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 5097.23 295.32 299.01 297.26 980.16 14898.99 195.15 199.14 296.47 35
TDRefinement93.52 393.39 593.88 295.94 1590.26 495.70 496.46 390.58 992.86 5396.29 2288.16 3694.17 10586.07 5698.48 1897.22 18
LTVRE_ROB86.10 193.04 493.44 491.82 2293.73 6885.72 3496.79 195.51 1088.86 1695.63 1096.99 1384.81 7993.16 14991.10 297.53 8096.58 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 5388.23 992.87 1394.32 2290.25 1195.65 995.74 3387.75 4395.72 3889.60 598.27 2892.08 233
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 2889.13 798.26 3091.76 244
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 2889.13 798.26 3091.76 244
HPM-MVS_fast92.50 892.54 1092.37 695.93 1685.81 3392.99 1294.23 2885.21 4492.51 6195.13 5290.65 1095.34 5888.06 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 8190.26 498.44 2093.63 145
SR-MVS-dyc-post92.41 1092.41 1192.39 594.13 5988.95 692.87 1394.16 3388.75 1893.79 3394.43 7688.83 2795.51 4987.16 3897.60 7492.73 190
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 4086.82 4397.34 8492.19 228
HPM-MVScopyleft92.13 1292.20 1491.91 1795.58 2684.67 4693.51 894.85 1682.88 7191.77 7593.94 10890.55 1395.73 3788.50 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 8785.17 3992.47 2795.05 1587.65 2893.21 4694.39 8190.09 1895.08 6986.67 4597.60 7494.18 113
COLMAP_ROBcopyleft83.01 391.97 1491.95 1592.04 1193.68 6986.15 2493.37 1095.10 1490.28 1092.11 6795.03 5489.75 2194.93 7379.95 13298.27 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 6494.14 9389.15 2695.62 4187.35 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 11483.09 6891.54 7794.25 8787.67 4695.51 4987.21 3798.11 4093.12 173
CP-MVS91.67 1791.58 2491.96 1495.29 3187.62 1393.38 993.36 7383.16 6791.06 8794.00 10188.26 3395.71 3987.28 3698.39 2392.55 203
XVS91.54 1891.36 2992.08 995.64 2486.25 2292.64 2093.33 7685.07 4589.99 10994.03 9986.57 5895.80 3087.35 3397.62 7294.20 110
MTAPA91.52 1991.60 2391.29 3096.59 486.29 2192.02 3791.81 14184.07 5592.00 7094.40 8086.63 5795.28 6188.59 1198.31 2692.30 220
UA-Net91.49 2091.53 2591.39 2794.98 3582.95 5893.52 792.79 10688.22 2388.53 14697.64 683.45 9394.55 8986.02 6098.60 1396.67 30
ACMMPR91.49 2091.35 3191.92 1695.74 2085.88 3092.58 2393.25 8281.99 7791.40 7994.17 9287.51 4795.87 2087.74 2297.76 6093.99 121
LPG-MVS_test91.47 2291.68 2190.82 3794.75 4181.69 6390.00 6694.27 2582.35 7593.67 3894.82 6091.18 595.52 4785.36 6798.73 795.23 66
region2R91.44 2391.30 3591.87 1995.75 1985.90 2992.63 2293.30 8081.91 7990.88 9494.21 8887.75 4395.87 2087.60 2797.71 6393.83 130
HFP-MVS91.30 2491.39 2891.02 3395.43 2984.66 4792.58 2393.29 8181.99 7791.47 7893.96 10588.35 3295.56 4487.74 2297.74 6292.85 187
ZNCC-MVS91.26 2591.34 3291.01 3495.73 2183.05 5692.18 3294.22 3080.14 10091.29 8393.97 10287.93 4295.87 2088.65 1097.96 5194.12 117
APDe-MVScopyleft91.22 2691.92 1689.14 6892.97 8978.04 9692.84 1694.14 3783.33 6593.90 2995.73 3488.77 2896.41 387.60 2797.98 4892.98 183
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 6693.90 4980.32 9791.74 7694.41 7988.17 3595.98 1386.37 4997.99 4693.96 123
SteuartSystems-ACMMP91.16 2891.36 2990.55 4193.91 6480.97 7091.49 4493.48 7182.82 7292.60 6093.97 10288.19 3496.29 687.61 2698.20 3694.39 104
Skip Steuart: Steuart Systems R&D Blog.
MP-MVScopyleft91.14 2990.91 4591.83 2096.18 1186.88 1792.20 3193.03 9682.59 7388.52 14794.37 8286.74 5695.41 5686.32 5098.21 3493.19 168
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 4293.74 5880.98 9091.38 8093.80 11287.20 5195.80 3087.10 4097.69 6593.93 124
MP-MVS-pluss90.81 3191.08 3889.99 5095.97 1479.88 7788.13 10994.51 1975.79 15892.94 5094.96 5588.36 3195.01 7190.70 398.40 2295.09 73
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+77.89 1190.73 3291.50 2688.44 8293.00 8876.26 12289.65 7995.55 987.72 2793.89 3194.94 5691.62 393.44 14078.35 15498.76 495.61 55
ACMMP_NAP90.65 3391.07 4089.42 6295.93 1679.54 8289.95 7093.68 6377.65 13591.97 7194.89 5788.38 3095.45 5489.27 697.87 5693.27 163
ACMM79.39 990.65 3390.99 4289.63 5895.03 3483.53 5189.62 8093.35 7579.20 11393.83 3293.60 12290.81 892.96 15685.02 7498.45 1992.41 210
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D90.60 3590.34 5491.38 2889.03 20284.23 4993.58 694.68 1890.65 890.33 10393.95 10784.50 8195.37 5780.87 12295.50 15694.53 94
ACMP79.16 1090.54 3690.60 5290.35 4594.36 5080.98 6989.16 9194.05 4279.03 11692.87 5293.74 11790.60 1295.21 6482.87 10098.76 494.87 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVScopyleft90.53 3791.08 3888.88 7193.38 7878.65 9089.15 9294.05 4284.68 4993.90 2994.11 9588.13 3796.30 584.51 8397.81 5891.70 248
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 10894.18 5472.65 15590.47 5993.69 6183.77 5894.11 2794.27 8390.28 1595.84 2686.03 5797.92 5292.29 222
SMA-MVScopyleft90.31 3990.48 5389.83 5595.31 3079.52 8390.98 5193.24 8375.37 16792.84 5495.28 4885.58 7196.09 887.92 1897.76 6093.88 127
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 9377.09 11191.19 4895.74 681.38 8592.28 6693.80 11286.89 5594.64 8485.52 6697.51 8194.30 109
v7n90.13 4190.96 4387.65 9891.95 12171.06 18889.99 6893.05 9386.53 3594.29 2396.27 2382.69 10194.08 10886.25 5397.63 7097.82 8
ME-MVS90.09 4290.66 5088.38 8492.82 9676.12 12689.40 8993.70 6083.72 6092.39 6493.18 13188.02 4095.47 5284.99 7597.69 6593.54 155
PMVScopyleft80.48 690.08 4390.66 5088.34 8696.71 392.97 290.31 6389.57 21888.51 2190.11 10595.12 5390.98 788.92 27877.55 16897.07 9183.13 405
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++90.07 4491.09 3787.00 10691.55 13872.64 15796.19 294.10 4085.33 4293.49 4094.64 6881.12 13695.88 1887.41 3195.94 13692.48 206
DVP-MVScopyleft90.06 4591.32 3386.29 12094.16 5772.56 16190.54 5691.01 16983.61 6293.75 3594.65 6589.76 1995.78 3486.42 4797.97 4990.55 285
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 17296.56 658.83 35889.04 9392.74 10891.40 696.12 596.06 2987.23 5095.57 4379.42 14298.74 699.00 2
PEN-MVS90.03 4791.88 1984.48 17196.57 558.88 35588.95 9493.19 8591.62 596.01 796.16 2787.02 5395.60 4278.69 15098.72 998.97 3
OurMVSNet-221017-090.01 4889.74 5990.83 3693.16 8580.37 7491.91 4093.11 8981.10 8895.32 1497.24 1072.94 25194.85 7585.07 7197.78 5997.26 16
DTE-MVSNet89.98 4991.91 1884.21 18196.51 757.84 36688.93 9592.84 10491.92 496.16 496.23 2486.95 5495.99 1279.05 14698.57 1598.80 6
XVG-ACMP-BASELINE89.98 4989.84 5790.41 4394.91 3784.50 4889.49 8593.98 4479.68 10592.09 6893.89 11083.80 8893.10 15282.67 10498.04 4193.64 144
TestfortrainingZip a89.97 5190.77 4887.58 9994.38 4873.21 14992.12 3393.85 5377.53 13993.24 4393.18 13187.06 5295.85 2487.89 1997.69 6593.68 139
3Dnovator+83.92 289.97 5189.66 6090.92 3591.27 14781.66 6691.25 4694.13 3888.89 1588.83 13894.26 8677.55 17795.86 2384.88 7795.87 14295.24 65
WR-MVS_H89.91 5391.31 3485.71 13796.32 962.39 29989.54 8393.31 7990.21 1295.57 1195.66 3781.42 13395.90 1780.94 12198.80 398.84 5
OPM-MVS89.80 5489.97 5589.27 6494.76 4079.86 7886.76 13692.78 10778.78 11992.51 6193.64 12188.13 3793.84 11984.83 7997.55 7794.10 118
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets89.78 5589.27 6691.30 2993.51 7284.79 4489.89 7290.63 17970.00 25494.55 1996.67 1787.94 4193.59 13184.27 8595.97 13295.52 56
anonymousdsp89.73 5688.88 7692.27 889.82 18386.67 1890.51 5890.20 20069.87 25595.06 1596.14 2884.28 8493.07 15387.68 2496.34 11497.09 20
test_djsdf89.62 5789.01 7091.45 2692.36 10682.98 5791.98 3890.08 20371.54 23394.28 2596.54 1981.57 13194.27 9586.26 5196.49 10897.09 20
XVG-OURS-SEG-HR89.59 5889.37 6490.28 4694.47 4385.95 2786.84 13293.91 4880.07 10186.75 19693.26 12893.64 290.93 21684.60 8290.75 31393.97 122
APD-MVScopyleft89.54 5989.63 6189.26 6592.57 9981.34 6890.19 6593.08 9280.87 9291.13 8593.19 13086.22 6595.97 1482.23 11097.18 8990.45 287
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax89.41 6088.81 7991.19 3293.38 7884.72 4589.70 7590.29 19769.27 26294.39 2196.38 2186.02 6893.52 13683.96 8795.92 13895.34 60
CPTT-MVS89.39 6188.98 7290.63 4095.09 3386.95 1692.09 3692.30 12379.74 10487.50 18092.38 16681.42 13393.28 14583.07 9697.24 8791.67 249
ACMH76.49 1489.34 6291.14 3683.96 18892.50 10270.36 19789.55 8193.84 5581.89 8094.70 1795.44 4490.69 988.31 29583.33 9298.30 2793.20 167
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testf189.30 6389.12 6789.84 5388.67 21385.64 3590.61 5493.17 8686.02 3893.12 4795.30 4684.94 7689.44 27074.12 21896.10 12794.45 98
APD_test289.30 6389.12 6789.84 5388.67 21385.64 3590.61 5493.17 8686.02 3893.12 4795.30 4684.94 7689.44 27074.12 21896.10 12794.45 98
CP-MVSNet89.27 6590.91 4584.37 17396.34 858.61 36188.66 10292.06 13090.78 795.67 895.17 5181.80 12895.54 4679.00 14798.69 1098.95 4
