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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
LCM-MVSNet99.95 199.95 199.95 199.99 199.99 199.95 299.97 399.99 1100.00 199.98 899.78 8100.00 199.92 3100.00 199.87 10
test_djsdf99.84 999.81 1099.91 299.94 1599.84 1899.77 1399.80 6099.73 4299.97 699.92 1799.77 999.98 799.43 53100.00 199.90 5
ANet_high99.88 499.87 499.91 299.99 199.91 399.65 54100.00 199.90 6100.00 199.97 1099.61 1799.97 1699.75 31100.00 199.84 15
anonymousdsp99.80 1399.77 1499.90 499.96 599.88 899.73 2199.85 2999.70 4999.92 3199.93 1499.45 2399.97 1699.36 61100.00 199.85 14
mvs_tets99.90 299.90 299.90 499.96 599.79 3399.72 2599.88 1899.92 599.98 399.93 1499.94 299.98 799.77 30100.00 199.92 3
Anonymous2023121199.83 1199.81 1099.89 699.97 499.95 299.88 499.93 699.87 1399.94 2099.98 899.55 2199.95 4199.21 7999.98 3699.78 31
PS-MVSNAJss99.84 999.82 999.89 699.96 599.77 3799.68 4199.85 2999.95 399.98 399.92 1799.28 3999.98 799.75 31100.00 199.94 2
jajsoiax99.89 399.89 399.89 699.96 599.78 3599.70 2999.86 2299.89 1099.98 399.90 2399.94 299.98 799.75 31100.00 199.90 5
v5299.85 799.84 799.89 699.96 599.89 699.87 599.81 5699.85 1999.96 899.90 2399.27 4299.95 4199.93 199.99 2099.82 23
PS-CasMVS99.66 3699.58 4599.89 699.80 6999.85 1399.66 4999.73 9299.62 7199.84 6099.71 11198.62 12899.96 3399.30 7199.96 5999.86 12
PEN-MVS99.66 3699.59 4399.89 699.83 4699.87 999.66 4999.73 9299.70 4999.84 6099.73 9898.56 13499.96 3399.29 7499.94 7799.83 18
v7n99.82 1299.80 1299.88 1299.96 599.84 1899.82 1099.82 4899.84 2399.94 2099.91 2099.13 5799.96 3399.83 2099.99 2099.83 18
V499.85 799.84 799.88 1299.96 599.89 699.87 599.81 5699.85 1999.96 899.90 2399.27 4299.95 4199.93 1100.00 199.82 23
DTE-MVSNet99.68 3399.61 4199.88 1299.80 6999.87 999.67 4699.71 10499.72 4599.84 6099.78 7998.67 12099.97 1699.30 7199.95 6599.80 25
LTVRE_ROB99.19 199.88 499.87 499.88 1299.91 2199.90 499.96 199.92 799.90 699.97 699.87 3799.81 799.95 4199.54 4499.99 2099.80 25
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
CP-MVSNet99.54 5999.43 7899.87 1699.76 10399.82 2799.57 6899.61 14799.54 8599.80 7499.64 15297.79 20199.95 4199.21 7999.94 7799.84 15
WR-MVS_H99.61 4499.53 6199.87 1699.80 6999.83 2299.67 4699.75 8499.58 8399.85 5799.69 12498.18 17799.94 5599.28 7699.95 6599.83 18
UA-Net99.78 1599.76 1899.86 1899.72 13099.71 5299.91 399.95 599.96 299.71 10699.91 2099.15 5399.97 1699.50 48100.00 199.90 5
FC-MVSNet-test99.70 2899.65 3499.86 1899.88 2899.86 1299.72 2599.78 7099.90 699.82 6599.83 5198.45 15399.87 15899.51 4799.97 4799.86 12
APDe-MVS99.48 7099.36 9099.85 2099.55 19699.81 2899.50 7499.69 11398.99 16299.75 9099.71 11198.79 9899.93 6698.46 14999.85 12999.80 25
FIs99.65 4199.58 4599.84 2199.84 4299.85 1399.66 4999.75 8499.86 1699.74 9899.79 7098.27 16899.85 19499.37 6099.93 8599.83 18
v74899.76 1799.74 2199.84 2199.95 1399.83 2299.82 1099.80 6099.82 2799.95 1699.87 3798.72 11299.93 6699.72 3499.98 3699.75 40
OurMVSNet-221017-099.75 1999.71 2599.84 2199.96 599.83 2299.83 899.85 2999.80 3199.93 2699.93 1498.54 14099.93 6699.59 3999.98 3699.76 37
pmmvs699.86 699.86 699.83 2499.94 1599.90 499.83 899.91 1199.85 1999.94 2099.95 1299.73 1099.90 10999.65 3599.97 4799.69 56
nrg03099.70 2899.66 3399.82 2599.76 10399.84 1899.61 6099.70 10799.93 499.78 8299.68 13699.10 5999.78 26999.45 5199.96 5999.83 18
Baseline_NR-MVSNet99.49 6899.37 8799.82 2599.91 2199.84 1898.83 22499.86 2299.68 5699.65 12699.88 3497.67 21099.87 15899.03 10599.86 12699.76 37
TransMVSNet (Re)99.78 1599.77 1499.81 2799.91 2199.85 1399.75 1799.86 2299.70 4999.91 3399.89 3199.60 1999.87 15899.59 3999.74 19099.71 49
XXY-MVS99.71 2799.67 3299.81 2799.89 2799.72 5199.59 6599.82 4899.39 11199.82 6599.84 5099.38 2899.91 9299.38 5899.93 8599.80 25
MP-MVS-pluss99.14 15598.92 17899.80 2999.83 4699.83 2298.61 24299.63 14096.84 29799.44 17799.58 18598.81 9199.91 9297.70 19899.82 15299.67 69
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
zzz-MVS99.30 11499.14 12499.80 2999.81 6199.81 2898.73 23899.53 19099.27 12399.42 18399.63 16098.21 17399.95 4197.83 19199.79 16999.65 89
MTAPA99.35 10299.20 12099.80 2999.81 6199.81 2899.33 10899.53 19099.27 12399.42 18399.63 16098.21 17399.95 4197.83 19199.79 16999.65 89
HPM-MVS_fast99.43 8199.30 10199.80 2999.83 4699.81 2899.52 7299.70 10798.35 22999.51 16999.50 21599.31 3599.88 13998.18 17199.84 13399.69 56
MIMVSNet199.66 3699.62 3899.80 2999.94 1599.87 999.69 3899.77 7399.78 3499.93 2699.89 3197.94 19099.92 8399.65 3599.98 3699.62 113
ACMMP_Plus99.28 11799.11 13299.79 3499.75 11199.81 2898.95 20899.53 19098.27 23899.53 16599.73 9898.75 10899.87 15897.70 19899.83 14399.68 62
VPA-MVSNet99.66 3699.62 3899.79 3499.68 14999.75 4499.62 5699.69 11399.85 1999.80 7499.81 6198.81 9199.91 9299.47 5099.88 11299.70 53
Vis-MVSNetpermissive99.75 1999.74 2199.79 3499.88 2899.66 7199.69 3899.92 799.67 5899.77 8699.75 9299.61 1799.98 799.35 6299.98 3699.72 46
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
pm-mvs199.79 1499.79 1399.78 3799.91 2199.83 2299.76 1699.87 2099.73 4299.89 3899.87 3799.63 1599.87 15899.54 4499.92 8899.63 99
HPM-MVScopyleft99.25 12499.07 14699.78 3799.81 6199.75 4499.61 6099.67 11997.72 26499.35 20599.25 26499.23 4699.92 8397.21 23299.82 15299.67 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
region2R99.23 12899.05 15399.77 3999.76 10399.70 5999.31 11899.59 16598.41 21999.32 21399.36 24198.73 11199.93 6697.29 22499.74 19099.67 69
PGM-MVS99.20 14299.01 16399.77 3999.75 11199.71 5299.16 16599.72 10197.99 24999.42 18399.60 17798.81 9199.93 6696.91 24499.74 19099.66 79
TDRefinement99.72 2599.70 2899.77 3999.90 2599.85 1399.86 799.92 799.69 5399.78 8299.92 1799.37 3099.88 13998.93 12199.95 6599.60 124
HSP-MVS99.01 17898.76 19899.76 4299.78 8899.73 5099.35 9999.31 25598.54 20999.54 16298.99 29996.81 24799.93 6696.97 24299.53 24099.61 118
HFP-MVS99.25 12499.08 14299.76 4299.73 12099.70 5999.31 11899.59 16598.36 22499.36 20399.37 23698.80 9599.91 9297.43 21799.75 18399.68 62
#test#99.12 15898.90 18199.76 4299.73 12099.70 5999.10 18199.59 16597.60 27299.36 20399.37 23698.80 9599.91 9296.84 24899.75 18399.68 62
ACMMPR99.23 12899.06 14899.76 4299.74 11799.69 6399.31 11899.59 16598.36 22499.35 20599.38 23598.61 13099.93 6697.43 21799.75 18399.67 69
MP-MVScopyleft99.06 16698.83 19299.76 4299.76 10399.71 5299.32 11199.50 20598.35 22998.97 25599.48 21798.37 16199.92 8395.95 28999.75 18399.63 99
TranMVSNet+NR-MVSNet99.54 5999.47 6999.76 4299.58 17499.64 7799.30 12199.63 14099.61 7599.71 10699.56 19798.76 10599.96 3399.14 9899.92 8899.68 62
mPP-MVS99.19 14599.00 16599.76 4299.76 10399.68 6699.38 9299.54 18598.34 23399.01 25299.50 21598.53 14499.93 6697.18 23499.78 17499.66 79
