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

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

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

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

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




Method Infoallhigh-res
multi-view
indooroutdoorbotani.boulde.bridgedoorexhibi.lectur.living.loungeobserv.old co.statueterrac.
sort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
LCM-MVSNet99.86 199.86 199.87 199.99 199.77 199.77 199.80 199.97 199.97 199.95 199.74 199.98 199.56 1100.00 199.85 3
pmmvs699.07 499.24 498.56 4999.81 296.38 6398.87 999.30 1199.01 1699.63 999.66 399.27 299.68 12597.75 3099.89 2299.62 25
UniMVSNet_ETH3D99.12 399.28 398.65 4399.77 396.34 6599.18 599.20 1699.67 299.73 399.65 499.15 399.86 2097.22 4699.92 1499.77 8
OurMVSNet-221017-098.61 1698.61 2398.63 4599.77 396.35 6499.17 699.05 4398.05 4199.61 1199.52 593.72 18099.88 1898.72 999.88 2399.65 23
Gipumacopyleft98.07 4098.31 2997.36 15099.76 596.28 6898.51 2299.10 3198.76 2396.79 20199.34 1796.61 7898.82 30696.38 7499.50 11596.98 315
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet198.51 2098.45 2698.67 4199.72 696.71 5298.76 1198.89 7998.49 2899.38 1799.14 3395.44 12999.84 2596.47 7199.80 3699.47 62
LTVRE_ROB96.88 199.18 299.34 298.72 3899.71 796.99 4699.69 299.57 499.02 1599.62 1099.36 1498.53 799.52 18298.58 1299.95 599.66 22
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
mvs_tets98.90 598.94 698.75 3399.69 896.48 6198.54 2099.22 1396.23 11299.71 499.48 798.77 699.93 298.89 399.95 599.84 5
PS-MVSNAJss98.53 1998.63 1998.21 7899.68 994.82 12998.10 4999.21 1496.91 8599.75 299.45 995.82 10899.92 498.80 499.96 499.89 1
jajsoiax98.77 998.79 1298.74 3599.66 1096.48 6198.45 2699.12 2895.83 13999.67 699.37 1298.25 1099.92 498.77 599.94 899.82 6
v7n98.73 1198.99 597.95 9699.64 1194.20 15598.67 1399.14 2699.08 1099.42 1599.23 2196.53 8399.91 1299.27 299.93 1099.73 15
test_djsdf98.73 1198.74 1698.69 4099.63 1296.30 6798.67 1399.02 5296.50 10099.32 2099.44 1097.43 3199.92 498.73 799.95 599.86 2
anonymousdsp98.72 1498.63 1998.99 1399.62 1397.29 3998.65 1699.19 1895.62 14799.35 1999.37 1297.38 3399.90 1398.59 1199.91 1799.77 8
FOURS199.59 1498.20 499.03 799.25 1298.96 1898.87 40
PEN-MVS98.75 1098.85 1098.44 5699.58 1595.67 8998.45 2699.15 2499.33 599.30 2199.00 4197.27 3899.92 497.64 3499.92 1499.75 13
EGC-MVSNET83.08 33877.93 34198.53 5199.57 1697.55 2798.33 3298.57 1614.71 37410.38 37598.90 5095.60 12199.50 18795.69 10699.61 7598.55 231
Baseline_NR-MVSNet97.72 7697.79 5597.50 13299.56 1793.29 18595.44 19498.86 9098.20 3898.37 7699.24 2094.69 15099.55 17395.98 9399.79 3899.65 23
SixPastTwentyTwo97.49 9297.57 8197.26 15699.56 1792.33 20498.28 3696.97 28098.30 3499.45 1499.35 1688.43 26799.89 1698.01 2099.76 4299.54 38
PS-CasMVS98.73 1198.85 1098.39 6099.55 1995.47 10198.49 2399.13 2799.22 899.22 2798.96 4597.35 3499.92 497.79 2899.93 1099.79 7
DTE-MVSNet98.79 898.86 898.59 4799.55 1996.12 7298.48 2599.10 3199.36 499.29 2399.06 3997.27 3899.93 297.71 3299.91 1799.70 18
HPM-MVS_fast98.32 2798.13 3398.88 2499.54 2197.48 3298.35 2999.03 5095.88 13497.88 13898.22 11098.15 1299.74 7596.50 7099.62 6999.42 81
TDRefinement98.90 598.86 899.02 999.54 2198.06 899.34 499.44 898.85 2099.00 3699.20 2397.42 3299.59 16097.21 4899.76 4299.40 84
pm-mvs198.47 2198.67 1797.86 10399.52 2394.58 13998.28 3699.00 6097.57 6199.27 2499.22 2298.32 999.50 18797.09 5499.75 4699.50 45
TransMVSNet (Re)98.38 2598.67 1797.51 12999.51 2493.39 18498.20 4498.87 8798.23 3699.48 1299.27 1998.47 899.55 17396.52 6899.53 10199.60 26
WR-MVS_H98.65 1598.62 2198.75 3399.51 2496.61 5798.55 1999.17 1999.05 1399.17 2998.79 5595.47 12799.89 1697.95 2199.91 1799.75 13
PMVScopyleft89.60 1796.71 14296.97 11995.95 22599.51 2497.81 1797.42 9297.49 26197.93 4495.95 24298.58 6996.88 6696.91 36289.59 29199.36 15993.12 362
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MP-MVS-pluss97.69 7897.36 9498.70 3999.50 2796.84 4995.38 20198.99 6392.45 25298.11 11098.31 9097.25 4199.77 5396.60 6499.62 6999.48 59
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FC-MVSNet-test98.16 3398.37 2797.56 12499.49 2893.10 19198.35 2999.21 1498.43 2998.89 3998.83 5494.30 16599.81 3297.87 2499.91 1799.77 8
VPNet97.26 10897.49 8896.59 19299.47 2990.58 23996.27 14698.53 16497.77 4798.46 6998.41 8294.59 15699.68 12594.61 16999.29 18299.52 42
CP-MVSNet98.42 2398.46 2498.30 6899.46 3095.22 11798.27 3898.84 9999.05 1399.01 3598.65 6795.37 13099.90 1397.57 3699.91 1799.77 8
XXY-MVS97.54 8897.70 6297.07 16599.46 3092.21 20897.22 10199.00 6094.93 17898.58 5898.92 4897.31 3699.41 21794.44 17699.43 14199.59 27
zzz-MVS98.01 4497.66 6799.06 499.44 3297.90 1295.66 18498.73 12997.69 5797.90 13597.96 14095.81 11299.82 2996.13 8199.61 7599.45 69
MTAPA98.14 3497.84 5199.06 499.44 3297.90 1297.25 9898.73 12997.69 5797.90 13597.96 14095.81 11299.82 2996.13 8199.61 7599.45 69
SteuartSystems-ACMMP98.02 4397.76 5998.79 3199.43 3497.21 4397.15 10498.90 7896.58 9698.08 11697.87 15497.02 5399.76 5895.25 13799.59 8199.40 84
Skip Steuart: Steuart Systems R&D Blog.
