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.
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fmvsm_l_conf0.5_n_a99.09 199.08 199.11 6199.43 6397.48 8998.88 12799.30 1498.47 1899.85 1199.43 4396.71 1999.96 499.86 199.80 2599.89 6
SED-MVS99.09 198.91 499.63 599.71 2499.24 699.02 8498.87 8597.65 3999.73 2299.48 3397.53 999.94 1498.43 6799.81 1699.70 67
DVP-MVS++99.08 398.89 599.64 499.17 11199.23 899.69 198.88 7897.32 6399.53 3799.47 3597.81 399.94 1498.47 6399.72 6899.74 50
fmvsm_l_conf0.5_n99.07 499.05 299.14 5799.41 6697.54 8798.89 12099.31 1398.49 1799.86 899.42 4496.45 2799.96 499.86 199.74 5999.90 5
DVP-MVScopyleft99.03 598.83 1099.63 599.72 1799.25 398.97 9598.58 17797.62 4199.45 3999.46 4097.42 1199.94 1498.47 6399.81 1699.69 70
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
MED-MVS99.02 698.85 899.52 1399.77 298.86 2299.32 2299.24 2097.00 8999.30 5099.35 6097.61 699.92 4398.30 7599.80 2599.79 28
TestfortrainingZip a99.02 698.79 1299.70 299.77 299.30 299.32 2299.24 2096.41 12199.30 5099.35 6097.61 699.92 4398.35 7299.80 2599.88 10
APDe-MVScopyleft99.02 698.84 999.55 1099.57 3998.96 1799.39 1198.93 6597.38 6099.41 4299.54 2096.66 2099.84 8898.86 3999.85 699.87 11
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
lecture98.95 998.78 1499.45 1999.75 698.63 3099.43 1099.38 897.60 4499.58 3399.47 3595.36 6499.93 3498.87 3899.57 10099.78 33
reproduce_model98.94 1098.81 1199.34 3199.52 4598.26 5498.94 10598.84 9698.06 2599.35 4699.61 596.39 3099.94 1498.77 4299.82 1499.83 18
reproduce-ours98.93 1198.78 1499.38 2399.49 5298.38 4098.86 13698.83 9898.06 2599.29 5399.58 1696.40 2899.94 1498.68 4599.81 1699.81 24
our_new_method98.93 1198.78 1499.38 2399.49 5298.38 4098.86 13698.83 9898.06 2599.29 5399.58 1696.40 2899.94 1498.68 4599.81 1699.81 24
test_fmvsmconf_n98.92 1398.87 699.04 6798.88 14797.25 11198.82 14999.34 1198.75 1199.80 1499.61 595.16 7799.95 999.70 1799.80 2599.93 1
DPE-MVScopyleft98.92 1398.67 2099.65 399.58 3799.20 1098.42 26298.91 7297.58 4599.54 3699.46 4097.10 1499.94 1497.64 11999.84 1199.83 18
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
fmvsm_l_conf0.5_n_998.90 1598.79 1299.24 4599.34 7197.83 7898.70 19099.26 1698.85 699.92 199.51 2693.91 10699.95 999.86 199.79 3599.92 2
fmvsm_l_conf0.5_n_398.90 1598.74 1899.37 2799.36 6898.25 5598.89 12099.24 2098.77 1099.89 399.59 1393.39 11299.96 499.78 1099.76 4899.89 6
SteuartSystems-ACMMP98.90 1598.75 1799.36 2999.22 10698.43 3899.10 6898.87 8597.38 6099.35 4699.40 4797.78 599.87 7997.77 10799.85 699.78 33
Skip Steuart: Steuart Systems R&D Blog.
test_fmvsm_n_192098.87 1899.01 398.45 12399.42 6496.43 15598.96 10199.36 1098.63 1399.86 899.51 2695.91 4699.97 199.72 1499.75 5598.94 228
ME-MVS98.83 1998.60 2499.52 1399.58 3798.86 2298.69 19398.93 6597.00 8999.17 6299.35 6096.62 2399.90 6498.30 7599.80 2599.79 28
TSAR-MVS + MP.98.78 2098.62 2299.24 4599.69 2998.28 5399.14 5998.66 15496.84 9699.56 3499.31 7196.34 3199.70 14298.32 7499.73 6399.73 55
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS98.78 2098.56 2899.45 1999.32 7798.87 2098.47 24998.81 10797.72 3498.76 9599.16 10597.05 1599.78 12498.06 8999.66 7999.69 70
MSP-MVS98.74 2298.55 2999.29 3899.75 698.23 5699.26 3298.88 7897.52 4899.41 4298.78 18496.00 4299.79 12197.79 10699.59 9699.85 15
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
fmvsm_s_conf0.5_n_898.73 2398.62 2299.05 6699.35 7097.27 10598.80 15899.23 2898.93 399.79 1599.59 1392.34 12999.95 999.82 699.71 7099.92 2
XVS98.70 2498.49 3699.34 3199.70 2798.35 4999.29 2798.88 7897.40 5798.46 11899.20 9295.90 4899.89 6897.85 10299.74 5999.78 33
fmvsm_s_conf0.5_n_1098.66 2598.54 3199.02 6899.36 6897.21 11498.86 13699.23 2898.90 599.83 1299.59 1391.57 16099.94 1499.79 999.74 5999.89 6
fmvsm_s_conf0.5_n_698.65 2698.55 2998.95 7798.50 18797.30 10198.79 16699.16 4098.14 2399.86 899.41 4693.71 10999.91 5699.71 1599.64 8799.65 83
MCST-MVS98.65 2698.37 4599.48 1799.60 3698.87 2098.41 26398.68 14697.04 8698.52 11698.80 17896.78 1899.83 9097.93 9699.61 9299.74 50
SD-MVS98.64 2898.68 1998.53 11299.33 7498.36 4898.90 11698.85 9597.28 6799.72 2599.39 4896.63 2297.60 42398.17 8499.85 699.64 86
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
fmvsm_s_conf0.5_n_998.63 2998.66 2198.54 10999.40 6795.83 19998.79 16699.17 3898.94 299.92 199.61 592.49 12499.93 3499.86 199.76 4899.86 12
HFP-MVS98.63 2998.40 4299.32 3799.72 1798.29 5299.23 3798.96 6096.10 13898.94 7799.17 10296.06 3999.92 4397.62 12099.78 4099.75 48
ACMMP_NAP98.61 3198.30 6099.55 1099.62 3598.95 1898.82 14998.81 10795.80 15299.16 6699.47 3595.37 6399.92 4397.89 10099.75 5599.79 28
region2R98.61 3198.38 4499.29 3899.74 1298.16 6299.23 3798.93 6596.15 13498.94 7799.17 10295.91 4699.94 1497.55 13099.79 3599.78 33
NCCC98.61 3198.35 4899.38 2399.28 9298.61 3198.45 25198.76 12597.82 3398.45 12198.93 15696.65 2199.83 9097.38 15199.41 12999.71 63
SF-MVS98.59 3498.32 5999.41 2299.54 4198.71 2699.04 7898.81 10795.12 20399.32 4999.39 4896.22 3399.84 8897.72 11099.73 6399.67 79
ACMMPR98.59 3498.36 4699.29 3899.74 1298.15 6399.23 3798.95 6196.10 13898.93 8199.19 9895.70 5299.94 1497.62 12099.79 3599.78 33
fmvsm_s_conf0.5_n_1198.58 3698.57 2698.62 9999.42 6497.16 11798.97 9598.86 9198.91 499.87 499.66 391.82 15299.95 999.82 699.82 1498.75 249
test_fmvsmconf0.1_n98.58 3698.44 4098.99 7097.73 29897.15 11898.84 14598.97 5798.75 1199.43 4199.54 2093.29 11499.93 3499.64 2099.79 3599.89 6
SMA-MVScopyleft98.58 3698.25 6399.56 999.51 4699.04 1698.95 10298.80 11493.67 29799.37 4599.52 2396.52 2599.89 6898.06 8999.81 1699.76 47
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
MTAPA98.58 3698.29 6199.46 1899.76 598.64 2998.90 11698.74 12997.27 7198.02 14799.39 4894.81 8799.96 497.91 9899.79 3599.77 40
HPM-MVS++copyleft98.58 3698.25 6399.55 1099.50 4899.08 1298.72 18598.66 15497.51 4998.15 13298.83 17595.70 5299.92 4397.53 13299.67 7699.66 82
SR-MVS98.57 4198.35 4899.24 4599.53 4298.18 6099.09 6998.82 10196.58 11299.10 6899.32 6995.39 6199.82 9797.70 11599.63 8999.72 59
CP-MVS98.57 4198.36 4699.19 5099.66 3197.86 7499.34 1798.87 8595.96 14498.60 11299.13 11296.05 4099.94 1497.77 10799.86 299.77 40
MSLP-MVS++98.56 4398.57 2698.55 10799.26 9596.80 13398.71 18699.05 5097.28 6798.84 8799.28 7696.47 2699.40 20698.52 6199.70 7299.47 115
DeepC-MVS_fast96.70 198.55 4498.34 5499.18 5299.25 9698.04 6898.50 24498.78 12197.72 3498.92 8399.28 7695.27 7099.82 9797.55 13099.77 4299.69 70
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post98.54 4598.35 4899.13 5899.49 5297.86 7499.11 6598.80 11496.49 11699.17 6299.35 6095.34 6699.82 9797.72 11099.65 8299.71 63
fmvsm_s_conf0.5_n_598.53 4698.35 4899.08 6399.07 12697.46 9398.68 19699.20 3497.50 5099.87 499.50 2991.96 14999.96 499.76 1199.65 8299.82 22
fmvsm_s_conf0.5_n_398.53 4698.45 3998.79 8599.23 10497.32 9898.80 15899.26 1698.82 799.87 499.60 1090.95 19399.93 3499.76 1199.73 6399.12 198
APD-MVS_3200maxsize98.53 4698.33 5899.15 5699.50 4897.92 7399.15 5698.81 10796.24 13099.20 5999.37 5495.30 6899.80 10997.73 10999.67 7699.72 59
MM98.51 4998.24 6599.33 3599.12 12098.14 6598.93 11197.02 41098.96 199.17 6299.47 3591.97 14899.94 1499.85 599.69 7399.91 4
mPP-MVS98.51 4998.26 6299.25 4499.75 698.04 6899.28 2998.81 10796.24 13098.35 12899.23 8695.46 5899.94 1497.42 14699.81 1699.77 40
ZNCC-MVS98.49 5198.20 7199.35 3099.73 1698.39 3999.19 4998.86 9195.77 15498.31 13199.10 12095.46 5899.93 3497.57 12999.81 1699.74 50
SPE-MVS-test98.49 5198.50 3498.46 12299.20 10997.05 12399.64 498.50 19997.45 5698.88 8499.14 10995.25 7299.15 25398.83 4099.56 10899.20 182
PGM-MVS98.49 5198.23 6799.27 4399.72 1798.08 6798.99 9199.49 595.43 17999.03 6999.32 6995.56 5599.94 1496.80 18499.77 4299.78 33
EI-MVSNet-Vis-set98.47 5498.39 4398.69 9399.46 5896.49 15298.30 27698.69 14397.21 7498.84 8799.36 5895.41 6099.78 12498.62 4999.65 8299.80 27
MVS_111021_HR98.47 5498.34 5498.88 8299.22 10697.32 9897.91 33399.58 397.20 7598.33 12999.00 14495.99 4399.64 15698.05 9199.76 4899.69 70
balanced_conf0398.45 5698.35 4898.74 8998.65 17697.55 8599.19 4998.60 16596.72 10699.35 4698.77 18795.06 8299.55 17998.95 3599.87 199.12 198
test_fmvsmvis_n_192098.44 5798.51 3298.23 14498.33 21896.15 16998.97 9599.15 4298.55 1698.45 12199.55 1894.26 10099.97 199.65 1899.66 7998.57 274
CS-MVS98.44 5798.49 3698.31 13699.08 12596.73 13799.67 398.47 20697.17 7898.94 7799.10 12095.73 5199.13 25898.71 4499.49 11999.09 206
GST-MVS98.43 5998.12 7599.34 3199.72 1798.38 4099.09 6998.82 10195.71 15898.73 9899.06 13595.27 7099.93 3497.07 16199.63 8999.72 59
fmvsm_s_conf0.5_n98.42 6098.51 3298.13 16099.30 8395.25 23498.85 14199.39 797.94 2999.74 2199.62 492.59 12399.91 5699.65 1899.52 11499.25 175
EI-MVSNet-UG-set98.41 6198.34 5498.61 10199.45 6196.32 16298.28 27998.68 14697.17 7898.74 9699.37 5495.25 7299.79 12198.57 5299.54 11199.73 55
DELS-MVS98.40 6298.20 7198.99 7099.00 13497.66 8097.75 35498.89 7597.71 3698.33 12998.97 14694.97 8499.88 7798.42 6999.76 4899.42 130
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_s_conf0.5_n_a98.38 6398.42 4198.27 13899.09 12495.41 22298.86 13699.37 997.69 3899.78 1799.61 592.38 12799.91 5699.58 2399.43 12799.49 111
