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

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

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

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

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




Method Infoallhigh-res
multi-view
indooroutdoorbotani.boulde.bridgedoorexhibi.lectur.living.loungeobserv.old co.statueterrac.
sort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
DVP-MVS++98.06 197.99 198.28 1098.67 6795.39 1299.29 198.28 5294.78 6198.93 2098.87 3196.04 299.86 997.45 4699.58 2399.59 32
SED-MVS98.05 297.99 198.24 1199.42 1095.30 1898.25 4098.27 5595.13 4099.19 1398.89 2895.54 599.85 2197.52 4299.66 1099.56 40
TestfortrainingZip a97.92 397.70 1098.58 399.56 196.08 598.69 1198.70 1693.45 11898.73 3098.53 5195.46 799.86 996.63 6999.58 2399.80 1
MED-MVS97.91 497.88 498.00 2399.56 194.50 3598.69 1198.70 1694.23 8798.73 3098.53 5195.46 799.86 997.40 5099.58 2399.65 20
DVP-MVScopyleft97.91 497.81 598.22 1499.45 695.36 1498.21 4797.85 13794.92 5098.73 3098.87 3195.08 1099.84 2697.52 4299.67 699.48 56
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
DPE-MVScopyleft97.86 697.65 1198.47 699.17 3895.78 897.21 19798.35 4295.16 3898.71 3598.80 3895.05 1299.89 396.70 6899.73 199.73 12
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVScopyleft97.82 797.73 998.08 1999.15 3994.82 2998.81 898.30 4894.76 6498.30 4398.90 2593.77 1999.68 7597.93 2999.69 399.75 7
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CNVR-MVS97.68 897.44 2498.37 898.90 5995.86 797.27 18898.08 9395.81 2097.87 5898.31 8194.26 1599.68 7597.02 5799.49 4399.57 36
fmvsm_l_conf0.5_n97.65 997.75 897.34 6198.21 10692.75 9297.83 9898.73 1095.04 4599.30 798.84 3693.34 2499.78 4999.32 799.13 9899.50 52
fmvsm_l_conf0.5_n_397.64 1097.60 1397.79 3498.14 11393.94 5697.93 8398.65 2496.70 899.38 599.07 1189.92 9199.81 3599.16 1499.43 5399.61 30
fmvsm_l_conf0.5_n_a97.63 1197.76 797.26 6898.25 9992.59 10097.81 10398.68 1994.93 4899.24 1098.87 3193.52 2299.79 4699.32 799.21 8399.40 66
SteuartSystems-ACMMP97.62 1297.53 1897.87 2898.39 8894.25 4498.43 2798.27 5595.34 3298.11 4798.56 4794.53 1499.71 6796.57 7399.62 1799.65 20
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_l_conf0.5_n_997.59 1397.79 696.97 8698.28 9491.49 14497.61 13898.71 1397.10 599.70 198.93 2290.95 7699.77 5299.35 699.53 3399.65 20
MSP-MVS97.59 1397.54 1797.73 4299.40 1493.77 6198.53 1998.29 5095.55 2798.56 3897.81 13693.90 1799.65 7996.62 7099.21 8399.77 3
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
lecture97.58 1597.63 1297.43 5899.37 1992.93 8698.86 798.85 595.27 3498.65 3698.90 2591.97 5299.80 4097.63 3899.21 8399.57 36
test_fmvsm_n_192097.55 1697.89 396.53 10598.41 8591.73 13098.01 6699.02 196.37 1399.30 798.92 2392.39 4499.79 4699.16 1499.46 4698.08 228
ME-MVS97.54 1797.39 2798.00 2399.21 3694.50 3597.75 11098.34 4494.23 8798.15 4698.53 5193.32 2799.84 2697.40 5099.58 2399.65 20
reproduce-ours97.53 1897.51 2097.60 5198.97 5393.31 7397.71 12098.20 6995.80 2197.88 5598.98 1892.91 3099.81 3597.68 3399.43 5399.67 15
our_new_method97.53 1897.51 2097.60 5198.97 5393.31 7397.71 12098.20 6995.80 2197.88 5598.98 1892.91 3099.81 3597.68 3399.43 5399.67 15
reproduce_model97.51 2097.51 2097.50 5498.99 5293.01 8297.79 10698.21 6795.73 2497.99 5199.03 1592.63 3999.82 3397.80 3199.42 5699.67 15
test_fmvsmconf_n97.49 2197.56 1697.29 6497.44 16592.37 10797.91 8598.88 495.83 1998.92 2399.05 1491.45 6199.80 4099.12 1699.46 4699.69 14
TSAR-MVS + MP.97.42 2297.33 2997.69 4699.25 3294.24 4598.07 6097.85 13793.72 10398.57 3798.35 7293.69 2099.40 13397.06 5699.46 4699.44 61
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD-MVS97.41 2397.53 1897.06 8298.57 7894.46 3897.92 8498.14 8394.82 5799.01 1798.55 4994.18 1697.41 39796.94 5899.64 1499.32 74
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
SF-MVS97.39 2497.13 3198.17 1699.02 4895.28 2098.23 4498.27 5592.37 17198.27 4498.65 4593.33 2599.72 6596.49 7599.52 3599.51 49
SMA-MVScopyleft97.35 2597.03 4098.30 999.06 4495.42 1197.94 8198.18 7690.57 25698.85 2798.94 2193.33 2599.83 3196.72 6699.68 499.63 26
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
HPM-MVS++copyleft97.34 2696.97 4398.47 699.08 4296.16 497.55 14997.97 12195.59 2596.61 9797.89 11892.57 4199.84 2695.95 9999.51 3899.40 66
fmvsm_s_conf0.5_n_997.33 2797.57 1596.62 10198.43 8290.32 20297.80 10498.53 3097.24 499.62 299.14 288.65 10999.80 4099.54 199.15 9599.74 9
fmvsm_s_conf0.5_n_897.32 2897.48 2396.85 8898.28 9491.07 16997.76 10898.62 2697.53 299.20 1299.12 588.24 11799.81 3599.41 399.17 9199.67 15
fmvsm_s_conf0.5_n_1197.30 2997.59 1496.43 11998.42 8391.37 15198.04 6398.00 11797.30 399.45 499.21 189.28 9799.80 4099.27 1099.35 6998.12 220
NCCC97.30 2997.03 4098.11 1898.77 6295.06 2697.34 17798.04 10895.96 1597.09 7997.88 12393.18 2899.71 6795.84 10499.17 9199.56 40
fmvsm_s_conf0.5_n_1097.29 3197.40 2696.97 8698.24 10091.96 12697.89 8898.72 1296.77 799.46 399.06 1287.78 12799.84 2699.40 499.27 7599.12 92
MM97.29 3196.98 4298.23 1298.01 12395.03 2798.07 6095.76 35397.78 197.52 6298.80 3888.09 11999.86 999.44 299.37 6799.80 1
ACMMP_NAP97.20 3396.86 4998.23 1299.09 4095.16 2397.60 13998.19 7492.82 15497.93 5498.74 4291.60 5999.86 996.26 8099.52 3599.67 15
XVS97.18 3496.96 4597.81 3299.38 1794.03 5498.59 1798.20 6994.85 5396.59 9998.29 8491.70 5699.80 4095.66 10899.40 6199.62 27
MCST-MVS97.18 3496.84 5198.20 1599.30 2995.35 1697.12 20498.07 9893.54 11296.08 12597.69 14993.86 1899.71 6796.50 7499.39 6399.55 43
fmvsm_s_conf0.5_n_397.15 3697.36 2896.52 10797.98 12691.19 16197.84 9598.65 2497.08 699.25 999.10 687.88 12599.79 4699.32 799.18 9098.59 171
HFP-MVS97.14 3796.92 4797.83 3099.42 1094.12 5098.52 2098.32 4693.21 12797.18 7398.29 8492.08 4999.83 3195.63 11399.59 1999.54 45
test_fmvsmconf0.1_n97.09 3897.06 3597.19 7395.67 30792.21 11497.95 8098.27 5595.78 2398.40 4299.00 1689.99 8999.78 4999.06 1899.41 5999.59 32
fmvsm_s_conf0.5_n_697.08 3997.17 3096.81 8997.28 17091.73 13097.75 11098.50 3194.86 5299.22 1198.78 4089.75 9499.76 5499.10 1799.29 7398.94 121
MTAPA97.08 3996.78 5997.97 2799.37 1994.42 4097.24 19098.08 9395.07 4496.11 12398.59 4690.88 7999.90 296.18 9299.50 4099.58 35
region2R97.07 4196.84 5197.77 3899.46 593.79 5998.52 2098.24 6393.19 13097.14 7698.34 7591.59 6099.87 795.46 11999.59 1999.64 25
ACMMPR97.07 4196.84 5197.79 3499.44 993.88 5798.52 2098.31 4793.21 12797.15 7598.33 7891.35 6599.86 995.63 11399.59 1999.62 27
CP-MVS97.02 4396.81 5697.64 4999.33 2693.54 6498.80 998.28 5292.99 14096.45 11198.30 8391.90 5399.85 2195.61 11599.68 499.54 45
SR-MVS97.01 4496.86 4997.47 5699.09 4093.27 7597.98 7198.07 9893.75 10297.45 6498.48 6191.43 6399.59 9596.22 8399.27 7599.54 45
fmvsm_s_conf0.5_n_597.00 4596.97 4397.09 7997.58 16192.56 10197.68 12498.47 3594.02 9398.90 2598.89 2888.94 10399.78 4999.18 1299.03 10798.93 125
ZNCC-MVS96.96 4696.67 6497.85 2999.37 1994.12 5098.49 2498.18 7692.64 16196.39 11398.18 9191.61 5899.88 495.59 11899.55 3099.57 36
APD-MVScopyleft96.95 4796.60 6698.01 2199.03 4794.93 2897.72 11898.10 9191.50 20698.01 5098.32 8092.33 4599.58 9894.85 13799.51 3899.53 48
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSLP-MVS++96.94 4897.06 3596.59 10298.72 6491.86 12897.67 12598.49 3294.66 6997.24 7298.41 6792.31 4798.94 19596.61 7199.46 4698.96 114
DeepC-MVS_fast93.89 296.93 4996.64 6597.78 3698.64 7394.30 4197.41 16798.04 10894.81 5996.59 9998.37 7091.24 6899.64 8795.16 12499.52 3599.42 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SPE-MVS-test96.89 5097.04 3996.45 11898.29 9391.66 13799.03 497.85 13795.84 1896.90 8397.97 10991.24 6898.75 23196.92 5999.33 7098.94 121
SR-MVS-dyc-post96.88 5196.80 5797.11 7899.02 4892.34 10897.98 7198.03 11093.52 11597.43 6798.51 5691.40 6499.56 10696.05 9499.26 7899.43 63
CS-MVS96.86 5297.06 3596.26 13598.16 11291.16 16699.09 397.87 13295.30 3397.06 8098.03 10191.72 5498.71 24197.10 5599.17 9198.90 130
mPP-MVS96.86 5296.60 6697.64 4999.40 1493.44 6698.50 2398.09 9293.27 12695.95 13198.33 7891.04 7399.88 495.20 12299.57 2999.60 31
fmvsm_s_conf0.5_n96.85 5497.13 3196.04 14998.07 12090.28 20397.97 7798.76 994.93 4898.84 2899.06 1288.80 10699.65 7999.06 1898.63 12398.18 213
GST-MVS96.85 5496.52 7097.82 3199.36 2394.14 4998.29 3498.13 8492.72 15796.70 9198.06 9891.35 6599.86 994.83 14099.28 7499.47 58
balanced_conf0396.84 5696.89 4896.68 9397.63 15392.22 11398.17 5397.82 14394.44 7998.23 4597.36 17990.97 7599.22 15197.74 3299.66 1098.61 168
patch_mono-296.83 5797.44 2495.01 22599.05 4585.39 37196.98 21798.77 894.70 6697.99 5198.66 4393.61 2199.91 197.67 3799.50 4099.72 13
APD-MVS_3200maxsize96.81 5896.71 6397.12 7699.01 5192.31 11097.98 7198.06 10193.11 13697.44 6598.55 4990.93 7799.55 10896.06 9399.25 8099.51 49
PGM-MVS96.81 5896.53 6997.65 4799.35 2593.53 6597.65 12998.98 292.22 17797.14 7698.44 6491.17 7199.85 2194.35 16399.46 4699.57 36
MP-MVScopyleft96.77 6096.45 7797.72 4399.39 1693.80 5898.41 2898.06 10193.37 12295.54 14998.34 7590.59 8399.88 494.83 14099.54 3299.49 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS96.77 6096.46 7697.71 4598.40 8694.07 5298.21 4798.45 3789.86 27497.11 7898.01 10492.52 4299.69 7396.03 9799.53 3399.36 72
fmvsm_s_conf0.5_n_496.75 6297.07 3495.79 17397.76 14289.57 23297.66 12898.66 2295.36 3099.03 1698.90 2588.39 11499.73 6199.17 1398.66 12198.08 228
