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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
LCM-MVSNet99.95 199.95 199.95 199.99 199.99 199.95 299.97 299.99 1100.00 199.98 1099.78 6100.00 199.92 1100.00 199.87 9
test_djsdf99.84 899.81 999.91 299.94 1099.84 2199.77 1299.80 5199.73 4699.97 699.92 1899.77 799.98 899.43 43100.00 199.90 4
ANet_high99.88 499.87 499.91 299.99 199.91 299.65 51100.00 199.90 8100.00 199.97 1199.61 1799.97 1999.75 13100.00 199.84 14
UniMVSNet_ETH3D99.85 799.83 799.90 499.89 2199.91 299.89 499.71 9799.93 599.95 1199.89 2799.71 999.96 3799.51 3599.97 3399.84 14
anonymousdsp99.80 1199.77 1299.90 499.96 499.88 999.73 2199.85 2899.70 5599.92 1999.93 1599.45 2499.97 1999.36 55100.00 199.85 13
mvs_tets99.90 299.90 299.90 499.96 499.79 3999.72 2499.88 1999.92 799.98 399.93 1599.94 199.98 899.77 12100.00 199.92 3
PS-MVSNAJss99.84 899.82 899.89 799.96 499.77 4599.68 3899.85 2899.95 399.98 399.92 1899.28 4299.98 899.75 13100.00 199.94 2
jajsoiax99.89 399.89 399.89 799.96 499.78 4299.70 2999.86 2499.89 1499.98 399.90 2399.94 199.98 899.75 13100.00 199.90 4
PS-CasMVS99.66 2799.58 4099.89 799.80 5899.85 1699.66 4699.73 8599.62 7699.84 4699.71 10898.62 12799.96 3799.30 6699.96 4599.86 11
PEN-MVS99.66 2799.59 3699.89 799.83 3999.87 1099.66 4699.73 8599.70 5599.84 4699.73 9598.56 13699.96 3799.29 6999.94 6799.83 18
v7n99.82 1099.80 1099.88 1199.96 499.84 2199.82 899.82 4199.84 3099.94 1299.91 2199.13 6099.96 3799.83 999.99 1299.83 18
DTE-MVSNet99.68 2499.61 3399.88 1199.80 5899.87 1099.67 4299.71 9799.72 4999.84 4699.78 7398.67 12199.97 1999.30 6699.95 5499.80 25
LTVRE_ROB99.19 199.88 499.87 499.88 1199.91 1599.90 599.96 199.92 999.90 899.97 699.87 3399.81 599.95 4799.54 3099.99 1299.80 25
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
CP-MVSNet99.54 5099.43 6699.87 1499.76 8799.82 2999.57 7099.61 14999.54 9099.80 6399.64 14997.79 21699.95 4799.21 7699.94 6799.84 14
WR-MVS_H99.61 3899.53 5299.87 1499.80 5899.83 2599.67 4299.75 7799.58 8999.85 4399.69 12198.18 18799.94 6199.28 7199.95 5499.83 18
UA-Net99.78 1399.76 1499.86 1699.72 11199.71 7199.91 399.95 799.96 299.71 10499.91 2199.15 5599.97 1999.50 37100.00 199.90 4
FC-MVSNet-test99.70 1999.65 2599.86 1699.88 2599.86 1399.72 2499.78 6299.90 899.82 5399.83 4898.45 15499.87 18199.51 3599.97 3399.86 11
APDe-MVS99.48 5899.36 7999.85 1899.55 18499.81 3299.50 7799.69 10898.99 17399.75 8499.71 10898.79 10499.93 7598.46 14899.85 12799.80 25
FIs99.65 3299.58 4099.84 1999.84 3599.85 1699.66 4699.75 7799.86 2299.74 9399.79 6698.27 17599.85 21999.37 5499.93 7599.83 18
OurMVSNet-221017-099.75 1599.71 1699.84 1999.96 499.83 2599.83 699.85 2899.80 3999.93 1599.93 1598.54 13999.93 7599.59 2399.98 2499.76 41
test_0728_SECOND99.83 2199.70 12299.79 3999.14 16899.61 14999.92 9597.88 19599.72 20599.77 37
pmmvs699.86 699.86 699.83 2199.94 1099.90 599.83 699.91 1299.85 2799.94 1299.95 1399.73 899.90 13799.65 1799.97 3399.69 57
DPE-MVScopyleft99.14 15498.92 18499.82 2399.57 17399.77 4598.74 24499.60 16198.55 22399.76 7899.69 12198.23 18199.92 9596.39 30099.75 18499.76 41
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
nrg03099.70 1999.66 2399.82 2399.76 8799.84 2199.61 5999.70 10299.93 599.78 7199.68 13299.10 6199.78 28499.45 4199.96 4599.83 18
Baseline_NR-MVSNet99.49 5699.37 7699.82 2399.91 1599.84 2198.83 22899.86 2499.68 6099.65 12599.88 3097.67 22499.87 18199.03 10399.86 12399.76 41
MSP-MVS99.04 17598.79 20299.81 2699.78 7599.73 6599.35 10399.57 17998.54 22699.54 17198.99 32896.81 26199.93 7596.97 26799.53 26799.77 37
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
TransMVSNet (Re)99.78 1399.77 1299.81 2699.91 1599.85 1699.75 1699.86 2499.70 5599.91 2199.89 2799.60 1999.87 18199.59 2399.74 19299.71 50
XXY-MVS99.71 1899.67 2299.81 2699.89 2199.72 6999.59 6699.82 4199.39 11799.82 5399.84 4799.38 3099.91 11799.38 5199.93 7599.80 25
MP-MVS-pluss99.14 15498.92 18499.80 2999.83 3999.83 2598.61 25199.63 13996.84 32699.44 19599.58 19498.81 9799.91 11797.70 21799.82 15199.67 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