XVG-OURS89.18 6688.83 7890.23 4794.28 5186.11 2685.91 15193.60 6680.16 9989.13 13493.44 12483.82 8790.98 21383.86 8995.30 16493.60 148
DeepC-MVS82.31 489.15 6789.08 6989.37 6393.64 7079.07 8688.54 10594.20 3173.53 19289.71 11794.82 6085.09 7595.77 3684.17 8698.03 4393.26 165
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 17997.00 264.33 27089.67 7888.38 23888.84 1794.29 2397.57 790.48 1491.26 20472.57 24997.65 6997.34 15
MSP-MVS89.08 6988.16 8691.83 2095.76 1886.14 2592.75 1793.90 4978.43 12489.16 13292.25 17572.03 26596.36 488.21 1390.93 30592.98 183
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 12491.63 13277.07 11289.82 7393.77 5778.90 11792.88 5192.29 17386.11 6690.22 24486.24 5497.24 8791.36 257
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 7591.27 16178.20 12786.69 20092.28 17480.36 14695.06 7086.17 5596.49 10890.22 291
Elysia88.71 7288.89 7488.19 8991.26 14872.96 15188.10 11093.59 6784.31 5190.42 9994.10 9674.07 22894.82 7688.19 1495.92 13896.80 27
StellarMVS88.71 7288.89 7488.19 8991.26 14872.96 15188.10 11093.59 6784.31 5190.42 9994.10 9674.07 22894.82 7688.19 1495.92 13896.80 27
test_040288.65 7489.58 6385.88 13392.55 10072.22 16984.01 19989.44 22188.63 2094.38 2295.77 3286.38 6493.59 13179.84 13395.21 16591.82 242
DP-MVS88.60 7589.01 7087.36 10191.30 14577.50 10487.55 11892.97 10087.95 2689.62 12192.87 14984.56 8093.89 11677.65 16696.62 10390.70 277
APD_test188.40 7687.91 8889.88 5289.50 18986.65 2089.98 6991.91 13684.26 5390.87 9593.92 10982.18 11689.29 27473.75 22694.81 18493.70 138
Anonymous2023121188.40 7689.62 6284.73 16390.46 16865.27 25988.86 9693.02 9787.15 3093.05 4997.10 1182.28 11492.02 18276.70 17897.99 4696.88 26
PS-MVSNAJss88.31 7887.90 8989.56 6093.31 8077.96 9987.94 11491.97 13370.73 24594.19 2696.67 1776.94 19094.57 8783.07 9696.28 11696.15 38
OMC-MVS88.19 7987.52 9390.19 4891.94 12381.68 6587.49 12193.17 8676.02 15288.64 14391.22 21484.24 8593.37 14377.97 16497.03 9295.52 56
CS-MVS88.14 8087.67 9289.54 6189.56 18779.18 8590.47 5994.77 1779.37 11184.32 26689.33 28083.87 8694.53 9082.45 10694.89 18094.90 76
TSAR-MVS + MP.88.14 8087.82 9089.09 6995.72 2276.74 11592.49 2691.19 16467.85 28986.63 20194.84 5979.58 15495.96 1587.62 2594.50 19394.56 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 21993.26 8263.94 27491.10 4989.64 21585.07 4590.91 9191.09 21989.16 2591.87 18782.03 11195.87 14293.13 170
EC-MVSNet88.01 8388.32 8587.09 10389.28 19472.03 17290.31 6396.31 480.88 9185.12 24189.67 27384.47 8295.46 5382.56 10596.26 11993.77 136
RPSCF88.00 8486.93 10791.22 3190.08 17689.30 589.68 7791.11 16579.26 11289.68 11894.81 6382.44 10587.74 30676.54 18388.74 34996.61 32
AllTest87.97 8587.40 9789.68 5691.59 13383.40 5289.50 8495.44 1179.47 10788.00 16293.03 14082.66 10291.47 19570.81 26396.14 12494.16 114
TranMVSNet+NR-MVSNet87.86 8688.76 8085.18 14994.02 6264.13 27184.38 19191.29 15784.88 4892.06 6993.84 11186.45 6193.73 12173.22 24098.66 1197.69 9
nrg03087.85 8788.49 8185.91 13190.07 17869.73 20587.86 11594.20 3174.04 18492.70 5994.66 6485.88 6991.50 19479.72 13597.32 8596.50 34
CNVR-MVS87.81 8887.68 9188.21 8892.87 9177.30 11085.25 16891.23 16277.31 14287.07 19091.47 20482.94 9894.71 8084.67 8196.27 11892.62 198
HQP_MVS87.75 8987.43 9688.70 7793.45 7476.42 11989.45 8693.61 6479.44 10986.55 20292.95 14674.84 21595.22 6280.78 12495.83 14494.46 96
sc_t187.70 9088.94 7383.99 18693.47 7367.15 23685.05 17388.21 24586.81 3291.87 7397.65 585.51 7387.91 30174.22 21397.63 7096.92 25
MM87.64 9187.15 9989.09 6989.51 18876.39 12188.68 10186.76 27684.54 5083.58 28593.78 11473.36 24696.48 287.98 1796.21 12094.41 103
MVSMamba_PlusPlus87.53 9288.86 7783.54 20592.03 11962.26 30391.49 4492.62 11288.07 2588.07 15996.17 2672.24 26095.79 3384.85 7894.16 20692.58 201
NCCC87.36 9386.87 10888.83 7292.32 10978.84 8986.58 14091.09 16778.77 12084.85 25390.89 23080.85 13995.29 5981.14 11995.32 16192.34 218
DeepPCF-MVS81.24 587.28 9486.21 11890.49 4291.48 14284.90 4283.41 22292.38 11970.25 25189.35 12990.68 24082.85 10094.57 8779.55 13995.95 13592.00 237
SixPastTwentyTwo87.20 9587.45 9586.45 11792.52 10169.19 21587.84 11688.05 24681.66 8294.64 1896.53 2065.94 30294.75 7983.02 9896.83 9795.41 58
fmvsm_s_conf0.5_n_987.04 9687.02 10487.08 10489.67 18575.87 12884.60 18489.74 21074.40 18189.92 11393.41 12580.45 14490.63 23186.66 4694.37 19994.73 87
SPE-MVS-test87.00 9786.43 11488.71 7689.46 19077.46 10589.42 8895.73 777.87 13381.64 32687.25 32582.43 10694.53 9077.65 16696.46 11094.14 116
UniMVSNet (Re)86.87 9886.98 10686.55 11593.11 8668.48 22583.80 20992.87 10280.37 9589.61 12391.81 19077.72 17394.18 10375.00 20698.53 1696.99 24
Vis-MVSNetpermissive86.86 9986.58 11187.72 9692.09 11677.43 10787.35 12292.09 12978.87 11884.27 27194.05 9878.35 16593.65 12480.54 12891.58 29092.08 233
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 12792.86 9367.02 24082.55 24991.56 14783.08 6990.92 8991.82 18978.25 16693.99 11074.16 21698.35 2497.49 13
DU-MVS86.80 10186.99 10586.21 12593.24 8367.02 24083.16 23292.21 12481.73 8190.92 8991.97 18177.20 18493.99 11074.16 21698.35 2497.61 10
casdiffmvs_mvgpermissive86.72 10287.51 9484.36 17587.09 26465.22 26084.16 19594.23 2877.89 13191.28 8493.66 12084.35 8392.71 16280.07 12994.87 18395.16 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 10285.94 30278.30 9286.93 12992.20 12565.94 30989.16 13293.16 13583.10 9689.89 25987.81 2194.43 19793.35 158
tt0320-xc86.67 10488.41 8381.44 26293.45 7460.44 33283.96 20188.50 23487.26 2990.90 9397.90 385.61 7086.40 33270.14 27598.01 4597.47 14
IS-MVSNet86.66 10586.82 11086.17 12792.05 11866.87 24491.21 4788.64 23186.30 3789.60 12492.59 15869.22 28394.91 7473.89 22397.89 5596.72 29
tt032086.63 10688.36 8481.41 26393.57 7160.73 32984.37 19288.61 23387.00 3190.75 9697.98 285.54 7286.45 33069.75 28097.70 6497.06 22
v1086.54 10787.10 10184.84 15788.16 22863.28 28186.64 13992.20 12575.42 16692.81 5694.50 7274.05 23194.06 10983.88 8896.28 11697.17 19
pmmvs686.52 10888.06 8781.90 24892.22 11262.28 30284.66 18389.15 22583.54 6489.85 11497.32 888.08 3986.80 32370.43 27297.30 8696.62 31
NormalMVS86.47 10985.32 14189.94 5194.43 4480.42 7288.63 10393.59 6774.56 17685.12 24190.34 25366.19 29994.20 10076.57 18198.44 2095.19 68
PHI-MVS86.38 11085.81 12888.08 9188.44 22277.34 10889.35 9093.05 9373.15 20584.76 25587.70 31478.87 15994.18 10380.67 12696.29 11592.73 190
CSCG86.26 11186.47 11385.60 13990.87 16074.26 13887.98 11391.85 13780.35 9689.54 12788.01 30179.09 15792.13 17875.51 19995.06 17290.41 288
DeepC-MVS_fast80.27 886.23 11285.65 13487.96 9491.30 14576.92 11387.19 12491.99 13270.56 24684.96 24890.69 23980.01 15095.14 6778.37 15395.78 14891.82 242
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 17587.82 23662.35 30186.42 14391.33 15676.78 14692.73 5894.48 7473.41 24393.72 12283.10 9595.41 15797.01 23
Anonymous2024052986.20 11487.13 10083.42 20790.19 17364.55 26784.55 18690.71 17685.85 4089.94 11295.24 5082.13 11790.40 23969.19 28796.40 11395.31 62
fmvsm_s_conf0.5_n_386.19 11587.27 9882.95 22186.91 27270.38 19685.31 16792.61 11375.59 16288.32 15492.87 14982.22 11588.63 28788.80 992.82 25389.83 301
test_fmvsmconf0.1_n86.18 11685.88 12687.08 10485.26 31878.25 9385.82 15591.82 13965.33 32488.55 14592.35 17282.62 10489.80 26186.87 4294.32 20193.18 169
CDPH-MVS86.17 11785.54 13588.05 9392.25 11075.45 13183.85 20692.01 13165.91 31186.19 21391.75 19483.77 8994.98 7277.43 17196.71 10193.73 137
NR-MVSNet86.00 11886.22 11785.34 14693.24 8364.56 26682.21 26390.46 18580.99 8988.42 15091.97 18177.56 17693.85 11772.46 25098.65 1297.61 10
train_agg85.98 11985.28 14288.07 9292.34 10779.70 8083.94 20290.32 19265.79 31384.49 26090.97 22481.93 12393.63 12681.21 11896.54 10690.88 271
KinetiMVS85.95 12086.10 12185.50 14387.56 24669.78 20383.70 21289.83 20980.42 9487.76 17393.24 12973.76 23791.54 19385.03 7393.62 22795.19 68
FC-MVSNet-test85.93 12187.05 10382.58 23292.25 11056.44 37785.75 15693.09 9177.33 14191.94 7294.65 6574.78 21793.41 14275.11 20598.58 1497.88 7
test_fmvsmconf_n85.88 12285.51 13686.99 10784.77 32778.21 9485.40 16591.39 15465.32 32587.72 17591.81 19082.33 10989.78 26286.68 4494.20 20492.99 181
Effi-MVS+-dtu85.82 12383.38 19293.14 487.13 25991.15 387.70 11788.42 23774.57 17583.56 28685.65 34978.49 16494.21 9972.04 25292.88 24994.05 120
TAPA-MVS77.73 1285.71 12484.83 15288.37 8588.78 21279.72 7987.15 12693.50 7069.17 26385.80 22489.56 27480.76 14092.13 17873.21 24595.51 15593.25 166
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda85.50 12586.14 11983.58 20187.97 23067.13 23787.55 11894.32 2273.44 19588.47 14887.54 31786.45 6191.06 21175.76 19693.76 21892.54 204
canonicalmvs85.50 12586.14 11983.58 20187.97 23067.13 23787.55 11894.32 2273.44 19588.47 14887.54 31786.45 6191.06 21175.76 19693.76 21892.54 204