fmvsm_s_conf0.5_n_a96.75 6296.93 4696.20 14097.64 15190.72 18598.00 6798.73 1094.55 7398.91 2499.08 888.22 11899.63 8898.91 2198.37 13698.25 208
MGCNet96.74 6496.31 8198.02 2096.87 20394.65 3197.58 14094.39 42096.47 1297.16 7498.39 6887.53 13699.87 798.97 2099.41 5999.55 43
test_fmvsmvis_n_192096.70 6596.84 5196.31 12996.62 23091.73 13097.98 7198.30 4896.19 1496.10 12498.95 2089.42 9599.76 5498.90 2299.08 10297.43 268
MP-MVS-pluss96.70 6596.27 8397.98 2699.23 3594.71 3096.96 21998.06 10190.67 24695.55 14798.78 4091.07 7299.86 996.58 7299.55 3099.38 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.96.69 6796.49 7197.27 6798.31 9293.39 6796.79 24296.72 29794.17 8997.44 6597.66 15392.76 3499.33 13996.86 6297.76 16299.08 98
HPM-MVScopyleft96.69 6796.45 7797.40 5999.36 2393.11 8098.87 698.06 10191.17 22596.40 11297.99 10790.99 7499.58 9895.61 11599.61 1899.49 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_HR96.68 6996.58 6896.99 8498.46 7992.31 11096.20 30598.90 394.30 8695.86 13497.74 14492.33 4599.38 13696.04 9699.42 5699.28 77
fmvsm_s_conf0.5_n_296.62 7096.82 5596.02 15197.98 12690.43 19597.50 15398.59 2796.59 1099.31 699.08 884.47 20499.75 5899.37 598.45 13397.88 241
DELS-MVS96.61 7196.38 8097.30 6397.79 14093.19 7895.96 31998.18 7695.23 3595.87 13397.65 15491.45 6199.70 7295.87 10099.44 5299.00 109
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
DeepPCF-MVS93.97 196.61 7197.09 3395.15 21698.09 11686.63 33796.00 31798.15 8195.43 2897.95 5398.56 4793.40 2399.36 13796.77 6399.48 4499.45 59
fmvsm_s_conf0.1_n96.58 7396.77 6096.01 15496.67 22890.25 20497.91 8598.38 3894.48 7798.84 2899.14 288.06 12099.62 8998.82 2398.60 12598.15 217
MVSMamba_PlusPlus96.51 7496.48 7296.59 10298.07 12091.97 12498.14 5497.79 14590.43 26197.34 7097.52 16991.29 6799.19 15498.12 2899.64 1498.60 169
EI-MVSNet-Vis-set96.51 7496.47 7396.63 9898.24 10091.20 16096.89 22797.73 15294.74 6596.49 10698.49 5890.88 7999.58 9896.44 7698.32 13899.13 89
HPM-MVS_fast96.51 7496.27 8397.22 7099.32 2792.74 9398.74 1098.06 10190.57 25696.77 8898.35 7290.21 8699.53 11294.80 14499.63 1699.38 70
fmvsm_s_conf0.5_n_796.45 7796.80 5795.37 20897.29 16988.38 28597.23 19498.47 3595.14 3998.43 4199.09 787.58 13399.72 6598.80 2599.21 8398.02 232
EC-MVSNet96.42 7896.47 7396.26 13597.01 19291.52 14398.89 597.75 14994.42 8096.64 9697.68 15089.32 9698.60 25797.45 4699.11 10198.67 166
fmvsm_s_conf0.1_n_a96.40 7996.47 7396.16 14295.48 31690.69 18697.91 8598.33 4594.07 9198.93 2099.14 287.44 14199.61 9098.63 2698.32 13898.18 213
CANet96.39 8096.02 8897.50 5497.62 15493.38 6897.02 21097.96 12295.42 2994.86 17297.81 13687.38 14399.82 3396.88 6099.20 8899.29 75
dcpmvs_296.37 8197.05 3894.31 27298.96 5584.11 39297.56 14497.51 19393.92 9797.43 6798.52 5592.75 3599.32 14197.32 5499.50 4099.51 49
NormalMVS96.36 8296.11 8697.12 7699.37 1992.90 8797.99 6897.63 16695.92 1696.57 10297.93 11185.34 18699.50 12094.99 12999.21 8398.97 111
EI-MVSNet-UG-set96.34 8396.30 8296.47 11598.20 10790.93 17596.86 23097.72 15494.67 6896.16 12298.46 6290.43 8499.58 9896.23 8297.96 15598.90 130
fmvsm_s_conf0.1_n_296.33 8496.44 7996.00 15597.30 16890.37 20197.53 15097.92 12796.52 1199.14 1599.08 883.21 22699.74 5999.22 1198.06 15097.88 241
train_agg96.30 8595.83 9397.72 4398.70 6594.19 4696.41 28098.02 11388.58 32196.03 12697.56 16692.73 3799.59 9595.04 12699.37 6799.39 68
ACMMPcopyleft96.27 8695.93 8997.28 6699.24 3392.62 9898.25 4098.81 692.99 14094.56 18298.39 6888.96 10299.85 2194.57 15797.63 16399.36 72
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
MVS_111021_LR96.24 8796.19 8596.39 12498.23 10591.35 15396.24 30298.79 793.99 9595.80 13697.65 15489.92 9199.24 14995.87 10099.20 8898.58 172
test_fmvsmconf0.01_n96.15 8895.85 9297.03 8392.66 43191.83 12997.97 7797.84 14195.57 2697.53 6199.00 1684.20 21099.76 5498.82 2399.08 10299.48 56
DeepC-MVS93.07 396.06 8995.66 9497.29 6497.96 12893.17 7997.30 18298.06 10193.92 9793.38 22298.66 4386.83 15099.73 6195.60 11799.22 8298.96 114
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CSCG96.05 9095.91 9096.46 11799.24 3390.47 19298.30 3398.57 2989.01 30393.97 20397.57 16492.62 4099.76 5494.66 15199.27 7599.15 87
sasdasda96.02 9195.45 10197.75 4097.59 15795.15 2498.28 3597.60 17194.52 7596.27 11796.12 25987.65 13099.18 15796.20 8894.82 25698.91 127
ETV-MVS96.02 9195.89 9196.40 12297.16 17692.44 10597.47 16297.77 14894.55 7396.48 10794.51 34191.23 7098.92 19895.65 11198.19 14497.82 249
canonicalmvs96.02 9195.45 10197.75 4097.59 15795.15 2498.28 3597.60 17194.52 7596.27 11796.12 25987.65 13099.18 15796.20 8894.82 25698.91 127
CDPH-MVS95.97 9495.38 10697.77 3898.93 5694.44 3996.35 28997.88 13086.98 36896.65 9597.89 11891.99 5199.47 12592.26 20399.46 4699.39 68
UA-Net95.95 9595.53 9797.20 7297.67 14792.98 8497.65 12998.13 8494.81 5996.61 9798.35 7288.87 10499.51 11790.36 25597.35 17499.11 94
SymmetryMVS95.94 9695.54 9697.15 7497.85 13692.90 8797.99 6896.91 28495.92 1696.57 10297.93 11185.34 18699.50 12094.99 12996.39 22199.05 102
MGCFI-Net95.94 9695.40 10597.56 5397.59 15794.62 3298.21 4797.57 17894.41 8196.17 12196.16 25787.54 13599.17 15996.19 9094.73 26198.91 127
BP-MVS195.89 9895.49 9897.08 8196.67 22893.20 7798.08 5896.32 32394.56 7296.32 11497.84 13084.07 21399.15 16396.75 6498.78 11698.90 130
VNet95.89 9895.45 10197.21 7198.07 12092.94 8597.50 15398.15 8193.87 9997.52 6297.61 16085.29 18899.53 11295.81 10595.27 24799.16 85
alignmvs95.87 10095.23 11197.78 3697.56 16395.19 2297.86 9197.17 24894.39 8396.47 10896.40 24485.89 16999.20 15396.21 8795.11 25298.95 118
casdiffmvs_mvgpermissive95.81 10195.57 9596.51 11196.87 20391.49 14497.50 15397.56 18693.99 9595.13 16297.92 11487.89 12498.78 21595.97 9897.33 17599.26 79
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DPM-MVS95.69 10294.92 12498.01 2198.08 11995.71 1095.27 36197.62 17090.43 26195.55 14797.07 19991.72 5499.50 12089.62 27198.94 11198.82 146
DP-MVS Recon95.68 10395.12 11697.37 6099.19 3794.19 4697.03 20898.08 9388.35 33095.09 16397.65 15489.97 9099.48 12492.08 21498.59 12698.44 190
casdiffmvspermissive95.64 10495.49 9896.08 14596.76 22590.45 19397.29 18397.44 21394.00 9495.46 15297.98 10887.52 13898.73 23595.64 11297.33 17599.08 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GDP-MVS95.62 10595.13 11497.09 7996.79 21493.26 7697.89 8897.83 14293.58 10796.80 8597.82 13483.06 23399.16 16194.40 16097.95 15698.87 140
MG-MVS95.61 10695.38 10696.31 12998.42 8390.53 19096.04 31497.48 19893.47 11795.67 14498.10 9489.17 9999.25 14891.27 23298.77 11799.13 89
baseline95.58 10795.42 10496.08 14596.78 21990.41 19697.16 20197.45 20993.69 10695.65 14597.85 12887.29 14498.68 24595.66 10897.25 18199.13 89
CPTT-MVS95.57 10895.19 11296.70 9299.27 3191.48 14698.33 3198.11 8987.79 34995.17 16198.03 10187.09 14899.61 9093.51 18199.42 5699.02 103
EIA-MVS95.53 10995.47 10095.71 18497.06 18489.63 22897.82 10097.87 13293.57 10893.92 20495.04 31390.61 8298.95 19394.62 15398.68 12098.54 175
3Dnovator+91.43 495.40 11094.48 15098.16 1796.90 20195.34 1798.48 2597.87 13294.65 7088.53 35398.02 10383.69 21799.71 6793.18 18998.96 11099.44 61
PS-MVSNAJ95.37 11195.33 10895.49 20297.35 16790.66 18895.31 35897.48 19893.85 10096.51 10595.70 28488.65 10999.65 7994.80 14498.27 14196.17 307
MVSFormer95.37 11195.16 11395.99 15696.34 26791.21 15898.22 4597.57 17891.42 21096.22 11997.32 18086.20 16497.92 34194.07 16699.05 10498.85 142
diffmvs_AUTHOR95.33 11395.27 11095.50 20196.37 26589.08 25996.08 31297.38 22493.09 13896.53 10497.74 14486.45 15898.68 24596.32 7897.48 16698.75 157
xiu_mvs_v2_base95.32 11495.29 10995.40 20797.22 17290.50 19195.44 35197.44 21393.70 10596.46 10996.18 25488.59 11399.53 11294.79 14797.81 15996.17 307
E3new95.28 11595.11 11795.80 17097.03 18989.76 22296.78 24697.54 19092.06 18795.40 15397.75 14187.49 13998.76 22594.85 13797.10 18798.88 138
PVSNet_Blended_VisFu95.27 11694.91 12596.38 12598.20 10790.86 17897.27 18898.25 6190.21 26594.18 19697.27 18687.48 14099.73 6193.53 18097.77 16198.55 174
viewcassd2359sk1195.26 11795.09 11895.80 17096.95 19889.72 22496.80 24197.56 18692.21 17995.37 15497.80 13887.17 14798.77 21994.82 14297.10 18798.90 130
KinetiMVS95.26 11794.75 13696.79 9096.99 19492.05 12097.82 10097.78 14694.77 6396.46 10997.70 14780.62 28899.34 13892.37 20298.28 14098.97 111
diffmvspermissive95.25 11995.13 11495.63 18796.43 26089.34 24695.99 31897.35 22992.83 15396.31 11597.37 17886.44 15998.67 24896.26 8097.19 18498.87 140
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmanbaseed2359cas95.24 12095.02 12095.91 15996.87 20389.98 21396.82 23697.49 19692.26 17595.47 15197.82 13486.47 15798.69 24394.80 14497.20 18399.06 101
Vis-MVSNetpermissive95.23 12194.81 13196.51 11197.18 17591.58 14198.26 3998.12 8694.38 8494.90 17198.15 9382.28 25498.92 19891.45 22998.58 12799.01 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet95.22 12295.04 11995.76 17797.49 16489.56 23398.67 1597.00 27490.69 24494.24 19297.62 15989.79 9398.81 21193.39 18696.49 21498.92 126
E295.20 12395.00 12195.79 17396.79 21489.66 22596.82 23697.58 17592.35 17295.28 15697.83 13286.68 15298.76 22594.79 14796.92 19398.95 118
E395.20 12395.00 12195.79 17396.77 22189.66 22596.82 23697.58 17592.35 17295.28 15697.83 13286.69 15198.76 22594.79 14796.92 19398.95 118
EPNet95.20 12394.56 14397.14 7592.80 42892.68 9797.85 9494.87 40496.64 992.46 23997.80 13886.23 16199.65 7993.72 17698.62 12499.10 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator91.36 595.19 12694.44 15297.44 5796.56 24393.36 7098.65 1698.36 3994.12 9089.25 33598.06 9882.20 25699.77 5293.41 18599.32 7199.18 84
