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 6090.86 196.85 8099.61 696.03 2799.06 999.07 7
No_MVS96.52 197.78 6090.86 196.85 8099.61 696.03 2799.06 999.07 7
OPU-MVS96.21 398.00 4890.85 397.13 1997.08 7092.59 298.94 9292.25 9298.99 1498.84 19
HPM-MVS++copyleft95.14 1394.91 2695.83 498.25 3589.65 495.92 8796.96 6891.75 1394.02 7296.83 8288.12 2899.55 2093.41 6698.94 1698.28 62
MM95.10 1494.91 2695.68 596.09 11688.34 1096.68 3894.37 30495.08 194.68 5897.72 4182.94 10099.64 397.85 598.76 3299.06 9
SMA-MVScopyleft95.20 1095.07 2095.59 698.14 4188.48 996.26 5497.28 4085.90 21097.67 498.10 1488.41 2499.56 1694.66 4899.19 198.71 25
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 10591.37 12795.55 795.63 14388.73 797.07 2396.77 9290.84 2684.02 33796.62 9575.95 21999.34 4287.77 18597.68 9698.59 29
CNVR-MVS95.40 895.37 1195.50 898.11 4288.51 895.29 13296.96 6892.09 1095.32 5097.08 7089.49 1699.33 4595.10 4398.85 2098.66 26
TestfortrainingZip95.40 997.32 7488.97 697.32 1096.82 8589.07 9295.69 4596.49 10089.27 1899.29 5095.80 14297.95 98
MGCNet94.18 5093.80 6495.34 1094.91 18387.62 1595.97 8293.01 35592.58 694.22 6397.20 6480.56 14199.59 1097.04 2098.68 4098.81 22
ACMMP_NAP94.74 2594.56 3395.28 1198.02 4787.70 1295.68 10797.34 3088.28 12495.30 5197.67 4385.90 5599.54 2493.91 5698.95 1598.60 28
DPE-MVScopyleft95.57 595.67 595.25 1298.36 3187.28 1995.56 11997.51 1089.13 9197.14 1797.91 3491.64 899.62 494.61 4999.17 298.86 16
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS94.97 1794.90 2895.20 1397.84 5687.76 1196.65 3997.48 1587.76 15495.71 4497.70 4288.28 2799.35 4193.89 5798.78 2998.48 35
MCST-MVS94.45 3494.20 5195.19 1498.46 2287.50 1795.00 15697.12 5587.13 17592.51 11396.30 10689.24 1999.34 4293.46 6398.62 4998.73 23
NCCC94.81 2294.69 3295.17 1597.83 5787.46 1895.66 11096.93 7292.34 793.94 7396.58 9787.74 3199.44 3392.83 7598.40 5798.62 27
DPM-MVS92.58 10091.74 11195.08 1696.19 10789.31 592.66 31496.56 11383.44 28591.68 14095.04 19186.60 4798.99 8285.60 21997.92 8496.93 191
ZNCC-MVS94.47 3394.28 4595.03 1798.52 1886.96 2196.85 3397.32 3488.24 12593.15 8897.04 7386.17 5299.62 492.40 8698.81 2698.52 31
test_0728_SECOND95.01 1898.79 586.43 4097.09 2197.49 1199.61 695.62 3499.08 798.99 11
MTAPA94.42 3994.22 4895.00 1998.42 2486.95 2294.36 21196.97 6591.07 2293.14 8997.56 4584.30 8199.56 1693.43 6498.75 3398.47 38
TestfortrainingZip a95.33 995.44 1094.99 2098.88 186.26 4897.32 1097.43 2590.76 2996.80 2698.09 1889.00 2299.58 1393.66 6096.99 11199.14 2
MSP-MVS95.42 795.56 794.98 2198.49 2086.52 3796.91 3097.47 1691.73 1496.10 3696.69 8789.90 1399.30 4894.70 4798.04 7999.13 4
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 3694.27 4794.92 2298.65 1186.67 3196.92 2997.23 4388.60 11593.58 8097.27 5885.22 6499.54 2492.21 9498.74 3498.56 30
APDe-MVScopyleft95.46 695.64 694.91 2398.26 3486.29 4797.46 797.40 2689.03 9796.20 3598.10 1489.39 1799.34 4295.88 2999.03 1199.10 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR94.43 3694.28 4594.91 2398.63 1286.69 2996.94 2597.32 3488.63 11293.53 8397.26 6085.04 6899.54 2492.35 8998.78 2998.50 32
MED-MVS95.95 296.29 294.90 2598.88 185.89 6597.32 1097.86 190.76 2997.21 1498.09 1892.42 499.67 195.27 4098.85 2099.14 2
GST-MVS94.21 4593.97 6094.90 2598.41 2586.82 2596.54 4197.19 4488.24 12593.26 8596.83 8285.48 6099.59 1091.43 12098.40 5798.30 56
HFP-MVS94.52 3194.40 3894.86 2798.61 1386.81 2696.94 2597.34 3088.63 11293.65 7897.21 6286.10 5399.49 3092.35 8998.77 3198.30 56
sasdasda93.27 8292.75 9294.85 2895.70 13987.66 1396.33 4496.41 12390.00 5494.09 6894.60 21682.33 11098.62 13392.40 8692.86 23698.27 65
MP-MVS-pluss94.21 4594.00 5994.85 2898.17 3986.65 3294.82 16997.17 4986.26 20292.83 9897.87 3685.57 5999.56 1694.37 5298.92 1798.34 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs93.27 8292.75 9294.85 2895.70 13987.66 1396.33 4496.41 12390.00 5494.09 6894.60 21682.33 11098.62 13392.40 8692.86 23698.27 65
XVS94.45 3494.32 4194.85 2898.54 1686.60 3596.93 2797.19 4490.66 3692.85 9697.16 6885.02 6999.49 3091.99 10598.56 5398.47 38
X-MVStestdata88.31 23986.13 28894.85 2898.54 1686.60 3596.93 2797.19 4490.66 3692.85 9623.41 52685.02 6999.49 3091.99 10598.56 5398.47 38
SteuartSystems-ACMMP95.20 1095.32 1394.85 2896.99 8286.33 4397.33 897.30 3791.38 1995.39 4997.46 5088.98 2399.40 3494.12 5398.89 1898.82 21
Skip Steuart: Steuart Systems R&D Blog.
MED-MVS test94.84 3498.88 185.89 6597.32 1097.86 188.11 13397.21 1497.54 4699.67 195.27 4098.85 2098.95 13
DVP-MVS++95.98 196.36 194.82 3597.78 6086.00 5498.29 197.49 1190.75 3197.62 898.06 2492.59 299.61 695.64 3299.02 1298.86 16
ME-MVS95.17 1295.29 1494.81 3698.39 2885.89 6595.91 8897.55 889.01 9995.86 4297.54 4689.24 1999.59 1095.27 4098.85 2098.95 13
alignmvs93.08 9092.50 9994.81 3695.62 14487.61 1695.99 7996.07 17089.77 6794.12 6794.87 20080.56 14198.66 12592.42 8593.10 23198.15 77
SED-MVS95.91 396.28 394.80 3898.77 885.99 5697.13 1997.44 2090.31 4497.71 298.07 2292.31 599.58 1395.66 3099.13 398.84 19
DeepC-MVS_fast89.43 294.04 5393.79 6594.80 3897.48 7086.78 2795.65 11296.89 7789.40 7992.81 9996.97 7585.37 6299.24 5290.87 13098.69 3898.38 48
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 4294.07 5694.77 4098.47 2186.31 4596.71 3696.98 6489.04 9591.98 12497.19 6585.43 6199.56 1692.06 10398.79 2798.44 43
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVScopyleft94.24 4394.07 5694.75 4198.06 4586.90 2495.88 9096.94 7185.68 21795.05 5697.18 6687.31 3999.07 6591.90 11198.61 5198.28 62
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS94.34 4094.21 5094.74 4298.39 2886.64 3397.60 597.24 4188.53 11792.73 10497.23 6185.20 6599.32 4692.15 9798.83 2598.25 70
PGM-MVS93.96 5893.72 7094.68 4398.43 2386.22 4995.30 13097.78 387.45 16593.26 8597.33 5684.62 7899.51 2890.75 13398.57 5298.32 55
DVP-MVScopyleft95.67 496.02 494.64 4498.78 685.93 5997.09 2196.73 9890.27 4897.04 2198.05 2791.47 999.55 2095.62 3499.08 798.45 42
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 5693.78 6694.63 4598.50 1985.90 6496.87 3196.91 7588.70 11091.83 13497.17 6783.96 8599.55 2091.44 11998.64 4898.43 44
PHI-MVS93.89 6093.65 7494.62 4696.84 8586.43 4096.69 3797.49 1185.15 24193.56 8296.28 10785.60 5899.31 4792.45 8398.79 2798.12 82
TSAR-MVS + MP.94.85 1994.94 2494.58 4798.25 3586.33 4396.11 6796.62 10888.14 13096.10 3696.96 7689.09 2198.94 9294.48 5098.68 4098.48 35
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CANet93.54 6993.20 8394.55 4895.65 14185.73 7294.94 15996.69 10491.89 1290.69 16795.88 13981.99 12399.54 2493.14 7097.95 8398.39 46
train_agg93.44 7593.08 8594.52 4997.53 6786.49 3894.07 23196.78 9081.86 33092.77 10196.20 11087.63 3399.12 6392.14 9898.69 3897.94 99
CDPH-MVS92.83 9492.30 10394.44 5097.79 5886.11 5394.06 23396.66 10580.09 36192.77 10196.63 9486.62 4599.04 6987.40 19298.66 4498.17 75
3Dnovator86.66 591.73 12390.82 14294.44 5094.59 21086.37 4297.18 1797.02 6289.20 8884.31 33296.66 9073.74 26099.17 5786.74 20297.96 8297.79 123
SR-MVS94.23 4494.17 5494.43 5298.21 3885.78 7096.40 4396.90 7688.20 12894.33 6297.40 5384.75 7799.03 7093.35 6797.99 8198.48 35
HPM-MVScopyleft94.02 5493.88 6194.43 5298.39 2885.78 7097.25 1597.07 6086.90 18592.62 11096.80 8684.85 7599.17 5792.43 8498.65 4798.33 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + GP.93.66 6793.41 7894.41 5496.59 9286.78 2794.40 20393.93 32289.77 6794.21 6495.59 16087.35 3898.61 13592.72 7896.15 13697.83 119
reproduce-ours94.82 2094.97 2294.38 5597.91 5385.46 7595.86 9197.15 5189.82 6095.23 5398.10 1487.09 4199.37 3795.30 3898.25 6698.30 56
our_new_method94.82 2094.97 2294.38 5597.91 5385.46 7595.86 9197.15 5189.82 6095.23 5398.10 1487.09 4199.37 3795.30 3898.25 6698.30 56
NormalMVS93.46 7293.16 8494.37 5798.40 2686.20 5096.30 4796.27 13691.65 1792.68 10696.13 12177.97 18998.84 10690.75 13398.26 6298.07 84
test1294.34 5897.13 8086.15 5296.29 13291.04 16385.08 6799.01 7598.13 7497.86 114
SymmetryMVS92.81 9792.31 10294.32 5996.15 10886.20 5096.30 4794.43 30091.65 1792.68 10696.13 12177.97 18998.84 10690.75 13394.72 17097.92 108
ACMMPcopyleft93.24 8492.88 9094.30 6098.09 4485.33 7996.86 3297.45 1988.33 12190.15 18697.03 7481.44 13199.51 2890.85 13195.74 14598.04 91
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
reproduce_model94.76 2494.92 2594.29 6197.92 4985.18 8195.95 8597.19 4489.67 7095.27 5298.16 686.53 4899.36 4095.42 3798.15 7298.33 51
DeepC-MVS88.79 393.31 8192.99 8894.26 6296.07 11885.83 6894.89 16296.99 6389.02 9889.56 19597.37 5582.51 10799.38 3592.20 9598.30 6097.57 138
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 9192.63 9694.23 6395.62 14485.92 6196.08 6996.33 13089.86 5893.89 7594.66 21382.11 11898.50 14192.33 9192.82 23998.27 65
fmvsm_l_conf0.5_n_394.80 2395.01 2194.15 6495.64 14285.08 8296.09 6897.36 2890.98 2497.09 1998.12 1084.98 7398.94 9297.07 1797.80 9198.43 44
EPNet91.79 11491.02 13694.10 6590.10 41685.25 8096.03 7692.05 38292.83 587.39 24395.78 15079.39 16799.01 7588.13 17997.48 9998.05 90
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lecture95.10 1495.46 994.01 6698.40 2684.36 10797.70 397.78 391.19 2096.22 3498.08 2186.64 4499.37 3794.91 4598.26 6298.29 61
test_fmvsmconf_n94.60 2894.81 3093.98 6794.62 20684.96 8596.15 6297.35 2989.37 8096.03 3998.11 1186.36 4999.01 7597.45 1097.83 8997.96 97
DELS-MVS93.43 7993.25 8193.97 6895.42 15285.04 8393.06 29797.13 5490.74 3391.84 13295.09 19086.32 5099.21 5591.22 12298.45 5597.65 132
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 11191.28 13093.96 6998.33 3385.92 6194.66 18296.66 10582.69 30790.03 18895.82 14682.30 11299.03 7084.57 23896.48 12996.91 193
HPM-MVS_fast93.40 8093.22 8293.94 7098.36 3184.83 8797.15 1896.80 8985.77 21492.47 11497.13 6982.38 10899.07 6590.51 13898.40 5797.92 108
test_fmvsmconf0.1_n94.20 4794.31 4393.88 7192.46 33084.80 8896.18 5996.82 8589.29 8595.68 4698.11 1185.10 6698.99 8297.38 1197.75 9597.86 114
SD-MVS94.96 1895.33 1293.88 7197.25 7986.69 2996.19 5797.11 5890.42 4096.95 2397.27 5889.53 1596.91 32194.38 5198.85 2098.03 92
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 7493.31 7993.84 7396.99 8284.84 8693.24 28897.24 4188.76 10791.60 14195.85 14386.07 5498.66 12591.91 10998.16 7098.03 92
