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 12698.73 12999.05 14298.76 34397.81 20099.25 4399.30 24398.57 17298.55 30099.33 11897.95 13999.90 8197.16 24899.67 23999.44 207
3Dnovator+97.89 398.69 15098.51 16999.24 10698.81 33698.40 12099.02 7099.19 28198.99 12398.07 34699.28 12997.11 21499.84 17796.84 28299.32 34599.47 195
DeepC-MVS97.60 498.97 9898.93 10099.10 12899.35 19697.98 17398.01 21299.46 16697.56 26799.54 7999.50 6898.97 2999.84 17798.06 16099.92 7199.49 176
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 21998.01 25399.23 10898.39 40798.97 7395.03 46099.18 28596.88 33699.33 13698.78 28398.16 12199.28 46696.74 29099.62 26199.44 207
DeepC-MVS_fast96.85 698.30 22398.15 23898.75 20998.61 37797.23 25297.76 25599.09 30597.31 29998.75 26698.66 31297.56 17699.64 36396.10 35099.55 28999.39 229
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 34496.68 35798.32 28798.32 41197.16 26598.86 9299.37 20689.48 50996.29 46199.15 17496.56 25199.90 8192.90 45099.20 37097.89 457
ACMH96.65 799.25 4099.24 5399.26 10199.72 4598.38 12299.07 6599.55 12098.30 19299.65 6399.45 8499.22 1799.76 27098.44 13099.77 17099.64 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+96.62 999.08 7899.00 9399.33 8999.71 4998.83 8698.60 12199.58 9999.11 9999.53 8399.18 16298.81 3999.67 33896.71 29599.77 17099.50 168
COLMAP_ROBcopyleft96.50 1098.99 9398.85 11799.41 6999.58 9499.10 6598.74 9999.56 11699.09 10999.33 13699.19 15898.40 8699.72 30695.98 35399.76 18699.42 216
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 36895.95 39198.65 22698.93 30798.09 15596.93 35599.28 25583.58 52698.13 34097.78 41496.13 27499.40 44793.52 43399.29 35398.45 422
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMM96.08 1298.91 10598.73 12999.48 5799.55 11799.14 5798.07 19999.37 20697.62 25899.04 20098.96 23798.84 3799.79 24697.43 22899.65 24999.49 176
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HY-MVS95.94 1395.90 40895.35 41697.55 37897.95 44494.79 38898.81 9896.94 45892.28 48595.17 48798.57 33089.90 42399.75 28291.20 48697.33 48798.10 446
OpenMVS_ROBcopyleft95.38 1495.84 41195.18 42797.81 34298.41 40697.15 26697.37 31898.62 38583.86 52598.65 27998.37 35794.29 34999.68 33388.41 50598.62 43196.60 497
ACMP95.32 1598.41 20198.09 24399.36 7499.51 13398.79 8997.68 26799.38 20295.76 39898.81 25698.82 27598.36 8999.82 20794.75 39299.77 17099.48 187
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLCcopyleft94.65 1696.51 37395.73 39698.85 17998.75 34597.91 18396.42 39099.06 30990.94 50195.59 47597.38 44294.41 34199.59 38690.93 49198.04 46399.05 338
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet93.40 1795.67 41595.70 39795.57 47398.83 33088.57 50692.50 51697.72 42592.69 48096.49 45896.44 46793.72 36599.43 44393.61 42899.28 35498.71 398
PCF-MVS92.86 1894.36 44593.00 46498.42 27598.70 35797.56 22193.16 51399.11 30279.59 53097.55 38897.43 43992.19 39499.73 29679.85 52799.45 31797.97 454
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IB-MVS91.63 1992.24 48490.90 48896.27 44197.22 48791.24 48394.36 48593.33 51492.37 48392.24 52194.58 50666.20 52599.89 9793.16 44494.63 52097.66 472
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 27697.94 26397.65 36399.71 4997.94 18098.52 13098.68 37998.99 12397.52 39199.35 11197.41 19298.18 50391.59 47999.67 23996.82 493
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PVSNet_089.98 2191.15 49090.30 49293.70 50297.72 45784.34 52790.24 52397.42 43590.20 50593.79 51093.09 51690.90 41598.89 49086.57 51472.76 53597.87 459
MVEpermissive83.40 2292.50 47991.92 48194.25 49398.83 33091.64 47192.71 51483.52 53795.92 38786.46 53295.46 49095.20 31495.40 52980.51 52698.64 42795.73 509
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CMPMVSbinary75.91 2396.29 38895.44 41198.84 18596.25 51698.69 9897.02 34699.12 30088.90 51397.83 36898.86 26289.51 42898.90 48991.92 47199.51 30198.92 365
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dtuonlycased97.70 29198.19 23196.24 44399.75 3489.51 50394.69 47299.64 7698.23 19999.46 10198.57 33098.25 10699.85 15895.65 37099.44 32499.36 248
dtuonly96.49 37697.28 31294.10 49698.80 33983.27 53093.66 50399.48 15195.10 42397.87 36398.30 36895.61 30099.68 33396.98 26799.75 19099.33 263
dtuplus98.32 21998.39 19398.10 31299.15 25895.29 36696.68 37099.51 13797.32 29799.18 17899.15 17497.61 17199.62 36997.19 24599.74 19299.38 238
SIFT-UM-Cal96.49 37696.62 36496.12 45498.13 43597.89 18693.35 50998.44 39895.48 41098.63 28198.34 36195.45 30897.45 51392.22 46899.50 30993.02 524
SIFT-NCM-Cal96.56 37196.68 35796.20 44798.27 41898.44 11894.40 48396.67 46595.29 41797.63 38098.17 38196.40 25996.59 52593.61 42899.66 24793.57 517
SIFT-CM-Cal96.28 38996.31 38296.16 45198.39 40798.11 15193.46 50896.47 47194.81 43398.49 30798.43 35094.48 33997.34 51692.60 46299.70 22393.02 524
SIFT-PCN-Cal96.34 38496.46 37696.01 45898.17 42996.89 28493.48 50797.35 44094.84 43199.35 13098.30 36894.70 33497.92 50792.03 46999.88 9593.21 523
SIFT-NN-UMatch95.38 42895.26 42195.75 46798.25 41997.78 20293.24 51295.66 49194.01 45895.10 48997.47 43793.12 37596.78 52292.42 46598.04 46392.69 529
SIFT-NN-NCMNet95.39 42795.22 42495.92 46098.29 41498.34 12993.58 50594.60 49994.07 45694.84 49397.53 42994.37 34596.62 52391.01 48998.64 42792.80 527
SIFT-NN-CMatch95.63 41895.48 40796.08 45598.24 42198.00 16892.71 51494.29 50394.20 45095.85 47197.26 44795.72 29797.01 51891.99 47099.02 39493.23 521
SIFT-NN-PointCN96.06 39796.11 38895.91 46197.88 44897.73 20893.49 50697.51 43493.22 46896.57 44898.26 37296.23 27096.60 52492.54 46399.27 35593.40 519
XFeat-NN89.63 49289.13 49591.14 51390.93 53790.02 50084.90 53094.05 50988.10 51892.89 51693.33 51578.74 49990.89 53483.46 52095.72 51492.52 530
ALIKED-NN94.29 44993.41 45896.94 41496.18 51797.66 21394.90 46498.68 37988.85 51490.43 52596.81 45889.82 42496.59 52586.67 51398.33 44196.58 498
SP-NN94.67 44194.44 44395.36 48195.12 52595.23 37194.27 48796.10 47894.46 44190.91 52495.76 48291.47 40893.87 53295.23 38296.62 49897.00 488
SIFT-NN92.96 47392.79 46793.46 50496.92 49696.45 31191.89 52094.39 50192.91 47692.54 51895.46 49088.26 44090.71 53585.22 51697.52 47493.22 522
hybridcas99.08 7899.13 7098.92 17099.54 12397.61 21998.22 17699.66 6999.27 7399.40 11799.24 14498.47 7799.70 31498.59 11899.80 15199.46 197
GLUNet-SfM86.26 49684.68 49891.01 51480.58 54083.56 52878.04 53193.59 51176.70 53195.29 48694.72 50477.51 50594.26 53166.39 53499.33 34295.20 511
PDCNetPlus95.22 43294.73 43996.70 42897.85 45091.14 48693.94 49799.97 193.06 47398.95 22098.89 25774.32 50999.14 47695.63 37199.93 5799.82 36
hybrid98.22 23598.27 21798.08 31799.13 26295.24 36896.61 37699.53 13097.43 28698.46 31098.97 23396.75 24299.65 35897.84 18499.69 22799.35 254
RoMa-SfM98.46 19698.27 21799.02 14899.35 19698.32 13097.56 28999.70 5295.88 38999.38 12198.65 31496.41 25899.46 43697.78 18999.71 21499.28 281
DKM98.18 24397.95 26098.85 17999.35 19698.31 13196.68 37099.69 5596.90 33598.61 28798.77 28594.41 34198.93 48697.32 23699.84 11499.32 267
ELoFTR97.81 28597.74 27998.04 32399.39 18295.79 34097.28 33099.58 9994.13 45299.38 12199.37 10493.31 37099.60 38197.23 24299.96 2898.74 396
MatchFormer97.07 34696.92 33897.49 38498.44 40095.92 33296.79 36199.14 29893.08 47299.32 14299.10 18893.89 35999.03 47992.78 45699.78 16397.52 477
LoFTR97.97 26697.79 27598.53 25998.80 33997.47 22997.01 34799.55 12095.55 40599.46 10199.22 15194.22 35199.44 44196.45 32599.82 13398.68 405
ALIKED-LG97.10 34296.63 36398.50 26697.96 44398.68 9997.75 25899.68 6295.86 39098.36 32398.33 36591.58 40499.04 47890.87 49499.31 34797.77 466
SP-DiffGlue96.87 35896.76 35197.21 39995.17 52496.88 28696.12 41298.93 33496.51 35498.37 32197.55 42893.65 36797.83 50896.11 34998.45 43996.92 489
SP-LightGlue97.22 33497.01 33297.88 33697.33 48497.19 25996.38 39299.08 30797.28 30296.53 45197.50 43392.36 39098.70 49597.84 18498.76 41597.74 468
SP-SuperGlue97.31 32497.23 31797.57 37796.96 49597.24 25196.26 40398.76 36997.68 25396.88 43397.85 40994.32 34798.01 50597.76 19598.57 43497.45 480
SIFT-UMatch96.33 38596.47 37495.89 46298.29 41497.95 17893.84 49997.24 44595.78 39798.72 26998.04 39493.45 36996.81 52193.14 44599.73 19692.91 526
SIFT-NCMNet96.30 38796.40 37896.03 45797.80 45597.68 21292.34 51896.94 45895.55 40598.84 24998.63 32094.17 35297.63 51293.57 43299.71 21492.77 528
SIFT-ConvMatch96.57 37096.62 36496.43 43498.20 42598.27 13493.88 49896.88 46195.29 41798.88 24098.25 37395.18 31697.43 51493.22 44399.83 12693.59 516
SIFT-PointCN96.45 38196.47 37496.39 43698.13 43597.54 22393.31 51097.23 44694.67 43698.68 27598.32 36694.64 33597.81 50993.50 43599.77 17093.83 514
XFeat-MNN93.41 46592.98 46594.68 48992.63 53192.92 45089.72 52795.81 48592.10 48797.23 41196.29 47184.95 46697.31 51789.60 50298.54 43693.81 515
ALIKED-MNN95.97 40595.30 42098.00 32697.66 46798.12 15096.98 35099.41 19491.11 49994.04 50697.30 44691.56 40598.61 49789.99 49999.63 25797.28 485
SP-MNN96.46 38096.24 38797.10 40596.71 50395.98 32996.00 41797.33 44195.82 39494.93 49297.10 45593.70 36698.01 50596.30 33698.30 44597.30 484
SIFT-MNN95.92 40795.97 39095.74 46998.18 42798.00 16894.17 48996.99 45395.74 39997.16 41297.90 40590.71 41695.79 52793.71 42699.21 36893.44 518
casdiffseed41469214799.09 7299.12 7199.01 15099.55 11797.91 18398.30 16499.68 6299.04 11899.19 17399.37 10498.98 2899.61 37798.13 15399.83 12699.50 168
gbinet_0.2-2-1-0.0295.44 42594.55 44098.14 30895.99 52195.34 36494.71 46898.29 40796.00 38396.05 46890.50 53084.99 46599.79 24697.33 23497.07 49299.28 281
0.3-1-1-0.01587.27 49584.50 49995.57 47391.70 53390.77 49289.41 52892.04 52188.98 51282.46 53581.35 53360.36 53699.50 42192.96 44781.23 53196.45 499
0.4-1-1-0.188.42 49385.91 49695.94 45993.08 53091.54 47290.99 52292.04 52189.96 50884.83 53383.25 53263.75 53299.52 41493.25 44182.07 52996.75 494
0.4-1-1-0.287.49 49484.89 49795.31 48291.33 53690.08 49988.47 52992.07 52088.70 51584.06 53481.08 53463.62 53399.49 42592.93 44981.71 53096.37 500
wanda-best-256-51295.48 42394.74 43797.68 35796.53 50794.12 41194.17 48998.57 39095.84 39196.71 44091.16 52686.05 45599.76 27097.57 21296.09 50699.17 320
usedtu_dtu_shiyan298.99 9398.86 11499.39 7299.73 3898.71 9799.05 6899.47 16199.16 9399.49 9499.12 18396.34 26599.93 5398.05 16299.36 33599.54 143
usedtu_dtu_shiyan197.37 31897.13 32598.11 31099.03 28695.40 35994.47 48098.99 32796.87 33797.97 35597.81 41292.12 39699.75 28297.49 22599.43 32699.16 326
