This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.




Method Infoallhigh-res
multi-view
indooroutdoorbotani.boulde.bridgedoorexhibi.lectur.living.loungeobserv.old co.statueterrac.
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted by
APDe-MVS99.66 199.57 199.92 199.77 3899.89 199.75 3499.56 4699.02 1199.88 399.85 2699.18 599.96 1899.22 3099.92 999.90 1
UA-Net99.42 2999.29 3699.80 2999.62 9199.55 5199.50 11699.70 1598.79 4099.77 2399.96 197.45 9199.96 1898.92 5499.90 2299.89 2
CHOSEN 1792x268899.19 5599.10 5599.45 9399.89 898.52 17999.39 16299.94 198.73 4499.11 15199.89 1095.50 14399.94 4099.50 799.97 299.89 2
DP-MVS99.16 6098.95 7599.78 3399.77 3899.53 5599.41 15599.50 9797.03 18299.04 16599.88 1497.39 9299.92 6398.66 8699.90 2299.87 4
EI-MVSNet-UG-set99.58 399.57 199.64 6199.78 3499.14 9499.60 7799.45 14599.01 1499.90 199.83 3798.98 1899.93 5599.59 199.95 599.86 5
Test_1112_low_res98.89 9498.66 10899.57 7099.69 7098.95 12099.03 25199.47 12596.98 18499.15 14699.23 24696.77 11099.89 9298.83 6898.78 15299.86 5
HyFIR lowres test99.11 7198.92 7799.65 5699.90 399.37 7399.02 25499.91 397.67 12899.59 5999.75 9095.90 13399.73 14799.53 599.02 13299.86 5
EI-MVSNet-Vis-set99.58 399.56 399.64 6199.78 3499.15 9399.61 7699.45 14599.01 1499.89 299.82 4499.01 1199.92 6399.56 499.95 599.85 8
CVMVSNet98.57 12798.67 10598.30 22499.35 14395.59 26899.50 11699.55 5398.60 5199.39 9399.83 3794.48 19699.45 19398.75 7498.56 16199.85 8
HPM-MVS_fast99.51 1299.40 1499.85 1799.91 199.79 1699.76 2799.56 4697.72 12399.76 2699.75 9099.13 699.92 6399.07 4399.92 999.85 8
MG-MVS99.13 6299.02 6699.45 9399.57 10298.63 16799.07 23999.34 20198.99 1999.61 5599.82 4497.98 8099.87 9797.00 21799.80 6799.85 8
ACMMP_Plus99.47 2099.34 2499.88 499.87 1599.86 399.47 13199.48 11198.05 9899.76 2699.86 2298.82 3299.93 5598.82 7199.91 1599.84 12
HFP-MVS99.49 1399.37 1799.86 1299.87 1599.80 1299.66 5499.67 2298.15 8099.68 3499.69 11199.06 899.96 1898.69 8399.87 3799.84 12
region2R99.48 1799.35 2299.87 699.88 1199.80 1299.65 6499.66 2598.13 8299.66 4599.68 11698.96 2099.96 1898.62 9199.87 3799.84 12
#test#99.43 2799.29 3699.86 1299.87 1599.80 1299.55 10099.67 2297.83 11299.68 3499.69 11199.06 899.96 1898.39 11499.87 3799.84 12
Regformer-499.59 299.54 499.73 4599.76 4199.41 7099.58 8299.49 10299.02 1199.88 399.80 6499.00 1799.94 4099.45 1499.92 999.84 12
XVS99.53 999.42 1199.87 699.85 2399.83 799.69 4499.68 1998.98 2099.37 9799.74 9498.81 3399.94 4098.79 7299.86 4799.84 12
X-MVStestdata96.55 24895.45 26999.87 699.85 2399.83 799.69 4499.68 1998.98 2099.37 9764.01 33398.81 3399.94 4098.79 7299.86 4799.84 12
ACMMPR99.49 1399.36 1999.86 1299.87 1599.79 1699.66 5499.67 2298.15 8099.67 4099.69 11198.95 2399.96 1898.69 8399.87 3799.84 12
HPM-MVS99.42 2999.28 3899.83 2299.90 399.72 2599.81 1599.54 6197.59 13199.68 3499.63 13898.91 2699.94 4098.58 9699.91 1599.84 12
SteuartSystems-ACMMP99.54 799.42 1199.87 699.82 2999.81 1199.59 7999.51 8498.62 4999.79 1899.83 3799.28 399.97 1098.48 10899.90 2299.84 12
Skip Steuart: Steuart Systems R&D Blog.
1112_ss98.98 8998.77 9599.59 6799.68 7199.02 10699.25 20699.48 11197.23 16499.13 14799.58 15496.93 10599.90 8498.87 6098.78 15299.84 12
MP-MVS-pluss99.37 3799.20 4699.88 499.90 399.87 299.30 18699.52 7597.18 16799.60 5699.79 7298.79 3599.95 3398.83 6899.91 1599.83 23
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MPTG99.49 1399.36 1999.89 299.90 399.86 399.36 17399.47 12598.79 4099.68 3499.81 5398.43 6199.97 1098.88 5699.90 2299.83 23
MTAPA99.52 1199.39 1599.89 299.90 399.86 399.66 5499.47 12598.79 4099.68 3499.81 5398.43 6199.97 1098.88 5699.90 2299.83 23
Regformer-399.57 699.53 599.68 5099.76 4199.29 8099.58 8299.44 15399.01 1499.87 699.80 6498.97 1999.91 7299.44 1599.92 999.83 23
PGM-MVS99.45 2299.31 3199.86 1299.87 1599.78 2099.58 8299.65 3097.84 11199.71 2999.80 6499.12 799.97 1098.33 12199.87 3799.83 23
mPP-MVS99.44 2599.30 3399.86 1299.88 1199.79 1699.69 4499.48 11198.12 8499.50 7299.75 9098.78 3699.97 1098.57 9899.89 3099.83 23
CP-MVS99.45 2299.32 2699.85 1799.83 2899.75 2199.69 4499.52 7598.07 9399.53 6799.63 13898.93 2599.97 1098.74 7599.91 1599.83 23
TSAR-MVS + MP.99.58 399.50 799.81 2799.91 199.66 3499.63 6799.39 17598.91 3099.78 2299.85 2699.36 299.94 4098.84 6699.88 3399.82 30
MP-MVScopyleft99.33 4199.15 5099.87 699.88 1199.82 1099.66 5499.46 13498.09 8999.48 7699.74 9498.29 7099.96 1897.93 14899.87 3799.82 30
MCST-MVS99.43 2799.30 3399.82 2499.79 3399.74 2499.29 19099.40 17298.79 4099.52 6999.62 14398.91 2699.90 8498.64 8899.75 7799.82 30
DeepC-MVS_fast98.69 199.49 1399.39 1599.77 3599.63 8799.59 4699.36 17399.46 13499.07 1099.79 1899.82 4498.85 3099.92 6398.68 8599.87 3799.82 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HSP-MVS99.41 3299.26 4399.85 1799.89 899.80 1299.67 5199.37 18898.70 4599.77 2399.49 17998.21 7399.95 3398.46 11199.77 7499.81 34
CPTT-MVS99.11 7198.90 8099.74 4399.80 3299.46 6599.59 7999.49 10297.03 18299.63 5099.69 11197.27 9799.96 1897.82 15699.84 5699.81 34
ACMMPcopyleft99.45 2299.32 2699.82 2499.89 899.67 3299.62 7099.69 1898.12 8499.63 5099.84 3598.73 4699.96 1898.55 10399.83 6099.81 34
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
DeepPCF-MVS98.18 398.81 10799.37 1797.12 27999.60 9791.75 30698.61 29799.44 15399.35 199.83 1199.85 2698.70 4899.81 12499.02 4799.91 1599.81 34
3Dnovator+97.12 1399.18 5798.97 7199.82 2499.17 18299.68 3099.81 1599.51 8499.20 698.72 20699.89 1095.68 14099.97 1098.86 6399.86 4799.81 34
Regformer-199.53 999.47 899.72 4799.71 6399.44 6799.49 12299.46 13498.95 2599.83 1199.76 8599.01 1199.93 5599.17 3599.87 3799.80 39
Regformer-299.54 799.47 899.75 3899.71 6399.52 5899.49 12299.49 10298.94 2699.83 1199.76 8599.01 1199.94 4099.15 3799.87 3799.80 39
APD-MVScopyleft99.27 4999.08 5699.84 2199.75 4799.79 1699.50 11699.50 9797.16 16999.77 2399.82 4498.78 3699.94 4097.56 18199.86 4799.80 39
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC99.34 4099.19 4799.79 3299.61 9599.65 3799.30 18699.48 11198.86 3299.21 13799.63 13898.72 4799.90 8498.25 12599.63 10099.80 39
