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 bysort bysorted bysort by
MCST-MVS98.18 297.95 998.86 599.85 396.60 999.70 1997.98 4797.18 295.96 8599.33 1992.62 26100.00 198.99 2099.93 199.98 6
NCCC98.12 598.11 398.13 2399.76 694.46 4799.81 797.88 4996.54 998.84 1999.46 1092.55 2799.98 998.25 3799.93 199.94 18
DVP-MVS++98.18 298.09 598.44 1599.61 2495.38 2199.55 3797.68 8093.01 6099.23 899.45 1495.12 899.98 999.25 1599.92 399.97 7
PC_three_145294.60 2799.41 299.12 4295.50 799.96 2899.84 299.92 399.97 7
OPU-MVS99.49 499.64 1798.51 499.77 1099.19 2895.12 899.97 2199.90 199.92 399.99 1
MSLP-MVS++97.50 1497.45 1597.63 3799.65 1693.21 7199.70 1998.13 3994.61 2697.78 4799.46 1089.85 4999.81 7097.97 4099.91 699.88 26
DPE-MVScopyleft98.11 698.00 698.44 1599.50 4295.39 2099.29 7297.72 7194.50 2898.64 2299.54 393.32 1999.97 2199.58 999.90 799.95 15
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CNVR-MVS98.46 198.38 198.72 899.80 496.19 1499.80 997.99 4697.05 599.41 299.59 292.89 25100.00 198.99 2099.90 799.96 10
test9_res98.60 2599.87 999.90 22
agg_prior297.84 4499.87 999.91 21
HPM-MVS++copyleft97.72 1097.59 1198.14 2299.53 4094.76 4199.19 7697.75 6695.66 1798.21 3299.29 2091.10 3399.99 597.68 4599.87 999.68 54
MG-MVS97.24 1796.83 2798.47 1499.79 595.71 1799.07 9899.06 994.45 3096.42 7998.70 8788.81 5999.74 7895.35 9199.86 1299.97 7
MSP-MVS97.77 998.18 296.53 8899.54 3690.14 13299.41 5997.70 7695.46 2198.60 2399.19 2895.71 499.49 10298.15 3899.85 1399.95 15
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
train_agg97.20 2097.08 2097.57 4199.57 3393.17 7299.38 6297.66 8390.18 12498.39 2899.18 3190.94 3599.66 8498.58 2899.85 1399.88 26
MSC_two_6792asdad99.51 299.61 2498.60 297.69 7899.98 999.55 1099.83 1599.96 10
No_MVS99.51 299.61 2498.60 297.69 7899.98 999.55 1099.83 1599.96 10
SMA-MVScopyleft97.24 1796.99 2198.00 2899.30 5494.20 5499.16 8297.65 8889.55 14499.22 1099.52 890.34 4699.99 598.32 3599.83 1599.82 31
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
TSAR-MVS + MP.97.44 1597.46 1497.39 4599.12 6593.49 6898.52 15697.50 12194.46 2998.99 1498.64 9091.58 3099.08 13898.49 2999.83 1599.60 65
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test_241102_TWO97.72 7194.17 3399.23 899.54 393.14 2499.98 999.70 399.82 1999.99 1
DVP-MVScopyleft98.07 798.00 698.29 1899.66 1295.20 2999.72 1697.47 12693.95 3899.07 1299.46 1093.18 2299.97 2199.64 699.82 1999.69 53
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_THIRD93.01 6099.07 1299.46 1094.66 1499.97 2199.25 1599.82 1999.95 15
test_0728_SECOND98.77 799.66 1296.37 1399.72 1697.68 8099.98 999.64 699.82 1999.96 10
SED-MVS98.18 298.10 498.41 1799.63 1895.24 2499.77 1097.72 7194.17 3399.30 699.54 393.32 1999.98 999.70 399.81 2399.99 1
IU-MVS99.63 1895.38 2197.73 7095.54 1999.54 199.69 599.81 2399.99 1
test_prior299.57 3591.43 9598.12 3698.97 5690.43 4398.33 3499.81 23
DPM-MVS97.86 897.25 1899.68 198.25 9399.10 199.76 1397.78 6396.61 898.15 3399.53 793.62 17100.00 191.79 14799.80 2699.94 18
APDe-MVS97.53 1197.47 1397.70 3599.58 3093.63 6399.56 3697.52 11693.59 5398.01 4299.12 4290.80 3999.55 9699.26 1499.79 2799.93 20
CDPH-MVS96.56 3596.18 3997.70 3599.59 2893.92 5999.13 9397.44 13289.02 15697.90 4599.22 2588.90 5899.49 10294.63 10999.79 2799.68 54
region2R96.30 4296.17 4296.70 7899.70 790.31 12799.46 5097.66 8390.55 11497.07 6299.07 4786.85 9599.97 2195.43 8999.74 2999.81 32
SD-MVS97.51 1397.40 1697.81 3399.01 7293.79 6299.33 6997.38 13993.73 4998.83 2099.02 5390.87 3899.88 4998.69 2399.74 2999.77 42
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
HFP-MVS96.42 3896.26 3796.90 6699.69 890.96 11499.47 4697.81 5890.54 11596.88 6499.05 5087.57 7699.96 2895.65 8299.72 3199.78 37
ACMMPR96.28 4396.14 4696.73 7599.68 990.47 12599.47 4697.80 6090.54 11596.83 6999.03 5286.51 10699.95 3195.65 8299.72 3199.75 45
CP-MVS96.22 4496.15 4596.42 9399.67 1089.62 14999.70 1997.61 9690.07 13096.00 8499.16 3487.43 7999.92 3996.03 7899.72 3199.70 51
test1297.83 3299.33 5394.45 4897.55 10997.56 4888.60 6199.50 10199.71 3499.55 70
ZD-MVS99.67 1093.28 7097.61 9687.78 19897.41 5299.16 3490.15 4799.56 9598.35 3399.70 35
DeepC-MVS_fast93.52 297.16 2196.84 2698.13 2399.61 2494.45 4898.85 12097.64 8996.51 1195.88 8899.39 1887.35 8599.99 596.61 6799.69 3699.96 10
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APD-MVScopyleft96.95 2696.72 2897.63 3799.51 4193.58 6499.16 8297.44 13290.08 12998.59 2499.07 4789.06 5599.42 11397.92 4199.66 3799.88 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SF-MVS97.22 1996.92 2298.12 2599.11 6694.88 3499.44 5397.45 12989.60 14098.70 2199.42 1790.42 4499.72 7998.47 3099.65 3899.77 42
HPM-MVScopyleft95.41 6895.22 6795.99 10999.29 5589.14 15499.17 8197.09 16787.28 21195.40 9898.48 10284.93 12999.38 11895.64 8699.65 3899.47 77
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test22298.32 9291.21 10298.08 20597.58 10483.74 26995.87 8999.02 5386.74 9899.64 4099.81 32
mPP-MVS95.90 5495.75 5696.38 9599.58 3089.41 15399.26 7397.41 13690.66 10994.82 10798.95 6286.15 11399.98 995.24 9499.64 4099.74 46
SteuartSystems-ACMMP97.25 1697.34 1797.01 5797.38 12091.46 9999.75 1497.66 8394.14 3798.13 3499.26 2192.16 2999.66 8497.91 4299.64 4099.90 22
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HPM-MVS_fast94.89 7794.62 7695.70 11899.11 6688.44 17499.14 9097.11 16385.82 23595.69 9498.47 10383.46 14799.32 12593.16 13399.63 4399.35 85
9.1496.87 2499.34 5099.50 4397.49 12389.41 14798.59 2499.43 1689.78 5099.69 8198.69 2399.62 44
新几何197.40 4498.92 7792.51 8797.77 6585.52 24096.69 7499.06 4988.08 7099.89 4884.88 22599.62 4499.79 35
原ACMM196.18 10099.03 7190.08 13597.63 9388.98 15797.00 6398.97 5688.14 6999.71 8088.23 18799.62 4498.76 141
PHI-MVS96.65 3396.46 3397.21 5199.34 5091.77 9299.70 1998.05 4286.48 22898.05 3999.20 2789.33 5399.96 2898.38 3199.62 4499.90 22
DELS-MVS97.12 2296.60 3198.68 998.03 10296.57 1099.84 497.84 5296.36 1395.20 10298.24 11188.17 6699.83 6496.11 7699.60 4899.64 60
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
MP-MVScopyleft96.00 4895.82 5296.54 8799.47 4690.13 13499.36 6697.41 13690.64 11295.49 9798.95 6285.51 12199.98 996.00 7999.59 4999.52 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS96.09 4695.81 5496.95 6599.42 4791.19 10399.55 3797.53 11389.72 13595.86 9098.94 6586.59 10299.97 2195.13 9599.56 5099.68 54
MVS_111021_HR96.69 3196.69 2996.72 7798.58 8891.00 11399.14 9099.45 193.86 4495.15 10398.73 8188.48 6299.76 7697.23 5399.56 5099.40 81
DeepPCF-MVS93.56 196.55 3697.84 1092.68 21098.71 8578.11 32999.70 1997.71 7598.18 197.36 5499.76 190.37 4599.94 3499.27 1399.54 5299.99 1
