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
CHOSEN 280x42099.12 6799.13 5199.08 13299.66 8297.89 20398.43 30499.71 1398.88 3199.62 5399.76 8596.63 11499.70 16399.46 1399.99 199.66 85
CHOSEN 1792x268899.19 5599.10 5599.45 9399.89 898.52 17999.39 16299.94 198.73 4499.11 15199.89 1095.50 14399.94 4099.50 799.97 299.89 2
DeepC-MVS98.35 299.30 4499.19 4799.64 6199.82 2999.23 8799.62 7099.55 5398.94 2699.63 5099.95 295.82 13699.94 4099.37 1699.97 299.73 63
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CSCG99.32 4299.32 2699.32 10899.85 2398.29 19199.71 4199.66 2598.11 8699.41 8899.80 6498.37 6799.96 1898.99 4999.96 499.72 69
EI-MVSNet-UG-set99.58 399.57 199.64 6199.78 3499.14 9499.60 7799.45 14599.01 1499.90 199.83 3798.98 1899.93 5599.59 199.95 599.86 5
EI-MVSNet-Vis-set99.58 399.56 399.64 6199.78 3499.15 9399.61 7699.45 14599.01 1499.89 299.82 4499.01 1199.92 6399.56 499.95 599.85 8
UGNet98.87 9598.69 10399.40 10199.22 17098.72 15999.44 13999.68 1999.24 499.18 14599.42 20092.74 23999.96 1899.34 2199.94 799.53 115
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
SD-MVS99.41 3299.52 699.05 13699.74 5399.68 3099.46 13499.52 7599.11 899.88 399.91 599.43 197.70 30798.72 8099.93 899.77 49
MVS_030599.24 5299.13 5199.57 7099.44 12699.12 9699.29 19099.55 5398.93 2899.52 6999.61 14696.36 12099.97 1099.57 299.92 999.63 96
Regformer-399.57 699.53 599.68 5099.76 4199.29 8099.58 8299.44 15399.01 1499.87 699.80 6498.97 1999.91 7299.44 1599.92 999.83 23
Regformer-499.59 299.54 499.73 4599.76 4199.41 7099.58 8299.49 10299.02 1199.88 399.80 6499.00 1799.94 4099.45 1499.92 999.84 12
APDe-MVS99.66 199.57 199.92 199.77 3899.89 199.75 3499.56 4699.02 1199.88 399.85 2699.18 599.96 1899.22 3099.92 999.90 1
HPM-MVS_fast99.51 1299.40 1499.85 1799.91 199.79 1699.76 2799.56 4697.72 12399.76 2699.75 9099.13 699.92 6399.07 4399.92 999.85 8
3Dnovator97.25 999.24 5299.05 5899.81 2799.12 19099.66 3499.84 999.74 1099.09 998.92 18499.90 795.94 13199.98 598.95 5299.92 999.79 43
MP-MVS-pluss99.37 3799.20 4699.88 499.90 399.87 299.30 18699.52 7597.18 16799.60 5699.79 7298.79 3599.95 3398.83 6899.91 1599.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_Plus99.47 2099.34 2499.88 499.87 1599.86 399.47 13199.48 11198.05 9899.76 2699.86 2298.82 3299.93 5598.82 7199.91 1599.84 12
HPM-MVS99.42 2999.28 3899.83 2299.90 399.72 2599.81 1599.54 6197.59 13199.68 3499.63 13898.91 2699.94 4098.58 9699.91 1599.84 12
114514_t98.93 9298.67 10599.72 4799.85 2399.53 5599.62 7099.59 3792.65 29799.71 2999.78 7798.06 7899.90 8498.84 6699.91 1599.74 58
CP-MVS99.45 2299.32 2699.85 1799.83 2899.75 2199.69 4499.52 7598.07 9399.53 6799.63 13898.93 2599.97 1098.74 7599.91 1599.83 23
PHI-MVS99.30 4499.17 4999.70 4999.56 10599.52 5899.58 8299.80 897.12 17399.62 5399.73 9798.58 5599.90 8498.61 9399.91 1599.68 81
DeepPCF-MVS98.18 398.81 10799.37 1797.12 27999.60 9791.75 30698.61 29799.44 15399.35 199.83 1199.85 2698.70 4899.81 12499.02 4799.91 1599.81 34
MPTG99.49 1399.36 1999.89 299.90 399.86 399.36 17399.47 12598.79 4099.68 3499.81 5398.43 6199.97 1098.88 5699.90 2299.83 23
MTAPA99.52 1199.39 1599.89 299.90 399.86 399.66 5499.47 12598.79 4099.68 3499.81 5398.43 6199.97 1098.88 5699.90 2299.83 23
UA-Net99.42 2999.29 3699.80 2999.62 9199.55 5199.50 11699.70 1598.79 4099.77 2399.96 197.45 9199.96 1898.92 5499.90 2299.89 2
jason99.13 6299.03 6399.45 9399.46 12198.87 13199.12 22899.26 23598.03 10199.79 1899.65 12897.02 10299.85 10399.02 4799.90 2299.65 88
jason: jason.
SteuartSystems-ACMMP99.54 799.42 1199.87 699.82 2999.81 1199.59 7999.51 8498.62 4999.79 1899.83 3799.28 399.97 1098.48 10899.90 2299.84 12
Skip Steuart: Steuart Systems R&D Blog.
DP-MVS99.16 6098.95 7599.78 3399.77 3899.53 5599.41 15599.50 9797.03 18299.04 16599.88 1497.39 9299.92 6398.66 8699.90 2299.87 4
MSDG98.98 8998.80 9299.53 7899.76 4199.19 8898.75 28999.55 5397.25 16199.47 7799.77 8297.82 8399.87 9796.93 22499.90 2299.54 111
COLMAP_ROBcopyleft97.56 698.86 9898.75 9899.17 12699.88 1198.53 17699.34 17999.59 3797.55 13698.70 21399.89 1095.83 13599.90 8498.10 13399.90 2299.08 163
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MVS_test032698.79 11198.62 11399.28 11899.00 21098.41 18999.01 25899.09 25499.23 598.67 21699.68 11694.31 20399.95 3398.74 7599.89 3099.46 132
mPP-MVS99.44 2599.30 3399.86 1299.88 1199.79 1699.69 4499.48 11198.12 8499.50 7299.75 9098.78 3699.97 1098.57 9899.89 3099.83 23
MVS_111021_LR99.41 3299.33 2599.65 5699.77 3899.51 6098.94 27699.85 698.82 3699.65 4899.74 9498.51 5699.80 12798.83 6899.89 3099.64 93
TSAR-MVS + MP.99.58 399.50 799.81 2799.91 199.66 3499.63 6799.39 17598.91 3099.78 2299.85 2699.36 299.94 4098.84 6699.88 3399.82 30
abl_699.44 2599.31 3199.83 2299.85 2399.75 2199.66 5499.59 3798.13 8299.82 1499.81 5398.60 5499.96 1898.46 11199.88 3399.79 43
QAPM98.67 12298.30 13599.80 2999.20 17399.67 3299.77 2499.72 1194.74 26698.73 20599.90 795.78 13799.98 596.96 22199.88 3399.76 52
MVS_111021_HR99.41 3299.32 2699.66 5399.72 6199.47 6498.95 27499.85 698.82 3699.54 6699.73 9798.51 5699.74 13998.91 5599.88 3399.77 49
HFP-MVS99.49 1399.37 1799.86 1299.87 1599.80 1299.66 5499.67 2298.15 8099.68 3499.69 11199.06 899.96 1898.69 8399.87 3799.84 12
region2R99.48 1799.35 2299.87 699.88 1199.80 1299.65 6499.66 2598.13 8299.66 4599.68 11698.96 2099.96 1898.62 9199.87 3799.84 12
#test#99.43 2799.29 3699.86 1299.87 1599.80 1299.55 10099.67 2297.83 11299.68 3499.69 11199.06 899.96 1898.39 11499.87 3799.84 12
Regformer-199.53 999.47 899.72 4799.71 6399.44 6799.49 12299.46 13498.95 2599.83 1199.76 8599.01 1199.93 5599.17 3599.87 3799.80 39
Regformer-299.54 799.47 899.75 3899.71 6399.52 5899.49 12299.49 10298.94 2699.83 1199.76 8599.01 1199.94 4099.15 3799.87 3799.80 39
ACMMPR99.49 1399.36 1999.86 1299.87 1599.79 1699.66 5499.67 2298.15 8099.67 4099.69 11198.95 2399.96 1898.69 8399.87 3799.84 12
MP-MVScopyleft99.33 4199.15 5099.87 699.88 1199.82 1099.66 5499.46 13498.09 8999.48 7699.74 9498.29 7099.96 1897.93 14899.87 3799.82 30
PGM-MVS99.45 2299.31 3199.86 1299.87 1599.78 2099.58 8299.65 3097.84 11199.71 2999.80 6499.12 799.97 1098.33 12199.87 3799.83 23
