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 bysorted bysort bysort bysort bysort bysort by
UA-Net97.96 4597.62 4898.98 4898.86 8897.47 5998.89 6499.08 1996.67 4998.72 3599.54 193.15 7999.81 5094.87 13398.83 9899.65 50
APDe-MVS99.02 198.84 199.55 199.57 2398.96 299.39 598.93 3597.38 1899.41 399.54 196.66 599.84 4298.86 199.85 299.87 1
DeepC-MVS95.98 397.88 4997.58 5098.77 5899.25 6496.93 7798.83 7498.75 7496.96 4196.89 11299.50 390.46 12499.87 3497.84 3599.76 2399.52 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP_Plus98.61 1398.30 2599.55 199.62 2198.95 398.82 7698.81 5895.80 7299.16 1299.47 495.37 4099.92 1397.89 3199.75 2999.79 4
MP-MVS-pluss98.31 3997.92 4299.49 499.72 1198.88 498.43 14898.78 6894.10 13097.69 8499.42 595.25 4599.92 1398.09 2399.80 999.67 46
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SteuartSystems-ACMMP98.90 298.75 299.36 1299.22 7198.43 1699.10 4498.87 4897.38 1899.35 599.40 697.78 199.87 3497.77 3899.85 299.78 7
Skip Steuart: Steuart Systems R&D Blog.
MPTG98.55 2298.25 2999.46 699.76 198.64 898.55 13398.74 7597.27 2698.02 6399.39 794.81 5499.96 197.91 2899.79 1099.77 14
MTAPA98.58 1898.29 2699.46 699.76 198.64 898.90 6098.74 7597.27 2698.02 6399.39 794.81 5499.96 197.91 2899.79 1099.77 14
VDDNet95.36 15094.53 16197.86 11198.10 13295.13 15198.85 7197.75 22590.46 24798.36 5199.39 773.27 30699.64 10097.98 2696.58 15998.81 136
SD-MVS98.64 1098.68 398.53 7299.33 4298.36 2198.90 6098.85 5297.28 2299.72 199.39 796.63 797.60 27498.17 2299.85 299.64 53
DeepPCF-MVS96.37 297.93 4898.48 1396.30 21099.00 8289.54 27197.43 24298.87 4898.16 299.26 699.38 1196.12 1799.64 10098.30 2099.77 1799.72 30
EI-MVSNet-UG-set98.41 3098.34 2198.61 6699.45 3396.32 10298.28 16598.68 9397.17 3198.74 3499.37 1295.25 4599.79 6998.57 799.54 6399.73 27
APD-MVS_3200maxsize98.53 2598.33 2499.15 3799.50 2797.92 4599.15 3698.81 5896.24 5899.20 1099.37 1295.30 4399.80 5797.73 4099.67 3999.72 30
abl_698.30 4098.03 3899.13 3899.56 2497.76 5099.13 4098.82 5596.14 6199.26 699.37 1293.33 7699.93 996.96 6699.67 3999.69 35
LS3D97.16 8496.66 9198.68 6298.53 11297.19 6998.93 5898.90 4192.83 18795.99 14199.37 1292.12 9599.87 3493.67 16699.57 5498.97 125
EI-MVSNet-Vis-set98.47 2898.39 1598.69 6199.46 3296.49 9598.30 16398.69 9097.21 2898.84 2799.36 1695.41 3999.78 7498.62 599.65 4399.80 3
ACMMPcopyleft98.23 4197.95 4199.09 4299.74 797.62 5499.03 4899.41 695.98 6797.60 9099.36 1694.45 6499.93 997.14 6098.85 9799.70 34
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
DP-MVS96.59 10595.93 11498.57 6899.34 3996.19 10698.70 11098.39 15089.45 27394.52 16399.35 1891.85 10199.85 4092.89 19198.88 9499.68 41
VDD-MVS95.82 13195.23 14197.61 12798.84 9193.98 20698.68 11597.40 25895.02 10297.95 6999.34 1974.37 30499.78 7498.64 396.80 15499.08 117
PGM-MVS98.49 2798.23 3299.27 2399.72 1198.08 3898.99 5199.49 595.43 8699.03 1599.32 2095.56 3599.94 396.80 7899.77 1799.78 7
TSAR-MVS + MP.98.78 398.62 499.24 2599.69 1798.28 2799.14 3798.66 10396.84 4399.56 299.31 2196.34 1099.70 9198.32 1999.73 3499.73 27
Regformer-398.59 1698.50 1198.86 5699.43 3597.05 7398.40 15198.68 9397.43 1499.06 1499.31 2195.80 3299.77 7998.62 599.76 2399.78 7
Regformer-498.64 1098.53 798.99 4699.43 3597.37 6298.40 15198.79 6697.46 1399.09 1399.31 2195.86 3199.80 5798.64 399.76 2399.79 4
XVG-OURS96.55 10796.41 9796.99 15098.75 9593.76 21297.50 23998.52 12695.67 7696.83 11499.30 2488.95 15099.53 11395.88 10496.26 16897.69 173
MSLP-MVS++98.56 2198.57 598.55 7099.26 6396.80 8298.71 10799.05 2297.28 2298.84 2799.28 2596.47 999.40 12198.52 1399.70 3799.47 77
DeepC-MVS_fast96.70 198.55 2298.34 2199.18 3299.25 6498.04 3998.50 14198.78 6897.72 498.92 2699.28 2595.27 4499.82 4897.55 4999.77 1799.69 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RPSCF94.87 17295.40 12993.26 28598.89 8582.06 30998.33 15698.06 21290.30 25196.56 12399.26 2787.09 20799.49 11593.82 16296.32 16698.24 161
APD-MVScopyleft98.35 3598.00 4099.42 999.51 2698.72 798.80 8598.82 5594.52 12099.23 899.25 2895.54 3799.80 5796.52 8899.77 1799.74 25
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MP-MVScopyleft98.33 3898.01 3999.28 2099.75 398.18 3399.22 2798.79 6696.13 6297.92 7299.23 2994.54 5999.94 396.74 8099.78 1499.73 27
mPP-MVS98.51 2698.26 2899.25 2499.75 398.04 3999.28 1698.81 5896.24 5898.35 5299.23 2995.46 3899.94 397.42 5499.81 899.77 14
MG-MVS97.81 5397.60 4998.44 7999.12 8095.97 11597.75 22398.78 6896.89 4298.46 4599.22 3193.90 7399.68 9594.81 13699.52 6599.67 46
Regformer-198.66 898.51 1099.12 4099.35 3797.81 4998.37 15398.76 7197.49 1099.20 1099.21 3296.08 1999.79 6998.42 1599.73 3499.75 20
Regformer-298.69 798.52 899.19 2899.35 3798.01 4198.37 15398.81 5897.48 1299.21 999.21 3296.13 1699.80 5798.40 1799.73 3499.75 20
Vis-MVSNetpermissive97.42 7297.11 7098.34 8598.66 10296.23 10599.22 2799.00 2596.63 5198.04 6299.21 3288.05 18599.35 12596.01 10199.21 8399.45 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
XVS98.70 598.49 1299.34 1399.70 1598.35 2299.29 1498.88 4697.40 1598.46 4599.20 3595.90 2999.89 2597.85 3399.74 3299.78 7
LFMVS95.86 12994.98 15098.47 7798.87 8796.32 10298.84 7396.02 29993.40 16698.62 3999.20 3574.99 29999.63 10397.72 4197.20 14899.46 81
HPM-MVS_fast98.38 3298.13 3599.12 4099.75 397.86 4699.44 498.82 5594.46 12498.94 2199.20 3595.16 4899.74 8597.58 4699.85 299.77 14
ACMMPR98.59 1698.36 1899.29 1899.74 798.15 3599.23 2198.95 3296.10 6598.93 2599.19 3895.70 3399.94 397.62 4499.79 1099.78 7
HFP-MVS98.63 1298.40 1499.32 1699.72 1198.29 2599.23 2198.96 3096.10 6598.94 2199.17 3996.06 2099.92 1397.62 4499.78 1499.75 20
region2R98.61 1398.38 1699.29 1899.74 798.16 3499.23 2198.93 3596.15 6098.94 2199.17 3995.91 2899.94 397.55 4999.79 1099.78 7
