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 7999.70 38100.00 199.73 109100.00 199.89 4199.79 2299.88 23599.98 1100.00 199.98 5
test_fmvs299.72 5399.85 1799.34 29499.91 3198.08 38599.48 109100.00 199.90 4999.99 799.91 3199.50 6299.98 2699.98 199.99 1699.96 13
test_fmvs399.83 2199.93 299.53 22699.96 798.62 34099.67 53100.00 199.95 32100.00 199.95 1699.85 1499.99 799.98 199.99 1699.98 5
test_fmvsmconf0.01_n99.89 399.88 799.91 399.98 399.76 7099.12 242100.00 1100.00 199.99 799.91 3199.98 1100.00 199.97 4100.00 199.99 2
test_vis1_n_192099.72 5399.88 799.27 31999.93 2497.84 39899.34 148100.00 199.99 399.99 799.82 9099.87 1399.99 799.97 499.99 1699.97 10
test_vis1_n99.68 6499.79 3499.36 28999.94 1898.18 37499.52 94100.00 199.86 65100.00 199.88 5098.99 14899.96 6899.97 499.96 8799.95 14
test_fmvs1_n99.68 6499.81 2899.28 31499.95 1597.93 39499.49 107100.00 199.82 8599.99 799.89 4199.21 10399.98 2699.97 499.98 5099.93 20
test_f99.75 4999.88 799.37 28499.96 798.21 37199.51 101100.00 199.94 36100.00 199.93 2299.58 5099.94 9799.97 499.99 1699.97 10
fmvsm_l_conf0.5_n_999.83 2199.81 2899.89 1199.86 5999.80 5198.94 30999.96 2899.98 1899.96 3499.78 13299.88 1199.98 2699.96 999.99 1699.90 29
fmvsm_l_conf0.5_n_399.85 1299.83 2199.92 299.88 4599.86 1899.08 25899.97 2099.98 1899.96 3499.79 11999.90 999.99 799.96 999.99 1699.90 29
test_fmvsmconf0.1_n99.87 999.86 1399.91 399.97 699.74 8799.01 28299.99 1199.99 399.98 1499.88 5099.97 299.99 799.96 9100.00 199.98 5
test_fmvsmvis_n_192099.84 1799.86 1399.81 5499.88 4599.55 17099.17 21799.98 1299.99 399.96 3499.84 7699.96 399.99 799.96 999.99 1699.88 40
test_cas_vis1_n_192099.76 4699.86 1399.45 25299.93 2498.40 35999.30 16699.98 1299.94 3699.99 799.89 4199.80 2199.97 4399.96 999.97 7399.97 10
fmvsm_s_conf0.5_n_1099.77 4499.73 5499.88 1999.81 10799.75 7999.06 26499.85 8299.99 399.97 2499.84 7699.12 12099.98 2699.95 1499.99 1699.90 29
fmvsm_s_conf0.5_n_799.73 5299.78 3999.60 19199.74 17998.93 30698.85 32399.96 2899.96 2899.97 2499.76 15099.82 1899.96 6899.95 1499.98 5099.90 29
fmvsm_l_conf0.5_n99.80 3099.78 3999.85 3299.88 4599.66 12099.11 24799.91 5199.98 1899.96 3499.64 23799.60 4499.99 799.95 1499.99 1699.88 40
test_fmvsm_n_192099.84 1799.85 1799.83 4199.82 9599.70 10899.17 21799.97 2099.99 399.96 3499.82 9099.94 4100.00 199.95 14100.00 199.80 65
test_fmvs199.48 13099.65 7498.97 36199.54 28397.16 42699.11 24799.98 1299.78 10299.96 3499.81 9798.72 19199.97 4399.95 1499.97 7399.79 73
mvsany_test399.85 1299.88 799.75 9799.95 1599.37 22399.53 9299.98 1299.77 10699.99 799.95 1699.85 1499.94 9799.95 1499.98 5099.94 17
fmvsm_s_conf0.5_n_999.82 2499.82 2599.82 4699.83 8699.59 15798.97 30099.92 4299.99 399.97 2499.84 7699.90 999.94 9799.94 2099.99 1699.92 24
fmvsm_s_conf0.1_n_299.81 2899.78 3999.89 1199.93 2499.76 7098.92 31399.98 1299.99 399.99 799.88 5099.43 6799.94 9799.94 2099.99 1699.99 2
fmvsm_l_conf0.5_n_a99.80 3099.79 3499.84 3899.88 4599.64 13399.12 24299.91 5199.98 1899.95 4599.67 22199.67 3499.99 799.94 2099.99 1699.88 40
MM99.18 23499.05 24399.55 21699.35 35498.81 31899.05 26597.79 46899.99 399.48 28499.59 28996.29 36599.95 8099.94 2099.98 5099.88 40
test_fmvsmconf_n99.85 1299.84 2099.88 1999.91 3199.73 9098.97 30099.98 1299.99 399.96 3499.85 6899.93 799.99 799.94 2099.99 1699.93 20
fmvsm_s_conf0.5_n_1199.76 4699.75 5199.81 5499.81 10799.53 17399.15 22699.89 6099.99 399.98 1499.86 6399.13 11799.98 2699.93 2599.99 1699.92 24
fmvsm_s_conf0.5_n_599.78 3799.76 4999.85 3299.79 13099.72 9598.84 32599.96 2899.96 2899.96 3499.72 17699.71 2899.99 799.93 2599.98 5099.85 49
fmvsm_s_conf0.5_n_299.78 3799.75 5199.88 1999.82 9599.76 7098.88 31799.92 4299.98 1899.98 1499.85 6899.42 6999.94 9799.93 2599.98 5099.94 17
fmvsm_s_conf0.1_n_a99.85 1299.83 2199.91 399.95 1599.82 4299.10 25099.98 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 5799.07 26399.98 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 7399.82 4299.03 27399.96 2899.99 399.97 2499.84 7699.58 5099.93 11999.92 3099.98 5099.93 20
fmvsm_s_conf0.5_n99.83 2199.81 2899.87 2699.85 7399.78 5799.03 27399.96 2899.99 399.97 2499.84 7699.78 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 5599.88 1999.82 9599.75 7999.02 27799.87 6999.98 1899.98 1499.81 9799.07 13199.97 4399.91 3399.99 1699.92 24
fmvsm_s_conf0.5_n_699.80 3099.78 3999.85 3299.78 13899.78 5799.00 28899.97 2099.96 2899.97 2499.56 30399.92 899.93 11999.91 3399.99 1699.83 56
fmvsm_s_conf0.5_n_499.78 3799.78 3999.79 7199.75 17199.56 16698.98 29899.94 3899.92 4599.97 2499.72 17699.84 1699.92 15099.91 3399.98 5099.89 37
MVStest198.22 36898.09 36398.62 40499.04 42696.23 45099.20 20299.92 4299.44 20099.98 1499.87 5685.87 47199.67 44399.91 3399.57 34999.95 14
v192192099.56 10599.57 10399.55 21699.75 17199.11 27999.05 26599.61 24699.15 26299.88 8299.71 18699.08 12899.87 25099.90 3799.97 7399.66 148
v124099.56 10599.58 9899.51 23299.80 11699.00 29399.00 28899.65 22499.15 26299.90 6799.75 15899.09 12499.88 23599.90 3799.96 8799.67 133
v1099.69 5999.69 6099.66 15199.81 10799.39 21699.66 5799.75 16199.60 16699.92 5999.87 5698.75 18699.86 26999.90 3799.99 1699.73 93
v119299.57 10199.57 10399.57 20599.77 15199.22 26099.04 27099.60 25799.18 25199.87 9299.72 17699.08 12899.85 28899.89 4099.98 5099.66 148
fmvsm_s_conf0.5_n_399.79 3499.77 4599.85 3299.81 10799.71 10098.97 30099.92 4299.98 1899.97 2499.86 6399.53 5899.95 8099.88 4199.99 1699.89 37
v14419299.55 11099.54 11399.58 19799.78 13899.20 26699.11 24799.62 23999.18 25199.89 7299.72 17698.66 20099.87 25099.88 4199.97 7399.66 148
v899.68 6499.69 6099.65 15899.80 11699.40 21399.66 5799.76 15699.64 15099.93 5399.85 6898.66 20099.84 30599.88 4199.99 1699.71 102
mvs5depth99.88 699.91 399.80 6499.92 2999.42 20599.94 3100.00 199.97 2599.89 7299.99 1299.63 3799.97 4399.87 4499.99 16100.00 1
v114499.54 11499.53 11799.59 19499.79 13099.28 24199.10 25099.61 24699.20 24899.84 10199.73 16898.67 19899.84 30599.86 4599.98 5099.64 169
mmtdpeth99.78 3799.83 2199.66 15199.85 7399.05 29299.79 1599.97 20100.00 199.43 29699.94 1999.64 3599.94 9799.83 4699.99 1699.98 5
SSC-MVS99.52 12099.42 14499.83 4199.86 5999.65 12699.52 9499.81 11799.87 6299.81 11599.79 11996.78 34499.99 799.83 4699.51 36599.86 46
v7n99.82 2499.80 3299.88 1999.96 799.84 2699.82 1099.82 10499.84 7599.94 4899.91 3199.13 11799.96 6899.83 4699.99 1699.83 56
v2v48299.50 12399.47 12999.58 19799.78 13899.25 24999.14 23099.58 27299.25 23999.81 11599.62 26198.24 25999.84 30599.83 4699.97 7399.64 169
test_vis1_rt99.45 14699.46 13499.41 27099.71 19298.63 33998.99 29599.96 2899.03 27599.95 4599.12 41198.75 18699.84 30599.82 5099.82 23399.77 79
tt080599.63 8599.57 10399.81 5499.87 5499.88 1299.58 8298.70 42999.72 11399.91 6299.60 27999.43 6799.81 35899.81 5199.53 36199.73 93
VortexMVS99.13 24799.24 19798.79 39399.67 22896.60 44299.24 19199.80 12299.85 7199.93 5399.84 7695.06 38499.89 22099.80 5299.98 5099.89 37
V4299.56 10599.54 11399.63 17299.79 13099.46 19099.39 12999.59 26399.24 24199.86 9599.70 19698.55 21599.82 34299.79 5399.95 11199.60 205
SSC-MVS3.299.64 8499.67 6599.56 20999.75 17198.98 29698.96 30499.87 6999.88 6099.84 10199.64 23799.32 8799.91 17999.78 5499.96 8799.80 65
mvs_tets99.90 299.90 499.90 899.96 799.79 5499.72 3399.88 6599.92 4599.98 1499.93 2299.94 499.98 2699.77 55100.00 199.92 24
WB-MVS99.44 15099.32 17399.80 6499.81 10799.61 15199.47 11299.81 11799.82 8599.71 18399.72 17696.60 34999.98 2699.75 5699.23 40699.82 63
PS-MVSNAJss99.84 1799.82 2599.89 1199.96 799.77 6399.68 4899.85 8299.95 3299.98 1499.92 2799.28 9299.98 2699.75 56100.00 199.94 17
jajsoiax99.89 399.89 699.89 1199.96 799.78 5799.70 3899.86 7699.89 5599.98 1499.90 3699.94 499.98 2699.75 56100.00 199.90 29
ANet_high99.88 699.87 1199.91 399.99 199.91 499.65 62100.00 199.90 49100.00 199.97 1499.61 4199.97 4399.75 56100.00 199.84 52
AstraMVS99.15 24499.06 23899.42 26299.85 7398.59 34399.13 23797.26 47699.84 7599.87 9299.77 14296.11 36899.93 11999.71 6099.96 8799.74 89
Elysia99.69 5999.65 7499.81 5499.86 5999.72 9599.34 14899.77 14899.94 3699.91 6299.76 15098.55 21599.99 799.70 6199.98 5099.72 97
StellarMVS99.69 5999.65 7499.81 5499.86 5999.72 9599.34 14899.77 14899.94 3699.91 6299.76 15098.55 21599.99 799.70 6199.98 5099.72 97
tt0320-xc99.82 2499.82 2599.82 4699.82 9599.84 2699.82 1099.92 4299.94 3699.94 4899.93 2299.34 8499.92 15099.70 6199.96 8799.70 105
reproduce_monomvs97.40 40797.46 39597.20 46099.05 42391.91 48999.20 20299.18 40099.84 7599.86 9599.75 15880.67 47999.83 32599.69 6499.95 11199.85 49
SPE-MVS-test99.68 6499.70 5799.64 16599.57 26799.83 3499.78 1799.97 2099.92 4599.50 28199.38 35799.57 5299.95 8099.69 6499.90 16099.15 379
guyue99.12 25099.02 25299.41 27099.84 7898.56 34499.19 20898.30 45499.82 8599.84 10199.75 15894.84 38799.92 15099.68 6699.94 12899.74 89
tt032099.79 3499.79 3499.81 5499.82 9599.84 2699.82 1099.90 5799.94 3699.94 4899.94 1999.07 13199.92 15099.68 6699.97 7399.67 133
