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 bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort by
patch_mono-298.36 4398.87 496.82 20299.53 3690.68 30498.64 15999.29 997.88 899.19 3299.52 496.80 1599.97 199.11 699.86 199.82 11
dcpmvs_298.08 5298.59 1196.56 22699.57 3390.34 31199.15 4798.38 18496.82 6399.29 2699.49 1095.78 4199.57 13298.94 999.86 199.77 22
test_0728_THIRD97.32 3399.45 1899.46 1697.88 199.94 598.47 2899.86 199.85 5
CP-MVS98.57 2398.36 2499.19 3999.66 2697.86 6199.34 1898.87 6195.96 9998.60 7199.13 7296.05 3199.94 597.77 6799.86 199.77 22
CHOSEN 280x42097.18 10197.18 8697.20 17498.81 12893.27 26195.78 35199.15 2195.25 13796.79 16398.11 19292.29 11099.07 19598.56 1999.85 599.25 125
SD-MVS98.64 1398.68 898.53 7999.33 5798.36 4098.90 9798.85 7097.28 3699.72 699.39 2296.63 1997.60 33398.17 4299.85 599.64 65
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
APDe-MVS99.02 498.84 599.55 999.57 3398.96 1699.39 1298.93 4297.38 3099.41 2099.54 296.66 1799.84 5798.86 1199.85 599.87 2
HPM-MVS_fast98.38 4198.13 4699.12 4899.75 397.86 6199.44 1198.82 7394.46 17498.94 4499.20 5795.16 6699.74 10197.58 8199.85 599.77 22
SteuartSystems-ACMMP98.90 698.75 799.36 2199.22 8698.43 3399.10 5898.87 6197.38 3099.35 2499.40 2197.78 599.87 4897.77 6799.85 599.78 16
Skip Steuart: Steuart Systems R&D Blog.
DPE-MVScopyleft98.92 598.67 999.65 299.58 3299.20 998.42 19298.91 4897.58 1999.54 1599.46 1697.10 1299.94 597.64 7799.84 1099.83 8
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HPM-MVScopyleft98.36 4398.10 4999.13 4699.74 797.82 6599.53 898.80 8594.63 16698.61 7098.97 9595.13 6799.77 9697.65 7699.83 1199.79 14
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SED-MVS99.09 198.91 299.63 499.71 1999.24 599.02 7498.87 6197.65 1499.73 499.48 1197.53 799.94 598.43 3299.81 1299.70 47
IU-MVS99.71 1999.23 798.64 12895.28 13599.63 1198.35 3799.81 1299.83 8
ZNCC-MVS98.49 3098.20 4499.35 2299.73 1198.39 3499.19 4298.86 6795.77 10998.31 8999.10 7695.46 4999.93 2197.57 8499.81 1299.74 31
DVP-MVScopyleft99.03 398.83 699.63 499.72 1299.25 298.97 8498.58 14097.62 1699.45 1899.46 1697.42 999.94 598.47 2899.81 1299.69 50
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
test_0728_SECOND99.71 199.72 1299.35 198.97 8498.88 5499.94 598.47 2899.81 1299.84 7
SMA-MVScopyleft98.58 2098.25 3899.56 899.51 3999.04 1598.95 9098.80 8593.67 21399.37 2399.52 496.52 2199.89 3998.06 4799.81 1299.76 28
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
mPP-MVS98.51 2998.26 3799.25 3499.75 398.04 5699.28 2498.81 7896.24 8798.35 8699.23 5295.46 4999.94 597.42 9299.81 1299.77 22
MSC_two_6792asdad99.62 699.17 9199.08 1198.63 13099.94 598.53 2099.80 1999.86 3
No_MVS99.62 699.17 9199.08 1198.63 13099.94 598.53 2099.80 1999.86 3
test_241102_TWO98.87 6197.65 1499.53 1699.48 1197.34 1199.94 598.43 3299.80 1999.83 8
MP-MVS-pluss98.31 4997.92 5599.49 1299.72 1298.88 1898.43 19098.78 9294.10 18297.69 12599.42 2095.25 6299.92 2698.09 4699.80 1999.67 59
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MTAPA98.58 2098.29 3699.46 1499.76 298.64 2598.90 9798.74 10097.27 4098.02 10199.39 2294.81 7499.96 497.91 5699.79 2399.77 22
region2R98.61 1598.38 2299.29 2899.74 798.16 5199.23 3198.93 4296.15 9198.94 4499.17 6495.91 3799.94 597.55 8599.79 2399.78 16
ACMMPR98.59 1898.36 2499.29 2899.74 798.15 5299.23 3198.95 3896.10 9498.93 4899.19 6295.70 4399.94 597.62 7899.79 2399.78 16
HFP-MVS98.63 1498.40 2099.32 2799.72 1298.29 4499.23 3198.96 3796.10 9498.94 4499.17 6496.06 3099.92 2697.62 7899.78 2699.75 29
MP-MVScopyleft98.33 4898.01 5299.28 3199.75 398.18 4999.22 3598.79 9096.13 9297.92 11299.23 5294.54 7799.94 596.74 12699.78 2699.73 36
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PGM-MVS98.49 3098.23 4199.27 3399.72 1298.08 5598.99 8199.49 595.43 12599.03 3899.32 3995.56 4699.94 596.80 12399.77 2899.78 16
APD-MVScopyleft98.35 4598.00 5399.42 1699.51 3998.72 2198.80 12598.82 7394.52 17199.23 2999.25 5195.54 4899.80 7896.52 13199.77 2899.74 31
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
114514_t96.93 11096.27 12698.92 5899.50 4197.63 6998.85 11198.90 4984.80 35897.77 11699.11 7492.84 10299.66 11894.85 18199.77 2899.47 93
CPTT-MVS97.72 6697.32 8198.92 5899.64 2897.10 8999.12 5398.81 7892.34 26498.09 9499.08 8493.01 10199.92 2696.06 14599.77 2899.75 29
DeepPCF-MVS96.37 297.93 5898.48 1996.30 25299.00 10989.54 32397.43 28298.87 6198.16 599.26 2899.38 2796.12 2999.64 12198.30 3999.77 2899.72 39
DeepC-MVS_fast96.70 198.55 2698.34 2999.18 4199.25 7898.04 5698.50 18198.78 9297.72 1098.92 4999.28 4495.27 6099.82 6697.55 8599.77 2899.69 50
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS98.40 4098.20 4498.99 5399.00 10997.66 6797.75 26198.89 5197.71 1298.33 8798.97 9594.97 7199.88 4798.42 3499.76 3499.42 104
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
MVS_111021_HR98.47 3398.34 2998.88 6199.22 8697.32 7897.91 24599.58 397.20 4398.33 8799.00 9395.99 3499.64 12198.05 4999.76 3499.69 50
PHI-MVS98.34 4698.06 5099.18 4199.15 9798.12 5499.04 6899.09 2493.32 22798.83 5499.10 7696.54 2099.83 5997.70 7499.76 3499.59 73
DeepC-MVS95.98 397.88 5997.58 6498.77 6399.25 7896.93 9498.83 11598.75 9896.96 5796.89 15799.50 890.46 15599.87 4897.84 6399.76 3499.52 80
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_fmvsm_n_192098.87 799.01 198.45 8799.42 5496.43 12098.96 8999.36 798.63 299.86 299.51 695.91 3799.97 199.72 299.75 3898.94 164
ACMMP_NAP98.61 1598.30 3599.55 999.62 3098.95 1798.82 11798.81 7895.80 10899.16 3599.47 1395.37 5499.92 2697.89 5899.75 3899.79 14
MVS_111021_LR98.34 4698.23 4198.67 6899.27 7596.90 9697.95 24199.58 397.14 4898.44 8199.01 9295.03 7099.62 12797.91 5699.75 3899.50 85
3Dnovator94.51 597.46 8296.93 9699.07 5097.78 21297.64 6899.35 1799.06 2797.02 5493.75 26199.16 6789.25 17899.92 2697.22 9999.75 3899.64 65
XVS98.70 1198.49 1799.34 2399.70 2298.35 4199.29 2298.88 5497.40 2798.46 7699.20 5795.90 3999.89 3997.85 6199.74 4299.78 16
X-MVStestdata94.06 27292.30 29399.34 2399.70 2298.35 4199.29 2298.88 5497.40 2798.46 7643.50 38195.90 3999.89 3997.85 6199.74 4299.78 16
OPU-MVS99.37 2099.24 8499.05 1499.02 7499.16 6797.81 399.37 16097.24 9799.73 4499.70 47
SF-MVS98.59 1898.32 3499.41 1799.54 3598.71 2299.04 6898.81 7895.12 14399.32 2599.39 2296.22 2499.84 5797.72 7099.73 4499.67 59
TSAR-MVS + MP.98.78 898.62 1099.24 3599.69 2498.28 4599.14 4998.66 12396.84 6199.56 1399.31 4196.34 2399.70 10998.32 3899.73 4499.73 36
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DVP-MVS++99.08 298.89 399.64 399.17 9199.23 799.69 198.88 5497.32 3399.53 1699.47 1397.81 399.94 598.47 2899.72 4799.74 31
PC_three_145295.08 14899.60 1299.16 6797.86 298.47 26597.52 8899.72 4799.74 31
9.1498.06 5099.47 4798.71 14598.82 7394.36 17699.16 3599.29 4396.05 3199.81 7197.00 10499.71 49
MVS_030498.47 3398.22 4399.21 3899.00 10997.80 6698.88 10495.32 35398.86 198.53 7499.44 1994.38 8499.94 599.86 199.70 5099.90 1
MSLP-MVS++98.56 2598.57 1298.55 7599.26 7796.80 9998.71 14599.05 2997.28 3698.84 5299.28 4496.47 2299.40 15898.52 2699.70 5099.47 93
test_vis1_n_192096.71 11996.84 10096.31 25199.11 10089.74 31899.05 6598.58 14098.08 699.87 199.37 2878.48 32899.93 2199.29 499.69 5299.27 121
