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

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

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

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

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




Method Infoallhigh-res
multi-view
indooroutdoorbotani.boulde.bridgedoorexhibi.lectur.living.loungeobserv.old co.statueterrac.
sort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
AdaColmapbinary97.23 13096.80 13898.51 13399.99 195.60 20299.09 33198.84 6593.32 20996.74 22399.72 9586.04 264100.00 198.01 15299.43 12999.94 87
CNVR-MVS99.40 199.26 199.84 799.98 299.51 799.98 2498.69 8298.20 999.93 399.98 296.82 26100.00 199.75 41100.00 199.99 26
TestfortrainingZip99.90 599.97 399.70 599.97 4298.89 5296.02 9999.99 199.96 397.97 5100.00 199.65 96100.00 1
MCST-MVS99.32 399.14 499.86 699.97 399.59 699.97 4298.64 9198.47 399.13 10799.92 1696.38 36100.00 199.74 43100.00 1100.00 1
mPP-MVS98.39 5698.20 5498.97 9399.97 396.92 13999.95 7598.38 18495.04 12498.61 14099.80 5993.39 117100.00 198.64 114100.00 199.98 57
CPTT-MVS97.64 11097.32 11498.58 12299.97 395.77 19199.96 5698.35 19089.90 35398.36 15599.79 6391.18 18099.99 3998.37 13099.99 2199.99 26
DP-MVS Recon98.41 5398.02 6899.56 3099.97 398.70 5399.92 10398.44 14892.06 28198.40 15499.84 4995.68 48100.00 198.19 14199.71 9199.97 67
PAPR98.52 4398.16 5899.58 2999.97 398.77 4799.95 7598.43 15695.35 11898.03 17099.75 8194.03 10299.98 5198.11 14699.83 8099.99 26
MED-MVS test99.60 2499.96 998.79 4299.97 4298.88 5596.36 9099.07 11299.93 12100.00 199.98 999.96 4699.99 26
MED-MVS99.24 899.11 799.60 2499.96 998.79 4299.97 4298.88 5596.91 6299.07 11299.92 1697.36 18100.00 199.98 999.96 46100.00 1
TestfortrainingZip a99.01 1698.78 2199.69 1799.96 999.09 2599.97 4298.74 7696.91 6299.86 1699.92 1696.29 3799.99 3998.32 13399.09 149100.00 1
HFP-MVS98.56 3998.37 4399.14 7399.96 997.43 11599.95 7598.61 9994.77 13499.31 9599.85 3894.22 95100.00 198.70 10999.98 3299.98 57
region2R98.54 4198.37 4399.05 8399.96 997.18 12599.96 5698.55 11994.87 13199.45 8199.85 3894.07 101100.00 198.67 111100.00 199.98 57
ACMMPR98.50 4498.32 4799.05 8399.96 997.18 12599.95 7598.60 10194.77 13499.31 9599.84 4993.73 111100.00 198.70 10999.98 3299.98 57
NCCC99.37 299.25 299.71 1699.96 999.15 2399.97 4298.62 9898.02 2299.90 799.95 497.33 19100.00 199.54 58100.00 1100.00 1
CP-MVS98.45 4898.32 4798.87 9899.96 996.62 15499.97 4298.39 18094.43 15298.90 12199.87 3294.30 92100.00 199.04 8599.99 2199.99 26
test_one_060199.94 1799.30 1398.41 17396.63 7599.75 4299.93 1297.49 11
test_0728_SECOND99.82 899.94 1799.47 899.95 7598.43 156100.00 199.99 5100.00 1100.00 1
XVS98.70 3298.55 3199.15 7199.94 1797.50 11199.94 9398.42 16896.22 9399.41 8799.78 6794.34 8999.96 7698.92 9499.95 5399.99 26
X-MVStestdata93.83 28892.06 32399.15 7199.94 1797.50 11199.94 9398.42 16896.22 9399.41 8741.37 54294.34 8999.96 7698.92 9499.95 5399.99 26
test_prior99.43 4199.94 1798.49 6698.65 8899.80 14399.99 26
MSLP-MVS++99.13 999.01 1299.49 3799.94 1798.46 6799.98 2498.86 5997.10 5399.80 2899.94 595.92 44100.00 199.51 59100.00 1100.00 1
APDe-MVScopyleft99.06 1398.91 1599.51 3499.94 1798.76 5099.91 11198.39 18097.20 5199.46 8099.85 3895.53 5299.79 14599.86 27100.00 199.99 26
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MP-MVScopyleft98.23 7197.97 7299.03 8599.94 1797.17 12899.95 7598.39 18094.70 13898.26 16199.81 5891.84 171100.00 198.85 10099.97 4299.93 88
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS98.65 3598.36 4599.49 3799.94 1798.73 5199.87 13398.33 19593.97 17899.76 4199.87 3294.99 6899.75 15498.55 118100.00 199.98 57
PAPM_NR98.12 7597.93 7898.70 10999.94 1796.13 18099.82 16698.43 15694.56 14297.52 18999.70 10194.40 8499.98 5197.00 19699.98 3299.99 26
MG-MVS98.91 2298.65 2799.68 1899.94 1799.07 2699.64 23999.44 1997.33 4499.00 11799.72 9594.03 10299.98 5198.73 108100.00 1100.00 1
ME-MVS99.07 1198.89 1799.59 2799.93 2898.79 4299.95 7598.80 7195.89 10399.28 9999.93 1296.28 3899.98 5199.98 999.96 4699.99 26
SED-MVS99.28 599.11 799.77 999.93 2899.30 1399.96 5698.43 15697.27 4799.80 2899.94 596.71 29100.00 1100.00 1100.00 1100.00 1
IU-MVS99.93 2899.31 1198.41 17397.71 3199.84 23100.00 1100.00 1100.00 1
test_241102_ONE99.93 2899.30 1398.43 15697.26 4999.80 2899.88 2996.71 29100.00 1
DVP-MVScopyleft99.30 499.16 399.73 1399.93 2899.29 1699.95 7598.32 19797.28 4599.83 2499.91 1997.22 21100.00 199.99 5100.00 199.89 97
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
test072699.93 2899.29 1699.96 5698.42 16897.28 4599.86 1699.94 597.22 21
MSP-MVS99.09 1099.12 598.98 9299.93 2897.24 12299.95 7598.42 16897.50 3899.52 7699.88 2997.43 1799.71 16099.50 6199.98 32100.00 1
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
agg_prior99.93 2898.77 4798.43 15699.63 5999.85 130
FOURS199.92 3697.66 10599.95 7598.36 18895.58 11299.52 76
ZD-MVS99.92 3698.57 6198.52 12892.34 26999.31 9599.83 5195.06 6399.80 14399.70 4999.97 42
GST-MVS98.27 6397.97 7299.17 6699.92 3697.57 10799.93 10098.39 18094.04 17698.80 12699.74 8892.98 133100.00 198.16 14399.76 8899.93 88
TEST999.92 3698.92 3199.96 5698.43 15693.90 18499.71 4999.86 3495.88 4599.85 130
train_agg98.88 2398.65 2799.59 2799.92 3698.92 3199.96 5698.43 15694.35 15799.71 4999.86 3495.94 4299.85 13099.69 5099.98 3299.99 26
test_899.92 3698.88 3499.96 5698.43 15694.35 15799.69 5199.85 3895.94 4299.85 130
PGM-MVS98.34 5898.13 6098.99 9099.92 3697.00 13599.75 19899.50 1793.90 18499.37 9299.76 7393.24 126100.00 197.75 17399.96 4699.98 57
ACMMPcopyleft97.74 10397.44 10798.66 11399.92 3696.13 18099.18 32399.45 1894.84 13296.41 24299.71 9891.40 17499.99 3997.99 15498.03 19099.87 100
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
DVP-MVS++99.26 699.09 1099.77 999.91 4499.31 1199.95 7598.43 15696.48 8099.80 2899.93 1297.44 15100.00 199.92 1699.98 32100.00 1
MSC_two_6792asdad99.93 299.91 4499.80 298.41 173100.00 199.96 12100.00 1100.00 1
No_MVS99.93 299.91 4499.80 298.41 173100.00 199.96 12100.00 1100.00 1
HPM-MVS++copyleft99.07 1198.88 1899.63 1999.90 4799.02 2799.95 7598.56 11397.56 3799.44 8299.85 3895.38 56100.00 199.31 7199.99 2199.87 100
APD-MVScopyleft98.62 3698.35 4699.41 4499.90 4798.51 6499.87 13398.36 18894.08 17199.74 4599.73 9294.08 10099.74 15699.42 6799.99 2199.99 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast96.59 198.81 2698.54 3299.62 2299.90 4798.85 3799.24 31898.47 14098.14 1699.08 11099.91 1993.09 130100.00 199.04 8599.99 21100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OPU-MVS99.93 299.89 5099.80 299.96 5699.80 5997.44 15100.00 1100.00 199.98 32100.00 1
DPE-MVScopyleft99.26 699.10 999.74 1299.89 5099.24 2099.87 13398.44 14897.48 3999.64 5899.94 596.68 3199.99 3999.99 5100.00 199.99 26
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part299.89 5099.25 1999.49 79
CSCG97.10 13697.04 12697.27 24299.89 5091.92 33899.90 11799.07 3788.67 37795.26 27599.82 5493.17 12999.98 5198.15 14499.47 12499.90 96
ZNCC-MVS98.31 6098.03 6799.17 6699.88 5497.59 10699.94 9398.44 14894.31 16098.50 14799.82 5493.06 13199.99 3998.30 13599.99 2199.93 88
SR-MVS98.46 4798.30 5098.93 9699.88 5497.04 13499.84 15298.35 19094.92 12899.32 9499.80 5993.35 11999.78 14799.30 7299.95 5399.96 75
9.1498.38 4199.87 5699.91 11198.33 19593.22 21299.78 3999.89 2794.57 8099.85 13099.84 2999.97 42
SMA-MVScopyleft98.76 2998.48 3599.62 2299.87 5698.87 3599.86 14498.38 18493.19 21499.77 4099.94 595.54 50100.00 199.74 4399.99 21100.00 1
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
NormalMVS97.90 8597.85 8598.04 16699.86 5895.39 21299.61 24697.78 27096.52 7898.61 14099.31 15792.73 14199.67 16896.77 21299.48 12199.06 253
lecture98.67 3398.46 3699.28 5399.86 5897.88 9299.97 4299.25 3096.07 9799.79 3799.70 10192.53 15099.98 5199.51 5999.48 12199.97 67
PHI-MVS98.41 5398.21 5399.03 8599.86 5897.10 13299.98 2498.80 7190.78 33199.62 6299.78 6795.30 57100.00 199.80 3299.93 6499.99 26
MTAPA98.29 6297.96 7599.30 5299.85 6197.93 9099.39 29198.28 20495.76 10697.18 20499.88 2992.74 140100.00 198.67 11199.88 7699.99 26
LS3D95.84 21195.11 22898.02 16799.85 6195.10 23298.74 38198.50 13787.22 40293.66 29899.86 3487.45 23999.95 8590.94 33499.81 8699.02 261
HPM-MVScopyleft97.96 8097.72 9098.68 11099.84 6396.39 16699.90 11798.17 22292.61 25098.62 13999.57 13191.87 17099.67 16898.87 9999.99 2199.99 26
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EI-MVSNet-Vis-set98.27 6398.11 6298.75 10699.83 6496.59 15899.40 28798.51 13195.29 12098.51 14699.76 7393.60 11599.71 16098.53 12199.52 11499.95 83
save fliter99.82 6598.79 4299.96 5698.40 17797.66 33
PLCcopyleft95.54 397.93 8397.89 8298.05 16599.82 6594.77 24499.92 10398.46 14293.93 18197.20 20299.27 16595.44 5599.97 6497.41 18099.51 11799.41 198
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
APD-MVS_3200maxsize98.25 6898.08 6498.78 10399.81 6796.60 15699.82 16698.30 20293.95 18099.37 9299.77 7192.84 13799.76 15398.95 9099.92 6799.97 67
EI-MVSNet-UG-set98.14 7497.99 7098.60 11899.80 6896.27 16999.36 29798.50 13795.21 12298.30 15899.75 8193.29 12399.73 15998.37 13099.30 13899.81 109
SR-MVS-dyc-post98.31 6098.17 5798.71 10899.79 6996.37 16799.76 19298.31 19994.43 15299.40 8999.75 8193.28 12499.78 14798.90 9799.92 6799.97 67
RE-MVS-def98.13 6099.79 6996.37 16799.76 19298.31 19994.43 15299.40 8999.75 8192.95 13498.90 9799.92 6799.97 67
HPM-MVS_fast97.80 9797.50 10398.68 11099.79 6996.42 16299.88 13098.16 22791.75 29398.94 11999.54 13491.82 17299.65 17297.62 17799.99 2199.99 26
SF-MVS98.67 3398.40 3999.50 3599.77 7298.67 5499.90 11798.21 21793.53 19699.81 2699.89 2794.70 7699.86 12999.84 2999.93 6499.96 75
MGCNet99.06 1398.84 1999.72 1499.76 7399.21 2299.99 899.34 2598.70 299.44 8299.75 8193.24 12699.99 3999.94 1499.41 13199.95 83
旧先验199.76 7397.52 10998.64 9199.85 3895.63 4999.94 5899.99 26
OMC-MVS97.28 12697.23 11897.41 23199.76 7393.36 30499.65 23597.95 24996.03 9897.41 19599.70 10189.61 20699.51 17896.73 21498.25 18099.38 201
新几何199.42 4399.75 7698.27 7198.63 9792.69 24599.55 7199.82 5494.40 84100.00 191.21 32699.94 5899.99 26
MP-MVS-pluss98.07 7897.64 9699.38 4999.74 7798.41 6999.74 20298.18 22193.35 20796.45 23599.85 3892.64 14599.97 6498.91 9699.89 7399.77 116
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.98.93 2098.77 2299.41 4499.74 7798.67 5499.77 18698.38 18496.73 7199.88 1399.74 8894.89 7099.59 17499.80 3299.98 3299.97 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test1299.43 4199.74 7798.56 6298.40 17799.65 5594.76 7399.75 15499.98 3299.99 26
