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 61
MM95.10 1494.91 2695.68 596.09 11688.34 1096.68 3894.37 30195.08 194.68 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 20897.67 498.10 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 10491.37 12695.55 795.63 14388.73 797.07 2396.77 9290.84 2684.02 33496.62 9575.95 21699.34 4287.77 18397.68 9698.59 29
CNVR-MVS95.40 895.37 1195.50 898.11 4288.51 895.29 13196.96 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 9195.69 4596.49 10089.27 1899.29 5095.80 14297.95 97
MGCNet94.18 5093.80 6495.34 1094.91 18387.62 1595.97 8293.01 35292.58 694.22 6397.20 6480.56 14099.59 1097.04 2098.68 4098.81 22
ACMMP_NAP94.74 2594.56 3395.28 1198.02 4787.70 1295.68 10797.34 3088.28 12395.30 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 9097.14 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 15395.71 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 15597.12 5587.13 17492.51 11396.30 10589.24 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 9991.74 11095.08 1696.19 10789.31 592.66 31396.56 11383.44 28391.68 13995.04 18886.60 4798.99 8285.60 21797.92 8496.93 188
ZNCC-MVS94.47 3394.28 4595.03 1798.52 1886.96 2196.85 3397.32 3488.24 12493.15 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 21096.97 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 11493.58 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 9696.20 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 11193.53 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 12493.26 8596.83 8285.48 6099.59 1091.43 12098.40 5798.30 55
HFP-MVS94.52 3194.40 3894.86 2798.61 1386.81 2696.94 2597.34 3088.63 11193.65 7897.21 6286.10 5399.49 3092.35 8998.77 3198.30 55
sasdasda93.27 8292.75 9294.85 2895.70 13987.66 1396.33 4496.41 12390.00 5394.09 6894.60 21382.33 10998.62 13392.40 8692.86 23398.27 64
MP-MVS-pluss94.21 4594.00 5994.85 2898.17 3986.65 3294.82 16897.17 4986.26 20092.83 9897.87 3685.57 5999.56 1694.37 5298.92 1798.34 48
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 5394.09 6894.60 21382.33 10998.62 13392.40 8692.86 23398.27 64
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 23686.13 28594.85 2898.54 1686.60 3596.93 2797.19 4490.66 3692.85 9623.41 51785.02 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 13297.21 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 9895.86 4297.54 4689.24 1999.59 1095.27 4098.85 2098.95 13
alignmvs93.08 9092.50 9894.81 3695.62 14487.61 1695.99 7996.07 16989.77 6694.12 6794.87 19780.56 14098.66 12592.42 8593.10 22998.15 76
SED-MVS95.91 396.28 394.80 3898.77 885.99 5697.13 1997.44 2090.31 4397.71 298.07 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 7892.81 9996.97 7585.37 6299.24 5290.87 12998.69 3898.38 47
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 9491.98 12497.19 6585.43 6199.56 1692.06 10398.79 2798.44 42
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 21595.05 5697.18 6687.31 3999.07 6591.90 11198.61 5198.28 61
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 11692.73 10497.23 6185.20 6599.32 4692.15 9798.83 2598.25 69
PGM-MVS93.96 5893.72 7094.68 4398.43 2386.22 4995.30 12997.78 387.45 16493.26 8597.33 5684.62 7899.51 2890.75 13198.57 5298.32 54
DVP-MVScopyleft95.67 496.02 494.64 4498.78 685.93 5997.09 2196.73 9890.27 4797.04 2198.05 2791.47 999.55 2095.62 3499.08 798.45 41
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 10991.83 13497.17 6783.96 8599.55 2091.44 11998.64 4898.43 43
PHI-MVS93.89 6093.65 7494.62 4696.84 8586.43 4096.69 3797.49 1185.15 23993.56 8296.28 10685.60 5899.31 4792.45 8398.79 2798.12 81
TSAR-MVS + MP.94.85 1994.94 2494.58 4798.25 3586.33 4396.11 6796.62 10888.14 12996.10 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 15896.69 10491.89 1290.69 16695.88 13881.99 12299.54 2493.14 7097.95 8398.39 45
train_agg93.44 7593.08 8594.52 4997.53 6786.49 3894.07 23096.78 9081.86 32792.77 10196.20 10987.63 3399.12 6392.14 9898.69 3897.94 98
CDPH-MVS92.83 9492.30 10294.44 5097.79 5886.11 5394.06 23296.66 10580.09 35892.77 10196.63 9486.62 4599.04 6987.40 19098.66 4498.17 74
3Dnovator86.66 591.73 12290.82 14194.44 5094.59 20986.37 4297.18 1797.02 6289.20 8784.31 32996.66 9073.74 25799.17 5786.74 20097.96 8297.79 122
SR-MVS94.23 4494.17 5494.43 5298.21 3885.78 7096.40 4396.90 7688.20 12794.33 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 18492.62 11096.80 8684.85 7599.17 5792.43 8498.65 4798.33 50
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 20293.93 31989.77 6694.21 6495.59 15987.35 3898.61 13592.72 7896.15 13697.83 118
reproduce-ours94.82 2094.97 2294.38 5597.91 5385.46 7595.86 9197.15 5189.82 5995.23 5398.10 1487.09 4199.37 3795.30 3898.25 6698.30 55
our_new_method94.82 2094.97 2294.38 5597.91 5385.46 7595.86 9197.15 5189.82 5995.23 5398.10 1487.09 4199.37 3795.30 3898.25 6698.30 55
NormalMVS93.46 7293.16 8494.37 5798.40 2686.20 5096.30 4796.27 13691.65 1792.68 10696.13 12077.97 18698.84 10690.75 13198.26 6298.07 83
test1294.34 5897.13 8086.15 5296.29 13291.04 16285.08 6799.01 7598.13 7497.86 113
SymmetryMVS92.81 9692.31 10194.32 5996.15 10886.20 5096.30 4794.43 29791.65 1792.68 10696.13 12077.97 18698.84 10690.75 13194.72 16997.92 107
ACMMPcopyleft93.24 8492.88 9094.30 6098.09 4485.33 7996.86 3297.45 1988.33 12090.15 18397.03 7481.44 13099.51 2890.85 13095.74 14598.04 90
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 6995.27 5298.16 686.53 4899.36 4095.42 3798.15 7298.33 50
DeepC-MVS88.79 393.31 8192.99 8894.26 6296.07 11885.83 6894.89 16196.99 6389.02 9789.56 19297.37 5582.51 10699.38 3592.20 9598.30 6097.57 137
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 9594.23 6395.62 14485.92 6196.08 6996.33 13089.86 5793.89 7594.66 21082.11 11798.50 14192.33 9192.82 23698.27 64
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 43
EPNet91.79 11391.02 13594.10 6590.10 41385.25 8096.03 7692.05 37992.83 587.39 24095.78 14979.39 16699.01 7588.13 17797.48 9998.05 89
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 60
test_fmvsmconf_n94.60 2894.81 3093.98 6794.62 20584.96 8596.15 6297.35 2989.37 7996.03 3998.11 1186.36 4999.01 7597.45 1097.83 8997.96 96
DELS-MVS93.43 7993.25 8193.97 6895.42 15285.04 8393.06 29697.13 5490.74 3391.84 13295.09 18786.32 5099.21 5591.22 12198.45 5597.65 131
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 11091.28 12993.96 6998.33 3385.92 6194.66 18196.66 10582.69 30590.03 18595.82 14582.30 11199.03 7084.57 23596.48 12996.91 190
HPM-MVS_fast93.40 8093.22 8293.94 7098.36 3184.83 8797.15 1896.80 8985.77 21292.47 11497.13 6982.38 10799.07 6590.51 13698.40 5797.92 107
test_fmvsmconf0.1_n94.20 4794.31 4393.88 7192.46 32784.80 8896.18 5996.82 8589.29 8495.68 4698.11 1185.10 6698.99 8297.38 1197.75 9597.86 113
SD-MVS94.96 1895.33 1293.88 7197.25 7986.69 2996.19 5797.11 5890.42 3996.95 2397.27 5889.53 1596.91 31894.38 5198.85 2098.03 91
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 28797.24 4188.76 10691.60 14095.85 14286.07 5498.66 12591.91 10998.16 7098.03 91
SR-MVS-dyc-post93.82 6293.82 6393.82 7497.92 4984.57 9496.28 5196.76 9387.46 16293.75 7697.43 5184.24 8299.01 7592.73 7697.80 9197.88 111
test_prior93.82 7497.29 7784.49 9896.88 7898.87 10098.11 82
APD-MVS_3200maxsize93.78 6393.77 6793.80 7697.92 4984.19 11196.30 4796.87 7986.96 18093.92 7497.47 4983.88 8698.96 8992.71 7997.87 8798.26 68
fmvsm_l_conf0.5_n94.29 4194.46 3693.79 7795.28 15885.43 7795.68 10796.43 12186.56 19296.84 2597.81 3987.56 3698.77 11597.14 1596.82 11997.16 169
CSCG93.23 8593.05 8693.76 7898.04 4684.07 11396.22 5697.37 2784.15 26490.05 18495.66 15587.77 3099.15 6189.91 14798.27 6198.07 83
GDP-MVS92.04 10891.46 12393.75 7994.55 21584.69 9195.60 11896.56 11387.83 15093.07 9295.89 13773.44 26198.65 12790.22 14096.03 13897.91 109
BP-MVS192.48 10192.07 10593.72 8094.50 21984.39 10695.90 8994.30 30490.39 4092.67 10895.94 13374.46 24098.65 12793.14 7097.35 10398.13 78
test_fmvsmconf0.01_n93.19 8693.02 8793.71 8189.25 42684.42 10596.06 7396.29 13289.06 9294.68 5898.13 779.22 16898.98 8697.22 1397.24 10597.74 125
UA-Net92.83 9492.54 9793.68 8296.10 11584.71 9095.66 11096.39 12591.92 1193.22 8796.49 10083.16 9598.87 10084.47 23795.47 15297.45 144
fmvsm_l_conf0.5_n_a94.20 4794.40 3893.60 8395.29 15784.98 8495.61 11596.28 13586.31 19896.75 2897.86 3787.40 3798.74 11997.07 1797.02 11097.07 174
QAPM89.51 19388.15 22093.59 8494.92 18184.58 9396.82 3496.70 10378.43 38583.41 35296.19 11373.18 26699.30 4877.11 36196.54 12696.89 191
test_fmvsm_n_192094.71 2695.11 1993.50 8595.79 13384.62 9296.15 6297.64 589.85 5897.19 1697.89 3586.28 5198.71 12297.11 1698.08 7897.17 162
fmvsm_s_conf0.5_n_994.99 1695.50 893.44 8696.51 10082.25 18595.76 10296.92 7393.37 397.63 798.43 184.82 7699.16 6098.15 197.92 8498.90 15
KinetiMVS91.82 11291.30 12793.39 8794.72 19783.36 13895.45 12296.37 12790.33 4292.17 11996.03 12772.32 27898.75 11687.94 18096.34 13198.07 83
casdiffmvs_mvgpermissive92.96 9392.83 9193.35 8894.59 20983.40 13695.00 15596.34 12990.30 4592.05 12296.05 12483.43 8998.15 17792.07 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 13796.23 3397.84 3883.36 9398.83 10997.49 897.34 10497.25 155
EI-MVSNet-Vis-set93.01 9292.92 8993.29 9095.01 17283.51 13394.48 19095.77 19790.87 2592.52 11296.67 8984.50 7999.00 8091.99 10594.44 18297.36 147
Vis-MVSNetpermissive91.75 12091.23 13093.29 9095.32 15683.78 12396.14 6495.98 17689.89 5590.45 17096.58 9775.09 22998.31 16884.75 22996.90 11597.78 123
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 5995.56 4895.51 16284.50 7998.79 11394.83 4698.86 1997.72 127
SPE-MVS-test94.02 5494.29 4493.24 9396.69 8883.24 14197.49 696.92 7392.14 992.90 9495.77 15085.02 6998.33 16593.03 7298.62 4998.13 78
VNet92.24 10691.91 10893.24 9396.59 9283.43 13494.84 16796.44 12089.19 8894.08 7195.90 13677.85 19298.17 17588.90 16793.38 21898.13 78
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 12499.22 5397.86 497.91 8697.20 160
VDD-MVS90.74 15089.92 16593.20 9596.27 10583.02 15695.73 10493.86 32388.42 11992.53 11196.84 8162.09 39298.64 13090.95 12792.62 24397.93 106
Elysia90.12 17089.10 18893.18 9793.16 29684.05 11595.22 13896.27 13685.16 23790.59 16794.68 20664.64 37198.37 15886.38 20695.77 14397.12 171
StellarMVS90.12 17089.10 18893.18 9793.16 29684.05 11595.22 13896.27 13685.16 23790.59 16794.68 20664.64 37198.37 15886.38 20695.77 14397.12 171
CS-MVS94.12 5194.44 3793.17 9996.55 9583.08 15397.63 496.95 7091.71 1593.50 8496.21 10885.61 5798.24 17093.64 6198.17 6998.19 72
nrg03091.08 14590.39 14993.17 9993.07 30386.91 2396.41 4296.26 14088.30 12288.37 21794.85 20082.19 11697.64 24191.09 12282.95 36994.96 276
MVSMamba_PlusPlus93.44 7593.54 7693.14 10196.58 9483.05 15496.06 7396.50 11884.42 26194.09 6895.56 16185.01 7298.69 12494.96 4498.66 4497.67 130
EI-MVSNet-UG-set92.74 9792.62 9693.12 10294.86 18683.20 14394.40 20295.74 20090.71 3592.05 12296.60 9684.00 8498.99 8291.55 11793.63 20797.17 162
test_fmvsmvis_n_192093.44 7593.55 7593.10 10393.67 28284.26 10995.83 9596.14 16089.00 9992.43 11597.50 4883.37 9298.72 12096.61 2497.44 10096.32 216
新几何193.10 10397.30 7684.35 10895.56 21871.09 46291.26 15096.24 10782.87 10298.86 10279.19 33998.10 7596.07 232
OMC-MVS91.23 13690.62 14693.08 10596.27 10584.07 11393.52 26995.93 18286.95 18189.51 19396.13 12078.50 18098.35 16285.84 21592.90 23296.83 198
OpenMVScopyleft83.78 1188.74 22387.29 24293.08 10592.70 32185.39 7896.57 4096.43 12178.74 38080.85 38496.07 12369.64 31599.01 7578.01 35296.65 12494.83 284
MAR-MVS90.30 16689.37 18193.07 10796.61 9184.48 9995.68 10795.67 20982.36 31087.85 22792.85 28076.63 20598.80 11180.01 31996.68 12395.91 238
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 14690.21 15393.03 10893.86 26783.88 12092.81 30793.86 32379.84 36191.76 13694.29 22777.92 18998.04 19990.48 13797.11 10697.17 162
Effi-MVS+91.59 13091.11 13293.01 10994.35 23483.39 13794.60 18395.10 25487.10 17590.57 16993.10 27581.43 13198.07 19389.29 15994.48 18097.59 136
fmvsm_s_conf0.5_n_a93.57 6893.76 6893.00 11095.02 17183.67 12696.19 5796.10 16687.27 16895.98 4098.05 2783.07 9998.45 15196.68 2395.51 14996.88 192
MVS_111021_LR92.47 10292.29 10392.98 11195.99 12584.43 10393.08 29396.09 16788.20 12791.12 15595.72 15381.33 13297.76 23091.74 11397.37 10296.75 200
fmvsm_s_conf0.1_n_a93.19 8693.26 8092.97 11292.49 32583.62 12996.02 7795.72 20486.78 18696.04 3898.19 482.30 11198.43 15596.38 2595.42 15596.86 193
ETV-MVS92.74 9792.66 9492.97 11295.20 16484.04 11795.07 15096.51 11790.73 3492.96 9391.19 34184.06 8398.34 16391.72 11496.54 12696.54 211
LFMVS90.08 17389.13 18792.95 11496.71 8782.32 18496.08 6989.91 43786.79 18592.15 12196.81 8462.60 39098.34 16387.18 19493.90 19698.19 72
UGNet89.95 18088.95 19692.95 11494.51 21783.31 13995.70 10695.23 24689.37 7987.58 23493.94 24364.00 37998.78 11483.92 24596.31 13296.74 201
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 14890.10 15792.90 11693.04 30683.53 13293.08 29394.15 31280.22 35591.41 14694.91 19476.87 19997.93 21990.28 13896.90 11597.24 156
jason: jason.