HPM-MVS++99.39 3699.23 4599.87 699.75 4799.84 699.43 14499.51 8498.68 4799.27 12399.53 16798.64 5299.96 1898.44 11399.80 6799.79 43
abl_699.44 2599.31 3199.83 2299.85 2399.75 2199.66 5499.59 3798.13 8299.82 1499.81 5398.60 5499.96 1898.46 11199.88 3399.79 43
PVSNet_Blended_VisFu99.36 3899.28 3899.61 6599.86 2099.07 10199.47 13199.93 297.66 12999.71 2999.86 2297.73 8699.96 1899.47 1299.82 6499.79 43
3Dnovator97.25 999.24 5299.05 5899.81 2799.12 19099.66 3499.84 999.74 1099.09 998.92 18499.90 795.94 13199.98 598.95 5299.92 999.79 43
APD-MVS_3200maxsize99.48 1799.35 2299.85 1799.76 4199.83 799.63 6799.54 6198.36 6599.79 1899.82 4498.86 2999.95 3398.62 9199.81 6599.78 47
CDPH-MVS99.13 6298.91 7999.80 2999.75 4799.71 2699.15 22499.41 16596.60 20699.60 5699.55 16398.83 3199.90 8497.48 18999.83 6099.78 47
SD-MVS99.41 3299.52 699.05 13699.74 5399.68 3099.46 13499.52 7599.11 899.88 399.91 599.43 197.70 30798.72 8099.93 899.77 49
CNVR-MVS99.42 2999.30 3399.78 3399.62 9199.71 2699.26 20599.52 7598.82 3699.39 9399.71 10298.96 2099.85 10398.59 9599.80 6799.77 49
MVS_111021_HR99.41 3299.32 2699.66 5399.72 6199.47 6498.95 27499.85 698.82 3699.54 6699.73 9798.51 5699.74 13998.91 5599.88 3399.77 49
QAPM98.67 12298.30 13599.80 2999.20 17399.67 3299.77 2499.72 1194.74 26698.73 20599.90 795.78 13799.98 596.96 22199.88 3399.76 52
test9_res97.49 18899.72 8399.75 53
train_agg99.02 8498.77 9599.77 3599.67 7299.65 3799.05 24599.41 16596.28 22998.95 17999.49 17998.76 4199.91 7297.63 17599.72 8399.75 53
agg_prior398.97 9198.71 10199.75 3899.67 7299.60 4499.04 25099.41 16595.93 25198.87 19099.48 18598.61 5399.91 7297.63 17599.72 8399.75 53
agg_prior199.01 8798.76 9799.76 3799.67 7299.62 4098.99 26199.40 17296.26 23298.87 19099.49 17998.77 3999.91 7297.69 17299.72 8399.75 53
agg_prior297.21 20499.73 8299.75 53
test_prior399.21 5499.05 5899.68 5099.67 7299.48 6298.96 27099.56 4698.34 6699.01 16899.52 17198.68 4999.83 11597.96 14599.74 7999.74 58
test_prior99.68 5099.67 7299.48 6299.56 4699.83 11599.74 58
test1299.75 3899.64 8599.61 4299.29 22199.21 13798.38 6599.89 9299.74 7999.74 58
114514_t98.93 9298.67 10599.72 4799.85 2399.53 5599.62 7099.59 3792.65 29799.71 2999.78 7798.06 7899.90 8498.84 6699.91 1599.74 58
Vis-MVSNetpermissive99.12 6798.97 7199.56 7399.78 3499.10 9899.68 4999.66 2598.49 5699.86 799.87 1994.77 18299.84 10899.19 3299.41 10799.74 58
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
旧先验199.74 5399.59 4699.54 6199.69 11198.47 5899.68 9399.73 63
112199.09 7598.87 8499.75 3899.74 5399.60 4499.27 19799.48 11196.82 19399.25 12799.65 12898.38 6599.93 5597.53 18499.67 9499.73 63
EPNet98.86 9898.71 10199.30 11297.20 30498.18 19599.62 7098.91 27799.28 298.63 22599.81 5395.96 12899.99 199.24 2999.72 8399.73 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet99.05 8098.87 8499.57 7099.73 5899.32 7699.75 3499.20 24298.02 10299.56 6399.86 2296.54 11699.67 16898.09 13499.13 12399.73 63
F-COLMAP99.19 5599.04 6199.64 6199.78 3499.27 8399.42 15199.54 6197.29 15899.41 8899.59 15198.42 6499.93 5598.19 12799.69 9099.73 63
DeepC-MVS98.35 299.30 4499.19 4799.64 6199.82 2999.23 8799.62 7099.55 5398.94 2699.63 5099.95 295.82 13699.94 4099.37 1699.97 299.73 63
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
新几何199.75 3899.75 4799.59 4699.54 6196.76 19499.29 11599.64 13498.43 6199.94 4096.92 22599.66 9599.72 69
无先验98.99 26199.51 8496.89 18999.93 5597.53 18499.72 69
test22299.75 4799.49 6198.91 27999.49 10296.42 22199.34 10799.65 12898.28 7199.69 9099.72 69
testdata99.54 7499.75 4798.95 12099.51 8497.07 17899.43 8499.70 10698.87 2899.94 4097.76 16299.64 9899.72 69
VNet99.11 7198.90 8099.73 4599.52 10899.56 4999.41 15599.39 17599.01 1499.74 2899.78 7795.56 14199.92 6399.52 698.18 17499.72 69
WTY-MVS99.06 7998.88 8399.61 6599.62 9199.16 9199.37 16999.56 4698.04 9999.53 6799.62 14396.84 10699.94 4098.85 6598.49 16599.72 69
CSCG99.32 4299.32 2699.32 10899.85 2398.29 19199.71 4199.66 2598.11 8699.41 8899.80 6498.37 6799.96 1898.99 4999.96 499.72 69
原ACMM199.65 5699.73 5899.33 7599.47 12597.46 14299.12 14999.66 12798.67 5199.91 7297.70 17199.69 9099.71 76
LFMVS97.90 18597.35 21899.54 7499.52 10899.01 10899.39 16298.24 30897.10 17799.65 4899.79 7284.79 31199.91 7299.28 2698.38 16999.69 77
EPNet_dtu98.03 16897.96 15698.23 23598.27 28895.54 27199.23 20998.75 28899.02 1197.82 26199.71 10296.11 12799.48 19093.04 29299.65 9799.69 77
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PAPM_NR99.04 8198.84 8899.66 5399.74 5399.44 6799.39 16299.38 18197.70 12699.28 11999.28 24098.34 6899.85 10396.96 22199.45 10499.69 77
EPP-MVSNet99.13 6298.99 6899.53 7899.65 8499.06 10299.81 1599.33 20997.43 14699.60 5699.88 1497.14 9999.84 10899.13 3898.94 13999.69 77
sss99.17 5899.05 5899.53 7899.62 9198.97 11599.36 17399.62 3197.83 11299.67 4099.65 12897.37 9599.95 3399.19 3299.19 12099.68 81
PHI-MVS99.30 4499.17 4999.70 4999.56 10599.52 5899.58 8299.80 897.12 17399.62 5399.73 9798.58 5599.90 8498.61 9399.91 1599.68 81
PVSNet_094.43 1996.09 26695.47 26897.94 25399.31 15494.34 29097.81 31699.70 1597.12 17397.46 26598.75 28089.71 27899.79 13097.69 17281.69 32099.68 81
TAMVS99.12 6799.08 5699.24 12199.46 12198.55 17499.51 11199.46 13498.09 8999.45 8099.82 4498.34 6899.51 18998.70 8198.93 14099.67 84
CHOSEN 280x42099.12 6799.13 5199.08 13299.66 8297.89 20398.43 30499.71 1398.88 3199.62 5399.76 8596.63 11499.70 16399.46 1399.99 199.66 85
CDS-MVSNet99.09 7599.03 6399.25 12099.42 12898.73 15899.45 13599.46 13498.11 8699.46 7999.77 8298.01 7999.37 20698.70 8198.92 14299.66 85
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR98.63 12698.34 13199.51 8499.40 13599.03 10598.80 28599.36 18996.33 22699.00 17599.12 25698.46 5999.84 10895.23 26699.37 11299.66 85
TSAR-MVS + GP.99.36 3899.36 1999.36 10399.67 7298.61 17299.07 23999.33 20999.00 1899.82 1499.81 5399.06 899.84 10899.09 4199.42 10699.65 88
MVSFormer99.17 5899.12 5399.29 11699.51 11098.94 12399.88 199.46 13497.55 13699.80 1699.65 12897.39 9299.28 22999.03 4599.85 5199.65 88
jason99.13 6299.03 6399.45 9399.46 12198.87 13199.12 22899.26 23598.03 10199.79 1899.65 12897.02 10299.85 10399.02 4799.90 2299.65 88
jason: jason.
