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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
AdaColmapbinary97.23 13096.80 13998.51 13299.99 195.60 20099.09 30798.84 6593.32 20396.74 21499.72 9486.04 259100.00 198.01 15299.43 12999.94 86
CNVR-MVS99.40 199.26 199.84 699.98 299.51 699.98 2198.69 8198.20 999.93 299.98 296.82 26100.00 199.75 41100.00 199.99 24
MCST-MVS99.32 399.14 499.86 599.97 399.59 599.97 3998.64 9098.47 399.13 10499.92 1796.38 36100.00 199.74 43100.00 1100.00 1
mPP-MVS98.39 5698.20 5498.97 9299.97 396.92 13899.95 7298.38 18395.04 12398.61 13899.80 5893.39 117100.00 198.64 114100.00 199.98 56
CPTT-MVS97.64 11097.32 11498.58 12199.97 395.77 18999.96 5398.35 18989.90 33898.36 15399.79 6291.18 17699.99 3998.37 13099.99 2199.99 24
DP-MVS Recon98.41 5398.02 6899.56 2999.97 398.70 5299.92 10098.44 14792.06 27098.40 15299.84 4895.68 47100.00 198.19 14199.71 9299.97 66
PAPR98.52 4398.16 5899.58 2899.97 398.77 4699.95 7298.43 15595.35 11798.03 16799.75 8094.03 10299.98 5098.11 14699.83 8199.99 24
MED-MVS test99.60 2399.96 898.79 4199.97 3998.88 5496.36 8899.07 10999.93 11100.00 199.98 999.96 4699.99 24
MED-MVS99.15 899.00 1299.60 2399.96 898.79 4199.97 3998.88 5495.89 10199.07 10999.93 1197.36 17100.00 199.98 999.96 4699.99 24
TestfortrainingZip a99.09 1098.87 1999.76 1099.96 899.27 1899.97 3998.88 5496.36 8899.07 10999.93 1197.36 17100.00 198.32 13399.96 46100.00 1
HFP-MVS98.56 3998.37 4399.14 7299.96 897.43 11499.95 7298.61 9894.77 13399.31 9299.85 3794.22 95100.00 198.70 10999.98 3299.98 56
region2R98.54 4198.37 4399.05 8299.96 897.18 12499.96 5398.55 11894.87 13099.45 7999.85 3794.07 101100.00 198.67 111100.00 199.98 56
ACMMPR98.50 4498.32 4799.05 8299.96 897.18 12499.95 7298.60 10094.77 13399.31 9299.84 4893.73 111100.00 198.70 10999.98 3299.98 56
NCCC99.37 299.25 299.71 1699.96 899.15 2399.97 3998.62 9798.02 2299.90 699.95 397.33 19100.00 199.54 58100.00 1100.00 1
CP-MVS98.45 4898.32 4798.87 9799.96 896.62 15299.97 3998.39 17994.43 15098.90 11999.87 3194.30 92100.00 199.04 8499.99 2199.99 24
test_one_060199.94 1699.30 1298.41 17296.63 7399.75 4099.93 1197.49 10
test_0728_SECOND99.82 799.94 1699.47 799.95 7298.43 155100.00 199.99 5100.00 1100.00 1
XVS98.70 3298.55 3199.15 7099.94 1697.50 11099.94 9098.42 16796.22 9299.41 8499.78 6694.34 8999.96 7598.92 9499.95 5499.99 24
X-MVStestdata93.83 27392.06 30899.15 7099.94 1697.50 11099.94 9098.42 16796.22 9299.41 8441.37 48494.34 8999.96 7598.92 9499.95 5499.99 24
test_prior99.43 4099.94 1698.49 6598.65 8799.80 14299.99 24
MSLP-MVS++99.13 999.01 1199.49 3699.94 1698.46 6699.98 2198.86 5997.10 5399.80 2699.94 495.92 43100.00 199.51 59100.00 1100.00 1
APDe-MVScopyleft99.06 1498.91 1599.51 3399.94 1698.76 4999.91 10898.39 17997.20 5199.46 7899.85 3795.53 5199.79 14499.86 27100.00 199.99 24
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MP-MVScopyleft98.23 7197.97 7299.03 8499.94 1697.17 12799.95 7298.39 17994.70 13798.26 15999.81 5791.84 167100.00 198.85 10099.97 4299.93 87
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CDPH-MVS98.65 3598.36 4599.49 3699.94 1698.73 5099.87 13098.33 19493.97 17599.76 3999.87 3194.99 6799.75 15398.55 118100.00 199.98 56
PAPM_NR98.12 7597.93 7898.70 10899.94 1696.13 17899.82 16098.43 15594.56 14197.52 18499.70 10094.40 8499.98 5097.00 19299.98 3299.99 24
MG-MVS98.91 2298.65 2799.68 1799.94 1699.07 2599.64 22399.44 1997.33 4499.00 11599.72 9494.03 10299.98 5098.73 108100.00 1100.00 1
ME-MVS99.07 1298.89 1799.59 2699.93 2798.79 4199.95 7298.80 7195.89 10199.28 9699.93 1196.28 3799.98 5099.98 999.96 4699.99 24
SED-MVS99.28 599.11 799.77 899.93 2799.30 1299.96 5398.43 15597.27 4799.80 2699.94 496.71 29100.00 1100.00 1100.00 1100.00 1
IU-MVS99.93 2799.31 1098.41 17297.71 3199.84 21100.00 1100.00 1100.00 1
test_241102_ONE99.93 2799.30 1298.43 15597.26 4999.80 2699.88 2896.71 29100.00 1
DVP-MVScopyleft99.30 499.16 399.73 1399.93 2799.29 1599.95 7298.32 19697.28 4599.83 2299.91 1897.22 21100.00 199.99 5100.00 199.89 96
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
test072699.93 2799.29 1599.96 5398.42 16797.28 4599.86 1599.94 497.22 21
MSP-MVS99.09 1099.12 598.98 9199.93 2797.24 12199.95 7298.42 16797.50 3899.52 7499.88 2897.43 1699.71 15999.50 6199.98 32100.00 1
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
agg_prior99.93 2798.77 4698.43 15599.63 5799.85 129
FOURS199.92 3597.66 10499.95 7298.36 18795.58 11199.52 74
ZD-MVS99.92 3598.57 6098.52 12792.34 25899.31 9299.83 5095.06 6299.80 14299.70 4999.97 42
GST-MVS98.27 6397.97 7299.17 6599.92 3597.57 10699.93 9798.39 17994.04 17398.80 12499.74 8792.98 133100.00 198.16 14399.76 8999.93 87
TEST999.92 3598.92 3099.96 5398.43 15593.90 18199.71 4799.86 3395.88 4499.85 129
train_agg98.88 2398.65 2799.59 2699.92 3598.92 3099.96 5398.43 15594.35 15599.71 4799.86 3395.94 4199.85 12999.69 5099.98 3299.99 24
test_899.92 3598.88 3399.96 5398.43 15594.35 15599.69 4999.85 3795.94 4199.85 129
PGM-MVS98.34 5898.13 6098.99 8999.92 3597.00 13499.75 18599.50 1793.90 18199.37 8999.76 7293.24 126100.00 197.75 17199.96 4699.98 56
ACMMPcopyleft97.74 10397.44 10798.66 11299.92 3596.13 17899.18 30099.45 1894.84 13196.41 22999.71 9791.40 17099.99 3997.99 15498.03 18999.87 99
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
DVP-MVS++99.26 699.09 999.77 899.91 4399.31 1099.95 7298.43 15596.48 7899.80 2699.93 1197.44 14100.00 199.92 1699.98 32100.00 1
MSC_two_6792asdad99.93 299.91 4399.80 298.41 172100.00 199.96 12100.00 1100.00 1
No_MVS99.93 299.91 4399.80 298.41 172100.00 199.96 12100.00 1100.00 1
HPM-MVS++copyleft99.07 1298.88 1899.63 1899.90 4699.02 2699.95 7298.56 11297.56 3799.44 8099.85 3795.38 55100.00 199.31 7199.99 2199.87 99
APD-MVScopyleft98.62 3698.35 4699.41 4399.90 4698.51 6399.87 13098.36 18794.08 16899.74 4399.73 9194.08 10099.74 15599.42 6799.99 2199.99 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast96.59 198.81 2698.54 3299.62 2199.90 4698.85 3699.24 29598.47 13998.14 1699.08 10799.91 1893.09 130100.00 199.04 8499.99 21100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OPU-MVS99.93 299.89 4999.80 299.96 5399.80 5897.44 14100.00 1100.00 199.98 32100.00 1
DPE-MVScopyleft99.26 699.10 899.74 1299.89 4999.24 2099.87 13098.44 14797.48 3999.64 5699.94 496.68 3199.99 3999.99 5100.00 199.99 24
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part299.89 4999.25 1999.49 77
CSCG97.10 13697.04 12697.27 23199.89 4991.92 32199.90 11499.07 3788.67 36295.26 26199.82 5393.17 12999.98 5098.15 14499.47 12499.90 95
ZNCC-MVS98.31 6098.03 6799.17 6599.88 5397.59 10599.94 9098.44 14794.31 15898.50 14599.82 5393.06 13199.99 3998.30 13599.99 2199.93 87
SR-MVS98.46 4798.30 5098.93 9599.88 5397.04 13399.84 14998.35 18994.92 12799.32 9199.80 5893.35 11999.78 14699.30 7299.95 5499.96 74
9.1498.38 4199.87 5599.91 10898.33 19493.22 20699.78 3799.89 2694.57 8099.85 12999.84 2999.97 42
SMA-MVScopyleft98.76 2998.48 3599.62 2199.87 5598.87 3499.86 14198.38 18393.19 20899.77 3899.94 495.54 49100.00 199.74 4399.99 21100.00 1
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
NormalMVS97.90 8597.85 8598.04 16599.86 5795.39 21099.61 23097.78 26596.52 7698.61 13899.31 15692.73 14199.67 16796.77 20399.48 12199.06 243
lecture98.67 3398.46 3699.28 5299.86 5797.88 9199.97 3999.25 3096.07 9699.79 3599.70 10092.53 14999.98 5099.51 5999.48 12199.97 66
PHI-MVS98.41 5398.21 5399.03 8499.86 5797.10 13199.98 2198.80 7190.78 31799.62 6099.78 6695.30 56100.00 199.80 3299.93 6599.99 24
MTAPA98.29 6297.96 7599.30 5199.85 6097.93 8999.39 27398.28 20395.76 10597.18 19999.88 2892.74 140100.00 198.67 11199.88 7799.99 24
LS3D95.84 20595.11 21898.02 16699.85 6095.10 22898.74 35798.50 13687.22 38493.66 28299.86 3387.45 23599.95 8490.94 32099.81 8799.02 247
HPM-MVScopyleft97.96 8097.72 9098.68 10999.84 6296.39 16499.90 11498.17 21892.61 24398.62 13799.57 13091.87 16699.67 16798.87 9999.99 2199.99 24
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EI-MVSNet-Vis-set98.27 6398.11 6298.75 10599.83 6396.59 15699.40 26998.51 13095.29 11998.51 14499.76 7293.60 11599.71 15998.53 12199.52 11499.95 82
save fliter99.82 6498.79 4199.96 5398.40 17697.66 33
PLCcopyleft95.54 397.93 8397.89 8298.05 16499.82 6494.77 23999.92 10098.46 14193.93 17897.20 19799.27 16295.44 5499.97 6397.41 17799.51 11799.41 193
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
APD-MVS_3200maxsize98.25 6898.08 6498.78 10299.81 6696.60 15499.82 16098.30 20193.95 17799.37 8999.77 7092.84 13799.76 15298.95 9099.92 6899.97 66
EI-MVSNet-UG-set98.14 7497.99 7098.60 11799.80 6796.27 16799.36 27998.50 13695.21 12198.30 15699.75 8093.29 12399.73 15898.37 13099.30 13899.81 108
SR-MVS-dyc-post98.31 6098.17 5798.71 10799.79 6896.37 16599.76 18198.31 19894.43 15099.40 8699.75 8093.28 12499.78 14698.90 9799.92 6899.97 66
RE-MVS-def98.13 6099.79 6896.37 16599.76 18198.31 19894.43 15099.40 8699.75 8092.95 13498.90 9799.92 6899.97 66
HPM-MVS_fast97.80 9797.50 10398.68 10999.79 6896.42 16099.88 12798.16 22391.75 28198.94 11799.54 13391.82 16899.65 17197.62 17499.99 2199.99 24
SF-MVS98.67 3398.40 3999.50 3499.77 7198.67 5399.90 11498.21 21393.53 19399.81 2499.89 2694.70 7699.86 12899.84 2999.93 6599.96 74