DP-MVS87.25 27785.36 31692.90 11697.65 6483.24 14194.81 16992.00 38174.99 42981.92 37395.00 19072.66 27199.05 6766.92 44292.33 24896.40 213
fmvsm_s_conf0.5_n_894.56 3095.12 1892.87 11895.96 12881.32 21595.76 10297.57 793.48 297.53 1098.32 381.78 12799.13 6297.91 297.81 9098.16 75
fmvsm_s_conf0.5_n93.76 6494.06 5892.86 11995.62 14483.17 14596.14 6496.12 16488.13 13095.82 4398.04 3083.43 8998.48 14396.97 2196.23 13396.92 189
fmvsm_s_conf0.1_n93.46 7293.66 7392.85 12093.75 27483.13 14796.02 7795.74 20087.68 15695.89 4198.17 582.78 10398.46 14796.71 2296.17 13596.98 183
casdiffseed41469214791.11 14390.55 14792.81 12194.27 24282.58 17794.81 16996.03 17487.93 14390.17 18195.62 15778.51 17997.90 22384.18 24193.45 21697.94 98
CANet_DTU90.26 16889.41 18092.81 12193.46 28983.01 15793.48 27094.47 29689.43 7787.76 23294.23 23270.54 30399.03 7084.97 22496.39 13096.38 214
MVSFormer91.68 12891.30 12792.80 12393.86 26783.88 12095.96 8395.90 18684.66 25791.76 13694.91 19477.92 18997.30 28389.64 15597.11 10697.24 156
PVSNet_Blended_VisFu91.38 13390.91 13892.80 12396.39 10283.17 14594.87 16396.66 10583.29 28889.27 19994.46 22280.29 14399.17 5787.57 18795.37 15696.05 235
fmvsm_l_conf0.5_n_994.65 2795.28 1592.77 12595.95 12981.83 19795.53 12097.12 5591.68 1697.89 198.06 2485.71 5698.65 12797.32 1298.26 6297.83 118
LuminaMVS90.55 16289.81 16792.77 12592.78 31984.21 11094.09 22894.17 31185.82 20991.54 14194.14 23469.93 30997.92 22091.62 11694.21 19096.18 224
balanced_ft_v192.23 10792.05 10692.77 12595.40 15381.78 20195.80 9695.69 20887.94 14191.92 12995.04 18875.91 21798.71 12293.83 5896.94 11297.82 120
fmvsm_s_conf0.5_n_694.11 5294.56 3392.76 12894.98 17681.96 19495.79 9897.29 3989.31 8297.52 1197.61 4483.25 9498.88 9997.05 1998.22 6897.43 146
VDDNet89.56 19288.49 21192.76 12895.07 17082.09 18896.30 4793.19 34781.05 34991.88 13096.86 8061.16 40898.33 16588.43 17492.49 24797.84 117
viewdifsd2359ckpt0991.18 13990.65 14592.75 13094.61 20882.36 18394.32 21195.74 20084.72 25489.66 19195.15 18579.69 16198.04 19987.70 18494.27 18997.85 116
h-mvs3390.80 14890.15 15692.75 13096.01 12182.66 17095.43 12395.53 22289.80 6293.08 9095.64 15675.77 21899.00 8092.07 10078.05 42696.60 206
hybridcas92.43 10392.33 10092.74 13294.51 21781.84 19695.05 15396.16 15889.60 7191.40 14796.20 10982.23 11398.09 18889.95 14695.87 14098.28 61
casdiffmvspermissive92.51 10092.43 9992.74 13294.41 22981.98 19294.54 18796.23 14489.57 7391.96 12696.17 11482.58 10598.01 20690.95 12795.45 15498.23 70
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 15390.02 16392.71 13495.72 13782.41 18194.11 22495.12 25285.63 21691.49 14394.70 20474.75 23398.42 15686.13 21092.53 24597.31 148
DCV-MVSNet90.69 15390.02 16392.71 13495.72 13782.41 18194.11 22495.12 25285.63 21691.49 14394.70 20474.75 23398.42 15686.13 21092.53 24597.31 148
PCF-MVS84.11 1087.74 25186.08 28992.70 13694.02 25684.43 10389.27 41495.87 19173.62 44484.43 32194.33 22478.48 18298.86 10270.27 41694.45 18194.81 285
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 13796.05 12082.00 19096.31 4696.71 10192.27 896.68 3098.39 285.32 6398.92 9597.20 1498.16 7097.17 162
SSM_040490.73 15190.08 15892.69 13795.00 17583.13 14794.32 21195.00 26285.41 22789.84 18695.35 17176.13 20897.98 21185.46 22094.18 19196.95 185
baseline92.39 10592.29 10392.69 13794.46 22481.77 20294.14 22196.27 13689.22 8691.88 13096.00 12882.35 10897.99 20891.05 12395.27 16098.30 55
MSLP-MVS++93.72 6694.08 5592.65 14097.31 7583.43 13495.79 9897.33 3290.03 5293.58 8096.96 7684.87 7497.76 23092.19 9698.66 4496.76 199
EC-MVSNet93.44 7593.71 7192.63 14195.21 16382.43 17897.27 1496.71 10190.57 3892.88 9595.80 14683.16 9598.16 17693.68 5998.14 7397.31 148
ab-mvs89.41 20088.35 21392.60 14295.15 16882.65 17492.20 33495.60 21683.97 26888.55 21393.70 25774.16 24898.21 17482.46 26989.37 29296.94 187
LS3D87.89 24686.32 27892.59 14396.07 11882.92 16095.23 13694.92 27175.66 42182.89 35995.98 13072.48 27599.21 5568.43 43095.23 16195.64 252
Anonymous2024052988.09 24286.59 26792.58 14496.53 9781.92 19595.99 7995.84 19374.11 43989.06 20395.21 18061.44 40098.81 11083.67 25287.47 32397.01 181
fmvsm_s_conf0.5_n_394.49 3295.13 1792.56 14595.49 15081.10 22595.93 8697.16 5092.96 497.39 1298.13 783.63 8898.80 11197.89 397.61 9897.78 123
CPTT-MVS91.99 10991.80 10992.55 14698.24 3781.98 19296.76 3596.49 11981.89 32690.24 17596.44 10378.59 17798.61 13589.68 15397.85 8897.06 175
viewdifsd2359ckpt1391.20 13890.75 14392.54 14794.30 24082.13 18794.03 23495.89 18885.60 21890.20 17795.36 17079.69 16197.90 22387.85 18293.86 19797.61 133
114514_t89.51 19388.50 20992.54 14798.11 4281.99 19195.16 14696.36 12870.19 46685.81 27495.25 17676.70 20398.63 13282.07 27996.86 11897.00 182
PAPM_NR91.22 13790.78 14292.52 14997.60 6581.46 21194.37 20896.24 14386.39 19787.41 23794.80 20282.06 12098.48 14382.80 26495.37 15697.61 133
mamba_040889.06 21387.92 22792.50 15094.76 19182.66 17079.84 48694.64 28985.18 23288.96 20595.00 19076.00 21397.98 21183.74 24993.15 22696.85 194
DeepPCF-MVS89.96 194.20 4794.77 3192.49 15196.52 9880.00 27694.00 23997.08 5990.05 5195.65 4797.29 5789.66 1498.97 8793.95 5598.71 3598.50 32
SSM_040790.47 16489.80 16892.46 15294.76 19182.66 17093.98 24195.00 26285.41 22788.96 20595.35 17176.13 20897.88 22585.46 22093.15 22696.85 194
IS-MVSNet91.43 13291.09 13492.46 15295.87 13281.38 21496.95 2493.69 33689.72 6889.50 19595.98 13078.57 17897.77 22983.02 25896.50 12898.22 71
API-MVS90.66 15790.07 15992.45 15496.36 10384.57 9496.06 7395.22 24882.39 30889.13 20094.27 23080.32 14298.46 14780.16 31796.71 12294.33 308
xiu_mvs_v1_base_debu90.64 15890.05 16092.40 15593.97 26284.46 10093.32 27895.46 22685.17 23492.25 11694.03 23570.59 29998.57 13890.97 12494.67 17194.18 312
xiu_mvs_v1_base90.64 15890.05 16092.40 15593.97 26284.46 10093.32 27895.46 22685.17 23492.25 11694.03 23570.59 29998.57 13890.97 12494.67 17194.18 312
xiu_mvs_v1_base_debi90.64 15890.05 16092.40 15593.97 26284.46 10093.32 27895.46 22685.17 23492.25 11694.03 23570.59 29998.57 13890.97 12494.67 17194.18 312
fmvsm_s_conf0.5_n_293.47 7193.83 6292.39 15895.36 15481.19 22195.20 14396.56 11390.37 4197.13 1898.03 3177.47 19598.96 8997.79 696.58 12597.03 178
viewmacassd2359aftdt91.67 12991.43 12592.37 15993.95 26581.00 22993.90 24995.97 17987.75 15491.45 14596.04 12679.92 14997.97 21389.26 16094.67 17198.14 77
viewmanbaseed2359cas91.78 11691.58 11592.37 15994.32 23781.07 22693.76 25595.96 18087.26 16991.50 14295.88 13880.92 13897.97 21389.70 15294.92 16598.07 83
fmvsm_s_conf0.1_n_293.16 8893.42 7792.37 15994.62 20581.13 22395.23 13695.89 18890.30 4596.74 2998.02 3276.14 20798.95 9197.64 796.21 13497.03 178
AdaColmapbinary89.89 18389.07 19092.37 15997.41 7183.03 15594.42 19795.92 18382.81 30286.34 26394.65 21173.89 25399.02 7380.69 30695.51 14995.05 271
CNLPA89.07 21287.98 22492.34 16396.87 8484.78 8994.08 22993.24 34481.41 34084.46 31995.13 18675.57 22596.62 33577.21 35993.84 19995.61 255
fmvsm_s_conf0.5_n_493.86 6194.37 4092.33 16495.13 16980.95 23295.64 11396.97 6589.60 7196.85 2497.77 4083.08 9898.92 9597.49 896.78 12097.13 170
ET-MVSNet_ETH3D87.51 26585.91 29792.32 16593.70 28183.93 11892.33 32790.94 41384.16 26372.09 46492.52 29369.90 31095.85 38789.20 16188.36 31097.17 162
E491.74 12191.55 11892.31 16694.27 24280.80 24293.81 25296.17 15687.97 13991.11 15696.05 12480.75 13998.08 19189.78 14894.02 19398.06 88
E291.79 11391.61 11392.31 16694.49 22080.86 23893.74 25796.19 14987.63 15991.16 15195.94 13381.31 13398.06 19489.76 14994.29 18797.99 93
Anonymous20240521187.68 25286.13 28592.31 16696.66 8980.74 24494.87 16391.49 39880.47 35489.46 19695.44 16554.72 44998.23 17182.19 27589.89 28297.97 95
E391.78 11691.61 11392.30 16994.48 22180.86 23893.73 25896.19 14987.63 15991.16 15195.95 13281.30 13498.06 19489.76 14994.29 18797.99 93
CHOSEN 1792x268888.84 21987.69 23292.30 16996.14 10981.42 21390.01 40195.86 19274.52 43487.41 23793.94 24375.46 22698.36 16080.36 31295.53 14897.12 171
viewcassd2359sk1191.79 11391.62 11292.29 17194.62 20580.88 23693.70 26296.18 15587.38 16691.13 15495.85 14281.62 12998.06 19489.71 15194.40 18397.94 98
HY-MVS83.01 1289.03 21587.94 22692.29 17194.86 18682.77 16292.08 33994.49 29581.52 33986.93 24492.79 28678.32 18498.23 17179.93 32090.55 26995.88 241
CDS-MVSNet89.45 19688.51 20892.29 17193.62 28483.61 13193.01 29794.68 28781.95 32187.82 23093.24 26978.69 17596.99 31280.34 31393.23 22396.28 219
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR90.02 17689.27 18692.29 17195.78 13480.95 23292.68 31296.22 14581.91 32386.66 25493.75 25582.23 11398.44 15379.40 33894.79 16897.48 142
E3new91.76 11991.58 11592.28 17594.69 20280.90 23593.68 26596.17 15687.15 17291.09 16195.70 15481.75 12898.05 19889.67 15494.35 18497.90 110
mvsmamba90.33 16589.69 17192.25 17695.17 16581.64 20495.27 13493.36 34284.88 24789.51 19394.27 23069.29 32497.42 26789.34 15896.12 13797.68 129
E5new91.71 12391.55 11892.20 17794.33 23580.62 24894.41 19896.19 14988.06 13391.11 15696.16 11579.92 14998.03 20290.00 14193.80 20197.94 98