DeepC-MVS_fast98.69 199.49 1399.39 1599.77 3599.63 8799.59 4699.36 17399.46 13499.07 1099.79 1899.82 4498.85 3099.92 6398.68 8599.87 3799.82 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TAPA-MVS97.07 1597.74 20697.34 22198.94 14899.70 6897.53 21199.25 20699.51 8491.90 30199.30 11199.63 13898.78 3699.64 17488.09 30899.87 3799.65 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
XVS99.53 999.42 1199.87 699.85 2399.83 799.69 4499.68 1998.98 2099.37 9799.74 9498.81 3399.94 4098.79 7299.86 4799.84 12
X-MVStestdata96.55 24895.45 26999.87 699.85 2399.83 799.69 4499.68 1998.98 2099.37 9764.01 33398.81 3399.94 4098.79 7299.86 4799.84 12
APD-MVScopyleft99.27 4999.08 5699.84 2199.75 4799.79 1699.50 11699.50 9797.16 16999.77 2399.82 4498.78 3699.94 4097.56 18199.86 4799.80 39
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
3Dnovator+97.12 1399.18 5798.97 7199.82 2499.17 18299.68 3099.81 1599.51 8499.20 698.72 20699.89 1095.68 14099.97 1098.86 6399.86 4799.81 34
MVSFormer99.17 5899.12 5399.29 11699.51 11098.94 12399.88 199.46 13497.55 13699.80 1699.65 12897.39 9299.28 22999.03 4599.85 5199.65 88
lupinMVS99.13 6299.01 6799.46 9299.51 11098.94 12399.05 24599.16 24697.86 10799.80 1699.56 16097.39 9299.86 10098.94 5399.85 5199.58 107
PVSNet_Blended99.08 7798.97 7199.42 10099.76 4198.79 15498.78 28699.91 396.74 19599.67 4099.49 17997.53 8999.88 9598.98 5099.85 5199.60 101
MVS-HIRNet95.75 26995.16 27397.51 27399.30 15593.69 29798.88 28195.78 32485.09 31698.78 20192.65 32091.29 26499.37 20694.85 27199.85 5199.46 132
PCF-MVS97.08 1497.66 21897.06 23599.47 9099.61 9599.09 9998.04 31599.25 23791.24 30498.51 23199.70 10694.55 19399.91 7292.76 29499.85 5199.42 140
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MSLP-MVS++99.46 2199.47 899.44 9699.60 9799.16 9199.41 15599.71 1398.98 2099.45 8099.78 7799.19 499.54 18899.28 2699.84 5699.63 96
DELS-MVS99.48 1799.42 1199.65 5699.72 6199.40 7299.05 24599.66 2599.14 799.57 6299.80 6498.46 5999.94 4099.57 299.84 5699.60 101
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
CPTT-MVS99.11 7198.90 8099.74 4399.80 3299.46 6599.59 7999.49 10297.03 18299.63 5099.69 11197.27 9799.96 1897.82 15699.84 5699.81 34
LS3D99.27 4999.12 5399.74 4399.18 17799.75 2199.56 9599.57 4398.45 5999.49 7599.85 2697.77 8599.94 4098.33 12199.84 5699.52 116
AllTest98.87 9598.72 9999.31 10999.86 2098.48 18499.56 9599.61 3297.85 10999.36 10199.85 2695.95 12999.85 10396.66 23799.83 6099.59 105
TestCases99.31 10999.86 2098.48 18499.61 3297.85 10999.36 10199.85 2695.95 12999.85 10396.66 23799.83 6099.59 105
CDPH-MVS99.13 6298.91 7999.80 2999.75 4799.71 2699.15 22499.41 16596.60 20699.60 5699.55 16398.83 3199.90 8497.48 18999.83 6099.78 47
ACMMPcopyleft99.45 2299.32 2699.82 2499.89 899.67 3299.62 7099.69 1898.12 8499.63 5099.84 3598.73 4699.96 1898.55 10399.83 6099.81 34
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
PVSNet_Blended_VisFu99.36 3899.28 3899.61 6599.86 2099.07 10199.47 13199.93 297.66 12999.71 2999.86 2297.73 8699.96 1899.47 1299.82 6499.79 43
APD-MVS_3200maxsize99.48 1799.35 2299.85 1799.76 4199.83 799.63 6799.54 6198.36 6599.79 1899.82 4498.86 2999.95 3398.62 9199.81 6599.78 47
OMC-MVS99.08 7799.04 6199.20 12599.67 7298.22 19499.28 19499.52 7598.07 9399.66 4599.81 5397.79 8499.78 13297.79 15899.81 6599.60 101
MS-PatchMatch97.24 23997.32 22496.99 28098.45 28593.51 29998.82 28499.32 21597.41 14998.13 24999.30 23788.99 28499.56 18595.68 25799.80 6797.90 293
HPM-MVS++99.39 3699.23 4599.87 699.75 4799.84 699.43 14499.51 8498.68 4799.27 12399.53 16798.64 5299.96 1898.44 11399.80 6799.79 43
CNVR-MVS99.42 2999.30 3399.78 3399.62 9199.71 2699.26 20599.52 7598.82 3699.39 9399.71 10298.96 2099.85 10398.59 9599.80 6799.77 49
MG-MVS99.13 6299.02 6699.45 9399.57 10298.63 16799.07 23999.34 20198.99 1999.61 5599.82 4497.98 8099.87 9797.00 21799.80 6799.85 8
MVS_dtu98.77 11498.60 11999.30 11298.95 22498.47 18699.08 23899.27 23399.26 398.94 18199.71 10293.54 22699.96 1898.86 6399.79 7199.45 136
MVP-Stereo97.81 19597.75 18297.99 25197.53 29796.60 24998.96 27098.85 28397.22 16597.23 26999.36 22295.28 14899.46 19295.51 26099.78 7297.92 292
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
API-MVS99.04 8199.03 6399.06 13499.40 13599.31 7999.55 10099.56 4698.54 5399.33 10899.39 21198.76 4199.78 13296.98 21999.78 7298.07 283
HSP-MVS99.41 3299.26 4399.85 1799.89 899.80 1299.67 5199.37 18898.70 4599.77 2399.49 17998.21 7399.95 3398.46 11199.77 7499.81 34
AdaColmapbinary99.01 8798.80 9299.66 5399.56 10599.54 5299.18 21999.70 1598.18 7999.35 10499.63 13896.32 12199.90 8497.48 18999.77 7499.55 109
OpenMVScopyleft96.50 1698.47 12998.12 14399.52 8299.04 20599.53 5599.82 1399.72 1194.56 27298.08 25199.88 1494.73 18599.98 597.47 19199.76 7699.06 168
MCST-MVS99.43 2799.30 3399.82 2499.79 3399.74 2499.29 19099.40 17298.79 4099.52 6999.62 14398.91 2699.90 8498.64 8899.75 7799.82 30
CNLPA99.14 6198.99 6899.59 6799.58 10099.41 7099.16 22199.44 15398.45 5999.19 14399.49 17998.08 7799.89 9297.73 16699.75 7799.48 125
test_prior399.21 5499.05 5899.68 5099.67 7299.48 6298.96 27099.56 4698.34 6699.01 16899.52 17198.68 4999.83 11597.96 14599.74 7999.74 58
test_prior298.96 27098.34 6699.01 16899.52 17198.68 4997.96 14599.74 79
test1299.75 3899.64 8599.61 4299.29 22199.21 13798.38 6599.89 9299.74 7999.74 58
agg_prior297.21 20499.73 8299.75 53
test9_res97.49 18899.72 8399.75 53
train_agg99.02 8498.77 9599.77 3599.67 7299.65 3799.05 24599.41 16596.28 22998.95 17999.49 17998.76 4199.91 7297.63 17599.72 8399.75 53
agg_prior398.97 9198.71 10199.75 3899.67 7299.60 4499.04 25099.41 16595.93 25198.87 19099.48 18598.61 5399.91 7297.63 17599.72 8399.75 53
agg_prior199.01 8798.76 9799.76 3799.67 7299.62 4098.99 26199.40 17296.26 23298.87 19099.49 17998.77 3999.91 7297.69 17299.72 8399.75 53
EPNet98.86 9898.71 10199.30 11297.20 30498.18 19599.62 7098.91 27799.28 298.63 22599.81 5395.96 12899.99 199.24 2999.72 8399.73 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DP-MVS Recon99.12 6798.95 7599.65 5699.74 5399.70 2899.27 19799.57 4396.40 22499.42 8699.68 11698.75 4499.80 12797.98 14499.72 8399.44 137
PVSNet96.02 1798.85 10498.84 8898.89 16499.73 5897.28 21498.32 30899.60 3497.86 10799.50 7299.57 15896.75 11199.86 10098.56 10199.70 8999.54 111
