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 2499.85 1699.11 6299.90 199.78 1099.63 1499.78 1299.67 1999.48 699.81 17299.30 2299.97 1299.77 19
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 7298.73 6999.05 13298.76 25397.81 17799.25 3999.30 15198.57 11298.55 20499.33 7497.95 8499.90 5497.16 14399.67 15699.44 143
3Dnovator+97.89 398.69 9398.51 10199.24 9998.81 24898.40 11399.02 6599.19 18698.99 8298.07 24299.28 7997.11 14799.84 13396.84 17699.32 24299.47 133
DeepC-MVS97.60 498.97 5398.93 5299.10 11899.35 13197.98 15698.01 16599.46 8597.56 18099.54 3599.50 4498.97 1699.84 13398.06 9699.92 4299.49 115
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 14798.01 16999.23 10198.39 31098.97 6995.03 33999.18 19096.88 23699.33 7498.78 19598.16 6899.28 35396.74 18499.62 17099.44 143
DeepC-MVS_fast96.85 698.30 14998.15 15698.75 17598.61 28397.23 20897.76 18999.09 21597.31 20798.75 17798.66 21697.56 11199.64 28196.10 23499.55 19999.39 164
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 24796.68 25798.32 22398.32 31397.16 21798.86 7999.37 11289.48 36196.29 33499.15 10696.56 17999.90 5492.90 32399.20 26197.89 336
ACMH96.65 799.25 2799.24 2799.26 9499.72 3798.38 11599.07 6099.55 5298.30 12399.65 2699.45 5599.22 999.76 22098.44 7599.77 10599.64 48
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+96.62 999.08 4299.00 4799.33 8099.71 3998.83 8098.60 9699.58 3499.11 6399.53 3899.18 9698.81 2499.67 26396.71 18999.77 10599.50 111
COLMAP_ROBcopyleft96.50 1098.99 4898.85 5999.41 6599.58 6099.10 6398.74 8399.56 4899.09 7399.33 7499.19 9498.40 4799.72 24395.98 23799.76 11599.42 150
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 27195.95 28298.65 18298.93 21998.09 14096.93 25799.28 16183.58 37698.13 23697.78 30296.13 19799.40 33793.52 31399.29 24998.45 314
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMM96.08 1298.91 6098.73 6999.48 5599.55 7599.14 5598.07 15399.37 11297.62 17399.04 12398.96 15098.84 2299.79 19497.43 13199.65 16299.49 115
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HY-MVS95.94 1395.90 29395.35 30197.55 27897.95 33394.79 28198.81 8296.94 34292.28 33995.17 35798.57 23489.90 30599.75 22791.20 35197.33 35298.10 328
OpenMVS_ROBcopyleft95.38 1495.84 29595.18 30697.81 25798.41 30997.15 21897.37 22698.62 28983.86 37598.65 18698.37 25994.29 26099.68 26088.41 36498.62 31696.60 366
ACMP95.32 1598.41 13898.09 16199.36 6999.51 8498.79 8597.68 19699.38 10895.76 27798.81 17098.82 18998.36 4999.82 15894.75 27399.77 10599.48 125
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLCcopyleft94.65 1696.51 27595.73 28698.85 15898.75 25597.91 16596.42 28799.06 21990.94 35495.59 34597.38 32794.41 25699.59 29790.93 35498.04 33799.05 245
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet93.40 1795.67 29895.70 28795.57 33798.83 24388.57 36192.50 37397.72 32292.69 33496.49 33096.44 34893.72 27299.43 33593.61 31099.28 25098.71 299
PCF-MVS92.86 1894.36 31893.00 33598.42 21598.70 26697.56 19293.16 37199.11 21179.59 37997.55 27797.43 32492.19 29199.73 23579.85 38099.45 22397.97 335
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
IB-MVS91.63 1992.24 34490.90 34896.27 32297.22 36391.24 35394.36 35893.33 37092.37 33792.24 37594.58 37566.20 38899.89 6493.16 32194.63 37597.66 349
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 18797.94 17597.65 26899.71 3997.94 16498.52 10698.68 28598.99 8297.52 28099.35 6897.41 12798.18 37791.59 34599.67 15696.82 363
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PVSNet_089.98 2191.15 34790.30 35093.70 35697.72 34484.34 38090.24 37697.42 32890.20 35893.79 37093.09 37990.90 29998.89 37186.57 36972.76 38397.87 338
MVEpermissive83.40 2292.50 34091.92 34394.25 35098.83 24391.64 34492.71 37283.52 38695.92 27286.46 38395.46 36495.20 23495.40 38280.51 37998.64 31495.73 375
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CMPMVSbinary75.91 2396.29 28495.44 29798.84 15996.25 37798.69 9397.02 25099.12 20988.90 36497.83 25798.86 17789.51 30798.90 37091.92 33999.51 21098.92 270
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVS299.12 3999.41 1498.25 22999.76 2895.07 27799.05 6499.94 197.78 16399.82 1099.84 298.56 3899.71 24499.96 199.96 1599.97 1
mvsany_test98.87 6598.92 5398.74 17999.38 12096.94 22598.58 10099.10 21396.49 25099.96 299.81 598.18 6499.45 33298.97 4299.79 9699.83 11
FMVS199.25 2799.16 3199.51 4699.89 699.63 398.71 8899.69 1898.90 9299.43 5399.35 6898.86 2099.67 26397.81 11199.81 8199.24 215
APD_test99.25 2799.16 3199.51 4699.89 699.63 398.71 8899.69 1898.90 9299.43 5399.35 6898.86 2099.67 26397.81 11199.81 8199.24 215
FMVS98.67 10098.87 5598.05 24699.72 3795.59 25798.51 11099.81 996.30 25999.78 1299.82 496.14 19698.63 37499.82 299.93 3299.95 2
FE-MVS95.66 29994.95 31197.77 26098.53 29595.28 26899.40 1596.09 35393.11 32897.96 24999.26 8379.10 36999.77 21292.40 33598.71 30998.27 322
FA-MVS(test-final)96.99 25796.82 24897.50 28298.70 26694.78 28299.34 1996.99 33995.07 29198.48 21199.33 7488.41 31899.65 27896.13 23398.92 29998.07 330
iter_conf_final97.10 24596.65 26298.45 21298.53 29596.08 24798.30 13099.11 21198.10 14398.85 16098.95 15479.38 36799.87 9298.68 6199.91 4899.40 162
bld_raw_dy_0_6499.07 4399.00 4799.29 8599.85 1698.18 13299.11 5699.40 10399.33 4499.38 6499.44 5695.21 23399.97 499.31 2099.98 999.73 29
patch_mono-298.51 12898.63 8598.17 23599.38 12094.78 28297.36 22799.69 1898.16 14198.49 21099.29 7897.06 14899.97 498.29 8499.91 4899.76 23
EGC-MVSNET85.24 34880.54 35199.34 7799.77 2599.20 3599.08 5799.29 15812.08 38420.84 38599.42 5897.55 11299.85 11697.08 15299.72 12998.96 263
test250692.39 34191.89 34493.89 35499.38 12082.28 38399.32 2266.03 39099.08 7598.77 17499.57 3266.26 38799.84 13398.71 5899.95 1999.54 92
test111196.49 27896.82 24895.52 33899.42 11587.08 36999.22 4187.14 38299.11 6399.46 4899.58 3188.69 31299.86 10198.80 5199.95 1999.62 52
ECVR-MVScopyleft96.42 28196.61 26395.85 33099.38 12088.18 36599.22 4186.00 38499.08 7599.36 6999.57 3288.47 31799.82 15898.52 7099.95 1999.54 92
test_blank0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
DVP-MVS++98.90 6298.70 7699.51 4698.43 30599.15 5099.43 1199.32 13598.17 13899.26 8999.02 12998.18 6499.88 7597.07 15399.45 22399.49 115
FOURS199.73 3199.67 299.43 1199.54 5799.43 3499.26 89
MSC_two_6792asdad99.32 8298.43 30598.37 11698.86 26099.89 6497.14 14799.60 17899.71 31
PC_three_145293.27 32599.40 6098.54 23698.22 6097.00 38095.17 26599.45 22399.49 115
No_MVS99.32 8298.43 30598.37 11698.86 26099.89 6497.14 14799.60 17899.71 31
test_one_060199.39 11999.20 3599.31 14198.49 11498.66 18599.02 12997.64 104
eth-test20.00 392
eth-test0.00 392
GeoE99.05 4498.99 5099.25 9799.44 11098.35 12098.73 8599.56 4898.42 11798.91 14798.81 19198.94 1899.91 4998.35 8099.73 12299.49 115
test_method79.78 34979.50 35280.62 36580.21 38845.76 39070.82 37998.41 30031.08 38380.89 38497.71 30684.85 33897.37 37991.51 34780.03 38298.75 296
Anonymous2024052198.69 9398.87 5598.16 23799.77 2595.11 27699.08 5799.44 9199.34 4399.33 7499.55 3694.10 26699.94 2699.25 2599.96 1599.42 150
h-mvs3397.77 19897.33 21999.10 11899.21 15397.84 17198.35 12898.57 29199.11 6398.58 19899.02 12988.65 31599.96 1198.11 9196.34 36399.49 115
hse-mvs297.46 21897.07 23198.64 18398.73 25797.33 20297.45 22297.64 32799.11 6398.58 19897.98 29088.65 31599.79 19498.11 9197.39 34798.81 285