TSAR-MVS + GP.98.38 6398.24 6598.81 8499.22 10697.25 11198.11 30898.29 26897.19 7698.99 7599.02 13896.22 3399.67 14998.52 6198.56 18399.51 104
HPM-MVS_fast98.38 6398.13 7499.12 6099.75 697.86 7499.44 998.82 10194.46 25198.94 7799.20 9295.16 7799.74 13497.58 12599.85 699.77 40
patch_mono-298.36 6698.87 696.82 27299.53 4290.68 38798.64 20799.29 1597.88 3099.19 6199.52 2396.80 1799.97 199.11 3199.86 299.82 22
HPM-MVScopyleft98.36 6698.10 7899.13 5899.74 1297.82 7999.53 698.80 11494.63 23898.61 11198.97 14695.13 7999.77 12997.65 11899.83 1399.79 28
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_498.35 6898.50 3497.90 18699.16 11595.08 24398.75 17199.24 2098.39 1999.81 1399.52 2392.35 12899.90 6499.74 1399.51 11698.71 255
APD-MVScopyleft98.35 6898.00 8499.42 2199.51 4698.72 2598.80 15898.82 10194.52 24699.23 5899.25 8595.54 5799.80 10996.52 19399.77 4299.74 50
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_LR98.34 7098.23 6798.67 9599.27 9396.90 12997.95 32699.58 397.14 8198.44 12399.01 14295.03 8399.62 16397.91 9899.75 5599.50 106
PHI-MVS98.34 7098.06 7999.18 5299.15 11898.12 6699.04 7899.09 4593.32 31498.83 9099.10 12096.54 2499.83 9097.70 11599.76 4899.59 94
MP-MVScopyleft98.33 7298.01 8399.28 4199.75 698.18 6099.22 4198.79 11996.13 13597.92 16199.23 8694.54 9099.94 1496.74 18799.78 4099.73 55
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVSMamba_PlusPlus98.31 7398.19 7398.67 9598.96 14197.36 9699.24 3598.57 17994.81 22698.99 7598.90 16295.22 7599.59 16699.15 3099.84 1199.07 214
MP-MVS-pluss98.31 7397.92 8699.49 1699.72 1798.88 1998.43 25998.78 12194.10 26297.69 18399.42 4495.25 7299.92 4398.09 8899.80 2599.67 79
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_s_conf0.5_n_298.30 7598.21 6998.57 10499.25 9697.11 12098.66 20399.20 3498.82 799.79 1599.60 1089.38 23699.92 4399.80 899.38 13498.69 257
fmvsm_s_conf0.5_n_798.23 7698.35 4897.89 18898.86 15194.99 24998.58 22099.00 5398.29 2099.73 2299.60 1091.70 15599.92 4399.63 2199.73 6398.76 248
MGCNet98.23 7697.91 8799.21 4998.06 26197.96 7298.58 22095.51 44998.58 1498.87 8599.26 8092.99 11899.95 999.62 2299.67 7699.73 55
ACMMPcopyleft98.23 7697.95 8599.09 6299.74 1297.62 8399.03 8199.41 695.98 14397.60 19599.36 5894.45 9599.93 3497.14 15898.85 16799.70 67
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
EC-MVSNet98.21 7998.11 7698.49 11998.34 21597.26 11099.61 598.43 22496.78 9998.87 8598.84 17193.72 10899.01 28298.91 3799.50 11799.19 186
fmvsm_s_conf0.1_n98.18 8098.21 6998.11 16598.54 18595.24 23598.87 13099.24 2097.50 5099.70 2699.67 191.33 17299.89 6899.47 2599.54 11199.21 181
fmvsm_s_conf0.1_n_298.14 8198.02 8298.53 11298.88 14797.07 12298.69 19398.82 10198.78 999.77 1899.61 588.83 25699.91 5699.71 1599.07 15098.61 267
fmvsm_s_conf0.1_n_a98.08 8298.04 8198.21 14597.66 30495.39 22598.89 12099.17 3897.24 7299.76 2099.67 191.13 18499.88 7799.39 2699.41 12999.35 144
dcpmvs_298.08 8298.59 2596.56 30199.57 3990.34 39999.15 5698.38 24296.82 9899.29 5399.49 3295.78 5099.57 16998.94 3699.86 299.77 40
NormalMVS98.07 8497.90 8898.59 10399.75 696.60 14398.94 10598.60 16597.86 3198.71 10199.08 13091.22 17999.80 10997.40 14899.57 10099.37 139
CANet98.05 8597.76 9198.90 8198.73 16197.27 10598.35 26698.78 12197.37 6297.72 18098.96 15191.53 16599.92 4398.79 4199.65 8299.51 104
train_agg97.97 8697.52 10499.33 3599.31 7998.50 3497.92 33198.73 13292.98 33097.74 17798.68 20096.20 3599.80 10996.59 18899.57 10099.68 75
ETV-MVS97.96 8797.81 8998.40 13198.42 19897.27 10598.73 18198.55 18496.84 9698.38 12597.44 32295.39 6199.35 21197.62 12098.89 16198.58 273
UA-Net97.96 8797.62 9598.98 7298.86 15197.47 9198.89 12099.08 4696.67 10998.72 10099.54 2093.15 11699.81 10294.87 25198.83 16899.65 83
CDPH-MVS97.94 8997.49 10699.28 4199.47 5698.44 3697.91 33398.67 15192.57 34698.77 9498.85 17095.93 4599.72 13695.56 22999.69 7399.68 75
DeepPCF-MVS96.37 297.93 9098.48 3896.30 32799.00 13489.54 41597.43 37798.87 8598.16 2299.26 5799.38 5396.12 3899.64 15698.30 7599.77 4299.72 59
DeepC-MVS95.98 397.88 9197.58 9798.77 8799.25 9696.93 12798.83 14798.75 12796.96 9296.89 22799.50 2990.46 20499.87 7997.84 10499.76 4899.52 101
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_fmvsmconf0.01_n97.86 9297.54 10398.83 8395.48 42896.83 13298.95 10298.60 16598.58 1498.93 8199.55 1888.57 26199.91 5699.54 2499.61 9299.77 40
DP-MVS Recon97.86 9297.46 10999.06 6599.53 4298.35 4998.33 26898.89 7592.62 34398.05 14298.94 15495.34 6699.65 15396.04 20999.42 12899.19 186
CSCG97.85 9497.74 9298.20 14799.67 3095.16 23899.22 4199.32 1293.04 32897.02 22098.92 16095.36 6499.91 5697.43 14499.64 8799.52 101
SymmetryMVS97.84 9597.58 9798.62 9999.01 13296.60 14398.94 10598.44 21397.86 3198.71 10199.08 13091.22 17999.80 10997.40 14897.53 24899.47 115
BP-MVS197.82 9697.51 10598.76 8898.25 23197.39 9599.15 5697.68 34296.69 10798.47 11799.10 12090.29 21099.51 18698.60 5099.35 13799.37 139
MG-MVS97.81 9797.60 9698.44 12599.12 12095.97 18097.75 35498.78 12196.89 9598.46 11899.22 8893.90 10799.68 14894.81 25599.52 11499.67 79
VNet97.79 9897.40 11498.96 7598.88 14797.55 8598.63 21098.93 6596.74 10399.02 7098.84 17190.33 20999.83 9098.53 5596.66 27199.50 106
EIA-MVS97.75 9997.58 9798.27 13898.38 20596.44 15499.01 8698.60 16595.88 14897.26 20697.53 31694.97 8499.33 21497.38 15199.20 14699.05 215
PS-MVSNAJ97.73 10097.77 9097.62 21898.68 17195.58 21297.34 38698.51 19497.29 6598.66 10897.88 28094.51 9199.90 6497.87 10199.17 14897.39 317
casdiffmvs_mvgpermissive97.72 10197.48 10898.44 12598.42 19896.59 14798.92 11398.44 21396.20 13297.76 17499.20 9291.66 15899.23 24098.27 8298.41 20399.49 111
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CPTT-MVS97.72 10197.32 12098.92 7899.64 3397.10 12199.12 6398.81 10792.34 35498.09 13799.08 13093.01 11799.92 4396.06 20899.77 4299.75 48
PVSNet_Blended_VisFu97.70 10397.46 10998.44 12599.27 9395.91 18898.63 21099.16 4094.48 25097.67 18498.88 16692.80 12099.91 5697.11 15999.12 14999.50 106
mvsany_test197.69 10497.70 9397.66 21498.24 23294.18 29297.53 37097.53 36395.52 17499.66 2899.51 2694.30 9899.56 17298.38 7098.62 17899.23 177
sasdasda97.67 10597.23 12998.98 7298.70 16698.38 4099.34 1798.39 23796.76 10197.67 18497.40 32692.26 13399.49 19098.28 7996.28 28999.08 210
canonicalmvs97.67 10597.23 12998.98 7298.70 16698.38 4099.34 1798.39 23796.76 10197.67 18497.40 32692.26 13399.49 19098.28 7996.28 28999.08 210
xiu_mvs_v2_base97.66 10797.70 9397.56 22298.61 18095.46 22097.44 37498.46 20797.15 8098.65 10998.15 25594.33 9799.80 10997.84 10498.66 17797.41 315
GDP-MVS97.64 10897.28 12298.71 9298.30 22397.33 9799.05 7498.52 19196.34 12798.80 9199.05 13689.74 22399.51 18696.86 18098.86 16599.28 165
baseline97.64 10897.44 11198.25 14298.35 21096.20 16699.00 8898.32 25596.33 12998.03 14599.17 10291.35 17199.16 24998.10 8798.29 21299.39 135
casdiffmvspermissive97.63 11097.41 11398.28 13798.33 21896.14 17098.82 14998.32 25596.38 12597.95 15699.21 9091.23 17899.23 24098.12 8698.37 20599.48 113
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MGCFI-Net97.62 11197.19 13298.92 7898.66 17398.20 5899.32 2298.38 24296.69 10797.58 19797.42 32592.10 14299.50 18998.28 7996.25 29299.08 210
xiu_mvs_v1_base_debu97.60 11297.56 10097.72 20398.35 21095.98 17597.86 34398.51 19497.13 8299.01 7298.40 22791.56 16199.80 10998.53 5598.68 17397.37 319
xiu_mvs_v1_base97.60 11297.56 10097.72 20398.35 21095.98 17597.86 34398.51 19497.13 8299.01 7298.40 22791.56 16199.80 10998.53 5598.68 17397.37 319
xiu_mvs_v1_base_debi97.60 11297.56 10097.72 20398.35 21095.98 17597.86 34398.51 19497.13 8299.01 7298.40 22791.56 16199.80 10998.53 5598.68 17397.37 319
diffmvs_AUTHOR97.59 11597.44 11198.01 17798.26 23095.47 21998.12 30598.36 24896.38 12598.84 8799.10 12091.13 18499.26 22798.24 8398.56 18399.30 158
diffmvspermissive97.58 11697.40 11498.13 16098.32 22195.81 20298.06 31498.37 24496.20 13298.74 9698.89 16591.31 17499.25 23198.16 8598.52 18799.34 146
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
guyue97.57 11797.37 11698.20 14798.50 18795.86 19698.89 12097.03 40797.29 6598.73 9898.90 16289.41 23599.32 21598.68 4598.86 16599.42 130
MVSFormer97.57 11797.49 10697.84 19098.07 25895.76 20699.47 798.40 23294.98 21598.79 9298.83 17592.34 12998.41 35696.91 16899.59 9699.34 146
alignmvs97.56 11997.07 14299.01 6998.66 17398.37 4798.83 14798.06 32096.74 10398.00 15197.65 30390.80 19599.48 19598.37 7196.56 27599.19 186
E3new97.55 12097.35 11898.16 15198.48 19295.85 19798.55 23398.41 22995.42 18198.06 14099.12 11592.23 13699.24 23697.43 14498.45 19399.39 135
DPM-MVS97.55 12096.99 14999.23 4899.04 12898.55 3297.17 40398.35 24994.85 22597.93 16098.58 21095.07 8199.71 14192.60 33199.34 13899.43 127
OMC-MVS97.55 12097.34 11998.20 14799.33 7495.92 18798.28 27998.59 17295.52 17497.97 15499.10 12093.28 11599.49 19095.09 24698.88 16299.19 186
viewcassd2359sk1197.53 12397.32 12098.16 15198.45 19595.83 19998.57 22998.42 22895.52 17498.07 13899.12 11591.81 15399.25 23197.46 14298.48 19299.41 133
LuminaMVS97.49 12497.18 13398.42 12997.50 31997.15 11898.45 25197.68 34296.56 11598.68 10398.78 18489.84 22099.32 21598.60 5098.57 18298.79 240
