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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
test_0728_SECOND99.71 199.72 1299.35 198.97 8498.88 5499.94 598.47 2899.81 1299.84 7
DPE-MVScopyleft98.92 598.67 999.65 299.58 3299.20 998.42 19298.91 4897.58 1999.54 1599.46 1697.10 1299.94 597.64 7799.84 1099.83 8
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS++99.08 298.89 399.64 399.17 9199.23 799.69 198.88 5497.32 3399.53 1699.47 1397.81 399.94 598.47 2899.72 4799.74 31
SED-MVS99.09 198.91 299.63 499.71 1999.24 599.02 7498.87 6197.65 1499.73 499.48 1197.53 799.94 598.43 3299.81 1299.70 47
DVP-MVScopyleft99.03 398.83 699.63 499.72 1299.25 298.97 8498.58 14097.62 1699.45 1899.46 1697.42 999.94 598.47 2899.81 1299.69 50
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
MSC_two_6792asdad99.62 699.17 9199.08 1198.63 13099.94 598.53 2099.80 1999.86 3
No_MVS99.62 699.17 9199.08 1198.63 13099.94 598.53 2099.80 1999.86 3
SMA-MVScopyleft98.58 2098.25 3899.56 899.51 3999.04 1598.95 9098.80 8593.67 21399.37 2399.52 496.52 2199.89 3998.06 4799.81 1299.76 28
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
ACMMP_NAP98.61 1598.30 3599.55 999.62 3098.95 1798.82 11798.81 7895.80 10899.16 3599.47 1395.37 5499.92 2697.89 5899.75 3899.79 14
HPM-MVS++copyleft98.58 2098.25 3899.55 999.50 4199.08 1198.72 14498.66 12397.51 2298.15 9098.83 11595.70 4399.92 2697.53 8799.67 5499.66 62
APDe-MVS99.02 498.84 599.55 999.57 3398.96 1699.39 1298.93 4297.38 3099.41 2099.54 296.66 1799.84 5798.86 1199.85 599.87 2
MP-MVS-pluss98.31 4997.92 5599.49 1299.72 1298.88 1898.43 19098.78 9294.10 18297.69 12599.42 2095.25 6299.92 2698.09 4699.80 1999.67 59
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MCST-MVS98.65 1298.37 2399.48 1399.60 3198.87 1998.41 19398.68 11597.04 5398.52 7598.80 11896.78 1699.83 5997.93 5499.61 6799.74 31
MTAPA98.58 2098.29 3699.46 1499.76 298.64 2598.90 9798.74 10097.27 4098.02 10199.39 2294.81 7499.96 497.91 5699.79 2399.77 22
CNVR-MVS98.78 898.56 1399.45 1599.32 6098.87 1998.47 18498.81 7897.72 1098.76 5899.16 6797.05 1399.78 9198.06 4799.66 5699.69 50
APD-MVScopyleft98.35 4598.00 5399.42 1699.51 3998.72 2198.80 12598.82 7394.52 17199.23 2999.25 5195.54 4899.80 7896.52 13199.77 2899.74 31
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SF-MVS98.59 1898.32 3499.41 1799.54 3598.71 2299.04 6898.81 7895.12 14399.32 2599.39 2296.22 2499.84 5797.72 7099.73 4499.67 59
NCCC98.61 1598.35 2699.38 1899.28 7498.61 2698.45 18598.76 9697.82 998.45 7998.93 10496.65 1899.83 5997.38 9499.41 9799.71 43
3Dnovator+94.38 697.43 8796.78 10499.38 1897.83 21098.52 2899.37 1498.71 10897.09 5292.99 28699.13 7289.36 17499.89 3996.97 10699.57 7499.71 43
OPU-MVS99.37 2099.24 8499.05 1499.02 7499.16 6797.81 399.37 16097.24 9799.73 4499.70 47
SteuartSystems-ACMMP98.90 698.75 799.36 2199.22 8698.43 3399.10 5898.87 6197.38 3099.35 2499.40 2197.78 599.87 4897.77 6799.85 599.78 16
Skip Steuart: Steuart Systems R&D Blog.
ZNCC-MVS98.49 3098.20 4499.35 2299.73 1198.39 3499.19 4298.86 6795.77 10998.31 8999.10 7695.46 4999.93 2197.57 8499.81 1299.74 31
GST-MVS98.43 3898.12 4799.34 2399.72 1298.38 3599.09 5998.82 7395.71 11398.73 6199.06 8695.27 6099.93 2197.07 10399.63 6499.72 39
XVS98.70 1198.49 1799.34 2399.70 2298.35 4199.29 2298.88 5497.40 2798.46 7699.20 5795.90 3999.89 3997.85 6199.74 4299.78 16
X-MVStestdata94.06 27292.30 29399.34 2399.70 2298.35 4199.29 2298.88 5497.40 2798.46 7643.50 38195.90 3999.89 3997.85 6199.74 4299.78 16
train_agg97.97 5497.52 6999.33 2699.31 6298.50 2997.92 24398.73 10392.98 24197.74 12098.68 13296.20 2699.80 7896.59 12799.57 7499.68 55
HFP-MVS98.63 1498.40 2099.32 2799.72 1298.29 4499.23 3198.96 3796.10 9498.94 4499.17 6496.06 3099.92 2697.62 7899.78 2699.75 29
MSP-MVS98.74 1098.55 1499.29 2899.75 398.23 4699.26 2798.88 5497.52 2199.41 2098.78 12096.00 3399.79 8897.79 6699.59 7099.85 5
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
region2R98.61 1598.38 2299.29 2899.74 798.16 5199.23 3198.93 4296.15 9198.94 4499.17 6495.91 3799.94 597.55 8599.79 2399.78 16
ACMMPR98.59 1898.36 2499.29 2899.74 798.15 5299.23 3198.95 3896.10 9498.93 4899.19 6295.70 4399.94 597.62 7899.79 2399.78 16
MP-MVScopyleft98.33 4898.01 5299.28 3199.75 398.18 4999.22 3598.79 9096.13 9297.92 11299.23 5294.54 7799.94 596.74 12699.78 2699.73 36
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS97.94 5797.49 7099.28 3199.47 4798.44 3197.91 24598.67 12092.57 25698.77 5798.85 11295.93 3699.72 10395.56 16399.69 5299.68 55
PGM-MVS98.49 3098.23 4199.27 3399.72 1298.08 5598.99 8199.49 595.43 12599.03 3899.32 3995.56 4699.94 596.80 12399.77 2899.78 16
mPP-MVS98.51 2998.26 3799.25 3499.75 398.04 5699.28 2498.81 7896.24 8798.35 8699.23 5295.46 4999.94 597.42 9299.81 1299.77 22
SR-MVS98.57 2398.35 2699.24 3599.53 3698.18 4999.09 5998.82 7396.58 7399.10 3799.32 3995.39 5299.82 6697.70 7499.63 6499.72 39
TSAR-MVS + MP.98.78 898.62 1099.24 3599.69 2498.28 4599.14 4998.66 12396.84 6199.56 1399.31 4196.34 2399.70 10998.32 3899.73 4499.73 36
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS97.55 8096.99 9499.23 3799.04 10498.55 2797.17 30698.35 18894.85 15897.93 11198.58 14395.07 6999.71 10892.60 25199.34 10399.43 102
MVS_030498.47 3398.22 4399.21 3899.00 10997.80 6698.88 10495.32 35398.86 198.53 7499.44 1994.38 8499.94 599.86 199.70 5099.90 1
test_prior99.19 3999.31 6298.22 4798.84 7199.70 10999.65 63
CP-MVS98.57 2398.36 2499.19 3999.66 2697.86 6199.34 1898.87 6195.96 9998.60 7199.13 7296.05 3199.94 597.77 6799.86 199.77 22
test1299.18 4199.16 9598.19 4898.53 15098.07 9595.13 6799.72 10399.56 8099.63 67
PHI-MVS98.34 4698.06 5099.18 4199.15 9798.12 5499.04 6899.09 2493.32 22798.83 5499.10 7696.54 2099.83 5997.70 7499.76 3499.59 73
DeepC-MVS_fast96.70 198.55 2698.34 2999.18 4199.25 7898.04 5698.50 18198.78 9297.72 1098.92 4999.28 4495.27 6099.82 6697.55 8599.77 2899.69 50
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
新几何199.16 4499.34 5598.01 5898.69 11290.06 32198.13 9198.95 10294.60 7699.89 3991.97 27199.47 9199.59 73
APD-MVS_3200maxsize98.53 2898.33 3399.15 4599.50 4197.92 6099.15 4798.81 7896.24 8799.20 3099.37 2895.30 5899.80 7897.73 6999.67 5499.72 39
SR-MVS-dyc-post98.54 2798.35 2699.13 4699.49 4597.86 6199.11 5598.80 8596.49 7699.17 3399.35 3495.34 5699.82 6697.72 7099.65 5999.71 43
HPM-MVScopyleft98.36 4398.10 4999.13 4699.74 797.82 6599.53 898.80 8594.63 16698.61 7098.97 9595.13 6799.77 9697.65 7699.83 1199.79 14
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HPM-MVS_fast98.38 4198.13 4699.12 4899.75 397.86 6199.44 1198.82 7394.46 17498.94 4499.20 5795.16 6699.74 10197.58 8199.85 599.77 22
ACMMPcopyleft98.23 5097.95 5499.09 4999.74 797.62 7099.03 7199.41 695.98 9797.60 13399.36 3294.45 8299.93 2197.14 10098.85 12599.70 47
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
3Dnovator94.51 597.46 8296.93 9699.07 5097.78 21297.64 6899.35 1799.06 2797.02 5493.75 26199.16 6789.25 17899.92 2697.22 9999.75 3899.64 65
DP-MVS Recon97.86 6097.46 7399.06 5199.53 3698.35 4198.33 19798.89 5192.62 25398.05 9698.94 10395.34 5699.65 11996.04 14699.42 9699.19 133
alignmvs97.56 7997.07 9199.01 5298.66 14298.37 3998.83 11598.06 24896.74 6798.00 10597.65 23490.80 14999.48 15298.37 3696.56 19799.19 133