blended_shiyan895.98 40395.33 41797.94 33197.05 49494.87 38695.34 45098.59 38796.17 37197.09 41692.39 52187.62 44499.76 27097.65 20496.05 51299.20 306
E5new99.05 8299.11 7398.85 17999.60 8897.30 24298.42 15199.63 7998.73 15099.26 15599.39 10098.71 5199.70 31498.43 13299.84 11499.54 143
FE-blended-shiyan795.48 42394.74 43797.68 35796.53 50794.12 41194.17 48998.57 39095.84 39196.71 44091.16 52686.05 45599.76 27097.57 21296.09 50699.17 320
E6new99.05 8299.11 7398.85 17999.60 8897.30 24298.42 15199.63 7998.73 15099.26 15599.39 10098.71 5199.70 31498.43 13299.84 11499.54 143
blended_shiyan695.99 40295.33 41797.95 33097.06 49294.89 38495.34 45098.58 38896.17 37197.06 41892.41 52087.64 44399.76 27097.64 20596.09 50699.19 312
usedtu_blend_shiyan596.20 39595.62 40097.94 33196.53 50794.93 38298.83 9699.59 9698.89 13796.71 44091.16 52686.05 45599.73 29696.70 29696.09 50699.17 320
blend_shiyan492.09 48690.16 49397.88 33696.78 50194.93 38295.24 45498.58 38896.22 36996.07 46691.42 52563.46 53499.73 29696.70 29676.98 53498.98 352
E699.05 8299.11 7398.85 17999.60 8897.30 24298.42 15199.63 7998.73 15099.26 15599.39 10098.71 5199.70 31498.43 13299.84 11499.54 143
E599.05 8299.11 7398.85 17999.60 8897.30 24298.42 15199.63 7998.73 15099.26 15599.39 10098.71 5199.70 31498.43 13299.84 11499.54 143
FE-MVSNET397.37 31897.13 32598.11 31099.03 28695.40 35994.47 48098.99 32796.87 33797.97 35597.81 41292.12 39699.75 28297.49 22599.43 32699.16 326
E498.87 11198.88 10798.81 19099.52 13097.23 25297.62 27899.61 8898.58 17099.18 17899.33 11898.29 9899.69 32397.99 17099.83 12699.52 160
E3new98.41 20198.34 20398.62 23499.19 24296.90 28397.32 32299.50 14197.40 28998.63 28198.92 24597.21 20799.65 35897.34 23299.52 29899.31 272
FE-MVSNET299.15 5799.22 5498.94 16499.70 5797.49 22598.62 11899.67 6898.85 14499.34 13399.54 6298.47 7799.81 22498.93 9299.91 8099.51 164
fmvsm_s_conf0.5_n_1199.21 4799.34 3598.80 19399.48 15596.56 30497.97 22599.69 5599.63 2899.84 3099.54 6298.21 11499.94 4199.76 2399.95 3999.88 20
E298.70 14698.68 14098.73 21599.40 18097.10 26997.48 30199.57 10798.09 22199.00 20599.20 15597.90 14299.67 33897.73 19999.77 17099.43 211
MED-MVS test99.45 6499.58 9498.93 7998.68 10999.60 9096.46 36099.53 8398.77 28599.83 19596.67 30099.64 25199.58 117
MED-MVS99.01 8998.84 11899.52 4499.58 9498.93 7998.68 10999.60 9098.85 14499.53 8399.16 16897.87 14899.83 19596.67 30099.64 25199.81 41
E398.69 15098.68 14098.73 21599.40 18097.10 26997.48 30199.57 10798.09 22199.00 20599.20 15597.90 14299.67 33897.73 19999.77 17099.43 211
TestfortrainingZip a99.09 7298.92 10199.61 1399.58 9499.17 4398.68 10999.27 25898.85 14499.61 7099.16 16897.14 21199.86 14498.39 13799.57 28199.81 41
TestfortrainingZip98.97 15998.30 41398.43 11998.68 10998.26 40897.76 24798.86 24698.16 38395.15 31799.47 43297.55 47399.02 345
fmvsm_s_conf0.5_n_1099.15 5799.27 4798.78 20099.47 15896.56 30497.75 25899.71 4799.60 3599.74 4699.44 8597.96 13899.95 2599.86 499.94 5199.82 36
viewdifsd2359ckpt0798.71 14198.86 11498.26 29399.43 17395.65 34397.20 33799.66 6999.20 8399.29 14799.01 22098.29 9899.73 29697.92 17599.75 19099.39 229
viewdifsd2359ckpt0998.13 24997.92 26698.77 20599.18 25097.35 23797.29 32699.53 13095.81 39598.09 34498.47 34696.34 26599.66 35197.02 26099.51 30199.29 278
viewdifsd2359ckpt1398.39 21098.29 21398.70 21999.26 22597.19 25997.51 29799.48 15196.94 33098.58 29498.82 27597.47 19099.55 40297.21 24499.33 34299.34 257
viewcassd2359sk1198.55 18198.51 16998.67 22499.29 21096.99 27597.39 31299.54 12697.73 24998.81 25699.08 19597.55 17799.66 35197.52 21999.67 23999.36 248
viewdifsd2359ckpt1198.84 11899.04 8698.24 29799.56 11195.51 34997.38 31499.70 5299.16 9399.57 7299.40 9798.26 10499.71 30798.55 12599.82 13399.50 168
viewmacassd2359aftdt98.86 11598.87 11098.83 18699.53 12797.32 24197.70 26599.64 7698.22 20199.25 16399.27 13198.40 8699.61 37797.98 17199.87 10099.55 137
viewmsd2359difaftdt98.84 11899.04 8698.24 29799.56 11195.51 34997.38 31499.70 5299.16 9399.57 7299.40 9798.26 10499.71 30798.55 12599.82 13399.50 168
diffmvs_AUTHOR98.50 19298.59 15998.23 30099.35 19695.48 35396.61 37699.60 9098.37 18398.90 23399.00 22497.37 19599.76 27098.22 14799.85 10999.46 197
FE-MVSNET98.59 17398.50 17298.87 17699.58 9497.30 24298.08 19599.74 4396.94 33098.97 21499.10 18896.94 22499.74 28997.33 23499.86 10799.55 137
fmvsm_l_conf0.5_n_999.32 3299.43 2498.98 15799.59 9297.18 26297.44 30999.83 2699.56 3999.91 1299.34 11599.36 1399.93 5399.83 1099.98 1299.85 30
mamba_040898.80 12898.88 10798.55 25299.27 21696.50 30798.00 21399.60 9098.93 13199.22 16898.84 27098.59 6799.89 9797.74 19799.72 20599.27 284
icg_test_0407_298.20 24098.38 19697.65 36399.03 28694.03 41795.78 43399.45 17098.16 21399.06 19098.71 29698.27 10299.68 33397.50 22099.45 31799.22 301
SSM_0407298.80 12898.88 10798.56 25099.27 21696.50 30798.00 21399.60 9098.93 13199.22 16898.84 27098.59 6799.90 8197.74 19799.72 20599.27 284
SSM_040798.86 11598.96 9998.55 25299.27 21696.50 30798.04 20499.66 6999.09 10999.22 16899.02 20998.79 4399.87 13597.87 18199.72 20599.27 284
viewmambaseed2359dif98.19 24198.26 22097.99 32899.02 29395.03 37996.59 37999.53 13096.21 37099.00 20598.99 22697.62 16999.61 37797.62 20799.72 20599.33 263
IMVS_040798.39 21098.64 14897.66 36199.03 28694.03 41798.10 19299.45 17098.16 21399.06 19098.71 29698.27 10299.71 30797.50 22099.45 31799.22 301
viewmanbaseed2359cas98.58 17598.54 16598.70 21999.28 21397.13 26897.47 30599.55 12097.55 26998.96 21998.92 24597.77 15699.59 38697.59 21199.77 17099.39 229
IMVS_040498.07 25498.20 22797.69 35699.03 28694.03 41796.67 37299.45 17098.16 21398.03 35198.71 29696.80 23599.82 20797.50 22099.45 31799.22 301
SSM_040498.90 10799.01 9198.57 24599.42 17596.59 29998.13 18599.66 6999.09 10999.30 14699.02 20998.79 4399.89 9797.87 18199.80 15199.23 296
IMVS_040398.34 21498.56 16297.66 36199.03 28694.03 41797.98 22199.45 17098.16 21398.89 23698.71 29697.90 14299.74 28997.50 22099.45 31799.22 301
SD_040396.28 38995.83 39397.64 36698.72 34994.30 40498.87 8998.77 36797.80 24396.53 45198.02 39697.34 19799.47 43276.93 53099.48 31399.16 326
fmvsm_s_conf0.5_n_999.17 5299.38 2898.53 25999.51 13395.82 33897.62 27899.78 3699.72 1499.90 1499.48 7598.66 5999.89 9799.85 699.93 5799.89 16
ME-MVS98.61 16998.33 20899.44 6599.24 22798.93 7997.45 30799.06 30998.14 21999.06 19098.77 28596.97 22399.82 20796.67 30099.64 25199.58 117
NormalMVS98.26 23097.97 25999.15 12199.64 7797.83 19298.28 16699.43 18499.24 7698.80 25898.85 26589.76 42599.94 4198.04 16399.67 23999.68 73
lecture99.25 4099.12 7199.62 999.64 7799.40 1198.89 8899.51 13799.19 8899.37 12599.25 14298.36 8999.88 11598.23 14699.67 23999.59 109
SymmetryMVS98.05 25697.71 28499.09 13299.29 21097.83 19298.28 16697.64 43299.24 7698.80 25898.85 26589.76 42599.94 4198.04 16399.50 30999.49 176
Elysia99.15 5799.14 6899.18 11399.63 8397.92 18198.50 13799.43 18499.67 2099.70 5199.13 18096.66 24699.98 499.54 4499.96 2899.64 86
StellarMVS99.15 5799.14 6899.18 11399.63 8397.92 18198.50 13799.43 18499.67 2099.70 5199.13 18096.66 24699.98 499.54 4499.96 2899.64 86
KinetiMVS99.03 8799.02 8999.03 14599.70 5797.48 22898.43 14899.29 25199.70 1599.60 7199.07 19696.13 27499.94 4199.42 5599.87 10099.68 73
LuminaMVS98.39 21098.20 22798.98 15799.50 13997.49 22597.78 24997.69 42798.75 14999.49 9499.25 14292.30 39399.94 4199.14 7599.88 9599.50 168
VortexMVS97.98 26598.31 21097.02 40998.88 32191.45 47598.03 20699.47 16198.65 15899.55 7799.47 7891.49 40799.81 22499.32 6099.91 8099.80 45
AstraMVS98.16 24898.07 24898.41 27699.51 13395.86 33598.00 21395.14 49498.97 12699.43 10899.24 14493.25 37199.84 17799.21 7099.87 10099.54 143
guyue98.01 26097.93 26598.26 29399.45 16695.48 35398.08 19596.24 47498.89 13799.34 13399.14 17891.32 41099.82 20799.07 8099.83 12699.48 187
sc_t199.62 799.66 899.53 3899.82 1999.09 6899.50 1199.63 7999.88 499.86 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 21399.51 13396.44 31297.65 27399.65 7499.66 2399.78 3999.48 7597.92 14199.93 5399.72 3099.95 3999.87 22
fmvsm_s_conf0.5_n_798.83 12199.04 8698.20 30299.30 20894.83 38797.23 33299.36 21098.64 15999.84 3099.43 8898.10 12699.91 7499.56 4199.96 2899.87 22
fmvsm_s_conf0.5_n_699.08 7899.21 5798.69 22199.36 19196.51 30697.62 27899.68 6298.43 18199.85 2799.10 18899.12 2399.88 11599.77 2299.92 7199.67 78
fmvsm_s_conf0.5_n_599.07 8199.10 7998.99 15399.47 15897.22 25597.40 31199.83 2697.61 26199.85 2799.30 12598.80 4199.95 2599.71 3299.90 8899.78 50
fmvsm_s_conf0.5_n_499.01 8999.22 5498.38 28099.31 20495.48 35397.56 28999.73 4498.87 13999.75 4499.27 13198.80 4199.86 14499.80 1799.90 8899.81 41
SSC-MVS3.298.53 18698.79 12397.74 35199.46 16193.62 44096.45 38699.34 22299.33 6598.93 22998.70 30397.90 14299.90 8199.12 7699.92 7199.69 72
testing3-293.78 45893.91 44993.39 50798.82 33381.72 53697.76 25595.28 49298.60 16696.54 45096.66 46165.85 52799.62 36996.65 30498.99 39998.82 378
myMVS_eth3d2892.92 47592.31 47194.77 48797.84 45187.59 51396.19 40696.11 47797.08 32294.27 50093.49 51366.07 52698.78 49291.78 47497.93 46797.92 456
UWE-MVS-2890.22 49189.28 49493.02 51194.50 52882.87 53296.52 38387.51 53295.21 42192.36 52096.04 47371.57 51398.25 50272.04 53297.77 46997.94 455
fmvsm_l_conf0.5_n_399.45 1899.48 1899.34 8399.59 9298.21 14397.82 24399.84 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 20099.46 16196.58 30297.65 27399.72 4599.47 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 23299.49 14796.08 32697.38 31499.81 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 22699.69 6196.08 32697.49 30099.90 1299.53 4199.88 2199.64 3798.51 7699.90 8199.83 1099.98 1299.97 4
GDP-MVS97.50 30497.11 32798.67 22499.02 29396.85 28798.16 18299.71 4798.32 19098.52 30598.54 33383.39 48099.95 2598.79 10199.56 28599.19 312
BP-MVS197.40 31696.97 33498.71 21899.07 27496.81 28998.34 16397.18 44798.58 17098.17 33398.61 32584.01 47699.94 4198.97 8999.78 16399.37 241
reproduce_monomvs95.00 43895.25 42294.22 49497.51 47883.34 52997.86 23998.44 39898.51 17799.29 14799.30 12567.68 52099.56 39898.89 9699.81 14099.77 53
mmtdpeth99.30 3399.42 2598.92 17099.58 9496.89 28499.48 1399.92 899.92 298.26 33099.80 1198.33 9599.91 7499.56 4199.95 3999.97 4
reproduce_model99.15 5798.97 9799.67 499.33 20299.44 998.15 18399.47 16199.12 9899.52 8799.32 12398.31 9699.90 8197.78 18999.73 19699.66 80
reproduce-ours99.09 7298.90 10499.67 499.27 21699.49 598.00 21399.42 19099.05 11699.48 9699.27 13198.29 9899.89 9797.61 20899.71 21499.62 92