E297.48 12597.25 12498.16 15198.40 20295.79 20398.58 22098.44 21395.58 16598.00 15199.14 10991.21 18399.24 23697.50 13798.43 19799.45 122
E397.48 12597.25 12498.16 15198.38 20595.79 20398.58 22098.44 21395.58 16598.00 15199.14 10991.25 17799.24 23697.50 13798.44 19499.45 122
KinetiMVS97.48 12597.05 14498.78 8698.37 20897.30 10198.99 9198.70 14197.18 7799.02 7099.01 14287.50 29199.67 14995.33 23699.33 14099.37 139
viewmanbaseed2359cas97.47 12897.25 12498.14 15598.41 20095.84 19898.57 22998.43 22495.55 17197.97 15499.12 11591.26 17699.15 25397.42 14698.53 18699.43 127
PAPM_NR97.46 12997.11 13998.50 11799.50 4896.41 15798.63 21098.60 16595.18 19697.06 21898.06 26194.26 10099.57 16993.80 29798.87 16499.52 101
EPP-MVSNet97.46 12997.28 12297.99 17998.64 17795.38 22699.33 2198.31 25993.61 30297.19 21099.07 13494.05 10399.23 24096.89 17298.43 19799.37 139
3Dnovator94.51 597.46 12996.93 15399.07 6497.78 29297.64 8199.35 1699.06 4897.02 8793.75 34599.16 10589.25 24099.92 4397.22 15799.75 5599.64 86
CNLPA97.45 13297.03 14698.73 9099.05 12797.44 9498.07 31398.53 18895.32 18996.80 23298.53 21593.32 11399.72 13694.31 27899.31 14199.02 219
lupinMVS97.44 13397.22 13198.12 16398.07 25895.76 20697.68 35997.76 33994.50 24998.79 9298.61 20592.34 12999.30 22097.58 12599.59 9699.31 154
3Dnovator+94.38 697.43 13496.78 16499.38 2397.83 28998.52 3399.37 1398.71 13797.09 8592.99 37599.13 11289.36 23799.89 6896.97 16499.57 10099.71 63
Vis-MVSNetpermissive97.42 13597.11 13998.34 13498.66 17396.23 16599.22 4199.00 5396.63 11198.04 14499.21 9088.05 27899.35 21196.01 21199.21 14599.45 122
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS97.41 13697.25 12497.91 18598.70 16696.80 13398.82 14998.69 14394.53 24498.11 13598.28 24294.50 9499.57 16994.12 28699.49 11997.37 319
sss97.39 13796.98 15198.61 10198.60 18196.61 14298.22 28598.93 6593.97 27298.01 15098.48 22091.98 14699.85 8496.45 19598.15 21999.39 135
test_cas_vis1_n_192097.38 13897.36 11797.45 22698.95 14293.25 33199.00 8898.53 18897.70 3799.77 1899.35 6084.71 34799.85 8498.57 5299.66 7999.26 173
PVSNet_Blended97.38 13897.12 13898.14 15599.25 9695.35 22997.28 39199.26 1693.13 32497.94 15898.21 25092.74 12199.81 10296.88 17499.40 13299.27 166
E697.37 14097.16 13597.98 18098.28 22895.40 22498.87 13098.45 21195.55 17197.84 16799.20 9290.44 20599.25 23197.61 12398.22 21699.29 161
E597.37 14097.16 13597.98 18098.30 22395.41 22298.87 13098.45 21195.56 16797.84 16799.19 9890.39 20699.25 23197.61 12398.22 21699.29 161
E497.37 14097.13 13798.12 16398.27 22995.70 20898.59 21698.44 21395.56 16797.80 17199.18 10090.57 20299.26 22797.45 14398.28 21499.40 134
WTY-MVS97.37 14096.92 15498.72 9198.86 15196.89 13198.31 27398.71 13795.26 19297.67 18498.56 21492.21 13899.78 12495.89 21396.85 26599.48 113
AstraMVS97.34 14497.24 12897.65 21598.13 25294.15 29398.94 10596.25 43997.47 5498.60 11299.28 7689.67 22599.41 20598.73 4398.07 22399.38 138
viewmacassd2359aftdt97.32 14597.07 14298.08 16898.30 22395.69 20998.62 21398.44 21395.56 16797.86 16699.22 8889.91 21899.14 25697.29 15498.43 19799.42 130
jason97.32 14597.08 14198.06 17297.45 32595.59 21197.87 34197.91 33194.79 22898.55 11598.83 17591.12 18699.23 24097.58 12599.60 9499.34 146
jason: jason.
MVS_Test97.28 14797.00 14798.13 16098.33 21895.97 18098.74 17598.07 31594.27 25798.44 12398.07 26092.48 12599.26 22796.43 19698.19 21899.16 192
EPNet97.28 14796.87 15698.51 11494.98 43796.14 17098.90 11697.02 41098.28 2195.99 26799.11 11891.36 17099.89 6896.98 16399.19 14799.50 106
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SSM_040497.26 14997.00 14798.03 17498.46 19395.99 17498.62 21398.44 21394.77 22997.24 20798.93 15691.22 17999.28 22496.54 19098.74 17298.84 236
mvsmamba97.25 15096.99 14998.02 17698.34 21595.54 21699.18 5397.47 36995.04 20998.15 13298.57 21389.46 23299.31 21997.68 11799.01 15599.22 179
viewdifsd2359ckpt1397.24 15196.97 15298.06 17298.43 19695.77 20598.59 21698.34 25294.81 22697.60 19598.94 15490.78 19999.09 26896.93 16798.33 20899.32 153
test_yl97.22 15296.78 16498.54 10998.73 16196.60 14398.45 25198.31 25994.70 23298.02 14798.42 22590.80 19599.70 14296.81 18196.79 26799.34 146
DCV-MVSNet97.22 15296.78 16498.54 10998.73 16196.60 14398.45 25198.31 25994.70 23298.02 14798.42 22590.80 19599.70 14296.81 18196.79 26799.34 146
IS-MVSNet97.22 15296.88 15598.25 14298.85 15496.36 16099.19 4997.97 32595.39 18397.23 20898.99 14591.11 18798.93 29494.60 26698.59 18099.47 115
viewdifsd2359ckpt0797.20 15597.05 14497.65 21598.40 20294.33 28598.39 26498.43 22495.67 16097.66 18899.08 13090.04 21599.32 21597.47 14198.29 21299.31 154
PLCcopyleft95.07 497.20 15596.78 16498.44 12599.29 8896.31 16498.14 30298.76 12592.41 35296.39 25598.31 24094.92 8699.78 12494.06 28998.77 17199.23 177
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x42097.18 15797.18 13397.20 23998.81 15793.27 32895.78 44999.15 4295.25 19396.79 23398.11 25892.29 13299.07 27198.56 5499.85 699.25 175
SSM_040797.17 15896.87 15698.08 16898.19 24095.90 18998.52 23698.44 21394.77 22996.75 23498.93 15691.22 17999.22 24496.54 19098.43 19799.10 203
LS3D97.16 15996.66 17398.68 9498.53 18697.19 11598.93 11198.90 7392.83 33795.99 26799.37 5492.12 14199.87 7993.67 30199.57 10098.97 224
AdaColmapbinary97.15 16096.70 16998.48 12099.16 11596.69 13998.01 32098.89 7594.44 25296.83 22898.68 20090.69 20099.76 13094.36 27499.29 14298.98 223
viewdifsd2359ckpt0997.13 16196.79 16298.14 15598.43 19695.90 18998.52 23698.37 24494.32 25597.33 20298.86 16990.23 21399.16 24996.81 18198.25 21599.36 143
mamv497.13 16198.11 7694.17 41398.97 14083.70 45898.66 20398.71 13794.63 23897.83 16998.90 16296.25 3299.55 17999.27 2899.76 4899.27 166
Effi-MVS+97.12 16396.69 17098.39 13298.19 24096.72 13897.37 38298.43 22493.71 29097.65 18998.02 26492.20 13999.25 23196.87 17797.79 23299.19 186
CHOSEN 1792x268897.12 16396.80 16098.08 16899.30 8394.56 27498.05 31599.71 193.57 30497.09 21498.91 16188.17 27299.89 6896.87 17799.56 10899.81 24
F-COLMAP97.09 16596.80 16097.97 18299.45 6194.95 25398.55 23398.62 16493.02 32996.17 26298.58 21094.01 10499.81 10293.95 29198.90 16099.14 196
RRT-MVS97.03 16696.78 16497.77 19997.90 28594.34 28399.12 6398.35 24995.87 14998.06 14098.70 19886.45 31099.63 15998.04 9298.54 18599.35 144
TAMVS97.02 16796.79 16297.70 20698.06 26195.31 23298.52 23698.31 25993.95 27397.05 21998.61 20593.49 11198.52 33895.33 23697.81 23199.29 161
viewmambaseed2359dif97.01 16896.84 15897.51 22498.19 24094.21 29198.16 29898.23 28093.61 30297.78 17299.13 11290.79 19899.18 24897.24 15598.40 20499.15 193
CDS-MVSNet96.99 16996.69 17097.90 18698.05 26395.98 17598.20 28898.33 25493.67 29796.95 22198.49 21993.54 11098.42 34995.24 24397.74 23599.31 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU96.96 17096.55 17898.21 14598.17 24996.07 17397.98 32498.21 28297.24 7297.13 21298.93 15686.88 30299.91 5695.00 24999.37 13698.66 263
114514_t96.93 17196.27 19198.92 7899.50 4897.63 8298.85 14198.90 7384.80 45497.77 17399.11 11892.84 11999.66 15294.85 25299.77 4299.47 115
MAR-MVS96.91 17296.40 18598.45 12398.69 16996.90 12998.66 20398.68 14692.40 35397.07 21797.96 27191.54 16499.75 13293.68 29998.92 15998.69 257
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
HyFIR lowres test96.90 17396.49 18298.14 15599.33 7495.56 21397.38 38099.65 292.34 35497.61 19298.20 25189.29 23999.10 26796.97 16497.60 24099.77 40
Vis-MVSNet (Re-imp)96.87 17496.55 17897.83 19198.73 16195.46 22099.20 4798.30 26694.96 21796.60 24398.87 16790.05 21498.59 33393.67 30198.60 17999.46 120
SDMVSNet96.85 17596.42 18398.14 15599.30 8396.38 15899.21 4499.23 2895.92 14595.96 26998.76 19285.88 32299.44 20297.93 9695.59 30498.60 268
PAPR96.84 17696.24 19398.65 9798.72 16596.92 12897.36 38498.57 17993.33 31396.67 23897.57 31294.30 9899.56 17291.05 37498.59 18099.47 115
HY-MVS93.96 896.82 17796.23 19498.57 10498.46 19397.00 12498.14 30298.21 28293.95 27396.72 23797.99 26891.58 15999.76 13094.51 27096.54 27698.95 227
mamba_040896.81 17896.38 18698.09 16798.19 24095.90 18995.69 45098.32 25594.51 24796.75 23498.73 19490.99 19199.27 22695.83 21698.43 19799.10 203
UGNet96.78 17996.30 19098.19 15098.24 23295.89 19498.88 12798.93 6597.39 5996.81 23197.84 28482.60 37699.90 6496.53 19299.49 11998.79 240
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
IMVS_040796.74 18096.64 17497.05 25497.99 27292.82 34398.45 25198.27 26995.16 19797.30 20398.79 18091.53 16599.06 27294.74 25797.54 24499.27 166
IMVS_040396.74 18096.61 17597.12 24897.99 27292.82 34398.47 24998.27 26995.16 19797.13 21298.79 18091.44 16899.26 22794.74 25797.54 24499.27 166
PVSNet_BlendedMVS96.73 18296.60 17697.12 24899.25 9695.35 22998.26 28299.26 1694.28 25697.94 15897.46 31992.74 12199.81 10296.88 17493.32 34296.20 414
SSM_0407296.71 18396.38 18697.68 20998.19 24095.90 18995.69 45098.32 25594.51 24796.75 23498.73 19490.99 19198.02 39795.83 21698.43 19799.10 203
test_vis1_n_192096.71 18396.84 15896.31 32699.11 12289.74 40899.05 7498.58 17798.08 2499.87 499.37 5478.48 40899.93 3499.29 2799.69 7399.27 166
mvs_anonymous96.70 18596.53 18097.18 24298.19 24093.78 30398.31 27398.19 28694.01 26994.47 30198.27 24592.08 14498.46 34497.39 15097.91 22799.31 154
Elysia96.64 18696.02 20398.51 11498.04 26597.30 10198.74 17598.60 16595.04 20997.91 16298.84 17183.59 37199.48 19594.20 28299.25 14398.75 249
