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

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

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

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

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




Method Infoallhigh-res
multi-view
indooroutdoorbotani.boulde.bridgedoorexhibi.lectur.living.loungeobserv.old co.statueterrac.
sort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
LCM-MVSNet95.70 196.40 193.61 398.67 185.39 3795.54 597.36 196.97 199.04 199.05 196.61 195.92 1685.07 7199.27 199.54 1
mamv495.37 294.51 297.96 196.31 1098.41 191.05 5197.23 295.32 299.01 297.26 980.16 15098.99 195.15 199.14 296.47 35
TDRefinement93.52 393.39 593.88 295.94 1590.26 495.70 496.46 390.58 992.86 5496.29 2288.16 3694.17 10686.07 5698.48 1897.22 18
LTVRE_ROB86.10 193.04 493.44 491.82 2293.73 6985.72 3496.79 195.51 1088.86 1695.63 1096.99 1384.81 8093.16 15091.10 297.53 8196.58 33
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
reproduce_model92.89 593.18 892.01 1394.20 5488.23 992.87 1394.32 2290.25 1195.65 995.74 3387.75 4395.72 3989.60 598.27 2892.08 236
reproduce-ours92.86 693.22 691.76 2394.39 4687.71 1192.40 2894.38 2089.82 1395.51 1295.49 4289.64 2295.82 2989.13 798.26 3091.76 247
our_new_method92.86 693.22 691.76 2394.39 4687.71 1192.40 2894.38 2089.82 1395.51 1295.49 4289.64 2295.82 2989.13 798.26 3091.76 247
HPM-MVS_fast92.50 892.54 1092.37 695.93 1685.81 3392.99 1294.23 2885.21 4492.51 6295.13 5290.65 1095.34 5988.06 1698.15 3995.95 46
lecture92.43 993.50 389.21 6694.43 4479.31 8492.69 1995.72 888.48 2294.43 2095.73 3491.34 494.68 8290.26 498.44 2093.63 146
SR-MVS-dyc-post92.41 1092.41 1192.39 594.13 6088.95 692.87 1394.16 3388.75 1893.79 3394.43 7688.83 2795.51 5087.16 3897.60 7592.73 192
SR-MVS92.23 1192.34 1291.91 1794.89 3887.85 1092.51 2593.87 5288.20 2493.24 4394.02 10090.15 1795.67 4186.82 4397.34 8592.19 231
HPM-MVScopyleft92.13 1292.20 1491.91 1795.58 2684.67 4693.51 894.85 1682.88 7291.77 7693.94 10890.55 1395.73 3888.50 1298.23 3395.33 61
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS_3200maxsize92.05 1392.24 1391.48 2593.02 8885.17 3992.47 2795.05 1587.65 2893.21 4794.39 8190.09 1895.08 7086.67 4597.60 7594.18 114
COLMAP_ROBcopyleft83.01 391.97 1491.95 1592.04 1193.68 7086.15 2493.37 1095.10 1490.28 1092.11 6895.03 5489.75 2194.93 7479.95 13498.27 2895.04 74
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMPcopyleft91.91 1591.87 2092.03 1295.53 2785.91 2893.35 1194.16 3382.52 7592.39 6594.14 9389.15 2695.62 4287.35 3398.24 3294.56 90
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
mPP-MVS91.69 1691.47 2792.37 696.04 1388.48 892.72 1892.60 11783.09 6991.54 7894.25 8787.67 4695.51 5087.21 3798.11 4093.12 175
CP-MVS91.67 1791.58 2491.96 1495.29 3187.62 1393.38 993.36 7483.16 6891.06 8894.00 10188.26 3395.71 4087.28 3698.39 2392.55 206
XVS91.54 1891.36 2992.08 995.64 2486.25 2292.64 2093.33 7885.07 4589.99 11094.03 9986.57 5995.80 3187.35 3397.62 7394.20 111
MTAPA91.52 1991.60 2391.29 3096.59 486.29 2192.02 3891.81 14484.07 5592.00 7194.40 8086.63 5895.28 6288.59 1198.31 2692.30 223
UA-Net91.49 2091.53 2591.39 2794.98 3582.95 5893.52 792.79 10888.22 2388.53 14797.64 683.45 9494.55 9086.02 6098.60 1396.67 30
ACMMPR91.49 2091.35 3191.92 1695.74 2085.88 3092.58 2393.25 8481.99 7891.40 8094.17 9287.51 4795.87 2087.74 2297.76 6093.99 122
LPG-MVS_test91.47 2291.68 2190.82 3794.75 4181.69 6390.00 6794.27 2582.35 7693.67 3894.82 6091.18 595.52 4885.36 6798.73 795.23 66
region2R91.44 2391.30 3591.87 1995.75 1985.90 2992.63 2293.30 8281.91 8090.88 9594.21 8887.75 4395.87 2087.60 2797.71 6393.83 131
HFP-MVS91.30 2491.39 2891.02 3395.43 2984.66 4792.58 2393.29 8381.99 7891.47 7993.96 10588.35 3295.56 4587.74 2297.74 6292.85 189
ZNCC-MVS91.26 2591.34 3291.01 3495.73 2183.05 5692.18 3294.22 3080.14 10191.29 8493.97 10287.93 4295.87 2088.65 1097.96 5194.12 118
APDe-MVScopyleft91.22 2691.92 1689.14 6892.97 9078.04 9692.84 1694.14 3783.33 6693.90 2995.73 3488.77 2896.41 387.60 2797.98 4892.98 185
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PGM-MVS91.20 2790.95 4591.93 1595.67 2385.85 3190.00 6793.90 4980.32 9891.74 7794.41 7988.17 3595.98 1386.37 4997.99 4693.96 124
SteuartSystems-ACMMP91.16 2891.36 2990.55 4193.91 6580.97 7091.49 4593.48 7282.82 7392.60 6193.97 10288.19 3496.29 687.61 2698.20 3694.39 105
Skip Steuart: Steuart Systems R&D Blog.
MP-MVScopyleft91.14 2990.91 4691.83 2096.18 1186.88 1792.20 3193.03 9882.59 7488.52 14894.37 8286.74 5795.41 5786.32 5098.21 3493.19 170
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS90.96 3091.01 4290.82 3795.45 2882.73 5991.75 4393.74 5980.98 9191.38 8193.80 11287.20 5195.80 3187.10 4097.69 6593.93 125
MP-MVS-pluss90.81 3191.08 3989.99 5095.97 1479.88 7788.13 11094.51 1975.79 15992.94 5194.96 5588.36 3195.01 7290.70 398.40 2295.09 73
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+77.89 1190.73 3291.50 2688.44 8393.00 8976.26 12289.65 8095.55 987.72 2793.89 3194.94 5691.62 393.44 14178.35 15698.76 495.61 55
ACMMP_NAP90.65 3391.07 4189.42 6295.93 1679.54 8289.95 7193.68 6477.65 13691.97 7294.89 5788.38 3095.45 5589.27 697.87 5693.27 165
ACMM79.39 990.65 3390.99 4389.63 5895.03 3483.53 5189.62 8193.35 7779.20 11493.83 3293.60 12290.81 892.96 15785.02 7498.45 1992.41 213
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D90.60 3590.34 5591.38 2889.03 20384.23 4993.58 694.68 1890.65 890.33 10493.95 10784.50 8295.37 5880.87 12495.50 15794.53 94
ACMP79.16 1090.54 3690.60 5390.35 4594.36 5180.98 6989.16 9294.05 4279.03 11792.87 5393.74 11790.60 1295.21 6582.87 10298.76 494.87 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVScopyleft90.53 3791.08 3988.88 7193.38 7978.65 9089.15 9394.05 4284.68 4993.90 2994.11 9588.13 3796.30 584.51 8497.81 5891.70 251
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MED-MVS90.48 3891.14 3688.50 8094.38 4876.12 12692.12 3393.85 5383.72 6093.24 4393.18 13187.06 5295.85 2484.99 7597.69 6593.54 156
SED-MVS90.46 3991.64 2286.93 10994.18 5572.65 15690.47 6093.69 6283.77 5894.11 2794.27 8390.28 1595.84 2786.03 5797.92 5292.29 225
SMA-MVScopyleft90.31 4090.48 5489.83 5595.31 3079.52 8390.98 5293.24 8575.37 16892.84 5595.28 4885.58 7296.09 887.92 1897.76 6093.88 128
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
SF-MVS90.27 4190.80 4888.68 7892.86 9477.09 11191.19 4995.74 681.38 8692.28 6793.80 11286.89 5694.64 8585.52 6697.51 8294.30 110
v7n90.13 4290.96 4487.65 9991.95 12271.06 19089.99 6993.05 9586.53 3594.29 2396.27 2382.69 10394.08 10986.25 5397.63 7197.82 8
ME-MVS90.09 4390.66 5188.38 8592.82 9776.12 12689.40 9093.70 6183.72 6092.39 6593.18 13188.02 4095.47 5384.99 7597.69 6593.54 156
PMVScopyleft80.48 690.08 4490.66 5188.34 8796.71 392.97 290.31 6489.57 22188.51 2190.11 10695.12 5390.98 788.92 28077.55 17097.07 9283.13 408
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++90.07 4591.09 3887.00 10791.55 13972.64 15896.19 294.10 4085.33 4293.49 4094.64 6881.12 13895.88 1887.41 3195.94 13792.48 209
DVP-MVScopyleft90.06 4691.32 3386.29 12194.16 5872.56 16290.54 5791.01 17283.61 6393.75 3594.65 6589.76 1995.78 3586.42 4797.97 4990.55 288
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
PS-CasMVS90.06 4691.92 1684.47 17396.56 658.83 36189.04 9492.74 11091.40 696.12 596.06 2987.23 5095.57 4479.42 14498.74 699.00 2
PEN-MVS90.03 4891.88 1984.48 17296.57 558.88 35888.95 9593.19 8791.62 596.01 796.16 2787.02 5495.60 4378.69 15298.72 998.97 3
OurMVSNet-221017-090.01 4989.74 6090.83 3693.16 8680.37 7491.91 4193.11 9181.10 8995.32 1497.24 1072.94 25494.85 7685.07 7197.78 5997.26 16
DTE-MVSNet89.98 5091.91 1884.21 18296.51 757.84 36988.93 9692.84 10691.92 496.16 496.23 2486.95 5595.99 1279.05 14898.57 1598.80 6
XVG-ACMP-BASELINE89.98 5089.84 5890.41 4394.91 3784.50 4889.49 8693.98 4479.68 10692.09 6993.89 11083.80 8993.10 15382.67 10698.04 4193.64 145
TestfortrainingZip a89.97 5290.77 4987.58 10094.38 4873.21 15092.12 3393.85 5377.53 14093.24 4393.18 13187.06 5295.85 2487.89 1997.69 6593.68 140
3Dnovator+83.92 289.97 5289.66 6190.92 3591.27 14881.66 6691.25 4794.13 3888.89 1588.83 13994.26 8677.55 17995.86 2384.88 7895.87 14395.24 65
WR-MVS_H89.91 5491.31 3485.71 13896.32 962.39 30189.54 8493.31 8190.21 1295.57 1195.66 3781.42 13595.90 1780.94 12398.80 398.84 5
OPM-MVS89.80 5589.97 5689.27 6494.76 4079.86 7886.76 13792.78 10978.78 12092.51 6293.64 12188.13 3793.84 12084.83 8097.55 7894.10 119
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets89.78 5689.27 6791.30 2993.51 7384.79 4489.89 7390.63 18270.00 25794.55 1996.67 1787.94 4193.59 13284.27 8695.97 13395.52 56
anonymousdsp89.73 5788.88 7792.27 889.82 18486.67 1890.51 5990.20 20369.87 25895.06 1596.14 2884.28 8593.07 15487.68 2496.34 11597.09 20
test_djsdf89.62 5889.01 7191.45 2692.36 10782.98 5791.98 3990.08 20671.54 23594.28 2596.54 1981.57 13394.27 9686.26 5196.49 10997.09 20
XVG-OURS-SEG-HR89.59 5989.37 6590.28 4694.47 4385.95 2786.84 13393.91 4880.07 10286.75 19793.26 12893.64 290.93 21884.60 8390.75 31693.97 123
