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 bysorted bysort bysort bysort bysort bysort bysort by
SED-MVS99.61 299.52 799.88 699.84 3399.90 299.60 7799.48 14699.08 1699.91 199.81 6699.20 799.96 2098.91 7599.85 5999.79 61
test_241102_ONE99.84 3399.90 299.48 14699.07 1899.91 199.74 12399.20 799.76 183
EI-MVSNet-UG-set99.58 599.57 299.64 8099.78 4799.14 13399.60 7799.45 18699.01 2399.90 399.83 4698.98 2799.93 7399.59 599.95 799.86 14
EI-MVSNet-Vis-set99.58 599.56 499.64 8099.78 4799.15 13299.61 7699.45 18699.01 2399.89 499.82 5399.01 1999.92 8499.56 899.95 799.85 17
DVP-MVS++99.59 399.50 999.88 699.51 16099.88 899.87 699.51 10598.99 3099.88 599.81 6699.27 599.96 2098.85 8999.80 8899.81 45
test_241102_TWO99.48 14699.08 1699.88 599.81 6698.94 3599.96 2098.91 7599.84 6699.88 8
DPE-MVScopyleft99.46 2699.32 3599.91 299.78 4799.88 899.36 20299.51 10598.73 6099.88 599.84 4298.72 6599.96 2098.16 17699.87 4199.88 8
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
Regformer-499.59 399.54 599.73 6199.76 5699.41 10199.58 9299.49 13399.02 2099.88 599.80 8299.00 2599.94 5899.45 2199.92 1299.84 21
SD-MVS99.41 4699.52 799.05 17099.74 7599.68 5499.46 15899.52 9299.11 1199.88 599.91 899.43 197.70 35898.72 11099.93 1199.77 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
APDe-MVS99.66 199.57 299.92 199.77 5299.89 499.75 3299.56 5899.02 2099.88 599.85 3399.18 1099.96 2099.22 4399.92 1299.90 1
Regformer-399.57 899.53 699.68 6899.76 5699.29 11499.58 9299.44 19599.01 2399.87 1199.80 8298.97 2899.91 9599.44 2399.92 1299.83 32
test072699.85 2699.89 499.62 7099.50 12599.10 1299.86 1299.82 5398.94 35
Vis-MVSNetpermissive99.12 8998.97 9599.56 9499.78 4799.10 13899.68 4699.66 2798.49 7699.86 1299.87 2494.77 21799.84 14199.19 4699.41 14299.74 82
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PC_three_145298.18 11199.84 1499.70 13999.31 398.52 34398.30 16699.80 8899.81 45
IU-MVS99.84 3399.88 899.32 25998.30 9799.84 1498.86 8799.85 5999.89 2
xiu_mvs_v1_base_debu99.29 6199.27 5599.34 13399.63 12898.97 15399.12 26299.51 10598.86 4799.84 1499.47 24198.18 10299.99 199.50 1299.31 15199.08 207
xiu_mvs_v1_base99.29 6199.27 5599.34 13399.63 12898.97 15399.12 26299.51 10598.86 4799.84 1499.47 24198.18 10299.99 199.50 1299.31 15199.08 207
xiu_mvs_v1_base_debi99.29 6199.27 5599.34 13399.63 12898.97 15399.12 26299.51 10598.86 4799.84 1499.47 24198.18 10299.99 199.50 1299.31 15199.08 207
Regformer-199.53 1299.47 1299.72 6499.71 9499.44 9899.49 14499.46 17498.95 3999.83 1999.76 11299.01 1999.93 7399.17 4999.87 4199.80 55
Regformer-299.54 1099.47 1299.75 5499.71 9499.52 8899.49 14499.49 13398.94 4099.83 1999.76 11299.01 1999.94 5899.15 5299.87 4199.80 55
DeepPCF-MVS98.18 398.81 13599.37 2497.12 32599.60 14291.75 36298.61 33699.44 19599.35 199.83 1999.85 3398.70 6799.81 16399.02 6399.91 1799.81 45
TSAR-MVS + GP.99.36 5399.36 2699.36 13299.67 10998.61 19499.07 27299.33 24999.00 2799.82 2299.81 6699.06 1699.84 14199.09 5799.42 14199.65 121
abl_699.44 3299.31 4299.83 3699.85 2699.75 4399.66 5299.59 4498.13 11599.82 2299.81 6698.60 7499.96 2098.46 15099.88 3799.79 61
FOURS199.91 199.93 199.87 699.56 5899.10 1299.81 24
DVP-MVScopyleft99.57 899.47 1299.88 699.85 2699.89 499.57 9899.37 23299.10 1299.81 2499.80 8298.94 3599.96 2098.93 7299.86 5299.81 45
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_THIRD98.99 3099.81 2499.80 8299.09 1499.96 2098.85 8999.90 2499.88 8
MVSFormer99.17 7799.12 7199.29 14699.51 16098.94 16299.88 299.46 17497.55 18199.80 2799.65 16897.39 12399.28 28199.03 6199.85 5999.65 121
lupinMVS99.13 8399.01 9099.46 12199.51 16098.94 16299.05 27799.16 29097.86 14599.80 2799.56 20797.39 12399.86 12998.94 7099.85 5999.58 146
tttt051798.42 16098.14 17299.28 14999.66 11898.38 21599.74 3596.85 36397.68 16899.79 2999.74 12391.39 30799.89 11898.83 9599.56 13499.57 147
APD-MVS_3200maxsize99.48 2199.35 2999.85 2899.76 5699.83 1799.63 6499.54 7598.36 9099.79 2999.82 5398.86 4499.95 4798.62 12499.81 8499.78 69
jason99.13 8399.03 8399.45 12299.46 18198.87 16999.12 26299.26 27598.03 13599.79 2999.65 16897.02 13799.85 13599.02 6399.90 2499.65 121
jason: jason.
SteuartSystems-ACMMP99.54 1099.42 1699.87 1299.82 3899.81 2799.59 8499.51 10598.62 6699.79 2999.83 4699.28 499.97 1298.48 14699.90 2499.84 21
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS_fast98.69 199.49 1799.39 2099.77 5099.63 12899.59 7399.36 20299.46 17499.07 1899.79 2999.82 5398.85 4599.92 8498.68 11799.87 4199.82 39
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SMA-MVScopyleft99.44 3299.30 4599.85 2899.73 8399.83 1799.56 10599.47 16497.45 19399.78 3499.82 5399.18 1099.91 9598.79 10199.89 3499.81 45
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
TSAR-MVS + MP.99.58 599.50 999.81 4199.91 199.66 5999.63 6499.39 21798.91 4599.78 3499.85 3399.36 299.94 5898.84 9299.88 3799.82 39
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test250696.81 29396.65 29197.29 32199.74 7592.21 36199.60 7785.06 38099.13 899.77 3699.93 487.82 35099.85 13599.38 2699.38 14399.80 55
CS-MVS99.50 1699.49 1199.52 10899.76 5699.35 10699.90 199.55 6798.56 7099.77 3699.70 13998.75 6099.77 17799.64 299.78 9599.42 180
test_part299.81 4199.83 1799.77 36
MSP-MVS99.42 4199.27 5599.88 699.89 999.80 2999.67 4899.50 12598.70 6299.77 3699.49 23298.21 10099.95 4798.46 15099.77 10099.88 8
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
CS-MVS-test99.42 4199.39 2099.52 10899.77 5299.35 10699.80 2099.57 5298.56 7099.77 3699.44 24798.16 10599.77 17799.64 299.78 9599.42 180
UA-Net99.42 4199.29 4999.80 4399.62 13499.55 8099.50 13499.70 1598.79 5699.77 3699.96 197.45 12299.96 2098.92 7499.90 2499.89 2
APD-MVScopyleft99.27 6499.08 7699.84 3599.75 6799.79 3399.50 13499.50 12597.16 22199.77 3699.82 5398.78 5299.94 5897.56 23099.86 5299.80 55
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS-dyc-post99.45 2899.31 4299.85 2899.76 5699.82 2399.63 6499.52 9298.38 8699.76 4399.82 5398.53 7799.95 4798.61 12799.81 8499.77 71
RE-MVS-def99.34 3199.76 5699.82 2399.63 6499.52 9298.38 8699.76 4399.82 5398.75 6098.61 12799.81 8499.77 71
ACMMP_NAP99.47 2499.34 3199.88 699.87 1699.86 1399.47 15599.48 14698.05 13299.76 4399.86 2798.82 4899.93 7398.82 9999.91 1799.84 21
HPM-MVS_fast99.51 1599.40 1999.85 2899.91 199.79 3399.76 3199.56 5897.72 16499.76 4399.75 11799.13 1299.92 8499.07 5999.92 1299.85 17
test117299.43 3699.29 4999.85 2899.75 6799.82 2399.60 7799.56 5898.28 9899.74 4799.79 9498.53 7799.95 4798.55 14199.78 9599.79 61
VNet99.11 9498.90 10499.73 6199.52 15899.56 7899.41 17899.39 21799.01 2399.74 4799.78 10195.56 18699.92 8499.52 1098.18 21999.72 95
patch_mono-299.26 6699.62 198.16 27899.81 4194.59 34099.52 12399.64 3399.33 299.73 4999.90 1099.00 2599.99 199.69 199.98 299.89 2
SR-MVS99.43 3699.29 4999.86 2199.75 6799.83 1799.59 8499.62 3498.21 10799.73 4999.79 9498.68 6899.96 2098.44 15299.77 10099.79 61
thisisatest053098.35 16798.03 18699.31 13999.63 12898.56 19699.54 11796.75 36597.53 18699.73 4999.65 16891.25 31099.89 11898.62 12499.56 13499.48 168
DROMVSNet99.44 3299.39 2099.58 9099.56 15299.49 9199.88 299.58 5098.38 8699.73 4999.69 14898.20 10199.70 20999.64 299.82 8199.54 151
