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.
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MSC_two_6792asdad96.52 197.78 5790.86 196.85 7799.61 496.03 2699.06 999.07 5
No_MVS96.52 197.78 5790.86 196.85 7799.61 496.03 2699.06 999.07 5
OPU-MVS96.21 398.00 4590.85 397.13 1697.08 6692.59 298.94 8892.25 8898.99 1498.84 16
HPM-MVS++copyleft95.14 1194.91 2395.83 498.25 3289.65 495.92 8396.96 6591.75 1294.02 6796.83 7888.12 2599.55 1793.41 6298.94 1698.28 58
MM95.10 1294.91 2395.68 596.09 11288.34 996.68 3594.37 28295.08 194.68 5397.72 3882.94 9799.64 197.85 598.76 3099.06 7
SMA-MVScopyleft95.20 895.07 1795.59 698.14 3888.48 896.26 5097.28 3785.90 19297.67 498.10 1388.41 2199.56 1394.66 4699.19 198.71 22
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
3Dnovator+87.14 492.42 10091.37 11495.55 795.63 13988.73 697.07 2096.77 8890.84 2584.02 31896.62 9175.95 20299.34 3987.77 16997.68 9398.59 26
CNVR-MVS95.40 795.37 995.50 898.11 3988.51 795.29 12696.96 6592.09 995.32 4597.08 6689.49 1599.33 4295.10 4198.85 2098.66 23
MGCNet94.18 4793.80 6195.34 994.91 17887.62 1495.97 7893.01 32692.58 694.22 5897.20 6080.56 13299.59 897.04 1998.68 3898.81 19
ACMMP_NAP94.74 2394.56 3095.28 1098.02 4487.70 1195.68 10297.34 2788.28 11895.30 4697.67 4085.90 5299.54 2193.91 5498.95 1598.60 25
DPE-MVScopyleft95.57 495.67 495.25 1198.36 2887.28 1895.56 11497.51 889.13 8697.14 1597.91 3191.64 799.62 294.61 4799.17 298.86 13
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS94.97 1594.90 2595.20 1297.84 5387.76 1096.65 3697.48 1387.76 14095.71 4097.70 3988.28 2499.35 3893.89 5598.78 2798.48 32
MCST-MVS94.45 3194.20 4895.19 1398.46 1987.50 1695.00 14997.12 5287.13 15892.51 10896.30 10089.24 1799.34 3993.46 5998.62 4798.73 20
NCCC94.81 2094.69 2995.17 1497.83 5487.46 1795.66 10596.93 6992.34 793.94 6896.58 9387.74 2899.44 3092.83 7198.40 5598.62 24
DPM-MVS92.58 9691.74 10695.08 1596.19 10389.31 592.66 29896.56 10883.44 26691.68 13395.04 17386.60 4498.99 7885.60 20397.92 8196.93 172
ZNCC-MVS94.47 3094.28 4295.03 1698.52 1586.96 2096.85 3097.32 3188.24 11993.15 8397.04 6986.17 4999.62 292.40 8298.81 2498.52 28
test_0728_SECOND95.01 1798.79 286.43 3997.09 1897.49 999.61 495.62 3399.08 798.99 9
MTAPA94.42 3694.22 4595.00 1898.42 2186.95 2194.36 19996.97 6291.07 2193.14 8497.56 4284.30 7899.56 1393.43 6098.75 3198.47 35
MSP-MVS95.42 695.56 694.98 1998.49 1786.52 3696.91 2797.47 1491.73 1396.10 3296.69 8389.90 1299.30 4594.70 4598.04 7699.13 2
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
region2R94.43 3394.27 4494.92 2098.65 886.67 3096.92 2697.23 4088.60 10993.58 7597.27 5485.22 6199.54 2192.21 9098.74 3298.56 27
APDe-MVScopyleft95.46 595.64 594.91 2198.26 3186.29 4697.46 797.40 2389.03 9196.20 3198.10 1389.39 1699.34 3995.88 2899.03 1199.10 4
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR94.43 3394.28 4294.91 2198.63 986.69 2896.94 2297.32 3188.63 10693.53 7897.26 5685.04 6599.54 2192.35 8598.78 2798.50 29
GST-MVS94.21 4293.97 5794.90 2398.41 2286.82 2496.54 3897.19 4188.24 11993.26 8096.83 7885.48 5899.59 891.43 11698.40 5598.30 52
HFP-MVS94.52 2894.40 3594.86 2498.61 1086.81 2596.94 2297.34 2788.63 10693.65 7397.21 5886.10 5099.49 2792.35 8598.77 2998.30 52
sasdasda93.27 7992.75 8994.85 2595.70 13487.66 1296.33 4196.41 11890.00 5094.09 6394.60 19782.33 10698.62 12792.40 8292.86 21798.27 60
MP-MVS-pluss94.21 4294.00 5694.85 2598.17 3686.65 3194.82 16297.17 4686.26 18492.83 9397.87 3385.57 5799.56 1394.37 5098.92 1798.34 45
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs93.27 7992.75 8994.85 2595.70 13487.66 1296.33 4196.41 11890.00 5094.09 6394.60 19782.33 10698.62 12792.40 8292.86 21798.27 60
XVS94.45 3194.32 3894.85 2598.54 1386.60 3496.93 2497.19 4190.66 3392.85 9197.16 6485.02 6699.49 2791.99 10198.56 5198.47 35
X-MVStestdata88.31 22286.13 27194.85 2598.54 1386.60 3496.93 2497.19 4190.66 3392.85 9123.41 47085.02 6699.49 2791.99 10198.56 5198.47 35
SteuartSystems-ACMMP95.20 895.32 1194.85 2596.99 7886.33 4297.33 897.30 3491.38 1895.39 4497.46 4688.98 2099.40 3194.12 5198.89 1898.82 18
Skip Steuart: Steuart Systems R&D Blog.
DVP-MVS++95.98 196.36 194.82 3197.78 5786.00 5298.29 197.49 990.75 2897.62 898.06 2192.59 299.61 495.64 3199.02 1298.86 13
MED-MVS95.17 1095.29 1294.81 3298.39 2585.89 6395.91 8497.55 689.01 9395.86 3897.54 4389.24 1799.59 895.27 3998.85 2098.95 11
alignmvs93.08 8792.50 9594.81 3295.62 14087.61 1595.99 7596.07 15589.77 6394.12 6294.87 18180.56 13298.66 11992.42 8193.10 21398.15 72
SED-MVS95.91 296.28 294.80 3498.77 585.99 5497.13 1697.44 1890.31 4097.71 298.07 1992.31 499.58 1195.66 2999.13 398.84 16
DeepC-MVS_fast89.43 294.04 5093.79 6294.80 3497.48 6786.78 2695.65 10796.89 7489.40 7492.81 9496.97 7185.37 6099.24 4890.87 12598.69 3698.38 44
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVScopyleft94.25 3994.07 5394.77 3698.47 1886.31 4496.71 3396.98 6189.04 8991.98 11997.19 6185.43 5999.56 1392.06 9998.79 2598.44 39
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVScopyleft94.24 4094.07 5394.75 3798.06 4286.90 2395.88 8696.94 6885.68 19995.05 5197.18 6287.31 3699.07 6191.90 10798.61 4998.28 58
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS94.34 3794.21 4794.74 3898.39 2586.64 3297.60 597.24 3888.53 11192.73 9997.23 5785.20 6299.32 4392.15 9398.83 2398.25 65
PGM-MVS93.96 5593.72 6794.68 3998.43 2086.22 4795.30 12497.78 187.45 14993.26 8097.33 5284.62 7599.51 2590.75 12798.57 5098.32 51
DVP-MVScopyleft95.67 396.02 394.64 4098.78 385.93 5797.09 1896.73 9490.27 4497.04 1998.05 2491.47 899.55 1795.62 3399.08 798.45 38
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
mPP-MVS93.99 5393.78 6394.63 4198.50 1685.90 6296.87 2896.91 7288.70 10491.83 12897.17 6383.96 8299.55 1791.44 11598.64 4698.43 40
PHI-MVS93.89 5793.65 7194.62 4296.84 8186.43 3996.69 3497.49 985.15 22393.56 7796.28 10185.60 5699.31 4492.45 7998.79 2598.12 77
TSAR-MVS + MP.94.85 1794.94 2194.58 4398.25 3286.33 4296.11 6396.62 10388.14 12496.10 3296.96 7289.09 1998.94 8894.48 4898.68 3898.48 32
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CANet93.54 6693.20 8094.55 4495.65 13785.73 6894.94 15296.69 9991.89 1190.69 15195.88 12581.99 11899.54 2193.14 6697.95 8098.39 42
train_agg93.44 7293.08 8294.52 4597.53 6486.49 3794.07 21996.78 8681.86 30892.77 9696.20 10487.63 3099.12 5992.14 9498.69 3697.94 90
CDPH-MVS92.83 9192.30 9894.44 4697.79 5586.11 5194.06 22196.66 10080.09 33992.77 9696.63 9086.62 4299.04 6587.40 17698.66 4298.17 70
3Dnovator86.66 591.73 11390.82 12994.44 4694.59 20386.37 4197.18 1497.02 5989.20 8384.31 31396.66 8673.74 24299.17 5386.74 18697.96 7997.79 107
SR-MVS94.23 4194.17 5194.43 4898.21 3585.78 6696.40 4096.90 7388.20 12294.33 5797.40 4984.75 7499.03 6693.35 6397.99 7898.48 32
HPM-MVScopyleft94.02 5193.88 5894.43 4898.39 2585.78 6697.25 1297.07 5786.90 16892.62 10596.80 8284.85 7299.17 5392.43 8098.65 4598.33 47
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + GP.93.66 6493.41 7594.41 5096.59 8886.78 2694.40 19193.93 30089.77 6394.21 5995.59 14487.35 3598.61 12992.72 7496.15 13197.83 104
reproduce-ours94.82 1894.97 1994.38 5197.91 5085.46 7195.86 8797.15 4889.82 5695.23 4898.10 1387.09 3899.37 3495.30 3798.25 6498.30 52
our_new_method94.82 1894.97 1994.38 5197.91 5085.46 7195.86 8797.15 4889.82 5695.23 4898.10 1387.09 3899.37 3495.30 3798.25 6498.30 52
NormalMVS93.46 6993.16 8194.37 5398.40 2386.20 4896.30 4396.27 13191.65 1692.68 10196.13 11077.97 17298.84 10190.75 12798.26 6098.07 79
test1294.34 5497.13 7686.15 5096.29 12791.04 14785.08 6499.01 7198.13 7197.86 99
SymmetryMVS92.81 9392.31 9794.32 5596.15 10486.20 4896.30 4394.43 27891.65 1692.68 10196.13 11077.97 17298.84 10190.75 12794.72 16397.92 94
ACMMPcopyleft93.24 8192.88 8794.30 5698.09 4185.33 7596.86 2997.45 1788.33 11590.15 16697.03 7081.44 12599.51 2590.85 12695.74 13898.04 85
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
reproduce_model94.76 2294.92 2294.29 5797.92 4685.18 7795.95 8197.19 4189.67 6695.27 4798.16 586.53 4599.36 3795.42 3698.15 6998.33 47
