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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MSC_two_6792asdad96.52 197.78 6090.86 196.85 8099.61 796.03 2799.06 999.07 5
No_MVS96.52 197.78 6090.86 196.85 8099.61 796.03 2799.06 999.07 5
OPU-MVS96.21 398.00 4890.85 397.13 1997.08 7092.59 298.94 9192.25 9298.99 1498.84 19
HPM-MVS++copyleft95.14 1394.91 2695.83 498.25 3589.65 495.92 8796.96 6891.75 1394.02 7196.83 8288.12 2799.55 2093.41 6698.94 1698.28 61
MM95.10 1494.91 2695.68 596.09 11588.34 996.68 3894.37 29495.08 194.68 5797.72 3982.94 10099.64 497.85 598.76 3399.06 7
SMA-MVScopyleft95.20 1095.07 2095.59 698.14 4188.48 896.26 5497.28 4085.90 20297.67 498.10 1488.41 2399.56 1694.66 4999.19 198.71 25
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
3Dnovator+87.14 492.42 10391.37 12395.55 795.63 14388.73 697.07 2396.77 9190.84 2684.02 32896.62 9575.95 21199.34 4287.77 17997.68 9798.59 29
TestfortrainingZip a95.70 495.76 595.51 898.88 187.98 1097.32 1097.86 188.11 12997.21 1497.54 4492.42 499.67 193.66 6098.85 2098.89 15
CNVR-MVS95.40 995.37 1195.50 998.11 4288.51 795.29 13096.96 6892.09 1095.32 4997.08 7089.49 1799.33 4595.10 4498.85 2098.66 26
MGCNet94.18 5093.80 6495.34 1094.91 18287.62 1595.97 8293.01 33992.58 694.22 6297.20 6480.56 13999.59 1197.04 2098.68 4198.81 22
ACMMP_NAP94.74 2594.56 3395.28 1198.02 4787.70 1295.68 10697.34 3088.28 12095.30 5097.67 4185.90 5499.54 2493.91 5798.95 1598.60 28
DPE-MVScopyleft95.57 695.67 695.25 1298.36 3187.28 1995.56 11897.51 1189.13 8797.14 1897.91 3291.64 999.62 594.61 5099.17 298.86 16
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS94.97 1794.90 2895.20 1397.84 5687.76 1196.65 3997.48 1687.76 14795.71 4497.70 4088.28 2699.35 4193.89 5898.78 3098.48 35
MCST-MVS94.45 3494.20 5195.19 1498.46 2287.50 1795.00 15397.12 5587.13 16892.51 11296.30 10489.24 1999.34 4293.46 6398.62 5098.73 23
NCCC94.81 2294.69 3295.17 1597.83 5787.46 1895.66 10996.93 7292.34 793.94 7296.58 9787.74 3099.44 3392.83 7598.40 5898.62 27
DPM-MVS92.58 9991.74 10995.08 1696.19 10689.31 592.66 30996.56 11283.44 27691.68 13795.04 18386.60 4698.99 8185.60 21397.92 8596.93 182
ZNCC-MVS94.47 3394.28 4595.03 1798.52 1886.96 2196.85 3397.32 3488.24 12193.15 8797.04 7386.17 5199.62 592.40 8698.81 2798.52 31
test_0728_SECOND95.01 1898.79 586.43 4097.09 2197.49 1299.61 795.62 3499.08 798.99 9
MTAPA94.42 3994.22 4895.00 1998.42 2486.95 2294.36 20596.97 6591.07 2293.14 8897.56 4384.30 8199.56 1693.43 6498.75 3498.47 38
MSP-MVS95.42 895.56 894.98 2098.49 2086.52 3796.91 3097.47 1791.73 1496.10 3696.69 8789.90 1499.30 4894.70 4898.04 8099.13 2
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
region2R94.43 3694.27 4794.92 2198.65 1186.67 3196.92 2997.23 4388.60 11193.58 7997.27 5885.22 6499.54 2492.21 9498.74 3598.56 30
APDe-MVScopyleft95.46 795.64 794.91 2298.26 3486.29 4797.46 797.40 2689.03 9296.20 3598.10 1489.39 1899.34 4295.88 2999.03 1199.10 4
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR94.43 3694.28 4594.91 2298.63 1286.69 2996.94 2597.32 3488.63 10893.53 8297.26 6085.04 6899.54 2492.35 8998.78 3098.50 32
GST-MVS94.21 4593.97 6094.90 2498.41 2586.82 2596.54 4197.19 4488.24 12193.26 8496.83 8285.48 6099.59 1191.43 12098.40 5898.30 55
HFP-MVS94.52 3194.40 3894.86 2598.61 1386.81 2696.94 2597.34 3088.63 10893.65 7797.21 6286.10 5299.49 3092.35 8998.77 3298.30 55
sasdasda93.27 8292.75 9294.85 2695.70 13887.66 1396.33 4496.41 12290.00 5194.09 6794.60 20782.33 10998.62 13192.40 8692.86 22798.27 63
MP-MVS-pluss94.21 4594.00 5994.85 2698.17 3986.65 3294.82 16697.17 4986.26 19492.83 9797.87 3485.57 5999.56 1694.37 5398.92 1798.34 48
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs93.27 8292.75 9294.85 2695.70 13887.66 1396.33 4496.41 12290.00 5194.09 6794.60 20782.33 10998.62 13192.40 8692.86 22798.27 63
XVS94.45 3494.32 4194.85 2698.54 1686.60 3596.93 2797.19 4490.66 3492.85 9597.16 6885.02 6999.49 3091.99 10598.56 5498.47 38
X-MVStestdata88.31 23186.13 28094.85 2698.54 1686.60 3596.93 2797.19 4490.66 3492.85 9523.41 48385.02 6999.49 3091.99 10598.56 5498.47 38
SteuartSystems-ACMMP95.20 1095.32 1394.85 2696.99 8186.33 4397.33 897.30 3791.38 1995.39 4897.46 5088.98 2299.40 3494.12 5498.89 1898.82 21
Skip Steuart: Steuart Systems R&D Blog.
MED-MVS test94.84 3298.88 185.89 6497.32 1097.86 188.11 12997.21 1497.54 4499.67 195.27 4098.85 2098.95 11
MED-MVS95.74 396.04 394.84 3298.88 185.89 6497.32 1097.86 189.01 9497.21 1497.54 4492.42 499.67 195.27 4098.85 2098.95 11
DVP-MVS++95.98 196.36 194.82 3497.78 6086.00 5398.29 197.49 1290.75 2997.62 898.06 2292.59 299.61 795.64 3299.02 1298.86 16
ME-MVS95.17 1295.29 1494.81 3598.39 2885.89 6495.91 8897.55 989.01 9495.86 4297.54 4489.24 1999.59 1195.27 4098.85 2098.95 11
alignmvs93.08 9092.50 9894.81 3595.62 14487.61 1695.99 7996.07 16589.77 6494.12 6694.87 19180.56 13998.66 12392.42 8593.10 22398.15 75
SED-MVS95.91 296.28 294.80 3798.77 885.99 5597.13 1997.44 2190.31 4197.71 298.07 2092.31 699.58 1495.66 3099.13 398.84 19
DeepC-MVS_fast89.43 294.04 5393.79 6594.80 3797.48 7086.78 2795.65 11196.89 7789.40 7592.81 9896.97 7585.37 6299.24 5190.87 12998.69 3998.38 47
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVScopyleft94.25 4294.07 5694.77 3998.47 2186.31 4596.71 3696.98 6489.04 9091.98 12397.19 6585.43 6199.56 1692.06 10398.79 2898.44 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVScopyleft94.24 4394.07 5694.75 4098.06 4586.90 2495.88 9096.94 7185.68 20995.05 5597.18 6687.31 3899.07 6491.90 11198.61 5298.28 61
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS94.34 4094.21 5094.74 4198.39 2886.64 3397.60 597.24 4188.53 11392.73 10397.23 6185.20 6599.32 4692.15 9798.83 2698.25 68
PGM-MVS93.96 5893.72 7094.68 4298.43 2386.22 4895.30 12897.78 487.45 15893.26 8497.33 5684.62 7899.51 2890.75 13198.57 5398.32 54
DVP-MVScopyleft95.67 596.02 494.64 4398.78 685.93 5897.09 2196.73 9790.27 4597.04 2298.05 2591.47 1099.55 2095.62 3499.08 798.45 41
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
mPP-MVS93.99 5693.78 6694.63 4498.50 1985.90 6396.87 3196.91 7588.70 10691.83 13297.17 6783.96 8599.55 2091.44 11998.64 4998.43 43
PHI-MVS93.89 6093.65 7494.62 4596.84 8486.43 4096.69 3797.49 1285.15 23393.56 8196.28 10585.60 5899.31 4792.45 8398.79 2898.12 80
TSAR-MVS + MP.94.85 1994.94 2494.58 4698.25 3586.33 4396.11 6796.62 10788.14 12696.10 3696.96 7689.09 2198.94 9194.48 5198.68 4198.48 35
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CANet93.54 6993.20 8394.55 4795.65 14185.73 7194.94 15696.69 10391.89 1290.69 16195.88 13481.99 12199.54 2493.14 7097.95 8498.39 45
train_agg93.44 7593.08 8594.52 4897.53 6786.49 3894.07 22596.78 8981.86 32092.77 10096.20 10887.63 3299.12 6292.14 9898.69 3997.94 96
CDPH-MVS92.83 9492.30 10194.44 4997.79 5886.11 5294.06 22796.66 10480.09 35192.77 10096.63 9486.62 4499.04 6887.40 18698.66 4598.17 73
3Dnovator86.66 591.73 12090.82 13894.44 4994.59 20886.37 4297.18 1797.02 6289.20 8484.31 32396.66 9073.74 25199.17 5686.74 19697.96 8397.79 116
SR-MVS94.23 4494.17 5494.43 5198.21 3885.78 6996.40 4396.90 7688.20 12494.33 6197.40 5384.75 7799.03 6993.35 6797.99 8298.48 35
HPM-MVScopyleft94.02 5493.88 6194.43 5198.39 2885.78 6997.25 1597.07 6086.90 17892.62 10996.80 8684.85 7599.17 5692.43 8498.65 4898.33 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + GP.93.66 6793.41 7894.41 5396.59 9186.78 2794.40 19793.93 31289.77 6494.21 6395.59 15487.35 3798.61 13392.72 7896.15 13597.83 113
reproduce-ours94.82 2094.97 2294.38 5497.91 5385.46 7495.86 9197.15 5189.82 5795.23 5298.10 1487.09 4099.37 3795.30 3898.25 6798.30 55
our_new_method94.82 2094.97 2294.38 5497.91 5385.46 7495.86 9197.15 5189.82 5795.23 5298.10 1487.09 4099.37 3795.30 3898.25 6798.30 55
NormalMVS93.46 7293.16 8494.37 5698.40 2686.20 4996.30 4796.27 13591.65 1792.68 10596.13 11677.97 18198.84 10590.75 13198.26 6398.07 82
test1294.34 5797.13 7986.15 5196.29 13191.04 15785.08 6799.01 7498.13 7597.86 108
SymmetryMVS92.81 9692.31 10094.32 5896.15 10786.20 4996.30 4794.43 29091.65 1792.68 10596.13 11677.97 18198.84 10590.75 13194.72 16797.92 102
ACMMPcopyleft93.24 8492.88 9094.30 5998.09 4485.33 7896.86 3297.45 2088.33 11790.15 17697.03 7481.44 12999.51 2890.85 13095.74 14298.04 89
