30295.53 19272.68 34996.85 26188.43 36888.45 17373.14 33390.63 29775.82 21694.38 33192.95 13595.71 14998.48 154
EPP-MVSNet93.75 10993.67 10194.01 18195.86 18085.70 24298.67 13997.66 8384.46 25891.36 15897.18 15591.16 3197.79 19392.93 13693.75 16698.53 151
CostFormer92.89 13492.48 12994.12 17594.99 21885.89 23792.89 32597.00 17686.98 21695.00 10690.78 28990.05 4897.51 21692.92 13791.73 19498.96 117
XVG-OURS-SEG-HR90.95 17390.66 16991.83 22595.18 20781.14 30995.92 29195.92 23988.40 17790.33 17597.85 11970.66 26099.38 11892.83 13888.83 21394.98 227
mvsmamba89.99 19489.42 18591.69 23290.64 30386.34 22198.40 17492.27 34291.01 10284.80 22494.93 21276.12 21596.51 25792.81 13983.84 24792.21 251
sss94.85 7993.94 9597.58 3996.43 15694.09 5898.93 11499.16 889.50 14595.27 10097.85 11981.50 18399.65 8892.79 14094.02 16498.99 114
test_vis1_rt81.31 30380.05 30685.11 32391.29 29570.66 35598.98 11177.39 38185.76 23768.80 34782.40 35436.56 36899.44 10992.67 14186.55 22385.24 358
MAR-MVS94.43 9394.09 8895.45 12699.10 6887.47 19498.39 17897.79 6288.37 17894.02 12099.17 3378.64 20599.91 4292.48 14298.85 8798.96 117
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
API-MVS94.78 8194.18 8696.59 8399.21 6190.06 13998.80 12597.78 6383.59 27393.85 12399.21 2683.79 14299.97 2192.37 14399.00 7899.74 46
nrg03090.23 18688.87 19594.32 16791.53 29193.54 6698.79 12995.89 24788.12 18884.55 22794.61 21978.80 20396.88 23992.35 14475.21 29692.53 240
OMC-MVS93.90 10493.62 10294.73 15198.63 8787.00 20798.04 20896.56 19392.19 8092.46 13998.73 8179.49 19799.14 13592.16 14594.34 16298.03 173
131493.44 11891.98 13997.84 3195.24 20094.38 5196.22 28497.92 4890.18 12482.28 25797.71 12977.63 21099.80 7291.94 14698.67 9499.34 87
DPM-MVS97.86 897.25 1899.68 198.25 9399.10 199.76 1397.78 6396.61 898.15 3399.53 793.62 17100.00 191.79 14799.80 2699.94 18
mvs_anonymous92.50 14391.65 14695.06 13796.60 15089.64 14897.06 25396.44 20186.64 22484.14 23193.93 23082.49 16896.17 28391.47 14896.08 14399.35 85
baseline294.04 9893.80 9994.74 15093.07 27090.25 12898.12 19998.16 3689.86 13286.53 21296.95 16595.56 698.05 17991.44 14994.53 15995.93 221
IB-MVS89.43 692.12 15290.83 16595.98 11095.40 19790.78 11799.81 798.06 4191.23 10085.63 21893.66 23890.63 4098.78 14691.22 15071.85 33198.36 162
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
ab-mvs91.05 17289.17 19096.69 7995.96 17891.72 9492.62 32997.23 14985.61 23989.74 18293.89 23268.55 27099.42 11391.09 15187.84 21698.92 125
XVG-OURS90.83 17590.49 17191.86 22495.23 20181.25 30695.79 29995.92 23988.96 15890.02 17998.03 11871.60 25499.35 12391.06 15287.78 21794.98 227
3Dnovator87.35 1193.17 13091.77 14497.37 4695.41 19693.07 7598.82 12397.85 5191.53 9182.56 24997.58 13671.97 24999.82 6791.01 15399.23 6999.22 98
VPA-MVSNet89.10 20587.66 21893.45 19392.56 27391.02 11297.97 21298.32 2786.92 21886.03 21492.01 26568.84 26997.10 23190.92 15475.34 29592.23 249
PAPM_NR95.43 6695.05 7296.57 8699.42 4790.14 13298.58 15397.51 11890.65 11192.44 14098.90 6887.77 7599.90 4690.88 15599.32 6499.68 54
3Dnovator+87.72 893.43 11991.84 14298.17 2195.73 18595.08 3198.92 11697.04 17091.42 9681.48 27497.60 13474.60 22399.79 7390.84 15698.97 7999.64 60
test_fmvs285.10 27485.45 25284.02 33189.85 31365.63 36398.49 16292.59 33890.45 11785.43 22193.32 24443.94 35996.59 24990.81 15784.19 24489.85 322
gm-plane-assit94.69 22888.14 17788.22 18597.20 15398.29 16590.79 158
MVSTER92.71 13692.32 13093.86 18597.29 12492.95 8099.01 10796.59 18990.09 12885.51 21994.00 22894.61 1696.56 25390.77 15983.03 25792.08 258
ACMP87.39 1088.71 21988.24 21090.12 26993.91 24981.06 31098.50 16095.67 26189.43 14680.37 28295.55 20065.67 29497.83 19090.55 16084.51 23891.47 276
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
RRT_MVS88.91 20988.56 20589.93 27590.31 30781.61 29998.08 20596.38 20389.30 14882.41 25494.84 21573.15 23896.04 28990.38 16182.23 26492.15 254
ECVR-MVScopyleft92.29 14791.33 15295.15 13496.41 15787.84 18398.10 20294.84 29790.82 10691.42 15797.28 14765.61 29698.49 15890.33 16297.19 12499.12 105
testdata95.26 13398.20 9587.28 20197.60 9885.21 24498.48 2799.15 3688.15 6898.72 15290.29 16399.45 5799.78 37
LPG-MVS_test88.86 21188.47 20890.06 27093.35 26580.95 31198.22 19095.94 23687.73 20283.17 24196.11 19166.28 29197.77 19590.19 16485.19 23491.46 277
LGP-MVS_train90.06 27093.35 26580.95 31195.94 23687.73 20283.17 24196.11 19166.28 29197.77 19590.19 16485.19 23491.46 277
MVSFormer94.71 8694.08 8996.61 8295.05 21694.87 3597.77 22396.17 21986.84 21998.04 4098.52 9785.52 11995.99 29089.83 16698.97 7998.96 117
test_djsdf88.26 22687.73 21689.84 27888.05 33782.21 29397.77 22396.17 21986.84 21982.41 25491.95 26972.07 24895.99 29089.83 16684.50 23991.32 284
test250694.80 8094.21 8396.58 8496.41 15792.18 9098.01 20998.96 1090.82 10693.46 12897.28 14785.92 11598.45 15989.82 16897.19 12499.12 105
tpmrst92.78 13592.16 13494.65 15396.27 16487.45 19591.83 33397.10 16689.10 15594.68 11090.69 29388.22 6597.73 20389.78 16991.80 19298.77 140