#test#98.54 2498.27 2799.32 1699.72 1198.29 2598.98 5498.96 3095.65 7898.94 2199.17 3996.06 2099.92 1397.21 5999.78 1499.75 20
CNVR-MVS98.78 398.56 699.45 899.32 4598.87 598.47 14498.81 5897.72 498.76 3399.16 4297.05 299.78 7498.06 2499.66 4299.69 35
3Dnovator94.51 597.46 6696.93 7799.07 4397.78 15297.64 5299.35 1099.06 2097.02 3993.75 20899.16 4289.25 13999.92 1397.22 5899.75 2999.64 53
CP-MVS98.57 2098.36 1899.19 2899.66 1997.86 4699.34 1198.87 4895.96 6898.60 4199.13 4496.05 2299.94 397.77 3899.86 199.77 14
3Dnovator+94.38 697.43 7196.78 8499.38 1097.83 14998.52 1199.37 798.71 8697.09 3792.99 22999.13 4489.36 13699.89 2596.97 6499.57 5499.71 32
EPNet97.28 7996.87 8098.51 7394.98 28996.14 10798.90 6097.02 27998.28 195.99 14199.11 4691.36 11099.89 2596.98 6399.19 8499.50 70
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t96.93 9196.27 10498.92 5299.50 2797.63 5398.85 7198.90 4184.80 30197.77 7799.11 4692.84 8199.66 9794.85 13499.77 1799.47 77
testdata98.26 8899.20 7495.36 14298.68 9391.89 21798.60 4199.10 4894.44 6599.82 4894.27 15199.44 7399.58 63
PHI-MVS98.34 3698.06 3799.18 3299.15 7898.12 3799.04 4799.09 1893.32 16998.83 2999.10 4896.54 899.83 4397.70 4299.76 2399.59 61
OMC-MVS97.55 6597.34 6298.20 9299.33 4295.92 12298.28 16598.59 11195.52 8397.97 6899.10 4893.28 7899.49 11595.09 13298.88 9499.19 103
COLMAP_ROBcopyleft93.27 1295.33 15394.87 15596.71 16599.29 5593.24 22598.58 12698.11 19889.92 26193.57 21199.10 4886.37 22099.79 6990.78 23498.10 12897.09 186
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
旧先验199.29 5597.48 5898.70 8999.09 5295.56 3599.47 6899.61 56
XVG-OURS-SEG-HR96.51 10896.34 10097.02 14998.77 9393.76 21297.79 22198.50 13395.45 8596.94 10799.09 5287.87 19199.55 11296.76 7995.83 17797.74 170
CPTT-MVS97.72 5697.32 6398.92 5299.64 2097.10 7299.12 4298.81 5892.34 20798.09 5899.08 5493.01 8099.92 1396.06 9899.77 1799.75 20
EPP-MVSNet97.46 6697.28 6497.99 10698.64 10495.38 14199.33 1398.31 15893.61 16097.19 9899.07 5594.05 7099.23 13196.89 7098.43 11799.37 86
OpenMVScopyleft93.04 1395.83 13095.00 14898.32 8697.18 19297.32 6399.21 3098.97 2889.96 25891.14 25699.05 5686.64 21599.92 1393.38 17199.47 6897.73 171
EI-MVSNet95.96 12495.83 11796.36 20597.93 14293.70 21698.12 18398.27 16493.70 15395.07 14999.02 5792.23 9198.54 20094.68 13893.46 20696.84 209
CVMVSNet95.43 14596.04 11193.57 28197.93 14283.62 30398.12 18398.59 11195.68 7596.56 12399.02 5787.51 20197.51 27793.56 16997.44 14599.60 59
TSAR-MVS + GP.98.38 3298.24 3198.81 5799.22 7197.25 6798.11 18598.29 16397.19 3098.99 2099.02 5796.22 1199.67 9698.52 1398.56 11099.51 69
QAPM96.29 11695.40 12998.96 5097.85 14897.60 5599.23 2198.93 3589.76 26593.11 22699.02 5789.11 14399.93 991.99 21299.62 4599.34 87
MVS_111021_LR98.34 3698.23 3298.67 6399.27 6196.90 7997.95 20099.58 397.14 3398.44 4999.01 6195.03 5199.62 10597.91 2899.75 2999.50 70
MVS_111021_HR98.47 2898.34 2198.88 5499.22 7197.32 6397.91 20599.58 397.20 2998.33 5399.00 6295.99 2499.64 10098.05 2599.76 2399.69 35
IS-MVSNet97.22 8196.88 7998.25 9098.85 9096.36 10099.19 3397.97 21795.39 8897.23 9798.99 6391.11 11598.93 16894.60 14198.59 10899.47 77
原ACMM198.65 6499.32 4596.62 8898.67 10093.27 17297.81 7698.97 6495.18 4799.83 4393.84 16199.46 7199.50 70
112197.37 7696.77 8699.16 3599.34 3997.99 4498.19 17498.68 9390.14 25498.01 6598.97 6494.80 5699.87 3493.36 17299.46 7199.61 56
HPM-MVS98.36 3498.10 3699.13 3899.74 797.82 4899.53 198.80 6594.63 11798.61 4098.97 6495.13 4999.77 7997.65 4399.83 799.79 4
DELS-MVS98.40 3198.20 3498.99 4699.00 8297.66 5197.75 22398.89 4397.71 698.33 5398.97 6494.97 5299.88 3398.42 1599.76 2399.42 84
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
test22299.23 7097.17 7197.40 24398.66 10388.68 28098.05 6098.96 6894.14 6999.53 6499.61 56
新几何199.16 3599.34 3998.01 4198.69 9090.06 25698.13 5698.95 6994.60 5899.89 2591.97 21399.47 6899.59 61
DP-MVS Recon97.86 5197.46 5899.06 4499.53 2598.35 2298.33 15698.89 4392.62 19098.05 6098.94 7095.34 4299.65 9896.04 9999.42 7499.19 103
NCCC98.61 1398.35 2099.38 1099.28 6098.61 1098.45 14598.76 7197.82 398.45 4898.93 7196.65 699.83 4397.38 5699.41 7599.71 32
MVS_030598.00 4397.71 4698.87 5598.77 9397.19 6998.28 16598.71 8697.57 797.70 8298.92 7291.16 11399.93 998.71 299.60 4799.48 75
CSCG97.85 5297.74 4598.20 9299.67 1895.16 14999.22 2799.32 793.04 17797.02 10598.92 7295.36 4199.91 2197.43 5399.64 4499.52 66
CHOSEN 1792x268897.12 8696.80 8198.08 10199.30 5294.56 19098.05 19099.71 193.57 16197.09 9998.91 7488.17 18099.89 2596.87 7699.56 6099.81 2
PVSNet_Blended_VisFu97.70 5797.46 5898.44 7999.27 6195.91 12498.63 12099.16 1694.48 12397.67 8598.88 7592.80 8299.91 2197.11 6199.12 8699.50 70
Vis-MVSNet (Re-imp)96.87 9496.55 9497.83 11398.73 9695.46 13999.20 3198.30 16194.96 10596.60 12298.87 7690.05 13198.59 19693.67 16698.60 10799.46 81
MVS_dtu96.84 9596.38 9998.24 9197.81 15096.01 11097.98 19798.09 20697.49 1096.55 12598.86 7786.53 21699.89 2595.19 13198.89 9398.82 135
CDPH-MVS97.94 4797.49 5699.28 2099.47 3198.44 1497.91 20598.67 10092.57 19398.77 3298.85 7895.93 2799.72 8695.56 11799.69 3899.68 41
VNet97.79 5497.40 6198.96 5098.88 8697.55 5698.63 12098.93 3596.74 4699.02 1698.84 7990.33 12799.83 4398.53 996.66 15699.50 70
HPM-MVS++98.58 1898.25 2999.55 199.50 2799.08 198.72 10698.66 10397.51 998.15 5598.83 8095.70 3399.92 1397.53 5199.67 3999.66 48
MVSFormer97.57 6397.49 5697.84 11298.07 13395.76 12999.47 298.40 14894.98 10398.79 3098.83 8092.34 8698.41 23196.91 6899.59 5199.34 87
jason97.32 7897.08 7298.06 10497.45 17495.59 13297.87 21397.91 22094.79 11098.55 4398.83 8091.12 11499.23 13197.58 4699.60 4799.34 87
jason: jason.