CDPH-MVS97.94 5797.49 7099.28 3199.47 4798.44 3197.91 24598.67 12092.57 25698.77 5798.85 11295.93 3699.72 10395.56 16399.69 5299.68 55
HPM-MVS++copyleft98.58 2098.25 3899.55 999.50 4199.08 1198.72 14498.66 12397.51 2298.15 9098.83 11595.70 4399.92 2697.53 8799.67 5499.66 62
APD-MVS_3200maxsize98.53 2898.33 3399.15 4599.50 4197.92 6099.15 4798.81 7896.24 8799.20 3099.37 2895.30 5899.80 7897.73 6999.67 5499.72 39
test_fmvsmvis_n_192098.44 3698.51 1598.23 10698.33 17196.15 13598.97 8499.15 2198.55 398.45 7999.55 194.26 8899.97 199.65 399.66 5698.57 194
test_cas_vis1_n_192097.38 9197.36 7997.45 16098.95 11693.25 26399.00 7898.53 15097.70 1399.77 399.35 3484.71 27699.85 5398.57 1799.66 5699.26 123
CNVR-MVS98.78 898.56 1399.45 1599.32 6098.87 1998.47 18498.81 7897.72 1098.76 5899.16 6797.05 1399.78 9198.06 4799.66 5699.69 50
SR-MVS-dyc-post98.54 2798.35 2699.13 4699.49 4597.86 6199.11 5598.80 8596.49 7699.17 3399.35 3495.34 5699.82 6697.72 7099.65 5999.71 43
RE-MVS-def98.34 2999.49 4597.86 6199.11 5598.80 8596.49 7699.17 3399.35 3495.29 5997.72 7099.65 5999.71 43
CANet98.05 5397.76 5898.90 6098.73 13297.27 8098.35 19598.78 9297.37 3297.72 12398.96 10091.53 13499.92 2698.79 1399.65 5999.51 83
EI-MVSNet-Vis-set98.47 3398.39 2198.69 6699.46 4996.49 11798.30 20498.69 11297.21 4298.84 5299.36 3295.41 5199.78 9198.62 1699.65 5999.80 13
CSCG97.85 6197.74 5998.20 10899.67 2595.16 18199.22 3599.32 893.04 23997.02 15098.92 10695.36 5599.91 3497.43 9199.64 6399.52 80
SR-MVS98.57 2398.35 2699.24 3599.53 3698.18 4999.09 5998.82 7396.58 7399.10 3799.32 3995.39 5299.82 6697.70 7499.63 6499.72 39
GST-MVS98.43 3898.12 4799.34 2399.72 1298.38 3599.09 5998.82 7395.71 11398.73 6199.06 8695.27 6099.93 2197.07 10399.63 6499.72 39
QAPM96.29 13895.40 16198.96 5697.85 20997.60 7199.23 3198.93 4289.76 32693.11 28399.02 8889.11 18399.93 2191.99 27099.62 6699.34 108
MCST-MVS98.65 1298.37 2399.48 1399.60 3198.87 1998.41 19398.68 11597.04 5398.52 7598.80 11896.78 1699.83 5997.93 5499.61 6799.74 31
test_prior297.80 25796.12 9397.89 11498.69 13195.96 3596.89 11399.60 68
jason97.32 9497.08 9098.06 12097.45 24195.59 16397.87 25197.91 26394.79 15998.55 7398.83 11591.12 14299.23 17197.58 8199.60 6899.34 108
jason: jason.
MSP-MVS98.74 1098.55 1499.29 2899.75 398.23 4699.26 2798.88 5497.52 2199.41 2098.78 12096.00 3399.79 8897.79 6699.59 7099.85 5
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
MVSFormer97.57 7897.49 7097.84 13098.07 19595.76 15999.47 998.40 17994.98 15198.79 5598.83 11592.34 10898.41 27996.91 10999.59 7099.34 108
lupinMVS97.44 8697.22 8598.12 11698.07 19595.76 15997.68 26697.76 26994.50 17298.79 5598.61 13892.34 10899.30 16597.58 8199.59 7099.31 114
ZD-MVS99.46 4998.70 2398.79 9093.21 23298.67 6398.97 9595.70 4399.83 5996.07 14299.58 73
test_fmvs196.42 13196.67 11195.66 27798.82 12788.53 34098.80 12598.20 21396.39 8399.64 1099.20 5780.35 31899.67 11699.04 799.57 7498.78 176
test9_res96.39 13699.57 7499.69 50
train_agg97.97 5497.52 6999.33 2699.31 6298.50 2997.92 24398.73 10392.98 24197.74 12098.68 13296.20 2699.80 7896.59 12799.57 7499.68 55
agg_prior295.87 15299.57 7499.68 55
3Dnovator+94.38 697.43 8796.78 10499.38 1897.83 21098.52 2899.37 1498.71 10897.09 5292.99 28699.13 7289.36 17499.89 3996.97 10699.57 7499.71 43
LS3D97.16 10296.66 11298.68 6798.53 15297.19 8798.93 9498.90 4992.83 24895.99 19099.37 2892.12 11799.87 4893.67 22399.57 7498.97 160
CS-MVS-test98.49 3098.50 1698.46 8699.20 8997.05 9099.64 498.50 16097.45 2698.88 5099.14 7195.25 6299.15 18198.83 1299.56 8099.20 129
test1299.18 4199.16 9598.19 4898.53 15098.07 9595.13 6799.72 10399.56 8099.63 67
CHOSEN 1792x268897.12 10496.80 10198.08 11899.30 6694.56 21498.05 23299.71 193.57 21897.09 14498.91 10788.17 20699.89 3996.87 11899.56 8099.81 12
EI-MVSNet-UG-set98.41 3998.34 2998.61 7199.45 5296.32 12898.28 20798.68 11597.17 4598.74 5999.37 2895.25 6299.79 8898.57 1799.54 8399.73 36
test22299.23 8597.17 8897.40 28398.66 12388.68 33898.05 9698.96 10094.14 9099.53 8499.61 69
MG-MVS97.81 6297.60 6398.44 8999.12 9995.97 14597.75 26198.78 9296.89 6098.46 7699.22 5493.90 9499.68 11594.81 18499.52 8599.67 59
test_fmvs1_n95.90 15895.99 13895.63 27898.67 14188.32 34499.26 2798.22 21096.40 8299.67 799.26 4773.91 35599.70 10999.02 899.50 8698.87 168
EC-MVSNet98.21 5198.11 4898.49 8398.34 16997.26 8499.61 598.43 17596.78 6498.87 5198.84 11393.72 9599.01 20598.91 1099.50 8699.19 133
CS-MVS98.44 3698.49 1798.31 9999.08 10296.73 10399.67 398.47 16697.17 4598.94 4499.10 7695.73 4299.13 18498.71 1499.49 8899.09 147
UGNet96.78 11796.30 12598.19 11098.24 17795.89 15598.88 10498.93 4297.39 2996.81 16197.84 21682.60 30299.90 3796.53 13099.49 8898.79 173
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
API-MVS97.41 8997.25 8397.91 12798.70 13796.80 9998.82 11798.69 11294.53 16998.11 9298.28 17794.50 8199.57 13294.12 20899.49 8897.37 231
新几何199.16 4499.34 5598.01 5898.69 11290.06 32198.13 9198.95 10294.60 7699.89 3991.97 27199.47 9199.59 73
旧先验199.29 7097.48 7498.70 11199.09 8295.56 4699.47 9199.61 69
OpenMVScopyleft93.04 1395.83 16295.00 18698.32 9897.18 26097.32 7899.21 3898.97 3589.96 32291.14 31999.05 8786.64 23899.92 2693.38 22999.47 9197.73 221
原ACMM198.65 6999.32 6096.62 10698.67 12093.27 23197.81 11598.97 9595.18 6599.83 5993.84 21799.46 9499.50 85
testdata98.26 10399.20 8995.36 17398.68 11591.89 27898.60 7199.10 7694.44 8399.82 6694.27 20399.44 9599.58 77
DP-MVS Recon97.86 6097.46 7399.06 5199.53 3698.35 4198.33 19798.89 5192.62 25398.05 9698.94 10395.34 5699.65 11996.04 14699.42 9699.19 133
NCCC98.61 1598.35 2699.38 1899.28 7498.61 2698.45 18598.76 9697.82 998.45 7998.93 10496.65 1899.83 5997.38 9499.41 9799.71 43
TAPA-MVS93.98 795.35 19194.56 20697.74 14199.13 9894.83 20098.33 19798.64 12886.62 34696.29 18298.61 13894.00 9399.29 16680.00 36299.41 9799.09 147
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_vis1_n95.47 17995.13 17996.49 23597.77 21390.41 30999.27 2698.11 23396.58 7399.66 899.18 6367.00 36599.62 12799.21 599.40 9999.44 100
PVSNet_Blended97.38 9197.12 8798.14 11199.25 7895.35 17597.28 29699.26 1093.13 23597.94 10998.21 18592.74 10499.81 7196.88 11599.40 9999.27 121
MS-PatchMatch93.84 27693.63 26494.46 31696.18 31289.45 32497.76 26098.27 20392.23 26992.13 30997.49 24679.50 32298.69 24189.75 30799.38 10195.25 341
CANet_DTU96.96 10996.55 11598.21 10798.17 18996.07 13897.98 23998.21 21197.24 4197.13 14398.93 10486.88 23599.91 3495.00 17999.37 10298.66 185
DPM-MVS97.55 8096.99 9499.23 3799.04 10498.55 2797.17 30698.35 18894.85 15897.93 11198.58 14395.07 6999.71 10892.60 25199.34 10399.43 102
MVP-Stereo94.28 25793.92 24295.35 28894.95 34592.60 27497.97 24097.65 27491.61 28690.68 32497.09 27486.32 24598.42 27189.70 30999.34 10395.02 348
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CNLPA97.45 8597.03 9298.73 6499.05 10397.44 7798.07 23098.53 15095.32 13396.80 16298.53 14793.32 9899.72 10394.31 20299.31 10599.02 155
AdaColmapbinary97.15 10396.70 10898.48 8499.16 9596.69 10598.01 23698.89 5194.44 17596.83 15898.68 13290.69 15299.76 9794.36 19899.29 10698.98 159
Vis-MVSNetpermissive97.42 8897.11 8898.34 9798.66 14296.23 13199.22 3599.00 3296.63 7298.04 9899.21 5588.05 21199.35 16196.01 14899.21 10799.45 99
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EIA-MVS97.75 6497.58 6498.27 10198.38 16196.44 11999.01 7698.60 13395.88 10597.26 13997.53 24594.97 7199.33 16397.38 9499.20 10899.05 153