SR-MVS-dyc-post93.82 6293.82 6393.82 7497.92 4984.57 9496.28 5196.76 9387.46 16393.75 7697.43 5184.24 8299.01 7592.73 7697.80 9197.88 112
test_prior93.82 7497.29 7784.49 9896.88 7898.87 10098.11 83
APD-MVS_3200maxsize93.78 6393.77 6793.80 7697.92 4984.19 11196.30 4796.87 7986.96 18193.92 7497.47 4983.88 8698.96 8992.71 7997.87 8798.26 69
fmvsm_l_conf0.5_n94.29 4194.46 3693.79 7795.28 15885.43 7795.68 10796.43 12186.56 19396.84 2597.81 3987.56 3698.77 11597.14 1596.82 11997.16 172
CSCG93.23 8593.05 8693.76 7898.04 4684.07 11396.22 5697.37 2784.15 26690.05 18795.66 15687.77 3099.15 6189.91 14998.27 6198.07 84
GDP-MVS92.04 10991.46 12493.75 7994.55 21684.69 9195.60 11896.56 11387.83 15193.07 9295.89 13873.44 26498.65 12790.22 14296.03 13897.91 110
BP-MVS192.48 10292.07 10693.72 8094.50 22084.39 10695.90 8994.30 30790.39 4192.67 10895.94 13474.46 24398.65 12793.14 7097.35 10398.13 79
test_fmvsmconf0.01_n93.19 8693.02 8793.71 8189.25 42984.42 10596.06 7396.29 13289.06 9394.68 5898.13 779.22 16998.98 8697.22 1397.24 10597.74 126
UA-Net92.83 9492.54 9893.68 8296.10 11584.71 9095.66 11096.39 12591.92 1193.22 8796.49 10083.16 9598.87 10084.47 24095.47 15397.45 146
fmvsm_l_conf0.5_n_a94.20 4794.40 3893.60 8395.29 15784.98 8495.61 11596.28 13586.31 20096.75 2897.86 3787.40 3798.74 11997.07 1797.02 11097.07 177
QAPM89.51 19688.15 22393.59 8494.92 18184.58 9396.82 3496.70 10378.43 38983.41 35596.19 11473.18 26999.30 4877.11 36596.54 12696.89 194
test_fmvsm_n_192094.71 2695.11 1993.50 8595.79 13384.62 9296.15 6297.64 589.85 5997.19 1697.89 3586.28 5198.71 12297.11 1698.08 7897.17 165
fmvsm_s_conf0.5_n_994.99 1695.50 893.44 8696.51 10082.25 18695.76 10296.92 7393.37 397.63 798.43 184.82 7699.16 6098.15 197.92 8498.90 15
KinetiMVS91.82 11391.30 12893.39 8794.72 19883.36 13895.45 12296.37 12790.33 4392.17 11996.03 12872.32 28198.75 11687.94 18296.34 13198.07 84
casdiffmvs_mvgpermissive92.96 9392.83 9193.35 8894.59 21083.40 13695.00 15696.34 12990.30 4692.05 12296.05 12583.43 8998.15 17892.07 10095.67 14698.49 34
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 5894.18 5393.30 8994.79 19083.81 12295.77 10096.74 9788.02 13896.23 3397.84 3883.36 9398.83 10997.49 897.34 10497.25 157
EI-MVSNet-Vis-set93.01 9292.92 8993.29 9095.01 17283.51 13394.48 19195.77 19890.87 2592.52 11296.67 8984.50 7999.00 8091.99 10594.44 18397.36 149
Vis-MVSNetpermissive91.75 12191.23 13193.29 9095.32 15683.78 12396.14 6495.98 17789.89 5690.45 17196.58 9775.09 23298.31 16884.75 23296.90 11597.78 124
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
BridgeMVS93.98 5794.22 4893.26 9296.13 11083.29 14096.27 5396.52 11689.82 6095.56 4895.51 16484.50 7998.79 11394.83 4698.86 1997.72 128
SPE-MVS-test94.02 5494.29 4493.24 9396.69 8883.24 14197.49 696.92 7392.14 992.90 9495.77 15185.02 6998.33 16593.03 7298.62 4998.13 79
VNet92.24 10791.91 10993.24 9396.59 9283.43 13494.84 16896.44 12089.19 8994.08 7195.90 13777.85 19598.17 17688.90 16993.38 22098.13 79
fmvsm_s_conf0.5_n_1094.43 3694.84 2993.20 9595.73 13683.19 14495.99 7997.31 3691.08 2197.67 498.11 1181.87 12599.22 5397.86 497.91 8697.20 163
VDD-MVS90.74 15289.92 16793.20 9596.27 10583.02 15695.73 10493.86 32688.42 12092.53 11196.84 8162.09 39798.64 13090.95 12892.62 24697.93 107
Elysia90.12 17289.10 19193.18 9793.16 29784.05 11595.22 13996.27 13685.16 23990.59 16894.68 20964.64 37598.37 15886.38 20895.77 14397.12 174
StellarMVS90.12 17289.10 19193.18 9793.16 29784.05 11595.22 13996.27 13685.16 23990.59 16894.68 20964.64 37598.37 15886.38 20895.77 14397.12 174
CS-MVS94.12 5194.44 3793.17 9996.55 9583.08 15397.63 496.95 7091.71 1593.50 8496.21 10985.61 5798.24 17093.64 6198.17 6998.19 73
nrg03091.08 14690.39 15193.17 9993.07 30486.91 2396.41 4296.26 14088.30 12388.37 22094.85 20382.19 11797.64 24291.09 12382.95 37394.96 279
MVSMamba_PlusPlus93.44 7593.54 7693.14 10196.58 9483.05 15496.06 7396.50 11884.42 26394.09 6895.56 16285.01 7298.69 12494.96 4498.66 4497.67 131
EI-MVSNet-UG-set92.74 9892.62 9793.12 10294.86 18683.20 14394.40 20395.74 20190.71 3592.05 12296.60 9684.00 8498.99 8291.55 11793.63 20997.17 165
test_fmvsmvis_n_192093.44 7593.55 7593.10 10393.67 28384.26 10995.83 9596.14 16189.00 10092.43 11597.50 4883.37 9298.72 12096.61 2497.44 10096.32 219
新几何193.10 10397.30 7684.35 10895.56 21971.09 46791.26 15196.24 10882.87 10298.86 10279.19 34298.10 7596.07 235
OMC-MVS91.23 13790.62 14893.08 10596.27 10584.07 11393.52 27095.93 18386.95 18289.51 19696.13 12178.50 18398.35 16285.84 21792.90 23596.83 201
OpenMVScopyleft83.78 1188.74 22687.29 24593.08 10592.70 32485.39 7896.57 4096.43 12178.74 38380.85 38896.07 12469.64 31899.01 7578.01 35696.65 12494.83 287
MAR-MVS90.30 16889.37 18493.07 10796.61 9184.48 9995.68 10795.67 21082.36 31287.85 23092.85 28476.63 20898.80 11180.01 32296.68 12395.91 241
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
cashybrid292.82 9692.75 9293.03 10894.79 19082.44 17895.39 12496.24 14390.58 3891.79 13696.43 10482.73 10498.19 17591.31 12195.54 14898.46 41
lupinMVS90.92 14890.21 15593.03 10893.86 26883.88 12092.81 30893.86 32679.84 36491.76 13794.29 23077.92 19298.04 20090.48 13997.11 10697.17 165
Effi-MVS+91.59 13191.11 13393.01 11094.35 23583.39 13794.60 18495.10 25687.10 17690.57 17093.10 27981.43 13298.07 19489.29 16194.48 18197.59 137
fmvsm_s_conf0.5_n_a93.57 6893.76 6893.00 11195.02 17183.67 12696.19 5796.10 16787.27 16995.98 4098.05 2783.07 9998.45 15196.68 2395.51 15096.88 195
MVS_111021_LR92.47 10392.29 10492.98 11295.99 12584.43 10393.08 29496.09 16888.20 12891.12 15695.72 15481.33 13397.76 23191.74 11397.37 10296.75 203
fmvsm_s_conf0.1_n_a93.19 8693.26 8092.97 11392.49 32883.62 12996.02 7795.72 20586.78 18796.04 3898.19 482.30 11298.43 15596.38 2595.42 15696.86 196
ETV-MVS92.74 9892.66 9592.97 11395.20 16484.04 11795.07 15196.51 11790.73 3492.96 9391.19 34584.06 8398.34 16391.72 11496.54 12696.54 214
LFMVS90.08 17589.13 19092.95 11596.71 8782.32 18596.08 6989.91 44286.79 18692.15 12196.81 8462.60 39598.34 16387.18 19693.90 19898.19 73
UGNet89.95 18288.95 19992.95 11594.51 21883.31 13995.70 10695.23 24789.37 8087.58 23793.94 24664.00 38398.78 11483.92 24896.31 13296.74 204
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 15090.10 15992.90 11793.04 30783.53 13293.08 29494.15 31580.22 35891.41 14794.91 19776.87 20297.93 22090.28 14096.90 11597.24 158
jason: jason.
DP-MVS87.25 28085.36 31992.90 11797.65 6483.24 14194.81 17092.00 38474.99 43481.92 37795.00 19372.66 27499.05 6766.92 44892.33 25196.40 216
fmvsm_s_conf0.5_n_894.56 3095.12 1892.87 11995.96 12881.32 21695.76 10297.57 793.48 297.53 1098.32 381.78 12899.13 6297.91 297.81 9098.16 76
fmvsm_s_conf0.5_n93.76 6494.06 5892.86 12095.62 14483.17 14596.14 6496.12 16588.13 13195.82 4398.04 3083.43 8998.48 14396.97 2196.23 13396.92 192
fmvsm_s_conf0.1_n93.46 7293.66 7392.85 12193.75 27583.13 14796.02 7795.74 20187.68 15795.89 4198.17 582.78 10398.46 14796.71 2296.17 13596.98 186
casdiffseed41469214791.11 14490.55 14992.81 12294.27 24382.58 17794.81 17096.03 17587.93 14490.17 18495.62 15878.51 18297.90 22484.18 24493.45 21897.94 99
CANet_DTU90.26 17089.41 18392.81 12293.46 29083.01 15793.48 27194.47 29989.43 7887.76 23594.23 23570.54 30699.03 7084.97 22796.39 13096.38 217
MVSFormer91.68 12991.30 12892.80 12493.86 26883.88 12095.96 8395.90 18784.66 25991.76 13794.91 19777.92 19297.30 28689.64 15797.11 10697.24 158
PVSNet_Blended_VisFu91.38 13490.91 13992.80 12496.39 10283.17 14594.87 16496.66 10583.29 29089.27 20294.46 22580.29 14499.17 5787.57 18995.37 15796.05 238
fmvsm_l_conf0.5_n_994.65 2795.28 1592.77 12695.95 12981.83 19895.53 12097.12 5591.68 1697.89 198.06 2485.71 5698.65 12797.32 1298.26 6297.83 119
LuminaMVS90.55 16489.81 16992.77 12692.78 32184.21 11094.09 22994.17 31485.82 21191.54 14294.14 23769.93 31297.92 22191.62 11694.21 19196.18 227
balanced_ft_v192.23 10892.05 10792.77 12695.40 15381.78 20295.80 9695.69 20987.94 14291.92 12995.04 19175.91 22098.71 12293.83 5896.94 11297.82 121
fmvsm_s_conf0.5_n_694.11 5294.56 3392.76 12994.98 17681.96 19595.79 9897.29 3989.31 8397.52 1197.61 4483.25 9498.88 9997.05 1998.22 6897.43 148
VDDNet89.56 19588.49 21492.76 12995.07 17082.09 18996.30 4793.19 35081.05 35291.88 13096.86 8061.16 41398.33 16588.43 17692.49 25097.84 118
viewdifsd2359ckpt0991.18 14090.65 14792.75 13194.61 20982.36 18494.32 21295.74 20184.72 25689.66 19495.15 18879.69 16298.04 20087.70 18694.27 19097.85 117
h-mvs3390.80 15090.15 15892.75 13196.01 12182.66 17095.43 12395.53 22389.80 6393.08 9095.64 15775.77 22199.00 8092.07 10078.05 43096.60 209
hybridcas92.43 10492.33 10192.74 13394.51 21881.84 19795.05 15496.16 15989.60 7291.40 14896.20 11082.23 11498.09 18989.95 14895.87 14098.28 62
casdiffmvspermissive92.51 10192.43 10092.74 13394.41 23081.98 19394.54 18896.23 14589.57 7491.96 12696.17 11582.58 10698.01 20790.95 12895.45 15598.23 71
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 15590.02 16592.71 13595.72 13782.41 18294.11 22595.12 25485.63 21891.49 14494.70 20774.75 23698.42 15686.13 21292.53 24897.31 150
DCV-MVSNet90.69 15590.02 16592.71 13595.72 13782.41 18294.11 22595.12 25485.63 21891.49 14494.70 20774.75 23698.42 15686.13 21292.53 24897.31 150
PCF-MVS84.11 1087.74 25486.08 29292.70 13794.02 25784.43 10389.27 41795.87 19273.62 44984.43 32494.33 22778.48 18598.86 10270.27 42294.45 18294.81 288
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
fmvsm_s_conf0.5_n_1194.60 2895.23 1692.69 13896.05 12082.00 19196.31 4696.71 10192.27 896.68 3098.39 285.32 6398.92 9597.20 1498.16 7097.17 165
SSM_040490.73 15390.08 16092.69 13895.00 17583.13 14794.32 21295.00 26485.41 22989.84 18995.35 17476.13 21197.98 21285.46 22294.18 19296.95 188
baseline92.39 10692.29 10492.69 13894.46 22581.77 20394.14 22296.27 13689.22 8791.88 13096.00 12982.35 10997.99 20991.05 12495.27 16198.30 56
MSLP-MVS++93.72 6694.08 5592.65 14197.31 7583.43 13495.79 9897.33 3290.03 5393.58 8096.96 7684.87 7497.76 23192.19 9698.66 4496.76 202
EC-MVSNet93.44 7593.71 7192.63 14295.21 16382.43 17997.27 1496.71 10190.57 3992.88 9595.80 14783.16 9598.16 17793.68 5998.14 7397.31 150
