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++98.06 197.99 198.28 998.67 6495.39 1199.29 198.28 4994.78 6098.93 1998.87 3096.04 299.86 997.45 4599.58 2399.59 29
SED-MVS98.05 297.99 198.24 1099.42 795.30 1798.25 3698.27 5295.13 3999.19 1298.89 2795.54 599.85 1897.52 4199.66 1099.56 37
DVP-MVScopyleft97.91 397.81 498.22 1399.45 395.36 1398.21 4397.85 13394.92 4998.73 2998.87 3095.08 899.84 2397.52 4199.67 699.48 53
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-MVScopyleft97.86 497.65 998.47 599.17 3595.78 797.21 18898.35 3995.16 3798.71 3198.80 3795.05 1099.89 396.70 6599.73 199.73 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVScopyleft97.82 597.73 898.08 1899.15 3694.82 2898.81 898.30 4594.76 6398.30 3998.90 2493.77 1799.68 7197.93 2899.69 399.75 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CNVR-MVS97.68 697.44 2198.37 798.90 5695.86 697.27 17998.08 9095.81 1997.87 5498.31 7794.26 1399.68 7197.02 5499.49 4099.57 33
fmvsm_l_conf0.5_n97.65 797.75 797.34 5898.21 10292.75 8997.83 9398.73 1095.04 4499.30 698.84 3593.34 2299.78 4599.32 799.13 9499.50 49
fmvsm_l_conf0.5_n_397.64 897.60 1197.79 3198.14 10993.94 5397.93 7898.65 2196.70 799.38 499.07 1089.92 8999.81 3299.16 1399.43 5099.61 27
fmvsm_l_conf0.5_n_a97.63 997.76 697.26 6598.25 9592.59 9797.81 9898.68 1694.93 4799.24 998.87 3093.52 2099.79 4299.32 799.21 7999.40 63
SteuartSystems-ACMMP97.62 1097.53 1597.87 2598.39 8494.25 4198.43 2398.27 5295.34 3198.11 4398.56 4694.53 1299.71 6396.57 6999.62 1799.65 19
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_l_conf0.5_n_997.59 1197.79 596.97 8398.28 9091.49 14197.61 13398.71 1397.10 499.70 198.93 2190.95 7499.77 4899.35 699.53 3099.65 19
MSP-MVS97.59 1197.54 1497.73 3999.40 1193.77 5898.53 1598.29 4795.55 2698.56 3497.81 12593.90 1599.65 7596.62 6699.21 7999.77 2
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
lecture97.58 1397.63 1097.43 5599.37 1692.93 8398.86 798.85 595.27 3398.65 3298.90 2491.97 5099.80 3797.63 3799.21 7999.57 33
test_fmvsm_n_192097.55 1497.89 396.53 10298.41 8191.73 12798.01 6199.02 196.37 1299.30 698.92 2292.39 4299.79 4299.16 1399.46 4398.08 216
ME-MVS97.54 1597.39 2498.00 2299.21 3394.50 3497.75 10598.34 4194.23 8698.15 4298.53 5093.32 2599.84 2397.40 4999.58 2399.65 19
reproduce-ours97.53 1697.51 1797.60 4898.97 5093.31 7097.71 11598.20 6695.80 2097.88 5198.98 1792.91 2899.81 3297.68 3299.43 5099.67 14
our_new_method97.53 1697.51 1797.60 4898.97 5093.31 7097.71 11598.20 6695.80 2097.88 5198.98 1792.91 2899.81 3297.68 3299.43 5099.67 14
reproduce_model97.51 1897.51 1797.50 5198.99 4993.01 7997.79 10198.21 6495.73 2397.99 4799.03 1492.63 3799.82 3097.80 3099.42 5399.67 14
test_fmvsmconf_n97.49 1997.56 1397.29 6197.44 16192.37 10497.91 8098.88 495.83 1898.92 2299.05 1391.45 5999.80 3799.12 1599.46 4399.69 13
TSAR-MVS + MP.97.42 2097.33 2697.69 4399.25 2994.24 4298.07 5697.85 13393.72 10198.57 3398.35 6893.69 1899.40 12997.06 5399.46 4399.44 58
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 2197.53 1597.06 7998.57 7594.46 3597.92 7998.14 8094.82 5699.01 1698.55 4894.18 1497.41 37996.94 5599.64 1499.32 71
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 2297.13 2898.17 1599.02 4595.28 1998.23 4098.27 5292.37 16298.27 4098.65 4493.33 2399.72 6196.49 7199.52 3299.51 46
SMA-MVScopyleft97.35 2397.03 3798.30 899.06 4195.42 1097.94 7698.18 7390.57 24498.85 2698.94 2093.33 2399.83 2896.72 6399.68 499.63 23
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 2496.97 4098.47 599.08 3996.16 497.55 14497.97 11795.59 2496.61 9397.89 11292.57 3999.84 2395.95 9599.51 3599.40 63
fmvsm_s_conf0.5_n_997.33 2597.57 1296.62 9898.43 7990.32 19797.80 9998.53 2797.24 399.62 299.14 188.65 10699.80 3799.54 199.15 9199.74 8
fmvsm_s_conf0.5_n_897.32 2697.48 2096.85 8598.28 9091.07 16597.76 10398.62 2397.53 299.20 1199.12 488.24 11499.81 3299.41 399.17 8799.67 14
NCCC97.30 2797.03 3798.11 1798.77 5995.06 2597.34 17298.04 10595.96 1497.09 7597.88 11593.18 2699.71 6395.84 10099.17 8799.56 37
fmvsm_s_conf0.5_n_1097.29 2897.40 2396.97 8398.24 9691.96 12397.89 8398.72 1296.77 699.46 399.06 1187.78 12499.84 2399.40 499.27 7199.12 89
MM97.29 2896.98 3998.23 1198.01 11995.03 2698.07 5695.76 33597.78 197.52 5898.80 3788.09 11699.86 999.44 299.37 6499.80 1
ACMMP_NAP97.20 3096.86 4698.23 1199.09 3795.16 2297.60 13498.19 7192.82 15097.93 5098.74 4191.60 5799.86 996.26 7699.52 3299.67 14
XVS97.18 3196.96 4297.81 2999.38 1494.03 5198.59 1398.20 6694.85 5296.59 9598.29 8091.70 5499.80 3795.66 10499.40 5899.62 24
MCST-MVS97.18 3196.84 4898.20 1499.30 2695.35 1597.12 19598.07 9593.54 11096.08 12197.69 13793.86 1699.71 6396.50 7099.39 6099.55 40
fmvsm_s_conf0.5_n_397.15 3397.36 2596.52 10497.98 12291.19 15797.84 9098.65 2197.08 599.25 899.10 587.88 12299.79 4299.32 799.18 8698.59 160
HFP-MVS97.14 3496.92 4497.83 2799.42 794.12 4798.52 1698.32 4393.21 12397.18 6998.29 8092.08 4799.83 2895.63 10999.59 1999.54 42
test_fmvsmconf0.1_n97.09 3597.06 3297.19 7095.67 29592.21 11197.95 7598.27 5295.78 2298.40 3899.00 1589.99 8799.78 4599.06 1799.41 5699.59 29
fmvsm_s_conf0.5_n_697.08 3697.17 2796.81 8697.28 16691.73 12797.75 10598.50 2894.86 5199.22 1098.78 3989.75 9299.76 5099.10 1699.29 6998.94 115
MTAPA97.08 3696.78 5697.97 2499.37 1694.42 3797.24 18198.08 9095.07 4396.11 11998.59 4590.88 7799.90 296.18 8899.50 3799.58 32
region2R97.07 3896.84 4897.77 3599.46 293.79 5698.52 1698.24 6093.19 12697.14 7298.34 7191.59 5899.87 795.46 11599.59 1999.64 22
ACMMPR97.07 3896.84 4897.79 3199.44 693.88 5498.52 1698.31 4493.21 12397.15 7198.33 7491.35 6399.86 995.63 10999.59 1999.62 24
CP-MVS97.02 4096.81 5397.64 4699.33 2393.54 6198.80 998.28 4992.99 13696.45 10798.30 7991.90 5199.85 1895.61 11199.68 499.54 42
SR-MVS97.01 4196.86 4697.47 5399.09 3793.27 7297.98 6698.07 9593.75 10097.45 6098.48 5791.43 6199.59 9196.22 7999.27 7199.54 42
fmvsm_s_conf0.5_n_597.00 4296.97 4097.09 7697.58 15792.56 9897.68 11998.47 3294.02 9198.90 2498.89 2788.94 10099.78 4599.18 1199.03 10398.93 119
ZNCC-MVS96.96 4396.67 6197.85 2699.37 1694.12 4798.49 2098.18 7392.64 15796.39 10998.18 8791.61 5699.88 495.59 11499.55 2799.57 33
APD-MVScopyleft96.95 4496.60 6398.01 2099.03 4494.93 2797.72 11398.10 8891.50 19498.01 4698.32 7692.33 4399.58 9494.85 12999.51 3599.53 45
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSLP-MVS++96.94 4597.06 3296.59 9998.72 6191.86 12597.67 12098.49 2994.66 6897.24 6898.41 6392.31 4598.94 19196.61 6799.46 4398.96 111
DeepC-MVS_fast93.89 296.93 4696.64 6297.78 3398.64 7094.30 3897.41 16298.04 10594.81 5896.59 9598.37 6691.24 6699.64 8395.16 12099.52 3299.42 62
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 4797.04 3696.45 11598.29 8991.66 13499.03 497.85 13395.84 1796.90 7997.97 10591.24 6698.75 21996.92 5699.33 6698.94 115
SR-MVS-dyc-post96.88 4896.80 5497.11 7599.02 4592.34 10597.98 6698.03 10793.52 11397.43 6398.51 5291.40 6299.56 10296.05 9099.26 7499.43 60
CS-MVS96.86 4997.06 3296.26 13198.16 10891.16 16299.09 397.87 12895.30 3297.06 7698.03 9791.72 5298.71 22997.10 5299.17 8798.90 124
mPP-MVS96.86 4996.60 6397.64 4699.40 1193.44 6398.50 1998.09 8993.27 12295.95 12798.33 7491.04 7199.88 495.20 11899.57 2699.60 28
fmvsm_s_conf0.5_n96.85 5197.13 2896.04 14598.07 11690.28 19897.97 7298.76 994.93 4798.84 2799.06 1188.80 10399.65 7599.06 1798.63 11998.18 202
GST-MVS96.85 5196.52 6797.82 2899.36 2094.14 4698.29 3098.13 8192.72 15396.70 8798.06 9491.35 6399.86 994.83 13199.28 7099.47 55
balanced_conf0396.84 5396.89 4596.68 9097.63 14992.22 11098.17 4997.82 13994.44 7898.23 4197.36 16790.97 7399.22 14797.74 3199.66 1098.61 157
patch_mono-296.83 5497.44 2195.01 21399.05 4285.39 35296.98 20898.77 894.70 6597.99 4798.66 4293.61 1999.91 197.67 3699.50 3799.72 12
APD-MVS_3200maxsize96.81 5596.71 6097.12 7399.01 4892.31 10797.98 6698.06 9893.11 13297.44 6198.55 4890.93 7599.55 10496.06 8999.25 7699.51 46
PGM-MVS96.81 5596.53 6697.65 4499.35 2293.53 6297.65 12498.98 292.22 16697.14 7298.44 6091.17 6999.85 1894.35 15199.46 4399.57 33
MP-MVScopyleft96.77 5796.45 7497.72 4099.39 1393.80 5598.41 2498.06 9893.37 11895.54 14598.34 7190.59 8199.88 494.83 13199.54 2999.49 51
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PHI-MVS96.77 5796.46 7397.71 4298.40 8294.07 4998.21 4398.45 3489.86 26297.11 7498.01 10092.52 4099.69 6996.03 9399.53 3099.36 69
fmvsm_s_conf0.5_n_496.75 5997.07 3195.79 16897.76 13889.57 22497.66 12398.66 1995.36 2999.03 1598.90 2488.39 11199.73 5799.17 1298.66 11798.08 216
fmvsm_s_conf0.5_n_a96.75 5996.93 4396.20 13697.64 14790.72 18098.00 6298.73 1094.55 7298.91 2399.08 788.22 11599.63 8498.91 2098.37 13298.25 197
MGCNet96.74 6196.31 7898.02 1996.87 19894.65 3097.58 13594.39 40196.47 1197.16 7098.39 6487.53 13399.87 798.97 1999.41 5699.55 40
test_fmvsmvis_n_192096.70 6296.84 4896.31 12596.62 22391.73 12797.98 6698.30 4596.19 1396.10 12098.95 1989.42 9399.76 5098.90 2199.08 9897.43 256
MP-MVS-pluss96.70 6296.27 8097.98 2399.23 3294.71 2996.96 21098.06 9890.67 23495.55 14398.78 3991.07 7099.86 996.58 6899.55 2799.38 67
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + GP.96.69 6496.49 6897.27 6498.31 8893.39 6496.79 23096.72 28494.17 8797.44 6197.66 14192.76 3299.33 13596.86 5997.76 15899.08 95
HPM-MVScopyleft96.69 6496.45 7497.40 5699.36 2093.11 7798.87 698.06 9891.17 21396.40 10897.99 10390.99 7299.58 9495.61 11199.61 1899.49 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVS_111021_HR96.68 6696.58 6596.99 8198.46 7692.31 10796.20 29198.90 394.30 8595.86 13097.74 13292.33 4399.38 13296.04 9299.42 5399.28 74
fmvsm_s_conf0.5_n_296.62 6796.82 5296.02 14797.98 12290.43 19097.50 14898.59 2496.59 999.31 599.08 784.47 19399.75 5499.37 598.45 12997.88 229
DELS-MVS96.61 6896.38 7797.30 6097.79 13693.19 7595.96 30598.18 7395.23 3495.87 12997.65 14291.45 5999.70 6895.87 9699.44 4999.00 106
