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
FMVS299.83 1199.93 299.53 16399.96 498.62 26899.67 47100.00 199.95 4100.00 199.95 1399.85 399.99 699.98 199.99 1299.98 1
FMVS99.75 1799.88 599.37 21599.96 498.21 29199.51 84100.00 199.94 8100.00 199.93 1799.58 2299.94 6299.97 299.99 1299.97 2
mvsany_test99.85 799.88 599.75 6099.95 1299.37 16899.53 8199.98 499.77 5299.99 599.95 1399.85 399.94 6299.95 399.98 2899.94 4
LCM-MVSNet99.95 199.95 199.95 199.99 199.99 199.95 299.97 599.99 1100.00 199.98 1099.78 8100.00 199.92 4100.00 199.87 12
v192192099.56 5399.57 5199.55 15799.75 10699.11 21899.05 20199.61 15499.15 16499.88 3999.71 11799.08 7299.87 18999.90 599.97 3999.66 84
v124099.56 5399.58 4899.51 16999.80 6699.00 23099.00 21199.65 13699.15 16499.90 2999.75 9899.09 6899.88 17699.90 599.96 5399.67 74
v1099.69 2699.69 2499.66 10599.81 6199.39 16299.66 5299.75 8299.60 9399.92 2299.87 3698.75 11799.86 20999.90 599.99 1299.73 50
v119299.57 5099.57 5199.57 15099.77 9199.22 20499.04 20399.60 16799.18 15499.87 4899.72 11099.08 7299.85 22799.89 899.98 2899.66 84
v14419299.55 5699.54 5699.58 14499.78 8399.20 21099.11 18999.62 14799.18 15499.89 3399.72 11098.66 12899.87 18999.88 999.97 3999.66 84
v899.68 2999.69 2499.65 11099.80 6699.40 16099.66 5299.76 7599.64 7999.93 1899.85 4698.66 12899.84 24499.88 999.99 1299.71 54
v114499.54 5899.53 6099.59 14099.79 7699.28 18799.10 19099.61 15499.20 15299.84 5499.73 10498.67 12699.84 24499.86 1199.98 2899.64 101
v7n99.82 1299.80 1399.88 1299.96 499.84 2299.82 899.82 4599.84 3599.94 1599.91 2499.13 6599.96 3899.83 1299.99 1299.83 21
v2v48299.50 6299.47 6499.58 14499.78 8399.25 19599.14 17799.58 18499.25 14399.81 6699.62 17698.24 18499.84 24499.83 1299.97 3999.64 101
V4299.56 5399.54 5699.63 12499.79 7699.46 14199.39 10399.59 17499.24 14599.86 4999.70 12498.55 14399.82 26699.79 1499.95 6299.60 131
mvs_tets99.90 299.90 399.90 599.96 499.79 4399.72 2999.88 2399.92 1199.98 699.93 1799.94 199.98 1099.77 15100.00 199.92 6
PS-MVSNAJss99.84 999.82 1199.89 899.96 499.77 4999.68 4399.85 3299.95 499.98 699.92 2199.28 4699.98 1099.75 16100.00 199.94 4
jajsoiax99.89 399.89 499.89 899.96 499.78 4699.70 3499.86 2899.89 1899.98 699.90 2699.94 199.98 1099.75 16100.00 199.90 7
ANet_high99.88 499.87 799.91 299.99 199.91 499.65 58100.00 199.90 13100.00 199.97 1199.61 1999.97 2099.75 16100.00 199.84 17
CS-MVS-test99.68 2999.70 2099.64 11799.57 18099.83 2799.78 1199.97 599.92 1199.50 19299.38 26899.57 2399.95 4899.69 1999.90 9599.15 284
RRT_MVS99.67 3299.59 4499.91 299.94 1499.88 1199.78 1199.27 29499.87 2499.91 2499.87 3698.04 20299.96 3899.68 2099.99 1299.90 7
CS-MVS99.67 3299.70 2099.58 14499.53 19899.84 2299.79 1099.96 999.90 1399.61 15399.41 25899.51 2799.95 4899.66 2199.89 10498.96 317
pmmvs699.86 699.86 999.83 2499.94 1499.90 799.83 699.91 1599.85 3299.94 1599.95 1399.73 1099.90 14399.65 2299.97 3999.69 61
MIMVSNet199.66 3499.62 3599.80 3299.94 1499.87 1399.69 4099.77 7099.78 4999.93 1899.89 3097.94 21199.92 9999.65 2299.98 2899.62 117
DROMVSNet99.69 2699.69 2499.68 9599.71 12299.91 499.76 1899.96 999.86 2799.51 19099.39 26699.57 2399.93 7999.64 2499.86 13199.20 273
K. test v398.87 21398.60 22399.69 9499.93 2099.46 14199.74 2394.97 37799.78 4999.88 3999.88 3393.66 31499.97 2099.61 2599.95 6299.64 101
KD-MVS_self_test99.63 4099.59 4499.76 5099.84 4399.90 799.37 10999.79 6299.83 3899.88 3999.85 4698.42 16499.90 14399.60 2699.73 20799.49 195
Anonymous2024052199.44 7799.42 7699.49 17599.89 2998.96 23699.62 6199.76 7599.85 3299.82 5999.88 3396.39 28199.97 2099.59 2799.98 2899.55 157
TransMVSNet (Re)99.78 1599.77 1599.81 2999.91 2399.85 1799.75 2199.86 2899.70 6299.91 2499.89 3099.60 2199.87 18999.59 2799.74 20099.71 54
OurMVSNet-221017-099.75 1799.71 1999.84 2299.96 499.83 2799.83 699.85 3299.80 4499.93 1899.93 1798.54 14599.93 7999.59 2799.98 2899.76 45
EU-MVSNet99.39 9299.62 3598.72 30399.88 3396.44 34499.56 7899.85 3299.90 1399.90 2999.85 4698.09 19899.83 25699.58 3099.95 6299.90 7
mvsmamba99.74 2099.70 2099.85 1999.93 2099.83 2799.76 1899.81 5499.96 299.91 2499.81 6298.60 13699.94 6299.58 3099.98 2899.77 40
mvs_anonymous99.28 11999.39 7998.94 27799.19 30997.81 31399.02 20799.55 19799.78 4999.85 5199.80 6598.24 18499.86 20999.57 3299.50 27999.15 284
test111197.74 29998.16 27296.49 35899.60 16189.86 38799.71 3391.21 38499.89 1899.88 3999.87 3693.73 31399.90 14399.56 3399.99 1299.70 57
lessismore_v099.64 11799.86 3999.38 16590.66 38599.89 3399.83 5294.56 30499.97 2099.56 3399.92 8599.57 151
bld_raw_dy_0_6499.70 2399.65 3099.85 1999.95 1299.77 4999.66 5299.71 10299.95 499.91 2499.77 8998.35 173100.00 199.54 3599.99 1299.79 34
pm-mvs199.79 1499.79 1499.78 4099.91 2399.83 2799.76 1899.87 2599.73 5499.89 3399.87 3699.63 1699.87 18999.54 3599.92 8599.63 106
LTVRE_ROB99.19 199.88 499.87 799.88 1299.91 2399.90 799.96 199.92 1299.90 1399.97 999.87 3699.81 799.95 4899.54 3599.99 1299.80 28
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 6699.65 3098.95 27699.71 12297.27 32899.50 8599.82 4599.59 9599.41 21799.85 4699.62 18100.00 199.53 3899.89 10499.59 140
test250694.73 34894.59 35095.15 36499.59 16585.90 38999.75 2174.01 39099.89 1899.71 11199.86 4379.00 38899.90 14399.52 3999.99 1299.65 92
UniMVSNet_ETH3D99.85 799.83 1099.90 599.89 2999.91 499.89 499.71 10299.93 999.95 1499.89 3099.71 1199.96 3899.51 4099.97 3999.84 17
FC-MVSNet-test99.70 2399.65 3099.86 1799.88 3399.86 1699.72 2999.78 6799.90 1399.82 5999.83 5298.45 16099.87 18999.51 4099.97 3999.86 14
UA-Net99.78 1599.76 1799.86 1799.72 11999.71 7699.91 399.95 1199.96 299.71 11199.91 2499.15 6099.97 2099.50 42100.00 199.90 7
PMMVS299.48 6699.45 6999.57 15099.76 9598.99 23198.09 31199.90 1898.95 18699.78 7799.58 20299.57 2399.93 7999.48 4399.95 6299.79 34
VPA-MVSNet99.66 3499.62 3599.79 3799.68 14299.75 6199.62 6199.69 11499.85 3299.80 6999.81 6298.81 10299.91 12399.47 4499.88 11399.70 57
ECVR-MVScopyleft97.73 30098.04 27896.78 35299.59 16590.81 38399.72 2990.43 38699.89 1899.86 4999.86 4393.60 31599.89 16199.46 4599.99 1299.65 92
nrg03099.70 2399.66 2899.82 2699.76 9599.84 2299.61 6699.70 10899.93 999.78 7799.68 14199.10 6699.78 29399.45 4699.96 5399.83 21
TAMVS99.49 6499.45 6999.63 12499.48 22699.42 15599.45 9499.57 18699.66 7599.78 7799.83 5297.85 22099.86 20999.44 4799.96 5399.61 127
GeoE99.69 2699.66 2899.78 4099.76 9599.76 5799.60 7199.82 4599.46 11299.75 9299.56 21599.63 1699.95 4899.43 4899.88 11399.62 117
new-patchmatchnet99.35 10299.57 5198.71 30599.82 5496.62 34298.55 27099.75 8299.50 10199.88 3999.87 3699.31 4299.88 17699.43 48100.00 199.62 117
test20.0399.55 5699.54 5699.58 14499.79 7699.37 16899.02 20799.89 1999.60 9399.82 5999.62 17698.81 10299.89 16199.43 4899.86 13199.47 205
MVSFormer99.41 8599.44 7199.31 23099.57 18098.40 28099.77 1499.80 5699.73 5499.63 13899.30 28998.02 20599.98 1099.43 4899.69 22199.55 157
test_djsdf99.84 999.81 1299.91 299.94 1499.84 2299.77 1499.80 5699.73 5499.97 999.92 2199.77 999.98 1099.43 48100.00 199.90 7
Anonymous2023121199.62 4599.57 5199.76 5099.61 15999.60 11599.81 999.73 9099.82 4099.90 2999.90 2697.97 21099.86 20999.42 5399.96 5399.80 28
SixPastTwentyTwo99.42 8199.30 10099.76 5099.92 2299.67 9299.70 3499.14 31799.65 7799.89 3399.90 2696.20 28699.94 6299.42 5399.92 8599.67 74