fmvsm_s_conf0.5_n_885.48 12785.75 13184.68 16687.10 26269.98 20184.28 19392.68 10974.77 17287.90 16692.36 17173.94 23290.41 23885.95 6292.74 25593.66 140
EPP-MVSNet85.47 12885.04 14786.77 11291.52 14169.37 21091.63 4387.98 24981.51 8487.05 19191.83 18866.18 30195.29 5970.75 26696.89 9495.64 53
GeoE85.45 12985.81 12884.37 17390.08 17667.07 23985.86 15491.39 15472.33 22387.59 17790.25 25884.85 7892.37 17278.00 16291.94 28093.66 140
MGCNet85.37 13084.58 16287.75 9585.28 31773.36 14386.54 14285.71 29377.56 13881.78 32492.47 16470.29 27796.02 1185.59 6595.96 13393.87 128
FIs85.35 13186.27 11682.60 23191.86 12557.31 37085.10 17293.05 9375.83 15791.02 8893.97 10273.57 23992.91 16073.97 22298.02 4497.58 12
test_fmvsmvis_n_192085.22 13285.36 14084.81 15985.80 30576.13 12585.15 17192.32 12261.40 36091.33 8190.85 23383.76 9086.16 33884.31 8493.28 23692.15 231
casdiffmvspermissive85.21 13385.85 12783.31 21086.17 29462.77 28883.03 23493.93 4774.69 17488.21 15692.68 15782.29 11391.89 18677.87 16593.75 22195.27 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 15486.01 30071.31 18484.96 17491.76 14369.10 26588.90 13592.56 16173.84 23590.63 23186.88 4193.26 23793.13 170
baseline85.20 13485.93 12483.02 21786.30 28962.37 30084.55 18693.96 4574.48 17887.12 18592.03 18082.30 11191.94 18378.39 15294.21 20394.74 86
SSM_040485.16 13685.09 14585.36 14590.14 17569.52 20886.17 14891.58 14574.41 17986.55 20291.49 20178.54 16093.97 11273.71 22793.21 24192.59 200
K. test v385.14 13784.73 15486.37 11891.13 15469.63 20785.45 16376.68 37884.06 5692.44 6396.99 1362.03 32994.65 8380.58 12793.24 23894.83 83
mmtdpeth85.13 13885.78 13083.17 21584.65 32974.71 13485.87 15390.35 19177.94 13083.82 27896.96 1577.75 17180.03 39778.44 15196.21 12094.79 85
EI-MVSNet-Vis-set85.12 13984.53 16586.88 10984.01 34272.76 15483.91 20585.18 30380.44 9388.75 14085.49 35380.08 14991.92 18482.02 11290.85 31095.97 44
fmvsm_l_conf0.5_n_385.11 14084.96 14985.56 14087.49 24975.69 13084.71 18190.61 18167.64 29384.88 25192.05 17982.30 11188.36 29383.84 9091.10 29892.62 198
MGCFI-Net85.04 14185.95 12382.31 24187.52 24763.59 27786.23 14793.96 4573.46 19388.07 15987.83 31286.46 6090.87 22176.17 19093.89 21492.47 208
EI-MVSNet-UG-set85.04 14184.44 16886.85 11083.87 34672.52 16383.82 20785.15 30480.27 9888.75 14085.45 35579.95 15191.90 18581.92 11590.80 31296.13 39
X-MVStestdata85.04 14182.70 21192.08 995.64 2486.25 2292.64 2093.33 7685.07 4589.99 10916.05 47486.57 5895.80 3087.35 3397.62 7294.20 110
MSLP-MVS++85.00 14486.03 12281.90 24891.84 12871.56 18286.75 13793.02 9775.95 15587.12 18589.39 27877.98 16889.40 27377.46 16994.78 18584.75 377
F-COLMAP84.97 14583.42 19189.63 5892.39 10583.40 5288.83 9791.92 13573.19 20480.18 34889.15 28477.04 18893.28 14565.82 32092.28 26992.21 227
SSM_040784.89 14684.85 15185.01 15589.13 19868.97 21885.60 16091.58 14574.41 17985.68 22591.49 20178.54 16093.69 12373.71 22793.47 22992.38 215
balanced_conf0384.80 14785.40 13883.00 21888.95 20561.44 31190.42 6292.37 12171.48 23588.72 14293.13 13670.16 27995.15 6679.26 14494.11 20792.41 210
3Dnovator80.37 784.80 14784.71 15785.06 15286.36 28774.71 13488.77 9990.00 20575.65 16084.96 24893.17 13474.06 23091.19 20678.28 15691.09 29989.29 311
SymmetryMVS84.79 14983.54 18688.55 7992.44 10480.42 7288.63 10382.37 33874.56 17685.12 24190.34 25366.19 29994.20 10076.57 18195.68 15291.03 265
IterMVS-LS84.73 15084.98 14883.96 18887.35 25263.66 27583.25 22789.88 20876.06 15089.62 12192.37 16973.40 24592.52 16778.16 15994.77 18795.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR84.63 15184.34 17385.49 14490.18 17475.86 12979.23 31787.13 26673.35 19785.56 23289.34 27983.60 9290.50 23576.64 18094.05 21190.09 297
HQP-MVS84.61 15284.06 17886.27 12191.19 15070.66 19184.77 17692.68 10973.30 20080.55 34090.17 26372.10 26194.61 8577.30 17394.47 19593.56 152
v119284.57 15384.69 15984.21 18187.75 23862.88 28583.02 23591.43 15169.08 26689.98 11190.89 23072.70 25593.62 12982.41 10794.97 17796.13 39
fmvsm_s_conf0.5_n_584.56 15484.71 15784.11 18487.92 23372.09 17184.80 17588.64 23164.43 33488.77 13991.78 19278.07 16787.95 30085.85 6392.18 27392.30 220
FMVSNet184.55 15585.45 13781.85 25090.27 17261.05 32086.83 13388.27 24278.57 12389.66 12095.64 3875.43 20790.68 22869.09 28895.33 16093.82 131
v114484.54 15684.72 15684.00 18587.67 24262.55 29282.97 23790.93 17270.32 25089.80 11590.99 22373.50 24093.48 13881.69 11794.65 19195.97 44
Gipumacopyleft84.44 15786.33 11578.78 30884.20 33973.57 14289.55 8190.44 18684.24 5484.38 26394.89 5776.35 20380.40 39476.14 19196.80 9982.36 415
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 17484.74 16287.25 25570.84 19083.55 21788.45 23668.64 27586.29 21291.31 21074.97 21388.42 29187.87 2090.07 32894.95 75
MCST-MVS84.36 15983.93 18285.63 13891.59 13371.58 18083.52 21892.13 12761.82 35383.96 27689.75 27179.93 15293.46 13978.33 15594.34 20091.87 241
VDDNet84.35 16085.39 13981.25 26595.13 3259.32 34685.42 16481.11 34986.41 3687.41 18196.21 2573.61 23890.61 23366.33 31396.85 9593.81 134
ETV-MVS84.31 16183.91 18385.52 14188.58 21870.40 19584.50 19093.37 7278.76 12184.07 27478.72 42880.39 14595.13 6873.82 22592.98 24791.04 264
v124084.30 16284.51 16683.65 19887.65 24361.26 31682.85 24191.54 14867.94 28690.68 9890.65 24471.71 26993.64 12582.84 10194.78 18596.07 41
MVS_111021_LR84.28 16383.76 18485.83 13589.23 19683.07 5580.99 28583.56 32672.71 21586.07 21689.07 28681.75 13086.19 33777.11 17593.36 23288.24 330
h-mvs3384.25 16482.76 21088.72 7591.82 13082.60 6084.00 20084.98 31071.27 23686.70 19890.55 24963.04 32693.92 11578.26 15794.20 20489.63 303
v14419284.24 16584.41 16983.71 19787.59 24561.57 31082.95 23891.03 16867.82 29089.80 11590.49 25073.28 24793.51 13781.88 11694.89 18096.04 43
dcpmvs_284.23 16685.14 14481.50 26088.61 21761.98 30782.90 24093.11 8968.66 27492.77 5792.39 16578.50 16387.63 30976.99 17792.30 26694.90 76
v192192084.23 16684.37 17183.79 19387.64 24461.71 30982.91 23991.20 16367.94 28690.06 10690.34 25372.04 26493.59 13182.32 10894.91 17896.07 41
VDD-MVS84.23 16684.58 16283.20 21391.17 15365.16 26283.25 22784.97 31179.79 10387.18 18494.27 8374.77 21890.89 21969.24 28496.54 10693.55 154
v2v48284.09 16984.24 17583.62 19987.13 25961.40 31282.71 24489.71 21372.19 22689.55 12591.41 20570.70 27593.20 14781.02 12093.76 21896.25 37
EG-PatchMatch MVS84.08 17084.11 17783.98 18792.22 11272.61 16082.20 26587.02 27272.63 21688.86 13691.02 22278.52 16291.11 20973.41 23591.09 29988.21 331
E384.06 17184.61 16082.40 24087.49 24961.30 31481.03 28493.36 7371.83 23186.01 21891.87 18382.91 9991.36 20275.66 19891.33 29494.53 94
fmvsm_s_conf0.5_n_684.05 17284.14 17683.81 19187.75 23871.17 18683.42 22191.10 16667.90 28884.53 25890.70 23873.01 25088.73 28485.09 7093.72 22391.53 254
DP-MVS Recon84.05 17283.22 19586.52 11691.73 13175.27 13283.23 22992.40 11772.04 22882.04 31588.33 29777.91 17093.95 11466.17 31495.12 17090.34 290
viewmacassd2359aftdt84.04 17484.78 15381.81 25386.43 28160.32 33481.95 26792.82 10571.56 23286.06 21792.98 14281.79 12990.28 24076.18 18993.24 23894.82 84
TransMVSNet (Re)84.02 17585.74 13278.85 30791.00 15755.20 38982.29 25987.26 26179.65 10688.38 15295.52 4183.00 9786.88 32167.97 30296.60 10494.45 98
Baseline_NR-MVSNet84.00 17685.90 12578.29 31991.47 14353.44 40182.29 25987.00 27579.06 11589.55 12595.72 3677.20 18486.14 33972.30 25198.51 1795.28 63
fmvsm_l_conf0.5_n_983.98 17784.46 16782.53 23586.11 29770.65 19382.45 25489.17 22467.72 29286.74 19791.49 20179.20 15585.86 34884.71 8092.60 25991.07 263
TSAR-MVS + GP.83.95 17882.69 21287.72 9689.27 19581.45 6783.72 21181.58 34774.73 17385.66 22886.06 34472.56 25792.69 16475.44 20195.21 16589.01 324
LuminaMVS83.94 17983.51 18785.23 14789.78 18471.74 17584.76 17987.27 26072.60 21789.31 13090.60 24864.04 31590.95 21479.08 14594.11 20792.99 181
alignmvs83.94 17983.98 18083.80 19287.80 23767.88 23284.54 18891.42 15373.27 20388.41 15187.96 30272.33 25890.83 22276.02 19394.11 20792.69 194
Effi-MVS+83.90 18184.01 17983.57 20387.22 25765.61 25886.55 14192.40 11778.64 12281.34 33184.18 37483.65 9192.93 15874.22 21387.87 36392.17 230
fmvsm_s_conf0.1_n_283.82 18283.49 18884.84 15785.99 30170.19 19980.93 28687.58 25667.26 29987.94 16592.37 16971.40 27188.01 29786.03 5791.87 28196.31 36
mvs5depth83.82 18284.54 16481.68 25682.23 37168.65 22386.89 13089.90 20780.02 10287.74 17497.86 464.19 31482.02 38276.37 18595.63 15494.35 105
CANet83.79 18482.85 20986.63 11386.17 29472.21 17083.76 21091.43 15177.24 14374.39 40387.45 32175.36 20895.42 5577.03 17692.83 25292.25 226
pm-mvs183.69 18584.95 15079.91 29390.04 18059.66 34382.43 25587.44 25775.52 16487.85 16995.26 4981.25 13585.65 35268.74 29496.04 12994.42 102
