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 4997.23 295.32 299.01 297.26 980.16 14798.99 195.15 199.14 296.47 35
TDRefinement93.52 393.39 593.88 295.94 1590.26 495.70 496.46 390.58 992.86 5296.29 2288.16 3694.17 10486.07 5698.48 1897.22 18
LTVRE_ROB86.10 193.04 493.44 491.82 2293.73 6785.72 3496.79 195.51 1088.86 1695.63 1096.99 1384.81 7993.16 14891.10 297.53 7996.58 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 5288.23 992.87 1394.32 2290.25 1195.65 995.74 3387.75 4395.72 3789.60 598.27 2892.08 231
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 2789.13 798.26 3091.76 242
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 2789.13 798.26 3091.76 242
HPM-MVS_fast92.50 892.54 1092.37 695.93 1685.81 3392.99 1294.23 2885.21 4492.51 6095.13 5290.65 1095.34 5788.06 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 8090.26 498.44 2093.63 144
SR-MVS-dyc-post92.41 1092.41 1192.39 594.13 5888.95 692.87 1394.16 3388.75 1893.79 3394.43 7688.83 2795.51 4887.16 3897.60 7392.73 188
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 3986.82 4397.34 8392.19 226
HPM-MVScopyleft92.13 1292.20 1491.91 1795.58 2684.67 4693.51 894.85 1682.88 7191.77 7493.94 10890.55 1395.73 3688.50 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 8685.17 3992.47 2795.05 1587.65 2893.21 4594.39 8190.09 1895.08 6886.67 4597.60 7394.18 112
COLMAP_ROBcopyleft83.01 391.97 1491.95 1592.04 1193.68 6886.15 2493.37 1095.10 1490.28 1092.11 6695.03 5489.75 2194.93 7279.95 13198.27 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 7492.39 6394.14 9389.15 2695.62 4087.35 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 11283.09 6891.54 7694.25 8787.67 4695.51 4887.21 3798.11 4093.12 171
CP-MVS91.67 1791.58 2491.96 1495.29 3187.62 1393.38 993.36 7283.16 6791.06 8694.00 10188.26 3395.71 3887.28 3698.39 2392.55 201
XVS91.54 1891.36 2992.08 995.64 2486.25 2292.64 2093.33 7485.07 4589.99 10894.03 9986.57 5895.80 2987.35 3397.62 7194.20 109
MTAPA91.52 1991.60 2391.29 3096.59 486.29 2192.02 3691.81 13984.07 5592.00 6994.40 8086.63 5795.28 6088.59 1198.31 2692.30 218
UA-Net91.49 2091.53 2591.39 2794.98 3582.95 5893.52 792.79 10488.22 2388.53 14597.64 683.45 9394.55 8886.02 6098.60 1396.67 30
ACMMPR91.49 2091.35 3191.92 1695.74 2085.88 3092.58 2393.25 8081.99 7791.40 7894.17 9287.51 4795.87 2087.74 2297.76 6093.99 120
LPG-MVS_test91.47 2291.68 2190.82 3794.75 4181.69 6390.00 6594.27 2582.35 7593.67 3894.82 6091.18 595.52 4685.36 6798.73 795.23 66
region2R91.44 2391.30 3591.87 1995.75 1985.90 2992.63 2293.30 7881.91 7990.88 9394.21 8887.75 4395.87 2087.60 2797.71 6393.83 129
HFP-MVS91.30 2491.39 2891.02 3395.43 2984.66 4792.58 2393.29 7981.99 7791.47 7793.96 10588.35 3295.56 4387.74 2297.74 6292.85 185
ZNCC-MVS91.26 2591.34 3291.01 3495.73 2183.05 5692.18 3294.22 3080.14 10091.29 8293.97 10287.93 4295.87 2088.65 1097.96 5194.12 116
APDe-MVScopyleft91.22 2691.92 1689.14 6892.97 8878.04 9692.84 1694.14 3783.33 6593.90 2995.73 3488.77 2896.41 387.60 2797.98 4892.98 181
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PGM-MVS91.20 2790.95 4491.93 1595.67 2385.85 3190.00 6593.90 4980.32 9791.74 7594.41 7988.17 3595.98 1386.37 4997.99 4693.96 122
SteuartSystems-ACMMP91.16 2891.36 2990.55 4193.91 6380.97 7091.49 4393.48 7082.82 7292.60 5993.97 10288.19 3496.29 687.61 2698.20 3694.39 103
Skip Steuart: Steuart Systems R&D Blog.
MP-MVScopyleft91.14 2990.91 4591.83 2096.18 1186.88 1792.20 3193.03 9482.59 7388.52 14694.37 8286.74 5695.41 5586.32 5098.21 3493.19 166
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS90.96 3091.01 4190.82 3795.45 2882.73 5991.75 4193.74 5780.98 9091.38 7993.80 11287.20 5195.80 2987.10 4097.69 6593.93 123
MP-MVS-pluss90.81 3191.08 3889.99 5095.97 1479.88 7788.13 10894.51 1975.79 15792.94 4994.96 5588.36 3195.01 7090.70 398.40 2295.09 73
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+77.89 1190.73 3291.50 2688.44 8193.00 8776.26 12289.65 7895.55 987.72 2793.89 3194.94 5691.62 393.44 13978.35 15398.76 495.61 55
ACMMP_NAP90.65 3391.07 4089.42 6295.93 1679.54 8289.95 6993.68 6277.65 13591.97 7094.89 5788.38 3095.45 5389.27 697.87 5693.27 161
ACMM79.39 990.65 3390.99 4289.63 5895.03 3483.53 5189.62 7993.35 7379.20 11393.83 3293.60 12290.81 892.96 15585.02 7498.45 1992.41 208
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D90.60 3590.34 5491.38 2889.03 20184.23 4993.58 694.68 1890.65 890.33 10293.95 10784.50 8195.37 5680.87 12195.50 15594.53 94
ACMP79.16 1090.54 3690.60 5290.35 4594.36 4980.98 6989.16 9094.05 4279.03 11692.87 5193.74 11790.60 1295.21 6382.87 9998.76 494.87 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVScopyleft90.53 3791.08 3888.88 7193.38 7778.65 9089.15 9194.05 4284.68 4993.90 2994.11 9588.13 3796.30 584.51 8297.81 5891.70 246
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SED-MVS90.46 3891.64 2286.93 10794.18 5372.65 15490.47 5893.69 6083.77 5894.11 2794.27 8390.28 1595.84 2586.03 5797.92 5292.29 220
SMA-MVScopyleft90.31 3990.48 5389.83 5595.31 3079.52 8390.98 5093.24 8175.37 16692.84 5395.28 4885.58 7196.09 887.92 1897.76 6093.88 126
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 4090.80 4788.68 7892.86 9277.09 11191.19 4795.74 681.38 8592.28 6593.80 11286.89 5594.64 8385.52 6697.51 8094.30 108
v7n90.13 4190.96 4387.65 9791.95 12071.06 18789.99 6793.05 9186.53 3594.29 2396.27 2382.69 10094.08 10786.25 5397.63 6997.82 8
ME-MVS90.09 4290.66 5088.38 8392.82 9576.12 12689.40 8893.70 5983.72 6092.39 6393.18 13188.02 4095.47 5184.99 7597.69 6593.54 154
PMVScopyleft80.48 690.08 4390.66 5088.34 8596.71 392.97 290.31 6289.57 21688.51 2190.11 10495.12 5390.98 788.92 27677.55 16797.07 9083.13 403
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++90.07 4491.09 3787.00 10591.55 13772.64 15696.19 294.10 4085.33 4293.49 4094.64 6881.12 13595.88 1887.41 3195.94 13592.48 204
DVP-MVScopyleft90.06 4591.32 3386.29 11994.16 5672.56 16090.54 5591.01 16783.61 6293.75 3594.65 6589.76 1995.78 3386.42 4797.97 4990.55 283
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 4591.92 1684.47 17196.56 658.83 35689.04 9292.74 10691.40 696.12 596.06 2987.23 5095.57 4279.42 14198.74 699.00 2
PEN-MVS90.03 4791.88 1984.48 17096.57 558.88 35388.95 9393.19 8391.62 596.01 796.16 2787.02 5395.60 4178.69 14998.72 998.97 3
OurMVSNet-221017-090.01 4889.74 5990.83 3693.16 8480.37 7491.91 3993.11 8781.10 8895.32 1497.24 1072.94 25094.85 7485.07 7197.78 5997.26 16
DTE-MVSNet89.98 4991.91 1884.21 18096.51 757.84 36488.93 9492.84 10291.92 496.16 496.23 2486.95 5495.99 1279.05 14598.57 1598.80 6
XVG-ACMP-BASELINE89.98 4989.84 5790.41 4394.91 3784.50 4889.49 8493.98 4479.68 10592.09 6793.89 11083.80 8893.10 15182.67 10398.04 4193.64 143
TestfortrainingZip a89.97 5190.77 4887.58 9894.38 4873.21 14892.12 3393.85 5377.53 13993.24 4393.18 13187.06 5295.85 2487.89 1997.69 6593.68 138
3Dnovator+83.92 289.97 5189.66 6090.92 3591.27 14681.66 6691.25 4594.13 3888.89 1588.83 13794.26 8677.55 17695.86 2384.88 7695.87 14195.24 65
WR-MVS_H89.91 5391.31 3485.71 13696.32 962.39 29889.54 8293.31 7790.21 1295.57 1195.66 3781.42 13295.90 1780.94 12098.80 398.84 5
OPM-MVS89.80 5489.97 5589.27 6494.76 4079.86 7886.76 13592.78 10578.78 11992.51 6093.64 12188.13 3793.84 11884.83 7897.55 7694.10 117
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets89.78 5589.27 6691.30 2993.51 7184.79 4489.89 7190.63 17770.00 25294.55 1996.67 1787.94 4193.59 13084.27 8495.97 13195.52 56
anonymousdsp89.73 5688.88 7692.27 889.82 18286.67 1890.51 5790.20 19869.87 25395.06 1596.14 2884.28 8493.07 15287.68 2496.34 11397.09 20
test_djsdf89.62 5789.01 7091.45 2692.36 10582.98 5791.98 3790.08 20171.54 23194.28 2596.54 1981.57 13094.27 9486.26 5196.49 10797.09 20
XVG-OURS-SEG-HR89.59 5889.37 6490.28 4694.47 4385.95 2786.84 13193.91 4880.07 10186.75 19593.26 12893.64 290.93 21484.60 8190.75 31193.97 121
APD-MVScopyleft89.54 5989.63 6189.26 6592.57 9881.34 6890.19 6493.08 9080.87 9291.13 8493.19 13086.22 6595.97 1482.23 10997.18 8890.45 285
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax89.41 6088.81 7991.19 3293.38 7784.72 4589.70 7490.29 19569.27 26094.39 2196.38 2186.02 6893.52 13583.96 8695.92 13795.34 60
CPTT-MVS89.39 6188.98 7290.63 4095.09 3386.95 1692.09 3592.30 12179.74 10487.50 17992.38 16581.42 13293.28 14483.07 9597.24 8691.67 247
ACMH76.49 1489.34 6291.14 3683.96 18792.50 10170.36 19689.55 8093.84 5481.89 8094.70 1795.44 4490.69 988.31 29383.33 9198.30 2793.20 165
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testf189.30 6389.12 6789.84 5388.67 21285.64 3590.61 5393.17 8486.02 3893.12 4695.30 4684.94 7689.44 26874.12 21696.10 12694.45 97
APD_test289.30 6389.12 6789.84 5388.67 21285.64 3590.61 5393.17 8486.02 3893.12 4695.30 4684.94 7689.44 26874.12 21696.10 12694.45 97