ab-mvs89.41 20388.35 21692.60 14395.15 16882.65 17492.20 33795.60 21783.97 27088.55 21693.70 26074.16 25198.21 17482.46 27289.37 29696.94 190
LS3D87.89 24986.32 28192.59 14496.07 11882.92 16095.23 13794.92 27475.66 42682.89 36395.98 13172.48 27899.21 5568.43 43695.23 16295.64 255
Anonymous2024052988.09 24586.59 27092.58 14596.53 9781.92 19695.99 7995.84 19474.11 44489.06 20695.21 18361.44 40598.81 11083.67 25587.47 32797.01 184
fmvsm_s_conf0.5_n_394.49 3295.13 1792.56 14695.49 15081.10 22695.93 8697.16 5092.96 497.39 1298.13 783.63 8898.80 11197.89 397.61 9897.78 124
CPTT-MVS91.99 11091.80 11092.55 14798.24 3781.98 19396.76 3596.49 11981.89 32990.24 17796.44 10378.59 17998.61 13589.68 15597.85 8897.06 178
viewdifsd2359ckpt1391.20 13990.75 14492.54 14894.30 24182.13 18894.03 23595.89 18985.60 22090.20 17995.36 17379.69 16297.90 22487.85 18493.86 19997.61 134
114514_t89.51 19688.50 21292.54 14898.11 4281.99 19295.16 14796.36 12870.19 47185.81 27795.25 17976.70 20698.63 13282.07 28296.86 11897.00 185
PAPM_NR91.22 13890.78 14392.52 15097.60 6581.46 21294.37 20996.24 14386.39 19987.41 24094.80 20582.06 12198.48 14382.80 26795.37 15797.61 134
mamba_040889.06 21687.92 23092.50 15194.76 19282.66 17079.84 49294.64 29285.18 23488.96 20895.00 19376.00 21697.98 21283.74 25293.15 22896.85 197
DeepPCF-MVS89.96 194.20 4794.77 3192.49 15296.52 9880.00 27794.00 24097.08 5990.05 5295.65 4797.29 5789.66 1498.97 8793.95 5598.71 3598.50 32
SSM_040790.47 16689.80 17092.46 15394.76 19282.66 17093.98 24295.00 26485.41 22988.96 20895.35 17476.13 21197.88 22685.46 22293.15 22896.85 197
IS-MVSNet91.43 13391.09 13592.46 15395.87 13281.38 21596.95 2493.69 33989.72 6989.50 19895.98 13178.57 18097.77 23083.02 26196.50 12898.22 72
API-MVS90.66 15990.07 16192.45 15596.36 10384.57 9496.06 7395.22 24982.39 31089.13 20394.27 23380.32 14398.46 14780.16 32096.71 12294.33 311
xiu_mvs_v1_base_debu90.64 16090.05 16292.40 15693.97 26384.46 10093.32 27995.46 22785.17 23692.25 11694.03 23870.59 30298.57 13890.97 12594.67 17294.18 315
xiu_mvs_v1_base90.64 16090.05 16292.40 15693.97 26384.46 10093.32 27995.46 22785.17 23692.25 11694.03 23870.59 30298.57 13890.97 12594.67 17294.18 315
xiu_mvs_v1_base_debi90.64 16090.05 16292.40 15693.97 26384.46 10093.32 27995.46 22785.17 23692.25 11694.03 23870.59 30298.57 13890.97 12594.67 17294.18 315
fmvsm_s_conf0.5_n_293.47 7193.83 6292.39 15995.36 15481.19 22295.20 14496.56 11390.37 4297.13 1898.03 3177.47 19898.96 8997.79 696.58 12597.03 181
viewmacassd2359aftdt91.67 13091.43 12692.37 16093.95 26681.00 23093.90 25095.97 18087.75 15591.45 14696.04 12779.92 15097.97 21489.26 16294.67 17298.14 78
viewmanbaseed2359cas91.78 11791.58 11692.37 16094.32 23881.07 22793.76 25695.96 18187.26 17091.50 14395.88 13980.92 13997.97 21489.70 15494.92 16698.07 84
fmvsm_s_conf0.1_n_293.16 8893.42 7792.37 16094.62 20681.13 22495.23 13795.89 18990.30 4696.74 2998.02 3276.14 21098.95 9197.64 796.21 13497.03 181
AdaColmapbinary89.89 18589.07 19392.37 16097.41 7183.03 15594.42 19895.92 18482.81 30486.34 26694.65 21473.89 25699.02 7380.69 30995.51 15095.05 274
CNLPA89.07 21587.98 22792.34 16496.87 8484.78 8994.08 23093.24 34781.41 34384.46 32295.13 18975.57 22896.62 33877.21 36393.84 20195.61 258
fmvsm_s_conf0.5_n_493.86 6194.37 4092.33 16595.13 16980.95 23395.64 11396.97 6589.60 7296.85 2497.77 4083.08 9898.92 9597.49 896.78 12097.13 173
ET-MVSNet_ETH3D87.51 26885.91 30092.32 16693.70 28283.93 11892.33 32990.94 41784.16 26572.09 46992.52 29769.90 31395.85 39089.20 16388.36 31497.17 165
E491.74 12291.55 11992.31 16794.27 24380.80 24393.81 25396.17 15787.97 14091.11 15796.05 12580.75 14098.08 19289.78 15094.02 19498.06 89
E291.79 11491.61 11492.31 16794.49 22180.86 23993.74 25896.19 15087.63 16091.16 15295.94 13481.31 13498.06 19589.76 15194.29 18897.99 94
Anonymous20240521187.68 25586.13 28892.31 16796.66 8980.74 24594.87 16491.49 40180.47 35789.46 19995.44 16754.72 45498.23 17182.19 27889.89 28697.97 96
E391.78 11791.61 11492.30 17094.48 22280.86 23993.73 25996.19 15087.63 16091.16 15295.95 13381.30 13598.06 19589.76 15194.29 18897.99 94
CHOSEN 1792x268888.84 22287.69 23592.30 17096.14 10981.42 21490.01 40495.86 19374.52 43987.41 24093.94 24675.46 22998.36 16080.36 31595.53 14997.12 174
viewcassd2359sk1191.79 11491.62 11392.29 17294.62 20680.88 23793.70 26396.18 15687.38 16791.13 15595.85 14381.62 13098.06 19589.71 15394.40 18497.94 99
HY-MVS83.01 1289.03 21887.94 22992.29 17294.86 18682.77 16292.08 34294.49 29881.52 34286.93 24792.79 29078.32 18798.23 17179.93 32390.55 27395.88 244
CDS-MVSNet89.45 19988.51 21192.29 17293.62 28583.61 13193.01 29894.68 29081.95 32487.82 23393.24 27378.69 17796.99 31580.34 31693.23 22596.28 222
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR90.02 17889.27 18992.29 17295.78 13480.95 23392.68 31396.22 14681.91 32686.66 25793.75 25882.23 11498.44 15379.40 34194.79 16997.48 144
E3new91.76 12091.58 11692.28 17694.69 20380.90 23693.68 26696.17 15787.15 17391.09 16295.70 15581.75 12998.05 19989.67 15694.35 18597.90 111
mvsmamba90.33 16789.69 17392.25 17795.17 16581.64 20595.27 13593.36 34584.88 24989.51 19694.27 23369.29 32897.42 26889.34 16096.12 13797.68 130
E5new91.71 12491.55 11992.20 17894.33 23680.62 24994.41 19996.19 15088.06 13491.11 15796.16 11679.92 15098.03 20390.00 14393.80 20397.94 99
E6new91.71 12491.55 11992.20 17894.32 23880.62 24994.41 19996.19 15088.06 13491.11 15796.16 11679.92 15098.03 20390.00 14393.80 20397.94 99
E691.71 12491.55 11992.20 17894.32 23880.62 24994.41 19996.19 15088.06 13491.11 15796.16 11679.92 15098.03 20390.00 14393.80 20397.94 99
E591.71 12491.55 11992.20 17894.33 23680.62 24994.41 19996.19 15088.06 13491.11 15796.16 11679.92 15098.03 20390.00 14393.80 20397.94 99
PLCcopyleft84.53 789.06 21688.03 22592.15 18297.27 7882.69 16994.29 21495.44 23279.71 36684.01 33894.18 23676.68 20798.75 11677.28 36293.41 21995.02 275
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet91.70 12891.56 11892.13 18395.88 13080.50 25697.33 895.25 24686.15 20589.76 19395.60 15983.42 9198.32 16787.37 19493.25 22497.56 139
patch_mono-293.74 6594.32 4192.01 18497.54 6678.37 32893.40 27597.19 4488.02 13894.99 5797.21 6288.35 2598.44 15394.07 5498.09 7699.23 1
原ACMM192.01 18497.34 7381.05 22896.81 8878.89 37790.45 17195.92 13682.65 10598.84 10680.68 31098.26 6296.14 229
UniMVSNet (Re)89.80 18889.07 19392.01 18493.60 28684.52 9794.78 17397.47 1689.26 8686.44 26392.32 30382.10 11997.39 27984.81 23180.84 40794.12 319
MG-MVS91.77 11991.70 11292.00 18797.08 8180.03 27593.60 26895.18 25287.85 15090.89 16596.47 10282.06 12198.36 16085.07 22697.04 10997.62 133
EIA-MVS91.95 11191.94 10891.98 18895.16 16680.01 27695.36 12596.73 9888.44 11889.34 20092.16 30883.82 8798.45 15189.35 15997.06 10897.48 144
PVSNet_Blended90.73 15390.32 15391.98 18896.12 11181.25 21892.55 31896.83 8382.04 32289.10 20492.56 29681.04 13798.85 10486.72 20495.91 13995.84 246
guyue91.12 14390.84 14191.96 19094.59 21080.57 25494.87 16493.71 33888.96 10191.14 15495.22 18073.22 26897.76 23192.01 10493.81 20297.54 142
PS-MVSNAJ91.18 14090.92 13891.96 19095.26 16182.60 17692.09 34195.70 20786.27 20191.84 13292.46 29879.70 15998.99 8289.08 16495.86 14194.29 312
TAMVS89.21 20988.29 22091.96 19093.71 28082.62 17593.30 28394.19 31282.22 31687.78 23493.94 24678.83 17496.95 31877.70 35892.98 23396.32 219
SDMVSNet90.19 17189.61 17691.93 19396.00 12283.09 15292.89 30595.98 17788.73 10886.85 25395.20 18472.09 28597.08 30688.90 16989.85 28895.63 256
FA-MVS(test-final)89.66 19188.91 20191.93 19394.57 21480.27 26091.36 36294.74 28784.87 25089.82 19092.61 29574.72 23998.47 14683.97 24793.53 21397.04 180
MVS_Test91.31 13691.11 13391.93 19394.37 23180.14 26593.46 27395.80 19686.46 19691.35 15093.77 25682.21 11698.09 18987.57 18994.95 16597.55 140
NR-MVSNet88.58 23287.47 24191.93 19393.04 30784.16 11294.77 17496.25 14289.05 9480.04 40293.29 27179.02 17297.05 31181.71 29380.05 41794.59 295
HyFIR lowres test88.09 24586.81 25891.93 19396.00 12280.63 24790.01 40495.79 19773.42 45187.68 23692.10 31473.86 25797.96 21680.75 30891.70 25697.19 164
GeoE90.05 17689.43 18191.90 19895.16 16680.37 25995.80 9694.65 29183.90 27187.55 23994.75 20678.18 18897.62 24481.28 29893.63 20997.71 129
thisisatest053088.67 22787.61 23791.86 19994.87 18580.07 27094.63 18389.90 44384.00 26988.46 21893.78 25566.88 35298.46 14783.30 25792.65 24197.06 178
xiu_mvs_v2_base91.13 14290.89 14091.86 19994.97 17782.42 18092.24 33495.64 21586.11 20991.74 13993.14 27779.67 16498.89 9889.06 16595.46 15494.28 313
DU-MVS89.34 20888.50 21291.85 20193.04 30783.72 12494.47 19496.59 11089.50 7586.46 26093.29 27177.25 20097.23 29584.92 22881.02 40394.59 295
AstraMVS90.69 15590.30 15491.84 20293.81 27179.85 28394.76 17592.39 37088.96 10191.01 16495.87 14270.69 30097.94 21992.49 8292.70 24097.73 127
OPM-MVS90.12 17289.56 17791.82 20393.14 29983.90 11994.16 22195.74 20188.96 10187.86 22995.43 16972.48 27897.91 22288.10 18190.18 28093.65 353
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS90.60 16390.19 15691.82 20394.70 20182.73 16695.85 9396.22 14690.81 2786.91 24994.86 20174.23 24798.12 17988.15 17789.99 28294.63 292
UniMVSNet_NR-MVSNet89.92 18489.29 18791.81 20593.39 29283.72 12494.43 19797.12 5589.80 6386.46 26093.32 26883.16 9597.23 29584.92 22881.02 40394.49 305
diffmvspermissive91.37 13591.23 13191.77 20693.09 30280.27 26092.36 32495.52 22487.03 17891.40 14894.93 19680.08 14797.44 26692.13 9994.56 17897.61 134
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 13291.44 12591.73 20793.09 30280.27 26092.51 31995.58 21887.22 17191.80 13595.57 16179.96 14997.48 25892.23 9394.97 16497.45 146
1112_ss88.42 23487.33 24491.72 20894.92 18180.98 23192.97 30294.54 29578.16 39583.82 34193.88 25178.78 17697.91 22279.45 33789.41 29596.26 223
Fast-Effi-MVS+89.41 20388.64 20791.71 20994.74 19580.81 24293.54 26995.10 25683.11 29486.82 25590.67 36879.74 15897.75 23580.51 31393.55 21196.57 212
WTY-MVS89.60 19388.92 20091.67 21095.47 15181.15 22392.38 32394.78 28583.11 29489.06 20694.32 22878.67 17896.61 34181.57 29490.89 26997.24 158
TAPA-MVS84.62 688.16 24387.01 25391.62 21196.64 9080.65 24694.39 20596.21 14976.38 41886.19 27095.44 16779.75 15798.08 19262.75 46695.29 15996.13 230
