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 bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort by
MM89.16 689.23 788.97 490.79 9573.65 1092.66 2391.17 12786.57 187.39 4994.97 1971.70 5597.68 192.19 195.63 2895.57 1
fmvsm_s_conf0.5_n_485.39 6885.75 6184.30 11886.70 23965.83 18488.77 12489.78 17075.46 10188.35 2893.73 6569.19 8793.06 18391.30 288.44 14394.02 59
fmvsm_s_conf0.5_n_386.36 4787.46 2783.09 17687.08 23165.21 20089.09 11390.21 15879.67 1889.98 1895.02 1873.17 3891.71 23691.30 291.60 8992.34 138
fmvsm_s_conf0.5_n_886.56 4287.17 3384.73 10387.76 20865.62 19189.20 10492.21 8979.94 1689.74 2094.86 2068.63 9694.20 12290.83 491.39 9494.38 42
MVS_030487.69 2087.55 2488.12 1389.45 13071.76 5191.47 4989.54 17982.14 386.65 5794.28 3868.28 10197.46 690.81 595.31 3495.15 7
test_fmvsmconf_n85.92 5486.04 5585.57 7685.03 27569.51 9389.62 8990.58 14273.42 15887.75 4294.02 5272.85 4393.24 16790.37 690.75 10493.96 61
test_fmvsmconf0.1_n85.61 6285.65 6285.50 7782.99 32469.39 10089.65 8690.29 15673.31 16187.77 4194.15 4671.72 5493.23 16890.31 790.67 10693.89 67
test_fmvsmconf0.01_n84.73 7984.52 8185.34 8080.25 36569.03 10389.47 9289.65 17673.24 16586.98 5494.27 3966.62 11693.23 16890.26 889.95 11993.78 74
fmvsm_s_conf0.5_n_284.04 8484.11 8583.81 15186.17 24865.00 20886.96 18687.28 24474.35 13288.25 3194.23 4261.82 17392.60 19789.85 988.09 14893.84 70
fmvsm_s_conf0.5_n_585.22 7185.55 6484.25 12586.26 24567.40 15389.18 10589.31 18772.50 17488.31 2993.86 6169.66 8191.96 22489.81 1091.05 9993.38 93
fmvsm_s_conf0.1_n_283.80 8883.79 8983.83 15085.62 25964.94 21087.03 18486.62 26074.32 13387.97 3994.33 3660.67 19792.60 19789.72 1187.79 15093.96 61
MSC_two_6792asdad89.16 194.34 2775.53 292.99 4997.53 289.67 1296.44 994.41 39
No_MVS89.16 194.34 2775.53 292.99 4997.53 289.67 1296.44 994.41 39
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
fmvsm_s_conf0.5_n_685.55 6386.20 4883.60 15587.32 22465.13 20388.86 12091.63 11275.41 10288.23 3293.45 7268.56 9792.47 20489.52 1592.78 7393.20 104
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
test_0728_THIRD78.38 3592.12 995.78 481.46 797.40 989.42 1696.57 794.67 28
APDe-MVScopyleft89.15 789.63 687.73 2894.49 1871.69 5293.83 493.96 1375.70 9791.06 1696.03 176.84 1497.03 1789.09 1895.65 2794.47 38
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DVP-MVScopyleft89.60 390.35 387.33 4095.27 571.25 5993.49 992.73 6477.33 5392.12 995.78 480.98 997.40 989.08 1996.41 1293.33 97
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND87.71 3295.34 171.43 5893.49 994.23 397.49 489.08 1996.41 1294.21 50
SED-MVS90.08 290.85 287.77 2695.30 270.98 6693.57 794.06 1077.24 5693.10 195.72 882.99 197.44 789.07 2196.63 494.88 15
test_241102_TWO94.06 1077.24 5692.78 495.72 881.26 897.44 789.07 2196.58 694.26 49
fmvsm_l_conf0.5_n_386.02 4986.32 4585.14 8587.20 22768.54 12389.57 9090.44 14775.31 10687.49 4694.39 3572.86 4292.72 19489.04 2390.56 10794.16 51
IU-MVS95.30 271.25 5992.95 5566.81 27792.39 688.94 2496.63 494.85 20
fmvsm_l_conf0.5_n84.47 8084.54 7984.27 12285.42 26368.81 10988.49 13687.26 24668.08 26788.03 3693.49 6872.04 5091.77 23288.90 2589.14 13092.24 145
fmvsm_s_conf0.5_n83.80 8883.71 9084.07 13586.69 24067.31 15689.46 9383.07 31271.09 20086.96 5593.70 6669.02 9391.47 24888.79 2684.62 19393.44 92
MP-MVS-pluss87.67 2187.72 2087.54 3693.64 4472.04 4889.80 8193.50 2575.17 11286.34 5995.29 1570.86 6796.00 5488.78 2796.04 1694.58 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_fmvsmvis_n_192084.02 8583.87 8784.49 11084.12 29369.37 10188.15 15187.96 22870.01 22483.95 9693.23 7768.80 9591.51 24688.61 2889.96 11892.57 128
SMA-MVScopyleft89.08 889.23 788.61 694.25 3173.73 992.40 2493.63 2174.77 12392.29 795.97 274.28 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
fmvsm_s_conf0.1_n83.56 9683.38 9584.10 12984.86 27767.28 15789.40 9883.01 31370.67 20887.08 5293.96 5868.38 9991.45 24988.56 3084.50 19493.56 87
test_fmvsm_n_192085.29 7085.34 6885.13 8886.12 25069.93 8688.65 13290.78 13869.97 22688.27 3093.98 5771.39 6091.54 24388.49 3190.45 10993.91 64
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 45
fmvsm_l_conf0.5_n_a84.13 8384.16 8484.06 13785.38 26468.40 12688.34 14386.85 25667.48 27487.48 4793.40 7370.89 6691.61 23788.38 3389.22 12892.16 149
fmvsm_s_conf0.5_n_a83.63 9483.41 9484.28 12086.14 24968.12 13389.43 9482.87 31770.27 21987.27 5193.80 6469.09 8891.58 23988.21 3483.65 21393.14 108
fmvsm_s_conf0.1_n_a83.32 10482.99 10284.28 12083.79 30168.07 13589.34 10182.85 31869.80 23087.36 5094.06 5068.34 10091.56 24187.95 3583.46 21993.21 103
TSAR-MVS + MP.88.02 1788.11 1687.72 3093.68 4372.13 4691.41 5092.35 8274.62 12788.90 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
ACMMP_NAP88.05 1688.08 1787.94 1993.70 4173.05 2290.86 5793.59 2376.27 8788.14 3395.09 1771.06 6596.67 2987.67 3796.37 1494.09 55
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 14287.63 3894.27 5993.65 81
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
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.
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 4096.44 993.05 113
DPE-MVScopyleft89.48 589.98 488.01 1694.80 1172.69 3191.59 4394.10 875.90 9392.29 795.66 1081.67 697.38 1187.44 4196.34 1593.95 63
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS88.46 1288.74 1287.64 3592.78 6471.95 4992.40 2494.74 275.71 9589.16 2195.10 1675.65 2196.19 4687.07 4296.01 1794.79 22
9.1488.26 1592.84 6391.52 4894.75 173.93 14488.57 2794.67 2375.57 2295.79 5886.77 4395.76 23
MTAPA87.23 3187.00 3487.90 2294.18 3574.25 586.58 20192.02 9479.45 2185.88 6194.80 2168.07 10296.21 4586.69 4495.34 3293.23 100
fmvsm_s_conf0.5_n_783.34 10384.03 8681.28 22685.73 25665.13 20385.40 23589.90 16874.96 11782.13 11993.89 6066.65 11587.92 31586.56 4591.05 9990.80 186
reproduce-ours87.47 2387.61 2287.07 4593.27 5071.60 5391.56 4693.19 3574.98 11588.96 2295.54 1271.20 6396.54 3686.28 4693.49 6593.06 111
our_new_method87.47 2387.61 2287.07 4593.27 5071.60 5391.56 4693.19 3574.98 11588.96 2295.54 1271.20 6396.54 3686.28 4693.49 6593.06 111
reproduce_model87.28 3087.39 2886.95 4893.10 5671.24 6391.60 4293.19 3574.69 12488.80 2595.61 1170.29 7496.44 3986.20 4893.08 6993.16 106
DeepPCF-MVS80.84 188.10 1388.56 1386.73 5392.24 7169.03 10389.57 9093.39 3077.53 4989.79 1994.12 4778.98 1296.58 3585.66 4995.72 2494.58 33
MP-MVScopyleft87.71 1987.64 2187.93 2194.36 2673.88 692.71 2292.65 7077.57 4583.84 9894.40 3472.24 4796.28 4385.65 5095.30 3593.62 84
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
balanced_conf0386.78 3786.99 3586.15 6391.24 8367.61 14690.51 6292.90 5677.26 5587.44 4891.63 11471.27 6296.06 4985.62 5195.01 3794.78 23
ZNCC-MVS87.94 1887.85 1988.20 1294.39 2473.33 1993.03 1493.81 1776.81 6985.24 6894.32 3771.76 5396.93 1985.53 5295.79 2294.32 46
HPM-MVScopyleft87.11 3386.98 3687.50 3893.88 3972.16 4592.19 3393.33 3176.07 9083.81 9993.95 5969.77 8096.01 5385.15 5394.66 4794.32 46
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
train_agg86.43 4486.20 4887.13 4493.26 5272.96 2588.75 12691.89 10268.69 25885.00 7193.10 7974.43 2695.41 7384.97 5495.71 2593.02 115
test9_res84.90 5595.70 2692.87 120
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 54
MCST-MVS87.37 2987.25 3087.73 2894.53 1772.46 3889.82 7993.82 1673.07 16784.86 7692.89 8676.22 1796.33 4184.89 5795.13 3694.40 41
DeepC-MVS79.81 287.08 3586.88 4087.69 3391.16 8472.32 4390.31 7193.94 1477.12 6182.82 11394.23 4272.13 4997.09 1684.83 5895.37 3193.65 81
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS86.73 3886.67 4186.91 4994.11 3772.11 4792.37 2892.56 7574.50 12886.84 5694.65 2467.31 11195.77 5984.80 5992.85 7292.84 121
MVSMamba_PlusPlus85.99 5185.96 5686.05 6691.09 8567.64 14589.63 8892.65 7072.89 17284.64 8191.71 11071.85 5196.03 5084.77 6094.45 5494.49 37
ZD-MVS94.38 2572.22 4492.67 6770.98 20387.75 4294.07 4974.01 3296.70 2784.66 6194.84 44
PC_three_145268.21 26692.02 1294.00 5482.09 595.98 5684.58 6296.68 294.95 11
HFP-MVS87.58 2287.47 2687.94 1994.58 1673.54 1593.04 1293.24 3376.78 7184.91 7394.44 3270.78 6896.61 3284.53 6394.89 4293.66 77
ACMMPR87.44 2587.23 3188.08 1594.64 1373.59 1293.04 1293.20 3476.78 7184.66 8094.52 2568.81 9496.65 3084.53 6394.90 4194.00 60