SixPastTwentyTwo99.42 8499.30 10199.76 4299.92 1999.67 6899.70 2999.14 28099.65 6599.89 3899.90 2396.20 26299.94 5599.42 5799.92 8899.67 69
SteuartSystems-ACMMP99.30 11499.14 12499.76 4299.87 3299.66 7199.18 15299.60 16198.55 20899.57 14999.67 14299.03 7199.94 5597.01 24099.80 16699.69 56
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XVS99.27 12299.11 13299.75 5199.71 13399.71 5299.37 9699.61 14799.29 12098.76 28099.47 22098.47 15099.88 13997.62 20599.73 19699.67 69
X-MVStestdata96.09 31894.87 32699.75 5199.71 13399.71 5299.37 9699.61 14799.29 12098.76 28061.30 36098.47 15099.88 13997.62 20599.73 19699.67 69
abl_699.36 10099.23 11799.75 5199.71 13399.74 4999.33 10899.76 7999.07 15899.65 12699.63 16099.09 6199.92 8397.13 23699.76 18099.58 139
CP-MVS99.23 12899.05 15399.75 5199.66 15499.66 7199.38 9299.62 14398.38 22299.06 24999.27 26098.79 9899.94 5597.51 21399.82 15299.66 79
SMA-MVS99.23 12899.06 14899.74 5599.46 23199.76 4199.13 17799.58 17397.62 27099.68 11299.64 15299.02 7299.83 22697.61 20799.82 15299.63 99
ESAPD98.87 20198.58 21199.74 5599.62 16599.67 6898.74 23599.53 19097.71 26599.55 15999.57 19298.40 15899.90 10994.47 32299.68 20799.66 79
HPM-MVS++copyleft98.96 18798.70 20199.74 5599.52 20299.71 5298.86 21899.19 27598.47 21598.59 29499.06 29498.08 18199.91 9296.94 24399.60 22699.60 124
APD-MVS_3200maxsize99.31 11399.16 12199.74 5599.53 20099.75 4499.27 13399.61 14799.19 13999.57 14999.64 15298.76 10599.90 10997.29 22499.62 22199.56 144
LPG-MVS_test99.22 13799.05 15399.74 5599.82 5399.63 8199.16 16599.73 9297.56 27499.64 12899.69 12499.37 3099.89 12496.66 25899.87 11999.69 56
LGP-MVS_train99.74 5599.82 5399.63 8199.73 9297.56 27499.64 12899.69 12499.37 3099.89 12496.66 25899.87 11999.69 56
DP-MVS99.48 7099.39 8299.74 5599.57 18399.62 8399.29 12999.61 14799.87 1399.74 9899.76 8898.69 11599.87 15898.20 16799.80 16699.75 40
ACMMPcopyleft99.25 12499.08 14299.74 5599.79 8299.68 6699.50 7499.65 13298.07 24599.52 16799.69 12498.57 13399.92 8397.18 23499.79 16999.63 99
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
v1399.76 1799.77 1499.73 6399.86 3599.55 9699.77 1399.86 2299.79 3399.96 899.91 2098.90 8499.87 15899.91 5100.00 199.78 31
GBi-Net99.42 8499.31 9699.73 6399.49 21699.77 3799.68 4199.70 10799.44 10199.62 13899.83 5197.21 23499.90 10998.96 11599.90 10099.53 157
test199.42 8499.31 9699.73 6399.49 21699.77 3799.68 4199.70 10799.44 10199.62 13899.83 5197.21 23499.90 10998.96 11599.90 10099.53 157
FMVSNet199.66 3699.63 3799.73 6399.78 8899.77 3799.68 4199.70 10799.67 5899.82 6599.83 5198.98 7499.90 10999.24 7899.97 4799.53 157
HyFIR lowres test98.91 19598.64 20699.73 6399.85 3999.47 10698.07 29999.83 4098.64 20199.89 3899.60 17792.57 294100.00 199.33 6599.97 4799.72 46
v1299.75 1999.77 1499.72 6899.85 3999.53 9999.75 1799.86 2299.78 3499.96 899.90 2398.88 8799.86 17899.91 5100.00 199.77 34
UniMVSNet_NR-MVSNet99.37 9799.25 11499.72 6899.47 22799.56 9398.97 20699.61 14799.43 10699.67 11699.28 25897.85 19799.95 4199.17 8899.81 16199.65 89
ACMM98.09 1199.46 7799.38 8499.72 6899.80 6999.69 6399.13 17799.65 13298.99 16299.64 12899.72 10499.39 2499.86 17898.23 16499.81 16199.60 124
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH98.42 699.59 4599.54 5399.72 6899.86 3599.62 8399.56 7099.79 6898.77 18799.80 7499.85 4599.64 1499.85 19498.70 13799.89 10699.70 53
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1199.75 1999.76 1899.71 7299.85 3999.49 10299.73 2199.84 3799.75 3999.95 1699.90 2398.93 8099.86 17899.92 3100.00 199.77 34
VPNet99.46 7799.37 8799.71 7299.82 5399.59 8899.48 7899.70 10799.81 2899.69 11099.58 18597.66 21499.86 17899.17 8899.44 25099.67 69
V999.74 2399.75 2099.71 7299.84 4299.50 10099.74 1999.86 2299.76 3899.96 899.90 2398.83 9099.85 19499.91 5100.00 199.77 34
DU-MVS99.33 11099.21 11999.71 7299.43 23899.56 9398.83 22499.53 19099.38 11299.67 11699.36 24197.67 21099.95 4199.17 8899.81 16199.63 99
APD-MVScopyleft98.87 20198.59 20999.71 7299.50 21199.62 8399.01 19699.57 17596.80 29999.54 16299.63 16098.29 16699.91 9295.24 31399.71 20299.61 118
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMH+98.40 899.50 6699.43 7899.71 7299.86 3599.76 4199.32 11199.77 7399.53 8799.77 8699.76 8899.26 4599.78 26997.77 19499.88 11299.60 124
COLMAP_ROBcopyleft98.06 1299.45 7999.37 8799.70 7899.83 4699.70 5999.38 9299.78 7099.53 8799.67 11699.78 7999.19 4999.86 17897.32 22299.87 11999.55 147
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
V1499.73 2499.74 2199.69 7999.83 4699.48 10599.72 2599.85 2999.74 4099.96 899.89 3198.79 9899.85 19499.91 5100.00 199.76 37
K. test v398.87 20198.60 20899.69 7999.93 1899.46 11099.74 1994.97 35399.78 3499.88 4699.88 3493.66 28599.97 1699.61 3899.95 6599.64 95
wuykxyi23d99.65 4199.64 3699.69 7999.92 1999.20 18698.89 21399.99 298.73 19599.95 1699.80 6399.84 499.99 499.64 3799.98 3699.89 9
v1599.72 2599.73 2499.68 8299.82 5399.44 11799.70 2999.85 2999.72 4599.95 1699.88 3498.76 10599.84 21099.90 9100.00 199.75 40
UniMVSNet (Re)99.37 9799.26 11299.68 8299.51 20699.58 9098.98 20599.60 16199.43 10699.70 10899.36 24197.70 20599.88 13999.20 8299.87 11999.59 135
NR-MVSNet99.40 9099.31 9699.68 8299.43 23899.55 9699.73 2199.50 20599.46 9999.88 4699.36 24197.54 21799.87 15898.97 11499.87 11999.63 99
v1799.70 2899.71 2599.67 8599.81 6199.44 11799.70 2999.83 4099.69 5399.94 2099.87 3798.70 11399.84 21099.88 1499.99 2099.73 43
v1699.70 2899.71 2599.67 8599.81 6199.43 12399.70 2999.83 4099.70 4999.94 2099.87 3798.69 11599.84 21099.88 1499.99 2099.73 43
LCM-MVSNet-Re99.28 11799.15 12399.67 8599.33 26899.76 4199.34 10699.97 398.93 16899.91 3399.79 7098.68 11799.93 6696.80 25099.56 22999.30 230
1112_ss99.05 16998.84 18999.67 8599.66 15499.29 16198.52 25699.82 4897.65 26999.43 18199.16 27796.42 25799.91 9299.07 10399.84 13399.80 25
DeepPCF-MVS98.42 699.18 14799.02 16099.67 8599.22 28499.75 4497.25 33799.47 21498.72 19699.66 12099.70 11899.29 3799.63 33198.07 17999.81 16199.62 113
DeepC-MVS98.90 499.62 4399.61 4199.67 8599.72 13099.44 11799.24 14099.71 10499.27 12399.93 2699.90 2399.70 1299.93 6698.99 10899.99 2099.64 95
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMP97.51 1499.05 16998.84 18999.67 8599.78 8899.55 9698.88 21599.66 12397.11 29399.47 17499.60 17799.07 6699.89 12496.18 27599.85 12999.58 139
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
3Dnovator+98.92 399.35 10299.24 11599.67 8599.35 25499.47 10699.62 5699.50 20599.44 10199.12 24299.78 7998.77 10499.94 5597.87 18899.72 20199.62 113
v1099.69 3299.69 2999.66 9399.81 6199.39 13599.66 4999.75 8499.60 8099.92 3199.87 3798.75 10899.86 17899.90 999.99 2099.73 43
