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 2799.85 1699.11 6599.90 199.78 3699.63 2999.78 4099.67 3099.48 1099.81 22299.30 6399.97 2199.77 50
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 12298.73 12399.05 14298.76 33097.81 18699.25 4399.30 22898.57 16598.55 28199.33 11497.95 13499.90 8197.16 23099.67 22199.44 201
3Dnovator+97.89 398.69 14698.51 16599.24 10698.81 32598.40 11799.02 6999.19 26498.99 12198.07 32399.28 12597.11 20999.84 17496.84 26399.32 31999.47 190
DeepC-MVS97.60 498.97 9298.93 9799.10 12899.35 18997.98 16298.01 20699.46 15297.56 25799.54 7999.50 6998.97 2899.84 17498.06 15699.92 6999.49 171
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 21498.01 24499.23 10898.39 39398.97 7495.03 43999.18 26896.88 32299.33 13098.78 27098.16 11699.28 44396.74 27199.62 24299.44 201
DeepC-MVS_fast96.85 698.30 21798.15 22998.75 20298.61 36497.23 22897.76 24999.09 28797.31 28798.75 25198.66 30097.56 17299.64 34996.10 32799.55 26999.39 223
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 32896.68 33998.32 27898.32 39697.16 24098.86 9199.37 19189.48 46396.29 42499.15 16696.56 24599.90 8192.90 41599.20 34197.89 435
ACMH96.65 799.25 4199.24 5499.26 10199.72 4398.38 11999.07 6499.55 11198.30 18499.65 6499.45 8599.22 1799.76 26798.44 12999.77 16099.64 84
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+96.62 999.08 7799.00 9099.33 8999.71 4798.83 8798.60 11999.58 9199.11 9899.53 8399.18 15698.81 3899.67 32596.71 27699.77 16099.50 164
COLMAP_ROBcopyleft96.50 1098.99 8898.85 11299.41 7099.58 9099.10 6698.74 9799.56 10799.09 10899.33 13099.19 15298.40 8299.72 29795.98 33099.76 17599.42 210
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 35095.95 36198.65 21998.93 29698.09 14696.93 34499.28 24083.58 47698.13 31797.78 38596.13 26499.40 42493.52 40499.29 32698.45 401
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMM96.08 1298.91 10098.73 12399.48 5799.55 11399.14 5898.07 19399.37 19197.62 24899.04 18998.96 22598.84 3699.79 24497.43 21499.65 23099.49 171
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HY-MVS95.94 1395.90 37495.35 38497.55 35597.95 41694.79 34898.81 9696.94 42292.28 44295.17 44798.57 31689.90 38799.75 27591.20 44497.33 44798.10 424
OpenMVS_ROBcopyleft95.38 1495.84 37795.18 39097.81 32298.41 39297.15 24197.37 31098.62 36283.86 47598.65 26298.37 34194.29 32799.68 32188.41 45998.62 39996.60 466
ACMP95.32 1598.41 19698.09 23499.36 7499.51 12798.79 9097.68 26099.38 18795.76 37498.81 24198.82 26298.36 8599.82 20594.75 36699.77 16099.48 182
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLCcopyleft94.65 1696.51 35395.73 36698.85 17598.75 33297.91 17196.42 37599.06 29090.94 45695.59 43697.38 40994.41 32299.59 36990.93 44898.04 42699.05 319
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet93.40 1795.67 38195.70 36795.57 43098.83 31988.57 45792.50 47497.72 39592.69 43796.49 42196.44 43093.72 34099.43 42093.61 40199.28 32798.71 378
PCF-MVS92.86 1894.36 40393.00 42198.42 26698.70 34497.56 20293.16 47299.11 28479.59 48097.55 36297.43 40692.19 36399.73 28879.85 47899.45 29697.97 432
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IB-MVS91.63 1992.24 43990.90 44396.27 41197.22 45491.24 43994.36 45993.33 46692.37 44092.24 47594.58 46566.20 47799.89 9793.16 41294.63 47297.66 448
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 26697.94 25397.65 34199.71 4797.94 16898.52 12898.68 35798.99 12197.52 36599.35 10797.41 18898.18 47491.59 43799.67 22196.82 463
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PVSNet_089.98 2191.15 44590.30 44793.70 45497.72 42684.34 47890.24 47897.42 40490.20 46093.79 46693.09 47490.90 38098.89 46386.57 46772.76 48497.87 437
MVEpermissive83.40 2292.50 43491.92 43694.25 44698.83 31991.64 42892.71 47383.52 48695.92 36986.46 48495.46 45195.20 30095.40 48280.51 47798.64 39695.73 475
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CMPMVSbinary75.91 2396.29 36195.44 37998.84 17896.25 47598.69 9897.02 33799.12 28288.90 46697.83 34398.86 24989.51 39198.90 46291.92 42999.51 28198.92 346
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
blend_shiyan492.09 44190.16 44897.88 31796.78 46494.93 34595.24 43398.58 36496.22 35496.07 42991.42 48163.46 48499.73 28896.70 27776.98 48398.98 333
E699.05 8099.11 7298.85 17599.60 8697.30 22198.42 14999.63 7398.73 14599.26 14899.39 10098.71 5099.70 30598.43 13199.84 11299.54 142
E599.05 8099.11 7298.85 17599.60 8697.30 22198.42 14999.63 7398.73 14599.26 14899.39 10098.71 5099.70 30598.43 13199.84 11299.54 142
FE-MVSNET397.37 30697.13 31198.11 30099.03 27695.40 32794.47 45698.99 30896.87 32397.97 33297.81 38492.12 36599.75 27597.49 21299.43 30499.16 308
E498.87 10798.88 10398.81 18399.52 12497.23 22897.62 27199.61 8098.58 16399.18 16899.33 11498.29 9499.69 31197.99 16599.83 12199.52 156
E3new98.41 19698.34 19798.62 22799.19 23496.90 25897.32 31499.50 12997.40 27898.63 26498.92 23397.21 20399.65 34597.34 21899.52 27899.31 261
FE-MVSNET299.15 5899.22 5598.94 16199.70 5597.49 20598.62 11699.67 6498.85 14199.34 12799.54 6398.47 7499.81 22298.93 9399.91 7899.51 160
fmvsm_s_conf0.5_n_1199.21 4899.34 3698.80 18699.48 14996.56 27797.97 21999.69 5499.63 2999.84 3099.54 6398.21 10999.94 4299.76 2399.95 3899.88 20
E298.70 14298.68 13698.73 20899.40 17497.10 24497.48 29399.57 9898.09 21399.00 19499.20 14997.90 13799.67 32597.73 19099.77 16099.43 205
MED-MVS test99.45 6499.58 9098.93 8098.68 10799.60 8296.46 34499.53 8398.77 27299.83 19296.67 28099.64 23299.58 115
MED-MVS98.90 10298.72 12599.45 6499.58 9098.93 8098.68 10799.60 8298.14 21099.53 8398.77 27297.87 14399.83 19296.67 28099.64 23299.58 115
E398.69 14698.68 13698.73 20899.40 17497.10 24497.48 29399.57 9898.09 21399.00 19499.20 14997.90 13799.67 32597.73 19099.77 16099.43 205
TestfortrainingZip a98.95 9598.72 12599.64 999.58 9099.32 2298.68 10799.60 8296.46 34499.53 8398.77 27297.87 14399.83 19298.39 13499.64 23299.77 50
TestfortrainingZip98.68 107
fmvsm_s_conf0.5_n_1099.15 5899.27 4898.78 19399.47 15296.56 27797.75 25299.71 4799.60 3699.74 4799.44 8697.96 13399.95 2699.86 499.94 5099.82 36
viewdifsd2359ckpt0798.71 13798.86 11098.26 28499.43 16795.65 31197.20 32899.66 6599.20 8399.29 14099.01 20998.29 9499.73 28897.92 17099.75 17999.39 223
viewdifsd2359ckpt0998.13 24097.92 25698.77 19899.18 24297.35 21697.29 31899.53 12095.81 37298.09 32198.47 33196.34 25799.66 33897.02 24299.51 28199.29 267
viewdifsd2359ckpt1398.39 20598.29 20798.70 21299.26 21797.19 23597.51 28999.48 13996.94 31798.58 27598.82 26297.47 18699.55 38597.21 22799.33 31799.34 248
viewcassd2359sk1198.55 17798.51 16598.67 21799.29 20296.99 25097.39 30499.54 11697.73 24098.81 24199.08 18497.55 17399.66 33897.52 20699.67 22199.36 241
viewdifsd2359ckpt1198.84 11499.04 8398.24 28899.56 10795.51 31797.38 30699.70 5299.16 9399.57 7299.40 9798.26 10099.71 29898.55 12499.82 12699.50 164
viewmacassd2359aftdt98.86 11198.87 10698.83 17999.53 12197.32 22097.70 25899.64 7198.22 19299.25 15499.27 12798.40 8299.61 36297.98 16699.87 9899.55 136
viewmsd2359difaftdt98.84 11499.04 8398.24 28899.56 10795.51 31797.38 30699.70 5299.16 9399.57 7299.40 9798.26 10099.71 29898.55 12499.82 12699.50 164
diffmvs_AUTHOR98.50 18898.59 15598.23 29199.35 18995.48 32196.61 36299.60 8298.37 17698.90 22199.00 21397.37 19199.76 26798.22 14499.85 10799.46 192
FE-MVSNET98.59 16998.50 16898.87 17299.58 9097.30 22198.08 18999.74 4396.94 31798.97 20399.10 17896.94 21999.74 28197.33 22099.86 10599.55 136
fmvsm_l_conf0.5_n_999.32 3399.43 2498.98 15599.59 8897.18 23797.44 30199.83 2599.56 4099.91 1299.34 11199.36 1399.93 5499.83 1099.98 1299.85 30
mamba_040898.80 12498.88 10398.55 24599.27 20896.50 28098.00 20799.60 8298.93 12999.22 15998.84 25798.59 6499.89 9797.74 18899.72 19199.27 271
icg_test_0407_298.20 23298.38 19197.65 34199.03 27694.03 37595.78 41499.45 15698.16 20499.06 17998.71 28498.27 9899.68 32197.50 20799.45 29699.22 288
SSM_0407298.80 12498.88 10398.56 24399.27 20896.50 28098.00 20799.60 8298.93 12999.22 15998.84 25798.59 6499.90 8197.74 18899.72 19199.27 271
SSM_040798.86 11198.96 9698.55 24599.27 20896.50 28098.04 19899.66 6599.09 10899.22 15999.02 19898.79 4299.87 13497.87 17699.72 19199.27 271
viewmambaseed2359dif98.19 23398.26 21297.99 31299.02 28295.03 34296.59 36499.53 12096.21 35599.00 19498.99 21597.62 16699.61 36297.62 19699.72 19199.33 254
IMVS_040798.39 20598.64 14497.66 33999.03 27694.03 37598.10 18699.45 15698.16 20499.06 17998.71 28498.27 9899.71 29897.50 20799.45 29699.22 288
viewmanbaseed2359cas98.58 17198.54 16198.70 21299.28 20597.13 24397.47 29799.55 11197.55 25998.96 20898.92 23397.77 15399.59 36997.59 20099.77 16099.39 223
IMVS_040498.07 24598.20 21997.69 33699.03 27694.03 37596.67 35899.45 15698.16 20498.03 32898.71 28496.80 23099.82 20597.50 20799.45 29699.22 288
SSM_040498.90 10299.01 8898.57 23899.42 16996.59 27298.13 17999.66 6599.09 10899.30 13999.02 19898.79 4299.89 9797.87 17699.80 14399.23 283
IMVS_040398.34 20998.56 15897.66 33999.03 27694.03 37597.98 21599.45 15698.16 20498.89 22498.71 28497.90 13799.74 28197.50 20799.45 29699.22 288
SD_040396.28 36295.83 36397.64 34498.72 33694.30 36498.87 8898.77 34697.80 23596.53 41598.02 37097.34 19399.47 41276.93 48199.48 29299.16 308
