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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test_fmvsm_n_192097.55 1697.89 396.53 10598.41 8591.73 13098.01 6699.02 196.37 1399.30 798.92 2392.39 4499.79 4699.16 1499.46 4698.08 225
PGM-MVS96.81 5896.53 6997.65 4799.35 2593.53 6597.65 12998.98 292.22 17497.14 7698.44 6491.17 7199.85 2194.35 16099.46 4699.57 36
MVS_111021_HR96.68 6996.58 6896.99 8498.46 7992.31 11096.20 30298.90 394.30 8695.86 13497.74 14192.33 4599.38 13696.04 9699.42 5699.28 77
test_fmvsmconf_n97.49 2197.56 1697.29 6497.44 16592.37 10797.91 8598.88 495.83 1998.92 2399.05 1491.45 6199.80 4099.12 1699.46 4699.69 14
lecture97.58 1597.63 1297.43 5899.37 1992.93 8698.86 798.85 595.27 3498.65 3698.90 2591.97 5299.80 4097.63 3899.21 8399.57 36
ACMMPcopyleft96.27 8695.93 8997.28 6699.24 3392.62 9898.25 4098.81 692.99 14094.56 17998.39 6888.96 10299.85 2194.57 15497.63 16399.36 72
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
MVS_111021_LR96.24 8796.19 8596.39 12498.23 10591.35 15396.24 29998.79 793.99 9595.80 13697.65 15189.92 9199.24 14995.87 10099.20 8898.58 169
patch_mono-296.83 5797.44 2495.01 22299.05 4585.39 36396.98 21498.77 894.70 6697.99 5198.66 4393.61 2199.91 197.67 3799.50 4099.72 13
fmvsm_s_conf0.5_n96.85 5497.13 3196.04 14998.07 12090.28 20397.97 7798.76 994.93 4898.84 2899.06 1288.80 10699.65 7999.06 1898.63 12398.18 210
fmvsm_l_conf0.5_n97.65 997.75 897.34 6198.21 10692.75 9297.83 9898.73 1095.04 4599.30 798.84 3693.34 2499.78 4999.32 799.13 9899.50 52
fmvsm_s_conf0.5_n_a96.75 6296.93 4696.20 14097.64 15190.72 18598.00 6798.73 1094.55 7398.91 2499.08 888.22 11899.63 8898.91 2198.37 13698.25 205
fmvsm_s_conf0.5_n_1097.29 3197.40 2696.97 8698.24 10091.96 12697.89 8898.72 1296.77 799.46 399.06 1287.78 12799.84 2699.40 499.27 7599.12 92
fmvsm_l_conf0.5_n_997.59 1397.79 696.97 8698.28 9491.49 14497.61 13898.71 1397.10 599.70 198.93 2290.95 7699.77 5299.35 699.53 3399.65 20
FC-MVSNet-test93.94 18093.57 17295.04 22095.48 31391.45 14998.12 5598.71 1393.37 12290.23 29396.70 21987.66 12997.85 34491.49 22490.39 33395.83 319
UniMVSNet (Re)93.31 20792.55 22095.61 18995.39 31993.34 7197.39 17298.71 1393.14 13590.10 30294.83 32187.71 12898.03 31791.67 22283.99 40895.46 338
MED-MVS test98.00 2399.56 194.50 3598.69 1198.70 1693.45 11898.73 3098.53 5199.86 997.40 5099.58 2399.65 20
MED-MVS97.91 497.88 498.00 2399.56 194.50 3598.69 1198.70 1694.23 8798.73 3098.53 5195.46 799.86 997.40 5099.58 2399.65 20
TestfortrainingZip a97.92 397.70 1098.58 399.56 196.08 598.69 1198.70 1693.45 11898.73 3098.53 5195.46 799.86 996.63 6999.58 2399.80 1
fmvsm_l_conf0.5_n_a97.63 1197.76 797.26 6898.25 9992.59 10097.81 10398.68 1994.93 4899.24 1098.87 3193.52 2299.79 4699.32 799.21 8399.40 66
FIs94.09 17193.70 16895.27 20995.70 30292.03 12298.10 5698.68 1993.36 12490.39 29096.70 21987.63 13297.94 33592.25 20290.50 33295.84 318
WR-MVS_H92.00 26491.35 26193.95 29195.09 34689.47 23998.04 6398.68 1991.46 20588.34 35494.68 32885.86 17097.56 37485.77 34984.24 40694.82 384
fmvsm_s_conf0.5_n_496.75 6297.07 3495.79 17397.76 14289.57 23297.66 12898.66 2295.36 3099.03 1698.90 2588.39 11499.73 6199.17 1398.66 12198.08 225
VPA-MVSNet93.24 20992.48 22595.51 19695.70 30292.39 10697.86 9198.66 2292.30 17192.09 25195.37 29680.49 28898.40 27193.95 16685.86 37995.75 327
fmvsm_l_conf0.5_n_397.64 1097.60 1397.79 3498.14 11393.94 5697.93 8398.65 2496.70 899.38 599.07 1189.92 9199.81 3599.16 1499.43 5399.61 30
fmvsm_s_conf0.5_n_397.15 3697.36 2896.52 10797.98 12691.19 16197.84 9598.65 2497.08 699.25 999.10 687.88 12599.79 4699.32 799.18 9098.59 168
fmvsm_s_conf0.5_n_897.32 2897.48 2396.85 8898.28 9491.07 16997.76 10898.62 2697.53 299.20 1299.12 588.24 11799.81 3599.41 399.17 9199.67 15
fmvsm_s_conf0.5_n_296.62 7096.82 5596.02 15197.98 12690.43 19597.50 15398.59 2796.59 1099.31 699.08 884.47 20199.75 5899.37 598.45 13397.88 238
UniMVSNet_NR-MVSNet93.37 20592.67 21495.47 20295.34 32592.83 8997.17 19798.58 2892.98 14590.13 29895.80 27288.37 11697.85 34491.71 21983.93 40995.73 329
CSCG96.05 9095.91 9096.46 11799.24 3390.47 19298.30 3398.57 2989.01 30093.97 20097.57 16192.62 4099.76 5494.66 14899.27 7599.15 87
fmvsm_s_conf0.5_n_997.33 2797.57 1596.62 10198.43 8290.32 20297.80 10498.53 3097.24 499.62 299.14 288.65 10999.80 4099.54 199.15 9599.74 9
fmvsm_s_conf0.5_n_697.08 3997.17 3096.81 8997.28 17091.73 13097.75 11098.50 3194.86 5299.22 1198.78 4089.75 9499.76 5499.10 1799.29 7398.94 121
MSLP-MVS++96.94 4897.06 3596.59 10298.72 6491.86 12897.67 12598.49 3294.66 6997.24 7298.41 6792.31 4798.94 19596.61 7199.46 4698.96 114
HyFIR lowres test93.66 19292.92 20295.87 16298.24 10089.88 21894.58 37998.49 3285.06 39893.78 20395.78 27682.86 23698.67 24591.77 21795.71 23199.07 100
CHOSEN 1792x268894.15 16693.51 17896.06 14798.27 9689.38 24495.18 36498.48 3485.60 38893.76 20497.11 19483.15 22699.61 9091.33 22798.72 11999.19 83
fmvsm_s_conf0.5_n_796.45 7796.80 5795.37 20597.29 16988.38 27897.23 19198.47 3595.14 3998.43 4199.09 787.58 13399.72 6598.80 2599.21 8398.02 229
fmvsm_s_conf0.5_n_597.00 4596.97 4397.09 7997.58 16192.56 10197.68 12498.47 3594.02 9398.90 2598.89 2888.94 10399.78 4999.18 1299.03 10798.93 125
PHI-MVS96.77 6096.46 7697.71 4598.40 8694.07 5298.21 4798.45 3789.86 27197.11 7898.01 10492.52 4299.69 7396.03 9799.53 3399.36 72
fmvsm_s_conf0.1_n96.58 7396.77 6096.01 15496.67 22890.25 20497.91 8598.38 3894.48 7798.84 2899.14 288.06 12099.62 8998.82 2398.60 12598.15 214
PVSNet_BlendedMVS94.06 17293.92 16294.47 25898.27 9689.46 24196.73 24798.36 3990.17 26394.36 18595.24 30488.02 12199.58 9893.44 18090.72 32894.36 404
PVSNet_Blended94.87 14194.56 14095.81 16998.27 9689.46 24195.47 34698.36 3988.84 30994.36 18596.09 26188.02 12199.58 9893.44 18098.18 14598.40 190
3Dnovator91.36 595.19 12694.44 14997.44 5796.56 24093.36 7098.65 1698.36 3994.12 9089.25 33298.06 9882.20 25399.77 5293.41 18299.32 7199.18 84
FOURS199.55 493.34 7199.29 198.35 4294.98 4698.49 39
DPE-MVScopyleft97.86 697.65 1198.47 699.17 3895.78 897.21 19498.35 4295.16 3898.71 3598.80 3895.05 1299.89 396.70 6899.73 199.73 12
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ME-MVS97.54 1797.39 2798.00 2399.21 3694.50 3597.75 11098.34 4494.23 8798.15 4698.53 5193.32 2799.84 2697.40 5099.58 2399.65 20
fmvsm_s_conf0.1_n_a96.40 7996.47 7396.16 14295.48 31390.69 18697.91 8598.33 4594.07 9198.93 2099.14 287.44 14199.61 9098.63 2698.32 13898.18 210
HFP-MVS97.14 3796.92 4797.83 3099.42 1094.12 5098.52 2098.32 4693.21 12797.18 7398.29 8492.08 4999.83 3195.63 11399.59 1999.54 45
ACMMPR97.07 4196.84 5197.79 3499.44 993.88 5798.52 2098.31 4793.21 12797.15 7598.33 7891.35 6599.86 995.63 11399.59 1999.62 27
test_fmvsmvis_n_192096.70 6596.84 5196.31 12996.62 23091.73 13097.98 7198.30 4896.19 1496.10 12498.95 2089.42 9599.76 5498.90 2299.08 10297.43 265
APDe-MVScopyleft97.82 797.73 998.08 1999.15 3994.82 2998.81 898.30 4894.76 6498.30 4398.90 2593.77 1999.68 7597.93 2999.69 399.75 7
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test072699.45 695.36 1498.31 3298.29 5094.92 5098.99 1898.92 2395.08 10
MSP-MVS97.59 1397.54 1797.73 4299.40 1493.77 6198.53 1998.29 5095.55 2798.56 3897.81 13393.90 1799.65 7996.62 7099.21 8399.77 3
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
DVP-MVS++98.06 197.99 198.28 1098.67 6795.39 1299.29 198.28 5294.78 6198.93 2098.87 3196.04 299.86 997.45 4699.58 2399.59 32
test_0728_SECOND98.51 599.45 695.93 698.21 4798.28 5299.86 997.52 4299.67 699.75 7
CP-MVS97.02 4396.81 5697.64 4999.33 2693.54 6498.80 998.28 5292.99 14096.45 11198.30 8391.90 5399.85 2195.61 11599.68 499.54 45
test_fmvsmconf0.1_n97.09 3897.06 3597.19 7395.67 30492.21 11497.95 8098.27 5595.78 2398.40 4299.00 1689.99 8999.78 4999.06 1899.41 5999.59 32
SED-MVS98.05 297.99 198.24 1199.42 1095.30 1898.25 4098.27 5595.13 4099.19 1398.89 2895.54 599.85 2197.52 4299.66 1099.56 40
test_241102_TWO98.27 5595.13 4098.93 2098.89 2894.99 1399.85 2197.52 4299.65 1399.74 9
test_241102_ONE99.42 1095.30 1898.27 5595.09 4399.19 1398.81 3795.54 599.65 79
SF-MVS97.39 2497.13 3198.17 1699.02 4895.28 2098.23 4498.27 5592.37 16898.27 4498.65 4593.33 2599.72 6596.49 7599.52 3599.51 49
SteuartSystems-ACMMP97.62 1297.53 1897.87 2898.39 8894.25 4498.43 2798.27 5595.34 3298.11 4798.56 4794.53 1499.71 6796.57 7399.62 1799.65 20
Skip Steuart: Steuart Systems R&D Blog.
