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

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

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

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

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




Method Infoallhigh-res
multi-view
indooroutdoorbotani.boulde.bridgedoorexhibi.lectur.living.loungeobserv.old co.statueterrac.
sort bysort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort by
CS-MVS99.50 3199.48 2299.54 12599.76 8299.42 11899.90 199.55 10098.56 11899.78 8199.70 21398.65 7499.79 24299.65 4199.78 13499.41 261
mmtdpeth96.95 37896.71 37797.67 39899.33 28894.90 42599.89 299.28 34498.15 17599.72 10298.57 43486.56 43699.90 14899.82 2989.02 46198.20 429
SPE-MVS-test99.49 3399.48 2299.54 12599.78 7099.30 13899.89 299.58 7898.56 11899.73 9799.69 22498.55 8199.82 22499.69 3599.85 9499.48 240
MVSFormer99.17 10999.12 9799.29 20499.51 22698.94 19799.88 499.46 23597.55 27499.80 7499.65 24597.39 12599.28 36399.03 13199.85 9499.65 172
test_djsdf98.67 21198.57 21298.98 24298.70 41898.91 20499.88 499.46 23597.55 27499.22 25599.88 5495.73 22099.28 36399.03 13197.62 32298.75 332
OurMVSNet-221017-097.88 29497.77 28598.19 35798.71 41796.53 37899.88 499.00 38797.79 24498.78 33899.94 691.68 37399.35 35397.21 34196.99 35898.69 349
EC-MVSNet99.44 5099.39 4099.58 11699.56 20599.49 10999.88 499.58 7898.38 13799.73 9799.69 22498.20 10399.70 28399.64 4399.82 11799.54 216
DVP-MVS++99.59 1599.50 1999.88 1599.51 22699.88 1099.87 899.51 15398.99 6999.88 4399.81 13199.27 799.96 4198.85 16399.80 12599.81 79
FOURS199.91 199.93 199.87 899.56 9099.10 4899.81 69
K. test v397.10 37596.79 37598.01 37098.72 41596.33 38599.87 897.05 46597.59 26896.16 44299.80 14988.71 41399.04 40796.69 37396.55 36598.65 373
FC-MVSNet-test98.75 20498.62 20599.15 22699.08 35799.45 11599.86 1199.60 6798.23 16598.70 35099.82 11696.80 16299.22 37899.07 12696.38 36898.79 322
v7n97.87 29697.52 31498.92 25398.76 41198.58 25099.84 1299.46 23596.20 39198.91 31599.70 21394.89 25899.44 33396.03 39093.89 42698.75 332
DTE-MVSNet97.51 35197.19 36098.46 32898.63 42598.13 28399.84 1299.48 20196.68 35397.97 40499.67 23892.92 33698.56 44196.88 36692.60 44498.70 345
3Dnovator97.25 999.24 9799.05 11299.81 6099.12 34699.66 7199.84 1299.74 1399.09 5598.92 31499.90 3495.94 20799.98 2098.95 14399.92 3999.79 92
FIs98.78 19998.63 20099.23 21699.18 33099.54 9899.83 1599.59 7398.28 15098.79 33799.81 13196.75 16599.37 34699.08 12596.38 36898.78 324
MGCFI-Net99.01 16398.85 17199.50 14999.42 26099.26 14499.82 1699.48 20198.60 11599.28 23798.81 42397.04 14799.76 25499.29 9297.87 31199.47 246
test_fmvs392.10 43191.77 43493.08 44696.19 46386.25 46699.82 1698.62 43996.65 35695.19 45096.90 46655.05 48095.93 47396.63 37890.92 45397.06 462
jajsoiax98.43 22598.28 23298.88 26798.60 42998.43 26999.82 1699.53 12598.19 17098.63 36299.80 14993.22 33199.44 33399.22 10197.50 33498.77 328
OpenMVScopyleft96.50 1698.47 22298.12 24399.52 13999.04 36599.53 10199.82 1699.72 1494.56 43198.08 39799.88 5494.73 27199.98 2097.47 32599.76 14099.06 303
SDMVSNet99.11 13898.90 15799.75 7799.81 5799.59 8899.81 2099.65 3998.78 9899.64 14199.88 5494.56 28399.93 11099.67 3798.26 28999.72 134
nrg03098.64 21598.42 22299.28 20899.05 36399.69 6399.81 2099.46 23598.04 21399.01 29799.82 11696.69 16799.38 34399.34 8194.59 41398.78 324
HPM-MVScopyleft99.42 5599.28 6999.83 5699.90 499.72 5699.81 2099.54 10997.59 26899.68 11699.63 25798.91 3999.94 9298.58 20799.91 4699.84 53
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPP-MVSNet99.13 12498.99 13599.53 13399.65 15799.06 17199.81 2099.33 31997.43 29199.60 15699.88 5497.14 13899.84 19699.13 11698.94 23899.69 151
3Dnovator+97.12 1399.18 10498.97 13999.82 5799.17 33899.68 6499.81 2099.51 15399.20 3398.72 34399.89 4395.68 22299.97 2998.86 16199.86 8799.81 79
sasdasda99.02 15998.86 16899.51 14499.42 26099.32 13199.80 2599.48 20198.63 11099.31 22998.81 42397.09 14399.75 25799.27 9697.90 30899.47 246
FA-MVS(test-final)98.75 20498.53 21699.41 17799.55 20999.05 17399.80 2599.01 38696.59 36699.58 16099.59 27195.39 23299.90 14897.78 29199.49 17799.28 278
GeoE98.85 19098.62 20599.53 13399.61 18699.08 16899.80 2599.51 15397.10 32399.31 22999.78 17395.23 24399.77 25098.21 24799.03 23299.75 110
canonicalmvs99.02 15998.86 16899.51 14499.42 26099.32 13199.80 2599.48 20198.63 11099.31 22998.81 42397.09 14399.75 25799.27 9697.90 30899.47 246
v897.95 28597.63 30498.93 25198.95 38098.81 22899.80 2599.41 27196.03 40599.10 28099.42 32994.92 25599.30 36196.94 36194.08 42398.66 371
Vis-MVSNet (Re-imp)98.87 17898.72 18699.31 19699.71 11798.88 21099.80 2599.44 25597.91 22699.36 21999.78 17395.49 22999.43 33797.91 27699.11 21899.62 187
Anonymous2024052196.20 39495.89 39797.13 41697.72 45094.96 42499.79 3199.29 34293.01 44697.20 42799.03 40289.69 40398.36 44591.16 45296.13 37498.07 436
PS-MVSNAJss98.92 17298.92 15298.90 25998.78 40498.53 25499.78 3299.54 10998.07 19999.00 30199.76 18699.01 2099.37 34699.13 11697.23 35198.81 321
PEN-MVS97.76 31797.44 33098.72 29398.77 40998.54 25399.78 3299.51 15397.06 32798.29 38799.64 25192.63 34998.89 43298.09 26093.16 43698.72 338
anonymousdsp98.44 22498.28 23298.94 24998.50 43598.96 18799.77 3499.50 17697.07 32598.87 32399.77 18294.76 26899.28 36398.66 19397.60 32398.57 399
SixPastTwentyTwo97.50 35297.33 34898.03 36798.65 42396.23 39099.77 3498.68 43597.14 31697.90 40799.93 1090.45 39299.18 38697.00 35596.43 36798.67 362
QAPM98.67 21198.30 23199.80 6499.20 32499.67 6899.77 3499.72 1494.74 42898.73 34299.90 3495.78 21899.98 2096.96 35999.88 7699.76 107
SSC-MVS92.73 43093.73 42489.72 45695.02 47481.38 47699.76 3799.23 35494.87 42592.80 46398.93 41594.71 27391.37 48074.49 47993.80 42796.42 466
test_vis3_rt87.04 43885.81 44190.73 45393.99 47781.96 47499.76 3790.23 48892.81 44981.35 47691.56 47640.06 48499.07 40494.27 42488.23 46391.15 476
dcpmvs_299.23 9899.58 998.16 35999.83 4794.68 43099.76 3799.52 13199.07 5899.98 1399.88 5498.56 8099.93 11099.67 3799.98 499.87 40
RRT-MVS98.91 17398.75 18299.39 18399.46 25098.61 24899.76 3799.50 17698.06 20399.81 6999.88 5493.91 31599.94 9299.11 11999.27 19499.61 189
HPM-MVS_fast99.51 2999.40 3899.85 4399.91 199.79 4199.76 3799.56 9097.72 25399.76 9199.75 19199.13 1499.92 12399.07 12699.92 3999.85 46
lecture99.60 1499.50 1999.89 1199.89 899.90 399.75 4299.59 7399.06 6199.88 4399.85 8298.41 9399.96 4199.28 9399.84 10299.83 63
MVSMamba_PlusPlus99.46 4299.41 3799.64 10199.68 13299.50 10899.75 4299.50 17698.27 15299.87 4999.92 1898.09 10899.94 9299.65 4199.95 2399.47 246
v1097.85 29997.52 31498.86 27498.99 37398.67 23999.75 4299.41 27195.70 40998.98 30499.41 33394.75 26999.23 37396.01 39294.63 41298.67 362
APDe-MVScopyleft99.66 699.57 1099.92 199.77 7899.89 699.75 4299.56 9099.02 6299.88 4399.85 8299.18 1299.96 4199.22 10199.92 3999.90 25
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
IS-MVSNet99.05 15598.87 16599.57 12099.73 10799.32 13199.75 4299.20 36098.02 21899.56 16499.86 7596.54 17799.67 29198.09 26099.13 21199.73 124
test_vis1_n97.92 28997.44 33099.34 18899.53 21798.08 28699.74 4799.49 18999.15 38100.00 199.94 679.51 46899.98 2099.88 2699.76 14099.97 4
test_fmvs1_n98.41 22898.14 24099.21 21799.82 5397.71 31299.74 4799.49 18999.32 2999.99 299.95 385.32 44699.97 2999.82 2999.84 10299.96 7
balanced_conf0399.46 4299.39 4099.67 9099.55 20999.58 9399.74 4799.51 15398.42 13499.87 4999.84 9798.05 11199.91 13599.58 4799.94 3199.52 223
tttt051798.42 22698.14 24099.28 20899.66 14898.38 27299.74 4796.85 46797.68 25999.79 7699.74 19691.39 38199.89 16398.83 16999.56 17099.57 210
WB-MVS93.10 42894.10 42090.12 45595.51 47181.88 47599.73 5199.27 34795.05 42093.09 46298.91 41994.70 27491.89 47976.62 47794.02 42596.58 465
test_fmvs297.25 36997.30 35197.09 41899.43 25893.31 45199.73 5198.87 40998.83 8899.28 23799.80 14984.45 45199.66 29497.88 27897.45 33998.30 422
SD_040397.55 34697.53 31397.62 40099.61 18693.64 44899.72 5399.44 25598.03 21598.62 36599.39 34196.06 19999.57 31587.88 46599.01 23599.66 166
MonoMVSNet98.38 23298.47 22098.12 36498.59 43196.19 39299.72 5398.79 42097.89 22899.44 19199.52 29996.13 19698.90 43198.64 19597.54 32999.28 278
baseline99.15 11599.02 12699.53 13399.66 14899.14 16099.72 5399.48 20198.35 14299.42 19799.84 9796.07 19899.79 24299.51 5699.14 20899.67 161
RPSCF98.22 24398.62 20596.99 42099.82 5391.58 46099.72 5399.44 25596.61 36199.66 12799.89 4395.92 20899.82 22497.46 32699.10 22599.57 210
CSCG99.32 7999.32 5499.32 19499.85 3198.29 27499.71 5799.66 3298.11 19099.41 20299.80 14998.37 9699.96 4198.99 13599.96 1799.72 134
dmvs_re98.08 26198.16 23797.85 38599.55 20994.67 43199.70 5898.92 39798.15 17599.06 29199.35 35393.67 32399.25 37097.77 29497.25 35099.64 179
WR-MVS_H98.13 25497.87 27498.90 25999.02 36798.84 22099.70 5899.59 7397.27 30598.40 37999.19 38595.53 22799.23 37398.34 23793.78 42898.61 393
mvsmamba99.06 15198.96 14399.36 18599.47 24898.64 24399.70 5899.05 38197.61 26799.65 13699.83 10396.54 17799.92 12399.19 10599.62 16599.51 232