原ACMM199.65 5699.73 5899.33 7599.47 12597.46 14299.12 14999.66 12798.67 5199.91 7297.70 17199.69 9099.71 76
test22299.75 4799.49 6198.91 27999.49 10296.42 22199.34 10799.65 12898.28 7199.69 9099.72 69
F-COLMAP99.19 5599.04 6199.64 6199.78 3499.27 8399.42 15199.54 6197.29 15899.41 8899.59 15198.42 6499.93 5598.19 12799.69 9099.73 63
旧先验199.74 5399.59 4699.54 6199.69 11198.47 5899.68 9399.73 63
112199.09 7598.87 8499.75 3899.74 5399.60 4499.27 19799.48 11196.82 19399.25 12799.65 12898.38 6599.93 5597.53 18499.67 9499.73 63
PS-MVSNAJ99.32 4299.32 2699.30 11299.57 10298.94 12398.97 26899.46 13498.92 2999.71 2999.24 24599.01 1199.98 599.35 1799.66 9598.97 177
新几何199.75 3899.75 4799.59 4699.54 6196.76 19499.29 11599.64 13498.43 6199.94 4096.92 22599.66 9599.72 69
EPNet_dtu98.03 16897.96 15698.23 23598.27 28895.54 27199.23 20998.75 28899.02 1197.82 26199.71 10296.11 12799.48 19093.04 29299.65 9799.69 77
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testdata99.54 7499.75 4798.95 12099.51 8497.07 17899.43 8499.70 10698.87 2899.94 4097.76 16299.64 9899.72 69
PatchMatch-RL98.84 10698.62 11399.52 8299.71 6399.28 8199.06 24399.77 997.74 12199.50 7299.53 16795.41 14599.84 10897.17 20999.64 9899.44 137
NCCC99.34 4099.19 4799.79 3299.61 9599.65 3799.30 18699.48 11198.86 3299.21 13799.63 13898.72 4799.90 8498.25 12599.63 10099.80 39
PLCcopyleft97.94 499.02 8498.85 8799.53 7899.66 8299.01 10899.24 20899.52 7596.85 19199.27 12399.48 18598.25 7299.91 7297.76 16299.62 10199.65 88
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-RMVSNet98.41 13498.08 14799.40 10199.41 13198.83 13899.30 18698.77 28797.70 12698.94 18199.65 12892.91 23599.74 13996.52 24199.55 10299.64 93
MAR-MVS98.86 9898.63 11099.54 7499.37 14099.66 3499.45 13599.54 6196.61 20499.01 16899.40 20797.09 10099.86 10097.68 17499.53 10399.10 158
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
Fast-Effi-MVS+-dtu98.77 11498.83 9198.60 19599.41 13196.99 23399.52 10799.49 10298.11 8699.24 12899.34 22996.96 10499.79 13097.95 14799.45 10499.02 172
PAPM_NR99.04 8198.84 8899.66 5399.74 5399.44 6799.39 16299.38 18197.70 12699.28 11999.28 24098.34 6899.85 10396.96 22199.45 10499.69 77
TSAR-MVS + GP.99.36 3899.36 1999.36 10399.67 7298.61 17299.07 23999.33 20999.00 1899.82 1499.81 5399.06 899.84 10899.09 4199.42 10699.65 88
Vis-MVSNetpermissive99.12 6798.97 7199.56 7399.78 3499.10 9899.68 4999.66 2598.49 5699.86 799.87 1994.77 18299.84 10899.19 3299.41 10799.74 58
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+-dtu98.78 11298.89 8298.47 20999.33 14796.91 23999.57 8899.30 21898.47 5799.41 8898.99 26596.78 10899.74 13998.73 7899.38 10898.74 196
test-LLR98.06 15997.90 16098.55 20298.79 25497.10 22298.67 29397.75 31697.34 15398.61 22898.85 27294.45 19799.45 19397.25 20299.38 10899.10 158
TESTMET0.1,197.55 22197.27 22998.40 21798.93 23396.53 25098.67 29397.61 32096.96 18598.64 22499.28 24088.63 29199.45 19397.30 20199.38 10899.21 153
test-mter97.49 23097.13 23398.55 20298.79 25497.10 22298.67 29397.75 31696.65 20198.61 22898.85 27288.23 29699.45 19397.25 20299.38 10899.10 158
PAPR98.63 12698.34 13199.51 8499.40 13599.03 10598.80 28599.36 18996.33 22699.00 17599.12 25698.46 5999.84 10895.23 26699.37 11299.66 85
xiu_mvs_v1_base_debu99.29 4699.27 4099.34 10499.63 8798.97 11599.12 22899.51 8498.86 3299.84 899.47 18998.18 7499.99 199.50 799.31 11399.08 163
xiu_mvs_v1_base99.29 4699.27 4099.34 10499.63 8798.97 11599.12 22899.51 8498.86 3299.84 899.47 18998.18 7499.99 199.50 799.31 11399.08 163
xiu_mvs_v1_base_debi99.29 4699.27 4099.34 10499.63 8798.97 11599.12 22899.51 8498.86 3299.84 899.47 18998.18 7499.99 199.50 799.31 11399.08 163
131498.68 12198.54 12399.11 13198.89 24198.65 16599.27 19799.49 10296.89 18997.99 25699.56 16097.72 8799.83 11597.74 16599.27 11698.84 183
xiu_mvs_v2_base99.26 5199.25 4499.29 11699.53 10798.91 12899.02 25499.45 14598.80 3999.71 2999.26 24398.94 2499.98 599.34 2199.23 11798.98 176
PatchmatchNetpermissive98.31 13998.36 12998.19 24099.16 18495.32 27699.27 19798.92 27497.37 15299.37 9799.58 15494.90 17099.70 16397.43 19599.21 11899.54 111
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test198.16 14998.14 14198.22 23799.30 15595.55 26999.07 23998.97 26897.57 13499.43 8499.60 14992.72 24099.60 18297.38 19799.20 11999.50 122
sss99.17 5899.05 5899.53 7899.62 9198.97 11599.36 17399.62 3197.83 11299.67 4099.65 12897.37 9599.95 3399.19 3299.19 12099.68 81
MVS97.28 23796.55 24499.48 8798.78 25898.95 12099.27 19799.39 17583.53 31798.08 25199.54 16696.97 10399.87 9794.23 28599.16 12199.63 96
BH-untuned98.42 13398.36 12998.59 19699.49 11696.70 24599.27 19799.13 25097.24 16398.80 19999.38 21295.75 13899.74 13997.07 21499.16 12199.33 147
IS-MVSNet99.05 8098.87 8499.57 7099.73 5899.32 7699.75 3499.20 24298.02 10299.56 6399.86 2296.54 11699.67 16898.09 13499.13 12399.73 63
Patchmatch-test97.93 18097.65 18898.77 18799.18 17797.07 22699.03 25199.14 24996.16 24198.74 20499.57 15894.56 19299.72 15193.36 29199.11 12499.52 116
Vis-MVSNet (Re-imp)98.87 9598.72 9999.31 10999.71 6398.88 13099.80 1999.44 15397.91 10599.36 10199.78 7795.49 14499.43 20197.91 14999.11 12499.62 99
RPSCF98.22 14398.62 11396.99 28099.82 2991.58 30799.72 3999.44 15396.61 20499.66 4599.89 1095.92 13299.82 12097.46 19299.10 12699.57 108
gg-mvs-nofinetune96.17 26495.32 27198.73 18998.79 25498.14 19799.38 16794.09 32891.07 30698.07 25491.04 32489.62 28099.35 21396.75 23199.09 12798.68 214
EPMVS97.82 19497.65 18898.35 22098.88 24295.98 26399.49 12294.71 32797.57 13499.26 12699.48 18592.46 25399.71 15797.87 15299.08 12899.35 145
MVS_Test99.10 7498.97 7199.48 8799.49 11699.14 9499.67 5199.34 20197.31 15699.58 6099.76 8597.65 8899.82 12098.87 6099.07 12999.46 132
ADS-MVSNet298.02 17098.07 14997.87 25899.33 14795.19 27999.23 20999.08 25596.24 23499.10 15499.67 12194.11 21198.93 27896.81 22899.05 13099.48 125
ADS-MVSNet98.20 14698.08 14798.56 20099.33 14796.48 25299.23 20999.15 24796.24 23499.10 15499.67 12194.11 21199.71 15796.81 22899.05 13099.48 125
mvs-test198.86 9898.84 8898.89 16499.33 14797.77 20799.44 13999.30 21898.47 5799.10 15499.43 19896.78 10899.95 3398.73 7899.02 13298.96 179
HyFIR lowres test99.11 7198.92 7799.65 5699.90 399.37 7399.02 25499.91 397.67 12899.59 5999.75 9095.90 13399.73 14799.53 599.02 13299.86 5