DeepC-MVS88.79 393.31 7892.99 8594.26 5896.07 11485.83 6494.89 15596.99 6089.02 9289.56 17597.37 5182.51 10399.38 3292.20 9198.30 5897.57 122
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MGCFI-Net93.03 8892.63 9294.23 5995.62 14085.92 5996.08 6596.33 12589.86 5493.89 7094.66 19482.11 11398.50 13592.33 8792.82 22098.27 60
fmvsm_l_conf0.5_n_394.80 2195.01 1894.15 6095.64 13885.08 7896.09 6497.36 2590.98 2397.09 1798.12 984.98 7098.94 8897.07 1697.80 8898.43 40
EPNet91.79 10891.02 12394.10 6190.10 39585.25 7696.03 7292.05 35392.83 587.39 22495.78 13679.39 15499.01 7188.13 16397.48 9698.05 84
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lecture95.10 1295.46 894.01 6298.40 2384.36 10397.70 397.78 191.19 1996.22 3098.08 1886.64 4199.37 3494.91 4398.26 6098.29 57
test_fmvsmconf_n94.60 2694.81 2793.98 6394.62 19984.96 8196.15 5897.35 2689.37 7596.03 3598.11 1086.36 4699.01 7197.45 1097.83 8697.96 89
DELS-MVS93.43 7693.25 7893.97 6495.42 14885.04 7993.06 28197.13 5190.74 3091.84 12695.09 17286.32 4799.21 5191.22 11798.45 5397.65 116
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
DP-MVS Recon91.95 10591.28 11793.96 6598.33 3085.92 5994.66 17496.66 10082.69 28690.03 16895.82 13282.30 10899.03 6684.57 22196.48 12496.91 174
HPM-MVS_fast93.40 7793.22 7993.94 6698.36 2884.83 8397.15 1596.80 8585.77 19692.47 10997.13 6582.38 10499.07 6190.51 13298.40 5597.92 94
test_fmvsmconf0.1_n94.20 4494.31 4093.88 6792.46 31184.80 8496.18 5596.82 8289.29 8095.68 4198.11 1085.10 6398.99 7897.38 1197.75 9297.86 99
SD-MVS94.96 1695.33 1093.88 6797.25 7586.69 2896.19 5397.11 5590.42 3696.95 2197.27 5489.53 1496.91 29994.38 4998.85 2098.03 86
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
MVS_111021_HR93.45 7193.31 7693.84 6996.99 7884.84 8293.24 27297.24 3888.76 10191.60 13495.85 12986.07 5198.66 11991.91 10598.16 6898.03 86
SR-MVS-dyc-post93.82 5993.82 6093.82 7097.92 4684.57 9096.28 4796.76 8987.46 14793.75 7197.43 4784.24 7999.01 7192.73 7297.80 8897.88 97
test_prior93.82 7097.29 7384.49 9496.88 7598.87 9598.11 78
APD-MVS_3200maxsize93.78 6093.77 6493.80 7297.92 4684.19 10796.30 4396.87 7686.96 16493.92 6997.47 4583.88 8398.96 8592.71 7597.87 8498.26 64
fmvsm_l_conf0.5_n94.29 3894.46 3393.79 7395.28 15385.43 7395.68 10296.43 11686.56 17696.84 2397.81 3687.56 3398.77 11097.14 1496.82 11497.16 153
CSCG93.23 8293.05 8393.76 7498.04 4384.07 10996.22 5297.37 2484.15 24790.05 16795.66 14187.77 2799.15 5789.91 13798.27 5998.07 79
GDP-MVS92.04 10391.46 11193.75 7594.55 20984.69 8795.60 11396.56 10887.83 13793.07 8795.89 12473.44 24698.65 12190.22 13596.03 13397.91 96
BP-MVS192.48 9892.07 10193.72 7694.50 21284.39 10295.90 8594.30 28590.39 3792.67 10395.94 12174.46 22598.65 12193.14 6697.35 10098.13 74
test_fmvsmconf0.01_n93.19 8393.02 8493.71 7789.25 40884.42 10196.06 6996.29 12789.06 8794.68 5398.13 679.22 15698.98 8297.22 1397.24 10297.74 110
UA-Net92.83 9192.54 9493.68 7896.10 11184.71 8695.66 10596.39 12091.92 1093.22 8296.49 9683.16 9298.87 9584.47 22395.47 14597.45 128
fmvsm_l_conf0.5_n_a94.20 4494.40 3593.60 7995.29 15284.98 8095.61 11096.28 13086.31 18296.75 2597.86 3487.40 3498.74 11497.07 1697.02 10797.07 158
QAPM89.51 17988.15 20693.59 8094.92 17684.58 8996.82 3196.70 9878.43 36683.41 33496.19 10773.18 25199.30 4577.11 33496.54 12196.89 175
test_fmvsm_n_192094.71 2495.11 1693.50 8195.79 12884.62 8896.15 5897.64 389.85 5597.19 1497.89 3286.28 4898.71 11797.11 1598.08 7597.17 147
fmvsm_s_conf0.5_n_994.99 1495.50 793.44 8296.51 9682.25 18095.76 9796.92 7093.37 397.63 798.43 184.82 7399.16 5698.15 197.92 8198.90 12
KinetiMVS91.82 10791.30 11593.39 8394.72 19283.36 13495.45 11796.37 12290.33 3992.17 11496.03 11672.32 26398.75 11187.94 16696.34 12698.07 79
casdiffmvs_mvgpermissive92.96 9092.83 8893.35 8494.59 20383.40 13295.00 14996.34 12490.30 4292.05 11796.05 11483.43 8698.15 17192.07 9695.67 13998.49 31
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_593.96 5594.18 5093.30 8594.79 18583.81 11895.77 9596.74 9388.02 12796.23 2997.84 3583.36 9098.83 10497.49 897.34 10197.25 140
EI-MVSNet-Vis-set93.01 8992.92 8693.29 8695.01 16783.51 12994.48 18395.77 18390.87 2492.52 10796.67 8584.50 7699.00 7691.99 10194.44 17697.36 131
Vis-MVSNetpermissive91.75 11291.23 11893.29 8695.32 15183.78 11996.14 6095.98 16289.89 5290.45 15596.58 9375.09 21498.31 16284.75 21596.90 11097.78 108
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
balanced_conf0393.98 5494.22 4593.26 8896.13 10683.29 13696.27 4996.52 11189.82 5695.56 4395.51 14784.50 7698.79 10894.83 4498.86 1997.72 112
SPE-MVS-test94.02 5194.29 4193.24 8996.69 8483.24 13797.49 696.92 7092.14 892.90 8995.77 13785.02 6698.33 15993.03 6898.62 4798.13 74
VNet92.24 10291.91 10393.24 8996.59 8883.43 13094.84 16196.44 11589.19 8494.08 6695.90 12377.85 17898.17 16988.90 15393.38 20298.13 74
fmvsm_s_conf0.5_n_1094.43 3394.84 2693.20 9195.73 13183.19 14095.99 7597.31 3391.08 2097.67 498.11 1081.87 12099.22 4997.86 497.91 8397.20 145
VDD-MVS90.74 13789.92 15193.20 9196.27 10183.02 15295.73 9993.86 30488.42 11492.53 10696.84 7762.09 36998.64 12490.95 12392.62 22797.93 93
Elysia90.12 15689.10 17493.18 9393.16 28184.05 11195.22 13396.27 13185.16 22190.59 15294.68 19064.64 35298.37 15286.38 19295.77 13697.12 155
StellarMVS90.12 15689.10 17493.18 9393.16 28184.05 11195.22 13396.27 13185.16 22190.59 15294.68 19064.64 35298.37 15286.38 19295.77 13697.12 155
CS-MVS94.12 4894.44 3493.17 9596.55 9183.08 14997.63 496.95 6791.71 1493.50 7996.21 10385.61 5598.24 16493.64 5798.17 6798.19 68
nrg03091.08 13190.39 13593.17 9593.07 28886.91 2296.41 3996.26 13588.30 11788.37 20094.85 18482.19 11297.64 22591.09 11882.95 35194.96 260
MVSMamba_PlusPlus93.44 7293.54 7393.14 9796.58 9083.05 15096.06 6996.50 11384.42 24494.09 6395.56 14685.01 6998.69 11894.96 4298.66 4297.67 115
EI-MVSNet-UG-set92.74 9492.62 9393.12 9894.86 18183.20 13994.40 19195.74 18690.71 3292.05 11796.60 9284.00 8198.99 7891.55 11393.63 19297.17 147
test_fmvsmvis_n_192093.44 7293.55 7293.10 9993.67 26784.26 10595.83 9196.14 14689.00 9492.43 11097.50 4483.37 8998.72 11596.61 2397.44 9796.32 200
新几何193.10 9997.30 7284.35 10495.56 20371.09 43391.26 14396.24 10282.87 9998.86 9779.19 31398.10 7296.07 216
OMC-MVS91.23 12390.62 13493.08 10196.27 10184.07 10993.52 25495.93 16886.95 16589.51 17696.13 11078.50 16698.35 15685.84 20192.90 21696.83 182
OpenMVScopyleft83.78 1188.74 20987.29 22893.08 10192.70 30685.39 7496.57 3796.43 11678.74 36180.85 36696.07 11369.64 29899.01 7178.01 32596.65 11994.83 268
MAR-MVS90.30 15289.37 16793.07 10396.61 8784.48 9595.68 10295.67 19482.36 29187.85 21192.85 26476.63 19198.80 10680.01 30196.68 11895.91 222
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
lupinMVS90.92 13290.21 13993.03 10493.86 25283.88 11692.81 29393.86 30479.84 34291.76 13094.29 21177.92 17598.04 18890.48 13397.11 10397.17 147
Effi-MVS+91.59 11791.11 12093.01 10594.35 22583.39 13394.60 17695.10 23887.10 15990.57 15493.10 25981.43 12698.07 18589.29 14594.48 17497.59 121
fmvsm_s_conf0.5_n_a93.57 6593.76 6593.00 10695.02 16683.67 12296.19 5396.10 15287.27 15395.98 3698.05 2483.07 9698.45 14596.68 2295.51 14296.88 176
MVS_111021_LR92.47 9992.29 9992.98 10795.99 12084.43 9993.08 27896.09 15388.20 12291.12 14695.72 14081.33 12797.76 21491.74 10997.37 9996.75 184
fmvsm_s_conf0.1_n_a93.19 8393.26 7792.97 10892.49 30983.62 12596.02 7395.72 19086.78 17096.04 3498.19 382.30 10898.43 14996.38 2495.42 14896.86 177
ETV-MVS92.74 9492.66 9192.97 10895.20 15984.04 11395.07 14596.51 11290.73 3192.96 8891.19 32584.06 8098.34 15791.72 11096.54 12196.54 195
LFMVS90.08 15989.13 17392.95 11096.71 8382.32 17996.08 6589.91 41086.79 16992.15 11696.81 8062.60 36798.34 15787.18 18093.90 18698.19 68
UGNet89.95 16688.95 18292.95 11094.51 21183.31 13595.70 10195.23 23189.37 7587.58 21893.94 22764.00 35798.78 10983.92 23096.31 12796.74 185
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
jason90.80 13590.10 14392.90 11293.04 29183.53 12893.08 27894.15 29380.22 33691.41 14094.91 17876.87 18597.93 20490.28 13496.90 11097.24 141
jason: jason.