patch_mono-299.51 6199.46 6899.64 11799.70 13099.11 21899.04 20399.87 2599.71 5899.47 19799.79 7598.24 18499.98 1099.38 5599.96 5399.83 21
UGNet99.38 9499.34 8999.49 17598.90 34498.90 24699.70 3499.35 27699.86 2798.57 32699.81 6298.50 15599.93 7999.38 5599.98 2899.66 84
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 2299.67 2799.81 2999.89 2999.72 7499.59 7299.82 4599.39 12599.82 5999.84 5199.38 3499.91 12399.38 5599.93 8199.80 28
iter_conf_final98.75 22598.54 23399.40 20499.33 28198.75 25599.26 13999.59 17499.80 4499.76 8499.58 20290.17 35399.92 9999.37 5899.97 3999.54 165
FIs99.65 3999.58 4899.84 2299.84 4399.85 1799.66 5299.75 8299.86 2799.74 10199.79 7598.27 18299.85 22799.37 5899.93 8199.83 21
anonymousdsp99.80 1399.77 1599.90 599.96 499.88 1199.73 2699.85 3299.70 6299.92 2299.93 1799.45 2899.97 2099.36 60100.00 199.85 16
Vis-MVSNetpermissive99.75 1799.74 1899.79 3799.88 3399.66 9499.69 4099.92 1299.67 7199.77 8299.75 9899.61 1999.98 1099.35 6199.98 2899.72 51
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dcpmvs_299.61 4799.64 3399.53 16399.79 7698.82 25099.58 7499.97 599.95 499.96 1199.76 9398.44 16199.99 699.34 6299.96 5399.78 36
test_part198.63 23698.26 26099.75 6099.40 25499.49 13499.67 4799.68 11799.86 2799.88 3999.86 4386.73 37299.93 7999.34 6299.97 3999.81 27
CHOSEN 1792x268899.39 9299.30 10099.65 11099.88 3399.25 19598.78 24999.88 2398.66 21999.96 1199.79 7597.45 24399.93 7999.34 6299.99 1299.78 36
CDS-MVSNet99.22 13999.13 13199.50 17299.35 26699.11 21898.96 22299.54 20399.46 11299.61 15399.70 12496.31 28399.83 25699.34 6299.88 11399.55 157
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT99.00 19299.16 12498.51 31099.75 10695.90 35298.07 31499.84 3899.84 3599.89 3399.73 10496.01 29099.99 699.33 66100.00 199.63 106
HyFIR lowres test98.91 20598.64 22099.73 7799.85 4299.47 13798.07 31499.83 4098.64 22199.89 3399.60 19492.57 324100.00 199.33 6699.97 3999.72 51
pmmvs599.19 14999.11 13899.42 19699.76 9598.88 24798.55 27099.73 9098.82 20499.72 10699.62 17696.56 27299.82 26699.32 6899.95 6299.56 154
v14899.40 8899.41 7799.39 20899.76 9598.94 23899.09 19599.59 17499.17 15899.81 6699.61 18598.41 16599.69 32699.32 6899.94 7399.53 171
baseline99.63 4099.62 3599.66 10599.80 6699.62 10799.44 9799.80 5699.71 5899.72 10699.69 13099.15 6099.83 25699.32 6899.94 7399.53 171
iter_conf0598.46 26198.23 26299.15 25599.04 33297.99 30499.10 19099.61 15499.79 4799.76 8499.58 20287.88 36399.92 9999.31 7199.97 3999.53 171
CVMVSNet98.61 23898.88 19897.80 33499.58 17093.60 36999.26 13999.64 14299.66 7599.72 10699.67 14693.26 31799.93 7999.30 7299.81 16799.87 12
PS-CasMVS99.66 3499.58 4899.89 899.80 6699.85 1799.66 5299.73 9099.62 8399.84 5499.71 11798.62 13299.96 3899.30 7299.96 5399.86 14
DTE-MVSNet99.68 2999.61 3999.88 1299.80 6699.87 1399.67 4799.71 10299.72 5799.84 5499.78 8298.67 12699.97 2099.30 7299.95 6299.80 28
tmp_tt95.75 34495.42 34296.76 35389.90 38994.42 36498.86 23297.87 36278.01 38099.30 24599.69 13097.70 22795.89 38499.29 7598.14 36399.95 3
PEN-MVS99.66 3499.59 4499.89 899.83 4799.87 1399.66 5299.73 9099.70 6299.84 5499.73 10498.56 14299.96 3899.29 7599.94 7399.83 21
WR-MVS_H99.61 4799.53 6099.87 1599.80 6699.83 2799.67 4799.75 8299.58 9699.85 5199.69 13098.18 19499.94 6299.28 7799.95 6299.83 21
IterMVS98.97 19699.16 12498.42 31499.74 11295.64 35598.06 31699.83 4099.83 3899.85 5199.74 10096.10 28999.99 699.27 78100.00 199.63 106
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs3398.61 23898.34 25299.44 19099.60 16198.67 26199.27 13799.44 24899.68 6799.32 23699.49 24092.50 327100.00 199.24 7996.51 37799.65 92
hse-mvs298.52 25298.30 25799.16 25399.29 29098.60 26998.77 25099.02 32499.68 6799.32 23699.04 33192.50 32799.85 22799.24 7997.87 36899.03 310
FMVSNet199.66 3499.63 3499.73 7799.78 8399.77 4999.68 4399.70 10899.67 7199.82 5999.83 5298.98 8399.90 14399.24 7999.97 3999.53 171
casdiffmvs99.63 4099.61 3999.67 9899.79 7699.59 11899.13 18399.85 3299.79 4799.76 8499.72 11099.33 4199.82 26699.21 8299.94 7399.59 140
CP-MVSNet99.54 5899.43 7499.87 1599.76 9599.82 3399.57 7699.61 15499.54 9799.80 6999.64 15797.79 22499.95 4899.21 8299.94 7399.84 17
DELS-MVS99.34 10799.30 10099.48 17999.51 20999.36 17298.12 30799.53 21299.36 12999.41 21799.61 18599.22 5499.87 18999.21 8299.68 22699.20 273
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 9799.26 11299.68 9599.51 20999.58 12198.98 22099.60 16799.43 12099.70 11499.36 27597.70 22799.88 17699.20 8599.87 12499.59 140
CANet99.11 17099.05 15999.28 23598.83 35398.56 27098.71 25899.41 25599.25 14399.23 25399.22 30897.66 23699.94 6299.19 8699.97 3999.33 245
EI-MVSNet-UG-set99.48 6699.50 6299.42 19699.57 18098.65 26799.24 14699.46 24399.68 6799.80 6999.66 15098.99 8299.89 16199.19 8699.90 9599.72 51
xiu_mvs_v1_base_debu99.23 13099.34 8998.91 28399.59 16598.23 28898.47 27999.66 12699.61 8799.68 11998.94 34899.39 3099.97 2099.18 8899.55 26698.51 346
xiu_mvs_v1_base99.23 13099.34 8998.91 28399.59 16598.23 28898.47 27999.66 12699.61 8799.68 11998.94 34899.39 3099.97 2099.18 8899.55 26698.51 346
xiu_mvs_v1_base_debi99.23 13099.34 8998.91 28399.59 16598.23 28898.47 27999.66 12699.61 8799.68 11998.94 34899.39 3099.97 2099.18 8899.55 26698.51 346
MVS_030498.88 21198.71 21499.39 20898.85 35198.91 24599.45 9499.30 28898.56 22897.26 37099.68 14196.18 28799.96 3899.17 9199.94 7399.29 256
VPNet99.46 7399.37 8499.71 8999.82 5499.59 11899.48 8999.70 10899.81 4199.69 11799.58 20297.66 23699.86 20999.17 9199.44 28799.67 74
UniMVSNet_NR-MVSNet99.37 9799.25 11499.72 8399.47 23299.56 12498.97 22199.61 15499.43 12099.67 12499.28 29497.85 22099.95 4899.17 9199.81 16799.65 92
DU-MVS99.33 11199.21 11999.71 8999.43 24599.56 12498.83 23799.53 21299.38 12699.67 12499.36 27597.67 23299.95 4899.17 9199.81 16799.63 106
EI-MVSNet-Vis-set99.47 7299.49 6399.42 19699.57 18098.66 26499.24 14699.46 24399.67 7199.79 7499.65 15598.97 8599.89 16199.15 9599.89 10499.71 54
EI-MVSNet99.38 9499.44 7199.21 24799.58 17098.09 30099.26 13999.46 24399.62 8399.75 9299.67 14698.54 14599.85 22799.15 9599.92 8599.68 67
VNet99.18 15399.06 15599.56 15499.24 30099.36 17299.33 11699.31 28599.67 7199.47 19799.57 21296.48 27599.84 24499.15 9599.30 30899.47 205
EG-PatchMatch MVS99.57 5099.56 5599.62 13399.77 9199.33 17999.26 13999.76 7599.32 13499.80 6999.78 8299.29 4499.87 18999.15 9599.91 9499.66 84
PVSNet_Blended_VisFu99.40 8899.38 8199.44 19099.90 2798.66 26498.94 22599.91 1597.97 28199.79 7499.73 10499.05 7799.97 2099.15 9599.99 1299.68 67
IterMVS-LS99.41 8599.47 6499.25 24299.81 6198.09 30098.85 23499.76 7599.62 8399.83 5899.64 15798.54 14599.97 2099.15 9599.99 1299.68 67
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet99.54 5899.47 6499.76 5099.58 17099.64 10199.30 12699.63 14499.61 8799.71 11199.56 21598.76 11599.96 3899.14 10199.92 8599.68 67
MVSTER98.47 26098.22 26499.24 24499.06 32998.35 28599.08 19899.46 24399.27 13999.75 9299.66 15088.61 36199.85 22799.14 10199.92 8599.52 182
Anonymous2023120699.35 10299.31 9599.47 18199.74 11299.06 22999.28 13499.74 8799.23 14799.72 10699.53 22697.63 23899.88 17699.11 10399.84 14099.48 200
MVS_Test99.28 11999.31 9599.19 25099.35 26698.79 25399.36 11299.49 23399.17 15899.21 25999.67 14698.78 11199.66 34699.09 10499.66 23799.10 295