LCM-MVSNet-Re97.83 19198.15 14096.87 28499.30 15592.25 30599.59 7998.26 30797.43 14696.20 28199.13 25396.27 12398.73 28398.17 12998.99 13499.64 93
mvs_anonymous99.03 8398.99 6899.16 12799.38 13898.52 17999.51 11199.38 18197.79 11599.38 9599.81 5397.30 9699.45 19399.35 1798.99 13499.51 119
test_normal97.44 23296.77 24299.44 9697.75 29699.00 11099.10 23698.64 29797.71 12493.93 30098.82 27587.39 30199.83 11598.61 9398.97 13699.49 123
diffmvs98.72 11898.49 12499.43 9999.48 11999.19 8899.62 7099.42 16295.58 25799.37 9799.67 12196.14 12699.74 13998.14 13198.96 13799.37 144
Test495.05 27693.67 28499.22 12496.07 30698.94 12399.20 21799.27 23397.71 12489.96 31597.59 30666.18 32399.25 23898.06 14198.96 13799.47 129
EPP-MVSNet99.13 6298.99 6899.53 7899.65 8499.06 10299.81 1599.33 20997.43 14699.60 5699.88 1497.14 9999.84 10899.13 3898.94 13999.69 77
MIMVSNet97.73 20797.45 20598.57 19899.45 12597.50 21299.02 25498.98 26796.11 24699.41 8899.14 25290.28 27198.74 28295.74 25598.93 14099.47 129
TAMVS99.12 6799.08 5699.24 12199.46 12198.55 17499.51 11199.46 13498.09 8999.45 8099.82 4498.34 6899.51 18998.70 8198.93 14099.67 84
CDS-MVSNet99.09 7599.03 6399.25 12099.42 12898.73 15899.45 13599.46 13498.11 8699.46 7999.77 8298.01 7999.37 20698.70 8198.92 14299.66 85
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPM97.59 22097.09 23499.07 13399.06 20198.26 19398.30 30999.10 25294.88 26398.08 25199.34 22996.27 12399.64 17489.87 30298.92 14299.31 148
DI_MVS_plusplus_test97.45 23196.79 24099.44 9697.76 29599.04 10499.21 21598.61 30097.74 12194.01 29798.83 27487.38 30299.83 11598.63 8998.90 14499.44 137
XVG-OURS-SEG-HR98.69 12098.62 11398.89 16499.71 6397.74 20899.12 22899.54 6198.44 6299.42 8699.71 10294.20 20699.92 6398.54 10598.90 14499.00 173
PMMVS98.80 11098.62 11399.34 10499.27 16398.70 16098.76 28899.31 21697.34 15399.21 13799.07 25897.20 9899.82 12098.56 10198.87 14699.52 116
DSMNet-mixed97.25 23897.35 21896.95 28297.84 29393.61 29899.57 8896.63 32396.13 24598.87 19098.61 28394.59 19197.70 30795.08 26898.86 14799.55 109
XVG-OURS98.73 11798.68 10498.88 16799.70 6897.73 20998.92 27799.55 5398.52 5599.45 8099.84 3595.27 14999.91 7298.08 13898.84 14899.00 173
Fast-Effi-MVS+98.70 11998.43 12699.51 8499.51 11099.28 8199.52 10799.47 12596.11 24699.01 16899.34 22996.20 12599.84 10897.88 15198.82 14999.39 143
ab-mvs98.86 9898.63 11099.54 7499.64 8599.19 8899.44 13999.54 6197.77 11799.30 11199.81 5394.20 20699.93 5599.17 3598.82 14999.49 123
MDTV_nov1_ep1398.32 13399.11 19294.44 28899.27 19798.74 29197.51 14099.40 9299.62 14394.78 17899.76 13797.59 17798.81 151
Test_1112_low_res98.89 9498.66 10899.57 7099.69 7098.95 12099.03 25199.47 12596.98 18499.15 14699.23 24696.77 11099.89 9298.83 6898.78 15299.86 5
1112_ss98.98 8998.77 9599.59 6799.68 7199.02 10699.25 20699.48 11197.23 16499.13 14799.58 15496.93 10599.90 8498.87 6098.78 15299.84 12
PatchT97.03 24496.44 24598.79 18598.99 21298.34 19099.16 22199.07 25892.13 29899.52 6997.31 31194.54 19498.98 27088.54 30698.73 15499.03 170
tpmrst98.33 13898.48 12597.90 25799.16 18494.78 28499.31 18499.11 25197.27 15999.45 8099.59 15195.33 14699.84 10898.48 10898.61 15599.09 162
BH-w/o98.00 17497.89 16498.32 22299.35 14396.20 26199.01 25898.90 27996.42 22198.38 23899.00 26495.26 15099.72 15196.06 24998.61 15599.03 170
cascas97.69 21297.43 21198.48 20798.60 27897.30 21398.18 31399.39 17592.96 29498.41 23698.78 27993.77 22299.27 23298.16 13098.61 15598.86 182
CR-MVSNet98.17 14897.93 15998.87 17199.18 17798.49 18299.22 21399.33 20996.96 18599.56 6399.38 21294.33 20199.00 26894.83 27298.58 15899.14 155
RPMNet96.61 24795.85 25598.87 17199.18 17798.49 18299.22 21399.08 25588.72 31399.56 6397.38 30994.08 21399.00 26886.87 31398.58 15899.14 155
dp97.75 20497.80 16997.59 27199.10 19593.71 29699.32 18198.88 28196.48 21799.08 15899.55 16392.67 24499.82 12096.52 24198.58 15899.24 152
CVMVSNet98.57 12798.67 10598.30 22499.35 14395.59 26899.50 11699.55 5398.60 5199.39 9399.83 3794.48 19699.45 19398.75 7498.56 16199.85 8
Effi-MVS+98.81 10798.59 12099.48 8799.46 12199.12 9698.08 31499.50 9797.50 14199.38 9599.41 20396.37 11999.81 12499.11 4098.54 16299.51 119
testgi97.65 21997.50 19998.13 24399.36 14296.45 25399.42 15199.48 11197.76 11897.87 25999.45 19691.09 26598.81 28194.53 27698.52 16399.13 157
tpm cat197.39 23497.36 21697.50 27499.17 18293.73 29499.43 14499.31 21691.27 30398.71 20799.08 25794.31 20399.77 13496.41 24598.50 16499.00 173
WTY-MVS99.06 7998.88 8399.61 6599.62 9199.16 9199.37 16999.56 4698.04 9999.53 6799.62 14396.84 10699.94 4098.85 6598.49 16599.72 69
testus94.61 27995.30 27292.54 30196.44 30584.18 31698.36 30599.03 26394.18 28196.49 27898.57 28588.74 28695.09 31987.41 31098.45 16698.36 278
tpmvs97.98 17598.02 15297.84 26199.04 20594.73 28699.31 18499.20 24296.10 24998.76 20399.42 20094.94 16599.81 12496.97 22098.45 16698.97 177
LP97.04 24396.80 23997.77 26698.90 23895.23 27798.97 26899.06 26094.02 28298.09 25099.41 20393.88 21898.82 28090.46 30098.42 16899.26 151
LFMVS97.90 18597.35 21899.54 7499.52 10899.01 10899.39 16298.24 30897.10 17799.65 4899.79 7284.79 31199.91 7299.28 2698.38 16999.69 77
GA-MVS97.85 18897.47 20399.00 14199.38 13897.99 20198.57 29999.15 24797.04 18198.90 18799.30 23789.83 27799.38 20396.70 23498.33 17099.62 99
VDD-MVS97.73 20797.35 21898.88 16799.47 12097.12 22199.34 17998.85 28398.19 7699.67 4099.85 2682.98 31599.92 6399.49 1198.32 17199.60 101
GG-mvs-BLEND98.45 21198.55 28198.16 19699.43 14493.68 32997.23 26998.46 28789.30 28299.22 24495.43 26298.22 17297.98 288
HY-MVS97.30 798.85 10498.64 10999.47 9099.42 12899.08 10099.62 7099.36 18997.39 15199.28 11999.68 11696.44 11799.92 6398.37 11798.22 17299.40 142
VNet99.11 7198.90 8099.73 4599.52 10899.56 4999.41 15599.39 17599.01 1499.74 2899.78 7795.56 14199.92 6399.52 698.18 17499.72 69
PatchFormer-LS_test98.01 17398.05 15097.87 25899.15 18794.76 28599.42 15198.93 27297.12 17398.84 19698.59 28493.74 22599.80 12798.55 10398.17 17599.06 168
DWT-MVSNet_test97.53 22397.40 21497.93 25499.03 20794.86 28399.57 8898.63 29896.59 20898.36 24098.79 27789.32 28199.74 13998.14 13198.16 17699.20 154