PLCcopyleft97.94 499.02 8498.85 8799.53 7899.66 8299.01 10899.24 20899.52 7596.85 19199.27 12399.48 18598.25 7299.91 7297.76 16299.62 10199.65 88
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS97.07 1597.74 20697.34 22198.94 14899.70 6897.53 21199.25 20699.51 8491.90 30199.30 11199.63 13898.78 3699.64 17488.09 30899.87 3799.65 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LCM-MVSNet-Re97.83 19198.15 14096.87 28499.30 15592.25 30599.59 7998.26 30797.43 14696.20 28199.13 25396.27 12398.73 28398.17 12998.99 13499.64 93
BH-RMVSNet98.41 13498.08 14799.40 10199.41 13198.83 13899.30 18698.77 28797.70 12698.94 18199.65 12892.91 23599.74 13996.52 24199.55 10299.64 93
MVS_111021_LR99.41 3299.33 2599.65 5699.77 3899.51 6098.94 27699.85 698.82 3699.65 4899.74 9498.51 5699.80 12798.83 6899.89 3099.64 93
MVS_030599.24 5299.13 5199.57 7099.44 12699.12 9699.29 19099.55 5398.93 2899.52 6999.61 14696.36 12099.97 1099.57 299.92 999.63 96
MVS97.28 23796.55 24499.48 8798.78 25898.95 12099.27 19799.39 17583.53 31798.08 25199.54 16696.97 10399.87 9794.23 28599.16 12199.63 96
MSLP-MVS++99.46 2199.47 899.44 9699.60 9799.16 9199.41 15599.71 1398.98 2099.45 8099.78 7799.19 499.54 18899.28 2699.84 5699.63 96
GA-MVS97.85 18897.47 20399.00 14199.38 13897.99 20198.57 29999.15 24797.04 18198.90 18799.30 23789.83 27799.38 20396.70 23498.33 17099.62 99
Vis-MVSNet (Re-imp)98.87 9598.72 9999.31 10999.71 6398.88 13099.80 1999.44 15397.91 10599.36 10199.78 7795.49 14499.43 20197.91 14999.11 12499.62 99
VDD-MVS97.73 20797.35 21898.88 16799.47 12097.12 22199.34 17998.85 28398.19 7699.67 4099.85 2682.98 31599.92 6399.49 1198.32 17199.60 101
DELS-MVS99.48 1799.42 1199.65 5699.72 6199.40 7299.05 24599.66 2599.14 799.57 6299.80 6498.46 5999.94 4099.57 299.84 5699.60 101
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
PVSNet_Blended99.08 7798.97 7199.42 10099.76 4198.79 15498.78 28699.91 396.74 19599.67 4099.49 17997.53 8999.88 9598.98 5099.85 5199.60 101
OMC-MVS99.08 7799.04 6199.20 12599.67 7298.22 19499.28 19499.52 7598.07 9399.66 4599.81 5397.79 8499.78 13297.79 15899.81 6599.60 101
AllTest98.87 9598.72 9999.31 10999.86 2098.48 18499.56 9599.61 3297.85 10999.36 10199.85 2695.95 12999.85 10396.66 23799.83 6099.59 105
TestCases99.31 10999.86 2098.48 18499.61 3297.85 10999.36 10199.85 2695.95 12999.85 10396.66 23799.83 6099.59 105
lupinMVS99.13 6299.01 6799.46 9299.51 11098.94 12399.05 24599.16 24697.86 10799.80 1699.56 16097.39 9299.86 10098.94 5399.85 5199.58 107
RPSCF98.22 14398.62 11396.99 28099.82 2991.58 30799.72 3999.44 15396.61 20499.66 4599.89 1095.92 13299.82 12097.46 19299.10 12699.57 108
DSMNet-mixed97.25 23897.35 21896.95 28297.84 29393.61 29899.57 8896.63 32396.13 24598.87 19098.61 28394.59 19197.70 30795.08 26898.86 14799.55 109
AdaColmapbinary99.01 8798.80 9299.66 5399.56 10599.54 5299.18 21999.70 1598.18 7999.35 10499.63 13896.32 12199.90 8497.48 18999.77 7499.55 109
alignmvs98.81 10798.56 12299.58 6999.43 12799.42 6999.51 11198.96 27098.61 5099.35 10498.92 27194.78 17899.77 13499.35 1798.11 17899.54 111
PatchmatchNetpermissive98.31 13998.36 12998.19 24099.16 18495.32 27699.27 19798.92 27497.37 15299.37 9799.58 15494.90 17099.70 16397.43 19599.21 11899.54 111
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PVSNet96.02 1798.85 10498.84 8898.89 16499.73 5897.28 21498.32 30899.60 3497.86 10799.50 7299.57 15896.75 11199.86 10098.56 10199.70 8999.54 111
MSDG98.98 8998.80 9299.53 7899.76 4199.19 8898.75 28999.55 5397.25 16199.47 7799.77 8297.82 8399.87 9796.93 22499.90 2299.54 111
UGNet98.87 9598.69 10399.40 10199.22 17098.72 15999.44 13999.68 1999.24 499.18 14599.42 20092.74 23999.96 1899.34 2199.94 799.53 115
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
Patchmatch-test97.93 18097.65 18898.77 18799.18 17797.07 22699.03 25199.14 24996.16 24198.74 20499.57 15894.56 19299.72 15193.36 29199.11 12499.52 116
PMMVS98.80 11098.62 11399.34 10499.27 16398.70 16098.76 28899.31 21697.34 15399.21 13799.07 25897.20 9899.82 12098.56 10198.87 14699.52 116
LS3D99.27 4999.12 5399.74 4399.18 17799.75 2199.56 9599.57 4398.45 5999.49 7599.85 2697.77 8599.94 4098.33 12199.84 5699.52 116
Effi-MVS+98.81 10798.59 12099.48 8799.46 12199.12 9698.08 31499.50 9797.50 14199.38 9599.41 20396.37 11999.81 12499.11 4098.54 16299.51 119
Patchmatch-RL test95.84 26895.81 25795.95 29195.61 30790.57 30898.24 31098.39 30495.10 26295.20 28798.67 28294.78 17897.77 30596.28 24790.02 30099.51 119
mvs_anonymous99.03 8398.99 6899.16 12799.38 13898.52 17999.51 11199.38 18197.79 11599.38 9599.81 5397.30 9699.45 19399.35 1798.99 13499.51 119
Patchmatch-test198.16 14998.14 14198.22 23799.30 15595.55 26999.07 23998.97 26897.57 13499.43 8499.60 14992.72 24099.60 18297.38 19799.20 11999.50 122
test_normal97.44 23296.77 24299.44 9697.75 29699.00 11099.10 23698.64 29797.71 12493.93 30098.82 27587.39 30199.83 11598.61 9398.97 13699.49 123
ab-mvs98.86 9898.63 11099.54 7499.64 8599.19 8899.44 13999.54 6197.77 11799.30 11199.81 5394.20 20699.93 5599.17 3598.82 14999.49 123
ADS-MVSNet298.02 17098.07 14997.87 25899.33 14795.19 27999.23 20999.08 25596.24 23499.10 15499.67 12194.11 21198.93 27896.81 22899.05 13099.48 125
ADS-MVSNet98.20 14698.08 14798.56 20099.33 14796.48 25299.23 20999.15 24796.24 23499.10 15499.67 12194.11 21199.71 15796.81 22899.05 13099.48 125
tpm97.67 21797.55 19498.03 24699.02 20895.01 28299.43 14498.54 30396.44 21999.12 14999.34 22991.83 25899.60 18297.75 16496.46 22999.48 125
CNLPA99.14 6198.99 6899.59 6799.58 10099.41 7099.16 22199.44 15398.45 5999.19 14399.49 17998.08 7799.89 9297.73 16699.75 7799.48 125
canonicalmvs99.02 8498.86 8699.51 8499.42 12899.32 7699.80 1999.48 11198.63 4899.31 11098.81 27697.09 10099.75 13899.27 2897.90 18499.47 129
Test495.05 27693.67 28499.22 12496.07 30698.94 12399.20 21799.27 23397.71 12489.96 31597.59 30666.18 32399.25 23898.06 14198.96 13799.47 129
MIMVSNet97.73 20797.45 20598.57 19899.45 12597.50 21299.02 25498.98 26796.11 24699.41 8899.14 25290.28 27198.74 28295.74 25598.93 14099.47 129
MVS_test032698.79 11198.62 11399.28 11899.00 21098.41 18999.01 25899.09 25499.23 598.67 21699.68 11694.31 20399.95 3398.74 7599.89 3099.46 132
MVS_Test99.10 7498.97 7199.48 8799.49 11699.14 9499.67 5199.34 20197.31 15699.58 6099.76 8597.65 8899.82 12098.87 6099.07 12999.46 132
