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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort by
CS-MVS99.50 1699.49 1299.52 10899.76 5799.35 10699.90 199.55 6798.56 7199.77 3799.70 14098.75 6099.77 17899.64 399.78 9699.42 181
MVSFormer99.17 7899.12 7299.29 14699.51 16198.94 16299.88 299.46 17597.55 18299.80 2899.65 16997.39 12499.28 28299.03 6299.85 6099.65 122
test_djsdf98.67 14998.57 15098.98 17998.70 32398.91 16699.88 299.46 17597.55 18299.22 18199.88 1995.73 18299.28 28299.03 6297.62 23798.75 244
OurMVSNet-221017-097.88 22397.77 21698.19 27698.71 32296.53 29699.88 299.00 30797.79 15798.78 25899.94 391.68 30199.35 27197.21 25496.99 26998.69 260
DROMVSNet99.44 3299.39 2199.58 9099.56 15399.49 9199.88 299.58 5098.38 8799.73 5099.69 14998.20 10299.70 21099.64 399.82 8299.54 152
DVP-MVS++99.59 399.50 1099.88 699.51 16199.88 899.87 699.51 10698.99 3199.88 699.81 6799.27 599.96 2098.85 9099.80 8999.81 46
FOURS199.91 199.93 199.87 699.56 5899.10 1299.81 25
K. test v397.10 29096.79 29198.01 28998.72 32096.33 30399.87 697.05 36397.59 17796.16 34899.80 8388.71 33799.04 31796.69 28796.55 27598.65 282
FC-MVSNet-test98.75 14398.62 14399.15 16399.08 27299.45 9799.86 999.60 4198.23 10598.70 27099.82 5496.80 14499.22 29299.07 6096.38 28098.79 235
v7n97.87 22597.52 24198.92 18998.76 31698.58 19599.84 1099.46 17596.20 29798.91 23899.70 14094.89 21099.44 25196.03 30093.89 33198.75 244
DTE-MVSNet97.51 27597.19 28298.46 25198.63 32998.13 22699.84 1099.48 14796.68 25997.97 31999.67 16292.92 26698.56 34396.88 27992.60 34698.70 256
3Dnovator97.25 999.24 7099.05 7999.81 4199.12 26399.66 5999.84 1099.74 1099.09 1598.92 23799.90 1095.94 17399.98 798.95 7099.92 1399.79 62
FIs98.78 14098.63 13899.23 15699.18 25099.54 8299.83 1399.59 4498.28 9998.79 25799.81 6796.75 14899.37 26399.08 5996.38 28098.78 236
jajsoiax98.43 16098.28 16798.88 20198.60 33398.43 21299.82 1499.53 8698.19 10998.63 28199.80 8393.22 26299.44 25199.22 4497.50 24898.77 240
OpenMVScopyleft96.50 1698.47 15798.12 17599.52 10899.04 27999.53 8599.82 1499.72 1194.56 33398.08 31399.88 1994.73 22199.98 797.47 24099.76 10499.06 214
nrg03098.64 15298.42 15799.28 14999.05 27899.69 5299.81 1699.46 17598.04 13499.01 22199.82 5496.69 15099.38 26099.34 3394.59 32098.78 236
HPM-MVScopyleft99.42 4199.28 5499.83 3699.90 499.72 4799.81 1699.54 7597.59 17799.68 6299.63 18298.91 4099.94 5898.58 13499.91 1899.84 22
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPP-MVSNet99.13 8498.99 9299.53 10299.65 12499.06 14399.81 1699.33 25097.43 19899.60 9499.88 1997.14 13399.84 14299.13 5498.94 18199.69 108
3Dnovator+97.12 1399.18 7698.97 9699.82 3899.17 25699.68 5499.81 1699.51 10699.20 598.72 26399.89 1495.68 18499.97 1298.86 8899.86 5399.81 46
GeoE98.85 13298.62 14399.53 10299.61 13999.08 14099.80 2099.51 10697.10 23099.31 15899.78 10295.23 20199.77 17898.21 17099.03 17599.75 78
CS-MVS-test99.42 4199.39 2199.52 10899.77 5399.35 10699.80 2099.57 5298.56 7199.77 3799.44 24898.16 10699.77 17899.64 399.78 9699.42 181
canonicalmvs99.02 10998.86 11399.51 11299.42 18999.32 10999.80 2099.48 14798.63 6699.31 15898.81 33497.09 13599.75 18699.27 4197.90 23099.47 174
v897.95 21697.63 23298.93 18798.95 29298.81 17999.80 2099.41 20896.03 31299.10 20599.42 25494.92 20899.30 28096.94 27494.08 32998.66 280
Vis-MVSNet (Re-imp)98.87 12198.72 12799.31 13999.71 9598.88 16899.80 2099.44 19697.91 14499.36 14999.78 10295.49 19099.43 25597.91 19699.11 16699.62 135
Anonymous2024052196.20 30695.89 30797.13 32597.72 35194.96 33599.79 2599.29 27293.01 34797.20 33699.03 32189.69 32998.36 34691.16 35296.13 28598.07 341
PS-MVSNAJss98.92 11998.92 10298.90 19598.78 31298.53 19999.78 2699.54 7598.07 12899.00 22699.76 11399.01 1999.37 26399.13 5497.23 26298.81 233
PEN-MVS97.76 24397.44 25598.72 22598.77 31598.54 19899.78 2699.51 10697.06 23498.29 30699.64 17692.63 27998.89 33998.09 18193.16 33998.72 250
anonymousdsp98.44 15998.28 16798.94 18598.50 33898.96 15799.77 2899.50 12697.07 23298.87 24599.77 10994.76 21999.28 28298.66 12197.60 23898.57 309
SixPastTwentyTwo97.50 27697.33 27298.03 28698.65 32796.23 30699.77 2898.68 34197.14 22397.90 32099.93 490.45 31899.18 30097.00 26896.43 27998.67 272
QAPM98.67 14998.30 16699.80 4399.20 24599.67 5799.77 2899.72 1194.74 33098.73 26299.90 1095.78 18099.98 796.96 27299.88 3899.76 77
dcpmvs_299.23 7199.58 298.16 27899.83 3794.68 34099.76 3199.52 9299.07 1899.98 199.88 1998.56 7799.93 7399.67 299.98 299.87 13
HPM-MVS_fast99.51 1599.40 2099.85 2899.91 199.79 3399.76 3199.56 5897.72 16599.76 4499.75 11899.13 1299.92 8599.07 6099.92 1399.85 18
v1097.85 22897.52 24198.86 20898.99 28598.67 18799.75 3399.41 20895.70 31598.98 22899.41 25894.75 22099.23 28996.01 30194.63 31998.67 272
APDe-MVS99.66 199.57 399.92 199.77 5399.89 499.75 3399.56 5899.02 2199.88 699.85 3499.18 1099.96 2099.22 4499.92 1399.90 1
IS-MVSNet99.05 10598.87 10999.57 9299.73 8499.32 10999.75 3399.20 28698.02 13799.56 10299.86 2896.54 15499.67 21698.09 18199.13 16599.73 90
tttt051798.42 16198.14 17399.28 14999.66 11998.38 21599.74 3696.85 36497.68 16999.79 3099.74 12491.39 30899.89 11998.83 9699.56 13599.57 148
baseline99.15 8199.02 8799.53 10299.66 11999.14 13399.72 3799.48 14798.35 9299.42 13099.84 4396.07 16799.79 17299.51 1299.14 16499.67 115
RPSCF98.22 17698.62 14396.99 32799.82 3991.58 36499.72 3799.44 19696.61 26699.66 7399.89 1495.92 17499.82 16097.46 24199.10 16999.57 148
CSCG99.32 5799.32 3699.32 13899.85 2698.29 21799.71 3999.66 2798.11 12099.41 13499.80 8398.37 9499.96 2098.99 6699.96 799.72 96
WR-MVS_H98.13 18897.87 20798.90 19599.02 28298.84 17399.70 4099.59 4497.27 21298.40 29899.19 30595.53 18899.23 28998.34 16293.78 33298.61 303
LTVRE_ROB97.16 1298.02 20597.90 20298.40 25999.23 23796.80 28899.70 4099.60 4197.12 22698.18 31099.70 14091.73 30099.72 19898.39 15597.45 25398.68 265
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
XVS99.53 1299.42 1799.87 1299.85 2699.83 1799.69 4299.68 1998.98 3499.37 14699.74 12498.81 4999.94 5898.79 10299.86 5399.84 22
X-MVStestdata96.55 29895.45 31399.87 1299.85 2699.83 1799.69 4299.68 1998.98 3499.37 14664.01 37798.81 4999.94 5898.79 10299.86 5399.84 22
V4298.06 19597.79 21198.86 20898.98 28898.84 17399.69 4299.34 24396.53 27299.30 16099.37 26994.67 22499.32 27797.57 23094.66 31898.42 324
mPP-MVS99.44 3299.30 4699.86 2199.88 1299.79 3399.69 4299.48 14798.12 11899.50 11499.75 11898.78 5299.97 1298.57 13699.89 3599.83 33
CP-MVS99.45 2899.32 3699.85 2899.83 3799.75 4399.69 4299.52 9298.07 12899.53 10999.63 18298.93 3999.97 1298.74 10799.91 1899.83 33