MGCNet99.06 1498.84 2099.72 1499.76 7299.21 2299.99 599.34 2598.70 299.44 8099.75 8093.24 12699.99 3999.94 1499.41 13199.95 82
旧先验199.76 7297.52 10898.64 9099.85 3795.63 4899.94 5999.99 24
OMC-MVS97.28 12697.23 11897.41 22199.76 7293.36 28899.65 21997.95 24596.03 9797.41 19099.70 10089.61 20299.51 17796.73 20598.25 17999.38 195
新几何199.42 4299.75 7598.27 7098.63 9692.69 23899.55 6999.82 5394.40 84100.00 191.21 31299.94 5999.99 24
MP-MVS-pluss98.07 7897.64 9699.38 4899.74 7698.41 6899.74 18898.18 21793.35 20196.45 22699.85 3792.64 14499.97 6398.91 9699.89 7499.77 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.98.93 2098.77 2299.41 4399.74 7698.67 5399.77 17598.38 18396.73 6999.88 1299.74 8794.89 6999.59 17399.80 3299.98 3299.97 66
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test1299.43 4099.74 7698.56 6198.40 17699.65 5394.76 7299.75 15399.98 3299.99 24
原ACMM198.96 9399.73 7996.99 13598.51 13094.06 17199.62 6099.85 3794.97 6899.96 7595.11 23399.95 5499.92 92
TSAR-MVS + GP.98.60 3798.51 3498.86 9899.73 7996.63 15199.97 3997.92 25098.07 1998.76 13099.55 13195.00 6699.94 9399.91 1997.68 19699.99 24
CANet98.27 6397.82 8799.63 1899.72 8199.10 2499.98 2198.51 13097.00 5998.52 14299.71 9787.80 22699.95 8499.75 4199.38 13399.83 104
reproduce_model98.75 3098.66 2699.03 8499.71 8297.10 13199.73 19598.23 21197.02 5899.18 10299.90 2294.54 8199.99 3999.77 3799.90 7399.99 24
F-COLMAP96.93 14896.95 12996.87 24499.71 8291.74 32699.85 14497.95 24593.11 21595.72 25099.16 18092.35 15599.94 9395.32 22999.35 13698.92 255
reproduce-ours98.78 2798.67 2499.09 7999.70 8497.30 11899.74 18898.25 20797.10 5399.10 10599.90 2294.59 7799.99 3999.77 3799.91 7199.99 24
our_new_method98.78 2798.67 2499.09 7999.70 8497.30 11899.74 18898.25 20797.10 5399.10 10599.90 2294.59 7799.99 3999.77 3799.91 7199.99 24
SD-MVS98.92 2198.70 2399.56 2999.70 8498.73 5099.94 9098.34 19396.38 8499.81 2499.76 7294.59 7799.98 5099.84 2999.96 4699.97 66
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
patch_mono-298.24 6999.12 595.59 28699.67 8786.91 41099.95 7298.89 5297.60 3499.90 699.76 7296.54 3499.98 5099.94 1499.82 8599.88 97
ACMMP_NAP98.49 4598.14 5999.54 3199.66 8898.62 5999.85 14498.37 18694.68 13899.53 7299.83 5092.87 136100.00 198.66 11399.84 8099.99 24
DeepPCF-MVS95.94 297.71 10798.98 1393.92 35299.63 8981.76 44599.96 5398.56 11299.47 199.19 10199.99 194.16 99100.00 199.92 1699.93 65100.00 1
EPNet98.49 4598.40 3998.77 10499.62 9096.80 14599.90 11499.51 1697.60 3499.20 9999.36 15193.71 11299.91 11097.99 15498.71 16499.61 146
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MM98.83 2498.53 3399.76 1099.59 9199.33 899.99 599.76 698.39 499.39 8899.80 5890.49 19199.96 7599.89 2199.43 12999.98 56
PVSNet_BlendedMVS96.05 19595.82 18996.72 25099.59 9196.99 13599.95 7299.10 3494.06 17198.27 15795.80 35689.00 21499.95 8499.12 7887.53 34893.24 411
PVSNet_Blended97.94 8297.64 9698.83 9999.59 9196.99 135100.00 199.10 3495.38 11698.27 15799.08 18489.00 21499.95 8499.12 7899.25 14099.57 157
PatchMatch-RL96.04 19695.40 20597.95 16899.59 9195.22 22399.52 25199.07 3793.96 17696.49 22498.35 27182.28 30999.82 14190.15 33699.22 14398.81 262
dcpmvs_297.42 12198.09 6395.42 29399.58 9587.24 40699.23 29696.95 38494.28 16198.93 11899.73 9194.39 8799.16 20599.89 2199.82 8599.86 101
test22299.55 9697.41 11699.34 28198.55 11891.86 27699.27 9799.83 5093.84 10999.95 5499.99 24
CNLPA97.76 10197.38 11098.92 9699.53 9796.84 14099.87 13098.14 22793.78 18596.55 22299.69 10492.28 15799.98 5097.13 18799.44 12899.93 87
API-MVS97.86 8897.66 9498.47 13499.52 9895.41 20899.47 26198.87 5891.68 28298.84 12199.85 3792.34 15699.99 3998.44 12699.96 46100.00 1
PVSNet91.05 1397.13 13596.69 14598.45 13799.52 9895.81 18799.95 7299.65 1294.73 13599.04 11399.21 17384.48 29199.95 8494.92 23998.74 16399.58 155
114514_t97.41 12296.83 13699.14 7299.51 10097.83 9399.89 12498.27 20588.48 36699.06 11299.66 11590.30 19499.64 17296.32 21499.97 4299.96 74
cl2293.77 27893.25 28295.33 29799.49 10194.43 24999.61 23098.09 23090.38 32689.16 35395.61 36490.56 18997.34 33891.93 30384.45 37094.21 356
testdata98.42 14199.47 10295.33 21498.56 11293.78 18599.79 3599.85 3793.64 11499.94 9394.97 23799.94 59100.00 1
MAR-MVS97.43 11797.19 12098.15 15799.47 10294.79 23899.05 31898.76 7392.65 24198.66 13599.82 5388.52 22099.98 5098.12 14599.63 9999.67 129
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
DP-MVS94.54 25093.42 27297.91 17499.46 10494.04 26598.93 33697.48 30281.15 43990.04 32499.55 13187.02 24399.95 8488.97 34898.11 18599.73 119
MVS_111021_LR98.42 5298.38 4198.53 12999.39 10595.79 18899.87 13099.86 296.70 7098.78 12599.79 6292.03 16399.90 11299.17 7799.86 7999.88 97
CHOSEN 280x42099.01 1799.03 1098.95 9499.38 10698.87 3498.46 37699.42 2197.03 5799.02 11499.09 18399.35 298.21 30099.73 4599.78 8899.77 115
MVS_111021_HR98.72 3198.62 2999.01 8899.36 10797.18 12499.93 9799.90 196.81 6798.67 13499.77 7093.92 10499.89 11799.27 7399.94 5999.96 74
fmvsm_s_conf0.5_n_1198.03 7997.89 8298.46 13699.35 10897.76 9799.99 598.04 23698.20 999.90 699.78 6686.21 25799.95 8499.89 2199.68 9497.65 300
DPM-MVS98.83 2498.46 3699.97 199.33 10999.92 199.96 5398.44 14797.96 2399.55 6999.94 497.18 23100.00 193.81 27099.94 5999.98 56
TAPA-MVS92.12 894.42 25893.60 26496.90 24399.33 10991.78 32599.78 17098.00 23989.89 33994.52 26799.47 13791.97 16499.18 20269.90 45699.52 11499.73 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
reproduce_monomvs95.38 22295.07 22096.32 26599.32 11196.60 15499.76 18198.85 6296.65 7287.83 37596.05 35399.52 198.11 30596.58 20981.07 39994.25 350
fmvsm_s_conf0.5_n_998.15 7398.02 6898.55 12399.28 11295.84 18699.99 598.57 10698.17 1399.93 299.74 8787.04 24299.97 6399.86 2799.59 10899.83 104
SPE-MVS-test97.88 8697.94 7797.70 19299.28 11295.20 22499.98 2197.15 35195.53 11399.62 6099.79 6292.08 16298.38 28398.75 10799.28 13999.52 169
test_fmvsm_n_192098.44 4998.61 3097.92 17299.27 11495.18 225100.00 198.90 5098.05 2099.80 2699.73 9192.64 14499.99 3999.58 5799.51 11798.59 272
fmvsm_s_conf0.5_n_1098.24 6997.90 8099.26 5499.24 11597.88 9199.99 598.76 7398.20 999.92 499.74 8785.97 26199.94 9399.72 4699.53 11399.96 74
fmvsm_l_conf0.5_n_a99.00 1898.91 1599.28 5299.21 11697.91 9099.98 2198.85 6298.25 599.92 499.75 8094.72 7499.97 6399.87 2599.64 9799.95 82
fmvsm_s_conf0.5_n_898.38 5798.05 6699.35 4999.20 11798.12 7699.98 2198.81 6798.22 799.80 2699.71 9787.37 23799.97 6399.91 1999.48 12199.97 66
test_yl97.83 9297.37 11199.21 5999.18 11897.98 8599.64 22399.27 2791.43 29197.88 17498.99 19795.84 4599.84 13798.82 10195.32 27599.79 111
DCV-MVSNet97.83 9297.37 11199.21 5999.18 11897.98 8599.64 22399.27 2791.43 29197.88 17498.99 19795.84 4599.84 13798.82 10195.32 27599.79 111
fmvsm_l_conf0.5_n98.94 1998.84 2099.25 5599.17 12097.81 9599.98 2198.86 5998.25 599.90 699.76 7294.21 9799.97 6399.87 2599.52 11499.98 56
DeepC-MVS94.51 496.92 14996.40 15998.45 13799.16 12195.90 18499.66 21898.06 23396.37 8794.37 27399.49 13683.29 30299.90 11297.63 17399.61 10499.55 159
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS98.54 4198.22 5299.50 3499.15 12298.65 57100.00 198.58 10497.70 3298.21 16299.24 16992.58 14799.94 9398.63 11699.94 5999.92 92
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
fmvsm_l_conf0.5_n_398.41 5398.08 6499.39 4599.12 12398.29 6999.98 2198.64 9098.14 1699.86 1599.76 7287.99 22599.97 6399.72 4699.54 11199.91 94
fmvsm_l_conf0.5_n_998.55 4098.23 5199.49 3699.10 12498.50 6499.99 598.70 7998.14 1699.94 199.68 11189.02 21399.98 5099.89 2199.61 10499.99 24
CS-MVS97.79 9997.91 7997.43 21999.10 12494.42 25099.99 597.10 36395.07 12299.68 5099.75 8092.95 13498.34 28798.38 12899.14 14599.54 163
Anonymous20240521193.10 29691.99 30996.40 26199.10 12489.65 37598.88 34297.93 24783.71 42394.00 27998.75 23368.79 41599.88 12395.08 23491.71 30899.68 127
fmvsm_s_conf0.5_n97.80 9797.85 8597.67 19399.06 12794.41 25199.98 2198.97 4397.34 4299.63 5799.69 10487.27 23899.97 6399.62 5599.06 15098.62 271
HyFIR lowres test96.66 16596.43 15697.36 22699.05 12893.91 27099.70 20999.80 390.54 32296.26 23298.08 28492.15 16098.23 29996.84 20295.46 27099.93 87
LFMVS94.75 24493.56 26798.30 14799.03 12995.70 19498.74 35797.98 24287.81 37798.47 14699.39 14867.43 42499.53 17498.01 15295.20 27899.67 129
fmvsm_s_conf0.5_n_497.75 10297.86 8497.42 22099.01 13094.69 24199.97 3998.76 7397.91 2599.87 1399.76 7286.70 24999.93 10399.67 5299.12 14897.64 301
fmvsm_s_conf0.5_n_297.59 11297.28 11598.53 12999.01 13098.15 7199.98 2198.59 10298.17 1399.75 4099.63 12181.83 31599.94 9399.78 3598.79 16197.51 309
AllTest92.48 31291.64 31595.00 30699.01 13088.43 39398.94 33496.82 39886.50 39388.71 35898.47 26674.73 39099.88 12385.39 38996.18 24596.71 315
TestCases95.00 30699.01 13088.43 39396.82 39886.50 39388.71 35898.47 26674.73 39099.88 12385.39 38996.18 24596.71 315
COLMAP_ROBcopyleft90.47 1492.18 31991.49 32194.25 34099.00 13488.04 39998.42 38296.70 40582.30 43488.43 36799.01 19476.97 36599.85 12986.11 38596.50 23794.86 326
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_397.95 8197.66 9498.81 10098.99 13598.07 7999.98 2198.81 6798.18 1299.89 1099.70 10084.15 29499.97 6399.76 4099.50 11998.39 279