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
reproduce_model94.76 2494.92 2594.29 6097.92 4985.18 8095.95 8597.19 4489.67 6795.27 5198.16 686.53 4799.36 4095.42 3798.15 7398.33 50
DeepC-MVS88.79 393.31 8192.99 8894.26 6196.07 11785.83 6794.89 15996.99 6389.02 9389.56 18597.37 5582.51 10699.38 3592.20 9598.30 6197.57 131
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MGCFI-Net93.03 9192.63 9594.23 6295.62 14485.92 6096.08 6996.33 12989.86 5593.89 7494.66 20482.11 11698.50 13992.33 9192.82 23098.27 63
fmvsm_l_conf0.5_n_394.80 2395.01 2194.15 6395.64 14285.08 8196.09 6897.36 2890.98 2497.09 2098.12 1084.98 7398.94 9197.07 1797.80 9298.43 43
EPNet91.79 11191.02 13294.10 6490.10 40685.25 7996.03 7692.05 36692.83 587.39 23495.78 14579.39 16399.01 7488.13 17397.48 10098.05 88
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lecture95.10 1495.46 1094.01 6598.40 2684.36 10697.70 397.78 491.19 2096.22 3498.08 1986.64 4399.37 3794.91 4698.26 6398.29 60
test_fmvsmconf_n94.60 2894.81 3093.98 6694.62 20484.96 8496.15 6297.35 2989.37 7696.03 3998.11 1186.36 4899.01 7497.45 1097.83 9097.96 95
DELS-MVS93.43 7993.25 8193.97 6795.42 15285.04 8293.06 29197.13 5490.74 3191.84 13095.09 18286.32 4999.21 5491.22 12198.45 5697.65 125
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
DP-MVS Recon91.95 10891.28 12693.96 6898.33 3385.92 6094.66 17896.66 10482.69 29790.03 17895.82 14182.30 11199.03 6984.57 23196.48 12896.91 184
HPM-MVS_fast93.40 8093.22 8293.94 6998.36 3184.83 8697.15 1896.80 8885.77 20692.47 11397.13 6982.38 10799.07 6490.51 13698.40 5897.92 102
test_fmvsmconf0.1_n94.20 4794.31 4393.88 7092.46 32184.80 8796.18 5996.82 8589.29 8195.68 4598.11 1185.10 6698.99 8197.38 1197.75 9697.86 108
SD-MVS94.96 1895.33 1293.88 7097.25 7886.69 2996.19 5797.11 5890.42 3796.95 2497.27 5889.53 1696.91 30994.38 5298.85 2098.03 90
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
MVS_111021_HR93.45 7493.31 7993.84 7296.99 8184.84 8593.24 28297.24 4188.76 10391.60 13895.85 13886.07 5398.66 12391.91 10998.16 7198.03 90
SR-MVS-dyc-post93.82 6293.82 6393.82 7397.92 4984.57 9396.28 5196.76 9287.46 15693.75 7597.43 5184.24 8299.01 7492.73 7697.80 9297.88 106
test_prior93.82 7397.29 7684.49 9796.88 7898.87 9998.11 81
APD-MVS_3200maxsize93.78 6393.77 6793.80 7597.92 4984.19 11096.30 4796.87 7986.96 17493.92 7397.47 4983.88 8698.96 8892.71 7997.87 8898.26 67
fmvsm_l_conf0.5_n94.29 4194.46 3693.79 7695.28 15785.43 7695.68 10696.43 12086.56 18696.84 2697.81 3787.56 3598.77 11497.14 1596.82 11897.16 163
CSCG93.23 8593.05 8693.76 7798.04 4684.07 11296.22 5697.37 2784.15 25790.05 17795.66 15187.77 2999.15 6089.91 14398.27 6298.07 82
GDP-MVS92.04 10691.46 12093.75 7894.55 21484.69 9095.60 11796.56 11287.83 14493.07 9195.89 13373.44 25598.65 12590.22 13996.03 13797.91 104
BP-MVS192.48 10192.07 10493.72 7994.50 21784.39 10595.90 8994.30 29790.39 3892.67 10795.94 12974.46 23498.65 12593.14 7097.35 10498.13 77
test_fmvsmconf0.01_n93.19 8693.02 8793.71 8089.25 41984.42 10496.06 7396.29 13189.06 8894.68 5798.13 779.22 16598.98 8597.22 1397.24 10697.74 119
UA-Net92.83 9492.54 9793.68 8196.10 11484.71 8995.66 10996.39 12491.92 1193.22 8696.49 10083.16 9598.87 9984.47 23395.47 14997.45 137
fmvsm_l_conf0.5_n_a94.20 4794.40 3893.60 8295.29 15684.98 8395.61 11496.28 13486.31 19296.75 2897.86 3587.40 3698.74 11897.07 1797.02 11197.07 168
QAPM89.51 18888.15 21593.59 8394.92 18084.58 9296.82 3496.70 10278.43 37883.41 34596.19 11173.18 26099.30 4877.11 34696.54 12596.89 185
test_fmvsm_n_192094.71 2695.11 1993.50 8495.79 13284.62 9196.15 6297.64 689.85 5697.19 1797.89 3386.28 5098.71 12197.11 1698.08 7997.17 156
fmvsm_s_conf0.5_n_994.99 1695.50 993.44 8596.51 9982.25 18395.76 10196.92 7393.37 397.63 798.43 184.82 7699.16 5998.15 197.92 8598.90 14
KinetiMVS91.82 11091.30 12493.39 8694.72 19683.36 13795.45 12196.37 12690.33 4092.17 11896.03 12372.32 27298.75 11587.94 17696.34 13098.07 82
casdiffmvs_mvgpermissive92.96 9392.83 9193.35 8794.59 20883.40 13595.00 15396.34 12890.30 4392.05 12196.05 12083.43 8998.15 17592.07 10095.67 14398.49 34
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_593.96 5894.18 5393.30 8894.79 18983.81 12195.77 9996.74 9688.02 13396.23 3397.84 3683.36 9398.83 10897.49 897.34 10597.25 149
EI-MVSNet-Vis-set93.01 9292.92 8993.29 8995.01 17183.51 13294.48 18795.77 19390.87 2592.52 11196.67 8984.50 7999.00 7991.99 10594.44 18097.36 140
Vis-MVSNetpermissive91.75 11891.23 12793.29 8995.32 15583.78 12296.14 6495.98 17289.89 5390.45 16596.58 9775.09 22398.31 16684.75 22596.90 11497.78 117
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
balanced_conf0393.98 5794.22 4893.26 9196.13 10983.29 13996.27 5396.52 11589.82 5795.56 4795.51 15784.50 7998.79 11294.83 4798.86 1997.72 121
SPE-MVS-test94.02 5494.29 4493.24 9296.69 8783.24 14097.49 696.92 7392.14 992.90 9395.77 14685.02 6998.33 16393.03 7298.62 5098.13 77
VNet92.24 10591.91 10693.24 9296.59 9183.43 13394.84 16596.44 11989.19 8594.08 7095.90 13277.85 18798.17 17388.90 16393.38 21298.13 77
fmvsm_s_conf0.5_n_1094.43 3694.84 2993.20 9495.73 13583.19 14395.99 7997.31 3691.08 2197.67 498.11 1181.87 12399.22 5297.86 497.91 8797.20 154
VDD-MVS90.74 14689.92 16093.20 9496.27 10483.02 15595.73 10393.86 31688.42 11692.53 11096.84 8162.09 37998.64 12890.95 12792.62 23797.93 101
Elysia90.12 16589.10 18393.18 9693.16 29184.05 11495.22 13796.27 13585.16 23190.59 16294.68 20064.64 36298.37 15686.38 20295.77 14097.12 165
StellarMVS90.12 16589.10 18393.18 9693.16 29184.05 11495.22 13796.27 13585.16 23190.59 16294.68 20064.64 36298.37 15686.38 20295.77 14097.12 165
CS-MVS94.12 5194.44 3793.17 9896.55 9483.08 15297.63 496.95 7091.71 1593.50 8396.21 10785.61 5798.24 16893.64 6198.17 7098.19 71
nrg03091.08 14090.39 14493.17 9893.07 29886.91 2396.41 4296.26 13988.30 11988.37 21094.85 19482.19 11597.64 23591.09 12282.95 36294.96 270
MVSMamba_PlusPlus93.44 7593.54 7693.14 10096.58 9383.05 15396.06 7396.50 11784.42 25494.09 6795.56 15685.01 7298.69 12294.96 4598.66 4597.67 124
EI-MVSNet-UG-set92.74 9792.62 9693.12 10194.86 18583.20 14294.40 19795.74 19690.71 3392.05 12196.60 9684.00 8498.99 8191.55 11793.63 20297.17 156
test_fmvsmvis_n_192093.44 7593.55 7593.10 10293.67 27784.26 10895.83 9596.14 15689.00 9692.43 11497.50 4883.37 9298.72 11996.61 2497.44 10196.32 210
新几何193.10 10297.30 7584.35 10795.56 21371.09 44691.26 14796.24 10682.87 10298.86 10179.19 32598.10 7696.07 226
OMC-MVS91.23 13290.62 14393.08 10496.27 10484.07 11293.52 26495.93 17886.95 17589.51 18696.13 11678.50 17598.35 16085.84 21192.90 22696.83 192
OpenMVScopyleft83.78 1188.74 21887.29 23793.08 10492.70 31685.39 7796.57 4096.43 12078.74 37380.85 37796.07 11969.64 30899.01 7478.01 33796.65 12394.83 278
MAR-MVS90.30 16189.37 17693.07 10696.61 9084.48 9895.68 10695.67 20482.36 30287.85 22192.85 27476.63 20098.80 11080.01 31196.68 12295.91 232
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
lupinMVS90.92 14190.21 14893.03 10793.86 26283.88 11992.81 30393.86 31679.84 35491.76 13494.29 22177.92 18498.04 19690.48 13797.11 10797.17 156
Effi-MVS+91.59 12691.11 12993.01 10894.35 23283.39 13694.60 18095.10 24887.10 16990.57 16493.10 26981.43 13098.07 19089.29 15594.48 17897.59 130
fmvsm_s_conf0.5_n_a93.57 6893.76 6893.00 10995.02 17083.67 12596.19 5796.10 16287.27 16295.98 4098.05 2583.07 9998.45 14996.68 2395.51 14696.88 186
MVS_111021_LR92.47 10292.29 10292.98 11095.99 12484.43 10293.08 28896.09 16388.20 12491.12 15295.72 14981.33 13197.76 22491.74 11397.37 10396.75 194
fmvsm_s_conf0.1_n_a93.19 8693.26 8092.97 11192.49 31983.62 12896.02 7795.72 20086.78 18096.04 3898.19 482.30 11198.43 15396.38 2595.42 15296.86 187
ETV-MVS92.74 9792.66 9492.97 11195.20 16384.04 11695.07 14996.51 11690.73 3292.96 9291.19 33584.06 8398.34 16191.72 11496.54 12596.54 205
LFMVS90.08 16889.13 18292.95 11396.71 8682.32 18296.08 6989.91 42386.79 17992.15 12096.81 8462.60 37798.34 16187.18 19093.90 19498.19 71
UGNet89.95 17588.95 19192.95 11394.51 21683.31 13895.70 10595.23 24189.37 7687.58 22893.94 23764.00 36798.78 11383.92 24096.31 13196.74 195
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
jason90.80 14490.10 15292.90 11593.04 30183.53 13193.08 28894.15 30580.22 34891.41 14494.91 18876.87 19497.93 21490.28 13896.90 11497.24 150
jason: jason.