fmvsm_s_conf0.5_n_999.17 5399.38 2998.53 25299.51 12795.82 30797.62 27199.78 3699.72 1599.90 1499.48 7698.66 5699.89 9799.85 699.93 5699.89 16
ME-MVS98.61 16598.33 20299.44 6699.24 21998.93 8097.45 29999.06 29098.14 21099.06 17998.77 27296.97 21899.82 20596.67 28099.64 23299.58 115
NormalMVS98.26 22397.97 25099.15 12199.64 7597.83 17898.28 16199.43 17099.24 7698.80 24398.85 25289.76 38899.94 4298.04 15899.67 22199.68 71
lecture99.25 4199.12 7199.62 1099.64 7599.40 1298.89 8799.51 12699.19 8899.37 12099.25 13898.36 8599.88 11598.23 14399.67 22199.59 107
SymmetryMVS98.05 24797.71 27299.09 13299.29 20297.83 17898.28 16197.64 40299.24 7698.80 24398.85 25289.76 38899.94 4298.04 15899.50 28999.49 171
Elysia99.15 5899.14 6999.18 11399.63 8197.92 16998.50 13599.43 17099.67 2199.70 5299.13 17196.66 24099.98 499.54 4499.96 2899.64 84
StellarMVS99.15 5899.14 6999.18 11399.63 8197.92 16998.50 13599.43 17099.67 2199.70 5299.13 17196.66 24099.98 499.54 4499.96 2899.64 84
KinetiMVS99.03 8399.02 8699.03 14599.70 5597.48 20898.43 14699.29 23699.70 1699.60 7199.07 18596.13 26499.94 4299.42 5699.87 9899.68 71
LuminaMVS98.39 20598.20 21998.98 15599.50 13397.49 20597.78 24397.69 39798.75 14499.49 9599.25 13892.30 36299.94 4299.14 7699.88 9499.50 164
VortexMVS97.98 25698.31 20497.02 38398.88 31091.45 43198.03 20099.47 14898.65 15199.55 7799.47 7991.49 37399.81 22299.32 6199.91 7899.80 42
AstraMVS98.16 23998.07 23998.41 26799.51 12795.86 30498.00 20795.14 45198.97 12499.43 10699.24 14093.25 34299.84 17499.21 7199.87 9899.54 142
guyue98.01 25197.93 25598.26 28499.45 16095.48 32198.08 18996.24 43498.89 13599.34 12799.14 16991.32 37599.82 20599.07 8199.83 12199.48 182
sc_t199.62 799.66 899.53 3999.82 1999.09 6999.50 1199.63 7399.88 499.86 2499.80 1199.03 2499.89 9799.48 5399.93 5699.60 100
tt0320-xc99.64 599.68 599.50 5499.72 4398.98 7299.51 1099.85 1899.86 699.88 2199.82 599.02 2699.90 8199.54 4499.95 3899.61 98
tt032099.61 899.65 999.48 5799.71 4798.94 7999.54 899.83 2599.87 599.89 1899.82 598.75 4699.90 8199.54 4499.95 3899.59 107
fmvsm_s_conf0.5_n_899.13 6799.26 5198.74 20699.51 12796.44 28497.65 26699.65 6999.66 2499.78 4099.48 7697.92 13699.93 5499.72 3099.95 3899.87 22
fmvsm_s_conf0.5_n_798.83 11799.04 8398.20 29399.30 19994.83 34797.23 32399.36 19598.64 15299.84 3099.43 8998.10 12199.91 7499.56 4199.96 2899.87 22
fmvsm_s_conf0.5_n_699.08 7799.21 5898.69 21499.36 18496.51 27997.62 27199.68 6098.43 17499.85 2799.10 17899.12 2399.88 11599.77 2299.92 6999.67 76
fmvsm_s_conf0.5_n_599.07 7999.10 7698.99 15199.47 15297.22 23197.40 30399.83 2597.61 25199.85 2799.30 12198.80 4099.95 2699.71 3299.90 8699.78 47
fmvsm_s_conf0.5_n_499.01 8599.22 5598.38 27199.31 19595.48 32197.56 28299.73 4498.87 13699.75 4599.27 12798.80 4099.86 14399.80 1799.90 8699.81 40
SSC-MVS3.298.53 18298.79 11797.74 33199.46 15593.62 39896.45 37199.34 20799.33 6698.93 21798.70 29197.90 13799.90 8199.12 7799.92 6999.69 70
testing3-293.78 41593.91 40793.39 45898.82 32281.72 48597.76 24995.28 44998.60 15996.54 41496.66 42465.85 47999.62 35596.65 28498.99 36998.82 359
myMVS_eth3d2892.92 43092.31 42694.77 44197.84 42187.59 46496.19 38996.11 43797.08 30994.27 45793.49 47266.07 47898.78 46591.78 43297.93 42997.92 434
UWE-MVS-2890.22 44689.28 44993.02 46294.50 48382.87 48196.52 36887.51 48195.21 39192.36 47496.04 43571.57 46598.25 47372.04 48397.77 43197.94 433
fmvsm_l_conf0.5_n_399.45 1899.48 1899.34 8399.59 8898.21 13697.82 23799.84 2299.41 5899.92 899.41 9499.51 899.95 2699.84 999.97 2199.87 22
fmvsm_s_conf0.5_n_399.22 4799.37 3298.78 19399.46 15596.58 27597.65 26699.72 4599.47 4899.86 2499.50 6998.94 3099.89 9799.75 2699.97 2199.86 28
fmvsm_s_conf0.5_n_299.14 6399.31 4298.63 22599.49 14196.08 29797.38 30699.81 3199.48 4599.84 3099.57 4998.46 7899.89 9799.82 1299.97 2199.91 13
fmvsm_s_conf0.1_n_299.20 5199.38 2998.65 21999.69 5996.08 29797.49 29299.90 1199.53 4299.88 2199.64 3798.51 7399.90 8199.83 1099.98 1299.97 4
GDP-MVS97.50 29297.11 31298.67 21799.02 28296.85 26098.16 17699.71 4798.32 18298.52 28698.54 31883.39 43599.95 2698.79 10299.56 26599.19 298
BP-MVS197.40 30496.97 31898.71 21199.07 26496.81 26298.34 15997.18 41298.58 16398.17 31098.61 31184.01 43199.94 4298.97 9099.78 15499.37 234
reproduce_monomvs95.00 39795.25 38694.22 44797.51 44683.34 47997.86 23398.44 37198.51 17099.29 14099.30 12167.68 47299.56 38198.89 9799.81 13299.77 50
mmtdpeth99.30 3499.42 2598.92 16799.58 9096.89 25999.48 1399.92 799.92 298.26 30799.80 1198.33 9199.91 7499.56 4199.95 3899.97 4
reproduce_model99.15 5898.97 9499.67 499.33 19399.44 1098.15 17799.47 14899.12 9799.52 8899.32 11998.31 9299.90 8197.78 18299.73 18399.66 78
reproduce-ours99.09 7398.90 10099.67 499.27 20899.49 698.00 20799.42 17699.05 11599.48 9699.27 12798.29 9499.89 9797.61 19799.71 20099.62 90
our_new_method99.09 7398.90 10099.67 499.27 20899.49 698.00 20799.42 17699.05 11599.48 9699.27 12798.29 9499.89 9797.61 19799.71 20099.62 90
mmdepth0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
monomultidepth0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
mvs5depth99.30 3499.59 1298.44 26499.65 6995.35 32999.82 399.94 299.83 799.42 11099.94 298.13 11999.96 1499.63 3699.96 28100.00 1
MVStest195.86 37595.60 37196.63 40195.87 47991.70 42797.93 22198.94 31198.03 21699.56 7499.66 3271.83 46498.26 47299.35 5999.24 33399.91 13
ttmdpeth97.91 25898.02 24397.58 35098.69 34994.10 37198.13 17998.90 32097.95 22297.32 38099.58 4795.95 27998.75 46696.41 30799.22 33799.87 22
WBMVS95.18 39294.78 39896.37 40797.68 43489.74 45495.80 41398.73 35497.54 26198.30 30198.44 33470.06 46699.82 20596.62 28699.87 9899.54 142
dongtai76.24 45075.95 45377.12 46792.39 48567.91 49190.16 47959.44 49282.04 47889.42 48094.67 46449.68 48981.74 48548.06 48577.66 48281.72 481
kuosan69.30 45168.95 45470.34 46887.68 48965.00 49291.11 47759.90 49169.02 48174.46 48688.89 48348.58 49068.03 48728.61 48672.33 48577.99 482
MVSMamba_PlusPlus98.83 11798.98 9398.36 27599.32 19496.58 27598.90 8399.41 18099.75 1198.72 25499.50 6996.17 26299.94 4299.27 6599.78 15498.57 394
MGCFI-Net98.34 20998.28 20898.51 25498.47 38297.59 20198.96 7799.48 13999.18 9197.40 37595.50 44898.66 5699.50 40398.18 14798.71 38998.44 404
testing9193.32 42292.27 42796.47 40597.54 43991.25 43896.17 39396.76 42697.18 30393.65 46893.50 47165.11 48199.63 35293.04 41397.45 43898.53 395
testing1193.08 42792.02 43296.26 41297.56 43790.83 44696.32 38195.70 44596.47 34392.66 47293.73 46864.36 48299.59 36993.77 39997.57 43498.37 413
testing9993.04 42891.98 43596.23 41497.53 44190.70 44896.35 37995.94 44196.87 32393.41 46993.43 47363.84 48399.59 36993.24 41197.19 44898.40 409
UBG93.25 42492.32 42596.04 42197.72 42690.16 45195.92 40795.91 44296.03 36493.95 46593.04 47569.60 46899.52 39790.72 45297.98 42798.45 401
UWE-MVS92.38 43691.76 43994.21 44897.16 45584.65 47495.42 42888.45 48095.96 36796.17 42595.84 44366.36 47599.71 29891.87 43198.64 39698.28 416
ETVMVS92.60 43391.08 44297.18 37597.70 43193.65 39796.54 36595.70 44596.51 33994.68 45392.39 47861.80 48599.50 40386.97 46497.41 44198.40 409
sasdasda98.34 20998.26 21298.58 23598.46 38497.82 18398.96 7799.46 15299.19 8897.46 37095.46 45198.59 6499.46 41598.08 15498.71 38998.46 398
testing22291.96 44290.37 44596.72 40097.47 44892.59 41396.11 39594.76 45396.83 32692.90 47192.87 47657.92 48699.55 38586.93 46597.52 43598.00 431
WB-MVSnew95.73 38095.57 37496.23 41496.70 46690.70 44896.07 39793.86 46395.60 37897.04 38995.45 45496.00 27199.55 38591.04 44698.31 40898.43 406
fmvsm_l_conf0.5_n_a99.19 5299.27 4898.94 16199.65 6997.05 24697.80 24199.76 3998.70 15099.78 4099.11 17598.79 4299.95 2699.85 699.96 2899.83 33
fmvsm_l_conf0.5_n99.21 4899.28 4799.02 14899.64 7597.28 22597.82 23799.76 3998.73 14599.82 3499.09 18398.81 3899.95 2699.86 499.96 2899.83 33
fmvsm_s_conf0.1_n_a99.17 5399.30 4598.80 18699.75 3496.59 27297.97 21999.86 1698.22 19299.88 2199.71 2298.59 6499.84 17499.73 2899.98 1299.98 3
fmvsm_s_conf0.1_n99.16 5799.33 3898.64 22199.71 4796.10 29297.87 23299.85 1898.56 16899.90 1499.68 2598.69 5499.85 15699.72 3099.98 1299.97 4
fmvsm_s_conf0.5_n_a99.10 7299.20 5998.78 19399.55 11396.59 27297.79 24299.82 3098.21 19499.81 3799.53 6598.46 7899.84 17499.70 3399.97 2199.90 15
fmvsm_s_conf0.5_n99.09 7399.26 5198.61 23199.55 11396.09 29597.74 25399.81 3198.55 16999.85 2799.55 5798.60 6399.84 17499.69 3599.98 1299.89 16
MM98.22 22897.99 24698.91 16898.66 35996.97 25197.89 22894.44 45699.54 4198.95 20999.14 16993.50 34199.92 6599.80 1799.96 2899.85 30
WAC-MVS90.90 44491.37 441
Syy-MVS96.04 36995.56 37597.49 36197.10 45794.48 35996.18 39196.58 42995.65 37694.77 45192.29 47991.27 37699.36 42998.17 14998.05 42498.63 388
test_fmvsmconf0.1_n99.49 1599.54 1499.34 8399.78 2498.11 14397.77 24699.90 1199.33 6699.97 399.66 3299.71 399.96 1499.79 1999.99 599.96 8
test_fmvsmconf0.01_n99.57 1099.63 1099.36 7499.87 1298.13 14298.08 18999.95 199.45 5199.98 299.75 1699.80 199.97 799.82 1299.99 599.99 2
myMVS_eth3d91.92 44390.45 44496.30 40997.10 45790.90 44496.18 39196.58 42995.65 37694.77 45192.29 47953.88 48799.36 42989.59 45798.05 42498.63 388
testing393.51 41992.09 43097.75 32998.60 36694.40 36197.32 31495.26 45097.56 25796.79 40695.50 44853.57 48899.77 26195.26 35698.97 37399.08 315
