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.
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MM89.16 689.23 788.97 490.79 9573.65 1092.66 2391.17 13086.57 187.39 4994.97 1971.70 5597.68 192.19 195.63 2895.57 1
casdiffmvs_mvgpermissive85.99 5186.09 5485.70 7487.65 21267.22 16588.69 13293.04 4179.64 2085.33 6792.54 9573.30 3594.50 11483.49 7491.14 9895.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
dcpmvs_285.63 6186.15 5284.06 14091.71 7864.94 21486.47 20991.87 10673.63 15386.60 5893.02 8476.57 1591.87 23483.36 7592.15 8195.35 3
casdiffmvspermissive85.11 7385.14 7385.01 9487.20 22765.77 19287.75 16692.83 6077.84 4084.36 8892.38 9772.15 4893.93 13881.27 9990.48 10895.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
3Dnovator+77.84 485.48 6484.47 8288.51 791.08 8673.49 1693.18 1193.78 1880.79 876.66 21893.37 7460.40 20596.75 2677.20 13893.73 6495.29 5
BP-MVS184.32 8183.71 9086.17 6187.84 20167.85 14289.38 9989.64 18077.73 4283.98 9592.12 10256.89 23095.43 7084.03 7191.75 8895.24 6
MVS_030487.69 2087.55 2488.12 1389.45 13071.76 5191.47 4989.54 18382.14 386.65 5794.28 3868.28 10197.46 690.81 595.31 3495.15 7
CS-MVS86.69 3986.95 3785.90 7190.76 9667.57 15192.83 1793.30 3279.67 1884.57 8492.27 9871.47 5895.02 9384.24 6893.46 6795.13 8
TSAR-MVS + MP.88.02 1788.11 1687.72 3093.68 4372.13 4691.41 5092.35 8274.62 12888.90 2493.85 6275.75 2096.00 5487.80 3694.63 4895.04 9
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
baseline84.93 7684.98 7484.80 10487.30 22565.39 20187.30 18092.88 5777.62 4484.04 9492.26 9971.81 5293.96 13281.31 9790.30 11195.03 10
DVP-MVS++90.23 191.01 187.89 2494.34 2771.25 5995.06 194.23 378.38 3592.78 495.74 682.45 397.49 489.42 1696.68 294.95 11
PC_three_145268.21 27392.02 1294.00 5482.09 595.98 5684.58 6296.68 294.95 11
IS-MVSNet83.15 10782.81 10584.18 13089.94 11663.30 25291.59 4388.46 22579.04 2779.49 15892.16 10065.10 13594.28 11967.71 23591.86 8794.95 11
SteuartSystems-ACMMP88.72 1188.86 1188.32 992.14 7272.96 2593.73 593.67 2080.19 1288.10 3494.80 2173.76 3397.11 1587.51 3995.82 2194.90 14
Skip Steuart: Steuart Systems R&D Blog.
SED-MVS90.08 290.85 287.77 2695.30 270.98 6693.57 794.06 1077.24 5793.10 195.72 882.99 197.44 789.07 2196.63 494.88 15
OPU-MVS89.06 394.62 1575.42 493.57 794.02 5282.45 396.87 2083.77 7396.48 894.88 15
SMA-MVScopyleft89.08 889.23 788.61 694.25 3173.73 992.40 2493.63 2174.77 12492.29 795.97 274.28 2997.24 1388.58 2996.91 194.87 17
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
test250677.30 24376.49 24079.74 26990.08 10952.02 38787.86 16563.10 42974.88 12080.16 15192.79 9138.29 39392.35 21568.74 22892.50 7894.86 18
ECVR-MVScopyleft79.61 18079.26 17380.67 24990.08 10954.69 37087.89 16377.44 38274.88 12080.27 14892.79 9148.96 31992.45 20968.55 22992.50 7894.86 18
IU-MVS95.30 271.25 5992.95 5566.81 28492.39 688.94 2496.63 494.85 20
test111179.43 18779.18 17680.15 26189.99 11453.31 38387.33 17977.05 38675.04 11480.23 15092.77 9348.97 31892.33 21768.87 22692.40 8094.81 21
SF-MVS88.46 1288.74 1287.64 3592.78 6471.95 4992.40 2494.74 275.71 9689.16 2195.10 1675.65 2196.19 4687.07 4296.01 1794.79 22
balanced_conf0386.78 3786.99 3586.15 6391.24 8367.61 14990.51 6292.90 5677.26 5687.44 4891.63 11571.27 6296.06 4985.62 5195.01 3794.78 23
sasdasda85.91 5585.87 5886.04 6789.84 11869.44 9890.45 6893.00 4676.70 7688.01 3791.23 12773.28 3693.91 13981.50 9588.80 13794.77 24
SPE-MVS-test86.29 4886.48 4385.71 7391.02 8867.21 16692.36 2993.78 1878.97 3083.51 10591.20 13070.65 7195.15 8481.96 9294.89 4294.77 24
canonicalmvs85.91 5585.87 5886.04 6789.84 11869.44 9890.45 6893.00 4676.70 7688.01 3791.23 12773.28 3693.91 13981.50 9588.80 13794.77 24
GDP-MVS83.52 9782.64 10886.16 6288.14 18568.45 12589.13 11192.69 6572.82 17683.71 10091.86 10855.69 23795.35 7980.03 11089.74 12394.69 27
test_0728_THIRD78.38 3592.12 995.78 481.46 797.40 989.42 1696.57 794.67 28
MSP-MVS89.51 489.91 588.30 1094.28 3073.46 1792.90 1694.11 680.27 1091.35 1494.16 4578.35 1396.77 2489.59 1494.22 6094.67 28
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
RRT-MVS82.60 11882.10 11784.10 13287.98 19562.94 26387.45 17591.27 12677.42 5379.85 15390.28 15156.62 23394.70 10979.87 11388.15 15094.67 28
MGCFI-Net85.06 7585.51 6583.70 15789.42 13163.01 25889.43 9492.62 7376.43 8087.53 4591.34 12572.82 4493.42 16581.28 9888.74 14094.66 31
alignmvs85.48 6485.32 7085.96 7089.51 12769.47 9589.74 8392.47 7676.17 8987.73 4491.46 12270.32 7393.78 14581.51 9488.95 13494.63 32
MP-MVS-pluss87.67 2187.72 2087.54 3693.64 4472.04 4889.80 8193.50 2575.17 11386.34 5995.29 1570.86 6796.00 5488.78 2796.04 1694.58 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepPCF-MVS80.84 188.10 1388.56 1386.73 5392.24 7169.03 10389.57 9093.39 3077.53 5089.79 1994.12 4778.98 1296.58 3585.66 4995.72 2494.58 33
VDD-MVS83.01 11282.36 11384.96 9691.02 8866.40 17688.91 11888.11 22877.57 4684.39 8793.29 7652.19 27193.91 13977.05 14188.70 14194.57 35
KinetiMVS83.31 10582.61 10985.39 8287.08 23167.56 15288.06 15591.65 11477.80 4182.21 11991.79 10957.27 22594.07 13077.77 13289.89 12194.56 36
VDDNet81.52 13780.67 13884.05 14390.44 10164.13 23289.73 8485.91 27771.11 20583.18 10793.48 6950.54 29793.49 15973.40 18188.25 14894.54 37
MVSMamba_PlusPlus85.99 5185.96 5686.05 6691.09 8567.64 14889.63 8892.65 7072.89 17584.64 8191.71 11171.85 5196.03 5084.77 6094.45 5494.49 38
APDe-MVScopyleft89.15 789.63 687.73 2894.49 1871.69 5293.83 493.96 1375.70 9891.06 1696.03 176.84 1497.03 1789.09 1895.65 2794.47 39
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSC_two_6792asdad89.16 194.34 2775.53 292.99 4997.53 289.67 1296.44 994.41 40
No_MVS89.16 194.34 2775.53 292.99 4997.53 289.67 1296.44 994.41 40
MCST-MVS87.37 2987.25 3087.73 2894.53 1772.46 3889.82 7993.82 1673.07 17084.86 7692.89 8676.22 1796.33 4184.89 5795.13 3694.40 42
fmvsm_s_conf0.5_n_886.56 4287.17 3384.73 10687.76 20865.62 19589.20 10492.21 8979.94 1689.74 2094.86 2068.63 9694.20 12490.83 491.39 9494.38 43
CANet86.45 4386.10 5387.51 3790.09 10870.94 7089.70 8592.59 7481.78 481.32 13291.43 12370.34 7297.23 1484.26 6693.36 6894.37 44
PHI-MVS86.43 4486.17 5187.24 4190.88 9270.96 6892.27 3294.07 972.45 17885.22 6991.90 10569.47 8396.42 4083.28 7795.94 1994.35 45
CNVR-MVS88.93 1089.13 1088.33 894.77 1273.82 890.51 6293.00 4680.90 788.06 3594.06 5076.43 1696.84 2188.48 3295.99 1894.34 46
ZNCC-MVS87.94 1887.85 1988.20 1294.39 2473.33 1993.03 1493.81 1776.81 7085.24 6894.32 3771.76 5396.93 1985.53 5295.79 2294.32 47
HPM-MVScopyleft87.11 3386.98 3687.50 3893.88 3972.16 4592.19 3393.33 3176.07 9183.81 9993.95 5969.77 8096.01 5385.15 5394.66 4794.32 47
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CDPH-MVS85.76 5985.29 7287.17 4393.49 4771.08 6488.58 13692.42 8068.32 27284.61 8293.48 6972.32 4696.15 4879.00 11795.43 3094.28 49
test_241102_TWO94.06 1077.24 5792.78 495.72 881.26 897.44 789.07 2196.58 694.26 50
test_0728_SECOND87.71 3295.34 171.43 5893.49 994.23 397.49 489.08 1996.41 1294.21 51
fmvsm_l_conf0.5_n_386.02 4986.32 4585.14 8887.20 22768.54 12389.57 9090.44 15075.31 10787.49 4694.39 3572.86 4292.72 19789.04 2390.56 10794.16 52
DeepC-MVS_fast79.65 386.91 3686.62 4287.76 2793.52 4672.37 4191.26 5193.04 4176.62 7884.22 8993.36 7571.44 5996.76 2580.82 10395.33 3394.16 52
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet83.40 10183.02 10184.57 10990.13 10764.47 22592.32 3090.73 14274.45 13279.35 16091.10 13369.05 9195.12 8572.78 18887.22 16294.13 54
NCCC88.06 1488.01 1888.24 1194.41 2273.62 1191.22 5492.83 6081.50 585.79 6393.47 7173.02 4197.00 1884.90 5594.94 4094.10 55
ACMMP_NAP88.05 1688.08 1787.94 1993.70 4173.05 2290.86 5793.59 2376.27 8888.14 3395.09 1771.06 6596.67 2987.67 3796.37 1494.09 56
XVS87.18 3286.91 3988.00 1794.42 2073.33 1992.78 1892.99 4979.14 2383.67 10294.17 4467.45 10996.60 3383.06 7894.50 5194.07 57
X-MVStestdata80.37 16977.83 20688.00 1794.42 2073.33 1992.78 1892.99 4979.14 2383.67 10212.47 44367.45 10996.60 3383.06 7894.50 5194.07 57
region2R87.42 2787.20 3288.09 1494.63 1473.55 1393.03 1493.12 4076.73 7584.45 8594.52 2569.09 8896.70 2784.37 6594.83 4594.03 59
fmvsm_s_conf0.5_n_485.39 6885.75 6184.30 12186.70 24065.83 18888.77 12689.78 17375.46 10288.35 2893.73 6569.19 8793.06 18691.30 288.44 14694.02 60
ACMMPR87.44 2587.23 3188.08 1594.64 1373.59 1293.04 1293.20 3476.78 7284.66 8094.52 2568.81 9496.65 3084.53 6394.90 4194.00 61
fmvsm_s_conf0.1_n_283.80 8883.79 8983.83 15385.62 26364.94 21487.03 18786.62 26674.32 13487.97 3994.33 3660.67 19792.60 20089.72 1187.79 15393.96 62