CP-MVSNet89.27 6590.91 4584.37 17296.34 858.61 35988.66 10192.06 12890.78 795.67 895.17 5181.80 12795.54 4579.00 14698.69 1098.95 4
XVG-OURS89.18 6688.83 7890.23 4794.28 5086.11 2685.91 15093.60 6580.16 9989.13 13393.44 12483.82 8790.98 21183.86 8895.30 16393.60 147
DeepC-MVS82.31 489.15 6789.08 6989.37 6393.64 6979.07 8688.54 10494.20 3173.53 19189.71 11694.82 6085.09 7595.77 3584.17 8598.03 4393.26 163
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 6890.72 4984.31 17897.00 264.33 26989.67 7788.38 23688.84 1794.29 2397.57 790.48 1491.26 20272.57 24797.65 6897.34 15
MSP-MVS89.08 6988.16 8691.83 2095.76 1886.14 2592.75 1793.90 4978.43 12489.16 13192.25 17472.03 26496.36 488.21 1390.93 30392.98 181
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 7089.88 5686.22 12391.63 13177.07 11289.82 7293.77 5678.90 11792.88 5092.29 17286.11 6690.22 24286.24 5497.24 8691.36 255
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 7188.45 8290.38 4494.92 3685.85 3189.70 7491.27 15978.20 12786.69 19992.28 17380.36 14595.06 6986.17 5596.49 10790.22 289
Elysia88.71 7288.89 7488.19 8891.26 14772.96 15088.10 10993.59 6684.31 5190.42 9894.10 9674.07 22794.82 7588.19 1495.92 13796.80 27
StellarMVS88.71 7288.89 7488.19 8891.26 14772.96 15088.10 10993.59 6684.31 5190.42 9894.10 9674.07 22794.82 7588.19 1495.92 13796.80 27
test_040288.65 7489.58 6385.88 13292.55 9972.22 16884.01 19889.44 21988.63 2094.38 2295.77 3286.38 6493.59 13079.84 13295.21 16491.82 240
DP-MVS88.60 7589.01 7087.36 10091.30 14477.50 10487.55 11792.97 9887.95 2689.62 12092.87 14884.56 8093.89 11577.65 16596.62 10290.70 275
APD_test188.40 7687.91 8889.88 5289.50 18886.65 2089.98 6891.91 13484.26 5390.87 9493.92 10982.18 11589.29 27273.75 22494.81 18393.70 137
Anonymous2023121188.40 7689.62 6284.73 16290.46 16765.27 25888.86 9593.02 9587.15 3093.05 4897.10 1182.28 11392.02 18176.70 17797.99 4696.88 26
PS-MVSNAJss88.31 7887.90 8989.56 6093.31 7977.96 9987.94 11391.97 13170.73 24394.19 2696.67 1776.94 18994.57 8683.07 9596.28 11596.15 38
OMC-MVS88.19 7987.52 9390.19 4891.94 12281.68 6587.49 12093.17 8476.02 15188.64 14291.22 21284.24 8593.37 14277.97 16397.03 9195.52 56
CS-MVS88.14 8087.67 9289.54 6189.56 18679.18 8590.47 5894.77 1779.37 11184.32 26489.33 27883.87 8694.53 8982.45 10594.89 17994.90 76
TSAR-MVS + MP.88.14 8087.82 9089.09 6995.72 2276.74 11592.49 2691.19 16267.85 28786.63 20094.84 5979.58 15395.96 1587.62 2594.50 19294.56 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 8289.79 5882.98 21893.26 8163.94 27391.10 4889.64 21385.07 4590.91 9091.09 21789.16 2591.87 18682.03 11095.87 14193.13 168
EC-MVSNet88.01 8388.32 8587.09 10289.28 19372.03 17190.31 6296.31 480.88 9185.12 23989.67 27184.47 8295.46 5282.56 10496.26 11893.77 135
RPSCF88.00 8486.93 10791.22 3190.08 17589.30 589.68 7691.11 16379.26 11289.68 11794.81 6382.44 10487.74 30476.54 18288.74 34796.61 32
AllTest87.97 8587.40 9789.68 5691.59 13283.40 5289.50 8395.44 1179.47 10788.00 16193.03 13982.66 10191.47 19470.81 26196.14 12394.16 113
TranMVSNet+NR-MVSNet87.86 8688.76 8085.18 14894.02 6164.13 27084.38 19091.29 15584.88 4892.06 6893.84 11186.45 6193.73 12073.22 23898.66 1197.69 9
nrg03087.85 8788.49 8185.91 13090.07 17769.73 20487.86 11494.20 3174.04 18392.70 5894.66 6485.88 6991.50 19379.72 13497.32 8496.50 34
CNVR-MVS87.81 8887.68 9188.21 8792.87 9077.30 11085.25 16791.23 16077.31 14187.07 18991.47 20282.94 9894.71 7984.67 8096.27 11792.62 196
HQP_MVS87.75 8987.43 9688.70 7793.45 7376.42 11989.45 8593.61 6379.44 10986.55 20192.95 14574.84 21495.22 6180.78 12395.83 14394.46 95
sc_t187.70 9088.94 7383.99 18593.47 7267.15 23585.05 17288.21 24386.81 3291.87 7297.65 585.51 7387.91 29974.22 21197.63 6996.92 25
MM87.64 9187.15 9989.09 6989.51 18776.39 12188.68 10086.76 27484.54 5083.58 28393.78 11473.36 24596.48 287.98 1796.21 11994.41 102
MVSMamba_PlusPlus87.53 9288.86 7783.54 20492.03 11862.26 30291.49 4392.62 11088.07 2588.07 15896.17 2672.24 25995.79 3284.85 7794.16 20592.58 199
NCCC87.36 9386.87 10888.83 7292.32 10878.84 8986.58 13991.09 16578.77 12084.85 25190.89 22880.85 13895.29 5881.14 11895.32 16092.34 216
DeepPCF-MVS81.24 587.28 9486.21 11890.49 4291.48 14184.90 4283.41 22192.38 11770.25 24989.35 12890.68 23882.85 9994.57 8679.55 13895.95 13492.00 235
SixPastTwentyTwo87.20 9587.45 9586.45 11692.52 10069.19 21487.84 11588.05 24481.66 8294.64 1896.53 2065.94 30194.75 7883.02 9796.83 9695.41 58
fmvsm_s_conf0.5_n_987.04 9687.02 10487.08 10389.67 18475.87 12784.60 18389.74 20874.40 18089.92 11293.41 12580.45 14390.63 22986.66 4694.37 19894.73 87
SPE-MVS-test87.00 9786.43 11488.71 7689.46 18977.46 10589.42 8795.73 777.87 13381.64 32487.25 32382.43 10594.53 8977.65 16596.46 10994.14 115
UniMVSNet (Re)86.87 9886.98 10686.55 11493.11 8568.48 22483.80 20892.87 10080.37 9589.61 12291.81 18877.72 17294.18 10275.00 20498.53 1696.99 24
Vis-MVSNetpermissive86.86 9986.58 11187.72 9592.09 11577.43 10787.35 12192.09 12778.87 11884.27 26994.05 9878.35 16493.65 12380.54 12791.58 28992.08 231
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet86.84 10087.06 10286.17 12692.86 9267.02 23982.55 24891.56 14583.08 6990.92 8891.82 18778.25 16593.99 10974.16 21498.35 2497.49 13
DU-MVS86.80 10186.99 10586.21 12493.24 8267.02 23983.16 23192.21 12281.73 8190.92 8891.97 18077.20 18393.99 10974.16 21498.35 2497.61 10
casdiffmvs_mvgpermissive86.72 10287.51 9484.36 17487.09 26265.22 25984.16 19494.23 2877.89 13191.28 8393.66 12084.35 8392.71 16180.07 12894.87 18295.16 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 10386.52 11287.18 10185.94 30078.30 9286.93 12892.20 12365.94 30789.16 13193.16 13483.10 9689.89 25787.81 2194.43 19693.35 156
tt0320-xc86.67 10488.41 8381.44 26093.45 7360.44 33083.96 20088.50 23287.26 2990.90 9297.90 385.61 7086.40 33070.14 27398.01 4597.47 14
IS-MVSNet86.66 10586.82 11086.17 12692.05 11766.87 24391.21 4688.64 22986.30 3789.60 12392.59 15769.22 28294.91 7373.89 22197.89 5596.72 29
tt032086.63 10688.36 8481.41 26193.57 7060.73 32784.37 19188.61 23187.00 3190.75 9597.98 285.54 7286.45 32869.75 27897.70 6497.06 22
v1086.54 10787.10 10184.84 15688.16 22763.28 28086.64 13892.20 12375.42 16592.81 5594.50 7274.05 23094.06 10883.88 8796.28 11597.17 19
pmmvs686.52 10888.06 8781.90 24692.22 11162.28 30184.66 18289.15 22383.54 6489.85 11397.32 888.08 3986.80 32170.43 27097.30 8596.62 31
NormalMVS86.47 10985.32 14189.94 5194.43 4480.42 7288.63 10293.59 6674.56 17585.12 23990.34 25166.19 29894.20 9976.57 18098.44 2095.19 68
PHI-MVS86.38 11085.81 12888.08 9088.44 22177.34 10889.35 8993.05 9173.15 20484.76 25387.70 31278.87 15894.18 10280.67 12596.29 11492.73 188
CSCG86.26 11186.47 11385.60 13890.87 15974.26 13787.98 11291.85 13580.35 9689.54 12688.01 29979.09 15692.13 17775.51 19795.06 17190.41 286
DeepC-MVS_fast80.27 886.23 11285.65 13487.96 9391.30 14476.92 11387.19 12391.99 13070.56 24484.96 24690.69 23780.01 14995.14 6678.37 15295.78 14791.82 240
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v886.22 11386.83 10984.36 17487.82 23562.35 30086.42 14291.33 15476.78 14592.73 5794.48 7473.41 24293.72 12183.10 9495.41 15697.01 23
Anonymous2024052986.20 11487.13 10083.42 20690.19 17264.55 26684.55 18590.71 17485.85 4089.94 11195.24 5082.13 11690.40 23769.19 28596.40 11295.31 62
fmvsm_s_conf0.5_n_386.19 11587.27 9882.95 22086.91 27070.38 19585.31 16692.61 11175.59 16188.32 15392.87 14882.22 11488.63 28588.80 992.82 25289.83 299
test_fmvsmconf0.1_n86.18 11685.88 12687.08 10385.26 31678.25 9385.82 15491.82 13765.33 32288.55 14492.35 17182.62 10389.80 25986.87 4294.32 20093.18 167
CDPH-MVS86.17 11785.54 13588.05 9292.25 10975.45 13083.85 20592.01 12965.91 30986.19 21291.75 19283.77 8994.98 7177.43 17096.71 10093.73 136
NR-MVSNet86.00 11886.22 11785.34 14593.24 8264.56 26582.21 26290.46 18380.99 8988.42 14991.97 18077.56 17593.85 11672.46 24898.65 1297.61 10
train_agg85.98 11985.28 14288.07 9192.34 10679.70 8083.94 20190.32 19065.79 31184.49 25890.97 22281.93 12293.63 12581.21 11796.54 10590.88 269
KinetiMVS85.95 12086.10 12185.50 14287.56 24569.78 20283.70 21189.83 20780.42 9487.76 17293.24 12973.76 23691.54 19285.03 7393.62 22695.19 68
FC-MVSNet-test85.93 12187.05 10382.58 23192.25 10956.44 37585.75 15593.09 8977.33 14091.94 7194.65 6574.78 21693.41 14175.11 20398.58 1497.88 7
test_fmvsmconf_n85.88 12285.51 13686.99 10684.77 32578.21 9485.40 16491.39 15265.32 32387.72 17491.81 18882.33 10889.78 26086.68 4494.20 20392.99 179
Effi-MVS+-dtu85.82 12383.38 19193.14 487.13 25791.15 387.70 11688.42 23574.57 17483.56 28485.65 34778.49 16394.21 9872.04 25092.88 24894.05 119
TAPA-MVS77.73 1285.71 12484.83 15288.37 8488.78 21179.72 7987.15 12593.50 6969.17 26185.80 22289.56 27280.76 13992.13 17773.21 24395.51 15493.25 164