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet89.62 19288.96 19891.60 21293.86 26882.89 16195.46 12197.33 3287.91 14588.43 21993.31 26974.17 25097.40 27687.32 19582.86 37894.52 300
FE-MVS87.40 27386.02 29491.57 21394.56 21579.69 29190.27 39193.72 33780.57 35588.80 21291.62 33465.32 36898.59 13774.97 38894.33 18796.44 215
hybridnocas0790.93 14790.72 14591.54 21492.75 32279.72 28992.35 32695.21 25086.41 19890.44 17495.40 17079.17 17197.39 27990.83 13293.94 19797.50 143
XVG-OURS89.40 20588.70 20691.52 21594.06 25581.46 21291.27 36796.07 17086.14 20688.89 21195.77 15168.73 33797.26 29287.39 19389.96 28495.83 247
hse-mvs289.88 18689.34 18591.51 21694.83 18881.12 22593.94 24493.91 32589.80 6393.08 9093.60 26175.77 22197.66 23992.07 10077.07 43795.74 251
TranMVSNet+NR-MVSNet88.84 22287.95 22891.49 21792.68 32583.01 15794.92 16196.31 13189.88 5785.53 28693.85 25376.63 20896.96 31781.91 28679.87 42094.50 303
AUN-MVS87.78 25386.54 27391.48 21894.82 18981.05 22893.91 24893.93 32283.00 29986.93 24793.53 26369.50 32297.67 23786.14 21077.12 43695.73 253
XVG-OURS-SEG-HR89.95 18289.45 17991.47 21994.00 26181.21 22191.87 34696.06 17285.78 21388.55 21695.73 15374.67 24097.27 29088.71 17389.64 29395.91 241
MVS87.44 27186.10 29191.44 22092.61 32783.62 12992.63 31595.66 21267.26 47881.47 38092.15 30977.95 19198.22 17379.71 32695.48 15292.47 404
hybrid90.69 15590.45 15091.43 22192.67 32679.42 29992.28 33395.21 25085.15 24190.39 17595.37 17278.93 17397.32 28590.27 14193.74 20797.55 140
viewdifsd2359ckpt0791.11 14491.02 13691.41 22294.21 24878.37 32892.91 30495.71 20687.50 16290.32 17695.88 13980.27 14597.99 20988.78 17293.55 21197.86 114
F-COLMAP87.95 24886.80 25991.40 22396.35 10480.88 23794.73 17795.45 23079.65 36782.04 37594.61 21571.13 29298.50 14176.24 37591.05 26794.80 289
dcpmvs_293.49 7094.19 5291.38 22497.69 6376.78 37294.25 21696.29 13288.33 12194.46 6096.88 7988.07 2998.64 13093.62 6298.09 7698.73 23
thisisatest051587.33 27685.99 29591.37 22593.49 28879.55 29290.63 38389.56 45180.17 35987.56 23890.86 35867.07 34998.28 16981.50 29593.02 23296.29 221
HQP-MVS89.80 18889.28 18891.34 22694.17 25081.56 20694.39 20596.04 17388.81 10485.43 29593.97 24573.83 25897.96 21687.11 19989.77 29194.50 303
fmvsm_s_conf0.5_n_793.15 8993.76 6891.31 22794.42 22979.48 29494.52 18997.14 5389.33 8294.17 6698.09 1881.83 12697.49 25796.33 2698.02 8096.95 188
RRT-MVS90.85 14990.70 14691.30 22894.25 24576.83 37194.85 16796.13 16489.04 9590.23 17894.88 19970.15 31198.72 12091.86 11294.88 16798.34 49
FMVSNet387.40 27386.11 29091.30 22893.79 27483.64 12894.20 22094.81 28383.89 27284.37 32591.87 32568.45 34096.56 35078.23 35385.36 34693.70 352
FMVSNet287.19 28685.82 30391.30 22894.01 25883.67 12694.79 17294.94 26983.57 28083.88 34092.05 31866.59 35796.51 35477.56 36085.01 34993.73 350
RPMNet83.95 36681.53 37791.21 23190.58 40479.34 30485.24 46996.76 9371.44 46585.55 28482.97 47070.87 29798.91 9761.01 47089.36 29795.40 262
IB-MVS80.51 1585.24 34283.26 36191.19 23292.13 33979.86 28291.75 35091.29 40783.28 29180.66 39288.49 41761.28 40798.46 14780.99 30479.46 42495.25 268
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 19888.90 20291.18 23394.22 24782.07 19092.13 33996.09 16887.90 14685.37 30192.45 29974.38 24597.56 24987.15 19790.43 27593.93 330
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 19988.90 20291.12 23494.47 22381.49 21095.30 13096.14 16186.73 18985.45 29295.16 18669.89 31498.10 18187.70 18689.23 30093.77 346
LGP-MVS_train91.12 23494.47 22381.49 21096.14 16186.73 18985.45 29295.16 18669.89 31498.10 18187.70 18689.23 30093.77 346
ACMM84.12 989.14 21188.48 21591.12 23494.65 20581.22 22095.31 12896.12 16585.31 23385.92 27594.34 22670.19 31098.06 19585.65 21888.86 30594.08 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt051788.61 22987.78 23491.11 23794.96 17877.81 34795.35 12689.69 44685.09 24488.05 22794.59 21866.93 35098.48 14383.27 25892.13 25397.03 181
GBi-Net87.26 27885.98 29691.08 23894.01 25883.10 14995.14 14894.94 26983.57 28084.37 32591.64 33066.59 35796.34 36878.23 35385.36 34693.79 341
test187.26 27885.98 29691.08 23894.01 25883.10 14995.14 14894.94 26983.57 28084.37 32591.64 33066.59 35796.34 36878.23 35385.36 34693.79 341
FMVSNet185.85 32784.11 34891.08 23892.81 31983.10 14995.14 14894.94 26981.64 33782.68 36591.64 33059.01 42996.34 36875.37 38283.78 36293.79 341
Test_1112_low_res87.65 25786.51 27491.08 23894.94 18079.28 30891.77 34994.30 30776.04 42483.51 35192.37 30177.86 19497.73 23678.69 34889.13 30296.22 224
PS-MVSNAJss89.97 18089.62 17591.02 24291.90 34880.85 24195.26 13695.98 17786.26 20286.21 26994.29 23079.70 15997.65 24088.87 17188.10 31694.57 297
BH-RMVSNet88.37 23787.48 24091.02 24295.28 15879.45 29692.89 30593.07 35385.45 22886.91 24994.84 20470.35 30797.76 23173.97 39794.59 17795.85 245
UniMVSNet_ETH3D87.53 26786.37 27891.00 24492.44 33178.96 31394.74 17695.61 21684.07 26885.36 30294.52 22059.78 42197.34 28382.93 26287.88 32196.71 205
FIs90.51 16590.35 15290.99 24593.99 26280.98 23195.73 10497.54 989.15 9086.72 25694.68 20981.83 12697.24 29485.18 22488.31 31594.76 290
ACMP84.23 889.01 22088.35 21690.99 24594.73 19681.27 21795.07 15195.89 18986.48 19483.67 34694.30 22969.33 32497.99 20987.10 20188.55 30793.72 351
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous2023121186.59 31085.13 32590.98 24796.52 9881.50 20896.14 6496.16 15973.78 44783.65 34792.15 30963.26 38997.37 28282.82 26681.74 39294.06 324
IMVS_040389.97 18089.64 17490.96 24893.72 27677.75 35293.00 29995.34 24185.53 22488.77 21394.49 22178.49 18497.84 22784.75 23292.65 24197.28 153
sss88.93 22188.26 22290.94 24994.05 25680.78 24491.71 35195.38 23681.55 34188.63 21593.91 25075.04 23395.47 40982.47 27191.61 25796.57 212
IMVS_040789.85 18789.51 17890.88 25093.72 27677.75 35293.07 29695.34 24185.53 22488.34 22194.49 22177.69 19697.60 24584.75 23292.65 24197.28 153
dtuplus89.78 19089.43 18190.85 25192.83 31877.91 34192.32 33194.97 26682.33 31490.20 17995.53 16378.56 18197.38 28185.15 22592.95 23497.24 158
viewmambaseed2359dif90.04 17789.78 17190.83 25292.85 31777.92 34092.23 33595.01 26081.90 32790.20 17995.45 16679.64 16697.34 28387.52 19193.17 22697.23 162
sd_testset88.59 23187.85 23390.83 25296.00 12280.42 25892.35 32694.71 28888.73 10886.85 25395.20 18467.31 34496.43 36279.64 32989.85 28895.63 256
PVSNet_BlendedMVS89.98 17989.70 17290.82 25496.12 11181.25 21893.92 24696.83 8383.49 28489.10 20492.26 30681.04 13798.85 10486.72 20487.86 32292.35 411
cascas86.43 31884.98 32890.80 25592.10 34180.92 23590.24 39595.91 18673.10 45483.57 35088.39 41865.15 37097.46 26284.90 23091.43 25994.03 326
ECVR-MVScopyleft89.09 21488.53 21090.77 25695.62 14475.89 38596.16 6084.22 48087.89 14890.20 17996.65 9163.19 39198.10 18185.90 21596.94 11298.33 51
GA-MVS86.61 30885.27 32290.66 25791.33 37178.71 31790.40 39093.81 33285.34 23285.12 30589.57 39961.25 40897.11 30480.99 30489.59 29496.15 228
thres600view787.65 25786.67 26590.59 25896.08 11778.72 31594.88 16391.58 39787.06 17788.08 22592.30 30468.91 33498.10 18170.05 42991.10 26294.96 279
thres40087.62 26286.64 26690.57 25995.99 12578.64 31894.58 18591.98 38686.94 18388.09 22391.77 32669.18 33098.10 18170.13 42691.10 26294.96 279
baseline188.10 24487.28 24690.57 25994.96 17880.07 27094.27 21591.29 40786.74 18887.41 24094.00 24376.77 20596.20 37380.77 30779.31 42695.44 260
viewdifsd2359ckpt1189.43 20189.05 19590.56 26192.89 31577.00 36792.81 30894.52 29687.03 17889.77 19195.79 14874.67 24097.51 25388.97 16784.98 35097.17 165
viewmsd2359difaftdt89.43 20189.05 19590.56 26192.89 31577.00 36792.81 30894.52 29687.03 17889.77 19195.79 14874.67 24097.51 25388.97 16784.98 35097.17 165
usedtu_dtu_shiyan186.84 29785.61 31190.53 26390.50 40881.80 20090.97 37594.96 26783.05 29683.50 35290.32 37572.15 28296.65 33279.49 33485.55 34493.15 376
FE-MVSNET386.84 29785.61 31190.53 26390.50 40881.80 20090.97 37594.96 26783.05 29683.50 35290.32 37572.15 28296.65 33279.49 33485.55 34493.15 376
FC-MVSNet-test90.27 16990.18 15790.53 26393.71 28079.85 28395.77 10097.59 689.31 8386.27 26794.67 21281.93 12497.01 31484.26 24288.09 31894.71 291
PAPM86.68 30785.39 31790.53 26393.05 30679.33 30789.79 40794.77 28678.82 38081.95 37693.24 27376.81 20397.30 28666.94 44693.16 22794.95 283
WR-MVS88.38 23687.67 23690.52 26793.30 29480.18 26393.26 28695.96 18188.57 11685.47 29192.81 28876.12 21396.91 32181.24 29982.29 38394.47 308
SSM_0407288.57 23387.92 23090.51 26894.76 19282.66 17079.84 49294.64 29285.18 23488.96 20895.00 19376.00 21692.03 46083.74 25293.15 22896.85 197
MVSTER88.84 22288.29 22090.51 26892.95 31280.44 25793.73 25995.01 26084.66 25987.15 24493.12 27872.79 27397.21 29787.86 18387.36 33093.87 335
testdata90.49 27096.40 10177.89 34495.37 23872.51 45993.63 7996.69 8782.08 12097.65 24083.08 25997.39 10195.94 240
test111189.10 21288.64 20790.48 27195.53 14974.97 39596.08 6984.89 47888.13 13190.16 18596.65 9163.29 38898.10 18186.14 21096.90 11598.39 46
tt080586.92 29485.74 30990.48 27192.22 33579.98 27895.63 11494.88 27783.83 27484.74 31492.80 28957.61 43697.67 23785.48 22184.42 35593.79 341
jajsoiax88.24 24187.50 23990.48 27190.89 39280.14 26595.31 12895.65 21484.97 24784.24 33394.02 24165.31 36997.42 26888.56 17488.52 30993.89 331
PatchMatch-RL86.77 30485.54 31390.47 27495.88 13082.71 16890.54 38692.31 37479.82 36584.32 33091.57 33868.77 33696.39 36473.16 40393.48 21792.32 412
0.4-1-1-0.181.55 39878.59 42190.42 27587.55 45179.90 28088.56 43089.19 45677.01 41079.72 40977.71 48654.84 45197.11 30480.50 31472.20 45194.26 314
tfpn200view987.58 26586.64 26690.41 27695.99 12578.64 31894.58 18591.98 38686.94 18388.09 22391.77 32669.18 33098.10 18170.13 42691.10 26294.48 306
VPNet88.20 24287.47 24190.39 27793.56 28779.46 29594.04 23495.54 22288.67 11186.96 24694.58 21969.33 32497.15 29984.05 24680.53 41294.56 298
ACMH80.38 1785.36 33783.68 35590.39 27794.45 22680.63 24794.73 17794.85 27982.09 31877.24 43792.65 29360.01 41997.58 24772.25 40884.87 35292.96 383
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view90087.63 26086.71 26290.38 27996.12 11178.55 32195.03 15591.58 39787.15 17388.06 22692.29 30568.91 33498.10 18170.13 42691.10 26294.48 306