LTVRE_ROB97.16 1298.02 27397.90 26998.40 33899.23 31796.80 36799.70 5899.60 6797.12 31998.18 39499.70 21391.73 37299.72 27098.39 23097.45 33998.68 354
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
MED-MVS test99.87 2199.88 1399.81 3399.69 6299.87 699.34 2699.90 3499.83 10399.95 7698.83 16999.89 6899.83 63
MED-MVS99.66 699.60 899.87 2199.88 1399.81 3399.69 6299.87 699.18 3499.90 3499.83 10399.30 499.95 7698.83 16999.89 6899.83 63
TestfortrainingZip a99.73 199.67 199.92 199.88 1399.91 299.69 6299.87 699.34 2699.90 3499.83 10399.30 499.95 7699.32 8499.89 6899.90 25
TestfortrainingZip99.69 62
test_f91.90 43291.26 43693.84 44295.52 47085.92 46799.69 6298.53 44395.31 41493.87 45796.37 46955.33 47998.27 44695.70 39890.98 45297.32 461
XVS99.53 2799.42 3299.87 2199.85 3199.83 2299.69 6299.68 2498.98 7299.37 21399.74 19698.81 4999.94 9298.79 17799.86 8799.84 53
X-MVStestdata96.55 38695.45 40599.87 2199.85 3199.83 2299.69 6299.68 2498.98 7299.37 21364.01 48598.81 4999.94 9298.79 17799.86 8799.84 53
V4298.06 26397.79 28098.86 27498.98 37698.84 22099.69 6299.34 31196.53 36899.30 23399.37 34794.67 27699.32 35897.57 31594.66 41198.42 414
mPP-MVS99.44 5099.30 6299.86 3499.88 1399.79 4199.69 6299.48 20198.12 18899.50 17899.75 19198.78 5399.97 2998.57 21099.89 6899.83 63
CP-MVS99.45 4699.32 5499.85 4399.83 4799.75 5199.69 6299.52 13198.07 19999.53 17399.63 25798.93 3899.97 2998.74 18199.91 4699.83 63
FE-MVS98.48 22198.17 23699.40 17899.54 21698.96 18799.68 7298.81 41695.54 41199.62 14899.70 21393.82 31899.93 11097.35 33599.46 17899.32 275
PS-CasMVS97.93 28697.59 30898.95 24798.99 37399.06 17199.68 7299.52 13197.13 31798.31 38499.68 23292.44 35899.05 40698.51 21894.08 42398.75 332
Vis-MVSNetpermissive99.12 13298.97 13999.56 12299.78 7099.10 16499.68 7299.66 3298.49 12599.86 5399.87 6794.77 26799.84 19699.19 10599.41 18299.74 115
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS99.12 13298.92 15299.70 8799.67 13599.40 12199.67 7599.63 4698.73 10299.94 2899.81 13194.54 28699.96 4198.40 22999.93 3399.74 115
BP-MVS199.12 13298.94 14999.65 9599.51 22699.30 13899.67 7598.92 39798.48 12699.84 5699.69 22494.96 25099.92 12399.62 4499.79 13299.71 145
test_vis1_n_192098.63 21698.40 22499.31 19699.86 2597.94 29999.67 7599.62 5199.43 1799.99 299.91 2687.29 431100.00 199.92 2499.92 3999.98 2
EIA-MVS99.18 10499.09 10499.45 16699.49 24099.18 15299.67 7599.53 12597.66 26299.40 20799.44 32598.10 10799.81 22998.94 14499.62 16599.35 270
MSP-MVS99.42 5599.27 7399.88 1599.89 899.80 3899.67 7599.50 17698.70 10699.77 8599.49 30998.21 10299.95 7698.46 22499.77 13799.88 35
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
MVS_Test99.10 14398.97 13999.48 15799.49 24099.14 16099.67 7599.34 31197.31 30299.58 16099.76 18697.65 12199.82 22498.87 15699.07 22999.46 251
CP-MVSNet98.09 25897.78 28399.01 23898.97 37899.24 14799.67 7599.46 23597.25 30798.48 37699.64 25193.79 31999.06 40598.63 19794.10 42298.74 336
MTAPA99.52 2899.39 4099.89 1199.90 499.86 1899.66 8299.47 22398.79 9599.68 11699.81 13198.43 8999.97 2998.88 15399.90 5799.83 63
HFP-MVS99.49 3399.37 4499.86 3499.87 2099.80 3899.66 8299.67 2798.15 17599.68 11699.69 22499.06 1899.96 4198.69 18999.87 7999.84 53
mvs_tets98.40 23198.23 23498.91 25798.67 42298.51 26099.66 8299.53 12598.19 17098.65 35999.81 13192.75 34099.44 33399.31 8697.48 33898.77 328
EU-MVSNet97.98 28098.03 25597.81 39198.72 41596.65 37499.66 8299.66 3298.09 19498.35 38299.82 11695.25 24198.01 45297.41 33195.30 39998.78 324
ACMMPR99.49 3399.36 4699.86 3499.87 2099.79 4199.66 8299.67 2798.15 17599.67 12299.69 22498.95 3299.96 4198.69 18999.87 7999.84 53
MP-MVScopyleft99.33 7799.15 9399.87 2199.88 1399.82 2899.66 8299.46 23598.09 19499.48 18299.74 19698.29 9999.96 4197.93 27599.87 7999.82 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NormalMVS99.27 8999.19 8899.52 13999.89 898.83 22399.65 8899.52 13199.10 4899.84 5699.76 18695.80 21699.99 499.30 8999.84 10299.74 115
SymmetryMVS99.15 11599.02 12699.52 13999.72 11198.83 22399.65 8899.34 31199.10 4899.84 5699.76 18695.80 21699.99 499.30 8998.72 25999.73 124
Elysia98.88 17598.65 19799.58 11699.58 19699.34 12799.65 8899.52 13198.26 15599.83 6499.87 6793.37 32699.90 14897.81 28899.91 4699.49 237
StellarMVS98.88 17598.65 19799.58 11699.58 19699.34 12799.65 8899.52 13198.26 15599.83 6499.87 6793.37 32699.90 14897.81 28899.91 4699.49 237
test_cas_vis1_n_192099.16 11199.01 13199.61 10999.81 5798.86 21799.65 8899.64 4299.39 2299.97 2599.94 693.20 33299.98 2099.55 5099.91 4699.99 1
region2R99.48 3799.35 4899.87 2199.88 1399.80 3899.65 8899.66 3298.13 18399.66 12799.68 23298.96 2799.96 4198.62 19899.87 7999.84 53
TranMVSNet+NR-MVSNet97.93 28697.66 29998.76 29098.78 40498.62 24699.65 8899.49 18997.76 24898.49 37599.60 26994.23 29998.97 42398.00 27192.90 43898.70 345
GDP-MVS99.08 14698.89 16199.64 10199.53 21799.34 12799.64 9599.48 20198.32 14799.77 8599.66 24395.14 24699.93 11098.97 14199.50 17699.64 179
ttmdpeth97.80 31397.63 30498.29 34898.77 40997.38 32399.64 9599.36 29998.78 9896.30 44099.58 27592.34 36199.39 34198.36 23595.58 39298.10 434
mvsany_test393.77 42593.45 42894.74 43995.78 46688.01 46599.64 9598.25 44898.28 15094.31 45497.97 45668.89 47298.51 44397.50 32190.37 45497.71 451
ZNCC-MVS99.47 4099.33 5299.87 2199.87 2099.81 3399.64 9599.67 2798.08 19899.55 17099.64 25198.91 3999.96 4198.72 18499.90 5799.82 72
tfpnnormal97.84 30397.47 32298.98 24299.20 32499.22 14999.64 9599.61 6096.32 38298.27 38899.70 21393.35 32899.44 33395.69 39995.40 39798.27 424
casdiffmvs_mvgpermissive99.15 11599.02 12699.55 12499.66 14899.09 16599.64 9599.56 9098.26 15599.45 18699.87 6796.03 20199.81 22999.54 5199.15 20799.73 124
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SR-MVS-dyc-post99.45 4699.31 6099.85 4399.76 8299.82 2899.63 10199.52 13198.38 13799.76 9199.82 11698.53 8299.95 7698.61 20199.81 12099.77 100
RE-MVS-def99.34 5099.76 8299.82 2899.63 10199.52 13198.38 13799.76 9199.82 11698.75 6098.61 20199.81 12099.77 100
TSAR-MVS + MP.99.58 1699.50 1999.81 6099.91 199.66 7199.63 10199.39 28198.91 8299.78 8199.85 8299.36 299.94 9298.84 16699.88 7699.82 72
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Anonymous2023120696.22 39296.03 39396.79 42897.31 45694.14 44099.63 10199.08 37596.17 39497.04 43199.06 39893.94 31297.76 45886.96 46995.06 40498.47 408
APD-MVS_3200maxsize99.48 3799.35 4899.85 4399.76 8299.83 2299.63 10199.54 10998.36 14199.79 7699.82 11698.86 4399.95 7698.62 19899.81 12099.78 98
test072699.85 3199.89 699.62 10699.50 17699.10 4899.86 5399.82 11698.94 34
EPNet98.86 18198.71 18899.30 20197.20 45898.18 27999.62 10698.91 40299.28 3198.63 36299.81 13195.96 20499.99 499.24 10099.72 14899.73 124
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t98.93 17198.67 19299.72 8699.85 3199.53 10199.62 10699.59 7392.65 45199.71 10999.78 17398.06 11099.90 14898.84 16699.91 4699.74 115
HY-MVS97.30 798.85 19098.64 19999.47 16399.42 26099.08 16899.62 10699.36 29997.39 29699.28 23799.68 23296.44 18399.92 12398.37 23398.22 29299.40 263
ACMMPcopyleft99.45 4699.32 5499.82 5799.89 899.67 6899.62 10699.69 2298.12 18899.63 14499.84 9798.73 6699.96 4198.55 21699.83 11399.81 79
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
DeepC-MVS98.35 299.30 8399.19 8899.64 10199.82 5399.23 14899.62 10699.55 10098.94 7899.63 14499.95 395.82 21499.94 9299.37 7599.97 999.73 124
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-Vis-set99.58 1699.56 1299.64 10199.78 7099.15 15999.61 11299.45 24699.01 6499.89 4099.82 11699.01 2099.92 12399.56 4999.95 2399.85 46
E699.15 11599.03 11799.50 14999.66 14898.90 20799.60 11399.53 12598.13 18399.72 10299.91 2696.31 19099.84 19699.30 8999.10 22599.76 107
reproduce_monomvs97.89 29397.87 27497.96 37699.51 22695.45 41099.60 11399.25 35099.17 3698.85 32999.49 30989.29 40799.64 30399.35 7696.31 37198.78 324
test250696.81 38296.65 37897.29 41399.74 10092.21 45899.60 11385.06 48999.13 4199.77 8599.93 1087.82 42999.85 18799.38 7499.38 18399.80 88
SED-MVS99.61 1099.52 1499.88 1599.84 3899.90 399.60 11399.48 20199.08 5699.91 3199.81 13199.20 999.96 4198.91 15099.85 9499.79 92
OPU-MVS99.64 10199.56 20599.72 5699.60 11399.70 21399.27 799.42 33998.24 24699.80 12599.79 92
GST-MVS99.40 6499.24 7899.85 4399.86 2599.79 4199.60 11399.67 2797.97 22199.63 14499.68 23298.52 8399.95 7698.38 23199.86 8799.81 79
EI-MVSNet-UG-set99.58 1699.57 1099.64 10199.78 7099.14 16099.60 11399.45 24699.01 6499.90 3499.83 10398.98 2699.93 11099.59 4599.95 2399.86 42
ACMH97.28 898.10 25797.99 25998.44 33399.41 26596.96 35599.60 11399.56 9098.09 19498.15 39599.91 2690.87 38999.70 28398.88 15397.45 33998.67 362
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VortexMVS98.67 21198.66 19598.68 29999.62 17597.96 29499.59 12199.41 27198.13 18399.31 22999.70 21395.48 23099.27 36699.40 7197.32 34898.79 322
guyue99.16 11199.04 11499.52 13999.69 12798.92 20399.59 12198.81 41698.73 10299.90 3499.87 6795.34 23599.88 16899.66 4099.81 12099.74 115
ECVR-MVScopyleft98.04 26998.05 25398.00 37299.74 10094.37 43799.59 12194.98 47799.13 4199.66 12799.93 1090.67 39199.84 19699.40 7199.38 18399.80 88
