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_rt99.89 399.90 499.87 2699.98 399.75 8099.70 38100.00 199.73 109100.00 199.89 4199.79 2299.88 23599.98 1100.00 199.98 5
test_fmvs299.72 5499.85 1799.34 29199.91 3198.08 38299.48 109100.00 199.90 5099.99 799.91 3199.50 6199.98 2799.98 199.99 1699.96 13
test_fmvs399.83 2199.93 299.53 22699.96 798.62 33799.67 53100.00 199.95 32100.00 199.95 1699.85 1499.99 899.98 199.99 1699.98 5
test_fmvsmconf0.01_n99.89 399.88 799.91 399.98 399.76 7199.12 241100.00 1100.00 199.99 799.91 3199.98 1100.00 199.97 4100.00 199.99 2
test_vis1_n_192099.72 5499.88 799.27 31699.93 2497.84 39599.34 148100.00 199.99 399.99 799.82 9199.87 1399.99 899.97 499.99 1699.97 10
test_vis1_n99.68 6599.79 3499.36 28799.94 1898.18 37199.52 94100.00 199.86 66100.00 199.88 5098.99 14899.96 6999.97 499.96 8799.95 14
test_fmvs1_n99.68 6599.81 2899.28 31199.95 1597.93 39199.49 107100.00 199.82 8699.99 799.89 4199.21 10299.98 2799.97 499.98 5099.93 20
test_f99.75 4999.88 799.37 28299.96 798.21 36899.51 101100.00 199.94 36100.00 199.93 2299.58 4999.94 9899.97 499.99 1699.97 10
fmvsm_l_conf0.5_n_999.83 2199.81 2899.89 1199.86 5999.80 5298.94 30899.96 2899.98 1899.96 3499.78 13299.88 1199.98 2799.96 999.99 1699.90 29
fmvsm_l_conf0.5_n_399.85 1299.83 2199.92 299.88 4599.86 1999.08 25799.97 2099.98 1899.96 3499.79 11999.90 999.99 899.96 999.99 1699.90 29
test_fmvsmconf0.1_n99.87 999.86 1399.91 399.97 699.74 8899.01 28199.99 1199.99 399.98 1499.88 5099.97 299.99 899.96 9100.00 199.98 5
test_fmvsmvis_n_192099.84 1799.86 1399.81 5499.88 4599.55 17099.17 21699.98 1299.99 399.96 3499.84 7799.96 399.99 899.96 999.99 1699.88 40
test_cas_vis1_n_192099.76 4699.86 1399.45 25299.93 2498.40 35699.30 16599.98 1299.94 3699.99 799.89 4199.80 2199.97 4499.96 999.97 7399.97 10
fmvsm_s_conf0.5_n_1099.77 4499.73 5599.88 1999.81 10699.75 8099.06 26399.85 8299.99 399.97 2499.84 7799.12 11999.98 2799.95 1499.99 1699.90 29
fmvsm_s_conf0.5_n_799.73 5299.78 3999.60 19199.74 17898.93 30498.85 32299.96 2899.96 2899.97 2499.76 14999.82 1899.96 6999.95 1499.98 5099.90 29
fmvsm_l_conf0.5_n99.80 3099.78 3999.85 3299.88 4599.66 12199.11 24699.91 5299.98 1899.96 3499.64 23699.60 4399.99 899.95 1499.99 1699.88 40
test_fmvsm_n_192099.84 1799.85 1799.83 4199.82 9499.70 10999.17 21699.97 2099.99 399.96 3499.82 9199.94 4100.00 199.95 14100.00 199.80 65
test_fmvs199.48 13099.65 7498.97 35899.54 28297.16 42299.11 24699.98 1299.78 10399.96 3499.81 9898.72 19199.97 4499.95 1499.97 7399.79 73
mvsany_test399.85 1299.88 799.75 9799.95 1599.37 22299.53 9299.98 1299.77 10799.99 799.95 1699.85 1499.94 9899.95 1499.98 5099.94 17
fmvsm_s_conf0.5_n_999.82 2499.82 2599.82 4699.83 8599.59 15798.97 29999.92 4399.99 399.97 2499.84 7799.90 999.94 9899.94 2099.99 1699.92 24
fmvsm_s_conf0.1_n_299.81 2899.78 3999.89 1199.93 2499.76 7198.92 31299.98 1299.99 399.99 799.88 5099.43 6699.94 9899.94 2099.99 1699.99 2
fmvsm_l_conf0.5_n_a99.80 3099.79 3499.84 3899.88 4599.64 13399.12 24199.91 5299.98 1899.95 4599.67 22099.67 3499.99 899.94 2099.99 1699.88 40
MM99.18 23399.05 24299.55 21699.35 35398.81 31599.05 26497.79 46399.99 399.48 28399.59 28896.29 36399.95 8199.94 2099.98 5099.88 40
test_fmvsmconf_n99.85 1299.84 2099.88 1999.91 3199.73 9198.97 29999.98 1299.99 399.96 3499.85 6999.93 799.99 899.94 2099.99 1699.93 20
fmvsm_s_conf0.5_n_1199.76 4699.75 5199.81 5499.81 10699.53 17399.15 22599.89 6199.99 399.98 1499.86 6399.13 11699.98 2799.93 2599.99 1699.92 24
fmvsm_s_conf0.5_n_599.78 3799.76 4999.85 3299.79 12999.72 9698.84 32499.96 2899.96 2899.96 3499.72 17599.71 2899.99 899.93 2599.98 5099.85 49
fmvsm_s_conf0.5_n_299.78 3799.75 5199.88 1999.82 9499.76 7198.88 31699.92 4399.98 1899.98 1499.85 6999.42 6899.94 9899.93 2599.98 5099.94 17
fmvsm_s_conf0.1_n_a99.85 1299.83 2199.91 399.95 1599.82 4399.10 24999.98 1299.99 399.98 1499.91 3199.68 3399.93 11999.93 2599.99 1699.99 2
fmvsm_s_conf0.1_n99.86 1099.85 1799.89 1199.93 2499.78 5899.07 26299.98 1299.99 399.98 1499.90 3699.88 1199.92 15099.93 2599.99 1699.98 5
fmvsm_s_conf0.5_n_a99.82 2499.79 3499.89 1199.85 7299.82 4399.03 27299.96 2899.99 399.97 2499.84 7799.58 4999.93 11999.92 3099.98 5099.93 20
fmvsm_s_conf0.5_n99.83 2199.81 2899.87 2699.85 7299.78 5899.03 27299.96 2899.99 399.97 2499.84 7799.78 2399.92 15099.92 3099.99 1699.92 24
LCM-MVSNet99.95 199.95 199.95 199.99 199.99 199.95 299.97 2099.99 3100.00 199.98 1399.78 23100.00 199.92 30100.00 199.87 44
fmvsm_s_conf0.5_n_899.76 4699.72 5699.88 1999.82 9499.75 8099.02 27699.87 7099.98 1899.98 1499.81 9899.07 13099.97 4499.91 3399.99 1699.92 24
fmvsm_s_conf0.5_n_699.80 3099.78 3999.85 3299.78 13799.78 5899.00 28799.97 2099.96 2899.97 2499.56 30299.92 899.93 11999.91 3399.99 1699.83 56
fmvsm_s_conf0.5_n_499.78 3799.78 3999.79 7199.75 17099.56 16698.98 29799.94 3899.92 4699.97 2499.72 17599.84 1699.92 15099.91 3399.98 5099.89 37
MVStest198.22 36698.09 36198.62 39999.04 42396.23 44599.20 20199.92 4399.44 19999.98 1499.87 5685.87 46699.67 43699.91 3399.57 34799.95 14
v192192099.56 10599.57 10399.55 21699.75 17099.11 27799.05 26499.61 24499.15 26199.88 8399.71 18599.08 12799.87 25099.90 3799.97 7399.66 147
v124099.56 10599.58 9899.51 23299.80 11599.00 29199.00 28799.65 22399.15 26199.90 6899.75 15799.09 12399.88 23599.90 3799.96 8799.67 133
v1099.69 6099.69 6199.66 15199.81 10699.39 21699.66 5799.75 16199.60 16599.92 6099.87 5698.75 18699.86 26999.90 3799.99 1699.73 93
v119299.57 10199.57 10399.57 20599.77 15099.22 25899.04 26999.60 25599.18 25099.87 9399.72 17599.08 12799.85 28899.89 4099.98 5099.66 147
fmvsm_s_conf0.5_n_399.79 3499.77 4599.85 3299.81 10699.71 10198.97 29999.92 4399.98 1899.97 2499.86 6399.53 5799.95 8199.88 4199.99 1699.89 37
v14419299.55 11099.54 11399.58 19799.78 13799.20 26499.11 24699.62 23799.18 25099.89 7399.72 17598.66 20099.87 25099.88 4199.97 7399.66 147
v899.68 6599.69 6199.65 15899.80 11599.40 21399.66 5799.76 15699.64 14999.93 5399.85 6998.66 20099.84 30499.88 4199.99 1699.71 102
mvs5depth99.88 699.91 399.80 6499.92 2999.42 20599.94 3100.00 199.97 2599.89 7399.99 1299.63 3799.97 4499.87 4499.99 16100.00 1
v114499.54 11499.53 11799.59 19499.79 12999.28 24099.10 24999.61 24499.20 24799.84 10299.73 16798.67 19899.84 30499.86 4599.98 5099.64 168
mmtdpeth99.78 3799.83 2199.66 15199.85 7299.05 29099.79 1599.97 20100.00 199.43 29599.94 1999.64 3599.94 9899.83 4699.99 1699.98 5
SSC-MVS99.52 12099.42 14499.83 4199.86 5999.65 12799.52 9499.81 11799.87 6399.81 11699.79 11996.78 34299.99 899.83 4699.51 36399.86 46
v7n99.82 2499.80 3299.88 1999.96 799.84 2799.82 1099.82 10499.84 7699.94 4899.91 3199.13 11699.96 6999.83 4699.99 1699.83 56
v2v48299.50 12399.47 12999.58 19799.78 13799.25 24899.14 22999.58 27099.25 23899.81 11699.62 26098.24 25999.84 30499.83 4699.97 7399.64 168
test_vis1_rt99.45 14699.46 13499.41 27099.71 19198.63 33698.99 29499.96 2899.03 27499.95 4599.12 40898.75 18699.84 30499.82 5099.82 23299.77 79
tt080599.63 8599.57 10399.81 5499.87 5499.88 1299.58 8298.70 42499.72 11399.91 6399.60 27899.43 6699.81 35599.81 5199.53 35999.73 93
VortexMVS99.13 24699.24 19698.79 38999.67 22796.60 43799.24 19099.80 12299.85 7299.93 5399.84 7795.06 38199.89 22099.80 5299.98 5099.89 37
V4299.56 10599.54 11399.63 17299.79 12999.46 19099.39 12999.59 26199.24 24099.86 9699.70 19598.55 21599.82 33999.79 5399.95 11199.60 204
SSC-MVS3.299.64 8499.67 6599.56 20999.75 17098.98 29498.96 30399.87 7099.88 6199.84 10299.64 23699.32 8699.91 17999.78 5499.96 8799.80 65
mvs_tets99.90 299.90 499.90 899.96 799.79 5599.72 3399.88 6699.92 4699.98 1499.93 2299.94 499.98 2799.77 55100.00 199.92 24
WB-MVS99.44 15099.32 17299.80 6499.81 10699.61 15199.47 11299.81 11799.82 8699.71 18399.72 17596.60 34799.98 2799.75 5699.23 40499.82 63
PS-MVSNAJss99.84 1799.82 2599.89 1199.96 799.77 6499.68 4999.85 8299.95 3299.98 1499.92 2799.28 9199.98 2799.75 56100.00 199.94 17
jajsoiax99.89 399.89 699.89 1199.96 799.78 5899.70 3899.86 7699.89 5699.98 1499.90 3699.94 499.98 2799.75 56100.00 199.90 29
ANet_high99.88 699.87 1199.91 399.99 199.91 499.65 62100.00 199.90 50100.00 199.97 1499.61 4199.97 4499.75 56100.00 199.84 52
AstraMVS99.15 24399.06 23799.42 26299.85 7298.59 34099.13 23697.26 47199.84 7699.87 9399.77 14296.11 36699.93 11999.71 6099.96 8799.74 89
Elysia99.69 6099.65 7499.81 5499.86 5999.72 9699.34 14899.77 14899.94 3699.91 6399.76 14998.55 21599.99 899.70 6199.98 5099.72 97
StellarMVS99.69 6099.65 7499.81 5499.86 5999.72 9699.34 14899.77 14899.94 3699.91 6399.76 14998.55 21599.99 899.70 6199.98 5099.72 97
tt0320-xc99.82 2499.82 2599.82 4699.82 9499.84 2799.82 1099.92 4399.94 3699.94 4899.93 2299.34 8399.92 15099.70 6199.96 8799.70 105
reproduce_monomvs97.40 40397.46 39397.20 45399.05 42091.91 48199.20 20199.18 39699.84 7699.86 9699.75 15780.67 47499.83 32399.69 6499.95 11199.85 49
SPE-MVS-test99.68 6599.70 5899.64 16599.57 26699.83 3599.78 1799.97 2099.92 4699.50 28099.38 35599.57 5199.95 8199.69 6499.90 15999.15 375