EPNet97.28 9596.87 9998.51 8094.98 34496.14 13698.90 9797.02 32198.28 495.99 19099.11 7491.36 13699.89 3996.98 10599.19 10999.50 85
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PS-MVSNAJ97.73 6597.77 5797.62 15398.68 14095.58 16497.34 29198.51 15597.29 3598.66 6797.88 21294.51 7899.90 3797.87 6099.17 11097.39 229
PVSNet_Blended_VisFu97.70 6897.46 7398.44 8999.27 7595.91 15398.63 16199.16 2094.48 17397.67 12698.88 10992.80 10399.91 3497.11 10199.12 11199.50 85
BH-RMVSNet95.92 15795.32 17097.69 14698.32 17494.64 20698.19 21897.45 29694.56 16796.03 18898.61 13885.02 26999.12 18690.68 29399.06 11299.30 117
test250694.44 24793.91 24496.04 26099.02 10688.99 33399.06 6379.47 38896.96 5798.36 8499.26 4777.21 34099.52 14696.78 12499.04 11399.59 73
test111195.94 15595.78 14596.41 24498.99 11390.12 31399.04 6892.45 37496.99 5698.03 9999.27 4681.40 30799.48 15296.87 11899.04 11399.63 67
ECVR-MVScopyleft95.95 15395.71 15296.65 21299.02 10690.86 29999.03 7191.80 37596.96 5798.10 9399.26 4781.31 30899.51 14796.90 11299.04 11399.59 73
PVSNet91.96 1896.35 13696.15 13096.96 19299.17 9192.05 28096.08 34498.68 11593.69 20997.75 11997.80 22288.86 19299.69 11494.26 20499.01 11699.15 140
PatchMatch-RL96.59 12396.03 13698.27 10199.31 6296.51 11697.91 24599.06 2793.72 20596.92 15598.06 19588.50 20199.65 11991.77 27599.00 11798.66 185
PCF-MVS93.45 1194.68 22693.43 27398.42 9398.62 14696.77 10195.48 35598.20 21384.63 35993.34 27498.32 17488.55 19999.81 7184.80 34898.96 11898.68 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MAR-MVS96.91 11196.40 12198.45 8798.69 13996.90 9698.66 15798.68 11592.40 26397.07 14797.96 20591.54 13399.75 9993.68 22198.92 11998.69 181
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
F-COLMAP97.09 10696.80 10197.97 12499.45 5294.95 19498.55 17498.62 13293.02 24096.17 18598.58 14394.01 9299.81 7193.95 21398.90 12099.14 142
ETV-MVS97.96 5597.81 5698.40 9498.42 15897.27 8098.73 14098.55 14696.84 6198.38 8397.44 25195.39 5299.35 16197.62 7898.89 12198.58 193
DP-MVS96.59 12395.93 14098.57 7399.34 5596.19 13498.70 14998.39 18189.45 33194.52 21999.35 3491.85 12399.85 5392.89 24798.88 12299.68 55
OMC-MVS97.55 8097.34 8098.20 10899.33 5795.92 15298.28 20798.59 13595.52 12197.97 10699.10 7693.28 9999.49 14895.09 17798.88 12299.19 133
PAPM_NR97.46 8297.11 8898.50 8199.50 4196.41 12398.63 16198.60 13395.18 14097.06 14898.06 19594.26 8899.57 13293.80 21998.87 12499.52 80
ACMMPcopyleft98.23 5097.95 5499.09 4999.74 797.62 7099.03 7199.41 695.98 9797.60 13399.36 3294.45 8299.93 2197.14 10098.85 12599.70 47
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
UA-Net97.96 5597.62 6298.98 5498.86 12397.47 7598.89 10199.08 2596.67 7098.72 6299.54 293.15 10099.81 7194.87 18098.83 12699.65 63
MSDG95.93 15695.30 17397.83 13198.90 11995.36 17396.83 33198.37 18591.32 29694.43 22698.73 12890.27 15999.60 12990.05 30298.82 12798.52 195
EPNet_dtu95.21 19994.95 19095.99 26296.17 31390.45 30898.16 22297.27 30896.77 6593.14 28298.33 17390.34 15798.42 27185.57 34198.81 12899.09 147
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PLCcopyleft95.07 497.20 10096.78 10498.44 8999.29 7096.31 13098.14 22398.76 9692.41 26296.39 18098.31 17594.92 7399.78 9194.06 21198.77 12999.23 126
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
xiu_mvs_v1_base_debu97.60 7497.56 6697.72 14298.35 16495.98 14097.86 25298.51 15597.13 4999.01 4098.40 16291.56 13099.80 7898.53 2098.68 13097.37 231
xiu_mvs_v1_base97.60 7497.56 6697.72 14298.35 16495.98 14097.86 25298.51 15597.13 4999.01 4098.40 16291.56 13099.80 7898.53 2098.68 13097.37 231
xiu_mvs_v1_base_debi97.60 7497.56 6697.72 14298.35 16495.98 14097.86 25298.51 15597.13 4999.01 4098.40 16291.56 13099.80 7898.53 2098.68 13097.37 231
MVS-HIRNet89.46 32388.40 32392.64 33597.58 22782.15 36594.16 36893.05 37375.73 37090.90 32182.52 37379.42 32398.33 28783.53 35398.68 13097.43 226
xiu_mvs_v2_base97.66 7197.70 6097.56 15798.61 14795.46 17097.44 28098.46 16797.15 4798.65 6898.15 18994.33 8599.80 7897.84 6398.66 13497.41 227
mvsany_test197.69 6997.70 6097.66 15198.24 17794.18 23097.53 27797.53 28895.52 12199.66 899.51 694.30 8699.56 13598.38 3598.62 13599.23 126
Vis-MVSNet (Re-imp)96.87 11396.55 11597.83 13198.73 13295.46 17099.20 4098.30 20094.96 15396.60 16998.87 11090.05 16198.59 25193.67 22398.60 13699.46 97
IS-MVSNet97.22 9796.88 9898.25 10498.85 12596.36 12699.19 4297.97 25695.39 12797.23 14098.99 9491.11 14398.93 21794.60 19198.59 13799.47 93
PAPR96.84 11596.24 12898.65 6998.72 13696.92 9597.36 28998.57 14293.33 22696.67 16597.57 24294.30 8699.56 13591.05 28898.59 13799.47 93
TSAR-MVS + GP.98.38 4198.24 4098.81 6299.22 8697.25 8598.11 22898.29 20297.19 4498.99 4399.02 8896.22 2499.67 11698.52 2698.56 13999.51 83
diffmvspermissive97.58 7797.40 7798.13 11498.32 17495.81 15898.06 23198.37 18596.20 8998.74 5998.89 10891.31 13999.25 16898.16 4398.52 14099.34 108
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BH-untuned95.95 15395.72 14996.65 21298.55 15192.26 27698.23 21197.79 26893.73 20494.62 21698.01 20088.97 19099.00 20693.04 24098.51 14198.68 182
test-LLR95.10 20594.87 19495.80 27296.77 28289.70 31996.91 32195.21 35495.11 14494.83 21195.72 33787.71 21998.97 20793.06 23898.50 14298.72 178
TESTMET0.1,194.18 26393.69 26295.63 27896.92 27489.12 32996.91 32194.78 35993.17 23494.88 20896.45 31778.52 32798.92 21893.09 23798.50 14298.85 169
test-mter94.08 27093.51 27095.80 27296.77 28289.70 31996.91 32195.21 35492.89 24594.83 21195.72 33777.69 33598.97 20793.06 23898.50 14298.72 178
131496.25 14295.73 14897.79 13597.13 26395.55 16798.19 21898.59 13593.47 22192.03 31197.82 22091.33 13899.49 14894.62 19098.44 14598.32 204
LCM-MVSNet-Re95.22 19895.32 17094.91 29998.18 18787.85 35098.75 13395.66 35095.11 14488.96 33796.85 30190.26 16097.65 33195.65 16198.44 14599.22 128
EPP-MVSNet97.46 8297.28 8297.99 12398.64 14495.38 17299.33 2198.31 19493.61 21797.19 14199.07 8594.05 9199.23 17196.89 11398.43 14799.37 107
casdiffmvs_mvgpermissive97.72 6697.48 7298.44 8998.42 15896.59 11198.92 9598.44 17196.20 8997.76 11799.20 5791.66 12899.23 17198.27 4198.41 14899.49 90
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvspermissive97.63 7397.41 7698.28 10098.33 17196.14 13698.82 11798.32 19296.38 8497.95 10799.21 5591.23 14199.23 17198.12 4498.37 14999.48 91
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PatchmatchNetpermissive95.71 16895.52 15996.29 25397.58 22790.72 30396.84 33097.52 28994.06 18397.08 14596.96 29289.24 17998.90 22292.03 26998.37 14999.26 123
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVS94.67 22993.54 26998.08 11896.88 27896.56 11398.19 21898.50 16078.05 36892.69 29498.02 19891.07 14599.63 12490.09 29998.36 15198.04 212
FE-MVS95.62 17494.90 19297.78 13698.37 16394.92 19597.17 30697.38 30290.95 30797.73 12297.70 22885.32 26699.63 12491.18 28398.33 15298.79 173
gg-mvs-nofinetune92.21 29990.58 30797.13 18096.75 28595.09 18595.85 34989.40 38185.43 35694.50 22081.98 37480.80 31598.40 28592.16 26398.33 15297.88 215
SCA95.46 18095.13 17996.46 24197.67 22191.29 29497.33 29297.60 27894.68 16396.92 15597.10 27083.97 29298.89 22392.59 25398.32 15499.20 129