region2R87.42 2787.20 3288.09 1494.63 1473.55 1393.03 1493.12 4076.73 7484.45 8594.52 2569.09 8896.70 2784.37 6594.83 4594.03 58
CANet86.45 4386.10 5387.51 3790.09 10870.94 7089.70 8592.59 7481.78 481.32 13091.43 12270.34 7297.23 1484.26 6693.36 6894.37 43
APD-MVScopyleft87.44 2587.52 2587.19 4294.24 3272.39 3991.86 4092.83 6073.01 16988.58 2694.52 2573.36 3496.49 3884.26 6695.01 3792.70 123
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CS-MVS86.69 3986.95 3785.90 7190.76 9667.57 14892.83 1793.30 3279.67 1884.57 8492.27 9871.47 5895.02 9384.24 6893.46 6795.13 8
CP-MVS87.11 3386.92 3887.68 3494.20 3473.86 793.98 392.82 6376.62 7783.68 10194.46 2967.93 10495.95 5784.20 6994.39 5593.23 100
GST-MVS87.42 2787.26 2987.89 2494.12 3672.97 2492.39 2693.43 2876.89 6784.68 7793.99 5670.67 7096.82 2284.18 7095.01 3793.90 66
BP-MVS184.32 8183.71 9086.17 6187.84 20167.85 13989.38 9989.64 17777.73 4183.98 9592.12 10256.89 22995.43 7084.03 7191.75 8895.24 6
EC-MVSNet86.01 5086.38 4484.91 9789.31 13966.27 17592.32 3093.63 2179.37 2284.17 9191.88 10669.04 9295.43 7083.93 7293.77 6393.01 116
OPU-MVS89.06 394.62 1575.42 493.57 794.02 5282.45 396.87 2083.77 7396.48 894.88 15
casdiffmvs_mvgpermissive85.99 5186.09 5485.70 7487.65 21267.22 16188.69 13093.04 4179.64 2085.33 6792.54 9573.30 3594.50 11283.49 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 13791.71 7864.94 21086.47 20491.87 10473.63 15086.60 5893.02 8476.57 1591.87 23083.36 7592.15 8195.35 3
test_prior288.85 12275.41 10284.91 7393.54 6774.28 2983.31 7695.86 20
PHI-MVS86.43 4486.17 5187.24 4190.88 9270.96 6892.27 3294.07 972.45 17585.22 6991.90 10569.47 8396.42 4083.28 7795.94 1994.35 44
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 56
X-MVStestdata80.37 16377.83 19988.00 1794.42 2073.33 1992.78 1892.99 4979.14 2383.67 10212.47 43667.45 10996.60 3383.06 7894.50 5194.07 56
mamv476.81 24378.23 19172.54 35986.12 25065.75 18978.76 34882.07 32664.12 31672.97 28891.02 13867.97 10368.08 42483.04 8078.02 28383.80 370
APD-MVS_3200maxsize85.97 5385.88 5786.22 6092.69 6669.53 9291.93 3792.99 4973.54 15485.94 6094.51 2865.80 13095.61 6283.04 8092.51 7793.53 90
agg_prior282.91 8295.45 2992.70 123
mPP-MVS86.67 4186.32 4587.72 3094.41 2273.55 1392.74 2092.22 8876.87 6882.81 11494.25 4166.44 12096.24 4482.88 8394.28 5893.38 93
SR-MVS-dyc-post85.77 5885.61 6386.23 5993.06 5870.63 7691.88 3892.27 8473.53 15585.69 6494.45 3065.00 13895.56 6382.75 8491.87 8592.50 132
RE-MVS-def85.48 6693.06 5870.63 7691.88 3892.27 8473.53 15585.69 6494.45 3063.87 14482.75 8491.87 8592.50 132
h-mvs3383.15 10682.19 11486.02 6990.56 9870.85 7388.15 15189.16 19576.02 9184.67 7891.39 12361.54 17895.50 6682.71 8675.48 32091.72 158
hse-mvs281.72 12880.94 13484.07 13588.72 16367.68 14485.87 22187.26 24676.02 9184.67 7888.22 20561.54 17893.48 15782.71 8673.44 34891.06 177
PGM-MVS86.68 4086.27 4787.90 2294.22 3373.38 1890.22 7393.04 4175.53 9983.86 9794.42 3367.87 10696.64 3182.70 8894.57 5093.66 77
ACMMPcopyleft85.89 5785.39 6787.38 3993.59 4572.63 3392.74 2093.18 3976.78 7180.73 13993.82 6364.33 14096.29 4282.67 8990.69 10593.23 100
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
diffmvspermissive82.10 12081.88 12282.76 19783.00 32263.78 23483.68 27389.76 17272.94 17082.02 12189.85 15865.96 12990.79 26782.38 9087.30 15893.71 76
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
patch_mono-283.65 9284.54 7980.99 23590.06 11365.83 18484.21 26588.74 21471.60 19085.01 7092.44 9674.51 2583.50 35982.15 9192.15 8193.64 83
SPE-MVS-test86.29 4886.48 4385.71 7391.02 8867.21 16292.36 2993.78 1878.97 3083.51 10591.20 12970.65 7195.15 8481.96 9294.89 4294.77 24
TSAR-MVS + GP.85.71 6085.33 6986.84 5091.34 8172.50 3689.07 11487.28 24476.41 8085.80 6290.22 15374.15 3195.37 7881.82 9391.88 8492.65 127
alignmvs85.48 6485.32 7085.96 7089.51 12769.47 9589.74 8392.47 7676.17 8887.73 4491.46 12170.32 7393.78 14281.51 9488.95 13194.63 32
sasdasda85.91 5585.87 5886.04 6789.84 11869.44 9890.45 6893.00 4676.70 7588.01 3791.23 12673.28 3693.91 13681.50 9588.80 13494.77 24
canonicalmvs85.91 5585.87 5886.04 6789.84 11869.44 9890.45 6893.00 4676.70 7588.01 3791.23 12673.28 3693.91 13681.50 9588.80 13494.77 24
baseline84.93 7684.98 7484.80 10187.30 22565.39 19787.30 17792.88 5777.62 4384.04 9492.26 9971.81 5293.96 12981.31 9790.30 11195.03 10
MGCFI-Net85.06 7585.51 6583.70 15389.42 13163.01 25289.43 9492.62 7376.43 7987.53 4591.34 12472.82 4493.42 16281.28 9888.74 13794.66 31
casdiffmvspermissive85.11 7385.14 7385.01 9187.20 22765.77 18887.75 16392.83 6077.84 4084.36 8892.38 9772.15 4893.93 13581.27 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
MVS_111021_HR85.14 7284.75 7786.32 5891.65 7972.70 3085.98 21790.33 15376.11 8982.08 12091.61 11671.36 6194.17 12581.02 10092.58 7692.08 151
HPM-MVS_fast85.35 6984.95 7686.57 5693.69 4270.58 7892.15 3591.62 11373.89 14582.67 11694.09 4862.60 15995.54 6580.93 10192.93 7193.57 86
CPTT-MVS83.73 9083.33 9784.92 9693.28 4970.86 7292.09 3690.38 14968.75 25779.57 15192.83 8860.60 20193.04 18680.92 10291.56 9290.86 185
ETV-MVS84.90 7884.67 7885.59 7589.39 13468.66 12088.74 12892.64 7279.97 1584.10 9285.71 27169.32 8595.38 7580.82 10391.37 9592.72 122
DeepC-MVS_fast79.65 386.91 3686.62 4287.76 2793.52 4672.37 4191.26 5193.04 4176.62 7784.22 8993.36 7571.44 5996.76 2580.82 10395.33 3394.16 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
nrg03083.88 8683.53 9284.96 9386.77 23769.28 10290.46 6792.67 6774.79 12282.95 10991.33 12572.70 4593.09 18180.79 10579.28 27192.50 132
EI-MVSNet-Vis-set84.19 8283.81 8885.31 8188.18 18267.85 13987.66 16589.73 17480.05 1482.95 10989.59 16670.74 6994.82 10180.66 10684.72 19193.28 99
MSLP-MVS++85.43 6685.76 6084.45 11191.93 7570.24 7990.71 5992.86 5877.46 5184.22 8992.81 9067.16 11392.94 18880.36 10794.35 5790.16 215
MVS_111021_LR82.61 11582.11 11584.11 12888.82 15771.58 5585.15 23886.16 26874.69 12480.47 14191.04 13562.29 16690.55 27180.33 10890.08 11690.20 214
DELS-MVS85.41 6785.30 7185.77 7288.49 17067.93 13885.52 23493.44 2778.70 3183.63 10489.03 18174.57 2495.71 6180.26 10994.04 6193.66 77
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
GDP-MVS83.52 9782.64 10886.16 6288.14 18568.45 12589.13 11192.69 6572.82 17383.71 10091.86 10855.69 23495.35 7980.03 11089.74 12294.69 27
EI-MVSNet-UG-set83.81 8783.38 9585.09 8987.87 19967.53 14987.44 17389.66 17579.74 1782.23 11889.41 17570.24 7594.74 10479.95 11183.92 20592.99 118
CSCG86.41 4686.19 5087.07 4592.91 6172.48 3790.81 5893.56 2473.95 14283.16 10891.07 13475.94 1895.19 8279.94 11294.38 5693.55 88
RRT-MVS82.60 11782.10 11684.10 12987.98 19562.94 25787.45 17291.27 12377.42 5279.85 14790.28 14956.62 23194.70 10779.87 11388.15 14794.67 28
OPM-MVS83.50 9882.95 10385.14 8588.79 16070.95 6989.13 11191.52 11677.55 4880.96 13791.75 10960.71 19594.50 11279.67 11486.51 17089.97 231
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CDPH-MVS85.76 5985.29 7287.17 4393.49 4771.08 6488.58 13492.42 8068.32 26584.61 8293.48 6972.32 4696.15 4879.00 11595.43 3094.28 48
MVSFormer82.85 11282.05 11885.24 8387.35 21870.21 8090.50 6490.38 14968.55 26081.32 13089.47 16961.68 17593.46 15978.98 11690.26 11292.05 152
test_djsdf80.30 16479.32 16583.27 16783.98 29765.37 19890.50 6490.38 14968.55 26076.19 22588.70 18856.44 23293.46 15978.98 11680.14 26190.97 182
test_vis1_n_192075.52 26575.78 24174.75 33879.84 37157.44 32683.26 28485.52 27562.83 33379.34 15586.17 26445.10 34479.71 37978.75 11881.21 24587.10 316
HQP_MVS83.64 9383.14 9885.14 8590.08 10968.71 11691.25 5292.44 7779.12 2578.92 16091.00 13960.42 20395.38 7578.71 11986.32 17291.33 169
plane_prior592.44 7795.38 7578.71 11986.32 17291.33 169
LPG-MVS_test82.08 12181.27 12784.50 10889.23 14368.76 11290.22 7391.94 10075.37 10476.64 21391.51 11854.29 24794.91 9578.44 12183.78 20689.83 236
LGP-MVS_train84.50 10889.23 14368.76 11291.94 10075.37 10476.64 21391.51 11854.29 24794.91 9578.44 12183.78 20689.83 236
lupinMVS81.39 13780.27 14684.76 10287.35 21870.21 8085.55 23086.41 26262.85 33281.32 13088.61 19261.68 17592.24 21678.41 12390.26 11291.83 155
jason81.39 13780.29 14584.70 10486.63 24269.90 8885.95 21886.77 25763.24 32581.07 13689.47 16961.08 19192.15 21878.33 12490.07 11792.05 152
jason: jason.