ACMH93.61 998.44 2298.76 1397.51 12999.43 3493.54 18098.23 3999.05 4397.40 7399.37 1899.08 3798.79 599.47 19597.74 3199.71 5499.50 45
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVScopyleft98.11 3897.83 5398.92 2299.42 3697.46 3398.57 1799.05 4395.43 15797.41 16697.50 18897.98 1599.79 3995.58 11799.57 8699.50 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
K. test v396.44 15696.28 15796.95 17099.41 3791.53 22497.65 7490.31 35898.89 1998.93 3899.36 1484.57 29699.92 497.81 2699.56 8999.39 86
VDDNet96.98 12096.84 12697.41 14699.40 3893.26 18697.94 5695.31 31599.26 798.39 7599.18 2787.85 27699.62 15195.13 14999.09 21199.35 98
ACMH+93.58 1098.23 3298.31 2997.98 9599.39 3995.22 11797.55 8199.20 1698.21 3799.25 2598.51 7698.21 1199.40 21994.79 16399.72 5199.32 101
TSAR-MVS + MP.97.42 9797.23 10498.00 9499.38 4095.00 12497.63 7698.20 20493.00 24098.16 10498.06 13095.89 10399.72 8695.67 10899.10 21099.28 115
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
FIs97.93 5598.07 3697.48 13699.38 4092.95 19498.03 5499.11 2998.04 4298.62 5298.66 6593.75 17999.78 4397.23 4599.84 2999.73 15
lessismore_v097.05 16699.36 4292.12 21284.07 37098.77 4798.98 4385.36 29099.74 7597.34 4499.37 15699.30 107
Anonymous2024052197.07 11497.51 8595.76 23399.35 4388.18 27797.78 6598.40 18197.11 8098.34 8299.04 4089.58 25499.79 3998.09 1899.93 1099.30 107
ACMMP_NAP97.89 6197.63 7498.67 4199.35 4396.84 4996.36 14298.79 11695.07 17197.88 13898.35 8697.24 4299.72 8696.05 8699.58 8399.45 69
Vis-MVSNetpermissive98.27 2998.34 2898.07 8799.33 4595.21 11998.04 5299.46 797.32 7597.82 14699.11 3496.75 7299.86 2097.84 2599.36 15999.15 140
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ANet_high98.31 2898.94 696.41 20599.33 4589.64 25197.92 5999.56 599.27 699.66 899.50 697.67 2599.83 2897.55 3799.98 299.77 8
ZNCC-MVS97.92 5697.62 7698.83 2699.32 4797.24 4197.45 8898.84 9995.76 14196.93 19697.43 19497.26 4099.79 3996.06 8499.53 10199.45 69
MP-MVScopyleft97.64 8097.18 10799.00 1299.32 4797.77 1897.49 8798.73 12996.27 10995.59 25797.75 16696.30 9699.78 4393.70 21099.48 12399.45 69
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PVSNet_Blended_VisFu95.95 17695.80 17896.42 20399.28 4990.62 23895.31 20799.08 3788.40 29896.97 19498.17 11592.11 21799.78 4393.64 21199.21 19198.86 199
tfpnnormal97.72 7697.97 4196.94 17199.26 5092.23 20797.83 6498.45 17198.25 3599.13 3098.66 6596.65 7599.69 11793.92 20299.62 6998.91 188
MSP-MVS97.45 9596.92 12399.03 899.26 5097.70 1997.66 7398.89 7995.65 14598.51 6296.46 26292.15 21599.81 3295.14 14798.58 26499.58 28
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
testgi96.07 16996.50 15094.80 27399.26 5087.69 29095.96 16798.58 16095.08 17098.02 12496.25 27397.92 1697.60 35988.68 30598.74 24999.11 155
IS-MVSNet96.93 12296.68 13597.70 11599.25 5394.00 16198.57 1796.74 28998.36 3198.14 10897.98 13988.23 26999.71 10093.10 22299.72 5199.38 88
DVP-MVScopyleft97.78 7297.65 6998.16 7999.24 5495.51 9696.74 12598.23 20095.92 13198.40 7398.28 9997.06 5099.71 10095.48 12199.52 10699.26 120
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test072699.24 5495.51 9696.89 11898.89 7995.92 13198.64 5198.31 9097.06 50
test_0728_SECOND98.25 7399.23 5695.49 10096.74 12598.89 7999.75 6595.48 12199.52 10699.53 41
GST-MVS97.82 6997.49 8898.81 2999.23 5697.25 4097.16 10398.79 11695.96 12897.53 15297.40 19696.93 6099.77 5395.04 15399.35 16499.42 81
ACMMPcopyleft98.05 4197.75 6198.93 2199.23 5697.60 2398.09 5098.96 7195.75 14397.91 13498.06 13096.89 6499.76 5895.32 13399.57 8699.43 80
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
KD-MVS_self_test97.86 6598.07 3697.25 15799.22 5992.81 19797.55 8198.94 7497.10 8198.85 4198.88 5195.03 14199.67 13097.39 4399.65 6499.26 120
SED-MVS97.94 5297.90 4598.07 8799.22 5995.35 10796.79 12298.83 10696.11 11899.08 3198.24 10597.87 2099.72 8695.44 12599.51 11199.14 143
IU-MVS99.22 5995.40 10298.14 21685.77 32398.36 7995.23 13999.51 11199.49 53
test_241102_ONE99.22 5995.35 10798.83 10696.04 12399.08 3198.13 11797.87 2099.33 240
nrg03098.54 1898.62 2198.32 6599.22 5995.66 9097.90 6099.08 3798.31 3399.02 3498.74 5997.68 2499.61 15897.77 2999.85 2899.70 18
region2R97.92 5697.59 7998.92 2299.22 5997.55 2797.60 7798.84 9996.00 12697.22 17097.62 17896.87 6799.76 5895.48 12199.43 14199.46 64
mPP-MVS97.91 5997.53 8399.04 799.22 5997.87 1597.74 7098.78 12096.04 12397.10 18097.73 16996.53 8399.78 4395.16 14499.50 11599.46 64
COLMAP_ROBcopyleft94.48 698.25 3198.11 3498.64 4499.21 6697.35 3797.96 5599.16 2098.34 3298.78 4598.52 7597.32 3599.45 20294.08 19399.67 6199.13 146
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMPR97.95 5097.62 7698.94 1899.20 6797.56 2697.59 7898.83 10696.05 12197.46 16397.63 17796.77 7199.76 5895.61 11499.46 12899.49 53
PGM-MVS97.88 6297.52 8498.96 1699.20 6797.62 2297.09 10999.06 4195.45 15597.55 15197.94 14597.11 4499.78 4394.77 16699.46 12899.48 59
test_040297.84 6697.97 4197.47 13799.19 6994.07 15896.71 13098.73 12998.66 2598.56 5998.41 8296.84 6999.69 11794.82 16199.81 3398.64 221
EPP-MVSNet96.84 12896.58 14097.65 11999.18 7093.78 17198.68 1296.34 29397.91 4597.30 16898.06 13088.46 26699.85 2293.85 20499.40 15199.32 101
abl_698.42 2398.19 3299.09 399.16 7198.10 697.73 7299.11 2997.76 5098.62 5298.27 10397.88 1999.80 3895.67 10899.50 11599.38 88
XVG-ACMP-BASELINE97.58 8697.28 10098.49 5399.16 7196.90 4896.39 13998.98 6695.05 17298.06 11898.02 13495.86 10499.56 16994.37 18199.64 6699.00 171
CHOSEN 1792x268894.10 25493.41 26196.18 21699.16 7190.04 24492.15 31898.68 14479.90 35596.22 23297.83 15787.92 27599.42 20889.18 29799.65 6499.08 160
HFP-MVS97.94 5297.64 7298.83 2699.15 7497.50 3097.59 7898.84 9996.05 12197.49 15797.54 18397.07 4899.70 10995.61 11499.46 12899.30 107
#test#97.62 8297.22 10598.83 2699.15 7497.50 3096.81 12198.84 9994.25 20097.49 15797.54 18397.07 4899.70 10994.37 18199.46 12899.30 107
XVS97.96 4697.63 7498.94 1899.15 7497.66 2097.77 6698.83 10697.42 6996.32 22597.64 17696.49 8699.72 8695.66 11099.37 15699.45 69
X-MVStestdata92.86 28090.83 30598.94 1899.15 7497.66 2097.77 6698.83 10697.42 6996.32 22536.50 37296.49 8699.72 8695.66 11099.37 15699.45 69
LPG-MVS_test97.94 5297.67 6698.74 3599.15 7497.02 4497.09 10999.02 5295.15 16798.34 8298.23 10797.91 1799.70 10994.41 17899.73 4899.50 45
LGP-MVS_train98.74 3599.15 7497.02 4499.02 5295.15 16798.34 8298.23 10797.91 1799.70 10994.41 17899.73 4899.50 45
RPSCF97.87 6397.51 8598.95 1799.15 7498.43 397.56 8099.06 4196.19 11598.48 6698.70 6294.72 14999.24 25994.37 18199.33 17499.17 136
ACMM93.33 1198.05 4197.79 5598.85 2599.15 7497.55 2796.68 13198.83 10695.21 16398.36 7998.13 11798.13 1499.62 15196.04 8799.54 9899.39 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet197.95 5098.08 3597.56 12499.14 8293.67 17498.23 3998.66 14997.41 7299.00 3699.19 2495.47 12799.73 8195.83 10299.76 4299.30 107
Vis-MVSNet (Re-imp)95.11 20994.85 21095.87 23099.12 8389.17 25997.54 8694.92 31796.50 10096.58 21297.27 21183.64 30199.48 19288.42 30899.67 6198.97 175
OPM-MVS97.54 8897.25 10198.41 5899.11 8496.61 5795.24 21498.46 17094.58 19098.10 11398.07 12597.09 4799.39 22495.16 14499.44 13399.21 129
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UA-Net98.88 798.76 1399.22 299.11 8497.89 1499.47 399.32 1099.08 1097.87 14199.67 296.47 8899.92 497.88 2399.98 299.85 3
AllTest97.20 11296.92 12398.06 8999.08 8696.16 7097.14 10699.16 2094.35 19697.78 14798.07 12595.84 10599.12 27491.41 24799.42 14498.91 188