testgi99.29 11899.26 11299.37 21599.75 10698.81 25198.84 23599.89 1998.38 24899.75 9299.04 33199.36 3999.86 20999.08 10599.25 31599.45 211
1112_ss99.05 18098.84 20399.67 9899.66 14899.29 18598.52 27599.82 4597.65 29899.43 20799.16 31596.42 27899.91 12399.07 10699.84 14099.80 28
CANet_DTU98.91 20598.85 20199.09 26498.79 35898.13 29598.18 30099.31 28599.48 10398.86 30099.51 23296.56 27299.95 4899.05 10799.95 6299.19 276
Baseline_NR-MVSNet99.49 6499.37 8499.82 2699.91 2399.84 2298.83 23799.86 2899.68 6799.65 13299.88 3397.67 23299.87 18999.03 10899.86 13199.76 45
FMVSNet299.35 10299.28 10799.55 15799.49 22099.35 17699.45 9499.57 18699.44 11599.70 11499.74 10097.21 25599.87 18999.03 10899.94 7399.44 216
Test_1112_low_res98.95 20298.73 21299.63 12499.68 14299.15 21598.09 31199.80 5697.14 32699.46 20199.40 26296.11 28899.89 16199.01 11099.84 14099.84 17
VDD-MVS99.20 14699.11 13899.44 19099.43 24598.98 23299.50 8598.32 35499.80 4499.56 17199.69 13096.99 26599.85 22798.99 11199.73 20799.50 190
DeepC-MVS98.90 499.62 4599.61 3999.67 9899.72 11999.44 14899.24 14699.71 10299.27 13999.93 1899.90 2699.70 1399.93 7998.99 11199.99 1299.64 101
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 6699.47 6499.51 16999.77 9199.41 15998.81 24299.66 12699.42 12499.75 9299.66 15099.20 5599.76 30398.98 11399.99 1299.36 239
EPNet_dtu97.62 30597.79 29897.11 35196.67 38492.31 37498.51 27698.04 35799.24 14595.77 37899.47 24893.78 31299.66 34698.98 11399.62 24599.37 236
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs99.34 10799.32 9499.39 20899.67 14798.77 25498.57 26899.81 5499.61 8799.48 19599.41 25898.47 15699.86 20998.97 11599.90 9599.53 171
NR-MVSNet99.40 8899.31 9599.68 9599.43 24599.55 12799.73 2699.50 22899.46 11299.88 3999.36 27597.54 24099.87 18998.97 11599.87 12499.63 106
GBi-Net99.42 8199.31 9599.73 7799.49 22099.77 4999.68 4399.70 10899.44 11599.62 14799.83 5297.21 25599.90 14398.96 11799.90 9599.53 171
FMVSNet597.80 29697.25 31299.42 19698.83 35398.97 23499.38 10599.80 5698.87 19899.25 24999.69 13080.60 38399.91 12398.96 11799.90 9599.38 233
test199.42 8199.31 9599.73 7799.49 22099.77 4999.68 4399.70 10899.44 11599.62 14799.83 5297.21 25599.90 14398.96 11799.90 9599.53 171
FMVSNet398.80 22098.63 22299.32 22799.13 31798.72 25899.10 19099.48 23599.23 14799.62 14799.64 15792.57 32499.86 20998.96 11799.90 9599.39 231
UnsupCasMVSNet_eth98.83 21698.57 22999.59 14099.68 14299.45 14698.99 21699.67 12299.48 10399.55 17699.36 27594.92 29899.86 20998.95 12196.57 37699.45 211
CHOSEN 280x42098.41 26698.41 24498.40 31599.34 27695.89 35396.94 37099.44 24898.80 20799.25 24999.52 22893.51 31699.98 1098.94 12299.98 2899.32 248
TDRefinement99.72 2199.70 2099.77 4399.90 2799.85 1799.86 599.92 1299.69 6599.78 7799.92 2199.37 3699.88 17698.93 12399.95 6299.60 131
Regformer-499.45 7599.44 7199.50 17299.52 20498.94 23899.17 16799.53 21299.64 7999.76 8499.60 19498.96 8899.90 14398.91 12499.84 14099.67 74
Regformer-399.41 8599.41 7799.40 20499.52 20498.70 25999.17 16799.44 24899.62 8399.75 9299.60 19498.90 9599.85 22798.89 12599.84 14099.65 92
alignmvs98.28 27697.96 28499.25 24299.12 31998.93 24299.03 20698.42 35099.64 7998.72 31597.85 38090.86 34599.62 35698.88 12699.13 32099.19 276
sss98.90 20798.77 21199.27 23799.48 22698.44 27798.72 25699.32 28197.94 28599.37 22599.35 28096.31 28399.91 12398.85 12799.63 24499.47 205
xiu_mvs_v2_base99.02 18699.11 13898.77 30099.37 26298.09 30098.13 30699.51 22499.47 10899.42 20998.54 36999.38 3499.97 2098.83 12899.33 30598.24 358
PS-MVSNAJ99.00 19299.08 14998.76 30199.37 26298.10 29998.00 32199.51 22499.47 10899.41 21798.50 37199.28 4699.97 2098.83 12899.34 30398.20 362
D2MVS99.22 13999.19 12199.29 23399.69 13498.74 25798.81 24299.41 25598.55 23099.68 11999.69 13098.13 19699.87 18998.82 13099.98 2899.24 262
PatchT98.45 26398.32 25598.83 29598.94 34298.29 28699.24 14698.82 33299.84 3599.08 27799.76 9391.37 33599.94 6298.82 13099.00 32998.26 357
FMVS199.63 4099.60 4299.72 8399.94 1499.95 299.47 9199.89 1999.43 12099.88 3999.80 6599.26 5099.90 14398.81 13299.88 11399.32 248
APD_test99.63 4099.60 4299.72 8399.94 1499.95 299.47 9199.89 1999.43 12099.88 3999.80 6599.26 5099.90 14398.81 13299.88 11399.32 248
Effi-MVS+99.06 17798.97 18399.34 22199.31 28498.98 23298.31 29299.91 1598.81 20598.79 30898.94 34899.14 6399.84 24498.79 13498.74 34499.20 273
canonicalmvs99.02 18699.00 17499.09 26499.10 32598.70 25999.61 6699.66 12699.63 8298.64 32097.65 38299.04 7899.54 36598.79 13498.92 33399.04 309
VDDNet98.97 19698.82 20699.42 19699.71 12298.81 25199.62 6198.68 33799.81 4199.38 22499.80 6594.25 30699.85 22798.79 13499.32 30699.59 140
CR-MVSNet98.35 27398.20 26698.83 29599.05 33098.12 29699.30 12699.67 12297.39 31399.16 26699.79 7591.87 33299.91 12398.78 13798.77 34098.44 351
test_method91.72 34992.32 35289.91 36693.49 38870.18 39090.28 37999.56 19161.71 38395.39 38099.52 22893.90 30899.94 6298.76 13898.27 35899.62 117
RPMNet98.60 24098.53 23598.83 29599.05 33098.12 29699.30 12699.62 14799.86 2799.16 26699.74 10092.53 32699.92 9998.75 13998.77 34098.44 351
pmmvs499.13 16499.06 15599.36 21999.57 18099.10 22398.01 31999.25 30098.78 21099.58 16199.44 25598.24 18499.76 30398.74 14099.93 8199.22 267
tttt051797.62 30597.20 31398.90 28999.76 9597.40 32599.48 8994.36 37999.06 17799.70 11499.49 24084.55 37899.94 6298.73 14199.65 24099.36 239
EPP-MVSNet99.17 15799.00 17499.66 10599.80 6699.43 15299.70 3499.24 30399.48 10399.56 17199.77 8994.89 29999.93 7998.72 14299.89 10499.63 106
Anonymous2024052999.42 8199.34 8999.65 11099.53 19899.60 11599.63 6099.39 26599.47 10899.76 8499.78 8298.13 19699.86 20998.70 14399.68 22699.49 195
ACMH98.42 699.59 4999.54 5699.72 8399.86 3999.62 10799.56 7899.79 6298.77 21199.80 6999.85 4699.64 1599.85 22798.70 14399.89 10499.70 57
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs99.33 11199.28 10799.47 18199.57 18099.39 16299.78 1199.43 25298.87 19899.57 16499.82 5998.06 20199.87 18998.69 14599.73 20799.15 284
LFMVS98.46 26198.19 26999.26 23999.24 30098.52 27399.62 6196.94 37099.87 2499.31 24099.58 20291.04 34099.81 28298.68 14699.42 29299.45 211
WR-MVS99.11 17098.93 18899.66 10599.30 28899.42 15598.42 28599.37 27299.04 17899.57 16499.20 31296.89 26799.86 20998.66 14799.87 12499.70 57
Anonymous20240521198.75 22598.46 23999.63 12499.34 27699.66 9499.47 9197.65 36399.28 13899.56 17199.50 23593.15 31899.84 24498.62 14899.58 26099.40 228
EPNet98.13 28497.77 29999.18 25294.57 38797.99 30499.24 14697.96 35999.74 5397.29 36999.62 17693.13 31999.97 2098.59 14999.83 15099.58 145
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++99.05 18099.09 14798.91 28399.21 30498.36 28498.82 24199.47 23998.85 20098.90 29599.56 21598.78 11199.09 37898.57 15099.68 22699.26 259
Patchmatch-RL test98.60 24098.36 24999.33 22399.77 9199.07 22798.27 29599.87 2598.91 19399.74 10199.72 11090.57 34999.79 29098.55 15199.85 13599.11 293
pmmvs398.08 28797.80 29698.91 28399.41 25197.69 31897.87 33599.66 12695.87 34799.50 19299.51 23290.35 35199.97 2098.55 15199.47 28499.08 301
ETV-MVS99.18 15399.18 12299.16 25399.34 27699.28 18799.12 18799.79 6299.48 10398.93 28998.55 36899.40 2999.93 7998.51 15399.52 27698.28 356
jason99.16 15899.11 13899.32 22799.75 10698.44 27798.26 29699.39 26598.70 21799.74 10199.30 28998.54 14599.97 2098.48 15499.82 15999.55 157
jason: jason.