xiu_mvs_v1_base_debu80.80 14979.72 15584.03 14287.35 21870.19 8285.56 22788.77 21069.06 25081.83 12288.16 20650.91 28792.85 19078.29 12587.56 15289.06 255
xiu_mvs_v1_base80.80 14979.72 15584.03 14287.35 21870.19 8285.56 22788.77 21069.06 25081.83 12288.16 20650.91 28792.85 19078.29 12587.56 15289.06 255
xiu_mvs_v1_base_debi80.80 14979.72 15584.03 14287.35 21870.19 8285.56 22788.77 21069.06 25081.83 12288.16 20650.91 28792.85 19078.29 12587.56 15289.06 255
Effi-MVS+83.62 9583.08 9985.24 8388.38 17667.45 15088.89 11989.15 19675.50 10082.27 11788.28 20269.61 8294.45 11477.81 12887.84 14993.84 70
PS-MVSNAJss82.07 12281.31 12684.34 11686.51 24367.27 15889.27 10291.51 11771.75 18579.37 15390.22 15363.15 15394.27 11877.69 12982.36 23391.49 165
ACMP74.13 681.51 13680.57 13884.36 11489.42 13168.69 11989.97 7791.50 12074.46 13075.04 26090.41 14853.82 25294.54 10977.56 13082.91 22589.86 235
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
BP-MVS77.47 131
HQP-MVS82.61 11582.02 11984.37 11389.33 13666.98 16589.17 10692.19 9176.41 8077.23 19890.23 15260.17 20695.11 8777.47 13185.99 18091.03 179
MVS_Test83.15 10683.06 10083.41 16386.86 23363.21 24886.11 21592.00 9674.31 13482.87 11189.44 17470.03 7693.21 17077.39 13388.50 14293.81 72
3Dnovator+77.84 485.48 6484.47 8288.51 791.08 8673.49 1693.18 1193.78 1880.79 876.66 21293.37 7460.40 20596.75 2677.20 13493.73 6495.29 5
anonymousdsp78.60 20377.15 21782.98 18480.51 36367.08 16387.24 17989.53 18065.66 29775.16 25587.19 23352.52 26192.25 21577.17 13579.34 27089.61 243
mmtdpeth74.16 28073.01 28477.60 30683.72 30461.13 27685.10 24085.10 27972.06 18377.21 20280.33 36643.84 35385.75 33777.14 13652.61 41485.91 339
VDD-MVS83.01 11182.36 11284.96 9391.02 8866.40 17288.91 11888.11 22377.57 4584.39 8793.29 7652.19 26793.91 13677.05 13788.70 13894.57 35
XVG-OURS-SEG-HR80.81 14779.76 15483.96 14785.60 26068.78 11183.54 28090.50 14570.66 21176.71 21191.66 11160.69 19691.26 25476.94 13881.58 24191.83 155
jajsoiax79.29 18677.96 19483.27 16784.68 28266.57 17189.25 10390.16 16069.20 24675.46 24089.49 16845.75 33993.13 17976.84 13980.80 25190.11 219
SDMVSNet80.38 16180.18 14780.99 23589.03 15264.94 21080.45 32489.40 18375.19 11076.61 21589.98 15560.61 20087.69 31976.83 14083.55 21590.33 209
mvs_tets79.13 19077.77 20383.22 17184.70 28166.37 17389.17 10690.19 15969.38 23975.40 24389.46 17144.17 35193.15 17776.78 14180.70 25390.14 216
DPM-MVS84.93 7684.29 8386.84 5090.20 10673.04 2387.12 18193.04 4169.80 23082.85 11291.22 12873.06 4096.02 5276.72 14294.63 4891.46 168
test_cas_vis1_n_192073.76 28673.74 27573.81 34775.90 39359.77 29680.51 32282.40 32258.30 37481.62 12885.69 27244.35 35076.41 39776.29 14378.61 27485.23 349
ET-MVSNet_ETH3D78.63 20276.63 23284.64 10586.73 23869.47 9585.01 24284.61 28569.54 23666.51 36586.59 25150.16 29691.75 23376.26 14484.24 20292.69 125
v2v48280.23 16579.29 16683.05 18083.62 30564.14 22787.04 18389.97 16573.61 15178.18 17887.22 23161.10 19093.82 14076.11 14576.78 30091.18 173
test_fmvs1_n70.86 31970.24 31672.73 35772.51 41555.28 35881.27 31079.71 35651.49 40478.73 16284.87 29327.54 41177.02 39176.06 14679.97 26385.88 340
CLD-MVS82.31 11881.65 12484.29 11988.47 17167.73 14385.81 22592.35 8275.78 9478.33 17486.58 25364.01 14394.35 11576.05 14787.48 15590.79 187
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EPNet83.72 9182.92 10486.14 6584.22 29169.48 9491.05 5685.27 27781.30 676.83 20791.65 11266.09 12595.56 6376.00 14893.85 6293.38 93
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvs170.93 31870.52 31172.16 36173.71 40455.05 36080.82 31378.77 36551.21 40578.58 16784.41 30131.20 40676.94 39275.88 14980.12 26284.47 361
XVG-OURS80.41 16079.23 16883.97 14685.64 25869.02 10583.03 29290.39 14871.09 20077.63 18991.49 12054.62 24691.35 25275.71 15083.47 21891.54 162
V4279.38 18578.24 18982.83 18981.10 35765.50 19485.55 23089.82 16971.57 19178.21 17686.12 26560.66 19893.18 17675.64 15175.46 32289.81 238
PS-MVSNAJ81.69 13081.02 13283.70 15389.51 12768.21 13284.28 26490.09 16270.79 20581.26 13485.62 27663.15 15394.29 11675.62 15288.87 13388.59 279
xiu_mvs_v2_base81.69 13081.05 13183.60 15589.15 14668.03 13784.46 25890.02 16370.67 20881.30 13386.53 25663.17 15294.19 12475.60 15388.54 14088.57 280
EIA-MVS83.31 10582.80 10684.82 9989.59 12365.59 19288.21 14792.68 6674.66 12678.96 15886.42 25869.06 9095.26 8075.54 15490.09 11593.62 84
AUN-MVS79.21 18877.60 20984.05 14088.71 16467.61 14685.84 22387.26 24669.08 24977.23 19888.14 21053.20 25993.47 15875.50 15573.45 34791.06 177
mvsmamba80.60 15579.38 16284.27 12289.74 12167.24 16087.47 17086.95 25270.02 22375.38 24488.93 18251.24 28492.56 20075.47 15689.22 12893.00 117
reproduce_monomvs75.40 26974.38 26678.46 28983.92 29957.80 32083.78 27186.94 25373.47 15772.25 29984.47 29938.74 38289.27 29275.32 15770.53 36788.31 285
OMC-MVS82.69 11381.97 12184.85 9888.75 16267.42 15187.98 15490.87 13674.92 11879.72 14991.65 11262.19 16993.96 12975.26 15886.42 17193.16 106
v114480.03 16979.03 17283.01 18283.78 30264.51 21887.11 18290.57 14471.96 18478.08 18186.20 26361.41 18293.94 13274.93 15977.23 29190.60 197
MVSTER79.01 19377.88 19882.38 20383.07 31964.80 21484.08 26988.95 20669.01 25378.69 16387.17 23454.70 24492.43 20674.69 16080.57 25589.89 234
test_vis1_n69.85 33369.21 32271.77 36372.66 41455.27 35981.48 30676.21 38452.03 40175.30 25183.20 33128.97 40976.22 39974.60 16178.41 28083.81 369
test_fmvs268.35 34667.48 34570.98 37269.50 41851.95 38280.05 33076.38 38349.33 40774.65 26784.38 30223.30 42075.40 40874.51 16275.17 33185.60 343
PVSNet_Blended_VisFu82.62 11481.83 12384.96 9390.80 9469.76 9088.74 12891.70 11169.39 23878.96 15888.46 19765.47 13294.87 10074.42 16388.57 13990.24 213
v879.97 17179.02 17382.80 19284.09 29464.50 22087.96 15590.29 15674.13 14175.24 25386.81 24062.88 15893.89 13974.39 16475.40 32590.00 227
v14419279.47 17978.37 18582.78 19583.35 31063.96 23086.96 18690.36 15269.99 22577.50 19085.67 27460.66 19893.77 14474.27 16576.58 30190.62 195
ACMM73.20 880.78 15279.84 15383.58 15789.31 13968.37 12789.99 7691.60 11470.28 21877.25 19689.66 16253.37 25793.53 15574.24 16682.85 22688.85 268
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
旧先验286.56 20258.10 37787.04 5388.98 29974.07 167
v119279.59 17678.43 18483.07 17983.55 30764.52 21786.93 18990.58 14270.83 20477.78 18685.90 26759.15 20993.94 13273.96 16877.19 29390.76 189
v1079.74 17378.67 17782.97 18584.06 29564.95 20987.88 16190.62 14173.11 16675.11 25786.56 25461.46 18194.05 12873.68 16975.55 31889.90 233
v192192079.22 18778.03 19382.80 19283.30 31263.94 23186.80 19390.33 15369.91 22877.48 19185.53 27858.44 21393.75 14673.60 17076.85 29890.71 193
cl2278.07 21677.01 21981.23 22882.37 33861.83 27083.55 27887.98 22768.96 25475.06 25983.87 31361.40 18391.88 22973.53 17176.39 30589.98 230
Effi-MVS+-dtu80.03 16978.57 18084.42 11285.13 27368.74 11488.77 12488.10 22474.99 11474.97 26183.49 32657.27 22593.36 16373.53 17180.88 24991.18 173
c3_l78.75 19877.91 19681.26 22782.89 32661.56 27384.09 26889.13 19869.97 22675.56 23684.29 30566.36 12192.09 22073.47 17375.48 32090.12 218
VDDNet81.52 13480.67 13784.05 14090.44 10164.13 22889.73 8485.91 27171.11 19983.18 10793.48 6950.54 29393.49 15673.40 17488.25 14594.54 36
CANet_DTU80.61 15479.87 15282.83 18985.60 26063.17 25187.36 17488.65 21676.37 8475.88 23188.44 19853.51 25593.07 18273.30 17589.74 12292.25 143
miper_ehance_all_eth78.59 20477.76 20481.08 23382.66 33161.56 27383.65 27489.15 19668.87 25575.55 23783.79 31766.49 11992.03 22173.25 17676.39 30589.64 242
3Dnovator76.31 583.38 10282.31 11386.59 5587.94 19672.94 2890.64 6092.14 9377.21 5875.47 23892.83 8858.56 21294.72 10573.24 17792.71 7592.13 150
v124078.99 19477.78 20282.64 19883.21 31463.54 23986.62 20090.30 15569.74 23577.33 19485.68 27357.04 22793.76 14573.13 17876.92 29590.62 195
miper_enhance_ethall77.87 22376.86 22380.92 23881.65 34561.38 27582.68 29388.98 20365.52 29975.47 23882.30 34665.76 13192.00 22372.95 17976.39 30589.39 248
MG-MVS83.41 10083.45 9383.28 16692.74 6562.28 26488.17 14989.50 18175.22 10781.49 12992.74 9466.75 11495.11 8772.85 18091.58 9192.45 135
EPP-MVSNet83.40 10183.02 10184.57 10690.13 10764.47 22192.32 3090.73 13974.45 13179.35 15491.10 13269.05 9195.12 8572.78 18187.22 15994.13 53