TestCases98.06 8999.08 8696.16 7099.16 2094.35 19697.78 14798.07 12595.84 10599.12 27491.41 24799.42 14498.91 188
TranMVSNet+NR-MVSNet98.33 2698.30 3198.43 5799.07 8895.87 8096.73 12999.05 4398.67 2498.84 4298.45 8097.58 2899.88 1896.45 7299.86 2599.54 38
test111194.53 23994.81 21493.72 29999.06 8981.94 34898.31 3383.87 37196.37 10598.49 6599.17 2981.49 30799.73 8196.64 6299.86 2599.49 53
VPA-MVSNet98.27 2998.46 2497.70 11599.06 8993.80 16997.76 6899.00 6098.40 3099.07 3398.98 4396.89 6499.75 6597.19 5199.79 3899.55 37
114514_t93.96 25893.22 26596.19 21599.06 8990.97 23295.99 16498.94 7473.88 36893.43 31896.93 23392.38 21399.37 23089.09 29899.28 18398.25 260
EG-PatchMatch MVS97.69 7897.79 5597.40 14799.06 8993.52 18195.96 16798.97 7094.55 19198.82 4398.76 5897.31 3699.29 25197.20 5099.44 13399.38 88
test_one_060199.05 9395.50 9998.87 8797.21 7998.03 12298.30 9496.93 60
ACMP92.54 1397.47 9497.10 11198.55 5099.04 9496.70 5396.24 15098.89 7993.71 21697.97 12997.75 16697.44 3099.63 14393.22 21999.70 5799.32 101
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_part299.03 9596.07 7498.08 116
XVG-OURS-SEG-HR97.38 10097.07 11498.30 6899.01 9697.41 3694.66 24499.02 5295.20 16498.15 10697.52 18698.83 498.43 33994.87 15996.41 33199.07 162
XVG-OURS97.12 11396.74 13298.26 7098.99 9797.45 3493.82 27999.05 4395.19 16598.32 8797.70 17295.22 13698.41 34094.27 18698.13 27998.93 183
CP-MVS97.92 5697.56 8298.99 1398.99 9797.82 1697.93 5798.96 7196.11 11896.89 19997.45 19296.85 6899.78 4395.19 14099.63 6899.38 88
test250689.86 31889.16 32391.97 33298.95 9976.83 36598.54 2061.07 37996.20 11397.07 18599.16 3055.19 37899.69 11796.43 7399.83 3199.38 88
ECVR-MVScopyleft94.37 24594.48 23294.05 29698.95 9983.10 34098.31 3382.48 37296.20 11398.23 9799.16 3081.18 31099.66 13695.95 9499.83 3199.38 88
CSCG97.40 9997.30 9797.69 11798.95 9994.83 12897.28 9798.99 6396.35 10898.13 10995.95 29095.99 10199.66 13694.36 18499.73 4898.59 227
SF-MVS97.60 8497.39 9298.22 7598.93 10295.69 8697.05 11199.10 3195.32 16097.83 14497.88 15296.44 9099.72 8694.59 17399.39 15399.25 124
HyFIR lowres test93.72 26392.65 27896.91 17498.93 10291.81 22191.23 33598.52 16582.69 34396.46 21996.52 26080.38 31599.90 1390.36 28198.79 24499.03 168
PM-MVS97.36 10397.10 11198.14 8398.91 10496.77 5196.20 15298.63 15593.82 21398.54 6098.33 8893.98 17399.05 28495.99 9299.45 13298.61 226
CPTT-MVS96.69 14396.08 16798.49 5398.89 10596.64 5697.25 9898.77 12192.89 24696.01 24197.13 21792.23 21499.67 13092.24 23199.34 16799.17 136
GeoE97.75 7497.70 6297.89 10098.88 10694.53 14097.10 10898.98 6695.75 14397.62 14997.59 18097.61 2799.77 5396.34 7699.44 13399.36 96
DPE-MVScopyleft97.64 8097.35 9598.50 5298.85 10796.18 6995.21 21698.99 6395.84 13898.78 4598.08 12396.84 6999.81 3293.98 20099.57 8699.52 42
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVScopyleft97.48 9397.11 11098.60 4698.83 10896.67 5496.74 12598.73 12991.61 26398.48 6698.36 8596.53 8399.68 12595.17 14299.54 9899.45 69
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
SR-MVS-dyc-post98.14 3497.84 5199.02 998.81 10998.05 997.55 8198.86 9097.77 4798.20 9998.07 12596.60 8099.76 5895.49 11899.20 19299.26 120
RE-MVS-def97.88 4998.81 10998.05 997.55 8198.86 9097.77 4798.20 9998.07 12596.94 5895.49 11899.20 19299.26 120
UniMVSNet (Re)97.83 6797.65 6998.35 6498.80 11195.86 8195.92 17199.04 4997.51 6698.22 9897.81 16194.68 15299.78 4397.14 5399.75 4699.41 83
Anonymous2023121198.55 1798.76 1397.94 9798.79 11294.37 14798.84 1099.15 2499.37 399.67 699.43 1195.61 12099.72 8698.12 1699.86 2599.73 15
APD-MVS_3200maxsize98.13 3797.90 4598.79 3198.79 11297.31 3897.55 8198.92 7697.72 5498.25 9498.13 11797.10 4599.75 6595.44 12599.24 19099.32 101
test117298.08 3997.76 5999.05 698.78 11498.07 797.41 9398.85 9497.57 6198.15 10697.96 14096.60 8099.76 5895.30 13499.18 19799.33 100
DeepC-MVS95.41 497.82 6997.70 6298.16 7998.78 11495.72 8496.23 15199.02 5293.92 21198.62 5298.99 4297.69 2399.62 15196.18 8099.87 2499.15 140
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS98.00 4597.66 6799.01 1198.77 11697.93 1197.38 9498.83 10697.32 7598.06 11897.85 15596.65 7599.77 5395.00 15699.11 20899.32 101
MCST-MVS96.24 16295.80 17897.56 12498.75 11794.13 15794.66 24498.17 21090.17 28196.21 23396.10 28395.14 13799.43 20794.13 19298.85 23999.13 146
DU-MVS97.79 7197.60 7898.36 6298.73 11895.78 8295.65 18798.87 8797.57 6198.31 8997.83 15794.69 15099.85 2297.02 5799.71 5499.46 64
NR-MVSNet97.96 4697.86 5098.26 7098.73 11895.54 9498.14 4798.73 12997.79 4699.42 1597.83 15794.40 16399.78 4395.91 9799.76 4299.46 64
Anonymous2023120695.27 20395.06 20195.88 22998.72 12089.37 25695.70 18097.85 23788.00 30396.98 19397.62 17891.95 22299.34 23789.21 29699.53 10198.94 179
APDe-MVS98.14 3498.03 4098.47 5598.72 12096.04 7598.07 5199.10 3195.96 12898.59 5798.69 6396.94 5899.81 3296.64 6299.58 8399.57 32
UniMVSNet_NR-MVSNet97.83 6797.65 6998.37 6198.72 12095.78 8295.66 18499.02 5298.11 4098.31 8997.69 17494.65 15499.85 2297.02 5799.71 5499.48 59
tttt051793.31 27492.56 28195.57 24098.71 12387.86 28497.44 8987.17 36695.79 14097.47 16296.84 23864.12 36799.81 3296.20 7999.32 17699.02 170
v897.60 8498.06 3896.23 21298.71 12389.44 25597.43 9198.82 11497.29 7798.74 4899.10 3593.86 17599.68 12598.61 1099.94 899.56 35
HQP_MVS96.66 14696.33 15697.68 11898.70 12594.29 14996.50 13598.75 12596.36 10696.16 23596.77 24491.91 22699.46 19892.59 22899.20 19299.28 115
plane_prior798.70 12594.67 137
Anonymous2024052997.96 4698.04 3997.71 11398.69 12794.28 15297.86 6298.31 19498.79 2299.23 2698.86 5395.76 11599.61 15895.49 11899.36 15999.23 127
VDD-MVS97.37 10197.25 10197.74 11198.69 12794.50 14397.04 11295.61 30998.59 2698.51 6298.72 6092.54 20899.58 16296.02 8999.49 11999.12 151
DROMVSNet97.90 6097.94 4497.79 10798.66 12995.14 12098.31 3399.66 297.57 6195.95 24297.01 22996.99 5599.82 2997.66 3399.64 6698.39 241
HPM-MVS++copyleft96.99 11796.38 15398.81 2998.64 13097.59 2495.97 16698.20 20495.51 15395.06 26696.53 25894.10 17099.70 10994.29 18599.15 19999.13 146
ab-mvs96.59 14996.59 13896.60 19198.64 13092.21 20898.35 2997.67 24994.45 19296.99 19198.79 5594.96 14499.49 18990.39 28099.07 21498.08 269
F-COLMAP95.30 20294.38 23798.05 9298.64 13096.04 7595.61 19098.66 14989.00 29193.22 32296.40 26692.90 19699.35 23587.45 32297.53 30698.77 210
ITE_SJBPF97.85 10498.64 13096.66 5598.51 16795.63 14697.22 17097.30 21095.52 12398.55 33390.97 25798.90 23198.34 249
v14896.58 15096.97 11995.42 24898.63 13487.57 29195.09 22197.90 23495.91 13398.24 9697.96 14093.42 18599.39 22496.04 8799.52 10699.29 114
ETH3D-3000-0.196.89 12796.46 15198.16 7998.62 13595.69 8695.96 16798.98 6693.36 22497.04 18797.31 20994.93 14599.63 14392.60 22699.34 16799.17 136
UnsupCasMVSNet_bld94.72 22894.26 23996.08 21998.62 13590.54 24293.38 29498.05 22990.30 27997.02 18996.80 24389.54 25599.16 27088.44 30796.18 33498.56 229
DP-MVS97.87 6397.89 4897.81 10698.62 13594.82 12997.13 10798.79 11698.98 1798.74 4898.49 7795.80 11499.49 18995.04 15399.44 13399.11 155
v1097.55 8797.97 4196.31 20998.60 13889.64 25197.44 8999.02 5296.60 9498.72 5099.16 3093.48 18499.72 8698.76 699.92 1499.58 28
Test_1112_low_res93.53 27092.86 27095.54 24398.60 13888.86 26592.75 30698.69 14282.66 34492.65 33296.92 23584.75 29499.56 16990.94 25897.76 29298.19 265
V4297.04 11597.16 10896.68 18998.59 14091.05 22996.33 14498.36 18694.60 18797.99 12598.30 9493.32 18699.62 15197.40 4299.53 10199.38 88