zzz-MVS99.30 10899.14 12099.80 2999.81 5399.81 3298.73 24699.53 20599.27 13299.42 20199.63 15998.21 18299.95 4797.83 20499.79 16999.65 88
MTAPA99.35 9499.20 11299.80 2999.81 5399.81 3299.33 10699.53 20599.27 13299.42 20199.63 15998.21 18299.95 4797.83 20499.79 16999.65 88
HPM-MVS_fast99.43 7099.30 9299.80 2999.83 3999.81 3299.52 7599.70 10298.35 24899.51 18399.50 22699.31 3899.88 16898.18 17299.84 13299.69 57
MIMVSNet199.66 2799.62 2999.80 2999.94 1099.87 1099.69 3599.77 6599.78 4299.93 1599.89 2797.94 20399.92 9599.65 1799.98 2499.62 113
ACMMP_NAP99.28 11199.11 13099.79 3499.75 9899.81 3298.95 21499.53 20598.27 25799.53 17699.73 9598.75 11299.87 18197.70 21799.83 14299.68 63
VPA-MVSNet99.66 2799.62 2999.79 3499.68 13599.75 5699.62 5499.69 10899.85 2799.80 6399.81 5898.81 9799.91 11799.47 3999.88 10799.70 53
Vis-MVSNetpermissive99.75 1599.74 1599.79 3499.88 2599.66 8999.69 3599.92 999.67 6499.77 7699.75 8899.61 1799.98 899.35 5699.98 2499.72 47
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GeoE99.69 2199.66 2399.78 3799.76 8799.76 5299.60 6499.82 4199.46 10699.75 8499.56 20599.63 1499.95 4799.43 4399.88 10799.62 113
pm-mvs199.79 1299.79 1199.78 3799.91 1599.83 2599.76 1499.87 2199.73 4699.89 2799.87 3399.63 1499.87 18199.54 3099.92 7999.63 102
HPM-MVScopyleft99.25 11899.07 14599.78 3799.81 5399.75 5699.61 5999.67 11697.72 28899.35 22099.25 29199.23 4899.92 9597.21 25699.82 15199.67 70
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DVP-MVS++99.38 8699.25 10699.77 4099.03 32399.77 4599.74 1899.61 14999.18 14799.76 7899.61 17799.00 7599.92 9597.72 21299.60 24899.62 113
SED-MVS99.40 8099.28 9999.77 4099.69 12699.82 2999.20 14799.54 19699.13 15999.82 5399.63 15998.91 8799.92 9597.85 20199.70 21199.58 142
ZNCC-MVS99.22 13199.04 15799.77 4099.76 8799.73 6599.28 12499.56 18498.19 26299.14 26199.29 28298.84 9699.92 9597.53 23399.80 16499.64 97
DVP-MVScopyleft99.32 10599.17 11599.77 4099.69 12699.80 3799.14 16899.31 27899.16 15399.62 13999.61 17798.35 16799.91 11797.88 19599.72 20599.61 124
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
region2R99.23 12299.05 15199.77 4099.76 8799.70 7899.31 11399.59 16898.41 23799.32 22799.36 26598.73 11599.93 7597.29 24599.74 19299.67 70
PGM-MVS99.20 13899.01 16399.77 4099.75 9899.71 7199.16 16499.72 9497.99 27299.42 20199.60 18698.81 9799.93 7596.91 27099.74 19299.66 80
TDRefinement99.72 1799.70 1799.77 4099.90 1999.85 1699.86 599.92 999.69 5899.78 7199.92 1899.37 3299.88 16898.93 11899.95 5499.60 128
KD-MVS_self_test99.63 3399.59 3699.76 4799.84 3599.90 599.37 9999.79 5799.83 3399.88 3399.85 4298.42 15899.90 13799.60 2299.73 19999.49 190
Anonymous2023121199.62 3699.57 4399.76 4799.61 15299.60 11099.81 999.73 8599.82 3599.90 2399.90 2397.97 20299.86 20199.42 4899.96 4599.80 25
HFP-MVS99.25 11899.08 14199.76 4799.73 10799.70 7899.31 11399.59 16898.36 24399.36 21899.37 26098.80 10199.91 11797.43 23899.75 18499.68 63
#test#99.12 15898.90 18899.76 4799.73 10799.70 7899.10 18199.59 16897.60 29399.36 21899.37 26098.80 10199.91 11796.84 27699.75 18499.68 63
ACMMPR99.23 12299.06 14799.76 4799.74 10499.69 8299.31 11399.59 16898.36 24399.35 22099.38 25998.61 12999.93 7597.43 23899.75 18499.67 70
MP-MVScopyleft99.06 16998.83 19799.76 4799.76 8799.71 7199.32 10999.50 22198.35 24898.97 27699.48 23498.37 16599.92 9595.95 32099.75 18499.63 102
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TranMVSNet+NR-MVSNet99.54 5099.47 5699.76 4799.58 16399.64 9699.30 11699.63 13999.61 8099.71 10499.56 20598.76 11099.96 3799.14 9699.92 7999.68 63
mPP-MVS99.19 14199.00 16699.76 4799.76 8799.68 8599.38 9599.54 19698.34 25299.01 27499.50 22698.53 14399.93 7597.18 25899.78 17599.66 80
SixPastTwentyTwo99.42 7399.30 9299.76 4799.92 1499.67 8799.70 2999.14 30999.65 7099.89 2799.90 2396.20 27899.94 6199.42 4899.92 7999.67 70
SteuartSystems-ACMMP99.30 10899.14 12099.76 4799.87 2999.66 8999.18 15399.60 16198.55 22399.57 15799.67 13899.03 7499.94 6197.01 26599.80 16499.69 57
Skip Steuart: Steuart Systems R&D Blog.