DP-MVS87.25 27285.36 31092.90 11597.65 6483.24 14094.81 16792.00 36874.99 41481.92 36695.00 18472.66 26599.05 6666.92 42792.33 24296.40 207
fmvsm_s_conf0.5_n_894.56 3095.12 1892.87 11795.96 12781.32 21095.76 10197.57 893.48 297.53 1098.32 381.78 12699.13 6197.91 297.81 9198.16 74
fmvsm_s_conf0.5_n93.76 6494.06 5892.86 11895.62 14483.17 14496.14 6496.12 16088.13 12795.82 4398.04 2883.43 8998.48 14196.97 2196.23 13296.92 183
fmvsm_s_conf0.1_n93.46 7293.66 7392.85 11993.75 26983.13 14696.02 7795.74 19687.68 15095.89 4198.17 582.78 10398.46 14596.71 2296.17 13496.98 177
CANet_DTU90.26 16389.41 17592.81 12093.46 28483.01 15693.48 26594.47 28989.43 7487.76 22694.23 22670.54 29699.03 6984.97 22096.39 12996.38 208
MVSFormer91.68 12491.30 12492.80 12193.86 26283.88 11995.96 8395.90 18284.66 25091.76 13494.91 18877.92 18497.30 27689.64 15197.11 10797.24 150
PVSNet_Blended_VisFu91.38 12990.91 13592.80 12196.39 10183.17 14494.87 16196.66 10483.29 28189.27 19294.46 21680.29 14299.17 5687.57 18395.37 15396.05 229
fmvsm_l_conf0.5_n_994.65 2795.28 1592.77 12395.95 12881.83 19495.53 11997.12 5591.68 1697.89 198.06 2285.71 5698.65 12597.32 1298.26 6397.83 113
LuminaMVS90.55 15789.81 16292.77 12392.78 31484.21 10994.09 22394.17 30485.82 20391.54 13994.14 22869.93 30297.92 21591.62 11694.21 18896.18 218
fmvsm_s_conf0.5_n_694.11 5294.56 3392.76 12594.98 17581.96 19295.79 9797.29 3989.31 7997.52 1197.61 4283.25 9498.88 9897.05 1998.22 6997.43 139
VDDNet89.56 18788.49 20692.76 12595.07 16982.09 18696.30 4793.19 33481.05 34291.88 12896.86 8061.16 39598.33 16388.43 17092.49 24197.84 112
viewdifsd2359ckpt0991.18 13590.65 14292.75 12794.61 20782.36 18194.32 20695.74 19684.72 24789.66 18495.15 18079.69 15898.04 19687.70 18094.27 18797.85 111
h-mvs3390.80 14490.15 15192.75 12796.01 12082.66 16995.43 12295.53 21789.80 6093.08 8995.64 15275.77 21299.00 7992.07 10078.05 41996.60 200
casdiffmvspermissive92.51 10092.43 9992.74 12994.41 22781.98 19094.54 18496.23 14389.57 7091.96 12596.17 11282.58 10598.01 20190.95 12795.45 15198.23 69
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_yl90.69 14990.02 15892.71 13095.72 13682.41 17994.11 21995.12 24685.63 21091.49 14194.70 19874.75 22798.42 15486.13 20692.53 23997.31 141
DCV-MVSNet90.69 14990.02 15892.71 13095.72 13682.41 17994.11 21995.12 24685.63 21091.49 14194.70 19874.75 22798.42 15486.13 20692.53 23997.31 141
PCF-MVS84.11 1087.74 24686.08 28492.70 13294.02 25184.43 10289.27 40195.87 18773.62 42884.43 31594.33 21878.48 17798.86 10170.27 40194.45 17994.81 279
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
fmvsm_s_conf0.5_n_1194.60 2895.23 1692.69 13396.05 11982.00 18896.31 4696.71 10092.27 896.68 3098.39 285.32 6398.92 9497.20 1498.16 7197.17 156
SSM_040490.73 14790.08 15392.69 13395.00 17483.13 14694.32 20695.00 25685.41 22189.84 17995.35 16576.13 20397.98 20685.46 21694.18 18996.95 179
baseline92.39 10492.29 10292.69 13394.46 22281.77 19794.14 21696.27 13589.22 8391.88 12896.00 12482.35 10897.99 20391.05 12395.27 15798.30 55
MSLP-MVS++93.72 6694.08 5592.65 13697.31 7483.43 13395.79 9797.33 3290.03 5093.58 7996.96 7684.87 7497.76 22492.19 9698.66 4596.76 193
EC-MVSNet93.44 7593.71 7192.63 13795.21 16282.43 17697.27 1496.71 10090.57 3692.88 9495.80 14283.16 9598.16 17493.68 5998.14 7497.31 141
ab-mvs89.41 19588.35 20892.60 13895.15 16782.65 17392.20 32895.60 21183.97 26188.55 20693.70 25174.16 24298.21 17282.46 26489.37 28696.94 181
LS3D87.89 24186.32 27392.59 13996.07 11782.92 15995.23 13594.92 26475.66 40682.89 35295.98 12672.48 26999.21 5468.43 41595.23 15895.64 246
Anonymous2024052988.09 23786.59 26292.58 14096.53 9681.92 19395.99 7995.84 18974.11 42389.06 19695.21 17561.44 38798.81 10983.67 24787.47 31797.01 175
fmvsm_s_conf0.5_n_394.49 3295.13 1792.56 14195.49 15081.10 22095.93 8697.16 5092.96 497.39 1298.13 783.63 8898.80 11097.89 397.61 9997.78 117
CPTT-MVS91.99 10791.80 10792.55 14298.24 3781.98 19096.76 3596.49 11881.89 31990.24 16996.44 10278.59 17398.61 13389.68 14997.85 8997.06 169
viewdifsd2359ckpt1391.20 13490.75 14092.54 14394.30 23682.13 18594.03 22995.89 18485.60 21290.20 17195.36 16479.69 15897.90 21887.85 17893.86 19597.61 127
114514_t89.51 18888.50 20492.54 14398.11 4281.99 18995.16 14596.36 12770.19 45085.81 26895.25 17176.70 19898.63 13082.07 27496.86 11797.00 176
PAPM_NR91.22 13390.78 13992.52 14597.60 6581.46 20694.37 20396.24 14286.39 19187.41 23194.80 19682.06 11998.48 14182.80 25995.37 15397.61 127
mamba_040889.06 20887.92 22292.50 14694.76 19082.66 16979.84 47094.64 28285.18 22688.96 19895.00 18476.00 20897.98 20683.74 24493.15 22096.85 188
DeepPCF-MVS89.96 194.20 4794.77 3192.49 14796.52 9780.00 26794.00 23497.08 5990.05 4995.65 4697.29 5789.66 1598.97 8693.95 5698.71 3698.50 32
SSM_040790.47 15989.80 16392.46 14894.76 19082.66 16993.98 23695.00 25685.41 22188.96 19895.35 16576.13 20397.88 21985.46 21693.15 22096.85 188
IS-MVSNet91.43 12891.09 13192.46 14895.87 13181.38 20996.95 2493.69 32589.72 6689.50 18895.98 12678.57 17497.77 22383.02 25396.50 12798.22 70
API-MVS90.66 15290.07 15492.45 15096.36 10284.57 9396.06 7395.22 24382.39 30089.13 19394.27 22480.32 14198.46 14580.16 31096.71 12194.33 302
xiu_mvs_v1_base_debu90.64 15390.05 15592.40 15193.97 25784.46 9993.32 27395.46 22185.17 22892.25 11594.03 22970.59 29298.57 13690.97 12494.67 16994.18 305
xiu_mvs_v1_base90.64 15390.05 15592.40 15193.97 25784.46 9993.32 27395.46 22185.17 22892.25 11594.03 22970.59 29298.57 13690.97 12494.67 16994.18 305
xiu_mvs_v1_base_debi90.64 15390.05 15592.40 15193.97 25784.46 9993.32 27395.46 22185.17 22892.25 11594.03 22970.59 29298.57 13690.97 12494.67 16994.18 305
fmvsm_s_conf0.5_n_293.47 7193.83 6292.39 15495.36 15381.19 21695.20 14296.56 11290.37 3997.13 1998.03 2977.47 19098.96 8897.79 696.58 12497.03 172
viewmacassd2359aftdt91.67 12591.43 12292.37 15593.95 26081.00 22493.90 24495.97 17587.75 14891.45 14396.04 12279.92 14897.97 20889.26 15694.67 16998.14 76
viewmanbaseed2359cas91.78 11491.58 11492.37 15594.32 23481.07 22193.76 25095.96 17687.26 16391.50 14095.88 13480.92 13797.97 20889.70 14894.92 16398.07 82
fmvsm_s_conf0.1_n_293.16 8893.42 7792.37 15594.62 20481.13 21895.23 13595.89 18490.30 4396.74 2998.02 3076.14 20298.95 9097.64 796.21 13397.03 172
AdaColmapbinary89.89 17889.07 18592.37 15597.41 7183.03 15494.42 19495.92 17982.81 29486.34 25794.65 20573.89 24799.02 7280.69 30195.51 14695.05 265
CNLPA89.07 20787.98 21992.34 15996.87 8384.78 8894.08 22493.24 33181.41 33384.46 31395.13 18175.57 21996.62 32377.21 34493.84 19795.61 249
fmvsm_s_conf0.5_n_493.86 6194.37 4092.33 16095.13 16880.95 22795.64 11296.97 6589.60 6996.85 2597.77 3883.08 9898.92 9497.49 896.78 11997.13 164
ET-MVSNet_ETH3D87.51 26085.91 29292.32 16193.70 27683.93 11792.33 32290.94 40084.16 25672.09 44892.52 28769.90 30395.85 37189.20 15788.36 30497.17 156
E491.74 11991.55 11792.31 16294.27 23880.80 23793.81 24796.17 15387.97 13591.11 15396.05 12080.75 13898.08 18889.78 14494.02 19198.06 87
E291.79 11191.61 11292.31 16294.49 21880.86 23393.74 25296.19 14887.63 15391.16 14895.94 12981.31 13298.06 19189.76 14594.29 18597.99 92
Anonymous20240521187.68 24786.13 28092.31 16296.66 8880.74 23994.87 16191.49 38580.47 34789.46 18995.44 16054.72 43498.23 16982.19 27089.89 27697.97 94
E391.78 11491.61 11292.30 16594.48 21980.86 23393.73 25396.19 14887.63 15391.16 14895.95 12881.30 13398.06 19189.76 14594.29 18597.99 92
CHOSEN 1792x268888.84 21487.69 22792.30 16596.14 10881.42 20890.01 38895.86 18874.52 41987.41 23193.94 23775.46 22098.36 15880.36 30695.53 14597.12 165
viewcassd2359sk1191.79 11191.62 11192.29 16794.62 20480.88 23193.70 25796.18 15287.38 16091.13 15195.85 13881.62 12898.06 19189.71 14794.40 18197.94 96
HY-MVS83.01 1289.03 21087.94 22192.29 16794.86 18582.77 16192.08 33394.49 28881.52 33286.93 23892.79 28078.32 17998.23 16979.93 31290.55 26395.88 235
CDS-MVSNet89.45 19188.51 20392.29 16793.62 27983.61 13093.01 29294.68 28081.95 31387.82 22493.24 26378.69 17196.99 30380.34 30793.23 21796.28 213
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR90.02 17189.27 18192.29 16795.78 13380.95 22792.68 30896.22 14481.91 31586.66 24893.75 24982.23 11398.44 15179.40 32494.79 16697.48 135
E3new91.76 11791.58 11492.28 17194.69 20180.90 23093.68 26096.17 15387.15 16691.09 15695.70 15081.75 12798.05 19589.67 15094.35 18297.90 105
mvsmamba90.33 16089.69 16692.25 17295.17 16481.64 19995.27 13393.36 33084.88 24089.51 18694.27 22469.29 31797.42 26189.34 15496.12 13697.68 123
E691.71 12191.55 11792.20 17394.32 23480.62 24394.41 19596.19 14888.06 13191.11 15396.16 11379.92 14898.03 19990.00 14093.80 19997.94 96
E591.71 12191.55 11792.20 17394.33 23380.62 24394.41 19596.19 14888.06 13191.11 15396.16 11379.92 14898.03 19990.00 14093.80 19997.94 96
PLCcopyleft84.53 789.06 20888.03 21792.15 17597.27 7782.69 16894.29 20895.44 22679.71 35684.01 32994.18 22776.68 19998.75 11577.28 34393.41 21195.02 266
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet91.70 12391.56 11692.13 17695.88 12980.50 24897.33 895.25 24086.15 19789.76 18395.60 15383.42 9198.32 16587.37 18893.25 21697.56 132
patch_mono-293.74 6594.32 4192.01 17797.54 6678.37 30993.40 26997.19 4488.02 13394.99 5697.21 6288.35 2498.44 15194.07 5598.09 7799.23 1
原ACMM192.01 17797.34 7381.05 22296.81 8778.89 36790.45 16595.92 13182.65 10498.84 10580.68 30298.26 6396.14 220
UniMVSNet (Re)89.80 18189.07 18592.01 17793.60 28084.52 9694.78 16997.47 1789.26 8286.44 25492.32 29382.10 11797.39 27284.81 22480.84 39694.12 309