APDe-MVS99.48 6699.36 8799.85 1999.55 19299.81 3699.50 8599.69 11498.99 18099.75 9299.71 11798.79 10999.93 7998.46 15599.85 13599.80 28
CL-MVSNet_self_test98.71 23198.56 23299.15 25599.22 30298.66 26497.14 36599.51 22498.09 27499.54 17899.27 29696.87 26899.74 30998.43 15698.96 33099.03 310
our_test_398.85 21599.09 14798.13 32699.66 14894.90 36297.72 34099.58 18499.07 17399.64 13499.62 17698.19 19299.93 7998.41 15799.95 6299.55 157
Gipumacopyleft99.57 5099.59 4499.49 17599.98 399.71 7699.72 2999.84 3899.81 4199.94 1599.78 8298.91 9299.71 31898.41 15799.95 6299.05 308
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 197.37 31396.91 32398.74 30297.72 38097.57 32097.60 34697.36 36998.00 27799.21 25998.02 37890.04 35599.79 29098.37 15995.89 38098.86 327
Regformer-199.32 11399.27 11099.47 18199.41 25198.95 23798.99 21699.48 23599.48 10399.66 12899.52 22898.78 11199.87 18998.36 16099.74 20099.60 131
PM-MVS99.36 10099.29 10599.58 14499.83 4799.66 9498.95 22399.86 2898.85 20099.81 6699.73 10498.40 16999.92 9998.36 16099.83 15099.17 280
baseline197.73 30097.33 30998.96 27599.30 28897.73 31699.40 10198.42 35099.33 13399.46 20199.21 31091.18 33899.82 26698.35 16291.26 38299.32 248
MVS-HIRNet97.86 29398.22 26496.76 35399.28 29391.53 37998.38 28792.60 38399.13 16699.31 24099.96 1297.18 25999.68 33798.34 16399.83 15099.07 306
GA-MVS97.99 29297.68 30298.93 28099.52 20498.04 30397.19 36499.05 32398.32 26198.81 30598.97 34489.89 35799.41 37598.33 16499.05 32599.34 244
Fast-Effi-MVS+99.02 18698.87 19999.46 18499.38 25999.50 13399.04 20399.79 6297.17 32498.62 32198.74 36199.34 4099.95 4898.32 16599.41 29398.92 322
Regformer-299.34 10799.27 11099.53 16399.41 25199.10 22398.99 21699.53 21299.47 10899.66 12899.52 22898.80 10699.89 16198.31 16699.74 20099.60 131
MDA-MVSNet_test_wron98.95 20298.99 17998.85 29199.64 15297.16 33198.23 29899.33 27998.93 19099.56 17199.66 15097.39 24799.83 25698.29 16799.88 11399.55 157
N_pmnet98.73 22998.53 23599.35 22099.72 11998.67 26198.34 28894.65 37898.35 25599.79 7499.68 14198.03 20399.93 7998.28 16899.92 8599.44 216
ET-MVSNet_ETH3D96.78 32496.07 33398.91 28399.26 29797.92 31197.70 34296.05 37497.96 28492.37 38398.43 37287.06 36699.90 14398.27 16997.56 37198.91 323
thisisatest053097.45 31096.95 32098.94 27799.68 14297.73 31699.09 19594.19 38198.61 22599.56 17199.30 28984.30 37999.93 7998.27 16999.54 27299.16 282
YYNet198.95 20298.99 17998.84 29399.64 15297.14 33298.22 29999.32 28198.92 19299.59 15999.66 15097.40 24599.83 25698.27 16999.90 9599.55 157
ACMM98.09 1199.46 7399.38 8199.72 8399.80 6699.69 8799.13 18399.65 13698.99 18099.64 13499.72 11099.39 3099.86 20998.23 17299.81 16799.60 131
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS98.96 19998.87 19999.24 24499.57 18098.40 28098.12 30799.18 31398.28 26399.63 13899.13 31798.02 20599.97 2098.22 17399.69 22199.35 242
3Dnovator99.15 299.43 7899.36 8799.65 11099.39 25699.42 15599.70 3499.56 19199.23 14799.35 22899.80 6599.17 5899.95 4898.21 17499.84 14099.59 140
Fast-Effi-MVS+-dtu99.20 14699.12 13599.43 19499.25 29899.69 8799.05 20199.82 4599.50 10198.97 28599.05 32898.98 8399.98 1098.20 17599.24 31798.62 339
MS-PatchMatch99.00 19298.97 18399.09 26499.11 32498.19 29298.76 25299.33 27998.49 23899.44 20399.58 20298.21 18999.69 32698.20 17599.62 24599.39 231
TSAR-MVS + GP.99.12 16699.04 16599.38 21299.34 27699.16 21398.15 30399.29 29098.18 27099.63 13899.62 17699.18 5799.68 33798.20 17599.74 20099.30 253
DP-MVS99.48 6699.39 7999.74 6799.57 18099.62 10799.29 13399.61 15499.87 2499.74 10199.76 9398.69 12299.87 18998.20 17599.80 17299.75 48
MVP-Stereo99.16 15899.08 14999.43 19499.48 22699.07 22799.08 19899.55 19798.63 22299.31 24099.68 14198.19 19299.78 29398.18 17999.58 26099.45 211
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast99.43 7899.30 10099.80 3299.83 4799.81 3699.52 8299.70 10898.35 25599.51 19099.50 23599.31 4299.88 17698.18 17999.84 14099.69 61
MDA-MVSNet-bldmvs99.06 17799.05 15999.07 26899.80 6697.83 31298.89 22799.72 9999.29 13599.63 13899.70 12496.47 27699.89 16198.17 18199.82 15999.50 190
JIA-IIPM98.06 28897.92 29198.50 31198.59 36797.02 33498.80 24598.51 34699.88 2397.89 35799.87 3691.89 33199.90 14398.16 18297.68 37098.59 341
EIA-MVS99.12 16699.01 17199.45 18899.36 26499.62 10799.34 11499.79 6298.41 24498.84 30298.89 35398.75 11799.84 24498.15 18399.51 27798.89 324
miper_lstm_enhance98.65 23598.60 22398.82 29899.20 30797.33 32797.78 33899.66 12699.01 17999.59 15999.50 23594.62 30399.85 22798.12 18499.90 9599.26 259
Effi-MVS+-dtu99.07 17698.92 19299.52 16698.89 34799.78 4699.15 17599.66 12699.34 13098.92 29299.24 30697.69 22999.98 1098.11 18599.28 31198.81 331
mvs-test198.83 21698.70 21799.22 24698.89 34799.65 9998.88 22899.66 12699.34 13098.29 33798.94 34897.69 22999.96 3898.11 18598.54 35298.04 366
tpm97.15 31696.95 32097.75 33698.91 34394.24 36599.32 11997.96 35997.71 29698.29 33799.32 28586.72 37399.92 9998.10 18796.24 37999.09 298
DeepPCF-MVS98.42 699.18 15399.02 16899.67 9899.22 30299.75 6197.25 36299.47 23998.72 21699.66 12899.70 12499.29 4499.63 35598.07 18899.81 16799.62 117
ppachtmachnet_test98.89 21099.12 13598.20 32499.66 14895.24 35997.63 34499.68 11799.08 17199.78 7799.62 17698.65 13099.88 17698.02 18999.96 5399.48 200
tpmrst97.73 30098.07 27796.73 35598.71 36492.00 37599.10 19098.86 32998.52 23498.92 29299.54 22491.90 33099.82 26698.02 18999.03 32798.37 353
CSCG99.37 9799.29 10599.60 13899.71 12299.46 14199.43 9999.85 3298.79 20899.41 21799.60 19498.92 9099.92 9998.02 18999.92 8599.43 222
eth_miper_zixun_eth98.68 23398.71 21498.60 30799.10 32596.84 33997.52 35299.54 20398.94 18799.58 16199.48 24396.25 28599.76 30398.01 19299.93 8199.21 269
Patchmtry98.78 22198.54 23399.49 17598.89 34799.19 21199.32 11999.67 12299.65 7799.72 10699.79 7591.87 33299.95 4898.00 19399.97 3999.33 245
PVSNet_BlendedMVS99.03 18499.01 17199.09 26499.54 19397.99 30498.58 26499.82 4597.62 29999.34 23199.71 11798.52 15299.77 30197.98 19499.97 3999.52 182
PVSNet_Blended98.70 23298.59 22599.02 27299.54 19397.99 30497.58 34799.82 4595.70 35299.34 23198.98 34198.52 15299.77 30197.98 19499.83 15099.30 253
cl____98.54 25098.41 24498.92 28199.03 33497.80 31497.46 35499.59 17498.90 19499.60 15699.46 25193.85 31099.78 29397.97 19699.89 10499.17 280
DIV-MVS_self_test98.54 25098.42 24398.92 28199.03 33497.80 31497.46 35499.59 17498.90 19499.60 15699.46 25193.87 30999.78 29397.97 19699.89 10499.18 278
AUN-MVS97.82 29597.38 30899.14 25899.27 29598.53 27198.72 25699.02 32498.10 27297.18 37299.03 33589.26 35999.85 22797.94 19897.91 36699.03 310
FA-MVS(test-final)98.52 25298.32 25599.10 26399.48 22698.67 26199.77 1498.60 34397.35 31599.63 13899.80 6593.07 32099.84 24497.92 19999.30 30898.78 334
ambc99.20 24999.35 26698.53 27199.17 16799.46 24399.67 12499.80 6598.46 15999.70 32097.92 19999.70 21899.38 233
USDC98.96 19998.93 18899.05 27099.54 19397.99 30497.07 36899.80 5698.21 26799.75 9299.77 8998.43 16299.64 35497.90 20199.88 11399.51 184
OPM-MVS99.26 12599.13 13199.63 12499.70 13099.61 11398.58 26499.48 23598.50 23699.52 18599.63 16799.14 6399.76 30397.89 20299.77 18799.51 184
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVScopyleft99.32 11399.17 12399.77 4399.69 13499.80 4199.14 17799.31 28599.16 16099.62 14799.61 18598.35 17399.91 12397.88 20399.72 21399.61 127