guyue99.12 24999.02 25199.41 27099.84 7798.56 34199.19 20798.30 44999.82 8699.84 10299.75 15794.84 38499.92 15099.68 6699.94 12799.74 89
tt032099.79 3499.79 3499.81 5499.82 9499.84 2799.82 1099.90 5899.94 3699.94 4899.94 1999.07 13099.92 15099.68 6699.97 7399.67 133
MGCNet98.61 32498.30 34599.52 22897.88 48698.95 30098.76 34194.11 48799.84 7699.32 32699.57 29895.57 37599.95 8199.68 6699.98 5099.68 124
CS-MVS99.67 7699.70 5899.58 19799.53 28999.84 2799.79 1599.96 2899.90 5099.61 23699.41 34599.51 6099.95 8199.66 6999.89 17398.96 417
mamv499.73 5299.74 5399.70 13299.66 22999.87 1599.69 4599.93 3999.93 4399.93 5399.86 6399.07 130100.00 199.66 6999.92 14599.24 350
KinetiMVS99.66 7799.63 8299.76 8699.89 3999.57 16599.37 14099.82 10499.95 3299.90 6899.63 25198.57 21199.97 4499.65 7199.94 12799.74 89
pmmvs699.86 1099.86 1399.83 4199.94 1899.90 799.83 799.91 5299.85 7299.94 4899.95 1699.73 2799.90 19899.65 7199.97 7399.69 117
MIMVSNet199.66 7799.62 8499.80 6499.94 1899.87 1599.69 4599.77 14899.78 10399.93 5399.89 4197.94 28799.92 15099.65 7199.98 5099.62 186
LuminaMVS99.39 16999.28 18799.73 11399.83 8599.49 18099.00 28799.05 40899.81 9299.89 7399.79 11996.54 35199.97 4499.64 7499.98 5099.73 93
sc_t199.81 2899.80 3299.82 4699.88 4599.88 1299.83 799.79 13199.94 3699.93 5399.92 2799.35 8299.92 15099.64 7499.94 12799.68 124
EC-MVSNet99.69 6099.69 6199.68 14099.71 19199.91 499.76 2399.96 2899.86 6699.51 27799.39 35399.57 5199.93 11999.64 7499.86 20499.20 363
K. test v398.87 30098.60 31099.69 13899.93 2499.46 19099.74 2794.97 48299.78 10399.88 8399.88 5093.66 39999.97 4499.61 7799.95 11199.64 168
KD-MVS_self_test99.63 8599.59 9499.76 8699.84 7799.90 799.37 14099.79 13199.83 8299.88 8399.85 6998.42 23999.90 19899.60 7899.73 28699.49 269
Anonymous2024052199.44 15099.42 14499.49 23899.89 3998.96 29999.62 6799.76 15699.85 7299.82 10999.88 5096.39 35899.97 4499.59 7999.98 5099.55 229
TransMVSNet (Re)99.78 3799.77 4599.81 5499.91 3199.85 2299.75 2599.86 7699.70 12499.91 6399.89 4199.60 4399.87 25099.59 7999.74 28099.71 102
OurMVSNet-221017-099.75 4999.71 5799.84 3899.96 799.83 3599.83 799.85 8299.80 9699.93 5399.93 2298.54 21999.93 11999.59 7999.98 5099.76 84
EU-MVSNet99.39 16999.62 8498.72 39499.88 4596.44 43999.56 8799.85 8299.90 5099.90 6899.85 6998.09 27599.83 32399.58 8299.95 11199.90 29
mvs_anonymous99.28 19799.39 14998.94 36299.19 39697.81 39799.02 27699.55 28399.78 10399.85 9999.80 10898.24 25999.86 26999.57 8399.50 36699.15 375
test111197.74 38798.16 35796.49 46499.60 24489.86 49599.71 3791.21 49199.89 5699.88 8399.87 5693.73 39899.90 19899.56 8499.99 1699.70 105
lessismore_v099.64 16599.86 5999.38 21890.66 49299.89 7399.83 8494.56 38999.97 4499.56 8499.92 14599.57 222
mvsany_test199.44 15099.45 13699.40 27399.37 34698.64 33597.90 43799.59 26199.27 23499.92 6099.82 9199.74 2699.93 11999.55 8699.87 19699.63 174
MVSMamba_PlusPlus99.55 11099.58 9899.47 24599.68 22099.40 21399.52 9499.70 19299.92 4699.77 14599.86 6398.28 25599.96 6999.54 8799.90 15999.05 404
pm-mvs199.79 3499.79 3499.78 7599.91 3199.83 3599.76 2399.87 7099.73 10999.89 7399.87 5699.63 3799.87 25099.54 8799.92 14599.63 174
LTVRE_ROB99.19 199.88 699.87 1199.88 1999.91 3199.90 799.96 199.92 4399.90 5099.97 2499.87 5699.81 2099.95 8199.54 8799.99 1699.80 65
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 13099.65 7498.95 36199.71 19197.27 41999.50 10299.82 10499.59 16799.41 30499.85 6999.62 40100.00 199.53 9099.89 17399.59 211
test250694.73 45494.59 45495.15 47199.59 25085.90 49799.75 2574.01 49999.89 5699.71 18399.86 6379.00 48499.90 19899.52 9199.99 1699.65 156
UniMVSNet_ETH3D99.85 1299.83 2199.90 899.89 3999.91 499.89 599.71 18399.93 4399.95 4599.89 4199.71 2899.96 6999.51 9299.97 7399.84 52
FC-MVSNet-test99.70 5899.65 7499.86 3099.88 4599.86 1999.72 3399.78 14299.90 5099.82 10999.83 8498.45 23599.87 25099.51 9299.97 7399.86 46
BP-MVS198.72 31698.46 32699.50 23499.53 28999.00 29199.34 14898.53 43499.65 14599.73 17399.38 35590.62 44099.96 6999.50 9499.86 20499.55 229
UA-Net99.78 3799.76 4999.86 3099.72 18799.71 10199.91 499.95 3699.96 2899.71 18399.91 3199.15 11199.97 4499.50 94100.00 199.90 29
viewdifsd2359ckpt1199.62 9299.64 7999.56 20999.86 5999.19 26599.02 27699.93 3999.83 8299.88 8399.81 9898.99 14899.83 32399.48 9699.96 8799.65 156
viewmsd2359difaftdt99.62 9299.64 7999.56 20999.86 5999.19 26599.02 27699.93 3999.83 8299.88 8399.81 9898.99 14899.83 32399.48 9699.96 8799.65 156
PMMVS299.48 13099.45 13699.57 20599.76 15498.99 29398.09 41499.90 5898.95 28499.78 13399.58 29199.57 5199.93 11999.48 9699.95 11199.79 73
VPA-MVSNet99.66 7799.62 8499.79 7199.68 22099.75 8099.62 6799.69 20099.85 7299.80 12399.81 9898.81 17499.91 17999.47 9999.88 18399.70 105
GDP-MVS98.81 30798.57 31699.50 23499.53 28999.12 27699.28 17499.86 7699.53 17699.57 24799.32 37290.88 43699.98 2799.46 10099.74 28099.42 308
ECVR-MVScopyleft97.73 38898.04 36496.78 45799.59 25090.81 49099.72 3390.43 49399.89 5699.86 9699.86 6393.60 40099.89 22099.46 10099.99 1699.65 156
nrg03099.70 5899.66 7299.82 4699.76 15499.84 2799.61 7399.70 19299.93 4399.78 13399.68 21699.10 12199.78 36999.45 10299.96 8799.83 56
FE-MVSNET299.68 6599.67 6599.72 12199.86 5999.68 11699.46 11699.88 6699.62 15499.87 9399.85 6999.06 13799.85 28899.44 10399.98 5099.63 174
TAMVS99.49 12899.45 13699.63 17299.48 31499.42 20599.45 11799.57 27299.66 14199.78 13399.83 8497.85 29499.86 26999.44 10399.96 8799.61 200
GeoE99.69 6099.66 7299.78 7599.76 15499.76 7199.60 7999.82 10499.46 19499.75 15899.56 30299.63 3799.95 8199.43 10599.88 18399.62 186
new-patchmatchnet99.35 18299.57 10398.71 39799.82 9496.62 43598.55 36999.75 16199.50 18199.88 8399.87 5699.31 8799.88 23599.43 105100.00 199.62 186
test20.0399.55 11099.54 11399.58 19799.79 12999.37 22299.02 27699.89 6199.60 16599.82 10999.62 26098.81 17499.89 22099.43 10599.86 20499.47 277
MVSFormer99.41 16399.44 14099.31 30399.57 26698.40 35699.77 1999.80 12299.73 10999.63 22099.30 37798.02 28099.98 2799.43 10599.69 30699.55 229
test_djsdf99.84 1799.81 2899.91 399.94 1899.84 2799.77 1999.80 12299.73 10999.97 2499.92 2799.77 2599.98 2799.43 105100.00 199.90 29
SDMVSNet99.77 4499.77 4599.76 8699.80 11599.65 12799.63 6499.86 7699.97 2599.89 7399.89 4199.52 5999.99 899.42 11099.96 8799.65 156
Anonymous2023121199.62 9299.57 10399.76 8699.61 24299.60 15599.81 1399.73 17199.82 8699.90 6899.90 3697.97 28699.86 26999.42 11099.96 8799.80 65
SixPastTwentyTwo99.42 15799.30 17999.76 8699.92 2999.67 11999.70 3899.14 40199.65 14599.89 7399.90 3696.20 36599.94 9899.42 11099.92 14599.67 133
balanced_conf0399.50 12399.50 12299.50 23499.42 33799.49 18099.52 9499.75 16199.86 6699.78 13399.71 18598.20 26799.90 19899.39 11399.88 18399.10 386
patch_mono-299.51 12199.46 13499.64 16599.70 20699.11 27799.04 26999.87 7099.71 11899.47 28599.79 11998.24 25999.98 2799.38 11499.96 8799.83 56
UGNet99.38 17299.34 16699.49 23898.90 43598.90 30899.70 3899.35 35599.86 6698.57 41999.81 9898.50 23099.93 11999.38 11499.98 5099.66 147
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 5799.67 6599.81 5499.89 3999.72 9699.59 8099.82 10499.39 21599.82 10999.84 7799.38 7499.91 17999.38 11499.93 13999.80 65
FIs99.65 8399.58 9899.84 3899.84 7799.85 2299.66 5799.75 16199.86 6699.74 16899.79 11998.27 25799.85 28899.37 11799.93 13999.83 56
sd_testset99.78 3799.78 3999.80 6499.80 11599.76 7199.80 1499.79 13199.97 2599.89 7399.89 4199.53 5799.99 899.36 11899.96 8799.65 156
anonymousdsp99.80 3099.77 4599.90 899.96 799.88 1299.73 3099.85 8299.70 12499.92 6099.93 2299.45 6299.97 4499.36 118100.00 199.85 49
casdiffmvs_mvgpermissive99.68 6599.68 6499.69 13899.81 10699.59 15799.29 17299.90 5899.71 11899.79 12999.73 16799.54 5499.84 30499.36 11899.96 8799.65 156
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 4999.74 5399.79 7199.88 4599.66 12199.69 4599.92 4399.67 13799.77 14599.75 15799.61 4199.98 2799.35 12199.98 5099.72 97
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dcpmvs_299.61 9699.64 7999.53 22699.79 12998.82 31499.58 8299.97 2099.95 3299.96 3499.76 14998.44 23699.99 899.34 12299.96 8799.78 75
CHOSEN 1792x268899.39 16999.30 17999.65 15899.88 4599.25 24898.78 33999.88 6698.66 32699.96 3499.79 11997.45 31699.93 11999.34 12299.99 1699.78 75
CDS-MVSNet99.22 21999.13 21299.50 23499.35 35399.11 27798.96 30399.54 28999.46 19499.61 23699.70 19596.31 36199.83 32399.34 12299.88 18399.55 229
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT99.00 28099.16 20598.51 40599.75 17095.90 45198.07 41799.84 8999.84 7699.89 7399.73 16796.01 36999.99 899.33 125100.00 199.63 174
HyFIR lowres test98.91 29398.64 30799.73 11399.85 7299.47 18498.07 41799.83 9898.64 32999.89 7399.60 27892.57 413100.00 199.33 12599.97 7399.72 97
pmmvs599.19 22999.11 21999.42 26299.76 15498.88 31098.55 36999.73 17198.82 30599.72 17899.62 26096.56 34899.82 33999.32 12799.95 11199.56 225
v14899.40 16599.41 14799.39 27699.76 15498.94 30199.09 25499.59 26199.17 25599.81 11699.61 27098.41 24099.69 41999.32 12799.94 12799.53 245