DELS-MVS98.40 4098.20 4498.99 5399.00 10997.66 6797.75 26198.89 5197.71 1298.33 8798.97 9594.97 7199.88 4798.42 3499.76 3499.42 104
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
canonicalmvs97.67 7097.23 8498.98 5498.70 13798.38 3599.34 1898.39 18196.76 6697.67 12697.40 25492.26 11199.49 14898.28 4096.28 20999.08 151
UA-Net97.96 5597.62 6298.98 5498.86 12397.47 7598.89 10199.08 2596.67 7098.72 6299.54 293.15 10099.81 7194.87 18098.83 12699.65 63
VNet97.79 6397.40 7798.96 5698.88 12197.55 7298.63 16198.93 4296.74 6799.02 3998.84 11390.33 15899.83 5998.53 2096.66 19399.50 85
QAPM96.29 13895.40 16198.96 5697.85 20997.60 7199.23 3198.93 4289.76 32693.11 28399.02 8889.11 18399.93 2191.99 27099.62 6699.34 108
114514_t96.93 11096.27 12698.92 5899.50 4197.63 6998.85 11198.90 4984.80 35897.77 11699.11 7492.84 10299.66 11894.85 18199.77 2899.47 93
CPTT-MVS97.72 6697.32 8198.92 5899.64 2897.10 8999.12 5398.81 7892.34 26498.09 9499.08 8493.01 10199.92 2696.06 14599.77 2899.75 29
CANet98.05 5397.76 5898.90 6098.73 13297.27 8098.35 19598.78 9297.37 3297.72 12398.96 10091.53 13499.92 2698.79 1399.65 5999.51 83
MVS_111021_HR98.47 3398.34 2998.88 6199.22 8697.32 7897.91 24599.58 397.20 4398.33 8799.00 9395.99 3499.64 12198.05 4999.76 3499.69 50
TSAR-MVS + GP.98.38 4198.24 4098.81 6299.22 8697.25 8598.11 22898.29 20297.19 4498.99 4399.02 8896.22 2499.67 11698.52 2698.56 13999.51 83
DeepC-MVS95.98 397.88 5997.58 6498.77 6399.25 7896.93 9498.83 11598.75 9896.96 5796.89 15799.50 890.46 15599.87 4897.84 6399.76 3499.52 80
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CNLPA97.45 8597.03 9298.73 6499.05 10397.44 7798.07 23098.53 15095.32 13396.80 16298.53 14793.32 9899.72 10394.31 20299.31 10599.02 155
WTY-MVS97.37 9396.92 9798.72 6598.86 12396.89 9898.31 20298.71 10895.26 13697.67 12698.56 14692.21 11499.78 9195.89 15096.85 18899.48 91
EI-MVSNet-Vis-set98.47 3398.39 2198.69 6699.46 4996.49 11798.30 20498.69 11297.21 4298.84 5299.36 3295.41 5199.78 9198.62 1699.65 5999.80 13
LS3D97.16 10296.66 11298.68 6798.53 15297.19 8798.93 9498.90 4992.83 24895.99 19099.37 2892.12 11799.87 4893.67 22399.57 7498.97 160
MVS_111021_LR98.34 4698.23 4198.67 6899.27 7596.90 9697.95 24199.58 397.14 4898.44 8199.01 9295.03 7099.62 12797.91 5699.75 3899.50 85
原ACMM198.65 6999.32 6096.62 10698.67 12093.27 23197.81 11598.97 9595.18 6599.83 5993.84 21799.46 9499.50 85
PAPR96.84 11596.24 12898.65 6998.72 13696.92 9597.36 28998.57 14293.33 22696.67 16597.57 24294.30 8699.56 13591.05 28898.59 13799.47 93
EI-MVSNet-UG-set98.41 3998.34 2998.61 7199.45 5296.32 12898.28 20798.68 11597.17 4598.74 5999.37 2895.25 6299.79 8898.57 1799.54 8399.73 36
sss97.39 9096.98 9598.61 7198.60 14896.61 10898.22 21298.93 4293.97 19098.01 10498.48 15291.98 12199.85 5396.45 13398.15 15799.39 105
HY-MVS93.96 896.82 11696.23 12998.57 7398.46 15697.00 9198.14 22398.21 21193.95 19196.72 16497.99 20291.58 12999.76 9794.51 19596.54 19898.95 163
DP-MVS96.59 12395.93 14098.57 7399.34 5596.19 13498.70 14998.39 18189.45 33194.52 21999.35 3491.85 12399.85 5392.89 24798.88 12299.68 55
MSLP-MVS++98.56 2598.57 1298.55 7599.26 7796.80 9998.71 14599.05 2997.28 3698.84 5299.28 4496.47 2299.40 15898.52 2699.70 5099.47 93
ab-mvs96.42 13195.71 15298.55 7598.63 14596.75 10297.88 25098.74 10093.84 19696.54 17498.18 18885.34 26499.75 9995.93 14996.35 20399.15 140
test_yl97.22 9796.78 10498.54 7798.73 13296.60 10998.45 18598.31 19494.70 16098.02 10198.42 16090.80 14999.70 10996.81 12196.79 19099.34 108
DCV-MVSNet97.22 9796.78 10498.54 7798.73 13296.60 10998.45 18598.31 19494.70 16098.02 10198.42 16090.80 14999.70 10996.81 12196.79 19099.34 108
SD-MVS98.64 1398.68 898.53 7999.33 5798.36 4098.90 9798.85 7097.28 3699.72 699.39 2296.63 1997.60 33398.17 4299.85 599.64 65
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
EPNet97.28 9596.87 9998.51 8094.98 34496.14 13698.90 9797.02 32198.28 495.99 19099.11 7491.36 13699.89 3996.98 10599.19 10999.50 85
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
1112_ss96.63 12196.00 13798.50 8198.56 14996.37 12598.18 22198.10 23692.92 24494.84 20998.43 15892.14 11699.58 13194.35 19996.51 19999.56 79
PAPM_NR97.46 8297.11 8898.50 8199.50 4196.41 12398.63 16198.60 13395.18 14097.06 14898.06 19594.26 8899.57 13293.80 21998.87 12499.52 80
EC-MVSNet98.21 5198.11 4898.49 8398.34 16997.26 8499.61 598.43 17596.78 6498.87 5198.84 11393.72 9599.01 20598.91 1099.50 8699.19 133
AdaColmapbinary97.15 10396.70 10898.48 8499.16 9596.69 10598.01 23698.89 5194.44 17596.83 15898.68 13290.69 15299.76 9794.36 19899.29 10698.98 159
LFMVS95.86 16094.98 18898.47 8598.87 12296.32 12898.84 11496.02 34493.40 22498.62 6999.20 5774.99 35099.63 12497.72 7097.20 18399.46 97
CS-MVS-test98.49 3098.50 1698.46 8699.20 8997.05 9099.64 498.50 16097.45 2698.88 5099.14 7195.25 6299.15 18198.83 1299.56 8099.20 129
test_fmvsm_n_192098.87 799.01 198.45 8799.42 5496.43 12098.96 8999.36 798.63 299.86 299.51 695.91 3799.97 199.72 299.75 3898.94 164
MAR-MVS96.91 11196.40 12198.45 8798.69 13996.90 9698.66 15798.68 11592.40 26397.07 14797.96 20591.54 13399.75 9993.68 22198.92 11998.69 181
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
casdiffmvs_mvgpermissive97.72 6697.48 7298.44 8998.42 15896.59 11198.92 9598.44 17196.20 8997.76 11799.20 5791.66 12899.23 17198.27 4198.41 14899.49 90
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_Blended_VisFu97.70 6897.46 7398.44 8999.27 7595.91 15398.63 16199.16 2094.48 17397.67 12698.88 10992.80 10399.91 3497.11 10199.12 11199.50 85
MG-MVS97.81 6297.60 6398.44 8999.12 9995.97 14597.75 26198.78 9296.89 6098.46 7699.22 5493.90 9499.68 11594.81 18499.52 8599.67 59
PLCcopyleft95.07 497.20 10096.78 10498.44 8999.29 7096.31 13098.14 22398.76 9692.41 26296.39 18098.31 17594.92 7399.78 9194.06 21198.77 12999.23 126
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS93.45 1194.68 22693.43 27398.42 9398.62 14696.77 10195.48 35598.20 21384.63 35993.34 27498.32 17488.55 19999.81 7184.80 34898.96 11898.68 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ETV-MVS97.96 5597.81 5698.40 9498.42 15897.27 8098.73 14098.55 14696.84 6198.38 8397.44 25195.39 5299.35 16197.62 7898.89 12198.58 193
Effi-MVS+97.12 10496.69 10998.39 9598.19 18596.72 10497.37 28798.43 17593.71 20697.65 12998.02 19892.20 11599.25 16896.87 11897.79 16999.19 133
Test_1112_low_res96.34 13795.66 15798.36 9698.56 14995.94 14897.71 26498.07 24392.10 27394.79 21397.29 25991.75 12599.56 13594.17 20696.50 20099.58 77
Vis-MVSNetpermissive97.42 8897.11 8898.34 9798.66 14296.23 13199.22 3599.00 3296.63 7298.04 9899.21 5588.05 21199.35 16196.01 14899.21 10799.45 99
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVScopyleft93.04 1395.83 16295.00 18698.32 9897.18 26097.32 7899.21 3898.97 3589.96 32291.14 31999.05 8786.64 23899.92 2693.38 22999.47 9197.73 221
CS-MVS98.44 3698.49 1798.31 9999.08 10296.73 10399.67 398.47 16697.17 4598.94 4499.10 7695.73 4299.13 18498.71 1499.49 8899.09 147
casdiffmvspermissive97.63 7397.41 7698.28 10098.33 17196.14 13698.82 11798.32 19296.38 8497.95 10799.21 5591.23 14199.23 17198.12 4498.37 14999.48 91
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EIA-MVS97.75 6497.58 6498.27 10198.38 16196.44 11999.01 7698.60 13395.88 10597.26 13997.53 24594.97 7199.33 16397.38 9499.20 10899.05 153