our_new_method99.09 7298.90 10499.67 499.27 21699.49 598.00 21399.42 19099.05 11699.48 9699.27 13198.29 9899.89 9797.61 20899.71 21499.62 92
mmdepth0.00 5070.00 5100.00 5220.00 5450.00 5470.00 5330.00 5460.00 5400.00 5410.00 5400.00 5440.00 5410.00 5390.00 5390.00 537
monomultidepth0.00 5070.00 5100.00 5220.00 5450.00 5470.00 5330.00 5460.00 5400.00 5410.00 5400.00 5440.00 5410.00 5390.00 5390.00 537
mvs5depth99.30 3399.59 1298.44 27399.65 7195.35 36299.82 399.94 399.83 799.42 11299.94 298.13 12499.96 1399.63 3699.96 28100.00 1
MVStest195.86 40995.60 40296.63 42995.87 52291.70 47097.93 22798.94 33198.03 22499.56 7499.66 3271.83 51298.26 50199.35 5899.24 36199.91 13
ttmdpeth97.91 26898.02 25297.58 37298.69 36294.10 41398.13 18598.90 34197.95 23097.32 40799.58 4795.95 28998.75 49396.41 32899.22 36599.87 22
WBMVS95.18 43394.78 43596.37 43797.68 46589.74 50295.80 43298.73 37697.54 27198.30 32498.44 34970.06 51499.82 20796.62 30699.87 10099.54 143
dongtai76.24 50075.95 50377.12 51892.39 53267.91 54290.16 52459.44 54382.04 52889.42 52894.67 50549.68 54081.74 53648.06 53577.66 53381.72 532
kuosan69.30 50168.95 50470.34 51987.68 53965.00 54391.11 52159.90 54269.02 53274.46 53788.89 53148.58 54168.03 53828.61 53672.33 53677.99 533
MVSMamba_PlusPlus98.83 12198.98 9698.36 28499.32 20396.58 30298.90 8499.41 19499.75 1098.72 26999.50 6896.17 27299.94 4199.27 6499.78 16398.57 415
MGCFI-Net98.34 21498.28 21498.51 26298.47 39597.59 22098.96 7899.48 15199.18 9197.40 40295.50 48798.66 5999.50 42198.18 15098.71 42098.44 425
testing9193.32 46692.27 47296.47 43397.54 47191.25 48296.17 41096.76 46497.18 31693.65 51293.50 51265.11 52999.63 36693.04 44697.45 47898.53 416
testing1193.08 47192.02 47796.26 44297.56 46990.83 49196.32 39795.70 48796.47 35992.66 51793.73 50964.36 53099.59 38693.77 42597.57 47298.37 434
testing9993.04 47291.98 48096.23 44597.53 47390.70 49496.35 39595.94 48296.87 33793.41 51393.43 51463.84 53199.59 38693.24 44297.19 48898.40 430
UBG93.25 46892.32 47096.04 45697.72 45790.16 49795.92 42695.91 48396.03 38193.95 50993.04 51769.60 51699.52 41490.72 49697.98 46598.45 422
UWE-MVS92.38 48191.76 48494.21 49597.16 48884.65 52395.42 44788.45 53195.96 38596.17 46295.84 48166.36 52399.71 30791.87 47398.64 42798.28 437
ETVMVS92.60 47891.08 48797.18 40097.70 46293.65 43996.54 38095.70 48796.51 35494.68 49692.39 52161.80 53599.50 42186.97 51097.41 48198.40 430
sasdasda98.34 21498.26 22098.58 24298.46 39797.82 19798.96 7899.46 16699.19 8897.46 39695.46 49098.59 6799.46 43698.08 15898.71 42098.46 419
testing22291.96 48790.37 49096.72 42797.47 48092.59 45696.11 41394.76 49696.83 34192.90 51592.87 51857.92 53799.55 40286.93 51197.52 47498.00 453
WB-MVSnew95.73 41495.57 40596.23 44596.70 50490.70 49496.07 41593.86 51095.60 40397.04 42095.45 49496.00 28199.55 40291.04 48898.31 44498.43 427
fmvsm_l_conf0.5_n_a99.19 5199.27 4798.94 16499.65 7197.05 27197.80 24799.76 3998.70 15799.78 3999.11 18598.79 4399.95 2599.85 699.96 2899.83 33
fmvsm_l_conf0.5_n99.21 4799.28 4699.02 14899.64 7797.28 24897.82 24399.76 3998.73 15099.82 3499.09 19498.81 3999.95 2599.86 499.96 2899.83 33
fmvsm_s_conf0.1_n_a99.17 5299.30 4498.80 19399.75 3496.59 29997.97 22599.86 1798.22 20199.88 2199.71 2298.59 6799.84 17799.73 2899.98 1299.98 3
fmvsm_s_conf0.1_n99.16 5699.33 3798.64 22899.71 4996.10 32197.87 23899.85 1998.56 17599.90 1499.68 2598.69 5799.85 15899.72 3099.98 1299.97 4
fmvsm_s_conf0.5_n_a99.10 7199.20 5898.78 20099.55 11796.59 29997.79 24899.82 3198.21 20399.81 3699.53 6498.46 8299.84 17799.70 3399.97 2199.90 15
fmvsm_s_conf0.5_n99.09 7299.26 5098.61 23899.55 11796.09 32497.74 26099.81 3298.55 17699.85 2799.55 5698.60 6699.84 17799.69 3599.98 1299.89 16
MM98.22 23597.99 25598.91 17298.66 37296.97 27697.89 23494.44 50099.54 4098.95 22099.14 17893.50 36899.92 6599.80 1799.96 2899.85 30
WAC-MVS90.90 48991.37 483
Syy-MVS96.04 39995.56 40697.49 38497.10 49094.48 39996.18 40896.58 46895.65 40194.77 49492.29 52391.27 41199.36 45298.17 15298.05 46198.63 409
test_fmvsmconf0.1_n99.49 1599.54 1499.34 8399.78 2498.11 15197.77 25299.90 1299.33 6599.97 399.66 3299.71 399.96 1399.79 1999.99 599.96 8
test_fmvsmconf0.01_n99.57 1099.63 1099.36 7499.87 1298.13 14998.08 19599.95 299.45 5099.98 299.75 1699.80 199.97 699.82 1299.99 599.99 2
myMVS_eth3d91.92 48890.45 48996.30 43997.10 49090.90 48996.18 40896.58 46895.65 40194.77 49492.29 52353.88 53899.36 45289.59 50398.05 46198.63 409
testing393.51 46292.09 47597.75 34998.60 37994.40 40197.32 32295.26 49397.56 26796.79 43895.50 48753.57 53999.77 26495.26 38198.97 40399.08 334
SSC-MVS98.71 14198.74 12798.62 23499.72 4596.08 32698.74 9998.64 38499.74 1299.67 5999.24 14494.57 33799.95 2599.11 7799.24 36199.82 36
test_fmvsmconf_n99.44 1999.48 1899.31 9499.64 7798.10 15497.68 26799.84 2399.29 7199.92 899.57 4999.60 599.96 1399.74 2799.98 1299.89 16
WB-MVS98.52 19098.55 16398.43 27499.65 7195.59 34498.52 13098.77 36799.65 2599.52 8799.00 22494.34 34699.93 5398.65 11498.83 41199.76 58
test_fmvsmvis_n_192099.26 3999.49 1698.54 25799.66 7096.97 27698.00 21399.85 1999.24 7699.92 899.50 6899.39 1299.95 2599.89 399.98 1298.71 398
dmvs_re95.98 40395.39 41497.74 35198.86 32497.45 23298.37 15995.69 48997.95 23096.56 44995.95 47690.70 41797.68 51188.32 50696.13 50598.11 445
SDMVSNet99.23 4599.32 3998.96 16199.68 6497.35 23798.84 9599.48 15199.69 1799.63 6699.68 2599.03 2499.96 1397.97 17299.92 7199.57 124
dmvs_testset92.94 47492.21 47495.13 48498.59 38290.99 48897.65 27392.09 51996.95 32994.00 50793.55 51192.34 39296.97 52072.20 53192.52 52597.43 481
sd_testset99.28 3699.31 4199.19 11299.68 6498.06 16499.41 1799.30 24399.69 1799.63 6699.68 2599.25 1699.96 1397.25 24199.92 7199.57 124
test_fmvsm_n_192099.33 3099.45 2398.99 15399.57 10397.73 20897.93 22799.83 2699.22 7999.93 699.30 12599.42 1199.96 1399.85 699.99 599.29 278
test_cas_vis1_n_192098.33 21898.68 14097.27 39699.69 6192.29 46498.03 20699.85 1997.62 25899.96 499.62 4093.98 35899.74 28999.52 4999.86 10799.79 47
test_vis1_n_192098.40 20498.92 10196.81 42399.74 3790.76 49398.15 18399.91 1098.33 18899.89 1899.55 5695.07 32099.88 11599.76 2399.93 5799.79 47
test_vis1_n98.31 22298.50 17297.73 35499.76 3094.17 40998.68 10999.91 1096.31 36699.79 3899.57 4992.85 38499.42 44599.79 1999.84 11499.60 102
test_fmvs1_n98.09 25298.28 21497.52 38199.68 6493.47 44298.63 11699.93 695.41 41599.68 5799.64 3791.88 40199.48 42999.82 1299.87 10099.62 92
mvsany_test197.60 29897.54 29697.77 34597.72 45795.35 36295.36 44997.13 45094.13 45299.71 4999.33 11897.93 14099.30 46297.60 21098.94 40698.67 407
APD_test198.83 12198.66 14599.34 8399.78 2499.47 898.42 15199.45 17098.28 19798.98 21099.19 15897.76 15799.58 39396.57 31199.55 28998.97 356
test_vis1_rt97.75 28797.72 28397.83 34098.81 33696.35 31597.30 32599.69 5594.61 43797.87 36398.05 39396.26 26998.32 50098.74 10798.18 45098.82 378
test_vis3_rt99.14 6299.17 6099.07 13599.78 2498.38 12298.92 8399.94 397.80 24399.91 1299.67 3097.15 21098.91 48899.76 2399.56 28599.92 12
test_fmvs298.70 14698.97 9797.89 33599.54 12394.05 41498.55 12699.92 896.78 34499.72 4799.78 1396.60 25099.67 33899.91 299.90 8899.94 10
test_fmvs197.72 28997.94 26397.07 40898.66 37292.39 46197.68 26799.81 3295.20 42299.54 7999.44 8591.56 40599.41 44699.78 2199.77 17099.40 228
test_fmvs399.12 6999.41 2698.25 29599.76 3095.07 37899.05 6899.94 397.78 24699.82 3499.84 398.56 7399.71 30799.96 199.96 2899.97 4
mvsany_test398.87 11198.92 10198.74 21399.38 18496.94 28098.58 12399.10 30396.49 35799.96 499.81 898.18 11799.45 43998.97 8999.79 15899.83 33
testf199.25 4099.16 6299.51 4999.89 699.63 398.71 10699.69 5598.90 13599.43 10899.35 11198.86 3599.67 33897.81 18699.81 14099.24 294
APD_test299.25 4099.16 6299.51 4999.89 699.63 398.71 10699.69 5598.90 13599.43 10899.35 11198.86 3599.67 33897.81 18699.81 14099.24 294
test_f98.67 15998.87 11098.05 32299.72 4595.59 34498.51 13599.81 3296.30 36899.78 3999.82 596.14 27398.63 49699.82 1299.93 5799.95 9
FE-MVS95.66 41694.95 43297.77 34598.53 39195.28 36799.40 1996.09 47993.11 47197.96 35799.26 13779.10 49899.77 26492.40 46698.71 42098.27 438
FA-MVS(test-final)96.99 35496.82 34797.50 38398.70 35794.78 38999.34 2396.99 45395.07 42498.48 30999.33 11888.41 43999.65 35896.13 34898.92 40898.07 448
BridgeMVS98.63 16598.72 13198.38 28098.66 37296.68 29898.90 8499.42 19098.99 12398.97 21499.19 15895.81 29499.85 15898.77 10599.77 17098.60 411
MonoMVSNet96.25 39296.53 37295.39 47996.57 50691.01 48798.82 9797.68 42998.57 17298.03 35199.37 10490.92 41497.78 51094.99 38693.88 52397.38 482
patch_mono-298.51 19198.63 15098.17 30599.38 18494.78 38997.36 31999.69 5598.16 21398.49 30799.29 12897.06 21599.97 698.29 14399.91 8099.76 58
EGC-MVSNET85.24 49780.54 50099.34 8399.77 2799.20 3899.08 6299.29 25112.08 53720.84 53899.42 8997.55 17799.85 15897.08 25699.72 20598.96 358
test250692.39 48091.89 48293.89 50099.38 18482.28 53499.32 2666.03 54199.08 11398.77 26399.57 4966.26 52499.84 17798.71 11099.95 3999.54 143
test111196.49 37696.82 34795.52 47599.42 17587.08 51599.22 4687.14 53399.11 9999.46 10199.58 4788.69 43399.86 14498.80 10099.95 3999.62 92
ECVR-MVScopyleft96.42 38296.61 36695.85 46499.38 18488.18 51099.22 4686.00 53599.08 11399.36 12899.57 4988.47 43899.82 20798.52 12799.95 3999.54 143
test_blank0.00 5070.00 5100.00 5220.00 5450.00 5470.00 5330.00 5460.00 5400.00 5410.00 5400.00 5440.00 5410.00 5390.00 5390.00 537
tt080598.69 15098.62 15298.90 17599.75 3499.30 2199.15 5796.97 45598.86 14198.87 24597.62 42598.63 6398.96 48499.41 5698.29 44698.45 422
DVP-MVS++98.90 10798.70 13799.51 4998.43 40299.15 5299.43 1599.32 23098.17 21099.26 15599.02 20998.18 11799.88 11597.07 25799.45 31799.49 176
FOURS199.73 3899.67 299.43 1599.54 12699.43 5499.26 155
MSC_two_6792asdad99.32 9198.43 40298.37 12498.86 35299.89 9797.14 25199.60 26899.71 65
PC_three_145293.27 46799.40 11798.54 33398.22 11297.00 51995.17 38399.45 31799.49 176
No_MVS99.32 9198.43 40298.37 12498.86 35299.89 9797.14 25199.60 26899.71 65
test_one_060199.39 18299.20 3899.31 23598.49 17898.66 27899.02 20997.64 167
eth-test20.00 545
eth-test0.00 545
GeoE99.05 8298.99 9599.25 10499.44 16898.35 12898.73 10399.56 11698.42 18298.91 23298.81 27898.94 3199.91 7498.35 13999.73 19699.49 176
test_method79.78 49879.50 50180.62 51680.21 54145.76 54470.82 53298.41 40331.08 53680.89 53697.71 41884.85 46797.37 51591.51 48180.03 53298.75 394