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 2499.70 13099.79 4399.14 17799.61 15499.92 9997.88 20399.72 21399.77 40
c3_l98.72 23098.71 21498.72 30399.12 31997.22 33097.68 34399.56 19198.90 19499.54 17899.48 24396.37 28299.73 31297.88 20399.88 11399.21 269
3Dnovator+98.92 399.35 10299.24 11699.67 9899.35 26699.47 13799.62 6199.50 22899.44 11599.12 27399.78 8298.77 11499.94 6297.87 20699.72 21399.62 117
miper_ehance_all_eth98.59 24398.59 22598.59 30898.98 34097.07 33397.49 35399.52 22098.50 23699.52 18599.37 27096.41 28099.71 31897.86 20799.62 24599.00 316
WTY-MVS98.59 24398.37 24899.26 23999.43 24598.40 28098.74 25399.13 31998.10 27299.21 25999.24 30694.82 30099.90 14397.86 20798.77 34099.49 195
SED-MVS99.40 8899.28 10799.77 4399.69 13499.82 3399.20 15699.54 20399.13 16699.82 5999.63 16798.91 9299.92 9997.85 20999.70 21899.58 145
test_241102_TWO99.54 20399.13 16699.76 8499.63 16798.32 17999.92 9997.85 20999.69 22199.75 48
MVS_111021_HR99.12 16699.02 16899.40 20499.50 21599.11 21897.92 33299.71 10298.76 21499.08 27799.47 24899.17 5899.54 36597.85 20999.76 18999.54 165
zzz-MVS99.30 11699.14 12899.80 3299.81 6199.81 3698.73 25599.53 21299.27 13999.42 20999.63 16798.21 18999.95 4897.83 21299.79 17799.65 92
MTAPA99.35 10299.20 12099.80 3299.81 6199.81 3699.33 11699.53 21299.27 13999.42 20999.63 16798.21 18999.95 4897.83 21299.79 17799.65 92
MSC_two_6792asdad99.74 6799.03 33499.53 12999.23 30499.92 9997.77 21499.69 22199.78 36
No_MVS99.74 6799.03 33499.53 12999.23 30499.92 9997.77 21499.69 22199.78 36
TESTMET0.1,196.24 33695.84 33897.41 34398.24 37593.84 36897.38 35695.84 37598.43 24197.81 36198.56 36779.77 38499.89 16197.77 21498.77 34098.52 345
ACMH+98.40 899.50 6299.43 7499.71 8999.86 3999.76 5799.32 11999.77 7099.53 9999.77 8299.76 9399.26 5099.78 29397.77 21499.88 11399.60 131
IU-MVS99.69 13499.77 4999.22 30797.50 30799.69 11797.75 21899.70 21899.77 40
114514_t98.49 25898.11 27599.64 11799.73 11599.58 12199.24 14699.76 7589.94 37699.42 20999.56 21597.76 22699.86 20997.74 21999.82 15999.47 205
DVP-MVS++99.38 9499.25 11499.77 4399.03 33499.77 4999.74 2399.61 15499.18 15499.76 8499.61 18599.00 8099.92 9997.72 22099.60 25599.62 117
test_0728_THIRD99.18 15499.62 14799.61 18598.58 13999.91 12397.72 22099.80 17299.77 40
EGC-MVSNET89.05 35085.52 35399.64 11799.89 2999.78 4699.56 7899.52 22024.19 38449.96 38599.83 5299.15 6099.92 9997.71 22299.85 13599.21 269
miper_enhance_ethall98.03 28997.94 28998.32 31998.27 37496.43 34596.95 36999.41 25596.37 34299.43 20798.96 34694.74 30199.69 32697.71 22299.62 24598.83 330
TSAR-MVS + MP.99.34 10799.24 11699.63 12499.82 5499.37 16899.26 13999.35 27698.77 21199.57 16499.70 12499.27 4999.88 17697.71 22299.75 19299.65 92
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl2297.56 30897.28 31098.40 31598.37 37296.75 34097.24 36399.37 27297.31 31899.41 21799.22 30887.30 36499.37 37697.70 22599.62 24599.08 301
MP-MVS-pluss99.14 16298.92 19299.80 3299.83 4799.83 2798.61 26099.63 14496.84 33499.44 20399.58 20298.81 10299.91 12397.70 22599.82 15999.67 74
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP99.28 11999.11 13899.79 3799.75 10699.81 3698.95 22399.53 21298.27 26499.53 18399.73 10498.75 11799.87 18997.70 22599.83 15099.68 67
UnsupCasMVSNet_bld98.55 24998.27 25999.40 20499.56 19199.37 16897.97 32799.68 11797.49 30899.08 27799.35 28095.41 29799.82 26697.70 22598.19 36199.01 315
MVS_111021_LR99.13 16499.03 16799.42 19699.58 17099.32 18197.91 33499.73 9098.68 21899.31 24099.48 24399.09 6899.66 34697.70 22599.77 18799.29 256
IS-MVSNet99.03 18498.85 20199.55 15799.80 6699.25 19599.73 2699.15 31699.37 12799.61 15399.71 11794.73 30299.81 28297.70 22599.88 11399.58 145
test-LLR97.15 31696.95 32097.74 33798.18 37795.02 36097.38 35696.10 37198.00 27797.81 36198.58 36490.04 35599.91 12397.69 23198.78 33898.31 354
test-mter96.23 33795.73 33997.74 33798.18 37795.02 36097.38 35696.10 37197.90 28697.81 36198.58 36479.12 38799.91 12397.69 23198.78 33898.31 354
XVS99.27 12399.11 13899.75 6099.71 12299.71 7699.37 10999.61 15499.29 13598.76 31299.47 24898.47 15699.88 17697.62 23399.73 20799.67 74
X-MVStestdata96.09 33894.87 34799.75 6099.71 12299.71 7699.37 10999.61 15499.29 13598.76 31261.30 39198.47 15699.88 17697.62 23399.73 20799.67 74
SMA-MVScopyleft99.19 14999.00 17499.73 7799.46 23799.73 7099.13 18399.52 22097.40 31299.57 16499.64 15798.93 8999.83 25697.61 23599.79 17799.63 106
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 32796.79 32696.46 35998.90 34490.71 38499.41 10098.68 33794.69 36698.14 34899.34 28386.32 37599.80 28797.60 23698.07 36598.88 325
PVSNet97.47 1598.42 26598.44 24198.35 31799.46 23796.26 34696.70 37399.34 27897.68 29799.00 28499.13 31797.40 24599.72 31497.59 23799.68 22699.08 301
new_pmnet98.88 21198.89 19798.84 29399.70 13097.62 31998.15 30399.50 22897.98 28099.62 14799.54 22498.15 19599.94 6297.55 23899.84 14098.95 319
IB-MVS95.41 2095.30 34794.46 35197.84 33398.76 36295.33 35897.33 35996.07 37396.02 34695.37 38197.41 38476.17 38999.96 3897.54 23995.44 38198.22 359
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 12999.11 13899.61 13698.38 37199.79 4399.57 7699.68 11799.61 8799.15 26899.71 11798.70 12199.91 12397.54 23999.68 22699.13 292
ZNCC-MVS99.22 13999.04 16599.77 4399.76 9599.73 7099.28 13499.56 19198.19 26999.14 27099.29 29298.84 10199.92 9997.53 24199.80 17299.64 101
CP-MVS99.23 13099.05 15999.75 6099.66 14899.66 9499.38 10599.62 14798.38 24899.06 28199.27 29698.79 10999.94 6297.51 24299.82 15999.66 84
SD-MVS99.01 19099.30 10098.15 32599.50 21599.40 16098.94 22599.61 15499.22 15199.75 9299.82 5999.54 2695.51 38597.48 24399.87 12499.54 165
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 25898.29 25899.11 26198.96 34198.42 27997.54 34899.32 28197.53 30598.47 33398.15 37797.88 21799.82 26697.46 24499.24 31799.09 298
DeepC-MVS_fast98.47 599.23 13099.12 13599.56 15499.28 29399.22 20498.99 21699.40 26299.08 17199.58 16199.64 15798.90 9599.83 25697.44 24599.75 19299.63 106
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 12699.08 14999.76 5099.73 11599.70 8399.31 12399.59 17498.36 25099.36 22699.37 27098.80 10699.91 12397.43 24699.75 19299.68 67
ACMMPR99.23 13099.06 15599.76 5099.74 11299.69 8799.31 12399.59 17498.36 25099.35 22899.38 26898.61 13499.93 7997.43 24699.75 19299.67 74
Vis-MVSNet (Re-imp)98.77 22298.58 22899.34 22199.78 8398.88 24799.61 6699.56 19199.11 17099.24 25299.56 21593.00 32299.78 29397.43 24699.89 10499.35 242
MIMVSNet98.43 26498.20 26699.11 26199.53 19898.38 28399.58 7498.61 34198.96 18599.33 23399.76 9390.92 34299.81 28297.38 24999.76 18999.15 284
XVG-OURS-SEG-HR99.16 15898.99 17999.66 10599.84 4399.64 10198.25 29799.73 9098.39 24799.63 13899.43 25699.70 1399.90 14397.34 25098.64 34899.44 216
COLMAP_ROBcopyleft98.06 1299.45 7599.37 8499.70 9399.83 4799.70 8399.38 10599.78 6799.53 9999.67 12499.78 8299.19 5699.86 20997.32 25199.87 12499.55 157
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS99.02 18698.81 20799.65 11099.58 17099.49 13498.58 26499.07 32098.40 24699.04 28299.25 30198.51 15499.80 28797.31 25299.51 27799.65 92
region2R99.23 13099.05 15999.77 4399.76 9599.70 8399.31 12399.59 17498.41 24499.32 23699.36 27598.73 12099.93 7997.29 25399.74 20099.67 74