E6new91.71 12391.55 11892.20 17794.32 23780.62 24894.41 19896.19 14988.06 13391.11 15696.16 11579.92 14998.03 20290.00 14193.80 20197.94 98
E691.71 12391.55 11892.20 17794.32 23780.62 24894.41 19896.19 14988.06 13391.11 15696.16 11579.92 14998.03 20290.00 14193.80 20197.94 98
E591.71 12391.55 11892.20 17794.33 23580.62 24894.41 19896.19 14988.06 13391.11 15696.16 11579.92 14998.03 20290.00 14193.80 20197.94 98
PLCcopyleft84.53 789.06 21388.03 22292.15 18197.27 7882.69 16994.29 21395.44 23179.71 36384.01 33594.18 23376.68 20498.75 11677.28 35893.41 21795.02 272
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet91.70 12791.56 11792.13 18295.88 13080.50 25597.33 895.25 24586.15 20389.76 19095.60 15883.42 9198.32 16787.37 19293.25 22297.56 138
patch_mono-293.74 6594.32 4192.01 18397.54 6678.37 32693.40 27497.19 4488.02 13794.99 5797.21 6288.35 2598.44 15394.07 5498.09 7699.23 1
原ACMM192.01 18397.34 7381.05 22796.81 8878.89 37490.45 17095.92 13582.65 10498.84 10680.68 30798.26 6296.14 226
UniMVSNet (Re)89.80 18689.07 19092.01 18393.60 28584.52 9794.78 17297.47 1689.26 8586.44 26092.32 29982.10 11897.39 27884.81 22880.84 40394.12 316
MG-MVS91.77 11891.70 11192.00 18697.08 8180.03 27493.60 26795.18 25087.85 14990.89 16496.47 10282.06 12098.36 16085.07 22397.04 10997.62 132
EIA-MVS91.95 11091.94 10791.98 18795.16 16680.01 27595.36 12496.73 9888.44 11789.34 19792.16 30483.82 8798.45 15189.35 15797.06 10897.48 142
PVSNet_Blended90.73 15190.32 15191.98 18796.12 11181.25 21792.55 31796.83 8382.04 31989.10 20192.56 29281.04 13698.85 10486.72 20295.91 13995.84 243
guyue91.12 14290.84 14091.96 18994.59 20980.57 25394.87 16393.71 33588.96 10091.14 15395.22 17773.22 26597.76 23092.01 10493.81 20097.54 141
PS-MVSNAJ91.18 13990.92 13791.96 18995.26 16182.60 17692.09 33895.70 20686.27 19991.84 13292.46 29479.70 15898.99 8289.08 16295.86 14194.29 309
TAMVS89.21 20688.29 21791.96 18993.71 27982.62 17593.30 28294.19 30982.22 31387.78 23193.94 24378.83 17296.95 31577.70 35492.98 23196.32 216
SDMVSNet90.19 16989.61 17491.93 19296.00 12283.09 15292.89 30495.98 17688.73 10786.85 25095.20 18172.09 28297.08 30388.90 16789.85 28495.63 253
FA-MVS(test-final)89.66 18888.91 19891.93 19294.57 21380.27 25991.36 35994.74 28484.87 24889.82 18792.61 29174.72 23698.47 14683.97 24493.53 21197.04 177
MVS_Test91.31 13591.11 13291.93 19294.37 23080.14 26493.46 27295.80 19586.46 19591.35 14993.77 25382.21 11598.09 18887.57 18794.95 16497.55 139
NR-MVSNet88.58 22987.47 23891.93 19293.04 30684.16 11294.77 17396.25 14289.05 9380.04 39893.29 26779.02 17097.05 30881.71 29080.05 41394.59 292
HyFIR lowres test88.09 24286.81 25591.93 19296.00 12280.63 24690.01 40195.79 19673.42 44687.68 23392.10 31073.86 25497.96 21580.75 30591.70 25297.19 161
GeoE90.05 17489.43 17991.90 19795.16 16680.37 25895.80 9694.65 28883.90 26987.55 23694.75 20378.18 18597.62 24381.28 29593.63 20797.71 128
thisisatest053088.67 22487.61 23491.86 19894.87 18580.07 26994.63 18289.90 43884.00 26788.46 21593.78 25266.88 34898.46 14783.30 25492.65 23897.06 175
xiu_mvs_v2_base91.13 14190.89 13991.86 19894.97 17782.42 17992.24 33195.64 21486.11 20791.74 13893.14 27379.67 16398.89 9889.06 16395.46 15394.28 310
DU-MVS89.34 20588.50 20991.85 20093.04 30683.72 12494.47 19396.59 11089.50 7486.46 25793.29 26777.25 19797.23 29284.92 22581.02 39994.59 292
AstraMVS90.69 15390.30 15291.84 20193.81 27079.85 28294.76 17492.39 36788.96 10091.01 16395.87 14170.69 29797.94 21892.49 8292.70 23797.73 126
OPM-MVS90.12 17089.56 17591.82 20293.14 29883.90 11994.16 22095.74 20088.96 10087.86 22695.43 16772.48 27597.91 22188.10 17990.18 27693.65 349
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS90.60 16190.19 15491.82 20294.70 20082.73 16695.85 9396.22 14590.81 2786.91 24694.86 19874.23 24498.12 17888.15 17589.99 27894.63 289
UniMVSNet_NR-MVSNet89.92 18289.29 18491.81 20493.39 29183.72 12494.43 19697.12 5589.80 6286.46 25793.32 26483.16 9597.23 29284.92 22581.02 39994.49 302
diffmvspermissive91.37 13491.23 13091.77 20593.09 30180.27 25992.36 32395.52 22387.03 17791.40 14794.93 19380.08 14697.44 26592.13 9994.56 17797.61 133
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 13191.44 12491.73 20693.09 30180.27 25992.51 31895.58 21787.22 17091.80 13595.57 16079.96 14897.48 25792.23 9394.97 16397.45 144
1112_ss88.42 23187.33 24191.72 20794.92 18180.98 23092.97 30194.54 29278.16 39183.82 33893.88 24878.78 17497.91 22179.45 33489.41 29196.26 220
Fast-Effi-MVS+89.41 20088.64 20491.71 20894.74 19480.81 24193.54 26895.10 25483.11 29286.82 25290.67 36479.74 15797.75 23480.51 31093.55 20996.57 209
WTY-MVS89.60 19088.92 19791.67 20995.47 15181.15 22292.38 32294.78 28283.11 29289.06 20394.32 22578.67 17696.61 33881.57 29190.89 26597.24 156
TAPA-MVS84.62 688.16 24087.01 25091.62 21096.64 9080.65 24594.39 20496.21 14876.38 41386.19 26795.44 16579.75 15698.08 19162.75 46095.29 15896.13 227
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet89.62 18988.96 19591.60 21193.86 26782.89 16195.46 12197.33 3287.91 14488.43 21693.31 26574.17 24797.40 27587.32 19382.86 37494.52 297
FE-MVS87.40 27086.02 29191.57 21294.56 21479.69 28990.27 38893.72 33480.57 35288.80 20991.62 33065.32 36498.59 13774.97 38494.33 18696.44 212
XVG-OURS89.40 20288.70 20391.52 21394.06 25481.46 21191.27 36496.07 16986.14 20488.89 20895.77 15068.73 33397.26 28987.39 19189.96 28095.83 244
hse-mvs289.88 18489.34 18291.51 21494.83 18881.12 22493.94 24393.91 32289.80 6293.08 9093.60 25875.77 21897.66 23892.07 10077.07 43395.74 248
TranMVSNet+NR-MVSNet88.84 21987.95 22591.49 21592.68 32283.01 15794.92 16096.31 13189.88 5685.53 28393.85 25076.63 20596.96 31481.91 28379.87 41694.50 300
AUN-MVS87.78 25086.54 27091.48 21694.82 18981.05 22793.91 24793.93 31983.00 29786.93 24493.53 25969.50 31897.67 23686.14 20877.12 43295.73 250
XVG-OURS-SEG-HR89.95 18089.45 17791.47 21794.00 26081.21 22091.87 34396.06 17185.78 21188.55 21395.73 15274.67 23797.27 28788.71 17189.64 28995.91 238
MVS87.44 26886.10 28891.44 21892.61 32483.62 12992.63 31495.66 21167.26 47281.47 37692.15 30577.95 18898.22 17379.71 32395.48 15192.47 400
hybrid90.69 15390.45 14891.43 21992.67 32379.42 29792.28 33095.21 24985.15 23990.39 17395.37 16978.93 17197.32 28290.27 13993.74 20597.55 139
viewdifsd2359ckpt0791.11 14391.02 13591.41 22094.21 24778.37 32692.91 30395.71 20587.50 16190.32 17495.88 13880.27 14497.99 20888.78 17093.55 20997.86 113
F-COLMAP87.95 24586.80 25691.40 22196.35 10480.88 23694.73 17695.45 22979.65 36482.04 37194.61 21271.13 28998.50 14176.24 37191.05 26394.80 286
dcpmvs_293.49 7094.19 5291.38 22297.69 6376.78 36994.25 21596.29 13288.33 12094.46 6096.88 7988.07 2998.64 13093.62 6298.09 7698.73 23
thisisatest051587.33 27385.99 29291.37 22393.49 28779.55 29090.63 38089.56 44680.17 35687.56 23590.86 35467.07 34598.28 16981.50 29293.02 23096.29 218
HQP-MVS89.80 18689.28 18591.34 22494.17 24981.56 20594.39 20496.04 17288.81 10385.43 29293.97 24273.83 25597.96 21587.11 19789.77 28794.50 300
fmvsm_s_conf0.5_n_793.15 8993.76 6891.31 22594.42 22879.48 29294.52 18897.14 5389.33 8194.17 6698.09 1881.83 12597.49 25696.33 2698.02 8096.95 185
RRT-MVS90.85 14790.70 14491.30 22694.25 24476.83 36894.85 16696.13 16389.04 9490.23 17694.88 19670.15 30898.72 12091.86 11294.88 16698.34 48
FMVSNet387.40 27086.11 28791.30 22693.79 27383.64 12894.20 21994.81 28083.89 27084.37 32291.87 32168.45 33696.56 34778.23 34985.36 34293.70 348
FMVSNet287.19 28385.82 30091.30 22694.01 25783.67 12694.79 17194.94 26683.57 27883.88 33792.05 31466.59 35396.51 35177.56 35685.01 34593.73 346
RPMNet83.95 36281.53 37391.21 22990.58 40179.34 30285.24 46496.76 9371.44 46085.55 28182.97 46470.87 29498.91 9761.01 46489.36 29395.40 259
IB-MVS80.51 1585.24 33983.26 35791.19 23092.13 33679.86 28191.75 34791.29 40383.28 28980.66 38888.49 41361.28 40298.46 14780.99 30179.46 42095.25 265
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 19588.90 19991.18 23194.22 24682.07 18992.13 33696.09 16787.90 14585.37 29892.45 29574.38 24297.56 24887.15 19590.43 27193.93 326
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 19688.90 19991.12 23294.47 22281.49 20995.30 12996.14 16086.73 18885.45 28995.16 18369.89 31198.10 18087.70 18489.23 29693.77 342
LGP-MVS_train91.12 23294.47 22281.49 20996.14 16086.73 18885.45 28995.16 18369.89 31198.10 18087.70 18489.23 29693.77 342
ACMM84.12 989.14 20888.48 21291.12 23294.65 20481.22 21995.31 12796.12 16485.31 23185.92 27294.34 22370.19 30798.06 19485.65 21688.86 30194.08 320
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt051788.61 22687.78 23191.11 23594.96 17877.81 34495.35 12589.69 44185.09 24288.05 22494.59 21566.93 34698.48 14383.27 25592.13 25097.03 178
GBi-Net87.26 27585.98 29391.08 23694.01 25783.10 14995.14 14794.94 26683.57 27884.37 32291.64 32666.59 35396.34 36578.23 34985.36 34293.79 337
test187.26 27585.98 29391.08 23694.01 25783.10 14995.14 14794.94 26683.57 27884.37 32291.64 32666.59 35396.34 36578.23 34985.36 34293.79 337
FMVSNet185.85 32484.11 34491.08 23692.81 31783.10 14995.14 14794.94 26681.64 33482.68 36191.64 32659.01 42496.34 36575.37 37883.78 35893.79 337