CL-MVSNet_self_test97.44 22197.22 22498.08 24298.57 29095.78 25594.30 35998.79 27396.58 24898.60 19498.19 27594.74 25199.64 28196.41 21598.84 30198.82 282
KD-MVS_2432*160092.87 33891.99 34195.51 33991.37 38589.27 35994.07 36198.14 31195.42 28597.25 29496.44 34867.86 38399.24 35591.28 34996.08 36798.02 332
KD-MVS_self_test99.25 2799.18 2999.44 6199.63 5799.06 6798.69 9099.54 5799.31 4699.62 3199.53 4097.36 13199.86 10199.24 2799.71 13499.39 164
AUN-MVS96.24 28795.45 29698.60 19198.70 26697.22 21097.38 22597.65 32595.95 27195.53 35397.96 29482.11 35899.79 19496.31 22097.44 34598.80 290
ZD-MVS99.01 20698.84 7999.07 21894.10 31498.05 24598.12 28096.36 19299.86 10192.70 33199.19 265
test117298.76 8198.49 10699.57 1899.18 16799.37 1198.39 12499.31 14198.43 11698.90 14898.88 17397.49 12299.86 10196.43 21399.37 23599.48 125
SR-MVS-dyc-post98.81 7298.55 9699.57 1899.20 15799.38 898.48 11699.30 15198.64 10298.95 13898.96 15097.49 12299.86 10196.56 20199.39 23199.45 139
RE-MVS-def98.58 9499.20 15799.38 898.48 11699.30 15198.64 10298.95 13898.96 15097.75 9596.56 20199.39 23199.45 139
SED-MVS98.91 6098.72 7199.49 5299.49 9499.17 4198.10 15099.31 14198.03 14699.66 2399.02 12998.36 4999.88 7596.91 16599.62 17099.41 153
IU-MVS99.49 9499.15 5098.87 25592.97 32999.41 5796.76 18299.62 17099.66 43
OPU-MVS98.82 16198.59 28798.30 12198.10 15098.52 23998.18 6498.75 37394.62 27799.48 22099.41 153
test_241102_TWO99.30 15198.03 14699.26 8999.02 12997.51 11899.88 7596.91 16599.60 17899.66 43
test_241102_ONE99.49 9499.17 4199.31 14197.98 14899.66 2398.90 16498.36 4999.48 327
xxxxxxxxxxxxxcwj98.44 13598.24 14399.06 13099.11 18097.97 15796.53 27999.54 5798.24 12998.83 16498.90 16497.80 9299.82 15895.68 25399.52 20799.38 171
SF-MVS98.53 12598.27 14099.32 8299.31 13498.75 8698.19 14099.41 10196.77 24098.83 16498.90 16497.80 9299.82 15895.68 25399.52 20799.38 171
ETH3D cwj APD-0.1697.55 21197.00 23599.19 10598.51 29898.64 9496.85 26399.13 20794.19 31297.65 26898.40 25495.78 21699.81 17293.37 31899.16 26899.12 239
cl2295.79 29695.39 30096.98 30396.77 37092.79 33094.40 35798.53 29394.59 30197.89 25398.17 27682.82 35499.24 35596.37 21699.03 28698.92 270
miper_ehance_all_eth97.06 24997.03 23397.16 29897.83 34093.06 32494.66 34999.09 21595.99 27098.69 18198.45 25192.73 28799.61 29296.79 17899.03 28698.82 282
miper_enhance_ethall96.01 29095.74 28596.81 31396.41 37592.27 33993.69 36898.89 25291.14 35298.30 22497.35 33090.58 30099.58 30296.31 22099.03 28698.60 307
ZNCC-MVS98.68 9798.40 12299.54 3099.57 6499.21 2998.46 11899.29 15897.28 21098.11 23998.39 25698.00 7899.87 9296.86 17599.64 16499.55 88
ETH3 D test640096.46 28095.59 29299.08 12298.88 23398.21 13196.53 27999.18 19088.87 36597.08 29997.79 30193.64 27499.77 21288.92 36399.40 23099.28 206
dcpmvs_298.78 7799.11 3797.78 25999.56 7193.67 31899.06 6299.86 699.50 2599.66 2399.26 8397.21 14399.99 298.00 10199.91 4899.68 39
cl____97.02 25396.83 24797.58 27497.82 34194.04 30294.66 34999.16 19997.04 22998.63 18898.71 20588.68 31499.69 25197.00 15799.81 8199.00 256
DIV-MVS_self_test97.02 25396.84 24697.58 27497.82 34194.03 30394.66 34999.16 19997.04 22998.63 18898.71 20588.69 31299.69 25197.00 15799.81 8199.01 253
eth_miper_zixun_eth97.23 23797.25 22197.17 29698.00 33292.77 33194.71 34699.18 19097.27 21198.56 20298.74 20191.89 29599.69 25197.06 15599.81 8199.05 245
9.1497.78 18499.07 19197.53 21399.32 13595.53 28298.54 20698.70 20897.58 10999.76 22094.32 29099.46 221
testtj97.79 19797.25 22199.42 6299.03 20298.85 7797.78 18499.18 19095.83 27598.12 23798.50 24495.50 22699.86 10192.23 33899.07 28199.54 92
uanet_test0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
ETH3D-3000-0.198.03 17197.62 19899.29 8599.11 18098.80 8497.47 22099.32 13595.54 28098.43 21798.62 22796.61 17899.77 21293.95 30199.49 21899.30 201
DCPMVS0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
save fliter99.11 18097.97 15796.53 27999.02 23298.24 129
ET-MVSNet_ETH3D94.30 32193.21 33197.58 27498.14 32494.47 29394.78 34593.24 37194.72 29989.56 37995.87 35778.57 37299.81 17296.91 16597.11 35598.46 312
UniMVSNet_ETH3D99.69 299.69 499.69 399.84 1899.34 1799.69 499.58 3499.90 299.86 899.78 899.58 399.95 1799.00 4099.95 1999.78 17
EIA-MVS98.00 17597.74 18798.80 16598.72 25998.09 14098.05 15799.60 3197.39 19996.63 32195.55 36197.68 9899.80 18196.73 18699.27 25198.52 310
miper_refine_blended92.87 33891.99 34195.51 33991.37 38589.27 35994.07 36198.14 31195.42 28597.25 29496.44 34867.86 38399.24 35591.28 34996.08 36798.02 332
miper_lstm_enhance97.18 24197.16 22797.25 29498.16 32392.85 32995.15 33799.31 14197.25 21398.74 17998.78 19590.07 30399.78 20697.19 14199.80 9199.11 241
ETV-MVS98.03 17197.86 18198.56 19998.69 27198.07 14697.51 21699.50 6798.10 14397.50 28295.51 36298.41 4699.88 7596.27 22399.24 25697.71 348
CS-MVS99.13 3799.10 3999.24 9999.06 19599.15 5099.36 1899.88 499.36 4298.21 22998.46 25098.68 3199.93 3199.03 3899.85 6498.64 306
D2MVS97.84 19397.84 18297.83 25699.14 17694.74 28496.94 25598.88 25395.84 27498.89 15198.96 15094.40 25799.69 25197.55 12499.95 1999.05 245
DVP-MVScopyleft98.77 8098.52 9999.52 4299.50 8799.21 2998.02 16298.84 26497.97 14999.08 11499.02 12997.61 10799.88 7596.99 15999.63 16799.48 125
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 13899.08 11499.02 12997.89 8599.88 7597.07 15399.71 13499.70 36
test_0728_SECOND99.60 1399.50 8799.23 2798.02 16299.32 13599.88 7596.99 15999.63 16799.68 39
test072699.50 8799.21 2998.17 14499.35 12297.97 14999.26 8999.06 11697.61 107
SR-MVS98.71 8898.43 11899.57 1899.18 16799.35 1498.36 12799.29 15898.29 12698.88 15698.85 18097.53 11599.87 9296.14 23199.31 24499.48 125
DPM-MVS96.32 28395.59 29298.51 20698.76 25397.21 21294.54 35598.26 30491.94 34196.37 33297.25 33193.06 28199.43 33591.42 34898.74 30598.89 274
GST-MVS98.61 10998.30 13799.52 4299.51 8499.20 3598.26 13499.25 17097.44 19598.67 18398.39 25697.68 9899.85 11696.00 23599.51 21099.52 104
test_yl96.69 26796.29 27597.90 25298.28 31595.24 26997.29 23297.36 33098.21 13298.17 23097.86 29786.27 32699.55 30994.87 27198.32 32298.89 274
thisisatest053095.27 30794.45 31697.74 26499.19 16094.37 29497.86 17990.20 37997.17 22398.22 22897.65 31073.53 38099.90 5496.90 17099.35 23898.95 264
Anonymous2024052998.93 5898.87 5599.12 11499.19 16098.22 13099.01 6698.99 23999.25 5199.54 3599.37 6497.04 14999.80 18197.89 10599.52 20799.35 184
Anonymous20240521197.90 18197.50 20499.08 12298.90 22798.25 12498.53 10596.16 35198.87 9499.11 10898.86 17790.40 30299.78 20697.36 13499.31 24499.19 228
DCV-MVSNet96.69 26796.29 27597.90 25298.28 31595.24 26997.29 23297.36 33098.21 13298.17 23097.86 29786.27 32699.55 30994.87 27198.32 32298.89 274
tttt051795.64 30094.98 30997.64 27099.36 12793.81 31498.72 8690.47 37898.08 14598.67 18398.34 26373.88 37999.92 3997.77 11599.51 21099.20 223
our_test_397.39 22497.73 18996.34 32098.70 26689.78 35894.61 35298.97 24196.50 24999.04 12398.85 18095.98 20799.84 13397.26 13999.67 15699.41 153
thisisatest051594.12 32593.16 33296.97 30498.60 28592.90 32893.77 36790.61 37794.10 31496.91 30895.87 35774.99 37899.80 18194.52 28099.12 27898.20 324
ppachtmachnet_test97.50 21397.74 18796.78 31498.70 26691.23 35494.55 35499.05 22396.36 25599.21 9898.79 19496.39 18899.78 20696.74 18499.82 7799.34 186
SMA-MVScopyleft98.40 14098.03 16899.51 4699.16 17199.21 2998.05 15799.22 17894.16 31398.98 13299.10 11397.52 11799.79 19496.45 21199.64 16499.53 100