guyue95.17 12794.96 12395.82 16896.97 19689.65 22797.56 14495.58 36594.82 5795.72 13997.42 17582.90 23898.84 20796.71 6796.93 19298.96 114
E495.09 12894.86 13095.77 17696.58 23889.56 23396.85 23197.56 18692.50 16695.03 16897.86 12686.03 16798.78 21594.71 15096.65 20798.96 114
OMC-MVS95.09 12894.70 13796.25 13898.46 7991.28 15496.43 27697.57 17892.04 18894.77 17797.96 11087.01 14999.09 17491.31 23196.77 19898.36 197
viewmacassd2359aftdt95.07 13094.80 13295.87 16296.53 24889.84 21996.90 22697.48 19892.44 16895.36 15597.89 11885.23 18998.68 24594.40 16097.00 19199.09 96
E5new95.04 13194.88 12695.52 19596.62 23089.02 26197.29 18397.57 17892.54 16295.04 16497.89 11885.65 17898.77 21994.92 13296.44 21798.78 149
E6new95.04 13194.88 12695.52 19596.60 23489.02 26197.29 18397.57 17892.54 16295.04 16497.90 11685.66 17698.77 21994.92 13296.44 21798.78 149
E695.04 13194.88 12695.52 19596.60 23489.02 26197.29 18397.57 17892.54 16295.04 16497.90 11685.66 17698.77 21994.92 13296.44 21798.78 149
E595.04 13194.88 12695.52 19596.62 23089.02 26197.29 18397.57 17892.54 16295.04 16497.89 11885.65 17898.77 21994.92 13296.44 21798.78 149
xiu_mvs_v1_base_debu95.01 13594.76 13395.75 17996.58 23891.71 13396.25 29997.35 22992.99 14096.70 9196.63 23182.67 24499.44 12996.22 8397.46 16796.11 313
xiu_mvs_v1_base95.01 13594.76 13395.75 17996.58 23891.71 13396.25 29997.35 22992.99 14096.70 9196.63 23182.67 24499.44 12996.22 8397.46 16796.11 313
xiu_mvs_v1_base_debi95.01 13594.76 13395.75 17996.58 23891.71 13396.25 29997.35 22992.99 14096.70 9196.63 23182.67 24499.44 12996.22 8397.46 16796.11 313
PAPM_NR95.01 13594.59 14196.26 13598.89 6090.68 18797.24 19097.73 15291.80 19392.93 23696.62 23489.13 10099.14 16689.21 28497.78 16098.97 111
lupinMVS94.99 13994.56 14396.29 13396.34 26791.21 15895.83 32796.27 33088.93 30996.22 11996.88 21386.20 16498.85 20595.27 12199.05 10498.82 146
Effi-MVS+94.93 14094.45 15196.36 12796.61 23391.47 14796.41 28097.41 21991.02 23394.50 18595.92 26887.53 13698.78 21593.89 17296.81 19798.84 145
IS-MVSNet94.90 14194.52 14796.05 14897.67 14790.56 18998.44 2696.22 33493.21 12793.99 20197.74 14485.55 18398.45 27189.98 26097.86 15799.14 88
LuminaMVS94.89 14294.35 15596.53 10595.48 31692.80 9196.88 22996.18 33892.85 15295.92 13296.87 21581.44 27198.83 20896.43 7797.10 18797.94 237
MVS_Test94.89 14294.62 14095.68 18596.83 20989.55 23596.70 25497.17 24891.17 22595.60 14696.11 26387.87 12698.76 22593.01 19797.17 18598.72 161
viewdifsd2359ckpt1394.87 14494.52 14795.90 16096.88 20290.19 20696.92 22397.36 22791.26 21894.65 17997.46 17085.79 17398.64 25293.64 17896.76 19998.88 138
PVSNet_Blended94.87 14494.56 14395.81 16998.27 9689.46 24195.47 35098.36 3988.84 31294.36 18896.09 26488.02 12199.58 9893.44 18398.18 14598.40 193
jason94.84 14694.39 15396.18 14195.52 31490.93 17596.09 31196.52 31289.28 29496.01 12997.32 18084.70 20098.77 21995.15 12598.91 11398.85 142
jason: jason.
API-MVS94.84 14694.49 14995.90 16097.90 13492.00 12397.80 10497.48 19889.19 29794.81 17596.71 22088.84 10599.17 15988.91 29198.76 11896.53 296
AstraMVS94.82 14894.64 13995.34 21096.36 26688.09 29897.58 14094.56 41394.98 4695.70 14297.92 11481.93 26498.93 19696.87 6195.88 22898.99 110
viewdifsd2359ckpt0994.81 14994.37 15496.12 14496.91 19990.75 18496.94 22097.31 23490.51 25994.31 19097.38 17785.70 17598.71 24193.54 17996.75 20098.90 130
test_yl94.78 15094.23 15896.43 11997.74 14391.22 15696.85 23197.10 25591.23 22295.71 14096.93 20884.30 20799.31 14393.10 19095.12 25098.75 157
DCV-MVSNet94.78 15094.23 15896.43 11997.74 14391.22 15696.85 23197.10 25591.23 22295.71 14096.93 20884.30 20799.31 14393.10 19095.12 25098.75 157
viewdifsd2359ckpt0794.76 15294.68 13895.01 22596.76 22587.41 31396.38 28697.43 21692.65 15994.52 18397.75 14185.55 18398.81 21194.36 16296.69 20498.82 146
SSM_040494.73 15394.31 15795.98 15797.05 18690.90 17797.01 21397.29 23591.24 21994.17 19797.60 16185.03 19398.76 22592.14 20897.30 17898.29 206
WTY-MVS94.71 15494.02 16396.79 9097.71 14592.05 12096.59 26997.35 22990.61 25294.64 18096.93 20886.41 16099.39 13491.20 23494.71 26298.94 121
mamv494.66 15596.10 8790.37 41898.01 12373.41 46996.82 23697.78 14689.95 27294.52 18397.43 17492.91 3099.09 17498.28 2799.16 9498.60 169
mvsmamba94.57 15694.14 16095.87 16297.03 18989.93 21797.84 9595.85 34991.34 21394.79 17696.80 21680.67 28698.81 21194.85 13798.12 14898.85 142
SSM_040794.54 15794.12 16295.80 17096.79 21490.38 19896.79 24297.29 23591.24 21993.68 20897.60 16185.03 19398.67 24892.14 20896.51 21098.35 199
RRT-MVS94.51 15894.35 15594.98 22996.40 26186.55 34097.56 14497.41 21993.19 13094.93 17097.04 20179.12 31699.30 14596.19 9097.32 17799.09 96
sss94.51 15893.80 16796.64 9497.07 18191.97 12496.32 29498.06 10188.94 30894.50 18596.78 21784.60 20199.27 14791.90 21596.02 22498.68 165
test_cas_vis1_n_192094.48 16094.55 14694.28 27496.78 21986.45 34397.63 13597.64 16493.32 12597.68 6098.36 7173.75 37999.08 17796.73 6599.05 10497.31 275
CANet_DTU94.37 16193.65 17396.55 10496.46 25892.13 11896.21 30396.67 30494.38 8493.53 21697.03 20679.34 31299.71 6790.76 24498.45 13397.82 249
AdaColmapbinary94.34 16293.68 17296.31 12998.59 7591.68 13696.59 26997.81 14489.87 27392.15 25097.06 20083.62 22099.54 11089.34 27898.07 14997.70 254
viewmambaseed2359dif94.28 16394.14 16094.71 24796.21 27186.97 32795.93 32197.11 25489.00 30495.00 16997.70 14786.02 16898.59 26193.71 17796.59 20998.57 173
CNLPA94.28 16393.53 17896.52 10798.38 8992.55 10296.59 26996.88 28890.13 26991.91 25897.24 18885.21 19099.09 17487.64 32297.83 15897.92 238
MAR-MVS94.22 16593.46 18396.51 11198.00 12592.19 11797.67 12597.47 20288.13 33893.00 23195.84 27284.86 19999.51 11787.99 30498.17 14697.83 248
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
PAPR94.18 16693.42 18896.48 11497.64 15191.42 15095.55 34597.71 15888.99 30592.34 24695.82 27489.19 9899.11 16986.14 34997.38 17298.90 130
SDMVSNet94.17 16793.61 17495.86 16598.09 11691.37 15197.35 17698.20 6993.18 13291.79 26297.28 18479.13 31598.93 19694.61 15492.84 29497.28 276
test_vis1_n_192094.17 16794.58 14292.91 34897.42 16682.02 41997.83 9897.85 13794.68 6798.10 4898.49 5870.15 40799.32 14197.91 3098.82 11497.40 270
h-mvs3394.15 16993.52 18096.04 14997.81 13990.22 20597.62 13797.58 17595.19 3696.74 8997.45 17183.67 21899.61 9095.85 10279.73 43598.29 206
CHOSEN 1792x268894.15 16993.51 18196.06 14798.27 9689.38 24495.18 37098.48 3485.60 39193.76 20797.11 19783.15 22999.61 9091.33 23098.72 11999.19 83
Vis-MVSNet (Re-imp)94.15 16993.88 16694.95 23397.61 15587.92 30298.10 5695.80 35292.22 17793.02 23097.45 17184.53 20397.91 34488.24 30097.97 15499.02 103
CDS-MVSNet94.14 17293.54 17795.93 15896.18 27991.46 14896.33 29397.04 26988.97 30793.56 21396.51 23887.55 13497.89 34589.80 26595.95 22698.44 190
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLCcopyleft91.00 694.11 17393.43 18696.13 14398.58 7791.15 16796.69 25697.39 22187.29 36391.37 27296.71 22088.39 11499.52 11687.33 33097.13 18697.73 252
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FIs94.09 17493.70 17195.27 21295.70 30592.03 12298.10 5698.68 1993.36 12490.39 29396.70 22287.63 13297.94 33892.25 20590.50 33595.84 321
PVSNet_BlendedMVS94.06 17593.92 16594.47 26198.27 9689.46 24196.73 25098.36 3990.17 26694.36 18895.24 30788.02 12199.58 9893.44 18390.72 33194.36 412
nrg03094.05 17693.31 19096.27 13495.22 33994.59 3398.34 3097.46 20492.93 14791.21 28296.64 22787.23 14698.22 29194.99 12985.80 38395.98 317
UGNet94.04 17793.28 19196.31 12996.85 20691.19 16197.88 9097.68 15994.40 8293.00 23196.18 25473.39 38399.61 9091.72 22198.46 13298.13 218
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
TAMVS94.01 17893.46 18395.64 18696.16 28190.45 19396.71 25396.89 28789.27 29593.46 22096.92 21187.29 14497.94 33888.70 29695.74 23298.53 176
Elysia94.00 17993.12 19696.64 9496.08 29192.72 9597.50 15397.63 16691.15 22794.82 17397.12 19574.98 36699.06 18390.78 24298.02 15198.12 220
StellarMVS94.00 17993.12 19696.64 9496.08 29192.72 9597.50 15397.63 16691.15 22794.82 17397.12 19574.98 36699.06 18390.78 24298.02 15198.12 220
IMVS_040393.98 18193.79 16894.55 25796.19 27586.16 35296.35 28997.24 24291.54 20193.59 21297.04 20185.86 17098.73 23590.68 24795.59 23898.76 153
114514_t93.95 18293.06 19996.63 9899.07 4391.61 13897.46 16497.96 12277.99 46093.00 23197.57 16486.14 16699.33 13989.22 28399.15 9598.94 121
IMVS_040793.94 18393.75 16994.49 26096.19 27586.16 35296.35 28997.24 24291.54 20193.50 21797.04 20185.64 18198.54 26490.68 24795.59 23898.76 153
FC-MVSNet-test93.94 18393.57 17595.04 22395.48 31691.45 14998.12 5598.71 1393.37 12290.23 29696.70 22287.66 12997.85 34791.49 22790.39 33695.83 322
mvsany_test193.93 18593.98 16493.78 30694.94 35686.80 33094.62 38492.55 45388.77 31896.85 8498.49 5888.98 10198.08 30995.03 12795.62 23796.46 301
GeoE93.89 18693.28 19195.72 18396.96 19789.75 22398.24 4396.92 28389.47 28892.12 25297.21 19084.42 20598.39 27987.71 31496.50 21399.01 106
HY-MVS89.66 993.87 18792.95 20496.63 9897.10 18092.49 10495.64 34296.64 30589.05 30293.00 23195.79 27885.77 17499.45 12889.16 28794.35 26497.96 235
XVG-OURS-SEG-HR93.86 18893.55 17694.81 23997.06 18488.53 28095.28 35997.45 20991.68 19894.08 20097.68 15082.41 25298.90 20193.84 17492.47 30096.98 284