test_one_060199.32 2795.20 2198.25 6195.13 4098.48 4098.87 3195.16 9
PVSNet_Blended_VisFu95.27 11694.91 12596.38 12598.20 10790.86 17897.27 18598.25 6190.21 26294.18 19397.27 18387.48 14099.73 6193.53 17797.77 16198.55 171
region2R97.07 4196.84 5197.77 3899.46 593.79 5998.52 2098.24 6393.19 13097.14 7698.34 7591.59 6099.87 795.46 11999.59 1999.64 25
PS-CasMVS91.55 28590.84 28593.69 30894.96 35088.28 28197.84 9598.24 6391.46 20588.04 36595.80 27279.67 30497.48 38287.02 32984.54 40395.31 352
DU-MVS92.90 22792.04 23695.49 19994.95 35192.83 8997.16 19898.24 6393.02 13990.13 29895.71 27983.47 21897.85 34491.71 21983.93 40995.78 323
9.1496.75 6198.93 5697.73 11598.23 6691.28 21497.88 5598.44 6493.00 2999.65 7995.76 10699.47 45
reproduce_model97.51 2097.51 2097.50 5498.99 5293.01 8297.79 10698.21 6795.73 2497.99 5199.03 1592.63 3999.82 3397.80 3199.42 5699.67 15
D2MVS91.30 30290.95 27992.35 35694.71 36685.52 35796.18 30498.21 6788.89 30786.60 39493.82 37779.92 30097.95 33389.29 27790.95 32593.56 419
reproduce-ours97.53 1897.51 2097.60 5198.97 5393.31 7397.71 12098.20 6995.80 2197.88 5598.98 1892.91 3099.81 3597.68 3399.43 5399.67 15
our_new_method97.53 1897.51 2097.60 5198.97 5393.31 7397.71 12098.20 6995.80 2197.88 5598.98 1892.91 3099.81 3597.68 3399.43 5399.67 15
SDMVSNet94.17 16493.61 17195.86 16598.09 11691.37 15197.35 17698.20 6993.18 13291.79 25997.28 18179.13 31298.93 19694.61 15192.84 29197.28 273
XVS97.18 3496.96 4597.81 3299.38 1794.03 5498.59 1798.20 6994.85 5396.59 9998.29 8491.70 5699.80 4095.66 10899.40 6199.62 27
X-MVStestdata91.71 27389.67 34097.81 3299.38 1794.03 5498.59 1798.20 6994.85 5396.59 9932.69 48191.70 5699.80 4095.66 10899.40 6199.62 27
ACMMP_NAP97.20 3396.86 4998.23 1299.09 4095.16 2397.60 13998.19 7492.82 15497.93 5498.74 4291.60 5999.86 996.26 8099.52 3599.67 15
CP-MVSNet91.89 26991.24 26893.82 30095.05 34788.57 27197.82 10098.19 7491.70 19488.21 36095.76 27781.96 25897.52 38087.86 30384.65 39795.37 348
ZNCC-MVS96.96 4696.67 6497.85 2999.37 1994.12 5098.49 2498.18 7692.64 16196.39 11398.18 9191.61 5899.88 495.59 11899.55 3099.57 36
SMA-MVScopyleft97.35 2597.03 4098.30 999.06 4495.42 1197.94 8198.18 7690.57 25398.85 2798.94 2193.33 2599.83 3196.72 6699.68 499.63 26
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
PEN-MVS91.20 30790.44 30393.48 31994.49 37487.91 29697.76 10898.18 7691.29 21187.78 36995.74 27880.35 29197.33 39385.46 35382.96 41995.19 363
DELS-MVS96.61 7196.38 8097.30 6397.79 14093.19 7895.96 31698.18 7695.23 3595.87 13397.65 15191.45 6199.70 7295.87 10099.44 5299.00 109
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
tfpnnormal89.70 35988.40 36593.60 31295.15 34290.10 20797.56 14498.16 8087.28 36186.16 40094.63 33277.57 34098.05 31374.48 44084.59 40192.65 432
VNet95.89 9895.45 10197.21 7198.07 12092.94 8597.50 15398.15 8193.87 9997.52 6297.61 15785.29 18599.53 11295.81 10595.27 24499.16 85
DeepPCF-MVS93.97 196.61 7197.09 3395.15 21398.09 11686.63 32996.00 31498.15 8195.43 2897.95 5398.56 4793.40 2399.36 13796.77 6399.48 4499.45 59
SD-MVS97.41 2397.53 1897.06 8298.57 7894.46 3897.92 8498.14 8394.82 5799.01 1798.55 4994.18 1697.41 38996.94 5899.64 1499.32 74
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
GST-MVS96.85 5496.52 7097.82 3199.36 2394.14 4998.29 3498.13 8492.72 15796.70 9198.06 9891.35 6599.86 994.83 13799.28 7499.47 58
UA-Net95.95 9595.53 9797.20 7297.67 14792.98 8497.65 12998.13 8494.81 5996.61 9798.35 7288.87 10499.51 11790.36 25297.35 17499.11 94
QAPM93.45 20392.27 23096.98 8596.77 22192.62 9898.39 2998.12 8684.50 40688.27 35897.77 13782.39 25099.81 3585.40 35498.81 11598.51 176
Vis-MVSNetpermissive95.23 12194.81 12896.51 11197.18 17591.58 14198.26 3998.12 8694.38 8494.90 16898.15 9382.28 25198.92 19891.45 22698.58 12799.01 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVScopyleft89.19 1292.86 23091.68 25196.40 12295.34 32592.73 9498.27 3798.12 8684.86 40185.78 40297.75 13878.89 32299.74 5987.50 31998.65 12296.73 290
TranMVSNet+NR-MVSNet92.50 23991.63 25295.14 21494.76 36292.07 11997.53 15098.11 8992.90 15189.56 32096.12 25683.16 22597.60 37289.30 27683.20 41895.75 327
CPTT-MVS95.57 10895.19 11296.70 9299.27 3191.48 14698.33 3198.11 8987.79 34695.17 16198.03 10187.09 14899.61 9093.51 17899.42 5699.02 103
APD-MVScopyleft96.95 4796.60 6698.01 2199.03 4794.93 2897.72 11898.10 9191.50 20398.01 5098.32 8092.33 4599.58 9894.85 13499.51 3899.53 48
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mPP-MVS96.86 5296.60 6697.64 4999.40 1493.44 6698.50 2398.09 9293.27 12695.95 13198.33 7891.04 7399.88 495.20 12299.57 2999.60 31
ZD-MVS99.05 4594.59 3398.08 9389.22 29397.03 8198.10 9492.52 4299.65 7994.58 15399.31 72
MTGPAbinary98.08 93
MTAPA97.08 3996.78 5997.97 2799.37 1994.42 4097.24 18798.08 9395.07 4496.11 12398.59 4690.88 7999.90 296.18 9299.50 4099.58 35
CNVR-MVS97.68 897.44 2498.37 898.90 5995.86 797.27 18598.08 9395.81 2097.87 5898.31 8194.26 1599.68 7597.02 5799.49 4399.57 36
DP-MVS Recon95.68 10395.12 11697.37 6099.19 3794.19 4697.03 20598.08 9388.35 32795.09 16397.65 15189.97 9099.48 12492.08 21198.59 12698.44 187
SR-MVS97.01 4496.86 4997.47 5699.09 4093.27 7597.98 7198.07 9893.75 10297.45 6498.48 6191.43 6399.59 9596.22 8399.27 7599.54 45
MCST-MVS97.18 3496.84 5198.20 1599.30 2995.35 1697.12 20198.07 9893.54 11296.08 12597.69 14693.86 1899.71 6796.50 7499.39 6399.55 43
NR-MVSNet92.34 24891.27 26795.53 19494.95 35193.05 8197.39 17298.07 9892.65 15984.46 41395.71 27985.00 19297.77 35589.71 26483.52 41595.78 323
MP-MVS-pluss96.70 6596.27 8397.98 2699.23 3594.71 3096.96 21698.06 10190.67 24395.55 14798.78 4091.07 7299.86 996.58 7299.55 3099.38 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
APD-MVS_3200maxsize96.81 5896.71 6397.12 7699.01 5192.31 11097.98 7198.06 10193.11 13697.44 6598.55 4990.93 7799.55 10896.06 9399.25 8099.51 49
MP-MVScopyleft96.77 6096.45 7797.72 4399.39 1693.80 5898.41 2898.06 10193.37 12295.54 14998.34 7590.59 8399.88 494.83 13799.54 3299.49 54
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS_fast96.51 7496.27 8397.22 7099.32 2792.74 9398.74 1098.06 10190.57 25396.77 8898.35 7290.21 8699.53 11294.80 14199.63 1699.38 70
HPM-MVScopyleft96.69 6796.45 7797.40 5999.36 2393.11 8098.87 698.06 10191.17 22296.40 11297.99 10790.99 7499.58 9895.61 11599.61 1899.49 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
sss94.51 15593.80 16496.64 9497.07 18191.97 12496.32 29198.06 10188.94 30594.50 18296.78 21484.60 19899.27 14791.90 21296.02 22198.68 162
DeepC-MVS93.07 396.06 8995.66 9497.29 6497.96 12893.17 7997.30 18298.06 10193.92 9793.38 21998.66 4386.83 15099.73 6195.60 11799.22 8298.96 114
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NCCC97.30 2997.03 4098.11 1898.77 6295.06 2697.34 17798.04 10895.96 1597.09 7997.88 12093.18 2899.71 6795.84 10499.17 9199.56 40
DeepC-MVS_fast93.89 296.93 4996.64 6597.78 3698.64 7394.30 4197.41 16798.04 10894.81 5996.59 9998.37 7091.24 6899.64 8795.16 12499.52 3599.42 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post96.88 5196.80 5797.11 7899.02 4892.34 10897.98 7198.03 11093.52 11597.43 6798.51 5691.40 6499.56 10696.05 9499.26 7899.43 63
RE-MVS-def96.72 6299.02 4892.34 10897.98 7198.03 11093.52 11597.43 6798.51 5690.71 8196.05 9499.26 7899.43 63
RPMNet88.98 36587.05 37994.77 24194.45 37687.19 31390.23 45598.03 11077.87 45492.40 23787.55 46180.17 29599.51 11768.84 46193.95 27797.60 258
save fliter98.91 5894.28 4297.02 20798.02 11395.35 31
TEST998.70 6594.19 4696.41 27798.02 11388.17 33196.03 12697.56 16392.74 3699.59 95
train_agg96.30 8595.83 9397.72 4398.70 6594.19 4696.41 27798.02 11388.58 31896.03 12697.56 16392.73 3799.59 9595.04 12699.37 6799.39 68
test_898.67 6794.06 5396.37 28598.01 11688.58 31895.98 13097.55 16592.73 3799.58 98
fmvsm_s_conf0.5_n_1197.30 2997.59 1496.43 11998.42 8391.37 15198.04 6398.00 11797.30 399.45 499.21 189.28 9799.80 4099.27 1099.35 6998.12 217
agg_prior98.67 6793.79 5998.00 11795.68 14399.57 105
test_prior97.23 6998.67 6792.99 8398.00 11799.41 13299.29 75
WR-MVS92.34 24891.53 25694.77 24195.13 34490.83 17996.40 28197.98 12091.88 18989.29 32995.54 29082.50 24697.80 35189.79 26385.27 38895.69 330
HPM-MVS++copyleft97.34 2696.97 4398.47 699.08 4296.16 497.55 14997.97 12195.59 2596.61 9797.89 11792.57 4199.84 2695.95 9999.51 3899.40 66
CANet96.39 8096.02 8897.50 5497.62 15493.38 6897.02 20797.96 12295.42 2994.86 16997.81 13387.38 14399.82 3396.88 6099.20 8899.29 75
114514_t93.95 17993.06 19696.63 9899.07 4391.61 13897.46 16497.96 12277.99 45293.00 22897.57 16186.14 16699.33 13989.22 28099.15 9598.94 121
IU-MVS99.42 1095.39 1297.94 12490.40 26098.94 1997.41 4999.66 1099.74 9
MSC_two_6792asdad98.86 198.67 6796.94 197.93 12599.86 997.68 3399.67 699.77 3
No_MVS98.86 198.67 6796.94 197.93 12599.86 997.68 3399.67 699.77 3
fmvsm_s_conf0.1_n_296.33 8496.44 7996.00 15597.30 16890.37 20197.53 15097.92 12796.52 1199.14 1599.08 883.21 22399.74 5999.22 1198.06 15097.88 238