PLCcopyleft91.07 394.23 9694.01 9094.87 14499.17 6387.49 19399.25 7496.55 19488.43 17691.26 15998.21 11485.92 11599.86 5789.77 17097.57 11597.24 193
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test111192.12 15291.19 15594.94 14296.15 17187.36 19898.12 19994.84 29790.85 10590.97 16297.26 14965.60 29798.37 16189.74 17197.14 12799.07 111
CDS-MVSNet93.47 11793.04 11694.76 14894.75 22789.45 15298.82 12397.03 17287.91 19590.97 16296.48 18189.06 5596.36 26789.50 17292.81 17598.49 153
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Effi-MVS+-dtu89.97 19590.68 16887.81 30795.15 20871.98 35197.87 21795.40 27791.92 8587.57 19791.44 27774.27 22996.84 24089.45 17393.10 17194.60 229
jajsoiax87.35 23986.51 23689.87 27687.75 34281.74 29797.03 25495.98 23088.47 17080.15 28593.80 23461.47 31296.36 26789.44 17484.47 24191.50 275
mvs_tets87.09 24286.22 23989.71 28187.87 33881.39 30396.73 26895.90 24588.19 18679.99 28793.61 23959.96 31996.31 27589.40 17584.34 24291.43 279
PS-MVSNAJss89.54 20189.05 19291.00 24488.77 32884.36 26597.39 23695.97 23188.47 17081.88 26793.80 23482.48 16996.50 25889.34 17683.34 25692.15 254
VPNet88.30 22486.57 23493.49 19291.95 28391.35 10098.18 19497.20 15588.61 16784.52 22894.89 21362.21 31096.76 24589.34 17672.26 32892.36 244
114514_t94.06 9793.05 11597.06 5599.08 6992.26 8898.97 11297.01 17582.58 29192.57 13898.22 11280.68 18999.30 12689.34 17699.02 7799.63 62
OPM-MVS89.76 19789.15 19191.57 23490.53 30485.58 24598.11 20195.93 23892.88 6686.05 21396.47 18267.06 28597.87 18889.29 17986.08 22991.26 287
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVS_Test93.67 11392.67 12596.69 7996.72 14892.66 8297.22 24896.03 22887.69 20495.12 10494.03 22681.55 18298.28 16689.17 18096.46 13299.14 102
BH-w/o92.32 14691.79 14393.91 18496.85 14186.18 22799.11 9595.74 25688.13 18784.81 22397.00 16377.26 21297.91 18489.16 18198.03 10697.64 181
TAMVS92.62 13992.09 13794.20 17294.10 23987.68 18698.41 17196.97 17887.53 20889.74 18296.04 19384.77 13496.49 26088.97 18292.31 18398.42 155
CNLPA93.64 11492.74 12396.36 9698.96 7590.01 14299.19 7695.89 24786.22 23189.40 18598.85 7380.66 19099.84 6188.57 18396.92 12899.24 95
baseline192.61 14091.28 15396.58 8497.05 13794.63 4597.72 22796.20 21589.82 13388.56 19196.85 17186.85 9597.82 19188.42 18480.10 27397.30 191
CANet_DTU94.31 9593.35 10697.20 5297.03 13894.71 4398.62 14595.54 26895.61 1897.21 5798.47 10371.88 25099.84 6188.38 18597.46 12097.04 200
thisisatest051594.75 8294.19 8496.43 9296.13 17692.64 8599.47 4697.60 9887.55 20793.17 13197.59 13594.71 1398.42 16088.28 18693.20 16998.24 167
原ACMM196.18 10099.03 7190.08 13597.63 9388.98 15797.00 6398.97 5688.14 6999.71 8088.23 18799.62 4498.76 141
UGNet91.91 15690.85 16295.10 13597.06 13688.69 16998.01 20998.24 3192.41 7592.39 14193.61 23960.52 31799.68 8288.14 18897.25 12296.92 203
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
AUN-MVS90.17 18989.50 18292.19 21796.21 16782.67 28997.76 22597.53 11388.05 18991.67 14896.15 18983.10 15697.47 21788.11 18966.91 34796.43 214
Vis-MVSNet (Re-imp)93.26 12793.00 11994.06 17896.14 17386.71 21298.68 13796.70 18488.30 18289.71 18497.64 13385.43 12596.39 26588.06 19096.32 13699.08 109
bld_raw_dy_0_6487.82 22986.71 23391.15 24089.54 31985.61 24397.37 23989.16 36689.26 14983.42 23794.50 22065.79 29396.18 28188.00 19183.37 25491.67 265
PVSNet87.13 1293.69 11092.83 12296.28 9897.99 10390.22 13099.38 6298.93 1191.42 9693.66 12697.68 13071.29 25799.64 9087.94 19297.20 12398.98 115
FIs90.70 17889.87 17893.18 19792.29 27691.12 10698.17 19698.25 2989.11 15483.44 23694.82 21682.26 17496.17 28387.76 19382.76 25992.25 247
tpm291.77 15791.09 15693.82 18794.83 22585.56 24692.51 33097.16 15884.00 26493.83 12490.66 29587.54 7797.17 22787.73 19491.55 19798.72 142
无先验98.52 15697.82 5587.20 21299.90 4687.64 19599.85 30
Anonymous20240521188.84 21287.03 22894.27 16898.14 9984.18 26898.44 16795.58 26676.79 33589.34 18696.88 17053.42 34299.54 9887.53 19687.12 22099.09 108
IS-MVSNet93.00 13392.51 12894.49 15896.14 17387.36 19898.31 18595.70 25888.58 16990.17 17697.50 13983.02 15897.22 22687.06 19796.07 14498.90 126
MDTV_nov1_ep13_2view91.17 10591.38 34087.45 20993.08 13386.67 10087.02 19898.95 121
Anonymous2024052987.66 23685.58 24993.92 18397.59 11685.01 25798.13 19797.13 16166.69 36588.47 19296.01 19455.09 33699.51 10087.00 19984.12 24597.23 194
UniMVSNet_NR-MVSNet89.60 19988.55 20692.75 20792.17 27990.07 13698.74 13298.15 3788.37 17883.21 23993.98 22982.86 16095.93 29486.95 20072.47 32592.25 247
DU-MVS88.83 21487.51 21992.79 20591.46 29290.07 13698.71 13397.62 9588.87 16383.21 23993.68 23674.63 22195.93 29486.95 20072.47 32592.36 244
FA-MVS(test-final)92.22 15191.08 15795.64 12196.05 17788.98 15891.60 33797.25 14586.99 21391.84 14592.12 26183.03 15799.00 14086.91 20293.91 16598.93 123
ACMM86.95 1388.77 21788.22 21190.43 26193.61 25781.34 30498.50 16095.92 23987.88 19683.85 23495.20 21067.20 28397.89 18686.90 20384.90 23692.06 259
