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 bysorted bysort bysort by
test_vis3_rt99.89 399.90 399.87 1599.98 399.75 6299.70 34100.00 199.73 57100.00 199.89 3199.79 899.88 17299.98 1100.00 199.98 1
test_fmvs299.72 2299.85 1199.34 21199.91 2698.08 29299.48 92100.00 199.90 1399.99 799.91 2499.50 3199.98 1099.98 199.99 1399.96 4
test_fmvs399.83 1299.93 299.53 15899.96 598.62 25699.67 48100.00 199.95 4100.00 199.95 1399.85 399.99 699.98 199.99 1399.98 1
test_vis1_n99.68 3199.79 1799.36 20899.94 1698.18 28299.52 83100.00 199.86 28100.00 199.88 3698.99 8799.96 4199.97 499.96 5699.95 5
test_fmvs1_n99.68 3199.81 1499.28 22699.95 1397.93 30199.49 91100.00 199.82 4199.99 799.89 3199.21 6099.98 1099.97 499.98 3099.93 9
test_f99.75 1899.88 699.37 20499.96 598.21 27999.51 86100.00 199.94 8100.00 199.93 1799.58 2499.94 6499.97 499.99 1399.97 3
test_fmvs199.48 7299.65 3598.97 26699.54 19897.16 32299.11 19099.98 899.78 5199.96 1599.81 6698.72 12299.97 2299.95 799.97 4299.79 37
mvsany_test399.85 899.88 699.75 6099.95 1399.37 16399.53 8299.98 899.77 5599.99 799.95 1399.85 399.94 6499.95 799.98 3099.94 7
LCM-MVSNet99.95 199.95 199.95 199.99 199.99 199.95 299.97 1099.99 1100.00 199.98 1099.78 9100.00 199.92 9100.00 199.87 16
v192192099.56 5999.57 5799.55 15399.75 11299.11 20899.05 20299.61 15899.15 16699.88 4799.71 12499.08 7799.87 18699.90 1099.97 4299.66 88
v124099.56 5999.58 5499.51 16299.80 7299.00 21999.00 21299.65 14099.15 16699.90 3799.75 10399.09 7499.88 17299.90 1099.96 5699.67 79
v1099.69 2899.69 2899.66 10299.81 6799.39 15899.66 5299.75 8799.60 9799.92 2899.87 4098.75 11799.86 20499.90 1099.99 1399.73 55
v119299.57 5699.57 5799.57 14799.77 9799.22 19599.04 20499.60 17099.18 15599.87 5599.72 11799.08 7799.85 22199.89 1399.98 3099.66 88
v14419299.55 6299.54 6399.58 14199.78 8999.20 20099.11 19099.62 15199.18 15599.89 4199.72 11798.66 13099.87 18699.88 1499.97 4299.66 88
v899.68 3199.69 2899.65 10799.80 7299.40 15699.66 5299.76 8299.64 8599.93 2499.85 4998.66 13099.84 23599.88 1499.99 1399.71 60
v114499.54 6499.53 6799.59 13899.79 8299.28 18199.10 19299.61 15899.20 15399.84 6199.73 11098.67 12899.84 23599.86 1699.98 3099.64 104
v7n99.82 1399.80 1699.88 1299.96 599.84 2499.82 899.82 5299.84 3699.94 2199.91 2499.13 7199.96 4199.83 1799.99 1399.83 25
v2v48299.50 6899.47 7199.58 14199.78 8999.25 18899.14 17899.58 18699.25 14499.81 7399.62 18198.24 18699.84 23599.83 1799.97 4299.64 104
test_vis1_rt99.45 8299.46 7599.41 19199.71 12798.63 25598.99 21799.96 1499.03 17999.95 1999.12 31598.75 11799.84 23599.82 1999.82 16399.77 44
tt080599.63 4599.57 5799.81 3099.87 4299.88 1299.58 7498.70 32899.72 6199.91 3199.60 19899.43 3399.81 27499.81 2099.53 27199.73 55
V4299.56 5999.54 6399.63 12199.79 8299.46 13799.39 10799.59 17699.24 14699.86 5699.70 13198.55 14599.82 25999.79 2199.95 6799.60 134
mvs_tets99.90 299.90 399.90 599.96 599.79 4499.72 2999.88 3099.92 1199.98 1099.93 1799.94 199.98 1099.77 22100.00 199.92 10
PS-MVSNAJss99.84 1099.82 1399.89 899.96 599.77 5099.68 4499.85 3999.95 499.98 1099.92 2199.28 5199.98 1099.75 23100.00 199.94 7
jajsoiax99.89 399.89 599.89 899.96 599.78 4799.70 3499.86 3599.89 1999.98 1099.90 2799.94 199.98 1099.75 23100.00 199.90 11
ANet_high99.88 599.87 899.91 299.99 199.91 499.65 58100.00 199.90 13100.00 199.97 1199.61 2199.97 2299.75 23100.00 199.84 21
CS-MVS-test99.68 3199.70 2499.64 11499.57 18599.83 2999.78 1199.97 1099.92 1199.50 19699.38 26699.57 2599.95 5199.69 2699.90 10099.15 276
RRT_MVS99.67 3799.59 5099.91 299.94 1699.88 1299.78 1199.27 28699.87 2599.91 3199.87 4098.04 20399.96 4199.68 2799.99 1399.90 11
CS-MVS99.67 3799.70 2499.58 14199.53 20499.84 2499.79 1099.96 1499.90 1399.61 15799.41 25699.51 3099.95 5199.66 2899.89 10998.96 310
pmmvs699.86 799.86 1099.83 2599.94 1699.90 799.83 699.91 2199.85 3399.94 2199.95 1399.73 1299.90 14199.65 2999.97 4299.69 67
MIMVSNet199.66 3999.62 4199.80 3499.94 1699.87 1599.69 4199.77 7799.78 5199.93 2499.89 3197.94 21199.92 10199.65 2999.98 3099.62 120
DROMVSNet99.69 2899.69 2899.68 9299.71 12799.91 499.76 1899.96 1499.86 2899.51 19499.39 26499.57 2599.93 8199.64 3199.86 13799.20 265
K. test v398.87 21498.60 22399.69 9099.93 2399.46 13799.74 2394.97 36999.78 5199.88 4799.88 3693.66 30799.97 2299.61 3299.95 6799.64 104
KD-MVS_self_test99.63 4599.59 5099.76 5199.84 4999.90 799.37 11399.79 6999.83 3999.88 4799.85 4998.42 16699.90 14199.60 3399.73 20799.49 193
Anonymous2024052199.44 8499.42 8499.49 16599.89 3398.96 22599.62 6199.76 8299.85 3399.82 6699.88 3696.39 27599.97 2299.59 3499.98 3099.55 156
TransMVSNet (Re)99.78 1699.77 1999.81 3099.91 2699.85 1999.75 2199.86 3599.70 6899.91 3199.89 3199.60 2399.87 18699.59 3499.74 20299.71 60
OurMVSNet-221017-099.75 1899.71 2399.84 2399.96 599.83 2999.83 699.85 3999.80 4699.93 2499.93 1798.54 14799.93 8199.59 3499.98 3099.76 50
EU-MVSNet99.39 9999.62 4198.72 29599.88 3896.44 33699.56 7999.85 3999.90 1399.90 3799.85 4998.09 19999.83 25099.58 3799.95 6799.90 11
mvsmamba99.74 2199.70 2499.85 2099.93 2399.83 2999.76 1899.81 6199.96 299.91 3199.81 6698.60 13899.94 6499.58 3799.98 3099.77 44
mvs_anonymous99.28 12399.39 8698.94 26999.19 30397.81 30499.02 20899.55 19999.78 5199.85 5899.80 7098.24 18699.86 20499.57 3999.50 27799.15 276
test111197.74 29198.16 26796.49 35099.60 16689.86 37999.71 3391.21 37699.89 1999.88 4799.87 4093.73 30699.90 14199.56 4099.99 1399.70 63
lessismore_v099.64 11499.86 4599.38 16090.66 37799.89 4199.83 5594.56 29799.97 2299.56 4099.92 9099.57 151
mvsany_test199.44 8499.45 7799.40 19399.37 25798.64 25497.90 32699.59 17699.27 14099.92 2899.82 6299.74 1199.93 8199.55 4299.87 12999.63 109
bld_raw_dy_0_6499.70 2599.65 3599.85 2099.95 1399.77 5099.66 5299.71 10899.95 499.91 3199.77 9498.35 175100.00 199.54 4399.99 1399.79 37
pm-mvs199.79 1599.79 1799.78 4199.91 2699.83 2999.76 1899.87 3299.73 5799.89 4199.87 4099.63 1899.87 18699.54 4399.92 9099.63 109
LTVRE_ROB99.19 199.88 599.87 899.88 1299.91 2699.90 799.96 199.92 1899.90 1399.97 1399.87 4099.81 799.95 5199.54 4399.99 1399.80 31
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
DSMNet-mixed99.48 7299.65 3598.95 26899.71 12797.27 31999.50 8799.82 5299.59 9999.41 21899.85 4999.62 20100.00 199.53 4699.89 10999.59 141
test250694.73 34094.59 34295.15 35699.59 17085.90 38199.75 2174.01 38299.89 1999.71 11799.86 4779.00 38099.90 14199.52 4799.99 1399.65 96
UniMVSNet_ETH3D99.85 899.83 1299.90 599.89 3399.91 499.89 499.71 10899.93 999.95 1999.89 3199.71 1399.96 4199.51 4899.97 4299.84 21
FC-MVSNet-test99.70 2599.65 3599.86 1899.88 3899.86 1899.72 2999.78 7499.90 1399.82 6699.83 5598.45 16299.87 18699.51 4899.97 4299.86 18
UA-Net99.78 1699.76 2199.86 1899.72 12499.71 7699.91 399.95 1799.96 299.71 11799.91 2499.15 6699.97 2299.50 50100.00 199.90 11
PMMVS299.48 7299.45 7799.57 14799.76 10198.99 22098.09 30499.90 2498.95 18699.78 8599.58 20599.57 2599.93 8199.48 5199.95 6799.79 37
VPA-MVSNet99.66 3999.62 4199.79 3899.68 14799.75 6299.62 6199.69 12099.85 3399.80 7699.81 6698.81 10599.91 12399.47 5299.88 11899.70 63
ECVR-MVScopyleft97.73 29298.04 27296.78 34499.59 17090.81 37599.72 2990.43 37899.89 1999.86 5699.86 4793.60 30899.89 15899.46 5399.99 1399.65 96