SR-MVS99.43 5399.29 6699.86 3499.75 9299.83 2299.59 12199.62 5198.21 16899.73 9799.79 16698.68 7099.96 4198.44 22699.77 13799.79 92
thres100view90097.76 31797.45 32598.69 29899.72 11197.86 30399.59 12198.74 42697.93 22499.26 24898.62 43191.75 37099.83 21593.22 43798.18 29798.37 420
thres600view797.86 29897.51 31698.92 25399.72 11197.95 29799.59 12198.74 42697.94 22399.27 24398.62 43191.75 37099.86 18193.73 43198.19 29698.96 314
LCM-MVSNet-Re97.83 30698.15 23996.87 42699.30 29792.25 45799.59 12198.26 44797.43 29196.20 44199.13 39196.27 19298.73 43898.17 25298.99 23699.64 179
baseline198.31 23797.95 26499.38 18499.50 23898.74 23399.59 12198.93 39498.41 13599.14 27299.60 26994.59 28199.79 24298.48 22093.29 43399.61 189
SteuartSystems-ACMMP99.54 2499.42 3299.87 2199.82 5399.81 3399.59 12199.51 15398.62 11299.79 7699.83 10399.28 699.97 2998.48 22099.90 5799.84 53
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CPTT-MVS99.11 13898.90 15799.74 8099.80 6399.46 11499.59 12199.49 18997.03 33199.63 14499.69 22497.27 13399.96 4197.82 28699.84 10299.81 79
IMVS_040398.86 18198.89 16198.78 28899.55 20996.93 35699.58 13199.44 25598.05 20699.68 11699.80 14996.81 16199.80 23698.15 25598.92 24199.60 192
test_fmvsmvis_n_192099.65 899.61 799.77 7499.38 27599.37 12399.58 13199.62 5199.41 2199.87 4999.92 1898.81 49100.00 199.97 299.93 3399.94 17
dmvs_testset95.02 41396.12 39091.72 45099.10 35180.43 47899.58 13197.87 45797.47 28395.22 44898.82 42293.99 31095.18 47588.09 46394.91 40999.56 213
test_fmvsm_n_192099.69 599.66 499.78 7199.84 3899.44 11699.58 13199.69 2299.43 1799.98 1399.91 2698.62 76100.00 199.97 299.95 2399.90 25
test111198.04 26998.11 24497.83 38899.74 10093.82 44299.58 13195.40 47699.12 4699.65 13699.93 1090.73 39099.84 19699.43 6999.38 18399.82 72
PGM-MVS99.45 4699.31 6099.86 3499.87 2099.78 4799.58 13199.65 3997.84 23799.71 10999.80 14999.12 1599.97 2998.33 23899.87 7999.83 63
LPG-MVS_test98.22 24398.13 24298.49 32099.33 28897.05 34299.58 13199.55 10097.46 28499.24 25099.83 10392.58 35099.72 27098.09 26097.51 33298.68 354
PHI-MVS99.30 8399.17 9199.70 8799.56 20599.52 10599.58 13199.80 1197.12 31999.62 14899.73 20298.58 7899.90 14898.61 20199.91 4699.68 157
fmvsm_s_conf0.5_n_1199.32 7999.16 9299.80 6499.83 4799.70 6099.57 13999.56 9099.45 1199.99 299.93 1094.18 30399.99 499.96 1399.98 499.73 124
AstraMVS99.09 14499.03 11799.25 21199.66 14898.13 28399.57 13998.24 44998.82 8999.91 3199.88 5495.81 21599.90 14899.72 3299.67 15899.74 115
SF-MVS99.38 6799.24 7899.79 6899.79 6899.68 6499.57 13999.54 10997.82 24399.71 10999.80 14998.95 3299.93 11098.19 24999.84 10299.74 115
DVP-MVScopyleft99.57 2099.47 2499.88 1599.85 3199.89 699.57 13999.37 29799.10 4899.81 6999.80 14998.94 3499.96 4198.93 14799.86 8799.81 79
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND99.91 699.84 3899.89 699.57 13999.51 15399.96 4198.93 14799.86 8799.88 35
Effi-MVS+-dtu98.78 19998.89 16198.47 32799.33 28896.91 36199.57 13999.30 33898.47 12799.41 20298.99 40896.78 16399.74 26098.73 18399.38 18398.74 336
v2v48298.06 26397.77 28598.92 25398.90 38698.82 22699.57 13999.36 29996.65 35699.19 26499.35 35394.20 30099.25 37097.72 30194.97 40698.69 349
DSMNet-mixed97.25 36997.35 34296.95 42397.84 44693.61 44999.57 13996.63 47196.13 39998.87 32398.61 43394.59 28197.70 45995.08 41398.86 24999.55 214
FE-MVSNET94.07 42493.36 42996.22 43494.05 47694.71 42999.56 14798.36 44593.15 44593.76 45897.55 45986.47 43796.49 47087.48 46689.83 45997.48 459
reproduce_model99.63 999.54 1399.90 899.78 7099.88 1099.56 14799.55 10099.15 3899.90 3499.90 3499.00 2499.97 2999.11 11999.91 4699.86 42
MVStest196.08 39895.48 40397.89 38298.93 38196.70 36999.56 14799.35 30692.69 45091.81 46799.46 32289.90 40098.96 42595.00 41592.61 44398.00 443
fmvsm_l_conf0.5_n_a99.71 299.67 199.85 4399.86 2599.61 8599.56 14799.63 4699.48 399.98 1399.83 10398.75 6099.99 499.97 299.96 1799.94 17
fmvsm_l_conf0.5_n99.71 299.67 199.85 4399.84 3899.63 8299.56 14799.63 4699.47 499.98 1399.82 11698.75 6099.99 499.97 299.97 999.94 17
sd_testset98.75 20498.57 21299.29 20499.81 5798.26 27699.56 14799.62 5198.78 9899.64 14199.88 5492.02 36499.88 16899.54 5198.26 28999.72 134
KD-MVS_self_test95.00 41494.34 41996.96 42297.07 46195.39 41399.56 14799.44 25595.11 41797.13 42997.32 46491.86 36897.27 46490.35 45581.23 47398.23 428
ETV-MVS99.26 9299.21 8499.40 17899.46 25099.30 13899.56 14799.52 13198.52 12299.44 19199.27 37598.41 9399.86 18199.10 12299.59 16899.04 304
SMA-MVScopyleft99.44 5099.30 6299.85 4399.73 10799.83 2299.56 14799.47 22397.45 28799.78 8199.82 11699.18 1299.91 13598.79 17799.89 6899.81 79
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
AllTest98.87 17898.72 18699.31 19699.86 2598.48 26599.56 14799.61 6097.85 23499.36 21999.85 8295.95 20599.85 18796.66 37599.83 11399.59 203
casdiffmvspermissive99.13 12498.98 13899.56 12299.65 15799.16 15599.56 14799.50 17698.33 14599.41 20299.86 7595.92 20899.83 21599.45 6899.16 20499.70 148
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
XXY-MVS98.38 23298.09 24899.24 21499.26 30999.32 13199.56 14799.55 10097.45 28798.71 34499.83 10393.23 32999.63 30998.88 15396.32 37098.76 330
ACMH+97.24 1097.92 28997.78 28398.32 34599.46 25096.68 37399.56 14799.54 10998.41 13597.79 41399.87 6790.18 39899.66 29498.05 26897.18 35498.62 384
ACMM97.58 598.37 23498.34 22798.48 32299.41 26597.10 33699.56 14799.45 24698.53 12199.04 29499.85 8293.00 33499.71 27698.74 18197.45 33998.64 375
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D99.27 8999.12 9799.74 8099.18 33099.75 5199.56 14799.57 8598.45 13099.49 18199.85 8297.77 11899.94 9298.33 23899.84 10299.52 223
testing3-297.84 30397.70 29598.24 35499.53 21795.37 41499.55 16298.67 43698.46 12899.27 24399.34 35786.58 43599.83 21599.32 8498.63 26299.52 223
test_fmvsmconf0.01_n99.22 10099.03 11799.79 6898.42 43899.48 11199.55 16299.51 15399.39 2299.78 8199.93 1094.80 26299.95 7699.93 2399.95 2399.94 17
test_fmvs198.88 17598.79 17999.16 22299.69 12797.61 31699.55 16299.49 18999.32 2999.98 1399.91 2691.41 38099.96 4199.82 2999.92 3999.90 25
v14419297.92 28997.60 30798.87 27198.83 39898.65 24199.55 16299.34 31196.20 39199.32 22899.40 33794.36 29399.26 36996.37 38695.03 40598.70 345
API-MVS99.04 15699.03 11799.06 23299.40 27099.31 13599.55 16299.56 9098.54 12099.33 22799.39 34198.76 5799.78 24896.98 35799.78 13498.07 436
fmvsm_l_conf0.5_n_399.61 1099.51 1899.92 199.84 3899.82 2899.54 16799.66 3299.46 799.98 1399.89 4397.27 13399.99 499.97 299.95 2399.95 11
fmvsm_s_conf0.1_n_a99.26 9299.06 11099.85 4399.52 22399.62 8399.54 16799.62 5198.69 10799.99 299.96 194.47 29099.94 9299.88 2699.92 3999.98 2
APD_test195.87 40096.49 38294.00 44199.53 21784.01 47099.54 16799.32 32995.91 40797.99 40299.85 8285.49 44499.88 16891.96 44898.84 25198.12 433
thisisatest053098.35 23598.03 25599.31 19699.63 16698.56 25199.54 16796.75 46997.53 27899.73 9799.65 24591.25 38599.89 16398.62 19899.56 17099.48 240
MTMP99.54 16798.88 407
v114497.98 28097.69 29698.85 27798.87 39198.66 24099.54 16799.35 30696.27 38699.23 25499.35 35394.67 27699.23 37396.73 37095.16 40298.68 354
v14897.79 31597.55 30998.50 31998.74 41297.72 30999.54 16799.33 31996.26 38798.90 31799.51 30394.68 27599.14 39197.83 28593.15 43798.63 382
CostFormer97.72 32797.73 29297.71 39699.15 34494.02 44199.54 16799.02 38594.67 42999.04 29499.35 35392.35 36099.77 25098.50 21997.94 30799.34 273
MVSTER98.49 22098.32 22999.00 24099.35 28299.02 17599.54 16799.38 28997.41 29499.20 26199.73 20293.86 31799.36 35098.87 15697.56 32798.62 384
fmvsm_s_conf0.5_n_1099.41 5999.24 7899.92 199.83 4799.84 2099.53 17699.56 9099.45 1199.99 299.92 1894.92 25599.99 499.97 299.97 999.95 11
fmvsm_s_conf0.1_n99.29 8599.10 9999.86 3499.70 12299.65 7599.53 17699.62 5198.74 10199.99 299.95 394.53 28899.94 9299.89 2599.96 1799.97 4
E499.13 12499.01 13199.49 15399.68 13298.90 20799.52 17899.52 13198.13 18399.71 10999.90 3496.32 18899.84 19699.21 10399.11 21899.75 110
reproduce-ours99.61 1099.52 1499.90 899.76 8299.88 1099.52 17899.54 10999.13 4199.89 4099.89 4398.96 2799.96 4199.04 12999.90 5799.85 46
our_new_method99.61 1099.52 1499.90 899.76 8299.88 1099.52 17899.54 10999.13 4199.89 4099.89 4398.96 2799.96 4199.04 12999.90 5799.85 46
fmvsm_s_conf0.5_n_a99.56 2199.47 2499.85 4399.83 4799.64 8199.52 17899.65 3999.10 4899.98 1399.92 1897.35 12999.96 4199.94 2199.92 3999.95 11
MM99.40 6499.28 6999.74 8099.67 13599.31 13599.52 17898.87 40999.55 199.74 9599.80 14996.47 18099.98 2099.97 299.97 999.94 17
patch_mono-299.26 9299.62 698.16 35999.81 5794.59 43399.52 17899.64 4299.33 2899.73 9799.90 3499.00 2499.99 499.69 3599.98 499.89 29
Fast-Effi-MVS+-dtu98.77 20398.83 17598.60 30499.41 26596.99 35199.52 17899.49 18998.11 19099.24 25099.34 35796.96 15299.79 24297.95 27499.45 17999.02 307
Fast-Effi-MVS+98.70 20898.43 22199.51 14499.51 22699.28 14199.52 17899.47 22396.11 40099.01 29799.34 35796.20 19499.84 19697.88 27898.82 25399.39 264
v192192097.80 31397.45 32598.84 27898.80 40098.53 25499.52 17899.34 31196.15 39799.24 25099.47 31893.98 31199.29 36295.40 40795.13 40398.69 349
MIMVSNet195.51 40695.04 41196.92 42597.38 45395.60 40399.52 17899.50 17693.65 43996.97 43399.17 38685.28 44796.56 46988.36 46295.55 39498.60 396