PS-CasMVS97.93 21797.59 23698.95 18498.99 28599.06 14399.68 4799.52 9297.13 22498.31 30499.68 15692.44 28899.05 31698.51 14594.08 32998.75 244
Vis-MVSNetpermissive99.12 9098.97 9699.56 9499.78 4899.10 13899.68 4799.66 2798.49 7799.86 1399.87 2594.77 21899.84 14299.19 4799.41 14399.74 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EIA-MVS99.18 7699.09 7699.45 12299.49 17399.18 12599.67 4999.53 8697.66 17399.40 13999.44 24898.10 10899.81 16498.94 7199.62 13299.35 189
MSP-MVS99.42 4199.27 5699.88 699.89 999.80 2999.67 4999.50 12698.70 6399.77 3799.49 23398.21 10199.95 4798.46 15199.77 10199.88 8
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
MVS_Test99.10 9898.97 9699.48 11699.49 17399.14 13399.67 4999.34 24397.31 20899.58 9999.76 11397.65 12099.82 16098.87 8399.07 17299.46 176
CP-MVSNet98.09 19297.78 21499.01 17598.97 29099.24 12099.67 4999.46 17597.25 21498.48 29399.64 17693.79 25399.06 31598.63 12494.10 32898.74 248
MTAPA99.52 1499.39 2199.89 499.90 499.86 1399.66 5399.47 16598.79 5799.68 6299.81 6798.43 8799.97 1298.88 7999.90 2599.83 33
HFP-MVS99.49 1799.37 2599.86 2199.87 1699.80 2999.66 5399.67 2298.15 11499.68 6299.69 14999.06 1699.96 2098.69 11699.87 4299.84 22
mvs_tets98.40 16598.23 16998.91 19398.67 32698.51 20599.66 5399.53 8698.19 10998.65 27999.81 6792.75 27099.44 25199.31 3697.48 25298.77 240
EU-MVSNet97.98 21298.03 18797.81 30598.72 32096.65 29399.66 5399.66 2798.09 12398.35 30299.82 5495.25 20098.01 35297.41 24695.30 30798.78 236
ACMMPR99.49 1799.36 2799.86 2199.87 1699.79 3399.66 5399.67 2298.15 11499.67 6899.69 14998.95 3299.96 2098.69 11699.87 4299.84 22
MP-MVScopyleft99.33 5699.15 6999.87 1299.88 1299.82 2399.66 5399.46 17598.09 12399.48 11899.74 12498.29 9899.96 2097.93 19599.87 4299.82 40
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
abl_699.44 3299.31 4399.83 3699.85 2699.75 4399.66 5399.59 4498.13 11699.82 2399.81 6798.60 7499.96 2098.46 15199.88 3899.79 62
test_part197.75 24797.24 28099.29 14699.59 14599.63 6599.65 6099.49 13496.17 30098.44 29599.69 14989.80 32799.47 24398.68 11893.66 33398.78 236
region2R99.48 2199.35 3099.87 1299.88 1299.80 2999.65 6099.66 2798.13 11699.66 7399.68 15698.96 2999.96 2098.62 12599.87 4299.84 22
TranMVSNet+NR-MVSNet97.93 21797.66 22898.76 22298.78 31298.62 19299.65 6099.49 13497.76 16098.49 29299.60 19594.23 23998.97 33398.00 19092.90 34198.70 256
ZNCC-MVS99.47 2499.33 3499.87 1299.87 1699.81 2799.64 6399.67 2298.08 12799.55 10699.64 17698.91 4099.96 2098.72 11199.90 2599.82 40
tfpnnormal97.84 23197.47 24798.98 17999.20 24599.22 12299.64 6399.61 3696.32 28798.27 30799.70 14093.35 25999.44 25195.69 30795.40 30598.27 333
SR-MVS-dyc-post99.45 2899.31 4399.85 2899.76 5799.82 2399.63 6599.52 9298.38 8799.76 4499.82 5498.53 7899.95 4798.61 12899.81 8599.77 72
RE-MVS-def99.34 3299.76 5799.82 2399.63 6599.52 9298.38 8799.76 4499.82 5498.75 6098.61 12899.81 8599.77 72
TSAR-MVS + MP.99.58 599.50 1099.81 4199.91 199.66 5999.63 6599.39 21898.91 4699.78 3599.85 3499.36 299.94 5898.84 9399.88 3899.82 40
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Anonymous2023120696.22 30496.03 30396.79 33497.31 35794.14 34799.63 6599.08 30096.17 30097.04 34099.06 31893.94 24997.76 35886.96 36695.06 31298.47 317
APD-MVS_3200maxsize99.48 2199.35 3099.85 2899.76 5799.83 1799.63 6599.54 7598.36 9199.79 3099.82 5498.86 4499.95 4798.62 12599.81 8599.78 70
RRT_test8_iter0597.72 25397.60 23498.08 28399.23 23796.08 31099.63 6599.49 13497.54 18598.94 23499.81 6787.99 34799.35 27199.21 4696.51 27798.81 233
test072699.85 2699.89 499.62 7199.50 12699.10 1299.86 1399.82 5498.94 35
EPNet98.86 12498.71 12999.30 14397.20 35998.18 22299.62 7198.91 31999.28 398.63 28199.81 6795.96 17099.99 199.24 4399.72 11299.73 90
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t98.93 11898.67 13399.72 6499.85 2699.53 8599.62 7199.59 4492.65 34999.71 5599.78 10298.06 11099.90 11198.84 9399.91 1899.74 83
HY-MVS97.30 798.85 13298.64 13799.47 11999.42 18999.08 14099.62 7199.36 23497.39 20399.28 16599.68 15696.44 15899.92 8598.37 15998.22 21599.40 186
ACMMPcopyleft99.45 2899.32 3699.82 3899.89 999.67 5799.62 7199.69 1898.12 11899.63 8499.84 4398.73 6499.96 2098.55 14299.83 7699.81 46
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
DeepC-MVS98.35 299.30 5999.19 6699.64 8099.82 3999.23 12199.62 7199.55 6798.94 4199.63 8499.95 295.82 17999.94 5899.37 2899.97 599.73 90
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-Vis-set99.58 599.56 599.64 8099.78 4899.15 13299.61 7799.45 18799.01 2499.89 599.82 5499.01 1999.92 8599.56 999.95 899.85 18
test250696.81 29496.65 29297.29 32299.74 7692.21 36299.60 7885.06 38199.13 899.77 3799.93 487.82 35199.85 13699.38 2799.38 14499.80 56
test117299.43 3699.29 5099.85 2899.75 6899.82 2399.60 7899.56 5898.28 9999.74 4899.79 9598.53 7899.95 4798.55 14299.78 9699.79 62
SED-MVS99.61 299.52 899.88 699.84 3399.90 299.60 7899.48 14799.08 1699.91 299.81 6799.20 799.96 2098.91 7699.85 6099.79 62
OPU-MVS99.64 8099.56 15399.72 4799.60 7899.70 14099.27 599.42 25698.24 16999.80 8999.79 62
GST-MVS99.40 4999.24 6199.85 2899.86 2299.79 3399.60 7899.67 2297.97 13999.63 8499.68 15698.52 8099.95 4798.38 15799.86 5399.81 46
EI-MVSNet-UG-set99.58 599.57 399.64 8099.78 4899.14 13399.60 7899.45 18799.01 2499.90 499.83 4798.98 2799.93 7399.59 699.95 899.86 15
ACMH97.28 898.10 19197.99 19198.44 25599.41 19296.96 28299.60 7899.56 5898.09 12398.15 31199.91 890.87 31599.70 21098.88 7997.45 25398.67 272
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ECVR-MVScopyleft98.04 20198.05 18598.00 29199.74 7694.37 34499.59 8594.98 37299.13 899.66 7399.93 490.67 31799.84 14299.40 2699.38 14499.80 56
SR-MVS99.43 3699.29 5099.86 2199.75 6899.83 1799.59 8599.62 3498.21 10899.73 5099.79 9598.68 6899.96 2098.44 15399.77 10199.79 62
thres100view90097.76 24397.45 25098.69 22799.72 8997.86 24199.59 8598.74 33397.93 14299.26 17398.62 34191.75 29899.83 15393.22 34098.18 22098.37 330
thres600view797.86 22797.51 24398.92 18999.72 8997.95 23699.59 8598.74 33397.94 14199.27 16898.62 34191.75 29899.86 13093.73 33598.19 21998.96 225
LCM-MVSNet-Re97.83 23398.15 17296.87 33299.30 22092.25 36199.59 8598.26 34897.43 19896.20 34799.13 31196.27 16398.73 34298.17 17698.99 17999.64 129
baseline198.31 17097.95 19799.38 13199.50 17198.74 18299.59 8598.93 31498.41 8599.14 19799.60 19594.59 22799.79 17298.48 14793.29 33799.61 137
SteuartSystems-ACMMP99.54 1099.42 1799.87 1299.82 3999.81 2799.59 8599.51 10698.62 6799.79 3099.83 4799.28 499.97 1298.48 14799.90 2599.84 22
Skip Steuart: Steuart Systems R&D Blog.