nrg03099.70 2599.66 3399.82 2799.76 10199.84 2499.61 6699.70 11499.93 999.78 8599.68 14899.10 7299.78 28699.45 5499.96 5699.83 25
TAMVS99.49 7099.45 7799.63 12199.48 22799.42 15199.45 9899.57 18899.66 8199.78 8599.83 5597.85 21899.86 20499.44 5599.96 5699.61 130
GeoE99.69 2899.66 3399.78 4199.76 10199.76 5899.60 7199.82 5299.46 11499.75 9999.56 21799.63 1899.95 5199.43 5699.88 11899.62 120
new-patchmatchnet99.35 10999.57 5798.71 29799.82 6096.62 33498.55 26699.75 8799.50 10599.88 4799.87 4099.31 4799.88 17299.43 56100.00 199.62 120
test20.0399.55 6299.54 6399.58 14199.79 8299.37 16399.02 20899.89 2699.60 9799.82 6699.62 18198.81 10599.89 15899.43 5699.86 13799.47 201
MVSFormer99.41 9399.44 8099.31 22199.57 18598.40 26899.77 1499.80 6399.73 5799.63 14299.30 28598.02 20599.98 1099.43 5699.69 22299.55 156
test_djsdf99.84 1099.81 1499.91 299.94 1699.84 2499.77 1499.80 6399.73 5799.97 1399.92 2199.77 1099.98 1099.43 56100.00 199.90 11
Anonymous2023121199.62 5199.57 5799.76 5199.61 16499.60 11399.81 999.73 9699.82 4199.90 3799.90 2797.97 21099.86 20499.42 6199.96 5699.80 31
SixPastTwentyTwo99.42 8999.30 10799.76 5199.92 2599.67 9199.70 3499.14 30899.65 8399.89 4199.90 2796.20 28099.94 6499.42 6199.92 9099.67 79
patch_mono-299.51 6799.46 7599.64 11499.70 13599.11 20899.04 20499.87 3299.71 6399.47 20099.79 8098.24 18699.98 1099.38 6399.96 5699.83 25
UGNet99.38 10199.34 9699.49 16598.90 33798.90 23399.70 3499.35 26899.86 2898.57 32399.81 6698.50 15799.93 8199.38 6399.98 3099.66 88
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
XXY-MVS99.71 2499.67 3299.81 3099.89 3399.72 7499.59 7299.82 5299.39 12799.82 6699.84 5499.38 3999.91 12399.38 6399.93 8699.80 31
iter_conf_final98.75 22498.54 23399.40 19399.33 27598.75 24299.26 14399.59 17699.80 4699.76 9299.58 20590.17 34699.92 10199.37 6699.97 4299.54 164
FIs99.65 4499.58 5499.84 2399.84 4999.85 1999.66 5299.75 8799.86 2899.74 10799.79 8098.27 18499.85 22199.37 6699.93 8699.83 25
anonymousdsp99.80 1499.77 1999.90 599.96 599.88 1299.73 2699.85 3999.70 6899.92 2899.93 1799.45 3299.97 2299.36 68100.00 199.85 20
casdiffmvs_mvgpermissive99.68 3199.68 3199.69 9099.81 6799.59 11599.29 13699.90 2499.71 6399.79 8199.73 11099.54 2899.84 23599.36 6899.96 5699.65 96
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNetpermissive99.75 1899.74 2299.79 3899.88 3899.66 9399.69 4199.92 1899.67 7799.77 9099.75 10399.61 2199.98 1099.35 7099.98 3099.72 57
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dcpmvs_299.61 5399.64 3999.53 15899.79 8298.82 23799.58 7499.97 1099.95 499.96 1599.76 9898.44 16399.99 699.34 7199.96 5699.78 40
CHOSEN 1792x268899.39 9999.30 10799.65 10799.88 3899.25 18898.78 24799.88 3098.66 21999.96 1599.79 8097.45 23999.93 8199.34 7199.99 1399.78 40
CDS-MVSNet99.22 14299.13 13599.50 16499.35 26299.11 20898.96 22299.54 20599.46 11499.61 15799.70 13196.31 27799.83 25099.34 7199.88 11899.55 156
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT99.00 19399.16 12998.51 30299.75 11295.90 34498.07 30799.84 4599.84 3699.89 4199.73 11096.01 28499.99 699.33 74100.00 199.63 109
HyFIR lowres test98.91 20698.64 22099.73 7499.85 4899.47 13398.07 30799.83 4798.64 22199.89 4199.60 19892.57 317100.00 199.33 7499.97 4299.72 57
pmmvs599.19 15299.11 14299.42 18499.76 10198.88 23498.55 26699.73 9698.82 20499.72 11299.62 18196.56 26699.82 25999.32 7699.95 6799.56 153
v14899.40 9599.41 8599.39 19799.76 10198.94 22699.09 19699.59 17699.17 16099.81 7399.61 19098.41 16799.69 31999.32 7699.94 7899.53 170
baseline99.63 4599.62 4199.66 10299.80 7299.62 10599.44 10199.80 6399.71 6399.72 11299.69 13799.15 6699.83 25099.32 7699.94 7899.53 170
iter_conf0598.46 25598.23 25899.15 24699.04 32697.99 29499.10 19299.61 15899.79 4999.76 9299.58 20587.88 35699.92 10199.31 7999.97 4299.53 170
CVMVSNet98.61 23598.88 19997.80 32699.58 17593.60 36199.26 14399.64 14699.66 8199.72 11299.67 15393.26 31099.93 8199.30 8099.81 17299.87 16
PS-CasMVS99.66 3999.58 5499.89 899.80 7299.85 1999.66 5299.73 9699.62 8899.84 6199.71 12498.62 13499.96 4199.30 8099.96 5699.86 18
DTE-MVSNet99.68 3199.61 4599.88 1299.80 7299.87 1599.67 4899.71 10899.72 6199.84 6199.78 8798.67 12899.97 2299.30 8099.95 6799.80 31
tmp_tt95.75 33695.42 33496.76 34589.90 38194.42 35698.86 23097.87 35478.01 37299.30 24599.69 13797.70 22595.89 37699.29 8398.14 35599.95 5
PEN-MVS99.66 3999.59 5099.89 899.83 5399.87 1599.66 5299.73 9699.70 6899.84 6199.73 11098.56 14499.96 4199.29 8399.94 7899.83 25
WR-MVS_H99.61 5399.53 6799.87 1599.80 7299.83 2999.67 4899.75 8799.58 10099.85 5899.69 13798.18 19599.94 6499.28 8599.95 6799.83 25
IterMVS98.97 19799.16 12998.42 30699.74 11895.64 34798.06 30999.83 4799.83 3999.85 5899.74 10696.10 28399.99 699.27 86100.00 199.63 109
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs3398.61 23598.34 25199.44 17899.60 16698.67 24799.27 14199.44 24399.68 7399.32 23699.49 23992.50 320100.00 199.24 8796.51 36999.65 96
hse-mvs298.52 24798.30 25599.16 24499.29 28498.60 25798.77 24899.02 31599.68 7399.32 23699.04 32592.50 32099.85 22199.24 8797.87 36099.03 303
FMVSNet199.66 3999.63 4099.73 7499.78 8999.77 5099.68 4499.70 11499.67 7799.82 6699.83 5598.98 8999.90 14199.24 8799.97 4299.53 170
casdiffmvspermissive99.63 4599.61 4599.67 9599.79 8299.59 11599.13 18499.85 3999.79 4999.76 9299.72 11799.33 4699.82 25999.21 9099.94 7899.59 141
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CP-MVSNet99.54 6499.43 8299.87 1599.76 10199.82 3599.57 7799.61 15899.54 10199.80 7699.64 16497.79 22299.95 5199.21 9099.94 7899.84 21
DELS-MVS99.34 11499.30 10799.48 16999.51 21199.36 16798.12 30099.53 21499.36 13199.41 21899.61 19099.22 5999.87 18699.21 9099.68 22799.20 265
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
UniMVSNet (Re)99.37 10499.26 11899.68 9299.51 21199.58 11998.98 22099.60 17099.43 12299.70 12099.36 27297.70 22599.88 17299.20 9399.87 12999.59 141
CANet99.11 17299.05 16399.28 22698.83 34598.56 25898.71 25499.41 24999.25 14499.23 25399.22 30397.66 23399.94 6499.19 9499.97 4299.33 238
EI-MVSNet-UG-set99.48 7299.50 6999.42 18499.57 18598.65 25399.24 15099.46 23899.68 7399.80 7699.66 15798.99 8799.89 15899.19 9499.90 10099.72 57
xiu_mvs_v1_base_debu99.23 13499.34 9698.91 27599.59 17098.23 27698.47 27599.66 13199.61 9199.68 12698.94 34199.39 3599.97 2299.18 9699.55 26498.51 339
xiu_mvs_v1_base99.23 13499.34 9698.91 27599.59 17098.23 27698.47 27599.66 13199.61 9199.68 12698.94 34199.39 3599.97 2299.18 9699.55 26498.51 339
xiu_mvs_v1_base_debi99.23 13499.34 9698.91 27599.59 17098.23 27698.47 27599.66 13199.61 9199.68 12698.94 34199.39 3599.97 2299.18 9699.55 26498.51 339
MVS_030498.88 21298.71 21599.39 19798.85 34398.91 23299.45 9899.30 28098.56 22897.26 36299.68 14896.18 28199.96 4199.17 9999.94 7899.29 249
VPNet99.46 8099.37 9199.71 8599.82 6099.59 11599.48 9299.70 11499.81 4399.69 12399.58 20597.66 23399.86 20499.17 9999.44 28499.67 79
UniMVSNet_NR-MVSNet99.37 10499.25 12099.72 8099.47 23399.56 12298.97 22199.61 15899.43 12299.67 13199.28 28997.85 21899.95 5199.17 9999.81 17299.65 96