GST-MVS99.16 15098.96 17799.75 5799.73 10799.73 6599.20 14799.55 19098.22 25999.32 22799.35 27098.65 12599.91 11796.86 27399.74 19299.62 113
test_part198.63 22998.26 25299.75 5799.40 24599.49 12999.67 4299.68 11199.86 2299.88 3399.86 3986.73 36299.93 7599.34 5799.97 3399.81 24
XVS99.27 11599.11 13099.75 5799.71 11499.71 7199.37 9999.61 14999.29 12898.76 30399.47 23998.47 15099.88 16897.62 22599.73 19999.67 70
X-MVStestdata96.09 32994.87 33999.75 5799.71 11499.71 7199.37 9999.61 14999.29 12898.76 30361.30 38398.47 15099.88 16897.62 22599.73 19999.67 70
abl_699.36 9299.23 11099.75 5799.71 11499.74 6299.33 10699.76 7099.07 16699.65 12599.63 15999.09 6399.92 9597.13 26199.76 18199.58 142
CP-MVS99.23 12299.05 15199.75 5799.66 14199.66 8999.38 9599.62 14298.38 24199.06 27299.27 28698.79 10499.94 6197.51 23499.82 15199.66 80
MSC_two_6792asdad99.74 6399.03 32399.53 12499.23 29699.92 9597.77 20699.69 21499.78 33
No_MVS99.74 6399.03 32399.53 12499.23 29699.92 9597.77 20699.69 21499.78 33
test117299.23 12299.05 15199.74 6399.52 19799.75 5699.20 14799.61 14998.97 17599.48 18799.58 19498.41 15999.91 11797.15 26099.55 25999.57 148
SR-MVS99.19 14199.00 16699.74 6399.51 20299.72 6999.18 15399.60 16198.85 19399.47 18999.58 19498.38 16499.92 9596.92 26999.54 26599.57 148
HPM-MVS++copyleft98.96 19198.70 20999.74 6399.52 19799.71 7198.86 22399.19 30498.47 23398.59 31599.06 31798.08 19399.91 11796.94 26899.60 24899.60 128
APD-MVS_3200maxsize99.31 10799.16 11699.74 6399.53 19299.75 5699.27 12799.61 14999.19 14699.57 15799.64 14998.76 11099.90 13797.29 24599.62 23899.56 151
LPG-MVS_test99.22 13199.05 15199.74 6399.82 4699.63 10099.16 16499.73 8597.56 29499.64 12799.69 12199.37 3299.89 15396.66 28699.87 11699.69 57
LGP-MVS_train99.74 6399.82 4699.63 10099.73 8597.56 29499.64 12799.69 12199.37 3299.89 15396.66 28699.87 11699.69 57
DP-MVS99.48 5899.39 7199.74 6399.57 17399.62 10299.29 12399.61 14999.87 2099.74 9399.76 8398.69 11799.87 18198.20 16899.80 16499.75 44
ACMMPcopyleft99.25 11899.08 14199.74 6399.79 6899.68 8599.50 7799.65 13198.07 26899.52 17899.69 12198.57 13499.92 9597.18 25899.79 16999.63 102
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
SR-MVS-dyc-post99.27 11599.11 13099.73 7399.54 18699.74 6299.26 12999.62 14299.16 15399.52 17899.64 14998.41 15999.91 11797.27 24899.61 24599.54 162
SMA-MVScopyleft99.19 14199.00 16699.73 7399.46 22999.73 6599.13 17499.52 21397.40 30599.57 15799.64 14998.93 8499.83 24797.61 22799.79 16999.63 102
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
GBi-Net99.42 7399.31 8799.73 7399.49 21399.77 4599.68 3899.70 10299.44 10999.62 13999.83 4897.21 24799.90 13798.96 11299.90 8999.53 167
test199.42 7399.31 8799.73 7399.49 21399.77 4599.68 3899.70 10299.44 10999.62 13999.83 4897.21 24799.90 13798.96 11299.90 8999.53 167
FMVSNet199.66 2799.63 2899.73 7399.78 7599.77 4599.68 3899.70 10299.67 6499.82 5399.83 4898.98 7899.90 13799.24 7399.97 3399.53 167
HyFIR lowres test98.91 19798.64 21299.73 7399.85 3499.47 13298.07 30599.83 3698.64 21499.89 2799.60 18692.57 316100.00 199.33 6199.97 3399.72 47
testtj98.56 23998.17 26299.72 7999.45 23299.60 11098.88 21999.50 22196.88 32399.18 25699.48 23497.08 25499.92 9593.69 35799.38 28999.63 102
UniMVSNet_NR-MVSNet99.37 8999.25 10699.72 7999.47 22499.56 11998.97 21299.61 14999.43 11499.67 11799.28 28497.85 21299.95 4799.17 8699.81 15999.65 88
ACMM98.09 1199.46 6599.38 7399.72 7999.80 5899.69 8299.13 17499.65 13198.99 17399.64 12799.72 10199.39 2699.86 20198.23 16599.81 15999.60 128
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH98.42 699.59 4099.54 4899.72 7999.86 3199.62 10299.56 7299.79 5798.77 20499.80 6399.85 4299.64 1399.85 21998.70 13699.89 9999.70 53
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPNet99.46 6599.37 7699.71 8399.82 4699.59 11399.48 8199.70 10299.81 3699.69 11099.58 19497.66 22899.86 20199.17 8699.44 28099.67 70
DU-MVS99.33 10399.21 11199.71 8399.43 23699.56 11998.83 22899.53 20599.38 11899.67 11799.36 26597.67 22499.95 4799.17 8699.81 15999.63 102
APD-MVScopyleft98.87 20598.59 21799.71 8399.50 20899.62 10299.01 19999.57 17996.80 32899.54 17199.63 15998.29 17399.91 11795.24 33799.71 20999.61 124
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMH+98.40 899.50 5499.43 6699.71 8399.86 3199.76 5299.32 10999.77 6599.53 9299.77 7699.76 8399.26 4699.78 28497.77 20699.88 10799.60 128
COLMAP_ROBcopyleft98.06 1299.45 6799.37 7699.70 8799.83 3999.70 7899.38 9599.78 6299.53 9299.67 11799.78 7399.19 5199.86 20197.32 24399.87 11699.55 154
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
K. test v398.87 20598.60 21599.69 8899.93 1399.46 13699.74 1894.97 36999.78 4299.88 3399.88 3093.66 30799.97 1999.61 2199.95 5499.64 97
UniMVSNet (Re)99.37 8999.26 10499.68 8999.51 20299.58 11698.98 21099.60 16199.43 11499.70 10799.36 26597.70 21999.88 16899.20 7999.87 11699.59 137
NR-MVSNet99.40 8099.31 8799.68 8999.43 23699.55 12299.73 2199.50 22199.46 10699.88 3399.36 26597.54 23299.87 18198.97 11099.87 11699.63 102
DROMVSNet99.69 2199.69 1999.68 8999.71 11499.91 299.76 1499.96 599.86 2299.51 18399.39 25799.57 2099.93 7599.64 2099.86 12399.20 266