baseline99.63 8599.62 8499.66 15199.80 11599.62 14199.44 11999.80 12299.71 11899.72 17899.69 20499.15 11199.83 32399.32 12799.94 12799.53 245
CVMVSNet98.61 32498.88 28597.80 43699.58 25693.60 47499.26 18399.64 23199.66 14199.72 17899.67 22093.26 40499.93 11999.30 13099.81 24299.87 44
PS-CasMVS99.66 7799.58 9899.89 1199.80 11599.85 2299.66 5799.73 17199.62 15499.84 10299.71 18598.62 20499.96 6999.30 13099.96 8799.86 46
DTE-MVSNet99.68 6599.61 8899.88 1999.80 11599.87 1599.67 5399.71 18399.72 11399.84 10299.78 13298.67 19899.97 4499.30 13099.95 11199.80 65
tmp_tt95.75 44695.42 44196.76 45889.90 49894.42 46898.86 32097.87 46178.01 48999.30 33699.69 20497.70 30295.89 49199.29 13398.14 46299.95 14
PEN-MVS99.66 7799.59 9499.89 1199.83 8599.87 1599.66 5799.73 17199.70 12499.84 10299.73 16798.56 21499.96 6999.29 13399.94 12799.83 56
WR-MVS_H99.61 9699.53 11799.87 2699.80 11599.83 3599.67 5399.75 16199.58 16999.85 9999.69 20498.18 27099.94 9899.28 13599.95 11199.83 56
IterMVS98.97 28499.16 20598.42 41099.74 17895.64 45598.06 41999.83 9899.83 8299.85 9999.74 16296.10 36899.99 899.27 136100.00 199.63 174
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NormalMVS99.09 25798.91 28399.62 18199.78 13799.11 27799.36 14499.77 14899.82 8699.68 19599.53 31493.30 40299.99 899.24 13799.76 26999.74 89
SymmetryMVS99.01 27798.82 29399.58 19799.65 23499.11 27799.36 14499.20 39499.82 8699.68 19599.53 31493.30 40299.99 899.24 13799.63 32799.64 168
WBMVS97.50 39997.18 40498.48 40798.85 44395.89 45298.44 38699.52 30499.53 17699.52 27099.42 34480.10 47799.86 26999.24 13799.95 11199.68 124
h-mvs3398.61 32498.34 34099.44 25699.60 24498.67 32799.27 17899.44 33099.68 12999.32 32699.49 32792.50 416100.00 199.24 13796.51 48299.65 156
hse-mvs298.52 33798.30 34599.16 33299.29 37598.60 33898.77 34099.02 41099.68 12999.32 32699.04 41892.50 41699.85 28899.24 13797.87 46999.03 408
FMVSNet199.66 7799.63 8299.73 11399.78 13799.77 6499.68 4999.70 19299.67 13799.82 10999.83 8498.98 15299.90 19899.24 13799.97 7399.53 245
casdiffmvspermissive99.63 8599.61 8899.67 14499.79 12999.59 15799.13 23699.85 8299.79 10099.76 15399.72 17599.33 8599.82 33999.21 14399.94 12799.59 211
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 11499.43 14299.87 2699.76 15499.82 4399.57 8599.61 24499.54 17499.80 12399.64 23697.79 29899.95 8199.21 14399.94 12799.84 52
DELS-MVS99.34 18799.30 17999.48 24399.51 29899.36 22698.12 41099.53 29999.36 22099.41 30499.61 27099.22 10199.87 25099.21 14399.68 31199.20 363
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
viewmambaseed2359dif99.47 14099.50 12299.37 28299.70 20698.80 31898.67 35099.92 4399.49 18399.77 14599.71 18599.08 12799.78 36999.20 14699.94 12799.54 239
UniMVSNet (Re)99.37 17699.26 19299.68 14099.51 29899.58 16298.98 29799.60 25599.43 20699.70 18799.36 36397.70 30299.88 23599.20 14699.87 19699.59 211
CANet99.11 25399.05 24299.28 31198.83 44598.56 34198.71 34899.41 33699.25 23899.23 34599.22 39597.66 31099.94 9899.19 14899.97 7399.33 331
EI-MVSNet-UG-set99.48 13099.50 12299.42 26299.57 26698.65 33399.24 19099.46 32499.68 12999.80 12399.66 22598.99 14899.89 22099.19 14899.90 15999.72 97
xiu_mvs_v1_base_debu99.23 21099.34 16698.91 37199.59 25098.23 36598.47 38199.66 21399.61 15999.68 19598.94 43499.39 7099.97 4499.18 15099.55 35298.51 456
xiu_mvs_v1_base99.23 21099.34 16698.91 37199.59 25098.23 36598.47 38199.66 21399.61 15999.68 19598.94 43499.39 7099.97 4499.18 15099.55 35298.51 456
xiu_mvs_v1_base_debi99.23 21099.34 16698.91 37199.59 25098.23 36598.47 38199.66 21399.61 15999.68 19598.94 43499.39 7099.97 4499.18 15099.55 35298.51 456
VPNet99.46 14299.37 15599.71 12799.82 9499.59 15799.48 10999.70 19299.81 9299.69 19099.58 29197.66 31099.86 26999.17 15399.44 37399.67 133
UniMVSNet_NR-MVSNet99.37 17699.25 19499.72 12199.47 32099.56 16698.97 29999.61 24499.43 20699.67 20299.28 38197.85 29499.95 8199.17 15399.81 24299.65 156
DU-MVS99.33 19099.21 19999.71 12799.43 33299.56 16698.83 32799.53 29999.38 21699.67 20299.36 36397.67 30699.95 8199.17 15399.81 24299.63 174
usedtu_dtu_shiyan299.44 15099.33 17199.78 7599.86 5999.76 7199.54 9099.79 13199.66 14199.66 20899.79 11996.76 34399.96 6999.15 15699.72 29299.62 186
EI-MVSNet-Vis-set99.47 14099.49 12699.42 26299.57 26698.66 33099.24 19099.46 32499.67 13799.79 12999.65 23498.97 15499.89 22099.15 15699.89 17399.71 102
EI-MVSNet99.38 17299.44 14099.21 32699.58 25698.09 37999.26 18399.46 32499.62 15499.75 15899.67 22098.54 21999.85 28899.15 15699.92 14599.68 124
VNet99.18 23399.06 23799.56 20999.24 38699.36 22699.33 15499.31 36899.67 13799.47 28599.57 29896.48 35299.84 30499.15 15699.30 39299.47 277
EG-PatchMatch MVS99.57 10199.56 10899.62 18199.77 15099.33 23299.26 18399.76 15699.32 22599.80 12399.78 13299.29 8999.87 25099.15 15699.91 15799.66 147
PVSNet_Blended_VisFu99.40 16599.38 15299.44 25699.90 3798.66 33098.94 30899.91 5297.97 39099.79 12999.73 16799.05 13999.97 4499.15 15699.99 1699.68 124
IterMVS-LS99.41 16399.47 12999.25 32299.81 10698.09 37998.85 32299.76 15699.62 15499.83 10899.64 23698.54 21999.97 4499.15 15699.99 1699.68 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet99.54 11499.47 12999.76 8699.58 25699.64 13399.30 16599.63 23499.61 15999.71 18399.56 30298.76 18499.96 6999.14 16399.92 14599.68 124
MVSTER98.47 34498.22 35099.24 32499.06 41998.35 36299.08 25799.46 32499.27 23499.75 15899.66 22588.61 45399.85 28899.14 16399.92 14599.52 256
E5new99.68 6599.67 6599.70 13299.87 5499.62 14199.41 12299.84 8999.68 12999.77 14599.81 9899.59 4599.78 36999.13 16599.96 8799.70 105
E6new99.68 6599.67 6599.70 13299.86 5999.62 14199.41 12299.84 8999.68 12999.77 14599.81 9899.59 4599.78 36999.13 16599.96 8799.70 105
E699.68 6599.67 6599.70 13299.86 5999.62 14199.41 12299.84 8999.68 12999.77 14599.81 9899.59 4599.78 36999.13 16599.96 8799.70 105
E599.68 6599.67 6599.70 13299.87 5499.62 14199.41 12299.84 8999.68 12999.77 14599.81 9899.59 4599.78 36999.13 16599.96 8799.70 105
diffmvs_AUTHOR99.48 13099.48 12799.47 24599.80 11598.89 30998.71 34899.82 10499.79 10099.66 20899.63 25198.87 17099.88 23599.13 16599.95 11199.62 186
Anonymous2023120699.35 18299.31 17499.47 24599.74 17899.06 28999.28 17499.74 16799.23 24299.72 17899.53 31497.63 31299.88 23599.11 17099.84 21499.48 273
Syy-MVS98.17 36997.85 38199.15 33498.50 46898.79 31998.60 35799.21 39197.89 39996.76 47696.37 49995.47 37899.57 46299.10 17198.73 43899.09 391
ttmdpeth99.48 13099.55 11099.29 30899.76 15498.16 37399.33 15499.95 3699.79 10099.36 31599.89 4199.13 11699.77 38299.09 17299.64 32499.93 20
MVS_Test99.28 19799.31 17499.19 32999.35 35398.79 31999.36 14499.49 31799.17 25599.21 35099.67 22098.78 18199.66 44199.09 17299.66 32099.10 386
FE-MVSNET398.87 30098.71 30299.35 28999.59 25098.88 31097.17 47199.64 23198.94 28599.27 33899.22 39595.57 37599.83 32399.08 17499.92 14599.35 326
testgi99.29 19699.26 19299.37 28299.75 17098.81 31598.84 32499.89 6198.38 35799.75 15899.04 41899.36 7999.86 26999.08 17499.25 40099.45 284
1112_ss99.05 26598.84 29099.67 14499.66 22999.29 23898.52 37599.82 10497.65 41299.43 29599.16 40296.42 35599.91 17999.07 17699.84 21499.80 65
CANet_DTU98.91 29398.85 28899.09 34398.79 45198.13 37498.18 40299.31 36899.48 18698.86 39099.51 32096.56 34899.95 8199.05 17799.95 11199.19 366
blended_shiyan897.82 38297.45 39598.92 36698.06 48297.45 41397.73 44399.35 35597.96 39398.35 43097.34 48292.76 41299.84 30499.04 17896.49 48499.47 277
blended_shiyan697.82 38297.46 39398.92 36698.08 48197.46 41197.73 44399.34 35897.96 39398.33 43197.35 48192.78 41099.84 30499.04 17896.53 47999.46 282
Baseline_NR-MVSNet99.49 12899.37 15599.82 4699.91 3199.84 2798.83 32799.86 7699.68 12999.65 21299.88 5097.67 30699.87 25099.03 18099.86 20499.76 84
FMVSNet299.35 18299.28 18799.55 21699.49 30999.35 22999.45 11799.57 27299.44 19999.70 18799.74 16297.21 32799.87 25099.03 18099.94 12799.44 298
FE-blended-shiyan797.53 39897.14 40698.72 39497.71 48896.86 43197.00 47699.34 35897.73 40898.18 43896.82 49291.92 41999.84 30499.02 18296.53 47999.45 284
Test_1112_low_res98.95 29098.73 30099.63 17299.68 22099.15 27398.09 41499.80 12297.14 43899.46 28999.40 34996.11 36699.89 22099.01 18399.84 21499.84 52
VDD-MVS99.20 22699.11 21999.44 25699.43 33298.98 29499.50 10298.32 44899.80 9699.56 25599.69 20496.99 33799.85 28898.99 18499.73 28699.50 264
DeepC-MVS98.90 499.62 9299.61 8899.67 14499.72 18799.44 19899.24 19099.71 18399.27 23499.93 5399.90 3699.70 3199.93 11998.99 18499.99 1699.64 168
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 13099.47 12999.51 23299.77 15099.41 21298.81 33299.66 21399.42 21099.75 15899.66 22599.20 10399.76 38698.98 18699.99 1699.36 323
EPNet_dtu97.62 39397.79 38497.11 45696.67 49392.31 47998.51 37698.04 45599.24 24095.77 48599.47 33493.78 39799.66 44198.98 18699.62 32999.37 320
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvspermissive99.34 18799.32 17299.39 27699.67 22798.77 32198.57 36699.81 11799.61 15999.48 28399.41 34598.47 23199.86 26998.97 18899.90 15999.53 245