DP-MVS87.25 26385.36 30092.90 11297.65 6183.24 13794.81 16392.00 35574.99 40181.92 35595.00 17472.66 25699.05 6366.92 41492.33 23296.40 197
fmvsm_s_conf0.5_n_894.56 2795.12 1592.87 11495.96 12381.32 20595.76 9797.57 593.48 297.53 1098.32 281.78 12399.13 5897.91 297.81 8798.16 71
fmvsm_s_conf0.5_n93.76 6194.06 5592.86 11595.62 14083.17 14196.14 6096.12 15088.13 12595.82 3998.04 2783.43 8698.48 13796.97 2096.23 12896.92 173
fmvsm_s_conf0.1_n93.46 6993.66 7092.85 11693.75 25983.13 14396.02 7395.74 18687.68 14395.89 3798.17 482.78 10098.46 14196.71 2196.17 13096.98 167
CANet_DTU90.26 15489.41 16692.81 11793.46 27483.01 15393.48 25594.47 27789.43 7387.76 21694.23 21670.54 28699.03 6684.97 21096.39 12596.38 198
MVSFormer91.68 11591.30 11592.80 11893.86 25283.88 11695.96 7995.90 17284.66 24091.76 13094.91 17877.92 17597.30 26689.64 14197.11 10397.24 141
PVSNet_Blended_VisFu91.38 12090.91 12692.80 11896.39 9883.17 14194.87 15796.66 10083.29 27189.27 18294.46 20680.29 13599.17 5387.57 17395.37 14996.05 219
fmvsm_l_conf0.5_n_994.65 2595.28 1392.77 12095.95 12481.83 19095.53 11597.12 5291.68 1597.89 198.06 2185.71 5498.65 12197.32 1298.26 6097.83 104
LuminaMVS90.55 14889.81 15392.77 12092.78 30484.21 10694.09 21794.17 29285.82 19391.54 13594.14 21869.93 29297.92 20591.62 11294.21 18196.18 208
fmvsm_s_conf0.5_n_694.11 4994.56 3092.76 12294.98 17181.96 18895.79 9397.29 3689.31 7897.52 1197.61 4183.25 9198.88 9497.05 1898.22 6697.43 130
VDDNet89.56 17888.49 19792.76 12295.07 16582.09 18396.30 4393.19 32181.05 33091.88 12496.86 7661.16 38598.33 15988.43 16092.49 23197.84 103
viewdifsd2359ckpt0991.18 12690.65 13392.75 12494.61 20282.36 17894.32 20095.74 18684.72 23789.66 17495.15 17079.69 14998.04 18887.70 17094.27 18097.85 102
h-mvs3390.80 13590.15 14292.75 12496.01 11682.66 16695.43 11895.53 20789.80 5993.08 8595.64 14275.77 20399.00 7692.07 9678.05 40896.60 190
casdiffmvspermissive92.51 9792.43 9692.74 12694.41 22081.98 18694.54 18096.23 13989.57 6991.96 12196.17 10882.58 10298.01 19190.95 12395.45 14798.23 66
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_yl90.69 14090.02 14992.71 12795.72 13282.41 17694.11 21395.12 23685.63 20091.49 13794.70 18874.75 21898.42 15086.13 19692.53 22997.31 132
DCV-MVSNet90.69 14090.02 14992.71 12795.72 13282.41 17694.11 21395.12 23685.63 20091.49 13794.70 18874.75 21898.42 15086.13 19692.53 22997.31 132
PCF-MVS84.11 1087.74 23786.08 27592.70 12994.02 24184.43 9989.27 38895.87 17773.62 41584.43 30594.33 20878.48 16898.86 9770.27 38894.45 17594.81 269
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SSM_040490.73 13890.08 14492.69 13095.00 17083.13 14394.32 20095.00 24685.41 21189.84 16995.35 15576.13 19497.98 19685.46 20694.18 18296.95 169
baseline92.39 10192.29 9992.69 13094.46 21581.77 19294.14 21096.27 13189.22 8291.88 12496.00 11782.35 10597.99 19391.05 11995.27 15398.30 52
MSLP-MVS++93.72 6394.08 5292.65 13297.31 7183.43 13095.79 9397.33 2990.03 4993.58 7596.96 7284.87 7197.76 21492.19 9298.66 4296.76 183
EC-MVSNet93.44 7293.71 6892.63 13395.21 15882.43 17397.27 1196.71 9790.57 3592.88 9095.80 13383.16 9298.16 17093.68 5698.14 7097.31 132
ab-mvs89.41 18688.35 19992.60 13495.15 16382.65 17092.20 31795.60 20183.97 25188.55 19693.70 24174.16 23398.21 16882.46 25489.37 27696.94 171
LS3D87.89 23286.32 26492.59 13596.07 11482.92 15695.23 13194.92 25375.66 39382.89 34195.98 11972.48 26099.21 5168.43 40295.23 15495.64 236
Anonymous2024052988.09 22886.59 25392.58 13696.53 9381.92 18995.99 7595.84 17974.11 41089.06 18695.21 16561.44 37798.81 10583.67 23787.47 30797.01 165
fmvsm_s_conf0.5_n_394.49 2995.13 1492.56 13795.49 14681.10 21595.93 8297.16 4792.96 497.39 1298.13 683.63 8598.80 10697.89 397.61 9597.78 108
CPTT-MVS91.99 10491.80 10492.55 13898.24 3481.98 18696.76 3296.49 11481.89 30790.24 15996.44 9878.59 16498.61 12989.68 14097.85 8597.06 159
viewdifsd2359ckpt1391.20 12590.75 13192.54 13994.30 22782.13 18294.03 22395.89 17485.60 20290.20 16195.36 15479.69 14997.90 20887.85 16893.86 18797.61 118
114514_t89.51 17988.50 19592.54 13998.11 3981.99 18595.16 14196.36 12370.19 43785.81 25895.25 16176.70 18998.63 12682.07 26496.86 11397.00 166
PAPM_NR91.22 12490.78 13092.52 14197.60 6281.46 20194.37 19796.24 13886.39 18187.41 22194.80 18682.06 11698.48 13782.80 24995.37 14997.61 118
mamba_040889.06 19987.92 21392.50 14294.76 18682.66 16679.84 45794.64 27185.18 21688.96 18895.00 17476.00 19997.98 19683.74 23493.15 21096.85 178
DeepPCF-MVS89.96 194.20 4494.77 2892.49 14396.52 9480.00 25594.00 22897.08 5690.05 4895.65 4297.29 5389.66 1398.97 8393.95 5398.71 3398.50 29
SSM_040790.47 15089.80 15492.46 14494.76 18682.66 16693.98 23095.00 24685.41 21188.96 18895.35 15576.13 19497.88 20985.46 20693.15 21096.85 178
IS-MVSNet91.43 11991.09 12292.46 14495.87 12781.38 20496.95 2193.69 31289.72 6589.50 17895.98 11978.57 16597.77 21383.02 24396.50 12398.22 67
API-MVS90.66 14390.07 14592.45 14696.36 9984.57 9096.06 6995.22 23382.39 28989.13 18394.27 21480.32 13498.46 14180.16 30096.71 11794.33 292
xiu_mvs_v1_base_debu90.64 14490.05 14692.40 14793.97 24784.46 9693.32 26395.46 21185.17 21892.25 11194.03 21970.59 28298.57 13290.97 12094.67 16594.18 295
xiu_mvs_v1_base90.64 14490.05 14692.40 14793.97 24784.46 9693.32 26395.46 21185.17 21892.25 11194.03 21970.59 28298.57 13290.97 12094.67 16594.18 295
xiu_mvs_v1_base_debi90.64 14490.05 14692.40 14793.97 24784.46 9693.32 26395.46 21185.17 21892.25 11194.03 21970.59 28298.57 13290.97 12094.67 16594.18 295
fmvsm_s_conf0.5_n_293.47 6893.83 5992.39 15095.36 14981.19 21195.20 13896.56 10890.37 3897.13 1698.03 2877.47 18198.96 8597.79 696.58 12097.03 162
viewmacassd2359aftdt91.67 11691.43 11392.37 15193.95 25081.00 21993.90 23895.97 16587.75 14191.45 13996.04 11579.92 14197.97 19889.26 14694.67 16598.14 73
viewmanbaseed2359cas91.78 11091.58 10992.37 15194.32 22681.07 21693.76 24395.96 16687.26 15491.50 13695.88 12580.92 13197.97 19889.70 13994.92 15998.07 79
fmvsm_s_conf0.1_n_293.16 8593.42 7492.37 15194.62 19981.13 21395.23 13195.89 17490.30 4296.74 2698.02 2976.14 19398.95 8797.64 796.21 12997.03 162
AdaColmapbinary89.89 16989.07 17692.37 15197.41 6883.03 15194.42 19095.92 16982.81 28386.34 24794.65 19573.89 23899.02 6980.69 29195.51 14295.05 255
CNLPA89.07 19887.98 21092.34 15596.87 8084.78 8594.08 21893.24 31881.41 32184.46 30395.13 17175.57 21096.62 31277.21 33293.84 18995.61 239
fmvsm_s_conf0.5_n_493.86 5894.37 3792.33 15695.13 16480.95 22295.64 10896.97 6289.60 6896.85 2297.77 3783.08 9598.92 9197.49 896.78 11597.13 154
ET-MVSNet_ETH3D87.51 25185.91 28392.32 15793.70 26683.93 11492.33 31190.94 38784.16 24672.09 43592.52 27769.90 29395.85 35989.20 14788.36 29497.17 147
Anonymous20240521187.68 23886.13 27192.31 15896.66 8580.74 23094.87 15791.49 37280.47 33589.46 17995.44 15054.72 42298.23 16582.19 26089.89 26697.97 88
CHOSEN 1792x268888.84 20587.69 21892.30 15996.14 10581.42 20390.01 37595.86 17874.52 40687.41 22193.94 22775.46 21198.36 15480.36 29695.53 14197.12 155
viewcassd2359sk1191.79 10891.62 10892.29 16094.62 19980.88 22593.70 24896.18 14487.38 15191.13 14595.85 12981.62 12498.06 18689.71 13894.40 17797.94 90
HY-MVS83.01 1289.03 20187.94 21292.29 16094.86 18182.77 15892.08 32294.49 27681.52 32086.93 22892.79 27078.32 17098.23 16579.93 30290.55 25395.88 225
CDS-MVSNet89.45 18288.51 19492.29 16093.62 26983.61 12793.01 28294.68 26981.95 30187.82 21493.24 25378.69 16296.99 29380.34 29793.23 20796.28 203
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR90.02 16289.27 17292.29 16095.78 12980.95 22292.68 29796.22 14081.91 30386.66 23893.75 23982.23 11098.44 14779.40 31294.79 16297.48 126
mvsmamba90.33 15189.69 15792.25 16495.17 16081.64 19495.27 12993.36 31784.88 23089.51 17694.27 21469.29 30797.42 25189.34 14496.12 13297.68 114
PLCcopyleft84.53 789.06 19988.03 20892.15 16597.27 7482.69 16594.29 20295.44 21679.71 34484.01 31994.18 21776.68 19098.75 11177.28 33193.41 20195.02 256
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet91.70 11491.56 11092.13 16695.88 12580.50 23797.33 895.25 23086.15 18789.76 17395.60 14383.42 8898.32 16187.37 17893.25 20697.56 123
patch_mono-293.74 6294.32 3892.01 16797.54 6378.37 29793.40 25997.19 4188.02 12794.99 5297.21 5888.35 2298.44 14794.07 5298.09 7399.23 1
原ACMM192.01 16797.34 7081.05 21796.81 8478.89 35590.45 15595.92 12282.65 10198.84 10180.68 29298.26 6096.14 210
UniMVSNet (Re)89.80 17289.07 17692.01 16793.60 27084.52 9394.78 16597.47 1489.26 8186.44 24492.32 28382.10 11497.39 26284.81 21480.84 38594.12 299
MG-MVS91.77 11191.70 10792.00 17097.08 7780.03 25393.60 25295.18 23487.85 13690.89 14996.47 9782.06 11698.36 15485.07 20997.04 10697.62 117
EIA-MVS91.95 10591.94 10291.98 17195.16 16180.01 25495.36 11996.73 9488.44 11289.34 18092.16 28883.82 8498.45 14589.35 14397.06 10597.48 126
PVSNet_Blended90.73 13890.32 13791.98 17196.12 10781.25 20792.55 30296.83 8082.04 29989.10 18492.56 27681.04 12998.85 9986.72 18895.91 13495.84 227
guyue91.12 12990.84 12891.96 17394.59 20380.57 23594.87 15793.71 31188.96 9591.14 14495.22 16273.22 25097.76 21492.01 10093.81 19097.54 125
PS-MVSNAJ91.18 12690.92 12591.96 17395.26 15682.60 17292.09 32195.70 19286.27 18391.84 12692.46 27879.70 14698.99 7889.08 14895.86 13594.29 293
TAMVS89.21 19288.29 20391.96 17393.71 26482.62 17193.30 26794.19 29082.22 29487.78 21593.94 22778.83 15996.95 29677.70 32792.98 21596.32 200