diffmvs99.14 8199.02 8699.51 11299.61 13898.96 15799.28 22499.49 13398.46 7999.72 5399.71 13596.50 15499.88 12399.31 3599.11 16599.67 114
xxxxxxxxxxxxxcwj99.43 3699.32 3599.75 5499.76 5699.59 7399.14 26099.53 8699.00 2799.71 5499.80 8298.95 3299.93 7398.19 17199.84 6699.74 82
SF-MVS99.38 5199.24 6099.79 4699.79 4599.68 5499.57 9899.54 7597.82 15599.71 5499.80 8298.95 3299.93 7398.19 17199.84 6699.74 82
xiu_mvs_v2_base99.26 6699.25 5999.29 14699.53 15698.91 16699.02 28699.45 18698.80 5599.71 5499.26 29698.94 3599.98 799.34 3299.23 15698.98 221
PS-MVSNAJ99.32 5799.32 3599.30 14399.57 14898.94 16298.97 30099.46 17498.92 4499.71 5499.24 29899.01 1999.98 799.35 2899.66 12598.97 222
PGM-MVS99.45 2899.31 4299.86 2199.87 1699.78 4099.58 9299.65 3297.84 14999.71 5499.80 8299.12 1399.97 1298.33 16299.87 4199.83 32
114514_t98.93 11798.67 13299.72 6499.85 2699.53 8599.62 7099.59 4492.65 34899.71 5499.78 10198.06 10999.90 11098.84 9299.91 1799.74 82
PVSNet_Blended_VisFu99.36 5399.28 5399.61 8599.86 2299.07 14299.47 15599.93 297.66 17299.71 5499.86 2797.73 11799.96 2099.47 1999.82 8199.79 61
zzz-MVS99.49 1799.36 2699.89 499.90 499.86 1399.36 20299.47 16498.79 5699.68 6199.81 6698.43 8699.97 1298.88 7899.90 2499.83 32
MTAPA99.52 1499.39 2099.89 499.90 499.86 1399.66 5299.47 16498.79 5699.68 6199.81 6698.43 8699.97 1298.88 7899.90 2499.83 32
HFP-MVS99.49 1799.37 2499.86 2199.87 1699.80 2999.66 5299.67 2298.15 11399.68 6199.69 14899.06 1699.96 2098.69 11599.87 4199.84 21
#test#99.43 3699.29 4999.86 2199.87 1699.80 2999.55 11499.67 2297.83 15099.68 6199.69 14899.06 1699.96 2098.39 15499.87 4199.84 21
VDDNet97.55 26997.02 28699.16 16199.49 17298.12 22799.38 19599.30 26695.35 31899.68 6199.90 1082.62 36399.93 7399.31 3598.13 22499.42 180
HPM-MVScopyleft99.42 4199.28 5399.83 3699.90 499.72 4799.81 1699.54 7597.59 17699.68 6199.63 18198.91 4099.94 5898.58 13399.91 1799.84 21
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
VDD-MVS97.73 25097.35 26698.88 20199.47 18097.12 26499.34 21198.85 32498.19 10899.67 6799.85 3382.98 36199.92 8499.49 1698.32 21299.60 138
ACMMPR99.49 1799.36 2699.86 2199.87 1699.79 3399.66 5299.67 2298.15 11399.67 6799.69 14898.95 3299.96 2098.69 11599.87 4199.84 21
PVSNet_BlendedMVS98.86 12398.80 11999.03 17399.76 5698.79 18099.28 22499.91 397.42 19999.67 6799.37 26897.53 12099.88 12398.98 6697.29 26098.42 323
PVSNet_Blended99.08 10098.97 9599.42 12799.76 5698.79 18098.78 32299.91 396.74 25499.67 6799.49 23297.53 12099.88 12398.98 6699.85 5999.60 138
sss99.17 7799.05 7899.53 10299.62 13498.97 15399.36 20299.62 3497.83 15099.67 6799.65 16897.37 12799.95 4799.19 4699.19 15999.68 111
ECVR-MVScopyleft98.04 20098.05 18498.00 29099.74 7594.37 34399.59 8494.98 37199.13 899.66 7299.93 490.67 31699.84 14199.40 2599.38 14399.80 55
h-mvs3397.70 25797.28 27598.97 18199.70 10197.27 25899.36 20299.45 18698.94 4099.66 7299.64 17594.93 20599.99 199.48 1784.36 35999.65 121
hse-mvs297.50 27597.14 28298.59 23299.49 17297.05 27199.28 22499.22 28198.94 4099.66 7299.42 25394.93 20599.65 22299.48 1783.80 36199.08 207
region2R99.48 2199.35 2999.87 1299.88 1299.80 2999.65 5999.66 2798.13 11599.66 7299.68 15598.96 2999.96 2098.62 12499.87 4199.84 21
RPSCF98.22 17598.62 14296.99 32699.82 3891.58 36399.72 3699.44 19596.61 26599.66 7299.89 1495.92 17399.82 15997.46 24099.10 16899.57 147
OMC-MVS99.08 10099.04 8199.20 15799.67 10998.22 22199.28 22499.52 9298.07 12799.66 7299.81 6697.79 11599.78 17597.79 20599.81 8499.60 138
test111198.04 20098.11 17597.83 30199.74 7593.82 34899.58 9295.40 37099.12 1099.65 7899.93 490.73 31599.84 14199.43 2499.38 14399.82 39
test_one_060199.81 4199.88 899.49 13398.97 3699.65 7899.81 6699.09 14
LFMVS97.90 22197.35 26699.54 9699.52 15899.01 14899.39 19098.24 34897.10 22999.65 7899.79 9484.79 35899.91 9599.28 3898.38 20799.69 107
MVS_111021_LR99.41 4699.33 3399.65 7599.77 5299.51 9098.94 30799.85 698.82 5199.65 7899.74 12398.51 8099.80 16898.83 9599.89 3499.64 128
9.1499.10 7399.72 8899.40 18699.51 10597.53 18699.64 8299.78 10198.84 4699.91 9597.63 22199.82 81
GST-MVS99.40 4999.24 6099.85 2899.86 2299.79 3399.60 7799.67 2297.97 13899.63 8399.68 15598.52 7999.95 4798.38 15699.86 5299.81 45
CPTT-MVS99.11 9498.90 10499.74 5999.80 4499.46 9699.59 8499.49 13397.03 23699.63 8399.69 14897.27 13099.96 2097.82 20399.84 6699.81 45
ACMMPcopyleft99.45 2899.32 3599.82 3899.89 999.67 5799.62 7099.69 1898.12 11799.63 8399.84 4298.73 6499.96 2098.55 14199.83 7599.81 45
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
DeepC-MVS98.35 299.30 5999.19 6599.64 8099.82 3899.23 12199.62 7099.55 6798.94 4099.63 8399.95 295.82 17899.94 5899.37 2799.97 499.73 89
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CHOSEN 280x42099.12 8999.13 7099.08 16699.66 11897.89 23898.43 34699.71 1398.88 4699.62 8799.76 11296.63 15099.70 20999.46 2099.99 199.66 117
PHI-MVS99.30 5999.17 6799.70 6799.56 15299.52 8899.58 9299.80 897.12 22599.62 8799.73 13098.58 7599.90 11098.61 12799.91 1799.68 111
ETH3D-3000-0.199.21 7199.02 8699.77 5099.73 8399.69 5299.38 19599.51 10597.45 19399.61 8999.75 11798.51 8099.91 9597.45 24299.83 7599.71 102
test_yl98.86 12398.63 13799.54 9699.49 17299.18 12599.50 13499.07 30198.22 10599.61 8999.51 22695.37 19299.84 14198.60 13098.33 20899.59 142
DCV-MVSNet98.86 12398.63 13799.54 9699.49 17299.18 12599.50 13499.07 30198.22 10599.61 8999.51 22695.37 19299.84 14198.60 13098.33 20899.59 142
MG-MVS99.13 8399.02 8699.45 12299.57 14898.63 19199.07 27299.34 24298.99 3099.61 8999.82 5397.98 11199.87 12697.00 26799.80 8899.85 17
MP-MVS-pluss99.37 5299.20 6499.88 699.90 499.87 1299.30 21899.52 9297.18 21999.60 9399.79 9498.79 5199.95 4798.83 9599.91 1799.83 32
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CDPH-MVS99.13 8398.91 10399.80 4399.75 6799.71 4999.15 25899.41 20796.60 26799.60 9399.55 21098.83 4799.90 11097.48 23799.83 7599.78 69
EPP-MVSNet99.13 8398.99 9199.53 10299.65 12399.06 14399.81 1699.33 24997.43 19799.60 9399.88 1997.14 13299.84 14199.13 5398.94 18099.69 107
testtj99.12 8998.87 10899.86 2199.72 8899.79 3399.44 16399.51 10597.29 20999.59 9699.74 12398.15 10699.96 2096.74 28299.69 11799.81 45
HyFIR lowres test99.11 9498.92 10199.65 7599.90 499.37 10499.02 28699.91 397.67 17199.59 9699.75 11795.90 17599.73 19399.53 999.02 17699.86 14
MVS_Test99.10 9798.97 9599.48 11699.49 17299.14 13399.67 4899.34 24297.31 20799.58 9899.76 11297.65 11999.82 15998.87 8299.07 17199.46 175
MDTV_nov1_ep13_2view95.18 33199.35 20896.84 24999.58 9895.19 20197.82 20399.46 175
DELS-MVS99.48 2199.42 1699.65 7599.72 8899.40 10399.05 27799.66 2799.14 799.57 10099.80 8298.46 8499.94 5899.57 799.84 6699.60 138
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
ZD-MVS99.71 9499.79 3399.61 3696.84 24999.56 10199.54 21598.58 7599.96 2096.93 27499.75 104
CR-MVSNet98.17 18297.93 19998.87 20599.18 24998.49 20799.22 24899.33 24996.96 24099.56 10199.38 26594.33 23599.00 32394.83 32398.58 19899.14 199
RPMNet96.72 29595.90 30599.19 15899.18 24998.49 20799.22 24899.52 9288.72 35999.56 10197.38 35694.08 24599.95 4786.87 36698.58 19899.14 199