test_fmvs195.35 22395.68 19694.36 33698.99 13584.98 42199.96 5396.65 40797.60 3499.73 4598.96 20371.58 40599.93 10398.31 13499.37 13498.17 284
HY-MVS92.50 797.79 9997.17 12299.63 1898.98 13799.32 997.49 41199.52 1495.69 10898.32 15597.41 30493.32 12199.77 14998.08 14995.75 26099.81 108
VNet97.21 13196.57 15099.13 7698.97 13897.82 9499.03 32199.21 3294.31 15899.18 10298.88 21586.26 25699.89 11798.93 9294.32 28899.69 126
thres20096.96 14596.21 16699.22 5898.97 13898.84 3799.85 14499.71 793.17 21096.26 23298.88 21589.87 19999.51 17794.26 25894.91 28099.31 212
tfpn200view996.79 15395.99 17399.19 6198.94 14098.82 3899.78 17099.71 792.86 22596.02 24098.87 22289.33 20699.50 17993.84 26794.57 28499.27 221
thres40096.78 15595.99 17399.16 6898.94 14098.82 3899.78 17099.71 792.86 22596.02 24098.87 22289.33 20699.50 17993.84 26794.57 28499.16 231
sasdasda97.09 13896.32 16099.39 4598.93 14298.95 2899.72 19997.35 31594.45 14697.88 17499.42 14186.71 24799.52 17598.48 12393.97 29499.72 121
Anonymous2023121189.86 36988.44 37794.13 34398.93 14290.68 35398.54 37398.26 20676.28 45186.73 38995.54 36870.60 41197.56 33190.82 32380.27 40894.15 364
canonicalmvs97.09 13896.32 16099.39 4598.93 14298.95 2899.72 19997.35 31594.45 14697.88 17499.42 14186.71 24799.52 17598.48 12393.97 29499.72 121
SDMVSNet94.80 23993.96 25497.33 22998.92 14595.42 20799.59 23598.99 4092.41 25492.55 29797.85 29575.81 38098.93 22097.90 16091.62 30997.64 301
sd_testset93.55 28592.83 28995.74 28498.92 14590.89 34998.24 39098.85 6292.41 25492.55 29797.85 29571.07 41098.68 25293.93 26491.62 30997.64 301
EPNet_dtu95.71 21195.39 20696.66 25298.92 14593.41 28499.57 24098.90 5096.19 9497.52 18498.56 25692.65 14397.36 33677.89 43798.33 17499.20 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WTY-MVS98.10 7697.60 9899.60 2398.92 14599.28 1799.89 12499.52 1495.58 11198.24 16199.39 14893.33 12099.74 15597.98 15695.58 26999.78 114
CHOSEN 1792x268896.81 15296.53 15197.64 19698.91 14993.07 29099.65 21999.80 395.64 10995.39 25798.86 22484.35 29399.90 11296.98 19499.16 14499.95 82
thres100view90096.74 16095.92 18599.18 6298.90 15098.77 4699.74 18899.71 792.59 24595.84 24498.86 22489.25 20899.50 17993.84 26794.57 28499.27 221
thres600view796.69 16395.87 18899.14 7298.90 15098.78 4599.74 18899.71 792.59 24595.84 24498.86 22489.25 20899.50 17993.44 28094.50 28799.16 231
MSDG94.37 26093.36 27997.40 22298.88 15293.95 26999.37 27797.38 31185.75 40490.80 31699.17 17784.11 29699.88 12386.35 38198.43 17298.36 281
MGCFI-Net97.00 14396.22 16599.34 5098.86 15398.80 4099.67 21797.30 32794.31 15897.77 18099.41 14586.36 25499.50 17998.38 12893.90 29699.72 121
h-mvs3394.92 23694.36 24096.59 25498.85 15491.29 34198.93 33698.94 4495.90 9998.77 12798.42 26990.89 18499.77 14997.80 16470.76 44798.72 268
Anonymous2024052992.10 32090.65 33296.47 25698.82 15590.61 35598.72 35998.67 8675.54 45593.90 28198.58 25466.23 42899.90 11294.70 24890.67 31298.90 258
PVSNet_Blended_VisFu97.27 12796.81 13898.66 11298.81 15696.67 15099.92 10098.64 9094.51 14396.38 23098.49 26289.05 21299.88 12397.10 18998.34 17399.43 190
PS-MVSNAJ98.44 4998.20 5499.16 6898.80 15798.92 3099.54 24998.17 21897.34 4299.85 1899.85 3791.20 17399.89 11799.41 6899.67 9598.69 269
CANet_DTU96.76 15696.15 16898.60 11798.78 15897.53 10799.84 14997.63 28097.25 5099.20 9999.64 11881.36 32199.98 5092.77 29198.89 15598.28 283
mvsany_test197.82 9597.90 8097.55 20798.77 15993.04 29399.80 16697.93 24796.95 6199.61 6799.68 11190.92 18199.83 13999.18 7698.29 17899.80 110
alignmvs97.81 9697.33 11399.25 5598.77 15998.66 5599.99 598.44 14794.40 15498.41 15099.47 13793.65 11399.42 18998.57 11794.26 29099.67 129
SymmetryMVS97.64 11097.46 10498.17 15398.74 16195.39 21099.61 23099.26 2996.52 7698.61 13899.31 15692.73 14199.67 16796.77 20395.63 26799.45 186
SteuartSystems-ACMMP99.02 1698.97 1499.18 6298.72 16297.71 9999.98 2198.44 14796.85 6299.80 2699.91 1897.57 899.85 12999.44 6699.99 2199.99 24
Skip Steuart: Steuart Systems R&D Blog.
xiu_mvs_v2_base98.23 7197.97 7299.02 8798.69 16398.66 5599.52 25198.08 23297.05 5699.86 1599.86 3390.65 18699.71 15999.39 7098.63 16598.69 269
miper_enhance_ethall94.36 26293.98 25395.49 28798.68 16495.24 22199.73 19597.29 33193.28 20589.86 32995.97 35494.37 8897.05 35992.20 29584.45 37094.19 357
fmvsm_s_conf0.5_n_598.08 7797.71 9299.17 6598.67 16597.69 10399.99 598.57 10697.40 4099.89 1099.69 10485.99 26099.96 7599.80 3299.40 13299.85 102
ETVMVS97.03 14296.64 14698.20 15298.67 16597.12 12899.89 12498.57 10691.10 30398.17 16398.59 25193.86 10898.19 30195.64 22695.24 27799.28 219
test250697.53 11497.19 12098.58 12198.66 16796.90 13998.81 35199.77 594.93 12597.95 16998.96 20392.51 15099.20 20094.93 23898.15 18299.64 135
ECVR-MVScopyleft95.66 21495.05 22197.51 21298.66 16793.71 27498.85 34898.45 14294.93 12596.86 21098.96 20375.22 38699.20 20095.34 22898.15 18299.64 135
mamv495.24 22696.90 13190.25 41498.65 16972.11 46398.28 38797.64 27989.99 33795.93 24298.25 27994.74 7399.11 20699.01 8999.64 9799.53 167
balanced_conf0398.27 6397.99 7099.11 7798.64 17098.43 6799.47 26197.79 26294.56 14199.74 4398.35 27194.33 9199.25 19499.12 7899.96 4699.64 135
fmvsm_s_conf0.5_n_a97.73 10597.72 9097.77 18698.63 17194.26 25899.96 5398.92 4997.18 5299.75 4099.69 10487.00 24499.97 6399.46 6498.89 15599.08 241
MVSMamba_PlusPlus97.83 9297.45 10698.99 8998.60 17298.15 7199.58 23797.74 27090.34 32999.26 9898.32 27494.29 9399.23 19599.03 8799.89 7499.58 155
testing22297.08 14196.75 14198.06 16398.56 17396.82 14199.85 14498.61 9892.53 24998.84 12198.84 22893.36 11898.30 29195.84 22294.30 28999.05 245
test111195.57 21794.98 22497.37 22498.56 17393.37 28798.86 34698.45 14294.95 12496.63 21698.95 20875.21 38799.11 20695.02 23598.14 18499.64 135
MVSTER95.53 21895.22 21396.45 25998.56 17397.72 9899.91 10897.67 27592.38 25791.39 30797.14 31197.24 2097.30 34394.80 24487.85 34194.34 345
testing3-297.72 10697.43 10998.60 11798.55 17697.11 130100.00 199.23 3193.78 18597.90 17198.73 23595.50 5299.69 16398.53 12194.63 28298.99 249
VDD-MVS93.77 27892.94 28796.27 26698.55 17690.22 36498.77 35697.79 26290.85 30996.82 21299.42 14161.18 44899.77 14998.95 9094.13 29198.82 261
tpmvs94.28 26493.57 26696.40 26198.55 17691.50 33995.70 44898.55 11887.47 37992.15 30094.26 42091.42 16998.95 21988.15 36095.85 25698.76 264
UGNet95.33 22494.57 23697.62 20098.55 17694.85 23398.67 36599.32 2695.75 10696.80 21396.27 34372.18 40299.96 7594.58 25199.05 15198.04 289
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
PCF-MVS94.20 595.18 22894.10 24798.43 13998.55 17695.99 18297.91 40497.31 32690.35 32889.48 34299.22 17085.19 27699.89 11790.40 33398.47 17199.41 193
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-2895.95 19996.49 15294.34 33798.51 18189.99 36999.39 27398.57 10693.14 21297.33 19398.31 27693.44 11694.68 43793.69 27795.98 25098.34 282
UWE-MVS96.79 15396.72 14397.00 23898.51 18193.70 27599.71 20298.60 10092.96 22097.09 20098.34 27396.67 3398.85 22692.11 30196.50 23798.44 277
myMVS_eth3d2897.86 8897.59 10098.68 10998.50 18397.26 12099.92 10098.55 11893.79 18498.26 15998.75 23395.20 5799.48 18598.93 9296.40 24099.29 217
test_vis1_n_192095.44 22095.31 20995.82 28198.50 18388.74 38799.98 2197.30 32797.84 2899.85 1899.19 17566.82 42699.97 6398.82 10199.46 12698.76 264
BH-w/o95.71 21195.38 20796.68 25198.49 18592.28 31299.84 14997.50 30092.12 26792.06 30398.79 23184.69 28798.67 25495.29 23099.66 9699.09 239
baseline195.78 20794.86 22798.54 12798.47 18698.07 7999.06 31497.99 24092.68 23994.13 27898.62 24893.28 12498.69 25193.79 27285.76 35798.84 260
fmvsm_s_conf0.5_n_797.70 10897.74 8997.59 20598.44 18795.16 22799.97 3998.65 8797.95 2499.62 6099.78 6686.09 25899.94 9399.69 5099.50 11997.66 299
EPMVS96.53 17296.01 17298.09 16198.43 18896.12 18096.36 43599.43 2093.53 19397.64 18295.04 39694.41 8398.38 28391.13 31498.11 18599.75 117
kuosan93.17 29392.60 29594.86 31398.40 18989.54 37798.44 37898.53 12584.46 41888.49 36397.92 29290.57 18897.05 35983.10 40693.49 29997.99 290
WBMVS94.52 25394.03 25195.98 27298.38 19096.68 14999.92 10097.63 28090.75 31889.64 33795.25 38996.77 2796.90 37194.35 25683.57 37794.35 343
UBG97.84 9197.69 9398.29 14898.38 19096.59 15699.90 11498.53 12593.91 18098.52 14298.42 26996.77 2799.17 20398.54 11996.20 24499.11 238
sss97.57 11397.03 12799.18 6298.37 19298.04 8299.73 19599.38 2293.46 19798.76 13099.06 18891.21 17299.89 11796.33 21397.01 22799.62 142
testing1197.48 11697.27 11698.10 16098.36 19396.02 18199.92 10098.45 14293.45 19998.15 16498.70 23895.48 5399.22 19697.85 16295.05 27999.07 242
BH-untuned95.18 22894.83 22896.22 26798.36 19391.22 34299.80 16697.32 32590.91 30791.08 31098.67 24083.51 29998.54 26594.23 25999.61 10498.92 255
testing9197.16 13396.90 13197.97 16798.35 19595.67 19799.91 10898.42 16792.91 22397.33 19398.72 23694.81 7199.21 19796.98 19494.63 28299.03 246
testing9997.17 13296.91 13097.95 16898.35 19595.70 19499.91 10898.43 15592.94 22197.36 19198.72 23694.83 7099.21 19797.00 19294.64 28198.95 251
ET-MVSNet_ETH3D94.37 26093.28 28197.64 19698.30 19797.99 8499.99 597.61 28694.35 15571.57 46199.45 14096.23 3895.34 42796.91 20085.14 36499.59 149
AUN-MVS93.28 29092.60 29595.34 29698.29 19890.09 36799.31 28598.56 11291.80 28096.35 23198.00 28789.38 20598.28 29492.46 29269.22 45397.64 301