1112_ss94.12 25393.42 26096.23 21298.59 14090.85 23394.24 25898.85 9485.49 32592.97 32594.94 31386.01 28699.64 14191.78 24197.92 28698.20 264
v2v48296.78 13597.06 11595.95 22598.57 14288.77 26895.36 20298.26 19795.18 16697.85 14398.23 10792.58 20599.63 14397.80 2799.69 5899.45 69
WR-MVS96.90 12596.81 12897.16 15998.56 14392.20 21094.33 25398.12 21997.34 7498.20 9997.33 20792.81 19799.75 6594.79 16399.81 3399.54 38
APD-MVScopyleft97.00 11696.53 14698.41 5898.55 14496.31 6696.32 14598.77 12192.96 24597.44 16597.58 18295.84 10599.74 7591.96 23499.35 16499.19 133
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Patchmatch-RL test94.66 23294.49 23195.19 25698.54 14588.91 26392.57 31098.74 12791.46 26698.32 8797.75 16677.31 33198.81 30896.06 8499.61 7597.85 288
9.1496.69 13498.53 14696.02 16298.98 6693.23 22997.18 17497.46 19196.47 8899.62 15192.99 22399.32 176
baseline97.44 9697.78 5896.43 20298.52 14790.75 23796.84 11999.03 5096.51 9997.86 14298.02 13496.67 7499.36 23297.09 5499.47 12599.19 133
casdiffmvs97.50 9197.81 5496.56 19698.51 14891.04 23095.83 17699.09 3697.23 7898.33 8698.30 9497.03 5299.37 23096.58 6699.38 15599.28 115
IterMVS-LS96.92 12397.29 9895.79 23298.51 14888.13 28095.10 21998.66 14996.99 8298.46 6998.68 6492.55 20699.74 7596.91 6099.79 3899.50 45
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon95.55 18995.13 19696.80 18098.51 14893.99 16294.60 24698.69 14290.20 28095.78 25196.21 27692.73 20098.98 29390.58 27498.86 23797.42 305
h-mvs3396.29 16095.63 18498.26 7098.50 15196.11 7396.90 11797.09 27596.58 9697.21 17298.19 11284.14 29799.78 4395.89 9896.17 33598.89 192
test20.0396.58 15096.61 13796.48 20098.49 15291.72 22295.68 18397.69 24896.81 8898.27 9397.92 14894.18 16998.71 31790.78 26499.66 6399.00 171
plane_prior198.49 152
xxxxxxxxxxxxxcwj97.24 11097.03 11797.89 10098.48 15494.71 13394.53 24999.07 4095.02 17497.83 14497.88 15296.44 9099.72 8694.59 17399.39 15399.25 124
save fliter98.48 15494.71 13394.53 24998.41 17995.02 174
MDA-MVSNet-bldmvs95.69 18395.67 18295.74 23498.48 15488.76 26992.84 30397.25 26796.00 12697.59 15097.95 14491.38 23199.46 19893.16 22196.35 33298.99 174
UnsupCasMVSNet_eth95.91 17795.73 18196.44 20198.48 15491.52 22595.31 20798.45 17195.76 14197.48 16097.54 18389.53 25798.69 31994.43 17794.61 35099.13 146
testtj96.69 14396.13 16398.36 6298.46 15896.02 7796.44 13798.70 13994.26 19996.79 20197.13 21794.07 17199.75 6590.53 27598.80 24399.31 106
ZD-MVS98.43 15995.94 7998.56 16290.72 27596.66 20997.07 22395.02 14299.74 7591.08 25498.93 229
thisisatest053092.71 28391.76 29195.56 24298.42 16088.23 27596.03 16187.35 36594.04 20796.56 21495.47 30364.03 36899.77 5394.78 16599.11 20898.68 220
v114496.84 12897.08 11396.13 21898.42 16089.28 25895.41 19898.67 14794.21 20197.97 12998.31 9093.06 19199.65 13898.06 1999.62 6999.45 69
plane_prior698.38 16294.37 14791.91 226
FPMVS89.92 31788.63 32593.82 29798.37 16396.94 4791.58 32693.34 33188.00 30390.32 35097.10 22170.87 35991.13 37171.91 36996.16 33693.39 361
PAPM_NR94.61 23594.17 24495.96 22398.36 16491.23 22795.93 17097.95 23192.98 24193.42 31994.43 32590.53 24098.38 34387.60 31896.29 33398.27 258
MVS_111021_HR96.73 13996.54 14597.27 15498.35 16593.66 17793.42 29198.36 18694.74 18296.58 21296.76 24696.54 8298.99 29194.87 15999.27 18599.15 140
TAMVS95.49 19194.94 20497.16 15998.31 16693.41 18395.07 22496.82 28591.09 27297.51 15497.82 16089.96 25099.42 20888.42 30899.44 13398.64 221
OMC-MVS96.48 15496.00 17097.91 9998.30 16796.01 7894.86 23698.60 15791.88 26097.18 17497.21 21596.11 9999.04 28590.49 27999.34 16798.69 218
新几何197.25 15798.29 16894.70 13697.73 24577.98 36194.83 27396.67 25192.08 21999.45 20288.17 31298.65 25897.61 299
jason94.39 24494.04 24895.41 25098.29 16887.85 28692.74 30896.75 28885.38 33095.29 26296.15 27888.21 27099.65 13894.24 18799.34 16798.74 212
jason: jason.
v119296.83 13197.06 11596.15 21798.28 17089.29 25795.36 20298.77 12193.73 21598.11 11098.34 8793.02 19599.67 13098.35 1499.58 8399.50 45
CDPH-MVS95.45 19694.65 22097.84 10598.28 17094.96 12593.73 28398.33 19185.03 33395.44 25996.60 25495.31 13399.44 20590.01 28599.13 20499.11 155
MVS_111021_LR96.82 13296.55 14397.62 12198.27 17295.34 10993.81 28198.33 19194.59 18996.56 21496.63 25396.61 7898.73 31594.80 16299.34 16798.78 207
CLD-MVS95.47 19495.07 19996.69 18798.27 17292.53 20191.36 32998.67 14791.22 27195.78 25194.12 32995.65 11998.98 29390.81 26299.72 5198.57 228
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
112194.26 24693.26 26397.27 15498.26 17494.73 13195.86 17297.71 24777.96 36294.53 28096.71 24891.93 22499.40 21987.71 31498.64 25997.69 296
Anonymous20240521196.34 15995.98 17297.43 14498.25 17593.85 16796.74 12594.41 32297.72 5498.37 7698.03 13387.15 28099.53 17894.06 19499.07 21498.92 187
pmmvs-eth3d96.49 15396.18 16297.42 14598.25 17594.29 14994.77 24198.07 22789.81 28497.97 12998.33 8893.11 19099.08 28195.46 12499.84 2998.89 192
v14419296.69 14396.90 12596.03 22098.25 17588.92 26295.49 19298.77 12193.05 23898.09 11498.29 9892.51 21099.70 10998.11 1799.56 8999.47 62
ambc96.56 19698.23 17891.68 22397.88 6198.13 21898.42 7298.56 7294.22 16899.04 28594.05 19799.35 16498.95 177
thres100view90091.76 29991.26 29893.26 30898.21 17984.50 33296.39 13990.39 35696.87 8696.33 22493.08 33873.44 35199.42 20878.85 36097.74 29395.85 342
v192192096.72 14096.96 12195.99 22198.21 17988.79 26795.42 19698.79 11693.22 23098.19 10298.26 10492.68 20199.70 10998.34 1599.55 9599.49 53
thres600view792.03 29591.43 29393.82 29798.19 18184.61 33196.27 14690.39 35696.81 8896.37 22393.11 33473.44 35199.49 18980.32 35697.95 28597.36 306
PatchMatch-RL94.61 23593.81 25597.02 16998.19 18195.72 8493.66 28497.23 26888.17 30194.94 27195.62 29991.43 23098.57 33087.36 32397.68 29996.76 328
LF4IMVS96.07 16995.63 18497.36 15098.19 18195.55 9395.44 19498.82 11492.29 25495.70 25596.55 25692.63 20498.69 31991.75 24399.33 17497.85 288
v124096.74 13797.02 11895.91 22898.18 18488.52 27095.39 20098.88 8593.15 23698.46 6998.40 8492.80 19899.71 10098.45 1399.49 11999.49 53
TAPA-MVS93.32 1294.93 21694.23 24097.04 16798.18 18494.51 14195.22 21598.73 12981.22 35096.25 23195.95 29093.80 17898.98 29389.89 28798.87 23597.62 298
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test22298.17 18693.24 18792.74 30897.61 25975.17 36694.65 27796.69 25090.96 23698.66 25697.66 297
MIMVSNet93.42 27192.86 27095.10 25998.17 18688.19 27698.13 4893.69 32592.07 25595.04 26998.21 11180.95 31399.03 28881.42 35498.06 28298.07 271
原ACMM196.58 19398.16 18892.12 21298.15 21585.90 32193.49 31496.43 26392.47 21199.38 22787.66 31798.62 26098.23 261
testdata95.70 23798.16 18890.58 23997.72 24680.38 35395.62 25697.02 22792.06 22098.98 29389.06 30098.52 26597.54 301
MVP-Stereo95.69 18395.28 19296.92 17298.15 19093.03 19295.64 18998.20 20490.39 27896.63 21197.73 16991.63 22999.10 27991.84 24097.31 31498.63 223
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS97.37 10197.70 6296.35 20698.14 19195.13 12196.54 13498.92 7695.94 13099.19 2898.08 12397.74 2295.06 36895.24 13899.54 9898.87 198
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
EU-MVSNet94.25 24794.47 23393.60 30298.14 19182.60 34397.24 10092.72 33885.08 33198.48 6698.94 4682.59 30498.76 31397.47 4099.53 10199.44 79
NP-MVS98.14 19193.72 17395.08 309
LCM-MVSNet-Re97.33 10497.33 9697.32 15298.13 19493.79 17096.99 11599.65 396.74 9099.47 1398.93 4796.91 6399.84 2590.11 28399.06 21798.32 250