WR-MVS99.11 16198.93 17599.66 9399.30 27499.42 12798.42 26999.37 24399.04 15999.57 14999.20 27596.89 24699.86 17898.66 14199.87 11999.70 53
XVG-OURS-SEG-HR99.16 15298.99 16899.66 9399.84 4299.64 7798.25 28099.73 9298.39 22199.63 13199.43 22699.70 1299.90 10997.34 22198.64 31299.44 196
EPP-MVSNet99.17 15099.00 16599.66 9399.80 6999.43 12399.70 2999.24 27199.48 9299.56 15699.77 8594.89 27599.93 6698.72 13699.89 10699.63 99
v1899.68 3399.69 2999.65 9799.79 8299.40 13299.68 4199.83 4099.66 6299.93 2699.85 4598.65 12499.84 21099.87 1899.99 2099.71 49
v899.68 3399.69 2999.65 9799.80 6999.40 13299.66 4999.76 7999.64 6799.93 2699.85 4598.66 12299.84 21099.88 1499.99 2099.71 49
MCST-MVS99.02 17498.81 19499.65 9799.58 17499.49 10298.58 24699.07 28398.40 22099.04 25099.25 26498.51 14899.80 25897.31 22399.51 24299.65 89
XVG-OURS99.21 14099.06 14899.65 9799.82 5399.62 8397.87 31999.74 8998.36 22499.66 12099.68 13699.71 1199.90 10996.84 24899.88 11299.43 202
CHOSEN 1792x268899.39 9399.30 10199.65 9799.88 2899.25 17398.78 23399.88 1898.66 19999.96 899.79 7097.45 22199.93 6699.34 6399.99 2099.78 31
QAPM98.40 24397.99 25499.65 9799.39 24699.47 10699.67 4699.52 20091.70 34498.78 27999.80 6398.55 13899.95 4194.71 32099.75 18399.53 157
3Dnovator99.15 299.43 8199.36 9099.65 9799.39 24699.42 12799.70 2999.56 17899.23 13499.35 20599.80 6399.17 5199.95 4198.21 16699.84 13399.59 135
lessismore_v099.64 10499.86 3599.38 14190.66 35699.89 3899.83 5194.56 27999.97 1699.56 4399.92 8899.57 143
114514_t98.49 23398.11 24899.64 10499.73 12099.58 9099.24 14099.76 7989.94 34799.42 18399.56 19797.76 20399.86 17897.74 19699.82 15299.47 185
CPTT-MVS98.74 21498.44 22099.64 10499.61 16799.38 14199.18 15299.55 18196.49 30799.27 22099.37 23697.11 24099.92 8395.74 29699.67 21399.62 113
RPSCF99.18 14799.02 16099.64 10499.83 4699.85 1399.44 8199.82 4898.33 23499.50 17199.78 7997.90 19299.65 32796.78 25199.83 14399.44 196
TSAR-MVS + MP.99.34 10799.24 11599.63 10899.82 5399.37 14499.26 13499.35 24698.77 18799.57 14999.70 11899.27 4299.88 13997.71 19799.75 18399.65 89
OPM-MVS99.26 12399.13 12799.63 10899.70 14099.61 8798.58 24699.48 21098.50 21299.52 16799.63 16099.14 5499.76 27797.89 18799.77 17899.51 168
AllTest99.21 14099.07 14699.63 10899.78 8899.64 7799.12 17999.83 4098.63 20299.63 13199.72 10498.68 11799.75 28396.38 26999.83 14399.51 168
TestCases99.63 10899.78 8899.64 7799.83 4098.63 20299.63 13199.72 10498.68 11799.75 28396.38 26999.83 14399.51 168
V4299.56 5099.54 5399.63 10899.79 8299.46 11099.39 8699.59 16599.24 13299.86 5699.70 11898.55 13899.82 23499.79 2699.95 6599.60 124
XVG-ACMP-BASELINE99.23 12899.10 13999.63 10899.82 5399.58 9098.83 22499.72 10198.36 22499.60 14599.71 11198.92 8199.91 9297.08 23799.84 13399.40 207
Test_1112_low_res98.95 19098.73 19999.63 10899.68 14999.15 19298.09 29599.80 6097.14 29099.46 17699.40 23196.11 26499.89 12499.01 10799.84 13399.84 15
TAMVS99.49 6899.45 7399.63 10899.48 22299.42 12799.45 7999.57 17599.66 6299.78 8299.83 5197.85 19799.86 17899.44 5299.96 5999.61 118
testing_299.58 4699.56 5199.62 11699.81 6199.44 11799.14 17299.43 22599.69 5399.82 6599.79 7099.14 5499.79 26199.31 7099.95 6599.63 99
EG-PatchMatch MVS99.57 4799.56 5199.62 11699.77 9899.33 15499.26 13499.76 7999.32 11999.80 7499.78 7999.29 3799.87 15899.15 9299.91 9899.66 79
F-COLMAP98.74 21498.45 21999.62 11699.57 18399.47 10698.84 22299.65 13296.31 30998.93 26399.19 27697.68 20999.87 15896.52 26499.37 26499.53 157
v1neww99.55 5499.54 5399.61 11999.80 6999.39 13599.32 11199.61 14799.18 14099.87 5199.69 12498.64 12699.82 23499.79 2699.94 7799.60 124
v7new99.55 5499.54 5399.61 11999.80 6999.39 13599.32 11199.61 14799.18 14099.87 5199.69 12498.64 12699.82 23499.79 2699.94 7799.60 124
v799.56 5099.54 5399.61 11999.80 6999.39 13599.30 12199.59 16599.14 14999.82 6599.72 10498.75 10899.84 21099.83 2099.94 7799.61 118
v699.55 5499.54 5399.61 11999.80 6999.39 13599.32 11199.60 16199.18 14099.87 5199.68 13698.65 12499.82 23499.79 2699.95 6599.61 118
CDPH-MVS98.56 22698.20 24299.61 11999.50 21199.46 11098.32 27699.41 22895.22 32699.21 23199.10 28598.34 16399.82 23495.09 31699.66 21699.56 144
LS3D99.24 12799.11 13299.61 11998.38 34299.79 3399.57 6899.68 11699.61 7599.15 23999.71 11198.70 11399.91 9297.54 21199.68 20799.13 257
tfpnnormal99.43 8199.38 8499.60 12599.87 3299.75 4499.59 6599.78 7099.71 4799.90 3599.69 12498.85 8999.90 10997.25 22899.78 17499.15 250
CSCG99.37 9799.29 10699.60 12599.71 13399.46 11099.43 8299.85 2998.79 18499.41 18999.60 17798.92 8199.92 8398.02 18099.92 8899.43 202
v114499.54 5999.53 6199.59 12799.79 8299.28 16399.10 18199.61 14799.20 13899.84 6099.73 9898.67 12099.84 21099.86 1999.98 3699.64 95
testmv99.53 6599.51 6699.59 12799.73 12099.31 15798.48 26099.92 799.57 8499.87 5199.79 7099.12 5899.91 9299.16 9199.99 2099.55 147
UnsupCasMVSNet_eth98.83 20498.57 21399.59 12799.68 14999.45 11598.99 20199.67 11999.48 9299.55 15999.36 24194.92 27499.86 17898.95 11996.57 34899.45 191
PHI-MVS99.11 16198.95 17499.59 12799.13 29599.59 8899.17 15999.65 13297.88 25599.25 22399.46 22398.97 7699.80 25897.26 22799.82 15299.37 216
v14419299.55 5499.54 5399.58 13199.78 8899.20 18699.11 18099.62 14399.18 14099.89 3899.72 10498.66 12299.87 15899.88 1499.97 4799.66 79
v2v48299.50 6699.47 6999.58 13199.78 8899.25 17399.14 17299.58 17399.25 13099.81 7199.62 16798.24 17099.84 21099.83 2099.97 4799.64 95
v199.54 5999.52 6399.58 13199.77 9899.28 16399.15 16799.61 14799.26 12799.88 4699.68 13698.56 13499.82 23499.82 2399.97 4799.63 99
test20.0399.55 5499.54 5399.58 13199.79 8299.37 14499.02 19499.89 1599.60 8099.82 6599.62 16798.81 9199.89 12499.43 5399.86 12699.47 185
PM-MVS99.36 10099.29 10699.58 13199.83 4699.66 7198.95 20899.86 2298.85 17699.81 7199.73 9898.40 15899.92 8398.36 15499.83 14399.17 248
NCCC98.82 20698.57 21399.58 13199.21 28599.31 15798.61 24299.25 26898.65 20098.43 30499.26 26297.86 19699.81 25396.55 26399.27 27699.61 118
train_agg98.35 24897.95 25899.57 13799.35 25499.35 15198.11 29399.41 22894.90 33097.92 32598.99 29998.02 18599.85 19495.38 31199.44 25099.50 174
agg_prior198.33 25197.92 26099.57 13799.35 25499.36 14797.99 30799.39 23794.85 33397.76 33598.98 30298.03 18399.85 19495.49 30699.44 25099.51 168
v119299.57 4799.57 4899.57 13799.77 9899.22 18099.04 19199.60 16199.18 14099.87 5199.72 10499.08 6499.85 19499.89 1399.98 3699.66 79
v114199.54 5999.52 6399.57 13799.78 8899.27 16799.15 16799.61 14799.26 12799.89 3899.69 12498.56 13499.82 23499.82 2399.97 4799.63 99
divwei89l23v2f11299.54 5999.52 6399.57 13799.78 8899.27 16799.15 16799.61 14799.26 12799.89 3899.69 12498.56 13499.82 23499.82 2399.96 5999.63 99