CPTT-MVS94.60 8994.43 7995.09 13699.66 1286.85 20999.44 5397.47 12683.22 27894.34 11598.96 6082.50 16799.55 9694.81 10399.50 5398.88 127
MP-MVS-pluss95.80 5795.30 6497.29 4798.95 7692.66 8298.59 15197.14 15988.95 15993.12 13299.25 2285.62 11899.94 3496.56 6999.48 5499.28 92
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP96.59 3496.18 3997.81 3398.82 8193.55 6598.88 11997.59 10290.66 10997.98 4399.14 3886.59 102100.00 196.47 7199.46 5599.89 25
PGM-MVS95.85 5595.65 6096.45 9199.50 4289.77 14698.22 19098.90 1289.19 15196.74 7298.95 6285.91 11799.92 3993.94 11899.46 5599.66 58
testdata95.26 13398.20 9587.28 20197.60 9885.21 24498.48 2799.15 3688.15 6898.72 15290.29 16399.45 5799.78 37
SR-MVS96.13 4596.16 4496.07 10599.42 4789.04 15698.59 15197.33 14390.44 11896.84 6799.12 4286.75 9799.41 11697.47 4899.44 5899.76 44
XVS96.47 3796.37 3596.77 7199.62 2290.66 12299.43 5697.58 10492.41 7596.86 6598.96 6087.37 8199.87 5295.65 8299.43 5999.78 37
X-MVStestdata90.69 17988.66 20196.77 7199.62 2290.66 12299.43 5697.58 10492.41 7596.86 6529.59 38587.37 8199.87 5295.65 8299.43 5999.78 37
MVS93.92 10292.28 13198.83 695.69 18696.82 796.22 28498.17 3484.89 25384.34 23098.61 9479.32 19899.83 6493.88 12099.43 5999.86 29
MTAPA96.09 4695.80 5596.96 6499.29 5591.19 10397.23 24797.45 12992.58 6994.39 11499.24 2486.43 10899.99 596.22 7399.40 6299.71 50
旧先验198.97 7392.90 8197.74 6799.15 3691.05 3499.33 6399.60 65
PAPM_NR95.43 6695.05 7296.57 8699.42 4790.14 13298.58 15397.51 11890.65 11192.44 14098.90 6887.77 7599.90 4690.88 15599.32 6499.68 54
SR-MVS-dyc-post95.75 6195.86 5195.41 12899.22 5987.26 20498.40 17497.21 15189.63 13896.67 7598.97 5686.73 9999.36 12096.62 6599.31 6599.60 65
RE-MVS-def95.70 5799.22 5987.26 20498.40 17497.21 15189.63 13896.67 7598.97 5685.24 12796.62 6599.31 6599.60 65
PAPM96.35 3995.94 4897.58 3994.10 23995.25 2398.93 11498.17 3494.26 3293.94 12198.72 8389.68 5197.88 18796.36 7299.29 6799.62 64
APD-MVS_3200maxsize95.64 6495.65 6095.62 12299.24 5887.80 18498.42 16997.22 15088.93 16196.64 7798.98 5585.49 12299.36 12096.68 6499.27 6899.70 51
3Dnovator87.35 1193.17 13091.77 14497.37 4695.41 19693.07 7598.82 12397.85 5191.53 9182.56 24997.58 13671.97 24999.82 6791.01 15399.23 6999.22 98
patch_mono-297.10 2397.97 894.49 15899.21 6183.73 27499.62 3098.25 2995.28 2299.38 498.91 6792.28 2899.94 3499.61 899.22 7099.78 37
dcpmvs_295.67 6396.18 3994.12 17598.82 8184.22 26797.37 23995.45 27390.70 10895.77 9298.63 9290.47 4298.68 15499.20 1799.22 7099.45 78
GST-MVS95.97 5195.66 5896.90 6699.49 4591.22 10199.45 5297.48 12489.69 13695.89 8798.72 8386.37 10999.95 3194.62 11099.22 7099.52 72
PS-MVSNAJ96.87 2896.40 3498.29 1897.35 12197.29 599.03 10497.11 16395.83 1598.97 1599.14 3882.48 16999.60 9398.60 2599.08 7398.00 174
test_fmvsm_n_192097.08 2497.55 1295.67 12097.94 10489.61 15099.93 198.48 2397.08 499.08 1199.13 4088.17 6699.93 3799.11 1899.06 7497.47 187
MVS_111021_LR95.78 5895.94 4895.28 13298.19 9787.69 18598.80 12599.26 793.39 5595.04 10598.69 8884.09 13999.76 7696.96 5999.06 7498.38 159
PAPR96.35 3995.82 5297.94 3099.63 1894.19 5599.42 5897.55 10992.43 7293.82 12599.12 4287.30 8699.91 4294.02 11699.06 7499.74 46
114514_t94.06 9793.05 11597.06 5599.08 6992.26 8898.97 11297.01 17582.58 29192.57 13898.22 11280.68 18999.30 12689.34 17699.02 7799.63 62
API-MVS94.78 8194.18 8696.59 8399.21 6190.06 13998.80 12597.78 6383.59 27393.85 12399.21 2683.79 14299.97 2192.37 14399.00 7899.74 46
MVSFormer94.71 8694.08 8996.61 8295.05 21694.87 3597.77 22396.17 21986.84 21998.04 4098.52 9785.52 11995.99 29089.83 16698.97 7998.96 117
lupinMVS96.32 4195.94 4897.44 4395.05 21694.87 3599.86 396.50 19793.82 4798.04 4098.77 7785.52 11998.09 17596.98 5898.97 7999.37 83
3Dnovator+87.72 893.43 11991.84 14298.17 2195.73 18595.08 3198.92 11697.04 17091.42 9681.48 27497.60 13474.60 22399.79 7390.84 15698.97 7999.64 60
GG-mvs-BLEND96.98 6296.53 15294.81 4087.20 35497.74 6793.91 12296.40 18396.56 296.94 23795.08 9698.95 8299.20 99
test_cas_vis1_n_192093.86 10693.74 10094.22 17195.39 19886.08 23199.73 1596.07 22696.38 1297.19 6097.78 12465.46 29999.86 5796.71 6298.92 8396.73 205
MVS_030497.53 1197.15 1998.67 1097.30 12396.52 1199.60 3198.88 1397.14 397.21 5798.94 6586.89 9499.91 4299.43 1298.91 8499.59 69
CS-MVS-test95.98 5096.34 3694.90 14398.06 10187.66 18899.69 2696.10 22393.66 5098.35 3199.05 5086.28 11097.66 20596.96 5998.90 8599.37 83
gg-mvs-nofinetune90.00 19387.71 21796.89 7096.15 17194.69 4485.15 36097.74 6768.32 36092.97 13560.16 37396.10 396.84 24093.89 11998.87 8699.14 102
MAR-MVS94.43 9394.09 8895.45 12699.10 6887.47 19498.39 17897.79 6288.37 17894.02 12099.17 3378.64 20599.91 4292.48 14298.85 8798.96 117
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
CSCG94.87 7894.71 7595.36 12999.54 3686.49 21499.34 6898.15 3782.71 28990.15 17799.25 2289.48 5299.86 5794.97 10198.82 8899.72 49
CHOSEN 280x42096.80 3096.85 2596.66 8197.85 10794.42 5094.76 30898.36 2692.50 7195.62 9697.52 13897.92 197.38 22398.31 3698.80 8998.20 170
CANet97.00 2596.49 3298.55 1198.86 8096.10 1599.83 597.52 11695.90 1497.21 5798.90 6882.66 16699.93 3798.71 2298.80 8999.63 62
test_vis1_n_192093.08 13293.42 10592.04 22296.31 16279.36 31799.83 596.06 22796.72 798.53 2698.10 11758.57 32299.91 4297.86 4398.79 9196.85 204
MVP-Stereo86.61 25185.83 24588.93 29988.70 33083.85 27396.07 28894.41 31382.15 30075.64 32091.96 26867.65 27996.45 26377.20 29398.72 9286.51 351
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
QAPM91.41 16389.49 18397.17 5395.66 18893.42 6998.60 14997.51 11880.92 31481.39 27597.41 14472.89 24299.87 5282.33 25698.68 9398.21 169
131493.44 11891.98 13997.84 3195.24 20094.38 5196.22 28497.92 4890.18 12482.28 25797.71 12977.63 21099.80 7291.94 14698.67 9499.34 87
CS-MVS95.75 6196.19 3894.40 16297.88 10686.22 22599.66 2796.12 22292.69 6898.07 3898.89 7087.09 8897.59 21196.71 6298.62 9599.39 82
EC-MVSNet95.09 7495.17 6894.84 14695.42 19588.17 17699.48 4495.92 23991.47 9397.34 5598.36 10682.77 16297.41 22297.24 5298.58 9698.94 122
DeepC-MVS91.02 494.56 9293.92 9696.46 9097.16 12990.76 11898.39 17897.11 16393.92 4088.66 19098.33 10778.14 20799.85 6095.02 9898.57 9798.78 139
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OpenMVScopyleft85.28 1490.75 17788.84 19696.48 8993.58 25893.51 6798.80 12597.41 13682.59 29078.62 30297.49 14068.00 27699.82 6784.52 23198.55 9896.11 219
EIA-MVS95.11 7395.27 6694.64 15596.34 16186.51 21399.59 3396.62 18692.51 7094.08 11998.64 9086.05 11498.24 16995.07 9798.50 9999.18 100
jason95.40 6994.86 7497.03 5692.91 27194.23 5399.70 1996.30 20893.56 5496.73 7398.52 9781.46 18597.91 18496.08 7798.47 10098.96 117
jason: jason.