原ACMM198.96 9499.73 8096.99 13698.51 13194.06 17499.62 6299.85 3894.97 6999.96 7695.11 24699.95 5399.92 93
TSAR-MVS + GP.98.60 3798.51 3498.86 9999.73 8096.63 15399.97 4297.92 25498.07 1998.76 13299.55 13295.00 6799.94 9499.91 1997.68 19799.99 26
CANet98.27 6397.82 8799.63 1999.72 8299.10 2499.98 2498.51 13197.00 5998.52 14499.71 9887.80 23099.95 8599.75 4199.38 13399.83 105
reproduce_model98.75 3098.66 2699.03 8599.71 8397.10 13299.73 20998.23 21297.02 5899.18 10599.90 2394.54 8199.99 3999.77 3799.90 7299.99 26
F-COLMAP96.93 14896.95 12996.87 26099.71 8391.74 34899.85 14797.95 24993.11 22295.72 26499.16 18592.35 15699.94 9495.32 24299.35 13698.92 269
reproduce-ours98.78 2798.67 2499.09 8099.70 8597.30 11999.74 20298.25 20897.10 5399.10 10899.90 2394.59 7799.99 3999.77 3799.91 7099.99 26
our_new_method98.78 2798.67 2499.09 8099.70 8597.30 11999.74 20298.25 20897.10 5399.10 10899.90 2394.59 7799.99 3999.77 3799.91 7099.99 26
SD-MVS98.92 2198.70 2399.56 3099.70 8598.73 5199.94 9398.34 19496.38 8699.81 2699.76 7394.59 7799.98 5199.84 2999.96 4699.97 67
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
patch_mono-298.24 6999.12 595.59 30499.67 8886.91 43599.95 7598.89 5297.60 3499.90 799.76 7396.54 3499.98 5199.94 1499.82 8499.88 98
ACMMP_NAP98.49 4598.14 5999.54 3299.66 8998.62 6099.85 14798.37 18794.68 13999.53 7499.83 5192.87 136100.00 198.66 11399.84 7999.99 26
DeepPCF-MVS95.94 297.71 10798.98 1393.92 37799.63 9081.76 47099.96 5698.56 11399.47 199.19 10499.99 194.16 99100.00 199.92 1699.93 64100.00 1
EPNet98.49 4598.40 3998.77 10599.62 9196.80 14799.90 11799.51 1697.60 3499.20 10299.36 15293.71 11299.91 11197.99 15498.71 16599.61 151
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MM98.83 2498.53 3399.76 1199.59 9299.33 999.99 899.76 698.39 499.39 9199.80 5990.49 19599.96 7699.89 2199.43 12999.98 57
PVSNet_BlendedMVS96.05 20195.82 19396.72 26699.59 9296.99 13699.95 7599.10 3494.06 17498.27 15995.80 37289.00 21899.95 8599.12 7987.53 36193.24 435
PVSNet_Blended97.94 8297.64 9698.83 10099.59 9296.99 136100.00 199.10 3495.38 11798.27 15999.08 19089.00 21899.95 8599.12 7999.25 14099.57 162
PatchMatch-RL96.04 20295.40 21197.95 17099.59 9295.22 22699.52 26899.07 3793.96 17996.49 23398.35 28482.28 32599.82 14290.15 35099.22 14398.81 277
dcpmvs_297.42 12198.09 6395.42 31199.58 9687.24 43199.23 31996.95 40594.28 16398.93 12099.73 9294.39 8799.16 20799.89 2199.82 8499.86 102
test22299.55 9797.41 11799.34 29998.55 11991.86 28799.27 10099.83 5193.84 10999.95 5399.99 26
CNLPA97.76 10197.38 11098.92 9799.53 9896.84 14199.87 13398.14 23193.78 18896.55 23199.69 10592.28 15899.98 5197.13 19199.44 12899.93 88
API-MVS97.86 8897.66 9498.47 13599.52 9995.41 21099.47 27898.87 5891.68 29598.84 12399.85 3892.34 15799.99 3998.44 12699.96 46100.00 1
PVSNet91.05 1397.13 13596.69 14498.45 13899.52 9995.81 18999.95 7599.65 1294.73 13699.04 11599.21 17784.48 30099.95 8594.92 25298.74 16499.58 160
114514_t97.41 12296.83 13599.14 7399.51 10197.83 9499.89 12798.27 20688.48 38299.06 11499.66 11690.30 19899.64 17396.32 22699.97 4299.96 75
cl2293.77 29393.25 29395.33 31599.49 10294.43 25699.61 24698.09 23490.38 34289.16 37095.61 38190.56 19397.34 35491.93 31784.45 38494.21 373
testdata98.42 14299.47 10395.33 21698.56 11393.78 18899.79 3799.85 3893.64 11499.94 9494.97 25099.94 58100.00 1
MAR-MVS97.43 11797.19 12098.15 15899.47 10394.79 24399.05 34298.76 7392.65 24898.66 13799.82 5488.52 22499.98 5198.12 14599.63 9899.67 133
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
DP-MVS94.54 26193.42 28397.91 17699.46 10594.04 27598.93 36097.48 30781.15 45990.04 34199.55 13287.02 24799.95 8588.97 36598.11 18699.73 120
MVS_111021_LR98.42 5298.38 4198.53 13099.39 10695.79 19099.87 13399.86 296.70 7298.78 12799.79 6392.03 16799.90 11399.17 7899.86 7899.88 98
CHOSEN 280x42099.01 1699.03 1198.95 9599.38 10798.87 3598.46 40099.42 2197.03 5799.02 11699.09 18999.35 298.21 31699.73 4599.78 8799.77 116
MVS_111021_HR98.72 3198.62 2999.01 8999.36 10897.18 12599.93 10099.90 196.81 6998.67 13699.77 7193.92 10499.89 11899.27 7499.94 5899.96 75
fmvsm_s_conf0.5_n_1198.03 7997.89 8298.46 13799.35 10997.76 9899.99 898.04 24098.20 999.90 799.78 6786.21 26299.95 8599.89 2199.68 9397.65 316
DPM-MVS98.83 2498.46 3699.97 199.33 11099.92 199.96 5698.44 14897.96 2399.55 7199.94 597.18 23100.00 193.81 28399.94 5899.98 57
TAPA-MVS92.12 894.42 26993.60 27596.90 25999.33 11091.78 34799.78 18098.00 24389.89 35494.52 28399.47 13891.97 16899.18 20469.90 48299.52 11499.73 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs95.38 23395.07 23096.32 28199.32 11296.60 15699.76 19298.85 6296.65 7487.83 39996.05 36999.52 198.11 32196.58 21881.07 41394.25 366
fmvsm_s_conf0.5_n_998.15 7398.02 6898.55 12499.28 11395.84 18899.99 898.57 10798.17 1399.93 399.74 8887.04 24699.97 6499.86 2799.59 10899.83 105
SPE-MVS-test97.88 8697.94 7797.70 19599.28 11395.20 22799.98 2497.15 36495.53 11499.62 6299.79 6392.08 16698.38 29898.75 10799.28 13999.52 173
test_fmvsm_n_192098.44 4998.61 3097.92 17499.27 11595.18 228100.00 198.90 5098.05 2099.80 2899.73 9292.64 14599.99 3999.58 5799.51 11798.59 287
fmvsm_s_conf0.5_n_1098.24 6997.90 8099.26 5599.24 11697.88 9299.99 898.76 7398.20 999.92 599.74 8885.97 26699.94 9499.72 4699.53 11399.96 75
fmvsm_l_conf0.5_n_a99.00 1898.91 1599.28 5399.21 11797.91 9199.98 2498.85 6298.25 599.92 599.75 8194.72 7499.97 6499.87 2599.64 9799.95 83
fmvsm_s_conf0.5_n_898.38 5798.05 6699.35 5099.20 11898.12 7799.98 2498.81 6798.22 799.80 2899.71 9887.37 24199.97 6499.91 1999.48 12199.97 67
test_yl97.83 9297.37 11199.21 6099.18 11997.98 8699.64 23999.27 2791.43 30497.88 17998.99 20795.84 4699.84 13898.82 10195.32 28899.79 112
DCV-MVSNet97.83 9297.37 11199.21 6099.18 11997.98 8699.64 23999.27 2791.43 30497.88 17998.99 20795.84 4699.84 13898.82 10195.32 28899.79 112
fmvsm_l_conf0.5_n98.94 1998.84 1999.25 5699.17 12197.81 9699.98 2498.86 5998.25 599.90 799.76 7394.21 9799.97 6499.87 2599.52 11499.98 57
DeepC-MVS94.51 496.92 14996.40 15998.45 13899.16 12295.90 18699.66 23498.06 23796.37 8994.37 28999.49 13783.29 31899.90 11397.63 17699.61 10499.55 164
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS98.54 4198.22 5299.50 3599.15 12398.65 58100.00 198.58 10597.70 3298.21 16599.24 17392.58 14899.94 9498.63 11699.94 5899.92 93
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
fmvsm_l_conf0.5_n_398.41 5398.08 6499.39 4699.12 12498.29 7099.98 2498.64 9198.14 1699.86 1699.76 7387.99 22999.97 6499.72 4699.54 11199.91 95
fmvsm_l_conf0.5_n_998.55 4098.23 5199.49 3799.10 12598.50 6599.99 898.70 8098.14 1699.94 299.68 11289.02 21799.98 5199.89 2199.61 10499.99 26
CS-MVS97.79 9997.91 7997.43 22799.10 12594.42 25799.99 897.10 37895.07 12399.68 5299.75 8192.95 13498.34 30298.38 12899.14 14599.54 168
Anonymous20240521193.10 31191.99 32496.40 27799.10 12589.65 40098.88 36697.93 25183.71 44294.00 29598.75 24568.79 43999.88 12495.08 24791.71 32199.68 131
fmvsm_s_conf0.5_n97.80 9797.85 8597.67 19699.06 12894.41 25899.98 2498.97 4397.34 4299.63 5999.69 10587.27 24299.97 6499.62 5599.06 15198.62 286
HyFIR lowres test96.66 16696.43 15697.36 23699.05 12993.91 28199.70 22599.80 390.54 33796.26 24598.08 29692.15 16498.23 31596.84 20695.46 28399.93 88
LFMVS94.75 25593.56 27898.30 14899.03 13095.70 19698.74 38197.98 24687.81 39598.47 14899.39 14967.43 44899.53 17598.01 15295.20 29199.67 133
fmvsm_s_conf0.5_n_497.75 10297.86 8497.42 22899.01 13194.69 24799.97 4298.76 7397.91 2599.87 1499.76 7386.70 25399.93 10499.67 5299.12 14897.64 317
fmvsm_s_conf0.5_n_297.59 11297.28 11598.53 13099.01 13198.15 7299.98 2498.59 10398.17 1399.75 4299.63 12281.83 33199.94 9499.78 3598.79 16297.51 325
AllTest92.48 32891.64 33195.00 32499.01 13188.43 41898.94 35896.82 42086.50 41288.71 37598.47 27974.73 41399.88 12485.39 41296.18 25796.71 331
TestCases95.00 32499.01 13188.43 41896.82 42086.50 41288.71 37598.47 27974.73 41399.88 12485.39 41296.18 25796.71 331
COLMAP_ROBcopyleft90.47 1492.18 33591.49 33794.25 35899.00 13588.04 42498.42 40696.70 42782.30 45488.43 38799.01 20076.97 38899.85 13086.11 40896.50 24894.86 342
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_397.95 8197.66 9498.81 10198.99 13698.07 8099.98 2498.81 6798.18 1299.89 1199.70 10184.15 30499.97 6499.76 4099.50 11998.39 294
test_fmvs195.35 23495.68 20094.36 35498.99 13684.98 44699.96 5696.65 42997.60 3499.73 4798.96 21371.58 42999.93 10498.31 13499.37 13498.17 300
HY-MVS92.50 797.79 9997.17 12299.63 1998.98 13899.32 1097.49 43599.52 1495.69 10998.32 15797.41 31793.32 12199.77 15098.08 14995.75 27399.81 109
VNet97.21 13196.57 14999.13 7798.97 13997.82 9599.03 34599.21 3294.31 16099.18 10598.88 22686.26 26199.89 11898.93 9294.32 30199.69 130
thres20096.96 14596.21 16799.22 5998.97 13998.84 3899.85 14799.71 793.17 21696.26 24598.88 22689.87 20399.51 17894.26 27194.91 29399.31 219
tfpn200view996.79 15495.99 17699.19 6298.94 14198.82 3999.78 18099.71 792.86 23296.02 25598.87 23389.33 21099.50 18093.84 28094.57 29799.27 228
thres40096.78 15695.99 17699.16 6998.94 14198.82 3999.78 18099.71 792.86 23296.02 25598.87 23389.33 21099.50 18093.84 28094.57 29799.16 241
sasdasda97.09 13896.32 16199.39 4698.93 14398.95 2999.72 21397.35 32194.45 14897.88 17999.42 14286.71 25199.52 17698.48 12393.97 30799.72 122
Anonymous2023121189.86 38688.44 39494.13 36698.93 14390.68 37898.54 39798.26 20776.28 47786.73 41395.54 38570.60 43597.56 34790.82 33780.27 42294.15 382
canonicalmvs97.09 13896.32 16199.39 4698.93 14398.95 2999.72 21397.35 32194.45 14897.88 17999.42 14286.71 25199.52 17698.48 12393.97 30799.72 122
SDMVSNet94.80 25093.96 26597.33 23998.92 14695.42 20999.59 25198.99 4092.41 26592.55 31397.85 30875.81 40398.93 22197.90 16191.62 32297.64 317
sd_testset93.55 30092.83 30495.74 30298.92 14690.89 37498.24 41398.85 6292.41 26592.55 31397.85 30871.07 43498.68 26193.93 27791.62 32297.64 317
EPNet_dtu95.71 22295.39 21296.66 26898.92 14693.41 30099.57 25798.90 5096.19 9597.52 18998.56 26992.65 14497.36 35277.89 46398.33 17599.20 238
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS98.10 7697.60 9899.60 2498.92 14699.28 1899.89 12799.52 1495.58 11298.24 16399.39 14993.33 12099.74 15697.98 15695.58 28299.78 115
CHOSEN 1792x268896.81 15396.53 15097.64 20098.91 15093.07 30799.65 23599.80 395.64 11095.39 27198.86 23584.35 30299.90 11396.98 19899.16 14499.95 83
thres100view90096.74 16195.92 18899.18 6398.90 15198.77 4799.74 20299.71 792.59 25295.84 25898.86 23589.25 21299.50 18093.84 28094.57 29799.27 228
thres600view796.69 16495.87 19299.14 7398.90 15198.78 4699.74 20299.71 792.59 25295.84 25898.86 23589.25 21299.50 18093.44 29394.50 30099.16 241