Anonymous2024052198.69 15098.87 11098.16 30799.77 2795.11 37799.08 6299.44 17899.34 6499.33 13699.55 5694.10 35799.94 4199.25 6799.96 2899.42 216
h-mvs3397.77 28697.33 31199.10 12899.21 23597.84 19198.35 16198.57 39099.11 9998.58 29499.02 20988.65 43699.96 1398.11 15596.34 50199.49 176
hse-mvs297.46 30997.07 32898.64 22898.73 34797.33 23997.45 30797.64 43299.11 9998.58 29497.98 39988.65 43699.79 24698.11 15597.39 48298.81 383
CL-MVSNet_self_test97.44 31297.22 31898.08 31798.57 38695.78 34194.30 48698.79 36496.58 35398.60 29098.19 38094.74 33399.64 36396.41 32898.84 41098.82 378
KD-MVS_2432*160092.87 47691.99 47895.51 47691.37 53489.27 50494.07 49298.14 41595.42 41297.25 40996.44 46767.86 51899.24 46891.28 48496.08 51098.02 450
KD-MVS_self_test99.25 4099.18 5999.44 6599.63 8399.06 7098.69 10899.54 12699.31 6899.62 6999.53 6497.36 19699.86 14499.24 6999.71 21499.39 229
AUN-MVS96.24 39495.45 41098.60 24098.70 35797.22 25597.38 31497.65 43095.95 38695.53 48297.96 40382.11 48899.79 24696.31 33497.44 47998.80 388
ZD-MVS99.01 29598.84 8599.07 30894.10 45498.05 34998.12 38696.36 26499.86 14492.70 45999.19 373
SR-MVS-dyc-post98.81 12698.55 16399.57 2199.20 23999.38 1298.48 14399.30 24398.64 15998.95 22098.96 23797.49 18899.86 14496.56 31599.39 33199.45 203
RE-MVS-def98.58 16099.20 23999.38 1298.48 14399.30 24398.64 15998.95 22098.96 23797.75 15896.56 31599.39 33199.45 203
SED-MVS98.91 10598.72 13199.49 5599.49 14799.17 4398.10 19299.31 23598.03 22499.66 6099.02 20998.36 8999.88 11596.91 27199.62 26199.41 219
IU-MVS99.49 14799.15 5298.87 34792.97 47499.41 11496.76 28899.62 26199.66 80
OPU-MVS98.82 18898.59 38298.30 13298.10 19298.52 33798.18 11798.75 49394.62 39699.48 31399.41 219
test_241102_TWO99.30 24398.03 22499.26 15599.02 20997.51 18499.88 11596.91 27199.60 26899.66 80
test_241102_ONE99.49 14799.17 4399.31 23597.98 22799.66 6098.90 25198.36 8999.48 429
SF-MVS98.53 18698.27 21799.32 9199.31 20498.75 9098.19 17799.41 19496.77 34598.83 25198.90 25197.80 15499.82 20795.68 36999.52 29899.38 238
cl2295.79 41295.39 41496.98 41296.77 50292.79 45394.40 48398.53 39494.59 43897.89 36198.17 38182.82 48599.24 46896.37 33099.03 39198.92 365
miper_ehance_all_eth97.06 34797.03 33097.16 40497.83 45293.06 44694.66 47399.09 30595.99 38498.69 27298.45 34892.73 38799.61 37796.79 28499.03 39198.82 378
miper_enhance_ethall96.01 40095.74 39596.81 42396.41 51492.27 46593.69 50298.89 34491.14 49898.30 32497.35 44590.58 41899.58 39396.31 33499.03 39198.60 411
ZNCC-MVS98.68 15698.40 19099.54 3199.57 10399.21 3298.46 14599.29 25197.28 30298.11 34298.39 35498.00 13399.87 13596.86 28199.64 25199.55 137
dcpmvs_298.78 13299.11 7397.78 34499.56 11193.67 43799.06 6699.86 1799.50 4399.66 6099.26 13797.21 20799.99 298.00 16899.91 8099.68 73
cl____97.02 35096.83 34697.58 37297.82 45394.04 41694.66 47399.16 29297.04 32498.63 28198.71 29688.68 43599.69 32397.00 26299.81 14099.00 350
DIV-MVS_self_test97.02 35096.84 34597.58 37297.82 45394.03 41794.66 47399.16 29297.04 32498.63 28198.71 29688.69 43399.69 32397.00 26299.81 14099.01 347
eth_miper_zixun_eth97.23 33397.25 31597.17 40298.00 44292.77 45494.71 46899.18 28597.27 30498.56 29898.74 29291.89 40099.69 32397.06 25999.81 14099.05 338
9.1497.78 27699.07 27497.53 29499.32 23095.53 40898.54 30298.70 30397.58 17499.76 27094.32 40999.46 315
uanet_test0.00 5070.00 5100.00 5220.00 5450.00 5470.00 5330.00 5460.00 5400.00 5410.00 5400.00 5440.00 5410.00 5390.00 5390.00 537
DCPMVS0.00 5070.00 5100.00 5220.00 5450.00 5470.00 5330.00 5460.00 5400.00 5410.00 5400.00 5440.00 5410.00 5390.00 5390.00 537
save fliter99.11 26597.97 17496.53 38299.02 32198.24 198
ET-MVSNet_ETH3D94.30 44893.21 46097.58 37298.14 43294.47 40094.78 46793.24 51594.72 43489.56 52795.87 47978.57 50299.81 22496.91 27197.11 49198.46 419
UniMVSNet_ETH3D99.69 299.69 499.69 399.84 1799.34 1999.69 599.58 9999.90 399.86 2499.78 1399.58 699.95 2599.00 8799.95 3999.78 50
EIA-MVS98.00 26197.74 27998.80 19398.72 34998.09 15598.05 20299.60 9097.39 29096.63 44595.55 48597.68 16199.80 23396.73 29299.27 35598.52 417
miper_refine_blended92.87 47691.99 47895.51 47691.37 53489.27 50494.07 49298.14 41595.42 41297.25 40996.44 46767.86 51899.24 46891.28 48496.08 51098.02 450
miper_lstm_enhance97.18 33897.16 32197.25 39898.16 43092.85 45295.15 45899.31 23597.25 30698.74 26898.78 28390.07 42199.78 25897.19 24599.80 15199.11 333
ETV-MVS98.03 25797.86 27298.56 25098.69 36298.07 16197.51 29799.50 14198.10 22097.50 39395.51 48698.41 8599.88 11596.27 33899.24 36197.71 471
CS-MVS99.13 6699.10 7999.24 10699.06 27999.15 5299.36 2299.88 1599.36 6398.21 33298.46 34798.68 5899.93 5399.03 8599.85 10998.64 408
D2MVS97.84 28297.84 27397.83 34099.14 26094.74 39196.94 35398.88 34595.84 39198.89 23698.96 23794.40 34399.69 32397.55 21499.95 3999.05 338
DVP-MVScopyleft98.77 13598.52 16899.52 4499.50 13999.21 3298.02 20998.84 35697.97 22899.08 18899.02 20997.61 17199.88 11596.99 26499.63 25799.48 187
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 21099.08 18899.02 20997.89 14699.88 11597.07 25799.71 21499.70 70
test_0728_SECOND99.60 1699.50 13999.23 3098.02 20999.32 23099.88 11596.99 26499.63 25799.68 73
test072699.50 13999.21 3298.17 18199.35 21697.97 22899.26 15599.06 19797.61 171
SR-MVS98.71 14198.43 18699.57 2199.18 25099.35 1698.36 16099.29 25198.29 19598.88 24098.85 26597.53 18199.87 13596.14 34699.31 34799.48 187
DPM-MVS96.32 38695.59 40498.51 26298.76 34397.21 25794.54 47998.26 40891.94 48896.37 45997.25 44893.06 37999.43 44391.42 48298.74 41698.89 370
GST-MVS98.61 16998.30 21199.52 4499.51 13399.20 3898.26 17099.25 26697.44 28598.67 27698.39 35497.68 16199.85 15896.00 35199.51 30199.52 160
test_yl96.69 36496.29 38397.90 33398.28 41695.24 36897.29 32697.36 43798.21 20398.17 33397.86 40786.27 45099.55 40294.87 39098.32 44298.89 370
thisisatest053095.27 43094.45 44297.74 35199.19 24294.37 40297.86 23990.20 52897.17 31798.22 33197.65 42273.53 51199.90 8196.90 27699.35 33898.95 359
Anonymous2024052998.93 10398.87 11099.12 12499.19 24298.22 14299.01 7198.99 32799.25 7599.54 7999.37 10497.04 21699.80 23397.89 17699.52 29899.35 254
Anonymous20240521197.90 26997.50 29999.08 13398.90 31598.25 13698.53 12996.16 47598.87 13999.11 18398.86 26290.40 42099.78 25897.36 23199.31 34799.19 312
DCV-MVSNet96.69 36496.29 38397.90 33398.28 41695.24 36897.29 32697.36 43798.21 20398.17 33397.86 40786.27 45099.55 40294.87 39098.32 44298.89 370
tttt051795.64 41794.98 43097.64 36699.36 19193.81 43298.72 10490.47 52798.08 22398.67 27698.34 36173.88 51099.92 6597.77 19199.51 30199.20 306
our_test_397.39 31797.73 28296.34 43898.70 35789.78 50194.61 47698.97 33096.50 35699.04 20098.85 26595.98 28699.84 17797.26 24099.67 23999.41 219
thisisatest051594.12 45393.16 46196.97 41398.60 37992.90 45193.77 50190.61 52694.10 45496.91 42795.87 47974.99 50899.80 23394.52 39999.12 38498.20 440
ppachtmachnet_test97.50 30497.74 27996.78 42598.70 35791.23 48494.55 47899.05 31396.36 36399.21 17198.79 28196.39 26099.78 25896.74 29099.82 13399.34 257
SMA-MVScopyleft98.40 20498.03 25199.51 4999.16 25499.21 3298.05 20299.22 27494.16 45198.98 21099.10 18897.52 18399.79 24696.45 32599.64 25199.53 157
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
GSMVS98.81 383
DPE-MVScopyleft98.59 17398.26 22099.57 2199.27 21699.15 5297.01 34799.39 20097.67 25499.44 10798.99 22697.53 18199.89 9795.40 37999.68 23399.66 80
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part299.36 19199.10 6599.05 198
thres100view90094.19 45093.67 45495.75 46799.06 27991.35 47898.03 20694.24 50698.33 18897.40 40294.98 49979.84 49299.62 36983.05 52198.08 45896.29 501
tfpnnormal98.90 10798.90 10498.91 17299.67 6897.82 19799.00 7399.44 17899.45 5099.51 9299.24 14498.20 11699.86 14495.92 35599.69 22799.04 342
tfpn200view994.03 45493.44 45695.78 46698.93 30791.44 47697.60 28494.29 50397.94 23297.10 41494.31 50779.67 49499.62 36983.05 52198.08 45896.29 501
c3_l97.36 32097.37 30797.31 39398.09 43793.25 44495.01 46199.16 29297.05 32398.77 26398.72 29592.88 38299.64 36396.93 27099.76 18699.05 338
CHOSEN 280x42095.51 42295.47 40895.65 47298.25 41988.27 50993.25 51198.88 34593.53 46494.65 49797.15 45186.17 45299.93 5397.41 22999.93 5798.73 397
CANet97.87 27597.76 27798.19 30497.75 45695.51 34996.76 36599.05 31397.74 24896.93 42498.21 37895.59 30299.89 9797.86 18399.93 5799.19 312
Fast-Effi-MVS+-dtu98.27 22898.09 24398.81 19098.43 40298.11 15197.61 28399.50 14198.64 15997.39 40497.52 43298.12 12599.95 2596.90 27698.71 42098.38 432
Effi-MVS+-dtu98.26 23097.90 26999.35 8098.02 44199.49 598.02 20999.16 29298.29 19597.64 37997.99 39896.44 25799.95 2596.66 30398.93 40798.60 411
CANet_DTU97.26 32997.06 32997.84 33997.57 46894.65 39696.19 40698.79 36497.23 31295.14 48898.24 37593.22 37399.84 17797.34 23299.84 11499.04 342
MGCNet97.44 31297.01 33298.72 21796.42 51396.74 29497.20 33791.97 52398.46 18098.30 32498.79 28192.74 38699.91 7499.30 6299.94 5199.52 160
MP-MVS-pluss98.57 17698.23 22599.60 1699.69 6199.35 1697.16 34299.38 20294.87 43098.97 21498.99 22698.01 13299.88 11597.29 23899.70 22399.58 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS98.40 20498.00 25499.61 1399.57 10399.25 2898.57 12499.35 21697.55 26999.31 14597.71 41894.61 33699.88 11596.14 34699.19 37399.70 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 46998.81 383
sam_mvs84.29 475
IterMVS-SCA-FT97.85 28198.18 23396.87 41999.27 21691.16 48595.53 44199.25 26699.10 10699.41 11499.35 11193.10 37799.96 1398.65 11499.94 5199.49 176
TSAR-MVS + MP.98.63 16598.49 17799.06 14199.64 7797.90 18598.51 13598.94 33196.96 32899.24 16598.89 25797.83 15099.81 22496.88 27899.49 31299.48 187
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 27698.17 23496.92 41698.98 30093.91 42796.45 38699.17 28997.85 24098.41 31697.14 45298.47 7799.92 6598.02 16599.05 38796.92 489
OPM-MVS98.56 17798.32 20999.25 10499.41 17898.73 9497.13 34499.18 28597.10 32198.75 26698.92 24598.18 11799.65 35896.68 29999.56 28599.37 241
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP98.75 13798.48 17899.57 2199.58 9499.29 2397.82 24399.25 26696.94 33098.78 26099.12 18398.02 13199.84 17797.13 25399.67 23999.59 109
ambc98.24 29798.82 33395.97 33198.62 11899.00 32699.27 15199.21 15396.99 22199.50 42196.55 31899.50 30999.26 290
MTGPAbinary99.20 277
SPE-MVS-test99.13 6699.09 8199.26 10199.13 26298.97 7399.31 3099.88 1599.44 5298.16 33698.51 33898.64 6199.93 5398.91 9399.85 10998.88 373