test_fmvsmconf_n85.92 5486.04 5585.57 7885.03 28269.51 9389.62 8990.58 14573.42 16187.75 4294.02 5272.85 4393.24 17090.37 690.75 10493.96 62
DPE-MVScopyleft89.48 589.98 488.01 1694.80 1172.69 3191.59 4394.10 875.90 9492.29 795.66 1081.67 697.38 1187.44 4196.34 1593.95 64
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_fmvsm_n_192085.29 7085.34 6885.13 9186.12 25269.93 8688.65 13490.78 14169.97 23388.27 3093.98 5771.39 6091.54 24888.49 3190.45 10993.91 65
test_prior86.33 5792.61 6869.59 9192.97 5495.48 6793.91 65
GST-MVS87.42 2787.26 2987.89 2494.12 3672.97 2492.39 2693.43 2876.89 6884.68 7793.99 5670.67 7096.82 2284.18 7095.01 3793.90 67
test_fmvsmconf0.1_n85.61 6285.65 6285.50 7982.99 33169.39 10089.65 8690.29 15973.31 16487.77 4194.15 4671.72 5493.23 17190.31 790.67 10693.89 68
Anonymous20240521178.25 21677.01 22681.99 21591.03 8760.67 29284.77 25483.90 30370.65 21880.00 15291.20 13041.08 37891.43 25565.21 25785.26 19193.85 69
LFMVS81.82 12881.23 12983.57 16291.89 7663.43 25089.84 7881.85 33677.04 6583.21 10693.10 7952.26 27093.43 16471.98 19489.95 11993.85 69
fmvsm_s_conf0.5_n_284.04 8484.11 8583.81 15586.17 25065.00 21286.96 19087.28 25074.35 13388.25 3194.23 4261.82 17392.60 20089.85 988.09 15193.84 71
Effi-MVS+83.62 9583.08 9985.24 8688.38 17667.45 15488.89 11989.15 20075.50 10182.27 11788.28 20769.61 8294.45 11677.81 13187.84 15293.84 71
Anonymous2024052980.19 17378.89 18184.10 13290.60 9764.75 21988.95 11790.90 13765.97 30180.59 14491.17 13249.97 30393.73 15169.16 22382.70 23693.81 73
MVS_Test83.15 10783.06 10083.41 16786.86 23463.21 25486.11 22192.00 9874.31 13582.87 11189.44 17970.03 7693.21 17377.39 13788.50 14593.81 73
ElysianMVS81.53 13580.16 15085.62 7585.51 26668.25 13188.84 12392.19 9171.31 19980.50 14589.83 16146.89 33094.82 10176.85 14389.57 12593.80 75
StellarMVS81.53 13580.16 15085.62 7585.51 26668.25 13188.84 12392.19 9171.31 19980.50 14589.83 16146.89 33094.82 10176.85 14389.57 12593.80 75
test_fmvsmconf0.01_n84.73 7984.52 8185.34 8380.25 37269.03 10389.47 9289.65 17973.24 16886.98 5494.27 3966.62 11693.23 17190.26 889.95 11993.78 77
GeoE81.71 13081.01 13483.80 15689.51 12764.45 22688.97 11688.73 22071.27 20278.63 17289.76 16466.32 12293.20 17669.89 21586.02 18393.74 78
diffmvspermissive82.10 12181.88 12382.76 20283.00 32963.78 24083.68 28089.76 17572.94 17382.02 12289.85 16065.96 12990.79 27382.38 9087.30 16193.71 79
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HFP-MVS87.58 2287.47 2687.94 1994.58 1673.54 1593.04 1293.24 3376.78 7284.91 7394.44 3270.78 6896.61 3284.53 6394.89 4293.66 80
VNet82.21 12082.41 11181.62 22190.82 9360.93 28784.47 26389.78 17376.36 8684.07 9391.88 10664.71 13990.26 28070.68 20688.89 13593.66 80
PGM-MVS86.68 4086.27 4787.90 2294.22 3373.38 1890.22 7393.04 4175.53 10083.86 9794.42 3367.87 10696.64 3182.70 8894.57 5093.66 80
DELS-MVS85.41 6785.30 7185.77 7288.49 17067.93 14185.52 24193.44 2778.70 3183.63 10489.03 18674.57 2495.71 6180.26 10994.04 6193.66 80
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
SD-MVS88.06 1488.50 1486.71 5492.60 6972.71 2991.81 4193.19 3577.87 3990.32 1794.00 5474.83 2393.78 14587.63 3894.27 5993.65 84
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
DeepC-MVS79.81 287.08 3586.88 4087.69 3391.16 8472.32 4390.31 7193.94 1477.12 6282.82 11394.23 4272.13 4997.09 1684.83 5895.37 3193.65 84
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
patch_mono-283.65 9284.54 7980.99 24190.06 11365.83 18884.21 27288.74 21971.60 19485.01 7092.44 9674.51 2583.50 36682.15 9192.15 8193.64 86
EIA-MVS83.31 10582.80 10684.82 10289.59 12365.59 19688.21 14992.68 6674.66 12778.96 16486.42 26469.06 9095.26 8075.54 16090.09 11593.62 87
MP-MVScopyleft87.71 1987.64 2187.93 2194.36 2673.88 692.71 2292.65 7077.57 4683.84 9894.40 3472.24 4796.28 4385.65 5095.30 3593.62 87
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS_fast85.35 6984.95 7686.57 5693.69 4270.58 7892.15 3591.62 11673.89 14782.67 11694.09 4862.60 15995.54 6580.93 10192.93 7193.57 89
fmvsm_s_conf0.1_n83.56 9683.38 9584.10 13284.86 28467.28 16189.40 9883.01 32070.67 21487.08 5293.96 5868.38 9991.45 25488.56 3084.50 19993.56 90
CSCG86.41 4686.19 5087.07 4592.91 6172.48 3790.81 5893.56 2473.95 14483.16 10891.07 13575.94 1895.19 8279.94 11294.38 5693.55 91
test1286.80 5292.63 6770.70 7591.79 11082.71 11571.67 5696.16 4794.50 5193.54 92
APD-MVS_3200maxsize85.97 5385.88 5786.22 6092.69 6669.53 9291.93 3792.99 4973.54 15785.94 6094.51 2865.80 13095.61 6283.04 8092.51 7793.53 93
mvs_anonymous79.42 18879.11 17780.34 25684.45 29557.97 32282.59 30187.62 24367.40 28276.17 23488.56 20068.47 9889.59 29370.65 20786.05 18293.47 94
fmvsm_s_conf0.5_n83.80 8883.71 9084.07 13886.69 24167.31 16089.46 9383.07 31971.09 20686.96 5593.70 6669.02 9391.47 25388.79 2684.62 19893.44 95
fmvsm_s_conf0.5_n_585.22 7185.55 6484.25 12886.26 24767.40 15789.18 10589.31 19172.50 17788.31 2993.86 6169.66 8191.96 22889.81 1091.05 9993.38 96
mPP-MVS86.67 4186.32 4587.72 3094.41 2273.55 1392.74 2092.22 8876.87 6982.81 11494.25 4166.44 12096.24 4482.88 8394.28 5893.38 96
EPNet83.72 9182.92 10486.14 6584.22 29869.48 9491.05 5685.27 28481.30 676.83 21391.65 11366.09 12595.56 6376.00 15493.85 6293.38 96
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNetpermissive83.46 9982.80 10685.43 8190.25 10568.74 11490.30 7290.13 16476.33 8780.87 14092.89 8661.00 19294.20 12472.45 19390.97 10193.35 99
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DVP-MVScopyleft89.60 390.35 387.33 4095.27 571.25 5993.49 992.73 6477.33 5492.12 995.78 480.98 997.40 989.08 1996.41 1293.33 100
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
UniMVSNet_ETH3D79.10 19778.24 19581.70 22086.85 23560.24 29987.28 18188.79 21474.25 13876.84 21290.53 14949.48 30991.56 24667.98 23382.15 24093.29 101
EI-MVSNet-Vis-set84.19 8283.81 8885.31 8488.18 18267.85 14287.66 16889.73 17780.05 1482.95 10989.59 17170.74 6994.82 10180.66 10684.72 19693.28 102
MTAPA87.23 3187.00 3487.90 2294.18 3574.25 586.58 20692.02 9679.45 2185.88 6194.80 2168.07 10296.21 4586.69 4495.34 3293.23 103
CP-MVS87.11 3386.92 3887.68 3494.20 3473.86 793.98 392.82 6376.62 7883.68 10194.46 2967.93 10495.95 5784.20 6994.39 5593.23 103
ACMMPcopyleft85.89 5785.39 6787.38 3993.59 4572.63 3392.74 2093.18 3976.78 7280.73 14393.82 6364.33 14096.29 4282.67 8990.69 10593.23 103
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
fmvsm_s_conf0.1_n_a83.32 10482.99 10284.28 12383.79 30868.07 13789.34 10182.85 32569.80 23787.36 5094.06 5068.34 10091.56 24687.95 3583.46 22593.21 106
fmvsm_s_conf0.5_n_685.55 6386.20 4883.60 15987.32 22465.13 20788.86 12091.63 11575.41 10388.23 3293.45 7268.56 9792.47 20889.52 1592.78 7393.20 107
PAPM_NR83.02 11182.41 11184.82 10292.47 7066.37 17787.93 16191.80 10973.82 14877.32 20190.66 14567.90 10594.90 9770.37 20989.48 12893.19 108
reproduce_model87.28 3087.39 2886.95 4893.10 5671.24 6391.60 4293.19 3574.69 12588.80 2595.61 1170.29 7496.44 3986.20 4893.08 6993.16 109
OMC-MVS82.69 11481.97 12284.85 10188.75 16267.42 15587.98 15790.87 13974.92 11979.72 15591.65 11362.19 16993.96 13275.26 16486.42 17593.16 109
fmvsm_s_conf0.5_n_a83.63 9483.41 9484.28 12386.14 25168.12 13589.43 9482.87 32470.27 22687.27 5193.80 6469.09 8891.58 24388.21 3483.65 21993.14 111
PAPR81.66 13380.89 13683.99 14890.27 10464.00 23386.76 20191.77 11268.84 26377.13 21189.50 17267.63 10794.88 9967.55 23788.52 14493.09 112
UA-Net85.08 7484.96 7585.45 8092.07 7368.07 13789.78 8290.86 14082.48 284.60 8393.20 7869.35 8495.22 8171.39 19990.88 10393.07 113
reproduce-ours87.47 2387.61 2287.07 4593.27 5071.60 5391.56 4693.19 3574.98 11688.96 2295.54 1271.20 6396.54 3686.28 4693.49 6593.06 114
our_new_method87.47 2387.61 2287.07 4593.27 5071.60 5391.56 4693.19 3574.98 11688.96 2295.54 1271.20 6396.54 3686.28 4693.49 6593.06 114
HPM-MVS++copyleft89.02 989.15 988.63 595.01 976.03 192.38 2792.85 5980.26 1187.78 4094.27 3975.89 1996.81 2387.45 4096.44 993.05 116
thisisatest053079.40 18977.76 21184.31 12087.69 21165.10 21087.36 17784.26 29970.04 22977.42 19888.26 20949.94 30494.79 10570.20 21084.70 19793.03 117
train_agg86.43 4486.20 4887.13 4493.26 5272.96 2588.75 12891.89 10468.69 26585.00 7193.10 7974.43 2695.41 7384.97 5495.71 2593.02 118
EC-MVSNet86.01 5086.38 4484.91 10089.31 13966.27 17992.32 3093.63 2179.37 2284.17 9191.88 10669.04 9295.43 7083.93 7293.77 6393.01 119
mvsmamba80.60 16179.38 16884.27 12589.74 12167.24 16487.47 17386.95 25870.02 23075.38 25088.93 18751.24 28892.56 20375.47 16289.22 13193.00 120