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet (Re)89.50 20288.32 20993.03 19992.21 27890.96 11498.90 11898.39 2589.13 15383.22 23892.03 26381.69 18196.34 27386.79 20472.53 32491.81 263
BH-untuned91.46 16290.84 16393.33 19596.51 15484.83 26098.84 12295.50 27086.44 23083.50 23596.70 17675.49 21997.77 19586.78 20597.81 10997.40 188
mvsany_test375.85 32674.52 32879.83 34373.53 37360.64 36791.73 33587.87 37083.91 26770.55 34282.52 35331.12 37093.66 33586.66 20662.83 35485.19 359
miper_enhance_ethall90.33 18489.70 17992.22 21597.12 13388.93 16298.35 18195.96 23388.60 16883.14 24392.33 26087.38 8096.18 28186.49 20777.89 28291.55 274
thisisatest053094.00 9993.52 10395.43 12795.76 18490.02 14198.99 10997.60 9886.58 22591.74 14797.36 14694.78 1298.34 16286.37 20892.48 18097.94 176
TESTMET0.1,193.82 10793.26 11095.49 12595.21 20390.25 12899.15 8797.54 11289.18 15291.79 14694.87 21489.13 5497.63 20886.21 20996.29 13998.60 149
anonymousdsp86.69 24885.75 24789.53 28686.46 34982.94 28296.39 27595.71 25783.97 26579.63 29290.70 29268.85 26895.94 29386.01 21084.02 24689.72 324
F-COLMAP92.07 15491.75 14593.02 20098.16 9882.89 28598.79 12995.97 23186.54 22787.92 19597.80 12278.69 20499.65 8885.97 21195.93 14696.53 211
cl2289.57 20088.79 19891.91 22397.94 10487.62 18997.98 21196.51 19685.03 24982.37 25691.79 27083.65 14396.50 25885.96 21277.89 28291.61 271
test-LLR93.11 13192.68 12494.40 16294.94 22187.27 20299.15 8797.25 14590.21 12291.57 15094.04 22484.89 13097.58 21285.94 21396.13 14098.36 162
test-mter93.27 12692.89 12194.40 16294.94 22187.27 20299.15 8797.25 14588.95 15991.57 15094.04 22488.03 7197.58 21285.94 21396.13 14098.36 162
FC-MVSNet-test90.22 18789.40 18692.67 21191.78 28789.86 14497.89 21498.22 3288.81 16482.96 24494.66 21881.90 18095.96 29285.89 21582.52 26292.20 253
Vis-MVSNetpermissive92.64 13891.85 14195.03 14095.12 20988.23 17598.48 16496.81 18191.61 8992.16 14497.22 15271.58 25598.00 18385.85 21697.81 10998.88 127
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
sd_testset89.23 20388.05 21492.74 20896.80 14485.33 25095.85 29797.03 17288.34 18085.73 21595.26 20861.12 31597.76 20085.61 21786.75 22195.14 224
test_fmvs375.09 32775.19 32474.81 34877.45 37154.08 37395.93 29090.64 35882.51 29473.29 33181.19 35822.29 37586.29 37085.50 21867.89 34384.06 361
WR-MVS88.54 22287.22 22692.52 21291.93 28589.50 15198.56 15497.84 5286.99 21381.87 26893.81 23374.25 23095.92 29685.29 21974.43 30592.12 256
XXY-MVS87.75 23386.02 24292.95 20390.46 30589.70 14797.71 22995.90 24584.02 26380.95 27694.05 22367.51 28197.10 23185.16 22078.41 27992.04 260
thres20093.69 11092.59 12796.97 6397.76 10894.74 4299.35 6799.36 289.23 15091.21 16196.97 16483.42 14898.77 14785.08 22190.96 20297.39 189
tttt051793.30 12493.01 11894.17 17395.57 18986.47 21598.51 15997.60 9885.99 23390.55 16997.19 15494.80 1198.31 16385.06 22291.86 19097.74 178
XVG-ACMP-BASELINE85.86 26384.95 25988.57 30189.90 31177.12 33294.30 31295.60 26587.40 21082.12 26092.99 25453.42 34297.66 20585.02 22383.83 24890.92 295
dmvs_re88.69 22088.06 21390.59 25593.83 25378.68 32395.75 30096.18 21887.99 19284.48 22996.32 18767.52 28096.94 23784.98 22485.49 23396.14 218
新几何197.40 4498.92 7792.51 8797.77 6585.52 24096.69 7499.06 4988.08 7099.89 4884.88 22599.62 4499.79 35
1112_ss92.71 13691.55 14896.20 9995.56 19091.12 10698.48 16494.69 30488.29 18386.89 20898.50 9987.02 9198.66 15584.75 22689.77 21198.81 135
miper_ehance_all_eth88.94 20888.12 21291.40 23595.32 19986.93 20897.85 21895.55 26784.19 26181.97 26591.50 27684.16 13895.91 29784.69 22777.89 28291.36 282
Test_1112_low_res92.27 14990.97 15996.18 10095.53 19291.10 10898.47 16694.66 30588.28 18486.83 20993.50 24387.00 9298.65 15684.69 22789.74 21298.80 136
TR-MVS90.77 17689.44 18494.76 14896.31 16288.02 18197.92 21395.96 23385.52 24088.22 19497.23 15166.80 28698.09 17584.58 22992.38 18198.17 171
tt080586.50 25484.79 26391.63 23391.97 28181.49 30096.49 27397.38 13982.24 29882.44 25195.82 19751.22 34798.25 16884.55 23080.96 26995.13 226
OpenMVScopyleft85.28 1490.75 17788.84 19696.48 8993.58 25893.51 6798.80 12597.41 13682.59 29078.62 30297.49 14068.00 27699.82 6784.52 23198.55 9896.11 219
UniMVSNet_ETH3D85.65 27083.79 27791.21 23890.41 30680.75 31395.36 30395.78 25378.76 32581.83 27194.33 22249.86 35296.66 24684.30 23283.52 25396.22 217
NR-MVSNet87.74 23586.00 24392.96 20291.46 29290.68 12196.65 27097.42 13588.02 19173.42 33093.68 23677.31 21195.83 30084.26 23371.82 33292.36 244
D2MVS87.96 22887.39 22189.70 28291.84 28683.40 27798.31 18598.49 2288.04 19078.23 30890.26 30873.57 23396.79 24484.21 23483.53 25288.90 334
testdata299.88 4984.16 235
Baseline_NR-MVSNet85.83 26484.82 26288.87 30088.73 32983.34 27898.63 14491.66 35180.41 31982.44 25191.35 27974.63 22195.42 31184.13 23671.39 33487.84 340
thres100view90093.34 12392.15 13596.90 6697.62 11394.84 3799.06 10099.36 287.96 19390.47 17296.78 17383.29 15198.75 14984.11 23790.69 20497.12 195