Test_1112_low_res87.65 25486.51 27191.08 23694.94 18079.28 30691.77 34694.30 30476.04 41983.51 34892.37 29777.86 19197.73 23578.69 34489.13 29896.22 221
PS-MVSNAJss89.97 17889.62 17391.02 24091.90 34580.85 24095.26 13595.98 17686.26 20086.21 26694.29 22779.70 15897.65 23988.87 16988.10 31294.57 294
BH-RMVSNet88.37 23487.48 23791.02 24095.28 15879.45 29492.89 30493.07 35085.45 22686.91 24694.84 20170.35 30497.76 23073.97 39394.59 17695.85 242
UniMVSNet_ETH3D87.53 26486.37 27591.00 24292.44 32878.96 31194.74 17595.61 21584.07 26685.36 29994.52 21759.78 41697.34 28082.93 25987.88 31796.71 202
FIs90.51 16390.35 15090.99 24393.99 26180.98 23095.73 10497.54 989.15 8986.72 25394.68 20681.83 12597.24 29185.18 22288.31 31194.76 287
ACMP84.23 889.01 21788.35 21390.99 24394.73 19581.27 21695.07 15095.89 18886.48 19383.67 34394.30 22669.33 32097.99 20887.10 19988.55 30393.72 347
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous2023121186.59 30785.13 32290.98 24596.52 9881.50 20796.14 6496.16 15873.78 44283.65 34492.15 30563.26 38597.37 27982.82 26381.74 38894.06 321
IMVS_040389.97 17889.64 17290.96 24693.72 27577.75 34993.00 29895.34 24085.53 22288.77 21094.49 21878.49 18197.84 22684.75 22992.65 23897.28 151
sss88.93 21888.26 21990.94 24794.05 25580.78 24391.71 34895.38 23581.55 33888.63 21293.91 24775.04 23095.47 40682.47 26891.61 25396.57 209
IMVS_040789.85 18589.51 17690.88 24893.72 27577.75 34993.07 29595.34 24085.53 22288.34 21894.49 21877.69 19397.60 24484.75 22992.65 23897.28 151
viewmambaseed2359dif90.04 17589.78 16990.83 24992.85 31677.92 33892.23 33295.01 25881.90 32490.20 17795.45 16479.64 16597.34 28087.52 18993.17 22497.23 159
sd_testset88.59 22887.85 23090.83 24996.00 12280.42 25792.35 32594.71 28588.73 10786.85 25095.20 18167.31 34096.43 35979.64 32689.85 28495.63 253
PVSNet_BlendedMVS89.98 17789.70 17090.82 25196.12 11181.25 21793.92 24596.83 8383.49 28289.10 20192.26 30281.04 13698.85 10486.72 20287.86 31892.35 407
cascas86.43 31584.98 32590.80 25292.10 33880.92 23490.24 39295.91 18573.10 44983.57 34788.39 41465.15 36697.46 26184.90 22791.43 25594.03 323
ECVR-MVScopyleft89.09 21188.53 20790.77 25395.62 14475.89 38296.16 6084.22 47587.89 14790.20 17796.65 9163.19 38798.10 18085.90 21396.94 11298.33 50
GA-MVS86.61 30585.27 31990.66 25491.33 36878.71 31590.40 38793.81 32985.34 23085.12 30289.57 39561.25 40397.11 30180.99 30189.59 29096.15 225
thres600view787.65 25486.67 26290.59 25596.08 11778.72 31394.88 16291.58 39487.06 17688.08 22292.30 30068.91 33098.10 18070.05 42391.10 25894.96 276
thres40087.62 25986.64 26390.57 25695.99 12578.64 31694.58 18491.98 38386.94 18288.09 22091.77 32269.18 32698.10 18070.13 42091.10 25894.96 276
baseline188.10 24187.28 24390.57 25694.96 17880.07 26994.27 21491.29 40386.74 18787.41 23794.00 24076.77 20296.20 37080.77 30479.31 42295.44 257
viewdifsd2359ckpt1189.43 19889.05 19290.56 25892.89 31477.00 36492.81 30794.52 29387.03 17789.77 18895.79 14774.67 23797.51 25288.97 16584.98 34697.17 162
viewmsd2359difaftdt89.43 19889.05 19290.56 25892.89 31477.00 36492.81 30794.52 29387.03 17789.77 18895.79 14774.67 23797.51 25288.97 16584.98 34697.17 162
usedtu_dtu_shiyan186.84 29485.61 30890.53 26090.50 40581.80 19990.97 37294.96 26483.05 29483.50 34990.32 37172.15 27996.65 32979.49 33185.55 34093.15 372
FE-MVSNET386.84 29485.61 30890.53 26090.50 40581.80 19990.97 37294.96 26483.05 29483.50 34990.32 37172.15 27996.65 32979.49 33185.55 34093.15 372
FC-MVSNet-test90.27 16790.18 15590.53 26093.71 27979.85 28295.77 10097.59 689.31 8286.27 26494.67 20981.93 12397.01 31184.26 23988.09 31494.71 288
PAPM86.68 30485.39 31490.53 26093.05 30579.33 30589.79 40494.77 28378.82 37781.95 37293.24 26976.81 20097.30 28366.94 44093.16 22594.95 280
WR-MVS88.38 23387.67 23390.52 26493.30 29380.18 26293.26 28595.96 18088.57 11585.47 28892.81 28476.12 21096.91 31881.24 29682.29 37994.47 305
SSM_0407288.57 23087.92 22790.51 26594.76 19182.66 17079.84 48694.64 28985.18 23288.96 20595.00 19076.00 21392.03 45683.74 24993.15 22696.85 194
MVSTER88.84 21988.29 21790.51 26592.95 31180.44 25693.73 25895.01 25884.66 25787.15 24193.12 27472.79 27097.21 29487.86 18187.36 32693.87 331
testdata90.49 26796.40 10177.89 34195.37 23772.51 45493.63 7996.69 8782.08 11997.65 23983.08 25697.39 10195.94 237
test111189.10 20988.64 20490.48 26895.53 14974.97 39296.08 6984.89 47388.13 13090.16 18296.65 9163.29 38498.10 18086.14 20896.90 11598.39 45
tt080586.92 29185.74 30690.48 26892.22 33279.98 27795.63 11494.88 27483.83 27284.74 31192.80 28557.61 43197.67 23685.48 21984.42 35193.79 337
jajsoiax88.24 23887.50 23690.48 26890.89 38980.14 26495.31 12795.65 21384.97 24584.24 33094.02 23865.31 36597.42 26788.56 17288.52 30593.89 327
PatchMatch-RL86.77 30185.54 31090.47 27195.88 13082.71 16890.54 38392.31 37179.82 36284.32 32791.57 33468.77 33296.39 36173.16 39993.48 21592.32 408
0.4-1-1-0.181.55 39478.59 41690.42 27287.55 44779.90 27988.56 42689.19 45177.01 40579.72 40577.71 47954.84 44697.11 30180.50 31172.20 44694.26 311
tfpn200view987.58 26286.64 26390.41 27395.99 12578.64 31694.58 18491.98 38386.94 18288.09 22091.77 32269.18 32698.10 18070.13 42091.10 25894.48 303
VPNet88.20 23987.47 23890.39 27493.56 28679.46 29394.04 23395.54 22188.67 11086.96 24394.58 21669.33 32097.15 29684.05 24380.53 40894.56 295
ACMH80.38 1785.36 33483.68 35190.39 27494.45 22580.63 24694.73 17694.85 27682.09 31577.24 43392.65 28960.01 41497.58 24672.25 40484.87 34892.96 379
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view90087.63 25786.71 25990.38 27696.12 11178.55 31995.03 15491.58 39487.15 17288.06 22392.29 30168.91 33098.10 18070.13 42091.10 25894.48 303
mvs_tets88.06 24487.28 24390.38 27690.94 38579.88 28095.22 13895.66 21185.10 24184.21 33193.94 24363.53 38297.40 27588.50 17388.40 30993.87 331
131487.51 26586.57 26890.34 27892.42 32979.74 28792.63 31495.35 23978.35 38680.14 39591.62 33074.05 24997.15 29681.05 29793.53 21194.12 316
LTVRE_ROB82.13 1386.26 31884.90 32890.34 27894.44 22681.50 20792.31 32994.89 27283.03 29679.63 40792.67 28869.69 31497.79 22871.20 40986.26 33591.72 418
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 40777.58 42190.25 28086.55 45179.72 28887.46 44789.48 44976.43 41277.93 42875.94 48152.31 45897.05 30880.25 31671.85 45093.99 325
test_djsdf89.03 21588.64 20490.21 28190.74 39679.28 30695.96 8395.90 18684.66 25785.33 30092.94 27974.02 25097.30 28389.64 15588.53 30494.05 322
v2v48287.84 24787.06 24790.17 28290.99 38179.23 30994.00 23995.13 25184.87 24885.53 28392.07 31374.45 24197.45 26284.71 23481.75 38793.85 334
pmmvs485.43 33283.86 34990.16 28390.02 41682.97 15990.27 38892.67 36275.93 42080.73 38691.74 32471.05 29095.73 39578.85 34383.46 36591.78 417
V4287.68 25286.86 25290.15 28490.58 40180.14 26494.24 21795.28 24483.66 27685.67 27891.33 33674.73 23597.41 27384.43 23881.83 38592.89 382
MSDG84.86 34783.09 36090.14 28593.80 27180.05 27189.18 41793.09 34978.89 37478.19 42491.91 31965.86 36397.27 28768.47 42988.45 30793.11 374
sc_t181.53 39578.67 41590.12 28690.78 39378.64 31693.91 24790.20 42768.42 46980.82 38589.88 38846.48 47396.76 32376.03 37471.47 45194.96 276
anonymousdsp87.84 24787.09 24690.12 28689.13 42780.54 25494.67 18095.55 21982.05 31783.82 33892.12 30771.47 28797.15 29687.15 19587.80 32192.67 389
thres20087.21 28186.24 28290.12 28695.36 15478.53 32093.26 28592.10 37786.42 19688.00 22591.11 34769.24 32598.00 20769.58 42491.04 26493.83 336
CR-MVSNet85.35 33583.76 35090.12 28690.58 40179.34 30285.24 46491.96 38578.27 38885.55 28187.87 42471.03 29195.61 39873.96 39489.36 29395.40 259
0.4-1-1-0.280.84 40677.77 41990.06 29086.18 45579.35 30086.75 45289.54 44776.23 41778.59 42375.46 48455.03 44596.99 31280.11 31872.05 44893.85 334
v114487.61 26086.79 25790.06 29091.01 38079.34 30293.95 24295.42 23483.36 28785.66 27991.31 33974.98 23197.42 26783.37 25382.06 38193.42 358
XXY-MVS87.65 25486.85 25390.03 29292.14 33580.60 25293.76 25595.23 24682.94 29984.60 31394.02 23874.27 24395.49 40581.04 29883.68 36194.01 324
Vis-MVSNet (Re-imp)89.59 19189.44 17890.03 29295.74 13575.85 38395.61 11590.80 41787.66 15887.83 22995.40 16876.79 20196.46 35678.37 34596.73 12197.80 121
test250687.21 28186.28 28090.02 29495.62 14473.64 40896.25 5571.38 49887.89 14790.45 17096.65 9155.29 44398.09 18886.03 21296.94 11298.33 50
BH-untuned88.60 22788.13 22190.01 29595.24 16278.50 32293.29 28394.15 31284.75 25384.46 31993.40 26175.76 22097.40 27577.59 35594.52 17994.12 316
v119287.25 27786.33 27790.00 29690.76 39579.04 31093.80 25395.48 22482.57 30685.48 28791.18 34373.38 26497.42 26782.30 27282.06 38193.53 352
v7n86.81 29685.76 30489.95 29790.72 39779.25 30895.07 15095.92 18384.45 26082.29 36590.86 35472.60 27497.53 25079.42 33780.52 40993.08 376
testing9187.11 28686.18 28389.92 29894.43 22775.38 39191.53 35492.27 37386.48 19386.50 25590.24 37461.19 40697.53 25082.10 27790.88 26696.84 197
IMVS_040487.60 26186.84 25489.89 29993.72 27577.75 34988.56 42695.34 24085.53 22279.98 39994.49 21866.54 35694.64 41984.75 22992.65 23897.28 151