Effi-MVS+98.02 25897.82 27498.62 23498.53 39197.19 25997.33 32199.68 6297.30 30096.68 44397.46 43898.56 7399.80 23396.63 30598.20 44998.86 375
xiu_mvs_v2_base97.16 34097.49 30096.17 44998.54 38992.46 45995.45 44598.84 35697.25 30697.48 39596.49 46498.31 9699.90 8196.34 33398.68 42596.15 505
xiu_mvs_v1_base97.86 27698.17 23496.92 41698.98 30093.91 42796.45 38699.17 28997.85 24098.41 31697.14 45298.47 7799.92 6598.02 16599.05 38796.92 489
new-patchmatchnet98.35 21398.74 12797.18 40099.24 22792.23 46696.42 39099.48 15198.30 19299.69 5599.53 6497.44 19199.82 20798.84 9999.77 17099.49 176
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 29697.49 30098.08 31799.14 26095.12 37696.70 36999.05 31393.77 46198.62 28598.83 27293.23 37299.75 28298.33 14299.76 18699.36 248
test_post197.59 28620.48 53983.07 48399.66 35194.16 410
test_post21.25 53883.86 47899.70 314
Fast-Effi-MVS+97.67 29497.38 30698.57 24598.71 35397.43 23497.23 33299.45 17094.82 43296.13 46396.51 46398.52 7599.91 7496.19 34298.83 41198.37 434
patchmatchnet-post98.77 28584.37 47299.85 158
Anonymous2023121199.27 3799.27 4799.26 10199.29 21098.18 14499.49 1299.51 13799.70 1599.80 3799.68 2596.84 22999.83 19599.21 7099.91 8099.77 53
pmmvs-eth3d98.47 19598.34 20398.86 17899.30 20897.76 20497.16 34299.28 25595.54 40799.42 11299.19 15897.27 20299.63 36697.89 17699.97 2199.20 306
GG-mvs-BLEND94.76 48894.54 52792.13 46799.31 3080.47 53988.73 53091.01 52967.59 52198.16 50482.30 52594.53 52193.98 513
xiu_mvs_v1_base_debi97.86 27698.17 23496.92 41698.98 30093.91 42796.45 38699.17 28997.85 24098.41 31697.14 45298.47 7799.92 6598.02 16599.05 38796.92 489
Anonymous2023120698.21 23898.21 22698.20 30299.51 13395.43 35898.13 18599.32 23096.16 37598.93 22998.82 27596.00 28199.83 19597.32 23699.73 19699.36 248
MTAPA98.88 11098.64 14899.61 1399.67 6899.36 1598.43 14899.20 27798.83 14898.89 23698.90 25196.98 22299.92 6597.16 24899.70 22399.56 130
MTMP97.93 22791.91 524
gm-plane-assit94.83 52681.97 53588.07 51994.99 49899.60 38191.76 475
test9_res93.28 44099.15 37899.38 238
MVP-Stereo98.08 25397.92 26698.57 24598.96 30396.79 29097.90 23399.18 28596.41 36298.46 31098.95 24195.93 29099.60 38196.51 32198.98 40299.31 272
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST998.71 35398.08 15995.96 42199.03 31891.40 49495.85 47197.53 42996.52 25399.76 270
train_agg97.10 34296.45 37799.07 13598.71 35398.08 15995.96 42199.03 31891.64 48995.85 47197.53 42996.47 25599.76 27093.67 42799.16 37699.36 248
gg-mvs-nofinetune92.37 48291.20 48695.85 46495.80 52392.38 46299.31 3081.84 53899.75 1091.83 52299.74 1868.29 51799.02 48187.15 50997.12 49096.16 504
SCA96.41 38396.66 36195.67 47098.24 42188.35 50895.85 43096.88 46196.11 37697.67 37898.67 30993.10 37799.85 15894.16 41099.22 36598.81 383
Patchmatch-test96.55 37296.34 38097.17 40298.35 40993.06 44698.40 15697.79 42397.33 29598.41 31698.67 30983.68 47999.69 32395.16 38499.31 34798.77 391
test_898.67 36798.01 16795.91 42799.02 32191.64 48995.79 47497.50 43396.47 25599.76 270
MS-PatchMatch97.68 29397.75 27897.45 38898.23 42493.78 43397.29 32698.84 35696.10 37798.64 28098.65 31496.04 27899.36 45296.84 28299.14 37999.20 306
Patchmatch-RL test97.26 32997.02 33197.99 32899.52 13095.53 34896.13 41199.71 4797.47 27799.27 15199.16 16884.30 47499.62 36997.89 17699.77 17098.81 383
cdsmvs_eth3d_5k24.66 50232.88 5050.00 5220.00 5450.00 5470.00 53399.10 3030.00 5400.00 54197.58 42699.21 180.00 5410.00 5390.00 5390.00 537
pcd_1.5k_mvsjas8.17 50510.90 5080.00 5220.00 5450.00 5470.00 5330.00 5460.00 5400.00 5410.00 54098.07 1270.00 5410.00 5390.00 5390.00 537
agg_prior292.50 46499.16 37699.37 241
agg_prior98.68 36697.99 17099.01 32495.59 47599.77 264
tmp_tt78.77 49978.73 50278.90 51758.45 54274.76 54194.20 48878.26 54039.16 53586.71 53192.82 51980.50 49075.19 53786.16 51592.29 52686.74 531
canonicalmvs98.34 21498.26 22098.58 24298.46 39797.82 19798.96 7899.46 16699.19 8897.46 39695.46 49098.59 6799.46 43698.08 15898.71 42098.46 419
anonymousdsp99.51 1499.47 2199.62 999.88 999.08 6999.34 2399.69 5598.93 13199.65 6399.72 2198.93 3399.95 2599.11 77100.00 199.82 36
alignmvs97.35 32196.88 34298.78 20098.54 38998.09 15597.71 26397.69 42799.20 8397.59 38495.90 47888.12 44299.55 40298.18 15098.96 40498.70 401
nrg03099.40 2599.35 3399.54 3199.58 9499.13 6098.98 7699.48 15199.68 1999.46 10199.26 13798.62 6499.73 29699.17 7499.92 7199.76 58
v14419298.54 18498.57 16198.45 27199.21 23595.98 32997.63 27799.36 21097.15 32099.32 14299.18 16295.84 29399.84 17799.50 5099.91 8099.54 143
FIs99.14 6299.09 8199.29 9599.70 5798.28 13399.13 5999.52 13699.48 4499.24 16599.41 9496.79 23699.82 20798.69 11299.88 9599.76 58
v192192098.54 18498.60 15798.38 28099.20 23995.76 34297.56 28999.36 21097.23 31299.38 12199.17 16696.02 27999.84 17799.57 3999.90 8899.54 143
UA-Net99.47 1699.40 2799.70 299.49 14799.29 2399.80 499.72 4599.82 899.04 20099.81 898.05 13099.96 1398.85 9899.99 599.86 28
v119298.60 17198.66 14598.41 27699.27 21695.88 33497.52 29599.36 21097.41 28799.33 13699.20 15596.37 26399.82 20799.57 3999.92 7199.55 137
FC-MVSNet-test99.27 3799.25 5299.34 8399.77 2798.37 12499.30 3599.57 10799.61 3499.40 11799.50 6897.12 21299.85 15899.02 8699.94 5199.80 45
v114498.60 17198.66 14598.41 27699.36 19195.90 33397.58 28799.34 22297.51 27399.27 15199.15 17496.34 26599.80 23399.47 5399.93 5799.51 164
sosnet-low-res0.00 5070.00 5100.00 5220.00 5450.00 5470.00 5330.00 5460.00 5400.00 5410.00 5400.00 5440.00 5410.00 5390.00 5390.00 537
HFP-MVS98.71 14198.44 18599.51 4999.49 14799.16 4898.52 13099.31 23597.47 27798.58 29498.50 34297.97 13799.85 15896.57 31199.59 27299.53 157
v14898.45 19898.60 15798.00 32699.44 16894.98 38097.44 30999.06 30998.30 19299.32 14298.97 23396.65 24899.62 36998.37 13899.85 10999.39 229
sosnet0.00 5070.00 5100.00 5220.00 5450.00 5470.00 5330.00 5460.00 5400.00 5410.00 5400.00 5440.00 5410.00 5390.00 5390.00 537
uncertanet0.00 5070.00 5100.00 5220.00 5450.00 5470.00 5330.00 5460.00 5400.00 5410.00 5400.00 5440.00 5410.00 5390.00 5390.00 537
AllTest98.44 19998.20 22799.16 11899.50 13998.55 10898.25 17199.58 9996.80 34298.88 24099.06 19797.65 16499.57 39594.45 40299.61 26699.37 241
TestCases99.16 11899.50 13998.55 10899.58 9996.80 34298.88 24099.06 19797.65 16499.57 39594.45 40299.61 26699.37 241
v7n99.53 1299.57 1399.41 6999.88 998.54 11199.45 1499.61 8899.66 2399.68 5799.66 3298.44 8499.95 2599.73 2899.96 2899.75 62
region2R98.69 15098.40 19099.54 3199.53 12799.17 4398.52 13099.31 23597.46 28298.44 31398.51 33897.83 15099.88 11596.46 32499.58 27799.58 117
RRT-MVS97.88 27397.98 25697.61 36998.15 43193.77 43498.97 7799.64 7699.16 9398.69 27299.42 8991.60 40299.89 9797.63 20698.52 43799.16 326
balanced_ft_v198.28 22798.35 20298.10 31298.08 43896.23 31999.23 4599.26 26498.34 18697.46 39699.42 8995.38 31199.88 11598.60 11799.34 34098.17 442
PS-MVSNAJss99.46 1799.49 1699.35 8099.90 498.15 14699.20 4999.65 7499.48 4499.92 899.71 2298.07 12799.96 1399.53 48100.00 199.93 11
PS-MVSNAJ97.08 34597.39 30596.16 45198.56 38792.46 45995.24 45498.85 35597.25 30697.49 39495.99 47598.07 12799.90 8196.37 33098.67 42696.12 506
jajsoiax99.58 999.61 1199.48 5799.87 1298.61 10399.28 4099.66 6999.09 10999.89 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 4799.27 7399.90 1499.74 1899.68 499.97 699.55 4399.99 599.88 20
EI-MVSNet-UG-set98.69 15098.71 13498.62 23499.10 26796.37 31497.23 33298.87 34799.20 8399.19 17398.99 22697.30 19999.85 15898.77 10599.79 15899.65 85
EI-MVSNet-Vis-set98.68 15698.70 13798.63 23299.09 27096.40 31397.23 33298.86 35299.20 8399.18 17898.97 23397.29 20199.85 15898.72 10999.78 16399.64 86
HPM-MVS++copyleft98.10 25097.64 29199.48 5799.09 27099.13 6097.52 29598.75 37397.46 28296.90 43097.83 41196.01 28099.84 17795.82 36399.35 33899.46 197
test_prior497.97 17495.86 428
XVS98.72 14098.45 18399.53 3899.46 16199.21 3298.65 11499.34 22298.62 16497.54 38998.63 32097.50 18599.83 19596.79 28499.53 29599.56 130
v124098.55 18198.62 15298.32 28799.22 23395.58 34697.51 29799.45 17097.16 31899.45 10699.24 14496.12 27699.85 15899.60 3799.88 9599.55 137
pm-mvs199.44 1999.48 1899.33 8999.80 2198.63 10099.29 3699.63 7999.30 7099.65 6399.60 4599.16 2299.82 20799.07 8099.83 12699.56 130
test_prior295.74 43596.48 35896.11 46497.63 42495.92 29194.16 41099.20 370
X-MVStestdata94.32 44692.59 46899.53 3899.46 16199.21 3298.65 11499.34 22298.62 16497.54 38945.85 53597.50 18599.83 19596.79 28499.53 29599.56 130
test_prior98.95 16398.69 36297.95 17899.03 31899.59 38699.30 276
旧先验295.76 43488.56 51797.52 39199.66 35194.48 400
新几何295.93 424
新几何198.91 17298.94 30597.76 20498.76 36987.58 52096.75 43998.10 38894.80 33099.78 25892.73 45899.00 39799.20 306
旧先验198.82 33397.45 23298.76 36998.34 36195.50 30699.01 39699.23 296
无先验95.74 43598.74 37589.38 51099.73 29692.38 46799.22 301
原ACMM295.53 441
原ACMM198.35 28598.90 31596.25 31898.83 36092.48 48296.07 46698.10 38895.39 31099.71 30792.61 46198.99 39999.08 334
test22298.92 31196.93 28195.54 44098.78 36685.72 52396.86 43498.11 38794.43 34099.10 38699.23 296
testdata299.79 24692.80 455
segment_acmp97.02 219
testdata98.09 31498.93 30795.40 35998.80 36390.08 50697.45 39998.37 35795.26 31399.70 31493.58 43198.95 40599.17 320
testdata195.44 44696.32 365
v899.01 8999.16 6298.57 24599.47 15896.31 31798.90 8499.47 16199.03 12099.52 8799.57 4996.93 22599.81 22499.60 3799.98 1299.60 102
131495.74 41395.60 40296.17 44997.53 47392.75 45598.07 19998.31 40691.22 49694.25 50196.68 46095.53 30399.03 47991.64 47897.18 48996.74 495
LFMVS97.20 33696.72 35498.64 22898.72 34996.95 27998.93 8294.14 50899.74 1298.78 26099.01 22084.45 47199.73 29697.44 22799.27 35599.25 291
VDD-MVS98.56 17798.39 19399.07 13599.13 26298.07 16198.59 12297.01 45299.59 3699.11 18399.27 13194.82 32799.79 24698.34 14099.63 25799.34 257
VDDNet98.21 23897.95 26099.01 15099.58 9497.74 20699.01 7197.29 44399.67 2098.97 21499.50 6890.45 41999.80 23397.88 17999.20 37099.48 187
v1098.97 9899.11 7398.55 25299.44 16896.21 32098.90 8499.55 12098.73 15099.48 9699.60 4596.63 24999.83 19599.70 3399.99 599.61 100
VPNet98.87 11198.83 11999.01 15099.70 5797.62 21898.43 14899.35 21699.47 4799.28 14999.05 20496.72 24399.82 20798.09 15799.36 33599.59 109
MVS93.19 46992.09 47596.50 43296.91 49794.03 41798.07 19998.06 41968.01 53394.56 49996.48 46595.96 28899.30 46283.84 51996.89 49596.17 503