MCST-MVS98.65 998.37 1799.48 599.60 2298.87 598.41 15098.68 9397.04 3898.52 4498.80 8396.78 499.83 4397.93 2799.61 4699.74 25
HSP-MVS98.70 598.52 899.24 2599.75 398.23 2899.26 1798.58 11697.52 899.41 398.78 8496.00 2399.79 6997.79 3799.59 5199.69 35
OPM-MVS95.69 13795.33 13696.76 16396.16 25894.63 18398.43 14898.39 15096.64 5095.02 15198.78 8485.15 23899.05 15195.21 13094.20 18896.60 243
AllTest95.24 15594.65 15996.99 15099.25 6493.21 22698.59 12498.18 18091.36 23293.52 21398.77 8684.67 24399.72 8689.70 25597.87 13498.02 166
TestCases96.99 15099.25 6493.21 22698.18 18091.36 23293.52 21398.77 8684.67 24399.72 8689.70 25597.87 13498.02 166
LPG-MVS_test95.62 14095.34 13496.47 19797.46 17193.54 21798.99 5198.54 12194.67 11394.36 17698.77 8685.39 23399.11 14495.71 11294.15 19196.76 216
LGP-MVS_train96.47 19797.46 17193.54 21798.54 12194.67 11394.36 17698.77 8685.39 23399.11 14495.71 11294.15 19196.76 216
MSDG95.93 12695.30 13997.83 11398.90 8495.36 14296.83 27798.37 15391.32 23694.43 17398.73 9090.27 12899.60 10690.05 24798.82 9998.52 151
test_prior398.22 4297.90 4399.19 2899.31 4798.22 3097.80 21998.84 5396.12 6397.89 7498.69 9195.96 2599.70 9196.89 7099.60 4799.65 50
test_prior297.80 21996.12 6397.89 7498.69 9195.96 2596.89 7099.60 47
TEST999.31 4798.50 1297.92 20298.73 7992.63 18997.74 8098.68 9396.20 1299.80 57
train_agg97.97 4497.52 5499.33 1599.31 4798.50 1297.92 20298.73 7992.98 18097.74 8098.68 9396.20 1299.80 5796.59 8499.57 5499.68 41
AdaColmapbinary97.15 8596.70 8798.48 7699.16 7696.69 8798.01 19498.89 4394.44 12596.83 11498.68 9390.69 12299.76 8194.36 14799.29 8298.98 124
test_899.29 5598.44 1497.89 21098.72 8192.98 18097.70 8298.66 9696.20 1299.80 57
agg_prior197.95 4697.51 5599.28 2099.30 5298.38 1797.81 21898.72 8193.16 17497.57 9298.66 9696.14 1599.81 5096.63 8399.56 6099.66 48
agg_prior397.87 5097.42 6099.23 2799.29 5598.23 2897.92 20298.72 8192.38 20697.59 9198.64 9896.09 1899.79 6996.59 8499.57 5499.68 41
cdsmvs_eth3d_5k23.98 30931.98 3090.00 3240.00 3370.00 3380.00 32998.59 1110.00 3330.00 33498.61 9990.60 1230.00 3360.00 3330.00 3340.00 332
lupinMVS97.44 7097.22 6798.12 9898.07 13395.76 12997.68 22897.76 22494.50 12198.79 3098.61 9992.34 8699.30 12697.58 4699.59 5199.31 90
BH-RMVSNet95.92 12795.32 13797.69 12398.32 11994.64 18298.19 17497.45 25394.56 11896.03 13998.61 9985.02 23999.12 14090.68 23699.06 8799.30 93
TAMVS97.02 8996.79 8397.70 12298.06 13595.31 14698.52 13698.31 15893.95 13897.05 10498.61 9993.49 7598.52 20795.33 12397.81 13799.29 95
TAPA-MVS93.98 795.35 15194.56 16097.74 11899.13 7994.83 16898.33 15698.64 10886.62 28996.29 13398.61 9994.00 7299.29 12780.00 30199.41 7599.09 114
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
F-COLMAP97.09 8896.80 8197.97 10799.45 3394.95 15898.55 13398.62 10993.02 17896.17 13598.58 10494.01 7199.81 5093.95 15898.90 9299.14 111
WTY-MVS97.37 7696.92 7898.72 6098.86 8896.89 8198.31 16198.71 8695.26 9197.67 8598.56 10592.21 9299.78 7495.89 10396.85 15399.48 75
CNLPA97.45 6997.03 7498.73 5999.05 8197.44 6198.07 18998.53 12495.32 8996.80 11898.53 10693.32 7799.72 8694.31 15099.31 8099.02 120
ACMP93.49 1095.34 15294.98 15096.43 20197.67 15693.48 21998.73 10498.44 14294.94 10892.53 23998.53 10684.50 24699.14 13895.48 12094.00 19696.66 234
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH92.88 1694.55 19693.95 19396.34 20897.63 15893.26 22498.81 8298.49 13793.43 16589.74 26898.53 10681.91 26499.08 14993.69 16493.30 21296.70 225
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-094.21 21194.00 18994.85 26195.60 27889.22 27698.89 6497.43 25595.29 9092.18 24998.52 10982.86 26098.59 19693.46 17091.76 22996.74 218
MVS_test032696.78 9896.28 10398.26 8897.92 14496.13 10997.88 21198.07 20997.38 1896.05 13898.49 11086.68 21499.87 3494.78 13799.30 8198.79 137
CDS-MVSNet96.99 9096.69 8897.90 11098.05 13695.98 11198.20 17298.33 15793.67 15896.95 10698.49 11093.54 7498.42 22495.24 12997.74 14199.31 90
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
sss97.39 7496.98 7698.61 6698.60 10896.61 9098.22 17098.93 3593.97 13798.01 6598.48 11291.98 9999.85 4096.45 9098.15 12699.39 85
ACMH+92.99 1494.30 20793.77 20495.88 22597.81 15092.04 24098.71 10798.37 15393.99 13590.60 26398.47 11380.86 27199.05 15192.75 19392.40 22196.55 250
ACMM93.85 995.69 13795.38 13396.61 18297.61 16093.84 21098.91 5998.44 14295.25 9294.28 18498.47 11386.04 22799.12 14095.50 11993.95 19896.87 206
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
1112_ss96.63 10296.00 11398.50 7498.56 10996.37 9998.18 17898.10 20392.92 18294.84 15498.43 11592.14 9499.58 10794.35 14896.51 16299.56 65
ab-mvs-re8.20 31210.94 3130.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 33498.43 1150.00 3410.00 3360.00 3330.00 3340.00 332
xiu_mvs_v1_base_debu97.60 6097.56 5197.72 11998.35 11495.98 11197.86 21498.51 12897.13 3499.01 1798.40 11791.56 10699.80 5798.53 998.68 10297.37 181
xiu_mvs_v1_base97.60 6097.56 5197.72 11998.35 11495.98 11197.86 21498.51 12897.13 3499.01 1798.40 11791.56 10699.80 5798.53 998.68 10297.37 181
xiu_mvs_v1_base_debi97.60 6097.56 5197.72 11998.35 11495.98 11197.86 21498.51 12897.13 3499.01 1798.40 11791.56 10699.80 5798.53 998.68 10297.37 181
mvs_tets95.41 14895.00 14896.65 17695.58 27994.42 19399.00 5098.55 12095.73 7493.21 22198.38 12083.45 25898.63 19397.09 6294.00 19696.91 201
FC-MVSNet-test96.42 11196.05 11097.53 13096.95 20297.27 6599.36 899.23 1295.83 7193.93 20198.37 12192.00 9898.32 24096.02 10092.72 21997.00 191
jajsoiax95.45 14495.03 14796.73 16495.42 28494.63 18399.14 3798.52 12695.74 7393.22 22098.36 12283.87 25598.65 19296.95 6794.04 19496.91 201
nrg03096.28 11895.72 12097.96 10896.90 20698.15 3599.39 598.31 15895.47 8494.42 17498.35 12392.09 9698.69 18897.50 5289.05 25397.04 189
FIs96.51 10896.12 10997.67 12597.13 19597.54 5799.36 899.22 1495.89 6994.03 19998.35 12391.98 9998.44 22196.40 9292.76 21897.01 190
ITE_SJBPF95.44 24197.42 17591.32 25097.50 24495.09 10093.59 20998.35 12381.70 26598.88 17589.71 25493.39 21096.12 268
LTVRE_ROB92.95 1594.60 19293.90 19696.68 17197.41 17894.42 19398.52 13698.59 11191.69 22291.21 25598.35 12384.87 24199.04 15591.06 23093.44 20996.60 243
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
PS-MVSNAJss96.43 11096.26 10596.92 15895.84 27195.08 15399.16 3598.50 13395.87 7093.84 20698.34 12794.51 6098.61 19496.88 7393.45 20897.06 187