LCM-MVSNet-Re99.28 11199.15 11999.67 9299.33 27299.76 5299.34 10499.97 298.93 18399.91 2199.79 6698.68 11899.93 7596.80 27899.56 25599.30 246
casdiffmvs99.63 3399.61 3399.67 9299.79 6899.59 11399.13 17499.85 2899.79 4199.76 7899.72 10199.33 3799.82 25799.21 7699.94 6799.59 137
1112_ss99.05 17298.84 19599.67 9299.66 14199.29 17998.52 26699.82 4197.65 29199.43 19999.16 30596.42 27099.91 11799.07 10199.84 13299.80 25
DeepPCF-MVS98.42 699.18 14599.02 16099.67 9299.22 29299.75 5697.25 35399.47 23298.72 20999.66 12199.70 11599.29 4099.63 34798.07 18199.81 15999.62 113
DeepC-MVS98.90 499.62 3699.61 3399.67 9299.72 11199.44 14399.24 13799.71 9799.27 13299.93 1599.90 2399.70 1199.93 7598.99 10699.99 1299.64 97
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMP97.51 1499.05 17298.84 19599.67 9299.78 7599.55 12298.88 21999.66 12097.11 32099.47 18999.60 18699.07 6999.89 15396.18 30999.85 12799.58 142
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
3Dnovator+98.92 399.35 9499.24 10899.67 9299.35 25799.47 13299.62 5499.50 22199.44 10999.12 26499.78 7398.77 10999.94 6197.87 19899.72 20599.62 113
v1099.69 2199.69 1999.66 9999.81 5399.39 15799.66 4699.75 7799.60 8699.92 1999.87 3398.75 11299.86 20199.90 299.99 1299.73 46
WR-MVS99.11 16298.93 18099.66 9999.30 27899.42 15098.42 27699.37 26599.04 17199.57 15799.20 30296.89 25999.86 20198.66 14099.87 11699.70 53
XVG-OURS-SEG-HR99.16 15098.99 17199.66 9999.84 3599.64 9698.25 28899.73 8598.39 24099.63 13199.43 24799.70 1199.90 13797.34 24298.64 33999.44 211
baseline99.63 3399.62 2999.66 9999.80 5899.62 10299.44 8799.80 5199.71 5099.72 9999.69 12199.15 5599.83 24799.32 6399.94 6799.53 167
EPP-MVSNet99.17 14999.00 16699.66 9999.80 5899.43 14799.70 2999.24 29599.48 9799.56 16499.77 8094.89 29299.93 7598.72 13599.89 9999.63 102
Anonymous2024052999.42 7399.34 8199.65 10499.53 19299.60 11099.63 5399.39 25899.47 10299.76 7899.78 7398.13 18999.86 20198.70 13699.68 21999.49 190
v899.68 2499.69 1999.65 10499.80 5899.40 15599.66 4699.76 7099.64 7299.93 1599.85 4298.66 12399.84 23699.88 699.99 1299.71 50
MCST-MVS99.02 17898.81 19999.65 10499.58 16399.49 12998.58 25599.07 31298.40 23999.04 27399.25 29198.51 14899.80 27897.31 24499.51 27099.65 88
XVG-OURS99.21 13699.06 14799.65 10499.82 4699.62 10297.87 32699.74 8298.36 24399.66 12199.68 13299.71 999.90 13796.84 27699.88 10799.43 217
CHOSEN 1792x268899.39 8499.30 9299.65 10499.88 2599.25 18998.78 24099.88 1998.66 21299.96 899.79 6697.45 23599.93 7599.34 5799.99 1299.78 33
QAPM98.40 25997.99 27299.65 10499.39 24799.47 13299.67 4299.52 21391.70 36598.78 30199.80 6098.55 13799.95 4794.71 34599.75 18499.53 167
3Dnovator99.15 299.43 7099.36 7999.65 10499.39 24799.42 15099.70 2999.56 18499.23 14099.35 22099.80 6099.17 5399.95 4798.21 16799.84 13299.59 137
patch_mono-299.51 5399.46 6099.64 11199.70 12299.11 21299.04 19399.87 2199.71 5099.47 18999.79 6698.24 17799.98 899.38 5199.96 4599.83 18
EGC-MVSNET89.05 34285.52 34599.64 11199.89 2199.78 4299.56 7299.52 21324.19 37649.96 37799.83 4899.15 5599.92 9597.71 21499.85 12799.21 262
lessismore_v099.64 11199.86 3199.38 16090.66 37799.89 2799.83 4894.56 29799.97 1999.56 2899.92 7999.57 148
114514_t98.49 25098.11 26699.64 11199.73 10799.58 11699.24 13799.76 7089.94 36899.42 20199.56 20597.76 21899.86 20197.74 21199.82 15199.47 200
CPTT-MVS98.74 21998.44 23499.64 11199.61 15299.38 16099.18 15399.55 19096.49 33199.27 23899.37 26097.11 25399.92 9595.74 32799.67 22699.62 113
RPSCF99.18 14599.02 16099.64 11199.83 3999.85 1699.44 8799.82 4198.33 25399.50 18599.78 7397.90 20699.65 34496.78 27999.83 14299.44 211
Anonymous20240521198.75 21798.46 23199.63 11799.34 26799.66 8999.47 8397.65 35499.28 13199.56 16499.50 22693.15 31199.84 23698.62 14199.58 25399.40 223
TSAR-MVS + MP.99.34 9999.24 10899.63 11799.82 4699.37 16399.26 12999.35 26998.77 20499.57 15799.70 11599.27 4599.88 16897.71 21499.75 18499.65 88
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
OPM-MVS99.26 11799.13 12399.63 11799.70 12299.61 10898.58 25599.48 22898.50 22999.52 17899.63 15999.14 5899.76 29497.89 19499.77 17999.51 179
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
AllTest99.21 13699.07 14599.63 11799.78 7599.64 9699.12 17899.83 3698.63 21599.63 13199.72 10198.68 11899.75 29896.38 30199.83 14299.51 179
TestCases99.63 11799.78 7599.64 9699.83 3698.63 21599.63 13199.72 10198.68 11899.75 29896.38 30199.83 14299.51 179
V4299.56 4599.54 4899.63 11799.79 6899.46 13699.39 9399.59 16899.24 13899.86 4199.70 11598.55 13799.82 25799.79 1199.95 5499.60 128
XVG-ACMP-BASELINE99.23 12299.10 13899.63 11799.82 4699.58 11698.83 22899.72 9498.36 24399.60 14799.71 10898.92 8599.91 11797.08 26399.84 13299.40 223
Test_1112_low_res98.95 19498.73 20499.63 11799.68 13599.15 20998.09 30299.80 5197.14 31899.46 19399.40 25396.11 28099.89 15399.01 10599.84 13299.84 14
TAMVS99.49 5699.45 6199.63 11799.48 21999.42 15099.45 8499.57 17999.66 6899.78 7199.83 4897.85 21299.86 20199.44 4299.96 4599.61 124