SDMVSNet90.19 15589.61 16091.93 17696.00 11783.09 14892.89 29095.98 16288.73 10286.85 23495.20 16672.09 26597.08 28588.90 15389.85 26895.63 237
FA-MVS(test-final)89.66 17488.91 18491.93 17694.57 20780.27 24191.36 33894.74 26684.87 23189.82 17092.61 27574.72 22198.47 14083.97 22993.53 19697.04 161
MVS_Test91.31 12291.11 12091.93 17694.37 22180.14 24693.46 25795.80 18186.46 17991.35 14293.77 23782.21 11198.09 18287.57 17394.95 15897.55 124
NR-MVSNet88.58 21587.47 22491.93 17693.04 29184.16 10894.77 16696.25 13789.05 8880.04 38093.29 25179.02 15897.05 29081.71 27580.05 39594.59 276
HyFIR lowres test88.09 22886.81 24191.93 17696.00 11780.63 23290.01 37595.79 18273.42 41787.68 21792.10 29473.86 23997.96 20080.75 29091.70 23697.19 146
GeoE90.05 16089.43 16591.90 18195.16 16180.37 24095.80 9294.65 27083.90 25287.55 22094.75 18778.18 17197.62 22781.28 28093.63 19297.71 113
thisisatest053088.67 21087.61 22091.86 18294.87 18080.07 24994.63 17589.90 41184.00 25088.46 19893.78 23666.88 33198.46 14183.30 23992.65 22297.06 159
xiu_mvs_v2_base91.13 12890.89 12791.86 18294.97 17282.42 17492.24 31495.64 19986.11 19191.74 13293.14 25779.67 15198.89 9389.06 14995.46 14694.28 294
DU-MVS89.34 19188.50 19591.85 18493.04 29183.72 12094.47 18696.59 10589.50 7086.46 24193.29 25177.25 18397.23 27584.92 21181.02 38194.59 276
AstraMVS90.69 14090.30 13891.84 18593.81 25579.85 26094.76 16792.39 34188.96 9591.01 14895.87 12870.69 28097.94 20392.49 7892.70 22197.73 111
OPM-MVS90.12 15689.56 16191.82 18693.14 28383.90 11594.16 20995.74 18688.96 9587.86 21095.43 15272.48 26097.91 20688.10 16590.18 26093.65 330
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS90.60 14790.19 14091.82 18694.70 19582.73 16295.85 8996.22 14090.81 2686.91 23094.86 18274.23 22998.12 17288.15 16189.99 26294.63 273
UniMVSNet_NR-MVSNet89.92 16889.29 17091.81 18893.39 27683.72 12094.43 18997.12 5289.80 5986.46 24193.32 24883.16 9297.23 27584.92 21181.02 38194.49 286
diffmvspermissive91.37 12191.23 11891.77 18993.09 28680.27 24192.36 30895.52 20887.03 16191.40 14194.93 17780.08 13897.44 24992.13 9594.56 17197.61 118
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs_AUTHOR91.51 11891.44 11291.73 19093.09 28680.27 24192.51 30395.58 20287.22 15591.80 12995.57 14579.96 14097.48 24192.23 8994.97 15797.45 128
1112_ss88.42 21787.33 22791.72 19194.92 17680.98 22092.97 28694.54 27378.16 37283.82 32293.88 23278.78 16197.91 20679.45 30889.41 27596.26 204
Fast-Effi-MVS+89.41 18688.64 19091.71 19294.74 18980.81 22893.54 25395.10 23883.11 27586.82 23690.67 34879.74 14597.75 21880.51 29593.55 19496.57 193
WTY-MVS89.60 17688.92 18391.67 19395.47 14781.15 21292.38 30794.78 26483.11 27589.06 18694.32 20978.67 16396.61 31581.57 27690.89 24997.24 141
TAPA-MVS84.62 688.16 22687.01 23691.62 19496.64 8680.65 23194.39 19396.21 14376.38 38686.19 25195.44 15079.75 14498.08 18462.75 43295.29 15196.13 211
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet89.62 17588.96 18191.60 19593.86 25282.89 15795.46 11697.33 2987.91 13188.43 19993.31 24974.17 23297.40 25987.32 17982.86 35694.52 281
FE-MVS87.40 25686.02 27791.57 19694.56 20879.69 26490.27 36293.72 31080.57 33388.80 19291.62 31465.32 34798.59 13174.97 35794.33 17996.44 196
XVG-OURS89.40 18888.70 18991.52 19794.06 23981.46 20191.27 34296.07 15586.14 18888.89 19195.77 13768.73 31697.26 27287.39 17789.96 26495.83 228
hse-mvs289.88 17089.34 16891.51 19894.83 18381.12 21493.94 23293.91 30389.80 5993.08 8593.60 24275.77 20397.66 22292.07 9677.07 41595.74 232
TranMVSNet+NR-MVSNet88.84 20587.95 21191.49 19992.68 30783.01 15394.92 15496.31 12689.88 5385.53 26793.85 23476.63 19196.96 29581.91 26879.87 39894.50 284
AUN-MVS87.78 23686.54 25691.48 20094.82 18481.05 21793.91 23693.93 30083.00 27886.93 22893.53 24369.50 30197.67 22086.14 19477.12 41495.73 234
XVG-OURS-SEG-HR89.95 16689.45 16391.47 20194.00 24581.21 21091.87 32696.06 15785.78 19588.55 19695.73 13974.67 22297.27 27088.71 15789.64 27395.91 222
MVS87.44 25486.10 27491.44 20292.61 30883.62 12592.63 29995.66 19667.26 44381.47 35892.15 28977.95 17498.22 16779.71 30495.48 14492.47 373
viewdifsd2359ckpt0791.11 13091.02 12391.41 20394.21 23278.37 29792.91 28995.71 19187.50 14690.32 15895.88 12580.27 13697.99 19388.78 15693.55 19497.86 99
F-COLMAP87.95 23186.80 24291.40 20496.35 10080.88 22594.73 16995.45 21479.65 34582.04 35394.61 19671.13 27298.50 13576.24 34491.05 24794.80 270
dcpmvs_293.49 6794.19 4991.38 20597.69 6076.78 33994.25 20496.29 12788.33 11594.46 5596.88 7588.07 2698.64 12493.62 5898.09 7398.73 20
thisisatest051587.33 25985.99 27891.37 20693.49 27279.55 26590.63 35689.56 41980.17 33787.56 21990.86 33867.07 32898.28 16381.50 27793.02 21496.29 202
HQP-MVS89.80 17289.28 17191.34 20794.17 23481.56 19594.39 19396.04 15888.81 9885.43 27693.97 22673.83 24097.96 20087.11 18389.77 27194.50 284
fmvsm_s_conf0.5_n_793.15 8693.76 6591.31 20894.42 21979.48 26794.52 18197.14 5089.33 7794.17 6198.09 1781.83 12197.49 24096.33 2598.02 7796.95 169
RRT-MVS90.85 13490.70 13291.30 20994.25 22976.83 33894.85 16096.13 14989.04 8990.23 16094.88 18070.15 29198.72 11591.86 10894.88 16098.34 45
FMVSNet387.40 25686.11 27391.30 20993.79 25883.64 12494.20 20894.81 26283.89 25384.37 30691.87 30568.45 31996.56 32078.23 32285.36 32493.70 329
FMVSNet287.19 26985.82 28691.30 20994.01 24283.67 12294.79 16494.94 24883.57 26183.88 32192.05 29866.59 33696.51 32477.56 32985.01 32793.73 327
RPMNet83.95 34681.53 35791.21 21290.58 38579.34 27385.24 43596.76 8971.44 43185.55 26582.97 44370.87 27798.91 9261.01 43689.36 27795.40 243
IB-MVS80.51 1585.24 32383.26 34191.19 21392.13 32079.86 25991.75 32991.29 37783.28 27280.66 37088.49 39561.28 37998.46 14180.99 28679.46 40295.25 249
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
CLD-MVS89.47 18188.90 18591.18 21494.22 23182.07 18492.13 31996.09 15387.90 13285.37 28292.45 27974.38 22797.56 23287.15 18190.43 25593.93 308
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test89.45 18288.90 18591.12 21594.47 21381.49 19995.30 12496.14 14686.73 17285.45 27395.16 16869.89 29498.10 17487.70 17089.23 28093.77 323
LGP-MVS_train91.12 21594.47 21381.49 19996.14 14686.73 17285.45 27395.16 16869.89 29498.10 17487.70 17089.23 28093.77 323
ACMM84.12 989.14 19488.48 19891.12 21594.65 19881.22 20995.31 12296.12 15085.31 21585.92 25694.34 20770.19 29098.06 18685.65 20288.86 28594.08 303
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt051788.61 21287.78 21791.11 21894.96 17377.81 31595.35 12089.69 41485.09 22588.05 20894.59 19966.93 32998.48 13783.27 24092.13 23497.03 162
GBi-Net87.26 26185.98 27991.08 21994.01 24283.10 14595.14 14294.94 24883.57 26184.37 30691.64 31066.59 33696.34 33778.23 32285.36 32493.79 318
test187.26 26185.98 27991.08 21994.01 24283.10 14595.14 14294.94 24883.57 26184.37 30691.64 31066.59 33696.34 33778.23 32285.36 32493.79 318
FMVSNet185.85 30884.11 32891.08 21992.81 30283.10 14595.14 14294.94 24881.64 31582.68 34391.64 31059.01 40196.34 33775.37 35183.78 34093.79 318
Test_1112_low_res87.65 24086.51 25791.08 21994.94 17579.28 27791.77 32894.30 28576.04 39183.51 33292.37 28177.86 17797.73 21978.69 31789.13 28296.22 205
PS-MVSNAJss89.97 16489.62 15991.02 22391.90 32980.85 22795.26 13095.98 16286.26 18486.21 25094.29 21179.70 14697.65 22388.87 15588.10 29694.57 278
BH-RMVSNet88.37 22087.48 22391.02 22395.28 15379.45 26992.89 29093.07 32485.45 21086.91 23094.84 18570.35 28797.76 21473.97 36594.59 17095.85 226
UniMVSNet_ETH3D87.53 25086.37 26191.00 22592.44 31278.96 28294.74 16895.61 20084.07 24985.36 28394.52 20159.78 39397.34 26482.93 24487.88 30196.71 186
FIs90.51 14990.35 13690.99 22693.99 24680.98 22095.73 9997.54 789.15 8586.72 23794.68 19081.83 12197.24 27485.18 20888.31 29594.76 271
ACMP84.23 889.01 20388.35 19990.99 22694.73 19081.27 20695.07 14595.89 17486.48 17783.67 32794.30 21069.33 30397.99 19387.10 18588.55 28793.72 328
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous2023121186.59 29185.13 30690.98 22896.52 9481.50 19796.14 6096.16 14573.78 41383.65 32892.15 28963.26 36397.37 26382.82 24881.74 37094.06 304
IMVS_040389.97 16489.64 15890.96 22993.72 26077.75 32093.00 28395.34 22585.53 20688.77 19394.49 20278.49 16797.84 21084.75 21592.65 22297.28 135
sss88.93 20488.26 20590.94 23094.05 24080.78 22991.71 33095.38 22081.55 31988.63 19593.91 23175.04 21595.47 37882.47 25391.61 23796.57 193
IMVS_040789.85 17189.51 16290.88 23193.72 26077.75 32093.07 28095.34 22585.53 20688.34 20194.49 20277.69 17997.60 22884.75 21592.65 22297.28 135
viewmambaseed2359dif90.04 16189.78 15590.83 23292.85 30177.92 30992.23 31595.01 24281.90 30490.20 16195.45 14979.64 15397.34 26487.52 17593.17 20897.23 144
sd_testset88.59 21487.85 21690.83 23296.00 11780.42 23992.35 30994.71 26788.73 10286.85 23495.20 16667.31 32396.43 33179.64 30689.85 26895.63 237
PVSNet_BlendedMVS89.98 16389.70 15690.82 23496.12 10781.25 20793.92 23496.83 8083.49 26589.10 18492.26 28681.04 12998.85 9986.72 18887.86 30292.35 379
cascas86.43 29984.98 30990.80 23592.10 32280.92 22490.24 36695.91 17173.10 42083.57 33188.39 39665.15 34997.46 24584.90 21391.43 23994.03 306
ECVR-MVScopyleft89.09 19788.53 19390.77 23695.62 14075.89 35296.16 5684.22 44587.89 13490.20 16196.65 8763.19 36498.10 17485.90 19996.94 10898.33 47