VDD-MVS93.82 18993.08 19896.02 15197.88 13589.96 21697.72 11895.85 34992.43 16995.86 13498.44 6468.42 42499.39 13496.31 7994.85 25498.71 163
mvs_anonymous93.82 18993.74 17094.06 28496.44 25985.41 36995.81 32997.05 26789.85 27690.09 30696.36 24687.44 14197.75 36193.97 16896.69 20499.02 103
HQP_MVS93.78 19193.43 18694.82 23796.21 27189.99 21197.74 11397.51 19394.85 5391.34 27396.64 22781.32 27398.60 25793.02 19592.23 30395.86 318
PS-MVSNAJss93.74 19293.51 18194.44 26393.91 39489.28 25197.75 11097.56 18692.50 16689.94 30996.54 23788.65 10998.18 29693.83 17590.90 32995.86 318
XVG-OURS93.72 19393.35 18994.80 24297.07 18188.61 27494.79 38197.46 20491.97 19193.99 20197.86 12681.74 26798.88 20292.64 20192.67 29996.92 288
mamba_040893.70 19492.99 20095.83 16796.79 21490.38 19888.69 47297.07 26190.96 23593.68 20897.31 18284.97 19698.76 22590.95 23896.51 21098.35 199
HyFIR lowres test93.66 19592.92 20595.87 16298.24 10089.88 21894.58 38698.49 3285.06 40193.78 20695.78 27982.86 23998.67 24891.77 22095.71 23499.07 100
LFMVS93.60 19692.63 21996.52 10798.13 11591.27 15597.94 8193.39 44190.57 25696.29 11698.31 8169.00 41799.16 16194.18 16595.87 22999.12 92
icg_test_0407_293.58 19793.46 18393.94 29696.19 27586.16 35293.73 42297.24 24291.54 20193.50 21797.04 20185.64 18196.91 41790.68 24795.59 23898.76 153
F-COLMAP93.58 19792.98 20395.37 20898.40 8688.98 26597.18 19997.29 23587.75 35290.49 29197.10 19885.21 19099.50 12086.70 34096.72 20397.63 256
ab-mvs93.57 19992.55 22396.64 9497.28 17091.96 12695.40 35297.45 20989.81 27893.22 22896.28 25079.62 30999.46 12690.74 24593.11 29198.50 180
LS3D93.57 19992.61 22196.47 11597.59 15791.61 13897.67 12597.72 15485.17 39990.29 29598.34 7584.60 20199.73 6183.85 38598.27 14198.06 230
FA-MVS(test-final)93.52 20192.92 20595.31 21196.77 22188.54 27894.82 38096.21 33689.61 28394.20 19495.25 30683.24 22599.14 16690.01 25996.16 22398.25 208
SSM_0407293.51 20292.99 20095.05 22196.79 21490.38 19888.69 47297.07 26190.96 23593.68 20897.31 18284.97 19696.42 42890.95 23896.51 21098.35 199
viewdifsd2359ckpt1193.46 20393.22 19494.17 27796.11 28885.42 36796.43 27697.07 26192.91 14894.20 19498.00 10580.82 28498.73 23594.42 15889.04 35098.34 203
viewmsd2359difaftdt93.46 20393.23 19394.17 27796.12 28685.42 36796.43 27697.08 25892.91 14894.21 19398.00 10580.82 28498.74 23394.41 15989.05 34898.34 203
Fast-Effi-MVS+93.46 20392.75 21395.59 19096.77 22190.03 20896.81 24097.13 25088.19 33391.30 27694.27 35986.21 16398.63 25487.66 32196.46 21698.12 220
hse-mvs293.45 20692.99 20094.81 23997.02 19188.59 27596.69 25696.47 31595.19 3696.74 8996.16 25783.67 21898.48 27095.85 10279.13 43997.35 273
QAPM93.45 20692.27 23396.98 8596.77 22192.62 9898.39 2998.12 8684.50 40988.27 36197.77 14082.39 25399.81 3585.40 36298.81 11598.51 179
UniMVSNet_NR-MVSNet93.37 20892.67 21795.47 20595.34 32892.83 8997.17 20098.58 2892.98 14590.13 30195.80 27588.37 11697.85 34791.71 22283.93 41295.73 332
1112_ss93.37 20892.42 23096.21 13997.05 18690.99 17096.31 29596.72 29786.87 37189.83 31396.69 22486.51 15699.14 16688.12 30193.67 28598.50 180
UniMVSNet (Re)93.31 21092.55 22395.61 18995.39 32293.34 7197.39 17298.71 1393.14 13590.10 30594.83 32487.71 12898.03 32091.67 22583.99 41195.46 341
OPM-MVS93.28 21192.76 21194.82 23794.63 37290.77 18296.65 26097.18 24693.72 10391.68 26697.26 18779.33 31398.63 25492.13 21192.28 30295.07 370
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VPA-MVSNet93.24 21292.48 22895.51 19995.70 30592.39 10697.86 9198.66 2292.30 17492.09 25495.37 29980.49 29198.40 27493.95 16985.86 38295.75 330
test_fmvs193.21 21393.53 17892.25 37196.55 24581.20 42697.40 17196.96 27690.68 24596.80 8598.04 10069.25 41598.40 27497.58 4198.50 12897.16 281
MVSTER93.20 21492.81 21094.37 26696.56 24389.59 23197.06 20797.12 25191.24 21991.30 27695.96 26682.02 26098.05 31693.48 18290.55 33395.47 340
test111193.19 21592.82 20994.30 27397.58 16184.56 38698.21 4789.02 47293.53 11394.58 18198.21 8872.69 38699.05 18693.06 19398.48 13199.28 77
ECVR-MVScopyleft93.19 21592.73 21594.57 25697.66 14985.41 36998.21 4788.23 47493.43 12094.70 17898.21 8872.57 38799.07 18193.05 19498.49 12999.25 80
HQP-MVS93.19 21592.74 21494.54 25895.86 29789.33 24796.65 26097.39 22193.55 10990.14 29795.87 27080.95 27898.50 26792.13 21192.10 30895.78 326
CHOSEN 280x42093.12 21892.72 21694.34 26996.71 22787.27 31790.29 46297.72 15486.61 37591.34 27395.29 30184.29 20998.41 27393.25 18798.94 11197.35 273
sd_testset93.10 21992.45 22995.05 22198.09 11689.21 25396.89 22797.64 16493.18 13291.79 26297.28 18475.35 36398.65 25188.99 28992.84 29497.28 276
Effi-MVS+-dtu93.08 22093.21 19592.68 35996.02 29483.25 40297.14 20396.72 29793.85 10091.20 28393.44 39883.08 23198.30 28691.69 22495.73 23396.50 298
test_djsdf93.07 22192.76 21194.00 28893.49 41088.70 27198.22 4597.57 17891.42 21090.08 30795.55 29282.85 24097.92 34194.07 16691.58 31595.40 348
VDDNet93.05 22292.07 23796.02 15196.84 20790.39 19798.08 5895.85 34986.22 38395.79 13798.46 6267.59 42799.19 15494.92 13294.85 25498.47 185
thisisatest053093.03 22392.21 23595.49 20297.07 18189.11 25897.49 16192.19 45590.16 26794.09 19996.41 24376.43 35499.05 18690.38 25495.68 23598.31 205
EI-MVSNet93.03 22392.88 20793.48 32795.77 30386.98 32696.44 27497.12 25190.66 24891.30 27697.64 15786.56 15498.05 31689.91 26290.55 33395.41 345
CLD-MVS92.98 22592.53 22594.32 27096.12 28689.20 25495.28 35997.47 20292.66 15889.90 31095.62 28880.58 28998.40 27492.73 20092.40 30195.38 350
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tttt051792.96 22692.33 23294.87 23697.11 17987.16 32397.97 7792.09 45690.63 25093.88 20597.01 20776.50 35199.06 18390.29 25795.45 24498.38 195
ACMM89.79 892.96 22692.50 22794.35 26796.30 26988.71 27097.58 14097.36 22791.40 21290.53 29096.65 22679.77 30598.75 23191.24 23391.64 31395.59 336
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test92.94 22892.56 22294.10 28296.16 28188.26 28997.65 12997.46 20491.29 21490.12 30397.16 19279.05 31898.73 23592.25 20591.89 31195.31 355
BH-untuned92.94 22892.62 22093.92 30097.22 17286.16 35296.40 28496.25 33390.06 27089.79 31496.17 25683.19 22798.35 28287.19 33397.27 18097.24 278
DU-MVS92.90 23092.04 23995.49 20294.95 35492.83 8997.16 20198.24 6393.02 13990.13 30195.71 28283.47 22197.85 34791.71 22283.93 41295.78 326
PatchMatch-RL92.90 23092.02 24195.56 19198.19 10990.80 18095.27 36197.18 24687.96 34091.86 26195.68 28580.44 29298.99 19184.01 38097.54 16596.89 289
VortexMVS92.88 23292.64 21893.58 32196.58 23887.53 31296.93 22297.28 23892.78 15689.75 31594.99 31482.73 24397.76 35994.60 15588.16 35995.46 341
PMMVS92.86 23392.34 23194.42 26594.92 35786.73 33394.53 38896.38 32184.78 40694.27 19195.12 31283.13 23098.40 27491.47 22896.49 21498.12 220
OpenMVScopyleft89.19 1292.86 23391.68 25496.40 12295.34 32892.73 9498.27 3798.12 8684.86 40485.78 41097.75 14178.89 32599.74 5987.50 32798.65 12296.73 293
Test_1112_low_res92.84 23591.84 24895.85 16697.04 18889.97 21595.53 34796.64 30585.38 39489.65 32095.18 30885.86 17099.10 17187.70 31593.58 29098.49 182
baseline192.82 23691.90 24695.55 19397.20 17490.77 18297.19 19894.58 41292.20 18092.36 24396.34 24784.16 21198.21 29289.20 28583.90 41597.68 255
131492.81 23792.03 24095.14 21795.33 33189.52 23896.04 31497.44 21387.72 35386.25 40295.33 30083.84 21598.79 21489.26 28197.05 19097.11 282
DP-MVS92.76 23891.51 26296.52 10798.77 6290.99 17097.38 17496.08 34182.38 43589.29 33297.87 12483.77 21699.69 7381.37 40996.69 20498.89 136
test_fmvs1_n92.73 23992.88 20792.29 36896.08 29181.05 42797.98 7197.08 25890.72 24396.79 8798.18 9163.07 45298.45 27197.62 4098.42 13597.36 271
BH-RMVSNet92.72 24091.97 24394.97 23197.16 17687.99 30096.15 30995.60 36390.62 25191.87 26097.15 19478.41 33198.57 26283.16 38797.60 16498.36 197
ACMP89.59 1092.62 24192.14 23694.05 28596.40 26188.20 29397.36 17597.25 24191.52 20588.30 35996.64 22778.46 33098.72 24091.86 21891.48 31795.23 362
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LCM-MVSNet-Re92.50 24292.52 22692.44 36196.82 21181.89 42096.92 22393.71 43892.41 17084.30 42394.60 33685.08 19297.03 41191.51 22697.36 17398.40 193
TranMVSNet+NR-MVSNet92.50 24291.63 25595.14 21794.76 36592.07 11997.53 15098.11 8992.90 15189.56 32396.12 25983.16 22897.60 37589.30 27983.20 42195.75 330
thres600view792.49 24491.60 25695.18 21597.91 13389.47 23997.65 12994.66 40992.18 18493.33 22394.91 31978.06 33899.10 17181.61 40294.06 27996.98 284
IMVS_040492.44 24591.92 24594.00 28896.19 27586.16 35293.84 41997.24 24291.54 20188.17 36597.04 20176.96 34897.09 40890.68 24795.59 23898.76 153
thres100view90092.43 24691.58 25794.98 22997.92 13289.37 24597.71 12094.66 40992.20 18093.31 22494.90 32078.06 33899.08 17781.40 40694.08 27596.48 299
jajsoiax92.42 24791.89 24794.03 28793.33 41888.50 28197.73 11597.53 19192.00 19088.85 34596.50 23975.62 36198.11 30393.88 17391.56 31695.48 338
thres40092.42 24791.52 26095.12 21997.85 13689.29 24997.41 16794.88 40192.19 18293.27 22694.46 34678.17 33499.08 17781.40 40694.08 27596.98 284
tfpn200view992.38 24991.52 26094.95 23397.85 13689.29 24997.41 16794.88 40192.19 18293.27 22694.46 34678.17 33499.08 17781.40 40694.08 27596.48 299
test_vis1_n92.37 25092.26 23492.72 35694.75 36682.64 40998.02 6596.80 29491.18 22497.77 5997.93 11158.02 46298.29 28797.63 3898.21 14397.23 279
WR-MVS92.34 25191.53 25994.77 24495.13 34790.83 17996.40 28497.98 12091.88 19289.29 33295.54 29382.50 24997.80 35489.79 26685.27 39195.69 333