MDTV_nov1_ep13_2view95.18 28099.35 17796.84 19299.58 6095.19 15497.82 15699.46 132
MVS-HIRNet95.75 26995.16 27397.51 27399.30 15593.69 29798.88 28195.78 32485.09 31698.78 20192.65 32091.29 26499.37 20694.85 27199.85 5199.46 132
MVS_dtu98.77 11498.60 11999.30 11298.95 22498.47 18699.08 23899.27 23399.26 398.94 18199.71 10293.54 22699.96 1898.86 6399.79 7199.45 136
DI_MVS_plusplus_test97.45 23196.79 24099.44 9697.76 29599.04 10499.21 21598.61 30097.74 12194.01 29798.83 27487.38 30299.83 11598.63 8998.90 14499.44 137
DP-MVS Recon99.12 6798.95 7599.65 5699.74 5399.70 2899.27 19799.57 4396.40 22499.42 8699.68 11698.75 4499.80 12797.98 14499.72 8399.44 137
PatchMatch-RL98.84 10698.62 11399.52 8299.71 6399.28 8199.06 24399.77 997.74 12199.50 7299.53 16795.41 14599.84 10897.17 20999.64 9899.44 137
VDDNet97.55 22197.02 23699.16 12799.49 11698.12 19999.38 16799.30 21895.35 25999.68 3499.90 782.62 31799.93 5599.31 2498.13 17799.42 140
PCF-MVS97.08 1497.66 21897.06 23599.47 9099.61 9599.09 9998.04 31599.25 23791.24 30498.51 23199.70 10694.55 19399.91 7292.76 29499.85 5199.42 140
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HY-MVS97.30 798.85 10498.64 10999.47 9099.42 12899.08 10099.62 7099.36 18997.39 15199.28 11999.68 11696.44 11799.92 6398.37 11798.22 17299.40 142
Fast-Effi-MVS+98.70 11998.43 12699.51 8499.51 11099.28 8199.52 10799.47 12596.11 24699.01 16899.34 22996.20 12599.84 10897.88 15198.82 14999.39 143
diffmvs98.72 11898.49 12499.43 9999.48 11999.19 8899.62 7099.42 16295.58 25799.37 9799.67 12196.14 12699.74 13998.14 13198.96 13799.37 144
EPMVS97.82 19497.65 18898.35 22098.88 24295.98 26399.49 12294.71 32797.57 13499.26 12699.48 18592.46 25399.71 15797.87 15299.08 12899.35 145
CostFormer97.72 20997.73 18397.71 26999.15 18794.02 29299.54 10399.02 26494.67 26799.04 16599.35 22692.35 25599.77 13498.50 10797.94 18399.34 146
BH-untuned98.42 13398.36 12998.59 19699.49 11696.70 24599.27 19799.13 25097.24 16398.80 19999.38 21295.75 13899.74 13997.07 21499.16 12199.33 147
PAPM97.59 22097.09 23499.07 13399.06 20198.26 19398.30 30999.10 25294.88 26398.08 25199.34 22996.27 12399.64 17489.87 30298.92 14299.31 148
tpm297.44 23297.34 22197.74 26899.15 18794.36 28999.45 13598.94 27193.45 29298.90 18799.44 19791.35 26399.59 18497.31 20098.07 17999.29 149
JIA-IIPM97.50 22897.02 23698.93 15198.73 26497.80 20699.30 18698.97 26891.73 30298.91 18594.86 31895.10 15799.71 15797.58 17897.98 18299.28 150
LP97.04 24396.80 23997.77 26698.90 23895.23 27798.97 26899.06 26094.02 28298.09 25099.41 20393.88 21898.82 28090.46 30098.42 16899.26 151
dp97.75 20497.80 16997.59 27199.10 19593.71 29699.32 18198.88 28196.48 21799.08 15899.55 16392.67 24499.82 12096.52 24198.58 15899.24 152
TESTMET0.1,197.55 22197.27 22998.40 21798.93 23396.53 25098.67 29397.61 32096.96 18598.64 22499.28 24088.63 29199.45 19397.30 20199.38 10899.21 153
DWT-MVSNet_test97.53 22397.40 21497.93 25499.03 20794.86 28399.57 8898.63 29896.59 20898.36 24098.79 27789.32 28199.74 13998.14 13198.16 17699.20 154
CR-MVSNet98.17 14897.93 15998.87 17199.18 17798.49 18299.22 21399.33 20996.96 18599.56 6399.38 21294.33 20199.00 26894.83 27298.58 15899.14 155
RPMNet96.61 24795.85 25598.87 17199.18 17798.49 18299.22 21399.08 25588.72 31399.56 6397.38 30994.08 21399.00 26886.87 31398.58 15899.14 155
testgi97.65 21997.50 19998.13 24399.36 14296.45 25399.42 15199.48 11197.76 11897.87 25999.45 19691.09 26598.81 28194.53 27698.52 16399.13 157
test-LLR98.06 15997.90 16098.55 20298.79 25497.10 22298.67 29397.75 31697.34 15398.61 22898.85 27294.45 19799.45 19397.25 20299.38 10899.10 158
test-mter97.49 23097.13 23398.55 20298.79 25497.10 22298.67 29397.75 31696.65 20198.61 22898.85 27288.23 29699.45 19397.25 20299.38 10899.10 158
IB-MVS95.67 1896.22 26295.44 27098.57 19899.21 17196.70 24598.65 29697.74 31896.71 19797.27 26898.54 28686.03 30599.92 6398.47 11086.30 31699.10 158
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
MAR-MVS98.86 9898.63 11099.54 7499.37 14099.66 3499.45 13599.54 6196.61 20499.01 16899.40 20797.09 10099.86 10097.68 17499.53 10399.10 158
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
tpmrst98.33 13898.48 12597.90 25799.16 18494.78 28499.31 18499.11 25197.27 15999.45 8099.59 15195.33 14699.84 10898.48 10898.61 15599.09 162
xiu_mvs_v1_base_debu99.29 4699.27 4099.34 10499.63 8798.97 11599.12 22899.51 8498.86 3299.84 899.47 18998.18 7499.99 199.50 799.31 11399.08 163
xiu_mvs_v1_base99.29 4699.27 4099.34 10499.63 8798.97 11599.12 22899.51 8498.86 3299.84 899.47 18998.18 7499.99 199.50 799.31 11399.08 163
xiu_mvs_v1_base_debi99.29 4699.27 4099.34 10499.63 8798.97 11599.12 22899.51 8498.86 3299.84 899.47 18998.18 7499.99 199.50 799.31 11399.08 163
COLMAP_ROBcopyleft97.56 698.86 9898.75 9899.17 12699.88 1198.53 17699.34 17999.59 3797.55 13698.70 21399.89 1095.83 13599.90 8498.10 13399.90 2299.08 163
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tpmp4_e2397.34 23597.29 22797.52 27299.25 16793.73 29499.58 8299.19 24594.00 28398.20 24699.41 20390.74 26999.74 13997.13 21098.07 17999.07 167
PatchFormer-LS_test98.01 17398.05 15097.87 25899.15 18794.76 28599.42 15198.93 27297.12 17398.84 19698.59 28493.74 22599.80 12798.55 10398.17 17599.06 168
OpenMVScopyleft96.50 1698.47 12998.12 14399.52 8299.04 20599.53 5599.82 1399.72 1194.56 27298.08 25199.88 1494.73 18599.98 597.47 19199.76 7699.06 168
PatchT97.03 24496.44 24598.79 18598.99 21298.34 19099.16 22199.07 25892.13 29899.52 6997.31 31194.54 19498.98 27088.54 30698.73 15499.03 170
BH-w/o98.00 17497.89 16498.32 22299.35 14396.20 26199.01 25898.90 27996.42 22198.38 23899.00 26495.26 15099.72 15196.06 24998.61 15599.03 170
Fast-Effi-MVS+-dtu98.77 11498.83 9198.60 19599.41 13196.99 23399.52 10799.49 10298.11 8699.24 12899.34 22996.96 10499.79 13097.95 14799.45 10499.02 172
XVG-OURS-SEG-HR98.69 12098.62 11398.89 16499.71 6397.74 20899.12 22899.54 6198.44 6299.42 8699.71 10294.20 20699.92 6398.54 10598.90 14499.00 173
XVG-OURS98.73 11798.68 10498.88 16799.70 6897.73 20998.92 27799.55 5398.52 5599.45 8099.84 3595.27 14999.91 7298.08 13898.84 14899.00 173
tpm cat197.39 23497.36 21697.50 27499.17 18293.73 29499.43 14499.31 21691.27 30398.71 20799.08 25794.31 20399.77 13496.41 24598.50 16499.00 173