GA-MVS86.61 28985.27 30390.66 23791.33 35278.71 28690.40 36193.81 30785.34 21485.12 28689.57 37761.25 38097.11 28480.99 28689.59 27496.15 209
thres600view787.65 24086.67 24890.59 23896.08 11378.72 28494.88 15691.58 36887.06 16088.08 20692.30 28468.91 31398.10 17470.05 39591.10 24294.96 260
thres40087.62 24586.64 24990.57 23995.99 12078.64 28794.58 17791.98 35786.94 16688.09 20491.77 30669.18 30998.10 17470.13 39291.10 24294.96 260
baseline188.10 22787.28 22990.57 23994.96 17380.07 24994.27 20391.29 37786.74 17187.41 22194.00 22476.77 18896.20 34280.77 28979.31 40495.44 241
viewdifsd2359ckpt1189.43 18489.05 17890.56 24192.89 29977.00 33492.81 29394.52 27487.03 16189.77 17195.79 13474.67 22297.51 23688.97 15184.98 32897.17 147
viewmsd2359difaftdt89.43 18489.05 17890.56 24192.89 29977.00 33492.81 29394.52 27487.03 16189.77 17195.79 13474.67 22297.51 23688.97 15184.98 32897.17 147
FC-MVSNet-test90.27 15390.18 14190.53 24393.71 26479.85 26095.77 9597.59 489.31 7886.27 24894.67 19381.93 11997.01 29284.26 22588.09 29894.71 272
PAPM86.68 28885.39 29890.53 24393.05 29079.33 27689.79 37894.77 26578.82 35881.95 35493.24 25376.81 18697.30 26666.94 41293.16 20994.95 264
WR-MVS88.38 21987.67 21990.52 24593.30 27880.18 24493.26 27095.96 16688.57 11085.47 27292.81 26876.12 19696.91 29981.24 28182.29 36194.47 289
SSM_0407288.57 21687.92 21390.51 24694.76 18682.66 16679.84 45794.64 27185.18 21688.96 18895.00 17476.00 19992.03 42683.74 23493.15 21096.85 178
MVSTER88.84 20588.29 20390.51 24692.95 29680.44 23893.73 24595.01 24284.66 24087.15 22593.12 25872.79 25597.21 27787.86 16787.36 31093.87 313
testdata90.49 24896.40 9777.89 31295.37 22272.51 42593.63 7496.69 8382.08 11597.65 22383.08 24197.39 9895.94 221
test111189.10 19588.64 19090.48 24995.53 14574.97 36296.08 6584.89 44388.13 12590.16 16596.65 8763.29 36298.10 17486.14 19496.90 11098.39 42
tt080586.92 27785.74 29290.48 24992.22 31679.98 25695.63 10994.88 25683.83 25584.74 29592.80 26957.61 40797.67 22085.48 20584.42 33393.79 318
jajsoiax88.24 22487.50 22290.48 24990.89 37380.14 24695.31 12295.65 19884.97 22884.24 31494.02 22265.31 34897.42 25188.56 15888.52 28993.89 309
PatchMatch-RL86.77 28585.54 29490.47 25295.88 12582.71 16490.54 35992.31 34579.82 34384.32 31191.57 31868.77 31596.39 33373.16 37193.48 20092.32 380
tfpn200view987.58 24886.64 24990.41 25395.99 12078.64 28794.58 17791.98 35786.94 16688.09 20491.77 30669.18 30998.10 17470.13 39291.10 24294.48 287
VPNet88.20 22587.47 22490.39 25493.56 27179.46 26894.04 22295.54 20688.67 10586.96 22794.58 20069.33 30397.15 27984.05 22880.53 39094.56 279
ACMH80.38 1785.36 31883.68 33590.39 25494.45 21680.63 23294.73 16994.85 25882.09 29677.24 40592.65 27360.01 39197.58 23072.25 37684.87 33092.96 358
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view90087.63 24386.71 24590.38 25696.12 10778.55 29095.03 14891.58 36887.15 15788.06 20792.29 28568.91 31398.10 17470.13 39291.10 24294.48 287
mvs_tets88.06 23087.28 22990.38 25690.94 36979.88 25895.22 13395.66 19685.10 22484.21 31593.94 22763.53 36097.40 25988.50 15988.40 29393.87 313
131487.51 25186.57 25490.34 25892.42 31379.74 26392.63 29995.35 22478.35 36780.14 37791.62 31474.05 23497.15 27981.05 28293.53 19694.12 299
LTVRE_ROB82.13 1386.26 30284.90 31290.34 25894.44 21781.50 19792.31 31394.89 25483.03 27779.63 38792.67 27269.69 29797.79 21271.20 38186.26 31991.72 390
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
test_djsdf89.03 20188.64 19090.21 26090.74 38079.28 27795.96 7995.90 17284.66 24085.33 28492.94 26374.02 23597.30 26689.64 14188.53 28894.05 305
v2v48287.84 23387.06 23390.17 26190.99 36579.23 28094.00 22895.13 23584.87 23185.53 26792.07 29774.45 22697.45 24684.71 22081.75 36993.85 316
pmmvs485.43 31683.86 33390.16 26290.02 39882.97 15590.27 36292.67 33675.93 39280.73 36891.74 30871.05 27395.73 36778.85 31683.46 34791.78 389
V4287.68 23886.86 23890.15 26390.58 38580.14 24694.24 20695.28 22983.66 25985.67 26291.33 32074.73 22097.41 25784.43 22481.83 36792.89 361
MSDG84.86 33183.09 34490.14 26493.80 25680.05 25189.18 39193.09 32378.89 35578.19 39791.91 30365.86 34697.27 27068.47 40188.45 29193.11 353
sc_t181.53 37078.67 39190.12 26590.78 37778.64 28793.91 23690.20 40068.42 44080.82 36789.88 37046.48 44596.76 30476.03 34771.47 42994.96 260
anonymousdsp87.84 23387.09 23290.12 26589.13 40980.54 23694.67 17395.55 20482.05 29783.82 32292.12 29171.47 27097.15 27987.15 18187.80 30592.67 367
thres20087.21 26786.24 26890.12 26595.36 14978.53 29193.26 27092.10 35186.42 18088.00 20991.11 33169.24 30898.00 19269.58 39691.04 24893.83 317
CR-MVSNet85.35 31983.76 33490.12 26590.58 38579.34 27385.24 43591.96 35978.27 36985.55 26587.87 40671.03 27495.61 37073.96 36689.36 27795.40 243
v114487.61 24686.79 24390.06 26991.01 36479.34 27393.95 23195.42 21983.36 27085.66 26391.31 32374.98 21697.42 25183.37 23882.06 36393.42 339
XXY-MVS87.65 24086.85 23990.03 27092.14 31980.60 23493.76 24395.23 23182.94 28084.60 29794.02 22274.27 22895.49 37781.04 28383.68 34394.01 307
Vis-MVSNet (Re-imp)89.59 17789.44 16490.03 27095.74 13075.85 35395.61 11090.80 39187.66 14587.83 21395.40 15376.79 18796.46 32978.37 31896.73 11697.80 106
test250687.21 26786.28 26690.02 27295.62 14073.64 37896.25 5171.38 46887.89 13490.45 15596.65 8755.29 41998.09 18286.03 19896.94 10898.33 47
BH-untuned88.60 21388.13 20790.01 27395.24 15778.50 29393.29 26894.15 29384.75 23684.46 30393.40 24575.76 20597.40 25977.59 32894.52 17394.12 299
v119287.25 26386.33 26390.00 27490.76 37979.04 28193.80 24195.48 20982.57 28785.48 27191.18 32773.38 24997.42 25182.30 25782.06 36393.53 333
v7n86.81 28085.76 29089.95 27590.72 38179.25 27995.07 14595.92 16984.45 24382.29 34790.86 33872.60 25997.53 23479.42 31180.52 39193.08 355
testing9187.11 27286.18 26989.92 27694.43 21875.38 36191.53 33592.27 34786.48 17786.50 23990.24 35661.19 38397.53 23482.10 26290.88 25096.84 181
IMVS_040487.60 24786.84 24089.89 27793.72 26077.75 32088.56 40095.34 22585.53 20679.98 38194.49 20266.54 33994.64 39184.75 21592.65 22297.28 135
v887.50 25386.71 24589.89 27791.37 34979.40 27094.50 18295.38 22084.81 23483.60 33091.33 32076.05 19797.42 25182.84 24780.51 39292.84 363
v1087.25 26386.38 26089.85 27991.19 35579.50 26694.48 18395.45 21483.79 25783.62 32991.19 32575.13 21397.42 25181.94 26780.60 38792.63 369
baseline286.50 29585.39 29889.84 28091.12 36076.70 34191.88 32588.58 42382.35 29279.95 38290.95 33673.42 24797.63 22680.27 29989.95 26595.19 250
pm-mvs186.61 28985.54 29489.82 28191.44 34480.18 24495.28 12894.85 25883.84 25481.66 35692.62 27472.45 26296.48 32679.67 30578.06 40792.82 364
TR-MVS86.78 28285.76 29089.82 28194.37 22178.41 29592.47 30492.83 33081.11 32986.36 24592.40 28068.73 31697.48 24173.75 36989.85 26893.57 332
ACMH+81.04 1485.05 32683.46 33889.82 28194.66 19779.37 27194.44 18894.12 29682.19 29578.04 39992.82 26758.23 40497.54 23373.77 36882.90 35592.54 370
EI-MVSNet89.10 19588.86 18789.80 28491.84 33178.30 30093.70 24895.01 24285.73 19787.15 22595.28 15979.87 14397.21 27783.81 23287.36 31093.88 312
v14419287.19 26986.35 26289.74 28590.64 38378.24 30293.92 23495.43 21781.93 30285.51 26991.05 33474.21 23197.45 24682.86 24681.56 37193.53 333
COLMAP_ROBcopyleft80.39 1683.96 34582.04 35489.74 28595.28 15379.75 26294.25 20492.28 34675.17 39978.02 40093.77 23758.60 40397.84 21065.06 42385.92 32091.63 392
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA86.32 30185.18 30589.73 28792.15 31876.60 34291.12 34691.69 36483.53 26485.50 27088.81 38966.79 33296.48 32676.65 33790.35 25796.12 212
IterMVS-LS88.36 22187.91 21589.70 28893.80 25678.29 30193.73 24595.08 24085.73 19784.75 29491.90 30479.88 14296.92 29883.83 23182.51 35793.89 309
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing1186.44 29885.35 30189.69 28994.29 22875.40 36091.30 34090.53 39584.76 23585.06 28890.13 36258.95 40297.45 24682.08 26391.09 24696.21 207
testing9986.72 28685.73 29389.69 28994.23 23074.91 36491.35 33990.97 38586.14 18886.36 24590.22 35759.41 39697.48 24182.24 25990.66 25296.69 188
v192192086.97 27686.06 27689.69 28990.53 38878.11 30593.80 24195.43 21781.90 30485.33 28491.05 33472.66 25697.41 25782.05 26581.80 36893.53 333
icg_test_0407_289.15 19388.97 18089.68 29293.72 26077.75 32088.26 40595.34 22585.53 20688.34 20194.49 20277.69 17993.99 40284.75 21592.65 22297.28 135
VortexMVS88.42 21788.01 20989.63 29393.89 25178.82 28393.82 24095.47 21086.67 17484.53 30191.99 30072.62 25896.65 31089.02 15084.09 33793.41 340
Fast-Effi-MVS+-dtu87.44 25486.72 24489.63 29392.04 32377.68 32594.03 22393.94 29985.81 19482.42 34691.32 32270.33 28897.06 28880.33 29890.23 25994.14 298
v124086.78 28285.85 28589.56 29590.45 39077.79 31793.61 25195.37 22281.65 31485.43 27691.15 32971.50 26997.43 25081.47 27882.05 36593.47 337
Effi-MVS+-dtu88.65 21188.35 19989.54 29693.33 27776.39 34694.47 18694.36 28387.70 14285.43 27689.56 37873.45 24597.26 27285.57 20491.28 24194.97 257
AllTest83.42 35281.39 35889.52 29795.01 16777.79 31793.12 27490.89 38977.41 37676.12 41493.34 24654.08 42597.51 23668.31 40384.27 33593.26 343
TestCases89.52 29795.01 16777.79 31790.89 38977.41 37676.12 41493.34 24654.08 42597.51 23668.31 40384.27 33593.26 343
mvs_anonymous89.37 19089.32 16989.51 29993.47 27374.22 37191.65 33394.83 26082.91 28185.45 27393.79 23581.23 12896.36 33686.47 19094.09 18397.94 90