SF-MVS99.10 16698.93 18099.62 12699.58 16399.51 12799.13 17499.65 13197.97 27499.42 20199.61 17798.86 9399.87 18196.45 29899.68 21999.49 190
EG-PatchMatch MVS99.57 4299.56 4799.62 12699.77 8399.33 17399.26 12999.76 7099.32 12699.80 6399.78 7399.29 4099.87 18199.15 9099.91 8899.66 80
F-COLMAP98.74 21998.45 23299.62 12699.57 17399.47 13298.84 22699.65 13196.31 33598.93 28099.19 30497.68 22399.87 18196.52 29399.37 29399.53 167
CDPH-MVS98.56 23998.20 25799.61 12999.50 20899.46 13698.32 28299.41 24895.22 34999.21 25099.10 31498.34 16999.82 25795.09 34099.66 23099.56 151
LS3D99.24 12199.11 13099.61 12998.38 36299.79 3999.57 7099.68 11199.61 8099.15 25999.71 10898.70 11699.91 11797.54 23199.68 21999.13 284
tfpnnormal99.43 7099.38 7399.60 13199.87 2999.75 5699.59 6699.78 6299.71 5099.90 2399.69 12198.85 9599.90 13797.25 25399.78 17599.15 277
CS-MVS-test99.59 4099.59 3699.60 13199.55 18499.86 1399.60 6499.94 899.90 899.59 15098.89 34399.24 4799.95 4799.66 1699.90 8998.98 309
CSCG99.37 8999.29 9799.60 13199.71 11499.46 13699.43 8999.85 2898.79 20199.41 20999.60 18698.92 8599.92 9598.02 18299.92 7999.43 217
ETH3D-3000-0.198.77 21498.50 22999.59 13499.47 22499.53 12498.77 24199.60 16197.33 30999.23 24499.50 22697.91 20599.83 24795.02 34199.67 22699.41 221
CS-MVS99.67 2699.70 1799.59 13499.54 18699.86 1399.80 1099.96 599.90 899.59 15099.41 24999.51 2399.95 4799.65 1799.90 8998.97 310
v114499.54 5099.53 5299.59 13499.79 6899.28 18199.10 18199.61 14999.20 14599.84 4699.73 9598.67 12199.84 23699.86 899.98 2499.64 97
UnsupCasMVSNet_eth98.83 20898.57 22199.59 13499.68 13599.45 14198.99 20699.67 11699.48 9799.55 16999.36 26594.92 29199.86 20198.95 11696.57 36899.45 206
PHI-MVS99.11 16298.95 17999.59 13499.13 30799.59 11399.17 15899.65 13197.88 28099.25 24099.46 24298.97 8099.80 27897.26 25099.82 15199.37 231
v14419299.55 4899.54 4899.58 13999.78 7599.20 20499.11 18099.62 14299.18 14799.89 2799.72 10198.66 12399.87 18199.88 699.97 3399.66 80
v2v48299.50 5499.47 5699.58 13999.78 7599.25 18999.14 16899.58 17799.25 13699.81 6099.62 16898.24 17799.84 23699.83 999.97 3399.64 97
test20.0399.55 4899.54 4899.58 13999.79 6899.37 16399.02 19799.89 1699.60 8699.82 5399.62 16898.81 9799.89 15399.43 4399.86 12399.47 200
PM-MVS99.36 9299.29 9799.58 13999.83 3999.66 8998.95 21499.86 2498.85 19399.81 6099.73 9598.40 16399.92 9598.36 15399.83 14299.17 273
NCCC98.82 21098.57 22199.58 13999.21 29499.31 17698.61 25199.25 29298.65 21398.43 32599.26 28997.86 21099.81 27396.55 29199.27 30699.61 124
train_agg98.35 26497.95 27699.57 14499.35 25799.35 17098.11 30099.41 24894.90 35397.92 34698.99 32898.02 19799.85 21995.38 33599.44 28099.50 185
agg_prior198.33 26697.92 28299.57 14499.35 25799.36 16697.99 31499.39 25894.85 35697.76 35598.98 33198.03 19599.85 21995.49 33199.44 28099.51 179
v119299.57 4299.57 4399.57 14499.77 8399.22 19899.04 19399.60 16199.18 14799.87 4099.72 10199.08 6799.85 21999.89 599.98 2499.66 80
PMMVS299.48 5899.45 6199.57 14499.76 8798.99 22598.09 30299.90 1598.95 17999.78 7199.58 19499.57 2099.93 7599.48 3899.95 5499.79 31
VNet99.18 14599.06 14799.56 14899.24 29099.36 16699.33 10699.31 27899.67 6499.47 18999.57 20296.48 26799.84 23699.15 9099.30 30199.47 200
CNVR-MVS98.99 18798.80 20199.56 14899.25 28899.43 14798.54 26499.27 28798.58 22098.80 29899.43 24798.53 14399.70 31197.22 25599.59 25299.54 162
DeepC-MVS_fast98.47 599.23 12299.12 12799.56 14899.28 28399.22 19898.99 20699.40 25599.08 16499.58 15499.64 14998.90 9099.83 24797.44 23799.75 18499.63 102
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v192192099.56 4599.57 4399.55 15199.75 9899.11 21299.05 19199.61 14999.15 15799.88 3399.71 10899.08 6799.87 18199.90 299.97 3399.66 80
HQP_MVS98.90 19998.68 21199.55 15199.58 16399.24 19498.80 23699.54 19698.94 18099.14 26199.25 29197.24 24599.82 25795.84 32399.78 17599.60 128
FMVSNet299.35 9499.28 9999.55 15199.49 21399.35 17099.45 8499.57 17999.44 10999.70 10799.74 9197.21 24799.87 18199.03 10399.94 6799.44 211
IS-MVSNet99.03 17698.85 19399.55 15199.80 5899.25 18999.73 2199.15 30899.37 11999.61 14599.71 10894.73 29599.81 27397.70 21799.88 10799.58 142
xxxxxxxxxxxxxcwj99.11 16298.96 17799.54 15599.53 19299.25 18998.29 28499.76 7099.07 16699.42 20199.61 17798.86 9399.87 18196.45 29899.68 21999.49 190
test1299.54 15599.29 28099.33 17399.16 30798.43 32597.54 23299.82 25799.47 27799.48 195
dcpmvs_299.61 3899.64 2799.53 15799.79 6898.82 24499.58 6899.97 299.95 399.96 899.76 8398.44 15599.99 599.34 5799.96 4599.78 33
Regformer-299.34 9999.27 10299.53 15799.41 24299.10 21798.99 20699.53 20599.47 10299.66 12199.52 21998.80 10199.89 15398.31 15999.74 19299.60 128
Effi-MVS+-dtu99.07 16898.92 18499.52 15998.89 33699.78 4299.15 16699.66 12099.34 12298.92 28399.24 29697.69 22199.98 898.11 17899.28 30398.81 324
新几何199.52 15999.50 20899.22 19899.26 28995.66 34598.60 31499.28 28497.67 22499.89 15395.95 32099.32 29999.45 206
112198.56 23998.24 25399.52 15999.49 21399.24 19499.30 11699.22 29995.77 34298.52 32099.29 28297.39 23999.85 21995.79 32599.34 29699.46 204
ETH3D cwj APD-0.1698.50 24798.16 26399.51 16299.04 32299.39 15798.47 27099.47 23296.70 33098.78 30199.33 27497.62 23199.86 20194.69 34699.38 28999.28 251