VDDNet97.55 22197.02 23699.16 12799.49 11698.12 19999.38 16799.30 21895.35 25999.68 3499.90 782.62 31799.93 5599.31 2498.13 17799.42 140
alignmvs98.81 10798.56 12299.58 6999.43 12799.42 6999.51 11198.96 27098.61 5099.35 10498.92 27194.78 17899.77 13499.35 1798.11 17899.54 111
tpm297.44 23297.34 22197.74 26899.15 18794.36 28999.45 13598.94 27193.45 29298.90 18799.44 19791.35 26399.59 18497.31 20098.07 17999.29 149
tpmp4_e2397.34 23597.29 22797.52 27299.25 16793.73 29499.58 8299.19 24594.00 28398.20 24699.41 20390.74 26999.74 13997.13 21098.07 17999.07 167
test235694.07 28594.46 28092.89 29995.18 31086.13 31497.60 31999.06 26093.61 28896.15 28498.28 28885.60 30893.95 32186.68 31498.00 18198.59 261
JIA-IIPM97.50 22897.02 23698.93 15198.73 26497.80 20699.30 18698.97 26891.73 30298.91 18594.86 31895.10 15799.71 15797.58 17897.98 18299.28 150
CostFormer97.72 20997.73 18397.71 26999.15 18794.02 29299.54 10399.02 26494.67 26799.04 16599.35 22692.35 25599.77 13498.50 10797.94 18399.34 146
canonicalmvs99.02 8498.86 8699.51 8499.42 12899.32 7699.80 1999.48 11198.63 4899.31 11098.81 27697.09 10099.75 13899.27 2897.90 18499.47 129
OpenMVS_ROBcopyleft92.34 2094.38 28293.70 28396.41 29097.38 29993.17 30099.06 24398.75 28886.58 31494.84 29098.26 28981.53 31899.32 22089.01 30597.87 18596.76 309
TR-MVS97.76 20197.41 21398.82 18199.06 20197.87 20498.87 28298.56 30296.63 20398.68 21599.22 24792.49 24999.65 17295.40 26397.79 18698.95 180
DeepMVS_CXcopyleft93.34 29799.29 15882.27 32199.22 24085.15 31596.33 28099.05 26190.97 26799.73 14793.57 28997.77 18798.01 287
CLD-MVS98.16 14998.10 14498.33 22199.29 15896.82 24298.75 28999.44 15397.83 11299.13 14799.55 16392.92 23399.67 16898.32 12397.69 18898.48 270
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP_MVS98.27 14298.22 13998.44 21499.29 15896.97 23599.39 16299.47 12598.97 2399.11 15199.61 14692.71 24199.69 16697.78 15997.63 18998.67 225
plane_prior599.47 12599.69 16697.78 15997.63 18998.67 225
test_djsdf98.67 12298.57 12198.98 14398.70 26998.91 12899.88 199.46 13497.55 13699.22 13599.88 1495.73 13999.28 22999.03 4597.62 19198.75 193
anonymousdsp98.44 13198.28 13698.94 14898.50 28398.96 11999.77 2499.50 9797.07 17898.87 19099.77 8294.76 18399.28 22998.66 8697.60 19298.57 265
plane_prior96.97 23599.21 21598.45 5997.60 192
HQP3-MVS99.39 17597.58 194
HQP-MVS98.02 17097.90 16098.37 21999.19 17496.83 24098.98 26599.39 17598.24 7298.66 21799.40 20792.47 25099.64 17497.19 20697.58 19498.64 241
EI-MVSNet98.67 12298.67 10598.68 19199.35 14397.97 20299.50 11699.38 18196.93 18899.20 14099.83 3797.87 8199.36 21098.38 11697.56 19698.71 200
MVSTER98.49 12898.32 13399.00 14199.35 14399.02 10699.54 10399.38 18197.41 14999.20 14099.73 9793.86 22099.36 21098.87 6097.56 19698.62 249
OPM-MVS98.19 14798.10 14498.45 21198.88 24297.07 22699.28 19499.38 18198.57 5299.22 13599.81 5392.12 25699.66 17098.08 13897.54 19898.61 258
LPG-MVS_test98.22 14398.13 14298.49 20599.33 14797.05 22899.58 8299.55 5397.46 14299.24 12899.83 3792.58 24699.72 15198.09 13497.51 19998.68 214
LGP-MVS_train98.49 20599.33 14797.05 22899.55 5397.46 14299.24 12899.83 3792.58 24699.72 15198.09 13497.51 19998.68 214
jajsoiax98.43 13298.28 13698.88 16798.60 27898.43 18799.82 1399.53 7198.19 7698.63 22599.80 6493.22 22999.44 19899.22 3097.50 20198.77 190
EG-PatchMatch MVS95.97 26795.69 26196.81 28597.78 29492.79 30299.16 22198.93 27296.16 24194.08 29499.22 24782.72 31699.47 19195.67 25897.50 20198.17 281
test_040296.64 24696.24 24797.85 26098.85 24996.43 25499.44 13999.26 23593.52 28996.98 27599.52 17188.52 29299.20 24992.58 29697.50 20197.93 291
ACMP97.20 1198.06 15997.94 15898.45 21199.37 14097.01 23199.44 13999.49 10297.54 13998.45 23599.79 7291.95 25799.72 15197.91 14997.49 20498.62 249
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
mvs_tets98.40 13598.23 13898.91 15898.67 27398.51 18199.66 5499.53 7198.19 7698.65 22399.81 5392.75 23799.44 19899.31 2497.48 20598.77 190
ACMM97.58 598.37 13798.34 13198.48 20799.41 13197.10 22299.56 9599.45 14598.53 5499.04 16599.85 2693.00 23199.71 15798.74 7597.45 20698.64 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH97.28 898.10 15697.99 15498.44 21499.41 13196.96 23799.60 7799.56 4698.09 8998.15 24899.91 590.87 26899.70 16398.88 5697.45 20698.67 225
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB97.16 1298.02 17097.90 16098.40 21799.23 16896.80 24399.70 4299.60 3497.12 17398.18 24799.70 10691.73 25999.72 15198.39 11497.45 20698.68 214
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
ACMMP++97.43 209
testpf95.66 27096.02 25394.58 29498.35 28792.32 30497.25 32197.91 31592.83 29597.03 27498.99 26588.69 28898.61 28495.72 25697.40 21092.80 318
ITE_SJBPF98.08 24499.29 15896.37 25598.92 27498.34 6698.83 19799.75 9091.09 26599.62 18095.82 25397.40 21098.25 280
XVG-ACMP-BASELINE97.83 19197.71 18598.20 23999.11 19296.33 25799.41 15599.52 7598.06 9799.05 16499.50 17689.64 27999.73 14797.73 16697.38 21298.53 267
USDC97.34 23597.20 23197.75 26799.07 19995.20 27898.51 30299.04 26297.99 10398.31 24399.86 2289.02 28399.55 18795.67 25897.36 21398.49 269
pcd1.5k->3k40.85 30743.49 30932.93 32198.95 2240.00 3380.00 32999.53 710.00 3330.00 3340.27 33595.32 1470.00 3360.00 33397.30 21498.80 185
PVSNet_BlendedMVS98.86 9898.80 9299.03 13799.76 4198.79 15499.28 19499.91 397.42 14899.67 4099.37 21597.53 8999.88 9598.98 5097.29 21598.42 274
PS-MVSNAJss98.92 9398.92 7798.90 16298.78 25898.53 17699.78 2299.54 6198.07 9399.00 17599.76 8599.01 1199.37 20699.13 3897.23 21698.81 184
TinyColmap97.12 24196.89 23897.83 26299.07 19995.52 27298.57 29998.74 29197.58 13397.81 26299.79 7288.16 29799.56 18595.10 26797.21 21798.39 277
ACMMP++_ref97.19 218
ACMH+97.24 1097.92 18397.78 17298.32 22299.46 12196.68 24799.56 9599.54 6198.41 6397.79 26399.87 1990.18 27599.66 17098.05 14297.18 21998.62 249
test0.0.03 197.71 21197.42 21298.56 20098.41 28697.82 20598.78 28698.63 29897.34 15398.05 25598.98 26894.45 19798.98 27095.04 26997.15 22098.89 181
CMPMVSbinary69.68 2394.13 28394.90 27591.84 30397.24 30380.01 32398.52 30199.48 11189.01 31191.99 30999.67 12185.67 30799.13 25495.44 26197.03 22196.39 311
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OurMVSNet-221017-097.88 18697.77 17698.19 24098.71 26896.53 25099.88 199.00 26597.79 11598.78 20199.94 391.68 26099.35 21397.21 20496.99 22298.69 209