PatchMatch-RL96.59 12396.03 13698.27 10199.31 6296.51 11697.91 24599.06 2793.72 20596.92 15598.06 19588.50 20199.65 11991.77 27599.00 11798.66 185
testdata98.26 10399.20 8995.36 17398.68 11591.89 27898.60 7199.10 7694.44 8399.82 6694.27 20399.44 9599.58 77
baseline97.64 7297.44 7598.25 10498.35 16496.20 13299.00 7898.32 19296.33 8698.03 9999.17 6491.35 13799.16 17898.10 4598.29 15599.39 105
IS-MVSNet97.22 9796.88 9898.25 10498.85 12596.36 12699.19 4297.97 25695.39 12797.23 14098.99 9491.11 14398.93 21794.60 19198.59 13799.47 93
test_fmvsmvis_n_192098.44 3698.51 1598.23 10698.33 17196.15 13598.97 8499.15 2198.55 398.45 7999.55 194.26 8899.97 199.65 399.66 5698.57 194
CANet_DTU96.96 10996.55 11598.21 10798.17 18996.07 13897.98 23998.21 21197.24 4197.13 14398.93 10486.88 23599.91 3495.00 17999.37 10298.66 185
CSCG97.85 6197.74 5998.20 10899.67 2595.16 18199.22 3599.32 893.04 23997.02 15098.92 10695.36 5599.91 3497.43 9199.64 6399.52 80
OMC-MVS97.55 8097.34 8098.20 10899.33 5795.92 15298.28 20798.59 13595.52 12197.97 10699.10 7693.28 9999.49 14895.09 17798.88 12299.19 133
UGNet96.78 11796.30 12598.19 11098.24 17795.89 15598.88 10498.93 4297.39 2996.81 16197.84 21682.60 30299.90 3796.53 13099.49 8898.79 173
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
SDMVSNet96.85 11496.42 11998.14 11199.30 6696.38 12499.21 3899.23 1495.92 10095.96 19298.76 12685.88 25299.44 15797.93 5495.59 21998.60 189
PVSNet_Blended97.38 9197.12 8798.14 11199.25 7895.35 17597.28 29699.26 1093.13 23597.94 10998.21 18592.74 10499.81 7196.88 11599.40 9999.27 121
HyFIR lowres test96.90 11296.49 11898.14 11199.33 5795.56 16597.38 28599.65 292.34 26497.61 13298.20 18689.29 17699.10 19296.97 10697.60 17799.77 22
MVS_Test97.28 9597.00 9398.13 11498.33 17195.97 14598.74 13698.07 24394.27 17898.44 8198.07 19492.48 10699.26 16796.43 13498.19 15699.16 139
diffmvspermissive97.58 7797.40 7798.13 11498.32 17495.81 15898.06 23198.37 18596.20 8998.74 5998.89 10891.31 13999.25 16898.16 4398.52 14099.34 108
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
lupinMVS97.44 8697.22 8598.12 11698.07 19595.76 15997.68 26697.76 26994.50 17298.79 5598.61 13892.34 10899.30 16597.58 8199.59 7099.31 114
GeoE96.58 12596.07 13398.10 11798.35 16495.89 15599.34 1898.12 23093.12 23696.09 18698.87 11089.71 16798.97 20792.95 24398.08 16099.43 102
MVS94.67 22993.54 26998.08 11896.88 27896.56 11398.19 21898.50 16078.05 36892.69 29498.02 19891.07 14599.63 12490.09 29998.36 15198.04 212
CHOSEN 1792x268897.12 10496.80 10198.08 11899.30 6694.56 21498.05 23299.71 193.57 21897.09 14498.91 10788.17 20699.89 3996.87 11899.56 8099.81 12
jason97.32 9497.08 9098.06 12097.45 24195.59 16397.87 25197.91 26394.79 15998.55 7398.83 11591.12 14299.23 17197.58 8199.60 6899.34 108
jason: jason.
Fast-Effi-MVS+96.28 14095.70 15498.03 12198.29 17695.97 14598.58 16798.25 20891.74 28195.29 20197.23 26491.03 14699.15 18192.90 24597.96 16398.97 160
baseline195.84 16195.12 18198.01 12298.49 15595.98 14098.73 14097.03 31995.37 13096.22 18398.19 18789.96 16399.16 17894.60 19187.48 33098.90 167
EPP-MVSNet97.46 8297.28 8297.99 12398.64 14495.38 17299.33 2198.31 19493.61 21797.19 14199.07 8594.05 9199.23 17196.89 11398.43 14799.37 107
thisisatest053096.01 14995.36 16697.97 12498.38 16195.52 16898.88 10494.19 36694.04 18497.64 13098.31 17583.82 29799.46 15595.29 17297.70 17498.93 165
F-COLMAP97.09 10696.80 10197.97 12499.45 5294.95 19498.55 17498.62 13293.02 24096.17 18598.58 14394.01 9299.81 7193.95 21398.90 12099.14 142
nrg03096.28 14095.72 14997.96 12696.90 27798.15 5299.39 1298.31 19495.47 12394.42 22798.35 16892.09 11898.69 24197.50 8989.05 31497.04 238
API-MVS97.41 8997.25 8397.91 12798.70 13796.80 9998.82 11798.69 11294.53 16998.11 9298.28 17794.50 8199.57 13294.12 20899.49 8897.37 231
CDS-MVSNet96.99 10896.69 10997.90 12898.05 19895.98 14098.20 21598.33 19193.67 21396.95 15198.49 15193.54 9698.42 27195.24 17597.74 17299.31 114
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
VDDNet95.36 19094.53 20797.86 12998.10 19495.13 18498.85 11197.75 27090.46 31398.36 8499.39 2273.27 35799.64 12197.98 5096.58 19698.81 172
MVSFormer97.57 7897.49 7097.84 13098.07 19595.76 15999.47 998.40 17994.98 15198.79 5598.83 11592.34 10898.41 27996.91 10999.59 7099.34 108
Vis-MVSNet (Re-imp)96.87 11396.55 11597.83 13198.73 13295.46 17099.20 4098.30 20094.96 15396.60 16998.87 11090.05 16198.59 25193.67 22398.60 13699.46 97
MSDG95.93 15695.30 17397.83 13198.90 11995.36 17396.83 33198.37 18591.32 29694.43 22698.73 12890.27 15999.60 12990.05 30298.82 12798.52 195
FA-MVS(test-final)96.41 13595.94 13997.82 13398.21 18195.20 18097.80 25797.58 27993.21 23297.36 13797.70 22889.47 17199.56 13594.12 20897.99 16198.71 180
h-mvs3396.17 14395.62 15897.81 13499.03 10594.45 21698.64 15998.75 9897.48 2398.67 6398.72 12989.76 16599.86 5297.95 5281.59 35799.11 145
131496.25 14295.73 14897.79 13597.13 26395.55 16798.19 21898.59 13593.47 22192.03 31197.82 22091.33 13899.49 14894.62 19098.44 14598.32 204
FE-MVS95.62 17494.90 19297.78 13698.37 16394.92 19597.17 30697.38 30290.95 30797.73 12297.70 22885.32 26699.63 12491.18 28398.33 15298.79 173
tttt051796.07 14795.51 16097.78 13698.41 16094.84 19899.28 2494.33 36494.26 17997.64 13098.64 13684.05 29099.47 15495.34 16897.60 17799.03 154
PAPM94.95 21594.00 23797.78 13697.04 26795.65 16296.03 34798.25 20891.23 30194.19 24097.80 22291.27 14098.86 22882.61 35697.61 17698.84 171
thisisatest051595.61 17794.89 19397.76 13998.15 19195.15 18396.77 33294.41 36292.95 24397.18 14297.43 25284.78 27499.45 15694.63 18897.73 17398.68 182
Anonymous2024052995.10 20594.22 22297.75 14099.01 10894.26 22698.87 10898.83 7285.79 35496.64 16698.97 9578.73 32699.85 5396.27 13794.89 22499.12 144
TAPA-MVS93.98 795.35 19194.56 20697.74 14199.13 9894.83 20098.33 19798.64 12886.62 34696.29 18298.61 13894.00 9399.29 16680.00 36299.41 9799.09 147
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
xiu_mvs_v1_base_debu97.60 7497.56 6697.72 14298.35 16495.98 14097.86 25298.51 15597.13 4999.01 4098.40 16291.56 13099.80 7898.53 2098.68 13097.37 231
xiu_mvs_v1_base97.60 7497.56 6697.72 14298.35 16495.98 14097.86 25298.51 15597.13 4999.01 4098.40 16291.56 13099.80 7898.53 2098.68 13097.37 231
xiu_mvs_v1_base_debi97.60 7497.56 6697.72 14298.35 16495.98 14097.86 25298.51 15597.13 4999.01 4098.40 16291.56 13099.80 7898.53 2098.68 13097.37 231
TAMVS97.02 10796.79 10397.70 14598.06 19795.31 17798.52 17698.31 19493.95 19197.05 14998.61 13893.49 9798.52 25995.33 16997.81 16899.29 119
VPA-MVSNet95.75 16595.11 18297.69 14697.24 25297.27 8098.94 9299.23 1495.13 14295.51 19897.32 25785.73 25598.91 21997.33 9689.55 30696.89 255
BH-RMVSNet95.92 15795.32 17097.69 14698.32 17494.64 20698.19 21897.45 29694.56 16796.03 18898.61 13885.02 26999.12 18690.68 29399.06 11299.30 117
Anonymous20240521195.28 19594.49 20997.67 14899.00 10993.75 24298.70 14997.04 31890.66 30996.49 17698.80 11878.13 33299.83 5996.21 14195.36 22399.44 100
FIs96.51 12896.12 13197.67 14897.13 26397.54 7399.36 1599.22 1795.89 10394.03 24898.35 16891.98 12198.44 26996.40 13592.76 26897.01 240
thres600view795.49 17894.77 19697.67 14898.98 11495.02 18798.85 11196.90 32795.38 12896.63 16796.90 29784.29 28299.59 13088.65 32396.33 20498.40 199