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
GSMVS98.81 285
DPE-MVScopyleft98.59 11498.26 14199.57 1899.27 14199.15 5097.01 25199.39 10697.67 16999.44 5298.99 14197.53 11599.89 6495.40 26399.68 15099.66 43
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part299.36 12799.10 6399.05 121
test_part197.91 18097.46 21099.27 9198.80 25098.18 13299.07 6099.36 11699.75 599.63 2999.49 4782.20 35799.89 6498.87 4899.95 1999.74 28
thres100view90094.19 32293.67 32695.75 33399.06 19591.35 34998.03 16094.24 36598.33 12197.40 28994.98 37079.84 36299.62 28683.05 37498.08 33496.29 367
tfpnnormal98.90 6298.90 5498.91 15099.67 4997.82 17599.00 6899.44 9199.45 3199.51 4399.24 8898.20 6399.86 10195.92 23999.69 14599.04 249
tfpn200view994.03 32693.44 32895.78 33298.93 21991.44 34797.60 20594.29 36397.94 15197.10 29794.31 37679.67 36499.62 28683.05 37498.08 33496.29 367
c3_l97.36 22597.37 21497.31 29098.09 32793.25 32295.01 34099.16 19997.05 22898.77 17498.72 20492.88 28499.64 28196.93 16499.76 11599.05 245
CHOSEN 280x42095.51 30495.47 29495.65 33698.25 31788.27 36493.25 37098.88 25393.53 32294.65 36197.15 33586.17 32899.93 3197.41 13299.93 3298.73 298
CANet97.87 18697.76 18598.19 23497.75 34395.51 26196.76 26999.05 22397.74 16496.93 30598.21 27395.59 22299.89 6497.86 11099.93 3299.19 228
Fast-Effi-MVS+-dtu98.27 15398.09 16198.81 16398.43 30598.11 13997.61 20499.50 6798.64 10297.39 29097.52 31898.12 7199.95 1796.90 17098.71 30998.38 318
Effi-MVS+-dtu98.26 15597.90 17899.35 7498.02 33099.49 598.02 16299.16 19998.29 12697.64 26997.99 28996.44 18699.95 1796.66 19298.93 29898.60 307
CANet_DTU97.26 23397.06 23297.84 25597.57 35094.65 28996.19 29998.79 27397.23 21995.14 35898.24 27093.22 27699.84 13397.34 13599.84 6899.04 249
MVS_030497.64 20597.35 21698.52 20497.87 33996.69 23498.59 9898.05 31697.44 19593.74 37298.85 18093.69 27399.88 7598.11 9199.81 8198.98 258
MP-MVS-pluss98.57 11598.23 14599.60 1399.69 4799.35 1497.16 24699.38 10894.87 29798.97 13598.99 14198.01 7799.88 7597.29 13799.70 13999.58 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS98.40 14098.00 17099.61 999.57 6499.25 2598.57 10199.35 12297.55 18199.31 8297.71 30694.61 25299.88 7596.14 23199.19 26599.70 36
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 34098.81 285
sam_mvs84.29 346
IterMVS-SCA-FT97.85 19298.18 15096.87 30999.27 14191.16 35595.53 32599.25 17099.10 7099.41 5799.35 6893.10 27999.96 1198.65 6299.94 2899.49 115
TSAR-MVS + MP.98.63 10698.49 10699.06 13099.64 5597.90 16698.51 11098.94 24296.96 23299.24 9498.89 17297.83 8899.81 17296.88 17299.49 21899.48 125
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 18798.17 15196.92 30698.98 21193.91 30996.45 28499.17 19697.85 15998.41 21897.14 33698.47 4299.92 3998.02 9899.05 28296.92 360
OPM-MVS98.56 11698.32 13699.25 9799.41 11798.73 9097.13 24899.18 19097.10 22798.75 17798.92 16098.18 6499.65 27896.68 19199.56 19799.37 174
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP98.75 8398.48 10899.57 1899.58 6099.29 2097.82 18299.25 17096.94 23398.78 17199.12 11098.02 7699.84 13397.13 14999.67 15699.59 64
ambc98.24 23198.82 24695.97 24998.62 9499.00 23899.27 8599.21 9196.99 15499.50 32396.55 20499.50 21799.26 211
zzz-MVS98.79 7498.52 9999.61 999.67 4999.36 1297.33 22999.20 18198.83 9898.89 15198.90 16496.98 15599.92 3997.16 14399.70 13999.56 80
MTGPAbinary99.20 181
mvs-test197.83 19597.48 20898.89 15398.02 33099.20 3597.20 24099.16 19998.29 12696.46 33197.17 33396.44 18699.92 3996.66 19297.90 33997.54 354
CS-MVS-test99.13 3799.09 4099.26 9499.13 17898.97 6999.31 2699.88 499.44 3298.16 23298.51 24098.64 3299.93 3198.91 4499.85 6498.88 277
Effi-MVS+98.02 17397.82 18398.62 18898.53 29597.19 21497.33 22999.68 2297.30 20896.68 31997.46 32398.56 3899.80 18196.63 19498.20 32698.86 279
xiu_mvs_v2_base97.16 24397.49 20596.17 32598.54 29392.46 33595.45 32998.84 26497.25 21397.48 28496.49 34598.31 5499.90 5496.34 21998.68 31296.15 371
xiu_mvs_v1_base97.86 18798.17 15196.92 30698.98 21193.91 30996.45 28499.17 19697.85 15998.41 21897.14 33698.47 4299.92 3998.02 9899.05 28296.92 360
new-patchmatchnet98.35 14598.74 6897.18 29599.24 14692.23 34096.42 28799.48 7798.30 12399.69 2099.53 4097.44 12699.82 15898.84 5099.77 10599.49 115
pmmvs699.67 399.70 399.60 1399.90 499.27 2399.53 799.76 1299.64 1299.84 999.83 399.50 599.87 9299.36 1799.92 4299.64 48
pmmvs597.64 20597.49 20598.08 24299.14 17695.12 27596.70 27399.05 22393.77 31998.62 19098.83 18693.23 27599.75 22798.33 8399.76 11599.36 180
test_post197.59 20720.48 38683.07 35299.66 27394.16 291
test_post21.25 38583.86 34899.70 247
Fast-Effi-MVS+97.67 20397.38 21398.57 19598.71 26297.43 19997.23 23699.45 8894.82 29896.13 33596.51 34498.52 4199.91 4996.19 22798.83 30298.37 320
patchmatchnet-post98.77 19784.37 34399.85 116
Anonymous2023121199.27 2599.27 2599.26 9499.29 13898.18 13299.49 899.51 6599.70 899.80 1199.68 1796.84 16199.83 14899.21 2899.91 4899.77 19
pmmvs-eth3d98.47 13298.34 13298.86 15799.30 13797.76 18097.16 24699.28 16195.54 28099.42 5699.19 9497.27 13699.63 28497.89 10599.97 1299.20 223
GG-mvs-BLEND94.76 34694.54 38392.13 34199.31 2680.47 38888.73 38191.01 38167.59 38598.16 37882.30 37894.53 37693.98 378
xiu_mvs_v1_base_debi97.86 18798.17 15196.92 30698.98 21193.91 30996.45 28499.17 19697.85 15998.41 21897.14 33698.47 4299.92 3998.02 9899.05 28296.92 360
Anonymous2023120698.21 16098.21 14698.20 23399.51 8495.43 26598.13 14599.32 13596.16 26398.93 14598.82 18996.00 20399.83 14897.32 13699.73 12299.36 180
MTAPA98.88 6498.64 8499.61 999.67 4999.36 1298.43 12199.20 18198.83 9898.89 15198.90 16496.98 15599.92 3997.16 14399.70 13999.56 80
MTMP97.93 17091.91 375
gm-plane-assit94.83 38281.97 38488.07 36894.99 36999.60 29391.76 341
test9_res93.28 32099.15 27199.38 171
MVP-Stereo98.08 16997.92 17698.57 19598.96 21496.79 22997.90 17499.18 19096.41 25498.46 21298.95 15495.93 21099.60 29396.51 20798.98 29599.31 198
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TEST998.71 26298.08 14495.96 30699.03 22891.40 34895.85 34297.53 31696.52 18199.76 220
train_agg97.10 24596.45 27099.07 12598.71 26298.08 14495.96 30699.03 22891.64 34395.85 34297.53 31696.47 18499.76 22093.67 30999.16 26899.36 180
gg-mvs-nofinetune92.37 34291.20 34795.85 33095.80 38192.38 33799.31 2681.84 38799.75 591.83 37699.74 1168.29 38299.02 36587.15 36797.12 35496.16 370
SCA96.41 28296.66 26095.67 33498.24 31888.35 36395.85 31496.88 34496.11 26497.67 26798.67 21393.10 27999.85 11694.16 29199.22 25898.81 285
Patchmatch-test96.55 27396.34 27397.17 29698.35 31193.06 32498.40 12397.79 32097.33 20498.41 21898.67 21383.68 34999.69 25195.16 26699.31 24498.77 293
test_898.67 27698.01 15195.91 31199.02 23291.64 34395.79 34497.50 31996.47 18499.76 220
MS-PatchMatch97.68 20297.75 18697.45 28598.23 32093.78 31597.29 23298.84 26496.10 26598.64 18798.65 21896.04 20099.36 34296.84 17699.14 27299.20 223
Patchmatch-RL test97.26 23397.02 23497.99 25099.52 8295.53 26096.13 30099.71 1597.47 18799.27 8599.16 10284.30 34599.62 28697.89 10599.77 10598.81 285
cdsmvs_eth3d_5k24.66 35132.88 3540.00 3690.00 3920.00 3930.00 38099.10 2130.00 3870.00 38897.58 31499.21 100.00 3880.00 3860.00 3860.00 384
pcd_1.5k_mvsjas8.17 35410.90 3570.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 38798.07 720.00 3880.00 3860.00 3860.00 384