tfpn200view993.43 11992.27 13296.90 6697.68 11194.84 3799.18 7899.36 288.45 17390.79 16496.90 16883.31 14998.75 14984.11 23790.69 20497.12 195
thres40093.39 12192.27 13296.73 7597.68 11194.84 3799.18 7899.36 288.45 17390.79 16496.90 16883.31 14998.75 14984.11 23790.69 20496.61 206
c3_l88.19 22787.23 22591.06 24294.97 21986.17 22897.72 22795.38 27883.43 27581.68 27291.37 27882.81 16195.72 30384.04 24073.70 31391.29 286
UA-Net93.30 12492.62 12695.34 13096.27 16488.53 17395.88 29496.97 17890.90 10495.37 9997.07 16082.38 17399.10 13783.91 24194.86 15898.38 159
IterMVS-LS88.34 22387.44 22091.04 24394.10 23985.85 23998.10 20295.48 27185.12 24582.03 26491.21 28281.35 18695.63 30683.86 24275.73 29491.63 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet89.87 19689.38 18791.36 23794.32 23585.87 23897.61 23296.59 18985.10 24685.51 21997.10 15881.30 18796.56 25383.85 24383.03 25791.64 266
tpm89.67 19888.95 19491.82 22692.54 27481.43 30192.95 32495.92 23987.81 19790.50 17189.44 32184.99 12895.65 30583.67 24482.71 26098.38 159
eth_miper_zixun_eth87.76 23287.00 22990.06 27094.67 22982.65 29097.02 25695.37 27984.19 26181.86 27091.58 27581.47 18495.90 29883.24 24573.61 31491.61 271
Fast-Effi-MVS+91.72 15890.79 16694.49 15895.89 17987.40 19799.54 4295.70 25885.01 25189.28 18795.68 19977.75 20997.57 21583.22 24695.06 15698.51 152
test_post190.74 34841.37 38485.38 12696.36 26783.16 247
SCA90.64 18089.25 18994.83 14794.95 22088.83 16496.26 28197.21 15190.06 13190.03 17890.62 29866.61 28796.81 24283.16 24794.36 16198.84 130
TranMVSNet+NR-MVSNet87.75 23386.31 23892.07 22190.81 30088.56 17098.33 18297.18 15687.76 19981.87 26893.90 23172.45 24495.43 31083.13 24971.30 33592.23 249
CMPMVSbinary58.40 2180.48 30680.11 30581.59 34185.10 35359.56 36894.14 31595.95 23568.54 35960.71 36593.31 24555.35 33597.87 18883.06 25084.85 23787.33 345
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
thres600view793.18 12992.00 13896.75 7397.62 11394.92 3299.07 9899.36 287.96 19390.47 17296.78 17383.29 15198.71 15382.93 25190.47 20896.61 206
pmmvs487.58 23886.17 24191.80 22789.58 31788.92 16397.25 24595.28 28282.54 29280.49 28193.17 25075.62 21896.05 28882.75 25278.90 27790.42 309
CVMVSNet90.30 18590.91 16188.46 30394.32 23573.58 34597.61 23297.59 10290.16 12788.43 19397.10 15876.83 21492.86 34282.64 25393.54 16898.93 123
Anonymous2023121184.72 27882.65 28890.91 24697.71 11084.55 26397.28 24396.67 18566.88 36479.18 29890.87 28858.47 32396.60 24882.61 25474.20 30991.59 273
GA-MVS90.10 19188.69 20094.33 16692.44 27587.97 18299.08 9796.26 21289.65 13786.92 20793.11 25168.09 27496.96 23582.54 25590.15 20998.05 172
QAPM91.41 16389.49 18397.17 5395.66 18893.42 6998.60 14997.51 11880.92 31481.39 27597.41 14472.89 24299.87 5282.33 25698.68 9398.21 169
Patchmatch-RL test81.90 30180.13 30487.23 31280.71 36570.12 35884.07 36688.19 36983.16 28070.57 34182.18 35687.18 8792.59 34782.28 25762.78 35598.98 115
v2v48287.27 24185.76 24691.78 23189.59 31687.58 19098.56 15495.54 26884.53 25782.51 25091.78 27173.11 23996.47 26182.07 25874.14 31191.30 285
Fast-Effi-MVS+-dtu88.84 21288.59 20489.58 28593.44 26378.18 32798.65 14194.62 30688.46 17284.12 23295.37 20768.91 26796.52 25682.06 25991.70 19594.06 230
pmmvs585.87 26284.40 27290.30 26688.53 33284.23 26698.60 14993.71 32481.53 30680.29 28392.02 26464.51 30295.52 30882.04 26078.34 28091.15 289
V4287.00 24385.68 24890.98 24589.91 31086.08 23198.32 18495.61 26483.67 27282.72 24690.67 29474.00 23296.53 25581.94 26174.28 30890.32 311
EPMVS92.59 14191.59 14795.59 12497.22 12690.03 14091.78 33498.04 4390.42 11991.66 14990.65 29686.49 10797.46 21881.78 26296.31 13799.28 92
DIV-MVS_self_test87.82 22986.81 23190.87 24994.87 22485.39 24997.81 21995.22 29182.92 28780.76 27891.31 28081.99 17795.81 30181.36 26375.04 29891.42 280
cl____87.82 22986.79 23290.89 24894.88 22385.43 24797.81 21995.24 28682.91 28880.71 27991.22 28181.97 17995.84 29981.34 26475.06 29791.40 281
RPSCF85.33 27285.55 25084.67 32894.63 23062.28 36593.73 31893.76 32274.38 34485.23 22297.06 16164.09 30398.31 16380.98 26586.08 22993.41 235
OurMVSNet-221017-084.13 28983.59 27885.77 32187.81 33970.24 35694.89 30793.65 32686.08 23276.53 31293.28 24761.41 31396.14 28580.95 26677.69 28790.93 294
v14886.38 25685.06 25690.37 26589.47 32284.10 26998.52 15695.48 27183.80 26880.93 27790.22 31274.60 22396.31 27580.92 26771.55 33390.69 304
PatchMatch-RL91.47 16190.54 17094.26 16998.20 9586.36 22096.94 25797.14 15987.75 20088.98 18895.75 19871.80 25299.40 11780.92 26797.39 12197.02 201
FE-MVS91.38 16490.16 17595.05 13996.46 15587.53 19289.69 35197.84 5282.97 28392.18 14392.00 26784.07 14098.93 14380.71 26995.52 15198.68 145
miper_lstm_enhance86.90 24486.20 24089.00 29794.53 23181.19 30796.74 26795.24 28682.33 29780.15 28590.51 30581.99 17794.68 32880.71 26973.58 31591.12 290
PCF-MVS89.78 591.26 16589.63 18096.16 10395.44 19491.58 9895.29 30496.10 22385.07 24882.75 24597.45 14278.28 20699.78 7480.60 27195.65 15097.12 195