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 6897.09 3095.15 20498.09 11286.63 31996.00 30398.15 7895.43 2797.95 4998.56 4693.40 2199.36 13396.77 6099.48 4199.45 56
fmvsm_s_conf0.1_n96.58 7096.77 5796.01 15096.67 22190.25 19997.91 8098.38 3594.48 7698.84 2799.14 188.06 11799.62 8598.82 2298.60 12198.15 206
MVSMamba_PlusPlus96.51 7196.48 6996.59 9998.07 11691.97 12198.14 5097.79 14190.43 24997.34 6697.52 15791.29 6599.19 15098.12 2799.64 1498.60 158
EI-MVSNet-Vis-set96.51 7196.47 7096.63 9598.24 9691.20 15696.89 21897.73 14894.74 6496.49 10298.49 5490.88 7799.58 9496.44 7298.32 13499.13 86
HPM-MVS_fast96.51 7196.27 8097.22 6799.32 2492.74 9098.74 1098.06 9890.57 24496.77 8498.35 6890.21 8499.53 10894.80 13599.63 1699.38 67
fmvsm_s_conf0.5_n_796.45 7496.80 5495.37 19697.29 16588.38 26897.23 18598.47 3295.14 3898.43 3799.09 687.58 13099.72 6198.80 2499.21 7998.02 220
EC-MVSNet96.42 7596.47 7096.26 13197.01 18791.52 14098.89 597.75 14594.42 7996.64 9297.68 13889.32 9498.60 24597.45 4599.11 9798.67 155
fmvsm_s_conf0.1_n_a96.40 7696.47 7096.16 13895.48 30490.69 18197.91 8098.33 4294.07 8998.93 1999.14 187.44 13799.61 8698.63 2598.32 13498.18 202
CANet96.39 7796.02 8597.50 5197.62 15093.38 6597.02 20197.96 11895.42 2894.86 16097.81 12587.38 13999.82 3096.88 5799.20 8499.29 72
dcpmvs_296.37 7897.05 3594.31 25998.96 5284.11 37397.56 13997.51 18193.92 9597.43 6398.52 5192.75 3399.32 13797.32 5199.50 3799.51 46
NormalMVS96.36 7996.11 8397.12 7399.37 1692.90 8497.99 6397.63 16295.92 1596.57 9897.93 10785.34 17599.50 11694.99 12599.21 7998.97 108
EI-MVSNet-UG-set96.34 8096.30 7996.47 11298.20 10390.93 17096.86 22197.72 15094.67 6796.16 11898.46 5890.43 8299.58 9496.23 7897.96 15198.90 124
fmvsm_s_conf0.1_n_296.33 8196.44 7696.00 15197.30 16490.37 19697.53 14597.92 12396.52 1099.14 1499.08 783.21 21599.74 5599.22 1098.06 14697.88 229
train_agg96.30 8295.83 9097.72 4098.70 6294.19 4396.41 26798.02 11088.58 30896.03 12297.56 15492.73 3599.59 9195.04 12299.37 6499.39 65
ACMMPcopyleft96.27 8395.93 8697.28 6399.24 3092.62 9598.25 3698.81 692.99 13694.56 17098.39 6488.96 9999.85 1894.57 14597.63 15999.36 69
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 8496.19 8296.39 12098.23 10191.35 14996.24 28998.79 793.99 9395.80 13297.65 14289.92 8999.24 14595.87 9699.20 8498.58 161
test_fmvsmconf0.01_n96.15 8595.85 8997.03 8092.66 41891.83 12697.97 7297.84 13795.57 2597.53 5799.00 1584.20 19999.76 5098.82 2299.08 9899.48 53
DeepC-MVS93.07 396.06 8695.66 9197.29 6197.96 12493.17 7697.30 17798.06 9893.92 9593.38 21098.66 4286.83 14699.73 5795.60 11399.22 7898.96 111
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CSCG96.05 8795.91 8796.46 11499.24 3090.47 18798.30 2998.57 2689.01 29093.97 19197.57 15292.62 3899.76 5094.66 13999.27 7199.15 84
sasdasda96.02 8895.45 9897.75 3797.59 15395.15 2398.28 3197.60 16794.52 7496.27 11396.12 24787.65 12799.18 15396.20 8494.82 24498.91 121
ETV-MVS96.02 8895.89 8896.40 11897.16 17292.44 10297.47 15797.77 14494.55 7296.48 10394.51 32991.23 6898.92 19495.65 10798.19 14097.82 237
canonicalmvs96.02 8895.45 9897.75 3797.59 15395.15 2398.28 3197.60 16794.52 7496.27 11396.12 24787.65 12799.18 15396.20 8494.82 24498.91 121
CDPH-MVS95.97 9195.38 10397.77 3598.93 5394.44 3696.35 27697.88 12686.98 35496.65 9197.89 11291.99 4999.47 12192.26 19199.46 4399.39 65
UA-Net95.95 9295.53 9497.20 6997.67 14392.98 8197.65 12498.13 8194.81 5896.61 9398.35 6888.87 10199.51 11390.36 24397.35 17099.11 91
SymmetryMVS95.94 9395.54 9397.15 7197.85 13292.90 8497.99 6396.91 27195.92 1596.57 9897.93 10785.34 17599.50 11694.99 12596.39 20999.05 99
MGCFI-Net95.94 9395.40 10297.56 5097.59 15394.62 3198.21 4397.57 17294.41 8096.17 11796.16 24587.54 13299.17 15596.19 8694.73 24998.91 121
BP-MVS195.89 9595.49 9597.08 7896.67 22193.20 7498.08 5496.32 30994.56 7196.32 11097.84 12184.07 20299.15 15996.75 6198.78 11298.90 124
VNet95.89 9595.45 9897.21 6898.07 11692.94 8297.50 14898.15 7893.87 9797.52 5897.61 14885.29 17799.53 10895.81 10195.27 23599.16 82
alignmvs95.87 9795.23 10897.78 3397.56 15995.19 2197.86 8697.17 23694.39 8296.47 10496.40 23285.89 16299.20 14996.21 8395.11 24098.95 114
casdiffmvs_mvgpermissive95.81 9895.57 9296.51 10896.87 19891.49 14197.50 14897.56 17693.99 9395.13 15597.92 11087.89 12198.78 21195.97 9497.33 17199.26 76
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 9994.92 11898.01 2098.08 11595.71 995.27 34697.62 16690.43 24995.55 14397.07 18791.72 5299.50 11689.62 25998.94 10798.82 139
DP-MVS Recon95.68 10095.12 11397.37 5799.19 3494.19 4397.03 19998.08 9088.35 31795.09 15697.65 14289.97 8899.48 12092.08 20298.59 12298.44 179
casdiffmvspermissive95.64 10195.49 9596.08 14196.76 21890.45 18897.29 17897.44 20194.00 9295.46 14897.98 10487.52 13598.73 22395.64 10897.33 17199.08 95
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 10295.13 11197.09 7696.79 20993.26 7397.89 8397.83 13893.58 10596.80 8197.82 12383.06 22299.16 15794.40 14897.95 15298.87 133
MG-MVS95.61 10395.38 10396.31 12598.42 8090.53 18596.04 30097.48 18693.47 11595.67 14098.10 9089.17 9699.25 14491.27 22098.77 11399.13 86
baseline95.58 10495.42 10196.08 14196.78 21390.41 19197.16 19297.45 19793.69 10495.65 14197.85 11987.29 14098.68 23395.66 10497.25 17799.13 86
CPTT-MVS95.57 10595.19 10996.70 8999.27 2891.48 14398.33 2798.11 8687.79 33595.17 15498.03 9787.09 14499.61 8693.51 16999.42 5399.02 100
EIA-MVS95.53 10695.47 9795.71 17697.06 18089.63 22097.82 9597.87 12893.57 10693.92 19295.04 30190.61 8098.95 18994.62 14198.68 11698.54 164
3Dnovator+91.43 495.40 10794.48 13998.16 1696.90 19695.34 1698.48 2197.87 12894.65 6988.53 34098.02 9983.69 20699.71 6393.18 17798.96 10699.44 58
PS-MVSNAJ95.37 10895.33 10595.49 19097.35 16390.66 18395.31 34397.48 18693.85 9896.51 10195.70 27288.65 10699.65 7594.80 13598.27 13796.17 295
MVSFormer95.37 10895.16 11095.99 15296.34 25591.21 15498.22 4197.57 17291.42 19896.22 11597.32 16886.20 15897.92 32994.07 15499.05 10098.85 135
diffmvs_AUTHOR95.33 11095.27 10795.50 18996.37 25389.08 25096.08 29897.38 21293.09 13496.53 10097.74 13286.45 15298.68 23396.32 7497.48 16298.75 146
xiu_mvs_v2_base95.32 11195.29 10695.40 19597.22 16890.50 18695.44 33697.44 20193.70 10396.46 10596.18 24288.59 11099.53 10894.79 13897.81 15596.17 295
PVSNet_Blended_VisFu95.27 11294.91 11996.38 12198.20 10390.86 17397.27 17998.25 5890.21 25394.18 18497.27 17487.48 13699.73 5793.53 16897.77 15798.55 163
viewcassd2359sk1195.26 11395.09 11495.80 16696.95 19389.72 21896.80 22997.56 17692.21 16895.37 14997.80 12787.17 14398.77 21494.82 13397.10 18398.90 124
KinetiMVS95.26 11394.75 12596.79 8796.99 18992.05 11797.82 9597.78 14294.77 6296.46 10597.70 13580.62 27699.34 13492.37 19098.28 13698.97 108
diffmvspermissive95.25 11595.13 11195.63 17996.43 24889.34 23795.99 30497.35 21792.83 14996.31 11197.37 16686.44 15398.67 23696.26 7697.19 18098.87 133
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmanbaseed2359cas95.24 11695.02 11695.91 15596.87 19889.98 20896.82 22697.49 18492.26 16495.47 14797.82 12386.47 15198.69 23194.80 13597.20 17999.06 98
Vis-MVSNetpermissive95.23 11794.81 12096.51 10897.18 17191.58 13898.26 3598.12 8394.38 8394.90 15998.15 8982.28 24398.92 19491.45 21798.58 12399.01 103
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet95.22 11895.04 11595.76 16997.49 16089.56 22598.67 1197.00 26190.69 23294.24 18097.62 14789.79 9198.81 20793.39 17496.49 20698.92 120
EPNet95.20 11994.56 13297.14 7292.80 41592.68 9497.85 8994.87 38596.64 892.46 22797.80 12786.23 15599.65 7593.72 16498.62 12099.10 92
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator91.36 595.19 12094.44 14197.44 5496.56 23193.36 6798.65 1298.36 3694.12 8889.25 32398.06 9482.20 24599.77 4893.41 17399.32 6799.18 81
guyue95.17 12194.96 11795.82 16496.97 19189.65 21997.56 13995.58 34794.82 5695.72 13597.42 16382.90 22798.84 20396.71 6496.93 18798.96 111
OMC-MVS95.09 12294.70 12696.25 13498.46 7691.28 15096.43 26397.57 17292.04 17694.77 16597.96 10687.01 14599.09 17091.31 21996.77 19198.36 186
viewmacassd2359aftdt95.07 12394.80 12195.87 15896.53 23689.84 21496.90 21797.48 18692.44 15995.36 15097.89 11285.23 17898.68 23394.40 14897.00 18699.09 93
xiu_mvs_v1_base_debu95.01 12494.76 12295.75 17196.58 22791.71 13096.25 28697.35 21792.99 13696.70 8796.63 21982.67 23399.44 12596.22 7997.46 16396.11 301
xiu_mvs_v1_base95.01 12494.76 12295.75 17196.58 22791.71 13096.25 28697.35 21792.99 13696.70 8796.63 21982.67 23399.44 12596.22 7997.46 16396.11 301
xiu_mvs_v1_base_debi95.01 12494.76 12295.75 17196.58 22791.71 13096.25 28697.35 21792.99 13696.70 8796.63 21982.67 23399.44 12596.22 7997.46 16396.11 301
PAPM_NR95.01 12494.59 13096.26 13198.89 5790.68 18297.24 18197.73 14891.80 18192.93 22496.62 22289.13 9799.14 16289.21 27297.78 15698.97 108
lupinMVS94.99 12894.56 13296.29 12996.34 25591.21 15495.83 31396.27 31388.93 29696.22 11596.88 20186.20 15898.85 20195.27 11799.05 10098.82 139
Effi-MVS+94.93 12994.45 14096.36 12396.61 22491.47 14496.41 26797.41 20791.02 22194.50 17395.92 25687.53 13398.78 21193.89 16096.81 19098.84 138
IS-MVSNet94.90 13094.52 13696.05 14497.67 14390.56 18498.44 2296.22 31693.21 12393.99 18997.74 13285.55 17298.45 25989.98 24897.86 15399.14 85
LuminaMVS94.89 13194.35 14496.53 10295.48 30492.80 8896.88 22096.18 32092.85 14895.92 12896.87 20381.44 26098.83 20496.43 7397.10 18397.94 225
MVS_Test94.89 13194.62 12995.68 17796.83 20489.55 22696.70 24197.17 23691.17 21395.60 14296.11 25187.87 12398.76 21693.01 18597.17 18198.72 150
viewdifsd2359ckpt1394.87 13394.52 13695.90 15696.88 19790.19 20196.92 21497.36 21591.26 20694.65 16797.46 15885.79 16698.64 24093.64 16696.76 19298.88 132
PVSNet_Blended94.87 13394.56 13295.81 16598.27 9289.46 23295.47 33598.36 3688.84 29994.36 17696.09 25288.02 11899.58 9493.44 17198.18 14198.40 182
jason94.84 13594.39 14296.18 13795.52 30290.93 17096.09 29796.52 29989.28 28196.01 12597.32 16884.70 18998.77 21495.15 12198.91 10998.85 135
jason: jason.