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda85.50 12586.14 11983.58 20087.97 22967.13 23687.55 11794.32 2273.44 19488.47 14787.54 31586.45 6191.06 20975.76 19593.76 21792.54 202
canonicalmvs85.50 12586.14 11983.58 20087.97 22967.13 23687.55 11794.32 2273.44 19488.47 14787.54 31586.45 6191.06 20975.76 19593.76 21792.54 202
fmvsm_s_conf0.5_n_885.48 12785.75 13184.68 16587.10 26069.98 20084.28 19292.68 10774.77 17187.90 16592.36 17073.94 23190.41 23685.95 6292.74 25493.66 139
EPP-MVSNet85.47 12885.04 14786.77 11191.52 14069.37 20991.63 4287.98 24781.51 8487.05 19091.83 18666.18 30095.29 5870.75 26496.89 9395.64 53
GeoE85.45 12985.81 12884.37 17290.08 17567.07 23885.86 15391.39 15272.33 22287.59 17690.25 25684.85 7892.37 17178.00 16191.94 27993.66 139
MGCNet85.37 13084.58 16187.75 9485.28 31573.36 14286.54 14185.71 29177.56 13881.78 32292.47 16370.29 27696.02 1185.59 6595.96 13293.87 127
FIs85.35 13186.27 11682.60 23091.86 12457.31 36885.10 17193.05 9175.83 15691.02 8793.97 10273.57 23892.91 15973.97 22098.02 4497.58 12
test_fmvsmvis_n_192085.22 13285.36 14084.81 15885.80 30376.13 12585.15 17092.32 12061.40 35891.33 8090.85 23183.76 9086.16 33684.31 8393.28 23592.15 229
casdiffmvspermissive85.21 13385.85 12783.31 20986.17 29262.77 28783.03 23393.93 4774.69 17388.21 15592.68 15682.29 11291.89 18577.87 16493.75 22095.27 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 13485.25 14385.02 15386.01 29871.31 18384.96 17391.76 14169.10 26388.90 13492.56 16073.84 23490.63 22986.88 4193.26 23693.13 168
baseline85.20 13485.93 12483.02 21686.30 28762.37 29984.55 18593.96 4574.48 17787.12 18492.03 17982.30 11091.94 18278.39 15194.21 20294.74 86
SSM_040485.16 13685.09 14585.36 14490.14 17469.52 20786.17 14791.58 14374.41 17886.55 20191.49 19978.54 15993.97 11173.71 22593.21 24092.59 198
K. test v385.14 13784.73 15486.37 11791.13 15369.63 20685.45 16276.68 37684.06 5692.44 6296.99 1362.03 32894.65 8280.58 12693.24 23794.83 83
mmtdpeth85.13 13885.78 13083.17 21484.65 32774.71 13385.87 15290.35 18977.94 13083.82 27696.96 1577.75 17080.03 39578.44 15096.21 11994.79 85
EI-MVSNet-Vis-set85.12 13984.53 16486.88 10884.01 34072.76 15383.91 20485.18 30180.44 9388.75 13985.49 35180.08 14891.92 18382.02 11190.85 30895.97 44
fmvsm_l_conf0.5_n_385.11 14084.96 14985.56 13987.49 24875.69 12984.71 18090.61 17967.64 29184.88 24992.05 17882.30 11088.36 29183.84 8991.10 29692.62 196
MGCFI-Net85.04 14185.95 12382.31 23987.52 24663.59 27686.23 14693.96 4573.46 19288.07 15887.83 31086.46 6090.87 21976.17 18993.89 21392.47 206
EI-MVSNet-UG-set85.04 14184.44 16786.85 10983.87 34472.52 16283.82 20685.15 30280.27 9888.75 13985.45 35379.95 15091.90 18481.92 11490.80 31096.13 39
X-MVStestdata85.04 14182.70 21092.08 995.64 2486.25 2292.64 2093.33 7485.07 4589.99 10816.05 47286.57 5895.80 2987.35 3397.62 7194.20 109
MSLP-MVS++85.00 14486.03 12281.90 24691.84 12771.56 18186.75 13693.02 9575.95 15487.12 18489.39 27677.98 16789.40 27177.46 16894.78 18484.75 375
F-COLMAP84.97 14583.42 19089.63 5892.39 10483.40 5288.83 9691.92 13373.19 20380.18 34689.15 28277.04 18793.28 14465.82 31892.28 26892.21 225
SSM_040784.89 14684.85 15185.01 15489.13 19768.97 21785.60 15991.58 14374.41 17885.68 22391.49 19978.54 15993.69 12273.71 22593.47 22892.38 213
balanced_conf0384.80 14785.40 13883.00 21788.95 20461.44 31090.42 6192.37 11971.48 23388.72 14193.13 13570.16 27895.15 6579.26 14394.11 20692.41 208
3Dnovator80.37 784.80 14784.71 15785.06 15186.36 28574.71 13388.77 9890.00 20375.65 15984.96 24693.17 13374.06 22991.19 20478.28 15591.09 29789.29 309
SymmetryMVS84.79 14983.54 18588.55 7992.44 10380.42 7288.63 10282.37 33674.56 17585.12 23990.34 25166.19 29894.20 9976.57 18095.68 15191.03 263
IterMVS-LS84.73 15084.98 14883.96 18787.35 25063.66 27483.25 22689.88 20676.06 14989.62 12092.37 16873.40 24492.52 16678.16 15894.77 18695.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR84.63 15184.34 17285.49 14390.18 17375.86 12879.23 31587.13 26473.35 19685.56 23089.34 27783.60 9290.50 23376.64 17994.05 21090.09 295
HQP-MVS84.61 15284.06 17786.27 12091.19 14970.66 19084.77 17592.68 10773.30 19980.55 33890.17 26172.10 26094.61 8477.30 17294.47 19493.56 151
v119284.57 15384.69 15984.21 18087.75 23762.88 28483.02 23491.43 14969.08 26489.98 11090.89 22872.70 25493.62 12882.41 10694.97 17696.13 39
fmvsm_s_conf0.5_n_584.56 15484.71 15784.11 18387.92 23272.09 17084.80 17488.64 22964.43 33288.77 13891.78 19078.07 16687.95 29885.85 6392.18 27292.30 218
FMVSNet184.55 15585.45 13781.85 24890.27 17161.05 31886.83 13288.27 24078.57 12389.66 11995.64 3875.43 20690.68 22669.09 28695.33 15993.82 130
v114484.54 15684.72 15684.00 18487.67 24162.55 29182.97 23690.93 17070.32 24889.80 11490.99 22173.50 23993.48 13781.69 11694.65 19095.97 44
Gipumacopyleft84.44 15786.33 11578.78 30684.20 33773.57 14189.55 8090.44 18484.24 5484.38 26194.89 5776.35 20280.40 39276.14 19096.80 9882.36 413
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_484.38 15884.27 17384.74 16187.25 25370.84 18983.55 21688.45 23468.64 27386.29 21191.31 20874.97 21288.42 28987.87 2090.07 32694.95 75
MCST-MVS84.36 15983.93 18185.63 13791.59 13271.58 17983.52 21792.13 12561.82 35183.96 27489.75 26979.93 15193.46 13878.33 15494.34 19991.87 239
VDDNet84.35 16085.39 13981.25 26395.13 3259.32 34485.42 16381.11 34786.41 3687.41 18096.21 2573.61 23790.61 23166.33 31196.85 9493.81 133
ETV-MVS84.31 16183.91 18285.52 14088.58 21770.40 19484.50 18993.37 7178.76 12184.07 27278.72 42680.39 14495.13 6773.82 22392.98 24691.04 262
v124084.30 16284.51 16583.65 19787.65 24261.26 31482.85 24091.54 14667.94 28490.68 9790.65 24271.71 26893.64 12482.84 10094.78 18496.07 41
MVS_111021_LR84.28 16383.76 18385.83 13489.23 19583.07 5580.99 28383.56 32472.71 21486.07 21589.07 28481.75 12986.19 33577.11 17493.36 23188.24 328
h-mvs3384.25 16482.76 20988.72 7591.82 12982.60 6084.00 19984.98 30871.27 23486.70 19790.55 24763.04 32593.92 11478.26 15694.20 20389.63 301
v14419284.24 16584.41 16883.71 19687.59 24461.57 30982.95 23791.03 16667.82 28889.80 11490.49 24873.28 24693.51 13681.88 11594.89 17996.04 43
dcpmvs_284.23 16685.14 14481.50 25888.61 21661.98 30682.90 23993.11 8768.66 27292.77 5692.39 16478.50 16287.63 30776.99 17692.30 26594.90 76
v192192084.23 16684.37 17083.79 19287.64 24361.71 30882.91 23891.20 16167.94 28490.06 10590.34 25172.04 26393.59 13082.32 10794.91 17796.07 41
VDD-MVS84.23 16684.58 16183.20 21291.17 15265.16 26183.25 22684.97 30979.79 10387.18 18394.27 8374.77 21790.89 21769.24 28296.54 10593.55 153
v2v48284.09 16984.24 17483.62 19887.13 25761.40 31182.71 24389.71 21172.19 22589.55 12491.41 20370.70 27493.20 14681.02 11993.76 21796.25 37
EG-PatchMatch MVS84.08 17084.11 17683.98 18692.22 11172.61 15982.20 26487.02 27072.63 21588.86 13591.02 22078.52 16191.11 20773.41 23391.09 29788.21 329
fmvsm_s_conf0.5_n_684.05 17184.14 17583.81 19087.75 23771.17 18583.42 22091.10 16467.90 28684.53 25690.70 23673.01 24988.73 28285.09 7093.72 22291.53 252
DP-MVS Recon84.05 17183.22 19486.52 11591.73 13075.27 13183.23 22892.40 11572.04 22782.04 31388.33 29577.91 16993.95 11366.17 31295.12 16990.34 288
viewmacassd2359aftdt84.04 17384.78 15381.81 25186.43 27960.32 33281.95 26692.82 10371.56 23086.06 21692.98 14181.79 12890.28 23876.18 18893.24 23794.82 84
TransMVSNet (Re)84.02 17485.74 13278.85 30591.00 15655.20 38782.29 25887.26 25979.65 10688.38 15195.52 4183.00 9786.88 31967.97 30096.60 10394.45 97
Baseline_NR-MVSNet84.00 17585.90 12578.29 31791.47 14253.44 39982.29 25887.00 27379.06 11589.55 12495.72 3677.20 18386.14 33772.30 24998.51 1795.28 63
fmvsm_l_conf0.5_n_983.98 17684.46 16682.53 23486.11 29570.65 19282.45 25389.17 22267.72 29086.74 19691.49 19979.20 15485.86 34684.71 7992.60 25891.07 261
TSAR-MVS + GP.83.95 17782.69 21187.72 9589.27 19481.45 6783.72 21081.58 34574.73 17285.66 22686.06 34272.56 25692.69 16375.44 19995.21 16489.01 322
LuminaMVS83.94 17883.51 18685.23 14689.78 18371.74 17484.76 17887.27 25872.60 21689.31 12990.60 24664.04 31490.95 21279.08 14494.11 20692.99 179
alignmvs83.94 17883.98 17983.80 19187.80 23667.88 23184.54 18791.42 15173.27 20288.41 15087.96 30072.33 25790.83 22076.02 19294.11 20692.69 192
Effi-MVS+83.90 18084.01 17883.57 20287.22 25565.61 25786.55 14092.40 11578.64 12281.34 32984.18 37283.65 9192.93 15774.22 21187.87 36192.17 228
fmvsm_s_conf0.1_n_283.82 18183.49 18784.84 15685.99 29970.19 19880.93 28487.58 25467.26 29787.94 16492.37 16871.40 27088.01 29586.03 5791.87 28096.31 36
mvs5depth83.82 18184.54 16381.68 25482.23 36968.65 22286.89 12989.90 20580.02 10287.74 17397.86 464.19 31382.02 38076.37 18495.63 15394.35 104
CANet83.79 18382.85 20886.63 11286.17 29272.21 16983.76 20991.43 14977.24 14274.39 40187.45 31975.36 20795.42 5477.03 17592.83 25192.25 224