XVG-ACMP-BASELINE86.00 30484.84 31489.45 30091.20 35478.00 30791.70 33195.55 20485.05 22682.97 34092.25 28754.49 42397.48 24182.93 24487.45 30992.89 361
testing22284.84 33283.32 33989.43 30194.15 23775.94 35191.09 34789.41 42184.90 22985.78 25989.44 37952.70 43096.28 34070.80 38791.57 23896.07 216
MVP-Stereo85.97 30584.86 31389.32 30290.92 37182.19 18192.11 32094.19 29078.76 36078.77 39691.63 31368.38 32096.56 32075.01 35693.95 18589.20 430
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNetpermissive85.85 30884.70 31689.29 30391.76 33575.54 35788.49 40191.30 37681.63 31685.05 28988.70 39371.71 26696.24 34174.61 36189.05 28396.08 215
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v14887.04 27486.32 26489.21 30490.94 36977.26 33093.71 24794.43 27884.84 23384.36 30990.80 34276.04 19897.05 29082.12 26179.60 40193.31 342
tfpnnormal84.72 33483.23 34289.20 30592.79 30380.05 25194.48 18395.81 18082.38 29081.08 36491.21 32469.01 31296.95 29661.69 43480.59 38890.58 417
cl2286.78 28285.98 27989.18 30692.34 31477.62 32690.84 35294.13 29581.33 32383.97 32090.15 36173.96 23696.60 31784.19 22682.94 35293.33 341
BH-w/o87.57 24987.05 23489.12 30794.90 17977.90 31192.41 30593.51 31482.89 28283.70 32691.34 31975.75 20697.07 28775.49 34993.49 19892.39 377
WR-MVS_H87.80 23587.37 22689.10 30893.23 27978.12 30495.61 11097.30 3487.90 13283.72 32592.01 29979.65 15296.01 35176.36 34180.54 38993.16 351
miper_enhance_ethall86.90 27886.18 26989.06 30991.66 34077.58 32790.22 36894.82 26179.16 35184.48 30289.10 38379.19 15796.66 30984.06 22782.94 35292.94 359
c3_l87.14 27186.50 25889.04 31092.20 31777.26 33091.22 34594.70 26882.01 30084.34 31090.43 35378.81 16096.61 31583.70 23681.09 37893.25 345
miper_ehance_all_eth87.22 26686.62 25289.02 31192.13 32077.40 32990.91 35194.81 26281.28 32484.32 31190.08 36479.26 15596.62 31283.81 23282.94 35293.04 356
gg-mvs-nofinetune81.77 36479.37 37988.99 31290.85 37577.73 32486.29 42779.63 45674.88 40483.19 33969.05 45960.34 38896.11 34675.46 35094.64 16993.11 353
ETVMVS84.43 33982.92 34888.97 31394.37 22174.67 36591.23 34488.35 42583.37 26986.06 25489.04 38455.38 41795.67 36967.12 41091.34 24096.58 192
pmmvs683.42 35281.60 35688.87 31488.01 42477.87 31394.96 15194.24 28974.67 40578.80 39591.09 33260.17 39096.49 32577.06 33675.40 42192.23 382
test_cas_vis1_n_192088.83 20888.85 18888.78 31591.15 35976.72 34093.85 23994.93 25283.23 27492.81 9496.00 11761.17 38494.45 39291.67 11194.84 16195.17 251
MIMVSNet82.59 35880.53 36388.76 31691.51 34278.32 29986.57 42690.13 40379.32 34780.70 36988.69 39452.98 42993.07 41866.03 41888.86 28594.90 265
cl____86.52 29485.78 28788.75 31792.03 32476.46 34490.74 35394.30 28581.83 31083.34 33690.78 34375.74 20896.57 31881.74 27381.54 37293.22 347
DIV-MVS_self_test86.53 29385.78 28788.75 31792.02 32576.45 34590.74 35394.30 28581.83 31083.34 33690.82 34175.75 20696.57 31881.73 27481.52 37393.24 346
CP-MVSNet87.63 24387.26 23188.74 31993.12 28476.59 34395.29 12696.58 10688.43 11383.49 33392.98 26275.28 21295.83 36078.97 31481.15 37793.79 318
eth_miper_zixun_eth86.50 29585.77 28988.68 32091.94 32675.81 35490.47 36094.89 25482.05 29784.05 31790.46 35275.96 20196.77 30382.76 25079.36 40393.46 338
CHOSEN 280x42085.15 32483.99 33188.65 32192.47 31078.40 29679.68 45992.76 33374.90 40381.41 36089.59 37669.85 29695.51 37479.92 30395.29 15192.03 385
PS-CasMVS87.32 26086.88 23788.63 32292.99 29476.33 34895.33 12196.61 10488.22 12183.30 33893.07 26073.03 25395.79 36478.36 31981.00 38393.75 325
TransMVSNet (Re)84.43 33983.06 34688.54 32391.72 33678.44 29495.18 13992.82 33282.73 28579.67 38692.12 29173.49 24495.96 35371.10 38568.73 44091.21 404
tt0320-xc79.63 39376.66 40288.52 32491.03 36378.72 28493.00 28389.53 42066.37 44476.11 41687.11 41746.36 44795.32 38272.78 37367.67 44191.51 396
EG-PatchMatch MVS82.37 36080.34 36688.46 32590.27 39279.35 27292.80 29694.33 28477.14 38073.26 43290.18 36047.47 44296.72 30570.25 38987.32 31289.30 427
PEN-MVS86.80 28186.27 26788.40 32692.32 31575.71 35695.18 13996.38 12187.97 12982.82 34293.15 25673.39 24895.92 35576.15 34579.03 40693.59 331
Baseline_NR-MVSNet87.07 27386.63 25188.40 32691.44 34477.87 31394.23 20792.57 33884.12 24885.74 26192.08 29577.25 18396.04 34782.29 25879.94 39691.30 402
UBG85.51 31484.57 32188.35 32894.21 23271.78 40390.07 37389.66 41682.28 29385.91 25789.01 38561.30 37897.06 28876.58 34092.06 23596.22 205
D2MVS85.90 30685.09 30788.35 32890.79 37677.42 32891.83 32795.70 19280.77 33280.08 37990.02 36666.74 33496.37 33481.88 26987.97 30091.26 403
pmmvs584.21 34182.84 35188.34 33088.95 41176.94 33692.41 30591.91 36175.63 39480.28 37491.18 32764.59 35495.57 37177.09 33583.47 34692.53 371
mamv490.92 13291.78 10588.33 33195.67 13670.75 41692.92 28896.02 16181.90 30488.11 20395.34 15785.88 5396.97 29495.22 4095.01 15697.26 139
tt032080.13 38677.41 39588.29 33290.50 38978.02 30693.10 27790.71 39366.06 44776.75 40986.97 41849.56 43795.40 37971.65 37771.41 43091.46 399
LCM-MVSNet-Re88.30 22388.32 20288.27 33394.71 19472.41 39893.15 27390.98 38487.77 13979.25 39091.96 30178.35 16995.75 36583.04 24295.62 14096.65 189
CostFormer85.77 31184.94 31188.26 33491.16 35872.58 39689.47 38691.04 38376.26 38986.45 24389.97 36870.74 27996.86 30282.35 25687.07 31595.34 247
ITE_SJBPF88.24 33591.88 33077.05 33392.92 32785.54 20480.13 37893.30 25057.29 40896.20 34272.46 37584.71 33191.49 397
PVSNet78.82 1885.55 31384.65 31788.23 33694.72 19271.93 39987.12 42292.75 33478.80 35984.95 29190.53 35064.43 35596.71 30774.74 35993.86 18796.06 218
IterMVS-SCA-FT85.45 31584.53 32288.18 33791.71 33776.87 33790.19 37092.65 33785.40 21381.44 35990.54 34966.79 33295.00 38881.04 28381.05 37992.66 368
EPNet_dtu86.49 29785.94 28288.14 33890.24 39372.82 38894.11 21392.20 34986.66 17579.42 38992.36 28273.52 24395.81 36271.26 38093.66 19195.80 230
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry82.71 35680.93 36288.06 33990.05 39776.37 34784.74 44091.96 35972.28 42881.32 36287.87 40671.03 27495.50 37668.97 39880.15 39492.32 380
test_vis1_n_192089.39 18989.84 15288.04 34092.97 29572.64 39394.71 17196.03 16086.18 18691.94 12396.56 9561.63 37395.74 36693.42 6195.11 15595.74 232
DTE-MVSNet86.11 30385.48 29687.98 34191.65 34174.92 36394.93 15395.75 18587.36 15282.26 34893.04 26172.85 25495.82 36174.04 36477.46 41293.20 349
PMMVS85.71 31284.96 31087.95 34288.90 41277.09 33288.68 39890.06 40572.32 42786.47 24090.76 34472.15 26494.40 39481.78 27293.49 19892.36 378
GG-mvs-BLEND87.94 34389.73 40477.91 31087.80 41178.23 46180.58 37183.86 43659.88 39295.33 38171.20 38192.22 23390.60 416
MonoMVSNet86.89 27986.55 25587.92 34489.46 40773.75 37594.12 21193.10 32287.82 13885.10 28790.76 34469.59 29994.94 38986.47 19082.50 35895.07 254
reproduce_monomvs86.37 30085.87 28487.87 34593.66 26873.71 37693.44 25895.02 24188.61 10882.64 34591.94 30257.88 40696.68 30889.96 13679.71 40093.22 347
pmmvs-eth3d80.97 37978.72 39087.74 34684.99 44279.97 25790.11 37291.65 36675.36 39673.51 43086.03 42559.45 39593.96 40575.17 35372.21 42689.29 429
MS-PatchMatch85.05 32684.16 32687.73 34791.42 34778.51 29291.25 34393.53 31377.50 37580.15 37691.58 31661.99 37095.51 37475.69 34894.35 17889.16 431
mmtdpeth85.04 32884.15 32787.72 34893.11 28575.74 35594.37 19792.83 33084.98 22789.31 18186.41 42261.61 37597.14 28292.63 7762.11 45190.29 418
test_040281.30 37579.17 38487.67 34993.19 28078.17 30392.98 28591.71 36275.25 39876.02 41790.31 35559.23 39796.37 33450.22 45483.63 34488.47 439
IterMVS84.88 33083.98 33287.60 35091.44 34476.03 35090.18 37192.41 34083.24 27381.06 36590.42 35466.60 33594.28 39879.46 30780.98 38492.48 372
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test81.37 37379.30 38087.58 35190.92 37174.16 37380.99 45287.68 43070.52 43576.63 41188.81 38971.21 27192.76 42160.01 44086.93 31695.83 228
EPMVS83.90 34882.70 35287.51 35290.23 39472.67 39188.62 39981.96 45181.37 32285.01 29088.34 39766.31 34094.45 39275.30 35287.12 31395.43 242
ADS-MVSNet281.66 36779.71 37687.50 35391.35 35074.19 37283.33 44588.48 42472.90 42282.24 34985.77 42864.98 35093.20 41664.57 42583.74 34195.12 252
OurMVSNet-221017-085.35 31984.64 31987.49 35490.77 37872.59 39594.01 22694.40 28184.72 23779.62 38893.17 25561.91 37196.72 30581.99 26681.16 37593.16 351
tpm284.08 34382.94 34787.48 35591.39 34871.27 40889.23 39090.37 39771.95 42984.64 29689.33 38067.30 32496.55 32275.17 35387.09 31494.63 273
RPSCF85.07 32584.27 32387.48 35592.91 29870.62 41891.69 33292.46 33976.20 39082.67 34495.22 16263.94 35897.29 26977.51 33085.80 32194.53 280
myMVS_eth3d2885.80 31085.26 30487.42 35794.73 19069.92 42390.60 35790.95 38687.21 15686.06 25490.04 36559.47 39496.02 34974.89 35893.35 20596.33 199
WBMVS84.97 32984.18 32587.34 35894.14 23871.62 40790.20 36992.35 34281.61 31784.06 31690.76 34461.82 37296.52 32378.93 31583.81 33993.89 309
miper_lstm_enhance85.27 32284.59 32087.31 35991.28 35374.63 36687.69 41694.09 29781.20 32881.36 36189.85 37274.97 21794.30 39781.03 28579.84 39993.01 357
FMVSNet581.52 37179.60 37787.27 36091.17 35677.95 30891.49 33692.26 34876.87 38276.16 41387.91 40551.67 43192.34 42467.74 40781.16 37591.52 395
USDC82.76 35581.26 36087.26 36191.17 35674.55 36789.27 38893.39 31678.26 37075.30 42192.08 29554.43 42496.63 31171.64 37885.79 32290.61 414
test-LLR85.87 30785.41 29787.25 36290.95 36771.67 40589.55 38289.88 41283.41 26784.54 29987.95 40367.25 32595.11 38581.82 27093.37 20394.97 257