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 16599.31 17499.68 14099.43 33299.55 17099.73 3099.50 31399.46 19499.88 8399.36 36397.54 31399.87 25098.97 18899.87 19699.63 174
TestfortrainingZip a99.61 9699.53 11799.85 3299.76 15499.84 2799.38 13299.78 14299.58 16999.81 11699.66 22599.02 14399.90 19898.96 19099.79 25499.81 64
viewdifsd2359ckpt0799.51 12199.50 12299.52 22899.80 11599.19 26598.92 31299.88 6699.72 11399.64 21599.62 26099.06 13799.81 35598.96 19099.94 12799.56 225
GBi-Net99.42 15799.31 17499.73 11399.49 30999.77 6499.68 4999.70 19299.44 19999.62 23099.83 8497.21 32799.90 19898.96 19099.90 15999.53 245
FMVSNet597.80 38597.25 40299.42 26298.83 44598.97 29799.38 13299.80 12298.87 29799.25 34199.69 20480.60 47699.91 17998.96 19099.90 15999.38 317
test199.42 15799.31 17499.73 11399.49 30999.77 6499.68 4999.70 19299.44 19999.62 23099.83 8497.21 32799.90 19898.96 19099.90 15999.53 245
FMVSNet398.80 30898.63 30999.32 29999.13 40598.72 32499.10 24999.48 31899.23 24299.62 23099.64 23692.57 41399.86 26998.96 19099.90 15999.39 315
UnsupCasMVSNet_eth98.83 30498.57 31699.59 19499.68 22099.45 19698.99 29499.67 20899.48 18699.55 26099.36 36394.92 38299.86 26998.95 19696.57 47899.45 284
CHOSEN 280x42098.41 34998.41 33298.40 41199.34 36295.89 45296.94 47899.44 33098.80 30999.25 34199.52 31893.51 40199.98 2798.94 19799.98 5099.32 335
E499.61 9699.59 9499.66 15199.84 7799.53 17399.08 25799.84 8999.65 14599.74 16899.80 10899.45 6299.77 38298.93 19899.95 11199.69 117
TDRefinement99.72 5499.70 5899.77 7999.90 3799.85 2299.86 699.92 4399.69 12799.78 13399.92 2799.37 7699.88 23598.93 19899.95 11199.60 204
viewmacassd2359aftdt99.63 8599.61 8899.68 14099.84 7799.61 15199.14 22999.87 7099.71 11899.75 15899.77 14299.54 5499.72 40398.91 20099.96 8799.70 105
alignmvs98.28 35997.96 37099.25 32299.12 40798.93 30499.03 27298.42 44199.64 14998.72 40597.85 47390.86 43799.62 45398.88 20199.13 40699.19 366
testing3-296.51 42596.43 42096.74 46099.36 34991.38 48799.10 24997.87 46199.48 18698.57 41998.71 44976.65 48799.66 44198.87 20299.26 39999.18 368
MGCFI-Net99.02 27199.01 25599.06 35099.11 41298.60 33899.63 6499.67 20899.63 15198.58 41797.65 47699.07 13099.57 46298.85 20398.92 42299.03 408
sss98.90 29598.77 29999.27 31699.48 31498.44 35398.72 34699.32 36497.94 39699.37 31499.35 36896.31 36199.91 17998.85 20399.63 32799.47 277
xiu_mvs_v2_base99.02 27199.11 21998.77 39199.37 34698.09 37998.13 40999.51 30999.47 19199.42 29898.54 45899.38 7499.97 4498.83 20599.33 38898.24 468
PS-MVSNAJ99.00 28099.08 23198.76 39299.37 34698.10 37898.00 42599.51 30999.47 19199.41 30498.50 46099.28 9199.97 4498.83 20599.34 38798.20 472
E299.54 11499.51 12099.62 18199.78 13799.47 18499.01 28199.82 10499.55 17299.69 19099.77 14299.26 9599.76 38698.82 20799.93 13999.62 186
E399.54 11499.51 12099.62 18199.78 13799.47 18499.01 28199.82 10499.55 17299.69 19099.77 14299.25 9999.76 38698.82 20799.93 13999.62 186
D2MVS99.22 21999.19 20299.29 30899.69 21298.74 32398.81 33299.41 33698.55 33899.68 19599.69 20498.13 27299.87 25098.82 20799.98 5099.24 350
PatchT98.45 34698.32 34298.83 38598.94 43398.29 36399.24 19098.82 41899.84 7699.08 36799.76 14991.37 42699.94 9898.82 20799.00 41798.26 467
testf199.63 8599.60 9299.72 12199.94 1899.95 299.47 11299.89 6199.43 20699.88 8399.80 10899.26 9599.90 19898.81 21199.88 18399.32 335
APD_test299.63 8599.60 9299.72 12199.94 1899.95 299.47 11299.89 6199.43 20699.88 8399.80 10899.26 9599.90 19898.81 21199.88 18399.32 335
usedtu_blend_shiyan597.97 37997.65 39198.92 36697.71 48897.49 40899.53 9299.81 11799.52 18098.18 43896.82 49291.92 41999.83 32398.79 21396.53 47999.45 284
blend_shiyan495.04 45393.76 45798.88 38097.92 48497.49 40897.72 44599.34 35897.93 39797.65 46597.11 48677.69 48599.83 32398.79 21379.72 49299.33 331
sasdasda99.02 27199.00 25999.09 34399.10 41498.70 32599.61 7399.66 21399.63 15198.64 41197.65 47699.04 14099.54 46698.79 21398.92 42299.04 406
Effi-MVS+99.06 26298.97 27099.34 29199.31 36998.98 29498.31 39499.91 5298.81 30798.79 39998.94 43499.14 11499.84 30498.79 21398.74 43599.20 363
canonicalmvs99.02 27199.00 25999.09 34399.10 41498.70 32599.61 7399.66 21399.63 15198.64 41197.65 47699.04 14099.54 46698.79 21398.92 42299.04 406
VDDNet98.97 28498.82 29399.42 26299.71 19198.81 31599.62 6798.68 42599.81 9299.38 31299.80 10894.25 39199.85 28898.79 21399.32 39099.59 211
CR-MVSNet98.35 35698.20 35298.83 38599.05 42098.12 37599.30 16599.67 20897.39 42699.16 35699.79 11991.87 42399.91 17998.78 21998.77 43198.44 461
test_method91.72 45592.32 45889.91 47493.49 49770.18 50090.28 48999.56 27761.71 49295.39 48799.52 31893.90 39399.94 9898.76 22098.27 45599.62 186
RPMNet98.60 32798.53 32298.83 38599.05 42098.12 37599.30 16599.62 23799.86 6699.16 35699.74 16292.53 41599.92 15098.75 22198.77 43198.44 461
mamba_040899.54 11499.55 11099.54 22299.71 19199.24 25299.27 17899.79 13199.72 11399.78 13399.64 23699.36 7999.93 11998.74 22299.90 15999.45 284
SSM_0407299.55 11099.55 11099.55 21699.71 19199.24 25299.27 17899.79 13199.72 11399.78 13399.64 23699.36 7999.97 4498.74 22299.90 15999.45 284
SSM_040799.56 10599.56 10899.54 22299.71 19199.24 25299.15 22599.84 8999.80 9699.78 13399.70 19599.44 6499.93 11998.74 22299.90 15999.45 284
SSM_040499.57 10199.58 9899.54 22299.76 15499.28 24099.19 20799.84 8999.80 9699.78 13399.70 19599.44 6499.93 11998.74 22299.95 11199.41 309
pmmvs499.13 24699.06 23799.36 28799.57 26699.10 28498.01 42399.25 38198.78 31299.58 24499.44 34198.24 25999.76 38698.74 22299.93 13999.22 356
viewmanbaseed2359cas99.50 12399.47 12999.61 18799.73 18299.52 17799.03 27299.83 9899.49 18399.65 21299.64 23699.18 10599.71 40898.73 22799.92 14599.58 216
tttt051797.62 39397.20 40398.90 37799.76 15497.40 41699.48 10994.36 48499.06 27299.70 18799.49 32784.55 46999.94 9898.73 22799.65 32299.36 323
viewcassd2359sk1199.48 13099.45 13699.58 19799.73 18299.42 20598.96 30399.80 12299.44 19999.63 22099.74 16299.09 12399.76 38698.72 22999.91 15799.57 222
EPP-MVSNet99.17 23899.00 25999.66 15199.80 11599.43 20299.70 3899.24 38499.48 18699.56 25599.77 14294.89 38399.93 11998.72 22999.89 17399.63 174
FE-MVSNET99.45 14699.36 16099.71 12799.84 7799.64 13399.16 22299.91 5298.65 32799.73 17399.73 16798.54 21999.82 33998.71 23199.96 8799.67 133
Anonymous2024052999.42 15799.34 16699.65 15899.53 28999.60 15599.63 6499.39 34699.47 19199.76 15399.78 13298.13 27299.86 26998.70 23299.68 31199.49 269
ACMH98.42 699.59 10099.54 11399.72 12199.86 5999.62 14199.56 8799.79 13198.77 31499.80 12399.85 6999.64 3599.85 28898.70 23299.89 17399.70 105
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs99.33 19099.28 18799.47 24599.57 26699.39 21699.78 1799.43 33398.87 29799.57 24799.82 9198.06 27899.87 25098.69 23499.73 28699.15 375
LFMVS98.46 34598.19 35599.26 31999.24 38698.52 34999.62 6796.94 47399.87 6399.31 33199.58 29191.04 43199.81 35598.68 23599.42 37799.45 284
WR-MVS99.11 25398.93 27599.66 15199.30 37399.42 20598.42 38799.37 35199.04 27399.57 24799.20 40096.89 33999.86 26998.66 23699.87 19699.70 105
mvsmamba99.08 25898.95 27399.45 25299.36 34999.18 27099.39 12998.81 41999.37 21799.35 31799.70 19596.36 36099.94 9898.66 23699.59 34399.22 356
viewdifsd2359ckpt1399.42 15799.37 15599.57 20599.72 18799.46 19099.01 28199.80 12299.20 24799.51 27799.60 27898.92 16199.70 41298.65 23899.90 15999.55 229
RRT-MVS99.08 25899.00 25999.33 29499.27 38098.65 33399.62 6799.93 3999.66 14199.67 20299.82 9195.27 38099.93 11998.64 23999.09 41099.41 309
E3new99.42 15799.37 15599.56 20999.68 22099.38 21898.93 31199.79 13199.30 22999.55 26099.69 20498.88 16899.76 38698.63 24099.89 17399.53 245
Anonymous20240521198.75 31298.46 32699.63 17299.34 36299.66 12199.47 11297.65 46499.28 23399.56 25599.50 32393.15 40599.84 30498.62 24199.58 34599.40 312
lecture99.56 10599.48 12799.81 5499.78 13799.86 1999.50 10299.70 19299.59 16799.75 15899.71 18598.94 15799.92 15098.59 24299.76 26999.66 147
EPNet98.13 37097.77 38599.18 33194.57 49697.99 38599.24 19097.96 45799.74 10897.29 46999.62 26093.13 40699.97 4498.59 24299.83 22299.58 216
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++99.05 26599.09 22998.91 37199.21 39198.36 36198.82 33199.47 32198.85 30098.90 38599.56 30298.78 18199.09 48298.57 24499.68 31199.26 347
Patchmatch-RL test98.60 32798.36 33799.33 29499.77 15099.07 28798.27 39699.87 7098.91 29299.74 16899.72 17590.57 44299.79 36698.55 24599.85 20999.11 384
pmmvs398.08 37397.80 38298.91 37199.41 33997.69 40397.87 43899.66 21395.87 45799.50 28099.51 32090.35 44499.97 4498.55 24599.47 37099.08 397
ETV-MVS99.18 23399.18 20399.16 33299.34 36299.28 24099.12 24199.79 13199.48 18698.93 37998.55 45799.40 6999.93 11998.51 24799.52 36298.28 466
viewdifsd2359ckpt0999.24 20899.16 20599.49 23899.70 20699.22 25898.88 31699.81 11798.70 32299.38 31299.37 35898.22 26499.76 38698.48 24899.88 18399.51 258
jason99.16 23999.11 21999.32 29999.75 17098.44 35398.26 39899.39 34698.70 32299.74 16899.30 37798.54 21999.97 4498.48 24899.82 23299.55 229
jason: jason.