SSC-MVS98.71 13798.74 12198.62 22799.72 4396.08 29798.74 9798.64 36199.74 1399.67 6099.24 14094.57 31999.95 2699.11 7899.24 33399.82 36
test_fmvsmconf_n99.44 1999.48 1899.31 9499.64 7598.10 14597.68 26099.84 2299.29 7299.92 899.57 4999.60 599.96 1499.74 2799.98 1299.89 16
WB-MVS98.52 18698.55 15998.43 26599.65 6995.59 31298.52 12898.77 34699.65 2699.52 8899.00 21394.34 32599.93 5498.65 11598.83 38199.76 56
test_fmvsmvis_n_192099.26 4099.49 1698.54 25099.66 6896.97 25198.00 20799.85 1899.24 7699.92 899.50 6999.39 1299.95 2699.89 399.98 1298.71 378
dmvs_re95.98 37295.39 38297.74 33198.86 31397.45 21198.37 15595.69 44797.95 22296.56 41395.95 43890.70 38197.68 47788.32 46096.13 46398.11 423
SDMVSNet99.23 4699.32 4098.96 15899.68 6297.35 21698.84 9499.48 13999.69 1899.63 6799.68 2599.03 2499.96 1497.97 16799.92 6999.57 123
dmvs_testset92.94 42992.21 42995.13 43898.59 36990.99 44397.65 26692.09 47196.95 31694.00 46393.55 47092.34 36196.97 48072.20 48292.52 47797.43 455
sd_testset99.28 3799.31 4299.19 11299.68 6298.06 15599.41 1799.30 22899.69 1899.63 6799.68 2599.25 1699.96 1497.25 22599.92 6999.57 123
test_fmvsm_n_192099.33 3199.45 2398.99 15199.57 9997.73 19397.93 22199.83 2599.22 7999.93 699.30 12199.42 1199.96 1499.85 699.99 599.29 267
test_cas_vis1_n_192098.33 21398.68 13697.27 37299.69 5992.29 42198.03 20099.85 1897.62 24899.96 499.62 4093.98 33499.74 28199.52 5099.86 10599.79 44
test_vis1_n_192098.40 19998.92 9896.81 39699.74 3690.76 44798.15 17799.91 998.33 18099.89 1899.55 5795.07 30499.88 11599.76 2399.93 5699.79 44
test_vis1_n98.31 21698.50 16897.73 33499.76 3094.17 36998.68 10799.91 996.31 35199.79 3999.57 4992.85 35499.42 42299.79 1999.84 11299.60 100
test_fmvs1_n98.09 24398.28 20897.52 35899.68 6293.47 40098.63 11499.93 595.41 38799.68 5899.64 3791.88 36999.48 40999.82 1299.87 9899.62 90
mvsany_test197.60 28697.54 28497.77 32597.72 42695.35 32995.36 43097.13 41594.13 41699.71 5099.33 11497.93 13599.30 43997.60 19998.94 37698.67 386
APD_test198.83 11798.66 14199.34 8399.78 2499.47 998.42 14999.45 15698.28 18998.98 19999.19 15297.76 15499.58 37696.57 29199.55 26998.97 337
test_vis1_rt97.75 27697.72 27197.83 32098.81 32596.35 28797.30 31799.69 5494.61 40397.87 33998.05 36896.26 26098.32 47198.74 10898.18 41398.82 359
test_vis3_rt99.14 6399.17 6199.07 13599.78 2498.38 11998.92 8299.94 297.80 23599.91 1299.67 3097.15 20698.91 46199.76 2399.56 26599.92 12
test_fmvs298.70 14298.97 9497.89 31699.54 11894.05 37298.55 12499.92 796.78 32999.72 4899.78 1396.60 24499.67 32599.91 299.90 8699.94 10
test_fmvs197.72 27897.94 25397.07 38298.66 35992.39 41897.68 26099.81 3195.20 39299.54 7999.44 8691.56 37299.41 42399.78 2199.77 16099.40 222
test_fmvs399.12 7099.41 2698.25 28699.76 3095.07 34199.05 6799.94 297.78 23899.82 3499.84 398.56 7099.71 29899.96 199.96 2899.97 4
mvsany_test398.87 10798.92 9898.74 20699.38 17796.94 25598.58 12199.10 28596.49 34199.96 499.81 898.18 11299.45 41798.97 9099.79 14999.83 33
testf199.25 4199.16 6399.51 4999.89 699.63 498.71 10499.69 5498.90 13399.43 10699.35 10798.86 3499.67 32597.81 17999.81 13299.24 281
APD_test299.25 4199.16 6399.51 4999.89 699.63 498.71 10499.69 5498.90 13399.43 10699.35 10798.86 3499.67 32597.81 17999.81 13299.24 281
test_f98.67 15598.87 10698.05 30899.72 4395.59 31298.51 13399.81 3196.30 35399.78 4099.82 596.14 26398.63 46899.82 1299.93 5699.95 9
FE-MVS95.66 38294.95 39597.77 32598.53 37895.28 33299.40 1996.09 43893.11 43197.96 33399.26 13379.10 45399.77 26192.40 42798.71 38998.27 417
FA-MVS(test-final)96.99 33796.82 33097.50 36098.70 34494.78 34999.34 2396.99 41895.07 39398.48 28999.33 11488.41 40299.65 34596.13 32698.92 37898.07 426
balanced_conf0398.63 16198.72 12598.38 27198.66 35996.68 27198.90 8399.42 17698.99 12198.97 20399.19 15295.81 28499.85 15698.77 10699.77 16098.60 390
MonoMVSNet96.25 36496.53 35095.39 43596.57 46891.01 44298.82 9597.68 39998.57 16598.03 32899.37 10290.92 37997.78 47694.99 36093.88 47597.38 456
patch_mono-298.51 18798.63 14698.17 29699.38 17794.78 34997.36 31199.69 5498.16 20498.49 28899.29 12497.06 21099.97 798.29 14099.91 7899.76 56
EGC-MVSNET85.24 44780.54 45099.34 8399.77 2799.20 4099.08 6199.29 23612.08 48620.84 48799.42 9097.55 17399.85 15697.08 23899.72 19198.96 339
test250692.39 43591.89 43793.89 45299.38 17782.28 48399.32 2666.03 49099.08 11298.77 24899.57 4966.26 47699.84 17498.71 11199.95 3899.54 142
test111196.49 35696.82 33095.52 43199.42 16987.08 46699.22 4587.14 48299.11 9899.46 10199.58 4788.69 39699.86 14398.80 10199.95 3899.62 90
ECVR-MVScopyleft96.42 35896.61 34495.85 42399.38 17788.18 46199.22 4586.00 48499.08 11299.36 12399.57 4988.47 40199.82 20598.52 12699.95 3899.54 142
test_blank0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
tt080598.69 14698.62 14898.90 17199.75 3499.30 2399.15 5696.97 41998.86 13898.87 23297.62 39698.63 6098.96 45899.41 5798.29 40998.45 401
DVP-MVS++98.90 10298.70 13399.51 4998.43 38899.15 5399.43 1599.32 21598.17 20199.26 14899.02 19898.18 11299.88 11597.07 23999.45 29699.49 171
FOURS199.73 3799.67 399.43 1599.54 11699.43 5599.26 148
MSC_two_6792asdad99.32 9198.43 38898.37 12198.86 33199.89 9797.14 23399.60 24999.71 63
PC_three_145293.27 42899.40 11598.54 31898.22 10797.00 47995.17 35799.45 29699.49 171
No_MVS99.32 9198.43 38898.37 12198.86 33199.89 9797.14 23399.60 24999.71 63
test_one_060199.39 17699.20 4099.31 22098.49 17198.66 26199.02 19897.64 164
eth-test20.00 494
eth-test0.00 494
GeoE99.05 8098.99 9299.25 10499.44 16298.35 12598.73 10199.56 10798.42 17598.91 22098.81 26598.94 3099.91 7498.35 13699.73 18399.49 171
test_method79.78 44879.50 45180.62 46580.21 49045.76 49370.82 48298.41 37531.08 48580.89 48597.71 38984.85 42297.37 47891.51 43980.03 48198.75 375
Anonymous2024052198.69 14698.87 10698.16 29899.77 2795.11 34099.08 6199.44 16499.34 6599.33 13099.55 5794.10 33399.94 4299.25 6899.96 2899.42 210
h-mvs3397.77 27597.33 29999.10 12899.21 22797.84 17798.35 15798.57 36599.11 9898.58 27599.02 19888.65 39999.96 1498.11 15196.34 45999.49 171
hse-mvs297.46 29797.07 31398.64 22198.73 33497.33 21897.45 29997.64 40299.11 9898.58 27597.98 37388.65 39999.79 24498.11 15197.39 44298.81 364
CL-MVSNet_self_test97.44 30097.22 30498.08 30498.57 37395.78 30994.30 46098.79 34396.58 33898.60 27198.19 35794.74 31799.64 34996.41 30798.84 38098.82 359
KD-MVS_2432*160092.87 43191.99 43395.51 43291.37 48689.27 45594.07 46298.14 38595.42 38497.25 38296.44 43067.86 47099.24 44591.28 44296.08 46498.02 428
KD-MVS_self_test99.25 4199.18 6099.44 6699.63 8199.06 7198.69 10699.54 11699.31 6999.62 7099.53 6597.36 19299.86 14399.24 7099.71 20099.39 223
AUN-MVS96.24 36695.45 37898.60 23398.70 34497.22 23197.38 30697.65 40095.95 36895.53 44397.96 37782.11 44399.79 24496.31 31397.44 43998.80 369
ZD-MVS99.01 28498.84 8699.07 28994.10 41798.05 32698.12 36196.36 25699.86 14392.70 42399.19 344
SR-MVS-dyc-post98.81 12298.55 15999.57 2299.20 23199.38 1398.48 14199.30 22898.64 15298.95 20998.96 22597.49 18499.86 14396.56 29599.39 30899.45 197
RE-MVS-def98.58 15699.20 23199.38 1398.48 14199.30 22898.64 15298.95 20998.96 22597.75 15596.56 29599.39 30899.45 197
SED-MVS98.91 10098.72 12599.49 5599.49 14199.17 4598.10 18699.31 22098.03 21699.66 6199.02 19898.36 8599.88 11596.91 25299.62 24299.41 213
IU-MVS99.49 14199.15 5398.87 32692.97 43299.41 11296.76 26999.62 24299.66 78
OPU-MVS98.82 18198.59 36998.30 12698.10 18698.52 32298.18 11298.75 46694.62 37099.48 29299.41 213
test_241102_TWO99.30 22898.03 21699.26 14899.02 19897.51 18099.88 11596.91 25299.60 24999.66 78
test_241102_ONE99.49 14199.17 4599.31 22097.98 21999.66 6198.90 23998.36 8599.48 409
SF-MVS98.53 18298.27 21199.32 9199.31 19598.75 9198.19 17199.41 18096.77 33098.83 23698.90 23997.80 15199.82 20595.68 34699.52 27899.38 232
cl2295.79 37895.39 38296.98 38696.77 46592.79 41094.40 45898.53 36794.59 40497.89 33798.17 35882.82 44099.24 44596.37 30999.03 36298.92 346
miper_ehance_all_eth97.06 33097.03 31597.16 37997.83 42293.06 40494.66 44999.09 28795.99 36698.69 25698.45 33392.73 35799.61 36296.79 26599.03 36298.82 359
miper_enhance_ethall96.01 37095.74 36596.81 39696.41 47392.27 42293.69 46998.89 32391.14 45498.30 30197.35 41290.58 38299.58 37696.31 31399.03 36298.60 390
ZNCC-MVS98.68 15298.40 18699.54 3299.57 9999.21 3498.46 14399.29 23697.28 29098.11 31998.39 33898.00 12899.87 13496.86 26299.64 23299.55 136
dcpmvs_298.78 12899.11 7297.78 32499.56 10793.67 39599.06 6599.86 1699.50 4499.66 6199.26 13397.21 20399.99 298.00 16399.91 7899.68 71
cl____97.02 33396.83 32997.58 35097.82 42394.04 37494.66 44999.16 27597.04 31198.63 26498.71 28488.68 39899.69 31197.00 24499.81 13299.00 331
DIV-MVS_self_test97.02 33396.84 32897.58 35097.82 42394.03 37594.66 44999.16 27597.04 31198.63 26498.71 28488.69 39699.69 31197.00 24499.81 13299.01 327
eth_miper_zixun_eth97.23 31997.25 30297.17 37798.00 41592.77 41194.71 44699.18 26897.27 29198.56 27998.74 28091.89 36899.69 31197.06 24199.81 13299.05 319
9.1497.78 26599.07 26497.53 28699.32 21595.53 38198.54 28398.70 29197.58 17099.76 26794.32 38399.46 294
uanet_test0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