PMMVS299.48 7099.45 7399.57 13799.76 10398.99 20798.09 29599.90 1498.95 16599.78 8299.58 18599.57 2099.93 6699.48 4999.95 6599.79 30
VNet99.18 14799.06 14899.56 14399.24 28299.36 14799.33 10899.31 25599.67 5899.47 17499.57 19296.48 25499.84 21099.15 9299.30 27199.47 185
CNVR-MVS98.99 18398.80 19699.56 14399.25 28099.43 12398.54 25499.27 26398.58 20698.80 27699.43 22698.53 14499.70 29697.22 23099.59 22799.54 154
DeepC-MVS_fast98.47 599.23 12899.12 13099.56 14399.28 27799.22 18098.99 20199.40 23499.08 15799.58 14799.64 15298.90 8499.83 22697.44 21699.75 18399.63 99
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v192192099.56 5099.57 4899.55 14699.75 11199.11 19599.05 18999.61 14799.15 14799.88 4699.71 11199.08 6499.87 15899.90 999.97 4799.66 79
HQP_MVS98.90 19798.68 20399.55 14699.58 17499.24 17698.80 22999.54 18598.94 16699.14 24099.25 26497.24 23199.82 23495.84 29299.78 17499.60 124
FMVSNet299.35 10299.28 10899.55 14699.49 21699.35 15199.45 7999.57 17599.44 10199.70 10899.74 9497.21 23499.87 15899.03 10599.94 7799.44 196
IS-MVSNet99.03 17298.85 18799.55 14699.80 6999.25 17399.73 2199.15 27999.37 11399.61 14399.71 11194.73 27799.81 25397.70 19899.88 11299.58 139
test1299.54 15099.29 27599.33 15499.16 27898.43 30497.54 21799.82 23499.47 24799.48 181
agg_prior398.24 25397.81 26699.53 15199.34 26499.26 16998.09 29599.39 23794.21 33897.77 33498.96 30797.74 20499.84 21095.38 31199.44 25099.50 174
Regformer-299.34 10799.27 11099.53 15199.41 24299.10 19898.99 20199.53 19099.47 9699.66 12099.52 20898.80 9599.89 12498.31 15999.74 19099.60 124
Effi-MVS+-dtu99.07 16598.92 17899.52 15398.89 31699.78 3599.15 16799.66 12399.34 11698.92 26599.24 26997.69 20799.98 798.11 17699.28 27398.81 292
新几何199.52 15399.50 21199.22 18099.26 26595.66 32298.60 29399.28 25897.67 21099.89 12495.95 28999.32 26999.45 191
112198.56 22698.24 23899.52 15399.49 21699.24 17699.30 12199.22 27395.77 31898.52 29899.29 25797.39 22599.85 19495.79 29499.34 26699.46 189
pmmvs-eth3d99.48 7099.47 6999.51 15699.77 9899.41 13198.81 22899.66 12399.42 10899.75 9099.66 14699.20 4899.76 27798.98 11099.99 2099.36 219
v124099.56 5099.58 4599.51 15699.80 6999.00 20699.00 19899.65 13299.15 14799.90 3599.75 9299.09 6199.88 13999.90 999.96 5999.67 69
Regformer-499.45 7999.44 7599.50 15899.52 20298.94 21399.17 15999.53 19099.64 6799.76 8999.60 17798.96 7999.90 10998.91 12299.84 13399.67 69
CDS-MVSNet99.22 13799.13 12799.50 15899.35 25499.11 19598.96 20799.54 18599.46 9999.61 14399.70 11896.31 25999.83 22699.34 6399.88 11299.55 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Patchmtry98.78 21198.54 21699.49 16098.89 31699.19 18899.32 11199.67 11999.65 6599.72 10299.79 7091.87 30099.95 4198.00 18299.97 4799.33 224
UGNet99.38 9599.34 9299.49 16098.90 31298.90 22099.70 2999.35 24699.86 1698.57 29699.81 6198.50 14999.93 6699.38 5899.98 3699.66 79
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
Gipumacopyleft99.57 4799.59 4399.49 16099.98 399.71 5299.72 2599.84 3799.81 2899.94 2099.78 7998.91 8399.71 29598.41 15199.95 6599.05 275
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DELS-MVS99.34 10799.30 10199.48 16399.51 20699.36 14798.12 29199.53 19099.36 11599.41 18999.61 17499.22 4799.87 15899.21 7999.68 20799.20 241
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
PLCcopyleft97.35 1698.36 24597.99 25499.48 16399.32 26999.24 17698.50 25899.51 20295.19 32898.58 29598.96 30796.95 24599.83 22695.63 30399.25 27799.37 216
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Anonymous2023120699.35 10299.31 9699.47 16599.74 11799.06 20599.28 13099.74 8999.23 13499.72 10299.53 20697.63 21699.88 13999.11 10099.84 13399.48 181
Regformer-199.32 11299.27 11099.47 16599.41 24298.95 21298.99 20199.48 21099.48 9299.66 12099.52 20898.78 10199.87 15898.36 15499.74 19099.60 124
ab-mvs99.33 11099.28 10899.47 16599.57 18399.39 13599.78 1299.43 22598.87 17499.57 14999.82 5898.06 18299.87 15898.69 13899.73 19699.15 250
Fast-Effi-MVS+99.02 17498.87 18499.46 16899.38 24999.50 10099.04 19199.79 6897.17 28898.62 29198.74 32399.34 3499.95 4198.32 15899.41 25998.92 284
test_prior398.62 22098.34 23399.46 16899.35 25499.22 18097.95 31299.39 23797.87 25698.05 32099.05 29597.90 19299.69 30295.99 28599.49 24599.48 181
test_prior99.46 16899.35 25499.22 18099.39 23799.69 30299.48 181
TAPA-MVS97.92 1398.03 26497.55 27699.46 16899.47 22799.44 11798.50 25899.62 14386.79 34899.07 24899.26 26298.26 16999.62 33297.28 22699.73 19699.31 229
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
no-one99.28 11799.23 11799.45 17299.87 3299.08 20198.95 20899.52 20098.88 17399.77 8699.83 5197.78 20299.90 10998.46 14999.99 2099.38 212
test_040299.22 13799.14 12499.45 17299.79 8299.43 12399.28 13099.68 11699.54 8599.40 19399.56 19799.07 6699.82 23496.01 28399.96 5999.11 259
VDD-MVS99.20 14299.11 13299.44 17499.43 23898.98 20899.50 7498.32 31599.80 3199.56 15699.69 12496.99 24499.85 19498.99 10899.73 19699.50 174
PVSNet_Blended_VisFu99.40 9099.38 8499.44 17499.90 2598.66 23698.94 21199.91 1197.97 25199.79 7999.73 9899.05 6999.97 1699.15 9299.99 2099.68 62
OMC-MVS98.90 19798.72 20099.44 17499.39 24699.42 12798.58 24699.64 13797.31 28699.44 17799.62 16798.59 13299.69 30296.17 27699.79 16999.22 237
Fast-Effi-MVS+-dtu99.20 14299.12 13099.43 17799.25 28099.69 6399.05 18999.82 4899.50 9098.97 25599.05 29598.98 7499.98 798.20 16799.24 27998.62 297
MVP-Stereo99.16 15299.08 14299.43 17799.48 22299.07 20399.08 18699.55 18198.63 20299.31 21599.68 13698.19 17699.78 26998.18 17199.58 22899.45 191
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs599.19 14599.11 13299.42 17999.76 10398.88 22398.55 25199.73 9298.82 18099.72 10299.62 16796.56 25199.82 23499.32 6899.95 6599.56 144
EI-MVSNet-UG-set99.48 7099.50 6799.42 17999.57 18398.65 23899.24 14099.46 21799.68 5699.80 7499.66 14698.99 7399.89 12499.19 8399.90 10099.72 46
EI-MVSNet-Vis-set99.47 7699.49 6899.42 17999.57 18398.66 23699.24 14099.46 21799.67 5899.79 7999.65 15198.97 7699.89 12499.15 9299.89 10699.71 49
testdata99.42 17999.51 20698.93 21799.30 25896.20 31098.87 27099.40 23198.33 16599.89 12496.29 27299.28 27399.44 196
VDDNet98.97 18498.82 19399.42 17999.71 13398.81 22999.62 5698.68 30199.81 2899.38 20199.80 6394.25 28199.85 19498.79 12999.32 26999.59 135
FMVSNet597.80 26897.25 27999.42 17998.83 32298.97 21099.38 9299.80 6098.87 17499.25 22399.69 12480.60 35699.91 9298.96 11599.90 10099.38 212
MVS_111021_LR99.13 15699.03 15999.42 17999.58 17499.32 15697.91 31899.73 9298.68 19899.31 21599.48 21799.09 6199.66 32097.70 19899.77 17899.29 233
CMPMVSbinary77.52 2398.50 23198.19 24599.41 18698.33 34399.56 9399.01 19699.59 16595.44 32399.57 14999.80 6395.64 26999.46 34796.47 26899.92 8899.21 240