FMVSNet392.69 30791.58 31795.99 27198.29 19897.42 11599.26 29497.62 28389.80 34089.68 33395.32 38381.62 31996.27 40387.01 37785.65 35894.29 347
PMMVS96.76 15696.76 14096.76 24898.28 20092.10 31699.91 10897.98 24294.12 16699.53 7299.39 14886.93 24598.73 24496.95 19797.73 19399.45 186
hse-mvs294.38 25994.08 25095.31 29898.27 20190.02 36899.29 29098.56 11295.90 9998.77 12798.00 28790.89 18498.26 29897.80 16469.20 45497.64 301
PVSNet_088.03 1991.80 32790.27 34196.38 26398.27 20190.46 35999.94 9099.61 1393.99 17486.26 39997.39 30671.13 40999.89 11798.77 10567.05 46098.79 263
UA-Net96.54 17195.96 17998.27 14998.23 20395.71 19398.00 40298.45 14293.72 18998.41 15099.27 16288.71 21999.66 17091.19 31397.69 19499.44 189
test_cas_vis1_n_192096.59 16896.23 16397.65 19598.22 20494.23 25999.99 597.25 33697.77 2999.58 6899.08 18477.10 36099.97 6397.64 17299.45 12798.74 266
FE-MVS95.70 21395.01 22397.79 18298.21 20594.57 24395.03 44998.69 8188.90 35697.50 18696.19 34592.60 14699.49 18489.99 33897.94 19199.31 212
GG-mvs-BLEND98.54 12798.21 20598.01 8393.87 45498.52 12797.92 17097.92 29299.02 397.94 31898.17 14299.58 10999.67 129
mvs_anonymous95.65 21595.03 22297.53 20998.19 20795.74 19199.33 28297.49 30190.87 30890.47 31997.10 31388.23 22297.16 35095.92 22097.66 19799.68 127
MVS_Test96.46 17495.74 19298.61 11698.18 20897.23 12299.31 28597.15 35191.07 30498.84 12197.05 31788.17 22398.97 21694.39 25397.50 19999.61 146
BH-RMVSNet95.18 22894.31 24397.80 18098.17 20995.23 22299.76 18197.53 29692.52 25094.27 27699.25 16876.84 36798.80 23490.89 32299.54 11199.35 203
dongtai91.55 33391.13 32692.82 38298.16 21086.35 41199.47 26198.51 13083.24 42685.07 40997.56 30090.33 19394.94 43376.09 44591.73 30797.18 312
RPSCF91.80 32792.79 29188.83 42698.15 21169.87 46598.11 39896.60 40983.93 42194.33 27499.27 16279.60 34399.46 18891.99 30293.16 30497.18 312
ETV-MVS97.92 8497.80 8898.25 15098.14 21296.48 15899.98 2197.63 28095.61 11099.29 9599.46 13992.55 14898.82 23099.02 8898.54 16999.46 181
IS-MVSNet96.29 18795.90 18697.45 21598.13 21394.80 23799.08 30997.61 28692.02 27295.54 25598.96 20390.64 18798.08 30793.73 27597.41 20399.47 179
test_fmvsmconf_n98.43 5198.32 4798.78 10298.12 21496.41 16199.99 598.83 6698.22 799.67 5199.64 11891.11 17799.94 9399.67 5299.62 10099.98 56
fmvsm_s_conf0.1_n_297.25 12896.85 13598.43 13998.08 21598.08 7899.92 10097.76 26998.05 2099.65 5399.58 12780.88 32899.93 10399.59 5698.17 18097.29 310
ab-mvs94.69 24593.42 27298.51 13298.07 21696.26 16896.49 43398.68 8390.31 33094.54 26697.00 31976.30 37599.71 15995.98 21993.38 30299.56 158
XVG-OURS-SEG-HR94.79 24094.70 23595.08 30398.05 21789.19 37999.08 30997.54 29493.66 19094.87 26499.58 12778.78 35199.79 14497.31 18093.40 30196.25 319
EIA-MVS97.53 11497.46 10497.76 18898.04 21894.84 23499.98 2197.61 28694.41 15397.90 17199.59 12492.40 15498.87 22498.04 15199.13 14699.59 149
XVG-OURS94.82 23794.74 23495.06 30498.00 21989.19 37999.08 30997.55 29294.10 16794.71 26599.62 12280.51 33499.74 15596.04 21893.06 30696.25 319
mvsmamba96.94 14696.73 14297.55 20797.99 22094.37 25599.62 22697.70 27293.13 21398.42 14997.92 29288.02 22498.75 24298.78 10499.01 15299.52 169
dp95.05 23194.43 23896.91 24197.99 22092.73 30196.29 43897.98 24289.70 34195.93 24294.67 41093.83 11098.45 27186.91 38096.53 23699.54 163
tpmrst96.27 18995.98 17597.13 23397.96 22293.15 28996.34 43698.17 21892.07 26898.71 13395.12 39393.91 10598.73 24494.91 24196.62 23499.50 175
TR-MVS94.54 25093.56 26797.49 21497.96 22294.34 25698.71 36097.51 29990.30 33194.51 26898.69 23975.56 38198.77 23892.82 29095.99 24999.35 203
Vis-MVSNet (Re-imp)96.32 18495.98 17597.35 22897.93 22494.82 23699.47 26198.15 22691.83 27795.09 26299.11 18291.37 17197.47 33493.47 27997.43 20099.74 118
MDTV_nov1_ep1395.69 19497.90 22594.15 26295.98 44498.44 14793.12 21497.98 16895.74 35895.10 6098.58 26190.02 33796.92 229
Fast-Effi-MVS+95.02 23394.19 24597.52 21197.88 22694.55 24499.97 3997.08 36788.85 35894.47 26997.96 29184.59 28898.41 27589.84 34097.10 22099.59 149
ADS-MVSNet293.80 27793.88 25793.55 36597.87 22785.94 41594.24 45096.84 39590.07 33496.43 22794.48 41590.29 19595.37 42687.44 36797.23 21199.36 199
ADS-MVSNet94.79 24094.02 25297.11 23597.87 22793.79 27194.24 45098.16 22390.07 33496.43 22794.48 41590.29 19598.19 30187.44 36797.23 21199.36 199
Effi-MVS+96.30 18695.69 19498.16 15497.85 22996.26 16897.41 41397.21 34390.37 32798.65 13698.58 25486.61 25198.70 25097.11 18897.37 20599.52 169
PatchmatchNetpermissive95.94 20095.45 20297.39 22397.83 23094.41 25196.05 44298.40 17692.86 22597.09 20095.28 38894.21 9798.07 30989.26 34698.11 18599.70 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cascas94.64 24893.61 26297.74 19097.82 23196.26 16899.96 5397.78 26585.76 40294.00 27997.54 30176.95 36699.21 19797.23 18595.43 27297.76 298
1112_ss96.01 19795.20 21498.42 14197.80 23296.41 16199.65 21996.66 40692.71 23692.88 29399.40 14692.16 15999.30 19291.92 30493.66 29799.55 159
E3new96.75 15896.43 15697.71 19197.79 23394.83 23599.80 16697.33 31993.52 19597.49 18799.31 15687.73 22798.83 22797.52 17597.40 20499.48 178
Test_1112_low_res95.72 20994.83 22898.42 14197.79 23396.41 16199.65 21996.65 40792.70 23792.86 29496.13 34992.15 16099.30 19291.88 30593.64 29899.55 159
Effi-MVS+-dtu94.53 25295.30 21092.22 39097.77 23582.54 43899.59 23597.06 37194.92 12795.29 25995.37 38185.81 26297.89 31994.80 24497.07 22196.23 321
tpm cat193.51 28692.52 30196.47 25697.77 23591.47 34096.13 44098.06 23380.98 44092.91 29293.78 42489.66 20098.87 22487.03 37696.39 24199.09 239
FA-MVS(test-final)95.86 20395.09 21998.15 15797.74 23795.62 19996.31 43798.17 21891.42 29396.26 23296.13 34990.56 18999.47 18792.18 29697.07 22199.35 203
xiu_mvs_v1_base_debu97.43 11797.06 12398.55 12397.74 23798.14 7399.31 28597.86 25696.43 8199.62 6099.69 10485.56 26999.68 16499.05 8198.31 17597.83 294
xiu_mvs_v1_base97.43 11797.06 12398.55 12397.74 23798.14 7399.31 28597.86 25696.43 8199.62 6099.69 10485.56 26999.68 16499.05 8198.31 17597.83 294
xiu_mvs_v1_base_debi97.43 11797.06 12398.55 12397.74 23798.14 7399.31 28597.86 25696.43 8199.62 6099.69 10485.56 26999.68 16499.05 8198.31 17597.83 294
EPP-MVSNet96.69 16396.60 14896.96 24097.74 23793.05 29299.37 27798.56 11288.75 36095.83 24699.01 19496.01 3998.56 26396.92 19897.20 21399.25 224
gg-mvs-nofinetune93.51 28691.86 31398.47 13497.72 24297.96 8892.62 46098.51 13074.70 45897.33 19369.59 47598.91 497.79 32297.77 16999.56 11099.67 129
IB-MVS92.85 694.99 23493.94 25598.16 15497.72 24295.69 19699.99 598.81 6794.28 16192.70 29596.90 32195.08 6199.17 20396.07 21773.88 44099.60 148
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
thisisatest051597.41 12297.02 12898.59 12097.71 24497.52 10899.97 3998.54 12291.83 27797.45 18899.04 19097.50 999.10 20894.75 24696.37 24299.16 231
VortexMVS94.11 26693.50 26995.94 27497.70 24596.61 15399.35 28097.18 34693.52 19589.57 34095.74 35887.55 23296.97 36795.76 22585.13 36594.23 352
viewdifsd2359ckpt0996.21 19195.77 19097.53 20997.69 24694.50 24799.78 17097.23 34192.88 22496.58 21999.26 16684.85 28198.66 25796.61 20797.02 22699.43 190
Syy-MVS90.00 36790.63 33388.11 43397.68 24774.66 46199.71 20298.35 18990.79 31592.10 30198.67 24079.10 34993.09 45363.35 46895.95 25396.59 317
myMVS_eth3d94.46 25794.76 23393.55 36597.68 24790.97 34499.71 20298.35 18990.79 31592.10 30198.67 24092.46 15393.09 45387.13 37395.95 25396.59 317
test_fmvs1_n94.25 26594.36 24093.92 35297.68 24783.70 42899.90 11496.57 41097.40 4099.67 5198.88 21561.82 44599.92 10998.23 14099.13 14698.14 287
fmvsm_s_conf0.5_n_698.27 6397.96 7599.23 5797.66 25098.11 7799.98 2198.64 9097.85 2799.87 1399.72 9488.86 21699.93 10399.64 5499.36 13599.63 141
RRT-MVS96.24 19095.68 19697.94 17197.65 25194.92 23299.27 29397.10 36392.79 23197.43 18997.99 28981.85 31499.37 19198.46 12598.57 16699.53 167
diffmvspermissive97.00 14396.64 14698.09 16197.64 25296.17 17799.81 16297.19 34494.67 13998.95 11699.28 15986.43 25298.76 24098.37 13097.42 20299.33 206
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewcassd2359sk1196.59 16896.23 16397.66 19497.63 25394.70 24099.77 17597.33 31993.41 20097.34 19299.17 17786.72 24698.83 22797.40 17897.32 20899.46 181
viewdifsd2359ckpt1396.19 19295.77 19097.45 21597.62 25494.40 25399.70 20997.23 34192.76 23396.63 21699.05 18984.96 28098.64 25896.65 20697.35 20699.31 212
Vis-MVSNetpermissive95.72 20995.15 21797.45 21597.62 25494.28 25799.28 29198.24 20994.27 16396.84 21198.94 21079.39 34498.76 24093.25 28198.49 17099.30 215
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thisisatest053097.10 13696.72 14398.22 15197.60 25696.70 14699.92 10098.54 12291.11 30297.07 20298.97 20197.47 1299.03 21193.73 27596.09 24798.92 255
GDP-MVS97.88 8697.59 10098.75 10597.59 25797.81 9599.95 7297.37 31494.44 14999.08 10799.58 12797.13 2599.08 20994.99 23698.17 18099.37 197
miper_ehance_all_eth93.16 29492.60 29594.82 31497.57 25893.56 27999.50 25597.07 37088.75 36088.85 35795.52 37090.97 18096.74 38190.77 32484.45 37094.17 358
guyue97.15 13496.82 13798.15 15797.56 25996.25 17299.71 20297.84 25995.75 10698.13 16598.65 24387.58 23198.82 23098.29 13697.91 19299.36 199
viewmanbaseed2359cas96.45 17596.07 16997.59 20597.55 26094.59 24299.70 20997.33 31993.62 19297.00 20699.32 15385.57 26898.71 24797.26 18497.33 20799.47 179