baseline97.64 7297.44 7598.25 10498.35 16496.20 13299.00 7898.32 19296.33 8698.03 9999.17 6491.35 13799.16 17898.10 4598.29 15599.39 105
MVS_Test97.28 9597.00 9398.13 11498.33 17195.97 14598.74 13698.07 24394.27 17898.44 8198.07 19492.48 10699.26 16796.43 13498.19 15699.16 139
sss97.39 9096.98 9598.61 7198.60 14896.61 10898.22 21298.93 4293.97 19098.01 10498.48 15291.98 12199.85 5396.45 13398.15 15799.39 105
Patchmatch-test94.42 24893.68 26396.63 21697.60 22691.76 28494.83 36197.49 29389.45 33194.14 24297.10 27088.99 18698.83 23185.37 34498.13 15899.29 119
COLMAP_ROBcopyleft93.27 1295.33 19394.87 19496.71 20799.29 7093.24 26498.58 16798.11 23389.92 32393.57 26599.10 7686.37 24499.79 8890.78 29198.10 15997.09 236
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GeoE96.58 12596.07 13398.10 11798.35 16495.89 15599.34 1898.12 23093.12 23696.09 18698.87 11089.71 16798.97 20792.95 24398.08 16099.43 102
FA-MVS(test-final)96.41 13595.94 13997.82 13398.21 18195.20 18097.80 25797.58 27993.21 23297.36 13797.70 22889.47 17199.56 13594.12 20897.99 16198.71 180
Effi-MVS+-dtu96.29 13896.56 11495.51 28197.89 20890.22 31298.80 12598.10 23696.57 7596.45 17996.66 30890.81 14898.91 21995.72 15797.99 16197.40 228
Fast-Effi-MVS+96.28 14095.70 15498.03 12198.29 17695.97 14598.58 16798.25 20891.74 28195.29 20197.23 26491.03 14699.15 18192.90 24597.96 16398.97 160
mvs_anonymous96.70 12096.53 11797.18 17698.19 18593.78 23998.31 20298.19 21594.01 18794.47 22198.27 18092.08 11998.46 26697.39 9397.91 16499.31 114
PMMVS96.60 12296.33 12397.41 16497.90 20793.93 23597.35 29098.41 17792.84 24797.76 11797.45 25091.10 14499.20 17596.26 13897.91 16499.11 145
AllTest95.24 19794.65 20296.99 18899.25 7893.21 26598.59 16598.18 21891.36 29293.52 26798.77 12284.67 27799.72 10389.70 30997.87 16698.02 213
TestCases96.99 18899.25 7893.21 26598.18 21891.36 29293.52 26798.77 12284.67 27799.72 10389.70 30997.87 16698.02 213
TAMVS97.02 10796.79 10397.70 14598.06 19795.31 17798.52 17698.31 19493.95 19197.05 14998.61 13893.49 9798.52 25995.33 16997.81 16899.29 119
Effi-MVS+97.12 10496.69 10998.39 9598.19 18596.72 10497.37 28798.43 17593.71 20697.65 12998.02 19892.20 11599.25 16896.87 11897.79 16999.19 133
Fast-Effi-MVS+-dtu95.87 15995.85 14295.91 26797.74 21791.74 28698.69 15198.15 22695.56 11994.92 20797.68 23388.98 18998.79 23593.19 23597.78 17097.20 235
DSMNet-mixed92.52 29792.58 28992.33 33794.15 35382.65 36498.30 20494.26 36589.08 33692.65 29595.73 33585.01 27095.76 36186.24 33697.76 17198.59 191
CDS-MVSNet96.99 10896.69 10997.90 12898.05 19895.98 14098.20 21598.33 19193.67 21396.95 15198.49 15193.54 9698.42 27195.24 17597.74 17299.31 114
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thisisatest051595.61 17794.89 19397.76 13998.15 19195.15 18396.77 33294.41 36292.95 24397.18 14297.43 25284.78 27499.45 15694.63 18897.73 17398.68 182
thisisatest053096.01 14995.36 16697.97 12498.38 16195.52 16898.88 10494.19 36694.04 18497.64 13098.31 17583.82 29799.46 15595.29 17297.70 17498.93 165
BH-w/o95.38 18795.08 18396.26 25498.34 16991.79 28397.70 26597.43 29892.87 24694.24 23797.22 26588.66 19598.84 22991.55 27997.70 17498.16 210
PAPM94.95 21594.00 23797.78 13697.04 26795.65 16296.03 34798.25 20891.23 30194.19 24097.80 22291.27 14098.86 22882.61 35697.61 17698.84 171
tttt051796.07 14795.51 16097.78 13698.41 16094.84 19899.28 2494.33 36494.26 17997.64 13098.64 13684.05 29099.47 15495.34 16897.60 17799.03 154
HyFIR lowres test96.90 11296.49 11898.14 11199.33 5795.56 16597.38 28599.65 292.34 26497.61 13298.20 18689.29 17699.10 19296.97 10697.60 17799.77 22
CVMVSNet95.43 18396.04 13593.57 32497.93 20583.62 36198.12 22698.59 13595.68 11496.56 17099.02 8887.51 22397.51 33893.56 22797.44 17999.60 71
MDTV_nov1_ep1395.40 16197.48 23688.34 34396.85 32997.29 30693.74 20397.48 13697.26 26089.18 18099.05 19691.92 27297.43 180
baseline295.11 20494.52 20896.87 19996.65 29193.56 24898.27 20994.10 36893.45 22292.02 31297.43 25287.45 22799.19 17693.88 21697.41 18197.87 216
EPMVS94.99 21194.48 21096.52 23397.22 25491.75 28597.23 29891.66 37694.11 18197.28 13896.81 30385.70 25698.84 22993.04 24097.28 18298.97 160
LFMVS95.86 16094.98 18898.47 8598.87 12296.32 12898.84 11496.02 34493.40 22498.62 6999.20 5774.99 35099.63 12497.72 7097.20 18399.46 97
ADS-MVSNet294.58 23594.40 21895.11 29498.00 19988.74 33696.04 34597.30 30590.15 31996.47 17796.64 31187.89 21497.56 33690.08 30097.06 18499.02 155
ADS-MVSNet95.00 21094.45 21496.63 21698.00 19991.91 28296.04 34597.74 27190.15 31996.47 17796.64 31187.89 21498.96 21190.08 30097.06 18499.02 155
GG-mvs-BLEND96.59 22296.34 30794.98 19196.51 34188.58 38293.10 28494.34 35280.34 31998.05 31189.53 31296.99 18696.74 272
cascas94.63 23193.86 24896.93 19496.91 27694.27 22596.00 34898.51 15585.55 35594.54 21896.23 32384.20 28898.87 22695.80 15596.98 18797.66 224
WTY-MVS97.37 9396.92 9798.72 6598.86 12396.89 9898.31 20298.71 10895.26 13697.67 12698.56 14692.21 11499.78 9195.89 15096.85 18899.48 91
VDD-MVS95.82 16395.23 17597.61 15498.84 12693.98 23498.68 15297.40 30095.02 15097.95 10799.34 3874.37 35499.78 9198.64 1596.80 18999.08 151
test_yl97.22 9796.78 10498.54 7798.73 13296.60 10998.45 18598.31 19494.70 16098.02 10198.42 16090.80 14999.70 10996.81 12196.79 19099.34 108
DCV-MVSNet97.22 9796.78 10498.54 7798.73 13296.60 10998.45 18598.31 19494.70 16098.02 10198.42 16090.80 14999.70 10996.81 12196.79 19099.34 108
PatchT93.06 29291.97 29696.35 24896.69 28892.67 27394.48 36597.08 31486.62 34697.08 14592.23 36587.94 21397.90 32178.89 36696.69 19298.49 196
VNet97.79 6397.40 7798.96 5698.88 12197.55 7298.63 16198.93 4296.74 6799.02 3998.84 11390.33 15899.83 5998.53 2096.66 19399.50 85
CR-MVSNet94.76 22394.15 22896.59 22297.00 26893.43 25494.96 35797.56 28192.46 25796.93 15396.24 32188.15 20797.88 32587.38 33096.65 19498.46 197
RPMNet92.81 29491.34 30197.24 17297.00 26893.43 25494.96 35798.80 8582.27 36396.93 15392.12 36686.98 23399.82 6676.32 37096.65 19498.46 197
VDDNet95.36 19094.53 20797.86 12998.10 19495.13 18498.85 11197.75 27090.46 31398.36 8499.39 2273.27 35799.64 12197.98 5096.58 19698.81 172
alignmvs97.56 7997.07 9199.01 5298.66 14298.37 3998.83 11598.06 24896.74 6798.00 10597.65 23490.80 14999.48 15298.37 3696.56 19799.19 133
HY-MVS93.96 896.82 11696.23 12998.57 7398.46 15697.00 9198.14 22398.21 21193.95 19196.72 16497.99 20291.58 12999.76 9794.51 19596.54 19898.95 163
1112_ss96.63 12196.00 13798.50 8198.56 14996.37 12598.18 22198.10 23692.92 24494.84 20998.43 15892.14 11699.58 13194.35 19996.51 19999.56 79
thres20095.25 19694.57 20597.28 17198.81 12894.92 19598.20 21597.11 31395.24 13996.54 17496.22 32584.58 27999.53 14387.93 32896.50 20097.39 229
Test_1112_low_res96.34 13795.66 15798.36 9698.56 14995.94 14897.71 26498.07 24392.10 27394.79 21397.29 25991.75 12599.56 13594.17 20696.50 20099.58 77
tpmrst95.63 17395.69 15595.44 28597.54 23288.54 33996.97 31697.56 28193.50 22097.52 13596.93 29689.49 16999.16 17895.25 17496.42 20298.64 187
ab-mvs96.42 13195.71 15298.55 7598.63 14596.75 10297.88 25098.74 10093.84 19696.54 17498.18 18885.34 26499.75 9995.93 14996.35 20399.15 140
thres600view795.49 17894.77 19697.67 14898.98 11495.02 18798.85 11196.90 32795.38 12896.63 16796.90 29784.29 28299.59 13088.65 32396.33 20498.40 199