pmmvs-eth3d99.48 5899.47 5699.51 16299.77 8399.41 15498.81 23399.66 12099.42 11699.75 8499.66 14299.20 5099.76 29498.98 10899.99 1299.36 234
v124099.56 4599.58 4099.51 16299.80 5899.00 22499.00 20199.65 13199.15 15799.90 2399.75 8899.09 6399.88 16899.90 299.96 4599.67 70
ETH3 D test640097.76 28897.19 30599.50 16599.38 25099.26 18598.34 27999.49 22692.99 36298.54 31999.20 30295.92 28499.82 25791.14 36499.66 23099.40 223
Regformer-499.45 6799.44 6399.50 16599.52 19798.94 23299.17 15899.53 20599.64 7299.76 7899.60 18698.96 8399.90 13798.91 11999.84 13299.67 70
CDS-MVSNet99.22 13199.13 12399.50 16599.35 25799.11 21298.96 21399.54 19699.46 10699.61 14599.70 11596.31 27599.83 24799.34 5799.88 10799.55 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Anonymous2024052199.44 6999.42 6899.49 16899.89 2198.96 23099.62 5499.76 7099.85 2799.82 5399.88 3096.39 27399.97 1999.59 2399.98 2499.55 154
Patchmtry98.78 21398.54 22599.49 16898.89 33699.19 20599.32 10999.67 11699.65 7099.72 9999.79 6691.87 32599.95 4798.00 18699.97 3399.33 240
UGNet99.38 8699.34 8199.49 16898.90 33398.90 24099.70 2999.35 26999.86 2298.57 31799.81 5898.50 14999.93 7599.38 5199.98 2499.66 80
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
Gipumacopyleft99.57 4299.59 3699.49 16899.98 399.71 7199.72 2499.84 3499.81 3699.94 1299.78 7398.91 8799.71 30998.41 15099.95 5499.05 300
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DELS-MVS99.34 9999.30 9299.48 17299.51 20299.36 16698.12 29899.53 20599.36 12199.41 20999.61 17799.22 4999.87 18199.21 7699.68 21999.20 266
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
PLCcopyleft97.35 1698.36 26197.99 27299.48 17299.32 27399.24 19498.50 26899.51 21795.19 35198.58 31698.96 33696.95 25899.83 24795.63 32899.25 30799.37 231
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Anonymous2023120699.35 9499.31 8799.47 17499.74 10499.06 22399.28 12499.74 8299.23 14099.72 9999.53 21797.63 23099.88 16899.11 9899.84 13299.48 195
Regformer-199.32 10599.27 10299.47 17499.41 24298.95 23198.99 20699.48 22899.48 9799.66 12199.52 21998.78 10699.87 18198.36 15399.74 19299.60 128
ab-mvs99.33 10399.28 9999.47 17499.57 17399.39 15799.78 1199.43 24598.87 19199.57 15799.82 5598.06 19499.87 18198.69 13899.73 19999.15 277
Fast-Effi-MVS+99.02 17898.87 19199.46 17799.38 25099.50 12899.04 19399.79 5797.17 31698.62 31298.74 35299.34 3699.95 4798.32 15899.41 28698.92 315
test_prior398.62 23098.34 24599.46 17799.35 25799.22 19897.95 31999.39 25897.87 28198.05 34199.05 31897.90 20699.69 31795.99 31699.49 27499.48 195
test_prior99.46 17799.35 25799.22 19899.39 25899.69 31799.48 195
TAPA-MVS97.92 1398.03 28097.55 29699.46 17799.47 22499.44 14398.50 26899.62 14286.79 36999.07 27199.26 28998.26 17699.62 34897.28 24799.73 19999.31 245
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EIA-MVS99.12 15899.01 16399.45 18199.36 25599.62 10299.34 10499.79 5798.41 23798.84 29398.89 34398.75 11299.84 23698.15 17699.51 27098.89 317
test_040299.22 13199.14 12099.45 18199.79 6899.43 14799.28 12499.68 11199.54 9099.40 21499.56 20599.07 6999.82 25796.01 31499.96 4599.11 285
h-mvs3398.61 23198.34 24599.44 18399.60 15498.67 25399.27 12799.44 24199.68 6099.32 22799.49 23192.50 319100.00 199.24 7396.51 36999.65 88
VDD-MVS99.20 13899.11 13099.44 18399.43 23698.98 22699.50 7798.32 34599.80 3999.56 16499.69 12196.99 25799.85 21998.99 10699.73 19999.50 185
PVSNet_Blended_VisFu99.40 8099.38 7399.44 18399.90 1998.66 25598.94 21699.91 1297.97 27499.79 6899.73 9599.05 7299.97 1999.15 9099.99 1299.68 63
OMC-MVS98.90 19998.72 20599.44 18399.39 24799.42 15098.58 25599.64 13797.31 31099.44 19599.62 16898.59 13199.69 31796.17 31099.79 16999.22 260
Fast-Effi-MVS+-dtu99.20 13899.12 12799.43 18799.25 28899.69 8299.05 19199.82 4199.50 9598.97 27699.05 31898.98 7899.98 898.20 16899.24 30998.62 330
MVP-Stereo99.16 15099.08 14199.43 18799.48 21999.07 22199.08 18899.55 19098.63 21599.31 23199.68 13298.19 18599.78 28498.18 17299.58 25399.45 206
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs599.19 14199.11 13099.42 18999.76 8798.88 24198.55 26199.73 8598.82 19799.72 9999.62 16896.56 26499.82 25799.32 6399.95 5499.56 151
bset_n11_16_dypcd98.69 22598.45 23299.42 18999.69 12698.52 26496.06 36796.80 36299.71 5099.73 9799.54 21495.14 29099.96 3799.39 5099.95 5499.79 31
EI-MVSNet-UG-set99.48 5899.50 5499.42 18999.57 17398.65 25899.24 13799.46 23699.68 6099.80 6399.66 14298.99 7799.89 15399.19 8199.90 8999.72 47
EI-MVSNet-Vis-set99.47 6499.49 5599.42 18999.57 17398.66 25599.24 13799.46 23699.67 6499.79 6899.65 14798.97 8099.89 15399.15 9099.89 9999.71 50
testdata99.42 18999.51 20298.93 23699.30 28196.20 33698.87 29099.40 25398.33 17199.89 15396.29 30499.28 30399.44 211
VDDNet98.97 18898.82 19899.42 18999.71 11498.81 24599.62 5498.68 32999.81 3699.38 21699.80 6094.25 29999.85 21998.79 12799.32 29999.59 137
FMVSNet597.80 28697.25 30299.42 18998.83 34298.97 22899.38 9599.80 5198.87 19199.25 24099.69 12180.60 37499.91 11798.96 11299.90 8999.38 228