EPNet_dtu95.21 15794.95 15395.99 22096.17 25590.45 26398.16 17997.27 26996.77 4493.14 22598.33 12890.34 12698.42 22485.57 28998.81 10099.09 114
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PCF-MVS93.45 1194.68 18893.43 22498.42 8298.62 10696.77 8495.48 30098.20 17684.63 30293.34 21898.32 12988.55 17299.81 5084.80 29298.96 9098.68 144
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLCcopyleft95.07 497.20 8296.78 8498.44 7999.29 5596.31 10498.14 18098.76 7192.41 20496.39 13198.31 13094.92 5399.78 7494.06 15698.77 10199.23 101
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
HQP_MVS96.14 12195.90 11596.85 15997.42 17594.60 18898.80 8598.56 11897.28 2295.34 14598.28 13187.09 20799.03 15696.07 9694.27 18596.92 196
plane_prior498.28 131
API-MVS97.41 7397.25 6597.91 10998.70 9896.80 8298.82 7698.69 9094.53 11998.11 5798.28 13194.50 6399.57 10894.12 15599.49 6697.37 181
mvs_anonymous96.70 10196.53 9597.18 14098.19 12793.78 21198.31 16198.19 17794.01 13394.47 16598.27 13492.08 9798.46 21697.39 5597.91 13299.31 90
XXY-MVS95.20 15894.45 16697.46 13196.75 21496.56 9298.86 7098.65 10793.30 17193.27 21998.27 13484.85 24298.87 17694.82 13591.26 23596.96 193
SixPastTwentyTwo93.34 23492.86 23294.75 26595.67 27689.41 27498.75 9796.67 29193.89 14090.15 26698.25 13680.87 27098.27 24790.90 23390.64 23796.57 247
VPNet94.99 16494.19 17797.40 13497.16 19396.57 9198.71 10798.97 2895.67 7694.84 15498.24 13780.36 27698.67 19196.46 8987.32 27796.96 193
PVSNet_Blended97.38 7597.12 6998.14 9599.25 6495.35 14497.28 25599.26 893.13 17597.94 7098.21 13892.74 8399.81 5096.88 7399.40 7799.27 97
HyFIR lowres test96.90 9396.49 9698.14 9599.33 4295.56 13597.38 24599.65 292.34 20797.61 8998.20 13989.29 13899.10 14796.97 6497.60 14499.77 14
ab-mvs96.42 11195.71 12398.55 7098.63 10596.75 8597.88 21198.74 7593.84 14396.54 12698.18 14085.34 23699.75 8395.93 10296.35 16599.15 109
xiu_mvs_v2_base97.66 5997.70 4797.56 12998.61 10795.46 13997.44 24098.46 13897.15 3298.65 3898.15 14194.33 6699.80 5797.84 3598.66 10697.41 178
USDC93.33 23592.71 23595.21 25196.83 21090.83 25596.91 26997.50 24493.84 14390.72 26198.14 14277.69 28798.82 18289.51 25993.21 21595.97 272
EU-MVSNet93.66 23094.14 18092.25 29095.96 26583.38 30498.52 13698.12 19394.69 11192.61 23698.13 14387.36 20596.39 30391.82 21690.00 24196.98 192
CHOSEN 280x42097.18 8397.18 6897.20 13898.81 9293.27 22395.78 29899.15 1795.25 9296.79 11998.11 14492.29 8899.07 15098.56 899.85 299.25 99
MVSTER96.06 12295.72 12097.08 14798.23 12395.93 12198.73 10498.27 16494.86 10995.07 14998.09 14588.21 17998.54 20096.59 8493.46 20696.79 213
MVS_Test97.28 7997.00 7598.13 9798.33 11895.97 11598.74 10198.07 20994.27 12798.44 4998.07 14692.48 8599.26 12896.43 9198.19 12599.16 108
PAPM_NR97.46 6697.11 7098.50 7499.50 2796.41 9898.63 12098.60 11095.18 9497.06 10398.06 14794.26 6899.57 10893.80 16398.87 9699.52 66
PatchMatch-RL96.59 10596.03 11298.27 8799.31 4796.51 9497.91 20599.06 2093.72 15096.92 11098.06 14788.50 17599.65 9891.77 21899.00 8998.66 146
Effi-MVS+97.12 8696.69 8898.39 8398.19 12796.72 8697.37 24798.43 14593.71 15197.65 8898.02 14992.20 9399.25 12996.87 7697.79 13899.19 103
MVS94.67 18993.54 21898.08 10196.88 20796.56 9298.19 17498.50 13378.05 31592.69 23498.02 14991.07 11799.63 10390.09 24498.36 11998.04 165
BH-untuned95.95 12595.72 12096.65 17698.55 11192.26 23698.23 16997.79 22393.73 14994.62 16098.01 15188.97 14999.00 15993.04 18298.51 11198.68 144
CLD-MVS95.62 14095.34 13496.46 20097.52 16893.75 21497.27 25698.46 13895.53 8294.42 17498.00 15286.21 22298.97 16096.25 9594.37 18396.66 234
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HY-MVS93.96 896.82 9796.23 10798.57 6898.46 11397.00 7498.14 18098.21 17493.95 13896.72 12097.99 15391.58 10599.76 8194.51 14596.54 16198.95 129
MAR-MVS96.91 9296.40 9898.45 7898.69 10096.90 7998.66 11898.68 9392.40 20597.07 10297.96 15491.54 10999.75 8393.68 16598.92 9198.69 143
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
PS-CasMVS94.67 18993.99 19196.71 16596.68 21895.26 14799.13 4099.03 2393.68 15692.33 24597.95 15585.35 23598.10 25393.59 16888.16 26896.79 213
mvs-test196.60 10396.68 9096.37 20497.89 14691.81 24298.56 13198.10 20396.57 5296.52 12797.94 15690.81 11999.45 12095.72 11098.01 12997.86 169
TranMVSNet+NR-MVSNet95.14 16094.48 16297.11 14596.45 22896.36 10099.03 4899.03 2395.04 10193.58 21097.93 15788.27 17898.03 25894.13 15486.90 28496.95 195
testgi93.06 24192.45 23894.88 26096.43 22989.90 26698.75 9797.54 23795.60 7991.63 25497.91 15874.46 30397.02 28486.10 28593.67 20197.72 172
CP-MVSNet94.94 17094.30 17196.83 16096.72 21695.56 13599.11 4398.95 3293.89 14092.42 24497.90 15987.19 20698.12 25294.32 14988.21 26696.82 212
XVG-ACMP-BASELINE94.54 19794.14 18095.75 23196.55 22291.65 24798.11 18598.44 14294.96 10594.22 18897.90 15979.18 28299.11 14494.05 15793.85 19996.48 258
PS-MVSNAJ97.73 5597.77 4497.62 12698.68 10195.58 13397.34 25198.51 12897.29 2198.66 3797.88 16194.51 6099.90 2397.87 3299.17 8597.39 180
TransMVSNet (Re)92.67 24391.51 24796.15 21596.58 22194.65 18198.90 6096.73 28790.86 24589.46 27197.86 16285.62 23198.09 25586.45 28381.12 30195.71 278
test_djsdf96.00 12395.69 12596.93 15695.72 27595.49 13899.47 298.40 14894.98 10394.58 16197.86 16289.16 14298.41 23196.91 6894.12 19396.88 205
TinyColmap92.31 24791.53 24694.65 26796.92 20389.75 26896.92 26796.68 29090.45 24889.62 26997.85 16476.06 29598.81 18386.74 28192.51 22095.41 283
pm-mvs193.94 22693.06 22996.59 18496.49 22695.16 14998.95 5698.03 21692.32 20991.08 25797.84 16584.54 24598.41 23192.16 20586.13 29096.19 267
UGNet96.78 9896.30 10298.19 9498.24 12295.89 12698.88 6698.93 3597.39 1796.81 11797.84 16582.60 26199.90 2396.53 8799.49 6698.79 137
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
TDRefinement91.06 26789.68 27095.21 25185.35 31991.49 24898.51 14097.07 27591.47 22688.83 27697.84 16577.31 29199.09 14892.79 19277.98 31395.04 287
PEN-MVS94.42 20293.73 20896.49 19596.28 24894.84 16699.17 3499.00 2593.51 16292.23 24797.83 16886.10 22497.90 26592.55 19986.92 28396.74 218
131496.25 12095.73 11997.79 11697.13 19595.55 13798.19 17498.59 11193.47 16492.03 25197.82 16991.33 11199.49 11594.62 14098.44 11598.32 160
DTE-MVSNet93.98 22593.26 22896.14 21696.06 26194.39 19599.20 3198.86 5193.06 17691.78 25297.81 17085.87 22897.58 27590.53 23986.17 28896.46 259
PAPM94.95 16894.00 18997.78 11797.04 19895.65 13196.03 29398.25 16991.23 24194.19 19097.80 17191.27 11298.86 17882.61 29697.61 14398.84 134