Anonymous2023121190.63 33189.42 34794.27 27298.24 10089.19 25698.05 6297.89 12879.95 44488.25 35994.96 31372.56 38198.13 29689.70 26585.14 39095.49 334
原ACMM196.38 12598.59 7591.09 16897.89 12887.41 35795.22 16097.68 14790.25 8599.54 11087.95 30299.12 10098.49 179
CDPH-MVS95.97 9495.38 10697.77 3898.93 5694.44 3996.35 28697.88 13086.98 36596.65 9597.89 11791.99 5199.47 12592.26 20099.46 4699.39 68
test1197.88 130
EIA-MVS95.53 10995.47 10095.71 18497.06 18489.63 22897.82 10097.87 13293.57 10893.92 20195.04 31090.61 8298.95 19394.62 15098.68 12098.54 172
CS-MVS96.86 5297.06 3596.26 13598.16 11291.16 16699.09 397.87 13295.30 3397.06 8098.03 10191.72 5498.71 23897.10 5599.17 9198.90 130
无先验95.79 32797.87 13283.87 41499.65 7987.68 31298.89 136
3Dnovator+91.43 495.40 11094.48 14798.16 1796.90 20195.34 1798.48 2597.87 13294.65 7088.53 35098.02 10383.69 21499.71 6793.18 18698.96 11099.44 61
VPNet92.23 25691.31 26494.99 22495.56 30990.96 17297.22 19397.86 13692.96 14690.96 28196.62 23175.06 36198.20 29091.90 21283.65 41495.80 321
test_vis1_n_192094.17 16494.58 13992.91 34097.42 16682.02 41197.83 9897.85 13794.68 6798.10 4898.49 5870.15 40099.32 14197.91 3098.82 11497.40 267
DVP-MVScopyleft97.91 497.81 598.22 1499.45 695.36 1498.21 4797.85 13794.92 5098.73 3098.87 3195.08 1099.84 2697.52 4299.67 699.48 56
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
TSAR-MVS + MP.97.42 2297.33 2997.69 4699.25 3294.24 4598.07 6097.85 13793.72 10398.57 3798.35 7293.69 2099.40 13397.06 5699.46 4699.44 61
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SPE-MVS-test96.89 5097.04 3996.45 11898.29 9391.66 13799.03 497.85 13795.84 1896.90 8397.97 10991.24 6898.75 22896.92 5999.33 7098.94 121
test_fmvsmconf0.01_n96.15 8895.85 9297.03 8392.66 42891.83 12997.97 7797.84 14195.57 2697.53 6199.00 1684.20 20799.76 5498.82 2399.08 10299.48 56
GDP-MVS95.62 10595.13 11497.09 7996.79 21493.26 7697.89 8897.83 14293.58 10796.80 8597.82 13183.06 23099.16 16194.40 15797.95 15698.87 140
balanced_conf0396.84 5696.89 4896.68 9397.63 15392.22 11398.17 5397.82 14394.44 7998.23 4597.36 17690.97 7599.22 15197.74 3299.66 1098.61 165
AdaColmapbinary94.34 15993.68 16996.31 12998.59 7591.68 13696.59 26697.81 14489.87 27092.15 24797.06 19783.62 21799.54 11089.34 27598.07 14997.70 251
MVSMamba_PlusPlus96.51 7496.48 7296.59 10298.07 12091.97 12498.14 5497.79 14590.43 25897.34 7097.52 16691.29 6799.19 15498.12 2899.64 1498.60 166
KinetiMVS95.26 11794.75 13396.79 9096.99 19492.05 12097.82 10097.78 14694.77 6396.46 10997.70 14480.62 28599.34 13892.37 19998.28 14098.97 111
mamv494.66 15296.10 8790.37 41098.01 12373.41 46196.82 23397.78 14689.95 26994.52 18097.43 17192.91 3099.09 17498.28 2799.16 9498.60 166
ETV-MVS96.02 9195.89 9196.40 12297.16 17692.44 10597.47 16297.77 14894.55 7396.48 10794.51 33891.23 7098.92 19895.65 11198.19 14497.82 246
新几何197.32 6298.60 7493.59 6397.75 14981.58 43595.75 13897.85 12590.04 8899.67 7786.50 33599.13 9898.69 161
旧先验198.38 8993.38 6897.75 14998.09 9692.30 4899.01 10899.16 85
EC-MVSNet96.42 7896.47 7396.26 13597.01 19291.52 14398.89 597.75 14994.42 8096.64 9697.68 14789.32 9698.60 25497.45 4699.11 10198.67 163
EI-MVSNet-Vis-set96.51 7496.47 7396.63 9898.24 10091.20 16096.89 22497.73 15294.74 6596.49 10698.49 5890.88 7999.58 9896.44 7698.32 13899.13 89
PAPM_NR95.01 13294.59 13896.26 13598.89 6090.68 18797.24 18797.73 15291.80 19092.93 23396.62 23189.13 10099.14 16689.21 28197.78 16098.97 111
Anonymous2024052991.98 26590.73 29295.73 18298.14 11389.40 24397.99 6897.72 15479.63 44693.54 21297.41 17369.94 40299.56 10691.04 23491.11 32198.22 207
CHOSEN 280x42093.12 21592.72 21394.34 26696.71 22787.27 30990.29 45497.72 15486.61 37291.34 27095.29 29884.29 20698.41 27093.25 18498.94 11197.35 270
EI-MVSNet-UG-set96.34 8396.30 8296.47 11598.20 10790.93 17596.86 22797.72 15494.67 6896.16 12298.46 6290.43 8499.58 9896.23 8297.96 15598.90 130
LS3D93.57 19692.61 21896.47 11597.59 15791.61 13897.67 12597.72 15485.17 39690.29 29298.34 7584.60 19899.73 6183.85 37798.27 14198.06 227
PAPR94.18 16393.42 18596.48 11497.64 15191.42 15095.55 34197.71 15888.99 30292.34 24395.82 27189.19 9899.11 16986.14 34197.38 17298.90 130
UGNet94.04 17493.28 18896.31 12996.85 20691.19 16197.88 9097.68 15994.40 8293.00 22896.18 25173.39 37899.61 9091.72 21898.46 13298.13 215
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
testdata95.46 20398.18 11188.90 26497.66 16082.73 42697.03 8198.07 9790.06 8798.85 20589.67 26698.98 10998.64 164
test1297.65 4798.46 7994.26 4397.66 16095.52 15090.89 7899.46 12699.25 8099.22 82
DTE-MVSNet90.56 33289.75 33893.01 33693.95 38987.25 31097.64 13397.65 16290.74 23887.12 38295.68 28279.97 29997.00 40683.33 37881.66 42594.78 391
TAPA-MVS90.10 792.30 25191.22 27095.56 19198.33 9189.60 23096.79 23997.65 16281.83 43291.52 26597.23 18687.94 12398.91 20071.31 45598.37 13698.17 213
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sd_testset93.10 21692.45 22695.05 21898.09 11689.21 25396.89 22497.64 16493.18 13291.79 25997.28 18175.35 36098.65 24888.99 28692.84 29197.28 273
test_cas_vis1_n_192094.48 15794.55 14394.28 27196.78 21986.45 33597.63 13597.64 16493.32 12597.68 6098.36 7173.75 37699.08 17796.73 6599.05 10497.31 272
NormalMVS96.36 8296.11 8697.12 7699.37 1992.90 8797.99 6897.63 16695.92 1696.57 10297.93 11185.34 18399.50 12094.99 12999.21 8398.97 111
Elysia94.00 17693.12 19396.64 9496.08 28892.72 9597.50 15397.63 16691.15 22494.82 17097.12 19274.98 36399.06 18390.78 23998.02 15198.12 217
StellarMVS94.00 17693.12 19396.64 9496.08 28892.72 9597.50 15397.63 16691.15 22494.82 17097.12 19274.98 36399.06 18390.78 23998.02 15198.12 217
cdsmvs_eth3d_5k23.24 45030.99 4520.00 4690.00 4920.00 4940.00 48197.63 1660.00 4870.00 48896.88 21084.38 2030.00 4880.00 4870.00 4860.00 484
DPM-MVS95.69 10294.92 12498.01 2198.08 11995.71 1095.27 35797.62 17090.43 25895.55 14797.07 19691.72 5499.50 12089.62 26898.94 11198.82 146
sasdasda96.02 9195.45 10197.75 4097.59 15795.15 2498.28 3597.60 17194.52 7596.27 11796.12 25687.65 13099.18 15796.20 8894.82 25398.91 127
canonicalmvs96.02 9195.45 10197.75 4097.59 15795.15 2498.28 3597.60 17194.52 7596.27 11796.12 25687.65 13099.18 15796.20 8894.82 25398.91 127
test22298.24 10092.21 11495.33 35297.60 17179.22 44895.25 15897.84 12788.80 10699.15 9598.72 158
cascas91.20 30790.08 32094.58 25294.97 34989.16 25793.65 41997.59 17479.90 44589.40 32492.92 40475.36 35998.36 27892.14 20594.75 25696.23 300
E295.20 12395.00 12195.79 17396.79 21489.66 22596.82 23397.58 17592.35 16995.28 15697.83 12986.68 15298.76 22294.79 14496.92 19398.95 118
E395.20 12395.00 12195.79 17396.77 22189.66 22596.82 23397.58 17592.35 16995.28 15697.83 12986.69 15198.76 22294.79 14496.92 19398.95 118
h-mvs3394.15 16693.52 17796.04 14997.81 13990.22 20597.62 13797.58 17595.19 3696.74 8997.45 16883.67 21599.61 9095.85 10279.73 43298.29 203
E695.04 13194.88 12695.52 19596.60 23289.02 26197.29 18397.57 17892.54 16295.04 16497.90 11685.66 17698.77 21994.92 13296.44 21798.78 149
MGCFI-Net95.94 9695.40 10597.56 5397.59 15794.62 3298.21 4797.57 17894.41 8196.17 12196.16 25487.54 13599.17 15996.19 9094.73 25898.91 127
MVSFormer95.37 11195.16 11395.99 15696.34 26491.21 15898.22 4597.57 17891.42 20796.22 11997.32 17786.20 16497.92 33894.07 16399.05 10498.85 142
test_djsdf93.07 21892.76 20894.00 28593.49 40788.70 26898.22 4597.57 17891.42 20790.08 30495.55 28982.85 23797.92 33894.07 16391.58 31295.40 345
OMC-MVS95.09 12894.70 13496.25 13898.46 7991.28 15496.43 27397.57 17892.04 18594.77 17497.96 11087.01 14999.09 17491.31 22896.77 19898.36 194
E495.09 12894.86 12795.77 17696.58 23589.56 23396.85 22897.56 18392.50 16395.03 16597.86 12386.03 16798.78 21594.71 14796.65 20798.96 114
viewcassd2359sk1195.26 11795.09 11895.80 17096.95 19889.72 22496.80 23897.56 18392.21 17695.37 15497.80 13587.17 14798.77 21994.82 13997.10 18798.90 130
PS-MVSNAJss93.74 18993.51 17894.44 26093.91 39189.28 25197.75 11097.56 18392.50 16389.94 30696.54 23488.65 10998.18 29393.83 17290.90 32695.86 315
casdiffmvs_mvgpermissive95.81 10195.57 9596.51 11196.87 20391.49 14497.50 15397.56 18393.99 9595.13 16297.92 11487.89 12498.78 21595.97 9897.33 17599.26 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
E3new95.28 11595.11 11795.80 17097.03 18989.76 22296.78 24397.54 18792.06 18495.40 15397.75 13887.49 13998.76 22294.85 13497.10 18798.88 138
jajsoiax92.42 24491.89 24494.03 28493.33 41588.50 27597.73 11597.53 18892.00 18788.85 34296.50 23675.62 35898.11 30093.88 17091.56 31395.48 335
mvs_tets92.31 25091.76 24793.94 29393.41 41288.29 28097.63 13597.53 18892.04 18588.76 34596.45 23874.62 36898.09 30593.91 16891.48 31495.45 340
dcpmvs_296.37 8197.05 3894.31 26998.96 5584.11 38497.56 14497.51 19093.92 9797.43 6798.52 5592.75 3599.32 14197.32 5499.50 4099.51 49
HQP_MVS93.78 18893.43 18394.82 23496.21 26889.99 21197.74 11397.51 19094.85 5391.34 27096.64 22481.32 27098.60 25493.02 19292.23 30095.86 315
