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.
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
LTVRE_ROB98.40 199.67 399.71 299.56 2699.85 1699.11 6499.90 199.78 3699.63 2899.78 3999.67 3099.48 1099.81 22499.30 6299.97 2199.77 53
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
3Dnovator98.27 298.81 12798.73 13099.05 14598.76 35497.81 20499.25 4399.30 25498.57 17498.55 30999.33 11897.95 14099.90 8197.16 25499.67 24599.44 210
3Dnovator+97.89 398.69 15198.51 17299.24 10698.81 34798.40 12099.02 7099.19 29298.99 12498.07 35799.28 12997.11 21699.84 17796.84 29099.32 35599.47 197
DeepC-MVS97.60 498.97 9998.93 10199.10 13099.35 19997.98 17698.01 21399.46 17597.56 27199.54 7999.50 6898.97 2999.84 17798.06 16399.92 7199.49 177
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepPCF-MVS96.93 598.32 22398.01 25899.23 10898.39 41898.97 7395.03 47199.18 29696.88 34399.33 13898.78 28998.16 12299.28 47796.74 29999.62 26799.44 210
DeepC-MVS_fast96.85 698.30 22898.15 24398.75 21398.61 38897.23 26097.76 25799.09 31697.31 30498.75 27098.66 32197.56 17799.64 36996.10 36099.55 29699.39 232
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OpenMVScopyleft96.65 797.09 35596.68 36898.32 29698.32 42297.16 27398.86 9299.37 21789.48 52096.29 47399.15 17696.56 25599.90 8192.90 46299.20 38097.89 468
ACMH96.65 799.25 4099.24 5399.26 10199.72 4598.38 12399.07 6599.55 12698.30 19499.65 6399.45 8499.22 1799.76 27098.44 13199.77 17299.64 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+96.62 999.08 7999.00 9499.33 8999.71 4998.83 8698.60 12199.58 10399.11 10099.53 8399.18 16398.81 3999.67 34496.71 30499.77 17299.50 169
COLMAP_ROBcopyleft96.50 1098.99 9498.85 11899.41 6999.58 9499.10 6598.74 9999.56 12199.09 11099.33 13899.19 15998.40 8699.72 30695.98 36399.76 18899.42 219
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TAPA-MVS96.21 1196.63 37995.95 40298.65 23498.93 31898.09 15796.93 35799.28 26683.58 53898.13 35197.78 42696.13 28099.40 45793.52 44599.29 36398.45 433
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMM96.08 1298.91 10698.73 13099.48 5799.55 11799.14 5798.07 20099.37 21797.62 26299.04 20298.96 24298.84 3799.79 24697.43 23499.65 25599.49 177
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HY-MVS95.94 1395.90 42095.35 42897.55 38997.95 45594.79 40098.81 9896.94 47092.28 49695.17 49998.57 34089.90 43599.75 28291.20 49897.33 49998.10 457
OpenMVS_ROBcopyleft95.38 1495.84 42395.18 43997.81 35398.41 41797.15 27497.37 32098.62 39683.86 53798.65 28698.37 36794.29 35799.68 33988.41 51798.62 44396.60 509
ACMP95.32 1598.41 20498.09 24899.36 7499.51 13498.79 8997.68 26999.38 21395.76 40798.81 26098.82 28198.36 9099.82 20794.75 40499.77 17299.48 188
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLCcopyleft94.65 1696.51 38495.73 40898.85 18298.75 35697.91 18796.42 39899.06 32090.94 51295.59 48797.38 45494.41 34999.59 39490.93 50398.04 47599.05 345
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet93.40 1795.67 42795.70 40995.57 48598.83 34188.57 51892.50 52897.72 43792.69 49196.49 47096.44 47993.72 37399.43 45393.61 44099.28 36498.71 408
PCF-MVS92.86 1894.36 45793.00 47698.42 28398.70 36897.56 22793.16 52599.11 31379.59 54297.55 39997.43 45192.19 40599.73 29679.85 53999.45 32697.97 465
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IB-MVS91.63 1992.24 49690.90 50096.27 45397.22 49891.24 49594.36 49693.33 52692.37 49492.24 53394.58 51866.20 53799.89 9793.16 45694.63 53297.66 483
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
PMVScopyleft91.26 2097.86 28597.94 26897.65 37499.71 4997.94 18398.52 13098.68 39098.99 12497.52 40299.35 11197.41 19498.18 51491.59 49199.67 24596.82 505
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PVSNet_089.98 2191.15 50290.30 50493.70 51497.72 46884.34 53990.24 53597.42 44790.20 51693.79 52293.09 52890.90 42798.89 50186.57 52672.76 54797.87 470
MVEpermissive83.40 2292.50 49191.92 49394.25 50598.83 34191.64 48392.71 52683.52 54995.92 39686.46 54495.46 50295.20 32195.40 54180.51 53898.64 43995.73 521
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CMPMVSbinary75.91 2396.29 39995.44 42398.84 18896.25 52898.69 9897.02 34899.12 31188.90 52497.83 37998.86 26889.51 44098.90 50091.92 48399.51 30998.92 373
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
RoMa-HiRes98.68 15798.52 17099.16 11899.50 14198.35 12998.01 21399.71 4896.94 33599.35 13098.66 32196.38 26699.63 37298.39 13899.71 21799.48 188
DKM-HiRes98.14 25497.80 28199.16 11899.51 13498.40 12096.70 37399.63 8297.55 27397.45 41098.74 29893.27 38099.54 41797.78 19399.55 29699.53 157
ArgMatch-Sym97.83 29397.54 30598.71 22398.98 31097.65 22096.25 41299.43 19395.60 41298.85 25097.98 41095.72 30399.56 40695.54 38799.50 31798.92 373
PMatch-Up-SfM97.79 29697.48 31398.72 22199.03 29497.78 20696.05 42699.48 15996.90 34198.72 27499.18 16392.00 41199.71 30897.15 25798.77 42598.69 412
onestephybrid0198.40 20798.39 19698.42 28399.05 28996.23 32796.73 37199.41 20498.18 21298.65 28699.02 21397.02 22199.69 32797.73 20499.70 22799.33 268
nocashy0298.57 17898.66 14698.31 29899.20 24495.89 34396.92 35999.57 11198.71 15899.02 20699.04 20997.48 19099.71 30898.28 14699.70 22799.35 258
PMatch-SfM97.89 27997.64 29998.66 23299.26 22997.44 24096.08 42499.51 14496.72 35498.47 31999.13 18293.62 37699.70 31797.14 25898.80 42498.83 386
DenseAffine98.10 25697.86 27798.84 18899.32 20697.93 18496.62 38299.76 3996.68 35898.65 28698.72 30294.46 34799.33 46896.76 29699.75 19299.25 297
ArgMatch-SfM97.96 27497.72 29098.66 23299.02 30297.33 24696.49 39299.52 14295.46 42198.71 27898.29 38196.14 27899.69 32796.30 34599.56 29198.97 363
MASt3R-SfM96.02 41195.82 40596.60 44197.03 50694.90 39594.26 49998.53 40588.40 52998.41 32698.67 31792.39 40097.62 52495.31 39199.41 33897.29 496
hybridnocas0798.32 22398.37 20298.17 31599.14 26695.51 35996.67 37799.56 12197.85 24398.75 27098.95 24696.65 25199.63 37298.00 17199.78 16499.37 244
cashybrid299.12 6999.12 7199.09 13499.53 12798.08 16198.34 16399.66 7199.35 6499.35 13099.23 15098.39 8899.72 30698.46 12999.81 14099.47 197
dtuonlycased97.70 30298.19 23696.24 45599.75 3489.51 51594.69 48399.64 7998.23 20199.46 10198.57 34098.25 10799.85 15895.65 38099.44 33399.36 252
dtuonly96.49 38797.28 32394.10 50898.80 35083.27 54293.66 51599.48 15995.10 43497.87 37498.30 37895.61 30799.68 33996.98 27599.75 19299.33 268
dtuplus98.32 22398.39 19698.10 32399.15 26495.29 37796.68 37599.51 14497.32 30299.18 18099.15 17697.61 17299.62 37797.19 25199.74 19599.38 241
SIFT-UM-Cal96.49 38796.62 37596.12 46698.13 44697.89 19093.35 52198.44 41095.48 42098.63 29098.34 37195.45 31597.45 52592.22 48099.50 31793.02 536
SIFT-NCM-Cal96.56 38296.68 36896.20 45998.27 42998.44 11894.40 49496.67 47795.29 42897.63 39198.17 39296.40 26396.59 53793.61 44099.66 25393.57 529
SIFT-CM-Cal96.28 40096.31 39396.16 46398.39 41898.11 15393.46 52096.47 48394.81 44498.49 31698.43 36094.48 34697.34 52892.60 47499.70 22793.02 536
SIFT-PCN-Cal96.34 39596.46 38796.01 47098.17 44096.89 29293.48 51997.35 45294.84 44299.35 13098.30 37894.70 34197.92 51892.03 48199.88 9593.21 535
SIFT-NN-UMatch95.38 44095.26 43395.75 47998.25 43097.78 20693.24 52495.66 50394.01 46995.10 50197.47 44993.12 38596.78 53492.42 47798.04 47592.69 541
SIFT-NN-NCMNet95.39 43995.22 43695.92 47298.29 42598.34 13193.58 51794.60 51194.07 46794.84 50597.53 44194.37 35396.62 53591.01 50198.64 43992.80 539
SIFT-NN-CMatch95.63 43095.48 41996.08 46798.24 43298.00 17192.71 52694.29 51594.20 46195.85 48397.26 45995.72 30397.01 53091.99 48299.02 40493.23 533
SIFT-NN-PointCN96.06 40896.11 39995.91 47397.88 45997.73 21393.49 51897.51 44693.22 47996.57 46098.26 38396.23 27596.60 53692.54 47599.27 36593.40 531
XFeat-NN89.63 50489.13 50791.14 52590.93 54990.02 51284.90 54294.05 52188.10 53092.89 52893.33 52778.74 51190.89 54683.46 53295.72 52692.52 542
ALIKED-NN94.29 46193.41 47096.94 42596.18 52997.66 21894.90 47598.68 39088.85 52590.43 53796.81 47089.82 43696.59 53786.67 52598.33 45396.58 510
SP-NN94.67 45394.44 45595.36 49395.12 53795.23 38294.27 49896.10 49094.46 45290.91 53695.76 49491.47 42093.87 54495.23 39496.62 51097.00 500
SIFT-NN92.96 48592.79 47993.46 51696.92 50896.45 31991.89 53294.39 51392.91 48792.54 53095.46 50288.26 45290.71 54785.22 52897.52 48693.22 534
hybridcas99.08 7999.13 7098.92 17399.54 12397.61 22598.22 17799.66 7199.27 7499.40 11799.24 14498.47 7799.70 31798.59 11899.80 15299.46 200
GLUNet-SfM86.26 50884.68 51091.01 52680.58 55283.56 54078.04 54393.59 52376.70 54395.29 49894.72 51677.51 51794.26 54366.39 54699.33 35295.20 523
PDCNetPlus95.22 44494.73 45196.70 43997.85 46191.14 49893.94 50999.97 193.06 48498.95 22398.89 26374.32 52199.14 48795.63 38199.93 5799.82 36
hybrid98.22 24098.27 22298.08 32899.13 26995.24 37996.61 38399.53 13697.43 29198.46 32098.97 23896.75 24599.65 36497.84 18899.69 23399.35 258
RoMa-SfM98.46 19998.27 22299.02 15199.35 19998.32 13297.56 29199.70 5495.88 39899.38 12198.65 32496.41 26299.46 44697.78 19399.71 21799.28 287
DKM98.18 24897.95 26598.85 18299.35 19998.31 13396.68 37599.69 5796.90 34198.61 29698.77 29194.41 34998.93 49797.32 24299.84 11499.32 273
ELoFTR97.81 29597.74 28698.04 33499.39 18595.79 35097.28 33299.58 10394.13 46399.38 12199.37 10493.31 37999.60 38997.23 24899.96 2898.74 406
MatchFormer97.07 35796.92 34997.49 39598.44 41195.92 34196.79 36499.14 30993.08 48399.32 14499.10 19193.89 36799.03 49092.78 46899.78 16497.52 488
LoFTR97.97 27397.79 28298.53 26798.80 35097.47 23597.01 34999.55 12695.55 41599.46 10199.22 15294.22 35999.44 45196.45 33499.82 13398.68 416
ALIKED-LG97.10 35396.63 37498.50 27497.96 45498.68 9997.75 26099.68 6495.86 39998.36 33498.33 37591.58 41699.04 48990.87 50699.31 35797.77 477
SP-DiffGlue96.87 36996.76 36297.21 41095.17 53696.88 29496.12 42198.93 34596.51 36398.37 33297.55 44093.65 37597.83 51996.11 35998.45 45196.92 501
SP-LightGlue97.22 34597.01 34397.88 34797.33 49597.19 26796.38 40099.08 31897.28 30796.53 46397.50 44592.36 40198.70 50697.84 18898.76 42797.74 479