APD-MVS_3200maxsize99.31 11599.16 12499.74 6799.53 19899.75 6199.27 13799.61 15499.19 15399.57 16499.64 15798.76 11599.90 14397.29 25399.62 24599.56 154
TAPA-MVS97.92 1398.03 28997.55 30599.46 18499.47 23299.44 14898.50 27799.62 14786.79 37799.07 28099.26 29998.26 18399.62 35697.28 25599.73 20799.31 252
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post99.27 12399.11 13899.73 7799.54 19399.74 6799.26 13999.62 14799.16 16099.52 18599.64 15798.41 16599.91 12397.27 25699.61 25299.54 165
RE-MVS-def99.13 13199.54 19399.74 6799.26 13999.62 14799.16 16099.52 18599.64 15798.57 14097.27 25699.61 25299.54 165
test_yl98.25 27897.95 28599.13 25999.17 31298.47 27499.00 21198.67 33998.97 18299.22 25799.02 33691.31 33699.69 32697.26 25898.93 33199.24 262
DCV-MVSNet98.25 27897.95 28599.13 25999.17 31298.47 27499.00 21198.67 33998.97 18299.22 25799.02 33691.31 33699.69 32697.26 25898.93 33199.24 262
PHI-MVS99.11 17098.95 18799.59 14099.13 31799.59 11899.17 16799.65 13697.88 28799.25 24999.46 25198.97 8599.80 28797.26 25899.82 15999.37 236
tfpnnormal99.43 7899.38 8199.60 13899.87 3799.75 6199.59 7299.78 6799.71 5899.90 2999.69 13098.85 10099.90 14397.25 26199.78 18399.15 284
PatchmatchNetpermissive97.65 30497.80 29697.18 34998.82 35692.49 37399.17 16798.39 35298.12 27198.79 30899.58 20290.71 34799.89 16197.23 26299.41 29399.16 282
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS98.99 19598.80 20999.56 15499.25 29899.43 15298.54 27399.27 29498.58 22798.80 30799.43 25698.53 14999.70 32097.22 26399.59 25999.54 165
HPM-MVScopyleft99.25 12699.07 15399.78 4099.81 6199.75 6199.61 6699.67 12297.72 29599.35 22899.25 30199.23 5399.92 9997.21 26499.82 15999.67 74
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS99.19 14999.00 17499.76 5099.76 9599.68 9099.38 10599.54 20398.34 25999.01 28399.50 23598.53 14999.93 7997.18 26599.78 18399.66 84
ACMMPcopyleft99.25 12699.08 14999.74 6799.79 7699.68 9099.50 8599.65 13698.07 27599.52 18599.69 13098.57 14099.92 9997.18 26599.79 17799.63 106
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
test117299.23 13099.05 15999.74 6799.52 20499.75 6199.20 15699.61 15498.97 18299.48 19599.58 20298.41 16599.91 12397.15 26799.55 26699.57 151
abl_699.36 10099.23 11899.75 6099.71 12299.74 6799.33 11699.76 7599.07 17399.65 13299.63 16799.09 6899.92 9997.13 26899.76 18999.58 145
thisisatest051596.98 32096.42 32798.66 30699.42 25097.47 32297.27 36194.30 38097.24 32099.15 26898.86 35585.01 37699.87 18997.10 26999.39 29598.63 338
XVG-ACMP-BASELINE99.23 13099.10 14699.63 12499.82 5499.58 12198.83 23799.72 9998.36 25099.60 15699.71 11798.92 9099.91 12397.08 27099.84 14099.40 228
MSDG99.08 17598.98 18299.37 21599.60 16199.13 21697.54 34899.74 8798.84 20399.53 18399.55 22299.10 6699.79 29097.07 27199.86 13199.18 278
SteuartSystems-ACMMP99.30 11699.14 12899.76 5099.87 3799.66 9499.18 16299.60 16798.55 23099.57 16499.67 14699.03 7999.94 6297.01 27299.80 17299.69 61
Skip Steuart: Steuart Systems R&D Blog.
EPMVS96.53 33096.32 32897.17 35098.18 37792.97 37299.39 10389.95 38798.21 26798.61 32299.59 20086.69 37499.72 31496.99 27399.23 31998.81 331
MSP-MVS99.04 18398.79 21099.81 2999.78 8399.73 7099.35 11399.57 18698.54 23399.54 17898.99 33896.81 26999.93 7996.97 27499.53 27499.77 40
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 19998.70 21799.74 6799.52 20499.71 7698.86 23299.19 31298.47 24098.59 32499.06 32798.08 20099.91 12396.94 27599.60 25599.60 131
SR-MVS99.19 14999.00 17499.74 6799.51 20999.72 7499.18 16299.60 16798.85 20099.47 19799.58 20298.38 17099.92 9996.92 27699.54 27299.57 151
PGM-MVS99.20 14699.01 17199.77 4399.75 10699.71 7699.16 17399.72 9997.99 27999.42 20999.60 19498.81 10299.93 7996.91 27799.74 20099.66 84
HY-MVS98.23 998.21 28397.95 28598.99 27399.03 33498.24 28799.61 6698.72 33696.81 33598.73 31499.51 23294.06 30799.86 20996.91 27798.20 35998.86 327
MDTV_nov1_ep1397.73 30098.70 36590.83 38299.15 17598.02 35898.51 23598.82 30499.61 18590.98 34199.66 34696.89 27998.92 333
GST-MVS99.16 15898.96 18599.75 6099.73 11599.73 7099.20 15699.55 19798.22 26699.32 23699.35 28098.65 13099.91 12396.86 28099.74 20099.62 117
test_post199.14 17751.63 39389.54 35899.82 26696.86 280
SCA98.11 28598.36 24997.36 34499.20 30792.99 37198.17 30298.49 34898.24 26599.10 27699.57 21296.01 29099.94 6296.86 28099.62 24599.14 289
#test#99.12 16698.90 19699.76 5099.73 11599.70 8399.10 19099.59 17497.60 30099.36 22699.37 27098.80 10699.91 12396.84 28399.75 19299.68 67
XVG-OURS99.21 14499.06 15599.65 11099.82 5499.62 10797.87 33599.74 8798.36 25099.66 12899.68 14199.71 1199.90 14396.84 28399.88 11399.43 222
LCM-MVSNet-Re99.28 11999.15 12799.67 9899.33 28199.76 5799.34 11499.97 598.93 19099.91 2499.79 7598.68 12399.93 7996.80 28599.56 26299.30 253
RPSCF99.18 15399.02 16899.64 11799.83 4799.85 1799.44 9799.82 4598.33 26099.50 19299.78 8297.90 21499.65 35296.78 28699.83 15099.44 216
旧先验297.94 33095.33 35698.94 28899.88 17696.75 287
MDTV_nov1_ep13_2view91.44 38099.14 17797.37 31499.21 25991.78 33496.75 28799.03 310
CLD-MVS98.76 22498.57 22999.33 22399.57 18098.97 23497.53 35099.55 19796.41 34099.27 24799.13 31799.07 7499.78 29396.73 28999.89 10499.23 265
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 28697.98 28398.48 31299.27 29596.48 34399.40 10199.07 32098.81 20599.23 25399.57 21290.11 35499.87 18996.69 29099.64 24299.09 298
baseline296.83 32396.28 32998.46 31399.09 32796.91 33798.83 23793.87 38297.23 32196.23 37798.36 37388.12 36299.90 14396.68 29198.14 36398.57 344
cascas96.99 31996.82 32597.48 34097.57 38395.64 35596.43 37599.56 19191.75 37297.13 37397.61 38395.58 29698.63 38196.68 29199.11 32298.18 363
PC_three_145297.56 30199.68 11999.41 25899.09 6897.09 38396.66 29399.60 25599.62 117
LPG-MVS_test99.22 13999.05 15999.74 6799.82 5499.63 10599.16 17399.73 9097.56 30199.64 13499.69 13099.37 3699.89 16196.66 29399.87 12499.69 61
LGP-MVS_train99.74 6799.82 5499.63 10599.73 9097.56 30199.64 13499.69 13099.37 3699.89 16196.66 29399.87 12499.69 61
TinyColmap98.97 19698.93 18899.07 26899.46 23798.19 29297.75 33999.75 8298.79 20899.54 17899.70 12498.97 8599.62 35696.63 29699.83 15099.41 226
LF4IMVS99.01 19098.92 19299.27 23799.71 12299.28 18798.59 26399.77 7098.32 26199.39 22399.41 25898.62 13299.84 24496.62 29799.84 14098.69 337
NCCC98.82 21898.57 22999.58 14499.21 30499.31 18298.61 26099.25 30098.65 22098.43 33499.26 29997.86 21899.81 28296.55 29899.27 31499.61 127
OPU-MVS99.29 23399.12 31999.44 14899.20 15699.40 26299.00 8098.84 38096.54 29999.60 25599.58 145
F-COLMAP98.74 22798.45 24099.62 13399.57 18099.47 13798.84 23599.65 13696.31 34398.93 28999.19 31497.68 23199.87 18996.52 30099.37 30099.53 171
ADS-MVSNet297.78 29797.66 30498.12 32799.14 31595.36 35799.22 15398.75 33596.97 32998.25 34099.64 15790.90 34399.94 6296.51 30199.56 26299.08 301
ADS-MVSNet97.72 30397.67 30397.86 33299.14 31594.65 36399.22 15398.86 32996.97 32998.25 34099.64 15790.90 34399.84 24496.51 30199.56 26299.08 301
PatchMatch-RL98.68 23398.47 23899.30 23299.44 24299.28 18798.14 30599.54 20397.12 32799.11 27499.25 30197.80 22399.70 32096.51 30199.30 30898.93 321
CMPMVSbinary77.52 2398.50 25598.19 26999.41 20398.33 37399.56 12499.01 20999.59 17495.44 35499.57 16499.80 6595.64 29499.46 37496.47 30499.92 8599.21 269
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