MG-MVS91.77 11691.70 11092.00 18097.08 8080.03 26593.60 26295.18 24487.85 14390.89 15996.47 10182.06 11998.36 15885.07 21997.04 11097.62 126
EIA-MVS91.95 10891.94 10591.98 18195.16 16580.01 26695.36 12396.73 9788.44 11489.34 19092.16 29883.82 8798.45 14989.35 15397.06 10997.48 135
PVSNet_Blended90.73 14790.32 14691.98 18196.12 11081.25 21292.55 31396.83 8382.04 31189.10 19492.56 28681.04 13598.85 10386.72 19895.91 13895.84 237
guyue91.12 13890.84 13791.96 18394.59 20880.57 24694.87 16193.71 32488.96 9791.14 15095.22 17273.22 25997.76 22492.01 10493.81 19897.54 134
PS-MVSNAJ91.18 13590.92 13491.96 18395.26 16082.60 17592.09 33295.70 20286.27 19391.84 13092.46 28879.70 15598.99 8189.08 15895.86 13994.29 303
TAMVS89.21 20188.29 21291.96 18393.71 27482.62 17493.30 27794.19 30282.22 30587.78 22593.94 23778.83 16896.95 30677.70 33992.98 22596.32 210
SDMVSNet90.19 16489.61 16991.93 18696.00 12183.09 15192.89 30095.98 17288.73 10486.85 24495.20 17672.09 27597.08 29588.90 16389.85 27895.63 247
FA-MVS(test-final)89.66 18388.91 19391.93 18694.57 21280.27 25291.36 34994.74 27784.87 24189.82 18092.61 28574.72 23098.47 14483.97 23993.53 20697.04 171
MVS_Test91.31 13191.11 12991.93 18694.37 22880.14 25793.46 26795.80 19186.46 18991.35 14693.77 24782.21 11498.09 18687.57 18394.95 16297.55 133
NR-MVSNet88.58 22487.47 23391.93 18693.04 30184.16 11194.77 17096.25 14189.05 8980.04 39193.29 26179.02 16797.05 30081.71 28580.05 40694.59 286
HyFIR lowres test88.09 23786.81 25091.93 18696.00 12180.63 24190.01 38895.79 19273.42 43087.68 22792.10 30473.86 24897.96 21080.75 30091.70 24697.19 155
GeoE90.05 16989.43 17491.90 19195.16 16580.37 25195.80 9694.65 28183.90 26287.55 23094.75 19778.18 18097.62 23781.28 29093.63 20297.71 122
thisisatest053088.67 21987.61 22991.86 19294.87 18480.07 26194.63 17989.90 42484.00 26088.46 20893.78 24666.88 34198.46 14583.30 24992.65 23297.06 169
xiu_mvs_v2_base91.13 13790.89 13691.86 19294.97 17682.42 17792.24 32595.64 20986.11 20191.74 13693.14 26779.67 16098.89 9789.06 15995.46 15094.28 304
DU-MVS89.34 20088.50 20491.85 19493.04 30183.72 12394.47 19096.59 10989.50 7186.46 25193.29 26177.25 19297.23 28584.92 22181.02 39294.59 286
AstraMVS90.69 14990.30 14791.84 19593.81 26579.85 27294.76 17192.39 35488.96 9791.01 15895.87 13770.69 29097.94 21392.49 8292.70 23197.73 120
OPM-MVS90.12 16589.56 17091.82 19693.14 29383.90 11894.16 21595.74 19688.96 9787.86 22095.43 16272.48 26997.91 21688.10 17590.18 27093.65 340
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS90.60 15690.19 14991.82 19694.70 19982.73 16595.85 9396.22 14490.81 2786.91 24094.86 19274.23 23898.12 17688.15 17189.99 27294.63 283
UniMVSNet_NR-MVSNet89.92 17789.29 17991.81 19893.39 28683.72 12394.43 19397.12 5589.80 6086.46 25193.32 25883.16 9597.23 28584.92 22181.02 39294.49 296
diffmvspermissive91.37 13091.23 12791.77 19993.09 29680.27 25292.36 31995.52 21887.03 17191.40 14594.93 18780.08 14597.44 25992.13 9994.56 17597.61 127
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs_AUTHOR91.51 12791.44 12191.73 20093.09 29680.27 25292.51 31495.58 21287.22 16491.80 13395.57 15579.96 14797.48 25192.23 9394.97 16197.45 137
1112_ss88.42 22687.33 23691.72 20194.92 18080.98 22592.97 29694.54 28578.16 38483.82 33293.88 24278.78 17097.91 21679.45 32089.41 28596.26 214
Fast-Effi-MVS+89.41 19588.64 19991.71 20294.74 19380.81 23693.54 26395.10 24883.11 28586.82 24690.67 35879.74 15497.75 22880.51 30593.55 20496.57 203
WTY-MVS89.60 18588.92 19291.67 20395.47 15181.15 21792.38 31894.78 27583.11 28589.06 19694.32 21978.67 17296.61 32681.57 28690.89 25997.24 150
TAPA-MVS84.62 688.16 23587.01 24591.62 20496.64 8980.65 24094.39 19996.21 14776.38 39986.19 26195.44 16079.75 15398.08 18862.75 44595.29 15596.13 221
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet89.62 18488.96 19091.60 20593.86 26282.89 16095.46 12097.33 3287.91 13888.43 20993.31 25974.17 24197.40 26987.32 18982.86 36794.52 291
FE-MVS87.40 26586.02 28691.57 20694.56 21379.69 27690.27 37593.72 32380.57 34588.80 20291.62 32465.32 35798.59 13574.97 36994.33 18496.44 206
XVG-OURS89.40 19788.70 19891.52 20794.06 24981.46 20691.27 35496.07 16586.14 19888.89 20195.77 14668.73 32697.26 28287.39 18789.96 27495.83 238
hse-mvs289.88 17989.34 17791.51 20894.83 18781.12 21993.94 23893.91 31589.80 6093.08 8993.60 25275.77 21297.66 23292.07 10077.07 42695.74 242
TranMVSNet+NR-MVSNet88.84 21487.95 22091.49 20992.68 31783.01 15694.92 15896.31 13089.88 5485.53 27793.85 24476.63 20096.96 30581.91 27879.87 40994.50 294
AUN-MVS87.78 24586.54 26591.48 21094.82 18881.05 22293.91 24293.93 31283.00 28986.93 23893.53 25369.50 31197.67 23086.14 20477.12 42595.73 244
XVG-OURS-SEG-HR89.95 17589.45 17291.47 21194.00 25581.21 21591.87 33796.06 16785.78 20588.55 20695.73 14874.67 23197.27 28088.71 16789.64 28395.91 232
MVS87.44 26386.10 28391.44 21292.61 31883.62 12892.63 31095.66 20667.26 45681.47 36992.15 29977.95 18398.22 17179.71 31495.48 14892.47 384
viewdifsd2359ckpt0791.11 13991.02 13291.41 21394.21 24278.37 30992.91 29995.71 20187.50 15590.32 16895.88 13480.27 14397.99 20388.78 16693.55 20497.86 108
F-COLMAP87.95 24086.80 25191.40 21496.35 10380.88 23194.73 17395.45 22479.65 35782.04 36494.61 20671.13 28298.50 13976.24 35691.05 25794.80 280
dcpmvs_293.49 7094.19 5291.38 21597.69 6376.78 35294.25 21096.29 13188.33 11794.46 5996.88 7988.07 2898.64 12893.62 6298.09 7798.73 23
thisisatest051587.33 26885.99 28791.37 21693.49 28279.55 27790.63 36989.56 43280.17 34987.56 22990.86 34867.07 33898.28 16781.50 28793.02 22496.29 212
HQP-MVS89.80 18189.28 18091.34 21794.17 24481.56 20094.39 19996.04 16888.81 10085.43 28693.97 23673.83 24997.96 21087.11 19389.77 28194.50 294
fmvsm_s_conf0.5_n_793.15 8993.76 6891.31 21894.42 22679.48 27994.52 18597.14 5389.33 7894.17 6598.09 1881.83 12497.49 25096.33 2698.02 8196.95 179
RRT-MVS90.85 14390.70 14191.30 21994.25 23976.83 35194.85 16496.13 15989.04 9090.23 17094.88 19070.15 30198.72 11991.86 11294.88 16498.34 48
FMVSNet387.40 26586.11 28291.30 21993.79 26883.64 12794.20 21494.81 27383.89 26384.37 31691.87 31568.45 32996.56 33278.23 33485.36 33593.70 339
FMVSNet287.19 27885.82 29591.30 21994.01 25283.67 12594.79 16894.94 25983.57 27183.88 33192.05 30866.59 34696.51 33677.56 34185.01 33893.73 337
RPMNet83.95 35681.53 36791.21 22290.58 39579.34 28585.24 44896.76 9271.44 44485.55 27582.97 45570.87 28798.91 9661.01 44989.36 28795.40 253
IB-MVS80.51 1585.24 33383.26 35191.19 22392.13 33079.86 27191.75 34091.29 39083.28 28280.66 38188.49 40661.28 38998.46 14580.99 29679.46 41395.25 259
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
CLD-MVS89.47 19088.90 19491.18 22494.22 24182.07 18792.13 33096.09 16387.90 13985.37 29292.45 28974.38 23697.56 24287.15 19190.43 26593.93 318
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test89.45 19188.90 19491.12 22594.47 22081.49 20495.30 12896.14 15686.73 18285.45 28395.16 17869.89 30498.10 17887.70 18089.23 29093.77 333
LGP-MVS_train91.12 22594.47 22081.49 20496.14 15686.73 18285.45 28395.16 17869.89 30498.10 17887.70 18089.23 29093.77 333
ACMM84.12 989.14 20388.48 20791.12 22594.65 20381.22 21495.31 12696.12 16085.31 22585.92 26694.34 21770.19 30098.06 19185.65 21288.86 29594.08 313
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt051788.61 22187.78 22691.11 22894.96 17777.81 32795.35 12489.69 42785.09 23588.05 21894.59 20966.93 33998.48 14183.27 25092.13 24497.03 172
GBi-Net87.26 27085.98 28891.08 22994.01 25283.10 14895.14 14694.94 25983.57 27184.37 31691.64 32066.59 34696.34 34978.23 33485.36 33593.79 328
test187.26 27085.98 28891.08 22994.01 25283.10 14895.14 14694.94 25983.57 27184.37 31691.64 32066.59 34696.34 34978.23 33485.36 33593.79 328
FMVSNet185.85 31884.11 33891.08 22992.81 31283.10 14895.14 14694.94 25981.64 32782.68 35491.64 32059.01 41196.34 34975.37 36383.78 35193.79 328
Test_1112_low_res87.65 24986.51 26691.08 22994.94 17979.28 28991.77 33994.30 29776.04 40483.51 34292.37 29177.86 18697.73 22978.69 32989.13 29296.22 215
PS-MVSNAJss89.97 17389.62 16891.02 23391.90 33980.85 23595.26 13495.98 17286.26 19486.21 26094.29 22179.70 15597.65 23388.87 16588.10 30694.57 288
BH-RMVSNet88.37 22987.48 23291.02 23395.28 15779.45 28192.89 30093.07 33785.45 22086.91 24094.84 19570.35 29797.76 22473.97 37894.59 17495.85 236
UniMVSNet_ETH3D87.53 25986.37 27091.00 23592.44 32278.96 29494.74 17295.61 21084.07 25985.36 29394.52 21159.78 40397.34 27482.93 25487.88 31196.71 196
FIs90.51 15890.35 14590.99 23693.99 25680.98 22595.73 10397.54 1089.15 8686.72 24794.68 20081.83 12497.24 28485.18 21888.31 30594.76 281
ACMP84.23 889.01 21288.35 20890.99 23694.73 19481.27 21195.07 14995.89 18486.48 18783.67 33794.30 22069.33 31397.99 20387.10 19588.55 29793.72 338
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous2023121186.59 30185.13 31690.98 23896.52 9781.50 20296.14 6496.16 15573.78 42683.65 33892.15 29963.26 37397.37 27382.82 25881.74 38194.06 314
IMVS_040389.97 17389.64 16790.96 23993.72 27077.75 33293.00 29395.34 23585.53 21688.77 20394.49 21278.49 17697.84 22084.75 22592.65 23297.28 144
sss88.93 21388.26 21490.94 24094.05 25080.78 23891.71 34195.38 23081.55 33188.63 20593.91 24175.04 22495.47 39082.47 26391.61 24796.57 203
IMVS_040789.85 18089.51 17190.88 24193.72 27077.75 33293.07 29095.34 23585.53 21688.34 21194.49 21277.69 18897.60 23884.75 22592.65 23297.28 144
viewmambaseed2359dif90.04 17089.78 16490.83 24292.85 31177.92 32192.23 32695.01 25281.90 31690.20 17195.45 15979.64 16297.34 27487.52 18593.17 21897.23 153