APDe-MVScopyleft99.48 13099.36 16099.85 3299.55 28099.81 4899.50 10299.69 20098.99 27799.75 15899.71 18598.79 17999.93 11998.46 25099.85 20999.80 65
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
icg_test_0407_299.30 19499.29 18499.31 30399.71 19198.55 34398.17 40499.71 18399.41 21199.73 17399.60 27899.17 10799.92 15098.45 25199.70 29899.45 284
IMVS_040799.38 17299.42 14499.28 31199.71 19198.55 34399.27 17899.71 18399.41 21199.73 17399.60 27899.17 10799.83 32398.45 25199.70 29899.45 284
IMVS_040499.23 21099.20 20099.32 29999.71 19198.55 34398.57 36699.71 18399.41 21199.52 27099.60 27898.12 27499.95 8198.45 25199.70 29899.45 284
IMVS_040399.37 17699.39 14999.28 31199.71 19198.55 34399.19 20799.71 18399.41 21199.67 20299.60 27899.12 11999.84 30498.45 25199.70 29899.45 284
CL-MVSNet_self_test98.71 31898.56 32099.15 33499.22 38998.66 33097.14 47299.51 30998.09 38399.54 26399.27 38396.87 34099.74 39898.43 25598.96 41999.03 408
our_test_398.85 30399.09 22998.13 42499.66 22994.90 46697.72 44599.58 27099.07 27099.64 21599.62 26098.19 26899.93 11998.41 25699.95 11199.55 229
Gipumacopyleft99.57 10199.59 9499.49 23899.98 399.71 10199.72 3399.84 8999.81 9299.94 4899.78 13298.91 16499.71 40898.41 25699.95 11199.05 404
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 197.37 40596.91 41498.74 39397.72 48797.57 40597.60 45297.36 47098.00 38699.21 35098.02 46990.04 44799.79 36698.37 25895.89 48798.86 431
PM-MVS99.36 18099.29 18499.58 19799.83 8599.66 12198.95 30699.86 7698.85 30099.81 11699.73 16798.40 24499.92 15098.36 25999.83 22299.17 371
baseline197.73 38897.33 39998.96 35999.30 37397.73 40199.40 12798.42 44199.33 22499.46 28999.21 39891.18 42999.82 33998.35 26091.26 49099.32 335
MVS-HIRNet97.86 38098.22 35096.76 45899.28 37891.53 48598.38 38992.60 49099.13 26399.31 33199.96 1597.18 33199.68 43198.34 26199.83 22299.07 402
GA-MVS97.99 37897.68 38898.93 36599.52 29698.04 38397.19 47099.05 40898.32 37098.81 39598.97 43089.89 44999.41 47798.33 26299.05 41399.34 330
Fast-Effi-MVS+99.02 27198.87 28699.46 24999.38 34499.50 17999.04 26999.79 13197.17 43698.62 41398.74 44899.34 8399.95 8198.32 26399.41 37898.92 424
MDA-MVSNet_test_wron98.95 29098.99 26698.85 38199.64 23597.16 42298.23 40099.33 36298.93 28999.56 25599.66 22597.39 32099.83 32398.29 26499.88 18399.55 229
N_pmnet98.73 31598.53 32299.35 28999.72 18798.67 32798.34 39194.65 48398.35 36499.79 12999.68 21698.03 27999.93 11998.28 26599.92 14599.44 298
ET-MVSNet_ETH3D96.78 41796.07 42798.91 37199.26 38397.92 39297.70 44896.05 47897.96 39392.37 49198.43 46187.06 45799.90 19898.27 26697.56 47298.91 425
thisisatest053097.45 40096.95 41198.94 36299.68 22097.73 40199.09 25494.19 48698.61 33499.56 25599.30 37784.30 47199.93 11998.27 26699.54 35799.16 373
YYNet198.95 29098.99 26698.84 38399.64 23597.14 42498.22 40199.32 36498.92 29199.59 24299.66 22597.40 31899.83 32398.27 26699.90 15999.55 229
reproduce_model99.50 12399.40 14899.83 4199.60 24499.83 3599.12 24199.68 20399.49 18399.80 12399.79 11999.01 14599.93 11998.24 26999.82 23299.73 93
ACMM98.09 1199.46 14299.38 15299.72 12199.80 11599.69 11399.13 23699.65 22398.99 27799.64 21599.72 17599.39 7099.86 26998.23 27099.81 24299.60 204
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS98.96 28798.87 28699.24 32499.57 26698.40 35698.12 41099.18 39698.28 37299.63 22099.13 40498.02 28099.97 4498.22 27199.69 30699.35 326
3Dnovator99.15 299.43 15499.36 16099.65 15899.39 34199.42 20599.70 3899.56 27799.23 24299.35 31799.80 10899.17 10799.95 8198.21 27299.84 21499.59 211
Fast-Effi-MVS+-dtu99.20 22699.12 21699.43 26099.25 38499.69 11399.05 26499.82 10499.50 18198.97 37599.05 41698.98 15299.98 2798.20 27399.24 40298.62 446
MS-PatchMatch99.00 28098.97 27099.09 34399.11 41298.19 36998.76 34199.33 36298.49 34799.44 29199.58 29198.21 26599.69 41998.20 27399.62 32999.39 315
TSAR-MVS + GP.99.12 24999.04 24899.38 27999.34 36299.16 27198.15 40699.29 37298.18 37999.63 22099.62 26099.18 10599.68 43198.20 27399.74 28099.30 341
DP-MVS99.48 13099.39 14999.74 10299.57 26699.62 14199.29 17299.61 24499.87 6399.74 16899.76 14998.69 19499.87 25098.20 27399.80 24999.75 87
MVP-Stereo99.16 23999.08 23199.43 26099.48 31499.07 28799.08 25799.55 28398.63 33099.31 33199.68 21698.19 26899.78 36998.18 27799.58 34599.45 284
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast99.43 15499.30 17999.80 6499.83 8599.81 4899.52 9499.70 19298.35 36499.51 27799.50 32399.31 8799.88 23598.18 27799.84 21499.69 117
MDA-MVSNet-bldmvs99.06 26299.05 24299.07 34899.80 11597.83 39698.89 31599.72 18099.29 23099.63 22099.70 19596.47 35399.89 22098.17 27999.82 23299.50 264
JIA-IIPM98.06 37497.92 37798.50 40698.59 46497.02 42698.80 33598.51 43699.88 6197.89 45399.87 5691.89 42299.90 19898.16 28097.68 47198.59 449
EIA-MVS99.12 24999.01 25599.45 25299.36 34999.62 14199.34 14899.79 13198.41 35398.84 39298.89 43898.75 18699.84 30498.15 28199.51 36398.89 428
miper_lstm_enhance98.65 32398.60 31098.82 38899.20 39497.33 41897.78 44199.66 21399.01 27699.59 24299.50 32394.62 38899.85 28898.12 28299.90 15999.26 347
reproduce-ours99.46 14299.35 16499.82 4699.56 27799.83 3599.05 26499.65 22399.45 19799.78 13399.78 13298.93 15899.93 11998.11 28399.81 24299.70 105
our_new_method99.46 14299.35 16499.82 4699.56 27799.83 3599.05 26499.65 22399.45 19799.78 13399.78 13298.93 15899.93 11998.11 28399.81 24299.70 105
Effi-MVS+-dtu99.07 26198.92 27999.52 22898.89 43899.78 5899.15 22599.66 21399.34 22198.92 38299.24 39397.69 30499.98 2798.11 28399.28 39598.81 435
tpm97.15 40996.95 41197.75 43898.91 43494.24 46999.32 15797.96 45797.71 41098.29 43299.32 37286.72 46399.92 15098.10 28696.24 48599.09 391
DeepPCF-MVS98.42 699.18 23399.02 25199.67 14499.22 38999.75 8097.25 46899.47 32198.72 31999.66 20899.70 19599.29 8999.63 45298.07 28799.81 24299.62 186
ppachtmachnet_test98.89 29899.12 21698.20 42299.66 22995.24 46297.63 45099.68 20399.08 26899.78 13399.62 26098.65 20299.88 23598.02 28899.96 8799.48 273
tpmrst97.73 38898.07 36396.73 46198.71 46092.00 48099.10 24998.86 41598.52 34398.92 38299.54 31291.90 42199.82 33998.02 28899.03 41598.37 463
CSCG99.37 17699.29 18499.60 19199.71 19199.46 19099.43 12199.85 8298.79 31099.41 30499.60 27898.92 16199.92 15098.02 28899.92 14599.43 304
eth_miper_zixun_eth98.68 32198.71 30298.60 40199.10 41496.84 43297.52 45899.54 28998.94 28599.58 24499.48 33096.25 36499.76 38698.01 29199.93 13999.21 359
Patchmtry98.78 30998.54 32199.49 23898.89 43899.19 26599.32 15799.67 20899.65 14599.72 17899.79 11991.87 42399.95 8198.00 29299.97 7399.33 331
PVSNet_BlendedMVS99.03 26999.01 25599.09 34399.54 28297.99 38598.58 36299.82 10497.62 41399.34 32199.71 18598.52 22799.77 38297.98 29399.97 7399.52 256
PVSNet_Blended98.70 31998.59 31299.02 35399.54 28297.99 38597.58 45399.82 10495.70 46199.34 32198.98 42898.52 22799.77 38297.98 29399.83 22299.30 341
cl____98.54 33598.41 33298.92 36699.03 42497.80 39997.46 46099.59 26198.90 29399.60 23999.46 33793.85 39599.78 36997.97 29599.89 17399.17 371
DIV-MVS_self_test98.54 33598.42 33198.92 36699.03 42497.80 39997.46 46099.59 26198.90 29399.60 23999.46 33793.87 39499.78 36997.97 29599.89 17399.18 368
AUN-MVS97.82 38297.38 39899.14 33799.27 38098.53 34798.72 34699.02 41098.10 38197.18 47299.03 42289.26 45199.85 28897.94 29797.91 46799.03 408
FA-MVS(test-final)98.52 33798.32 34299.10 34299.48 31498.67 32799.77 1998.60 43297.35 42899.63 22099.80 10893.07 40799.84 30497.92 29899.30 39298.78 438
ambc99.20 32899.35 35398.53 34799.17 21699.46 32499.67 20299.80 10898.46 23499.70 41297.92 29899.70 29899.38 317
USDC98.96 28798.93 27599.05 35199.54 28297.99 38597.07 47599.80 12298.21 37699.75 15899.77 14298.43 23799.64 45097.90 30099.88 18399.51 258
OPM-MVS99.26 20399.13 21299.63 17299.70 20699.61 15198.58 36299.48 31898.50 34599.52 27099.63 25199.14 11499.76 38697.89 30199.77 26799.51 258
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVScopyleft99.32 19299.17 20499.77 7999.69 21299.80 5299.14 22999.31 36899.16 25799.62 23099.61 27098.35 24899.91 17997.88 30299.72 29299.61 200
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 4199.70 20699.79 5599.14 22999.61 24499.92 15097.88 30299.72 29299.77 79
c3_l98.72 31698.71 30298.72 39499.12 40797.22 42197.68 44999.56 27798.90 29399.54 26399.48 33096.37 35999.73 40197.88 30299.88 18399.21 359
3Dnovator+98.92 399.35 18299.24 19699.67 14499.35 35399.47 18499.62 6799.50 31399.44 19999.12 36399.78 13298.77 18399.94 9897.87 30599.72 29299.62 186
miper_ehance_all_eth98.59 33098.59 31298.59 40298.98 43097.07 42597.49 45999.52 30498.50 34599.52 27099.37 35896.41 35799.71 40897.86 30699.62 32999.00 415
WTY-MVS98.59 33098.37 33699.26 31999.43 33298.40 35698.74 34499.13 40398.10 38199.21 35099.24 39394.82 38599.90 19897.86 30698.77 43199.49 269
APD_test199.36 18099.28 18799.61 18799.89 3999.89 1099.32 15799.74 16799.18 25099.69 19099.75 15798.41 24099.84 30497.85 30899.70 29899.10 386
SED-MVS99.40 16599.28 18799.77 7999.69 21299.82 4399.20 20199.54 28999.13 26399.82 10999.63 25198.91 16499.92 15097.85 30899.70 29899.58 216
test_241102_TWO99.54 28999.13 26399.76 15399.63 25198.32 25399.92 15097.85 30899.69 30699.75 87
MVS_111021_HR99.12 24999.02 25199.40 27399.50 30499.11 27797.92 43499.71 18398.76 31799.08 36799.47 33499.17 10799.54 46697.85 30899.76 26999.54 239
MTAPA99.35 18299.20 20099.80 6499.81 10699.81 4899.33 15499.53 29999.27 23499.42 29899.63 25198.21 26599.95 8197.83 31299.79 25499.65 156
MSC_two_6792asdad99.74 10299.03 42499.53 17399.23 38599.92 15097.77 31399.69 30699.78 75
No_MVS99.74 10299.03 42499.53 17399.23 38599.92 15097.77 31399.69 30699.78 75
TESTMET0.1,196.24 43295.84 43397.41 44798.24 47593.84 47297.38 46295.84 47998.43 35097.81 45998.56 45679.77 48099.89 22097.77 31398.77 43198.52 455