IS-MVSNet99.05 10498.87 10899.57 9299.73 8399.32 10999.75 3299.20 28598.02 13699.56 10199.86 2796.54 15399.67 21598.09 18099.13 16499.73 89
ZNCC-MVS99.47 2499.33 3399.87 1299.87 1699.81 2799.64 6299.67 2298.08 12699.55 10599.64 17598.91 4099.96 2098.72 11099.90 2499.82 39
thisisatest051598.14 18697.79 21099.19 15899.50 17098.50 20698.61 33696.82 36496.95 24299.54 10699.43 25091.66 30399.86 12998.08 18499.51 13899.22 196
MVS_111021_HR99.41 4699.32 3599.66 7199.72 8899.47 9598.95 30599.85 698.82 5199.54 10699.73 13098.51 8099.74 18698.91 7599.88 3799.77 71
CP-MVS99.45 2899.32 3599.85 2899.83 3799.75 4399.69 4199.52 9298.07 12799.53 10899.63 18198.93 3999.97 1298.74 10699.91 1799.83 32
WTY-MVS99.06 10298.88 10799.61 8599.62 13499.16 12899.37 19899.56 5898.04 13399.53 10899.62 18796.84 14299.94 5898.85 8998.49 20599.72 95
MCST-MVS99.43 3699.30 4599.82 3899.79 4599.74 4699.29 22299.40 21398.79 5699.52 11099.62 18798.91 4099.90 11098.64 12299.75 10499.82 39
PatchT97.03 29096.44 29598.79 21998.99 28498.34 21699.16 25499.07 30192.13 34999.52 11097.31 35994.54 23098.98 32588.54 36098.73 19499.03 215
CANet99.25 6999.14 6999.59 8799.41 19199.16 12899.35 20899.57 5298.82 5199.51 11299.61 19196.46 15599.95 4799.59 599.98 299.65 121
mPP-MVS99.44 3299.30 4599.86 2199.88 1299.79 3399.69 4199.48 14698.12 11799.50 11399.75 11798.78 5299.97 1298.57 13599.89 3499.83 32
PatchMatch-RL98.84 13498.62 14299.52 10899.71 9499.28 11599.06 27599.77 997.74 16399.50 11399.53 21995.41 19099.84 14197.17 26099.64 12899.44 178
PVSNet96.02 1798.85 13198.84 11498.89 19899.73 8397.28 25798.32 35299.60 4197.86 14599.50 11399.57 20496.75 14799.86 12998.56 13899.70 11699.54 151
LS3D99.27 6499.12 7199.74 5999.18 24999.75 4399.56 10599.57 5298.45 8099.49 11699.85 3397.77 11699.94 5898.33 16299.84 6699.52 157
MP-MVScopyleft99.33 5699.15 6899.87 1299.88 1299.82 2399.66 5299.46 17498.09 12299.48 11799.74 12398.29 9799.96 2097.93 19499.87 4199.82 39
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
旧先验298.96 30196.70 25799.47 11899.94 5898.19 171
MSDG98.98 11398.80 11999.53 10299.76 5699.19 12398.75 32599.55 6797.25 21399.47 11899.77 10897.82 11499.87 12696.93 27499.90 2499.54 151
CDS-MVSNet99.09 9899.03 8399.25 15299.42 18898.73 18399.45 15999.46 17498.11 11999.46 12099.77 10898.01 11099.37 26298.70 11298.92 18399.66 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MSLP-MVS++99.46 2699.47 1299.44 12699.60 14299.16 12899.41 17899.71 1398.98 3399.45 12199.78 10199.19 999.54 23999.28 3899.84 6699.63 132
XVG-OURS98.73 14398.68 13198.88 20199.70 10197.73 24698.92 30899.55 6798.52 7499.45 12199.84 4295.27 19699.91 9598.08 18498.84 18899.00 218
tpmrst98.33 16898.48 15397.90 29799.16 25794.78 33899.31 21699.11 29597.27 21199.45 12199.59 19795.33 19499.84 14198.48 14698.61 19599.09 206
TAMVS99.12 8999.08 7699.24 15499.46 18198.55 19799.51 12899.46 17498.09 12299.45 12199.82 5398.34 9499.51 24098.70 11298.93 18199.67 114
ETV-MVS99.26 6699.21 6399.40 12899.46 18199.30 11399.56 10599.52 9298.52 7499.44 12599.27 29498.41 9099.86 12999.10 5699.59 13399.04 214
CANet_DTU98.97 11598.87 10899.25 15299.33 21098.42 21499.08 27199.30 26699.16 699.43 12699.75 11795.27 19699.97 1298.56 13899.95 799.36 187
SCA98.19 17998.16 17098.27 27399.30 21995.55 31999.07 27298.97 30997.57 17999.43 12699.57 20492.72 27299.74 18697.58 22599.20 15899.52 157
testdata99.54 9699.75 6798.95 15999.51 10597.07 23199.43 12699.70 13998.87 4399.94 5897.76 20899.64 12899.72 95
DPM-MVS98.95 11698.71 12899.66 7199.63 12899.55 8098.64 33599.10 29697.93 14199.42 12999.55 21098.67 7199.80 16895.80 30499.68 12299.61 136
XVG-OURS-SEG-HR98.69 14698.62 14298.89 19899.71 9497.74 24599.12 26299.54 7598.44 8399.42 12999.71 13594.20 23999.92 8498.54 14398.90 18599.00 218
baseline99.15 8099.02 8699.53 10299.66 11899.14 13399.72 3699.48 14698.35 9199.42 12999.84 4296.07 16699.79 17199.51 1199.14 16399.67 114
DP-MVS Recon99.12 8998.95 9999.65 7599.74 7599.70 5199.27 22999.57 5296.40 28499.42 12999.68 15598.75 6099.80 16897.98 19099.72 11199.44 178
Effi-MVS+-dtu98.78 13998.89 10698.47 25099.33 21096.91 28499.57 9899.30 26698.47 7799.41 13398.99 32496.78 14499.74 18698.73 10899.38 14398.74 247
casdiffmvs99.13 8398.98 9499.56 9499.65 12399.16 12899.56 10599.50 12598.33 9599.41 13399.86 2795.92 17399.83 15299.45 2199.16 16099.70 104
MIMVSNet97.73 25097.45 24998.57 23699.45 18697.50 25299.02 28698.98 30896.11 30699.41 13399.14 30990.28 31898.74 34095.74 30598.93 18199.47 173
CSCG99.32 5799.32 3599.32 13899.85 2698.29 21799.71 3899.66 2798.11 11999.41 13399.80 8298.37 9399.96 2098.99 6599.96 699.72 95
F-COLMAP99.19 7399.04 8199.64 8099.78 4799.27 11799.42 17699.54 7597.29 20999.41 13399.59 19798.42 8999.93 7398.19 17199.69 11799.73 89
EIA-MVS99.18 7599.09 7599.45 12299.49 17299.18 12599.67 4899.53 8697.66 17299.40 13899.44 24798.10 10799.81 16398.94 7099.62 13199.35 188
MDTV_nov1_ep1398.32 16399.11 26494.44 34299.27 22998.74 33297.51 18899.40 13899.62 18794.78 21499.76 18397.59 22498.81 191
ETH3D cwj APD-0.1699.06 10298.84 11499.72 6499.51 16099.60 7099.23 24399.44 19597.04 23499.39 14099.67 16198.30 9699.92 8497.27 24999.69 11799.64 128
CVMVSNet98.57 15498.67 13298.30 26899.35 20595.59 31899.50 13499.55 6798.60 6899.39 14099.83 4694.48 23199.45 24598.75 10598.56 20199.85 17
CNVR-MVS99.42 4199.30 4599.78 4899.62 13499.71 4999.26 23899.52 9298.82 5199.39 14099.71 13598.96 2999.85 13598.59 13299.80 8899.77 71
Effi-MVS+98.81 13598.59 14899.48 11699.46 18199.12 13798.08 35899.50 12597.50 18999.38 14399.41 25796.37 15999.81 16399.11 5598.54 20299.51 163
mvs_anonymous99.03 10798.99 9199.16 16199.38 20098.52 20399.51 12899.38 22397.79 15699.38 14399.81 6697.30 12899.45 24599.35 2898.99 17899.51 163
XVS99.53 1299.42 1699.87 1299.85 2699.83 1799.69 4199.68 1998.98 3399.37 14599.74 12398.81 4999.94 5898.79 10199.86 5299.84 21
X-MVStestdata96.55 29795.45 31299.87 1299.85 2699.83 1799.69 4199.68 1998.98 3399.37 14564.01 37698.81 4999.94 5898.79 10199.86 5299.84 21
PatchmatchNetpermissive98.31 16998.36 15898.19 27699.16 25795.32 32799.27 22998.92 31597.37 20399.37 14599.58 20094.90 20899.70 20997.43 24499.21 15799.54 151
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
AllTest98.87 12098.72 12699.31 13999.86 2298.48 20999.56 10599.61 3697.85 14799.36 14899.85 3395.95 17099.85 13596.66 28899.83 7599.59 142
TestCases99.31 13999.86 2298.48 20999.61 3697.85 14799.36 14899.85 3395.95 17099.85 13596.66 28899.83 7599.59 142
Vis-MVSNet (Re-imp)98.87 12098.72 12699.31 13999.71 9498.88 16899.80 2099.44 19597.91 14399.36 14899.78 10195.49 18999.43 25497.91 19599.11 16599.62 134
alignmvs98.81 13598.56 15099.58 9099.43 18799.42 10099.51 12898.96 31198.61 6799.35 15198.92 33094.78 21499.77 17799.35 2898.11 22599.54 151
VPA-MVSNet98.29 17297.95 19699.30 14399.16 25799.54 8299.50 13499.58 5098.27 10099.35 15199.37 26892.53 28199.65 22299.35 2894.46 32098.72 249
AdaColmapbinary99.01 11198.80 11999.66 7199.56 15299.54 8299.18 25299.70 1598.18 11199.35 15199.63 18196.32 16099.90 11097.48 23799.77 10099.55 149
test22299.75 6799.49 9198.91 31099.49 13396.42 28299.34 15499.65 16898.28 9899.69 11799.72 95