EI-MVSNet-UG-set83.81 8783.38 9585.09 9287.87 19967.53 15387.44 17689.66 17879.74 1782.23 11889.41 18070.24 7594.74 10679.95 11183.92 21192.99 121
tttt051779.40 18977.91 20283.90 15288.10 18863.84 23888.37 14484.05 30171.45 19776.78 21589.12 18349.93 30694.89 9870.18 21183.18 22992.96 122
test9_res84.90 5595.70 2692.87 123
AstraMVS80.81 15180.14 15282.80 19686.05 25563.96 23486.46 21085.90 27873.71 15180.85 14190.56 14754.06 25491.57 24579.72 11483.97 21092.86 124
SR-MVS86.73 3886.67 4186.91 4994.11 3772.11 4792.37 2892.56 7574.50 12986.84 5694.65 2467.31 11195.77 5984.80 5992.85 7292.84 125
ETV-MVS84.90 7884.67 7885.59 7789.39 13468.66 12088.74 13092.64 7279.97 1584.10 9285.71 27769.32 8595.38 7580.82 10391.37 9592.72 126
agg_prior282.91 8295.45 2992.70 127
APD-MVScopyleft87.44 2587.52 2587.19 4294.24 3272.39 3991.86 4092.83 6073.01 17288.58 2694.52 2573.36 3496.49 3884.26 6695.01 3792.70 127
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ET-MVSNet_ETH3D78.63 20876.63 23984.64 10886.73 23969.47 9585.01 24984.61 29269.54 24366.51 37286.59 25750.16 30091.75 23776.26 15084.24 20792.69 129
Vis-MVSNet (Re-imp)78.36 21578.45 18878.07 30288.64 16651.78 39386.70 20279.63 36474.14 14175.11 26390.83 14361.29 18689.75 29058.10 32491.60 8992.69 129
TSAR-MVS + GP.85.71 6085.33 6986.84 5091.34 8172.50 3689.07 11487.28 25076.41 8185.80 6290.22 15574.15 3195.37 7881.82 9391.88 8492.65 131
test_fmvsmvis_n_192084.02 8583.87 8784.49 11384.12 30069.37 10188.15 15387.96 23370.01 23183.95 9693.23 7768.80 9591.51 25188.61 2889.96 11892.57 132
FA-MVS(test-final)80.96 14779.91 15684.10 13288.30 17965.01 21184.55 26290.01 16773.25 16779.61 15687.57 22658.35 21494.72 10771.29 20086.25 17892.56 133
guyue81.13 14480.64 13982.60 20586.52 24463.92 23786.69 20387.73 24173.97 14380.83 14289.69 16556.70 23191.33 25978.26 13085.40 19092.54 134
test_yl81.17 14280.47 14383.24 17389.13 14763.62 24186.21 21889.95 16972.43 18181.78 12789.61 16957.50 22293.58 15370.75 20486.90 16692.52 135
DCV-MVSNet81.17 14280.47 14383.24 17389.13 14763.62 24186.21 21889.95 16972.43 18181.78 12789.61 16957.50 22293.58 15370.75 20486.90 16692.52 135
SR-MVS-dyc-post85.77 5885.61 6386.23 5993.06 5870.63 7691.88 3892.27 8473.53 15885.69 6494.45 3065.00 13895.56 6382.75 8491.87 8592.50 137
RE-MVS-def85.48 6693.06 5870.63 7691.88 3892.27 8473.53 15885.69 6494.45 3063.87 14482.75 8491.87 8592.50 137
nrg03083.88 8683.53 9284.96 9686.77 23869.28 10290.46 6792.67 6774.79 12382.95 10991.33 12672.70 4593.09 18480.79 10579.28 27792.50 137
MG-MVS83.41 10083.45 9383.28 17092.74 6562.28 27188.17 15189.50 18575.22 10881.49 13092.74 9466.75 11495.11 8772.85 18791.58 9192.45 140
FIs82.07 12382.42 11081.04 24088.80 15958.34 31688.26 14893.49 2676.93 6778.47 17791.04 13669.92 7892.34 21669.87 21684.97 19392.44 141
testing3-275.12 28075.19 26274.91 34190.40 10245.09 42280.29 33478.42 37478.37 3776.54 22387.75 22044.36 35687.28 33157.04 33483.49 22392.37 142
fmvsm_s_conf0.5_n_386.36 4787.46 2783.09 18087.08 23165.21 20489.09 11390.21 16179.67 1889.98 1895.02 1873.17 3891.71 24091.30 291.60 8992.34 143
FC-MVSNet-test81.52 13782.02 12080.03 26388.42 17555.97 35587.95 15993.42 2977.10 6377.38 19990.98 14269.96 7791.79 23568.46 23184.50 19992.33 144
Fast-Effi-MVS+80.81 15179.92 15583.47 16388.85 15464.51 22285.53 23989.39 18870.79 21178.49 17685.06 29767.54 10893.58 15367.03 24586.58 17292.32 145
TranMVSNet+NR-MVSNet80.84 14980.31 14682.42 20887.85 20062.33 26987.74 16791.33 12580.55 977.99 18989.86 15965.23 13492.62 19867.05 24475.24 33792.30 146
ab-mvs79.51 18378.97 18081.14 23788.46 17260.91 28883.84 27789.24 19670.36 22179.03 16388.87 19063.23 15190.21 28265.12 25882.57 23792.28 147
CANet_DTU80.61 16079.87 15782.83 19385.60 26463.17 25787.36 17788.65 22176.37 8575.88 23788.44 20353.51 25993.07 18573.30 18289.74 12392.25 148
UniMVSNet_NR-MVSNet81.88 12681.54 12682.92 19088.46 17263.46 24887.13 18392.37 8180.19 1278.38 17889.14 18271.66 5793.05 18770.05 21276.46 31092.25 148
fmvsm_l_conf0.5_n84.47 8084.54 7984.27 12585.42 26968.81 10988.49 13887.26 25268.08 27488.03 3693.49 6872.04 5091.77 23688.90 2589.14 13392.24 150
DU-MVS81.12 14580.52 14282.90 19187.80 20363.46 24887.02 18891.87 10679.01 2878.38 17889.07 18465.02 13693.05 18770.05 21276.46 31092.20 151
NR-MVSNet80.23 17179.38 16882.78 20087.80 20363.34 25186.31 21591.09 13479.01 2872.17 30789.07 18467.20 11292.81 19666.08 25175.65 32392.20 151
TAPA-MVS73.13 979.15 19577.94 20182.79 19989.59 12362.99 26288.16 15291.51 12065.77 30277.14 21091.09 13460.91 19393.21 17350.26 37687.05 16492.17 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
fmvsm_l_conf0.5_n_a84.13 8384.16 8484.06 14085.38 27068.40 12688.34 14586.85 26267.48 28187.48 4793.40 7370.89 6691.61 24188.38 3389.22 13192.16 154
3Dnovator76.31 583.38 10282.31 11486.59 5587.94 19672.94 2890.64 6092.14 9577.21 5975.47 24492.83 8858.56 21294.72 10773.24 18492.71 7592.13 155
MVS_111021_HR85.14 7284.75 7786.32 5891.65 7972.70 3085.98 22390.33 15676.11 9082.08 12191.61 11771.36 6194.17 12781.02 10092.58 7692.08 156
MVSFormer82.85 11382.05 11985.24 8687.35 21870.21 8090.50 6490.38 15268.55 26781.32 13289.47 17461.68 17593.46 16278.98 11890.26 11292.05 157
jason81.39 14080.29 14784.70 10786.63 24369.90 8885.95 22486.77 26363.24 33281.07 13889.47 17461.08 19192.15 22278.33 12690.07 11792.05 157
jason: jason.
HyFIR lowres test77.53 23875.40 25783.94 15189.59 12366.62 17380.36 33288.64 22256.29 39676.45 22485.17 29457.64 22093.28 16861.34 29483.10 23091.91 159
XVG-OURS-SEG-HR80.81 15179.76 15983.96 15085.60 26468.78 11183.54 28790.50 14870.66 21776.71 21791.66 11260.69 19691.26 26076.94 14281.58 24791.83 160
lupinMVS81.39 14080.27 14884.76 10587.35 21870.21 8085.55 23786.41 26862.85 33981.32 13288.61 19761.68 17592.24 22078.41 12590.26 11291.83 160
WR-MVS79.49 18479.22 17580.27 25888.79 16058.35 31585.06 24888.61 22378.56 3277.65 19488.34 20563.81 14690.66 27764.98 26077.22 29891.80 162
h-mvs3383.15 10782.19 11586.02 6990.56 9870.85 7388.15 15389.16 19976.02 9284.67 7891.39 12461.54 17895.50 6682.71 8675.48 32791.72 163
UniMVSNet (Re)81.60 13481.11 13183.09 18088.38 17664.41 22787.60 16993.02 4578.42 3478.56 17488.16 21169.78 7993.26 16969.58 21976.49 30991.60 164
UGNet80.83 15079.59 16484.54 11088.04 19168.09 13689.42 9688.16 22776.95 6676.22 23089.46 17649.30 31393.94 13568.48 23090.31 11091.60 164
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
testing9176.54 25375.66 25279.18 28188.43 17455.89 35681.08 31883.00 32173.76 15075.34 25284.29 31246.20 34090.07 28464.33 26484.50 19991.58 166
XVG-OURS80.41 16679.23 17483.97 14985.64 26269.02 10583.03 29990.39 15171.09 20677.63 19591.49 12154.62 24991.35 25775.71 15683.47 22491.54 167
LCM-MVSNet-Re77.05 24576.94 22977.36 31587.20 22751.60 39480.06 33680.46 35275.20 11067.69 35286.72 24962.48 16288.98 30663.44 27089.25 13091.51 168
DP-MVS Recon83.11 11082.09 11886.15 6394.44 1970.92 7188.79 12592.20 9070.53 21979.17 16291.03 13864.12 14296.03 5068.39 23290.14 11491.50 169
PS-MVSNAJss82.07 12381.31 12784.34 11986.51 24567.27 16289.27 10291.51 12071.75 18979.37 15990.22 15563.15 15394.27 12077.69 13382.36 23991.49 170
testing9976.09 26575.12 26479.00 28288.16 18355.50 36280.79 32281.40 34173.30 16575.17 26084.27 31544.48 35590.02 28564.28 26584.22 20891.48 171
thisisatest051577.33 24275.38 25883.18 17685.27 27463.80 23982.11 30683.27 31365.06 31175.91 23683.84 32249.54 30894.27 12067.24 24186.19 17991.48 171
DPM-MVS84.93 7684.29 8386.84 5090.20 10673.04 2387.12 18493.04 4169.80 23782.85 11291.22 12973.06 4096.02 5276.72 14894.63 4891.46 173
HQP_MVS83.64 9383.14 9885.14 8890.08 10968.71 11691.25 5292.44 7779.12 2578.92 16691.00 14060.42 20395.38 7578.71 12186.32 17691.33 174
plane_prior592.44 7795.38 7578.71 12186.32 17691.33 174
GA-MVS76.87 24975.17 26381.97 21682.75 33562.58 26681.44 31586.35 27172.16 18574.74 27182.89 34446.20 34092.02 22668.85 22781.09 25291.30 176
VPA-MVSNet80.60 16180.55 14180.76 24788.07 19060.80 29086.86 19591.58 11875.67 9980.24 14989.45 17863.34 14790.25 28170.51 20879.22 27891.23 177
Effi-MVS+-dtu80.03 17578.57 18684.42 11585.13 27968.74 11488.77 12688.10 22974.99 11574.97 26883.49 33357.27 22593.36 16673.53 17880.88 25591.18 178
v2v48280.23 17179.29 17283.05 18483.62 31264.14 23187.04 18689.97 16873.61 15478.18 18487.22 23761.10 19093.82 14376.11 15176.78 30691.18 178
FE-MVS77.78 23175.68 25084.08 13788.09 18966.00 18383.13 29487.79 23968.42 27178.01 18885.23 29245.50 34995.12 8559.11 31285.83 18791.11 180