RPSCF94.87 21995.40 16193.26 33098.89 12082.06 36698.33 19798.06 24890.30 31896.56 17099.26 4787.09 23099.49 14893.82 21896.32 20598.24 205
thres100view90095.38 18794.70 20097.41 16498.98 11494.92 19598.87 10896.90 32795.38 12896.61 16896.88 29884.29 28299.56 13588.11 32496.29 20697.76 218
tfpn200view995.32 19494.62 20397.43 16298.94 11794.98 19198.68 15296.93 32595.33 13196.55 17296.53 31484.23 28699.56 13588.11 32496.29 20697.76 218
thres40095.38 18794.62 20397.65 15298.94 11794.98 19198.68 15296.93 32595.33 13196.55 17296.53 31484.23 28699.56 13588.11 32496.29 20698.40 199
canonicalmvs97.67 7097.23 8498.98 5498.70 13798.38 3599.34 1898.39 18196.76 6697.67 12697.40 25492.26 11199.49 14898.28 4096.28 20999.08 151
XVG-OURS96.55 12796.41 12096.99 18898.75 13193.76 24097.50 27998.52 15395.67 11596.83 15899.30 4288.95 19199.53 14395.88 15196.26 21097.69 223
GA-MVS94.81 22094.03 23397.14 17997.15 26293.86 23796.76 33397.58 27994.00 18894.76 21497.04 28380.91 31298.48 26291.79 27496.25 21199.09 147
tpm294.19 26193.76 25795.46 28497.23 25389.04 33197.31 29496.85 33387.08 34596.21 18496.79 30483.75 29898.74 23892.43 26196.23 21298.59 191
MIMVSNet93.26 28792.21 29496.41 24497.73 21893.13 26795.65 35297.03 31991.27 30094.04 24796.06 32875.33 34897.19 34386.56 33496.23 21298.92 166
TR-MVS94.94 21794.20 22397.17 17797.75 21494.14 23197.59 27497.02 32192.28 26895.75 19597.64 23683.88 29498.96 21189.77 30696.15 21498.40 199
CostFormer94.95 21594.73 19995.60 28097.28 25089.06 33097.53 27796.89 32989.66 32896.82 16096.72 30686.05 24998.95 21695.53 16596.13 21598.79 173
tpmvs94.60 23294.36 21995.33 28997.46 23888.60 33896.88 32797.68 27291.29 29893.80 25996.42 31888.58 19699.24 17091.06 28696.04 21698.17 209
tpm cat193.36 28292.80 28495.07 29697.58 22787.97 34896.76 33397.86 26582.17 36493.53 26696.04 32986.13 24799.13 18489.24 31795.87 21798.10 211
XVG-OURS-SEG-HR96.51 12896.34 12297.02 18798.77 13093.76 24097.79 25998.50 16095.45 12496.94 15299.09 8287.87 21699.55 14296.76 12595.83 21897.74 220
SDMVSNet96.85 11496.42 11998.14 11199.30 6696.38 12499.21 3899.23 1495.92 10095.96 19298.76 12685.88 25299.44 15797.93 5495.59 21998.60 189
sd_testset96.17 14395.76 14797.42 16399.30 6694.34 22398.82 11799.08 2595.92 10095.96 19298.76 12682.83 30199.32 16495.56 16395.59 21998.60 189
test_vis1_rt91.29 30590.65 30593.19 33297.45 24186.25 35698.57 17290.90 37993.30 22986.94 34993.59 35662.07 36999.11 18897.48 9095.58 22194.22 355
JIA-IIPM93.35 28392.49 29095.92 26696.48 30090.65 30595.01 35696.96 32385.93 35296.08 18787.33 37187.70 22198.78 23691.35 28195.58 22198.34 202
Anonymous20240521195.28 19594.49 20997.67 14899.00 10993.75 24298.70 14997.04 31890.66 30996.49 17698.80 11878.13 33299.83 5996.21 14195.36 22399.44 100
Anonymous2024052995.10 20594.22 22297.75 14099.01 10894.26 22698.87 10898.83 7285.79 35496.64 16698.97 9578.73 32699.85 5396.27 13794.89 22499.12 144
CLD-MVS95.62 17495.34 16796.46 24197.52 23593.75 24297.27 29798.46 16795.53 12094.42 22798.00 20186.21 24698.97 20796.25 14094.37 22596.66 285
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
dp94.15 26493.90 24594.90 30097.31 24986.82 35596.97 31697.19 31291.22 30296.02 18996.61 31385.51 26099.02 20390.00 30494.30 22698.85 169
HQP_MVS96.14 14595.90 14196.85 20097.42 24394.60 21298.80 12598.56 14497.28 3695.34 19998.28 17787.09 23099.03 20096.07 14294.27 22796.92 247
plane_prior598.56 14499.03 20096.07 14294.27 22796.92 247
plane_prior94.60 21298.44 18896.74 6794.22 229
OPM-MVS95.69 17195.33 16996.76 20596.16 31594.63 20798.43 19098.39 18196.64 7195.02 20698.78 12085.15 26899.05 19695.21 17694.20 23096.60 290
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP3-MVS98.46 16794.18 231
HQP-MVS95.72 16795.40 16196.69 21097.20 25694.25 22798.05 23298.46 16796.43 7994.45 22297.73 22586.75 23698.96 21195.30 17094.18 23196.86 261
LPG-MVS_test95.62 17495.34 16796.47 23897.46 23893.54 24998.99 8198.54 14894.67 16494.36 23098.77 12285.39 26199.11 18895.71 15894.15 23396.76 270
LGP-MVS_train96.47 23897.46 23893.54 24998.54 14894.67 16494.36 23098.77 12285.39 26199.11 18895.71 15894.15 23396.76 270
test_djsdf96.00 15095.69 15596.93 19495.72 32995.49 16999.47 998.40 17994.98 15194.58 21797.86 21389.16 18198.41 27996.91 10994.12 23596.88 256
jajsoiax95.45 18295.03 18596.73 20695.42 34094.63 20799.14 4998.52 15395.74 11093.22 27798.36 16783.87 29598.65 24696.95 10894.04 23696.91 252
anonymousdsp95.42 18494.91 19196.94 19395.10 34395.90 15499.14 4998.41 17793.75 20193.16 27997.46 24887.50 22598.41 27995.63 16294.03 23796.50 309
mvs_tets95.41 18695.00 18696.65 21295.58 33394.42 21899.00 7898.55 14695.73 11293.21 27898.38 16583.45 29998.63 24797.09 10294.00 23896.91 252
ACMP93.49 1095.34 19294.98 18896.43 24397.67 22193.48 25398.73 14098.44 17194.94 15692.53 29998.53 14784.50 28199.14 18395.48 16794.00 23896.66 285
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM93.85 995.69 17195.38 16596.61 21997.61 22593.84 23898.91 9698.44 17195.25 13794.28 23498.47 15486.04 25199.12 18695.50 16693.95 24096.87 258
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet_ETH3D94.24 25893.33 27596.97 19197.19 25993.38 25898.74 13698.57 14291.21 30393.81 25898.58 14372.85 35898.77 23795.05 17893.93 24198.77 177
XVG-ACMP-BASELINE94.54 23794.14 22995.75 27596.55 29591.65 28898.11 22898.44 17194.96 15394.22 23897.90 20979.18 32599.11 18894.05 21293.85 24296.48 312
EG-PatchMatch MVS91.13 30890.12 31194.17 32194.73 35089.00 33298.13 22597.81 26789.22 33585.32 36096.46 31667.71 36398.42 27187.89 32993.82 24395.08 346
iter_conf_final96.42 13196.12 13197.34 16998.46 15696.55 11599.08 6198.06 24896.03 9695.63 19698.46 15687.72 21898.59 25197.84 6393.80 24496.87 258
iter_conf0596.13 14695.79 14497.15 17898.16 19095.99 13998.88 10497.98 25495.91 10295.58 19798.46 15685.53 25998.59 25197.88 5993.75 24596.86 261
test_fmvs293.43 28193.58 26692.95 33496.97 27183.91 36099.19 4297.24 31095.74 11095.20 20298.27 18069.65 36098.72 24096.26 13893.73 24696.24 322
testgi93.06 29292.45 29194.88 30196.43 30389.90 31598.75 13397.54 28795.60 11791.63 31697.91 20874.46 35397.02 34586.10 33793.67 24797.72 222
test0.0.03 194.08 27093.51 27095.80 27295.53 33592.89 27297.38 28595.97 34695.11 14492.51 30196.66 30887.71 21996.94 34787.03 33293.67 24797.57 225
mvsmamba96.57 12696.32 12497.32 17096.60 29296.43 12099.54 797.98 25496.49 7695.20 20298.64 13690.82 14798.55 25597.97 5193.65 24996.98 242
CMPMVSbinary66.06 2189.70 31989.67 31589.78 34493.19 36076.56 36997.00 31598.35 18880.97 36581.57 36597.75 22474.75 35198.61 24889.85 30593.63 25094.17 356
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ACMMP++93.61 251
D2MVS95.18 20195.08 18395.48 28297.10 26592.07 27998.30 20499.13 2394.02 18692.90 28796.73 30589.48 17098.73 23994.48 19693.60 25295.65 337
bld_raw_dy_0_6495.74 16695.31 17297.03 18696.35 30695.76 15999.12 5397.37 30395.97 9894.70 21598.48 15285.80 25498.49 26196.55 12993.48 25396.84 263
EI-MVSNet95.96 15295.83 14396.36 24797.93 20593.70 24698.12 22698.27 20393.70 20895.07 20499.02 8892.23 11398.54 25794.68 18693.46 25496.84 263
MVSTER96.06 14895.72 14997.08 18498.23 17995.93 15198.73 14098.27 20394.86 15795.07 20498.09 19388.21 20598.54 25796.59 12793.46 25496.79 267
PS-MVSNAJss96.43 13096.26 12796.92 19795.84 32795.08 18699.16 4698.50 16095.87 10693.84 25798.34 17294.51 7898.61 24896.88 11593.45 25697.06 237