FE-MVSNET295.10 41294.44 41897.08 41995.08 47295.97 39699.51 18899.37 29795.02 42194.10 45597.57 45886.18 43997.66 46193.28 43689.86 45897.61 454
viewmacassd2359aftdt99.08 14698.94 14999.50 14999.66 14898.96 18799.51 18899.54 10998.27 15299.42 19799.89 4395.88 21299.80 23699.20 10499.11 21899.76 107
SSM_040799.13 12499.03 11799.43 17499.62 17598.88 21099.51 18899.50 17698.14 18099.37 21399.85 8296.85 15599.83 21599.19 10599.25 19799.60 192
fmvsm_s_conf0.5_n_899.54 2499.42 3299.89 1199.83 4799.74 5499.51 18899.62 5199.46 799.99 299.90 3496.60 17299.98 2099.95 1699.95 2399.96 7
fmvsm_s_conf0.5_n99.51 2999.40 3899.85 4399.84 3899.65 7599.51 18899.67 2799.13 4199.98 1399.92 1896.60 17299.96 4199.95 1699.96 1799.95 11
UniMVSNet_ETH3D97.32 36696.81 37498.87 27199.40 27097.46 32099.51 18899.53 12595.86 40898.54 37299.77 18282.44 46099.66 29498.68 19197.52 33199.50 236
alignmvs98.81 19498.56 21499.58 11699.43 25899.42 11899.51 18898.96 39298.61 11399.35 22298.92 41894.78 26499.77 25099.35 7698.11 30299.54 216
v119297.81 31197.44 33098.91 25798.88 38898.68 23899.51 18899.34 31196.18 39399.20 26199.34 35794.03 30999.36 35095.32 40995.18 40198.69 349
test20.0396.12 39695.96 39596.63 42997.44 45295.45 41099.51 18899.38 28996.55 36796.16 44299.25 37893.76 32196.17 47187.35 46894.22 41998.27 424
mvs_anonymous99.03 15898.99 13599.16 22299.38 27598.52 25899.51 18899.38 28997.79 24499.38 21199.81 13197.30 13199.45 32899.35 7698.99 23699.51 232
TAMVS99.12 13299.08 10599.24 21499.46 25098.55 25299.51 18899.46 23598.09 19499.45 18699.82 11698.34 9799.51 32298.70 18698.93 23999.67 161
viewdifsd2359ckpt1399.06 15198.93 15199.45 16699.63 16698.96 18799.50 19999.51 15397.83 23899.28 23799.80 14996.68 16999.71 27699.05 12899.12 21699.68 157
viewdifsd2359ckpt1198.78 19998.74 18498.89 26399.67 13597.04 34599.50 19999.58 7898.26 15599.56 16499.90 3494.36 29399.87 17599.49 6198.32 28599.77 100
viewmsd2359difaftdt98.78 19998.74 18498.90 25999.67 13597.04 34599.50 19999.58 7898.26 15599.56 16499.90 3494.36 29399.87 17599.49 6198.32 28599.77 100
IMVS_040798.86 18198.91 15598.72 29399.55 20996.93 35699.50 19999.44 25598.05 20699.66 12799.80 14997.13 13999.65 29998.15 25598.92 24199.60 192
viewmanbaseed2359cas99.18 10499.07 10999.50 14999.62 17599.01 17799.50 19999.52 13198.25 16099.68 11699.82 11696.93 15399.80 23699.15 11599.11 21899.70 148
fmvsm_s_conf0.5_n_699.54 2499.44 3199.85 4399.51 22699.67 6899.50 19999.64 4299.43 1799.98 1399.78 17397.26 13699.95 7699.95 1699.93 3399.92 23
test_fmvsmconf0.1_n99.55 2399.45 3099.86 3499.44 25799.65 7599.50 19999.61 6099.45 1199.87 4999.92 1897.31 13099.97 2999.95 1699.99 199.97 4
test_yl98.86 18198.63 20099.54 12599.49 24099.18 15299.50 19999.07 37898.22 16699.61 15399.51 30395.37 23399.84 19698.60 20498.33 28199.59 203
DCV-MVSNet98.86 18198.63 20099.54 12599.49 24099.18 15299.50 19999.07 37898.22 16699.61 15399.51 30395.37 23399.84 19698.60 20498.33 28199.59 203
tfpn200view997.72 32797.38 33898.72 29399.69 12797.96 29499.50 19998.73 43297.83 23899.17 26998.45 43891.67 37499.83 21593.22 43798.18 29798.37 420
UA-Net99.42 5599.29 6699.80 6499.62 17599.55 9699.50 19999.70 1898.79 9599.77 8599.96 197.45 12499.96 4198.92 14999.90 5799.89 29
pm-mvs197.68 33597.28 35498.88 26799.06 36098.62 24699.50 19999.45 24696.32 38297.87 40999.79 16692.47 35499.35 35397.54 31893.54 43098.67 362
EI-MVSNet98.67 21198.67 19298.68 29999.35 28297.97 29299.50 19999.38 28996.93 34099.20 26199.83 10397.87 11499.36 35098.38 23197.56 32798.71 340
CVMVSNet98.57 21898.67 19298.30 34799.35 28295.59 40499.50 19999.55 10098.60 11599.39 20999.83 10394.48 28999.45 32898.75 18098.56 26999.85 46
VPA-MVSNet98.29 24097.95 26499.30 20199.16 34099.54 9899.50 19999.58 7898.27 15299.35 22299.37 34792.53 35299.65 29999.35 7694.46 41498.72 338
thres40097.77 31697.38 33898.92 25399.69 12797.96 29499.50 19998.73 43297.83 23899.17 26998.45 43891.67 37499.83 21593.22 43798.18 29798.96 314
APD-MVScopyleft99.27 8999.08 10599.84 5599.75 9299.79 4199.50 19999.50 17697.16 31599.77 8599.82 11698.78 5399.94 9297.56 31699.86 8799.80 88
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
E299.15 11599.03 11799.49 15399.65 15798.93 20299.49 21699.52 13198.14 18099.72 10299.88 5496.57 17699.84 19699.17 11199.13 21199.72 134
E399.15 11599.03 11799.49 15399.62 17598.91 20499.49 21699.52 13198.13 18399.72 10299.88 5496.61 17199.84 19699.17 11199.13 21199.72 134
SSM_040499.16 11199.06 11099.44 17199.65 15798.96 18799.49 21699.50 17698.14 18099.62 14899.85 8296.85 15599.85 18799.19 10599.26 19699.52 223
fmvsm_s_conf0.5_n_499.36 7299.24 7899.73 8399.78 7099.53 10199.49 21699.60 6799.42 2099.99 299.86 7595.15 24599.95 7699.95 1699.89 6899.73 124
test_vis1_rt95.81 40295.65 40196.32 43399.67 13591.35 46199.49 21696.74 47098.25 16095.24 44798.10 45374.96 46999.90 14899.53 5398.85 25097.70 453
TransMVSNet (Re)97.15 37396.58 37998.86 27499.12 34698.85 21899.49 21698.91 40295.48 41297.16 42899.80 14993.38 32599.11 40094.16 42791.73 44798.62 384
UniMVSNet (Re)98.29 24098.00 25899.13 22799.00 37099.36 12699.49 21699.51 15397.95 22298.97 30699.13 39196.30 19199.38 34398.36 23593.34 43298.66 371
EPMVS97.82 30997.65 30098.35 34298.88 38895.98 39599.49 21694.71 47997.57 27199.26 24899.48 31592.46 35799.71 27697.87 28099.08 22899.35 270
viewcassd2359sk1199.18 10499.08 10599.49 15399.65 15798.95 19399.48 22499.51 15398.10 19399.72 10299.87 6797.13 13999.84 19699.13 11699.14 20899.69 151
fmvsm_s_conf0.5_n_999.41 5999.28 6999.81 6099.84 3899.52 10599.48 22499.62 5199.46 799.99 299.92 1895.24 24299.96 4199.97 299.97 999.96 7
SSC-MVS3.297.34 36497.15 36197.93 37899.02 36795.76 40199.48 22499.58 7897.62 26699.09 28399.53 29587.95 42599.27 36696.42 38295.66 39098.75 332
fmvsm_s_conf0.5_n_399.37 6899.20 8699.87 2199.75 9299.70 6099.48 22499.66 3299.45 1199.99 299.93 1094.64 28099.97 2999.94 2199.97 999.95 11
test_fmvsmconf_n99.70 499.64 599.87 2199.80 6399.66 7199.48 22499.64 4299.45 1199.92 3099.92 1898.62 7699.99 499.96 1399.99 199.96 7
Anonymous2023121197.88 29497.54 31298.90 25999.71 11798.53 25499.48 22499.57 8594.16 43498.81 33399.68 23293.23 32999.42 33998.84 16694.42 41698.76 330
v124097.69 33297.32 34998.79 28698.85 39598.43 26999.48 22499.36 29996.11 40099.27 24399.36 35093.76 32199.24 37294.46 42195.23 40098.70 345
VPNet97.84 30397.44 33099.01 23899.21 32298.94 19799.48 22499.57 8598.38 13799.28 23799.73 20288.89 41099.39 34199.19 10593.27 43498.71 340
UniMVSNet_NR-MVSNet98.22 24397.97 26198.96 24598.92 38398.98 18099.48 22499.53 12597.76 24898.71 34499.46 32296.43 18499.22 37898.57 21092.87 44098.69 349
TDRefinement95.42 40894.57 41697.97 37489.83 48296.11 39499.48 22498.75 42396.74 34996.68 43699.88 5488.65 41699.71 27698.37 23382.74 47198.09 435
fmvsm_l_conf0.5_n_999.58 1699.47 2499.92 199.85 3199.82 2899.47 23499.63 4699.45 1199.98 1399.89 4397.02 14899.99 499.98 199.96 1799.95 11
ACMMP_NAP99.47 4099.34 5099.88 1599.87 2099.86 1899.47 23499.48 20198.05 20699.76 9199.86 7598.82 4899.93 11098.82 17699.91 4699.84 53
NR-MVSNet97.97 28397.61 30699.02 23798.87 39199.26 14499.47 23499.42 26897.63 26497.08 43099.50 30695.07 24899.13 39497.86 28193.59 42998.68 354
PVSNet_Blended_VisFu99.36 7299.28 6999.61 10999.86 2599.07 17099.47 23499.93 297.66 26299.71 10999.86 7597.73 11999.96 4199.47 6699.82 11799.79 92
E3new99.18 10499.08 10599.48 15799.63 16698.94 19799.46 23899.50 17698.06 20399.72 10299.84 9797.27 13399.84 19699.10 12299.13 21199.67 161
LuminaMVS99.23 9899.10 9999.61 10999.35 28299.31 13599.46 23899.13 36998.61 11399.86 5399.89 4396.41 18699.91 13599.67 3799.51 17499.63 184
fmvsm_s_conf0.1_n_299.37 6899.22 8399.81 6099.77 7899.75 5199.46 23899.60 6799.47 499.98 1399.94 694.98 24999.95 7699.97 299.79 13299.73 124
SD-MVS99.41 5999.52 1499.05 23499.74 10099.68 6499.46 23899.52 13199.11 4799.88 4399.91 2699.43 197.70 45998.72 18499.93 3399.77 100
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
viewdifsd2359ckpt0799.11 13899.00 13499.43 17499.63 16698.73 23499.45 24299.54 10998.33 14599.62 14899.81 13196.17 19599.87 17599.27 9699.14 20899.69 151
testing397.28 36796.76 37698.82 28099.37 27898.07 28799.45 24299.36 29997.56 27397.89 40898.95 41383.70 45498.82 43396.03 39098.56 26999.58 207
tt080597.97 28397.77 28598.57 30999.59 19496.61 37699.45 24299.08 37598.21 16898.88 32099.80 14988.66 41599.70 28398.58 20797.72 31799.39 264
tpm297.44 35997.34 34597.74 39599.15 34494.36 43899.45 24298.94 39393.45 44398.90 31799.44 32591.35 38299.59 31397.31 33698.07 30399.29 277
FMVSNet297.72 32797.36 34098.80 28599.51 22698.84 22099.45 24299.42 26896.49 37098.86 32899.29 37090.26 39498.98 41696.44 38196.56 36498.58 398
CDS-MVSNet99.09 14499.03 11799.25 21199.42 26098.73 23499.45 24299.46 23598.11 19099.46 18599.77 18298.01 11299.37 34698.70 18698.92 24199.66 166
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MAR-MVS98.86 18198.63 20099.54 12599.37 27899.66 7199.45 24299.54 10996.61 36199.01 29799.40 33797.09 14399.86 18197.68 30699.53 17399.10 292
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