CPTT-MVS99.11 9598.90 10599.74 5999.80 4599.46 9699.59 8599.49 13497.03 23799.63 8499.69 14997.27 13199.96 2097.82 20499.84 6799.81 46
test111198.04 20198.11 17697.83 30299.74 7693.82 34999.58 9395.40 37199.12 1099.65 7999.93 490.73 31699.84 14299.43 2599.38 14499.82 40
Regformer-399.57 899.53 799.68 6899.76 5799.29 11499.58 9399.44 19699.01 2499.87 1299.80 8398.97 2899.91 9699.44 2499.92 1399.83 33
Regformer-499.59 399.54 699.73 6199.76 5799.41 10199.58 9399.49 13499.02 2199.88 699.80 8399.00 2599.94 5899.45 2299.92 1399.84 22
PGM-MVS99.45 2899.31 4399.86 2199.87 1699.78 4099.58 9399.65 3297.84 15099.71 5599.80 8399.12 1399.97 1298.33 16399.87 4299.83 33
LPG-MVS_test98.22 17698.13 17498.49 24499.33 21197.05 27199.58 9399.55 6797.46 19199.24 17699.83 4792.58 28099.72 19898.09 18197.51 24698.68 265
PHI-MVS99.30 5999.17 6899.70 6799.56 15399.52 8899.58 9399.80 897.12 22699.62 8899.73 13198.58 7599.90 11198.61 12899.91 1899.68 112
SF-MVS99.38 5199.24 6199.79 4699.79 4699.68 5499.57 9999.54 7597.82 15699.71 5599.80 8398.95 3299.93 7398.19 17299.84 6799.74 83
DVP-MVScopyleft99.57 899.47 1399.88 699.85 2699.89 499.57 9999.37 23399.10 1299.81 2599.80 8398.94 3599.96 2098.93 7399.86 5399.81 46
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_SECOND99.91 299.84 3399.89 499.57 9999.51 10699.96 2098.93 7399.86 5399.88 8
Effi-MVS+-dtu98.78 14098.89 10798.47 25099.33 21196.91 28499.57 9999.30 26798.47 7899.41 13498.99 32596.78 14599.74 18798.73 10999.38 14498.74 248
v2v48298.06 19597.77 21698.92 18998.90 29598.82 17799.57 9999.36 23496.65 26299.19 19099.35 27594.20 24099.25 28797.72 21594.97 31498.69 260
DWT-MVSNet_test97.53 27297.40 26197.93 29599.03 28194.86 33799.57 9998.63 34296.59 27098.36 30198.79 33589.32 33299.74 18798.14 17998.16 22499.20 199
DSMNet-mixed97.25 28697.35 26796.95 33097.84 34993.61 35599.57 9996.63 36796.13 30698.87 24598.61 34394.59 22797.70 35995.08 32098.86 18899.55 150
KD-MVS_self_test95.00 31894.34 32396.96 32997.07 36295.39 32699.56 10699.44 19695.11 32297.13 33897.32 35991.86 29697.27 36290.35 35581.23 36598.23 337
ETV-MVS99.26 6699.21 6499.40 12899.46 18299.30 11399.56 10699.52 9298.52 7599.44 12699.27 29598.41 9199.86 13099.10 5799.59 13499.04 215
SMA-MVScopyleft99.44 3299.30 4699.85 2899.73 8499.83 1799.56 10699.47 16597.45 19499.78 3599.82 5499.18 1099.91 9698.79 10299.89 3599.81 46
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
AllTest98.87 12198.72 12799.31 13999.86 2298.48 20999.56 10699.61 3697.85 14899.36 14999.85 3495.95 17199.85 13696.66 28999.83 7699.59 143
casdiffmvs99.13 8498.98 9599.56 9499.65 12499.16 12899.56 10699.50 12698.33 9699.41 13499.86 2895.92 17499.83 15399.45 2299.16 16199.70 105
XXY-MVS98.38 16698.09 18099.24 15499.26 23199.32 10999.56 10699.55 6797.45 19498.71 26499.83 4793.23 26099.63 23198.88 7996.32 28298.76 242
ACMH+97.24 1097.92 22097.78 21498.32 26699.46 18296.68 29299.56 10699.54 7598.41 8597.79 32599.87 2590.18 32499.66 21998.05 18997.18 26598.62 294
ACMM97.58 598.37 16798.34 16298.48 24699.41 19297.10 26599.56 10699.45 18798.53 7499.04 21899.85 3493.00 26499.71 20498.74 10797.45 25398.64 284
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D99.27 6499.12 7299.74 5999.18 25099.75 4399.56 10699.57 5298.45 8199.49 11799.85 3497.77 11799.94 5898.33 16399.84 6799.52 158
v14419297.92 22097.60 23498.87 20598.83 30798.65 18999.55 11599.34 24396.20 29799.32 15799.40 26194.36 23599.26 28696.37 29695.03 31398.70 256
#test#99.43 3699.29 5099.86 2199.87 1699.80 2999.55 11599.67 2297.83 15199.68 6299.69 14999.06 1699.96 2098.39 15599.87 4299.84 22
API-MVS99.04 10699.03 8499.06 16899.40 19799.31 11299.55 11599.56 5898.54 7399.33 15699.39 26598.76 5799.78 17696.98 27099.78 9698.07 341
thisisatest053098.35 16898.03 18799.31 13999.63 12998.56 19699.54 11896.75 36697.53 18799.73 5099.65 16991.25 31199.89 11998.62 12599.56 13599.48 169
MTMP99.54 11898.88 323
v114497.98 21297.69 22598.85 21198.87 30198.66 18899.54 11899.35 23996.27 29199.23 18099.35 27594.67 22499.23 28996.73 28495.16 31098.68 265
v14897.79 24197.55 23798.50 24398.74 31797.72 24799.54 11899.33 25096.26 29298.90 24099.51 22794.68 22399.14 30397.83 20393.15 34098.63 292
CostFormer97.72 25397.73 22297.71 30999.15 26194.02 34899.54 11899.02 30694.67 33199.04 21899.35 27592.35 29099.77 17898.50 14697.94 22999.34 191
MVSTER98.49 15698.32 16499.00 17799.35 20699.02 14699.54 11899.38 22497.41 20199.20 18799.73 13193.86 25299.36 26798.87 8397.56 24298.62 294
patch_mono-299.26 6699.62 198.16 27899.81 4294.59 34199.52 12499.64 3399.33 299.73 5099.90 1099.00 2599.99 199.69 199.98 299.89 2
Fast-Effi-MVS+-dtu98.77 14298.83 11998.60 23199.41 19296.99 27899.52 12499.49 13498.11 12099.24 17699.34 27896.96 14199.79 17297.95 19499.45 14099.02 218
Fast-Effi-MVS+98.70 14598.43 15699.51 11299.51 16199.28 11599.52 12499.47 16596.11 30799.01 22199.34 27896.20 16599.84 14297.88 19898.82 19099.39 187
v192192097.80 24097.45 25098.84 21298.80 30898.53 19999.52 12499.34 24396.15 30499.24 17699.47 24293.98 24899.29 28195.40 31495.13 31198.69 260
MIMVSNet195.51 31395.04 31796.92 33197.38 35495.60 31799.52 12499.50 12693.65 34196.97 34299.17 30685.28 35896.56 36788.36 36295.55 30298.60 306
UniMVSNet_ETH3D97.32 28496.81 29098.87 20599.40 19797.46 25399.51 12999.53 8695.86 31498.54 28999.77 10982.44 36599.66 21998.68 11897.52 24599.50 167
alignmvs98.81 13698.56 15199.58 9099.43 18899.42 10099.51 12998.96 31298.61 6899.35 15298.92 33194.78 21599.77 17899.35 2998.11 22699.54 152
v119297.81 23897.44 25598.91 19398.88 29798.68 18699.51 12999.34 24396.18 29999.20 18799.34 27894.03 24799.36 26795.32 31795.18 30998.69 260
test20.0396.12 30895.96 30596.63 33597.44 35395.45 32499.51 12999.38 22496.55 27196.16 34899.25 29893.76 25596.17 36887.35 36594.22 32698.27 333
mvs_anonymous99.03 10898.99 9299.16 16199.38 20198.52 20399.51 12999.38 22497.79 15799.38 14499.81 6797.30 12999.45 24699.35 2998.99 17999.51 164
TAMVS99.12 9099.08 7799.24 15499.46 18298.55 19799.51 12999.46 17598.09 12399.45 12299.82 5498.34 9599.51 24198.70 11398.93 18299.67 115
test_yl98.86 12498.63 13899.54 9699.49 17399.18 12599.50 13599.07 30298.22 10699.61 9099.51 22795.37 19399.84 14298.60 13198.33 20999.59 143
DCV-MVSNet98.86 12498.63 13899.54 9699.49 17399.18 12599.50 13599.07 30298.22 10699.61 9099.51 22795.37 19399.84 14298.60 13198.33 20999.59 143
tfpn200view997.72 25397.38 26398.72 22599.69 10597.96 23499.50 13598.73 33897.83 15199.17 19498.45 34691.67 30299.83 15393.22 34098.18 22098.37 330
UA-Net99.42 4199.29 5099.80 4399.62 13599.55 8099.50 13599.70 1598.79 5799.77 3799.96 197.45 12399.96 2098.92 7599.90 2599.89 2
pm-mvs197.68 26197.28 27698.88 20199.06 27598.62 19299.50 13599.45 18796.32 28797.87 32199.79 9592.47 28499.35 27197.54 23393.54 33598.67 272
EI-MVSNet98.67 14998.67 13398.68 22899.35 20697.97 23299.50 13599.38 22496.93 24699.20 18799.83 4797.87 11399.36 26798.38 15797.56 24298.71 252
CVMVSNet98.57 15598.67 13398.30 26899.35 20695.59 31899.50 13599.55 6798.60 6999.39 14199.83 4794.48 23299.45 24698.75 10698.56 20299.85 18
VPA-MVSNet98.29 17397.95 19799.30 14399.16 25899.54 8299.50 13599.58 5098.27 10199.35 15299.37 26992.53 28299.65 22399.35 2994.46 32198.72 250
thres40097.77 24297.38 26398.92 18999.69 10597.96 23499.50 13598.73 33897.83 15199.17 19498.45 34691.67 30299.83 15393.22 34098.18 22098.96 225