MGCNet98.61 32698.30 34799.52 22897.88 48998.95 30298.76 34294.11 49599.84 7599.32 32899.57 29995.57 37799.95 8099.68 6699.98 5099.68 124
CS-MVS99.67 7699.70 5799.58 19799.53 29099.84 2699.79 1599.96 2899.90 4999.61 23799.41 34799.51 6199.95 8099.66 6999.89 17498.96 421
KinetiMVS99.66 7799.63 8299.76 8699.89 3999.57 16599.37 14099.82 10499.95 3299.90 6799.63 25298.57 21199.97 4399.65 7099.94 12899.74 89
pmmvs699.86 1099.86 1399.83 4199.94 1899.90 799.83 799.91 5199.85 7199.94 4899.95 1699.73 2799.90 19899.65 7099.97 7399.69 117
MIMVSNet199.66 7799.62 8499.80 6499.94 1899.87 1599.69 4599.77 14899.78 10299.93 5399.89 4197.94 28899.92 15099.65 7099.98 5099.62 187
LuminaMVS99.39 16999.28 18899.73 11399.83 8699.49 18099.00 28899.05 41299.81 9199.89 7299.79 11996.54 35399.97 4399.64 7399.98 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 8399.92 15099.64 7399.94 12899.68 124
EC-MVSNet99.69 5999.69 6099.68 13999.71 19299.91 499.76 2399.96 2899.86 6599.51 27899.39 35599.57 5299.93 11999.64 7399.86 20599.20 367
K. test v398.87 30198.60 31299.69 13799.93 2499.46 19099.74 2794.97 49099.78 10299.88 8299.88 5093.66 40299.97 4399.61 7699.95 11199.64 169
KD-MVS_self_test99.63 8599.59 9499.76 8699.84 7899.90 799.37 14099.79 13199.83 8199.88 8299.85 6898.42 23999.90 19899.60 7799.73 28799.49 270
Anonymous2024052199.44 15099.42 14499.49 23899.89 3998.96 30199.62 6799.76 15699.85 7199.82 10899.88 5096.39 36099.97 4399.59 7899.98 5099.55 230
TransMVSNet (Re)99.78 3799.77 4599.81 5499.91 3199.85 2199.75 2599.86 7699.70 12599.91 6299.89 4199.60 4499.87 25099.59 7899.74 28199.71 102
OurMVSNet-221017-099.75 4999.71 5699.84 3899.96 799.83 3499.83 799.85 8299.80 9599.93 5399.93 2298.54 21999.93 11999.59 7899.98 5099.76 84
EU-MVSNet99.39 16999.62 8498.72 39899.88 4596.44 44499.56 8799.85 8299.90 4999.90 6799.85 6898.09 27699.83 32599.58 8199.95 11199.90 29
mvs_anonymous99.28 19899.39 14998.94 36599.19 39797.81 40099.02 27799.55 28599.78 10299.85 9899.80 10798.24 25999.86 26999.57 8299.50 36899.15 379
test111197.74 38998.16 35996.49 47299.60 24489.86 50399.71 3791.21 49999.89 5599.88 8299.87 5693.73 40199.90 19899.56 8399.99 1699.70 105
lessismore_v099.64 16599.86 5999.38 21890.66 50099.89 7299.83 8394.56 39299.97 4399.56 8399.92 14699.57 223
mvsany_test199.44 15099.45 13699.40 27399.37 34798.64 33897.90 43899.59 26399.27 23599.92 5999.82 9099.74 2699.93 11999.55 8599.87 19799.63 175
MVSMamba_PlusPlus99.55 11099.58 9899.47 24599.68 22199.40 21399.52 9499.70 19399.92 4599.77 14499.86 6398.28 25599.96 6899.54 8699.90 16099.05 408
pm-mvs199.79 3499.79 3499.78 7599.91 3199.83 3499.76 2399.87 6999.73 10999.89 7299.87 5699.63 3799.87 25099.54 8699.92 14699.63 175
LTVRE_ROB99.19 199.88 699.87 1199.88 1999.91 3199.90 799.96 199.92 4299.90 4999.97 2499.87 5699.81 2099.95 8099.54 8699.99 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 36499.71 19297.27 42399.50 10299.82 10499.59 16899.41 30599.85 6899.62 40100.00 199.53 8999.89 17499.59 212
test250694.73 45894.59 45895.15 47999.59 25085.90 50599.75 2574.01 50799.89 5599.71 18399.86 6379.00 48999.90 19899.52 9099.99 1699.65 157
balanced_ft_v199.37 17699.36 16099.38 27999.10 41699.38 21899.68 4899.72 18099.72 11399.36 31699.77 14297.66 31199.94 9799.52 9099.73 28798.83 438
UniMVSNet_ETH3D99.85 1299.83 2199.90 899.89 3999.91 499.89 599.71 18499.93 4399.95 4599.89 4199.71 2899.96 6899.51 9299.97 7399.84 52
FC-MVSNet-test99.70 5799.65 7499.86 3099.88 4599.86 1899.72 3399.78 14299.90 4999.82 10899.83 8398.45 23599.87 25099.51 9299.97 7399.86 46
BP-MVS198.72 31898.46 32899.50 23499.53 29099.00 29399.34 14898.53 43999.65 14699.73 17399.38 35790.62 44599.96 6899.50 9499.86 20599.55 230
UA-Net99.78 3799.76 4999.86 3099.72 18899.71 10099.91 499.95 3699.96 2899.71 18399.91 3199.15 11299.97 4399.50 94100.00 199.90 29
viewdifsd2359ckpt1199.62 9299.64 7999.56 20999.86 5999.19 26799.02 27799.93 3999.83 8199.88 8299.81 9798.99 14899.83 32599.48 9699.96 8799.65 157
viewmsd2359difaftdt99.62 9299.64 7999.56 20999.86 5999.19 26799.02 27799.93 3999.83 8199.88 8299.81 9798.99 14899.83 32599.48 9699.96 8799.65 157
PMMVS299.48 13099.45 13699.57 20599.76 15598.99 29598.09 41599.90 5798.95 28599.78 13299.58 29299.57 5299.93 11999.48 9699.95 11199.79 73
VPA-MVSNet99.66 7799.62 8499.79 7199.68 22199.75 7999.62 6799.69 20199.85 7199.80 12299.81 9798.81 17499.91 17999.47 9999.88 18499.70 105
GDP-MVS98.81 30998.57 31899.50 23499.53 29099.12 27899.28 17599.86 7699.53 17799.57 24899.32 37490.88 44099.98 2699.46 10099.74 28199.42 311
ECVR-MVScopyleft97.73 39098.04 36696.78 46599.59 25090.81 49899.72 3390.43 50199.89 5599.86 9599.86 6393.60 40399.89 22099.46 10099.99 1699.65 157
nrg03099.70 5799.66 7299.82 4699.76 15599.84 2699.61 7399.70 19399.93 4399.78 13299.68 21799.10 12299.78 37299.45 10299.96 8799.83 56
FE-MVSNET299.68 6499.67 6599.72 12199.86 5999.68 11599.46 11699.88 6599.62 15599.87 9299.85 6899.06 13799.85 28899.44 10399.98 5099.63 175
TAMVS99.49 12899.45 13699.63 17299.48 31599.42 20599.45 11799.57 27499.66 14299.78 13299.83 8397.85 29599.86 26999.44 10399.96 8799.61 201
GeoE99.69 5999.66 7299.78 7599.76 15599.76 7099.60 7999.82 10499.46 19599.75 15899.56 30399.63 3799.95 8099.43 10599.88 18499.62 187
new-patchmatchnet99.35 18399.57 10398.71 40299.82 9596.62 44098.55 37099.75 16199.50 18299.88 8299.87 5699.31 8899.88 23599.43 105100.00 199.62 187
test20.0399.55 11099.54 11399.58 19799.79 13099.37 22399.02 27799.89 6099.60 16699.82 10899.62 26198.81 17499.89 22099.43 10599.86 20599.47 278
MVSFormer99.41 16399.44 14099.31 30699.57 26798.40 35999.77 1999.80 12299.73 10999.63 22199.30 37998.02 28199.98 2699.43 10599.69 30899.55 230
test_djsdf99.84 1799.81 2899.91 399.94 1899.84 2699.77 1999.80 12299.73 10999.97 2499.92 2799.77 2599.98 2699.43 105100.00 199.90 29
SDMVSNet99.77 4499.77 4599.76 8699.80 11699.65 12699.63 6499.86 7699.97 2599.89 7299.89 4199.52 6099.99 799.42 11099.96 8799.65 157
Anonymous2023121199.62 9299.57 10399.76 8699.61 24299.60 15599.81 1399.73 17199.82 8599.90 6799.90 3697.97 28799.86 26999.42 11099.96 8799.80 65
SixPastTwentyTwo99.42 15799.30 18099.76 8699.92 2999.67 11899.70 3899.14 40599.65 14699.89 7299.90 3696.20 36799.94 9799.42 11099.92 14699.67 133
balanced_conf0399.50 12399.50 12299.50 23499.42 33899.49 18099.52 9499.75 16199.86 6599.78 13299.71 18698.20 26799.90 19899.39 11399.88 18499.10 390
patch_mono-299.51 12199.46 13499.64 16599.70 20799.11 27999.04 27099.87 6999.71 11999.47 28699.79 11998.24 25999.98 2699.38 11499.96 8799.83 56
UGNet99.38 17299.34 16799.49 23898.90 43898.90 31099.70 3899.35 35799.86 6598.57 42299.81 9798.50 23099.93 11999.38 11499.98 5099.66 148
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 5699.67 6599.81 5499.89 3999.72 9599.59 8099.82 10499.39 21699.82 10899.84 7699.38 7599.91 17999.38 11499.93 14099.80 65
FIs99.65 8399.58 9899.84 3899.84 7899.85 2199.66 5799.75 16199.86 6599.74 16899.79 11998.27 25799.85 28899.37 11799.93 14099.83 56
sd_testset99.78 3799.78 3999.80 6499.80 11699.76 7099.80 1499.79 13199.97 2599.89 7299.89 4199.53 5899.99 799.36 11899.96 8799.65 157
anonymousdsp99.80 3099.77 4599.90 899.96 799.88 1299.73 3099.85 8299.70 12599.92 5999.93 2299.45 6399.97 4399.36 118100.00 199.85 49
casdiffmvs_mvgpermissive99.68 6499.68 6399.69 13799.81 10799.59 15799.29 17399.90 5799.71 11999.79 12899.73 16899.54 5599.84 30599.36 11899.96 8799.65 157
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 12099.69 4599.92 4299.67 13899.77 14499.75 15899.61 4199.98 2699.35 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 13098.82 31799.58 8299.97 2099.95 3299.96 3499.76 15098.44 23699.99 799.34 12299.96 8799.78 75
CHOSEN 1792x268899.39 16999.30 18099.65 15899.88 4599.25 24998.78 34099.88 6598.66 32999.96 3499.79 11997.45 31899.93 11999.34 12299.99 1699.78 75
CDS-MVSNet99.22 22099.13 21399.50 23499.35 35499.11 27998.96 30499.54 29199.46 19599.61 23799.70 19696.31 36399.83 32599.34 12299.88 18499.55 230
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT99.00 28199.16 20698.51 41099.75 17195.90 45698.07 41899.84 8999.84 7599.89 7299.73 16896.01 37199.99 799.33 125100.00 199.63 175
HyFIR lowres test98.91 29498.64 30999.73 11399.85 7399.47 18498.07 41899.83 9898.64 33299.89 7299.60 27992.57 416100.00 199.33 12599.97 7399.72 97
pmmvs599.19 23099.11 22099.42 26299.76 15598.88 31298.55 37099.73 17198.82 30899.72 17899.62 26196.56 35099.82 34299.32 12799.95 11199.56 226
v14899.40 16599.41 14799.39 27699.76 15598.94 30399.09 25599.59 26399.17 25699.81 11599.61 27198.41 24099.69 42599.32 12799.94 12899.53 246
baseline99.63 8599.62 8499.66 15199.80 11699.62 14199.44 11999.80 12299.71 11999.72 17899.69 20599.15 11299.83 32599.32 12799.94 12899.53 246
CVMVSNet98.61 32698.88 28697.80 44199.58 25793.60 48299.26 18499.64 23299.66 14299.72 17899.67 22193.26 40799.93 11999.30 13099.81 24399.87 44
PS-CasMVS99.66 7799.58 9899.89 1199.80 11699.85 2199.66 5799.73 17199.62 15599.84 10199.71 18698.62 20499.96 6899.30 13099.96 8799.86 46