API-MVS94.84 13594.49 13895.90 15697.90 13092.00 12097.80 9997.48 18689.19 28494.81 16396.71 20888.84 10299.17 15588.91 27998.76 11496.53 284
AstraMVS94.82 13794.64 12895.34 19896.36 25488.09 28097.58 13594.56 39494.98 4595.70 13897.92 11081.93 25398.93 19296.87 5895.88 21698.99 107
viewdifsd2359ckpt0994.81 13894.37 14396.12 14096.91 19490.75 17996.94 21197.31 22290.51 24794.31 17897.38 16585.70 16898.71 22993.54 16796.75 19398.90 124
test_yl94.78 13994.23 14796.43 11697.74 13991.22 15296.85 22297.10 24291.23 21095.71 13696.93 19684.30 19699.31 13993.10 17895.12 23898.75 146
DCV-MVSNet94.78 13994.23 14796.43 11697.74 13991.22 15296.85 22297.10 24291.23 21095.71 13696.93 19684.30 19699.31 13993.10 17895.12 23898.75 146
viewdifsd2359ckpt0794.76 14194.68 12795.01 21396.76 21887.41 29596.38 27397.43 20492.65 15594.52 17197.75 13085.55 17298.81 20794.36 15096.69 19798.82 139
SSM_040494.73 14294.31 14695.98 15397.05 18290.90 17297.01 20497.29 22391.24 20794.17 18597.60 14985.03 18298.76 21692.14 19697.30 17498.29 195
WTY-MVS94.71 14394.02 15296.79 8797.71 14192.05 11796.59 25697.35 21790.61 24094.64 16896.93 19686.41 15499.39 13091.20 22294.71 25098.94 115
mamv494.66 14496.10 8490.37 39998.01 11973.41 45096.82 22697.78 14289.95 26094.52 17197.43 16292.91 2899.09 17098.28 2699.16 9098.60 158
mvsmamba94.57 14594.14 14995.87 15897.03 18589.93 21297.84 9095.85 33191.34 20194.79 16496.80 20480.67 27498.81 20794.85 12998.12 14498.85 135
SSM_040794.54 14694.12 15195.80 16696.79 20990.38 19396.79 23097.29 22391.24 20793.68 19697.60 14985.03 18298.67 23692.14 19696.51 20298.35 188
RRT-MVS94.51 14794.35 14494.98 21796.40 24986.55 32297.56 13997.41 20793.19 12694.93 15897.04 18979.12 30499.30 14196.19 8697.32 17399.09 93
sss94.51 14793.80 15696.64 9197.07 17791.97 12196.32 28198.06 9888.94 29594.50 17396.78 20584.60 19099.27 14391.90 20396.02 21298.68 154
test_cas_vis1_n_192094.48 14994.55 13594.28 26196.78 21386.45 32497.63 13097.64 16093.32 12197.68 5698.36 6773.75 36799.08 17396.73 6299.05 10097.31 263
CANet_DTU94.37 15093.65 16296.55 10196.46 24692.13 11596.21 29096.67 29194.38 8393.53 20497.03 19479.34 30099.71 6390.76 23298.45 12997.82 237
AdaColmapbinary94.34 15193.68 16196.31 12598.59 7291.68 13396.59 25697.81 14089.87 26192.15 23897.06 18883.62 20999.54 10689.34 26698.07 14597.70 242
viewmambaseed2359dif94.28 15294.14 14994.71 23596.21 25986.97 30995.93 30797.11 24189.00 29195.00 15797.70 13586.02 16198.59 24993.71 16596.59 20198.57 162
CNLPA94.28 15293.53 16796.52 10498.38 8592.55 9996.59 25696.88 27590.13 25791.91 24697.24 17685.21 17999.09 17087.64 30597.83 15497.92 226
MAR-MVS94.22 15493.46 17296.51 10898.00 12192.19 11497.67 12097.47 19088.13 32593.00 21995.84 26084.86 18899.51 11387.99 29298.17 14297.83 236
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 15593.42 17796.48 11197.64 14791.42 14795.55 33097.71 15488.99 29292.34 23495.82 26289.19 9599.11 16586.14 33197.38 16898.90 124
SDMVSNet94.17 15693.61 16395.86 16198.09 11291.37 14897.35 17198.20 6693.18 12891.79 25097.28 17279.13 30398.93 19294.61 14292.84 28297.28 264
test_vis1_n_192094.17 15694.58 13192.91 33097.42 16282.02 40097.83 9397.85 13394.68 6698.10 4498.49 5470.15 39199.32 13797.91 2998.82 11097.40 258
h-mvs3394.15 15893.52 16996.04 14597.81 13590.22 20097.62 13297.58 17195.19 3596.74 8597.45 15983.67 20799.61 8695.85 9879.73 42298.29 195
CHOSEN 1792x268894.15 15893.51 17096.06 14398.27 9289.38 23595.18 35398.48 3185.60 37793.76 19597.11 18583.15 21899.61 8691.33 21898.72 11599.19 80
Vis-MVSNet (Re-imp)94.15 15893.88 15594.95 22197.61 15187.92 28498.10 5295.80 33492.22 16693.02 21897.45 15984.53 19297.91 33288.24 28897.97 15099.02 100
CDS-MVSNet94.14 16193.54 16695.93 15496.18 26791.46 14596.33 28097.04 25688.97 29493.56 20196.51 22687.55 13197.89 33389.80 25395.95 21498.44 179
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLCcopyleft91.00 694.11 16293.43 17596.13 13998.58 7491.15 16396.69 24397.39 20987.29 34991.37 26096.71 20888.39 11199.52 11287.33 31297.13 18297.73 240
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
FIs94.09 16393.70 16095.27 20095.70 29392.03 11998.10 5298.68 1693.36 12090.39 28196.70 21087.63 12997.94 32692.25 19390.50 32395.84 309
PVSNet_BlendedMVS94.06 16493.92 15494.47 24898.27 9289.46 23296.73 23798.36 3690.17 25494.36 17695.24 29588.02 11899.58 9493.44 17190.72 31994.36 394
nrg03094.05 16593.31 17996.27 13095.22 32794.59 3298.34 2697.46 19292.93 14391.21 27096.64 21587.23 14298.22 27994.99 12585.80 37095.98 305
UGNet94.04 16693.28 18096.31 12596.85 20191.19 15797.88 8597.68 15594.40 8193.00 21996.18 24273.39 36999.61 8691.72 20998.46 12898.13 207
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 16793.46 17295.64 17896.16 26990.45 18896.71 24096.89 27489.27 28293.46 20896.92 19987.29 14097.94 32688.70 28495.74 22098.53 165
Elysia94.00 16893.12 18596.64 9196.08 27992.72 9297.50 14897.63 16291.15 21594.82 16197.12 18374.98 35499.06 17990.78 23098.02 14798.12 209
StellarMVS94.00 16893.12 18596.64 9196.08 27992.72 9297.50 14897.63 16291.15 21594.82 16197.12 18374.98 35499.06 17990.78 23098.02 14798.12 209
IMVS_040393.98 17093.79 15794.55 24496.19 26386.16 33396.35 27697.24 23091.54 18993.59 20097.04 18985.86 16398.73 22390.68 23595.59 22698.76 142
114514_t93.95 17193.06 18896.63 9599.07 4091.61 13597.46 15997.96 11877.99 44193.00 21997.57 15286.14 16099.33 13589.22 27199.15 9198.94 115
IMVS_040793.94 17293.75 15894.49 24796.19 26386.16 33396.35 27697.24 23091.54 18993.50 20597.04 18985.64 17098.54 25290.68 23595.59 22698.76 142
FC-MVSNet-test93.94 17293.57 16495.04 21195.48 30491.45 14698.12 5198.71 1393.37 11890.23 28496.70 21087.66 12697.85 33591.49 21590.39 32495.83 310
mvsany_test193.93 17493.98 15393.78 29394.94 34486.80 31294.62 36592.55 43488.77 30596.85 8098.49 5488.98 9898.08 29795.03 12395.62 22596.46 289
GeoE93.89 17593.28 18095.72 17596.96 19289.75 21798.24 3996.92 27089.47 27592.12 24097.21 17884.42 19498.39 26787.71 29996.50 20599.01 103
HY-MVS89.66 993.87 17692.95 19396.63 9597.10 17692.49 10195.64 32796.64 29289.05 28993.00 21995.79 26685.77 16799.45 12489.16 27594.35 25297.96 223
XVG-OURS-SEG-HR93.86 17793.55 16594.81 22797.06 18088.53 26495.28 34497.45 19791.68 18694.08 18897.68 13882.41 24198.90 19793.84 16292.47 28896.98 272
VDD-MVS93.82 17893.08 18796.02 14797.88 13189.96 21197.72 11395.85 33192.43 16095.86 13098.44 6068.42 40899.39 13096.31 7594.85 24298.71 152
mvs_anonymous93.82 17893.74 15994.06 27196.44 24785.41 35095.81 31497.05 25489.85 26490.09 29496.36 23487.44 13797.75 34993.97 15696.69 19799.02 100
HQP_MVS93.78 18093.43 17594.82 22596.21 25989.99 20697.74 10897.51 18194.85 5291.34 26196.64 21581.32 26298.60 24593.02 18392.23 29195.86 306
PS-MVSNAJss93.74 18193.51 17094.44 25093.91 38289.28 24297.75 10597.56 17692.50 15889.94 29796.54 22588.65 10698.18 28493.83 16390.90 31795.86 306
XVG-OURS93.72 18293.35 17894.80 23097.07 17788.61 25994.79 36297.46 19291.97 17993.99 18997.86 11881.74 25698.88 19892.64 18992.67 28796.92 276
mamba_040893.70 18392.99 18995.83 16396.79 20990.38 19388.69 45297.07 24890.96 22393.68 19697.31 17084.97 18598.76 21690.95 22696.51 20298.35 188
HyFIR lowres test93.66 18492.92 19495.87 15898.24 9689.88 21394.58 36798.49 2985.06 38793.78 19495.78 26782.86 22898.67 23691.77 20895.71 22299.07 97
LFMVS93.60 18592.63 20896.52 10498.13 11191.27 15197.94 7693.39 42290.57 24496.29 11298.31 7769.00 40199.16 15794.18 15395.87 21799.12 89
icg_test_0407_293.58 18693.46 17293.94 28396.19 26386.16 33393.73 40297.24 23091.54 18993.50 20597.04 18985.64 17096.91 39990.68 23595.59 22698.76 142
F-COLMAP93.58 18692.98 19295.37 19698.40 8288.98 25297.18 19097.29 22387.75 33890.49 27997.10 18685.21 17999.50 11686.70 32296.72 19697.63 244
ab-mvs93.57 18892.55 21296.64 9197.28 16691.96 12395.40 33797.45 19789.81 26693.22 21696.28 23879.62 29799.46 12290.74 23393.11 27998.50 169
LS3D93.57 18892.61 21096.47 11297.59 15391.61 13597.67 12097.72 15085.17 38590.29 28398.34 7184.60 19099.73 5783.85 36798.27 13798.06 218
FA-MVS(test-final)93.52 19092.92 19495.31 19996.77 21588.54 26394.82 36196.21 31889.61 27094.20 18295.25 29483.24 21499.14 16290.01 24796.16 21198.25 197
SSM_0407293.51 19192.99 18995.05 20996.79 20990.38 19388.69 45297.07 24890.96 22393.68 19697.31 17084.97 18596.42 41090.95 22696.51 20298.35 188
viewdifsd2359ckpt1193.46 19293.22 18394.17 26496.11 27685.42 34896.43 26397.07 24892.91 14494.20 18298.00 10180.82 27298.73 22394.42 14689.04 33798.34 192
viewmsd2359difaftdt93.46 19293.23 18294.17 26496.12 27485.42 34896.43 26397.08 24592.91 14494.21 18198.00 10180.82 27298.74 22194.41 14789.05 33598.34 192