Anonymous2023121178.97 20177.69 21482.81 19590.54 9964.29 22990.11 7591.51 12065.01 31376.16 23588.13 21650.56 29693.03 19069.68 21877.56 29691.11 180
hse-mvs281.72 12980.94 13584.07 13888.72 16367.68 14785.87 22787.26 25276.02 9284.67 7888.22 21061.54 17893.48 16082.71 8673.44 35591.06 182
AUN-MVS79.21 19477.60 21684.05 14388.71 16467.61 14985.84 22987.26 25269.08 25677.23 20488.14 21553.20 26393.47 16175.50 16173.45 35491.06 182
HQP4-MVS77.24 20395.11 8791.03 184
HQP-MVS82.61 11682.02 12084.37 11689.33 13666.98 16989.17 10692.19 9176.41 8177.23 20490.23 15460.17 20695.11 8777.47 13585.99 18491.03 184
RPSCF73.23 30371.46 30778.54 29282.50 34159.85 30282.18 30582.84 32658.96 37571.15 31989.41 18045.48 35084.77 35758.82 31671.83 36791.02 186
LuminaMVS80.68 15879.62 16383.83 15385.07 28168.01 14086.99 18988.83 21270.36 22181.38 13187.99 21850.11 30192.51 20779.02 11686.89 16890.97 187
test_djsdf80.30 17079.32 17183.27 17183.98 30465.37 20290.50 6490.38 15268.55 26776.19 23188.70 19356.44 23493.46 16278.98 11880.14 26790.97 187
PCF-MVS73.52 780.38 16778.84 18285.01 9487.71 20968.99 10683.65 28191.46 12463.00 33677.77 19390.28 15166.10 12495.09 9161.40 29288.22 14990.94 189
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VPNet78.69 20778.66 18478.76 28688.31 17855.72 35984.45 26686.63 26576.79 7178.26 18190.55 14859.30 20889.70 29266.63 24677.05 30090.88 190
CPTT-MVS83.73 9083.33 9784.92 9993.28 4970.86 7292.09 3690.38 15268.75 26479.57 15792.83 8860.60 20193.04 18980.92 10291.56 9290.86 191
fmvsm_s_conf0.5_n_783.34 10384.03 8681.28 23285.73 26065.13 20785.40 24289.90 17174.96 11882.13 12093.89 6066.65 11587.92 32286.56 4591.05 9990.80 192
tt080578.73 20577.83 20681.43 22685.17 27560.30 29889.41 9790.90 13771.21 20377.17 20988.73 19246.38 33593.21 17372.57 19178.96 27990.79 193
CLD-MVS82.31 11981.65 12584.29 12288.47 17167.73 14685.81 23192.35 8275.78 9578.33 18086.58 25964.01 14394.35 11776.05 15387.48 15890.79 193
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v119279.59 18278.43 19083.07 18383.55 31464.52 22186.93 19390.58 14570.83 21077.78 19285.90 27359.15 20993.94 13573.96 17577.19 29990.76 195
IterMVS-LS80.06 17479.38 16882.11 21285.89 25663.20 25586.79 19889.34 18974.19 13975.45 24786.72 24966.62 11692.39 21272.58 19076.86 30390.75 196
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d2873.62 29473.53 28473.90 35388.20 18147.41 41278.06 36679.37 36674.29 13773.98 28284.29 31244.67 35283.54 36551.47 36687.39 15990.74 197
EI-MVSNet80.52 16579.98 15482.12 21184.28 29663.19 25686.41 21188.95 21074.18 14078.69 16987.54 22966.62 11692.43 21072.57 19180.57 26190.74 197
v192192079.22 19378.03 19982.80 19683.30 31963.94 23686.80 19790.33 15669.91 23577.48 19785.53 28458.44 21393.75 14973.60 17776.85 30490.71 199
QAPM80.88 14879.50 16685.03 9388.01 19468.97 10791.59 4392.00 9866.63 29375.15 26292.16 10057.70 21995.45 6863.52 26888.76 13990.66 200
v14419279.47 18578.37 19182.78 20083.35 31763.96 23486.96 19090.36 15569.99 23277.50 19685.67 28060.66 19893.77 14774.27 17276.58 30790.62 201
v124078.99 20077.78 20982.64 20383.21 32163.54 24586.62 20590.30 15869.74 24277.33 20085.68 27957.04 22893.76 14873.13 18576.92 30190.62 201
v114480.03 17579.03 17883.01 18683.78 30964.51 22287.11 18590.57 14771.96 18878.08 18786.20 26961.41 18293.94 13574.93 16677.23 29790.60 203
1112_ss77.40 24176.43 24280.32 25789.11 15160.41 29783.65 28187.72 24262.13 34973.05 29486.72 24962.58 16189.97 28662.11 28680.80 25790.59 204
CP-MVSNet78.22 21778.34 19277.84 30687.83 20254.54 37287.94 16091.17 13077.65 4373.48 28988.49 20162.24 16888.43 31662.19 28374.07 34690.55 205
testing22274.04 28972.66 29578.19 29987.89 19855.36 36381.06 31979.20 36971.30 20174.65 27483.57 33239.11 38888.67 31351.43 36885.75 18890.53 206
PS-CasMVS78.01 22678.09 19877.77 30887.71 20954.39 37488.02 15691.22 12777.50 5173.26 29188.64 19660.73 19488.41 31761.88 28773.88 35090.53 206
CDS-MVSNet79.07 19877.70 21383.17 17787.60 21368.23 13384.40 26986.20 27367.49 28076.36 22786.54 26161.54 17890.79 27361.86 28887.33 16090.49 208
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS78.89 20377.51 21883.03 18587.80 20367.79 14584.72 25585.05 28867.63 27776.75 21687.70 22262.25 16790.82 27258.53 31987.13 16390.49 208
PEN-MVS77.73 23277.69 21477.84 30687.07 23353.91 37787.91 16291.18 12977.56 4873.14 29388.82 19161.23 18789.17 30259.95 30372.37 36190.43 210
Test_1112_low_res76.40 26075.44 25579.27 27889.28 14158.09 31881.69 31087.07 25659.53 37072.48 30286.67 25461.30 18589.33 29760.81 29880.15 26690.41 211
HY-MVS69.67 1277.95 22777.15 22480.36 25587.57 21760.21 30083.37 28987.78 24066.11 29775.37 25187.06 24463.27 14990.48 27961.38 29382.43 23890.40 212
sc_t172.19 31569.51 32680.23 25984.81 28561.09 28584.68 25680.22 35860.70 35971.27 31683.58 33136.59 39989.24 30060.41 29963.31 39990.37 213
CHOSEN 1792x268877.63 23775.69 24983.44 16489.98 11568.58 12278.70 35687.50 24656.38 39575.80 23986.84 24558.67 21191.40 25661.58 29185.75 18890.34 214
SDMVSNet80.38 16780.18 14980.99 24189.03 15264.94 21480.45 33189.40 18775.19 11176.61 22189.98 15760.61 20087.69 32676.83 14683.55 22190.33 215
sd_testset77.70 23577.40 21978.60 28989.03 15260.02 30179.00 35185.83 27975.19 11176.61 22189.98 15754.81 24285.46 35062.63 27983.55 22190.33 215
114514_t80.68 15879.51 16584.20 12994.09 3867.27 16289.64 8791.11 13358.75 37974.08 28190.72 14458.10 21595.04 9269.70 21789.42 12990.30 217
eth_miper_zixun_eth77.92 22876.69 23781.61 22383.00 32961.98 27483.15 29389.20 19869.52 24474.86 27084.35 31161.76 17492.56 20371.50 19872.89 35990.28 218
PVSNet_Blended_VisFu82.62 11581.83 12484.96 9690.80 9469.76 9088.74 13091.70 11369.39 24578.96 16488.46 20265.47 13294.87 10074.42 17088.57 14290.24 219
MVS_111021_LR82.61 11682.11 11684.11 13188.82 15771.58 5585.15 24586.16 27474.69 12580.47 14791.04 13662.29 16690.55 27880.33 10890.08 11690.20 220
MSLP-MVS++85.43 6685.76 6084.45 11491.93 7570.24 7990.71 5992.86 5877.46 5284.22 8992.81 9067.16 11392.94 19180.36 10794.35 5790.16 221
mvs_tets79.13 19677.77 21083.22 17584.70 28866.37 17789.17 10690.19 16269.38 24675.40 24989.46 17644.17 35893.15 18076.78 14780.70 25990.14 222
BH-RMVSNet79.61 18078.44 18983.14 17889.38 13565.93 18584.95 25187.15 25573.56 15678.19 18389.79 16356.67 23293.36 16659.53 30886.74 17090.13 223
c3_l78.75 20477.91 20281.26 23382.89 33361.56 28084.09 27589.13 20269.97 23375.56 24284.29 31266.36 12192.09 22473.47 18075.48 32790.12 224
v7n78.97 20177.58 21783.14 17883.45 31665.51 19788.32 14691.21 12873.69 15272.41 30386.32 26757.93 21693.81 14469.18 22275.65 32390.11 225
jajsoiax79.29 19277.96 20083.27 17184.68 28966.57 17589.25 10390.16 16369.20 25375.46 24689.49 17345.75 34693.13 18276.84 14580.80 25790.11 225
v14878.72 20677.80 20881.47 22582.73 33661.96 27586.30 21688.08 23073.26 16676.18 23285.47 28662.46 16392.36 21471.92 19573.82 35190.09 227
GBi-Net78.40 21377.40 21981.40 22887.60 21363.01 25888.39 14189.28 19271.63 19175.34 25287.28 23354.80 24391.11 26362.72 27579.57 27190.09 227
test178.40 21377.40 21981.40 22887.60 21363.01 25888.39 14189.28 19271.63 19175.34 25287.28 23354.80 24391.11 26362.72 27579.57 27190.09 227
FMVSNet177.44 23976.12 24681.40 22886.81 23763.01 25888.39 14189.28 19270.49 22074.39 27887.28 23349.06 31791.11 26360.91 29678.52 28290.09 227
WR-MVS_H78.51 21278.49 18778.56 29188.02 19256.38 34988.43 13992.67 6777.14 6173.89 28387.55 22866.25 12389.24 30058.92 31473.55 35390.06 231
DTE-MVSNet76.99 24676.80 23277.54 31486.24 24853.06 38687.52 17190.66 14377.08 6472.50 30188.67 19560.48 20289.52 29457.33 33170.74 37390.05 232
v879.97 17779.02 17982.80 19684.09 30164.50 22487.96 15890.29 15974.13 14275.24 25986.81 24662.88 15893.89 14274.39 17175.40 33290.00 233
thres600view776.50 25575.44 25579.68 27189.40 13357.16 33585.53 23983.23 31473.79 14976.26 22987.09 24251.89 28091.89 23248.05 39083.72 21890.00 233
thres40076.50 25575.37 25979.86 26689.13 14757.65 32985.17 24383.60 30673.41 16276.45 22486.39 26552.12 27291.95 22948.33 38583.75 21590.00 233
cl2278.07 22377.01 22681.23 23482.37 34561.83 27783.55 28587.98 23268.96 26175.06 26583.87 32061.40 18391.88 23373.53 17876.39 31289.98 236
OPM-MVS83.50 9882.95 10385.14 8888.79 16070.95 6989.13 11191.52 11977.55 4980.96 13991.75 11060.71 19594.50 11479.67 11586.51 17489.97 237
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline275.70 26973.83 28181.30 23183.26 32061.79 27882.57 30280.65 34866.81 28466.88 36383.42 33457.86 21892.19 22163.47 26979.57 27189.91 238