MSDG94.37 27193.36 29097.40 23298.88 15393.95 28099.37 29597.38 31685.75 42390.80 33299.17 18284.11 30699.88 12486.35 40498.43 17398.36 296
MGCFI-Net97.00 14396.22 16699.34 5198.86 15498.80 4199.67 23397.30 33394.31 16097.77 18599.41 14686.36 25999.50 18098.38 12893.90 30999.72 122
h-mvs3394.92 24794.36 25096.59 27098.85 15591.29 36698.93 36098.94 4495.90 10198.77 12998.42 28290.89 18899.77 15097.80 16670.76 46798.72 283
Anonymous2024052992.10 33690.65 34896.47 27298.82 15690.61 38098.72 38398.67 8775.54 48193.90 29798.58 26766.23 45399.90 11394.70 26190.67 32598.90 272
PVSNet_Blended_VisFu97.27 12796.81 13798.66 11398.81 15796.67 15299.92 10398.64 9194.51 14496.38 24398.49 27589.05 21699.88 12497.10 19398.34 17499.43 194
PS-MVSNAJ98.44 4998.20 5499.16 6998.80 15898.92 3199.54 26698.17 22297.34 4299.85 2099.85 3891.20 17799.89 11899.41 6899.67 9498.69 284
CANet_DTU96.76 15796.15 17098.60 11898.78 15997.53 10899.84 15297.63 28497.25 5099.20 10299.64 11981.36 33799.98 5192.77 30598.89 15698.28 298
mvsany_test197.82 9597.90 8097.55 21198.77 16093.04 31099.80 17497.93 25196.95 6199.61 6999.68 11290.92 18599.83 14099.18 7798.29 17999.80 111
alignmvs97.81 9697.33 11399.25 5698.77 16098.66 5699.99 898.44 14894.40 15698.41 15299.47 13893.65 11399.42 19098.57 11794.26 30399.67 133
SymmetryMVS97.64 11097.46 10498.17 15498.74 16295.39 21299.61 24699.26 2996.52 7898.61 14099.31 15792.73 14199.67 16896.77 21295.63 28099.45 190
SteuartSystems-ACMMP99.02 1598.97 1499.18 6398.72 16397.71 10099.98 2498.44 14896.85 6499.80 2899.91 1997.57 999.85 13099.44 6699.99 2199.99 26
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base98.23 7197.97 7299.02 8898.69 16498.66 5699.52 26898.08 23697.05 5699.86 1699.86 3490.65 19099.71 16099.39 7098.63 16698.69 284
miper_enhance_ethall94.36 27393.98 26495.49 30598.68 16595.24 22499.73 20997.29 34193.28 21189.86 34695.97 37094.37 8897.05 37592.20 30984.45 38494.19 374
fmvsm_s_conf0.5_n_598.08 7797.71 9299.17 6698.67 16697.69 10499.99 898.57 10797.40 4099.89 1199.69 10585.99 26599.96 7699.80 3299.40 13299.85 103
ETVMVS97.03 14296.64 14598.20 15398.67 16697.12 12999.89 12798.57 10791.10 31798.17 16698.59 26493.86 10898.19 31795.64 23995.24 29099.28 226
test250697.53 11497.19 12098.58 12298.66 16896.90 14098.81 37599.77 594.93 12697.95 17398.96 21392.51 15199.20 20294.93 25198.15 18399.64 139
ECVR-MVScopyleft95.66 22595.05 23197.51 21698.66 16893.71 28598.85 37298.45 14394.93 12696.86 21698.96 21375.22 40999.20 20295.34 24198.15 18399.64 139
BridgeMVS98.27 6397.99 7099.11 7898.64 17098.43 6899.47 27897.79 26694.56 14299.74 4598.35 28494.33 9199.25 19699.12 7999.96 4699.64 139
fmvsm_s_conf0.5_n_a97.73 10597.72 9097.77 18998.63 17194.26 26699.96 5698.92 4997.18 5299.75 4299.69 10587.00 24899.97 6499.46 6498.89 15699.08 251
MVSMamba_PlusPlus97.83 9297.45 10698.99 9098.60 17298.15 7299.58 25397.74 27590.34 34599.26 10198.32 28794.29 9399.23 19799.03 8899.89 7399.58 160
balanced_ft_v196.88 15096.52 15197.96 16998.60 17294.94 23699.41 28697.56 29693.53 19699.42 8697.89 30783.33 31799.31 19399.29 7399.62 9999.64 139
testing22297.08 14196.75 14098.06 16498.56 17496.82 14299.85 14798.61 9992.53 26098.84 12398.84 23993.36 11898.30 30795.84 23594.30 30299.05 255
test111195.57 22894.98 23497.37 23498.56 17493.37 30398.86 37098.45 14394.95 12596.63 22598.95 21875.21 41099.11 20895.02 24898.14 18599.64 139
MVSTER95.53 22995.22 22396.45 27598.56 17497.72 9999.91 11197.67 28092.38 26891.39 32397.14 32497.24 2097.30 35994.80 25787.85 35494.34 361
testing3-297.72 10697.43 10998.60 11898.55 17797.11 131100.00 199.23 3193.78 18897.90 17598.73 24795.50 5399.69 16498.53 12194.63 29598.99 263
VDD-MVS93.77 29392.94 30296.27 28298.55 17790.22 38998.77 38097.79 26690.85 32396.82 22099.42 14261.18 47399.77 15098.95 9094.13 30498.82 276
tpmvs94.28 27593.57 27796.40 27798.55 17791.50 36495.70 47498.55 11987.47 39792.15 31694.26 43991.42 17398.95 22088.15 38295.85 26898.76 279
UGNet95.33 23594.57 24697.62 20498.55 17794.85 23898.67 38999.32 2695.75 10796.80 22296.27 35972.18 42699.96 7694.58 26499.05 15298.04 305
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
PCF-MVS94.20 595.18 23894.10 25898.43 14098.55 17795.99 18497.91 42897.31 33290.35 34489.48 35999.22 17485.19 28299.89 11890.40 34798.47 17299.41 198
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-2895.95 20596.49 15294.34 35598.51 18289.99 39499.39 29198.57 10793.14 21997.33 19898.31 28993.44 11694.68 46493.69 29095.98 26298.34 297
UWE-MVS96.79 15496.72 14297.00 25398.51 18293.70 28699.71 21898.60 10192.96 22797.09 20698.34 28696.67 3398.85 22892.11 31596.50 24898.44 292
myMVS_eth3d2897.86 8897.59 10098.68 11098.50 18497.26 12199.92 10398.55 11993.79 18798.26 16198.75 24595.20 5899.48 18698.93 9296.40 25199.29 224
test_vis1_n_192095.44 23195.31 21995.82 29998.50 18488.74 41299.98 2497.30 33397.84 2899.85 2099.19 18066.82 45199.97 6498.82 10199.46 12698.76 279
BH-w/o95.71 22295.38 21796.68 26798.49 18692.28 32999.84 15297.50 30592.12 27892.06 31998.79 24384.69 29598.67 26395.29 24399.66 9599.09 249
baseline195.78 21894.86 23798.54 12898.47 18798.07 8099.06 33897.99 24492.68 24694.13 29498.62 26193.28 12498.69 26093.79 28585.76 37198.84 275
fmvsm_s_conf0.5_n_797.70 10897.74 8997.59 20998.44 18895.16 23099.97 4298.65 8897.95 2499.62 6299.78 6786.09 26399.94 9499.69 5099.50 11997.66 315
EPMVS96.53 17596.01 17598.09 16298.43 18996.12 18296.36 46199.43 2093.53 19697.64 18795.04 41394.41 8398.38 29891.13 32898.11 18699.75 118
kuosan93.17 30892.60 31094.86 33198.40 19089.54 40298.44 40298.53 12684.46 43788.49 38297.92 30490.57 19297.05 37583.10 42993.49 31297.99 306
WBMVS94.52 26494.03 26295.98 28998.38 19196.68 15199.92 10397.63 28490.75 33289.64 35495.25 40696.77 2796.90 38894.35 26983.57 39194.35 359
UBG97.84 9197.69 9398.29 14998.38 19196.59 15899.90 11798.53 12693.91 18398.52 14498.42 28296.77 2799.17 20598.54 11996.20 25699.11 248
sss97.57 11397.03 12799.18 6398.37 19398.04 8399.73 20999.38 2293.46 20198.76 13299.06 19491.21 17699.89 11896.33 22597.01 23499.62 147
testing1197.48 11697.27 11698.10 16198.36 19496.02 18399.92 10398.45 14393.45 20398.15 16798.70 25195.48 5499.22 19897.85 16395.05 29299.07 252
BH-untuned95.18 23894.83 23896.22 28398.36 19491.22 36799.80 17497.32 33190.91 32191.08 32698.67 25383.51 31098.54 28094.23 27299.61 10498.92 269
testing9197.16 13396.90 13197.97 16898.35 19695.67 19999.91 11198.42 16892.91 23097.33 19898.72 24894.81 7299.21 19996.98 19894.63 29599.03 260
testing9997.17 13296.91 13097.95 17098.35 19695.70 19699.91 11198.43 15692.94 22897.36 19698.72 24894.83 7199.21 19997.00 19694.64 29498.95 265
ET-MVSNet_ETH3D94.37 27193.28 29297.64 20098.30 19897.99 8599.99 897.61 29094.35 15771.57 48999.45 14196.23 3995.34 45496.91 20485.14 37899.59 154
AUN-MVS93.28 30592.60 31095.34 31498.29 19990.09 39299.31 30598.56 11391.80 29196.35 24498.00 29989.38 20998.28 31092.46 30669.22 47497.64 317
FMVSNet392.69 32391.58 33395.99 28898.29 19997.42 11699.26 31797.62 28789.80 35589.68 35095.32 40081.62 33596.27 42987.01 40085.65 37294.29 363
PMMVS96.76 15796.76 13996.76 26498.28 20192.10 33399.91 11197.98 24694.12 16999.53 7499.39 14986.93 24998.73 25196.95 20197.73 19499.45 190
hse-mvs294.38 27094.08 26195.31 31698.27 20290.02 39399.29 31298.56 11395.90 10198.77 12998.00 29990.89 18898.26 31497.80 16669.20 47597.64 317
PVSNet_088.03 1991.80 34390.27 35796.38 27998.27 20290.46 38499.94 9399.61 1393.99 17786.26 42397.39 31971.13 43399.89 11898.77 10567.05 48198.79 278
UA-Net96.54 17495.96 18298.27 15098.23 20495.71 19598.00 42598.45 14393.72 19298.41 15299.27 16588.71 22399.66 17191.19 32797.69 19599.44 193
test_cas_vis1_n_192096.59 17096.23 16497.65 19998.22 20594.23 26899.99 897.25 34697.77 2999.58 7099.08 19077.10 38399.97 6497.64 17599.45 12798.74 281
FE-MVS95.70 22495.01 23397.79 18598.21 20694.57 24995.03 47598.69 8288.90 37197.50 19196.19 36192.60 14799.49 18589.99 35297.94 19299.31 219
GG-mvs-BLEND98.54 12898.21 20698.01 8493.87 48098.52 12897.92 17497.92 30499.02 397.94 33498.17 14299.58 10999.67 133
mvs_anonymous95.65 22695.03 23297.53 21398.19 20895.74 19399.33 30097.49 30690.87 32290.47 33597.10 32688.23 22697.16 36695.92 23397.66 19899.68 131
MVS_Test96.46 17895.74 19698.61 11798.18 20997.23 12399.31 30597.15 36491.07 31898.84 12397.05 33088.17 22798.97 21794.39 26697.50 20099.61 151
BH-RMVSNet95.18 23894.31 25397.80 18398.17 21095.23 22599.76 19297.53 30192.52 26194.27 29299.25 17276.84 39098.80 24090.89 33699.54 11199.35 209
dongtai91.55 34991.13 34292.82 40798.16 21186.35 43699.47 27898.51 13183.24 44585.07 43497.56 31390.33 19794.94 46076.09 47191.73 32097.18 328
RPSCF91.80 34392.79 30688.83 45098.15 21269.87 49398.11 42196.60 43183.93 44094.33 29099.27 16579.60 36199.46 18991.99 31693.16 31797.18 328
ETV-MVS97.92 8497.80 8898.25 15198.14 21396.48 16099.98 2497.63 28495.61 11199.29 9899.46 14092.55 14998.82 23299.02 8998.54 17099.46 185
IS-MVSNet96.29 19195.90 18997.45 22398.13 21494.80 24299.08 33397.61 29092.02 28395.54 26998.96 21390.64 19198.08 32393.73 28897.41 20499.47 183
test_fmvsmconf_n98.43 5198.32 4798.78 10398.12 21596.41 16399.99 898.83 6698.22 799.67 5399.64 11991.11 18199.94 9499.67 5299.62 9999.98 57
fmvsm_s_conf0.1_n_297.25 12896.85 13498.43 14098.08 21698.08 7999.92 10397.76 27498.05 2099.65 5599.58 12880.88 34599.93 10499.59 5698.17 18197.29 326
ab-mvs94.69 25693.42 28398.51 13398.07 21796.26 17096.49 45998.68 8490.31 34694.54 28297.00 33376.30 39899.71 16095.98 23293.38 31599.56 163
XVG-OURS-SEG-HR94.79 25194.70 24595.08 32198.05 21889.19 40499.08 33397.54 29993.66 19394.87 27899.58 12878.78 36999.79 14597.31 18393.40 31496.25 335
EIA-MVS97.53 11497.46 10497.76 19198.04 21994.84 23999.98 2497.61 29094.41 15597.90 17599.59 12592.40 15598.87 22598.04 15199.13 14699.59 154
XVG-OURS94.82 24894.74 24495.06 32298.00 22089.19 40499.08 33397.55 29794.10 17094.71 28099.62 12380.51 35299.74 15696.04 23193.06 31996.25 335
mvsmamba96.94 14696.73 14197.55 21197.99 22194.37 26299.62 24297.70 27793.13 22098.42 15197.92 30488.02 22898.75 24998.78 10499.01 15399.52 173
dp95.05 24294.43 24896.91 25797.99 22192.73 31896.29 46497.98 24689.70 35695.93 25794.67 42893.83 11098.45 28686.91 40396.53 24799.54 168
tpmrst96.27 19395.98 17897.13 24897.96 22393.15 30696.34 46298.17 22292.07 27998.71 13595.12 41093.91 10598.73 25194.91 25496.62 24599.50 179
TR-MVS94.54 26193.56 27897.49 22197.96 22394.34 26498.71 38497.51 30490.30 34794.51 28498.69 25275.56 40498.77 24592.82 30495.99 26199.35 209