DU-MVS99.33 11799.21 12499.71 8599.43 24499.56 12298.83 23599.53 21499.38 12899.67 13199.36 27297.67 22999.95 5199.17 9999.81 17299.63 109
EI-MVSNet-Vis-set99.47 7999.49 7099.42 18499.57 18598.66 25099.24 15099.46 23899.67 7799.79 8199.65 16298.97 9199.89 15899.15 10399.89 10999.71 60
EI-MVSNet99.38 10199.44 8099.21 23899.58 17598.09 28999.26 14399.46 23899.62 8899.75 9999.67 15398.54 14799.85 22199.15 10399.92 9099.68 73
VNet99.18 15699.06 15999.56 15099.24 29499.36 16799.33 12099.31 27799.67 7799.47 20099.57 21496.48 26999.84 23599.15 10399.30 30199.47 201
EG-PatchMatch MVS99.57 5699.56 6299.62 13099.77 9799.33 17399.26 14399.76 8299.32 13599.80 7699.78 8799.29 4999.87 18699.15 10399.91 9999.66 88
PVSNet_Blended_VisFu99.40 9599.38 8899.44 17899.90 3198.66 25098.94 22599.91 2197.97 28199.79 8199.73 11099.05 8299.97 2299.15 10399.99 1399.68 73
IterMVS-LS99.41 9399.47 7199.25 23499.81 6798.09 28998.85 23299.76 8299.62 8899.83 6599.64 16498.54 14799.97 2299.15 10399.99 1399.68 73
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet99.54 6499.47 7199.76 5199.58 17599.64 9999.30 13099.63 14899.61 9199.71 11799.56 21798.76 11599.96 4199.14 10999.92 9099.68 73
MVSTER98.47 25498.22 26099.24 23699.06 32398.35 27399.08 19999.46 23899.27 14099.75 9999.66 15788.61 35499.85 22199.14 10999.92 9099.52 181
Anonymous2023120699.35 10999.31 10299.47 17199.74 11899.06 21899.28 13899.74 9299.23 14899.72 11299.53 22897.63 23599.88 17299.11 11199.84 14699.48 197
MVS_Test99.28 12399.31 10299.19 24199.35 26298.79 24099.36 11699.49 23199.17 16099.21 25899.67 15398.78 11299.66 33899.09 11299.66 23699.10 287
testgi99.29 12299.26 11899.37 20499.75 11298.81 23898.84 23399.89 2698.38 24899.75 9999.04 32599.36 4499.86 20499.08 11399.25 30899.45 206
1112_ss99.05 18198.84 20499.67 9599.66 15399.29 17998.52 27199.82 5297.65 29799.43 21099.16 30996.42 27299.91 12399.07 11499.84 14699.80 31
CANet_DTU98.91 20698.85 20299.09 25598.79 35098.13 28498.18 29399.31 27799.48 10798.86 29899.51 23296.56 26699.95 5199.05 11599.95 6799.19 268
Baseline_NR-MVSNet99.49 7099.37 9199.82 2799.91 2699.84 2498.83 23599.86 3599.68 7399.65 13799.88 3697.67 22999.87 18699.03 11699.86 13799.76 50
FMVSNet299.35 10999.28 11499.55 15399.49 22299.35 17099.45 9899.57 18899.44 11799.70 12099.74 10697.21 25099.87 18699.03 11699.94 7899.44 211
Test_1112_low_res98.95 20398.73 21399.63 12199.68 14799.15 20598.09 30499.80 6397.14 32399.46 20499.40 26096.11 28299.89 15899.01 11899.84 14699.84 21
VDD-MVS99.20 14999.11 14299.44 17899.43 24498.98 22199.50 8798.32 34699.80 4699.56 17599.69 13796.99 25999.85 22198.99 11999.73 20799.50 188
DeepC-MVS98.90 499.62 5199.61 4599.67 9599.72 12499.44 14499.24 15099.71 10899.27 14099.93 2499.90 2799.70 1599.93 8198.99 11999.99 1399.64 104
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
pmmvs-eth3d99.48 7299.47 7199.51 16299.77 9799.41 15598.81 24099.66 13199.42 12699.75 9999.66 15799.20 6199.76 29698.98 12199.99 1399.36 232
EPNet_dtu97.62 29797.79 29197.11 34396.67 37692.31 36698.51 27298.04 34999.24 14695.77 37099.47 24693.78 30599.66 33898.98 12199.62 24399.37 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvspermissive99.34 11499.32 10199.39 19799.67 15298.77 24198.57 26499.81 6199.61 9199.48 19999.41 25698.47 15899.86 20498.97 12399.90 10099.53 170
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
NR-MVSNet99.40 9599.31 10299.68 9299.43 24499.55 12599.73 2699.50 22799.46 11499.88 4799.36 27297.54 23699.87 18698.97 12399.87 12999.63 109
GBi-Net99.42 8999.31 10299.73 7499.49 22299.77 5099.68 4499.70 11499.44 11799.62 15199.83 5597.21 25099.90 14198.96 12599.90 10099.53 170
FMVSNet597.80 28997.25 30599.42 18498.83 34598.97 22399.38 10999.80 6398.87 19899.25 24999.69 13780.60 37599.91 12398.96 12599.90 10099.38 226
test199.42 8999.31 10299.73 7499.49 22299.77 5099.68 4499.70 11499.44 11799.62 15199.83 5597.21 25099.90 14198.96 12599.90 10099.53 170
FMVSNet398.80 22098.63 22299.32 21899.13 31198.72 24599.10 19299.48 23299.23 14899.62 15199.64 16492.57 31799.86 20498.96 12599.90 10099.39 224
UnsupCasMVSNet_eth98.83 21798.57 22999.59 13899.68 14799.45 14298.99 21799.67 12799.48 10799.55 18099.36 27294.92 29199.86 20498.95 12996.57 36899.45 206
CHOSEN 280x42098.41 26098.41 24398.40 30799.34 27095.89 34596.94 36299.44 24398.80 20799.25 24999.52 23093.51 30999.98 1098.94 13099.98 3099.32 241
TDRefinement99.72 2299.70 2499.77 4499.90 3199.85 1999.86 599.92 1899.69 7199.78 8599.92 2199.37 4199.88 17298.93 13199.95 6799.60 134
alignmvs98.28 26997.96 27899.25 23499.12 31398.93 22999.03 20798.42 34299.64 8598.72 31297.85 37290.86 33899.62 34898.88 13299.13 31399.19 268
sss98.90 20898.77 21299.27 22999.48 22798.44 26598.72 25299.32 27397.94 28599.37 22699.35 27796.31 27799.91 12398.85 13399.63 24299.47 201
xiu_mvs_v2_base99.02 18799.11 14298.77 29299.37 25798.09 28998.13 29999.51 22399.47 11199.42 21298.54 36199.38 3999.97 2298.83 13499.33 29898.24 351
PS-MVSNAJ99.00 19399.08 15398.76 29399.37 25798.10 28898.00 31499.51 22399.47 11199.41 21898.50 36399.28 5199.97 2298.83 13499.34 29798.20 355
D2MVS99.22 14299.19 12699.29 22499.69 13998.74 24498.81 24099.41 24998.55 23099.68 12699.69 13798.13 19799.87 18698.82 13699.98 3099.24 254
PatchT98.45 25798.32 25398.83 28798.94 33598.29 27499.24 15098.82 32399.84 3699.08 27599.76 9891.37 32899.94 6498.82 13699.00 32298.26 350
testf199.63 4599.60 4899.72 8099.94 1699.95 299.47 9599.89 2699.43 12299.88 4799.80 7099.26 5599.90 14198.81 13899.88 11899.32 241
APD_test299.63 4599.60 4899.72 8099.94 1699.95 299.47 9599.89 2699.43 12299.88 4799.80 7099.26 5599.90 14198.81 13899.88 11899.32 241
Effi-MVS+99.06 17898.97 18699.34 21199.31 27898.98 22198.31 28699.91 2198.81 20598.79 30698.94 34199.14 6999.84 23598.79 14098.74 33799.20 265
canonicalmvs99.02 18799.00 17799.09 25599.10 31998.70 24699.61 6699.66 13199.63 8798.64 31797.65 37499.04 8399.54 35798.79 14098.92 32699.04 302
VDDNet98.97 19798.82 20799.42 18499.71 12798.81 23899.62 6198.68 32999.81 4399.38 22599.80 7094.25 29999.85 22198.79 14099.32 29999.59 141
CR-MVSNet98.35 26798.20 26298.83 28799.05 32498.12 28599.30 13099.67 12797.39 31199.16 26499.79 8091.87 32599.91 12398.78 14398.77 33398.44 344
test_method91.72 34192.32 34489.91 35893.49 38070.18 38290.28 37199.56 19361.71 37595.39 37299.52 23093.90 30199.94 6498.76 14498.27 35099.62 120
RPMNet98.60 23798.53 23598.83 28799.05 32498.12 28599.30 13099.62 15199.86 2899.16 26499.74 10692.53 31999.92 10198.75 14598.77 33398.44 344
pmmvs499.13 16799.06 15999.36 20899.57 18599.10 21398.01 31299.25 29298.78 21099.58 16599.44 25398.24 18699.76 29698.74 14699.93 8699.22 259
tttt051797.62 29797.20 30698.90 28199.76 10197.40 31699.48 9294.36 37199.06 17799.70 12099.49 23984.55 37099.94 6498.73 14799.65 23899.36 232
EPP-MVSNet99.17 16099.00 17799.66 10299.80 7299.43 14899.70 3499.24 29599.48 10799.56 17599.77 9494.89 29299.93 8198.72 14899.89 10999.63 109
Anonymous2024052999.42 8999.34 9699.65 10799.53 20499.60 11399.63 6099.39 25999.47 11199.76 9299.78 8798.13 19799.86 20498.70 14999.68 22799.49 193