v887.50 26786.71 25989.89 29991.37 36579.40 29894.50 18995.38 23584.81 25183.60 34691.33 33676.05 21197.42 26782.84 26280.51 41092.84 384
v1087.25 27786.38 27489.85 30191.19 37179.50 29194.48 19095.45 22983.79 27483.62 34591.19 34175.13 22897.42 26781.94 28280.60 40592.63 391
baseline286.50 31185.39 31489.84 30291.12 37676.70 37191.88 34288.58 45382.35 31179.95 40090.95 35273.42 26297.63 24280.27 31589.95 28195.19 266
pm-mvs186.61 30585.54 31089.82 30391.44 36080.18 26295.28 13394.85 27683.84 27181.66 37492.62 29072.45 27796.48 35379.67 32578.06 42592.82 385
TR-MVS86.78 29885.76 30489.82 30394.37 23078.41 32492.47 31992.83 35681.11 34886.36 26192.40 29668.73 33397.48 25773.75 39789.85 28493.57 351
ACMH+81.04 1485.05 34283.46 35489.82 30394.66 20379.37 29994.44 19594.12 31582.19 31478.04 42692.82 28358.23 42797.54 24973.77 39682.90 37392.54 397
EI-MVSNet89.10 20988.86 20189.80 30691.84 34778.30 32993.70 26295.01 25885.73 21387.15 24195.28 17479.87 15597.21 29483.81 24787.36 32693.88 330
gbinet_0.2-2-1-0.0282.59 37680.19 38889.77 30785.23 46680.05 27191.59 35393.52 33877.60 39479.78 40482.87 46663.26 38596.45 35778.93 34168.97 46192.81 386
usedtu_blend_shiyan582.39 38179.93 39589.75 30885.12 46780.08 26792.36 32393.26 34374.29 43779.00 41582.72 46764.29 37696.60 34279.60 32768.75 46592.55 394
v14419287.19 28386.35 27689.74 30990.64 39978.24 33193.92 24595.43 23281.93 32285.51 28591.05 35074.21 24697.45 26282.86 26181.56 38993.53 352
COLMAP_ROBcopyleft80.39 1683.96 36182.04 37089.74 30995.28 15879.75 28694.25 21592.28 37275.17 42778.02 42793.77 25358.60 42697.84 22665.06 45185.92 33691.63 420
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA86.32 31785.18 32189.73 31192.15 33476.60 37291.12 36891.69 39083.53 28185.50 28688.81 40766.79 34996.48 35376.65 36490.35 27396.12 228
blend_shiyan481.94 38479.35 40389.70 31285.52 46280.08 26791.29 36293.82 32677.12 40379.31 41182.94 46554.81 44796.60 34279.60 32769.78 45692.41 403
IterMVS-LS88.36 23587.91 22989.70 31293.80 27178.29 33093.73 25895.08 25685.73 21384.75 31091.90 32079.88 15496.92 31783.83 24682.51 37593.89 327
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
blended_shiyan882.79 37180.49 38189.69 31485.50 46379.83 28491.38 35793.82 32677.14 40079.39 41083.73 45764.95 37096.63 33279.75 32268.77 46492.62 393
testing1186.44 31485.35 31789.69 31494.29 24175.40 39091.30 36190.53 42284.76 25285.06 30490.13 38058.95 42597.45 26282.08 27891.09 26296.21 223
testing9986.72 30285.73 30789.69 31494.23 24574.91 39491.35 36090.97 41186.14 20486.36 26190.22 37559.41 41997.48 25782.24 27490.66 26896.69 204
v192192086.97 29086.06 29089.69 31490.53 40478.11 33493.80 25395.43 23281.90 32485.33 30091.05 35072.66 27197.41 27382.05 28081.80 38693.53 352
icg_test_0407_289.15 20788.97 19489.68 31893.72 27577.75 34988.26 43295.34 24085.53 22288.34 21894.49 21877.69 19393.99 43184.75 22992.65 23897.28 151
blended_shiyan682.78 37280.48 38289.67 31985.53 46179.76 28591.37 35893.82 32677.14 40079.30 41283.73 45764.96 36996.63 33279.68 32468.75 46592.63 391
VortexMVS88.42 23188.01 22389.63 32093.89 26678.82 31293.82 25195.47 22586.67 19084.53 31791.99 31672.62 27396.65 32989.02 16484.09 35593.41 359
Fast-Effi-MVS+-dtu87.44 26886.72 25889.63 32092.04 33977.68 35494.03 23493.94 31885.81 21082.42 36491.32 33870.33 30597.06 30680.33 31490.23 27594.14 315
v124086.78 29885.85 29989.56 32290.45 40877.79 34693.61 26695.37 23781.65 33385.43 29291.15 34571.50 28697.43 26681.47 29382.05 38393.47 356
Effi-MVS+-dtu88.65 22588.35 21389.54 32393.33 29276.39 37694.47 19394.36 30287.70 15585.43 29289.56 39673.45 26097.26 28985.57 21891.28 25794.97 273
wanda-best-256-51282.44 37880.07 39089.53 32485.12 46779.44 29590.49 38493.75 33276.97 40679.00 41582.72 46764.29 37696.61 33879.56 32968.75 46592.55 394
FE-blended-shiyan782.44 37880.07 39089.53 32485.12 46779.44 29590.49 38493.75 33276.97 40679.00 41582.72 46764.29 37696.61 33879.56 32968.75 46592.55 394
AllTest83.42 36881.39 37489.52 32695.01 17277.79 34693.12 28990.89 41577.41 39676.12 44293.34 26254.08 45297.51 25268.31 43184.27 35393.26 362
TestCases89.52 32695.01 17277.79 34690.89 41577.41 39676.12 44293.34 26254.08 45297.51 25268.31 43184.27 35393.26 362
mvs_anonymous89.37 20489.32 18389.51 32893.47 28874.22 40191.65 35194.83 27882.91 30085.45 28993.79 25181.23 13596.36 36486.47 20494.09 19297.94 98
XVG-ACMP-BASELINE86.00 32084.84 33089.45 32991.20 37078.00 33691.70 34995.55 21985.05 24382.97 35892.25 30354.49 45097.48 25782.93 25987.45 32592.89 382
testing22284.84 34883.32 35589.43 33094.15 25275.94 38191.09 36989.41 45084.90 24685.78 27589.44 39752.70 45796.28 36870.80 41591.57 25496.07 232
MVP-Stereo85.97 32184.86 32989.32 33190.92 38782.19 18692.11 33794.19 30978.76 37978.77 42291.63 32968.38 33796.56 34775.01 38393.95 19589.20 460
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNetpermissive85.85 32484.70 33289.29 33291.76 35175.54 38788.49 42891.30 40281.63 33585.05 30588.70 41171.71 28396.24 36974.61 38989.05 29996.08 231
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v14887.04 28886.32 27889.21 33390.94 38577.26 36093.71 26194.43 29784.84 25084.36 32590.80 35876.04 21297.05 30882.12 27679.60 41993.31 361
tfpnnormal84.72 35083.23 35889.20 33492.79 31880.05 27194.48 19095.81 19482.38 30981.08 38291.21 34069.01 32996.95 31561.69 46280.59 40690.58 446
cl2286.78 29885.98 29389.18 33592.34 33077.62 35590.84 37694.13 31481.33 34283.97 33690.15 37973.96 25196.60 34284.19 24082.94 37093.33 360
BH-w/o87.57 26387.05 24889.12 33694.90 18477.90 34092.41 32093.51 33982.89 30183.70 34291.34 33575.75 22197.07 30575.49 37693.49 21392.39 405
WR-MVS_H87.80 24987.37 24089.10 33793.23 29478.12 33395.61 11597.30 3787.90 14583.72 34192.01 31579.65 16496.01 37976.36 36880.54 40793.16 370
miper_enhance_ethall86.90 29286.18 28389.06 33891.66 35677.58 35690.22 39494.82 27979.16 37084.48 31889.10 40179.19 16996.66 32884.06 24282.94 37092.94 380
c3_l87.14 28586.50 27289.04 33992.20 33377.26 36091.22 36794.70 28682.01 32084.34 32690.43 36978.81 17396.61 33883.70 25181.09 39693.25 364
miper_ehance_all_eth87.22 28086.62 26689.02 34092.13 33677.40 35890.91 37594.81 28081.28 34384.32 32790.08 38279.26 16796.62 33583.81 24782.94 37093.04 377
gg-mvs-nofinetune81.77 38879.37 40288.99 34190.85 39177.73 35386.29 45679.63 48674.88 43283.19 35769.05 49460.34 41196.11 37475.46 37794.64 17593.11 374
ETVMVS84.43 35582.92 36488.97 34294.37 23074.67 39591.23 36688.35 45583.37 28686.06 27089.04 40255.38 44195.67 39767.12 43891.34 25696.58 208
pmmvs683.42 36881.60 37288.87 34388.01 44277.87 34294.96 15794.24 30874.67 43378.80 42191.09 34860.17 41396.49 35277.06 36375.40 43992.23 410
test_cas_vis1_n_192088.83 22288.85 20288.78 34491.15 37576.72 37093.85 25094.93 27083.23 29192.81 9996.00 12861.17 40794.45 42091.67 11594.84 16795.17 267
MIMVSNet82.59 37680.53 37988.76 34591.51 35878.32 32886.57 45590.13 43079.32 36680.70 38788.69 41252.98 45693.07 44766.03 44688.86 30194.90 281
cl____86.52 31085.78 30188.75 34692.03 34076.46 37490.74 37794.30 30481.83 32983.34 35490.78 35975.74 22396.57 34581.74 28881.54 39093.22 366
DIV-MVS_self_test86.53 30985.78 30188.75 34692.02 34176.45 37590.74 37794.30 30481.83 32983.34 35490.82 35775.75 22196.57 34581.73 28981.52 39193.24 365
CP-MVSNet87.63 25787.26 24588.74 34893.12 29976.59 37395.29 13196.58 11188.43 11883.49 35192.98 27875.28 22795.83 38878.97 34081.15 39593.79 337
eth_miper_zixun_eth86.50 31185.77 30388.68 34991.94 34275.81 38490.47 38694.89 27282.05 31784.05 33390.46 36875.96 21596.77 32282.76 26579.36 42193.46 357
CHOSEN 280x42085.15 34083.99 34788.65 35092.47 32678.40 32579.68 48892.76 35974.90 43181.41 37889.59 39469.85 31395.51 40279.92 32195.29 15892.03 413
PS-CasMVS87.32 27486.88 25188.63 35192.99 30976.33 37895.33 12696.61 10988.22 12683.30 35693.07 27673.03 26895.79 39278.36 34681.00 40193.75 344
TransMVSNet (Re)84.43 35583.06 36288.54 35291.72 35278.44 32395.18 14492.82 35882.73 30479.67 40692.12 30773.49 25995.96 38171.10 41368.73 46991.21 433
tt0320-xc79.63 42076.66 42988.52 35391.03 37978.72 31393.00 29889.53 44866.37 47476.11 44487.11 43546.36 47595.32 41072.78 40167.67 47091.51 425
EG-PatchMatch MVS82.37 38280.34 38488.46 35490.27 41079.35 30092.80 31094.33 30377.14 40073.26 46190.18 37847.47 47096.72 32470.25 41787.32 32889.30 457
PEN-MVS86.80 29786.27 28188.40 35592.32 33175.71 38695.18 14496.38 12687.97 13982.82 36093.15 27273.39 26395.92 38376.15 37279.03 42493.59 350
Baseline_NR-MVSNet87.07 28786.63 26588.40 35591.44 36077.87 34294.23 21892.57 36484.12 26585.74 27792.08 31177.25 19796.04 37582.29 27379.94 41491.30 431
UBG85.51 33084.57 33788.35 35794.21 24771.78 43390.07 39989.66 44382.28 31285.91 27389.01 40361.30 40197.06 30676.58 36792.06 25196.22 221
D2MVS85.90 32285.09 32388.35 35790.79 39277.42 35791.83 34595.70 20680.77 35180.08 39790.02 38466.74 35196.37 36281.88 28487.97 31691.26 432
pmmvs584.21 35782.84 36788.34 35988.95 42976.94 36692.41 32091.91 38775.63 42280.28 39291.18 34364.59 37395.57 39977.09 36283.47 36492.53 398
tt032080.13 41377.41 42288.29 36090.50 40578.02 33593.10 29290.71 42066.06 47776.75 43786.97 43649.56 46595.40 40771.65 40571.41 45291.46 428