viewdifsd2359ckpt0999.01 16398.87 16599.40 17899.62 17598.79 22999.44 24999.51 15397.76 24899.35 22299.69 22496.42 18599.75 25798.97 14199.11 21899.66 166
fmvsm_s_conf0.5_n_299.32 7999.13 9599.89 1199.80 6399.77 4899.44 24999.58 7899.47 499.99 299.93 1094.04 30899.96 4199.96 1399.93 3399.93 22
UGNet98.87 17898.69 19099.40 17899.22 32198.72 23699.44 24999.68 2499.24 3299.18 26899.42 32992.74 34299.96 4199.34 8199.94 3199.53 222
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
ab-mvs98.86 18198.63 20099.54 12599.64 16299.19 15099.44 24999.54 10997.77 24799.30 23399.81 13194.20 30099.93 11099.17 11198.82 25399.49 237
test_040296.64 38596.24 38797.85 38598.85 39596.43 38299.44 24999.26 34893.52 44096.98 43299.52 29988.52 41999.20 38592.58 44797.50 33497.93 448
ACMP97.20 1198.06 26397.94 26698.45 33099.37 27897.01 34999.44 24999.49 18997.54 27798.45 37799.79 16691.95 36699.72 27097.91 27697.49 33798.62 384
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GG-mvs-BLEND98.45 33098.55 43398.16 28099.43 25593.68 48197.23 42498.46 43789.30 40699.22 37895.43 40698.22 29297.98 445
HPM-MVS++copyleft99.39 6699.23 8299.87 2199.75 9299.84 2099.43 25599.51 15398.68 10999.27 24399.53 29598.64 7599.96 4198.44 22699.80 12599.79 92
tpm cat197.39 36197.36 34097.50 40799.17 33893.73 44499.43 25599.31 33391.27 45598.71 34499.08 39594.31 29899.77 25096.41 38498.50 27399.00 308
tpm97.67 33897.55 30998.03 36799.02 36795.01 42299.43 25598.54 44296.44 37699.12 27599.34 35791.83 36999.60 31297.75 29796.46 36699.48 240
GBi-Net97.68 33597.48 31998.29 34899.51 22697.26 32999.43 25599.48 20196.49 37099.07 28699.32 36590.26 39498.98 41697.10 34996.65 36198.62 384
test197.68 33597.48 31998.29 34899.51 22697.26 32999.43 25599.48 20196.49 37099.07 28699.32 36590.26 39498.98 41697.10 34996.65 36198.62 384
FMVSNet196.84 38196.36 38598.29 34899.32 29597.26 32999.43 25599.48 20195.11 41798.55 37199.32 36583.95 45398.98 41695.81 39596.26 37298.62 384
fmvsm_s_conf0.5_n_799.34 7599.29 6699.48 15799.70 12298.63 24499.42 26299.63 4699.46 799.98 1399.88 5495.59 22599.96 4199.97 299.98 499.85 46
fmvsm_s_conf0.5_n_599.37 6899.21 8499.86 3499.80 6399.68 6499.42 26299.61 6099.37 2499.97 2599.86 7594.96 25099.99 499.97 299.93 3399.92 23
mamv499.33 7799.42 3299.07 23099.67 13597.73 30799.42 26299.60 6798.15 17599.94 2899.91 2698.42 9199.94 9299.72 3299.96 1799.54 216
testgi97.65 34097.50 31798.13 36399.36 28196.45 38199.42 26299.48 20197.76 24897.87 40999.45 32491.09 38698.81 43494.53 42098.52 27299.13 291
F-COLMAP99.19 10199.04 11499.64 10199.78 7099.27 14399.42 26299.54 10997.29 30499.41 20299.59 27198.42 9199.93 11098.19 24999.69 15399.73 124
Anonymous20240521198.30 23997.98 26099.26 21099.57 20198.16 28099.41 26798.55 44196.03 40599.19 26499.74 19691.87 36799.92 12399.16 11498.29 28899.70 148
MSLP-MVS++99.46 4299.47 2499.44 17199.60 19299.16 15599.41 26799.71 1698.98 7299.45 18699.78 17399.19 1199.54 32099.28 9399.84 10299.63 184
VNet99.11 13898.90 15799.73 8399.52 22399.56 9499.41 26799.39 28199.01 6499.74 9599.78 17395.56 22699.92 12399.52 5598.18 29799.72 134
baseline297.87 29697.55 30998.82 28099.18 33098.02 28999.41 26796.58 47396.97 33496.51 43799.17 38693.43 32499.57 31597.71 30299.03 23298.86 318
DU-MVS98.08 26197.79 28098.96 24598.87 39198.98 18099.41 26799.45 24697.87 23098.71 34499.50 30694.82 26099.22 37898.57 21092.87 44098.68 354
Baseline_NR-MVSNet97.76 31797.45 32598.68 29999.09 35498.29 27499.41 26798.85 41195.65 41098.63 36299.67 23894.82 26099.10 40298.07 26792.89 43998.64 375
XVG-ACMP-BASELINE97.83 30697.71 29498.20 35699.11 34896.33 38599.41 26799.52 13198.06 20399.05 29399.50 30689.64 40499.73 26697.73 29997.38 34698.53 401
DP-MVS99.16 11198.95 14799.78 7199.77 7899.53 10199.41 26799.50 17697.03 33199.04 29499.88 5497.39 12599.92 12398.66 19399.90 5799.87 40
9.1499.10 9999.72 11199.40 27599.51 15397.53 27899.64 14199.78 17398.84 4699.91 13597.63 30799.82 117
D2MVS98.41 22898.50 21898.15 36299.26 30996.62 37599.40 27599.61 6097.71 25498.98 30499.36 35096.04 20099.67 29198.70 18697.41 34498.15 432
Anonymous2024052998.09 25897.68 29799.34 18899.66 14898.44 26899.40 27599.43 26693.67 43899.22 25599.89 4390.23 39799.93 11099.26 9998.33 28199.66 166
FMVSNet398.03 27197.76 28998.84 27899.39 27398.98 18099.40 27599.38 28996.67 35499.07 28699.28 37292.93 33598.98 41697.10 34996.65 36198.56 400
LFMVS97.90 29297.35 34299.54 12599.52 22399.01 17799.39 27998.24 44997.10 32399.65 13699.79 16684.79 44999.91 13599.28 9398.38 27899.69 151
HQP_MVS98.27 24298.22 23598.44 33399.29 30196.97 35399.39 27999.47 22398.97 7599.11 27799.61 26692.71 34599.69 28897.78 29197.63 32098.67 362
plane_prior299.39 27998.97 75
CHOSEN 1792x268899.19 10199.10 9999.45 16699.89 898.52 25899.39 27999.94 198.73 10299.11 27799.89 4395.50 22899.94 9299.50 5799.97 999.89 29
PAPM_NR99.04 15698.84 17399.66 9199.74 10099.44 11699.39 27999.38 28997.70 25799.28 23799.28 37298.34 9799.85 18796.96 35999.45 17999.69 151
gg-mvs-nofinetune96.17 39595.32 40798.73 29198.79 40198.14 28299.38 28494.09 48091.07 45898.07 40091.04 47889.62 40599.35 35396.75 36999.09 22798.68 354
VDDNet97.55 34697.02 36899.16 22299.49 24098.12 28599.38 28499.30 33895.35 41399.68 11699.90 3482.62 45999.93 11099.31 8698.13 30199.42 258
ME-MVS99.56 2199.46 2899.86 3499.80 6399.81 3399.37 28699.70 1899.18 3499.83 6499.83 10398.74 6599.93 11098.83 16999.89 6899.83 63
MGCNet99.15 11598.96 14399.73 8398.92 38399.37 12399.37 28696.92 46699.51 299.66 12799.78 17396.69 16799.97 2999.84 2899.97 999.84 53
pmmvs696.53 38796.09 39297.82 39098.69 42095.47 40999.37 28699.47 22393.46 44297.41 41899.78 17387.06 43399.33 35696.92 36492.70 44298.65 373
PM-MVS92.96 42992.23 43395.14 43895.61 46789.98 46499.37 28698.21 45194.80 42795.04 45297.69 45765.06 47397.90 45594.30 42289.98 45797.54 458
WTY-MVS99.06 15198.88 16499.61 10999.62 17599.16 15599.37 28699.56 9098.04 21399.53 17399.62 26296.84 15999.94 9298.85 16398.49 27499.72 134
IterMVS-LS98.46 22398.42 22298.58 30899.59 19498.00 29099.37 28699.43 26696.94 33999.07 28699.59 27197.87 11499.03 40998.32 24095.62 39198.71 340
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs3397.70 33197.28 35498.97 24499.70 12297.27 32799.36 29299.45 24698.94 7899.66 12799.64 25194.93 25399.99 499.48 6484.36 46899.65 172
DPE-MVScopyleft99.46 4299.32 5499.91 699.78 7099.88 1099.36 29299.51 15398.73 10299.88 4399.84 9798.72 6799.96 4198.16 25399.87 7999.88 35
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UnsupCasMVSNet_eth96.44 38996.12 39097.40 41098.65 42395.65 40299.36 29299.51 15397.13 31796.04 44498.99 40888.40 42098.17 44896.71 37190.27 45598.40 417
sss99.17 10999.05 11299.53 13399.62 17598.97 18399.36 29299.62 5197.83 23899.67 12299.65 24597.37 12899.95 7699.19 10599.19 20399.68 157
DeepC-MVS_fast98.69 199.49 3399.39 4099.77 7499.63 16699.59 8899.36 29299.46 23599.07 5899.79 7699.82 11698.85 4499.92 12398.68 19199.87 7999.82 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet99.25 9699.14 9499.59 11399.41 26599.16 15599.35 29799.57 8598.82 8999.51 17799.61 26696.46 18199.95 7699.59 4599.98 499.65 172
pmmvs-eth3d95.34 41094.73 41397.15 41495.53 46995.94 39799.35 29799.10 37295.13 41593.55 45997.54 46088.15 42497.91 45494.58 41989.69 46097.61 454
MDTV_nov1_ep13_2view95.18 41999.35 29796.84 34499.58 16095.19 24497.82 28699.46 251
VDD-MVS97.73 32597.35 34298.88 26799.47 24897.12 33599.34 30098.85 41198.19 17099.67 12299.85 8282.98 45799.92 12399.49 6198.32 28599.60 192
COLMAP_ROBcopyleft97.56 698.86 18198.75 18299.17 22199.88 1398.53 25499.34 30099.59 7397.55 27498.70 35099.89 4395.83 21399.90 14898.10 25999.90 5799.08 297
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
viewmambaseed2359dif99.01 16398.90 15799.32 19499.58 19698.51 26099.33 30299.54 10997.85 23499.44 19199.85 8296.01 20299.79 24299.41 7099.13 21199.67 161
myMVS_eth3d2897.69 33297.34 34598.73 29199.27 30697.52 31899.33 30298.78 42198.03 21598.82 33298.49 43686.64 43499.46 32698.44 22698.24 29199.23 285
EGC-MVSNET82.80 44277.86 44897.62 40097.91 44496.12 39399.33 30299.28 3448.40 48625.05 48799.27 37584.11 45299.33 35689.20 45898.22 29297.42 460
diffmvs_AUTHOR99.19 10199.10 9999.48 15799.64 16298.85 21899.32 30599.48 20198.50 12499.81 6999.81 13196.82 16099.88 16899.40 7199.12 21699.71 145
ETVMVS97.50 35296.90 37299.29 20499.23 31798.78 23299.32 30598.90 40497.52 28098.56 37098.09 45484.72 45099.69 28897.86 28197.88 31099.39 264
FMVSNet596.43 39096.19 38997.15 41499.11 34895.89 39899.32 30599.52 13194.47 43398.34 38399.07 39687.54 43097.07 46592.61 44695.72 38898.47 408
dp97.75 32197.80 27997.59 40499.10 35193.71 44599.32 30598.88 40796.48 37399.08 28599.55 28692.67 34899.82 22496.52 37998.58 26699.24 284
tpmvs97.98 28098.02 25797.84 38799.04 36594.73 42799.31 30999.20 36096.10 40498.76 34099.42 32994.94 25299.81 22996.97 35898.45 27598.97 312
tpmrst98.33 23698.48 21997.90 38199.16 34094.78 42699.31 30999.11 37197.27 30599.45 18699.59 27195.33 23699.84 19698.48 22098.61 26399.09 296
testing9997.36 36296.94 37198.63 30299.18 33096.70 36999.30 31198.93 39497.71 25498.23 38998.26 44684.92 44899.84 19698.04 26997.85 31399.35 270