mvsany_test197.69 6997.70 6097.66 15198.24 17794.18 23097.53 27797.53 28895.52 12199.66 899.51 694.30 8699.56 13598.38 3598.62 13599.23 126
thres40095.38 18794.62 20397.65 15298.94 11794.98 19198.68 15296.93 32595.33 13196.55 17296.53 31484.23 28699.56 13588.11 32496.29 20698.40 199
PS-MVSNAJ97.73 6597.77 5797.62 15398.68 14095.58 16497.34 29198.51 15597.29 3598.66 6797.88 21294.51 7899.90 3797.87 6099.17 11097.39 229
VDD-MVS95.82 16395.23 17597.61 15498.84 12693.98 23498.68 15297.40 30095.02 15097.95 10799.34 3874.37 35499.78 9198.64 1596.80 18999.08 151
ET-MVSNet_ETH3D94.13 26592.98 28197.58 15598.22 18096.20 13297.31 29495.37 35294.53 16979.56 36797.63 23886.51 23997.53 33796.91 10990.74 29099.02 155
UniMVSNet (Re)95.78 16495.19 17797.58 15596.99 27097.47 7598.79 13099.18 1995.60 11793.92 25297.04 28391.68 12698.48 26295.80 15587.66 32996.79 267
xiu_mvs_v2_base97.66 7197.70 6097.56 15798.61 14795.46 17097.44 28098.46 16797.15 4798.65 6898.15 18994.33 8599.80 7897.84 6398.66 13497.41 227
FC-MVSNet-test96.42 13196.05 13497.53 15896.95 27297.27 8099.36 1599.23 1495.83 10793.93 25198.37 16692.00 12098.32 28896.02 14792.72 26997.00 241
XXY-MVS95.20 20094.45 21497.46 15996.75 28596.56 11398.86 11098.65 12793.30 22993.27 27698.27 18084.85 27398.87 22694.82 18391.26 28596.96 244
test_cas_vis1_n_192097.38 9197.36 7997.45 16098.95 11693.25 26399.00 7898.53 15097.70 1399.77 399.35 3484.71 27699.85 5398.57 1799.66 5699.26 123
NR-MVSNet94.98 21394.16 22797.44 16196.53 29697.22 8698.74 13698.95 3894.96 15389.25 33697.69 23089.32 17598.18 30094.59 19387.40 33296.92 247
tfpn200view995.32 19494.62 20397.43 16298.94 11794.98 19198.68 15296.93 32595.33 13196.55 17296.53 31484.23 28699.56 13588.11 32496.29 20697.76 218
sd_testset96.17 14395.76 14797.42 16399.30 6694.34 22398.82 11799.08 2595.92 10095.96 19298.76 12682.83 30199.32 16495.56 16395.59 21998.60 189
thres100view90095.38 18794.70 20097.41 16498.98 11494.92 19598.87 10896.90 32795.38 12896.61 16896.88 29884.29 28299.56 13588.11 32496.29 20697.76 218
PMMVS96.60 12296.33 12397.41 16497.90 20793.93 23597.35 29098.41 17792.84 24797.76 11797.45 25091.10 14499.20 17596.26 13897.91 16499.11 145
VPNet94.99 21194.19 22497.40 16697.16 26196.57 11298.71 14598.97 3595.67 11594.84 20998.24 18480.36 31798.67 24596.46 13287.32 33496.96 244
UniMVSNet_NR-MVSNet95.71 16895.15 17897.40 16696.84 28096.97 9298.74 13699.24 1295.16 14193.88 25497.72 22791.68 12698.31 29095.81 15387.25 33596.92 247
DU-MVS95.42 18494.76 19797.40 16696.53 29696.97 9298.66 15798.99 3495.43 12593.88 25497.69 23088.57 19798.31 29095.81 15387.25 33596.92 247
iter_conf_final96.42 13196.12 13197.34 16998.46 15696.55 11599.08 6198.06 24896.03 9695.63 19698.46 15687.72 21898.59 25197.84 6393.80 24496.87 258
mvsmamba96.57 12696.32 12497.32 17096.60 29296.43 12099.54 797.98 25496.49 7695.20 20298.64 13690.82 14798.55 25597.97 5193.65 24996.98 242
thres20095.25 19694.57 20597.28 17198.81 12894.92 19598.20 21597.11 31395.24 13996.54 17496.22 32584.58 27999.53 14387.93 32896.50 20097.39 229
RPMNet92.81 29491.34 30197.24 17297.00 26893.43 25494.96 35798.80 8582.27 36396.93 15392.12 36686.98 23399.82 6676.32 37096.65 19498.46 197
WR-MVS95.15 20294.46 21297.22 17396.67 29096.45 11898.21 21398.81 7894.15 18093.16 27997.69 23087.51 22398.30 29295.29 17288.62 32096.90 254
CHOSEN 280x42097.18 10197.18 8697.20 17498.81 12893.27 26195.78 35199.15 2195.25 13796.79 16398.11 19292.29 11099.07 19598.56 1999.85 599.25 125
IB-MVS91.98 1793.27 28691.97 29697.19 17597.47 23793.41 25697.09 31195.99 34593.32 22792.47 30295.73 33578.06 33399.53 14394.59 19382.98 35298.62 188
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
mvs_anonymous96.70 12096.53 11797.18 17698.19 18593.78 23998.31 20298.19 21594.01 18794.47 22198.27 18092.08 11998.46 26697.39 9397.91 16499.31 114
TR-MVS94.94 21794.20 22397.17 17797.75 21494.14 23197.59 27497.02 32192.28 26895.75 19597.64 23683.88 29498.96 21189.77 30696.15 21498.40 199
iter_conf0596.13 14695.79 14497.15 17898.16 19095.99 13998.88 10497.98 25495.91 10295.58 19798.46 15685.53 25998.59 25197.88 5993.75 24596.86 261
GA-MVS94.81 22094.03 23397.14 17997.15 26293.86 23796.76 33397.58 27994.00 18894.76 21497.04 28380.91 31298.48 26291.79 27496.25 21199.09 147
gg-mvs-nofinetune92.21 29990.58 30797.13 18096.75 28595.09 18595.85 34989.40 38185.43 35694.50 22081.98 37480.80 31598.40 28592.16 26398.33 15297.88 215
PVSNet_BlendedMVS96.73 11896.60 11397.12 18199.25 7895.35 17598.26 21099.26 1094.28 17797.94 10997.46 24892.74 10499.81 7196.88 11593.32 25996.20 324
TranMVSNet+NR-MVSNet95.14 20394.48 21097.11 18296.45 30296.36 12699.03 7199.03 3095.04 14993.58 26497.93 20788.27 20498.03 31294.13 20786.90 34096.95 246
FMVSNet394.97 21494.26 22197.11 18298.18 18796.62 10698.56 17398.26 20793.67 21394.09 24497.10 27084.25 28498.01 31392.08 26592.14 27296.70 279
MVSTER96.06 14895.72 14997.08 18498.23 17995.93 15198.73 14098.27 20394.86 15795.07 20498.09 19388.21 20598.54 25796.59 12793.46 25496.79 267
FMVSNet294.47 24593.61 26597.04 18598.21 18196.43 12098.79 13098.27 20392.46 25793.50 27097.09 27481.16 30998.00 31591.09 28491.93 27596.70 279
bld_raw_dy_0_6495.74 16695.31 17297.03 18696.35 30695.76 15999.12 5397.37 30395.97 9894.70 21598.48 15285.80 25498.49 26196.55 12993.48 25396.84 263
XVG-OURS-SEG-HR96.51 12896.34 12297.02 18798.77 13093.76 24097.79 25998.50 16095.45 12496.94 15299.09 8287.87 21699.55 14296.76 12595.83 21897.74 220
AllTest95.24 19794.65 20296.99 18899.25 7893.21 26598.59 16598.18 21891.36 29293.52 26798.77 12284.67 27799.72 10389.70 30997.87 16698.02 213
TestCases96.99 18899.25 7893.21 26598.18 21891.36 29293.52 26798.77 12284.67 27799.72 10389.70 30997.87 16698.02 213
XVG-OURS96.55 12796.41 12096.99 18898.75 13193.76 24097.50 27998.52 15395.67 11596.83 15899.30 4288.95 19199.53 14395.88 15196.26 21097.69 223
UniMVSNet_ETH3D94.24 25893.33 27596.97 19197.19 25993.38 25898.74 13698.57 14291.21 30393.81 25898.58 14372.85 35898.77 23795.05 17893.93 24198.77 177
PVSNet91.96 1896.35 13696.15 13096.96 19299.17 9192.05 28096.08 34498.68 11593.69 20997.75 11997.80 22288.86 19299.69 11494.26 20499.01 11699.15 140
anonymousdsp95.42 18494.91 19196.94 19395.10 34395.90 15499.14 4998.41 17793.75 20193.16 27997.46 24887.50 22598.41 27995.63 16294.03 23796.50 309
hse-mvs295.71 16895.30 17396.93 19498.50 15393.53 25198.36 19498.10 23697.48 2398.67 6397.99 20289.76 16599.02 20397.95 5280.91 36198.22 207
test_djsdf96.00 15095.69 15596.93 19495.72 32995.49 16999.47 998.40 17994.98 15194.58 21797.86 21389.16 18198.41 27996.91 10994.12 23596.88 256
cascas94.63 23193.86 24896.93 19496.91 27694.27 22596.00 34898.51 15585.55 35594.54 21896.23 32384.20 28898.87 22695.80 15596.98 18797.66 224
AUN-MVS94.53 23993.73 25996.92 19798.50 15393.52 25298.34 19698.10 23693.83 19895.94 19497.98 20485.59 25899.03 20094.35 19980.94 36098.22 207
PS-MVSNAJss96.43 13096.26 12796.92 19795.84 32795.08 18699.16 4698.50 16095.87 10693.84 25798.34 17294.51 7898.61 24896.88 11593.45 25697.06 237
baseline295.11 20494.52 20896.87 19996.65 29193.56 24898.27 20994.10 36893.45 22292.02 31297.43 25287.45 22799.19 17693.88 21697.41 18197.87 216
HQP_MVS96.14 14595.90 14196.85 20097.42 24394.60 21298.80 12598.56 14497.28 3695.34 19998.28 17787.09 23099.03 20096.07 14294.27 22796.92 247