PVSNet91.96 1896.35 11396.15 10896.96 15399.17 7592.05 23996.08 29098.68 9393.69 15497.75 7997.80 17188.86 15399.69 9494.26 15299.01 8899.15 109
CMPMVSbinary66.06 2189.70 27689.67 27189.78 29593.19 30176.56 31497.00 26498.35 15580.97 31181.57 30897.75 17374.75 30198.61 19489.85 25093.63 20394.17 303
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
diffmvs96.32 11595.74 11898.07 10398.26 12196.14 10798.53 13598.23 17290.10 25596.88 11397.73 17490.16 13099.15 13693.90 16097.85 13698.91 131
NP-MVS97.28 18394.51 19197.73 174
HQP-MVS95.72 13495.40 12996.69 16897.20 18994.25 20198.05 19098.46 13896.43 5494.45 16697.73 17486.75 21298.96 16395.30 12494.18 18996.86 208
UniMVSNet_NR-MVSNet95.71 13595.15 14497.40 13496.84 20996.97 7598.74 10199.24 1095.16 9593.88 20397.72 17791.68 10398.31 24295.81 10687.25 27996.92 196
DU-MVS95.42 14694.76 15797.40 13496.53 22396.97 7598.66 11898.99 2795.43 8693.88 20397.69 17888.57 17098.31 24295.81 10687.25 27996.92 196
WR-MVS95.15 15994.46 16497.22 13796.67 21996.45 9698.21 17198.81 5894.15 12893.16 22297.69 17887.51 20198.30 24495.29 12688.62 26396.90 203
NR-MVSNet94.98 16694.16 17897.44 13296.53 22397.22 6898.74 10198.95 3294.96 10589.25 27397.69 17889.32 13798.18 25094.59 14287.40 27696.92 196
Fast-Effi-MVS+-dtu95.87 12895.85 11695.91 22397.74 15491.74 24698.69 11198.15 18895.56 8194.92 15297.68 18188.98 14898.79 18593.19 17797.78 13997.20 185
alignmvs97.56 6497.07 7399.01 4598.66 10298.37 2098.83 7498.06 21296.74 4698.00 6797.65 18290.80 12199.48 11998.37 1896.56 16099.19 103
LF4IMVS93.14 24092.79 23494.20 27695.88 26988.67 28497.66 23097.07 27593.81 14591.71 25397.65 18277.96 28698.81 18391.47 22591.92 22795.12 285
lessismore_v094.45 27494.93 29188.44 28891.03 32586.77 28497.64 18476.23 29498.42 22490.31 24285.64 29296.51 255
TR-MVS94.94 17094.20 17697.17 14197.75 15394.14 20397.59 23497.02 27992.28 21195.75 14397.64 18483.88 25498.96 16389.77 25196.15 17298.40 157
Baseline_NR-MVSNet94.35 20593.81 20095.96 22196.20 25394.05 20598.61 12396.67 29191.44 22893.85 20597.60 18688.57 17098.14 25194.39 14686.93 28295.68 279
pmmvs494.69 18593.99 19196.81 16195.74 27395.94 11997.40 24397.67 22890.42 24993.37 21797.59 18789.08 14498.20 24992.97 18491.67 23096.30 265
K. test v392.55 24491.91 24594.48 27195.64 27789.24 27599.07 4594.88 31194.04 13286.78 28397.59 18777.64 29097.64 27392.08 20789.43 24996.57 247
PAPR96.84 9596.24 10698.65 6498.72 9796.92 7897.36 24998.57 11793.33 16896.67 12197.57 18994.30 6799.56 11091.05 23298.59 10899.47 77
pmmvs691.77 26090.63 26195.17 25394.69 29591.24 25298.67 11697.92 21986.14 29289.62 26997.56 19075.79 29698.34 23890.75 23584.56 29595.94 273
MS-PatchMatch93.84 22893.63 21294.46 27396.18 25489.45 27297.76 22298.27 16492.23 21292.13 25097.49 19179.50 27998.69 18889.75 25399.38 7895.25 284
semantic-postprocess94.85 26197.98 14190.56 26298.11 19893.75 14692.58 23797.48 19283.91 25397.41 27992.48 20291.30 23396.58 245
anonymousdsp95.42 14694.91 15496.94 15595.10 28895.90 12599.14 3798.41 14693.75 14693.16 22297.46 19387.50 20398.41 23195.63 11694.03 19596.50 256
PVSNet_BlendedMVS96.73 10096.60 9297.12 14499.25 6495.35 14498.26 16899.26 894.28 12697.94 7097.46 19392.74 8399.81 5096.88 7393.32 21196.20 266
PMMVS96.60 10396.33 10197.41 13397.90 14593.93 20797.35 25098.41 14692.84 18697.76 7897.45 19591.10 11699.20 13396.26 9497.91 13299.11 113
canonicalmvs97.67 5897.23 6698.98 4898.70 9898.38 1799.34 1198.39 15096.76 4597.67 8597.40 19692.26 8999.49 11598.28 2196.28 16799.08 117
IterMVS94.09 22093.85 19994.80 26497.99 13990.35 26497.18 26098.12 19393.68 15692.46 24397.34 19784.05 25197.41 27992.51 20191.33 23296.62 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VPA-MVSNet95.75 13395.11 14597.69 12397.24 18597.27 6598.94 5799.23 1295.13 9695.51 14497.32 19885.73 22998.91 17097.33 5789.55 24796.89 204
IterMVS-LS95.46 14395.21 14296.22 21398.12 13193.72 21598.32 16098.13 19193.71 15194.26 18597.31 19992.24 9098.10 25394.63 13990.12 23996.84 209
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Test_1112_low_res96.34 11495.66 12798.36 8498.56 10995.94 11997.71 22598.07 20992.10 21394.79 15897.29 20091.75 10299.56 11094.17 15396.50 16399.58 63
pmmvs593.65 23192.97 23195.68 23295.49 28292.37 23598.20 17297.28 26889.66 26992.58 23797.26 20182.14 26298.09 25593.18 17890.95 23696.58 245
MDTV_nov1_ep1395.40 12997.48 16988.34 28996.85 27597.29 26793.74 14897.48 9597.26 20189.18 14199.05 15191.92 21597.43 146
Fast-Effi-MVS+96.28 11895.70 12498.03 10598.29 12095.97 11598.58 12698.25 16991.74 22195.29 14897.23 20391.03 11899.15 13692.90 18997.96 13198.97 125
BH-w/o95.38 14995.08 14696.26 21298.34 11791.79 24397.70 22697.43 25592.87 18594.24 18797.22 20488.66 16898.84 17991.55 22297.70 14298.16 163
v192192094.20 21293.47 22396.40 20395.98 26494.08 20498.52 13698.15 18891.33 23594.25 18697.20 20586.41 21998.42 22490.04 24889.39 25096.69 230
v794.69 18594.04 18696.62 18196.41 23094.79 17698.78 9298.13 19191.89 21794.30 18297.16 20688.13 18398.45 21891.96 21489.65 24496.61 241
v2v48294.69 18594.03 18796.65 17696.17 25594.79 17698.67 11698.08 20892.72 18894.00 20097.16 20687.69 19898.45 21892.91 18888.87 25896.72 221
v7n94.19 21393.43 22496.47 19795.90 26794.38 19699.26 1798.34 15691.99 21592.76 23397.13 20888.31 17798.52 20789.48 26087.70 27396.52 253
Patchmatch-test94.42 20293.68 21196.63 17997.60 16191.76 24494.83 30897.49 25089.45 27394.14 19397.10 20988.99 14598.83 18185.37 29198.13 12799.29 95
FMVSNet394.97 16794.26 17297.11 14598.18 12996.62 8898.56 13198.26 16893.67 15894.09 19597.10 20984.25 24898.01 25992.08 20792.14 22296.70 225
MVP-Stereo94.28 21093.92 19495.35 24994.95 29092.60 23497.97 19897.65 22991.61 22390.68 26297.09 21186.32 22198.42 22489.70 25599.34 7995.02 288
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FMVSNet294.47 20093.61 21497.04 14898.21 12496.43 9798.79 9098.27 16492.46 19493.50 21597.09 21181.16 26698.00 26091.09 22891.93 22696.70 225
GBi-Net94.49 19893.80 20196.56 18998.21 12495.00 15498.82 7698.18 18092.46 19494.09 19597.07 21381.16 26697.95 26292.08 20792.14 22296.72 221
test194.49 19893.80 20196.56 18998.21 12495.00 15498.82 7698.18 18092.46 19494.09 19597.07 21381.16 26697.95 26292.08 20792.14 22296.72 221
FMVSNet193.19 23992.07 24296.56 18997.54 16695.00 15498.82 7698.18 18090.38 25092.27 24697.07 21373.68 30597.95 26289.36 26291.30 23396.72 221