pm-mvs183.69 18484.95 15079.91 29190.04 17959.66 34182.43 25487.44 25575.52 16387.85 16895.26 4981.25 13485.65 35068.74 29296.04 12894.42 101
AdaColmapbinary83.66 18583.69 18483.57 20290.05 17872.26 16786.29 14490.00 20378.19 12881.65 32387.16 32583.40 9494.24 9761.69 35494.76 18784.21 385
viewdifsd2359ckpt0983.64 18683.18 19785.03 15287.26 25266.99 24185.32 16593.83 5565.57 31784.99 24589.40 27577.30 17993.57 13371.16 26093.80 21694.54 93
MIMVSNet183.63 18784.59 16080.74 27494.06 6062.77 28782.72 24284.53 31677.57 13790.34 10195.92 3176.88 19585.83 34761.88 35297.42 8193.62 145
fmvsm_s_conf0.5_n_283.62 18883.29 19384.62 16685.43 31370.18 19980.61 29087.24 26067.14 29887.79 17091.87 18271.79 26787.98 29786.00 6191.77 28395.71 50
test_fmvsm_n_192083.60 18982.89 20585.74 13585.22 31777.74 10284.12 19690.48 18159.87 37886.45 21091.12 21675.65 20485.89 34482.28 10890.87 30693.58 149
WR-MVS83.56 19084.40 16981.06 26893.43 7654.88 38878.67 32485.02 30681.24 8690.74 9691.56 19772.85 25191.08 20868.00 29998.04 4197.23 17
CNLPA83.55 19183.10 20084.90 15589.34 19283.87 5084.54 18788.77 22679.09 11483.54 28588.66 29274.87 21381.73 38266.84 30692.29 26789.11 315
viewcassd2359sk1183.53 19283.96 18082.25 24086.97 26961.13 31680.80 28893.22 8270.97 24085.36 23491.08 21881.84 12691.29 20174.79 20690.58 32294.33 106
LCM-MVSNet-Re83.48 19385.06 14678.75 30785.94 30055.75 38180.05 29694.27 2576.47 14696.09 694.54 7183.31 9589.75 26359.95 36594.89 17990.75 272
hse-mvs283.47 19481.81 22688.47 8091.03 15582.27 6182.61 24483.69 32271.27 23486.70 19786.05 34363.04 32592.41 16978.26 15693.62 22690.71 274
V4283.47 19483.37 19283.75 19483.16 36363.33 27981.31 27790.23 19769.51 25790.91 9090.81 23374.16 22692.29 17580.06 12990.22 32495.62 54
VPA-MVSNet83.47 19484.73 15479.69 29690.29 17057.52 36781.30 27988.69 22876.29 14787.58 17894.44 7580.60 14287.20 31366.60 30996.82 9794.34 105
mamba_040883.44 19782.88 20685.11 14989.13 19768.97 21772.73 39991.28 15672.90 20885.68 22390.61 24476.78 19693.97 11173.37 23593.47 22892.38 213
viewdifsd2359ckpt0783.41 19884.35 17180.56 28085.84 30258.93 35279.47 30791.28 15673.01 20787.59 17692.07 17785.24 7488.68 28373.59 23091.11 29594.09 118
PAPM_NR83.23 19983.19 19683.33 20890.90 15865.98 25388.19 10790.78 17378.13 12980.87 33487.92 30473.49 24192.42 16870.07 27488.40 35091.60 249
CLD-MVS83.18 20082.64 21284.79 15989.05 20067.82 23277.93 33492.52 11368.33 27685.07 24281.54 40182.06 11992.96 15569.35 28197.91 5493.57 150
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 20185.68 13375.65 35581.24 38145.26 44379.94 29892.91 9983.83 5791.33 8096.88 1680.25 14685.92 34068.89 28995.89 14095.76 48
FA-MVS(test-final)83.13 20283.02 20183.43 20586.16 29466.08 25288.00 11188.36 23775.55 16285.02 24392.75 15465.12 30792.50 16774.94 20591.30 29391.72 244
114514_t83.10 20382.54 21584.77 16092.90 8969.10 21686.65 13790.62 17854.66 41081.46 32690.81 23376.98 18894.38 9272.62 24696.18 12190.82 271
RRT-MVS82.97 20483.44 18881.57 25685.06 32058.04 36287.20 12290.37 18777.88 13288.59 14393.70 11963.17 32293.05 15376.49 18388.47 34993.62 145
viewmanbaseed2359cas82.95 20583.43 18981.52 25785.18 31860.03 33781.36 27692.38 11769.55 25684.84 25291.38 20479.85 15290.09 25174.22 21192.09 27494.43 100
BP-MVS182.81 20681.67 22886.23 12187.88 23468.53 22386.06 14984.36 31775.65 15985.14 23890.19 25845.84 41494.42 9185.18 6994.72 18895.75 49
UGNet82.78 20781.64 22986.21 12486.20 29176.24 12386.86 13085.68 29277.07 14373.76 40592.82 15069.64 27991.82 18869.04 28893.69 22390.56 282
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 20881.93 22485.19 14782.08 37080.15 7685.53 16088.76 22768.01 28185.58 22987.75 31171.80 26686.85 32074.02 21993.87 21488.58 325
EI-MVSNet82.61 20982.42 21783.20 21283.25 36063.66 27483.50 21885.07 30376.06 14986.55 20185.10 35973.41 24290.25 23978.15 16090.67 31795.68 52
QAPM82.59 21082.59 21482.58 23186.44 27866.69 24489.94 7090.36 18867.97 28384.94 24892.58 15972.71 25392.18 17670.63 26787.73 36488.85 323
fmvsm_s_conf0.1_n_a82.58 21181.93 22484.50 16987.68 24073.35 14386.14 14877.70 36561.64 35685.02 24391.62 19477.75 17086.24 33282.79 10187.07 37293.91 125
Fast-Effi-MVS+-dtu82.54 21281.41 23885.90 13185.60 30876.53 11883.07 23289.62 21573.02 20679.11 35683.51 37780.74 14090.24 24168.76 29189.29 33790.94 266
MVS_Test82.47 21383.22 19480.22 28782.62 36857.75 36682.54 24991.96 13271.16 23882.89 29692.52 16277.41 17790.50 23380.04 13087.84 36392.40 210
viewdifsd2359ckpt1182.46 21482.98 20380.88 27183.53 34761.00 32179.46 30885.97 28769.48 25887.89 16691.31 20882.10 11788.61 28674.28 20992.86 24993.02 175
viewmsd2359difaftdt82.46 21482.99 20280.88 27183.52 34861.00 32179.46 30885.97 28769.48 25887.89 16691.31 20882.10 11788.61 28674.28 20992.86 24993.02 175
v14882.31 21682.48 21681.81 25185.59 30959.66 34181.47 27486.02 28572.85 21088.05 16090.65 24270.73 27390.91 21675.15 20291.79 28194.87 78
API-MVS82.28 21782.61 21381.30 26286.29 28869.79 20188.71 9987.67 25378.42 12582.15 30984.15 37377.98 16791.59 19165.39 32192.75 25382.51 412
MVSFormer82.23 21881.57 23484.19 18285.54 31069.26 21191.98 3790.08 20171.54 23176.23 38185.07 36258.69 35094.27 9486.26 5188.77 34589.03 320
viewdifsd2359ckpt1382.22 21981.98 22382.95 22085.48 31264.44 26783.17 23092.11 12665.97 30683.72 27989.73 27077.60 17490.80 22270.61 26889.42 33593.59 148
fmvsm_s_conf0.5_n_a82.21 22081.51 23784.32 17786.56 27573.35 14385.46 16177.30 36961.81 35284.51 25790.88 23077.36 17886.21 33482.72 10286.97 37793.38 155
EIA-MVS82.19 22181.23 24585.10 15087.95 23169.17 21583.22 22993.33 7470.42 24578.58 36179.77 41777.29 18094.20 9971.51 25688.96 34391.93 238
GDP-MVS82.17 22280.85 25386.15 12888.65 21468.95 22085.65 15893.02 9568.42 27483.73 27889.54 27345.07 42594.31 9379.66 13693.87 21495.19 68
fmvsm_s_conf0.1_n82.17 22281.59 23283.94 18986.87 27371.57 18085.19 16977.42 36862.27 35084.47 26091.33 20676.43 19985.91 34283.14 9287.14 37094.33 106
PCF-MVS74.62 1582.15 22480.92 25185.84 13389.43 19072.30 16680.53 29191.82 13757.36 39487.81 16989.92 26677.67 17393.63 12558.69 37095.08 17091.58 250
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLCcopyleft73.85 1682.09 22580.31 26087.45 9990.86 16080.29 7585.88 15190.65 17668.17 27976.32 38086.33 33773.12 24892.61 16561.40 35790.02 32889.44 304
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n82.06 22681.54 23683.60 19983.94 34173.90 13983.35 22386.10 28158.97 38083.80 27790.36 25074.23 22486.94 31882.90 9890.22 32489.94 297
fmvsm_s_conf0.5_n_782.04 22782.05 22182.01 24486.98 26871.07 18678.70 32289.45 21868.07 28078.14 36391.61 19574.19 22585.92 34079.61 13791.73 28489.05 319
GBi-Net82.02 22882.07 21981.85 24886.38 28261.05 31886.83 13288.27 24072.43 21786.00 21795.64 3863.78 31890.68 22665.95 31493.34 23293.82 130
test182.02 22882.07 21981.85 24886.38 28261.05 31886.83 13288.27 24072.43 21786.00 21795.64 3863.78 31890.68 22665.95 31493.34 23293.82 130
OpenMVScopyleft76.72 1381.98 23082.00 22281.93 24584.42 33268.22 22688.50 10589.48 21766.92 30181.80 32091.86 18372.59 25590.16 24571.19 25991.25 29487.40 345
KD-MVS_self_test81.93 23183.14 19978.30 31684.75 32652.75 40380.37 29389.42 22070.24 25090.26 10393.39 12674.55 22386.77 32268.61 29496.64 10195.38 59
fmvsm_s_conf0.5_n81.91 23281.30 24283.75 19486.02 29771.56 18184.73 17977.11 37262.44 34784.00 27390.68 23876.42 20085.89 34483.14 9287.11 37193.81 133
SDMVSNet81.90 23383.17 19878.10 32088.81 20962.45 29776.08 36886.05 28473.67 18883.41 28693.04 13782.35 10780.65 38970.06 27595.03 17291.21 257
tfpnnormal81.79 23482.95 20478.31 31588.93 20555.40 38380.83 28782.85 33176.81 14485.90 22194.14 9374.58 22186.51 32666.82 30795.68 15193.01 178
AstraMVS81.67 23581.40 23982.48 23687.06 26566.47 24781.41 27581.68 34268.78 26988.00 16190.95 22665.70 30387.86 30376.66 17892.38 26293.12 171
c3_l81.64 23681.59 23281.79 25380.86 38759.15 34978.61 32590.18 19968.36 27587.20 18287.11 32769.39 28091.62 19078.16 15894.43 19694.60 89
guyue81.57 23781.37 24182.15 24186.39 28066.13 25181.54 27383.21 32669.79 25487.77 17189.95 26465.36 30687.64 30675.88 19392.49 26092.67 193
PVSNet_Blended_VisFu81.55 23880.49 25884.70 16491.58 13573.24 14784.21 19391.67 14262.86 34180.94 33287.16 32567.27 29292.87 16069.82 27788.94 34487.99 335
fmvsm_l_conf0.5_n_a81.46 23980.87 25283.25 21083.73 34673.21 14883.00 23585.59 29458.22 38682.96 29590.09 26372.30 25886.65 32481.97 11389.95 32989.88 298
SSM_0407281.44 24082.88 20677.10 33589.13 19768.97 21772.73 39991.28 15672.90 20885.68 22390.61 24476.78 19669.94 43273.37 23593.47 22892.38 213
DELS-MVS81.44 24081.25 24382.03 24384.27 33662.87 28576.47 36292.49 11470.97 24081.64 32483.83 37475.03 21092.70 16274.29 20892.22 27190.51 284
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 24281.61 23180.41 28386.38 28258.75 35783.93 20386.58 27672.43 21787.65 17592.98 14163.78 31890.22 24266.86 30493.92 21292.27 222