DTE-MVSNet99.68 6499.61 8899.88 1999.80 11699.87 1599.67 5399.71 18499.72 11399.84 10199.78 13298.67 19899.97 4399.30 13099.95 11199.80 65
tmp_tt95.75 45095.42 44596.76 46689.90 50694.42 47498.86 32197.87 46678.01 49799.30 33899.69 20597.70 30395.89 49999.29 13398.14 46599.95 14
PEN-MVS99.66 7799.59 9499.89 1199.83 8699.87 1599.66 5799.73 17199.70 12599.84 10199.73 16898.56 21499.96 6899.29 13399.94 12899.83 56
WR-MVS_H99.61 9699.53 11799.87 2699.80 11699.83 3499.67 5399.75 16199.58 17099.85 9899.69 20598.18 27099.94 9799.28 13599.95 11199.83 56
IterMVS98.97 28599.16 20698.42 41599.74 17995.64 46098.06 42099.83 9899.83 8199.85 9899.74 16396.10 37099.99 799.27 136100.00 199.63 175
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NormalMVS99.09 25898.91 28499.62 18199.78 13899.11 27999.36 14499.77 14899.82 8599.68 19599.53 31593.30 40599.99 799.24 13799.76 27099.74 89
SymmetryMVS99.01 27898.82 29499.58 19799.65 23499.11 27999.36 14499.20 39899.82 8599.68 19599.53 31593.30 40599.99 799.24 13799.63 32999.64 169
WBMVS97.50 40397.18 40698.48 41298.85 44695.89 45798.44 38799.52 30699.53 17799.52 27199.42 34680.10 48299.86 26999.24 13799.95 11199.68 124
h-mvs3398.61 32698.34 34299.44 25699.60 24498.67 33099.27 17999.44 33299.68 13099.32 32899.49 32892.50 419100.00 199.24 13796.51 48799.65 157
hse-mvs298.52 33998.30 34799.16 33599.29 37698.60 34198.77 34199.02 41499.68 13099.32 32899.04 42192.50 41999.85 28899.24 13797.87 47299.03 412
FMVSNet199.66 7799.63 8299.73 11399.78 13899.77 6399.68 4899.70 19399.67 13899.82 10899.83 8398.98 15299.90 19899.24 13799.97 7399.53 246
casdiffseed41469214799.68 6499.68 6399.67 14399.86 5999.65 12699.32 15799.87 6999.75 10799.77 14499.80 10799.61 4199.68 43799.21 14399.95 11199.67 133
casdiffmvspermissive99.63 8599.61 8899.67 14399.79 13099.59 15799.13 23799.85 8299.79 9999.76 15399.72 17699.33 8699.82 34299.21 14399.94 12899.59 212
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 15599.82 4299.57 8599.61 24699.54 17599.80 12299.64 23797.79 29999.95 8099.21 14399.94 12899.84 52
DELS-MVS99.34 18899.30 18099.48 24399.51 29999.36 22798.12 41199.53 30199.36 22199.41 30599.61 27199.22 10299.87 25099.21 14399.68 31399.20 367
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 28499.70 20798.80 32198.67 35199.92 4299.49 18499.77 14499.71 18699.08 12899.78 37299.20 14799.94 12899.54 240
UniMVSNet (Re)99.37 17699.26 19399.68 13999.51 29999.58 16298.98 29899.60 25799.43 20799.70 18799.36 36597.70 30399.88 23599.20 14799.87 19799.59 212
CANet99.11 25499.05 24399.28 31498.83 44898.56 34498.71 34999.41 33899.25 23999.23 34899.22 39797.66 31199.94 9799.19 14999.97 7399.33 335
EI-MVSNet-UG-set99.48 13099.50 12299.42 26299.57 26798.65 33699.24 19199.46 32699.68 13099.80 12299.66 22698.99 14899.89 22099.19 14999.90 16099.72 97
xiu_mvs_v1_base_debu99.23 21199.34 16798.91 37499.59 25098.23 36898.47 38299.66 21499.61 16099.68 19598.94 43799.39 7199.97 4399.18 15199.55 35498.51 461
xiu_mvs_v1_base99.23 21199.34 16798.91 37499.59 25098.23 36898.47 38299.66 21499.61 16099.68 19598.94 43799.39 7199.97 4399.18 15199.55 35498.51 461
xiu_mvs_v1_base_debi99.23 21199.34 16798.91 37499.59 25098.23 36898.47 38299.66 21499.61 16099.68 19598.94 43799.39 7199.97 4399.18 15199.55 35498.51 461
VPNet99.46 14299.37 15599.71 12799.82 9599.59 15799.48 10999.70 19399.81 9199.69 19099.58 29297.66 31199.86 26999.17 15499.44 37599.67 133
UniMVSNet_NR-MVSNet99.37 17699.25 19599.72 12199.47 32199.56 16698.97 30099.61 24699.43 20799.67 20399.28 38397.85 29599.95 8099.17 15499.81 24399.65 157
DU-MVS99.33 19199.21 20099.71 12799.43 33399.56 16698.83 32899.53 30199.38 21799.67 20399.36 36597.67 30799.95 8099.17 15499.81 24399.63 175
usedtu_dtu_shiyan299.44 15099.33 17299.78 7599.86 5999.76 7099.54 9099.79 13199.66 14299.66 20999.79 11996.76 34599.96 6899.15 15799.72 29499.62 187
EI-MVSNet-Vis-set99.47 14099.49 12699.42 26299.57 26798.66 33399.24 19199.46 32699.67 13899.79 12899.65 23598.97 15499.89 22099.15 15799.89 17499.71 102
EI-MVSNet99.38 17299.44 14099.21 32999.58 25798.09 38299.26 18499.46 32699.62 15599.75 15899.67 22198.54 21999.85 28899.15 15799.92 14699.68 124
VNet99.18 23499.06 23899.56 20999.24 38799.36 22799.33 15499.31 37199.67 13899.47 28699.57 29996.48 35499.84 30599.15 15799.30 39499.47 278
EG-PatchMatch MVS99.57 10199.56 10899.62 18199.77 15199.33 23399.26 18499.76 15699.32 22699.80 12299.78 13299.29 9099.87 25099.15 15799.91 15899.66 148
PVSNet_Blended_VisFu99.40 16599.38 15299.44 25699.90 3798.66 33398.94 30999.91 5197.97 39399.79 12899.73 16899.05 13999.97 4399.15 15799.99 1699.68 124
IterMVS-LS99.41 16399.47 12999.25 32599.81 10798.09 38298.85 32399.76 15699.62 15599.83 10799.64 23798.54 21999.97 4399.15 15799.99 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 25799.64 13399.30 16699.63 23699.61 16099.71 18399.56 30398.76 18499.96 6899.14 16499.92 14699.68 124
MVSTER98.47 34698.22 35299.24 32799.06 42298.35 36599.08 25899.46 32699.27 23599.75 15899.66 22688.61 45899.85 28899.14 16499.92 14699.52 257
E5new99.68 6499.67 6599.70 13299.87 5499.62 14199.41 12299.84 8999.68 13099.77 14499.81 9799.59 4699.78 37299.13 16699.96 8799.70 105
E6new99.68 6499.67 6599.70 13299.86 5999.62 14199.41 12299.84 8999.68 13099.77 14499.81 9799.59 4699.78 37299.13 16699.96 8799.70 105
E699.68 6499.67 6599.70 13299.86 5999.62 14199.41 12299.84 8999.68 13099.77 14499.81 9799.59 4699.78 37299.13 16699.96 8799.70 105
E599.68 6499.67 6599.70 13299.87 5499.62 14199.41 12299.84 8999.68 13099.77 14499.81 9799.59 4699.78 37299.13 16699.96 8799.70 105
diffmvs_AUTHOR99.48 13099.48 12799.47 24599.80 11698.89 31198.71 34999.82 10499.79 9999.66 20999.63 25298.87 17099.88 23599.13 16699.95 11199.62 187
Anonymous2023120699.35 18399.31 17599.47 24599.74 17999.06 29199.28 17599.74 16799.23 24399.72 17899.53 31597.63 31499.88 23599.11 17199.84 21599.48 274
Syy-MVS98.17 37197.85 38399.15 33798.50 47198.79 32298.60 35899.21 39597.89 40296.76 48196.37 50495.47 38199.57 46999.10 17298.73 44099.09 395
ttmdpeth99.48 13099.55 11099.29 31199.76 15598.16 37699.33 15499.95 3699.79 9999.36 31699.89 4199.13 11799.77 38599.09 17399.64 32699.93 20
MVS_Test99.28 19899.31 17599.19 33299.35 35498.79 32299.36 14499.49 31999.17 25699.21 35399.67 22198.78 18199.66 44899.09 17399.66 32299.10 390
usedtu_dtu_shiyan198.87 30198.71 30399.35 29199.59 25098.88 31297.17 47299.64 23298.94 28699.27 34099.22 39795.57 37799.83 32599.08 17599.92 14699.35 329
FE-MVSNET398.87 30198.71 30399.35 29199.59 25098.88 31297.17 47299.64 23298.94 28699.27 34099.22 39795.57 37799.83 32599.08 17599.92 14699.35 329
testgi99.29 19799.26 19399.37 28499.75 17198.81 31898.84 32599.89 6098.38 36099.75 15899.04 42199.36 8099.86 26999.08 17599.25 40299.45 285
1112_ss99.05 26698.84 29199.67 14399.66 23099.29 23998.52 37699.82 10497.65 41799.43 29699.16 40596.42 35799.91 17999.07 17899.84 21599.80 65
CANet_DTU98.91 29498.85 28999.09 34698.79 45498.13 37798.18 40399.31 37199.48 18798.86 39399.51 32196.56 35099.95 8099.05 17999.95 11199.19 370
blended_shiyan897.82 38497.45 39798.92 36998.06 48597.45 41697.73 44499.35 35797.96 39698.35 43497.34 48592.76 41599.84 30599.04 18096.49 48999.47 278
blended_shiyan697.82 38497.46 39598.92 36998.08 48497.46 41497.73 44499.34 36097.96 39698.33 43597.35 48492.78 41399.84 30599.04 18096.53 48399.46 283
Baseline_NR-MVSNet99.49 12899.37 15599.82 4699.91 3199.84 2698.83 32899.86 7699.68 13099.65 21399.88 5097.67 30799.87 25099.03 18299.86 20599.76 84
FMVSNet299.35 18399.28 18899.55 21699.49 31099.35 23099.45 11799.57 27499.44 20099.70 18799.74 16397.21 32999.87 25099.03 18299.94 12899.44 300
wanda-best-256-51297.53 40097.14 40898.72 39897.71 49196.86 43597.00 47999.34 36097.73 41298.18 44296.82 49591.92 42299.84 30599.02 18496.53 48399.45 285
FE-blended-shiyan797.53 40097.14 40898.72 39897.71 49196.86 43597.00 47999.34 36097.73 41298.18 44296.82 49591.92 42299.84 30599.02 18496.53 48399.45 285
Test_1112_low_res98.95 29198.73 30199.63 17299.68 22199.15 27598.09 41599.80 12297.14 44399.46 29099.40 35196.11 36899.89 22099.01 18699.84 21599.84 52
VDD-MVS99.20 22799.11 22099.44 25699.43 33398.98 29699.50 10298.32 45399.80 9599.56 25699.69 20596.99 33999.85 28898.99 18799.73 28799.50 265
DeepC-MVS98.90 499.62 9299.61 8899.67 14399.72 18899.44 19899.24 19199.71 18499.27 23599.93 5399.90 3699.70 3199.93 11998.99 18799.99 1699.64 169
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 15199.41 21298.81 33399.66 21499.42 21199.75 15899.66 22699.20 10499.76 39098.98 18999.99 1699.36 326
EPNet_dtu97.62 39597.79 38697.11 46496.67 49892.31 48798.51 37798.04 46099.24 24195.77 49099.47 33693.78 40099.66 44898.98 18999.62 33199.37 323
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvspermissive99.34 18899.32 17399.39 27699.67 22898.77 32498.57 36799.81 11799.61 16099.48 28499.41 34798.47 23199.86 26998.97 19199.90 16099.53 246
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 17599.68 13999.43 33399.55 17099.73 3099.50 31599.46 19599.88 8299.36 36597.54 31599.87 25098.97 19199.87 19799.63 175