v119294.32 20693.58 21696.53 19296.10 25994.45 19298.50 14198.17 18591.54 22594.19 19097.06 21686.95 21198.43 22390.14 24389.57 24596.70 225
v1neww94.83 17394.22 17396.68 17196.39 23194.85 16198.87 6798.11 19892.45 19994.45 16697.06 21688.82 15898.54 20092.93 18688.91 25696.65 236
v7new94.83 17394.22 17396.68 17196.39 23194.85 16198.87 6798.11 19892.45 19994.45 16697.06 21688.82 15898.54 20092.93 18688.91 25696.65 236
V4294.78 17894.14 18096.70 16796.33 24295.22 14898.97 5598.09 20692.32 20994.31 18097.06 21688.39 17698.55 19992.90 18988.87 25896.34 263
v694.83 17394.21 17596.69 16896.36 23594.85 16198.87 6798.11 19892.46 19494.44 17297.05 22088.76 16498.57 19892.95 18588.92 25596.65 236
GA-MVS94.81 17794.03 18797.14 14297.15 19493.86 20996.76 27897.58 23194.00 13494.76 15997.04 22180.91 26998.48 21191.79 21796.25 16999.09 114
UniMVSNet (Re)95.78 13295.19 14397.58 12896.99 20197.47 5998.79 9099.18 1595.60 7993.92 20297.04 22191.68 10398.48 21195.80 10887.66 27496.79 213
v14419294.39 20493.70 20996.48 19696.06 26194.35 19798.58 12698.16 18791.45 22794.33 17897.02 22387.50 20398.45 21891.08 22989.11 25296.63 239
v114494.59 19493.92 19496.60 18396.21 25294.78 17898.59 12498.14 19091.86 22094.21 18997.02 22387.97 18698.41 23191.72 21989.57 24596.61 241
v124094.06 22393.29 22796.34 20896.03 26393.90 20898.44 14698.17 18591.18 24394.13 19497.01 22586.05 22598.42 22489.13 26589.50 24896.70 225
v1094.29 20893.55 21796.51 19496.39 23194.80 17398.99 5198.19 17791.35 23493.02 22896.99 22688.09 18498.41 23190.50 24088.41 26596.33 264
test_040291.32 26390.27 26594.48 27196.60 22091.12 25398.50 14197.22 27286.10 29388.30 27896.98 22777.65 28997.99 26178.13 30792.94 21794.34 301
v894.47 20093.77 20496.57 18896.36 23594.83 16899.05 4698.19 17791.92 21693.16 22296.97 22888.82 15898.48 21191.69 22087.79 27296.39 260
PatchmatchNetpermissive95.71 13595.52 12896.29 21197.58 16390.72 25896.84 27697.52 23894.06 13197.08 10096.96 22989.24 14098.90 17392.03 21198.37 11899.26 98
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test195.32 15494.97 15296.35 20697.67 15691.29 25197.33 25297.60 23094.68 11296.92 11096.95 23083.97 25298.50 21091.33 22798.32 12199.25 99
v14894.29 20893.76 20695.91 22396.10 25992.93 23098.58 12697.97 21792.59 19293.47 21696.95 23088.53 17398.32 24092.56 19887.06 28196.49 257
gm-plane-assit95.88 26987.47 29589.74 26796.94 23299.19 13493.32 174
v114194.75 18194.11 18496.67 17496.27 25094.86 16098.69 11198.12 19392.43 20294.31 18096.94 23288.78 16398.48 21192.63 19688.85 26096.67 231
divwei89l23v2f11294.76 17994.12 18396.67 17496.28 24894.85 16198.69 11198.12 19392.44 20194.29 18396.94 23288.85 15598.48 21192.67 19488.79 26296.67 231
v194.75 18194.11 18496.69 16896.27 25094.87 15998.69 11198.12 19392.43 20294.32 17996.94 23288.71 16798.54 20092.66 19588.84 26196.67 231
tpmrst95.63 13995.69 12595.44 24197.54 16688.54 28796.97 26597.56 23293.50 16397.52 9496.93 23689.49 13399.16 13595.25 12896.42 16498.64 147
v5294.18 21593.52 21996.13 21795.95 26694.29 19999.23 2198.21 17491.42 22992.84 23196.89 23787.85 19298.53 20691.51 22387.81 27095.57 282
V494.18 21593.52 21996.13 21795.89 26894.31 19899.23 2198.22 17391.42 22992.82 23296.89 23787.93 18898.52 20791.51 22387.81 27095.58 281
LCM-MVSNet-Re95.22 15695.32 13794.91 25898.18 12987.85 29498.75 9795.66 30595.11 9788.96 27596.85 23990.26 12997.65 27295.65 11598.44 11599.22 102
WR-MVS_H95.05 16294.46 16496.81 16196.86 20895.82 12899.24 2099.24 1093.87 14292.53 23996.84 24090.37 12598.24 24893.24 17587.93 26996.38 261
EPMVS94.99 16494.48 16296.52 19397.22 18791.75 24597.23 25791.66 32494.11 12997.28 9696.81 24185.70 23098.84 17993.04 18297.28 14798.97 125
tpm294.19 21393.76 20695.46 23997.23 18689.04 27997.31 25496.85 28687.08 28896.21 13496.79 24283.75 25798.74 18792.43 20396.23 17098.59 149
tpmp4_e2393.91 22793.42 22695.38 24797.62 15988.59 28697.52 23897.34 26287.94 28494.17 19296.79 24282.91 25999.05 15190.62 23895.91 17598.50 152
CostFormer94.95 16894.73 15895.60 23497.28 18389.06 27897.53 23796.89 28389.66 26996.82 11696.72 24486.05 22598.95 16795.53 11896.13 17398.79 137
test20.0390.89 26990.38 26392.43 28893.48 30088.14 29198.33 15697.56 23293.40 16687.96 27996.71 24580.69 27394.13 31279.15 30486.17 28895.01 289
Effi-MVS+-dtu96.29 11696.56 9395.51 23597.89 14690.22 26598.80 8598.10 20396.57 5296.45 13096.66 24690.81 11998.91 17095.72 11097.99 13097.40 179
test0.0.03 194.08 22193.51 22195.80 22895.53 28192.89 23197.38 24595.97 30195.11 9792.51 24196.66 24687.71 19596.94 28587.03 28093.67 20197.57 175
ADS-MVSNet294.58 19594.40 16895.11 25598.00 13788.74 28296.04 29197.30 26690.15 25296.47 12896.64 24887.89 18997.56 27690.08 24597.06 14999.02 120
ADS-MVSNet95.00 16394.45 16696.63 17998.00 13791.91 24196.04 29197.74 22690.15 25296.47 12896.64 24887.89 18998.96 16390.08 24597.06 14999.02 120
dp94.15 21893.90 19694.90 25997.31 18286.82 29996.97 26597.19 27391.22 24296.02 14096.61 25085.51 23299.02 15890.00 24994.30 18498.85 132
v74893.75 22993.06 22995.82 22795.73 27492.64 23399.25 1998.24 17191.60 22492.22 24896.52 25187.60 20098.46 21690.64 23785.72 29196.36 262
EG-PatchMatch MVS91.13 26590.12 26694.17 27894.73 29489.00 28098.13 18297.81 22289.22 27785.32 29296.46 25267.71 31498.42 22487.89 27693.82 20095.08 286
TESTMET0.1,194.18 21593.69 21095.63 23396.92 20389.12 27796.91 26994.78 31293.17 17394.88 15396.45 25378.52 28398.92 16993.09 17998.50 11298.85 132
DWT-MVSNet_test94.82 17694.36 16996.20 21497.35 18090.79 25698.34 15596.57 29492.91 18395.33 14796.44 25482.00 26399.12 14094.52 14495.78 17898.70 142
tpmvs94.60 19294.36 16995.33 25097.46 17188.60 28596.88 27497.68 22791.29 23893.80 20796.42 25588.58 16999.24 13091.06 23096.04 17498.17 162
Anonymous2023120691.66 26191.10 24993.33 28394.02 29987.35 29698.58 12697.26 27090.48 24690.16 26596.31 25683.83 25696.53 30179.36 30389.90 24296.12 268
tpm94.13 21993.80 20195.12 25496.50 22587.91 29397.44 24095.89 30492.62 19096.37 13296.30 25784.13 25098.30 24493.24 17591.66 23199.14 111
CR-MVSNet94.76 17994.15 17996.59 18497.00 19993.43 22094.96 30497.56 23292.46 19496.93 10896.24 25888.15 18197.88 26987.38 27796.65 15798.46 154
Patchmtry93.22 23892.35 23995.84 22696.77 21193.09 22994.66 31097.56 23287.37 28792.90 23096.24 25888.15 18197.90 26587.37 27890.10 24096.53 252
tmp_tt68.90 30066.97 30074.68 31550.78 33459.95 33087.13 32283.47 33338.80 32962.21 32396.23 26064.70 31876.91 33288.91 26630.49 32987.19 317