LCM-MVSNet-Re88.30 23788.32 21688.27 36194.71 19972.41 42893.15 28890.98 41087.77 15279.25 41391.96 31778.35 18395.75 39383.04 25795.62 14796.65 205
CostFormer85.77 32784.94 32788.26 36291.16 37472.58 42689.47 41291.04 40976.26 41686.45 25989.97 38670.74 29696.86 32182.35 27187.07 33195.34 263
ITE_SJBPF88.24 36391.88 34677.05 36392.92 35385.54 22080.13 39693.30 26657.29 43296.20 37072.46 40384.71 34991.49 426
PVSNet78.82 1885.55 32984.65 33388.23 36494.72 19771.93 42987.12 45092.75 36078.80 37884.95 30790.53 36664.43 37496.71 32674.74 38693.86 19796.06 234
IterMVS-SCA-FT85.45 33184.53 33888.18 36591.71 35376.87 36790.19 39692.65 36385.40 22981.44 37790.54 36566.79 34995.00 41681.04 29881.05 39792.66 390
EPNet_dtu86.49 31385.94 29688.14 36690.24 41172.82 41894.11 22492.20 37586.66 19179.42 40992.36 29873.52 25895.81 39071.26 40893.66 20695.80 246
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry82.71 37480.93 37888.06 36790.05 41576.37 37784.74 46991.96 38572.28 45781.32 38087.87 42471.03 29195.50 40468.97 42680.15 41292.32 408
test_vis1_n_192089.39 20389.84 16688.04 36892.97 31072.64 42394.71 17896.03 17486.18 20291.94 12896.56 9961.63 39695.74 39493.42 6595.11 16295.74 248
DTE-MVSNet86.11 31985.48 31287.98 36991.65 35774.92 39394.93 15995.75 19987.36 16782.26 36693.04 27772.85 26995.82 38974.04 39277.46 43093.20 368
PMMVS85.71 32884.96 32687.95 37088.90 43077.09 36288.68 42490.06 43272.32 45686.47 25690.76 36072.15 27994.40 42381.78 28793.49 21392.36 406
GG-mvs-BLEND87.94 37189.73 42277.91 33987.80 43878.23 49180.58 38983.86 45559.88 41595.33 40971.20 40992.22 24990.60 445
MonoMVSNet86.89 29386.55 26987.92 37289.46 42573.75 40594.12 22293.10 34887.82 15185.10 30390.76 36069.59 31694.94 41786.47 20482.50 37695.07 270
reproduce_monomvs86.37 31685.87 29887.87 37393.66 28373.71 40693.44 27395.02 25788.61 11382.64 36391.94 31857.88 42996.68 32789.96 14579.71 41893.22 366
pmmvs-eth3d80.97 40478.72 41487.74 37484.99 47079.97 27890.11 39891.65 39275.36 42473.51 45986.03 44459.45 41893.96 43475.17 38072.21 44589.29 459
MS-PatchMatch85.05 34284.16 34287.73 37591.42 36378.51 32191.25 36593.53 33777.50 39580.15 39491.58 33261.99 39395.51 40275.69 37594.35 18489.16 461
mmtdpeth85.04 34484.15 34387.72 37693.11 30075.74 38594.37 20892.83 35684.98 24489.31 19886.41 44161.61 39897.14 29992.63 8162.11 48190.29 447
test_040281.30 40079.17 40887.67 37793.19 29578.17 33292.98 30091.71 38875.25 42676.02 44590.31 37359.23 42096.37 36250.22 48483.63 36288.47 469
IterMVS84.88 34683.98 34887.60 37891.44 36076.03 38090.18 39792.41 36683.24 29081.06 38390.42 37066.60 35294.28 42779.46 33380.98 40292.48 399
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test81.37 39879.30 40487.58 37990.92 38774.16 40380.99 48187.68 46070.52 46476.63 43988.81 40771.21 28892.76 45160.01 46986.93 33295.83 244
EPMVS83.90 36482.70 36887.51 38090.23 41272.67 42188.62 42581.96 48181.37 34185.01 30688.34 41566.31 35794.45 42075.30 37987.12 32995.43 258
ADS-MVSNet281.66 39179.71 39987.50 38191.35 36674.19 40283.33 47488.48 45472.90 45182.24 36785.77 44764.98 36793.20 44564.57 45383.74 35995.12 268
OurMVSNet-221017-085.35 33584.64 33587.49 38290.77 39472.59 42594.01 23794.40 30084.72 25479.62 40893.17 27161.91 39496.72 32481.99 28181.16 39393.16 370
tpm284.08 35982.94 36387.48 38391.39 36471.27 43889.23 41690.37 42471.95 45884.64 31289.33 39867.30 34196.55 34975.17 38087.09 33094.63 289
RPSCF85.07 34184.27 33987.48 38392.91 31370.62 44791.69 35092.46 36576.20 41882.67 36295.22 17763.94 38097.29 28677.51 35785.80 33794.53 296
myMVS_eth3d2885.80 32685.26 32087.42 38594.73 19569.92 45390.60 38190.95 41287.21 17186.06 27090.04 38359.47 41796.02 37774.89 38593.35 22196.33 215
FE-MVSNET281.82 38779.99 39387.34 38684.74 47177.36 35992.72 31194.55 29182.09 31573.79 45886.46 43857.80 43094.45 42074.65 38773.10 44190.20 448
WBMVS84.97 34584.18 34187.34 38694.14 25371.62 43790.20 39592.35 36881.61 33684.06 33290.76 36061.82 39596.52 35078.93 34183.81 35793.89 327
miper_lstm_enhance85.27 33884.59 33687.31 38891.28 36974.63 39687.69 44394.09 31681.20 34781.36 37989.85 39074.97 23294.30 42681.03 30079.84 41793.01 378
FMVSNet581.52 39679.60 40087.27 38991.17 37277.95 33791.49 35592.26 37476.87 40876.16 44187.91 42351.67 45992.34 45467.74 43581.16 39391.52 424
USDC82.76 37381.26 37687.26 39091.17 37274.55 39789.27 41493.39 34178.26 38975.30 44992.08 31154.43 45196.63 33271.64 40685.79 33890.61 443
test-LLR85.87 32385.41 31387.25 39190.95 38371.67 43589.55 40889.88 43983.41 28484.54 31587.95 42167.25 34295.11 41381.82 28593.37 21994.97 273
test-mter84.54 35483.64 35287.25 39190.95 38371.67 43589.55 40889.88 43979.17 36984.54 31587.95 42155.56 43895.11 41381.82 28593.37 21994.97 273
JIA-IIPM81.04 40178.98 41287.25 39188.64 43173.48 41081.75 48089.61 44573.19 44882.05 37073.71 48866.07 36295.87 38671.18 41184.60 35092.41 403
TDRefinement79.81 41777.34 42387.22 39479.24 48775.48 38893.12 28992.03 38076.45 41175.01 45091.58 33249.19 46696.44 35870.22 41969.18 46089.75 453
tpmvs83.35 37082.07 36987.20 39591.07 37871.00 44488.31 43191.70 38978.91 37280.49 39187.18 43369.30 32397.08 30368.12 43483.56 36393.51 355
ppachtmachnet_test81.84 38680.07 39087.15 39688.46 43574.43 40089.04 42092.16 37675.33 42577.75 43088.99 40466.20 35995.37 40865.12 45077.60 42891.65 419
dmvs_re84.20 35883.22 35987.14 39791.83 34977.81 34490.04 40090.19 42884.70 25681.49 37589.17 40064.37 37591.13 46771.58 40785.65 33992.46 401
tpm cat181.96 38380.27 38587.01 39891.09 37771.02 44387.38 44891.53 39766.25 47580.17 39386.35 44368.22 33896.15 37369.16 42582.29 37993.86 333
test_fmvs1_n87.03 28987.04 24986.97 39989.74 42171.86 43094.55 18694.43 29778.47 38391.95 12795.50 16351.16 46193.81 43593.02 7394.56 17795.26 264
OpenMVS_ROBcopyleft74.94 1979.51 42177.03 42886.93 40087.00 44976.23 37992.33 32790.74 41968.93 46874.52 45488.23 41849.58 46496.62 33557.64 47584.29 35287.94 472
SixPastTwentyTwo83.91 36382.90 36586.92 40190.99 38170.67 44693.48 27091.99 38285.54 22077.62 43292.11 30960.59 41096.87 32076.05 37377.75 42793.20 368
ADS-MVSNet81.56 39379.78 39686.90 40291.35 36671.82 43183.33 47489.16 45272.90 45182.24 36785.77 44764.98 36793.76 43664.57 45383.74 35995.12 268
PatchT82.68 37581.27 37586.89 40390.09 41470.94 44584.06 47190.15 42974.91 43085.63 28083.57 45969.37 31994.87 41865.19 44888.50 30694.84 283
tpm84.73 34984.02 34686.87 40490.33 40968.90 45689.06 41989.94 43680.85 35085.75 27689.86 38968.54 33595.97 38077.76 35384.05 35695.75 247
Patchmatch-RL test81.67 39079.96 39486.81 40585.42 46471.23 43982.17 47987.50 46178.47 38377.19 43482.50 47170.81 29593.48 44082.66 26672.89 44495.71 251
test_vis1_n86.56 30886.49 27386.78 40688.51 43272.69 42094.68 17993.78 33179.55 36590.70 16595.31 17348.75 46793.28 44393.15 6993.99 19494.38 307
testing3-286.72 30286.71 25986.74 40796.11 11465.92 46893.39 27589.65 44489.46 7587.84 22892.79 28659.17 42297.60 24481.31 29490.72 26796.70 203
test_fmvs187.34 27287.56 23586.68 40890.59 40071.80 43294.01 23794.04 31778.30 38791.97 12595.22 17756.28 43693.71 43792.89 7494.71 17094.52 297
MDA-MVSNet-bldmvs78.85 42676.31 43186.46 40989.76 42073.88 40488.79 42290.42 42379.16 37059.18 48588.33 41660.20 41294.04 42962.00 46168.96 46291.48 427
mvs5depth80.98 40379.15 40986.45 41084.57 47273.29 41387.79 43991.67 39180.52 35382.20 36989.72 39255.14 44495.93 38273.93 39566.83 47290.12 450
tpmrst85.35 33584.99 32486.43 41190.88 39067.88 46188.71 42391.43 40080.13 35786.08 26988.80 40973.05 26796.02 37782.48 26783.40 36795.40 259
TESTMET0.1,183.74 36682.85 36686.42 41289.96 41771.21 44089.55 40887.88 45777.41 39683.37 35387.31 42956.71 43493.65 43980.62 30892.85 23594.40 306
our_test_381.93 38580.46 38386.33 41388.46 43573.48 41088.46 42991.11 40576.46 41076.69 43888.25 41766.89 34794.36 42468.75 42779.08 42391.14 435
lessismore_v086.04 41488.46 43568.78 45780.59 48473.01 46290.11 38155.39 44096.43 35975.06 38265.06 47692.90 381
TinyColmap79.76 41877.69 42085.97 41591.71 35373.12 41489.55 40890.36 42575.03 42872.03 46590.19 37746.22 47696.19 37263.11 45781.03 39888.59 468
KD-MVS_2432*160078.50 42776.02 43585.93 41686.22 45374.47 39884.80 46792.33 36979.29 36776.98 43585.92 44553.81 45493.97 43267.39 43657.42 48689.36 455
miper_refine_blended78.50 42776.02 43585.93 41686.22 45374.47 39884.80 46792.33 36979.29 36776.98 43585.92 44553.81 45493.97 43267.39 43657.42 48689.36 455
K. test v381.59 39280.15 38985.91 41889.89 41969.42 45592.57 31687.71 45985.56 21973.44 46089.71 39355.58 43795.52 40177.17 36069.76 45792.78 387
SSC-MVS3.284.60 35384.19 34085.85 41992.74 32068.07 45888.15 43493.81 32987.42 16583.76 34091.07 34962.91 38895.73 39574.56 39083.24 36893.75 344
mvsany_test185.42 33385.30 31885.77 42087.95 44475.41 38987.61 44680.97 48376.82 40988.68 21195.83 14477.44 19690.82 46985.90 21386.51 33391.08 439
MIMVSNet179.38 42277.28 42485.69 42186.35 45273.67 40791.61 35292.75 36078.11 39272.64 46388.12 41948.16 46891.97 46060.32 46677.49 42991.43 429