mvs_tets88.06 24787.28 24690.38 27990.94 38879.88 28195.22 13995.66 21285.10 24384.21 33493.94 24663.53 38697.40 27688.50 17588.40 31393.87 335
131487.51 26886.57 27190.34 28192.42 33279.74 28892.63 31595.35 24078.35 39080.14 39991.62 33474.05 25297.15 29981.05 30093.53 21394.12 319
LTVRE_ROB82.13 1386.26 32184.90 33190.34 28194.44 22781.50 20892.31 33294.89 27583.03 29879.63 41192.67 29269.69 31797.79 22971.20 41486.26 33991.72 422
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
0.3-1-1-0.01580.75 41177.58 42690.25 28386.55 45679.72 28987.46 45189.48 45476.43 41777.93 43275.94 48952.31 46397.05 31180.25 31971.85 45593.99 328
test_djsdf89.03 21888.64 20790.21 28490.74 39979.28 30895.96 8395.90 18784.66 25985.33 30392.94 28374.02 25397.30 28689.64 15788.53 30894.05 325
v2v48287.84 25087.06 25090.17 28590.99 38479.23 31194.00 24095.13 25384.87 25085.53 28692.07 31774.45 24497.45 26384.71 23781.75 39193.85 338
pmmvs485.43 33583.86 35390.16 28690.02 41982.97 15990.27 39192.67 36575.93 42580.73 39091.74 32871.05 29395.73 39878.85 34783.46 36991.78 421
V4287.68 25586.86 25590.15 28790.58 40480.14 26594.24 21895.28 24583.66 27885.67 28191.33 34074.73 23897.41 27484.43 24181.83 38992.89 386
MSDG84.86 35083.09 36490.14 28893.80 27280.05 27289.18 42093.09 35278.89 37778.19 42891.91 32365.86 36797.27 29068.47 43588.45 31193.11 378
sc_t181.53 39978.67 42090.12 28990.78 39678.64 31893.91 24890.20 43268.42 47480.82 38989.88 39246.48 47896.76 32676.03 37871.47 45694.96 279
anonymousdsp87.84 25087.09 24990.12 28989.13 43080.54 25594.67 18195.55 22082.05 32083.82 34192.12 31171.47 29097.15 29987.15 19787.80 32592.67 393
thres20087.21 28486.24 28590.12 28995.36 15478.53 32293.26 28692.10 38086.42 19788.00 22891.11 35169.24 32998.00 20869.58 43091.04 26893.83 340
CR-MVSNet85.35 33883.76 35490.12 28990.58 40479.34 30485.24 46991.96 38878.27 39285.55 28487.87 42871.03 29495.61 40173.96 39889.36 29795.40 262
0.4-1-1-0.280.84 41077.77 42490.06 29386.18 46079.35 30286.75 45789.54 45276.23 42278.59 42775.46 49255.03 45096.99 31580.11 32172.05 45393.85 338
v114487.61 26386.79 26090.06 29391.01 38379.34 30493.95 24395.42 23583.36 28985.66 28291.31 34374.98 23497.42 26883.37 25682.06 38593.42 362
XXY-MVS87.65 25786.85 25690.03 29592.14 33880.60 25393.76 25695.23 24782.94 30184.60 31694.02 24174.27 24695.49 40881.04 30183.68 36594.01 327
Vis-MVSNet (Re-imp)89.59 19489.44 18090.03 29595.74 13575.85 38695.61 11590.80 42187.66 15987.83 23295.40 17076.79 20496.46 35978.37 34996.73 12197.80 122
test250687.21 28486.28 28390.02 29795.62 14473.64 41196.25 5571.38 50487.89 14890.45 17196.65 9155.29 44898.09 18986.03 21496.94 11298.33 51
BH-untuned88.60 23088.13 22490.01 29895.24 16278.50 32493.29 28494.15 31584.75 25584.46 32293.40 26575.76 22397.40 27677.59 35994.52 18094.12 319
v119287.25 28086.33 28090.00 29990.76 39879.04 31293.80 25495.48 22582.57 30885.48 29091.18 34773.38 26797.42 26882.30 27582.06 38593.53 356
v7n86.81 29985.76 30789.95 30090.72 40079.25 31095.07 15195.92 18484.45 26282.29 36990.86 35872.60 27797.53 25179.42 34080.52 41393.08 380
testing9187.11 28986.18 28689.92 30194.43 22875.38 39491.53 35792.27 37686.48 19486.50 25890.24 37861.19 41197.53 25182.10 28090.88 27096.84 200
IMVS_040487.60 26486.84 25789.89 30293.72 27677.75 35288.56 43095.34 24185.53 22479.98 40394.49 22166.54 36094.64 42284.75 23292.65 24197.28 153
v887.50 27086.71 26289.89 30291.37 36879.40 30094.50 19095.38 23684.81 25383.60 34991.33 34076.05 21497.42 26882.84 26580.51 41492.84 388
v1087.25 28086.38 27789.85 30491.19 37479.50 29394.48 19195.45 23083.79 27683.62 34891.19 34575.13 23197.42 26881.94 28580.60 40992.63 395
baseline286.50 31485.39 31789.84 30591.12 37976.70 37491.88 34588.58 45882.35 31379.95 40490.95 35673.42 26597.63 24380.27 31889.95 28595.19 269
pm-mvs186.61 30885.54 31389.82 30691.44 36380.18 26395.28 13494.85 27983.84 27381.66 37892.62 29472.45 28096.48 35679.67 32878.06 42992.82 389
TR-MVS86.78 30185.76 30789.82 30694.37 23178.41 32692.47 32092.83 35981.11 35186.36 26492.40 30068.73 33797.48 25873.75 40189.85 28893.57 355
ACMH+81.04 1485.05 34583.46 35889.82 30694.66 20479.37 30194.44 19694.12 31882.19 31778.04 43092.82 28758.23 43297.54 25073.77 40082.90 37792.54 401
EI-MVSNet89.10 21288.86 20489.80 30991.84 35078.30 33193.70 26395.01 26085.73 21587.15 24495.28 17779.87 15697.21 29783.81 25087.36 33093.88 334
gbinet_0.2-2-1-0.0282.59 38080.19 39289.77 31085.23 47180.05 27291.59 35693.52 34177.60 39979.78 40882.87 47263.26 38996.45 36078.93 34568.97 46692.81 390
usedtu_blend_shiyan582.39 38579.93 39989.75 31185.12 47280.08 26892.36 32493.26 34674.29 44279.00 41982.72 47364.29 38096.60 34579.60 33068.75 47092.55 398
v14419287.19 28686.35 27989.74 31290.64 40278.24 33393.92 24695.43 23381.93 32585.51 28891.05 35474.21 24997.45 26382.86 26481.56 39393.53 356
COLMAP_ROBcopyleft80.39 1683.96 36582.04 37489.74 31295.28 15879.75 28794.25 21692.28 37575.17 43278.02 43193.77 25658.60 43197.84 22765.06 45785.92 34091.63 424
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA86.32 32085.18 32489.73 31492.15 33776.60 37591.12 37191.69 39383.53 28385.50 28988.81 41166.79 35396.48 35676.65 36890.35 27796.12 231
blend_shiyan481.94 38879.35 40789.70 31585.52 46780.08 26891.29 36593.82 32977.12 40879.31 41582.94 47154.81 45296.60 34579.60 33069.78 46192.41 407
IterMVS-LS88.36 23887.91 23289.70 31593.80 27278.29 33293.73 25995.08 25885.73 21584.75 31391.90 32479.88 15596.92 32083.83 24982.51 37993.89 331
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
blended_shiyan882.79 37580.49 38589.69 31785.50 46879.83 28591.38 36093.82 32977.14 40579.39 41483.73 46364.95 37496.63 33579.75 32568.77 46992.62 397
testing1186.44 31785.35 32089.69 31794.29 24275.40 39391.30 36490.53 42784.76 25485.06 30790.13 38458.95 43097.45 26382.08 28191.09 26696.21 226
testing9986.72 30585.73 31089.69 31794.23 24674.91 39791.35 36390.97 41586.14 20686.36 26490.22 37959.41 42497.48 25882.24 27790.66 27296.69 207
v192192086.97 29386.06 29389.69 31790.53 40778.11 33693.80 25495.43 23381.90 32785.33 30391.05 35472.66 27497.41 27482.05 28381.80 39093.53 356
icg_test_0407_289.15 21088.97 19789.68 32193.72 27677.75 35288.26 43695.34 24185.53 22488.34 22194.49 22177.69 19693.99 43584.75 23292.65 24197.28 153
blended_shiyan682.78 37680.48 38689.67 32285.53 46679.76 28691.37 36193.82 32977.14 40579.30 41683.73 46364.96 37396.63 33579.68 32768.75 47092.63 395
VortexMVS88.42 23488.01 22689.63 32393.89 26778.82 31493.82 25295.47 22686.67 19184.53 32091.99 32072.62 27696.65 33289.02 16684.09 35993.41 363
Fast-Effi-MVS+-dtu87.44 27186.72 26189.63 32392.04 34277.68 35794.03 23593.94 32185.81 21282.42 36891.32 34270.33 30897.06 30980.33 31790.23 27994.14 318
v124086.78 30185.85 30289.56 32590.45 41177.79 34993.61 26795.37 23881.65 33685.43 29591.15 34971.50 28997.43 26781.47 29682.05 38793.47 360
Effi-MVS+-dtu88.65 22888.35 21689.54 32693.33 29376.39 37994.47 19494.36 30587.70 15685.43 29589.56 40073.45 26397.26 29285.57 22091.28 26194.97 276
wanda-best-256-51282.44 38280.07 39489.53 32785.12 47279.44 29790.49 38793.75 33576.97 41179.00 41982.72 47364.29 38096.61 34179.56 33268.75 47092.55 398
FE-blended-shiyan782.44 38280.07 39489.53 32785.12 47279.44 29790.49 38793.75 33576.97 41179.00 41982.72 47364.29 38096.61 34179.56 33268.75 47092.55 398
AllTest83.42 37281.39 37889.52 32995.01 17277.79 34993.12 29090.89 41977.41 40176.12 44693.34 26654.08 45797.51 25368.31 43784.27 35793.26 366
TestCases89.52 32995.01 17277.79 34990.89 41977.41 40176.12 44693.34 26654.08 45797.51 25368.31 43784.27 35793.26 366
mvs_anonymous89.37 20789.32 18689.51 33193.47 28974.22 40491.65 35494.83 28182.91 30285.45 29293.79 25481.23 13696.36 36786.47 20694.09 19397.94 99
XVG-ACMP-BASELINE86.00 32384.84 33389.45 33291.20 37378.00 33891.70 35295.55 22085.05 24582.97 36292.25 30754.49 45597.48 25882.93 26287.45 32992.89 386
testing22284.84 35183.32 35989.43 33394.15 25375.94 38491.09 37289.41 45584.90 24885.78 27889.44 40152.70 46296.28 37170.80 42091.57 25896.07 235
MVP-Stereo85.97 32484.86 33289.32 33490.92 39082.19 18792.11 34094.19 31278.76 38278.77 42691.63 33368.38 34196.56 35075.01 38793.95 19689.20 464
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNetpermissive85.85 32784.70 33589.29 33591.76 35475.54 39088.49 43291.30 40681.63 33885.05 30888.70 41571.71 28696.24 37274.61 39389.05 30396.08 234
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v14887.04 29186.32 28189.21 33690.94 38877.26 36393.71 26294.43 30084.84 25284.36 32890.80 36276.04 21597.05 31182.12 27979.60 42393.31 365
tfpnnormal84.72 35383.23 36289.20 33792.79 32080.05 27294.48 19195.81 19582.38 31181.08 38691.21 34469.01 33396.95 31861.69 46880.59 41090.58 450
cl2286.78 30185.98 29689.18 33892.34 33377.62 35890.84 37994.13 31781.33 34583.97 33990.15 38373.96 25496.60 34584.19 24382.94 37493.33 364
BH-w/o87.57 26687.05 25189.12 33994.90 18477.90 34392.41 32193.51 34282.89 30383.70 34591.34 33975.75 22497.07 30875.49 38093.49 21592.39 409
WR-MVS_H87.80 25287.37 24389.10 34093.23 29578.12 33595.61 11597.30 3787.90 14683.72 34492.01 31979.65 16596.01 38276.36 37280.54 41193.16 374
miper_enhance_ethall86.90 29586.18 28689.06 34191.66 35977.58 35990.22 39794.82 28279.16 37384.48 32189.10 40579.19 17096.66 33184.06 24582.94 37492.94 384
c3_l87.14 28886.50 27589.04 34292.20 33677.26 36391.22 37094.70 28982.01 32384.34 32990.43 37378.81 17596.61 34183.70 25481.09 40093.25 368
miper_ehance_all_eth87.22 28386.62 26989.02 34392.13 33977.40 36190.91 37894.81 28381.28 34684.32 33090.08 38679.26 16896.62 33883.81 25082.94 37493.04 381
gg-mvs-nofinetune81.77 39279.37 40688.99 34490.85 39477.73 35686.29 46179.63 49174.88 43783.19 36169.05 50260.34 41696.11 37775.46 38194.64 17693.11 378
ETVMVS84.43 35882.92 36888.97 34594.37 23174.67 39891.23 36988.35 46083.37 28886.06 27389.04 40655.38 44695.67 40067.12 44491.34 26096.58 211
pmmvs683.42 37281.60 37688.87 34688.01 44677.87 34594.96 15894.24 31174.67 43878.80 42591.09 35260.17 41896.49 35577.06 36775.40 44392.23 414
test_cas_vis1_n_192088.83 22588.85 20588.78 34791.15 37876.72 37393.85 25194.93 27383.23 29392.81 9996.00 12961.17 41294.45 42391.67 11594.84 16895.17 270