LF4IMVS97.52 22497.46 20497.70 27098.98 21695.55 26999.29 19098.82 28698.07 9398.66 21799.64 13489.97 27699.61 18197.01 21696.68 22397.94 290
GBi-Net97.68 21497.48 20198.29 22599.51 11097.26 21699.43 14499.48 11196.49 21199.07 15999.32 23490.26 27298.98 27097.10 21196.65 22498.62 249
test197.68 21497.48 20198.29 22599.51 11097.26 21699.43 14499.48 11196.49 21199.07 15999.32 23490.26 27298.98 27097.10 21196.65 22498.62 249
FMVSNet398.03 16897.76 17998.84 17999.39 13798.98 11299.40 16199.38 18196.67 20099.07 15999.28 24092.93 23298.98 27097.10 21196.65 22498.56 266
FMVSNet297.72 20997.36 21698.80 18499.51 11098.84 13599.45 13599.42 16296.49 21198.86 19599.29 23990.26 27298.98 27096.44 24396.56 22798.58 264
K. test v397.10 24296.79 24098.01 24998.72 26696.33 25799.87 497.05 32297.59 13196.16 28299.80 6488.71 28799.04 26396.69 23596.55 22898.65 239
tpm97.67 21797.55 19498.03 24699.02 20895.01 28299.43 14498.54 30396.44 21999.12 14999.34 22991.83 25899.60 18297.75 16496.46 22999.48 125
SixPastTwentyTwo97.50 22897.33 22398.03 24698.65 27496.23 26099.77 2498.68 29697.14 17097.90 25899.93 490.45 27099.18 25097.00 21796.43 23098.67 225
FIs98.78 11298.63 11099.23 12399.18 17799.54 5299.83 1299.59 3798.28 7098.79 20099.81 5396.75 11199.37 20699.08 4296.38 23198.78 187
FC-MVSNet-test98.75 11698.62 11399.15 12999.08 19899.45 6699.86 899.60 3498.23 7598.70 21399.82 4496.80 10799.22 24499.07 4396.38 23198.79 186
XXY-MVS98.38 13698.09 14699.24 12199.26 16599.32 7699.56 9599.55 5397.45 14598.71 20799.83 3793.23 22899.63 17998.88 5696.32 23398.76 192
FMVSNet196.84 24596.36 24698.29 22599.32 15397.26 21699.43 14499.48 11195.11 26198.55 23099.32 23483.95 31498.98 27095.81 25496.26 23498.62 249
N_pmnet94.95 27895.83 25692.31 30298.47 28479.33 32499.12 22892.81 33393.87 28597.68 26499.13 25393.87 21999.01 26791.38 29896.19 23598.59 261
pmmvs498.13 15197.90 16098.81 18298.61 27798.87 13198.99 26199.21 24196.44 21999.06 16399.58 15495.90 13399.11 25797.18 20896.11 23698.46 273
testing_294.44 28192.93 28798.98 14394.16 31499.00 11099.42 15199.28 22896.60 20684.86 31796.84 31270.91 32099.27 23298.23 12696.08 23798.68 214
IterMVS97.83 19197.77 17698.02 24899.58 10096.27 25999.02 25499.48 11197.22 16598.71 20799.70 10692.75 23799.13 25497.46 19296.00 23898.67 225
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs597.52 22497.30 22698.16 24298.57 28096.73 24499.27 19798.90 27996.14 24498.37 23999.53 16791.54 26299.14 25197.51 18695.87 23998.63 247
semantic-postprocess98.06 24599.57 10296.36 25699.49 10297.18 16798.71 20799.72 10192.70 24399.14 25197.44 19495.86 24098.67 225
new_pmnet96.38 25596.03 25197.41 27598.13 29195.16 28199.05 24599.20 24293.94 28497.39 26798.79 27791.61 26199.04 26390.43 30195.77 24198.05 284
FMVSNet596.43 25196.19 24897.15 27799.11 19295.89 26599.32 18199.52 7594.47 27698.34 24299.07 25887.54 30097.07 31092.61 29595.72 24298.47 271
Gipumacopyleft90.99 29190.15 29293.51 29698.73 26490.12 30993.98 32599.45 14579.32 32092.28 30894.91 31769.61 32197.98 30087.42 30995.67 24392.45 320
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
IterMVS-LS98.46 13098.42 12798.58 19799.59 9998.00 20099.37 16999.43 16196.94 18799.07 15999.59 15197.87 8199.03 26598.32 12395.62 24498.71 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmtry97.75 20497.40 21498.81 18299.10 19598.87 13199.11 23499.33 20994.83 26498.81 19899.38 21294.33 20199.02 26696.10 24895.57 24598.53 267
MIMVSNet195.51 27195.04 27496.92 28397.38 29995.60 26799.52 10799.50 9793.65 28796.97 27699.17 25085.28 30996.56 31488.36 30795.55 24698.60 260
test123567892.91 28893.30 28591.71 30593.14 31783.01 31898.75 28998.58 30192.80 29692.45 30797.91 29288.51 29393.54 32282.26 31895.35 24798.59 261
EU-MVSNet97.98 17598.03 15197.81 26498.72 26696.65 24899.66 5499.66 2598.09 8998.35 24199.82 4495.25 15198.01 29997.41 19695.30 24898.78 187
v124097.69 21297.32 22498.79 18598.85 24998.43 18799.48 12799.36 18996.11 24699.27 12399.36 22293.76 22399.24 24094.46 27895.23 24998.70 204
v119297.81 19597.44 20898.91 15898.88 24298.68 16199.51 11199.34 20196.18 23999.20 14099.34 22994.03 21499.36 21095.32 26595.18 25098.69 209
v114497.98 17597.69 18698.85 17898.87 24598.66 16499.54 10399.35 19396.27 23199.23 13399.35 22694.67 18899.23 24196.73 23295.16 25198.68 214
v798.05 16597.78 17298.87 17198.99 21298.67 16299.64 6699.34 20196.31 22899.29 11599.51 17494.78 17899.27 23297.03 21595.15 25298.66 236
v192192097.80 19797.45 20598.84 17998.80 25298.53 17699.52 10799.34 20196.15 24399.24 12899.47 18993.98 21599.29 22895.40 26395.13 25398.69 209
Anonymous2023120696.22 26296.03 25196.79 28697.31 30294.14 29199.63 6799.08 25596.17 24097.04 27399.06 26093.94 21697.76 30686.96 31295.06 25498.47 271
v14419297.92 18397.60 19298.87 17198.83 25198.65 16599.55 10099.34 20196.20 23799.32 10999.40 20794.36 20099.26 23796.37 24695.03 25598.70 204
v2v48298.06 15997.77 17698.92 15698.90 23898.82 14599.57 8899.36 18996.65 20199.19 14399.35 22694.20 20699.25 23897.72 17094.97 25698.69 209
FPMVS84.93 29685.65 29682.75 31686.77 32763.39 33398.35 30798.92 27474.11 32283.39 31998.98 26850.85 32992.40 32684.54 31694.97 25692.46 319
lessismore_v097.79 26598.69 27095.44 27594.75 32695.71 28699.87 1988.69 28899.32 22095.89 25294.93 25898.62 249
v698.12 15397.84 16698.94 14898.94 22898.83 13899.66 5499.34 20196.49 21199.30 11199.37 21594.95 16499.34 21697.77 16194.74 25998.67 225
v1neww98.12 15397.84 16698.93 15198.97 21998.81 14799.66 5499.35 19396.49 21199.29 11599.37 21595.02 16099.32 22097.73 16694.73 26098.67 225
v7new98.12 15397.84 16698.93 15198.97 21998.81 14799.66 5499.35 19396.49 21199.29 11599.37 21595.02 16099.32 22097.73 16694.73 26098.67 225
v114198.05 16597.76 17998.91 15898.91 23798.78 15699.57 8899.35 19396.41 22399.23 13399.36 22294.93 16799.27 23297.38 19794.72 26298.68 214
v198.05 16597.76 17998.93 15198.92 23598.80 15299.57 8899.35 19396.39 22599.28 11999.36 22294.86 17399.32 22097.38 19794.72 26298.68 214
divwei89l23v2f11298.06 15997.78 17298.91 15898.90 23898.77 15799.57 8899.35 19396.45 21899.24 12899.37 21594.92 16899.27 23297.50 18794.71 26498.68 214