APD-MVScopyleft99.27 6499.08 7799.84 3599.75 6899.79 3399.50 13599.50 12697.16 22299.77 3799.82 5498.78 5299.94 5897.56 23199.86 5399.80 56
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
RRT_MVS98.60 15498.44 15599.05 17098.88 29799.14 13399.49 14599.38 22497.76 16099.29 16399.86 2895.38 19299.36 26798.81 10197.16 26698.64 284
Regformer-199.53 1299.47 1399.72 6499.71 9599.44 9899.49 14599.46 17598.95 4099.83 2099.76 11399.01 1999.93 7399.17 5099.87 4299.80 56
Regformer-299.54 1099.47 1399.75 5499.71 9599.52 8899.49 14599.49 13498.94 4199.83 2099.76 11399.01 1999.94 5899.15 5399.87 4299.80 56
TransMVSNet (Re)97.15 28896.58 29398.86 20899.12 26398.85 17299.49 14598.91 31995.48 31797.16 33799.80 8393.38 25899.11 31194.16 33291.73 34898.62 294
UniMVSNet (Re)98.29 17398.00 19099.13 16499.00 28499.36 10599.49 14599.51 10697.95 14098.97 23099.13 31196.30 16299.38 26098.36 16193.34 33698.66 280
EPMVS97.82 23697.65 22998.35 26398.88 29795.98 31199.49 14594.71 37497.57 18099.26 17399.48 23992.46 28799.71 20497.87 19999.08 17199.35 189
Anonymous2023121197.88 22397.54 24098.90 19599.71 9598.53 19999.48 15199.57 5294.16 33698.81 25399.68 15693.23 26099.42 25698.84 9394.42 32398.76 242
v124097.69 25997.32 27398.79 21998.85 30598.43 21299.48 15199.36 23496.11 30799.27 16899.36 27293.76 25599.24 28894.46 32795.23 30898.70 256
VPNet97.84 23197.44 25599.01 17599.21 24398.94 16299.48 15199.57 5298.38 8799.28 16599.73 13188.89 33699.39 25899.19 4793.27 33898.71 252
UniMVSNet_NR-MVSNet98.22 17697.97 19398.96 18298.92 29498.98 15099.48 15199.53 8697.76 16098.71 26499.46 24696.43 15999.22 29298.57 13692.87 34398.69 260
TDRefinement95.42 31594.57 32197.97 29389.83 37496.11 30999.48 15198.75 33096.74 25596.68 34399.88 1988.65 33999.71 20498.37 15982.74 36398.09 340
ACMMP_NAP99.47 2499.34 3299.88 699.87 1699.86 1399.47 15699.48 14798.05 13399.76 4499.86 2898.82 4899.93 7398.82 10099.91 1899.84 22
NR-MVSNet97.97 21597.61 23399.02 17498.87 30199.26 11899.47 15699.42 20697.63 17597.08 33999.50 23095.07 20499.13 30697.86 20093.59 33498.68 265
PVSNet_Blended_VisFu99.36 5399.28 5499.61 8599.86 2299.07 14299.47 15699.93 297.66 17399.71 5599.86 2897.73 11899.96 2099.47 2099.82 8299.79 62
SD-MVS99.41 4699.52 899.05 17099.74 7699.68 5499.46 15999.52 9299.11 1199.88 699.91 899.43 197.70 35998.72 11199.93 1299.77 72
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
tpm297.44 28197.34 27097.74 30899.15 26194.36 34599.45 16098.94 31393.45 34598.90 24099.44 24891.35 30999.59 23597.31 24898.07 22799.29 194
FMVSNet297.72 25397.36 26598.80 21899.51 16198.84 17399.45 16099.42 20696.49 27498.86 25099.29 29090.26 32098.98 32696.44 29396.56 27498.58 308
CDS-MVSNet99.09 9999.03 8499.25 15299.42 18998.73 18399.45 16099.46 17598.11 12099.46 12199.77 10998.01 11199.37 26398.70 11398.92 18499.66 118
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MAR-MVS98.86 12498.63 13899.54 9699.37 20399.66 5999.45 16099.54 7596.61 26699.01 22199.40 26197.09 13599.86 13097.68 22199.53 13899.10 203
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
testtj99.12 9098.87 10999.86 2199.72 8999.79 3399.44 16499.51 10697.29 21099.59 9799.74 12498.15 10799.96 2096.74 28399.69 11899.81 46
mvs-test198.86 12498.84 11598.89 19899.33 21197.77 24499.44 16499.30 26798.47 7899.10 20599.43 25196.78 14599.95 4798.73 10999.02 17798.96 225
UGNet98.87 12198.69 13199.40 12899.22 24198.72 18499.44 16499.68 1999.24 499.18 19399.42 25492.74 27299.96 2099.34 3399.94 1199.53 157
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
ab-mvs98.86 12498.63 13899.54 9699.64 12699.19 12399.44 16499.54 7597.77 15999.30 16099.81 6794.20 24099.93 7399.17 5098.82 19099.49 168
test_040296.64 29796.24 29997.85 30098.85 30596.43 30099.44 16499.26 27693.52 34296.98 34199.52 22388.52 34199.20 29992.58 34997.50 24897.93 352
ACMP97.20 1198.06 19597.94 19998.45 25299.37 20397.01 27699.44 16499.49 13497.54 18598.45 29499.79 9591.95 29499.72 19897.91 19697.49 25198.62 294
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GG-mvs-BLEND98.45 25298.55 33698.16 22399.43 17093.68 37697.23 33498.46 34589.30 33399.22 29295.43 31398.22 21597.98 349
HPM-MVS++copyleft99.39 5099.23 6399.87 1299.75 6899.84 1699.43 17099.51 10698.68 6599.27 16899.53 22098.64 7399.96 2098.44 15399.80 8999.79 62
tpm cat197.39 28297.36 26597.50 31799.17 25693.73 35199.43 17099.31 26391.27 35398.71 26499.08 31594.31 23899.77 17896.41 29598.50 20599.00 219
tpm97.67 26497.55 23798.03 28699.02 28295.01 33399.43 17098.54 34696.44 28199.12 20099.34 27891.83 29799.60 23497.75 21196.46 27899.48 169
GBi-Net97.68 26197.48 24598.29 26999.51 16197.26 26099.43 17099.48 14796.49 27499.07 21299.32 28590.26 32098.98 32697.10 26396.65 27198.62 294
test197.68 26197.48 24598.29 26999.51 16197.26 26099.43 17099.48 14796.49 27499.07 21299.32 28590.26 32098.98 32697.10 26396.65 27198.62 294
FMVSNet196.84 29396.36 29798.29 26999.32 21897.26 26099.43 17099.48 14795.11 32298.55 28899.32 28583.95 36198.98 32695.81 30496.26 28398.62 294
testgi97.65 26697.50 24498.13 28299.36 20596.45 29999.42 17799.48 14797.76 16097.87 32199.45 24791.09 31298.81 34094.53 32698.52 20499.13 202
F-COLMAP99.19 7499.04 8299.64 8099.78 4899.27 11799.42 17799.54 7597.29 21099.41 13499.59 19898.42 9099.93 7398.19 17299.69 11899.73 90
Anonymous20240521198.30 17297.98 19299.26 15199.57 14998.16 22399.41 17998.55 34596.03 31299.19 19099.74 12491.87 29599.92 8599.16 5298.29 21499.70 105
MSLP-MVS++99.46 2699.47 1399.44 12699.60 14399.16 12899.41 17999.71 1398.98 3499.45 12299.78 10299.19 999.54 24099.28 3999.84 6799.63 133
VNet99.11 9598.90 10599.73 6199.52 15999.56 7899.41 17999.39 21899.01 2499.74 4899.78 10295.56 18799.92 8599.52 1198.18 22099.72 96
baseline297.87 22597.55 23798.82 21499.18 25098.02 22999.41 17996.58 36896.97 24096.51 34499.17 30693.43 25799.57 23697.71 21699.03 17598.86 230
DU-MVS98.08 19497.79 21198.96 18298.87 30198.98 15099.41 17999.45 18797.87 14598.71 26499.50 23094.82 21299.22 29298.57 13692.87 34398.68 265
Baseline_NR-MVSNet97.76 24397.45 25098.68 22899.09 27098.29 21799.41 17998.85 32595.65 31698.63 28199.67 16294.82 21299.10 31398.07 18892.89 34298.64 284
XVG-ACMP-BASELINE97.83 23397.71 22498.20 27599.11 26596.33 30399.41 17999.52 9298.06 13299.05 21799.50 23089.64 33099.73 19497.73 21397.38 25998.53 311
DP-MVS99.16 8098.95 10099.78 4899.77 5399.53 8599.41 17999.50 12697.03 23799.04 21899.88 1997.39 12499.92 8598.66 12199.90 2599.87 13
9.1499.10 7499.72 8999.40 18799.51 10697.53 18799.64 8399.78 10298.84 4699.91 9697.63 22299.82 82
D2MVS98.41 16398.50 15398.15 28199.26 23196.62 29499.40 18799.61 3697.71 16698.98 22899.36 27296.04 16899.67 21698.70 11397.41 25798.15 339
Anonymous2024052998.09 19297.68 22699.34 13399.66 11998.44 21199.40 18799.43 20493.67 34099.22 18199.89 1490.23 32399.93 7399.26 4298.33 20999.66 118
FMVSNet398.03 20397.76 21998.84 21299.39 20098.98 15099.40 18799.38 22496.67 26099.07 21299.28 29292.93 26598.98 32697.10 26396.65 27198.56 310
LFMVS97.90 22297.35 26799.54 9699.52 15999.01 14899.39 19198.24 34997.10 23099.65 7999.79 9584.79 35999.91 9699.28 3998.38 20899.69 108
HQP_MVS98.27 17598.22 17098.44 25599.29 22496.97 28099.39 19199.47 16598.97 3799.11 20299.61 19292.71 27599.69 21497.78 20797.63 23598.67 272
plane_prior299.39 19198.97 37
CHOSEN 1792x268899.19 7499.10 7499.45 12299.89 998.52 20399.39 19199.94 198.73 6199.11 20299.89 1495.50 18999.94 5899.50 1399.97 599.89 2