TestfortrainingZip a99.61 9699.53 11799.85 3299.76 15599.84 2699.38 13299.78 14299.58 17099.81 11599.66 22699.02 14399.90 19898.96 19399.79 25599.81 64
viewdifsd2359ckpt0799.51 12199.50 12299.52 22899.80 11699.19 26798.92 31399.88 6599.72 11399.64 21699.62 26199.06 13799.81 35898.96 19399.94 12899.56 226
GBi-Net99.42 15799.31 17599.73 11399.49 31099.77 6399.68 4899.70 19399.44 20099.62 23199.83 8397.21 32999.90 19898.96 19399.90 16099.53 246
FMVSNet597.80 38797.25 40499.42 26298.83 44898.97 29999.38 13299.80 12298.87 30099.25 34499.69 20580.60 48199.91 17998.96 19399.90 16099.38 320
test199.42 15799.31 17599.73 11399.49 31099.77 6399.68 4899.70 19399.44 20099.62 23199.83 8397.21 32999.90 19898.96 19399.90 16099.53 246
FMVSNet398.80 31098.63 31199.32 30299.13 40798.72 32799.10 25099.48 32099.23 24399.62 23199.64 23792.57 41699.86 26998.96 19399.90 16099.39 318
UnsupCasMVSNet_eth98.83 30698.57 31899.59 19499.68 22199.45 19698.99 29599.67 20999.48 18799.55 26199.36 36594.92 38599.86 26998.95 19996.57 48299.45 285
CHOSEN 280x42098.41 35198.41 33498.40 41699.34 36395.89 45796.94 48299.44 33298.80 31299.25 34499.52 31993.51 40499.98 2698.94 20099.98 5099.32 339
E499.61 9699.59 9499.66 15199.84 7899.53 17399.08 25899.84 8999.65 14699.74 16899.80 10799.45 6399.77 38598.93 20199.95 11199.69 117
TDRefinement99.72 5399.70 5799.77 7999.90 3799.85 2199.86 699.92 4299.69 12899.78 13299.92 2799.37 7799.88 23598.93 20199.95 11199.60 205
viewmacassd2359aftdt99.63 8599.61 8899.68 13999.84 7899.61 15199.14 23099.87 6999.71 11999.75 15899.77 14299.54 5599.72 40998.91 20399.96 8799.70 105
alignmvs98.28 36197.96 37299.25 32599.12 40998.93 30699.03 27398.42 44699.64 15098.72 40897.85 47690.86 44199.62 46098.88 20499.13 40899.19 370
testing3-296.51 42996.43 42496.74 46899.36 35091.38 49599.10 25097.87 46699.48 18798.57 42298.71 45276.65 49499.66 44898.87 20599.26 40199.18 372
MGCFI-Net99.02 27299.01 25699.06 35399.11 41498.60 34199.63 6499.67 20999.63 15298.58 42097.65 47999.07 13199.57 46998.85 20698.92 42499.03 412
sss98.90 29698.77 30099.27 31999.48 31598.44 35698.72 34799.32 36797.94 39999.37 31599.35 37096.31 36399.91 17998.85 20699.63 32999.47 278
xiu_mvs_v2_base99.02 27299.11 22098.77 39599.37 34798.09 38298.13 41099.51 31199.47 19299.42 29998.54 46199.38 7599.97 4398.83 20899.33 39098.24 473
PS-MVSNAJ99.00 28199.08 23298.76 39699.37 34798.10 38198.00 42699.51 31199.47 19299.41 30598.50 46399.28 9299.97 4398.83 20899.34 38998.20 477
E299.54 11499.51 12099.62 18199.78 13899.47 18499.01 28299.82 10499.55 17399.69 19099.77 14299.26 9699.76 39098.82 21099.93 14099.62 187
E399.54 11499.51 12099.62 18199.78 13899.47 18499.01 28299.82 10499.55 17399.69 19099.77 14299.25 10099.76 39098.82 21099.93 14099.62 187
D2MVS99.22 22099.19 20399.29 31199.69 21398.74 32698.81 33399.41 33898.55 34199.68 19599.69 20598.13 27299.87 25098.82 21099.98 5099.24 355
PatchT98.45 34898.32 34498.83 38998.94 43698.29 36699.24 19198.82 42299.84 7599.08 37099.76 15091.37 43099.94 9798.82 21099.00 41998.26 472
testf199.63 8599.60 9299.72 12199.94 1899.95 299.47 11299.89 6099.43 20799.88 8299.80 10799.26 9699.90 19898.81 21499.88 18499.32 339
APD_test299.63 8599.60 9299.72 12199.94 1899.95 299.47 11299.89 6099.43 20799.88 8299.80 10799.26 9699.90 19898.81 21499.88 18499.32 339
gbinet_0.2-2-1-0.0297.52 40297.07 41098.88 38397.35 49797.35 42197.17 47299.25 38497.86 40798.41 43296.54 50190.74 44399.85 28898.80 21697.51 47699.43 306
usedtu_blend_shiyan597.97 38197.65 39398.92 36997.71 49197.49 41199.53 9299.81 11799.52 18198.18 44296.82 49591.92 42299.83 32598.79 21796.53 48399.45 285
blend_shiyan495.04 45793.76 46198.88 38397.92 48797.49 41197.72 44699.34 36097.93 40097.65 47097.11 48977.69 49299.83 32598.79 21779.72 50099.33 335
sasdasda99.02 27299.00 26099.09 34699.10 41698.70 32899.61 7399.66 21499.63 15298.64 41497.65 47999.04 14099.54 47498.79 21798.92 42499.04 410
Effi-MVS+99.06 26398.97 27199.34 29499.31 37098.98 29698.31 39599.91 5198.81 31098.79 40298.94 43799.14 11599.84 30598.79 21798.74 43799.20 367
canonicalmvs99.02 27299.00 26099.09 34699.10 41698.70 32899.61 7399.66 21499.63 15298.64 41497.65 47999.04 14099.54 47498.79 21798.92 42499.04 410
VDDNet98.97 28598.82 29499.42 26299.71 19298.81 31899.62 6798.68 43099.81 9199.38 31399.80 10794.25 39499.85 28898.79 21799.32 39299.59 212
CR-MVSNet98.35 35898.20 35498.83 38999.05 42398.12 37899.30 16699.67 20997.39 43199.16 35999.79 11991.87 42799.91 17998.78 22398.77 43398.44 466
test_method91.72 46292.32 46289.91 48293.49 50570.18 50890.28 49699.56 27961.71 50095.39 49299.52 31993.90 39699.94 9798.76 22498.27 45899.62 187
RPMNet98.60 32998.53 32498.83 38999.05 42398.12 37899.30 16699.62 23999.86 6599.16 35999.74 16392.53 41899.92 15098.75 22598.77 43398.44 466
mamba_040899.54 11499.55 11099.54 22299.71 19299.24 25499.27 17999.79 13199.72 11399.78 13299.64 23799.36 8099.93 11998.74 22699.90 16099.45 285
SSM_0407299.55 11099.55 11099.55 21699.71 19299.24 25499.27 17999.79 13199.72 11399.78 13299.64 23799.36 8099.97 4398.74 22699.90 16099.45 285
SSM_040799.56 10599.56 10899.54 22299.71 19299.24 25499.15 22699.84 8999.80 9599.78 13299.70 19699.44 6599.93 11998.74 22699.90 16099.45 285
SSM_040499.57 10199.58 9899.54 22299.76 15599.28 24199.19 20899.84 8999.80 9599.78 13299.70 19699.44 6599.93 11998.74 22699.95 11199.41 312
pmmvs499.13 24799.06 23899.36 28999.57 26799.10 28698.01 42499.25 38498.78 31599.58 24599.44 34398.24 25999.76 39098.74 22699.93 14099.22 360
viewmanbaseed2359cas99.50 12399.47 12999.61 18799.73 18399.52 17799.03 27399.83 9899.49 18499.65 21399.64 23799.18 10699.71 41498.73 23199.92 14699.58 217
tttt051797.62 39597.20 40598.90 38099.76 15597.40 41999.48 10994.36 49299.06 27399.70 18799.49 32884.55 47499.94 9798.73 23199.65 32499.36 326
viewcassd2359sk1199.48 13099.45 13699.58 19799.73 18399.42 20598.96 30499.80 12299.44 20099.63 22199.74 16399.09 12499.76 39098.72 23399.91 15899.57 223
EPP-MVSNet99.17 23999.00 26099.66 15199.80 11699.43 20299.70 3899.24 38899.48 18799.56 25699.77 14294.89 38699.93 11998.72 23399.89 17499.63 175
FE-MVSNET99.45 14699.36 16099.71 12799.84 7899.64 13399.16 22399.91 5198.65 33099.73 17399.73 16898.54 21999.82 34298.71 23599.96 8799.67 133
Anonymous2024052999.42 15799.34 16799.65 15899.53 29099.60 15599.63 6499.39 34899.47 19299.76 15399.78 13298.13 27299.86 26998.70 23699.68 31399.49 270
ACMH98.42 699.59 10099.54 11399.72 12199.86 5999.62 14199.56 8799.79 13198.77 31799.80 12299.85 6899.64 3599.85 28898.70 23699.89 17499.70 105
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs99.33 19199.28 18899.47 24599.57 26799.39 21699.78 1799.43 33598.87 30099.57 24899.82 9098.06 27999.87 25098.69 23899.73 28799.15 379
LFMVS98.46 34798.19 35799.26 32299.24 38798.52 35299.62 6796.94 47899.87 6299.31 33399.58 29291.04 43599.81 35898.68 23999.42 37999.45 285
WR-MVS99.11 25498.93 27699.66 15199.30 37499.42 20598.42 38899.37 35399.04 27499.57 24899.20 40396.89 34199.86 26998.66 24099.87 19799.70 105
mvsmamba99.08 25998.95 27499.45 25299.36 35099.18 27299.39 12998.81 42499.37 21899.35 31999.70 19696.36 36299.94 9798.66 24099.59 34599.22 360
viewdifsd2359ckpt1399.42 15799.37 15599.57 20599.72 18899.46 19099.01 28299.80 12299.20 24899.51 27899.60 27998.92 16199.70 41898.65 24299.90 16099.55 230
RRT-MVS99.08 25999.00 26099.33 29799.27 38198.65 33699.62 6799.93 3999.66 14299.67 20399.82 9095.27 38399.93 11998.64 24399.09 41299.41 312
E3new99.42 15799.37 15599.56 20999.68 22199.38 21898.93 31299.79 13199.30 23099.55 26199.69 20598.88 16899.76 39098.63 24499.89 17499.53 246
Anonymous20240521198.75 31498.46 32899.63 17299.34 36399.66 12099.47 11297.65 46999.28 23499.56 25699.50 32493.15 40899.84 30598.62 24599.58 34799.40 315
lecture99.56 10599.48 12799.81 5499.78 13899.86 1899.50 10299.70 19399.59 16899.75 15899.71 18698.94 15799.92 15098.59 24699.76 27099.66 148
EPNet98.13 37297.77 38799.18 33494.57 50497.99 38899.24 19197.96 46299.74 10897.29 47499.62 26193.13 40999.97 4398.59 24699.83 22399.58 217
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++99.05 26699.09 23098.91 37499.21 39298.36 36498.82 33299.47 32398.85 30398.90 38899.56 30398.78 18199.09 49098.57 24899.68 31399.26 352
Patchmatch-RL test98.60 32998.36 33999.33 29799.77 15199.07 28998.27 39799.87 6998.91 29599.74 16899.72 17690.57 44799.79 36998.55 24999.85 21099.11 388
pmmvs398.08 37597.80 38498.91 37499.41 34097.69 40697.87 43999.66 21495.87 46299.50 28199.51 32190.35 44999.97 4398.55 24999.47 37299.08 401
ETV-MVS99.18 23499.18 20499.16 33599.34 36399.28 24199.12 24299.79 13199.48 18798.93 38298.55 46099.40 7099.93 11998.51 25199.52 36498.28 471
viewdifsd2359ckpt0999.24 20999.16 20699.49 23899.70 20799.22 26098.88 31799.81 11798.70 32599.38 31399.37 36098.22 26499.76 39098.48 25299.88 18499.51 259
jason99.16 24099.11 22099.32 30299.75 17198.44 35698.26 39999.39 34898.70 32599.74 16899.30 37998.54 21999.97 4398.48 25299.82 23399.55 230
jason: jason.