Fast-Effi-MVS+93.46 19292.75 20295.59 18296.77 21590.03 20396.81 22897.13 23888.19 32091.30 26494.27 34786.21 15798.63 24287.66 30496.46 20898.12 209
hse-mvs293.45 19592.99 18994.81 22797.02 18688.59 26096.69 24396.47 30295.19 3596.74 8596.16 24583.67 20798.48 25895.85 9879.13 42697.35 261
QAPM93.45 19592.27 22296.98 8296.77 21592.62 9598.39 2598.12 8384.50 39588.27 34897.77 12982.39 24299.81 3285.40 34498.81 11198.51 168
UniMVSNet_NR-MVSNet93.37 19792.67 20695.47 19395.34 31692.83 8697.17 19198.58 2592.98 14190.13 28995.80 26388.37 11397.85 33591.71 21083.93 39995.73 320
1112_ss93.37 19792.42 21996.21 13597.05 18290.99 16696.31 28296.72 28486.87 35789.83 30196.69 21286.51 15099.14 16288.12 28993.67 27398.50 169
UniMVSNet (Re)93.31 19992.55 21295.61 18195.39 31093.34 6897.39 16798.71 1393.14 13190.10 29394.83 31287.71 12598.03 30891.67 21383.99 39895.46 329
OPM-MVS93.28 20092.76 20094.82 22594.63 36090.77 17796.65 24797.18 23493.72 10191.68 25497.26 17579.33 30198.63 24292.13 19992.28 29095.07 357
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
VPA-MVSNet93.24 20192.48 21795.51 18795.70 29392.39 10397.86 8698.66 1992.30 16392.09 24295.37 28780.49 27998.40 26293.95 15785.86 36995.75 318
test_fmvs193.21 20293.53 16792.25 35396.55 23381.20 40797.40 16696.96 26390.68 23396.80 8198.04 9669.25 39998.40 26297.58 4098.50 12497.16 269
MVSTER93.20 20392.81 19994.37 25396.56 23189.59 22397.06 19897.12 23991.24 20791.30 26495.96 25482.02 24998.05 30493.48 17090.55 32195.47 328
test111193.19 20492.82 19894.30 26097.58 15784.56 36798.21 4389.02 45393.53 11194.58 16998.21 8472.69 37099.05 18293.06 18198.48 12799.28 74
ECVR-MVScopyleft93.19 20492.73 20494.57 24397.66 14585.41 35098.21 4388.23 45593.43 11694.70 16698.21 8472.57 37199.07 17793.05 18298.49 12599.25 77
HQP-MVS93.19 20492.74 20394.54 24595.86 28589.33 23896.65 24797.39 20993.55 10790.14 28595.87 25880.95 26698.50 25592.13 19992.10 29695.78 314
CHOSEN 280x42093.12 20792.72 20594.34 25696.71 22087.27 29990.29 44297.72 15086.61 36191.34 26195.29 28984.29 19898.41 26193.25 17598.94 10797.35 261
sd_testset93.10 20892.45 21895.05 20998.09 11289.21 24496.89 21897.64 16093.18 12891.79 25097.28 17275.35 35198.65 23988.99 27792.84 28297.28 264
Effi-MVS+-dtu93.08 20993.21 18492.68 34196.02 28283.25 38397.14 19496.72 28493.85 9891.20 27193.44 38583.08 22098.30 27491.69 21295.73 22196.50 286
test_djsdf93.07 21092.76 20094.00 27593.49 39788.70 25898.22 4197.57 17291.42 19890.08 29595.55 28082.85 22997.92 32994.07 15491.58 30395.40 336
VDDNet93.05 21192.07 22696.02 14796.84 20290.39 19298.08 5495.85 33186.22 36995.79 13398.46 5867.59 41199.19 15094.92 12894.85 24298.47 174
thisisatest053093.03 21292.21 22495.49 19097.07 17789.11 24997.49 15692.19 43690.16 25594.09 18796.41 23176.43 34299.05 18290.38 24295.68 22398.31 194
EI-MVSNet93.03 21292.88 19693.48 30995.77 29186.98 30896.44 26197.12 23990.66 23691.30 26497.64 14586.56 14898.05 30489.91 25090.55 32195.41 333
CLD-MVS92.98 21492.53 21494.32 25796.12 27489.20 24595.28 34497.47 19092.66 15489.90 29895.62 27680.58 27798.40 26292.73 18892.40 28995.38 338
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tttt051792.96 21592.33 22194.87 22497.11 17587.16 30597.97 7292.09 43790.63 23893.88 19397.01 19576.50 33999.06 17990.29 24595.45 23298.38 184
ACMM89.79 892.96 21592.50 21694.35 25496.30 25788.71 25797.58 13597.36 21591.40 20090.53 27896.65 21479.77 29398.75 21991.24 22191.64 30195.59 324
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test92.94 21792.56 21194.10 26996.16 26988.26 27297.65 12497.46 19291.29 20290.12 29197.16 18079.05 30698.73 22392.25 19391.89 29995.31 343
BH-untuned92.94 21792.62 20993.92 28797.22 16886.16 33396.40 27196.25 31590.06 25889.79 30296.17 24483.19 21698.35 27087.19 31597.27 17697.24 266
DU-MVS92.90 21992.04 22895.49 19094.95 34292.83 8697.16 19298.24 6093.02 13590.13 28995.71 27083.47 21097.85 33591.71 21083.93 39995.78 314
PatchMatch-RL92.90 21992.02 23095.56 18398.19 10590.80 17595.27 34697.18 23487.96 32791.86 24995.68 27380.44 28098.99 18784.01 36297.54 16196.89 277
VortexMVS92.88 22192.64 20793.58 30496.58 22787.53 29496.93 21397.28 22692.78 15289.75 30394.99 30282.73 23297.76 34794.60 14388.16 34695.46 329
PMMVS92.86 22292.34 22094.42 25294.92 34586.73 31594.53 36996.38 30784.78 39294.27 17995.12 30083.13 21998.40 26291.47 21696.49 20698.12 209
OpenMVScopyleft89.19 1292.86 22291.68 24396.40 11895.34 31692.73 9198.27 3398.12 8384.86 39085.78 39297.75 13078.89 31399.74 5587.50 30998.65 11896.73 281
Test_1112_low_res92.84 22491.84 23795.85 16297.04 18489.97 21095.53 33296.64 29285.38 38089.65 30895.18 29685.86 16399.10 16787.70 30093.58 27898.49 171
baseline192.82 22591.90 23595.55 18597.20 17090.77 17797.19 18994.58 39392.20 16992.36 23196.34 23584.16 20098.21 28089.20 27383.90 40297.68 243
131492.81 22692.03 22995.14 20595.33 31989.52 22996.04 30097.44 20187.72 33986.25 38995.33 28883.84 20498.79 21089.26 26997.05 18597.11 270
DP-MVS92.76 22791.51 25196.52 10498.77 5990.99 16697.38 16996.08 32382.38 41789.29 32097.87 11683.77 20599.69 6981.37 39096.69 19798.89 130
test_fmvs1_n92.73 22892.88 19692.29 35096.08 27981.05 40897.98 6697.08 24590.72 23196.79 8398.18 8763.07 43498.45 25997.62 3998.42 13197.36 259
BH-RMVSNet92.72 22991.97 23294.97 21997.16 17287.99 28296.15 29595.60 34590.62 23991.87 24897.15 18278.41 31998.57 25083.16 36997.60 16098.36 186
ACMP89.59 1092.62 23092.14 22594.05 27296.40 24988.20 27597.36 17097.25 22991.52 19388.30 34696.64 21578.46 31898.72 22891.86 20691.48 30595.23 350
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LCM-MVSNet-Re92.50 23192.52 21592.44 34396.82 20681.89 40196.92 21493.71 41992.41 16184.30 40594.60 32485.08 18197.03 39391.51 21497.36 16998.40 182
TranMVSNet+NR-MVSNet92.50 23191.63 24495.14 20594.76 35392.07 11697.53 14598.11 8692.90 14789.56 31196.12 24783.16 21797.60 36289.30 26783.20 40895.75 318
thres600view792.49 23391.60 24595.18 20397.91 12989.47 23097.65 12494.66 39092.18 17393.33 21194.91 30778.06 32699.10 16781.61 38394.06 26796.98 272
IMVS_040492.44 23491.92 23494.00 27596.19 26386.16 33393.84 39997.24 23091.54 18988.17 35297.04 18976.96 33697.09 39090.68 23595.59 22698.76 142
thres100view90092.43 23591.58 24694.98 21797.92 12889.37 23697.71 11594.66 39092.20 16993.31 21294.90 30878.06 32699.08 17381.40 38794.08 26396.48 287
jajsoiax92.42 23691.89 23694.03 27493.33 40588.50 26597.73 11097.53 17992.00 17888.85 33296.50 22775.62 34998.11 29193.88 16191.56 30495.48 326
thres40092.42 23691.52 24995.12 20797.85 13289.29 24097.41 16294.88 38292.19 17193.27 21494.46 33478.17 32299.08 17381.40 38794.08 26396.98 272
tfpn200view992.38 23891.52 24994.95 22197.85 13289.29 24097.41 16294.88 38292.19 17193.27 21494.46 33478.17 32299.08 17381.40 38794.08 26396.48 287
test_vis1_n92.37 23992.26 22392.72 33894.75 35482.64 39098.02 6096.80 28191.18 21297.77 5597.93 10758.02 44498.29 27597.63 3798.21 13997.23 267
WR-MVS92.34 24091.53 24894.77 23295.13 33590.83 17496.40 27197.98 11691.88 18089.29 32095.54 28182.50 23897.80 34289.79 25485.27 37895.69 321
NR-MVSNet92.34 24091.27 25995.53 18694.95 34293.05 7897.39 16798.07 9592.65 15584.46 40395.71 27085.00 18497.77 34689.71 25583.52 40595.78 314
mvs_tets92.31 24291.76 23993.94 28393.41 40288.29 27097.63 13097.53 17992.04 17688.76 33596.45 22974.62 35998.09 29693.91 15991.48 30595.45 331
TAPA-MVS90.10 792.30 24391.22 26295.56 18398.33 8789.60 22296.79 23097.65 15881.83 42191.52 25697.23 17787.94 12098.91 19671.31 44498.37 13298.17 205
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest051592.29 24491.30 25795.25 20196.60 22588.90 25494.36 37892.32 43587.92 32893.43 20994.57 32577.28 33399.00 18689.42 26495.86 21897.86 233
Fast-Effi-MVS+-dtu92.29 24491.99 23193.21 32095.27 32385.52 34697.03 19996.63 29592.09 17489.11 32695.14 29880.33 28398.08 29787.54 30894.74 24896.03 304
IterMVS-LS92.29 24491.94 23393.34 31496.25 25886.97 30996.57 25997.05 25490.67 23489.50 31494.80 31486.59 14797.64 35789.91 25086.11 36895.40 336
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet86.66 1892.24 24791.74 24293.73 29497.77 13783.69 38092.88 42296.72 28487.91 32993.00 21994.86 31078.51 31799.05 18286.53 32397.45 16798.47 174
VPNet92.23 24891.31 25694.99 21595.56 30090.96 16897.22 18797.86 13292.96 14290.96 27296.62 22275.06 35298.20 28191.90 20383.65 40495.80 312
thres20092.23 24891.39 25294.75 23497.61 15189.03 25196.60 25595.09 37192.08 17593.28 21394.00 36278.39 32099.04 18581.26 39394.18 25996.19 294
anonymousdsp92.16 25091.55 24793.97 27992.58 42089.55 22697.51 14797.42 20689.42 27888.40 34294.84 31180.66 27597.88 33491.87 20591.28 30994.48 389