plane_prior597.51 19098.60 25493.02 19292.23 30095.86 315
viewmanbaseed2359cas95.24 12095.02 12095.91 15996.87 20389.98 21396.82 23397.49 19392.26 17295.47 15197.82 13186.47 15798.69 24094.80 14197.20 18399.06 101
reproduce_monomvs91.30 30291.10 27491.92 37096.82 21182.48 40597.01 21097.49 19394.64 7188.35 35395.27 30170.53 39598.10 30195.20 12284.60 40095.19 363
viewmacassd2359aftdt95.07 13094.80 12995.87 16296.53 24589.84 21996.90 22397.48 19592.44 16595.36 15597.89 11785.23 18698.68 24294.40 15797.00 19199.09 96
PS-MVSNAJ95.37 11195.33 10895.49 19997.35 16790.66 18895.31 35497.48 19593.85 10096.51 10595.70 28188.65 10999.65 7994.80 14198.27 14196.17 304
API-MVS94.84 14394.49 14695.90 16097.90 13492.00 12397.80 10497.48 19589.19 29494.81 17296.71 21788.84 10599.17 15988.91 28898.76 11896.53 293
MG-MVS95.61 10695.38 10696.31 12998.42 8390.53 19096.04 31197.48 19593.47 11795.67 14498.10 9489.17 9999.25 14891.27 22998.77 11799.13 89
MAR-MVS94.22 16293.46 18096.51 11198.00 12592.19 11797.67 12597.47 19988.13 33593.00 22895.84 26984.86 19699.51 11787.99 30198.17 14697.83 245
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
CLD-MVS92.98 22292.53 22294.32 26796.12 28389.20 25495.28 35597.47 19992.66 15889.90 30795.62 28580.58 28698.40 27192.73 19792.40 29895.38 347
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_ETH3D91.34 30090.22 31694.68 24594.86 35887.86 29797.23 19197.46 20187.99 33689.90 30796.92 20866.35 43098.23 28790.30 25390.99 32497.96 232
nrg03094.05 17393.31 18796.27 13495.22 33694.59 3398.34 3097.46 20192.93 14791.21 27996.64 22487.23 14698.22 28894.99 12985.80 38095.98 314
XVG-OURS93.72 19093.35 18694.80 23997.07 18188.61 26994.79 37497.46 20191.97 18893.99 19897.86 12381.74 26498.88 20292.64 19892.67 29696.92 285
LPG-MVS_test92.94 22592.56 21994.10 27996.16 27888.26 28297.65 12997.46 20191.29 21190.12 30097.16 18979.05 31598.73 23292.25 20291.89 30895.31 352
LGP-MVS_train94.10 27996.16 27888.26 28297.46 20191.29 21190.12 30097.16 18979.05 31598.73 23292.25 20291.89 30895.31 352
MVS91.71 27390.44 30395.51 19695.20 33891.59 14096.04 31197.45 20673.44 46287.36 37895.60 28685.42 18299.10 17185.97 34697.46 16795.83 319
XVG-OURS-SEG-HR93.86 18593.55 17394.81 23697.06 18488.53 27495.28 35597.45 20691.68 19594.08 19797.68 14782.41 24998.90 20193.84 17192.47 29796.98 281
baseline95.58 10795.42 10496.08 14596.78 21990.41 19697.16 19897.45 20693.69 10695.65 14597.85 12587.29 14498.68 24295.66 10897.25 18199.13 89
ab-mvs93.57 19692.55 22096.64 9497.28 17091.96 12695.40 34897.45 20689.81 27593.22 22596.28 24779.62 30699.46 12690.74 24293.11 28898.50 177
xiu_mvs_v2_base95.32 11495.29 10995.40 20497.22 17290.50 19195.44 34797.44 21093.70 10596.46 10996.18 25188.59 11399.53 11294.79 14497.81 15996.17 304
131492.81 23492.03 23795.14 21495.33 32889.52 23896.04 31197.44 21087.72 35086.25 39995.33 29783.84 21298.79 21489.26 27897.05 19097.11 279
casdiffmvspermissive95.64 10495.49 9896.08 14596.76 22590.45 19397.29 18397.44 21094.00 9495.46 15297.98 10887.52 13898.73 23295.64 11297.33 17599.08 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewdifsd2359ckpt0794.76 14994.68 13595.01 22296.76 22587.41 30596.38 28397.43 21392.65 15994.52 18097.75 13885.55 18098.81 21194.36 15996.69 20498.82 146
XXY-MVS92.16 25891.23 26994.95 23094.75 36390.94 17497.47 16297.43 21389.14 29588.90 33896.43 23979.71 30398.24 28689.56 26987.68 36195.67 331
anonymousdsp92.16 25891.55 25593.97 28992.58 43089.55 23597.51 15297.42 21589.42 28888.40 35294.84 32080.66 28497.88 34391.87 21491.28 31894.48 399
Effi-MVS+94.93 13794.45 14896.36 12796.61 23191.47 14796.41 27797.41 21691.02 23094.50 18295.92 26587.53 13698.78 21593.89 16996.81 19798.84 145
RRT-MVS94.51 15594.35 15294.98 22696.40 25886.55 33297.56 14497.41 21693.19 13094.93 16797.04 19879.12 31399.30 14596.19 9097.32 17799.09 96
HQP3-MVS97.39 21892.10 305
HQP-MVS93.19 21292.74 21194.54 25595.86 29489.33 24796.65 25797.39 21893.55 10990.14 29495.87 26780.95 27598.50 26492.13 20892.10 30595.78 323
PLCcopyleft91.00 694.11 17093.43 18396.13 14398.58 7791.15 16796.69 25397.39 21887.29 36091.37 26996.71 21788.39 11499.52 11687.33 32297.13 18697.73 249
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
diffmvs_AUTHOR95.33 11395.27 11095.50 19896.37 26289.08 25996.08 30997.38 22193.09 13896.53 10497.74 14186.45 15898.68 24296.32 7897.48 16698.75 154
v7n90.76 32489.86 33193.45 32193.54 40487.60 30397.70 12397.37 22288.85 30887.65 37194.08 36881.08 27498.10 30184.68 36383.79 41394.66 396
UnsupCasMVSNet_eth85.99 40284.45 40690.62 40689.97 44882.40 40893.62 42097.37 22289.86 27178.59 45292.37 41465.25 44095.35 44182.27 39270.75 46094.10 410
viewdifsd2359ckpt1394.87 14194.52 14495.90 16096.88 20290.19 20696.92 22097.36 22491.26 21594.65 17697.46 16785.79 17398.64 24993.64 17596.76 19998.88 138
ACMM89.79 892.96 22392.50 22494.35 26496.30 26688.71 26797.58 14097.36 22491.40 20990.53 28796.65 22379.77 30298.75 22891.24 23091.64 31095.59 333
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
xiu_mvs_v1_base_debu95.01 13294.76 13095.75 17996.58 23591.71 13396.25 29697.35 22692.99 14096.70 9196.63 22882.67 24199.44 12996.22 8397.46 16796.11 310
xiu_mvs_v1_base95.01 13294.76 13095.75 17996.58 23591.71 13396.25 29697.35 22692.99 14096.70 9196.63 22882.67 24199.44 12996.22 8397.46 16796.11 310
xiu_mvs_v1_base_debi95.01 13294.76 13095.75 17996.58 23591.71 13396.25 29697.35 22692.99 14096.70 9196.63 22882.67 24199.44 12996.22 8397.46 16796.11 310
diffmvspermissive95.25 11995.13 11495.63 18796.43 25789.34 24695.99 31597.35 22692.83 15396.31 11597.37 17586.44 15998.67 24596.26 8097.19 18498.87 140
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
WTY-MVS94.71 15194.02 16096.79 9097.71 14592.05 12096.59 26697.35 22690.61 24994.64 17796.93 20586.41 16099.39 13491.20 23194.71 25998.94 121
viewdifsd2359ckpt0994.81 14694.37 15196.12 14496.91 19990.75 18496.94 21797.31 23190.51 25694.31 18797.38 17485.70 17598.71 23893.54 17696.75 20098.90 130
SSM_040794.54 15494.12 15995.80 17096.79 21490.38 19896.79 23997.29 23291.24 21693.68 20597.60 15885.03 19098.67 24592.14 20596.51 21098.35 196
SSM_040494.73 15094.31 15495.98 15797.05 18690.90 17797.01 21097.29 23291.24 21694.17 19497.60 15885.03 19098.76 22292.14 20597.30 17898.29 203
F-COLMAP93.58 19492.98 20095.37 20598.40 8688.98 26297.18 19697.29 23287.75 34990.49 28897.10 19585.21 18799.50 12086.70 33296.72 20397.63 253
VortexMVS92.88 22992.64 21593.58 31496.58 23587.53 30496.93 21997.28 23592.78 15689.75 31294.99 31182.73 24097.76 35694.60 15288.16 35695.46 338
XVG-ACMP-BASELINE90.93 32090.21 31793.09 33494.31 38285.89 35095.33 35297.26 23691.06 22989.38 32595.44 29568.61 41398.60 25489.46 27191.05 32294.79 389
PCF-MVS89.48 1191.56 28489.95 32896.36 12796.60 23292.52 10392.51 43997.26 23679.41 44788.90 33896.56 23384.04 21199.55 10877.01 43197.30 17897.01 280
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMP89.59 1092.62 23892.14 23394.05 28296.40 25888.20 28597.36 17597.25 23891.52 20288.30 35696.64 22478.46 32798.72 23791.86 21591.48 31495.23 359
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
icg_test_0407_293.58 19493.46 18093.94 29396.19 27286.16 34493.73 41497.24 23991.54 19893.50 21497.04 19885.64 17896.91 40990.68 24495.59 23598.76 150
IMVS_040793.94 18093.75 16694.49 25796.19 27286.16 34496.35 28697.24 23991.54 19893.50 21497.04 19885.64 17898.54 26190.68 24495.59 23598.76 150
IMVS_040492.44 24291.92 24294.00 28596.19 27286.16 34493.84 41197.24 23991.54 19888.17 36297.04 19876.96 34597.09 40090.68 24495.59 23598.76 150
IMVS_040393.98 17893.79 16594.55 25496.19 27286.16 34496.35 28697.24 23991.54 19893.59 20997.04 19885.86 17098.73 23290.68 24495.59 23598.76 150
OPM-MVS93.28 20892.76 20894.82 23494.63 36990.77 18296.65 25797.18 24393.72 10391.68 26397.26 18479.33 31098.63 25192.13 20892.28 29995.07 367
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PatchMatch-RL92.90 22792.02 23895.56 19198.19 10990.80 18095.27 35797.18 24387.96 33791.86 25895.68 28280.44 28998.99 19184.01 37297.54 16596.89 286
alignmvs95.87 10095.23 11197.78 3697.56 16395.19 2297.86 9197.17 24594.39 8396.47 10896.40 24185.89 16999.20 15396.21 8795.11 24998.95 118
MVS_Test94.89 13994.62 13795.68 18596.83 20989.55 23596.70 25197.17 24591.17 22295.60 14696.11 26087.87 12698.76 22293.01 19497.17 18598.72 158
Fast-Effi-MVS+93.46 20092.75 21095.59 19096.77 22190.03 20896.81 23797.13 24788.19 33091.30 27394.27 35686.21 16398.63 25187.66 31396.46 21698.12 217
FE-MVSNET391.65 27790.67 29694.60 24793.65 40290.95 17394.86 37297.12 24889.69 27889.21 33393.62 38781.17 27397.67 36387.54 31789.14 34495.17 365
EI-MVSNet93.03 22092.88 20493.48 31995.77 30086.98 31896.44 27197.12 24890.66 24591.30 27397.64 15486.56 15498.05 31389.91 25990.55 33095.41 342
MVSTER93.20 21192.81 20794.37 26396.56 24089.59 23197.06 20497.12 24891.24 21691.30 27395.96 26382.02 25798.05 31393.48 17990.55 33095.47 337