CP-MVSNet94.94 21794.30 22096.83 20196.72 28795.56 16599.11 5598.95 3893.89 19392.42 30497.90 20987.19 22998.12 30594.32 20188.21 32396.82 266
patch_mono-298.36 4398.87 496.82 20299.53 3690.68 30498.64 15999.29 997.88 899.19 3299.52 496.80 1599.97 199.11 699.86 199.82 11
pmmvs494.69 22493.99 23996.81 20395.74 32895.94 14897.40 28397.67 27390.42 31593.37 27397.59 24089.08 18498.20 29992.97 24291.67 27996.30 321
WR-MVS_H95.05 20894.46 21296.81 20396.86 27995.82 15799.24 3099.24 1293.87 19592.53 29996.84 30290.37 15698.24 29893.24 23387.93 32696.38 317
OPM-MVS95.69 17195.33 16996.76 20596.16 31594.63 20798.43 19098.39 18196.64 7195.02 20698.78 12085.15 26899.05 19695.21 17694.20 23096.60 290
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
jajsoiax95.45 18295.03 18596.73 20695.42 34094.63 20799.14 4998.52 15395.74 11093.22 27798.36 16783.87 29598.65 24696.95 10894.04 23696.91 252
PS-CasMVS94.67 22993.99 23996.71 20796.68 28995.26 17899.13 5299.03 3093.68 21192.33 30597.95 20685.35 26398.10 30693.59 22588.16 32596.79 267
COLMAP_ROBcopyleft93.27 1295.33 19394.87 19496.71 20799.29 7093.24 26498.58 16798.11 23389.92 32393.57 26599.10 7686.37 24499.79 8890.78 29198.10 15997.09 236
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
V4294.78 22294.14 22996.70 20996.33 30895.22 17998.97 8498.09 24092.32 26694.31 23397.06 28088.39 20298.55 25592.90 24588.87 31896.34 318
HQP-MVS95.72 16795.40 16196.69 21097.20 25694.25 22798.05 23298.46 16796.43 7994.45 22297.73 22586.75 23698.96 21195.30 17094.18 23196.86 261
LTVRE_ROB92.95 1594.60 23293.90 24596.68 21197.41 24694.42 21898.52 17698.59 13591.69 28491.21 31898.35 16884.87 27299.04 19991.06 28693.44 25796.60 290
Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
ECVR-MVScopyleft95.95 15395.71 15296.65 21299.02 10690.86 29999.03 7191.80 37596.96 5798.10 9399.26 4781.31 30899.51 14796.90 11299.04 11399.59 73
mvs_tets95.41 18695.00 18696.65 21295.58 33394.42 21899.00 7898.55 14695.73 11293.21 27898.38 16583.45 29998.63 24797.09 10294.00 23896.91 252
v2v48294.69 22494.03 23396.65 21296.17 31394.79 20398.67 15598.08 24192.72 25094.00 24997.16 26887.69 22298.45 26792.91 24488.87 31896.72 275
BH-untuned95.95 15395.72 14996.65 21298.55 15192.26 27698.23 21197.79 26893.73 20494.62 21698.01 20088.97 19099.00 20693.04 24098.51 14198.68 182
tt080594.54 23793.85 24996.63 21697.98 20393.06 27098.77 13297.84 26693.67 21393.80 25998.04 19776.88 34398.96 21194.79 18592.86 26697.86 217
Patchmatch-test94.42 24893.68 26396.63 21697.60 22691.76 28494.83 36197.49 29389.45 33194.14 24297.10 27088.99 18698.83 23185.37 34498.13 15899.29 119
ADS-MVSNet95.00 21094.45 21496.63 21698.00 19991.91 28296.04 34597.74 27190.15 31996.47 17796.64 31187.89 21498.96 21190.08 30097.06 18499.02 155
Anonymous2023121194.10 26893.26 27896.61 21999.11 10094.28 22499.01 7698.88 5486.43 34892.81 28997.57 24281.66 30698.68 24494.83 18289.02 31696.88 256
ACMM93.85 995.69 17195.38 16596.61 21997.61 22593.84 23898.91 9698.44 17195.25 13794.28 23498.47 15486.04 25199.12 18695.50 16693.95 24096.87 258
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v114494.59 23493.92 24296.60 22196.21 31094.78 20498.59 16598.14 22891.86 28094.21 23997.02 28587.97 21298.41 27991.72 27689.57 30496.61 289
GG-mvs-BLEND96.59 22296.34 30794.98 19196.51 34188.58 38293.10 28494.34 35280.34 31998.05 31189.53 31296.99 18696.74 272
pm-mvs193.94 27593.06 28096.59 22296.49 29995.16 18198.95 9098.03 25192.32 26691.08 32097.84 21684.54 28098.41 27992.16 26386.13 34696.19 325
CR-MVSNet94.76 22394.15 22896.59 22297.00 26893.43 25494.96 35797.56 28192.46 25796.93 15396.24 32188.15 20797.88 32587.38 33096.65 19498.46 197
v894.47 24593.77 25596.57 22596.36 30594.83 20099.05 6598.19 21591.92 27793.16 27996.97 29088.82 19498.48 26291.69 27787.79 32796.39 316
dcpmvs_298.08 5298.59 1196.56 22699.57 3390.34 31199.15 4798.38 18496.82 6399.29 2699.49 1095.78 4199.57 13298.94 999.86 199.77 22
RRT_MVS95.98 15195.78 14596.56 22696.48 30094.22 22999.57 697.92 26195.89 10393.95 25098.70 13089.27 17798.42 27197.23 9893.02 26397.04 238
GBi-Net94.49 24293.80 25296.56 22698.21 18195.00 18898.82 11798.18 21892.46 25794.09 24497.07 27781.16 30997.95 31792.08 26592.14 27296.72 275
test194.49 24293.80 25296.56 22698.21 18195.00 18898.82 11798.18 21892.46 25794.09 24497.07 27781.16 30997.95 31792.08 26592.14 27296.72 275
FMVSNet193.19 29092.07 29596.56 22697.54 23295.00 18898.82 11798.18 21890.38 31692.27 30697.07 27773.68 35697.95 31789.36 31691.30 28396.72 275
tfpnnormal93.66 27792.70 28796.55 23196.94 27395.94 14898.97 8499.19 1891.04 30591.38 31797.34 25584.94 27198.61 24885.45 34389.02 31695.11 345
v119294.32 25393.58 26696.53 23296.10 31694.45 21698.50 18198.17 22391.54 28794.19 24097.06 28086.95 23498.43 27090.14 29889.57 30496.70 279
EPMVS94.99 21194.48 21096.52 23397.22 25491.75 28597.23 29891.66 37694.11 18197.28 13896.81 30385.70 25698.84 22993.04 24097.28 18298.97 160
v1094.29 25593.55 26896.51 23496.39 30494.80 20298.99 8198.19 21591.35 29493.02 28596.99 28888.09 20998.41 27990.50 29588.41 32296.33 320
test_vis1_n95.47 17995.13 17996.49 23597.77 21390.41 30999.27 2698.11 23396.58 7399.66 899.18 6367.00 36599.62 12799.21 599.40 9999.44 100
PEN-MVS94.42 24893.73 25996.49 23596.28 30994.84 19899.17 4599.00 3293.51 21992.23 30797.83 21986.10 24897.90 32192.55 25686.92 33996.74 272
v14419294.39 25093.70 26196.48 23796.06 31894.35 22298.58 16798.16 22591.45 28994.33 23297.02 28587.50 22598.45 26791.08 28589.11 31396.63 287
v7n94.19 26193.43 27396.47 23895.90 32494.38 22199.26 2798.34 19091.99 27592.76 29197.13 26988.31 20398.52 25989.48 31487.70 32896.52 304
LPG-MVS_test95.62 17495.34 16796.47 23897.46 23893.54 24998.99 8198.54 14894.67 16494.36 23098.77 12285.39 26199.11 18895.71 15894.15 23396.76 270
LGP-MVS_train96.47 23897.46 23893.54 24998.54 14894.67 16494.36 23098.77 12285.39 26199.11 18895.71 15894.15 23396.76 270
SCA95.46 18095.13 17996.46 24197.67 22191.29 29497.33 29297.60 27894.68 16396.92 15597.10 27083.97 29298.89 22392.59 25398.32 15499.20 129
CLD-MVS95.62 17495.34 16796.46 24197.52 23593.75 24297.27 29798.46 16795.53 12094.42 22798.00 20186.21 24698.97 20796.25 14094.37 22596.66 285
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ACMP93.49 1095.34 19294.98 18896.43 24397.67 22193.48 25398.73 14098.44 17194.94 15692.53 29998.53 14784.50 28199.14 18395.48 16794.00 23896.66 285
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test111195.94 15595.78 14596.41 24498.99 11390.12 31399.04 6892.45 37496.99 5698.03 9999.27 4681.40 30799.48 15296.87 11899.04 11399.63 67
MIMVSNet93.26 28792.21 29496.41 24497.73 21893.13 26795.65 35297.03 31991.27 30094.04 24796.06 32875.33 34897.19 34386.56 33496.23 21298.92 166
v192192094.20 26093.47 27296.40 24695.98 32194.08 23298.52 17698.15 22691.33 29594.25 23697.20 26786.41 24398.42 27190.04 30389.39 31096.69 284
EI-MVSNet95.96 15295.83 14396.36 24797.93 20593.70 24698.12 22698.27 20393.70 20895.07 20499.02 8892.23 11398.54 25794.68 18693.46 25496.84 263
PatchT93.06 29291.97 29696.35 24896.69 28892.67 27394.48 36597.08 31486.62 34697.08 14592.23 36587.94 21397.90 32178.89 36696.69 19298.49 196
v124094.06 27293.29 27796.34 24996.03 32093.90 23698.44 18898.17 22391.18 30494.13 24397.01 28786.05 24998.42 27189.13 31989.50 30896.70 279