SP-SuperGlue97.31 33597.23 32897.57 38896.96 50797.24 25996.26 41198.76 38097.68 25796.88 44597.85 42194.32 35598.01 51697.76 20098.57 44697.45 491
SIFT-UMatch96.33 39696.47 38595.89 47498.29 42597.95 18193.84 51197.24 45795.78 40698.72 27498.04 40593.45 37896.81 53393.14 45799.73 19992.91 538
SIFT-NCMNet96.30 39896.40 38996.03 46997.80 46697.68 21792.34 53096.94 47095.55 41598.84 25398.63 33094.17 36097.63 52393.57 44499.71 21792.77 540
SIFT-ConvMatch96.57 38196.62 37596.43 44698.20 43698.27 13693.88 51096.88 47395.29 42898.88 24398.25 38495.18 32397.43 52693.22 45599.83 12693.59 528
SIFT-PointCN96.45 39296.47 38596.39 44898.13 44697.54 22993.31 52297.23 45894.67 44798.68 28298.32 37694.64 34297.81 52093.50 44799.77 17293.83 526
XFeat-MNN93.41 47792.98 47794.68 50192.63 54392.92 46289.72 53995.81 49792.10 49897.23 42396.29 48384.95 47897.31 52989.60 51498.54 44893.81 527
ALIKED-MNN95.97 41795.30 43298.00 33797.66 47898.12 15296.98 35299.41 20491.11 51094.04 51897.30 45891.56 41798.61 50889.99 51199.63 26397.28 497
SP-MNN96.46 39196.24 39897.10 41696.71 51595.98 33896.00 42897.33 45395.82 40394.93 50497.10 46793.70 37498.01 51696.30 34598.30 45797.30 495
SIFT-MNN95.92 41995.97 40195.74 48198.18 43898.00 17194.17 50196.99 46595.74 40897.16 42497.90 41790.71 42895.79 53993.71 43899.21 37893.44 530
casdiffseed41469214799.09 7399.12 7199.01 15399.55 11797.91 18798.30 16599.68 6499.04 11999.19 17599.37 10498.98 2899.61 38598.13 15699.83 12699.50 169
gbinet_0.2-2-1-0.0295.44 43794.55 45298.14 31995.99 53395.34 37594.71 47998.29 41996.00 39296.05 48090.50 54284.99 47799.79 24697.33 24097.07 50499.28 287
0.3-1-1-0.01587.27 50784.50 51195.57 48591.70 54590.77 50489.41 54092.04 53388.98 52382.46 54781.35 54560.36 54899.50 43192.96 45981.23 54396.45 511
0.4-1-1-0.188.42 50585.91 50895.94 47193.08 54291.54 48490.99 53492.04 53389.96 51984.83 54583.25 54463.75 54499.52 42493.25 45382.07 54196.75 506
0.4-1-1-0.287.49 50684.89 50995.31 49491.33 54890.08 51188.47 54192.07 53288.70 52684.06 54681.08 54663.62 54599.49 43592.93 46181.71 54296.37 512
wanda-best-256-51295.48 43594.74 44997.68 36896.53 51994.12 42394.17 50198.57 40195.84 40096.71 45291.16 53886.05 46799.76 27097.57 21896.09 51899.17 327
usedtu_dtu_shiyan298.99 9498.86 11599.39 7299.73 3898.71 9799.05 6899.47 17099.16 9499.49 9499.12 18696.34 27099.93 5398.05 16599.36 34599.54 143
usedtu_dtu_shiyan197.37 32997.13 33698.11 32199.03 29495.40 37094.47 49198.99 33896.87 34497.97 36697.81 42492.12 40799.75 28297.49 23199.43 33599.16 333
blended_shiyan895.98 41595.33 42997.94 34297.05 50594.87 39895.34 46198.59 39896.17 38097.09 42892.39 53387.62 45699.76 27097.65 21096.05 52499.20 313
E5new99.05 8399.11 7498.85 18299.60 8897.30 25098.42 15199.63 8298.73 15199.26 15799.39 10098.71 5199.70 31798.43 13399.84 11499.54 143
FE-blended-shiyan795.48 43594.74 44997.68 36896.53 51994.12 42394.17 50198.57 40195.84 40096.71 45291.16 53886.05 46799.76 27097.57 21896.09 51899.17 327
E6new99.05 8399.11 7498.85 18299.60 8897.30 25098.42 15199.63 8298.73 15199.26 15799.39 10098.71 5199.70 31798.43 13399.84 11499.54 143
blended_shiyan695.99 41495.33 42997.95 34197.06 50394.89 39695.34 46198.58 39996.17 38097.06 43092.41 53287.64 45599.76 27097.64 21196.09 51899.19 319
usedtu_blend_shiyan596.20 40695.62 41297.94 34296.53 51994.93 39398.83 9699.59 10098.89 13896.71 45291.16 53886.05 46799.73 29696.70 30596.09 51899.17 327
blend_shiyan492.09 49890.16 50597.88 34796.78 51394.93 39395.24 46598.58 39996.22 37896.07 47891.42 53763.46 54699.73 29696.70 30576.98 54698.98 359
E699.05 8399.11 7498.85 18299.60 8897.30 25098.42 15199.63 8298.73 15199.26 15799.39 10098.71 5199.70 31798.43 13399.84 11499.54 143
E599.05 8399.11 7498.85 18299.60 8897.30 25098.42 15199.63 8298.73 15199.26 15799.39 10098.71 5199.70 31798.43 13399.84 11499.54 143
FE-MVSNET397.37 32997.13 33698.11 32199.03 29495.40 37094.47 49198.99 33896.87 34497.97 36697.81 42492.12 40799.75 28297.49 23199.43 33599.16 333
E498.87 11298.88 10898.81 19499.52 13197.23 26097.62 28099.61 9298.58 17299.18 18099.33 11898.29 9999.69 32797.99 17499.83 12699.52 161
E3new98.41 20498.34 20898.62 24299.19 24896.90 29197.32 32499.50 14997.40 29498.63 29098.92 25197.21 20999.65 36497.34 23899.52 30699.31 278
FE-MVSNET299.15 5799.22 5498.94 16799.70 5797.49 23198.62 11899.67 7098.85 14599.34 13599.54 6298.47 7799.81 22498.93 9299.91 8099.51 165
fmvsm_s_conf0.5_n_1199.21 4799.34 3598.80 19799.48 15896.56 31297.97 22799.69 5799.63 2899.84 3099.54 6298.21 11599.94 4199.76 2399.95 3999.88 20
E298.70 14798.68 14198.73 21999.40 18397.10 27797.48 30399.57 11198.09 22499.00 20899.20 15697.90 14399.67 34497.73 20499.77 17299.43 214
MED-MVS test99.45 6499.58 9498.93 7998.68 10999.60 9496.46 36999.53 8398.77 29199.83 19596.67 30999.64 25799.58 117
MED-MVS99.01 9098.84 11999.52 4499.58 9498.93 7998.68 10999.60 9498.85 14599.53 8399.16 17097.87 14999.83 19596.67 30999.64 25799.81 41
E398.69 15198.68 14198.73 21999.40 18397.10 27797.48 30399.57 11198.09 22499.00 20899.20 15697.90 14399.67 34497.73 20499.77 17299.43 214
TestfortrainingZip a99.09 7398.92 10299.61 1399.58 9499.17 4398.68 10999.27 26998.85 14599.61 7099.16 17097.14 21399.86 14498.39 13899.57 28799.81 41
TestfortrainingZip98.97 16298.30 42498.43 11998.68 10998.26 42097.76 25198.86 24998.16 39495.15 32499.47 44297.55 48599.02 352
fmvsm_s_conf0.5_n_1099.15 5799.27 4798.78 20499.47 16196.56 31297.75 26099.71 4899.60 3599.74 4699.44 8597.96 13999.95 2599.86 499.94 5199.82 36
viewdifsd2359ckpt0798.71 14298.86 11598.26 30399.43 17695.65 35397.20 33999.66 7199.20 8499.29 14999.01 22598.29 9999.73 29697.92 17999.75 19299.39 232
viewdifsd2359ckpt0998.13 25597.92 27198.77 20999.18 25697.35 24497.29 32899.53 13695.81 40498.09 35598.47 35696.34 27099.66 35797.02 26899.51 30999.29 284
viewdifsd2359ckpt1398.39 21498.29 21898.70 22599.26 22997.19 26797.51 29999.48 15996.94 33598.58 30398.82 28197.47 19299.55 41197.21 25099.33 35299.34 262
viewcassd2359sk1198.55 18498.51 17298.67 23099.29 21496.99 28397.39 31499.54 13297.73 25398.81 26099.08 19897.55 17899.66 35797.52 22599.67 24599.36 252
viewdifsd2359ckpt1198.84 11999.04 8798.24 30799.56 11195.51 35997.38 31699.70 5499.16 9499.57 7299.40 9798.26 10599.71 30898.55 12599.82 13399.50 169
viewmacassd2359aftdt98.86 11698.87 11198.83 19099.53 12797.32 24997.70 26799.64 7998.22 20399.25 16599.27 13198.40 8699.61 38597.98 17599.87 10099.55 137
viewmsd2359difaftdt98.84 11999.04 8798.24 30799.56 11195.51 35997.38 31699.70 5499.16 9499.57 7299.40 9798.26 10599.71 30898.55 12599.82 13399.50 169
diffmvs_AUTHOR98.50 19598.59 16198.23 31099.35 19995.48 36496.61 38399.60 9498.37 18598.90 23699.00 22997.37 19799.76 27098.22 15099.85 10999.46 200
FE-MVSNET98.59 17598.50 17598.87 17999.58 9497.30 25098.08 19699.74 4496.94 33598.97 21799.10 19196.94 22799.74 28997.33 24099.86 10799.55 137
fmvsm_l_conf0.5_n_999.32 3299.43 2498.98 16099.59 9297.18 27097.44 31199.83 2699.56 3999.91 1299.34 11599.36 1399.93 5399.83 1099.98 1299.85 30
mamba_040898.80 12998.88 10898.55 26099.27 22096.50 31598.00 21599.60 9498.93 13299.22 17098.84 27698.59 6799.89 9797.74 20299.72 20899.27 290
icg_test_0407_298.20 24598.38 20097.65 37499.03 29494.03 42995.78 44499.45 17998.16 21699.06 19298.71 30498.27 10399.68 33997.50 22699.45 32699.22 308
SSM_0407298.80 12998.88 10898.56 25899.27 22096.50 31598.00 21599.60 9498.93 13299.22 17098.84 27698.59 6799.90 8197.74 20299.72 20899.27 290
SSM_040798.86 11698.96 10098.55 26099.27 22096.50 31598.04 20599.66 7199.09 11099.22 17099.02 21398.79 4399.87 13597.87 18599.72 20899.27 290
viewmambaseed2359dif98.19 24698.26 22597.99 33999.02 30295.03 39096.59 38699.53 13696.21 37999.00 20898.99 23197.62 17099.61 38597.62 21399.72 20899.33 268
IMVS_040798.39 21498.64 15097.66 37299.03 29494.03 42998.10 19399.45 17998.16 21699.06 19298.71 30498.27 10399.71 30897.50 22699.45 32699.22 308
viewmanbaseed2359cas98.58 17798.54 16798.70 22599.28 21797.13 27697.47 30799.55 12697.55 27398.96 22298.92 25197.77 15799.59 39497.59 21799.77 17299.39 232
IMVS_040498.07 26198.20 23297.69 36799.03 29494.03 42996.67 37799.45 17998.16 21698.03 36298.71 30496.80 23899.82 20797.50 22699.45 32699.22 308
SSM_040498.90 10899.01 9298.57 25399.42 17896.59 30798.13 18699.66 7199.09 11099.30 14899.02 21398.79 4399.89 9797.87 18599.80 15299.23 303
IMVS_040398.34 21898.56 16497.66 37299.03 29494.03 42997.98 22399.45 17998.16 21698.89 23998.71 30497.90 14399.74 28997.50 22699.45 32699.22 308
SD_040396.28 40095.83 40497.64 37798.72 36094.30 41698.87 8998.77 37897.80 24796.53 46398.02 40797.34 19999.47 44276.93 54299.48 32299.16 333
fmvsm_s_conf0.5_n_999.17 5299.38 2898.53 26799.51 13495.82 34897.62 28099.78 3699.72 1499.90 1499.48 7598.66 5999.89 9799.85 699.93 5799.89 16
ME-MVS98.61 17198.33 21399.44 6599.24 23298.93 7997.45 30999.06 32098.14 22299.06 19298.77 29196.97 22699.82 20796.67 30999.64 25799.58 117
NormalMVS98.26 23597.97 26499.15 12399.64 7797.83 19698.28 16799.43 19399.24 7798.80 26298.85 27189.76 43799.94 4198.04 16699.67 24599.68 73
lecture99.25 4099.12 7199.62 999.64 7799.40 1198.89 8899.51 14499.19 8999.37 12599.25 14298.36 9099.88 11598.23 14999.67 24599.59 109
SymmetryMVS98.05 26397.71 29299.09 13499.29 21497.83 19698.28 16797.64 44499.24 7798.80 26298.85 27189.76 43799.94 4198.04 16699.50 31799.49 177
Elysia99.15 5799.14 6899.18 11399.63 8397.92 18598.50 13799.43 19399.67 2099.70 5199.13 18296.66 24999.98 499.54 4499.96 2899.64 86
StellarMVS99.15 5799.14 6899.18 11399.63 8397.92 18598.50 13799.43 19399.67 2099.70 5199.13 18296.66 24999.98 499.54 4499.96 2899.64 86
KinetiMVS99.03 8899.02 9099.03 14899.70 5797.48 23498.43 14899.29 26299.70 1599.60 7199.07 19996.13 28099.94 4199.42 5599.87 10099.68 73
LuminaMVS98.39 21498.20 23298.98 16099.50 14197.49 23197.78 25197.69 43998.75 15099.49 9499.25 14292.30 40499.94 4199.14 7599.88 9599.50 169