ACMH98.42 699.59 5599.54 6399.72 8099.86 4599.62 10599.56 7999.79 6998.77 21199.80 7699.85 4999.64 1799.85 22198.70 14999.89 10999.70 63
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs99.33 11799.28 11499.47 17199.57 18599.39 15899.78 1199.43 24698.87 19899.57 16899.82 6298.06 20299.87 18698.69 15199.73 20799.15 276
LFMVS98.46 25598.19 26599.26 23199.24 29498.52 26199.62 6196.94 36299.87 2599.31 24099.58 20591.04 33399.81 27498.68 15299.42 28899.45 206
WR-MVS99.11 17298.93 19099.66 10299.30 28299.42 15198.42 28099.37 26499.04 17899.57 16899.20 30796.89 26199.86 20498.66 15399.87 12999.70 63
Anonymous20240521198.75 22498.46 23899.63 12199.34 27099.66 9399.47 9597.65 35599.28 13999.56 17599.50 23593.15 31199.84 23598.62 15499.58 25899.40 222
EPNet98.13 27797.77 29299.18 24394.57 37997.99 29499.24 15097.96 35199.74 5697.29 36199.62 18193.13 31299.97 2298.59 15599.83 15499.58 146
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++99.05 18199.09 15198.91 27599.21 29898.36 27298.82 23999.47 23598.85 20098.90 29399.56 21798.78 11299.09 37098.57 15699.68 22799.26 251
Patchmatch-RL test98.60 23798.36 24899.33 21499.77 9799.07 21698.27 28899.87 3298.91 19399.74 10799.72 11790.57 34299.79 28398.55 15799.85 14199.11 285
pmmvs398.08 28097.80 28998.91 27599.41 25097.69 30997.87 32799.66 13195.87 34299.50 19699.51 23290.35 34499.97 2298.55 15799.47 28199.08 294
ETV-MVS99.18 15699.18 12799.16 24499.34 27099.28 18199.12 18899.79 6999.48 10798.93 28798.55 36099.40 3499.93 8198.51 15999.52 27498.28 349
jason99.16 16199.11 14299.32 21899.75 11298.44 26598.26 28999.39 25998.70 21799.74 10799.30 28598.54 14799.97 2298.48 16099.82 16399.55 156
jason: jason.
APDe-MVS99.48 7299.36 9499.85 2099.55 19799.81 3899.50 8799.69 12098.99 18199.75 9999.71 12498.79 11099.93 8198.46 16199.85 14199.80 31
CL-MVSNet_self_test98.71 23098.56 23299.15 24699.22 29698.66 25097.14 35799.51 22398.09 27499.54 18299.27 29196.87 26299.74 30298.43 16298.96 32399.03 303
our_test_398.85 21699.09 15198.13 31899.66 15394.90 35497.72 33299.58 18699.07 17599.64 13899.62 18198.19 19399.93 8198.41 16399.95 6799.55 156
Gipumacopyleft99.57 5699.59 5099.49 16599.98 399.71 7699.72 2999.84 4599.81 4399.94 2199.78 8798.91 9799.71 31198.41 16399.95 6799.05 301
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 197.37 30596.91 31598.74 29497.72 37297.57 31197.60 33897.36 36198.00 27799.21 25898.02 37090.04 34899.79 28398.37 16595.89 37298.86 320
PM-MVS99.36 10799.29 11299.58 14199.83 5399.66 9398.95 22399.86 3598.85 20099.81 7399.73 11098.40 17199.92 10198.36 16699.83 15499.17 272
baseline197.73 29297.33 30298.96 26799.30 28297.73 30799.40 10598.42 34299.33 13499.46 20499.21 30591.18 33199.82 25998.35 16791.26 37499.32 241
MVS-HIRNet97.86 28698.22 26096.76 34599.28 28791.53 37198.38 28292.60 37599.13 16899.31 24099.96 1297.18 25499.68 32998.34 16899.83 15499.07 299
GA-MVS97.99 28597.68 29598.93 27299.52 20998.04 29397.19 35699.05 31498.32 26198.81 30398.97 33789.89 35099.41 36798.33 16999.05 31899.34 237
Fast-Effi-MVS+99.02 18798.87 20099.46 17399.38 25599.50 13099.04 20499.79 6997.17 32198.62 31898.74 35399.34 4599.95 5198.32 17099.41 28998.92 315
MDA-MVSNet_test_wron98.95 20398.99 18298.85 28399.64 15797.16 32298.23 29199.33 27198.93 19099.56 17599.66 15797.39 24399.83 25098.29 17199.88 11899.55 156
N_pmnet98.73 22898.53 23599.35 21099.72 12498.67 24798.34 28394.65 37098.35 25599.79 8199.68 14898.03 20499.93 8198.28 17299.92 9099.44 211
ET-MVSNet_ETH3D96.78 31696.07 32598.91 27599.26 29197.92 30297.70 33496.05 36697.96 28492.37 37598.43 36487.06 35999.90 14198.27 17397.56 36398.91 316
thisisatest053097.45 30296.95 31298.94 26999.68 14797.73 30799.09 19694.19 37398.61 22599.56 17599.30 28584.30 37199.93 8198.27 17399.54 26999.16 274
YYNet198.95 20398.99 18298.84 28599.64 15797.14 32498.22 29299.32 27398.92 19299.59 16399.66 15797.40 24199.83 25098.27 17399.90 10099.55 156
ACMM98.09 1199.46 8099.38 8899.72 8099.80 7299.69 8699.13 18499.65 14098.99 18199.64 13899.72 11799.39 3599.86 20498.23 17699.81 17299.60 134
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS98.96 20098.87 20099.24 23699.57 18598.40 26898.12 30099.18 30498.28 26399.63 14299.13 31198.02 20599.97 2298.22 17799.69 22299.35 235
3Dnovator99.15 299.43 8699.36 9499.65 10799.39 25299.42 15199.70 3499.56 19399.23 14899.35 22899.80 7099.17 6499.95 5198.21 17899.84 14699.59 141
Fast-Effi-MVS+-dtu99.20 14999.12 13999.43 18299.25 29299.69 8699.05 20299.82 5299.50 10598.97 28399.05 32398.98 8999.98 1098.20 17999.24 31098.62 332
MS-PatchMatch99.00 19398.97 18699.09 25599.11 31898.19 28098.76 24999.33 27198.49 23899.44 20699.58 20598.21 19199.69 31998.20 17999.62 24399.39 224
TSAR-MVS + GP.99.12 16999.04 16899.38 20199.34 27099.16 20398.15 29699.29 28298.18 27099.63 14299.62 18199.18 6399.68 32998.20 17999.74 20299.30 246
DP-MVS99.48 7299.39 8699.74 6599.57 18599.62 10599.29 13699.61 15899.87 2599.74 10799.76 9898.69 12499.87 18698.20 17999.80 17799.75 53
MVP-Stereo99.16 16199.08 15399.43 18299.48 22799.07 21699.08 19999.55 19998.63 22299.31 24099.68 14898.19 19399.78 28698.18 18399.58 25899.45 206
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast99.43 8699.30 10799.80 3499.83 5399.81 3899.52 8399.70 11498.35 25599.51 19499.50 23599.31 4799.88 17298.18 18399.84 14699.69 67
MDA-MVSNet-bldmvs99.06 17899.05 16399.07 25999.80 7297.83 30398.89 22799.72 10599.29 13699.63 14299.70 13196.47 27099.89 15898.17 18599.82 16399.50 188
JIA-IIPM98.06 28197.92 28598.50 30398.59 35997.02 32698.80 24398.51 33899.88 2497.89 35099.87 4091.89 32499.90 14198.16 18697.68 36298.59 334
EIA-MVS99.12 16999.01 17499.45 17699.36 26099.62 10599.34 11899.79 6998.41 24498.84 30098.89 34598.75 11799.84 23598.15 18799.51 27598.89 317
miper_lstm_enhance98.65 23498.60 22398.82 29099.20 30197.33 31897.78 33099.66 13199.01 18099.59 16399.50 23594.62 29699.85 22198.12 18899.90 10099.26 251
Effi-MVS+-dtu99.07 17798.92 19499.52 16098.89 34099.78 4799.15 17699.66 13199.34 13298.92 29099.24 30197.69 22799.98 1098.11 18999.28 30498.81 324
tpm97.15 30896.95 31297.75 32898.91 33694.24 35799.32 12297.96 35197.71 29598.29 33299.32 28186.72 36599.92 10198.10 19096.24 37199.09 291
DeepPCF-MVS98.42 699.18 15699.02 17199.67 9599.22 29699.75 6297.25 35499.47 23598.72 21699.66 13599.70 13199.29 4999.63 34798.07 19199.81 17299.62 120
ppachtmachnet_test98.89 21199.12 13998.20 31699.66 15395.24 35197.63 33699.68 12399.08 17399.78 8599.62 18198.65 13299.88 17298.02 19299.96 5699.48 197
tpmrst97.73 29298.07 27196.73 34798.71 35692.00 36799.10 19298.86 32098.52 23498.92 29099.54 22691.90 32399.82 25998.02 19299.03 32098.37 346
CSCG99.37 10499.29 11299.60 13699.71 12799.46 13799.43 10399.85 3998.79 20899.41 21899.60 19898.92 9599.92 10198.02 19299.92 9099.43 217
eth_miper_zixun_eth98.68 23298.71 21598.60 29999.10 31996.84 33197.52 34499.54 20598.94 18799.58 16599.48 24296.25 27999.76 29698.01 19599.93 8699.21 261
Patchmtry98.78 22198.54 23399.49 16598.89 34099.19 20199.32 12299.67 12799.65 8399.72 11299.79 8091.87 32599.95 5198.00 19699.97 4299.33 238
PVSNet_BlendedMVS99.03 18599.01 17499.09 25599.54 19897.99 29498.58 26099.82 5297.62 29899.34 23199.71 12498.52 15499.77 29497.98 19799.97 4299.52 181