testing393.92 27194.23 24492.99 37997.54 26190.23 36399.99 599.16 3390.57 32191.33 30998.63 24792.99 13292.52 45782.46 41095.39 27396.22 322
SSM_040495.75 20895.16 21697.50 21397.53 26295.39 21099.11 30597.25 33690.81 31195.27 26098.83 22984.74 28498.67 25495.24 23197.69 19498.45 276
LCM-MVSNet-Re92.31 31692.60 29591.43 39997.53 26279.27 45599.02 32391.83 47092.07 26880.31 43394.38 41883.50 30095.48 42397.22 18697.58 19899.54 163
GBi-Net90.88 34489.82 35094.08 34497.53 26291.97 31798.43 37996.95 38487.05 38589.68 33394.72 40671.34 40696.11 40987.01 37785.65 35894.17 358
test190.88 34489.82 35094.08 34497.53 26291.97 31798.43 37996.95 38487.05 38589.68 33394.72 40671.34 40696.11 40987.01 37785.65 35894.17 358
FMVSNet291.02 34189.56 35595.41 29497.53 26295.74 19198.98 32697.41 30987.05 38588.43 36795.00 40071.34 40696.24 40585.12 39285.21 36394.25 350
tttt051796.85 15096.49 15297.92 17297.48 26795.89 18599.85 14498.54 12290.72 31996.63 21698.93 21397.47 1299.02 21293.03 28895.76 25998.85 259
BP-MVS198.33 5998.18 5698.81 10097.44 26897.98 8599.96 5398.17 21894.88 12998.77 12799.59 12497.59 799.08 20998.24 13998.93 15499.36 199
casdiffmvs_mvgpermissive96.43 17695.94 18397.89 17697.44 26895.47 20399.86 14197.29 33193.35 20196.03 23999.19 17585.39 27398.72 24697.89 16197.04 22399.49 177
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
E296.36 18195.95 18197.60 20297.41 27094.52 24599.71 20297.33 31993.20 20797.02 20399.07 18685.37 27498.82 23097.27 18197.14 21799.46 181
EC-MVSNet97.38 12497.24 11797.80 18097.41 27095.64 19899.99 597.06 37194.59 14099.63 5799.32 15389.20 21198.14 30398.76 10699.23 14299.62 142
viewdifsd2359ckpt0795.83 20695.42 20497.07 23697.40 27293.04 29399.60 23397.24 33992.39 25696.09 23899.14 18183.07 30598.93 22097.02 19196.87 23099.23 227
c3_l92.53 31191.87 31294.52 32697.40 27292.99 29599.40 26996.93 38987.86 37588.69 36095.44 37589.95 19896.44 39590.45 33080.69 40494.14 367
viewmambaseed2359dif95.92 20295.55 20097.04 23797.38 27493.41 28499.78 17096.97 38291.14 30196.58 21999.27 16284.85 28198.75 24296.87 20197.12 21998.97 250
fmvsm_s_conf0.1_n97.30 12597.21 11997.60 20297.38 27494.40 25399.90 11498.64 9096.47 8099.51 7699.65 11784.99 27999.93 10399.22 7599.09 14998.46 275
E396.36 18195.95 18197.60 20297.37 27694.52 24599.71 20297.33 31993.18 20997.02 20399.07 18685.45 27298.82 23097.27 18197.14 21799.46 181
CDS-MVSNet96.34 18396.07 16997.13 23397.37 27694.96 23099.53 25097.91 25191.55 28595.37 25898.32 27495.05 6397.13 35393.80 27195.75 26099.30 215
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TESTMET0.1,196.74 16096.26 16298.16 15497.36 27896.48 15899.96 5398.29 20291.93 27395.77 24798.07 28595.54 4998.29 29290.55 32898.89 15599.70 124
miper_lstm_enhance91.81 32491.39 32393.06 37897.34 27989.18 38199.38 27596.79 40086.70 39287.47 38195.22 39090.00 19795.86 41888.26 35781.37 39394.15 364
baseline96.43 17695.98 17597.76 18897.34 27995.17 22699.51 25397.17 34893.92 17996.90 20999.28 15985.37 27498.64 25897.50 17696.86 23299.46 181
cl____92.31 31691.58 31794.52 32697.33 28192.77 29799.57 24096.78 40186.97 38987.56 37995.51 37189.43 20496.62 38788.60 35182.44 38594.16 363
SD_040392.63 31093.38 27690.40 41397.32 28277.91 45797.75 40998.03 23891.89 27490.83 31598.29 27882.00 31193.79 44688.51 35595.75 26099.52 169
DIV-MVS_self_test92.32 31591.60 31694.47 33097.31 28392.74 29999.58 23796.75 40286.99 38887.64 37795.54 36889.55 20396.50 39288.58 35282.44 38594.17 358
casdiffmvspermissive96.42 17895.97 17897.77 18697.30 28494.98 22999.84 14997.09 36693.75 18896.58 21999.26 16685.07 27798.78 23797.77 16997.04 22399.54 163
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
GeoE94.36 26293.48 27096.99 23997.29 28593.54 28099.96 5396.72 40488.35 36993.43 28398.94 21082.05 31098.05 31088.12 36296.48 23999.37 197
eth_miper_zixun_eth92.41 31491.93 31093.84 35697.28 28690.68 35398.83 34996.97 38288.57 36589.19 35295.73 36189.24 21096.69 38589.97 33981.55 39194.15 364
MVSFormer96.94 14696.60 14897.95 16897.28 28697.70 10199.55 24797.27 33391.17 29899.43 8299.54 13390.92 18196.89 37294.67 24999.62 10099.25 224
lupinMVS97.85 9097.60 9898.62 11597.28 28697.70 10199.99 597.55 29295.50 11599.43 8299.67 11390.92 18198.71 24798.40 12799.62 10099.45 186
diffmvs_AUTHOR96.75 15896.41 15897.79 18297.20 28995.46 20499.69 21297.15 35194.46 14598.78 12599.21 17385.64 26698.77 23898.27 13797.31 20999.13 235
mamba_040894.98 23594.09 24897.64 19697.14 29095.31 21593.48 45797.08 36790.48 32394.40 27098.62 24884.49 28998.67 25493.99 26297.18 21498.93 252
SSM_0407294.77 24294.09 24896.82 24597.14 29095.31 21593.48 45797.08 36790.48 32394.40 27098.62 24884.49 28996.21 40693.99 26297.18 21498.93 252
SSM_040795.62 21694.95 22597.61 20197.14 29095.31 21599.00 32497.25 33690.81 31194.40 27098.83 22984.74 28498.58 26195.24 23197.18 21498.93 252
SCA94.69 24593.81 25997.33 22997.10 29394.44 24898.86 34698.32 19693.30 20496.17 23795.59 36676.48 37397.95 31691.06 31697.43 20099.59 149
viewmacassd2359aftdt95.93 20195.45 20297.36 22697.09 29494.12 26499.57 24097.26 33593.05 21896.50 22399.17 17782.76 30698.68 25296.61 20797.04 22399.28 219
KinetiMVS96.10 19395.29 21198.53 12997.08 29597.12 12899.56 24498.12 22994.78 13298.44 14798.94 21080.30 33899.39 19091.56 30998.79 16199.06 243
TAMVS95.85 20495.58 19896.65 25397.07 29693.50 28199.17 30197.82 26191.39 29595.02 26398.01 28692.20 15897.30 34393.75 27495.83 25799.14 234
Fast-Effi-MVS+-dtu93.72 28193.86 25893.29 37097.06 29786.16 41299.80 16696.83 39692.66 24092.58 29697.83 29781.39 32097.67 32789.75 34196.87 23096.05 324
E496.01 19795.53 20197.44 21897.05 29894.23 25999.57 24097.30 32792.72 23496.47 22599.03 19183.98 29798.83 22796.92 19896.77 23399.27 221
CostFormer96.10 19395.88 18796.78 24797.03 29992.55 30797.08 42297.83 26090.04 33698.72 13294.89 40495.01 6598.29 29296.54 21095.77 25899.50 175
test_fmvsmvis_n_192097.67 10997.59 10097.91 17497.02 30095.34 21399.95 7298.45 14297.87 2697.02 20399.59 12489.64 20199.98 5099.41 6899.34 13798.42 278
test-LLR96.47 17396.04 17197.78 18497.02 30095.44 20599.96 5398.21 21394.07 16995.55 25396.38 33893.90 10698.27 29690.42 33198.83 15999.64 135
test-mter96.39 17995.93 18497.78 18497.02 30095.44 20599.96 5398.21 21391.81 27995.55 25396.38 33895.17 5898.27 29690.42 33198.83 15999.64 135
icg_test_0407_295.04 23294.78 23295.84 28096.97 30391.64 33298.63 36897.12 35692.33 25995.60 25198.88 21585.65 26496.56 39092.12 29795.70 26399.32 208
IMVS_040795.21 22794.80 23196.46 25896.97 30391.64 33298.81 35197.12 35692.33 25995.60 25198.88 21585.65 26498.42 27392.12 29795.70 26399.32 208
IMVS_040493.83 27393.17 28395.80 28296.97 30391.64 33297.78 40897.12 35692.33 25990.87 31498.88 21576.78 36896.43 39692.12 29795.70 26399.32 208
IMVS_040395.25 22594.81 23096.58 25596.97 30391.64 33298.97 33197.12 35692.33 25995.43 25698.88 21585.78 26398.79 23592.12 29795.70 26399.32 208
gm-plane-assit96.97 30393.76 27391.47 28998.96 20398.79 23594.92 239
WB-MVSnew92.90 30092.77 29293.26 37296.95 30893.63 27799.71 20298.16 22391.49 28694.28 27598.14 28281.33 32296.48 39379.47 42795.46 27089.68 454
QAPM95.40 22194.17 24699.10 7896.92 30997.71 9999.40 26998.68 8389.31 34488.94 35698.89 21482.48 30899.96 7593.12 28799.83 8199.62 142
KD-MVS_2432*160088.00 38986.10 39393.70 36196.91 31094.04 26597.17 41997.12 35684.93 41381.96 42392.41 43792.48 15194.51 43979.23 42852.68 47492.56 423
miper_refine_blended88.00 38986.10 39393.70 36196.91 31094.04 26597.17 41997.12 35684.93 41381.96 42392.41 43792.48 15194.51 43979.23 42852.68 47492.56 423
tpm295.47 21995.18 21596.35 26496.91 31091.70 33096.96 42597.93 24788.04 37398.44 14795.40 37793.32 12197.97 31394.00 26195.61 26899.38 195
FMVSNet588.32 38587.47 38790.88 40296.90 31388.39 39597.28 41695.68 43282.60 43384.67 41192.40 43979.83 34191.16 46276.39 44481.51 39293.09 414
3Dnovator+91.53 1196.31 18595.24 21299.52 3296.88 31498.64 5899.72 19998.24 20995.27 12088.42 36998.98 19982.76 30699.94 9397.10 18999.83 8199.96 74
Patchmatch-test92.65 30991.50 32096.10 27096.85 31590.49 35891.50 46597.19 34482.76 43290.23 32095.59 36695.02 6498.00 31277.41 43996.98 22899.82 106
MVS96.60 16795.56 19999.72 1496.85 31599.22 2198.31 38598.94 4491.57 28490.90 31399.61 12386.66 25099.96 7597.36 17999.88 7799.99 24
3Dnovator91.47 1296.28 18895.34 20899.08 8196.82 31797.47 11399.45 26698.81 6795.52 11489.39 34399.00 19681.97 31299.95 8497.27 18199.83 8199.84 103
EI-MVSNet93.73 28093.40 27594.74 31596.80 31892.69 30299.06 31497.67 27588.96 35391.39 30799.02 19288.75 21897.30 34391.07 31587.85 34194.22 354
CVMVSNet94.68 24794.94 22693.89 35596.80 31886.92 40999.06 31498.98 4194.45 14694.23 27799.02 19285.60 26795.31 42890.91 32195.39 27399.43 190
IterMVS-LS92.69 30792.11 30694.43 33496.80 31892.74 29999.45 26696.89 39288.98 35189.65 33695.38 38088.77 21796.34 40090.98 31982.04 38894.22 354
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AstraMVS96.57 17096.46 15596.91 24196.79 32192.50 30899.90 11497.38 31196.02 9897.79 17999.32 15386.36 25498.99 21398.26 13896.33 24399.23 227
IterMVS90.91 34390.17 34593.12 37596.78 32290.42 36198.89 34097.05 37489.03 34886.49 39495.42 37676.59 37195.02 43087.22 37284.09 37393.93 385