V4298.06 15997.79 17098.86 17598.98 21698.84 13599.69 4499.34 20196.53 21099.30 11199.37 21594.67 18899.32 22097.57 18094.66 26598.42 274
test1235691.74 29092.19 29190.37 30891.22 31982.41 31998.61 29798.28 30690.66 30791.82 31097.92 29184.90 31092.61 32381.64 31994.66 26596.09 313
v1097.85 18897.52 19698.86 17598.99 21298.67 16299.75 3499.41 16595.70 25598.98 17799.41 20394.75 18499.23 24196.01 25194.63 26798.67 225
nrg03098.64 12598.42 12799.28 11899.05 20499.69 2999.81 1599.46 13498.04 9999.01 16899.82 4496.69 11399.38 20399.34 2194.59 26898.78 187
VPA-MVSNet98.29 14097.95 15799.30 11299.16 18499.54 5299.50 11699.58 4298.27 7199.35 10499.37 21592.53 24899.65 17299.35 1794.46 26998.72 198
MDA-MVSNet_test_wron95.45 27294.60 27798.01 24998.16 29097.21 22099.11 23499.24 23893.49 29080.73 32298.98 26893.02 23098.18 28794.22 28694.45 27098.64 241
MDA-MVSNet-bldmvs94.96 27793.98 28297.92 25598.24 28997.27 21599.15 22499.33 20993.80 28680.09 32399.03 26388.31 29597.86 30393.49 29094.36 27198.62 249
WR-MVS98.06 15997.73 18399.06 13498.86 24899.25 8599.19 21899.35 19397.30 15798.66 21799.43 19893.94 21699.21 24898.58 9694.28 27298.71 200
111192.30 28992.21 29092.55 30093.30 31586.27 31299.15 22498.74 29191.94 29990.85 31297.82 29384.18 31295.21 31779.65 32094.27 27396.19 312
test20.0396.12 26595.96 25496.63 28797.44 29895.45 27499.51 11199.38 18196.55 20996.16 28299.25 24493.76 22396.17 31587.35 31194.22 27498.27 279
YYNet195.36 27494.51 27997.92 25597.89 29297.10 22299.10 23699.23 23993.26 29380.77 32199.04 26292.81 23698.02 29894.30 28294.18 27598.64 241
CP-MVSNet98.09 15797.78 17299.01 13998.97 21999.24 8699.67 5199.46 13497.25 16198.48 23499.64 13493.79 22199.06 26198.63 8994.10 27698.74 196
v897.95 17997.63 19098.93 15198.95 22498.81 14799.80 1999.41 16596.03 25099.10 15499.42 20094.92 16899.30 22696.94 22394.08 27798.66 236
PS-CasMVS97.93 18097.59 19398.95 14798.99 21299.06 10299.68 4999.52 7597.13 17198.31 24399.68 11692.44 25499.05 26298.51 10694.08 27798.75 193
V497.80 19797.51 19898.67 19398.79 25498.63 16799.87 499.44 15395.87 25299.01 16899.46 19394.52 19599.33 21796.64 24093.97 27998.05 284
v5297.79 19997.50 19998.66 19498.80 25298.62 16999.87 499.44 15395.87 25299.01 16899.46 19394.44 19999.33 21796.65 23993.96 28098.05 284
v7n97.87 18797.52 19698.92 15698.76 26298.58 17399.84 999.46 13496.20 23798.91 18599.70 10694.89 17199.44 19896.03 25093.89 28198.75 193
WR-MVS_H98.13 15197.87 16598.90 16299.02 20898.84 13599.70 4299.59 3797.27 15998.40 23799.19 24995.53 14299.23 24198.34 12093.78 28298.61 258
NR-MVSNet97.97 17897.61 19199.02 13898.87 24599.26 8499.47 13199.42 16297.63 13097.08 27299.50 17695.07 15899.13 25497.86 15393.59 28398.68 214
pm-mvs197.68 21497.28 22898.88 16799.06 20198.62 16999.50 11699.45 14596.32 22797.87 25999.79 7292.47 25099.35 21397.54 18393.54 28498.67 225
UniMVSNet (Re)98.29 14098.00 15399.13 13099.00 21099.36 7499.49 12299.51 8497.95 10498.97 17899.13 25396.30 12299.38 20398.36 11993.34 28598.66 236
VPNet97.84 19097.44 20899.01 13999.21 17198.94 12399.48 12799.57 4398.38 6499.28 11999.73 9788.89 28599.39 20299.19 3293.27 28698.71 200
PEN-MVS97.76 20197.44 20898.72 19098.77 26198.54 17599.78 2299.51 8497.06 18098.29 24599.64 13492.63 24598.89 27998.09 13493.16 28798.72 198
v14897.79 19997.55 19498.50 20498.74 26397.72 21099.54 10399.33 20996.26 23298.90 18799.51 17494.68 18799.14 25197.83 15593.15 28898.63 247
TranMVSNet+NR-MVSNet97.93 18097.66 18798.76 18898.78 25898.62 16999.65 6499.49 10297.76 11898.49 23399.60 14994.23 20598.97 27798.00 14392.90 28998.70 204
Baseline_NR-MVSNet97.76 20197.45 20598.68 19199.09 19798.29 19199.41 15598.85 28395.65 25698.63 22599.67 12194.82 17599.10 25998.07 14092.89 29098.64 241
UniMVSNet_NR-MVSNet98.22 14397.97 15598.96 14598.92 23598.98 11299.48 12799.53 7197.76 11898.71 20799.46 19396.43 11899.22 24498.57 9892.87 29198.69 209
DU-MVS98.08 15897.79 17098.96 14598.87 24598.98 11299.41 15599.45 14597.87 10698.71 20799.50 17694.82 17599.22 24498.57 9892.87 29198.68 214
pmmvs696.53 24996.09 25097.82 26398.69 27095.47 27399.37 16999.47 12593.46 29197.41 26699.78 7787.06 30399.33 21796.92 22592.70 29398.65 239
DTE-MVSNet97.51 22797.19 23298.46 21098.63 27698.13 19899.84 999.48 11196.68 19997.97 25799.67 12192.92 23398.56 28596.88 22792.60 29498.70 204
v74897.52 22497.23 23098.41 21698.69 27097.23 21999.87 499.45 14595.72 25498.51 23199.53 16794.13 21099.30 22696.78 23092.39 29598.70 204
TransMVSNet (Re)97.15 24096.58 24398.86 17599.12 19098.85 13499.49 12298.91 27795.48 25897.16 27199.80 6493.38 22799.11 25794.16 28791.73 29698.62 249
ambc93.06 29892.68 31882.36 32098.47 30398.73 29595.09 28897.41 30855.55 32899.10 25996.42 24491.32 29797.71 304
PMVScopyleft70.75 2275.98 30574.97 30479.01 31870.98 33355.18 33493.37 32698.21 30965.08 32961.78 33093.83 31921.74 33992.53 32478.59 32291.12 29889.34 324
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UnsupCasMVSNet_eth96.44 25096.12 24997.40 27698.65 27495.65 26699.36 17399.51 8497.13 17196.04 28598.99 26588.40 29498.17 28896.71 23390.27 29998.40 276
Patchmatch-RL test95.84 26895.81 25795.95 29195.61 30790.57 30898.24 31098.39 30495.10 26295.20 28798.67 28294.78 17897.77 30596.28 24790.02 30099.51 119
PM-MVS92.96 28792.23 28995.14 29395.61 30789.98 31099.37 16998.21 30994.80 26595.04 28997.69 29865.06 32497.90 30294.30 28289.98 30197.54 308
pmmvs-eth3d95.34 27594.73 27697.15 27795.53 30995.94 26499.35 17799.10 25295.13 26093.55 30397.54 30788.15 29897.91 30194.58 27589.69 30297.61 305
testmv87.91 29387.80 29488.24 30987.68 32677.50 32699.07 23997.66 31989.27 30986.47 31696.22 31568.35 32292.49 32576.63 32488.82 30394.72 316
v1196.23 26195.57 26798.21 23898.93 23398.83 13899.72 3999.29 22194.29 28094.05 29597.64 30194.88 17298.04 29792.89 29388.43 30497.77 302
new-patchmatchnet94.48 28094.08 28195.67 29295.08 31192.41 30399.18 21999.28 22894.55 27393.49 30497.37 31087.86 29997.01 31191.57 29788.36 30597.61 305
v1896.42 25295.80 25998.26 22898.95 22498.82 14599.76 2799.28 22894.58 26994.12 29297.70 29695.22 15398.16 28994.83 27287.80 30697.79 301
v1696.39 25495.76 26098.26 22898.96 22298.81 14799.76 2799.28 22894.57 27094.10 29397.70 29695.04 15998.16 28994.70 27487.77 30797.80 296