NR-MVSNet92.34 25191.27 27095.53 19494.95 35493.05 8197.39 17298.07 9892.65 15984.46 42195.71 28285.00 19597.77 35889.71 26783.52 41895.78 326
mvs_tets92.31 25391.76 25093.94 29693.41 41588.29 28797.63 13597.53 19192.04 18888.76 34896.45 24174.62 37198.09 30893.91 17191.48 31795.45 343
TAPA-MVS90.10 792.30 25491.22 27395.56 19198.33 9189.60 23096.79 24297.65 16281.83 43991.52 26897.23 18987.94 12398.91 20071.31 46398.37 13698.17 216
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest051592.29 25591.30 26895.25 21396.60 23488.90 26794.36 39892.32 45487.92 34193.43 22194.57 33777.28 34599.00 19089.42 27695.86 23097.86 245
Fast-Effi-MVS+-dtu92.29 25591.99 24293.21 33895.27 33585.52 36597.03 20896.63 30892.09 18589.11 33995.14 31080.33 29598.08 30987.54 32594.74 26096.03 316
IterMVS-LS92.29 25591.94 24493.34 33296.25 27086.97 32796.57 27297.05 26790.67 24689.50 32694.80 32686.59 15397.64 37089.91 26286.11 38195.40 348
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet86.66 1892.24 25891.74 25393.73 30797.77 14183.69 39992.88 44296.72 29787.91 34293.00 23194.86 32278.51 32999.05 18686.53 34197.45 17198.47 185
VPNet92.23 25991.31 26794.99 22795.56 31290.96 17297.22 19697.86 13692.96 14690.96 28496.62 23475.06 36498.20 29391.90 21583.65 41795.80 324
thres20092.23 25991.39 26394.75 24697.61 15589.03 26096.60 26895.09 39092.08 18693.28 22594.00 37478.39 33299.04 18981.26 41294.18 27196.19 306
anonymousdsp92.16 26191.55 25893.97 29292.58 43389.55 23597.51 15297.42 21889.42 29188.40 35594.84 32380.66 28797.88 34691.87 21791.28 32194.48 407
XXY-MVS92.16 26191.23 27294.95 23394.75 36690.94 17497.47 16297.43 21689.14 29888.90 34196.43 24279.71 30698.24 28989.56 27287.68 36495.67 334
BH-w/o92.14 26391.75 25193.31 33396.99 19485.73 36295.67 33795.69 35888.73 31989.26 33494.82 32582.97 23698.07 31385.26 36596.32 22296.13 312
testing3-292.10 26492.05 23892.27 36997.71 14579.56 44697.42 16694.41 41993.53 11393.22 22895.49 29569.16 41699.11 16993.25 18794.22 26998.13 218
Anonymous20240521192.07 26590.83 28995.76 17798.19 10988.75 26997.58 14095.00 39386.00 38693.64 21197.45 17166.24 43999.53 11290.68 24792.71 29799.01 106
FE-MVS92.05 26691.05 27895.08 22096.83 20987.93 30193.91 41695.70 35686.30 38094.15 19894.97 31576.59 35099.21 15284.10 37896.86 19598.09 227
WR-MVS_H92.00 26791.35 26493.95 29495.09 34989.47 23998.04 6398.68 1991.46 20888.34 35794.68 33185.86 17097.56 37885.77 35784.24 40994.82 391
Anonymous2024052991.98 26890.73 29595.73 18298.14 11389.40 24397.99 6897.72 15479.63 45393.54 21597.41 17669.94 40999.56 10691.04 23791.11 32498.22 210
MonoMVSNet91.92 26991.77 24992.37 36392.94 42483.11 40597.09 20695.55 36792.91 14890.85 28694.55 33881.27 27596.52 42693.01 19787.76 36397.47 267
PatchmatchNetpermissive91.91 27091.35 26493.59 32095.38 32384.11 39293.15 43795.39 37389.54 28592.10 25393.68 38782.82 24198.13 29984.81 36995.32 24698.52 177
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing9191.90 27191.02 27994.53 25996.54 24686.55 34095.86 32595.64 36291.77 19591.89 25993.47 39769.94 40998.86 20390.23 25893.86 28298.18 213
CP-MVSNet91.89 27291.24 27193.82 30395.05 35088.57 27697.82 10098.19 7491.70 19788.21 36395.76 28081.96 26197.52 38887.86 30684.65 40095.37 351
SCA91.84 27391.18 27593.83 30295.59 31084.95 38294.72 38295.58 36590.82 23892.25 24893.69 38575.80 35898.10 30486.20 34795.98 22598.45 187
FMVSNet391.78 27490.69 29895.03 22496.53 24892.27 11297.02 21096.93 27989.79 27989.35 32994.65 33477.01 34697.47 39186.12 35088.82 35195.35 352
AUN-MVS91.76 27590.75 29394.81 23997.00 19388.57 27696.65 26096.49 31489.63 28292.15 25096.12 25978.66 32798.50 26790.83 24079.18 43897.36 271
X-MVStestdata91.71 27689.67 34397.81 3299.38 1794.03 5498.59 1798.20 6994.85 5396.59 9932.69 48991.70 5699.80 4095.66 10899.40 6199.62 27
MVS91.71 27690.44 30695.51 19995.20 34191.59 14096.04 31497.45 20973.44 47087.36 38195.60 28985.42 18599.10 17185.97 35497.46 16795.83 322
EPNet_dtu91.71 27691.28 26992.99 34593.76 39983.71 39896.69 25695.28 38093.15 13487.02 39095.95 26783.37 22497.38 39979.46 42596.84 19697.88 241
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing1191.68 27990.75 29394.47 26196.53 24886.56 33995.76 33394.51 41691.10 23191.24 28193.59 39268.59 42198.86 20391.10 23594.29 26798.00 234
FE-MVSNET391.65 28090.67 29994.60 25093.65 40590.95 17394.86 37997.12 25189.69 28189.21 33693.62 39081.17 27697.67 36687.54 32589.14 34795.17 368
baseline291.63 28190.86 28593.94 29694.33 38386.32 34595.92 32291.64 46089.37 29286.94 39394.69 33081.62 26998.69 24388.64 29794.57 26396.81 291
testing9991.62 28290.72 29694.32 27096.48 25586.11 35795.81 32994.76 40691.55 20091.75 26493.44 39868.55 42298.82 20990.43 25293.69 28498.04 231
test250691.60 28390.78 29094.04 28697.66 14983.81 39598.27 3775.53 49093.43 12095.23 15998.21 8867.21 43099.07 18193.01 19798.49 12999.25 80
miper_ehance_all_eth91.59 28491.13 27692.97 34695.55 31386.57 33894.47 39296.88 28887.77 35088.88 34394.01 37386.22 16297.54 38489.49 27386.93 37294.79 396
v2v48291.59 28490.85 28793.80 30493.87 39688.17 29596.94 22096.88 28889.54 28589.53 32494.90 32081.70 26898.02 32189.25 28285.04 39795.20 363
V4291.58 28690.87 28493.73 30794.05 39188.50 28197.32 18096.97 27588.80 31789.71 31694.33 35482.54 24898.05 31689.01 28885.07 39594.64 405
PCF-MVS89.48 1191.56 28789.95 33196.36 12796.60 23492.52 10392.51 44797.26 23979.41 45488.90 34196.56 23684.04 21499.55 10877.01 43997.30 17897.01 283
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UBG91.55 28890.76 29193.94 29696.52 25185.06 37895.22 36594.54 41490.47 26091.98 25692.71 40972.02 39098.74 23388.10 30295.26 24898.01 233
PS-CasMVS91.55 28890.84 28893.69 31194.96 35388.28 28897.84 9598.24 6391.46 20888.04 36895.80 27579.67 30797.48 39087.02 33784.54 40695.31 355
miper_enhance_ethall91.54 29091.01 28093.15 34095.35 32787.07 32593.97 41196.90 28586.79 37289.17 33793.43 40186.55 15597.64 37089.97 26186.93 37294.74 401
myMVS_eth3d2891.52 29190.97 28193.17 33996.91 19983.24 40395.61 34394.96 39792.24 17691.98 25693.28 40269.31 41498.40 27488.71 29595.68 23597.88 241
PAPM91.52 29190.30 31295.20 21495.30 33489.83 22093.38 43396.85 29186.26 38288.59 35195.80 27584.88 19898.15 29875.67 44495.93 22797.63 256
ET-MVSNet_ETH3D91.49 29390.11 32295.63 18796.40 26191.57 14295.34 35593.48 44090.60 25475.58 46595.49 29580.08 29996.79 42294.25 16489.76 34198.52 177
TR-MVS91.48 29490.59 30294.16 28096.40 26187.33 31495.67 33795.34 37987.68 35491.46 27095.52 29476.77 34998.35 28282.85 39293.61 28896.79 292
tpmrst91.44 29591.32 26691.79 38695.15 34579.20 45293.42 43295.37 37588.55 32493.49 21993.67 38882.49 25098.27 28890.41 25389.34 34597.90 239
test-LLR91.42 29691.19 27492.12 37494.59 37380.66 43094.29 40392.98 44691.11 22990.76 28892.37 41779.02 32098.07 31388.81 29296.74 20197.63 256
MSDG91.42 29690.24 31694.96 23297.15 17888.91 26693.69 42596.32 32385.72 39086.93 39496.47 24080.24 29698.98 19280.57 41695.05 25396.98 284
c3_l91.38 29890.89 28392.88 35095.58 31186.30 34694.68 38396.84 29288.17 33488.83 34794.23 36285.65 17897.47 39189.36 27784.63 40194.89 382
GA-MVS91.38 29890.31 31194.59 25194.65 37187.62 31094.34 39996.19 33790.73 24290.35 29493.83 37871.84 39297.96 33287.22 33293.61 28898.21 211
v114491.37 30090.60 30193.68 31393.89 39588.23 29196.84 23497.03 27188.37 32989.69 31894.39 34882.04 25997.98 32587.80 30985.37 38894.84 386
GBi-Net91.35 30190.27 31494.59 25196.51 25291.18 16397.50 15396.93 27988.82 31489.35 32994.51 34173.87 37597.29 40386.12 35088.82 35195.31 355
test191.35 30190.27 31494.59 25196.51 25291.18 16397.50 15396.93 27988.82 31489.35 32994.51 34173.87 37597.29 40386.12 35088.82 35195.31 355
UniMVSNet_ETH3D91.34 30390.22 31994.68 24894.86 36187.86 30597.23 19497.46 20487.99 33989.90 31096.92 21166.35 43798.23 29090.30 25690.99 32797.96 235
FMVSNet291.31 30490.08 32394.99 22796.51 25292.21 11497.41 16796.95 27788.82 31488.62 35094.75 32873.87 37597.42 39685.20 36688.55 35695.35 352
reproduce_monomvs91.30 30591.10 27791.92 37896.82 21182.48 41397.01 21397.49 19694.64 7188.35 35695.27 30470.53 40298.10 30495.20 12284.60 40395.19 366
D2MVS91.30 30590.95 28292.35 36494.71 36985.52 36596.18 30798.21 6788.89 31086.60 39793.82 38079.92 30397.95 33689.29 28090.95 32893.56 427
v891.29 30790.53 30593.57 32394.15 38788.12 29797.34 17797.06 26688.99 30588.32 35894.26 36183.08 23198.01 32287.62 32383.92 41494.57 406
CVMVSNet91.23 30891.75 25189.67 42795.77 30374.69 46496.44 27494.88 40185.81 38892.18 24997.64 15779.07 31795.58 44488.06 30395.86 23098.74 160
cl2291.21 30990.56 30493.14 34196.09 29086.80 33094.41 39696.58 31187.80 34888.58 35293.99 37580.85 28397.62 37389.87 26486.93 37294.99 373
PEN-MVS91.20 31090.44 30693.48 32794.49 37787.91 30497.76 10898.18 7691.29 21487.78 37295.74 28180.35 29497.33 40185.46 36182.96 42295.19 366
Baseline_NR-MVSNet91.20 31090.62 30092.95 34793.83 39788.03 29997.01 21395.12 38988.42 32889.70 31795.13 31183.47 22197.44 39489.66 27083.24 42093.37 431
cascas91.20 31090.08 32394.58 25594.97 35289.16 25793.65 42797.59 17479.90 45289.40 32792.92 40775.36 36298.36 28192.14 20894.75 25996.23 303
CostFormer91.18 31390.70 29792.62 36094.84 36281.76 42194.09 40994.43 41784.15 41292.72 23893.77 38279.43 31198.20 29390.70 24692.18 30697.90 239
tt080591.09 31490.07 32694.16 28095.61 30988.31 28697.56 14496.51 31389.56 28489.17 33795.64 28767.08 43498.38 28091.07 23688.44 35795.80 324