Vis-MVSNet (Re-imp)96.32 18895.98 17897.35 23897.93 22594.82 24199.47 27898.15 23091.83 28895.09 27699.11 18891.37 17597.47 35093.47 29297.43 20199.74 119
MDTV_nov1_ep1395.69 19897.90 22694.15 27295.98 47098.44 14893.12 22197.98 17295.74 37495.10 6198.58 27390.02 35196.92 236
Fast-Effi-MVS+95.02 24494.19 25697.52 21597.88 22794.55 25099.97 4297.08 38288.85 37394.47 28597.96 30384.59 29798.41 29089.84 35497.10 22499.59 154
ADS-MVSNet293.80 29293.88 26893.55 39097.87 22885.94 44094.24 47696.84 41790.07 35096.43 24094.48 43390.29 19995.37 45387.44 38997.23 21299.36 205
ADS-MVSNet94.79 25194.02 26397.11 25097.87 22893.79 28294.24 47698.16 22790.07 35096.43 24094.48 43390.29 19998.19 31787.44 38997.23 21299.36 205
Effi-MVS+96.30 19095.69 19898.16 15597.85 23096.26 17097.41 43897.21 35490.37 34398.65 13898.58 26786.61 25598.70 25897.11 19297.37 20699.52 173
PatchmatchNetpermissive95.94 20695.45 20797.39 23397.83 23194.41 25896.05 46898.40 17792.86 23297.09 20695.28 40594.21 9798.07 32589.26 36398.11 18699.70 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas94.64 25993.61 27397.74 19397.82 23296.26 17099.96 5697.78 27085.76 42194.00 29597.54 31476.95 38999.21 19997.23 18895.43 28597.76 314
1112_ss96.01 20395.20 22498.42 14297.80 23396.41 16399.65 23596.66 42892.71 24392.88 30999.40 14792.16 16399.30 19491.92 31893.66 31099.55 164
E3new96.75 15996.43 15697.71 19497.79 23494.83 24099.80 17497.33 32593.52 19997.49 19299.31 15787.73 23198.83 22997.52 17897.40 20599.48 182
Test_1112_low_res95.72 22094.83 23898.42 14297.79 23496.41 16399.65 23596.65 42992.70 24492.86 31096.13 36592.15 16499.30 19491.88 31993.64 31199.55 164
Effi-MVS+-dtu94.53 26395.30 22092.22 41597.77 23682.54 46399.59 25197.06 39194.92 12895.29 27395.37 39885.81 26797.89 33594.80 25797.07 22596.23 337
tpm cat193.51 30192.52 31696.47 27297.77 23691.47 36596.13 46698.06 23780.98 46092.91 30893.78 44489.66 20498.87 22587.03 39996.39 25299.09 249
FA-MVS(test-final)95.86 20995.09 22998.15 15897.74 23895.62 20196.31 46398.17 22291.42 30696.26 24596.13 36590.56 19399.47 18892.18 31097.07 22599.35 209
xiu_mvs_v1_base_debu97.43 11797.06 12398.55 12497.74 23898.14 7499.31 30597.86 26096.43 8399.62 6299.69 10585.56 27499.68 16599.05 8298.31 17697.83 310
xiu_mvs_v1_base97.43 11797.06 12398.55 12497.74 23898.14 7499.31 30597.86 26096.43 8399.62 6299.69 10585.56 27499.68 16599.05 8298.31 17697.83 310
xiu_mvs_v1_base_debi97.43 11797.06 12398.55 12497.74 23898.14 7499.31 30597.86 26096.43 8399.62 6299.69 10585.56 27499.68 16599.05 8298.31 17697.83 310
EPP-MVSNet96.69 16496.60 14796.96 25597.74 23893.05 30999.37 29598.56 11388.75 37595.83 26099.01 20096.01 4098.56 27696.92 20297.20 21499.25 232
gg-mvs-nofinetune93.51 30191.86 32898.47 13597.72 24397.96 8992.62 49198.51 13174.70 48497.33 19869.59 51598.91 497.79 33897.77 17199.56 11099.67 133
IB-MVS92.85 694.99 24593.94 26698.16 15597.72 24395.69 19899.99 898.81 6794.28 16392.70 31196.90 33795.08 6299.17 20596.07 23073.88 45599.60 153
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
thisisatest051597.41 12297.02 12898.59 12197.71 24597.52 10999.97 4298.54 12391.83 28897.45 19399.04 19697.50 1099.10 20994.75 25996.37 25399.16 241
VortexMVS94.11 27993.50 28095.94 29197.70 24696.61 15599.35 29897.18 35793.52 19989.57 35795.74 37487.55 23696.97 38395.76 23885.13 37994.23 368
viewdifsd2359ckpt0996.21 19695.77 19497.53 21397.69 24794.50 25399.78 18097.23 35192.88 23196.58 22899.26 16984.85 28898.66 26696.61 21697.02 23299.43 194
Syy-MVS90.00 38490.63 34988.11 45897.68 24874.66 48999.71 21898.35 19090.79 32992.10 31798.67 25379.10 36793.09 48063.35 49895.95 26596.59 333
myMVS_eth3d94.46 26894.76 24393.55 39097.68 24890.97 36999.71 21898.35 19090.79 32992.10 31798.67 25392.46 15493.09 48087.13 39695.95 26596.59 333
test_fmvs1_n94.25 27694.36 25093.92 37797.68 24883.70 45399.90 11796.57 43297.40 4099.67 5398.88 22661.82 47099.92 11098.23 14099.13 14698.14 303
fmvsm_s_conf0.5_n_698.27 6397.96 7599.23 5897.66 25198.11 7899.98 2498.64 9197.85 2799.87 1499.72 9588.86 22099.93 10499.64 5499.36 13599.63 146
RRT-MVS96.24 19595.68 20097.94 17397.65 25294.92 23799.27 31597.10 37892.79 23897.43 19497.99 30181.85 33099.37 19298.46 12598.57 16799.53 172
diffmvspermissive97.00 14396.64 14598.09 16297.64 25396.17 17999.81 16897.19 35594.67 14098.95 11899.28 16186.43 25698.76 24798.37 13097.42 20399.33 212
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewcassd2359sk1196.59 17096.23 16497.66 19897.63 25494.70 24599.77 18697.33 32593.41 20497.34 19799.17 18286.72 25098.83 22997.40 18197.32 20999.46 185
viewdifsd2359ckpt1396.19 19795.77 19497.45 22397.62 25594.40 26099.70 22597.23 35192.76 24096.63 22599.05 19584.96 28798.64 26996.65 21597.35 20799.31 219
Vis-MVSNetpermissive95.72 22095.15 22797.45 22397.62 25594.28 26599.28 31398.24 21094.27 16596.84 21898.94 22079.39 36298.76 24793.25 29598.49 17199.30 222
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest053097.10 13696.72 14298.22 15297.60 25796.70 14899.92 10398.54 12391.11 31697.07 20898.97 21197.47 1399.03 21293.73 28896.09 25998.92 269
GDP-MVS97.88 8697.59 10098.75 10697.59 25897.81 9699.95 7597.37 31994.44 15199.08 11099.58 12897.13 2599.08 21094.99 24998.17 18199.37 203
miper_ehance_all_eth93.16 30992.60 31094.82 33297.57 25993.56 29599.50 27297.07 39088.75 37588.85 37495.52 38790.97 18496.74 39990.77 33884.45 38494.17 376
guyue97.15 13496.82 13698.15 15897.56 26096.25 17499.71 21897.84 26395.75 10798.13 16898.65 25687.58 23598.82 23298.29 13697.91 19399.36 205
viewmanbaseed2359cas96.45 17996.07 17297.59 20997.55 26194.59 24899.70 22597.33 32593.62 19597.00 21299.32 15485.57 27398.71 25597.26 18797.33 20899.47 183
testing393.92 28594.23 25592.99 40497.54 26290.23 38899.99 899.16 3390.57 33691.33 32598.63 26092.99 13292.52 48482.46 43395.39 28696.22 338
SSM_040495.75 21995.16 22697.50 21897.53 26395.39 21299.11 32997.25 34690.81 32595.27 27498.83 24084.74 29298.67 26395.24 24497.69 19598.45 291
LCM-MVSNet-Re92.31 33292.60 31091.43 42497.53 26379.27 48199.02 34791.83 49892.07 27980.31 45994.38 43783.50 31195.48 45097.22 18997.58 19999.54 168
GBi-Net90.88 36089.82 36694.08 36897.53 26391.97 33498.43 40396.95 40587.05 40389.68 35094.72 42471.34 43096.11 43587.01 40085.65 37294.17 376
test190.88 36089.82 36694.08 36897.53 26391.97 33498.43 40396.95 40587.05 40389.68 35094.72 42471.34 43096.11 43587.01 40085.65 37294.17 376
FMVSNet291.02 35789.56 37195.41 31297.53 26395.74 19398.98 35097.41 31487.05 40388.43 38795.00 41871.34 43096.24 43185.12 41585.21 37794.25 366
tttt051796.85 15196.49 15297.92 17497.48 26895.89 18799.85 14798.54 12390.72 33396.63 22598.93 22397.47 1399.02 21393.03 30295.76 27298.85 274
cashybrid296.25 19495.89 19097.32 24197.45 26993.68 28899.80 17497.22 35393.38 20596.86 21699.28 16184.64 29698.87 22597.18 19097.19 21599.41 198
BP-MVS198.33 5998.18 5698.81 10197.44 27097.98 8699.96 5698.17 22294.88 13098.77 12999.59 12597.59 899.08 21098.24 13998.93 15599.36 205
casdiffmvs_mvgpermissive96.43 18095.94 18697.89 17897.44 27095.47 20599.86 14497.29 34193.35 20796.03 25399.19 18085.39 27898.72 25497.89 16297.04 22999.49 181
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
E296.36 18595.95 18497.60 20697.41 27294.52 25199.71 21897.33 32593.20 21397.02 20999.07 19285.37 27998.82 23297.27 18497.14 22199.46 185
EC-MVSNet97.38 12497.24 11797.80 18397.41 27295.64 20099.99 897.06 39194.59 14199.63 5999.32 15489.20 21598.14 31998.76 10699.23 14299.62 147
viewdifsd2359ckpt0795.83 21295.42 20997.07 25197.40 27493.04 31099.60 24997.24 34992.39 26796.09 25299.14 18783.07 32198.93 22197.02 19596.87 23799.23 235
c3_l92.53 32791.87 32794.52 34497.40 27492.99 31299.40 28796.93 41087.86 39388.69 37795.44 39289.95 20296.44 41790.45 34480.69 41894.14 386
hybrid96.53 17596.15 17097.67 19697.39 27695.12 23199.80 17497.15 36493.38 20598.23 16499.16 18585.20 28198.70 25897.92 15897.15 22099.20 238
viewmambaseed2359dif95.92 20895.55 20597.04 25297.38 27793.41 30099.78 18096.97 40391.14 31596.58 22899.27 16584.85 28898.75 24996.87 20597.12 22398.97 264
fmvsm_s_conf0.1_n97.30 12597.21 11997.60 20697.38 27794.40 26099.90 11798.64 9196.47 8299.51 7899.65 11884.99 28699.93 10499.22 7699.09 14998.46 290
hybridcas96.09 20095.62 20297.50 21897.37 27994.44 25499.84 15297.16 36193.16 21796.03 25399.21 17784.19 30398.65 26896.53 22097.07 22599.42 197
E396.36 18595.95 18497.60 20697.37 27994.52 25199.71 21897.33 32593.18 21597.02 20999.07 19285.45 27798.82 23297.27 18497.14 22199.46 185
CDS-MVSNet96.34 18796.07 17297.13 24897.37 27994.96 23499.53 26797.91 25591.55 29895.37 27298.32 28795.05 6497.13 36993.80 28495.75 27399.30 222
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
hybridnocas0796.57 17296.16 16997.81 18297.36 28295.32 21799.81 16897.12 37094.17 16798.02 17198.90 22485.05 28498.80 24097.85 16397.18 21699.32 214
TESTMET0.1,196.74 16196.26 16398.16 15597.36 28296.48 16099.96 5698.29 20391.93 28495.77 26198.07 29795.54 5098.29 30890.55 34298.89 15699.70 125
miper_lstm_enhance91.81 34091.39 33993.06 40397.34 28489.18 40699.38 29396.79 42286.70 41187.47 40595.22 40790.00 20195.86 44488.26 37881.37 40794.15 382
baseline96.43 18095.98 17897.76 19197.34 28495.17 22999.51 27097.17 35993.92 18296.90 21599.28 16185.37 27998.64 26997.50 17996.86 23999.46 185
cl____92.31 33291.58 33394.52 34497.33 28692.77 31499.57 25796.78 42386.97 40787.56 40395.51 38889.43 20896.62 40688.60 36882.44 39994.16 381
SD_040392.63 32693.38 28790.40 43897.32 28777.91 48397.75 43398.03 24291.89 28590.83 33198.29 29182.00 32793.79 47388.51 37395.75 27399.52 173
DIV-MVS_self_test92.32 33191.60 33294.47 34897.31 28892.74 31699.58 25396.75 42486.99 40687.64 40195.54 38589.55 20796.50 41288.58 36982.44 39994.17 376
casdiffmvspermissive96.42 18295.97 18197.77 18997.30 28994.98 23399.84 15297.09 38193.75 19196.58 22899.26 16985.07 28398.78 24497.77 17197.04 22999.54 168
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GeoE94.36 27393.48 28196.99 25497.29 29093.54 29699.96 5696.72 42688.35 38693.43 29998.94 22082.05 32698.05 32688.12 38496.48 25099.37 203
eth_miper_zixun_eth92.41 33091.93 32593.84 38197.28 29190.68 37898.83 37396.97 40388.57 38089.19 36995.73 37789.24 21496.69 40489.97 35381.55 40594.15 382
MVSFormer96.94 14696.60 14797.95 17097.28 29197.70 10299.55 26497.27 34391.17 31299.43 8499.54 13490.92 18596.89 38994.67 26299.62 9999.25 232
lupinMVS97.85 9097.60 9898.62 11697.28 29197.70 10299.99 897.55 29795.50 11699.43 8499.67 11490.92 18598.71 25598.40 12799.62 9999.45 190