viewmambaseed2359dif94.28 16094.14 15794.71 24496.21 26886.97 31995.93 31897.11 25189.00 30195.00 16697.70 14486.02 16898.59 25893.71 17496.59 20998.57 170
test_yl94.78 14794.23 15596.43 11997.74 14391.22 15696.85 22897.10 25291.23 21995.71 14096.93 20584.30 20499.31 14393.10 18795.12 24798.75 154
DCV-MVSNet94.78 14794.23 15596.43 11997.74 14391.22 15696.85 22897.10 25291.23 21995.71 14096.93 20584.30 20499.31 14393.10 18795.12 24798.75 154
LTVRE_ROB88.41 1390.99 31689.92 33094.19 27396.18 27689.55 23596.31 29297.09 25487.88 34085.67 40395.91 26678.79 32398.57 25981.50 39589.98 33594.44 402
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
viewmsd2359difaftdt93.46 20093.23 19094.17 27496.12 28385.42 35996.43 27397.08 25592.91 14894.21 19098.00 10580.82 28198.74 23094.41 15689.05 34598.34 200
test_fmvs1_n92.73 23692.88 20492.29 36096.08 28881.05 41997.98 7197.08 25590.72 24096.79 8798.18 9163.07 44498.45 26897.62 4098.42 13597.36 268
v1091.04 31490.23 31493.49 31894.12 38588.16 28897.32 18097.08 25588.26 32988.29 35794.22 36182.17 25497.97 32586.45 33684.12 40794.33 405
viewdifsd2359ckpt1193.46 20093.22 19194.17 27496.11 28585.42 35996.43 27397.07 25892.91 14894.20 19198.00 10580.82 28198.73 23294.42 15589.04 34798.34 200
mamba_040893.70 19192.99 19795.83 16796.79 21490.38 19888.69 46497.07 25890.96 23293.68 20597.31 17984.97 19398.76 22290.95 23596.51 21098.35 196
SSM_0407293.51 19992.99 19795.05 21896.79 21490.38 19888.69 46497.07 25890.96 23293.68 20597.31 17984.97 19396.42 42090.95 23596.51 21098.35 196
v14419291.06 31390.28 31093.39 32293.66 40087.23 31296.83 23297.07 25887.43 35689.69 31594.28 35581.48 26798.00 32087.18 32684.92 39694.93 375
v119291.07 31290.23 31493.58 31493.70 39787.82 29996.73 24797.07 25887.77 34789.58 31894.32 35380.90 27997.97 32586.52 33485.48 38394.95 371
v891.29 30490.53 30293.57 31694.15 38488.12 28997.34 17797.06 26388.99 30288.32 35594.26 35883.08 22898.01 31987.62 31583.92 41194.57 398
mvs_anonymous93.82 18693.74 16794.06 28196.44 25685.41 36195.81 32597.05 26489.85 27390.09 30396.36 24387.44 14197.75 35893.97 16596.69 20499.02 103
IterMVS-LS92.29 25291.94 24193.34 32496.25 26786.97 31996.57 26997.05 26490.67 24389.50 32394.80 32386.59 15397.64 36789.91 25986.11 37895.40 345
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v192192090.85 32290.03 32593.29 32693.55 40386.96 32196.74 24697.04 26687.36 35889.52 32294.34 35080.23 29497.97 32586.27 33785.21 38994.94 373
CDS-MVSNet94.14 16993.54 17495.93 15896.18 27691.46 14896.33 29097.04 26688.97 30493.56 21096.51 23587.55 13497.89 34289.80 26295.95 22398.44 187
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SSC-MVS3.289.74 35889.26 35191.19 39595.16 33980.29 43094.53 38197.03 26891.79 19188.86 34194.10 36569.94 40297.82 34885.29 35586.66 37495.45 340
v114491.37 29790.60 29893.68 30993.89 39288.23 28496.84 23197.03 26888.37 32689.69 31594.39 34582.04 25697.98 32287.80 30585.37 38594.84 381
v124090.70 32889.85 33293.23 32893.51 40686.80 32296.61 26397.02 27087.16 36389.58 31894.31 35479.55 30797.98 32285.52 35285.44 38494.90 378
EPP-MVSNet95.22 12295.04 11995.76 17797.49 16489.56 23398.67 1597.00 27190.69 24194.24 18997.62 15689.79 9398.81 21193.39 18396.49 21498.92 126
V4291.58 28390.87 28193.73 30494.05 38888.50 27597.32 18096.97 27288.80 31489.71 31394.33 35182.54 24598.05 31389.01 28585.07 39294.64 397
test_fmvs193.21 21093.53 17592.25 36396.55 24281.20 41897.40 17196.96 27390.68 24296.80 8598.04 10069.25 40898.40 27197.58 4198.50 12897.16 278
FMVSNet291.31 30190.08 32094.99 22496.51 24992.21 11497.41 16796.95 27488.82 31188.62 34794.75 32573.87 37297.42 38885.20 35888.55 35395.35 349
ACMH87.59 1690.53 33389.42 34793.87 29896.21 26887.92 29497.24 18796.94 27588.45 32483.91 42396.27 24871.92 38498.62 25384.43 36689.43 34195.05 369
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net91.35 29890.27 31194.59 24896.51 24991.18 16397.50 15396.93 27688.82 31189.35 32694.51 33873.87 37297.29 39586.12 34288.82 34895.31 352
test191.35 29890.27 31194.59 24896.51 24991.18 16397.50 15396.93 27688.82 31189.35 32694.51 33873.87 37297.29 39586.12 34288.82 34895.31 352
FMVSNet391.78 27190.69 29595.03 22196.53 24592.27 11297.02 20796.93 27689.79 27689.35 32694.65 33177.01 34397.47 38386.12 34288.82 34895.35 349
FMVSNet189.88 35388.31 36694.59 24895.41 31891.18 16397.50 15396.93 27686.62 37187.41 37694.51 33865.94 43597.29 39583.04 38187.43 36495.31 352
GeoE93.89 18393.28 18895.72 18396.96 19789.75 22398.24 4396.92 28089.47 28592.12 24997.21 18784.42 20298.39 27687.71 30896.50 21399.01 106
SymmetryMVS95.94 9695.54 9697.15 7497.85 13692.90 8797.99 6896.91 28195.92 1696.57 10297.93 11185.34 18399.50 12094.99 12996.39 21899.05 102
miper_enhance_ethall91.54 28791.01 27793.15 33295.35 32487.07 31793.97 40396.90 28286.79 36989.17 33493.43 39886.55 15597.64 36789.97 25886.93 36994.74 393
eth_miper_zixun_eth91.02 31590.59 29992.34 35895.33 32884.35 38094.10 40096.90 28288.56 32088.84 34394.33 35184.08 20997.60 37288.77 29184.37 40595.06 368
TAMVS94.01 17593.46 18095.64 18696.16 27890.45 19396.71 25096.89 28489.27 29293.46 21796.92 20887.29 14497.94 33588.70 29395.74 22998.53 173
miper_ehance_all_eth91.59 28191.13 27392.97 33895.55 31086.57 33094.47 38496.88 28587.77 34788.88 34094.01 37086.22 16297.54 37689.49 27086.93 36994.79 389
v2v48291.59 28190.85 28493.80 30193.87 39388.17 28796.94 21796.88 28589.54 28289.53 32194.90 31781.70 26598.02 31889.25 27985.04 39495.20 360
CNLPA94.28 16093.53 17596.52 10798.38 8992.55 10296.59 26696.88 28590.13 26691.91 25597.24 18585.21 18799.09 17487.64 31497.83 15897.92 235
PAPM91.52 28890.30 30995.20 21195.30 33189.83 22093.38 42596.85 28886.26 37988.59 34895.80 27284.88 19598.15 29575.67 43695.93 22497.63 253
c3_l91.38 29590.89 28092.88 34295.58 30886.30 33894.68 37696.84 28988.17 33188.83 34494.23 35985.65 17797.47 38389.36 27484.63 39894.89 379
pm-mvs190.72 32789.65 34293.96 29094.29 38389.63 22897.79 10696.82 29089.07 29786.12 40195.48 29478.61 32597.78 35386.97 33081.67 42494.46 400
test_vis1_n92.37 24792.26 23192.72 34894.75 36382.64 40198.02 6596.80 29191.18 22197.77 5997.93 11158.02 45498.29 28497.63 3898.21 14397.23 276
CMPMVSbinary62.92 2185.62 40784.92 40187.74 43389.14 45373.12 46394.17 39896.80 29173.98 45973.65 46194.93 31566.36 42997.61 37183.95 37491.28 31892.48 437
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch90.27 34089.77 33691.78 37994.33 38084.72 37795.55 34196.73 29386.17 38186.36 39895.28 30071.28 38997.80 35184.09 37198.14 14792.81 429
Effi-MVS+-dtu93.08 21793.21 19292.68 35196.02 29183.25 39497.14 20096.72 29493.85 10091.20 28093.44 39583.08 22898.30 28391.69 22195.73 23096.50 295
TSAR-MVS + GP.96.69 6796.49 7197.27 6798.31 9293.39 6796.79 23996.72 29494.17 8997.44 6597.66 15092.76 3499.33 13996.86 6297.76 16299.08 98
1112_ss93.37 20592.42 22796.21 13997.05 18690.99 17096.31 29296.72 29486.87 36889.83 31096.69 22186.51 15699.14 16688.12 29893.67 28298.50 177
PVSNet86.66 1892.24 25591.74 25093.73 30497.77 14183.69 39192.88 43496.72 29487.91 33993.00 22894.86 31978.51 32699.05 18686.53 33397.45 17198.47 182
miper_lstm_enhance90.50 33690.06 32491.83 37595.33 32883.74 38893.86 40996.70 29887.56 35487.79 36893.81 37883.45 22096.92 40887.39 32084.62 39994.82 384
v14890.99 31690.38 30592.81 34593.83 39485.80 35196.78 24396.68 29989.45 28788.75 34693.93 37482.96 23497.82 34887.83 30483.25 41694.80 387
ACMH+87.92 1490.20 34489.18 35393.25 32796.48 25286.45 33596.99 21396.68 29988.83 31084.79 41296.22 25070.16 39998.53 26284.42 36788.04 35794.77 392
CANet_DTU94.37 15893.65 17096.55 10496.46 25592.13 11896.21 30096.67 30194.38 8493.53 21397.03 20379.34 30999.71 6790.76 24198.45 13397.82 246
cl____90.96 31990.32 30792.89 34195.37 32286.21 34194.46 38696.64 30287.82 34388.15 36394.18 36282.98 23297.54 37687.70 30985.59 38194.92 377
HY-MVS89.66 993.87 18492.95 20196.63 9897.10 18092.49 10495.64 33896.64 30289.05 29993.00 22895.79 27585.77 17499.45 12889.16 28494.35 26197.96 232
Test_1112_low_res92.84 23291.84 24595.85 16697.04 18889.97 21595.53 34396.64 30285.38 39189.65 31795.18 30585.86 17099.10 17187.70 30993.58 28798.49 179
DIV-MVS_self_test90.97 31890.33 30692.88 34295.36 32386.19 34394.46 38696.63 30587.82 34388.18 36194.23 35982.99 23197.53 37887.72 30685.57 38294.93 375
Fast-Effi-MVS+-dtu92.29 25291.99 23993.21 33095.27 33285.52 35797.03 20596.63 30592.09 18289.11 33695.14 30780.33 29298.08 30687.54 31794.74 25796.03 313
UnsupCasMVSNet_bld82.13 42479.46 42990.14 41388.00 46182.47 40690.89 45296.62 30778.94 44975.61 45684.40 46756.63 45796.31 42277.30 42866.77 46891.63 448
cl2291.21 30690.56 30193.14 33396.09 28786.80 32294.41 38896.58 30887.80 34588.58 34993.99 37280.85 28097.62 37089.87 26186.93 36994.99 370
jason94.84 14394.39 15096.18 14195.52 31190.93 17596.09 30896.52 30989.28 29196.01 12997.32 17784.70 19798.77 21995.15 12598.91 11398.85 142
jason: jason.