agg_prior197.06 24996.40 27199.03 13598.68 27497.99 15295.76 31699.01 23591.73 34295.59 34597.50 31996.49 18399.77 21293.71 30899.14 27299.34 186
agg_prior292.50 33499.16 26899.37 174
agg_prior98.68 27497.99 15299.01 23595.59 34599.77 212
tmp_tt78.77 35078.73 35378.90 36658.45 38974.76 38994.20 36078.26 38939.16 38286.71 38292.82 38080.50 36075.19 38586.16 37092.29 37986.74 380
canonicalmvs98.34 14698.26 14198.58 19398.46 30297.82 17598.96 7299.46 8599.19 5997.46 28595.46 36498.59 3699.46 33198.08 9598.71 30998.46 312
anonymousdsp99.51 1099.47 1299.62 699.88 999.08 6699.34 1999.69 1898.93 9099.65 2699.72 1498.93 1999.95 1799.11 32100.00 199.82 12
alignmvs97.35 22696.88 24398.78 17098.54 29398.09 14097.71 19397.69 32499.20 5597.59 27395.90 35688.12 32099.55 30998.18 8998.96 29698.70 301
nrg03099.40 1899.35 1899.54 3099.58 6099.13 5898.98 7199.48 7799.68 999.46 4899.26 8398.62 3499.73 23599.17 3199.92 4299.76 23
v14419298.54 12398.57 9598.45 21299.21 15395.98 24897.63 20199.36 11697.15 22699.32 8099.18 9695.84 21599.84 13399.50 1299.91 4899.54 92
FIs99.14 3599.09 4099.29 8599.70 4598.28 12299.13 5399.52 6499.48 2799.24 9499.41 6196.79 16799.82 15898.69 6099.88 5999.76 23
v192192098.54 12398.60 9298.38 21999.20 15795.76 25697.56 21099.36 11697.23 21999.38 6499.17 10096.02 20199.84 13399.57 899.90 5599.54 92
UA-Net99.47 1199.40 1599.70 299.49 9499.29 2099.80 399.72 1499.82 399.04 12399.81 598.05 7599.96 1198.85 4999.99 599.86 9
v119298.60 11198.66 8298.41 21699.27 14195.88 25197.52 21499.36 11697.41 19799.33 7499.20 9396.37 19199.82 15899.57 899.92 4299.55 88
FC-MVSNet-test99.27 2599.25 2699.34 7799.77 2598.37 11699.30 3199.57 4199.61 1999.40 6099.50 4497.12 14599.85 11699.02 3999.94 2899.80 15
v114498.60 11198.66 8298.41 21699.36 12795.90 25097.58 20899.34 12897.51 18399.27 8599.15 10696.34 19399.80 18199.47 1499.93 3299.51 107
sosnet-low-res0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
HFP-MVS98.71 8898.44 11699.51 4699.49 9499.16 4598.52 10699.31 14197.47 18798.58 19898.50 24497.97 8299.85 11696.57 19899.59 18299.53 100
v14898.45 13498.60 9298.00 24999.44 11094.98 27897.44 22399.06 21998.30 12399.32 8098.97 14796.65 17699.62 28698.37 7999.85 6499.39 164
sosnet0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
uncertanet0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
AllTest98.44 13598.20 14799.16 10999.50 8798.55 10398.25 13599.58 3496.80 23898.88 15699.06 11697.65 10199.57 30394.45 28399.61 17699.37 174
TestCases99.16 10999.50 8798.55 10399.58 3496.80 23898.88 15699.06 11697.65 10199.57 30394.45 28399.61 17699.37 174
v7n99.53 899.57 899.41 6599.88 998.54 10699.45 1099.61 3099.66 1199.68 2299.66 2098.44 4599.95 1799.73 499.96 1599.75 26
region2R98.69 9398.40 12299.54 3099.53 8099.17 4198.52 10699.31 14197.46 19298.44 21498.51 24097.83 8899.88 7596.46 21099.58 18899.58 70
iter_conf0596.54 27496.07 27997.92 25197.90 33794.50 29297.87 17899.14 20697.73 16598.89 15198.95 15475.75 37799.87 9298.50 7199.92 4299.40 162
RRT_MVS99.09 4098.94 5199.55 2699.87 1298.82 8299.48 998.16 31099.49 2699.59 3299.65 2294.79 24999.95 1799.45 1599.96 1599.88 5
PS-MVSNAJss99.46 1299.49 1099.35 7499.90 498.15 13699.20 4499.65 2699.48 2799.92 499.71 1598.07 7299.96 1199.53 11100.00 199.93 3
PS-MVSNAJ97.08 24897.39 21296.16 32798.56 29192.46 33595.24 33498.85 26397.25 21397.49 28395.99 35498.07 7299.90 5496.37 21698.67 31396.12 372
jajsoiax99.58 699.61 799.48 5599.87 1298.61 9899.28 3699.66 2599.09 7399.89 799.68 1799.53 499.97 499.50 1299.99 599.87 7
mvs_tets99.63 599.67 599.49 5299.88 998.61 9899.34 1999.71 1599.27 5099.90 599.74 1199.68 299.97 499.55 1099.99 599.88 5
#test#98.50 12998.16 15499.51 4699.49 9499.16 4598.03 16099.31 14196.30 25998.58 19898.50 24497.97 8299.85 11695.68 25399.59 18299.53 100
EI-MVSNet-UG-set98.69 9398.71 7398.62 18899.10 18496.37 23997.23 23698.87 25599.20 5599.19 10098.99 14197.30 13399.85 11698.77 5599.79 9699.65 47
EI-MVSNet-Vis-set98.68 9798.70 7698.63 18699.09 18796.40 23897.23 23698.86 26099.20 5599.18 10498.97 14797.29 13599.85 11698.72 5799.78 10199.64 48
Regformer-398.61 10998.61 9098.63 18699.02 20496.53 23697.17 24498.84 26499.13 6299.10 11198.85 18097.24 14099.79 19498.41 7899.70 13999.57 75
Regformer-498.73 8698.68 7998.89 15399.02 20497.22 21097.17 24499.06 21999.21 5299.17 10598.85 18097.45 12599.86 10198.48 7399.70 13999.60 58
Regformer-198.55 12098.44 11698.87 15598.85 23897.29 20496.91 26098.99 23998.97 8598.99 13098.64 22197.26 13999.81 17297.79 11399.57 19299.51 107
Regformer-298.60 11198.46 11299.02 13898.85 23897.71 18596.91 26099.09 21598.98 8499.01 12798.64 22197.37 13099.84 13397.75 12099.57 19299.52 104
HPM-MVS++copyleft98.10 16797.64 19699.48 5599.09 18799.13 5897.52 21498.75 27997.46 19296.90 31197.83 30096.01 20299.84 13395.82 24799.35 23899.46 135
test_prior497.97 15795.86 312
XVS98.72 8798.45 11499.53 3799.46 10599.21 2998.65 9199.34 12898.62 10697.54 27898.63 22597.50 11999.83 14896.79 17899.53 20499.56 80
v124098.55 12098.62 8798.32 22399.22 15195.58 25897.51 21699.45 8897.16 22499.45 5199.24 8896.12 19899.85 11699.60 699.88 5999.55 88
test_prior397.48 21797.00 23598.95 14498.69 27197.95 16295.74 31899.03 22896.48 25196.11 33697.63 31295.92 21199.59 29794.16 29199.20 26199.30 201
pm-mvs199.44 1399.48 1199.33 8099.80 2298.63 9599.29 3299.63 2799.30 4899.65 2699.60 2999.16 1499.82 15899.07 3499.83 7499.56 80
test_prior295.74 31896.48 25196.11 33697.63 31295.92 21194.16 29199.20 261
X-MVStestdata94.32 31992.59 33799.53 3799.46 10599.21 2998.65 9199.34 12898.62 10697.54 27845.85 38297.50 11999.83 14896.79 17899.53 20499.56 80
test_prior98.95 14498.69 27197.95 16299.03 22899.59 29799.30 201
旧先验295.76 31688.56 36797.52 28099.66 27394.48 281
新几何295.93 309
新几何198.91 15098.94 21797.76 18098.76 27687.58 37096.75 31898.10 28294.80 24799.78 20692.73 33099.00 29299.20 223
旧先验198.82 24697.45 19898.76 27698.34 26395.50 22699.01 29199.23 218
无先验95.74 31898.74 28189.38 36299.73 23592.38 33699.22 222
原ACMM295.53 325
原ACMM198.35 22198.90 22796.25 24298.83 26992.48 33696.07 33998.10 28295.39 23099.71 24492.61 33398.99 29399.08 242
test22298.92 22396.93 22695.54 32498.78 27585.72 37396.86 31498.11 28194.43 25599.10 28099.23 218
testdata299.79 19492.80 328
segment_acmp97.02 152
testdata98.09 23998.93 21995.40 26698.80 27290.08 35997.45 28698.37 25995.26 23299.70 24793.58 31298.95 29799.17 234
testdata195.44 33096.32 257
v899.01 4699.16 3198.57 19599.47 10496.31 24198.90 7599.47 8399.03 7999.52 4099.57 3296.93 15799.81 17299.60 699.98 999.60 58
131495.74 29795.60 29196.17 32597.53 35392.75 33298.07 15398.31 30391.22 35094.25 36496.68 34295.53 22399.03 36491.64 34497.18 35396.74 364
112196.73 26696.00 28098.91 15098.95 21697.76 18098.07 15398.73 28287.65 36996.54 32498.13 27794.52 25499.73 23592.38 33699.02 28999.24 215
LFMVS97.20 23996.72 25498.64 18398.72 25996.95 22498.93 7494.14 36799.74 798.78 17199.01 13884.45 34299.73 23597.44 13099.27 25199.25 212
VDD-MVS98.56 11698.39 12599.07 12599.13 17898.07 14698.59 9897.01 33899.59 2099.11 10899.27 8194.82 24499.79 19498.34 8199.63 16799.34 186
VDDNet98.21 16097.95 17399.01 13999.58 6097.74 18399.01 6697.29 33499.67 1098.97 13599.50 4490.45 30199.80 18197.88 10899.20 26199.48 125
v1098.97 5399.11 3798.55 20099.44 11096.21 24398.90 7599.55 5298.73 10099.48 4599.60 2996.63 17799.83 14899.70 599.99 599.61 57