MIMVSNet82.59 38080.53 38388.76 34891.51 36178.32 33086.57 46090.13 43579.32 36980.70 39188.69 41652.98 46193.07 45166.03 45288.86 30594.90 284
cl____86.52 31385.78 30488.75 34992.03 34376.46 37790.74 38094.30 30781.83 33283.34 35790.78 36375.74 22696.57 34881.74 29181.54 39493.22 370
DIV-MVS_self_test86.53 31285.78 30488.75 34992.02 34476.45 37890.74 38094.30 30781.83 33283.34 35790.82 36175.75 22496.57 34881.73 29281.52 39593.24 369
CP-MVSNet87.63 26087.26 24888.74 35193.12 30076.59 37695.29 13296.58 11188.43 11983.49 35492.98 28275.28 23095.83 39178.97 34481.15 39993.79 341
eth_miper_zixun_eth86.50 31485.77 30688.68 35291.94 34575.81 38790.47 38994.89 27582.05 32084.05 33690.46 37275.96 21896.77 32582.76 26879.36 42593.46 361
CHOSEN 280x42085.15 34383.99 35188.65 35392.47 32978.40 32779.68 49492.76 36274.90 43681.41 38289.59 39869.85 31695.51 40579.92 32495.29 15992.03 417
PS-CasMVS87.32 27786.88 25488.63 35492.99 31076.33 38195.33 12796.61 10988.22 12783.30 35993.07 28073.03 27195.79 39578.36 35081.00 40593.75 348
TransMVSNet (Re)84.43 35883.06 36688.54 35591.72 35578.44 32595.18 14592.82 36182.73 30679.67 41092.12 31173.49 26295.96 38471.10 41868.73 47491.21 437
tt0320-xc79.63 42576.66 43488.52 35691.03 38278.72 31593.00 29989.53 45366.37 48076.11 44887.11 43946.36 48095.32 41372.78 40567.67 47591.51 429
EG-PatchMatch MVS82.37 38680.34 38888.46 35790.27 41379.35 30292.80 31194.33 30677.14 40573.26 46590.18 38247.47 47596.72 32770.25 42387.32 33289.30 461
PEN-MVS86.80 30086.27 28488.40 35892.32 33475.71 38995.18 14596.38 12687.97 14082.82 36493.15 27673.39 26695.92 38676.15 37679.03 42893.59 354
Baseline_NR-MVSNet87.07 29086.63 26888.40 35891.44 36377.87 34594.23 21992.57 36784.12 26785.74 28092.08 31577.25 20096.04 37882.29 27679.94 41891.30 435
UBG85.51 33384.57 34088.35 36094.21 24871.78 43690.07 40289.66 44882.28 31585.91 27689.01 40761.30 40697.06 30976.58 37192.06 25496.22 224
D2MVS85.90 32585.09 32688.35 36090.79 39577.42 36091.83 34895.70 20780.77 35480.08 40190.02 38866.74 35596.37 36581.88 28787.97 32091.26 436
pmmvs584.21 36182.84 37188.34 36288.95 43276.94 36992.41 32191.91 39075.63 42780.28 39691.18 34764.59 37795.57 40277.09 36683.47 36892.53 402
tt032080.13 41777.41 42788.29 36390.50 40878.02 33793.10 29390.71 42466.06 48376.75 44186.97 44049.56 47095.40 41071.65 40971.41 45791.46 432
LCM-MVSNet-Re88.30 24088.32 21988.27 36494.71 20072.41 43193.15 28990.98 41487.77 15379.25 41791.96 32178.35 18695.75 39683.04 26095.62 14796.65 208
CostFormer85.77 33084.94 33088.26 36591.16 37772.58 42989.47 41591.04 41376.26 42186.45 26289.97 39070.74 29996.86 32482.35 27487.07 33595.34 266
ITE_SJBPF88.24 36691.88 34977.05 36692.92 35685.54 22280.13 40093.30 27057.29 43796.20 37372.46 40784.71 35391.49 430
PVSNet78.82 1885.55 33284.65 33688.23 36794.72 19871.93 43287.12 45492.75 36378.80 38184.95 31090.53 37064.43 37896.71 32974.74 39093.86 19996.06 237
IterMVS-SCA-FT85.45 33484.53 34188.18 36891.71 35676.87 37090.19 39992.65 36685.40 23181.44 38190.54 36966.79 35395.00 41981.04 30181.05 40192.66 394
EPNet_dtu86.49 31685.94 29988.14 36990.24 41472.82 42194.11 22592.20 37886.66 19279.42 41392.36 30273.52 26195.81 39371.26 41393.66 20895.80 249
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry82.71 37880.93 38288.06 37090.05 41876.37 38084.74 47491.96 38872.28 46281.32 38487.87 42871.03 29495.50 40768.97 43280.15 41692.32 412
test_vis1_n_192089.39 20689.84 16888.04 37192.97 31172.64 42694.71 17996.03 17586.18 20491.94 12896.56 9961.63 40195.74 39793.42 6595.11 16395.74 251
DTE-MVSNet86.11 32285.48 31587.98 37291.65 36074.92 39694.93 16095.75 20087.36 16882.26 37093.04 28172.85 27295.82 39274.04 39677.46 43493.20 372
PMMVS85.71 33184.96 32987.95 37388.90 43377.09 36588.68 42890.06 43772.32 46186.47 25990.76 36472.15 28294.40 42681.78 29093.49 21592.36 410
GG-mvs-BLEND87.94 37489.73 42577.91 34187.80 44278.23 49680.58 39383.86 46159.88 42095.33 41271.20 41492.22 25290.60 449
MonoMVSNet86.89 29686.55 27287.92 37589.46 42873.75 40894.12 22393.10 35187.82 15285.10 30690.76 36469.59 31994.94 42086.47 20682.50 38095.07 273
reproduce_monomvs86.37 31985.87 30187.87 37693.66 28473.71 40993.44 27495.02 25988.61 11482.64 36791.94 32257.88 43496.68 33089.96 14779.71 42293.22 370
pmmvs-eth3d80.97 40878.72 41987.74 37784.99 47579.97 27990.11 40191.65 39575.36 42973.51 46386.03 44959.45 42393.96 43875.17 38472.21 45089.29 463
MS-PatchMatch85.05 34584.16 34687.73 37891.42 36678.51 32391.25 36893.53 34077.50 40080.15 39891.58 33661.99 39895.51 40575.69 37994.35 18589.16 465
mmtdpeth85.04 34784.15 34787.72 37993.11 30175.74 38894.37 20992.83 35984.98 24689.31 20186.41 44661.61 40397.14 30292.63 8162.11 48690.29 451
test_040281.30 40479.17 41287.67 38093.19 29678.17 33492.98 30191.71 39175.25 43176.02 44990.31 37759.23 42596.37 36550.22 49183.63 36688.47 474
IterMVS84.88 34983.98 35287.60 38191.44 36376.03 38390.18 40092.41 36983.24 29281.06 38790.42 37466.60 35694.28 43079.46 33680.98 40692.48 403
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test81.37 40279.30 40887.58 38290.92 39074.16 40680.99 48787.68 46570.52 46976.63 44388.81 41171.21 29192.76 45560.01 47586.93 33695.83 247
EPMVS83.90 36882.70 37287.51 38390.23 41572.67 42488.62 42981.96 48681.37 34485.01 30988.34 41966.31 36194.45 42375.30 38387.12 33395.43 261
ADS-MVSNet281.66 39579.71 40387.50 38491.35 36974.19 40583.33 48088.48 45972.90 45682.24 37185.77 45364.98 37193.20 44964.57 45983.74 36395.12 271
OurMVSNet-221017-085.35 33884.64 33887.49 38590.77 39772.59 42894.01 23894.40 30384.72 25679.62 41293.17 27561.91 39996.72 32781.99 28481.16 39793.16 374
tpm284.08 36382.94 36787.48 38691.39 36771.27 44189.23 41990.37 42971.95 46384.64 31589.33 40267.30 34596.55 35275.17 38487.09 33494.63 292
RPSCF85.07 34484.27 34387.48 38692.91 31470.62 45091.69 35392.46 36876.20 42382.67 36695.22 18063.94 38497.29 28977.51 36185.80 34194.53 299
myMVS_eth3d2885.80 32985.26 32387.42 38894.73 19669.92 45690.60 38490.95 41687.21 17286.06 27390.04 38759.47 42296.02 38074.89 38993.35 22396.33 218
FE-MVSNET281.82 39179.99 39787.34 38984.74 47677.36 36292.72 31294.55 29482.09 31873.79 46286.46 44357.80 43594.45 42374.65 39173.10 44590.20 452
WBMVS84.97 34884.18 34587.34 38994.14 25471.62 44090.20 39892.35 37181.61 33984.06 33590.76 36461.82 40096.52 35378.93 34583.81 36193.89 331
miper_lstm_enhance85.27 34184.59 33987.31 39191.28 37274.63 39987.69 44794.09 31981.20 35081.36 38389.85 39474.97 23594.30 42981.03 30379.84 42193.01 382
FMVSNet581.52 40079.60 40487.27 39291.17 37577.95 33991.49 35892.26 37776.87 41376.16 44587.91 42751.67 46492.34 45867.74 44181.16 39791.52 428
USDC82.76 37781.26 38087.26 39391.17 37574.55 40089.27 41793.39 34478.26 39375.30 45392.08 31554.43 45696.63 33571.64 41085.79 34290.61 447
test-LLR85.87 32685.41 31687.25 39490.95 38671.67 43889.55 41189.88 44483.41 28684.54 31887.95 42567.25 34695.11 41681.82 28893.37 22194.97 276
test-mter84.54 35783.64 35687.25 39490.95 38671.67 43889.55 41189.88 44479.17 37284.54 31887.95 42555.56 44395.11 41681.82 28893.37 22194.97 276
JIA-IIPM81.04 40578.98 41787.25 39488.64 43473.48 41381.75 48689.61 45073.19 45382.05 37473.71 49666.07 36695.87 38971.18 41684.60 35492.41 407
TDRefinement79.81 42177.34 42887.22 39779.24 49375.48 39193.12 29092.03 38376.45 41675.01 45491.58 33649.19 47196.44 36170.22 42569.18 46589.75 457
tpmvs83.35 37482.07 37387.20 39891.07 38171.00 44788.31 43591.70 39278.91 37580.49 39587.18 43769.30 32797.08 30668.12 44083.56 36793.51 359
ppachtmachnet_test81.84 39080.07 39487.15 39988.46 43874.43 40389.04 42392.16 37975.33 43077.75 43488.99 40866.20 36395.37 41165.12 45677.60 43291.65 423
dmvs_re84.20 36283.22 36387.14 40091.83 35277.81 34790.04 40390.19 43384.70 25881.49 37989.17 40464.37 37991.13 47171.58 41185.65 34392.46 405
tpm cat181.96 38780.27 38987.01 40191.09 38071.02 44687.38 45291.53 40066.25 48180.17 39786.35 44868.22 34296.15 37669.16 43182.29 38393.86 337
test_fmvs1_n87.03 29287.04 25286.97 40289.74 42471.86 43394.55 18794.43 30078.47 38791.95 12795.50 16551.16 46693.81 43993.02 7394.56 17895.26 267
OpenMVS_ROBcopyleft74.94 1979.51 42677.03 43386.93 40387.00 45376.23 38292.33 32990.74 42368.93 47374.52 45888.23 42249.58 46996.62 33857.64 48184.29 35687.94 477
SixPastTwentyTwo83.91 36782.90 36986.92 40490.99 38470.67 44993.48 27191.99 38585.54 22277.62 43692.11 31360.59 41596.87 32376.05 37777.75 43193.20 372
ADS-MVSNet81.56 39779.78 40086.90 40591.35 36971.82 43483.33 48089.16 45772.90 45682.24 37185.77 45364.98 37193.76 44064.57 45983.74 36395.12 271
PatchT82.68 37981.27 37986.89 40690.09 41770.94 44884.06 47790.15 43474.91 43585.63 28383.57 46569.37 32394.87 42165.19 45488.50 31094.84 286
tpm84.73 35284.02 35086.87 40790.33 41268.90 45989.06 42289.94 44180.85 35385.75 27989.86 39368.54 33995.97 38377.76 35784.05 36095.75 250
Patchmatch-RL test81.67 39479.96 39886.81 40885.42 46971.23 44282.17 48587.50 46678.47 38777.19 43882.50 47770.81 29893.48 44482.66 26972.89 44895.71 254
test_vis1_n86.56 31186.49 27686.78 40988.51 43572.69 42394.68 18093.78 33479.55 36890.70 16695.31 17648.75 47293.28 44793.15 6993.99 19594.38 310
testing3-286.72 30586.71 26286.74 41096.11 11465.92 47293.39 27689.65 44989.46 7687.84 23192.79 29059.17 42797.60 24581.31 29790.72 27196.70 206
test_fmvs187.34 27587.56 23886.68 41190.59 40371.80 43594.01 23894.04 32078.30 39191.97 12595.22 18056.28 44193.71 44192.89 7494.71 17194.52 300
MDA-MVSNet-bldmvs78.85 43176.31 43686.46 41289.76 42373.88 40788.79 42690.42 42879.16 37359.18 49188.33 42060.20 41794.04 43362.00 46768.96 46791.48 431
mvs5depth80.98 40779.15 41386.45 41384.57 47773.29 41687.79 44391.67 39480.52 35682.20 37389.72 39655.14 44995.93 38573.93 39966.83 47790.12 454
tpmrst85.35 33884.99 32786.43 41490.88 39367.88 46588.71 42791.43 40480.13 36086.08 27288.80 41373.05 27096.02 38082.48 27083.40 37195.40 262
TESTMET0.1,183.74 37082.85 37086.42 41589.96 42071.21 44389.55 41187.88 46277.41 40183.37 35687.31 43356.71 43993.65 44380.62 31192.85 23894.40 309