nocashy0296.61 16896.34 16097.42 22897.26 29494.37 26299.83 16097.16 36194.51 14497.89 17799.26 16986.38 25798.66 26697.70 17497.06 22899.23 235
dtuplus95.79 21795.42 20996.93 25697.24 29593.16 30599.78 18096.93 41091.69 29496.18 25099.29 16083.80 30898.73 25196.83 20797.02 23298.89 273
diffmvs_AUTHOR96.75 15996.41 15897.79 18597.20 29695.46 20699.69 22897.15 36494.46 14798.78 12799.21 17785.64 27198.77 24598.27 13797.31 21099.13 245
mamba_040894.98 24694.09 25997.64 20097.14 29795.31 21893.48 48697.08 38290.48 33994.40 28698.62 26184.49 29898.67 26393.99 27597.18 21698.93 266
SSM_0407294.77 25394.09 25996.82 26197.14 29795.31 21893.48 48697.08 38290.48 33994.40 28698.62 26184.49 29896.21 43293.99 27597.18 21698.93 266
SSM_040795.62 22794.95 23597.61 20597.14 29795.31 21899.00 34897.25 34690.81 32594.40 28698.83 24084.74 29298.58 27395.24 24497.18 21698.93 266
SCA94.69 25693.81 27097.33 23997.10 30094.44 25498.86 37098.32 19793.30 21096.17 25195.59 38376.48 39697.95 33291.06 33097.43 20199.59 154
viewmacassd2359aftdt95.93 20795.45 20797.36 23697.09 30194.12 27499.57 25797.26 34593.05 22596.50 23299.17 18282.76 32298.68 26196.61 21697.04 22999.28 226
KinetiMVS96.10 19895.29 22198.53 13097.08 30297.12 12999.56 26198.12 23394.78 13398.44 14998.94 22080.30 35699.39 19191.56 32398.79 16299.06 253
TAMVS95.85 21095.58 20396.65 26997.07 30393.50 29799.17 32497.82 26591.39 30895.02 27798.01 29892.20 16297.30 35993.75 28795.83 26999.14 244
Fast-Effi-MVS+-dtu93.72 29693.86 26993.29 39597.06 30486.16 43799.80 17496.83 41892.66 24792.58 31297.83 31081.39 33697.67 34389.75 35596.87 23796.05 340
E496.01 20395.53 20697.44 22697.05 30594.23 26899.57 25797.30 33392.72 24196.47 23499.03 19783.98 30798.83 22996.92 20296.77 24099.27 228
E5new95.83 21295.39 21297.15 24497.03 30693.59 29099.32 30397.30 33392.58 25496.45 23599.00 20483.37 31498.81 23696.81 20896.65 24399.04 256
E595.83 21295.39 21297.15 24497.03 30693.59 29099.32 30397.30 33392.58 25496.45 23599.00 20483.37 31498.81 23696.81 20896.65 24399.04 256
CostFormer96.10 19895.88 19196.78 26397.03 30692.55 32497.08 44797.83 26490.04 35298.72 13494.89 42295.01 6698.29 30896.54 21995.77 27199.50 179
test_fmvsmvis_n_192097.67 10997.59 10097.91 17697.02 30995.34 21599.95 7598.45 14397.87 2697.02 20999.59 12589.64 20599.98 5199.41 6899.34 13798.42 293
test-LLR96.47 17796.04 17497.78 18797.02 30995.44 20799.96 5698.21 21794.07 17295.55 26796.38 35493.90 10698.27 31290.42 34598.83 16099.64 139
test-mter96.39 18395.93 18797.78 18797.02 30995.44 20799.96 5698.21 21791.81 29095.55 26796.38 35495.17 5998.27 31290.42 34598.83 16099.64 139
casdiffseed41469214795.07 24194.26 25497.50 21897.01 31294.70 24599.58 25397.02 39591.27 31094.66 28198.82 24280.79 34798.55 27993.39 29495.79 27099.27 228
E6new95.83 21295.39 21297.14 24697.00 31393.58 29299.31 30597.30 33392.57 25696.45 23599.01 20083.44 31298.81 23696.80 21096.66 24199.04 256
E695.83 21295.39 21297.14 24697.00 31393.58 29299.31 30597.30 33392.57 25696.45 23599.01 20083.44 31298.81 23696.80 21096.66 24199.04 256
icg_test_0407_295.04 24394.78 24295.84 29896.97 31591.64 35698.63 39297.12 37092.33 27095.60 26598.88 22685.65 26996.56 40992.12 31195.70 27699.32 214
IMVS_040795.21 23794.80 24196.46 27496.97 31591.64 35698.81 37597.12 37092.33 27095.60 26598.88 22685.65 26998.42 28892.12 31195.70 27699.32 214
IMVS_040493.83 28893.17 29495.80 30096.97 31591.64 35697.78 43297.12 37092.33 27090.87 33098.88 22676.78 39196.43 41892.12 31195.70 27699.32 214
IMVS_040395.25 23694.81 24096.58 27196.97 31591.64 35698.97 35597.12 37092.33 27095.43 27098.88 22685.78 26898.79 24292.12 31195.70 27699.32 214
gm-plane-assit96.97 31593.76 28491.47 30298.96 21398.79 24294.92 252
WB-MVSnew92.90 31592.77 30793.26 39796.95 32093.63 28999.71 21898.16 22791.49 29994.28 29198.14 29481.33 33896.48 41579.47 45295.46 28389.68 482
QAPM95.40 23294.17 25799.10 7996.92 32197.71 10099.40 28798.68 8489.31 35988.94 37398.89 22582.48 32499.96 7693.12 30199.83 8099.62 147
KD-MVS_2432*160088.00 40686.10 41093.70 38696.91 32294.04 27597.17 44497.12 37084.93 43281.96 44892.41 46092.48 15294.51 46679.23 45452.68 51292.56 447
miper_refine_blended88.00 40686.10 41093.70 38696.91 32294.04 27597.17 44497.12 37084.93 43281.96 44892.41 46092.48 15294.51 46679.23 45452.68 51292.56 447
tpm295.47 23095.18 22596.35 28096.91 32291.70 35396.96 45097.93 25188.04 39198.44 14995.40 39493.32 12197.97 32994.00 27495.61 28199.38 201
FMVSNet588.32 40287.47 40490.88 42796.90 32588.39 42097.28 44195.68 45482.60 45384.67 43692.40 46279.83 35991.16 49076.39 47081.51 40693.09 438
3Dnovator+91.53 1196.31 18995.24 22299.52 3396.88 32698.64 5999.72 21398.24 21095.27 12188.42 38998.98 20982.76 32299.94 9497.10 19399.83 8099.96 75
Patchmatch-test92.65 32591.50 33696.10 28696.85 32790.49 38391.50 49797.19 35582.76 45290.23 33695.59 38395.02 6598.00 32877.41 46596.98 23599.82 107
MVS96.60 16995.56 20499.72 1496.85 32799.22 2198.31 40998.94 4491.57 29790.90 32999.61 12486.66 25499.96 7697.36 18299.88 7699.99 26
3Dnovator91.47 1296.28 19295.34 21899.08 8296.82 32997.47 11499.45 28398.81 6795.52 11589.39 36099.00 20481.97 32899.95 8597.27 18499.83 8099.84 104
EI-MVSNet93.73 29593.40 28694.74 33396.80 33092.69 31999.06 33897.67 28088.96 36891.39 32399.02 19888.75 22297.30 35991.07 32987.85 35494.22 371
CVMVSNet94.68 25894.94 23693.89 38096.80 33086.92 43499.06 33898.98 4194.45 14894.23 29399.02 19885.60 27295.31 45590.91 33595.39 28699.43 194
IterMVS-LS92.69 32392.11 32194.43 35296.80 33092.74 31699.45 28396.89 41488.98 36689.65 35395.38 39788.77 22196.34 42590.98 33382.04 40294.22 371
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS96.57 17296.46 15596.91 25796.79 33392.50 32599.90 11797.38 31696.02 9997.79 18499.32 15486.36 25998.99 21498.26 13896.33 25499.23 235
IterMVS90.91 35990.17 36193.12 40096.78 33490.42 38698.89 36497.05 39489.03 36386.49 41895.42 39376.59 39495.02 45787.22 39584.09 38793.93 409
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
131496.84 15295.96 18299.48 4096.74 33598.52 6398.31 40998.86 5995.82 10489.91 34498.98 20987.49 23899.96 7697.80 16699.73 9099.96 75
IterMVS-SCA-FT90.85 36290.16 36292.93 40596.72 33689.96 39598.89 36496.99 39988.95 36986.63 41595.67 37876.48 39695.00 45887.04 39884.04 39093.84 416
MVS-HIRNet86.22 41983.19 43495.31 31696.71 33790.29 38792.12 49397.33 32562.85 50186.82 41270.37 51369.37 43897.49 34975.12 47397.99 19198.15 301
viewdifsd2359ckpt1194.09 28193.63 27295.46 30996.68 33888.92 40999.62 24297.12 37093.07 22395.73 26299.22 17477.05 38498.88 22496.52 22187.69 35998.58 288
viewmsd2359difaftdt94.09 28193.64 27195.46 30996.68 33888.92 40999.62 24297.13 36993.07 22395.73 26299.22 17477.05 38498.89 22396.52 22187.70 35898.58 288
VDDNet93.12 31091.91 32696.76 26496.67 34092.65 32298.69 38798.21 21782.81 45197.75 18699.28 16161.57 47199.48 18698.09 14894.09 30598.15 301
dmvs_re93.20 30793.15 29693.34 39396.54 34183.81 45298.71 38498.51 13191.39 30892.37 31598.56 26978.66 37197.83 33793.89 27889.74 32698.38 295
Elysia94.50 26593.38 28797.85 18096.49 34296.70 14898.98 35097.78 27090.81 32596.19 24898.55 27173.63 42198.98 21589.41 35698.56 16897.88 308
StellarMVS94.50 26593.38 28797.85 18096.49 34296.70 14898.98 35097.78 27090.81 32596.19 24898.55 27173.63 42198.98 21589.41 35698.56 16897.88 308
MIMVSNet90.30 37588.67 39095.17 32096.45 34491.64 35692.39 49297.15 36485.99 41890.50 33493.19 45366.95 44994.86 46282.01 43793.43 31399.01 262
CR-MVSNet93.45 30492.62 30995.94 29196.29 34592.66 32092.01 49496.23 44092.62 24996.94 21393.31 45091.04 18296.03 44079.23 45495.96 26399.13 245
RPMNet89.76 38887.28 40597.19 24396.29 34592.66 32092.01 49498.31 19970.19 49296.94 21385.87 49987.25 24399.78 14762.69 49995.96 26399.13 245
tt080591.28 35290.18 36094.60 33996.26 34787.55 42798.39 40798.72 7889.00 36589.22 36698.47 27962.98 46698.96 21990.57 34188.00 35397.28 327
Patchmtry89.70 38988.49 39393.33 39496.24 34889.94 39891.37 49896.23 44078.22 47487.69 40093.31 45091.04 18296.03 44080.18 45182.10 40194.02 399
test_vis1_rt86.87 41686.05 41389.34 44696.12 34978.07 48299.87 13383.54 51592.03 28278.21 47189.51 48145.80 49399.91 11196.25 22793.11 31890.03 478
JIA-IIPM91.76 34690.70 34794.94 32696.11 35087.51 42893.16 48998.13 23275.79 48097.58 18877.68 50892.84 13797.97 32988.47 37496.54 24699.33 212
OpenMVScopyleft90.15 1594.77 25393.59 27698.33 14696.07 35197.48 11399.56 26198.57 10790.46 34186.51 41798.95 21878.57 37299.94 9493.86 27999.74 8997.57 322
PAPM98.60 3798.42 3899.14 7396.05 35298.96 2899.90 11799.35 2496.68 7398.35 15699.66 11696.45 3598.51 28199.45 6599.89 7399.96 75
CLD-MVS94.06 28493.90 26794.55 34396.02 35390.69 37799.98 2497.72 27696.62 7791.05 32898.85 23877.21 38298.47 28298.11 14689.51 33294.48 347
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT90.38 37288.75 38995.25 31895.99 35490.16 39091.22 49997.54 29976.80 47697.26 20186.01 49891.88 16996.07 43966.16 49295.91 26799.51 177
ACMH+89.98 1690.35 37389.54 37292.78 40995.99 35486.12 43898.81 37597.18 35789.38 35883.14 44497.76 31168.42 44398.43 28789.11 36486.05 37093.78 419
DeepMVS_CXcopyleft82.92 47495.98 35658.66 50796.01 44692.72 24178.34 47095.51 38858.29 47898.08 32382.57 43285.29 37592.03 457
ACMP92.05 992.74 32192.42 31893.73 38295.91 35788.72 41399.81 16897.53 30194.13 16887.00 41198.23 29274.07 41798.47 28296.22 22888.86 33993.99 404
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n93.61 29993.03 29995.35 31395.86 35886.94 43399.87 13396.36 43896.85 6499.54 7398.79 24352.41 48699.83 14098.64 11498.97 15499.29 224
HQP-NCC95.78 35999.87 13396.82 6693.37 300
ACMP_Plane95.78 35999.87 13396.82 6693.37 300
HQP-MVS94.61 26094.50 24794.92 32795.78 35991.85 34199.87 13397.89 25696.82 6693.37 30098.65 25680.65 35098.39 29497.92 15889.60 32794.53 343
NP-MVS95.77 36291.79 34598.65 256
test_fmvsmconf0.1_n97.74 10397.44 10798.64 11595.76 36396.20 17699.94 9398.05 23998.17 1398.89 12299.42 14287.65 23399.90 11399.50 6199.60 10799.82 107
plane_prior695.76 36391.72 35280.47 354
ACMM91.95 1092.88 31692.52 31693.98 37695.75 36589.08 40899.77 18697.52 30393.00 22689.95 34397.99 30176.17 40098.46 28593.63 29188.87 33894.39 355
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS93.83 28892.84 30396.80 26295.73 36693.57 29499.88 13097.24 34992.57 25692.92 30796.66 34678.73 37097.67 34387.75 38794.06 30699.17 240
plane_prior195.73 366
jason97.24 12996.86 13398.38 14595.73 36697.32 11899.97 4297.40 31595.34 11998.60 14399.54 13487.70 23298.56 27697.94 15799.47 12499.25 232
jason: jason.