test-mter84.54 33883.64 33687.25 36290.95 36771.67 40589.55 38289.88 41279.17 35084.54 29987.95 40355.56 41495.11 38581.82 27093.37 20394.97 257
JIA-IIPM81.04 37678.98 38887.25 36288.64 41373.48 38081.75 45189.61 41873.19 41982.05 35273.71 45566.07 34595.87 35871.18 38384.60 33292.41 376
TDRefinement79.81 39077.34 39687.22 36579.24 45875.48 35893.12 27492.03 35476.45 38575.01 42291.58 31649.19 43896.44 33070.22 39169.18 43789.75 423
tpmvs83.35 35482.07 35387.20 36691.07 36271.00 41488.31 40491.70 36378.91 35380.49 37387.18 41569.30 30697.08 28568.12 40683.56 34593.51 336
ppachtmachnet_test81.84 36380.07 37187.15 36788.46 41774.43 37089.04 39492.16 35075.33 39777.75 40288.99 38666.20 34295.37 38065.12 42277.60 41091.65 391
dmvs_re84.20 34283.22 34387.14 36891.83 33377.81 31590.04 37490.19 40184.70 23981.49 35789.17 38264.37 35691.13 43771.58 37985.65 32392.46 374
tpm cat181.96 36180.27 36787.01 36991.09 36171.02 41387.38 42091.53 37166.25 44580.17 37586.35 42468.22 32196.15 34569.16 39782.29 36193.86 315
test_fmvs1_n87.03 27587.04 23586.97 37089.74 40371.86 40094.55 17994.43 27878.47 36491.95 12295.50 14851.16 43393.81 40693.02 6994.56 17195.26 248
OpenMVS_ROBcopyleft74.94 1979.51 39477.03 40186.93 37187.00 43076.23 34992.33 31190.74 39268.93 43974.52 42688.23 40049.58 43696.62 31257.64 44684.29 33487.94 442
SixPastTwentyTwo83.91 34782.90 34986.92 37290.99 36570.67 41793.48 25591.99 35685.54 20477.62 40492.11 29360.59 38796.87 30176.05 34677.75 40993.20 349
ADS-MVSNet81.56 36979.78 37386.90 37391.35 35071.82 40183.33 44589.16 42272.90 42282.24 34985.77 42864.98 35093.76 40764.57 42583.74 34195.12 252
PatchT82.68 35781.27 35986.89 37490.09 39670.94 41584.06 44290.15 40274.91 40285.63 26483.57 43869.37 30294.87 39065.19 42088.50 29094.84 267
tpm84.73 33384.02 33086.87 37590.33 39168.90 42689.06 39389.94 40980.85 33185.75 26089.86 37168.54 31895.97 35277.76 32684.05 33895.75 231
Patchmatch-RL test81.67 36679.96 37286.81 37685.42 44071.23 40982.17 45087.50 43178.47 36477.19 40682.50 44570.81 27893.48 41182.66 25172.89 42595.71 235
test_vis1_n86.56 29286.49 25986.78 37788.51 41472.69 39094.68 17293.78 30979.55 34690.70 15095.31 15848.75 43993.28 41493.15 6593.99 18494.38 291
testing3-286.72 28686.71 24586.74 37896.11 11065.92 43893.39 26089.65 41789.46 7187.84 21292.79 27059.17 39997.60 22881.31 27990.72 25196.70 187
test_fmvs187.34 25887.56 22186.68 37990.59 38471.80 40294.01 22694.04 29878.30 36891.97 12095.22 16256.28 41293.71 40892.89 7094.71 16494.52 281
MDA-MVSNet-bldmvs78.85 39976.31 40486.46 38089.76 40273.88 37488.79 39690.42 39679.16 35159.18 45588.33 39860.20 38994.04 40062.00 43368.96 43891.48 398
mvs5depth80.98 37879.15 38586.45 38184.57 44373.29 38387.79 41291.67 36580.52 33482.20 35189.72 37455.14 42095.93 35473.93 36766.83 44390.12 420
tpmrst85.35 31984.99 30886.43 38290.88 37467.88 43188.71 39791.43 37480.13 33886.08 25388.80 39173.05 25296.02 34982.48 25283.40 34995.40 243
TESTMET0.1,183.74 35082.85 35086.42 38389.96 39971.21 41089.55 38287.88 42777.41 37683.37 33587.31 41156.71 41093.65 41080.62 29392.85 21994.40 290
our_test_381.93 36280.46 36586.33 38488.46 41773.48 38088.46 40291.11 37976.46 38476.69 41088.25 39966.89 33094.36 39568.75 39979.08 40591.14 406
lessismore_v086.04 38588.46 41768.78 42780.59 45473.01 43390.11 36355.39 41696.43 33175.06 35565.06 44692.90 360
TinyColmap79.76 39177.69 39485.97 38691.71 33773.12 38489.55 38290.36 39875.03 40072.03 43690.19 35946.22 44896.19 34463.11 42981.03 38088.59 438
KD-MVS_2432*160078.50 40076.02 40885.93 38786.22 43374.47 36884.80 43892.33 34379.29 34876.98 40785.92 42653.81 42793.97 40367.39 40857.42 45689.36 425
miper_refine_blended78.50 40076.02 40885.93 38786.22 43374.47 36884.80 43892.33 34379.29 34876.98 40785.92 42653.81 42793.97 40367.39 40857.42 45689.36 425
K. test v381.59 36880.15 37085.91 38989.89 40169.42 42592.57 30187.71 42985.56 20373.44 43189.71 37555.58 41395.52 37377.17 33369.76 43492.78 365
SSC-MVS3.284.60 33784.19 32485.85 39092.74 30568.07 42888.15 40793.81 30787.42 15083.76 32491.07 33362.91 36595.73 36774.56 36283.24 35093.75 325
mvsany_test185.42 31785.30 30285.77 39187.95 42675.41 35987.61 41980.97 45376.82 38388.68 19495.83 13177.44 18290.82 43985.90 19986.51 31791.08 410
MIMVSNet179.38 39577.28 39785.69 39286.35 43273.67 37791.61 33492.75 33478.11 37372.64 43488.12 40148.16 44091.97 43060.32 43777.49 41191.43 400
UWE-MVS83.69 35183.09 34485.48 39393.06 28965.27 44390.92 35086.14 43579.90 34186.26 24990.72 34757.17 40995.81 36271.03 38692.62 22795.35 246
UnsupCasMVSNet_eth80.07 38778.27 39385.46 39485.24 44172.63 39488.45 40394.87 25782.99 27971.64 43988.07 40256.34 41191.75 43273.48 37063.36 44992.01 386
CL-MVSNet_self_test81.74 36580.53 36385.36 39585.96 43572.45 39790.25 36493.07 32481.24 32679.85 38587.29 41270.93 27692.52 42266.95 41169.23 43691.11 408
MDA-MVSNet_test_wron79.21 39777.19 39985.29 39688.22 42172.77 38985.87 42990.06 40574.34 40762.62 45287.56 40966.14 34391.99 42966.90 41573.01 42391.10 409
YYNet179.22 39677.20 39885.28 39788.20 42272.66 39285.87 42990.05 40774.33 40862.70 45087.61 40866.09 34492.03 42666.94 41272.97 42491.15 405
WB-MVSnew83.77 34983.28 34085.26 39891.48 34371.03 41291.89 32487.98 42678.91 35384.78 29390.22 35769.11 31194.02 40164.70 42490.44 25490.71 412
dp81.47 37280.23 36885.17 39989.92 40065.49 44186.74 42490.10 40476.30 38881.10 36387.12 41662.81 36695.92 35568.13 40579.88 39794.09 302
UnsupCasMVSNet_bld76.23 41073.27 41485.09 40083.79 44572.92 38685.65 43293.47 31571.52 43068.84 44579.08 45049.77 43593.21 41566.81 41660.52 45389.13 433
SD_040384.71 33584.65 31784.92 40192.95 29665.95 43792.07 32393.23 31983.82 25679.03 39193.73 24073.90 23792.91 42063.02 43190.05 26195.89 224
Anonymous2023120681.03 37779.77 37584.82 40287.85 42770.26 42091.42 33792.08 35273.67 41477.75 40289.25 38162.43 36893.08 41761.50 43582.00 36691.12 407
FE-MVSNET78.19 40276.03 40784.69 40383.70 44673.31 38290.58 35890.00 40877.11 38171.91 43785.47 43055.53 41591.94 43159.69 44170.24 43288.83 435
test0.0.03 182.41 35981.69 35584.59 40488.23 42072.89 38790.24 36687.83 42883.41 26779.86 38489.78 37367.25 32588.99 44965.18 42183.42 34891.90 388
CMPMVSbinary59.16 2180.52 38179.20 38384.48 40583.98 44467.63 43489.95 37793.84 30664.79 44966.81 44791.14 33057.93 40595.17 38376.25 34388.10 29690.65 413
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet84.69 33684.79 31584.37 40691.84 33164.92 44493.70 24891.47 37366.19 44686.16 25295.28 15967.18 32793.33 41380.89 28890.42 25694.88 266
PVSNet_073.20 2077.22 40674.83 41284.37 40690.70 38271.10 41183.09 44789.67 41572.81 42473.93 42983.13 44060.79 38693.70 40968.54 40050.84 46188.30 440
LF4IMVS80.37 38479.07 38784.27 40886.64 43169.87 42489.39 38791.05 38276.38 38674.97 42390.00 36747.85 44194.25 39974.55 36380.82 38688.69 437
Anonymous2024052180.44 38379.21 38284.11 40985.75 43867.89 43092.86 29293.23 31975.61 39575.59 42087.47 41050.03 43494.33 39671.14 38481.21 37490.12 420
PM-MVS78.11 40376.12 40684.09 41083.54 44770.08 42188.97 39585.27 44279.93 34074.73 42586.43 42134.70 45993.48 41179.43 31072.06 42788.72 436
test_fmvs283.98 34484.03 32983.83 41187.16 42967.53 43593.93 23392.89 32877.62 37486.89 23393.53 24347.18 44392.02 42890.54 13086.51 31791.93 387
testgi80.94 38080.20 36983.18 41287.96 42566.29 43691.28 34190.70 39483.70 25878.12 39892.84 26551.37 43290.82 43963.34 42882.46 35992.43 375
KD-MVS_self_test80.20 38579.24 38183.07 41385.64 43965.29 44291.01 34993.93 30078.71 36276.32 41286.40 42359.20 39892.93 41972.59 37469.35 43591.00 411
testing380.46 38279.59 37883.06 41493.44 27564.64 44593.33 26285.47 44084.34 24579.93 38390.84 34044.35 45192.39 42357.06 44887.56 30692.16 384
ambc83.06 41479.99 45663.51 44977.47 46092.86 32974.34 42884.45 43528.74 46095.06 38773.06 37268.89 43990.61 414
test20.0379.95 38979.08 38682.55 41685.79 43767.74 43391.09 34791.08 38081.23 32774.48 42789.96 36961.63 37390.15 44160.08 43876.38 41789.76 422
MVStest172.91 41469.70 41982.54 41778.14 45973.05 38588.21 40686.21 43460.69 45364.70 44890.53 35046.44 44685.70 45658.78 44453.62 45888.87 434
test_vis1_rt77.96 40476.46 40382.48 41885.89 43671.74 40490.25 36478.89 45771.03 43471.30 44081.35 44742.49 45391.05 43884.55 22282.37 36084.65 445
EU-MVSNet81.32 37480.95 36182.42 41988.50 41663.67 44893.32 26391.33 37564.02 45080.57 37292.83 26661.21 38292.27 42576.34 34280.38 39391.32 401
myMVS_eth3d79.67 39278.79 38982.32 42091.92 32764.08 44689.75 38087.40 43281.72 31278.82 39387.20 41345.33 44991.29 43559.09 44387.84 30391.60 393
ttmdpeth76.55 40874.64 41382.29 42182.25 45267.81 43289.76 37985.69 43870.35 43675.76 41891.69 30946.88 44489.77 44366.16 41763.23 45089.30 427
pmmvs371.81 41768.71 42081.11 42275.86 46170.42 41986.74 42483.66 44658.95 45668.64 44680.89 44836.93 45789.52 44563.10 43063.59 44883.39 446
Syy-MVS80.07 38779.78 37380.94 42391.92 32759.93 45589.75 38087.40 43281.72 31278.82 39387.20 41366.29 34191.29 43547.06 45687.84 30391.60 393
UWE-MVS-2878.98 39878.38 39280.80 42488.18 42360.66 45490.65 35578.51 45878.84 35777.93 40190.93 33759.08 40089.02 44850.96 45390.33 25892.72 366
new-patchmatchnet76.41 40975.17 41180.13 42582.65 45159.61 45687.66 41791.08 38078.23 37169.85 44383.22 43954.76 42191.63 43464.14 42764.89 44789.16 431