APDe-MVScopyleft99.48 13099.36 16099.85 3299.55 28199.81 4799.50 10299.69 20198.99 27899.75 15899.71 18698.79 17999.93 11998.46 25499.85 21099.80 65
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
icg_test_0407_299.30 19599.29 18599.31 30699.71 19298.55 34698.17 40599.71 18499.41 21299.73 17399.60 27999.17 10899.92 15098.45 25599.70 30099.45 285
IMVS_040799.38 17299.42 14499.28 31499.71 19298.55 34699.27 17999.71 18499.41 21299.73 17399.60 27999.17 10899.83 32598.45 25599.70 30099.45 285
IMVS_040499.23 21199.20 20199.32 30299.71 19298.55 34698.57 36799.71 18499.41 21299.52 27199.60 27998.12 27499.95 8098.45 25599.70 30099.45 285
IMVS_040399.37 17699.39 14999.28 31499.71 19298.55 34699.19 20899.71 18499.41 21299.67 20399.60 27999.12 12099.84 30598.45 25599.70 30099.45 285
CL-MVSNet_self_test98.71 32098.56 32299.15 33799.22 39098.66 33397.14 47599.51 31198.09 38699.54 26499.27 38596.87 34299.74 40498.43 25998.96 42199.03 412
our_test_398.85 30599.09 23098.13 42999.66 23094.90 47297.72 44699.58 27299.07 27199.64 21699.62 26198.19 26899.93 11998.41 26099.95 11199.55 230
Gipumacopyleft99.57 10199.59 9499.49 23899.98 399.71 10099.72 3399.84 8999.81 9199.94 4899.78 13298.91 16499.71 41498.41 26099.95 11199.05 408
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 197.37 40996.91 41898.74 39797.72 49097.57 40897.60 45397.36 47598.00 38999.21 35398.02 47290.04 45299.79 36998.37 26295.89 49298.86 435
PM-MVS99.36 18199.29 18599.58 19799.83 8699.66 12098.95 30799.86 7698.85 30399.81 11599.73 16898.40 24499.92 15098.36 26399.83 22399.17 375
baseline197.73 39097.33 40198.96 36299.30 37497.73 40499.40 12798.42 44699.33 22599.46 29099.21 40191.18 43399.82 34298.35 26491.26 49599.32 339
MVS-HIRNet97.86 38298.22 35296.76 46699.28 37991.53 49398.38 39092.60 49899.13 26499.31 33399.96 1597.18 33399.68 43798.34 26599.83 22399.07 406
GA-MVS97.99 38097.68 39098.93 36899.52 29798.04 38697.19 47199.05 41298.32 37398.81 39898.97 43389.89 45499.41 48598.33 26699.05 41599.34 334
Fast-Effi-MVS+99.02 27298.87 28799.46 24999.38 34599.50 17999.04 27099.79 13197.17 44198.62 41698.74 45199.34 8499.95 8098.32 26799.41 38098.92 428
MDA-MVSNet_test_wron98.95 29198.99 26798.85 38599.64 23597.16 42698.23 40199.33 36598.93 29199.56 25699.66 22697.39 32299.83 32598.29 26899.88 18499.55 230
N_pmnet98.73 31798.53 32499.35 29199.72 18898.67 33098.34 39294.65 49198.35 36799.79 12899.68 21798.03 28099.93 11998.28 26999.92 14699.44 300
ET-MVSNet_ETH3D96.78 42196.07 43198.91 37499.26 38497.92 39597.70 44996.05 48397.96 39692.37 49698.43 46487.06 46299.90 19898.27 27097.56 47598.91 429
thisisatest053097.45 40496.95 41598.94 36599.68 22197.73 40499.09 25594.19 49498.61 33799.56 25699.30 37984.30 47699.93 11998.27 27099.54 35999.16 377
YYNet198.95 29198.99 26798.84 38799.64 23597.14 42898.22 40299.32 36798.92 29499.59 24399.66 22697.40 32099.83 32598.27 27099.90 16099.55 230
reproduce_model99.50 12399.40 14899.83 4199.60 24499.83 3499.12 24299.68 20499.49 18499.80 12299.79 11999.01 14599.93 11998.24 27399.82 23399.73 93
ACMM98.09 1199.46 14299.38 15299.72 12199.80 11699.69 11299.13 23799.65 22498.99 27899.64 21699.72 17699.39 7199.86 26998.23 27499.81 24399.60 205
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS98.96 28898.87 28799.24 32799.57 26798.40 35998.12 41199.18 40098.28 37599.63 22199.13 40798.02 28199.97 4398.22 27599.69 30899.35 329
3Dnovator99.15 299.43 15499.36 16099.65 15899.39 34299.42 20599.70 3899.56 27999.23 24399.35 31999.80 10799.17 10899.95 8098.21 27699.84 21599.59 212
Fast-Effi-MVS+-dtu99.20 22799.12 21799.43 26099.25 38599.69 11299.05 26599.82 10499.50 18298.97 37899.05 41998.98 15299.98 2698.20 27799.24 40498.62 451
MS-PatchMatch99.00 28198.97 27199.09 34699.11 41498.19 37298.76 34299.33 36598.49 35099.44 29299.58 29298.21 26599.69 42598.20 27799.62 33199.39 318
TSAR-MVS + GP.99.12 25099.04 24999.38 27999.34 36399.16 27398.15 40799.29 37598.18 38299.63 22199.62 26199.18 10699.68 43798.20 27799.74 28199.30 346
DP-MVS99.48 13099.39 14999.74 10299.57 26799.62 14199.29 17399.61 24699.87 6299.74 16899.76 15098.69 19499.87 25098.20 27799.80 25099.75 87
MVP-Stereo99.16 24099.08 23299.43 26099.48 31599.07 28999.08 25899.55 28598.63 33399.31 33399.68 21798.19 26899.78 37298.18 28199.58 34799.45 285
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast99.43 15499.30 18099.80 6499.83 8699.81 4799.52 9499.70 19398.35 36799.51 27899.50 32499.31 8899.88 23598.18 28199.84 21599.69 117
MDA-MVSNet-bldmvs99.06 26399.05 24399.07 35199.80 11697.83 39998.89 31699.72 18099.29 23199.63 22199.70 19696.47 35599.89 22098.17 28399.82 23399.50 265
JIA-IIPM98.06 37697.92 37998.50 41198.59 46797.02 43098.80 33698.51 44199.88 6097.89 45899.87 5691.89 42699.90 19898.16 28497.68 47498.59 454
EIA-MVS99.12 25099.01 25699.45 25299.36 35099.62 14199.34 14899.79 13198.41 35698.84 39598.89 44198.75 18699.84 30598.15 28599.51 36598.89 432
miper_lstm_enhance98.65 32598.60 31298.82 39299.20 39597.33 42297.78 44299.66 21499.01 27799.59 24399.50 32494.62 39199.85 28898.12 28699.90 16099.26 352
reproduce-ours99.46 14299.35 16599.82 4699.56 27899.83 3499.05 26599.65 22499.45 19899.78 13299.78 13298.93 15899.93 11998.11 28799.81 24399.70 105
our_new_method99.46 14299.35 16599.82 4699.56 27899.83 3499.05 26599.65 22499.45 19899.78 13299.78 13298.93 15899.93 11998.11 28799.81 24399.70 105
Effi-MVS+-dtu99.07 26298.92 28099.52 22898.89 44199.78 5799.15 22699.66 21499.34 22298.92 38599.24 39597.69 30599.98 2698.11 28799.28 39798.81 440
tpm97.15 41396.95 41597.75 44398.91 43794.24 47699.32 15797.96 46297.71 41598.29 43699.32 37486.72 46899.92 15098.10 29096.24 49099.09 395
DeepPCF-MVS98.42 699.18 23499.02 25299.67 14399.22 39099.75 7997.25 46999.47 32398.72 32299.66 20999.70 19699.29 9099.63 45998.07 29199.81 24399.62 187
ppachtmachnet_test98.89 29999.12 21798.20 42799.66 23095.24 46897.63 45199.68 20499.08 26999.78 13299.62 26198.65 20299.88 23598.02 29299.96 8799.48 274
tpmrst97.73 39098.07 36596.73 46998.71 46392.00 48899.10 25098.86 41998.52 34698.92 38599.54 31391.90 42599.82 34298.02 29299.03 41798.37 468
CSCG99.37 17699.29 18599.60 19199.71 19299.46 19099.43 12199.85 8298.79 31399.41 30599.60 27998.92 16199.92 15098.02 29299.92 14699.43 306
eth_miper_zixun_eth98.68 32398.71 30398.60 40699.10 41696.84 43797.52 45999.54 29198.94 28699.58 24599.48 33296.25 36699.76 39098.01 29599.93 14099.21 363
Patchmtry98.78 31198.54 32399.49 23898.89 44199.19 26799.32 15799.67 20999.65 14699.72 17899.79 11991.87 42799.95 8098.00 29699.97 7399.33 335
PVSNet_BlendedMVS99.03 27099.01 25699.09 34699.54 28397.99 38898.58 36399.82 10497.62 41899.34 32399.71 18698.52 22799.77 38597.98 29799.97 7399.52 257
PVSNet_Blended98.70 32198.59 31499.02 35699.54 28397.99 38897.58 45499.82 10495.70 46699.34 32398.98 43198.52 22799.77 38597.98 29799.83 22399.30 346
cl____98.54 33798.41 33498.92 36999.03 42797.80 40297.46 46199.59 26398.90 29699.60 24099.46 33993.85 39899.78 37297.97 29999.89 17499.17 375
DIV-MVS_self_test98.54 33798.42 33398.92 36999.03 42797.80 40297.46 46199.59 26398.90 29699.60 24099.46 33993.87 39799.78 37297.97 29999.89 17499.18 372
AUN-MVS97.82 38497.38 40099.14 34099.27 38198.53 35098.72 34799.02 41498.10 38497.18 47799.03 42589.26 45699.85 28897.94 30197.91 47099.03 412
FA-MVS(test-final)98.52 33998.32 34499.10 34599.48 31598.67 33099.77 1998.60 43797.35 43399.63 22199.80 10793.07 41099.84 30597.92 30299.30 39498.78 443
ambc99.20 33199.35 35498.53 35099.17 21799.46 32699.67 20399.80 10798.46 23499.70 41897.92 30299.70 30099.38 320
USDC98.96 28898.93 27699.05 35499.54 28397.99 38897.07 47899.80 12298.21 37999.75 15899.77 14298.43 23799.64 45797.90 30499.88 18499.51 259
OPM-MVS99.26 20499.13 21399.63 17299.70 20799.61 15198.58 36399.48 32098.50 34899.52 27199.63 25299.14 11599.76 39097.89 30599.77 26899.51 259
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVScopyleft99.32 19399.17 20599.77 7999.69 21399.80 5199.14 23099.31 37199.16 25899.62 23199.61 27198.35 24899.91 17997.88 30699.72 29499.61 201
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 20799.79 5499.14 23099.61 24699.92 15097.88 30699.72 29499.77 79
c3_l98.72 31898.71 30398.72 39899.12 40997.22 42597.68 45099.56 27998.90 29699.54 26499.48 33296.37 36199.73 40797.88 30699.88 18499.21 363
3Dnovator+98.92 399.35 18399.24 19799.67 14399.35 35499.47 18499.62 6799.50 31599.44 20099.12 36699.78 13298.77 18399.94 9797.87 30999.72 29499.62 187
miper_ehance_all_eth98.59 33298.59 31498.59 40798.98 43397.07 42997.49 46099.52 30698.50 34899.52 27199.37 36096.41 35999.71 41497.86 31099.62 33199.00 419
WTY-MVS98.59 33298.37 33899.26 32299.43 33398.40 35998.74 34599.13 40798.10 38499.21 35399.24 39594.82 38899.90 19897.86 31098.77 43399.49 270
APD_test199.36 18199.28 18899.61 18799.89 3999.89 1099.32 15799.74 16799.18 25199.69 19099.75 15898.41 24099.84 30597.85 31299.70 30099.10 390
SED-MVS99.40 16599.28 18899.77 7999.69 21399.82 4299.20 20299.54 29199.13 26499.82 10899.63 25298.91 16499.92 15097.85 31299.70 30099.58 217
test_241102_TWO99.54 29199.13 26499.76 15399.63 25298.32 25399.92 15097.85 31299.69 30899.75 87
MVS_111021_HR99.12 25099.02 25299.40 27399.50 30599.11 27997.92 43599.71 18498.76 32099.08 37099.47 33699.17 10899.54 47497.85 31299.76 27099.54 240
MTAPA99.35 18399.20 20199.80 6499.81 10799.81 4799.33 15499.53 30199.27 23599.42 29999.63 25298.21 26599.95 8097.83 31699.79 25599.65 157
MSC_two_6792asdad99.74 10299.03 42799.53 17399.23 38999.92 15097.77 31799.69 30899.78 75
No_MVS99.74 10299.03 42799.53 17399.23 38999.92 15097.77 31799.69 30899.78 75
TESTMET0.1,196.24 43695.84 43797.41 45398.24 47893.84 47997.38 46395.84 48798.43 35397.81 46498.56 45979.77 48599.89 22097.77 31798.77 43398.52 460
ACMH+98.40 899.50 12399.43 14299.71 12799.86 5999.76 7099.32 15799.77 14899.53 17799.77 14499.76 15099.26 9699.78 37297.77 31799.88 18499.60 205