cascas94.63 19193.86 19896.93 15696.91 20594.27 20096.00 29498.51 12885.55 29794.54 16296.23 26084.20 24998.87 17695.80 10896.98 15297.66 174
UnsupCasMVSNet_eth90.99 26889.92 26994.19 27794.08 29889.83 26797.13 26298.67 10093.69 15485.83 28996.19 26275.15 29896.74 29589.14 26479.41 30796.00 271
PatchFormer-LS_test95.47 14295.27 14096.08 21997.59 16290.66 25998.10 18797.34 26293.98 13696.08 13696.15 26387.65 19999.12 14095.27 12795.24 18198.44 156
MDA-MVSNet-bldmvs89.97 27588.35 28194.83 26395.21 28791.34 24997.64 23197.51 24188.36 28271.17 31996.13 26479.22 28196.63 30083.65 29386.27 28796.52 253
MIMVSNet93.26 23792.21 24196.41 20297.73 15593.13 22895.65 29997.03 27891.27 24094.04 19896.06 26575.33 29797.19 28286.56 28296.23 17098.92 130
tpm cat193.36 23292.80 23395.07 25697.58 16387.97 29296.76 27897.86 22182.17 30993.53 21296.04 26686.13 22399.13 13989.24 26395.87 17698.10 164
N_pmnet87.12 28687.77 28385.17 30695.46 28361.92 32897.37 24770.66 33585.83 29688.73 27796.04 26685.33 23797.76 27180.02 30090.48 23895.84 274
DI_MVS_plusplus_test94.74 18393.62 21398.09 10095.34 28595.92 12298.09 18897.34 26294.66 11585.89 28795.91 26880.49 27599.38 12396.66 8298.22 12398.97 125
test_normal94.72 18493.59 21598.11 9995.30 28695.95 11897.91 20597.39 26094.64 11685.70 29095.88 26980.52 27499.36 12496.69 8198.30 12299.01 123
MIMVSNet189.67 27788.28 28293.82 27992.81 30491.08 25498.01 19497.45 25387.95 28387.90 28095.87 27067.63 31594.56 31178.73 30688.18 26795.83 275
YYNet190.70 27189.39 27294.62 26894.79 29390.65 26097.20 25897.46 25187.54 28672.54 31795.74 27186.51 21796.66 29986.00 28686.76 28696.54 251
DSMNet-mixed92.52 24592.58 23692.33 28994.15 29782.65 30798.30 16394.26 31789.08 27892.65 23595.73 27285.01 24095.76 30686.24 28497.76 14098.59 149
IB-MVS91.98 1793.27 23691.97 24397.19 13997.47 17093.41 22297.09 26395.99 30093.32 16992.47 24295.73 27278.06 28599.53 11394.59 14282.98 29698.62 148
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
test-LLR95.10 16194.87 15595.80 22896.77 21189.70 26996.91 26995.21 30795.11 9794.83 15695.72 27487.71 19598.97 16093.06 18098.50 11298.72 140
test-mter94.08 22193.51 22195.80 22896.77 21189.70 26996.91 26995.21 30792.89 18494.83 15695.72 27477.69 28798.97 16093.06 18098.50 11298.72 140
MDA-MVSNet_test_wron90.71 27089.38 27394.68 26694.83 29290.78 25797.19 25997.46 25187.60 28572.41 31895.72 27486.51 21796.71 29885.92 28786.80 28596.56 249
FMVSNet591.81 25990.92 25394.49 27097.21 18892.09 23898.00 19697.55 23689.31 27690.86 26095.61 27774.48 30295.32 30885.57 28989.70 24396.07 270
PVSNet_088.72 1991.28 26490.03 26795.00 25797.99 13987.29 29794.84 30798.50 13392.06 21489.86 26795.19 27879.81 27899.39 12292.27 20469.79 32098.33 159
DeepMVS_CXcopyleft86.78 30297.09 19772.30 32195.17 31075.92 31684.34 30395.19 27870.58 31095.35 30779.98 30289.04 25492.68 311
testus88.91 28089.08 27588.40 29891.39 30676.05 31596.56 28496.48 29589.38 27589.39 27295.17 28070.94 30993.56 31577.04 30995.41 18095.61 280
patchmatchnet-post95.10 28189.42 13598.89 174
Patchmatch-RL test91.49 26290.85 25493.41 28291.37 30784.40 30192.81 31695.93 30391.87 21987.25 28194.87 28288.99 14596.53 30192.54 20082.00 29899.30 93
LP91.12 26689.99 26894.53 26996.35 23788.70 28393.86 31597.35 26184.88 30090.98 25894.77 28384.40 24797.43 27875.41 31391.89 22897.47 176
OpenMVS_ROBcopyleft86.42 2089.00 27987.43 28593.69 28093.08 30289.42 27397.91 20596.89 28378.58 31485.86 28894.69 28469.48 31198.29 24677.13 30893.29 21393.36 310
Test492.21 24890.34 26497.82 11592.83 30395.87 12797.94 20198.05 21594.50 12182.12 30694.48 28559.54 32198.54 20095.39 12298.22 12399.06 119
FPMVS77.62 29677.14 29479.05 31179.25 32560.97 32995.79 29795.94 30265.96 32067.93 32194.40 28637.73 32988.88 32568.83 31888.46 26487.29 316
testpf88.74 28189.09 27487.69 29995.78 27283.16 30684.05 32694.13 32085.22 29990.30 26494.39 28774.92 30095.80 30589.77 25193.28 21484.10 320
GG-mvs-BLEND96.59 18496.34 23894.98 15796.51 28888.58 32893.10 22794.34 28880.34 27798.05 25789.53 25896.99 15196.74 218
test235688.68 28288.61 27888.87 29789.90 31278.23 31295.11 30296.66 29388.66 28189.06 27494.33 28973.14 30792.56 31975.56 31295.11 18295.81 276
new_pmnet90.06 27489.00 27793.22 28694.18 29688.32 29096.42 28996.89 28386.19 29185.67 29193.62 29077.18 29297.10 28381.61 29889.29 25194.23 302
PM-MVS87.77 28486.55 28691.40 29391.03 30983.36 30596.92 26795.18 30991.28 23986.48 28693.42 29153.27 32296.74 29589.43 26181.97 29994.11 304
v1692.08 25190.94 25195.49 23796.38 23494.84 16698.81 8297.51 24189.94 26085.25 29593.28 29288.86 15396.91 28788.70 26979.78 30494.72 292
v1892.10 25090.97 25095.50 23696.34 23894.85 16198.82 7697.52 23889.99 25785.31 29493.26 29388.90 15296.92 28688.82 26779.77 30594.73 291
v1792.08 25190.94 25195.48 23896.34 23894.83 16898.81 8297.52 23889.95 25985.32 29293.24 29488.91 15196.91 28788.76 26879.63 30694.71 293
pmmvs-eth3d90.36 27389.05 27694.32 27591.10 30892.12 23797.63 23396.95 28288.86 27984.91 30293.13 29578.32 28496.74 29588.70 26981.81 30094.09 305
V1491.93 25490.76 25695.42 24696.33 24294.81 17298.77 9397.51 24189.86 26385.09 29793.13 29588.80 16296.83 29188.32 27279.06 31094.60 298
v1591.94 25390.77 25595.43 24396.31 24694.83 16898.77 9397.50 24489.92 26185.13 29693.08 29788.76 16496.86 28988.40 27179.10 30894.61 297
V991.91 25590.73 25795.45 24096.32 24594.80 17398.77 9397.50 24489.81 26485.03 29993.08 29788.76 16496.86 28988.24 27379.03 31194.69 294
v1191.85 25890.68 26095.36 24896.34 23894.74 18098.80 8597.43 25589.60 27185.09 29793.03 29988.53 17396.75 29487.37 27879.96 30394.58 299
v1291.89 25690.70 25895.43 24396.31 24694.80 17398.76 9697.50 24489.76 26584.95 30093.00 30088.82 15896.82 29388.23 27479.00 31294.68 296
v1391.88 25790.69 25995.43 24396.33 24294.78 17898.75 9797.50 24489.68 26884.93 30192.98 30188.84 15696.83 29188.14 27579.09 30994.69 294
test123567886.26 28885.81 28787.62 30086.97 31775.00 31996.55 28696.32 29886.08 29481.32 30992.98 30173.10 30892.05 32071.64 31687.32 27795.81 276
111184.94 28984.30 29086.86 30187.59 31575.10 31796.63 28196.43 29682.53 30680.75 31092.91 30368.94 31293.79 31368.24 31984.66 29491.70 312
.test124573.05 29876.31 29663.27 31987.59 31575.10 31796.63 28196.43 29682.53 30680.75 31092.91 30368.94 31293.79 31368.24 31912.72 33120.91 329