v1079.74 17978.67 18382.97 18984.06 30264.95 21387.88 16490.62 14473.11 16975.11 26386.56 26061.46 18194.05 13173.68 17675.55 32589.90 239
MVSTER79.01 19977.88 20582.38 20983.07 32664.80 21884.08 27688.95 21069.01 26078.69 16987.17 24054.70 24792.43 21074.69 16780.57 26189.89 240
ACMP74.13 681.51 13980.57 14084.36 11789.42 13168.69 11989.97 7791.50 12374.46 13175.04 26690.41 15053.82 25694.54 11177.56 13482.91 23189.86 241
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test82.08 12281.27 12884.50 11189.23 14368.76 11290.22 7391.94 10275.37 10576.64 21991.51 11954.29 25094.91 9578.44 12383.78 21289.83 242
LGP-MVS_train84.50 11189.23 14368.76 11291.94 10275.37 10576.64 21991.51 11954.29 25094.91 9578.44 12383.78 21289.83 242
V4279.38 19178.24 19582.83 19381.10 36465.50 19885.55 23789.82 17271.57 19578.21 18286.12 27160.66 19893.18 17975.64 15775.46 32989.81 244
MAR-MVS81.84 12780.70 13785.27 8591.32 8271.53 5689.82 7990.92 13669.77 23978.50 17586.21 26862.36 16594.52 11365.36 25692.05 8389.77 245
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
DIV-MVS_self_test77.72 23376.76 23480.58 25182.48 34360.48 29583.09 29587.86 23769.22 25174.38 27985.24 29162.10 17091.53 24971.09 20175.40 33289.74 246
cl____77.72 23376.76 23480.58 25182.49 34260.48 29583.09 29587.87 23669.22 25174.38 27985.22 29362.10 17091.53 24971.09 20175.41 33189.73 247
miper_ehance_all_eth78.59 21077.76 21181.08 23982.66 33861.56 28083.65 28189.15 20068.87 26275.55 24383.79 32466.49 11992.03 22573.25 18376.39 31289.64 248
anonymousdsp78.60 20977.15 22482.98 18880.51 37067.08 16787.24 18289.53 18465.66 30475.16 26187.19 23952.52 26592.25 21977.17 13979.34 27689.61 249
FMVSNet278.20 21977.21 22381.20 23587.60 21362.89 26487.47 17389.02 20571.63 19175.29 25887.28 23354.80 24391.10 26662.38 28079.38 27589.61 249
baseline176.98 24776.75 23677.66 30988.13 18655.66 36085.12 24681.89 33473.04 17176.79 21488.90 18862.43 16487.78 32563.30 27271.18 37189.55 251
ETVMVS72.25 31471.05 31375.84 32787.77 20751.91 39079.39 34474.98 39569.26 24973.71 28582.95 34240.82 38086.14 34146.17 39884.43 20489.47 252
FMVSNet377.88 22976.85 23180.97 24386.84 23662.36 26886.52 20888.77 21571.13 20475.34 25286.66 25554.07 25391.10 26662.72 27579.57 27189.45 253
miper_enhance_ethall77.87 23076.86 23080.92 24481.65 35261.38 28282.68 30088.98 20765.52 30675.47 24482.30 35365.76 13192.00 22772.95 18676.39 31289.39 254
testing1175.14 27974.01 27678.53 29388.16 18356.38 34980.74 32580.42 35470.67 21472.69 30083.72 32743.61 36289.86 28762.29 28283.76 21489.36 255
cascas76.72 25274.64 26782.99 18785.78 25965.88 18782.33 30389.21 19760.85 35872.74 29781.02 36447.28 32693.75 14967.48 23885.02 19289.34 256
Fast-Effi-MVS+-dtu78.02 22576.49 24082.62 20483.16 32566.96 17186.94 19287.45 24872.45 17871.49 31584.17 31754.79 24691.58 24367.61 23680.31 26489.30 257
IB-MVS68.01 1575.85 26873.36 28783.31 16984.76 28766.03 18183.38 28885.06 28770.21 22869.40 33881.05 36345.76 34594.66 11065.10 25975.49 32689.25 258
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
thres100view90076.50 25575.55 25479.33 27789.52 12656.99 33885.83 23083.23 31473.94 14576.32 22887.12 24151.89 28091.95 22948.33 38583.75 21589.07 259
tfpn200view976.42 25975.37 25979.55 27689.13 14757.65 32985.17 24383.60 30673.41 16276.45 22486.39 26552.12 27291.95 22948.33 38583.75 21589.07 259
xiu_mvs_v1_base_debu80.80 15479.72 16084.03 14587.35 21870.19 8285.56 23488.77 21569.06 25781.83 12388.16 21150.91 29192.85 19378.29 12787.56 15589.06 261
xiu_mvs_v1_base80.80 15479.72 16084.03 14587.35 21870.19 8285.56 23488.77 21569.06 25781.83 12388.16 21150.91 29192.85 19378.29 12787.56 15589.06 261
xiu_mvs_v1_base_debi80.80 15479.72 16084.03 14587.35 21870.19 8285.56 23488.77 21569.06 25781.83 12388.16 21150.91 29192.85 19378.29 12787.56 15589.06 261
EPNet_dtu75.46 27374.86 26577.23 31882.57 34054.60 37186.89 19483.09 31871.64 19066.25 37485.86 27555.99 23588.04 32154.92 34886.55 17389.05 264
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pm-mvs177.25 24476.68 23878.93 28484.22 29858.62 31386.41 21188.36 22671.37 19873.31 29088.01 21761.22 18889.15 30364.24 26673.01 35889.03 265
PVSNet_Blended80.98 14680.34 14582.90 19188.85 15465.40 19984.43 26792.00 9867.62 27878.11 18585.05 29866.02 12794.27 12071.52 19689.50 12789.01 266
PAPM77.68 23676.40 24381.51 22487.29 22661.85 27683.78 27889.59 18264.74 31571.23 31788.70 19362.59 16093.66 15252.66 36087.03 16589.01 266
WTY-MVS75.65 27075.68 25075.57 33186.40 24656.82 34077.92 36982.40 32965.10 31076.18 23287.72 22163.13 15680.90 38260.31 30181.96 24389.00 268
无先验87.48 17288.98 20760.00 36594.12 12867.28 24088.97 269
GSMVS88.96 270
sam_mvs151.32 28788.96 270
SCA74.22 28672.33 29979.91 26584.05 30362.17 27279.96 33979.29 36866.30 29672.38 30480.13 37651.95 27888.60 31459.25 31077.67 29588.96 270
miper_lstm_enhance74.11 28873.11 29077.13 31980.11 37459.62 30572.23 39986.92 26166.76 28670.40 32382.92 34356.93 22982.92 37069.06 22472.63 36088.87 273
ACMM73.20 880.78 15779.84 15883.58 16189.31 13968.37 12789.99 7691.60 11770.28 22577.25 20289.66 16753.37 26193.53 15874.24 17382.85 23288.85 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs674.69 28273.39 28578.61 28881.38 35957.48 33286.64 20487.95 23464.99 31470.18 32686.61 25650.43 29889.52 29462.12 28570.18 37688.83 275
原ACMM184.35 11893.01 6068.79 11092.44 7763.96 32981.09 13791.57 11866.06 12695.45 6867.19 24294.82 4688.81 276
CNLPA78.08 22276.79 23381.97 21690.40 10271.07 6587.59 17084.55 29366.03 30072.38 30489.64 16857.56 22186.04 34259.61 30783.35 22688.79 277
UWE-MVS72.13 31671.49 30674.03 35186.66 24247.70 41081.40 31676.89 38863.60 33175.59 24184.22 31639.94 38385.62 34748.98 38286.13 18188.77 278
UBG73.08 30572.27 30075.51 33388.02 19251.29 39878.35 36377.38 38365.52 30673.87 28482.36 35145.55 34786.48 33855.02 34784.39 20588.75 279
K. test v371.19 32168.51 33379.21 28083.04 32857.78 32884.35 27076.91 38772.90 17462.99 39482.86 34539.27 38591.09 26861.65 29052.66 42088.75 279
旧先验191.96 7465.79 19186.37 27093.08 8369.31 8692.74 7488.74 281
PatchmatchNetpermissive73.12 30471.33 31078.49 29583.18 32360.85 28979.63 34178.57 37364.13 32271.73 31179.81 38151.20 28985.97 34357.40 33076.36 31788.66 282
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SixPastTwentyTwo73.37 29871.26 31279.70 27085.08 28057.89 32485.57 23383.56 30871.03 20865.66 37685.88 27442.10 37292.57 20259.11 31263.34 39888.65 283
SSC-MVS3.273.35 30173.39 28573.23 35785.30 27349.01 40874.58 39281.57 33875.21 10973.68 28685.58 28352.53 26482.05 37554.33 35277.69 29488.63 284
PS-MVSNAJ81.69 13181.02 13383.70 15789.51 12768.21 13484.28 27190.09 16570.79 21181.26 13685.62 28263.15 15394.29 11875.62 15888.87 13688.59 285
xiu_mvs_v2_base81.69 13181.05 13283.60 15989.15 14668.03 13984.46 26590.02 16670.67 21481.30 13586.53 26263.17 15294.19 12675.60 15988.54 14388.57 286
MonoMVSNet76.49 25875.80 24778.58 29081.55 35558.45 31486.36 21486.22 27274.87 12274.73 27283.73 32651.79 28388.73 31170.78 20372.15 36488.55 287
CostFormer75.24 27873.90 27979.27 27882.65 33958.27 31780.80 32182.73 32761.57 35375.33 25683.13 33955.52 23891.07 26964.98 26078.34 28788.45 288
lessismore_v078.97 28381.01 36557.15 33665.99 42261.16 40082.82 34639.12 38791.34 25859.67 30646.92 42788.43 289
OpenMVScopyleft72.83 1079.77 17878.33 19384.09 13685.17 27569.91 8790.57 6190.97 13566.70 28772.17 30791.91 10454.70 24793.96 13261.81 28990.95 10288.41 290
reproduce_monomvs75.40 27674.38 27378.46 29683.92 30657.80 32783.78 27886.94 25973.47 16072.25 30684.47 30638.74 38989.27 29975.32 16370.53 37488.31 291
VortexMVS78.57 21177.89 20480.59 25085.89 25662.76 26585.61 23289.62 18172.06 18674.99 26785.38 28855.94 23690.77 27574.99 16576.58 30788.23 292
OurMVSNet-221017-074.26 28572.42 29879.80 26883.76 31059.59 30685.92 22686.64 26466.39 29566.96 36287.58 22539.46 38491.60 24265.76 25469.27 37988.22 293
LS3D76.95 24874.82 26683.37 16890.45 10067.36 15989.15 11086.94 25961.87 35269.52 33790.61 14651.71 28494.53 11246.38 39786.71 17188.21 294
WBMVS73.43 29772.81 29375.28 33787.91 19750.99 40078.59 35981.31 34365.51 30874.47 27784.83 30146.39 33486.68 33558.41 32077.86 29088.17 295
XVG-ACMP-BASELINE76.11 26474.27 27581.62 22183.20 32264.67 22083.60 28489.75 17669.75 24071.85 31087.09 24232.78 40892.11 22369.99 21480.43 26388.09 296
tpm273.26 30271.46 30778.63 28783.34 31856.71 34380.65 32780.40 35556.63 39473.55 28882.02 35851.80 28291.24 26156.35 34278.42 28587.95 297
MDTV_nov1_ep13_2view37.79 43675.16 38655.10 39966.53 36949.34 31253.98 35387.94 298