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Regformer-399.41 8799.41 8099.40 18799.52 20298.70 23399.17 15999.44 22299.62 7199.75 9099.60 17798.90 8499.85 19498.89 12399.84 13399.65 89
UnsupCasMVSNet_bld98.55 22898.27 23799.40 18799.56 19499.37 14497.97 31199.68 11697.49 27999.08 24599.35 24695.41 27399.82 23497.70 19898.19 33499.01 279
MVS_111021_HR99.12 15899.02 16099.40 18799.50 21199.11 19597.92 31699.71 10498.76 19099.08 24599.47 22099.17 5199.54 34097.85 19099.76 18099.54 154
v14899.40 9099.41 8099.39 19099.76 10398.94 21399.09 18599.59 16599.17 14599.81 7199.61 17498.41 15699.69 30299.32 6899.94 7799.53 157
HQP-MVS98.36 24598.02 25399.39 19099.31 27098.94 21397.98 30899.37 24397.45 28098.15 31498.83 31696.67 24999.70 29694.73 31899.67 21399.53 157
MVS_030499.17 15099.10 13999.38 19299.08 30398.86 22698.46 26599.73 9299.53 8799.35 20599.30 25497.11 24099.96 3399.33 6599.99 2099.33 224
TSAR-MVS + GP.99.12 15899.04 15899.38 19299.34 26499.16 19098.15 28799.29 25998.18 24299.63 13199.62 16799.18 5099.68 31098.20 16799.74 19099.30 230
AdaColmapbinary98.60 22298.35 23299.38 19299.12 29799.22 18098.67 24199.42 22797.84 26098.81 27499.27 26097.32 22999.81 25395.14 31499.53 24099.10 263
ITE_SJBPF99.38 19299.63 16099.44 11799.73 9298.56 20799.33 21199.53 20698.88 8799.68 31096.01 28399.65 21899.02 278
原ACMM199.37 19699.47 22798.87 22599.27 26396.74 30098.26 30999.32 24997.93 19199.82 23495.96 28899.38 26299.43 202
testgi99.29 11699.26 11299.37 19699.75 11198.81 22998.84 22299.89 1598.38 22299.75 9099.04 29899.36 3399.86 17899.08 10299.25 27799.45 191
MSDG99.08 16498.98 17199.37 19699.60 16899.13 19397.54 32799.74 8998.84 17999.53 16599.55 20299.10 5999.79 26197.07 23899.86 12699.18 246
pmmvs499.13 15699.06 14899.36 19999.57 18399.10 19898.01 30399.25 26898.78 18699.58 14799.44 22598.24 17099.76 27798.74 13499.93 8599.22 237
N_pmnet98.73 21698.53 21799.35 20099.72 13098.67 23598.34 27494.65 35498.35 22999.79 7999.68 13698.03 18399.93 6698.28 16299.92 8899.44 196
Effi-MVS+99.06 16698.97 17299.34 20199.31 27098.98 20898.31 27799.91 1198.81 18198.79 27798.94 31099.14 5499.84 21098.79 12998.74 30799.20 241
Vis-MVSNet (Re-imp)98.77 21298.58 21199.34 20199.78 8898.88 22399.61 6099.56 17899.11 15299.24 22699.56 19793.00 29299.78 26997.43 21799.89 10699.35 221
Patchmatch-RL test98.60 22298.36 23199.33 20399.77 9899.07 20398.27 27899.87 2098.91 17199.74 9899.72 10490.57 31499.79 26198.55 14599.85 12999.11 259
PAPM_NR98.36 24598.04 25299.33 20399.48 22298.93 21798.79 23299.28 26297.54 27798.56 29798.57 32897.12 23999.69 30294.09 32898.90 29499.38 212
PCF-MVS96.03 1896.73 30395.86 31599.33 20399.44 23699.16 19096.87 34199.44 22286.58 34998.95 26199.40 23194.38 28099.88 13987.93 34699.80 16698.95 281
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS98.76 21398.57 21399.33 20399.57 18398.97 21097.53 32999.55 18196.41 30899.27 22099.13 27999.07 6699.78 26996.73 25599.89 10699.23 236
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
jason99.16 15299.11 13299.32 20799.75 11198.44 24498.26 27999.39 23798.70 19799.74 9899.30 25498.54 14099.97 1698.48 14899.82 15299.55 147
jason: jason.
FMVSNet398.80 20998.63 20799.32 20799.13 29598.72 23299.10 18199.48 21099.23 13499.62 13899.64 15292.57 29499.86 17898.96 11599.90 10099.39 209
MVSFormer99.41 8799.44 7599.31 20999.57 18398.40 24799.77 1399.80 6099.73 4299.63 13199.30 25498.02 18599.98 799.43 5399.69 20599.55 147
DP-MVS Recon98.50 23198.23 23999.31 20999.49 21699.46 11098.56 25099.63 14094.86 33298.85 27299.37 23697.81 19999.59 33796.08 27899.44 25098.88 286
PatchMatch-RL98.68 21898.47 21899.30 21199.44 23699.28 16398.14 28999.54 18597.12 29299.11 24399.25 26497.80 20099.70 29696.51 26599.30 27198.93 283
CANet99.11 16199.05 15399.28 21298.83 32298.56 23998.71 24099.41 22899.25 13099.23 22799.22 27397.66 21499.94 5599.19 8399.97 4799.33 224
CNLPA98.57 22598.34 23399.28 21299.18 29199.10 19898.34 27499.41 22898.48 21498.52 29898.98 30297.05 24299.78 26995.59 30499.50 24398.96 280
test_normal98.82 20698.67 20499.27 21499.56 19498.83 22898.22 28298.01 31999.03 16099.49 17399.24 26996.21 26199.76 27798.69 13899.56 22999.22 237
DI_MVS_plusplus_test98.80 20998.65 20599.27 21499.57 18398.90 22098.44 26797.95 32299.02 16199.51 16999.23 27296.18 26399.76 27798.52 14799.42 25799.14 254
Test498.65 21998.44 22099.27 21499.57 18398.86 22698.43 26899.41 22898.85 17699.57 14998.95 30993.05 29099.75 28398.57 14399.56 22999.19 243
sss98.90 19798.77 19799.27 21499.48 22298.44 24498.72 23999.32 25197.94 25399.37 20299.35 24696.31 25999.91 9298.85 12599.63 22099.47 185
LF4IMVS99.01 17898.92 17899.27 21499.71 13399.28 16398.59 24599.77 7398.32 23599.39 19499.41 23098.62 12899.84 21096.62 26199.84 13398.69 296
LFMVS98.46 23698.19 24599.26 21999.24 28298.52 24299.62 5696.94 33899.87 1399.31 21599.58 18591.04 30599.81 25398.68 14099.42 25799.45 191
WTY-MVS98.59 22498.37 23099.26 21999.43 23898.40 24798.74 23599.13 28298.10 24499.21 23199.24 26994.82 27699.90 10997.86 18998.77 30399.49 180
OpenMVScopyleft98.12 1098.23 25597.89 26499.26 21999.19 28999.26 16999.65 5499.69 11391.33 34598.14 31899.77 8598.28 16799.96 3395.41 31099.55 23598.58 301
alignmvs98.28 25297.96 25799.25 22299.12 29798.93 21799.03 19398.42 31299.64 6798.72 28397.85 34090.86 31099.62 33298.88 12499.13 28399.19 243
IterMVS-LS99.41 8799.47 6999.25 22299.81 6198.09 27298.85 22199.76 7999.62 7199.83 6499.64 15298.54 14099.97 1699.15 9299.99 2099.68 62
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
lupinMVS98.96 18798.87 18499.24 22499.57 18398.40 24798.12 29199.18 27698.28 23799.63 13199.13 27998.02 18599.97 1698.22 16599.69 20599.35 221
MVSTER98.47 23598.22 24099.24 22499.06 30598.35 25299.08 18699.46 21799.27 12399.75 9099.66 14688.61 32499.85 19499.14 9899.92 8899.52 165
mvs-test198.83 20498.70 20199.22 22698.89 31699.65 7598.88 21599.66 12399.34 11698.29 30798.94 31097.69 20799.96 3398.11 17698.54 32398.04 324
EI-MVSNet99.38 9599.44 7599.21 22799.58 17498.09 27299.26 13499.46 21799.62 7199.75 9099.67 14298.54 14099.85 19499.15 9299.92 8899.68 62
BH-RMVSNet98.41 24198.14 24799.21 22799.21 28598.47 24398.60 24498.26 31698.35 22998.93 26399.31 25197.20 23799.66 32094.32 32499.10 28599.51 168
ambc99.20 22999.35 25498.53 24199.17 15999.46 21799.67 11699.80 6398.46 15299.70 29697.92 18599.70 20499.38 212
test123567898.93 19498.84 18999.19 23099.46 23198.55 24097.53 32999.77 7398.76 19099.69 11099.48 21796.69 24899.90 10998.30 16099.91 9899.11 259
MVS_Test99.28 11799.31 9699.19 23099.35 25498.79 23199.36 9899.49 20999.17 14599.21 23199.67 14298.78 10199.66 32099.09 10199.66 21699.10 263