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
131496.84 15195.96 17999.48 3996.74 32398.52 6298.31 38598.86 5995.82 10389.91 32798.98 19987.49 23499.96 7597.80 16499.73 9199.96 74
IterMVS-SCA-FT90.85 34690.16 34692.93 38096.72 32489.96 37098.89 34096.99 37888.95 35486.63 39195.67 36276.48 37395.00 43187.04 37584.04 37693.84 392
MVS-HIRNet86.22 39683.19 40995.31 29896.71 32590.29 36292.12 46297.33 31962.85 46986.82 38870.37 47469.37 41497.49 33375.12 44797.99 19098.15 285
viewdifsd2359ckpt1194.09 26893.63 26195.46 29196.68 32688.92 38499.62 22697.12 35693.07 21695.73 24899.22 17077.05 36198.88 22396.52 21187.69 34698.58 273
viewmsd2359difaftdt94.09 26893.64 26095.46 29196.68 32688.92 38499.62 22697.13 35593.07 21695.73 24899.22 17077.05 36198.89 22296.52 21187.70 34598.58 273
VDDNet93.12 29591.91 31196.76 24896.67 32892.65 30598.69 36398.21 21382.81 43197.75 18199.28 15961.57 44699.48 18598.09 14894.09 29298.15 285
dmvs_re93.20 29293.15 28493.34 36896.54 32983.81 42798.71 36098.51 13091.39 29592.37 29998.56 25678.66 35397.83 32193.89 26589.74 31398.38 280
Elysia94.50 25493.38 27697.85 17896.49 33096.70 14698.98 32697.78 26590.81 31196.19 23598.55 25873.63 39798.98 21489.41 34298.56 16797.88 292
StellarMVS94.50 25493.38 27697.85 17896.49 33096.70 14698.98 32697.78 26590.81 31196.19 23598.55 25873.63 39798.98 21489.41 34298.56 16797.88 292
MIMVSNet90.30 35988.67 37395.17 30296.45 33291.64 33292.39 46197.15 35185.99 39990.50 31893.19 43266.95 42594.86 43582.01 41493.43 30099.01 248
CR-MVSNet93.45 28992.62 29495.94 27496.29 33392.66 30392.01 46396.23 41892.62 24296.94 20793.31 43091.04 17896.03 41479.23 42895.96 25199.13 235
RPMNet89.76 37187.28 38897.19 23296.29 33392.66 30392.01 46398.31 19870.19 46596.94 20785.87 46787.25 23999.78 14662.69 46995.96 25199.13 235
tt080591.28 33690.18 34494.60 32196.26 33587.55 40298.39 38398.72 7789.00 35089.22 34998.47 26662.98 44198.96 21890.57 32788.00 34097.28 311
Patchmtry89.70 37288.49 37693.33 36996.24 33689.94 37391.37 46696.23 41878.22 44887.69 37693.31 43091.04 17896.03 41480.18 42682.10 38794.02 375
test_vis1_rt86.87 39486.05 39689.34 42296.12 33778.07 45699.87 13083.54 48292.03 27178.21 44489.51 45145.80 46799.91 11096.25 21593.11 30590.03 450
JIA-IIPM91.76 33090.70 33194.94 30896.11 33887.51 40393.16 45998.13 22875.79 45497.58 18377.68 47292.84 13797.97 31388.47 35696.54 23599.33 206
OpenMVScopyleft90.15 1594.77 24293.59 26598.33 14596.07 33997.48 11299.56 24498.57 10690.46 32586.51 39398.95 20878.57 35499.94 9393.86 26699.74 9097.57 306
PAPM98.60 3798.42 3899.14 7296.05 34098.96 2799.90 11499.35 2496.68 7198.35 15499.66 11596.45 3598.51 26699.45 6599.89 7499.96 74
CLD-MVS94.06 27093.90 25694.55 32596.02 34190.69 35299.98 2197.72 27196.62 7591.05 31298.85 22777.21 35998.47 26798.11 14689.51 31994.48 331
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PatchT90.38 35688.75 37295.25 30095.99 34290.16 36591.22 46797.54 29476.80 45097.26 19686.01 46691.88 16596.07 41366.16 46495.91 25599.51 173
ACMH+89.98 1690.35 35789.54 35692.78 38495.99 34286.12 41398.81 35197.18 34689.38 34383.14 41997.76 29868.42 41998.43 27289.11 34786.05 35693.78 395
DeepMVS_CXcopyleft82.92 44495.98 34458.66 47596.01 42492.72 23478.34 44395.51 37158.29 45398.08 30782.57 40985.29 36192.03 431
ACMP92.05 992.74 30592.42 30393.73 35795.91 34588.72 38899.81 16297.53 29694.13 16587.00 38798.23 28074.07 39498.47 26796.22 21688.86 32693.99 380
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n93.61 28493.03 28695.35 29595.86 34686.94 40899.87 13096.36 41696.85 6299.54 7198.79 23152.41 46199.83 13998.64 11498.97 15399.29 217
HQP-NCC95.78 34799.87 13096.82 6493.37 284
ACMP_Plane95.78 34799.87 13096.82 6493.37 284
HQP-MVS94.61 24994.50 23794.92 30995.78 34791.85 32299.87 13097.89 25296.82 6493.37 28498.65 24380.65 33298.39 27997.92 15889.60 31494.53 327
NP-MVS95.77 35091.79 32498.65 243
test_fmvsmconf0.1_n97.74 10397.44 10798.64 11495.76 35196.20 17499.94 9098.05 23598.17 1398.89 12099.42 14187.65 22999.90 11299.50 6199.60 10799.82 106
plane_prior695.76 35191.72 32980.47 336
ACMM91.95 1092.88 30192.52 30193.98 35195.75 35389.08 38399.77 17597.52 29893.00 21989.95 32697.99 28976.17 37798.46 27093.63 27888.87 32594.39 339
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GA-MVS93.83 27392.84 28896.80 24695.73 35493.57 27899.88 12797.24 33992.57 24792.92 29196.66 33078.73 35297.67 32787.75 36594.06 29399.17 230
plane_prior195.73 354
jason97.24 12996.86 13498.38 14495.73 35497.32 11799.97 3997.40 31095.34 11898.60 14199.54 13387.70 22898.56 26397.94 15799.47 12499.25 224
jason: jason.
mmtdpeth88.52 38387.75 38590.85 40495.71 35783.47 43398.94 33494.85 44788.78 35997.19 19889.58 45063.29 43998.97 21698.54 11962.86 46890.10 449
HQP_MVS94.49 25694.36 24094.87 31095.71 35791.74 32699.84 14997.87 25496.38 8493.01 28998.59 25180.47 33698.37 28597.79 16789.55 31794.52 329
plane_prior795.71 35791.59 338
ITE_SJBPF92.38 38795.69 36085.14 41995.71 43192.81 22889.33 34698.11 28370.23 41298.42 27385.91 38788.16 33893.59 403
fmvsm_s_conf0.1_n_a97.09 13896.90 13197.63 19995.65 36194.21 26199.83 15698.50 13696.27 9199.65 5399.64 11884.72 28699.93 10399.04 8498.84 15898.74 266
ACMH89.72 1790.64 35089.63 35393.66 36395.64 36288.64 39198.55 37197.45 30389.03 34881.62 42697.61 29969.75 41398.41 27589.37 34487.62 34793.92 386
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline296.71 16296.49 15297.37 22495.63 36395.96 18399.74 18898.88 5492.94 22191.61 30598.97 20197.72 698.62 26094.83 24398.08 18897.53 308
FMVSNet188.50 38486.64 39194.08 34495.62 36491.97 31798.43 37996.95 38483.00 42986.08 40194.72 40659.09 45296.11 40981.82 41684.07 37494.17 358
LuminaMVS96.63 16696.21 16697.87 17795.58 36596.82 14199.12 30397.67 27594.47 14497.88 17498.31 27687.50 23398.71 24798.07 15097.29 21098.10 288
LPG-MVS_test92.96 29892.71 29393.71 35995.43 36688.67 38999.75 18597.62 28392.81 22890.05 32298.49 26275.24 38498.40 27795.84 22289.12 32194.07 372
LGP-MVS_train93.71 35995.43 36688.67 38997.62 28392.81 22890.05 32298.49 26275.24 38498.40 27795.84 22289.12 32194.07 372
tpm93.70 28293.41 27494.58 32395.36 36887.41 40497.01 42396.90 39190.85 30996.72 21594.14 42190.40 19296.84 37690.75 32588.54 33399.51 173
D2MVS92.76 30492.59 29993.27 37195.13 36989.54 37799.69 21299.38 2292.26 26487.59 37894.61 41285.05 27897.79 32291.59 30888.01 33992.47 426
VPA-MVSNet92.70 30691.55 31996.16 26895.09 37096.20 17498.88 34299.00 3991.02 30691.82 30495.29 38776.05 37997.96 31595.62 22781.19 39494.30 346
LTVRE_ROB88.28 1890.29 36089.05 36794.02 34795.08 37190.15 36697.19 41897.43 30584.91 41583.99 41597.06 31674.00 39598.28 29484.08 39887.71 34393.62 402
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
TinyColmap87.87 39186.51 39291.94 39395.05 37285.57 41797.65 41094.08 45784.40 41981.82 42596.85 32562.14 44498.33 28880.25 42586.37 35491.91 433
test0.0.03 193.86 27293.61 26294.64 31995.02 37392.18 31599.93 9798.58 10494.07 16987.96 37398.50 26193.90 10694.96 43281.33 41793.17 30396.78 314
UniMVSNet (Re)93.07 29792.13 30595.88 27794.84 37496.24 17399.88 12798.98 4192.49 25289.25 34795.40 37787.09 24197.14 35293.13 28678.16 41894.26 348
USDC90.00 36788.96 36893.10 37794.81 37588.16 39798.71 36095.54 43693.66 19083.75 41797.20 31065.58 43098.31 29083.96 40187.49 34992.85 420
VPNet91.81 32490.46 33595.85 27994.74 37695.54 20298.98 32698.59 10292.14 26690.77 31797.44 30368.73 41797.54 33294.89 24277.89 42094.46 332
FIs94.10 26793.43 27196.11 26994.70 37796.82 14199.58 23798.93 4892.54 24889.34 34597.31 30787.62 23097.10 35694.22 26086.58 35294.40 338
UniMVSNet_ETH3D90.06 36688.58 37594.49 32994.67 37888.09 39897.81 40797.57 29183.91 42288.44 36597.41 30457.44 45497.62 32991.41 31088.59 33297.77 297
UniMVSNet_NR-MVSNet92.95 29992.11 30695.49 28794.61 37995.28 21999.83 15699.08 3691.49 28689.21 35096.86 32487.14 24096.73 38293.20 28277.52 42394.46 332
test_fmvs289.47 37689.70 35288.77 42994.54 38075.74 45899.83 15694.70 45394.71 13691.08 31096.82 32954.46 45797.78 32492.87 28988.27 33692.80 421
MonoMVSNet94.82 23794.43 23895.98 27294.54 38090.73 35199.03 32197.06 37193.16 21193.15 28895.47 37488.29 22197.57 33097.85 16291.33 31199.62 142
WR-MVS92.31 31691.25 32495.48 29094.45 38295.29 21899.60 23398.68 8390.10 33388.07 37296.89 32280.68 33196.80 38093.14 28579.67 41194.36 340
nrg03093.51 28692.53 30096.45 25994.36 38397.20 12399.81 16297.16 35091.60 28389.86 32997.46 30286.37 25397.68 32695.88 22180.31 40794.46 332
tfpnnormal89.29 37987.61 38694.34 33794.35 38494.13 26398.95 33398.94 4483.94 42084.47 41295.51 37174.84 38997.39 33577.05 44280.41 40591.48 436
FC-MVSNet-test93.81 27693.15 28495.80 28294.30 38596.20 17499.42 26898.89 5292.33 25989.03 35597.27 30987.39 23696.83 37893.20 28286.48 35394.36 340
SSC-MVS3.289.59 37488.66 37492.38 38794.29 38686.12 41399.49 25797.66 27890.28 33288.63 36295.18 39164.46 43596.88 37485.30 39182.66 38294.14 367
MS-PatchMatch90.65 34990.30 34091.71 39894.22 38785.50 41898.24 39097.70 27288.67 36286.42 39696.37 34067.82 42298.03 31183.62 40399.62 10091.60 434
WR-MVS_H91.30 33490.35 33894.15 34194.17 38892.62 30699.17 30198.94 4488.87 35786.48 39594.46 41784.36 29296.61 38888.19 35978.51 41693.21 412