PAPM_NR99.04 10698.84 11599.66 7199.74 7699.44 9899.39 19199.38 22497.70 16799.28 16599.28 29298.34 9599.85 13696.96 27299.45 14099.69 108
ETH3D-3000-0.199.21 7299.02 8799.77 5099.73 8499.69 5299.38 19699.51 10697.45 19499.61 9099.75 11898.51 8199.91 9697.45 24399.83 7699.71 103
gg-mvs-nofinetune96.17 30795.32 31598.73 22398.79 30998.14 22599.38 19694.09 37591.07 35698.07 31691.04 37089.62 33199.35 27196.75 28299.09 17098.68 265
VDDNet97.55 27097.02 28799.16 16199.49 17398.12 22799.38 19699.30 26795.35 31999.68 6299.90 1082.62 36499.93 7399.31 3698.13 22599.42 181
pmmvs696.53 29996.09 30297.82 30498.69 32495.47 32399.37 19999.47 16593.46 34497.41 33099.78 10287.06 35399.33 27596.92 27792.70 34598.65 282
PM-MVS92.96 32992.23 33295.14 34295.61 36589.98 36799.37 19998.21 35094.80 32995.04 35597.69 35465.06 37197.90 35594.30 32889.98 35397.54 359
WTY-MVS99.06 10398.88 10899.61 8599.62 13599.16 12899.37 19999.56 5898.04 13499.53 10999.62 18896.84 14399.94 5898.85 9098.49 20699.72 96
IterMVS-LS98.46 15898.42 15798.58 23599.59 14598.00 23099.37 19999.43 20496.94 24599.07 21299.59 19897.87 11399.03 31998.32 16595.62 30098.71 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
h-mvs3397.70 25897.28 27698.97 18199.70 10297.27 25899.36 20399.45 18798.94 4199.66 7399.64 17694.93 20699.99 199.48 1884.36 36099.65 122
DPE-MVScopyleft99.46 2699.32 3699.91 299.78 4899.88 899.36 20399.51 10698.73 6199.88 699.84 4398.72 6599.96 2098.16 17799.87 4299.88 8
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
zzz-MVS99.49 1799.36 2799.89 499.90 499.86 1399.36 20399.47 16598.79 5799.68 6299.81 6798.43 8799.97 1298.88 7999.90 2599.83 33
UnsupCasMVSNet_eth96.44 30196.12 30197.40 31998.65 32795.65 31699.36 20399.51 10697.13 22496.04 35098.99 32588.40 34298.17 34896.71 28590.27 35198.40 327
sss99.17 7899.05 7999.53 10299.62 13598.97 15399.36 20399.62 3497.83 15199.67 6899.65 16997.37 12899.95 4799.19 4799.19 16099.68 112
DeepC-MVS_fast98.69 199.49 1799.39 2199.77 5099.63 12999.59 7399.36 20399.46 17599.07 1899.79 3099.82 5498.85 4599.92 8598.68 11899.87 4299.82 40
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet99.25 6999.14 7099.59 8799.41 19299.16 12899.35 20999.57 5298.82 5299.51 11399.61 19296.46 15699.95 4799.59 699.98 299.65 122
pmmvs-eth3d95.34 31794.73 31997.15 32395.53 36795.94 31299.35 20999.10 29795.13 32093.55 35897.54 35588.15 34697.91 35494.58 32589.69 35497.61 356
MDTV_nov1_ep13_2view95.18 33199.35 20996.84 25099.58 9995.19 20297.82 20499.46 176
VDD-MVS97.73 25197.35 26798.88 20199.47 18197.12 26499.34 21298.85 32598.19 10999.67 6899.85 3482.98 36299.92 8599.49 1798.32 21399.60 139
COLMAP_ROBcopyleft97.56 698.86 12498.75 12699.17 16099.88 1298.53 19999.34 21299.59 4497.55 18298.70 27099.89 1495.83 17899.90 11198.10 18099.90 2599.08 208
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EGC-MVSNET82.80 33577.86 34197.62 31197.91 34796.12 30899.33 21499.28 2748.40 37825.05 37999.27 29584.11 36099.33 27589.20 35898.22 21597.42 360
FMVSNet596.43 30296.19 30097.15 32399.11 26595.89 31399.32 21599.52 9294.47 33598.34 30399.07 31687.54 35297.07 36392.61 34895.72 29898.47 317
dp97.75 24797.80 21097.59 31399.10 26893.71 35299.32 21598.88 32396.48 27899.08 21199.55 21192.67 27899.82 16096.52 29198.58 19999.24 196
tpmvs97.98 21298.02 18997.84 30199.04 27994.73 33999.31 21799.20 28696.10 31198.76 26099.42 25494.94 20599.81 16496.97 27198.45 20798.97 223
tpmrst98.33 16998.48 15497.90 29899.16 25894.78 33899.31 21799.11 29697.27 21299.45 12299.59 19895.33 19599.84 14298.48 14798.61 19699.09 207
MP-MVS-pluss99.37 5299.20 6599.88 699.90 499.87 1299.30 21999.52 9297.18 22099.60 9499.79 9598.79 5199.95 4798.83 9699.91 1899.83 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
NCCC99.34 5599.19 6699.79 4699.61 13999.65 6299.30 21999.48 14798.86 4899.21 18499.63 18298.72 6599.90 11198.25 16899.63 13199.80 56
JIA-IIPM97.50 27697.02 28798.93 18798.73 31897.80 24399.30 21998.97 31091.73 35298.91 23894.86 36595.10 20399.71 20497.58 22697.98 22899.28 195
BH-RMVSNet98.41 16398.08 18199.40 12899.41 19298.83 17699.30 21998.77 32997.70 16798.94 23499.65 16992.91 26899.74 18796.52 29199.55 13799.64 129
MCST-MVS99.43 3699.30 4699.82 3899.79 4699.74 4699.29 22399.40 21498.79 5799.52 11199.62 18898.91 4099.90 11198.64 12399.75 10599.82 40
LF4IMVS97.52 27397.46 24997.70 31098.98 28895.55 31999.29 22398.82 32898.07 12898.66 27399.64 17689.97 32599.61 23397.01 26796.68 27097.94 351
hse-mvs297.50 27697.14 28398.59 23299.49 17397.05 27199.28 22599.22 28298.94 4199.66 7399.42 25494.93 20699.65 22399.48 1883.80 36299.08 208
OPM-MVS98.19 18098.10 17798.45 25298.88 29797.07 26999.28 22599.38 22498.57 7099.22 18199.81 6792.12 29199.66 21998.08 18597.54 24498.61 303
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
diffmvs99.14 8299.02 8799.51 11299.61 13998.96 15799.28 22599.49 13498.46 8099.72 5499.71 13696.50 15599.88 12499.31 3699.11 16699.67 115
PVSNet_BlendedMVS98.86 12498.80 12099.03 17399.76 5798.79 18099.28 22599.91 397.42 20099.67 6899.37 26997.53 12199.88 12498.98 6797.29 26198.42 324
OMC-MVS99.08 10199.04 8299.20 15799.67 11098.22 22199.28 22599.52 9298.07 12899.66 7399.81 6797.79 11699.78 17697.79 20699.81 8599.60 139
AUN-MVS96.88 29296.31 29898.59 23299.48 18097.04 27499.27 23099.22 28297.44 19798.51 29099.41 25891.97 29399.66 21997.71 21683.83 36199.07 213
pmmvs597.52 27397.30 27598.16 27898.57 33596.73 28999.27 23098.90 32196.14 30598.37 30099.53 22091.54 30799.14 30397.51 23695.87 29398.63 292
131498.68 14898.54 15299.11 16598.89 29698.65 18999.27 23099.49 13496.89 24797.99 31899.56 20897.72 11999.83 15397.74 21299.27 15598.84 232
112199.09 9998.87 10999.75 5499.74 7699.60 7099.27 23099.48 14796.82 25399.25 17599.65 16998.38 9299.93 7397.53 23499.67 12599.73 90
MVS97.28 28596.55 29499.48 11698.78 31298.95 15999.27 23099.39 21883.53 36498.08 31399.54 21696.97 14099.87 12794.23 33099.16 16199.63 133
BH-untuned98.42 16198.36 15998.59 23299.49 17396.70 29099.27 23099.13 29597.24 21698.80 25599.38 26695.75 18199.74 18797.07 26699.16 16199.33 192
MDTV_nov1_ep1398.32 16499.11 26594.44 34399.27 23098.74 33397.51 18999.40 13999.62 18894.78 21599.76 18497.59 22598.81 192
DP-MVS Recon99.12 9098.95 10099.65 7599.74 7699.70 5199.27 23099.57 5296.40 28599.42 13099.68 15698.75 6099.80 16997.98 19199.72 11299.44 179
PatchmatchNetpermissive98.31 17098.36 15998.19 27699.16 25895.32 32799.27 23098.92 31697.37 20499.37 14699.58 20194.90 20999.70 21097.43 24599.21 15899.54 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres20097.61 26897.28 27698.62 23099.64 12698.03 22899.26 23998.74 33397.68 16999.09 21098.32 35091.66 30499.81 16492.88 34498.22 21598.03 344
CNVR-MVS99.42 4199.30 4699.78 4899.62 13599.71 4999.26 23999.52 9298.82 5299.39 14199.71 13698.96 2999.85 13698.59 13399.80 8999.77 72
1112_ss98.98 11498.77 12399.59 8799.68 10999.02 14699.25 24199.48 14797.23 21799.13 19899.58 20196.93 14299.90 11198.87 8398.78 19399.84 22
TAPA-MVS97.07 1597.74 25097.34 27098.94 18599.70 10297.53 25199.25 24199.51 10691.90 35199.30 16099.63 18298.78 5299.64 22688.09 36399.87 4299.65 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLCcopyleft97.94 499.02 10998.85 11499.53 10299.66 11999.01 14899.24 24399.52 9296.85 24999.27 16899.48 23998.25 10099.91 9697.76 20999.62 13299.65 122