DCPMVS0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
save fliter99.11 25597.97 16396.53 36799.02 30298.24 190
ET-MVSNet_ETH3D94.30 40693.21 41797.58 35098.14 40894.47 36094.78 44593.24 46794.72 40189.56 47995.87 44178.57 45699.81 22296.91 25297.11 45198.46 398
UniMVSNet_ETH3D99.69 299.69 499.69 399.84 1799.34 2099.69 599.58 9199.90 399.86 2499.78 1399.58 699.95 2699.00 8899.95 3899.78 47
EIA-MVS98.00 25297.74 26898.80 18698.72 33698.09 14698.05 19699.60 8297.39 27996.63 41095.55 44697.68 15899.80 23196.73 27399.27 32898.52 396
miper_refine_blended92.87 43191.99 43395.51 43291.37 48689.27 45594.07 46298.14 38595.42 38497.25 38296.44 43067.86 47099.24 44591.28 44296.08 46498.02 428
miper_lstm_enhance97.18 32397.16 30797.25 37498.16 40692.85 40995.15 43799.31 22097.25 29398.74 25398.78 27090.07 38599.78 25597.19 22899.80 14399.11 314
ETV-MVS98.03 24897.86 26298.56 24398.69 34998.07 15297.51 28999.50 12998.10 21297.50 36795.51 44798.41 8199.88 11596.27 31699.24 33397.71 447
CS-MVS99.13 6799.10 7699.24 10699.06 26999.15 5399.36 2299.88 1499.36 6498.21 30998.46 33298.68 5599.93 5499.03 8699.85 10798.64 387
D2MVS97.84 27297.84 26397.83 32099.14 25194.74 35196.94 34298.88 32495.84 37198.89 22498.96 22594.40 32399.69 31197.55 20199.95 3899.05 319
DVP-MVScopyleft98.77 13198.52 16499.52 4599.50 13399.21 3498.02 20398.84 33597.97 22099.08 17799.02 19897.61 16899.88 11596.99 24699.63 23999.48 182
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 20199.08 17799.02 19897.89 14199.88 11597.07 23999.71 20099.70 68
test_0728_SECOND99.60 1699.50 13399.23 3298.02 20399.32 21599.88 11596.99 24699.63 23999.68 71
test072699.50 13399.21 3498.17 17599.35 20197.97 22099.26 14899.06 18697.61 168
SR-MVS98.71 13798.43 18299.57 2299.18 24299.35 1798.36 15699.29 23698.29 18798.88 22898.85 25297.53 17799.87 13496.14 32499.31 32199.48 182
DPM-MVS96.32 36095.59 37398.51 25498.76 33097.21 23394.54 45598.26 37991.94 44496.37 42297.25 41393.06 34999.43 42091.42 44098.74 38598.89 351
GST-MVS98.61 16598.30 20599.52 4599.51 12799.20 4098.26 16599.25 24997.44 27598.67 25998.39 33897.68 15899.85 15696.00 32899.51 28199.52 156
test_yl96.69 34696.29 35697.90 31498.28 39895.24 33397.29 31897.36 40698.21 19498.17 31097.86 38086.27 41099.55 38594.87 36498.32 40698.89 351
thisisatest053095.27 39094.45 40197.74 33199.19 23494.37 36297.86 23390.20 47797.17 30498.22 30897.65 39373.53 46399.90 8196.90 25799.35 31498.95 340
Anonymous2024052998.93 9898.87 10699.12 12499.19 23498.22 13599.01 7098.99 30899.25 7599.54 7999.37 10297.04 21199.80 23197.89 17199.52 27899.35 246
Anonymous20240521197.90 25997.50 28799.08 13398.90 30498.25 12998.53 12796.16 43598.87 13699.11 17298.86 24990.40 38499.78 25597.36 21799.31 32199.19 298
DCV-MVSNet96.69 34696.29 35697.90 31498.28 39895.24 33397.29 31897.36 40698.21 19498.17 31097.86 38086.27 41099.55 38594.87 36498.32 40698.89 351
tttt051795.64 38394.98 39397.64 34499.36 18493.81 39098.72 10290.47 47698.08 21598.67 25998.34 34573.88 46299.92 6597.77 18399.51 28199.20 293
our_test_397.39 30597.73 27096.34 40898.70 34489.78 45394.61 45298.97 31096.50 34099.04 18998.85 25295.98 27699.84 17497.26 22499.67 22199.41 213
thisisatest051594.12 41093.16 41896.97 38798.60 36692.90 40893.77 46890.61 47594.10 41796.91 39695.87 44174.99 46199.80 23194.52 37399.12 35598.20 419
ppachtmachnet_test97.50 29297.74 26896.78 39898.70 34491.23 44094.55 45499.05 29496.36 34899.21 16298.79 26896.39 25299.78 25596.74 27199.82 12699.34 248
SMA-MVScopyleft98.40 19998.03 24299.51 4999.16 24699.21 3498.05 19699.22 25794.16 41598.98 19999.10 17897.52 17999.79 24496.45 30599.64 23299.53 153
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
GSMVS98.81 364
DPE-MVScopyleft98.59 16998.26 21299.57 2299.27 20899.15 5397.01 33899.39 18597.67 24499.44 10598.99 21597.53 17799.89 9795.40 35499.68 21599.66 78
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part299.36 18499.10 6699.05 187
thres100view90094.19 40793.67 41295.75 42699.06 26991.35 43498.03 20094.24 46098.33 18097.40 37594.98 45979.84 44799.62 35583.05 47298.08 42196.29 467
tfpnnormal98.90 10298.90 10098.91 16899.67 6697.82 18399.00 7299.44 16499.45 5199.51 9399.24 14098.20 11199.86 14395.92 33299.69 21099.04 323
tfpn200view994.03 41193.44 41495.78 42598.93 29691.44 43297.60 27794.29 45897.94 22497.10 38594.31 46679.67 44999.62 35583.05 47298.08 42196.29 467
c3_l97.36 30797.37 29597.31 36998.09 41193.25 40295.01 44099.16 27597.05 31098.77 24898.72 28392.88 35299.64 34996.93 25199.76 17599.05 319
CHOSEN 280x42095.51 38795.47 37695.65 42998.25 40088.27 46093.25 47198.88 32493.53 42594.65 45497.15 41686.17 41299.93 5497.41 21599.93 5698.73 377
CANet97.87 26597.76 26698.19 29597.75 42595.51 31796.76 35399.05 29497.74 23996.93 39398.21 35595.59 29099.89 9797.86 17899.93 5699.19 298
Fast-Effi-MVS+-dtu98.27 22198.09 23498.81 18398.43 38898.11 14397.61 27699.50 12998.64 15297.39 37797.52 40198.12 12099.95 2696.90 25798.71 38998.38 411
Effi-MVS+-dtu98.26 22397.90 25999.35 8098.02 41499.49 698.02 20399.16 27598.29 18797.64 35497.99 37296.44 25199.95 2696.66 28398.93 37798.60 390
CANet_DTU97.26 31597.06 31497.84 31997.57 43694.65 35696.19 38998.79 34397.23 29995.14 44898.24 35293.22 34499.84 17497.34 21899.84 11299.04 323
MGCNet97.44 30097.01 31798.72 21096.42 47296.74 26797.20 32891.97 47298.46 17398.30 30198.79 26892.74 35699.91 7499.30 6399.94 5099.52 156
MP-MVS-pluss98.57 17298.23 21799.60 1699.69 5999.35 1797.16 33399.38 18794.87 39998.97 20398.99 21598.01 12799.88 11597.29 22299.70 20799.58 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS98.40 19998.00 24599.61 1499.57 9999.25 3098.57 12299.35 20197.55 25999.31 13897.71 38994.61 31899.88 11596.14 32499.19 34499.70 68
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 42498.81 364
sam_mvs84.29 430
IterMVS-SCA-FT97.85 27198.18 22496.87 39299.27 20891.16 44195.53 42299.25 24999.10 10599.41 11299.35 10793.10 34799.96 1498.65 11599.94 5099.49 171
TSAR-MVS + MP.98.63 16198.49 17399.06 14199.64 7597.90 17298.51 13398.94 31196.96 31599.24 15698.89 24597.83 14699.81 22296.88 25999.49 29199.48 182
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 26698.17 22596.92 38998.98 28993.91 38596.45 37199.17 27297.85 23298.41 29597.14 41798.47 7499.92 6598.02 16099.05 35896.92 460
OPM-MVS98.56 17398.32 20399.25 10499.41 17298.73 9597.13 33599.18 26897.10 30898.75 25198.92 23398.18 11299.65 34596.68 27999.56 26599.37 234
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP98.75 13398.48 17499.57 2299.58 9099.29 2597.82 23799.25 24996.94 31798.78 24599.12 17498.02 12699.84 17497.13 23599.67 22199.59 107
ambc98.24 28898.82 32295.97 30198.62 11699.00 30799.27 14499.21 14796.99 21699.50 40396.55 29899.50 28999.26 277
MTGPAbinary99.20 260
SPE-MVS-test99.13 6799.09 7899.26 10199.13 25398.97 7499.31 3099.88 1499.44 5398.16 31398.51 32398.64 5899.93 5498.91 9499.85 10798.88 354
Effi-MVS+98.02 24997.82 26498.62 22798.53 37897.19 23597.33 31399.68 6097.30 28896.68 40897.46 40598.56 7099.80 23196.63 28598.20 41298.86 356
xiu_mvs_v2_base97.16 32597.49 28896.17 41798.54 37692.46 41695.45 42698.84 33597.25 29397.48 36996.49 42798.31 9299.90 8196.34 31298.68 39496.15 471
xiu_mvs_v1_base97.86 26698.17 22596.92 38998.98 28993.91 38596.45 37199.17 27297.85 23298.41 29597.14 41798.47 7499.92 6598.02 16099.05 35896.92 460
new-patchmatchnet98.35 20898.74 12197.18 37599.24 21992.23 42396.42 37599.48 13998.30 18499.69 5699.53 6597.44 18799.82 20598.84 10099.77 16099.49 171
pmmvs699.67 399.70 399.60 1699.90 499.27 2899.53 999.76 3999.64 2799.84 3099.83 499.50 999.87 13499.36 5899.92 6999.64 84
pmmvs597.64 28497.49 28898.08 30499.14 25195.12 33996.70 35799.05 29493.77 42298.62 26798.83 25993.23 34399.75 27598.33 13999.76 17599.36 241
test_post197.59 27920.48 48883.07 43899.66 33894.16 384
test_post21.25 48783.86 43399.70 305
Fast-Effi-MVS+97.67 28297.38 29498.57 23898.71 34097.43 21397.23 32399.45 15694.82 40096.13 42696.51 42698.52 7299.91 7496.19 32098.83 38198.37 413
patchmatchnet-post98.77 27284.37 42799.85 156
Anonymous2023121199.27 3899.27 4899.26 10199.29 20298.18 13799.49 1299.51 12699.70 1699.80 3899.68 2596.84 22499.83 19299.21 7199.91 7899.77 50
pmmvs-eth3d98.47 19198.34 19798.86 17499.30 19997.76 18997.16 33399.28 24095.54 38099.42 11099.19 15297.27 19899.63 35297.89 17199.97 2199.20 293
GG-mvs-BLEND94.76 44294.54 48292.13 42499.31 3080.47 48888.73 48291.01 48267.59 47398.16 47582.30 47694.53 47393.98 478
xiu_mvs_v1_base_debi97.86 26698.17 22596.92 38998.98 28993.91 38596.45 37199.17 27297.85 23298.41 29597.14 41798.47 7499.92 6598.02 16099.05 35896.92 460
Anonymous2023120698.21 23098.21 21898.20 29399.51 12795.43 32698.13 17999.32 21596.16 35898.93 21798.82 26296.00 27199.83 19297.32 22199.73 18399.36 241
MTAPA98.88 10698.64 14499.61 1499.67 6699.36 1698.43 14699.20 26098.83 14398.89 22498.90 23996.98 21799.92 6597.16 23099.70 20799.56 129
MTMP97.93 22191.91 473
gm-plane-assit94.83 48181.97 48488.07 46994.99 45899.60 36591.76 433
test9_res93.28 41099.15 34999.38 232
MVP-Stereo98.08 24497.92 25698.57 23898.96 29296.79 26397.90 22799.18 26896.41 34798.46 29098.95 22995.93 28099.60 36596.51 30198.98 37299.31 261