v2v48298.56 17798.62 15298.37 28399.42 17595.81 33997.58 28799.16 29297.90 23699.28 14999.01 22095.98 28699.79 24699.33 5999.90 8899.51 164
V4298.78 13298.78 12598.76 20799.44 16897.04 27298.27 16999.19 28197.87 23899.25 16399.16 16896.84 22999.78 25899.21 7099.84 11499.46 197
SD-MVS98.40 20498.68 14097.54 37998.96 30397.99 17097.88 23599.36 21098.20 20799.63 6699.04 20698.76 4695.33 53096.56 31599.74 19299.31 272
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 40995.32 41997.49 38498.60 37994.15 41093.83 50097.93 42195.49 40996.68 44397.42 44083.21 48199.30 46296.22 34098.55 43599.01 347
MSLP-MVS++98.02 25898.14 24097.64 36698.58 38495.19 37397.48 30199.23 27397.47 27797.90 36098.62 32397.04 21698.81 49197.55 21499.41 32998.94 363
APDe-MVScopyleft98.99 9398.79 12399.60 1699.21 23599.15 5298.87 8999.48 15197.57 26599.35 13099.24 14497.83 15099.89 9797.88 17999.70 22399.75 62
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize98.84 11898.61 15699.53 3899.19 24299.27 2698.49 14099.33 22898.64 15999.03 20398.98 23197.89 14699.85 15896.54 31999.42 32899.46 197
ADS-MVSNet295.43 42694.98 43096.76 42698.14 43291.74 46997.92 23097.76 42490.23 50296.51 45598.91 24885.61 46099.85 15892.88 45196.90 49398.69 402
EI-MVSNet98.40 20498.51 16998.04 32399.10 26794.73 39297.20 33798.87 34798.97 12699.06 19099.02 20996.00 28199.80 23398.58 11999.82 13399.60 102
Regformer0.00 5070.00 5100.00 5220.00 5450.00 5470.00 5330.00 5460.00 5400.00 5410.00 5400.00 5440.00 5410.00 5390.00 5390.00 537
CVMVSNet96.25 39297.21 31993.38 50899.10 26780.56 53897.20 33798.19 41496.94 33099.00 20599.02 20989.50 42999.80 23396.36 33299.59 27299.78 50
pmmvs497.58 30197.28 31298.51 26298.84 32896.93 28195.40 44898.52 39593.60 46398.61 28798.65 31495.10 31999.60 38196.97 26899.79 15898.99 351
EU-MVSNet97.66 29598.50 17295.13 48499.63 8385.84 51898.35 16198.21 41198.23 19999.54 7999.46 8095.02 32199.68 33398.24 14499.87 10099.87 22
VNet98.42 20098.30 21198.79 19798.79 34297.29 24798.23 17298.66 38199.31 6898.85 24798.80 27994.80 33099.78 25898.13 15399.13 38199.31 272
test-LLR93.90 45693.85 45094.04 49796.53 50784.62 52494.05 49492.39 51796.17 37194.12 50395.07 49582.30 48699.67 33895.87 35998.18 45097.82 460
TESTMET0.1,192.19 48591.77 48393.46 50496.48 51282.80 53394.05 49491.52 52594.45 44494.00 50794.88 50166.65 52299.56 39895.78 36498.11 45698.02 450
test-mter92.33 48391.76 48494.04 49796.53 50784.62 52494.05 49492.39 51794.00 45994.12 50395.07 49565.63 52899.67 33895.87 35998.18 45097.82 460
VPA-MVSNet99.30 3399.30 4499.28 9699.49 14798.36 12799.00 7399.45 17099.63 2899.52 8799.44 8598.25 10699.88 11599.09 7999.84 11499.62 92
ACMMPR98.70 14698.42 18899.54 3199.52 13099.14 5798.52 13099.31 23597.47 27798.56 29898.54 33397.75 15899.88 11596.57 31199.59 27299.58 117
testgi98.32 21998.39 19398.13 30999.57 10395.54 34797.78 24999.49 14997.37 29299.19 17397.65 42298.96 3099.49 42596.50 32298.99 39999.34 257
test20.0398.78 13298.77 12698.78 20099.46 16197.20 25897.78 24999.24 27199.04 11899.41 11498.90 25197.65 16499.76 27097.70 20199.79 15899.39 229
thres600view794.45 44493.83 45196.29 44099.06 27991.53 47397.99 22094.24 50698.34 18697.44 40095.01 49779.84 49299.67 33884.33 51898.23 44797.66 472
ADS-MVSNet95.24 43194.93 43396.18 44898.14 43290.10 49897.92 23097.32 44290.23 50296.51 45598.91 24885.61 46099.74 28992.88 45196.90 49398.69 402
MP-MVScopyleft98.46 19698.09 24399.54 3199.57 10399.22 3198.50 13799.19 28197.61 26197.58 38598.66 31297.40 19399.88 11594.72 39599.60 26899.54 143
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs17.12 50320.53 5066.87 52112.05 5434.20 54693.62 5046.73 5444.62 53910.41 53924.33 5368.28 5433.56 5409.69 53815.07 53712.86 536
thres40094.14 45293.44 45696.24 44398.93 30791.44 47697.60 28494.29 50397.94 23297.10 41494.31 50779.67 49499.62 36983.05 52198.08 45897.66 472
test12317.04 50420.11 5077.82 52010.25 5444.91 54594.80 4664.47 5454.93 53810.00 54024.28 5379.69 5423.64 53910.14 53712.43 53814.92 535
thres20093.72 46093.14 46295.46 47898.66 37291.29 48096.61 37694.63 49897.39 29096.83 43593.71 51079.88 49199.56 39882.40 52498.13 45595.54 510
test0.0.03 194.51 44393.69 45396.99 41196.05 51893.61 44194.97 46293.49 51296.17 37197.57 38794.88 50182.30 48699.01 48393.60 43094.17 52298.37 434
pmmvs395.03 43694.40 44496.93 41597.70 46292.53 45895.08 45997.71 42688.57 51697.71 37598.08 39179.39 49699.82 20796.19 34299.11 38598.43 427
EMVS93.83 45794.02 44893.23 50996.83 50084.96 52189.77 52696.32 47397.92 23497.43 40196.36 47086.17 45298.93 48687.68 50897.73 47095.81 508
E-PMN94.17 45194.37 44593.58 50396.86 49885.71 52090.11 52597.07 45198.17 21097.82 37097.19 44984.62 47098.94 48589.77 50097.68 47196.09 507
PGM-MVS98.66 16098.37 19899.55 2899.53 12799.18 4298.23 17299.49 14997.01 32798.69 27298.88 25998.00 13399.89 9795.87 35999.59 27299.58 117
LCM-MVSNet-Re98.64 16398.48 17899.11 12698.85 32798.51 11398.49 14099.83 2698.37 18399.69 5599.46 8098.21 11499.92 6594.13 41499.30 35198.91 368
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 26197.63 29299.10 12899.24 22798.17 14596.89 35898.73 37695.66 40097.92 35897.70 42097.17 20999.66 35196.18 34499.23 36499.47 195
mvs_anonymous97.83 28498.16 23796.87 41998.18 42791.89 46897.31 32498.90 34197.37 29298.83 25199.46 8096.28 26899.79 24698.90 9498.16 45398.95 359
MVS_Test98.18 24398.36 19997.67 35998.48 39494.73 39298.18 17899.02 32197.69 25298.04 35099.11 18597.22 20699.56 39898.57 12198.90 40998.71 398
MDA-MVSNet-bldmvs97.94 26797.91 26898.06 32099.44 16894.96 38196.63 37599.15 29798.35 18598.83 25199.11 18594.31 34899.85 15896.60 30898.72 41899.37 241
CDPH-MVS97.26 32996.66 36199.07 13599.00 29698.15 14696.03 41699.01 32491.21 49797.79 37197.85 40996.89 22799.69 32392.75 45799.38 33499.39 229
test1298.93 16798.58 38497.83 19298.66 38196.53 45195.51 30599.69 32399.13 38199.27 284
casdiffmvspermissive98.95 10199.00 9398.81 19099.38 18497.33 23997.82 24399.57 10799.17 9299.35 13099.17 16698.35 9399.69 32398.46 12999.73 19699.41 219
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 23598.24 22498.17 30599.00 29695.44 35796.38 39299.58 9997.79 24598.53 30398.50 34296.76 23999.74 28997.95 17499.64 25199.34 257
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 45992.83 46696.42 43597.70 46291.28 48196.84 36089.77 52993.96 46092.44 51995.93 47779.14 49799.77 26492.94 44896.76 49798.21 439
baseline195.96 40695.44 41197.52 38198.51 39393.99 42498.39 15796.09 47998.21 20398.40 32097.76 41686.88 44699.63 36695.42 37889.27 52898.95 359
YYNet197.60 29897.67 28697.39 39299.04 28393.04 44995.27 45298.38 40497.25 30698.92 23198.95 24195.48 30799.73 29696.99 26498.74 41699.41 219
PMMVS298.07 25498.08 24698.04 32399.41 17894.59 39894.59 47799.40 19897.50 27498.82 25498.83 27296.83 23199.84 17797.50 22099.81 14099.71 65
MDA-MVSNet_test_wron97.60 29897.66 28997.41 39199.04 28393.09 44595.27 45298.42 40197.26 30598.88 24098.95 24195.43 30999.73 29697.02 26098.72 41899.41 219
tpmvs95.02 43795.25 42294.33 49296.39 51585.87 51798.08 19596.83 46395.46 41195.51 48398.69 30585.91 45899.53 41094.16 41096.23 50397.58 475
PM-MVS98.82 12498.72 13199.12 12499.64 7798.54 11197.98 22199.68 6297.62 25899.34 13399.18 16297.54 17999.77 26497.79 18899.74 19299.04 342
HQP_MVS97.99 26497.67 28698.93 16799.19 24297.65 21597.77 25299.27 25898.20 20797.79 37197.98 39994.90 32399.70 31494.42 40499.51 30199.45 203
plane_prior799.19 24297.87 188
plane_prior698.99 29997.70 21194.90 323
plane_prior599.27 25899.70 31494.42 40499.51 30199.45 203
plane_prior497.98 399
plane_prior397.78 20297.41 28797.79 371
plane_prior297.77 25298.20 207
plane_prior199.05 282
plane_prior97.65 21597.07 34596.72 34799.36 335
PS-CasMVS99.40 2599.33 3799.62 999.71 4999.10 6599.29 3699.53 13099.53 4199.46 10199.41 9498.23 10999.95 2598.89 9699.95 3999.81 41
UniMVSNet_NR-MVSNet98.86 11598.68 14099.40 7199.17 25298.74 9197.68 26799.40 19899.14 9799.06 19098.59 32896.71 24499.93 5398.57 12199.77 17099.53 157
PEN-MVS99.41 2499.34 3599.62 999.73 3899.14 5799.29 3699.54 12699.62 3299.56 7499.42 8998.16 12199.96 1398.78 10299.93 5799.77 53
TransMVSNet (Re)99.44 1999.47 2199.36 7499.80 2198.58 10699.27 4299.57 10799.39 5899.75 4499.62 4099.17 2099.83 19599.06 8299.62 26199.66 80
DTE-MVSNet99.43 2299.35 3399.66 799.71 4999.30 2199.31 3099.51 13799.64 2699.56 7499.46 8098.23 10999.97 698.78 10299.93 5799.72 64
DU-MVS98.82 12498.63 15099.39 7299.16 25498.74 9197.54 29399.25 26698.84 14799.06 19098.76 29096.76 23999.93 5398.57 12199.77 17099.50 168
UniMVSNet (Re)98.87 11198.71 13499.35 8099.24 22798.73 9497.73 26299.38 20298.93 13199.12 18298.73 29396.77 23799.86 14498.63 11699.80 15199.46 197
CP-MVSNet99.21 4799.09 8199.56 2699.65 7198.96 7799.13 5999.34 22299.42 5599.33 13699.26 13797.01 22099.94 4198.74 10799.93 5799.79 47
WR-MVS_H99.33 3099.22 5499.65 899.71 4999.24 2999.32 2699.55 12099.46 4999.50 9399.34 11597.30 19999.93 5398.90 9499.93 5799.77 53
WR-MVS98.40 20498.19 23199.03 14599.00 29697.65 21596.85 35998.94 33198.57 17298.89 23698.50 34295.60 30199.85 15897.54 21699.85 10999.59 109
NR-MVSNet98.95 10198.82 12099.36 7499.16 25498.72 9699.22 4699.20 27799.10 10699.72 4798.76 29096.38 26299.86 14498.00 16899.82 13399.50 168
Baseline_NR-MVSNet98.98 9798.86 11499.36 7499.82 1998.55 10897.47 30599.57 10799.37 6099.21 17199.61 4396.76 23999.83 19598.06 16099.83 12699.71 65
TranMVSNet+NR-MVSNet99.17 5299.07 8499.46 6399.37 19098.87 8498.39 15799.42 19099.42 5599.36 12899.06 19798.38 8899.95 2598.34 14099.90 8899.57 124
TSAR-MVS + GP.98.18 24397.98 25698.77 20598.71 35397.88 18796.32 39798.66 38196.33 36499.23 16798.51 33897.48 18999.40 44797.16 24899.46 31599.02 345
n20.00 546
nn0.00 546
mPP-MVS98.64 16398.34 20399.54 3199.54 12399.17 4398.63 11699.24 27197.47 27798.09 34498.68 30797.62 16999.89 9796.22 34099.62 26199.57 124
door-mid99.57 107
XVG-OURS-SEG-HR98.49 19398.28 21499.14 12299.49 14798.83 8696.54 38099.48 15197.32 29799.11 18398.61 32599.33 1599.30 46296.23 33998.38 44099.28 281
mvsmamba97.57 30297.26 31498.51 26298.69 36296.73 29598.74 9997.25 44497.03 32697.88 36299.23 15090.95 41399.87 13596.61 30799.00 39798.91 368
MVSFormer98.26 23098.43 18697.77 34598.88 32193.89 43099.39 2099.56 11699.11 9998.16 33698.13 38493.81 36299.97 699.26 6599.57 28199.43 211
jason97.45 31197.35 30997.76 34899.24 22793.93 42695.86 42898.42 40194.24 44898.50 30698.13 38494.82 32799.91 7497.22 24399.73 19699.43 211
jason: jason.