APD-MVScopyleft89.54 6089.63 6289.26 6592.57 10081.34 6890.19 6693.08 9480.87 9391.13 8693.19 13086.22 6695.97 1482.23 11297.18 9090.45 290
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax89.41 6188.81 8091.19 3293.38 7984.72 4589.70 7690.29 20069.27 26594.39 2196.38 2186.02 6993.52 13783.96 8895.92 13995.34 60
CPTT-MVS89.39 6288.98 7390.63 4095.09 3386.95 1692.09 3792.30 12679.74 10587.50 18192.38 16781.42 13593.28 14683.07 9897.24 8891.67 252
ACMH76.49 1489.34 6391.14 3683.96 19092.50 10370.36 19989.55 8293.84 5681.89 8194.70 1795.44 4490.69 988.31 29883.33 9498.30 2793.20 169
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testf189.30 6489.12 6889.84 5388.67 21485.64 3590.61 5593.17 8886.02 3893.12 4895.30 4684.94 7789.44 27274.12 22196.10 12894.45 99
APD_test289.30 6489.12 6889.84 5388.67 21485.64 3590.61 5593.17 8886.02 3893.12 4895.30 4684.94 7789.44 27274.12 22196.10 12894.45 99
CP-MVSNet89.27 6690.91 4684.37 17496.34 858.61 36488.66 10392.06 13390.78 795.67 895.17 5181.80 13095.54 4779.00 14998.69 1098.95 4
XVG-OURS89.18 6788.83 7990.23 4794.28 5286.11 2685.91 15293.60 6780.16 10089.13 13593.44 12483.82 8890.98 21583.86 9095.30 16593.60 149
DeepC-MVS82.31 489.15 6889.08 7089.37 6393.64 7179.07 8688.54 10694.20 3173.53 19389.71 11894.82 6085.09 7695.77 3784.17 8798.03 4393.26 167
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet_ETH3D89.12 6990.72 5084.31 18097.00 264.33 27289.67 7988.38 24188.84 1794.29 2397.57 790.48 1491.26 20672.57 25297.65 7097.34 15
MSP-MVS89.08 7088.16 8791.83 2095.76 1886.14 2592.75 1793.90 4978.43 12589.16 13392.25 17672.03 26896.36 488.21 1390.93 30892.98 185
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
SD-MVS88.96 7189.88 5786.22 12591.63 13377.07 11289.82 7493.77 5878.90 11892.88 5292.29 17486.11 6790.22 24686.24 5497.24 8891.36 260
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
HPM-MVS++copyleft88.93 7288.45 8390.38 4494.92 3685.85 3189.70 7691.27 16478.20 12886.69 20192.28 17580.36 14895.06 7186.17 5596.49 10990.22 294
Elysia88.71 7388.89 7588.19 9091.26 14972.96 15288.10 11193.59 6884.31 5190.42 10094.10 9674.07 23194.82 7788.19 1495.92 13996.80 27
StellarMVS88.71 7388.89 7588.19 9091.26 14972.96 15288.10 11193.59 6884.31 5190.42 10094.10 9674.07 23194.82 7788.19 1495.92 13996.80 27
test_040288.65 7589.58 6485.88 13492.55 10172.22 17084.01 20189.44 22488.63 2094.38 2295.77 3286.38 6593.59 13279.84 13595.21 16691.82 245
DP-MVS88.60 7689.01 7187.36 10291.30 14677.50 10487.55 11992.97 10287.95 2689.62 12292.87 15084.56 8193.89 11777.65 16896.62 10490.70 280
APD_test188.40 7787.91 8989.88 5289.50 19086.65 2089.98 7091.91 13984.26 5390.87 9693.92 10982.18 11889.29 27673.75 22994.81 18593.70 139
Anonymous2023121188.40 7789.62 6384.73 16490.46 16965.27 26188.86 9793.02 9987.15 3093.05 5097.10 1182.28 11692.02 18376.70 18097.99 4696.88 26
PS-MVSNAJss88.31 7987.90 9089.56 6093.31 8177.96 9987.94 11591.97 13670.73 24794.19 2696.67 1776.94 19394.57 8883.07 9896.28 11796.15 38
OMC-MVS88.19 8087.52 9490.19 4891.94 12481.68 6587.49 12293.17 8876.02 15388.64 14491.22 21684.24 8693.37 14477.97 16697.03 9395.52 56
CS-MVS88.14 8187.67 9389.54 6189.56 18879.18 8590.47 6094.77 1779.37 11284.32 26889.33 28383.87 8794.53 9182.45 10894.89 18194.90 76
TSAR-MVS + MP.88.14 8187.82 9189.09 6995.72 2276.74 11592.49 2691.19 16767.85 29286.63 20294.84 5979.58 15695.96 1587.62 2594.50 19494.56 90
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
tt080588.09 8389.79 5982.98 22193.26 8363.94 27691.10 5089.64 21885.07 4590.91 9291.09 22289.16 2591.87 18882.03 11395.87 14393.13 172
EC-MVSNet88.01 8488.32 8687.09 10489.28 19572.03 17390.31 6496.31 480.88 9285.12 24389.67 27684.47 8395.46 5482.56 10796.26 12093.77 137
RPSCF88.00 8586.93 10891.22 3190.08 17789.30 589.68 7891.11 16879.26 11389.68 11994.81 6382.44 10787.74 30976.54 18588.74 35296.61 32
AllTest87.97 8687.40 9889.68 5691.59 13483.40 5289.50 8595.44 1179.47 10888.00 16393.03 14182.66 10491.47 19670.81 26696.14 12594.16 115
TranMVSNet+NR-MVSNet87.86 8788.76 8185.18 15094.02 6364.13 27384.38 19391.29 16084.88 4892.06 7093.84 11186.45 6293.73 12273.22 24398.66 1197.69 9
nrg03087.85 8888.49 8285.91 13290.07 17969.73 20787.86 11694.20 3174.04 18592.70 6094.66 6485.88 7091.50 19579.72 13797.32 8696.50 34
CNVR-MVS87.81 8987.68 9288.21 8992.87 9277.30 11085.25 17091.23 16577.31 14387.07 19191.47 20682.94 9994.71 8184.67 8296.27 11992.62 200
HQP_MVS87.75 9087.43 9788.70 7793.45 7576.42 11989.45 8793.61 6579.44 11086.55 20392.95 14774.84 21895.22 6380.78 12695.83 14594.46 97
sc_t187.70 9188.94 7483.99 18893.47 7467.15 23885.05 17588.21 24886.81 3291.87 7497.65 585.51 7487.91 30474.22 21697.63 7196.92 25
MM87.64 9287.15 10089.09 6989.51 18976.39 12188.68 10286.76 27984.54 5083.58 28793.78 11473.36 24996.48 287.98 1796.21 12194.41 104
MVSMamba_PlusPlus87.53 9388.86 7883.54 20792.03 12062.26 30591.49 4592.62 11488.07 2588.07 16096.17 2672.24 26395.79 3484.85 7994.16 20792.58 204
NCCC87.36 9486.87 10988.83 7292.32 11078.84 8986.58 14191.09 17078.77 12184.85 25590.89 23380.85 14195.29 6081.14 12195.32 16292.34 221
DeepPCF-MVS81.24 587.28 9586.21 11990.49 4291.48 14384.90 4283.41 22492.38 12270.25 25489.35 13090.68 24382.85 10294.57 8879.55 14195.95 13692.00 240
SixPastTwentyTwo87.20 9687.45 9686.45 11892.52 10269.19 21787.84 11788.05 24981.66 8394.64 1896.53 2065.94 30594.75 8083.02 10096.83 9895.41 58
fmvsm_s_conf0.5_n_987.04 9787.02 10587.08 10589.67 18675.87 12984.60 18689.74 21374.40 18289.92 11493.41 12580.45 14690.63 23386.66 4694.37 20094.73 87
SPE-MVS-test87.00 9886.43 11588.71 7689.46 19177.46 10589.42 8995.73 777.87 13481.64 32987.25 32882.43 10894.53 9177.65 16896.46 11194.14 117
UniMVSNet (Re)86.87 9986.98 10786.55 11693.11 8768.48 22783.80 21192.87 10480.37 9689.61 12491.81 19277.72 17594.18 10475.00 20998.53 1696.99 24
Vis-MVSNetpermissive86.86 10086.58 11287.72 9792.09 11777.43 10787.35 12392.09 13278.87 11984.27 27394.05 9878.35 16793.65 12580.54 13091.58 29292.08 236
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet86.84 10187.06 10386.17 12892.86 9467.02 24282.55 25191.56 15083.08 7090.92 9091.82 19178.25 16893.99 11174.16 21998.35 2497.49 13
DU-MVS86.80 10286.99 10686.21 12693.24 8467.02 24283.16 23492.21 12781.73 8290.92 9091.97 18277.20 18793.99 11174.16 21998.35 2497.61 10
casdiffmvs_mvgpermissive86.72 10387.51 9584.36 17687.09 26665.22 26284.16 19794.23 2877.89 13291.28 8593.66 12084.35 8492.71 16380.07 13194.87 18495.16 71
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsmconf0.01_n86.68 10486.52 11387.18 10385.94 30578.30 9286.93 13092.20 12865.94 31289.16 13393.16 13683.10 9789.89 26187.81 2194.43 19893.35 160
tt0320-xc86.67 10588.41 8481.44 26593.45 7560.44 33583.96 20388.50 23787.26 2990.90 9497.90 385.61 7186.40 33570.14 27898.01 4597.47 14
IS-MVSNet86.66 10686.82 11186.17 12892.05 11966.87 24691.21 4888.64 23486.30 3789.60 12592.59 15969.22 28694.91 7573.89 22697.89 5596.72 29
tt032086.63 10788.36 8581.41 26693.57 7260.73 33284.37 19488.61 23687.00 3190.75 9797.98 285.54 7386.45 33369.75 28397.70 6497.06 22
v1086.54 10887.10 10284.84 15888.16 22963.28 28386.64 14092.20 12875.42 16792.81 5794.50 7274.05 23494.06 11083.88 8996.28 11797.17 19
pmmvs686.52 10988.06 8881.90 25192.22 11362.28 30484.66 18589.15 22883.54 6589.85 11597.32 888.08 3986.80 32670.43 27597.30 8796.62 31
NormalMVS86.47 11085.32 14289.94 5194.43 4480.42 7288.63 10493.59 6874.56 17785.12 24390.34 25666.19 30294.20 10176.57 18398.44 2095.19 68
PHI-MVS86.38 11185.81 12988.08 9288.44 22377.34 10889.35 9193.05 9573.15 20684.76 25787.70 31778.87 16194.18 10480.67 12896.29 11692.73 192
CSCG86.26 11286.47 11485.60 14090.87 16174.26 13987.98 11491.85 14080.35 9789.54 12888.01 30479.09 15992.13 17975.51 20295.06 17390.41 291
DeepC-MVS_fast80.27 886.23 11385.65 13587.96 9591.30 14676.92 11387.19 12591.99 13570.56 24884.96 25090.69 24280.01 15295.14 6878.37 15595.78 14991.82 245
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v886.22 11486.83 11084.36 17687.82 23762.35 30386.42 14491.33 15976.78 14792.73 5994.48 7473.41 24693.72 12383.10 9795.41 15897.01 23
Anonymous2024052986.20 11587.13 10183.42 20990.19 17464.55 26984.55 18890.71 17985.85 4089.94 11395.24 5082.13 11990.40 24169.19 29096.40 11495.31 62
fmvsm_s_conf0.5_n_386.19 11687.27 9982.95 22386.91 27470.38 19885.31 16992.61 11675.59 16388.32 15592.87 15082.22 11788.63 28988.80 992.82 25489.83 304
test_fmvsmconf0.1_n86.18 11785.88 12787.08 10585.26 32178.25 9385.82 15691.82 14265.33 32788.55 14692.35 17382.62 10689.80 26386.87 4294.32 20293.18 171
CDPH-MVS86.17 11885.54 13688.05 9492.25 11175.45 13283.85 20892.01 13465.91 31486.19 21491.75 19683.77 9094.98 7377.43 17396.71 10293.73 138
NR-MVSNet86.00 11986.22 11885.34 14793.24 8464.56 26882.21 26590.46 18880.99 9088.42 15191.97 18277.56 17893.85 11872.46 25398.65 1297.61 10
train_agg85.98 12085.28 14388.07 9392.34 10879.70 8083.94 20490.32 19565.79 31684.49 26290.97 22781.93 12593.63 12781.21 12096.54 10790.88 274
KinetiMVS85.95 12186.10 12285.50 14487.56 24769.78 20583.70 21489.83 21280.42 9587.76 17493.24 12973.76 24091.54 19485.03 7393.62 22895.19 68