TinyColmap81.25 24382.34 21877.99 32385.33 31460.68 32882.32 25788.33 23871.26 23686.97 19192.22 17677.10 18686.98 31762.37 34695.17 16686.31 358
diffmvs_AUTHOR81.24 24481.55 23580.30 28580.61 39260.22 33377.98 33390.48 18167.77 28983.34 28889.50 27474.69 21987.42 30978.78 14890.81 30993.27 161
AUN-MVS81.18 24578.78 28388.39 8290.93 15782.14 6282.51 25083.67 32364.69 33180.29 34285.91 34651.07 38992.38 17076.29 18793.63 22590.65 279
IMVS_040781.08 24681.23 24580.62 27985.76 30462.46 29382.46 25187.91 24865.23 32482.12 31087.92 30477.27 18190.18 24471.67 25290.74 31289.20 310
tttt051781.07 24779.58 27385.52 14088.99 20366.45 24887.03 12775.51 38473.76 18788.32 15390.20 25737.96 44694.16 10679.36 14295.13 16795.93 47
Fast-Effi-MVS+81.04 24880.57 25582.46 23787.50 24763.22 28178.37 32889.63 21468.01 28181.87 31682.08 39582.31 10992.65 16467.10 30388.30 35691.51 253
BH-untuned80.96 24980.99 24980.84 27388.55 21868.23 22580.33 29488.46 23372.79 21386.55 20186.76 33174.72 21891.77 18961.79 35388.99 34282.52 411
IMVS_040380.93 25081.00 24880.72 27685.76 30462.46 29381.82 26787.91 24865.23 32482.07 31287.92 30475.91 20390.50 23371.67 25290.74 31289.20 310
eth_miper_zixun_eth80.84 25180.22 26482.71 22881.41 37960.98 32377.81 33690.14 20067.31 29686.95 19287.24 32464.26 31192.31 17375.23 20191.61 28794.85 82
xiu_mvs_v1_base_debu80.84 25180.14 26682.93 22388.31 22271.73 17579.53 30387.17 26165.43 31879.59 34882.73 38976.94 18990.14 24873.22 23888.33 35286.90 352
xiu_mvs_v1_base80.84 25180.14 26682.93 22388.31 22271.73 17579.53 30387.17 26165.43 31879.59 34882.73 38976.94 18990.14 24873.22 23888.33 35286.90 352
xiu_mvs_v1_base_debi80.84 25180.14 26682.93 22388.31 22271.73 17579.53 30387.17 26165.43 31879.59 34882.73 38976.94 18990.14 24873.22 23888.33 35286.90 352
IterMVS-SCA-FT80.64 25579.41 27484.34 17683.93 34269.66 20576.28 36481.09 34872.43 21786.47 20890.19 25860.46 33593.15 14977.45 16986.39 38390.22 289
BH-RMVSNet80.53 25680.22 26481.49 25987.19 25666.21 25077.79 33786.23 27974.21 18283.69 28088.50 29373.25 24790.75 22363.18 34287.90 36087.52 343
VortexMVS80.51 25780.63 25480.15 28983.36 35661.82 30780.63 28988.00 24667.11 29987.23 18189.10 28363.98 31588.00 29673.63 22992.63 25790.64 280
Anonymous20240521180.51 25781.19 24778.49 31288.48 21957.26 36976.63 35782.49 33481.21 8784.30 26792.24 17567.99 28886.24 33262.22 34795.13 16791.98 237
DIV-MVS_self_test80.43 25980.23 26281.02 26979.99 39759.25 34677.07 35087.02 27067.38 29386.19 21289.22 27963.09 32390.16 24576.32 18595.80 14593.66 139
cl____80.42 26080.23 26281.02 26979.99 39759.25 34677.07 35087.02 27067.37 29486.18 21489.21 28063.08 32490.16 24576.31 18695.80 14593.65 142
diffmvspermissive80.40 26180.48 25980.17 28879.02 41060.04 33577.54 34190.28 19666.65 30482.40 30387.33 32273.50 23987.35 31177.98 16289.62 33393.13 168
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPNet80.37 26278.41 29186.23 12176.75 42473.28 14587.18 12477.45 36776.24 14868.14 43588.93 28665.41 30593.85 11669.47 28096.12 12591.55 251
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth80.34 26380.04 26981.24 26579.82 40058.95 35177.66 33889.66 21265.75 31485.99 22085.11 35868.29 28791.42 19876.03 19192.03 27593.33 157
MG-MVS80.32 26480.94 25078.47 31388.18 22552.62 40682.29 25885.01 30772.01 22879.24 35592.54 16169.36 28193.36 14370.65 26689.19 34089.45 303
mvsmamba80.30 26578.87 28084.58 16888.12 22867.55 23392.35 3084.88 31063.15 33985.33 23590.91 22750.71 39195.20 6466.36 31087.98 35990.99 264
VPNet80.25 26681.68 22775.94 35192.46 10247.98 43076.70 35581.67 34373.45 19384.87 25092.82 15074.66 22086.51 32661.66 35596.85 9493.33 157
MAR-MVS80.24 26778.74 28584.73 16286.87 27378.18 9585.75 15587.81 25265.67 31677.84 36778.50 42773.79 23590.53 23261.59 35690.87 30685.49 368
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 26879.00 27983.78 19388.17 22686.66 1981.31 27766.81 44069.64 25588.33 15290.19 25864.58 30883.63 37171.99 25190.03 32781.06 431
Anonymous2024052180.18 26981.25 24376.95 33783.15 36460.84 32582.46 25185.99 28668.76 27086.78 19393.73 11859.13 34777.44 40673.71 22597.55 7692.56 200
LFMVS80.15 27080.56 25678.89 30489.19 19655.93 37785.22 16873.78 39682.96 7084.28 26892.72 15557.38 35990.07 25363.80 33695.75 14890.68 276
DPM-MVS80.10 27179.18 27882.88 22690.71 16369.74 20378.87 32090.84 17160.29 37475.64 39085.92 34567.28 29193.11 15071.24 25891.79 28185.77 364
MSDG80.06 27279.99 27180.25 28683.91 34368.04 23077.51 34289.19 22177.65 13581.94 31483.45 37976.37 20186.31 33163.31 34186.59 38086.41 356
FE-MVS79.98 27378.86 28183.36 20786.47 27766.45 24889.73 7384.74 31472.80 21284.22 27191.38 20444.95 42693.60 12963.93 33491.50 29090.04 296
sd_testset79.95 27481.39 24075.64 35688.81 20958.07 36176.16 36782.81 33273.67 18883.41 28693.04 13780.96 13777.65 40558.62 37195.03 17291.21 257
ab-mvs79.67 27580.56 25676.99 33688.48 21956.93 37184.70 18186.06 28368.95 26780.78 33593.08 13675.30 20884.62 35856.78 38090.90 30489.43 305
VNet79.31 27680.27 26176.44 34587.92 23253.95 39575.58 37484.35 31874.39 18182.23 30790.72 23572.84 25284.39 36360.38 36393.98 21190.97 265
thisisatest053079.07 27777.33 30184.26 17987.13 25764.58 26483.66 21375.95 37968.86 26885.22 23787.36 32138.10 44393.57 13375.47 19894.28 20194.62 88
cl2278.97 27878.21 29381.24 26577.74 41459.01 35077.46 34587.13 26465.79 31184.32 26485.10 35958.96 34990.88 21875.36 20092.03 27593.84 128
patch_mono-278.89 27979.39 27577.41 33284.78 32468.11 22875.60 37283.11 32860.96 36679.36 35289.89 26775.18 20972.97 42173.32 23792.30 26591.15 259
RPMNet78.88 28078.28 29280.68 27879.58 40162.64 28982.58 24694.16 3374.80 17075.72 38892.59 15748.69 39895.56 4373.48 23282.91 41983.85 390
PAPR78.84 28178.10 29481.07 26785.17 31960.22 33382.21 26290.57 18062.51 34375.32 39484.61 36774.99 21192.30 17459.48 36888.04 35890.68 276
viewmambaseed2359dif78.80 28278.47 29079.78 29280.26 39659.28 34577.31 34787.13 26460.42 37282.37 30488.67 29174.58 22187.87 30267.78 30287.73 36492.19 226
PVSNet_BlendedMVS78.80 28277.84 29581.65 25584.43 33063.41 27779.49 30690.44 18461.70 35575.43 39187.07 32869.11 28391.44 19660.68 36192.24 26990.11 294
FMVSNet378.80 28278.55 28779.57 29882.89 36756.89 37381.76 26885.77 29069.04 26586.00 21790.44 24951.75 38790.09 25165.95 31493.34 23291.72 244
test_yl78.71 28578.51 28879.32 30184.32 33458.84 35478.38 32685.33 29875.99 15282.49 30186.57 33358.01 35390.02 25562.74 34392.73 25589.10 316
DCV-MVSNet78.71 28578.51 28879.32 30184.32 33458.84 35478.38 32685.33 29875.99 15282.49 30186.57 33358.01 35390.02 25562.74 34392.73 25589.10 316
test111178.53 28778.85 28277.56 32992.22 11147.49 43282.61 24469.24 42872.43 21785.28 23694.20 8951.91 38590.07 25365.36 32296.45 11095.11 72
FE-MVSNET78.46 28879.36 27675.75 35386.53 27654.53 39078.03 33085.35 29769.01 26685.41 23390.68 23864.27 31085.73 34862.59 34592.35 26487.00 351
icg_test_0407_278.46 28879.68 27274.78 36385.76 30462.46 29368.51 42887.91 24865.23 32482.12 31087.92 30477.27 18172.67 42271.67 25290.74 31289.20 310
ECVR-MVScopyleft78.44 29078.63 28677.88 32591.85 12548.95 42683.68 21269.91 42472.30 22384.26 27094.20 8951.89 38689.82 25863.58 33796.02 12994.87 78
pmmvs-eth3d78.42 29177.04 30482.57 23387.44 24974.41 13680.86 28679.67 35655.68 40384.69 25490.31 25560.91 33385.42 35162.20 34891.59 28887.88 339
mvs_anonymous78.13 29278.76 28476.23 35079.24 40750.31 42278.69 32384.82 31261.60 35783.09 29492.82 15073.89 23387.01 31468.33 29886.41 38291.37 254
TAMVS78.08 29376.36 31183.23 21190.62 16472.87 15279.08 31680.01 35561.72 35481.35 32886.92 33063.96 31788.78 28050.61 41993.01 24588.04 334
miper_enhance_ethall77.83 29476.93 30580.51 28176.15 43158.01 36375.47 37688.82 22558.05 38883.59 28280.69 40564.41 30991.20 20373.16 24492.03 27592.33 217
Vis-MVSNet (Re-imp)77.82 29577.79 29677.92 32488.82 20851.29 41683.28 22471.97 41274.04 18382.23 30789.78 26857.38 35989.41 27057.22 37995.41 15693.05 174
CANet_DTU77.81 29677.05 30380.09 29081.37 38059.90 33983.26 22588.29 23969.16 26267.83 43883.72 37560.93 33289.47 26569.22 28489.70 33290.88 269
OpenMVS_ROBcopyleft70.19 1777.77 29777.46 29878.71 30884.39 33361.15 31581.18 28182.52 33362.45 34683.34 28887.37 32066.20 29788.66 28464.69 32985.02 39986.32 357
SSC-MVS77.55 29881.64 22965.29 42990.46 16720.33 47673.56 39268.28 43085.44 4188.18 15794.64 6870.93 27281.33 38471.25 25792.03 27594.20 109
MDA-MVSNet-bldmvs77.47 29976.90 30679.16 30379.03 40964.59 26366.58 44075.67 38273.15 20488.86 13588.99 28566.94 29381.23 38564.71 32888.22 35791.64 248
jason77.42 30075.75 31782.43 23887.10 26069.27 21077.99 33281.94 34051.47 43077.84 36785.07 36260.32 33789.00 27470.74 26589.27 33989.03 320
jason: jason.