our_test_381.93 38980.46 38786.33 41688.46 43873.48 41388.46 43391.11 40976.46 41576.69 44288.25 42166.89 35194.36 42768.75 43379.08 42791.14 439
lessismore_v086.04 41788.46 43868.78 46080.59 48973.01 46790.11 38555.39 44596.43 36275.06 38665.06 48192.90 385
TinyColmap79.76 42277.69 42585.97 41891.71 35673.12 41789.55 41190.36 43075.03 43372.03 47090.19 38146.22 48196.19 37563.11 46381.03 40288.59 473
KD-MVS_2432*160078.50 43276.02 44085.93 41986.22 45874.47 40184.80 47292.33 37279.29 37076.98 43985.92 45053.81 45993.97 43667.39 44257.42 49189.36 459
miper_refine_blended78.50 43276.02 44085.93 41986.22 45874.47 40184.80 47292.33 37279.29 37076.98 43985.92 45053.81 45993.97 43667.39 44257.42 49189.36 459
K. test v381.59 39680.15 39385.91 42189.89 42269.42 45892.57 31787.71 46485.56 22173.44 46489.71 39755.58 44295.52 40477.17 36469.76 46292.78 391
SSC-MVS3.284.60 35684.19 34485.85 42292.74 32368.07 46288.15 43893.81 33287.42 16683.76 34391.07 35362.91 39395.73 39874.56 39483.24 37293.75 348
mvsany_test185.42 33685.30 32185.77 42387.95 44875.41 39287.61 45080.97 48876.82 41488.68 21495.83 14577.44 19990.82 47485.90 21586.51 33791.08 443
MIMVSNet179.38 42777.28 42985.69 42486.35 45773.67 41091.61 35592.75 36378.11 39672.64 46888.12 42348.16 47391.97 46460.32 47277.49 43391.43 433
UWE-MVS83.69 37183.09 36485.48 42593.06 30565.27 47790.92 37786.14 47079.90 36386.26 26890.72 36757.17 43895.81 39371.03 41992.62 24695.35 265
UnsupCasMVSNet_eth80.07 41878.27 42385.46 42685.24 47072.63 42788.45 43494.87 27882.99 30071.64 47388.07 42456.34 44091.75 46673.48 40263.36 48492.01 418
CL-MVSNet_self_test81.74 39380.53 38385.36 42785.96 46172.45 43090.25 39393.07 35381.24 34879.85 40787.29 43470.93 29692.52 45666.95 44569.23 46491.11 441
MDA-MVSNet_test_wron79.21 42977.19 43185.29 42888.22 44372.77 42285.87 46390.06 43774.34 44062.62 48887.56 43166.14 36491.99 46366.90 44973.01 44691.10 442
YYNet179.22 42877.20 43085.28 42988.20 44472.66 42585.87 46390.05 43974.33 44162.70 48687.61 43066.09 36592.03 46066.94 44672.97 44791.15 438
WB-MVSnew83.77 36983.28 36085.26 43091.48 36271.03 44591.89 34487.98 46178.91 37584.78 31290.22 37969.11 33294.02 43464.70 45890.44 27490.71 445
dp81.47 40180.23 39085.17 43189.92 42165.49 47586.74 45890.10 43676.30 42081.10 38587.12 43862.81 39495.92 38668.13 43979.88 41994.09 322
UnsupCasMVSNet_bld76.23 44273.27 44685.09 43283.79 47972.92 41985.65 46693.47 34371.52 46468.84 47979.08 48449.77 46893.21 44866.81 45060.52 48889.13 467
usedtu_dtu_shiyan274.72 44471.30 44984.98 43377.78 49570.58 45191.85 34790.76 42267.24 47968.06 48182.17 47837.13 49092.78 45460.69 47166.03 47891.59 427
SD_040384.71 35484.65 33684.92 43492.95 31265.95 47192.07 34393.23 34883.82 27579.03 41893.73 25973.90 25592.91 45363.02 46590.05 28195.89 243
Anonymous2023120681.03 40679.77 40284.82 43587.85 44970.26 45391.42 35992.08 38173.67 44877.75 43489.25 40362.43 39693.08 45061.50 46982.00 38891.12 440
FE-MVSNET78.19 43476.03 43984.69 43683.70 48073.31 41590.58 38590.00 44077.11 40971.91 47185.47 45555.53 44491.94 46559.69 47670.24 45988.83 469
test0.0.03 182.41 38481.69 37584.59 43788.23 44272.89 42090.24 39587.83 46383.41 28679.86 40689.78 39567.25 34688.99 48465.18 45583.42 37091.90 420
CMPMVSbinary59.16 2180.52 41279.20 41184.48 43883.98 47867.63 46889.95 40693.84 32864.79 48566.81 48291.14 35057.93 43395.17 41476.25 37488.10 31690.65 446
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet84.69 35584.79 33484.37 43991.84 35064.92 47893.70 26391.47 40366.19 48286.16 27195.28 17767.18 34893.33 44680.89 30690.42 27694.88 285
PVSNet_073.20 2077.22 43874.83 44484.37 43990.70 40171.10 44483.09 48289.67 44772.81 45873.93 46183.13 46760.79 41493.70 44268.54 43450.84 49788.30 475
LF4IMVS80.37 41579.07 41584.27 44186.64 45469.87 45789.39 41691.05 41276.38 41874.97 45590.00 38947.85 47494.25 43174.55 39580.82 40888.69 471
Anonymous2024052180.44 41479.21 41084.11 44285.75 46467.89 46492.86 30793.23 34875.61 42875.59 45287.47 43250.03 46794.33 42871.14 41781.21 39690.12 454
PM-MVS78.11 43576.12 43884.09 44383.54 48170.08 45488.97 42485.27 47779.93 36274.73 45786.43 44534.70 49393.48 44479.43 33972.06 45288.72 470
dtuonly84.33 36084.48 34283.87 44486.63 45563.54 48386.79 45691.48 40278.02 39783.20 36093.56 26269.53 32194.11 43279.08 34392.02 25593.97 329
test_fmvs283.98 36484.03 34983.83 44587.16 45267.53 46993.93 24592.89 35777.62 39886.89 25293.53 26347.18 47692.02 46290.54 13686.51 33791.93 419
testgi80.94 40980.20 39183.18 44687.96 44766.29 47091.28 36690.70 42583.70 27778.12 42992.84 28551.37 46590.82 47463.34 46282.46 38192.43 406
KD-MVS_self_test80.20 41679.24 40983.07 44785.64 46565.29 47691.01 37493.93 32278.71 38476.32 44486.40 44759.20 42692.93 45272.59 40669.35 46391.00 444
testing380.46 41379.59 40583.06 44893.44 29164.64 47993.33 27885.47 47584.34 26479.93 40590.84 36044.35 48492.39 45757.06 48387.56 32692.16 416
ambc83.06 44879.99 49163.51 48477.47 49592.86 35874.34 46084.45 46028.74 49495.06 41873.06 40468.89 46890.61 447
test20.0379.95 42079.08 41482.55 45085.79 46367.74 46791.09 37291.08 41081.23 34974.48 45989.96 39161.63 40190.15 47660.08 47376.38 43989.76 456
MVStest172.91 44769.70 45282.54 45178.14 49473.05 41888.21 43786.21 46960.69 48964.70 48490.53 37046.44 47985.70 49258.78 47953.62 49488.87 468
test_vis1_rt77.96 43676.46 43582.48 45285.89 46271.74 43790.25 39378.89 49271.03 46871.30 47481.35 48042.49 48691.05 47284.55 23982.37 38284.65 481
EU-MVSNet81.32 40380.95 38182.42 45388.50 43763.67 48293.32 27991.33 40564.02 48680.57 39492.83 28661.21 41092.27 45976.34 37380.38 41591.32 434
myMVS_eth3d79.67 42378.79 41882.32 45491.92 34664.08 48089.75 40987.40 46781.72 33478.82 42387.20 43545.33 48291.29 46959.09 47887.84 32391.60 425
ttmdpeth76.55 44074.64 44582.29 45582.25 48667.81 46689.76 40885.69 47370.35 47075.76 45091.69 32946.88 47789.77 47866.16 45163.23 48589.30 461
dtuonlycased79.67 42379.05 41681.54 45688.34 44168.44 46188.96 42590.65 42678.48 38673.21 46685.88 45263.18 39291.00 47370.40 42172.32 44985.19 480
pmmvs371.81 45068.71 45381.11 45775.86 49770.42 45286.74 45883.66 48158.95 49268.64 48080.89 48236.93 49189.52 48063.10 46463.59 48383.39 482
Syy-MVS80.07 41879.78 40080.94 45891.92 34659.93 49189.75 40987.40 46781.72 33478.82 42387.20 43566.29 36291.29 46947.06 49687.84 32391.60 425
UWE-MVS-2878.98 43078.38 42280.80 45988.18 44560.66 49090.65 38278.51 49378.84 37977.93 43290.93 35759.08 42889.02 48350.96 48990.33 27892.72 392
new-patchmatchnet76.41 44175.17 44380.13 46082.65 48559.61 49287.66 44891.08 41078.23 39469.85 47783.22 46654.76 45391.63 46864.14 46164.89 48289.16 465
mvsany_test374.95 44373.26 44780.02 46174.61 49863.16 48585.53 46778.42 49474.16 44374.89 45686.46 44336.02 49289.09 48282.39 27366.91 47687.82 478
test_fmvs377.67 43777.16 43279.22 46279.52 49261.14 48792.34 32891.64 39673.98 44578.86 42286.59 44227.38 49787.03 48688.12 18075.97 44189.50 458
DSMNet-mixed76.94 43976.29 43778.89 46383.10 48356.11 50087.78 44479.77 49060.65 49075.64 45188.71 41461.56 40488.34 48560.07 47489.29 29992.21 415
EGC-MVSNET61.97 45956.37 46478.77 46489.63 42673.50 41289.12 42182.79 4830.21 5451.24 54684.80 45839.48 48790.04 47744.13 49875.94 44272.79 497
ArgMatch-SfM70.39 45167.69 45578.49 46581.44 48760.73 48884.71 47575.65 50368.09 47666.71 48386.79 44120.42 50386.05 49171.50 41253.87 49388.67 472
new_pmnet72.15 44870.13 45178.20 46682.95 48465.68 47383.91 47882.40 48562.94 48864.47 48579.82 48342.85 48586.26 49057.41 48274.44 44482.65 486
MVS-HIRNet73.70 44672.20 44878.18 46791.81 35356.42 49982.94 48382.58 48455.24 49368.88 47866.48 50455.32 44795.13 41558.12 48088.42 31283.01 484
LCM-MVSNet66.00 45662.16 46177.51 46864.51 51358.29 49483.87 47990.90 41848.17 49854.69 49473.31 49716.83 50786.75 48765.47 45361.67 48787.48 479
APD_test169.04 45266.26 45877.36 46980.51 49062.79 48685.46 46883.51 48254.11 49559.14 49284.79 45923.40 50089.61 47955.22 48470.24 45979.68 491
test_f71.95 44970.87 45075.21 47074.21 50159.37 49385.07 47185.82 47265.25 48470.42 47683.13 46723.62 49882.93 49878.32 35171.94 45483.33 483
ANet_high58.88 46354.22 46872.86 47156.50 51856.67 49680.75 48886.00 47173.09 45537.39 50964.63 50822.17 50179.49 50243.51 49923.96 51282.43 487
test_vis3_rt65.12 45762.60 45972.69 47271.44 50360.71 48987.17 45365.55 50663.80 48753.22 49565.65 50714.54 50889.44 48176.65 36865.38 48067.91 504
LoFTR57.22 46652.62 47071.00 47372.03 50248.57 50672.00 50370.08 50544.40 50340.92 50776.42 4888.12 51282.76 49942.28 50247.33 50081.66 488
FPMVS64.63 45862.55 46070.88 47470.80 50456.71 49584.42 47684.42 47951.78 49649.57 49681.61 47923.49 49981.48 50040.61 50476.25 44074.46 496
dmvs_testset74.57 44575.81 44270.86 47587.72 45040.47 51587.05 45577.90 49882.75 30571.15 47585.47 45567.98 34384.12 49645.26 49776.98 43888.00 476
DenseAffine56.77 46752.17 47170.54 47674.27 49953.25 50277.23 49650.43 51449.87 49747.26 50177.37 4877.99 51379.10 50350.35 49034.79 50679.28 492
N_pmnet68.89 45368.44 45470.23 47789.07 43128.79 52488.06 43919.50 52469.47 47271.86 47284.93 45761.24 40991.75 46654.70 48577.15 43590.15 453
testf159.54 46156.11 46569.85 47869.28 50556.61 49780.37 48976.55 50142.58 50545.68 50275.61 49011.26 50984.18 49443.20 50060.44 48968.75 502
APD_test259.54 46156.11 46569.85 47869.28 50556.61 49780.37 48976.55 50142.58 50545.68 50275.61 49011.26 50984.18 49443.20 50060.44 48968.75 502
WB-MVS67.92 45467.49 45669.21 48081.09 48841.17 51488.03 44078.00 49773.50 45062.63 48783.11 46963.94 38486.52 48825.66 51251.45 49679.94 490
PMMVS259.60 46056.40 46369.21 48068.83 50746.58 50773.02 50277.48 49955.07 49449.21 49772.95 49817.43 50680.04 50149.32 49344.33 50180.99 489
SSC-MVS67.06 45566.56 45768.56 48280.54 48940.06 51687.77 44577.37 50072.38 46061.75 48982.66 47663.37 38786.45 48924.48 51348.69 49979.16 493
Gipumacopyleft57.99 46554.91 46767.24 48388.51 43565.59 47452.21 51090.33 43143.58 50442.84 50551.18 51320.29 50485.07 49334.77 50670.45 45851.05 512
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
RoMa-SfM53.80 46849.39 47267.06 48467.87 50948.86 50475.04 49738.06 52047.23 50047.40 50078.96 4857.40 51476.66 50548.89 49433.62 50775.64 495