XXY-MVS92.16 25091.23 26194.95 22194.75 35490.94 16997.47 15797.43 20489.14 28588.90 32896.43 23079.71 29498.24 27789.56 26087.68 35195.67 322
BH-w/o92.14 25291.75 24093.31 31596.99 18985.73 34395.67 32295.69 34088.73 30689.26 32294.82 31382.97 22598.07 30185.26 34796.32 21096.13 300
testing3-292.10 25392.05 22792.27 35197.71 14179.56 42797.42 16194.41 40093.53 11193.22 21695.49 28369.16 40099.11 16593.25 17594.22 25798.13 207
Anonymous20240521192.07 25490.83 27895.76 16998.19 10588.75 25697.58 13595.00 37486.00 37293.64 19997.45 15966.24 42399.53 10890.68 23592.71 28599.01 103
FE-MVS92.05 25591.05 26795.08 20896.83 20487.93 28393.91 39695.70 33886.30 36694.15 18694.97 30376.59 33899.21 14884.10 36096.86 18898.09 215
WR-MVS_H92.00 25691.35 25393.95 28195.09 33789.47 23098.04 5998.68 1691.46 19688.34 34494.68 31985.86 16397.56 36485.77 33984.24 39694.82 374
Anonymous2024052991.98 25790.73 28495.73 17498.14 10989.40 23497.99 6397.72 15079.63 43593.54 20397.41 16469.94 39399.56 10291.04 22591.11 31298.22 199
MonoMVSNet91.92 25891.77 23892.37 34592.94 41183.11 38697.09 19795.55 34992.91 14490.85 27494.55 32681.27 26496.52 40893.01 18587.76 35097.47 255
PatchmatchNetpermissive91.91 25991.35 25393.59 30395.38 31184.11 37393.15 41795.39 35489.54 27292.10 24193.68 37582.82 23098.13 28784.81 35195.32 23498.52 166
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing9191.90 26091.02 26894.53 24696.54 23486.55 32295.86 31195.64 34491.77 18391.89 24793.47 38469.94 39398.86 19990.23 24693.86 27098.18 202
CP-MVSNet91.89 26191.24 26093.82 29095.05 33888.57 26197.82 9598.19 7191.70 18588.21 35095.76 26881.96 25097.52 37087.86 29484.65 38795.37 339
SCA91.84 26291.18 26493.83 28995.59 29884.95 36394.72 36395.58 34790.82 22692.25 23693.69 37375.80 34698.10 29286.20 32995.98 21398.45 176
FMVSNet391.78 26390.69 28795.03 21296.53 23692.27 10997.02 20196.93 26689.79 26789.35 31794.65 32277.01 33497.47 37386.12 33288.82 33895.35 340
AUN-MVS91.76 26490.75 28294.81 22797.00 18888.57 26196.65 24796.49 30189.63 26992.15 23896.12 24778.66 31598.50 25590.83 22879.18 42597.36 259
X-MVStestdata91.71 26589.67 33197.81 2999.38 1494.03 5198.59 1398.20 6694.85 5296.59 9532.69 47091.70 5499.80 3795.66 10499.40 5899.62 24
MVS91.71 26590.44 29495.51 18795.20 32991.59 13796.04 30097.45 19773.44 45187.36 36895.60 27785.42 17499.10 16785.97 33697.46 16395.83 310
EPNet_dtu91.71 26591.28 25892.99 32793.76 38783.71 37996.69 24395.28 36193.15 13087.02 37795.95 25583.37 21397.38 38179.46 40696.84 18997.88 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing1191.68 26890.75 28294.47 24896.53 23686.56 32195.76 31894.51 39791.10 21991.24 26993.59 37968.59 40598.86 19991.10 22394.29 25598.00 222
baseline291.63 26990.86 27493.94 28394.33 37186.32 32695.92 30891.64 44189.37 27986.94 38094.69 31881.62 25898.69 23188.64 28594.57 25196.81 279
testing9991.62 27090.72 28594.32 25796.48 24386.11 33895.81 31494.76 38791.55 18891.75 25293.44 38568.55 40698.82 20590.43 24093.69 27298.04 219
test250691.60 27190.78 27994.04 27397.66 14583.81 37698.27 3375.53 47193.43 11695.23 15298.21 8467.21 41499.07 17793.01 18598.49 12599.25 77
miper_ehance_all_eth91.59 27291.13 26592.97 32895.55 30186.57 32094.47 37296.88 27587.77 33688.88 33094.01 36186.22 15697.54 36689.49 26186.93 35994.79 379
v2v48291.59 27290.85 27693.80 29193.87 38488.17 27796.94 21196.88 27589.54 27289.53 31294.90 30881.70 25798.02 30989.25 27085.04 38495.20 351
V4291.58 27490.87 27393.73 29494.05 37988.50 26597.32 17596.97 26288.80 30489.71 30494.33 34282.54 23798.05 30489.01 27685.07 38294.64 387
PCF-MVS89.48 1191.56 27589.95 31996.36 12396.60 22592.52 10092.51 42797.26 22779.41 43688.90 32896.56 22484.04 20399.55 10477.01 42097.30 17497.01 271
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UBG91.55 27690.76 28093.94 28396.52 23985.06 35995.22 34994.54 39590.47 24891.98 24492.71 39672.02 37498.74 22188.10 29095.26 23698.01 221
PS-CasMVS91.55 27690.84 27793.69 29894.96 34188.28 27197.84 9098.24 6091.46 19688.04 35595.80 26379.67 29597.48 37287.02 31984.54 39395.31 343
miper_enhance_ethall91.54 27891.01 26993.15 32295.35 31587.07 30793.97 39196.90 27286.79 35889.17 32493.43 38886.55 14997.64 35789.97 24986.93 35994.74 383
myMVS_eth3d2891.52 27990.97 27093.17 32196.91 19483.24 38495.61 32894.96 37892.24 16591.98 24493.28 38969.31 39898.40 26288.71 28395.68 22397.88 229
PAPM91.52 27990.30 30095.20 20295.30 32289.83 21593.38 41396.85 27886.26 36888.59 33895.80 26384.88 18798.15 28675.67 42595.93 21597.63 244
ET-MVSNet_ETH3D91.49 28190.11 31095.63 17996.40 24991.57 13995.34 34093.48 42190.60 24275.58 44695.49 28380.08 28796.79 40494.25 15289.76 32998.52 166
TR-MVS91.48 28290.59 29094.16 26796.40 24987.33 29695.67 32295.34 36087.68 34091.46 25895.52 28276.77 33798.35 27082.85 37493.61 27696.79 280
tpmrst91.44 28391.32 25591.79 36895.15 33379.20 43393.42 41295.37 35688.55 31193.49 20793.67 37682.49 23998.27 27690.41 24189.34 33397.90 227
test-LLR91.42 28491.19 26392.12 35694.59 36180.66 41194.29 38392.98 42791.11 21790.76 27692.37 40479.02 30898.07 30188.81 28096.74 19497.63 244
MSDG91.42 28490.24 30494.96 22097.15 17488.91 25393.69 40596.32 30985.72 37686.93 38196.47 22880.24 28498.98 18880.57 39795.05 24196.98 272
c3_l91.38 28690.89 27292.88 33295.58 29986.30 32794.68 36496.84 27988.17 32188.83 33494.23 35085.65 16997.47 37389.36 26584.63 38894.89 369
GA-MVS91.38 28690.31 29994.59 23894.65 35987.62 29294.34 37996.19 31990.73 23090.35 28293.83 36671.84 37697.96 32087.22 31493.61 27698.21 200
v114491.37 28890.60 28993.68 29993.89 38388.23 27496.84 22497.03 25888.37 31689.69 30694.39 33682.04 24897.98 31387.80 29685.37 37594.84 371
GBi-Net91.35 28990.27 30294.59 23896.51 24091.18 15997.50 14896.93 26688.82 30189.35 31794.51 32973.87 36397.29 38586.12 33288.82 33895.31 343
test191.35 28990.27 30294.59 23896.51 24091.18 15997.50 14896.93 26688.82 30189.35 31794.51 32973.87 36397.29 38586.12 33288.82 33895.31 343
UniMVSNet_ETH3D91.34 29190.22 30794.68 23694.86 34987.86 28797.23 18597.46 19287.99 32689.90 29896.92 19966.35 42198.23 27890.30 24490.99 31597.96 223
FMVSNet291.31 29290.08 31194.99 21596.51 24092.21 11197.41 16296.95 26488.82 30188.62 33794.75 31673.87 36397.42 37885.20 34888.55 34395.35 340
reproduce_monomvs91.30 29391.10 26691.92 36096.82 20682.48 39497.01 20497.49 18494.64 7088.35 34395.27 29270.53 38698.10 29295.20 11884.60 39095.19 354
D2MVS91.30 29390.95 27192.35 34694.71 35785.52 34696.18 29398.21 6488.89 29786.60 38493.82 36879.92 29197.95 32489.29 26890.95 31693.56 409
v891.29 29590.53 29393.57 30694.15 37588.12 27997.34 17297.06 25388.99 29288.32 34594.26 34983.08 22098.01 31087.62 30683.92 40194.57 388
CVMVSNet91.23 29691.75 24089.67 40895.77 29174.69 44596.44 26194.88 38285.81 37492.18 23797.64 14579.07 30595.58 42688.06 29195.86 21898.74 149
cl2291.21 29790.56 29293.14 32396.09 27886.80 31294.41 37696.58 29887.80 33488.58 33993.99 36380.85 27197.62 36089.87 25286.93 35994.99 360
PEN-MVS91.20 29890.44 29493.48 30994.49 36587.91 28697.76 10398.18 7391.29 20287.78 35995.74 26980.35 28297.33 38385.46 34382.96 40995.19 354
Baseline_NR-MVSNet91.20 29890.62 28892.95 32993.83 38588.03 28197.01 20495.12 37088.42 31589.70 30595.13 29983.47 21097.44 37689.66 25883.24 40793.37 413
cascas91.20 29890.08 31194.58 24294.97 34089.16 24893.65 40797.59 17079.90 43489.40 31592.92 39475.36 35098.36 26992.14 19694.75 24796.23 291
CostFormer91.18 30190.70 28692.62 34294.84 35081.76 40294.09 38994.43 39884.15 39892.72 22693.77 37079.43 29998.20 28190.70 23492.18 29497.90 227
tt080591.09 30290.07 31494.16 26795.61 29788.31 26997.56 13996.51 30089.56 27189.17 32495.64 27567.08 41898.38 26891.07 22488.44 34495.80 312
v119291.07 30390.23 30593.58 30493.70 38887.82 28996.73 23797.07 24887.77 33689.58 30994.32 34480.90 27097.97 31686.52 32485.48 37394.95 361
v14419291.06 30490.28 30193.39 31293.66 39187.23 30296.83 22597.07 24887.43 34589.69 30694.28 34681.48 25998.00 31187.18 31684.92 38694.93 365
v1091.04 30590.23 30593.49 30894.12 37688.16 27897.32 17597.08 24588.26 31988.29 34794.22 35282.17 24697.97 31686.45 32684.12 39794.33 395
eth_miper_zixun_eth91.02 30690.59 29092.34 34895.33 31984.35 36994.10 38896.90 27288.56 31088.84 33394.33 34284.08 20197.60 36288.77 28284.37 39595.06 358
v14890.99 30790.38 29692.81 33593.83 38585.80 34096.78 23496.68 28989.45 27788.75 33693.93 36582.96 22697.82 33987.83 29583.25 40694.80 377
LTVRE_ROB88.41 1390.99 30789.92 32194.19 26396.18 26789.55 22696.31 28297.09 24487.88 33085.67 39395.91 25778.79 31498.57 25081.50 38489.98 32694.44 392
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 30990.33 29792.88 33295.36 31486.19 33294.46 37496.63 29587.82 33288.18 35194.23 35082.99 22397.53 36887.72 29785.57 37294.93 365