new-patchmatchnet88.50 28387.45 28491.67 29290.31 31085.89 30097.16 26197.33 26589.47 27283.63 30492.77 30576.38 29395.06 31082.70 29577.29 31494.06 306
pmmvs386.67 28784.86 28992.11 29188.16 31487.19 29896.63 28194.75 31379.88 31387.22 28292.75 30666.56 31695.20 30981.24 29976.56 31693.96 307
Anonymous2023121183.69 29081.50 29290.26 29489.23 31380.10 31197.97 19897.06 27772.79 31982.05 30792.57 30750.28 32396.32 30476.15 31175.38 31794.37 300
ambc89.49 29686.66 31875.78 31692.66 31796.72 28886.55 28592.50 30846.01 32597.90 26590.32 24182.09 29794.80 290
testing_290.61 27288.50 27996.95 15490.08 31195.57 13497.69 22798.06 21293.02 17876.55 31392.48 30961.18 32098.44 22195.45 12191.98 22596.84 209
test1235683.47 29183.37 29183.78 30784.43 32070.09 32495.12 30195.60 30682.98 30478.89 31292.43 31064.99 31791.41 32270.36 31785.55 29389.82 314
PatchT93.06 24191.97 24396.35 20696.69 21792.67 23294.48 31197.08 27486.62 28997.08 10092.23 31187.94 18797.90 26578.89 30596.69 15598.49 153
RPMNet92.52 24591.17 24896.59 18497.00 19993.43 22094.96 30497.26 27082.27 30896.93 10892.12 31286.98 21097.88 26976.32 31096.65 15798.46 154
UnsupCasMVSNet_bld87.17 28585.12 28893.31 28491.94 30588.77 28194.92 30698.30 16184.30 30382.30 30590.04 31363.96 31997.25 28185.85 28874.47 31993.93 308
LCM-MVSNet78.70 29376.24 29786.08 30377.26 32971.99 32294.34 31296.72 28861.62 32376.53 31489.33 31433.91 33292.78 31881.85 29774.60 31893.46 309
PMMVS277.95 29575.44 29885.46 30482.54 32174.95 32094.23 31393.08 32272.80 31874.68 31587.38 31536.36 33091.56 32173.95 31463.94 32189.87 313
JIA-IIPM93.35 23392.49 23795.92 22296.48 22790.65 26095.01 30396.96 28185.93 29596.08 13687.33 31687.70 19798.78 18691.35 22695.58 17998.34 158
testmv78.74 29277.35 29382.89 30978.16 32869.30 32595.87 29594.65 31481.11 31070.98 32087.11 31746.31 32490.42 32365.28 32276.72 31588.95 315
PMVScopyleft61.03 2365.95 30263.57 30473.09 31657.90 33351.22 33485.05 32593.93 32154.45 32544.32 32983.57 31813.22 33589.15 32458.68 32681.00 30278.91 324
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVS-HIRNet89.46 27888.40 28092.64 28797.58 16382.15 30894.16 31493.05 32375.73 31790.90 25982.52 31979.42 28098.33 23983.53 29498.68 10297.43 177
gg-mvs-nofinetune92.21 24890.58 26297.13 14396.75 21495.09 15295.85 29689.40 32785.43 29894.50 16481.98 32080.80 27298.40 23792.16 20598.33 12097.88 168
PNet_i23d67.70 30165.07 30275.60 31378.61 32659.61 33189.14 32188.24 32961.83 32252.37 32680.89 32118.91 33484.91 32762.70 32452.93 32382.28 321
Gipumacopyleft78.40 29476.75 29583.38 30895.54 28080.43 31079.42 32797.40 25864.67 32173.46 31680.82 32245.65 32693.14 31766.32 32187.43 27576.56 325
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
no-one74.41 29770.76 29985.35 30579.88 32476.83 31394.68 30994.22 31880.33 31263.81 32279.73 32335.45 33193.36 31671.78 31536.99 32885.86 319
ANet_high69.08 29965.37 30180.22 31065.99 33271.96 32390.91 32090.09 32682.62 30549.93 32878.39 32429.36 33381.75 32862.49 32538.52 32786.95 318
E-PMN64.94 30364.25 30367.02 31782.28 32259.36 33291.83 31985.63 33152.69 32660.22 32477.28 32541.06 32880.12 33046.15 32841.14 32561.57 327
EMVS64.07 30463.26 30566.53 31881.73 32358.81 33391.85 31884.75 33251.93 32859.09 32575.13 32643.32 32779.09 33142.03 32939.47 32661.69 326
MVEpermissive62.14 2263.28 30659.38 30674.99 31474.33 33065.47 32785.55 32480.50 33452.02 32751.10 32775.00 32710.91 33980.50 32951.60 32753.40 32278.99 323
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d63.73 30558.86 30778.35 31267.62 33167.90 32686.56 32387.81 33058.26 32442.49 33070.28 32811.55 33785.05 32663.66 32341.50 32482.11 322
X-MVStestdata94.06 22392.30 24099.34 1399.70 1598.35 2299.29 1498.88 4697.40 1598.46 4543.50 32995.90 2999.89 2597.85 3399.74 3299.78 7
testmvs21.48 31024.95 31111.09 32314.89 3356.47 33796.56 2849.87 3377.55 33117.93 33139.02 3309.43 3405.90 33516.56 33212.72 33120.91 329
test12320.95 31123.72 31212.64 32213.54 3368.19 33696.55 2866.13 3387.48 33216.74 33237.98 33112.97 3366.05 33416.69 3315.43 33323.68 328
test_post31.83 33288.83 15798.91 170
test_post196.68 28030.43 33387.85 19298.69 18892.59 197
wuyk23d30.17 30830.18 31030.16 32178.61 32643.29 33566.79 32814.21 33617.31 33014.82 33311.93 33411.55 33741.43 33337.08 33019.30 3305.76 331
pcd_1.5k_mvsjas7.88 31310.50 3140.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.00 33594.51 600.00 3360.00 3330.00 3340.00 332
pcd1.5k->3k39.42 30741.78 30832.35 32096.17 2550.00 3380.00 32998.54 1210.00 3330.00 3340.00 33587.78 1940.00 3360.00 33393.56 20597.06 187
sosnet-low-res0.00 3140.00 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.00 3350.00 3410.00 3360.00 3330.00 3340.00 332
sosnet0.00 3140.00 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.00 3350.00 3410.00 3360.00 3330.00 3340.00 332
uncertanet0.00 3140.00 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.00 3350.00 3410.00 3360.00 3330.00 3340.00 332
Regformer0.00 3140.00 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.00 3350.00 3410.00 3360.00 3330.00 3340.00 332
uanet0.00 3140.00 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.00 3350.00 3410.00 3360.00 3330.00 3340.00 332
sam_mvs189.45 134
sam_mvs88.99 145
MTGPAbinary98.74 75
MTMP94.14 319
test9_res96.39 9399.57 5499.69 35
agg_prior295.87 10599.57 5499.68 41
agg_prior99.30 5298.38 1798.72 8197.57 9299.81 50
test_prior498.01 4197.86 214
test_prior99.19 2899.31 4798.22 3098.84 5399.70 9199.65 50
旧先验297.57 23691.30 23798.67 3699.80 5795.70 114
新几何297.64 231
无先验97.58 23598.72 8191.38 23199.87 3493.36 17299.60 59
原ACMM297.67 229
testdata299.89 2591.65 221
segment_acmp96.85 3
testdata197.32 25396.34 57
test1299.18 3299.16 7698.19 3298.53 12498.07 5995.13 4999.72 8699.56 6099.63 55
plane_prior797.42 17594.63 183
plane_prior697.35 18094.61 18687.09 207
plane_prior598.56 11899.03 15696.07 9694.27 18596.92 196
plane_prior394.61 18697.02 3995.34 145
plane_prior298.80 8597.28 22
plane_prior197.37 179
plane_prior94.60 18898.44 14696.74 4694.22 187
n20.00 339
nn0.00 339
door-mid94.37 316
test1198.66 103
door94.64 315
HQP5-MVS94.25 201
HQP-NCC97.20 18998.05 19096.43 5494.45 166
ACMP_Plane97.20 18998.05 19096.43 5494.45 166
BP-MVS95.30 124
HQP4-MVS94.45 16698.96 16396.87 206
HQP3-MVS98.46 13894.18 189
HQP2-MVS86.75 212
MDTV_nov1_ep13_2view84.26 30296.89 27390.97 24497.90 7389.89 13293.91 15999.18 107
ACMMP++_ref92.97 216
ACMMP++93.61 204
Test By Simon94.64 57