StellarMVS96.64 18696.02 20398.51 11498.04 26597.30 10198.74 17598.60 16595.04 20997.91 16298.84 17183.59 37199.48 19594.20 28299.25 14398.75 249
1112_ss96.63 18896.00 20598.50 11798.56 18296.37 15998.18 29698.10 30892.92 33394.84 28998.43 22392.14 14099.58 16894.35 27596.51 27799.56 100
PMMVS96.60 18996.33 18997.41 23097.90 28593.93 29997.35 38598.41 22992.84 33697.76 17497.45 32191.10 18899.20 24596.26 20197.91 22799.11 201
DP-MVS96.59 19095.93 20898.57 10499.34 7196.19 16898.70 19098.39 23789.45 42494.52 29999.35 6091.85 15099.85 8492.89 32598.88 16299.68 75
PatchMatch-RL96.59 19096.03 20298.27 13899.31 7996.51 15197.91 33399.06 4893.72 28996.92 22598.06 26188.50 26699.65 15391.77 35699.00 15798.66 263
GeoE96.58 19296.07 19998.10 16698.35 21095.89 19499.34 1798.12 30293.12 32596.09 26398.87 16789.71 22498.97 28492.95 32198.08 22299.43 127
icg_test_0407_296.56 19396.50 18196.73 27897.99 27292.82 34397.18 40098.27 26995.16 19797.30 20398.79 18091.53 16598.10 38794.74 25797.54 24499.27 166
XVG-OURS96.55 19496.41 18496.99 25798.75 16093.76 30497.50 37398.52 19195.67 16096.83 22899.30 7488.95 25499.53 18295.88 21496.26 29197.69 308
FIs96.51 19596.12 19897.67 21197.13 34997.54 8799.36 1499.22 3395.89 14794.03 33098.35 23391.98 14698.44 34796.40 19792.76 35097.01 327
XVG-OURS-SEG-HR96.51 19596.34 18897.02 25698.77 15993.76 30497.79 35298.50 19995.45 17896.94 22299.09 12887.87 28399.55 17996.76 18695.83 30397.74 305
PS-MVSNAJss96.43 19796.26 19296.92 26795.84 41795.08 24399.16 5598.50 19995.87 14993.84 34098.34 23794.51 9198.61 32996.88 17493.45 33797.06 325
test_fmvs196.42 19896.67 17295.66 35798.82 15688.53 43598.80 15898.20 28496.39 12499.64 3099.20 9280.35 39699.67 14999.04 3399.57 10098.78 244
FC-MVSNet-test96.42 19896.05 20097.53 22396.95 35897.27 10599.36 1499.23 2895.83 15193.93 33398.37 23192.00 14598.32 36896.02 21092.72 35197.00 328
ab-mvs96.42 19895.71 21998.55 10798.63 17896.75 13697.88 34098.74 12993.84 27996.54 24898.18 25385.34 33399.75 13295.93 21296.35 28199.15 193
FA-MVS(test-final)96.41 20195.94 20797.82 19398.21 23695.20 23797.80 35097.58 35393.21 31997.36 20197.70 29689.47 23099.56 17294.12 28697.99 22498.71 255
PVSNet91.96 1896.35 20296.15 19596.96 26299.17 11192.05 36096.08 44298.68 14693.69 29397.75 17697.80 29088.86 25599.69 14794.26 28099.01 15599.15 193
Test_1112_low_res96.34 20395.66 22498.36 13398.56 18295.94 18397.71 35798.07 31592.10 36394.79 29397.29 33491.75 15499.56 17294.17 28496.50 27899.58 98
viewdifsd2359ckpt1196.30 20496.13 19696.81 27398.10 25592.10 35698.49 24798.40 23296.02 14097.61 19299.31 7186.37 31299.29 22297.52 13393.36 34199.04 216
viewmsd2359difaftdt96.30 20496.13 19696.81 27398.10 25592.10 35698.49 24798.40 23296.02 14097.61 19299.31 7186.37 31299.30 22097.52 13393.37 34099.04 216
Effi-MVS+-dtu96.29 20696.56 17795.51 36297.89 28790.22 40098.80 15898.10 30896.57 11496.45 25396.66 39190.81 19498.91 29795.72 22397.99 22497.40 316
QAPM96.29 20695.40 23098.96 7597.85 28897.60 8499.23 3798.93 6589.76 41893.11 37299.02 13889.11 24599.93 3491.99 35099.62 9199.34 146
Fast-Effi-MVS+96.28 20895.70 22198.03 17498.29 22695.97 18098.58 22098.25 27891.74 37195.29 28297.23 33991.03 19099.15 25392.90 32397.96 22698.97 224
nrg03096.28 20895.72 21697.96 18496.90 36398.15 6399.39 1198.31 25995.47 17794.42 30798.35 23392.09 14398.69 32197.50 13789.05 40297.04 326
131496.25 21095.73 21597.79 19597.13 34995.55 21598.19 29198.59 17293.47 30892.03 40297.82 28891.33 17299.49 19094.62 26598.44 19498.32 288
sd_testset96.17 21195.76 21497.42 22999.30 8394.34 28398.82 14999.08 4695.92 14595.96 26998.76 19282.83 37599.32 21595.56 22995.59 30498.60 268
h-mvs3396.17 21195.62 22597.81 19499.03 12994.45 27698.64 20798.75 12797.48 5298.67 10498.72 19789.76 22199.86 8397.95 9481.59 45299.11 201
HQP_MVS96.14 21395.90 20996.85 27097.42 32794.60 27298.80 15898.56 18297.28 6795.34 27898.28 24287.09 29799.03 27796.07 20594.27 31296.92 335
tttt051796.07 21495.51 22897.78 19698.41 20094.84 25799.28 2994.33 46294.26 25897.64 19098.64 20484.05 36299.47 19995.34 23597.60 24099.03 218
MVSTER96.06 21595.72 21697.08 25298.23 23495.93 18698.73 18198.27 26994.86 22395.07 28498.09 25988.21 27198.54 33696.59 18893.46 33596.79 354
thisisatest053096.01 21695.36 23597.97 18298.38 20595.52 21798.88 12794.19 46494.04 26497.64 19098.31 24083.82 36999.46 20095.29 24097.70 23798.93 229
test_djsdf96.00 21795.69 22296.93 26495.72 41995.49 21899.47 798.40 23294.98 21594.58 29797.86 28189.16 24398.41 35696.91 16894.12 32096.88 344
EI-MVSNet95.96 21895.83 21196.36 32297.93 28393.70 31098.12 30598.27 26993.70 29295.07 28499.02 13892.23 13698.54 33694.68 26193.46 33596.84 350
VortexMVS95.95 21995.79 21296.42 31898.29 22693.96 29898.68 19698.31 25996.02 14094.29 31597.57 31289.47 23098.37 36397.51 13691.93 35996.94 333
ECVR-MVScopyleft95.95 21995.71 21996.65 28699.02 13090.86 38299.03 8191.80 47596.96 9298.10 13699.26 8081.31 38299.51 18696.90 17199.04 15299.59 94
BH-untuned95.95 21995.72 21696.65 28698.55 18492.26 35298.23 28497.79 33893.73 28794.62 29698.01 26688.97 25399.00 28393.04 31898.51 18898.68 259
test111195.94 22295.78 21396.41 31998.99 13790.12 40199.04 7892.45 47496.99 9198.03 14599.27 7981.40 38199.48 19596.87 17799.04 15299.63 88
MSDG95.93 22395.30 24297.83 19198.90 14595.36 22796.83 42998.37 24491.32 38794.43 30698.73 19490.27 21199.60 16590.05 38898.82 16998.52 276
BH-RMVSNet95.92 22495.32 24097.69 20798.32 22194.64 26698.19 29197.45 37494.56 24296.03 26598.61 20585.02 33899.12 26190.68 37999.06 15199.30 158
test_fmvs1_n95.90 22595.99 20695.63 35898.67 17288.32 43999.26 3298.22 28196.40 12399.67 2799.26 8073.91 44899.70 14299.02 3499.50 11798.87 233
Fast-Effi-MVS+-dtu95.87 22695.85 21095.91 34497.74 29791.74 36698.69 19398.15 29895.56 16794.92 28797.68 30188.98 25298.79 31593.19 31397.78 23397.20 323
LFMVS95.86 22794.98 25798.47 12198.87 15096.32 16298.84 14596.02 44093.40 31198.62 11099.20 9274.99 44099.63 15997.72 11097.20 25399.46 120
baseline195.84 22895.12 25098.01 17798.49 19195.98 17598.73 18197.03 40795.37 18696.22 25898.19 25289.96 21799.16 24994.60 26687.48 41898.90 232
OpenMVScopyleft93.04 1395.83 22995.00 25598.32 13597.18 34697.32 9899.21 4498.97 5789.96 41491.14 41199.05 13686.64 30599.92 4393.38 30799.47 12297.73 306
IMVS_040495.82 23095.52 22696.73 27897.99 27292.82 34397.23 39398.27 26995.16 19794.31 31398.79 18085.63 32698.10 38794.74 25797.54 24499.27 166
VDD-MVS95.82 23095.23 24497.61 21998.84 15593.98 29798.68 19697.40 37895.02 21397.95 15699.34 6874.37 44699.78 12498.64 4896.80 26699.08 210
UniMVSNet (Re)95.78 23295.19 24697.58 22096.99 35697.47 9198.79 16699.18 3795.60 16393.92 33497.04 36191.68 15698.48 34095.80 22087.66 41796.79 354
VPA-MVSNet95.75 23395.11 25197.69 20797.24 33897.27 10598.94 10599.23 2895.13 20295.51 27697.32 33285.73 32498.91 29797.33 15389.55 39396.89 343
HQP-MVS95.72 23495.40 23096.69 28497.20 34294.25 28998.05 31598.46 20796.43 11894.45 30297.73 29386.75 30398.96 28895.30 23894.18 31696.86 349
hse-mvs295.71 23595.30 24296.93 26498.50 18793.53 31598.36 26598.10 30897.48 5298.67 10497.99 26889.76 22199.02 28097.95 9480.91 45898.22 291
UniMVSNet_NR-MVSNet95.71 23595.15 24797.40 23296.84 36696.97 12598.74 17599.24 2095.16 19793.88 33697.72 29591.68 15698.31 37095.81 21887.25 42396.92 335
PatchmatchNetpermissive95.71 23595.52 22696.29 32897.58 31090.72 38696.84 42897.52 36494.06 26397.08 21596.96 37189.24 24198.90 30092.03 34998.37 20599.26 173
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
OPM-MVS95.69 23895.33 23996.76 27796.16 40294.63 26798.43 25998.39 23796.64 11095.02 28698.78 18485.15 33799.05 27395.21 24594.20 31596.60 378
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMM93.85 995.69 23895.38 23496.61 29497.61 30793.84 30298.91 11598.44 21395.25 19394.28 31698.47 22186.04 32199.12 26195.50 23293.95 32596.87 347
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst95.63 24095.69 22295.44 36697.54 31588.54 43496.97 41497.56 35693.50 30697.52 19996.93 37589.49 22899.16 24995.25 24296.42 28098.64 265
FE-MVS95.62 24194.90 26197.78 19698.37 20894.92 25497.17 40397.38 38090.95 39897.73 17997.70 29685.32 33599.63 15991.18 36698.33 20898.79 240
LPG-MVS_test95.62 24195.34 23696.47 31297.46 32293.54 31398.99 9198.54 18694.67 23694.36 31098.77 18785.39 33099.11 26395.71 22494.15 31896.76 357
CLD-MVS95.62 24195.34 23696.46 31597.52 31893.75 30697.27 39298.46 20795.53 17394.42 30798.00 26786.21 31698.97 28496.25 20394.37 31096.66 372
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest051595.61 24494.89 26297.76 20098.15 25195.15 24096.77 43094.41 46092.95 33297.18 21197.43 32384.78 34499.45 20194.63 26397.73 23698.68 259
MonoMVSNet95.51 24595.45 22995.68 35595.54 42490.87 38198.92 11397.37 38195.79 15395.53 27597.38 32889.58 22797.68 41996.40 19792.59 35298.49 278
thres600view795.49 24694.77 26597.67 21198.98 13895.02 24598.85 14196.90 41795.38 18496.63 24096.90 37784.29 35499.59 16688.65 41296.33 28298.40 282
test_vis1_n95.47 24795.13 24896.49 30997.77 29390.41 39699.27 3198.11 30596.58 11299.66 2899.18 10067.00 46299.62 16399.21 2999.40 13299.44 125