v1796.42 25295.81 25798.25 23298.94 22898.80 15299.76 2799.28 22894.57 27094.18 29197.71 29595.23 15298.16 28994.86 27087.73 30897.80 296
UnsupCasMVSNet_bld93.53 28692.51 28896.58 28997.38 29993.82 29398.24 31099.48 11191.10 30593.10 30596.66 31374.89 31998.37 28694.03 28887.71 30997.56 307
pmmvs394.09 28493.25 28696.60 28894.76 31294.49 28798.92 27798.18 31189.66 30896.48 27998.06 29086.28 30497.33 30989.68 30387.20 31097.97 289
v1596.28 25695.62 26298.25 23298.94 22898.83 13899.76 2799.29 22194.52 27494.02 29697.61 30395.02 16098.13 29394.53 27686.92 31197.80 296
V1496.26 25795.60 26398.26 22898.94 22898.83 13899.76 2799.29 22194.49 27593.96 29897.66 29994.99 16398.13 29394.41 27986.90 31297.80 296
V996.25 25895.58 26498.26 22898.94 22898.83 13899.75 3499.29 22194.45 27793.96 29897.62 30294.94 16598.14 29294.40 28086.87 31397.81 294
v1396.24 25995.58 26498.25 23298.98 21698.83 13899.75 3499.29 22194.35 27993.89 30197.60 30495.17 15598.11 29594.27 28486.86 31497.81 294
v1296.24 25995.58 26498.23 23598.96 22298.81 14799.76 2799.29 22194.42 27893.85 30297.60 30495.12 15698.09 29694.32 28186.85 31597.80 296
IB-MVS95.67 1896.22 26295.44 27098.57 19899.21 17196.70 24598.65 29697.74 31896.71 19797.27 26898.54 28686.03 30599.92 6398.47 11086.30 31699.10 158
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
Anonymous2023121190.69 29289.39 29394.58 29494.25 31388.18 31199.29 19099.07 25882.45 31992.95 30697.65 30063.96 32697.79 30489.27 30485.63 31797.77 302
LCM-MVSNet86.80 29585.22 29891.53 30687.81 32580.96 32298.23 31298.99 26671.05 32390.13 31496.51 31448.45 33196.88 31290.51 29985.30 31896.76 309
TDRefinement95.42 27394.57 27897.97 25289.83 32396.11 26299.48 12798.75 28896.74 19596.68 27799.88 1488.65 29099.71 15798.37 11782.74 31998.09 282
PVSNet_094.43 1996.09 26695.47 26897.94 25399.31 15494.34 29097.81 31699.70 1597.12 17397.46 26598.75 28089.71 27899.79 13097.69 17281.69 32099.68 81
PMMVS286.87 29485.37 29791.35 30790.21 32283.80 31798.89 28097.45 32183.13 31891.67 31195.03 31648.49 33094.70 32085.86 31577.62 32195.54 314
PNet_i23d79.43 30277.68 30384.67 31286.18 32871.69 33196.50 32393.68 32975.17 32171.33 32691.18 32332.18 33590.62 32778.57 32374.34 32291.71 322
MVEpermissive76.82 2176.91 30474.31 30684.70 31185.38 33076.05 32996.88 32293.17 33167.39 32671.28 32789.01 32721.66 34087.69 32971.74 32772.29 32390.35 323
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN80.61 30079.88 30182.81 31590.75 32176.38 32897.69 31795.76 32566.44 32783.52 31892.25 32162.54 32787.16 33068.53 32861.40 32484.89 327
EMVS80.02 30179.22 30282.43 31791.19 32076.40 32797.55 32092.49 33566.36 32883.01 32091.27 32264.63 32585.79 33165.82 32960.65 32585.08 326
wuykxyi23d74.42 30671.19 30784.14 31476.16 33174.29 33096.00 32492.57 33469.57 32463.84 32987.49 32821.98 33788.86 32875.56 32657.50 32689.26 325
ANet_high77.30 30374.86 30584.62 31375.88 33277.61 32597.63 31893.15 33288.81 31264.27 32889.29 32536.51 33383.93 33275.89 32552.31 32792.33 321
no-one83.04 29880.12 30091.79 30489.44 32485.65 31599.32 18198.32 30589.06 31079.79 32589.16 32644.86 33296.67 31384.33 31746.78 32893.05 317
tmp_tt82.80 29981.52 29986.66 31066.61 33468.44 33292.79 32797.92 31368.96 32580.04 32499.85 2685.77 30696.15 31697.86 15343.89 32995.39 315
.test124583.42 29786.17 29575.15 31993.30 31586.27 31299.15 22498.74 29191.94 29990.85 31297.82 29384.18 31295.21 31779.65 32039.90 33043.98 329
testmvs39.17 30943.78 30825.37 32336.04 33616.84 33798.36 30526.56 33620.06 33138.51 33267.32 32929.64 33615.30 33537.59 33139.90 33043.98 329
test12339.01 31042.50 31028.53 32239.17 33520.91 33698.75 28919.17 33819.83 33238.57 33166.67 33033.16 33415.42 33437.50 33229.66 33249.26 328
wuyk23d40.18 30841.29 31136.84 32086.18 32849.12 33579.73 32822.81 33727.64 33025.46 33328.45 33421.98 33748.89 33355.80 33023.56 33312.51 331
cdsmvs_eth3d_5k24.64 31132.85 3120.00 3240.00 3370.00 3380.00 32999.51 840.00 3330.00 33499.56 16096.58 1150.00 3360.00 3330.00 3340.00 332
pcd_1.5k_mvsjas8.27 31311.03 3140.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 33599.01 110.00 3360.00 3330.00 3340.00 332
sosnet-low-res0.02 3140.03 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 3350.00 3410.00 3360.00 3330.00 3340.00 332
sosnet0.02 3140.03 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 3350.00 3410.00 3360.00 3330.00 3340.00 332
uncertanet0.02 3140.03 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 3350.00 3410.00 3360.00 3330.00 3340.00 332
Regformer0.02 3140.03 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 3350.00 3410.00 3360.00 3330.00 3340.00 332
ab-mvs-re8.30 31211.06 3130.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 33499.58 1540.00 3410.00 3360.00 3330.00 3340.00 332
uanet0.02 3140.03 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 3350.00 3410.00 3360.00 3330.00 3340.00 332
sam_mvs194.86 173
sam_mvs94.72 186
MTGPAbinary99.47 125
test_post199.23 20965.14 33294.18 20999.71 15797.58 178
test_post65.99 33194.65 19099.73 147
patchmatchnet-post98.70 28194.79 17799.74 139
MTMP98.88 281
gm-plane-assit98.54 28292.96 30194.65 26899.15 25199.64 17497.56 181
TEST999.67 7299.65 3799.05 24599.41 16596.22 23698.95 17999.49 17998.77 3999.91 72
test_899.67 7299.61 4299.03 25199.41 16596.28 22998.93 18399.48 18598.76 4199.91 72
agg_prior99.67 7299.62 4099.40 17298.87 19099.91 72
test_prior499.56 4998.99 261
test_prior99.68 5099.67 7299.48 6299.56 4699.83 11599.74 58
旧先验298.96 27096.70 19899.47 7799.94 4098.19 127
新几何299.01 258
无先验98.99 26199.51 8496.89 18999.93 5597.53 18499.72 69
原ACMM298.95 274
testdata299.95 3396.67 236
segment_acmp98.96 20
testdata198.85 28398.32 69
plane_prior799.29 15897.03 230
plane_prior699.27 16396.98 23492.71 241
plane_prior499.61 146
plane_prior397.00 23298.69 4699.11 151
plane_prior299.39 16298.97 23
plane_prior199.26 165
n20.00 339
nn0.00 339
door-mid98.05 312
test1199.35 193
door97.92 313
HQP5-MVS96.83 240
HQP-NCC99.19 17498.98 26598.24 7298.66 217
ACMP_Plane99.19 17498.98 26598.24 7298.66 217
BP-MVS97.19 206
HQP4-MVS98.66 21799.64 17498.64 241
HQP2-MVS92.47 250
NP-MVS99.23 16896.92 23899.40 207
MDTV_nov1_ep13_2view95.18 28099.35 17796.84 19299.58 6095.19 15497.82 15699.46 132
Test By Simon98.75 44