AdaColmapbinary83.66 18683.69 18583.57 20390.05 17972.26 16886.29 14590.00 20578.19 12881.65 32587.16 32783.40 9494.24 9861.69 35694.76 18884.21 387
viewdifsd2359ckpt0983.64 18783.18 19885.03 15387.26 25466.99 24285.32 16693.83 5665.57 31984.99 24789.40 27777.30 18093.57 13471.16 26293.80 21794.54 93
MIMVSNet183.63 18884.59 16180.74 27694.06 6162.77 28882.72 24384.53 31877.57 13790.34 10295.92 3176.88 19685.83 34961.88 35497.42 8293.62 146
fmvsm_s_conf0.5_n_283.62 18983.29 19484.62 16785.43 31570.18 20080.61 29287.24 26267.14 30087.79 17191.87 18371.79 26887.98 29986.00 6191.77 28495.71 50
test_fmvsm_n_192083.60 19082.89 20685.74 13685.22 31977.74 10284.12 19790.48 18359.87 38086.45 21191.12 21875.65 20585.89 34682.28 10990.87 30893.58 150
WR-MVS83.56 19184.40 17081.06 27093.43 7754.88 39078.67 32685.02 30881.24 8690.74 9791.56 19972.85 25291.08 21068.00 30198.04 4197.23 17
CNLPA83.55 19283.10 20184.90 15689.34 19383.87 5084.54 18888.77 22879.09 11483.54 28788.66 29474.87 21481.73 38466.84 30892.29 26889.11 317
viewcassd2359sk1183.53 19383.96 18182.25 24286.97 27161.13 31880.80 29093.22 8470.97 24285.36 23691.08 22081.84 12791.29 20374.79 20890.58 32494.33 107
LCM-MVSNet-Re83.48 19485.06 14678.75 30985.94 30255.75 38380.05 29894.27 2576.47 14796.09 694.54 7183.31 9589.75 26559.95 36794.89 18090.75 274
hse-mvs283.47 19581.81 22788.47 8191.03 15682.27 6182.61 24583.69 32471.27 23686.70 19886.05 34563.04 32692.41 17078.26 15793.62 22790.71 276
V4283.47 19583.37 19383.75 19583.16 36563.33 28081.31 27890.23 19969.51 25990.91 9190.81 23574.16 22792.29 17680.06 13090.22 32695.62 54
VPA-MVSNet83.47 19584.73 15479.69 29890.29 17157.52 36981.30 28088.69 23076.29 14887.58 17994.44 7580.60 14387.20 31566.60 31196.82 9894.34 106
mamba_040883.44 19882.88 20785.11 15089.13 19868.97 21872.73 40191.28 15872.90 20985.68 22590.61 24676.78 19793.97 11273.37 23793.47 22992.38 215
viewdifsd2359ckpt0783.41 19984.35 17280.56 28285.84 30458.93 35479.47 30991.28 15873.01 20887.59 17792.07 17885.24 7488.68 28573.59 23291.11 29794.09 119
PAPM_NR83.23 20083.19 19783.33 20990.90 15965.98 25488.19 10890.78 17578.13 12980.87 33687.92 30673.49 24292.42 16970.07 27688.40 35291.60 251
CLD-MVS83.18 20182.64 21384.79 16089.05 20167.82 23377.93 33692.52 11568.33 27885.07 24481.54 40382.06 12092.96 15669.35 28397.91 5493.57 151
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 20285.68 13375.65 35781.24 38345.26 44579.94 30092.91 10183.83 5791.33 8196.88 1680.25 14785.92 34268.89 29195.89 14195.76 48
FA-MVS(test-final)83.13 20383.02 20283.43 20686.16 29666.08 25388.00 11288.36 23975.55 16385.02 24592.75 15565.12 30892.50 16874.94 20791.30 29591.72 246
114514_t83.10 20482.54 21684.77 16192.90 9069.10 21786.65 13890.62 18054.66 41281.46 32890.81 23576.98 18994.38 9372.62 24896.18 12290.82 273
RRT-MVS82.97 20583.44 18981.57 25885.06 32258.04 36487.20 12390.37 18977.88 13288.59 14493.70 11963.17 32393.05 15476.49 18488.47 35193.62 146
viewmanbaseed2359cas82.95 20683.43 19081.52 25985.18 32060.03 33981.36 27792.38 11969.55 25884.84 25491.38 20679.85 15390.09 25374.22 21392.09 27594.43 101
BP-MVS182.81 20781.67 22986.23 12287.88 23568.53 22486.06 15084.36 31975.65 16085.14 24090.19 26045.84 41594.42 9285.18 6994.72 18995.75 49
UGNet82.78 20881.64 23086.21 12586.20 29376.24 12386.86 13185.68 29477.07 14473.76 40792.82 15169.64 28091.82 18969.04 29093.69 22490.56 284
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 20981.93 22585.19 14882.08 37280.15 7685.53 16188.76 22968.01 28385.58 23187.75 31371.80 26786.85 32274.02 22193.87 21588.58 327
EI-MVSNet82.61 21082.42 21883.20 21383.25 36263.66 27583.50 21985.07 30576.06 15086.55 20285.10 36173.41 24390.25 24178.15 16190.67 31995.68 52
QAPM82.59 21182.59 21582.58 23286.44 28066.69 24589.94 7190.36 19067.97 28584.94 25092.58 16072.71 25492.18 17770.63 26987.73 36688.85 325
fmvsm_s_conf0.1_n_a82.58 21281.93 22584.50 17087.68 24173.35 14486.14 14977.70 36761.64 35885.02 24591.62 19677.75 17186.24 33482.79 10287.07 37493.91 126
Fast-Effi-MVS+-dtu82.54 21381.41 23985.90 13285.60 31076.53 11883.07 23389.62 21773.02 20779.11 35883.51 37980.74 14190.24 24368.76 29389.29 33990.94 268
MVS_Test82.47 21483.22 19580.22 28982.62 37057.75 36882.54 25091.96 13471.16 24082.89 29892.52 16377.41 17890.50 23580.04 13187.84 36592.40 212
viewdifsd2359ckpt1182.46 21582.98 20480.88 27383.53 34961.00 32379.46 31085.97 28969.48 26087.89 16791.31 21082.10 11888.61 28874.28 21192.86 25093.02 177
viewmsd2359difaftdt82.46 21582.99 20380.88 27383.52 35061.00 32379.46 31085.97 28969.48 26087.89 16791.31 21082.10 11888.61 28874.28 21192.86 25093.02 177
v14882.31 21782.48 21781.81 25385.59 31159.66 34381.47 27586.02 28772.85 21188.05 16190.65 24470.73 27490.91 21875.15 20491.79 28294.87 78
API-MVS82.28 21882.61 21481.30 26486.29 29069.79 20288.71 10087.67 25578.42 12582.15 31184.15 37577.98 16891.59 19265.39 32392.75 25482.51 414
MVSFormer82.23 21981.57 23584.19 18385.54 31269.26 21291.98 3890.08 20371.54 23376.23 38385.07 36458.69 35194.27 9586.26 5188.77 34789.03 322
viewdifsd2359ckpt1382.22 22081.98 22482.95 22185.48 31464.44 26883.17 23192.11 12865.97 30883.72 28189.73 27277.60 17590.80 22470.61 27089.42 33793.59 149
fmvsm_s_conf0.5_n_a82.21 22181.51 23884.32 17886.56 27773.35 14485.46 16277.30 37161.81 35484.51 25990.88 23277.36 17986.21 33682.72 10386.97 37993.38 157
EIA-MVS82.19 22281.23 24685.10 15187.95 23269.17 21683.22 23093.33 7670.42 24778.58 36379.77 41977.29 18194.20 10071.51 25888.96 34591.93 240
GDP-MVS82.17 22380.85 25486.15 12988.65 21568.95 22185.65 15993.02 9768.42 27683.73 28089.54 27545.07 42694.31 9479.66 13793.87 21595.19 68
fmvsm_s_conf0.1_n82.17 22381.59 23383.94 19086.87 27571.57 18185.19 17077.42 37062.27 35284.47 26291.33 20876.43 20085.91 34483.14 9387.14 37294.33 107
PCF-MVS74.62 1582.15 22580.92 25285.84 13489.43 19172.30 16780.53 29391.82 13957.36 39687.81 17089.92 26877.67 17493.63 12658.69 37295.08 17191.58 252
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLCcopyleft73.85 1682.09 22680.31 26187.45 10090.86 16180.29 7585.88 15290.65 17868.17 28176.32 38286.33 33973.12 24992.61 16661.40 35990.02 33089.44 306
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n82.06 22781.54 23783.60 20083.94 34373.90 14083.35 22486.10 28358.97 38283.80 27990.36 25274.23 22586.94 32082.90 9990.22 32689.94 299
fmvsm_s_conf0.5_n_782.04 22882.05 22282.01 24686.98 27071.07 18778.70 32489.45 22068.07 28278.14 36591.61 19774.19 22685.92 34279.61 13891.73 28589.05 321
GBi-Net82.02 22982.07 22081.85 25086.38 28461.05 32086.83 13388.27 24272.43 21886.00 21995.64 3863.78 31990.68 22865.95 31693.34 23393.82 131
test182.02 22982.07 22081.85 25086.38 28461.05 32086.83 13388.27 24272.43 21886.00 21995.64 3863.78 31990.68 22865.95 31693.34 23393.82 131
OpenMVScopyleft76.72 1381.98 23182.00 22381.93 24784.42 33468.22 22788.50 10689.48 21966.92 30381.80 32291.86 18572.59 25690.16 24771.19 26191.25 29687.40 347
KD-MVS_self_test81.93 23283.14 20078.30 31884.75 32852.75 40580.37 29589.42 22270.24 25290.26 10493.39 12674.55 22486.77 32468.61 29696.64 10295.38 59
fmvsm_s_conf0.5_n81.91 23381.30 24383.75 19586.02 29971.56 18284.73 18077.11 37462.44 34984.00 27590.68 24076.42 20185.89 34683.14 9387.11 37393.81 134
SDMVSNet81.90 23483.17 19978.10 32288.81 21062.45 29876.08 37086.05 28673.67 18983.41 28893.04 13882.35 10880.65 39170.06 27795.03 17391.21 259
tfpnnormal81.79 23582.95 20578.31 31788.93 20655.40 38580.83 28982.85 33376.81 14585.90 22394.14 9374.58 22286.51 32866.82 30995.68 15293.01 180
AstraMVS81.67 23681.40 24082.48 23787.06 26766.47 24881.41 27681.68 34468.78 27188.00 16290.95 22865.70 30487.86 30576.66 17992.38 26393.12 173
c3_l81.64 23781.59 23381.79 25580.86 38959.15 35178.61 32790.18 20168.36 27787.20 18387.11 32969.39 28191.62 19178.16 15994.43 19794.60 89
guyue81.57 23881.37 24282.15 24386.39 28266.13 25281.54 27483.21 32869.79 25687.77 17289.95 26665.36 30787.64 30875.88 19492.49 26192.67 195
PVSNet_Blended_VisFu81.55 23980.49 25984.70 16591.58 13673.24 14884.21 19491.67 14462.86 34380.94 33487.16 32767.27 29392.87 16169.82 27988.94 34687.99 337
fmvsm_l_conf0.5_n_a81.46 24080.87 25383.25 21183.73 34873.21 14983.00 23685.59 29658.22 38882.96 29790.09 26572.30 25986.65 32681.97 11489.95 33189.88 300
SSM_0407281.44 24182.88 20777.10 33789.13 19868.97 21872.73 40191.28 15872.90 20985.68 22590.61 24676.78 19769.94 43473.37 23793.47 22992.38 215
DELS-MVS81.44 24181.25 24482.03 24584.27 33862.87 28676.47 36492.49 11670.97 24281.64 32683.83 37675.03 21192.70 16374.29 21092.22 27290.51 286
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 24381.61 23280.41 28586.38 28458.75 35983.93 20486.58 27872.43 21887.65 17692.98 14263.78 31990.22 24466.86 30693.92 21392.27 224