IU-MVS99.69 21399.77 6399.22 39297.50 42599.69 19097.75 32199.70 30099.77 79
114514_t98.49 34498.11 36299.64 16599.73 18399.58 16299.24 19199.76 15689.94 49299.42 29999.56 30397.76 30299.86 26997.74 32299.82 23399.47 278
DVP-MVS++99.38 17299.25 19599.77 7999.03 42799.77 6399.74 2799.61 24699.18 25199.76 15399.61 27199.00 14699.92 15097.72 32399.60 34199.62 187
test_0728_THIRD99.18 25199.62 23199.61 27198.58 21099.91 17997.72 32399.80 25099.77 79
EGC-MVSNET89.05 46485.52 46799.64 16599.89 3999.78 5799.56 8799.52 30624.19 50149.96 50299.83 8399.15 11299.92 15097.71 32599.85 21099.21 363
miper_enhance_ethall98.03 37797.94 37798.32 42198.27 47796.43 44596.95 48199.41 33896.37 45799.43 29698.96 43594.74 38999.69 42597.71 32599.62 33198.83 438
TSAR-MVS + MP.99.34 18899.24 19799.63 17299.82 9599.37 22399.26 18499.35 35798.77 31799.57 24899.70 19699.27 9599.88 23597.71 32599.75 27499.65 157
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl2297.56 39897.28 40298.40 41698.37 47596.75 43897.24 47099.37 35397.31 43599.41 30599.22 39787.30 46099.37 48697.70 32899.62 33199.08 401
MP-MVS-pluss99.14 24598.92 28099.80 6499.83 8699.83 3498.61 35699.63 23696.84 45099.44 29299.58 29298.81 17499.91 17997.70 32899.82 23399.67 133
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP99.28 19899.11 22099.79 7199.75 17199.81 4798.95 30799.53 30198.27 37699.53 26999.73 16898.75 18699.87 25097.70 32899.83 22399.68 124
UnsupCasMVSNet_bld98.55 33698.27 35099.40 27399.56 27899.37 22397.97 43199.68 20497.49 42699.08 37099.35 37095.41 38299.82 34297.70 32898.19 46299.01 418
MVS_111021_LR99.13 24799.03 25199.42 26299.58 25799.32 23597.91 43799.73 17198.68 32799.31 33399.48 33299.09 12499.66 44897.70 32899.77 26899.29 349
IS-MVSNet99.03 27098.85 28999.55 21699.80 11699.25 24999.73 3099.15 40499.37 21899.61 23799.71 18694.73 39099.81 35897.70 32899.88 18499.58 217
MED-MVS test99.74 10299.76 15599.65 12699.38 13299.78 14299.58 17099.81 11599.66 22699.90 19897.69 33499.79 25599.67 133
MED-MVS99.45 14699.36 16099.74 10299.76 15599.65 12699.38 13299.78 14299.31 22899.81 11599.66 22699.02 14399.90 19897.69 33499.79 25599.67 133
ME-MVS99.26 20499.10 22899.73 11399.60 24499.65 12698.75 34499.45 33199.31 22899.65 21399.66 22698.00 28699.86 26997.69 33499.79 25599.67 133
test-LLR97.15 41396.95 41597.74 44498.18 48095.02 47097.38 46396.10 48098.00 38997.81 46498.58 45690.04 45299.91 17997.69 33498.78 43198.31 469
test-mter96.23 43795.73 44097.74 44498.18 48095.02 47097.38 46396.10 48097.90 40197.81 46498.58 45679.12 48899.91 17997.69 33498.78 43198.31 469
MonoMVSNet98.23 36698.32 34497.99 43298.97 43496.62 44099.49 10798.42 44699.62 15599.40 31099.79 11995.51 38098.58 49797.68 33995.98 49198.76 446
XVS99.27 20299.11 22099.75 9799.71 19299.71 10099.37 14099.61 24699.29 23198.76 40599.47 33698.47 23199.88 23597.62 34099.73 28799.67 133
X-MVStestdata96.09 44194.87 45499.75 9799.71 19299.71 10099.37 14099.61 24699.29 23198.76 40561.30 51098.47 23199.88 23597.62 34099.73 28799.67 133
SMA-MVScopyleft99.19 23099.00 26099.73 11399.46 32599.73 9099.13 23799.52 30697.40 43099.57 24899.64 23798.93 15899.83 32597.61 34299.79 25599.63 175
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 42496.79 42396.46 47398.90 43890.71 49999.41 12298.68 43094.69 47998.14 44999.34 37386.32 47099.80 36697.60 34398.07 46898.88 433
PVSNet97.47 1598.42 35098.44 33198.35 41899.46 32596.26 44996.70 48599.34 36097.68 41699.00 37799.13 40797.40 32099.72 40997.59 34499.68 31399.08 401
new_pmnet98.88 30098.89 28598.84 38799.70 20797.62 40798.15 40799.50 31597.98 39299.62 23199.54 31398.15 27199.94 9797.55 34599.84 21598.95 423
IB-MVS95.41 2095.30 45694.46 46097.84 44098.76 45995.33 46597.33 46696.07 48296.02 46195.37 49397.41 48376.17 49599.96 6897.54 34695.44 49498.22 474
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 20999.11 22099.61 18798.38 47499.79 5499.57 8599.68 20499.61 16099.15 36199.71 18698.70 19399.91 17997.54 34699.68 31399.13 387
ZNCC-MVS99.22 22099.04 24999.77 7999.76 15599.73 9099.28 17599.56 27998.19 38199.14 36399.29 38298.84 17399.92 15097.53 34899.80 25099.64 169
CP-MVS99.23 21199.05 24399.75 9799.66 23099.66 12099.38 13299.62 23998.38 36099.06 37499.27 38598.79 17999.94 9797.51 34999.82 23399.66 148
SD-MVS99.01 27899.30 18098.15 42899.50 30599.40 21398.94 30999.61 24699.22 24799.75 15899.82 9099.54 5595.51 50197.48 35099.87 19799.54 240
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 34498.29 34999.11 34398.96 43598.42 35897.54 45599.32 36797.53 42398.47 42898.15 47197.88 29299.82 34297.46 35199.24 40499.09 395
DeepC-MVS_fast98.47 599.23 21199.12 21799.56 20999.28 37999.22 26098.99 29599.40 34599.08 26999.58 24599.64 23798.90 16799.83 32597.44 35299.75 27499.63 175
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 20699.08 23299.76 8699.73 18399.70 10899.31 16399.59 26398.36 36299.36 31699.37 36098.80 17899.91 17997.43 35399.75 27499.68 124
ACMMPR99.23 21199.06 23899.76 8699.74 17999.69 11299.31 16399.59 26398.36 36299.35 31999.38 35798.61 20699.93 11997.43 35399.75 27499.67 133
Vis-MVSNet (Re-imp)98.77 31298.58 31799.34 29499.78 13898.88 31299.61 7399.56 27999.11 26899.24 34799.56 30393.00 41299.78 37297.43 35399.89 17499.35 329
MIMVSNet98.43 34998.20 35499.11 34399.53 29098.38 36399.58 8298.61 43598.96 28299.33 32599.76 15090.92 43799.81 35897.38 35699.76 27099.15 379
WB-MVSnew98.34 36098.14 36098.96 36298.14 48397.90 39698.27 39797.26 47698.63 33398.80 40098.00 47497.77 30099.90 19897.37 35798.98 42099.09 395
XVG-OURS-SEG-HR99.16 24098.99 26799.66 15199.84 7899.64 13398.25 40099.73 17198.39 35999.63 22199.43 34499.70 3199.90 19897.34 35898.64 44499.44 300
COLMAP_ROBcopyleft98.06 1299.45 14699.37 15599.70 13299.83 8699.70 10899.38 13299.78 14299.53 17799.67 20399.78 13299.19 10599.86 26997.32 35999.87 19799.55 230
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
0.4-1-1-0.193.18 45991.66 46397.73 44695.83 49995.29 46695.30 49295.90 48593.59 48190.58 49894.40 50777.87 49099.77 38597.31 36084.20 49698.15 479
MCST-MVS99.02 27298.81 29699.65 15899.58 25799.49 18098.58 36399.07 40998.40 35899.04 37599.25 39098.51 22999.80 36697.31 36099.51 36599.65 157
region2R99.23 21199.05 24399.77 7999.76 15599.70 10899.31 16399.59 26398.41 35699.32 32899.36 36598.73 19099.93 11997.29 36299.74 28199.67 133
APD-MVS_3200maxsize99.31 19499.16 20699.74 10299.53 29099.75 7999.27 17999.61 24699.19 25099.57 24899.64 23798.76 18499.90 19897.29 36299.62 33199.56 226
TAPA-MVS97.92 1398.03 37797.55 39499.46 24999.47 32199.44 19898.50 37899.62 23986.79 49399.07 37399.26 38898.26 25899.62 46097.28 36499.73 28799.31 344
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post99.27 20299.11 22099.73 11399.54 28399.74 8799.26 18499.62 23999.16 25899.52 27199.64 23798.41 24099.91 17997.27 36599.61 33899.54 240
RE-MVS-def99.13 21399.54 28399.74 8799.26 18499.62 23999.16 25899.52 27199.64 23798.57 21197.27 36599.61 33899.54 240
testing1196.05 44395.41 44697.97 43498.78 45695.27 46798.59 36198.23 45698.86 30296.56 48496.91 49375.20 49799.69 42597.26 36798.29 45798.93 426
test_yl98.25 36397.95 37399.13 34199.17 40198.47 35399.00 28898.67 43298.97 28099.22 35199.02 42691.31 43199.69 42597.26 36798.93 42299.24 355
DCV-MVSNet98.25 36397.95 37399.13 34199.17 40198.47 35399.00 28898.67 43298.97 28099.22 35199.02 42691.31 43199.69 42597.26 36798.93 42299.24 355
PHI-MVS99.11 25498.95 27499.59 19499.13 40799.59 15799.17 21799.65 22497.88 40499.25 34499.46 33998.97 15499.80 36697.26 36799.82 23399.37 323
tfpnnormal99.43 15499.38 15299.60 19199.87 5499.75 7999.59 8099.78 14299.71 11999.90 6799.69 20598.85 17299.90 19897.25 37199.78 26499.15 379
PatchmatchNetpermissive97.65 39497.80 38497.18 46198.82 45192.49 48699.17 21798.39 44998.12 38398.79 40299.58 29290.71 44499.89 22097.23 37299.41 38099.16 377
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS98.99 28498.80 29899.56 20999.25 38599.43 20298.54 37399.27 37998.58 33998.80 40099.43 34498.53 22499.70 41897.22 37399.59 34599.54 240
testing396.48 43095.63 44299.01 35799.23 38997.81 40098.90 31599.10 40898.72 32297.84 46397.92 47572.44 50199.85 28897.21 37499.33 39099.35 329
HPM-MVScopyleft99.25 20699.07 23699.78 7599.81 10799.75 7999.61 7399.67 20997.72 41499.35 31999.25 39099.23 10199.92 15097.21 37499.82 23399.67 133
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
0.3-1-1-0.01592.36 46190.68 46597.39 45494.94 50294.41 47594.21 49495.89 48692.87 48488.87 50093.49 50975.30 49699.76 39097.19 37683.41 49898.02 482
0.4-1-1-0.292.59 46091.07 46497.15 46394.73 50393.68 48193.50 49595.91 48492.68 48590.48 49993.52 50877.77 49199.75 40097.19 37683.88 49798.01 483
mPP-MVS99.19 23099.00 26099.76 8699.76 15599.68 11599.38 13299.54 29198.34 37199.01 37699.50 32498.53 22499.93 11997.18 37899.78 26499.66 148
ACMMPcopyleft99.25 20699.08 23299.74 10299.79 13099.68 11599.50 10299.65 22498.07 38799.52 27199.69 20598.57 21199.92 15097.18 37899.79 25599.63 175
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 43795.74 43997.70 44798.86 44595.59 46298.66 35398.14 45898.96 28297.67 46997.06 49076.78 49398.92 49397.10 38098.41 45498.58 456
thisisatest051596.98 41796.42 42598.66 40399.42 33897.47 41397.27 46894.30 49397.24 43799.15 36198.86 44385.01 47299.87 25097.10 38099.39 38298.63 450
XVG-ACMP-BASELINE99.23 21199.10 22899.63 17299.82 9599.58 16298.83 32899.72 18098.36 36299.60 24099.71 18698.92 16199.91 17997.08 38299.84 21599.40 315
MSDG99.08 25998.98 27099.37 28499.60 24499.13 27697.54 45599.74 16798.84 30699.53 26999.55 31199.10 12299.79 36997.07 38399.86 20599.18 372
SteuartSystems-ACMMP99.30 19599.14 21199.76 8699.87 5499.66 12099.18 21299.60 25798.55 34199.57 24899.67 22199.03 14299.94 9797.01 38499.80 25099.69 117
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS96.21 43995.78 43897.49 44998.53 46993.83 48098.04 42193.94 49698.96 28298.46 42998.17 47079.86 48399.87 25096.99 38599.06 41398.78 443