API-MVS99.04 10599.03 8399.06 16899.40 19699.31 11299.55 11499.56 5898.54 7299.33 15599.39 26498.76 5799.78 17596.98 26999.78 9598.07 340
v14419297.92 21997.60 23398.87 20598.83 30698.65 18999.55 11499.34 24296.20 29699.32 15699.40 26094.36 23499.26 28596.37 29595.03 31298.70 255
GeoE98.85 13198.62 14299.53 10299.61 13899.08 14099.80 2099.51 10597.10 22999.31 15799.78 10195.23 20099.77 17798.21 16999.03 17499.75 77
canonicalmvs99.02 10898.86 11299.51 11299.42 18899.32 10999.80 2099.48 14698.63 6599.31 15798.81 33397.09 13499.75 18599.27 4097.90 22999.47 173
V4298.06 19497.79 21098.86 20898.98 28798.84 17399.69 4199.34 24296.53 27199.30 15999.37 26894.67 22399.32 27697.57 22994.66 31798.42 323
ab-mvs98.86 12398.63 13799.54 9699.64 12599.19 12399.44 16399.54 7597.77 15899.30 15999.81 6694.20 23999.93 7399.17 4998.82 18999.49 167
TAPA-MVS97.07 1597.74 24997.34 26998.94 18599.70 10197.53 25199.25 24099.51 10591.90 35099.30 15999.63 18198.78 5299.64 22588.09 36299.87 4199.65 121
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RRT_MVS98.60 15398.44 15499.05 17098.88 29699.14 13399.49 14499.38 22397.76 15999.29 16299.86 2795.38 19199.36 26698.81 10097.16 26598.64 283
新几何199.75 5499.75 6799.59 7399.54 7596.76 25399.29 16299.64 17598.43 8699.94 5896.92 27699.66 12599.72 95
VPNet97.84 23097.44 25499.01 17599.21 24298.94 16299.48 15099.57 5298.38 8699.28 16499.73 13088.89 33599.39 25799.19 4693.27 33798.71 251
HY-MVS97.30 798.85 13198.64 13699.47 11999.42 18899.08 14099.62 7099.36 23397.39 20299.28 16499.68 15596.44 15799.92 8498.37 15898.22 21499.40 185
PAPM_NR99.04 10598.84 11499.66 7199.74 7599.44 9899.39 19099.38 22397.70 16699.28 16499.28 29198.34 9499.85 13596.96 27199.45 13999.69 107
ETH3 D test640098.70 14498.35 16099.73 6199.69 10499.60 7099.16 25499.45 18695.42 31799.27 16799.60 19497.39 12399.91 9595.36 31599.83 7599.70 104
HPM-MVS++copyleft99.39 5099.23 6299.87 1299.75 6799.84 1699.43 16999.51 10598.68 6499.27 16799.53 21998.64 7399.96 2098.44 15299.80 8899.79 61
v124097.69 25897.32 27298.79 21998.85 30498.43 21299.48 15099.36 23396.11 30699.27 16799.36 27193.76 25499.24 28794.46 32695.23 30798.70 255
thres600view797.86 22697.51 24298.92 18999.72 8897.95 23699.59 8498.74 33297.94 14099.27 16798.62 34091.75 29799.86 12993.73 33498.19 21898.96 224
PLCcopyleft97.94 499.02 10898.85 11399.53 10299.66 11899.01 14899.24 24299.52 9296.85 24899.27 16799.48 23898.25 9999.91 9597.76 20899.62 13199.65 121
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
thres100view90097.76 24297.45 24998.69 22799.72 8897.86 24199.59 8498.74 33297.93 14199.26 17298.62 34091.75 29799.83 15293.22 33998.18 21998.37 329
EPMVS97.82 23597.65 22898.35 26398.88 29695.98 31199.49 14494.71 37397.57 17999.26 17299.48 23892.46 28699.71 20397.87 19899.08 17099.35 188
112199.09 9898.87 10899.75 5499.74 7599.60 7099.27 22999.48 14696.82 25299.25 17499.65 16898.38 9199.93 7397.53 23399.67 12499.73 89
Fast-Effi-MVS+-dtu98.77 14198.83 11898.60 23199.41 19196.99 27899.52 12399.49 13398.11 11999.24 17599.34 27796.96 14099.79 17197.95 19399.45 13999.02 217
v192192097.80 23997.45 24998.84 21298.80 30798.53 19999.52 12399.34 24296.15 30399.24 17599.47 24193.98 24799.29 28095.40 31395.13 31098.69 259
LPG-MVS_test98.22 17598.13 17398.49 24499.33 21097.05 27199.58 9299.55 6797.46 19099.24 17599.83 4692.58 27999.72 19798.09 18097.51 24598.68 264
LGP-MVS_train98.49 24499.33 21097.05 27199.55 6797.46 19099.24 17599.83 4692.58 27999.72 19798.09 18097.51 24598.68 264
v114497.98 21197.69 22498.85 21198.87 30098.66 18899.54 11799.35 23896.27 29099.23 17999.35 27494.67 22399.23 28896.73 28395.16 30998.68 264
Anonymous2024052998.09 19197.68 22599.34 13399.66 11898.44 21199.40 18699.43 20393.67 33999.22 18099.89 1490.23 32299.93 7399.26 4198.33 20899.66 117
OPM-MVS98.19 17998.10 17698.45 25298.88 29697.07 26999.28 22499.38 22398.57 6999.22 18099.81 6692.12 29099.66 21898.08 18497.54 24398.61 302
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test_djsdf98.67 14898.57 14998.98 17998.70 32298.91 16699.88 299.46 17497.55 18199.22 18099.88 1995.73 18199.28 28199.03 6197.62 23698.75 243
test1299.75 5499.64 12599.61 6899.29 27199.21 18398.38 9199.89 11899.74 10799.74 82
NCCC99.34 5599.19 6599.79 4699.61 13899.65 6299.30 21899.48 14698.86 4799.21 18399.63 18198.72 6599.90 11098.25 16799.63 13099.80 55
PMMVS98.80 13898.62 14299.34 13399.27 22898.70 18598.76 32499.31 26297.34 20499.21 18399.07 31597.20 13199.82 15998.56 13898.87 18699.52 157
v119297.81 23797.44 25498.91 19398.88 29698.68 18699.51 12899.34 24296.18 29899.20 18699.34 27794.03 24699.36 26695.32 31695.18 30898.69 259
EI-MVSNet98.67 14898.67 13298.68 22899.35 20597.97 23299.50 13499.38 22396.93 24599.20 18699.83 4697.87 11299.36 26698.38 15697.56 24198.71 251
MVSTER98.49 15598.32 16399.00 17799.35 20599.02 14699.54 11799.38 22397.41 20099.20 18699.73 13093.86 25199.36 26698.87 8297.56 24198.62 293
Anonymous20240521198.30 17197.98 19199.26 15199.57 14898.16 22399.41 17898.55 34496.03 31199.19 18999.74 12391.87 29499.92 8499.16 5198.29 21399.70 104
v2v48298.06 19497.77 21598.92 18998.90 29498.82 17799.57 9899.36 23396.65 26199.19 18999.35 27494.20 23999.25 28697.72 21494.97 31398.69 259
CNLPA99.14 8198.99 9199.59 8799.58 14699.41 10199.16 25499.44 19598.45 8099.19 18999.49 23298.08 10899.89 11897.73 21299.75 10499.48 168
UGNet98.87 12098.69 13099.40 12899.22 24098.72 18499.44 16399.68 1999.24 499.18 19299.42 25392.74 27199.96 2099.34 3299.94 1099.53 156
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
tfpn200view997.72 25297.38 26298.72 22599.69 10497.96 23499.50 13498.73 33797.83 15099.17 19398.45 34591.67 30199.83 15293.22 33998.18 21998.37 329
thres40097.77 24197.38 26298.92 18999.69 10497.96 23499.50 13498.73 33797.83 15099.17 19398.45 34591.67 30199.83 15293.22 33998.18 21998.96 224
Test_1112_low_res98.89 11998.66 13599.57 9299.69 10498.95 15999.03 28399.47 16496.98 23899.15 19599.23 29996.77 14699.89 11898.83 9598.78 19299.86 14
baseline198.31 16997.95 19699.38 13199.50 17098.74 18299.59 8498.93 31398.41 8499.14 19699.60 19494.59 22699.79 17198.48 14693.29 33699.61 136
1112_ss98.98 11398.77 12299.59 8799.68 10899.02 14699.25 24099.48 14697.23 21699.13 19799.58 20096.93 14199.90 11098.87 8298.78 19299.84 21
CLD-MVS98.16 18398.10 17698.33 26499.29 22396.82 28798.75 32599.44 19597.83 15099.13 19799.55 21092.92 26599.67 21598.32 16497.69 23398.48 314
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
原ACMM199.65 7599.73 8399.33 10899.47 16497.46 19099.12 19999.66 16798.67 7199.91 9597.70 21799.69 11799.71 102
tpm97.67 26397.55 23698.03 28599.02 28195.01 33399.43 16998.54 34596.44 28099.12 19999.34 27791.83 29699.60 23397.75 21096.46 27799.48 168
HQP_MVS98.27 17498.22 16998.44 25599.29 22396.97 28099.39 19099.47 16498.97 3699.11 20199.61 19192.71 27499.69 21397.78 20697.63 23498.67 271
plane_prior397.00 27798.69 6399.11 201
CHOSEN 1792x268899.19 7399.10 7399.45 12299.89 998.52 20399.39 19099.94 198.73 6099.11 20199.89 1495.50 18899.94 5899.50 1299.97 499.89 2
mvs-test198.86 12398.84 11498.89 19899.33 21097.77 24499.44 16399.30 26698.47 7799.10 20499.43 25096.78 14499.95 4798.73 10899.02 17698.96 224