VortexMVS97.98 27298.31 21597.02 42098.88 33291.45 48798.03 20799.47 17098.65 16099.55 7799.47 7891.49 41999.81 22499.32 6099.91 8099.80 45
AstraMVS98.16 25398.07 25398.41 28599.51 13495.86 34598.00 21595.14 50698.97 12799.43 10899.24 14493.25 38199.84 17799.21 7099.87 10099.54 143
guyue98.01 26797.93 27098.26 30399.45 16995.48 36498.08 19696.24 48698.89 13899.34 13599.14 18091.32 42299.82 20799.07 8099.83 12699.48 188
sc_t199.62 799.66 899.53 3899.82 1999.09 6899.50 1199.63 8299.88 499.86 2499.80 1199.03 2499.89 9799.48 5299.93 5799.60 102
tt0320-xc99.64 599.68 599.50 5499.72 4598.98 7199.51 1099.85 1999.86 699.88 2199.82 599.02 2699.90 8199.54 4499.95 3999.61 100
tt032099.61 899.65 999.48 5799.71 4998.94 7899.54 899.83 2699.87 599.89 1899.82 598.75 4799.90 8199.54 4499.95 3999.59 109
fmvsm_s_conf0.5_n_899.13 6699.26 5098.74 21799.51 13496.44 32097.65 27599.65 7799.66 2399.78 3999.48 7597.92 14299.93 5399.72 3099.95 3999.87 22
fmvsm_s_conf0.5_n_798.83 12299.04 8798.20 31299.30 21294.83 39997.23 33499.36 22198.64 16199.84 3099.43 8898.10 12799.91 7499.56 4199.96 2899.87 22
fmvsm_s_conf0.5_n_699.08 7999.21 5798.69 22799.36 19496.51 31497.62 28099.68 6498.43 18399.85 2799.10 19199.12 2399.88 11599.77 2299.92 7199.67 78
fmvsm_s_conf0.5_n_599.07 8299.10 8098.99 15699.47 16197.22 26397.40 31399.83 2697.61 26599.85 2799.30 12598.80 4199.95 2599.71 3299.90 8899.78 50
fmvsm_s_conf0.5_n_499.01 9099.22 5498.38 28999.31 20895.48 36497.56 29199.73 4598.87 14099.75 4499.27 13198.80 4199.86 14499.80 1799.90 8899.81 41
SSC-MVS3.298.53 18998.79 12497.74 36299.46 16493.62 45296.45 39499.34 23399.33 6698.93 23298.70 31197.90 14399.90 8199.12 7699.92 7199.69 72
testing3-293.78 47093.91 46193.39 51998.82 34481.72 54897.76 25795.28 50498.60 16896.54 46296.66 47365.85 53999.62 37796.65 31398.99 40998.82 388
myMVS_eth3d2892.92 48792.31 48394.77 49997.84 46287.59 52596.19 41596.11 48997.08 32794.27 51293.49 52566.07 53898.78 50391.78 48697.93 47997.92 467
UWE-MVS-2890.22 50389.28 50693.02 52394.50 54082.87 54496.52 39087.51 54495.21 43292.36 53296.04 48571.57 52598.25 51372.04 54497.77 48197.94 466
fmvsm_l_conf0.5_n_399.45 1899.48 1899.34 8399.59 9298.21 14597.82 24599.84 2399.41 5799.92 899.41 9499.51 899.95 2599.84 999.97 2199.87 22
fmvsm_s_conf0.5_n_399.22 4699.37 3198.78 20499.46 16496.58 31097.65 27599.72 4699.47 4799.86 2499.50 6898.94 3199.89 9799.75 2699.97 2199.86 28
fmvsm_s_conf0.5_n_299.14 6299.31 4198.63 24099.49 15096.08 33597.38 31699.81 3299.48 4499.84 3099.57 4998.46 8299.89 9799.82 1299.97 2199.91 13
fmvsm_s_conf0.1_n_299.20 5099.38 2898.65 23499.69 6196.08 33597.49 30299.90 1299.53 4199.88 2199.64 3798.51 7699.90 8199.83 1099.98 1299.97 4
GDP-MVS97.50 31597.11 33898.67 23099.02 30296.85 29598.16 18399.71 4898.32 19298.52 31498.54 34383.39 49299.95 2598.79 10199.56 29199.19 319
BP-MVS197.40 32796.97 34598.71 22399.07 28196.81 29798.34 16397.18 45998.58 17298.17 34498.61 33584.01 48899.94 4198.97 8999.78 16499.37 244
reproduce_monomvs95.00 45095.25 43494.22 50697.51 48983.34 54197.86 24198.44 41098.51 17999.29 14999.30 12567.68 53299.56 40698.89 9699.81 14099.77 53
mmtdpeth99.30 3399.42 2598.92 17399.58 9496.89 29299.48 1399.92 899.92 298.26 34199.80 1198.33 9699.91 7499.56 4199.95 3999.97 4
reproduce_model99.15 5798.97 9899.67 499.33 20599.44 998.15 18499.47 17099.12 9999.52 8799.32 12398.31 9799.90 8197.78 19399.73 19999.66 80
reproduce-ours99.09 7398.90 10599.67 499.27 22099.49 598.00 21599.42 20099.05 11799.48 9699.27 13198.29 9999.89 9797.61 21499.71 21799.62 92
our_new_method99.09 7398.90 10599.67 499.27 22099.49 598.00 21599.42 20099.05 11799.48 9699.27 13198.29 9999.89 9797.61 21499.71 21799.62 92
mmdepth0.00 5190.00 5220.00 5340.00 5570.00 5590.00 5450.00 5580.00 5520.00 5530.00 5520.00 5560.00 5530.00 5510.00 5510.00 549
monomultidepth0.00 5190.00 5220.00 5340.00 5570.00 5590.00 5450.00 5580.00 5520.00 5530.00 5520.00 5560.00 5530.00 5510.00 5510.00 549
mvs5depth99.30 3399.59 1298.44 28199.65 7195.35 37399.82 399.94 399.83 799.42 11299.94 298.13 12599.96 1399.63 3699.96 28100.00 1
MVStest195.86 42195.60 41496.63 44095.87 53491.70 48297.93 22998.94 34298.03 22799.56 7499.66 3271.83 52498.26 51299.35 5899.24 37199.91 13
ttmdpeth97.91 27698.02 25797.58 38398.69 37394.10 42598.13 18698.90 35297.95 23397.32 41999.58 4795.95 29598.75 50496.41 33799.22 37599.87 22
WBMVS95.18 44594.78 44796.37 44997.68 47689.74 51495.80 44398.73 38797.54 27698.30 33598.44 35970.06 52699.82 20796.62 31599.87 10099.54 143
dongtai76.24 51275.95 51577.12 53092.39 54467.91 55490.16 53659.44 55582.04 54089.42 54094.67 51749.68 55281.74 54848.06 54777.66 54581.72 544
kuosan69.30 51368.95 51670.34 53187.68 55165.00 55591.11 53359.90 55469.02 54474.46 54988.89 54348.58 55368.03 55028.61 54872.33 54877.99 545
MVSMamba_PlusPlus98.83 12298.98 9798.36 29399.32 20696.58 31098.90 8499.41 20499.75 1098.72 27499.50 6896.17 27799.94 4199.27 6499.78 16498.57 426
MGCFI-Net98.34 21898.28 21998.51 27098.47 40697.59 22698.96 7899.48 15999.18 9297.40 41495.50 49998.66 5999.50 43198.18 15398.71 43298.44 436
testing9193.32 47892.27 48496.47 44597.54 48291.25 49496.17 41996.76 47697.18 32193.65 52493.50 52465.11 54199.63 37293.04 45897.45 49098.53 427
testing1193.08 48392.02 48996.26 45497.56 48090.83 50396.32 40595.70 49996.47 36892.66 52993.73 52164.36 54299.59 39493.77 43797.57 48498.37 445
testing9993.04 48491.98 49296.23 45797.53 48490.70 50696.35 40395.94 49496.87 34493.41 52593.43 52663.84 54399.59 39493.24 45497.19 50098.40 441
UBG93.25 48092.32 48296.04 46897.72 46890.16 50995.92 43795.91 49596.03 39093.95 52193.04 52969.60 52899.52 42490.72 50897.98 47798.45 433
UWE-MVS92.38 49391.76 49694.21 50797.16 49984.65 53595.42 45888.45 54395.96 39496.17 47495.84 49366.36 53599.71 30891.87 48598.64 43998.28 448
ETVMVS92.60 49091.08 49997.18 41197.70 47393.65 45196.54 38795.70 49996.51 36394.68 50892.39 53361.80 54799.50 43186.97 52297.41 49398.40 441
sasdasda98.34 21898.26 22598.58 25098.46 40897.82 20198.96 7899.46 17599.19 8997.46 40795.46 50298.59 6799.46 44698.08 16198.71 43298.46 430
testing22291.96 49990.37 50296.72 43897.47 49192.59 46896.11 42294.76 50896.83 34892.90 52792.87 53057.92 54999.55 41186.93 52397.52 48698.00 464
WB-MVSnew95.73 42695.57 41796.23 45796.70 51690.70 50696.07 42593.86 52295.60 41297.04 43295.45 50696.00 28799.55 41191.04 50098.31 45698.43 438
fmvsm_l_conf0.5_n_a99.19 5199.27 4798.94 16799.65 7197.05 27997.80 24999.76 3998.70 15999.78 3999.11 18898.79 4399.95 2599.85 699.96 2899.83 33
fmvsm_l_conf0.5_n99.21 4799.28 4699.02 15199.64 7797.28 25697.82 24599.76 3998.73 15199.82 3499.09 19798.81 3999.95 2599.86 499.96 2899.83 33
fmvsm_s_conf0.1_n_a99.17 5299.30 4498.80 19799.75 3496.59 30797.97 22799.86 1798.22 20399.88 2199.71 2298.59 6799.84 17799.73 2899.98 1299.98 3
fmvsm_s_conf0.1_n99.16 5699.33 3798.64 23699.71 4996.10 33097.87 24099.85 1998.56 17799.90 1499.68 2598.69 5799.85 15899.72 3099.98 1299.97 4
fmvsm_s_conf0.5_n_a99.10 7299.20 5898.78 20499.55 11796.59 30797.79 25099.82 3198.21 20599.81 3699.53 6498.46 8299.84 17799.70 3399.97 2199.90 15
fmvsm_s_conf0.5_n99.09 7399.26 5098.61 24699.55 11796.09 33397.74 26299.81 3298.55 17899.85 2799.55 5698.60 6699.84 17799.69 3599.98 1299.89 16
MM98.22 24097.99 26098.91 17598.66 38396.97 28497.89 23694.44 51299.54 4098.95 22399.14 18093.50 37799.92 6599.80 1799.96 2899.85 30
WAC-MVS90.90 50191.37 495
Syy-MVS96.04 41095.56 41897.49 39597.10 50194.48 41196.18 41796.58 48095.65 41094.77 50692.29 53591.27 42399.36 46298.17 15598.05 47398.63 420
test_fmvsmconf0.1_n99.49 1599.54 1499.34 8399.78 2498.11 15397.77 25499.90 1299.33 6699.97 399.66 3299.71 399.96 1399.79 1999.99 599.96 8
test_fmvsmconf0.01_n99.57 1099.63 1099.36 7499.87 1298.13 15198.08 19699.95 299.45 5099.98 299.75 1699.80 199.97 699.82 1299.99 599.99 2
myMVS_eth3d91.92 50090.45 50196.30 45197.10 50190.90 50196.18 41796.58 48095.65 41094.77 50692.29 53553.88 55099.36 46289.59 51598.05 47398.63 420
testing393.51 47492.09 48797.75 36098.60 39094.40 41397.32 32495.26 50597.56 27196.79 45095.50 49953.57 55199.77 26495.26 39398.97 41399.08 341
SSC-MVS98.71 14298.74 12898.62 24299.72 4596.08 33598.74 9998.64 39599.74 1299.67 5999.24 14494.57 34499.95 2599.11 7799.24 37199.82 36
test_fmvsmconf_n99.44 1999.48 1899.31 9499.64 7798.10 15697.68 26999.84 2399.29 7299.92 899.57 4999.60 599.96 1399.74 2799.98 1299.89 16
WB-MVS98.52 19398.55 16598.43 28299.65 7195.59 35498.52 13098.77 37899.65 2599.52 8799.00 22994.34 35499.93 5398.65 11498.83 42199.76 58
test_fmvsmvis_n_192099.26 3999.49 1698.54 26599.66 7096.97 28498.00 21599.85 1999.24 7799.92 899.50 6899.39 1299.95 2599.89 399.98 1298.71 408
dmvs_re95.98 41595.39 42697.74 36298.86 33597.45 23898.37 15995.69 50197.95 23396.56 46195.95 48890.70 42997.68 52288.32 51896.13 51798.11 456
SDMVSNet99.23 4599.32 3998.96 16499.68 6497.35 24498.84 9599.48 15999.69 1799.63 6699.68 2599.03 2499.96 1397.97 17699.92 7199.57 124
dmvs_testset92.94 48692.21 48695.13 49698.59 39390.99 50097.65 27592.09 53196.95 33494.00 51993.55 52392.34 40396.97 53272.20 54392.52 53797.43 492
sd_testset99.28 3699.31 4199.19 11299.68 6498.06 16799.41 1799.30 25499.69 1799.63 6699.68 2599.25 1699.96 1397.25 24799.92 7199.57 124
test_fmvsm_n_192099.33 3099.45 2398.99 15699.57 10397.73 21397.93 22999.83 2699.22 8099.93 699.30 12599.42 1199.96 1399.85 699.99 599.29 284
test_cas_vis1_n_192098.33 22298.68 14197.27 40799.69 6192.29 47698.03 20799.85 1997.62 26299.96 499.62 4093.98 36699.74 28999.52 4999.86 10799.79 47
test_vis1_n_192098.40 20798.92 10296.81 43499.74 3790.76 50598.15 18499.91 1098.33 19099.89 1899.55 5695.07 32799.88 11599.76 2399.93 5799.79 47
test_vis1_n98.31 22798.50 17597.73 36599.76 3094.17 42198.68 10999.91 1096.31 37599.79 3899.57 4992.85 39499.42 45599.79 1999.84 11499.60 102
test_fmvs1_n98.09 25998.28 21997.52 39299.68 6493.47 45498.63 11699.93 695.41 42699.68 5799.64 3791.88 41399.48 43999.82 1299.87 10099.62 92