EPMVS96.53 42796.32 42697.17 46298.18 48092.97 48599.39 12989.95 50298.21 37998.61 41799.59 28986.69 46999.72 40996.99 38599.23 40698.81 440
MSP-MVS99.04 26998.79 29999.81 5499.78 13899.73 9099.35 14799.57 27498.54 34499.54 26498.99 42896.81 34399.93 11996.97 38799.53 36199.77 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 28898.70 30799.74 10299.52 29799.71 10098.86 32199.19 39998.47 35298.59 41999.06 41898.08 27899.91 17996.94 38899.60 34199.60 205
SR-MVS99.19 23099.00 26099.74 10299.51 29999.72 9599.18 21299.60 25798.85 30399.47 28699.58 29298.38 24599.92 15096.92 38999.54 35999.57 223
PGM-MVS99.20 22799.01 25699.77 7999.75 17199.71 10099.16 22399.72 18097.99 39199.42 29999.60 27998.81 17499.93 11996.91 39099.74 28199.66 148
HY-MVS98.23 998.21 37097.95 37398.99 35899.03 42798.24 36799.61 7398.72 42896.81 45198.73 40799.51 32194.06 39599.86 26996.91 39098.20 46098.86 435
MDTV_nov1_ep1397.73 38898.70 46490.83 49799.15 22698.02 46198.51 34798.82 39799.61 27190.98 43699.66 44896.89 39298.92 424
GST-MVS99.16 24098.96 27399.75 9799.73 18399.73 9099.20 20299.55 28598.22 37899.32 32899.35 37098.65 20299.91 17996.86 39399.74 28199.62 187
test_post199.14 23051.63 51289.54 45599.82 34296.86 393
SCA98.11 37398.36 33997.36 45599.20 39592.99 48498.17 40598.49 44398.24 37799.10 36999.57 29996.01 37199.94 9796.86 39399.62 33199.14 384
UBG96.53 42795.95 43398.29 42598.87 44496.31 44898.48 38198.07 45998.83 30797.32 47296.54 50179.81 48499.62 46096.84 39698.74 43798.95 423
XVG-OURS99.21 22599.06 23899.65 15899.82 9599.62 14197.87 43999.74 16798.36 36299.66 20999.68 21799.71 2899.90 19896.84 39699.88 18499.43 306
LCM-MVSNet-Re99.28 19899.15 21099.67 14399.33 36899.76 7099.34 14899.97 2098.93 29199.91 6299.79 11998.68 19599.93 11996.80 39899.56 35099.30 346
RPSCF99.18 23499.02 25299.64 16599.83 8699.85 2199.44 11999.82 10498.33 37299.50 28199.78 13297.90 29099.65 45596.78 39999.83 22399.44 300
旧先验297.94 43395.33 47098.94 38199.88 23596.75 400
MDTV_nov1_ep13_2view91.44 49499.14 23097.37 43299.21 35391.78 42996.75 40099.03 412
CLD-MVS98.76 31398.57 31899.33 29799.57 26798.97 29997.53 45799.55 28596.41 45599.27 34099.13 40799.07 13199.78 37296.73 40299.89 17499.23 358
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 37497.98 37198.48 41299.27 38196.48 44399.40 12799.07 40998.81 31099.23 34899.57 29990.11 45199.87 25096.69 40399.64 32699.09 395
baseline296.83 42096.28 42798.46 41499.09 42096.91 43398.83 32893.87 49797.23 43896.23 48998.36 46588.12 45999.90 19896.68 40498.14 46598.57 458
cascas96.99 41696.82 42297.48 45097.57 49695.64 46096.43 48799.56 27991.75 48897.13 47997.61 48295.58 37698.63 49596.68 40499.11 41098.18 478
PC_three_145297.56 41999.68 19599.41 34799.09 12497.09 49896.66 40699.60 34199.62 187
LPG-MVS_test99.22 22099.05 24399.74 10299.82 9599.63 13999.16 22399.73 17197.56 41999.64 21699.69 20599.37 7799.89 22096.66 40699.87 19799.69 117
LGP-MVS_train99.74 10299.82 9599.63 13999.73 17197.56 41999.64 21699.69 20599.37 7799.89 22096.66 40699.87 19799.69 117
ETVMVS96.14 44095.22 45198.89 38198.80 45298.01 38798.66 35398.35 45298.71 32497.18 47796.31 50674.23 50099.75 40096.64 40998.13 46798.90 430
TinyColmap98.97 28598.93 27699.07 35199.46 32598.19 37297.75 44399.75 16198.79 31399.54 26499.70 19698.97 15499.62 46096.63 41099.83 22399.41 312
LF4IMVS99.01 27898.92 28099.27 31999.71 19299.28 24198.59 36199.77 14898.32 37399.39 31299.41 34798.62 20499.84 30596.62 41199.84 21598.69 449
NCCC98.82 30798.57 31899.58 19799.21 39299.31 23698.61 35699.25 38498.65 33098.43 43099.26 38897.86 29399.81 35896.55 41299.27 40099.61 201
OPU-MVS99.29 31199.12 40999.44 19899.20 20299.40 35199.00 14698.84 49496.54 41399.60 34199.58 217
F-COLMAP98.74 31598.45 33099.62 18199.57 26799.47 18498.84 32599.65 22496.31 45898.93 38299.19 40497.68 30699.87 25096.52 41499.37 38599.53 246
testing9995.86 44895.19 45297.87 43898.76 45995.03 46998.62 35598.44 44598.68 32796.67 48396.66 50074.31 49999.69 42596.51 41598.03 46998.90 430
ADS-MVSNet297.78 38897.66 39298.12 43099.14 40595.36 46499.22 19998.75 42796.97 44698.25 43899.64 23790.90 43899.94 9796.51 41599.56 35099.08 401
ADS-MVSNet97.72 39397.67 39197.86 43999.14 40594.65 47399.22 19998.86 41996.97 44698.25 43899.64 23790.90 43899.84 30596.51 41599.56 35099.08 401
PatchMatch-RL98.68 32398.47 32799.30 31099.44 33099.28 24198.14 40999.54 29197.12 44499.11 36799.25 39097.80 29899.70 41896.51 41599.30 39498.93 426
CMPMVSbinary77.52 2398.50 34298.19 35799.41 27098.33 47699.56 16699.01 28299.59 26395.44 46899.57 24899.80 10795.64 37499.46 48496.47 41999.92 14699.21 363
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing9196.00 44495.32 44998.02 43198.76 45995.39 46398.38 39098.65 43498.82 30896.84 48096.71 49975.06 49899.71 41496.46 42098.23 45998.98 420
SF-MVS99.10 25798.93 27699.62 18199.58 25799.51 17899.13 23799.65 22497.97 39399.42 29999.61 27198.86 17199.87 25096.45 42199.68 31399.49 270
FE-MVS97.85 38397.42 39999.15 33799.44 33098.75 32599.77 1998.20 45795.85 46399.33 32599.80 10788.86 45799.88 23596.40 42299.12 40998.81 440
DPE-MVScopyleft99.14 24598.92 28099.82 4699.57 26799.77 6398.74 34599.60 25798.55 34199.76 15399.69 20598.23 26399.92 15096.39 42399.75 27499.76 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 49489.02 50493.47 48298.30 46699.84 30596.38 424
AllTest99.21 22599.07 23699.63 17299.78 13899.64 13399.12 24299.83 9898.63 33399.63 22199.72 17698.68 19599.75 40096.38 42499.83 22399.51 259
TestCases99.63 17299.78 13899.64 13399.83 9898.63 33399.63 22199.72 17698.68 19599.75 40096.38 42499.83 22399.51 259
testdata99.42 26299.51 29998.93 30699.30 37496.20 45998.87 39299.40 35198.33 25299.89 22096.29 42799.28 39799.44 300
dp96.86 41997.07 41096.24 47598.68 46590.30 50299.19 20898.38 45097.35 43398.23 44099.59 28987.23 46199.82 34296.27 42898.73 44098.59 454
tpmvs97.39 40897.69 38996.52 47198.41 47391.76 49099.30 16698.94 41897.74 41197.85 46299.55 31192.40 42199.73 40796.25 42998.73 44098.06 481
KD-MVS_2432*160095.89 44595.41 44697.31 45894.96 50093.89 47797.09 47699.22 39297.23 43898.88 38999.04 42179.23 48699.54 47496.24 43096.81 48098.50 464
miper_refine_blended95.89 44595.41 44697.31 45894.96 50093.89 47797.09 47699.22 39297.23 43898.88 38999.04 42179.23 48699.54 47496.24 43096.81 48098.50 464
ACMP97.51 1499.05 26698.84 29199.67 14399.78 13899.55 17098.88 31799.66 21497.11 44599.47 28699.60 27999.07 13199.89 22096.18 43299.85 21099.58 217
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS98.90 29698.72 30299.44 25699.39 34299.42 20598.58 36399.64 23297.31 43599.44 29299.62 26198.59 20899.69 42596.17 43399.79 25599.22 360
DP-MVS Recon98.50 34298.23 35199.31 30699.49 31099.46 19098.56 36999.63 23694.86 47798.85 39499.37 36097.81 29799.59 46796.08 43499.44 37598.88 433
tpm cat196.78 42196.98 41496.16 47698.85 44690.59 50099.08 25899.32 36792.37 48697.73 46899.46 33991.15 43499.69 42596.07 43598.80 43098.21 475
tpm296.35 43396.22 42896.73 46998.88 44391.75 49199.21 20198.51 44193.27 48397.89 45899.21 40184.83 47399.70 41896.04 43698.18 46398.75 447
dmvs_re98.69 32298.48 32699.31 30699.55 28199.42 20599.54 9098.38 45099.32 22698.72 40898.71 45296.76 34599.21 48896.01 43799.35 38899.31 344
test_040299.22 22099.14 21199.45 25299.79 13099.43 20299.28 17599.68 20499.54 17599.40 31099.56 30399.07 13199.82 34296.01 43799.96 8799.11 388
ITE_SJBPF99.38 27999.63 23799.44 19899.73 17198.56 34099.33 32599.53 31598.88 16899.68 43796.01 43799.65 32499.02 417
test_prior297.95 43297.87 40598.05 45199.05 41997.90 29095.99 44099.49 370
testdata299.89 22095.99 440
原ACMM199.37 28499.47 32198.87 31699.27 37996.74 45398.26 43799.32 37497.93 28999.82 34295.96 44299.38 38399.43 306
新几何199.52 22899.50 30599.22 26099.26 38195.66 46798.60 41899.28 38397.67 30799.89 22095.95 44399.32 39299.45 285
MP-MVScopyleft99.06 26398.83 29399.76 8699.76 15599.71 10099.32 15799.50 31598.35 36798.97 37899.48 33298.37 24699.92 15095.95 44399.75 27499.63 175
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testing22295.60 45594.59 45898.61 40598.66 46697.45 41698.54 37397.90 46598.53 34596.54 48596.47 50370.62 50499.81 35895.91 44598.15 46498.56 459
wuyk23d97.58 39799.13 21392.93 48099.69 21399.49 18099.52 9499.77 14897.97 39399.96 3499.79 11999.84 1699.94 9795.85 44699.82 23379.36 498
HQP_MVS98.90 29698.68 30899.55 21699.58 25799.24 25498.80 33699.54 29198.94 28699.14 36399.25 39097.24 32799.82 34295.84 44799.78 26499.60 205
plane_prior599.54 29199.82 34295.84 44799.78 26499.60 205
无先验98.01 42499.23 38995.83 46499.85 28895.79 44999.44 300
CPTT-MVS98.74 31598.44 33199.64 16599.61 24299.38 21899.18 21299.55 28596.49 45499.27 34099.37 36097.11 33599.92 15095.74 45099.67 31999.62 187
PLCcopyleft97.35 1698.36 35597.99 36999.48 24399.32 36999.24 25498.50 37899.51 31195.19 47398.58 42098.96 43596.95 34099.83 32595.63 45199.25 40299.37 323
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA98.57 33498.34 34299.28 31499.18 40099.10 28698.34 39299.41 33898.48 35198.52 42598.98 43197.05 33799.78 37295.59 45299.50 36898.96 421
131498.00 37997.90 38198.27 42698.90 43897.45 41699.30 16699.06 41194.98 47497.21 47699.12 41198.43 23799.67 44395.58 45398.56 44797.71 487
PVSNet_095.53 1995.85 44995.31 45097.47 45198.78 45693.48 48395.72 48999.40 34596.18 46097.37 47197.73 47795.73 37399.58 46895.49 45481.40 49999.36 326
MAR-MVS98.24 36597.92 37999.19 33298.78 45699.65 12699.17 21799.14 40595.36 46998.04 45298.81 44897.47 31799.72 40995.47 45599.06 41398.21 475
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 36697.89 38299.26 32299.19 39799.26 24699.65 6299.69 20191.33 49098.14 44999.77 14298.28 25599.96 6895.41 45699.55 35498.58 456
train_agg98.35 35897.95 37399.57 20599.35 35499.35 23098.11 41399.41 33894.90 47597.92 45698.99 42898.02 28199.85 28895.38 45799.44 37599.50 265