VPNet98.87 6598.83 6099.01 13999.70 4597.62 19198.43 12199.35 12299.47 2999.28 8399.05 12396.72 17399.82 15898.09 9499.36 23699.59 64
MVS93.19 33692.09 34096.50 31896.91 36694.03 30398.07 15398.06 31568.01 38094.56 36396.48 34695.96 20999.30 35083.84 37396.89 35896.17 369
v2v48298.56 11698.62 8798.37 22099.42 11595.81 25497.58 20899.16 19997.90 15599.28 8399.01 13895.98 20799.79 19499.33 1899.90 5599.51 107
V4298.78 7798.78 6598.76 17399.44 11097.04 22098.27 13399.19 18697.87 15799.25 9399.16 10296.84 16199.78 20699.21 2899.84 6899.46 135
SD-MVS98.40 14098.68 7997.54 27998.96 21497.99 15297.88 17599.36 11698.20 13599.63 2999.04 12598.76 2595.33 38396.56 20199.74 11999.31 198
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
GA-MVS95.86 29495.32 30297.49 28398.60 28594.15 30093.83 36697.93 31895.49 28396.68 31997.42 32583.21 35099.30 35096.22 22598.55 31999.01 253
MSLP-MVS++98.02 17398.14 15897.64 27098.58 28895.19 27297.48 21899.23 17797.47 18797.90 25298.62 22797.04 14998.81 37297.55 12499.41 22898.94 268
APDe-MVS98.99 4898.79 6499.60 1399.21 15399.15 5098.87 7799.48 7797.57 17899.35 7199.24 8897.83 8899.89 6497.88 10899.70 13999.75 26
APD-MVS_3200maxsize98.84 6998.61 9099.53 3799.19 16099.27 2398.49 11399.33 13398.64 10299.03 12698.98 14597.89 8599.85 11696.54 20599.42 22799.46 135
ADS-MVSNet295.43 30594.98 30996.76 31598.14 32491.74 34397.92 17197.76 32190.23 35596.51 32798.91 16185.61 33399.85 11692.88 32496.90 35698.69 302
EI-MVSNet98.40 14098.51 10198.04 24799.10 18494.73 28597.20 24098.87 25598.97 8599.06 11699.02 12996.00 20399.80 18198.58 6499.82 7799.60 58
Regformer0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
CVMVSNet96.25 28697.21 22593.38 36099.10 18480.56 38697.20 24098.19 30996.94 23399.00 12999.02 12989.50 30899.80 18196.36 21899.59 18299.78 17
pmmvs497.58 21097.28 22098.51 20698.84 24196.93 22695.40 33198.52 29493.60 32198.61 19298.65 21895.10 23799.60 29396.97 16299.79 9698.99 257
EU-MVSNet97.66 20498.50 10395.13 34499.63 5785.84 37298.35 12898.21 30698.23 13199.54 3599.46 5195.02 23899.68 26098.24 8599.87 6299.87 7
VNet98.42 13798.30 13798.79 16798.79 25297.29 20498.23 13698.66 28699.31 4698.85 16098.80 19294.80 24799.78 20698.13 9099.13 27599.31 198
test-LLR93.90 32893.85 32294.04 35196.53 37284.62 37794.05 36392.39 37396.17 26194.12 36695.07 36682.30 35599.67 26395.87 24398.18 32797.82 339
TESTMET0.1,192.19 34591.77 34593.46 35896.48 37482.80 38294.05 36391.52 37694.45 30694.00 36994.88 37266.65 38699.56 30695.78 24898.11 33298.02 332
test-mter92.33 34391.76 34694.04 35196.53 37284.62 37794.05 36392.39 37394.00 31794.12 36695.07 36665.63 38999.67 26395.87 24398.18 32797.82 339
VPA-MVSNet99.30 2499.30 2499.28 8899.49 9498.36 11999.00 6899.45 8899.63 1499.52 4099.44 5698.25 5599.88 7599.09 3399.84 6899.62 52
ACMMPR98.70 9198.42 12099.54 3099.52 8299.14 5598.52 10699.31 14197.47 18798.56 20298.54 23697.75 9599.88 7596.57 19899.59 18299.58 70
testgi98.32 14798.39 12598.13 23899.57 6495.54 25997.78 18499.49 7597.37 20199.19 10097.65 31098.96 1799.49 32496.50 20898.99 29399.34 186
test20.0398.78 7798.77 6798.78 17099.46 10597.20 21397.78 18499.24 17599.04 7899.41 5798.90 16497.65 10199.76 22097.70 12199.79 9699.39 164
thres600view794.45 31793.83 32396.29 32199.06 19591.53 34597.99 16694.24 36598.34 12097.44 28795.01 36879.84 36299.67 26384.33 37298.23 32497.66 349
ADS-MVSNet95.24 30894.93 31296.18 32498.14 32490.10 35797.92 17197.32 33390.23 35596.51 32798.91 16185.61 33399.74 23192.88 32496.90 35698.69 302
MP-MVScopyleft98.46 13398.09 16199.54 3099.57 6499.22 2898.50 11299.19 18697.61 17597.58 27498.66 21697.40 12899.88 7594.72 27699.60 17899.54 92
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs17.12 35220.53 3556.87 36812.05 3904.20 39293.62 3696.73 3914.62 38610.41 38624.33 3838.28 3913.56 3879.69 38515.07 38412.86 383
thres40094.14 32493.44 32896.24 32398.93 21991.44 34797.60 20594.29 36397.94 15197.10 29794.31 37679.67 36499.62 28683.05 37498.08 33497.66 349
test12317.04 35320.11 3567.82 36710.25 3914.91 39194.80 3444.47 3924.93 38510.00 38724.28 3849.69 3903.64 38610.14 38412.43 38514.92 382
thres20093.72 33193.14 33395.46 34198.66 28191.29 35196.61 27794.63 36197.39 19996.83 31593.71 37879.88 36199.56 30682.40 37798.13 33195.54 376
test0.0.03 194.51 31693.69 32596.99 30296.05 37893.61 32094.97 34193.49 36896.17 26197.57 27694.88 37282.30 35599.01 36793.60 31194.17 37798.37 320
pmmvs395.03 31194.40 31796.93 30597.70 34792.53 33495.08 33897.71 32388.57 36697.71 26498.08 28579.39 36699.82 15896.19 22799.11 27998.43 316
EMVS93.83 32994.02 32193.23 36196.83 36984.96 37589.77 37896.32 35097.92 15397.43 28896.36 35186.17 32898.93 36987.68 36697.73 34195.81 374
E-PMN94.17 32394.37 31893.58 35796.86 36785.71 37490.11 37797.07 33798.17 13897.82 25997.19 33284.62 34198.94 36889.77 36097.68 34296.09 373
PGM-MVS98.66 10198.37 12899.55 2699.53 8099.18 4098.23 13699.49 7597.01 23198.69 18198.88 17398.00 7899.89 6495.87 24399.59 18299.58 70
LCM-MVSNet-Re98.64 10498.48 10899.11 11698.85 23898.51 10898.49 11399.83 898.37 11899.69 2099.46 5198.21 6299.92 3994.13 29699.30 24798.91 273
LCM-MVSNet99.93 199.92 199.94 199.99 199.97 199.90 199.89 399.98 199.99 199.96 199.77 1100.00 199.81 3100.00 199.85 10
MCST-MVS98.00 17597.63 19799.10 11899.24 14698.17 13596.89 26298.73 28295.66 27897.92 25097.70 30897.17 14499.66 27396.18 22999.23 25799.47 133
mvs_anonymous97.83 19598.16 15496.87 30998.18 32291.89 34297.31 23198.90 25097.37 20198.83 16499.46 5196.28 19499.79 19498.90 4598.16 32998.95 264
MVS_Test98.18 16398.36 12997.67 26698.48 30094.73 28598.18 14199.02 23297.69 16898.04 24699.11 11197.22 14299.56 30698.57 6698.90 30098.71 299
MDA-MVSNet-bldmvs97.94 17997.91 17798.06 24499.44 11094.96 27996.63 27699.15 20598.35 11998.83 16499.11 11194.31 25999.85 11696.60 19598.72 30799.37 174
CDPH-MVS97.26 23396.66 26099.07 12599.00 20798.15 13696.03 30299.01 23591.21 35197.79 26097.85 29996.89 15999.69 25192.75 32999.38 23499.39 164
test1298.93 14798.58 28897.83 17298.66 28696.53 32595.51 22599.69 25199.13 27599.27 208
casdiffmvs98.95 5699.00 4798.81 16399.38 12097.33 20297.82 18299.57 4199.17 6099.35 7199.17 10098.35 5299.69 25198.46 7499.73 12299.41 153
diffmvs98.22 15998.24 14398.17 23599.00 20795.44 26496.38 28999.58 3497.79 16298.53 20798.50 24496.76 17099.74 23197.95 10499.64 16499.34 186
baseline293.73 33092.83 33696.42 31997.70 34791.28 35296.84 26589.77 38093.96 31892.44 37495.93 35579.14 36899.77 21292.94 32296.76 36098.21 323
baseline195.96 29295.44 29797.52 28198.51 29893.99 30698.39 12496.09 35398.21 13298.40 22297.76 30486.88 32299.63 28495.42 26289.27 38198.95 264
YYNet197.60 20897.67 19197.39 28999.04 19993.04 32795.27 33298.38 30197.25 21398.92 14698.95 15495.48 22899.73 23596.99 15998.74 30599.41 153
PMMVS298.07 17098.08 16498.04 24799.41 11794.59 29194.59 35399.40 10397.50 18498.82 16898.83 18696.83 16399.84 13397.50 12999.81 8199.71 31
MDA-MVSNet_test_wron97.60 20897.66 19497.41 28899.04 19993.09 32395.27 33298.42 29897.26 21298.88 15698.95 15495.43 22999.73 23597.02 15698.72 30799.41 153
tpmvs95.02 31295.25 30394.33 34996.39 37685.87 37198.08 15296.83 34595.46 28495.51 35498.69 20985.91 33199.53 31494.16 29196.23 36597.58 352
PM-MVS98.82 7098.72 7199.12 11499.64 5598.54 10697.98 16799.68 2297.62 17399.34 7399.18 9697.54 11399.77 21297.79 11399.74 11999.04 249