lupinMVS97.06 34796.86 34397.65 36398.88 32193.89 43095.48 44497.97 42093.53 46498.16 33697.58 42693.81 36299.91 7496.77 28799.57 28199.17 320
test_djsdf99.52 1399.51 1599.53 3899.86 1498.74 9199.39 2099.56 11699.11 9999.70 5199.73 2099.00 2799.97 699.26 6599.98 1299.89 16
HPM-MVS_fast99.01 8998.82 12099.57 2199.71 4999.35 1699.00 7399.50 14197.33 29598.94 22898.86 26298.75 4799.82 20797.53 21799.71 21499.56 130
K. test v398.00 26197.66 28999.03 14599.79 2397.56 22199.19 5392.47 51699.62 3299.52 8799.66 3289.61 42799.96 1399.25 6799.81 14099.56 130
lessismore_v098.97 15999.73 3897.53 22486.71 53499.37 12599.52 6789.93 42299.92 6598.99 8899.72 20599.44 207
SixPastTwentyTwo98.75 13798.62 15299.16 11899.83 1897.96 17799.28 4098.20 41299.37 6099.70 5199.65 3692.65 38899.93 5399.04 8499.84 11499.60 102
OurMVSNet-221017-099.37 2899.31 4199.53 3899.91 398.98 7199.63 799.58 9999.44 5299.78 3999.76 1596.39 26099.92 6599.44 5499.92 7199.68 73
HPM-MVScopyleft98.79 13098.53 16799.59 2099.65 7199.29 2399.16 5599.43 18496.74 34698.61 28798.38 35698.62 6499.87 13596.47 32399.67 23999.59 109
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS98.53 18698.34 20399.11 12699.50 13998.82 8895.97 41999.50 14197.30 30099.05 19898.98 23199.35 1499.32 45995.72 36699.68 23399.18 316
XVG-ACMP-BASELINE98.56 17798.34 20399.22 10999.54 12398.59 10597.71 26399.46 16697.25 30698.98 21098.99 22697.54 17999.84 17795.88 35699.74 19299.23 296
casdiffmvs_mvgpermissive99.12 6999.16 6298.99 15399.43 17397.73 20898.00 21399.62 8599.22 7999.55 7799.22 15198.93 3399.75 28298.66 11399.81 14099.50 168
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 14198.46 18299.47 6199.57 10398.97 7398.23 17299.48 15196.60 35199.10 18699.06 19798.71 5199.83 19595.58 37599.78 16399.62 92
LGP-MVS_train99.47 6199.57 10398.97 7399.48 15196.60 35199.10 18699.06 19798.71 5199.83 19595.58 37599.78 16399.62 92
baseline98.96 10099.02 8998.76 20799.38 18497.26 25098.49 14099.50 14198.86 14199.19 17399.06 19798.23 10999.69 32398.71 11099.76 18699.33 263
test1198.87 347
door99.41 194
EPNet_dtu94.93 43994.78 43595.38 48093.58 52987.68 51296.78 36395.69 48997.35 29489.14 52998.09 39088.15 44199.49 42594.95 38999.30 35198.98 352
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x268897.49 30797.14 32498.54 25799.68 6496.09 32496.50 38499.62 8591.58 49198.84 24998.97 23392.36 39099.88 11596.76 28899.95 3999.67 78
EPNet96.14 39695.44 41198.25 29590.76 53895.50 35297.92 23094.65 49798.97 12692.98 51498.85 26589.12 43199.87 13595.99 35299.68 23399.39 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS96.79 290
HQP-NCC98.67 36796.29 39996.05 37895.55 478
ACMP_Plane98.67 36796.29 39996.05 37895.55 478
APD-MVScopyleft98.10 25097.67 28699.42 6799.11 26598.93 7997.76 25599.28 25594.97 42798.72 26998.77 28597.04 21699.85 15893.79 42499.54 29199.49 176
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS92.82 453
HQP4-MVS95.56 47799.54 40899.32 267
HQP3-MVS99.04 31699.26 359
HQP2-MVS93.84 360
CNVR-MVS98.17 24697.87 27199.07 13598.67 36798.24 13797.01 34798.93 33497.25 30697.62 38198.34 36197.27 20299.57 39596.42 32799.33 34299.39 229
NCCC97.86 27697.47 30399.05 14298.61 37798.07 16196.98 35098.90 34197.63 25797.04 42097.93 40495.99 28599.66 35195.31 38098.82 41399.43 211
114514_t96.50 37595.77 39498.69 22199.48 15597.43 23497.84 24299.55 12081.42 52996.51 45598.58 32995.53 30399.67 33893.41 43899.58 27798.98 352
CP-MVS98.70 14698.42 18899.52 4499.36 19199.12 6298.72 10499.36 21097.54 27198.30 32498.40 35397.86 14999.89 9796.53 32099.72 20599.56 130
DSMNet-mixed97.42 31497.60 29496.87 41999.15 25891.46 47498.54 12899.12 30092.87 47897.58 38599.63 3996.21 27199.90 8195.74 36599.54 29199.27 284
tpm293.09 47092.58 46994.62 49097.56 46986.53 51697.66 27195.79 48686.15 52294.07 50598.23 37775.95 50699.53 41090.91 49296.86 49697.81 462
NP-MVS98.84 32897.39 23696.84 456
EG-PatchMatch MVS98.99 9399.01 9198.94 16499.50 13997.47 22998.04 20499.59 9698.15 21899.40 11799.36 11098.58 7299.76 27098.78 10299.68 23399.59 109
tpm cat193.29 46793.13 46393.75 50197.39 48284.74 52297.39 31297.65 43083.39 52794.16 50298.41 35282.86 48499.39 44991.56 48095.35 51797.14 487
SteuartSystems-ACMMP98.79 13098.54 16599.54 3199.73 3899.16 4898.23 17299.31 23597.92 23498.90 23398.90 25198.00 13399.88 11596.15 34599.72 20599.58 117
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CostFormer93.97 45593.78 45294.51 49197.53 47385.83 51997.98 22195.96 48189.29 51194.99 49198.63 32078.63 50199.62 36994.54 39896.50 49998.09 447
CR-MVSNet96.28 38995.95 39197.28 39597.71 46094.22 40598.11 19098.92 33892.31 48496.91 42799.37 10485.44 46399.81 22497.39 23097.36 48597.81 462
JIA-IIPM95.52 42195.03 42997.00 41096.85 49994.03 41796.93 35595.82 48499.20 8394.63 49899.71 2283.09 48299.60 38194.42 40494.64 51997.36 483
Patchmtry97.35 32196.97 33498.50 26697.31 48596.47 31098.18 17898.92 33898.95 13098.78 26099.37 10485.44 46399.85 15895.96 35499.83 12699.17 320
PatchT96.65 36796.35 37997.54 37997.40 48195.32 36597.98 22196.64 46799.33 6596.89 43199.42 8984.32 47399.81 22497.69 20397.49 47697.48 478
tpmrst95.07 43595.46 40993.91 49997.11 48984.36 52697.62 27896.96 45694.98 42696.35 46098.80 27985.46 46299.59 38695.60 37396.23 50397.79 465
BH-w/o95.13 43494.89 43495.86 46398.20 42591.31 47995.65 43797.37 43693.64 46296.52 45495.70 48393.04 38099.02 48188.10 50795.82 51397.24 486
tpm94.67 44194.34 44695.66 47197.68 46588.42 50797.88 23594.90 49594.46 44196.03 47098.56 33278.66 50099.79 24695.88 35695.01 51898.78 390
DELS-MVS98.27 22898.20 22798.48 26898.86 32496.70 29695.60 43999.20 27797.73 24998.45 31298.71 29697.50 18599.82 20798.21 14899.59 27298.93 364
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 36096.75 35397.08 40698.74 34693.33 44396.71 36898.26 40896.72 34798.44 31397.37 44395.20 31499.47 43291.89 47297.43 48098.44 425
RPMNet97.02 35096.93 33697.30 39497.71 46094.22 40598.11 19099.30 24399.37 6096.91 42799.34 11586.72 44799.87 13597.53 21797.36 48597.81 462
MVSTER96.86 35996.55 37097.79 34397.91 44794.21 40797.56 28998.87 34797.49 27699.06 19099.05 20480.72 48999.80 23398.44 13099.82 13399.37 241
CPTT-MVS97.84 28297.36 30899.27 9999.31 20498.46 11698.29 16599.27 25894.90 42997.83 36898.37 35794.90 32399.84 17793.85 42399.54 29199.51 164
GBi-Net98.65 16198.47 18099.17 11598.90 31598.24 13799.20 4999.44 17898.59 16798.95 22099.55 5694.14 35399.86 14497.77 19199.69 22799.41 219
PVSNet_Blended_VisFu98.17 24698.15 23898.22 30199.73 3895.15 37497.36 31999.68 6294.45 44498.99 20999.27 13196.87 22899.94 4197.13 25399.91 8099.57 124
PVSNet_BlendedMVS97.55 30397.53 29797.60 37098.92 31193.77 43496.64 37499.43 18494.49 43997.62 38199.18 16296.82 23299.67 33894.73 39399.93 5799.36 248
UnsupCasMVSNet_eth97.89 27197.60 29498.75 20999.31 20497.17 26497.62 27899.35 21698.72 15698.76 26598.68 30792.57 38999.74 28997.76 19595.60 51599.34 257
UnsupCasMVSNet_bld97.30 32696.92 33898.45 27199.28 21396.78 29396.20 40599.27 25895.42 41298.28 32898.30 36893.16 37499.71 30794.99 38697.37 48398.87 374
PVSNet_Blended96.88 35796.68 35797.47 38798.92 31193.77 43494.71 46899.43 18490.98 50097.62 38197.36 44496.82 23299.67 33894.73 39399.56 28598.98 352
FMVSNet596.01 40095.20 42698.41 27697.53 47396.10 32198.74 9999.50 14197.22 31598.03 35199.04 20669.80 51599.88 11597.27 23999.71 21499.25 291
test198.65 16198.47 18099.17 11598.90 31598.24 13799.20 4999.44 17898.59 16798.95 22099.55 5694.14 35399.86 14497.77 19199.69 22799.41 219
new_pmnet96.99 35496.76 35197.67 35998.72 34994.89 38495.95 42398.20 41292.62 48198.55 30098.54 33394.88 32699.52 41493.96 41899.44 32498.59 414
FMVSNet397.50 30497.24 31698.29 29198.08 43895.83 33797.86 23998.91 34097.89 23798.95 22098.95 24187.06 44599.81 22497.77 19199.69 22799.23 296
dp93.47 46393.59 45593.13 51096.64 50581.62 53797.66 27196.42 47292.80 47996.11 46498.64 31878.55 50399.59 38693.31 43992.18 52798.16 443
FMVSNet298.49 19398.40 19098.75 20998.90 31597.14 26798.61 12099.13 29998.59 16799.19 17399.28 12994.14 35399.82 20797.97 17299.80 15199.29 278
FMVSNet199.17 5299.17 6099.17 11599.55 11798.24 13799.20 4999.44 17899.21 8199.43 10899.55 5697.82 15399.86 14498.42 13699.89 9499.41 219
N_pmnet97.63 29797.17 32098.99 15399.27 21697.86 18995.98 41893.41 51395.25 41999.47 10098.90 25195.63 29999.85 15896.91 27199.73 19699.27 284
cascas94.79 44094.33 44796.15 45396.02 52092.36 46392.34 51899.26 26485.34 52495.08 49094.96 50092.96 38198.53 49894.41 40798.59 43297.56 476
BH-RMVSNet96.83 36096.58 36997.58 37298.47 39594.05 41496.67 37297.36 43796.70 34997.87 36397.98 39995.14 31899.44 44190.47 49798.58 43399.25 291
UGNet98.53 18698.45 18398.79 19797.94 44596.96 27899.08 6298.54 39399.10 10696.82 43699.47 7896.55 25299.84 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 36696.27 38597.87 33898.81 33694.61 39796.77 36497.92 42294.94 42897.12 41397.74 41791.11 41299.82 20793.89 42098.15 45499.18 316