SCA95.46 24895.13 24896.46 31597.67 30291.29 37497.33 38797.60 35294.68 23596.92 22597.10 34683.97 36498.89 30192.59 33398.32 21199.20 182
IterMVS-LS95.46 24895.21 24596.22 33098.12 25393.72 30998.32 27298.13 30193.71 29094.26 31797.31 33392.24 13598.10 38794.63 26390.12 38496.84 350
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing3-295.45 25095.34 23695.77 35398.69 16988.75 43098.87 13097.21 39496.13 13597.22 20997.68 30177.95 41699.65 15397.58 12596.77 26998.91 231
jajsoiax95.45 25095.03 25496.73 27895.42 43294.63 26799.14 5998.52 19195.74 15593.22 36598.36 23283.87 36798.65 32696.95 16694.04 32196.91 340
CVMVSNet95.43 25296.04 20193.57 42097.93 28383.62 45998.12 30598.59 17295.68 15996.56 24499.02 13887.51 28997.51 42893.56 30597.44 24999.60 92
anonymousdsp95.42 25394.91 26096.94 26395.10 43695.90 18999.14 5998.41 22993.75 28493.16 36897.46 31987.50 29198.41 35695.63 22894.03 32296.50 398
DU-MVS95.42 25394.76 26697.40 23296.53 38396.97 12598.66 20398.99 5695.43 17993.88 33697.69 29888.57 26198.31 37095.81 21887.25 42396.92 335
mvs_tets95.41 25595.00 25596.65 28695.58 42394.42 27899.00 8898.55 18495.73 15793.21 36698.38 23083.45 37398.63 32797.09 16094.00 32396.91 340
thres100view90095.38 25694.70 27097.41 23098.98 13894.92 25498.87 13096.90 41795.38 18496.61 24296.88 37884.29 35499.56 17288.11 41596.29 28697.76 303
thres40095.38 25694.62 27497.65 21598.94 14394.98 25098.68 19696.93 41595.33 18796.55 24696.53 39784.23 35899.56 17288.11 41596.29 28698.40 282
BH-w/o95.38 25695.08 25296.26 32998.34 21591.79 36397.70 35897.43 37692.87 33594.24 31997.22 34088.66 25998.84 30791.55 36297.70 23798.16 294
VDDNet95.36 25994.53 27997.86 18998.10 25595.13 24198.85 14197.75 34090.46 40598.36 12699.39 4873.27 45099.64 15697.98 9396.58 27498.81 239
TAPA-MVS93.98 795.35 26094.56 27897.74 20299.13 11994.83 25998.33 26898.64 15986.62 44296.29 25798.61 20594.00 10599.29 22280.00 45999.41 12999.09 206
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP93.49 1095.34 26194.98 25796.43 31797.67 30293.48 31798.73 18198.44 21394.94 22192.53 38898.53 21584.50 35399.14 25695.48 23394.00 32396.66 372
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
COLMAP_ROBcopyleft93.27 1295.33 26294.87 26396.71 28199.29 8893.24 33298.58 22098.11 30589.92 41593.57 35099.10 12086.37 31299.79 12190.78 37798.10 22197.09 324
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UBG95.32 26394.72 26997.13 24698.05 26393.26 32997.87 34197.20 39594.96 21796.18 26195.66 43180.97 38899.35 21194.47 27297.08 25698.78 244
tfpn200view995.32 26394.62 27497.43 22898.94 14394.98 25098.68 19696.93 41595.33 18796.55 24696.53 39784.23 35899.56 17288.11 41596.29 28697.76 303
Anonymous20240521195.28 26594.49 28197.67 21199.00 13493.75 30698.70 19097.04 40690.66 40196.49 25098.80 17878.13 41299.83 9096.21 20495.36 30899.44 125
thres20095.25 26694.57 27797.28 23698.81 15794.92 25498.20 28897.11 39995.24 19596.54 24896.22 40984.58 35199.53 18287.93 42096.50 27897.39 317
AllTest95.24 26794.65 27396.99 25799.25 9693.21 33398.59 21698.18 28991.36 38393.52 35298.77 18784.67 34899.72 13689.70 39597.87 22998.02 298
LCM-MVSNet-Re95.22 26895.32 24094.91 38398.18 24687.85 44598.75 17195.66 44795.11 20488.96 43196.85 38190.26 21297.65 42095.65 22798.44 19499.22 179
EPNet_dtu95.21 26994.95 25995.99 33896.17 40090.45 39498.16 29897.27 38996.77 10093.14 37198.33 23890.34 20898.42 34985.57 43498.81 17099.09 206
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XXY-MVS95.20 27094.45 28797.46 22596.75 37396.56 14998.86 13698.65 15893.30 31693.27 36498.27 24584.85 34298.87 30494.82 25491.26 37096.96 330
D2MVS95.18 27195.08 25295.48 36397.10 35192.07 35998.30 27699.13 4494.02 26692.90 37696.73 38789.48 22998.73 31994.48 27193.60 33495.65 428
WR-MVS95.15 27294.46 28497.22 23896.67 37896.45 15398.21 28698.81 10794.15 26093.16 36897.69 29887.51 28998.30 37295.29 24088.62 40896.90 342
TranMVSNet+NR-MVSNet95.14 27394.48 28297.11 25096.45 38996.36 16099.03 8199.03 5195.04 20993.58 34997.93 27488.27 27098.03 39694.13 28586.90 42896.95 332
myMVS_eth3d2895.12 27494.62 27496.64 29098.17 24992.17 35398.02 31997.32 38395.41 18296.22 25896.05 41578.01 41499.13 25895.22 24497.16 25498.60 268
baseline295.11 27594.52 28096.87 26996.65 37993.56 31298.27 28194.10 46693.45 30992.02 40397.43 32387.45 29499.19 24693.88 29497.41 25197.87 301
miper_enhance_ethall95.10 27694.75 26796.12 33497.53 31793.73 30896.61 43698.08 31392.20 36293.89 33596.65 39392.44 12698.30 37294.21 28191.16 37196.34 407
Anonymous2024052995.10 27694.22 29897.75 20199.01 13294.26 28898.87 13098.83 9885.79 45096.64 23998.97 14678.73 40599.85 8496.27 20094.89 30999.12 198
test-LLR95.10 27694.87 26395.80 35096.77 37089.70 41096.91 41995.21 45295.11 20494.83 29195.72 42887.71 28598.97 28493.06 31698.50 18998.72 252
WR-MVS_H95.05 27994.46 28496.81 27396.86 36595.82 20199.24 3599.24 2093.87 27892.53 38896.84 38290.37 20798.24 37893.24 31187.93 41496.38 406
miper_ehance_all_eth95.01 28094.69 27195.97 34197.70 30093.31 32797.02 41298.07 31592.23 35993.51 35496.96 37191.85 15098.15 38393.68 29991.16 37196.44 404
testing1195.00 28194.28 29497.16 24497.96 28093.36 32598.09 31197.06 40594.94 22195.33 28196.15 41176.89 42999.40 20695.77 22296.30 28598.72 252
ADS-MVSNet95.00 28194.45 28796.63 29198.00 27091.91 36296.04 44397.74 34190.15 41196.47 25196.64 39487.89 28198.96 28890.08 38697.06 25799.02 219
VPNet94.99 28394.19 30097.40 23297.16 34796.57 14898.71 18698.97 5795.67 16094.84 28998.24 24980.36 39598.67 32596.46 19487.32 42296.96 330
EPMVS94.99 28394.48 28296.52 30797.22 34091.75 36597.23 39391.66 47694.11 26197.28 20596.81 38485.70 32598.84 30793.04 31897.28 25298.97 224
testing9194.98 28594.25 29797.20 23997.94 28193.41 32098.00 32297.58 35394.99 21495.45 27796.04 41677.20 42499.42 20494.97 25096.02 29998.78 244
NR-MVSNet94.98 28594.16 30397.44 22796.53 38397.22 11398.74 17598.95 6194.96 21789.25 43097.69 29889.32 23898.18 38194.59 26887.40 42096.92 335
FMVSNet394.97 28794.26 29697.11 25098.18 24696.62 14098.56 23298.26 27793.67 29794.09 32697.10 34684.25 35698.01 39892.08 34592.14 35696.70 366
FE-MVSNET394.96 28894.28 29496.98 26095.93 41396.11 17297.08 40998.39 23793.62 30193.86 33896.40 40288.28 26998.21 37992.61 32992.36 35596.63 374
CostFormer94.95 28994.73 26895.60 36097.28 33689.06 42397.53 37096.89 41989.66 42096.82 23096.72 38886.05 31998.95 29395.53 23196.13 29798.79 240
PAPM94.95 28994.00 31697.78 19697.04 35395.65 21096.03 44598.25 27891.23 39294.19 32297.80 29091.27 17598.86 30682.61 45197.61 23998.84 236
CP-MVSNet94.94 29194.30 29396.83 27196.72 37595.56 21399.11 6598.95 6193.89 27692.42 39397.90 27787.19 29698.12 38694.32 27788.21 41196.82 353
TR-MVS94.94 29194.20 29997.17 24397.75 29494.14 29497.59 36797.02 41092.28 35895.75 27397.64 30683.88 36698.96 28889.77 39296.15 29698.40 282
RPSCF94.87 29395.40 23093.26 42698.89 14682.06 46598.33 26898.06 32090.30 41096.56 24499.26 8087.09 29799.49 19093.82 29696.32 28398.24 289
testing9994.83 29494.08 30897.07 25397.94 28193.13 33598.10 31097.17 39794.86 22395.34 27896.00 42076.31 43299.40 20695.08 24795.90 30098.68 259
GA-MVS94.81 29594.03 31297.14 24597.15 34893.86 30196.76 43197.58 35394.00 27094.76 29597.04 36180.91 38998.48 34091.79 35596.25 29299.09 206
c3_l94.79 29694.43 28995.89 34697.75 29493.12 33797.16 40598.03 32292.23 35993.46 35897.05 36091.39 16998.01 39893.58 30489.21 40096.53 389
V4294.78 29794.14 30596.70 28396.33 39495.22 23698.97 9598.09 31292.32 35694.31 31397.06 35788.39 26798.55 33592.90 32388.87 40696.34 407
reproduce_monomvs94.77 29894.67 27295.08 37898.40 20289.48 41698.80 15898.64 15997.57 4693.21 36697.65 30380.57 39498.83 31097.72 11089.47 39696.93 334
CR-MVSNet94.76 29994.15 30496.59 29797.00 35493.43 31894.96 45897.56 35692.46 34796.93 22396.24 40588.15 27397.88 41187.38 42396.65 27298.46 280
v2v48294.69 30094.03 31296.65 28696.17 40094.79 26298.67 20198.08 31392.72 33994.00 33197.16 34387.69 28898.45 34592.91 32288.87 40696.72 362
pmmvs494.69 30093.99 31896.81 27395.74 41895.94 18397.40 37897.67 34590.42 40793.37 36197.59 31089.08 24698.20 38092.97 32091.67 36496.30 410
cl2294.68 30294.19 30096.13 33398.11 25493.60 31196.94 41698.31 25992.43 35193.32 36396.87 38086.51 30698.28 37694.10 28891.16 37196.51 396
eth_miper_zixun_eth94.68 30294.41 29095.47 36497.64 30591.71 36796.73 43398.07 31592.71 34093.64 34697.21 34190.54 20398.17 38293.38 30789.76 38896.54 387
PCF-MVS93.45 1194.68 30293.43 35498.42 12998.62 17996.77 13595.48 45598.20 28484.63 45593.34 36298.32 23988.55 26499.81 10284.80 44398.96 15898.68 259
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS94.67 30593.54 34998.08 16896.88 36496.56 14998.19 29198.50 19978.05 46792.69 38398.02 26491.07 18999.63 15990.09 38598.36 20798.04 297
PS-CasMVS94.67 30593.99 31896.71 28196.68 37795.26 23399.13 6299.03 5193.68 29592.33 39497.95 27285.35 33298.10 38793.59 30388.16 41396.79 354
cascas94.63 30793.86 32896.93 26496.91 36294.27 28796.00 44698.51 19485.55 45194.54 29896.23 40784.20 36098.87 30495.80 22096.98 26297.66 309
tpmvs94.60 30894.36 29295.33 37097.46 32288.60 43396.88 42597.68 34291.29 38993.80 34296.42 40188.58 26099.24 23691.06 37296.04 29898.17 293