HQP_MVS97.99 17897.67 19198.93 14799.19 16097.65 18897.77 18799.27 16498.20 13597.79 26097.98 29094.90 24099.70 24794.42 28599.51 21099.45 139
plane_prior799.19 16097.87 168
plane_prior698.99 21097.70 18694.90 240
plane_prior599.27 16499.70 24794.42 28599.51 21099.45 139
plane_prior497.98 290
plane_prior397.78 17997.41 19797.79 260
plane_prior297.77 18798.20 135
plane_prior199.05 198
plane_prior97.65 18897.07 24996.72 24299.36 236
PS-CasMVS99.40 1899.33 2199.62 699.71 3999.10 6399.29 3299.53 6199.53 2499.46 4899.41 6198.23 5799.95 1798.89 4799.95 1999.81 14
UniMVSNet_NR-MVSNet98.86 6898.68 7999.40 6799.17 16998.74 8797.68 19699.40 10399.14 6199.06 11698.59 23296.71 17499.93 3198.57 6699.77 10599.53 100
PEN-MVS99.41 1799.34 2099.62 699.73 3199.14 5599.29 3299.54 5799.62 1799.56 3399.42 5898.16 6899.96 1198.78 5299.93 3299.77 19
TransMVSNet (Re)99.44 1399.47 1299.36 6999.80 2298.58 10199.27 3899.57 4199.39 3799.75 1599.62 2599.17 1299.83 14899.06 3599.62 17099.66 43
DTE-MVSNet99.43 1599.35 1899.66 499.71 3999.30 1999.31 2699.51 6599.64 1299.56 3399.46 5198.23 5799.97 498.78 5299.93 3299.72 30
DU-MVS98.82 7098.63 8599.39 6899.16 17198.74 8797.54 21299.25 17098.84 9799.06 11698.76 19996.76 17099.93 3198.57 6699.77 10599.50 111
UniMVSNet (Re)98.87 6598.71 7399.35 7499.24 14698.73 9097.73 19299.38 10898.93 9099.12 10798.73 20296.77 16899.86 10198.63 6399.80 9199.46 135
CP-MVSNet99.21 3299.09 4099.56 2499.65 5298.96 7399.13 5399.34 12899.42 3599.33 7499.26 8397.01 15399.94 2698.74 5699.93 3299.79 16
WR-MVS_H99.33 2399.22 2899.65 599.71 3999.24 2699.32 2299.55 5299.46 3099.50 4499.34 7297.30 13399.93 3198.90 4599.93 3299.77 19
WR-MVS98.40 14098.19 14999.03 13599.00 20797.65 18896.85 26398.94 24298.57 11298.89 15198.50 24495.60 22199.85 11697.54 12699.85 6499.59 64
NR-MVSNet98.95 5698.82 6199.36 6999.16 17198.72 9299.22 4199.20 18199.10 7099.72 1698.76 19996.38 19099.86 10198.00 10199.82 7799.50 111
Baseline_NR-MVSNet98.98 5298.86 5899.36 6999.82 2198.55 10397.47 22099.57 4199.37 3999.21 9899.61 2796.76 17099.83 14898.06 9699.83 7499.71 31
TranMVSNet+NR-MVSNet99.17 3399.07 4399.46 6099.37 12698.87 7698.39 12499.42 10099.42 3599.36 6999.06 11698.38 4899.95 1798.34 8199.90 5599.57 75
TSAR-MVS + GP.98.18 16397.98 17198.77 17298.71 26297.88 16796.32 29298.66 28696.33 25699.23 9798.51 24097.48 12499.40 33797.16 14399.46 22199.02 252
abl_698.99 4898.78 6599.61 999.45 10899.46 698.60 9699.50 6798.59 10899.24 9499.04 12598.54 4099.89 6496.45 21199.62 17099.50 111
n20.00 393
nn0.00 393
mPP-MVS98.64 10498.34 13299.54 3099.54 7899.17 4198.63 9399.24 17597.47 18798.09 24198.68 21197.62 10699.89 6496.22 22599.62 17099.57 75
door-mid99.57 41
XVG-OURS-SEG-HR98.49 13098.28 13999.14 11299.49 9498.83 8096.54 27899.48 7797.32 20699.11 10898.61 23099.33 899.30 35096.23 22498.38 32199.28 206
mvsmamba99.24 3199.15 3599.49 5299.83 1998.85 7799.41 1399.55 5299.54 2399.40 6099.52 4295.86 21499.91 4999.32 1999.95 1999.70 36
MVSFormer98.26 15598.43 11897.77 26098.88 23393.89 31299.39 1699.56 4899.11 6398.16 23298.13 27793.81 26999.97 499.26 2399.57 19299.43 147
jason97.45 22097.35 21697.76 26299.24 14693.93 30895.86 31298.42 29894.24 31098.50 20998.13 27794.82 24499.91 4997.22 14099.73 12299.43 147
jason: jason.
lupinMVS97.06 24996.86 24497.65 26898.88 23393.89 31295.48 32897.97 31793.53 32298.16 23297.58 31493.81 26999.91 4996.77 18199.57 19299.17 234
test_djsdf99.52 999.51 999.53 3799.86 1498.74 8799.39 1699.56 4899.11 6399.70 1899.73 1399.00 1599.97 499.26 2399.98 999.89 4
HPM-MVS_fast99.01 4698.82 6199.57 1899.71 3999.35 1499.00 6899.50 6797.33 20498.94 14498.86 17798.75 2699.82 15897.53 12799.71 13499.56 80
K. test v398.00 17597.66 19499.03 13599.79 2497.56 19299.19 4892.47 37299.62 1799.52 4099.66 2089.61 30699.96 1199.25 2599.81 8199.56 80
lessismore_v098.97 14299.73 3197.53 19486.71 38399.37 6799.52 4289.93 30499.92 3998.99 4199.72 12999.44 143
SixPastTwentyTwo98.75 8398.62 8799.16 10999.83 1997.96 16199.28 3698.20 30799.37 3999.70 1899.65 2292.65 28899.93 3199.04 3799.84 6899.60 58
OurMVSNet-221017-099.37 2199.31 2399.53 3799.91 398.98 6899.63 699.58 3499.44 3299.78 1299.76 996.39 18899.92 3999.44 1699.92 4299.68 39
HPM-MVScopyleft98.79 7498.53 9899.59 1799.65 5299.29 2099.16 5099.43 9796.74 24198.61 19298.38 25898.62 3499.87 9296.47 20999.67 15699.59 64
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS98.53 12598.34 13299.11 11699.50 8798.82 8295.97 30499.50 6797.30 20899.05 12198.98 14599.35 799.32 34795.72 25099.68 15099.18 230
XVG-ACMP-BASELINE98.56 11698.34 13299.22 10299.54 7898.59 10097.71 19399.46 8597.25 21398.98 13298.99 14197.54 11399.84 13395.88 24099.74 11999.23 218
LPG-MVS_test98.71 8898.46 11299.47 5899.57 6498.97 6998.23 13699.48 7796.60 24699.10 11199.06 11698.71 2999.83 14895.58 25999.78 10199.62 52
LGP-MVS_train99.47 5899.57 6498.97 6999.48 7796.60 24699.10 11199.06 11698.71 2999.83 14895.58 25999.78 10199.62 52
baseline98.96 5599.02 4598.76 17399.38 12097.26 20798.49 11399.50 6798.86 9599.19 10099.06 11698.23 5799.69 25198.71 5899.76 11599.33 192
test1198.87 255
door99.41 101
EPNet_dtu94.93 31394.78 31495.38 34293.58 38487.68 36796.78 26795.69 35897.35 20389.14 38098.09 28488.15 31999.49 32494.95 27099.30 24798.98 258
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x268897.49 21597.14 23098.54 20399.68 4896.09 24696.50 28299.62 2891.58 34598.84 16398.97 14792.36 29099.88 7596.76 18299.95 1999.67 42
EPNet96.14 28895.44 29798.25 22990.76 38795.50 26297.92 17194.65 36098.97 8592.98 37398.85 18089.12 31099.87 9295.99 23699.68 15099.39 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP5-MVS96.79 229
HQP-NCC98.67 27696.29 29396.05 26695.55 349
ACMP_Plane98.67 27696.29 29396.05 26695.55 349
APD-MVScopyleft98.10 16797.67 19199.42 6299.11 18098.93 7497.76 18999.28 16194.97 29498.72 18098.77 19797.04 14999.85 11693.79 30799.54 20099.49 115
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
BP-MVS92.82 326
HQP4-MVS95.56 34899.54 31299.32 194
HQP3-MVS99.04 22699.26 254
HQP2-MVS93.84 267
CNVR-MVS98.17 16597.87 18099.07 12598.67 27698.24 12597.01 25198.93 24497.25 21397.62 27098.34 26397.27 13699.57 30396.42 21499.33 24199.39 164
NCCC97.86 18797.47 20999.05 13298.61 28398.07 14696.98 25398.90 25097.63 17297.04 30297.93 29595.99 20699.66 27395.31 26498.82 30399.43 147
114514_t96.50 27795.77 28498.69 18099.48 10297.43 19997.84 18199.55 5281.42 37896.51 32798.58 23395.53 22399.67 26393.41 31799.58 18898.98 258
CP-MVS98.70 9198.42 12099.52 4299.36 12799.12 6098.72 8699.36 11697.54 18298.30 22498.40 25497.86 8799.89 6496.53 20699.72 12999.56 80
DSMNet-mixed97.42 22297.60 20096.87 30999.15 17591.46 34698.54 10499.12 20992.87 33297.58 27499.63 2496.21 19599.90 5495.74 24999.54 20099.27 208
tpm293.09 33792.58 33894.62 34797.56 35186.53 37097.66 19895.79 35786.15 37294.07 36898.23 27275.95 37599.53 31490.91 35596.86 35997.81 341
NP-MVS98.84 24197.39 20196.84 339
EG-PatchMatch MVS98.99 4899.01 4698.94 14699.50 8797.47 19698.04 15999.59 3298.15 14299.40 6099.36 6798.58 3799.76 22098.78 5299.68 15099.59 64
tpm cat193.29 33593.13 33493.75 35597.39 35984.74 37697.39 22497.65 32583.39 37794.16 36598.41 25382.86 35399.39 33991.56 34695.35 37297.14 359
SteuartSystems-ACMMP98.79 7498.54 9799.54 3099.73 3199.16 4598.23 13699.31 14197.92 15398.90 14898.90 16498.00 7899.88 7596.15 23099.72 12999.58 70
Skip Steuart: Steuart Systems R&D Blog.