sd_testset88.59 22387.85 22590.83 24296.00 12180.42 25092.35 32094.71 27888.73 10486.85 24495.20 17667.31 33396.43 34379.64 31689.85 27895.63 247
PVSNet_BlendedMVS89.98 17289.70 16590.82 24496.12 11081.25 21293.92 24096.83 8383.49 27589.10 19492.26 29681.04 13598.85 10386.72 19887.86 31292.35 391
cascas86.43 30984.98 31990.80 24592.10 33280.92 22990.24 37995.91 18173.10 43383.57 34188.39 40765.15 35997.46 25584.90 22391.43 24994.03 316
ECVR-MVScopyleft89.09 20688.53 20290.77 24695.62 14475.89 36596.16 6084.22 45887.89 14190.20 17196.65 9163.19 37498.10 17885.90 20996.94 11298.33 50
GA-MVS86.61 29985.27 31390.66 24791.33 36278.71 29890.40 37493.81 32085.34 22485.12 29689.57 38861.25 39097.11 29480.99 29689.59 28496.15 219
thres600view787.65 24986.67 25790.59 24896.08 11678.72 29694.88 16091.58 38187.06 17088.08 21692.30 29468.91 32398.10 17870.05 40891.10 25294.96 270
thres40087.62 25486.64 25890.57 24995.99 12478.64 29994.58 18191.98 37086.94 17688.09 21491.77 31669.18 31998.10 17870.13 40591.10 25294.96 270
baseline188.10 23687.28 23890.57 24994.96 17780.07 26194.27 20991.29 39086.74 18187.41 23194.00 23476.77 19796.20 35480.77 29979.31 41595.44 251
viewdifsd2359ckpt1189.43 19389.05 18790.56 25192.89 30977.00 34792.81 30394.52 28687.03 17189.77 18195.79 14374.67 23197.51 24688.97 16184.98 33997.17 156
viewmsd2359difaftdt89.43 19389.05 18790.56 25192.89 30977.00 34792.81 30394.52 28687.03 17189.77 18195.79 14374.67 23197.51 24688.97 16184.98 33997.17 156
FE-MVSNET386.84 28985.61 30390.53 25390.50 39981.80 19690.97 36294.96 25883.05 28783.50 34390.32 36572.15 27396.65 32079.49 31885.55 33493.15 363
FC-MVSNet-test90.27 16290.18 15090.53 25393.71 27479.85 27295.77 9997.59 789.31 7986.27 25894.67 20381.93 12297.01 30284.26 23588.09 30894.71 282
PAPM86.68 29885.39 30890.53 25393.05 30079.33 28889.79 39194.77 27678.82 37081.95 36593.24 26376.81 19597.30 27666.94 42593.16 21994.95 274
WR-MVS88.38 22887.67 22890.52 25693.30 28880.18 25593.26 28095.96 17688.57 11285.47 28292.81 27876.12 20596.91 30981.24 29182.29 37294.47 299
SSM_0407288.57 22587.92 22290.51 25794.76 19082.66 16979.84 47094.64 28285.18 22688.96 19895.00 18476.00 20892.03 43983.74 24493.15 22096.85 188
MVSTER88.84 21488.29 21290.51 25792.95 30680.44 24993.73 25395.01 25284.66 25087.15 23593.12 26872.79 26497.21 28787.86 17787.36 32093.87 323
testdata90.49 25996.40 10077.89 32495.37 23272.51 43893.63 7896.69 8782.08 11897.65 23383.08 25197.39 10295.94 231
test111189.10 20488.64 19990.48 26095.53 14974.97 37596.08 6984.89 45688.13 12790.16 17596.65 9163.29 37298.10 17886.14 20496.90 11498.39 45
tt080586.92 28685.74 30190.48 26092.22 32679.98 26895.63 11394.88 26783.83 26584.74 30592.80 27957.61 41897.67 23085.48 21584.42 34493.79 328
jajsoiax88.24 23387.50 23190.48 26090.89 38380.14 25795.31 12695.65 20884.97 23884.24 32494.02 23265.31 35897.42 26188.56 16888.52 29993.89 319
PatchMatch-RL86.77 29585.54 30490.47 26395.88 12982.71 16790.54 37292.31 35879.82 35584.32 32191.57 32868.77 32596.39 34573.16 38493.48 21092.32 392
tfpn200view987.58 25786.64 25890.41 26495.99 12478.64 29994.58 18191.98 37086.94 17688.09 21491.77 31669.18 31998.10 17870.13 40591.10 25294.48 297
VPNet88.20 23487.47 23390.39 26593.56 28179.46 28094.04 22895.54 21688.67 10786.96 23794.58 21069.33 31397.15 28984.05 23880.53 40194.56 289
ACMH80.38 1785.36 32883.68 34590.39 26594.45 22380.63 24194.73 17394.85 26982.09 30777.24 41792.65 28360.01 40197.58 24072.25 38984.87 34192.96 369
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view90087.63 25286.71 25490.38 26796.12 11078.55 30295.03 15291.58 38187.15 16688.06 21792.29 29568.91 32398.10 17870.13 40591.10 25294.48 297
mvs_tets88.06 23987.28 23890.38 26790.94 37979.88 27095.22 13795.66 20685.10 23484.21 32593.94 23763.53 37097.40 26988.50 16988.40 30393.87 323
131487.51 26086.57 26390.34 26992.42 32379.74 27592.63 31095.35 23478.35 37980.14 38891.62 32474.05 24397.15 28981.05 29293.53 20694.12 309
LTVRE_ROB82.13 1386.26 31284.90 32290.34 26994.44 22481.50 20292.31 32494.89 26583.03 28879.63 39892.67 28269.69 30797.79 22271.20 39486.26 32991.72 402
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
test_djsdf89.03 21088.64 19990.21 27190.74 39079.28 28995.96 8395.90 18284.66 25085.33 29492.94 27374.02 24497.30 27689.64 15188.53 29894.05 315
v2v48287.84 24287.06 24290.17 27290.99 37579.23 29294.00 23495.13 24584.87 24185.53 27792.07 30774.45 23597.45 25684.71 23081.75 38093.85 326
pmmvs485.43 32683.86 34390.16 27390.02 40982.97 15890.27 37592.67 34975.93 40580.73 37991.74 31871.05 28395.73 37978.85 32883.46 35891.78 401
V4287.68 24786.86 24790.15 27490.58 39580.14 25794.24 21295.28 23983.66 26985.67 27291.33 33074.73 22997.41 26784.43 23481.83 37892.89 372
MSDG84.86 34183.09 35490.14 27593.80 26680.05 26389.18 40493.09 33678.89 36778.19 40991.91 31365.86 35697.27 28068.47 41488.45 30193.11 364
sc_t181.53 38278.67 40390.12 27690.78 38778.64 29993.91 24290.20 41368.42 45380.82 37889.88 38146.48 45796.76 31476.03 35971.47 44194.96 270
anonymousdsp87.84 24287.09 24190.12 27689.13 42080.54 24794.67 17795.55 21482.05 30983.82 33292.12 30171.47 28097.15 28987.15 19187.80 31592.67 378
thres20087.21 27686.24 27790.12 27695.36 15378.53 30393.26 28092.10 36486.42 19088.00 21991.11 34169.24 31898.00 20269.58 40991.04 25893.83 327
CR-MVSNet85.35 32983.76 34490.12 27690.58 39579.34 28585.24 44891.96 37278.27 38185.55 27587.87 41771.03 28495.61 38273.96 37989.36 28795.40 253
v114487.61 25586.79 25290.06 28091.01 37479.34 28593.95 23795.42 22983.36 28085.66 27391.31 33374.98 22597.42 26183.37 24882.06 37493.42 349
XXY-MVS87.65 24986.85 24890.03 28192.14 32980.60 24593.76 25095.23 24182.94 29184.60 30794.02 23274.27 23795.49 38981.04 29383.68 35494.01 317
Vis-MVSNet (Re-imp)89.59 18689.44 17390.03 28195.74 13475.85 36695.61 11490.80 40487.66 15287.83 22395.40 16376.79 19696.46 34178.37 33096.73 12097.80 115
test250687.21 27686.28 27590.02 28395.62 14473.64 39196.25 5571.38 48187.89 14190.45 16596.65 9155.29 43098.09 18686.03 20896.94 11298.33 50
BH-untuned88.60 22288.13 21690.01 28495.24 16178.50 30593.29 27894.15 30584.75 24684.46 31393.40 25575.76 21497.40 26977.59 34094.52 17794.12 309
v119287.25 27286.33 27290.00 28590.76 38979.04 29393.80 24895.48 21982.57 29885.48 28191.18 33773.38 25897.42 26182.30 26782.06 37493.53 343
v7n86.81 29085.76 29989.95 28690.72 39179.25 29195.07 14995.92 17984.45 25382.29 35890.86 34872.60 26897.53 24479.42 32380.52 40293.08 366
testing9187.11 28186.18 27889.92 28794.43 22575.38 37491.53 34692.27 36086.48 18786.50 24990.24 36761.19 39397.53 24482.10 27290.88 26096.84 191
IMVS_040487.60 25686.84 24989.89 28893.72 27077.75 33288.56 41395.34 23585.53 21679.98 39294.49 21266.54 34994.64 40384.75 22592.65 23297.28 144
v887.50 26286.71 25489.89 28891.37 35979.40 28294.50 18695.38 23084.81 24483.60 34091.33 33076.05 20697.42 26182.84 25780.51 40392.84 374
v1087.25 27286.38 26989.85 29091.19 36579.50 27894.48 18795.45 22483.79 26783.62 33991.19 33575.13 22297.42 26181.94 27780.60 39892.63 380
baseline286.50 30585.39 30889.84 29191.12 37076.70 35491.88 33688.58 43682.35 30379.95 39390.95 34673.42 25697.63 23680.27 30989.95 27595.19 260
pm-mvs186.61 29985.54 30489.82 29291.44 35480.18 25595.28 13294.85 26983.84 26481.66 36792.62 28472.45 27196.48 33879.67 31578.06 41892.82 375
TR-MVS86.78 29285.76 29989.82 29294.37 22878.41 30792.47 31592.83 34381.11 34186.36 25592.40 29068.73 32697.48 25173.75 38289.85 27893.57 342
ACMH+81.04 1485.05 33683.46 34889.82 29294.66 20279.37 28394.44 19294.12 30882.19 30678.04 41192.82 27758.23 41497.54 24373.77 38182.90 36692.54 381
EI-MVSNet89.10 20488.86 19689.80 29591.84 34178.30 31293.70 25795.01 25285.73 20787.15 23595.28 16979.87 15297.21 28783.81 24287.36 32093.88 322
v14419287.19 27886.35 27189.74 29690.64 39378.24 31493.92 24095.43 22781.93 31485.51 27991.05 34474.21 24097.45 25682.86 25681.56 38293.53 343
COLMAP_ROBcopyleft80.39 1683.96 35582.04 36489.74 29695.28 15779.75 27494.25 21092.28 35975.17 41278.02 41293.77 24758.60 41397.84 22065.06 43685.92 33091.63 404
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA86.32 31185.18 31589.73 29892.15 32876.60 35591.12 35891.69 37783.53 27485.50 28088.81 40066.79 34296.48 33876.65 34990.35 26796.12 222
blend_shiyan481.94 37279.35 39189.70 29985.52 45180.08 26091.29 35293.82 31977.12 39379.31 40182.94 45654.81 43296.60 32879.60 31769.78 44692.41 387
IterMVS-LS88.36 23087.91 22489.70 29993.80 26678.29 31393.73 25395.08 25085.73 20784.75 30491.90 31479.88 15196.92 30883.83 24182.51 36893.89 319
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testing1186.44 30885.35 31189.69 30194.29 23775.40 37391.30 35190.53 40884.76 24585.06 29890.13 37358.95 41297.45 25682.08 27391.09 25696.21 217
testing9986.72 29685.73 30289.69 30194.23 24074.91 37791.35 35090.97 39886.14 19886.36 25590.22 36859.41 40697.48 25182.24 26990.66 26296.69 198
v192192086.97 28586.06 28589.69 30190.53 39878.11 31793.80 24895.43 22781.90 31685.33 29491.05 34472.66 26597.41 26782.05 27581.80 37993.53 343
icg_test_0407_289.15 20288.97 18989.68 30493.72 27077.75 33288.26 41895.34 23585.53 21688.34 21194.49 21277.69 18893.99 41584.75 22592.65 23297.28 144
VortexMVS88.42 22688.01 21889.63 30593.89 26178.82 29593.82 24695.47 22086.67 18484.53 31191.99 31072.62 26796.65 32089.02 16084.09 34893.41 350
Fast-Effi-MVS+-dtu87.44 26386.72 25389.63 30592.04 33377.68 33794.03 22993.94 31185.81 20482.42 35791.32 33270.33 29897.06 29880.33 30890.23 26994.14 308