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
BH-RMVSNet91.25 16789.99 17695.03 14096.75 14788.55 17198.65 14194.95 29487.74 20187.74 19697.80 12268.27 27398.14 17180.53 27297.49 11998.41 156
GeoE90.60 18189.56 18193.72 19195.10 21385.43 24799.41 5994.94 29583.96 26687.21 20396.83 17274.37 22797.05 23380.50 27393.73 16798.67 146
CP-MVSNet86.54 25285.45 25289.79 28091.02 29982.78 28897.38 23897.56 10885.37 24279.53 29493.03 25271.86 25195.25 31579.92 27473.43 31991.34 283
PatchmatchNetpermissive92.05 15591.04 15895.06 13796.17 17089.04 15691.26 34297.26 14489.56 14390.64 16890.56 30288.35 6497.11 22979.53 27596.07 14499.03 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v114486.83 24685.31 25491.40 23589.75 31487.21 20698.31 18595.45 27383.22 27882.70 24790.78 28973.36 23496.36 26779.49 27674.69 30290.63 306
IterMVS85.81 26584.67 26689.22 29293.51 25983.67 27596.32 27894.80 30085.09 24778.69 30090.17 31566.57 28993.17 34179.48 27777.42 28890.81 297
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT85.73 26884.64 26789.00 29793.46 26282.90 28496.27 27994.70 30385.02 25078.62 30290.35 30766.61 28793.33 33879.38 27877.36 28990.76 301
GBi-Net86.67 24984.96 25791.80 22795.11 21088.81 16596.77 26395.25 28382.94 28482.12 26090.25 30962.89 30794.97 31979.04 27980.24 27091.62 268
test186.67 24984.96 25791.80 22795.11 21088.81 16596.77 26395.25 28382.94 28482.12 26090.25 30962.89 30794.97 31979.04 27980.24 27091.62 268
FMVSNet388.81 21687.08 22793.99 18296.52 15394.59 4698.08 20596.20 21585.85 23482.12 26091.60 27474.05 23195.40 31279.04 27980.24 27091.99 261
LF4IMVS81.94 30081.17 29984.25 33087.23 34668.87 36193.35 32291.93 34983.35 27775.40 32193.00 25349.25 35596.65 24778.88 28278.11 28187.22 347
v886.11 25984.45 26991.10 24189.99 30986.85 20997.24 24695.36 28081.99 30179.89 28989.86 31774.53 22596.39 26578.83 28372.32 32790.05 318
pm-mvs184.68 27982.78 28590.40 26289.58 31785.18 25397.31 24194.73 30281.93 30376.05 31592.01 26565.48 29896.11 28678.75 28469.14 33889.91 321
test_f71.94 33270.82 33375.30 34772.77 37453.28 37491.62 33689.66 36475.44 33964.47 36178.31 36520.48 37689.56 36378.63 28566.02 35083.05 366
v14419286.40 25584.89 26090.91 24689.48 32185.59 24498.21 19295.43 27682.45 29582.62 24890.58 30172.79 24396.36 26778.45 28674.04 31290.79 299
PS-CasMVS85.81 26584.58 26889.49 28990.77 30182.11 29497.20 24997.36 14184.83 25479.12 29992.84 25567.42 28295.16 31778.39 28773.25 32091.21 288
tmp_tt53.66 34452.86 34656.05 36132.75 38941.97 38473.42 37576.12 38221.91 38239.68 37896.39 18542.59 36265.10 38178.00 28814.92 38261.08 374
JIA-IIPM85.97 26184.85 26189.33 29193.23 26773.68 34485.05 36197.13 16169.62 35691.56 15268.03 37188.03 7196.96 23577.89 28993.12 17097.34 190
MDTV_nov1_ep1390.47 17296.14 17388.55 17191.34 34197.51 11889.58 14192.24 14290.50 30686.99 9397.61 21077.64 29092.34 182
v119286.32 25784.71 26591.17 23989.53 32086.40 21798.13 19795.44 27582.52 29382.42 25390.62 29871.58 25596.33 27477.23 29174.88 29990.79 299
FMVSNet286.90 24484.79 26393.24 19695.11 21092.54 8697.67 23095.86 25182.94 28480.55 28091.17 28362.89 30795.29 31477.23 29179.71 27691.90 262
MVP-Stereo86.61 25185.83 24588.93 29988.70 33083.85 27396.07 28894.41 31382.15 30075.64 32091.96 26867.65 27996.45 26377.20 29398.72 9286.51 351
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpm cat188.89 21087.27 22493.76 18895.79 18285.32 25190.76 34797.09 16776.14 33785.72 21788.59 32782.92 15998.04 18076.96 29491.43 19897.90 177
v1085.73 26884.01 27590.87 24990.03 30886.73 21197.20 24995.22 29181.25 30979.85 29089.75 31873.30 23796.28 27976.87 29572.64 32389.61 326
v192192086.02 26084.44 27090.77 25289.32 32385.20 25298.10 20295.35 28182.19 29982.25 25890.71 29170.73 25896.30 27876.85 29674.49 30490.80 298
MS-PatchMatch86.75 24785.92 24489.22 29291.97 28182.47 29296.91 25896.14 22183.74 26977.73 30993.53 24258.19 32497.37 22576.75 29798.35 10187.84 340
K. test v381.04 30479.77 30784.83 32687.41 34370.23 35795.60 30293.93 32183.70 27167.51 35489.35 32355.76 33093.58 33776.67 29868.03 34290.67 305
PM-MVS74.88 32872.85 33180.98 34278.98 36964.75 36490.81 34685.77 37280.95 31368.23 35182.81 35229.08 37292.84 34376.54 29962.46 35785.36 356
WR-MVS_H86.53 25385.49 25189.66 28491.04 29883.31 27997.53 23498.20 3384.95 25279.64 29190.90 28778.01 20895.33 31376.29 30072.81 32190.35 310
ACMH+83.78 1584.21 28682.56 29089.15 29493.73 25679.16 31896.43 27494.28 31581.09 31174.00 32794.03 22654.58 33897.67 20476.10 30178.81 27890.63 306
PEN-MVS85.21 27383.93 27689.07 29689.89 31281.31 30597.09 25297.24 14884.45 25978.66 30192.68 25768.44 27294.87 32275.98 30270.92 33691.04 292
USDC84.74 27782.93 28190.16 26891.73 28883.54 27695.00 30693.30 33188.77 16573.19 33293.30 24653.62 34197.65 20775.88 30381.54 26789.30 329
EU-MVSNet84.19 28784.42 27183.52 33488.64 33167.37 36296.04 28995.76 25585.29 24378.44 30593.18 24970.67 25991.48 35875.79 30475.98 29291.70 264