LTVRE_ROB92.95 1594.60 30893.90 32496.68 28597.41 33094.42 27898.52 23698.59 17291.69 37491.21 41098.35 23384.87 34199.04 27691.06 37293.44 33896.60 378
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
v114494.59 31093.92 32196.60 29696.21 39694.78 26398.59 21698.14 30091.86 37094.21 32197.02 36487.97 27998.41 35691.72 35789.57 39196.61 377
ADS-MVSNet294.58 31194.40 29195.11 37698.00 27088.74 43196.04 44397.30 38590.15 41196.47 25196.64 39487.89 28197.56 42690.08 38697.06 25799.02 219
WBMVS94.56 31294.04 31096.10 33598.03 26793.08 33997.82 34998.18 28994.02 26693.77 34496.82 38381.28 38398.34 36595.47 23491.00 37496.88 344
ACMH92.88 1694.55 31393.95 32096.34 32497.63 30693.26 32998.81 15798.49 20493.43 31089.74 42498.53 21581.91 37899.08 27093.69 29893.30 34396.70 366
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt080594.54 31493.85 32996.63 29197.98 27893.06 34098.77 17097.84 33493.67 29793.80 34298.04 26376.88 43098.96 28894.79 25692.86 34897.86 302
XVG-ACMP-BASELINE94.54 31494.14 30595.75 35496.55 38291.65 36898.11 30898.44 21394.96 21794.22 32097.90 27779.18 40499.11 26394.05 29093.85 32796.48 401
AUN-MVS94.53 31693.73 33996.92 26798.50 18793.52 31698.34 26798.10 30893.83 28195.94 27197.98 27085.59 32899.03 27794.35 27580.94 45798.22 291
DIV-MVS_self_test94.52 31794.03 31295.99 33897.57 31493.38 32397.05 41097.94 32891.74 37192.81 37897.10 34689.12 24498.07 39492.60 33190.30 38196.53 389
cl____94.51 31894.01 31596.02 33797.58 31093.40 32297.05 41097.96 32791.73 37392.76 38097.08 35289.06 24798.13 38592.61 32990.29 38296.52 392
ETVMVS94.50 31993.44 35397.68 20998.18 24695.35 22998.19 29197.11 39993.73 28796.40 25495.39 43474.53 44398.84 30791.10 36896.31 28498.84 236
GBi-Net94.49 32093.80 33296.56 30198.21 23695.00 24698.82 14998.18 28992.46 34794.09 32697.07 35381.16 38497.95 40392.08 34592.14 35696.72 362
test194.49 32093.80 33296.56 30198.21 23695.00 24698.82 14998.18 28992.46 34794.09 32697.07 35381.16 38497.95 40392.08 34592.14 35696.72 362
dmvs_re94.48 32294.18 30295.37 36897.68 30190.11 40298.54 23597.08 40194.56 24294.42 30797.24 33884.25 35697.76 41791.02 37592.83 34998.24 289
v894.47 32393.77 33596.57 30096.36 39294.83 25999.05 7498.19 28691.92 36793.16 36896.97 36988.82 25898.48 34091.69 35887.79 41596.39 405
FMVSNet294.47 32393.61 34597.04 25598.21 23696.43 15598.79 16698.27 26992.46 34793.50 35597.09 35081.16 38498.00 40091.09 36991.93 35996.70 366
test250694.44 32593.91 32396.04 33699.02 13088.99 42699.06 7279.47 48896.96 9298.36 12699.26 8077.21 42399.52 18596.78 18599.04 15299.59 94
Patchmatch-test94.42 32693.68 34396.63 29197.60 30891.76 36494.83 46297.49 36889.45 42494.14 32497.10 34688.99 24998.83 31085.37 43798.13 22099.29 161
PEN-MVS94.42 32693.73 33996.49 30996.28 39594.84 25799.17 5499.00 5393.51 30592.23 39697.83 28786.10 31897.90 40792.55 33686.92 42796.74 359
v14419294.39 32893.70 34196.48 31196.06 40694.35 28298.58 22098.16 29791.45 38094.33 31297.02 36487.50 29198.45 34591.08 37189.11 40196.63 374
Baseline_NR-MVSNet94.35 32993.81 33195.96 34296.20 39794.05 29698.61 21596.67 42991.44 38193.85 33997.60 30988.57 26198.14 38494.39 27386.93 42695.68 427
miper_lstm_enhance94.33 33094.07 30995.11 37697.75 29490.97 37897.22 39598.03 32291.67 37592.76 38096.97 36990.03 21697.78 41692.51 33889.64 39096.56 384
v119294.32 33193.58 34696.53 30696.10 40494.45 27698.50 24498.17 29591.54 37894.19 32297.06 35786.95 30198.43 34890.14 38489.57 39196.70 366
UWE-MVS94.30 33293.89 32695.53 36197.83 28988.95 42797.52 37293.25 46894.44 25296.63 24097.07 35378.70 40699.28 22491.99 35097.56 24398.36 285
ACMH+92.99 1494.30 33293.77 33595.88 34797.81 29192.04 36198.71 18698.37 24493.99 27190.60 41798.47 22180.86 39199.05 27392.75 32892.40 35496.55 386
v14894.29 33493.76 33795.91 34496.10 40492.93 34198.58 22097.97 32592.59 34593.47 35796.95 37388.53 26598.32 36892.56 33587.06 42596.49 399
v1094.29 33493.55 34896.51 30896.39 39194.80 26198.99 9198.19 28691.35 38593.02 37496.99 36788.09 27598.41 35690.50 38188.41 41096.33 409
SD_040394.28 33694.46 28493.73 41798.02 26885.32 45498.31 27398.40 23294.75 23193.59 34798.16 25489.01 24896.54 44782.32 45297.58 24299.34 146
MVP-Stereo94.28 33693.92 32195.35 36994.95 43892.60 34897.97 32597.65 34691.61 37690.68 41697.09 35086.32 31598.42 34989.70 39599.34 13895.02 441
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UniMVSNet_ETH3D94.24 33893.33 35696.97 26197.19 34593.38 32398.74 17598.57 17991.21 39493.81 34198.58 21072.85 45198.77 31795.05 24893.93 32698.77 247
OurMVSNet-221017-094.21 33994.00 31694.85 38895.60 42289.22 42198.89 12097.43 37695.29 19092.18 39998.52 21882.86 37498.59 33393.46 30691.76 36296.74 359
v192192094.20 34093.47 35296.40 32195.98 41094.08 29598.52 23698.15 29891.33 38694.25 31897.20 34286.41 31198.42 34990.04 38989.39 39896.69 371
WB-MVSnew94.19 34194.04 31094.66 39696.82 36892.14 35497.86 34395.96 44393.50 30695.64 27496.77 38688.06 27797.99 40184.87 44096.86 26393.85 460
v7n94.19 34193.43 35496.47 31295.90 41494.38 28199.26 3298.34 25291.99 36592.76 38097.13 34588.31 26898.52 33889.48 40087.70 41696.52 392
tpm294.19 34193.76 33795.46 36597.23 33989.04 42497.31 38996.85 42387.08 44096.21 26096.79 38583.75 37098.74 31892.43 34196.23 29498.59 271
TESTMET0.1,194.18 34493.69 34295.63 35896.92 36089.12 42296.91 41994.78 45793.17 32194.88 28896.45 40078.52 40798.92 29593.09 31598.50 18998.85 234
dp94.15 34593.90 32494.90 38497.31 33586.82 45096.97 41497.19 39691.22 39396.02 26696.61 39685.51 32999.02 28090.00 39094.30 31198.85 234
ET-MVSNet_ETH3D94.13 34692.98 36497.58 22098.22 23596.20 16697.31 38995.37 45194.53 24479.56 46997.63 30886.51 30697.53 42796.91 16890.74 37699.02 219
tpm94.13 34693.80 33295.12 37596.50 38587.91 44497.44 37495.89 44692.62 34396.37 25696.30 40484.13 36198.30 37293.24 31191.66 36599.14 196
testing22294.12 34893.03 36397.37 23598.02 26894.66 26497.94 32996.65 43194.63 23895.78 27295.76 42371.49 45298.92 29591.17 36795.88 30198.52 276
IterMVS-SCA-FT94.11 34993.87 32794.85 38897.98 27890.56 39397.18 40098.11 30593.75 28492.58 38697.48 31883.97 36497.41 43092.48 34091.30 36896.58 380
Anonymous2023121194.10 35093.26 35996.61 29499.11 12294.28 28699.01 8698.88 7886.43 44492.81 37897.57 31281.66 38098.68 32494.83 25389.02 40496.88 344
IterMVS94.09 35193.85 32994.80 39297.99 27290.35 39897.18 40098.12 30293.68 29592.46 39297.34 32984.05 36297.41 43092.51 33891.33 36796.62 376
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-mter94.08 35293.51 35095.80 35096.77 37089.70 41096.91 41995.21 45292.89 33494.83 29195.72 42877.69 41898.97 28493.06 31698.50 18998.72 252
test0.0.03 194.08 35293.51 35095.80 35095.53 42692.89 34297.38 38095.97 44295.11 20492.51 39096.66 39187.71 28596.94 43787.03 42593.67 33097.57 313
v124094.06 35493.29 35896.34 32496.03 40893.90 30098.44 25798.17 29591.18 39594.13 32597.01 36686.05 31998.42 34989.13 40689.50 39596.70 366
X-MVStestdata94.06 35492.30 38099.34 3199.70 2798.35 4999.29 2798.88 7897.40 5798.46 11843.50 48395.90 4899.89 6897.85 10299.74 5999.78 33
DTE-MVSNet93.98 35693.26 35996.14 33296.06 40694.39 28099.20 4798.86 9193.06 32791.78 40497.81 28985.87 32397.58 42590.53 38086.17 43296.46 403
pm-mvs193.94 35793.06 36296.59 29796.49 38695.16 23898.95 10298.03 32292.32 35691.08 41297.84 28484.54 35298.41 35692.16 34386.13 43596.19 415
MS-PatchMatch93.84 35893.63 34494.46 40696.18 39989.45 41797.76 35398.27 26992.23 35992.13 40097.49 31779.50 40198.69 32189.75 39399.38 13495.25 433
tfpnnormal93.66 35992.70 37096.55 30596.94 35995.94 18398.97 9599.19 3691.04 39691.38 40997.34 32984.94 34098.61 32985.45 43689.02 40495.11 437
EU-MVSNet93.66 35994.14 30592.25 43795.96 41283.38 46198.52 23698.12 30294.69 23492.61 38598.13 25787.36 29596.39 45291.82 35490.00 38696.98 329
our_test_393.65 36193.30 35794.69 39495.45 43089.68 41296.91 41997.65 34691.97 36691.66 40796.88 37889.67 22597.93 40688.02 41891.49 36696.48 401
pmmvs593.65 36192.97 36595.68 35595.49 42792.37 34998.20 28897.28 38889.66 42092.58 38697.26 33582.14 37798.09 39193.18 31490.95 37596.58 380
SSC-MVS3.293.59 36393.13 36194.97 38196.81 36989.71 40997.95 32698.49 20494.59 24193.50 35596.91 37677.74 41798.37 36391.69 35890.47 37996.83 352
test_fmvs293.43 36493.58 34692.95 43196.97 35783.91 45799.19 4997.24 39195.74 15595.20 28398.27 24569.65 45498.72 32096.26 20193.73 32996.24 412
tpm cat193.36 36592.80 36795.07 37997.58 31087.97 44396.76 43197.86 33382.17 46293.53 35196.04 41686.13 31799.13 25889.24 40495.87 30298.10 296
JIA-IIPM93.35 36692.49 37695.92 34396.48 38790.65 38895.01 45796.96 41385.93 44896.08 26487.33 47387.70 28798.78 31691.35 36495.58 30698.34 286
SixPastTwentyTwo93.34 36792.86 36694.75 39395.67 42089.41 41998.75 17196.67 42993.89 27690.15 42298.25 24880.87 39098.27 37790.90 37690.64 37796.57 382
USDC93.33 36892.71 36995.21 37296.83 36790.83 38496.91 41997.50 36693.84 27990.72 41598.14 25677.69 41898.82 31289.51 39993.21 34595.97 421
IB-MVS91.98 1793.27 36991.97 38497.19 24197.47 32193.41 32097.09 40895.99 44193.32 31492.47 39195.73 42678.06 41399.53 18294.59 26882.98 44698.62 266
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