v124086.78 29285.85 29489.56 30790.45 40177.79 32993.61 26195.37 23281.65 32685.43 28691.15 33971.50 27997.43 26081.47 28882.05 37693.47 347
Effi-MVS+-dtu88.65 22088.35 20889.54 30893.33 28776.39 35994.47 19094.36 29587.70 14985.43 28689.56 38973.45 25497.26 28285.57 21491.28 25194.97 267
AllTest83.42 36281.39 36889.52 30995.01 17177.79 32993.12 28490.89 40277.41 38876.12 42693.34 25654.08 43797.51 24668.31 41684.27 34693.26 353
TestCases89.52 30995.01 17177.79 32990.89 40277.41 38876.12 42693.34 25654.08 43797.51 24668.31 41684.27 34693.26 353
mvs_anonymous89.37 19989.32 17889.51 31193.47 28374.22 38491.65 34494.83 27182.91 29285.45 28393.79 24581.23 13496.36 34886.47 20094.09 19097.94 96
XVG-ACMP-BASELINE86.00 31484.84 32489.45 31291.20 36478.00 31991.70 34295.55 21485.05 23682.97 35192.25 29754.49 43597.48 25182.93 25487.45 31992.89 372
testing22284.84 34283.32 34989.43 31394.15 24775.94 36491.09 35989.41 43484.90 23985.78 26989.44 39052.70 44296.28 35270.80 40091.57 24896.07 226
MVP-Stereo85.97 31584.86 32389.32 31490.92 38182.19 18492.11 33194.19 30278.76 37278.77 40891.63 32368.38 33096.56 33275.01 36893.95 19389.20 443
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNetpermissive85.85 31884.70 32689.29 31591.76 34575.54 37088.49 41491.30 38981.63 32885.05 29988.70 40471.71 27696.24 35374.61 37489.05 29396.08 225
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v14887.04 28386.32 27389.21 31690.94 37977.26 34393.71 25694.43 29084.84 24384.36 31990.80 35276.04 20797.05 30082.12 27179.60 41293.31 352
tfpnnormal84.72 34483.23 35289.20 31792.79 31380.05 26394.48 18795.81 19082.38 30181.08 37591.21 33469.01 32296.95 30661.69 44780.59 39990.58 429
cl2286.78 29285.98 28889.18 31892.34 32477.62 33890.84 36594.13 30781.33 33583.97 33090.15 37273.96 24596.60 32884.19 23682.94 36393.33 351
BH-w/o87.57 25887.05 24389.12 31994.90 18377.90 32392.41 31693.51 32782.89 29383.70 33691.34 32975.75 21597.07 29775.49 36193.49 20892.39 389
WR-MVS_H87.80 24487.37 23589.10 32093.23 28978.12 31695.61 11497.30 3787.90 13983.72 33592.01 30979.65 16196.01 36376.36 35380.54 40093.16 361
miper_enhance_ethall86.90 28786.18 27889.06 32191.66 35077.58 33990.22 38194.82 27279.16 36384.48 31289.10 39479.19 16696.66 31984.06 23782.94 36392.94 370
c3_l87.14 28086.50 26789.04 32292.20 32777.26 34391.22 35794.70 27982.01 31284.34 32090.43 36378.81 16996.61 32683.70 24681.09 38993.25 355
miper_ehance_all_eth87.22 27586.62 26189.02 32392.13 33077.40 34190.91 36494.81 27381.28 33684.32 32190.08 37579.26 16496.62 32383.81 24282.94 36393.04 367
gg-mvs-nofinetune81.77 37679.37 39088.99 32490.85 38577.73 33686.29 44079.63 46974.88 41783.19 35069.05 47260.34 39896.11 35875.46 36294.64 17393.11 364
ETVMVS84.43 34982.92 35888.97 32594.37 22874.67 37891.23 35688.35 43883.37 27986.06 26489.04 39555.38 42895.67 38167.12 42391.34 25096.58 202
pmmvs683.42 36281.60 36688.87 32688.01 43577.87 32594.96 15594.24 30174.67 41878.80 40791.09 34260.17 40096.49 33777.06 34875.40 43292.23 394
test_cas_vis1_n_192088.83 21788.85 19788.78 32791.15 36976.72 35393.85 24594.93 26383.23 28492.81 9896.00 12461.17 39494.45 40491.67 11594.84 16595.17 261
MIMVSNet82.59 36880.53 37388.76 32891.51 35278.32 31186.57 43990.13 41679.32 35980.70 38088.69 40552.98 44193.07 43166.03 43188.86 29594.90 275
cl____86.52 30485.78 29688.75 32992.03 33476.46 35790.74 36694.30 29781.83 32283.34 34790.78 35375.74 21796.57 33081.74 28381.54 38393.22 357
DIV-MVS_self_test86.53 30385.78 29688.75 32992.02 33576.45 35890.74 36694.30 29781.83 32283.34 34790.82 35175.75 21596.57 33081.73 28481.52 38493.24 356
CP-MVSNet87.63 25287.26 24088.74 33193.12 29476.59 35695.29 13096.58 11088.43 11583.49 34492.98 27275.28 22195.83 37278.97 32681.15 38893.79 328
eth_miper_zixun_eth86.50 30585.77 29888.68 33291.94 33675.81 36790.47 37394.89 26582.05 30984.05 32790.46 36275.96 21096.77 31382.76 26079.36 41493.46 348
CHOSEN 280x42085.15 33483.99 34188.65 33392.47 32078.40 30879.68 47292.76 34674.90 41681.41 37189.59 38769.85 30695.51 38679.92 31395.29 15592.03 397
PS-CasMVS87.32 26986.88 24688.63 33492.99 30476.33 36195.33 12596.61 10888.22 12383.30 34993.07 27073.03 26295.79 37678.36 33181.00 39493.75 335
TransMVSNet (Re)84.43 34983.06 35688.54 33591.72 34678.44 30695.18 14392.82 34582.73 29679.67 39792.12 30173.49 25395.96 36571.10 39868.73 45391.21 416
tt0320-xc79.63 40576.66 41488.52 33691.03 37378.72 29693.00 29389.53 43366.37 45776.11 42887.11 42846.36 45995.32 39472.78 38667.67 45491.51 408
EG-PatchMatch MVS82.37 37080.34 37688.46 33790.27 40379.35 28492.80 30694.33 29677.14 39273.26 44590.18 37147.47 45496.72 31570.25 40287.32 32289.30 440
PEN-MVS86.80 29186.27 27688.40 33892.32 32575.71 36995.18 14396.38 12587.97 13582.82 35393.15 26673.39 25795.92 36776.15 35779.03 41793.59 341
Baseline_NR-MVSNet87.07 28286.63 26088.40 33891.44 35477.87 32594.23 21392.57 35184.12 25885.74 27192.08 30577.25 19296.04 35982.29 26879.94 40791.30 414
UBG85.51 32484.57 33188.35 34094.21 24271.78 41690.07 38689.66 42982.28 30485.91 26789.01 39661.30 38897.06 29876.58 35292.06 24596.22 215
D2MVS85.90 31685.09 31788.35 34090.79 38677.42 34091.83 33895.70 20280.77 34480.08 39090.02 37766.74 34496.37 34681.88 27987.97 31091.26 415
pmmvs584.21 35182.84 36188.34 34288.95 42276.94 34992.41 31691.91 37475.63 40780.28 38591.18 33764.59 36495.57 38377.09 34783.47 35792.53 382
mamv490.92 14191.78 10888.33 34395.67 14070.75 42992.92 29896.02 17181.90 31688.11 21395.34 16785.88 5596.97 30495.22 4395.01 16097.26 148
tt032080.13 39877.41 40788.29 34490.50 39978.02 31893.10 28790.71 40666.06 46076.75 42186.97 42949.56 44995.40 39171.65 39071.41 44291.46 411
LCM-MVSNet-Re88.30 23288.32 21188.27 34594.71 19872.41 41193.15 28390.98 39787.77 14679.25 40291.96 31178.35 17895.75 37783.04 25295.62 14496.65 199
CostFormer85.77 32184.94 32188.26 34691.16 36872.58 40989.47 39991.04 39676.26 40286.45 25389.97 37970.74 28996.86 31282.35 26687.07 32595.34 257
ITE_SJBPF88.24 34791.88 34077.05 34692.92 34085.54 21480.13 38993.30 26057.29 41996.20 35472.46 38884.71 34291.49 409
PVSNet78.82 1885.55 32384.65 32788.23 34894.72 19671.93 41287.12 43592.75 34778.80 37184.95 30190.53 36064.43 36596.71 31774.74 37193.86 19596.06 228
IterMVS-SCA-FT85.45 32584.53 33288.18 34991.71 34776.87 35090.19 38392.65 35085.40 22381.44 37090.54 35966.79 34295.00 40081.04 29381.05 39092.66 379
EPNet_dtu86.49 30785.94 29188.14 35090.24 40472.82 40194.11 21992.20 36286.66 18579.42 40092.36 29273.52 25295.81 37471.26 39393.66 20195.80 240
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry82.71 36680.93 37288.06 35190.05 40876.37 36084.74 45391.96 37272.28 44181.32 37387.87 41771.03 28495.50 38868.97 41180.15 40592.32 392
test_vis1_n_192089.39 19889.84 16188.04 35292.97 30572.64 40694.71 17596.03 17086.18 19691.94 12796.56 9961.63 38395.74 37893.42 6595.11 15995.74 242
DTE-MVSNet86.11 31385.48 30687.98 35391.65 35174.92 37694.93 15795.75 19587.36 16182.26 35993.04 27172.85 26395.82 37374.04 37777.46 42393.20 359
PMMVS85.71 32284.96 32087.95 35488.90 42377.09 34588.68 41190.06 41872.32 44086.47 25090.76 35472.15 27394.40 40781.78 28293.49 20892.36 390
GG-mvs-BLEND87.94 35589.73 41577.91 32287.80 42478.23 47480.58 38283.86 44859.88 40295.33 39371.20 39492.22 24390.60 428
MonoMVSNet86.89 28886.55 26487.92 35689.46 41873.75 38894.12 21793.10 33587.82 14585.10 29790.76 35469.59 30994.94 40186.47 20082.50 36995.07 264
reproduce_monomvs86.37 31085.87 29387.87 35793.66 27873.71 38993.44 26895.02 25188.61 11082.64 35691.94 31257.88 41696.68 31889.96 14279.71 41193.22 357
pmmvs-eth3d80.97 39178.72 40287.74 35884.99 45479.97 26990.11 38591.65 37975.36 40973.51 44386.03 43759.45 40593.96 41875.17 36572.21 43889.29 442
MS-PatchMatch85.05 33684.16 33687.73 35991.42 35778.51 30491.25 35593.53 32677.50 38780.15 38791.58 32661.99 38095.51 38675.69 36094.35 18289.16 444
mmtdpeth85.04 33884.15 33787.72 36093.11 29575.74 36894.37 20392.83 34384.98 23789.31 19186.41 43461.61 38597.14 29292.63 8162.11 46490.29 430
test_040281.30 38779.17 39687.67 36193.19 29078.17 31592.98 29591.71 37575.25 41176.02 42990.31 36659.23 40796.37 34650.22 46783.63 35588.47 452
IterMVS84.88 34083.98 34287.60 36291.44 35476.03 36390.18 38492.41 35383.24 28381.06 37690.42 36466.60 34594.28 41179.46 31980.98 39592.48 383
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test81.37 38579.30 39287.58 36390.92 38174.16 38680.99 46587.68 44370.52 44876.63 42388.81 40071.21 28192.76 43460.01 45386.93 32695.83 238
EPMVS83.90 35882.70 36287.51 36490.23 40572.67 40488.62 41281.96 46481.37 33485.01 30088.34 40866.31 35094.45 40475.30 36487.12 32395.43 252
ADS-MVSNet281.66 37979.71 38787.50 36591.35 36074.19 38583.33 45888.48 43772.90 43582.24 36085.77 44064.98 36093.20 42964.57 43883.74 35295.12 262
OurMVSNet-221017-085.35 32984.64 32987.49 36690.77 38872.59 40894.01 23294.40 29384.72 24779.62 39993.17 26561.91 38196.72 31581.99 27681.16 38693.16 361
tpm284.08 35382.94 35787.48 36791.39 35871.27 42189.23 40390.37 41071.95 44284.64 30689.33 39167.30 33496.55 33475.17 36587.09 32494.63 283
RPSCF85.07 33584.27 33387.48 36792.91 30870.62 43191.69 34392.46 35276.20 40382.67 35595.22 17263.94 36897.29 27977.51 34285.80 33194.53 290
myMVS_eth3d2885.80 32085.26 31487.42 36994.73 19469.92 43690.60 37090.95 39987.21 16586.06 26490.04 37659.47 40496.02 36174.89 37093.35 21596.33 209