bset_n11_16_dypcd98.16 18397.97 19298.73 22398.26 34298.28 21997.99 36098.01 35397.68 16899.10 20499.63 18195.68 18399.15 30198.78 10496.55 27498.75 243
v897.95 21597.63 23198.93 18798.95 29198.81 17999.80 2099.41 20796.03 31199.10 20499.42 25394.92 20799.30 27996.94 27394.08 32898.66 279
ADS-MVSNet298.02 20498.07 18397.87 29899.33 21095.19 33099.23 24399.08 29996.24 29399.10 20499.67 16194.11 24398.93 33596.81 27999.05 17299.48 168
ADS-MVSNet98.20 17898.08 18098.56 23899.33 21096.48 29899.23 24399.15 29196.24 29399.10 20499.67 16194.11 24399.71 20396.81 27999.05 17299.48 168
thres20097.61 26797.28 27598.62 23099.64 12598.03 22899.26 23898.74 33297.68 16899.09 20998.32 34991.66 30399.81 16392.88 34398.22 21498.03 343
dp97.75 24697.80 20997.59 31299.10 26793.71 35199.32 21498.88 32296.48 27799.08 21099.55 21092.67 27799.82 15996.52 29098.58 19899.24 195
GBi-Net97.68 26097.48 24498.29 26999.51 16097.26 26099.43 16999.48 14696.49 27399.07 21199.32 28490.26 31998.98 32597.10 26296.65 27098.62 293
test197.68 26097.48 24498.29 26999.51 16097.26 26099.43 16999.48 14696.49 27399.07 21199.32 28490.26 31998.98 32597.10 26296.65 27098.62 293
FMVSNet398.03 20297.76 21898.84 21299.39 19998.98 15099.40 18699.38 22396.67 25999.07 21199.28 29192.93 26498.98 32597.10 26296.65 27098.56 309
IterMVS-LS98.46 15798.42 15698.58 23599.59 14498.00 23099.37 19899.43 20396.94 24499.07 21199.59 19797.87 11299.03 31898.32 16495.62 29998.71 251
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs498.13 18797.90 20198.81 21698.61 33198.87 16998.99 29399.21 28496.44 28099.06 21599.58 20095.90 17599.11 31097.18 25996.11 28598.46 320
XVG-ACMP-BASELINE97.83 23297.71 22398.20 27599.11 26496.33 30399.41 17899.52 9298.06 13199.05 21699.50 22989.64 32999.73 19397.73 21297.38 25898.53 310
CostFormer97.72 25297.73 22197.71 30899.15 26094.02 34799.54 11799.02 30594.67 33099.04 21799.35 27492.35 28999.77 17798.50 14597.94 22899.34 190
DP-MVS99.16 7998.95 9999.78 4899.77 5299.53 8599.41 17899.50 12597.03 23699.04 21799.88 1997.39 12399.92 8498.66 12099.90 2499.87 13
ACMM97.58 598.37 16698.34 16198.48 24699.41 19197.10 26599.56 10599.45 18698.53 7399.04 21799.85 3393.00 26399.71 20398.74 10697.45 25298.64 283
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Fast-Effi-MVS+98.70 14498.43 15599.51 11299.51 16099.28 11599.52 12399.47 16496.11 30699.01 22099.34 27796.20 16499.84 14197.88 19798.82 18999.39 186
nrg03098.64 15198.42 15699.28 14999.05 27799.69 5299.81 1699.46 17498.04 13399.01 22099.82 5396.69 14999.38 25999.34 3294.59 31998.78 235
test_prior399.21 7199.05 7899.68 6899.67 10999.48 9398.96 30199.56 5898.34 9299.01 22099.52 22298.68 6899.83 15297.96 19199.74 10799.74 82
test_prior298.96 30198.34 9299.01 22099.52 22298.68 6897.96 19199.74 107
MAR-MVS98.86 12398.63 13799.54 9699.37 20299.66 5999.45 15999.54 7596.61 26599.01 22099.40 26097.09 13499.86 12997.68 22099.53 13799.10 202
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
PS-MVSNAJss98.92 11898.92 10198.90 19598.78 31198.53 19999.78 2699.54 7598.07 12799.00 22599.76 11299.01 1999.37 26299.13 5397.23 26198.81 232
PAPR98.63 15298.34 16199.51 11299.40 19699.03 14598.80 32099.36 23396.33 28599.00 22599.12 31398.46 8499.84 14195.23 31799.37 15099.66 117
D2MVS98.41 16298.50 15298.15 28099.26 23096.62 29499.40 18699.61 3697.71 16598.98 22799.36 27196.04 16799.67 21598.70 11297.41 25698.15 338
v1097.85 22797.52 24098.86 20898.99 28498.67 18799.75 3299.41 20795.70 31498.98 22799.41 25794.75 21999.23 28896.01 30094.63 31898.67 271
miper_enhance_ethall98.16 18398.08 18098.41 25798.96 29097.72 24798.45 34599.32 25996.95 24298.97 22999.17 30597.06 13699.22 29197.86 19995.99 28898.29 331
UniMVSNet (Re)98.29 17298.00 18999.13 16499.00 28399.36 10599.49 14499.51 10597.95 13998.97 22999.13 31096.30 16199.38 25998.36 16093.34 33598.66 279
TEST999.67 10999.65 6299.05 27799.41 20796.22 29598.95 23199.49 23298.77 5599.91 95
train_agg99.02 10898.77 12299.77 5099.67 10999.65 6299.05 27799.41 20796.28 28898.95 23199.49 23298.76 5799.91 9597.63 22199.72 11199.75 77
RRT_test8_iter0597.72 25297.60 23398.08 28299.23 23696.08 31099.63 6499.49 13397.54 18498.94 23399.81 6687.99 34699.35 27099.21 4596.51 27698.81 232
BH-RMVSNet98.41 16298.08 18099.40 12899.41 19198.83 17699.30 21898.77 32897.70 16698.94 23399.65 16892.91 26799.74 18696.52 29099.55 13699.64 128
test_899.67 10999.61 6899.03 28399.41 20796.28 28898.93 23599.48 23898.76 5799.91 95
3Dnovator97.25 999.24 7099.05 7899.81 4199.12 26299.66 5999.84 1099.74 1099.09 1598.92 23699.90 1095.94 17299.98 798.95 6999.92 1299.79 61
v7n97.87 22497.52 24098.92 18998.76 31598.58 19599.84 1099.46 17496.20 29698.91 23799.70 13994.89 20999.44 25096.03 29993.89 33098.75 243
JIA-IIPM97.50 27597.02 28698.93 18798.73 31797.80 24399.30 21898.97 30991.73 35198.91 23794.86 36495.10 20299.71 20397.58 22597.98 22799.28 194
v14897.79 24097.55 23698.50 24398.74 31697.72 24799.54 11799.33 24996.26 29198.90 23999.51 22694.68 22299.14 30297.83 20293.15 33998.63 291
GA-MVS97.85 22797.47 24699.00 17799.38 20097.99 23198.57 33999.15 29197.04 23498.90 23999.30 28789.83 32599.38 25996.70 28598.33 20899.62 134
tpm297.44 28097.34 26997.74 30799.15 26094.36 34499.45 15998.94 31293.45 34498.90 23999.44 24791.35 30899.59 23497.31 24798.07 22699.29 193
miper_ehance_all_eth98.18 18198.10 17698.41 25799.23 23697.72 24798.72 32899.31 26296.60 26798.88 24299.29 28997.29 12999.13 30597.60 22395.99 28898.38 328
eth_miper_zixun_eth98.05 19997.96 19498.33 26499.26 23097.38 25598.56 34199.31 26296.65 26198.88 24299.52 22296.58 15199.12 30997.39 24695.53 30298.47 316
cl2297.85 22797.64 23098.48 24699.09 26997.87 23998.60 33899.33 24997.11 22898.87 24499.22 30092.38 28899.17 30098.21 16995.99 28898.42 323
agg_prior199.01 11198.76 12499.76 5399.67 10999.62 6698.99 29399.40 21396.26 29198.87 24499.49 23298.77 5599.91 9597.69 21899.72 11199.75 77
agg_prior99.67 10999.62 6699.40 21398.87 24499.91 95
anonymousdsp98.44 15898.28 16698.94 18598.50 33798.96 15799.77 2899.50 12597.07 23198.87 24499.77 10894.76 21899.28 28198.66 12097.60 23798.57 308
DSMNet-mixed97.25 28597.35 26696.95 32997.84 34893.61 35499.57 9896.63 36696.13 30598.87 24498.61 34294.59 22697.70 35895.08 31998.86 18799.55 149
FMVSNet297.72 25297.36 26498.80 21899.51 16098.84 17399.45 15999.42 20596.49 27398.86 24999.29 28990.26 31998.98 32596.44 29296.56 27398.58 307
c3_l98.12 18998.04 18598.38 26199.30 21997.69 25098.81 31999.33 24996.67 25998.83 25099.34 27797.11 13398.99 32497.58 22595.34 30598.48 314
ITE_SJBPF98.08 28299.29 22396.37 30198.92 31598.34 9298.83 25099.75 11791.09 31199.62 23195.82 30297.40 25798.25 334
Anonymous2023121197.88 22297.54 23998.90 19599.71 9498.53 19999.48 15099.57 5294.16 33598.81 25299.68 15593.23 25999.42 25598.84 9294.42 32298.76 241
Patchmtry97.75 24697.40 26098.81 21699.10 26798.87 16999.11 26899.33 24994.83 32798.81 25299.38 26594.33 23599.02 32096.10 29795.57 30098.53 310
miper_lstm_enhance98.00 20997.91 20098.28 27299.34 20997.43 25498.88 31299.36 23396.48 27798.80 25499.55 21095.98 16898.91 33697.27 24995.50 30398.51 312