tt080591.09 31190.07 32394.16 27795.61 30688.31 27997.56 14496.51 31089.56 28189.17 33495.64 28467.08 42798.38 27791.07 23388.44 35495.80 321
AUN-MVS91.76 27290.75 29094.81 23697.00 19388.57 27196.65 25796.49 31189.63 27992.15 24796.12 25678.66 32498.50 26490.83 23779.18 43597.36 268
hse-mvs293.45 20392.99 19794.81 23697.02 19188.59 27096.69 25396.47 31295.19 3696.74 8996.16 25483.67 21598.48 26795.85 10279.13 43697.35 270
SD_040390.01 34890.02 32689.96 41695.65 30576.76 45195.76 32996.46 31390.58 25286.59 39596.29 24682.12 25594.78 44573.00 45093.76 28098.35 196
EG-PatchMatch MVS87.02 38885.44 39391.76 38192.67 42785.00 37196.08 30996.45 31483.41 42279.52 44693.49 39257.10 45697.72 36079.34 41990.87 32792.56 434
KD-MVS_self_test85.95 40384.95 40088.96 42789.55 45279.11 44595.13 36596.42 31585.91 38484.07 42190.48 43770.03 40194.82 44480.04 41172.94 45792.94 427
FE-MVSNET286.36 39684.68 40591.39 38987.67 46386.47 33496.21 30096.41 31687.87 34179.31 44889.64 44565.29 43995.58 43682.42 39077.28 44292.14 445
pmmvs687.81 38086.19 38892.69 35091.32 44086.30 33897.34 17796.41 31680.59 44384.05 42294.37 34767.37 42297.67 36384.75 36279.51 43494.09 412
PMMVS92.86 23092.34 22894.42 26294.92 35486.73 32594.53 38196.38 31884.78 40394.27 18895.12 30983.13 22798.40 27191.47 22596.49 21498.12 217
RPSCF90.75 32590.86 28290.42 40996.84 20776.29 45495.61 33996.34 31983.89 41291.38 26897.87 12176.45 34998.78 21587.16 32792.23 30096.20 302
BP-MVS195.89 9895.49 9897.08 8196.67 22893.20 7798.08 5896.32 32094.56 7296.32 11497.84 12784.07 21099.15 16396.75 6498.78 11698.90 130
MSDG91.42 29390.24 31394.96 22997.15 17888.91 26393.69 41796.32 32085.72 38786.93 39196.47 23780.24 29398.98 19280.57 40895.05 25096.98 281
WBMVS90.69 33089.99 32792.81 34596.48 25285.00 37195.21 36296.30 32289.46 28689.04 33794.05 36972.45 38297.82 34889.46 27187.41 36695.61 332
OurMVSNet-221017-090.51 33590.19 31891.44 38793.41 41281.25 41696.98 21496.28 32391.68 19586.55 39696.30 24574.20 37197.98 32288.96 28787.40 36795.09 366
MVP-Stereo90.74 32690.08 32092.71 34993.19 41788.20 28595.86 32296.27 32486.07 38284.86 41194.76 32477.84 33897.75 35883.88 37698.01 15392.17 444
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lupinMVS94.99 13694.56 14096.29 13396.34 26491.21 15895.83 32496.27 32488.93 30696.22 11996.88 21086.20 16498.85 20595.27 12199.05 10498.82 146
BH-untuned92.94 22592.62 21793.92 29797.22 17286.16 34496.40 28196.25 32690.06 26789.79 31196.17 25383.19 22498.35 27987.19 32597.27 18097.24 275
CL-MVSNet_self_test86.31 39885.15 39789.80 41888.83 45681.74 41493.93 40696.22 32786.67 37085.03 40990.80 43578.09 33494.50 44674.92 43971.86 45993.15 425
IS-MVSNet94.90 13894.52 14496.05 14897.67 14790.56 18998.44 2696.22 32793.21 12793.99 19897.74 14185.55 18098.45 26889.98 25797.86 15799.14 88
FA-MVS(test-final)93.52 19892.92 20295.31 20896.77 22188.54 27394.82 37396.21 32989.61 28094.20 19195.25 30383.24 22299.14 16690.01 25696.16 22098.25 205
GA-MVS91.38 29590.31 30894.59 24894.65 36887.62 30294.34 39196.19 33090.73 23990.35 29193.83 37571.84 38597.96 32987.22 32493.61 28598.21 208
LuminaMVS94.89 13994.35 15296.53 10595.48 31392.80 9196.88 22696.18 33192.85 15295.92 13296.87 21281.44 26898.83 20896.43 7797.10 18797.94 234
IterMVS-SCA-FT90.31 33889.81 33491.82 37695.52 31184.20 38394.30 39496.15 33290.61 24987.39 37794.27 35675.80 35596.44 41987.34 32186.88 37394.82 384
IterMVS90.15 34689.67 34091.61 38395.48 31383.72 38994.33 39296.12 33389.99 26887.31 38094.15 36475.78 35796.27 42386.97 33086.89 37294.83 382
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS92.76 23591.51 25996.52 10798.77 6290.99 17097.38 17496.08 33482.38 42889.29 32997.87 12183.77 21399.69 7381.37 40196.69 20498.89 136
pmmvs490.93 32089.85 33294.17 27493.34 41490.79 18194.60 37896.02 33584.62 40487.45 37495.15 30681.88 26297.45 38587.70 30987.87 35994.27 409
ppachtmachnet_test88.35 37587.29 37491.53 38492.45 43383.57 39293.75 41395.97 33684.28 40785.32 40894.18 36279.00 32196.93 40775.71 43584.99 39594.10 410
Anonymous2024052186.42 39585.44 39389.34 42590.33 44579.79 43696.73 24795.92 33783.71 41783.25 42791.36 43263.92 44296.01 42478.39 42385.36 38692.22 442
ITE_SJBPF92.43 35495.34 32585.37 36495.92 33791.47 20487.75 37096.39 24271.00 39197.96 32982.36 39189.86 33793.97 415
test_fmvs289.77 35789.93 32989.31 42693.68 39976.37 45397.64 13395.90 33989.84 27491.49 26696.26 24958.77 45297.10 39994.65 14991.13 32094.46 400
USDC88.94 36687.83 37192.27 36194.66 36784.96 37393.86 40995.90 33987.34 35983.40 42595.56 28867.43 42198.19 29282.64 38989.67 33993.66 418
COLMAP_ROBcopyleft87.81 1590.40 33789.28 35093.79 30297.95 12987.13 31696.92 22095.89 34182.83 42586.88 39397.18 18873.77 37599.29 14678.44 42293.62 28494.95 371
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDD-MVS93.82 18693.08 19596.02 15197.88 13589.96 21697.72 11895.85 34292.43 16695.86 13498.44 6468.42 41799.39 13496.31 7994.85 25198.71 160
VDDNet93.05 21992.07 23496.02 15196.84 20790.39 19798.08 5895.85 34286.22 38095.79 13798.46 6267.59 42099.19 15494.92 13294.85 25198.47 182
mvsmamba94.57 15394.14 15795.87 16297.03 18989.93 21797.84 9595.85 34291.34 21094.79 17396.80 21380.67 28398.81 21194.85 13498.12 14898.85 142
Vis-MVSNet (Re-imp)94.15 16693.88 16394.95 23097.61 15587.92 29498.10 5695.80 34592.22 17493.02 22797.45 16884.53 20097.91 34188.24 29797.97 15499.02 103
MM97.29 3196.98 4298.23 1298.01 12395.03 2798.07 6095.76 34697.78 197.52 6298.80 3888.09 11999.86 999.44 299.37 6799.80 1
KD-MVS_2432*160084.81 41382.64 41691.31 39091.07 44285.34 36591.22 44795.75 34785.56 38983.09 42890.21 44067.21 42395.89 42677.18 42962.48 47292.69 430
miper_refine_blended84.81 41382.64 41691.31 39091.07 44285.34 36591.22 44795.75 34785.56 38983.09 42890.21 44067.21 42395.89 42677.18 42962.48 47292.69 430
FE-MVS92.05 26391.05 27595.08 21796.83 20987.93 29393.91 40895.70 34986.30 37794.15 19594.97 31276.59 34799.21 15284.10 37096.86 19598.09 224
tpm cat188.36 37487.21 37791.81 37795.13 34480.55 42592.58 43895.70 34974.97 45887.45 37491.96 42578.01 33798.17 29480.39 41088.74 35196.72 291
our_test_388.78 37087.98 37091.20 39492.45 43382.53 40393.61 42195.69 35185.77 38684.88 41093.71 38079.99 29896.78 41579.47 41686.24 37594.28 408
BH-w/o92.14 26091.75 24893.31 32596.99 19485.73 35495.67 33395.69 35188.73 31689.26 33194.82 32282.97 23398.07 31085.26 35796.32 21996.13 309
CR-MVSNet90.82 32389.77 33693.95 29194.45 37687.19 31390.23 45595.68 35386.89 36792.40 23792.36 41780.91 27797.05 40281.09 40593.95 27797.60 258
Patchmtry88.64 37287.25 37592.78 34794.09 38686.64 32689.82 45995.68 35380.81 44087.63 37292.36 41780.91 27797.03 40378.86 42085.12 39194.67 395
testing9191.90 26891.02 27694.53 25696.54 24386.55 33295.86 32295.64 35591.77 19291.89 25693.47 39469.94 40298.86 20390.23 25593.86 27998.18 210
BH-RMVSNet92.72 23791.97 24094.97 22897.16 17687.99 29296.15 30695.60 35690.62 24891.87 25797.15 19178.41 32898.57 25983.16 37997.60 16498.36 194
PVSNet_082.17 1985.46 40883.64 41190.92 39895.27 33279.49 44190.55 45395.60 35683.76 41683.00 43089.95 44271.09 39097.97 32582.75 38760.79 47495.31 352
guyue95.17 12794.96 12395.82 16896.97 19689.65 22797.56 14495.58 35894.82 5795.72 13997.42 17282.90 23598.84 20796.71 6796.93 19298.96 114
SCA91.84 27091.18 27293.83 29995.59 30784.95 37494.72 37595.58 35890.82 23592.25 24593.69 38275.80 35598.10 30186.20 33995.98 22298.45 184
MonoMVSNet91.92 26691.77 24692.37 35592.94 42183.11 39797.09 20395.55 36092.91 14890.85 28394.55 33581.27 27296.52 41893.01 19487.76 36097.47 264
AllTest90.23 34288.98 35693.98 28797.94 13086.64 32696.51 27095.54 36185.38 39185.49 40596.77 21570.28 39799.15 16380.02 41292.87 28996.15 307
TestCases93.98 28797.94 13086.64 32695.54 36185.38 39185.49 40596.77 21570.28 39799.15 16380.02 41292.87 28996.15 307
mmtdpeth89.70 35988.96 35791.90 37295.84 29984.42 37997.46 16495.53 36390.27 26194.46 18490.50 43669.74 40698.95 19397.39 5369.48 46392.34 438
tpmvs89.83 35689.15 35491.89 37394.92 35480.30 42993.11 43095.46 36486.28 37888.08 36492.65 40780.44 28998.52 26381.47 39789.92 33696.84 287
pmmvs589.86 35588.87 36092.82 34492.86 42386.23 34096.26 29595.39 36584.24 40887.12 38294.51 33874.27 37097.36 39287.61 31687.57 36294.86 380
PatchmatchNetpermissive91.91 26791.35 26193.59 31395.38 32084.11 38493.15 42995.39 36589.54 28292.10 25093.68 38482.82 23898.13 29684.81 36195.32 24398.52 174
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst91.44 29291.32 26391.79 37895.15 34279.20 44493.42 42495.37 36788.55 32193.49 21693.67 38582.49 24798.27 28590.41 25089.34 34297.90 236
Anonymous2023120687.09 38786.14 38989.93 41791.22 44180.35 42796.11 30795.35 36883.57 41984.16 41793.02 40273.54 37795.61 43472.16 45286.14 37793.84 417
MIMVSNet184.93 41183.05 41390.56 40789.56 45184.84 37695.40 34895.35 36883.91 41180.38 44292.21 42257.23 45593.34 45970.69 45882.75 42293.50 420
TDRefinement86.53 39184.76 40391.85 37482.23 47584.25 38196.38 28395.35 36884.97 40084.09 42094.94 31465.76 43698.34 28284.60 36574.52 45392.97 426
TR-MVS91.48 29190.59 29994.16 27796.40 25887.33 30695.67 33395.34 37187.68 35191.46 26795.52 29176.77 34698.35 27982.85 38493.61 28596.79 289
EPNet_dtu91.71 27391.28 26692.99 33793.76 39683.71 39096.69 25395.28 37293.15 13487.02 38795.95 26483.37 22197.38 39179.46 41796.84 19697.88 238
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet587.29 38485.79 39191.78 37994.80 36187.28 30895.49 34595.28 37284.09 41083.85 42491.82 42662.95 44594.17 45078.48 42185.34 38793.91 416
MDTV_nov1_ep1390.76 28895.22 33680.33 42893.03 43295.28 37288.14 33492.84 23493.83 37581.34 26998.08 30682.86 38294.34 262
LF4IMVS87.94 37887.25 37589.98 41592.38 43580.05 43594.38 38995.25 37587.59 35384.34 41494.74 32664.31 44197.66 36684.83 36087.45 36392.23 441
TransMVSNet (Re)88.94 36687.56 37293.08 33594.35 37988.45 27797.73 11595.23 37687.47 35584.26 41695.29 29879.86 30197.33 39379.44 41874.44 45493.45 422