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ETH3D cwj APD-0.1699.06 10398.84 11599.72 6499.51 16199.60 7099.23 24499.44 19697.04 23599.39 14199.67 16298.30 9799.92 8597.27 25099.69 11899.64 129
test_post199.23 24465.14 37694.18 24399.71 20497.58 226
ADS-MVSNet298.02 20598.07 18497.87 29999.33 21195.19 33099.23 24499.08 30096.24 29499.10 20599.67 16294.11 24498.93 33696.81 28099.05 17399.48 169
ADS-MVSNet98.20 17998.08 18198.56 23899.33 21196.48 29899.23 24499.15 29296.24 29499.10 20599.67 16294.11 24499.71 20496.81 28099.05 17399.48 169
EPNet_dtu98.03 20397.96 19598.23 27498.27 34295.54 32199.23 24498.75 33099.02 2197.82 32399.71 13696.11 16699.48 24293.04 34399.65 12899.69 108
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet98.17 18397.93 20098.87 20599.18 25098.49 20799.22 24999.33 25096.96 24199.56 10299.38 26694.33 23699.00 32494.83 32498.58 19999.14 200
RPMNet96.72 29695.90 30699.19 15899.18 25098.49 20799.22 24999.52 9288.72 36099.56 10297.38 35794.08 24699.95 4786.87 36798.58 19999.14 200
plane_prior96.97 28099.21 25198.45 8197.60 238
WR-MVS98.06 19597.73 22299.06 16898.86 30499.25 11999.19 25299.35 23997.30 20998.66 27399.43 25193.94 24999.21 29798.58 13494.28 32598.71 252
new-patchmatchnet94.48 32494.08 32495.67 34195.08 36892.41 36099.18 25399.28 27494.55 33493.49 35997.37 35887.86 35097.01 36491.57 35088.36 35597.61 356
AdaColmapbinary99.01 11298.80 12099.66 7199.56 15399.54 8299.18 25399.70 1598.18 11299.35 15299.63 18296.32 16199.90 11197.48 23899.77 10199.55 150
ETH3 D test640098.70 14598.35 16199.73 6199.69 10599.60 7099.16 25599.45 18795.42 31899.27 16899.60 19597.39 12499.91 9695.36 31699.83 7699.70 105
EG-PatchMatch MVS95.97 31095.69 31096.81 33397.78 35092.79 35999.16 25598.93 31496.16 30294.08 35799.22 30182.72 36399.47 24395.67 30997.50 24898.17 338
PatchT97.03 29196.44 29698.79 21998.99 28598.34 21699.16 25599.07 30292.13 35099.52 11197.31 36094.54 23198.98 32688.54 36198.73 19599.03 216
CNLPA99.14 8298.99 9299.59 8799.58 14799.41 10199.16 25599.44 19698.45 8199.19 19099.49 23398.08 10999.89 11997.73 21399.75 10599.48 169
MDA-MVSNet-bldmvs94.96 31993.98 32597.92 29698.24 34497.27 25899.15 25999.33 25093.80 33980.09 37199.03 32188.31 34397.86 35693.49 33894.36 32498.62 294
CDPH-MVS99.13 8498.91 10499.80 4399.75 6899.71 4999.15 25999.41 20896.60 26899.60 9499.55 21198.83 4799.90 11197.48 23899.83 7699.78 70
xxxxxxxxxxxxxcwj99.43 3699.32 3699.75 5499.76 5799.59 7399.14 26199.53 8699.00 2899.71 5599.80 8398.95 3299.93 7398.19 17299.84 6799.74 83
save fliter99.76 5799.59 7399.14 26199.40 21499.00 28
xiu_mvs_v1_base_debu99.29 6199.27 5699.34 13399.63 12998.97 15399.12 26399.51 10698.86 4899.84 1599.47 24298.18 10399.99 199.50 1399.31 15299.08 208
xiu_mvs_v1_base99.29 6199.27 5699.34 13399.63 12998.97 15399.12 26399.51 10698.86 4899.84 1599.47 24298.18 10399.99 199.50 1399.31 15299.08 208
xiu_mvs_v1_base_debi99.29 6199.27 5699.34 13399.63 12998.97 15399.12 26399.51 10698.86 4899.84 1599.47 24298.18 10399.99 199.50 1399.31 15299.08 208
XVG-OURS-SEG-HR98.69 14798.62 14398.89 19899.71 9597.74 24599.12 26399.54 7598.44 8499.42 13099.71 13694.20 24099.92 8598.54 14498.90 18699.00 219
jason99.13 8499.03 8499.45 12299.46 18298.87 16999.12 26399.26 27698.03 13699.79 3099.65 16997.02 13899.85 13699.02 6499.90 2599.65 122
jason: jason.
N_pmnet94.95 32095.83 30892.31 34698.47 33979.33 37399.12 26392.81 37993.87 33897.68 32699.13 31193.87 25199.01 32391.38 35196.19 28498.59 307
MDA-MVSNet_test_wron95.45 31494.60 32098.01 28998.16 34597.21 26399.11 26999.24 28093.49 34380.73 37098.98 32893.02 26398.18 34794.22 33194.45 32298.64 284
Patchmtry97.75 24797.40 26198.81 21699.10 26898.87 16999.11 26999.33 25094.83 32898.81 25399.38 26694.33 23699.02 32196.10 29895.57 30198.53 311
YYNet195.36 31694.51 32297.92 29697.89 34897.10 26599.10 27199.23 28193.26 34680.77 36999.04 32092.81 26998.02 35194.30 32894.18 32798.64 284
CANet_DTU98.97 11698.87 10999.25 15299.33 21198.42 21499.08 27299.30 26799.16 699.43 12799.75 11895.27 19799.97 1298.56 13999.95 899.36 188
SCA98.19 18098.16 17198.27 27399.30 22095.55 31999.07 27398.97 31097.57 18099.43 12799.57 20592.72 27399.74 18797.58 22699.20 15999.52 158
TSAR-MVS + GP.99.36 5399.36 2799.36 13299.67 11098.61 19499.07 27399.33 25099.00 2899.82 2399.81 6799.06 1699.84 14299.09 5899.42 14299.65 122
MG-MVS99.13 8499.02 8799.45 12299.57 14998.63 19199.07 27399.34 24398.99 3199.61 9099.82 5497.98 11299.87 12797.00 26899.80 8999.85 18
PatchMatch-RL98.84 13598.62 14399.52 10899.71 9599.28 11599.06 27699.77 997.74 16499.50 11499.53 22095.41 19199.84 14297.17 26199.64 12999.44 179
OpenMVS_ROBcopyleft92.34 2094.38 32593.70 32996.41 33897.38 35493.17 35799.06 27698.75 33086.58 36194.84 35698.26 35181.53 36699.32 27789.01 35997.87 23196.76 361
TEST999.67 11099.65 6299.05 27899.41 20896.22 29698.95 23299.49 23398.77 5599.91 96
train_agg99.02 10998.77 12399.77 5099.67 11099.65 6299.05 27899.41 20896.28 28998.95 23299.49 23398.76 5799.91 9697.63 22299.72 11299.75 78
lupinMVS99.13 8499.01 9199.46 12199.51 16198.94 16299.05 27899.16 29197.86 14699.80 2899.56 20897.39 12499.86 13098.94 7199.85 6099.58 147
DELS-MVS99.48 2199.42 1799.65 7599.72 8999.40 10399.05 27899.66 2799.14 799.57 10199.80 8398.46 8599.94 5899.57 899.84 6799.60 139
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
new_pmnet96.38 30396.03 30397.41 31898.13 34695.16 33299.05 27899.20 28693.94 33797.39 33198.79 33591.61 30699.04 31790.43 35495.77 29598.05 343
MVS_030496.79 29596.52 29597.59 31399.22 24194.92 33699.04 28399.59 4496.49 27498.43 29698.99 32580.48 36899.39 25897.15 26299.27 15598.47 317
Patchmatch-test97.93 21797.65 22998.77 22199.18 25097.07 26999.03 28499.14 29496.16 30298.74 26199.57 20594.56 22999.72 19893.36 33999.11 16699.52 158
test_899.67 11099.61 6899.03 28499.41 20896.28 28998.93 23699.48 23998.76 5799.91 96
Test_1112_low_res98.89 12098.66 13699.57 9299.69 10598.95 15999.03 28499.47 16596.98 23999.15 19699.23 30096.77 14799.89 11998.83 9698.78 19399.86 15
IterMVS-SCA-FT97.82 23697.75 22098.06 28599.57 14996.36 30299.02 28799.49 13497.18 22098.71 26499.72 13592.72 27399.14 30397.44 24495.86 29498.67 272
xiu_mvs_v2_base99.26 6699.25 6099.29 14699.53 15798.91 16699.02 28799.45 18798.80 5699.71 5599.26 29798.94 3599.98 799.34 3399.23 15798.98 222
MIMVSNet97.73 25197.45 25098.57 23699.45 18797.50 25299.02 28798.98 30996.11 30799.41 13499.14 31090.28 31998.74 34195.74 30698.93 18299.47 174
IterMVS97.83 23397.77 21698.02 28899.58 14796.27 30599.02 28799.48 14797.22 21898.71 26499.70 14092.75 27099.13 30697.46 24196.00 28898.67 272
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test99.11 9598.92 10299.65 7599.90 499.37 10499.02 28799.91 397.67 17299.59 9799.75 11895.90 17699.73 19499.53 1099.02 17799.86 15
新几何299.01 292
BH-w/o98.00 21097.89 20698.32 26699.35 20696.20 30799.01 29298.90 32196.42 28398.38 29999.00 32495.26 19999.72 19896.06 29998.61 19699.03 216
agg_prior199.01 11298.76 12599.76 5399.67 11099.62 6698.99 29499.40 21496.26 29298.87 24599.49 23398.77 5599.91 9697.69 21999.72 11299.75 78
test_prior499.56 7898.99 294
无先验98.99 29499.51 10696.89 24799.93 7397.53 23499.72 96
pmmvs498.13 18897.90 20298.81 21698.61 33298.87 16998.99 29499.21 28596.44 28199.06 21699.58 20195.90 17699.11 31197.18 26096.11 28698.46 321
HQP-NCC99.19 24798.98 29898.24 10298.66 273
ACMP_Plane99.19 24798.98 29898.24 10298.66 273