LTVRE_ROB92.95 1594.60 23293.90 24596.68 21197.41 24694.42 21898.52 17698.59 13591.69 28491.21 31898.35 16884.87 27299.04 19991.06 28693.44 25796.60 290
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
ITE_SJBPF95.44 28597.42 24391.32 29397.50 29195.09 14793.59 26398.35 16881.70 30598.88 22589.71 30893.39 25896.12 326
PVSNet_BlendedMVS96.73 11896.60 11397.12 18199.25 7895.35 17598.26 21099.26 1094.28 17797.94 10997.46 24892.74 10499.81 7196.88 11593.32 25996.20 324
ACMH92.88 1694.55 23693.95 24196.34 24997.63 22493.26 26298.81 12498.49 16593.43 22389.74 33198.53 14781.91 30499.08 19493.69 22093.30 26096.70 279
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OpenMVS_ROBcopyleft86.42 2089.00 32487.43 33293.69 32393.08 36189.42 32597.91 24596.89 32978.58 36785.86 35594.69 34769.48 36198.29 29577.13 36993.29 26193.36 364
USDC93.33 28592.71 28695.21 29096.83 28190.83 30196.91 32197.50 29193.84 19690.72 32398.14 19077.69 33598.82 23289.51 31393.21 26295.97 330
RRT_MVS95.98 15195.78 14596.56 22696.48 30094.22 22999.57 697.92 26195.89 10393.95 25098.70 13089.27 17798.42 27197.23 9893.02 26397.04 238
ACMMP++_ref92.97 264
test_040291.32 30490.27 31094.48 31496.60 29291.12 29698.50 18197.22 31186.10 35188.30 34396.98 28977.65 33797.99 31678.13 36892.94 26594.34 352
tt080594.54 23793.85 24996.63 21697.98 20393.06 27098.77 13297.84 26693.67 21393.80 25998.04 19776.88 34398.96 21194.79 18592.86 26697.86 217
dmvs_re94.48 24494.18 22695.37 28797.68 22090.11 31498.54 17597.08 31494.56 16794.42 22797.24 26384.25 28497.76 32991.02 28992.83 26798.24 205
FIs96.51 12896.12 13197.67 14897.13 26397.54 7399.36 1599.22 1795.89 10394.03 24898.35 16891.98 12198.44 26996.40 13592.76 26897.01 240
FC-MVSNet-test96.42 13196.05 13497.53 15896.95 27297.27 8099.36 1599.23 1495.83 10793.93 25198.37 16692.00 12098.32 28896.02 14792.72 26997.00 241
TinyColmap92.31 29891.53 29994.65 30996.92 27489.75 31796.92 31996.68 33790.45 31489.62 33297.85 21576.06 34698.81 23386.74 33392.51 27095.41 339
ACMH+92.99 1494.30 25493.77 25595.88 27097.81 21192.04 28198.71 14598.37 18593.99 18990.60 32598.47 15480.86 31499.05 19692.75 24992.40 27196.55 298
GBi-Net94.49 24293.80 25296.56 22698.21 18195.00 18898.82 11798.18 21892.46 25794.09 24497.07 27781.16 30997.95 31792.08 26592.14 27296.72 275
test194.49 24293.80 25296.56 22698.21 18195.00 18898.82 11798.18 21892.46 25794.09 24497.07 27781.16 30997.95 31792.08 26592.14 27296.72 275
FMVSNet394.97 21494.26 22197.11 18298.18 18796.62 10698.56 17398.26 20793.67 21394.09 24497.10 27084.25 28498.01 31392.08 26592.14 27296.70 279
FMVSNet294.47 24593.61 26597.04 18598.21 18196.43 12098.79 13098.27 20392.46 25793.50 27097.09 27481.16 30998.00 31591.09 28491.93 27596.70 279
LF4IMVS93.14 29192.79 28594.20 31995.88 32588.67 33797.66 26897.07 31693.81 19991.71 31497.65 23477.96 33498.81 23391.47 28091.92 27695.12 344
OurMVSNet-221017-094.21 25994.00 23794.85 30295.60 33289.22 32898.89 10197.43 29895.29 13492.18 30898.52 15082.86 30098.59 25193.46 22891.76 27796.74 272
EGC-MVSNET75.22 34369.54 34692.28 33894.81 34889.58 32297.64 27096.50 3411.82 3865.57 38795.74 33368.21 36296.26 35973.80 37291.71 27890.99 368
pmmvs494.69 22493.99 23996.81 20395.74 32895.94 14897.40 28397.67 27390.42 31593.37 27397.59 24089.08 18498.20 29992.97 24291.67 27996.30 321
tpm94.13 26593.80 25295.12 29396.50 29887.91 34997.44 28095.89 34992.62 25396.37 18196.30 32084.13 28998.30 29293.24 23391.66 28099.14 142
our_test_393.65 27993.30 27694.69 30795.45 33889.68 32196.91 32197.65 27491.97 27691.66 31596.88 29889.67 16897.93 32088.02 32791.49 28196.48 312
IterMVS94.09 26993.85 24994.80 30597.99 20190.35 31097.18 30498.12 23093.68 21192.46 30397.34 25584.05 29097.41 34092.51 25891.33 28296.62 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT94.11 26793.87 24794.85 30297.98 20390.56 30797.18 30498.11 23393.75 20192.58 29797.48 24783.97 29297.41 34092.48 26091.30 28396.58 292
FMVSNet193.19 29092.07 29596.56 22697.54 23295.00 18898.82 11798.18 21890.38 31692.27 30697.07 27773.68 35697.95 31789.36 31691.30 28396.72 275
XXY-MVS95.20 20094.45 21497.46 15996.75 28596.56 11398.86 11098.65 12793.30 22993.27 27698.27 18084.85 27398.87 22694.82 18391.26 28596.96 244
cl2294.68 22694.19 22496.13 25898.11 19393.60 24796.94 31898.31 19492.43 26193.32 27596.87 30086.51 23998.28 29694.10 21091.16 28696.51 307
miper_ehance_all_eth95.01 20994.69 20195.97 26497.70 21993.31 26097.02 31498.07 24392.23 26993.51 26996.96 29291.85 12398.15 30293.68 22191.16 28696.44 315
miper_enhance_ethall95.10 20594.75 19896.12 25997.53 23493.73 24496.61 33898.08 24192.20 27293.89 25396.65 31092.44 10798.30 29294.21 20591.16 28696.34 318
pmmvs593.65 27992.97 28295.68 27695.49 33692.37 27598.20 21597.28 30789.66 32892.58 29797.26 26082.14 30398.09 30893.18 23690.95 28996.58 292
ET-MVSNet_ETH3D94.13 26592.98 28197.58 15598.22 18096.20 13297.31 29495.37 35294.53 16979.56 36797.63 23886.51 23997.53 33796.91 10990.74 29099.02 155
SixPastTwentyTwo93.34 28492.86 28394.75 30695.67 33089.41 32698.75 13396.67 33893.89 19390.15 32998.25 18380.87 31398.27 29790.90 29090.64 29196.57 294
N_pmnet87.12 33287.77 33085.17 35295.46 33761.92 38397.37 28770.66 38985.83 35388.73 34296.04 32985.33 26597.76 32980.02 36190.48 29295.84 332
ppachtmachnet_test93.22 28892.63 28894.97 29895.45 33890.84 30096.88 32797.88 26490.60 31092.08 31097.26 26088.08 21097.86 32685.12 34590.33 29396.22 323
DIV-MVS_self_test94.52 24094.03 23395.99 26297.57 23193.38 25897.05 31297.94 25991.74 28192.81 28997.10 27089.12 18298.07 31092.60 25190.30 29496.53 301
cl____94.51 24194.01 23696.02 26197.58 22793.40 25797.05 31297.96 25891.73 28392.76 29197.08 27689.06 18598.13 30492.61 25090.29 29596.52 304
APD_test188.22 32788.01 32788.86 34695.98 32174.66 37597.21 30096.44 34283.96 36186.66 35297.90 20960.95 37097.84 32782.73 35490.23 29694.09 358
IterMVS-LS95.46 18095.21 17696.22 25598.12 19293.72 24598.32 20198.13 22993.71 20694.26 23597.31 25892.24 11298.10 30694.63 18890.12 29796.84 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmtry93.22 28892.35 29295.84 27196.77 28293.09 26994.66 36497.56 28187.37 34492.90 28796.24 32188.15 20797.90 32187.37 33190.10 29896.53 301
EU-MVSNet93.66 27794.14 22992.25 33995.96 32383.38 36298.52 17698.12 23094.69 16292.61 29698.13 19187.36 22896.39 35891.82 27390.00 29996.98 242
Anonymous2023120691.66 30291.10 30293.33 32894.02 35787.35 35298.58 16797.26 30990.48 31290.16 32896.31 31983.83 29696.53 35679.36 36489.90 30096.12 326
eth_miper_zixun_eth94.68 22694.41 21795.47 28397.64 22391.71 28796.73 33598.07 24392.71 25193.64 26297.21 26690.54 15498.17 30193.38 22989.76 30196.54 299
FMVSNet591.81 30090.92 30394.49 31397.21 25592.09 27898.00 23897.55 28689.31 33490.86 32295.61 34074.48 35295.32 36585.57 34189.70 30296.07 328
miper_lstm_enhance94.33 25294.07 23295.11 29497.75 21490.97 29897.22 29998.03 25191.67 28592.76 29196.97 29090.03 16297.78 32892.51 25889.64 30396.56 296
v119294.32 25393.58 26696.53 23296.10 31694.45 21698.50 18198.17 22391.54 28794.19 24097.06 28086.95 23498.43 27090.14 29889.57 30496.70 279
v114494.59 23493.92 24296.60 22196.21 31094.78 20498.59 16598.14 22891.86 28094.21 23997.02 28587.97 21298.41 27991.72 27689.57 30496.61 289