PVSNet_Blended98.70 23198.59 22599.02 26399.54 19897.99 29497.58 33999.82 5295.70 34699.34 23198.98 33598.52 15499.77 29497.98 19799.83 15499.30 246
cl____98.54 24598.41 24398.92 27399.03 32797.80 30597.46 34699.59 17698.90 19499.60 16099.46 24993.85 30399.78 28697.97 19999.89 10999.17 272
DIV-MVS_self_test98.54 24598.42 24298.92 27399.03 32797.80 30597.46 34699.59 17698.90 19499.60 16099.46 24993.87 30299.78 28697.97 19999.89 10999.18 270
AUN-MVS97.82 28897.38 30199.14 24999.27 28998.53 25998.72 25299.02 31598.10 27297.18 36499.03 32989.26 35299.85 22197.94 20197.91 35899.03 303
FA-MVS(test-final)98.52 24798.32 25399.10 25499.48 22798.67 24799.77 1498.60 33597.35 31399.63 14299.80 7093.07 31399.84 23597.92 20299.30 30198.78 327
ambc99.20 24099.35 26298.53 25999.17 17099.46 23899.67 13199.80 7098.46 16199.70 31397.92 20299.70 21899.38 226
USDC98.96 20098.93 19099.05 26199.54 19897.99 29497.07 36099.80 6398.21 26799.75 9999.77 9498.43 16499.64 34697.90 20499.88 11899.51 183
OPM-MVS99.26 12999.13 13599.63 12199.70 13599.61 11198.58 26099.48 23298.50 23699.52 18999.63 17499.14 6999.76 29697.89 20599.77 19199.51 183
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVScopyleft99.32 11999.17 12899.77 4499.69 13999.80 4299.14 17899.31 27799.16 16299.62 15199.61 19098.35 17599.91 12397.88 20699.72 21399.61 130
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND99.83 2599.70 13599.79 4499.14 17899.61 15899.92 10197.88 20699.72 21399.77 44
c3_l98.72 22998.71 21598.72 29599.12 31397.22 32197.68 33599.56 19398.90 19499.54 18299.48 24296.37 27699.73 30597.88 20699.88 11899.21 261
3Dnovator+98.92 399.35 10999.24 12299.67 9599.35 26299.47 13399.62 6199.50 22799.44 11799.12 27199.78 8798.77 11499.94 6497.87 20999.72 21399.62 120
miper_ehance_all_eth98.59 24098.59 22598.59 30098.98 33397.07 32597.49 34599.52 21998.50 23699.52 18999.37 26896.41 27499.71 31197.86 21099.62 24399.00 309
WTY-MVS98.59 24098.37 24799.26 23199.43 24498.40 26898.74 25099.13 31098.10 27299.21 25899.24 30194.82 29399.90 14197.86 21098.77 33399.49 193
APD_test199.36 10799.28 11499.61 13399.89 3399.89 1099.32 12299.74 9299.18 15599.69 12399.75 10398.41 16799.84 23597.85 21299.70 21899.10 287
SED-MVS99.40 9599.28 11499.77 4499.69 13999.82 3599.20 16099.54 20599.13 16899.82 6699.63 17498.91 9799.92 10197.85 21299.70 21899.58 146
test_241102_TWO99.54 20599.13 16899.76 9299.63 17498.32 18199.92 10197.85 21299.69 22299.75 53
MVS_111021_HR99.12 16999.02 17199.40 19399.50 21799.11 20897.92 32399.71 10898.76 21499.08 27599.47 24699.17 6499.54 35797.85 21299.76 19399.54 164
MTAPA99.35 10999.20 12599.80 3499.81 6799.81 3899.33 12099.53 21499.27 14099.42 21299.63 17498.21 19199.95 5197.83 21699.79 18299.65 96
MSC_two_6792asdad99.74 6599.03 32799.53 12799.23 29699.92 10197.77 21799.69 22299.78 40
No_MVS99.74 6599.03 32799.53 12799.23 29699.92 10197.77 21799.69 22299.78 40
TESTMET0.1,196.24 32895.84 33097.41 33598.24 36793.84 36097.38 34895.84 36798.43 24197.81 35498.56 35979.77 37699.89 15897.77 21798.77 33398.52 338
ACMH+98.40 899.50 6899.43 8299.71 8599.86 4599.76 5899.32 12299.77 7799.53 10399.77 9099.76 9899.26 5599.78 28697.77 21799.88 11899.60 134
IU-MVS99.69 13999.77 5099.22 29997.50 30599.69 12397.75 22199.70 21899.77 44
114514_t98.49 25298.11 26999.64 11499.73 12199.58 11999.24 15099.76 8289.94 36899.42 21299.56 21797.76 22499.86 20497.74 22299.82 16399.47 201
DVP-MVS++99.38 10199.25 12099.77 4499.03 32799.77 5099.74 2399.61 15899.18 15599.76 9299.61 19099.00 8599.92 10197.72 22399.60 25399.62 120
test_0728_THIRD99.18 15599.62 15199.61 19098.58 14199.91 12397.72 22399.80 17799.77 44
EGC-MVSNET89.05 34285.52 34599.64 11499.89 3399.78 4799.56 7999.52 21924.19 37649.96 37799.83 5599.15 6699.92 10197.71 22599.85 14199.21 261
miper_enhance_ethall98.03 28297.94 28398.32 31198.27 36696.43 33796.95 36199.41 24996.37 33799.43 21098.96 33994.74 29499.69 31997.71 22599.62 24398.83 323
TSAR-MVS + MP.99.34 11499.24 12299.63 12199.82 6099.37 16399.26 14399.35 26898.77 21199.57 16899.70 13199.27 5499.88 17297.71 22599.75 19599.65 96
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl2297.56 30097.28 30398.40 30798.37 36496.75 33297.24 35599.37 26497.31 31599.41 21899.22 30387.30 35799.37 36897.70 22899.62 24399.08 294
MP-MVS-pluss99.14 16598.92 19499.80 3499.83 5399.83 2998.61 25699.63 14896.84 33099.44 20699.58 20598.81 10599.91 12397.70 22899.82 16399.67 79
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP99.28 12399.11 14299.79 3899.75 11299.81 3898.95 22399.53 21498.27 26499.53 18799.73 11098.75 11799.87 18697.70 22899.83 15499.68 73
UnsupCasMVSNet_bld98.55 24498.27 25799.40 19399.56 19699.37 16397.97 31999.68 12397.49 30699.08 27599.35 27795.41 29099.82 25997.70 22898.19 35399.01 308
MVS_111021_LR99.13 16799.03 17099.42 18499.58 17599.32 17597.91 32599.73 9698.68 21899.31 24099.48 24299.09 7499.66 33897.70 22899.77 19199.29 249
IS-MVSNet99.03 18598.85 20299.55 15399.80 7299.25 18899.73 2699.15 30799.37 12999.61 15799.71 12494.73 29599.81 27497.70 22899.88 11899.58 146
test-LLR97.15 30896.95 31297.74 32998.18 36995.02 35297.38 34896.10 36398.00 27797.81 35498.58 35690.04 34899.91 12397.69 23498.78 33198.31 347
test-mter96.23 32995.73 33197.74 32998.18 36995.02 35297.38 34896.10 36397.90 28697.81 35498.58 35679.12 37999.91 12397.69 23498.78 33198.31 347
XVS99.27 12799.11 14299.75 6099.71 12799.71 7699.37 11399.61 15899.29 13698.76 30999.47 24698.47 15899.88 17297.62 23699.73 20799.67 79
X-MVStestdata96.09 33094.87 33999.75 6099.71 12799.71 7699.37 11399.61 15899.29 13698.76 30961.30 38398.47 15899.88 17297.62 23699.73 20799.67 79
SMA-MVScopyleft99.19 15299.00 17799.73 7499.46 23799.73 7099.13 18499.52 21997.40 31099.57 16899.64 16498.93 9499.83 25097.61 23899.79 18299.63 109
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
CostFormer96.71 31996.79 31896.46 35198.90 33790.71 37699.41 10498.68 32994.69 35998.14 34299.34 28086.32 36799.80 28097.60 23998.07 35798.88 318
PVSNet97.47 1598.42 25998.44 24098.35 30999.46 23796.26 33896.70 36599.34 27097.68 29699.00 28299.13 31197.40 24199.72 30797.59 24099.68 22799.08 294
new_pmnet98.88 21298.89 19898.84 28599.70 13597.62 31098.15 29699.50 22797.98 28099.62 15199.54 22698.15 19699.94 6497.55 24199.84 14698.95 312
IB-MVS95.41 2095.30 33994.46 34397.84 32598.76 35495.33 35097.33 35196.07 36596.02 34195.37 37397.41 37676.17 38199.96 4197.54 24295.44 37398.22 352
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
LS3D99.24 13399.11 14299.61 13398.38 36399.79 4499.57 7799.68 12399.61 9199.15 26699.71 12498.70 12399.91 12397.54 24299.68 22799.13 284
ZNCC-MVS99.22 14299.04 16899.77 4499.76 10199.73 7099.28 13899.56 19398.19 26999.14 26899.29 28898.84 10499.92 10197.53 24499.80 17799.64 104
CP-MVS99.23 13499.05 16399.75 6099.66 15399.66 9399.38 10999.62 15198.38 24899.06 27999.27 29198.79 11099.94 6497.51 24599.82 16399.66 88
SD-MVS99.01 19199.30 10798.15 31799.50 21799.40 15698.94 22599.61 15899.22 15299.75 9999.82 6299.54 2895.51 37797.48 24699.87 12999.54 164