cl____90.96 31090.32 29892.89 33195.37 31386.21 33094.46 37496.64 29287.82 33288.15 35394.18 35382.98 22497.54 36687.70 30085.59 37194.92 367
pmmvs490.93 31189.85 32394.17 26493.34 40490.79 17694.60 36696.02 32484.62 39387.45 36495.15 29781.88 25497.45 37587.70 30087.87 34994.27 399
XVG-ACMP-BASELINE90.93 31190.21 30893.09 32494.31 37385.89 33995.33 34197.26 22791.06 22089.38 31695.44 28668.61 40498.60 24589.46 26291.05 31394.79 379
v192192090.85 31390.03 31693.29 31693.55 39386.96 31196.74 23697.04 25687.36 34789.52 31394.34 34180.23 28597.97 31686.27 32785.21 37994.94 363
CR-MVSNet90.82 31489.77 32793.95 28194.45 36787.19 30390.23 44395.68 34286.89 35692.40 22892.36 40780.91 26897.05 39281.09 39493.95 26897.60 249
v7n90.76 31589.86 32293.45 31193.54 39487.60 29397.70 11897.37 21388.85 29887.65 36194.08 35981.08 26598.10 29284.68 35383.79 40394.66 386
RPSCF90.75 31690.86 27490.42 39896.84 20276.29 44395.61 32896.34 30883.89 40191.38 25997.87 11676.45 34098.78 21187.16 31792.23 29196.20 293
MVP-Stereo90.74 31790.08 31192.71 33993.19 40788.20 27595.86 31196.27 31386.07 37184.86 40194.76 31577.84 32997.75 34983.88 36698.01 14992.17 434
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pm-mvs190.72 31889.65 33393.96 28094.29 37489.63 22097.79 10196.82 28089.07 28786.12 39195.48 28578.61 31697.78 34486.97 32081.67 41494.46 390
v124090.70 31989.85 32393.23 31893.51 39686.80 31296.61 25397.02 26087.16 35289.58 30994.31 34579.55 29897.98 31385.52 34285.44 37494.90 368
EPMVS90.70 31989.81 32593.37 31394.73 35684.21 37193.67 40688.02 45689.50 27492.38 23093.49 38277.82 33097.78 34486.03 33592.68 28698.11 214
WBMVS90.69 32189.99 31892.81 33596.48 24385.00 36095.21 35196.30 31189.46 27689.04 32794.05 36072.45 37397.82 33989.46 26287.41 35695.61 323
Anonymous2023121190.63 32289.42 33894.27 26298.24 9689.19 24798.05 5897.89 12479.95 43388.25 34994.96 30472.56 37298.13 28789.70 25685.14 38095.49 325
DTE-MVSNet90.56 32389.75 32993.01 32693.95 38087.25 30097.64 12897.65 15890.74 22987.12 37295.68 27379.97 29097.00 39683.33 36881.66 41594.78 381
ACMH87.59 1690.53 32489.42 33893.87 28896.21 25987.92 28497.24 18196.94 26588.45 31483.91 41396.27 23971.92 37598.62 24484.43 35689.43 33295.05 359
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS90.52 32589.14 34694.67 23796.81 20887.85 28895.91 30993.97 41389.71 26892.34 23492.48 40265.41 42997.96 32081.37 39094.27 25698.21 200
OurMVSNet-221017-090.51 32690.19 30991.44 37793.41 40281.25 40596.98 20896.28 31291.68 18686.55 38696.30 23674.20 36297.98 31388.96 27887.40 35795.09 356
miper_lstm_enhance90.50 32790.06 31591.83 36595.33 31983.74 37793.86 39796.70 28887.56 34387.79 35893.81 36983.45 21296.92 39887.39 31084.62 38994.82 374
COLMAP_ROBcopyleft87.81 1590.40 32889.28 34193.79 29297.95 12587.13 30696.92 21495.89 33082.83 41486.88 38397.18 17973.77 36699.29 14278.44 41193.62 27594.95 361
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testing22290.31 32988.96 34894.35 25496.54 23487.29 29795.50 33393.84 41790.97 22291.75 25292.96 39362.18 43998.00 31182.86 37294.08 26397.76 239
IterMVS-SCA-FT90.31 32989.81 32591.82 36695.52 30284.20 37294.30 38296.15 32190.61 24087.39 36794.27 34775.80 34696.44 40987.34 31186.88 36394.82 374
MS-PatchMatch90.27 33189.77 32791.78 36994.33 37184.72 36695.55 33096.73 28386.17 37086.36 38895.28 29171.28 38097.80 34284.09 36198.14 14392.81 419
tpm90.25 33289.74 33091.76 37193.92 38179.73 42693.98 39093.54 42088.28 31891.99 24393.25 39077.51 33297.44 37687.30 31387.94 34898.12 209
AllTest90.23 33388.98 34793.98 27797.94 12686.64 31696.51 26095.54 35085.38 38085.49 39596.77 20670.28 38899.15 15980.02 40192.87 28096.15 298
dmvs_re90.21 33489.50 33692.35 34695.47 30885.15 35695.70 32194.37 40390.94 22588.42 34193.57 38074.63 35895.67 42382.80 37589.57 33196.22 292
ACMH+87.92 1490.20 33589.18 34493.25 31796.48 24386.45 32496.99 20796.68 28988.83 30084.79 40296.22 24170.16 39098.53 25384.42 35788.04 34794.77 382
test-mter90.19 33689.54 33592.12 35694.59 36180.66 41194.29 38392.98 42787.68 34090.76 27692.37 40467.67 41098.07 30188.81 28096.74 19497.63 244
IterMVS90.15 33789.67 33191.61 37395.48 30483.72 37894.33 38096.12 32289.99 25987.31 37094.15 35575.78 34896.27 41386.97 32086.89 36294.83 372
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,190.06 33889.42 33891.97 35994.41 36980.62 41394.29 38391.97 43987.28 35090.44 28092.47 40368.79 40297.67 35488.50 28796.60 20097.61 248
SD_040390.01 33990.02 31789.96 40595.65 29676.76 44095.76 31896.46 30390.58 24386.59 38596.29 23782.12 24794.78 43473.00 43993.76 27198.35 188
tpm289.96 34089.21 34392.23 35494.91 34781.25 40593.78 40094.42 39980.62 43191.56 25593.44 38576.44 34197.94 32685.60 34192.08 29897.49 253
UWE-MVS89.91 34189.48 33791.21 38195.88 28478.23 43894.91 36090.26 44989.11 28692.35 23394.52 32868.76 40397.96 32083.95 36495.59 22697.42 257
IB-MVS87.33 1789.91 34188.28 35894.79 23195.26 32687.70 29195.12 35593.95 41489.35 28087.03 37692.49 40170.74 38599.19 15089.18 27481.37 41697.49 253
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 34388.68 35393.53 30795.86 28584.89 36490.93 43895.07 37283.23 41291.28 26791.81 41779.01 31097.85 33579.52 40391.39 30797.84 234
WB-MVSnew89.88 34489.56 33490.82 39094.57 36483.06 38795.65 32692.85 42987.86 33190.83 27594.10 35679.66 29696.88 40076.34 42194.19 25892.54 425
FMVSNet189.88 34488.31 35794.59 23895.41 30991.18 15997.50 14896.93 26686.62 36087.41 36694.51 32965.94 42697.29 38583.04 37187.43 35495.31 343
pmmvs589.86 34688.87 35192.82 33492.86 41386.23 32996.26 28595.39 35484.24 39787.12 37294.51 32974.27 36197.36 38287.61 30787.57 35294.86 370
tpmvs89.83 34789.15 34591.89 36394.92 34580.30 41893.11 41895.46 35386.28 36788.08 35492.65 39780.44 28098.52 25481.47 38689.92 32796.84 278
test_fmvs289.77 34889.93 32089.31 41593.68 39076.37 44297.64 12895.90 32889.84 26591.49 25796.26 24058.77 44297.10 38994.65 14091.13 31194.46 390
SSC-MVS3.289.74 34989.26 34291.19 38495.16 33080.29 41994.53 36997.03 25891.79 18288.86 33194.10 35669.94 39397.82 33985.29 34586.66 36495.45 331
mmtdpeth89.70 35088.96 34891.90 36295.84 29084.42 36897.46 15995.53 35290.27 25294.46 17590.50 42669.74 39798.95 18997.39 5069.48 45292.34 428
tfpnnormal89.70 35088.40 35693.60 30295.15 33390.10 20297.56 13998.16 7787.28 35086.16 39094.63 32377.57 33198.05 30474.48 42984.59 39192.65 422
ADS-MVSNet289.45 35288.59 35492.03 35895.86 28582.26 39890.93 43894.32 40683.23 41291.28 26791.81 41779.01 31095.99 41579.52 40391.39 30797.84 234
Patchmatch-test89.42 35387.99 36093.70 29795.27 32385.11 35788.98 45094.37 40381.11 42587.10 37593.69 37382.28 24397.50 37174.37 43194.76 24698.48 173
test0.0.03 189.37 35488.70 35291.41 37892.47 42285.63 34495.22 34992.70 43291.11 21786.91 38293.65 37779.02 30893.19 45178.00 41389.18 33495.41 333
SixPastTwentyTwo89.15 35588.54 35590.98 38693.49 39780.28 42096.70 24194.70 38990.78 22784.15 40895.57 27871.78 37797.71 35284.63 35485.07 38294.94 363
RPMNet88.98 35687.05 37094.77 23294.45 36787.19 30390.23 44398.03 10777.87 44392.40 22887.55 45080.17 28699.51 11368.84 45093.95 26897.60 249
TransMVSNet (Re)88.94 35787.56 36393.08 32594.35 37088.45 26797.73 11095.23 36587.47 34484.26 40695.29 28979.86 29297.33 38379.44 40774.44 44393.45 412
USDC88.94 35787.83 36292.27 35194.66 35884.96 36293.86 39795.90 32887.34 34883.40 41595.56 27967.43 41298.19 28382.64 37989.67 33093.66 408
dp88.90 35988.26 35990.81 39194.58 36376.62 44192.85 42394.93 37985.12 38690.07 29693.07 39175.81 34598.12 29080.53 39887.42 35597.71 241
PatchT88.87 36087.42 36493.22 31994.08 37885.10 35889.51 44894.64 39281.92 42092.36 23188.15 44680.05 28897.01 39572.43 44093.65 27497.54 252
our_test_388.78 36187.98 36191.20 38392.45 42382.53 39293.61 40995.69 34085.77 37584.88 40093.71 37179.99 28996.78 40579.47 40586.24 36594.28 398
EU-MVSNet88.72 36288.90 35088.20 41993.15 40874.21 44796.63 25294.22 40885.18 38487.32 36995.97 25376.16 34394.98 43285.27 34686.17 36695.41 333
Patchmtry88.64 36387.25 36692.78 33794.09 37786.64 31689.82 44795.68 34280.81 42987.63 36292.36 40780.91 26897.03 39378.86 40985.12 38194.67 385
MIMVSNet88.50 36486.76 37493.72 29694.84 35087.77 29091.39 43394.05 41086.41 36487.99 35692.59 40063.27 43395.82 42077.44 41492.84 28297.57 251
tpm cat188.36 36587.21 36891.81 36795.13 33580.55 41492.58 42695.70 33874.97 44787.45 36491.96 41578.01 32898.17 28580.39 39988.74 34196.72 282
ppachtmachnet_test88.35 36687.29 36591.53 37492.45 42383.57 38193.75 40195.97 32584.28 39685.32 39894.18 35379.00 31296.93 39775.71 42484.99 38594.10 400
JIA-IIPM88.26 36787.04 37191.91 36193.52 39581.42 40489.38 44994.38 40280.84 42890.93 27380.74 45879.22 30297.92 32982.76 37691.62 30296.38 290