xxxxxxxxxxxxxcwj99.11 17098.96 18599.54 16199.53 19899.25 19598.29 29399.76 7599.07 17399.42 20999.61 18598.86 9899.87 18996.45 30599.68 22699.49 195
SF-MVS99.10 17498.93 18899.62 13399.58 17099.51 13299.13 18399.65 13697.97 28199.42 20999.61 18598.86 9899.87 18996.45 30599.68 22699.49 195
FE-MVS97.85 29497.42 30799.15 25599.44 24298.75 25599.77 1498.20 35695.85 34899.33 23399.80 6588.86 36099.88 17696.40 30799.12 32198.81 331
DPE-MVScopyleft99.14 16298.92 19299.82 2699.57 18099.77 4998.74 25399.60 16798.55 23099.76 8499.69 13098.23 18899.92 9996.39 30899.75 19299.76 45
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
gm-plane-assit97.59 38189.02 38893.47 36898.30 37499.84 24496.38 309
AllTest99.21 14499.07 15399.63 12499.78 8399.64 10199.12 18799.83 4098.63 22299.63 13899.72 11098.68 12399.75 30796.38 30999.83 15099.51 184
TestCases99.63 12499.78 8399.64 10199.83 4098.63 22299.63 13899.72 11098.68 12399.75 30796.38 30999.83 15099.51 184
testdata99.42 19699.51 20998.93 24299.30 28896.20 34498.87 29999.40 26298.33 17899.89 16196.29 31299.28 31199.44 216
dp96.86 32297.07 31696.24 36198.68 36690.30 38699.19 16198.38 35397.35 31598.23 34299.59 20087.23 36599.82 26696.27 31398.73 34698.59 341
tpmvs97.39 31297.69 30196.52 35798.41 37091.76 37699.30 12698.94 32897.74 29497.85 36099.55 22292.40 32999.73 31296.25 31498.73 34698.06 365
KD-MVS_2432*160095.89 34095.41 34397.31 34794.96 38593.89 36697.09 36699.22 30797.23 32198.88 29699.04 33179.23 38599.54 36596.24 31596.81 37498.50 349
miper_refine_blended95.89 34095.41 34397.31 34794.96 38593.89 36697.09 36699.22 30797.23 32198.88 29699.04 33179.23 38599.54 36596.24 31596.81 37498.50 349
ACMP97.51 1499.05 18098.84 20399.67 9899.78 8399.55 12798.88 22899.66 12697.11 32899.47 19799.60 19499.07 7499.89 16196.18 31799.85 13599.58 145
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS98.90 20798.72 21399.44 19099.39 25699.42 15598.58 26499.64 14297.31 31899.44 20399.62 17698.59 13799.69 32696.17 31899.79 17799.22 267
DP-MVS Recon98.50 25598.23 26299.31 23099.49 22099.46 14198.56 26999.63 14494.86 36398.85 30199.37 27097.81 22299.59 36296.08 31999.44 28798.88 325
tpm cat196.78 32496.98 31996.16 36298.85 35190.59 38599.08 19899.32 28192.37 37197.73 36699.46 25191.15 33999.69 32696.07 32098.80 33798.21 360
tpm296.35 33396.22 33096.73 35598.88 35091.75 37799.21 15598.51 34693.27 36997.89 35799.21 31084.83 37799.70 32096.04 32198.18 36298.75 336
test_040299.22 13999.14 12899.45 18899.79 7699.43 15299.28 13499.68 11799.54 9799.40 22299.56 21599.07 7499.82 26696.01 32299.96 5399.11 293
ITE_SJBPF99.38 21299.63 15499.44 14899.73 9098.56 22899.33 23399.53 22698.88 9799.68 33796.01 32299.65 24099.02 314
test_prior398.62 23798.34 25299.46 18499.35 26699.22 20497.95 32899.39 26597.87 28898.05 35099.05 32897.90 21499.69 32695.99 32499.49 28199.48 200
test_prior297.95 32897.87 28898.05 35099.05 32897.90 21495.99 32499.49 281
testdata299.89 16195.99 324
原ACMM199.37 21599.47 23298.87 24999.27 29496.74 33798.26 33999.32 28597.93 21299.82 26695.96 32799.38 29699.43 222
新几何199.52 16699.50 21599.22 20499.26 29795.66 35398.60 32399.28 29497.67 23299.89 16195.95 32899.32 30699.45 211
MP-MVScopyleft99.06 17798.83 20599.76 5099.76 9599.71 7699.32 11999.50 22898.35 25598.97 28599.48 24398.37 17199.92 9995.95 32899.75 19299.63 106
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
wuyk23d97.58 30799.13 13192.93 36599.69 13499.49 13499.52 8299.77 7097.97 28199.96 1199.79 7599.84 599.94 6295.85 33099.82 15979.36 381
HQP_MVS98.90 20798.68 21999.55 15799.58 17099.24 20098.80 24599.54 20398.94 18799.14 27099.25 30197.24 25399.82 26695.84 33199.78 18399.60 131
plane_prior599.54 20399.82 26695.84 33199.78 18399.60 131
无先验98.01 31999.23 30495.83 34999.85 22795.79 33399.44 216
112198.56 24698.24 26199.52 16699.49 22099.24 20099.30 12699.22 30795.77 35098.52 32999.29 29297.39 24799.85 22795.79 33399.34 30399.46 209
CPTT-MVS98.74 22798.44 24199.64 11799.61 15999.38 16599.18 16299.55 19796.49 33999.27 24799.37 27097.11 26199.92 9995.74 33599.67 23399.62 117
PLCcopyleft97.35 1698.36 27097.99 28199.48 17999.32 28399.24 20098.50 27799.51 22495.19 35998.58 32598.96 34696.95 26699.83 25695.63 33699.25 31599.37 236
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA98.57 24598.34 25299.28 23599.18 31199.10 22398.34 28899.41 25598.48 23998.52 32998.98 34197.05 26399.78 29395.59 33799.50 27998.96 317
131498.00 29197.90 29498.27 32398.90 34497.45 32499.30 12699.06 32294.98 36097.21 37199.12 32198.43 16299.67 34295.58 33898.56 35197.71 370
agg_prior198.33 27597.92 29199.57 15099.35 26699.36 17297.99 32399.39 26594.85 36497.76 36498.98 34198.03 20399.85 22795.49 33999.44 28799.51 184
PVSNet_095.53 1995.85 34395.31 34597.47 34198.78 36093.48 37095.72 37699.40 26296.18 34597.37 36797.73 38195.73 29399.58 36395.49 33981.40 38399.36 239
MAR-MVS98.24 28097.92 29199.19 25098.78 36099.65 9999.17 16799.14 31795.36 35598.04 35298.81 35897.47 24299.72 31495.47 34199.06 32498.21 360
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 28197.89 29599.26 23999.19 30999.26 19199.65 5899.69 11491.33 37498.14 34899.77 8998.28 18199.96 3895.41 34299.55 26698.58 343
train_agg98.35 27397.95 28599.57 15099.35 26699.35 17698.11 30999.41 25594.90 36197.92 35598.99 33898.02 20599.85 22795.38 34399.44 28799.50 190
9.1498.64 22099.45 24098.81 24299.60 16797.52 30699.28 24699.56 21598.53 14999.83 25695.36 34499.64 242
APD-MVScopyleft98.87 21398.59 22599.71 8999.50 21599.62 10799.01 20999.57 18696.80 33699.54 17899.63 16798.29 18099.91 12395.24 34599.71 21699.61 127
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
AdaColmapbinary98.60 24098.35 25199.38 21299.12 31999.22 20498.67 25999.42 25497.84 29298.81 30599.27 29697.32 25199.81 28295.14 34699.53 27499.10 295
test9_res95.10 34799.44 28799.50 190
CDPH-MVS98.56 24698.20 26699.61 13699.50 21599.46 14198.32 29199.41 25595.22 35799.21 25999.10 32498.34 17699.82 26695.09 34899.66 23799.56 154
ETH3D-3000-0.198.77 22298.50 23799.59 14099.47 23299.53 12998.77 25099.60 16797.33 31799.23 25399.50 23597.91 21399.83 25695.02 34999.67 23399.41 226
BH-untuned98.22 28298.09 27698.58 30999.38 25997.24 32998.55 27098.98 32797.81 29399.20 26498.76 36097.01 26499.65 35294.83 35098.33 35698.86 327
BP-MVS94.73 351
HQP-MVS98.36 27098.02 28099.39 20899.31 28498.94 23897.98 32499.37 27297.45 30998.15 34498.83 35696.67 27099.70 32094.73 35199.67 23399.53 171
QAPM98.40 26897.99 28199.65 11099.39 25699.47 13799.67 4799.52 22091.70 37398.78 31099.80 6598.55 14399.95 4894.71 35399.75 19299.53 171
ETH3D cwj APD-0.1698.50 25598.16 27299.51 16999.04 33299.39 16298.47 27999.47 23996.70 33898.78 31099.33 28497.62 23999.86 20994.69 35499.38 29699.28 258
agg_prior294.58 35599.46 28699.50 190
BH-RMVSNet98.41 26698.14 27499.21 24799.21 30498.47 27498.60 26298.26 35598.35 25598.93 28999.31 28797.20 25899.66 34694.32 35699.10 32399.51 184
E-PMN97.14 31897.43 30696.27 36098.79 35891.62 37895.54 37799.01 32699.44 11598.88 29699.12 32192.78 32399.68 33794.30 35799.03 32797.50 371
MG-MVS98.52 25298.39 24698.94 27799.15 31497.39 32698.18 30099.21 31198.89 19799.23 25399.63 16797.37 24999.74 30994.22 35899.61 25299.69 61
API-MVS98.38 26998.39 24698.35 31798.83 35399.26 19199.14 17799.18 31398.59 22698.66 31998.78 35998.61 13499.57 36494.14 35999.56 26296.21 378