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
PMMVS98.49 25298.29 25699.11 25298.96 33498.42 26797.54 34099.32 27397.53 30398.47 32898.15 36997.88 21599.82 25997.46 24799.24 31099.09 291
DeepC-MVS_fast98.47 599.23 13499.12 13999.56 15099.28 28799.22 19598.99 21799.40 25699.08 17399.58 16599.64 16498.90 10099.83 25097.44 24899.75 19599.63 109
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS99.25 13099.08 15399.76 5199.73 12199.70 8399.31 12799.59 17698.36 25099.36 22799.37 26898.80 10999.91 12397.43 24999.75 19599.68 73
ACMMPR99.23 13499.06 15999.76 5199.74 11899.69 8699.31 12799.59 17698.36 25099.35 22899.38 26698.61 13699.93 8197.43 24999.75 19599.67 79
Vis-MVSNet (Re-imp)98.77 22298.58 22899.34 21199.78 8998.88 23499.61 6699.56 19399.11 17299.24 25299.56 21793.00 31599.78 28697.43 24999.89 10999.35 235
MIMVSNet98.43 25898.20 26299.11 25299.53 20498.38 27199.58 7498.61 33398.96 18599.33 23399.76 9890.92 33599.81 27497.38 25299.76 19399.15 276
XVG-OURS-SEG-HR99.16 16198.99 18299.66 10299.84 4999.64 9998.25 29099.73 9698.39 24799.63 14299.43 25499.70 1599.90 14197.34 25398.64 34199.44 211
COLMAP_ROBcopyleft98.06 1299.45 8299.37 9199.70 8999.83 5399.70 8399.38 10999.78 7499.53 10399.67 13199.78 8799.19 6299.86 20497.32 25499.87 12999.55 156
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS99.02 18798.81 20899.65 10799.58 17599.49 13198.58 26099.07 31198.40 24699.04 28099.25 29698.51 15699.80 28097.31 25599.51 27599.65 96
region2R99.23 13499.05 16399.77 4499.76 10199.70 8399.31 12799.59 17698.41 24499.32 23699.36 27298.73 12199.93 8197.29 25699.74 20299.67 79
APD-MVS_3200maxsize99.31 12099.16 12999.74 6599.53 20499.75 6299.27 14199.61 15899.19 15499.57 16899.64 16498.76 11599.90 14197.29 25699.62 24399.56 153
TAPA-MVS97.92 1398.03 28297.55 29899.46 17399.47 23399.44 14498.50 27399.62 15186.79 36999.07 27899.26 29498.26 18599.62 34897.28 25899.73 20799.31 245
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post99.27 12799.11 14299.73 7499.54 19899.74 6899.26 14399.62 15199.16 16299.52 18999.64 16498.41 16799.91 12397.27 25999.61 25099.54 164
RE-MVS-def99.13 13599.54 19899.74 6899.26 14399.62 15199.16 16299.52 18999.64 16498.57 14297.27 25999.61 25099.54 164
test_yl98.25 27197.95 27999.13 25099.17 30698.47 26299.00 21298.67 33198.97 18399.22 25699.02 33091.31 32999.69 31997.26 26198.93 32499.24 254
DCV-MVSNet98.25 27197.95 27999.13 25099.17 30698.47 26299.00 21298.67 33198.97 18399.22 25699.02 33091.31 32999.69 31997.26 26198.93 32499.24 254
PHI-MVS99.11 17298.95 18999.59 13899.13 31199.59 11599.17 17099.65 14097.88 28799.25 24999.46 24998.97 9199.80 28097.26 26199.82 16399.37 229
tfpnnormal99.43 8699.38 8899.60 13699.87 4299.75 6299.59 7299.78 7499.71 6399.90 3799.69 13798.85 10399.90 14197.25 26499.78 18799.15 276
PatchmatchNetpermissive97.65 29697.80 28997.18 34198.82 34892.49 36599.17 17098.39 34498.12 27198.79 30699.58 20590.71 34099.89 15897.23 26599.41 28999.16 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS98.99 19698.80 21099.56 15099.25 29299.43 14898.54 26999.27 28698.58 22798.80 30599.43 25498.53 15199.70 31397.22 26699.59 25799.54 164
HPM-MVScopyleft99.25 13099.07 15799.78 4199.81 6799.75 6299.61 6699.67 12797.72 29499.35 22899.25 29699.23 5899.92 10197.21 26799.82 16399.67 79
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS99.19 15299.00 17799.76 5199.76 10199.68 8999.38 10999.54 20598.34 25999.01 28199.50 23598.53 15199.93 8197.18 26899.78 18799.66 88
ACMMPcopyleft99.25 13099.08 15399.74 6599.79 8299.68 8999.50 8799.65 14098.07 27599.52 18999.69 13798.57 14299.92 10197.18 26899.79 18299.63 109
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
thisisatest051596.98 31296.42 31998.66 29899.42 24997.47 31397.27 35394.30 37297.24 31799.15 26698.86 34785.01 36899.87 18697.10 27099.39 29198.63 331
XVG-ACMP-BASELINE99.23 13499.10 15099.63 12199.82 6099.58 11998.83 23599.72 10598.36 25099.60 16099.71 12498.92 9599.91 12397.08 27199.84 14699.40 222
MSDG99.08 17698.98 18599.37 20499.60 16699.13 20697.54 34099.74 9298.84 20399.53 18799.55 22499.10 7299.79 28397.07 27299.86 13799.18 270
SteuartSystems-ACMMP99.30 12199.14 13399.76 5199.87 4299.66 9399.18 16599.60 17098.55 23099.57 16899.67 15399.03 8499.94 6497.01 27399.80 17799.69 67
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EPMVS96.53 32296.32 32097.17 34298.18 36992.97 36499.39 10789.95 37998.21 26798.61 31999.59 20386.69 36699.72 30796.99 27499.23 31298.81 324
MSP-MVS99.04 18498.79 21199.81 3099.78 8999.73 7099.35 11799.57 18898.54 23399.54 18298.99 33296.81 26399.93 8196.97 27599.53 27199.77 44
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
HPM-MVS++copyleft98.96 20098.70 21899.74 6599.52 20999.71 7698.86 23099.19 30398.47 24098.59 32199.06 32298.08 20199.91 12396.94 27699.60 25399.60 134
SR-MVS99.19 15299.00 17799.74 6599.51 21199.72 7499.18 16599.60 17098.85 20099.47 20099.58 20598.38 17299.92 10196.92 27799.54 26999.57 151
PGM-MVS99.20 14999.01 17499.77 4499.75 11299.71 7699.16 17499.72 10597.99 27999.42 21299.60 19898.81 10599.93 8196.91 27899.74 20299.66 88
HY-MVS98.23 998.21 27697.95 27998.99 26499.03 32798.24 27599.61 6698.72 32796.81 33198.73 31199.51 23294.06 30099.86 20496.91 27898.20 35198.86 320
MDTV_nov1_ep1397.73 29398.70 35790.83 37499.15 17698.02 35098.51 23598.82 30299.61 19090.98 33499.66 33896.89 28098.92 326
GST-MVS99.16 16198.96 18899.75 6099.73 12199.73 7099.20 16099.55 19998.22 26699.32 23699.35 27798.65 13299.91 12396.86 28199.74 20299.62 120
test_post199.14 17851.63 38589.54 35199.82 25996.86 281
SCA98.11 27898.36 24897.36 33699.20 30192.99 36398.17 29598.49 34098.24 26599.10 27499.57 21496.01 28499.94 6496.86 28199.62 24399.14 281
XVG-OURS99.21 14799.06 15999.65 10799.82 6099.62 10597.87 32799.74 9298.36 25099.66 13599.68 14899.71 1399.90 14196.84 28499.88 11899.43 217
LCM-MVSNet-Re99.28 12399.15 13299.67 9599.33 27599.76 5899.34 11899.97 1098.93 19099.91 3199.79 8098.68 12599.93 8196.80 28599.56 26099.30 246
RPSCF99.18 15699.02 17199.64 11499.83 5399.85 1999.44 10199.82 5298.33 26099.50 19699.78 8797.90 21399.65 34496.78 28699.83 15499.44 211
旧先验297.94 32195.33 35098.94 28699.88 17296.75 287
MDTV_nov1_ep13_2view91.44 37299.14 17897.37 31299.21 25891.78 32796.75 28799.03 303
CLD-MVS98.76 22398.57 22999.33 21499.57 18598.97 22397.53 34299.55 19996.41 33599.27 24799.13 31199.07 7999.78 28696.73 28999.89 10999.23 257
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test98.10 27997.98 27798.48 30499.27 28996.48 33599.40 10599.07 31198.81 20599.23 25399.57 21490.11 34799.87 18696.69 29099.64 24099.09 291
baseline296.83 31596.28 32198.46 30599.09 32196.91 32998.83 23593.87 37497.23 31896.23 36998.36 36588.12 35599.90 14196.68 29198.14 35598.57 337
cascas96.99 31196.82 31797.48 33297.57 37595.64 34796.43 36799.56 19391.75 36497.13 36597.61 37595.58 28998.63 37396.68 29199.11 31598.18 356
PC_three_145297.56 29999.68 12699.41 25699.09 7497.09 37596.66 29399.60 25399.62 120
LPG-MVS_test99.22 14299.05 16399.74 6599.82 6099.63 10399.16 17499.73 9697.56 29999.64 13899.69 13799.37 4199.89 15896.66 29399.87 12999.69 67