MVS_111021_LR99.13 15699.03 15999.42 18999.58 16399.32 17597.91 32599.73 8598.68 21199.31 23199.48 23499.09 6399.66 33797.70 21799.77 17999.29 249
RRT_MVS98.75 21798.54 22599.41 19798.14 37198.61 25998.98 21099.66 12099.31 12799.84 4699.75 8891.98 32299.98 899.20 7999.95 5499.62 113
CMPMVSbinary77.52 2398.50 24798.19 26099.41 19798.33 36499.56 11999.01 19999.59 16895.44 34699.57 15799.80 6095.64 28699.46 36696.47 29799.92 7999.21 262
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Regformer-399.41 7799.41 6999.40 19999.52 19798.70 25199.17 15899.44 24199.62 7699.75 8499.60 18698.90 9099.85 21998.89 12099.84 13299.65 88
UnsupCasMVSNet_bld98.55 24298.27 25199.40 19999.56 18399.37 16397.97 31899.68 11197.49 30199.08 26899.35 27095.41 28999.82 25797.70 21798.19 35299.01 307
MVS_111021_HR99.12 15899.02 16099.40 19999.50 20899.11 21297.92 32399.71 9798.76 20799.08 26899.47 23999.17 5399.54 35797.85 20199.76 18199.54 162
MVS_030498.88 20398.71 20699.39 20298.85 34098.91 23999.45 8499.30 28198.56 22197.26 36199.68 13296.18 27999.96 3799.17 8699.94 6799.29 249
v14899.40 8099.41 6999.39 20299.76 8798.94 23299.09 18599.59 16899.17 15199.81 6099.61 17798.41 15999.69 31799.32 6399.94 6799.53 167
diffmvs99.34 9999.32 8699.39 20299.67 14098.77 24898.57 25999.81 5099.61 8099.48 18799.41 24998.47 15099.86 20198.97 11099.90 8999.53 167
HQP-MVS98.36 26198.02 27199.39 20299.31 27498.94 23297.98 31599.37 26597.45 30298.15 33598.83 34796.67 26299.70 31194.73 34399.67 22699.53 167
TSAR-MVS + GP.99.12 15899.04 15799.38 20699.34 26799.16 20798.15 29499.29 28398.18 26399.63 13199.62 16899.18 5299.68 32898.20 16899.74 19299.30 246
AdaColmapbinary98.60 23398.35 24499.38 20699.12 30999.22 19898.67 25099.42 24797.84 28598.81 29699.27 28697.32 24399.81 27395.14 33899.53 26799.10 287
ITE_SJBPF99.38 20699.63 14799.44 14399.73 8598.56 22199.33 22599.53 21798.88 9299.68 32896.01 31499.65 23399.02 306
原ACMM199.37 20999.47 22498.87 24399.27 28796.74 32998.26 33099.32 27597.93 20499.82 25795.96 31999.38 28999.43 217
testgi99.29 11099.26 10499.37 20999.75 9898.81 24598.84 22699.89 1698.38 24199.75 8499.04 32199.36 3599.86 20199.08 10099.25 30799.45 206
MSDG99.08 16798.98 17499.37 20999.60 15499.13 21097.54 33999.74 8298.84 19699.53 17699.55 21299.10 6199.79 28197.07 26499.86 12399.18 271
pmmvs499.13 15699.06 14799.36 21299.57 17399.10 21798.01 31099.25 29298.78 20399.58 15499.44 24698.24 17799.76 29498.74 13399.93 7599.22 260
N_pmnet98.73 22198.53 22799.35 21399.72 11198.67 25398.34 27994.65 37098.35 24899.79 6899.68 13298.03 19599.93 7598.28 16199.92 7999.44 211
Effi-MVS+99.06 16998.97 17599.34 21499.31 27498.98 22698.31 28399.91 1298.81 19898.79 29998.94 33899.14 5899.84 23698.79 12798.74 33599.20 266
Vis-MVSNet (Re-imp)98.77 21498.58 22099.34 21499.78 7598.88 24199.61 5999.56 18499.11 16399.24 24399.56 20593.00 31499.78 28497.43 23899.89 9999.35 237
Patchmatch-RL test98.60 23398.36 24299.33 21699.77 8399.07 22198.27 28699.87 2198.91 18699.74 9399.72 10190.57 34299.79 28198.55 14499.85 12799.11 285
PAPM_NR98.36 26198.04 26999.33 21699.48 21998.93 23698.79 23999.28 28697.54 29798.56 31898.57 35797.12 25299.69 31794.09 35298.90 32699.38 228
PCF-MVS96.03 1896.73 31795.86 32899.33 21699.44 23499.16 20796.87 36299.44 24186.58 37098.95 27899.40 25394.38 29899.88 16887.93 36999.80 16498.95 312
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS98.76 21698.57 22199.33 21699.57 17398.97 22897.53 34199.55 19096.41 33299.27 23899.13 30799.07 6999.78 28496.73 28299.89 9999.23 258
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DPM-MVS98.28 26797.94 28099.32 22099.36 25599.11 21297.31 35198.78 32696.88 32398.84 29399.11 31397.77 21799.61 35294.03 35499.36 29499.23 258
jason99.16 15099.11 13099.32 22099.75 9898.44 26998.26 28799.39 25898.70 21099.74 9399.30 27998.54 13999.97 1998.48 14799.82 15199.55 154
jason: jason.
FMVSNet398.80 21298.63 21499.32 22099.13 30798.72 25099.10 18199.48 22899.23 14099.62 13999.64 14992.57 31699.86 20198.96 11299.90 8999.39 226
MVSFormer99.41 7799.44 6399.31 22399.57 17398.40 27299.77 1299.80 5199.73 4699.63 13199.30 27998.02 19799.98 899.43 4399.69 21499.55 154
DP-MVS Recon98.50 24798.23 25499.31 22399.49 21399.46 13698.56 26099.63 13994.86 35598.85 29299.37 26097.81 21499.59 35496.08 31199.44 28098.88 318
PatchMatch-RL98.68 22698.47 23099.30 22599.44 23499.28 18198.14 29699.54 19697.12 31999.11 26599.25 29197.80 21599.70 31196.51 29499.30 30198.93 314
OPU-MVS99.29 22699.12 30999.44 14399.20 14799.40 25399.00 7598.84 37296.54 29299.60 24899.58 142
D2MVS99.22 13199.19 11399.29 22699.69 12698.74 24998.81 23399.41 24898.55 22399.68 11299.69 12198.13 18999.87 18198.82 12599.98 2499.24 255
CANet99.11 16299.05 15199.28 22898.83 34298.56 26198.71 24999.41 24899.25 13699.23 24499.22 29897.66 22899.94 6199.19 8199.97 3399.33 240
CNLPA98.57 23898.34 24599.28 22899.18 30199.10 21798.34 27999.41 24898.48 23298.52 32098.98 33197.05 25599.78 28495.59 32999.50 27298.96 311
sss98.90 19998.77 20399.27 23099.48 21998.44 26998.72 24799.32 27497.94 27899.37 21799.35 27096.31 27599.91 11798.85 12299.63 23799.47 200