ACMH+98.40 899.50 12399.43 14299.71 12799.86 5999.76 7199.32 15799.77 14899.53 17699.77 14599.76 14999.26 9599.78 36997.77 31399.88 18399.60 204
IU-MVS99.69 21299.77 6499.22 38897.50 42099.69 19097.75 31799.70 29899.77 79
114514_t98.49 34298.11 36099.64 16599.73 18299.58 16299.24 19099.76 15689.94 48499.42 29899.56 30297.76 30199.86 26997.74 31899.82 23299.47 277
DVP-MVS++99.38 17299.25 19499.77 7999.03 42499.77 6499.74 2799.61 24499.18 25099.76 15399.61 27099.00 14699.92 15097.72 31999.60 33999.62 186
test_0728_THIRD99.18 25099.62 23099.61 27098.58 21099.91 17997.72 31999.80 24999.77 79
EGC-MVSNET89.05 45785.52 46099.64 16599.89 3999.78 5899.56 8799.52 30424.19 49349.96 49499.83 8499.15 11199.92 15097.71 32199.85 20999.21 359
miper_enhance_ethall98.03 37597.94 37598.32 41698.27 47496.43 44096.95 47799.41 33696.37 45299.43 29598.96 43294.74 38699.69 41997.71 32199.62 32998.83 434
TSAR-MVS + MP.99.34 18799.24 19699.63 17299.82 9499.37 22299.26 18399.35 35598.77 31499.57 24799.70 19599.27 9499.88 23597.71 32199.75 27399.65 156
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl2297.56 39697.28 40098.40 41198.37 47296.75 43397.24 46999.37 35197.31 43099.41 30499.22 39587.30 45599.37 47897.70 32499.62 32999.08 397
MP-MVS-pluss99.14 24498.92 27999.80 6499.83 8599.83 3598.61 35599.63 23496.84 44599.44 29199.58 29198.81 17499.91 17997.70 32499.82 23299.67 133
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP99.28 19799.11 21999.79 7199.75 17099.81 4898.95 30699.53 29998.27 37399.53 26899.73 16798.75 18699.87 25097.70 32499.83 22299.68 124
UnsupCasMVSNet_bld98.55 33498.27 34899.40 27399.56 27799.37 22297.97 43099.68 20397.49 42199.08 36799.35 36895.41 37999.82 33997.70 32498.19 45999.01 414
MVS_111021_LR99.13 24699.03 25099.42 26299.58 25699.32 23497.91 43699.73 17198.68 32499.31 33199.48 33099.09 12399.66 44197.70 32499.77 26799.29 344
IS-MVSNet99.03 26998.85 28899.55 21699.80 11599.25 24899.73 3099.15 40099.37 21799.61 23699.71 18594.73 38799.81 35597.70 32499.88 18399.58 216
MED-MVS test99.74 10299.76 15499.65 12799.38 13299.78 14299.58 16999.81 11699.66 22599.90 19897.69 33099.79 25499.67 133
MED-MVS99.45 14699.36 16099.74 10299.76 15499.65 12799.38 13299.78 14299.31 22799.81 11699.66 22599.02 14399.90 19897.69 33099.79 25499.67 133
ME-MVS99.26 20399.10 22799.73 11399.60 24499.65 12798.75 34399.45 32999.31 22799.65 21299.66 22598.00 28599.86 26997.69 33099.79 25499.67 133
test-LLR97.15 40996.95 41197.74 43998.18 47795.02 46497.38 46296.10 47598.00 38697.81 45998.58 45390.04 44799.91 17997.69 33098.78 42998.31 464
test-mter96.23 43395.73 43697.74 43998.18 47795.02 46497.38 46296.10 47597.90 39897.81 45998.58 45379.12 48399.91 17997.69 33098.78 42998.31 464
MonoMVSNet98.23 36498.32 34297.99 42798.97 43196.62 43599.49 10798.42 44199.62 15499.40 30999.79 11995.51 37798.58 48997.68 33595.98 48698.76 441
XVS99.27 20199.11 21999.75 9799.71 19199.71 10199.37 14099.61 24499.29 23098.76 40299.47 33498.47 23199.88 23597.62 33699.73 28699.67 133
X-MVStestdata96.09 43794.87 45099.75 9799.71 19199.71 10199.37 14099.61 24499.29 23098.76 40261.30 50298.47 23199.88 23597.62 33699.73 28699.67 133
SMA-MVScopyleft99.19 22999.00 25999.73 11399.46 32499.73 9199.13 23699.52 30497.40 42599.57 24799.64 23698.93 15899.83 32397.61 33899.79 25499.63 174
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 42096.79 41996.46 46598.90 43590.71 49199.41 12298.68 42594.69 47498.14 44499.34 37186.32 46599.80 36397.60 33998.07 46598.88 429
PVSNet97.47 1598.42 34898.44 32998.35 41399.46 32496.26 44496.70 48199.34 35897.68 41199.00 37499.13 40497.40 31899.72 40397.59 34099.68 31199.08 397
new_pmnet98.88 29998.89 28498.84 38399.70 20697.62 40498.15 40699.50 31397.98 38999.62 23099.54 31298.15 27199.94 9897.55 34199.84 21498.95 419
IB-MVS95.41 2095.30 45294.46 45697.84 43598.76 45695.33 46097.33 46596.07 47796.02 45695.37 48897.41 48076.17 48899.96 6997.54 34295.44 48998.22 469
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 20899.11 21999.61 18798.38 47199.79 5599.57 8599.68 20399.61 15999.15 35899.71 18598.70 19399.91 17997.54 34299.68 31199.13 383
ZNCC-MVS99.22 21999.04 24899.77 7999.76 15499.73 9199.28 17499.56 27798.19 37899.14 36099.29 38098.84 17399.92 15097.53 34499.80 24999.64 168
CP-MVS99.23 21099.05 24299.75 9799.66 22999.66 12199.38 13299.62 23798.38 35799.06 37199.27 38398.79 17999.94 9897.51 34599.82 23299.66 147
SD-MVS99.01 27799.30 17998.15 42399.50 30499.40 21398.94 30899.61 24499.22 24699.75 15899.82 9199.54 5495.51 49397.48 34699.87 19699.54 239
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 34298.29 34799.11 34098.96 43298.42 35597.54 45499.32 36497.53 41898.47 42598.15 46897.88 29199.82 33997.46 34799.24 40299.09 391
DeepC-MVS_fast98.47 599.23 21099.12 21699.56 20999.28 37899.22 25898.99 29499.40 34399.08 26899.58 24499.64 23698.90 16799.83 32397.44 34899.75 27399.63 174
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 20599.08 23199.76 8699.73 18299.70 10999.31 16299.59 26198.36 35999.36 31599.37 35898.80 17899.91 17997.43 34999.75 27399.68 124
ACMMPR99.23 21099.06 23799.76 8699.74 17899.69 11399.31 16299.59 26198.36 35999.35 31799.38 35598.61 20699.93 11997.43 34999.75 27399.67 133
Vis-MVSNet (Re-imp)98.77 31098.58 31599.34 29199.78 13798.88 31099.61 7399.56 27799.11 26799.24 34499.56 30293.00 40999.78 36997.43 34999.89 17399.35 326
MIMVSNet98.43 34798.20 35299.11 34099.53 28998.38 36099.58 8298.61 43098.96 28199.33 32399.76 14990.92 43399.81 35597.38 35299.76 26999.15 375
WB-MVSnew98.34 35898.14 35898.96 35998.14 48097.90 39398.27 39697.26 47198.63 33098.80 39798.00 47197.77 29999.90 19897.37 35398.98 41899.09 391
XVG-OURS-SEG-HR99.16 23998.99 26699.66 15199.84 7799.64 13398.25 39999.73 17198.39 35699.63 22099.43 34299.70 3199.90 19897.34 35498.64 44299.44 298
COLMAP_ROBcopyleft98.06 1299.45 14699.37 15599.70 13299.83 8599.70 10999.38 13299.78 14299.53 17699.67 20299.78 13299.19 10499.86 26997.32 35599.87 19699.55 229
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS99.02 27198.81 29599.65 15899.58 25699.49 18098.58 36299.07 40598.40 35599.04 37299.25 38898.51 22999.80 36397.31 35699.51 36399.65 156
region2R99.23 21099.05 24299.77 7999.76 15499.70 10999.31 16299.59 26198.41 35399.32 32699.36 36398.73 19099.93 11997.29 35799.74 28099.67 133
APD-MVS_3200maxsize99.31 19399.16 20599.74 10299.53 28999.75 8099.27 17899.61 24499.19 24999.57 24799.64 23698.76 18499.90 19897.29 35799.62 32999.56 225
TAPA-MVS97.92 1398.03 37597.55 39299.46 24999.47 32099.44 19898.50 37799.62 23786.79 48599.07 37099.26 38698.26 25899.62 45397.28 35999.73 28699.31 339
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post99.27 20199.11 21999.73 11399.54 28299.74 8899.26 18399.62 23799.16 25799.52 27099.64 23698.41 24099.91 17997.27 36099.61 33699.54 239
RE-MVS-def99.13 21299.54 28299.74 8899.26 18399.62 23799.16 25799.52 27099.64 23698.57 21197.27 36099.61 33699.54 239
testing1196.05 43995.41 44297.97 42998.78 45395.27 46198.59 36098.23 45198.86 29996.56 47996.91 49075.20 48999.69 41997.26 36298.29 45498.93 422
test_yl98.25 36197.95 37199.13 33899.17 40098.47 35099.00 28798.67 42798.97 27999.22 34899.02 42391.31 42799.69 41997.26 36298.93 42099.24 350
DCV-MVSNet98.25 36197.95 37199.13 33899.17 40098.47 35099.00 28798.67 42798.97 27999.22 34899.02 42391.31 42799.69 41997.26 36298.93 42099.24 350
PHI-MVS99.11 25398.95 27399.59 19499.13 40599.59 15799.17 21699.65 22397.88 40199.25 34199.46 33798.97 15499.80 36397.26 36299.82 23299.37 320
tfpnnormal99.43 15499.38 15299.60 19199.87 5499.75 8099.59 8099.78 14299.71 11899.90 6899.69 20498.85 17299.90 19897.25 36699.78 26399.15 375
PatchmatchNetpermissive97.65 39297.80 38297.18 45498.82 44892.49 47899.17 21698.39 44498.12 38098.79 39999.58 29190.71 43999.89 22097.23 36799.41 37899.16 373
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS98.99 28398.80 29799.56 20999.25 38499.43 20298.54 37299.27 37698.58 33698.80 39799.43 34298.53 22499.70 41297.22 36899.59 34399.54 239
testing396.48 42695.63 43899.01 35499.23 38897.81 39798.90 31499.10 40498.72 31997.84 45897.92 47272.44 49399.85 28897.21 36999.33 38899.35 326
HPM-MVScopyleft99.25 20599.07 23599.78 7599.81 10699.75 8099.61 7399.67 20897.72 40999.35 31799.25 38899.23 10099.92 15097.21 36999.82 23299.67 133
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS99.19 22999.00 25999.76 8699.76 15499.68 11699.38 13299.54 28998.34 36899.01 37399.50 32398.53 22499.93 11997.18 37199.78 26399.66 147
ACMMPcopyleft99.25 20599.08 23199.74 10299.79 12999.68 11699.50 10299.65 22398.07 38499.52 27099.69 20498.57 21199.92 15097.18 37199.79 25499.63 174
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
myMVS_eth3d2896.23 43395.74 43597.70 44198.86 44295.59 45798.66 35298.14 45398.96 28197.67 46497.06 48776.78 48698.92 48597.10 37398.41 45198.58 451
thisisatest051596.98 41396.42 42198.66 39899.42 33797.47 41097.27 46794.30 48597.24 43299.15 35898.86 44085.01 46799.87 25097.10 37399.39 38098.63 445
XVG-ACMP-BASELINE99.23 21099.10 22799.63 17299.82 9499.58 16298.83 32799.72 18098.36 35999.60 23999.71 18598.92 16199.91 17997.08 37599.84 21499.40 312
MSDG99.08 25898.98 26999.37 28299.60 24499.13 27497.54 45499.74 16798.84 30399.53 26899.55 31099.10 12199.79 36697.07 37699.86 20499.18 368
SteuartSystems-ACMMP99.30 19499.14 21099.76 8699.87 5499.66 12199.18 21199.60 25598.55 33899.57 24799.67 22099.03 14299.94 9897.01 37799.80 24999.69 117
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UWE-MVS96.21 43595.78 43497.49 44398.53 46693.83 47398.04 42093.94 48898.96 28198.46 42698.17 46779.86 47899.87 25096.99 37899.06 41198.78 438
EPMVS96.53 42396.32 42297.17 45598.18 47792.97 47799.39 12989.95 49498.21 37698.61 41499.59 28886.69 46499.72 40396.99 37899.23 40498.81 435