ETH3 D test640094.77 22393.87 25497.47 13798.12 19593.73 17294.56 24898.70 13985.45 32894.70 27695.93 29291.77 22899.63 14386.45 32899.14 20099.05 166
3Dnovator+96.13 397.73 7597.59 7998.15 8298.11 19695.60 9298.04 5298.70 13998.13 3996.93 19698.45 8095.30 13499.62 15195.64 11298.96 22399.24 126
VNet96.84 12896.83 12796.88 17598.06 19792.02 21596.35 14397.57 26097.70 5697.88 13897.80 16292.40 21299.54 17694.73 16898.96 22399.08 160
test_part196.77 13696.53 14697.47 13798.04 19892.92 19597.93 5798.85 9498.83 2199.30 2199.07 3879.25 31899.79 3997.59 3599.93 1099.69 20
LFMVS95.32 20194.88 20996.62 19098.03 19991.47 22697.65 7490.72 35599.11 997.89 13798.31 9079.20 31999.48 19293.91 20399.12 20798.93 183
tfpn200view991.55 30191.00 30093.21 31198.02 20084.35 33495.70 18090.79 35396.26 11095.90 24792.13 35173.62 34899.42 20878.85 36097.74 29395.85 342
thres40091.68 30091.00 30093.71 30098.02 20084.35 33495.70 18090.79 35396.26 11095.90 24792.13 35173.62 34899.42 20878.85 36097.74 29397.36 306
OPU-MVS97.64 12098.01 20295.27 11296.79 12297.35 20596.97 5698.51 33691.21 25399.25 18799.14 143
xiu_mvs_v1_base_debu95.62 18695.96 17394.60 28098.01 20288.42 27193.99 27298.21 20192.98 24195.91 24494.53 32196.39 9299.72 8695.43 12898.19 27695.64 346
xiu_mvs_v1_base95.62 18695.96 17394.60 28098.01 20288.42 27193.99 27298.21 20192.98 24195.91 24494.53 32196.39 9299.72 8695.43 12898.19 27695.64 346
xiu_mvs_v1_base_debi95.62 18695.96 17394.60 28098.01 20288.42 27193.99 27298.21 20192.98 24195.91 24494.53 32196.39 9299.72 8695.43 12898.19 27695.64 346
CNVR-MVS96.92 12396.55 14398.03 9398.00 20695.54 9494.87 23598.17 21094.60 18796.38 22297.05 22595.67 11899.36 23295.12 15099.08 21299.19 133
PLCcopyleft91.02 1694.05 25792.90 26997.51 12998.00 20695.12 12294.25 25798.25 19886.17 31791.48 34395.25 30691.01 23499.19 26485.02 34196.69 32698.22 262
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GBi-Net96.99 11796.80 12997.56 12497.96 20893.67 17498.23 3998.66 14995.59 15097.99 12599.19 2489.51 25899.73 8194.60 17099.44 13399.30 107
test196.99 11796.80 12997.56 12497.96 20893.67 17498.23 3998.66 14995.59 15097.99 12599.19 2489.51 25899.73 8194.60 17099.44 13399.30 107
FMVSNet296.72 14096.67 13696.87 17697.96 20891.88 21897.15 10498.06 22895.59 15098.50 6498.62 6889.51 25899.65 13894.99 15799.60 7999.07 162
BH-untuned94.69 22994.75 21794.52 28597.95 21187.53 29294.07 26997.01 27893.99 20897.10 18095.65 29792.65 20398.95 29887.60 31896.74 32597.09 311
ETH3D cwj APD-0.1696.23 16395.61 18698.09 8697.91 21295.65 9194.94 23298.74 12791.31 26996.02 24097.08 22294.05 17299.69 11791.51 24698.94 22798.93 183
DPM-MVS93.68 26592.77 27696.42 20397.91 21292.54 20091.17 33697.47 26484.99 33493.08 32494.74 31789.90 25199.00 28987.54 32098.09 28197.72 294
QAPM95.88 17995.57 18796.80 18097.90 21491.84 22098.18 4698.73 12988.41 29796.42 22098.13 11794.73 14899.75 6588.72 30398.94 22798.81 203
TinyColmap96.00 17496.34 15594.96 26497.90 21487.91 28394.13 26798.49 16894.41 19398.16 10497.76 16396.29 9798.68 32290.52 27699.42 14498.30 254
CS-MVS-test96.62 14896.59 13896.69 18797.88 21693.16 18997.21 10299.53 695.61 14893.72 30495.33 30595.49 12499.69 11795.37 13299.19 19697.22 309
HQP-NCC97.85 21794.26 25493.18 23292.86 327
ACMP_Plane97.85 21794.26 25493.18 23292.86 327
N_pmnet95.18 20694.23 24098.06 8997.85 21796.55 5992.49 31291.63 34689.34 28798.09 11497.41 19590.33 24399.06 28391.58 24599.31 17898.56 229
HQP-MVS95.17 20894.58 22896.92 17297.85 21792.47 20294.26 25498.43 17493.18 23292.86 32795.08 30990.33 24399.23 26190.51 27798.74 24999.05 166
hse-mvs295.77 18295.09 19897.79 10797.84 22195.51 9695.66 18495.43 31496.58 9697.21 17296.16 27784.14 29799.54 17695.89 9896.92 31898.32 250
TEST997.84 22195.23 11493.62 28598.39 18286.81 31393.78 30095.99 28594.68 15299.52 182
train_agg95.46 19594.66 21997.88 10297.84 22195.23 11493.62 28598.39 18287.04 31193.78 30095.99 28594.58 15799.52 18291.76 24298.90 23198.89 192
MSLP-MVS++96.42 15896.71 13395.57 24097.82 22490.56 24195.71 17998.84 9994.72 18396.71 20797.39 20094.91 14698.10 35495.28 13599.02 21998.05 278
test_897.81 22595.07 12393.54 28898.38 18487.04 31193.71 30595.96 28994.58 15799.52 182
NCCC96.52 15295.99 17198.10 8597.81 22595.68 8895.00 23098.20 20495.39 15895.40 26196.36 26993.81 17799.45 20293.55 21398.42 26999.17 136
WTY-MVS93.55 26993.00 26895.19 25697.81 22587.86 28493.89 27796.00 29989.02 29094.07 29395.44 30486.27 28499.33 24087.69 31696.82 32298.39 241
CNLPA95.04 21294.47 23396.75 18397.81 22595.25 11394.12 26897.89 23594.41 19394.57 27895.69 29590.30 24698.35 34686.72 32798.76 24796.64 331
AUN-MVS93.95 26092.69 27797.74 11197.80 22995.38 10495.57 19195.46 31391.26 27092.64 33396.10 28374.67 34299.55 17393.72 20996.97 31798.30 254
EIA-MVS96.04 17195.77 18096.85 17797.80 22992.98 19396.12 15699.16 2094.65 18593.77 30291.69 35695.68 11799.67 13094.18 18998.85 23997.91 286
agg_prior195.39 19894.60 22597.75 11097.80 22994.96 12593.39 29398.36 18687.20 30993.49 31495.97 28894.65 15499.53 17891.69 24498.86 23798.77 210
agg_prior97.80 22994.96 12598.36 18693.49 31499.53 178
旧先验197.80 22993.87 16597.75 24497.04 22693.57 18398.68 25398.72 215
PCF-MVS89.43 1892.12 29490.64 30896.57 19597.80 22993.48 18289.88 35398.45 17174.46 36796.04 23995.68 29690.71 23999.31 24473.73 36699.01 22196.91 319
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_prior395.91 17795.39 19097.46 14097.79 23594.26 15393.33 29698.42 17794.21 20194.02 29596.25 27393.64 18199.34 23791.90 23698.96 22398.79 205
test_prior97.46 14097.79 23594.26 15398.42 17799.34 23798.79 205
PVSNet_BlendedMVS95.02 21594.93 20695.27 25297.79 23587.40 29594.14 26698.68 14488.94 29294.51 28198.01 13693.04 19299.30 24789.77 28999.49 11999.11 155
PVSNet_Blended93.96 25893.65 25794.91 26597.79 23587.40 29591.43 32898.68 14484.50 33894.51 28194.48 32493.04 19299.30 24789.77 28998.61 26198.02 281
USDC94.56 23794.57 23094.55 28497.78 23986.43 30992.75 30698.65 15485.96 31996.91 19897.93 14790.82 23798.74 31490.71 26999.59 8198.47 236
alignmvs96.01 17395.52 18897.50 13297.77 24094.71 13396.07 15896.84 28397.48 6796.78 20594.28 32885.50 28999.40 21996.22 7898.73 25298.40 239
ETV-MVS96.13 16895.90 17696.82 17997.76 24193.89 16495.40 19998.95 7395.87 13595.58 25891.00 36296.36 9599.72 8693.36 21498.83 24196.85 322
D2MVS95.18 20695.17 19595.21 25497.76 24187.76 28994.15 26497.94 23289.77 28596.99 19197.68 17587.45 27899.14 27295.03 15599.81 3398.74 212
DVP-MVS++97.96 4697.90 4598.12 8497.75 24395.40 10299.03 798.89 7996.62 9298.62 5298.30 9496.97 5699.75 6595.70 10499.25 18799.21 129
MSC_two_6792asdad98.22 7597.75 24395.34 10998.16 21399.75 6595.87 10099.51 11199.57 32
No_MVS98.22 7597.75 24395.34 10998.16 21399.75 6595.87 10099.51 11199.57 32
TSAR-MVS + GP.96.47 15596.12 16497.49 13597.74 24695.23 11494.15 26496.90 28293.26 22898.04 12196.70 24994.41 16298.89 30194.77 16699.14 20098.37 243
3Dnovator96.53 297.61 8397.64 7297.50 13297.74 24693.65 17898.49 2398.88 8596.86 8797.11 17998.55 7395.82 10899.73 8195.94 9599.42 14499.13 146
sss94.22 24893.72 25695.74 23497.71 24889.95 24693.84 27896.98 27988.38 29993.75 30395.74 29487.94 27198.89 30191.02 25698.10 28098.37 243
DeepC-MVS_fast94.34 796.74 13796.51 14997.44 14397.69 24994.15 15696.02 16298.43 17493.17 23597.30 16897.38 20295.48 12699.28 25393.74 20799.34 16798.88 196
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