FC-MVSNet-test85.93 12287.05 10482.58 23492.25 11156.44 38085.75 15793.09 9377.33 14291.94 7394.65 6574.78 22093.41 14375.11 20898.58 1497.88 7
test_fmvsmconf_n85.88 12385.51 13786.99 10884.77 33078.21 9485.40 16791.39 15765.32 32887.72 17691.81 19282.33 11189.78 26486.68 4494.20 20592.99 183
Effi-MVS+-dtu85.82 12483.38 19593.14 487.13 26191.15 387.70 11888.42 24074.57 17683.56 28885.65 35278.49 16694.21 10072.04 25592.88 25094.05 121
TAPA-MVS77.73 1285.71 12584.83 15388.37 8688.78 21379.72 7987.15 12793.50 7169.17 26685.80 22689.56 27780.76 14292.13 17973.21 24895.51 15693.25 168
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda85.50 12686.14 12083.58 20387.97 23167.13 23987.55 11994.32 2273.44 19688.47 14987.54 32086.45 6291.06 21375.76 19893.76 21992.54 207
canonicalmvs85.50 12686.14 12083.58 20387.97 23167.13 23987.55 11994.32 2273.44 19688.47 14987.54 32086.45 6291.06 21375.76 19893.76 21992.54 207
fmvsm_s_conf0.5_n_885.48 12885.75 13284.68 16787.10 26469.98 20384.28 19592.68 11174.77 17387.90 16792.36 17273.94 23590.41 24085.95 6292.74 25693.66 141
EPP-MVSNet85.47 12985.04 14886.77 11391.52 14269.37 21291.63 4487.98 25281.51 8587.05 19291.83 19066.18 30495.29 6070.75 26996.89 9595.64 53
GeoE85.45 13085.81 12984.37 17490.08 17767.07 24185.86 15591.39 15772.33 22487.59 17890.25 26184.85 7992.37 17378.00 16491.94 28193.66 141
MGCNet85.37 13184.58 16587.75 9685.28 32073.36 14486.54 14385.71 29677.56 13981.78 32792.47 16570.29 28096.02 1185.59 6595.96 13493.87 129
FIs85.35 13286.27 11782.60 23391.86 12657.31 37385.10 17493.05 9575.83 15891.02 8993.97 10273.57 24292.91 16173.97 22598.02 4497.58 12
test_fmvsmvis_n_192085.22 13385.36 14184.81 16085.80 30876.13 12585.15 17392.32 12561.40 36391.33 8290.85 23683.76 9186.16 34184.31 8593.28 23792.15 234
casdiffmvspermissive85.21 13485.85 12883.31 21286.17 29762.77 29083.03 23693.93 4774.69 17588.21 15792.68 15882.29 11591.89 18777.87 16793.75 22295.27 64
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_1085.20 13585.25 14485.02 15586.01 30371.31 18584.96 17691.76 14669.10 26888.90 13692.56 16273.84 23890.63 23386.88 4193.26 23893.13 172
baseline85.20 13585.93 12583.02 21986.30 29262.37 30284.55 18893.96 4574.48 17987.12 18692.03 18182.30 11391.94 18478.39 15494.21 20494.74 86
SSM_040485.16 13785.09 14685.36 14690.14 17669.52 21086.17 14991.58 14874.41 18086.55 20391.49 20378.54 16293.97 11373.71 23093.21 24292.59 203
K. test v385.14 13884.73 15586.37 11991.13 15569.63 20985.45 16576.68 38184.06 5692.44 6496.99 1362.03 33294.65 8480.58 12993.24 23994.83 83
mmtdpeth85.13 13985.78 13183.17 21784.65 33274.71 13585.87 15490.35 19477.94 13183.82 28096.96 1577.75 17380.03 40078.44 15396.21 12194.79 85
EI-MVSNet-Vis-set85.12 14084.53 16886.88 11084.01 34572.76 15583.91 20785.18 30680.44 9488.75 14185.49 35680.08 15191.92 18582.02 11490.85 31395.97 44
fmvsm_l_conf0.5_n_385.11 14184.96 15085.56 14187.49 25075.69 13184.71 18390.61 18467.64 29684.88 25392.05 18082.30 11388.36 29683.84 9191.10 30192.62 200
MGCFI-Net85.04 14285.95 12482.31 24487.52 24863.59 27986.23 14893.96 4573.46 19488.07 16087.83 31586.46 6190.87 22376.17 19293.89 21592.47 211
EI-MVSNet-UG-set85.04 14284.44 17186.85 11183.87 34972.52 16483.82 20985.15 30780.27 9988.75 14185.45 35879.95 15391.90 18681.92 11790.80 31596.13 39
X-MVStestdata85.04 14282.70 21492.08 995.64 2486.25 2292.64 2093.33 7885.07 4589.99 11016.05 47786.57 5995.80 3187.35 3397.62 7394.20 111
MSLP-MVS++85.00 14586.03 12381.90 25191.84 12971.56 18386.75 13893.02 9975.95 15687.12 18689.39 28177.98 17089.40 27577.46 17194.78 18684.75 380
F-COLMAP84.97 14683.42 19489.63 5892.39 10683.40 5288.83 9891.92 13873.19 20580.18 35189.15 28777.04 19193.28 14665.82 32392.28 27092.21 230
SSM_040784.89 14784.85 15285.01 15689.13 19968.97 22085.60 16191.58 14874.41 18085.68 22791.49 20378.54 16293.69 12473.71 23093.47 23092.38 218
balanced_conf0384.80 14885.40 13983.00 22088.95 20661.44 31390.42 6392.37 12471.48 23788.72 14393.13 13770.16 28295.15 6779.26 14694.11 20892.41 213
3Dnovator80.37 784.80 14884.71 15885.06 15386.36 29074.71 13588.77 10090.00 20875.65 16184.96 25093.17 13574.06 23391.19 20878.28 15891.09 30289.29 314
SymmetryMVS84.79 15083.54 18988.55 7992.44 10580.42 7288.63 10482.37 34174.56 17785.12 24390.34 25666.19 30294.20 10176.57 18395.68 15391.03 268
IterMVS-LS84.73 15184.98 14983.96 19087.35 25463.66 27783.25 22989.88 21176.06 15189.62 12292.37 17073.40 24892.52 16878.16 16194.77 18895.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR84.63 15284.34 17685.49 14590.18 17575.86 13079.23 32087.13 26973.35 19885.56 23489.34 28283.60 9390.50 23776.64 18294.05 21290.09 300
HQP-MVS84.61 15384.06 18186.27 12291.19 15170.66 19384.77 17892.68 11173.30 20180.55 34390.17 26672.10 26494.61 8677.30 17594.47 19693.56 153
v119284.57 15484.69 16084.21 18287.75 23962.88 28783.02 23791.43 15469.08 26989.98 11290.89 23372.70 25893.62 13082.41 10994.97 17896.13 39
fmvsm_s_conf0.5_n_1184.56 15584.69 16084.15 18586.53 28071.29 18685.53 16292.62 11470.54 24982.75 30491.20 21877.33 18288.55 29283.80 9291.93 28292.61 202
fmvsm_s_conf0.5_n_584.56 15584.71 15884.11 18687.92 23472.09 17284.80 17788.64 23464.43 33788.77 14091.78 19478.07 16987.95 30385.85 6392.18 27492.30 223
FMVSNet184.55 15785.45 13881.85 25390.27 17361.05 32386.83 13488.27 24578.57 12489.66 12195.64 3875.43 21090.68 23069.09 29195.33 16193.82 132
v114484.54 15884.72 15784.00 18787.67 24362.55 29482.97 23990.93 17570.32 25389.80 11690.99 22673.50 24393.48 13981.69 11994.65 19295.97 44
Gipumacopyleft84.44 15986.33 11678.78 31184.20 34273.57 14389.55 8290.44 18984.24 5484.38 26594.89 5776.35 20680.40 39776.14 19396.80 10082.36 418
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_484.38 16084.27 17784.74 16387.25 25770.84 19283.55 21988.45 23968.64 27886.29 21391.31 21274.97 21688.42 29487.87 2090.07 33194.95 75
MCST-MVS84.36 16183.93 18585.63 13991.59 13471.58 18183.52 22092.13 13061.82 35683.96 27889.75 27479.93 15493.46 14078.33 15794.34 20191.87 244
VDDNet84.35 16285.39 14081.25 26895.13 3259.32 34985.42 16681.11 35286.41 3687.41 18296.21 2573.61 24190.61 23566.33 31696.85 9693.81 135
ETV-MVS84.31 16383.91 18685.52 14288.58 21970.40 19784.50 19293.37 7378.76 12284.07 27678.72 43180.39 14795.13 6973.82 22892.98 24891.04 267
v124084.30 16484.51 16983.65 20087.65 24461.26 31982.85 24391.54 15167.94 28990.68 9990.65 24771.71 27293.64 12682.84 10394.78 18696.07 41
MVS_111021_LR84.28 16583.76 18785.83 13689.23 19783.07 5580.99 28883.56 32972.71 21686.07 21789.07 28981.75 13286.19 34077.11 17793.36 23388.24 333
h-mvs3384.25 16682.76 21388.72 7591.82 13182.60 6084.00 20284.98 31371.27 23886.70 19990.55 25263.04 32993.92 11678.26 15994.20 20589.63 306
v14419284.24 16784.41 17283.71 19987.59 24661.57 31282.95 24091.03 17167.82 29389.80 11690.49 25373.28 25093.51 13881.88 11894.89 18196.04 43
dcpmvs_284.23 16885.14 14581.50 26388.61 21861.98 30982.90 24293.11 9168.66 27792.77 5892.39 16678.50 16587.63 31276.99 17992.30 26794.90 76
v192192084.23 16884.37 17483.79 19587.64 24561.71 31182.91 24191.20 16667.94 28990.06 10790.34 25672.04 26793.59 13282.32 11094.91 17996.07 41
VDD-MVS84.23 16884.58 16583.20 21591.17 15465.16 26483.25 22984.97 31479.79 10487.18 18594.27 8374.77 22190.89 22169.24 28796.54 10793.55 155
v2v48284.09 17184.24 17883.62 20187.13 26161.40 31482.71 24689.71 21672.19 22789.55 12691.41 20770.70 27893.20 14881.02 12293.76 21996.25 37
EG-PatchMatch MVS84.08 17284.11 18083.98 18992.22 11372.61 16182.20 26787.02 27572.63 21788.86 13791.02 22578.52 16491.11 21173.41 23891.09 30288.21 334
E284.06 17384.61 16282.40 24287.49 25061.31 31681.03 28693.36 7471.83 23286.02 21991.87 18482.91 10091.37 20375.66 20091.33 29694.53 94
E384.06 17384.61 16282.40 24287.49 25061.30 31781.03 28693.36 7471.83 23286.01 22091.87 18482.91 10091.36 20475.66 20091.33 29694.53 94
fmvsm_s_conf0.5_n_684.05 17584.14 17983.81 19387.75 23971.17 18883.42 22391.10 16967.90 29184.53 26090.70 24173.01 25388.73 28685.09 7093.72 22491.53 257
DP-MVS Recon84.05 17583.22 19886.52 11791.73 13275.27 13383.23 23192.40 12072.04 22982.04 31888.33 30077.91 17293.95 11566.17 31795.12 17190.34 293
viewmacassd2359aftdt84.04 17784.78 15481.81 25686.43 28460.32 33781.95 26992.82 10771.56 23486.06 21892.98 14381.79 13190.28 24276.18 19193.24 23994.82 84
TransMVSNet (Re)84.02 17885.74 13378.85 31091.00 15855.20 39282.29 26187.26 26479.65 10788.38 15395.52 4183.00 9886.88 32467.97 30596.60 10594.45 99
Baseline_NR-MVSNet84.00 17985.90 12678.29 32291.47 14453.44 40482.29 26187.00 27879.06 11689.55 12695.72 3677.20 18786.14 34272.30 25498.51 1795.28 63
fmvsm_l_conf0.5_n_983.98 18084.46 17082.53 23786.11 30070.65 19582.45 25689.17 22767.72 29586.74 19891.49 20379.20 15785.86 35184.71 8192.60 26091.07 266
TSAR-MVS + GP.83.95 18182.69 21587.72 9789.27 19681.45 6783.72 21381.58 35074.73 17485.66 23086.06 34772.56 26092.69 16575.44 20495.21 16689.01 327
LuminaMVS83.94 18283.51 19085.23 14889.78 18571.74 17684.76 18187.27 26372.60 21889.31 13190.60 25164.04 31890.95 21679.08 14794.11 20892.99 183
alignmvs83.94 18283.98 18383.80 19487.80 23867.88 23484.54 19091.42 15673.27 20488.41 15287.96 30572.33 26190.83 22476.02 19594.11 20892.69 196