MSP-MVS99.04 26898.79 29899.81 5499.78 13799.73 9199.35 14799.57 27298.54 34199.54 26398.99 42596.81 34199.93 11996.97 38099.53 35999.77 79
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 28798.70 30599.74 10299.52 29699.71 10198.86 32099.19 39598.47 34998.59 41699.06 41598.08 27799.91 17996.94 38199.60 33999.60 204
SR-MVS99.19 22999.00 25999.74 10299.51 29899.72 9699.18 21199.60 25598.85 30099.47 28599.58 29198.38 24599.92 15096.92 38299.54 35799.57 222
PGM-MVS99.20 22699.01 25599.77 7999.75 17099.71 10199.16 22299.72 18097.99 38899.42 29899.60 27898.81 17499.93 11996.91 38399.74 28099.66 147
HY-MVS98.23 998.21 36897.95 37198.99 35599.03 42498.24 36499.61 7398.72 42396.81 44698.73 40499.51 32094.06 39299.86 26996.91 38398.20 45798.86 431
MDTV_nov1_ep1397.73 38698.70 46190.83 48999.15 22598.02 45698.51 34498.82 39499.61 27090.98 43299.66 44196.89 38598.92 422
GST-MVS99.16 23998.96 27299.75 9799.73 18299.73 9199.20 20199.55 28398.22 37599.32 32699.35 36898.65 20299.91 17996.86 38699.74 28099.62 186
test_post199.14 22951.63 50489.54 45099.82 33996.86 386
SCA98.11 37198.36 33797.36 44899.20 39492.99 47698.17 40498.49 43898.24 37499.10 36699.57 29896.01 36999.94 9896.86 38699.62 32999.14 380
UBG96.53 42395.95 42998.29 42098.87 44196.31 44398.48 38098.07 45498.83 30497.32 46796.54 49779.81 47999.62 45396.84 38998.74 43598.95 419
XVG-OURS99.21 22499.06 23799.65 15899.82 9499.62 14197.87 43899.74 16798.36 35999.66 20899.68 21699.71 2899.90 19896.84 38999.88 18399.43 304
LCM-MVSNet-Re99.28 19799.15 20999.67 14499.33 36799.76 7199.34 14899.97 2098.93 28999.91 6399.79 11998.68 19599.93 11996.80 39199.56 34899.30 341
RPSCF99.18 23399.02 25199.64 16599.83 8599.85 2299.44 11999.82 10498.33 36999.50 28099.78 13297.90 28999.65 44896.78 39299.83 22299.44 298
旧先验297.94 43295.33 46598.94 37899.88 23596.75 393
MDTV_nov1_ep13_2view91.44 48699.14 22997.37 42799.21 35091.78 42596.75 39399.03 408
CLD-MVS98.76 31198.57 31699.33 29499.57 26698.97 29797.53 45699.55 28396.41 45099.27 33899.13 40499.07 13099.78 36996.73 39599.89 17399.23 354
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 37297.98 36998.48 40799.27 38096.48 43899.40 12799.07 40598.81 30799.23 34599.57 29890.11 44699.87 25096.69 39699.64 32499.09 391
baseline296.83 41696.28 42398.46 40999.09 41796.91 42998.83 32793.87 48997.23 43396.23 48498.36 46288.12 45499.90 19896.68 39798.14 46298.57 453
cascas96.99 41296.82 41897.48 44497.57 49295.64 45596.43 48399.56 27791.75 48097.13 47497.61 47995.58 37498.63 48796.68 39799.11 40898.18 473
PC_three_145297.56 41499.68 19599.41 34599.09 12397.09 49096.66 39999.60 33999.62 186
LPG-MVS_test99.22 21999.05 24299.74 10299.82 9499.63 13999.16 22299.73 17197.56 41499.64 21599.69 20499.37 7699.89 22096.66 39999.87 19699.69 117
LGP-MVS_train99.74 10299.82 9499.63 13999.73 17197.56 41499.64 21599.69 20499.37 7699.89 22096.66 39999.87 19699.69 117
ETVMVS96.14 43695.22 44798.89 37898.80 44998.01 38498.66 35298.35 44798.71 32197.18 47296.31 50174.23 49299.75 39596.64 40298.13 46498.90 426
TinyColmap98.97 28498.93 27599.07 34899.46 32498.19 36997.75 44299.75 16198.79 31099.54 26399.70 19598.97 15499.62 45396.63 40399.83 22299.41 309
LF4IMVS99.01 27798.92 27999.27 31699.71 19199.28 24098.59 36099.77 14898.32 37099.39 31199.41 34598.62 20499.84 30496.62 40499.84 21498.69 444
NCCC98.82 30598.57 31699.58 19799.21 39199.31 23598.61 35599.25 38198.65 32798.43 42799.26 38697.86 29299.81 35596.55 40599.27 39899.61 200
OPU-MVS99.29 30899.12 40799.44 19899.20 20199.40 34999.00 14698.84 48696.54 40699.60 33999.58 216
F-COLMAP98.74 31398.45 32899.62 18199.57 26699.47 18498.84 32499.65 22396.31 45398.93 37999.19 40197.68 30599.87 25096.52 40799.37 38399.53 245
testing9995.86 44495.19 44897.87 43398.76 45695.03 46398.62 35498.44 44098.68 32496.67 47896.66 49674.31 49199.69 41996.51 40898.03 46698.90 426
ADS-MVSNet297.78 38697.66 39098.12 42599.14 40395.36 45999.22 19898.75 42296.97 44198.25 43499.64 23690.90 43499.94 9896.51 40899.56 34899.08 397
ADS-MVSNet97.72 39197.67 38997.86 43499.14 40394.65 46799.22 19898.86 41596.97 44198.25 43499.64 23690.90 43499.84 30496.51 40899.56 34899.08 397
PatchMatch-RL98.68 32198.47 32599.30 30799.44 32999.28 24098.14 40899.54 28997.12 43999.11 36499.25 38897.80 29799.70 41296.51 40899.30 39298.93 422
CMPMVSbinary77.52 2398.50 34098.19 35599.41 27098.33 47399.56 16699.01 28199.59 26195.44 46399.57 24799.80 10895.64 37299.46 47696.47 41299.92 14599.21 359
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing9196.00 44095.32 44598.02 42698.76 45695.39 45898.38 38998.65 42998.82 30596.84 47596.71 49575.06 49099.71 40896.46 41398.23 45698.98 416
SF-MVS99.10 25698.93 27599.62 18199.58 25699.51 17899.13 23699.65 22397.97 39099.42 29899.61 27098.86 17199.87 25096.45 41499.68 31199.49 269
FE-MVS97.85 38197.42 39799.15 33499.44 32998.75 32299.77 1998.20 45295.85 45899.33 32399.80 10888.86 45299.88 23596.40 41599.12 40798.81 435
DPE-MVScopyleft99.14 24498.92 27999.82 4699.57 26699.77 6498.74 34499.60 25598.55 33899.76 15399.69 20498.23 26399.92 15096.39 41699.75 27399.76 84
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
gm-plane-assit97.59 49089.02 49693.47 47698.30 46399.84 30496.38 417
AllTest99.21 22499.07 23599.63 17299.78 13799.64 13399.12 24199.83 9898.63 33099.63 22099.72 17598.68 19599.75 39596.38 41799.83 22299.51 258
TestCases99.63 17299.78 13799.64 13399.83 9898.63 33099.63 22099.72 17598.68 19599.75 39596.38 41799.83 22299.51 258
testdata99.42 26299.51 29898.93 30499.30 37196.20 45498.87 38999.40 34998.33 25299.89 22096.29 42099.28 39599.44 298
dp96.86 41597.07 40796.24 46798.68 46290.30 49499.19 20798.38 44597.35 42898.23 43699.59 28887.23 45699.82 33996.27 42198.73 43898.59 449
tpmvs97.39 40497.69 38796.52 46398.41 47091.76 48299.30 16598.94 41497.74 40797.85 45799.55 31092.40 41899.73 40196.25 42298.73 43898.06 475
KD-MVS_2432*160095.89 44195.41 44297.31 45194.96 49493.89 47097.09 47399.22 38897.23 43398.88 38699.04 41879.23 48199.54 46696.24 42396.81 47698.50 459
miper_refine_blended95.89 44195.41 44297.31 45194.96 49493.89 47097.09 47399.22 38897.23 43398.88 38699.04 41879.23 48199.54 46696.24 42396.81 47698.50 459
ACMP97.51 1499.05 26598.84 29099.67 14499.78 13799.55 17098.88 31699.66 21397.11 44099.47 28599.60 27899.07 13099.89 22096.18 42599.85 20999.58 216
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS98.90 29598.72 30199.44 25699.39 34199.42 20598.58 36299.64 23197.31 43099.44 29199.62 26098.59 20899.69 41996.17 42699.79 25499.22 356
DP-MVS Recon98.50 34098.23 34999.31 30399.49 30999.46 19098.56 36899.63 23494.86 47298.85 39199.37 35897.81 29699.59 46096.08 42799.44 37398.88 429
tpm cat196.78 41796.98 41096.16 46898.85 44390.59 49299.08 25799.32 36492.37 47897.73 46399.46 33791.15 43099.69 41996.07 42898.80 42898.21 470
tpm296.35 42996.22 42496.73 46198.88 44091.75 48399.21 20098.51 43693.27 47797.89 45399.21 39884.83 46899.70 41296.04 42998.18 46098.75 442
dmvs_re98.69 32098.48 32499.31 30399.55 28099.42 20599.54 9098.38 44599.32 22598.72 40598.71 44996.76 34399.21 48096.01 43099.35 38699.31 339
test_040299.22 21999.14 21099.45 25299.79 12999.43 20299.28 17499.68 20399.54 17499.40 30999.56 30299.07 13099.82 33996.01 43099.96 8799.11 384
ITE_SJBPF99.38 27999.63 23799.44 19899.73 17198.56 33799.33 32399.53 31498.88 16899.68 43196.01 43099.65 32299.02 413
test_prior297.95 43197.87 40298.05 44699.05 41697.90 28995.99 43399.49 368
testdata299.89 22095.99 433
原ACMM199.37 28299.47 32098.87 31399.27 37696.74 44898.26 43399.32 37297.93 28899.82 33995.96 43599.38 38199.43 304
新几何199.52 22899.50 30499.22 25899.26 37895.66 46298.60 41599.28 38197.67 30699.89 22095.95 43699.32 39099.45 284
MP-MVScopyleft99.06 26298.83 29299.76 8699.76 15499.71 10199.32 15799.50 31398.35 36498.97 37599.48 33098.37 24699.92 15095.95 43699.75 27399.63 174
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testing22295.60 45194.59 45498.61 40098.66 46397.45 41398.54 37297.90 46098.53 34296.54 48096.47 49870.62 49699.81 35595.91 43898.15 46198.56 454
wuyk23d97.58 39599.13 21292.93 47299.69 21299.49 18099.52 9499.77 14897.97 39099.96 3499.79 11999.84 1699.94 9895.85 43999.82 23279.36 490
HQP_MVS98.90 29598.68 30699.55 21699.58 25699.24 25298.80 33599.54 28998.94 28599.14 36099.25 38897.24 32599.82 33995.84 44099.78 26399.60 204
plane_prior599.54 28999.82 33995.84 44099.78 26399.60 204
无先验98.01 42399.23 38595.83 45999.85 28895.79 44299.44 298
CPTT-MVS98.74 31398.44 32999.64 16599.61 24299.38 21899.18 21199.55 28396.49 44999.27 33899.37 35897.11 33399.92 15095.74 44399.67 31799.62 186
PLCcopyleft97.35 1698.36 35397.99 36799.48 24399.32 36899.24 25298.50 37799.51 30995.19 46898.58 41798.96 43296.95 33899.83 32395.63 44499.25 40099.37 320
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA98.57 33298.34 34099.28 31199.18 39999.10 28498.34 39199.41 33698.48 34898.52 42298.98 42897.05 33599.78 36995.59 44599.50 36698.96 417
131498.00 37797.90 37998.27 42198.90 43597.45 41399.30 16599.06 40794.98 46997.21 47199.12 40898.43 23799.67 43695.58 44698.56 44597.71 479
PVSNet_095.53 1995.85 44595.31 44697.47 44598.78 45393.48 47595.72 48599.40 34396.18 45597.37 46697.73 47495.73 37199.58 46195.49 44781.40 49199.36 323
MAR-MVS98.24 36397.92 37799.19 32998.78 45399.65 12799.17 21699.14 40195.36 46498.04 44798.81 44597.47 31599.72 40395.47 44899.06 41198.21 470
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 36497.89 38099.26 31999.19 39699.26 24599.65 6299.69 20091.33 48298.14 44499.77 14298.28 25599.96 6995.41 44999.55 35298.58 451
train_agg98.35 35697.95 37199.57 20599.35 35399.35 22998.11 41299.41 33694.90 47097.92 45198.99 42598.02 28099.85 28895.38 45099.44 37399.50 264