IterMVS-SCA-FT95.86 18096.19 16194.85 27097.68 25085.53 31792.42 31497.63 25796.99 8298.36 7998.54 7487.94 27199.75 6597.07 5699.08 21299.27 119
MVSFormer96.14 16796.36 15495.49 24597.68 25087.81 28798.67 1399.02 5296.50 10094.48 28396.15 27886.90 28199.92 498.73 799.13 20498.74 212
lupinMVS93.77 26193.28 26295.24 25397.68 25087.81 28792.12 31996.05 29784.52 33794.48 28395.06 31186.90 28199.63 14393.62 21299.13 20498.27 258
Fast-Effi-MVS+95.49 19195.07 19996.75 18397.67 25392.82 19694.22 26098.60 15791.61 26393.42 31992.90 34196.73 7399.70 10992.60 22697.89 28997.74 293
canonicalmvs97.23 11197.21 10697.30 15397.65 25494.39 14597.84 6399.05 4397.42 6996.68 20893.85 33197.63 2699.33 24096.29 7798.47 26898.18 266
CDS-MVSNet94.88 21994.12 24597.14 16197.64 25593.57 17993.96 27597.06 27790.05 28296.30 22896.55 25686.10 28599.47 19590.10 28499.31 17898.40 239
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
pmmvs594.63 23494.34 23895.50 24497.63 25688.34 27494.02 27097.13 27387.15 31095.22 26497.15 21687.50 27799.27 25593.99 19999.26 18698.88 196
test1297.46 14097.61 25794.07 15897.78 24393.57 31293.31 18799.42 20898.78 24598.89 192
PMMVS293.66 26694.07 24692.45 32797.57 25880.67 35386.46 36296.00 29993.99 20897.10 18097.38 20289.90 25197.82 35688.76 30299.47 12598.86 199
BH-RMVSNet94.56 23794.44 23694.91 26597.57 25887.44 29493.78 28296.26 29493.69 21796.41 22196.50 26192.10 21899.00 28985.96 33097.71 29698.31 252
bset_n11_16_dypcd94.53 23993.95 25296.25 21197.56 26089.85 24888.52 35991.32 34894.90 17997.51 15496.38 26882.34 30599.78 4397.22 4699.80 3699.12 151
PVSNet86.72 1991.10 30590.97 30291.49 33497.56 26078.04 35987.17 36194.60 32084.65 33692.34 33792.20 35087.37 27998.47 33785.17 34097.69 29897.96 283
DELS-MVS96.17 16696.23 15995.99 22197.55 26290.04 24492.38 31698.52 16594.13 20496.55 21697.06 22494.99 14399.58 16295.62 11399.28 18398.37 243
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
IterMVS95.42 19795.83 17794.20 29397.52 26383.78 33892.41 31597.47 26495.49 15498.06 11898.49 7787.94 27199.58 16296.02 8999.02 21999.23 127
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CL-MVSNet_self_test95.04 21294.79 21695.82 23197.51 26489.79 24991.14 33796.82 28593.05 23896.72 20696.40 26690.82 23799.16 27091.95 23598.66 25698.50 234
new-patchmatchnet95.67 18596.58 14092.94 31997.48 26580.21 35492.96 30298.19 20994.83 18098.82 4398.79 5593.31 18799.51 18695.83 10299.04 21899.12 151
MDA-MVSNet_test_wron94.73 22494.83 21394.42 28797.48 26585.15 32490.28 34795.87 30392.52 24997.48 16097.76 16391.92 22599.17 26993.32 21596.80 32498.94 179
PHI-MVS96.96 12196.53 14698.25 7397.48 26596.50 6096.76 12498.85 9493.52 21996.19 23496.85 23795.94 10299.42 20893.79 20699.43 14198.83 201
DeepPCF-MVS94.58 596.90 12596.43 15298.31 6797.48 26597.23 4292.56 31198.60 15792.84 24798.54 6097.40 19696.64 7798.78 31094.40 18099.41 15098.93 183
thres20091.00 30790.42 31192.77 32197.47 26983.98 33794.01 27191.18 35195.12 16995.44 25991.21 36073.93 34499.31 24477.76 36397.63 30395.01 352
YYNet194.73 22494.84 21194.41 28897.47 26985.09 32690.29 34695.85 30492.52 24997.53 15297.76 16391.97 22199.18 26593.31 21696.86 32198.95 177
Effi-MVS+96.19 16596.01 16996.71 18597.43 27192.19 21196.12 15699.10 3195.45 15593.33 32194.71 31897.23 4399.56 16993.21 22097.54 30598.37 243
pmmvs494.82 22194.19 24396.70 18697.42 27292.75 19992.09 32196.76 28786.80 31495.73 25497.22 21489.28 26198.89 30193.28 21799.14 20098.46 238
MSDG95.33 20095.13 19695.94 22797.40 27391.85 21991.02 34098.37 18595.30 16196.31 22795.99 28594.51 16098.38 34389.59 29197.65 30297.60 300
EI-MVSNet-Vis-set97.32 10597.39 9297.11 16297.36 27492.08 21495.34 20497.65 25397.74 5198.29 9298.11 12195.05 13899.68 12597.50 3999.50 11599.56 35
PS-MVSNAJ94.10 25494.47 23393.00 31697.35 27584.88 32891.86 32397.84 23991.96 25894.17 28992.50 34895.82 10899.71 10091.27 25097.48 30894.40 356
Regformer-397.25 10997.29 9897.11 16297.35 27592.32 20595.26 21197.62 25897.67 5998.17 10397.89 15095.05 13899.56 16997.16 5299.42 14499.46 64
Regformer-497.53 9097.47 9097.71 11397.35 27593.91 16395.26 21198.14 21697.97 4398.34 8297.89 15095.49 12499.71 10097.41 4199.42 14499.51 44
diffmvs96.04 17196.23 15995.46 24797.35 27588.03 28293.42 29199.08 3794.09 20696.66 20996.93 23393.85 17699.29 25196.01 9198.67 25499.06 164
EI-MVSNet-UG-set97.32 10597.40 9197.09 16497.34 27992.01 21695.33 20597.65 25397.74 5198.30 9198.14 11695.04 14099.69 11797.55 3799.52 10699.58 28
baseline193.14 27892.64 27994.62 27997.34 27987.20 29996.67 13293.02 33394.71 18496.51 21795.83 29381.64 30698.60 32990.00 28688.06 36598.07 271
AdaColmapbinary95.11 20994.62 22496.58 19397.33 28194.45 14494.92 23398.08 22393.15 23693.98 29895.53 30294.34 16499.10 27985.69 33398.61 26196.20 339
xiu_mvs_v2_base94.22 24894.63 22392.99 31797.32 28284.84 32992.12 31997.84 23991.96 25894.17 28993.43 33296.07 10099.71 10091.27 25097.48 30894.42 355
OpenMVS_ROBcopyleft91.80 1493.64 26793.05 26695.42 24897.31 28391.21 22895.08 22396.68 29181.56 34796.88 20096.41 26490.44 24299.25 25885.39 33797.67 30095.80 344
CS-MVS95.98 17596.24 15895.20 25597.26 28489.88 24795.84 17599.39 993.89 21294.28 28695.15 30894.81 14799.62 15196.11 8399.40 15196.10 340
EI-MVSNet96.63 14796.93 12295.74 23497.26 28488.13 28095.29 20997.65 25396.99 8297.94 13298.19 11292.55 20699.58 16296.91 6099.56 8999.50 45
CVMVSNet92.33 29092.79 27390.95 33897.26 28475.84 36895.29 20992.33 34181.86 34596.27 22998.19 11281.44 30898.46 33894.23 18898.29 27498.55 231
Regformer-197.27 10797.16 10897.61 12297.21 28793.86 16694.85 23798.04 23097.62 6098.03 12297.50 18895.34 13199.63 14396.52 6899.31 17899.35 98
Regformer-297.41 9897.24 10397.93 9897.21 28794.72 13294.85 23798.27 19597.74 5198.11 11097.50 18895.58 12299.69 11796.57 6799.31 17899.37 95
Fast-Effi-MVS+-dtu96.44 15696.12 16497.39 14897.18 28994.39 14595.46 19398.73 12996.03 12594.72 27494.92 31596.28 9899.69 11793.81 20597.98 28498.09 268
OpenMVScopyleft94.22 895.48 19395.20 19396.32 20897.16 29091.96 21797.74 7098.84 9987.26 30794.36 28598.01 13693.95 17499.67 13090.70 27098.75 24897.35 308
BH-w/o92.14 29391.94 28792.73 32297.13 29185.30 32092.46 31395.64 30689.33 28894.21 28892.74 34489.60 25398.24 34981.68 35394.66 34994.66 354
MG-MVS94.08 25694.00 24994.32 29097.09 29285.89 31493.19 30095.96 30192.52 24994.93 27297.51 18789.54 25598.77 31187.52 32197.71 29698.31 252
thisisatest051590.43 31089.18 32294.17 29597.07 29385.44 31889.75 35487.58 36488.28 30093.69 30791.72 35565.27 36699.58 16290.59 27398.67 25497.50 303
MVS-HIRNet88.40 32890.20 31382.99 35397.01 29460.04 37793.11 30185.61 36984.45 33988.72 35999.09 3684.72 29598.23 35082.52 35296.59 32990.69 368
GA-MVS92.83 28192.15 28694.87 26996.97 29587.27 29890.03 34896.12 29691.83 26194.05 29494.57 31976.01 33898.97 29792.46 23097.34 31398.36 248
test_yl94.40 24294.00 24995.59 23896.95 29689.52 25394.75 24295.55 31196.18 11696.79 20196.14 28081.09 31199.18 26590.75 26597.77 29098.07 271
DCV-MVSNet94.40 24294.00 24995.59 23896.95 29689.52 25394.75 24295.55 31196.18 11696.79 20196.14 28081.09 31199.18 26590.75 26597.77 29098.07 271
MVS_Test96.27 16196.79 13194.73 27696.94 29886.63 30696.18 15398.33 19194.94 17696.07 23898.28 9995.25 13599.26 25697.21 4897.90 28898.30 254
MAR-MVS94.21 25093.03 26797.76 10996.94 29897.44 3596.97 11697.15 27287.89 30592.00 34092.73 34592.14 21699.12 27483.92 34697.51 30796.73 329
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