PAPM_NR98.36 27098.04 27899.33 22399.48 22698.93 24298.79 24899.28 29397.54 30498.56 32798.57 36697.12 26099.69 32694.09 36098.90 33599.38 233
ZD-MVS99.43 24599.61 11399.43 25296.38 34199.11 27499.07 32697.86 21899.92 9994.04 36199.49 281
DPM-MVS98.28 27697.94 28999.32 22799.36 26499.11 21897.31 36098.78 33496.88 33198.84 30299.11 32397.77 22599.61 36094.03 36299.36 30199.23 265
gg-mvs-nofinetune95.87 34295.17 34697.97 32998.19 37696.95 33599.69 4089.23 38899.89 1896.24 37699.94 1681.19 38199.51 37093.99 36398.20 35997.44 372
PMVScopyleft92.94 2198.82 21898.81 20798.85 29199.84 4397.99 30499.20 15699.47 23999.71 5899.42 20999.82 5998.09 19899.47 37293.88 36499.85 13599.07 306
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testtj98.56 24698.17 27199.72 8399.45 24099.60 11598.88 22899.50 22896.88 33199.18 26599.48 24397.08 26299.92 9993.69 36599.38 29699.63 106
EMVS96.96 32197.28 31095.99 36398.76 36291.03 38195.26 37898.61 34199.34 13098.92 29298.88 35493.79 31199.66 34692.87 36699.05 32597.30 375
BH-w/o97.20 31597.01 31897.76 33599.08 32895.69 35498.03 31898.52 34595.76 35197.96 35498.02 37895.62 29599.47 37292.82 36797.25 37398.12 364
TR-MVS97.44 31197.15 31598.32 31998.53 36997.46 32398.47 27997.91 36196.85 33398.21 34398.51 37096.42 27899.51 37092.16 36897.29 37297.98 367
OpenMVS_ROBcopyleft97.31 1797.36 31496.84 32498.89 29099.29 29099.45 14698.87 23199.48 23586.54 37999.44 20399.74 10097.34 25099.86 20991.61 36999.28 31197.37 374
GG-mvs-BLEND97.36 34497.59 38196.87 33899.70 3488.49 38994.64 38297.26 38780.66 38299.12 37791.50 37096.50 37896.08 380
DeepMVS_CXcopyleft97.98 32899.69 13496.95 33599.26 29775.51 38195.74 37998.28 37596.47 27699.62 35691.23 37197.89 36797.38 373
ETH3 D test640097.76 29897.19 31499.50 17299.38 25999.26 19198.34 28899.49 23392.99 37098.54 32899.20 31295.92 29299.82 26691.14 37299.66 23799.40 228
PAPR97.56 30897.07 31699.04 27198.80 35798.11 29897.63 34499.25 30094.56 36798.02 35398.25 37697.43 24499.68 33790.90 37398.74 34499.33 245
MVS95.72 34594.63 34998.99 27398.56 36897.98 31099.30 12698.86 32972.71 38297.30 36899.08 32598.34 17699.74 30989.21 37498.33 35699.26 259
thres600view796.60 32996.16 33197.93 33099.63 15496.09 35099.18 16297.57 36498.77 21198.72 31597.32 38587.04 36799.72 31488.57 37598.62 34997.98 367
FPMVS96.32 33495.50 34198.79 29999.60 16198.17 29498.46 28498.80 33397.16 32596.28 37499.63 16782.19 38099.09 37888.45 37698.89 33699.10 295
PCF-MVS96.03 1896.73 32695.86 33799.33 22399.44 24299.16 21396.87 37199.44 24886.58 37898.95 28799.40 26294.38 30599.88 17687.93 37799.80 17298.95 319
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view90096.39 33296.03 33497.47 34199.63 15495.93 35199.18 16297.57 36498.75 21598.70 31797.31 38687.04 36799.67 34287.62 37898.51 35396.81 376
tfpn200view996.30 33595.89 33597.53 33999.58 17096.11 34899.00 21197.54 36798.43 24198.52 32996.98 38886.85 36999.67 34287.62 37898.51 35396.81 376
thres40096.40 33195.89 33597.92 33199.58 17096.11 34899.00 21197.54 36798.43 24198.52 32996.98 38886.85 36999.67 34287.62 37898.51 35397.98 367
thres20096.09 33895.68 34097.33 34699.48 22696.22 34798.53 27497.57 36498.06 27698.37 33696.73 39086.84 37199.61 36086.99 38198.57 35096.16 379
MVEpermissive92.54 2296.66 32896.11 33298.31 32199.68 14297.55 32197.94 33095.60 37699.37 12790.68 38498.70 36296.56 27298.61 38286.94 38299.55 26698.77 335
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PAPM95.61 34694.71 34898.31 32199.12 31996.63 34196.66 37498.46 34990.77 37596.25 37598.68 36393.01 32199.69 32681.60 38397.86 36998.62 339
test12329.31 35133.05 35618.08 36725.93 39112.24 39197.53 35010.93 39211.78 38524.21 38650.08 39521.04 3908.60 38623.51 38432.43 38533.39 382
testmvs28.94 35233.33 35415.79 36826.03 3909.81 39296.77 37215.67 39111.55 38623.87 38750.74 39419.03 3918.53 38723.21 38533.07 38429.03 383
test_blank8.33 35511.11 3580.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 388100.00 10.00 3920.00 3880.00 3860.00 3860.00 384
uanet_test8.33 35511.11 3580.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 388100.00 10.00 3920.00 3880.00 3860.00 3860.00 384
DCPMVS8.33 35511.11 3580.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 388100.00 10.00 3920.00 3880.00 3860.00 3860.00 384
cdsmvs_eth3d_5k24.88 35333.17 3550.00 3690.00 3920.00 3930.00 38099.62 1470.00 3870.00 38899.13 31799.82 60.00 3880.00 3860.00 3860.00 384
pcd_1.5k_mvsjas16.61 35422.14 3570.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 388100.00 199.28 460.00 3880.00 3860.00 3860.00 384
sosnet-low-res8.33 35511.11 3580.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 388100.00 10.00 3920.00 3880.00 3860.00 3860.00 384
sosnet8.33 35511.11 3580.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 388100.00 10.00 3920.00 3880.00 3860.00 3860.00 384
uncertanet8.33 35511.11 3580.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 388100.00 10.00 3920.00 3880.00 3860.00 3860.00 384
Regformer8.33 35511.11 3580.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 388100.00 10.00 3920.00 3880.00 3860.00 3860.00 384
ab-mvs-re8.26 36311.02 3660.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 38899.16 3150.00 3920.00 3880.00 3860.00 3860.00 384
uanet8.33 35511.11 3580.00 3690.00 3920.00 3930.00 3800.00 3930.00 3870.00 388100.00 10.00 3920.00 3880.00 3860.00 3860.00 384
FOURS199.83 4799.89 1099.74 2399.71 10299.69 6599.63 138
test_one_060199.63 15499.76 5799.55 19799.23 14799.31 24099.61 18598.59 137
eth-test20.00 392
eth-test0.00 392
test_241102_ONE99.69 13499.82 3399.54 20399.12 16999.82 5999.49 24098.91 9299.52 369
save fliter99.53 19899.25 19598.29 29399.38 27199.07 173
test072699.69 13499.80 4199.24 14699.57 18699.16 16099.73 10599.65 15598.35 173
GSMVS99.14 289
test_part299.62 15899.67 9299.55 176
sam_mvs190.81 34699.14 289
sam_mvs90.52 350
MTGPAbinary99.53 212
test_post52.41 39290.25 35299.86 209
patchmatchnet-post99.62 17690.58 34899.94 62
MTMP99.09 19598.59 344
TEST999.35 26699.35 17698.11 30999.41 25594.83 36597.92 35598.99 33898.02 20599.85 227
test_899.34 27699.31 18298.08 31399.40 26294.90 36197.87 35998.97 34498.02 20599.84 244
agg_prior99.35 26699.36 17299.39 26597.76 36499.85 227
test_prior499.19 21198.00 321
test_prior99.46 18499.35 26699.22 20499.39 26599.69 32699.48 200
新几何298.04 317
旧先验199.49 22099.29 18599.26 29799.39 26697.67 23299.36 30199.46 209
原ACMM297.92 332
test22299.51 20999.08 22697.83 33799.29 29095.21 35898.68 31899.31 28797.28 25299.38 29699.43 222
segment_acmp98.37 171
testdata197.72 34097.86 291
test1299.54 16199.29 29099.33 17999.16 31598.43 33497.54 24099.82 26699.47 28499.48 200
plane_prior799.58 17099.38 165
plane_prior699.47 23299.26 19197.24 253
plane_prior499.25 301
plane_prior399.31 18298.36 25099.14 270
plane_prior298.80 24598.94 187
plane_prior199.51 209
plane_prior99.24 20098.42 28597.87 28899.71 216
n20.00 393
nn0.00 393
door-mid99.83 40
test1199.29 290
door99.77 70
HQP5-MVS98.94 238
HQP-NCC99.31 28497.98 32497.45 30998.15 344
ACMP_Plane99.31 28497.98 32497.45 30998.15 344
HQP4-MVS98.15 34499.70 32099.53 171
HQP3-MVS99.37 27299.67 233
HQP2-MVS96.67 270
NP-MVS99.40 25499.13 21698.83 356
ACMMP++_ref99.94 73
ACMMP++99.79 177
Test By Simon98.41 165