CostFormer93.97 32793.78 32494.51 34897.53 35385.83 37397.98 16795.96 35589.29 36394.99 36098.63 22578.63 37199.62 28694.54 27996.50 36198.09 329
CR-MVSNet96.28 28595.95 28297.28 29297.71 34594.22 29698.11 14898.92 24792.31 33896.91 30899.37 6485.44 33699.81 17297.39 13397.36 35097.81 341
JIA-IIPM95.52 30395.03 30897.00 30196.85 36894.03 30396.93 25795.82 35699.20 5594.63 36299.71 1583.09 35199.60 29394.42 28594.64 37497.36 357
Patchmtry97.35 22696.97 23798.50 20897.31 36196.47 23798.18 14198.92 24798.95 8998.78 17199.37 6485.44 33699.85 11695.96 23899.83 7499.17 234
PatchT96.65 27096.35 27297.54 27997.40 35895.32 26797.98 16796.64 34799.33 4496.89 31299.42 5884.32 34499.81 17297.69 12397.49 34397.48 355
tpmrst95.07 31095.46 29593.91 35397.11 36484.36 37997.62 20296.96 34094.98 29396.35 33398.80 19285.46 33599.59 29795.60 25796.23 36597.79 344
BH-w/o95.13 30994.89 31395.86 32998.20 32191.31 35095.65 32197.37 32993.64 32096.52 32695.70 35993.04 28299.02 36588.10 36595.82 36997.24 358
tpm94.67 31594.34 31995.66 33597.68 34988.42 36297.88 17594.90 35994.46 30496.03 34198.56 23578.66 37099.79 19495.88 24095.01 37398.78 292
DELS-MVS98.27 15398.20 14798.48 20998.86 23696.70 23395.60 32399.20 18197.73 16598.45 21398.71 20597.50 11999.82 15898.21 8799.59 18298.93 269
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 26296.75 25397.08 29998.74 25693.33 32196.71 27298.26 30496.72 24298.44 21497.37 32895.20 23499.47 32991.89 34097.43 34698.44 315
RPMNet97.02 25396.93 23897.30 29197.71 34594.22 29698.11 14899.30 15199.37 3996.91 30899.34 7286.72 32399.87 9297.53 12797.36 35097.81 341
MVSTER96.86 26196.55 26797.79 25897.91 33694.21 29897.56 21098.87 25597.49 18699.06 11699.05 12380.72 35999.80 18198.44 7599.82 7799.37 174
CPTT-MVS97.84 19397.36 21599.27 9199.31 13498.46 11198.29 13199.27 16494.90 29697.83 25798.37 25994.90 24099.84 13393.85 30699.54 20099.51 107
GBi-Net98.65 10298.47 11099.17 10698.90 22798.24 12599.20 4499.44 9198.59 10898.95 13899.55 3694.14 26299.86 10197.77 11599.69 14599.41 153
PVSNet_Blended_VisFu98.17 16598.15 15698.22 23299.73 3195.15 27397.36 22799.68 2294.45 30698.99 13099.27 8196.87 16099.94 2697.13 14999.91 4899.57 75
PVSNet_BlendedMVS97.55 21197.53 20297.60 27298.92 22393.77 31696.64 27599.43 9794.49 30297.62 27099.18 9696.82 16499.67 26394.73 27499.93 3299.36 180
UnsupCasMVSNet_eth97.89 18397.60 20098.75 17599.31 13497.17 21697.62 20299.35 12298.72 10198.76 17698.68 21192.57 28999.74 23197.76 11995.60 37099.34 186
UnsupCasMVSNet_bld97.30 23096.92 24098.45 21299.28 13996.78 23296.20 29899.27 16495.42 28598.28 22698.30 26793.16 27799.71 24494.99 26897.37 34898.87 278
PVSNet_Blended96.88 26096.68 25797.47 28498.92 22393.77 31694.71 34699.43 9790.98 35397.62 27097.36 32996.82 16499.67 26394.73 27499.56 19798.98 258
FMVSNet596.01 29095.20 30598.41 21697.53 35396.10 24498.74 8399.50 6797.22 22298.03 24799.04 12569.80 38199.88 7597.27 13899.71 13499.25 212
test198.65 10298.47 11099.17 10698.90 22798.24 12599.20 4499.44 9198.59 10898.95 13899.55 3694.14 26299.86 10197.77 11599.69 14599.41 153
new_pmnet96.99 25796.76 25297.67 26698.72 25994.89 28095.95 30898.20 30792.62 33598.55 20498.54 23694.88 24399.52 31893.96 30099.44 22698.59 309
FMVSNet397.50 21397.24 22398.29 22798.08 32895.83 25397.86 17998.91 24997.89 15698.95 13898.95 15487.06 32199.81 17297.77 11599.69 14599.23 218
dp93.47 33393.59 32793.13 36296.64 37181.62 38597.66 19896.42 34992.80 33396.11 33698.64 22178.55 37399.59 29793.31 31992.18 38098.16 326
FMVSNet298.49 13098.40 12298.75 17598.90 22797.14 21998.61 9599.13 20798.59 10899.19 10099.28 7994.14 26299.82 15897.97 10399.80 9199.29 205
FMVSNet199.17 3399.17 3099.17 10699.55 7598.24 12599.20 4499.44 9199.21 5299.43 5399.55 3697.82 9199.86 10198.42 7799.89 5899.41 153
N_pmnet97.63 20797.17 22698.99 14199.27 14197.86 16995.98 30393.41 36995.25 28999.47 4798.90 16495.63 22099.85 11696.91 16599.73 12299.27 208
cascas94.79 31494.33 32096.15 32896.02 38092.36 33892.34 37599.26 16985.34 37495.08 35994.96 37192.96 28398.53 37594.41 28898.59 31797.56 353
BH-RMVSNet96.83 26296.58 26697.58 27498.47 30194.05 30196.67 27497.36 33096.70 24497.87 25497.98 29095.14 23699.44 33490.47 35898.58 31899.25 212
UGNet98.53 12598.45 11498.79 16797.94 33496.96 22399.08 5798.54 29299.10 7096.82 31699.47 5096.55 18099.84 13398.56 6999.94 2899.55 88
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 26996.27 27797.87 25498.81 24894.61 29096.77 26897.92 31994.94 29597.12 29697.74 30591.11 29899.82 15893.89 30398.15 33099.18 230
XXY-MVS99.14 3599.15 3599.10 11899.76 2897.74 18398.85 8099.62 2898.48 11599.37 6799.49 4798.75 2699.86 10198.20 8899.80 9199.71 31
DROMVSNet99.09 4099.05 4499.20 10399.28 13998.93 7499.24 4099.84 799.08 7598.12 23798.37 25998.72 2899.90 5499.05 3699.77 10598.77 293
sss97.21 23896.93 23898.06 24498.83 24395.22 27196.75 27098.48 29694.49 30297.27 29397.90 29692.77 28699.80 18196.57 19899.32 24299.16 237
Test_1112_low_res96.99 25796.55 26798.31 22599.35 13195.47 26395.84 31599.53 6191.51 34796.80 31798.48 24991.36 29799.83 14896.58 19699.53 20499.62 52
1112_ss97.29 23296.86 24498.58 19399.34 13396.32 24096.75 27099.58 3493.14 32796.89 31297.48 32192.11 29399.86 10196.91 16599.54 20099.57 75
ab-mvs-re8.12 35510.83 3580.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 38897.48 3210.00 3920.00 3880.00 3860.00 3860.00 384
ab-mvs98.41 13898.36 12998.59 19299.19 16097.23 20899.32 2298.81 27097.66 17098.62 19099.40 6396.82 16499.80 18195.88 24099.51 21098.75 296
TR-MVS95.55 30295.12 30796.86 31297.54 35293.94 30796.49 28396.53 34894.36 30997.03 30396.61 34394.26 26199.16 36186.91 36896.31 36497.47 356
MDTV_nov1_ep13_2view74.92 38897.69 19590.06 36097.75 26385.78 33293.52 31398.69 302
MDTV_nov1_ep1395.22 30497.06 36583.20 38197.74 19196.16 35194.37 30896.99 30498.83 18683.95 34799.53 31493.90 30297.95 338
MIMVSNet199.38 2099.32 2299.55 2699.86 1499.19 3999.41 1399.59 3299.59 2099.71 1799.57 3297.12 14599.90 5499.21 2899.87 6299.54 92
MIMVSNet96.62 27296.25 27897.71 26599.04 19994.66 28899.16 5096.92 34397.23 21997.87 25499.10 11386.11 33099.65 27891.65 34399.21 26098.82 282
IterMVS-LS98.55 12098.70 7698.09 23999.48 10294.73 28597.22 23999.39 10698.97 8599.38 6499.31 7796.00 20399.93 3198.58 6499.97 1299.60 58
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet97.69 20197.35 21698.69 18098.73 25797.02 22296.92 25998.75 27995.89 27398.59 19698.67 21392.08 29499.74 23196.72 18799.81 8199.32 194
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ACMMP++_ref99.77 105
IterMVS97.73 19998.11 16096.57 31699.24 14690.28 35695.52 32799.21 17998.86 9599.33 7499.33 7493.11 27899.94 2698.49 7299.94 2899.48 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS Recon97.33 22896.92 24098.57 19599.09 18797.99 15296.79 26699.35 12293.18 32697.71 26498.07 28695.00 23999.31 34893.97 29999.13 27598.42 317