MP-MVS-pluss99.37 6899.20 8699.88 1599.90 499.87 1799.30 31199.52 13197.18 31399.60 15699.79 16698.79 5299.95 7698.83 16999.91 4699.83 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NCCC99.34 7599.19 8899.79 6899.61 18699.65 7599.30 31199.48 20198.86 8499.21 25899.63 25798.72 6799.90 14898.25 24599.63 16499.80 88
JIA-IIPM97.50 35297.02 36898.93 25198.73 41397.80 30599.30 31198.97 39091.73 45498.91 31594.86 47295.10 24799.71 27697.58 31197.98 30599.28 278
BH-RMVSNet98.41 22898.08 24999.40 17899.41 26598.83 22399.30 31198.77 42297.70 25798.94 31299.65 24592.91 33899.74 26096.52 37999.55 17299.64 179
testing1197.50 35297.10 36598.71 29699.20 32496.91 36199.29 31698.82 41497.89 22898.21 39298.40 44085.63 44399.83 21598.45 22598.04 30499.37 268
Syy-MVS97.09 37697.14 36296.95 42399.00 37092.73 45599.29 31699.39 28197.06 32797.41 41898.15 44993.92 31498.68 43991.71 44998.34 27999.45 254
myMVS_eth3d96.89 37996.37 38498.43 33599.00 37097.16 33399.29 31699.39 28197.06 32797.41 41898.15 44983.46 45698.68 43995.27 41098.34 27999.45 254
MCST-MVS99.43 5399.30 6299.82 5799.79 6899.74 5499.29 31699.40 27898.79 9599.52 17599.62 26298.91 3999.90 14898.64 19599.75 14299.82 72
LF4IMVS97.52 34997.46 32497.70 39798.98 37695.55 40599.29 31698.82 41498.07 19998.66 35399.64 25189.97 39999.61 31197.01 35496.68 36097.94 447
hse-mvs297.50 35297.14 36298.59 30599.49 24097.05 34299.28 32199.22 35698.94 7899.66 12799.42 32994.93 25399.65 29999.48 6483.80 47099.08 297
OPM-MVS98.19 24798.10 24598.45 33098.88 38897.07 34099.28 32199.38 28998.57 11799.22 25599.81 13192.12 36299.66 29498.08 26497.54 32998.61 393
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
diffmvspermissive99.14 12299.02 12699.51 14499.61 18698.96 18799.28 32199.49 18998.46 12899.72 10299.71 20996.50 17999.88 16899.31 8699.11 21899.67 161
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_BlendedMVS98.86 18198.80 17699.03 23699.76 8298.79 22999.28 32199.91 397.42 29399.67 12299.37 34797.53 12299.88 16898.98 13697.29 34998.42 414
OMC-MVS99.08 14699.04 11499.20 21899.67 13598.22 27899.28 32199.52 13198.07 19999.66 12799.81 13197.79 11799.78 24897.79 29099.81 12099.60 192
testing22297.16 37296.50 38199.16 22299.16 34098.47 26799.27 32698.66 43797.71 25498.23 38998.15 44982.28 46299.84 19697.36 33497.66 31999.18 288
AUN-MVS96.88 38096.31 38698.59 30599.48 24797.04 34599.27 32699.22 35697.44 29098.51 37399.41 33391.97 36599.66 29497.71 30283.83 46999.07 302
pmmvs597.52 34997.30 35198.16 35998.57 43296.73 36899.27 32698.90 40496.14 39898.37 38199.53 29591.54 37999.14 39197.51 32095.87 38398.63 382
131498.68 21098.54 21599.11 22898.89 38798.65 24199.27 32699.49 18996.89 34197.99 40299.56 28397.72 12099.83 21597.74 29899.27 19498.84 320
MVS97.28 36796.55 38099.48 15798.78 40498.95 19399.27 32699.39 28183.53 47298.08 39799.54 29196.97 15199.87 17594.23 42599.16 20499.63 184
BH-untuned98.42 22698.36 22598.59 30599.49 24096.70 36999.27 32699.13 36997.24 30998.80 33599.38 34495.75 21999.74 26097.07 35399.16 20499.33 274
MDTV_nov1_ep1398.32 22999.11 34894.44 43599.27 32698.74 42697.51 28199.40 20799.62 26294.78 26499.76 25497.59 31098.81 255
DP-MVS Recon99.12 13298.95 14799.65 9599.74 10099.70 6099.27 32699.57 8596.40 38099.42 19799.68 23298.75 6099.80 23697.98 27299.72 14899.44 256
PatchmatchNetpermissive98.31 23798.36 22598.19 35799.16 34095.32 41599.27 32698.92 39797.37 29799.37 21399.58 27594.90 25799.70 28397.43 33099.21 20199.54 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres20097.61 34397.28 35498.62 30399.64 16298.03 28899.26 33598.74 42697.68 25999.09 28398.32 44491.66 37699.81 22992.88 44298.22 29298.03 439
CNVR-MVS99.42 5599.30 6299.78 7199.62 17599.71 5899.26 33599.52 13198.82 8999.39 20999.71 20998.96 2799.85 18798.59 20699.80 12599.77 100
mamba_040899.08 14698.96 14399.44 17199.62 17598.88 21099.25 33799.47 22398.05 20699.37 21399.81 13196.85 15599.85 18798.98 13699.25 19799.60 192
SSM_0407299.06 15198.96 14399.35 18799.62 17598.88 21099.25 33799.47 22398.05 20699.37 21399.81 13196.85 15599.58 31498.98 13699.25 19799.60 192
tt032095.71 40595.07 40997.62 40099.05 36395.02 42199.25 33799.52 13186.81 46797.97 40499.72 20683.58 45599.15 38996.38 38593.35 43198.68 354
1112_ss98.98 16798.77 18099.59 11399.68 13299.02 17599.25 33799.48 20197.23 31099.13 27399.58 27596.93 15399.90 14898.87 15698.78 25699.84 53
TAPA-MVS97.07 1597.74 32397.34 34598.94 24999.70 12297.53 31799.25 33799.51 15391.90 45399.30 23399.63 25798.78 5399.64 30388.09 46399.87 7999.65 172
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UWE-MVS-2897.36 36297.24 35897.75 39398.84 39794.44 43599.24 34297.58 46297.98 22099.00 30199.00 40691.35 38299.53 32193.75 43098.39 27799.27 282
UBG97.85 29997.48 31998.95 24799.25 31397.64 31499.24 34298.74 42697.90 22798.64 36098.20 44888.65 41699.81 22998.27 24398.40 27699.42 258
PLCcopyleft97.94 499.02 15998.85 17199.53 13399.66 14899.01 17799.24 34299.52 13196.85 34399.27 24399.48 31598.25 10199.91 13597.76 29599.62 16599.65 172
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test_post199.23 34565.14 48494.18 30399.71 27697.58 311
ADS-MVSNet298.02 27398.07 25297.87 38399.33 28895.19 41899.23 34599.08 37596.24 38899.10 28099.67 23894.11 30598.93 42896.81 36799.05 23099.48 240
ADS-MVSNet98.20 24698.08 24998.56 31399.33 28896.48 38099.23 34599.15 36696.24 38899.10 28099.67 23894.11 30599.71 27696.81 36799.05 23099.48 240
EPNet_dtu98.03 27197.96 26298.23 35598.27 44095.54 40799.23 34598.75 42399.02 6297.82 41199.71 20996.11 19799.48 32393.04 44099.65 16199.69 151
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet98.17 25097.93 26798.87 27199.18 33098.49 26399.22 34999.33 31996.96 33599.56 16499.38 34494.33 29699.00 41494.83 41898.58 26699.14 289
RPMNet96.72 38395.90 39699.19 21999.18 33098.49 26399.22 34999.52 13188.72 46599.56 16497.38 46294.08 30799.95 7686.87 47098.58 26699.14 289
sc_t195.75 40395.05 41097.87 38398.83 39894.61 43299.21 35199.45 24687.45 46697.97 40499.85 8281.19 46599.43 33798.27 24393.20 43599.57 210
WBMVS97.74 32397.50 31798.46 32899.24 31597.43 32199.21 35199.42 26897.45 28798.96 30899.41 33388.83 41199.23 37398.94 14496.02 37698.71 340
plane_prior96.97 35399.21 35198.45 13097.60 323
IMVS_040498.53 21998.52 21798.55 31599.55 20996.93 35699.20 35499.44 25598.05 20698.96 30899.80 14994.66 27899.13 39498.15 25598.92 24199.60 192
tt0320-xc95.31 41194.59 41597.45 40898.92 38394.73 42799.20 35499.31 33386.74 46897.23 42499.72 20681.14 46698.95 42697.08 35291.98 44698.67 362
testing9197.44 35997.02 36898.71 29699.18 33096.89 36399.19 35699.04 38297.78 24698.31 38498.29 44585.41 44599.85 18798.01 27097.95 30699.39 264
WR-MVS98.06 26397.73 29299.06 23298.86 39499.25 14699.19 35699.35 30697.30 30398.66 35399.43 32793.94 31299.21 38398.58 20794.28 41898.71 340
new-patchmatchnet94.48 42094.08 42195.67 43795.08 47292.41 45699.18 35899.28 34494.55 43293.49 46097.37 46387.86 42897.01 46691.57 45088.36 46297.61 454
AdaColmapbinary99.01 16398.80 17699.66 9199.56 20599.54 9899.18 35899.70 1898.18 17399.35 22299.63 25796.32 18899.90 14897.48 32399.77 13799.55 214
EG-PatchMatch MVS95.97 39995.69 40096.81 42797.78 44792.79 45499.16 36098.93 39496.16 39594.08 45699.22 38182.72 45899.47 32495.67 40197.50 33498.17 430
PatchT97.03 37796.44 38398.79 28698.99 37398.34 27399.16 36099.07 37892.13 45299.52 17597.31 46594.54 28698.98 41688.54 46198.73 25899.03 305
CNLPA99.14 12298.99 13599.59 11399.58 19699.41 12099.16 36099.44 25598.45 13099.19 26499.49 30998.08 10999.89 16397.73 29999.75 14299.48 240
MDA-MVSNet-bldmvs94.96 41593.98 42297.92 37998.24 44197.27 32799.15 36399.33 31993.80 43780.09 47999.03 40288.31 42197.86 45693.49 43494.36 41798.62 384
CDPH-MVS99.13 12498.91 15599.80 6499.75 9299.71 5899.15 36399.41 27196.60 36499.60 15699.55 28698.83 4799.90 14897.48 32399.83 11399.78 98
save fliter99.76 8299.59 8899.14 36599.40 27899.00 67
WB-MVSnew97.65 34097.65 30097.63 39998.78 40497.62 31599.13 36698.33 44697.36 29899.07 28698.94 41495.64 22499.15 38992.95 44198.68 26196.12 470
testf190.42 43690.68 43789.65 45797.78 44773.97 48599.13 36698.81 41689.62 46091.80 46898.93 41562.23 47698.80 43586.61 47191.17 44996.19 468
APD_test290.42 43690.68 43789.65 45797.78 44773.97 48599.13 36698.81 41689.62 46091.80 46898.93 41562.23 47698.80 43586.61 47191.17 44996.19 468
xiu_mvs_v1_base_debu99.29 8599.27 7399.34 18899.63 16698.97 18399.12 36999.51 15398.86 8499.84 5699.47 31898.18 10499.99 499.50 5799.31 19199.08 297
xiu_mvs_v1_base99.29 8599.27 7399.34 18899.63 16698.97 18399.12 36999.51 15398.86 8499.84 5699.47 31898.18 10499.99 499.50 5799.31 19199.08 297
xiu_mvs_v1_base_debi99.29 8599.27 7399.34 18899.63 16698.97 18399.12 36999.51 15398.86 8499.84 5699.47 31898.18 10499.99 499.50 5799.31 19199.08 297
XVG-OURS-SEG-HR98.69 20998.62 20598.89 26399.71 11797.74 30699.12 36999.54 10998.44 13399.42 19799.71 20994.20 30099.92 12398.54 21798.90 24799.00 308
jason99.13 12499.03 11799.45 16699.46 25098.87 21499.12 36999.26 34898.03 21599.79 7699.65 24597.02 14899.85 18799.02 13399.90 5799.65 172
jason: jason.