DU-MVS92.46 31391.45 32295.49 28794.05 38995.28 21999.81 16298.74 7692.25 26589.21 35096.64 33281.66 31796.73 38293.20 28277.52 42394.46 332
NR-MVSNet91.56 33290.22 34295.60 28594.05 38995.76 19098.25 38998.70 7991.16 30080.78 43296.64 33283.23 30396.57 38991.41 31077.73 42294.46 332
CP-MVSNet91.23 33890.22 34294.26 33993.96 39192.39 31199.09 30798.57 10688.95 35486.42 39696.57 33579.19 34796.37 39890.29 33478.95 41394.02 375
XXY-MVS91.82 32390.46 33595.88 27793.91 39295.40 20998.87 34597.69 27488.63 36487.87 37497.08 31474.38 39397.89 31991.66 30784.07 37494.35 343
PS-CasMVS90.63 35189.51 35893.99 35093.83 39391.70 33098.98 32698.52 12788.48 36686.15 40096.53 33775.46 38296.31 40288.83 34978.86 41593.95 383
test_040285.58 39883.94 40390.50 41093.81 39485.04 42098.55 37195.20 44476.01 45279.72 43895.13 39264.15 43796.26 40466.04 46586.88 35190.21 447
XVG-ACMP-BASELINE91.22 33990.75 33092.63 38693.73 39585.61 41698.52 37597.44 30492.77 23289.90 32896.85 32566.64 42798.39 27992.29 29488.61 33093.89 388
TranMVSNet+NR-MVSNet91.68 33190.61 33494.87 31093.69 39693.98 26899.69 21298.65 8791.03 30588.44 36596.83 32880.05 34096.18 40790.26 33576.89 43194.45 337
TransMVSNet (Re)87.25 39285.28 39993.16 37493.56 39791.03 34398.54 37394.05 45983.69 42481.09 43096.16 34675.32 38396.40 39776.69 44368.41 45692.06 430
v1090.25 36188.82 37094.57 32493.53 39893.43 28399.08 30996.87 39485.00 41287.34 38594.51 41380.93 32797.02 36682.85 40879.23 41293.26 410
testgi89.01 38188.04 38291.90 39493.49 39984.89 42299.73 19595.66 43393.89 18385.14 40798.17 28159.68 45094.66 43877.73 43888.88 32496.16 323
v890.54 35389.17 36394.66 31893.43 40093.40 28699.20 29896.94 38885.76 40287.56 37994.51 41381.96 31397.19 34984.94 39478.25 41793.38 408
V4291.28 33690.12 34794.74 31593.42 40193.46 28299.68 21597.02 37587.36 38189.85 33195.05 39581.31 32397.34 33887.34 37080.07 40993.40 406
pm-mvs189.36 37887.81 38494.01 34893.40 40291.93 32098.62 36996.48 41486.25 39783.86 41696.14 34873.68 39697.04 36286.16 38475.73 43693.04 416
v114491.09 34089.83 34994.87 31093.25 40393.69 27699.62 22696.98 38086.83 39189.64 33794.99 40180.94 32697.05 35985.08 39381.16 39593.87 390
v119290.62 35289.25 36294.72 31793.13 40493.07 29099.50 25597.02 37586.33 39689.56 34195.01 39879.22 34697.09 35882.34 41281.16 39594.01 377
v2v48291.30 33490.07 34895.01 30593.13 40493.79 27199.77 17597.02 37588.05 37289.25 34795.37 38180.73 33097.15 35187.28 37180.04 41094.09 371
OPM-MVS93.21 29192.80 29094.44 33293.12 40690.85 35099.77 17597.61 28696.19 9491.56 30698.65 24375.16 38898.47 26793.78 27389.39 32093.99 380
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v14419290.79 34789.52 35794.59 32293.11 40792.77 29799.56 24496.99 37886.38 39589.82 33294.95 40380.50 33597.10 35683.98 40080.41 40593.90 387
PEN-MVS90.19 36389.06 36693.57 36493.06 40890.90 34899.06 31498.47 13988.11 37185.91 40296.30 34276.67 36995.94 41787.07 37476.91 43093.89 388
v124090.20 36288.79 37194.44 33293.05 40992.27 31399.38 27596.92 39085.89 40089.36 34494.87 40577.89 35897.03 36480.66 42181.08 39894.01 377
FE-MVSNET392.78 30391.73 31495.92 27693.03 41096.82 14199.83 15697.79 26290.58 32090.09 32195.04 39684.75 28396.72 38488.20 35886.23 35594.23 352
v14890.70 34889.63 35393.92 35292.97 41190.97 34499.75 18596.89 39287.51 37888.27 37095.01 39881.67 31697.04 36287.40 36977.17 42893.75 396
v192192090.46 35489.12 36494.50 32892.96 41292.46 30999.49 25796.98 38086.10 39889.61 33995.30 38478.55 35597.03 36482.17 41380.89 40394.01 377
MVStest185.03 40482.76 41391.83 39592.95 41389.16 38298.57 37094.82 44871.68 46368.54 46695.11 39483.17 30495.66 42174.69 44865.32 46390.65 443
tt0320-xc82.94 41980.35 42690.72 40892.90 41483.54 43196.85 42894.73 45163.12 46879.85 43793.77 42549.43 46595.46 42480.98 42071.54 44593.16 413
Baseline_NR-MVSNet90.33 35889.51 35892.81 38392.84 41589.95 37199.77 17593.94 46084.69 41789.04 35495.66 36381.66 31796.52 39190.99 31876.98 42991.97 432
test_method80.79 42579.70 42884.08 44192.83 41667.06 46799.51 25395.42 43854.34 47381.07 43193.53 42744.48 46892.22 45978.90 43377.23 42792.94 418
pmmvs492.10 32091.07 32895.18 30192.82 41794.96 23099.48 26096.83 39687.45 38088.66 36196.56 33683.78 29896.83 37889.29 34584.77 36893.75 396
LF4IMVS89.25 38088.85 36990.45 41292.81 41881.19 44898.12 39794.79 44991.44 29086.29 39897.11 31265.30 43398.11 30588.53 35485.25 36292.07 429
tt032083.56 41881.15 42190.77 40692.77 41983.58 43096.83 42995.52 43763.26 46781.36 42892.54 43553.26 45995.77 41980.45 42274.38 43992.96 417
DTE-MVSNet89.40 37788.24 38092.88 38192.66 42089.95 37199.10 30698.22 21287.29 38285.12 40896.22 34476.27 37695.30 42983.56 40475.74 43593.41 405
EU-MVSNet90.14 36590.34 33989.54 42192.55 42181.06 44998.69 36398.04 23691.41 29486.59 39296.84 32780.83 32993.31 45186.20 38381.91 38994.26 348
APD_test181.15 42380.92 42381.86 44592.45 42259.76 47496.04 44393.61 46373.29 46177.06 44796.64 33244.28 46996.16 40872.35 45282.52 38389.67 455
sc_t185.01 40582.46 41592.67 38592.44 42383.09 43497.39 41495.72 43065.06 46685.64 40596.16 34649.50 46497.34 33884.86 39575.39 43797.57 306
our_test_390.39 35589.48 36093.12 37592.40 42489.57 37699.33 28296.35 41787.84 37685.30 40694.99 40184.14 29596.09 41280.38 42384.56 36993.71 401
ppachtmachnet_test89.58 37588.35 37893.25 37392.40 42490.44 36099.33 28296.73 40385.49 40785.90 40395.77 35781.09 32596.00 41676.00 44682.49 38493.30 409
v7n89.65 37388.29 37993.72 35892.22 42690.56 35799.07 31397.10 36385.42 40986.73 38994.72 40680.06 33997.13 35381.14 41878.12 41993.49 404
dmvs_testset83.79 41486.07 39576.94 44992.14 42748.60 48496.75 43090.27 47489.48 34278.65 44198.55 25879.25 34586.65 47266.85 46282.69 38195.57 325
PS-MVSNAJss93.64 28393.31 28094.61 32092.11 42892.19 31499.12 30397.38 31192.51 25188.45 36496.99 32091.20 17397.29 34694.36 25487.71 34394.36 340
pmmvs590.17 36489.09 36593.40 36792.10 42989.77 37499.74 18895.58 43585.88 40187.24 38695.74 35873.41 39996.48 39388.54 35383.56 37893.95 383
N_pmnet80.06 42880.78 42477.89 44891.94 43045.28 48698.80 35456.82 48878.10 44980.08 43593.33 42877.03 36395.76 42068.14 46082.81 38092.64 422
test_djsdf92.83 30292.29 30494.47 33091.90 43192.46 30999.55 24797.27 33391.17 29889.96 32596.07 35281.10 32496.89 37294.67 24988.91 32394.05 374
SixPastTwentyTwo88.73 38288.01 38390.88 40291.85 43282.24 44098.22 39495.18 44588.97 35282.26 42296.89 32271.75 40496.67 38684.00 39982.98 37993.72 400
K. test v388.05 38887.24 38990.47 41191.82 43382.23 44198.96 33297.42 30789.05 34776.93 44995.60 36568.49 41895.42 42585.87 38881.01 40193.75 396
OurMVSNet-221017-089.81 37089.48 36090.83 40591.64 43481.21 44798.17 39695.38 44091.48 28885.65 40497.31 30772.66 40097.29 34688.15 36084.83 36793.97 382
mvs_tets91.81 32491.08 32794.00 34991.63 43590.58 35698.67 36597.43 30592.43 25387.37 38497.05 31771.76 40397.32 34194.75 24688.68 32994.11 370
Gipumacopyleft66.95 44165.00 44172.79 45491.52 43667.96 46666.16 47895.15 44647.89 47558.54 47267.99 47729.74 47387.54 47150.20 47677.83 42162.87 477
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.01_n96.39 17995.74 19298.32 14691.47 43795.56 20199.84 14997.30 32797.74 3097.89 17399.35 15279.62 34299.85 12999.25 7499.24 14199.55 159
jajsoiax91.92 32291.18 32594.15 34191.35 43890.95 34799.00 32497.42 30792.61 24387.38 38397.08 31472.46 40197.36 33694.53 25288.77 32794.13 369
MDA-MVSNet-bldmvs84.09 41281.52 41991.81 39691.32 43988.00 40098.67 36595.92 42680.22 44355.60 47593.32 42968.29 42093.60 44973.76 44976.61 43293.82 394
MVP-Stereo90.93 34290.45 33792.37 38991.25 44088.76 38698.05 40196.17 42087.27 38384.04 41395.30 38478.46 35697.27 34883.78 40299.70 9391.09 437
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet_test_wron85.51 40083.32 40892.10 39190.96 44188.58 39299.20 29896.52 41279.70 44557.12 47492.69 43479.11 34893.86 44577.10 44177.46 42593.86 391
YYNet185.50 40183.33 40792.00 39290.89 44288.38 39699.22 29796.55 41179.60 44657.26 47392.72 43379.09 35093.78 44777.25 44077.37 42693.84 392
anonymousdsp91.79 32990.92 32994.41 33590.76 44392.93 29698.93 33697.17 34889.08 34687.46 38295.30 38478.43 35796.92 37092.38 29388.73 32893.39 407
lessismore_v090.53 40990.58 44480.90 45095.80 42777.01 44895.84 35566.15 42996.95 36883.03 40775.05 43893.74 399
EG-PatchMatch MVS85.35 40283.81 40589.99 41990.39 44581.89 44398.21 39596.09 42281.78 43674.73 45593.72 42651.56 46397.12 35579.16 43188.61 33090.96 440
EGC-MVSNET69.38 43463.76 44486.26 43890.32 44681.66 44696.24 43993.85 4610.99 4853.22 48692.33 44052.44 46092.92 45559.53 47284.90 36684.21 466
CMPMVSbinary61.59 2184.75 40885.14 40083.57 44290.32 44662.54 47096.98 42497.59 29074.33 45969.95 46396.66 33064.17 43698.32 28987.88 36488.41 33589.84 452
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new_pmnet84.49 41182.92 41189.21 42390.03 44882.60 43796.89 42795.62 43480.59 44175.77 45489.17 45265.04 43494.79 43672.12 45381.02 40090.23 446
pmmvs685.69 39783.84 40491.26 40190.00 44984.41 42597.82 40696.15 42175.86 45381.29 42995.39 37961.21 44796.87 37583.52 40573.29 44192.50 425
ttmdpeth88.23 38787.06 39091.75 39789.91 45087.35 40598.92 33995.73 42987.92 37484.02 41496.31 34168.23 42196.84 37686.33 38276.12 43391.06 438