xiu_mvs_v2_base99.26 5199.25 4499.29 11699.53 10798.91 12899.02 25499.45 14598.80 3999.71 2999.26 24398.94 2499.98 599.34 2199.23 11798.98 176
PS-MVSNAJ99.32 4299.32 2699.30 11299.57 10298.94 12398.97 26899.46 13498.92 2999.71 2999.24 24599.01 1199.98 599.35 1799.66 9598.97 177
tpmvs97.98 17598.02 15297.84 26199.04 20594.73 28699.31 18499.20 24296.10 24998.76 20399.42 20094.94 16599.81 12496.97 22098.45 16698.97 177
mvs-test198.86 9898.84 8898.89 16499.33 14797.77 20799.44 13999.30 21898.47 5799.10 15499.43 19896.78 10899.95 3398.73 7899.02 13298.96 179
TR-MVS97.76 20197.41 21398.82 18199.06 20197.87 20498.87 28298.56 30296.63 20398.68 21599.22 24792.49 24999.65 17295.40 26397.79 18698.95 180
test0.0.03 197.71 21197.42 21298.56 20098.41 28697.82 20598.78 28698.63 29897.34 15398.05 25598.98 26894.45 19798.98 27095.04 26997.15 22098.89 181
cascas97.69 21297.43 21198.48 20798.60 27897.30 21398.18 31399.39 17592.96 29498.41 23698.78 27993.77 22299.27 23298.16 13098.61 15598.86 182
131498.68 12198.54 12399.11 13198.89 24198.65 16599.27 19799.49 10296.89 18997.99 25699.56 16097.72 8799.83 11597.74 16599.27 11698.84 183
PS-MVSNAJss98.92 9398.92 7798.90 16298.78 25898.53 17699.78 2299.54 6198.07 9399.00 17599.76 8599.01 1199.37 20699.13 3897.23 21698.81 184
pcd1.5k->3k40.85 30743.49 30932.93 32198.95 2240.00 3380.00 32999.53 710.00 3330.00 3340.27 33595.32 1470.00 3360.00 33397.30 21498.80 185
FC-MVSNet-test98.75 11698.62 11399.15 12999.08 19899.45 6699.86 899.60 3498.23 7598.70 21399.82 4496.80 10799.22 24499.07 4396.38 23198.79 186
nrg03098.64 12598.42 12799.28 11899.05 20499.69 2999.81 1599.46 13498.04 9999.01 16899.82 4496.69 11399.38 20399.34 2194.59 26898.78 187
FIs98.78 11298.63 11099.23 12399.18 17799.54 5299.83 1299.59 3798.28 7098.79 20099.81 5396.75 11199.37 20699.08 4296.38 23198.78 187
EU-MVSNet97.98 17598.03 15197.81 26498.72 26696.65 24899.66 5499.66 2598.09 8998.35 24199.82 4495.25 15198.01 29997.41 19695.30 24898.78 187
jajsoiax98.43 13298.28 13698.88 16798.60 27898.43 18799.82 1399.53 7198.19 7698.63 22599.80 6493.22 22999.44 19899.22 3097.50 20198.77 190
mvs_tets98.40 13598.23 13898.91 15898.67 27398.51 18199.66 5499.53 7198.19 7698.65 22399.81 5392.75 23799.44 19899.31 2497.48 20598.77 190
XXY-MVS98.38 13698.09 14699.24 12199.26 16599.32 7699.56 9599.55 5397.45 14598.71 20799.83 3793.23 22899.63 17998.88 5696.32 23398.76 192
v7n97.87 18797.52 19698.92 15698.76 26298.58 17399.84 999.46 13496.20 23798.91 18599.70 10694.89 17199.44 19896.03 25093.89 28198.75 193
PS-CasMVS97.93 18097.59 19398.95 14798.99 21299.06 10299.68 4999.52 7597.13 17198.31 24399.68 11692.44 25499.05 26298.51 10694.08 27798.75 193
test_djsdf98.67 12298.57 12198.98 14398.70 26998.91 12899.88 199.46 13497.55 13699.22 13599.88 1495.73 13999.28 22999.03 4597.62 19198.75 193
Effi-MVS+-dtu98.78 11298.89 8298.47 20999.33 14796.91 23999.57 8899.30 21898.47 5799.41 8898.99 26596.78 10899.74 13998.73 7899.38 10898.74 196
CP-MVSNet98.09 15797.78 17299.01 13998.97 21999.24 8699.67 5199.46 13497.25 16198.48 23499.64 13493.79 22199.06 26198.63 8994.10 27698.74 196
VPA-MVSNet98.29 14097.95 15799.30 11299.16 18499.54 5299.50 11699.58 4298.27 7199.35 10499.37 21592.53 24899.65 17299.35 1794.46 26998.72 198
PEN-MVS97.76 20197.44 20898.72 19098.77 26198.54 17599.78 2299.51 8497.06 18098.29 24599.64 13492.63 24598.89 27998.09 13493.16 28798.72 198
VPNet97.84 19097.44 20899.01 13999.21 17198.94 12399.48 12799.57 4398.38 6499.28 11999.73 9788.89 28599.39 20299.19 3293.27 28698.71 200
EI-MVSNet98.67 12298.67 10598.68 19199.35 14397.97 20299.50 11699.38 18196.93 18899.20 14099.83 3797.87 8199.36 21098.38 11697.56 19698.71 200
WR-MVS98.06 15997.73 18399.06 13498.86 24899.25 8599.19 21899.35 19397.30 15798.66 21799.43 19893.94 21699.21 24898.58 9694.28 27298.71 200
IterMVS-LS98.46 13098.42 12798.58 19799.59 9998.00 20099.37 16999.43 16196.94 18799.07 15999.59 15197.87 8199.03 26598.32 12395.62 24498.71 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v14419297.92 18397.60 19298.87 17198.83 25198.65 16599.55 10099.34 20196.20 23799.32 10999.40 20794.36 20099.26 23796.37 24695.03 25598.70 204
v74897.52 22497.23 23098.41 21698.69 27097.23 21999.87 499.45 14595.72 25498.51 23199.53 16794.13 21099.30 22696.78 23092.39 29598.70 204
v124097.69 21297.32 22498.79 18598.85 24998.43 18799.48 12799.36 18996.11 24699.27 12399.36 22293.76 22399.24 24094.46 27895.23 24998.70 204
DTE-MVSNet97.51 22797.19 23298.46 21098.63 27698.13 19899.84 999.48 11196.68 19997.97 25799.67 12192.92 23398.56 28596.88 22792.60 29498.70 204
TranMVSNet+NR-MVSNet97.93 18097.66 18798.76 18898.78 25898.62 16999.65 6499.49 10297.76 11898.49 23399.60 14994.23 20598.97 27798.00 14392.90 28998.70 204
v192192097.80 19797.45 20598.84 17998.80 25298.53 17699.52 10799.34 20196.15 24399.24 12899.47 18993.98 21599.29 22895.40 26395.13 25398.69 209
v119297.81 19597.44 20898.91 15898.88 24298.68 16199.51 11199.34 20196.18 23999.20 14099.34 22994.03 21499.36 21095.32 26595.18 25098.69 209
v2v48298.06 15997.77 17698.92 15698.90 23898.82 14599.57 8899.36 18996.65 20199.19 14399.35 22694.20 20699.25 23897.72 17094.97 25698.69 209
UniMVSNet_NR-MVSNet98.22 14397.97 15598.96 14598.92 23598.98 11299.48 12799.53 7197.76 11898.71 20799.46 19396.43 11899.22 24498.57 9892.87 29198.69 209
OurMVSNet-221017-097.88 18697.77 17698.19 24098.71 26896.53 25099.88 199.00 26597.79 11598.78 20199.94 391.68 26099.35 21397.21 20496.99 22298.69 209
gg-mvs-nofinetune96.17 26495.32 27198.73 18998.79 25498.14 19799.38 16794.09 32891.07 30698.07 25491.04 32489.62 28099.35 21396.75 23199.09 12798.68 214
v114497.98 17597.69 18698.85 17898.87 24598.66 16499.54 10399.35 19396.27 23199.23 13399.35 22694.67 18899.23 24196.73 23295.16 25198.68 214
v114198.05 16597.76 17998.91 15898.91 23798.78 15699.57 8899.35 19396.41 22399.23 13399.36 22294.93 16799.27 23297.38 19794.72 26298.68 214
testing_294.44 28192.93 28798.98 14394.16 31499.00 11099.42 15199.28 22896.60 20684.86 31796.84 31270.91 32099.27 23298.23 12696.08 23798.68 214
divwei89l23v2f11298.06 15997.78 17298.91 15898.90 23898.77 15799.57 8899.35 19396.45 21899.24 12899.37 21594.92 16899.27 23297.50 18794.71 26498.68 214
v198.05 16597.76 17998.93 15198.92 23598.80 15299.57 8899.35 19396.39 22599.28 11999.36 22294.86 17399.32 22097.38 19794.72 26298.68 214