MAR-MVS98.24 25397.92 26099.19 23098.78 32999.65 7599.17 15999.14 28095.36 32498.04 32298.81 31897.47 22099.72 29095.47 30899.06 28698.21 318
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
EPNet98.13 25997.77 27099.18 23394.57 35697.99 27699.24 14097.96 32099.74 4097.29 34199.62 16793.13 28999.97 1698.59 14299.83 14399.58 139
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs98.94 19398.87 18499.13 23499.37 25198.90 22099.25 13899.64 13797.55 27699.04 25099.58 18597.23 23399.64 32998.73 13599.44 25098.86 288
MIMVSNet98.43 23898.20 24299.11 23599.53 20098.38 25099.58 6798.61 30398.96 16499.33 21199.76 8890.92 30799.81 25397.38 22099.76 18099.15 250
PMMVS98.49 23398.29 23699.11 23598.96 30998.42 24697.54 32799.32 25197.53 27898.47 30398.15 33797.88 19599.82 23497.46 21599.24 27999.09 266
CANet_DTU98.91 19598.85 18799.09 23798.79 32798.13 26798.18 28499.31 25599.48 9298.86 27199.51 21296.56 25199.95 4199.05 10499.95 6599.19 243
MS-PatchMatch99.00 18198.97 17299.09 23799.11 30098.19 26498.76 23499.33 24998.49 21399.44 17799.58 18598.21 17399.69 30298.20 16799.62 22199.39 209
canonicalmvs99.02 17499.00 16599.09 23799.10 30298.70 23399.61 6099.66 12399.63 7098.64 29097.65 34799.04 7099.54 34098.79 12998.92 29299.04 276
PVSNet_BlendedMVS99.03 17299.01 16399.09 23799.54 19797.99 27698.58 24699.82 4897.62 27099.34 20999.71 11198.52 14699.77 27597.98 18399.97 4799.52 165
MDA-MVSNet-bldmvs99.06 16699.05 15399.07 24199.80 6997.83 28298.89 21399.72 10199.29 12099.63 13199.70 11896.47 25599.89 12498.17 17399.82 15299.50 174
TinyColmap98.97 18498.93 17599.07 24199.46 23198.19 26497.75 32299.75 8498.79 18499.54 16299.70 11898.97 7699.62 33296.63 26099.83 14399.41 206
USDC98.96 18798.93 17599.05 24399.54 19797.99 27697.07 33999.80 6098.21 24099.75 9099.77 8598.43 15499.64 32997.90 18699.88 11299.51 168
111197.29 28096.71 29999.04 24499.65 15697.72 28498.35 27299.80 6099.40 10999.66 12099.43 22675.10 36099.87 15898.98 11099.98 3699.52 165
PAPR97.56 27597.07 28199.04 24498.80 32698.11 27097.63 32499.25 26894.56 33698.02 32398.25 33697.43 22299.68 31090.90 33798.74 30799.33 224
PVSNet_Blended98.70 21798.59 20999.02 24699.54 19797.99 27697.58 32699.82 4895.70 32099.34 20998.98 30298.52 14699.77 27597.98 18399.83 14399.30 230
MVS95.72 32594.63 32898.99 24798.56 33897.98 28199.30 12198.86 29172.71 35397.30 34099.08 28698.34 16399.74 28789.21 34298.33 32999.26 234
HY-MVS98.23 998.21 25797.95 25898.99 24799.03 30898.24 26099.61 6098.72 29996.81 29898.73 28299.51 21294.06 28299.86 17896.91 24498.20 33298.86 288
DSMNet-mixed99.48 7099.65 3498.95 24999.71 13397.27 29599.50 7499.82 4899.59 8299.41 18999.85 4599.62 16100.00 199.53 4699.89 10699.59 135
mvs_anonymous99.28 11799.39 8298.94 25099.19 28997.81 28399.02 19499.55 18199.78 3499.85 5799.80 6398.24 17099.86 17899.57 4299.50 24399.15 250
MG-MVS98.52 23098.39 22798.94 25099.15 29297.39 29498.18 28499.21 27498.89 17299.23 22799.63 16097.37 22799.74 28794.22 32699.61 22599.69 56
GA-MVS97.99 26697.68 27398.93 25299.52 20298.04 27597.19 33899.05 28698.32 23598.81 27498.97 30589.89 32199.41 34898.33 15799.05 28799.34 223
xiu_mvs_v1_base_debu99.23 12899.34 9298.91 25399.59 17198.23 26198.47 26199.66 12399.61 7599.68 11298.94 31099.39 2499.97 1699.18 8599.55 23598.51 304
xiu_mvs_v1_base99.23 12899.34 9298.91 25399.59 17198.23 26198.47 26199.66 12399.61 7599.68 11298.94 31099.39 2499.97 1699.18 8599.55 23598.51 304
xiu_mvs_v1_base_debi99.23 12899.34 9298.91 25399.59 17198.23 26198.47 26199.66 12399.61 7599.68 11298.94 31099.39 2499.97 1699.18 8599.55 23598.51 304
MSLP-MVS++99.05 16999.09 14198.91 25399.21 28598.36 25198.82 22799.47 21498.85 17698.90 26899.56 19798.78 10199.09 35098.57 14399.68 20799.26 234
pmmvs398.08 26297.80 26798.91 25399.41 24297.69 28797.87 31999.66 12395.87 31599.50 17199.51 21290.35 31699.97 1698.55 14599.47 24799.08 269
OpenMVS_ROBcopyleft97.31 1797.36 27996.84 28998.89 25899.29 27599.45 11598.87 21799.48 21086.54 35099.44 17799.74 9497.34 22899.86 17891.61 33499.28 27397.37 341
MDA-MVSNet_test_wron98.95 19098.99 16898.85 25999.64 15897.16 29798.23 28199.33 24998.93 16899.56 15699.66 14697.39 22599.83 22698.29 16199.88 11299.55 147
PMVScopyleft92.94 2198.82 20698.81 19498.85 25999.84 4297.99 27699.20 15099.47 21499.71 4799.42 18399.82 5898.09 17999.47 34493.88 33099.85 12999.07 273
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
YYNet198.95 19098.99 16898.84 26199.64 15897.14 29898.22 28299.32 25198.92 17099.59 14699.66 14697.40 22399.83 22698.27 16399.90 10099.55 147
new_pmnet98.88 20098.89 18298.84 26199.70 14097.62 28998.15 28799.50 20597.98 25099.62 13899.54 20498.15 17899.94 5597.55 21099.84 13398.95 281
CR-MVSNet98.35 24898.20 24298.83 26399.05 30698.12 26899.30 12199.67 11997.39 28399.16 23799.79 7091.87 30099.91 9298.78 13298.77 30398.44 307
PatchT98.45 23798.32 23598.83 26398.94 31098.29 25999.24 14098.82 29499.84 2399.08 24599.76 8891.37 30399.94 5598.82 12899.00 29198.26 314
RPMNet98.53 22998.44 22098.83 26399.05 30698.12 26899.30 12198.78 29699.86 1699.16 23799.74 9492.53 29699.91 9298.75 13398.77 30398.44 307
FPMVS96.32 31395.50 32098.79 26699.60 16898.17 26698.46 26598.80 29597.16 28996.28 34699.63 16082.19 35199.09 35088.45 34498.89 29599.10 263
xiu_mvs_v2_base99.02 17499.11 13298.77 26799.37 25198.09 27298.13 29099.51 20299.47 9699.42 18398.54 33099.38 2899.97 1698.83 12699.33 26898.24 316
PS-MVSNAJ99.00 18199.08 14298.76 26899.37 25198.10 27198.00 30599.51 20299.47 9699.41 18998.50 33299.28 3999.97 1698.83 12699.34 26698.20 320
thresconf0.0297.25 28296.74 29398.75 26999.73 12098.35 25299.35 9995.78 34496.54 30199.39 19499.08 28686.57 33999.88 13995.69 29798.57 31598.02 325
tfpn_n40097.25 28296.74 29398.75 26999.73 12098.35 25299.35 9995.78 34496.54 30199.39 19499.08 28686.57 33999.88 13995.69 29798.57 31598.02 325
tfpnconf97.25 28296.74 29398.75 26999.73 12098.35 25299.35 9995.78 34496.54 30199.39 19499.08 28686.57 33999.88 13995.69 29798.57 31598.02 325
tfpnview1197.25 28296.74 29398.75 26999.73 12098.35 25299.35 9995.78 34496.54 30199.39 19499.08 28686.57 33999.88 13995.69 29798.57 31598.02 325
test0.0.03 197.37 27896.91 28898.74 27397.72 34997.57 29097.60 32597.36 33798.00 24799.21 23198.02 33890.04 31999.79 26198.37 15395.89 35198.86 288
EU-MVSNet99.39 9399.62 3898.72 27499.88 2896.44 30699.56 7099.85 2999.90 699.90 3599.85 4598.09 17999.83 22699.58 4199.95 6599.90 5
new-patchmatchnet99.35 10299.57 4898.71 27599.82 5396.62 30498.55 25199.75 8499.50 9099.88 4699.87 3799.31 3599.88 13999.43 53100.00 199.62 113
tfpn100097.28 28196.83 29098.64 27699.67 15397.68 28899.41 8395.47 35197.14 29099.43 18199.07 29385.87 34699.88 13996.78 25198.67 31198.34 311