LGP-MVS_train99.74 6599.82 6099.63 10399.73 9697.56 29999.64 13899.69 13799.37 4199.89 15896.66 29399.87 12999.69 67
TinyColmap98.97 19798.93 19099.07 25999.46 23798.19 28097.75 33199.75 8798.79 20899.54 18299.70 13198.97 9199.62 34896.63 29699.83 15499.41 221
LF4IMVS99.01 19198.92 19499.27 22999.71 12799.28 18198.59 25999.77 7798.32 26199.39 22499.41 25698.62 13499.84 23596.62 29799.84 14698.69 330
NCCC98.82 21898.57 22999.58 14199.21 29899.31 17698.61 25699.25 29298.65 22098.43 32999.26 29497.86 21699.81 27496.55 29899.27 30799.61 130
OPU-MVS99.29 22499.12 31399.44 14499.20 16099.40 26099.00 8598.84 37296.54 29999.60 25399.58 146
F-COLMAP98.74 22698.45 23999.62 13099.57 18599.47 13398.84 23399.65 14096.31 33898.93 28799.19 30897.68 22899.87 18696.52 30099.37 29499.53 170
ADS-MVSNet297.78 29097.66 29798.12 31999.14 30995.36 34999.22 15798.75 32696.97 32698.25 33499.64 16490.90 33699.94 6496.51 30199.56 26099.08 294
ADS-MVSNet97.72 29597.67 29697.86 32499.14 30994.65 35599.22 15798.86 32096.97 32698.25 33499.64 16490.90 33699.84 23596.51 30199.56 26099.08 294
PatchMatch-RL98.68 23298.47 23799.30 22399.44 24199.28 18198.14 29899.54 20597.12 32499.11 27299.25 29697.80 22199.70 31396.51 30199.30 30198.93 314
CMPMVSbinary77.52 2398.50 25098.19 26599.41 19198.33 36599.56 12299.01 21099.59 17695.44 34899.57 16899.80 7095.64 28799.46 36696.47 30499.92 9099.21 261
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SF-MVS99.10 17598.93 19099.62 13099.58 17599.51 12999.13 18499.65 14097.97 28199.42 21299.61 19098.86 10299.87 18696.45 30599.68 22799.49 193
FE-MVS97.85 28797.42 30099.15 24699.44 24198.75 24299.77 1498.20 34895.85 34399.33 23399.80 7088.86 35399.88 17296.40 30699.12 31498.81 324
DPE-MVScopyleft99.14 16598.92 19499.82 2799.57 18599.77 5098.74 25099.60 17098.55 23099.76 9299.69 13798.23 19099.92 10196.39 30799.75 19599.76 50
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
gm-plane-assit97.59 37389.02 38093.47 36198.30 36699.84 23596.38 308
AllTest99.21 14799.07 15799.63 12199.78 8999.64 9999.12 18899.83 4798.63 22299.63 14299.72 11798.68 12599.75 30096.38 30899.83 15499.51 183
TestCases99.63 12199.78 8999.64 9999.83 4798.63 22299.63 14299.72 11798.68 12599.75 30096.38 30899.83 15499.51 183
testdata99.42 18499.51 21198.93 22999.30 28096.20 33998.87 29799.40 26098.33 18099.89 15896.29 31199.28 30499.44 211
dp96.86 31497.07 30896.24 35398.68 35890.30 37899.19 16498.38 34597.35 31398.23 33699.59 20387.23 35899.82 25996.27 31298.73 33998.59 334
tpmvs97.39 30497.69 29496.52 34998.41 36291.76 36899.30 13098.94 31997.74 29397.85 35399.55 22492.40 32299.73 30596.25 31398.73 33998.06 358
KD-MVS_2432*160095.89 33295.41 33597.31 33994.96 37793.89 35897.09 35899.22 29997.23 31898.88 29499.04 32579.23 37799.54 35796.24 31496.81 36698.50 342
miper_refine_blended95.89 33295.41 33597.31 33994.96 37793.89 35897.09 35899.22 29997.23 31898.88 29499.04 32579.23 37799.54 35796.24 31496.81 36698.50 342
ACMP97.51 1499.05 18198.84 20499.67 9599.78 8999.55 12598.88 22899.66 13197.11 32599.47 20099.60 19899.07 7999.89 15896.18 31699.85 14199.58 146
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS98.90 20898.72 21499.44 17899.39 25299.42 15198.58 26099.64 14697.31 31599.44 20699.62 18198.59 13999.69 31996.17 31799.79 18299.22 259
DP-MVS Recon98.50 25098.23 25899.31 22199.49 22299.46 13798.56 26599.63 14894.86 35798.85 29999.37 26897.81 22099.59 35496.08 31899.44 28498.88 318
tpm cat196.78 31696.98 31196.16 35498.85 34390.59 37799.08 19999.32 27392.37 36397.73 35899.46 24991.15 33299.69 31996.07 31998.80 33098.21 353
tpm296.35 32596.22 32296.73 34798.88 34291.75 36999.21 15998.51 33893.27 36297.89 35099.21 30584.83 36999.70 31396.04 32098.18 35498.75 329
test_040299.22 14299.14 13399.45 17699.79 8299.43 14899.28 13899.68 12399.54 10199.40 22399.56 21799.07 7999.82 25996.01 32199.96 5699.11 285
ITE_SJBPF99.38 20199.63 15999.44 14499.73 9698.56 22899.33 23399.53 22898.88 10199.68 32996.01 32199.65 23899.02 307
test_prior297.95 32097.87 28898.05 34499.05 32397.90 21395.99 32399.49 279
testdata299.89 15895.99 323
原ACMM199.37 20499.47 23398.87 23699.27 28696.74 33398.26 33399.32 28197.93 21299.82 25995.96 32599.38 29299.43 217
新几何199.52 16099.50 21799.22 19599.26 28995.66 34798.60 32099.28 28997.67 22999.89 15895.95 32699.32 29999.45 206
MP-MVScopyleft99.06 17898.83 20699.76 5199.76 10199.71 7699.32 12299.50 22798.35 25598.97 28399.48 24298.37 17399.92 10195.95 32699.75 19599.63 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
wuyk23d97.58 29999.13 13592.93 35799.69 13999.49 13199.52 8399.77 7797.97 28199.96 1599.79 8099.84 599.94 6495.85 32899.82 16379.36 373
HQP_MVS98.90 20898.68 21999.55 15399.58 17599.24 19298.80 24399.54 20598.94 18799.14 26899.25 29697.24 24899.82 25995.84 32999.78 18799.60 134
plane_prior599.54 20599.82 25995.84 32999.78 18799.60 134
无先验98.01 31299.23 29695.83 34499.85 22195.79 33199.44 211
CPTT-MVS98.74 22698.44 24099.64 11499.61 16499.38 16099.18 16599.55 19996.49 33499.27 24799.37 26897.11 25699.92 10195.74 33299.67 23399.62 120
PLCcopyleft97.35 1698.36 26497.99 27599.48 16999.32 27799.24 19298.50 27399.51 22395.19 35398.58 32298.96 33996.95 26099.83 25095.63 33399.25 30899.37 229
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA98.57 24298.34 25199.28 22699.18 30599.10 21398.34 28399.41 24998.48 23998.52 32598.98 33597.05 25799.78 28695.59 33499.50 27798.96 310
131498.00 28497.90 28798.27 31598.90 33797.45 31599.30 13099.06 31394.98 35497.21 36399.12 31598.43 16499.67 33495.58 33598.56 34497.71 362
PVSNet_095.53 1995.85 33595.31 33797.47 33398.78 35293.48 36295.72 36899.40 25696.18 34097.37 35997.73 37395.73 28699.58 35595.49 33681.40 37599.36 232
MAR-MVS98.24 27397.92 28599.19 24198.78 35299.65 9899.17 17099.14 30895.36 34998.04 34598.81 35097.47 23899.72 30795.47 33799.06 31798.21 353
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
OpenMVScopyleft98.12 1098.23 27497.89 28899.26 23199.19 30399.26 18599.65 5899.69 12091.33 36698.14 34299.77 9498.28 18399.96 4195.41 33899.55 26498.58 336
train_agg98.35 26797.95 27999.57 14799.35 26299.35 17098.11 30299.41 24994.90 35597.92 34898.99 33298.02 20599.85 22195.38 33999.44 28499.50 188
9.1498.64 22099.45 24098.81 24099.60 17097.52 30499.28 24699.56 21798.53 15199.83 25095.36 34099.64 240
APD-MVScopyleft98.87 21498.59 22599.71 8599.50 21799.62 10599.01 21099.57 18896.80 33299.54 18299.63 17498.29 18299.91 12395.24 34199.71 21699.61 130
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
AdaColmapbinary98.60 23798.35 25099.38 20199.12 31399.22 19598.67 25599.42 24897.84 29198.81 30399.27 29197.32 24699.81 27495.14 34299.53 27199.10 287
test9_res95.10 34399.44 28499.50 188
CDPH-MVS98.56 24398.20 26299.61 13399.50 21799.46 13798.32 28599.41 24995.22 35199.21 25899.10 31998.34 17899.82 25995.09 34499.66 23699.56 153
BH-untuned98.22 27598.09 27098.58 30199.38 25597.24 32098.55 26698.98 31897.81 29299.20 26398.76 35297.01 25899.65 34494.83 34598.33 34898.86 320
BP-MVS94.73 346
HQP-MVS98.36 26498.02 27499.39 19799.31 27898.94 22697.98 31699.37 26497.45 30798.15 33898.83 34896.67 26499.70 31394.73 34699.67 23399.53 170