DKM50.92 47246.13 47665.30 48566.27 51145.98 50973.05 50131.91 52245.08 50142.04 50675.01 4944.95 52273.81 50747.90 49528.96 50976.09 494
MatchFormer51.11 47146.66 47564.46 48667.11 51043.39 51270.54 50463.67 50833.19 50937.22 51070.30 5006.67 51778.17 50430.29 50940.94 50371.81 500
PMVScopyleft47.18 2252.22 47048.46 47463.48 48745.72 52246.20 50873.41 50078.31 49541.03 50730.06 51465.68 5066.05 51883.43 49730.04 51065.86 47960.80 506
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai58.82 46458.24 46260.56 48883.13 48245.09 51182.32 48448.22 51667.61 47761.70 49069.15 50138.75 48876.05 50632.01 50841.31 50260.55 507
PDCNetPlus48.34 47445.15 47757.91 48961.43 51541.85 51365.98 50538.30 51947.59 49937.96 50871.85 49910.18 51166.85 51252.94 48720.14 52365.03 505
MVEpermissive39.65 2343.39 47638.59 48257.77 49056.52 51748.77 50555.38 50858.64 51129.33 51328.96 51552.65 5124.68 52564.62 51328.11 51133.07 50859.93 508
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method50.52 47348.47 47356.66 49152.26 52118.98 52941.51 51681.40 48710.10 51944.59 50475.01 49428.51 49568.16 50853.54 48649.31 49882.83 485
DeepMVS_CXcopyleft56.31 49274.23 50051.81 50356.67 51244.85 50248.54 49875.16 49327.87 49658.74 51540.92 50352.22 49558.39 510
ELoFTR40.15 47935.08 48355.36 49341.27 52928.17 52647.70 51243.76 51729.15 51430.35 51365.97 5052.17 53266.90 51134.51 50720.83 52271.00 501
kuosan53.51 46953.30 46954.13 49476.06 49645.36 51080.11 49148.36 51559.63 49154.84 49363.43 50937.41 48962.07 51420.73 51539.10 50454.96 511
PMatch-SfM38.18 48033.34 48452.72 49543.67 52428.18 52552.96 50916.29 52829.70 51231.24 51268.56 5031.08 54457.70 51638.73 50517.80 52572.30 499
MASt3R-SfM45.78 47543.96 47851.24 49645.04 52329.83 52357.88 50738.83 51831.88 51147.48 49981.30 4817.16 51551.15 51849.56 49236.51 50572.74 498
GLUNet-SfM31.36 48226.25 48746.70 49735.51 53124.89 52733.71 52136.36 52119.08 51523.78 51852.69 5113.82 53056.26 51719.75 51711.56 53458.95 509
E-PMN43.23 47742.29 47946.03 49865.58 51237.41 51773.51 49964.62 50733.99 50828.47 51647.87 51419.90 50567.91 50922.23 51424.45 51032.77 518
EMVS42.07 47841.12 48044.92 49963.45 51435.56 51973.65 49863.48 50933.05 51026.88 51745.45 51521.27 50267.14 51019.80 51623.02 51432.06 519
ALIKED-LG28.00 48326.54 48632.41 50058.12 51631.80 52047.26 51321.21 52314.15 51619.16 52041.93 5176.72 51635.73 5195.96 52624.32 51129.69 520
ALIKED-MNN26.28 48424.57 48931.39 50156.22 51931.73 52145.54 51419.13 52611.12 51717.11 52239.35 5195.01 52134.53 5205.54 52822.12 51627.92 521
ALIKED-NN26.07 48524.75 48830.02 50255.08 52030.61 52244.20 51519.22 52510.98 51817.98 52140.71 5185.39 52032.83 5215.59 52723.63 51326.63 522
tmp_tt35.64 48139.24 48124.84 50314.87 54823.90 52862.71 50651.51 5136.58 52736.66 51162.08 51044.37 48330.34 52352.40 48822.00 51720.27 524
wuyk23d21.27 48720.48 49023.63 50468.59 50836.41 51849.57 5116.85 5409.37 5207.89 5294.46 5454.03 52931.37 52217.47 51816.07 5273.12 540
SP-LightGlue20.24 48820.15 49220.49 50543.51 52512.27 53738.68 51814.56 5317.54 52412.90 52630.07 5234.75 52314.38 5277.60 52221.75 51834.82 513
SP-SuperGlue20.22 48920.18 49120.36 50643.26 52612.27 53738.71 51714.77 5307.64 52313.04 52530.21 5224.73 52414.21 5297.59 52321.65 51934.59 514
SP-DiffGlue20.02 49019.96 49320.21 50719.64 54513.14 53630.51 52215.49 5298.39 52119.98 51943.75 5165.48 51913.72 53013.75 51922.65 51533.78 516
SP-MNN19.61 49119.42 49420.19 50842.15 52711.42 54338.15 51914.24 5326.55 52811.64 52829.88 5254.16 52714.56 5267.09 52520.92 52134.58 515
SP-NN19.44 49219.37 49519.67 50941.70 52811.48 54237.75 52013.72 5346.86 52511.86 52729.97 5244.23 52614.25 5287.13 52421.07 52033.30 517
XFeat-MNN17.43 49316.95 49618.86 51016.90 54611.28 54427.31 52317.08 5278.08 52215.61 52435.73 5204.06 52822.95 52410.20 52017.59 52622.35 523
XFeat-NN15.96 49415.86 49716.25 51115.78 5479.87 54725.17 52413.83 5336.76 52615.68 52334.83 5213.61 53119.28 5259.22 52117.90 52419.58 525
SIFT-NN12.98 49513.18 49812.37 51236.49 53016.03 53022.41 5257.69 5364.89 5297.41 53020.48 5271.69 53311.46 5321.88 53115.70 5289.61 527
SIFT-MNN12.44 49612.55 49912.11 51334.55 53215.21 53120.91 5267.74 5354.86 5306.54 53220.09 5281.51 53411.47 5311.88 53114.87 5309.64 526
SIFT-NN-NCMNet12.12 49712.25 50011.75 51432.82 53414.83 53220.73 5277.58 5374.72 5326.60 53119.53 5291.49 53511.15 5341.74 53315.02 5299.28 528
SIFT-NCM-Cal11.58 49811.64 50111.40 51533.45 53314.10 53319.75 5296.89 5384.68 5354.55 53918.60 5341.34 53911.28 5331.53 53913.95 5318.82 532
SIFT-NN-CMatch11.26 49911.31 50311.13 51630.21 53813.40 53518.43 5306.79 5414.71 5336.47 53319.53 5291.43 53710.72 5361.71 53412.49 5339.26 529
SIFT-ConvMatch10.91 50110.94 50610.84 51732.07 53513.57 53417.23 5336.35 5424.71 5335.18 53618.94 5321.30 54010.76 5351.65 53711.02 5368.19 533
SIFT-NN-UMatch11.06 50011.19 50510.66 51828.66 54012.16 53919.79 5286.86 5394.73 5315.21 53519.47 5311.46 53610.70 5371.71 53412.79 5329.13 530
SIFT-UMatch10.58 50210.73 50710.15 51931.05 53611.65 54118.01 5315.92 5444.65 5364.72 53718.93 5331.25 54210.62 5381.66 53610.39 5378.16 534
SIFT-CM-Cal10.08 50410.13 5109.92 52030.71 53711.88 54015.35 5355.44 5454.59 5374.72 53718.04 5371.26 54110.19 5391.46 5419.60 5387.69 535
SIFT-NN-PointCN10.26 50310.46 5089.65 52127.18 5419.89 54617.89 5326.17 5434.40 5395.65 53418.29 5351.43 53710.09 5401.61 53811.55 5358.99 531
SIFT-UM-Cal9.80 50510.00 5119.22 52230.05 53910.15 54516.31 5344.85 5474.54 5384.19 54018.23 5361.19 5439.95 5411.52 5409.11 5407.57 536
SIFT-PCN-Cal8.65 5098.88 5137.98 52326.74 5427.47 54913.90 5374.61 5484.09 5413.82 54115.86 5381.01 5458.94 5421.34 5428.52 5417.53 537
SIFT-PointCN8.76 5079.03 5127.96 52426.50 5437.60 54814.94 5365.08 5464.10 5403.74 54215.46 5390.94 5468.92 5431.33 5439.14 5397.37 538
SIFT-NCMNet7.46 5117.71 5156.72 52525.03 5446.86 55011.42 5382.98 5494.05 5423.38 54313.68 5400.84 5477.65 5441.13 5446.87 5425.66 539
test1238.76 50711.22 5041.39 5260.85 5500.97 55185.76 4650.35 5510.54 5442.45 5458.14 5440.60 5480.48 5452.16 5300.17 5442.71 541
testmvs8.92 50611.52 5021.12 5271.06 5490.46 55286.02 4620.65 5500.62 5432.74 5449.52 5430.31 5490.45 5462.38 5290.39 5432.46 542
mmdepth0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
monomultidepth0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
test_blank0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
uanet_test0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
DCPMVS0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
cdsmvs_eth3d_5k22.14 48629.52 4850.00 5280.00 5510.00 5530.00 53995.76 1990.00 5460.00 54794.29 23075.66 2270.00 5470.00 5450.00 5450.00 543
pcd_1.5k_mvsjas6.64 5128.86 5140.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 54679.70 1590.00 5470.00 5450.00 5450.00 543
sosnet-low-res0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
sosnet0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
uncertanet0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
Regformer0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
ab-mvs-re7.82 51010.43 5090.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 54793.88 2510.00 5500.00 5470.00 5450.00 5450.00 543
uanet0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
WAC-MVS64.08 48059.14 477
FOURS198.86 485.54 7498.29 197.49 1189.79 6696.29 32
PC_three_145282.47 30997.09 1997.07 7292.72 198.04 20092.70 8099.02 1298.86 16
test_one_060198.58 1485.83 6897.44 2091.05 2396.78 2798.06 2491.45 12
eth-test20.00 551
eth-test0.00 551
ZD-MVS98.15 4086.62 3497.07 6083.63 27994.19 6596.91 7887.57 3599.26 5191.99 10598.44 56
RE-MVS-def93.68 7297.92 4984.57 9496.28 5196.76 9387.46 16393.75 7697.43 5182.94 10092.73 7697.80 9197.88 112
IU-MVS98.77 886.00 5496.84 8281.26 34797.26 1395.50 3699.13 399.03 10
test_241102_TWO97.44 2090.31 4497.62 898.07 2291.46 1199.58 1395.66 3099.12 698.98 12
test_241102_ONE98.77 885.99 5697.44 2090.26 5097.71 297.96 3392.31 599.38 35
9.1494.47 3597.79 5896.08 6997.44 2086.13 20895.10 5597.40 5388.34 2699.22 5393.25 6898.70 37
save fliter97.85 5585.63 7395.21 14296.82 8589.44 77
test_0728_THIRD90.75 3197.04 2198.05 2792.09 799.55 2095.64 3299.13 399.13 4
test072698.78 685.93 5997.19 1697.47 1690.27 4897.64 698.13 791.47 9
GSMVS96.12 231
test_part298.55 1587.22 2096.40 31
sam_mvs171.70 28796.12 231
sam_mvs70.60 301
MTGPAbinary96.97 65
test_post188.00 4419.81 54269.31 32695.53 40376.65 368
test_post10.29 54170.57 30595.91 388
patchmatchnet-post83.76 46271.53 28896.48 356
MTMP96.16 6060.64 510
gm-plane-assit89.60 42768.00 46377.28 40488.99 40897.57 24879.44 338
test9_res91.91 10998.71 3598.07 84
TEST997.53 6786.49 3894.07 23196.78 9081.61 33992.77 10196.20 11087.71 3299.12 63
test_897.49 6986.30 4694.02 23796.76 9381.86 33092.70 10596.20 11087.63 3399.02 73
agg_prior290.54 13698.68 4098.27 65
agg_prior97.38 7285.92 6196.72 10092.16 12098.97 87
test_prior485.96 5894.11 225
test_prior294.12 22387.67 15892.63 10996.39 10586.62 4591.50 11898.67 43
旧先验293.36 27771.25 46694.37 6197.13 30386.74 202
新几何293.11 292
旧先验196.79 8681.81 19995.67 21096.81 8486.69 4397.66 9796.97 187
无先验93.28 28596.26 14073.95 44699.05 6780.56 31296.59 210
原ACMM292.94 303
test22296.55 9581.70 20492.22 33695.01 26068.36 47590.20 17996.14 12080.26 14697.80 9196.05 238
testdata298.75 11678.30 352
segment_acmp87.16 40
testdata192.15 33887.94 142
plane_prior794.70 20182.74 165
plane_prior694.52 21782.75 16374.23 247
plane_prior596.22 14698.12 17988.15 17789.99 28294.63 292
plane_prior494.86 201
plane_prior382.75 16390.26 5086.91 249
plane_prior295.85 9390.81 27
plane_prior194.59 210
plane_prior82.73 16695.21 14289.66 7189.88 287
n20.00 552
nn0.00 552
door-mid85.49 474
test1196.57 112
door85.33 476
HQP5-MVS81.56 206
HQP-NCC94.17 25094.39 20588.81 10485.43 295
ACMP_Plane94.17 25094.39 20588.81 10485.43 295
BP-MVS87.11 199
HQP4-MVS85.43 29597.96 21694.51 302
HQP3-MVS96.04 17389.77 291
HQP2-MVS73.83 258
NP-MVS94.37 23182.42 18093.98 244
MDTV_nov1_ep13_2view55.91 50187.62 44973.32 45284.59 31770.33 30874.65 39195.50 259
MDTV_nov1_ep1383.56 35791.69 35869.93 45587.75 44691.54 39978.60 38584.86 31188.90 41069.54 32096.03 37970.25 42388.93 304
ACMMP++_ref87.47 327
ACMMP++88.01 319
Test By Simon80.02 148