TinyColmap81.25 24482.34 21977.99 32585.33 31660.68 33082.32 25888.33 24071.26 23886.97 19292.22 17777.10 18786.98 31962.37 34895.17 16786.31 360
diffmvs_AUTHOR81.24 24581.55 23680.30 28780.61 39460.22 33577.98 33590.48 18367.77 29183.34 29089.50 27674.69 22087.42 31178.78 14990.81 31193.27 163
AUN-MVS81.18 24678.78 28488.39 8390.93 15882.14 6282.51 25183.67 32564.69 33380.29 34485.91 34851.07 39092.38 17176.29 18893.63 22690.65 281
IMVS_040781.08 24781.23 24680.62 28185.76 30662.46 29482.46 25287.91 25065.23 32682.12 31287.92 30677.27 18290.18 24671.67 25490.74 31489.20 312
tttt051781.07 24879.58 27485.52 14188.99 20466.45 24987.03 12875.51 38673.76 18888.32 15490.20 25937.96 44794.16 10779.36 14395.13 16895.93 47
Fast-Effi-MVS+81.04 24980.57 25682.46 23887.50 24863.22 28278.37 33089.63 21668.01 28381.87 31882.08 39782.31 11092.65 16567.10 30588.30 35891.51 255
BH-untuned80.96 25080.99 25080.84 27588.55 21968.23 22680.33 29688.46 23572.79 21486.55 20286.76 33374.72 21991.77 19061.79 35588.99 34482.52 413
IMVS_040380.93 25181.00 24980.72 27885.76 30662.46 29481.82 26887.91 25065.23 32682.07 31487.92 30675.91 20490.50 23571.67 25490.74 31489.20 312
eth_miper_zixun_eth80.84 25280.22 26582.71 22981.41 38160.98 32577.81 33890.14 20267.31 29886.95 19387.24 32664.26 31292.31 17475.23 20391.61 28894.85 82
xiu_mvs_v1_base_debu80.84 25280.14 26782.93 22488.31 22371.73 17679.53 30587.17 26365.43 32079.59 35082.73 39176.94 19090.14 25073.22 24088.33 35486.90 354
xiu_mvs_v1_base80.84 25280.14 26782.93 22488.31 22371.73 17679.53 30587.17 26365.43 32079.59 35082.73 39176.94 19090.14 25073.22 24088.33 35486.90 354
xiu_mvs_v1_base_debi80.84 25280.14 26782.93 22488.31 22371.73 17679.53 30587.17 26365.43 32079.59 35082.73 39176.94 19090.14 25073.22 24088.33 35486.90 354
IterMVS-SCA-FT80.64 25679.41 27584.34 17783.93 34469.66 20676.28 36681.09 35072.43 21886.47 20990.19 26060.46 33693.15 15077.45 17086.39 38590.22 291
BH-RMVSNet80.53 25780.22 26581.49 26187.19 25866.21 25177.79 33986.23 28174.21 18383.69 28288.50 29573.25 24890.75 22563.18 34487.90 36287.52 345
VortexMVS80.51 25880.63 25580.15 29183.36 35861.82 30880.63 29188.00 24867.11 30187.23 18289.10 28563.98 31688.00 29873.63 23192.63 25890.64 282
Anonymous20240521180.51 25881.19 24878.49 31488.48 22057.26 37176.63 35982.49 33681.21 8784.30 26992.24 17667.99 28986.24 33462.22 34995.13 16891.98 239
DIV-MVS_self_test80.43 26080.23 26381.02 27179.99 39959.25 34877.07 35287.02 27267.38 29586.19 21389.22 28163.09 32490.16 24776.32 18695.80 14693.66 140
cl____80.42 26180.23 26381.02 27179.99 39959.25 34877.07 35287.02 27267.37 29686.18 21589.21 28263.08 32590.16 24776.31 18795.80 14693.65 143
diffmvspermissive80.40 26280.48 26080.17 29079.02 41260.04 33777.54 34390.28 19866.65 30682.40 30587.33 32473.50 24087.35 31377.98 16389.62 33593.13 170
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 26378.41 29286.23 12276.75 42673.28 14687.18 12577.45 36976.24 14968.14 43788.93 28865.41 30693.85 11769.47 28296.12 12691.55 253
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth80.34 26480.04 27081.24 26779.82 40258.95 35377.66 34089.66 21465.75 31685.99 22285.11 36068.29 28891.42 19976.03 19292.03 27693.33 159
MG-MVS80.32 26580.94 25178.47 31588.18 22652.62 40882.29 25985.01 30972.01 22979.24 35792.54 16269.36 28293.36 14470.65 26889.19 34289.45 305
mvsmamba80.30 26678.87 28184.58 16988.12 22967.55 23492.35 3084.88 31263.15 34185.33 23790.91 22950.71 39295.20 6566.36 31287.98 36190.99 266
VPNet80.25 26781.68 22875.94 35392.46 10347.98 43276.70 35781.67 34573.45 19484.87 25292.82 15174.66 22186.51 32861.66 35796.85 9593.33 159
MAR-MVS80.24 26878.74 28684.73 16386.87 27578.18 9585.75 15687.81 25465.67 31877.84 36978.50 42973.79 23690.53 23461.59 35890.87 30885.49 370
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 26979.00 28083.78 19488.17 22786.66 1981.31 27866.81 44269.64 25788.33 15390.19 26064.58 30983.63 37371.99 25390.03 32981.06 433
Anonymous2024052180.18 27081.25 24476.95 33983.15 36660.84 32782.46 25285.99 28868.76 27286.78 19493.73 11859.13 34877.44 40873.71 22797.55 7792.56 202
LFMVS80.15 27180.56 25778.89 30689.19 19755.93 37985.22 16973.78 39882.96 7084.28 27092.72 15657.38 36090.07 25563.80 33895.75 14990.68 278
DPM-MVS80.10 27279.18 27982.88 22790.71 16469.74 20478.87 32290.84 17360.29 37675.64 39285.92 34767.28 29293.11 15171.24 26091.79 28285.77 366
MSDG80.06 27379.99 27280.25 28883.91 34568.04 23177.51 34489.19 22377.65 13581.94 31683.45 38176.37 20286.31 33363.31 34386.59 38286.41 358
FE-MVS79.98 27478.86 28283.36 20886.47 27966.45 24989.73 7484.74 31672.80 21384.22 27391.38 20644.95 42793.60 13063.93 33691.50 29190.04 298
sd_testset79.95 27581.39 24175.64 35888.81 21058.07 36376.16 36982.81 33473.67 18983.41 28893.04 13880.96 13877.65 40758.62 37395.03 17391.21 259
ab-mvs79.67 27680.56 25776.99 33888.48 22056.93 37384.70 18286.06 28568.95 26980.78 33793.08 13775.30 20984.62 36056.78 38290.90 30689.43 307
VNet79.31 27780.27 26276.44 34787.92 23353.95 39775.58 37684.35 32074.39 18282.23 30990.72 23772.84 25384.39 36560.38 36593.98 21290.97 267
thisisatest053079.07 27877.33 30284.26 18087.13 25964.58 26583.66 21475.95 38168.86 27085.22 23987.36 32338.10 44493.57 13475.47 20094.28 20294.62 88
cl2278.97 27978.21 29481.24 26777.74 41659.01 35277.46 34787.13 26665.79 31384.32 26685.10 36158.96 35090.88 22075.36 20292.03 27693.84 129
patch_mono-278.89 28079.39 27677.41 33484.78 32668.11 22975.60 37483.11 33060.96 36879.36 35489.89 26975.18 21072.97 42373.32 23992.30 26691.15 261
RPMNet78.88 28178.28 29380.68 28079.58 40362.64 29082.58 24794.16 3374.80 17175.72 39092.59 15848.69 39995.56 4473.48 23482.91 42183.85 392
PAPR78.84 28278.10 29581.07 26985.17 32160.22 33582.21 26390.57 18262.51 34575.32 39684.61 36974.99 21292.30 17559.48 37088.04 36090.68 278
viewmambaseed2359dif78.80 28378.47 29179.78 29480.26 39859.28 34777.31 34987.13 26660.42 37482.37 30688.67 29374.58 22287.87 30467.78 30487.73 36692.19 228
PVSNet_BlendedMVS78.80 28377.84 29681.65 25784.43 33263.41 27879.49 30890.44 18661.70 35775.43 39387.07 33069.11 28491.44 19760.68 36392.24 27090.11 296
FMVSNet378.80 28378.55 28879.57 30082.89 36956.89 37581.76 26985.77 29269.04 26786.00 21990.44 25151.75 38890.09 25365.95 31693.34 23391.72 246
test_yl78.71 28678.51 28979.32 30384.32 33658.84 35678.38 32885.33 30075.99 15382.49 30386.57 33558.01 35490.02 25762.74 34592.73 25689.10 318
DCV-MVSNet78.71 28678.51 28979.32 30384.32 33658.84 35678.38 32885.33 30075.99 15382.49 30386.57 33558.01 35490.02 25762.74 34592.73 25689.10 318
test111178.53 28878.85 28377.56 33192.22 11247.49 43482.61 24569.24 43072.43 21885.28 23894.20 8951.91 38690.07 25565.36 32496.45 11195.11 72
FE-MVSNET78.46 28979.36 27775.75 35586.53 27854.53 39278.03 33285.35 29969.01 26885.41 23590.68 24064.27 31185.73 35062.59 34792.35 26587.00 353
icg_test_0407_278.46 28979.68 27374.78 36585.76 30662.46 29468.51 43087.91 25065.23 32682.12 31287.92 30677.27 18272.67 42471.67 25490.74 31489.20 312
ECVR-MVScopyleft78.44 29178.63 28777.88 32791.85 12648.95 42883.68 21369.91 42672.30 22484.26 27294.20 8951.89 38789.82 26063.58 33996.02 13094.87 78
pmmvs-eth3d78.42 29277.04 30582.57 23487.44 25174.41 13780.86 28879.67 35855.68 40584.69 25690.31 25760.91 33485.42 35362.20 35091.59 28987.88 341
mvs_anonymous78.13 29378.76 28576.23 35279.24 40950.31 42478.69 32584.82 31461.60 35983.09 29692.82 15173.89 23487.01 31668.33 30086.41 38491.37 256
TAMVS78.08 29476.36 31283.23 21290.62 16572.87 15379.08 31880.01 35761.72 35681.35 33086.92 33263.96 31888.78 28250.61 42193.01 24688.04 336
miper_enhance_ethall77.83 29576.93 30680.51 28376.15 43358.01 36575.47 37888.82 22758.05 39083.59 28480.69 40764.41 31091.20 20573.16 24692.03 27692.33 219
Vis-MVSNet (Re-imp)77.82 29677.79 29777.92 32688.82 20951.29 41883.28 22571.97 41474.04 18482.23 30989.78 27057.38 36089.41 27257.22 38195.41 15793.05 176
CANet_DTU77.81 29777.05 30480.09 29281.37 38259.90 34183.26 22688.29 24169.16 26467.83 44083.72 37760.93 33389.47 26769.22 28689.70 33490.88 271
OpenMVS_ROBcopyleft70.19 1777.77 29877.46 29978.71 31084.39 33561.15 31781.18 28282.52 33562.45 34883.34 29087.37 32266.20 29888.66 28664.69 33185.02 40186.32 359
SSC-MVS77.55 29981.64 23065.29 43190.46 16820.33 47873.56 39468.28 43285.44 4188.18 15894.64 6870.93 27381.33 38671.25 25992.03 27694.20 110
MDA-MVSNet-bldmvs77.47 30076.90 30779.16 30579.03 41164.59 26466.58 44275.67 38473.15 20588.86 13688.99 28766.94 29481.23 38764.71 33088.22 35991.64 250
jason77.42 30175.75 31882.43 23987.10 26269.27 21177.99 33481.94 34251.47 43277.84 36985.07 36460.32 33889.00 27670.74 26789.27 34189.03 322
jason: jason.