XXY-MVS99.14 6299.15 6799.10 12899.76 3097.74 20698.85 9399.62 8598.48 17999.37 12599.49 7498.75 4799.86 14498.20 14999.80 15199.71 65
EC-MVSNet99.09 7299.05 8599.20 11099.28 21398.93 7999.24 4499.84 2399.08 11398.12 34198.37 35798.72 5099.90 8199.05 8399.77 17098.77 391
sss97.21 33596.93 33698.06 32098.83 33095.22 37296.75 36698.48 39794.49 43997.27 40897.90 40592.77 38599.80 23396.57 31199.32 34599.16 326
Test_1112_low_res96.99 35496.55 37098.31 28999.35 19695.47 35695.84 43199.53 13091.51 49396.80 43798.48 34591.36 40999.83 19596.58 30999.53 29599.62 92
1112_ss97.29 32896.86 34398.58 24299.34 20196.32 31696.75 36699.58 9993.14 47096.89 43197.48 43592.11 39899.86 14496.91 27199.54 29199.57 124
ab-mvs-re8.12 50610.83 5090.00 5220.00 5450.00 5470.00 5330.00 5460.00 5400.00 54197.48 4350.00 5440.00 5410.00 5390.00 5390.00 537
ab-mvs98.41 20198.36 19998.59 24199.19 24297.23 25299.32 2698.81 36197.66 25598.62 28599.40 9796.82 23299.80 23395.88 35699.51 30198.75 394
TR-MVS95.55 42095.12 42896.86 42297.54 47193.94 42596.49 38596.53 47094.36 44797.03 42296.61 46294.26 35099.16 47486.91 51296.31 50297.47 479
MDTV_nov1_ep13_2view74.92 54097.69 26690.06 50797.75 37485.78 45993.52 43398.69 402
MDTV_nov1_ep1395.22 42497.06 49283.20 53197.74 26096.16 47594.37 44696.99 42398.83 27283.95 47799.53 41093.90 41997.95 466
MIMVSNet199.38 2799.32 3999.55 2899.86 1499.19 4199.41 1799.59 9699.59 3699.71 4999.57 4997.12 21299.90 8199.21 7099.87 10099.54 143
MIMVSNet96.62 36996.25 38697.71 35599.04 28394.66 39599.16 5596.92 46097.23 31297.87 36399.10 18886.11 45499.65 35891.65 47799.21 36898.82 378
IterMVS-LS98.55 18198.70 13798.09 31499.48 15594.73 39297.22 33699.39 20098.97 12699.38 12199.31 12496.00 28199.93 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 29297.35 30998.69 22198.73 34797.02 27496.92 35798.75 37395.89 38898.59 29298.67 30992.08 39999.74 28996.72 29399.81 14099.32 267
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref99.77 170
IterMVS97.73 28898.11 24296.57 43099.24 22790.28 49695.52 44399.21 27598.86 14199.33 13699.33 11893.11 37699.94 4198.49 12899.94 5199.48 187
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon97.33 32396.92 33898.57 24599.09 27097.99 17096.79 36199.35 21693.18 46997.71 37598.07 39295.00 32299.31 46093.97 41799.13 38198.42 429
MVS_111021_LR98.30 22398.12 24198.83 18699.16 25498.03 16696.09 41499.30 24397.58 26498.10 34398.24 37598.25 10699.34 45696.69 29899.65 24999.12 332
DP-MVS98.93 10398.81 12299.28 9699.21 23598.45 11798.46 14599.33 22899.63 2899.48 9699.15 17497.23 20599.75 28297.17 24799.66 24799.63 91
ACMMP++99.68 233
HQP-MVS97.00 35396.49 37398.55 25298.67 36796.79 29096.29 39999.04 31696.05 37895.55 47896.84 45693.84 36099.54 40892.82 45399.26 35999.32 267
QAPM97.31 32496.81 34998.82 18898.80 33997.49 22599.06 6699.19 28190.22 50497.69 37799.16 16896.91 22699.90 8190.89 49399.41 32999.07 336
Vis-MVSNetpermissive99.34 2999.36 3299.27 9999.73 3898.26 13599.17 5499.78 3699.11 9999.27 15199.48 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 44695.62 40090.42 51598.46 39775.36 53996.29 39989.13 53095.25 41995.38 48499.75 1692.88 38299.19 47294.07 41699.39 33196.72 496
IS-MVSNet98.19 24197.90 26999.08 13399.57 10397.97 17499.31 3098.32 40599.01 12298.98 21099.03 20891.59 40399.79 24695.49 37799.80 15199.48 187
HyFIR lowres test97.19 33796.60 36898.96 16199.62 8797.28 24895.17 45699.50 14194.21 44999.01 20498.32 36686.61 44899.99 297.10 25599.84 11499.60 102
EPMVS93.72 46093.27 45995.09 48696.04 51987.76 51198.13 18585.01 53694.69 43596.92 42598.64 31878.47 50499.31 46095.04 38596.46 50098.20 440
PAPM_NR96.82 36296.32 38198.30 29099.07 27496.69 29797.48 30198.76 36995.81 39596.61 44796.47 46694.12 35699.17 47390.82 49597.78 46899.06 337
TAMVS98.24 23498.05 24998.80 19399.07 27497.18 26297.88 23598.81 36196.66 35099.17 18199.21 15394.81 32999.77 26496.96 26999.88 9599.44 207
PAPR95.29 42994.47 44197.75 34997.50 47995.14 37594.89 46598.71 37891.39 49595.35 48595.48 48994.57 33799.14 47684.95 51797.37 48398.97 356
RPSCF98.62 16898.36 19999.42 6799.65 7199.42 1098.55 12699.57 10797.72 25198.90 23399.26 13796.12 27699.52 41495.72 36699.71 21499.32 267
Vis-MVSNet (Re-imp)97.46 30997.16 32198.34 28699.55 11796.10 32198.94 8198.44 39898.32 19098.16 33698.62 32388.76 43299.73 29693.88 42199.79 15899.18 316
test_040298.76 13698.71 13498.93 16799.56 11198.14 14898.45 14799.34 22299.28 7298.95 22098.91 24898.34 9499.79 24695.63 37199.91 8098.86 375
MVS_111021_HR98.25 23398.08 24698.75 20999.09 27097.46 23195.97 41999.27 25897.60 26397.99 35498.25 37398.15 12399.38 45196.87 27999.57 28199.42 216
CSCG98.68 15698.50 17299.20 11099.45 16698.63 10098.56 12599.57 10797.87 23898.85 24798.04 39497.66 16399.84 17796.72 29399.81 14099.13 331
PatchMatch-RL97.24 33296.78 35098.61 23899.03 28697.83 19296.36 39499.06 30993.49 46697.36 40697.78 41495.75 29599.49 42593.44 43798.77 41498.52 417
API-MVS97.04 34996.91 34197.42 39097.88 44898.23 14198.18 17898.50 39697.57 26597.39 40496.75 45996.77 23799.15 47590.16 49899.02 39494.88 512
Test By Simon96.52 253
TDRefinement99.42 2399.38 2899.55 2899.76 3099.33 2099.68 699.71 4799.38 5999.53 8399.61 4398.64 6199.80 23398.24 14499.84 11499.52 160
USDC97.41 31597.40 30497.44 38998.94 30593.67 43795.17 45699.53 13094.03 45798.97 21499.10 18895.29 31299.34 45695.84 36299.73 19699.30 276
EPP-MVSNet98.30 22398.04 25099.07 13599.56 11197.83 19299.29 3698.07 41899.03 12098.59 29299.13 18092.16 39599.90 8196.87 27999.68 23399.49 176
PMMVS96.51 37395.98 38998.09 31497.53 47395.84 33694.92 46398.84 35691.58 49196.05 46895.58 48495.68 29899.66 35195.59 37498.09 45798.76 393
PAPM91.88 48990.34 49196.51 43198.06 44092.56 45792.44 51797.17 44886.35 52190.38 52696.01 47486.61 44899.21 47170.65 53395.43 51697.75 467
ACMMPcopyleft98.75 13798.50 17299.52 4499.56 11199.16 4898.87 8999.37 20697.16 31898.82 25499.01 22097.71 16099.87 13596.29 33799.69 22799.54 143
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 33996.71 35598.55 25298.56 38798.05 16596.33 39698.93 33496.91 33497.06 41897.39 44194.38 34499.45 43991.66 47699.18 37598.14 444
PatchmatchNetpermissive95.58 41995.67 39995.30 48397.34 48387.32 51497.65 27396.65 46695.30 41697.07 41798.69 30584.77 46899.75 28294.97 38898.64 42798.83 377
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS98.29 22697.95 26099.34 8398.44 40099.16 4898.12 18999.38 20296.01 38298.06 34798.43 35097.80 15499.67 33895.69 36899.58 27799.20 306
F-COLMAP97.30 32696.68 35799.14 12299.19 24298.39 12197.27 33199.30 24392.93 47596.62 44698.00 39795.73 29699.68 33392.62 46098.46 43899.35 254
ANet_high99.57 1099.67 699.28 9699.89 698.09 15599.14 5899.93 699.82 899.93 699.81 899.17 2099.94 4199.31 61100.00 199.82 36
wuyk23d96.06 39797.62 29391.38 51298.65 37698.57 10798.85 9396.95 45796.86 34099.90 1499.16 16899.18 1998.40 49989.23 50499.77 17077.18 534
OMC-MVS97.88 27397.49 30099.04 14498.89 32098.63 10096.94 35399.25 26695.02 42598.53 30398.51 33897.27 20299.47 43293.50 43599.51 30199.01 347
MG-MVS96.77 36396.61 36697.26 39798.31 41293.06 44695.93 42498.12 41796.45 36197.92 35898.73 29393.77 36499.39 44991.19 48799.04 39099.33 263
AdaColmapbinary97.14 34196.71 35598.46 27098.34 41097.80 20196.95 35298.93 33495.58 40496.92 42597.66 42195.87 29299.53 41090.97 49099.14 37998.04 449
uanet0.00 5070.00 5100.00 5220.00 5450.00 5470.00 5330.00 5460.00 5400.00 5410.00 5400.00 5440.00 5410.00 5390.00 5390.00 537
ITE_SJBPF98.87 17699.22 23398.48 11599.35 21697.50 27498.28 32898.60 32797.64 16799.35 45593.86 42299.27 35598.79 389
DeepMVS_CXcopyleft93.44 50698.24 42194.21 40794.34 50264.28 53491.34 52394.87 50389.45 43092.77 53377.54 52993.14 52493.35 520
TinyColmap97.89 27197.98 25697.60 37098.86 32494.35 40396.21 40499.44 17897.45 28499.06 19098.88 25997.99 13699.28 46694.38 40899.58 27799.18 316
MAR-MVS96.47 37995.70 39798.79 19797.92 44699.12 6298.28 16698.60 38692.16 48695.54 48196.17 47294.77 33299.52 41489.62 50198.23 44797.72 470
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 26997.69 28598.52 26199.17 25297.66 21397.19 34199.47 16196.31 36697.85 36798.20 37996.71 24499.52 41494.62 39699.72 20598.38 432
MSDG97.71 29097.52 29898.28 29298.91 31496.82 28894.42 48299.37 20697.65 25698.37 32198.29 37197.40 19399.33 45894.09 41599.22 36598.68 405
LS3D98.63 16598.38 19699.36 7497.25 48699.38 1299.12 6199.32 23099.21 8198.44 31398.88 25997.31 19899.80 23396.58 30999.34 34098.92 365
CLD-MVS97.49 30797.16 32198.48 26899.07 27497.03 27394.71 46899.21 27594.46 44198.06 34797.16 45097.57 17599.48 42994.46 40199.78 16398.95 359
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS93.44 46492.23 47397.08 40699.25 22697.86 18995.61 43897.16 44992.90 47793.76 51198.65 31475.94 50795.66 52879.30 52897.49 47697.73 469
Gipumacopyleft99.03 8799.16 6298.64 22899.94 298.51 11399.32 2699.75 4299.58 3898.60 29099.62 4098.22 11299.51 42097.70 20199.73 19697.89 457
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015