Effi-MVS+83.90 18484.01 18283.57 20587.22 25965.61 26086.55 14292.40 12078.64 12381.34 33484.18 37783.65 9292.93 15974.22 21687.87 36692.17 233
fmvsm_s_conf0.1_n_283.82 18583.49 19184.84 15885.99 30470.19 20180.93 28987.58 25967.26 30287.94 16692.37 17071.40 27488.01 30086.03 5791.87 28396.31 36
mvs5depth83.82 18584.54 16781.68 25982.23 37468.65 22586.89 13189.90 21080.02 10387.74 17597.86 464.19 31782.02 38576.37 18795.63 15594.35 106
CANet83.79 18782.85 21286.63 11486.17 29772.21 17183.76 21291.43 15477.24 14474.39 40687.45 32475.36 21195.42 5677.03 17892.83 25392.25 229
pm-mvs183.69 18884.95 15179.91 29690.04 18159.66 34682.43 25787.44 26075.52 16587.85 17095.26 4981.25 13785.65 35568.74 29796.04 13094.42 103
AdaColmapbinary83.66 18983.69 18883.57 20590.05 18072.26 16986.29 14690.00 20878.19 12981.65 32887.16 33083.40 9594.24 9961.69 35994.76 18984.21 390
viewdifsd2359ckpt0983.64 19083.18 20185.03 15487.26 25666.99 24485.32 16893.83 5765.57 32284.99 24989.40 28077.30 18393.57 13571.16 26593.80 21894.54 93
MIMVSNet183.63 19184.59 16480.74 27994.06 6262.77 29082.72 24584.53 32177.57 13890.34 10395.92 3176.88 19985.83 35261.88 35797.42 8393.62 147
fmvsm_s_conf0.5_n_283.62 19283.29 19784.62 16885.43 31870.18 20280.61 29587.24 26567.14 30387.79 17291.87 18471.79 27187.98 30286.00 6191.77 28695.71 50
test_fmvsm_n_192083.60 19382.89 20985.74 13785.22 32277.74 10284.12 19990.48 18659.87 38386.45 21291.12 22175.65 20885.89 34982.28 11190.87 31193.58 151
WR-MVS83.56 19484.40 17381.06 27393.43 7854.88 39378.67 32985.02 31181.24 8790.74 9891.56 20172.85 25591.08 21268.00 30498.04 4197.23 17
CNLPA83.55 19583.10 20484.90 15789.34 19483.87 5084.54 19088.77 23179.09 11583.54 28988.66 29774.87 21781.73 38766.84 31192.29 26989.11 320
viewcassd2359sk1183.53 19683.96 18482.25 24586.97 27361.13 32180.80 29393.22 8670.97 24485.36 23891.08 22381.84 12991.29 20574.79 21190.58 32794.33 108
LCM-MVSNet-Re83.48 19785.06 14778.75 31285.94 30555.75 38680.05 30194.27 2576.47 14896.09 694.54 7183.31 9689.75 26759.95 37094.89 18190.75 277
hse-mvs283.47 19881.81 23088.47 8291.03 15782.27 6182.61 24783.69 32771.27 23886.70 19986.05 34863.04 32992.41 17178.26 15993.62 22890.71 279
V4283.47 19883.37 19683.75 19783.16 36863.33 28281.31 28090.23 20269.51 26290.91 9290.81 23874.16 23092.29 17780.06 13290.22 32995.62 54
VPA-MVSNet83.47 19884.73 15579.69 30190.29 17257.52 37281.30 28288.69 23376.29 14987.58 18094.44 7580.60 14587.20 31866.60 31496.82 9994.34 107
mamba_040883.44 20182.88 21085.11 15189.13 19968.97 22072.73 40491.28 16172.90 21085.68 22790.61 24976.78 20093.97 11373.37 24093.47 23092.38 218
viewdifsd2359ckpt0783.41 20284.35 17580.56 28585.84 30758.93 35779.47 31291.28 16173.01 20987.59 17892.07 17985.24 7588.68 28773.59 23591.11 30094.09 120
PAPM_NR83.23 20383.19 20083.33 21190.90 16065.98 25688.19 10990.78 17878.13 13080.87 33987.92 30973.49 24592.42 17070.07 27988.40 35591.60 254
CLD-MVS83.18 20482.64 21684.79 16189.05 20267.82 23577.93 33992.52 11868.33 28185.07 24681.54 40682.06 12292.96 15769.35 28697.91 5493.57 152
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high83.17 20585.68 13475.65 36081.24 38645.26 44879.94 30392.91 10383.83 5791.33 8296.88 1680.25 14985.92 34568.89 29495.89 14295.76 48
FA-MVS(test-final)83.13 20683.02 20583.43 20886.16 29966.08 25588.00 11388.36 24275.55 16485.02 24792.75 15665.12 31192.50 16974.94 21091.30 29891.72 249
114514_t83.10 20782.54 21984.77 16292.90 9169.10 21986.65 13990.62 18354.66 41581.46 33190.81 23876.98 19294.38 9472.62 25196.18 12390.82 276
RRT-MVS82.97 20883.44 19281.57 26185.06 32558.04 36787.20 12490.37 19277.88 13388.59 14593.70 11963.17 32693.05 15576.49 18688.47 35493.62 147
viewmanbaseed2359cas82.95 20983.43 19381.52 26285.18 32360.03 34281.36 27992.38 12269.55 26184.84 25691.38 20879.85 15590.09 25574.22 21692.09 27694.43 102
BP-MVS182.81 21081.67 23286.23 12387.88 23668.53 22686.06 15184.36 32275.65 16185.14 24290.19 26345.84 41894.42 9385.18 6994.72 19095.75 49
UGNet82.78 21181.64 23386.21 12686.20 29676.24 12386.86 13285.68 29777.07 14573.76 41092.82 15269.64 28391.82 19069.04 29393.69 22590.56 287
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
LF4IMVS82.75 21281.93 22885.19 14982.08 37580.15 7685.53 16288.76 23268.01 28685.58 23387.75 31671.80 27086.85 32574.02 22493.87 21688.58 330
EI-MVSNet82.61 21382.42 22183.20 21583.25 36563.66 27783.50 22185.07 30876.06 15186.55 20385.10 36473.41 24690.25 24378.15 16390.67 32295.68 52
QAPM82.59 21482.59 21882.58 23486.44 28366.69 24789.94 7290.36 19367.97 28884.94 25292.58 16172.71 25792.18 17870.63 27287.73 36988.85 328
fmvsm_s_conf0.1_n_a82.58 21581.93 22884.50 17187.68 24273.35 14586.14 15077.70 37061.64 36185.02 24791.62 19877.75 17386.24 33782.79 10487.07 37793.91 127
Fast-Effi-MVS+-dtu82.54 21681.41 24285.90 13385.60 31376.53 11883.07 23589.62 22073.02 20879.11 36183.51 38280.74 14390.24 24568.76 29689.29 34290.94 271
MVS_Test82.47 21783.22 19880.22 29282.62 37357.75 37182.54 25291.96 13771.16 24282.89 30092.52 16477.41 18090.50 23780.04 13387.84 36892.40 215
viewdifsd2359ckpt1182.46 21882.98 20780.88 27683.53 35261.00 32679.46 31385.97 29269.48 26387.89 16891.31 21282.10 12088.61 29074.28 21492.86 25193.02 179
viewmsd2359difaftdt82.46 21882.99 20680.88 27683.52 35361.00 32679.46 31385.97 29269.48 26387.89 16891.31 21282.10 12088.61 29074.28 21492.86 25193.02 179
v14882.31 22082.48 22081.81 25685.59 31459.66 34681.47 27786.02 29072.85 21288.05 16290.65 24770.73 27790.91 22075.15 20791.79 28494.87 78
API-MVS82.28 22182.61 21781.30 26786.29 29369.79 20488.71 10187.67 25878.42 12682.15 31484.15 37877.98 17091.59 19365.39 32692.75 25582.51 417
MVSFormer82.23 22281.57 23884.19 18485.54 31569.26 21491.98 3990.08 20671.54 23576.23 38685.07 36758.69 35494.27 9686.26 5188.77 35089.03 325
viewdifsd2359ckpt1382.22 22381.98 22782.95 22385.48 31764.44 27083.17 23392.11 13165.97 31183.72 28389.73 27577.60 17790.80 22670.61 27389.42 34093.59 150
fmvsm_s_conf0.5_n_a82.21 22481.51 24184.32 17986.56 27973.35 14585.46 16477.30 37461.81 35784.51 26190.88 23577.36 18186.21 33982.72 10586.97 38293.38 159
EIA-MVS82.19 22581.23 24985.10 15287.95 23369.17 21883.22 23293.33 7870.42 25078.58 36679.77 42277.29 18494.20 10171.51 26188.96 34891.93 243
GDP-MVS82.17 22680.85 25786.15 13088.65 21668.95 22385.65 16093.02 9968.42 27983.73 28289.54 27845.07 42994.31 9579.66 13993.87 21695.19 68
fmvsm_s_conf0.1_n82.17 22681.59 23683.94 19286.87 27771.57 18285.19 17277.42 37362.27 35584.47 26491.33 21076.43 20385.91 34783.14 9587.14 37594.33 108
PCF-MVS74.62 1582.15 22880.92 25585.84 13589.43 19272.30 16880.53 29691.82 14257.36 39987.81 17189.92 27177.67 17693.63 12758.69 37595.08 17291.58 255
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLCcopyleft73.85 1682.09 22980.31 26487.45 10190.86 16280.29 7585.88 15390.65 18168.17 28476.32 38586.33 34273.12 25292.61 16761.40 36290.02 33389.44 309
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n82.06 23081.54 24083.60 20283.94 34673.90 14183.35 22686.10 28658.97 38583.80 28190.36 25574.23 22886.94 32382.90 10190.22 32989.94 302
fmvsm_s_conf0.5_n_782.04 23182.05 22582.01 24986.98 27271.07 18978.70 32789.45 22368.07 28578.14 36891.61 19974.19 22985.92 34579.61 14091.73 28789.05 324
GBi-Net82.02 23282.07 22381.85 25386.38 28761.05 32386.83 13488.27 24572.43 21986.00 22195.64 3863.78 32290.68 23065.95 31993.34 23493.82 132
test182.02 23282.07 22381.85 25386.38 28761.05 32386.83 13488.27 24572.43 21986.00 22195.64 3863.78 32290.68 23065.95 31993.34 23493.82 132
OpenMVScopyleft76.72 1381.98 23482.00 22681.93 25084.42 33768.22 22988.50 10789.48 22266.92 30681.80 32591.86 18772.59 25990.16 24971.19 26491.25 29987.40 350
KD-MVS_self_test81.93 23583.14 20378.30 32184.75 33152.75 40880.37 29889.42 22570.24 25590.26 10593.39 12674.55 22786.77 32768.61 29996.64 10395.38 59
fmvsm_s_conf0.5_n81.91 23681.30 24683.75 19786.02 30271.56 18384.73 18277.11 37762.44 35284.00 27790.68 24376.42 20485.89 34983.14 9587.11 37693.81 135
SDMVSNet81.90 23783.17 20278.10 32588.81 21162.45 30076.08 37386.05 28973.67 19083.41 29093.04 13982.35 11080.65 39470.06 28095.03 17491.21 262
tfpnnormal81.79 23882.95 20878.31 32088.93 20755.40 38880.83 29282.85 33676.81 14685.90 22594.14 9374.58 22586.51 33166.82 31295.68 15393.01 182
AstraMVS81.67 23981.40 24382.48 23987.06 26966.47 25081.41 27881.68 34768.78 27488.00 16390.95 23165.70 30787.86 30876.66 18192.38 26493.12 175
c3_l81.64 24081.59 23681.79 25880.86 39259.15 35478.61 33090.18 20468.36 28087.20 18487.11 33269.39 28491.62 19278.16 16194.43 19894.60 89
guyue81.57 24181.37 24582.15 24686.39 28566.13 25481.54 27683.21 33169.79 25987.77 17389.95 26965.36 31087.64 31175.88 19692.49 26292.67 197
PVSNet_Blended_VisFu81.55 24280.49 26284.70 16691.58 13773.24 14984.21 19691.67 14762.86 34680.94 33787.16 33067.27 29692.87 16269.82 28288.94 34987.99 340
fmvsm_l_conf0.5_n_a81.46 24380.87 25683.25 21383.73 35173.21 15083.00 23885.59 29958.22 39182.96 29990.09 26872.30 26286.65 32981.97 11689.95 33489.88 303