FE-MVSNET281.82 37579.99 38287.34 37084.74 45577.36 34292.72 30794.55 28482.09 30773.79 44286.46 43157.80 41794.45 40474.65 37273.10 43490.20 431
WBMVS84.97 33984.18 33587.34 37094.14 24871.62 42090.20 38292.35 35581.61 32984.06 32690.76 35461.82 38296.52 33578.93 32783.81 35093.89 319
miper_lstm_enhance85.27 33284.59 33087.31 37291.28 36374.63 37987.69 42994.09 30981.20 34081.36 37289.85 38374.97 22694.30 41081.03 29579.84 41093.01 368
FMVSNet581.52 38379.60 38887.27 37391.17 36677.95 32091.49 34792.26 36176.87 39576.16 42587.91 41651.67 44392.34 43767.74 42081.16 38691.52 407
USDC82.76 36581.26 37087.26 37491.17 36674.55 38089.27 40193.39 32978.26 38275.30 43392.08 30554.43 43696.63 32271.64 39185.79 33290.61 426
test-LLR85.87 31785.41 30787.25 37590.95 37771.67 41889.55 39589.88 42583.41 27784.54 30987.95 41467.25 33595.11 39781.82 28093.37 21394.97 267
test-mter84.54 34883.64 34687.25 37590.95 37771.67 41889.55 39589.88 42579.17 36284.54 30987.95 41455.56 42595.11 39781.82 28093.37 21394.97 267
JIA-IIPM81.04 38878.98 40087.25 37588.64 42473.48 39381.75 46489.61 43173.19 43282.05 36373.71 46866.07 35595.87 37071.18 39684.60 34392.41 387
TDRefinement79.81 40277.34 40887.22 37879.24 47175.48 37193.12 28492.03 36776.45 39875.01 43491.58 32649.19 45096.44 34270.22 40469.18 45089.75 436
tpmvs83.35 36482.07 36387.20 37991.07 37271.00 42788.31 41791.70 37678.91 36580.49 38487.18 42669.30 31697.08 29568.12 41983.56 35693.51 346
ppachtmachnet_test81.84 37480.07 38187.15 38088.46 42874.43 38389.04 40792.16 36375.33 41077.75 41488.99 39766.20 35295.37 39265.12 43577.60 42191.65 403
dmvs_re84.20 35283.22 35387.14 38191.83 34377.81 32790.04 38790.19 41484.70 24981.49 36889.17 39364.37 36691.13 45071.58 39285.65 33392.46 385
tpm cat181.96 37180.27 37787.01 38291.09 37171.02 42687.38 43391.53 38466.25 45880.17 38686.35 43668.22 33196.15 35769.16 41082.29 37293.86 325
test_fmvs1_n87.03 28487.04 24486.97 38389.74 41471.86 41394.55 18394.43 29078.47 37691.95 12695.50 15851.16 44593.81 41993.02 7394.56 17595.26 258
OpenMVS_ROBcopyleft74.94 1979.51 40677.03 41386.93 38487.00 44176.23 36292.33 32290.74 40568.93 45274.52 43888.23 41149.58 44896.62 32357.64 45984.29 34587.94 455
SixPastTwentyTwo83.91 35782.90 35986.92 38590.99 37570.67 43093.48 26591.99 36985.54 21477.62 41692.11 30360.59 39796.87 31176.05 35877.75 42093.20 359
ADS-MVSNet81.56 38179.78 38486.90 38691.35 36071.82 41483.33 45889.16 43572.90 43582.24 36085.77 44064.98 36093.76 42064.57 43883.74 35295.12 262
PatchT82.68 36781.27 36986.89 38790.09 40770.94 42884.06 45590.15 41574.91 41585.63 27483.57 45069.37 31294.87 40265.19 43388.50 30094.84 277
tpm84.73 34384.02 34086.87 38890.33 40268.90 43989.06 40689.94 42280.85 34385.75 27089.86 38268.54 32895.97 36477.76 33884.05 34995.75 241
Patchmatch-RL test81.67 37879.96 38386.81 38985.42 45271.23 42282.17 46387.50 44478.47 37677.19 41882.50 45870.81 28893.48 42482.66 26172.89 43795.71 245
test_vis1_n86.56 30286.49 26886.78 39088.51 42572.69 40394.68 17693.78 32279.55 35890.70 16095.31 16848.75 45193.28 42793.15 6993.99 19294.38 301
testing3-286.72 29686.71 25486.74 39196.11 11365.92 45193.39 27089.65 43089.46 7287.84 22292.79 28059.17 40997.60 23881.31 28990.72 26196.70 197
test_fmvs187.34 26787.56 23086.68 39290.59 39471.80 41594.01 23294.04 31078.30 38091.97 12495.22 17256.28 42393.71 42192.89 7494.71 16894.52 291
MDA-MVSNet-bldmvs78.85 41176.31 41686.46 39389.76 41373.88 38788.79 40990.42 40979.16 36359.18 46888.33 40960.20 39994.04 41362.00 44668.96 45191.48 410
mvs5depth80.98 39079.15 39786.45 39484.57 45673.29 39687.79 42591.67 37880.52 34682.20 36289.72 38555.14 43195.93 36673.93 38066.83 45690.12 433
tpmrst85.35 32984.99 31886.43 39590.88 38467.88 44488.71 41091.43 38780.13 35086.08 26388.80 40273.05 26196.02 36182.48 26283.40 36095.40 253
TESTMET0.1,183.74 36082.85 36086.42 39689.96 41071.21 42389.55 39587.88 44077.41 38883.37 34687.31 42256.71 42193.65 42380.62 30392.85 22994.40 300
our_test_381.93 37380.46 37586.33 39788.46 42873.48 39388.46 41591.11 39276.46 39776.69 42288.25 41066.89 34094.36 40868.75 41279.08 41691.14 418
lessismore_v086.04 39888.46 42868.78 44080.59 46773.01 44690.11 37455.39 42796.43 34375.06 36765.06 45992.90 371
TinyColmap79.76 40377.69 40685.97 39991.71 34773.12 39789.55 39590.36 41175.03 41372.03 44990.19 37046.22 46096.19 35663.11 44281.03 39188.59 451
KD-MVS_2432*160078.50 41276.02 42085.93 40086.22 44474.47 38184.80 45192.33 35679.29 36076.98 41985.92 43853.81 43993.97 41667.39 42157.42 46989.36 438
miper_refine_blended78.50 41276.02 42085.93 40086.22 44474.47 38184.80 45192.33 35679.29 36076.98 41985.92 43853.81 43993.97 41667.39 42157.42 46989.36 438
K. test v381.59 38080.15 38085.91 40289.89 41269.42 43892.57 31287.71 44285.56 21373.44 44489.71 38655.58 42495.52 38577.17 34569.76 44792.78 376
SSC-MVS3.284.60 34784.19 33485.85 40392.74 31568.07 44188.15 42093.81 32087.42 15983.76 33491.07 34362.91 37595.73 37974.56 37583.24 36193.75 335
mvsany_test185.42 32785.30 31285.77 40487.95 43775.41 37287.61 43280.97 46676.82 39688.68 20495.83 14077.44 19190.82 45285.90 20986.51 32791.08 422
MIMVSNet179.38 40777.28 40985.69 40586.35 44373.67 39091.61 34592.75 34778.11 38572.64 44788.12 41248.16 45291.97 44360.32 45077.49 42291.43 412
UWE-MVS83.69 36183.09 35485.48 40693.06 29965.27 45690.92 36386.14 44879.90 35386.26 25990.72 35757.17 42095.81 37471.03 39992.62 23795.35 256
UnsupCasMVSNet_eth80.07 39978.27 40585.46 40785.24 45372.63 40788.45 41694.87 26882.99 29071.64 45288.07 41356.34 42291.75 44573.48 38363.36 46292.01 398
CL-MVSNet_self_test81.74 37780.53 37385.36 40885.96 44672.45 41090.25 37793.07 33781.24 33879.85 39687.29 42370.93 28692.52 43566.95 42469.23 44991.11 420
MDA-MVSNet_test_wron79.21 40977.19 41185.29 40988.22 43272.77 40285.87 44290.06 41874.34 42062.62 46587.56 42066.14 35391.99 44266.90 42873.01 43591.10 421
YYNet179.22 40877.20 41085.28 41088.20 43372.66 40585.87 44290.05 42074.33 42162.70 46387.61 41966.09 35492.03 43966.94 42572.97 43691.15 417
WB-MVSnew83.77 35983.28 35085.26 41191.48 35371.03 42591.89 33587.98 43978.91 36584.78 30390.22 36869.11 32194.02 41464.70 43790.44 26490.71 424
dp81.47 38480.23 37885.17 41289.92 41165.49 45486.74 43790.10 41776.30 40181.10 37487.12 42762.81 37695.92 36768.13 41879.88 40894.09 312
UnsupCasMVSNet_bld76.23 42273.27 42685.09 41383.79 45872.92 39985.65 44593.47 32871.52 44368.84 45879.08 46349.77 44793.21 42866.81 42960.52 46689.13 446
SD_040384.71 34584.65 32784.92 41492.95 30665.95 45092.07 33493.23 33283.82 26679.03 40393.73 25073.90 24692.91 43363.02 44490.05 27195.89 234
Anonymous2023120681.03 38979.77 38684.82 41587.85 43870.26 43391.42 34892.08 36573.67 42777.75 41489.25 39262.43 37893.08 43061.50 44882.00 37791.12 419
FE-MVSNET78.19 41476.03 41984.69 41683.70 45973.31 39590.58 37190.00 42177.11 39471.91 45085.47 44255.53 42691.94 44459.69 45470.24 44488.83 448
test0.0.03 182.41 36981.69 36584.59 41788.23 43172.89 40090.24 37987.83 44183.41 27779.86 39589.78 38467.25 33588.99 46265.18 43483.42 35991.90 400
CMPMVSbinary59.16 2180.52 39379.20 39584.48 41883.98 45767.63 44789.95 39093.84 31864.79 46266.81 46091.14 34057.93 41595.17 39576.25 35588.10 30690.65 425
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet84.69 34684.79 32584.37 41991.84 34164.92 45793.70 25791.47 38666.19 45986.16 26295.28 16967.18 33793.33 42680.89 29890.42 26694.88 276
PVSNet_073.20 2077.22 41874.83 42484.37 41990.70 39271.10 42483.09 46089.67 42872.81 43773.93 44183.13 45260.79 39693.70 42268.54 41350.84 47488.30 453
LF4IMVS80.37 39679.07 39984.27 42186.64 44269.87 43789.39 40091.05 39576.38 39974.97 43590.00 37847.85 45394.25 41274.55 37680.82 39788.69 450
Anonymous2024052180.44 39579.21 39484.11 42285.75 44967.89 44392.86 30293.23 33275.61 40875.59 43287.47 42150.03 44694.33 40971.14 39781.21 38590.12 433
PM-MVS78.11 41576.12 41884.09 42383.54 46070.08 43488.97 40885.27 45579.93 35274.73 43786.43 43334.70 47193.48 42479.43 32272.06 43988.72 449
test_fmvs283.98 35484.03 33983.83 42487.16 44067.53 44893.93 23992.89 34177.62 38686.89 24393.53 25347.18 45592.02 44190.54 13486.51 32791.93 399
testgi80.94 39280.20 37983.18 42587.96 43666.29 44991.28 35390.70 40783.70 26878.12 41092.84 27551.37 44490.82 45263.34 44182.46 37092.43 386
KD-MVS_self_test80.20 39779.24 39383.07 42685.64 45065.29 45591.01 36193.93 31278.71 37476.32 42486.40 43559.20 40892.93 43272.59 38769.35 44891.00 423
testing380.46 39479.59 38983.06 42793.44 28564.64 45893.33 27285.47 45384.34 25579.93 39490.84 35044.35 46392.39 43657.06 46187.56 31692.16 396
ambc83.06 42779.99 46963.51 46277.47 47392.86 34274.34 44084.45 44728.74 47295.06 39973.06 38568.89 45290.61 426
test20.0379.95 40179.08 39882.55 42985.79 44867.74 44691.09 35991.08 39381.23 33974.48 43989.96 38061.63 38390.15 45460.08 45176.38 42889.76 435
MVStest172.91 42669.70 43182.54 43078.14 47273.05 39888.21 41986.21 44760.69 46664.70 46190.53 36046.44 45885.70 46958.78 45753.62 47188.87 447
test_vis1_rt77.96 41676.46 41582.48 43185.89 44771.74 41790.25 37778.89 47071.03 44771.30 45381.35 46042.49 46591.05 45184.55 23282.37 37184.65 458
EU-MVSNet81.32 38680.95 37182.42 43288.50 42763.67 46193.32 27391.33 38864.02 46380.57 38392.83 27661.21 39292.27 43876.34 35480.38 40491.32 413
myMVS_eth3d79.67 40478.79 40182.32 43391.92 33764.08 45989.75 39387.40 44581.72 32478.82 40587.20 42445.33 46191.29 44859.09 45687.84 31391.60 405
ttmdpeth76.55 42074.64 42582.29 43482.25 46567.81 44589.76 39285.69 45170.35 44975.76 43091.69 31946.88 45689.77 45666.16 43063.23 46389.30 440