N_pmnet94.95 41695.83 39892.31 44898.47 43679.33 48099.12 36992.81 48693.87 43697.68 41499.13 39193.87 31699.01 41391.38 45196.19 37398.59 397
MDA-MVSNet_test_wron95.45 40794.60 41498.01 37098.16 44297.21 33299.11 37599.24 35393.49 44180.73 47898.98 41093.02 33398.18 44794.22 42694.45 41598.64 375
Patchmtry97.75 32197.40 33798.81 28399.10 35198.87 21499.11 37599.33 31994.83 42698.81 33399.38 34494.33 29699.02 41196.10 38895.57 39398.53 401
YYNet195.36 40994.51 41797.92 37997.89 44597.10 33699.10 37799.23 35493.26 44480.77 47799.04 40192.81 33998.02 45194.30 42294.18 42098.64 375
CANet_DTU98.97 16998.87 16599.25 21199.33 28898.42 27199.08 37899.30 33899.16 3799.43 19499.75 19195.27 23899.97 2998.56 21399.95 2399.36 269
icg_test_0407_298.79 19898.86 16898.57 30999.55 20996.93 35699.07 37999.44 25598.05 20699.66 12799.80 14997.13 13999.18 38698.15 25598.92 24199.60 192
SCA98.19 24798.16 23798.27 35399.30 29795.55 40599.07 37998.97 39097.57 27199.43 19499.57 28092.72 34399.74 26097.58 31199.20 20299.52 223
TSAR-MVS + GP.99.36 7299.36 4699.36 18599.67 13598.61 24899.07 37999.33 31999.00 6799.82 6899.81 13199.06 1899.84 19699.09 12499.42 18199.65 172
MG-MVS99.13 12499.02 12699.45 16699.57 20198.63 24499.07 37999.34 31198.99 6999.61 15399.82 11697.98 11399.87 17597.00 35599.80 12599.85 46
PatchMatch-RL98.84 19398.62 20599.52 13999.71 11799.28 14199.06 38399.77 1297.74 25299.50 17899.53 29595.41 23199.84 19697.17 34899.64 16299.44 256
OpenMVS_ROBcopyleft92.34 2094.38 42193.70 42796.41 43297.38 45393.17 45299.06 38398.75 42386.58 46994.84 45398.26 44681.53 46399.32 35889.01 45997.87 31196.76 463
TEST999.67 13599.65 7599.05 38599.41 27196.22 39098.95 31099.49 30998.77 5699.91 135
train_agg99.02 15998.77 18099.77 7499.67 13599.65 7599.05 38599.41 27196.28 38498.95 31099.49 30998.76 5799.91 13597.63 30799.72 14899.75 110
lupinMVS99.13 12499.01 13199.46 16599.51 22698.94 19799.05 38599.16 36597.86 23199.80 7499.56 28397.39 12599.86 18198.94 14499.85 9499.58 207
DELS-MVS99.48 3799.42 3299.65 9599.72 11199.40 12199.05 38599.66 3299.14 4099.57 16399.80 14998.46 8799.94 9299.57 4899.84 10299.60 192
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
new_pmnet96.38 39196.03 39397.41 40998.13 44395.16 42099.05 38599.20 36093.94 43597.39 42198.79 42691.61 37899.04 40790.43 45495.77 38598.05 438
Patchmatch-test97.93 28697.65 30098.77 28999.18 33097.07 34099.03 39099.14 36896.16 39598.74 34199.57 28094.56 28399.72 27093.36 43599.11 21899.52 223
test_899.67 13599.61 8599.03 39099.41 27196.28 38498.93 31399.48 31598.76 5799.91 135
Test_1112_low_res98.89 17498.66 19599.57 12099.69 12798.95 19399.03 39099.47 22396.98 33399.15 27199.23 38096.77 16499.89 16398.83 16998.78 25699.86 42
IterMVS-SCA-FT97.82 30997.75 29098.06 36699.57 20196.36 38499.02 39399.49 18997.18 31398.71 34499.72 20692.72 34399.14 39197.44 32995.86 38498.67 362
xiu_mvs_v2_base99.26 9299.25 7799.29 20499.53 21798.91 20499.02 39399.45 24698.80 9499.71 10999.26 37798.94 3499.98 2099.34 8199.23 20098.98 311
MIMVSNet97.73 32597.45 32598.57 30999.45 25697.50 31999.02 39398.98 38996.11 40099.41 20299.14 39090.28 39398.74 43795.74 39798.93 23999.47 246
IterMVS97.83 30697.77 28598.02 36999.58 19696.27 38899.02 39399.48 20197.22 31198.71 34499.70 21392.75 34099.13 39497.46 32696.00 37898.67 362
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test99.11 13898.92 15299.65 9599.90 499.37 12399.02 39399.91 397.67 26199.59 15999.75 19195.90 21099.73 26699.53 5399.02 23499.86 42
UWE-MVS97.58 34597.29 35398.48 32299.09 35496.25 38999.01 39896.61 47297.86 23199.19 26499.01 40588.72 41299.90 14897.38 33398.69 26099.28 278
新几何299.01 398
BH-w/o98.00 27897.89 27398.32 34599.35 28296.20 39199.01 39898.90 40496.42 37898.38 38099.00 40695.26 24099.72 27096.06 38998.61 26399.03 305
test_prior499.56 9498.99 401
无先验98.99 40199.51 15396.89 34199.93 11097.53 31999.72 134
pmmvs498.13 25497.90 26998.81 28398.61 42898.87 21498.99 40199.21 35996.44 37699.06 29199.58 27595.90 21099.11 40097.18 34796.11 37598.46 411
HQP-NCC99.19 32798.98 40498.24 16298.66 353
ACMP_Plane99.19 32798.98 40498.24 16298.66 353
HQP-MVS98.02 27397.90 26998.37 34199.19 32796.83 36498.98 40499.39 28198.24 16298.66 35399.40 33792.47 35499.64 30397.19 34597.58 32598.64 375
PS-MVSNAJ99.32 7999.32 5499.30 20199.57 20198.94 19798.97 40799.46 23598.92 8199.71 10999.24 37999.01 2099.98 2099.35 7699.66 15998.97 312
MVP-Stereo97.81 31197.75 29097.99 37397.53 45196.60 37798.96 40898.85 41197.22 31197.23 42499.36 35095.28 23799.46 32695.51 40399.78 13497.92 449
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior298.96 40898.34 14399.01 29799.52 29998.68 7097.96 27399.74 145
旧先验298.96 40896.70 35299.47 18399.94 9298.19 249
原ACMM298.95 411
MVS_111021_HR99.41 5999.32 5499.66 9199.72 11199.47 11398.95 41199.85 998.82 8999.54 17199.73 20298.51 8499.74 26098.91 15099.88 7699.77 100
mvsany_test199.50 3199.46 2899.62 10899.61 18699.09 16598.94 41399.48 20199.10 4899.96 2799.91 2698.85 4499.96 4199.72 3299.58 16999.82 72
MVS_111021_LR99.41 5999.33 5299.65 9599.77 7899.51 10798.94 41399.85 998.82 8999.65 13699.74 19698.51 8499.80 23698.83 16999.89 6899.64 179
pmmvs394.09 42393.25 43096.60 43094.76 47594.49 43498.92 41598.18 45389.66 45996.48 43898.06 45586.28 43897.33 46389.68 45787.20 46597.97 446
XVG-OURS98.73 20798.68 19198.88 26799.70 12297.73 30798.92 41599.55 10098.52 12299.45 18699.84 9795.27 23899.91 13598.08 26498.84 25199.00 308
test22299.75 9299.49 10998.91 41799.49 18996.42 37899.34 22699.65 24598.28 10099.69 15399.72 134
PMMVS286.87 43985.37 44391.35 45290.21 48183.80 47198.89 41897.45 46483.13 47391.67 47095.03 47048.49 48294.70 47685.86 47377.62 47595.54 471
miper_lstm_enhance98.00 27897.91 26898.28 35299.34 28797.43 32198.88 41999.36 29996.48 37398.80 33599.55 28695.98 20398.91 42997.27 33895.50 39698.51 404
MVS-HIRNet95.75 40395.16 40897.51 40699.30 29793.69 44698.88 41995.78 47485.09 47198.78 33892.65 47491.29 38499.37 34694.85 41799.85 9499.46 251
TR-MVS97.76 31797.41 33698.82 28099.06 36097.87 30198.87 42198.56 44096.63 36098.68 35299.22 38192.49 35399.65 29995.40 40797.79 31598.95 316
testdata198.85 42298.32 147
ET-MVSNet_ETH3D96.49 38895.64 40299.05 23499.53 21798.82 22698.84 42397.51 46397.63 26484.77 47299.21 38492.09 36398.91 42998.98 13692.21 44599.41 261
our_test_397.65 34097.68 29797.55 40598.62 42694.97 42398.84 42399.30 33896.83 34698.19 39399.34 35797.01 15099.02 41195.00 41596.01 37798.64 375
MS-PatchMatch97.24 37197.32 34996.99 42098.45 43793.51 45098.82 42599.32 32997.41 29498.13 39699.30 36888.99 40999.56 31795.68 40099.80 12597.90 450
c3_l98.12 25698.04 25498.38 34099.30 29797.69 31398.81 42699.33 31996.67 35498.83 33099.34 35797.11 14298.99 41597.58 31195.34 39898.48 406
ppachtmachnet_test97.49 35797.45 32597.61 40398.62 42695.24 41698.80 42799.46 23596.11 40098.22 39199.62 26296.45 18298.97 42393.77 42995.97 38298.61 393
PAPR98.63 21698.34 22799.51 14499.40 27099.03 17498.80 42799.36 29996.33 38199.00 30199.12 39498.46 8799.84 19695.23 41199.37 19099.66 166
test0.0.03 197.71 33097.42 33598.56 31398.41 43997.82 30498.78 42998.63 43897.34 29998.05 40198.98 41094.45 29198.98 41695.04 41497.15 35598.89 317
PVSNet_Blended99.08 14698.97 13999.42 17699.76 8298.79 22998.78 42999.91 396.74 34999.67 12299.49 30997.53 12299.88 16898.98 13699.85 9499.60 192
PMMVS98.80 19798.62 20599.34 18899.27 30698.70 23798.76 43199.31 33397.34 29999.21 25899.07 39697.20 13799.82 22498.56 21398.87 24899.52 223
test12339.01 45142.50 45328.53 46739.17 49020.91 49298.75 43219.17 49219.83 48538.57 48466.67 48233.16 48615.42 48637.50 48629.66 48449.26 481
MSDG98.98 16798.80 17699.53 13399.76 8299.19 15098.75 43299.55 10097.25 30799.47 18399.77 18297.82 11699.87 17596.93 36299.90 5799.54 216
CLD-MVS98.16 25198.10 24598.33 34399.29 30196.82 36698.75 43299.44 25597.83 23899.13 27399.55 28692.92 33699.67 29198.32 24097.69 31898.48 406
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
miper_ehance_all_eth98.18 24998.10 24598.41 33699.23 31797.72 30998.72 43599.31 33396.60 36498.88 32099.29 37097.29 13299.13 39497.60 30995.99 37998.38 419
cl____98.01 27697.84 27798.55 31599.25 31397.97 29298.71 43699.34 31196.47 37598.59 36999.54 29195.65 22399.21 38397.21 34195.77 38598.46 411
DIV-MVS_self_test98.01 27697.85 27698.48 32299.24 31597.95 29798.71 43699.35 30696.50 36998.60 36899.54 29195.72 22199.03 40997.21 34195.77 38598.46 411
test-LLR98.06 26397.90 26998.55 31598.79 40197.10 33698.67 43897.75 45897.34 29998.61 36698.85 42094.45 29199.45 32897.25 33999.38 18399.10 292
TESTMET0.1,197.55 34697.27 35798.40 33898.93 38196.53 37898.67 43897.61 46196.96 33598.64 36099.28 37288.63 41899.45 32897.30 33799.38 18399.21 287
test-mter97.49 35797.13 36498.55 31598.79 40197.10 33698.67 43897.75 45896.65 35698.61 36698.85 42088.23 42299.45 32897.25 33999.38 18399.10 292
mvs5depth96.66 38496.22 38897.97 37497.00 46296.28 38798.66 44199.03 38496.61 36196.93 43499.79 16687.20 43299.47 32496.65 37794.13 42198.16 431
IB-MVS95.67 1896.22 39295.44 40698.57 30999.21 32296.70 36998.65 44297.74 46096.71 35197.27 42398.54 43586.03 44099.92 12398.47 22386.30 46699.10 292
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
DPM-MVS98.95 17098.71 18899.66 9199.63 16699.55 9698.64 44399.10 37297.93 22499.42 19799.55 28698.67 7299.80 23695.80 39699.68 15699.61 189
thisisatest051598.14 25397.79 28099.19 21999.50 23898.50 26298.61 44496.82 46896.95 33799.54 17199.43 32791.66 37699.86 18198.08 26499.51 17499.22 286
DeepPCF-MVS98.18 398.81 19499.37 4497.12 41799.60 19291.75 45998.61 44499.44 25599.35 2599.83 6499.85 8298.70 6999.81 22999.02 13399.91 4699.81 79
cl2297.85 29997.64 30398.48 32299.09 35497.87 30198.60 44699.33 31997.11 32298.87 32399.22 38192.38 35999.17 38898.21 24795.99 37998.42 414