SSM_0407281.44 24482.88 21077.10 34089.13 19968.97 22072.73 40491.28 16172.90 21085.68 22790.61 24976.78 20069.94 43773.37 24093.47 23092.38 218
DELS-MVS81.44 24481.25 24782.03 24884.27 34162.87 28876.47 36792.49 11970.97 24481.64 32983.83 37975.03 21492.70 16474.29 21392.22 27390.51 289
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
FMVSNet281.31 24681.61 23580.41 28886.38 28758.75 36283.93 20686.58 28172.43 21987.65 17792.98 14363.78 32290.22 24666.86 30993.92 21492.27 227
TinyColmap81.25 24782.34 22277.99 32885.33 31960.68 33382.32 26088.33 24371.26 24086.97 19392.22 17877.10 19086.98 32262.37 35195.17 16886.31 363
diffmvs_AUTHOR81.24 24881.55 23980.30 29080.61 39760.22 33877.98 33890.48 18667.77 29483.34 29289.50 27974.69 22387.42 31478.78 15190.81 31493.27 165
AUN-MVS81.18 24978.78 28788.39 8490.93 15982.14 6282.51 25383.67 32864.69 33680.29 34785.91 35151.07 39392.38 17276.29 19093.63 22790.65 284
IMVS_040781.08 25081.23 24980.62 28485.76 30962.46 29682.46 25487.91 25365.23 32982.12 31587.92 30977.27 18590.18 24871.67 25790.74 31789.20 315
tttt051781.07 25179.58 27785.52 14288.99 20566.45 25187.03 12975.51 38973.76 18988.32 15590.20 26237.96 45094.16 10879.36 14595.13 16995.93 47
Fast-Effi-MVS+81.04 25280.57 25982.46 24087.50 24963.22 28478.37 33389.63 21968.01 28681.87 32182.08 40082.31 11292.65 16667.10 30888.30 36191.51 258
BH-untuned80.96 25380.99 25380.84 27888.55 22068.23 22880.33 29988.46 23872.79 21586.55 20386.76 33674.72 22291.77 19161.79 35888.99 34782.52 416
IMVS_040380.93 25481.00 25280.72 28185.76 30962.46 29681.82 27087.91 25365.23 32982.07 31787.92 30975.91 20790.50 23771.67 25790.74 31789.20 315
eth_miper_zixun_eth80.84 25580.22 26882.71 23181.41 38460.98 32877.81 34190.14 20567.31 30186.95 19487.24 32964.26 31592.31 17575.23 20691.61 29094.85 82
xiu_mvs_v1_base_debu80.84 25580.14 27082.93 22688.31 22471.73 17779.53 30887.17 26665.43 32379.59 35382.73 39476.94 19390.14 25273.22 24388.33 35786.90 357
xiu_mvs_v1_base80.84 25580.14 27082.93 22688.31 22471.73 17779.53 30887.17 26665.43 32379.59 35382.73 39476.94 19390.14 25273.22 24388.33 35786.90 357
xiu_mvs_v1_base_debi80.84 25580.14 27082.93 22688.31 22471.73 17779.53 30887.17 26665.43 32379.59 35382.73 39476.94 19390.14 25273.22 24388.33 35786.90 357
IterMVS-SCA-FT80.64 25979.41 27884.34 17883.93 34769.66 20876.28 36981.09 35372.43 21986.47 21090.19 26360.46 33993.15 15177.45 17286.39 38890.22 294
BH-RMVSNet80.53 26080.22 26881.49 26487.19 26066.21 25377.79 34286.23 28474.21 18483.69 28488.50 29873.25 25190.75 22763.18 34787.90 36587.52 348
VortexMVS80.51 26180.63 25880.15 29483.36 36161.82 31080.63 29488.00 25167.11 30487.23 18389.10 28863.98 31988.00 30173.63 23492.63 25990.64 285
Anonymous20240521180.51 26181.19 25178.49 31788.48 22157.26 37476.63 36282.49 33981.21 8884.30 27192.24 17767.99 29286.24 33762.22 35295.13 16991.98 242
DIV-MVS_self_test80.43 26380.23 26681.02 27479.99 40259.25 35177.07 35587.02 27567.38 29886.19 21489.22 28463.09 32790.16 24976.32 18895.80 14793.66 141
cl____80.42 26480.23 26681.02 27479.99 40259.25 35177.07 35587.02 27567.37 29986.18 21689.21 28563.08 32890.16 24976.31 18995.80 14793.65 144
diffmvspermissive80.40 26580.48 26380.17 29379.02 41560.04 34077.54 34690.28 20166.65 30982.40 30887.33 32773.50 24387.35 31677.98 16589.62 33893.13 172
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPNet80.37 26678.41 29586.23 12376.75 42973.28 14787.18 12677.45 37276.24 15068.14 44088.93 29165.41 30993.85 11869.47 28596.12 12791.55 256
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth80.34 26780.04 27381.24 27079.82 40558.95 35677.66 34389.66 21765.75 31985.99 22485.11 36368.29 29191.42 20076.03 19492.03 27793.33 161
MG-MVS80.32 26880.94 25478.47 31888.18 22752.62 41182.29 26185.01 31272.01 23079.24 36092.54 16369.36 28593.36 14570.65 27189.19 34589.45 308
mvsmamba80.30 26978.87 28484.58 17088.12 23067.55 23692.35 3084.88 31563.15 34485.33 23990.91 23250.71 39595.20 6666.36 31587.98 36490.99 269
VPNet80.25 27081.68 23175.94 35692.46 10447.98 43576.70 36081.67 34873.45 19584.87 25492.82 15274.66 22486.51 33161.66 36096.85 9693.33 161
MAR-MVS80.24 27178.74 28984.73 16486.87 27778.18 9585.75 15787.81 25765.67 32177.84 37278.50 43273.79 23990.53 23661.59 36190.87 31185.49 373
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
PM-MVS80.20 27279.00 28383.78 19688.17 22886.66 1981.31 28066.81 44569.64 26088.33 15490.19 26364.58 31283.63 37671.99 25690.03 33281.06 436
Anonymous2024052180.18 27381.25 24776.95 34283.15 36960.84 33082.46 25485.99 29168.76 27586.78 19593.73 11859.13 35177.44 41173.71 23097.55 7892.56 205
LFMVS80.15 27480.56 26078.89 30989.19 19855.93 38285.22 17173.78 40182.96 7184.28 27292.72 15757.38 36390.07 25763.80 34195.75 15090.68 281
DPM-MVS80.10 27579.18 28282.88 22990.71 16569.74 20678.87 32590.84 17660.29 37975.64 39585.92 35067.28 29593.11 15271.24 26391.79 28485.77 369
MSDG80.06 27679.99 27580.25 29183.91 34868.04 23377.51 34789.19 22677.65 13681.94 31983.45 38476.37 20586.31 33663.31 34686.59 38586.41 361
FE-MVS79.98 27778.86 28583.36 21086.47 28266.45 25189.73 7584.74 31972.80 21484.22 27591.38 20844.95 43093.60 13163.93 33991.50 29390.04 301
sd_testset79.95 27881.39 24475.64 36188.81 21158.07 36676.16 37282.81 33773.67 19083.41 29093.04 13980.96 14077.65 41058.62 37695.03 17491.21 262
ab-mvs79.67 27980.56 26076.99 34188.48 22156.93 37684.70 18486.06 28868.95 27280.78 34093.08 13875.30 21284.62 36356.78 38590.90 30989.43 310
VNet79.31 28080.27 26576.44 35087.92 23453.95 40075.58 37984.35 32374.39 18382.23 31290.72 24072.84 25684.39 36860.38 36893.98 21390.97 270
thisisatest053079.07 28177.33 30584.26 18187.13 26164.58 26783.66 21675.95 38468.86 27385.22 24187.36 32638.10 44793.57 13575.47 20394.28 20394.62 88
cl2278.97 28278.21 29781.24 27077.74 41959.01 35577.46 35087.13 26965.79 31684.32 26885.10 36458.96 35390.88 22275.36 20592.03 27793.84 130
patch_mono-278.89 28379.39 27977.41 33784.78 32968.11 23175.60 37783.11 33360.96 37179.36 35789.89 27275.18 21372.97 42673.32 24292.30 26791.15 264
RPMNet78.88 28478.28 29680.68 28379.58 40662.64 29282.58 24994.16 3374.80 17275.72 39392.59 15948.69 40295.56 4573.48 23782.91 42483.85 395
PAPR78.84 28578.10 29881.07 27285.17 32460.22 33882.21 26590.57 18562.51 34875.32 39984.61 37274.99 21592.30 17659.48 37388.04 36390.68 281
viewmambaseed2359dif78.80 28678.47 29479.78 29780.26 40159.28 35077.31 35287.13 26960.42 37782.37 30988.67 29674.58 22587.87 30767.78 30787.73 36992.19 231
PVSNet_BlendedMVS78.80 28677.84 29981.65 26084.43 33563.41 28079.49 31190.44 18961.70 36075.43 39687.07 33369.11 28791.44 19860.68 36692.24 27190.11 299
FMVSNet378.80 28678.55 29179.57 30382.89 37256.89 37881.76 27185.77 29569.04 27086.00 22190.44 25451.75 39190.09 25565.95 31993.34 23491.72 249
test_yl78.71 28978.51 29279.32 30684.32 33958.84 35978.38 33185.33 30375.99 15482.49 30686.57 33858.01 35790.02 25962.74 34892.73 25789.10 321
DCV-MVSNet78.71 28978.51 29279.32 30684.32 33958.84 35978.38 33185.33 30375.99 15482.49 30686.57 33858.01 35790.02 25962.74 34892.73 25789.10 321
test111178.53 29178.85 28677.56 33492.22 11347.49 43782.61 24769.24 43372.43 21985.28 24094.20 8951.91 38990.07 25765.36 32796.45 11295.11 72
FE-MVSNET78.46 29279.36 28075.75 35886.53 28054.53 39578.03 33585.35 30269.01 27185.41 23790.68 24364.27 31485.73 35362.59 35092.35 26687.00 356
icg_test_0407_278.46 29279.68 27674.78 36885.76 30962.46 29668.51 43387.91 25365.23 32982.12 31587.92 30977.27 18572.67 42771.67 25790.74 31789.20 315
ECVR-MVScopyleft78.44 29478.63 29077.88 33091.85 12748.95 43183.68 21569.91 42972.30 22584.26 27494.20 8951.89 39089.82 26263.58 34296.02 13194.87 78
pmmvs-eth3d78.42 29577.04 30882.57 23687.44 25374.41 13880.86 29179.67 36155.68 40884.69 25890.31 26060.91 33785.42 35662.20 35391.59 29187.88 344
mvs_anonymous78.13 29678.76 28876.23 35579.24 41250.31 42778.69 32884.82 31761.60 36283.09 29892.82 15273.89 23787.01 31968.33 30386.41 38791.37 259
TAMVS78.08 29776.36 31583.23 21490.62 16672.87 15479.08 32180.01 36061.72 35981.35 33386.92 33563.96 32188.78 28450.61 42493.01 24788.04 339
miper_enhance_ethall77.83 29876.93 30980.51 28676.15 43658.01 36875.47 38188.82 23058.05 39383.59 28680.69 41064.41 31391.20 20773.16 24992.03 27792.33 222
Vis-MVSNet (Re-imp)77.82 29977.79 30077.92 32988.82 21051.29 42183.28 22771.97 41774.04 18582.23 31289.78 27357.38 36389.41 27457.22 38495.41 15893.05 178
CANet_DTU77.81 30077.05 30780.09 29581.37 38559.90 34483.26 22888.29 24469.16 26767.83 44383.72 38060.93 33689.47 26969.22 28989.70 33790.88 274
OpenMVS_ROBcopyleft70.19 1777.77 30177.46 30278.71 31384.39 33861.15 32081.18 28482.52 33862.45 35183.34 29287.37 32566.20 30188.66 28864.69 33485.02 40486.32 362
SSC-MVS77.55 30281.64 23365.29 43490.46 16920.33 48173.56 39768.28 43585.44 4188.18 15994.64 6870.93 27681.33 38971.25 26292.03 27794.20 111
MDA-MVSNet-bldmvs77.47 30376.90 31079.16 30879.03 41464.59 26666.58 44575.67 38773.15 20688.86 13788.99 29066.94 29781.23 39064.71 33388.22 36291.64 253
jason77.42 30475.75 32182.43 24187.10 26469.27 21377.99 33781.94 34551.47 43577.84 37285.07 36760.32 34189.00 27870.74 27089.27 34489.03 325
jason: jason.