test_fmvs363.36 37061.82 37367.98 38762.51 42746.96 40877.37 36674.03 39445.24 41267.50 34778.79 38312.16 43272.98 41672.77 18266.02 38483.99 367
IterMVS-LS80.06 16879.38 16282.11 20685.89 25363.20 24986.79 19489.34 18574.19 13875.45 24186.72 24366.62 11692.39 20872.58 18376.86 29790.75 190
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tt080578.73 19977.83 19981.43 22085.17 26960.30 29189.41 9790.90 13471.21 19777.17 20388.73 18746.38 32893.21 17072.57 18478.96 27390.79 187
EI-MVSNet80.52 15979.98 14982.12 20584.28 28963.19 25086.41 20588.95 20674.18 13978.69 16387.54 22366.62 11692.43 20672.57 18480.57 25590.74 191
Vis-MVSNetpermissive83.46 9982.80 10685.43 7990.25 10568.74 11490.30 7290.13 16176.33 8680.87 13892.89 8661.00 19294.20 12272.45 18690.97 10193.35 96
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
LFMVS81.82 12781.23 12883.57 15891.89 7663.43 24489.84 7881.85 32977.04 6483.21 10693.10 7952.26 26693.43 16171.98 18789.95 11993.85 68
v14878.72 20077.80 20181.47 21982.73 32961.96 26886.30 21088.08 22573.26 16376.18 22685.47 28062.46 16392.36 21071.92 18873.82 34490.09 221
PVSNet_BlendedMVS80.60 15580.02 14882.36 20488.85 15465.40 19586.16 21492.00 9669.34 24078.11 17986.09 26666.02 12794.27 11871.52 18982.06 23687.39 303
PVSNet_Blended80.98 14280.34 14382.90 18788.85 15465.40 19584.43 26092.00 9667.62 27178.11 17985.05 29166.02 12794.27 11871.52 18989.50 12489.01 260
eth_miper_zixun_eth77.92 22176.69 23081.61 21783.00 32261.98 26783.15 28689.20 19469.52 23774.86 26384.35 30461.76 17492.56 20071.50 19172.89 35290.28 212
UA-Net85.08 7484.96 7585.45 7892.07 7368.07 13589.78 8290.86 13782.48 284.60 8393.20 7869.35 8495.22 8171.39 19290.88 10393.07 110
FA-MVS(test-final)80.96 14379.91 15184.10 12988.30 17965.01 20784.55 25590.01 16473.25 16479.61 15087.57 22058.35 21494.72 10571.29 19386.25 17492.56 129
cl____77.72 22676.76 22780.58 24482.49 33560.48 28883.09 28887.87 23169.22 24474.38 27285.22 28662.10 17091.53 24471.09 19475.41 32489.73 241
DIV-MVS_self_test77.72 22676.76 22780.58 24482.48 33660.48 28883.09 28887.86 23269.22 24474.38 27285.24 28462.10 17091.53 24471.09 19475.40 32589.74 240
MonoMVSNet76.49 25175.80 24078.58 28381.55 34858.45 30786.36 20886.22 26674.87 12174.73 26583.73 31951.79 27988.73 30470.78 19672.15 35788.55 281
test_yl81.17 13980.47 14183.24 16989.13 14763.62 23586.21 21289.95 16672.43 17881.78 12689.61 16457.50 22293.58 15070.75 19786.90 16392.52 130
DCV-MVSNet81.17 13980.47 14183.24 16989.13 14763.62 23586.21 21289.95 16672.43 17881.78 12689.61 16457.50 22293.58 15070.75 19786.90 16392.52 130
VNet82.21 11982.41 11081.62 21590.82 9360.93 28084.47 25689.78 17076.36 8584.07 9391.88 10664.71 13990.26 27370.68 19988.89 13293.66 77
mvs_anonymous79.42 18279.11 17180.34 24984.45 28857.97 31582.59 29487.62 23767.40 27576.17 22888.56 19568.47 9889.59 28670.65 20086.05 17893.47 91
VPA-MVSNet80.60 15580.55 13980.76 24188.07 19060.80 28386.86 19191.58 11575.67 9880.24 14389.45 17363.34 14790.25 27470.51 20179.22 27291.23 172
PAPM_NR83.02 11082.41 11084.82 9992.47 7066.37 17387.93 15891.80 10773.82 14677.32 19590.66 14467.90 10594.90 9770.37 20289.48 12593.19 105
thisisatest053079.40 18377.76 20484.31 11787.69 21165.10 20687.36 17484.26 29270.04 22277.42 19288.26 20449.94 29994.79 10370.20 20384.70 19293.03 114
tttt051779.40 18377.91 19683.90 14988.10 18863.84 23288.37 14284.05 29471.45 19376.78 20989.12 17849.93 30194.89 9870.18 20483.18 22392.96 119
UniMVSNet_NR-MVSNet81.88 12581.54 12582.92 18688.46 17263.46 24287.13 18092.37 8180.19 1278.38 17289.14 17771.66 5793.05 18470.05 20576.46 30392.25 143
DU-MVS81.12 14180.52 14082.90 18787.80 20363.46 24287.02 18591.87 10479.01 2878.38 17289.07 17965.02 13693.05 18470.05 20576.46 30392.20 146
XVG-ACMP-BASELINE76.11 25774.27 26881.62 21583.20 31564.67 21683.60 27789.75 17369.75 23371.85 30387.09 23632.78 40192.11 21969.99 20780.43 25788.09 289
GeoE81.71 12981.01 13383.80 15289.51 12764.45 22288.97 11688.73 21571.27 19678.63 16689.76 16066.32 12293.20 17369.89 20886.02 17993.74 75
FIs82.07 12282.42 10981.04 23488.80 15958.34 30988.26 14693.49 2676.93 6678.47 17191.04 13569.92 7892.34 21269.87 20984.97 18892.44 136
114514_t80.68 15379.51 15984.20 12694.09 3867.27 15889.64 8791.11 13058.75 37274.08 27490.72 14358.10 21595.04 9269.70 21089.42 12690.30 211
Anonymous2023121178.97 19577.69 20782.81 19190.54 9964.29 22590.11 7591.51 11765.01 30676.16 22988.13 21150.56 29293.03 18769.68 21177.56 29091.11 175
Patchmatch-RL test70.24 32767.78 34077.61 30477.43 38859.57 30071.16 39670.33 40262.94 33168.65 33872.77 40850.62 29185.49 34269.58 21266.58 38287.77 295
UniMVSNet (Re)81.60 13381.11 13083.09 17688.38 17664.41 22387.60 16693.02 4578.42 3478.56 16888.16 20669.78 7993.26 16669.58 21276.49 30291.60 159
IterMVS-SCA-FT75.43 26773.87 27380.11 25582.69 33064.85 21381.57 30583.47 30369.16 24770.49 31584.15 31151.95 27488.15 31269.23 21472.14 35887.34 305
v7n78.97 19577.58 21083.14 17483.45 30965.51 19388.32 14491.21 12573.69 14972.41 29686.32 26157.93 21693.81 14169.18 21575.65 31690.11 219
Anonymous2024052980.19 16778.89 17584.10 12990.60 9764.75 21588.95 11790.90 13465.97 29480.59 14091.17 13149.97 29893.73 14869.16 21682.70 23093.81 72
miper_lstm_enhance74.11 28173.11 28377.13 31280.11 36759.62 29872.23 39286.92 25566.76 27970.40 31682.92 33656.93 22882.92 36369.06 21772.63 35388.87 267
testdata79.97 25790.90 9164.21 22684.71 28359.27 36585.40 6692.91 8562.02 17289.08 29768.95 21891.37 9586.63 326
test111179.43 18179.18 17080.15 25489.99 11453.31 37687.33 17677.05 37975.04 11380.23 14492.77 9348.97 31392.33 21368.87 21992.40 8094.81 21
GA-MVS76.87 24275.17 25681.97 21082.75 32862.58 25981.44 30886.35 26572.16 18274.74 26482.89 33746.20 33392.02 22268.85 22081.09 24691.30 171
test250677.30 23676.49 23379.74 26290.08 10952.02 38087.86 16263.10 42274.88 11980.16 14592.79 9138.29 38692.35 21168.74 22192.50 7894.86 18
ECVR-MVScopyleft79.61 17479.26 16780.67 24390.08 10954.69 36387.89 16077.44 37574.88 11980.27 14292.79 9148.96 31492.45 20568.55 22292.50 7894.86 18
UGNet80.83 14679.59 15884.54 10788.04 19168.09 13489.42 9688.16 22276.95 6576.22 22489.46 17149.30 30893.94 13268.48 22390.31 11091.60 159
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
FC-MVSNet-test81.52 13482.02 11980.03 25688.42 17555.97 34887.95 15693.42 2977.10 6277.38 19390.98 14169.96 7791.79 23168.46 22484.50 19492.33 139
DP-MVS Recon83.11 10982.09 11786.15 6394.44 1970.92 7188.79 12392.20 9070.53 21379.17 15691.03 13764.12 14296.03 5068.39 22590.14 11491.50 164
UniMVSNet_ETH3D79.10 19178.24 18981.70 21486.85 23460.24 29287.28 17888.79 20974.25 13776.84 20690.53 14749.48 30491.56 24167.98 22682.15 23493.29 98
D2MVS74.82 27473.21 28179.64 26679.81 37262.56 26080.34 32687.35 24364.37 31368.86 33682.66 34146.37 32990.10 27667.91 22781.24 24486.25 329
IS-MVSNet83.15 10682.81 10584.18 12789.94 11663.30 24691.59 4388.46 22079.04 2779.49 15292.16 10065.10 13594.28 11767.71 22891.86 8794.95 11
Fast-Effi-MVS+-dtu78.02 21876.49 23382.62 19983.16 31866.96 16786.94 18887.45 24272.45 17571.49 30884.17 31054.79 24391.58 23967.61 22980.31 25889.30 251
PAPR81.66 13280.89 13583.99 14590.27 10464.00 22986.76 19791.77 11068.84 25677.13 20589.50 16767.63 10794.88 9967.55 23088.52 14193.09 109
cascas76.72 24574.64 26082.99 18385.78 25565.88 18382.33 29689.21 19360.85 35172.74 29081.02 35747.28 32193.75 14667.48 23185.02 18789.34 250
131476.53 24775.30 25480.21 25383.93 29862.32 26384.66 25088.81 20860.23 35670.16 32184.07 31255.30 23790.73 26967.37 23283.21 22287.59 300
无先验87.48 16988.98 20360.00 35894.12 12667.28 23388.97 263
thisisatest051577.33 23575.38 25183.18 17285.27 26863.80 23382.11 29983.27 30665.06 30475.91 23083.84 31549.54 30394.27 11867.24 23486.19 17591.48 166
原ACMM184.35 11593.01 6068.79 11092.44 7763.96 32281.09 13591.57 11766.06 12695.45 6867.19 23594.82 4688.81 270
Baseline_NR-MVSNet78.15 21478.33 18777.61 30485.79 25456.21 34686.78 19585.76 27373.60 15277.93 18487.57 22065.02 13688.99 29867.14 23675.33 32787.63 297
TranMVSNet+NR-MVSNet80.84 14580.31 14482.42 20287.85 20062.33 26287.74 16491.33 12280.55 977.99 18389.86 15765.23 13492.62 19567.05 23775.24 33092.30 141
Fast-Effi-MVS+80.81 14779.92 15083.47 15988.85 15464.51 21885.53 23289.39 18470.79 20578.49 17085.06 29067.54 10893.58 15067.03 23886.58 16892.32 140