Patchmatch-test64.82 37363.24 37469.57 38379.42 38649.82 40663.49 43069.05 41551.98 40959.95 40580.13 37650.91 29170.98 42440.66 41473.57 35287.90 299
PLCcopyleft70.83 1178.05 22476.37 24483.08 18291.88 7767.80 14488.19 15089.46 18664.33 32169.87 33488.38 20453.66 25793.58 15358.86 31582.73 23487.86 300
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm72.37 31271.71 30474.35 34882.19 34652.00 38879.22 34777.29 38464.56 31772.95 29683.68 32951.35 28683.26 36958.33 32275.80 32187.81 301
Patchmatch-RL test70.24 33467.78 34777.61 31177.43 39559.57 30771.16 40370.33 40962.94 33868.65 34572.77 41550.62 29585.49 34969.58 21966.58 38987.77 302
F-COLMAP76.38 26174.33 27482.50 20789.28 14166.95 17288.41 14089.03 20464.05 32666.83 36488.61 19746.78 33292.89 19257.48 32878.55 28187.67 303
Baseline_NR-MVSNet78.15 22178.33 19377.61 31185.79 25856.21 35386.78 19985.76 28073.60 15577.93 19087.57 22665.02 13688.99 30567.14 24375.33 33487.63 304
CL-MVSNet_self_test72.37 31271.46 30775.09 33979.49 38553.53 37980.76 32485.01 28969.12 25570.51 32182.05 35757.92 21784.13 36052.27 36266.00 39287.60 305
ACMH+68.96 1476.01 26674.01 27682.03 21488.60 16765.31 20388.86 12087.55 24470.25 22767.75 35187.47 23141.27 37693.19 17858.37 32175.94 32087.60 305
131476.53 25475.30 26180.21 26083.93 30562.32 27084.66 25788.81 21360.23 36370.16 32884.07 31955.30 24090.73 27667.37 23983.21 22887.59 307
API-MVS81.99 12581.23 12984.26 12790.94 9070.18 8591.10 5589.32 19071.51 19678.66 17188.28 20765.26 13395.10 9064.74 26291.23 9787.51 308
AdaColmapbinary80.58 16479.42 16784.06 14093.09 5768.91 10889.36 10088.97 20969.27 24875.70 24089.69 16557.20 22795.77 5963.06 27388.41 14787.50 309
PVSNet_BlendedMVS80.60 16180.02 15382.36 21088.85 15465.40 19986.16 22092.00 9869.34 24778.11 18586.09 27266.02 12794.27 12071.52 19682.06 24287.39 310
sss73.60 29573.64 28373.51 35682.80 33455.01 36876.12 37781.69 33762.47 34574.68 27385.85 27657.32 22478.11 39360.86 29780.93 25387.39 310
IterMVS-SCA-FT75.43 27473.87 28080.11 26282.69 33764.85 21781.57 31283.47 31069.16 25470.49 32284.15 31851.95 27888.15 31969.23 22172.14 36587.34 312
PVSNet64.34 1872.08 31770.87 31675.69 32986.21 24956.44 34774.37 39380.73 34762.06 35070.17 32782.23 35542.86 36683.31 36854.77 34984.45 20387.32 313
tt0320-xc70.11 33667.45 35378.07 30285.33 27259.51 30883.28 29078.96 37158.77 37767.10 36180.28 37436.73 39887.42 32956.83 33859.77 40987.29 314
新几何183.42 16593.13 5470.71 7485.48 28357.43 39081.80 12691.98 10363.28 14892.27 21864.60 26392.99 7087.27 315
TR-MVS77.44 23976.18 24581.20 23588.24 18063.24 25384.61 26086.40 26967.55 27977.81 19186.48 26354.10 25293.15 18057.75 32782.72 23587.20 316
TransMVSNet (Re)75.39 27774.56 26977.86 30585.50 26857.10 33786.78 19986.09 27672.17 18471.53 31487.34 23263.01 15789.31 29856.84 33761.83 40287.17 317
ACMH67.68 1675.89 26773.93 27881.77 21988.71 16466.61 17488.62 13589.01 20669.81 23666.78 36586.70 25341.95 37491.51 25155.64 34478.14 28887.17 317
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
KD-MVS_self_test68.81 34667.59 35172.46 36774.29 40845.45 41777.93 36887.00 25763.12 33363.99 38978.99 38942.32 36984.77 35756.55 34164.09 39787.16 319
EPMVS69.02 34568.16 33771.59 37179.61 38349.80 40777.40 37266.93 42062.82 34170.01 32979.05 38545.79 34477.86 39556.58 34075.26 33687.13 320
CR-MVSNet73.37 29871.27 31179.67 27281.32 36265.19 20575.92 37980.30 35659.92 36672.73 29881.19 36152.50 26686.69 33459.84 30477.71 29287.11 321
RPMNet73.51 29670.49 31982.58 20681.32 36265.19 20575.92 37992.27 8457.60 38872.73 29876.45 40352.30 26995.43 7048.14 38977.71 29287.11 321
test_vis1_n_192075.52 27275.78 24874.75 34579.84 37857.44 33383.26 29185.52 28262.83 34079.34 16186.17 27045.10 35179.71 38678.75 12081.21 25187.10 323
tt032070.49 33268.03 34077.89 30484.78 28659.12 31083.55 28580.44 35358.13 38367.43 35780.41 37239.26 38687.54 32855.12 34663.18 40086.99 324
XXY-MVS75.41 27575.56 25374.96 34083.59 31357.82 32680.59 32883.87 30466.54 29474.93 26988.31 20663.24 15080.09 38562.16 28476.85 30486.97 325
tpmrst72.39 31072.13 30173.18 36180.54 36949.91 40579.91 34079.08 37063.11 33471.69 31279.95 37855.32 23982.77 37165.66 25573.89 34986.87 326
thres20075.55 27174.47 27178.82 28587.78 20657.85 32583.07 29783.51 30972.44 18075.84 23884.42 30752.08 27591.75 23747.41 39283.64 22086.86 327
ITE_SJBPF78.22 29881.77 35160.57 29383.30 31269.25 25067.54 35387.20 23836.33 40187.28 33154.34 35174.62 34386.80 328
test22291.50 8068.26 13084.16 27383.20 31754.63 40179.74 15491.63 11558.97 21091.42 9386.77 329
MIMVSNet70.69 32869.30 32774.88 34284.52 29356.35 35175.87 38179.42 36564.59 31667.76 35082.41 35041.10 37781.54 37846.64 39681.34 24886.75 330
BH-untuned79.47 18578.60 18582.05 21389.19 14565.91 18686.07 22288.52 22472.18 18375.42 24887.69 22361.15 18993.54 15760.38 30086.83 16986.70 331
LTVRE_ROB69.57 1376.25 26274.54 27081.41 22788.60 16764.38 22879.24 34689.12 20370.76 21369.79 33687.86 21949.09 31693.20 17656.21 34380.16 26586.65 332
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
testdata79.97 26490.90 9164.21 23084.71 29059.27 37285.40 6692.91 8562.02 17289.08 30468.95 22591.37 9586.63 333
MIMVSNet168.58 34966.78 35973.98 35280.07 37551.82 39280.77 32384.37 29464.40 31959.75 40682.16 35636.47 40083.63 36442.73 40970.33 37586.48 334
tfpnnormal74.39 28373.16 28978.08 30186.10 25458.05 31984.65 25987.53 24570.32 22471.22 31885.63 28154.97 24189.86 28743.03 40875.02 33986.32 335
D2MVS74.82 28173.21 28879.64 27379.81 37962.56 26780.34 33387.35 24964.37 32068.86 34382.66 34846.37 33690.10 28367.91 23481.24 25086.25 336
tpm cat170.57 32968.31 33577.35 31682.41 34457.95 32378.08 36580.22 35852.04 40768.54 34777.66 39852.00 27787.84 32451.77 36372.07 36686.25 336
CVMVSNet72.99 30772.58 29674.25 34984.28 29650.85 40186.41 21183.45 31144.56 42073.23 29287.54 22949.38 31185.70 34565.90 25278.44 28486.19 338
AllTest70.96 32468.09 33979.58 27485.15 27763.62 24184.58 26179.83 36162.31 34660.32 40386.73 24732.02 40988.96 30850.28 37471.57 36986.15 339
TestCases79.58 27485.15 27763.62 24179.83 36162.31 34660.32 40386.73 24732.02 40988.96 30850.28 37471.57 36986.15 339
test-LLR72.94 30872.43 29774.48 34681.35 36058.04 32078.38 36077.46 38066.66 28869.95 33279.00 38748.06 32279.24 38766.13 24884.83 19486.15 339
test-mter71.41 32070.39 32274.48 34681.35 36058.04 32078.38 36077.46 38060.32 36269.95 33279.00 38736.08 40279.24 38766.13 24884.83 19486.15 339
IterMVS74.29 28472.94 29278.35 29781.53 35663.49 24781.58 31182.49 32868.06 27569.99 33183.69 32851.66 28585.54 34865.85 25371.64 36886.01 343
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS76.78 25174.57 26883.42 16593.29 4869.46 9788.55 13783.70 30563.98 32870.20 32588.89 18954.01 25594.80 10446.66 39481.88 24586.01 343
ppachtmachnet_test70.04 33767.34 35578.14 30079.80 38061.13 28379.19 34880.59 34959.16 37365.27 37979.29 38446.75 33387.29 33049.33 38066.72 38786.00 345
mmtdpeth74.16 28773.01 29177.60 31383.72 31161.13 28385.10 24785.10 28672.06 18677.21 20880.33 37343.84 36085.75 34477.14 14052.61 42185.91 346
test_fmvs1_n70.86 32670.24 32372.73 36472.51 42255.28 36581.27 31779.71 36351.49 41178.73 16884.87 30027.54 41877.02 39876.06 15279.97 26985.88 347
Patchmtry70.74 32769.16 33075.49 33480.72 36654.07 37674.94 39080.30 35658.34 38070.01 32981.19 36152.50 26686.54 33653.37 35771.09 37285.87 348
WB-MVSnew71.96 31871.65 30572.89 36284.67 29251.88 39182.29 30477.57 37962.31 34673.67 28783.00 34153.49 26081.10 38145.75 40182.13 24185.70 349
test_fmvs268.35 35367.48 35270.98 37969.50 42551.95 38980.05 33776.38 39049.33 41474.65 27484.38 30923.30 42775.40 41574.51 16975.17 33885.60 350
ambc75.24 33873.16 41750.51 40363.05 43187.47 24764.28 38577.81 39717.80 43389.73 29157.88 32660.64 40685.49 351
mvs5depth69.45 34267.45 35375.46 33573.93 40955.83 35779.19 34883.23 31466.89 28371.63 31383.32 33533.69 40785.09 35359.81 30555.34 41785.46 352
UnsupCasMVSNet_eth67.33 35865.99 36271.37 37373.48 41451.47 39675.16 38685.19 28565.20 30960.78 40180.93 36842.35 36877.20 39757.12 33253.69 41985.44 353
PatchT68.46 35267.85 34370.29 38180.70 36743.93 42572.47 39874.88 39660.15 36470.55 32076.57 40249.94 30481.59 37750.58 37074.83 34185.34 354
Anonymous2024052168.80 34767.22 35673.55 35574.33 40754.11 37583.18 29285.61 28158.15 38261.68 39880.94 36630.71 41481.27 38057.00 33573.34 35785.28 355
test_cas_vis1_n_192073.76 29373.74 28273.81 35475.90 40059.77 30380.51 32982.40 32958.30 38181.62 12985.69 27844.35 35776.41 40476.29 14978.61 28085.23 356