DU-MVS98.08 15897.79 17098.96 14598.87 24598.98 11299.41 15599.45 14597.87 10698.71 20799.50 17694.82 17599.22 24498.57 9892.87 29198.68 214
NR-MVSNet97.97 17897.61 19199.02 13898.87 24599.26 8499.47 13199.42 16297.63 13097.08 27299.50 17695.07 15899.13 25497.86 15393.59 28398.68 214
LPG-MVS_test98.22 14398.13 14298.49 20599.33 14797.05 22899.58 8299.55 5397.46 14299.24 12899.83 3792.58 24699.72 15198.09 13497.51 19998.68 214
LGP-MVS_train98.49 20599.33 14797.05 22899.55 5397.46 14299.24 12899.83 3792.58 24699.72 15198.09 13497.51 19998.68 214
LTVRE_ROB97.16 1298.02 17097.90 16098.40 21799.23 16896.80 24399.70 4299.60 3497.12 17398.18 24799.70 10691.73 25999.72 15198.39 11497.45 20698.68 214
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
semantic-postprocess98.06 24599.57 10296.36 25699.49 10297.18 16798.71 20799.72 10192.70 24399.14 25197.44 19495.86 24098.67 225
v1neww98.12 15397.84 16698.93 15198.97 21998.81 14799.66 5499.35 19396.49 21199.29 11599.37 21595.02 16099.32 22097.73 16694.73 26098.67 225
v7new98.12 15397.84 16698.93 15198.97 21998.81 14799.66 5499.35 19396.49 21199.29 11599.37 21595.02 16099.32 22097.73 16694.73 26098.67 225
pm-mvs197.68 21497.28 22898.88 16799.06 20198.62 16999.50 11699.45 14596.32 22797.87 25999.79 7292.47 25099.35 21397.54 18393.54 28498.67 225
v698.12 15397.84 16698.94 14898.94 22898.83 13899.66 5499.34 20196.49 21199.30 11199.37 21594.95 16499.34 21697.77 16194.74 25998.67 225
v1097.85 18897.52 19698.86 17598.99 21298.67 16299.75 3499.41 16595.70 25598.98 17799.41 20394.75 18499.23 24196.01 25194.63 26798.67 225
HQP_MVS98.27 14298.22 13998.44 21499.29 15896.97 23599.39 16299.47 12598.97 2399.11 15199.61 14692.71 24199.69 16697.78 15997.63 18998.67 225
plane_prior599.47 12599.69 16697.78 15997.63 18998.67 225
SixPastTwentyTwo97.50 22897.33 22398.03 24698.65 27496.23 26099.77 2498.68 29697.14 17097.90 25899.93 490.45 27099.18 25097.00 21796.43 23098.67 225
IterMVS97.83 19197.77 17698.02 24899.58 10096.27 25999.02 25499.48 11197.22 16598.71 20799.70 10692.75 23799.13 25497.46 19296.00 23898.67 225
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH97.28 898.10 15697.99 15498.44 21499.41 13196.96 23799.60 7799.56 4698.09 8998.15 24899.91 590.87 26899.70 16398.88 5697.45 20698.67 225
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v897.95 17997.63 19098.93 15198.95 22498.81 14799.80 1999.41 16596.03 25099.10 15499.42 20094.92 16899.30 22696.94 22394.08 27798.66 236
v798.05 16597.78 17298.87 17198.99 21298.67 16299.64 6699.34 20196.31 22899.29 11599.51 17494.78 17899.27 23297.03 21595.15 25298.66 236
UniMVSNet (Re)98.29 14098.00 15399.13 13099.00 21099.36 7499.49 12299.51 8497.95 10498.97 17899.13 25396.30 12299.38 20398.36 11993.34 28598.66 236
pmmvs696.53 24996.09 25097.82 26398.69 27095.47 27399.37 16999.47 12593.46 29197.41 26699.78 7787.06 30399.33 21796.92 22592.70 29398.65 239
K. test v397.10 24296.79 24098.01 24998.72 26696.33 25799.87 497.05 32297.59 13196.16 28299.80 6488.71 28799.04 26396.69 23596.55 22898.65 239
YYNet195.36 27494.51 27997.92 25597.89 29297.10 22299.10 23699.23 23993.26 29380.77 32199.04 26292.81 23698.02 29894.30 28294.18 27598.64 241
MDA-MVSNet_test_wron95.45 27294.60 27798.01 24998.16 29097.21 22099.11 23499.24 23893.49 29080.73 32298.98 26893.02 23098.18 28794.22 28694.45 27098.64 241
Baseline_NR-MVSNet97.76 20197.45 20598.68 19199.09 19798.29 19199.41 15598.85 28395.65 25698.63 22599.67 12194.82 17599.10 25998.07 14092.89 29098.64 241
HQP4-MVS98.66 21799.64 17498.64 241
HQP-MVS98.02 17097.90 16098.37 21999.19 17496.83 24098.98 26599.39 17598.24 7298.66 21799.40 20792.47 25099.64 17497.19 20697.58 19498.64 241
ACMM97.58 598.37 13798.34 13198.48 20799.41 13197.10 22299.56 9599.45 14598.53 5499.04 16599.85 2693.00 23199.71 15798.74 7597.45 20698.64 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs597.52 22497.30 22698.16 24298.57 28096.73 24499.27 19798.90 27996.14 24498.37 23999.53 16791.54 26299.14 25197.51 18695.87 23998.63 247
v14897.79 19997.55 19498.50 20498.74 26397.72 21099.54 10399.33 20996.26 23298.90 18799.51 17494.68 18799.14 25197.83 15593.15 28898.63 247
MDA-MVSNet-bldmvs94.96 27793.98 28297.92 25598.24 28997.27 21599.15 22499.33 20993.80 28680.09 32399.03 26388.31 29597.86 30393.49 29094.36 27198.62 249
TransMVSNet (Re)97.15 24096.58 24398.86 17599.12 19098.85 13499.49 12298.91 27795.48 25897.16 27199.80 6493.38 22799.11 25794.16 28791.73 29698.62 249
lessismore_v097.79 26598.69 27095.44 27594.75 32695.71 28699.87 1988.69 28899.32 22095.89 25294.93 25898.62 249
MVSTER98.49 12898.32 13399.00 14199.35 14399.02 10699.54 10399.38 18197.41 14999.20 14099.73 9793.86 22099.36 21098.87 6097.56 19698.62 249
GBi-Net97.68 21497.48 20198.29 22599.51 11097.26 21699.43 14499.48 11196.49 21199.07 15999.32 23490.26 27298.98 27097.10 21196.65 22498.62 249
test197.68 21497.48 20198.29 22599.51 11097.26 21699.43 14499.48 11196.49 21199.07 15999.32 23490.26 27298.98 27097.10 21196.65 22498.62 249
FMVSNet196.84 24596.36 24698.29 22599.32 15397.26 21699.43 14499.48 11195.11 26198.55 23099.32 23483.95 31498.98 27095.81 25496.26 23498.62 249
ACMP97.20 1198.06 15997.94 15898.45 21199.37 14097.01 23199.44 13999.49 10297.54 13998.45 23599.79 7291.95 25799.72 15197.91 14997.49 20498.62 249
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+97.24 1097.92 18397.78 17298.32 22299.46 12196.68 24799.56 9599.54 6198.41 6397.79 26399.87 1990.18 27599.66 17098.05 14297.18 21998.62 249
OPM-MVS98.19 14798.10 14498.45 21198.88 24297.07 22699.28 19499.38 18198.57 5299.22 13599.81 5392.12 25699.66 17098.08 13897.54 19898.61 258
WR-MVS_H98.13 15197.87 16598.90 16299.02 20898.84 13599.70 4299.59 3797.27 15998.40 23799.19 24995.53 14299.23 24198.34 12093.78 28298.61 258
MIMVSNet195.51 27195.04 27496.92 28397.38 29995.60 26799.52 10799.50 9793.65 28796.97 27699.17 25085.28 30996.56 31488.36 30795.55 24698.60 260
test235694.07 28594.46 28092.89 29995.18 31086.13 31497.60 31999.06 26093.61 28896.15 28498.28 28885.60 30893.95 32186.68 31498.00 18198.59 261
test123567892.91 28893.30 28591.71 30593.14 31783.01 31898.75 28998.58 30192.80 29692.45 30797.91 29288.51 29393.54 32282.26 31895.35 24798.59 261
N_pmnet94.95 27895.83 25692.31 30298.47 28479.33 32499.12 22892.81 33393.87 28597.68 26499.13 25393.87 21999.01 26791.38 29896.19 23598.59 261