FE-MVSNET398.09 25897.82 27898.89 26398.70 41898.90 20798.57 44799.47 22396.78 34798.87 32399.05 39994.75 26999.23 37397.45 32896.74 35998.53 401
GA-MVS97.85 29997.47 32299.00 24099.38 27597.99 29198.57 44799.15 36697.04 33098.90 31799.30 36889.83 40199.38 34396.70 37298.33 28199.62 187
TinyColmap97.12 37496.89 37397.83 38899.07 35895.52 40898.57 44798.74 42697.58 27097.81 41299.79 16688.16 42399.56 31795.10 41297.21 35298.39 418
eth_miper_zixun_eth98.05 26897.96 26298.33 34399.26 30997.38 32398.56 45099.31 33396.65 35698.88 32099.52 29996.58 17499.12 39997.39 33295.53 39598.47 408
CMPMVSbinary69.68 2394.13 42294.90 41291.84 44997.24 45780.01 47998.52 45199.48 20189.01 46391.99 46699.67 23885.67 44299.13 39495.44 40597.03 35796.39 467
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC97.34 36497.20 35997.75 39399.07 35895.20 41798.51 45299.04 38297.99 21998.31 38499.86 7589.02 40899.55 31995.67 40197.36 34798.49 405
ambc93.06 44792.68 47882.36 47298.47 45398.73 43295.09 45197.41 46155.55 47899.10 40296.42 38291.32 44897.71 451
miper_enhance_ethall98.16 25198.08 24998.41 33698.96 37997.72 30998.45 45499.32 32996.95 33798.97 30699.17 38697.06 14699.22 37897.86 28195.99 37998.29 423
CHOSEN 280x42099.12 13299.13 9599.08 22999.66 14897.89 30098.43 45599.71 1698.88 8399.62 14899.76 18696.63 17099.70 28399.46 6799.99 199.66 166
testmvs39.17 45043.78 45225.37 46836.04 49116.84 49398.36 45626.56 49020.06 48438.51 48567.32 48129.64 48715.30 48737.59 48539.90 48343.98 482
FPMVS84.93 44185.65 44282.75 46386.77 48463.39 48998.35 45798.92 39774.11 47583.39 47498.98 41050.85 48192.40 47884.54 47494.97 40692.46 473
KD-MVS_2432*160094.62 41793.72 42597.31 41197.19 45995.82 39998.34 45899.20 36095.00 42297.57 41598.35 44287.95 42598.10 44992.87 44377.00 47698.01 440
miper_refine_blended94.62 41793.72 42597.31 41197.19 45995.82 39998.34 45899.20 36095.00 42297.57 41598.35 44287.95 42598.10 44992.87 44377.00 47698.01 440
CL-MVSNet_self_test94.49 41993.97 42396.08 43596.16 46493.67 44798.33 46099.38 28995.13 41597.33 42298.15 44992.69 34796.57 46888.67 46079.87 47497.99 444
PVSNet96.02 1798.85 19098.84 17398.89 26399.73 10797.28 32698.32 46199.60 6797.86 23199.50 17899.57 28096.75 16599.86 18198.56 21399.70 15299.54 216
PAPM97.59 34497.09 36699.07 23099.06 36098.26 27698.30 46299.10 37294.88 42498.08 39799.34 35796.27 19299.64 30389.87 45698.92 24199.31 276
Patchmatch-RL test95.84 40195.81 39995.95 43695.61 46790.57 46298.24 46398.39 44495.10 41995.20 44998.67 43094.78 26497.77 45796.28 38790.02 45699.51 232
UnsupCasMVSNet_bld93.53 42692.51 43296.58 43197.38 45393.82 44298.24 46399.48 20191.10 45793.10 46196.66 46774.89 47098.37 44494.03 42887.71 46497.56 457
LCM-MVSNet86.80 44085.22 44491.53 45187.81 48380.96 47798.23 46598.99 38871.05 47690.13 47196.51 46848.45 48396.88 46790.51 45385.30 46796.76 463
cascas97.69 33297.43 33498.48 32298.60 42997.30 32598.18 46699.39 28192.96 44798.41 37898.78 42793.77 32099.27 36698.16 25398.61 26398.86 318
kuosan90.92 43590.11 44093.34 44498.78 40485.59 46998.15 46793.16 48489.37 46292.07 46598.38 44181.48 46495.19 47462.54 48397.04 35699.25 283
Effi-MVS+98.81 19498.59 21199.48 15799.46 25099.12 16398.08 46899.50 17697.50 28299.38 21199.41 33396.37 18799.81 22999.11 11998.54 27199.51 232
PCF-MVS97.08 1497.66 33997.06 36799.47 16399.61 18699.09 16598.04 46999.25 35091.24 45698.51 37399.70 21394.55 28599.91 13592.76 44599.85 9499.42 258
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet_094.43 1996.09 39795.47 40497.94 37799.31 29694.34 43997.81 47099.70 1897.12 31997.46 41798.75 42889.71 40299.79 24297.69 30581.69 47299.68 157
E-PMN80.61 44479.88 44682.81 46290.75 48076.38 48397.69 47195.76 47566.44 48083.52 47392.25 47562.54 47587.16 48268.53 48161.40 47984.89 480
dongtai93.26 42792.93 43194.25 44099.39 27385.68 46897.68 47293.27 48292.87 44896.85 43599.39 34182.33 46197.48 46276.78 47697.80 31499.58 207
ANet_high77.30 44674.86 45084.62 46175.88 48777.61 48197.63 47393.15 48588.81 46464.27 48289.29 47936.51 48583.93 48475.89 47852.31 48192.33 475
EMVS80.02 44579.22 44782.43 46491.19 47976.40 48297.55 47492.49 48766.36 48183.01 47591.27 47764.63 47485.79 48365.82 48260.65 48085.08 479
MVEpermissive76.82 2176.91 44774.31 45184.70 46085.38 48676.05 48496.88 47593.17 48367.39 47971.28 48189.01 48021.66 49087.69 48171.74 48072.29 47890.35 477
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method91.10 43391.36 43590.31 45495.85 46573.72 48794.89 47699.25 35068.39 47895.82 44599.02 40480.50 46798.95 42693.64 43294.89 41098.25 426
Gipumacopyleft90.99 43490.15 43993.51 44398.73 41390.12 46393.98 47799.45 24679.32 47492.28 46494.91 47169.61 47197.98 45387.42 46795.67 38992.45 474
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVScopyleft70.75 2275.98 44874.97 44979.01 46570.98 48855.18 49093.37 47898.21 45165.08 48261.78 48393.83 47321.74 48992.53 47778.59 47591.12 45189.34 478
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt82.80 44281.52 44586.66 45966.61 48968.44 48892.79 47997.92 45568.96 47780.04 48099.85 8285.77 44196.15 47297.86 28143.89 48295.39 472
wuyk23d40.18 44941.29 45436.84 46686.18 48549.12 49179.73 48022.81 49127.64 48325.46 48628.45 48621.98 48848.89 48555.80 48423.56 48512.51 483
mmdepth0.02 4560.03 4590.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.27 4880.00 4910.00 4880.00 4870.00 4860.00 484
monomultidepth0.02 4560.03 4590.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.27 4880.00 4910.00 4880.00 4870.00 4860.00 484
test_blank0.13 4550.17 4580.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4881.57 4870.00 4910.00 4880.00 4870.00 4860.00 484
uanet_test0.02 4560.03 4590.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.27 4880.00 4910.00 4880.00 4870.00 4860.00 484
DCPMVS0.02 4560.03 4590.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.27 4880.00 4910.00 4880.00 4870.00 4860.00 484
cdsmvs_eth3d_5k24.64 45232.85 4550.00 4690.00 4920.00 4940.00 48199.51 1530.00 4870.00 48899.56 28396.58 1740.00 4880.00 4870.00 4860.00 484
pcd_1.5k_mvsjas8.27 45411.03 4570.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.27 48899.01 200.00 4880.00 4870.00 4860.00 484
sosnet-low-res0.02 4560.03 4590.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.27 4880.00 4910.00 4880.00 4870.00 4860.00 484
sosnet0.02 4560.03 4590.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.27 4880.00 4910.00 4880.00 4870.00 4860.00 484
uncertanet0.02 4560.03 4590.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.27 4880.00 4910.00 4880.00 4870.00 4860.00 484
Regformer0.02 4560.03 4590.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.27 4880.00 4910.00 4880.00 4870.00 4860.00 484
ab-mvs-re8.30 45311.06 4560.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 48899.58 2750.00 4910.00 4880.00 4870.00 4860.00 484
uanet0.02 4560.03 4590.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.27 4880.00 4910.00 4880.00 4870.00 4860.00 484
WAC-MVS97.16 33395.47 404
MSC_two_6792asdad99.87 2199.51 22699.76 4999.33 31999.96 4198.87 15699.84 10299.89 29
PC_three_145298.18 17399.84 5699.70 21399.31 398.52 44298.30 24299.80 12599.81 79
No_MVS99.87 2199.51 22699.76 4999.33 31999.96 4198.87 15699.84 10299.89 29
test_one_060199.81 5799.88 1099.49 18998.97 7599.65 13699.81 13199.09 16
eth-test20.00 492
eth-test0.00 492
ZD-MVS99.71 11799.79 4199.61 6096.84 34499.56 16499.54 29198.58 7899.96 4196.93 36299.75 142
IU-MVS99.84 3899.88 1099.32 32998.30 14999.84 5698.86 16199.85 9499.89 29
test_241102_TWO99.48 20199.08 5699.88 4399.81 13198.94 3499.96 4198.91 15099.84 10299.88 35
test_241102_ONE99.84 3899.90 399.48 20199.07 5899.91 3199.74 19699.20 999.76 254
test_0728_THIRD98.99 6999.81 6999.80 14999.09 1699.96 4198.85 16399.90 5799.88 35
GSMVS99.52 223
test_part299.81 5799.83 2299.77 85
sam_mvs194.86 25999.52 223
sam_mvs94.72 272
MTGPAbinary99.47 223
test_post65.99 48394.65 27999.73 266
patchmatchnet-post98.70 42994.79 26399.74 260
gm-plane-assit98.54 43492.96 45394.65 43099.15 38999.64 30397.56 316
test9_res97.49 32299.72 14899.75 110
agg_prior297.21 34199.73 14799.75 110
agg_prior99.67 13599.62 8399.40 27898.87 32399.91 135
TestCases99.31 19699.86 2598.48 26599.61 6097.85 23499.36 21999.85 8295.95 20599.85 18796.66 37599.83 11399.59 203
test_prior99.68 8999.67 13599.48 11199.56 9099.83 21599.74 115
新几何199.75 7799.75 9299.59 8899.54 10996.76 34899.29 23699.64 25198.43 8999.94 9296.92 36499.66 15999.72 134
旧先验199.74 10099.59 8899.54 10999.69 22498.47 8699.68 15699.73 124
原ACMM199.65 9599.73 10799.33 13099.47 22397.46 28499.12 27599.66 24398.67 7299.91 13597.70 30499.69 15399.71 145
testdata299.95 7696.67 374
segment_acmp98.96 27
testdata99.54 12599.75 9298.95 19399.51 15397.07 32599.43 19499.70 21398.87 4299.94 9297.76 29599.64 16299.72 134
test1299.75 7799.64 16299.61 8599.29 34299.21 25898.38 9599.89 16399.74 14599.74 115
plane_prior799.29 30197.03 348
plane_prior699.27 30696.98 35292.71 345
plane_prior599.47 22399.69 28897.78 29197.63 32098.67 362
plane_prior499.61 266
plane_prior397.00 35098.69 10799.11 277
plane_prior199.26 309
n20.00 493
nn0.00 493
door-mid98.05 454
lessismore_v097.79 39298.69 42095.44 41294.75 47895.71 44699.87 6788.69 41499.32 35895.89 39394.93 40898.62 384
LGP-MVS_train98.49 32099.33 28897.05 34299.55 10097.46 28499.24 25099.83 10392.58 35099.72 27098.09 26097.51 33298.68 354
test1199.35 306
door97.92 455
HQP5-MVS96.83 364
BP-MVS97.19 345
HQP4-MVS98.66 35399.64 30398.64 375
HQP3-MVS99.39 28197.58 325
HQP2-MVS92.47 354
NP-MVS99.23 31796.92 36099.40 337
ACMMP++_ref97.19 353
ACMMP++97.43 343
Test By Simon98.75 60
ITE_SJBPF98.08 36599.29 30196.37 38398.92 39798.34 14398.83 33099.75 19191.09 38699.62 31095.82 39497.40 34598.25 426
DeepMVS_CXcopyleft93.34 44499.29 30182.27 47399.22 35685.15 47096.33 43999.05 39990.97 38899.73 26693.57 43397.77 31698.01 440