ACMH92.88 1694.55 23693.95 24196.34 24997.63 22493.26 26298.81 12498.49 16593.43 22389.74 33198.53 14781.91 30499.08 19493.69 22093.30 26096.70 279
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_vis1_n_192096.71 11996.84 10096.31 25199.11 10089.74 31899.05 6598.58 14098.08 699.87 199.37 2878.48 32899.93 2199.29 499.69 5299.27 121
DeepPCF-MVS96.37 297.93 5898.48 1996.30 25299.00 10989.54 32397.43 28298.87 6198.16 599.26 2899.38 2796.12 2999.64 12198.30 3999.77 2899.72 39
PatchmatchNetpermissive95.71 16895.52 15996.29 25397.58 22790.72 30396.84 33097.52 28994.06 18397.08 14596.96 29289.24 17998.90 22292.03 26998.37 14999.26 123
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
BH-w/o95.38 18795.08 18396.26 25498.34 16991.79 28397.70 26597.43 29892.87 24694.24 23797.22 26588.66 19598.84 22991.55 27997.70 17498.16 210
IterMVS-LS95.46 18095.21 17696.22 25598.12 19293.72 24598.32 20198.13 22993.71 20694.26 23597.31 25892.24 11298.10 30694.63 18890.12 29796.84 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TransMVSNet (Re)92.67 29591.51 30096.15 25696.58 29494.65 20598.90 9796.73 33490.86 30889.46 33597.86 21385.62 25798.09 30886.45 33581.12 35895.71 335
DTE-MVSNet93.98 27493.26 27896.14 25796.06 31894.39 22099.20 4098.86 6793.06 23891.78 31397.81 22185.87 25397.58 33590.53 29486.17 34496.46 314
cl2294.68 22694.19 22496.13 25898.11 19393.60 24796.94 31898.31 19492.43 26193.32 27596.87 30086.51 23998.28 29694.10 21091.16 28696.51 307
miper_enhance_ethall95.10 20594.75 19896.12 25997.53 23493.73 24496.61 33898.08 24192.20 27293.89 25396.65 31092.44 10798.30 29294.21 20591.16 28696.34 318
test250694.44 24793.91 24496.04 26099.02 10688.99 33399.06 6379.47 38896.96 5798.36 8499.26 4777.21 34099.52 14696.78 12499.04 11399.59 73
cl____94.51 24194.01 23696.02 26197.58 22793.40 25797.05 31297.96 25891.73 28392.76 29197.08 27689.06 18598.13 30492.61 25090.29 29596.52 304
DIV-MVS_self_test94.52 24094.03 23395.99 26297.57 23193.38 25897.05 31297.94 25991.74 28192.81 28997.10 27089.12 18298.07 31092.60 25190.30 29496.53 301
EPNet_dtu95.21 19994.95 19095.99 26296.17 31390.45 30898.16 22297.27 30896.77 6593.14 28298.33 17390.34 15798.42 27185.57 34198.81 12899.09 147
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth95.01 20994.69 20195.97 26497.70 21993.31 26097.02 31498.07 24392.23 26993.51 26996.96 29291.85 12398.15 30293.68 22191.16 28696.44 315
Baseline_NR-MVSNet94.35 25193.81 25195.96 26596.20 31194.05 23398.61 16496.67 33891.44 29093.85 25697.60 23988.57 19798.14 30394.39 19786.93 33895.68 336
JIA-IIPM93.35 28392.49 29095.92 26696.48 30090.65 30595.01 35696.96 32385.93 35296.08 18787.33 37187.70 22198.78 23691.35 28195.58 22198.34 202
Fast-Effi-MVS+-dtu95.87 15995.85 14295.91 26797.74 21791.74 28698.69 15198.15 22695.56 11994.92 20797.68 23388.98 18998.79 23593.19 23597.78 17097.20 235
v14894.29 25593.76 25795.91 26796.10 31692.93 27198.58 16797.97 25692.59 25593.47 27196.95 29488.53 20098.32 28892.56 25587.06 33796.49 310
c3_l94.79 22194.43 21695.89 26997.75 21493.12 26897.16 30898.03 25192.23 26993.46 27297.05 28291.39 13598.01 31393.58 22689.21 31296.53 301
ACMH+92.99 1494.30 25493.77 25595.88 27097.81 21192.04 28198.71 14598.37 18593.99 18990.60 32598.47 15480.86 31499.05 19692.75 24992.40 27196.55 298
Patchmtry93.22 28892.35 29295.84 27196.77 28293.09 26994.66 36497.56 28187.37 34492.90 28796.24 32188.15 20797.90 32187.37 33190.10 29896.53 301
test-LLR95.10 20594.87 19495.80 27296.77 28289.70 31996.91 32195.21 35495.11 14494.83 21195.72 33787.71 21998.97 20793.06 23898.50 14298.72 178
test-mter94.08 27093.51 27095.80 27296.77 28289.70 31996.91 32195.21 35492.89 24594.83 21195.72 33777.69 33598.97 20793.06 23898.50 14298.72 178
test0.0.03 194.08 27093.51 27095.80 27295.53 33592.89 27297.38 28595.97 34695.11 14492.51 30196.66 30887.71 21996.94 34787.03 33293.67 24797.57 225
XVG-ACMP-BASELINE94.54 23794.14 22995.75 27596.55 29591.65 28898.11 22898.44 17194.96 15394.22 23897.90 20979.18 32599.11 18894.05 21293.85 24296.48 312
pmmvs593.65 27992.97 28295.68 27695.49 33692.37 27598.20 21597.28 30789.66 32892.58 29797.26 26082.14 30398.09 30893.18 23690.95 28996.58 292
test_fmvs196.42 13196.67 11195.66 27798.82 12788.53 34098.80 12598.20 21396.39 8399.64 1099.20 5780.35 31899.67 11699.04 799.57 7498.78 176
test_fmvs1_n95.90 15895.99 13895.63 27898.67 14188.32 34499.26 2798.22 21096.40 8299.67 799.26 4773.91 35599.70 10999.02 899.50 8698.87 168
TESTMET0.1,194.18 26393.69 26295.63 27896.92 27489.12 32996.91 32194.78 35993.17 23494.88 20896.45 31778.52 32798.92 21893.09 23798.50 14298.85 169
CostFormer94.95 21594.73 19995.60 28097.28 25089.06 33097.53 27796.89 32989.66 32896.82 16096.72 30686.05 24998.95 21695.53 16596.13 21598.79 173
Effi-MVS+-dtu96.29 13896.56 11495.51 28197.89 20890.22 31298.80 12598.10 23696.57 7596.45 17996.66 30890.81 14898.91 21995.72 15797.99 16197.40 228
D2MVS95.18 20195.08 18395.48 28297.10 26592.07 27998.30 20499.13 2394.02 18692.90 28796.73 30589.48 17098.73 23994.48 19693.60 25295.65 337
eth_miper_zixun_eth94.68 22694.41 21795.47 28397.64 22391.71 28796.73 33598.07 24392.71 25193.64 26297.21 26690.54 15498.17 30193.38 22989.76 30196.54 299
tpm294.19 26193.76 25795.46 28497.23 25389.04 33197.31 29496.85 33387.08 34596.21 18496.79 30483.75 29898.74 23892.43 26196.23 21298.59 191
tpmrst95.63 17395.69 15595.44 28597.54 23288.54 33996.97 31697.56 28193.50 22097.52 13596.93 29689.49 16999.16 17895.25 17496.42 20298.64 187
ITE_SJBPF95.44 28597.42 24391.32 29397.50 29195.09 14793.59 26398.35 16881.70 30598.88 22589.71 30893.39 25896.12 326
dmvs_re94.48 24494.18 22695.37 28797.68 22090.11 31498.54 17597.08 31494.56 16794.42 22797.24 26384.25 28497.76 32991.02 28992.83 26798.24 205
MVP-Stereo94.28 25793.92 24295.35 28894.95 34592.60 27497.97 24097.65 27491.61 28690.68 32497.09 27486.32 24598.42 27189.70 30999.34 10395.02 348
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpmvs94.60 23294.36 21995.33 28997.46 23888.60 33896.88 32797.68 27291.29 29893.80 25996.42 31888.58 19699.24 17091.06 28696.04 21698.17 209
TDRefinement91.06 30989.68 31495.21 29085.35 37991.49 29198.51 18097.07 31691.47 28888.83 34197.84 21677.31 33999.09 19392.79 24877.98 36795.04 347
USDC93.33 28592.71 28695.21 29096.83 28190.83 30196.91 32197.50 29193.84 19690.72 32398.14 19077.69 33598.82 23289.51 31393.21 26295.97 330
pmmvs691.77 30190.63 30695.17 29294.69 35191.24 29598.67 15597.92 26186.14 35089.62 33297.56 24475.79 34798.34 28690.75 29284.56 34895.94 331
tpm94.13 26593.80 25295.12 29396.50 29887.91 34997.44 28095.89 34992.62 25396.37 18196.30 32084.13 28998.30 29293.24 23391.66 28099.14 142
miper_lstm_enhance94.33 25294.07 23295.11 29497.75 21490.97 29897.22 29998.03 25191.67 28592.76 29196.97 29090.03 16297.78 32892.51 25889.64 30396.56 296
ADS-MVSNet294.58 23594.40 21895.11 29498.00 19988.74 33696.04 34597.30 30590.15 31996.47 17796.64 31187.89 21497.56 33690.08 30097.06 18499.02 155
tpm cat193.36 28292.80 28495.07 29697.58 22787.97 34896.76 33397.86 26582.17 36493.53 26696.04 32986.13 24799.13 18489.24 31795.87 21798.10 211
PVSNet_088.72 1991.28 30690.03 31295.00 29797.99 20187.29 35394.84 36098.50 16092.06 27489.86 33095.19 34279.81 32199.39 15992.27 26269.79 37498.33 203