FMVSNet297.72 20997.36 21698.80 18499.51 11098.84 13599.45 13599.42 16296.49 21198.86 19599.29 23990.26 27298.98 27096.44 24396.56 22798.58 264
anonymousdsp98.44 13198.28 13698.94 14898.50 28398.96 11999.77 2499.50 9797.07 17898.87 19099.77 8294.76 18399.28 22998.66 8697.60 19298.57 265
FMVSNet398.03 16897.76 17998.84 17999.39 13798.98 11299.40 16199.38 18196.67 20099.07 15999.28 24092.93 23298.98 27097.10 21196.65 22498.56 266
XVG-ACMP-BASELINE97.83 19197.71 18598.20 23999.11 19296.33 25799.41 15599.52 7598.06 9799.05 16499.50 17689.64 27999.73 14797.73 16697.38 21298.53 267
Patchmtry97.75 20497.40 21498.81 18299.10 19598.87 13199.11 23499.33 20994.83 26498.81 19899.38 21294.33 20199.02 26696.10 24895.57 24598.53 267
USDC97.34 23597.20 23197.75 26799.07 19995.20 27898.51 30299.04 26297.99 10398.31 24399.86 2289.02 28399.55 18795.67 25897.36 21398.49 269
CLD-MVS98.16 14998.10 14498.33 22199.29 15896.82 24298.75 28999.44 15397.83 11299.13 14799.55 16392.92 23399.67 16898.32 12397.69 18898.48 270
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous2023120696.22 26296.03 25196.79 28697.31 30294.14 29199.63 6799.08 25596.17 24097.04 27399.06 26093.94 21697.76 30686.96 31295.06 25498.47 271
FMVSNet596.43 25196.19 24897.15 27799.11 19295.89 26599.32 18199.52 7594.47 27698.34 24299.07 25887.54 30097.07 31092.61 29595.72 24298.47 271
pmmvs498.13 15197.90 16098.81 18298.61 27798.87 13198.99 26199.21 24196.44 21999.06 16399.58 15495.90 13399.11 25797.18 20896.11 23698.46 273
V4298.06 15997.79 17098.86 17598.98 21698.84 13599.69 4499.34 20196.53 21099.30 11199.37 21594.67 18899.32 22097.57 18094.66 26598.42 274
PVSNet_BlendedMVS98.86 9898.80 9299.03 13799.76 4198.79 15499.28 19499.91 397.42 14899.67 4099.37 21597.53 8999.88 9598.98 5097.29 21598.42 274
UnsupCasMVSNet_eth96.44 25096.12 24997.40 27698.65 27495.65 26699.36 17399.51 8497.13 17196.04 28598.99 26588.40 29498.17 28896.71 23390.27 29998.40 276
TinyColmap97.12 24196.89 23897.83 26299.07 19995.52 27298.57 29998.74 29197.58 13397.81 26299.79 7288.16 29799.56 18595.10 26797.21 21798.39 277
testus94.61 27995.30 27292.54 30196.44 30584.18 31698.36 30599.03 26394.18 28196.49 27898.57 28588.74 28695.09 31987.41 31098.45 16698.36 278
test20.0396.12 26595.96 25496.63 28797.44 29895.45 27499.51 11199.38 18196.55 20996.16 28299.25 24493.76 22396.17 31587.35 31194.22 27498.27 279
ITE_SJBPF98.08 24499.29 15896.37 25598.92 27498.34 6698.83 19799.75 9091.09 26599.62 18095.82 25397.40 21098.25 280
EG-PatchMatch MVS95.97 26795.69 26196.81 28597.78 29492.79 30299.16 22198.93 27296.16 24194.08 29499.22 24782.72 31699.47 19195.67 25897.50 20198.17 281
TDRefinement95.42 27394.57 27897.97 25289.83 32396.11 26299.48 12798.75 28896.74 19596.68 27799.88 1488.65 29099.71 15798.37 11782.74 31998.09 282
API-MVS99.04 8199.03 6399.06 13499.40 13599.31 7999.55 10099.56 4698.54 5399.33 10899.39 21198.76 4199.78 13296.98 21999.78 7298.07 283
v5297.79 19997.50 19998.66 19498.80 25298.62 16999.87 499.44 15395.87 25299.01 16899.46 19394.44 19999.33 21796.65 23993.96 28098.05 284
V497.80 19797.51 19898.67 19398.79 25498.63 16799.87 499.44 15395.87 25299.01 16899.46 19394.52 19599.33 21796.64 24093.97 27998.05 284
new_pmnet96.38 25596.03 25197.41 27598.13 29195.16 28199.05 24599.20 24293.94 28497.39 26798.79 27791.61 26199.04 26390.43 30195.77 24198.05 284
DeepMVS_CXcopyleft93.34 29799.29 15882.27 32199.22 24085.15 31596.33 28099.05 26190.97 26799.73 14793.57 28997.77 18798.01 287
GG-mvs-BLEND98.45 21198.55 28198.16 19699.43 14493.68 32997.23 26998.46 28789.30 28299.22 24495.43 26298.22 17297.98 288
pmmvs394.09 28493.25 28696.60 28894.76 31294.49 28798.92 27798.18 31189.66 30896.48 27998.06 29086.28 30497.33 30989.68 30387.20 31097.97 289
LF4IMVS97.52 22497.46 20497.70 27098.98 21695.55 26999.29 19098.82 28698.07 9398.66 21799.64 13489.97 27699.61 18197.01 21696.68 22397.94 290
test_040296.64 24696.24 24797.85 26098.85 24996.43 25499.44 13999.26 23593.52 28996.98 27599.52 17188.52 29299.20 24992.58 29697.50 20197.93 291
MVP-Stereo97.81 19597.75 18297.99 25197.53 29796.60 24998.96 27098.85 28397.22 16597.23 26999.36 22295.28 14899.46 19295.51 26099.78 7297.92 292
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MS-PatchMatch97.24 23997.32 22496.99 28098.45 28593.51 29998.82 28499.32 21597.41 14998.13 24999.30 23788.99 28499.56 18595.68 25799.80 6797.90 293
v1396.24 25995.58 26498.25 23298.98 21698.83 13899.75 3499.29 22194.35 27993.89 30197.60 30495.17 15598.11 29594.27 28486.86 31497.81 294
V996.25 25895.58 26498.26 22898.94 22898.83 13899.75 3499.29 22194.45 27793.96 29897.62 30294.94 16598.14 29294.40 28086.87 31397.81 294
v1796.42 25295.81 25798.25 23298.94 22898.80 15299.76 2799.28 22894.57 27094.18 29197.71 29595.23 15298.16 28994.86 27087.73 30897.80 296
v1696.39 25495.76 26098.26 22898.96 22298.81 14799.76 2799.28 22894.57 27094.10 29397.70 29695.04 15998.16 28994.70 27487.77 30797.80 296
v1596.28 25695.62 26298.25 23298.94 22898.83 13899.76 2799.29 22194.52 27494.02 29697.61 30395.02 16098.13 29394.53 27686.92 31197.80 296
v1296.24 25995.58 26498.23 23598.96 22298.81 14799.76 2799.29 22194.42 27893.85 30297.60 30495.12 15698.09 29694.32 28186.85 31597.80 296
V1496.26 25795.60 26398.26 22898.94 22898.83 13899.76 2799.29 22194.49 27593.96 29897.66 29994.99 16398.13 29394.41 27986.90 31297.80 296
v1896.42 25295.80 25998.26 22898.95 22498.82 14599.76 2799.28 22894.58 26994.12 29297.70 29695.22 15398.16 28994.83 27287.80 30697.79 301
Anonymous2023121190.69 29289.39 29394.58 29494.25 31388.18 31199.29 19099.07 25882.45 31992.95 30697.65 30063.96 32697.79 30489.27 30485.63 31797.77 302
v1196.23 26195.57 26798.21 23898.93 23398.83 13899.72 3999.29 22194.29 28094.05 29597.64 30194.88 17298.04 29792.89 29388.43 30497.77 302
ambc93.06 29892.68 31882.36 32098.47 30398.73 29595.09 28897.41 30855.55 32899.10 25996.42 24491.32 29797.71 304
new-patchmatchnet94.48 28094.08 28195.67 29295.08 31192.41 30399.18 21999.28 22894.55 27393.49 30497.37 31087.86 29997.01 31191.57 29788.36 30597.61 305
pmmvs-eth3d95.34 27594.73 27697.15 27795.53 30995.94 26499.35 17799.10 25295.13 26093.55 30397.54 30788.15 29897.91 30194.58 27589.69 30297.61 305