MS-PatchMatch86.75 24785.92 24489.22 29291.97 28182.47 29296.91 25896.14 22183.74 26977.73 30993.53 24258.19 32497.37 22576.75 29798.35 10187.84 340
test_fmvsmvis_n_192095.47 6595.40 6395.70 11894.33 23490.22 13099.70 1996.98 17796.80 692.75 13698.89 7082.46 17299.92 3998.36 3298.33 10296.97 202
DP-MVS Recon95.85 5595.15 6997.95 2999.87 294.38 5199.60 3197.48 12486.58 22594.42 11399.13 4087.36 8499.98 993.64 12598.33 10299.48 76
test_fmvs192.35 14592.94 12090.57 25697.19 12775.43 33799.55 3794.97 29395.20 2396.82 7097.57 13759.59 32099.84 6197.30 5198.29 10496.46 213
xiu_mvs_v2_base96.66 3296.17 4298.11 2697.11 13496.96 699.01 10797.04 17095.51 2098.86 1899.11 4682.19 17699.36 12098.59 2798.14 10598.00 174
BH-w/o92.32 14691.79 14393.91 18496.85 14186.18 22799.11 9595.74 25688.13 18784.81 22397.00 16377.26 21297.91 18489.16 18198.03 10697.64 181
test_fmvs1_n91.07 17091.41 15190.06 27094.10 23974.31 34199.18 7894.84 29794.81 2596.37 8097.46 14150.86 35099.82 6797.14 5497.90 10796.04 220
TAPA-MVS87.50 990.35 18389.05 19294.25 17098.48 9185.17 25498.42 16996.58 19282.44 29687.24 20298.53 9682.77 16298.84 14559.09 36297.88 10898.72 142
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CHOSEN 1792x268894.35 9493.82 9895.95 11197.40 11988.74 16898.41 17198.27 2892.18 8191.43 15596.40 18378.88 20099.81 7093.59 12697.81 10999.30 90
BH-untuned91.46 16290.84 16393.33 19596.51 15484.83 26098.84 12295.50 27086.44 23083.50 23596.70 17675.49 21997.77 19586.78 20597.81 10997.40 188
Vis-MVSNetpermissive92.64 13891.85 14195.03 14095.12 20988.23 17598.48 16496.81 18191.61 8992.16 14497.22 15271.58 25598.00 18385.85 21697.81 10998.88 127
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPNet96.82 2996.68 3097.25 5098.65 8693.10 7499.48 4498.76 1496.54 997.84 4698.22 11287.49 7899.66 8495.35 9197.78 11299.00 113
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended95.94 5395.66 5896.75 7398.77 8391.61 9699.88 298.04 4393.64 5294.21 11697.76 12583.50 14599.87 5297.41 4997.75 11398.79 137
test_vis1_n90.40 18290.27 17390.79 25191.55 29076.48 33399.12 9494.44 30994.31 3197.34 5596.95 16543.60 36199.42 11397.57 4797.60 11496.47 212
ETV-MVS96.00 4896.00 4796.00 10896.56 15191.05 11199.63 2996.61 18793.26 5897.39 5398.30 10986.62 10198.13 17298.07 3997.57 11598.82 134
PLCcopyleft91.07 394.23 9694.01 9094.87 14499.17 6387.49 19399.25 7496.55 19488.43 17691.26 15998.21 11485.92 11599.86 5789.77 17097.57 11597.24 193
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D90.19 18888.72 19994.59 15798.97 7386.33 22296.90 25996.60 18874.96 34184.06 23398.74 8075.78 21799.83 6474.93 30897.57 11597.62 184
AdaColmapbinary93.82 10793.06 11496.10 10499.88 189.07 15598.33 18297.55 10986.81 22190.39 17498.65 8975.09 22099.98 993.32 13197.53 11899.26 94
BH-RMVSNet91.25 16789.99 17695.03 14096.75 14788.55 17198.65 14194.95 29487.74 20187.74 19697.80 12268.27 27398.14 17180.53 27297.49 11998.41 156
CANet_DTU94.31 9593.35 10697.20 5297.03 13894.71 4398.62 14595.54 26895.61 1897.21 5798.47 10371.88 25099.84 6188.38 18597.46 12097.04 200
PatchMatch-RL91.47 16190.54 17094.26 16998.20 9586.36 22096.94 25797.14 15987.75 20088.98 18895.75 19871.80 25299.40 11780.92 26797.39 12197.02 201
UGNet91.91 15690.85 16295.10 13597.06 13688.69 16998.01 20998.24 3192.41 7592.39 14193.61 23960.52 31799.68 8288.14 18897.25 12296.92 203
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
PVSNet87.13 1293.69 11092.83 12296.28 9897.99 10390.22 13099.38 6298.93 1191.42 9693.66 12697.68 13071.29 25799.64 9087.94 19297.20 12398.98 115
test250694.80 8094.21 8396.58 8496.41 15792.18 9098.01 20998.96 1090.82 10693.46 12897.28 14785.92 11598.45 15989.82 16897.19 12499.12 105
ECVR-MVScopyleft92.29 14791.33 15295.15 13496.41 15787.84 18398.10 20294.84 29790.82 10691.42 15797.28 14765.61 29698.49 15890.33 16297.19 12499.12 105
EI-MVSNet-Vis-set95.76 6095.63 6296.17 10299.14 6490.33 12698.49 16297.82 5591.92 8594.75 10898.88 7287.06 9099.48 10695.40 9097.17 12698.70 144
test111192.12 15291.19 15594.94 14296.15 17187.36 19898.12 19994.84 29790.85 10590.97 16297.26 14965.60 29798.37 16189.74 17197.14 12799.07 111
CNLPA93.64 11492.74 12396.36 9698.96 7590.01 14299.19 7695.89 24786.22 23189.40 18598.85 7380.66 19099.84 6188.57 18396.92 12899.24 95
xiu_mvs_v1_base_debu94.73 8393.98 9196.99 5995.19 20495.24 2498.62 14596.50 19792.99 6297.52 4998.83 7472.37 24599.15 13197.03 5596.74 12996.58 208
xiu_mvs_v1_base94.73 8393.98 9196.99 5995.19 20495.24 2498.62 14596.50 19792.99 6297.52 4998.83 7472.37 24599.15 13197.03 5596.74 12996.58 208
xiu_mvs_v1_base_debi94.73 8393.98 9196.99 5995.19 20495.24 2498.62 14596.50 19792.99 6297.52 4998.83 7472.37 24599.15 13197.03 5596.74 12996.58 208
MVS_Test93.67 11392.67 12596.69 7996.72 14892.66 8297.22 24896.03 22887.69 20495.12 10494.03 22681.55 18298.28 16689.17 18096.46 13299.14 102
EI-MVSNet-UG-set95.43 6695.29 6595.86 11399.07 7089.87 14398.43 16897.80 6091.78 8794.11 11898.77 7786.25 11299.48 10694.95 10296.45 13398.22 168
TSAR-MVS + GP.96.95 2696.91 2397.07 5498.88 7991.62 9599.58 3496.54 19595.09 2496.84 6798.63 9291.16 3199.77 7599.04 1996.42 13499.81 32
PVSNet_Blended_VisFu94.67 8794.11 8796.34 9797.14 13191.10 10899.32 7097.43 13492.10 8491.53 15496.38 18683.29 15199.68 8293.42 13096.37 13598.25 166
Vis-MVSNet (Re-imp)93.26 12793.00 11994.06 17896.14 17386.71 21298.68 13796.70 18488.30 18289.71 18497.64 13385.43 12596.39 26588.06 19096.32 13699.08 109
EPMVS92.59 14191.59 14795.59 12497.22 12690.03 14091.78 33498.04 4390.42 11991.66 14990.65 29686.49 10797.46 21881.78 26296.31 13799.28 92
PMMVS93.62 11593.90 9792.79 20596.79 14681.40 30298.85 12096.81 18191.25 9996.82 7098.15 11677.02 21398.13 17293.15 13496.30 13898.83 133
TESTMET0.1,193.82 10793.26 11095.49 12595.21 20390.25 12899.15 8797.54 11289.18 15291.79 14694.87 21489.13 5497.63 20886.21 20996.29 13998.60 149
test-LLR93.11 13192.68 12494.40 16294.94 22187.27 20299.15 8797.25 14590.21 12291.57 15094.04 22484.89 13097.58 21285.94 21396.13 14098.36 162
test-mter93.27 12692.89 12194.40 16294.94 22187.27 20299.15 8797.25 14588.95 15991.57 15094.04 22488.03 7197.58 21285.94 21396.13 14098.36 162
Effi-MVS+93.87 10593.15 11396.02 10795.79 18290.76 11896.70 26995.78 25386.98 21695.71 9397.17 15679.58 19498.01 18294.57 11196.09 14299.31 89
mvs_anonymous92.50 14391.65 14695.06 13796.60 15089.64 14897.06 25396.44 20186.64 22484.14 23193.93 23082.49 16896.17 28391.47 14896.08 14399.35 85
IS-MVSNet93.00 13392.51 12894.49 15896.14 17387.36 19898.31 18595.70 25888.58 16990.17 17697.50 13983.02 15897.22 22687.06 19796.07 14498.90 126
PatchmatchNetpermissive92.05 15591.04 15895.06 13796.17 17089.04 15691.26 34297.26 14489.56 14390.64 16890.56 30288.35 6497.11 22979.53 27596.07 14499.03 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
F-COLMAP92.07 15491.75 14593.02 20098.16 9882.89 28598.79 12995.97 23186.54 22787.92 19597.80 12278.69 20499.65 8885.97 21195.93 14696.53 211
diffmvspermissive94.59 9094.19 8495.81 11495.54 19190.69 12098.70 13595.68 26091.61 8995.96 8597.81 12180.11 19198.06 17796.52 7095.76 14798.67 146
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ACMMPcopyleft94.67 8794.30 8095.79 11599.25 5788.13 17898.41 17198.67 2190.38 12091.43 15598.72 8382.22 17599.95 3193.83 12295.76 14799.29 91
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
LCM-MVSNet-Re88.59 22188.61 20288.51 30295.53 19272.68 34996.85 26188.43 36888.45 17373.14 33390.63 29775.82 21694.38 33192.95 13595.71 14998.48 154