mvsany_test197.60 30997.54 30597.77 35697.72 46895.35 37395.36 46097.13 46294.13 46399.71 4999.33 11897.93 14199.30 47397.60 21698.94 41698.67 418
APD_test198.83 12298.66 14699.34 8399.78 2499.47 898.42 15199.45 17998.28 19998.98 21399.19 15997.76 15899.58 40196.57 32099.55 29698.97 363
test_vis1_rt97.75 29897.72 29097.83 35198.81 34796.35 32397.30 32799.69 5794.61 44897.87 37498.05 40496.26 27498.32 51198.74 10798.18 46298.82 388
test_vis3_rt99.14 6299.17 6099.07 13899.78 2498.38 12398.92 8399.94 397.80 24799.91 1299.67 3097.15 21298.91 49999.76 2399.56 29199.92 12
test_fmvs298.70 14798.97 9897.89 34699.54 12394.05 42698.55 12699.92 896.78 35199.72 4799.78 1396.60 25499.67 34499.91 299.90 8899.94 10
test_fmvs197.72 30097.94 26897.07 41998.66 38392.39 47397.68 26999.81 3295.20 43399.54 7999.44 8591.56 41799.41 45699.78 2199.77 17299.40 231
test_fmvs399.12 6999.41 2698.25 30599.76 3095.07 38999.05 6899.94 397.78 25099.82 3499.84 398.56 7399.71 30899.96 199.96 2899.97 4
mvsany_test398.87 11298.92 10298.74 21799.38 18796.94 28898.58 12399.10 31496.49 36699.96 499.81 898.18 11899.45 44998.97 8999.79 15999.83 33
testf199.25 4099.16 6299.51 4999.89 699.63 398.71 10699.69 5798.90 13699.43 10899.35 11198.86 3599.67 34497.81 19099.81 14099.24 301
APD_test299.25 4099.16 6299.51 4999.89 699.63 398.71 10699.69 5798.90 13699.43 10899.35 11198.86 3599.67 34497.81 19099.81 14099.24 301
test_f98.67 16198.87 11198.05 33399.72 4595.59 35498.51 13599.81 3296.30 37799.78 3999.82 596.14 27898.63 50799.82 1299.93 5799.95 9
FE-MVS95.66 42894.95 44497.77 35698.53 40295.28 37899.40 1996.09 49193.11 48297.96 36899.26 13779.10 51099.77 26492.40 47898.71 43298.27 449
FA-MVS(test-final)96.99 36596.82 35897.50 39498.70 36894.78 40199.34 2396.99 46595.07 43598.48 31899.33 11888.41 45199.65 36496.13 35898.92 41898.07 459
BridgeMVS98.63 16798.72 13298.38 28998.66 38396.68 30698.90 8499.42 20098.99 12498.97 21799.19 15995.81 30099.85 15898.77 10599.77 17298.60 422
MonoMVSNet96.25 40396.53 38395.39 49196.57 51891.01 49998.82 9797.68 44198.57 17498.03 36299.37 10490.92 42697.78 52194.99 39893.88 53597.38 493
patch_mono-298.51 19498.63 15298.17 31599.38 18794.78 40197.36 32199.69 5798.16 21698.49 31699.29 12897.06 21799.97 698.29 14599.91 8099.76 58
EGC-MVSNET85.24 50980.54 51299.34 8399.77 2799.20 3899.08 6299.29 26212.08 54920.84 55099.42 8997.55 17899.85 15897.08 26499.72 20898.96 366
test250692.39 49291.89 49493.89 51299.38 18782.28 54699.32 2666.03 55399.08 11498.77 26799.57 4966.26 53699.84 17798.71 11099.95 3999.54 143
test111196.49 38796.82 35895.52 48799.42 17887.08 52799.22 4687.14 54599.11 10099.46 10199.58 4788.69 44599.86 14498.80 10099.95 3999.62 92
ECVR-MVScopyleft96.42 39396.61 37795.85 47699.38 18788.18 52299.22 4686.00 54799.08 11499.36 12899.57 4988.47 45099.82 20798.52 12799.95 3999.54 143
test_blank0.00 5190.00 5220.00 5340.00 5570.00 5590.00 5450.00 5580.00 5520.00 5530.00 5520.00 5560.00 5530.00 5510.00 5510.00 549
tt080598.69 15198.62 15498.90 17899.75 3499.30 2199.15 5796.97 46798.86 14298.87 24897.62 43798.63 6398.96 49599.41 5698.29 45898.45 433
DVP-MVS++98.90 10898.70 13899.51 4998.43 41399.15 5299.43 1599.32 24198.17 21399.26 15799.02 21398.18 11899.88 11597.07 26599.45 32699.49 177
FOURS199.73 3899.67 299.43 1599.54 13299.43 5499.26 157
MSC_two_6792asdad99.32 9198.43 41398.37 12598.86 36399.89 9797.14 25899.60 27499.71 65
PC_three_145293.27 47899.40 11798.54 34398.22 11397.00 53195.17 39599.45 32699.49 177
No_MVS99.32 9198.43 41398.37 12598.86 36399.89 9797.14 25899.60 27499.71 65
test_one_060199.39 18599.20 3899.31 24698.49 18098.66 28599.02 21397.64 168
eth-test20.00 557
eth-test0.00 557
GeoE99.05 8398.99 9699.25 10499.44 17198.35 12998.73 10399.56 12198.42 18498.91 23598.81 28498.94 3199.91 7498.35 14199.73 19999.49 177
test_method79.78 51079.50 51380.62 52880.21 55345.76 55670.82 54498.41 41531.08 54880.89 54897.71 43084.85 47997.37 52791.51 49380.03 54498.75 404
Anonymous2024052198.69 15198.87 11198.16 31899.77 2795.11 38899.08 6299.44 18799.34 6599.33 13899.55 5694.10 36599.94 4199.25 6799.96 2899.42 219
h-mvs3397.77 29797.33 32299.10 13099.21 24097.84 19598.35 16198.57 40199.11 10098.58 30399.02 21388.65 44899.96 1398.11 15896.34 51399.49 177
hse-mvs297.46 32097.07 33998.64 23698.73 35897.33 24697.45 30997.64 44499.11 10098.58 30397.98 41088.65 44899.79 24698.11 15897.39 49498.81 393
CL-MVSNet_self_test97.44 32397.22 32998.08 32898.57 39795.78 35194.30 49798.79 37596.58 36298.60 29998.19 39194.74 34099.64 36996.41 33798.84 42098.82 388
KD-MVS_2432*160092.87 48891.99 49095.51 48891.37 54689.27 51694.07 50498.14 42795.42 42397.25 42196.44 47967.86 53099.24 47991.28 49696.08 52298.02 461
KD-MVS_self_test99.25 4099.18 5999.44 6599.63 8399.06 7098.69 10899.54 13299.31 6999.62 6999.53 6497.36 19899.86 14499.24 6999.71 21799.39 232
AUN-MVS96.24 40595.45 42298.60 24898.70 36897.22 26397.38 31697.65 44295.95 39595.53 49497.96 41582.11 50099.79 24696.31 34397.44 49198.80 398
ZD-MVS99.01 30598.84 8599.07 31994.10 46598.05 36098.12 39796.36 26999.86 14492.70 47199.19 383
SR-MVS-dyc-post98.81 12798.55 16599.57 2199.20 24499.38 1298.48 14399.30 25498.64 16198.95 22398.96 24297.49 18999.86 14496.56 32499.39 34199.45 206
RE-MVS-def98.58 16299.20 24499.38 1298.48 14399.30 25498.64 16198.95 22398.96 24297.75 15996.56 32499.39 34199.45 206
SED-MVS98.91 10698.72 13299.49 5599.49 15099.17 4398.10 19399.31 24698.03 22799.66 6099.02 21398.36 9099.88 11596.91 27999.62 26799.41 222
IU-MVS99.49 15099.15 5298.87 35892.97 48599.41 11496.76 29699.62 26799.66 80
OPU-MVS98.82 19298.59 39398.30 13498.10 19398.52 34798.18 11898.75 50494.62 40899.48 32299.41 222
test_241102_TWO99.30 25498.03 22799.26 15799.02 21397.51 18599.88 11596.91 27999.60 27499.66 80
test_241102_ONE99.49 15099.17 4399.31 24697.98 23099.66 6098.90 25798.36 9099.48 439
SF-MVS98.53 18998.27 22299.32 9199.31 20898.75 9098.19 17899.41 20496.77 35298.83 25598.90 25797.80 15599.82 20795.68 37999.52 30699.38 241
cl2295.79 42495.39 42696.98 42396.77 51492.79 46594.40 49498.53 40594.59 44997.89 37298.17 39282.82 49799.24 47996.37 33999.03 40198.92 373
miper_ehance_all_eth97.06 35897.03 34197.16 41597.83 46393.06 45894.66 48499.09 31695.99 39398.69 27998.45 35892.73 39799.61 38596.79 29299.03 40198.82 388
miper_enhance_ethall96.01 41295.74 40796.81 43496.41 52692.27 47793.69 51498.89 35591.14 50998.30 33597.35 45790.58 43099.58 40196.31 34399.03 40198.60 422
ZNCC-MVS98.68 15798.40 19399.54 3199.57 10399.21 3298.46 14599.29 26297.28 30798.11 35398.39 36498.00 13499.87 13596.86 28999.64 25799.55 137
dcpmvs_298.78 13399.11 7497.78 35599.56 11193.67 44999.06 6699.86 1799.50 4399.66 6099.26 13797.21 20999.99 298.00 17199.91 8099.68 73
cl____97.02 36196.83 35797.58 38397.82 46494.04 42894.66 48499.16 30397.04 32998.63 29098.71 30488.68 44799.69 32797.00 27099.81 14099.00 357
DIV-MVS_self_test97.02 36196.84 35697.58 38397.82 46494.03 42994.66 48499.16 30397.04 32998.63 29098.71 30488.69 44599.69 32797.00 27099.81 14099.01 354
eth_miper_zixun_eth97.23 34497.25 32697.17 41398.00 45392.77 46694.71 47999.18 29697.27 30998.56 30798.74 29891.89 41299.69 32797.06 26799.81 14099.05 345
9.1497.78 28399.07 28197.53 29699.32 24195.53 41898.54 31198.70 31197.58 17599.76 27094.32 42199.46 324
uanet_test0.00 5190.00 5220.00 5340.00 5570.00 5590.00 5450.00 5580.00 5520.00 5530.00 5520.00 5560.00 5530.00 5510.00 5510.00 549
DCPMVS0.00 5190.00 5220.00 5340.00 5570.00 5590.00 5450.00 5580.00 5520.00 5530.00 5520.00 5560.00 5530.00 5510.00 5510.00 549
save fliter99.11 27297.97 17796.53 38999.02 33298.24 200
ET-MVSNet_ETH3D94.30 46093.21 47297.58 38398.14 44394.47 41294.78 47893.24 52794.72 44589.56 53995.87 49178.57 51499.81 22496.91 27997.11 50398.46 430
UniMVSNet_ETH3D99.69 299.69 499.69 399.84 1799.34 1999.69 599.58 10399.90 399.86 2499.78 1399.58 699.95 2599.00 8799.95 3999.78 50
EIA-MVS98.00 26897.74 28698.80 19798.72 36098.09 15798.05 20399.60 9497.39 29596.63 45795.55 49797.68 16299.80 23396.73 30199.27 36598.52 428
miper_refine_blended92.87 48891.99 49095.51 48891.37 54689.27 51694.07 50498.14 42795.42 42397.25 42196.44 47967.86 53099.24 47991.28 49696.08 52298.02 461
miper_lstm_enhance97.18 34997.16 33297.25 40998.16 44192.85 46495.15 46999.31 24697.25 31198.74 27398.78 28990.07 43399.78 25897.19 25199.80 15299.11 340
ETV-MVS98.03 26497.86 27798.56 25898.69 37398.07 16497.51 29999.50 14998.10 22397.50 40495.51 49898.41 8599.88 11596.27 34899.24 37197.71 482
CS-MVS99.13 6699.10 8099.24 10699.06 28699.15 5299.36 2299.88 1599.36 6398.21 34398.46 35798.68 5899.93 5399.03 8599.85 10998.64 419
D2MVS97.84 29197.84 27997.83 35199.14 26694.74 40396.94 35598.88 35695.84 40098.89 23998.96 24294.40 35199.69 32797.55 22099.95 3999.05 345
DVP-MVScopyleft98.77 13698.52 17099.52 4499.50 14199.21 3298.02 21098.84 36797.97 23199.08 19099.02 21397.61 17299.88 11596.99 27299.63 26399.48 188
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_THIRD98.17 21399.08 19099.02 21397.89 14799.88 11597.07 26599.71 21799.70 70
test_0728_SECOND99.60 1699.50 14199.23 3098.02 21099.32 24199.88 11596.99 27299.63 26399.68 73
test072699.50 14199.21 3298.17 18299.35 22797.97 23199.26 15799.06 20097.61 172
SR-MVS98.71 14298.43 18999.57 2199.18 25699.35 1698.36 16099.29 26298.29 19798.88 24398.85 27197.53 18299.87 13596.14 35699.31 35799.48 188
DPM-MVS96.32 39795.59 41698.51 27098.76 35497.21 26594.54 49098.26 42091.94 49996.37 47197.25 46093.06 38999.43 45391.42 49498.74 42898.89 379
GST-MVS98.61 17198.30 21699.52 4499.51 13499.20 3898.26 17199.25 27797.44 29098.67 28398.39 36497.68 16299.85 15896.00 36199.51 30999.52 161
test_yl96.69 37596.29 39497.90 34498.28 42795.24 37997.29 32897.36 44998.21 20598.17 34497.86 41986.27 46299.55 41194.87 40298.32 45498.89 379
thisisatest053095.27 44294.45 45497.74 36299.19 24894.37 41497.86 24190.20 54097.17 32298.22 34297.65 43473.53 52399.90 8196.90 28499.35 34898.95 367