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST998.71 34098.08 15095.96 40299.03 29991.40 45095.85 43397.53 39996.52 24799.76 267
train_agg97.10 32796.45 35299.07 13598.71 34098.08 15095.96 40299.03 29991.64 44595.85 43397.53 39996.47 24999.76 26793.67 40099.16 34799.36 241
gg-mvs-nofinetune92.37 43791.20 44195.85 42395.80 48092.38 41999.31 3081.84 48799.75 1191.83 47699.74 1868.29 46999.02 45587.15 46397.12 45096.16 470
SCA96.41 35996.66 34295.67 42798.24 40188.35 45995.85 41196.88 42496.11 35997.67 35398.67 29793.10 34799.85 15694.16 38499.22 33798.81 364
Patchmatch-test96.55 35296.34 35497.17 37798.35 39493.06 40498.40 15297.79 39397.33 28498.41 29598.67 29783.68 43499.69 31195.16 35899.31 32198.77 372
test_898.67 35498.01 15895.91 40899.02 30291.64 44595.79 43597.50 40296.47 24999.76 267
MS-PatchMatch97.68 28197.75 26797.45 36498.23 40393.78 39197.29 31898.84 33596.10 36098.64 26398.65 30296.04 26899.36 42996.84 26399.14 35099.20 293
Patchmatch-RL test97.26 31597.02 31697.99 31299.52 12495.53 31696.13 39499.71 4797.47 26799.27 14499.16 16284.30 42999.62 35597.89 17199.77 16098.81 364
cdsmvs_eth3d_5k24.66 45232.88 4550.00 4710.00 4940.00 4960.00 48399.10 2850.00 4890.00 49097.58 39799.21 180.00 4900.00 4890.00 4880.00 486
pcd_1.5k_mvsjas8.17 45510.90 4580.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 48998.07 1220.00 4900.00 4890.00 4880.00 486
agg_prior292.50 42699.16 34799.37 234
agg_prior98.68 35397.99 15999.01 30595.59 43699.77 261
tmp_tt78.77 44978.73 45278.90 46658.45 49174.76 49094.20 46178.26 48939.16 48486.71 48392.82 47780.50 44575.19 48686.16 46892.29 47886.74 480
canonicalmvs98.34 20998.26 21298.58 23598.46 38497.82 18398.96 7799.46 15299.19 8897.46 37095.46 45198.59 6499.46 41598.08 15498.71 38998.46 398
anonymousdsp99.51 1499.47 2199.62 1099.88 999.08 7099.34 2399.69 5498.93 12999.65 6499.72 2198.93 3299.95 2699.11 78100.00 199.82 36
alignmvs97.35 30896.88 32598.78 19398.54 37698.09 14697.71 25697.69 39799.20 8397.59 35895.90 44088.12 40499.55 38598.18 14798.96 37498.70 381
nrg03099.40 2699.35 3499.54 3299.58 9099.13 6198.98 7599.48 13999.68 2099.46 10199.26 13398.62 6199.73 28899.17 7599.92 6999.76 56
v14419298.54 18098.57 15798.45 26299.21 22795.98 30097.63 27099.36 19597.15 30799.32 13699.18 15695.84 28399.84 17499.50 5199.91 7899.54 142
FIs99.14 6399.09 7899.29 9599.70 5598.28 12799.13 5899.52 12599.48 4599.24 15699.41 9496.79 23199.82 20598.69 11399.88 9499.76 56
v192192098.54 18098.60 15398.38 27199.20 23195.76 31097.56 28299.36 19597.23 29999.38 11899.17 16096.02 26999.84 17499.57 3999.90 8699.54 142
UA-Net99.47 1699.40 2799.70 299.49 14199.29 2599.80 499.72 4599.82 899.04 18999.81 898.05 12599.96 1498.85 9999.99 599.86 28
v119298.60 16798.66 14198.41 26799.27 20895.88 30397.52 28799.36 19597.41 27699.33 13099.20 14996.37 25599.82 20599.57 3999.92 6999.55 136
FC-MVSNet-test99.27 3899.25 5399.34 8399.77 2798.37 12199.30 3599.57 9899.61 3599.40 11599.50 6997.12 20799.85 15699.02 8799.94 5099.80 42
v114498.60 16798.66 14198.41 26799.36 18495.90 30297.58 28099.34 20797.51 26399.27 14499.15 16696.34 25799.80 23199.47 5499.93 5699.51 160
sosnet-low-res0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
HFP-MVS98.71 13798.44 18199.51 4999.49 14199.16 4998.52 12899.31 22097.47 26798.58 27598.50 32797.97 13299.85 15696.57 29199.59 25399.53 153
v14898.45 19398.60 15398.00 31199.44 16294.98 34397.44 30199.06 29098.30 18499.32 13698.97 22296.65 24299.62 35598.37 13599.85 10799.39 223
sosnet0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
uncertanet0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
AllTest98.44 19498.20 21999.16 11899.50 13398.55 10798.25 16699.58 9196.80 32798.88 22899.06 18697.65 16199.57 37894.45 37699.61 24799.37 234
TestCases99.16 11899.50 13398.55 10799.58 9196.80 32798.88 22899.06 18697.65 16199.57 37894.45 37699.61 24799.37 234
v7n99.53 1299.57 1399.41 7099.88 998.54 11099.45 1499.61 8099.66 2499.68 5899.66 3298.44 8099.95 2699.73 2899.96 2899.75 60
region2R98.69 14698.40 18699.54 3299.53 12199.17 4598.52 12899.31 22097.46 27298.44 29298.51 32397.83 14699.88 11596.46 30499.58 25899.58 115
RRT-MVS97.88 26397.98 24797.61 34798.15 40793.77 39298.97 7699.64 7199.16 9398.69 25699.42 9091.60 37099.89 9797.63 19598.52 40399.16 308
mamv499.44 1999.39 2899.58 2199.30 19999.74 299.04 6899.81 3199.77 1099.82 3499.57 4997.82 14999.98 499.53 4899.89 9299.01 327
PS-MVSNAJss99.46 1799.49 1699.35 8099.90 498.15 13999.20 4899.65 6999.48 4599.92 899.71 2298.07 12299.96 1499.53 48100.00 199.93 11
PS-MVSNAJ97.08 32997.39 29396.16 41998.56 37492.46 41695.24 43398.85 33497.25 29397.49 36895.99 43798.07 12299.90 8196.37 30998.67 39596.12 472
jajsoiax99.58 999.61 1199.48 5799.87 1298.61 10299.28 4099.66 6599.09 10899.89 1899.68 2599.53 799.97 799.50 5199.99 599.87 22
mvs_tets99.63 699.67 699.49 5599.88 998.61 10299.34 2399.71 4799.27 7499.90 1499.74 1899.68 499.97 799.55 4399.99 599.88 20
EI-MVSNet-UG-set98.69 14698.71 13098.62 22799.10 25796.37 28697.23 32398.87 32699.20 8399.19 16498.99 21597.30 19599.85 15698.77 10699.79 14999.65 83
EI-MVSNet-Vis-set98.68 15298.70 13398.63 22599.09 26096.40 28597.23 32398.86 33199.20 8399.18 16898.97 22297.29 19799.85 15698.72 11099.78 15499.64 84
HPM-MVS++copyleft98.10 24197.64 27999.48 5799.09 26099.13 6197.52 28798.75 35197.46 27296.90 39997.83 38396.01 27099.84 17495.82 34099.35 31499.46 192
test_prior497.97 16395.86 409
XVS98.72 13698.45 17999.53 3999.46 15599.21 3498.65 11299.34 20798.62 15797.54 36398.63 30797.50 18199.83 19296.79 26599.53 27599.56 129
v124098.55 17798.62 14898.32 27899.22 22595.58 31497.51 28999.45 15697.16 30599.45 10499.24 14096.12 26699.85 15699.60 3799.88 9499.55 136
pm-mvs199.44 1999.48 1899.33 8999.80 2198.63 9999.29 3699.63 7399.30 7199.65 6499.60 4599.16 2299.82 20599.07 8199.83 12199.56 129
test_prior295.74 41696.48 34296.11 42797.63 39595.92 28194.16 38499.20 341
X-MVStestdata94.32 40492.59 42399.53 3999.46 15599.21 3498.65 11299.34 20798.62 15797.54 36345.85 48497.50 18199.83 19296.79 26599.53 27599.56 129
test_prior98.95 16098.69 34997.95 16799.03 29999.59 36999.30 265
旧先验295.76 41588.56 46897.52 36599.66 33894.48 374
新几何295.93 405
新几何198.91 16898.94 29497.76 18998.76 34887.58 47096.75 40798.10 36394.80 31499.78 25592.73 42299.00 36799.20 293
旧先验198.82 32297.45 21198.76 34898.34 34595.50 29499.01 36699.23 283
无先验95.74 41698.74 35389.38 46499.73 28892.38 42899.22 288
原ACMM295.53 422
原ACMM198.35 27698.90 30496.25 29098.83 33992.48 43996.07 42998.10 36395.39 29799.71 29892.61 42598.99 36999.08 315
test22298.92 30096.93 25695.54 42198.78 34585.72 47396.86 40298.11 36294.43 32199.10 35799.23 283
testdata299.79 24492.80 420
segment_acmp97.02 214
testdata98.09 30198.93 29695.40 32798.80 34290.08 46197.45 37298.37 34195.26 29999.70 30593.58 40398.95 37599.17 305
testdata195.44 42796.32 350
v899.01 8599.16 6398.57 23899.47 15296.31 28998.90 8399.47 14899.03 11899.52 8899.57 4996.93 22099.81 22299.60 3799.98 1299.60 100
131495.74 37995.60 37196.17 41797.53 44192.75 41298.07 19398.31 37891.22 45294.25 45896.68 42395.53 29199.03 45491.64 43697.18 44996.74 464
LFMVS97.20 32196.72 33698.64 22198.72 33696.95 25498.93 8194.14 46299.74 1398.78 24599.01 20984.45 42699.73 28897.44 21399.27 32899.25 278
VDD-MVS98.56 17398.39 18999.07 13599.13 25398.07 15298.59 12097.01 41799.59 3799.11 17299.27 12794.82 31199.79 24498.34 13799.63 23999.34 248
VDDNet98.21 23097.95 25199.01 14999.58 9097.74 19199.01 7097.29 41099.67 2198.97 20399.50 6990.45 38399.80 23197.88 17499.20 34199.48 182
v1098.97 9299.11 7298.55 24599.44 16296.21 29198.90 8399.55 11198.73 14599.48 9699.60 4596.63 24399.83 19299.70 3399.99 599.61 98
VPNet98.87 10798.83 11399.01 14999.70 5597.62 20098.43 14699.35 20199.47 4899.28 14299.05 19396.72 23799.82 20598.09 15399.36 31299.59 107
MVS93.19 42592.09 43096.50 40496.91 46094.03 37598.07 19398.06 38968.01 48294.56 45696.48 42895.96 27899.30 43983.84 47196.89 45496.17 469
v2v48298.56 17398.62 14898.37 27499.42 16995.81 30897.58 28099.16 27597.90 22899.28 14299.01 20995.98 27699.79 24499.33 6099.90 8699.51 160
V4298.78 12898.78 11998.76 20099.44 16297.04 24798.27 16499.19 26497.87 23099.25 15499.16 16296.84 22499.78 25599.21 7199.84 11299.46 192
SD-MVS98.40 19998.68 13697.54 35698.96 29297.99 15997.88 22999.36 19598.20 19899.63 6799.04 19598.76 4595.33 48396.56 29599.74 18099.31 261
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 37595.32 38597.49 36198.60 36694.15 37093.83 46797.93 39195.49 38296.68 40897.42 40783.21 43699.30 43996.22 31898.55 40299.01 327
MSLP-MVS++98.02 24998.14 23197.64 34498.58 37195.19 33697.48 29399.23 25697.47 26797.90 33698.62 30997.04 21198.81 46497.55 20199.41 30698.94 344
APDe-MVScopyleft98.99 8898.79 11799.60 1699.21 22799.15 5398.87 8899.48 13997.57 25599.35 12599.24 14097.83 14699.89 9797.88 17499.70 20799.75 60
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
APD-MVS_3200maxsize98.84 11498.61 15299.53 3999.19 23499.27 2898.49 13899.33 21398.64 15299.03 19298.98 22097.89 14199.85 15696.54 29999.42 30599.46 192
ADS-MVSNet295.43 38894.98 39396.76 39998.14 40891.74 42697.92 22497.76 39490.23 45796.51 41898.91 23685.61 41799.85 15692.88 41696.90 45298.69 382