UWE-MVS83.69 36783.09 36085.48 42293.06 30465.27 47390.92 37486.14 46579.90 36086.26 26590.72 36357.17 43395.81 39071.03 41492.62 24395.35 262
UnsupCasMVSNet_eth80.07 41478.27 41885.46 42385.24 46572.63 42488.45 43094.87 27582.99 29871.64 46888.07 42056.34 43591.75 46273.48 39863.36 47992.01 414
CL-MVSNet_self_test81.74 38980.53 37985.36 42485.96 45672.45 42790.25 39093.07 35081.24 34579.85 40387.29 43070.93 29392.52 45266.95 43969.23 45991.11 437
MDA-MVSNet_test_wron79.21 42477.19 42685.29 42588.22 43972.77 41985.87 45890.06 43274.34 43562.62 48287.56 42766.14 36091.99 45966.90 44373.01 44291.10 438
YYNet179.22 42377.20 42585.28 42688.20 44072.66 42285.87 45890.05 43474.33 43662.70 48087.61 42666.09 36192.03 45666.94 44072.97 44391.15 434
WB-MVSnew83.77 36583.28 35685.26 42791.48 35971.03 44291.89 34187.98 45678.91 37284.78 30990.22 37569.11 32894.02 43064.70 45290.44 27090.71 441
dp81.47 39780.23 38685.17 42889.92 41865.49 47186.74 45390.10 43176.30 41581.10 38187.12 43462.81 38995.92 38368.13 43379.88 41594.09 319
UnsupCasMVSNet_bld76.23 43773.27 44185.09 42983.79 47472.92 41685.65 46193.47 34071.52 45968.84 47479.08 47749.77 46393.21 44466.81 44460.52 48389.13 463
usedtu_dtu_shiyan274.72 43971.30 44484.98 43077.78 48970.58 44891.85 34490.76 41867.24 47368.06 47682.17 47237.13 48592.78 45060.69 46566.03 47391.59 423
SD_040384.71 35184.65 33384.92 43192.95 31165.95 46792.07 34093.23 34583.82 27379.03 41493.73 25673.90 25292.91 44963.02 45990.05 27795.89 240
Anonymous2023120681.03 40279.77 39884.82 43287.85 44570.26 45091.42 35692.08 37873.67 44377.75 43089.25 39962.43 39193.08 44661.50 46382.00 38491.12 436
FE-MVSNET78.19 42976.03 43484.69 43383.70 47573.31 41290.58 38290.00 43577.11 40471.91 46685.47 44955.53 43991.94 46159.69 47070.24 45488.83 465
test0.0.03 182.41 38081.69 37184.59 43488.23 43872.89 41790.24 39287.83 45883.41 28479.86 40289.78 39167.25 34288.99 47965.18 44983.42 36691.90 416
CMPMVSbinary59.16 2180.52 40879.20 40784.48 43583.98 47367.63 46489.95 40393.84 32564.79 47966.81 47791.14 34657.93 42895.17 41176.25 37088.10 31290.65 442
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet84.69 35284.79 33184.37 43691.84 34764.92 47493.70 26291.47 39966.19 47686.16 26895.28 17467.18 34493.33 44280.89 30390.42 27294.88 282
PVSNet_073.20 2077.22 43374.83 43984.37 43690.70 39871.10 44183.09 47689.67 44272.81 45373.93 45783.13 46160.79 40993.70 43868.54 42850.84 49188.30 470
LF4IMVS80.37 41179.07 41184.27 43886.64 45069.87 45489.39 41391.05 40876.38 41374.97 45190.00 38547.85 46994.25 42874.55 39180.82 40488.69 467
Anonymous2024052180.44 41079.21 40684.11 43985.75 45967.89 46092.86 30693.23 34575.61 42375.59 44887.47 42850.03 46294.33 42571.14 41281.21 39290.12 450
PM-MVS78.11 43076.12 43384.09 44083.54 47670.08 45188.97 42185.27 47279.93 35974.73 45386.43 44034.70 48893.48 44079.43 33672.06 44788.72 466
test_fmvs283.98 36084.03 34583.83 44187.16 44867.53 46593.93 24492.89 35477.62 39386.89 24993.53 25947.18 47192.02 45890.54 13486.51 33391.93 415
testgi80.94 40580.20 38783.18 44287.96 44366.29 46691.28 36390.70 42183.70 27578.12 42592.84 28151.37 46090.82 46963.34 45682.46 37792.43 402
KD-MVS_self_test80.20 41279.24 40583.07 44385.64 46065.29 47291.01 37193.93 31978.71 38176.32 44086.40 44259.20 42192.93 44872.59 40269.35 45891.00 440
testing380.46 40979.59 40183.06 44493.44 29064.64 47593.33 27785.47 47084.34 26279.93 40190.84 35644.35 47992.39 45357.06 47787.56 32292.16 412
ambc83.06 44479.99 48563.51 47977.47 48992.86 35574.34 45684.45 45428.74 48995.06 41573.06 40068.89 46390.61 443
test20.0379.95 41679.08 41082.55 44685.79 45867.74 46391.09 36991.08 40681.23 34674.48 45589.96 38761.63 39690.15 47160.08 46776.38 43589.76 452
MVStest172.91 44269.70 44782.54 44778.14 48873.05 41588.21 43386.21 46460.69 48364.70 47890.53 36646.44 47485.70 48658.78 47353.62 48888.87 464
test_vis1_rt77.96 43176.46 43082.48 44885.89 45771.74 43490.25 39078.89 48771.03 46371.30 46981.35 47442.49 48191.05 46884.55 23682.37 37884.65 475
EU-MVSNet81.32 39980.95 37782.42 44988.50 43463.67 47893.32 27891.33 40164.02 48080.57 39092.83 28261.21 40592.27 45576.34 36980.38 41191.32 430
myMVS_eth3d79.67 41978.79 41382.32 45091.92 34364.08 47689.75 40687.40 46281.72 33178.82 41987.20 43145.33 47791.29 46559.09 47287.84 31991.60 421
ttmdpeth76.55 43574.64 44082.29 45182.25 48167.81 46289.76 40585.69 46870.35 46575.76 44691.69 32546.88 47289.77 47366.16 44563.23 48089.30 457
pmmvs371.81 44568.71 44881.11 45275.86 49170.42 44986.74 45383.66 47658.95 48668.64 47580.89 47536.93 48689.52 47563.10 45863.59 47883.39 476
Syy-MVS80.07 41479.78 39680.94 45391.92 34359.93 48589.75 40687.40 46281.72 33178.82 41987.20 43166.29 35891.29 46547.06 48887.84 31991.60 421
UWE-MVS-2878.98 42578.38 41780.80 45488.18 44160.66 48490.65 37978.51 48878.84 37677.93 42890.93 35359.08 42389.02 47850.96 48390.33 27492.72 388
new-patchmatchnet76.41 43675.17 43880.13 45582.65 48059.61 48687.66 44491.08 40678.23 39069.85 47283.22 46054.76 44891.63 46464.14 45564.89 47789.16 461
mvsany_test374.95 43873.26 44280.02 45674.61 49263.16 48085.53 46278.42 48974.16 43874.89 45286.46 43836.02 48789.09 47782.39 27066.91 47187.82 473
test_fmvs377.67 43277.16 42779.22 45779.52 48661.14 48292.34 32691.64 39373.98 44078.86 41886.59 43727.38 49287.03 48188.12 17875.97 43789.50 454
DSMNet-mixed76.94 43476.29 43278.89 45883.10 47856.11 49487.78 44079.77 48560.65 48475.64 44788.71 41061.56 39988.34 48060.07 46889.29 29592.21 411
EGC-MVSNET61.97 45356.37 45878.77 45989.63 42373.50 40989.12 41882.79 4780.21 5361.24 53784.80 45239.48 48290.04 47244.13 49075.94 43872.79 490
new_pmnet72.15 44370.13 44678.20 46082.95 47965.68 46983.91 47282.40 48062.94 48264.47 47979.82 47642.85 48086.26 48557.41 47674.44 44082.65 480
MVS-HIRNet73.70 44172.20 44378.18 46191.81 35056.42 49382.94 47782.58 47955.24 48768.88 47366.48 49555.32 44295.13 41258.12 47488.42 30883.01 478
LCM-MVSNet66.00 45062.16 45577.51 46264.51 50658.29 48883.87 47390.90 41448.17 49154.69 48873.31 48916.83 50186.75 48265.47 44761.67 48287.48 474
APD_test169.04 44666.26 45277.36 46380.51 48462.79 48185.46 46383.51 47754.11 48959.14 48684.79 45323.40 49589.61 47455.22 47870.24 45479.68 485
test_f71.95 44470.87 44575.21 46474.21 49459.37 48785.07 46685.82 46765.25 47870.42 47183.13 46123.62 49382.93 49278.32 34771.94 44983.33 477
ANet_high58.88 45754.22 46272.86 46556.50 51156.67 49080.75 48286.00 46673.09 45037.39 50164.63 49922.17 49679.49 49643.51 49123.96 50482.43 481
test_vis3_rt65.12 45162.60 45372.69 46671.44 49660.71 48387.17 44965.55 50063.80 48153.22 48965.65 49814.54 50289.44 47676.65 36465.38 47567.91 495
LoFTR57.22 46052.62 46471.00 46772.03 49548.57 49972.00 49670.08 49944.40 49640.92 49976.42 4808.12 50682.76 49342.28 49447.33 49481.66 482
FPMVS64.63 45262.55 45470.88 46870.80 49756.71 48984.42 47084.42 47451.78 49049.57 49081.61 47323.49 49481.48 49440.61 49676.25 43674.46 489
dmvs_testset74.57 44075.81 43770.86 46987.72 44640.47 50887.05 45177.90 49382.75 30371.15 47085.47 44967.98 33984.12 49045.26 48976.98 43488.00 471
N_pmnet68.89 44768.44 44970.23 47089.07 42828.79 51688.06 43519.50 51669.47 46771.86 46784.93 45161.24 40491.75 46254.70 47977.15 43190.15 449
testf159.54 45556.11 45969.85 47169.28 49856.61 49180.37 48376.55 49642.58 49845.68 49475.61 48211.26 50384.18 48843.20 49260.44 48468.75 493
APD_test259.54 45556.11 45969.85 47169.28 49856.61 49180.37 48376.55 49642.58 49845.68 49475.61 48211.26 50384.18 48843.20 49260.44 48468.75 493
WB-MVS67.92 44867.49 45069.21 47381.09 48241.17 50788.03 43678.00 49273.50 44562.63 48183.11 46363.94 38086.52 48325.66 50351.45 49079.94 484
PMMVS259.60 45456.40 45769.21 47368.83 50046.58 50073.02 49577.48 49455.07 48849.21 49172.95 49017.43 50080.04 49549.32 48544.33 49580.99 483
SSC-MVS67.06 44966.56 45168.56 47580.54 48340.06 50987.77 44177.37 49572.38 45561.75 48382.66 47063.37 38386.45 48424.48 50448.69 49379.16 486
Gipumacopyleft57.99 45954.91 46167.24 47688.51 43265.59 47052.21 50190.33 42643.58 49742.84 49751.18 50420.29 49885.07 48734.77 49770.45 45351.05 503
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
RoMa-SfM53.80 46149.39 46567.06 47767.87 50248.86 49775.04 49038.06 51247.23 49347.40 49378.96 4787.40 50776.66 49848.89 48633.62 49975.64 488
DKM50.92 46546.13 46965.30 47866.27 50445.98 50273.05 49431.91 51445.08 49442.04 49875.01 4864.95 51473.81 50047.90 48728.96 50176.09 487
MatchFormer51.11 46446.66 46864.46 47967.11 50343.39 50570.54 49763.67 50233.19 50237.22 50270.30 4926.67 50978.17 49730.29 50040.94 49771.81 491
PMVScopyleft47.18 2252.22 46348.46 46763.48 48045.72 51546.20 50173.41 49378.31 49041.03 50030.06 50565.68 4976.05 51083.43 49130.04 50165.86 47460.80 497
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai58.82 45858.24 45660.56 48183.13 47745.09 50482.32 47848.22 50967.61 47161.70 48469.15 49338.75 48376.05 49932.01 49941.31 49660.55 498
PDCNetPlus48.34 46745.15 47057.91 48261.43 50841.85 50665.98 49838.30 51147.59 49237.96 50071.85 49110.18 50566.85 50552.94 48120.14 51565.03 496