testgi87.97 36887.21 36890.24 40192.86 41380.76 40996.67 24694.97 37691.74 18485.52 39495.83 26162.66 43794.47 43776.25 42288.36 34595.48 326
LF4IMVS87.94 36987.25 36689.98 40492.38 42580.05 42494.38 37795.25 36487.59 34284.34 40494.74 31764.31 43197.66 35684.83 35087.45 35392.23 431
gg-mvs-nofinetune87.82 37085.61 38394.44 25094.46 36689.27 24391.21 43784.61 46580.88 42789.89 30074.98 46171.50 37897.53 36885.75 34097.21 17896.51 285
pmmvs687.81 37186.19 37992.69 34091.32 43086.30 32797.34 17296.41 30680.59 43284.05 41294.37 33867.37 41397.67 35484.75 35279.51 42494.09 402
testing387.67 37286.88 37390.05 40396.14 27280.71 41097.10 19692.85 42990.15 25687.54 36394.55 32655.70 44994.10 44073.77 43594.10 26295.35 340
K. test v387.64 37386.75 37590.32 40093.02 41079.48 43196.61 25392.08 43890.66 23680.25 43494.09 35867.21 41496.65 40785.96 33780.83 41894.83 372
Patchmatch-RL test87.38 37486.24 37890.81 39188.74 44878.40 43788.12 45793.17 42487.11 35382.17 42489.29 43781.95 25195.60 42588.64 28577.02 43298.41 181
FMVSNet587.29 37585.79 38291.78 36994.80 35287.28 29895.49 33495.28 36184.09 39983.85 41491.82 41662.95 43594.17 43978.48 41085.34 37793.91 406
myMVS_eth3d87.18 37686.38 37789.58 40995.16 33079.53 42895.00 35793.93 41588.55 31186.96 37891.99 41356.23 44894.00 44175.47 42794.11 26095.20 351
Syy-MVS87.13 37787.02 37287.47 42395.16 33073.21 45195.00 35793.93 41588.55 31186.96 37891.99 41375.90 34494.00 44161.59 45794.11 26095.20 351
Anonymous2023120687.09 37886.14 38089.93 40691.22 43180.35 41696.11 29695.35 35783.57 40884.16 40793.02 39273.54 36895.61 42472.16 44186.14 36793.84 407
EG-PatchMatch MVS87.02 37985.44 38491.76 37192.67 41785.00 36096.08 29896.45 30483.41 41179.52 43693.49 38257.10 44697.72 35179.34 40890.87 31892.56 424
TinyColmap86.82 38085.35 38791.21 38194.91 34782.99 38893.94 39394.02 41283.58 40781.56 42694.68 31962.34 43898.13 28775.78 42387.35 35892.52 426
UWE-MVS-2886.81 38186.41 37688.02 42192.87 41274.60 44695.38 33986.70 46188.17 32187.28 37194.67 32170.83 38493.30 44967.45 45194.31 25496.17 295
mvs5depth86.53 38285.08 38990.87 38888.74 44882.52 39391.91 43194.23 40786.35 36587.11 37493.70 37266.52 41997.76 34781.37 39075.80 43792.31 430
TDRefinement86.53 38284.76 39491.85 36482.23 46484.25 37096.38 27395.35 35784.97 38984.09 41094.94 30565.76 42798.34 27384.60 35574.52 44292.97 416
sc_t186.48 38484.10 40093.63 30093.45 40085.76 34296.79 23094.71 38873.06 45286.45 38794.35 33955.13 45097.95 32484.38 35878.55 42997.18 268
test_040286.46 38584.79 39391.45 37695.02 33985.55 34596.29 28494.89 38180.90 42682.21 42393.97 36468.21 40997.29 38562.98 45588.68 34291.51 439
Anonymous2024052186.42 38685.44 38489.34 41490.33 43579.79 42596.73 23795.92 32683.71 40683.25 41791.36 42263.92 43296.01 41478.39 41285.36 37692.22 432
DSMNet-mixed86.34 38786.12 38187.00 42789.88 43970.43 45394.93 35990.08 45077.97 44285.42 39792.78 39574.44 36093.96 44374.43 43095.14 23796.62 283
CL-MVSNet_self_test86.31 38885.15 38889.80 40788.83 44681.74 40393.93 39496.22 31686.67 35985.03 39990.80 42578.09 32594.50 43574.92 42871.86 44893.15 415
pmmvs-eth3d86.22 38984.45 39691.53 37488.34 45087.25 30094.47 37295.01 37383.47 40979.51 43789.61 43569.75 39695.71 42183.13 37076.73 43591.64 436
test_vis1_rt86.16 39085.06 39089.46 41193.47 39980.46 41596.41 26786.61 46285.22 38379.15 43888.64 44152.41 45497.06 39193.08 18090.57 32090.87 445
test20.0386.14 39185.40 38688.35 41790.12 43680.06 42395.90 31095.20 36688.59 30781.29 42793.62 37871.43 37992.65 45271.26 44581.17 41792.34 428
UnsupCasMVSNet_eth85.99 39284.45 39690.62 39589.97 43882.40 39793.62 40897.37 21389.86 26278.59 44192.37 40465.25 43095.35 43082.27 38170.75 44994.10 400
KD-MVS_self_test85.95 39384.95 39188.96 41689.55 44279.11 43495.13 35496.42 30585.91 37384.07 41190.48 42770.03 39294.82 43380.04 40072.94 44692.94 417
ttmdpeth85.91 39484.76 39489.36 41389.14 44380.25 42195.66 32593.16 42683.77 40483.39 41695.26 29366.24 42395.26 43180.65 39675.57 43892.57 423
YYNet185.87 39584.23 39890.78 39492.38 42582.46 39693.17 41595.14 36982.12 41967.69 45492.36 40778.16 32495.50 42877.31 41679.73 42294.39 393
MDA-MVSNet_test_wron85.87 39584.23 39890.80 39392.38 42582.57 39193.17 41595.15 36882.15 41867.65 45692.33 41078.20 32195.51 42777.33 41579.74 42194.31 397
CMPMVSbinary62.92 2185.62 39784.92 39287.74 42289.14 44373.12 45294.17 38696.80 28173.98 44873.65 45094.93 30666.36 42097.61 36183.95 36491.28 30992.48 427
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_082.17 1985.46 39883.64 40190.92 38795.27 32379.49 43090.55 44195.60 34583.76 40583.00 42089.95 43271.09 38197.97 31682.75 37760.79 46395.31 343
tt032085.39 39983.12 40292.19 35593.44 40185.79 34196.19 29294.87 38571.19 45482.92 42191.76 41958.43 44396.81 40381.03 39578.26 43093.98 404
MDA-MVSNet-bldmvs85.00 40082.95 40591.17 38593.13 40983.33 38294.56 36895.00 37484.57 39465.13 46092.65 39770.45 38795.85 41873.57 43677.49 43194.33 395
MIMVSNet184.93 40183.05 40390.56 39689.56 44184.84 36595.40 33795.35 35783.91 40080.38 43292.21 41257.23 44593.34 44870.69 44782.75 41293.50 410
tt0320-xc84.83 40282.33 41092.31 34993.66 39186.20 33196.17 29494.06 40971.26 45382.04 42592.22 41155.07 45196.72 40681.49 38575.04 44194.02 403
KD-MVS_2432*160084.81 40382.64 40691.31 37991.07 43285.34 35491.22 43595.75 33685.56 37883.09 41890.21 43067.21 41495.89 41677.18 41862.48 46192.69 420
miper_refine_blended84.81 40382.64 40691.31 37991.07 43285.34 35491.22 43595.75 33685.56 37883.09 41890.21 43067.21 41495.89 41677.18 41862.48 46192.69 420
OpenMVS_ROBcopyleft81.14 2084.42 40582.28 41190.83 38990.06 43784.05 37595.73 32094.04 41173.89 45080.17 43591.53 42159.15 44197.64 35766.92 45389.05 33590.80 446
FE-MVSNET83.85 40681.97 41289.51 41087.19 45483.19 38595.21 35193.17 42483.45 41078.90 43989.05 43965.46 42893.84 44569.71 44975.56 43991.51 439
mvsany_test383.59 40782.44 40987.03 42683.80 45973.82 44893.70 40390.92 44786.42 36382.51 42290.26 42946.76 45995.71 42190.82 22976.76 43491.57 438
PM-MVS83.48 40881.86 41488.31 41887.83 45277.59 43993.43 41191.75 44086.91 35580.63 43089.91 43344.42 46095.84 41985.17 34976.73 43591.50 441
test_fmvs383.21 40983.02 40483.78 43286.77 45668.34 45896.76 23594.91 38086.49 36284.14 40989.48 43636.04 46491.73 45491.86 20680.77 41991.26 444
new-patchmatchnet83.18 41081.87 41387.11 42586.88 45575.99 44493.70 40395.18 36785.02 38877.30 44488.40 44365.99 42593.88 44474.19 43370.18 45091.47 442
new_pmnet82.89 41181.12 41688.18 42089.63 44080.18 42291.77 43292.57 43376.79 44575.56 44788.23 44561.22 44094.48 43671.43 44382.92 41089.87 449
MVS-HIRNet82.47 41281.21 41586.26 42995.38 31169.21 45688.96 45189.49 45166.28 45880.79 42974.08 46368.48 40797.39 38071.93 44295.47 23192.18 433
MVStest182.38 41380.04 41789.37 41287.63 45382.83 38995.03 35693.37 42373.90 44973.50 45194.35 33962.89 43693.25 45073.80 43465.92 45892.04 435
UnsupCasMVSNet_bld82.13 41479.46 41990.14 40288.00 45182.47 39590.89 44096.62 29778.94 43875.61 44584.40 45656.63 44796.31 41277.30 41766.77 45791.63 437
dmvs_testset81.38 41582.60 40877.73 43891.74 42951.49 47393.03 42084.21 46689.07 28778.28 44291.25 42376.97 33588.53 46156.57 46182.24 41393.16 414
test_f80.57 41679.62 41883.41 43383.38 46267.80 46093.57 41093.72 41880.80 43077.91 44387.63 44933.40 46592.08 45387.14 31879.04 42790.34 448
pmmvs379.97 41777.50 42287.39 42482.80 46379.38 43292.70 42590.75 44870.69 45578.66 44087.47 45151.34 45593.40 44773.39 43769.65 45189.38 450
APD_test179.31 41877.70 42184.14 43189.11 44569.07 45792.36 43091.50 44269.07 45673.87 44992.63 39939.93 46294.32 43870.54 44880.25 42089.02 451
N_pmnet78.73 41978.71 42078.79 43792.80 41546.50 47694.14 38743.71 47878.61 43980.83 42891.66 42074.94 35696.36 41167.24 45284.45 39493.50 410
WB-MVS76.77 42076.63 42377.18 43985.32 45756.82 47194.53 36989.39 45282.66 41671.35 45289.18 43875.03 35388.88 45935.42 46866.79 45685.84 453
SSC-MVS76.05 42175.83 42476.72 44384.77 45856.22 47294.32 38188.96 45481.82 42270.52 45388.91 44074.79 35788.71 46033.69 46964.71 45985.23 454
test_vis3_rt72.73 42270.55 42579.27 43680.02 46568.13 45993.92 39574.30 47376.90 44458.99 46473.58 46420.29 47395.37 42984.16 35972.80 44774.31 461
LCM-MVSNet72.55 42369.39 42782.03 43470.81 47465.42 46390.12 44594.36 40555.02 46465.88 45881.72 45724.16 47289.96 45574.32 43268.10 45590.71 447
FPMVS71.27 42469.85 42675.50 44474.64 46959.03 46991.30 43491.50 44258.80 46157.92 46588.28 44429.98 46885.53 46453.43 46282.84 41181.95 457
PMMVS270.19 42566.92 42980.01 43576.35 46865.67 46286.22 45887.58 45864.83 46062.38 46180.29 46026.78 47088.49 46263.79 45454.07 46585.88 452