HQP-MVS98.02 20597.90 20298.37 26299.19 24796.83 28598.98 29899.39 21898.24 10298.66 27399.40 26192.47 28499.64 22697.19 25897.58 24098.64 284
PS-MVSNAJ99.32 5799.32 3699.30 14399.57 14998.94 16298.97 30199.46 17598.92 4599.71 5599.24 29999.01 1999.98 799.35 2999.66 12698.97 223
MVP-Stereo97.81 23897.75 22097.99 29297.53 35296.60 29598.96 30298.85 32597.22 21897.23 33499.36 27295.28 19699.46 24595.51 31199.78 9697.92 353
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_prior399.21 7299.05 7999.68 6899.67 11099.48 9398.96 30299.56 5898.34 9399.01 22199.52 22398.68 6899.83 15397.96 19299.74 10899.74 83
test_prior298.96 30298.34 9399.01 22199.52 22398.68 6897.96 19299.74 108
旧先验298.96 30296.70 25899.47 11999.94 5898.19 172
原ACMM298.95 306
MVS_111021_HR99.41 4699.32 3699.66 7199.72 8999.47 9598.95 30699.85 698.82 5299.54 10799.73 13198.51 8199.74 18798.91 7699.88 3899.77 72
MVS_111021_LR99.41 4699.33 3499.65 7599.77 5399.51 9098.94 30899.85 698.82 5299.65 7999.74 12498.51 8199.80 16998.83 9699.89 3599.64 129
pmmvs394.09 32793.25 33096.60 33694.76 36994.49 34298.92 30998.18 35289.66 35796.48 34598.06 35386.28 35497.33 36189.68 35787.20 35797.97 350
XVG-OURS98.73 14498.68 13298.88 20199.70 10297.73 24698.92 30999.55 6798.52 7599.45 12299.84 4395.27 19799.91 9698.08 18598.84 18999.00 219
test22299.75 6899.49 9198.91 31199.49 13496.42 28399.34 15599.65 16998.28 9999.69 11899.72 96
PMMVS286.87 33285.37 33691.35 34990.21 37383.80 36898.89 31297.45 36283.13 36591.67 36395.03 36348.49 37694.70 37085.86 36877.62 36795.54 364
miper_lstm_enhance98.00 21097.91 20198.28 27299.34 21097.43 25498.88 31399.36 23496.48 27898.80 25599.55 21195.98 16998.91 33797.27 25095.50 30498.51 313
MVS-HIRNet95.75 31295.16 31697.51 31699.30 22093.69 35398.88 31395.78 36985.09 36398.78 25892.65 36791.29 31099.37 26394.85 32399.85 6099.46 176
TR-MVS97.76 24397.41 26098.82 21499.06 27597.87 23998.87 31598.56 34496.63 26598.68 27299.22 30192.49 28399.65 22395.40 31497.79 23298.95 228
testdata198.85 31698.32 97
ET-MVSNet_ETH3D96.49 30095.64 31199.05 17099.53 15798.82 17798.84 31797.51 36197.63 17584.77 36599.21 30492.09 29298.91 33798.98 6792.21 34799.41 185
our_test_397.65 26697.68 22697.55 31598.62 33094.97 33498.84 31799.30 26796.83 25298.19 30999.34 27897.01 13999.02 32195.00 32296.01 28798.64 284
MS-PatchMatch97.24 28797.32 27396.99 32798.45 34093.51 35698.82 31999.32 26097.41 20198.13 31299.30 28888.99 33599.56 23795.68 30899.80 8997.90 354
c3_l98.12 19098.04 18698.38 26199.30 22097.69 25098.81 32099.33 25096.67 26098.83 25199.34 27897.11 13498.99 32597.58 22695.34 30698.48 315
ppachtmachnet_test97.49 27997.45 25097.61 31298.62 33095.24 32898.80 32199.46 17596.11 30798.22 30899.62 18896.45 15798.97 33393.77 33495.97 29298.61 303
PAPR98.63 15398.34 16299.51 11299.40 19799.03 14598.80 32199.36 23496.33 28699.00 22699.12 31498.46 8599.84 14295.23 31899.37 15199.66 118
test0.0.03 197.71 25797.42 25998.56 23898.41 34197.82 24298.78 32398.63 34297.34 20598.05 31798.98 32894.45 23398.98 32695.04 32197.15 26798.89 229
PVSNet_Blended99.08 10198.97 9699.42 12799.76 5798.79 18098.78 32399.91 396.74 25599.67 6899.49 23397.53 12199.88 12498.98 6799.85 6099.60 139
PMMVS98.80 13998.62 14399.34 13399.27 22998.70 18598.76 32599.31 26397.34 20599.21 18499.07 31697.20 13299.82 16098.56 13998.87 18799.52 158
test12339.01 34442.50 34628.53 35939.17 38220.91 38398.75 32619.17 38419.83 37738.57 37666.67 37433.16 37915.42 37837.50 37729.66 37649.26 373
MSDG98.98 11498.80 12099.53 10299.76 5799.19 12398.75 32699.55 6797.25 21499.47 11999.77 10997.82 11599.87 12796.93 27599.90 2599.54 152
CLD-MVS98.16 18498.10 17798.33 26499.29 22496.82 28798.75 32699.44 19697.83 15199.13 19899.55 21192.92 26699.67 21698.32 16597.69 23498.48 315
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
miper_ehance_all_eth98.18 18298.10 17798.41 25799.23 23797.72 24798.72 32999.31 26396.60 26898.88 24399.29 29097.29 13099.13 30697.60 22495.99 28998.38 329
cl____98.01 20897.84 20998.55 24099.25 23597.97 23298.71 33099.34 24396.47 28098.59 28799.54 21695.65 18699.21 29797.21 25495.77 29598.46 321
DIV-MVS_self_test98.01 20897.85 20898.48 24699.24 23697.95 23698.71 33099.35 23996.50 27398.60 28699.54 21695.72 18399.03 31997.21 25495.77 29598.46 321
test-LLR98.06 19597.90 20298.55 24098.79 30997.10 26598.67 33297.75 35797.34 20598.61 28498.85 33294.45 23399.45 24697.25 25299.38 14499.10 203
TESTMET0.1,197.55 27097.27 27998.40 25998.93 29396.53 29698.67 33297.61 36096.96 24198.64 28099.28 29288.63 34099.45 24697.30 24999.38 14499.21 198
test-mter97.49 27997.13 28498.55 24098.79 30997.10 26598.67 33297.75 35796.65 26298.61 28498.85 33288.23 34499.45 24697.25 25299.38 14499.10 203
IB-MVS95.67 1896.22 30495.44 31498.57 23699.21 24396.70 29098.65 33597.74 35996.71 25797.27 33398.54 34486.03 35599.92 8598.47 15086.30 35899.10 203
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
DPM-MVS98.95 11798.71 12999.66 7199.63 12999.55 8098.64 33699.10 29797.93 14299.42 13099.55 21198.67 7199.80 16995.80 30599.68 12399.61 137
thisisatest051598.14 18797.79 21199.19 15899.50 17198.50 20698.61 33796.82 36596.95 24399.54 10799.43 25191.66 30499.86 13098.08 18599.51 13999.22 197
DeepPCF-MVS98.18 398.81 13699.37 2597.12 32699.60 14391.75 36398.61 33799.44 19699.35 199.83 2099.85 3498.70 6799.81 16499.02 6499.91 1899.81 46
cl2297.85 22897.64 23198.48 24699.09 27097.87 23998.60 33999.33 25097.11 22998.87 24599.22 30192.38 28999.17 30198.21 17095.99 28998.42 324
GA-MVS97.85 22897.47 24799.00 17799.38 20197.99 23198.57 34099.15 29297.04 23598.90 24099.30 28889.83 32699.38 26096.70 28698.33 20999.62 135
TinyColmap97.12 28996.89 28997.83 30299.07 27395.52 32298.57 34098.74 33397.58 17997.81 32499.79 9588.16 34599.56 23795.10 31997.21 26398.39 328
eth_miper_zixun_eth98.05 20097.96 19598.33 26499.26 23197.38 25598.56 34299.31 26396.65 26298.88 24399.52 22396.58 15299.12 31097.39 24795.53 30398.47 317
CMPMVSbinary69.68 2394.13 32694.90 31891.84 34797.24 35880.01 37298.52 34399.48 14789.01 35891.99 36299.67 16285.67 35799.13 30695.44 31297.03 26896.39 363
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC97.34 28397.20 28197.75 30799.07 27395.20 32998.51 34499.04 30597.99 13898.31 30499.86 2889.02 33499.55 23995.67 30997.36 26098.49 314
ambc93.06 34592.68 37082.36 36998.47 34598.73 33895.09 35497.41 35655.55 37499.10 31396.42 29491.32 34997.71 355
miper_enhance_ethall98.16 18498.08 18198.41 25798.96 29197.72 24798.45 34699.32 26096.95 24398.97 23099.17 30697.06 13799.22 29297.86 20095.99 28998.29 332
CHOSEN 280x42099.12 9099.13 7199.08 16699.66 11997.89 23898.43 34799.71 1398.88 4799.62 8899.76 11396.63 15199.70 21099.46 2199.99 199.66 118
testmvs39.17 34343.78 34525.37 36036.04 38316.84 38498.36 34826.56 38220.06 37638.51 37767.32 37329.64 38015.30 37937.59 37639.90 37543.98 374
FPMVS84.93 33485.65 33582.75 35586.77 37663.39 38098.35 34998.92 31674.11 36783.39 36798.98 32850.85 37592.40 37284.54 36994.97 31492.46 366
KD-MVS_2432*160094.62 32193.72 32797.31 32097.19 36095.82 31498.34 35099.20 28695.00 32597.57 32798.35 34887.95 34898.10 34992.87 34577.00 36898.01 345
miper_refine_blended94.62 32193.72 32797.31 32097.19 36095.82 31498.34 35099.20 28695.00 32597.57 32798.35 34887.95 34898.10 34992.87 34577.00 36898.01 345