MVEpermissive39.65 2343.39 46838.59 47457.77 48356.52 51048.77 49855.38 50058.64 50529.33 50428.96 50652.65 5034.68 51764.62 50628.11 50233.07 50059.93 499
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method50.52 46648.47 46656.66 48452.26 51418.98 52041.51 50781.40 48210.10 51044.59 49675.01 48628.51 49068.16 50153.54 48049.31 49282.83 479
DeepMVS_CXcopyleft56.31 48574.23 49351.81 49656.67 50644.85 49548.54 49275.16 48527.87 49158.74 50840.92 49552.22 48958.39 501
ELoFTR40.15 47135.08 47555.36 48641.27 52028.17 51747.70 50343.76 51029.15 50530.35 50465.97 4962.17 52466.90 50434.51 49820.83 51471.00 492
kuosan53.51 46253.30 46354.13 48776.06 49045.36 50380.11 48548.36 50859.63 48554.84 48763.43 50037.41 48462.07 50720.73 50639.10 49854.96 502
GLUNet-SfM31.36 47326.25 47846.70 48835.51 52224.89 51833.71 51236.36 51319.08 50623.78 50952.69 5023.82 52256.26 50919.75 50811.56 52558.95 500
E-PMN43.23 46942.29 47146.03 48965.58 50537.41 51073.51 49264.62 50133.99 50128.47 50747.87 50519.90 49967.91 50222.23 50524.45 50232.77 509
EMVS42.07 47041.12 47244.92 49063.45 50735.56 51273.65 49163.48 50333.05 50326.88 50845.45 50621.27 49767.14 50319.80 50723.02 50632.06 510
ALIKED-LG28.00 47426.54 47732.41 49158.12 50931.80 51347.26 50421.21 51514.15 50719.16 51141.93 5086.72 50835.73 5105.96 51724.32 50329.69 511
ALIKED-MNN26.28 47524.57 48031.39 49256.22 51231.73 51445.54 50519.13 51811.12 50817.11 51339.35 5105.01 51334.53 5115.54 51922.12 50827.92 512
ALIKED-NN26.07 47624.75 47930.02 49355.08 51330.61 51544.20 50619.22 51710.98 50917.98 51240.71 5095.39 51232.83 5125.59 51823.63 50526.63 513
tmp_tt35.64 47239.24 47324.84 49414.87 53923.90 51962.71 49951.51 5076.58 51836.66 50362.08 50144.37 47830.34 51452.40 48222.00 50920.27 515
wuyk23d21.27 47820.48 48123.63 49568.59 50136.41 51149.57 5026.85 5319.37 5117.89 5204.46 5364.03 52131.37 51317.47 50916.07 5183.12 531
SP-LightGlue20.24 47920.15 48320.49 49643.51 51612.27 52838.68 50914.56 5227.54 51512.90 51730.07 5144.75 51514.38 5187.60 51321.75 51034.82 504
SP-SuperGlue20.22 48020.18 48220.36 49743.26 51712.27 52838.71 50814.77 5217.64 51413.04 51630.21 5134.73 51614.21 5207.59 51421.65 51134.59 505
SP-DiffGlue20.02 48119.96 48420.21 49819.64 53613.14 52730.51 51315.49 5208.39 51219.98 51043.75 5075.48 51113.72 52113.75 51022.65 50733.78 507
SP-MNN19.61 48219.42 48520.19 49942.15 51811.42 53438.15 51014.24 5236.55 51911.64 51929.88 5164.16 51914.56 5177.09 51620.92 51334.58 506
SP-NN19.44 48319.37 48619.67 50041.70 51911.48 53337.75 51113.72 5256.86 51611.86 51829.97 5154.23 51814.25 5197.13 51521.07 51233.30 508
XFeat-MNN17.43 48416.95 48718.86 50116.90 53711.28 53527.31 51417.08 5198.08 51315.61 51535.73 5114.06 52022.95 51510.20 51117.59 51722.35 514
XFeat-NN15.96 48515.86 48816.25 50215.78 5389.87 53825.17 51513.83 5246.76 51715.68 51434.83 5123.61 52319.28 5169.22 51217.90 51619.58 516
SIFT-NN12.98 48613.18 48912.37 50336.49 52116.03 52122.41 5167.69 5274.89 5207.41 52120.48 5181.69 52511.46 5231.88 52215.70 5199.61 518
SIFT-MNN12.44 48712.55 49012.11 50434.55 52315.21 52220.91 5177.74 5264.86 5216.54 52320.09 5191.51 52611.47 5221.88 52214.87 5219.64 517
SIFT-NN-NCMNet12.12 48812.25 49111.75 50532.82 52514.83 52320.73 5187.58 5284.72 5236.60 52219.53 5201.49 52711.15 5251.74 52415.02 5209.28 519
SIFT-NCM-Cal11.58 48911.64 49211.40 50633.45 52414.10 52419.75 5206.89 5294.68 5264.55 53018.60 5251.34 53111.28 5241.53 53013.95 5228.82 523
SIFT-NN-CMatch11.26 49011.31 49411.13 50730.21 52913.40 52618.43 5216.79 5324.71 5246.47 52419.53 5201.43 52910.72 5271.71 52512.49 5249.26 520
SIFT-ConvMatch10.91 49210.94 49710.84 50832.07 52613.57 52517.23 5246.35 5334.71 5245.18 52718.94 5231.30 53210.76 5261.65 52811.02 5278.19 524
SIFT-NN-UMatch11.06 49111.19 49610.66 50928.66 53112.16 53019.79 5196.86 5304.73 5225.21 52619.47 5221.46 52810.70 5281.71 52512.79 5239.13 521
SIFT-UMatch10.58 49310.73 49810.15 51031.05 52711.65 53218.01 5225.92 5354.65 5274.72 52818.93 5241.25 53410.62 5291.66 52710.39 5288.16 525
SIFT-CM-Cal10.08 49510.13 5019.92 51130.71 52811.88 53115.35 5265.44 5364.59 5284.72 52818.04 5281.26 53310.19 5301.46 5329.60 5297.69 526
SIFT-NN-PointCN10.26 49410.46 4999.65 51227.18 5329.89 53717.89 5236.17 5344.40 5305.65 52518.29 5261.43 52910.09 5311.61 52911.55 5268.99 522
SIFT-UM-Cal9.80 49610.00 5029.22 51330.05 53010.15 53616.31 5254.85 5384.54 5294.19 53118.23 5271.19 5359.95 5321.52 5319.11 5317.57 527
SIFT-PCN-Cal8.65 5008.88 5047.98 51426.74 5337.47 54013.90 5284.61 5394.09 5323.82 53215.86 5291.01 5368.94 5331.34 5338.52 5327.53 528
SIFT-PointCN8.76 4989.03 5037.96 51526.50 5347.60 53914.94 5275.08 5374.10 5313.74 53315.46 5300.94 5378.92 5341.33 5349.14 5307.37 529
SIFT-NCMNet7.46 5027.71 5066.72 51625.03 5356.86 54111.42 5292.98 5404.05 5333.38 53413.68 5310.84 5387.65 5351.13 5356.87 5335.66 530
test1238.76 49811.22 4951.39 5170.85 5410.97 54285.76 4600.35 5420.54 5352.45 5368.14 5350.60 5390.48 5362.16 5210.17 5352.71 532
testmvs8.92 49711.52 4931.12 5181.06 5400.46 54386.02 4570.65 5410.62 5342.74 5359.52 5340.31 5400.45 5372.38 5200.39 5342.46 533
mmdepth0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
monomultidepth0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
test_blank0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
uanet_test0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
DCPMVS0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
cdsmvs_eth3d_5k22.14 47729.52 4760.00 5190.00 5420.00 5440.00 53095.76 1980.00 5370.00 53894.29 22775.66 2240.00 5380.00 5360.00 5360.00 534
pcd_1.5k_mvsjas6.64 5038.86 5050.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 53779.70 1580.00 5380.00 5360.00 5360.00 534
sosnet-low-res0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
sosnet0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
uncertanet0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
Regformer0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
ab-mvs-re7.82 50110.43 5000.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 53893.88 2480.00 5410.00 5380.00 5360.00 5360.00 534
uanet0.00 5040.00 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.00 5370.00 5410.00 5380.00 5360.00 5360.00 534
WAC-MVS64.08 47659.14 471
FOURS198.86 485.54 7498.29 197.49 1189.79 6596.29 32
PC_three_145282.47 30797.09 1997.07 7292.72 198.04 19992.70 8099.02 1298.86 16
test_one_060198.58 1485.83 6897.44 2091.05 2396.78 2798.06 2491.45 12
eth-test20.00 542
eth-test0.00 542
ZD-MVS98.15 4086.62 3497.07 6083.63 27794.19 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 16293.75 7697.43 5182.94 10092.73 7697.80 9197.88 111
IU-MVS98.77 886.00 5496.84 8281.26 34497.26 1395.50 3699.13 399.03 10
test_241102_TWO97.44 2090.31 4397.62 898.07 2291.46 1199.58 1395.66 3099.12 698.98 12
test_241102_ONE98.77 885.99 5697.44 2090.26 4997.71 297.96 3392.31 599.38 35
9.1494.47 3597.79 5896.08 6997.44 2086.13 20695.10 5597.40 5388.34 2699.22 5393.25 6898.70 37
save fliter97.85 5585.63 7395.21 14196.82 8589.44 76
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 4797.64 698.13 791.47 9
GSMVS96.12 228
test_part298.55 1587.22 2096.40 31
sam_mvs171.70 28496.12 228
sam_mvs70.60 298
MTGPAbinary96.97 65
test_post188.00 4379.81 53369.31 32295.53 40076.65 364
test_post10.29 53270.57 30295.91 385
patchmatchnet-post83.76 45671.53 28596.48 353
MTMP96.16 6060.64 504
gm-plane-assit89.60 42468.00 45977.28 39988.99 40497.57 24779.44 335
test9_res91.91 10998.71 3598.07 83
TEST997.53 6786.49 3894.07 23096.78 9081.61 33692.77 10196.20 10987.71 3299.12 63
test_897.49 6986.30 4694.02 23696.76 9381.86 32792.70 10596.20 10987.63 3399.02 73
agg_prior290.54 13498.68 4098.27 64
agg_prior97.38 7285.92 6196.72 10092.16 12098.97 87
test_prior485.96 5894.11 224
test_prior294.12 22287.67 15792.63 10996.39 10486.62 4591.50 11898.67 43
旧先验293.36 27671.25 46194.37 6197.13 30086.74 200
新几何293.11 291
旧先验196.79 8681.81 19895.67 20996.81 8486.69 4397.66 9796.97 184
无先验93.28 28496.26 14073.95 44199.05 6780.56 30996.59 207
原ACMM292.94 302
test22296.55 9581.70 20392.22 33395.01 25868.36 47090.20 17796.14 11980.26 14597.80 9196.05 235
testdata298.75 11678.30 348
segment_acmp87.16 40
testdata192.15 33587.94 141
plane_prior794.70 20082.74 165
plane_prior694.52 21682.75 16374.23 244
plane_prior596.22 14598.12 17888.15 17589.99 27894.63 289
plane_prior494.86 198
plane_prior382.75 16390.26 4986.91 246
plane_prior295.85 9390.81 27
plane_prior194.59 209
plane_prior82.73 16695.21 14189.66 7089.88 283
n20.00 543
nn0.00 543
door-mid85.49 469
test1196.57 112
door85.33 471
HQP5-MVS81.56 205
HQP-NCC94.17 24994.39 20488.81 10385.43 292
ACMP_Plane94.17 24994.39 20488.81 10385.43 292
BP-MVS87.11 197
HQP4-MVS85.43 29297.96 21594.51 299
HQP3-MVS96.04 17289.77 287
HQP2-MVS73.83 255
NP-MVS94.37 23082.42 17993.98 241
MDTV_nov1_ep13_2view55.91 49587.62 44573.32 44784.59 31470.33 30574.65 38795.50 256
MDTV_nov1_ep1383.56 35391.69 35569.93 45287.75 44291.54 39678.60 38284.86 30888.90 40669.54 31796.03 37670.25 41788.93 300
ACMMP++_ref87.47 323
ACMMP++88.01 315
Test By Simon80.02 147