Anonymous2024052191.18 30790.44 30893.42 32593.70 35888.47 34198.94 9297.56 28188.46 33989.56 33495.08 34577.15 34296.97 34683.92 35189.55 30694.82 350
VPA-MVSNet95.75 16595.11 18297.69 14697.24 25297.27 8098.94 9299.23 1495.13 14295.51 19897.32 25785.73 25598.91 21997.33 9689.55 30696.89 255
v124094.06 27293.29 27796.34 24996.03 32093.90 23698.44 18898.17 22391.18 30494.13 24397.01 28786.05 24998.42 27189.13 31989.50 30896.70 279
K. test v392.55 29691.91 29894.48 31495.64 33189.24 32799.07 6294.88 35894.04 18486.78 35097.59 24077.64 33897.64 33292.08 26589.43 30996.57 294
v192192094.20 26093.47 27296.40 24695.98 32194.08 23298.52 17698.15 22691.33 29594.25 23697.20 26786.41 24398.42 27190.04 30389.39 31096.69 284
new_pmnet90.06 31789.00 32193.22 33194.18 35288.32 34496.42 34396.89 32986.19 34985.67 35793.62 35577.18 34197.10 34481.61 35889.29 31194.23 354
c3_l94.79 22194.43 21695.89 26997.75 21493.12 26897.16 30898.03 25192.23 26993.46 27297.05 28291.39 13598.01 31393.58 22689.21 31296.53 301
v14419294.39 25093.70 26196.48 23796.06 31894.35 22298.58 16798.16 22591.45 28994.33 23297.02 28587.50 22598.45 26791.08 28589.11 31396.63 287
nrg03096.28 14095.72 14997.96 12696.90 27798.15 5299.39 1298.31 19495.47 12394.42 22798.35 16892.09 11898.69 24197.50 8989.05 31497.04 238
DeepMVS_CXcopyleft86.78 34997.09 26672.30 37695.17 35775.92 36984.34 36295.19 34270.58 35995.35 36379.98 36389.04 31592.68 367
tfpnnormal93.66 27792.70 28796.55 23196.94 27395.94 14898.97 8499.19 1891.04 30591.38 31797.34 25584.94 27198.61 24885.45 34389.02 31695.11 345
Anonymous2023121194.10 26893.26 27896.61 21999.11 10094.28 22499.01 7698.88 5486.43 34892.81 28997.57 24281.66 30698.68 24494.83 18289.02 31696.88 256
v2v48294.69 22494.03 23396.65 21296.17 31394.79 20398.67 15598.08 24192.72 25094.00 24997.16 26887.69 22298.45 26792.91 24488.87 31896.72 275
V4294.78 22294.14 22996.70 20996.33 30895.22 17998.97 8498.09 24092.32 26694.31 23397.06 28088.39 20298.55 25592.90 24588.87 31896.34 318
WR-MVS95.15 20294.46 21297.22 17396.67 29096.45 11898.21 21398.81 7894.15 18093.16 27997.69 23087.51 22398.30 29295.29 17288.62 32096.90 254
FPMVS77.62 34277.14 34279.05 36079.25 38360.97 38495.79 35095.94 34765.96 37467.93 37694.40 34937.73 38088.88 37968.83 37688.46 32187.29 372
v1094.29 25593.55 26896.51 23496.39 30494.80 20298.99 8198.19 21591.35 29493.02 28596.99 28888.09 20998.41 27990.50 29588.41 32296.33 320
CP-MVSNet94.94 21794.30 22096.83 20196.72 28795.56 16599.11 5598.95 3893.89 19392.42 30497.90 20987.19 22998.12 30594.32 20188.21 32396.82 266
MIMVSNet189.67 32088.28 32593.82 32292.81 36391.08 29798.01 23697.45 29687.95 34187.90 34595.87 33267.63 36494.56 36978.73 36788.18 32495.83 333
PS-CasMVS94.67 22993.99 23996.71 20796.68 28995.26 17899.13 5299.03 3093.68 21192.33 30597.95 20685.35 26398.10 30693.59 22588.16 32596.79 267
WR-MVS_H95.05 20894.46 21296.81 20396.86 27995.82 15799.24 3099.24 1293.87 19592.53 29996.84 30290.37 15698.24 29893.24 23387.93 32696.38 317
v894.47 24593.77 25596.57 22596.36 30594.83 20099.05 6598.19 21591.92 27793.16 27996.97 29088.82 19498.48 26291.69 27787.79 32796.39 316
v7n94.19 26193.43 27396.47 23895.90 32494.38 22199.26 2798.34 19091.99 27592.76 29197.13 26988.31 20398.52 25989.48 31487.70 32896.52 304
UniMVSNet (Re)95.78 16495.19 17797.58 15596.99 27097.47 7598.79 13099.18 1995.60 11793.92 25297.04 28391.68 12698.48 26295.80 15587.66 32996.79 267
baseline195.84 16195.12 18198.01 12298.49 15595.98 14098.73 14097.03 31995.37 13096.22 18398.19 18789.96 16399.16 17894.60 19187.48 33098.90 167
Gipumacopyleft78.40 34076.75 34383.38 35595.54 33480.43 36879.42 37897.40 30064.67 37573.46 37280.82 37645.65 37593.14 37466.32 37787.43 33176.56 378
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
NR-MVSNet94.98 21394.16 22797.44 16196.53 29697.22 8698.74 13698.95 3894.96 15389.25 33697.69 23089.32 17598.18 30094.59 19387.40 33296.92 247
dmvs_testset87.64 32988.93 32283.79 35495.25 34163.36 38297.20 30191.17 37793.07 23785.64 35895.98 33185.30 26791.52 37769.42 37587.33 33396.49 310
VPNet94.99 21194.19 22497.40 16697.16 26196.57 11298.71 14598.97 3595.67 11594.84 20998.24 18480.36 31798.67 24596.46 13287.32 33496.96 244
UniMVSNet_NR-MVSNet95.71 16895.15 17897.40 16696.84 28096.97 9298.74 13699.24 1295.16 14193.88 25497.72 22791.68 12698.31 29095.81 15387.25 33596.92 247
DU-MVS95.42 18494.76 19797.40 16696.53 29696.97 9298.66 15798.99 3495.43 12593.88 25497.69 23088.57 19798.31 29095.81 15387.25 33596.92 247
v14894.29 25593.76 25795.91 26796.10 31692.93 27198.58 16797.97 25692.59 25593.47 27196.95 29488.53 20098.32 28892.56 25587.06 33796.49 310
Baseline_NR-MVSNet94.35 25193.81 25195.96 26596.20 31194.05 23398.61 16496.67 33891.44 29093.85 25697.60 23988.57 19798.14 30394.39 19786.93 33895.68 336
PEN-MVS94.42 24893.73 25996.49 23596.28 30994.84 19899.17 4599.00 3293.51 21992.23 30797.83 21986.10 24897.90 32192.55 25686.92 33996.74 272
TranMVSNet+NR-MVSNet95.14 20394.48 21097.11 18296.45 30296.36 12699.03 7199.03 3095.04 14993.58 26497.93 20788.27 20498.03 31294.13 20786.90 34096.95 246
MDA-MVSNet_test_wron90.71 31289.38 31794.68 30894.83 34790.78 30297.19 30397.46 29487.60 34272.41 37495.72 33786.51 23996.71 35385.92 33986.80 34196.56 296
YYNet190.70 31389.39 31694.62 31094.79 34990.65 30597.20 30197.46 29487.54 34372.54 37395.74 33386.51 23996.66 35486.00 33886.76 34296.54 299
MDA-MVSNet-bldmvs89.97 31888.35 32494.83 30495.21 34291.34 29297.64 27097.51 29088.36 34071.17 37596.13 32779.22 32496.63 35583.65 35286.27 34396.52 304
test20.0390.89 31190.38 30992.43 33693.48 35988.14 34798.33 19797.56 28193.40 22487.96 34496.71 30780.69 31694.13 37079.15 36586.17 34495.01 349
DTE-MVSNet93.98 27493.26 27896.14 25796.06 31894.39 22099.20 4098.86 6793.06 23891.78 31397.81 22185.87 25397.58 33590.53 29486.17 34496.46 314
pm-mvs193.94 27593.06 28096.59 22296.49 29995.16 18198.95 9098.03 25192.32 26691.08 32097.84 21684.54 28098.41 27992.16 26386.13 34696.19 325
lessismore_v094.45 31794.93 34688.44 34291.03 37886.77 35197.64 23676.23 34598.42 27190.31 29785.64 34796.51 307
test_fmvs387.17 33087.06 33387.50 34891.21 36775.66 37199.05 6596.61 34092.79 24988.85 34092.78 36143.72 37693.49 37193.95 21384.56 34893.34 365
pmmvs691.77 30190.63 30695.17 29294.69 35191.24 29598.67 15597.92 26186.14 35089.62 33297.56 24475.79 34798.34 28690.75 29284.56 34895.94 331
test_f86.07 33485.39 33588.10 34789.28 37275.57 37297.73 26396.33 34389.41 33385.35 35991.56 36743.31 37895.53 36291.32 28284.23 35093.21 366
mvsany_test388.80 32588.04 32691.09 34389.78 37181.57 36797.83 25695.49 35193.81 19987.53 34693.95 35456.14 37297.43 33994.68 18683.13 35194.26 353
IB-MVS91.98 1793.27 28691.97 29697.19 17597.47 23793.41 25697.09 31195.99 34593.32 22792.47 30295.73 33578.06 33399.53 14394.59 19382.98 35298.62 188
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
ambc89.49 34586.66 37675.78 37092.66 37096.72 33586.55 35392.50 36446.01 37497.90 32190.32 29682.09 35394.80 351
Patchmatch-RL test91.49 30390.85 30493.41 32691.37 36684.40 35892.81 36995.93 34891.87 27987.25 34794.87 34688.99 18696.53 35692.54 25782.00 35499.30 117
PM-MVS87.77 32886.55 33491.40 34291.03 36983.36 36396.92 31995.18 35691.28 29986.48 35493.42 35753.27 37396.74 35089.43 31581.97 35594.11 357