9.1498.64 30999.45 32998.81 33399.60 25797.52 42499.28 33999.56 30398.53 22499.83 32595.36 45899.64 326
APD-MVScopyleft98.87 30198.59 31499.71 12799.50 30599.62 14199.01 28299.57 27496.80 45299.54 26499.63 25298.29 25499.91 17995.24 45999.71 29899.61 201
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS96.36 44695.20 460
AdaColmapbinary98.60 32998.35 34199.38 27999.12 40999.22 26098.67 35199.42 33797.84 40998.81 39899.27 38597.32 32599.81 35895.14 46199.53 36199.10 390
test9_res95.10 46299.44 37599.50 265
CDPH-MVS98.56 33598.20 35499.61 18799.50 30599.46 19098.32 39499.41 33895.22 47199.21 35399.10 41598.34 25099.82 34295.09 46399.66 32299.56 226
BH-untuned98.22 36898.09 36398.58 40999.38 34597.24 42498.55 37098.98 41797.81 41099.20 35898.76 45097.01 33899.65 45594.83 46498.33 45598.86 435
BP-MVS94.73 465
HQP-MVS98.36 35598.02 36899.39 27699.31 37098.94 30397.98 42899.37 35397.45 42798.15 44598.83 44596.67 34799.70 41894.73 46599.67 31999.53 246
QAPM98.40 35397.99 36999.65 15899.39 34299.47 18499.67 5399.52 30691.70 48998.78 40499.80 10798.55 21599.95 8094.71 46799.75 27499.53 246
agg_prior294.58 46899.46 37499.50 265
myMVS_eth3d95.63 45394.73 45598.34 42098.50 47196.36 44698.60 35899.21 39597.89 40296.76 48196.37 50472.10 50299.57 46994.38 46998.73 44099.09 395
BH-RMVSNet98.41 35198.14 36099.21 32999.21 39298.47 35398.60 35898.26 45598.35 36798.93 38299.31 37797.20 33299.66 44894.32 47099.10 41199.51 259
E-PMN97.14 41597.43 39896.27 47498.79 45491.62 49295.54 49099.01 41699.44 20098.88 38999.12 41192.78 41399.68 43794.30 47199.03 41797.50 488
MG-MVS98.52 33998.39 33698.94 36599.15 40497.39 42098.18 40399.21 39598.89 29999.23 34899.63 25297.37 32399.74 40494.22 47299.61 33899.69 117
API-MVS98.38 35498.39 33698.35 41898.83 44899.26 24699.14 23099.18 40098.59 33898.66 41398.78 44998.61 20699.57 46994.14 47399.56 35096.21 495
PAPM_NR98.36 35598.04 36699.33 29799.48 31598.93 30698.79 33999.28 37897.54 42298.56 42498.57 45897.12 33499.69 42594.09 47498.90 42899.38 320
ZD-MVS99.43 33399.61 15199.43 33596.38 45699.11 36799.07 41797.86 29399.92 15094.04 47599.49 370
DPM-MVS98.28 36197.94 37799.32 30299.36 35099.11 27997.31 46798.78 42696.88 44898.84 39599.11 41497.77 30099.61 46594.03 47699.36 38699.23 358
gg-mvs-nofinetune95.87 44795.17 45397.97 43498.19 47996.95 43199.69 4589.23 50399.89 5596.24 48899.94 1981.19 47899.51 48093.99 47798.20 46097.44 489
PMVScopyleft92.94 2198.82 30798.81 29698.85 38599.84 7897.99 38899.20 20299.47 32399.71 11999.42 29999.82 9098.09 27699.47 48293.88 47899.85 21099.07 406
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS96.96 41897.28 40295.99 47898.76 45991.03 49695.26 49398.61 43599.34 22298.92 38598.88 44293.79 39999.66 44892.87 47999.05 41597.30 492
BH-w/o97.20 41297.01 41397.76 44299.08 42195.69 45998.03 42398.52 44095.76 46597.96 45598.02 47295.62 37599.47 48292.82 48097.25 47998.12 480
TR-MVS97.44 40597.15 40798.32 42198.53 46997.46 41498.47 38297.91 46496.85 44998.21 44198.51 46296.42 35799.51 48092.16 48197.29 47897.98 484
OpenMVS_ROBcopyleft97.31 1797.36 41096.84 42098.89 38199.29 37699.45 19698.87 32099.48 32086.54 49599.44 29299.74 16397.34 32499.86 26991.61 48299.28 39797.37 491
GG-mvs-BLEND97.36 45597.59 49496.87 43499.70 3888.49 50494.64 49497.26 48880.66 48099.12 48991.50 48396.50 48896.08 497
DeepMVS_CXcopyleft97.98 43399.69 21396.95 43199.26 38175.51 49895.74 49198.28 46796.47 35599.62 46091.23 48497.89 47197.38 490
PAPR97.56 39897.07 41099.04 35598.80 45298.11 38097.63 45199.25 38494.56 48098.02 45498.25 46897.43 31999.68 43790.90 48598.74 43799.33 335
MVS95.72 45194.63 45798.99 35898.56 46897.98 39399.30 16698.86 41972.71 49997.30 47399.08 41698.34 25099.74 40489.21 48698.33 45599.26 352
UWE-MVS-2895.64 45295.47 44496.14 47797.98 48690.39 50198.49 38095.81 48899.02 27698.03 45398.19 46984.49 47599.28 48788.75 48798.47 45298.75 447
thres600view796.60 42696.16 42997.93 43699.63 23796.09 45499.18 21297.57 47098.77 31798.72 40897.32 48687.04 46399.72 40988.57 48898.62 44597.98 484
FPMVS96.32 43495.50 44398.79 39399.60 24498.17 37598.46 38698.80 42597.16 44296.28 48699.63 25282.19 47799.09 49088.45 48998.89 42999.10 390
PCF-MVS96.03 1896.73 42395.86 43699.33 29799.44 33099.16 27396.87 48399.44 33286.58 49498.95 38099.40 35194.38 39399.88 23587.93 49099.80 25098.95 423
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view90096.39 43296.03 43297.47 45199.63 23795.93 45599.18 21297.57 47098.75 32198.70 41197.31 48787.04 46399.67 44387.62 49198.51 44996.81 493
tfpn200view996.30 43595.89 43497.53 44899.58 25796.11 45299.00 28897.54 47398.43 35398.52 42596.98 49186.85 46599.67 44387.62 49198.51 44996.81 493
thres40096.40 43195.89 43497.92 43799.58 25796.11 45299.00 28897.54 47398.43 35398.52 42596.98 49186.85 46599.67 44387.62 49198.51 44997.98 484
thres20096.09 44195.68 44197.33 45799.48 31596.22 45198.53 37597.57 47098.06 38898.37 43396.73 49886.84 46799.61 46586.99 49498.57 44696.16 496
MVEpermissive92.54 2296.66 42596.11 43098.31 42399.68 22197.55 40997.94 43395.60 48999.37 21890.68 49798.70 45496.56 35098.61 49686.94 49599.55 35498.77 445
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset97.27 41196.83 42198.59 40799.46 32597.55 40999.25 19096.84 47998.78 31597.24 47597.67 47897.11 33598.97 49286.59 49698.54 44899.27 350
PAPM95.61 45494.71 45698.31 42399.12 40996.63 43996.66 48698.46 44490.77 49196.25 48798.68 45593.01 41199.69 42581.60 49797.86 47398.62 451
SD_040397.42 40696.90 41998.98 36099.54 28397.90 39699.52 9499.54 29199.34 22297.87 46098.85 44498.72 19199.64 45778.93 49899.83 22399.40 315
dongtai89.37 46388.91 46690.76 48199.19 39777.46 50695.47 49187.82 50592.28 48794.17 49598.82 44771.22 50395.54 50063.85 49997.34 47799.27 350
kuosan85.65 46584.57 46888.90 48397.91 48877.11 50796.37 48887.62 50685.24 49685.45 50196.83 49469.94 50590.98 50245.90 50095.83 49398.62 451
test12329.31 46633.05 47118.08 48425.93 50812.24 50997.53 45710.93 50911.78 50224.21 50350.08 51421.04 5068.60 50323.51 50132.43 50233.39 499
testmvs28.94 46733.33 46915.79 48526.03 5079.81 51096.77 48415.67 50811.55 50323.87 50450.74 51319.03 5078.53 50423.21 50233.07 50129.03 500
mmdepth8.33 47011.11 4730.00 4860.00 5090.00 5110.00 4970.00 5100.00 5040.00 505100.00 10.00 5080.00 5050.00 5030.00 5030.00 501
monomultidepth8.33 47011.11 4730.00 4860.00 5090.00 5110.00 4970.00 5100.00 5040.00 505100.00 10.00 5080.00 5050.00 5030.00 5030.00 501
test_blank8.33 47011.11 4730.00 4860.00 5090.00 5110.00 4970.00 5100.00 5040.00 505100.00 10.00 5080.00 5050.00 5030.00 5030.00 501
uanet_test8.33 47011.11 4730.00 4860.00 5090.00 5110.00 4970.00 5100.00 5040.00 505100.00 10.00 5080.00 5050.00 5030.00 5030.00 501
DCPMVS8.33 47011.11 4730.00 4860.00 5090.00 5110.00 4970.00 5100.00 5040.00 505100.00 10.00 5080.00 5050.00 5030.00 5030.00 501
cdsmvs_eth3d_5k24.88 46833.17 4700.00 4860.00 5090.00 5110.00 49799.62 2390.00 5040.00 50599.13 40799.82 180.00 5050.00 5030.00 5030.00 501
pcd_1.5k_mvsjas16.61 46922.14 4720.00 4860.00 5090.00 5110.00 4970.00 5100.00 5040.00 505100.00 199.28 920.00 5050.00 5030.00 5030.00 501
sosnet-low-res8.33 47011.11 4730.00 4860.00 5090.00 5110.00 4970.00 5100.00 5040.00 505100.00 10.00 5080.00 5050.00 5030.00 5030.00 501
sosnet8.33 47011.11 4730.00 4860.00 5090.00 5110.00 4970.00 5100.00 5040.00 505100.00 10.00 5080.00 5050.00 5030.00 5030.00 501
uncertanet8.33 47011.11 4730.00 4860.00 5090.00 5110.00 4970.00 5100.00 5040.00 505100.00 10.00 5080.00 5050.00 5030.00 5030.00 501
Regformer8.33 47011.11 4730.00 4860.00 5090.00 5110.00 4970.00 5100.00 5040.00 505100.00 10.00 5080.00 5050.00 5030.00 5030.00 501
ab-mvs-re8.26 48011.02 4830.00 4860.00 5090.00 5110.00 4970.00 5100.00 5040.00 50599.16 4050.00 5080.00 5050.00 5030.00 5030.00 501
uanet8.33 47011.11 4730.00 4860.00 5090.00 5110.00 4970.00 5100.00 5040.00 505100.00 10.00 5080.00 5050.00 5030.00 5030.00 501
TestfortrainingZip99.38 27999.17 40199.25 24999.38 13298.82 42298.93 29199.68 19599.49 32898.11 27599.56 47398.44 45399.32 339
FOURS199.83 8699.89 1099.74 2799.71 18499.69 12899.63 221
test_one_060199.63 23799.76 7099.55 28599.23 24399.31 33399.61 27198.59 208
eth-test20.00 509
eth-test0.00 509
test_241102_ONE99.69 21399.82 4299.54 29199.12 26799.82 10899.49 32898.91 16499.52 479
save fliter99.53 29099.25 24998.29 39699.38 35299.07 271
test072699.69 21399.80 5199.24 19199.57 27499.16 25899.73 17399.65 23598.35 248
GSMVS99.14 384
test_part299.62 24199.67 11899.55 261
sam_mvs190.81 44299.14 384
sam_mvs90.52 448
MTGPAbinary99.53 301
test_post52.41 51190.25 45099.86 269
patchmatchnet-post99.62 26190.58 44699.94 97
MTMP99.09 25598.59 438
TEST999.35 35499.35 23098.11 41399.41 33894.83 47897.92 45698.99 42898.02 28199.85 288
test_899.34 36399.31 23698.08 41799.40 34594.90 47597.87 46098.97 43398.02 28199.84 305
agg_prior99.35 35499.36 22799.39 34897.76 46799.85 288
test_prior499.19 26798.00 426
test_prior99.46 24999.35 35499.22 26099.39 34899.69 42599.48 274
新几何298.04 421
旧先验199.49 31099.29 23999.26 38199.39 35597.67 30799.36 38699.46 283
原ACMM297.92 435
test22299.51 29999.08 28897.83 44199.29 37595.21 47298.68 41299.31 37797.28 32699.38 38399.43 306
segment_acmp98.37 246
testdata197.72 44697.86 407
test1299.54 22299.29 37699.33 23399.16 40398.43 43097.54 31599.82 34299.47 37299.48 274
plane_prior799.58 25799.38 218
plane_prior699.47 32199.26 24697.24 327
plane_prior499.25 390
plane_prior399.31 23698.36 36299.14 363
plane_prior298.80 33698.94 286
plane_prior199.51 299
plane_prior99.24 25498.42 38897.87 40599.71 298
n20.00 510
nn0.00 510
door-mid99.83 98
test1199.29 375
door99.77 148
HQP5-MVS98.94 303
HQP-NCC99.31 37097.98 42897.45 42798.15 445
ACMP_Plane99.31 37097.98 42897.45 42798.15 445
HQP4-MVS98.15 44599.70 41899.53 246
HQP3-MVS99.37 35399.67 319
HQP2-MVS96.67 347
NP-MVS99.40 34199.13 27698.83 445
ACMMP++_ref99.94 128
ACMMP++99.79 255
Test By Simon98.41 240