Effi-MVS+-dtu96.81 13396.09 16698.99 1396.90 30098.69 296.42 13898.09 22195.86 13695.15 26595.54 30194.26 16699.81 3294.06 19498.51 26798.47 236
mvs-test196.20 16495.50 18998.32 6596.90 30098.16 595.07 22498.09 22195.86 13693.63 30894.32 32794.26 16699.71 10094.06 19497.27 31697.07 312
MS-PatchMatch94.83 22094.91 20894.57 28396.81 30287.10 30094.23 25997.34 26688.74 29597.14 17697.11 22091.94 22398.23 35092.99 22397.92 28698.37 243
UGNet96.81 13396.56 14297.58 12396.64 30393.84 16897.75 6997.12 27496.47 10393.62 30998.88 5193.22 18999.53 17895.61 11499.69 5899.36 96
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
API-MVS95.09 21195.01 20395.31 25196.61 30494.02 16096.83 12097.18 27195.60 14995.79 24994.33 32694.54 15998.37 34585.70 33298.52 26593.52 359
PAPM87.64 33485.84 33993.04 31496.54 30584.99 32788.42 36095.57 31079.52 35683.82 36893.05 34080.57 31498.41 34062.29 37292.79 35695.71 345
FMVSNet395.26 20494.94 20496.22 21496.53 30690.06 24395.99 16497.66 25194.11 20597.99 12597.91 14980.22 31699.63 14394.60 17099.44 13398.96 176
HY-MVS91.43 1592.58 28491.81 29094.90 26796.49 30788.87 26497.31 9594.62 31985.92 32090.50 34996.84 23885.05 29199.40 21983.77 34995.78 34096.43 336
TR-MVS92.54 28592.20 28593.57 30396.49 30786.66 30593.51 28994.73 31889.96 28394.95 27093.87 33090.24 24898.61 32781.18 35594.88 34795.45 350
ET-MVSNet_ETH3D91.12 30489.67 31695.47 24696.41 30989.15 26191.54 32790.23 35989.07 28986.78 36792.84 34269.39 36299.44 20594.16 19096.61 32897.82 290
CANet95.86 18095.65 18396.49 19996.41 30990.82 23494.36 25298.41 17994.94 17692.62 33596.73 24792.68 20199.71 10095.12 15099.60 7998.94 179
mvs_anonymous95.36 19996.07 16893.21 31196.29 31181.56 34994.60 24697.66 25193.30 22796.95 19598.91 4993.03 19499.38 22796.60 6497.30 31598.69 218
MVS_030495.50 19095.05 20296.84 17896.28 31293.12 19097.00 11496.16 29595.03 17389.22 35797.70 17290.16 24999.48 19294.51 17599.34 16797.93 285
SCA93.38 27393.52 25992.96 31896.24 31381.40 35093.24 29894.00 32491.58 26594.57 27896.97 23087.94 27199.42 20889.47 29397.66 30198.06 275
LS3D97.77 7397.50 8798.57 4896.24 31397.58 2598.45 2698.85 9498.58 2797.51 15497.94 14595.74 11699.63 14395.19 14098.97 22298.51 233
new_pmnet92.34 28991.69 29294.32 29096.23 31589.16 26092.27 31792.88 33584.39 34095.29 26296.35 27085.66 28896.74 36684.53 34497.56 30497.05 313
MVEpermissive73.61 2286.48 33685.92 33888.18 35096.23 31585.28 32281.78 36875.79 37486.01 31882.53 37091.88 35392.74 19987.47 37371.42 37094.86 34891.78 364
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
c3_l95.20 20595.32 19194.83 27296.19 31786.43 30991.83 32498.35 19093.47 22197.36 16797.26 21288.69 26499.28 25395.41 13199.36 15998.78 207
DSMNet-mixed92.19 29291.83 28993.25 30996.18 31883.68 33996.27 14693.68 32776.97 36592.54 33699.18 2789.20 26398.55 33383.88 34798.60 26397.51 302
miper_lstm_enhance94.81 22294.80 21594.85 27096.16 31986.45 30891.14 33798.20 20493.49 22097.03 18897.37 20484.97 29399.26 25695.28 13599.56 8998.83 201
our_test_394.20 25294.58 22893.07 31396.16 31981.20 35190.42 34596.84 28390.72 27597.14 17697.13 21790.47 24199.11 27794.04 19898.25 27598.91 188
ppachtmachnet_test94.49 24194.84 21193.46 30596.16 31982.10 34590.59 34397.48 26390.53 27797.01 19097.59 18091.01 23499.36 23293.97 20199.18 19798.94 179
Patchmatch-test93.60 26893.25 26494.63 27896.14 32287.47 29396.04 16094.50 32193.57 21896.47 21896.97 23076.50 33498.61 32790.67 27198.41 27097.81 292
wuyk23d93.25 27695.20 19387.40 35296.07 32395.38 10497.04 11294.97 31695.33 15999.70 598.11 12198.14 1391.94 37077.76 36399.68 6074.89 370
eth_miper_zixun_eth94.89 21894.93 20694.75 27595.99 32486.12 31291.35 33098.49 16893.40 22297.12 17897.25 21386.87 28399.35 23595.08 15298.82 24298.78 207
CANet_DTU94.65 23394.21 24295.96 22395.90 32589.68 25093.92 27697.83 24193.19 23190.12 35295.64 29888.52 26599.57 16893.27 21899.47 12598.62 224
DIV-MVS_self_test94.73 22494.64 22195.01 26295.86 32687.00 30191.33 33198.08 22393.34 22597.10 18097.34 20684.02 29999.31 24495.15 14699.55 9598.72 215
cl____94.73 22494.64 22195.01 26295.85 32787.00 30191.33 33198.08 22393.34 22597.10 18097.33 20784.01 30099.30 24795.14 14799.56 8998.71 217
MVSTER94.21 25093.93 25395.05 26195.83 32886.46 30795.18 21797.65 25392.41 25397.94 13298.00 13872.39 35499.58 16296.36 7599.56 8999.12 151
FMVSNet593.39 27292.35 28396.50 19895.83 32890.81 23697.31 9598.27 19592.74 24896.27 22998.28 9962.23 36999.67 13090.86 26099.36 15999.03 168
miper_ehance_all_eth94.69 22994.70 21894.64 27795.77 33086.22 31191.32 33398.24 19991.67 26297.05 18696.65 25288.39 26899.22 26394.88 15898.34 27198.49 235
PVSNet_081.89 2184.49 33783.21 34088.34 34995.76 33174.97 37183.49 36592.70 33978.47 36087.94 36286.90 36983.38 30296.63 36773.44 36766.86 37393.40 360
PAPR92.22 29191.27 29795.07 26095.73 33288.81 26691.97 32297.87 23685.80 32290.91 34592.73 34591.16 23298.33 34779.48 35795.76 34198.08 269
baseline289.65 32088.44 32793.25 30995.62 33382.71 34193.82 27985.94 36888.89 29387.35 36592.54 34771.23 35799.33 24086.01 32994.60 35197.72 294
CHOSEN 280x42089.98 31589.19 32192.37 32895.60 33481.13 35286.22 36397.09 27581.44 34987.44 36493.15 33373.99 34399.47 19588.69 30499.07 21496.52 335
ADS-MVSNet291.47 30290.51 31094.36 28995.51 33585.63 31595.05 22795.70 30583.46 34192.69 33096.84 23879.15 32099.41 21785.66 33490.52 36098.04 279
ADS-MVSNet90.95 30890.26 31293.04 31495.51 33582.37 34495.05 22793.41 33083.46 34192.69 33096.84 23879.15 32098.70 31885.66 33490.52 36098.04 279
CR-MVSNet93.29 27592.79 27394.78 27495.44 33788.15 27896.18 15397.20 26984.94 33594.10 29198.57 7077.67 32699.39 22495.17 14295.81 33796.81 326
RPMNet94.68 23194.60 22594.90 26795.44 33788.15 27896.18 15398.86 9097.43 6894.10 29198.49 7779.40 31799.76 5895.69 10695.81 33796.81 326
131492.38 28892.30 28492.64 32395.42 33985.15 32495.86 17296.97 28085.40 32990.62 34693.06 33991.12 23397.80 35786.74 32695.49 34494.97 353
RRT_test8_iter0592.46 28692.52 28292.29 33095.33 34077.43 36295.73 17898.55 16394.41 19397.46 16397.72 17157.44 37299.74 7596.92 5999.14 20099.69 20
tpm91.08 30690.85 30491.75 33395.33 34078.09 35895.03 22991.27 35088.75 29493.53 31397.40 19671.24 35699.30 24791.25 25293.87 35397.87 287
IB-MVS85.98 2088.63 32686.95 33593.68 30195.12 34284.82 33090.85 34190.17 36087.55 30688.48 36091.34 35958.01 37199.59 16087.24 32493.80 35496.63 333
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
PatchT93.75 26293.57 25894.29 29295.05 34387.32 29796.05 15992.98 33497.54 6594.25 28798.72 6075.79 33999.24 25995.92 9695.81 33796.32 337
tpm288.47 32787.69 33190.79 33994.98 34477.34 36395.09 22191.83 34477.51 36489.40 35596.41 26467.83 36498.73 31583.58 35192.60 35896.29 338
Patchmtry95.03 21494.59 22796.33 20794.83 34590.82 23496.38 14197.20 26996.59 9597.49 15798.57 7077.67 32699.38 22792.95 22599.62 6998.80 204
MVS90.02 31389.20 32092.47 32694.71 34686.90 30395.86 17296.74 28964.72 37090.62 34692.77 34392.54 20898.39 34279.30 35895.56 34392.12 363
CostFormer89.75 31989.25 31791.26 33794.69 34778.00 36095.32 20691.98 34381.50 34890.55 34896.96 23271.06 35898.89 30188.59 30692.63 35796.87 320
PatchmatchNetpermissive91.98 29691.87 28892.30 32994.60 34879.71 35595.12 21893.59 32989.52 28693.61 31097.02 22777.94 32499.18 26590.84 26194.57 35298.01 282
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat188.01 33187.33 33290.05 34494.48 34976.28 36794.47 25194.35 32373.84 36989.26 35695.61 30073.64 34798.30 34884.13 34586.20 36895.57 349