dongtai69.99 42669.33 42871.98 44788.78 44761.64 46789.86 44659.93 47775.67 44674.96 44885.45 45350.19 45681.66 46643.86 46555.27 46472.63 462
testf169.31 42766.76 43076.94 44178.61 46661.93 46588.27 45586.11 46355.62 46259.69 46285.31 45420.19 47489.32 45657.62 45869.44 45379.58 458
APD_test269.31 42766.76 43076.94 44178.61 46661.93 46588.27 45586.11 46355.62 46259.69 46285.31 45420.19 47489.32 45657.62 45869.44 45379.58 458
EGC-MVSNET68.77 42963.01 43586.07 43092.49 42182.24 39993.96 39290.96 4460.71 4752.62 47690.89 42453.66 45293.46 44657.25 46084.55 39282.51 456
Gipumacopyleft67.86 43065.41 43275.18 44592.66 41873.45 44966.50 46694.52 39653.33 46557.80 46666.07 46630.81 46689.20 45848.15 46478.88 42862.90 466
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method66.11 43164.89 43369.79 44872.62 47235.23 48065.19 46792.83 43120.35 47065.20 45988.08 44743.14 46182.70 46573.12 43863.46 46091.45 443
kuosan65.27 43264.66 43467.11 45083.80 45961.32 46888.53 45460.77 47668.22 45767.67 45580.52 45949.12 45770.76 47229.67 47153.64 46669.26 464
ANet_high63.94 43359.58 43677.02 44061.24 47666.06 46185.66 46087.93 45778.53 44042.94 46871.04 46525.42 47180.71 46752.60 46330.83 46984.28 455
PMVScopyleft53.92 2258.58 43455.40 43768.12 44951.00 47748.64 47478.86 46387.10 46046.77 46635.84 47274.28 4628.76 47686.34 46342.07 46673.91 44469.38 463
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN53.28 43552.56 43955.43 45274.43 47047.13 47583.63 46276.30 47042.23 46742.59 46962.22 46828.57 46974.40 46931.53 47031.51 46844.78 467
MVEpermissive50.73 2353.25 43648.81 44166.58 45165.34 47557.50 47072.49 46570.94 47440.15 46939.28 47163.51 4676.89 47873.48 47138.29 46742.38 46768.76 465
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS52.08 43751.31 44054.39 45372.62 47245.39 47783.84 46175.51 47241.13 46840.77 47059.65 46930.08 46773.60 47028.31 47229.90 47044.18 468
tmp_tt51.94 43853.82 43846.29 45433.73 47845.30 47878.32 46467.24 47518.02 47150.93 46787.05 45252.99 45353.11 47370.76 44625.29 47140.46 469
wuyk23d25.11 43924.57 44326.74 45573.98 47139.89 47957.88 4689.80 47912.27 47210.39 4736.97 4757.03 47736.44 47425.43 47317.39 4723.89 472
cdsmvs_eth3d_5k23.24 44030.99 4420.00 4580.00 4810.00 4830.00 46997.63 1620.00 4760.00 47796.88 20184.38 1950.00 4770.00 4760.00 4750.00 473
testmvs13.36 44116.33 4444.48 4575.04 4792.26 48293.18 4143.28 4802.70 4738.24 47421.66 4712.29 4802.19 4757.58 4742.96 4739.00 471
test12313.04 44215.66 4455.18 4564.51 4803.45 48192.50 4281.81 4812.50 4747.58 47520.15 4723.67 4792.18 4767.13 4751.07 4749.90 470
ab-mvs-re8.06 44310.74 4460.00 4580.00 4810.00 4830.00 4690.00 4820.00 4760.00 47796.69 2120.00 4810.00 4770.00 4760.00 4750.00 473
pcd_1.5k_mvsjas7.39 4449.85 4470.00 4580.00 4810.00 4830.00 4690.00 4820.00 4760.00 4770.00 47688.65 1060.00 4770.00 4760.00 4750.00 473
mmdepth0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4690.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
monomultidepth0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4690.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
test_blank0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4690.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
uanet_test0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4690.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
DCPMVS0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4690.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
sosnet-low-res0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4690.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
sosnet0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4690.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
uncertanet0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4690.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
Regformer0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4690.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
uanet0.00 4450.00 4480.00 4580.00 4810.00 4830.00 4690.00 4820.00 4760.00 4770.00 4760.00 4810.00 4770.00 4760.00 4750.00 473
WAC-MVS79.53 42875.56 426
FOURS199.55 193.34 6899.29 198.35 3994.98 4598.49 35
MSC_two_6792asdad98.86 198.67 6496.94 197.93 12199.86 997.68 3299.67 699.77 2
PC_three_145290.77 22898.89 2598.28 8296.24 198.35 27095.76 10299.58 2399.59 29
No_MVS98.86 198.67 6496.94 197.93 12199.86 997.68 3299.67 699.77 2
test_one_060199.32 2495.20 2098.25 5895.13 3998.48 3698.87 3095.16 7
eth-test20.00 481
eth-test0.00 481
ZD-MVS99.05 4294.59 3298.08 9089.22 28397.03 7798.10 9092.52 4099.65 7594.58 14499.31 68
RE-MVS-def96.72 5999.02 4592.34 10597.98 6698.03 10793.52 11397.43 6398.51 5290.71 7996.05 9099.26 7499.43 60
IU-MVS99.42 795.39 1197.94 12090.40 25198.94 1897.41 4899.66 1099.74 8
OPU-MVS98.55 398.82 5896.86 398.25 3698.26 8396.04 299.24 14595.36 11699.59 1999.56 37
test_241102_TWO98.27 5295.13 3998.93 1998.89 2794.99 1199.85 1897.52 4199.65 1399.74 8
test_241102_ONE99.42 795.30 1798.27 5295.09 4299.19 1298.81 3695.54 599.65 75
9.1496.75 5898.93 5397.73 11098.23 6391.28 20597.88 5198.44 6093.00 2799.65 7595.76 10299.47 42
save fliter98.91 5594.28 3997.02 20198.02 11095.35 30
test_0728_THIRD94.78 6098.73 2998.87 3095.87 499.84 2397.45 4599.72 299.77 2
test_0728_SECOND98.51 499.45 395.93 598.21 4398.28 4999.86 997.52 4199.67 699.75 6
test072699.45 395.36 1398.31 2898.29 4794.92 4998.99 1798.92 2295.08 8
GSMVS98.45 176
test_part299.28 2795.74 898.10 44
sam_mvs182.76 23198.45 176
sam_mvs81.94 252
ambc86.56 42883.60 46170.00 45585.69 45994.97 37680.60 43188.45 44237.42 46396.84 40282.69 37875.44 44092.86 418
MTGPAbinary98.08 90
test_post192.81 42416.58 47480.53 27897.68 35386.20 329
test_post17.58 47381.76 25598.08 297
patchmatchnet-post90.45 42882.65 23698.10 292
GG-mvs-BLEND93.62 30193.69 38989.20 24592.39 42983.33 46787.98 35789.84 43471.00 38296.87 40182.08 38295.40 23394.80 377
MTMP97.86 8682.03 468
gm-plane-assit93.22 40678.89 43684.82 39193.52 38198.64 24087.72 297
test9_res94.81 13499.38 6199.45 56
TEST998.70 6294.19 4396.41 26798.02 11088.17 32196.03 12297.56 15492.74 3499.59 91
test_898.67 6494.06 5096.37 27598.01 11388.58 30895.98 12697.55 15692.73 3599.58 94
agg_prior293.94 15899.38 6199.50 49
agg_prior98.67 6493.79 5698.00 11495.68 13999.57 101
TestCases93.98 27797.94 12686.64 31695.54 35085.38 38085.49 39596.77 20670.28 38899.15 15980.02 40192.87 28096.15 298
test_prior493.66 5996.42 266
test_prior296.35 27692.80 15196.03 12297.59 15192.01 4895.01 12499.38 61
test_prior97.23 6698.67 6492.99 8098.00 11499.41 12899.29 72
旧先验295.94 30681.66 42397.34 6698.82 20592.26 191
新几何295.79 316
新几何197.32 5998.60 7193.59 6097.75 14581.58 42495.75 13497.85 11990.04 8699.67 7386.50 32599.13 9498.69 153
旧先验198.38 8593.38 6597.75 14598.09 9292.30 4699.01 10499.16 82
无先验95.79 31697.87 12883.87 40399.65 7587.68 30398.89 130
原ACMM295.67 322
原ACMM196.38 12198.59 7291.09 16497.89 12487.41 34695.22 15397.68 13890.25 8399.54 10687.95 29399.12 9698.49 171
test22298.24 9692.21 11195.33 34197.60 16779.22 43795.25 15197.84 12188.80 10399.15 9198.72 150
testdata299.67 7385.96 337
segment_acmp92.89 31
testdata95.46 19498.18 10788.90 25497.66 15682.73 41597.03 7798.07 9390.06 8598.85 20189.67 25798.98 10598.64 156
testdata195.26 34893.10 133
test1297.65 4498.46 7694.26 4097.66 15695.52 14690.89 7699.46 12299.25 7699.22 79
plane_prior796.21 25989.98 208
plane_prior696.10 27790.00 20481.32 262
plane_prior597.51 18198.60 24593.02 18392.23 29195.86 306
plane_prior496.64 215
plane_prior390.00 20494.46 7791.34 261
plane_prior297.74 10894.85 52
plane_prior196.14 272
plane_prior89.99 20697.24 18194.06 9092.16 295
n20.00 482
nn0.00 482
door-mid91.06 445
lessismore_v090.45 39791.96 42879.09 43587.19 45980.32 43394.39 33666.31 42297.55 36584.00 36376.84 43394.70 384
LGP-MVS_train94.10 26996.16 26988.26 27297.46 19291.29 20290.12 29197.16 18079.05 30698.73 22392.25 19391.89 29995.31 343
test1197.88 126
door91.13 444
HQP5-MVS89.33 238
HQP-NCC95.86 28596.65 24793.55 10790.14 285
ACMP_Plane95.86 28596.65 24793.55 10790.14 285
BP-MVS92.13 199
HQP4-MVS90.14 28598.50 25595.78 314
HQP3-MVS97.39 20992.10 296
HQP2-MVS80.95 266
NP-MVS95.99 28389.81 21695.87 258
MDTV_nov1_ep13_2view70.35 45493.10 41983.88 40293.55 20282.47 24086.25 32898.38 184
MDTV_nov1_ep1390.76 28095.22 32780.33 41793.03 42095.28 36188.14 32492.84 22593.83 36681.34 26198.08 29782.86 37294.34 253
ACMMP++_ref90.30 325
ACMMP++91.02 314
Test By Simon88.73 105
ITE_SJBPF92.43 34495.34 31685.37 35395.92 32691.47 19587.75 36096.39 23371.00 38297.96 32082.36 38089.86 32893.97 405
DeepMVS_CXcopyleft74.68 44690.84 43464.34 46481.61 46965.34 45967.47 45788.01 44848.60 45880.13 46862.33 45673.68 44579.58 458