VPNet78.69 20178.66 17878.76 27988.31 17855.72 35284.45 25986.63 25976.79 7078.26 17590.55 14659.30 20889.70 28566.63 23977.05 29490.88 184
PM-MVS66.41 35864.14 36173.20 35373.92 40356.45 33978.97 34564.96 41963.88 32364.72 37680.24 36819.84 42483.44 36066.24 24064.52 38979.71 400
test-LLR72.94 30172.43 29074.48 33981.35 35358.04 31378.38 35377.46 37366.66 28169.95 32579.00 38048.06 31779.24 38066.13 24184.83 18986.15 332
test-mter71.41 31370.39 31574.48 33981.35 35358.04 31378.38 35377.46 37360.32 35569.95 32579.00 38036.08 39579.24 38066.13 24184.83 18986.15 332
MVS78.19 21376.99 22181.78 21285.66 25766.99 16484.66 25090.47 14655.08 39372.02 30285.27 28363.83 14594.11 12766.10 24389.80 12184.24 363
NR-MVSNet80.23 16579.38 16282.78 19587.80 20363.34 24586.31 20991.09 13179.01 2872.17 30089.07 17967.20 11292.81 19366.08 24475.65 31692.20 146
CVMVSNet72.99 30072.58 28974.25 34284.28 28950.85 39486.41 20583.45 30444.56 41373.23 28587.54 22349.38 30685.70 33865.90 24578.44 27886.19 331
IterMVS74.29 27772.94 28578.35 29081.53 34963.49 24181.58 30482.49 32168.06 26869.99 32483.69 32151.66 28185.54 34165.85 24671.64 36186.01 336
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-074.26 27872.42 29179.80 26183.76 30359.59 29985.92 22086.64 25866.39 28866.96 35587.58 21939.46 37791.60 23865.76 24769.27 37288.22 286
tpmrst72.39 30372.13 29473.18 35480.54 36249.91 39879.91 33379.08 36363.11 32771.69 30579.95 37155.32 23682.77 36465.66 24873.89 34286.87 319
MAR-MVS81.84 12680.70 13685.27 8291.32 8271.53 5689.82 7990.92 13369.77 23278.50 16986.21 26262.36 16594.52 11165.36 24992.05 8389.77 239
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
Anonymous20240521178.25 20977.01 21981.99 20991.03 8760.67 28584.77 24783.90 29670.65 21280.00 14691.20 12941.08 37191.43 25065.21 25085.26 18693.85 68
ab-mvs79.51 17778.97 17481.14 23188.46 17260.91 28183.84 27089.24 19270.36 21579.03 15788.87 18563.23 15190.21 27565.12 25182.57 23192.28 142
IB-MVS68.01 1575.85 26173.36 28083.31 16584.76 28066.03 17783.38 28185.06 28070.21 22169.40 33181.05 35645.76 33894.66 10865.10 25275.49 31989.25 252
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
WR-MVS79.49 17879.22 16980.27 25188.79 16058.35 30885.06 24188.61 21878.56 3277.65 18888.34 20063.81 14690.66 27064.98 25377.22 29291.80 157
CostFormer75.24 27173.90 27279.27 27182.65 33258.27 31080.80 31482.73 32061.57 34675.33 25083.13 33255.52 23591.07 26364.98 25378.34 28188.45 282
API-MVS81.99 12481.23 12884.26 12490.94 9070.18 8591.10 5589.32 18671.51 19278.66 16588.28 20265.26 13395.10 9064.74 25591.23 9787.51 301
新几何183.42 16193.13 5470.71 7485.48 27657.43 38381.80 12591.98 10363.28 14892.27 21464.60 25692.99 7087.27 308
testing9176.54 24675.66 24579.18 27488.43 17455.89 34981.08 31183.00 31473.76 14875.34 24684.29 30546.20 33390.07 27764.33 25784.50 19491.58 161
testing9976.09 25875.12 25779.00 27588.16 18355.50 35580.79 31581.40 33473.30 16275.17 25484.27 30844.48 34890.02 27864.28 25884.22 20391.48 166
pm-mvs177.25 23776.68 23178.93 27784.22 29158.62 30686.41 20588.36 22171.37 19473.31 28388.01 21261.22 18889.15 29664.24 25973.01 35189.03 259
TESTMET0.1,169.89 33269.00 32472.55 35879.27 38156.85 33278.38 35374.71 39257.64 38068.09 34277.19 39337.75 38876.70 39363.92 26084.09 20484.10 366
QAPM80.88 14479.50 16085.03 9088.01 19468.97 10791.59 4392.00 9666.63 28675.15 25692.16 10057.70 21995.45 6863.52 26188.76 13690.66 194
baseline275.70 26273.83 27481.30 22583.26 31361.79 27182.57 29580.65 34166.81 27766.88 35683.42 32757.86 21892.19 21763.47 26279.57 26589.91 232
LCM-MVSNet-Re77.05 23876.94 22277.36 30887.20 22751.60 38780.06 32980.46 34575.20 10967.69 34586.72 24362.48 16288.98 29963.44 26389.25 12791.51 163
gm-plane-assit81.40 35153.83 37162.72 33680.94 35992.39 20863.40 264
baseline176.98 24076.75 22977.66 30288.13 18655.66 35385.12 23981.89 32773.04 16876.79 20888.90 18362.43 16487.78 31863.30 26571.18 36489.55 245
AdaColmapbinary80.58 15879.42 16184.06 13793.09 5768.91 10889.36 10088.97 20569.27 24175.70 23489.69 16157.20 22695.77 5963.06 26688.41 14487.50 302
test_vis1_rt60.28 37558.42 37865.84 39267.25 42155.60 35470.44 40160.94 42544.33 41459.00 40066.64 41524.91 41568.67 42262.80 26769.48 37073.25 411
GBi-Net78.40 20677.40 21281.40 22287.60 21363.01 25288.39 13989.28 18871.63 18775.34 24687.28 22754.80 24091.11 25762.72 26879.57 26590.09 221
test178.40 20677.40 21281.40 22287.60 21363.01 25288.39 13989.28 18871.63 18775.34 24687.28 22754.80 24091.11 25762.72 26879.57 26590.09 221
FMVSNet377.88 22276.85 22480.97 23786.84 23562.36 26186.52 20388.77 21071.13 19875.34 24686.66 24954.07 25091.10 26062.72 26879.57 26589.45 247
CMPMVSbinary51.72 2170.19 32868.16 33076.28 31773.15 41157.55 32479.47 33683.92 29548.02 40956.48 40984.81 29543.13 35786.42 33262.67 27181.81 24084.89 356
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sd_testset77.70 22877.40 21278.60 28289.03 15260.02 29479.00 34485.83 27275.19 11076.61 21589.98 15554.81 23985.46 34362.63 27283.55 21590.33 209
FMVSNet278.20 21277.21 21681.20 22987.60 21362.89 25887.47 17089.02 20171.63 18775.29 25287.28 22754.80 24091.10 26062.38 27379.38 26989.61 243
testdata291.01 26462.37 274
testing1175.14 27274.01 26978.53 28688.16 18356.38 34280.74 31880.42 34770.67 20872.69 29383.72 32043.61 35589.86 28062.29 27583.76 20889.36 249
CP-MVSNet78.22 21078.34 18677.84 29987.83 20254.54 36587.94 15791.17 12777.65 4273.48 28288.49 19662.24 16888.43 30962.19 27674.07 33990.55 199
XXY-MVS75.41 26875.56 24674.96 33383.59 30657.82 31980.59 32183.87 29766.54 28774.93 26288.31 20163.24 15080.09 37862.16 27776.85 29886.97 318
pmmvs674.69 27573.39 27878.61 28181.38 35257.48 32586.64 19987.95 22964.99 30770.18 31986.61 25050.43 29489.52 28762.12 27870.18 36988.83 269
1112_ss77.40 23476.43 23580.32 25089.11 15160.41 29083.65 27487.72 23662.13 34273.05 28786.72 24362.58 16189.97 27962.11 27980.80 25190.59 198
PS-CasMVS78.01 21978.09 19277.77 30187.71 20954.39 36788.02 15391.22 12477.50 5073.26 28488.64 19160.73 19488.41 31061.88 28073.88 34390.53 200
CDS-MVSNet79.07 19277.70 20683.17 17387.60 21368.23 13184.40 26286.20 26767.49 27376.36 22186.54 25561.54 17890.79 26761.86 28187.33 15790.49 202
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OpenMVScopyleft72.83 1079.77 17278.33 18784.09 13385.17 26969.91 8790.57 6190.97 13266.70 28072.17 30091.91 10454.70 24493.96 12961.81 28290.95 10288.41 284
K. test v371.19 31468.51 32679.21 27383.04 32157.78 32184.35 26376.91 38072.90 17162.99 38782.86 33839.27 37891.09 26261.65 28352.66 41388.75 273
CHOSEN 1792x268877.63 23075.69 24283.44 16089.98 11568.58 12278.70 34987.50 24056.38 38875.80 23386.84 23958.67 21191.40 25161.58 28485.75 18490.34 208
PCF-MVS73.52 780.38 16178.84 17685.01 9187.71 20968.99 10683.65 27491.46 12163.00 32977.77 18790.28 14966.10 12495.09 9161.40 28588.22 14690.94 183
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HY-MVS69.67 1277.95 22077.15 21780.36 24887.57 21760.21 29383.37 28287.78 23566.11 29075.37 24587.06 23863.27 14990.48 27261.38 28682.43 23290.40 206
HyFIR lowres test77.53 23175.40 25083.94 14889.59 12366.62 16980.36 32588.64 21756.29 38976.45 21885.17 28757.64 22093.28 16561.34 28783.10 22491.91 154
PMMVS69.34 33668.67 32571.35 36875.67 39562.03 26675.17 37873.46 39550.00 40668.68 33779.05 37852.07 27278.13 38561.16 28882.77 22773.90 410
FMVSNet177.44 23276.12 23981.40 22286.81 23663.01 25288.39 13989.28 18870.49 21474.39 27187.28 22749.06 31291.11 25760.91 28978.52 27690.09 221
sss73.60 28873.64 27673.51 34982.80 32755.01 36176.12 37081.69 33062.47 33874.68 26685.85 27057.32 22478.11 38660.86 29080.93 24787.39 303
Test_1112_low_res76.40 25375.44 24879.27 27189.28 14158.09 31181.69 30387.07 25059.53 36372.48 29586.67 24861.30 18589.33 29060.81 29180.15 26090.41 205
sc_t172.19 30869.51 31980.23 25284.81 27861.09 27884.68 24980.22 35160.70 35271.27 30983.58 32436.59 39289.24 29360.41 29263.31 39290.37 207
BH-untuned79.47 17978.60 17982.05 20789.19 14565.91 18286.07 21688.52 21972.18 18075.42 24287.69 21761.15 18993.54 15460.38 29386.83 16586.70 324
WTY-MVS75.65 26375.68 24375.57 32486.40 24456.82 33377.92 36282.40 32265.10 30376.18 22687.72 21563.13 15680.90 37560.31 29481.96 23789.00 262
pmmvs474.03 28471.91 29580.39 24781.96 34168.32 12881.45 30782.14 32459.32 36469.87 32785.13 28852.40 26488.13 31360.21 29574.74 33584.73 359