EI-MVSNet98.40 19998.51 16598.04 30999.10 25794.73 35297.20 32898.87 32698.97 12499.06 17999.02 19896.00 27199.80 23198.58 11899.82 12699.60 100
Regformer0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
CVMVSNet96.25 36497.21 30593.38 45999.10 25780.56 48797.20 32898.19 38496.94 31799.00 19499.02 19889.50 39299.80 23196.36 31199.59 25399.78 47
pmmvs497.58 28997.28 30098.51 25498.84 31796.93 25695.40 42998.52 36893.60 42498.61 26998.65 30295.10 30399.60 36596.97 24999.79 14998.99 332
EU-MVSNet97.66 28398.50 16895.13 43899.63 8185.84 46998.35 15798.21 38198.23 19199.54 7999.46 8195.02 30599.68 32198.24 14199.87 9899.87 22
VNet98.42 19598.30 20598.79 19098.79 32997.29 22498.23 16798.66 35899.31 6998.85 23398.80 26694.80 31499.78 25598.13 15099.13 35299.31 261
test-LLR93.90 41393.85 40894.04 44996.53 46984.62 47594.05 46492.39 46996.17 35694.12 46095.07 45582.30 44199.67 32595.87 33698.18 41397.82 438
TESTMET0.1,192.19 44091.77 43893.46 45696.48 47182.80 48294.05 46491.52 47494.45 40994.00 46394.88 46166.65 47499.56 38195.78 34198.11 41998.02 428
test-mter92.33 43891.76 43994.04 44996.53 46984.62 47594.05 46492.39 46994.00 42094.12 46095.07 45565.63 48099.67 32595.87 33698.18 41397.82 438
VPA-MVSNet99.30 3499.30 4599.28 9699.49 14198.36 12499.00 7299.45 15699.63 2999.52 8899.44 8698.25 10299.88 11599.09 8099.84 11299.62 90
ACMMPR98.70 14298.42 18499.54 3299.52 12499.14 5898.52 12899.31 22097.47 26798.56 27998.54 31897.75 15599.88 11596.57 29199.59 25399.58 115
testgi98.32 21498.39 18998.13 29999.57 9995.54 31597.78 24399.49 13797.37 28199.19 16497.65 39398.96 2999.49 40696.50 30298.99 36999.34 248
test20.0398.78 12898.77 12098.78 19399.46 15597.20 23497.78 24399.24 25499.04 11799.41 11298.90 23997.65 16199.76 26797.70 19299.79 14999.39 223
thres600view794.45 40293.83 40996.29 41099.06 26991.53 42997.99 21494.24 46098.34 17997.44 37395.01 45779.84 44799.67 32584.33 47098.23 41097.66 448
ADS-MVSNet95.24 39194.93 39696.18 41698.14 40890.10 45297.92 22497.32 40990.23 45796.51 41898.91 23685.61 41799.74 28192.88 41696.90 45298.69 382
MP-MVScopyleft98.46 19298.09 23499.54 3299.57 9999.22 3398.50 13599.19 26497.61 25197.58 35998.66 30097.40 18999.88 11594.72 36999.60 24999.54 142
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs17.12 45320.53 4566.87 47012.05 4924.20 49593.62 4706.73 4934.62 48810.41 48824.33 4858.28 4923.56 4899.69 48815.07 48612.86 485
thres40094.14 40993.44 41496.24 41398.93 29691.44 43297.60 27794.29 45897.94 22497.10 38594.31 46679.67 44999.62 35583.05 47298.08 42197.66 448
test12317.04 45420.11 4577.82 46910.25 4934.91 49494.80 4444.47 4944.93 48710.00 48924.28 4869.69 4913.64 48810.14 48712.43 48714.92 484
thres20093.72 41793.14 41995.46 43498.66 35991.29 43696.61 36294.63 45597.39 27996.83 40393.71 46979.88 44699.56 38182.40 47598.13 41895.54 476
test0.0.03 194.51 40193.69 41196.99 38596.05 47693.61 39994.97 44193.49 46496.17 35697.57 36194.88 46182.30 44199.01 45793.60 40294.17 47498.37 413
pmmvs395.03 39594.40 40296.93 38897.70 43192.53 41595.08 43897.71 39688.57 46797.71 35098.08 36679.39 45199.82 20596.19 32099.11 35698.43 406
EMVS93.83 41494.02 40693.23 46096.83 46384.96 47289.77 48196.32 43397.92 22697.43 37496.36 43386.17 41298.93 46087.68 46297.73 43295.81 474
E-PMN94.17 40894.37 40393.58 45596.86 46185.71 47190.11 48097.07 41698.17 20197.82 34597.19 41484.62 42598.94 45989.77 45597.68 43396.09 473
PGM-MVS98.66 15698.37 19399.55 2999.53 12199.18 4498.23 16799.49 13797.01 31498.69 25698.88 24698.00 12899.89 9795.87 33699.59 25399.58 115
LCM-MVSNet-Re98.64 15998.48 17499.11 12698.85 31698.51 11298.49 13899.83 2598.37 17699.69 5699.46 8198.21 10999.92 6594.13 38899.30 32498.91 349
LCM-MVSNet99.93 199.92 199.94 199.99 199.97 199.90 199.89 1399.98 199.99 199.96 199.77 2100.00 199.81 16100.00 199.85 30
MCST-MVS98.00 25297.63 28099.10 12899.24 21998.17 13896.89 34798.73 35495.66 37597.92 33497.70 39197.17 20599.66 33896.18 32299.23 33699.47 190
mvs_anonymous97.83 27498.16 22896.87 39298.18 40591.89 42597.31 31698.90 32097.37 28198.83 23699.46 8196.28 25999.79 24498.90 9598.16 41698.95 340
MVS_Test98.18 23598.36 19497.67 33798.48 38194.73 35298.18 17299.02 30297.69 24398.04 32799.11 17597.22 20299.56 38198.57 12098.90 37998.71 378
MDA-MVSNet-bldmvs97.94 25797.91 25898.06 30699.44 16294.96 34496.63 36199.15 28098.35 17898.83 23699.11 17594.31 32699.85 15696.60 28898.72 38799.37 234
CDPH-MVS97.26 31596.66 34299.07 13599.00 28598.15 13996.03 39899.01 30591.21 45397.79 34697.85 38296.89 22299.69 31192.75 42199.38 31199.39 223
test1298.93 16498.58 37197.83 17898.66 35896.53 41595.51 29399.69 31199.13 35299.27 271
casdiffmvspermissive98.95 9599.00 9098.81 18399.38 17797.33 21897.82 23799.57 9899.17 9299.35 12599.17 16098.35 8999.69 31198.46 12899.73 18399.41 213
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 22898.24 21698.17 29699.00 28595.44 32596.38 37799.58 9197.79 23798.53 28498.50 32796.76 23499.74 28197.95 16999.64 23299.34 248
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 41692.83 42296.42 40697.70 43191.28 43796.84 34989.77 47893.96 42192.44 47395.93 43979.14 45299.77 26192.94 41496.76 45698.21 418
baseline195.96 37395.44 37997.52 35898.51 38093.99 38298.39 15396.09 43898.21 19498.40 29997.76 38786.88 40699.63 35295.42 35389.27 48098.95 340
YYNet197.60 28697.67 27497.39 36899.04 27393.04 40795.27 43198.38 37697.25 29398.92 21998.95 22995.48 29599.73 28896.99 24698.74 38599.41 213
PMMVS298.07 24598.08 23798.04 30999.41 17294.59 35894.59 45399.40 18397.50 26498.82 23998.83 25996.83 22699.84 17497.50 20799.81 13299.71 63
MDA-MVSNet_test_wron97.60 28697.66 27797.41 36799.04 27393.09 40395.27 43198.42 37397.26 29298.88 22898.95 22995.43 29699.73 28897.02 24298.72 38799.41 213
tpmvs95.02 39695.25 38694.33 44596.39 47485.87 46898.08 18996.83 42595.46 38395.51 44498.69 29385.91 41599.53 39394.16 38496.23 46197.58 451
PM-MVS98.82 12098.72 12599.12 12499.64 7598.54 11097.98 21599.68 6097.62 24899.34 12799.18 15697.54 17599.77 26197.79 18199.74 18099.04 323
HQP_MVS97.99 25597.67 27498.93 16499.19 23497.65 19797.77 24699.27 24398.20 19897.79 34697.98 37394.90 30799.70 30594.42 37899.51 28199.45 197
plane_prior799.19 23497.87 174
plane_prior698.99 28897.70 19594.90 307
plane_prior599.27 24399.70 30594.42 37899.51 28199.45 197
plane_prior497.98 373
plane_prior397.78 18897.41 27697.79 346
plane_prior297.77 24698.20 198
plane_prior199.05 272
plane_prior97.65 19797.07 33696.72 33299.36 312
PS-CasMVS99.40 2699.33 3899.62 1099.71 4799.10 6699.29 3699.53 12099.53 4299.46 10199.41 9498.23 10499.95 2698.89 9799.95 3899.81 40
UniMVSNet_NR-MVSNet98.86 11198.68 13699.40 7299.17 24498.74 9297.68 26099.40 18399.14 9699.06 17998.59 31496.71 23899.93 5498.57 12099.77 16099.53 153
PEN-MVS99.41 2599.34 3699.62 1099.73 3799.14 5899.29 3699.54 11699.62 3399.56 7499.42 9098.16 11699.96 1498.78 10399.93 5699.77 50
TransMVSNet (Re)99.44 1999.47 2199.36 7499.80 2198.58 10599.27 4299.57 9899.39 5999.75 4599.62 4099.17 2099.83 19299.06 8399.62 24299.66 78
DTE-MVSNet99.43 2399.35 3499.66 799.71 4799.30 2399.31 3099.51 12699.64 2799.56 7499.46 8198.23 10499.97 798.78 10399.93 5699.72 62
DU-MVS98.82 12098.63 14699.39 7399.16 24698.74 9297.54 28599.25 24998.84 14299.06 17998.76 27896.76 23499.93 5498.57 12099.77 16099.50 164
UniMVSNet (Re)98.87 10798.71 13099.35 8099.24 21998.73 9597.73 25599.38 18798.93 12999.12 17198.73 28196.77 23299.86 14398.63 11799.80 14399.46 192
CP-MVSNet99.21 4899.09 7899.56 2799.65 6998.96 7899.13 5899.34 20799.42 5699.33 13099.26 13397.01 21599.94 4298.74 10899.93 5699.79 44
WR-MVS_H99.33 3199.22 5599.65 899.71 4799.24 3199.32 2699.55 11199.46 5099.50 9499.34 11197.30 19599.93 5498.90 9599.93 5699.77 50
WR-MVS98.40 19998.19 22399.03 14599.00 28597.65 19796.85 34898.94 31198.57 16598.89 22498.50 32795.60 28999.85 15697.54 20399.85 10799.59 107
NR-MVSNet98.95 9598.82 11499.36 7499.16 24698.72 9799.22 4599.20 26099.10 10599.72 4898.76 27896.38 25499.86 14398.00 16399.82 12699.50 164
Baseline_NR-MVSNet98.98 9198.86 11099.36 7499.82 1998.55 10797.47 29799.57 9899.37 6199.21 16299.61 4396.76 23499.83 19298.06 15699.83 12199.71 63
TranMVSNet+NR-MVSNet99.17 5399.07 8199.46 6399.37 18398.87 8598.39 15399.42 17699.42 5699.36 12399.06 18698.38 8499.95 2698.34 13799.90 8699.57 123
TSAR-MVS + GP.98.18 23597.98 24798.77 19898.71 34097.88 17396.32 38198.66 35896.33 34999.23 15898.51 32397.48 18599.40 42497.16 23099.46 29499.02 326
n20.00 495
nn0.00 495
mPP-MVS98.64 15998.34 19799.54 3299.54 11899.17 4598.63 11499.24 25497.47 26798.09 32198.68 29597.62 16699.89 9796.22 31899.62 24299.57 123
door-mid99.57 98
XVG-OURS-SEG-HR98.49 18998.28 20899.14 12299.49 14198.83 8796.54 36599.48 13997.32 28699.11 17298.61 31199.33 1599.30 43996.23 31798.38 40599.28 270
mvsmamba97.57 29097.26 30198.51 25498.69 34996.73 26898.74 9797.25 41197.03 31397.88 33899.23 14590.95 37899.87 13496.61 28799.00 36798.91 349
MVSFormer98.26 22398.43 18297.77 32598.88 31093.89 38899.39 2099.56 10799.11 9898.16 31398.13 35993.81 33799.97 799.26 6699.57 26299.43 205
jason97.45 29997.35 29797.76 32899.24 21993.93 38495.86 40998.42 37394.24 41398.50 28798.13 35994.82 31199.91 7497.22 22699.73 18399.43 205
jason: jason.