9.1498.64 30799.45 32898.81 33299.60 25597.52 41999.28 33799.56 30298.53 22499.83 32395.36 45199.64 324
APD-MVScopyleft98.87 30098.59 31299.71 12799.50 30499.62 14199.01 28199.57 27296.80 44799.54 26399.63 25198.29 25499.91 17995.24 45299.71 29699.61 200
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS96.36 44195.20 453
AdaColmapbinary98.60 32798.35 33999.38 27999.12 40799.22 25898.67 35099.42 33597.84 40598.81 39599.27 38397.32 32399.81 35595.14 45499.53 35999.10 386
test9_res95.10 45599.44 37399.50 264
CDPH-MVS98.56 33398.20 35299.61 18799.50 30499.46 19098.32 39399.41 33695.22 46699.21 35099.10 41298.34 25099.82 33995.09 45699.66 32099.56 225
BH-untuned98.22 36698.09 36198.58 40499.38 34497.24 42098.55 36998.98 41397.81 40699.20 35598.76 44797.01 33699.65 44894.83 45798.33 45298.86 431
BP-MVS94.73 458
HQP-MVS98.36 35398.02 36699.39 27699.31 36998.94 30197.98 42799.37 35197.45 42298.15 44098.83 44296.67 34599.70 41294.73 45899.67 31799.53 245
QAPM98.40 35197.99 36799.65 15899.39 34199.47 18499.67 5399.52 30491.70 48198.78 40199.80 10898.55 21599.95 8194.71 46099.75 27399.53 245
agg_prior294.58 46199.46 37299.50 264
myMVS_eth3d95.63 44994.73 45198.34 41598.50 46896.36 44198.60 35799.21 39197.89 39996.76 47696.37 49972.10 49499.57 46294.38 46298.73 43899.09 391
BH-RMVSNet98.41 34998.14 35899.21 32699.21 39198.47 35098.60 35798.26 45098.35 36498.93 37999.31 37597.20 33099.66 44194.32 46399.10 40999.51 258
E-PMN97.14 41197.43 39696.27 46698.79 45191.62 48495.54 48699.01 41299.44 19998.88 38699.12 40892.78 41099.68 43194.30 46499.03 41597.50 480
MG-MVS98.52 33798.39 33498.94 36299.15 40297.39 41798.18 40299.21 39198.89 29699.23 34599.63 25197.37 32199.74 39894.22 46599.61 33699.69 117
API-MVS98.38 35298.39 33498.35 41398.83 44599.26 24599.14 22999.18 39698.59 33598.66 41098.78 44698.61 20699.57 46294.14 46699.56 34896.21 487
PAPM_NR98.36 35398.04 36499.33 29499.48 31498.93 30498.79 33899.28 37597.54 41798.56 42198.57 45597.12 33299.69 41994.09 46798.90 42699.38 317
ZD-MVS99.43 33299.61 15199.43 33396.38 45199.11 36499.07 41497.86 29299.92 15094.04 46899.49 368
DPM-MVS98.28 35997.94 37599.32 29999.36 34999.11 27797.31 46698.78 42196.88 44398.84 39299.11 41197.77 29999.61 45894.03 46999.36 38499.23 354
gg-mvs-nofinetune95.87 44395.17 44997.97 42998.19 47696.95 42799.69 4589.23 49599.89 5696.24 48399.94 1981.19 47399.51 47293.99 47098.20 45797.44 481
PMVScopyleft92.94 2198.82 30598.81 29598.85 38199.84 7797.99 38599.20 20199.47 32199.71 11899.42 29899.82 9198.09 27599.47 47493.88 47199.85 20999.07 402
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS96.96 41497.28 40095.99 47098.76 45691.03 48895.26 48898.61 43099.34 22198.92 38298.88 43993.79 39699.66 44192.87 47299.05 41397.30 484
BH-w/o97.20 40897.01 40997.76 43799.08 41895.69 45498.03 42298.52 43595.76 46097.96 45098.02 46995.62 37399.47 47492.82 47397.25 47598.12 474
TR-MVS97.44 40197.15 40598.32 41698.53 46697.46 41198.47 38197.91 45996.85 44498.21 43798.51 45996.42 35599.51 47292.16 47497.29 47497.98 476
OpenMVS_ROBcopyleft97.31 1797.36 40696.84 41698.89 37899.29 37599.45 19698.87 31999.48 31886.54 48799.44 29199.74 16297.34 32299.86 26991.61 47599.28 39597.37 483
GG-mvs-BLEND97.36 44897.59 49096.87 43099.70 3888.49 49694.64 48997.26 48580.66 47599.12 48191.50 47696.50 48396.08 489
DeepMVS_CXcopyleft97.98 42899.69 21296.95 42799.26 37875.51 49095.74 48698.28 46496.47 35399.62 45391.23 47797.89 46897.38 482
PAPR97.56 39697.07 40799.04 35298.80 44998.11 37797.63 45099.25 38194.56 47598.02 44998.25 46597.43 31799.68 43190.90 47898.74 43599.33 331
MVS95.72 44794.63 45398.99 35598.56 46597.98 39099.30 16598.86 41572.71 49197.30 46899.08 41398.34 25099.74 39889.21 47998.33 45299.26 347
UWE-MVS-2895.64 44895.47 44096.14 46997.98 48390.39 49398.49 37995.81 48099.02 27598.03 44898.19 46684.49 47099.28 47988.75 48098.47 45098.75 442
thres600view796.60 42296.16 42597.93 43199.63 23796.09 44999.18 21197.57 46598.77 31498.72 40597.32 48387.04 45899.72 40388.57 48198.62 44397.98 476
FPMVS96.32 43095.50 43998.79 38999.60 24498.17 37298.46 38598.80 42097.16 43796.28 48199.63 25182.19 47299.09 48288.45 48298.89 42799.10 386
PCF-MVS96.03 1896.73 41995.86 43299.33 29499.44 32999.16 27196.87 47999.44 33086.58 48698.95 37799.40 34994.38 39099.88 23587.93 48399.80 24998.95 419
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view90096.39 42896.03 42897.47 44599.63 23795.93 45099.18 21197.57 46598.75 31898.70 40897.31 48487.04 45899.67 43687.62 48498.51 44796.81 485
tfpn200view996.30 43195.89 43097.53 44299.58 25696.11 44799.00 28797.54 46898.43 35098.52 42296.98 48886.85 46099.67 43687.62 48498.51 44796.81 485
thres40096.40 42795.89 43097.92 43299.58 25696.11 44799.00 28797.54 46898.43 35098.52 42296.98 48886.85 46099.67 43687.62 48498.51 44797.98 476
thres20096.09 43795.68 43797.33 45099.48 31496.22 44698.53 37497.57 46598.06 38598.37 42996.73 49486.84 46299.61 45886.99 48798.57 44496.16 488
MVEpermissive92.54 2296.66 42196.11 42698.31 41899.68 22097.55 40697.94 43295.60 48199.37 21790.68 49298.70 45196.56 34898.61 48886.94 48899.55 35298.77 440
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset97.27 40796.83 41798.59 40299.46 32497.55 40699.25 18996.84 47498.78 31297.24 47097.67 47597.11 33398.97 48486.59 48998.54 44699.27 345
PAPM95.61 45094.71 45298.31 41899.12 40796.63 43496.66 48298.46 43990.77 48396.25 48298.68 45293.01 40899.69 41981.60 49097.86 47098.62 446
SD_040397.42 40296.90 41598.98 35799.54 28297.90 39399.52 9499.54 28999.34 22197.87 45598.85 44198.72 19199.64 45078.93 49199.83 22299.40 312
dongtai89.37 45688.91 45990.76 47399.19 39677.46 49895.47 48787.82 49792.28 47994.17 49098.82 44471.22 49595.54 49263.85 49297.34 47399.27 345
kuosan85.65 45884.57 46188.90 47597.91 48577.11 49996.37 48487.62 49885.24 48885.45 49396.83 49169.94 49790.98 49445.90 49395.83 48898.62 446
test12329.31 45933.05 46418.08 47625.93 50012.24 50197.53 45610.93 50111.78 49424.21 49550.08 50621.04 4988.60 49523.51 49432.43 49433.39 491
testmvs28.94 46033.33 46215.79 47726.03 4999.81 50296.77 48015.67 50011.55 49523.87 49650.74 50519.03 4998.53 49623.21 49533.07 49329.03 492
mmdepth8.33 46311.11 4660.00 4780.00 5010.00 5030.00 4900.00 5020.00 4960.00 497100.00 10.00 5000.00 4970.00 4960.00 4950.00 493
monomultidepth8.33 46311.11 4660.00 4780.00 5010.00 5030.00 4900.00 5020.00 4960.00 497100.00 10.00 5000.00 4970.00 4960.00 4950.00 493
test_blank8.33 46311.11 4660.00 4780.00 5010.00 5030.00 4900.00 5020.00 4960.00 497100.00 10.00 5000.00 4970.00 4960.00 4950.00 493
uanet_test8.33 46311.11 4660.00 4780.00 5010.00 5030.00 4900.00 5020.00 4960.00 497100.00 10.00 5000.00 4970.00 4960.00 4950.00 493
DCPMVS8.33 46311.11 4660.00 4780.00 5010.00 5030.00 4900.00 5020.00 4960.00 497100.00 10.00 5000.00 4970.00 4960.00 4950.00 493
cdsmvs_eth3d_5k24.88 46133.17 4630.00 4780.00 5010.00 5030.00 49099.62 2370.00 4960.00 49799.13 40499.82 180.00 4970.00 4960.00 4950.00 493
pcd_1.5k_mvsjas16.61 46222.14 4650.00 4780.00 5010.00 5030.00 4900.00 5020.00 4960.00 497100.00 199.28 910.00 4970.00 4960.00 4950.00 493
sosnet-low-res8.33 46311.11 4660.00 4780.00 5010.00 5030.00 4900.00 5020.00 4960.00 497100.00 10.00 5000.00 4970.00 4960.00 4950.00 493
sosnet8.33 46311.11 4660.00 4780.00 5010.00 5030.00 4900.00 5020.00 4960.00 497100.00 10.00 5000.00 4970.00 4960.00 4950.00 493
uncertanet8.33 46311.11 4660.00 4780.00 5010.00 5030.00 4900.00 5020.00 4960.00 497100.00 10.00 5000.00 4970.00 4960.00 4950.00 493
Regformer8.33 46311.11 4660.00 4780.00 5010.00 5030.00 4900.00 5020.00 4960.00 497100.00 10.00 5000.00 4970.00 4960.00 4950.00 493
ab-mvs-re8.26 47311.02 4760.00 4780.00 5010.00 5030.00 4900.00 5020.00 4960.00 49799.16 4020.00 5000.00 4970.00 4960.00 4950.00 493
uanet8.33 46311.11 4660.00 4780.00 5010.00 5030.00 4900.00 5020.00 4960.00 497100.00 10.00 5000.00 4970.00 4960.00 4950.00 493
TestfortrainingZip99.38 132
FOURS199.83 8599.89 1099.74 2799.71 18399.69 12799.63 220
test_one_060199.63 23799.76 7199.55 28399.23 24299.31 33199.61 27098.59 208
eth-test20.00 501
eth-test0.00 501
test_241102_ONE99.69 21299.82 4399.54 28999.12 26699.82 10999.49 32798.91 16499.52 471
save fliter99.53 28999.25 24898.29 39599.38 35099.07 270
test072699.69 21299.80 5299.24 19099.57 27299.16 25799.73 17399.65 23498.35 248
GSMVS99.14 380
test_part299.62 24199.67 11999.55 260
sam_mvs190.81 43899.14 380
sam_mvs90.52 443
MTGPAbinary99.53 299
test_post52.41 50390.25 44599.86 269
patchmatchnet-post99.62 26090.58 44199.94 98
MTMP99.09 25498.59 433
TEST999.35 35399.35 22998.11 41299.41 33694.83 47397.92 45198.99 42598.02 28099.85 288
test_899.34 36299.31 23598.08 41699.40 34394.90 47097.87 45598.97 43098.02 28099.84 304
agg_prior99.35 35399.36 22699.39 34697.76 46299.85 288
test_prior499.19 26598.00 425
test_prior99.46 24999.35 35399.22 25899.39 34699.69 41999.48 273
新几何298.04 420
旧先验199.49 30999.29 23899.26 37899.39 35397.67 30699.36 38499.46 282
原ACMM297.92 434
test22299.51 29899.08 28697.83 44099.29 37295.21 46798.68 40999.31 37597.28 32499.38 38199.43 304
segment_acmp98.37 246
testdata197.72 44597.86 404
test1299.54 22299.29 37599.33 23299.16 39998.43 42797.54 31399.82 33999.47 37099.48 273
plane_prior799.58 25699.38 218
plane_prior699.47 32099.26 24597.24 325
plane_prior499.25 388
plane_prior399.31 23598.36 35999.14 360
plane_prior298.80 33598.94 285
plane_prior199.51 298
plane_prior99.24 25298.42 38797.87 40299.71 296
n20.00 502
nn0.00 502
door-mid99.83 98
test1199.29 372
door99.77 148
HQP5-MVS98.94 301
HQP-NCC99.31 36997.98 42797.45 42298.15 440
ACMP_Plane99.31 36997.98 42797.45 42298.15 440
HQP4-MVS98.15 44099.70 41299.53 245
HQP3-MVS99.37 35199.67 317
HQP2-MVS96.67 345
NP-MVS99.40 34099.13 27498.83 442
ACMMP++_ref99.94 127
ACMMP++99.79 254
Test By Simon98.41 240