PCF-MVS89.78 591.26 16589.63 18096.16 10395.44 19491.58 9895.29 30496.10 22385.07 24882.75 24597.45 14278.28 20699.78 7480.60 27195.65 15097.12 195
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FE-MVS91.38 16490.16 17595.05 13996.46 15587.53 19289.69 35197.84 5282.97 28392.18 14392.00 26784.07 14098.93 14380.71 26995.52 15198.68 145
mvsany_test194.57 9195.09 7192.98 20195.84 18182.07 29598.76 13195.24 28692.87 6796.45 7898.71 8684.81 13299.15 13197.68 4595.49 15297.73 179
casdiffmvspermissive93.98 10193.43 10495.61 12395.07 21589.86 14498.80 12595.84 25290.98 10392.74 13797.66 13279.71 19398.10 17494.72 10695.37 15398.87 129
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs_mvgpermissive94.00 9993.33 10796.03 10695.22 20290.90 11699.09 9695.99 22990.58 11391.55 15397.37 14579.91 19298.06 17795.01 9995.22 15499.13 104
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline93.91 10393.30 10895.72 11795.10 21390.07 13697.48 23595.91 24491.03 10193.54 12797.68 13079.58 19498.02 18194.27 11495.14 15599.08 109
Fast-Effi-MVS+91.72 15890.79 16694.49 15895.89 17987.40 19799.54 4295.70 25885.01 25189.28 18795.68 19977.75 20997.57 21583.22 24695.06 15698.51 152
EPNet_dtu92.28 14892.15 13592.70 20997.29 12484.84 25998.64 14397.82 5592.91 6593.02 13497.02 16285.48 12495.70 30472.25 32794.89 15797.55 186
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UA-Net93.30 12492.62 12695.34 13096.27 16488.53 17395.88 29496.97 17890.90 10495.37 9997.07 16082.38 17399.10 13783.91 24194.86 15898.38 159
baseline294.04 9893.80 9994.74 15093.07 27090.25 12898.12 19998.16 3689.86 13286.53 21296.95 16595.56 698.05 17991.44 14994.53 15995.93 221
MVS-HIRNet79.01 31375.13 32590.66 25493.82 25481.69 29885.16 35993.75 32354.54 36974.17 32659.15 37557.46 32696.58 25263.74 35194.38 16093.72 232
SCA90.64 18089.25 18994.83 14794.95 22088.83 16496.26 28197.21 15190.06 13190.03 17890.62 29866.61 28796.81 24283.16 24794.36 16198.84 130
OMC-MVS93.90 10493.62 10294.73 15198.63 8787.00 20798.04 20896.56 19392.19 8092.46 13998.73 8179.49 19799.14 13592.16 14594.34 16298.03 173
DP-MVS88.75 21886.56 23595.34 13098.92 7787.45 19597.64 23193.52 32870.55 35281.49 27397.25 15074.43 22699.88 4971.14 33094.09 16398.67 146
sss94.85 7993.94 9597.58 3996.43 15694.09 5898.93 11499.16 889.50 14595.27 10097.85 11981.50 18399.65 8892.79 14094.02 16498.99 114
FA-MVS(test-final)92.22 15191.08 15795.64 12196.05 17788.98 15891.60 33797.25 14586.99 21391.84 14592.12 26183.03 15799.00 14086.91 20293.91 16598.93 123
EPP-MVSNet93.75 10993.67 10194.01 18195.86 18085.70 24298.67 13997.66 8384.46 25891.36 15897.18 15591.16 3197.79 19392.93 13693.75 16698.53 151
GeoE90.60 18189.56 18193.72 19195.10 21385.43 24799.41 5994.94 29583.96 26687.21 20396.83 17274.37 22797.05 23380.50 27393.73 16798.67 146
CVMVSNet90.30 18590.91 16188.46 30394.32 23573.58 34597.61 23297.59 10290.16 12788.43 19397.10 15876.83 21492.86 34282.64 25393.54 16898.93 123
thisisatest051594.75 8294.19 8496.43 9296.13 17692.64 8599.47 4697.60 9887.55 20793.17 13197.59 13594.71 1398.42 16088.28 18693.20 16998.24 167
JIA-IIPM85.97 26184.85 26189.33 29193.23 26773.68 34485.05 36197.13 16169.62 35691.56 15268.03 37188.03 7196.96 23577.89 28993.12 17097.34 190
Effi-MVS+-dtu89.97 19590.68 16887.81 30795.15 20871.98 35197.87 21795.40 27791.92 8587.57 19791.44 27774.27 22996.84 24089.45 17393.10 17194.60 229
HY-MVS88.56 795.29 7094.23 8298.48 1397.72 10996.41 1294.03 31698.74 1592.42 7495.65 9594.76 21786.52 10599.49 10295.29 9392.97 17299.53 71
LFMVS92.23 15090.84 16396.42 9398.24 9491.08 11098.24 18996.22 21483.39 27694.74 10998.31 10861.12 31598.85 14494.45 11292.82 17399.32 88
HyFIR lowres test93.68 11293.29 10994.87 14497.57 11788.04 18098.18 19498.47 2487.57 20691.24 16095.05 21185.49 12297.46 21893.22 13292.82 17399.10 107
CDS-MVSNet93.47 11793.04 11694.76 14894.75 22789.45 15298.82 12397.03 17287.91 19590.97 16296.48 18189.06 5596.36 26789.50 17292.81 17598.49 153
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
WTY-MVS95.97 5195.11 7098.54 1297.62 11396.65 899.44 5398.74 1592.25 7995.21 10198.46 10586.56 10499.46 10895.00 10092.69 17699.50 75
test_yl95.27 7194.60 7797.28 4898.53 8992.98 7899.05 10198.70 1886.76 22294.65 11197.74 12787.78 7399.44 10995.57 8792.61 17799.44 79
DCV-MVSNet95.27 7194.60 7797.28 4898.53 8992.98 7899.05 10198.70 1886.76 22294.65 11197.74 12787.78 7399.44 10995.57 8792.61 17799.44 79
MSDG88.29 22586.37 23794.04 18096.90 14086.15 22996.52 27294.36 31477.89 33179.22 29796.95 16569.72 26399.59 9473.20 32392.58 17996.37 216
thisisatest053094.00 9993.52 10395.43 12795.76 18490.02 14198.99 10997.60 9886.58 22591.74 14797.36 14694.78 1298.34 16286.37 20892.48 18097.94 176
TR-MVS90.77 17689.44 18494.76 14896.31 16288.02 18197.92 21395.96 23385.52 24088.22 19497.23 15166.80 28698.09 17584.58 22992.38 18198.17 171
MDTV_nov1_ep1390.47 17296.14 17388.55 17191.34 34197.51 11889.58 14192.24 14290.50 30686.99 9397.61 21077.64 29092.34 182
TAMVS92.62 13992.09 13794.20 17294.10 23987.68 18698.41 17196.97 17887.53 20889.74 18296.04 19384.77 13496.49 26088.97 18292.31 18398.42 155
ADS-MVSNet287.62 23786.88 23089.86 27796.21 16779.14 31987.15 35592.99 33283.01 28189.91 18087.27 33778.87 20192.80 34574.20 31592.27 18497.64 181
ADS-MVSNet88.99 20687.30 22394.07 17796.21 16787.56 19187.15 35596.78 18383.01 28189.91 18087.27 33778.87 20197.01 23474.20 31592.27 18497.64 181
cascas90.93 17489.33 18895.76 11695.69 18693.03 7798.99 10996.59 18980.49 31686.79 21094.45 22165.23 30098.60 15793.52 12792.18 18695.66 223
CR-MVSNet88.83 21487.38 22293.16 19893.47 26086.24 22384.97 36294.20 31788.92 16290.76 16686.88 34184.43 13594.82 32470.64 33192.17 18798.41 156
RPMNet85.07 27581.88 29194.64 15593.47 26086.24 22384.97 36297.21 15164.85 36790.76 16678.80 36480.95 18899.27 12753.76 36892.17 18798.41 156
DSMNet-mixed81.60 30281.43 29682.10 33884.36 35560.79 36693.63 32086.74 37179.00 32179.32 29687.15 33963.87 30589.78 36266.89 34491.92 18995.73 222
tttt051793.30 12493.01 11894.17 17395.57 18986.47 21598.51 15997.60 9885.99 23390.55 16997.19 15494.80 1198.31 16385.06 22291.86 19097.74 178
VNet95.08 7594.26 8197.55 4298.07 10093.88 6098.68 13798.73 1790.33 12197.16 6197.43 14379.19 19999.53 9996.91 6191.85 19199.24 95
tpmrst92.78 13592.16 13494.65 15396.27 16487.45 19591.83 33397.10 16689.10 15594.68 11090.69 29388.22 6597.73 20389.78 16991.80 19298.77 140
alignmvs95.77 5995.00 7398.06 2797.35 12195.68 1899.71 1897.50 12191.50 9296.16 8398.61 9486.28 11099.00 14096.19 7491.74 19399.51 74
CostFormer92.89 13492.48 12994.12 17594.99 21885.89 23792.89 32597.00 17686.98 21695.00 10690.78 28990.05 4897.51 21692.92 13791.73 19498.96 117
Fast-Effi-MVS+-dtu88.84 21288.59 20489.58 28593.44 26378.18 32798.65 14194.62 30688.46 17284.12 23295.37 20768.91 26796.52 25682.06 25991.70 19594.06 230
PatchT85.44 27183.19 27992.22 21593.13 26983.00 28183.80 36896.37 20470.62 35190.55 16979.63 36384.81 13294.87 32258.18 36491.59 19698.79 137
tpm291.77 15791.09 15693.82 18794.83 22585.56 24692.51 33097.16 15884.00 26493.83 12490.66 29587.54 7797.17 22787.73 19491.55 19798.72 142
tpm cat188.89 21087.27 22493.76 18895.79 18285.32 25190.76 34797.09 16776.14 33785.72 21788.59 32782.92 15998.04 18076.96 29491.43 19897.90 177
canonicalmvs95.02 7693.96 9498.20 2097.53 11895.92 1698.71 13396.19 21791.78 8795.86 9098.49 10179.53 19699.03 13996.12 7591.42 19999.66 58