MDTV_nov1_ep1391.28 29694.31 35073.51 37294.80 23993.16 33286.75 31593.45 31797.40 19676.37 33598.55 33388.85 30196.43 330
cl2293.25 27692.84 27294.46 28694.30 35186.00 31391.09 33996.64 29290.74 27495.79 24996.31 27178.24 32398.77 31194.15 19198.34 27198.62 224
cascas91.89 29791.35 29593.51 30494.27 35285.60 31688.86 35898.61 15679.32 35792.16 33991.44 35889.22 26298.12 35390.80 26397.47 31096.82 325
test-LLR89.97 31689.90 31490.16 34294.24 35374.98 36989.89 35089.06 36192.02 25689.97 35390.77 36373.92 34598.57 33091.88 23897.36 31196.92 317
test-mter87.92 33287.17 33390.16 34294.24 35374.98 36989.89 35089.06 36186.44 31689.97 35390.77 36354.96 37998.57 33091.88 23897.36 31196.92 317
pmmvs390.00 31488.90 32493.32 30694.20 35585.34 31991.25 33492.56 34078.59 35993.82 29995.17 30767.36 36598.69 31989.08 29998.03 28395.92 341
tpmrst90.31 31190.61 30989.41 34594.06 35672.37 37495.06 22693.69 32588.01 30292.32 33896.86 23677.45 32898.82 30691.04 25587.01 36797.04 314
test0.0.03 190.11 31289.21 31992.83 32093.89 35786.87 30491.74 32588.74 36392.02 25694.71 27591.14 36173.92 34594.48 36983.75 35092.94 35597.16 310
JIA-IIPM91.79 29890.69 30795.11 25893.80 35890.98 23194.16 26391.78 34596.38 10490.30 35199.30 1872.02 35598.90 29988.28 31090.17 36295.45 350
miper_enhance_ethall93.14 27892.78 27594.20 29393.65 35985.29 32189.97 34997.85 23785.05 33296.15 23794.56 32085.74 28799.14 27293.74 20798.34 27198.17 267
TESTMET0.1,187.20 33586.57 33789.07 34693.62 36072.84 37389.89 35087.01 36785.46 32789.12 35890.20 36556.00 37797.72 35890.91 25996.92 31896.64 331
CMPMVSbinary73.10 2392.74 28291.39 29496.77 18293.57 36194.67 13794.21 26197.67 24980.36 35493.61 31096.60 25482.85 30397.35 36084.86 34298.78 24598.29 257
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
RRT_MVS94.90 21794.07 24697.39 14893.18 36293.21 18895.26 21197.49 26193.94 21098.25 9497.85 15572.96 35399.84 2597.90 2299.78 4199.14 143
DWT-MVSNet_test87.92 33286.77 33691.39 33593.18 36278.62 35795.10 21991.42 34785.58 32488.00 36188.73 36760.60 37098.90 29990.60 27287.70 36696.65 330
E-PMN89.52 32189.78 31588.73 34793.14 36477.61 36183.26 36692.02 34294.82 18193.71 30593.11 33475.31 34096.81 36385.81 33196.81 32391.77 365
PMMVS92.39 28791.08 29996.30 21093.12 36592.81 19790.58 34495.96 30179.17 35891.85 34292.27 34990.29 24798.66 32489.85 28896.68 32797.43 304
EMVS89.06 32389.22 31888.61 34893.00 36677.34 36382.91 36790.92 35294.64 18692.63 33491.81 35476.30 33697.02 36183.83 34896.90 32091.48 366
dp88.08 33088.05 32888.16 35192.85 36768.81 37694.17 26292.88 33585.47 32691.38 34496.14 28068.87 36398.81 30886.88 32583.80 37096.87 320
gg-mvs-nofinetune88.28 32986.96 33492.23 33192.84 36884.44 33398.19 4574.60 37599.08 1087.01 36699.47 856.93 37398.23 35078.91 35995.61 34294.01 357
tpmvs90.79 30990.87 30390.57 34192.75 36976.30 36695.79 17793.64 32891.04 27391.91 34196.26 27277.19 33298.86 30589.38 29589.85 36396.56 334
EPMVS89.26 32288.55 32691.39 33592.36 37079.11 35695.65 18779.86 37388.60 29693.12 32396.53 25870.73 36098.10 35490.75 26589.32 36496.98 315
gm-plane-assit91.79 37171.40 37581.67 34690.11 36698.99 29184.86 342
GG-mvs-BLEND90.60 34091.00 37284.21 33698.23 3972.63 37882.76 36984.11 37056.14 37696.79 36472.20 36892.09 35990.78 367
DeepMVS_CXcopyleft77.17 35490.94 37385.28 32274.08 37752.51 37180.87 37288.03 36875.25 34170.63 37459.23 37384.94 36975.62 369
EPNet_dtu91.39 30390.75 30693.31 30790.48 37482.61 34294.80 23992.88 33593.39 22381.74 37194.90 31681.36 30999.11 27788.28 31098.87 23598.21 263
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
KD-MVS_2432*160088.93 32487.74 32992.49 32488.04 37581.99 34689.63 35595.62 30791.35 26795.06 26693.11 33456.58 37498.63 32585.19 33895.07 34596.85 322
miper_refine_blended88.93 32487.74 32992.49 32488.04 37581.99 34689.63 35595.62 30791.35 26795.06 26693.11 33456.58 37498.63 32585.19 33895.07 34596.85 322
EPNet93.72 26392.62 28097.03 16887.61 37792.25 20696.27 14691.28 34996.74 9087.65 36397.39 20085.00 29299.64 14192.14 23299.48 12399.20 132
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_method66.88 33966.13 34269.11 35562.68 37825.73 38049.76 36996.04 29814.32 37364.27 37491.69 35673.45 35088.05 37276.06 36566.94 37293.54 358
tmp_tt57.23 34062.50 34341.44 35634.77 37949.21 37983.93 36460.22 38015.31 37271.11 37379.37 37170.09 36144.86 37564.76 37182.93 37130.25 371
test12312.59 34215.49 3453.87 3576.07 3802.55 38190.75 3422.59 3822.52 3755.20 37713.02 3744.96 3801.85 3775.20 3749.09 3747.23 372
testmvs12.33 34315.23 3463.64 3585.77 3812.23 38288.99 3573.62 3812.30 3765.29 37613.09 3734.52 3811.95 3765.16 3758.32 3756.75 373
test_blank0.00 3460.00 3490.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 3770.00 3820.00 3780.00 3760.00 3760.00 374
eth-test20.00 382
eth-test0.00 382
uanet_test0.00 3460.00 3490.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 3770.00 3820.00 3780.00 3760.00 3760.00 374
cdsmvs_eth3d_5k24.22 34132.30 3440.00 3590.00 3820.00 3830.00 37098.10 2200.00 3770.00 37895.06 31197.54 290.00 3780.00 3760.00 3760.00 374
pcd_1.5k_mvsjas7.98 34410.65 3470.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 37795.82 1080.00 3780.00 3760.00 3760.00 374
sosnet-low-res0.00 3460.00 3490.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 3770.00 3820.00 3780.00 3760.00 3760.00 374
sosnet0.00 3460.00 3490.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 3770.00 3820.00 3780.00 3760.00 3760.00 374
uncertanet0.00 3460.00 3490.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 3770.00 3820.00 3780.00 3760.00 3760.00 374
Regformer0.00 3460.00 3490.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 3770.00 3820.00 3780.00 3760.00 3760.00 374
ab-mvs-re7.91 34510.55 3480.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 37894.94 3130.00 3820.00 3780.00 3760.00 3760.00 374
uanet0.00 3460.00 3490.00 3590.00 3820.00 3830.00 3700.00 3830.00 3770.00 3780.00 3770.00 3820.00 3780.00 3760.00 3760.00 374
PC_three_145287.24 30898.37 7697.44 19397.00 5496.78 36592.01 23399.25 18799.21 129
test_241102_TWO98.83 10696.11 11898.62 5298.24 10596.92 6299.72 8695.44 12599.49 11999.49 53
test_0728_THIRD96.62 9298.40 7398.28 9997.10 4599.71 10095.70 10499.62 6999.58 28
GSMVS98.06 275
sam_mvs177.80 32598.06 275
sam_mvs77.38 329
MTGPAbinary98.73 129
test_post194.98 23110.37 37676.21 33799.04 28589.47 293
test_post10.87 37576.83 33399.07 282
patchmatchnet-post96.84 23877.36 33099.42 208
MTMP96.55 13374.60 375
test9_res91.29 24998.89 23499.00 171
agg_prior290.34 28298.90 23199.10 159
test_prior495.38 10493.61 287
test_prior293.33 29694.21 20194.02 29596.25 27393.64 18191.90 23698.96 223
旧先验293.35 29577.95 36395.77 25398.67 32390.74 268
新几何293.43 290
无先验93.20 29997.91 23380.78 35199.40 21987.71 31497.94 284
原ACMM292.82 304
testdata299.46 19887.84 313
segment_acmp95.34 131
testdata192.77 30593.78 214
plane_prior598.75 12599.46 19892.59 22899.20 19299.28 115
plane_prior496.77 244
plane_prior394.51 14195.29 16296.16 235
plane_prior296.50 13596.36 106
plane_prior94.29 14995.42 19694.31 19898.93 229
n20.00 383
nn0.00 383
door-mid98.17 210
test1198.08 223
door97.81 242
HQP5-MVS92.47 202
BP-MVS90.51 277
HQP4-MVS92.87 32699.23 26199.06 164
HQP3-MVS98.43 17498.74 249
HQP2-MVS90.33 243
MDTV_nov1_ep13_2view57.28 37894.89 23480.59 35294.02 29578.66 32285.50 33697.82 290
ACMMP++_ref99.52 106
ACMMP++99.55 95
Test By Simon94.51 160