PEN-MVS77.73 22577.69 20777.84 29987.07 23253.91 37087.91 15991.18 12677.56 4773.14 28688.82 18661.23 18789.17 29559.95 29672.37 35490.43 204
CR-MVSNet73.37 29171.27 30479.67 26581.32 35565.19 20175.92 37280.30 34959.92 35972.73 29181.19 35452.50 26286.69 32759.84 29777.71 28687.11 314
mvs5depth69.45 33567.45 34675.46 32873.93 40255.83 35079.19 34183.23 30766.89 27671.63 30683.32 32833.69 40085.09 34659.81 29855.34 41085.46 345
lessismore_v078.97 27681.01 35857.15 32965.99 41561.16 39382.82 33939.12 38091.34 25359.67 29946.92 42088.43 283
CNLPA78.08 21576.79 22681.97 21090.40 10271.07 6587.59 16784.55 28666.03 29372.38 29789.64 16357.56 22186.04 33559.61 30083.35 22088.79 271
BH-RMVSNet79.61 17478.44 18383.14 17489.38 13565.93 18184.95 24487.15 24973.56 15378.19 17789.79 15956.67 23093.36 16359.53 30186.74 16690.13 217
MS-PatchMatch73.83 28572.67 28777.30 31083.87 30066.02 17881.82 30084.66 28461.37 34968.61 33982.82 33947.29 32088.21 31159.27 30284.32 20177.68 404
test_post178.90 3475.43 43848.81 31685.44 34459.25 303
SCA74.22 27972.33 29279.91 25884.05 29662.17 26579.96 33279.29 36166.30 28972.38 29780.13 36951.95 27488.60 30759.25 30377.67 28988.96 264
FE-MVS77.78 22475.68 24384.08 13488.09 18966.00 17983.13 28787.79 23468.42 26478.01 18285.23 28545.50 34295.12 8559.11 30585.83 18391.11 175
SixPastTwentyTwo73.37 29171.26 30579.70 26385.08 27457.89 31785.57 22683.56 30171.03 20265.66 36985.88 26842.10 36592.57 19959.11 30563.34 39188.65 277
WR-MVS_H78.51 20578.49 18178.56 28488.02 19256.38 34288.43 13792.67 6777.14 6073.89 27687.55 22266.25 12389.24 29358.92 30773.55 34690.06 225
PLCcopyleft70.83 1178.05 21776.37 23783.08 17891.88 7767.80 14188.19 14889.46 18264.33 31469.87 32788.38 19953.66 25393.58 15058.86 30882.73 22887.86 293
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
RPSCF73.23 29671.46 30078.54 28582.50 33459.85 29582.18 29882.84 31958.96 36871.15 31289.41 17545.48 34384.77 35058.82 30971.83 36091.02 181
EU-MVSNet68.53 34467.61 34371.31 36978.51 38547.01 40784.47 25684.27 29142.27 41666.44 36684.79 29640.44 37483.76 35558.76 31068.54 37783.17 375
pmmvs-eth3d70.50 32467.83 33878.52 28777.37 38966.18 17681.82 30081.51 33258.90 36963.90 38380.42 36442.69 36086.28 33358.56 31165.30 38783.11 377
TAMVS78.89 19777.51 21183.03 18187.80 20367.79 14284.72 24885.05 28167.63 27076.75 21087.70 21662.25 16790.82 26658.53 31287.13 16090.49 202
WBMVS73.43 29072.81 28675.28 33087.91 19750.99 39378.59 35281.31 33665.51 30174.47 27084.83 29446.39 32786.68 32858.41 31377.86 28488.17 288
ACMH+68.96 1476.01 25974.01 26982.03 20888.60 16765.31 19988.86 12087.55 23870.25 22067.75 34487.47 22541.27 36993.19 17558.37 31475.94 31387.60 298
tpm72.37 30571.71 29774.35 34182.19 33952.00 38179.22 34077.29 37764.56 31072.95 28983.68 32251.35 28283.26 36258.33 31575.80 31487.81 294
BH-w/o78.21 21177.33 21580.84 23988.81 15865.13 20384.87 24587.85 23369.75 23374.52 26984.74 29761.34 18493.11 18058.24 31685.84 18284.27 362
Vis-MVSNet (Re-imp)78.36 20878.45 18278.07 29588.64 16651.78 38686.70 19879.63 35774.14 14075.11 25790.83 14261.29 18689.75 28358.10 31791.60 8992.69 125
MVP-Stereo76.12 25674.46 26581.13 23285.37 26569.79 8984.42 26187.95 22965.03 30567.46 34885.33 28253.28 25891.73 23558.01 31883.27 22181.85 389
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ambc75.24 33173.16 41050.51 39663.05 42487.47 24164.28 37877.81 39017.80 42689.73 28457.88 31960.64 39985.49 344
TR-MVS77.44 23276.18 23881.20 22988.24 18063.24 24784.61 25386.40 26367.55 27277.81 18586.48 25754.10 24993.15 17757.75 32082.72 22987.20 309
F-COLMAP76.38 25474.33 26782.50 20189.28 14166.95 16888.41 13889.03 20064.05 31966.83 35788.61 19246.78 32592.89 18957.48 32178.55 27587.67 296
EG-PatchMatch MVS74.04 28271.82 29680.71 24284.92 27667.42 15185.86 22288.08 22566.04 29264.22 37983.85 31435.10 39792.56 20057.44 32280.83 25082.16 388
PatchmatchNetpermissive73.12 29771.33 30378.49 28883.18 31660.85 28279.63 33478.57 36664.13 31571.73 30479.81 37451.20 28585.97 33657.40 32376.36 31088.66 276
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DTE-MVSNet76.99 23976.80 22577.54 30786.24 24653.06 37987.52 16890.66 14077.08 6372.50 29488.67 19060.48 20289.52 28757.33 32470.74 36690.05 226
UnsupCasMVSNet_eth67.33 35165.99 35571.37 36673.48 40751.47 38975.16 37985.19 27865.20 30260.78 39480.93 36142.35 36177.20 39057.12 32553.69 41285.44 346
pmmvs571.55 31270.20 31775.61 32377.83 38656.39 34181.74 30280.89 33757.76 37967.46 34884.49 29849.26 30985.32 34557.08 32675.29 32885.11 353
testing3-275.12 27375.19 25574.91 33490.40 10245.09 41580.29 32778.42 36778.37 3776.54 21787.75 21444.36 34987.28 32457.04 32783.49 21792.37 137
Anonymous2024052168.80 34067.22 34973.55 34874.33 40054.11 36883.18 28585.61 27458.15 37561.68 39180.94 35930.71 40781.27 37357.00 32873.34 35085.28 348
mvsany_test162.30 37261.26 37665.41 39369.52 41754.86 36266.86 41349.78 43346.65 41068.50 34183.21 33049.15 31066.28 42556.93 32960.77 39875.11 409
TransMVSNet (Re)75.39 27074.56 26277.86 29885.50 26257.10 33086.78 19586.09 27072.17 18171.53 30787.34 22663.01 15789.31 29156.84 33061.83 39587.17 310
tt0320-xc70.11 32967.45 34678.07 29585.33 26659.51 30183.28 28378.96 36458.77 37067.10 35480.28 36736.73 39187.42 32256.83 33159.77 40287.29 307
test_vis3_rt49.26 39247.02 39456.00 40454.30 43345.27 41466.76 41548.08 43436.83 42344.38 42253.20 4277.17 43964.07 42756.77 33255.66 40758.65 423
EPMVS69.02 33868.16 33071.59 36479.61 37649.80 40077.40 36566.93 41362.82 33470.01 32279.05 37845.79 33777.86 38856.58 33375.26 32987.13 313
KD-MVS_self_test68.81 33967.59 34472.46 36074.29 40145.45 41077.93 36187.00 25163.12 32663.99 38278.99 38242.32 36284.77 35056.55 33464.09 39087.16 312
tpm273.26 29571.46 30078.63 28083.34 31156.71 33680.65 32080.40 34856.63 38773.55 28182.02 35151.80 27891.24 25556.35 33578.42 27987.95 290
LTVRE_ROB69.57 1376.25 25574.54 26381.41 22188.60 16764.38 22479.24 33989.12 19970.76 20769.79 32987.86 21349.09 31193.20 17356.21 33680.16 25986.65 325
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
ACMH67.68 1675.89 26073.93 27181.77 21388.71 16466.61 17088.62 13389.01 20269.81 22966.78 35886.70 24741.95 36791.51 24655.64 33778.14 28287.17 310
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x42066.51 35764.71 35971.90 36281.45 35063.52 24057.98 42668.95 40953.57 39662.59 38976.70 39446.22 33275.29 40955.25 33879.68 26476.88 406
tt032070.49 32568.03 33377.89 29784.78 27959.12 30383.55 27880.44 34658.13 37667.43 35080.41 36539.26 37987.54 32155.12 33963.18 39386.99 317
UBG73.08 29872.27 29375.51 32688.02 19251.29 39178.35 35677.38 37665.52 29973.87 27782.36 34445.55 34086.48 33155.02 34084.39 20088.75 273
EPNet_dtu75.46 26674.86 25877.23 31182.57 33354.60 36486.89 19083.09 31171.64 18666.25 36785.86 26955.99 23388.04 31454.92 34186.55 16989.05 258
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsany_test353.99 38351.45 38861.61 39855.51 43244.74 41763.52 42245.41 43743.69 41558.11 40476.45 39617.99 42563.76 42854.77 34247.59 41976.34 407
PVSNet64.34 1872.08 31070.87 30975.69 32286.21 24756.44 34074.37 38680.73 34062.06 34370.17 32082.23 34842.86 35983.31 36154.77 34284.45 19887.32 306
ITE_SJBPF78.22 29181.77 34460.57 28683.30 30569.25 24367.54 34687.20 23236.33 39487.28 32454.34 34474.62 33686.80 321
SSC-MVS3.273.35 29473.39 27873.23 35085.30 26749.01 40174.58 38581.57 33175.21 10873.68 27985.58 27752.53 26082.05 36854.33 34577.69 28888.63 278
MDTV_nov1_ep13_2view37.79 42975.16 37955.10 39266.53 36249.34 30753.98 34687.94 291
gg-mvs-nofinetune69.95 33167.96 33475.94 31983.07 31954.51 36677.23 36770.29 40363.11 32770.32 31762.33 41743.62 35488.69 30553.88 34787.76 15184.62 360
PatchMatch-RL72.38 30470.90 30876.80 31588.60 16767.38 15479.53 33576.17 38562.75 33569.36 33282.00 35245.51 34184.89 34953.62 34880.58 25478.12 403
test_f52.09 38850.82 38955.90 40553.82 43542.31 42459.42 42558.31 42936.45 42456.12 41170.96 41212.18 43157.79 43153.51 34956.57 40667.60 416
Patchmtry70.74 32069.16 32375.49 32780.72 35954.07 36974.94 38380.30 34958.34 37370.01 32281.19 35452.50 26286.54 32953.37 35071.09 36585.87 341
USDC70.33 32668.37 32776.21 31880.60 36156.23 34579.19 34186.49 26160.89 35061.29 39285.47 28031.78 40489.47 28953.37 35076.21 31182.94 381