Patchmatch-test86.25 25884.06 27492.82 20494.42 23282.88 28682.88 36994.23 31671.58 34979.39 29590.62 29889.00 5796.42 26463.03 35491.37 20099.16 101
dp90.16 19088.83 19794.14 17496.38 16086.42 21691.57 33897.06 16984.76 25588.81 18990.19 31484.29 13797.43 22175.05 30791.35 20198.56 150
VDDNet90.08 19288.54 20794.69 15294.41 23387.68 18698.21 19296.40 20276.21 33693.33 13097.75 12654.93 33798.77 14794.71 10790.96 20297.61 185
thres20093.69 11092.59 12796.97 6397.76 10894.74 4299.35 6799.36 289.23 15091.21 16196.97 16483.42 14898.77 14785.08 22190.96 20297.39 189
thres100view90093.34 12392.15 13596.90 6697.62 11394.84 3799.06 10099.36 287.96 19390.47 17296.78 17383.29 15198.75 14984.11 23790.69 20497.12 195
tfpn200view993.43 11992.27 13296.90 6697.68 11194.84 3799.18 7899.36 288.45 17390.79 16496.90 16883.31 14998.75 14984.11 23790.69 20497.12 195
thres40093.39 12192.27 13296.73 7597.68 11194.84 3799.18 7899.36 288.45 17390.79 16496.90 16883.31 14998.75 14984.11 23790.69 20496.61 206
VDD-MVS91.24 16890.18 17494.45 16197.08 13585.84 24098.40 17496.10 22386.99 21393.36 12998.16 11554.27 33999.20 12896.59 6890.63 20798.31 165
thres600view793.18 12992.00 13896.75 7397.62 11394.92 3299.07 9899.36 287.96 19390.47 17296.78 17383.29 15198.71 15382.93 25190.47 20896.61 206
GA-MVS90.10 19188.69 20094.33 16692.44 27587.97 18299.08 9796.26 21289.65 13786.92 20793.11 25168.09 27496.96 23582.54 25590.15 20998.05 172
tpmvs89.16 20487.76 21593.35 19497.19 12784.75 26190.58 34997.36 14181.99 30184.56 22689.31 32483.98 14198.17 17074.85 31090.00 21097.12 195
1112_ss92.71 13691.55 14896.20 9995.56 19091.12 10698.48 16494.69 30488.29 18386.89 20898.50 9987.02 9198.66 15584.75 22689.77 21198.81 135
Test_1112_low_res92.27 14990.97 15996.18 10095.53 19291.10 10898.47 16694.66 30588.28 18486.83 20993.50 24387.00 9298.65 15684.69 22789.74 21298.80 136
XVG-OURS-SEG-HR90.95 17390.66 16991.83 22595.18 20781.14 30995.92 29195.92 23988.40 17790.33 17597.85 11970.66 26099.38 11892.83 13888.83 21394.98 227
COLMAP_ROBcopyleft82.69 1884.54 28282.82 28289.70 28296.72 14878.85 32095.89 29292.83 33671.55 35077.54 31195.89 19659.40 32199.14 13567.26 34288.26 21491.11 291
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet84.48 28381.83 29292.42 21391.73 28887.36 19885.52 35894.42 31281.40 30781.91 26687.58 33151.92 34592.81 34473.84 31888.15 21597.08 199
ab-mvs91.05 17289.17 19096.69 7995.96 17891.72 9492.62 32997.23 14985.61 23989.74 18293.89 23268.55 27099.42 11391.09 15187.84 21698.92 125
XVG-OURS90.83 17590.49 17191.86 22495.23 20181.25 30695.79 29995.92 23988.96 15890.02 17998.03 11871.60 25499.35 12391.06 15287.78 21794.98 227
AllTest84.97 27683.12 28090.52 25996.82 14278.84 32195.89 29292.17 34477.96 32975.94 31695.50 20155.48 33299.18 12971.15 32887.14 21893.55 233
TestCases90.52 25996.82 14278.84 32192.17 34477.96 32975.94 31695.50 20155.48 33299.18 12971.15 32887.14 21893.55 233
Anonymous20240521188.84 21287.03 22894.27 16898.14 9984.18 26898.44 16795.58 26676.79 33589.34 18696.88 17053.42 34299.54 9887.53 19687.12 22099.09 108
SDMVSNet91.09 16989.91 17794.65 15396.80 14490.54 12497.78 22197.81 5888.34 18085.73 21595.26 20866.44 29098.26 16794.25 11586.75 22195.14 224
sd_testset89.23 20388.05 21492.74 20896.80 14485.33 25095.85 29797.03 17288.34 18085.73 21595.26 20861.12 31597.76 20085.61 21786.75 22195.14 224
test_vis1_rt81.31 30380.05 30685.11 32391.29 29570.66 35598.98 11177.39 38185.76 23768.80 34782.40 35436.56 36899.44 10992.67 14186.55 22385.24 358
HQP3-MVS96.37 20486.29 224
HQP-MVS91.50 16091.23 15492.29 21493.95 24486.39 21899.16 8296.37 20493.92 4087.57 19796.67 17773.34 23597.77 19593.82 12386.29 22492.72 236
plane_prior86.07 23399.14 9093.81 4886.26 226
HQP_MVS91.26 16590.95 16092.16 21893.84 25186.07 23399.02 10596.30 20893.38 5686.99 20496.52 17972.92 24097.75 20193.46 12886.17 22792.67 238
plane_prior596.30 20897.75 20193.46 12886.17 22792.67 238
OPM-MVS89.76 19789.15 19191.57 23490.53 30485.58 24598.11 20195.93 23892.88 6686.05 21396.47 18267.06 28597.87 18889.29 17986.08 22991.26 287
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
RPSCF85.33 27285.55 25084.67 32894.63 23062.28 36593.73 31893.76 32274.38 34485.23 22297.06 16164.09 30398.31 16380.98 26586.08 22993.41 235
CLD-MVS91.06 17190.71 16792.10 22094.05 24386.10 23099.55 3796.29 21194.16 3584.70 22597.17 15669.62 26597.82 19194.74 10586.08 22992.39 242
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test0.0.03 188.96 20788.61 20290.03 27491.09 29784.43 26498.97 11297.02 17490.21 12280.29 28396.31 18884.89 13091.93 35672.98 32485.70 23293.73 231
dmvs_re88.69 22088.06 21390.59 25593.83 25378.68 32395.75 30096.18 21887.99 19284.48 22996.32 18767.52 28096.94 23784.98 22485.49 23396.14 218
LPG-MVS_test88.86 21188.47 20890.06 27093.35 26580.95 31198.22 19095.94 23687.73 20283.17 24196.11 19166.28 29197.77 19590.19 16485.19 23491.46 277
LGP-MVS_train90.06 27093.35 26580.95 31195.94 23687.73 20283.17 24196.11 19166.28 29197.77 19590.19 16485.19 23491.46 277
ACMM86.95 1388.77 21788.22 21190.43 26193.61 25781.34 30498.50 16095.92 23987.88 19683.85 23495.20 21067.20 28397.89 18686.90 20384.90 23692.06 259
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CMPMVSbinary58.40 2180.48 30680.11 30581.59 34185.10 35359.56 36894.14 31595.95 23568.54 35960.71 36593.31 24555.35 33597.87 18883.06 25084.85 23787.33 345
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ACMP87.39 1088.71 21988.24 21090.12 26993.91 24981.06 31098.50 16095.67 26189.43 14680.37 28295.55 20065.67 29497.83 19090.55 16084.51 23891.47 276
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_djsdf88.26 22687.73 21689.84 27888.05 33782.21 29397.77 22396.17 21986.84 21982.41 25491.95 26972.07 24895.99 29089.83 16684.50 23991.32 284
iter_conf_final93.22 12893.04 11693.76 18897.03 13892.22 8999.05 10193.31 33092.11 8386.93 20695.42 20495.01 1096.59 24993.98 11784.48 24092.46 241
jajsoiax87.35 23986.51 23689.87 27687.75 34281.74 29797.03 25495.98 23088.47 17080.15 28593.80 23461.47 31296.36 26789.44 17484.47 24191.50 275
mvs_tets87.09 24286.22 23989.71 28187.87 33881.39 30396.73 26895.90 24588.19 18679.99 28793.61 23959.96 31996.31 27589.40 17584.34 24291.43 279
iter_conf0593.48 11693.18 11294.39 16597.15 13094.17 5699.30 7192.97 33392.38 7886.70 21195.42 20495.67 596.59 24994.67 10884.32 24392.39 242
test_fmvs285.10 27485.45 25284.02 33189.85 31365.63 36398.49 16292.59 33890.45 11785.43 22193.32 24443.94 35996.59 24990.81 15784.19 24489.85 322
Anonymous2024052987.66 23685.58 24993.92 18397.59 11685.01 25798.13 19797.13 16166.69 36588.47 19296.01 19455.09 33699.51 10087.00 19984.12 24597.23 194
anonymousdsp86.69 24885.75 24789.53 28686.46 34982.94 28296.39 27595.71 25783.97 26579.63 29290.70 29268.85 26895.94 29386.01 21084.02 24689.72 324
mvsmamba89.99 19489.42 18591.69 23290.64 30386.34 22198.40 17492.27 34291.01 10284.80 22494.93 21276.12 21596.51 25792.81 13983.84 24792.21 251
XVG-ACMP-BASELINE85.86 26384.95 25988.57 30189.90 31177.12 33294.30 31295.60 26587.40 21082.12 26092.99 25453.42 34297.66 20585.02 22383.83 24890.92 295
ACMMP++83.83 248
ET-MVSNet_ETH3D92.56 14291.45 15095.88 11296.39 15994.13 5799.46 5096.97 17892.18 8166.94 35698.29 11094.65 1594.28 33294.34 11383.82 25099.24 95
EG-PatchMatch MVS79.92 30877.59 31386.90 31487.06 34777.90 33196.20 28694.06 31974.61 34266.53 35888.76 32640.40 36696.20 28067.02 34383.66 25186.61 349
D2MVS87.96 22887.39 22189.70 28291.84 28683.40 27798.31 18598.49 2288.04 19078.23 30890.26 30873.57 23396.79 24484.21 23483.53 25288.90 334