v124085.77 26784.11 27390.73 25389.26 32485.15 25597.88 21695.23 29081.89 30482.16 25990.55 30369.60 26696.31 27575.59 30574.87 30090.72 303
ITE_SJBPF87.93 30592.26 27776.44 33493.47 32987.67 20579.95 28895.49 20356.50 32997.38 22375.24 30682.33 26389.98 320
dp90.16 19088.83 19794.14 17496.38 16086.42 21691.57 33897.06 16984.76 25588.81 18990.19 31484.29 13797.43 22175.05 30791.35 20198.56 150
LS3D90.19 18888.72 19994.59 15798.97 7386.33 22296.90 25996.60 18874.96 34184.06 23398.74 8075.78 21799.83 6474.93 30897.57 11597.62 184
TDRefinement78.01 31975.31 32386.10 31970.06 37673.84 34393.59 32191.58 35374.51 34373.08 33591.04 28449.63 35497.12 22874.88 30959.47 36187.33 345
tpmvs89.16 20487.76 21593.35 19497.19 12784.75 26190.58 34997.36 14181.99 30184.56 22689.31 32483.98 14198.17 17074.85 31090.00 21097.12 195
pmmvs679.90 30977.31 31587.67 30884.17 35678.13 32895.86 29693.68 32567.94 36172.67 33889.62 32050.98 34995.75 30274.80 31166.04 34989.14 332
SixPastTwentyTwo82.63 29681.58 29485.79 32088.12 33671.01 35495.17 30592.54 33984.33 26072.93 33792.08 26260.41 31895.61 30774.47 31274.15 31090.75 302
ACMH83.09 1784.60 28082.61 28990.57 25693.18 26882.94 28296.27 27994.92 29681.01 31272.61 33993.61 23956.54 32897.79 19374.31 31381.07 26890.99 293
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_vis3_rt61.29 33758.75 34068.92 35567.41 37752.84 37691.18 34459.23 38866.96 36341.96 37658.44 37611.37 38494.72 32774.25 31457.97 36459.20 375
ADS-MVSNet287.62 23786.88 23089.86 27796.21 16779.14 31987.15 35592.99 33283.01 28189.91 18087.27 33778.87 20192.80 34574.20 31592.27 18497.64 181
ADS-MVSNet88.99 20687.30 22394.07 17796.21 16787.56 19187.15 35596.78 18383.01 28189.91 18087.27 33778.87 20197.01 23474.20 31592.27 18497.64 181
lessismore_v085.08 32485.59 35269.28 35990.56 35967.68 35390.21 31354.21 34095.46 30973.88 31762.64 35690.50 308
MIMVSNet84.48 28381.83 29292.42 21391.73 28887.36 19885.52 35894.42 31281.40 30781.91 26687.58 33151.92 34592.81 34473.84 31888.15 21597.08 199
v7n84.42 28582.75 28689.43 29088.15 33581.86 29696.75 26695.67 26180.53 31578.38 30689.43 32269.89 26196.35 27273.83 31972.13 32990.07 316
ambc79.60 34472.76 37556.61 37076.20 37392.01 34868.25 35080.23 36123.34 37494.73 32673.78 32060.81 35987.48 342
pmmvs-eth3d78.71 31676.16 32186.38 31680.25 36781.19 30794.17 31492.13 34677.97 32866.90 35782.31 35555.76 33092.56 34873.63 32162.31 35885.38 355
FMVSNet183.94 29081.32 29891.80 22791.94 28488.81 16596.77 26395.25 28377.98 32778.25 30790.25 30950.37 35194.97 31973.27 32277.81 28691.62 268
MSDG88.29 22586.37 23794.04 18096.90 14086.15 22996.52 27294.36 31477.89 33179.22 29796.95 16569.72 26399.59 9473.20 32392.58 17996.37 216
test0.0.03 188.96 20788.61 20290.03 27491.09 29784.43 26498.97 11297.02 17490.21 12280.29 28396.31 18884.89 13091.93 35672.98 32485.70 23293.73 231
UnsupCasMVSNet_eth78.90 31476.67 31985.58 32282.81 36174.94 33991.98 33296.31 20784.64 25665.84 36087.71 33051.33 34692.23 35272.89 32556.50 36789.56 327
DTE-MVSNet84.14 28882.80 28388.14 30488.95 32779.87 31696.81 26296.24 21383.50 27477.60 31092.52 25967.89 27894.24 33372.64 32669.05 33990.32 311
EPNet_dtu92.28 14892.15 13592.70 20997.29 12484.84 25998.64 14397.82 5592.91 6593.02 13497.02 16285.48 12495.70 30472.25 32794.89 15797.55 186
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
AllTest84.97 27683.12 28090.52 25996.82 14278.84 32195.89 29292.17 34477.96 32975.94 31695.50 20155.48 33299.18 12971.15 32887.14 21893.55 233
TestCases90.52 25996.82 14278.84 32192.17 34477.96 32975.94 31695.50 20155.48 33299.18 12971.15 32887.14 21893.55 233
DP-MVS88.75 21886.56 23595.34 13098.92 7787.45 19597.64 23193.52 32870.55 35281.49 27397.25 15074.43 22699.88 4971.14 33094.09 16398.67 146
CR-MVSNet88.83 21487.38 22293.16 19893.47 26086.24 22384.97 36294.20 31788.92 16290.76 16686.88 34184.43 13594.82 32470.64 33192.17 18798.41 156
KD-MVS_2432*160082.98 29480.52 30290.38 26394.32 23588.98 15892.87 32695.87 24980.46 31773.79 32887.49 33482.76 16493.29 33970.56 33246.53 37588.87 335
miper_refine_blended82.98 29480.52 30290.38 26394.32 23588.98 15892.87 32695.87 24980.46 31773.79 32887.49 33482.76 16493.29 33970.56 33246.53 37588.87 335
test_method70.10 33468.66 33774.41 35086.30 35155.84 37194.47 30989.82 36235.18 37766.15 35984.75 34930.54 37177.96 37870.40 33460.33 36089.44 328
LTVRE_ROB81.71 1984.59 28182.72 28790.18 26792.89 27283.18 28093.15 32394.74 30178.99 32275.14 32392.69 25665.64 29597.63 20869.46 33581.82 26689.74 323
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
FMVSNet582.29 29780.54 30187.52 30993.79 25584.01 27093.73 31892.47 34076.92 33474.27 32586.15 34563.69 30689.24 36469.07 33674.79 30189.29 330
our_test_384.47 28482.80 28389.50 28789.01 32583.90 27297.03 25494.56 30781.33 30875.36 32290.52 30471.69 25394.54 33068.81 33776.84 29090.07 316
UnsupCasMVSNet_bld73.85 33070.14 33484.99 32579.44 36875.73 33588.53 35295.24 28670.12 35561.94 36474.81 36841.41 36493.62 33668.65 33851.13 37485.62 354