CDS-MVSNet77.32 30175.40 32183.06 21589.00 20272.48 16377.90 33582.17 33860.81 36778.94 35883.49 37859.30 34588.76 28154.64 39992.37 26387.93 338
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_040477.24 30277.75 29775.73 35485.76 30462.46 29370.84 41487.91 24865.23 32472.21 41387.92 30467.48 29075.53 41471.67 25290.74 31289.20 310
xiu_mvs_v2_base77.19 30376.75 30878.52 31187.01 26661.30 31375.55 37587.12 26861.24 36374.45 40078.79 42577.20 18390.93 21464.62 33184.80 40683.32 399
MVSTER77.09 30475.70 31881.25 26375.27 43961.08 31777.49 34485.07 30360.78 36886.55 20188.68 28943.14 43590.25 23973.69 22890.67 31792.42 207
PS-MVSNAJ77.04 30576.53 31078.56 31087.09 26261.40 31175.26 37787.13 26461.25 36274.38 40277.22 43976.94 18990.94 21364.63 33084.83 40583.35 398
IterMVS76.91 30676.34 31278.64 30980.91 38564.03 27176.30 36379.03 35964.88 33083.11 29289.16 28159.90 34184.46 36168.61 29485.15 39787.42 344
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS76.84 30775.67 31980.34 28480.48 39462.16 30573.50 39384.80 31357.61 39282.24 30687.54 31551.31 38887.65 30570.40 27193.19 24191.23 256
CL-MVSNet_self_test76.81 30877.38 30075.12 35986.90 27151.34 41473.20 39680.63 35268.30 27781.80 32088.40 29466.92 29480.90 38655.35 39394.90 17893.12 171
TR-MVS76.77 30975.79 31679.72 29586.10 29665.79 25577.14 34883.02 32965.20 32881.40 32782.10 39366.30 29690.73 22555.57 39085.27 39382.65 406
MonoMVSNet76.66 31077.26 30274.86 36179.86 39954.34 39286.26 14586.08 28271.08 23985.59 22888.68 28953.95 37785.93 33963.86 33580.02 43584.32 381
USDC76.63 31176.73 30976.34 34783.46 35157.20 37080.02 29788.04 24552.14 42683.65 28191.25 21163.24 32186.65 32454.66 39894.11 20685.17 370
BH-w/o76.57 31276.07 31578.10 32086.88 27265.92 25477.63 33986.33 27765.69 31580.89 33379.95 41468.97 28590.74 22453.01 40985.25 39477.62 442
Patchmtry76.56 31377.46 29873.83 36979.37 40646.60 43682.41 25576.90 37373.81 18685.56 23092.38 16548.07 40183.98 36863.36 34095.31 16290.92 267
PVSNet_Blended76.49 31475.40 32179.76 29484.43 33063.41 27775.14 37890.44 18457.36 39475.43 39178.30 42869.11 28391.44 19660.68 36187.70 36684.42 380
miper_lstm_enhance76.45 31576.10 31477.51 33076.72 42560.97 32464.69 44485.04 30563.98 33583.20 29188.22 29656.67 36378.79 40273.22 23893.12 24292.78 187
lupinMVS76.37 31674.46 33082.09 24285.54 31069.26 21176.79 35380.77 35150.68 43776.23 38182.82 38758.69 35088.94 27569.85 27688.77 34588.07 331
cascas76.29 31774.81 32680.72 27684.47 32962.94 28373.89 39087.34 25655.94 40175.16 39676.53 44463.97 31691.16 20565.00 32590.97 30288.06 333
SD_040376.08 31876.77 30773.98 36787.08 26449.45 42583.62 21484.68 31563.31 33675.13 39787.47 31871.85 26584.56 35949.97 42187.86 36287.94 337
WB-MVS76.06 31980.01 27064.19 43289.96 18120.58 47572.18 40368.19 43183.21 6686.46 20993.49 12370.19 27778.97 40065.96 31390.46 32393.02 175
thres600view775.97 32075.35 32377.85 32787.01 26651.84 41280.45 29273.26 40175.20 16783.10 29386.31 33945.54 41689.05 27355.03 39692.24 26992.66 194
GA-MVS75.83 32174.61 32779.48 30081.87 37259.25 34673.42 39482.88 33068.68 27179.75 34781.80 39850.62 39289.46 26666.85 30585.64 39089.72 300
MVP-Stereo75.81 32273.51 33982.71 22889.35 19173.62 14080.06 29585.20 30060.30 37373.96 40387.94 30157.89 35789.45 26752.02 41374.87 45385.06 372
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs375.72 32375.20 32477.27 33375.01 44269.47 20878.93 31784.88 31046.67 44487.08 18887.84 30950.44 39471.62 42777.42 17188.53 34890.72 273
thres100view90075.45 32475.05 32576.66 34387.27 25151.88 41181.07 28273.26 40175.68 15883.25 29086.37 33645.54 41688.80 27751.98 41490.99 29989.31 307
ET-MVSNet_ETH3D75.28 32572.77 34882.81 22783.03 36668.11 22877.09 34976.51 37760.67 37077.60 37280.52 40938.04 44491.15 20670.78 26390.68 31689.17 314
thres40075.14 32674.23 33277.86 32686.24 28952.12 40879.24 31373.87 39473.34 19781.82 31884.60 36846.02 40988.80 27751.98 41490.99 29992.66 194
wuyk23d75.13 32779.30 27762.63 43575.56 43575.18 13280.89 28573.10 40375.06 16994.76 1695.32 4587.73 4552.85 46734.16 46597.11 8959.85 463
EU-MVSNet75.12 32874.43 33177.18 33483.11 36559.48 34385.71 15782.43 33539.76 46485.64 22788.76 28744.71 42887.88 30173.86 22285.88 38984.16 386
HyFIR lowres test75.12 32872.66 35082.50 23591.44 14365.19 26072.47 40187.31 25746.79 44380.29 34284.30 37052.70 38292.10 18051.88 41886.73 37890.22 289
CMPMVSbinary59.41 2075.12 32873.57 33779.77 29375.84 43467.22 23481.21 28082.18 33750.78 43576.50 37787.66 31355.20 37382.99 37462.17 35090.64 32189.09 318
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs474.92 33172.98 34680.73 27584.95 32171.71 17876.23 36577.59 36652.83 42077.73 37186.38 33556.35 36684.97 35557.72 37887.05 37385.51 367
tfpn200view974.86 33274.23 33276.74 34286.24 28952.12 40879.24 31373.87 39473.34 19781.82 31884.60 36846.02 40988.80 27751.98 41490.99 29989.31 307
1112_ss74.82 33373.74 33578.04 32289.57 18560.04 33576.49 36187.09 26954.31 41173.66 40679.80 41560.25 33886.76 32358.37 37284.15 41087.32 346
EGC-MVSNET74.79 33469.99 37889.19 6794.89 3887.00 1591.89 4086.28 2781.09 4732.23 47595.98 3081.87 12589.48 26479.76 13395.96 13291.10 260
ppachtmachnet_test74.73 33574.00 33476.90 33980.71 39056.89 37371.53 40978.42 36158.24 38579.32 35482.92 38657.91 35684.26 36565.60 32091.36 29289.56 302
Patchmatch-RL test74.48 33673.68 33676.89 34084.83 32366.54 24572.29 40269.16 42957.70 39086.76 19486.33 33745.79 41582.59 37569.63 27990.65 32081.54 422
PatchMatch-RL74.48 33673.22 34378.27 31887.70 23985.26 3875.92 37070.09 42264.34 33376.09 38481.25 40365.87 30278.07 40453.86 40183.82 41271.48 451
XXY-MVS74.44 33876.19 31369.21 40484.61 32852.43 40771.70 40677.18 37160.73 36980.60 33690.96 22475.44 20569.35 43556.13 38588.33 35285.86 363
test250674.12 33973.39 34076.28 34891.85 12544.20 44684.06 19748.20 47172.30 22381.90 31594.20 8927.22 47189.77 26164.81 32796.02 12994.87 78
reproduce_monomvs74.09 34073.23 34276.65 34476.52 42654.54 38977.50 34381.40 34665.85 31082.86 29886.67 33227.38 46984.53 36070.24 27290.66 31990.89 268
CR-MVSNet74.00 34173.04 34576.85 34179.58 40162.64 28982.58 24676.90 37350.50 43875.72 38892.38 16548.07 40184.07 36768.72 29382.91 41983.85 390
SSC-MVS3.273.90 34275.67 31968.61 41284.11 33941.28 45464.17 44672.83 40472.09 22679.08 35787.94 30170.31 27573.89 42055.99 38694.49 19390.67 278
Test_1112_low_res73.90 34273.08 34476.35 34690.35 16955.95 37673.40 39586.17 28050.70 43673.14 40785.94 34458.31 35285.90 34356.51 38283.22 41687.20 348
test20.0373.75 34474.59 32971.22 39081.11 38351.12 41870.15 42072.10 41170.42 24580.28 34491.50 19864.21 31274.72 41846.96 43994.58 19187.82 341
test_fmvs273.57 34572.80 34775.90 35272.74 45668.84 22177.07 35084.32 31945.14 45082.89 29684.22 37148.37 39970.36 43173.40 23487.03 37488.52 326
SCA73.32 34672.57 35275.58 35781.62 37655.86 37978.89 31971.37 41761.73 35374.93 39883.42 38060.46 33587.01 31458.11 37682.63 42483.88 387
baseline173.26 34773.54 33872.43 38384.92 32247.79 43179.89 29974.00 39265.93 30878.81 35986.28 34056.36 36581.63 38356.63 38179.04 44287.87 340
131473.22 34872.56 35375.20 35880.41 39557.84 36481.64 27185.36 29651.68 42973.10 40876.65 44361.45 33085.19 35363.54 33879.21 44082.59 407
MVS73.21 34972.59 35175.06 36080.97 38460.81 32681.64 27185.92 28946.03 44871.68 41677.54 43468.47 28689.77 26155.70 38985.39 39174.60 448
HY-MVS64.64 1873.03 35072.47 35474.71 36483.36 35654.19 39382.14 26581.96 33956.76 40069.57 43086.21 34160.03 33984.83 35749.58 42682.65 42285.11 371
thisisatest051573.00 35170.52 37080.46 28281.45 37859.90 33973.16 39774.31 39157.86 38976.08 38577.78 43137.60 44792.12 17965.00 32591.45 29189.35 306
EPNet_dtu72.87 35271.33 36477.49 33177.72 41560.55 32982.35 25675.79 38066.49 30558.39 46681.06 40453.68 37885.98 33853.55 40492.97 24785.95 361
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet72.62 35371.41 36376.28 34883.25 36060.34 33183.50 21879.02 36037.77 46876.33 37985.10 35949.60 39787.41 31070.54 26977.54 44881.08 429
CHOSEN 1792x268872.45 35470.56 36978.13 31990.02 18063.08 28268.72 42783.16 32742.99 45875.92 38685.46 35257.22 36185.18 35449.87 42481.67 42686.14 359
testgi72.36 35574.61 32765.59 42680.56 39342.82 45168.29 42973.35 40066.87 30281.84 31789.93 26572.08 26266.92 44946.05 44392.54 25987.01 350
thres20072.34 35671.55 36274.70 36583.48 35051.60 41375.02 37973.71 39770.14 25178.56 36280.57 40846.20 40788.20 29446.99 43889.29 33784.32 381
FPMVS72.29 35772.00 35673.14 37488.63 21585.00 4074.65 38367.39 43471.94 22977.80 36987.66 31350.48 39375.83 41249.95 42279.51 43658.58 465
FMVSNet572.10 35871.69 35873.32 37281.57 37753.02 40276.77 35478.37 36263.31 33676.37 37891.85 18436.68 44878.98 39947.87 43592.45 26187.95 336
our_test_371.85 35971.59 35972.62 38080.71 39053.78 39669.72 42371.71 41658.80 38278.03 36480.51 41056.61 36478.84 40162.20 34886.04 38885.23 369
PAPM71.77 36070.06 37676.92 33886.39 28053.97 39476.62 35886.62 27553.44 41563.97 45584.73 36657.79 35892.34 17239.65 45581.33 43084.45 379
ttmdpeth71.72 36170.67 36774.86 36173.08 45355.88 37877.41 34669.27 42755.86 40278.66 36093.77 11638.01 44575.39 41560.12 36489.87 33093.31 159