mvsany_test374.95 41173.26 41580.02 42674.61 46263.16 45085.53 43378.42 45974.16 40974.89 42486.46 42036.02 45889.09 44782.39 25566.91 44287.82 443
test_fmvs377.67 40577.16 40079.22 42779.52 45761.14 45292.34 31091.64 36773.98 41178.86 39286.59 41927.38 46387.03 45188.12 16475.97 41989.50 424
DSMNet-mixed76.94 40776.29 40578.89 42883.10 44956.11 46487.78 41379.77 45560.65 45475.64 41988.71 39261.56 37688.34 45060.07 43989.29 27992.21 383
EGC-MVSNET61.97 42556.37 43078.77 42989.63 40573.50 37989.12 39282.79 4480.21 4751.24 47684.80 43339.48 45490.04 44244.13 45875.94 42072.79 457
new_pmnet72.15 41570.13 41878.20 43082.95 45065.68 43983.91 44382.40 45062.94 45264.47 44979.82 44942.85 45286.26 45557.41 44774.44 42282.65 450
MVS-HIRNet73.70 41372.20 41678.18 43191.81 33456.42 46382.94 44882.58 44955.24 45768.88 44466.48 46055.32 41895.13 38458.12 44588.42 29283.01 448
LCM-MVSNet66.00 42262.16 42777.51 43264.51 47258.29 45883.87 44490.90 38848.17 46154.69 45873.31 45616.83 47286.75 45265.47 41961.67 45287.48 444
APD_test169.04 41866.26 42477.36 43380.51 45562.79 45185.46 43483.51 44754.11 45959.14 45684.79 43423.40 46689.61 44455.22 44970.24 43279.68 454
test_f71.95 41670.87 41775.21 43474.21 46459.37 45785.07 43785.82 43765.25 44870.42 44283.13 44023.62 46482.93 46278.32 32071.94 42883.33 447
ANet_high58.88 42954.22 43472.86 43556.50 47556.67 46080.75 45386.00 43673.09 42137.39 46764.63 46322.17 46779.49 46543.51 45923.96 46982.43 451
test_vis3_rt65.12 42362.60 42572.69 43671.44 46560.71 45387.17 42165.55 46963.80 45153.22 45965.65 46214.54 47389.44 44676.65 33765.38 44567.91 460
FPMVS64.63 42462.55 42670.88 43770.80 46656.71 45984.42 44184.42 44451.78 46049.57 46081.61 44623.49 46581.48 46340.61 46376.25 41874.46 456
dmvs_testset74.57 41275.81 41070.86 43887.72 42840.47 47387.05 42377.90 46382.75 28471.15 44185.47 43067.98 32284.12 46045.26 45776.98 41688.00 441
N_pmnet68.89 41968.44 42170.23 43989.07 41028.79 47888.06 40819.50 47869.47 43871.86 43884.93 43261.24 38191.75 43254.70 45077.15 41390.15 419
testf159.54 42756.11 43169.85 44069.28 46756.61 46180.37 45476.55 46642.58 46445.68 46375.61 45111.26 47484.18 45843.20 46060.44 45468.75 458
APD_test259.54 42756.11 43169.85 44069.28 46756.61 46180.37 45476.55 46642.58 46445.68 46375.61 45111.26 47484.18 45843.20 46060.44 45468.75 458
WB-MVS67.92 42067.49 42269.21 44281.09 45341.17 47288.03 40978.00 46273.50 41662.63 45183.11 44263.94 35886.52 45325.66 46851.45 46079.94 453
PMMVS259.60 42656.40 42969.21 44268.83 46946.58 46873.02 46477.48 46455.07 45849.21 46172.95 45717.43 47180.04 46449.32 45544.33 46480.99 452
SSC-MVS67.06 42166.56 42368.56 44480.54 45440.06 47487.77 41477.37 46572.38 42661.75 45382.66 44463.37 36186.45 45424.48 46948.69 46379.16 455
Gipumacopyleft57.99 43154.91 43367.24 44588.51 41465.59 44052.21 46790.33 39943.58 46342.84 46651.18 46720.29 46985.07 45734.77 46470.45 43151.05 466
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVScopyleft47.18 2252.22 43348.46 43763.48 44645.72 47746.20 46973.41 46378.31 46041.03 46630.06 46965.68 4616.05 47683.43 46130.04 46665.86 44460.80 461
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai58.82 43058.24 42860.56 44783.13 44845.09 47182.32 44948.22 47767.61 44261.70 45469.15 45838.75 45576.05 46632.01 46541.31 46560.55 462
MVEpermissive39.65 2343.39 43538.59 44157.77 44856.52 47448.77 46755.38 46658.64 47329.33 46928.96 47052.65 4664.68 47764.62 47028.11 46733.07 46759.93 463
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method50.52 43448.47 43656.66 44952.26 47618.98 48041.51 46981.40 45210.10 47044.59 46575.01 45428.51 46168.16 46753.54 45149.31 46282.83 449
DeepMVS_CXcopyleft56.31 45074.23 46351.81 46656.67 47444.85 46248.54 46275.16 45327.87 46258.74 47240.92 46252.22 45958.39 464
kuosan53.51 43253.30 43554.13 45176.06 46045.36 47080.11 45648.36 47659.63 45554.84 45763.43 46437.41 45662.07 47120.73 47139.10 46654.96 465
E-PMN43.23 43642.29 43846.03 45265.58 47137.41 47573.51 46264.62 47033.99 46728.47 47147.87 46819.90 47067.91 46822.23 47024.45 46832.77 467
EMVS42.07 43741.12 43944.92 45363.45 47335.56 47773.65 46163.48 47133.05 46826.88 47245.45 46921.27 46867.14 46919.80 47223.02 47032.06 468
tmp_tt35.64 43839.24 44024.84 45414.87 47823.90 47962.71 46551.51 4756.58 47236.66 46862.08 46544.37 45030.34 47452.40 45222.00 47120.27 469
wuyk23d21.27 44020.48 44323.63 45568.59 47036.41 47649.57 4686.85 4799.37 4717.89 4734.46 4754.03 47831.37 47317.47 47316.07 4723.12 470
test1238.76 44211.22 4451.39 4560.85 4800.97 48185.76 4310.35 4810.54 4742.45 4758.14 4740.60 4790.48 4752.16 4750.17 4742.71 471
testmvs8.92 44111.52 4441.12 4571.06 4790.46 48286.02 4280.65 4800.62 4732.74 4749.52 4730.31 4800.45 4762.38 4740.39 4732.46 472
mmdepth0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4700.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
monomultidepth0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4700.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
test_blank0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4700.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
uanet_test0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4700.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
DCPMVS0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4700.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
cdsmvs_eth3d_5k22.14 43929.52 4420.00 4580.00 4810.00 4830.00 47095.76 1840.00 4760.00 47794.29 21175.66 2090.00 4770.00 4760.00 4750.00 473
pcd_1.5k_mvsjas6.64 4448.86 4470.00 4580.00 4810.00 4830.00 4700.00 4820.00 4760.00 4770.00 47679.70 1460.00 4770.00 4760.00 4750.00 473
sosnet-low-res0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4700.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
sosnet0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4700.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
uncertanet0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4700.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
Regformer0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4700.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
ab-mvs-re7.82 44310.43 4460.00 4580.00 4810.00 4830.00 4700.00 4820.00 4760.00 47793.88 2320.00 4810.00 4770.00 4760.00 4750.00 473
uanet0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4700.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
TestfortrainingZip97.32 10
WAC-MVS64.08 44659.14 442
FOURS198.86 185.54 7098.29 197.49 989.79 6296.29 28
PC_three_145282.47 28897.09 1797.07 6892.72 198.04 18892.70 7699.02 1298.86 13
test_one_060198.58 1185.83 6497.44 1891.05 2296.78 2498.06 2191.45 11
eth-test20.00 481
eth-test0.00 481
ZD-MVS98.15 3786.62 3397.07 5783.63 26094.19 6096.91 7487.57 3299.26 4791.99 10198.44 54
RE-MVS-def93.68 6997.92 4684.57 9096.28 4796.76 8987.46 14793.75 7197.43 4782.94 9792.73 7297.80 8897.88 97
IU-MVS98.77 586.00 5296.84 7981.26 32597.26 1395.50 3599.13 399.03 8
test_241102_TWO97.44 1890.31 4097.62 898.07 1991.46 1099.58 1195.66 2999.12 698.98 10
test_241102_ONE98.77 585.99 5497.44 1890.26 4697.71 297.96 3092.31 499.38 32
9.1494.47 3297.79 5596.08 6597.44 1886.13 19095.10 5097.40 4988.34 2399.22 4993.25 6498.70 35
save fliter97.85 5285.63 6995.21 13696.82 8289.44 72
test_0728_THIRD90.75 2897.04 1998.05 2492.09 699.55 1795.64 3199.13 399.13 2
test072698.78 385.93 5797.19 1397.47 1490.27 4497.64 698.13 691.47 8
GSMVS96.12 212
test_part298.55 1287.22 1996.40 27
sam_mvs171.70 26796.12 212
sam_mvs70.60 281
MTGPAbinary96.97 62
test_post188.00 4109.81 47269.31 30595.53 37276.65 337
test_post10.29 47170.57 28595.91 357
patchmatchnet-post83.76 43771.53 26896.48 326
MTMP96.16 5660.64 472
gm-plane-assit89.60 40668.00 42977.28 37988.99 38697.57 23179.44 309
test9_res91.91 10598.71 3398.07 79
TEST997.53 6486.49 3794.07 21996.78 8681.61 31792.77 9696.20 10487.71 2999.12 59
test_897.49 6686.30 4594.02 22596.76 8981.86 30892.70 10096.20 10487.63 3099.02 69
agg_prior290.54 13098.68 3898.27 60
agg_prior97.38 6985.92 5996.72 9692.16 11598.97 83
test_prior485.96 5694.11 213
test_prior294.12 21187.67 14492.63 10496.39 9986.62 4291.50 11498.67 41
旧先验293.36 26171.25 43294.37 5697.13 28386.74 186
新几何293.11 276
旧先验196.79 8281.81 19195.67 19496.81 8086.69 4097.66 9496.97 168
无先验93.28 26996.26 13573.95 41299.05 6380.56 29496.59 191
原ACMM292.94 287
test22296.55 9181.70 19392.22 31695.01 24268.36 44190.20 16196.14 10980.26 13797.80 8896.05 219
testdata298.75 11178.30 321
segment_acmp87.16 37
testdata192.15 31887.94 130
plane_prior794.70 19582.74 161
plane_prior694.52 21082.75 15974.23 229
plane_prior596.22 14098.12 17288.15 16189.99 26294.63 273
plane_prior494.86 182
plane_prior382.75 15990.26 4686.91 230
plane_prior295.85 8990.81 26
plane_prior194.59 203
plane_prior82.73 16295.21 13689.66 6789.88 267
n20.00 482
nn0.00 482
door-mid85.49 439
test1196.57 107
door85.33 441
HQP5-MVS81.56 195
HQP-NCC94.17 23494.39 19388.81 9885.43 276
ACMP_Plane94.17 23494.39 19388.81 9885.43 276
BP-MVS87.11 183
HQP4-MVS85.43 27697.96 20094.51 283
HQP3-MVS96.04 15889.77 271
HQP2-MVS73.83 240
NP-MVS94.37 22182.42 17493.98 225
MDTV_nov1_ep13_2view55.91 46587.62 41873.32 41884.59 29870.33 28874.65 36095.50 240
MDTV_nov1_ep1383.56 33791.69 33969.93 42287.75 41591.54 37078.60 36384.86 29288.90 38869.54 30096.03 34870.25 38988.93 284
ACMMP++_ref87.47 307
ACMMP++88.01 299
Test By Simon80.02 139