lupinMVS97.06 33096.86 32697.65 34198.88 31093.89 38895.48 42597.97 39093.53 42598.16 31397.58 39793.81 33799.91 7496.77 26899.57 26299.17 305
test_djsdf99.52 1399.51 1599.53 3999.86 1498.74 9299.39 2099.56 10799.11 9899.70 5299.73 2099.00 2799.97 799.26 6699.98 1299.89 16
HPM-MVS_fast99.01 8598.82 11499.57 2299.71 4799.35 1799.00 7299.50 12997.33 28498.94 21698.86 24998.75 4699.82 20597.53 20499.71 20099.56 129
K. test v398.00 25297.66 27799.03 14599.79 2397.56 20299.19 5292.47 46899.62 3399.52 8899.66 3289.61 39099.96 1499.25 6899.81 13299.56 129
lessismore_v098.97 15799.73 3797.53 20486.71 48399.37 12099.52 6889.93 38699.92 6598.99 8999.72 19199.44 201
SixPastTwentyTwo98.75 13398.62 14899.16 11899.83 1897.96 16699.28 4098.20 38299.37 6199.70 5299.65 3692.65 35899.93 5499.04 8599.84 11299.60 100
OurMVSNet-221017-099.37 2999.31 4299.53 3999.91 398.98 7299.63 799.58 9199.44 5399.78 4099.76 1596.39 25299.92 6599.44 5599.92 6999.68 71
HPM-MVScopyleft98.79 12698.53 16399.59 2099.65 6999.29 2599.16 5499.43 17096.74 33198.61 26998.38 34098.62 6199.87 13496.47 30399.67 22199.59 107
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS98.53 18298.34 19799.11 12699.50 13398.82 8995.97 40099.50 12997.30 28899.05 18798.98 22099.35 1499.32 43695.72 34399.68 21599.18 301
XVG-ACMP-BASELINE98.56 17398.34 19799.22 10999.54 11898.59 10497.71 25699.46 15297.25 29398.98 19998.99 21597.54 17599.84 17495.88 33399.74 18099.23 283
casdiffmvs_mvgpermissive99.12 7099.16 6398.99 15199.43 16797.73 19398.00 20799.62 7799.22 7999.55 7799.22 14698.93 3299.75 27598.66 11499.81 13299.50 164
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 13798.46 17899.47 6199.57 9998.97 7498.23 16799.48 13996.60 33699.10 17599.06 18698.71 5099.83 19295.58 35099.78 15499.62 90
LGP-MVS_train99.47 6199.57 9998.97 7499.48 13996.60 33699.10 17599.06 18698.71 5099.83 19295.58 35099.78 15499.62 90
baseline98.96 9499.02 8698.76 20099.38 17797.26 22798.49 13899.50 12998.86 13899.19 16499.06 18698.23 10499.69 31198.71 11199.76 17599.33 254
test1198.87 326
door99.41 180
EPNet_dtu94.93 39894.78 39895.38 43693.58 48487.68 46396.78 35195.69 44797.35 28389.14 48198.09 36588.15 40399.49 40694.95 36399.30 32498.98 333
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x268897.49 29597.14 31098.54 25099.68 6296.09 29596.50 36999.62 7791.58 44798.84 23598.97 22292.36 36099.88 11596.76 26999.95 3899.67 76
EPNet96.14 36795.44 37998.25 28690.76 48895.50 32097.92 22494.65 45498.97 12492.98 47098.85 25289.12 39499.87 13495.99 32999.68 21599.39 223
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS96.79 263
HQP-NCC98.67 35496.29 38396.05 36195.55 439
ACMP_Plane98.67 35496.29 38396.05 36195.55 439
APD-MVScopyleft98.10 24197.67 27499.42 6899.11 25598.93 8097.76 24999.28 24094.97 39698.72 25498.77 27297.04 21199.85 15693.79 39899.54 27199.49 171
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS92.82 418
HQP4-MVS95.56 43899.54 39199.32 257
HQP3-MVS99.04 29799.26 331
HQP2-MVS93.84 335
CNVR-MVS98.17 23797.87 26199.07 13598.67 35498.24 13097.01 33898.93 31497.25 29397.62 35598.34 34597.27 19899.57 37896.42 30699.33 31799.39 223
NCCC97.86 26697.47 29199.05 14298.61 36498.07 15296.98 34098.90 32097.63 24797.04 38997.93 37895.99 27599.66 33895.31 35598.82 38399.43 205
114514_t96.50 35595.77 36498.69 21499.48 14997.43 21397.84 23699.55 11181.42 47996.51 41898.58 31595.53 29199.67 32593.41 40899.58 25898.98 333
CP-MVS98.70 14298.42 18499.52 4599.36 18499.12 6398.72 10299.36 19597.54 26198.30 30198.40 33797.86 14599.89 9796.53 30099.72 19199.56 129
DSMNet-mixed97.42 30297.60 28296.87 39299.15 25091.46 43098.54 12699.12 28292.87 43597.58 35999.63 3996.21 26199.90 8195.74 34299.54 27199.27 271
tpm293.09 42692.58 42494.62 44397.56 43786.53 46797.66 26495.79 44486.15 47294.07 46298.23 35475.95 45999.53 39390.91 44996.86 45597.81 440
NP-MVS98.84 31797.39 21596.84 420
EG-PatchMatch MVS98.99 8899.01 8898.94 16199.50 13397.47 20998.04 19899.59 8998.15 20999.40 11599.36 10698.58 6999.76 26798.78 10399.68 21599.59 107
tpm cat193.29 42393.13 42093.75 45397.39 45084.74 47397.39 30497.65 40083.39 47794.16 45998.41 33682.86 43999.39 42691.56 43895.35 46997.14 459
SteuartSystems-ACMMP98.79 12698.54 16199.54 3299.73 3799.16 4998.23 16799.31 22097.92 22698.90 22198.90 23998.00 12899.88 11596.15 32399.72 19199.58 115
Skip Steuart: Steuart Systems R&D Blog.
CostFormer93.97 41293.78 41094.51 44497.53 44185.83 47097.98 21595.96 44089.29 46594.99 45098.63 30778.63 45599.62 35594.54 37296.50 45798.09 425
CR-MVSNet96.28 36295.95 36197.28 37197.71 42994.22 36598.11 18498.92 31792.31 44196.91 39699.37 10285.44 42099.81 22297.39 21697.36 44597.81 440
JIA-IIPM95.52 38695.03 39297.00 38496.85 46294.03 37596.93 34495.82 44399.20 8394.63 45599.71 2283.09 43799.60 36594.42 37894.64 47197.36 457
Patchmtry97.35 30896.97 31898.50 25897.31 45296.47 28398.18 17298.92 31798.95 12898.78 24599.37 10285.44 42099.85 15695.96 33199.83 12199.17 305
PatchT96.65 34996.35 35397.54 35697.40 44995.32 33197.98 21596.64 42899.33 6696.89 40099.42 9084.32 42899.81 22297.69 19497.49 43697.48 453
tpmrst95.07 39495.46 37793.91 45197.11 45684.36 47797.62 27196.96 42094.98 39596.35 42398.80 26685.46 41999.59 36995.60 34896.23 46197.79 443
BH-w/o95.13 39394.89 39795.86 42298.20 40491.31 43595.65 41897.37 40593.64 42396.52 41795.70 44493.04 35099.02 45588.10 46195.82 46697.24 458
tpm94.67 40094.34 40495.66 42897.68 43488.42 45897.88 22994.90 45294.46 40796.03 43298.56 31778.66 45499.79 24495.88 33395.01 47098.78 371
DELS-MVS98.27 22198.20 21998.48 25998.86 31396.70 26995.60 42099.20 26097.73 24098.45 29198.71 28497.50 18199.82 20598.21 14599.59 25398.93 345
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 34296.75 33597.08 38098.74 33393.33 40196.71 35698.26 37996.72 33298.44 29297.37 41095.20 30099.47 41291.89 43097.43 44098.44 404
RPMNet97.02 33396.93 32097.30 37097.71 42994.22 36598.11 18499.30 22899.37 6196.91 39699.34 11186.72 40799.87 13497.53 20497.36 44597.81 440
MVSTER96.86 34196.55 34897.79 32397.91 41994.21 36797.56 28298.87 32697.49 26699.06 17999.05 19380.72 44499.80 23198.44 12999.82 12699.37 234
CPTT-MVS97.84 27297.36 29699.27 9999.31 19598.46 11598.29 16099.27 24394.90 39897.83 34398.37 34194.90 30799.84 17493.85 39799.54 27199.51 160
GBi-Net98.65 15798.47 17699.17 11598.90 30498.24 13099.20 4899.44 16498.59 16098.95 20999.55 5794.14 32999.86 14397.77 18399.69 21099.41 213
PVSNet_Blended_VisFu98.17 23798.15 22998.22 29299.73 3795.15 33797.36 31199.68 6094.45 40998.99 19899.27 12796.87 22399.94 4297.13 23599.91 7899.57 123
PVSNet_BlendedMVS97.55 29197.53 28597.60 34898.92 30093.77 39296.64 36099.43 17094.49 40597.62 35599.18 15696.82 22799.67 32594.73 36799.93 5699.36 241
UnsupCasMVSNet_eth97.89 26197.60 28298.75 20299.31 19597.17 23997.62 27199.35 20198.72 14998.76 25098.68 29592.57 35999.74 28197.76 18795.60 46799.34 248
UnsupCasMVSNet_bld97.30 31296.92 32298.45 26299.28 20596.78 26696.20 38899.27 24395.42 38498.28 30598.30 34993.16 34599.71 29894.99 36097.37 44398.87 355
PVSNet_Blended96.88 34096.68 33997.47 36398.92 30093.77 39294.71 44699.43 17090.98 45597.62 35597.36 41196.82 22799.67 32594.73 36799.56 26598.98 333
FMVSNet596.01 37095.20 38998.41 26797.53 44196.10 29298.74 9799.50 12997.22 30298.03 32899.04 19569.80 46799.88 11597.27 22399.71 20099.25 278
test198.65 15798.47 17699.17 11598.90 30498.24 13099.20 4899.44 16498.59 16098.95 20999.55 5794.14 32999.86 14397.77 18399.69 21099.41 213
new_pmnet96.99 33796.76 33497.67 33798.72 33694.89 34695.95 40498.20 38292.62 43898.55 28198.54 31894.88 31099.52 39793.96 39299.44 30398.59 393
FMVSNet397.50 29297.24 30398.29 28298.08 41295.83 30697.86 23398.91 31997.89 22998.95 20998.95 22987.06 40599.81 22297.77 18399.69 21099.23 283
dp93.47 42093.59 41393.13 46196.64 46781.62 48697.66 26496.42 43292.80 43696.11 42798.64 30578.55 45799.59 36993.31 40992.18 47998.16 421
FMVSNet298.49 18998.40 18698.75 20298.90 30497.14 24298.61 11899.13 28198.59 16099.19 16499.28 12594.14 32999.82 20597.97 16799.80 14399.29 267
FMVSNet199.17 5399.17 6199.17 11599.55 11398.24 13099.20 4899.44 16499.21 8199.43 10699.55 5797.82 14999.86 14398.42 13399.89 9299.41 213
N_pmnet97.63 28597.17 30698.99 15199.27 20897.86 17595.98 39993.41 46595.25 38999.47 10098.90 23995.63 28899.85 15696.91 25299.73 18399.27 271
cascas94.79 39994.33 40596.15 42096.02 47892.36 42092.34 47699.26 24885.34 47495.08 44994.96 46092.96 35198.53 46994.41 38198.59 40097.56 452
BH-RMVSNet96.83 34296.58 34797.58 35098.47 38294.05 37296.67 35897.36 40696.70 33497.87 33997.98 37395.14 30299.44 41990.47 45398.58 40199.25 278
UGNet98.53 18298.45 17998.79 19097.94 41796.96 25399.08 6198.54 36699.10 10596.82 40499.47 7996.55 24699.84 17498.56 12399.94 5099.55 136
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 34896.27 35897.87 31898.81 32594.61 35796.77 35297.92 39294.94 39797.12 38497.74 38891.11 37799.82 20593.89 39498.15 41799.18 301