ppachtmachnet_test93.22 28892.63 28894.97 29895.45 33890.84 30096.88 32797.88 26490.60 31092.08 31097.26 26088.08 21097.86 32685.12 34590.33 29396.22 323
LCM-MVSNet-Re95.22 19895.32 17094.91 29998.18 18787.85 35098.75 13395.66 35095.11 14488.96 33796.85 30190.26 16097.65 33195.65 16198.44 14599.22 128
dp94.15 26493.90 24594.90 30097.31 24986.82 35596.97 31697.19 31291.22 30296.02 18996.61 31385.51 26099.02 20390.00 30494.30 22698.85 169
testgi93.06 29292.45 29194.88 30196.43 30389.90 31598.75 13397.54 28795.60 11791.63 31697.91 20874.46 35397.02 34586.10 33793.67 24797.72 222
IterMVS-SCA-FT94.11 26793.87 24794.85 30297.98 20390.56 30797.18 30498.11 23393.75 20192.58 29797.48 24783.97 29297.41 34092.48 26091.30 28396.58 292
OurMVSNet-221017-094.21 25994.00 23794.85 30295.60 33289.22 32898.89 10197.43 29895.29 13492.18 30898.52 15082.86 30098.59 25193.46 22891.76 27796.74 272
MDA-MVSNet-bldmvs89.97 31888.35 32494.83 30495.21 34291.34 29297.64 27097.51 29088.36 34071.17 37596.13 32779.22 32496.63 35583.65 35286.27 34396.52 304
IterMVS94.09 26993.85 24994.80 30597.99 20190.35 31097.18 30498.12 23093.68 21192.46 30397.34 25584.05 29097.41 34092.51 25891.33 28296.62 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo93.34 28492.86 28394.75 30695.67 33089.41 32698.75 13396.67 33893.89 19390.15 32998.25 18380.87 31398.27 29790.90 29090.64 29196.57 294
our_test_393.65 27993.30 27694.69 30795.45 33889.68 32196.91 32197.65 27491.97 27691.66 31596.88 29889.67 16897.93 32088.02 32791.49 28196.48 312
MDA-MVSNet_test_wron90.71 31289.38 31794.68 30894.83 34790.78 30297.19 30397.46 29487.60 34272.41 37495.72 33786.51 23996.71 35385.92 33986.80 34196.56 296
TinyColmap92.31 29891.53 29994.65 30996.92 27489.75 31796.92 31996.68 33790.45 31489.62 33297.85 21576.06 34698.81 23386.74 33392.51 27095.41 339
YYNet190.70 31389.39 31694.62 31094.79 34990.65 30597.20 30197.46 29487.54 34372.54 37395.74 33386.51 23996.66 35486.00 33886.76 34296.54 299
KD-MVS_2432*160089.61 32187.96 32894.54 31194.06 35591.59 28995.59 35397.63 27689.87 32488.95 33894.38 35078.28 33096.82 34884.83 34668.05 37595.21 342
miper_refine_blended89.61 32187.96 32894.54 31194.06 35591.59 28995.59 35397.63 27689.87 32488.95 33894.38 35078.28 33096.82 34884.83 34668.05 37595.21 342
FMVSNet591.81 30090.92 30394.49 31397.21 25592.09 27898.00 23897.55 28689.31 33490.86 32295.61 34074.48 35295.32 36585.57 34189.70 30296.07 328
K. test v392.55 29691.91 29894.48 31495.64 33189.24 32799.07 6294.88 35894.04 18486.78 35097.59 24077.64 33897.64 33292.08 26589.43 30996.57 294
test_040291.32 30490.27 31094.48 31496.60 29291.12 29698.50 18197.22 31186.10 35188.30 34396.98 28977.65 33797.99 31678.13 36892.94 26594.34 352
MS-PatchMatch93.84 27693.63 26494.46 31696.18 31289.45 32497.76 26098.27 20392.23 26992.13 30997.49 24679.50 32298.69 24189.75 30799.38 10195.25 341
lessismore_v094.45 31794.93 34688.44 34291.03 37886.77 35197.64 23676.23 34598.42 27190.31 29785.64 34796.51 307
pmmvs-eth3d90.36 31589.05 32094.32 31891.10 36892.12 27797.63 27396.95 32488.86 33784.91 36193.13 36078.32 32996.74 35088.70 32281.81 35694.09 358
LF4IMVS93.14 29192.79 28594.20 31995.88 32588.67 33797.66 26897.07 31693.81 19991.71 31497.65 23477.96 33498.81 23391.47 28091.92 27695.12 344
UnsupCasMVSNet_eth90.99 31089.92 31394.19 32094.08 35489.83 31697.13 31098.67 12093.69 20985.83 35696.19 32675.15 34996.74 35089.14 31879.41 36396.00 329
EG-PatchMatch MVS91.13 30890.12 31194.17 32194.73 35089.00 33298.13 22597.81 26789.22 33585.32 36096.46 31667.71 36398.42 27187.89 32993.82 24395.08 346
MIMVSNet189.67 32088.28 32593.82 32292.81 36391.08 29798.01 23697.45 29687.95 34187.90 34595.87 33267.63 36494.56 36978.73 36788.18 32495.83 333
OpenMVS_ROBcopyleft86.42 2089.00 32487.43 33293.69 32393.08 36189.42 32597.91 24596.89 32978.58 36785.86 35594.69 34769.48 36198.29 29577.13 36993.29 26193.36 364
CVMVSNet95.43 18396.04 13593.57 32497.93 20583.62 36198.12 22698.59 13595.68 11496.56 17099.02 8887.51 22397.51 33893.56 22797.44 17999.60 71
Anonymous2024052191.18 30790.44 30893.42 32593.70 35888.47 34198.94 9297.56 28188.46 33989.56 33495.08 34577.15 34296.97 34683.92 35189.55 30694.82 350
Patchmatch-RL test91.49 30390.85 30493.41 32691.37 36684.40 35892.81 36995.93 34891.87 27987.25 34794.87 34688.99 18696.53 35692.54 25782.00 35499.30 117
KD-MVS_self_test90.38 31489.38 31793.40 32792.85 36288.94 33497.95 24197.94 25990.35 31790.25 32793.96 35379.82 32095.94 36084.62 35076.69 36995.33 340
Anonymous2023120691.66 30291.10 30293.33 32894.02 35787.35 35298.58 16797.26 30990.48 31290.16 32896.31 31983.83 29696.53 35679.36 36489.90 30096.12 326
UnsupCasMVSNet_bld87.17 33085.12 33693.31 32991.94 36488.77 33594.92 35998.30 20084.30 36082.30 36490.04 36863.96 36897.25 34285.85 34074.47 37393.93 362
RPSCF94.87 21995.40 16193.26 33098.89 12082.06 36698.33 19798.06 24890.30 31896.56 17099.26 4787.09 23099.49 14893.82 21896.32 20598.24 205
new_pmnet90.06 31789.00 32193.22 33194.18 35288.32 34496.42 34396.89 32986.19 34985.67 35793.62 35577.18 34197.10 34481.61 35889.29 31194.23 354
test_vis1_rt91.29 30590.65 30593.19 33297.45 24186.25 35698.57 17290.90 37993.30 22986.94 34993.59 35662.07 36999.11 18897.48 9095.58 22194.22 355
CL-MVSNet_self_test90.11 31689.14 31993.02 33391.86 36588.23 34696.51 34198.07 24390.49 31190.49 32694.41 34884.75 27595.34 36480.79 36074.95 37195.50 338
test_fmvs293.43 28193.58 26692.95 33496.97 27183.91 36099.19 4297.24 31095.74 11095.20 20298.27 18069.65 36098.72 24096.26 13893.73 24696.24 322
MVS-HIRNet89.46 32388.40 32392.64 33597.58 22782.15 36594.16 36893.05 37375.73 37090.90 32182.52 37379.42 32398.33 28783.53 35398.68 13097.43 226
test20.0390.89 31190.38 30992.43 33693.48 35988.14 34798.33 19797.56 28193.40 22487.96 34496.71 30780.69 31694.13 37079.15 36586.17 34495.01 349
DSMNet-mixed92.52 29792.58 28992.33 33794.15 35382.65 36498.30 20494.26 36589.08 33692.65 29595.73 33585.01 27095.76 36186.24 33697.76 17198.59 191
EGC-MVSNET75.22 34369.54 34692.28 33894.81 34889.58 32297.64 27096.50 3411.82 3865.57 38795.74 33368.21 36296.26 35973.80 37291.71 27890.99 368
EU-MVSNet93.66 27794.14 22992.25 33995.96 32383.38 36298.52 17698.12 23094.69 16292.61 29698.13 19187.36 22896.39 35891.82 27390.00 29996.98 242
pmmvs386.67 33384.86 33792.11 34088.16 37387.19 35496.63 33794.75 36079.88 36687.22 34892.75 36366.56 36695.20 36681.24 35976.56 37093.96 361
new-patchmatchnet88.50 32687.45 33191.67 34190.31 37085.89 35797.16 30897.33 30489.47 33083.63 36392.77 36276.38 34495.06 36782.70 35577.29 36894.06 360
PM-MVS87.77 32886.55 33491.40 34291.03 36983.36 36396.92 31995.18 35691.28 29986.48 35493.42 35753.27 37396.74 35089.43 31581.97 35594.11 357
mvsany_test388.80 32588.04 32691.09 34389.78 37181.57 36797.83 25695.49 35193.81 19987.53 34693.95 35456.14 37297.43 33994.68 18683.13 35194.26 353
CMPMVSbinary66.06 2189.70 31989.67 31589.78 34493.19 36076.56 36997.00 31598.35 18880.97 36581.57 36597.75 22474.75 35198.61 24889.85 30593.63 25094.17 356
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ambc89.49 34586.66 37675.78 37092.66 37096.72 33586.55 35392.50 36446.01 37497.90 32190.32 29682.09 35394.80 351