CL-MVSNet_self_test94.49 32393.97 32696.08 33996.16 36393.67 35498.33 35299.38 22495.13 32097.33 33298.15 35292.69 27796.57 36688.67 36079.87 36697.99 348
PVSNet96.02 1798.85 13298.84 11598.89 19899.73 8497.28 25798.32 35399.60 4197.86 14699.50 11499.57 20596.75 14899.86 13098.56 13999.70 11799.54 152
PAPM97.59 26997.09 28599.07 16799.06 27598.26 22098.30 35499.10 29794.88 32798.08 31399.34 27896.27 16399.64 22689.87 35698.92 18499.31 193
Patchmatch-RL test95.84 31195.81 30995.95 34095.61 36590.57 36598.24 35598.39 34795.10 32495.20 35398.67 34094.78 21597.77 35796.28 29790.02 35299.51 164
UnsupCasMVSNet_bld93.53 32892.51 33196.58 33797.38 35493.82 34998.24 35599.48 14791.10 35593.10 36096.66 36174.89 36998.37 34594.03 33387.71 35697.56 358
LCM-MVSNet86.80 33385.22 33791.53 34887.81 37580.96 37198.23 35798.99 30871.05 36890.13 36496.51 36248.45 37796.88 36590.51 35385.30 35996.76 361
cascas97.69 25997.43 25898.48 24698.60 33397.30 25698.18 35899.39 21892.96 34898.41 29798.78 33793.77 25499.27 28598.16 17798.61 19698.86 230
Effi-MVS+98.81 13698.59 14999.48 11699.46 18299.12 13798.08 35999.50 12697.50 19099.38 14499.41 25896.37 16099.81 16499.11 5698.54 20399.51 164
PCF-MVS97.08 1497.66 26597.06 28699.47 11999.61 13999.09 13998.04 36099.25 27891.24 35498.51 29099.70 14094.55 23099.91 9692.76 34799.85 6099.42 181
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
bset_n11_16_dypcd98.16 18497.97 19398.73 22398.26 34398.28 21997.99 36198.01 35497.68 16999.10 20599.63 18295.68 18499.15 30298.78 10596.55 27598.75 244
PVSNet_094.43 1996.09 30995.47 31297.94 29499.31 21994.34 34697.81 36299.70 1597.12 22697.46 32998.75 33889.71 32899.79 17297.69 21981.69 36499.68 112
E-PMN80.61 33779.88 33982.81 35490.75 37276.38 37697.69 36395.76 37066.44 37283.52 36692.25 36862.54 37387.16 37468.53 37361.40 37184.89 372
ANet_high77.30 33974.86 34384.62 35375.88 37977.61 37497.63 36493.15 37888.81 35964.27 37489.29 37136.51 37883.93 37675.89 37152.31 37392.33 368
EMVS80.02 33879.22 34082.43 35691.19 37176.40 37597.55 36592.49 38066.36 37383.01 36891.27 36964.63 37285.79 37565.82 37460.65 37285.08 371
MVEpermissive76.82 2176.91 34074.31 34484.70 35285.38 37876.05 37796.88 36693.17 37767.39 37171.28 37389.01 37221.66 38387.69 37371.74 37272.29 37090.35 369
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method91.10 33091.36 33390.31 35095.85 36473.72 37894.89 36799.25 27868.39 37095.82 35199.02 32380.50 36798.95 33593.64 33694.89 31798.25 335
Gipumacopyleft90.99 33190.15 33493.51 34398.73 31890.12 36693.98 36899.45 18779.32 36692.28 36194.91 36469.61 37097.98 35387.42 36495.67 29992.45 367
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVScopyleft70.75 2275.98 34174.97 34279.01 35770.98 38055.18 38193.37 36998.21 35065.08 37461.78 37593.83 36621.74 38292.53 37178.59 37091.12 35089.34 370
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt82.80 33581.52 33886.66 35166.61 38168.44 37992.79 37097.92 35568.96 36980.04 37299.85 3485.77 35696.15 36997.86 20043.89 37495.39 365
wuyk23d40.18 34241.29 34736.84 35886.18 37749.12 38279.73 37122.81 38327.64 37525.46 37828.45 37821.98 38148.89 37755.80 37523.56 37712.51 375
test_blank0.13 3480.17 3510.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3801.57 3790.00 3840.00 3800.00 3780.00 3780.00 376
uanet_test0.02 3490.03 3520.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.27 3800.00 3840.00 3800.00 3780.00 3780.00 376
DCPMVS0.02 3490.03 3520.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.27 3800.00 3840.00 3800.00 3780.00 3780.00 376
cdsmvs_eth3d_5k24.64 34532.85 3480.00 3610.00 3840.00 3850.00 37299.51 1060.00 3790.00 38099.56 20896.58 1520.00 3800.00 3780.00 3780.00 376
pcd_1.5k_mvsjas8.27 34711.03 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.27 38099.01 190.00 3800.00 3780.00 3780.00 376
sosnet-low-res0.02 3490.03 3520.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.27 3800.00 3840.00 3800.00 3780.00 3780.00 376
sosnet0.02 3490.03 3520.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.27 3800.00 3840.00 3800.00 3780.00 3780.00 376
uncertanet0.02 3490.03 3520.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.27 3800.00 3840.00 3800.00 3780.00 3780.00 376
Regformer0.02 3490.03 3520.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.27 3800.00 3840.00 3800.00 3780.00 3780.00 376
ab-mvs-re8.30 34611.06 3490.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 38099.58 2010.00 3840.00 3800.00 3780.00 3780.00 376
uanet0.02 3490.03 3520.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 3800.27 3800.00 3840.00 3800.00 3780.00 3780.00 376
MSC_two_6792asdad99.87 1299.51 16199.76 4199.33 25099.96 2098.87 8399.84 6799.89 2
PC_three_145298.18 11299.84 1599.70 14099.31 398.52 34498.30 16799.80 8999.81 46
No_MVS99.87 1299.51 16199.76 4199.33 25099.96 2098.87 8399.84 6799.89 2
test_one_060199.81 4299.88 899.49 13498.97 3799.65 7999.81 6799.09 14
eth-test20.00 384
eth-test0.00 384
ZD-MVS99.71 9599.79 3399.61 3696.84 25099.56 10299.54 21698.58 7599.96 2096.93 27599.75 105
IU-MVS99.84 3399.88 899.32 26098.30 9899.84 1598.86 8899.85 6099.89 2
test_241102_TWO99.48 14799.08 1699.88 699.81 6798.94 3599.96 2098.91 7699.84 6799.88 8
test_241102_ONE99.84 3399.90 299.48 14799.07 1899.91 299.74 12499.20 799.76 184
test_0728_THIRD98.99 3199.81 2599.80 8399.09 1499.96 2098.85 9099.90 2599.88 8
GSMVS99.52 158
test_part299.81 4299.83 1799.77 37
sam_mvs194.86 21199.52 158
sam_mvs94.72 222
MTGPAbinary99.47 165
test_post65.99 37594.65 22699.73 194
patchmatchnet-post98.70 33994.79 21499.74 187
gm-plane-assit98.54 33792.96 35894.65 33299.15 30999.64 22697.56 231
test9_res97.49 23799.72 11299.75 78
agg_prior297.21 25499.73 11199.75 78
agg_prior99.67 11099.62 6699.40 21498.87 24599.91 96
TestCases99.31 13999.86 2298.48 20999.61 3697.85 14899.36 14999.85 3495.95 17199.85 13696.66 28999.83 7699.59 143
test_prior99.68 6899.67 11099.48 9399.56 5899.83 15399.74 83
新几何199.75 5499.75 6899.59 7399.54 7596.76 25499.29 16399.64 17698.43 8799.94 5896.92 27799.66 12699.72 96
旧先验199.74 7699.59 7399.54 7599.69 14998.47 8499.68 12399.73 90
原ACMM199.65 7599.73 8499.33 10899.47 16597.46 19199.12 20099.66 16898.67 7199.91 9697.70 21899.69 11899.71 103
testdata299.95 4796.67 288
segment_acmp98.96 29
testdata99.54 9699.75 6898.95 15999.51 10697.07 23299.43 12799.70 14098.87 4399.94 5897.76 20999.64 12999.72 96
test1299.75 5499.64 12699.61 6899.29 27299.21 18498.38 9299.89 11999.74 10899.74 83
plane_prior799.29 22497.03 275
plane_prior699.27 22996.98 27992.71 275
plane_prior599.47 16599.69 21497.78 20797.63 23598.67 272
plane_prior499.61 192
plane_prior397.00 27798.69 6499.11 202
plane_prior199.26 231
n20.00 385
nn0.00 385
door-mid98.05 353
lessismore_v097.79 30698.69 32495.44 32594.75 37395.71 35299.87 2588.69 33899.32 27795.89 30294.93 31698.62 294
LGP-MVS_train98.49 24499.33 21197.05 27199.55 6797.46 19199.24 17699.83 4792.58 28099.72 19898.09 18197.51 24698.68 265
test1199.35 239
door97.92 355
HQP5-MVS96.83 285
BP-MVS97.19 258
HQP4-MVS98.66 27399.64 22698.64 284
HQP3-MVS99.39 21897.58 240
HQP2-MVS92.47 284
NP-MVS99.23 23796.92 28399.40 261
ACMMP++_ref97.19 264
ACMMP++97.43 256
Test By Simon98.75 60
ITE_SJBPF98.08 28399.29 22496.37 30198.92 31698.34 9398.83 25199.75 11891.09 31299.62 23295.82 30397.40 25898.25 335
DeepMVS_CXcopyleft93.34 34499.29 22482.27 37099.22 28285.15 36296.33 34699.05 31990.97 31499.73 19493.57 33797.77 23398.01 345