v119291.07 31590.23 31793.58 32193.70 40087.82 30796.73 25097.07 26187.77 35089.58 32194.32 35680.90 28297.97 32886.52 34285.48 38694.95 374
v14419291.06 31690.28 31393.39 33093.66 40387.23 32096.83 23597.07 26187.43 35989.69 31894.28 35881.48 27098.00 32387.18 33484.92 39994.93 378
v1091.04 31790.23 31793.49 32694.12 38888.16 29697.32 18097.08 25888.26 33288.29 36094.22 36482.17 25797.97 32886.45 34484.12 41094.33 413
eth_miper_zixun_eth91.02 31890.59 30292.34 36695.33 33184.35 38894.10 40896.90 28588.56 32388.84 34694.33 35484.08 21297.60 37588.77 29484.37 40895.06 371
v14890.99 31990.38 30892.81 35393.83 39785.80 35996.78 24696.68 30289.45 29088.75 34993.93 37782.96 23797.82 35187.83 30783.25 41994.80 394
LTVRE_ROB88.41 1390.99 31989.92 33394.19 27696.18 27989.55 23596.31 29597.09 25787.88 34385.67 41195.91 26978.79 32698.57 26281.50 40389.98 33894.44 410
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
DIV-MVS_self_test90.97 32190.33 30992.88 35095.36 32686.19 35194.46 39496.63 30887.82 34688.18 36494.23 36282.99 23497.53 38687.72 31285.57 38594.93 378
cl____90.96 32290.32 31092.89 34995.37 32586.21 34994.46 39496.64 30587.82 34688.15 36694.18 36582.98 23597.54 38487.70 31585.59 38494.92 380
pmmvs490.93 32389.85 33594.17 27793.34 41790.79 18194.60 38596.02 34284.62 40787.45 37795.15 30981.88 26597.45 39387.70 31587.87 36294.27 417
XVG-ACMP-BASELINE90.93 32390.21 32093.09 34294.31 38585.89 35895.33 35697.26 23991.06 23289.38 32895.44 29868.61 42098.60 25789.46 27491.05 32594.79 396
v192192090.85 32590.03 32893.29 33493.55 40686.96 32996.74 24997.04 26987.36 36189.52 32594.34 35380.23 29797.97 32886.27 34585.21 39294.94 376
CR-MVSNet90.82 32689.77 33993.95 29494.45 37987.19 32190.23 46395.68 36086.89 37092.40 24092.36 42080.91 28097.05 41081.09 41393.95 28097.60 261
v7n90.76 32789.86 33493.45 32993.54 40787.60 31197.70 12397.37 22588.85 31187.65 37494.08 37181.08 27798.10 30484.68 37183.79 41694.66 404
RPSCF90.75 32890.86 28590.42 41796.84 20776.29 46295.61 34396.34 32283.89 41591.38 27197.87 12476.45 35298.78 21587.16 33592.23 30396.20 305
MVP-Stereo90.74 32990.08 32392.71 35793.19 42088.20 29395.86 32596.27 33086.07 38584.86 41994.76 32777.84 34197.75 36183.88 38498.01 15392.17 452
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pm-mvs190.72 33089.65 34593.96 29394.29 38689.63 22897.79 10696.82 29389.07 30086.12 40695.48 29778.61 32897.78 35686.97 33881.67 42794.46 408
v124090.70 33189.85 33593.23 33693.51 40986.80 33096.61 26697.02 27387.16 36689.58 32194.31 35779.55 31097.98 32585.52 36085.44 38794.90 381
EPMVS90.70 33189.81 33793.37 33194.73 36884.21 39093.67 42688.02 47589.50 28792.38 24293.49 39577.82 34297.78 35686.03 35392.68 29898.11 226
WBMVS90.69 33389.99 33092.81 35396.48 25585.00 37995.21 36796.30 32589.46 28989.04 34094.05 37272.45 38997.82 35189.46 27487.41 36995.61 335
Anonymous2023121190.63 33489.42 35094.27 27598.24 10089.19 25698.05 6297.89 12879.95 45188.25 36294.96 31672.56 38898.13 29989.70 26885.14 39395.49 337
DTE-MVSNet90.56 33589.75 34193.01 34493.95 39287.25 31897.64 13397.65 16290.74 24187.12 38595.68 28579.97 30297.00 41483.33 38681.66 42894.78 398
ACMH87.59 1690.53 33689.42 35093.87 30196.21 27187.92 30297.24 19096.94 27888.45 32783.91 43196.27 25171.92 39198.62 25684.43 37489.43 34495.05 372
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS90.52 33789.14 35894.67 24996.81 21387.85 30695.91 32393.97 43289.71 28092.34 24692.48 41565.41 44597.96 33281.37 40994.27 26898.21 211
OurMVSNet-221017-090.51 33890.19 32191.44 39593.41 41581.25 42496.98 21796.28 32991.68 19886.55 39996.30 24874.20 37497.98 32588.96 29087.40 37095.09 369
miper_lstm_enhance90.50 33990.06 32791.83 38395.33 33183.74 39693.86 41796.70 30187.56 35787.79 37193.81 38183.45 22396.92 41687.39 32884.62 40294.82 391
COLMAP_ROBcopyleft87.81 1590.40 34089.28 35393.79 30597.95 12987.13 32496.92 22395.89 34882.83 42886.88 39697.18 19173.77 37899.29 14678.44 43093.62 28794.95 374
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testing22290.31 34188.96 36094.35 26796.54 24687.29 31595.50 34893.84 43690.97 23491.75 26492.96 40662.18 45798.00 32382.86 39094.08 27597.76 251
IterMVS-SCA-FT90.31 34189.81 33791.82 38495.52 31484.20 39194.30 40296.15 33990.61 25287.39 38094.27 35975.80 35896.44 42787.34 32986.88 37694.82 391
MS-PatchMatch90.27 34389.77 33991.78 38794.33 38384.72 38595.55 34596.73 29686.17 38486.36 40195.28 30371.28 39697.80 35484.09 37998.14 14792.81 437
tpm90.25 34489.74 34291.76 38993.92 39379.73 44593.98 41093.54 43988.28 33191.99 25593.25 40377.51 34497.44 39487.30 33187.94 36198.12 220
AllTest90.23 34588.98 35993.98 29097.94 13086.64 33496.51 27395.54 36885.38 39485.49 41396.77 21870.28 40499.15 16380.02 42092.87 29296.15 310
dmvs_re90.21 34689.50 34892.35 36495.47 32085.15 37595.70 33694.37 42290.94 23788.42 35493.57 39374.63 37095.67 44182.80 39389.57 34396.22 304
ACMH+87.92 1490.20 34789.18 35693.25 33596.48 25586.45 34396.99 21696.68 30288.83 31384.79 42096.22 25370.16 40698.53 26584.42 37588.04 36094.77 399
test-mter90.19 34889.54 34792.12 37494.59 37380.66 43094.29 40392.98 44687.68 35490.76 28892.37 41767.67 42698.07 31388.81 29296.74 20197.63 256
IterMVS90.15 34989.67 34391.61 39195.48 31683.72 39794.33 40096.12 34089.99 27187.31 38394.15 36775.78 36096.27 43186.97 33886.89 37594.83 387
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,190.06 35089.42 35091.97 37794.41 38180.62 43294.29 40391.97 45887.28 36490.44 29292.47 41668.79 41897.67 36688.50 29996.60 20897.61 260
SD_040390.01 35190.02 32989.96 42495.65 30876.76 45995.76 33396.46 31690.58 25586.59 39896.29 24982.12 25894.78 45373.00 45893.76 28398.35 199
tpm289.96 35289.21 35592.23 37294.91 35981.25 42493.78 42094.42 41880.62 44991.56 26793.44 39876.44 35397.94 33885.60 35992.08 31097.49 265
UWE-MVS89.91 35389.48 34991.21 40095.88 29678.23 45794.91 37890.26 46889.11 29992.35 24594.52 34068.76 41997.96 33283.95 38295.59 23897.42 269
IB-MVS87.33 1789.91 35388.28 37094.79 24395.26 33887.70 30995.12 37393.95 43389.35 29387.03 38992.49 41470.74 40199.19 15489.18 28681.37 42997.49 265
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
ADS-MVSNet89.89 35588.68 36593.53 32495.86 29784.89 38390.93 45895.07 39183.23 42691.28 27991.81 43079.01 32297.85 34779.52 42291.39 31997.84 246
WB-MVSnew89.88 35689.56 34690.82 40994.57 37683.06 40695.65 34192.85 44887.86 34590.83 28794.10 36879.66 30896.88 41876.34 44094.19 27092.54 443
FMVSNet189.88 35688.31 36994.59 25195.41 32191.18 16397.50 15396.93 27986.62 37487.41 37994.51 34165.94 44297.29 40383.04 38987.43 36795.31 355
pmmvs589.86 35888.87 36392.82 35292.86 42686.23 34896.26 29895.39 37384.24 41187.12 38594.51 34174.27 37397.36 40087.61 32487.57 36594.86 383
tpmvs89.83 35989.15 35791.89 38194.92 35780.30 43793.11 43895.46 37286.28 38188.08 36792.65 41080.44 29298.52 26681.47 40589.92 33996.84 290
test_fmvs289.77 36089.93 33289.31 43493.68 40276.37 46197.64 13395.90 34689.84 27791.49 26996.26 25258.77 46097.10 40794.65 15291.13 32394.46 408
SSC-MVS3.289.74 36189.26 35491.19 40395.16 34280.29 43894.53 38897.03 27191.79 19488.86 34494.10 36869.94 40997.82 35185.29 36386.66 37795.45 343
mmtdpeth89.70 36288.96 36091.90 38095.84 30284.42 38797.46 16495.53 37190.27 26494.46 18790.50 43969.74 41398.95 19397.39 5369.48 47192.34 446
tfpnnormal89.70 36288.40 36893.60 31995.15 34590.10 20797.56 14498.16 8087.28 36486.16 40494.63 33577.57 34398.05 31674.48 44884.59 40492.65 440
ADS-MVSNet289.45 36488.59 36692.03 37695.86 29782.26 41790.93 45894.32 42583.23 42691.28 27991.81 43079.01 32295.99 43379.52 42291.39 31997.84 246
Patchmatch-test89.42 36587.99 37293.70 31095.27 33585.11 37688.98 47094.37 42281.11 44387.10 38893.69 38582.28 25497.50 38974.37 45094.76 25898.48 184
test0.0.03 189.37 36688.70 36491.41 39692.47 43585.63 36395.22 36592.70 45191.11 22986.91 39593.65 38979.02 32093.19 47078.00 43289.18 34695.41 345
SixPastTwentyTwo89.15 36788.54 36790.98 40593.49 41080.28 43996.70 25494.70 40890.78 23984.15 42695.57 29071.78 39397.71 36484.63 37285.07 39594.94 376
RPMNet88.98 36887.05 38294.77 24494.45 37987.19 32190.23 46398.03 11077.87 46292.40 24087.55 46680.17 29899.51 11768.84 46993.95 28097.60 261
TransMVSNet (Re)88.94 36987.56 37593.08 34394.35 38288.45 28497.73 11595.23 38487.47 35884.26 42495.29 30179.86 30497.33 40179.44 42674.44 45793.45 430
USDC88.94 36987.83 37492.27 36994.66 37084.96 38193.86 41795.90 34687.34 36283.40 43395.56 29167.43 42898.19 29582.64 39789.67 34293.66 426
dp88.90 37188.26 37190.81 41094.58 37576.62 46092.85 44394.93 39885.12 40090.07 30893.07 40475.81 35798.12 30280.53 41787.42 36897.71 253
PatchT88.87 37287.42 37693.22 33794.08 39085.10 37789.51 46894.64 41181.92 43892.36 24388.15 46080.05 30097.01 41372.43 45993.65 28697.54 264
our_test_388.78 37387.98 37391.20 40292.45 43682.53 41193.61 42995.69 35885.77 38984.88 41893.71 38379.99 30196.78 42379.47 42486.24 37894.28 416
EU-MVSNet88.72 37488.90 36288.20 43893.15 42174.21 46696.63 26594.22 42785.18 39887.32 38295.97 26576.16 35594.98 45185.27 36486.17 37995.41 345
Patchmtry88.64 37587.25 37892.78 35594.09 38986.64 33489.82 46795.68 36080.81 44787.63 37592.36 42080.91 28097.03 41178.86 42885.12 39494.67 403
MIMVSNet88.50 37686.76 38693.72 30994.84 36287.77 30891.39 45394.05 42986.41 37887.99 36992.59 41363.27 45195.82 43877.44 43392.84 29497.57 263