CDS-MVSNet77.32 30575.40 32583.06 21889.00 20472.48 16577.90 34082.17 34360.81 37278.94 36383.49 38359.30 34988.76 28554.64 40492.37 26587.93 343
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_040477.24 30677.75 30175.73 35985.76 30962.46 29670.84 41987.91 25365.23 32972.21 41887.92 30967.48 29475.53 41971.67 25790.74 31789.20 315
xiu_mvs_v2_base77.19 30776.75 31278.52 31687.01 27061.30 31775.55 38087.12 27361.24 36874.45 40578.79 43077.20 18790.93 21864.62 33684.80 41183.32 404
MVSTER77.09 30875.70 32281.25 26875.27 44461.08 32277.49 34985.07 30860.78 37386.55 20388.68 29443.14 43990.25 24373.69 23390.67 32292.42 212
PS-MVSNAJ77.04 30976.53 31478.56 31587.09 26661.40 31475.26 38287.13 26961.25 36774.38 40777.22 44476.94 19390.94 21764.63 33584.83 41083.35 403
IterMVS76.91 31076.34 31678.64 31480.91 39064.03 27476.30 36879.03 36464.88 33583.11 29689.16 28659.90 34584.46 36668.61 29985.15 40287.42 349
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS76.84 31175.67 32380.34 28980.48 39962.16 30873.50 39884.80 31857.61 39782.24 31187.54 32051.31 39287.65 31070.40 27693.19 24391.23 261
CL-MVSNet_self_test76.81 31277.38 30475.12 36486.90 27551.34 41973.20 40180.63 35768.30 28281.80 32588.40 29966.92 29880.90 39155.35 39894.90 18093.12 175
TR-MVS76.77 31375.79 32079.72 30086.10 30165.79 25877.14 35383.02 33465.20 33381.40 33282.10 39866.30 30090.73 22955.57 39585.27 39882.65 411
MonoMVSNet76.66 31477.26 30674.86 36679.86 40454.34 39786.26 14786.08 28771.08 24385.59 23288.68 29453.95 38185.93 34463.86 34080.02 44084.32 386
USDC76.63 31576.73 31376.34 35283.46 35657.20 37580.02 30288.04 25052.14 43183.65 28591.25 21563.24 32586.65 32954.66 40394.11 20885.17 375
BH-w/o76.57 31676.07 31978.10 32586.88 27665.92 25777.63 34486.33 28265.69 32080.89 33879.95 41968.97 28990.74 22853.01 41485.25 39977.62 447
Patchmtry76.56 31777.46 30273.83 37479.37 41146.60 44182.41 25876.90 37873.81 18885.56 23492.38 16748.07 40583.98 37363.36 34595.31 16490.92 272
PVSNet_Blended76.49 31875.40 32579.76 29984.43 33563.41 28075.14 38390.44 18957.36 39975.43 39678.30 43369.11 28791.44 19860.68 36687.70 37184.42 385
miper_lstm_enhance76.45 31976.10 31877.51 33576.72 43060.97 32964.69 44985.04 31063.98 34083.20 29588.22 30156.67 36778.79 40773.22 24393.12 24492.78 191
lupinMVS76.37 32074.46 33482.09 24785.54 31569.26 21476.79 35880.77 35650.68 44276.23 38682.82 39258.69 35488.94 27969.85 28188.77 35088.07 336
cascas76.29 32174.81 33080.72 28184.47 33462.94 28673.89 39587.34 26155.94 40675.16 40176.53 44963.97 32091.16 20965.00 33090.97 30788.06 338
SD_040376.08 32276.77 31173.98 37287.08 26849.45 43083.62 21784.68 32063.31 34175.13 40287.47 32371.85 26984.56 36449.97 42687.86 36787.94 342
WB-MVS76.06 32380.01 27464.19 43789.96 18320.58 48072.18 40868.19 43683.21 6786.46 21193.49 12370.19 28178.97 40565.96 31890.46 32893.02 179
thres600view775.97 32475.35 32777.85 33287.01 27051.84 41780.45 29773.26 40675.20 16983.10 29786.31 34445.54 42089.05 27755.03 40192.24 27192.66 198
GA-MVS75.83 32574.61 33179.48 30581.87 37759.25 35173.42 39982.88 33568.68 27679.75 35281.80 40350.62 39689.46 27066.85 31085.64 39589.72 305
MVP-Stereo75.81 32673.51 34382.71 23189.35 19373.62 14280.06 30085.20 30560.30 37873.96 40887.94 30657.89 36189.45 27152.02 41874.87 45885.06 377
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs375.72 32775.20 32877.27 33875.01 44769.47 21178.93 32284.88 31546.67 44987.08 19087.84 31450.44 39871.62 43277.42 17488.53 35390.72 278
thres100view90075.45 32875.05 32976.66 34887.27 25551.88 41681.07 28573.26 40675.68 16083.25 29486.37 34145.54 42088.80 28151.98 41990.99 30489.31 312
ET-MVSNet_ETH3D75.28 32972.77 35282.81 23083.03 37168.11 23177.09 35476.51 38260.67 37577.60 37780.52 41438.04 44891.15 21070.78 26890.68 32189.17 319
thres40075.14 33074.23 33677.86 33186.24 29452.12 41379.24 31873.87 39973.34 19981.82 32384.60 37346.02 41388.80 28151.98 41990.99 30492.66 198
wuyk23d75.13 33179.30 28162.63 44075.56 44075.18 13480.89 29073.10 40875.06 17194.76 1695.32 4587.73 4552.85 47234.16 47097.11 9159.85 468
EU-MVSNet75.12 33274.43 33577.18 33983.11 37059.48 34885.71 15982.43 34039.76 46985.64 23188.76 29244.71 43287.88 30673.86 22785.88 39484.16 391
HyFIR lowres test75.12 33272.66 35482.50 23891.44 14565.19 26372.47 40687.31 26246.79 44880.29 34784.30 37552.70 38692.10 18251.88 42386.73 38390.22 294
CMPMVSbinary59.41 2075.12 33273.57 34179.77 29875.84 43967.22 23781.21 28382.18 34250.78 44076.50 38287.66 31855.20 37782.99 37962.17 35590.64 32689.09 323
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs474.92 33572.98 35080.73 28084.95 32671.71 18076.23 37077.59 37152.83 42577.73 37686.38 34056.35 37084.97 36057.72 38387.05 37885.51 372
tfpn200view974.86 33674.23 33676.74 34786.24 29452.12 41379.24 31873.87 39973.34 19981.82 32384.60 37346.02 41388.80 28151.98 41990.99 30489.31 312
1112_ss74.82 33773.74 33978.04 32789.57 18760.04 34076.49 36687.09 27454.31 41673.66 41179.80 42060.25 34286.76 32858.37 37784.15 41587.32 351
EGC-MVSNET74.79 33869.99 38289.19 6794.89 3887.00 1591.89 4286.28 2831.09 4782.23 48095.98 3081.87 12889.48 26879.76 13695.96 13491.10 265
ppachtmachnet_test74.73 33974.00 33876.90 34480.71 39556.89 37871.53 41478.42 36658.24 39079.32 35982.92 39157.91 36084.26 37065.60 32591.36 29589.56 307
Patchmatch-RL test74.48 34073.68 34076.89 34584.83 32866.54 24872.29 40769.16 43457.70 39586.76 19686.33 34245.79 41982.59 38069.63 28490.65 32581.54 427
PatchMatch-RL74.48 34073.22 34778.27 32387.70 24185.26 3875.92 37570.09 42764.34 33876.09 38981.25 40865.87 30678.07 40953.86 40683.82 41771.48 456
XXY-MVS74.44 34276.19 31769.21 40984.61 33352.43 41271.70 41177.18 37660.73 37480.60 34190.96 22975.44 20969.35 44056.13 39088.33 35785.86 368
test250674.12 34373.39 34476.28 35391.85 12744.20 45184.06 20048.20 47672.30 22581.90 32094.20 8927.22 47589.77 26564.81 33296.02 13194.87 78
reproduce_monomvs74.09 34473.23 34676.65 34976.52 43154.54 39477.50 34881.40 35165.85 31582.86 30286.67 33727.38 47384.53 36570.24 27790.66 32490.89 273
CR-MVSNet74.00 34573.04 34976.85 34679.58 40662.64 29282.58 24976.90 37850.50 44375.72 39392.38 16748.07 40584.07 37268.72 29882.91 42483.85 395
SSC-MVS3.273.90 34675.67 32368.61 41784.11 34441.28 45964.17 45172.83 40972.09 22879.08 36287.94 30670.31 27973.89 42555.99 39194.49 19590.67 283
Test_1112_low_res73.90 34673.08 34876.35 35190.35 17155.95 38173.40 40086.17 28550.70 44173.14 41285.94 34958.31 35685.90 34856.51 38783.22 42187.20 353
test20.0373.75 34874.59 33371.22 39581.11 38851.12 42370.15 42572.10 41670.42 25080.28 34991.50 20264.21 31674.72 42346.96 44494.58 19387.82 346
test_fmvs273.57 34972.80 35175.90 35772.74 46168.84 22477.07 35584.32 32445.14 45582.89 30084.22 37648.37 40370.36 43673.40 23987.03 37988.52 331
SCA73.32 35072.57 35675.58 36281.62 38155.86 38478.89 32471.37 42261.73 35874.93 40383.42 38560.46 33987.01 31958.11 38182.63 42983.88 392
baseline173.26 35173.54 34272.43 38884.92 32747.79 43679.89 30474.00 39765.93 31378.81 36486.28 34556.36 36981.63 38856.63 38679.04 44787.87 345
131473.22 35272.56 35775.20 36380.41 40057.84 36981.64 27485.36 30151.68 43473.10 41376.65 44861.45 33485.19 35863.54 34379.21 44582.59 412
MVS73.21 35372.59 35575.06 36580.97 38960.81 33181.64 27485.92 29446.03 45371.68 42177.54 43968.47 29089.77 26555.70 39485.39 39674.60 453
HY-MVS64.64 1873.03 35472.47 35874.71 36983.36 36154.19 39882.14 26881.96 34456.76 40569.57 43586.21 34660.03 34384.83 36249.58 43182.65 42785.11 376
thisisatest051573.00 35570.52 37480.46 28781.45 38359.90 34473.16 40274.31 39657.86 39476.08 39077.78 43637.60 45192.12 18165.00 33091.45 29489.35 311
EPNet_dtu72.87 35671.33 36877.49 33677.72 42060.55 33482.35 25975.79 38566.49 31058.39 47181.06 40953.68 38285.98 34353.55 40992.97 24985.95 366
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet72.62 35771.41 36776.28 35383.25 36560.34 33683.50 22179.02 36537.77 47376.33 38485.10 36449.60 40187.41 31570.54 27477.54 45381.08 434
CHOSEN 1792x268872.45 35870.56 37378.13 32490.02 18263.08 28568.72 43283.16 33242.99 46375.92 39185.46 35757.22 36585.18 35949.87 42981.67 43186.14 364
testgi72.36 35974.61 33165.59 43180.56 39842.82 45668.29 43473.35 40566.87 30781.84 32289.93 27072.08 26666.92 45446.05 44892.54 26187.01 355
thres20072.34 36071.55 36674.70 37083.48 35551.60 41875.02 38473.71 40270.14 25678.56 36780.57 41346.20 41188.20 29946.99 44389.29 34284.32 386
FPMVS72.29 36172.00 36073.14 37988.63 21785.00 4074.65 38867.39 43971.94 23177.80 37487.66 31850.48 39775.83 41749.95 42779.51 44158.58 470
FMVSNet572.10 36271.69 36273.32 37781.57 38253.02 40776.77 35978.37 36763.31 34176.37 38391.85 18836.68 45278.98 40447.87 44092.45 26387.95 341
our_test_371.85 36371.59 36372.62 38580.71 39553.78 40169.72 42871.71 42158.80 38778.03 36980.51 41556.61 36878.84 40662.20 35386.04 39385.23 374
PAPM71.77 36470.06 38076.92 34386.39 28553.97 39976.62 36386.62 28053.44 42063.97 46084.73 37157.79 36292.34 17439.65 46081.33 43584.45 384