MVS_111021_LR98.30 14998.12 15998.83 16099.16 17198.03 15096.09 30199.30 15197.58 17798.10 24098.24 27098.25 5599.34 34496.69 19099.65 16299.12 239
DP-MVS98.93 5898.81 6399.28 8899.21 15398.45 11298.46 11899.33 13399.63 1499.48 4599.15 10697.23 14199.75 22797.17 14299.66 16199.63 51
ACMMP++99.68 150
HQP-MVS97.00 25696.49 26998.55 20098.67 27696.79 22996.29 29399.04 22696.05 26695.55 34996.84 33993.84 26799.54 31292.82 32699.26 25499.32 194
QAPM97.31 22996.81 25098.82 16198.80 25097.49 19599.06 6299.19 18690.22 35797.69 26699.16 10296.91 15899.90 5490.89 35699.41 22899.07 243
Vis-MVSNetpermissive99.34 2299.36 1799.27 9199.73 3198.26 12399.17 4999.78 1099.11 6399.27 8599.48 4998.82 2399.95 1798.94 4399.93 3299.59 64
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS-HIRNet94.32 31995.62 29090.42 36498.46 30275.36 38796.29 29389.13 38195.25 28995.38 35599.75 1092.88 28499.19 35994.07 29899.39 23196.72 365
IS-MVSNet98.19 16297.90 17899.08 12299.57 6497.97 15799.31 2698.32 30299.01 8198.98 13299.03 12891.59 29699.79 19495.49 26199.80 9199.48 125
HyFIR lowres test97.19 24096.60 26598.96 14399.62 5997.28 20695.17 33599.50 6794.21 31199.01 12798.32 26686.61 32499.99 297.10 15199.84 6899.60 58
EPMVS93.72 33193.27 33095.09 34596.04 37987.76 36698.13 14585.01 38594.69 30096.92 30698.64 22178.47 37499.31 34895.04 26796.46 36298.20 324
PAPM_NR96.82 26496.32 27498.30 22699.07 19196.69 23497.48 21898.76 27695.81 27696.61 32396.47 34794.12 26599.17 36090.82 35797.78 34099.06 244
TAMVS98.24 15898.05 16698.80 16599.07 19197.18 21597.88 17598.81 27096.66 24599.17 10599.21 9194.81 24699.77 21296.96 16399.88 5999.44 143
PAPR95.29 30694.47 31597.75 26397.50 35795.14 27494.89 34398.71 28491.39 34995.35 35695.48 36394.57 25399.14 36384.95 37197.37 34898.97 262
RPSCF98.62 10898.36 12999.42 6299.65 5299.42 798.55 10399.57 4197.72 16798.90 14899.26 8396.12 19899.52 31895.72 25099.71 13499.32 194
Vis-MVSNet (Re-imp)97.46 21897.16 22798.34 22299.55 7596.10 24498.94 7398.44 29798.32 12298.16 23298.62 22788.76 31199.73 23593.88 30499.79 9699.18 230
test_040298.76 8198.71 7398.93 14799.56 7198.14 13898.45 12099.34 12899.28 4998.95 13898.91 16198.34 5399.79 19495.63 25699.91 4898.86 279
MVS_111021_HR98.25 15798.08 16498.75 17599.09 18797.46 19795.97 30499.27 16497.60 17697.99 24898.25 26998.15 7099.38 34196.87 17399.57 19299.42 150
CSCG98.68 9798.50 10399.20 10399.45 10898.63 9598.56 10299.57 4197.87 15798.85 16098.04 28797.66 10099.84 13396.72 18799.81 8199.13 238
PatchMatch-RL97.24 23696.78 25198.61 19099.03 20297.83 17296.36 29099.06 21993.49 32497.36 29297.78 30295.75 21799.49 32493.44 31698.77 30498.52 310
API-MVS97.04 25296.91 24297.42 28797.88 33898.23 12998.18 14198.50 29597.57 17897.39 29096.75 34196.77 16899.15 36290.16 35999.02 28994.88 377
Test By Simon96.52 181
TDRefinement99.42 1699.38 1699.55 2699.76 2899.33 1899.68 599.71 1599.38 3899.53 3899.61 2798.64 3299.80 18198.24 8599.84 6899.52 104
USDC97.41 22397.40 21197.44 28698.94 21793.67 31895.17 33599.53 6194.03 31698.97 13599.10 11395.29 23199.34 34495.84 24699.73 12299.30 201
EPP-MVSNet98.30 14998.04 16799.07 12599.56 7197.83 17299.29 3298.07 31499.03 7998.59 19699.13 10992.16 29299.90 5496.87 17399.68 15099.49 115
PMMVS96.51 27595.98 28198.09 23997.53 35395.84 25294.92 34298.84 26491.58 34596.05 34095.58 36095.68 21999.66 27395.59 25898.09 33398.76 295
PAPM91.88 34690.34 34996.51 31798.06 32992.56 33392.44 37497.17 33586.35 37190.38 37896.01 35386.61 32499.21 35870.65 38395.43 37197.75 345
ACMMPcopyleft98.75 8398.50 10399.52 4299.56 7199.16 4598.87 7799.37 11297.16 22498.82 16899.01 13897.71 9799.87 9296.29 22299.69 14599.54 92
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 24296.71 25598.55 20098.56 29198.05 14996.33 29198.93 24496.91 23597.06 30197.39 32694.38 25899.45 33291.66 34299.18 26798.14 327
PatchmatchNetpermissive95.58 30195.67 28995.30 34397.34 36087.32 36897.65 20096.65 34695.30 28897.07 30098.69 20984.77 33999.75 22794.97 26998.64 31498.83 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PHI-MVS98.29 15297.95 17399.34 7798.44 30499.16 4598.12 14799.38 10896.01 26998.06 24398.43 25297.80 9299.67 26395.69 25299.58 18899.20 223
F-COLMAP97.30 23096.68 25799.14 11299.19 16098.39 11497.27 23599.30 15192.93 33096.62 32298.00 28895.73 21899.68 26092.62 33298.46 32099.35 184
ANet_high99.57 799.67 599.28 8899.89 698.09 14099.14 5299.93 299.82 399.93 399.81 599.17 1299.94 2699.31 20100.00 199.82 12
wuyk23d96.06 28997.62 19891.38 36398.65 28298.57 10298.85 8096.95 34196.86 23799.90 599.16 10299.18 1198.40 37689.23 36299.77 10577.18 381
OMC-MVS97.88 18597.49 20599.04 13498.89 23298.63 9596.94 25599.25 17095.02 29298.53 20798.51 24097.27 13699.47 32993.50 31599.51 21099.01 253
MG-MVS96.77 26596.61 26397.26 29398.31 31493.06 32495.93 30998.12 31396.45 25397.92 25098.73 20293.77 27199.39 33991.19 35299.04 28599.33 192
AdaColmapbinary97.14 24496.71 25598.46 21198.34 31297.80 17896.95 25498.93 24495.58 27996.92 30697.66 30995.87 21399.53 31490.97 35399.14 27298.04 331
uanet0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
ITE_SJBPF98.87 15599.22 15198.48 11099.35 12297.50 18498.28 22698.60 23197.64 10499.35 34393.86 30599.27 25198.79 291
DeepMVS_CXcopyleft93.44 35998.24 31894.21 29894.34 36264.28 38191.34 37794.87 37489.45 30992.77 38477.54 38293.14 37893.35 379
TinyColmap97.89 18397.98 17197.60 27298.86 23694.35 29596.21 29799.44 9197.45 19499.06 11698.88 17397.99 8199.28 35394.38 28999.58 18899.18 230
MAR-MVS96.47 27995.70 28798.79 16797.92 33599.12 6098.28 13298.60 29092.16 34095.54 35296.17 35294.77 25099.52 31889.62 36198.23 32497.72 347
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 18197.69 19098.52 20499.17 16997.66 18797.19 24399.47 8396.31 25897.85 25698.20 27496.71 17499.52 31894.62 27799.72 12998.38 318
MSDG97.71 20097.52 20398.28 22898.91 22696.82 22894.42 35699.37 11297.65 17198.37 22398.29 26897.40 12899.33 34694.09 29799.22 25898.68 305
LS3D98.63 10698.38 12799.36 6997.25 36299.38 899.12 5599.32 13599.21 5298.44 21498.88 17397.31 13299.80 18196.58 19699.34 24098.92 270
CLD-MVS97.49 21597.16 22798.48 20999.07 19197.03 22194.71 34699.21 17994.46 30498.06 24397.16 33497.57 11099.48 32794.46 28299.78 10198.95 264
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS93.44 33492.23 33997.08 29999.25 14597.86 16995.61 32297.16 33692.90 33193.76 37198.65 21875.94 37695.66 38179.30 38197.49 34397.73 346
Gipumacopyleft99.03 4599.16 3198.64 18399.94 298.51 10899.32 2299.75 1399.58 2298.60 19499.62 2598.22 6099.51 32297.70 12199.73 12297.89 336
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015