pmmvs371.81 42968.71 43281.11 43575.86 47470.42 43286.74 43783.66 45958.95 46968.64 45980.89 46136.93 46989.52 45863.10 44363.59 46183.39 459
Syy-MVS80.07 39979.78 38480.94 43691.92 33759.93 46889.75 39387.40 44581.72 32478.82 40587.20 42466.29 35191.29 44847.06 46987.84 31391.60 405
UWE-MVS-2878.98 41078.38 40480.80 43788.18 43460.66 46790.65 36878.51 47178.84 36977.93 41390.93 34759.08 41089.02 46150.96 46690.33 26892.72 377
new-patchmatchnet76.41 42175.17 42380.13 43882.65 46459.61 46987.66 43091.08 39378.23 38369.85 45683.22 45154.76 43391.63 44764.14 44064.89 46089.16 444
mvsany_test374.95 42373.26 42780.02 43974.61 47563.16 46385.53 44678.42 47274.16 42274.89 43686.46 43136.02 47089.09 46082.39 26566.91 45587.82 456
test_fmvs377.67 41777.16 41279.22 44079.52 47061.14 46592.34 32191.64 38073.98 42478.86 40486.59 43027.38 47587.03 46488.12 17475.97 43089.50 437
DSMNet-mixed76.94 41976.29 41778.89 44183.10 46256.11 47787.78 42679.77 46860.65 46775.64 43188.71 40361.56 38688.34 46360.07 45289.29 28992.21 395
EGC-MVSNET61.97 43756.37 44278.77 44289.63 41673.50 39289.12 40582.79 4610.21 4881.24 48984.80 44539.48 46690.04 45544.13 47175.94 43172.79 470
new_pmnet72.15 42770.13 43078.20 44382.95 46365.68 45283.91 45682.40 46362.94 46564.47 46279.82 46242.85 46486.26 46857.41 46074.44 43382.65 463
MVS-HIRNet73.70 42572.20 42878.18 44491.81 34456.42 47682.94 46182.58 46255.24 47068.88 45766.48 47355.32 42995.13 39658.12 45888.42 30283.01 461
LCM-MVSNet66.00 43462.16 43977.51 44564.51 48558.29 47183.87 45790.90 40148.17 47454.69 47173.31 46916.83 48486.75 46565.47 43261.67 46587.48 457
APD_test169.04 43066.26 43677.36 44680.51 46862.79 46485.46 44783.51 46054.11 47259.14 46984.79 44623.40 47889.61 45755.22 46270.24 44479.68 467
test_f71.95 42870.87 42975.21 44774.21 47759.37 47085.07 45085.82 45065.25 46170.42 45583.13 45223.62 47682.93 47578.32 33271.94 44083.33 460
ANet_high58.88 44154.22 44672.86 44856.50 48856.67 47380.75 46686.00 44973.09 43437.39 48064.63 47622.17 47979.49 47843.51 47223.96 48282.43 464
test_vis3_rt65.12 43562.60 43772.69 44971.44 47860.71 46687.17 43465.55 48263.80 46453.22 47265.65 47514.54 48589.44 45976.65 34965.38 45867.91 473
FPMVS64.63 43662.55 43870.88 45070.80 47956.71 47284.42 45484.42 45751.78 47349.57 47381.61 45923.49 47781.48 47640.61 47676.25 42974.46 469
dmvs_testset74.57 42475.81 42270.86 45187.72 43940.47 48687.05 43677.90 47682.75 29571.15 45485.47 44267.98 33284.12 47345.26 47076.98 42788.00 454
N_pmnet68.89 43168.44 43370.23 45289.07 42128.79 49188.06 42119.50 49169.47 45171.86 45184.93 44461.24 39191.75 44554.70 46377.15 42490.15 432
testf159.54 43956.11 44369.85 45369.28 48056.61 47480.37 46776.55 47942.58 47745.68 47675.61 46411.26 48684.18 47143.20 47360.44 46768.75 471
APD_test259.54 43956.11 44369.85 45369.28 48056.61 47480.37 46776.55 47942.58 47745.68 47675.61 46411.26 48684.18 47143.20 47360.44 46768.75 471
WB-MVS67.92 43267.49 43469.21 45581.09 46641.17 48588.03 42278.00 47573.50 42962.63 46483.11 45463.94 36886.52 46625.66 48151.45 47379.94 466
PMMVS259.60 43856.40 44169.21 45568.83 48246.58 48173.02 47777.48 47755.07 47149.21 47472.95 47017.43 48380.04 47749.32 46844.33 47780.99 465
SSC-MVS67.06 43366.56 43568.56 45780.54 46740.06 48787.77 42777.37 47872.38 43961.75 46682.66 45763.37 37186.45 46724.48 48248.69 47679.16 468
Gipumacopyleft57.99 44354.91 44567.24 45888.51 42565.59 45352.21 48090.33 41243.58 47642.84 47951.18 48020.29 48185.07 47034.77 47770.45 44351.05 479
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVScopyleft47.18 2252.22 44548.46 44963.48 45945.72 49046.20 48273.41 47678.31 47341.03 47930.06 48265.68 4746.05 48883.43 47430.04 47965.86 45760.80 474
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai58.82 44258.24 44060.56 46083.13 46145.09 48482.32 46248.22 49067.61 45561.70 46769.15 47138.75 46776.05 47932.01 47841.31 47860.55 475
MVEpermissive39.65 2343.39 44738.59 45357.77 46156.52 48748.77 48055.38 47958.64 48629.33 48228.96 48352.65 4794.68 48964.62 48328.11 48033.07 48059.93 476
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method50.52 44648.47 44856.66 46252.26 48918.98 49341.51 48281.40 46510.10 48344.59 47875.01 46728.51 47368.16 48053.54 46449.31 47582.83 462
DeepMVS_CXcopyleft56.31 46374.23 47651.81 47956.67 48744.85 47548.54 47575.16 46627.87 47458.74 48540.92 47552.22 47258.39 477
kuosan53.51 44453.30 44754.13 46476.06 47345.36 48380.11 46948.36 48959.63 46854.84 47063.43 47737.41 46862.07 48420.73 48439.10 47954.96 478
E-PMN43.23 44842.29 45046.03 46565.58 48437.41 48873.51 47564.62 48333.99 48028.47 48447.87 48119.90 48267.91 48122.23 48324.45 48132.77 480
EMVS42.07 44941.12 45144.92 46663.45 48635.56 49073.65 47463.48 48433.05 48126.88 48545.45 48221.27 48067.14 48219.80 48523.02 48332.06 481
tmp_tt35.64 45039.24 45224.84 46714.87 49123.90 49262.71 47851.51 4886.58 48536.66 48162.08 47844.37 46230.34 48752.40 46522.00 48420.27 482
wuyk23d21.27 45220.48 45523.63 46868.59 48336.41 48949.57 4816.85 4929.37 4847.89 4864.46 4884.03 49031.37 48617.47 48616.07 4853.12 483
test1238.76 45411.22 4571.39 4690.85 4930.97 49485.76 4440.35 4940.54 4872.45 4888.14 4870.60 4910.48 4882.16 4880.17 4872.71 484
testmvs8.92 45311.52 4561.12 4701.06 4920.46 49586.02 4410.65 4930.62 4862.74 4879.52 4860.31 4920.45 4892.38 4870.39 4862.46 485
mmdepth0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
monomultidepth0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
test_blank0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
uanet_test0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
DCPMVS0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
cdsmvs_eth3d_5k22.14 45129.52 4540.00 4710.00 4940.00 4960.00 48395.76 1940.00 4890.00 49094.29 22175.66 2180.00 4900.00 4890.00 4880.00 486
pcd_1.5k_mvsjas6.64 4568.86 4590.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 48979.70 1550.00 4900.00 4890.00 4880.00 486
sosnet-low-res0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
sosnet0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
uncertanet0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
Regformer0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
ab-mvs-re7.82 45510.43 4580.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 49093.88 2420.00 4930.00 4900.00 4890.00 4880.00 486
uanet0.00 4570.00 4600.00 4710.00 4940.00 4960.00 4830.00 4950.00 4890.00 4900.00 4890.00 4930.00 4900.00 4890.00 4880.00 486
TestfortrainingZip97.32 10
WAC-MVS64.08 45959.14 455
FOURS198.86 485.54 7398.29 197.49 1289.79 6396.29 32
PC_three_145282.47 29997.09 2097.07 7292.72 198.04 19692.70 8099.02 1298.86 16
test_one_060198.58 1485.83 6797.44 2191.05 2396.78 2798.06 2291.45 13
eth-test20.00 494
eth-test0.00 494
ZD-MVS98.15 4086.62 3497.07 6083.63 27094.19 6496.91 7887.57 3499.26 5091.99 10598.44 57
RE-MVS-def93.68 7297.92 4984.57 9396.28 5196.76 9287.46 15693.75 7597.43 5182.94 10092.73 7697.80 9297.88 106
IU-MVS98.77 886.00 5396.84 8281.26 33797.26 1395.50 3699.13 399.03 8
test_241102_TWO97.44 2190.31 4197.62 898.07 2091.46 1299.58 1495.66 3099.12 698.98 10
test_241102_ONE98.77 885.99 5597.44 2190.26 4797.71 297.96 3192.31 699.38 35
9.1494.47 3597.79 5896.08 6997.44 2186.13 20095.10 5497.40 5388.34 2599.22 5293.25 6898.70 38
save fliter97.85 5585.63 7295.21 14096.82 8589.44 73
test_0728_THIRD90.75 2997.04 2298.05 2592.09 899.55 2095.64 3299.13 399.13 2
test072698.78 685.93 5897.19 1697.47 1790.27 4597.64 698.13 791.47 10
GSMVS96.12 222
test_part298.55 1587.22 2096.40 31
sam_mvs171.70 27796.12 222
sam_mvs70.60 291
MTGPAbinary96.97 65
test_post188.00 4239.81 48569.31 31595.53 38476.65 349
test_post10.29 48470.57 29595.91 369
patchmatchnet-post83.76 44971.53 27896.48 338
MTMP96.16 6060.64 485
gm-plane-assit89.60 41768.00 44277.28 39188.99 39797.57 24179.44 321
test9_res91.91 10998.71 3698.07 82
TEST997.53 6786.49 3894.07 22596.78 8981.61 32992.77 10096.20 10887.71 3199.12 62
test_897.49 6986.30 4694.02 23196.76 9281.86 32092.70 10496.20 10887.63 3299.02 72
agg_prior290.54 13498.68 4198.27 63
agg_prior97.38 7285.92 6096.72 9992.16 11998.97 86
test_prior485.96 5794.11 219
test_prior294.12 21787.67 15192.63 10896.39 10386.62 4491.50 11898.67 44
旧先验293.36 27171.25 44594.37 6097.13 29386.74 196
新几何293.11 286
旧先验196.79 8581.81 19595.67 20496.81 8486.69 4297.66 9896.97 178
无先验93.28 27996.26 13973.95 42599.05 6680.56 30496.59 201
原ACMM292.94 297
test22296.55 9481.70 19892.22 32795.01 25268.36 45490.20 17196.14 11580.26 14497.80 9296.05 229
testdata298.75 11578.30 333
segment_acmp87.16 39
testdata192.15 32987.94 137
plane_prior794.70 19982.74 164
plane_prior694.52 21582.75 16274.23 238
plane_prior596.22 14498.12 17688.15 17189.99 27294.63 283
plane_prior494.86 192
plane_prior382.75 16290.26 4786.91 240
plane_prior295.85 9390.81 27
plane_prior194.59 208
plane_prior82.73 16595.21 14089.66 6889.88 277
n20.00 495
nn0.00 495
door-mid85.49 452
test1196.57 111
door85.33 454
HQP5-MVS81.56 200
HQP-NCC94.17 24494.39 19988.81 10085.43 286
ACMP_Plane94.17 24494.39 19988.81 10085.43 286
BP-MVS87.11 193
HQP4-MVS85.43 28697.96 21094.51 293
HQP3-MVS96.04 16889.77 281
HQP2-MVS73.83 249
NP-MVS94.37 22882.42 17793.98 235
MDTV_nov1_ep13_2view55.91 47887.62 43173.32 43184.59 30870.33 29874.65 37295.50 250
MDTV_nov1_ep1383.56 34791.69 34969.93 43587.75 42891.54 38378.60 37584.86 30288.90 39969.54 31096.03 36070.25 40288.93 294
ACMMP++_ref87.47 317
ACMMP++88.01 309
Test By Simon80.02 146