ttmdpeth71.72 36570.67 37174.86 36673.08 45855.88 38377.41 35169.27 43255.86 40778.66 36593.77 11638.01 44975.39 42060.12 36989.87 33593.31 163
IB-MVS62.13 1971.64 36668.97 39279.66 30280.80 39462.26 30573.94 39476.90 37863.27 34368.63 43976.79 44633.83 45691.84 18959.28 37487.26 37384.88 378
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
UnsupCasMVSNet_eth71.63 36772.30 35969.62 40676.47 43352.70 41070.03 42680.97 35459.18 38479.36 35788.21 30260.50 33869.12 44158.33 37977.62 45287.04 354
testing371.53 36870.79 37073.77 37588.89 20941.86 45876.60 36559.12 46572.83 21380.97 33582.08 40019.80 48187.33 31765.12 32991.68 28992.13 235
test_vis3_rt71.42 36970.67 37173.64 37669.66 46870.46 19666.97 44489.73 21442.68 46588.20 15883.04 38743.77 43460.07 46665.35 32886.66 38490.39 292
Anonymous2023120671.38 37071.88 36169.88 40386.31 29154.37 39670.39 42374.62 39252.57 42776.73 38188.76 29259.94 34472.06 42944.35 45293.23 24183.23 406
test_vis1_n_192071.30 37171.58 36570.47 39877.58 42259.99 34374.25 38984.22 32551.06 43774.85 40479.10 42655.10 37868.83 44368.86 29579.20 44682.58 413
MIMVSNet71.09 37271.59 36369.57 40787.23 25850.07 42878.91 32371.83 41860.20 38171.26 42291.76 19555.08 37976.09 41541.06 45787.02 38082.54 415
test_fmvs1_n70.94 37370.41 37772.53 38773.92 44966.93 24575.99 37484.21 32643.31 46279.40 35679.39 42443.47 43568.55 44569.05 29284.91 40782.10 421
MS-PatchMatch70.93 37470.22 37873.06 38081.85 37862.50 29573.82 39677.90 36852.44 42875.92 39181.27 40755.67 37481.75 38655.37 39777.70 45174.94 452
pmmvs570.73 37570.07 37972.72 38377.03 42752.73 40974.14 39075.65 38850.36 44472.17 41985.37 36155.42 37680.67 39352.86 41587.59 37284.77 379
testing3-270.72 37670.97 36969.95 40288.93 20734.80 47269.85 42766.59 44678.42 12677.58 37885.55 35331.83 46282.08 38446.28 44593.73 22392.98 185
PatchT70.52 37772.76 35363.79 43979.38 41033.53 47377.63 34465.37 45073.61 19271.77 42092.79 15544.38 43375.65 41864.53 33785.37 39782.18 420
test_vis1_n70.29 37869.99 38271.20 39675.97 43866.50 24976.69 36180.81 35544.22 45875.43 39677.23 44350.00 39968.59 44466.71 31382.85 42678.52 446
N_pmnet70.20 37968.80 39474.38 37180.91 39084.81 4359.12 46276.45 38355.06 41175.31 40082.36 39755.74 37354.82 47147.02 44287.24 37483.52 399
tpmvs70.16 38069.56 38571.96 39174.71 44848.13 43379.63 30675.45 39065.02 33470.26 43081.88 40245.34 42585.68 35458.34 37875.39 45782.08 422
new-patchmatchnet70.10 38173.37 34560.29 44881.23 38716.95 48359.54 46074.62 39262.93 34580.97 33587.93 30862.83 33171.90 43055.24 39995.01 17792.00 240
YYNet170.06 38270.44 37568.90 41173.76 45153.42 40558.99 46367.20 44158.42 38987.10 18885.39 36059.82 34667.32 45159.79 37183.50 42085.96 365
MVStest170.05 38369.26 38672.41 38958.62 48055.59 38776.61 36465.58 44853.44 42089.28 13293.32 12722.91 47971.44 43474.08 22389.52 33990.21 298
MDA-MVSNet_test_wron70.05 38370.44 37568.88 41273.84 45053.47 40358.93 46467.28 44058.43 38887.09 18985.40 35959.80 34767.25 45259.66 37283.54 41985.92 367
CostFormer69.98 38568.68 39573.87 37377.14 42550.72 42579.26 31774.51 39451.94 43370.97 42584.75 37045.16 42887.49 31355.16 40079.23 44483.40 402
testing9169.94 38668.99 39172.80 38283.81 35045.89 44471.57 41373.64 40468.24 28370.77 42877.82 43534.37 45584.44 36753.64 40887.00 38188.07 336
baseline269.77 38766.89 40478.41 31979.51 40858.09 36576.23 37069.57 43057.50 39864.82 45877.45 44146.02 41388.44 29353.08 41177.83 44988.70 329
PatchmatchNetpermissive69.71 38868.83 39372.33 39077.66 42153.60 40279.29 31669.99 42857.66 39672.53 41682.93 39046.45 41080.08 39960.91 36572.09 46183.31 405
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs169.57 38969.05 38971.14 39769.15 46965.77 25973.98 39383.32 33042.83 46477.77 37578.27 43443.39 43868.50 44668.39 30284.38 41479.15 444
JIA-IIPM69.41 39066.64 40877.70 33373.19 45571.24 18775.67 37665.56 44970.42 25065.18 45492.97 14633.64 45883.06 37753.52 41069.61 46778.79 445
Syy-MVS69.40 39170.03 38167.49 42281.72 37938.94 46471.00 41661.99 45661.38 36470.81 42672.36 46061.37 33579.30 40264.50 33885.18 40084.22 388
testing9969.27 39268.15 39972.63 38483.29 36345.45 44671.15 41571.08 42367.34 30070.43 42977.77 43732.24 46184.35 36953.72 40786.33 38988.10 335
UnsupCasMVSNet_bld69.21 39369.68 38467.82 42079.42 40951.15 42267.82 43875.79 38554.15 41777.47 37985.36 36259.26 35070.64 43548.46 43779.35 44381.66 425
test_cas_vis1_n_192069.20 39469.12 38769.43 40873.68 45262.82 28970.38 42477.21 37546.18 45280.46 34678.95 42852.03 38865.53 45965.77 32477.45 45479.95 442
gg-mvs-nofinetune68.96 39569.11 38868.52 41876.12 43745.32 44783.59 21855.88 47086.68 3364.62 45997.01 1230.36 46683.97 37444.78 45182.94 42376.26 449
WBMVS68.76 39668.43 39669.75 40583.29 36340.30 46267.36 44072.21 41557.09 40277.05 38085.53 35533.68 45780.51 39548.79 43590.90 30988.45 332
WB-MVSnew68.72 39769.01 39067.85 41983.22 36743.98 45274.93 38565.98 44755.09 41073.83 40979.11 42565.63 30871.89 43138.21 46585.04 40387.69 347
tpm268.45 39866.83 40573.30 37878.93 41648.50 43279.76 30571.76 41947.50 44769.92 43283.60 38142.07 44188.40 29548.44 43879.51 44183.01 409
tpm67.95 39968.08 40067.55 42178.74 41743.53 45475.60 37767.10 44454.92 41272.23 41788.10 30342.87 44075.97 41652.21 41780.95 43983.15 407
WTY-MVS67.91 40068.35 39766.58 42780.82 39348.12 43465.96 44672.60 41053.67 41971.20 42381.68 40558.97 35269.06 44248.57 43681.67 43182.55 414
testing1167.38 40165.93 40971.73 39383.37 36046.60 44170.95 41869.40 43162.47 35066.14 44776.66 44731.22 46384.10 37149.10 43384.10 41684.49 382
test-LLR67.21 40266.74 40668.63 41576.45 43455.21 39067.89 43567.14 44262.43 35365.08 45572.39 45843.41 43669.37 43861.00 36384.89 40881.31 429
testing22266.93 40365.30 41671.81 39283.38 35945.83 44572.06 40967.50 43864.12 33969.68 43476.37 45027.34 47483.00 37838.88 46188.38 35686.62 360
sss66.92 40467.26 40265.90 42977.23 42451.10 42464.79 44871.72 42052.12 43270.13 43180.18 41757.96 35965.36 46050.21 42581.01 43781.25 431
KD-MVS_2432*160066.87 40565.81 41270.04 40067.50 47047.49 43762.56 45479.16 36261.21 36977.98 37080.61 41125.29 47782.48 38153.02 41284.92 40580.16 440
miper_refine_blended66.87 40565.81 41270.04 40067.50 47047.49 43762.56 45479.16 36261.21 36977.98 37080.61 41125.29 47782.48 38153.02 41284.92 40580.16 440
dmvs_re66.81 40766.98 40366.28 42876.87 42858.68 36371.66 41272.24 41360.29 37969.52 43673.53 45752.38 38764.40 46244.90 45081.44 43475.76 450
tpm cat166.76 40865.21 41771.42 39477.09 42650.62 42678.01 33673.68 40344.89 45668.64 43879.00 42745.51 42282.42 38349.91 42870.15 46481.23 433
UWE-MVS66.43 40965.56 41569.05 41084.15 34340.98 46073.06 40364.71 45254.84 41376.18 38879.62 42329.21 46880.50 39638.54 46489.75 33685.66 370
PVSNet58.17 2166.41 41065.63 41468.75 41381.96 37649.88 42962.19 45672.51 41251.03 43868.04 44175.34 45450.84 39474.77 42145.82 44982.96 42281.60 426
tpmrst66.28 41166.69 40765.05 43572.82 46039.33 46378.20 33470.69 42653.16 42367.88 44280.36 41648.18 40474.75 42258.13 38070.79 46381.08 434
Patchmatch-test65.91 41267.38 40161.48 44575.51 44143.21 45568.84 43163.79 45462.48 34972.80 41583.42 38544.89 43159.52 46848.27 43986.45 38681.70 424
ADS-MVSNet265.87 41363.64 42272.55 38673.16 45656.92 37767.10 44274.81 39149.74 44566.04 44982.97 38846.71 40877.26 41242.29 45469.96 46583.46 400
myMVS_eth3d2865.83 41465.85 41065.78 43083.42 35835.71 47067.29 44168.01 43767.58 29769.80 43377.72 43832.29 46074.30 42437.49 46689.06 34687.32 351
test_vis1_rt65.64 41564.09 41970.31 39966.09 47470.20 20061.16 45781.60 34938.65 47072.87 41469.66 46352.84 38460.04 46756.16 38977.77 45080.68 438
mvsany_test365.48 41662.97 42573.03 38169.99 46776.17 12464.83 44743.71 47843.68 46080.25 35087.05 33452.83 38563.09 46551.92 42272.44 46079.84 443
test-mter65.00 41763.79 42168.63 41576.45 43455.21 39067.89 43567.14 44250.98 43965.08 45572.39 45828.27 47169.37 43861.00 36384.89 40881.31 429
ETVMVS64.67 41863.34 42468.64 41483.44 35741.89 45769.56 43061.70 46161.33 36668.74 43775.76 45228.76 46979.35 40134.65 46986.16 39284.67 381
myMVS_eth3d64.66 41963.89 42066.97 42581.72 37937.39 46771.00 41661.99 45661.38 36470.81 42672.36 46020.96 48079.30 40249.59 43085.18 40084.22 388
test0.0.03 164.66 41964.36 41865.57 43275.03 44646.89 44064.69 44961.58 46262.43 35371.18 42477.54 43943.41 43668.47 44740.75 45982.65 42781.35 428
UBG64.34 42163.35 42367.30 42383.50 35440.53 46167.46 43965.02 45154.77 41467.54 44574.47 45632.99 45978.50 40840.82 45883.58 41882.88 410
test_f64.31 42265.85 41059.67 44966.54 47362.24 30757.76 46670.96 42440.13 46784.36 26682.09 39946.93 40751.67 47361.99 35681.89 43065.12 464
pmmvs362.47 42360.02 43669.80 40471.58 46464.00 27570.52 42258.44 46839.77 46866.05 44875.84 45127.10 47672.28 42846.15 44784.77 41273.11 454
EPMVS62.47 42362.63 42762.01 44170.63 46638.74 46574.76 38652.86 47253.91 41867.71 44480.01 41839.40 44566.60 45555.54 39668.81 46980.68 438
ADS-MVSNet61.90 42562.19 42961.03 44673.16 45636.42 46967.10 44261.75 45949.74 44566.04 44982.97 38846.71 40863.21 46342.29 45469.96 46583.46 400