XXY-MVS99.14 6399.15 6899.10 12899.76 3097.74 19198.85 9299.62 7798.48 17299.37 12099.49 7598.75 4699.86 14398.20 14699.80 14399.71 63
EC-MVSNet99.09 7399.05 8299.20 11099.28 20598.93 8099.24 4499.84 2299.08 11298.12 31898.37 34198.72 4999.90 8199.05 8499.77 16098.77 372
sss97.21 32096.93 32098.06 30698.83 31995.22 33596.75 35498.48 37094.49 40597.27 38197.90 37992.77 35599.80 23196.57 29199.32 31999.16 308
Test_1112_low_res96.99 33796.55 34898.31 28099.35 18995.47 32495.84 41299.53 12091.51 44996.80 40598.48 33091.36 37499.83 19296.58 28999.53 27599.62 90
1112_ss97.29 31496.86 32698.58 23599.34 19296.32 28896.75 35499.58 9193.14 43096.89 40097.48 40392.11 36699.86 14396.91 25299.54 27199.57 123
ab-mvs-re8.12 45610.83 4590.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 49097.48 4030.00 4930.00 4900.00 4890.00 4880.00 486
ab-mvs98.41 19698.36 19498.59 23499.19 23497.23 22899.32 2698.81 34097.66 24598.62 26799.40 9796.82 22799.80 23195.88 33399.51 28198.75 375
TR-MVS95.55 38595.12 39196.86 39597.54 43993.94 38396.49 37096.53 43194.36 41297.03 39196.61 42594.26 32899.16 45186.91 46696.31 46097.47 454
MDTV_nov1_ep13_2view74.92 48997.69 25990.06 46297.75 34985.78 41693.52 40498.69 382
MDTV_nov1_ep1395.22 38897.06 45983.20 48097.74 25396.16 43594.37 41196.99 39298.83 25983.95 43299.53 39393.90 39397.95 428
MIMVSNet199.38 2899.32 4099.55 2999.86 1499.19 4399.41 1799.59 8999.59 3799.71 5099.57 4997.12 20799.90 8199.21 7199.87 9899.54 142
MIMVSNet96.62 35196.25 35997.71 33599.04 27394.66 35599.16 5496.92 42397.23 29997.87 33999.10 17886.11 41499.65 34591.65 43599.21 34098.82 359
IterMVS-LS98.55 17798.70 13398.09 30199.48 14994.73 35297.22 32799.39 18598.97 12499.38 11899.31 12096.00 27199.93 5498.58 11899.97 2199.60 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet97.69 28097.35 29798.69 21498.73 33497.02 24996.92 34698.75 35195.89 37098.59 27398.67 29792.08 36799.74 28196.72 27499.81 13299.32 257
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref99.77 160
IterMVS97.73 27798.11 23396.57 40299.24 21990.28 45095.52 42499.21 25898.86 13899.33 13099.33 11493.11 34699.94 4298.49 12799.94 5099.48 182
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon97.33 31096.92 32298.57 23899.09 26097.99 15996.79 35099.35 20193.18 42997.71 35098.07 36795.00 30699.31 43793.97 39199.13 35298.42 408
MVS_111021_LR98.30 21798.12 23298.83 17999.16 24698.03 15796.09 39699.30 22897.58 25498.10 32098.24 35298.25 10299.34 43396.69 27899.65 23099.12 313
DP-MVS98.93 9898.81 11699.28 9699.21 22798.45 11698.46 14399.33 21399.63 2999.48 9699.15 16697.23 20199.75 27597.17 22999.66 22999.63 89
ACMMP++99.68 215
HQP-MVS97.00 33696.49 35198.55 24598.67 35496.79 26396.29 38399.04 29796.05 36195.55 43996.84 42093.84 33599.54 39192.82 41899.26 33199.32 257
QAPM97.31 31196.81 33298.82 18198.80 32897.49 20599.06 6599.19 26490.22 45997.69 35299.16 16296.91 22199.90 8190.89 45099.41 30699.07 317
Vis-MVSNetpermissive99.34 3099.36 3399.27 9999.73 3798.26 12899.17 5399.78 3699.11 9899.27 14499.48 7698.82 3799.95 2698.94 9299.93 5699.59 107
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS-HIRNet94.32 40495.62 37090.42 46498.46 38475.36 48896.29 38389.13 47995.25 38995.38 44599.75 1692.88 35299.19 44994.07 39099.39 30896.72 465
IS-MVSNet98.19 23397.90 25999.08 13399.57 9997.97 16399.31 3098.32 37799.01 12098.98 19999.03 19791.59 37199.79 24495.49 35299.80 14399.48 182
HyFIR lowres test97.19 32296.60 34698.96 15899.62 8597.28 22595.17 43599.50 12994.21 41499.01 19398.32 34886.61 40899.99 297.10 23799.84 11299.60 100
EPMVS93.72 41793.27 41695.09 44096.04 47787.76 46298.13 17985.01 48594.69 40296.92 39498.64 30578.47 45899.31 43795.04 35996.46 45898.20 419
PAPM_NR96.82 34496.32 35598.30 28199.07 26496.69 27097.48 29398.76 34895.81 37296.61 41296.47 42994.12 33299.17 45090.82 45197.78 43099.06 318
TAMVS98.24 22798.05 24098.80 18699.07 26497.18 23797.88 22998.81 34096.66 33599.17 17099.21 14794.81 31399.77 26196.96 25099.88 9499.44 201
PAPR95.29 38994.47 40097.75 32997.50 44795.14 33894.89 44398.71 35691.39 45195.35 44695.48 45094.57 31999.14 45384.95 46997.37 44398.97 337
RPSCF98.62 16498.36 19499.42 6899.65 6999.42 1198.55 12499.57 9897.72 24298.90 22199.26 13396.12 26699.52 39795.72 34399.71 20099.32 257
Vis-MVSNet (Re-imp)97.46 29797.16 30798.34 27799.55 11396.10 29298.94 8098.44 37198.32 18298.16 31398.62 30988.76 39599.73 28893.88 39599.79 14999.18 301
test_040298.76 13298.71 13098.93 16499.56 10798.14 14198.45 14599.34 20799.28 7398.95 20998.91 23698.34 9099.79 24495.63 34799.91 7898.86 356
MVS_111021_HR98.25 22698.08 23798.75 20299.09 26097.46 21095.97 40099.27 24397.60 25397.99 33198.25 35198.15 11899.38 42896.87 26099.57 26299.42 210
CSCG98.68 15298.50 16899.20 11099.45 16098.63 9998.56 12399.57 9897.87 23098.85 23398.04 36997.66 16099.84 17496.72 27499.81 13299.13 312
PatchMatch-RL97.24 31896.78 33398.61 23199.03 27697.83 17896.36 37899.06 29093.49 42797.36 37997.78 38595.75 28599.49 40693.44 40798.77 38498.52 396
API-MVS97.04 33296.91 32497.42 36697.88 42098.23 13498.18 17298.50 36997.57 25597.39 37796.75 42296.77 23299.15 45290.16 45499.02 36594.88 477
Test By Simon96.52 247
TDRefinement99.42 2499.38 2999.55 2999.76 3099.33 2199.68 699.71 4799.38 6099.53 8399.61 4398.64 5899.80 23198.24 14199.84 11299.52 156
USDC97.41 30397.40 29297.44 36598.94 29493.67 39595.17 43599.53 12094.03 41998.97 20399.10 17895.29 29899.34 43395.84 33999.73 18399.30 265
EPP-MVSNet98.30 21798.04 24199.07 13599.56 10797.83 17899.29 3698.07 38899.03 11898.59 27399.13 17192.16 36499.90 8196.87 26099.68 21599.49 171
PMMVS96.51 35395.98 36098.09 30197.53 44195.84 30594.92 44298.84 33591.58 44796.05 43195.58 44595.68 28799.66 33895.59 34998.09 42098.76 374
PAPM91.88 44490.34 44696.51 40398.06 41392.56 41492.44 47597.17 41386.35 47190.38 47896.01 43686.61 40899.21 44870.65 48495.43 46897.75 444
ACMMPcopyleft98.75 13398.50 16899.52 4599.56 10799.16 4998.87 8899.37 19197.16 30598.82 23999.01 20997.71 15799.87 13496.29 31599.69 21099.54 142
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 32496.71 33798.55 24598.56 37498.05 15696.33 38098.93 31496.91 32197.06 38897.39 40894.38 32499.45 41791.66 43499.18 34698.14 422
PatchmatchNetpermissive95.58 38495.67 36995.30 43797.34 45187.32 46597.65 26696.65 42795.30 38897.07 38798.69 29384.77 42399.75 27594.97 36298.64 39698.83 358
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS98.29 22097.95 25199.34 8398.44 38799.16 4998.12 18399.38 18796.01 36598.06 32498.43 33597.80 15199.67 32595.69 34599.58 25899.20 293
F-COLMAP97.30 31296.68 33999.14 12299.19 23498.39 11897.27 32299.30 22892.93 43396.62 41198.00 37195.73 28699.68 32192.62 42498.46 40499.35 246
ANet_high99.57 1099.67 699.28 9699.89 698.09 14699.14 5799.93 599.82 899.93 699.81 899.17 2099.94 4299.31 62100.00 199.82 36
wuyk23d96.06 36897.62 28191.38 46398.65 36398.57 10698.85 9296.95 42196.86 32599.90 1499.16 16299.18 1998.40 47089.23 45899.77 16077.18 483
OMC-MVS97.88 26397.49 28899.04 14498.89 30998.63 9996.94 34299.25 24995.02 39498.53 28498.51 32397.27 19899.47 41293.50 40699.51 28199.01 327
MG-MVS96.77 34596.61 34497.26 37398.31 39793.06 40495.93 40598.12 38796.45 34697.92 33498.73 28193.77 33999.39 42691.19 44599.04 36199.33 254
AdaColmapbinary97.14 32696.71 33798.46 26198.34 39597.80 18796.95 34198.93 31495.58 37996.92 39497.66 39295.87 28299.53 39390.97 44799.14 35098.04 427
uanet0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
ITE_SJBPF98.87 17299.22 22598.48 11499.35 20197.50 26498.28 30598.60 31397.64 16499.35 43293.86 39699.27 32898.79 370
DeepMVS_CXcopyleft93.44 45798.24 40194.21 36794.34 45764.28 48391.34 47794.87 46389.45 39392.77 48477.54 48093.14 47693.35 479
TinyColmap97.89 26197.98 24797.60 34898.86 31394.35 36396.21 38799.44 16497.45 27499.06 17998.88 24697.99 13199.28 44394.38 38299.58 25899.18 301
MAR-MVS96.47 35795.70 36798.79 19097.92 41899.12 6398.28 16198.60 36392.16 44395.54 44296.17 43494.77 31699.52 39789.62 45698.23 41097.72 446
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 25997.69 27398.52 25399.17 24497.66 19697.19 33299.47 14896.31 35197.85 34298.20 35696.71 23899.52 39794.62 37099.72 19198.38 411
MSDG97.71 27997.52 28698.28 28398.91 30396.82 26194.42 45799.37 19197.65 24698.37 30098.29 35097.40 18999.33 43594.09 38999.22 33798.68 385
LS3D98.63 16198.38 19199.36 7497.25 45399.38 1399.12 6099.32 21599.21 8198.44 29298.88 24697.31 19499.80 23196.58 28999.34 31698.92 346
CLD-MVS97.49 29597.16 30798.48 25999.07 26497.03 24894.71 44699.21 25894.46 40798.06 32497.16 41597.57 17199.48 40994.46 37599.78 15498.95 340
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS93.44 42192.23 42897.08 38099.25 21897.86 17595.61 41997.16 41492.90 43493.76 46798.65 30275.94 46095.66 48179.30 47997.49 43697.73 445
Gipumacopyleft99.03 8399.16 6398.64 22199.94 298.51 11299.32 2699.75 4299.58 3998.60 27199.62 4098.22 10799.51 40297.70 19299.73 18397.89 435
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015