CDS-MVSNet77.32 30275.40 32283.06 21689.00 20372.48 16477.90 33782.17 34060.81 36978.94 36083.49 38059.30 34688.76 28354.64 40192.37 26487.93 340
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_040477.24 30377.75 29875.73 35685.76 30662.46 29470.84 41687.91 25065.23 32672.21 41587.92 30667.48 29175.53 41671.67 25490.74 31489.20 312
xiu_mvs_v2_base77.19 30476.75 30978.52 31387.01 26861.30 31475.55 37787.12 27061.24 36574.45 40278.79 42777.20 18490.93 21664.62 33384.80 40883.32 401
MVSTER77.09 30575.70 31981.25 26575.27 44161.08 31977.49 34685.07 30560.78 37086.55 20288.68 29143.14 43690.25 24173.69 23090.67 31992.42 209
PS-MVSNAJ77.04 30676.53 31178.56 31287.09 26461.40 31275.26 37987.13 26661.25 36474.38 40477.22 44176.94 19090.94 21564.63 33284.83 40783.35 400
IterMVS76.91 30776.34 31378.64 31180.91 38764.03 27276.30 36579.03 36164.88 33283.11 29489.16 28359.90 34284.46 36368.61 29685.15 39987.42 346
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS76.84 30875.67 32080.34 28680.48 39662.16 30673.50 39584.80 31557.61 39482.24 30887.54 31751.31 38987.65 30770.40 27393.19 24291.23 258
CL-MVSNet_self_test76.81 30977.38 30175.12 36186.90 27351.34 41673.20 39880.63 35468.30 27981.80 32288.40 29666.92 29580.90 38855.35 39594.90 17993.12 173
TR-MVS76.77 31075.79 31779.72 29786.10 29865.79 25677.14 35083.02 33165.20 33081.40 32982.10 39566.30 29790.73 22755.57 39285.27 39582.65 408
MonoMVSNet76.66 31177.26 30374.86 36379.86 40154.34 39486.26 14686.08 28471.08 24185.59 23088.68 29153.95 37885.93 34163.86 33780.02 43784.32 383
USDC76.63 31276.73 31076.34 34983.46 35357.20 37280.02 29988.04 24752.14 42883.65 28391.25 21363.24 32286.65 32654.66 40094.11 20785.17 372
BH-w/o76.57 31376.07 31678.10 32286.88 27465.92 25577.63 34186.33 27965.69 31780.89 33579.95 41668.97 28690.74 22653.01 41185.25 39677.62 444
Patchmtry76.56 31477.46 29973.83 37179.37 40846.60 43882.41 25676.90 37573.81 18785.56 23292.38 16648.07 40283.98 37063.36 34295.31 16390.92 269
PVSNet_Blended76.49 31575.40 32279.76 29684.43 33263.41 27875.14 38090.44 18657.36 39675.43 39378.30 43069.11 28491.44 19760.68 36387.70 36884.42 382
miper_lstm_enhance76.45 31676.10 31577.51 33276.72 42760.97 32664.69 44685.04 30763.98 33783.20 29388.22 29856.67 36478.79 40473.22 24093.12 24392.78 189
lupinMVS76.37 31774.46 33182.09 24485.54 31269.26 21276.79 35580.77 35350.68 43976.23 38382.82 38958.69 35188.94 27769.85 27888.77 34788.07 333
cascas76.29 31874.81 32780.72 27884.47 33162.94 28473.89 39287.34 25855.94 40375.16 39876.53 44663.97 31791.16 20765.00 32790.97 30488.06 335
SD_040376.08 31976.77 30873.98 36987.08 26649.45 42783.62 21584.68 31763.31 33875.13 39987.47 32071.85 26684.56 36149.97 42387.86 36487.94 339
WB-MVS76.06 32080.01 27164.19 43489.96 18220.58 47772.18 40568.19 43383.21 6686.46 21093.49 12370.19 27878.97 40265.96 31590.46 32593.02 177
thres600view775.97 32175.35 32477.85 32987.01 26851.84 41480.45 29473.26 40375.20 16883.10 29586.31 34145.54 41789.05 27555.03 39892.24 27092.66 196
GA-MVS75.83 32274.61 32879.48 30281.87 37459.25 34873.42 39682.88 33268.68 27379.75 34981.80 40050.62 39389.46 26866.85 30785.64 39289.72 302
MVP-Stereo75.81 32373.51 34082.71 22989.35 19273.62 14180.06 29785.20 30260.30 37573.96 40587.94 30357.89 35889.45 26952.02 41574.87 45585.06 374
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs375.72 32475.20 32577.27 33575.01 44469.47 20978.93 31984.88 31246.67 44687.08 18987.84 31150.44 39571.62 42977.42 17288.53 35090.72 275
thres100view90075.45 32575.05 32676.66 34587.27 25351.88 41381.07 28373.26 40375.68 15983.25 29286.37 33845.54 41788.80 27951.98 41690.99 30189.31 309
ET-MVSNet_ETH3D75.28 32672.77 34982.81 22883.03 36868.11 22977.09 35176.51 37960.67 37277.60 37480.52 41138.04 44591.15 20870.78 26590.68 31889.17 316
thres40075.14 32774.23 33377.86 32886.24 29152.12 41079.24 31573.87 39673.34 19881.82 32084.60 37046.02 41088.80 27951.98 41690.99 30192.66 196
wuyk23d75.13 32879.30 27862.63 43775.56 43775.18 13380.89 28773.10 40575.06 17094.76 1695.32 4587.73 4552.85 46934.16 46797.11 9059.85 465
EU-MVSNet75.12 32974.43 33277.18 33683.11 36759.48 34585.71 15882.43 33739.76 46685.64 22988.76 28944.71 42987.88 30373.86 22485.88 39184.16 388
HyFIR lowres test75.12 32972.66 35182.50 23691.44 14465.19 26172.47 40387.31 25946.79 44580.29 34484.30 37252.70 38392.10 18151.88 42086.73 38090.22 291
CMPMVSbinary59.41 2075.12 32973.57 33879.77 29575.84 43667.22 23581.21 28182.18 33950.78 43776.50 37987.66 31555.20 37482.99 37662.17 35290.64 32389.09 320
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs474.92 33272.98 34780.73 27784.95 32371.71 17976.23 36777.59 36852.83 42277.73 37386.38 33756.35 36784.97 35757.72 38087.05 37585.51 369
tfpn200view974.86 33374.23 33376.74 34486.24 29152.12 41079.24 31573.87 39673.34 19881.82 32084.60 37046.02 41088.80 27951.98 41690.99 30189.31 309
1112_ss74.82 33473.74 33678.04 32489.57 18660.04 33776.49 36387.09 27154.31 41373.66 40879.80 41760.25 33986.76 32558.37 37484.15 41287.32 348
EGC-MVSNET74.79 33569.99 37989.19 6794.89 3887.00 1591.89 4186.28 2801.09 4752.23 47795.98 3081.87 12689.48 26679.76 13495.96 13391.10 262
ppachtmachnet_test74.73 33674.00 33576.90 34180.71 39256.89 37571.53 41178.42 36358.24 38779.32 35682.92 38857.91 35784.26 36765.60 32291.36 29389.56 304
Patchmatch-RL test74.48 33773.68 33776.89 34284.83 32566.54 24672.29 40469.16 43157.70 39286.76 19586.33 33945.79 41682.59 37769.63 28190.65 32281.54 424
PatchMatch-RL74.48 33773.22 34478.27 32087.70 24085.26 3875.92 37270.09 42464.34 33576.09 38681.25 40565.87 30378.07 40653.86 40383.82 41471.48 453
XXY-MVS74.44 33976.19 31469.21 40684.61 33052.43 40971.70 40877.18 37360.73 37180.60 33890.96 22675.44 20669.35 43756.13 38788.33 35485.86 365
test250674.12 34073.39 34176.28 35091.85 12644.20 44884.06 19848.20 47372.30 22481.90 31794.20 8927.22 47289.77 26364.81 32996.02 13094.87 78
reproduce_monomvs74.09 34173.23 34376.65 34676.52 42854.54 39177.50 34581.40 34865.85 31282.86 30086.67 33427.38 47084.53 36270.24 27490.66 32190.89 270
CR-MVSNet74.00 34273.04 34676.85 34379.58 40362.64 29082.58 24776.90 37550.50 44075.72 39092.38 16648.07 40284.07 36968.72 29582.91 42183.85 392
SSC-MVS3.273.90 34375.67 32068.61 41484.11 34141.28 45664.17 44872.83 40672.09 22779.08 35987.94 30370.31 27673.89 42255.99 38894.49 19490.67 280
Test_1112_low_res73.90 34373.08 34576.35 34890.35 17055.95 37873.40 39786.17 28250.70 43873.14 40985.94 34658.31 35385.90 34556.51 38483.22 41887.20 350
test20.0373.75 34574.59 33071.22 39281.11 38551.12 42070.15 42272.10 41370.42 24780.28 34691.50 20064.21 31374.72 42046.96 44194.58 19287.82 343
test_fmvs273.57 34672.80 34875.90 35472.74 45868.84 22277.07 35284.32 32145.14 45282.89 29884.22 37348.37 40070.36 43373.40 23687.03 37688.52 328
SCA73.32 34772.57 35375.58 35981.62 37855.86 38178.89 32171.37 41961.73 35574.93 40083.42 38260.46 33687.01 31658.11 37882.63 42683.88 389
baseline173.26 34873.54 33972.43 38584.92 32447.79 43379.89 30174.00 39465.93 31078.81 36186.28 34256.36 36681.63 38556.63 38379.04 44487.87 342
131473.22 34972.56 35475.20 36080.41 39757.84 36681.64 27285.36 29851.68 43173.10 41076.65 44561.45 33185.19 35563.54 34079.21 44282.59 409
MVS73.21 35072.59 35275.06 36280.97 38660.81 32881.64 27285.92 29146.03 45071.68 41877.54 43668.47 28789.77 26355.70 39185.39 39374.60 450
HY-MVS64.64 1873.03 35172.47 35574.71 36683.36 35854.19 39582.14 26681.96 34156.76 40269.57 43286.21 34360.03 34084.83 35949.58 42882.65 42485.11 373
thisisatest051573.00 35270.52 37180.46 28481.45 38059.90 34173.16 39974.31 39357.86 39176.08 38777.78 43337.60 44892.12 18065.00 32791.45 29289.35 308
EPNet_dtu72.87 35371.33 36577.49 33377.72 41760.55 33182.35 25775.79 38266.49 30758.39 46881.06 40653.68 37985.98 34053.55 40692.97 24885.95 363
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet72.62 35471.41 36476.28 35083.25 36260.34 33383.50 21979.02 36237.77 47076.33 38185.10 36149.60 39887.41 31270.54 27177.54 45081.08 431
CHOSEN 1792x268872.45 35570.56 37078.13 32190.02 18163.08 28368.72 42983.16 32942.99 46075.92 38885.46 35457.22 36285.18 35649.87 42681.67 42886.14 361
testgi72.36 35674.61 32865.59 42880.56 39542.82 45368.29 43173.35 40266.87 30481.84 31989.93 26772.08 26366.92 45146.05 44592.54 26087.01 352
thres20072.34 35771.55 36374.70 36783.48 35251.60 41575.02 38173.71 39970.14 25378.56 36480.57 41046.20 40888.20 29646.99 44089.29 33984.32 383
FPMVS72.29 35872.00 35773.14 37688.63 21685.00 4074.65 38567.39 43671.94 23077.80 37187.66 31550.48 39475.83 41449.95 42479.51 43858.58 467
FMVSNet572.10 35971.69 35973.32 37481.57 37953.02 40476.77 35678.37 36463.31 33876.37 38091.85 18636.68 44978.98 40147.87 43792.45 26287.95 338
our_test_371.85 36071.59 36072.62 38280.71 39253.78 39869.72 42571.71 41858.80 38478.03 36680.51 41256.61 36578.84 40362.20 35086.04 39085.23 371
PAPM71.77 36170.06 37776.92 34086.39 28253.97 39676.62 36086.62 27753.44 41763.97 45784.73 36857.79 35992.34 17339.65 45781.33 43284.45 381
ttmdpeth71.72 36270.67 36874.86 36373.08 45555.88 38077.41 34869.27 42955.86 40478.66 36293.77 11638.01 44675.39 41760.12 36689.87 33293.31 161