tpm cat188.36 37787.21 38091.81 38595.13 34780.55 43392.58 44695.70 35674.97 46687.45 37791.96 42878.01 34098.17 29780.39 41888.74 35496.72 294
ppachtmachnet_test88.35 37887.29 37791.53 39292.45 43683.57 40093.75 42195.97 34384.28 41085.32 41694.18 36579.00 32496.93 41575.71 44384.99 39894.10 418
JIA-IIPM88.26 37987.04 38391.91 37993.52 40881.42 42389.38 46994.38 42180.84 44690.93 28580.74 47779.22 31497.92 34182.76 39491.62 31496.38 302
testgi87.97 38087.21 38090.24 42092.86 42680.76 42896.67 25994.97 39591.74 19685.52 41295.83 27362.66 45594.47 45676.25 44188.36 35895.48 338
LF4IMVS87.94 38187.25 37889.98 42392.38 43880.05 44394.38 39795.25 38387.59 35684.34 42294.74 32964.31 44997.66 36984.83 36887.45 36692.23 449
gg-mvs-nofinetune87.82 38285.61 39594.44 26394.46 37889.27 25291.21 45784.61 48480.88 44589.89 31274.98 48071.50 39497.53 38685.75 35897.21 18296.51 297
pmmvs687.81 38386.19 39192.69 35891.32 44386.30 34697.34 17796.41 31980.59 45084.05 43094.37 35067.37 42997.67 36684.75 37079.51 43794.09 420
testing387.67 38486.88 38590.05 42296.14 28480.71 42997.10 20592.85 44890.15 26887.54 37694.55 33855.70 46794.10 45973.77 45494.10 27495.35 352
K. test v387.64 38586.75 38790.32 41993.02 42379.48 45096.61 26692.08 45790.66 24880.25 45294.09 37067.21 43096.65 42585.96 35580.83 43194.83 387
blended_shiyan887.58 38685.55 39693.66 31588.76 46388.54 27895.21 36796.29 32882.81 42986.25 40287.73 46373.70 38097.58 37787.81 30871.42 46494.85 385
blended_shiyan687.55 38785.52 39793.64 31688.78 46188.50 28195.23 36496.30 32582.80 43086.09 40787.70 46473.69 38197.56 37887.70 31571.36 46594.86 383
Patchmatch-RL test87.38 38886.24 39090.81 41088.74 46478.40 45688.12 47793.17 44387.11 36782.17 44289.29 45181.95 26295.60 44388.64 29777.02 44698.41 192
FE-blended-shiyan787.29 38985.21 40293.53 32488.54 46688.21 29294.51 39196.27 33082.69 43385.92 40886.89 47073.03 38497.55 38087.68 31971.36 46594.83 387
FMVSNet587.29 38985.79 39491.78 38794.80 36487.28 31695.49 34995.28 38084.09 41383.85 43291.82 42962.95 45394.17 45878.48 42985.34 39093.91 424
myMVS_eth3d87.18 39186.38 38989.58 42895.16 34279.53 44795.00 37593.93 43488.55 32486.96 39191.99 42656.23 46694.00 46075.47 44694.11 27295.20 363
Syy-MVS87.13 39287.02 38487.47 44295.16 34273.21 47095.00 37593.93 43488.55 32486.96 39191.99 42675.90 35694.00 46061.59 47694.11 27295.20 363
Anonymous2023120687.09 39386.14 39289.93 42591.22 44480.35 43596.11 31095.35 37683.57 42284.16 42593.02 40573.54 38295.61 44272.16 46086.14 38093.84 425
usedtu_blend_shiyan587.06 39484.84 40893.69 31188.54 46688.70 27195.83 32795.54 36878.74 45785.92 40886.89 47073.03 38497.55 38087.73 31071.36 46594.83 387
EG-PatchMatch MVS87.02 39585.44 39891.76 38992.67 43085.00 37996.08 31296.45 31783.41 42579.52 45493.49 39557.10 46497.72 36379.34 42790.87 33092.56 442
blend_shiyan486.87 39684.61 41393.67 31488.87 45988.70 27195.17 37196.30 32582.80 43086.16 40487.11 46865.12 44897.55 38087.73 31072.21 46294.75 400
TinyColmap86.82 39785.35 40191.21 40094.91 35982.99 40793.94 41394.02 43183.58 42181.56 44494.68 33162.34 45698.13 29975.78 44287.35 37192.52 444
UWE-MVS-2886.81 39886.41 38888.02 44092.87 42574.60 46595.38 35486.70 48088.17 33487.28 38494.67 33370.83 40093.30 46867.45 47094.31 26696.17 307
mvs5depth86.53 39985.08 40490.87 40788.74 46482.52 41291.91 45194.23 42686.35 37987.11 38793.70 38466.52 43597.76 35981.37 40975.80 45192.31 448
TDRefinement86.53 39984.76 41091.85 38282.23 48384.25 38996.38 28695.35 37684.97 40384.09 42894.94 31765.76 44398.34 28584.60 37374.52 45692.97 434
sc_t186.48 40184.10 41893.63 31793.45 41385.76 36196.79 24294.71 40773.06 47186.45 40094.35 35155.13 46897.95 33684.38 37678.55 44297.18 280
test_040286.46 40284.79 40991.45 39495.02 35185.55 36496.29 29794.89 40080.90 44482.21 44193.97 37668.21 42597.29 40362.98 47488.68 35591.51 458
Anonymous2024052186.42 40385.44 39889.34 43390.33 44879.79 44496.73 25095.92 34483.71 42083.25 43591.36 43563.92 45096.01 43278.39 43185.36 38992.22 450
FE-MVSNET286.36 40484.68 41291.39 39787.67 47186.47 34296.21 30396.41 31987.87 34479.31 45689.64 44865.29 44695.58 44482.42 39877.28 44592.14 453
DSMNet-mixed86.34 40586.12 39387.00 44689.88 45270.43 47294.93 37790.08 46977.97 46185.42 41592.78 40874.44 37293.96 46274.43 44995.14 24996.62 295
CL-MVSNet_self_test86.31 40685.15 40389.80 42688.83 46081.74 42293.93 41496.22 33486.67 37385.03 41790.80 43878.09 33794.50 45474.92 44771.86 46393.15 433
pmmvs-eth3d86.22 40784.45 41491.53 39288.34 46887.25 31894.47 39295.01 39283.47 42379.51 45589.61 44969.75 41295.71 43983.13 38876.73 44991.64 455
test_vis1_rt86.16 40885.06 40589.46 43093.47 41280.46 43496.41 28086.61 48185.22 39779.15 45788.64 45552.41 47297.06 40993.08 19290.57 33290.87 464
test20.0386.14 40985.40 40088.35 43690.12 44980.06 44295.90 32495.20 38588.59 32081.29 44593.62 39071.43 39592.65 47171.26 46481.17 43092.34 446
UnsupCasMVSNet_eth85.99 41084.45 41490.62 41489.97 45182.40 41693.62 42897.37 22589.86 27478.59 46092.37 41765.25 44795.35 44982.27 40070.75 46894.10 418
KD-MVS_self_test85.95 41184.95 40688.96 43589.55 45579.11 45395.13 37296.42 31885.91 38784.07 42990.48 44070.03 40894.82 45280.04 41972.94 46092.94 435
ttmdpeth85.91 41284.76 41089.36 43289.14 45680.25 44095.66 34093.16 44583.77 41883.39 43495.26 30566.24 43995.26 45080.65 41575.57 45292.57 441
YYNet185.87 41384.23 41690.78 41392.38 43882.46 41593.17 43595.14 38882.12 43767.69 47392.36 42078.16 33695.50 44777.31 43579.73 43594.39 411
MDA-MVSNet_test_wron85.87 41384.23 41690.80 41292.38 43882.57 41093.17 43595.15 38782.15 43667.65 47592.33 42378.20 33395.51 44677.33 43479.74 43494.31 415
CMPMVSbinary62.92 2185.62 41584.92 40787.74 44189.14 45673.12 47194.17 40696.80 29473.98 46773.65 46994.93 31866.36 43697.61 37483.95 38291.28 32192.48 445
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_082.17 1985.46 41683.64 41990.92 40695.27 33579.49 44990.55 46195.60 36383.76 41983.00 43889.95 44571.09 39797.97 32882.75 39560.79 48295.31 355
tt032085.39 41783.12 42092.19 37393.44 41485.79 36096.19 30694.87 40471.19 47382.92 43991.76 43258.43 46196.81 42181.03 41478.26 44393.98 422
MDA-MVSNet-bldmvs85.00 41882.95 42391.17 40493.13 42283.33 40194.56 38795.00 39384.57 40865.13 47992.65 41070.45 40395.85 43673.57 45577.49 44494.33 413
MIMVSNet184.93 41983.05 42190.56 41589.56 45484.84 38495.40 35295.35 37683.91 41480.38 45092.21 42557.23 46393.34 46770.69 46682.75 42593.50 428
tt0320-xc84.83 42082.33 42892.31 36793.66 40386.20 35096.17 30894.06 42871.26 47282.04 44392.22 42455.07 46996.72 42481.49 40475.04 45594.02 421
KD-MVS_2432*160084.81 42182.64 42491.31 39891.07 44585.34 37391.22 45595.75 35485.56 39283.09 43690.21 44367.21 43095.89 43477.18 43762.48 48092.69 438
miper_refine_blended84.81 42182.64 42491.31 39891.07 44585.34 37391.22 45595.75 35485.56 39283.09 43690.21 44367.21 43095.89 43477.18 43762.48 48092.69 438
OpenMVS_ROBcopyleft81.14 2084.42 42382.28 42990.83 40890.06 45084.05 39495.73 33594.04 43073.89 46980.17 45391.53 43459.15 45997.64 37066.92 47289.05 34890.80 465
FE-MVSNET83.85 42481.97 43089.51 42987.19 47383.19 40495.21 36793.17 44383.45 42478.90 45889.05 45365.46 44493.84 46469.71 46875.56 45391.51 458
mvsany_test383.59 42582.44 42787.03 44583.80 47873.82 46793.70 42390.92 46686.42 37782.51 44090.26 44246.76 47795.71 43990.82 24176.76 44891.57 457
PM-MVS83.48 42681.86 43288.31 43787.83 47077.59 45893.43 43191.75 45986.91 36980.63 44889.91 44644.42 47895.84 43785.17 36776.73 44991.50 460
test_fmvs383.21 42783.02 42283.78 45186.77 47568.34 47796.76 24894.91 39986.49 37684.14 42789.48 45036.04 48291.73 47391.86 21880.77 43291.26 463
new-patchmatchnet83.18 42881.87 43187.11 44486.88 47475.99 46393.70 42395.18 38685.02 40277.30 46388.40 45765.99 44193.88 46374.19 45270.18 46991.47 461
new_pmnet82.89 42981.12 43488.18 43989.63 45380.18 44191.77 45292.57 45276.79 46475.56 46688.23 45961.22 45894.48 45571.43 46282.92 42389.87 468
MVS-HIRNet82.47 43081.21 43386.26 44895.38 32369.21 47588.96 47189.49 47066.28 47780.79 44774.08 48268.48 42397.39 39871.93 46195.47 24392.18 451
MVStest182.38 43180.04 43589.37 43187.63 47282.83 40895.03 37493.37 44273.90 46873.50 47094.35 35162.89 45493.25 46973.80 45365.92 47792.04 454
UnsupCasMVSNet_bld82.13 43279.46 43790.14 42188.00 46982.47 41490.89 46096.62 31078.94 45675.61 46484.40 47556.63 46596.31 43077.30 43666.77 47691.63 456
dmvs_testset81.38 43382.60 42677.73 45791.74 44251.49 49293.03 44084.21 48589.07 30078.28 46191.25 43676.97 34788.53 48056.57 48082.24 42693.16 432
test_f80.57 43479.62 43683.41 45283.38 48167.80 47993.57 43093.72 43780.80 44877.91 46287.63 46533.40 48392.08 47287.14 33679.04 44090.34 467
pmmvs379.97 43577.50 44087.39 44382.80 48279.38 45192.70 44590.75 46770.69 47478.66 45987.47 46751.34 47393.40 46673.39 45669.65 47089.38 469
APD_test179.31 43677.70 43984.14 45089.11 45869.07 47692.36 45091.50 46169.07 47573.87 46892.63 41239.93 48094.32 45770.54 46780.25 43389.02 470
N_pmnet78.73 43778.71 43878.79 45692.80 42846.50 49594.14 40743.71 49778.61 45880.83 44691.66 43374.94 36896.36 42967.24 47184.45 40793.50 428
WB-MVS76.77 43876.63 44177.18 45885.32 47656.82 49094.53 38889.39 47182.66 43471.35 47189.18 45275.03 36588.88 47835.42 48766.79 47585.84 472
SSC-MVS76.05 43975.83 44276.72 46284.77 47756.22 49194.32 40188.96 47381.82 44070.52 47288.91 45474.79 36988.71 47933.69 48864.71 47885.23 473
test_vis3_rt72.73 44070.55 44379.27 45580.02 48468.13 47893.92 41574.30 49276.90 46358.99 48373.58 48320.29 49195.37 44884.16 37772.80 46174.31 480