Anonymous2024052998.93 10498.87 11199.12 12699.19 24898.22 14499.01 7198.99 33899.25 7699.54 7999.37 10497.04 21899.80 23397.89 18099.52 30699.35 258
Anonymous20240521197.90 27797.50 30999.08 13698.90 32698.25 13898.53 12996.16 48798.87 14099.11 18598.86 26890.40 43299.78 25897.36 23799.31 35799.19 319
DCV-MVSNet96.69 37596.29 39497.90 34498.28 42795.24 37997.29 32897.36 44998.21 20598.17 34497.86 41986.27 46299.55 41194.87 40298.32 45498.89 379
tttt051795.64 42994.98 44297.64 37799.36 19493.81 44498.72 10490.47 53998.08 22698.67 28398.34 37173.88 52299.92 6597.77 19699.51 30999.20 313
our_test_397.39 32897.73 28996.34 45098.70 36889.78 51394.61 48798.97 34196.50 36599.04 20298.85 27195.98 29299.84 17797.26 24699.67 24599.41 222
thisisatest051594.12 46593.16 47396.97 42498.60 39092.90 46393.77 51390.61 53894.10 46596.91 43995.87 49174.99 52099.80 23394.52 41199.12 39498.20 451
ppachtmachnet_test97.50 31597.74 28696.78 43698.70 36891.23 49694.55 48999.05 32496.36 37299.21 17398.79 28796.39 26499.78 25896.74 29999.82 13399.34 262
SMA-MVScopyleft98.40 20798.03 25699.51 4999.16 26099.21 3298.05 20399.22 28594.16 46298.98 21399.10 19197.52 18499.79 24696.45 33499.64 25799.53 157
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
GSMVS98.81 393
DPE-MVScopyleft98.59 17598.26 22599.57 2199.27 22099.15 5297.01 34999.39 21197.67 25899.44 10798.99 23197.53 18299.89 9795.40 39099.68 23999.66 80
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part299.36 19499.10 6599.05 200
thres100view90094.19 46293.67 46695.75 47999.06 28691.35 49098.03 20794.24 51898.33 19097.40 41494.98 51179.84 50499.62 37783.05 53398.08 47096.29 513
tfpnnormal98.90 10898.90 10598.91 17599.67 6897.82 20199.00 7399.44 18799.45 5099.51 9299.24 14498.20 11799.86 14495.92 36599.69 23399.04 349
tfpn200view994.03 46693.44 46895.78 47898.93 31891.44 48897.60 28694.29 51597.94 23597.10 42694.31 51979.67 50699.62 37783.05 53398.08 47096.29 513
c3_l97.36 33197.37 31897.31 40498.09 44893.25 45695.01 47299.16 30397.05 32898.77 26798.72 30292.88 39299.64 36996.93 27899.76 18899.05 345
CHOSEN 280x42095.51 43495.47 42095.65 48498.25 43088.27 52193.25 52398.88 35693.53 47594.65 50997.15 46386.17 46499.93 5397.41 23599.93 5798.73 407
CANet97.87 28497.76 28498.19 31497.75 46795.51 35996.76 36899.05 32497.74 25296.93 43698.21 38995.59 30999.89 9797.86 18799.93 5799.19 319
Fast-Effi-MVS+-dtu98.27 23398.09 24898.81 19498.43 41398.11 15397.61 28599.50 14998.64 16197.39 41697.52 44498.12 12699.95 2596.90 28498.71 43298.38 443
Effi-MVS+-dtu98.26 23597.90 27499.35 8098.02 45299.49 598.02 21099.16 30398.29 19797.64 39097.99 40996.44 26199.95 2596.66 31298.93 41798.60 422
CANet_DTU97.26 34097.06 34097.84 35097.57 47994.65 40896.19 41598.79 37597.23 31795.14 50098.24 38693.22 38399.84 17797.34 23899.84 11499.04 349
MGCNet97.44 32397.01 34398.72 22196.42 52596.74 30297.20 33991.97 53598.46 18298.30 33598.79 28792.74 39699.91 7499.30 6299.94 5199.52 161
MP-MVS-pluss98.57 17898.23 23099.60 1699.69 6199.35 1697.16 34499.38 21394.87 44198.97 21798.99 23198.01 13399.88 11597.29 24499.70 22799.58 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS98.40 20798.00 25999.61 1399.57 10399.25 2898.57 12499.35 22797.55 27399.31 14797.71 43094.61 34399.88 11596.14 35699.19 38399.70 70
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
sam_mvs184.74 48198.81 393
sam_mvs84.29 487
IterMVS-SCA-FT97.85 29098.18 23896.87 43099.27 22091.16 49795.53 45299.25 27799.10 10799.41 11499.35 11193.10 38799.96 1398.65 11499.94 5199.49 177
TSAR-MVS + MP.98.63 16798.49 18099.06 14499.64 7797.90 18998.51 13598.94 34296.96 33399.24 16798.89 26397.83 15199.81 22496.88 28699.49 32199.48 188
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
xiu_mvs_v1_base_debu97.86 28598.17 23996.92 42798.98 31093.91 43996.45 39499.17 30097.85 24398.41 32697.14 46498.47 7799.92 6598.02 16899.05 39796.92 501
OPM-MVS98.56 18098.32 21499.25 10499.41 18198.73 9497.13 34699.18 29697.10 32698.75 27098.92 25198.18 11899.65 36496.68 30899.56 29199.37 244
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP98.75 13898.48 18199.57 2199.58 9499.29 2397.82 24599.25 27796.94 33598.78 26499.12 18698.02 13299.84 17797.13 26199.67 24599.59 109
ambc98.24 30798.82 34495.97 34098.62 11899.00 33799.27 15399.21 15496.99 22499.50 43196.55 32799.50 31799.26 296
MTGPAbinary99.20 288
SPE-MVS-test99.13 6699.09 8299.26 10199.13 26998.97 7399.31 3099.88 1599.44 5298.16 34798.51 34898.64 6199.93 5398.91 9399.85 10998.88 382
Effi-MVS+98.02 26597.82 28098.62 24298.53 40297.19 26797.33 32399.68 6497.30 30596.68 45597.46 45098.56 7399.80 23396.63 31498.20 46198.86 384
xiu_mvs_v2_base97.16 35197.49 31096.17 46198.54 40092.46 47195.45 45698.84 36797.25 31197.48 40696.49 47698.31 9799.90 8196.34 34298.68 43796.15 517
xiu_mvs_v1_base97.86 28598.17 23996.92 42798.98 31093.91 43996.45 39499.17 30097.85 24398.41 32697.14 46498.47 7799.92 6598.02 16899.05 39796.92 501
new-patchmatchnet98.35 21798.74 12897.18 41199.24 23292.23 47896.42 39899.48 15998.30 19499.69 5599.53 6497.44 19399.82 20798.84 9999.77 17299.49 177
pmmvs699.67 399.70 399.60 1699.90 499.27 2699.53 999.76 3999.64 2699.84 3099.83 499.50 999.87 13599.36 5799.92 7199.64 86
pmmvs597.64 30797.49 31098.08 32899.14 26695.12 38796.70 37399.05 32493.77 47298.62 29498.83 27893.23 38299.75 28298.33 14499.76 18899.36 252
test_post197.59 28820.48 55183.07 49599.66 35794.16 422
test_post21.25 55083.86 49099.70 317
Fast-Effi-MVS+97.67 30597.38 31798.57 25398.71 36497.43 24197.23 33499.45 17994.82 44396.13 47596.51 47598.52 7599.91 7496.19 35298.83 42198.37 445
patchmatchnet-post98.77 29184.37 48499.85 158
Anonymous2023121199.27 3799.27 4799.26 10199.29 21498.18 14699.49 1299.51 14499.70 1599.80 3799.68 2596.84 23299.83 19599.21 7099.91 8099.77 53
pmmvs-eth3d98.47 19898.34 20898.86 18199.30 21297.76 20997.16 34499.28 26695.54 41799.42 11299.19 15997.27 20499.63 37297.89 18099.97 2199.20 313
GG-mvs-BLEND94.76 50094.54 53992.13 47999.31 3080.47 55188.73 54291.01 54167.59 53398.16 51582.30 53794.53 53393.98 525
xiu_mvs_v1_base_debi97.86 28598.17 23996.92 42798.98 31093.91 43996.45 39499.17 30097.85 24398.41 32697.14 46498.47 7799.92 6598.02 16899.05 39796.92 501
Anonymous2023120698.21 24398.21 23198.20 31299.51 13495.43 36998.13 18699.32 24196.16 38498.93 23298.82 28196.00 28799.83 19597.32 24299.73 19999.36 252
MTAPA98.88 11198.64 15099.61 1399.67 6899.36 1598.43 14899.20 28898.83 14998.89 23998.90 25796.98 22599.92 6597.16 25499.70 22799.56 130
MTMP97.93 22991.91 536
gm-plane-assit94.83 53881.97 54788.07 53194.99 51099.60 38991.76 487
test9_res93.28 45299.15 38899.38 241
MVP-Stereo98.08 26097.92 27198.57 25398.96 31496.79 29897.90 23599.18 29696.41 37198.46 32098.95 24695.93 29699.60 38996.51 33098.98 41299.31 278
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST998.71 36498.08 16195.96 43299.03 32991.40 50595.85 48397.53 44196.52 25799.76 270
train_agg97.10 35396.45 38899.07 13898.71 36498.08 16195.96 43299.03 32991.64 50095.85 48397.53 44196.47 25999.76 27093.67 43999.16 38699.36 252
gg-mvs-nofinetune92.37 49491.20 49895.85 47695.80 53592.38 47499.31 3081.84 55099.75 1091.83 53499.74 1868.29 52999.02 49287.15 52197.12 50296.16 516
SCA96.41 39496.66 37295.67 48298.24 43288.35 52095.85 44196.88 47396.11 38597.67 38998.67 31793.10 38799.85 15894.16 42299.22 37598.81 393
Patchmatch-test96.55 38396.34 39197.17 41398.35 42093.06 45898.40 15697.79 43597.33 30098.41 32698.67 31783.68 49199.69 32795.16 39699.31 35798.77 401
test_898.67 37898.01 17095.91 43899.02 33291.64 50095.79 48697.50 44596.47 25999.76 270
MS-PatchMatch97.68 30497.75 28597.45 39998.23 43593.78 44597.29 32898.84 36796.10 38698.64 28998.65 32496.04 28499.36 46296.84 29099.14 38999.20 313
Patchmatch-RL test97.26 34097.02 34297.99 33999.52 13195.53 35896.13 42099.71 4897.47 28299.27 15399.16 17084.30 48699.62 37797.89 18099.77 17298.81 393
cdsmvs_eth3d_5k24.66 51432.88 5170.00 5340.00 5570.00 5590.00 54599.10 3140.00 5520.00 55397.58 43899.21 180.00 5530.00 5510.00 5510.00 549
pcd_1.5k_mvsjas8.17 51710.90 5200.00 5340.00 5570.00 5590.00 5450.00 5580.00 5520.00 5530.00 55298.07 1280.00 5530.00 5510.00 5510.00 549
agg_prior292.50 47699.16 38699.37 244
agg_prior98.68 37797.99 17399.01 33595.59 48799.77 264
tmp_tt78.77 51178.73 51478.90 52958.45 55474.76 55394.20 50078.26 55239.16 54786.71 54392.82 53180.50 50275.19 54986.16 52792.29 53886.74 543
canonicalmvs98.34 21898.26 22598.58 25098.46 40897.82 20198.96 7899.46 17599.19 8997.46 40795.46 50298.59 6799.46 44698.08 16198.71 43298.46 430
anonymousdsp99.51 1499.47 2199.62 999.88 999.08 6999.34 2399.69 5798.93 13299.65 6399.72 2198.93 3399.95 2599.11 77100.00 199.82 36
alignmvs97.35 33296.88 35398.78 20498.54 40098.09 15797.71 26597.69 43999.20 8497.59 39595.90 49088.12 45499.55 41198.18 15398.96 41498.70 411
nrg03099.40 2599.35 3399.54 3199.58 9499.13 6098.98 7699.48 15999.68 1999.46 10199.26 13798.62 6499.73 29699.17 7499.92 7199.76 58
v14419298.54 18798.57 16398.45 27999.21 24095.98 33897.63 27999.36 22197.15 32599.32 14499.18 16395.84 29999.84 17799.50 5099.91 8099.54 143
FIs99.14 6299.09 8299.29 9599.70 5798.28 13599.13 5999.52 14299.48 4499.24 16799.41 9496.79 23999.82 20798.69 11299.88 9599.76 58
v192192098.54 18798.60 15998.38 28999.20 24495.76 35297.56 29199.36 22197.23 31799.38 12199.17 16896.02 28599.84 17799.57 3999.90 8899.54 143
UA-Net99.47 1699.40 2799.70 299.49 15099.29 2399.80 499.72 4699.82 899.04 20299.81 898.05 13199.96 1398.85 9899.99 599.86 28
v119298.60 17398.66 14698.41 28599.27 22095.88 34497.52 29799.36 22197.41 29299.33 13899.20 15696.37 26899.82 20799.57 3999.92 7199.55 137
FC-MVSNet-test99.27 3799.25 5299.34 8399.77 2798.37 12599.30 3599.57 11199.61 3499.40 11799.50 6897.12 21499.85 15899.02 8699.94 5199.80 45
v114498.60 17398.66 14698.41 28599.36 19495.90 34297.58 28999.34 23397.51 27899.27 15399.15 17696.34 27099.80 23399.47 5399.93 5799.51 165
sosnet-low-res0.00 5190.00 5220.00 5340.00 5570.00 5590.00 5450.00 5580.00 5520.00 5530.00 5520.00 5560.00 5530.00 5510.00 5510.00 549