APD_test188.22 32788.01 32788.86 34695.98 32174.66 37597.21 30096.44 34283.96 36186.66 35297.90 20960.95 37097.84 32782.73 35490.23 29694.09 358
test_f86.07 33485.39 33588.10 34789.28 37275.57 37297.73 26396.33 34389.41 33385.35 35991.56 36743.31 37895.53 36291.32 28284.23 35093.21 366
test_fmvs387.17 33087.06 33387.50 34891.21 36775.66 37199.05 6596.61 34092.79 24988.85 34092.78 36143.72 37693.49 37193.95 21384.56 34893.34 365
DeepMVS_CXcopyleft86.78 34997.09 26672.30 37695.17 35775.92 36984.34 36295.19 34270.58 35995.35 36379.98 36389.04 31592.68 367
LCM-MVSNet78.70 33976.24 34486.08 35077.26 38571.99 37794.34 36696.72 33561.62 37676.53 36889.33 36933.91 38492.78 37581.85 35774.60 37293.46 363
PMMVS277.95 34175.44 34585.46 35182.54 38074.95 37394.23 36793.08 37272.80 37174.68 36987.38 37036.36 38191.56 37673.95 37163.94 37789.87 369
N_pmnet87.12 33287.77 33085.17 35295.46 33761.92 38397.37 28770.66 38985.83 35388.73 34296.04 32985.33 26597.76 32980.02 36190.48 29295.84 332
test_vis3_rt79.22 33577.40 34184.67 35386.44 37774.85 37497.66 26881.43 38684.98 35767.12 37781.91 37528.09 38697.60 33388.96 32080.04 36281.55 375
dmvs_testset87.64 32988.93 32283.79 35495.25 34163.36 38297.20 30191.17 37793.07 23785.64 35895.98 33185.30 26791.52 37769.42 37587.33 33396.49 310
Gipumacopyleft78.40 34076.75 34383.38 35595.54 33480.43 36879.42 37897.40 30064.67 37573.46 37280.82 37645.65 37593.14 37466.32 37787.43 33176.56 378
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf179.02 33777.70 33982.99 35688.10 37466.90 37994.67 36293.11 37071.08 37274.02 37093.41 35834.15 38293.25 37272.25 37378.50 36588.82 370
APD_test279.02 33777.70 33982.99 35688.10 37466.90 37994.67 36293.11 37071.08 37274.02 37093.41 35834.15 38293.25 37272.25 37378.50 36588.82 370
test_method79.03 33678.17 33881.63 35886.06 37854.40 38882.75 37796.89 32939.54 38180.98 36695.57 34158.37 37194.73 36884.74 34978.61 36495.75 334
ANet_high69.08 34465.37 34880.22 35965.99 38771.96 37890.91 37390.09 38082.62 36249.93 38278.39 37729.36 38581.75 38062.49 37838.52 38186.95 374
FPMVS77.62 34277.14 34279.05 36079.25 38360.97 38495.79 35095.94 34765.96 37467.93 37694.40 34937.73 38088.88 37968.83 37688.46 32187.29 372
MVEpermissive62.14 2263.28 34959.38 35274.99 36174.33 38665.47 38185.55 37580.50 38752.02 37951.10 38175.00 38010.91 39080.50 38151.60 38053.40 37878.99 376
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt68.90 34566.97 34774.68 36250.78 38959.95 38587.13 37483.47 38538.80 38262.21 37896.23 32364.70 36776.91 38488.91 32130.49 38287.19 373
PMVScopyleft61.03 2365.95 34663.57 35073.09 36357.90 38851.22 38985.05 37693.93 36954.45 37744.32 38383.57 37213.22 38789.15 37858.68 37981.00 35978.91 377
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN64.94 34764.25 34967.02 36482.28 38159.36 38691.83 37285.63 38352.69 37860.22 37977.28 37841.06 37980.12 38246.15 38141.14 37961.57 380
EMVS64.07 34863.26 35166.53 36581.73 38258.81 38791.85 37184.75 38451.93 38059.09 38075.13 37943.32 37779.09 38342.03 38239.47 38061.69 379
wuyk23d30.17 35030.18 35430.16 36678.61 38443.29 39066.79 37914.21 39017.31 38314.82 38611.93 38611.55 38941.43 38537.08 38319.30 3835.76 383
test12320.95 35323.72 35612.64 36713.54 3918.19 39196.55 3406.13 3927.48 38516.74 38537.98 38312.97 3886.05 38616.69 3845.43 38523.68 381
testmvs21.48 35224.95 35511.09 36814.89 3906.47 39296.56 3399.87 3917.55 38417.93 38439.02 3829.43 3915.90 38716.56 38512.72 38420.91 382
test_blank0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
uanet_test0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
DCPMVS0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
cdsmvs_eth3d_5k23.98 35131.98 3530.00 3690.00 3920.00 3930.00 38098.59 1350.00 3870.00 38898.61 13890.60 1530.00 3880.00 3860.00 3860.00 384
pcd_1.5k_mvsjas7.88 35510.50 3580.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 38794.51 780.00 3880.00 3860.00 3860.00 384
sosnet-low-res0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
sosnet0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
uncertanet0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
Regformer0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
ab-mvs-re8.20 35410.94 3570.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 38898.43 1580.00 3920.00 3880.00 3860.00 3860.00 384
uanet0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
FOURS199.82 198.66 2499.69 198.95 3897.46 2599.39 22
PC_three_145295.08 14899.60 1299.16 6797.86 298.47 26597.52 8899.72 4799.74 31
test_one_060199.66 2699.25 298.86 6797.55 2099.20 3099.47 1397.57 6
eth-test20.00 392
eth-test0.00 392
ZD-MVS99.46 4998.70 2398.79 9093.21 23298.67 6398.97 9595.70 4399.83 5996.07 14299.58 73
RE-MVS-def98.34 2999.49 4597.86 6199.11 5598.80 8596.49 7699.17 3399.35 3495.29 5997.72 7099.65 5999.71 43
IU-MVS99.71 1999.23 798.64 12895.28 13599.63 1198.35 3799.81 1299.83 8
test_241102_TWO98.87 6197.65 1499.53 1699.48 1197.34 1199.94 598.43 3299.80 1999.83 8
test_241102_ONE99.71 1999.24 598.87 6197.62 1699.73 499.39 2297.53 799.74 101
9.1498.06 5099.47 4798.71 14598.82 7394.36 17699.16 3599.29 4396.05 3199.81 7197.00 10499.71 49
save fliter99.46 4998.38 3598.21 21398.71 10897.95 7
test_0728_THIRD97.32 3399.45 1899.46 1697.88 199.94 598.47 2899.86 199.85 5
test072699.72 1299.25 299.06 6398.88 5497.62 1699.56 1399.50 897.42 9
GSMVS99.20 129
test_part299.63 2999.18 1099.27 27
sam_mvs189.45 17299.20 129
sam_mvs88.99 186
MTGPAbinary98.74 100
test_post196.68 33630.43 38587.85 21798.69 24192.59 253
test_post31.83 38488.83 19398.91 219
patchmatchnet-post95.10 34489.42 17398.89 223
MTMP98.89 10194.14 367
gm-plane-assit95.88 32587.47 35189.74 32796.94 29599.19 17693.32 232
test9_res96.39 13699.57 7499.69 50
TEST999.31 6298.50 2997.92 24398.73 10392.63 25297.74 12098.68 13296.20 2699.80 78
test_899.29 7098.44 3197.89 24998.72 10592.98 24197.70 12498.66 13596.20 2699.80 78
agg_prior295.87 15299.57 7499.68 55
agg_prior99.30 6698.38 3598.72 10597.57 13499.81 71
test_prior498.01 5897.86 252
test_prior297.80 25796.12 9397.89 11498.69 13195.96 3596.89 11399.60 68
旧先验297.57 27691.30 29798.67 6399.80 7895.70 160
新几何297.64 270
旧先验199.29 7097.48 7498.70 11199.09 8295.56 4699.47 9199.61 69
无先验97.58 27598.72 10591.38 29199.87 4893.36 23199.60 71
原ACMM297.67 267
test22299.23 8597.17 8897.40 28398.66 12388.68 33898.05 9698.96 10094.14 9099.53 8499.61 69
testdata299.89 3991.65 278
segment_acmp96.85 14
testdata197.32 29396.34 85
plane_prior797.42 24394.63 207
plane_prior697.35 24894.61 21087.09 230
plane_prior598.56 14499.03 20096.07 14294.27 22796.92 247
plane_prior498.28 177
plane_prior394.61 21097.02 5495.34 199
plane_prior298.80 12597.28 36
plane_prior197.37 247
plane_prior94.60 21298.44 18896.74 6794.22 229
n20.00 393
nn0.00 393
door-mid94.37 363
test1198.66 123
door94.64 361
HQP5-MVS94.25 227
HQP-NCC97.20 25698.05 23296.43 7994.45 222
ACMP_Plane97.20 25698.05 23296.43 7994.45 222
BP-MVS95.30 170
HQP4-MVS94.45 22298.96 21196.87 258
HQP3-MVS98.46 16794.18 231
HQP2-MVS86.75 236
NP-MVS97.28 25094.51 21597.73 225
MDTV_nov1_ep13_2view84.26 35996.89 32690.97 30697.90 11389.89 16493.91 21599.18 138
MDTV_nov1_ep1395.40 16197.48 23688.34 34396.85 32997.29 30693.74 20397.48 13697.26 26089.18 18099.05 19691.92 27297.43 180
ACMMP++_ref92.97 264
ACMMP++93.61 251
Test By Simon94.64 75