ADS-MVSNet266.20 36963.33 37374.82 34379.92 37658.75 31267.55 41875.19 39453.37 40465.25 38075.86 40642.32 36980.53 38441.57 41268.91 38185.18 357
ADS-MVSNet64.36 37462.88 37768.78 38979.92 37647.17 41367.55 41871.18 40853.37 40465.25 38075.86 40642.32 36973.99 42041.57 41268.91 38185.18 357
FMVSNet569.50 34167.96 34174.15 35082.97 33255.35 36480.01 33882.12 33262.56 34463.02 39281.53 36036.92 39781.92 37648.42 38474.06 34785.17 359
pmmvs571.55 31970.20 32475.61 33077.83 39356.39 34881.74 30980.89 34457.76 38667.46 35584.49 30549.26 31485.32 35257.08 33375.29 33585.11 360
testing368.56 35067.67 34971.22 37787.33 22342.87 42783.06 29871.54 40770.36 22169.08 34284.38 30930.33 41585.69 34637.50 42075.45 33085.09 361
UWE-MVS-2865.32 37064.93 36466.49 39878.70 39038.55 43577.86 37064.39 42762.00 35164.13 38783.60 33041.44 37576.00 40831.39 42780.89 25484.92 362
CMPMVSbinary51.72 2170.19 33568.16 33776.28 32473.15 41857.55 33179.47 34383.92 30248.02 41656.48 41684.81 30243.13 36486.42 33962.67 27881.81 24684.89 363
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testgi66.67 36366.53 36067.08 39775.62 40341.69 43275.93 37876.50 38966.11 29765.20 38286.59 25735.72 40374.71 41743.71 40673.38 35684.84 364
MSDG73.36 30070.99 31480.49 25384.51 29465.80 19080.71 32686.13 27565.70 30365.46 37783.74 32544.60 35390.91 27151.13 36976.89 30284.74 365
pmmvs474.03 29171.91 30280.39 25481.96 34868.32 12881.45 31482.14 33159.32 37169.87 33485.13 29552.40 26888.13 32060.21 30274.74 34284.73 366
gg-mvs-nofinetune69.95 33867.96 34175.94 32683.07 32654.51 37377.23 37470.29 41063.11 33470.32 32462.33 42443.62 36188.69 31253.88 35487.76 15484.62 367
test_fmvs170.93 32570.52 31872.16 36873.71 41155.05 36780.82 32078.77 37251.21 41278.58 17384.41 30831.20 41376.94 39975.88 15580.12 26884.47 368
BH-w/o78.21 21877.33 22280.84 24588.81 15865.13 20784.87 25287.85 23869.75 24074.52 27684.74 30461.34 18493.11 18358.24 32385.84 18684.27 369
MVS78.19 22076.99 22881.78 21885.66 26166.99 16884.66 25790.47 14955.08 40072.02 30985.27 29063.83 14594.11 12966.10 25089.80 12284.24 370
COLMAP_ROBcopyleft66.92 1773.01 30670.41 32180.81 24687.13 23065.63 19488.30 14784.19 30062.96 33763.80 39187.69 22338.04 39492.56 20346.66 39474.91 34084.24 370
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
new-patchmatchnet61.73 38061.73 38161.70 40472.74 42024.50 44769.16 41378.03 37661.40 35456.72 41575.53 40938.42 39176.48 40345.95 40057.67 41084.13 372
TESTMET0.1,169.89 33969.00 33172.55 36579.27 38856.85 33978.38 36074.71 39957.64 38768.09 34977.19 40037.75 39576.70 40063.92 26784.09 20984.10 373
test_fmvs363.36 37761.82 38067.98 39462.51 43446.96 41577.37 37374.03 40145.24 41967.50 35478.79 39012.16 43972.98 42372.77 18966.02 39183.99 374
our_test_369.14 34467.00 35775.57 33179.80 38058.80 31177.96 36777.81 37759.55 36962.90 39578.25 39447.43 32483.97 36151.71 36467.58 38683.93 375
test_vis1_n69.85 34069.21 32971.77 37072.66 42155.27 36681.48 31376.21 39152.03 40875.30 25783.20 33828.97 41676.22 40674.60 16878.41 28683.81 376
mamv476.81 25078.23 19772.54 36686.12 25265.75 19378.76 35582.07 33364.12 32372.97 29591.02 13967.97 10368.08 43183.04 8078.02 28983.80 377
tpmvs71.09 32369.29 32876.49 32382.04 34756.04 35478.92 35381.37 34264.05 32667.18 36078.28 39349.74 30789.77 28949.67 37972.37 36183.67 378
test20.0367.45 35766.95 35868.94 38675.48 40444.84 42377.50 37177.67 37866.66 28863.01 39383.80 32347.02 32878.40 39142.53 41168.86 38383.58 379
test0.0.03 168.00 35567.69 34868.90 38777.55 39447.43 41175.70 38272.95 40666.66 28866.56 36882.29 35448.06 32275.87 41044.97 40574.51 34483.41 380
Anonymous2023120668.60 34867.80 34671.02 37880.23 37350.75 40278.30 36480.47 35156.79 39366.11 37582.63 34946.35 33778.95 38943.62 40775.70 32283.36 381
EU-MVSNet68.53 35167.61 35071.31 37678.51 39247.01 41484.47 26384.27 29842.27 42366.44 37384.79 30340.44 38183.76 36258.76 31768.54 38483.17 382
dp66.80 36165.43 36370.90 38079.74 38248.82 40975.12 38874.77 39759.61 36864.08 38877.23 39942.89 36580.72 38348.86 38366.58 38983.16 383
pmmvs-eth3d70.50 33167.83 34578.52 29477.37 39666.18 18081.82 30781.51 33958.90 37663.90 39080.42 37142.69 36786.28 34058.56 31865.30 39483.11 384
YYNet165.03 37162.91 37671.38 37275.85 40156.60 34569.12 41474.66 40057.28 39154.12 41977.87 39645.85 34374.48 41849.95 37761.52 40483.05 385
MDA-MVSNet-bldmvs66.68 36263.66 37275.75 32879.28 38760.56 29473.92 39578.35 37564.43 31850.13 42579.87 38044.02 35983.67 36346.10 39956.86 41183.03 386
MDA-MVSNet_test_wron65.03 37162.92 37571.37 37375.93 39956.73 34169.09 41574.73 39857.28 39154.03 42077.89 39545.88 34274.39 41949.89 37861.55 40382.99 387
USDC70.33 33368.37 33476.21 32580.60 36856.23 35279.19 34886.49 26760.89 35761.29 39985.47 28631.78 41189.47 29653.37 35776.21 31882.94 388
Syy-MVS68.05 35467.85 34368.67 39084.68 28940.97 43378.62 35773.08 40466.65 29166.74 36679.46 38252.11 27482.30 37332.89 42576.38 31582.75 389
myMVS_eth3d67.02 36066.29 36169.21 38584.68 28942.58 42878.62 35773.08 40466.65 29166.74 36679.46 38231.53 41282.30 37339.43 41776.38 31582.75 389
ttmdpeth59.91 38357.10 38768.34 39267.13 42946.65 41674.64 39167.41 41948.30 41562.52 39785.04 29920.40 42975.93 40942.55 41045.90 43082.44 391
OpenMVS_ROBcopyleft64.09 1970.56 33068.19 33677.65 31080.26 37159.41 30985.01 24982.96 32358.76 37865.43 37882.33 35237.63 39691.23 26245.34 40476.03 31982.32 392
JIA-IIPM66.32 36662.82 37876.82 32177.09 39761.72 27965.34 42675.38 39358.04 38564.51 38462.32 42542.05 37386.51 33751.45 36769.22 38082.21 393
dmvs_re71.14 32270.58 31772.80 36381.96 34859.68 30475.60 38379.34 36768.55 26769.27 34180.72 36949.42 31076.54 40152.56 36177.79 29182.19 394
EG-PatchMatch MVS74.04 28971.82 30380.71 24884.92 28367.42 15585.86 22888.08 23066.04 29964.22 38683.85 32135.10 40492.56 20357.44 32980.83 25682.16 395
MVP-Stereo76.12 26374.46 27281.13 23885.37 27169.79 8984.42 26887.95 23465.03 31267.46 35585.33 28953.28 26291.73 23958.01 32583.27 22781.85 396
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TDRefinement67.49 35664.34 36776.92 32073.47 41561.07 28684.86 25382.98 32259.77 36758.30 41085.13 29526.06 41987.89 32347.92 39160.59 40781.81 397
GG-mvs-BLEND75.38 33681.59 35455.80 35879.32 34569.63 41267.19 35973.67 41343.24 36388.90 31050.41 37184.50 19981.45 398
KD-MVS_2432*160066.22 36763.89 37073.21 35875.47 40553.42 38170.76 40684.35 29564.10 32466.52 37078.52 39134.55 40584.98 35450.40 37250.33 42481.23 399
miper_refine_blended66.22 36763.89 37073.21 35875.47 40553.42 38170.76 40684.35 29564.10 32466.52 37078.52 39134.55 40584.98 35450.40 37250.33 42481.23 399
test_040272.79 30970.44 32079.84 26788.13 18665.99 18485.93 22584.29 29765.57 30567.40 35885.49 28546.92 32992.61 19935.88 42274.38 34580.94 401
MVStest156.63 38752.76 39368.25 39361.67 43553.25 38571.67 40168.90 41738.59 42850.59 42483.05 34025.08 42170.66 42536.76 42138.56 43180.83 402
UnsupCasMVSNet_bld63.70 37661.53 38270.21 38273.69 41251.39 39772.82 39781.89 33455.63 39857.81 41271.80 41738.67 39078.61 39049.26 38152.21 42280.63 403
LCM-MVSNet54.25 38949.68 39967.97 39553.73 44345.28 42066.85 42180.78 34635.96 43239.45 43362.23 4268.70 44378.06 39448.24 38851.20 42380.57 404
N_pmnet52.79 39453.26 39251.40 41878.99 3897.68 45269.52 4103.89 45151.63 41057.01 41474.98 41040.83 37965.96 43337.78 41964.67 39580.56 405
TinyColmap67.30 35964.81 36574.76 34481.92 35056.68 34480.29 33481.49 34060.33 36156.27 41783.22 33624.77 42387.66 32745.52 40269.47 37879.95 406
PM-MVS66.41 36564.14 36873.20 36073.92 41056.45 34678.97 35264.96 42663.88 33064.72 38380.24 37519.84 43183.44 36766.24 24764.52 39679.71 407
ANet_high50.57 39846.10 40263.99 40148.67 44639.13 43470.99 40580.85 34561.39 35531.18 43557.70 43117.02 43473.65 42231.22 42815.89 44379.18 408
LF4IMVS64.02 37562.19 37969.50 38470.90 42353.29 38476.13 37677.18 38552.65 40658.59 40880.98 36523.55 42676.52 40253.06 35966.66 38878.68 409
PatchMatch-RL72.38 31170.90 31576.80 32288.60 16767.38 15879.53 34276.17 39262.75 34269.36 33982.00 35945.51 34884.89 35653.62 35580.58 26078.12 410
MS-PatchMatch73.83 29272.67 29477.30 31783.87 30766.02 18281.82 30784.66 29161.37 35668.61 34682.82 34647.29 32588.21 31859.27 30984.32 20677.68 411
DSMNet-mixed57.77 38656.90 38860.38 40667.70 42735.61 43769.18 41253.97 43832.30 43657.49 41379.88 37940.39 38268.57 43038.78 41872.37 36176.97 412
CHOSEN 280x42066.51 36464.71 36671.90 36981.45 35763.52 24657.98 43368.95 41653.57 40362.59 39676.70 40146.22 33975.29 41655.25 34579.68 27076.88 413