UniMVSNet_ETH3D85.65 27083.79 27791.21 23890.41 30680.75 31395.36 30395.78 25378.76 32581.83 27194.33 22249.86 35296.66 24684.30 23283.52 25396.22 217
bld_raw_dy_0_6487.82 22986.71 23391.15 24089.54 31985.61 24397.37 23989.16 36689.26 14983.42 23794.50 22065.79 29396.18 28188.00 19183.37 25491.67 265
PVSNet_BlendedMVS93.36 12293.20 11193.84 18698.77 8391.61 9699.47 4698.04 4391.44 9494.21 11692.63 25883.50 14599.87 5297.41 4983.37 25490.05 318
PS-MVSNAJss89.54 20189.05 19291.00 24488.77 32884.36 26597.39 23695.97 23188.47 17081.88 26793.80 23482.48 16996.50 25889.34 17683.34 25692.15 254
EI-MVSNet89.87 19689.38 18791.36 23794.32 23585.87 23897.61 23296.59 18985.10 24685.51 21997.10 15881.30 18796.56 25383.85 24383.03 25791.64 266
MVSTER92.71 13692.32 13093.86 18597.29 12492.95 8099.01 10796.59 18990.09 12885.51 21994.00 22894.61 1696.56 25390.77 15983.03 25792.08 258
FIs90.70 17889.87 17893.18 19792.29 27691.12 10698.17 19698.25 2989.11 15483.44 23694.82 21682.26 17496.17 28387.76 19382.76 25992.25 247
tpm89.67 19888.95 19491.82 22692.54 27481.43 30192.95 32495.92 23987.81 19790.50 17189.44 32184.99 12895.65 30583.67 24482.71 26098.38 159
ACMMP++_ref82.64 261
FC-MVSNet-test90.22 18789.40 18692.67 21191.78 28789.86 14497.89 21498.22 3288.81 16482.96 24494.66 21881.90 18095.96 29285.89 21582.52 26292.20 253
ITE_SJBPF87.93 30592.26 27776.44 33493.47 32987.67 20579.95 28895.49 20356.50 32997.38 22375.24 30682.33 26389.98 320
RRT_MVS88.91 20988.56 20589.93 27590.31 30781.61 29998.08 20596.38 20389.30 14882.41 25494.84 21573.15 23896.04 28990.38 16182.23 26492.15 254
OpenMVS_ROBcopyleft73.86 2077.99 32075.06 32686.77 31583.81 35877.94 33096.38 27691.53 35467.54 36268.38 34987.13 34043.94 35996.08 28755.03 36781.83 26586.29 352
LTVRE_ROB81.71 1984.59 28182.72 28790.18 26792.89 27283.18 28093.15 32394.74 30178.99 32275.14 32392.69 25665.64 29597.63 20869.46 33581.82 26689.74 323
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
USDC84.74 27782.93 28190.16 26891.73 28883.54 27695.00 30693.30 33188.77 16573.19 33293.30 24653.62 34197.65 20775.88 30381.54 26789.30 329
ACMH83.09 1784.60 28082.61 28990.57 25693.18 26882.94 28296.27 27994.92 29681.01 31272.61 33993.61 23956.54 32897.79 19374.31 31381.07 26890.99 293
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt080586.50 25484.79 26391.63 23391.97 28181.49 30096.49 27397.38 13982.24 29882.44 25195.82 19751.22 34798.25 16884.55 23080.96 26995.13 226
GBi-Net86.67 24984.96 25791.80 22795.11 21088.81 16596.77 26395.25 28382.94 28482.12 26090.25 30962.89 30794.97 31979.04 27980.24 27091.62 268
test186.67 24984.96 25791.80 22795.11 21088.81 16596.77 26395.25 28382.94 28482.12 26090.25 30962.89 30794.97 31979.04 27980.24 27091.62 268
FMVSNet388.81 21687.08 22793.99 18296.52 15394.59 4698.08 20596.20 21585.85 23482.12 26091.60 27474.05 23195.40 31279.04 27980.24 27091.99 261
baseline192.61 14091.28 15396.58 8497.05 13794.63 4597.72 22796.20 21589.82 13388.56 19196.85 17186.85 9597.82 19188.42 18480.10 27397.30 191
testgi82.29 29781.00 30086.17 31887.24 34574.84 34097.39 23691.62 35288.63 16675.85 31995.42 20446.07 35891.55 35766.87 34579.94 27492.12 256
test_040278.81 31576.33 32086.26 31791.18 29678.44 32695.88 29491.34 35568.55 35870.51 34389.91 31652.65 34494.99 31847.14 37279.78 27585.34 357
FMVSNet286.90 24484.79 26393.24 19695.11 21092.54 8697.67 23095.86 25182.94 28480.55 28091.17 28362.89 30795.29 31477.23 29179.71 27691.90 262
pmmvs487.58 23886.17 24191.80 22789.58 31788.92 16397.25 24595.28 28282.54 29280.49 28193.17 25075.62 21896.05 28882.75 25278.90 27790.42 309
ACMH+83.78 1584.21 28682.56 29089.15 29493.73 25679.16 31896.43 27494.28 31581.09 31174.00 32794.03 22654.58 33897.67 20476.10 30178.81 27890.63 306
XXY-MVS87.75 23386.02 24292.95 20390.46 30589.70 14797.71 22995.90 24584.02 26380.95 27694.05 22367.51 28197.10 23185.16 22078.41 27992.04 260
pmmvs585.87 26284.40 27290.30 26688.53 33284.23 26698.60 14993.71 32481.53 30680.29 28392.02 26464.51 30295.52 30882.04 26078.34 28091.15 289
LF4IMVS81.94 30081.17 29984.25 33087.23 34668.87 36193.35 32291.93 34983.35 27775.40 32193.00 25349.25 35596.65 24778.88 28278.11 28187.22 347
cl2289.57 20088.79 19891.91 22397.94 10487.62 18997.98 21196.51 19685.03 24982.37 25691.79 27083.65 14396.50 25885.96 21277.89 28291.61 271
miper_ehance_all_eth88.94 20888.12 21291.40 23595.32 19986.93 20897.85 21895.55 26784.19 26181.97 26591.50 27684.16 13895.91 29784.69 22777.89 28291.36 282
miper_enhance_ethall90.33 18489.70 17992.22 21597.12 13388.93 16298.35 18195.96 23388.60 16883.14 24392.33 26087.38 8096.18 28186.49 20777.89 28291.55 274
TinyColmap80.42 30777.94 31287.85 30692.09 28078.58 32493.74 31789.94 36174.99 34069.77 34491.78 27146.09 35797.58 21265.17 35077.89 28287.38 343
FMVSNet183.94 29081.32 29891.80 22791.94 28488.81 16596.77 26395.25 28377.98 32778.25 30790.25 30950.37 35194.97 31973.27 32277.81 28691.62 268
OurMVSNet-221017-084.13 28983.59 27885.77 32187.81 33970.24 35694.89 30793.65 32686.08 23276.53 31293.28 24761.41 31396.14 28580.95 26677.69 28790.93 294
IterMVS85.81 26584.67 26689.22 29293.51 25983.67 27596.32 27894.80 30085.09 24778.69 30090.17 31566.57 28993.17 34179.48 27777.42 28890.81 297
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT85.73 26884.64 26789.00 29793.46 26282.90 28496.27 27994.70 30385.02 25078.62 30290.35 30766.61 28793.33 33879.38 27877.36 28990.76 301
our_test_384.47 28482.80 28389.50 28789.01 32583.90 27297.03 25494.56 30781.33 30875.36 32290.52 30471.69 25394.54 33068.81 33776.84 29090.07 316
dmvs_testset77.17 32378.99 31071.71 35187.25 34438.55 38591.44 33981.76 37785.77 23669.49 34595.94 19569.71 26484.37 37152.71 37076.82 29192.21 251
EU-MVSNet84.19 28784.42 27183.52 33488.64 33167.37 36296.04 28995.76 25585.29 24378.44 30593.18 24970.67 25991.48 35875.79 30475.98 29291.70 264
Anonymous2023120680.76 30579.42 30984.79 32784.78 35472.98 34696.53 27192.97 33379.56 32074.33 32488.83 32561.27 31492.15 35360.59 35975.92 29389.24 331
IterMVS-LS88.34 22387.44 22091.04 24394.10 23985.85 23998.10 20295.48 27185.12 24582.03 26491.21 28281.35 18695.63 30683.86 24275.73 29491.63 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VPA-MVSNet89.10 20587.66 21893.45 19392.56 27391.02 11297.97 21298.32 2786.92 21886.03 21492.01 26568.84 26997.10 23190.92 15475.34 29592.23 249
nrg03090.23 18688.87 19594.32 16791.53 29193.54 6698.79 12995.89 24788.12 18884.55 22794.61 21978.80 20396.88 23992.35 14475.21 29692.53 240
cl____87.82 22986.79 23290.89 24894.88 22385.43 24797.81 21995.24 28682.91 28880.71 27991.22 28181.97 17995.84 29981.34 26475.06 29791.40 281
DIV-MVS_self_test87.82 22986.81 23190.87 24994.87 22485.39 24997.81 21995.22 29182.92 28780.76 27891.31 28081.99 17795.81 30181.36 26375.04 29891.42 280
v119286.32 25784.71 26591.17 23989.53 32086.40 21798.13 19795.44 27582.52 29382.42 25390.62 29871.58 25596.33 27477.23 29174.88 29990.79 299
v124085.77 26784.11 27390.73 25389.26 32485.15 25597.88 21695.23 29081.89 30482.16 25990.55 30369.60 26696.31 27575.59 30574.87 30090.72 303
FMVSNet582.29 29780.54 30187.52 30993.79 25584.01 27093.73 31892.47 34076.92 33474.27 32586.15 34563.69 30689.24 36469.07 33674.79 30189.29 330
v114486.83 24685.31 25491.40 23589.75 31487.21 20698.31 18595.45 27383.22 27882.70 24790.78 28973.36 23496.36 26779.49 27674.69 30290.63 306
Anonymous2024052178.63 31776.90 31883.82 33282.82 36072.86 34795.72 30193.57 32773.55 34772.17 34084.79 34849.69 35392.51 34965.29 34974.50 30386.09 353