LF4IMVS64.02 36862.19 37269.50 37770.90 41653.29 37776.13 36977.18 37852.65 39958.59 40180.98 35823.55 41976.52 39553.06 35266.66 38178.68 402
PAPM77.68 22976.40 23681.51 21887.29 22661.85 26983.78 27189.59 17864.74 30871.23 31088.70 18862.59 16093.66 14952.66 35387.03 16289.01 260
dmvs_re71.14 31570.58 31072.80 35681.96 34159.68 29775.60 37679.34 36068.55 26069.27 33480.72 36249.42 30576.54 39452.56 35477.79 28582.19 387
CL-MVSNet_self_test72.37 30571.46 30075.09 33279.49 37853.53 37280.76 31785.01 28269.12 24870.51 31482.05 35057.92 21784.13 35352.27 35566.00 38587.60 298
tpm cat170.57 32268.31 32877.35 30982.41 33757.95 31678.08 35880.22 35152.04 40068.54 34077.66 39152.00 27387.84 31751.77 35672.07 35986.25 329
our_test_369.14 33767.00 35075.57 32479.80 37358.80 30477.96 36077.81 37059.55 36262.90 38878.25 38747.43 31983.97 35451.71 35767.58 37983.93 368
MDTV_nov1_ep1369.97 31883.18 31653.48 37377.10 36880.18 35360.45 35369.33 33380.44 36348.89 31586.90 32651.60 35878.51 277
myMVS_eth3d2873.62 28773.53 27773.90 34688.20 18147.41 40578.06 35979.37 35974.29 13673.98 27584.29 30544.67 34583.54 35851.47 35987.39 15690.74 191
JIA-IIPM66.32 35962.82 37176.82 31477.09 39061.72 27265.34 41975.38 38658.04 37864.51 37762.32 41842.05 36686.51 33051.45 36069.22 37382.21 386
testing22274.04 28272.66 28878.19 29287.89 19855.36 35681.06 31279.20 36271.30 19574.65 26783.57 32539.11 38188.67 30651.43 36185.75 18490.53 200
MSDG73.36 29370.99 30780.49 24684.51 28765.80 18680.71 31986.13 26965.70 29665.46 37083.74 31844.60 34690.91 26551.13 36276.89 29684.74 358
PatchT68.46 34567.85 33670.29 37480.70 36043.93 41872.47 39174.88 38960.15 35770.55 31376.57 39549.94 29981.59 37050.58 36374.83 33485.34 347
GG-mvs-BLEND75.38 32981.59 34755.80 35179.32 33869.63 40567.19 35273.67 40643.24 35688.90 30350.41 36484.50 19481.45 391
KD-MVS_2432*160066.22 36063.89 36373.21 35175.47 39853.42 37470.76 39984.35 28864.10 31766.52 36378.52 38434.55 39884.98 34750.40 36550.33 41781.23 392
miper_refine_blended66.22 36063.89 36373.21 35175.47 39853.42 37470.76 39984.35 28864.10 31766.52 36378.52 38434.55 39884.98 34750.40 36550.33 41781.23 392
AllTest70.96 31768.09 33279.58 26785.15 27163.62 23584.58 25479.83 35462.31 33960.32 39686.73 24132.02 40288.96 30150.28 36771.57 36286.15 332
TestCases79.58 26785.15 27163.62 23579.83 35462.31 33960.32 39686.73 24132.02 40288.96 30150.28 36771.57 36286.15 332
TAPA-MVS73.13 979.15 18977.94 19582.79 19489.59 12362.99 25688.16 15091.51 11765.77 29577.14 20491.09 13360.91 19393.21 17050.26 36987.05 16192.17 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
YYNet165.03 36462.91 36971.38 36575.85 39456.60 33869.12 40774.66 39357.28 38454.12 41277.87 38945.85 33674.48 41149.95 37061.52 39783.05 378
MDA-MVSNet_test_wron65.03 36462.92 36871.37 36675.93 39256.73 33469.09 40874.73 39157.28 38454.03 41377.89 38845.88 33574.39 41249.89 37161.55 39682.99 380
tpmvs71.09 31669.29 32176.49 31682.04 34056.04 34778.92 34681.37 33564.05 31967.18 35378.28 38649.74 30289.77 28249.67 37272.37 35483.67 371
ppachtmachnet_test70.04 33067.34 34878.14 29379.80 37361.13 27679.19 34180.59 34259.16 36665.27 37279.29 37746.75 32687.29 32349.33 37366.72 38086.00 338
UnsupCasMVSNet_bld63.70 36961.53 37570.21 37573.69 40551.39 39072.82 39081.89 32755.63 39157.81 40571.80 41038.67 38378.61 38349.26 37452.21 41580.63 396
UWE-MVS72.13 30971.49 29974.03 34486.66 24147.70 40381.40 30976.89 38163.60 32475.59 23584.22 30939.94 37685.62 34048.98 37586.13 17788.77 272
dp66.80 35465.43 35670.90 37379.74 37548.82 40275.12 38174.77 39059.61 36164.08 38177.23 39242.89 35880.72 37648.86 37666.58 38283.16 376
FMVSNet569.50 33467.96 33474.15 34382.97 32555.35 35780.01 33182.12 32562.56 33763.02 38581.53 35336.92 39081.92 36948.42 37774.06 34085.17 352
thres100view90076.50 24875.55 24779.33 27089.52 12656.99 33185.83 22483.23 30773.94 14376.32 22287.12 23551.89 27691.95 22548.33 37883.75 20989.07 253
tfpn200view976.42 25275.37 25279.55 26989.13 14757.65 32285.17 23683.60 29973.41 15976.45 21886.39 25952.12 26891.95 22548.33 37883.75 20989.07 253
thres40076.50 24875.37 25279.86 25989.13 14757.65 32285.17 23683.60 29973.41 15976.45 21886.39 25952.12 26891.95 22548.33 37883.75 20990.00 227
LCM-MVSNet54.25 38249.68 39267.97 38853.73 43645.28 41366.85 41480.78 33935.96 42539.45 42662.23 4198.70 43678.06 38748.24 38151.20 41680.57 397
RPMNet73.51 28970.49 31282.58 20081.32 35565.19 20175.92 37292.27 8457.60 38172.73 29176.45 39652.30 26595.43 7048.14 38277.71 28687.11 314
thres600view776.50 24875.44 24879.68 26489.40 13357.16 32885.53 23283.23 30773.79 14776.26 22387.09 23651.89 27691.89 22848.05 38383.72 21290.00 227
TDRefinement67.49 34964.34 36076.92 31373.47 40861.07 27984.86 24682.98 31559.77 36058.30 40385.13 28826.06 41287.89 31647.92 38460.59 40081.81 390
thres20075.55 26474.47 26478.82 27887.78 20657.85 31883.07 29083.51 30272.44 17775.84 23284.42 30052.08 27191.75 23347.41 38583.64 21486.86 320
PVSNet_057.27 2061.67 37459.27 37768.85 38179.61 37657.44 32668.01 40973.44 39655.93 39058.54 40270.41 41344.58 34777.55 38947.01 38635.91 42571.55 413
DP-MVS76.78 24474.57 26183.42 16193.29 4869.46 9788.55 13583.70 29863.98 32170.20 31888.89 18454.01 25194.80 10246.66 38781.88 23986.01 336
COLMAP_ROBcopyleft66.92 1773.01 29970.41 31480.81 24087.13 23065.63 19088.30 14584.19 29362.96 33063.80 38487.69 21738.04 38792.56 20046.66 38774.91 33384.24 363
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet70.69 32169.30 32074.88 33584.52 28656.35 34475.87 37479.42 35864.59 30967.76 34382.41 34341.10 37081.54 37146.64 38981.34 24286.75 323
LS3D76.95 24174.82 25983.37 16490.45 10067.36 15589.15 11086.94 25361.87 34569.52 33090.61 14551.71 28094.53 11046.38 39086.71 16788.21 287
ETVMVS72.25 30771.05 30675.84 32087.77 20751.91 38379.39 33774.98 38869.26 24273.71 27882.95 33540.82 37386.14 33446.17 39184.43 19989.47 246
MDA-MVSNet-bldmvs66.68 35563.66 36575.75 32179.28 38060.56 28773.92 38878.35 36864.43 31150.13 41879.87 37344.02 35283.67 35646.10 39256.86 40483.03 379
new-patchmatchnet61.73 37361.73 37461.70 39772.74 41324.50 44069.16 40678.03 36961.40 34756.72 40875.53 40238.42 38476.48 39645.95 39357.67 40384.13 365
WB-MVSnew71.96 31171.65 29872.89 35584.67 28551.88 38482.29 29777.57 37262.31 33973.67 28083.00 33453.49 25681.10 37445.75 39482.13 23585.70 342
TinyColmap67.30 35264.81 35874.76 33781.92 34356.68 33780.29 32781.49 33360.33 35456.27 41083.22 32924.77 41687.66 32045.52 39569.47 37179.95 399
pmmvs357.79 37854.26 38368.37 38464.02 42656.72 33575.12 38165.17 41740.20 41852.93 41469.86 41420.36 42375.48 40645.45 39655.25 41172.90 412
OpenMVS_ROBcopyleft64.09 1970.56 32368.19 32977.65 30380.26 36459.41 30285.01 24282.96 31658.76 37165.43 37182.33 34537.63 38991.23 25645.34 39776.03 31282.32 385
test0.0.03 168.00 34867.69 34168.90 38077.55 38747.43 40475.70 37572.95 39966.66 28166.56 36182.29 34748.06 31775.87 40344.97 39874.51 33783.41 373
testgi66.67 35666.53 35367.08 39075.62 39641.69 42575.93 37176.50 38266.11 29065.20 37586.59 25135.72 39674.71 41043.71 39973.38 34984.84 357
Anonymous2023120668.60 34167.80 33971.02 37180.23 36650.75 39578.30 35780.47 34456.79 38666.11 36882.63 34246.35 33078.95 38243.62 40075.70 31583.36 374
tfpnnormal74.39 27673.16 28278.08 29486.10 25258.05 31284.65 25287.53 23970.32 21771.22 31185.63 27554.97 23889.86 28043.03 40175.02 33286.32 328
MIMVSNet168.58 34266.78 35273.98 34580.07 36851.82 38580.77 31684.37 28764.40 31259.75 39982.16 34936.47 39383.63 35742.73 40270.33 36886.48 327
ttmdpeth59.91 37657.10 38068.34 38567.13 42246.65 40974.64 38467.41 41248.30 40862.52 39085.04 29220.40 42275.93 40242.55 40345.90 42382.44 384
test20.0367.45 35066.95 35168.94 37975.48 39744.84 41677.50 36477.67 37166.66 28163.01 38683.80 31647.02 32378.40 38442.53 40468.86 37683.58 372
ADS-MVSNet266.20 36263.33 36674.82 33679.92 36958.75 30567.55 41175.19 38753.37 39765.25 37375.86 39942.32 36280.53 37741.57 40568.91 37485.18 350
ADS-MVSNet64.36 36762.88 37068.78 38279.92 36947.17 40667.55 41171.18 40153.37 39765.25 37375.86 39942.32 36273.99 41341.57 40568.91 37485.18 350
Patchmatch-test64.82 36663.24 36769.57 37679.42 37949.82 39963.49 42369.05 40851.98 40259.95 39880.13 36950.91 28770.98 41740.66 40773.57 34587.90 292
MVS-HIRNet59.14 37757.67 37963.57 39581.65 34543.50 41971.73 39365.06 41839.59 42051.43 41557.73 42338.34 38582.58 36539.53 40873.95 34164.62 419