IB-MVS62.13 1971.64 36368.97 38979.66 29980.80 39162.26 30373.94 39176.90 37563.27 34068.63 43676.79 44333.83 45391.84 18859.28 37187.26 37084.88 375
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 36472.30 35669.62 40376.47 43052.70 40770.03 42380.97 35159.18 38179.36 35488.21 29960.50 33569.12 43858.33 37677.62 44987.04 351
testing371.53 36570.79 36773.77 37288.89 20841.86 45576.60 36259.12 46272.83 21280.97 33282.08 39719.80 47887.33 31465.12 32691.68 28792.13 232
test_vis3_rt71.42 36670.67 36873.64 37369.66 46570.46 19466.97 44189.73 21142.68 46288.20 15783.04 38443.77 43160.07 46365.35 32586.66 38190.39 289
Anonymous2023120671.38 36771.88 35869.88 40086.31 28854.37 39370.39 42074.62 38952.57 42476.73 37888.76 28959.94 34172.06 42644.35 44993.23 24083.23 403
test_vis1_n_192071.30 36871.58 36270.47 39577.58 41959.99 34074.25 38684.22 32251.06 43474.85 40179.10 42355.10 37568.83 44068.86 29279.20 44382.58 410
MIMVSNet71.09 36971.59 36069.57 40487.23 25650.07 42578.91 32071.83 41560.20 37871.26 41991.76 19355.08 37676.09 41241.06 45487.02 37782.54 412
test_fmvs1_n70.94 37070.41 37472.53 38473.92 44666.93 24375.99 37184.21 32343.31 45979.40 35379.39 42143.47 43268.55 44269.05 28984.91 40482.10 418
MS-PatchMatch70.93 37170.22 37573.06 37781.85 37562.50 29373.82 39377.90 36552.44 42575.92 38881.27 40455.67 37181.75 38355.37 39477.70 44874.94 449
pmmvs570.73 37270.07 37672.72 38077.03 42452.73 40674.14 38775.65 38550.36 44172.17 41685.37 35855.42 37380.67 39052.86 41287.59 36984.77 376
testing3-270.72 37370.97 36669.95 39988.93 20634.80 46969.85 42466.59 44378.42 12577.58 37585.55 35031.83 45982.08 38146.28 44293.73 22292.98 183
PatchT70.52 37472.76 35063.79 43679.38 40733.53 47077.63 34165.37 44773.61 19171.77 41792.79 15444.38 43075.65 41564.53 33485.37 39482.18 417
test_vis1_n70.29 37569.99 37971.20 39375.97 43566.50 24776.69 35880.81 35244.22 45575.43 39377.23 44050.00 39668.59 44166.71 31082.85 42378.52 443
N_pmnet70.20 37668.80 39174.38 36880.91 38784.81 4359.12 45976.45 38055.06 40875.31 39782.36 39455.74 37054.82 46847.02 43987.24 37183.52 396
tpmvs70.16 37769.56 38271.96 38874.71 44548.13 43079.63 30375.45 38765.02 33170.26 42781.88 39945.34 42285.68 35158.34 37575.39 45482.08 419
new-patchmatchnet70.10 37873.37 34260.29 44581.23 38416.95 48059.54 45774.62 38962.93 34280.97 33287.93 30562.83 32871.90 42755.24 39695.01 17692.00 237
YYNet170.06 37970.44 37268.90 40873.76 44853.42 40258.99 46067.20 43858.42 38687.10 18785.39 35759.82 34367.32 44859.79 36883.50 41785.96 362
MVStest170.05 38069.26 38372.41 38658.62 47755.59 38476.61 36165.58 44553.44 41789.28 13193.32 12722.91 47671.44 43174.08 22089.52 33690.21 295
MDA-MVSNet_test_wron70.05 38070.44 37268.88 40973.84 44753.47 40058.93 46167.28 43758.43 38587.09 18885.40 35659.80 34467.25 44959.66 36983.54 41685.92 364
CostFormer69.98 38268.68 39273.87 37077.14 42250.72 42279.26 31474.51 39151.94 43070.97 42284.75 36745.16 42587.49 31055.16 39779.23 44183.40 399
testing9169.94 38368.99 38872.80 37983.81 34745.89 44171.57 41073.64 40168.24 28070.77 42577.82 43234.37 45284.44 36453.64 40587.00 37888.07 333
baseline269.77 38466.89 40178.41 31679.51 40558.09 36276.23 36769.57 42757.50 39564.82 45577.45 43846.02 41088.44 29053.08 40877.83 44688.70 326
PatchmatchNetpermissive69.71 38568.83 39072.33 38777.66 41853.60 39979.29 31369.99 42557.66 39372.53 41382.93 38746.45 40780.08 39660.91 36272.09 45883.31 402
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs169.57 38669.05 38671.14 39469.15 46665.77 25773.98 39083.32 32742.83 46177.77 37278.27 43143.39 43568.50 44368.39 29984.38 41179.15 441
JIA-IIPM69.41 38766.64 40577.70 33073.19 45271.24 18575.67 37365.56 44670.42 24765.18 45192.97 14533.64 45583.06 37453.52 40769.61 46478.79 442
Syy-MVS69.40 38870.03 37867.49 41981.72 37638.94 46171.00 41361.99 45361.38 36170.81 42372.36 45761.37 33279.30 39964.50 33585.18 39784.22 385
testing9969.27 38968.15 39672.63 38183.29 36045.45 44371.15 41271.08 42067.34 29770.43 42677.77 43432.24 45884.35 36653.72 40486.33 38688.10 332
UnsupCasMVSNet_bld69.21 39069.68 38167.82 41779.42 40651.15 41967.82 43575.79 38254.15 41477.47 37685.36 35959.26 34770.64 43248.46 43479.35 44081.66 422
test_cas_vis1_n_192069.20 39169.12 38469.43 40573.68 44962.82 28770.38 42177.21 37246.18 44980.46 34378.95 42552.03 38565.53 45665.77 32177.45 45179.95 439
gg-mvs-nofinetune68.96 39269.11 38568.52 41576.12 43445.32 44483.59 21655.88 46786.68 3364.62 45697.01 1230.36 46383.97 37144.78 44882.94 42076.26 446
WBMVS68.76 39368.43 39369.75 40283.29 36040.30 45967.36 43772.21 41257.09 39977.05 37785.53 35233.68 45480.51 39248.79 43290.90 30688.45 329
WB-MVSnew68.72 39469.01 38767.85 41683.22 36443.98 44974.93 38265.98 44455.09 40773.83 40679.11 42265.63 30571.89 42838.21 46285.04 40087.69 344
tpm268.45 39566.83 40273.30 37578.93 41348.50 42979.76 30271.76 41647.50 44469.92 42983.60 37842.07 43888.40 29248.44 43579.51 43883.01 406
tpm67.95 39668.08 39767.55 41878.74 41443.53 45175.60 37467.10 44154.92 40972.23 41488.10 30042.87 43775.97 41352.21 41480.95 43683.15 404
WTY-MVS67.91 39768.35 39466.58 42480.82 39048.12 43165.96 44372.60 40753.67 41671.20 42081.68 40258.97 34969.06 43948.57 43381.67 42882.55 411
testing1167.38 39865.93 40671.73 39083.37 35746.60 43870.95 41569.40 42862.47 34766.14 44476.66 44431.22 46084.10 36849.10 43084.10 41384.49 379
test-LLR67.21 39966.74 40368.63 41276.45 43155.21 38767.89 43267.14 43962.43 35065.08 45272.39 45543.41 43369.37 43561.00 36084.89 40581.31 426
testing22266.93 40065.30 41371.81 38983.38 35645.83 44272.06 40667.50 43564.12 33669.68 43176.37 44727.34 47183.00 37538.88 45888.38 35386.62 357
sss66.92 40167.26 39965.90 42677.23 42151.10 42164.79 44571.72 41752.12 42970.13 42880.18 41457.96 35665.36 45750.21 42281.01 43481.25 428
KD-MVS_2432*160066.87 40265.81 40970.04 39767.50 46747.49 43462.56 45179.16 35961.21 36677.98 36780.61 40825.29 47482.48 37853.02 40984.92 40280.16 437
miper_refine_blended66.87 40265.81 40970.04 39767.50 46747.49 43462.56 45179.16 35961.21 36677.98 36780.61 40825.29 47482.48 37853.02 40984.92 40280.16 437
dmvs_re66.81 40466.98 40066.28 42576.87 42558.68 36071.66 40972.24 41060.29 37669.52 43373.53 45452.38 38464.40 45944.90 44781.44 43175.76 447
tpm cat166.76 40565.21 41471.42 39177.09 42350.62 42378.01 33373.68 40044.89 45368.64 43579.00 42445.51 41982.42 38049.91 42570.15 46181.23 430
UWE-MVS66.43 40665.56 41269.05 40784.15 34040.98 45773.06 40064.71 44954.84 41076.18 38579.62 42029.21 46580.50 39338.54 46189.75 33385.66 367
PVSNet58.17 2166.41 40765.63 41168.75 41081.96 37349.88 42662.19 45372.51 40951.03 43568.04 43875.34 45150.84 39174.77 41845.82 44682.96 41981.60 423
tpmrst66.28 40866.69 40465.05 43272.82 45739.33 46078.20 33170.69 42353.16 42067.88 43980.36 41348.18 40174.75 41958.13 37770.79 46081.08 431
Patchmatch-test65.91 40967.38 39861.48 44275.51 43843.21 45268.84 42863.79 45162.48 34672.80 41283.42 38244.89 42859.52 46548.27 43686.45 38381.70 421
ADS-MVSNet265.87 41063.64 41972.55 38373.16 45356.92 37467.10 43974.81 38849.74 44266.04 44682.97 38546.71 40577.26 40942.29 45169.96 46283.46 397
myMVS_eth3d2865.83 41165.85 40765.78 42783.42 35535.71 46767.29 43868.01 43467.58 29469.80 43077.72 43532.29 45774.30 42137.49 46389.06 34387.32 348
test_vis1_rt65.64 41264.09 41670.31 39666.09 47170.20 19861.16 45481.60 34638.65 46772.87 41169.66 46052.84 38160.04 46456.16 38677.77 44780.68 435
mvsany_test365.48 41362.97 42273.03 37869.99 46476.17 12464.83 44443.71 47543.68 45780.25 34787.05 33152.83 38263.09 46251.92 41972.44 45779.84 440
test-mter65.00 41463.79 41868.63 41276.45 43155.21 38767.89 43267.14 43950.98 43665.08 45272.39 45528.27 46869.37 43561.00 36084.89 40581.31 426
ETVMVS64.67 41563.34 42168.64 41183.44 35441.89 45469.56 42761.70 45861.33 36368.74 43475.76 44928.76 46679.35 39834.65 46686.16 38984.67 378
myMVS_eth3d64.66 41663.89 41766.97 42281.72 37637.39 46471.00 41361.99 45361.38 36170.81 42372.36 45720.96 47779.30 39949.59 42785.18 39784.22 385
test0.0.03 164.66 41664.36 41565.57 42975.03 44346.89 43764.69 44661.58 45962.43 35071.18 42177.54 43643.41 43368.47 44440.75 45682.65 42481.35 425
UBG64.34 41863.35 42067.30 42083.50 35140.53 45867.46 43665.02 44854.77 41167.54 44274.47 45332.99 45678.50 40540.82 45583.58 41582.88 407
test_f64.31 41965.85 40759.67 44666.54 47062.24 30557.76 46370.96 42140.13 46484.36 26482.09 39646.93 40451.67 47061.99 35381.89 42765.12 461
pmmvs362.47 42060.02 43369.80 40171.58 46164.00 27370.52 41958.44 46539.77 46566.05 44575.84 44827.10 47372.28 42546.15 44484.77 40973.11 451
EPMVS62.47 42062.63 42462.01 43870.63 46338.74 46274.76 38352.86 46953.91 41567.71 44180.01 41539.40 44266.60 45255.54 39368.81 46680.68 435
ADS-MVSNet61.90 42262.19 42661.03 44373.16 45336.42 46667.10 43961.75 45649.74 44266.04 44682.97 38546.71 40563.21 46042.29 45169.96 46283.46 397
PMMVS61.65 42360.38 43065.47 43065.40 47469.26 21263.97 44961.73 45736.80 47160.11 46368.43 46259.42 34566.35 45348.97 43178.57 44560.81 464
E-PMN61.59 42461.62 42761.49 44166.81 46955.40 38553.77 46660.34 46166.80 30558.90 46665.50 46540.48 44166.12 45455.72 39086.25 38762.95 463
TESTMET0.1,161.29 42560.32 43164.19 43472.06 45951.30 41767.89 43262.09 45245.27 45160.65 46269.01 46127.93 46964.74 45856.31 38581.65 43076.53 445