Patchmtry83.61 29381.64 29389.50 28793.36 26482.84 28784.10 36594.20 31769.47 35779.57 29386.88 34184.43 13594.78 32568.48 33974.30 30790.88 296
KD-MVS_self_test77.47 32275.88 32282.24 33681.59 36268.93 36092.83 32894.02 32077.03 33373.14 33383.39 35155.44 33490.42 35967.95 34057.53 36587.38 343
TransMVSNet (Re)81.97 29979.61 30889.08 29589.70 31584.01 27097.26 24491.85 35078.84 32373.07 33691.62 27367.17 28495.21 31667.50 34159.46 36288.02 339
COLMAP_ROBcopyleft82.69 1884.54 28282.82 28289.70 28296.72 14878.85 32095.89 29292.83 33671.55 35077.54 31195.89 19659.40 32199.14 13567.26 34288.26 21491.11 291
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EG-PatchMatch MVS79.92 30877.59 31386.90 31487.06 34777.90 33196.20 28694.06 31974.61 34266.53 35888.76 32640.40 36696.20 28067.02 34383.66 25186.61 349
DSMNet-mixed81.60 30281.43 29682.10 33884.36 35560.79 36693.63 32086.74 37179.00 32179.32 29687.15 33963.87 30589.78 36266.89 34491.92 18995.73 222
testgi82.29 29781.00 30086.17 31887.24 34574.84 34097.39 23691.62 35288.63 16675.85 31995.42 20446.07 35891.55 35766.87 34579.94 27492.12 256
MDA-MVSNet_test_wron79.65 31177.05 31687.45 31087.79 34180.13 31496.25 28294.44 30973.87 34551.80 36987.47 33668.04 27592.12 35466.02 34667.79 34490.09 314
YYNet179.64 31277.04 31787.43 31187.80 34079.98 31596.23 28394.44 30973.83 34651.83 36887.53 33267.96 27792.07 35566.00 34767.75 34590.23 313
DeepMVS_CXcopyleft76.08 34690.74 30251.65 37890.84 35786.47 22957.89 36787.98 32835.88 36992.60 34665.77 34865.06 35283.97 362
Anonymous2024052178.63 31776.90 31883.82 33282.82 36072.86 34795.72 30193.57 32773.55 34772.17 34084.79 34849.69 35392.51 34965.29 34974.50 30386.09 353
TinyColmap80.42 30777.94 31287.85 30692.09 28078.58 32493.74 31789.94 36174.99 34069.77 34491.78 27146.09 35797.58 21265.17 35077.89 28287.38 343
MVS-HIRNet79.01 31375.13 32590.66 25493.82 25481.69 29885.16 35993.75 32354.54 36974.17 32659.15 37557.46 32696.58 25263.74 35194.38 16093.72 232
ppachtmachnet_test83.63 29281.57 29589.80 27989.01 32585.09 25697.13 25194.50 30878.84 32376.14 31491.00 28569.78 26294.61 32963.40 35274.36 30689.71 325
CL-MVSNet_self_test79.89 31078.34 31184.54 32981.56 36375.01 33896.88 26095.62 26381.10 31075.86 31885.81 34668.49 27190.26 36063.21 35356.51 36688.35 337
Patchmatch-test86.25 25884.06 27492.82 20494.42 23282.88 28682.88 36994.23 31671.58 34979.39 29590.62 29889.00 5796.42 26463.03 35491.37 20099.16 101
pmmvs372.86 33169.76 33682.17 33773.86 37274.19 34294.20 31389.01 36764.23 36867.72 35280.91 36041.48 36388.65 36662.40 35554.02 37083.68 363
new_pmnet76.02 32473.71 32982.95 33583.88 35772.85 34891.26 34292.26 34370.44 35362.60 36381.37 35747.64 35692.32 35161.85 35672.10 33083.68 363
tfpnnormal83.65 29181.35 29790.56 25891.37 29488.06 17997.29 24297.87 5078.51 32676.20 31390.91 28664.78 30196.47 26161.71 35773.50 31687.13 348
MDA-MVSNet-bldmvs77.82 32174.75 32787.03 31388.33 33378.52 32596.34 27792.85 33575.57 33848.87 37187.89 32957.32 32792.49 35060.79 35864.80 35390.08 315
Anonymous2023120680.76 30579.42 30984.79 32784.78 35472.98 34696.53 27192.97 33379.56 32074.33 32488.83 32561.27 31492.15 35360.59 35975.92 29389.24 331
new-patchmatchnet74.80 32972.40 33281.99 33978.36 37072.20 35094.44 31092.36 34177.06 33263.47 36279.98 36251.04 34888.85 36560.53 36054.35 36984.92 360
LCM-MVSNet60.07 33956.37 34171.18 35254.81 38548.67 37982.17 37089.48 36537.95 37549.13 37069.12 36913.75 38381.76 37259.28 36151.63 37383.10 365
TAPA-MVS87.50 990.35 18389.05 19294.25 17098.48 9185.17 25498.42 16996.58 19282.44 29687.24 20298.53 9682.77 16298.84 14559.09 36297.88 10898.72 142
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test20.0378.51 31877.48 31481.62 34083.07 35971.03 35396.11 28792.83 33681.66 30569.31 34689.68 31957.53 32587.29 36958.65 36368.47 34086.53 350
PatchT85.44 27183.19 27992.22 21593.13 26983.00 28183.80 36896.37 20470.62 35190.55 16979.63 36384.81 13294.87 32258.18 36491.59 19698.79 137
APD_test168.93 33566.98 33874.77 34980.62 36653.15 37587.97 35385.01 37453.76 37059.26 36687.52 33325.19 37389.95 36156.20 36567.33 34681.19 367
MIMVSNet175.92 32573.30 33083.81 33381.29 36475.57 33692.26 33192.05 34773.09 34867.48 35586.18 34440.87 36587.64 36855.78 36670.68 33788.21 338
OpenMVS_ROBcopyleft73.86 2077.99 32075.06 32686.77 31583.81 35877.94 33096.38 27691.53 35467.54 36268.38 34987.13 34043.94 35996.08 28755.03 36781.83 26586.29 352
RPMNet85.07 27581.88 29194.64 15593.47 26086.24 22384.97 36297.21 15164.85 36790.76 16678.80 36480.95 18899.27 12753.76 36892.17 18798.41 156
N_pmnet70.19 33369.87 33571.12 35388.24 33430.63 38995.85 29728.70 38970.18 35468.73 34886.55 34364.04 30493.81 33453.12 36973.46 31788.94 333
dmvs_testset77.17 32378.99 31071.71 35187.25 34438.55 38591.44 33981.76 37785.77 23669.49 34595.94 19569.71 26484.37 37152.71 37076.82 29192.21 251