BH-untuned98.42 16098.36 15898.59 23299.49 17296.70 29099.27 22999.13 29497.24 21598.80 25499.38 26595.75 18099.74 18697.07 26599.16 16099.33 191
FIs98.78 13998.63 13799.23 15699.18 24999.54 8299.83 1399.59 4498.28 9898.79 25699.81 6696.75 14799.37 26299.08 5896.38 27998.78 235
OurMVSNet-221017-097.88 22297.77 21598.19 27698.71 32196.53 29699.88 299.00 30697.79 15698.78 25799.94 391.68 30099.35 27097.21 25396.99 26898.69 259
MVS-HIRNet95.75 31195.16 31597.51 31599.30 21993.69 35298.88 31295.78 36885.09 36298.78 25792.65 36691.29 30999.37 26294.85 32299.85 5999.46 175
tpmvs97.98 21198.02 18897.84 30099.04 27894.73 33999.31 21699.20 28596.10 31098.76 25999.42 25394.94 20499.81 16396.97 27098.45 20698.97 222
Patchmatch-test97.93 21697.65 22898.77 22199.18 24997.07 26999.03 28399.14 29396.16 30198.74 26099.57 20494.56 22899.72 19793.36 33899.11 16599.52 157
QAPM98.67 14898.30 16599.80 4399.20 24499.67 5799.77 2899.72 1194.74 32998.73 26199.90 1095.78 17999.98 796.96 27199.88 3799.76 76
3Dnovator+97.12 1399.18 7598.97 9599.82 3899.17 25599.68 5499.81 1699.51 10599.20 598.72 26299.89 1495.68 18399.97 1298.86 8799.86 5299.81 45
IterMVS-SCA-FT97.82 23597.75 21998.06 28499.57 14896.36 30299.02 28699.49 13397.18 21998.71 26399.72 13492.72 27299.14 30297.44 24395.86 29398.67 271
UniMVSNet_NR-MVSNet98.22 17597.97 19298.96 18298.92 29398.98 15099.48 15099.53 8697.76 15998.71 26399.46 24596.43 15899.22 29198.57 13592.87 34298.69 259
DU-MVS98.08 19397.79 21098.96 18298.87 30098.98 15099.41 17899.45 18697.87 14498.71 26399.50 22994.82 21199.22 29198.57 13592.87 34298.68 264
tpm cat197.39 28197.36 26497.50 31699.17 25593.73 35099.43 16999.31 26291.27 35298.71 26399.08 31494.31 23799.77 17796.41 29498.50 20499.00 218
XXY-MVS98.38 16598.09 17999.24 15499.26 23099.32 10999.56 10599.55 6797.45 19398.71 26399.83 4693.23 25999.63 23098.88 7896.32 28198.76 241
IterMVS97.83 23297.77 21598.02 28799.58 14696.27 30599.02 28699.48 14697.22 21798.71 26399.70 13992.75 26999.13 30597.46 24096.00 28798.67 271
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FC-MVSNet-test98.75 14298.62 14299.15 16399.08 27199.45 9799.86 999.60 4198.23 10498.70 26999.82 5396.80 14399.22 29199.07 5996.38 27998.79 234
COLMAP_ROBcopyleft97.56 698.86 12398.75 12599.17 16099.88 1298.53 19999.34 21199.59 4497.55 18198.70 26999.89 1495.83 17799.90 11098.10 17999.90 2499.08 207
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TR-MVS97.76 24297.41 25998.82 21499.06 27497.87 23998.87 31498.56 34396.63 26498.68 27199.22 30092.49 28299.65 22295.40 31397.79 23198.95 227
WR-MVS98.06 19497.73 22199.06 16898.86 30399.25 11999.19 25199.35 23897.30 20898.66 27299.43 25093.94 24899.21 29698.58 13394.28 32498.71 251
HQP-NCC99.19 24698.98 29798.24 10198.66 272
ACMP_Plane99.19 24698.98 29798.24 10198.66 272
HQP4-MVS98.66 27299.64 22598.64 283
HQP-MVS98.02 20497.90 20198.37 26299.19 24696.83 28598.98 29799.39 21798.24 10198.66 27299.40 26092.47 28399.64 22597.19 25797.58 23998.64 283
LF4IMVS97.52 27297.46 24897.70 30998.98 28795.55 31999.29 22298.82 32798.07 12798.66 27299.64 17589.97 32499.61 23297.01 26696.68 26997.94 350
mvs_tets98.40 16498.23 16898.91 19398.67 32598.51 20599.66 5299.53 8698.19 10898.65 27899.81 6692.75 26999.44 25099.31 3597.48 25198.77 239
TESTMET0.1,197.55 26997.27 27898.40 25998.93 29296.53 29698.67 33197.61 35996.96 24098.64 27999.28 29188.63 33999.45 24597.30 24899.38 14399.21 197
jajsoiax98.43 15998.28 16698.88 20198.60 33298.43 21299.82 1499.53 8698.19 10898.63 28099.80 8293.22 26199.44 25099.22 4397.50 24798.77 239
Baseline_NR-MVSNet97.76 24297.45 24998.68 22899.09 26998.29 21799.41 17898.85 32495.65 31598.63 28099.67 16194.82 21199.10 31298.07 18792.89 34198.64 283
EPNet98.86 12398.71 12899.30 14397.20 35898.18 22299.62 7098.91 31899.28 398.63 28099.81 6695.96 16999.99 199.24 4299.72 11199.73 89
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test-LLR98.06 19497.90 20198.55 24098.79 30897.10 26598.67 33197.75 35697.34 20498.61 28398.85 33194.45 23299.45 24597.25 25199.38 14399.10 202
test-mter97.49 27897.13 28398.55 24098.79 30897.10 26598.67 33197.75 35696.65 26198.61 28398.85 33188.23 34399.45 24597.25 25199.38 14399.10 202
DIV-MVS_self_test98.01 20797.85 20798.48 24699.24 23597.95 23698.71 32999.35 23896.50 27298.60 28599.54 21595.72 18299.03 31897.21 25395.77 29498.46 320
cl____98.01 20797.84 20898.55 24099.25 23497.97 23298.71 32999.34 24296.47 27998.59 28699.54 21595.65 18599.21 29697.21 25395.77 29498.46 320
FMVSNet196.84 29296.36 29698.29 26999.32 21797.26 26099.43 16999.48 14695.11 32198.55 28799.32 28483.95 36098.98 32595.81 30396.26 28298.62 293
UniMVSNet_ETH3D97.32 28396.81 28998.87 20599.40 19697.46 25399.51 12899.53 8695.86 31398.54 28899.77 10882.44 36499.66 21898.68 11797.52 24499.50 166
AUN-MVS96.88 29196.31 29798.59 23299.48 17997.04 27499.27 22999.22 28197.44 19698.51 28999.41 25791.97 29299.66 21897.71 21583.83 36099.07 212
PCF-MVS97.08 1497.66 26497.06 28599.47 11999.61 13899.09 13998.04 35999.25 27791.24 35398.51 28999.70 13994.55 22999.91 9592.76 34699.85 5999.42 180
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TranMVSNet+NR-MVSNet97.93 21697.66 22798.76 22298.78 31198.62 19299.65 5999.49 13397.76 15998.49 29199.60 19494.23 23898.97 33298.00 18992.90 34098.70 255
CP-MVSNet98.09 19197.78 21399.01 17598.97 28999.24 12099.67 4899.46 17497.25 21398.48 29299.64 17593.79 25299.06 31498.63 12394.10 32798.74 247
ACMP97.20 1198.06 19497.94 19898.45 25299.37 20297.01 27699.44 16399.49 13397.54 18498.45 29399.79 9491.95 29399.72 19797.91 19597.49 25098.62 293
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_part197.75 24697.24 27999.29 14699.59 14499.63 6599.65 5999.49 13396.17 29998.44 29499.69 14889.80 32699.47 24298.68 11793.66 33298.78 235
MVS_030496.79 29496.52 29497.59 31299.22 24094.92 33699.04 28299.59 4496.49 27398.43 29598.99 32480.48 36799.39 25797.15 26199.27 15498.47 316
cascas97.69 25897.43 25798.48 24698.60 33297.30 25698.18 35799.39 21792.96 34798.41 29698.78 33693.77 25399.27 28498.16 17698.61 19598.86 229
WR-MVS_H98.13 18797.87 20698.90 19599.02 28198.84 17399.70 3999.59 4497.27 21198.40 29799.19 30495.53 18799.23 28898.34 16193.78 33198.61 302
BH-w/o98.00 20997.89 20598.32 26699.35 20596.20 30799.01 29198.90 32096.42 28298.38 29899.00 32395.26 19899.72 19796.06 29898.61 19599.03 215
pmmvs597.52 27297.30 27498.16 27898.57 33496.73 28999.27 22998.90 32096.14 30498.37 29999.53 21991.54 30699.14 30297.51 23595.87 29298.63 291
DWT-MVSNet_test97.53 27197.40 26097.93 29499.03 28094.86 33799.57 9898.63 34196.59 26998.36 30098.79 33489.32 33199.74 18698.14 17898.16 22399.20 198
EU-MVSNet97.98 21198.03 18697.81 30498.72 31996.65 29399.66 5299.66 2798.09 12298.35 30199.82 5395.25 19998.01 35197.41 24595.30 30698.78 235
FMVSNet596.43 30196.19 29997.15 32299.11 26495.89 31399.32 21499.52 9294.47 33498.34 30299.07 31587.54 35197.07 36292.61 34795.72 29798.47 316
PS-CasMVS97.93 21697.59 23598.95 18498.99 28499.06 14399.68 4699.52 9297.13 22398.31 30399.68 15592.44 28799.05 31598.51 14494.08 32898.75 243
USDC97.34 28297.20 28097.75 30699.07 27295.20 32998.51 34399.04 30497.99 13798.31 30399.86 2789.02 33399.55 23895.67 30897.36 25998.49 313