MVS-HIRNet61.16 42662.92 42355.87 44979.09 41035.34 46871.83 40757.98 46646.56 44759.05 46591.14 21749.95 39776.43 41138.74 45971.92 45955.84 468
EMVS61.10 42760.81 42961.99 43965.96 47255.86 38153.10 46758.97 46467.06 30256.89 47063.33 46640.98 43967.03 45054.79 39986.18 38863.08 462
DSMNet-mixed60.98 42861.61 42859.09 44872.88 45645.05 44674.70 38446.61 47426.20 47265.34 45090.32 25655.46 37263.12 46141.72 45381.30 43369.09 457
dp60.70 42960.29 43261.92 44072.04 46038.67 46370.83 41764.08 45051.28 43360.75 46177.28 43936.59 45071.58 43047.41 43862.34 46875.52 448
dmvs_testset60.59 43062.54 42554.72 45177.26 42027.74 47474.05 38961.00 46060.48 37365.62 44967.03 46455.93 36968.23 44632.07 47069.46 46568.17 458
CHOSEN 280x42059.08 43156.52 43766.76 42376.51 42964.39 26949.62 46859.00 46343.86 45655.66 47168.41 46335.55 45168.21 44743.25 45076.78 45367.69 459
mvsany_test158.48 43256.47 43864.50 43365.90 47368.21 22856.95 46442.11 47638.30 46865.69 44877.19 44256.96 36359.35 46646.16 44358.96 46965.93 460
UWE-MVS-2858.44 43357.71 43560.65 44473.58 45031.23 47169.68 42648.80 47253.12 42161.79 45978.83 42630.98 46168.40 44521.58 47380.99 43582.33 416
PVSNet_051.08 2256.10 43454.97 43959.48 44775.12 44253.28 40355.16 46561.89 45544.30 45459.16 46462.48 46754.22 37765.91 45535.40 46547.01 47059.25 466
new_pmnet55.69 43557.66 43649.76 45275.47 43930.59 47259.56 45651.45 47043.62 45862.49 45875.48 45040.96 44049.15 47237.39 46472.52 45669.55 456
PMMVS255.64 43659.27 43444.74 45364.30 47512.32 48140.60 46949.79 47153.19 41965.06 45484.81 36653.60 38049.76 47132.68 46989.41 33872.15 452
MVEpermissive40.22 2351.82 43750.47 44055.87 44962.66 47651.91 41231.61 47139.28 47740.65 46350.76 47274.98 45256.24 36844.67 47333.94 46864.11 46771.04 455
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai41.90 43842.65 44139.67 45470.86 46221.11 47661.01 45521.42 48157.36 39657.97 46950.06 47016.40 47958.73 46721.03 47427.69 47439.17 470
kuosan30.83 43932.17 44226.83 45653.36 47819.02 47957.90 46220.44 48238.29 46938.01 47337.82 47215.18 48033.45 4757.74 47620.76 47528.03 471
test_method30.46 44029.60 44333.06 45517.99 4803.84 48313.62 47273.92 3952.79 47418.29 47653.41 46928.53 46743.25 47422.56 47135.27 47252.11 469
cdsmvs_eth3d_5k20.81 44127.75 4440.00 4610.00 4840.00 4860.00 47385.44 2970.00 4790.00 48082.82 38981.46 1320.00 4800.00 4790.00 4780.00 476
tmp_tt20.25 44224.50 4457.49 4584.47 4818.70 48234.17 47025.16 4791.00 47632.43 47518.49 47339.37 4439.21 47721.64 47243.75 4714.57 473
ab-mvs-re6.65 4438.87 4460.00 4610.00 4840.00 4860.00 4730.00 4850.00 4790.00 48079.80 4170.00 4830.00 4800.00 4790.00 4780.00 476
pcd_1.5k_mvsjas6.41 4448.55 4470.00 4610.00 4840.00 4860.00 4730.00 4850.00 4790.00 4800.00 47976.94 1900.00 4800.00 4790.00 4780.00 476
test1236.27 4458.08 4480.84 4591.11 4830.57 48462.90 4500.82 4830.54 4771.07 4792.75 4781.26 4810.30 4781.04 4771.26 4771.66 474
testmvs5.91 4467.65 4490.72 4601.20 4820.37 48559.14 4580.67 4840.49 4781.11 4782.76 4770.94 4820.24 4791.02 4781.47 4761.55 475
mmdepth0.00 4470.00 4500.00 4610.00 4840.00 4860.00 4730.00 4850.00 4790.00 4800.00 4790.00 4830.00 4800.00 4790.00 4780.00 476
monomultidepth0.00 4470.00 4500.00 4610.00 4840.00 4860.00 4730.00 4850.00 4790.00 4800.00 4790.00 4830.00 4800.00 4790.00 4780.00 476
test_blank0.00 4470.00 4500.00 4610.00 4840.00 4860.00 4730.00 4850.00 4790.00 4800.00 4790.00 4830.00 4800.00 4790.00 4780.00 476
uanet_test0.00 4470.00 4500.00 4610.00 4840.00 4860.00 4730.00 4850.00 4790.00 4800.00 4790.00 4830.00 4800.00 4790.00 4780.00 476
DCPMVS0.00 4470.00 4500.00 4610.00 4840.00 4860.00 4730.00 4850.00 4790.00 4800.00 4790.00 4830.00 4800.00 4790.00 4780.00 476
sosnet-low-res0.00 4470.00 4500.00 4610.00 4840.00 4860.00 4730.00 4850.00 4790.00 4800.00 4790.00 4830.00 4800.00 4790.00 4780.00 476
sosnet0.00 4470.00 4500.00 4610.00 4840.00 4860.00 4730.00 4850.00 4790.00 4800.00 4790.00 4830.00 4800.00 4790.00 4780.00 476
uncertanet0.00 4470.00 4500.00 4610.00 4840.00 4860.00 4730.00 4850.00 4790.00 4800.00 4790.00 4830.00 4800.00 4790.00 4780.00 476
Regformer0.00 4470.00 4500.00 4610.00 4840.00 4860.00 4730.00 4850.00 4790.00 4800.00 4790.00 4830.00 4800.00 4790.00 4780.00 476
uanet0.00 4470.00 4500.00 4610.00 4840.00 4860.00 4730.00 4850.00 4790.00 4800.00 4790.00 4830.00 4800.00 4790.00 4780.00 476
MED-MVS test88.50 8094.38 4876.12 12692.12 3393.85 5377.53 13993.24 4393.18 13195.85 2484.99 7597.69 6593.54 155
TestfortrainingZip92.12 33
WAC-MVS37.39 46452.61 413
FOURS196.08 1287.41 1496.19 295.83 592.95 396.57 3
MSC_two_6792asdad88.81 7391.55 13877.99 9791.01 16996.05 987.45 2998.17 3792.40 212
PC_three_145258.96 38390.06 10691.33 20880.66 14293.03 15575.78 19595.94 13692.48 206
No_MVS88.81 7391.55 13877.99 9791.01 16996.05 987.45 2998.17 3792.40 212
test_one_060193.85 6673.27 14794.11 3986.57 3493.47 4294.64 6888.42 29
eth-test20.00 484
eth-test0.00 484
ZD-MVS92.22 11280.48 7191.85 13771.22 23990.38 10192.98 14286.06 6796.11 781.99 11396.75 100
RE-MVS-def92.61 994.13 5988.95 692.87 1394.16 3388.75 1893.79 3394.43 7690.64 1187.16 3897.60 7492.73 190
IU-MVS94.18 5472.64 15790.82 17456.98 40089.67 11985.78 6497.92 5293.28 162
OPU-MVS88.27 8791.89 12477.83 10090.47 5991.22 21481.12 13694.68 8174.48 20995.35 15992.29 222
test_241102_TWO93.71 5983.77 5893.49 4094.27 8389.27 2495.84 2686.03 5797.82 5792.04 235
test_241102_ONE94.18 5472.65 15593.69 6183.62 6194.11 2793.78 11490.28 1595.50 51
9.1489.29 6591.84 12888.80 9895.32 1375.14 16991.07 8692.89 14887.27 4993.78 12083.69 9197.55 77
save fliter93.75 6777.44 10686.31 14489.72 21270.80 244
test_0728_THIRD85.33 4293.75 3594.65 6587.44 4895.78 3487.41 3198.21 3492.98 183
test_0728_SECOND86.79 11194.25 5272.45 16590.54 5694.10 4095.88 1886.42 4797.97 4992.02 236
test072694.16 5772.56 16190.63 5393.90 4983.61 6293.75 3594.49 7389.76 19
GSMVS83.88 389
test_part293.86 6577.77 10192.84 54
sam_mvs146.11 40983.88 389
sam_mvs45.92 414
ambc82.98 21990.55 16764.86 26388.20 10789.15 22589.40 12893.96 10571.67 27091.38 20178.83 14896.55 10592.71 193
MTGPAbinary91.81 141
test_post178.85 3233.13 47545.19 42480.13 39558.11 378
test_post3.10 47645.43 42077.22 410
patchmatchnet-post81.71 40145.93 41387.01 316
GG-mvs-BLEND67.16 42173.36 45146.54 44084.15 19655.04 46858.64 46761.95 46829.93 46483.87 37238.71 46076.92 45271.07 454
MTMP90.66 5233.14 478
gm-plane-assit75.42 44044.97 44752.17 42672.36 45787.90 30254.10 402
test9_res80.83 12396.45 11190.57 283
TEST992.34 10779.70 8083.94 20290.32 19265.41 32384.49 26090.97 22482.03 12193.63 126
test_892.09 11678.87 8883.82 20790.31 19465.79 31384.36 26490.96 22681.93 12393.44 140
agg_prior279.68 13696.16 12390.22 291
agg_prior91.58 13677.69 10390.30 19584.32 26693.18 148
TestCases89.68 5691.59 13383.40 5295.44 1179.47 10788.00 16293.03 14082.66 10291.47 19570.81 26396.14 12494.16 114
test_prior478.97 8784.59 185
test_prior283.37 22375.43 16584.58 25791.57 19881.92 12579.54 14096.97 93
test_prior86.32 11990.59 16671.99 17392.85 10394.17 10592.80 188
旧先验281.73 27056.88 40186.54 20884.90 35872.81 247
新几何281.72 271
新几何182.95 22193.96 6378.56 9180.24 35555.45 40683.93 27791.08 22071.19 27288.33 29465.84 31993.07 24481.95 420
旧先验191.97 12071.77 17481.78 34391.84 18773.92 23393.65 22583.61 395
无先验82.81 24285.62 29558.09 38991.41 20067.95 30384.48 380
原ACMM282.26 262
原ACMM184.60 16892.81 9774.01 13991.50 14962.59 34482.73 30290.67 24376.53 19994.25 9769.24 28495.69 15185.55 368
test22293.31 8076.54 11679.38 31277.79 36652.59 42382.36 30790.84 23466.83 29691.69 28681.25 428
testdata286.43 33163.52 341
segment_acmp81.94 122
testdata79.54 30192.87 9172.34 16680.14 35659.91 37985.47 23491.75 19467.96 29085.24 35468.57 29892.18 27381.06 433
testdata179.62 30473.95 186
test1286.57 11490.74 16272.63 15990.69 17782.76 30179.20 15594.80 7895.32 16192.27 224
plane_prior793.45 7477.31 109
plane_prior692.61 9876.54 11674.84 215
plane_prior593.61 6495.22 6280.78 12495.83 14494.46 96
plane_prior492.95 146
plane_prior376.85 11477.79 13486.55 202
plane_prior289.45 8679.44 109
plane_prior192.83 95
plane_prior76.42 11987.15 12675.94 15695.03 173
n20.00 485
nn0.00 485
door-mid74.45 392
lessismore_v085.95 13091.10 15570.99 18970.91 42291.79 7494.42 7861.76 33092.93 15879.52 14193.03 24593.93 124
LGP-MVS_train90.82 3794.75 4181.69 6394.27 2582.35 7593.67 3894.82 6091.18 595.52 4785.36 6798.73 795.23 66
test1191.46 150
door72.57 408
HQP5-MVS70.66 191
HQP-NCC91.19 15084.77 17673.30 20080.55 340
ACMP_Plane91.19 15084.77 17673.30 20080.55 340
BP-MVS77.30 173
HQP4-MVS80.56 33994.61 8593.56 152
HQP3-MVS92.68 10994.47 195
HQP2-MVS72.10 261
NP-MVS91.95 12174.55 13690.17 263
MDTV_nov1_ep13_2view27.60 47570.76 41846.47 44861.27 46045.20 42349.18 42983.75 394
MDTV_nov1_ep1368.29 39578.03 41543.87 45074.12 38872.22 41152.17 42667.02 44385.54 35145.36 42180.85 38955.73 38984.42 410
ACMMP++_ref95.74 150
ACMMP++97.35 83
Test By Simon79.09 157
ITE_SJBPF90.11 4990.72 16384.97 4190.30 19581.56 8390.02 10891.20 21682.40 10790.81 22373.58 23394.66 19094.56 90
DeepMVS_CXcopyleft24.13 45732.95 47929.49 47321.63 48012.07 47337.95 47445.07 47130.84 46219.21 47617.94 47533.06 47323.69 472