IB-MVS62.13 1971.64 36268.97 38879.66 29780.80 38962.26 30273.94 38976.90 37363.27 33868.63 43476.79 44133.83 45291.84 18759.28 36987.26 36884.88 373
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 36372.30 35569.62 40176.47 42852.70 40570.03 42180.97 34959.18 37979.36 35288.21 29760.50 33469.12 43658.33 37477.62 44787.04 349
testing371.53 36470.79 36673.77 37088.89 20741.86 45376.60 36059.12 46072.83 21180.97 33082.08 39519.80 47787.33 31265.12 32491.68 28692.13 230
test_vis3_rt71.42 36570.67 36773.64 37169.66 46370.46 19366.97 43989.73 20942.68 46088.20 15683.04 38243.77 43060.07 46165.35 32386.66 37990.39 287
Anonymous2023120671.38 36671.88 35769.88 39886.31 28654.37 39170.39 41874.62 38752.57 42276.73 37688.76 28759.94 34072.06 42444.35 44793.23 23983.23 401
test_vis1_n_192071.30 36771.58 36170.47 39377.58 41759.99 33874.25 38484.22 32051.06 43274.85 39979.10 42155.10 37468.83 43868.86 29079.20 44182.58 408
MIMVSNet71.09 36871.59 35969.57 40287.23 25450.07 42378.91 31871.83 41360.20 37671.26 41791.76 19155.08 37576.09 41041.06 45287.02 37582.54 410
test_fmvs1_n70.94 36970.41 37372.53 38273.92 44466.93 24275.99 36984.21 32143.31 45779.40 35179.39 41943.47 43168.55 44069.05 28784.91 40282.10 416
MS-PatchMatch70.93 37070.22 37473.06 37581.85 37362.50 29273.82 39177.90 36352.44 42375.92 38681.27 40255.67 37081.75 38155.37 39277.70 44674.94 447
pmmvs570.73 37170.07 37572.72 37877.03 42252.73 40474.14 38575.65 38350.36 43972.17 41485.37 35655.42 37280.67 38852.86 41087.59 36784.77 374
testing3-270.72 37270.97 36569.95 39788.93 20534.80 46769.85 42266.59 44178.42 12577.58 37385.55 34831.83 45882.08 37946.28 44093.73 22192.98 181
PatchT70.52 37372.76 34963.79 43479.38 40533.53 46877.63 33965.37 44573.61 19071.77 41592.79 15344.38 42975.65 41364.53 33285.37 39282.18 415
test_vis1_n70.29 37469.99 37871.20 39175.97 43366.50 24676.69 35680.81 35044.22 45375.43 39177.23 43850.00 39568.59 43966.71 30882.85 42178.52 441
N_pmnet70.20 37568.80 39074.38 36680.91 38584.81 4359.12 45776.45 37855.06 40675.31 39582.36 39255.74 36954.82 46647.02 43787.24 36983.52 394
tpmvs70.16 37669.56 38171.96 38674.71 44348.13 42879.63 30175.45 38565.02 32970.26 42581.88 39745.34 42185.68 34958.34 37375.39 45282.08 417
new-patchmatchnet70.10 37773.37 34160.29 44381.23 38216.95 47859.54 45574.62 38762.93 34080.97 33087.93 30362.83 32771.90 42555.24 39495.01 17592.00 235
YYNet170.06 37870.44 37168.90 40673.76 44653.42 40058.99 45867.20 43658.42 38487.10 18685.39 35559.82 34267.32 44659.79 36683.50 41585.96 360
MVStest170.05 37969.26 38272.41 38458.62 47555.59 38276.61 35965.58 44353.44 41589.28 13093.32 12722.91 47571.44 42974.08 21889.52 33490.21 293
MDA-MVSNet_test_wron70.05 37970.44 37168.88 40773.84 44553.47 39858.93 45967.28 43558.43 38387.09 18785.40 35459.80 34367.25 44759.66 36783.54 41485.92 362
CostFormer69.98 38168.68 39173.87 36877.14 42050.72 42079.26 31274.51 38951.94 42870.97 42084.75 36545.16 42487.49 30855.16 39579.23 43983.40 397
testing9169.94 38268.99 38772.80 37783.81 34545.89 43971.57 40873.64 39968.24 27870.77 42377.82 43034.37 45184.44 36253.64 40387.00 37688.07 331
baseline269.77 38366.89 40078.41 31479.51 40358.09 36076.23 36569.57 42557.50 39364.82 45377.45 43646.02 40988.44 28853.08 40677.83 44488.70 324
PatchmatchNetpermissive69.71 38468.83 38972.33 38577.66 41653.60 39779.29 31169.99 42357.66 39172.53 41182.93 38546.45 40680.08 39460.91 36072.09 45683.31 400
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs169.57 38569.05 38571.14 39269.15 46465.77 25673.98 38883.32 32542.83 45977.77 37078.27 42943.39 43468.50 44168.39 29784.38 40979.15 439
JIA-IIPM69.41 38666.64 40477.70 32873.19 45071.24 18475.67 37165.56 44470.42 24565.18 44992.97 14433.64 45483.06 37253.52 40569.61 46278.79 440
Syy-MVS69.40 38770.03 37767.49 41781.72 37438.94 45971.00 41161.99 45161.38 35970.81 42172.36 45561.37 33179.30 39764.50 33385.18 39584.22 383
testing9969.27 38868.15 39572.63 37983.29 35845.45 44171.15 41071.08 41867.34 29570.43 42477.77 43232.24 45784.35 36453.72 40286.33 38488.10 330
UnsupCasMVSNet_bld69.21 38969.68 38067.82 41579.42 40451.15 41767.82 43375.79 38054.15 41277.47 37485.36 35759.26 34670.64 43048.46 43279.35 43881.66 420
test_cas_vis1_n_192069.20 39069.12 38369.43 40373.68 44762.82 28670.38 41977.21 37046.18 44780.46 34178.95 42352.03 38465.53 45465.77 31977.45 44979.95 437
gg-mvs-nofinetune68.96 39169.11 38468.52 41376.12 43245.32 44283.59 21555.88 46586.68 3364.62 45497.01 1230.36 46283.97 36944.78 44682.94 41876.26 444
WBMVS68.76 39268.43 39269.75 40083.29 35840.30 45767.36 43572.21 41057.09 39777.05 37585.53 35033.68 45380.51 39048.79 43090.90 30488.45 327
WB-MVSnew68.72 39369.01 38667.85 41483.22 36243.98 44774.93 38065.98 44255.09 40573.83 40479.11 42065.63 30471.89 42638.21 46085.04 39887.69 342
tpm268.45 39466.83 40173.30 37378.93 41148.50 42779.76 30071.76 41447.50 44269.92 42783.60 37642.07 43788.40 29048.44 43379.51 43683.01 404
tpm67.95 39568.08 39667.55 41678.74 41243.53 44975.60 37267.10 43954.92 40772.23 41288.10 29842.87 43675.97 41152.21 41280.95 43483.15 402
WTY-MVS67.91 39668.35 39366.58 42280.82 38848.12 42965.96 44172.60 40553.67 41471.20 41881.68 40058.97 34869.06 43748.57 43181.67 42682.55 409
testing1167.38 39765.93 40571.73 38883.37 35546.60 43670.95 41369.40 42662.47 34566.14 44276.66 44231.22 45984.10 36649.10 42884.10 41184.49 377
test-LLR67.21 39866.74 40268.63 41076.45 42955.21 38567.89 43067.14 43762.43 34865.08 45072.39 45343.41 43269.37 43361.00 35884.89 40381.31 424
testing22266.93 39965.30 41271.81 38783.38 35445.83 44072.06 40467.50 43364.12 33469.68 42976.37 44527.34 47083.00 37338.88 45688.38 35186.62 355
sss66.92 40067.26 39865.90 42477.23 41951.10 41964.79 44371.72 41552.12 42770.13 42680.18 41257.96 35565.36 45550.21 42081.01 43281.25 426
KD-MVS_2432*160066.87 40165.81 40870.04 39567.50 46547.49 43262.56 44979.16 35761.21 36477.98 36580.61 40625.29 47382.48 37653.02 40784.92 40080.16 435
miper_refine_blended66.87 40165.81 40870.04 39567.50 46547.49 43262.56 44979.16 35761.21 36477.98 36580.61 40625.29 47382.48 37653.02 40784.92 40080.16 435
dmvs_re66.81 40366.98 39966.28 42376.87 42358.68 35871.66 40772.24 40860.29 37469.52 43173.53 45252.38 38364.40 45744.90 44581.44 42975.76 445
tpm cat166.76 40465.21 41371.42 38977.09 42150.62 42178.01 33173.68 39844.89 45168.64 43379.00 42245.51 41882.42 37849.91 42370.15 45981.23 428
UWE-MVS66.43 40565.56 41169.05 40584.15 33840.98 45573.06 39864.71 44754.84 40876.18 38379.62 41829.21 46480.50 39138.54 45989.75 33185.66 365
PVSNet58.17 2166.41 40665.63 41068.75 40881.96 37149.88 42462.19 45172.51 40751.03 43368.04 43675.34 44950.84 39074.77 41645.82 44482.96 41781.60 421
tpmrst66.28 40766.69 40365.05 43072.82 45539.33 45878.20 32970.69 42153.16 41867.88 43780.36 41148.18 40074.75 41758.13 37570.79 45881.08 429
Patchmatch-test65.91 40867.38 39761.48 44075.51 43643.21 45068.84 42663.79 44962.48 34472.80 41083.42 38044.89 42759.52 46348.27 43486.45 38181.70 419
ADS-MVSNet265.87 40963.64 41872.55 38173.16 45156.92 37267.10 43774.81 38649.74 44066.04 44482.97 38346.71 40477.26 40742.29 44969.96 46083.46 395
myMVS_eth3d2865.83 41065.85 40665.78 42583.42 35335.71 46567.29 43668.01 43267.58 29269.80 42877.72 43332.29 45674.30 41937.49 46189.06 34187.32 346
test_vis1_rt65.64 41164.09 41570.31 39466.09 46970.20 19761.16 45281.60 34438.65 46572.87 40969.66 45852.84 38060.04 46256.16 38477.77 44580.68 433
mvsany_test365.48 41262.97 42173.03 37669.99 46276.17 12464.83 44243.71 47343.68 45580.25 34587.05 32952.83 38163.09 46051.92 41772.44 45579.84 438
test-mter65.00 41363.79 41768.63 41076.45 42955.21 38567.89 43067.14 43750.98 43465.08 45072.39 45328.27 46769.37 43361.00 35884.89 40381.31 424
ETVMVS64.67 41463.34 42068.64 40983.44 35241.89 45269.56 42561.70 45661.33 36168.74 43275.76 44728.76 46579.35 39634.65 46486.16 38784.67 376
myMVS_eth3d64.66 41563.89 41666.97 42081.72 37437.39 46271.00 41161.99 45161.38 35970.81 42172.36 45520.96 47679.30 39749.59 42585.18 39584.22 383
test0.0.03 164.66 41564.36 41465.57 42775.03 44146.89 43564.69 44461.58 45762.43 34871.18 41977.54 43443.41 43268.47 44240.75 45482.65 42281.35 423
UBG64.34 41763.35 41967.30 41883.50 34940.53 45667.46 43465.02 44654.77 40967.54 44074.47 45132.99 45578.50 40340.82 45383.58 41382.88 405
test_f64.31 41865.85 40659.67 44466.54 46862.24 30457.76 46170.96 41940.13 46284.36 26282.09 39446.93 40351.67 46861.99 35181.89 42565.12 459
pmmvs362.47 41960.02 43269.80 39971.58 45964.00 27270.52 41758.44 46339.77 46366.05 44375.84 44627.10 47272.28 42346.15 44284.77 40773.11 449
EPMVS62.47 41962.63 42362.01 43670.63 46138.74 46074.76 38152.86 46753.91 41367.71 43980.01 41339.40 44166.60 45055.54 39168.81 46480.68 433
ADS-MVSNet61.90 42162.19 42561.03 44173.16 45136.42 46467.10 43761.75 45449.74 44066.04 44482.97 38346.71 40463.21 45842.29 44969.96 46083.46 395
PMMVS61.65 42260.38 42965.47 42865.40 47269.26 21163.97 44761.73 45536.80 46960.11 46168.43 46059.42 34466.35 45148.97 42978.57 44360.81 462
E-PMN61.59 42361.62 42661.49 43966.81 46755.40 38353.77 46460.34 45966.80 30358.90 46465.50 46340.48 44066.12 45255.72 38886.25 38562.95 461