PEN-MVS97.76 24297.44 25498.72 22598.77 31498.54 19899.78 2699.51 10597.06 23398.29 30599.64 17592.63 27898.89 33898.09 18093.16 33898.72 249
tfpnnormal97.84 23097.47 24698.98 17999.20 24499.22 12299.64 6299.61 3696.32 28698.27 30699.70 13993.35 25899.44 25095.69 30695.40 30498.27 332
ppachtmachnet_test97.49 27897.45 24997.61 31198.62 32995.24 32898.80 32099.46 17496.11 30698.22 30799.62 18796.45 15698.97 33293.77 33395.97 29198.61 302
our_test_397.65 26597.68 22597.55 31498.62 32994.97 33498.84 31699.30 26696.83 25198.19 30899.34 27797.01 13899.02 32095.00 32196.01 28698.64 283
LTVRE_ROB97.16 1298.02 20497.90 20198.40 25999.23 23696.80 28899.70 3999.60 4197.12 22598.18 30999.70 13991.73 29999.72 19798.39 15497.45 25298.68 264
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
ACMH97.28 898.10 19097.99 19098.44 25599.41 19196.96 28299.60 7799.56 5898.09 12298.15 31099.91 890.87 31499.70 20998.88 7897.45 25298.67 271
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MS-PatchMatch97.24 28697.32 27296.99 32698.45 33993.51 35598.82 31899.32 25997.41 20098.13 31199.30 28788.99 33499.56 23695.68 30799.80 8897.90 353
MVS97.28 28496.55 29399.48 11698.78 31198.95 15999.27 22999.39 21783.53 36398.08 31299.54 21596.97 13999.87 12694.23 32999.16 16099.63 132
PAPM97.59 26897.09 28499.07 16799.06 27498.26 22098.30 35399.10 29694.88 32698.08 31299.34 27796.27 16299.64 22589.87 35598.92 18399.31 192
OpenMVScopyleft96.50 1698.47 15698.12 17499.52 10899.04 27899.53 8599.82 1499.72 1194.56 33298.08 31299.88 1994.73 22099.98 797.47 23999.76 10399.06 213
gg-mvs-nofinetune96.17 30695.32 31498.73 22398.79 30898.14 22599.38 19594.09 37491.07 35598.07 31591.04 36989.62 33099.35 27096.75 28199.09 16998.68 264
test0.0.03 197.71 25697.42 25898.56 23898.41 34097.82 24298.78 32298.63 34197.34 20498.05 31698.98 32794.45 23298.98 32595.04 32097.15 26698.89 228
131498.68 14798.54 15199.11 16598.89 29598.65 18999.27 22999.49 13396.89 24697.99 31799.56 20797.72 11899.83 15297.74 21199.27 15498.84 231
DTE-MVSNet97.51 27497.19 28198.46 25198.63 32898.13 22699.84 1099.48 14696.68 25897.97 31899.67 16192.92 26598.56 34296.88 27892.60 34598.70 255
SixPastTwentyTwo97.50 27597.33 27198.03 28598.65 32696.23 30699.77 2898.68 34097.14 22297.90 31999.93 490.45 31799.18 29997.00 26796.43 27898.67 271
pm-mvs197.68 26097.28 27598.88 20199.06 27498.62 19299.50 13499.45 18696.32 28697.87 32099.79 9492.47 28399.35 27097.54 23293.54 33498.67 271
testgi97.65 26597.50 24398.13 28199.36 20496.45 29999.42 17699.48 14697.76 15997.87 32099.45 24691.09 31198.81 33994.53 32598.52 20399.13 201
EPNet_dtu98.03 20297.96 19498.23 27498.27 34195.54 32199.23 24398.75 32999.02 2097.82 32299.71 13596.11 16599.48 24193.04 34299.65 12799.69 107
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TinyColmap97.12 28896.89 28897.83 30199.07 27295.52 32298.57 33998.74 33297.58 17897.81 32399.79 9488.16 34499.56 23695.10 31897.21 26298.39 327
ACMH+97.24 1097.92 21997.78 21398.32 26699.46 18196.68 29299.56 10599.54 7598.41 8497.79 32499.87 2490.18 32399.66 21898.05 18897.18 26498.62 293
N_pmnet94.95 31995.83 30792.31 34598.47 33879.33 37299.12 26292.81 37893.87 33797.68 32599.13 31093.87 25099.01 32291.38 35096.19 28398.59 306
KD-MVS_2432*160094.62 32093.72 32697.31 31997.19 35995.82 31498.34 34999.20 28595.00 32497.57 32698.35 34787.95 34798.10 34892.87 34477.00 36798.01 344
miper_refine_blended94.62 32093.72 32697.31 31997.19 35995.82 31498.34 34999.20 28595.00 32497.57 32698.35 34787.95 34798.10 34892.87 34477.00 36798.01 344
PVSNet_094.43 1996.09 30895.47 31197.94 29399.31 21894.34 34597.81 36199.70 1597.12 22597.46 32898.75 33789.71 32799.79 17197.69 21881.69 36399.68 111
pmmvs696.53 29896.09 30197.82 30398.69 32395.47 32399.37 19899.47 16493.46 34397.41 32999.78 10187.06 35299.33 27496.92 27692.70 34498.65 281
new_pmnet96.38 30296.03 30297.41 31798.13 34595.16 33299.05 27799.20 28593.94 33697.39 33098.79 33491.61 30599.04 31690.43 35395.77 29498.05 342
CL-MVSNet_self_test94.49 32293.97 32596.08 33896.16 36293.67 35398.33 35199.38 22395.13 31997.33 33198.15 35192.69 27696.57 36588.67 35979.87 36597.99 347
IB-MVS95.67 1896.22 30395.44 31398.57 23699.21 24296.70 29098.65 33497.74 35896.71 25697.27 33298.54 34386.03 35499.92 8498.47 14986.30 35799.10 202
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
GG-mvs-BLEND98.45 25298.55 33598.16 22399.43 16993.68 37597.23 33398.46 34489.30 33299.22 29195.43 31298.22 21497.98 348
MVP-Stereo97.81 23797.75 21997.99 29197.53 35196.60 29598.96 30198.85 32497.22 21797.23 33399.36 27195.28 19599.46 24495.51 31099.78 9597.92 352
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous2024052196.20 30595.89 30697.13 32497.72 35094.96 33599.79 2599.29 27193.01 34697.20 33599.03 32089.69 32898.36 34591.16 35196.13 28498.07 340
TransMVSNet (Re)97.15 28796.58 29298.86 20899.12 26298.85 17299.49 14498.91 31895.48 31697.16 33699.80 8293.38 25799.11 31094.16 33191.73 34798.62 293
KD-MVS_self_test95.00 31794.34 32296.96 32897.07 36195.39 32699.56 10599.44 19595.11 32197.13 33797.32 35891.86 29597.27 36190.35 35481.23 36498.23 336
NR-MVSNet97.97 21497.61 23299.02 17498.87 30099.26 11899.47 15599.42 20597.63 17497.08 33899.50 22995.07 20399.13 30597.86 19993.59 33398.68 264
Anonymous2023120696.22 30396.03 30296.79 33397.31 35694.14 34699.63 6499.08 29996.17 29997.04 33999.06 31793.94 24897.76 35786.96 36595.06 31198.47 316
test_040296.64 29696.24 29897.85 29998.85 30496.43 30099.44 16399.26 27593.52 34196.98 34099.52 22288.52 34099.20 29892.58 34897.50 24797.93 351
MIMVSNet195.51 31295.04 31696.92 33097.38 35395.60 31799.52 12399.50 12593.65 34096.97 34199.17 30585.28 35796.56 36688.36 36195.55 30198.60 305
TDRefinement95.42 31494.57 32097.97 29289.83 37396.11 30999.48 15098.75 32996.74 25496.68 34299.88 1988.65 33899.71 20398.37 15882.74 36298.09 339
baseline297.87 22497.55 23698.82 21499.18 24998.02 22999.41 17896.58 36796.97 23996.51 34399.17 30593.43 25699.57 23597.71 21599.03 17498.86 229
pmmvs394.09 32693.25 32996.60 33594.76 36894.49 34198.92 30898.18 35189.66 35696.48 34498.06 35286.28 35397.33 36089.68 35687.20 35697.97 349
DeepMVS_CXcopyleft93.34 34399.29 22382.27 36999.22 28185.15 36196.33 34599.05 31890.97 31399.73 19393.57 33697.77 23298.01 344
LCM-MVSNet-Re97.83 23298.15 17196.87 33199.30 21992.25 36099.59 8498.26 34797.43 19796.20 34699.13 31096.27 16298.73 34198.17 17598.99 17899.64 128
test20.0396.12 30795.96 30496.63 33497.44 35295.45 32499.51 12899.38 22396.55 27096.16 34799.25 29793.76 25496.17 36787.35 36494.22 32598.27 332
K. test v397.10 28996.79 29098.01 28898.72 31996.33 30399.87 697.05 36297.59 17696.16 34799.80 8288.71 33699.04 31696.69 28696.55 27498.65 281
UnsupCasMVSNet_eth96.44 30096.12 30097.40 31898.65 32695.65 31699.36 20299.51 10597.13 22396.04 34998.99 32488.40 34198.17 34796.71 28490.27 35098.40 326
test_method91.10 32991.36 33290.31 34995.85 36373.72 37794.89 36699.25 27768.39 36995.82 35099.02 32280.50 36698.95 33493.64 33594.89 31698.25 334
lessismore_v097.79 30598.69 32395.44 32594.75 37295.71 35199.87 2488.69 33799.32 27695.89 30194.93 31598.62 293