BH-untuned98.22 25698.09 24998.58 27799.38 24997.24 29698.55 25198.98 28997.81 26299.20 23698.76 32197.01 24399.65 32794.83 31798.33 32998.86 288
test1235698.43 23898.39 22798.55 27899.46 23196.36 30797.32 33699.81 5697.60 27299.62 13899.37 23694.57 27899.89 12497.80 19399.92 8899.40 207
conf0.0197.19 28796.74 29398.51 27999.73 12098.35 25299.35 9995.78 34496.54 30199.39 19499.08 28686.57 33999.88 13995.69 29798.57 31597.30 342
conf0.00297.19 28796.74 29398.51 27999.73 12098.35 25299.35 9995.78 34496.54 30199.39 19499.08 28686.57 33999.88 13995.69 29798.57 31597.30 342
semantic-postprocess98.51 27999.75 11195.90 31799.84 3799.84 2399.89 3899.73 9895.96 26799.99 499.33 65100.00 199.63 99
JIA-IIPM98.06 26397.92 26098.50 28298.59 33797.02 29998.80 22998.51 30799.88 1297.89 32799.87 3791.89 29999.90 10998.16 17497.68 34498.59 299
Patchmatch-test98.10 26197.98 25698.48 28399.27 27996.48 30599.40 8599.07 28398.81 18199.23 22799.57 19290.11 31899.87 15896.69 25699.64 21999.09 266
tfpn_ndepth96.93 29696.43 30498.42 28499.60 16897.72 28499.22 14695.16 35295.91 31499.26 22298.79 31985.56 34799.87 15896.03 28298.35 32897.68 337
IterMVS98.97 18499.16 12198.42 28499.74 11795.64 32498.06 30099.83 4099.83 2699.85 5799.74 9496.10 26599.99 499.27 77100.00 199.63 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 280x42098.41 24198.41 22598.40 28699.34 26495.89 31896.94 34099.44 22298.80 18399.25 22399.52 20893.51 28699.98 798.94 12099.98 3699.32 228
testus98.15 25898.06 25198.40 28699.11 30095.95 31396.77 34299.89 1595.83 31699.23 22798.47 33397.50 21999.84 21096.58 26299.20 28299.39 209
API-MVS98.38 24498.39 22798.35 28898.83 32299.26 16999.14 17299.18 27698.59 20598.66 28998.78 32098.61 13099.57 33994.14 32799.56 22996.21 349
PVSNet97.47 1598.42 24098.44 22098.35 28899.46 23196.26 30896.70 34499.34 24897.68 26899.00 25399.13 27997.40 22399.72 29097.59 20999.68 20799.08 269
TR-MVS97.44 27697.15 28098.32 29098.53 33997.46 29298.47 26197.91 32396.85 29698.21 31398.51 33196.42 25799.51 34292.16 33397.29 34597.98 329
PAPM95.61 32694.71 32798.31 29199.12 29796.63 30396.66 34598.46 31090.77 34696.25 34798.68 32593.01 29199.69 30281.60 35397.86 34298.62 297
MVEpermissive92.54 2296.66 30596.11 30998.31 29199.68 14997.55 29197.94 31495.60 35099.37 11390.68 35498.70 32496.56 25198.61 35486.94 35299.55 23598.77 294
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
131498.00 26597.90 26398.27 29398.90 31297.45 29399.30 12199.06 28594.98 32997.21 34299.12 28398.43 15499.67 31595.58 30598.56 32297.71 336
test235695.99 32195.26 32498.18 29496.93 35495.53 32695.31 34998.71 30095.67 32198.48 30297.83 34180.72 35499.88 13995.47 30898.21 33199.11 259
SD-MVS99.01 17899.30 10198.15 29599.50 21199.40 13298.94 21199.61 14799.22 13799.75 9099.82 5899.54 2295.51 35697.48 21499.87 11999.54 154
ADS-MVSNet297.78 26997.66 27598.12 29699.14 29395.36 32799.22 14698.75 29796.97 29498.25 31099.64 15290.90 30899.94 5596.51 26599.56 22999.08 269
LP98.34 25098.44 22098.05 29798.88 31995.31 32999.28 13098.74 29899.12 15198.98 25499.79 7093.40 28799.93 6698.38 15299.41 25998.90 285
DeepMVS_CXcopyleft97.98 29899.69 14296.95 30099.26 26575.51 35295.74 35198.28 33596.47 25599.62 33291.23 33697.89 34197.38 340
gg-mvs-nofinetune95.87 32295.17 32597.97 29998.19 34596.95 30099.69 3889.23 35899.89 1096.24 34899.94 1381.19 35299.51 34293.99 32998.20 33297.44 339
thres600view796.60 30696.16 30797.93 30099.63 16096.09 31299.18 15297.57 33098.77 18798.72 28397.32 35187.04 33099.72 29088.57 34398.62 31397.98 329
thres40096.40 31095.89 31397.92 30199.58 17496.11 31099.00 19897.54 33598.43 21698.52 29896.98 35786.85 33499.67 31587.62 34798.51 32497.98 329
view60096.86 29796.52 30097.88 30299.69 14295.87 31999.39 8697.68 32699.11 15298.96 25797.82 34287.40 32599.79 26189.78 33898.83 29697.98 329
view80096.86 29796.52 30097.88 30299.69 14295.87 31999.39 8697.68 32699.11 15298.96 25797.82 34287.40 32599.79 26189.78 33898.83 29697.98 329
conf0.05thres100096.86 29796.52 30097.88 30299.69 14295.87 31999.39 8697.68 32699.11 15298.96 25797.82 34287.40 32599.79 26189.78 33898.83 29697.98 329
tfpn96.86 29796.52 30097.88 30299.69 14295.87 31999.39 8697.68 32699.11 15298.96 25797.82 34287.40 32599.79 26189.78 33898.83 29697.98 329
ADS-MVSNet97.72 27197.67 27497.86 30699.14 29394.65 33299.22 14698.86 29196.97 29498.25 31099.64 15290.90 30899.84 21096.51 26599.56 22999.08 269
IB-MVS95.41 2095.30 32794.46 32997.84 30798.76 33195.33 32897.33 33596.07 34296.02 31295.37 35297.41 35076.17 35999.96 3397.54 21195.44 35298.22 317
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
CVMVSNet98.61 22198.88 18397.80 30899.58 17493.60 33699.26 13499.64 13799.66 6299.72 10299.67 14293.26 28899.93 6699.30 7199.81 16199.87 10
BH-w/o97.20 28697.01 28497.76 30999.08 30395.69 32398.03 30298.52 30695.76 31997.96 32498.02 33895.62 27099.47 34492.82 33297.25 34698.12 322
tpm97.15 28996.95 28697.75 31098.91 31194.24 33499.32 11197.96 32097.71 26598.29 30799.32 24986.72 33799.92 8398.10 17896.24 35099.09 266
test-LLR97.15 28996.95 28697.74 31198.18 34695.02 33097.38 33296.10 34098.00 24797.81 33198.58 32690.04 31999.91 9297.69 20398.78 30198.31 312
test-mter96.23 31695.73 31897.74 31198.18 34695.02 33097.38 33296.10 34097.90 25497.81 33198.58 32679.12 35899.91 9297.69 20398.78 30198.31 312
tfpn11196.50 30896.12 30897.65 31399.63 16095.93 31499.18 15297.57 33098.75 19298.70 28597.31 35287.04 33099.72 29088.27 34598.61 31497.30 342
conf200view1196.43 30996.03 31197.63 31499.63 16095.93 31499.18 15297.57 33098.75 19298.70 28597.31 35287.04 33099.67 31587.62 34798.51 32497.30 342
tfpn200view996.30 31495.89 31397.53 31599.58 17496.11 31099.00 19897.54 33598.43 21698.52 29896.98 35786.85 33499.67 31587.62 34798.51 32496.81 347
cascas96.99 29396.82 29197.48 31697.57 35295.64 32496.43 34699.56 17891.75 34397.13 34397.61 34895.58 27198.63 35396.68 25799.11 28498.18 321
thres100view90096.39 31196.03 31197.47 31799.63 16095.93 31499.18 15297.57 33098.75 19298.70 28597.31 35287.04 33099.67 31587.62 34798.51 32496.81 347
PVSNet_095.53 1995.85 32395.31 32297.47 31798.78 32993.48 33795.72 34799.40 23496.18 31197.37 33997.73 34695.73 26899.58 33895.49 30681.40 35399.36 219
TESTMET0.1,196.24 31595.84 31697.41 31998.24 34493.84 33597.38 33295.84 34398.43 21697.81 33198.56 32979.77 35799.89 12497.77 19498.77 30398.52 303
GG-mvs-BLEND97.36 32097.59 35096.87 30299.70 2988.49 35994.64 35397.26 35580.66 35599.12 34991.50 33596.50 34996.08 351
thres20096.09 31895.68 31997.33 32199.48 22296.22 30998.53 25597.57 33098.06 24698.37 30696.73 35986.84 33699.61 33686.99 35198.57 31596.16 350