v192192086.02 26084.44 27090.77 25289.32 32385.20 25298.10 20295.35 28182.19 29982.25 25890.71 29170.73 25896.30 27876.85 29674.49 30490.80 298
WR-MVS88.54 22287.22 22692.52 21291.93 28589.50 15198.56 15497.84 5286.99 21381.87 26893.81 23374.25 23095.92 29685.29 21974.43 30592.12 256
ppachtmachnet_test83.63 29281.57 29589.80 27989.01 32585.09 25697.13 25194.50 30878.84 32376.14 31491.00 28569.78 26294.61 32963.40 35274.36 30689.71 325
Patchmtry83.61 29381.64 29389.50 28793.36 26482.84 28784.10 36594.20 31769.47 35779.57 29386.88 34184.43 13594.78 32568.48 33974.30 30790.88 296
V4287.00 24385.68 24890.98 24589.91 31086.08 23198.32 18495.61 26483.67 27282.72 24690.67 29474.00 23296.53 25581.94 26174.28 30890.32 311
Anonymous2023121184.72 27882.65 28890.91 24697.71 11084.55 26397.28 24396.67 18566.88 36479.18 29890.87 28858.47 32396.60 24882.61 25474.20 30991.59 273
SixPastTwentyTwo82.63 29681.58 29485.79 32088.12 33671.01 35495.17 30592.54 33984.33 26072.93 33792.08 26260.41 31895.61 30774.47 31274.15 31090.75 302
v2v48287.27 24185.76 24691.78 23189.59 31687.58 19098.56 15495.54 26884.53 25782.51 25091.78 27173.11 23996.47 26182.07 25874.14 31191.30 285
v14419286.40 25584.89 26090.91 24689.48 32185.59 24498.21 19295.43 27682.45 29582.62 24890.58 30172.79 24396.36 26778.45 28674.04 31290.79 299
c3_l88.19 22787.23 22591.06 24294.97 21986.17 22897.72 22795.38 27883.43 27581.68 27291.37 27882.81 16195.72 30384.04 24073.70 31391.29 286
eth_miper_zixun_eth87.76 23287.00 22990.06 27094.67 22982.65 29097.02 25695.37 27984.19 26181.86 27091.58 27581.47 18495.90 29883.24 24573.61 31491.61 271
miper_lstm_enhance86.90 24486.20 24089.00 29794.53 23181.19 30796.74 26795.24 28682.33 29780.15 28590.51 30581.99 17794.68 32880.71 26973.58 31591.12 290
tfpnnormal83.65 29181.35 29790.56 25891.37 29488.06 17997.29 24297.87 5078.51 32676.20 31390.91 28664.78 30196.47 26161.71 35773.50 31687.13 348
N_pmnet70.19 33369.87 33571.12 35388.24 33430.63 38995.85 29728.70 38970.18 35468.73 34886.55 34364.04 30493.81 33453.12 36973.46 31788.94 333
EGC-MVSNET60.70 33855.37 34276.72 34586.35 35071.08 35289.96 35084.44 3760.38 3861.50 38784.09 35037.30 36788.10 36740.85 37673.44 31870.97 371
CP-MVSNet86.54 25285.45 25289.79 28091.02 29982.78 28897.38 23897.56 10885.37 24279.53 29493.03 25271.86 25195.25 31579.92 27473.43 31991.34 283
PS-CasMVS85.81 26584.58 26889.49 28990.77 30182.11 29497.20 24997.36 14184.83 25479.12 29992.84 25567.42 28295.16 31778.39 28773.25 32091.21 288
WR-MVS_H86.53 25385.49 25189.66 28491.04 29883.31 27997.53 23498.20 3384.95 25279.64 29190.90 28778.01 20895.33 31376.29 30072.81 32190.35 310
FPMVS61.57 33660.32 33965.34 35660.14 38342.44 38391.02 34589.72 36344.15 37242.63 37580.93 35919.02 37780.59 37742.50 37372.76 32273.00 369
v1085.73 26884.01 27590.87 24990.03 30886.73 21197.20 24995.22 29181.25 30979.85 29089.75 31873.30 23796.28 27976.87 29572.64 32389.61 326
UniMVSNet (Re)89.50 20288.32 20993.03 19992.21 27890.96 11498.90 11898.39 2589.13 15383.22 23892.03 26381.69 18196.34 27386.79 20472.53 32491.81 263
UniMVSNet_NR-MVSNet89.60 19988.55 20692.75 20792.17 27990.07 13698.74 13298.15 3788.37 17883.21 23993.98 22982.86 16095.93 29486.95 20072.47 32592.25 247
DU-MVS88.83 21487.51 21992.79 20591.46 29290.07 13698.71 13397.62 9588.87 16383.21 23993.68 23674.63 22195.93 29486.95 20072.47 32592.36 244
v886.11 25984.45 26991.10 24189.99 30986.85 20997.24 24695.36 28081.99 30179.89 28989.86 31774.53 22596.39 26578.83 28372.32 32790.05 318
VPNet88.30 22486.57 23493.49 19291.95 28391.35 10098.18 19497.20 15588.61 16784.52 22894.89 21362.21 31096.76 24589.34 17672.26 32892.36 244
v7n84.42 28582.75 28689.43 29088.15 33581.86 29696.75 26695.67 26180.53 31578.38 30689.43 32269.89 26196.35 27273.83 31972.13 32990.07 316
new_pmnet76.02 32473.71 32982.95 33583.88 35772.85 34891.26 34292.26 34370.44 35362.60 36381.37 35747.64 35692.32 35161.85 35672.10 33083.68 363
IB-MVS89.43 692.12 15290.83 16595.98 11095.40 19790.78 11799.81 798.06 4191.23 10085.63 21893.66 23890.63 4098.78 14691.22 15071.85 33198.36 162
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
NR-MVSNet87.74 23586.00 24392.96 20291.46 29290.68 12196.65 27097.42 13588.02 19173.42 33093.68 23677.31 21195.83 30084.26 23371.82 33292.36 244
v14886.38 25685.06 25690.37 26589.47 32284.10 26998.52 15695.48 27183.80 26880.93 27790.22 31274.60 22396.31 27580.92 26771.55 33390.69 304
Baseline_NR-MVSNet85.83 26484.82 26288.87 30088.73 32983.34 27898.63 14491.66 35180.41 31982.44 25191.35 27974.63 22195.42 31184.13 23671.39 33487.84 340
TranMVSNet+NR-MVSNet87.75 23386.31 23892.07 22190.81 30088.56 17098.33 18297.18 15687.76 19981.87 26893.90 23172.45 24495.43 31083.13 24971.30 33592.23 249
PEN-MVS85.21 27383.93 27689.07 29689.89 31281.31 30597.09 25297.24 14884.45 25978.66 30192.68 25768.44 27294.87 32275.98 30270.92 33691.04 292
MIMVSNet175.92 32573.30 33083.81 33381.29 36475.57 33692.26 33192.05 34773.09 34867.48 35586.18 34440.87 36587.64 36855.78 36670.68 33788.21 338
pm-mvs184.68 27982.78 28590.40 26289.58 31785.18 25397.31 24194.73 30281.93 30376.05 31592.01 26565.48 29896.11 28678.75 28469.14 33889.91 321
DTE-MVSNet84.14 28882.80 28388.14 30488.95 32779.87 31696.81 26296.24 21383.50 27477.60 31092.52 25967.89 27894.24 33372.64 32669.05 33990.32 311
test20.0378.51 31877.48 31481.62 34083.07 35971.03 35396.11 28792.83 33681.66 30569.31 34689.68 31957.53 32587.29 36958.65 36368.47 34086.53 350
h-mvs3392.47 14491.95 14094.05 17997.13 13285.01 25798.36 18098.08 4093.85 4596.27 8196.73 17583.19 15499.43 11295.81 8068.09 34197.70 180
K. test v381.04 30479.77 30784.83 32687.41 34370.23 35795.60 30293.93 32183.70 27167.51 35489.35 32355.76 33093.58 33776.67 29868.03 34290.67 305
test_fmvs375.09 32775.19 32474.81 34877.45 37154.08 37395.93 29090.64 35882.51 29473.29 33181.19 35822.29 37586.29 37085.50 21867.89 34384.06 361
MDA-MVSNet_test_wron79.65 31177.05 31687.45 31087.79 34180.13 31496.25 28294.44 30973.87 34551.80 36987.47 33668.04 27592.12 35466.02 34667.79 34490.09 314
YYNet179.64 31277.04 31787.43 31187.80 34079.98 31596.23 28394.44 30973.83 34651.83 36887.53 33267.96 27792.07 35566.00 34767.75 34590.23 313
APD_test168.93 33566.98 33874.77 34980.62 36653.15 37587.97 35385.01 37453.76 37059.26 36687.52 33325.19 37389.95 36156.20 36567.33 34681.19 367
AUN-MVS90.17 18989.50 18292.19 21796.21 16782.67 28997.76 22597.53 11388.05 18991.67 14896.15 18983.10 15697.47 21788.11 18966.91 34796.43 214
hse-mvs291.67 15991.51 14992.15 21996.22 16682.61 29197.74 22697.53 11393.85 4596.27 8196.15 18983.19 15497.44 22095.81 8066.86 34896.40 215
pmmvs679.90 30977.31 31587.67 30884.17 35678.13 32895.86 29693.68 32567.94 36172.67 33889.62 32050.98 34995.75 30274.80 31166.04 34989.14 332
test_f71.94 33270.82 33375.30 34772.77 37453.28 37491.62 33689.66 36475.44 33964.47 36178.31 36520.48 37689.56 36378.63 28566.02 35083.05 366
Gipumacopyleft54.77 34352.22 34762.40 36086.50 34859.37 36950.20 37890.35 36036.52 37641.20 37749.49 37818.33 37981.29 37332.10 37865.34 35146.54 378
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CXcopyleft76.08 34690.74 30251.65 37890.84 35786.47 22957.89 36787.98 32835.88 36992.60 34665.77 34865.06 35283.97 362
MDA-MVSNet-bldmvs77.82 32174.75 32787.03 31388.33 33378.52 32596.34 27792.85 33575.57 33848.87 37187.89 32957.32 32792.49 35060.79 35864.80 35390.08 315