LCM-MVSNet72.55 44169.39 44582.03 45370.81 49365.42 48290.12 46594.36 42455.02 48365.88 47781.72 47624.16 49089.96 47474.32 45168.10 47490.71 466
FPMVS71.27 44269.85 44475.50 46374.64 48859.03 48891.30 45491.50 46158.80 48057.92 48488.28 45829.98 48685.53 48353.43 48182.84 42481.95 476
PMMVS270.19 44366.92 44780.01 45476.35 48765.67 48186.22 47887.58 47764.83 47962.38 48080.29 47926.78 48888.49 48163.79 47354.07 48485.88 471
dongtai69.99 44469.33 44671.98 46688.78 46161.64 48689.86 46659.93 49675.67 46574.96 46785.45 47250.19 47481.66 48543.86 48455.27 48372.63 481
testf169.31 44566.76 44876.94 46078.61 48561.93 48488.27 47586.11 48255.62 48159.69 48185.31 47320.19 49289.32 47557.62 47769.44 47279.58 477
APD_test269.31 44566.76 44876.94 46078.61 48561.93 48488.27 47586.11 48255.62 48159.69 48185.31 47320.19 49289.32 47557.62 47769.44 47279.58 477
EGC-MVSNET68.77 44763.01 45386.07 44992.49 43482.24 41893.96 41290.96 4650.71 4942.62 49590.89 43753.66 47093.46 46557.25 47984.55 40582.51 475
Gipumacopyleft67.86 44865.41 45075.18 46492.66 43173.45 46866.50 48694.52 41553.33 48457.80 48566.07 48530.81 48489.20 47748.15 48378.88 44162.90 485
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method66.11 44964.89 45169.79 46772.62 49135.23 49965.19 48792.83 45020.35 48965.20 47888.08 46143.14 47982.70 48473.12 45763.46 47991.45 462
kuosan65.27 45064.66 45267.11 46983.80 47861.32 48788.53 47460.77 49568.22 47667.67 47480.52 47849.12 47570.76 49129.67 49053.64 48569.26 483
ANet_high63.94 45159.58 45477.02 45961.24 49566.06 48085.66 48087.93 47678.53 45942.94 48771.04 48425.42 48980.71 48652.60 48230.83 48884.28 474
PMVScopyleft53.92 2258.58 45255.40 45568.12 46851.00 49648.64 49378.86 48387.10 47946.77 48535.84 49174.28 4818.76 49486.34 48242.07 48573.91 45869.38 482
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN53.28 45352.56 45755.43 47174.43 48947.13 49483.63 48276.30 48942.23 48642.59 48862.22 48728.57 48774.40 48831.53 48931.51 48744.78 486
MVEpermissive50.73 2353.25 45448.81 45966.58 47065.34 49457.50 48972.49 48570.94 49340.15 48839.28 49063.51 4866.89 49673.48 49038.29 48642.38 48668.76 484
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS52.08 45551.31 45854.39 47272.62 49145.39 49683.84 48175.51 49141.13 48740.77 48959.65 48830.08 48573.60 48928.31 49129.90 48944.18 487
tmp_tt51.94 45653.82 45646.29 47333.73 49745.30 49778.32 48467.24 49418.02 49050.93 48687.05 46952.99 47153.11 49270.76 46525.29 49040.46 488
wuyk23d25.11 45724.57 46126.74 47473.98 49039.89 49857.88 4889.80 49812.27 49110.39 4926.97 4947.03 49536.44 49325.43 49217.39 4913.89 491
cdsmvs_eth3d_5k23.24 45830.99 4600.00 4770.00 5000.00 5020.00 48997.63 1660.00 4950.00 49696.88 21384.38 2060.00 4960.00 4950.00 4940.00 492
testmvs13.36 45916.33 4624.48 4765.04 4982.26 50193.18 4343.28 4992.70 4928.24 49321.66 4902.29 4982.19 4947.58 4932.96 4929.00 490
test12313.04 46015.66 4635.18 4754.51 4993.45 50092.50 4481.81 5002.50 4937.58 49420.15 4913.67 4972.18 4957.13 4941.07 4939.90 489
ab-mvs-re8.06 46110.74 4640.00 4770.00 5000.00 5020.00 4890.00 5010.00 4950.00 49696.69 2240.00 4990.00 4960.00 4950.00 4940.00 492
pcd_1.5k_mvsjas7.39 4629.85 4650.00 4770.00 5000.00 5020.00 4890.00 5010.00 4950.00 4960.00 49588.65 1090.00 4960.00 4950.00 4940.00 492
mmdepth0.00 4630.00 4660.00 4770.00 5000.00 5020.00 4890.00 5010.00 4950.00 4960.00 4950.00 4990.00 4960.00 4950.00 4940.00 492
monomultidepth0.00 4630.00 4660.00 4770.00 5000.00 5020.00 4890.00 5010.00 4950.00 4960.00 4950.00 4990.00 4960.00 4950.00 4940.00 492
test_blank0.00 4630.00 4660.00 4770.00 5000.00 5020.00 4890.00 5010.00 4950.00 4960.00 4950.00 4990.00 4960.00 4950.00 4940.00 492
uanet_test0.00 4630.00 4660.00 4770.00 5000.00 5020.00 4890.00 5010.00 4950.00 4960.00 4950.00 4990.00 4960.00 4950.00 4940.00 492
DCPMVS0.00 4630.00 4660.00 4770.00 5000.00 5020.00 4890.00 5010.00 4950.00 4960.00 4950.00 4990.00 4960.00 4950.00 4940.00 492
sosnet-low-res0.00 4630.00 4660.00 4770.00 5000.00 5020.00 4890.00 5010.00 4950.00 4960.00 4950.00 4990.00 4960.00 4950.00 4940.00 492
sosnet0.00 4630.00 4660.00 4770.00 5000.00 5020.00 4890.00 5010.00 4950.00 4960.00 4950.00 4990.00 4960.00 4950.00 4940.00 492
uncertanet0.00 4630.00 4660.00 4770.00 5000.00 5020.00 4890.00 5010.00 4950.00 4960.00 4950.00 4990.00 4960.00 4950.00 4940.00 492
Regformer0.00 4630.00 4660.00 4770.00 5000.00 5020.00 4890.00 5010.00 4950.00 4960.00 4950.00 4990.00 4960.00 4950.00 4940.00 492
uanet0.00 4630.00 4660.00 4770.00 5000.00 5020.00 4890.00 5010.00 4950.00 4960.00 4950.00 4990.00 4960.00 4950.00 4940.00 492
MED-MVS test98.00 2399.56 194.50 3598.69 1198.70 1693.45 11898.73 3098.53 5199.86 997.40 5099.58 2399.65 20
TestfortrainingZip98.69 11
WAC-MVS79.53 44775.56 445
FOURS199.55 493.34 7199.29 198.35 4294.98 4698.49 39
MSC_two_6792asdad98.86 198.67 6796.94 197.93 12599.86 997.68 3399.67 699.77 3
PC_three_145290.77 24098.89 2698.28 8696.24 198.35 28295.76 10699.58 2399.59 32
No_MVS98.86 198.67 6796.94 197.93 12599.86 997.68 3399.67 699.77 3
test_one_060199.32 2795.20 2198.25 6195.13 4098.48 4098.87 3195.16 9
eth-test20.00 500
eth-test0.00 500
ZD-MVS99.05 4594.59 3398.08 9389.22 29697.03 8198.10 9492.52 4299.65 7994.58 15699.31 72
RE-MVS-def96.72 6299.02 4892.34 10897.98 7198.03 11093.52 11597.43 6798.51 5690.71 8196.05 9499.26 7899.43 63
IU-MVS99.42 1095.39 1297.94 12490.40 26398.94 1997.41 4999.66 1099.74 9
OPU-MVS98.55 498.82 6196.86 398.25 4098.26 8796.04 299.24 14995.36 12099.59 1999.56 40
test_241102_TWO98.27 5595.13 4098.93 2098.89 2894.99 1399.85 2197.52 4299.65 1399.74 9
test_241102_ONE99.42 1095.30 1898.27 5595.09 4399.19 1398.81 3795.54 599.65 79
9.1496.75 6198.93 5697.73 11598.23 6691.28 21797.88 5598.44 6493.00 2999.65 7995.76 10699.47 45
save fliter98.91 5894.28 4297.02 21098.02 11395.35 31
test_0728_THIRD94.78 6198.73 3098.87 3195.87 499.84 2697.45 4699.72 299.77 3
test_0728_SECOND98.51 599.45 695.93 698.21 4798.28 5299.86 997.52 4299.67 699.75 7
test072699.45 695.36 1498.31 3298.29 5094.92 5098.99 1898.92 2395.08 10
GSMVS98.45 187
test_part299.28 3095.74 998.10 48
sam_mvs182.76 24298.45 187
sam_mvs81.94 263
ambc86.56 44783.60 48070.00 47485.69 47994.97 39580.60 44988.45 45637.42 48196.84 42082.69 39675.44 45492.86 436
MTGPAbinary98.08 93
test_post192.81 44416.58 49380.53 29097.68 36586.20 347
test_post17.58 49281.76 26698.08 309
patchmatchnet-post90.45 44182.65 24798.10 304
GG-mvs-BLEND93.62 31893.69 40189.20 25492.39 44983.33 48687.98 37089.84 44771.00 39896.87 41982.08 40195.40 24594.80 394
MTMP97.86 9182.03 487
gm-plane-assit93.22 41978.89 45584.82 40593.52 39498.64 25287.72 312
test9_res94.81 14399.38 6499.45 59
TEST998.70 6594.19 4696.41 28098.02 11388.17 33496.03 12697.56 16692.74 3699.59 95
test_898.67 6794.06 5396.37 28898.01 11688.58 32195.98 13097.55 16892.73 3799.58 98
agg_prior293.94 17099.38 6499.50 52
agg_prior98.67 6793.79 5998.00 11795.68 14399.57 105
TestCases93.98 29097.94 13086.64 33495.54 36885.38 39485.49 41396.77 21870.28 40499.15 16380.02 42092.87 29296.15 310
test_prior493.66 6296.42 279
test_prior296.35 28992.80 15596.03 12697.59 16392.01 5095.01 12899.38 64
test_prior97.23 6998.67 6792.99 8398.00 11799.41 13299.29 75
旧先验295.94 32081.66 44197.34 7098.82 20992.26 203
新几何295.79 331
新几何197.32 6298.60 7493.59 6397.75 14981.58 44295.75 13897.85 12890.04 8899.67 7786.50 34399.13 9898.69 164
旧先验198.38 8993.38 6897.75 14998.09 9692.30 4899.01 10899.16 85
无先验95.79 33197.87 13283.87 41799.65 7987.68 31998.89 136
原ACMM295.67 337
原ACMM196.38 12598.59 7591.09 16897.89 12887.41 36095.22 16097.68 15090.25 8599.54 11087.95 30599.12 10098.49 182
test22298.24 10092.21 11495.33 35697.60 17179.22 45595.25 15897.84 13088.80 10699.15 9598.72 161
testdata299.67 7785.96 355
segment_acmp92.89 33
testdata95.46 20698.18 11188.90 26797.66 16082.73 43297.03 8198.07 9790.06 8798.85 20589.67 26998.98 10998.64 167
testdata195.26 36393.10 137
test1297.65 4798.46 7994.26 4397.66 16095.52 15090.89 7899.46 12699.25 8099.22 82
plane_prior796.21 27189.98 213
plane_prior696.10 28990.00 20981.32 273
plane_prior597.51 19398.60 25793.02 19592.23 30395.86 318
plane_prior496.64 227
plane_prior390.00 20994.46 7891.34 273
plane_prior297.74 11394.85 53
plane_prior196.14 284
plane_prior89.99 21197.24 19094.06 9292.16 307
n20.00 501
nn0.00 501
door-mid91.06 464
lessismore_v090.45 41691.96 44179.09 45487.19 47880.32 45194.39 34866.31 43897.55 38084.00 38176.84 44794.70 402
LGP-MVS_train94.10 28296.16 28188.26 28997.46 20491.29 21490.12 30397.16 19279.05 31898.73 23592.25 20591.89 31195.31 355
test1197.88 130
door91.13 463
HQP5-MVS89.33 247
HQP-NCC95.86 29796.65 26093.55 10990.14 297
ACMP_Plane95.86 29796.65 26093.55 10990.14 297
BP-MVS92.13 211
HQP4-MVS90.14 29798.50 26795.78 326
HQP3-MVS97.39 22192.10 308
HQP2-MVS80.95 278
NP-MVS95.99 29589.81 22195.87 270
MDTV_nov1_ep13_2view70.35 47393.10 43983.88 41693.55 21482.47 25186.25 34698.38 195
MDTV_nov1_ep1390.76 29195.22 33980.33 43693.03 44095.28 38088.14 33792.84 23793.83 37881.34 27298.08 30982.86 39094.34 265
ACMMP++_ref90.30 337
ACMMP++91.02 326
Test By Simon88.73 108
ITE_SJBPF92.43 36295.34 32885.37 37295.92 34491.47 20787.75 37396.39 24571.00 39897.96 33282.36 39989.86 34093.97 423
DeepMVS_CXcopyleft74.68 46590.84 44764.34 48381.61 48865.34 47867.47 47688.01 46248.60 47680.13 48762.33 47573.68 45979.58 477