Patchmatch-test198.13 25998.40 22697.31 32299.20 28892.99 33898.17 28698.49 30998.24 23999.10 24499.52 20896.01 26699.83 22697.22 23099.62 22199.12 258
PatchmatchNetpermissive97.65 27297.80 26797.18 32398.82 32592.49 34099.17 15998.39 31398.12 24398.79 27799.58 18590.71 31299.89 12497.23 22999.41 25999.16 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS96.53 30796.32 30597.17 32498.18 34692.97 33999.39 8689.95 35798.21 24098.61 29299.59 18386.69 33899.72 29096.99 24199.23 28198.81 292
EPNet_dtu97.62 27397.79 26997.11 32596.67 35592.31 34198.51 25798.04 31799.24 13295.77 35099.47 22093.78 28499.66 32098.98 11099.62 22199.37 216
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tmp_tt95.75 32495.42 32196.76 32689.90 35794.42 33398.86 21897.87 32478.01 35199.30 21999.69 12497.70 20595.89 35599.29 7498.14 33699.95 1
MVS-HIRNet97.86 26798.22 24096.76 32699.28 27791.53 34898.38 27192.60 35599.13 15099.31 21599.96 1197.18 23899.68 31098.34 15699.83 14399.07 273
tpm296.35 31296.22 30696.73 32898.88 31991.75 34699.21 14998.51 30793.27 34197.89 32799.21 27484.83 34899.70 29696.04 28198.18 33598.75 295
tpmrst97.73 27098.07 25096.73 32898.71 33492.00 34299.10 18198.86 29198.52 21098.92 26599.54 20491.90 29899.82 23498.02 18099.03 28998.37 309
DWT-MVSNet_test96.03 32095.80 31796.71 33098.50 34091.93 34399.25 13897.87 32495.99 31396.81 34497.61 34881.02 35399.66 32097.20 23397.98 34098.54 302
PatchFormer-LS_test96.95 29597.07 28196.62 33198.76 33191.85 34499.18 15298.45 31197.29 28797.73 33797.22 35688.77 32399.76 27798.13 17598.04 33898.25 315
tpmvs97.39 27797.69 27296.52 33298.41 34191.76 34599.30 12198.94 29097.74 26397.85 33099.55 20292.40 29799.73 28996.25 27498.73 30998.06 323
CostFormer96.71 30496.79 29296.46 33398.90 31290.71 35299.41 8398.68 30194.69 33598.14 31899.34 24886.32 34599.80 25897.60 20898.07 33798.88 286
E-PMN97.14 29197.43 27796.27 33498.79 32791.62 34795.54 34899.01 28899.44 10198.88 26999.12 28392.78 29399.68 31094.30 32599.03 28997.50 338
tpmp4_e2396.11 31796.06 31096.27 33498.90 31290.70 35399.34 10699.03 28793.72 33996.56 34599.31 25183.63 34999.75 28396.06 28098.02 33998.35 310
dp96.86 29797.07 28196.24 33698.68 33690.30 35599.19 15198.38 31497.35 28598.23 31299.59 18387.23 32999.82 23496.27 27398.73 30998.59 299
tpm cat196.78 30296.98 28596.16 33798.85 32190.59 35499.08 18699.32 25192.37 34297.73 33799.46 22391.15 30499.69 30296.07 27998.80 30098.21 318
EMVS96.96 29497.28 27895.99 33898.76 33191.03 35095.26 35098.61 30399.34 11698.92 26598.88 31593.79 28399.66 32092.87 33199.05 28797.30 342
PNet_i23d97.02 29297.87 26594.49 33999.69 14284.81 35895.18 35199.85 2997.83 26199.32 21399.57 19295.53 27299.47 34496.09 27797.74 34399.18 246
wuyk23d97.58 27499.13 12792.93 34099.69 14299.49 10299.52 7299.77 7397.97 25199.96 899.79 7099.84 499.94 5595.85 29199.82 15279.36 353
testpf94.48 32895.31 32291.99 34197.22 35389.64 35698.86 21896.52 33994.36 33796.09 34998.76 32182.21 35098.73 35297.05 23996.74 34787.60 352
.test124585.84 32989.27 33075.54 34299.65 15697.72 28498.35 27299.80 6099.40 10999.66 12099.43 22675.10 36099.87 15898.98 11033.07 35429.03 355
pcd1.5k->3k49.97 33055.52 33133.31 34399.95 130.00 3610.00 35299.81 560.00 3560.00 357100.00 199.96 10.00 3590.00 356100.00 199.92 3
test12329.31 33133.05 33418.08 34425.93 35912.24 35997.53 32910.93 36111.78 35424.21 35550.08 36421.04 3628.60 35723.51 35432.43 35633.39 354
testmvs28.94 33233.33 33215.79 34526.03 3589.81 36096.77 34215.67 36011.55 35523.87 35650.74 36319.03 3638.53 35823.21 35533.07 35429.03 355
cdsmvs_eth3d_5k24.88 33333.17 3330.00 3460.00 3600.00 3610.00 35299.62 1430.00 3560.00 35799.13 27999.82 60.00 3590.00 3560.00 3570.00 357
pcd_1.5k_mvsjas16.61 33422.14 3350.00 3460.00 3600.00 3610.00 3520.00 3620.00 3560.00 357100.00 199.28 390.00 3590.00 3560.00 3570.00 357
sosnet-low-res8.33 33511.11 3360.00 3460.00 3600.00 3610.00 3520.00 3620.00 3560.00 357100.00 10.00 3640.00 3590.00 3560.00 3570.00 357
sosnet8.33 33511.11 3360.00 3460.00 3600.00 3610.00 3520.00 3620.00 3560.00 357100.00 10.00 3640.00 3590.00 3560.00 3570.00 357
uncertanet8.33 33511.11 3360.00 3460.00 3600.00 3610.00 3520.00 3620.00 3560.00 357100.00 10.00 3640.00 3590.00 3560.00 3570.00 357
Regformer8.33 33511.11 3360.00 3460.00 3600.00 3610.00 3520.00 3620.00 3560.00 357100.00 10.00 3640.00 3590.00 3560.00 3570.00 357
ab-mvs-re8.26 34011.02 3410.00 3460.00 3600.00 3610.00 3520.00 3620.00 3560.00 35799.16 2770.00 3640.00 3590.00 3560.00 3570.00 357
uanet8.33 33511.11 3360.00 3460.00 3600.00 3610.00 3520.00 3620.00 3560.00 357100.00 10.00 3640.00 3590.00 3560.00 3570.00 357
GSMVS99.14 254
test_part398.74 23597.71 26599.57 19299.90 10994.47 322
test_part299.62 16599.67 6899.55 159
test_part199.53 19098.40 15899.68 20799.66 79
sam_mvs190.81 31199.14 254
sam_mvs90.52 315
MTGPAbinary99.53 190
test_post199.14 17251.63 36289.54 32299.82 23496.86 247
test_post52.41 36190.25 31799.86 178
patchmatchnet-post99.62 16790.58 31399.94 55
MTMP98.59 305
gm-plane-assit97.59 35089.02 35793.47 34098.30 33499.84 21096.38 269
test9_res95.10 31599.44 25099.50 174
TEST999.35 25499.35 15198.11 29399.41 22894.83 33497.92 32598.99 29998.02 18599.85 194
test_899.34 26499.31 15798.08 29899.40 23494.90 33097.87 32998.97 30598.02 18599.84 210
agg_prior294.58 32199.46 24999.50 174
agg_prior99.35 25499.36 14799.39 23797.76 33599.85 194
test_prior499.19 18898.00 305
test_prior297.95 31297.87 25698.05 32099.05 29597.90 19295.99 28599.49 245
旧先验297.94 31495.33 32598.94 26299.88 13996.75 253
新几何298.04 301
旧先验199.49 21699.29 16199.26 26599.39 23497.67 21099.36 26599.46 189
无先验98.01 30399.23 27295.83 31699.85 19495.79 29499.44 196
原ACMM297.92 316
test22299.51 20699.08 20197.83 32199.29 25995.21 32798.68 28899.31 25197.28 23099.38 26299.43 202
testdata299.89 12495.99 285
segment_acmp98.37 161
testdata197.72 32397.86 259
plane_prior799.58 17499.38 141
plane_prior699.47 22799.26 16997.24 231
plane_prior599.54 18599.82 23495.84 29299.78 17499.60 124
plane_prior499.25 264
plane_prior399.31 15798.36 22499.14 240
plane_prior298.80 22998.94 166
plane_prior199.51 206
plane_prior99.24 17698.42 26997.87 25699.71 202
n20.00 362
nn0.00 362
door-mid99.83 40
test1199.29 259
door99.77 73
HQP5-MVS98.94 213
HQP-NCC99.31 27097.98 30897.45 28098.15 314
ACMP_Plane99.31 27097.98 30897.45 28098.15 314
BP-MVS94.73 318
HQP4-MVS98.15 31499.70 29699.53 157
HQP3-MVS99.37 24399.67 213
HQP2-MVS96.67 249
NP-MVS99.40 24599.13 19398.83 316
MDTV_nov1_ep13_2view91.44 34999.14 17297.37 28499.21 23191.78 30296.75 25399.03 277
MDTV_nov1_ep1397.73 27198.70 33590.83 35199.15 16798.02 31898.51 21198.82 27399.61 17490.98 30699.66 32096.89 24698.92 292
ACMMP++_ref99.94 77
ACMMP++99.79 169
Test By Simon98.41 156