QAPM98.40 26297.99 27599.65 10799.39 25299.47 13399.67 4899.52 21991.70 36598.78 30899.80 7098.55 14599.95 5194.71 34899.75 19599.53 170
agg_prior294.58 34999.46 28399.50 188
BH-RMVSNet98.41 26098.14 26899.21 23899.21 29898.47 26298.60 25898.26 34798.35 25598.93 28799.31 28397.20 25399.66 33894.32 35099.10 31699.51 183
E-PMN97.14 31097.43 29996.27 35298.79 35091.62 37095.54 36999.01 31799.44 11798.88 29499.12 31592.78 31699.68 32994.30 35199.03 32097.50 363
MG-MVS98.52 24798.39 24598.94 26999.15 30897.39 31798.18 29399.21 30298.89 19799.23 25399.63 17497.37 24499.74 30294.22 35299.61 25099.69 67
API-MVS98.38 26398.39 24598.35 30998.83 34599.26 18599.14 17899.18 30498.59 22698.66 31698.78 35198.61 13699.57 35694.14 35399.56 26096.21 370
PAPM_NR98.36 26498.04 27299.33 21499.48 22798.93 22998.79 24699.28 28597.54 30298.56 32498.57 35897.12 25599.69 31994.09 35498.90 32899.38 226
ZD-MVS99.43 24499.61 11199.43 24696.38 33699.11 27299.07 32197.86 21699.92 10194.04 35599.49 279
DPM-MVS98.28 26997.94 28399.32 21899.36 26099.11 20897.31 35298.78 32596.88 32898.84 30099.11 31897.77 22399.61 35294.03 35699.36 29599.23 257
gg-mvs-nofinetune95.87 33495.17 33897.97 32198.19 36896.95 32799.69 4189.23 38099.89 1996.24 36899.94 1681.19 37399.51 36293.99 35798.20 35197.44 364
PMVScopyleft92.94 2198.82 21898.81 20898.85 28399.84 4997.99 29499.20 16099.47 23599.71 6399.42 21299.82 6298.09 19999.47 36493.88 35899.85 14199.07 299
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS96.96 31397.28 30395.99 35598.76 35491.03 37395.26 37098.61 33399.34 13298.92 29098.88 34693.79 30499.66 33892.87 35999.05 31897.30 367
BH-w/o97.20 30797.01 31097.76 32799.08 32295.69 34698.03 31198.52 33795.76 34597.96 34798.02 37095.62 28899.47 36492.82 36097.25 36598.12 357
TR-MVS97.44 30397.15 30798.32 31198.53 36197.46 31498.47 27597.91 35396.85 32998.21 33798.51 36296.42 27299.51 36292.16 36197.29 36497.98 359
OpenMVS_ROBcopyleft97.31 1797.36 30696.84 31698.89 28299.29 28499.45 14298.87 22999.48 23286.54 37199.44 20699.74 10697.34 24599.86 20491.61 36299.28 30497.37 366
GG-mvs-BLEND97.36 33697.59 37396.87 33099.70 3488.49 38194.64 37497.26 37980.66 37499.12 36991.50 36396.50 37096.08 372
DeepMVS_CXcopyleft97.98 32099.69 13996.95 32799.26 28975.51 37395.74 37198.28 36796.47 27099.62 34891.23 36497.89 35997.38 365
PAPR97.56 30097.07 30899.04 26298.80 34998.11 28797.63 33699.25 29294.56 36098.02 34698.25 36897.43 24099.68 32990.90 36598.74 33799.33 238
MVS95.72 33794.63 34198.99 26498.56 36097.98 30099.30 13098.86 32072.71 37497.30 36099.08 32098.34 17899.74 30289.21 36698.33 34899.26 251
thres600view796.60 32196.16 32397.93 32299.63 15996.09 34299.18 16597.57 35698.77 21198.72 31297.32 37787.04 36099.72 30788.57 36798.62 34297.98 359
FPMVS96.32 32695.50 33398.79 29199.60 16698.17 28398.46 27998.80 32497.16 32296.28 36699.63 17482.19 37299.09 37088.45 36898.89 32999.10 287
PCF-MVS96.03 1896.73 31895.86 32999.33 21499.44 24199.16 20396.87 36399.44 24386.58 37098.95 28599.40 26094.38 29899.88 17287.93 36999.80 17798.95 312
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view90096.39 32496.03 32697.47 33399.63 15995.93 34399.18 16597.57 35698.75 21598.70 31497.31 37887.04 36099.67 33487.62 37098.51 34596.81 368
tfpn200view996.30 32795.89 32797.53 33199.58 17596.11 34099.00 21297.54 35998.43 24198.52 32596.98 38086.85 36299.67 33487.62 37098.51 34596.81 368
thres40096.40 32395.89 32797.92 32399.58 17596.11 34099.00 21297.54 35998.43 24198.52 32596.98 38086.85 36299.67 33487.62 37098.51 34597.98 359
thres20096.09 33095.68 33297.33 33899.48 22796.22 33998.53 27097.57 35698.06 27698.37 33196.73 38286.84 36499.61 35286.99 37398.57 34396.16 371
MVEpermissive92.54 2296.66 32096.11 32498.31 31399.68 14797.55 31297.94 32195.60 36899.37 12990.68 37698.70 35496.56 26698.61 37486.94 37499.55 26498.77 328
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PAPM95.61 33894.71 34098.31 31399.12 31396.63 33396.66 36698.46 34190.77 36796.25 36798.68 35593.01 31499.69 31981.60 37597.86 36198.62 332
test12329.31 34333.05 34818.08 35925.93 38312.24 38397.53 34210.93 38411.78 37724.21 37850.08 38721.04 3828.60 37823.51 37632.43 37733.39 374
testmvs28.94 34433.33 34615.79 36026.03 3829.81 38496.77 36415.67 38311.55 37823.87 37950.74 38619.03 3838.53 37923.21 37733.07 37629.03 375
test_blank8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
uanet_test8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
DCPMVS8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
cdsmvs_eth3d_5k24.88 34533.17 3470.00 3610.00 3840.00 3850.00 37299.62 1510.00 3790.00 38099.13 31199.82 60.00 3800.00 3780.00 3780.00 376
pcd_1.5k_mvsjas16.61 34622.14 3490.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 199.28 510.00 3800.00 3780.00 3780.00 376
sosnet-low-res8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
sosnet8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
uncertanet8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
Regformer8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
ab-mvs-re8.26 35511.02 3580.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 38099.16 3090.00 3840.00 3800.00 3780.00 3780.00 376
uanet8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
FOURS199.83 5399.89 1099.74 2399.71 10899.69 7199.63 142
test_one_060199.63 15999.76 5899.55 19999.23 14899.31 24099.61 19098.59 139
eth-test20.00 384
eth-test0.00 384
test_241102_ONE99.69 13999.82 3599.54 20599.12 17199.82 6699.49 23998.91 9799.52 361
save fliter99.53 20499.25 18898.29 28799.38 26399.07 175
test072699.69 13999.80 4299.24 15099.57 18899.16 16299.73 11199.65 16298.35 175
GSMVS99.14 281
test_part299.62 16399.67 9199.55 180
sam_mvs190.81 33999.14 281
sam_mvs90.52 343
MTGPAbinary99.53 214
test_post52.41 38490.25 34599.86 204
patchmatchnet-post99.62 18190.58 34199.94 64
MTMP99.09 19698.59 336
TEST999.35 26299.35 17098.11 30299.41 24994.83 35897.92 34898.99 33298.02 20599.85 221
test_899.34 27099.31 17698.08 30699.40 25694.90 35597.87 35298.97 33798.02 20599.84 235
agg_prior99.35 26299.36 16799.39 25997.76 35799.85 221
test_prior499.19 20198.00 314
test_prior99.46 17399.35 26299.22 19599.39 25999.69 31999.48 197
新几何298.04 310
旧先验199.49 22299.29 17999.26 28999.39 26497.67 22999.36 29599.46 205
原ACMM297.92 323
test22299.51 21199.08 21597.83 32999.29 28295.21 35298.68 31599.31 28397.28 24799.38 29299.43 217
segment_acmp98.37 173
testdata197.72 33297.86 290
test1299.54 15799.29 28499.33 17399.16 30698.43 32997.54 23699.82 25999.47 28199.48 197
plane_prior799.58 17599.38 160
plane_prior699.47 23399.26 18597.24 248
plane_prior499.25 296
plane_prior399.31 17698.36 25099.14 268
plane_prior298.80 24398.94 187
plane_prior199.51 211
plane_prior99.24 19298.42 28097.87 28899.71 216
n20.00 385
nn0.00 385
door-mid99.83 47
test1199.29 282
door99.77 77
HQP5-MVS98.94 226
HQP-NCC99.31 27897.98 31697.45 30798.15 338
ACMP_Plane99.31 27897.98 31697.45 30798.15 338
HQP4-MVS98.15 33899.70 31399.53 170
HQP3-MVS99.37 26499.67 233
HQP2-MVS96.67 264
NP-MVS99.40 25199.13 20698.83 348
ACMMP++_ref99.94 78
ACMMP++99.79 182
Test By Simon98.41 167