UnsupCasMVSNet_bld93.53 28692.51 28896.58 28997.38 29993.82 29398.24 31099.48 11191.10 30593.10 30596.66 31374.89 31998.37 28694.03 28887.71 30997.56 307
PM-MVS92.96 28792.23 28995.14 29395.61 30789.98 31099.37 16998.21 30994.80 26595.04 28997.69 29865.06 32497.90 30294.30 28289.98 30197.54 308
LCM-MVSNet86.80 29585.22 29891.53 30687.81 32580.96 32298.23 31298.99 26671.05 32390.13 31496.51 31448.45 33196.88 31290.51 29985.30 31896.76 309
OpenMVS_ROBcopyleft92.34 2094.38 28293.70 28396.41 29097.38 29993.17 30099.06 24398.75 28886.58 31494.84 29098.26 28981.53 31899.32 22089.01 30597.87 18596.76 309
CMPMVSbinary69.68 2394.13 28394.90 27591.84 30397.24 30380.01 32398.52 30199.48 11189.01 31191.99 30999.67 12185.67 30799.13 25495.44 26197.03 22196.39 311
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
111192.30 28992.21 29092.55 30093.30 31586.27 31299.15 22498.74 29191.94 29990.85 31297.82 29384.18 31295.21 31779.65 32094.27 27396.19 312
test1235691.74 29092.19 29190.37 30891.22 31982.41 31998.61 29798.28 30690.66 30791.82 31097.92 29184.90 31092.61 32381.64 31994.66 26596.09 313
PMMVS286.87 29485.37 29791.35 30790.21 32283.80 31798.89 28097.45 32183.13 31891.67 31195.03 31648.49 33094.70 32085.86 31577.62 32195.54 314
tmp_tt82.80 29981.52 29986.66 31066.61 33468.44 33292.79 32797.92 31368.96 32580.04 32499.85 2685.77 30696.15 31697.86 15343.89 32995.39 315
testmv87.91 29387.80 29488.24 30987.68 32677.50 32699.07 23997.66 31989.27 30986.47 31696.22 31568.35 32292.49 32576.63 32488.82 30394.72 316
no-one83.04 29880.12 30091.79 30489.44 32485.65 31599.32 18198.32 30589.06 31079.79 32589.16 32644.86 33296.67 31384.33 31746.78 32893.05 317
testpf95.66 27096.02 25394.58 29498.35 28792.32 30497.25 32197.91 31592.83 29597.03 27498.99 26588.69 28898.61 28495.72 25697.40 21092.80 318
FPMVS84.93 29685.65 29682.75 31686.77 32763.39 33398.35 30798.92 27474.11 32283.39 31998.98 26850.85 32992.40 32684.54 31694.97 25692.46 319
Gipumacopyleft90.99 29190.15 29293.51 29698.73 26490.12 30993.98 32599.45 14579.32 32092.28 30894.91 31769.61 32197.98 30087.42 30995.67 24392.45 320
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ANet_high77.30 30374.86 30584.62 31375.88 33277.61 32597.63 31893.15 33288.81 31264.27 32889.29 32536.51 33383.93 33275.89 32552.31 32792.33 321
PNet_i23d79.43 30277.68 30384.67 31286.18 32871.69 33196.50 32393.68 32975.17 32171.33 32691.18 32332.18 33590.62 32778.57 32374.34 32291.71 322
MVEpermissive76.82 2176.91 30474.31 30684.70 31185.38 33076.05 32996.88 32293.17 33167.39 32671.28 32789.01 32721.66 34087.69 32971.74 32772.29 32390.35 323
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVScopyleft70.75 2275.98 30574.97 30479.01 31870.98 33355.18 33493.37 32698.21 30965.08 32961.78 33093.83 31921.74 33992.53 32478.59 32291.12 29889.34 324
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d74.42 30671.19 30784.14 31476.16 33174.29 33096.00 32492.57 33469.57 32463.84 32987.49 32821.98 33788.86 32875.56 32657.50 32689.26 325
EMVS80.02 30179.22 30282.43 31791.19 32076.40 32797.55 32092.49 33566.36 32883.01 32091.27 32264.63 32585.79 33165.82 32960.65 32585.08 326
E-PMN80.61 30079.88 30182.81 31590.75 32176.38 32897.69 31795.76 32566.44 32783.52 31892.25 32162.54 32787.16 33068.53 32861.40 32484.89 327
test12339.01 31042.50 31028.53 32239.17 33520.91 33698.75 28919.17 33819.83 33238.57 33166.67 33033.16 33415.42 33437.50 33229.66 33249.26 328
.test124583.42 29786.17 29575.15 31993.30 31586.27 31299.15 22498.74 29191.94 29990.85 31297.82 29384.18 31295.21 31779.65 32039.90 33043.98 329
testmvs39.17 30943.78 30825.37 32336.04 33616.84 33798.36 30526.56 33620.06 33138.51 33267.32 32929.64 33615.30 33537.59 33139.90 33043.98 329
wuyk23d40.18 30841.29 31136.84 32086.18 32849.12 33579.73 32822.81 33727.64 33025.46 33328.45 33421.98 33748.89 33355.80 33023.56 33312.51 331
cdsmvs_eth3d_5k24.64 31132.85 3120.00 3240.00 3370.00 3380.00 32999.51 840.00 3330.00 33499.56 16096.58 1150.00 3360.00 3330.00 3340.00 332
pcd_1.5k_mvsjas8.27 31311.03 3140.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 33599.01 110.00 3360.00 3330.00 3340.00 332
sosnet-low-res0.02 3140.03 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 3350.00 3410.00 3360.00 3330.00 3340.00 332
sosnet0.02 3140.03 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 3350.00 3410.00 3360.00 3330.00 3340.00 332
uncertanet0.02 3140.03 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 3350.00 3410.00 3360.00 3330.00 3340.00 332
Regformer0.02 3140.03 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 3350.00 3410.00 3360.00 3330.00 3340.00 332
ab-mvs-re8.30 31211.06 3130.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 33499.58 1540.00 3410.00 3360.00 3330.00 3340.00 332
uanet0.02 3140.03 3150.00 3240.00 3370.00 3380.00 3290.00 3390.00 3330.00 3340.27 3350.00 3410.00 3360.00 3330.00 3340.00 332
sam_mvs194.86 173
sam_mvs94.72 186
MTGPAbinary99.47 125
test_post199.23 20965.14 33294.18 20999.71 15797.58 178
test_post65.99 33194.65 19099.73 147
patchmatchnet-post98.70 28194.79 17799.74 139
MTMP98.88 281
gm-plane-assit98.54 28292.96 30194.65 26899.15 25199.64 17497.56 181
TEST999.67 7299.65 3799.05 24599.41 16596.22 23698.95 17999.49 17998.77 3999.91 72
test_899.67 7299.61 4299.03 25199.41 16596.28 22998.93 18399.48 18598.76 4199.91 72
agg_prior99.67 7299.62 4099.40 17298.87 19099.91 72
test_prior499.56 4998.99 261
test_prior298.96 27098.34 6699.01 16899.52 17198.68 4997.96 14599.74 79
旧先验298.96 27096.70 19899.47 7799.94 4098.19 127
新几何299.01 258
原ACMM298.95 274
testdata299.95 3396.67 236
segment_acmp98.96 20
testdata198.85 28398.32 69
plane_prior799.29 15897.03 230
plane_prior699.27 16396.98 23492.71 241
plane_prior499.61 146
plane_prior397.00 23298.69 4699.11 151
plane_prior299.39 16298.97 23
plane_prior199.26 165
plane_prior96.97 23599.21 21598.45 5997.60 192
n20.00 339
nn0.00 339
door-mid98.05 312
test1199.35 193
door97.92 313
HQP5-MVS96.83 240
HQP-NCC99.19 17498.98 26598.24 7298.66 217
ACMP_Plane99.19 17498.98 26598.24 7298.66 217
BP-MVS97.19 206
HQP3-MVS99.39 17597.58 194
HQP2-MVS92.47 250
NP-MVS99.23 16896.92 23899.40 207
MDTV_nov1_ep1398.32 13399.11 19294.44 28899.27 19798.74 29197.51 14099.40 9299.62 14394.78 17899.76 13797.59 17798.81 151
ACMMP++_ref97.19 218
ACMMP++97.43 209
Test By Simon98.75 44