HFP-MVS98.71 14298.44 18899.51 4999.49 15099.16 4898.52 13099.31 24697.47 28298.58 30398.50 35297.97 13899.85 15896.57 32099.59 27899.53 157
v14898.45 20198.60 15998.00 33799.44 17194.98 39197.44 31199.06 32098.30 19499.32 14498.97 23896.65 25199.62 37798.37 14099.85 10999.39 232
sosnet0.00 5190.00 5220.00 5340.00 5570.00 5590.00 5450.00 5580.00 5520.00 5530.00 5520.00 5560.00 5530.00 5510.00 5510.00 549
uncertanet0.00 5190.00 5220.00 5340.00 5570.00 5590.00 5450.00 5580.00 5520.00 5530.00 5520.00 5560.00 5530.00 5510.00 5510.00 549
AllTest98.44 20298.20 23299.16 11899.50 14198.55 10898.25 17299.58 10396.80 34998.88 24399.06 20097.65 16599.57 40394.45 41499.61 27299.37 244
TestCases99.16 11899.50 14198.55 10899.58 10396.80 34998.88 24399.06 20097.65 16599.57 40394.45 41499.61 27299.37 244
v7n99.53 1299.57 1399.41 6999.88 998.54 11199.45 1499.61 9299.66 2399.68 5799.66 3298.44 8499.95 2599.73 2899.96 2899.75 62
region2R98.69 15198.40 19399.54 3199.53 12799.17 4398.52 13099.31 24697.46 28798.44 32398.51 34897.83 15199.88 11596.46 33399.58 28399.58 117
RRT-MVS97.88 28297.98 26197.61 38098.15 44293.77 44698.97 7799.64 7999.16 9498.69 27999.42 8991.60 41499.89 9797.63 21298.52 44999.16 333
balanced_ft_v198.28 23298.35 20798.10 32398.08 44996.23 32799.23 4599.26 27598.34 18897.46 40799.42 8995.38 31899.88 11598.60 11799.34 35098.17 453
PS-MVSNAJss99.46 1799.49 1699.35 8099.90 498.15 14899.20 4999.65 7799.48 4499.92 899.71 2298.07 12899.96 1399.53 48100.00 199.93 11
PS-MVSNAJ97.08 35697.39 31696.16 46398.56 39892.46 47195.24 46598.85 36697.25 31197.49 40595.99 48798.07 12899.90 8196.37 33998.67 43896.12 518
jajsoiax99.58 999.61 1199.48 5799.87 1298.61 10399.28 4099.66 7199.09 11099.89 1899.68 2599.53 799.97 699.50 5099.99 599.87 22
mvs_tets99.63 699.67 699.49 5599.88 998.61 10399.34 2399.71 4899.27 7499.90 1499.74 1899.68 499.97 699.55 4399.99 599.88 20
EI-MVSNet-UG-set98.69 15198.71 13598.62 24299.10 27496.37 32297.23 33498.87 35899.20 8499.19 17598.99 23197.30 20199.85 15898.77 10599.79 15999.65 85
EI-MVSNet-Vis-set98.68 15798.70 13898.63 24099.09 27796.40 32197.23 33498.86 36399.20 8499.18 18098.97 23897.29 20399.85 15898.72 10999.78 16499.64 86
HPM-MVS++copyleft98.10 25697.64 29999.48 5799.09 27799.13 6097.52 29798.75 38497.46 28796.90 44297.83 42396.01 28699.84 17795.82 37399.35 34899.46 200
test_prior497.97 17795.86 439
XVS98.72 14198.45 18699.53 3899.46 16499.21 3298.65 11499.34 23398.62 16697.54 40098.63 33097.50 18699.83 19596.79 29299.53 30399.56 130
v124098.55 18498.62 15498.32 29699.22 23895.58 35697.51 29999.45 17997.16 32399.45 10699.24 14496.12 28299.85 15899.60 3799.88 9599.55 137
pm-mvs199.44 1999.48 1899.33 8999.80 2198.63 10099.29 3699.63 8299.30 7199.65 6399.60 4599.16 2299.82 20799.07 8099.83 12699.56 130
test_prior295.74 44696.48 36796.11 47697.63 43695.92 29794.16 42299.20 380
X-MVStestdata94.32 45892.59 48099.53 3899.46 16499.21 3298.65 11499.34 23398.62 16697.54 40045.85 54797.50 18699.83 19596.79 29299.53 30399.56 130
test_prior98.95 16698.69 37397.95 18199.03 32999.59 39499.30 282
旧先验295.76 44588.56 52897.52 40299.66 35794.48 412
新几何295.93 435
新几何198.91 17598.94 31697.76 20998.76 38087.58 53296.75 45198.10 39994.80 33799.78 25892.73 47099.00 40799.20 313
旧先验198.82 34497.45 23898.76 38098.34 37195.50 31399.01 40699.23 303
无先验95.74 44698.74 38689.38 52199.73 29692.38 47999.22 308
原ACMM295.53 452
原ACMM198.35 29498.90 32696.25 32698.83 37192.48 49396.07 47898.10 39995.39 31799.71 30892.61 47398.99 40999.08 341
test22298.92 32296.93 28995.54 45198.78 37785.72 53596.86 44698.11 39894.43 34899.10 39699.23 303
testdata299.79 24692.80 467
segment_acmp97.02 221
testdata98.09 32598.93 31895.40 37098.80 37490.08 51797.45 41098.37 36795.26 32099.70 31793.58 44398.95 41599.17 327
testdata195.44 45796.32 374
v899.01 9099.16 6298.57 25399.47 16196.31 32598.90 8499.47 17099.03 12199.52 8799.57 4996.93 22899.81 22499.60 3799.98 1299.60 102
131495.74 42595.60 41496.17 46197.53 48492.75 46798.07 20098.31 41891.22 50794.25 51396.68 47295.53 31099.03 49091.64 49097.18 50196.74 507
LFMVS97.20 34796.72 36598.64 23698.72 36096.95 28798.93 8294.14 52099.74 1298.78 26499.01 22584.45 48399.73 29697.44 23399.27 36599.25 297
VDD-MVS98.56 18098.39 19699.07 13899.13 26998.07 16498.59 12297.01 46499.59 3699.11 18599.27 13194.82 33499.79 24698.34 14299.63 26399.34 262
VDDNet98.21 24397.95 26599.01 15399.58 9497.74 21199.01 7197.29 45599.67 2098.97 21799.50 6890.45 43199.80 23397.88 18399.20 38099.48 188
v1098.97 9999.11 7498.55 26099.44 17196.21 32998.90 8499.55 12698.73 15199.48 9699.60 4596.63 25399.83 19599.70 3399.99 599.61 100
VPNet98.87 11298.83 12099.01 15399.70 5797.62 22498.43 14899.35 22799.47 4799.28 15199.05 20796.72 24699.82 20798.09 16099.36 34599.59 109
MVS93.19 48192.09 48796.50 44496.91 50994.03 42998.07 20098.06 43168.01 54594.56 51196.48 47795.96 29499.30 47383.84 53196.89 50796.17 515
v2v48298.56 18098.62 15498.37 29299.42 17895.81 34997.58 28999.16 30397.90 23999.28 15199.01 22595.98 29299.79 24699.33 5999.90 8899.51 165
V4298.78 13398.78 12698.76 21199.44 17197.04 28098.27 17099.19 29297.87 24199.25 16599.16 17096.84 23299.78 25899.21 7099.84 11499.46 200
SD-MVS98.40 20798.68 14197.54 39098.96 31497.99 17397.88 23799.36 22198.20 20999.63 6699.04 20998.76 4695.33 54296.56 32499.74 19599.31 278
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
GA-MVS95.86 42195.32 43197.49 39598.60 39094.15 42293.83 51297.93 43395.49 41996.68 45597.42 45283.21 49399.30 47396.22 35098.55 44799.01 354
MSLP-MVS++98.02 26598.14 24597.64 37798.58 39595.19 38497.48 30399.23 28497.47 28297.90 37198.62 33397.04 21898.81 50297.55 22099.41 33898.94 371
APDe-MVScopyleft98.99 9498.79 12499.60 1699.21 24099.15 5298.87 8999.48 15997.57 26999.35 13099.24 14497.83 15199.89 9797.88 18399.70 22799.75 62
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize98.84 11998.61 15899.53 3899.19 24899.27 2698.49 14099.33 23998.64 16199.03 20598.98 23697.89 14799.85 15896.54 32899.42 33799.46 200
ADS-MVSNet295.43 43894.98 44296.76 43798.14 44391.74 48197.92 23297.76 43690.23 51396.51 46798.91 25485.61 47299.85 15892.88 46396.90 50598.69 412
EI-MVSNet98.40 20798.51 17298.04 33499.10 27494.73 40497.20 33998.87 35898.97 12799.06 19299.02 21396.00 28799.80 23398.58 11999.82 13399.60 102
Regformer0.00 5190.00 5220.00 5340.00 5570.00 5590.00 5450.00 5580.00 5520.00 5530.00 5520.00 5560.00 5530.00 5510.00 5510.00 549
CVMVSNet96.25 40397.21 33093.38 52099.10 27480.56 55097.20 33998.19 42696.94 33599.00 20899.02 21389.50 44199.80 23396.36 34199.59 27899.78 50
pmmvs497.58 31297.28 32398.51 27098.84 33996.93 28995.40 45998.52 40793.60 47498.61 29698.65 32495.10 32699.60 38996.97 27699.79 15998.99 358
EU-MVSNet97.66 30698.50 17595.13 49699.63 8385.84 53098.35 16198.21 42398.23 20199.54 7999.46 8095.02 32899.68 33998.24 14799.87 10099.87 22
VNet98.42 20398.30 21698.79 20198.79 35397.29 25598.23 17398.66 39299.31 6998.85 25098.80 28594.80 33799.78 25898.13 15699.13 39199.31 278
test-LLR93.90 46893.85 46294.04 50996.53 51984.62 53694.05 50692.39 52996.17 38094.12 51595.07 50782.30 49899.67 34495.87 36998.18 46297.82 471
TESTMET0.1,192.19 49791.77 49593.46 51696.48 52482.80 54594.05 50691.52 53794.45 45594.00 51994.88 51366.65 53499.56 40695.78 37498.11 46898.02 461
test-mter92.33 49591.76 49694.04 50996.53 51984.62 53694.05 50692.39 52994.00 47094.12 51595.07 50765.63 54099.67 34495.87 36998.18 46297.82 471
VPA-MVSNet99.30 3399.30 4499.28 9699.49 15098.36 12899.00 7399.45 17999.63 2899.52 8799.44 8598.25 10799.88 11599.09 7999.84 11499.62 92
ACMMPR98.70 14798.42 19199.54 3199.52 13199.14 5798.52 13099.31 24697.47 28298.56 30798.54 34397.75 15999.88 11596.57 32099.59 27899.58 117
testgi98.32 22398.39 19698.13 32099.57 10395.54 35797.78 25199.49 15797.37 29799.19 17597.65 43498.96 3099.49 43596.50 33198.99 40999.34 262
test20.0398.78 13398.77 12798.78 20499.46 16497.20 26697.78 25199.24 28299.04 11999.41 11498.90 25797.65 16599.76 27097.70 20799.79 15999.39 232
thres600view794.45 45693.83 46396.29 45299.06 28691.53 48597.99 22294.24 51898.34 18897.44 41295.01 50979.84 50499.67 34484.33 53098.23 45997.66 483
ADS-MVSNet95.24 44394.93 44596.18 46098.14 44390.10 51097.92 23297.32 45490.23 51396.51 46798.91 25485.61 47299.74 28992.88 46396.90 50598.69 412
MP-MVScopyleft98.46 19998.09 24899.54 3199.57 10399.22 3198.50 13799.19 29297.61 26597.58 39698.66 32197.40 19599.88 11594.72 40799.60 27499.54 143
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs17.12 51520.53 5186.87 53312.05 5554.20 55893.62 5166.73 5564.62 55110.41 55124.33 5488.28 5553.56 5529.69 55015.07 54912.86 548
thres40094.14 46493.44 46896.24 45598.93 31891.44 48897.60 28694.29 51597.94 23597.10 42694.31 51979.67 50699.62 37783.05 53398.08 47097.66 483
test12317.04 51620.11 5197.82 53210.25 5564.91 55794.80 4774.47 5574.93 55010.00 55224.28 5499.69 5543.64 55110.14 54912.43 55014.92 547
thres20093.72 47293.14 47495.46 49098.66 38391.29 49296.61 38394.63 51097.39 29596.83 44793.71 52279.88 50399.56 40682.40 53698.13 46795.54 522
test0.0.03 194.51 45593.69 46596.99 42296.05 53093.61 45394.97 47393.49 52496.17 38097.57 39894.88 51382.30 49899.01 49493.60 44294.17 53498.37 445
pmmvs395.03 44894.40 45696.93 42697.70 47392.53 47095.08 47097.71 43888.57 52797.71 38698.08 40279.39 50899.82 20796.19 35299.11 39598.43 438
EMVS93.83 46994.02 46093.23 52196.83 51284.96 53389.77 53896.32 48597.92 23797.43 41396.36 48286.17 46498.93 49787.68 52097.73 48295.81 520
E-PMN94.17 46394.37 45793.58 51596.86 51085.71 53290.11 53797.07 46398.17 21397.82 38197.19 46184.62 48298.94 49689.77 51297.68 48396.09 519
PGM-MVS98.66 16298.37 20299.55 2899.53 12799.18 4298.23 17399.49 15797.01 33298.69 27998.88 26598.00 13499.89 9795.87 36999.59 27899.58 117
LCM-MVSNet-Re98.64 16598.48 18199.11 12898.85 33898.51 11398.49 14099.83 2698.37 18599.69 5599.46 8098.21 11599.92 6594.13 42699.30 36198.91 377