DSMNet-mixed88.28 38688.24 38088.42 43189.64 45175.38 46098.06 40089.86 47585.59 40688.20 37192.14 44176.15 37891.95 46078.46 43596.05 24897.92 291
UnsupCasMVSNet_eth85.52 39983.99 40190.10 41789.36 45283.51 43296.65 43197.99 24089.14 34575.89 45393.83 42363.25 44093.92 44381.92 41567.90 45992.88 419
Anonymous2023120686.32 39585.42 39889.02 42589.11 45380.53 45399.05 31895.28 44185.43 40882.82 42093.92 42274.40 39293.44 45066.99 46181.83 39093.08 415
Anonymous2024052185.15 40383.81 40589.16 42488.32 45482.69 43698.80 35495.74 42879.72 44481.53 42790.99 44465.38 43294.16 44172.69 45181.11 39790.63 444
OpenMVS_ROBcopyleft79.82 2083.77 41581.68 41890.03 41888.30 45582.82 43598.46 37695.22 44373.92 46076.00 45291.29 44355.00 45696.94 36968.40 45988.51 33490.34 445
test20.0384.72 40983.99 40186.91 43688.19 45680.62 45298.88 34295.94 42588.36 36878.87 43994.62 41168.75 41689.11 46766.52 46375.82 43491.00 439
KD-MVS_self_test83.59 41682.06 41688.20 43286.93 45780.70 45197.21 41796.38 41582.87 43082.49 42188.97 45367.63 42392.32 45873.75 45062.30 47091.58 435
MIMVSNet182.58 42080.51 42588.78 42786.68 45884.20 42696.65 43195.41 43978.75 44778.59 44292.44 43651.88 46289.76 46665.26 46678.95 41392.38 428
CL-MVSNet_self_test84.50 41083.15 41088.53 43086.00 45981.79 44498.82 35097.35 31585.12 41183.62 41890.91 44676.66 37091.40 46169.53 45760.36 47192.40 427
UnsupCasMVSNet_bld79.97 43077.03 43588.78 42785.62 46081.98 44293.66 45597.35 31575.51 45670.79 46283.05 46948.70 46694.91 43478.31 43660.29 47289.46 458
mvs5depth84.87 40682.90 41290.77 40685.59 46184.84 42391.10 46893.29 46583.14 42785.07 40994.33 41962.17 44397.32 34178.83 43472.59 44490.14 448
Patchmatch-RL test86.90 39385.98 39789.67 42084.45 46275.59 45989.71 47192.43 46786.89 39077.83 44690.94 44594.22 9593.63 44887.75 36569.61 45099.79 111
pmmvs-eth3d84.03 41381.97 41790.20 41584.15 46387.09 40798.10 39994.73 45183.05 42874.10 45987.77 45965.56 43194.01 44281.08 41969.24 45289.49 457
test_fmvs379.99 42980.17 42779.45 44784.02 46462.83 46899.05 31893.49 46488.29 37080.06 43686.65 46428.09 47588.00 46888.63 35073.27 44287.54 464
PM-MVS80.47 42678.88 43085.26 43983.79 46572.22 46295.89 44691.08 47285.71 40576.56 45188.30 45536.64 47193.90 44482.39 41169.57 45189.66 456
new-patchmatchnet81.19 42279.34 42986.76 43782.86 46680.36 45497.92 40395.27 44282.09 43572.02 46086.87 46362.81 44290.74 46471.10 45463.08 46789.19 460
FE-MVSNET283.57 41781.36 42090.20 41582.83 46787.59 40198.28 38796.04 42385.33 41074.13 45887.45 46059.16 45193.26 45279.12 43269.91 44889.77 453
FE-MVSNET81.05 42478.81 43187.79 43481.98 46883.70 42898.23 39291.78 47181.27 43874.29 45787.44 46160.92 44990.67 46564.92 46768.43 45589.01 461
mvsany_test382.12 42181.14 42285.06 44081.87 46970.41 46497.09 42192.14 46891.27 29777.84 44588.73 45439.31 47095.49 42290.75 32571.24 44689.29 459
WB-MVS76.28 43277.28 43473.29 45381.18 47054.68 47897.87 40594.19 45681.30 43769.43 46490.70 44777.02 36482.06 47635.71 48168.11 45883.13 467
test_f78.40 43177.59 43380.81 44680.82 47162.48 47196.96 42593.08 46683.44 42574.57 45684.57 46827.95 47692.63 45684.15 39772.79 44387.32 465
SSC-MVS75.42 43376.40 43672.49 45780.68 47253.62 47997.42 41294.06 45880.42 44268.75 46590.14 44976.54 37281.66 47733.25 48266.34 46282.19 468
pmmvs380.27 42777.77 43287.76 43580.32 47382.43 43998.23 39291.97 46972.74 46278.75 44087.97 45857.30 45590.99 46370.31 45562.37 46989.87 451
testf168.38 43766.92 43872.78 45578.80 47450.36 48190.95 46987.35 48055.47 47158.95 47088.14 45620.64 48087.60 46957.28 47364.69 46480.39 470
APD_test268.38 43766.92 43872.78 45578.80 47450.36 48190.95 46987.35 48055.47 47158.95 47088.14 45620.64 48087.60 46957.28 47364.69 46480.39 470
ambc83.23 44377.17 47662.61 46987.38 47394.55 45576.72 45086.65 46430.16 47296.36 39984.85 39669.86 44990.73 442
test_vis3_rt68.82 43566.69 44075.21 45276.24 47760.41 47396.44 43468.71 48775.13 45750.54 47869.52 47616.42 48596.32 40180.27 42466.92 46168.89 474
TDRefinement84.76 40782.56 41491.38 40074.58 47884.80 42497.36 41594.56 45484.73 41680.21 43496.12 35163.56 43898.39 27987.92 36363.97 46690.95 441
E-PMN52.30 44552.18 44752.67 46371.51 47945.40 48593.62 45676.60 48536.01 47943.50 48064.13 47927.11 47767.31 48231.06 48326.06 47845.30 481
EMVS51.44 44751.22 44952.11 46470.71 48044.97 48794.04 45275.66 48635.34 48142.40 48161.56 48228.93 47465.87 48327.64 48424.73 47945.49 480
PMMVS267.15 44064.15 44376.14 45170.56 48162.07 47293.89 45387.52 47958.09 47060.02 46978.32 47122.38 47984.54 47459.56 47147.03 47681.80 469
FPMVS68.72 43668.72 43768.71 45965.95 48244.27 48895.97 44594.74 45051.13 47453.26 47690.50 44825.11 47883.00 47560.80 47080.97 40278.87 472
wuyk23d20.37 45120.84 45418.99 46765.34 48327.73 49050.43 4797.67 4919.50 4848.01 4856.34 4856.13 48826.24 48423.40 48510.69 4832.99 482
LCM-MVSNet67.77 43964.73 44276.87 45062.95 48456.25 47789.37 47293.74 46244.53 47661.99 46880.74 47020.42 48286.53 47369.37 45859.50 47387.84 462
MVEpermissive53.74 2251.54 44647.86 45062.60 46159.56 48550.93 48079.41 47677.69 48435.69 48036.27 48261.76 4815.79 48969.63 48037.97 48036.61 47767.24 475
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high56.10 44352.24 44667.66 46049.27 48656.82 47683.94 47482.02 48370.47 46433.28 48364.54 47817.23 48469.16 48145.59 47823.85 48077.02 473
tmp_tt65.23 44262.94 44572.13 45844.90 48750.03 48381.05 47589.42 47838.45 47748.51 47999.90 2254.09 45878.70 47991.84 30618.26 48187.64 463
PMVScopyleft49.05 2353.75 44451.34 44860.97 46240.80 48834.68 48974.82 47789.62 47737.55 47828.67 48472.12 4737.09 48781.63 47843.17 47968.21 45766.59 476
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12337.68 44939.14 45233.31 46519.94 48924.83 49198.36 3849.75 49015.53 48351.31 47787.14 46219.62 48317.74 48547.10 4773.47 48457.36 478
testmvs40.60 44844.45 45129.05 46619.49 49014.11 49299.68 21518.47 48920.74 48264.59 46798.48 26510.95 48617.09 48656.66 47511.01 48255.94 479
mmdepth0.00 4540.00 4570.00 4680.00 4910.00 4930.00 4800.00 4920.00 4860.00 4870.00 4870.00 4900.00 4870.00 4860.00 4850.00 483
monomultidepth0.00 4540.00 4570.00 4680.00 4910.00 4930.00 4800.00 4920.00 4860.00 4870.00 4870.00 4900.00 4870.00 4860.00 4850.00 483
test_blank0.00 4540.00 4570.00 4680.00 4910.00 4930.00 4800.00 4920.00 4860.00 4870.02 4860.00 4900.00 4870.00 4860.00 4850.00 483
eth-test20.00 491
eth-test0.00 491
uanet_test0.00 4540.00 4570.00 4680.00 4910.00 4930.00 4800.00 4920.00 4860.00 4870.00 4870.00 4900.00 4870.00 4860.00 4850.00 483
DCPMVS0.00 4540.00 4570.00 4680.00 4910.00 4930.00 4800.00 4920.00 4860.00 4870.00 4870.00 4900.00 4870.00 4860.00 4850.00 483
cdsmvs_eth3d_5k23.43 45031.24 4530.00 4680.00 4910.00 4930.00 48098.09 2300.00 4860.00 48799.67 11383.37 3010.00 4870.00 4860.00 4850.00 483
pcd_1.5k_mvsjas7.60 45310.13 4560.00 4680.00 4910.00 4930.00 4800.00 4920.00 4860.00 4870.00 48791.20 1730.00 4870.00 4860.00 4850.00 483
sosnet-low-res0.00 4540.00 4570.00 4680.00 4910.00 4930.00 4800.00 4920.00 4860.00 4870.00 4870.00 4900.00 4870.00 4860.00 4850.00 483
sosnet0.00 4540.00 4570.00 4680.00 4910.00 4930.00 4800.00 4920.00 4860.00 4870.00 4870.00 4900.00 4870.00 4860.00 4850.00 483
uncertanet0.00 4540.00 4570.00 4680.00 4910.00 4930.00 4800.00 4920.00 4860.00 4870.00 4870.00 4900.00 4870.00 4860.00 4850.00 483
Regformer0.00 4540.00 4570.00 4680.00 4910.00 4930.00 4800.00 4920.00 4860.00 4870.00 4870.00 4900.00 4870.00 4860.00 4850.00 483
ab-mvs-re8.28 45211.04 4550.00 4680.00 4910.00 4930.00 4800.00 4920.00 4860.00 48799.40 1460.00 4900.00 4870.00 4860.00 4850.00 483
uanet0.00 4540.00 4570.00 4680.00 4910.00 4930.00 4800.00 4920.00 4860.00 4870.00 4870.00 4900.00 4870.00 4860.00 4850.00 483
TestfortrainingZip99.97 39
WAC-MVS90.97 34486.10 386
PC_three_145296.96 6099.80 2699.79 6297.49 10100.00 199.99 599.98 32100.00 1
test_241102_TWO98.43 15597.27 4799.80 2699.94 497.18 23100.00 1100.00 1100.00 1100.00 1
test_0728_THIRD96.48 7899.83 2299.91 1897.87 5100.00 199.92 16100.00 1100.00 1
GSMVS99.59 149
sam_mvs194.72 7499.59 149
sam_mvs94.25 94
MTGPAbinary98.28 203
test_post195.78 44759.23 48393.20 12897.74 32591.06 316
test_post63.35 48094.43 8298.13 304
patchmatchnet-post91.70 44295.12 5997.95 316
MTMP99.87 13096.49 413
test9_res99.71 4899.99 21100.00 1
agg_prior299.48 63100.00 1100.00 1
test_prior498.05 8199.94 90
test_prior299.95 7295.78 10499.73 4599.76 7296.00 4099.78 35100.00 1
旧先验299.46 26594.21 16499.85 1899.95 8496.96 196
新几何299.40 269
无先验99.49 25798.71 7893.46 197100.00 194.36 25499.99 24
原ACMM299.90 114
testdata299.99 3990.54 329
segment_acmp96.68 31
testdata199.28 29196.35 90
plane_prior597.87 25498.37 28597.79 16789.55 31794.52 329
plane_prior498.59 251
plane_prior391.64 33296.63 7393.01 289
plane_prior299.84 14996.38 84
plane_prior91.74 32699.86 14196.76 6889.59 316
n20.00 492
nn0.00 492
door-mid89.69 476
test1198.44 147
door90.31 473
HQP5-MVS91.85 322
BP-MVS97.92 158
HQP4-MVS93.37 28498.39 27994.53 327
HQP3-MVS97.89 25289.60 314
HQP2-MVS80.65 332
MDTV_nov1_ep13_2view96.26 16896.11 44191.89 27498.06 16694.40 8494.30 25799.67 129
ACMMP++_ref87.04 350
ACMMP++88.23 337
Test By Simon92.82 139