PMMVS258.97 34055.07 34370.69 35462.72 38055.37 37285.97 35780.52 37849.48 37145.94 37268.31 37015.73 38180.78 37649.79 37137.12 37775.91 368
test_040278.81 31576.33 32086.26 31791.18 29678.44 32695.88 29491.34 35568.55 35870.51 34389.91 31652.65 34494.99 31847.14 37279.78 27585.34 357
FPMVS61.57 33660.32 33965.34 35660.14 38342.44 38391.02 34589.72 36344.15 37242.63 37580.93 35919.02 37780.59 37742.50 37372.76 32273.00 369
testf156.38 34153.73 34464.31 35864.84 37845.11 38080.50 37175.94 38338.87 37342.74 37375.07 36611.26 38581.19 37441.11 37453.27 37166.63 372
APD_test256.38 34153.73 34464.31 35864.84 37845.11 38080.50 37175.94 38338.87 37342.74 37375.07 36611.26 38581.19 37441.11 37453.27 37166.63 372
EGC-MVSNET60.70 33855.37 34276.72 34586.35 35071.08 35289.96 35084.44 3760.38 3861.50 38784.09 35037.30 36788.10 36740.85 37673.44 31870.97 371
ANet_high50.71 34546.17 34864.33 35744.27 38752.30 37776.13 37478.73 37964.95 36627.37 38055.23 37714.61 38267.74 38036.01 37718.23 38072.95 370
Gipumacopyleft54.77 34352.22 34762.40 36086.50 34859.37 36950.20 37890.35 36036.52 37641.20 37749.49 37818.33 37981.29 37332.10 37865.34 35146.54 378
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVScopyleft41.42 2345.67 34642.50 34955.17 36234.28 38832.37 38766.24 37678.71 38030.72 37822.04 38359.59 3744.59 38777.85 37927.49 37958.84 36355.29 376
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVEpermissive44.00 2241.70 34737.64 35253.90 36349.46 38643.37 38265.09 37766.66 38526.19 38125.77 38248.53 3793.58 38963.35 38226.15 38027.28 37854.97 377
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN41.02 34840.93 35041.29 36461.97 38133.83 38684.00 36765.17 38627.17 37927.56 37946.72 38017.63 38060.41 38319.32 38118.82 37929.61 379
EMVS39.96 34939.88 35140.18 36559.57 38432.12 38884.79 36464.57 38726.27 38026.14 38144.18 38318.73 37859.29 38417.03 38217.67 38129.12 380
wuyk23d16.71 35216.73 35616.65 36660.15 38225.22 39041.24 3795.17 3906.56 3835.48 3863.61 3863.64 38822.72 38515.20 3839.52 3831.99 383
testmvs18.81 35123.05 3546.10 3684.48 3902.29 39297.78 2213.00 3913.27 38418.60 38462.71 3721.53 3912.49 38714.26 3841.80 38413.50 382
test12316.58 35319.47 3557.91 3673.59 3915.37 39194.32 3111.39 3922.49 38513.98 38544.60 3822.91 3902.65 38611.35 3850.57 38515.70 381
test_blank0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
uanet_test0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
DCPMVS0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
cdsmvs_eth3d_5k22.52 35030.03 3530.00 3690.00 3920.00 3930.00 38097.17 1570.00 3870.00 38898.77 7774.35 2280.00 3880.00 3860.00 3860.00 384
pcd_1.5k_mvsjas6.87 3559.16 3580.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 38782.48 1690.00 3880.00 3860.00 3860.00 384
sosnet-low-res0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
sosnet0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
uncertanet0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
Regformer0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
ab-mvs-re8.21 35410.94 3570.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 38898.50 990.00 3920.00 3880.00 3860.00 3860.00 384
uanet0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
FOURS199.50 4288.94 16199.55 3797.47 12691.32 9898.12 36
test_one_060199.59 2894.89 3397.64 8993.14 5998.93 1799.45 1493.45 18
eth-test20.00 392
eth-test0.00 392
test_241102_ONE99.63 1895.24 2497.72 7194.16 3599.30 699.49 993.32 1999.98 9
save fliter99.34 5093.85 6199.65 2897.63 9395.69 16
test072699.66 1295.20 2999.77 1097.70 7693.95 3899.35 599.54 393.18 22
GSMVS98.84 130
test_part299.54 3695.42 1998.13 34
sam_mvs188.39 6398.84 130
sam_mvs87.08 89
MTGPAbinary97.45 129
test_post46.00 38187.37 8197.11 229
patchmatchnet-post84.86 34788.73 6096.81 242
MTMP99.21 7591.09 356
TEST999.57 3393.17 7299.38 6297.66 8389.57 14298.39 2899.18 3190.88 3799.66 84
test_899.55 3593.07 7599.37 6597.64 8990.18 12498.36 3099.19 2890.94 3599.64 90
agg_prior99.54 3692.66 8297.64 8997.98 4399.61 92
test_prior492.00 9199.41 59
test_prior97.01 5799.58 3091.77 9297.57 10799.49 10299.79 35
新几何298.26 188
旧先验198.97 7392.90 8197.74 6799.15 3691.05 3499.33 6399.60 65
原ACMM298.69 136
test22298.32 9291.21 10298.08 20597.58 10483.74 26995.87 8999.02 5386.74 9899.64 4099.81 32
segment_acmp90.56 41
testdata197.89 21492.43 72
test1297.83 3299.33 5394.45 4897.55 10997.56 4888.60 6199.50 10199.71 3499.55 70
plane_prior793.84 25185.73 241
plane_prior693.92 24886.02 23572.92 240
plane_prior496.52 179
plane_prior385.91 23693.65 5186.99 204
plane_prior299.02 10593.38 56
plane_prior193.90 250
plane_prior86.07 23399.14 9093.81 4886.26 226
n20.00 393
nn0.00 393
door-mid84.90 375
test1197.68 80
door85.30 373
HQP5-MVS86.39 218
HQP-NCC93.95 24499.16 8293.92 4087.57 197
ACMP_Plane93.95 24499.16 8293.92 4087.57 197
HQP4-MVS87.57 19797.77 19592.72 236
HQP3-MVS96.37 20486.29 224
HQP2-MVS73.34 235
NP-MVS93.94 24786.22 22596.67 177
ACMMP++_ref82.64 261
ACMMP++83.83 248
Test By Simon83.62 144