TESTMET0.1,161.29 42460.32 43064.19 43272.06 45751.30 41567.89 43062.09 45045.27 44960.65 46069.01 45927.93 46864.74 45656.31 38381.65 42876.53 443
MVS-HIRNet61.16 42562.92 42255.87 44779.09 40835.34 46671.83 40557.98 46446.56 44559.05 46391.14 21549.95 39676.43 40938.74 45771.92 45755.84 466
EMVS61.10 42660.81 42861.99 43765.96 47055.86 37953.10 46558.97 46267.06 30056.89 46863.33 46440.98 43867.03 44854.79 39786.18 38663.08 460
DSMNet-mixed60.98 42761.61 42759.09 44672.88 45445.05 44474.70 38246.61 47226.20 47065.34 44890.32 25455.46 37163.12 45941.72 45181.30 43169.09 455
dp60.70 42860.29 43161.92 43872.04 45838.67 46170.83 41564.08 44851.28 43160.75 45977.28 43736.59 44971.58 42847.41 43662.34 46675.52 446
dmvs_testset60.59 42962.54 42454.72 44977.26 41827.74 47274.05 38761.00 45860.48 37165.62 44767.03 46255.93 36868.23 44432.07 46869.46 46368.17 456
CHOSEN 280x42059.08 43056.52 43666.76 42176.51 42764.39 26849.62 46659.00 46143.86 45455.66 46968.41 46135.55 45068.21 44543.25 44876.78 45167.69 457
mvsany_test158.48 43156.47 43764.50 43165.90 47168.21 22756.95 46242.11 47438.30 46665.69 44677.19 44056.96 36259.35 46446.16 44158.96 46765.93 458
UWE-MVS-2858.44 43257.71 43460.65 44273.58 44831.23 46969.68 42448.80 47053.12 41961.79 45778.83 42430.98 46068.40 44321.58 47180.99 43382.33 414
PVSNet_051.08 2256.10 43354.97 43859.48 44575.12 44053.28 40155.16 46361.89 45344.30 45259.16 46262.48 46554.22 37665.91 45335.40 46347.01 46859.25 464
new_pmnet55.69 43457.66 43549.76 45075.47 43730.59 47059.56 45451.45 46843.62 45662.49 45675.48 44840.96 43949.15 47037.39 46272.52 45469.55 454
PMMVS255.64 43559.27 43344.74 45164.30 47312.32 47940.60 46749.79 46953.19 41765.06 45284.81 36453.60 37949.76 46932.68 46789.41 33672.15 450
MVEpermissive40.22 2351.82 43650.47 43955.87 44762.66 47451.91 41031.61 46939.28 47540.65 46150.76 47074.98 45056.24 36744.67 47133.94 46664.11 46571.04 453
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai41.90 43742.65 44039.67 45270.86 46021.11 47461.01 45321.42 47957.36 39457.97 46750.06 46816.40 47858.73 46521.03 47227.69 47239.17 468
kuosan30.83 43832.17 44126.83 45453.36 47619.02 47757.90 46020.44 48038.29 46738.01 47137.82 47015.18 47933.45 4737.74 47420.76 47328.03 469
test_method30.46 43929.60 44233.06 45317.99 4783.84 48113.62 47073.92 3932.79 47218.29 47453.41 46728.53 46643.25 47222.56 46935.27 47052.11 467
cdsmvs_eth3d_5k20.81 44027.75 4430.00 4590.00 4820.00 4840.00 47185.44 2950.00 4770.00 47882.82 38781.46 1310.00 4780.00 4770.00 4760.00 474
tmp_tt20.25 44124.50 4447.49 4564.47 4798.70 48034.17 46825.16 4771.00 47432.43 47318.49 47139.37 4429.21 47521.64 47043.75 4694.57 471
ab-mvs-re6.65 4428.87 4450.00 4590.00 4820.00 4840.00 4710.00 4830.00 4770.00 47879.80 4150.00 4820.00 4780.00 4770.00 4760.00 474
pcd_1.5k_mvsjas6.41 4438.55 4460.00 4590.00 4820.00 4840.00 4710.00 4830.00 4770.00 4780.00 47776.94 1890.00 4780.00 4770.00 4760.00 474
test1236.27 4448.08 4470.84 4571.11 4810.57 48262.90 4480.82 4810.54 4751.07 4772.75 4761.26 4800.30 4761.04 4751.26 4751.66 472
testmvs5.91 4457.65 4480.72 4581.20 4800.37 48359.14 4560.67 4820.49 4761.11 4762.76 4750.94 4810.24 4771.02 4761.47 4741.55 473
mmdepth0.00 4460.00 4490.00 4590.00 4820.00 4840.00 4710.00 4830.00 4770.00 4780.00 4770.00 4820.00 4780.00 4770.00 4760.00 474
monomultidepth0.00 4460.00 4490.00 4590.00 4820.00 4840.00 4710.00 4830.00 4770.00 4780.00 4770.00 4820.00 4780.00 4770.00 4760.00 474
test_blank0.00 4460.00 4490.00 4590.00 4820.00 4840.00 4710.00 4830.00 4770.00 4780.00 4770.00 4820.00 4780.00 4770.00 4760.00 474
uanet_test0.00 4460.00 4490.00 4590.00 4820.00 4840.00 4710.00 4830.00 4770.00 4780.00 4770.00 4820.00 4780.00 4770.00 4760.00 474
DCPMVS0.00 4460.00 4490.00 4590.00 4820.00 4840.00 4710.00 4830.00 4770.00 4780.00 4770.00 4820.00 4780.00 4770.00 4760.00 474
sosnet-low-res0.00 4460.00 4490.00 4590.00 4820.00 4840.00 4710.00 4830.00 4770.00 4780.00 4770.00 4820.00 4780.00 4770.00 4760.00 474
sosnet0.00 4460.00 4490.00 4590.00 4820.00 4840.00 4710.00 4830.00 4770.00 4780.00 4770.00 4820.00 4780.00 4770.00 4760.00 474
uncertanet0.00 4460.00 4490.00 4590.00 4820.00 4840.00 4710.00 4830.00 4770.00 4780.00 4770.00 4820.00 4780.00 4770.00 4760.00 474
Regformer0.00 4460.00 4490.00 4590.00 4820.00 4840.00 4710.00 4830.00 4770.00 4780.00 4770.00 4820.00 4780.00 4770.00 4760.00 474
uanet0.00 4460.00 4490.00 4590.00 4820.00 4840.00 4710.00 4830.00 4770.00 4780.00 4770.00 4820.00 4780.00 4770.00 4760.00 474
TestfortrainingZip92.12 33
WAC-MVS37.39 46252.61 411
FOURS196.08 1287.41 1496.19 295.83 592.95 396.57 3
MSC_two_6792asdad88.81 7391.55 13777.99 9791.01 16796.05 987.45 2998.17 3792.40 210
PC_three_145258.96 38190.06 10591.33 20680.66 14193.03 15475.78 19495.94 13592.48 204
No_MVS88.81 7391.55 13777.99 9791.01 16796.05 987.45 2998.17 3792.40 210
test_one_060193.85 6573.27 14694.11 3986.57 3493.47 4294.64 6888.42 29
eth-test20.00 482
eth-test0.00 482
ZD-MVS92.22 11180.48 7191.85 13571.22 23790.38 10092.98 14186.06 6796.11 781.99 11296.75 99
RE-MVS-def92.61 994.13 5888.95 692.87 1394.16 3388.75 1893.79 3394.43 7690.64 1187.16 3897.60 7392.73 188
IU-MVS94.18 5372.64 15690.82 17256.98 39889.67 11885.78 6497.92 5293.28 160
OPU-MVS88.27 8691.89 12377.83 10090.47 5891.22 21281.12 13594.68 8074.48 20795.35 15892.29 220
test_241102_TWO93.71 5883.77 5893.49 4094.27 8389.27 2495.84 2586.03 5797.82 5792.04 233
test_241102_ONE94.18 5372.65 15493.69 6083.62 6194.11 2793.78 11490.28 1595.50 50
9.1489.29 6591.84 12788.80 9795.32 1375.14 16891.07 8592.89 14787.27 4993.78 11983.69 9097.55 76
save fliter93.75 6677.44 10686.31 14389.72 21070.80 242
test_0728_THIRD85.33 4293.75 3594.65 6587.44 4895.78 3387.41 3198.21 3492.98 181
test_0728_SECOND86.79 11094.25 5172.45 16490.54 5594.10 4095.88 1886.42 4797.97 4992.02 234
test072694.16 5672.56 16090.63 5293.90 4983.61 6293.75 3594.49 7389.76 19
GSMVS83.88 387
test_part293.86 6477.77 10192.84 53
sam_mvs146.11 40883.88 387
sam_mvs45.92 413
ambc82.98 21890.55 16664.86 26288.20 10689.15 22389.40 12793.96 10571.67 26991.38 20078.83 14796.55 10492.71 191
MTGPAbinary91.81 139
test_post178.85 3213.13 47345.19 42380.13 39358.11 376
test_post3.10 47445.43 41977.22 408
patchmatchnet-post81.71 39945.93 41287.01 314
GG-mvs-BLEND67.16 41973.36 44946.54 43884.15 19555.04 46658.64 46561.95 46629.93 46383.87 37038.71 45876.92 45071.07 452
MTMP90.66 5133.14 476
gm-plane-assit75.42 43844.97 44552.17 42472.36 45587.90 30054.10 400
test9_res80.83 12296.45 11090.57 281
TEST992.34 10679.70 8083.94 20190.32 19065.41 32184.49 25890.97 22282.03 12093.63 125
test_892.09 11578.87 8883.82 20690.31 19265.79 31184.36 26290.96 22481.93 12293.44 139
agg_prior279.68 13596.16 12290.22 289
agg_prior91.58 13577.69 10390.30 19384.32 26493.18 147
TestCases89.68 5691.59 13283.40 5295.44 1179.47 10788.00 16193.03 13982.66 10191.47 19470.81 26196.14 12394.16 113
test_prior478.97 8784.59 184
test_prior283.37 22275.43 16484.58 25591.57 19681.92 12479.54 13996.97 92
test_prior86.32 11890.59 16571.99 17292.85 10194.17 10492.80 186
旧先验281.73 26956.88 39986.54 20784.90 35672.81 245
新几何281.72 270
新几何182.95 22093.96 6278.56 9180.24 35355.45 40483.93 27591.08 21871.19 27188.33 29265.84 31793.07 24381.95 418
旧先验191.97 11971.77 17381.78 34191.84 18573.92 23293.65 22483.61 393
无先验82.81 24185.62 29358.09 38791.41 19967.95 30184.48 378
原ACMM282.26 261
原ACMM184.60 16792.81 9674.01 13891.50 14762.59 34282.73 30090.67 24176.53 19894.25 9669.24 28295.69 15085.55 366
test22293.31 7976.54 11679.38 31077.79 36452.59 42182.36 30590.84 23266.83 29591.69 28581.25 426
testdata286.43 32963.52 339
segment_acmp81.94 121
testdata79.54 29992.87 9072.34 16580.14 35459.91 37785.47 23291.75 19267.96 28985.24 35268.57 29692.18 27281.06 431
testdata179.62 30273.95 185
test1286.57 11390.74 16172.63 15890.69 17582.76 29979.20 15494.80 7795.32 16092.27 222
plane_prior793.45 7377.31 109
plane_prior692.61 9776.54 11674.84 214
plane_prior593.61 6395.22 6180.78 12395.83 14394.46 95
plane_prior492.95 145
plane_prior376.85 11477.79 13486.55 201
plane_prior289.45 8579.44 109
plane_prior192.83 94
plane_prior76.42 11987.15 12575.94 15595.03 172
n20.00 483
nn0.00 483
door-mid74.45 390
lessismore_v085.95 12991.10 15470.99 18870.91 42091.79 7394.42 7861.76 32992.93 15779.52 14093.03 24493.93 123
LGP-MVS_train90.82 3794.75 4181.69 6394.27 2582.35 7593.67 3894.82 6091.18 595.52 4685.36 6798.73 795.23 66
test1191.46 148
door72.57 406
HQP5-MVS70.66 190
HQP-NCC91.19 14984.77 17573.30 19980.55 338
ACMP_Plane91.19 14984.77 17573.30 19980.55 338
BP-MVS77.30 172
HQP4-MVS80.56 33794.61 8493.56 151
HQP3-MVS92.68 10794.47 194
HQP2-MVS72.10 260
NP-MVS91.95 12074.55 13590.17 261
MDTV_nov1_ep13_2view27.60 47370.76 41646.47 44661.27 45845.20 42249.18 42783.75 392
MDTV_nov1_ep1368.29 39478.03 41343.87 44874.12 38672.22 40952.17 42467.02 44185.54 34945.36 42080.85 38755.73 38784.42 408
ACMMP++_ref95.74 149
ACMMP++97.35 82
Test By Simon79.09 156
ITE_SJBPF90.11 4990.72 16284.97 4190.30 19381.56 8390.02 10791.20 21482.40 10690.81 22173.58 23194.66 18994.56 90
DeepMVS_CXcopyleft24.13 45532.95 47729.49 47121.63 47812.07 47137.95 47245.07 46930.84 46119.21 47417.94 47333.06 47123.69 470