PMMVS61.65 42660.38 43365.47 43365.40 47769.26 21463.97 45261.73 46036.80 47460.11 46668.43 46559.42 34866.35 45648.97 43478.57 44860.81 467
E-PMN61.59 42761.62 43061.49 44466.81 47255.40 38853.77 46960.34 46466.80 30858.90 46965.50 46840.48 44466.12 45755.72 39386.25 39062.95 466
TESTMET0.1,161.29 42860.32 43464.19 43772.06 46251.30 42067.89 43562.09 45545.27 45460.65 46569.01 46427.93 47264.74 46156.31 38881.65 43376.53 448
MVS-HIRNet61.16 42962.92 42655.87 45279.09 41335.34 47171.83 41057.98 46946.56 45059.05 46891.14 22049.95 40076.43 41438.74 46271.92 46255.84 471
EMVS61.10 43060.81 43261.99 44265.96 47555.86 38453.10 47058.97 46767.06 30556.89 47363.33 46940.98 44267.03 45354.79 40286.18 39163.08 465
DSMNet-mixed60.98 43161.61 43159.09 45172.88 45945.05 44974.70 38746.61 47726.20 47565.34 45390.32 25955.46 37563.12 46441.72 45681.30 43669.09 460
dp60.70 43260.29 43561.92 44372.04 46338.67 46670.83 42064.08 45351.28 43660.75 46477.28 44236.59 45371.58 43347.41 44162.34 47175.52 451
dmvs_testset60.59 43362.54 42854.72 45477.26 42327.74 47774.05 39261.00 46360.48 37665.62 45267.03 46755.93 37268.23 44932.07 47369.46 46868.17 461
CHOSEN 280x42059.08 43456.52 44066.76 42676.51 43264.39 27149.62 47159.00 46643.86 45955.66 47468.41 46635.55 45468.21 45043.25 45376.78 45667.69 462
mvsany_test158.48 43556.47 44164.50 43665.90 47668.21 23056.95 46742.11 47938.30 47165.69 45177.19 44556.96 36659.35 46946.16 44658.96 47265.93 463
UWE-MVS-2858.44 43657.71 43860.65 44773.58 45331.23 47469.68 42948.80 47553.12 42461.79 46278.83 42930.98 46468.40 44821.58 47680.99 43882.33 419
PVSNet_051.08 2256.10 43754.97 44259.48 45075.12 44553.28 40655.16 46861.89 45844.30 45759.16 46762.48 47054.22 38065.91 45835.40 46847.01 47359.25 469
new_pmnet55.69 43857.66 43949.76 45575.47 44230.59 47559.56 45951.45 47343.62 46162.49 46175.48 45340.96 44349.15 47537.39 46772.52 45969.55 459
PMMVS255.64 43959.27 43744.74 45664.30 47812.32 48440.60 47249.79 47453.19 42265.06 45784.81 36953.60 38349.76 47432.68 47289.41 34172.15 455
MVEpermissive40.22 2351.82 44050.47 44355.87 45262.66 47951.91 41531.61 47439.28 48040.65 46650.76 47574.98 45556.24 37144.67 47633.94 47164.11 47071.04 458
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai41.90 44142.65 44439.67 45770.86 46521.11 47961.01 45821.42 48457.36 39957.97 47250.06 47316.40 48258.73 47021.03 47727.69 47739.17 473
kuosan30.83 44232.17 44526.83 45953.36 48119.02 48257.90 46520.44 48538.29 47238.01 47637.82 47515.18 48333.45 4787.74 47920.76 47828.03 474
test_method30.46 44329.60 44633.06 45817.99 4833.84 48613.62 47573.92 3982.79 47718.29 47953.41 47228.53 47043.25 47722.56 47435.27 47552.11 472
cdsmvs_eth3d_5k20.81 44427.75 4470.00 4640.00 4870.00 4890.00 47685.44 3000.00 4820.00 48382.82 39281.46 1340.00 4830.00 4820.00 4810.00 479
tmp_tt20.25 44524.50 4487.49 4614.47 4848.70 48534.17 47325.16 4821.00 47932.43 47818.49 47639.37 4469.21 48021.64 47543.75 4744.57 476
ab-mvs-re6.65 4468.87 4490.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 48379.80 4200.00 4860.00 4830.00 4820.00 4810.00 479
pcd_1.5k_mvsjas6.41 4478.55 4500.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 48276.94 1930.00 4830.00 4820.00 4810.00 479
test1236.27 4488.08 4510.84 4621.11 4860.57 48762.90 4530.82 4860.54 4801.07 4822.75 4811.26 4840.30 4811.04 4801.26 4801.66 477
testmvs5.91 4497.65 4520.72 4631.20 4850.37 48859.14 4610.67 4870.49 4811.11 4812.76 4800.94 4850.24 4821.02 4811.47 4791.55 478
mmdepth0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
monomultidepth0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
test_blank0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
uanet_test0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
DCPMVS0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
sosnet-low-res0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
sosnet0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
uncertanet0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
Regformer0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
uanet0.00 4500.00 4530.00 4640.00 4870.00 4890.00 4760.00 4880.00 4820.00 4830.00 4820.00 4860.00 4830.00 4820.00 4810.00 479
MED-MVS test88.50 8094.38 4876.12 12692.12 3393.85 5377.53 14093.24 4393.18 13195.85 2484.99 7597.69 6593.54 156
TestfortrainingZip92.12 33
WAC-MVS37.39 46752.61 416
FOURS196.08 1287.41 1496.19 295.83 592.95 396.57 3
MSC_two_6792asdad88.81 7391.55 13977.99 9791.01 17296.05 987.45 2998.17 3792.40 215
PC_three_145258.96 38690.06 10791.33 21080.66 14493.03 15675.78 19795.94 13792.48 209
No_MVS88.81 7391.55 13977.99 9791.01 17296.05 987.45 2998.17 3792.40 215
test_one_060193.85 6773.27 14894.11 3986.57 3493.47 4294.64 6888.42 29
eth-test20.00 487
eth-test0.00 487
ZD-MVS92.22 11380.48 7191.85 14071.22 24190.38 10292.98 14386.06 6896.11 781.99 11596.75 101
RE-MVS-def92.61 994.13 6088.95 692.87 1394.16 3388.75 1893.79 3394.43 7690.64 1187.16 3897.60 7592.73 192
IU-MVS94.18 5572.64 15890.82 17756.98 40389.67 12085.78 6497.92 5293.28 164
OPU-MVS88.27 8891.89 12577.83 10090.47 6091.22 21681.12 13894.68 8274.48 21295.35 16092.29 225
test_241102_TWO93.71 6083.77 5893.49 4094.27 8389.27 2495.84 2786.03 5797.82 5792.04 238
test_241102_ONE94.18 5572.65 15693.69 6283.62 6294.11 2793.78 11490.28 1595.50 52
9.1489.29 6691.84 12988.80 9995.32 1375.14 17091.07 8792.89 14987.27 4993.78 12183.69 9397.55 78
save fliter93.75 6877.44 10686.31 14589.72 21570.80 246
test_0728_THIRD85.33 4293.75 3594.65 6587.44 4895.78 3587.41 3198.21 3492.98 185
test_0728_SECOND86.79 11294.25 5372.45 16690.54 5794.10 4095.88 1886.42 4797.97 4992.02 239
test072694.16 5872.56 16290.63 5493.90 4983.61 6393.75 3594.49 7389.76 19
GSMVS83.88 392
test_part293.86 6677.77 10192.84 55
sam_mvs146.11 41283.88 392
sam_mvs45.92 417
ambc82.98 22190.55 16864.86 26588.20 10889.15 22889.40 12993.96 10571.67 27391.38 20278.83 15096.55 10692.71 195
MTGPAbinary91.81 144
test_post178.85 3263.13 47845.19 42780.13 39858.11 381
test_post3.10 47945.43 42377.22 413
patchmatchnet-post81.71 40445.93 41687.01 319
GG-mvs-BLEND67.16 42473.36 45446.54 44384.15 19855.04 47158.64 47061.95 47129.93 46783.87 37538.71 46376.92 45571.07 457
MTMP90.66 5333.14 481
gm-plane-assit75.42 44344.97 45052.17 42972.36 46087.90 30554.10 405
test9_res80.83 12596.45 11290.57 286
TEST992.34 10879.70 8083.94 20490.32 19565.41 32684.49 26290.97 22782.03 12393.63 127
test_892.09 11778.87 8883.82 20990.31 19765.79 31684.36 26690.96 22981.93 12593.44 141
agg_prior279.68 13896.16 12490.22 294
agg_prior91.58 13777.69 10390.30 19884.32 26893.18 149
TestCases89.68 5691.59 13483.40 5295.44 1179.47 10888.00 16393.03 14182.66 10491.47 19670.81 26696.14 12594.16 115
test_prior478.97 8784.59 187
test_prior283.37 22575.43 16684.58 25991.57 20081.92 12779.54 14296.97 94
test_prior86.32 12090.59 16771.99 17492.85 10594.17 10692.80 190
旧先验281.73 27256.88 40486.54 20984.90 36172.81 250
新几何281.72 273
新几何182.95 22393.96 6478.56 9180.24 35855.45 40983.93 27991.08 22371.19 27588.33 29765.84 32293.07 24581.95 423
旧先验191.97 12171.77 17581.78 34691.84 18973.92 23693.65 22683.61 398
无先验82.81 24485.62 29858.09 39291.41 20167.95 30684.48 383
原ACMM282.26 264
原ACMM184.60 16992.81 9874.01 14091.50 15262.59 34782.73 30590.67 24676.53 20294.25 9869.24 28795.69 15285.55 371
test22293.31 8176.54 11679.38 31577.79 36952.59 42682.36 31090.84 23766.83 29991.69 28881.25 431
testdata286.43 33463.52 344
segment_acmp81.94 124
testdata79.54 30492.87 9272.34 16780.14 35959.91 38285.47 23691.75 19667.96 29385.24 35768.57 30192.18 27481.06 436
testdata179.62 30773.95 187
test1286.57 11590.74 16372.63 16090.69 18082.76 30379.20 15794.80 7995.32 16292.27 227
plane_prior793.45 7577.31 109
plane_prior692.61 9976.54 11674.84 218
plane_prior593.61 6595.22 6380.78 12695.83 14594.46 97
plane_prior492.95 147
plane_prior376.85 11477.79 13586.55 203
plane_prior289.45 8779.44 110
plane_prior192.83 96
plane_prior76.42 11987.15 12775.94 15795.03 174
n20.00 488
nn0.00 488
door-mid74.45 395
lessismore_v085.95 13191.10 15670.99 19170.91 42591.79 7594.42 7861.76 33392.93 15979.52 14393.03 24693.93 125
LGP-MVS_train90.82 3794.75 4181.69 6394.27 2582.35 7693.67 3894.82 6091.18 595.52 4885.36 6798.73 795.23 66
test1191.46 153
door72.57 411
HQP5-MVS70.66 193
HQP-NCC91.19 15184.77 17873.30 20180.55 343
ACMP_Plane91.19 15184.77 17873.30 20180.55 343
BP-MVS77.30 175
HQP4-MVS80.56 34294.61 8693.56 153
HQP3-MVS92.68 11194.47 196
HQP2-MVS72.10 264
NP-MVS91.95 12274.55 13790.17 266
MDTV_nov1_ep13_2view27.60 47870.76 42146.47 45161.27 46345.20 42649.18 43283.75 397
MDTV_nov1_ep1368.29 39878.03 41843.87 45374.12 39172.22 41452.17 42967.02 44685.54 35445.36 42480.85 39255.73 39284.42 413
ACMMP++_ref95.74 151
ACMMP++97.35 84
Test By Simon79.09 159
ITE_SJBPF90.11 4990.72 16484.97 4190.30 19881.56 8490.02 10991.20 21882.40 10990.81 22573.58 23694.66 19194.56 90
DeepMVS_CXcopyleft24.13 46032.95 48229.49 47621.63 48312.07 47637.95 47745.07 47430.84 46519.21 47917.94 47833.06 47623.69 475