pmmvs-eth3d90.36 31589.05 32094.32 31891.10 36892.12 27797.63 27396.95 32488.86 33784.91 36193.13 36078.32 32996.74 35088.70 32281.81 35694.09 358
h-mvs3396.17 14395.62 15897.81 13499.03 10594.45 21698.64 15998.75 9897.48 2398.67 6398.72 12989.76 16599.86 5297.95 5281.59 35799.11 145
TransMVSNet (Re)92.67 29591.51 30096.15 25696.58 29494.65 20598.90 9796.73 33490.86 30889.46 33597.86 21385.62 25798.09 30886.45 33581.12 35895.71 335
PMVScopyleft61.03 2365.95 34663.57 35073.09 36357.90 38851.22 38985.05 37693.93 36954.45 37744.32 38383.57 37213.22 38789.15 37858.68 37981.00 35978.91 377
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
AUN-MVS94.53 23993.73 25996.92 19798.50 15393.52 25298.34 19698.10 23693.83 19895.94 19497.98 20485.59 25899.03 20094.35 19980.94 36098.22 207
hse-mvs295.71 16895.30 17396.93 19498.50 15393.53 25198.36 19498.10 23697.48 2398.67 6397.99 20289.76 16599.02 20397.95 5280.91 36198.22 207
test_vis3_rt79.22 33577.40 34184.67 35386.44 37774.85 37497.66 26881.43 38684.98 35767.12 37781.91 37528.09 38697.60 33388.96 32080.04 36281.55 375
UnsupCasMVSNet_eth90.99 31089.92 31394.19 32094.08 35489.83 31697.13 31098.67 12093.69 20985.83 35696.19 32675.15 34996.74 35089.14 31879.41 36396.00 329
test_method79.03 33678.17 33881.63 35886.06 37854.40 38882.75 37796.89 32939.54 38180.98 36695.57 34158.37 37194.73 36884.74 34978.61 36495.75 334
testf179.02 33777.70 33982.99 35688.10 37466.90 37994.67 36293.11 37071.08 37274.02 37093.41 35834.15 38293.25 37272.25 37378.50 36588.82 370
APD_test279.02 33777.70 33982.99 35688.10 37466.90 37994.67 36293.11 37071.08 37274.02 37093.41 35834.15 38293.25 37272.25 37378.50 36588.82 370
TDRefinement91.06 30989.68 31495.21 29085.35 37991.49 29198.51 18097.07 31691.47 28888.83 34197.84 21677.31 33999.09 19392.79 24877.98 36795.04 347
new-patchmatchnet88.50 32687.45 33191.67 34190.31 37085.89 35797.16 30897.33 30489.47 33083.63 36392.77 36276.38 34495.06 36782.70 35577.29 36894.06 360
KD-MVS_self_test90.38 31489.38 31793.40 32792.85 36288.94 33497.95 24197.94 25990.35 31790.25 32793.96 35379.82 32095.94 36084.62 35076.69 36995.33 340
pmmvs386.67 33384.86 33792.11 34088.16 37387.19 35496.63 33794.75 36079.88 36687.22 34892.75 36366.56 36695.20 36681.24 35976.56 37093.96 361
CL-MVSNet_self_test90.11 31689.14 31993.02 33391.86 36588.23 34696.51 34198.07 24390.49 31190.49 32694.41 34884.75 27595.34 36480.79 36074.95 37195.50 338
LCM-MVSNet78.70 33976.24 34486.08 35077.26 38571.99 37794.34 36696.72 33561.62 37676.53 36889.33 36933.91 38492.78 37581.85 35774.60 37293.46 363
UnsupCasMVSNet_bld87.17 33085.12 33693.31 32991.94 36488.77 33594.92 35998.30 20084.30 36082.30 36490.04 36863.96 36897.25 34285.85 34074.47 37393.93 362
PVSNet_088.72 1991.28 30690.03 31295.00 29797.99 20187.29 35394.84 36098.50 16092.06 27489.86 33095.19 34279.81 32199.39 15992.27 26269.79 37498.33 203
KD-MVS_2432*160089.61 32187.96 32894.54 31194.06 35591.59 28995.59 35397.63 27689.87 32488.95 33894.38 35078.28 33096.82 34884.83 34668.05 37595.21 342
miper_refine_blended89.61 32187.96 32894.54 31194.06 35591.59 28995.59 35397.63 27689.87 32488.95 33894.38 35078.28 33096.82 34884.83 34668.05 37595.21 342
PMMVS277.95 34175.44 34585.46 35182.54 38074.95 37394.23 36793.08 37272.80 37174.68 36987.38 37036.36 38191.56 37673.95 37163.94 37789.87 369
MVEpermissive62.14 2263.28 34959.38 35274.99 36174.33 38665.47 38185.55 37580.50 38752.02 37951.10 38175.00 38010.91 39080.50 38151.60 38053.40 37878.99 376
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN64.94 34764.25 34967.02 36482.28 38159.36 38691.83 37285.63 38352.69 37860.22 37977.28 37841.06 37980.12 38246.15 38141.14 37961.57 380
EMVS64.07 34863.26 35166.53 36581.73 38258.81 38791.85 37184.75 38451.93 38059.09 38075.13 37943.32 37779.09 38342.03 38239.47 38061.69 379
ANet_high69.08 34465.37 34880.22 35965.99 38771.96 37890.91 37390.09 38082.62 36249.93 38278.39 37729.36 38581.75 38062.49 37838.52 38186.95 374
tmp_tt68.90 34566.97 34774.68 36250.78 38959.95 38587.13 37483.47 38538.80 38262.21 37896.23 32364.70 36776.91 38488.91 32130.49 38287.19 373
wuyk23d30.17 35030.18 35430.16 36678.61 38443.29 39066.79 37914.21 39017.31 38314.82 38611.93 38611.55 38941.43 38537.08 38319.30 3835.76 383
testmvs21.48 35224.95 35511.09 36814.89 3906.47 39296.56 3399.87 3917.55 38417.93 38439.02 3829.43 3915.90 38716.56 38512.72 38420.91 382
test12320.95 35323.72 35612.64 36713.54 3918.19 39196.55 3406.13 3927.48 38516.74 38537.98 38312.97 3886.05 38616.69 3845.43 38523.68 381
test_blank0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
uanet_test0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
DCPMVS0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
cdsmvs_eth3d_5k23.98 35131.98 3530.00 3690.00 3920.00 3930.00 38098.59 1350.00 3870.00 38898.61 13890.60 1530.00 3880.00 3860.00 3860.00 384
pcd_1.5k_mvsjas7.88 35510.50 3580.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 38794.51 780.00 3880.00 3860.00 3860.00 384
sosnet-low-res0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
sosnet0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
uncertanet0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
Regformer0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
ab-mvs-re8.20 35410.94 3570.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 38898.43 1580.00 3920.00 3880.00 3860.00 3860.00 384
uanet0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
FOURS199.82 198.66 2499.69 198.95 3897.46 2599.39 22
test_one_060199.66 2699.25 298.86 6797.55 2099.20 3099.47 1397.57 6
eth-test20.00 392
eth-test0.00 392
test_241102_ONE99.71 1999.24 598.87 6197.62 1699.73 499.39 2297.53 799.74 101
save fliter99.46 4998.38 3598.21 21398.71 10897.95 7
test072699.72 1299.25 299.06 6398.88 5497.62 1699.56 1399.50 897.42 9
GSMVS99.20 129
test_part299.63 2999.18 1099.27 27
sam_mvs189.45 17299.20 129
sam_mvs88.99 186
MTGPAbinary98.74 100
test_post196.68 33630.43 38587.85 21798.69 24192.59 253
test_post31.83 38488.83 19398.91 219
patchmatchnet-post95.10 34489.42 17398.89 223
MTMP98.89 10194.14 367
gm-plane-assit95.88 32587.47 35189.74 32796.94 29599.19 17693.32 232
TEST999.31 6298.50 2997.92 24398.73 10392.63 25297.74 12098.68 13296.20 2699.80 78
test_899.29 7098.44 3197.89 24998.72 10592.98 24197.70 12498.66 13596.20 2699.80 78
agg_prior99.30 6698.38 3598.72 10597.57 13499.81 71
test_prior498.01 5897.86 252
test_prior99.19 3999.31 6298.22 4798.84 7199.70 10999.65 63
旧先验297.57 27691.30 29798.67 6399.80 7895.70 160
新几何297.64 270
无先验97.58 27598.72 10591.38 29199.87 4893.36 23199.60 71
原ACMM297.67 267
testdata299.89 3991.65 278
segment_acmp96.85 14
testdata197.32 29396.34 85
plane_prior797.42 24394.63 207
plane_prior697.35 24894.61 21087.09 230
plane_prior498.28 177
plane_prior394.61 21097.02 5495.34 199
plane_prior298.80 12597.28 36
plane_prior197.37 247
n20.00 393
nn0.00 393
door-mid94.37 363
test1198.66 123
door94.64 361
HQP5-MVS94.25 227
HQP-NCC97.20 25698.05 23296.43 7994.45 222
ACMP_Plane97.20 25698.05 23296.43 7994.45 222
BP-MVS95.30 170
HQP4-MVS94.45 22298.96 21196.87 258
HQP2-MVS86.75 236
NP-MVS97.28 25094.51 21597.73 225
MDTV_nov1_ep13_2view84.26 35996.89 32690.97 30697.90 11389.89 16493.91 21599.18 138
Test By Simon94.64 75