MIMVSNet93.26 37092.21 38196.41 31997.73 29893.13 33595.65 45297.03 40791.27 39194.04 32996.06 41475.33 43797.19 43386.56 42796.23 29498.92 230
ppachtmachnet_test93.22 37192.63 37194.97 38195.45 43090.84 38396.88 42597.88 33290.60 40292.08 40197.26 33588.08 27697.86 41285.12 43990.33 38096.22 413
Patchmtry93.22 37192.35 37995.84 34996.77 37093.09 33894.66 46597.56 35687.37 43992.90 37696.24 40588.15 27397.90 40787.37 42490.10 38596.53 389
testing393.19 37392.48 37795.30 37198.07 25892.27 35098.64 20797.17 39793.94 27593.98 33297.04 36167.97 45996.01 45688.40 41397.14 25597.63 310
FMVSNet193.19 37392.07 38296.56 30197.54 31595.00 24698.82 14998.18 28990.38 40892.27 39597.07 35373.68 44997.95 40389.36 40291.30 36896.72 362
LF4IMVS93.14 37592.79 36894.20 41195.88 41588.67 43297.66 36197.07 40393.81 28291.71 40597.65 30377.96 41598.81 31391.47 36391.92 36195.12 436
mmtdpeth93.12 37692.61 37294.63 39897.60 30889.68 41299.21 4497.32 38394.02 26697.72 18094.42 44577.01 42899.44 20299.05 3277.18 46994.78 446
testgi93.06 37792.45 37894.88 38696.43 39089.90 40498.75 17197.54 36295.60 16391.63 40897.91 27674.46 44597.02 43586.10 43093.67 33097.72 307
PatchT93.06 37791.97 38496.35 32396.69 37692.67 34794.48 46897.08 40186.62 44297.08 21592.23 46687.94 28097.90 40778.89 46396.69 27098.49 278
RPMNet92.81 37991.34 39097.24 23797.00 35493.43 31894.96 45898.80 11482.27 46196.93 22392.12 46786.98 30099.82 9776.32 46996.65 27298.46 280
UWE-MVS-2892.79 38092.51 37593.62 41996.46 38886.28 45197.93 33092.71 47394.17 25994.78 29497.16 34381.05 38796.43 45081.45 45596.86 26398.14 295
myMVS_eth3d92.73 38192.01 38394.89 38597.39 33190.94 37997.91 33397.46 37093.16 32293.42 35995.37 43568.09 45896.12 45488.34 41496.99 25997.60 311
TransMVSNet (Re)92.67 38291.51 38996.15 33196.58 38194.65 26598.90 11696.73 42590.86 39989.46 42997.86 28185.62 32798.09 39186.45 42881.12 45595.71 426
ttmdpeth92.61 38391.96 38694.55 40094.10 44890.60 39298.52 23697.29 38692.67 34190.18 42097.92 27579.75 40097.79 41491.09 36986.15 43495.26 432
Syy-MVS92.55 38492.61 37292.38 43497.39 33183.41 46097.91 33397.46 37093.16 32293.42 35995.37 43584.75 34596.12 45477.00 46896.99 25997.60 311
K. test v392.55 38491.91 38794.48 40495.64 42189.24 42099.07 7194.88 45694.04 26486.78 44697.59 31077.64 42197.64 42192.08 34589.43 39796.57 382
DSMNet-mixed92.52 38692.58 37492.33 43594.15 44782.65 46398.30 27694.26 46389.08 42992.65 38495.73 42685.01 33995.76 45886.24 42997.76 23498.59 271
TinyColmap92.31 38791.53 38894.65 39796.92 36089.75 40796.92 41796.68 42890.45 40689.62 42697.85 28376.06 43598.81 31386.74 42692.51 35395.41 430
gg-mvs-nofinetune92.21 38890.58 39697.13 24696.75 37395.09 24295.85 44789.40 48185.43 45294.50 30081.98 47680.80 39298.40 36292.16 34398.33 20897.88 300
FMVSNet591.81 38990.92 39294.49 40397.21 34192.09 35898.00 32297.55 36189.31 42790.86 41495.61 43274.48 44495.32 46285.57 43489.70 38996.07 419
pmmvs691.77 39090.63 39595.17 37494.69 44491.24 37598.67 20197.92 33086.14 44689.62 42697.56 31575.79 43698.34 36590.75 37884.56 43995.94 422
Anonymous2023120691.66 39191.10 39193.33 42494.02 45287.35 44798.58 22097.26 39090.48 40490.16 42196.31 40383.83 36896.53 44879.36 46189.90 38796.12 417
Patchmatch-RL test91.49 39290.85 39393.41 42291.37 46484.40 45592.81 47295.93 44591.87 36987.25 44294.87 44188.99 24996.53 44892.54 33782.00 44999.30 158
test_040291.32 39390.27 39994.48 40496.60 38091.12 37698.50 24497.22 39286.10 44788.30 43896.98 36877.65 42097.99 40178.13 46592.94 34794.34 448
test_vis1_rt91.29 39490.65 39493.19 42897.45 32586.25 45298.57 22990.90 47993.30 31686.94 44593.59 45462.07 47099.11 26397.48 14095.58 30694.22 451
PVSNet_088.72 1991.28 39590.03 40295.00 38097.99 27287.29 44894.84 46198.50 19992.06 36489.86 42395.19 43779.81 39999.39 20992.27 34269.79 47698.33 287
mvs5depth91.23 39690.17 40094.41 40892.09 46089.79 40695.26 45696.50 43390.73 40091.69 40697.06 35776.12 43498.62 32888.02 41884.11 44294.82 443
Anonymous2024052191.18 39790.44 39793.42 42193.70 45388.47 43698.94 10597.56 35688.46 43389.56 42895.08 44077.15 42696.97 43683.92 44689.55 39394.82 443
EG-PatchMatch MVS91.13 39890.12 40194.17 41394.73 44389.00 42598.13 30497.81 33789.22 42885.32 45696.46 39967.71 46098.42 34987.89 42293.82 32895.08 438
TDRefinement91.06 39989.68 40495.21 37285.35 48191.49 37198.51 24397.07 40391.47 37988.83 43597.84 28477.31 42299.09 26892.79 32777.98 46795.04 440
sc_t191.01 40089.39 40695.85 34895.99 40990.39 39798.43 25997.64 34878.79 46592.20 39897.94 27366.00 46498.60 33291.59 36185.94 43698.57 274
UnsupCasMVSNet_eth90.99 40189.92 40394.19 41294.08 44989.83 40597.13 40798.67 15193.69 29385.83 45296.19 41075.15 43996.74 44189.14 40579.41 46296.00 420
test20.0390.89 40290.38 39892.43 43393.48 45488.14 44298.33 26897.56 35693.40 31187.96 43996.71 38980.69 39394.13 46879.15 46286.17 43295.01 442
blend_shiyan490.76 40389.01 41295.99 33891.69 46393.35 32697.44 37497.83 33586.93 44192.23 39691.98 46875.19 43898.09 39192.88 32674.96 47296.52 392
MDA-MVSNet_test_wron90.71 40489.38 40894.68 39594.83 44090.78 38597.19 39997.46 37087.60 43772.41 47695.72 42886.51 30696.71 44485.92 43286.80 42996.56 384
YYNet190.70 40589.39 40694.62 39994.79 44290.65 38897.20 39797.46 37087.54 43872.54 47595.74 42486.51 30696.66 44586.00 43186.76 43096.54 387
KD-MVS_self_test90.38 40689.38 40893.40 42392.85 45788.94 42897.95 32697.94 32890.35 40990.25 41993.96 45179.82 39895.94 45784.62 44576.69 47095.33 431
pmmvs-eth3d90.36 40789.05 41194.32 41091.10 46692.12 35597.63 36696.95 41488.86 43184.91 45793.13 45978.32 40996.74 44188.70 41081.81 45194.09 454
FE-MVSNET290.29 40888.94 41494.36 40990.48 46892.27 35098.45 25197.82 33691.59 37784.90 45893.10 46073.92 44796.42 45187.92 42182.26 44794.39 447
tt032090.26 40988.73 41694.86 38796.12 40390.62 39098.17 29797.63 34977.46 46889.68 42596.04 41669.19 45697.79 41488.98 40785.29 43896.16 416
CL-MVSNet_self_test90.11 41089.14 41093.02 42991.86 46288.23 44196.51 43998.07 31590.49 40390.49 41894.41 44684.75 34595.34 46180.79 45774.95 47395.50 429
new_pmnet90.06 41189.00 41393.22 42794.18 44688.32 43996.42 44196.89 41986.19 44585.67 45393.62 45377.18 42597.10 43481.61 45489.29 39994.23 450
MDA-MVSNet-bldmvs89.97 41288.35 41894.83 39195.21 43491.34 37297.64 36397.51 36588.36 43571.17 47796.13 41279.22 40396.63 44683.65 44786.27 43196.52 392
tt0320-xc89.79 41388.11 42094.84 39096.19 39890.61 39198.16 29897.22 39277.35 46988.75 43696.70 39065.94 46597.63 42289.31 40383.39 44496.28 411
CMPMVSbinary66.06 2189.70 41489.67 40589.78 44293.19 45576.56 46897.00 41398.35 24980.97 46381.57 46497.75 29274.75 44298.61 32989.85 39193.63 33294.17 452
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet189.67 41588.28 41993.82 41692.81 45891.08 37798.01 32097.45 37487.95 43687.90 44095.87 42267.63 46194.56 46778.73 46488.18 41295.83 424
KD-MVS_2432*160089.61 41687.96 42494.54 40194.06 45091.59 36995.59 45397.63 34989.87 41688.95 43294.38 44878.28 41096.82 43984.83 44168.05 47795.21 434
miper_refine_blended89.61 41687.96 42494.54 40194.06 45091.59 36995.59 45397.63 34989.87 41688.95 43294.38 44878.28 41096.82 43984.83 44168.05 47795.21 434
MVStest189.53 41887.99 42394.14 41594.39 44590.42 39598.25 28396.84 42482.81 45881.18 46697.33 33177.09 42796.94 43785.27 43878.79 46395.06 439
MVS-HIRNet89.46 41988.40 41792.64 43297.58 31082.15 46494.16 47193.05 47275.73 47290.90 41382.52 47579.42 40298.33 36783.53 44898.68 17397.43 314
OpenMVS_ROBcopyleft86.42 2089.00 42087.43 42893.69 41893.08 45689.42 41897.91 33396.89 41978.58 46685.86 45194.69 44269.48 45598.29 37577.13 46793.29 34493.36 462
mvsany_test388.80 42188.04 42191.09 44189.78 47181.57 46697.83 34895.49 45093.81 28287.53 44193.95 45256.14 47397.43 42994.68 26183.13 44594.26 449
FE-MVSNET88.56 42287.09 42992.99 43089.93 47089.99 40398.15 30195.59 44888.42 43484.87 45992.90 46174.82 44194.99 46577.88 46681.21 45493.99 457
new-patchmatchnet88.50 42387.45 42791.67 43990.31 46985.89 45397.16 40597.33 38289.47 42383.63 46192.77 46376.38 43195.06 46482.70 45077.29 46894.06 456
APD_test188.22 42488.01 42288.86 44495.98 41074.66 47697.21 39696.44 43583.96 45786.66 44897.90 27760.95 47197.84 41382.73 44990.23 38394.09 454
PM-MVS87.77 42586.55 43191.40 44091.03 46783.36 46296.92 41795.18 45491.28 39086.48 45093.42 45553.27 47496.74 44189.43 40181.97 45094.11 453
dmvs_testset87.64 42688.93 41583.79 45395.25 43363.36 48597.20 39791.17 47793.07 32685.64 45495.98 42185.30 33691.52 47569.42 47487.33 42196.49 399
test_fmvs387.17 42787.06 43087.50 44691.21 46575.66 47199.05 7496.61 43292.79 33888.85 43492.78 46243.72 47793.49 46993.95 29184.56 43993.34 463
UnsupCasMVSNet_bld87.17 42785.12 43493.31 42591.94 46188.77 42994.92 46098.30 26684.30 45682.30 46290.04 47063.96 46897.25 43285.85 43374.47 47593.93 459
N_pmnet87.12 42987.77 42685.17 45095.46 42961.92 48697.37 38270.66 49185.83 44988.73 43796.04 41685.33 33497.76 41780.02 45890.48 37895.84 423
pmmvs386.67 43084.86 43592.11 43888.16 47587.19 44996.63 43594.75 45879.88 46487.22 44392.75 46466.56 46395.20 46381.24 45676.56 47193.96 458
test_f86.07 43185.39 43288.10 44589.28 47375.57 47297.73 35696.33 43789.41 42685.35 45591.56 46943.31 47995.53 45991.32 36584.23 44193.21 464