LCM-MVSNet99.93 199.92 199.94 199.99 199.97 199.90 199.89 1499.98 199.99 199.96 199.77 2100.00 199.81 16100.00 199.85 30
MCST-MVS98.00 26897.63 30199.10 13099.24 23298.17 14796.89 36198.73 38795.66 40997.92 36997.70 43297.17 21199.66 35796.18 35499.23 37499.47 197
mvs_anonymous97.83 29398.16 24296.87 43098.18 43891.89 48097.31 32698.90 35297.37 29798.83 25599.46 8096.28 27399.79 24698.90 9498.16 46598.95 367
MVS_Test98.18 24898.36 20497.67 37098.48 40594.73 40498.18 17999.02 33297.69 25698.04 36199.11 18897.22 20899.56 40698.57 12198.90 41998.71 408
MDA-MVSNet-bldmvs97.94 27597.91 27398.06 33199.44 17194.96 39296.63 38199.15 30898.35 18798.83 25599.11 18894.31 35699.85 15896.60 31798.72 43099.37 244
CDPH-MVS97.26 34096.66 37299.07 13899.00 30698.15 14896.03 42799.01 33591.21 50897.79 38297.85 42196.89 23099.69 32792.75 46999.38 34499.39 232
test1298.93 17098.58 39597.83 19698.66 39296.53 46395.51 31299.69 32799.13 39199.27 290
casdiffmvspermissive98.95 10299.00 9498.81 19499.38 18797.33 24697.82 24599.57 11199.17 9399.35 13099.17 16898.35 9499.69 32798.46 12999.73 19999.41 222
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvspermissive98.22 24098.24 22998.17 31599.00 30695.44 36896.38 40099.58 10397.79 24998.53 31298.50 35296.76 24299.74 28997.95 17899.64 25799.34 262
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline293.73 47192.83 47896.42 44797.70 47391.28 49396.84 36389.77 54193.96 47192.44 53195.93 48979.14 50999.77 26492.94 46096.76 50998.21 450
baseline195.96 41895.44 42397.52 39298.51 40493.99 43698.39 15796.09 49198.21 20598.40 33197.76 42886.88 45899.63 37295.42 38989.27 54098.95 367
YYNet197.60 30997.67 29497.39 40399.04 29193.04 46195.27 46398.38 41697.25 31198.92 23498.95 24695.48 31499.73 29696.99 27298.74 42899.41 222
PMMVS298.07 26198.08 25198.04 33499.41 18194.59 41094.59 48899.40 20997.50 27998.82 25898.83 27896.83 23499.84 17797.50 22699.81 14099.71 65
MDA-MVSNet_test_wron97.60 30997.66 29797.41 40299.04 29193.09 45795.27 46398.42 41397.26 31098.88 24398.95 24695.43 31699.73 29697.02 26898.72 43099.41 222
tpmvs95.02 44995.25 43494.33 50496.39 52785.87 52998.08 19696.83 47595.46 42195.51 49598.69 31385.91 47099.53 42094.16 42296.23 51597.58 486
PM-MVS98.82 12598.72 13299.12 12699.64 7798.54 11197.98 22399.68 6497.62 26299.34 13599.18 16397.54 18099.77 26497.79 19299.74 19599.04 349
HQP_MVS97.99 27197.67 29498.93 17099.19 24897.65 22097.77 25499.27 26998.20 20997.79 38297.98 41094.90 33099.70 31794.42 41699.51 30999.45 206
plane_prior799.19 24897.87 192
plane_prior698.99 30997.70 21694.90 330
plane_prior599.27 26999.70 31794.42 41699.51 30999.45 206
plane_prior497.98 410
plane_prior397.78 20697.41 29297.79 382
plane_prior297.77 25498.20 209
plane_prior199.05 289
plane_prior97.65 22097.07 34796.72 35499.36 345
PS-CasMVS99.40 2599.33 3799.62 999.71 4999.10 6599.29 3699.53 13699.53 4199.46 10199.41 9498.23 11099.95 2598.89 9699.95 3999.81 41
UniMVSNet_NR-MVSNet98.86 11698.68 14199.40 7199.17 25898.74 9197.68 26999.40 20999.14 9899.06 19298.59 33896.71 24799.93 5398.57 12199.77 17299.53 157
PEN-MVS99.41 2499.34 3599.62 999.73 3899.14 5799.29 3699.54 13299.62 3299.56 7499.42 8998.16 12299.96 1398.78 10299.93 5799.77 53
TransMVSNet (Re)99.44 1999.47 2199.36 7499.80 2198.58 10699.27 4299.57 11199.39 5899.75 4499.62 4099.17 2099.83 19599.06 8299.62 26799.66 80
DTE-MVSNet99.43 2299.35 3399.66 799.71 4999.30 2199.31 3099.51 14499.64 2699.56 7499.46 8098.23 11099.97 698.78 10299.93 5799.72 64
DU-MVS98.82 12598.63 15299.39 7299.16 26098.74 9197.54 29599.25 27798.84 14899.06 19298.76 29696.76 24299.93 5398.57 12199.77 17299.50 169
UniMVSNet (Re)98.87 11298.71 13599.35 8099.24 23298.73 9497.73 26499.38 21398.93 13299.12 18498.73 30096.77 24099.86 14498.63 11699.80 15299.46 200
CP-MVSNet99.21 4799.09 8299.56 2699.65 7198.96 7799.13 5999.34 23399.42 5599.33 13899.26 13797.01 22399.94 4198.74 10799.93 5799.79 47
WR-MVS_H99.33 3099.22 5499.65 899.71 4999.24 2999.32 2699.55 12699.46 4999.50 9399.34 11597.30 20199.93 5398.90 9499.93 5799.77 53
WR-MVS98.40 20798.19 23699.03 14899.00 30697.65 22096.85 36298.94 34298.57 17498.89 23998.50 35295.60 30899.85 15897.54 22299.85 10999.59 109
NR-MVSNet98.95 10298.82 12199.36 7499.16 26098.72 9699.22 4699.20 28899.10 10799.72 4798.76 29696.38 26699.86 14498.00 17199.82 13399.50 169
Baseline_NR-MVSNet98.98 9898.86 11599.36 7499.82 1998.55 10897.47 30799.57 11199.37 6099.21 17399.61 4396.76 24299.83 19598.06 16399.83 12699.71 65
TranMVSNet+NR-MVSNet99.17 5299.07 8599.46 6399.37 19398.87 8498.39 15799.42 20099.42 5599.36 12899.06 20098.38 8999.95 2598.34 14299.90 8899.57 124
TSAR-MVS + GP.98.18 24897.98 26198.77 20998.71 36497.88 19196.32 40598.66 39296.33 37399.23 16998.51 34897.48 19099.40 45797.16 25499.46 32499.02 352
n20.00 558
nn0.00 558
mPP-MVS98.64 16598.34 20899.54 3199.54 12399.17 4398.63 11699.24 28297.47 28298.09 35598.68 31597.62 17099.89 9796.22 35099.62 26799.57 124
door-mid99.57 111
XVG-OURS-SEG-HR98.49 19698.28 21999.14 12499.49 15098.83 8696.54 38799.48 15997.32 30299.11 18598.61 33599.33 1599.30 47396.23 34998.38 45299.28 287
mvsmamba97.57 31397.26 32598.51 27098.69 37396.73 30398.74 9997.25 45697.03 33197.88 37399.23 15090.95 42599.87 13596.61 31699.00 40798.91 377
MVSFormer98.26 23598.43 18997.77 35698.88 33293.89 44299.39 2099.56 12199.11 10098.16 34798.13 39593.81 37099.97 699.26 6599.57 28799.43 214
jason97.45 32297.35 32097.76 35999.24 23293.93 43895.86 43998.42 41394.24 45998.50 31598.13 39594.82 33499.91 7497.22 24999.73 19999.43 214
jason: jason.
lupinMVS97.06 35896.86 35497.65 37498.88 33293.89 44295.48 45597.97 43293.53 47598.16 34797.58 43893.81 37099.91 7496.77 29599.57 28799.17 327
test_djsdf99.52 1399.51 1599.53 3899.86 1498.74 9199.39 2099.56 12199.11 10099.70 5199.73 2099.00 2799.97 699.26 6599.98 1299.89 16
HPM-MVS_fast99.01 9098.82 12199.57 2199.71 4999.35 1699.00 7399.50 14997.33 30098.94 23198.86 26898.75 4799.82 20797.53 22399.71 21799.56 130
K. test v398.00 26897.66 29799.03 14899.79 2397.56 22799.19 5392.47 52899.62 3299.52 8799.66 3289.61 43999.96 1399.25 6799.81 14099.56 130
lessismore_v098.97 16299.73 3897.53 23086.71 54699.37 12599.52 6789.93 43499.92 6598.99 8899.72 20899.44 210
SixPastTwentyTwo98.75 13898.62 15499.16 11899.83 1897.96 18099.28 4098.20 42499.37 6099.70 5199.65 3692.65 39899.93 5399.04 8499.84 11499.60 102
OurMVSNet-221017-099.37 2899.31 4199.53 3899.91 398.98 7199.63 799.58 10399.44 5299.78 3999.76 1596.39 26499.92 6599.44 5499.92 7199.68 73
HPM-MVScopyleft98.79 13198.53 16999.59 2099.65 7199.29 2399.16 5599.43 19396.74 35398.61 29698.38 36698.62 6499.87 13596.47 33299.67 24599.59 109
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS98.53 18998.34 20899.11 12899.50 14198.82 8895.97 43099.50 14997.30 30599.05 20098.98 23699.35 1499.32 47095.72 37699.68 23999.18 323
XVG-ACMP-BASELINE98.56 18098.34 20899.22 10999.54 12398.59 10597.71 26599.46 17597.25 31198.98 21398.99 23197.54 18099.84 17795.88 36699.74 19599.23 303
casdiffmvs_mvgpermissive99.12 6999.16 6298.99 15699.43 17697.73 21398.00 21599.62 8999.22 8099.55 7799.22 15298.93 3399.75 28298.66 11399.81 14099.50 169
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LPG-MVS_test98.71 14298.46 18599.47 6199.57 10398.97 7398.23 17399.48 15996.60 36099.10 18899.06 20098.71 5199.83 19595.58 38599.78 16499.62 92
LGP-MVS_train99.47 6199.57 10398.97 7399.48 15996.60 36099.10 18899.06 20098.71 5199.83 19595.58 38599.78 16499.62 92
baseline98.96 10199.02 9098.76 21199.38 18797.26 25898.49 14099.50 14998.86 14299.19 17599.06 20098.23 11099.69 32798.71 11099.76 18899.33 268
test1198.87 358
door99.41 204
EPNet_dtu94.93 45194.78 44795.38 49293.58 54187.68 52496.78 36695.69 50197.35 29989.14 54198.09 40188.15 45399.49 43594.95 40199.30 36198.98 359
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x268897.49 31897.14 33598.54 26599.68 6496.09 33396.50 39199.62 8991.58 50298.84 25398.97 23892.36 40199.88 11596.76 29699.95 3999.67 78
EPNet96.14 40795.44 42398.25 30590.76 55095.50 36397.92 23294.65 50998.97 12792.98 52698.85 27189.12 44399.87 13595.99 36299.68 23999.39 232
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS96.79 298
HQP-NCC98.67 37896.29 40796.05 38795.55 490
ACMP_Plane98.67 37896.29 40796.05 38795.55 490
APD-MVScopyleft98.10 25697.67 29499.42 6799.11 27298.93 7997.76 25799.28 26694.97 43898.72 27498.77 29197.04 21899.85 15893.79 43699.54 29999.49 177
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS92.82 465
HQP4-MVS95.56 48999.54 41799.32 273
HQP3-MVS99.04 32799.26 369
HQP2-MVS93.84 368
CNVR-MVS98.17 25197.87 27699.07 13898.67 37898.24 13997.01 34998.93 34597.25 31197.62 39298.34 37197.27 20499.57 40396.42 33699.33 35299.39 232
NCCC97.86 28597.47 31499.05 14598.61 38898.07 16496.98 35298.90 35297.63 26197.04 43297.93 41695.99 29199.66 35795.31 39198.82 42399.43 214
114514_t96.50 38695.77 40698.69 22799.48 15897.43 24197.84 24499.55 12681.42 54196.51 46798.58 33995.53 31099.67 34493.41 45099.58 28398.98 359
CP-MVS98.70 14798.42 19199.52 4499.36 19499.12 6298.72 10499.36 22197.54 27698.30 33598.40 36397.86 15099.89 9796.53 32999.72 20899.56 130
DSMNet-mixed97.42 32597.60 30396.87 43099.15 26491.46 48698.54 12899.12 31192.87 48997.58 39699.63 3996.21 27699.90 8195.74 37599.54 29999.27 290
tpm293.09 48292.58 48194.62 50297.56 48086.53 52897.66 27395.79 49886.15 53494.07 51798.23 38875.95 51899.53 42090.91 50496.86 50897.81 473
NP-MVS98.84 33997.39 24396.84 468
EG-PatchMatch MVS98.99 9499.01 9298.94 16799.50 14197.47 23598.04 20599.59 10098.15 22199.40 11799.36 11098.58 7299.76 27098.78 10299.68 23999.59 109
tpm cat193.29 47993.13 47593.75 51397.39 49384.74 53497.39 31497.65 44283.39 53994.16 51498.41 36282.86 49699.39 45991.56 49295.35 52997.14 499
SteuartSystems-ACMMP98.79 13198.54 16799.54 3199.73 3899.16 4898.23 17399.31 24697.92 23798.90 23698.90 25798.00 13499.88 11596.15 35599.72 20899.58 117
Skip Steuart: Steuart Systems R&D Blog.