test20.0386.14 40185.40 39588.35 42890.12 44680.06 43495.90 32195.20 37788.59 31781.29 43793.62 38771.43 38892.65 46371.26 45681.17 42792.34 438
new-patchmatchnet83.18 42081.87 42387.11 43686.88 46675.99 45593.70 41595.18 37885.02 39977.30 45588.40 45465.99 43493.88 45574.19 44470.18 46191.47 453
MDA-MVSNet_test_wron85.87 40584.23 40890.80 40492.38 43582.57 40293.17 42795.15 37982.15 42967.65 46792.33 42078.20 33095.51 43877.33 42679.74 43194.31 407
YYNet185.87 40584.23 40890.78 40592.38 43582.46 40793.17 42795.14 38082.12 43067.69 46592.36 41778.16 33395.50 43977.31 42779.73 43294.39 403
Baseline_NR-MVSNet91.20 30790.62 29792.95 33993.83 39488.03 29197.01 21095.12 38188.42 32589.70 31495.13 30883.47 21897.44 38689.66 26783.24 41793.37 423
thres20092.23 25691.39 26094.75 24397.61 15589.03 26096.60 26595.09 38292.08 18393.28 22294.00 37178.39 32999.04 18981.26 40494.18 26896.19 303
ADS-MVSNet89.89 35288.68 36293.53 31795.86 29484.89 37590.93 45095.07 38383.23 42391.28 27691.81 42779.01 31997.85 34479.52 41491.39 31697.84 243
pmmvs-eth3d86.22 39984.45 40691.53 38488.34 46087.25 31094.47 38495.01 38483.47 42079.51 44789.61 44669.75 40595.71 43183.13 38076.73 44691.64 447
Anonymous20240521192.07 26290.83 28695.76 17798.19 10988.75 26697.58 14095.00 38586.00 38393.64 20897.45 16866.24 43299.53 11290.68 24492.71 29499.01 106
MDA-MVSNet-bldmvs85.00 41082.95 41591.17 39693.13 41983.33 39394.56 38095.00 38584.57 40565.13 47192.65 40770.45 39695.85 42873.57 44777.49 44194.33 405
ambc86.56 43983.60 47270.00 46685.69 47194.97 38780.60 44188.45 45337.42 47396.84 41282.69 38875.44 45192.86 428
testgi87.97 37787.21 37790.24 41292.86 42380.76 42096.67 25694.97 38791.74 19385.52 40495.83 27062.66 44794.47 44876.25 43388.36 35595.48 335
myMVS_eth3d2891.52 28890.97 27893.17 33196.91 19983.24 39595.61 33994.96 38992.24 17391.98 25393.28 39969.31 40798.40 27188.71 29295.68 23297.88 238
dp88.90 36888.26 36890.81 40294.58 37276.62 45292.85 43594.93 39085.12 39790.07 30593.07 40175.81 35498.12 29980.53 40987.42 36597.71 250
test_fmvs383.21 41983.02 41483.78 44386.77 46768.34 46996.76 24594.91 39186.49 37384.14 41989.48 44736.04 47491.73 46591.86 21580.77 42991.26 455
test_040286.46 39484.79 40291.45 38695.02 34885.55 35696.29 29494.89 39280.90 43782.21 43393.97 37368.21 41897.29 39562.98 46688.68 35291.51 450
tfpn200view992.38 24691.52 25794.95 23097.85 13689.29 24997.41 16794.88 39392.19 17993.27 22394.46 34378.17 33199.08 17781.40 39894.08 27296.48 296
CVMVSNet91.23 30591.75 24889.67 41995.77 30074.69 45696.44 27194.88 39385.81 38592.18 24697.64 15479.07 31495.58 43688.06 30095.86 22798.74 157
thres40092.42 24491.52 25795.12 21697.85 13689.29 24997.41 16794.88 39392.19 17993.27 22394.46 34378.17 33199.08 17781.40 39894.08 27296.98 281
tt032085.39 40983.12 41292.19 36593.44 41185.79 35296.19 30394.87 39671.19 46582.92 43191.76 42958.43 45396.81 41381.03 40678.26 44093.98 414
EPNet95.20 12394.56 14097.14 7592.80 42592.68 9797.85 9494.87 39696.64 992.46 23697.80 13586.23 16199.65 7993.72 17398.62 12499.10 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testing9991.62 27990.72 29394.32 26796.48 25286.11 34995.81 32594.76 39891.55 19791.75 26193.44 39568.55 41598.82 20990.43 24993.69 28198.04 228
sc_t186.48 39384.10 41093.63 31093.45 41085.76 35396.79 23994.71 39973.06 46386.45 39794.35 34855.13 46097.95 33384.38 36878.55 43997.18 277
SixPastTwentyTwo89.15 36488.54 36490.98 39793.49 40780.28 43196.70 25194.70 40090.78 23684.15 41895.57 28771.78 38697.71 36184.63 36485.07 39294.94 373
thres100view90092.43 24391.58 25494.98 22697.92 13289.37 24597.71 12094.66 40192.20 17793.31 22194.90 31778.06 33599.08 17781.40 39894.08 27296.48 296
thres600view792.49 24191.60 25395.18 21297.91 13389.47 23997.65 12994.66 40192.18 18193.33 22094.91 31678.06 33599.10 17181.61 39494.06 27696.98 281
PatchT88.87 36987.42 37393.22 32994.08 38785.10 36989.51 46094.64 40381.92 43192.36 24088.15 45780.05 29797.01 40572.43 45193.65 28397.54 261
baseline192.82 23391.90 24395.55 19397.20 17490.77 18297.19 19594.58 40492.20 17792.36 24096.34 24484.16 20898.21 28989.20 28283.90 41297.68 252
AstraMVS94.82 14594.64 13695.34 20796.36 26388.09 29097.58 14094.56 40594.98 4695.70 14297.92 11481.93 26198.93 19696.87 6195.88 22598.99 110
UBG91.55 28590.76 28893.94 29396.52 24885.06 37095.22 36094.54 40690.47 25791.98 25392.71 40672.02 38398.74 23088.10 29995.26 24598.01 230
Gipumacopyleft67.86 44065.41 44275.18 45692.66 42873.45 46066.50 47894.52 40753.33 47657.80 47766.07 47730.81 47689.20 46948.15 47578.88 43862.90 477
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testing1191.68 27690.75 29094.47 25896.53 24586.56 33195.76 32994.51 40891.10 22891.24 27893.59 38968.59 41498.86 20391.10 23294.29 26498.00 231
CostFormer91.18 31090.70 29492.62 35294.84 35981.76 41394.09 40194.43 40984.15 40992.72 23593.77 37979.43 30898.20 29090.70 24392.18 30397.90 236
tpm289.96 34989.21 35292.23 36494.91 35681.25 41693.78 41294.42 41080.62 44291.56 26493.44 39576.44 35097.94 33585.60 35192.08 30797.49 262
testing3-292.10 26192.05 23592.27 36197.71 14579.56 43897.42 16694.41 41193.53 11393.22 22595.49 29269.16 40999.11 16993.25 18494.22 26698.13 215
MGCNet96.74 6496.31 8198.02 2096.87 20394.65 3197.58 14094.39 41296.47 1297.16 7498.39 6887.53 13699.87 798.97 2099.41 5999.55 43
JIA-IIPM88.26 37687.04 38091.91 37193.52 40581.42 41589.38 46194.38 41380.84 43990.93 28280.74 46979.22 31197.92 33882.76 38691.62 31196.38 299
dmvs_re90.21 34389.50 34592.35 35695.47 31785.15 36795.70 33294.37 41490.94 23488.42 35193.57 39074.63 36795.67 43382.80 38589.57 34096.22 301
Patchmatch-test89.42 36287.99 36993.70 30795.27 33285.11 36888.98 46294.37 41481.11 43687.10 38593.69 38282.28 25197.50 38174.37 44294.76 25598.48 181
LCM-MVSNet72.55 43369.39 43782.03 44570.81 48565.42 47490.12 45794.36 41655.02 47565.88 46981.72 46824.16 48289.96 46674.32 44368.10 46690.71 458
ADS-MVSNet289.45 36188.59 36392.03 36895.86 29482.26 40990.93 45094.32 41783.23 42391.28 27691.81 42779.01 31995.99 42579.52 41491.39 31697.84 243
mvs5depth86.53 39185.08 39890.87 39988.74 45882.52 40491.91 44394.23 41886.35 37687.11 38493.70 38166.52 42897.76 35681.37 40175.80 44892.31 440
EU-MVSNet88.72 37188.90 35988.20 43093.15 41874.21 45896.63 26294.22 41985.18 39587.32 37995.97 26276.16 35294.98 44385.27 35686.17 37695.41 342
tt0320-xc84.83 41282.33 42092.31 35993.66 40086.20 34296.17 30594.06 42071.26 46482.04 43592.22 42155.07 46196.72 41681.49 39675.04 45294.02 413
MIMVSNet88.50 37386.76 38393.72 30694.84 35987.77 30091.39 44594.05 42186.41 37587.99 36692.59 41063.27 44395.82 43077.44 42592.84 29197.57 260
OpenMVS_ROBcopyleft81.14 2084.42 41582.28 42190.83 40090.06 44784.05 38695.73 33194.04 42273.89 46180.17 44591.53 43159.15 45197.64 36766.92 46489.05 34590.80 457
TinyColmap86.82 38985.35 39691.21 39294.91 35682.99 39993.94 40594.02 42383.58 41881.56 43694.68 32862.34 44898.13 29675.78 43487.35 36892.52 436
ETVMVS90.52 33489.14 35594.67 24696.81 21387.85 29895.91 32093.97 42489.71 27792.34 24392.48 41265.41 43897.96 32981.37 40194.27 26598.21 208
IB-MVS87.33 1789.91 35088.28 36794.79 24095.26 33587.70 30195.12 36693.95 42589.35 29087.03 38692.49 41170.74 39499.19 15489.18 28381.37 42697.49 262
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
Syy-MVS87.13 38687.02 38187.47 43495.16 33973.21 46295.00 36893.93 42688.55 32186.96 38891.99 42375.90 35394.00 45261.59 46894.11 26995.20 360
myMVS_eth3d87.18 38586.38 38689.58 42095.16 33979.53 43995.00 36893.93 42688.55 32186.96 38891.99 42356.23 45894.00 45275.47 43894.11 26995.20 360
testing22290.31 33888.96 35794.35 26496.54 24387.29 30795.50 34493.84 42890.97 23191.75 26192.96 40362.18 44998.00 32082.86 38294.08 27297.76 248
test_f80.57 42679.62 42883.41 44483.38 47367.80 47193.57 42293.72 42980.80 44177.91 45487.63 46033.40 47592.08 46487.14 32879.04 43790.34 459
LCM-MVSNet-Re92.50 23992.52 22392.44 35396.82 21181.89 41296.92 22093.71 43092.41 16784.30 41594.60 33385.08 18997.03 40391.51 22397.36 17398.40 190
tpm90.25 34189.74 33991.76 38193.92 39079.73 43793.98 40293.54 43188.28 32891.99 25293.25 40077.51 34197.44 38687.30 32387.94 35898.12 217
ET-MVSNet_ETH3D91.49 29090.11 31995.63 18796.40 25891.57 14295.34 35193.48 43290.60 25175.58 45795.49 29280.08 29696.79 41494.25 16189.76 33898.52 174
LFMVS93.60 19392.63 21696.52 10798.13 11591.27 15597.94 8193.39 43390.57 25396.29 11698.31 8169.00 41099.16 16194.18 16295.87 22699.12 92
MVStest182.38 42380.04 42789.37 42387.63 46482.83 40095.03 36793.37 43473.90 46073.50 46294.35 34862.89 44693.25 46173.80 44565.92 46992.04 446
FE-MVSNET83.85 41681.97 42289.51 42187.19 46583.19 39695.21 36293.17 43583.45 42178.90 45089.05 45065.46 43793.84 45669.71 46075.56 45091.51 450
Patchmatch-RL test87.38 38386.24 38790.81 40288.74 45878.40 44888.12 46993.17 43587.11 36482.17 43489.29 44881.95 25995.60 43588.64 29477.02 44398.41 189
ttmdpeth85.91 40484.76 40389.36 42489.14 45380.25 43295.66 33693.16 43783.77 41583.39 42695.26 30266.24 43295.26 44280.65 40775.57 44992.57 433
test-LLR91.42 29391.19 27192.12 36694.59 37080.66 42294.29 39592.98 43891.11 22690.76 28592.37 41479.02 31798.07 31088.81 28996.74 20197.63 253
test-mter90.19 34589.54 34492.12 36694.59 37080.66 42294.29 39592.98 43887.68 35190.76 28592.37 41467.67 41998.07 31088.81 28996.74 20197.63 253
WB-MVSnew89.88 35389.56 34390.82 40194.57 37383.06 39895.65 33792.85 44087.86 34290.83 28494.10 36579.66 30596.88 41076.34 43294.19 26792.54 435
testing387.67 38186.88 38290.05 41496.14 28180.71 42197.10 20292.85 44090.15 26587.54 37394.55 33555.70 45994.10 45173.77 44694.10 27195.35 349
test_method66.11 44164.89 44369.79 45972.62 48335.23 49165.19 47992.83 44220.35 48165.20 47088.08 45843.14 47182.70 47673.12 44963.46 47191.45 454
test0.0.03 189.37 36388.70 36191.41 38892.47 43285.63 35595.22 36092.70 44391.11 22686.91 39293.65 38679.02 31793.19 46278.00 42489.18 34395.41 342