mvsany_test353.99 39051.45 39561.61 40555.51 43944.74 42463.52 42945.41 44443.69 42258.11 41176.45 40317.99 43263.76 43554.77 34947.59 42676.34 414
dmvs_testset62.63 37864.11 36958.19 40878.55 39124.76 44675.28 38465.94 42367.91 27660.34 40276.01 40553.56 25873.94 42131.79 42667.65 38575.88 415
mvsany_test162.30 37961.26 38365.41 40069.52 42454.86 36966.86 42049.78 44046.65 41768.50 34883.21 33749.15 31566.28 43256.93 33660.77 40575.11 416
PMMVS69.34 34368.67 33271.35 37575.67 40262.03 27375.17 38573.46 40250.00 41368.68 34479.05 38552.07 27678.13 39261.16 29582.77 23373.90 417
test_vis1_rt60.28 38258.42 38565.84 39967.25 42855.60 36170.44 40860.94 43244.33 42159.00 40766.64 42224.91 42268.67 42962.80 27469.48 37773.25 418
pmmvs357.79 38554.26 39068.37 39164.02 43356.72 34275.12 38865.17 42440.20 42552.93 42169.86 42120.36 43075.48 41345.45 40355.25 41872.90 419
PVSNet_057.27 2061.67 38159.27 38468.85 38879.61 38357.44 33368.01 41673.44 40355.93 39758.54 40970.41 42044.58 35477.55 39647.01 39335.91 43271.55 420
WB-MVS54.94 38854.72 38955.60 41473.50 41320.90 44874.27 39461.19 43159.16 37350.61 42374.15 41147.19 32775.78 41117.31 43935.07 43370.12 421
SSC-MVS53.88 39153.59 39154.75 41672.87 41919.59 44973.84 39660.53 43357.58 38949.18 42773.45 41446.34 33875.47 41416.20 44232.28 43569.20 422
test_f52.09 39550.82 39655.90 41253.82 44242.31 43159.42 43258.31 43636.45 43156.12 41870.96 41912.18 43857.79 43853.51 35656.57 41367.60 423
PMMVS240.82 40538.86 40946.69 41953.84 44116.45 45048.61 43649.92 43937.49 42931.67 43460.97 4278.14 44556.42 43928.42 43030.72 43667.19 424
new_pmnet50.91 39750.29 39752.78 41768.58 42634.94 43963.71 42856.63 43739.73 42644.95 42865.47 42321.93 42858.48 43734.98 42356.62 41264.92 425
MVS-HIRNet59.14 38457.67 38663.57 40281.65 35243.50 42671.73 40065.06 42539.59 42751.43 42257.73 43038.34 39282.58 37239.53 41573.95 34864.62 426
APD_test153.31 39349.93 39863.42 40365.68 43050.13 40471.59 40266.90 42134.43 43340.58 43271.56 4188.65 44476.27 40534.64 42455.36 41663.86 427
test_method31.52 40829.28 41238.23 42227.03 4506.50 45320.94 44162.21 4304.05 44422.35 44252.50 43513.33 43647.58 44227.04 43234.04 43460.62 428
EGC-MVSNET52.07 39647.05 40067.14 39683.51 31560.71 29180.50 33067.75 4180.07 4460.43 44775.85 40824.26 42481.54 37828.82 42962.25 40159.16 429
test_vis3_rt49.26 39947.02 40156.00 41154.30 44045.27 42166.76 42248.08 44136.83 43044.38 42953.20 4347.17 44664.07 43456.77 33955.66 41458.65 430
FPMVS53.68 39251.64 39459.81 40765.08 43151.03 39969.48 41169.58 41341.46 42440.67 43172.32 41616.46 43570.00 42824.24 43565.42 39358.40 431
testf145.72 40041.96 40457.00 40956.90 43745.32 41866.14 42359.26 43426.19 43730.89 43660.96 4284.14 44770.64 42626.39 43346.73 42855.04 432
APD_test245.72 40041.96 40457.00 40956.90 43745.32 41866.14 42359.26 43426.19 43730.89 43660.96 4284.14 44770.64 42626.39 43346.73 42855.04 432
PMVScopyleft37.38 2244.16 40440.28 40855.82 41340.82 44842.54 43065.12 42763.99 42834.43 43324.48 43957.12 4323.92 44976.17 40717.10 44055.52 41548.75 434
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVEpermissive26.22 2330.37 41025.89 41443.81 42144.55 44735.46 43828.87 44039.07 44518.20 44118.58 44340.18 4382.68 45047.37 44317.07 44123.78 44048.60 435
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai45.42 40245.38 40345.55 42073.36 41626.85 44467.72 41734.19 44654.15 40249.65 42656.41 43325.43 42062.94 43619.45 43728.09 43746.86 436
kuosan39.70 40640.40 40737.58 42364.52 43226.98 44265.62 42533.02 44746.12 41842.79 43048.99 43624.10 42546.56 44412.16 44526.30 43839.20 437
Gipumacopyleft45.18 40341.86 40655.16 41577.03 39851.52 39532.50 43980.52 35032.46 43527.12 43835.02 4399.52 44275.50 41222.31 43660.21 40838.45 438
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CXcopyleft27.40 42640.17 44926.90 44324.59 45017.44 44223.95 44048.61 4379.77 44126.48 44518.06 43824.47 43928.83 439
E-PMN31.77 40730.64 41035.15 42452.87 44427.67 44157.09 43447.86 44224.64 43916.40 44433.05 44011.23 44054.90 44014.46 44318.15 44122.87 440
EMVS30.81 40929.65 41134.27 42550.96 44525.95 44556.58 43546.80 44324.01 44015.53 44530.68 44112.47 43754.43 44112.81 44417.05 44222.43 441
tmp_tt18.61 41221.40 41510.23 4284.82 45110.11 45134.70 43830.74 4491.48 44523.91 44126.07 44228.42 41713.41 44727.12 43115.35 4447.17 442
wuyk23d16.82 41315.94 41619.46 42758.74 43631.45 44039.22 4373.74 4526.84 4436.04 4462.70 4461.27 45124.29 44610.54 44614.40 4452.63 443
test1236.12 4158.11 4180.14 4290.06 4530.09 45471.05 4040.03 4540.04 4480.25 4491.30 4480.05 4520.03 4490.21 4480.01 4470.29 444
testmvs6.04 4168.02 4190.10 4300.08 4520.03 45569.74 4090.04 4530.05 4470.31 4481.68 4470.02 4530.04 4480.24 4470.02 4460.25 445
mmdepth0.00 4180.00 4210.00 4310.00 4540.00 4560.00 4420.00 4550.00 4490.00 4500.00 4490.00 4540.00 4500.00 4490.00 4480.00 446
monomultidepth0.00 4180.00 4210.00 4310.00 4540.00 4560.00 4420.00 4550.00 4490.00 4500.00 4490.00 4540.00 4500.00 4490.00 4480.00 446
test_blank0.00 4180.00 4210.00 4310.00 4540.00 4560.00 4420.00 4550.00 4490.00 4500.00 4490.00 4540.00 4500.00 4490.00 4480.00 446
uanet_test0.00 4180.00 4210.00 4310.00 4540.00 4560.00 4420.00 4550.00 4490.00 4500.00 4490.00 4540.00 4500.00 4490.00 4480.00 446
DCPMVS0.00 4180.00 4210.00 4310.00 4540.00 4560.00 4420.00 4550.00 4490.00 4500.00 4490.00 4540.00 4500.00 4490.00 4480.00 446
cdsmvs_eth3d_5k19.96 41126.61 4130.00 4310.00 4540.00 4560.00 44289.26 1950.00 4490.00 45088.61 19761.62 1770.00 4500.00 4490.00 4480.00 446
pcd_1.5k_mvsjas5.26 4177.02 4200.00 4310.00 4540.00 4560.00 4420.00 4550.00 4490.00 4500.00 44963.15 1530.00 4500.00 4490.00 4480.00 446
sosnet-low-res0.00 4180.00 4210.00 4310.00 4540.00 4560.00 4420.00 4550.00 4490.00 4500.00 4490.00 4540.00 4500.00 4490.00 4480.00 446
sosnet0.00 4180.00 4210.00 4310.00 4540.00 4560.00 4420.00 4550.00 4490.00 4500.00 4490.00 4540.00 4500.00 4490.00 4480.00 446
uncertanet0.00 4180.00 4210.00 4310.00 4540.00 4560.00 4420.00 4550.00 4490.00 4500.00 4490.00 4540.00 4500.00 4490.00 4480.00 446
Regformer0.00 4180.00 4210.00 4310.00 4540.00 4560.00 4420.00 4550.00 4490.00 4500.00 4490.00 4540.00 4500.00 4490.00 4480.00 446
ab-mvs-re7.23 4149.64 4170.00 4310.00 4540.00 4560.00 4420.00 4550.00 4490.00 45086.72 2490.00 4540.00 4500.00 4490.00 4480.00 446
uanet0.00 4180.00 4210.00 4310.00 4540.00 4560.00 4420.00 4550.00 4490.00 4500.00 4490.00 4540.00 4500.00 4490.00 4480.00 446
WAC-MVS42.58 42839.46 416
FOURS195.00 1072.39 3995.06 193.84 1574.49 13091.30 15
test_one_060195.07 771.46 5794.14 578.27 3892.05 1195.74 680.83 11
eth-test20.00 454
eth-test0.00 454
ZD-MVS94.38 2572.22 4492.67 6770.98 20987.75 4294.07 4974.01 3296.70 2784.66 6194.84 44
test_241102_ONE95.30 270.98 6694.06 1077.17 6093.10 195.39 1482.99 197.27 12
9.1488.26 1592.84 6391.52 4894.75 173.93 14688.57 2794.67 2375.57 2295.79 5886.77 4395.76 23
save fliter93.80 4072.35 4290.47 6691.17 13074.31 135
test072695.27 571.25 5993.60 694.11 677.33 5492.81 395.79 380.98 9
test_part295.06 872.65 3291.80 13
sam_mvs50.01 302
MTGPAbinary92.02 96
test_post178.90 3545.43 44548.81 32185.44 35159.25 310
test_post5.46 44450.36 29984.24 359
patchmatchnet-post74.00 41251.12 29088.60 314
MTMP92.18 3432.83 448
gm-plane-assit81.40 35853.83 37862.72 34380.94 36692.39 21263.40 271
TEST993.26 5272.96 2588.75 12891.89 10468.44 27085.00 7193.10 7974.36 2895.41 73
test_893.13 5472.57 3588.68 13391.84 10868.69 26584.87 7593.10 7974.43 2695.16 83
agg_prior92.85 6271.94 5091.78 11184.41 8694.93 94
test_prior472.60 3489.01 115
test_prior288.85 12275.41 10384.91 7393.54 6774.28 2983.31 7695.86 20
旧先验286.56 20758.10 38487.04 5388.98 30674.07 174
新几何286.29 217
原ACMM286.86 195
testdata291.01 27062.37 281
segment_acmp73.08 39
testdata184.14 27475.71 96
plane_prior790.08 10968.51 124
plane_prior689.84 11868.70 11860.42 203
plane_prior491.00 140
plane_prior368.60 12178.44 3378.92 166
plane_prior291.25 5279.12 25
plane_prior189.90 117
plane_prior68.71 11690.38 7077.62 4486.16 180
n20.00 455
nn0.00 455
door-mid69.98 411
test1192.23 87
door69.44 414
HQP5-MVS66.98 169
HQP-NCC89.33 13689.17 10676.41 8177.23 204
ACMP_Plane89.33 13689.17 10676.41 8177.23 204
BP-MVS77.47 135
HQP3-MVS92.19 9185.99 184
HQP2-MVS60.17 206
NP-MVS89.62 12268.32 12890.24 153
MDTV_nov1_ep1369.97 32583.18 32353.48 38077.10 37580.18 36060.45 36069.33 34080.44 37048.89 32086.90 33351.60 36578.51 283
ACMMP++_ref81.95 244
ACMMP++81.25 249
Test By Simon64.33 140