mmtdpeth88.52 40087.75 40290.85 42995.71 36983.47 45898.94 35894.85 47288.78 37497.19 20389.58 48063.29 46498.97 21798.54 11962.86 49090.10 477
HQP_MVS94.49 26794.36 25094.87 32895.71 36991.74 34899.84 15297.87 25896.38 8693.01 30598.59 26480.47 35498.37 30097.79 16989.55 33094.52 345
plane_prior795.71 36991.59 362
ITE_SJBPF92.38 41295.69 37285.14 44495.71 45392.81 23589.33 36398.11 29570.23 43698.42 28885.91 41088.16 35193.59 427
fmvsm_s_conf0.1_n_a97.09 13896.90 13197.63 20395.65 37394.21 27099.83 16098.50 13796.27 9299.65 5599.64 11984.72 29499.93 10499.04 8598.84 15998.74 281
ACMH89.72 1790.64 36689.63 36993.66 38895.64 37488.64 41698.55 39597.45 30889.03 36381.62 45197.61 31269.75 43798.41 29089.37 35887.62 36093.92 410
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline296.71 16396.49 15297.37 23495.63 37595.96 18599.74 20298.88 5592.94 22891.61 32198.97 21197.72 798.62 27194.83 25698.08 18997.53 324
FMVSNet188.50 40186.64 40894.08 36895.62 37691.97 33498.43 40396.95 40583.00 44986.08 42594.72 42459.09 47796.11 43581.82 43984.07 38894.17 376
LuminaMVS96.63 16796.21 16797.87 17995.58 37796.82 14299.12 32797.67 28094.47 14697.88 17998.31 28987.50 23798.71 25598.07 15097.29 21198.10 304
0.3-1-1-0.01594.22 27793.13 29897.49 22195.50 37894.17 271100.00 198.22 21388.44 38497.14 20597.04 33292.73 14198.59 27296.45 22372.65 46199.70 125
0.4-1-1-0.294.14 27893.02 30097.51 21695.45 37994.25 267100.00 198.22 21388.53 38196.83 21996.95 33592.25 16098.57 27596.34 22472.65 46199.70 125
LPG-MVS_test92.96 31392.71 30893.71 38495.43 38088.67 41499.75 19897.62 28792.81 23590.05 33998.49 27575.24 40798.40 29295.84 23589.12 33494.07 395
LGP-MVS_train93.71 38495.43 38088.67 41497.62 28792.81 23590.05 33998.49 27575.24 40798.40 29295.84 23589.12 33494.07 395
tpm93.70 29793.41 28594.58 34195.36 38287.41 42997.01 44896.90 41390.85 32396.72 22494.14 44190.40 19696.84 39390.75 33988.54 34699.51 177
0.4-1-1-0.194.07 28392.95 30197.42 22895.24 38394.00 278100.00 198.22 21388.27 38896.81 22196.93 33692.27 15998.56 27696.21 22972.63 46399.70 125
D2MVS92.76 32092.59 31493.27 39695.13 38489.54 40299.69 22899.38 2292.26 27587.59 40294.61 43085.05 28497.79 33891.59 32288.01 35292.47 451
VPA-MVSNet92.70 32291.55 33596.16 28495.09 38596.20 17698.88 36699.00 3991.02 32091.82 32095.29 40476.05 40297.96 33195.62 24081.19 40894.30 362
LTVRE_ROB88.28 1890.29 37689.05 38394.02 37195.08 38690.15 39197.19 44397.43 31084.91 43483.99 44097.06 32974.00 41898.28 31084.08 42187.71 35693.62 426
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
TinyColmap87.87 40886.51 40991.94 41895.05 38785.57 44297.65 43494.08 48384.40 43881.82 45096.85 34162.14 46998.33 30380.25 45086.37 36791.91 459
test0.0.03 193.86 28793.61 27394.64 33795.02 38892.18 33299.93 10098.58 10594.07 17287.96 39798.50 27493.90 10694.96 45981.33 44093.17 31696.78 330
UniMVSNet (Re)93.07 31292.13 32095.88 29594.84 38996.24 17599.88 13098.98 4192.49 26389.25 36495.40 39487.09 24597.14 36893.13 30078.16 43394.26 364
USDC90.00 38488.96 38493.10 40294.81 39088.16 42298.71 38495.54 45893.66 19383.75 44297.20 32365.58 45598.31 30583.96 42487.49 36292.85 444
VPNet91.81 34090.46 35195.85 29794.74 39195.54 20498.98 35098.59 10392.14 27790.77 33397.44 31668.73 44197.54 34894.89 25577.89 43594.46 348
FIs94.10 28093.43 28296.11 28594.70 39296.82 14299.58 25398.93 4892.54 25989.34 36297.31 32087.62 23497.10 37294.22 27386.58 36594.40 354
UniMVSNet_ETH3D90.06 38388.58 39294.49 34794.67 39388.09 42397.81 43197.57 29583.91 44188.44 38497.41 31757.44 47997.62 34591.41 32488.59 34597.77 313
UniMVSNet_NR-MVSNet92.95 31492.11 32195.49 30594.61 39495.28 22299.83 16099.08 3691.49 29989.21 36796.86 34087.14 24496.73 40093.20 29677.52 43894.46 348
test_fmvs289.47 39389.70 36888.77 45394.54 39575.74 48599.83 16094.70 47894.71 13791.08 32696.82 34554.46 48297.78 34092.87 30388.27 34992.80 445
MonoMVSNet94.82 24894.43 24895.98 28994.54 39590.73 37699.03 34597.06 39193.16 21793.15 30495.47 39188.29 22597.57 34697.85 16391.33 32499.62 147
WR-MVS92.31 33291.25 34095.48 30894.45 39795.29 22199.60 24998.68 8490.10 34988.07 39696.89 33880.68 34996.80 39793.14 29979.67 42594.36 356
dtuonly93.89 28693.16 29596.08 28794.37 39891.67 35599.15 32695.04 47091.79 29294.74 27998.72 24881.01 34298.31 30587.29 39396.33 25498.27 299
nrg03093.51 30192.53 31596.45 27594.36 39997.20 12499.81 16897.16 36191.60 29689.86 34697.46 31586.37 25897.68 34295.88 23480.31 42194.46 348
tfpnnormal89.29 39687.61 40394.34 35594.35 40094.13 27398.95 35798.94 4483.94 43984.47 43795.51 38874.84 41297.39 35177.05 46880.41 41991.48 462
FC-MVSNet-test93.81 29193.15 29695.80 30094.30 40196.20 17699.42 28598.89 5292.33 27089.03 37297.27 32287.39 24096.83 39593.20 29686.48 36694.36 356
SSC-MVS3.289.59 39188.66 39192.38 41294.29 40286.12 43899.49 27497.66 28390.28 34888.63 38095.18 40864.46 46096.88 39185.30 41482.66 39694.14 386
MS-PatchMatch90.65 36590.30 35691.71 42394.22 40385.50 44398.24 41397.70 27788.67 37786.42 42096.37 35667.82 44698.03 32783.62 42699.62 9991.60 460
WR-MVS_H91.30 35090.35 35494.15 36294.17 40492.62 32399.17 32498.94 4488.87 37286.48 41994.46 43584.36 30196.61 40788.19 38078.51 43093.21 436
DU-MVS92.46 32991.45 33895.49 30594.05 40595.28 22299.81 16898.74 7692.25 27689.21 36796.64 34881.66 33396.73 40093.20 29677.52 43894.46 348
NR-MVSNet91.56 34890.22 35895.60 30394.05 40595.76 19298.25 41298.70 8091.16 31480.78 45896.64 34883.23 31996.57 40891.41 32477.73 43794.46 348
CP-MVSNet91.23 35490.22 35894.26 35793.96 40792.39 32899.09 33198.57 10788.95 36986.42 42096.57 35179.19 36596.37 42390.29 34878.95 42794.02 399
XXY-MVS91.82 33990.46 35195.88 29593.91 40895.40 21198.87 36997.69 27988.63 37987.87 39897.08 32774.38 41697.89 33591.66 32184.07 38894.35 359
PS-CasMVS90.63 36789.51 37493.99 37493.83 40991.70 35398.98 35098.52 12888.48 38286.15 42496.53 35375.46 40596.31 42888.83 36678.86 42993.95 407
test_040285.58 42283.94 42890.50 43593.81 41085.04 44598.55 39595.20 46776.01 47879.72 46495.13 40964.15 46296.26 43066.04 49486.88 36490.21 474
XVG-ACMP-BASELINE91.22 35590.75 34692.63 41193.73 41185.61 44198.52 39997.44 30992.77 23989.90 34596.85 34166.64 45298.39 29492.29 30888.61 34393.89 412
TranMVSNet+NR-MVSNet91.68 34790.61 35094.87 32893.69 41293.98 27999.69 22898.65 8891.03 31988.44 38496.83 34480.05 35896.18 43390.26 34976.89 44694.45 353
TransMVSNet (Re)87.25 41485.28 42293.16 39993.56 41391.03 36898.54 39794.05 48583.69 44381.09 45596.16 36275.32 40696.40 42276.69 46968.41 47792.06 456
v1090.25 37788.82 38694.57 34293.53 41493.43 29999.08 33396.87 41685.00 43187.34 40994.51 43180.93 34497.02 38282.85 43179.23 42693.26 434
testgi89.01 39888.04 39991.90 41993.49 41584.89 44799.73 20995.66 45593.89 18685.14 43198.17 29359.68 47594.66 46577.73 46488.88 33796.16 339
v890.54 36989.17 37994.66 33693.43 41693.40 30299.20 32196.94 40985.76 42187.56 40394.51 43181.96 32997.19 36584.94 41778.25 43293.38 432
V4291.28 35290.12 36394.74 33393.42 41793.46 29899.68 23197.02 39587.36 39989.85 34895.05 41281.31 33997.34 35487.34 39280.07 42393.40 430
pm-mvs189.36 39587.81 40194.01 37293.40 41891.93 33798.62 39396.48 43686.25 41683.86 44196.14 36473.68 42097.04 37886.16 40775.73 45193.04 440
v114491.09 35689.83 36594.87 32893.25 41993.69 28799.62 24296.98 40186.83 40989.64 35494.99 41980.94 34397.05 37585.08 41681.16 40993.87 414
v119290.62 36889.25 37894.72 33593.13 42093.07 30799.50 27297.02 39586.33 41589.56 35895.01 41679.22 36497.09 37482.34 43581.16 40994.01 401
v2v48291.30 35090.07 36495.01 32393.13 42093.79 28299.77 18697.02 39588.05 39089.25 36495.37 39880.73 34897.15 36787.28 39480.04 42494.09 394
OPM-MVS93.21 30692.80 30594.44 35093.12 42290.85 37599.77 18697.61 29096.19 9591.56 32298.65 25675.16 41198.47 28293.78 28689.39 33393.99 404
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v14419290.79 36389.52 37394.59 34093.11 42392.77 31499.56 26196.99 39986.38 41489.82 34994.95 42180.50 35397.10 37283.98 42380.41 41993.90 411
PEN-MVS90.19 37989.06 38293.57 38993.06 42490.90 37399.06 33898.47 14088.11 38985.91 42696.30 35876.67 39295.94 44387.07 39776.91 44593.89 412
v124090.20 37888.79 38794.44 35093.05 42592.27 33099.38 29396.92 41285.89 41989.36 36194.87 42377.89 37997.03 38080.66 44581.08 41294.01 401
usedtu_dtu_shiyan192.78 31891.73 32995.92 29393.03 42696.82 14299.83 16097.79 26690.58 33490.09 33795.04 41384.75 29096.72 40288.19 38086.23 36894.23 368
FE-MVSNET392.78 31891.73 32995.92 29393.03 42696.82 14299.83 16097.79 26690.58 33490.09 33795.04 41384.75 29096.72 40288.20 37986.23 36894.23 368
ArgMatch-SfM85.25 42784.17 42588.48 45592.99 42877.23 48497.92 42694.24 48190.50 33885.08 43395.65 38049.84 48995.83 44581.06 44370.22 46892.39 453
v14890.70 36489.63 36993.92 37792.97 42990.97 36999.75 19896.89 41487.51 39688.27 39395.01 41681.67 33297.04 37887.40 39177.17 44393.75 420
v192192090.46 37089.12 38094.50 34692.96 43092.46 32699.49 27496.98 40186.10 41789.61 35695.30 40178.55 37397.03 38082.17 43680.89 41794.01 401
MVStest185.03 42982.76 43891.83 42092.95 43189.16 40798.57 39494.82 47371.68 48968.54 49495.11 41183.17 32095.66 44874.69 47465.32 48490.65 469
tt0320-xc82.94 44480.35 45190.72 43392.90 43283.54 45696.85 45394.73 47663.12 50079.85 46393.77 44549.43 49195.46 45180.98 44471.54 46593.16 437
Baseline_NR-MVSNet90.33 37489.51 37492.81 40892.84 43389.95 39699.77 18693.94 48684.69 43689.04 37195.66 37981.66 33396.52 41190.99 33276.98 44491.97 458
test_method80.79 45079.70 45384.08 46992.83 43467.06 49799.51 27095.42 46054.34 50981.07 45693.53 44744.48 49492.22 48778.90 45977.23 44292.94 442
pmmvs492.10 33691.07 34495.18 31992.82 43594.96 23499.48 27796.83 41887.45 39888.66 37996.56 35283.78 30996.83 39589.29 36184.77 38293.75 420
LF4IMVS89.25 39788.85 38590.45 43792.81 43681.19 47398.12 42094.79 47491.44 30386.29 42297.11 32565.30 45898.11 32188.53 37185.25 37692.07 455
tt032083.56 44381.15 44690.77 43192.77 43783.58 45596.83 45495.52 45963.26 49981.36 45392.54 45753.26 48495.77 44680.45 44674.38 45492.96 441