mvsany_test375.85 32674.52 32879.83 34373.53 37360.64 36791.73 33587.87 37083.91 26770.55 34282.52 35331.12 37093.66 33586.66 20662.83 35485.19 359
Patchmatch-RL test81.90 30180.13 30487.23 31280.71 36570.12 35884.07 36688.19 36983.16 28070.57 34182.18 35687.18 8792.59 34782.28 25762.78 35598.98 115
lessismore_v085.08 32485.59 35269.28 35990.56 35967.68 35390.21 31354.21 34095.46 30973.88 31762.64 35690.50 308
PM-MVS74.88 32872.85 33180.98 34278.98 36964.75 36490.81 34685.77 37280.95 31368.23 35182.81 35229.08 37292.84 34376.54 29962.46 35785.36 356
pmmvs-eth3d78.71 31676.16 32186.38 31680.25 36781.19 30794.17 31492.13 34677.97 32866.90 35782.31 35555.76 33092.56 34873.63 32162.31 35885.38 355
ambc79.60 34472.76 37556.61 37076.20 37392.01 34868.25 35080.23 36123.34 37494.73 32673.78 32060.81 35987.48 342
test_method70.10 33468.66 33774.41 35086.30 35155.84 37194.47 30989.82 36235.18 37766.15 35984.75 34930.54 37177.96 37870.40 33460.33 36089.44 328
TDRefinement78.01 31975.31 32386.10 31970.06 37673.84 34393.59 32191.58 35374.51 34373.08 33591.04 28449.63 35497.12 22874.88 30959.47 36187.33 345
TransMVSNet (Re)81.97 29979.61 30889.08 29589.70 31584.01 27097.26 24491.85 35078.84 32373.07 33691.62 27367.17 28495.21 31667.50 34159.46 36288.02 339
PMVScopyleft41.42 2345.67 34642.50 34955.17 36234.28 38832.37 38766.24 37678.71 38030.72 37822.04 38359.59 3744.59 38777.85 37927.49 37958.84 36355.29 376
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_vis3_rt61.29 33758.75 34068.92 35567.41 37752.84 37691.18 34459.23 38866.96 36341.96 37658.44 37611.37 38494.72 32774.25 31457.97 36459.20 375
KD-MVS_self_test77.47 32275.88 32282.24 33681.59 36268.93 36092.83 32894.02 32077.03 33373.14 33383.39 35155.44 33490.42 35967.95 34057.53 36587.38 343
CL-MVSNet_self_test79.89 31078.34 31184.54 32981.56 36375.01 33896.88 26095.62 26381.10 31075.86 31885.81 34668.49 27190.26 36063.21 35356.51 36688.35 337
UnsupCasMVSNet_eth78.90 31476.67 31985.58 32282.81 36174.94 33991.98 33296.31 20784.64 25665.84 36087.71 33051.33 34692.23 35272.89 32556.50 36789.56 327
PVSNet_083.28 1687.31 24085.16 25593.74 19094.78 22684.59 26298.91 11798.69 2089.81 13478.59 30493.23 24861.95 31199.34 12494.75 10455.72 36897.30 191
new-patchmatchnet74.80 32972.40 33281.99 33978.36 37072.20 35094.44 31092.36 34177.06 33263.47 36279.98 36251.04 34888.85 36560.53 36054.35 36984.92 360
pmmvs372.86 33169.76 33682.17 33773.86 37274.19 34294.20 31389.01 36764.23 36867.72 35280.91 36041.48 36388.65 36662.40 35554.02 37083.68 363
testf156.38 34153.73 34464.31 35864.84 37845.11 38080.50 37175.94 38338.87 37342.74 37375.07 36611.26 38581.19 37441.11 37453.27 37166.63 372
APD_test256.38 34153.73 34464.31 35864.84 37845.11 38080.50 37175.94 38338.87 37342.74 37375.07 36611.26 38581.19 37441.11 37453.27 37166.63 372
LCM-MVSNet60.07 33956.37 34171.18 35254.81 38548.67 37982.17 37089.48 36537.95 37549.13 37069.12 36913.75 38381.76 37259.28 36151.63 37383.10 365
UnsupCasMVSNet_bld73.85 33070.14 33484.99 32579.44 36875.73 33588.53 35295.24 28670.12 35561.94 36474.81 36841.41 36493.62 33668.65 33851.13 37485.62 354
KD-MVS_2432*160082.98 29480.52 30290.38 26394.32 23588.98 15892.87 32695.87 24980.46 31773.79 32887.49 33482.76 16493.29 33970.56 33246.53 37588.87 335
miper_refine_blended82.98 29480.52 30290.38 26394.32 23588.98 15892.87 32695.87 24980.46 31773.79 32887.49 33482.76 16493.29 33970.56 33246.53 37588.87 335
PMMVS258.97 34055.07 34370.69 35462.72 38055.37 37285.97 35780.52 37849.48 37145.94 37268.31 37015.73 38180.78 37649.79 37137.12 37775.91 368
MVEpermissive44.00 2241.70 34737.64 35253.90 36349.46 38643.37 38265.09 37766.66 38526.19 38125.77 38248.53 3793.58 38963.35 38226.15 38027.28 37854.97 377
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN41.02 34840.93 35041.29 36461.97 38133.83 38684.00 36765.17 38627.17 37927.56 37946.72 38017.63 38060.41 38319.32 38118.82 37929.61 379
ANet_high50.71 34546.17 34864.33 35744.27 38752.30 37776.13 37478.73 37964.95 36627.37 38055.23 37714.61 38267.74 38036.01 37718.23 38072.95 370
EMVS39.96 34939.88 35140.18 36559.57 38432.12 38884.79 36464.57 38726.27 38026.14 38144.18 38318.73 37859.29 38417.03 38217.67 38129.12 380
tmp_tt53.66 34452.86 34656.05 36132.75 38941.97 38473.42 37576.12 38221.91 38239.68 37896.39 18542.59 36265.10 38178.00 28814.92 38261.08 374
wuyk23d16.71 35216.73 35616.65 36660.15 38225.22 39041.24 3795.17 3906.56 3835.48 3863.61 3863.64 38822.72 38515.20 3839.52 3831.99 383
testmvs18.81 35123.05 3546.10 3684.48 3902.29 39297.78 2213.00 3913.27 38418.60 38462.71 3721.53 3912.49 38714.26 3841.80 38413.50 382
test12316.58 35319.47 3557.91 3673.59 3915.37 39194.32 3111.39 3922.49 38513.98 38544.60 3822.91 3902.65 38611.35 3850.57 38515.70 381
test_blank0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
uanet_test0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
DCPMVS0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
cdsmvs_eth3d_5k22.52 35030.03 3530.00 3690.00 3920.00 3930.00 38097.17 1570.00 3870.00 38898.77 7774.35 2280.00 3880.00 3860.00 3860.00 384
pcd_1.5k_mvsjas6.87 3559.16 3580.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 38782.48 1690.00 3880.00 3860.00 3860.00 384
sosnet-low-res0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
sosnet0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
uncertanet0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
Regformer0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
ab-mvs-re8.21 35410.94 3570.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 38898.50 990.00 3920.00 3880.00 3860.00 3860.00 384
uanet0.00 3560.00 3590.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 3880.00 3870.00 3920.00 3880.00 3860.00 3860.00 384
FOURS199.50 4288.94 16199.55 3797.47 12691.32 9898.12 36
test_one_060199.59 2894.89 3397.64 8993.14 5998.93 1799.45 1493.45 18
eth-test20.00 392
eth-test0.00 392
test_241102_ONE99.63 1895.24 2497.72 7194.16 3599.30 699.49 993.32 1999.98 9
save fliter99.34 5093.85 6199.65 2897.63 9395.69 16
test072699.66 1295.20 2999.77 1097.70 7693.95 3899.35 599.54 393.18 22
GSMVS98.84 130
test_part299.54 3695.42 1998.13 34
sam_mvs188.39 6398.84 130
sam_mvs87.08 89
MTGPAbinary97.45 129
test_post190.74 34841.37 38485.38 12696.36 26783.16 247
test_post46.00 38187.37 8197.11 229
patchmatchnet-post84.86 34788.73 6096.81 242
MTMP99.21 7591.09 356
gm-plane-assit94.69 22888.14 17788.22 18597.20 15398.29 16590.79 158
TEST999.57 3393.17 7299.38 6297.66 8389.57 14298.39 2899.18 3190.88 3799.66 84
test_899.55 3593.07 7599.37 6597.64 8990.18 12498.36 3099.19 2890.94 3599.64 90
agg_prior99.54 3692.66 8297.64 8997.98 4399.61 92
test_prior492.00 9199.41 59
test_prior97.01 5799.58 3091.77 9297.57 10799.49 10299.79 35
旧先验298.67 13985.75 23898.96 1698.97 14293.84 121
新几何298.26 188
无先验98.52 15697.82 5587.20 21299.90 4687.64 19599.85 30
原ACMM298.69 136
testdata299.88 4984.16 235
segment_acmp90.56 41
testdata197.89 21492.43 72
plane_prior793.84 25185.73 241
plane_prior693.92 24886.02 23572.92 240
plane_prior496.52 179
plane_prior385.91 23693.65 5186.99 204
plane_prior299.02 10593.38 56
plane_prior193.90 250
n20.00 393
nn0.00 393
door-mid84.90 375
test1197.68 80
door85.30 373
HQP5-MVS86.39 218
HQP-NCC93.95 24499.16 8293.92 4087.57 197
ACMP_Plane93.95 24499.16 8293.92 4087.57 197
BP-MVS93.82 123
HQP4-MVS87.57 19797.77 19592.72 236
HQP2-MVS73.34 235
NP-MVS93.94 24786.22 22596.67 177
MDTV_nov1_ep13_2view91.17 10591.38 34087.45 20993.08 13386.67 10087.02 19898.95 121
Test By Simon83.62 144