CostFormer93.97 46793.78 46494.51 50397.53 48485.83 53197.98 22395.96 49389.29 52294.99 50398.63 33078.63 51399.62 37794.54 41096.50 51198.09 458
CR-MVSNet96.28 40095.95 40297.28 40697.71 47194.22 41798.11 19198.92 34992.31 49596.91 43999.37 10485.44 47599.81 22497.39 23697.36 49797.81 473
JIA-IIPM95.52 43395.03 44197.00 42196.85 51194.03 42996.93 35795.82 49699.20 8494.63 51099.71 2283.09 49499.60 38994.42 41694.64 53197.36 494
Patchmtry97.35 33296.97 34598.50 27497.31 49696.47 31898.18 17998.92 34998.95 13198.78 26499.37 10485.44 47599.85 15895.96 36499.83 12699.17 327
PatchT96.65 37896.35 39097.54 39097.40 49295.32 37697.98 22396.64 47999.33 6696.89 44399.42 8984.32 48599.81 22497.69 20997.49 48897.48 489
tpmrst95.07 44795.46 42193.91 51197.11 50084.36 53897.62 28096.96 46894.98 43796.35 47298.80 28585.46 47499.59 39495.60 38396.23 51597.79 476
BH-w/o95.13 44694.89 44695.86 47598.20 43691.31 49195.65 44897.37 44893.64 47396.52 46695.70 49593.04 39099.02 49288.10 51995.82 52597.24 498
tpm94.67 45394.34 45895.66 48397.68 47688.42 51997.88 23794.90 50794.46 45296.03 48298.56 34278.66 51299.79 24695.88 36695.01 53098.78 400
DELS-MVS98.27 23398.20 23298.48 27698.86 33596.70 30495.60 45099.20 28897.73 25398.45 32298.71 30497.50 18699.82 20798.21 15199.59 27898.93 372
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
BH-untuned96.83 37196.75 36497.08 41798.74 35793.33 45596.71 37298.26 42096.72 35498.44 32397.37 45595.20 32199.47 44291.89 48497.43 49298.44 436
RPMNet97.02 36196.93 34797.30 40597.71 47194.22 41798.11 19199.30 25499.37 6096.91 43999.34 11586.72 45999.87 13597.53 22397.36 49797.81 473
MVSTER96.86 37096.55 38197.79 35497.91 45894.21 41997.56 29198.87 35897.49 28199.06 19299.05 20780.72 50199.80 23398.44 13199.82 13399.37 244
CPTT-MVS97.84 29197.36 31999.27 9999.31 20898.46 11698.29 16699.27 26994.90 44097.83 37998.37 36794.90 33099.84 17793.85 43599.54 29999.51 165
GBi-Net98.65 16398.47 18399.17 11598.90 32698.24 13999.20 4999.44 18798.59 16998.95 22399.55 5694.14 36199.86 14497.77 19699.69 23399.41 222
PVSNet_Blended_VisFu98.17 25198.15 24398.22 31199.73 3895.15 38597.36 32199.68 6494.45 45598.99 21299.27 13196.87 23199.94 4197.13 26199.91 8099.57 124
PVSNet_BlendedMVS97.55 31497.53 30797.60 38198.92 32293.77 44696.64 38099.43 19394.49 45097.62 39299.18 16396.82 23599.67 34494.73 40599.93 5799.36 252
UnsupCasMVSNet_eth97.89 27997.60 30398.75 21399.31 20897.17 27297.62 28099.35 22798.72 15798.76 26998.68 31592.57 39999.74 28997.76 20095.60 52799.34 262
UnsupCasMVSNet_bld97.30 33796.92 34998.45 27999.28 21796.78 30196.20 41499.27 26995.42 42398.28 33998.30 37893.16 38499.71 30894.99 39897.37 49598.87 383
PVSNet_Blended96.88 36896.68 36897.47 39898.92 32293.77 44694.71 47999.43 19390.98 51197.62 39297.36 45696.82 23599.67 34494.73 40599.56 29198.98 359
FMVSNet596.01 41295.20 43898.41 28597.53 48496.10 33098.74 9999.50 14997.22 32098.03 36299.04 20969.80 52799.88 11597.27 24599.71 21799.25 297
test198.65 16398.47 18399.17 11598.90 32698.24 13999.20 4999.44 18798.59 16998.95 22399.55 5694.14 36199.86 14497.77 19699.69 23399.41 222
new_pmnet96.99 36596.76 36297.67 37098.72 36094.89 39695.95 43498.20 42492.62 49298.55 30998.54 34394.88 33399.52 42493.96 43099.44 33398.59 425
FMVSNet397.50 31597.24 32798.29 30198.08 44995.83 34797.86 24198.91 35197.89 24098.95 22398.95 24687.06 45799.81 22497.77 19699.69 23399.23 303
dp93.47 47593.59 46793.13 52296.64 51781.62 54997.66 27396.42 48492.80 49096.11 47698.64 32878.55 51599.59 39493.31 45192.18 53998.16 454
FMVSNet298.49 19698.40 19398.75 21398.90 32697.14 27598.61 12099.13 31098.59 16999.19 17599.28 12994.14 36199.82 20797.97 17699.80 15299.29 284
FMVSNet199.17 5299.17 6099.17 11599.55 11798.24 13999.20 4999.44 18799.21 8299.43 10899.55 5697.82 15499.86 14498.42 13799.89 9499.41 222
N_pmnet97.63 30897.17 33198.99 15699.27 22097.86 19395.98 42993.41 52595.25 43099.47 10098.90 25795.63 30699.85 15896.91 27999.73 19999.27 290
cascas94.79 45294.33 45996.15 46596.02 53292.36 47592.34 53099.26 27585.34 53695.08 50294.96 51292.96 39198.53 50994.41 41998.59 44497.56 487
BH-RMVSNet96.83 37196.58 38097.58 38398.47 40694.05 42696.67 37797.36 44996.70 35797.87 37497.98 41095.14 32599.44 45190.47 50998.58 44599.25 297
UGNet98.53 18998.45 18698.79 20197.94 45696.96 28699.08 6298.54 40499.10 10796.82 44899.47 7896.55 25699.84 17798.56 12499.94 5199.55 137
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
WTY-MVS96.67 37796.27 39697.87 34998.81 34794.61 40996.77 36797.92 43494.94 43997.12 42597.74 42991.11 42499.82 20793.89 43298.15 46699.18 323
XXY-MVS99.14 6299.15 6799.10 13099.76 3097.74 21198.85 9399.62 8998.48 18199.37 12599.49 7498.75 4799.86 14498.20 15299.80 15299.71 65
EC-MVSNet99.09 7399.05 8699.20 11099.28 21798.93 7999.24 4499.84 2399.08 11498.12 35298.37 36798.72 5099.90 8199.05 8399.77 17298.77 401
sss97.21 34696.93 34798.06 33198.83 34195.22 38396.75 36998.48 40994.49 45097.27 42097.90 41792.77 39599.80 23396.57 32099.32 35599.16 333
Test_1112_low_res96.99 36596.55 38198.31 29899.35 19995.47 36795.84 44299.53 13691.51 50496.80 44998.48 35591.36 42199.83 19596.58 31899.53 30399.62 92
1112_ss97.29 33996.86 35498.58 25099.34 20496.32 32496.75 36999.58 10393.14 48196.89 44397.48 44792.11 40999.86 14496.91 27999.54 29999.57 124
ab-mvs-re8.12 51810.83 5210.00 5340.00 5570.00 5590.00 5450.00 5580.00 5520.00 55397.48 4470.00 5560.00 5530.00 5510.00 5510.00 549
ab-mvs98.41 20498.36 20498.59 24999.19 24897.23 26099.32 2698.81 37297.66 25998.62 29499.40 9796.82 23599.80 23395.88 36699.51 30998.75 404
TR-MVS95.55 43295.12 44096.86 43397.54 48293.94 43796.49 39296.53 48294.36 45897.03 43496.61 47494.26 35899.16 48586.91 52496.31 51497.47 490
MDTV_nov1_ep13_2view74.92 55297.69 26890.06 51897.75 38585.78 47193.52 44598.69 412
MDTV_nov1_ep1395.22 43697.06 50383.20 54397.74 26296.16 48794.37 45796.99 43598.83 27883.95 48999.53 42093.90 43197.95 478
MIMVSNet199.38 2799.32 3999.55 2899.86 1499.19 4199.41 1799.59 10099.59 3699.71 4999.57 4997.12 21499.90 8199.21 7099.87 10099.54 143
MIMVSNet96.62 38096.25 39797.71 36699.04 29194.66 40799.16 5596.92 47297.23 31797.87 37499.10 19186.11 46699.65 36491.65 48999.21 37898.82 388
IterMVS-LS98.55 18498.70 13898.09 32599.48 15894.73 40497.22 33899.39 21198.97 12799.38 12199.31 12496.00 28799.93 5398.58 11999.97 2199.60 102
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet97.69 30397.35 32098.69 22798.73 35897.02 28296.92 35998.75 38495.89 39798.59 30198.67 31792.08 41099.74 28996.72 30299.81 14099.32 273
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref99.77 172
IterMVS97.73 29998.11 24796.57 44299.24 23290.28 50895.52 45499.21 28698.86 14299.33 13899.33 11893.11 38699.94 4198.49 12899.94 5199.48 188
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon97.33 33496.92 34998.57 25399.09 27797.99 17396.79 36499.35 22793.18 48097.71 38698.07 40395.00 32999.31 47193.97 42999.13 39198.42 440
MVS_111021_LR98.30 22898.12 24698.83 19099.16 26098.03 16996.09 42399.30 25497.58 26898.10 35498.24 38698.25 10799.34 46696.69 30799.65 25599.12 339
DP-MVS98.93 10498.81 12399.28 9699.21 24098.45 11798.46 14599.33 23999.63 2899.48 9699.15 17697.23 20799.75 28297.17 25399.66 25399.63 91
ACMMP++99.68 239
HQP-MVS97.00 36496.49 38498.55 26098.67 37896.79 29896.29 40799.04 32796.05 38795.55 49096.84 46893.84 36899.54 41792.82 46599.26 36999.32 273
QAPM97.31 33596.81 36098.82 19298.80 35097.49 23199.06 6699.19 29290.22 51597.69 38899.16 17096.91 22999.90 8190.89 50599.41 33899.07 343
Vis-MVSNetpermissive99.34 2999.36 3299.27 9999.73 3898.26 13799.17 5499.78 3699.11 10099.27 15399.48 7598.82 3899.95 2598.94 9199.93 5799.59 109
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS-HIRNet94.32 45895.62 41290.42 52798.46 40875.36 55196.29 40789.13 54295.25 43095.38 49699.75 1692.88 39299.19 48394.07 42899.39 34196.72 508
IS-MVSNet98.19 24697.90 27499.08 13699.57 10397.97 17799.31 3098.32 41799.01 12398.98 21399.03 21291.59 41599.79 24695.49 38899.80 15299.48 188
HyFIR lowres test97.19 34896.60 37998.96 16499.62 8797.28 25695.17 46799.50 14994.21 46099.01 20798.32 37686.61 46099.99 297.10 26399.84 11499.60 102
EPMVS93.72 47293.27 47195.09 49896.04 53187.76 52398.13 18685.01 54894.69 44696.92 43798.64 32878.47 51699.31 47195.04 39796.46 51298.20 451
PAPM_NR96.82 37396.32 39298.30 30099.07 28196.69 30597.48 30398.76 38095.81 40496.61 45996.47 47894.12 36499.17 48490.82 50797.78 48099.06 344
TAMVS98.24 23998.05 25498.80 19799.07 28197.18 27097.88 23798.81 37296.66 35999.17 18399.21 15494.81 33699.77 26496.96 27799.88 9599.44 210
PAPR95.29 44194.47 45397.75 36097.50 49095.14 38694.89 47698.71 38991.39 50695.35 49795.48 50194.57 34499.14 48784.95 52997.37 49598.97 363
RPSCF98.62 17098.36 20499.42 6799.65 7199.42 1098.55 12699.57 11197.72 25598.90 23699.26 13796.12 28299.52 42495.72 37699.71 21799.32 273
Vis-MVSNet (Re-imp)97.46 32097.16 33298.34 29599.55 11796.10 33098.94 8198.44 41098.32 19298.16 34798.62 33388.76 44499.73 29693.88 43399.79 15999.18 323
test_040298.76 13798.71 13598.93 17099.56 11198.14 15098.45 14799.34 23399.28 7398.95 22398.91 25498.34 9599.79 24695.63 38199.91 8098.86 384
MVS_111021_HR98.25 23898.08 25198.75 21399.09 27797.46 23795.97 43099.27 26997.60 26797.99 36598.25 38498.15 12499.38 46196.87 28799.57 28799.42 219
CSCG98.68 15798.50 17599.20 11099.45 16998.63 10098.56 12599.57 11197.87 24198.85 25098.04 40597.66 16499.84 17796.72 30299.81 14099.13 338
PatchMatch-RL97.24 34396.78 36198.61 24699.03 29497.83 19696.36 40299.06 32093.49 47797.36 41897.78 42695.75 30199.49 43593.44 44998.77 42598.52 428
API-MVS97.04 36096.91 35297.42 40197.88 45998.23 14398.18 17998.50 40897.57 26997.39 41696.75 47196.77 24099.15 48690.16 51099.02 40494.88 524
Test By Simon96.52 257
TDRefinement99.42 2399.38 2899.55 2899.76 3099.33 2099.68 699.71 4899.38 5999.53 8399.61 4398.64 6199.80 23398.24 14799.84 11499.52 161
USDC97.41 32697.40 31597.44 40098.94 31693.67 44995.17 46799.53 13694.03 46898.97 21799.10 19195.29 31999.34 46695.84 37299.73 19999.30 282
EPP-MVSNet98.30 22898.04 25599.07 13899.56 11197.83 19699.29 3698.07 43099.03 12198.59 30199.13 18292.16 40699.90 8196.87 28799.68 23999.49 177
PMMVS96.51 38495.98 40098.09 32597.53 48495.84 34694.92 47498.84 36791.58 50296.05 48095.58 49695.68 30599.66 35795.59 38498.09 46998.76 403
PAPM91.88 50190.34 50396.51 44398.06 45192.56 46992.44 52997.17 46086.35 53390.38 53896.01 48686.61 46099.21 48270.65 54595.43 52897.75 478
ACMMPcopyleft98.75 13898.50 17599.52 4499.56 11199.16 4898.87 8999.37 21797.16 32398.82 25899.01 22597.71 16199.87 13596.29 34799.69 23399.54 143
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
CNLPA97.17 35096.71 36698.55 26098.56 39898.05 16896.33 40498.93 34596.91 34097.06 43097.39 45394.38 35299.45 44991.66 48899.18 38598.14 455
PatchmatchNetpermissive95.58 43195.67 41195.30 49597.34 49487.32 52697.65 27596.65 47895.30 42797.07 42998.69 31384.77 48099.75 28294.97 40098.64 43998.83 386
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS98.29 23197.95 26599.34 8398.44 41199.16 4898.12 19099.38 21396.01 39198.06 35898.43 36097.80 15599.67 34495.69 37899.58 28399.20 313
F-COLMAP97.30 33796.68 36899.14 12499.19 24898.39 12297.27 33399.30 25492.93 48696.62 45898.00 40895.73 30299.68 33992.62 47298.46 45099.35 258
ANet_high99.57 1099.67 699.28 9699.89 698.09 15799.14 5899.93 699.82 899.93 699.81 899.17 2099.94 4199.31 61100.00 199.82 36
wuyk23d96.06 40897.62 30291.38 52498.65 38798.57 10798.85 9396.95 46996.86 34799.90 1499.16 17099.18 1998.40 51089.23 51699.77 17277.18 546
OMC-MVS97.88 28297.49 31099.04 14798.89 33198.63 10096.94 35599.25 27795.02 43698.53 31298.51 34897.27 20499.47 44293.50 44799.51 30999.01 354
MG-MVS96.77 37496.61 37797.26 40898.31 42393.06 45895.93 43598.12 42996.45 37097.92 36998.73 30093.77 37299.39 45991.19 49999.04 40099.33 268
AdaColmapbinary97.14 35296.71 36698.46 27898.34 42197.80 20596.95 35498.93 34595.58 41496.92 43797.66 43395.87 29899.53 42090.97 50299.14 38998.04 460
uanet0.00 5190.00 5220.00 5340.00 5570.00 5590.00 5450.00 5580.00 5520.00 5530.00 5520.00 5560.00 5530.00 5510.00 5510.00 549
ITE_SJBPF98.87 17999.22 23898.48 11599.35 22797.50 27998.28 33998.60 33797.64 16899.35 46593.86 43499.27 36598.79 399
DeepMVS_CXcopyleft93.44 51898.24 43294.21 41994.34 51464.28 54691.34 53594.87 51589.45 44292.77 54577.54 54193.14 53693.35 532
TinyColmap97.89 27997.98 26197.60 38198.86 33594.35 41596.21 41399.44 18797.45 28999.06 19298.88 26597.99 13799.28 47794.38 42099.58 28399.18 323
MAR-MVS96.47 39095.70 40998.79 20197.92 45799.12 6298.28 16798.60 39792.16 49795.54 49396.17 48494.77 33999.52 42489.62 51398.23 45997.72 481
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
LF4IMVS97.90 27797.69 29398.52 26999.17 25897.66 21897.19 34399.47 17096.31 37597.85 37898.20 39096.71 24799.52 42494.62 40899.72 20898.38 443
MSDG97.71 30197.52 30898.28 30298.91 32596.82 29694.42 49399.37 21797.65 26098.37 33298.29 38197.40 19599.33 46894.09 42799.22 37598.68 416
LS3D98.63 16798.38 20099.36 7497.25 49799.38 1299.12 6199.32 24199.21 8298.44 32398.88 26597.31 20099.80 23396.58 31899.34 35098.92 373
CLD-MVS97.49 31897.16 33298.48 27699.07 28197.03 28194.71 47999.21 28694.46 45298.06 35897.16 46297.57 17699.48 43994.46 41399.78 16498.95 367
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS93.44 47692.23 48597.08 41799.25 23197.86 19395.61 44997.16 46192.90 48893.76 52398.65 32475.94 51995.66 54079.30 54097.49 48897.73 480
Gipumacopyleft99.03 8899.16 6298.64 23699.94 298.51 11399.32 2699.75 4399.58 3898.60 29999.62 4098.22 11399.51 43097.70 20799.73 19997.89 468
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015