WAC-MVS42.58 42139.46 409
myMVS_eth3d67.02 35366.29 35469.21 37884.68 28242.58 42178.62 35073.08 39766.65 28466.74 35979.46 37531.53 40582.30 36639.43 41076.38 30882.75 382
DSMNet-mixed57.77 37956.90 38160.38 39967.70 42035.61 43069.18 40553.97 43132.30 42957.49 40679.88 37240.39 37568.57 42338.78 41172.37 35476.97 405
N_pmnet52.79 38753.26 38551.40 41178.99 3827.68 44569.52 4033.89 44451.63 40357.01 40774.98 40340.83 37265.96 42637.78 41264.67 38880.56 398
testing368.56 34367.67 34271.22 37087.33 22342.87 42083.06 29171.54 40070.36 21569.08 33584.38 30230.33 40885.69 33937.50 41375.45 32385.09 354
MVStest156.63 38052.76 38668.25 38661.67 42853.25 37871.67 39468.90 41038.59 42150.59 41783.05 33325.08 41470.66 41836.76 41438.56 42480.83 395
test_040272.79 30270.44 31379.84 26088.13 18665.99 18085.93 21984.29 29065.57 29867.40 35185.49 27946.92 32492.61 19635.88 41574.38 33880.94 394
new_pmnet50.91 39050.29 39052.78 41068.58 41934.94 43263.71 42156.63 43039.73 41944.95 42165.47 41621.93 42158.48 43034.98 41656.62 40564.92 418
APD_test153.31 38649.93 39163.42 39665.68 42350.13 39771.59 39566.90 41434.43 42640.58 42571.56 4118.65 43776.27 39834.64 41755.36 40963.86 420
Syy-MVS68.05 34767.85 33668.67 38384.68 28240.97 42678.62 35073.08 39766.65 28466.74 35979.46 37552.11 27082.30 36632.89 41876.38 30882.75 382
dmvs_testset62.63 37164.11 36258.19 40178.55 38424.76 43975.28 37765.94 41667.91 26960.34 39576.01 39853.56 25473.94 41431.79 41967.65 37875.88 408
UWE-MVS-2865.32 36364.93 35766.49 39178.70 38338.55 42877.86 36364.39 42062.00 34464.13 38083.60 32341.44 36876.00 40131.39 42080.89 24884.92 355
ANet_high50.57 39146.10 39563.99 39448.67 43939.13 42770.99 39880.85 33861.39 34831.18 42857.70 42417.02 42773.65 41531.22 42115.89 43679.18 401
EGC-MVSNET52.07 38947.05 39367.14 38983.51 30860.71 28480.50 32367.75 4110.07 4390.43 44075.85 40124.26 41781.54 37128.82 42262.25 39459.16 422
PMMVS240.82 39838.86 40246.69 41253.84 43416.45 44348.61 42949.92 43237.49 42231.67 42760.97 4208.14 43856.42 43228.42 42330.72 42967.19 417
tmp_tt18.61 40521.40 40810.23 4214.82 44410.11 44434.70 43130.74 4421.48 43823.91 43426.07 43528.42 41013.41 44027.12 42415.35 4377.17 435
test_method31.52 40129.28 40538.23 41527.03 4436.50 44620.94 43462.21 4234.05 43722.35 43552.50 42813.33 42947.58 43527.04 42534.04 42760.62 421
testf145.72 39341.96 39757.00 40256.90 43045.32 41166.14 41659.26 42726.19 43030.89 42960.96 4214.14 44070.64 41926.39 42646.73 42155.04 425
APD_test245.72 39341.96 39757.00 40256.90 43045.32 41166.14 41659.26 42726.19 43030.89 42960.96 4214.14 44070.64 41926.39 42646.73 42155.04 425
FPMVS53.68 38551.64 38759.81 40065.08 42451.03 39269.48 40469.58 40641.46 41740.67 42472.32 40916.46 42870.00 42124.24 42865.42 38658.40 424
Gipumacopyleft45.18 39641.86 39955.16 40877.03 39151.52 38832.50 43280.52 34332.46 42827.12 43135.02 4329.52 43575.50 40522.31 42960.21 40138.45 431
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai45.42 39545.38 39645.55 41373.36 40926.85 43767.72 41034.19 43954.15 39549.65 41956.41 42625.43 41362.94 42919.45 43028.09 43046.86 429
DeepMVS_CXcopyleft27.40 41940.17 44226.90 43624.59 44317.44 43523.95 43348.61 4309.77 43426.48 43818.06 43124.47 43228.83 432
WB-MVS54.94 38154.72 38255.60 40773.50 40620.90 44174.27 38761.19 42459.16 36650.61 41674.15 40447.19 32275.78 40417.31 43235.07 42670.12 414
PMVScopyleft37.38 2244.16 39740.28 40155.82 40640.82 44142.54 42365.12 42063.99 42134.43 42624.48 43257.12 4253.92 44276.17 40017.10 43355.52 40848.75 427
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVEpermissive26.22 2330.37 40325.89 40743.81 41444.55 44035.46 43128.87 43339.07 43818.20 43418.58 43640.18 4312.68 44347.37 43617.07 43423.78 43348.60 428
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SSC-MVS53.88 38453.59 38454.75 40972.87 41219.59 44273.84 38960.53 42657.58 38249.18 42073.45 40746.34 33175.47 40716.20 43532.28 42869.20 415
E-PMN31.77 40030.64 40335.15 41752.87 43727.67 43457.09 42747.86 43524.64 43216.40 43733.05 43311.23 43354.90 43314.46 43618.15 43422.87 433
EMVS30.81 40229.65 40434.27 41850.96 43825.95 43856.58 42846.80 43624.01 43315.53 43830.68 43412.47 43054.43 43412.81 43717.05 43522.43 434
kuosan39.70 39940.40 40037.58 41664.52 42526.98 43565.62 41833.02 44046.12 41142.79 42348.99 42924.10 41846.56 43712.16 43826.30 43139.20 430
wuyk23d16.82 40615.94 40919.46 42058.74 42931.45 43339.22 4303.74 4456.84 4366.04 4392.70 4391.27 44424.29 43910.54 43914.40 4382.63 436
testmvs6.04 4098.02 4120.10 4230.08 4450.03 44869.74 4020.04 4460.05 4400.31 4411.68 4400.02 4460.04 4410.24 4400.02 4390.25 438
test1236.12 4088.11 4110.14 4220.06 4460.09 44771.05 3970.03 4470.04 4410.25 4421.30 4410.05 4450.03 4420.21 4410.01 4400.29 437
mmdepth0.00 4110.00 4140.00 4240.00 4470.00 4490.00 4350.00 4480.00 4420.00 4430.00 4420.00 4470.00 4430.00 4420.00 4410.00 439
monomultidepth0.00 4110.00 4140.00 4240.00 4470.00 4490.00 4350.00 4480.00 4420.00 4430.00 4420.00 4470.00 4430.00 4420.00 4410.00 439
test_blank0.00 4110.00 4140.00 4240.00 4470.00 4490.00 4350.00 4480.00 4420.00 4430.00 4420.00 4470.00 4430.00 4420.00 4410.00 439
uanet_test0.00 4110.00 4140.00 4240.00 4470.00 4490.00 4350.00 4480.00 4420.00 4430.00 4420.00 4470.00 4430.00 4420.00 4410.00 439
DCPMVS0.00 4110.00 4140.00 4240.00 4470.00 4490.00 4350.00 4480.00 4420.00 4430.00 4420.00 4470.00 4430.00 4420.00 4410.00 439
cdsmvs_eth3d_5k19.96 40426.61 4060.00 4240.00 4470.00 4490.00 43589.26 1910.00 4420.00 44388.61 19261.62 1770.00 4430.00 4420.00 4410.00 439
pcd_1.5k_mvsjas5.26 4107.02 4130.00 4240.00 4470.00 4490.00 4350.00 4480.00 4420.00 4430.00 44263.15 1530.00 4430.00 4420.00 4410.00 439
sosnet-low-res0.00 4110.00 4140.00 4240.00 4470.00 4490.00 4350.00 4480.00 4420.00 4430.00 4420.00 4470.00 4430.00 4420.00 4410.00 439
sosnet0.00 4110.00 4140.00 4240.00 4470.00 4490.00 4350.00 4480.00 4420.00 4430.00 4420.00 4470.00 4430.00 4420.00 4410.00 439
uncertanet0.00 4110.00 4140.00 4240.00 4470.00 4490.00 4350.00 4480.00 4420.00 4430.00 4420.00 4470.00 4430.00 4420.00 4410.00 439
Regformer0.00 4110.00 4140.00 4240.00 4470.00 4490.00 4350.00 4480.00 4420.00 4430.00 4420.00 4470.00 4430.00 4420.00 4410.00 439
ab-mvs-re7.23 4079.64 4100.00 4240.00 4470.00 4490.00 4350.00 4480.00 4420.00 44386.72 2430.00 4470.00 4430.00 4420.00 4410.00 439
uanet0.00 4110.00 4140.00 4240.00 4470.00 4490.00 4350.00 4480.00 4420.00 4430.00 4420.00 4470.00 4430.00 4420.00 4410.00 439
FOURS195.00 1072.39 3995.06 193.84 1574.49 12991.30 15
test_one_060195.07 771.46 5794.14 578.27 3892.05 1195.74 680.83 11
eth-test20.00 447
eth-test0.00 447
test_241102_ONE95.30 270.98 6694.06 1077.17 5993.10 195.39 1482.99 197.27 12
save fliter93.80 4072.35 4290.47 6691.17 12774.31 134
test072695.27 571.25 5993.60 694.11 677.33 5392.81 395.79 380.98 9
GSMVS88.96 264
test_part295.06 872.65 3291.80 13
sam_mvs151.32 28388.96 264
sam_mvs50.01 297
MTGPAbinary92.02 94
test_post5.46 43750.36 29584.24 352
patchmatchnet-post74.00 40551.12 28688.60 307
MTMP92.18 3432.83 441
TEST993.26 5272.96 2588.75 12691.89 10268.44 26385.00 7193.10 7974.36 2895.41 73
test_893.13 5472.57 3588.68 13191.84 10668.69 25884.87 7593.10 7974.43 2695.16 83
agg_prior92.85 6271.94 5091.78 10984.41 8694.93 94
test_prior472.60 3489.01 115
test_prior86.33 5792.61 6869.59 9192.97 5495.48 6793.91 64
新几何286.29 211
旧先验191.96 7465.79 18786.37 26493.08 8369.31 8692.74 7488.74 275
原ACMM286.86 191
test22291.50 8068.26 13084.16 26683.20 31054.63 39479.74 14891.63 11458.97 21091.42 9386.77 322
segment_acmp73.08 39
testdata184.14 26775.71 95
test1286.80 5292.63 6770.70 7591.79 10882.71 11571.67 5696.16 4794.50 5193.54 89
plane_prior790.08 10968.51 124
plane_prior689.84 11868.70 11860.42 203
plane_prior491.00 139
plane_prior368.60 12178.44 3378.92 160
plane_prior291.25 5279.12 25
plane_prior189.90 117
plane_prior68.71 11690.38 7077.62 4386.16 176
n20.00 448
nn0.00 448
door-mid69.98 404
test1192.23 87
door69.44 407
HQP5-MVS66.98 165
HQP-NCC89.33 13689.17 10676.41 8077.23 198
ACMP_Plane89.33 13689.17 10676.41 8077.23 198
HQP4-MVS77.24 19795.11 8791.03 179
HQP3-MVS92.19 9185.99 180
HQP2-MVS60.17 206
NP-MVS89.62 12268.32 12890.24 151
ACMMP++_ref81.95 238
ACMMP++81.25 243
Test By Simon64.33 140