new_pmnet82.89 42181.12 42688.18 43189.63 45080.18 43391.77 44492.57 44476.79 45675.56 45888.23 45661.22 45094.48 44771.43 45482.92 42089.87 460
mvsany_test193.93 18293.98 16193.78 30394.94 35386.80 32294.62 37792.55 44588.77 31596.85 8498.49 5888.98 10198.08 30695.03 12795.62 23496.46 298
thisisatest051592.29 25291.30 26595.25 21096.60 23288.90 26494.36 39092.32 44687.92 33893.43 21894.57 33477.28 34299.00 19089.42 27395.86 22797.86 242
thisisatest053093.03 22092.21 23295.49 19997.07 18189.11 25897.49 16192.19 44790.16 26494.09 19696.41 24076.43 35199.05 18690.38 25195.68 23298.31 202
tttt051792.96 22392.33 22994.87 23397.11 17987.16 31597.97 7792.09 44890.63 24793.88 20297.01 20476.50 34899.06 18390.29 25495.45 24198.38 192
K. test v387.64 38286.75 38490.32 41193.02 42079.48 44296.61 26392.08 44990.66 24580.25 44494.09 36767.21 42396.65 41785.96 34780.83 42894.83 382
TESTMET0.1,190.06 34789.42 34791.97 36994.41 37880.62 42494.29 39591.97 45087.28 36190.44 28992.47 41368.79 41197.67 36388.50 29696.60 20897.61 257
PM-MVS83.48 41881.86 42488.31 42987.83 46277.59 45093.43 42391.75 45186.91 36680.63 44089.91 44344.42 47095.84 42985.17 35976.73 44691.50 452
baseline291.63 27890.86 28293.94 29394.33 38086.32 33795.92 31991.64 45289.37 28986.94 39094.69 32781.62 26698.69 24088.64 29494.57 26096.81 288
APD_test179.31 42877.70 43184.14 44289.11 45569.07 46892.36 44291.50 45369.07 46773.87 46092.63 40939.93 47294.32 44970.54 45980.25 43089.02 462
FPMVS71.27 43469.85 43675.50 45574.64 48059.03 48091.30 44691.50 45358.80 47257.92 47688.28 45529.98 47885.53 47553.43 47382.84 42181.95 468
door91.13 455
door-mid91.06 456
EGC-MVSNET68.77 43963.01 44586.07 44192.49 43182.24 41093.96 40490.96 4570.71 4862.62 48790.89 43453.66 46293.46 45757.25 47184.55 40282.51 467
mvsany_test383.59 41782.44 41987.03 43783.80 47073.82 45993.70 41590.92 45886.42 37482.51 43290.26 43946.76 46995.71 43190.82 23876.76 44591.57 449
pmmvs379.97 42777.50 43287.39 43582.80 47479.38 44392.70 43790.75 45970.69 46678.66 45187.47 46251.34 46593.40 45873.39 44869.65 46289.38 461
UWE-MVS89.91 35089.48 34691.21 39295.88 29378.23 44994.91 37190.26 46089.11 29692.35 24294.52 33768.76 41297.96 32983.95 37495.59 23597.42 266
DSMNet-mixed86.34 39786.12 39087.00 43889.88 44970.43 46494.93 37090.08 46177.97 45385.42 40792.78 40574.44 36993.96 45474.43 44195.14 24696.62 292
MVS-HIRNet82.47 42281.21 42586.26 44095.38 32069.21 46788.96 46389.49 46266.28 46980.79 43974.08 47468.48 41697.39 39071.93 45395.47 24092.18 443
WB-MVS76.77 43076.63 43377.18 45085.32 46856.82 48294.53 38189.39 46382.66 42771.35 46389.18 44975.03 36288.88 47035.42 47966.79 46785.84 464
test111193.19 21292.82 20694.30 27097.58 16184.56 37898.21 4789.02 46493.53 11394.58 17898.21 8872.69 37999.05 18693.06 19098.48 13199.28 77
SSC-MVS76.05 43175.83 43476.72 45484.77 46956.22 48394.32 39388.96 46581.82 43370.52 46488.91 45174.79 36688.71 47133.69 48064.71 47085.23 465
ECVR-MVScopyleft93.19 21292.73 21294.57 25397.66 14985.41 36198.21 4788.23 46693.43 12094.70 17598.21 8872.57 38099.07 18193.05 19198.49 12999.25 80
EPMVS90.70 32889.81 33493.37 32394.73 36584.21 38293.67 41888.02 46789.50 28492.38 23993.49 39277.82 33997.78 35386.03 34592.68 29598.11 223
ANet_high63.94 44359.58 44677.02 45161.24 48766.06 47285.66 47287.93 46878.53 45142.94 47971.04 47625.42 48180.71 47852.60 47430.83 48084.28 466
PMMVS270.19 43566.92 43980.01 44676.35 47965.67 47386.22 47087.58 46964.83 47162.38 47280.29 47126.78 48088.49 47363.79 46554.07 47685.88 463
lessismore_v090.45 40891.96 43879.09 44687.19 47080.32 44394.39 34566.31 43197.55 37584.00 37376.84 44494.70 394
PMVScopyleft53.92 2258.58 44455.40 44768.12 46051.00 48848.64 48578.86 47587.10 47146.77 47735.84 48374.28 4738.76 48686.34 47442.07 47773.91 45569.38 474
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UWE-MVS-2886.81 39086.41 38588.02 43292.87 42274.60 45795.38 35086.70 47288.17 33187.28 38194.67 33070.83 39393.30 46067.45 46294.31 26396.17 304
test_vis1_rt86.16 40085.06 39989.46 42293.47 40980.46 42696.41 27786.61 47385.22 39479.15 44988.64 45252.41 46497.06 40193.08 18990.57 32990.87 456
testf169.31 43766.76 44076.94 45278.61 47761.93 47688.27 46786.11 47455.62 47359.69 47385.31 46520.19 48489.32 46757.62 46969.44 46479.58 469
APD_test269.31 43766.76 44076.94 45278.61 47761.93 47688.27 46786.11 47455.62 47359.69 47385.31 46520.19 48489.32 46757.62 46969.44 46479.58 469
gg-mvs-nofinetune87.82 37985.61 39294.44 26094.46 37589.27 25291.21 44984.61 47680.88 43889.89 30974.98 47271.50 38797.53 37885.75 35097.21 18296.51 294
dmvs_testset81.38 42582.60 41877.73 44991.74 43951.49 48493.03 43284.21 47789.07 29778.28 45391.25 43376.97 34488.53 47256.57 47282.24 42393.16 424
GG-mvs-BLEND93.62 31193.69 39889.20 25492.39 44183.33 47887.98 36789.84 44471.00 39196.87 41182.08 39395.40 24294.80 387
MTMP97.86 9182.03 479
DeepMVS_CXcopyleft74.68 45790.84 44464.34 47581.61 48065.34 47067.47 46888.01 45948.60 46880.13 47962.33 46773.68 45679.58 469
E-PMN53.28 44552.56 44955.43 46374.43 48147.13 48683.63 47476.30 48142.23 47842.59 48062.22 47928.57 47974.40 48031.53 48131.51 47944.78 478
test250691.60 28090.78 28794.04 28397.66 14983.81 38798.27 3775.53 48293.43 12095.23 15998.21 8867.21 42399.07 18193.01 19498.49 12999.25 80
EMVS52.08 44751.31 45054.39 46472.62 48345.39 48883.84 47375.51 48341.13 47940.77 48159.65 48030.08 47773.60 48128.31 48329.90 48144.18 479
test_vis3_rt72.73 43270.55 43579.27 44780.02 47668.13 47093.92 40774.30 48476.90 45558.99 47573.58 47520.29 48395.37 44084.16 36972.80 45874.31 472
MVEpermissive50.73 2353.25 44648.81 45166.58 46265.34 48657.50 48172.49 47770.94 48540.15 48039.28 48263.51 4786.89 48873.48 48238.29 47842.38 47868.76 476
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt51.94 44853.82 44846.29 46533.73 48945.30 48978.32 47667.24 48618.02 48250.93 47887.05 46352.99 46353.11 48470.76 45725.29 48240.46 480
kuosan65.27 44264.66 44467.11 46183.80 47061.32 47988.53 46660.77 48768.22 46867.67 46680.52 47049.12 46770.76 48329.67 48253.64 47769.26 475
dongtai69.99 43669.33 43871.98 45888.78 45761.64 47889.86 45859.93 48875.67 45774.96 45985.45 46450.19 46681.66 47743.86 47655.27 47572.63 473
N_pmnet78.73 42978.71 43078.79 44892.80 42546.50 48794.14 39943.71 48978.61 45080.83 43891.66 43074.94 36596.36 42167.24 46384.45 40493.50 420
wuyk23d25.11 44924.57 45326.74 46673.98 48239.89 49057.88 4809.80 49012.27 48310.39 4846.97 4867.03 48736.44 48525.43 48417.39 4833.89 483
testmvs13.36 45116.33 4544.48 4685.04 4902.26 49393.18 4263.28 4912.70 4848.24 48521.66 4822.29 4902.19 4867.58 4852.96 4849.00 482
test12313.04 45215.66 4555.18 4674.51 4913.45 49292.50 4401.81 4922.50 4857.58 48620.15 4833.67 4892.18 4877.13 4861.07 4859.90 481
mmdepth0.00 4550.00 4580.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.00 4870.00 4910.00 4880.00 4870.00 4860.00 484
monomultidepth0.00 4550.00 4580.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.00 4870.00 4910.00 4880.00 4870.00 4860.00 484
test_blank0.00 4550.00 4580.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.00 4870.00 4910.00 4880.00 4870.00 4860.00 484
uanet_test0.00 4550.00 4580.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.00 4870.00 4910.00 4880.00 4870.00 4860.00 484
DCPMVS0.00 4550.00 4580.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.00 4870.00 4910.00 4880.00 4870.00 4860.00 484
pcd_1.5k_mvsjas7.39 4549.85 4570.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.00 48788.65 1090.00 4880.00 4870.00 4860.00 484
sosnet-low-res0.00 4550.00 4580.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.00 4870.00 4910.00 4880.00 4870.00 4860.00 484
sosnet0.00 4550.00 4580.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.00 4870.00 4910.00 4880.00 4870.00 4860.00 484
uncertanet0.00 4550.00 4580.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.00 4870.00 4910.00 4880.00 4870.00 4860.00 484
Regformer0.00 4550.00 4580.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.00 4870.00 4910.00 4880.00 4870.00 4860.00 484
n20.00 493
nn0.00 493
ab-mvs-re8.06 45310.74 4560.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 48896.69 2210.00 4910.00 4880.00 4870.00 4860.00 484
uanet0.00 4550.00 4580.00 4690.00 4920.00 4940.00 4810.00 4930.00 4870.00 4880.00 4870.00 4910.00 4880.00 4870.00 4860.00 484
TestfortrainingZip98.69 11
WAC-MVS79.53 43975.56 437
PC_three_145290.77 23798.89 2698.28 8696.24 198.35 27995.76 10699.58 2399.59 32
eth-test20.00 492
eth-test0.00 492
OPU-MVS98.55 498.82 6196.86 398.25 4098.26 8796.04 299.24 14995.36 12099.59 1999.56 40
test_0728_THIRD94.78 6198.73 3098.87 3195.87 499.84 2697.45 4699.72 299.77 3
GSMVS98.45 184
test_part299.28 3095.74 998.10 48
sam_mvs182.76 23998.45 184
sam_mvs81.94 260
test_post192.81 43616.58 48580.53 28797.68 36286.20 339
test_post17.58 48481.76 26398.08 306
patchmatchnet-post90.45 43882.65 24498.10 301
gm-plane-assit93.22 41678.89 44784.82 40293.52 39198.64 24987.72 306
test9_res94.81 14099.38 6499.45 59
agg_prior293.94 16799.38 6499.50 52
test_prior493.66 6296.42 276
test_prior296.35 28692.80 15596.03 12697.59 16092.01 5095.01 12899.38 64
旧先验295.94 31781.66 43497.34 7098.82 20992.26 200
新几何295.79 327
原ACMM295.67 333
testdata299.67 7785.96 347
segment_acmp92.89 33
testdata195.26 35993.10 137
plane_prior796.21 26889.98 213
plane_prior696.10 28690.00 20981.32 270
plane_prior496.64 224
plane_prior390.00 20994.46 7891.34 270
plane_prior297.74 11394.85 53
plane_prior196.14 281
plane_prior89.99 21197.24 18794.06 9292.16 304
HQP5-MVS89.33 247
HQP-NCC95.86 29496.65 25793.55 10990.14 294
ACMP_Plane95.86 29496.65 25793.55 10990.14 294
BP-MVS92.13 208
HQP4-MVS90.14 29498.50 26495.78 323
HQP2-MVS80.95 275
NP-MVS95.99 29289.81 22195.87 267
MDTV_nov1_ep13_2view70.35 46593.10 43183.88 41393.55 21182.47 24886.25 33898.38 192
ACMMP++_ref90.30 334
ACMMP++91.02 323
Test By Simon88.73 108