WB-MVS84.86 43285.33 43383.46 45489.48 47269.56 48098.19 29196.42 43689.55 42281.79 46394.67 44384.80 34390.12 47652.44 48080.64 45990.69 467
SSC-MVS84.27 43384.71 43682.96 45889.19 47468.83 48198.08 31296.30 43889.04 43081.37 46594.47 44484.60 35089.89 47749.80 48279.52 46190.15 468
dongtai82.47 43481.88 43784.22 45295.19 43576.03 46994.59 46774.14 49082.63 45987.19 44496.09 41364.10 46787.85 48058.91 47884.11 44288.78 472
test_vis3_rt79.22 43577.40 44284.67 45186.44 47974.85 47597.66 36181.43 48684.98 45367.12 47981.91 47728.09 48797.60 42388.96 40880.04 46081.55 477
test_method79.03 43678.17 43881.63 45986.06 48054.40 49182.75 48096.89 41939.54 48380.98 46795.57 43358.37 47294.73 46684.74 44478.61 46495.75 425
testf179.02 43777.70 43982.99 45688.10 47666.90 48294.67 46393.11 46971.08 47474.02 47293.41 45634.15 48393.25 47072.25 47278.50 46588.82 470
APD_test279.02 43777.70 43982.99 45688.10 47666.90 48294.67 46393.11 46971.08 47474.02 47293.41 45634.15 48393.25 47072.25 47278.50 46588.82 470
LCM-MVSNet78.70 43976.24 44586.08 44877.26 48771.99 47894.34 46996.72 42661.62 47876.53 47089.33 47133.91 48592.78 47381.85 45374.60 47493.46 461
kuosan78.45 44077.69 44180.72 46092.73 45975.32 47394.63 46674.51 48975.96 47080.87 46893.19 45863.23 46979.99 48442.56 48481.56 45386.85 476
Gipumacopyleft78.40 44176.75 44483.38 45595.54 42480.43 46779.42 48197.40 37864.67 47773.46 47480.82 47845.65 47693.14 47266.32 47687.43 41976.56 480
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS277.95 44275.44 44685.46 44982.54 48274.95 47494.23 47093.08 47172.80 47374.68 47187.38 47236.36 48291.56 47473.95 47063.94 47989.87 469
FPMVS77.62 44377.14 44379.05 46279.25 48560.97 48795.79 44895.94 44465.96 47667.93 47894.40 44737.73 48188.88 47968.83 47588.46 40987.29 473
EGC-MVSNET75.22 44469.54 44792.28 43694.81 44189.58 41497.64 36396.50 4331.82 4885.57 48995.74 42468.21 45796.26 45373.80 47191.71 36390.99 466
ANet_high69.08 44565.37 44980.22 46165.99 48971.96 47990.91 47690.09 48082.62 46049.93 48478.39 47929.36 48681.75 48162.49 47738.52 48386.95 475
tmp_tt68.90 44666.97 44874.68 46450.78 49159.95 48887.13 47783.47 48538.80 48462.21 48096.23 40764.70 46676.91 48688.91 40930.49 48487.19 474
PMVScopyleft61.03 2365.95 44763.57 45173.09 46557.90 49051.22 49285.05 47993.93 46754.45 47944.32 48583.57 47413.22 48889.15 47858.68 47981.00 45678.91 479
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN64.94 44864.25 45067.02 46682.28 48359.36 48991.83 47585.63 48352.69 48060.22 48177.28 48041.06 48080.12 48346.15 48341.14 48161.57 482
EMVS64.07 44963.26 45266.53 46781.73 48458.81 49091.85 47484.75 48451.93 48259.09 48275.13 48143.32 47879.09 48542.03 48539.47 48261.69 481
MVEpermissive62.14 2263.28 45059.38 45374.99 46374.33 48865.47 48485.55 47880.50 48752.02 48151.10 48375.00 48210.91 49180.50 48251.60 48153.40 48078.99 478
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d30.17 45130.18 45530.16 46878.61 48643.29 49366.79 48214.21 49217.31 48514.82 48811.93 48811.55 49041.43 48737.08 48619.30 4855.76 485
cdsmvs_eth3d_5k23.98 45231.98 4540.00 4710.00 4940.00 4960.00 48398.59 1720.00 4890.00 49098.61 20590.60 2010.00 4900.00 4890.00 4880.00 486
testmvs21.48 45324.95 45611.09 47014.89 4926.47 49596.56 4379.87 4937.55 48617.93 48639.02 4849.43 4925.90 48916.56 48812.72 48620.91 484
test12320.95 45423.72 45712.64 46913.54 4938.19 49496.55 4386.13 4947.48 48716.74 48737.98 48512.97 4896.05 48816.69 4875.43 48723.68 483
ab-mvs-re8.20 45510.94 4580.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 49098.43 2230.00 4930.00 4900.00 4890.00 4880.00 486
pcd_1.5k_mvsjas7.88 45610.50 4590.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 48994.51 910.00 4900.00 4890.00 4880.00 486
mmdepth0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
monomultidepth0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
test_blank0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
uanet_test0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
DCPMVS0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
sosnet-low-res0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
sosnet0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
uncertanet0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
Regformer0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
uanet0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
MED-MVS test99.52 1399.77 298.86 2299.32 2299.24 2096.41 12199.30 5099.35 6099.92 4398.30 7599.80 2599.79 28
TestfortrainingZip99.32 22
WAC-MVS90.94 37988.66 411
FOURS199.82 198.66 2899.69 198.95 6197.46 5599.39 44
MSC_two_6792asdad99.62 799.17 11199.08 1298.63 16299.94 1498.53 5599.80 2599.86 12
PC_three_145295.08 20899.60 3299.16 10597.86 298.47 34397.52 13399.72 6899.74 50
No_MVS99.62 799.17 11199.08 1298.63 16299.94 1498.53 5599.80 2599.86 12
test_one_060199.66 3199.25 398.86 9197.55 4799.20 5999.47 3597.57 8
eth-test20.00 494
eth-test0.00 494
ZD-MVS99.46 5898.70 2798.79 11993.21 31998.67 10498.97 14695.70 5299.83 9096.07 20599.58 99
RE-MVS-def98.34 5499.49 5297.86 7499.11 6598.80 11496.49 11699.17 6299.35 6095.29 6997.72 11099.65 8299.71 63
IU-MVS99.71 2499.23 898.64 15995.28 19199.63 3198.35 7299.81 1699.83 18
OPU-MVS99.37 2799.24 10399.05 1599.02 8499.16 10597.81 399.37 21097.24 15599.73 6399.70 67
test_241102_TWO98.87 8597.65 3999.53 3799.48 3397.34 1399.94 1498.43 6799.80 2599.83 18
test_241102_ONE99.71 2499.24 698.87 8597.62 4199.73 2299.39 4897.53 999.74 134
9.1498.06 7999.47 5698.71 18698.82 10194.36 25499.16 6699.29 7596.05 4099.81 10297.00 16299.71 70
save fliter99.46 5898.38 4098.21 28698.71 13797.95 28
test_0728_THIRD97.32 6399.45 3999.46 4097.88 199.94 1498.47 6399.86 299.85 15
test_0728_SECOND99.71 199.72 1799.35 198.97 9598.88 7899.94 1498.47 6399.81 1699.84 17
test072699.72 1799.25 399.06 7298.88 7897.62 4199.56 3499.50 2997.42 11
GSMVS99.20 182
test_part299.63 3499.18 1199.27 56
sam_mvs189.45 23399.20 182
sam_mvs88.99 249
ambc89.49 44386.66 47875.78 47092.66 47396.72 42686.55 44992.50 46546.01 47597.90 40790.32 38282.09 44894.80 445
MTGPAbinary98.74 129
test_post196.68 43430.43 48787.85 28498.69 32192.59 333
test_post31.83 48688.83 25698.91 297
patchmatchnet-post95.10 43989.42 23498.89 301
GG-mvs-BLEND96.59 29796.34 39394.98 25096.51 43988.58 48293.10 37394.34 45080.34 39798.05 39589.53 39896.99 25996.74 359
MTMP98.89 12094.14 465
gm-plane-assit95.88 41587.47 44689.74 41996.94 37499.19 24693.32 310
test9_res96.39 19999.57 10099.69 70
TEST999.31 7998.50 3497.92 33198.73 13292.63 34297.74 17798.68 20096.20 3599.80 109
test_899.29 8898.44 3697.89 33998.72 13492.98 33097.70 18298.66 20396.20 3599.80 109
agg_prior295.87 21599.57 10099.68 75
agg_prior99.30 8398.38 4098.72 13497.57 19899.81 102
TestCases96.99 25799.25 9693.21 33398.18 28991.36 38393.52 35298.77 18784.67 34899.72 13689.70 39597.87 22998.02 298
test_prior498.01 7097.86 343
test_prior297.80 35096.12 13797.89 16598.69 19995.96 4496.89 17299.60 94
test_prior99.19 5099.31 7998.22 5798.84 9699.70 14299.65 83
旧先验297.57 36991.30 38898.67 10499.80 10995.70 226
新几何297.64 363
新几何199.16 5599.34 7198.01 7098.69 14390.06 41398.13 13498.95 15394.60 8999.89 6891.97 35299.47 12299.59 94
旧先验199.29 8897.48 8998.70 14199.09 12895.56 5599.47 12299.61 90
无先验97.58 36898.72 13491.38 38299.87 7993.36 30999.60 92
原ACMM297.67 360
原ACMM198.65 9799.32 7796.62 14098.67 15193.27 31897.81 17098.97 14695.18 7699.83 9093.84 29599.46 12599.50 106
test22299.23 10497.17 11697.40 37898.66 15488.68 43298.05 14298.96 15194.14 10299.53 11399.61 90
testdata299.89 6891.65 360
segment_acmp96.85 16
testdata98.26 14199.20 10995.36 22798.68 14691.89 36898.60 11299.10 12094.44 9699.82 9794.27 27999.44 12699.58 98
testdata197.32 38896.34 127
test1299.18 5299.16 11598.19 5998.53 18898.07 13895.13 7999.72 13699.56 10899.63 88
plane_prior797.42 32794.63 267
plane_prior697.35 33494.61 27087.09 297
plane_prior598.56 18299.03 27796.07 20594.27 31296.92 335
plane_prior498.28 242
plane_prior394.61 27097.02 8795.34 278
plane_prior298.80 15897.28 67
plane_prior197.37 333
plane_prior94.60 27298.44 25796.74 10394.22 314
n20.00 495
nn0.00 495
door-mid94.37 461
lessismore_v094.45 40794.93 43988.44 43791.03 47886.77 44797.64 30676.23 43398.42 34990.31 38385.64 43796.51 396
LGP-MVS_train96.47 31297.46 32293.54 31398.54 18694.67 23694.36 31098.77 18785.39 33099.11 26395.71 22494.15 31896.76 357
test1198.66 154
door94.64 459
HQP5-MVS94.25 289
HQP-NCC97.20 34298.05 31596.43 11894.45 302
ACMP_Plane97.20 34298.05 31596.43 11894.45 302
BP-MVS95.30 238
HQP4-MVS94.45 30298.96 28896.87 347
HQP3-MVS98.46 20794.18 316
HQP2-MVS86.75 303
NP-MVS97.28 33694.51 27597.73 293
MDTV_nov1_ep13_2view84.26 45696.89 42490.97 39797.90 16489.89 21993.91 29399.18 191
MDTV_nov1_ep1395.40 23097.48 32088.34 43896.85 42797.29 38693.74 28697.48 20097.26 33589.18 24299.05 27391.92 35397.43 250
ACMMP++_ref92.97 346
ACMMP++93.61 333
Test By Simon94.64 88
ITE_SJBPF95.44 36697.42 32791.32 37397.50 36695.09 20793.59 34798.35 23381.70 37998.88 30389.71 39493.39 33996.12 417
DeepMVS_CXcopyleft86.78 44797.09 35272.30 47795.17 45575.92 47184.34 46095.19 43770.58 45395.35 46079.98 46089.04 40392.68 465