DTE-MVSNet89.40 39488.24 39792.88 40692.66 43889.95 39699.10 33098.22 21387.29 40085.12 43296.22 36076.27 39995.30 45683.56 42775.74 45093.41 429
EU-MVSNet90.14 38190.34 35589.54 44592.55 43981.06 47498.69 38798.04 24091.41 30786.59 41696.84 34380.83 34693.31 47886.20 40681.91 40394.26 364
APD_test181.15 44880.92 44881.86 47592.45 44059.76 50696.04 46993.61 49073.29 48777.06 47496.64 34844.28 49596.16 43472.35 47882.52 39789.67 483
sc_t185.01 43082.46 44092.67 41092.44 44183.09 45997.39 43995.72 45265.06 49785.64 42996.16 36249.50 49097.34 35484.86 41875.39 45297.57 322
our_test_390.39 37189.48 37693.12 40092.40 44289.57 40199.33 30096.35 43987.84 39485.30 43094.99 41984.14 30596.09 43880.38 44884.56 38393.71 425
ppachtmachnet_test89.58 39288.35 39593.25 39892.40 44290.44 38599.33 30096.73 42585.49 42685.90 42795.77 37381.09 34196.00 44276.00 47282.49 39893.30 433
v7n89.65 39088.29 39693.72 38392.22 44490.56 38299.07 33797.10 37885.42 42886.73 41394.72 42480.06 35797.13 36981.14 44178.12 43493.49 428
dmvs_testset83.79 43986.07 41276.94 48292.14 44548.60 52096.75 45590.27 50289.48 35778.65 46898.55 27179.25 36386.65 50566.85 49082.69 39595.57 341
PS-MVSNAJss93.64 29893.31 29194.61 33892.11 44692.19 33199.12 32797.38 31692.51 26288.45 38396.99 33491.20 17797.29 36294.36 26787.71 35694.36 356
pmmvs590.17 38089.09 38193.40 39292.10 44789.77 39999.74 20295.58 45785.88 42087.24 41095.74 37473.41 42396.48 41588.54 37083.56 39293.95 407
N_pmnet80.06 45380.78 44977.89 48091.94 44845.28 52498.80 37856.82 52778.10 47580.08 46193.33 44877.03 38695.76 44768.14 48782.81 39492.64 446
test_djsdf92.83 31792.29 31994.47 34891.90 44992.46 32699.55 26497.27 34391.17 31289.96 34296.07 36881.10 34096.89 38994.67 26288.91 33694.05 398
SixPastTwentyTwo88.73 39988.01 40090.88 42791.85 45082.24 46598.22 41795.18 46888.97 36782.26 44796.89 33871.75 42896.67 40584.00 42282.98 39393.72 424
dtuonlycased86.10 42085.82 41586.95 46191.84 45179.57 48099.27 31594.89 47186.79 41079.46 46594.46 43566.85 45090.93 49380.41 44778.44 43190.34 471
K. test v388.05 40587.24 40690.47 43691.82 45282.23 46698.96 35697.42 31289.05 36276.93 47695.60 38268.49 44295.42 45285.87 41181.01 41593.75 420
OurMVSNet-221017-089.81 38789.48 37690.83 43091.64 45381.21 47298.17 41995.38 46291.48 30185.65 42897.31 32072.66 42497.29 36288.15 38284.83 38193.97 406
mvs_tets91.81 34091.08 34394.00 37391.63 45490.58 38198.67 38997.43 31092.43 26487.37 40897.05 33071.76 42797.32 35794.75 25988.68 34294.11 393
Gipumacopyleft66.95 47365.00 47372.79 48891.52 45567.96 49466.16 52495.15 46947.89 51158.54 50367.99 52029.74 50487.54 50450.20 51177.83 43662.87 519
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n96.39 18395.74 19698.32 14791.47 45695.56 20399.84 15297.30 33397.74 3097.89 17799.35 15379.62 36099.85 13099.25 7599.24 14199.55 164
jajsoiax91.92 33891.18 34194.15 36291.35 45790.95 37299.00 34897.42 31292.61 25087.38 40797.08 32772.46 42597.36 35294.53 26588.77 34094.13 391
MDA-MVSNet-bldmvs84.09 43781.52 44491.81 42191.32 45888.00 42598.67 38995.92 44880.22 46355.60 50693.32 44968.29 44493.60 47673.76 47576.61 44793.82 418
MVP-Stereo90.93 35890.45 35392.37 41491.25 45988.76 41198.05 42496.17 44287.27 40184.04 43895.30 40178.46 37497.27 36483.78 42599.70 9291.09 463
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron85.51 42483.32 43392.10 41690.96 46088.58 41799.20 32196.52 43479.70 46557.12 50592.69 45679.11 36693.86 47277.10 46777.46 44093.86 415
YYNet185.50 42583.33 43292.00 41790.89 46188.38 42199.22 32096.55 43379.60 46657.26 50492.72 45579.09 36893.78 47477.25 46677.37 44193.84 416
ALIKED-NN54.48 48352.67 48559.89 50590.79 46245.45 52281.25 51755.75 53134.99 52044.87 51771.98 51125.50 51274.36 51921.88 52947.04 51459.85 521
anonymousdsp91.79 34590.92 34594.41 35390.76 46392.93 31398.93 36097.17 35989.08 36187.46 40695.30 40178.43 37596.92 38692.38 30788.73 34193.39 431
lessismore_v090.53 43490.58 46480.90 47595.80 44977.01 47595.84 37166.15 45496.95 38483.03 43075.05 45393.74 423
EG-PatchMatch MVS85.35 42683.81 43089.99 44390.39 46581.89 46898.21 41896.09 44481.78 45674.73 48293.72 44651.56 48897.12 37179.16 45788.61 34390.96 466
EGC-MVSNET69.38 46663.76 47686.26 46590.32 46681.66 47196.24 46593.85 4870.99 5463.22 54792.33 46752.44 48592.92 48259.53 50584.90 38084.21 501
CMPMVSbinary61.59 2184.75 43385.14 42383.57 47090.32 46662.54 50296.98 44997.59 29474.33 48569.95 49196.66 34664.17 46198.32 30487.88 38688.41 34889.84 480
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ALIKED-MNN52.51 48750.15 49259.60 50790.05 46844.33 52681.60 51654.93 53232.36 52340.96 52468.77 51720.90 52175.30 51720.00 53041.78 51959.18 522
new_pmnet84.49 43682.92 43689.21 44790.03 46982.60 46296.89 45295.62 45680.59 46175.77 48189.17 48265.04 45994.79 46372.12 47981.02 41490.23 473
pmmvs685.69 42183.84 42991.26 42690.00 47084.41 45097.82 43096.15 44375.86 47981.29 45495.39 39661.21 47296.87 39283.52 42873.29 45792.50 450
ttmdpeth88.23 40487.06 40791.75 42289.91 47187.35 43098.92 36395.73 45187.92 39284.02 43996.31 35768.23 44596.84 39386.33 40576.12 44891.06 464
DSMNet-mixed88.28 40388.24 39788.42 45689.64 47275.38 48898.06 42389.86 50385.59 42588.20 39592.14 46976.15 40191.95 48878.46 46196.05 26097.92 307
DenseAffine75.91 45973.39 46383.47 47189.52 47371.86 49193.39 48889.29 50871.44 49066.83 49590.32 47730.65 50189.67 49768.20 48660.88 49988.88 491
UnsupCasMVSNet_eth85.52 42383.99 42690.10 44189.36 47483.51 45796.65 45697.99 24489.14 36075.89 48093.83 44363.25 46593.92 47081.92 43867.90 48092.88 443
Anonymous2023120686.32 41885.42 42189.02 44989.11 47580.53 47899.05 34295.28 46385.43 42782.82 44593.92 44274.40 41593.44 47766.99 48981.83 40493.08 439
ALIKED-LG54.29 48452.28 48660.32 50188.90 47645.51 52181.66 51556.33 52838.60 51342.62 52270.81 51225.00 51475.20 51819.87 53146.76 51660.24 520
Anonymous2024052185.15 42883.81 43089.16 44888.32 47782.69 46198.80 37895.74 45079.72 46481.53 45290.99 47265.38 45794.16 46872.69 47781.11 41190.63 470
OpenMVS_ROBcopyleft79.82 2083.77 44081.68 44390.03 44288.30 47882.82 46098.46 40095.22 46673.92 48676.00 47991.29 47155.00 48196.94 38568.40 48588.51 34790.34 471
test20.0384.72 43483.99 42686.91 46288.19 47980.62 47798.88 36695.94 44788.36 38578.87 46694.62 42968.75 44089.11 49966.52 49175.82 44991.00 465
RoMa-SfM74.91 46272.77 46481.35 47688.00 48067.35 49693.55 48586.23 51368.27 49566.79 49692.92 45430.40 50287.68 50166.14 49362.62 49189.02 489
gbinet_0.2-2-1-0.0287.63 41385.51 42093.99 37487.22 48191.56 36399.81 16897.36 32079.54 46788.60 38193.29 45273.76 41996.34 42589.27 36260.78 50094.06 397
blend_shiyan490.13 38288.79 38794.17 35987.12 48291.83 34399.75 19897.08 38279.27 47288.69 37792.53 45892.25 16096.50 41289.35 35973.04 45994.18 375
KD-MVS_self_test83.59 44182.06 44188.20 45786.93 48380.70 47697.21 44296.38 43782.87 45082.49 44688.97 48367.63 44792.32 48573.75 47662.30 49391.58 461
DKM72.18 46469.80 46779.34 47986.79 48465.15 49892.70 49084.00 51467.67 49661.97 49989.63 47923.69 51785.17 50767.39 48854.35 51087.70 495
MIMVSNet182.58 44580.51 45088.78 45186.68 48584.20 45196.65 45695.41 46178.75 47378.59 46992.44 45951.88 48789.76 49665.26 49578.95 42792.38 454
wanda-best-256-51287.82 40985.71 41694.15 36286.66 48691.88 33999.76 19297.08 38279.46 46888.37 39092.36 46378.01 37696.43 41888.39 37561.26 49594.14 386
FE-blended-shiyan787.82 40985.71 41694.15 36286.66 48691.88 33999.76 19297.08 38279.46 46888.37 39092.36 46378.01 37696.43 41888.39 37561.26 49594.14 386
usedtu_blend_shiyan586.75 41784.29 42494.16 36086.66 48691.83 34397.42 43695.23 46569.94 49388.37 39092.36 46378.01 37696.50 41289.35 35961.26 49594.14 386
SP-NN55.28 48253.59 48460.34 50086.63 48939.01 53186.70 50856.31 52931.08 52543.77 52068.45 51823.39 51860.24 52429.19 52456.76 50781.77 506
LoFTR74.41 46370.88 46684.99 46886.56 49067.85 49593.74 48189.63 50569.46 49454.95 50787.39 49330.76 50096.92 38661.37 50164.06 48790.19 475
blended_shiyan887.82 40985.71 41694.16 36086.54 49191.79 34599.72 21397.08 38279.32 47088.44 38492.35 46677.88 38096.56 40988.53 37161.51 49494.15 382
blended_shiyan687.74 41285.62 41994.09 36786.53 49291.73 35199.72 21397.08 38279.32 47088.22 39492.31 46877.82 38196.43 41888.31 37761.26 49594.13 391
CL-MVSNet_self_test84.50 43583.15 43588.53 45486.00 49381.79 46998.82 37497.35 32185.12 43083.62 44390.91 47476.66 39391.40 48969.53 48360.36 50192.40 452
MatchFormer70.84 46566.72 47183.19 47385.99 49464.61 49993.58 48488.62 50959.32 50450.64 51082.31 50528.00 50796.79 39852.52 51059.50 50388.18 492
UnsupCasMVSNet_bld79.97 45577.03 46188.78 45185.62 49581.98 46793.66 48297.35 32175.51 48270.79 49083.05 50248.70 49294.91 46178.31 46260.29 50289.46 486
mvs5depth84.87 43182.90 43790.77 43185.59 49684.84 44891.10 50093.29 49283.14 44785.07 43494.33 43862.17 46897.32 35778.83 46072.59 46490.14 476
SP-LightGlue55.29 48053.65 48360.20 50285.58 49739.12 53086.36 51157.52 52632.34 52444.34 51967.75 52124.36 51559.32 52729.62 52254.98 50882.17 504
SP-SuperGlue55.29 48053.71 48260.00 50485.11 49838.86 53286.96 50757.95 52532.77 52244.54 51868.00 51923.90 51659.51 52629.61 52354.59 50981.63 507
SP-MNN53.97 48552.04 48959.73 50684.72 49938.63 53386.51 50955.94 53029.25 52640.20 52567.48 52222.18 52059.59 52527.79 52554.33 51180.98 508
Patchmatch-RL test86.90 41585.98 41489.67 44484.45 50075.59 48689.71 50392.43 49486.89 40877.83 47390.94 47394.22 9593.63 47587.75 38769.61 47199.79 112
MASt3R-SfM78.94 45679.57 45477.07 48184.15 50150.74 51691.56 49692.34 49583.22 44680.84 45794.16 44036.67 49892.30 48679.45 45373.71 45688.16 493
pmmvs-eth3d84.03 43881.97 44290.20 43984.15 50187.09 43298.10 42294.73 47683.05 44874.10 48687.77 49065.56 45694.01 46981.08 44269.24 47389.49 485
test_fmvs379.99 45480.17 45279.45 47884.02 50362.83 50099.05 34293.49 49188.29 38780.06 46286.65 49628.09 50688.00 50088.63 36773.27 45887.54 497
PM-MVS80.47 45178.88 45685.26 46683.79 50472.22 49095.89 47291.08 50085.71 42476.56 47888.30 48636.64 49993.90 47182.39 43469.57 47289.66 484