Patchmatch-RL test95.84 31095.81 30895.95 33995.61 36490.57 36498.24 35498.39 34695.10 32395.20 35298.67 33994.78 21497.77 35696.28 29690.02 35199.51 163
ambc93.06 34492.68 36982.36 36898.47 34498.73 33795.09 35397.41 35555.55 37399.10 31296.42 29391.32 34897.71 354
PM-MVS92.96 32892.23 33195.14 34195.61 36489.98 36699.37 19898.21 34994.80 32895.04 35497.69 35365.06 37097.90 35494.30 32789.98 35297.54 358
OpenMVS_ROBcopyleft92.34 2094.38 32493.70 32896.41 33797.38 35393.17 35699.06 27598.75 32986.58 36094.84 35598.26 35081.53 36599.32 27689.01 35897.87 23096.76 360
EG-PatchMatch MVS95.97 30995.69 30996.81 33297.78 34992.79 35899.16 25498.93 31396.16 30194.08 35699.22 30082.72 36299.47 24295.67 30897.50 24798.17 337
pmmvs-eth3d95.34 31694.73 31897.15 32295.53 36695.94 31299.35 20899.10 29695.13 31993.55 35797.54 35488.15 34597.91 35394.58 32489.69 35397.61 355
new-patchmatchnet94.48 32394.08 32395.67 34095.08 36792.41 35999.18 25299.28 27394.55 33393.49 35897.37 35787.86 34997.01 36391.57 34988.36 35497.61 355
UnsupCasMVSNet_bld93.53 32792.51 33096.58 33697.38 35393.82 34898.24 35499.48 14691.10 35493.10 35996.66 36074.89 36898.37 34494.03 33287.71 35597.56 357
Gipumacopyleft90.99 33090.15 33393.51 34298.73 31790.12 36593.98 36799.45 18679.32 36592.28 36094.91 36369.61 36997.98 35287.42 36395.67 29892.45 366
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CMPMVSbinary69.68 2394.13 32594.90 31791.84 34697.24 35780.01 37198.52 34299.48 14689.01 35791.99 36199.67 16185.67 35699.13 30595.44 31197.03 26796.39 362
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PMMVS286.87 33185.37 33591.35 34890.21 37283.80 36798.89 31197.45 36183.13 36491.67 36295.03 36248.49 37594.70 36985.86 36777.62 36695.54 363
LCM-MVSNet86.80 33285.22 33691.53 34787.81 37480.96 37098.23 35698.99 30771.05 36790.13 36396.51 36148.45 37696.88 36490.51 35285.30 35896.76 360
ET-MVSNet_ETH3D96.49 29995.64 31099.05 17099.53 15698.82 17798.84 31697.51 36097.63 17484.77 36499.21 30392.09 29198.91 33698.98 6692.21 34699.41 184
E-PMN80.61 33679.88 33882.81 35390.75 37176.38 37597.69 36295.76 36966.44 37183.52 36592.25 36762.54 37287.16 37368.53 37261.40 37084.89 371
FPMVS84.93 33385.65 33482.75 35486.77 37563.39 37998.35 34898.92 31574.11 36683.39 36698.98 32750.85 37492.40 37184.54 36894.97 31392.46 365
EMVS80.02 33779.22 33982.43 35591.19 37076.40 37497.55 36492.49 37966.36 37283.01 36791.27 36864.63 37185.79 37465.82 37360.65 37185.08 370
YYNet195.36 31594.51 32197.92 29597.89 34797.10 26599.10 27099.23 28093.26 34580.77 36899.04 31992.81 26898.02 35094.30 32794.18 32698.64 283
MDA-MVSNet_test_wron95.45 31394.60 31998.01 28898.16 34497.21 26399.11 26899.24 27993.49 34280.73 36998.98 32793.02 26298.18 34694.22 33094.45 32198.64 283
MDA-MVSNet-bldmvs94.96 31893.98 32497.92 29598.24 34397.27 25899.15 25899.33 24993.80 33880.09 37099.03 32088.31 34297.86 35593.49 33794.36 32398.62 293
tmp_tt82.80 33481.52 33786.66 35066.61 38068.44 37892.79 36997.92 35468.96 36880.04 37199.85 3385.77 35596.15 36897.86 19943.89 37395.39 364
MVEpermissive76.82 2176.91 33974.31 34384.70 35185.38 37776.05 37696.88 36593.17 37667.39 37071.28 37289.01 37121.66 38287.69 37271.74 37172.29 36990.35 368
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high77.30 33874.86 34284.62 35275.88 37877.61 37397.63 36393.15 37788.81 35864.27 37389.29 37036.51 37783.93 37575.89 37052.31 37292.33 367
PMVScopyleft70.75 2275.98 34074.97 34179.01 35670.98 37955.18 38093.37 36898.21 34965.08 37361.78 37493.83 36521.74 38192.53 37078.59 36991.12 34989.34 369
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12339.01 34342.50 34528.53 35839.17 38120.91 38298.75 32519.17 38319.83 37638.57 37566.67 37333.16 37815.42 37737.50 37629.66 37549.26 372
testmvs39.17 34243.78 34425.37 35936.04 38216.84 38398.36 34726.56 38120.06 37538.51 37667.32 37229.64 37915.30 37837.59 37539.90 37443.98 373
wuyk23d40.18 34141.29 34636.84 35786.18 37649.12 38179.73 37022.81 38227.64 37425.46 37728.45 37721.98 38048.89 37655.80 37423.56 37612.51 374
EGC-MVSNET82.80 33477.86 34097.62 31097.91 34696.12 30899.33 21399.28 2738.40 37725.05 37899.27 29484.11 35999.33 27489.20 35798.22 21497.42 359
test_blank0.13 3470.17 3500.00 3600.00 3830.00 3840.00 3710.00 3840.00 3780.00 3791.57 3780.00 3830.00 3790.00 3770.00 3770.00 375
uanet_test0.02 3480.03 3510.00 3600.00 3830.00 3840.00 3710.00 3840.00 3780.00 3790.27 3790.00 3830.00 3790.00 3770.00 3770.00 375
cdsmvs_eth3d_5k24.64 34432.85 3470.00 3600.00 3830.00 3840.00 37199.51 1050.00 3780.00 37999.56 20796.58 1510.00 3790.00 3770.00 3770.00 375
pcd_1.5k_mvsjas8.27 34611.03 3490.00 3600.00 3830.00 3840.00 3710.00 3840.00 3780.00 3790.27 37999.01 190.00 3790.00 3770.00 3770.00 375
sosnet-low-res0.02 3480.03 3510.00 3600.00 3830.00 3840.00 3710.00 3840.00 3780.00 3790.27 3790.00 3830.00 3790.00 3770.00 3770.00 375
sosnet0.02 3480.03 3510.00 3600.00 3830.00 3840.00 3710.00 3840.00 3780.00 3790.27 3790.00 3830.00 3790.00 3770.00 3770.00 375
uncertanet0.02 3480.03 3510.00 3600.00 3830.00 3840.00 3710.00 3840.00 3780.00 3790.27 3790.00 3830.00 3790.00 3770.00 3770.00 375
Regformer0.02 3480.03 3510.00 3600.00 3830.00 3840.00 3710.00 3840.00 3780.00 3790.27 3790.00 3830.00 3790.00 3770.00 3770.00 375
ab-mvs-re8.30 34511.06 3480.00 3600.00 3830.00 3840.00 3710.00 3840.00 3780.00 37999.58 2000.00 3830.00 3790.00 3770.00 3770.00 375
uanet0.02 3480.03 3510.00 3600.00 3830.00 3840.00 3710.00 3840.00 3780.00 3790.27 3790.00 3830.00 3790.00 3770.00 3770.00 375
MSC_two_6792asdad99.87 1299.51 16099.76 4199.33 24999.96 2098.87 8299.84 6699.89 2
No_MVS99.87 1299.51 16099.76 4199.33 24999.96 2098.87 8299.84 6699.89 2
eth-test20.00 383
eth-test0.00 383
OPU-MVS99.64 8099.56 15299.72 4799.60 7799.70 13999.27 599.42 25598.24 16899.80 8899.79 61
save fliter99.76 5699.59 7399.14 26099.40 21399.00 27
test_0728_SECOND99.91 299.84 3399.89 499.57 9899.51 10599.96 2098.93 7299.86 5299.88 8
GSMVS99.52 157
sam_mvs194.86 21099.52 157
sam_mvs94.72 221
MTGPAbinary99.47 164
test_post199.23 24365.14 37594.18 24299.71 20397.58 225
test_post65.99 37494.65 22599.73 193
patchmatchnet-post98.70 33894.79 21399.74 186
MTMP99.54 11798.88 322
gm-plane-assit98.54 33692.96 35794.65 33199.15 30899.64 22597.56 230
test9_res97.49 23699.72 11199.75 77
agg_prior297.21 25399.73 11099.75 77
test_prior499.56 7898.99 293
test_prior99.68 6899.67 10999.48 9399.56 5899.83 15299.74 82
新几何299.01 291
旧先验199.74 7599.59 7399.54 7599.69 14898.47 8399.68 12299.73 89
无先验98.99 29399.51 10596.89 24699.93 7397.53 23399.72 95
原ACMM298.95 305
testdata299.95 4796.67 287
segment_acmp98.96 29
testdata198.85 31598.32 96
plane_prior799.29 22397.03 275
plane_prior699.27 22896.98 27992.71 274
plane_prior599.47 16499.69 21397.78 20697.63 23498.67 271
plane_prior499.61 191
plane_prior299.39 19098.97 36
plane_prior199.26 230
plane_prior96.97 28099.21 25098.45 8097.60 237
n20.00 384
nn0.00 384
door-mid98.05 352
test1199.35 238
door97.92 354
HQP5-MVS96.83 285
BP-MVS97.19 257
HQP3-MVS99.39 21797.58 239
HQP2-MVS92.47 283
NP-MVS99.23 23696.92 28399.40 260
ACMMP++_ref97.19 263
ACMMP++97.43 255
Test By Simon98.75 60