new-patchmatchnet81.19 44779.34 45586.76 46382.86 50580.36 47997.92 42695.27 46482.09 45572.02 48886.87 49562.81 46790.74 49471.10 48063.08 48989.19 488
FE-MVSNET283.57 44281.36 44590.20 43982.83 50687.59 42698.28 41196.04 44585.33 42974.13 48587.45 49159.16 47693.26 47979.12 45869.91 46989.77 481
FE-MVSNET81.05 44978.81 45787.79 45981.98 50783.70 45398.23 41591.78 49981.27 45874.29 48487.44 49260.92 47490.67 49564.92 49668.43 47689.01 490
mvsany_test382.12 44681.14 44785.06 46781.87 50870.41 49297.09 44692.14 49691.27 31077.84 47288.73 48439.31 49695.49 44990.75 33971.24 46689.29 487
WB-MVS76.28 45877.28 46073.29 48781.18 50954.68 51197.87 42994.19 48281.30 45769.43 49290.70 47577.02 38782.06 51035.71 51868.11 47983.13 502
test_f78.40 45777.59 45980.81 47780.82 51062.48 50396.96 45093.08 49383.44 44474.57 48384.57 50127.95 50892.63 48384.15 42072.79 46087.32 498
SSC-MVS75.42 46176.40 46272.49 49280.68 51153.62 51297.42 43694.06 48480.42 46268.75 49390.14 47876.54 39581.66 51133.25 51966.34 48382.19 503
pmmvs380.27 45277.77 45887.76 46080.32 51282.43 46498.23 41591.97 49772.74 48878.75 46787.97 48957.30 48090.99 49270.31 48162.37 49289.87 479
testf168.38 46966.92 46972.78 48978.80 51350.36 51790.95 50187.35 51155.47 50758.95 50188.14 48720.64 52387.60 50257.28 50664.69 48580.39 510
APD_test268.38 46966.92 46972.78 48978.80 51350.36 51790.95 50187.35 51155.47 50758.95 50188.14 48720.64 52387.60 50257.28 50664.69 48580.39 510
ambc83.23 47277.17 51562.61 50187.38 50594.55 48076.72 47786.65 49630.16 50396.36 42484.85 41969.86 47090.73 468
test_vis3_rt68.82 46766.69 47275.21 48676.24 51660.41 50596.44 46068.71 52175.13 48350.54 51169.52 51616.42 52996.32 42780.27 44966.92 48268.89 516
PDCNetPlus59.83 47757.26 48067.55 49676.18 51756.71 50987.01 50645.27 53559.54 50348.80 51383.01 50326.63 51076.54 51662.12 50026.78 52769.40 515
usedtu_dtu_shiyan275.87 46072.37 46586.39 46476.18 51775.49 48796.53 45893.82 48864.74 49872.53 48788.48 48537.67 49791.12 49164.13 49757.22 50592.56 447
TDRefinement84.76 43282.56 43991.38 42574.58 51984.80 44997.36 44094.56 47984.73 43580.21 46096.12 36763.56 46398.39 29487.92 38563.97 48890.95 467
PMatch-SfM62.12 47658.57 47972.76 49174.34 52052.97 51484.95 51265.57 52256.89 50646.61 51585.70 5009.51 53980.54 51360.53 50343.03 51884.77 499
SIFT-NN35.94 49636.54 49934.16 51273.93 52129.52 53562.74 52537.28 53619.65 53027.91 53249.19 53111.66 53246.35 5319.19 53237.30 52026.61 529
ELoFTR64.32 47560.56 47875.60 48573.46 52253.20 51386.50 51080.09 51760.74 50245.95 51682.48 50416.05 53089.20 49856.48 50943.34 51784.38 500
E-PMN52.30 48852.18 48852.67 50871.51 52345.40 52393.62 48376.60 51936.01 51743.50 52164.13 52527.11 50967.31 52231.06 52026.06 52845.30 528
EMVS51.44 49051.22 49152.11 50970.71 52444.97 52594.04 47875.66 52035.34 51942.40 52361.56 52928.93 50565.87 52327.64 52624.73 52945.49 526
PMMVS267.15 47264.15 47576.14 48470.56 52562.07 50493.89 47987.52 51058.09 50560.02 50078.32 50722.38 51984.54 50859.56 50447.03 51581.80 505
SIFT-MNN34.10 49734.41 50033.17 51468.99 52628.51 53660.22 52736.81 53719.08 53324.04 53447.28 53410.06 53645.04 5328.72 53334.47 52225.97 532
SIFT-NCM-Cal31.73 49931.67 50231.91 51767.18 52727.55 54258.36 52933.09 54118.38 53614.93 54145.16 5408.60 54043.82 5347.62 54231.68 52524.36 535
SIFT-NN-NCMNet33.88 49834.14 50133.10 51566.88 52828.42 53760.42 52636.72 53819.15 53124.06 53347.14 53510.24 53444.77 5338.72 53333.94 52426.10 531
FPMVS68.72 46868.72 46868.71 49465.95 52944.27 52795.97 47194.74 47551.13 51053.26 50890.50 47625.11 51383.00 50960.80 50280.97 41678.87 512
SP-DiffGlue56.84 47855.72 48160.19 50365.70 53040.86 52881.89 51460.28 52434.62 52150.39 51276.88 50926.61 51158.81 52848.21 51256.94 50680.90 509
wuyk23d20.37 51120.84 51418.99 52865.34 53127.73 54050.43 5377.67 5529.50 5458.01 5466.34 5466.13 54826.24 54523.40 52810.69 5432.99 543
SIFT-ConvMatch30.09 50229.76 50631.09 51965.16 53227.56 54154.13 53331.17 54218.55 53517.88 53745.89 5378.40 54142.26 5388.11 53818.51 53423.46 537
SIFT-CM-Cal28.34 50527.90 50929.63 52163.75 53325.98 54650.66 53626.18 54618.12 53916.88 53944.64 5418.08 54339.70 5397.65 54115.19 53923.22 538
LCM-MVSNet67.77 47164.73 47476.87 48362.95 53456.25 51089.37 50493.74 48944.53 51261.99 49880.74 50620.42 52586.53 50669.37 48459.50 50387.84 494
SIFT-NN-CMatch31.71 50031.56 50332.16 51662.58 53527.53 54356.45 53033.28 54019.00 53423.65 53547.34 53210.05 53742.72 5368.71 53522.96 53226.24 530
SIFT-UM-Cal27.47 50627.02 51028.83 52462.12 53624.58 54853.60 53423.46 54718.14 53812.85 54345.56 5387.49 54439.45 5407.68 54012.30 54022.45 539
SIFT-UMatch29.40 50428.87 50830.98 52062.08 53726.57 54556.09 53129.45 54418.31 53715.86 54046.00 5368.23 54242.54 5377.99 53915.81 53723.85 536
GLUNet-SfM51.10 49146.61 49464.56 49761.54 53839.88 52979.38 52065.13 52336.09 51633.36 52969.94 51414.50 53178.76 51442.46 51617.10 53675.02 514
SIFT-NN-UMatch31.23 50131.05 50531.79 51860.08 53927.23 54458.49 52833.65 53919.14 53217.30 53847.31 53310.12 53542.88 5358.67 53624.67 53025.27 533
XFeat-NN42.54 49242.87 49641.54 51159.73 54027.86 53969.53 52245.34 53424.36 52737.16 52664.79 52320.84 52251.40 53030.01 52134.12 52345.36 527
MVEpermissive53.74 2251.54 48947.86 49362.60 49859.56 54150.93 51579.41 51977.69 51835.69 51836.27 52761.76 5285.79 54969.63 52037.97 51736.61 52167.24 517
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SIFT-NN-PointCN29.63 50329.72 50729.36 52257.55 54223.55 54956.07 53230.57 54317.99 54020.99 53645.21 5399.94 53839.33 5418.40 53720.81 53325.20 534
SIFT-PointCN25.49 50725.71 51124.84 52556.17 54318.65 55051.37 53526.53 54516.31 54112.78 54439.87 5446.41 54734.09 5436.51 54415.42 53821.77 540
SIFT-PCN-Cal24.67 50824.81 51224.24 52656.13 54418.04 55149.05 53823.39 54816.07 54212.99 54240.17 5436.97 54634.68 5426.71 54311.81 54119.99 541
XFeat-MNN41.51 49341.24 49742.32 51055.40 54528.19 53869.39 52346.53 53323.57 52834.47 52863.21 52720.04 52652.41 52927.43 52731.08 52646.37 525
SIFT-NCMNet21.21 51021.22 51321.17 52752.99 54616.41 55242.12 53914.05 55015.89 54310.70 54535.85 5455.14 55029.82 5445.80 5458.44 54417.28 542
ANet_high56.10 47952.24 48767.66 49549.27 54756.82 50883.94 51382.02 51670.47 49133.28 53064.54 52417.23 52869.16 52145.59 51423.85 53177.02 513
tmp_tt65.23 47462.94 47772.13 49344.90 54850.03 51981.05 51889.42 50738.45 51448.51 51499.90 2354.09 48378.70 51591.84 32018.26 53587.64 496
PMVScopyleft49.05 2353.75 48651.34 49060.97 49940.80 54934.68 53474.82 52189.62 50637.55 51528.67 53172.12 5107.09 54581.63 51243.17 51568.21 47866.59 518
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12337.68 49539.14 49833.31 51319.94 55024.83 54798.36 4089.75 55115.53 54451.31 50987.14 49419.62 52717.74 54647.10 5133.47 54557.36 523
testmvs40.60 49444.45 49529.05 52319.49 55114.11 55399.68 23118.47 54920.74 52964.59 49798.48 27810.95 53317.09 54756.66 50811.01 54255.94 524
mmdepth0.00 5140.00 5170.00 5290.00 5520.00 5540.00 5400.00 5530.00 5470.00 5480.00 5480.00 5510.00 5480.00 5460.00 5460.00 544
monomultidepth0.00 5140.00 5170.00 5290.00 5520.00 5540.00 5400.00 5530.00 5470.00 5480.00 5480.00 5510.00 5480.00 5460.00 5460.00 544
test_blank0.00 5140.00 5170.00 5290.00 5520.00 5540.00 5400.00 5530.00 5470.00 5480.02 5470.00 5510.00 5480.00 5460.00 5460.00 544
eth-test20.00 552
eth-test0.00 552
uanet_test0.00 5140.00 5170.00 5290.00 5520.00 5540.00 5400.00 5530.00 5470.00 5480.00 5480.00 5510.00 5480.00 5460.00 5460.00 544
DCPMVS0.00 5140.00 5170.00 5290.00 5520.00 5540.00 5400.00 5530.00 5470.00 5480.00 5480.00 5510.00 5480.00 5460.00 5460.00 544
cdsmvs_eth3d_5k23.43 50931.24 5040.00 5290.00 5520.00 5540.00 54098.09 2340.00 5470.00 54899.67 11483.37 3140.00 5480.00 5460.00 5460.00 544
pcd_1.5k_mvsjas7.60 51310.13 5160.00 5290.00 5520.00 5540.00 5400.00 5530.00 5470.00 5480.00 54891.20 1770.00 5480.00 5460.00 5460.00 544
sosnet-low-res0.00 5140.00 5170.00 5290.00 5520.00 5540.00 5400.00 5530.00 5470.00 5480.00 5480.00 5510.00 5480.00 5460.00 5460.00 544
sosnet0.00 5140.00 5170.00 5290.00 5520.00 5540.00 5400.00 5530.00 5470.00 5480.00 5480.00 5510.00 5480.00 5460.00 5460.00 544
uncertanet0.00 5140.00 5170.00 5290.00 5520.00 5540.00 5400.00 5530.00 5470.00 5480.00 5480.00 5510.00 5480.00 5460.00 5460.00 544
Regformer0.00 5140.00 5170.00 5290.00 5520.00 5540.00 5400.00 5530.00 5470.00 5480.00 5480.00 5510.00 5480.00 5460.00 5460.00 544
ab-mvs-re8.28 51211.04 5150.00 5290.00 5520.00 5540.00 5400.00 5530.00 5470.00 54899.40 1470.00 5510.00 5480.00 5460.00 5460.00 544
uanet0.00 5140.00 5170.00 5290.00 5520.00 5540.00 5400.00 5530.00 5470.00 5480.00 5480.00 5510.00 5480.00 5460.00 5460.00 544
WAC-MVS90.97 36986.10 409
PC_three_145296.96 6099.80 2899.79 6397.49 11100.00 199.99 599.98 32100.00 1
test_241102_TWO98.43 15697.27 4799.80 2899.94 597.18 23100.00 1100.00 1100.00 1100.00 1
test_0728_THIRD96.48 8099.83 2499.91 1997.87 6100.00 199.92 16100.00 1100.00 1
GSMVS99.59 154
sam_mvs194.72 7499.59 154
sam_mvs94.25 94
MTGPAbinary98.28 204
test_post195.78 47359.23 53093.20 12897.74 34191.06 330
test_post63.35 52694.43 8298.13 320
patchmatchnet-post91.70 47095.12 6097.95 332
MTMP99.87 13396.49 435
test9_res99.71 4899.99 21100.00 1
agg_prior299.48 63100.00 1100.00 1
test_prior498.05 8299.94 93
test_prior299.95 7595.78 10599.73 4799.76 7396.00 4199.78 35100.00 1
旧先验299.46 28294.21 16699.85 2099.95 8596.96 200
新几何299.40 287
无先验99.49 27498.71 7993.46 201100.00 194.36 26799.99 26
原ACMM299.90 117
testdata299.99 3990.54 343
segment_acmp96.68 31
testdata199.28 31396.35 91
plane_prior597.87 25898.37 30097.79 16989.55 33094.52 345
plane_prior498.59 264
plane_prior391.64 35696.63 7593.01 305
plane_prior299.84 15296.38 86
plane_prior91.74 34899.86 14496.76 7089.59 329
n20.00 553
nn0.00 553
door-mid89.69 504
test1198.44 148
door90.31 501
HQP5-MVS91.85 341
BP-MVS97.92 158
HQP4-MVS93.37 30098.39 29494.53 343
HQP3-MVS97.89 25689.60 327
HQP2-MVS80.65 350
MDTV_nov1_ep13_2view96.26 17096.11 46791.89 28598.06 16994.40 8494.30 27099.67 133
ACMMP++_ref87.04 363
ACMMP++88.23 350
Test By Simon92.82 139