LF4IMVS99.01 18298.92 18499.27 23099.71 11499.28 18198.59 25499.77 6598.32 25499.39 21599.41 24998.62 12799.84 23696.62 29099.84 13298.69 328
LFMVS98.46 25398.19 26099.26 23299.24 29098.52 26499.62 5496.94 36199.87 2099.31 23199.58 19491.04 33399.81 27398.68 13999.42 28599.45 206
WTY-MVS98.59 23698.37 24199.26 23299.43 23698.40 27298.74 24499.13 31198.10 26599.21 25099.24 29694.82 29399.90 13797.86 19998.77 33199.49 190
OpenMVScopyleft98.12 1098.23 27297.89 28699.26 23299.19 29999.26 18599.65 5199.69 10891.33 36698.14 33999.77 8098.28 17499.96 3795.41 33499.55 25998.58 334
alignmvs98.28 26797.96 27599.25 23599.12 30998.93 23699.03 19698.42 34199.64 7298.72 30697.85 37190.86 33899.62 34898.88 12199.13 31299.19 269
IterMVS-LS99.41 7799.47 5699.25 23599.81 5398.09 29198.85 22599.76 7099.62 7699.83 5199.64 14998.54 13999.97 1999.15 9099.99 1299.68 63
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
lupinMVS98.96 19198.87 19199.24 23799.57 17398.40 27298.12 29899.18 30598.28 25699.63 13199.13 30798.02 19799.97 1998.22 16699.69 21499.35 237
MVSTER98.47 25298.22 25599.24 23799.06 31998.35 27799.08 18899.46 23699.27 13299.75 8499.66 14288.61 35299.85 21999.14 9699.92 7999.52 177
mvs-test198.83 20898.70 20999.22 23998.89 33699.65 9498.88 21999.66 12099.34 12298.29 32898.94 33897.69 22199.96 3798.11 17898.54 34398.04 358
EI-MVSNet99.38 8699.44 6399.21 24099.58 16398.09 29199.26 12999.46 23699.62 7699.75 8499.67 13898.54 13999.85 21999.15 9099.92 7999.68 63
BH-RMVSNet98.41 25798.14 26599.21 24099.21 29498.47 26698.60 25398.26 34698.35 24898.93 28099.31 27797.20 25099.66 33794.32 34899.10 31499.51 179
ambc99.20 24299.35 25798.53 26299.17 15899.46 23699.67 11799.80 6098.46 15399.70 31197.92 19299.70 21199.38 228
MVS_Test99.28 11199.31 8799.19 24399.35 25798.79 24799.36 10299.49 22699.17 15199.21 25099.67 13898.78 10699.66 33799.09 9999.66 23099.10 287
MAR-MVS98.24 27197.92 28299.19 24398.78 35099.65 9499.17 15899.14 30995.36 34798.04 34398.81 34997.47 23499.72 30595.47 33399.06 31598.21 352
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
EPNet98.13 27597.77 29099.18 24594.57 37997.99 29599.24 13797.96 34999.74 4597.29 36099.62 16893.13 31299.97 1998.59 14299.83 14299.58 142
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
hse-mvs298.52 24598.30 24999.16 24699.29 28098.60 26098.77 24199.02 31699.68 6099.32 22799.04 32192.50 31999.85 21999.24 7397.87 36099.03 302
ETV-MVS99.18 14599.18 11499.16 24699.34 26799.28 18199.12 17899.79 5799.48 9798.93 28098.55 35999.40 2599.93 7598.51 14699.52 26998.28 348
CL-MVSNet_self_test98.71 22398.56 22499.15 24899.22 29298.66 25597.14 35699.51 21798.09 26799.54 17199.27 28696.87 26099.74 30098.43 14998.96 32199.03 302
AUN-MVS97.82 28597.38 29899.14 24999.27 28598.53 26298.72 24799.02 31698.10 26597.18 36399.03 32589.26 35199.85 21997.94 19197.91 35899.03 302
test_yl98.25 26997.95 27699.13 25099.17 30298.47 26699.00 20198.67 33198.97 17599.22 24899.02 32691.31 32999.69 31797.26 25098.93 32299.24 255
DCV-MVSNet98.25 26997.95 27699.13 25099.17 30298.47 26699.00 20198.67 33198.97 17599.22 24899.02 32691.31 32999.69 31797.26 25098.93 32299.24 255
MIMVSNet98.43 25598.20 25799.11 25299.53 19298.38 27599.58 6898.61 33398.96 17899.33 22599.76 8390.92 33599.81 27397.38 24199.76 18199.15 277
PMMVS98.49 25098.29 25099.11 25298.96 33098.42 27197.54 33999.32 27497.53 29898.47 32498.15 36897.88 20999.82 25797.46 23699.24 30999.09 290
CANet_DTU98.91 19798.85 19399.09 25498.79 34898.13 28698.18 29199.31 27899.48 9798.86 29199.51 22396.56 26499.95 4799.05 10299.95 5499.19 269
MS-PatchMatch99.00 18498.97 17599.09 25499.11 31498.19 28398.76 24399.33 27298.49 23199.44 19599.58 19498.21 18299.69 31798.20 16899.62 23899.39 226
canonicalmvs99.02 17899.00 16699.09 25499.10 31598.70 25199.61 5999.66 12099.63 7598.64 31197.65 37399.04 7399.54 35798.79 12798.92 32499.04 301
PVSNet_BlendedMVS99.03 17699.01 16399.09 25499.54 18697.99 29598.58 25599.82 4197.62 29299.34 22399.71 10898.52 14699.77 29297.98 18799.97 3399.52 177
MDA-MVSNet-bldmvs99.06 16999.05 15199.07 25899.80 5897.83 30298.89 21899.72 9499.29 12899.63 13199.70 11596.47 26899.89 15398.17 17499.82 15199.50 185
TinyColmap98.97 18898.93 18099.07 25899.46 22998.19 28397.75 33099.75 7798.79 20199.54 17199.70 11598.97 8099.62 34896.63 28999.83 14299.41 221
USDC98.96 19198.93 18099.05 26099.54 18697.99 29597.07 35999.80 5198.21 26099.75 8499.77 8098.43 15699.64 34697.90 19399.88 10799.51 179
PAPR97.56 29897.07 30799.04 26198.80 34798.11 28997.63 33599.25 29294.56 35998.02 34498.25 36797.43 23699.68 32890.90 36598.74 33599.33 240
PVSNet_Blended98.70 22498.59 21799.02 26299.54 18697.99 29597.58 33899.82 4195.70 34499.34 22398.98 33198.52 14699.77 29297.98 18799.83 14299.30 246
MVS95.72 33794.63 34198.99 26398.56 35897.98 30099.30 11698.86 32172.71 37497.30 35999.08 31598.34 16999.74 30089.21 36698.33 34799.26 252
HY-MVS98.23 998.21 27497.95 27698.99 26399.03 32398.24 27999.61 5998.72 32896.81 32798.73 30599.51 22394.06 30099.86 20196.91 27098.20 35098.86 320
baseline197.73 29097.33 29998.96 26599.30 27897.73 30699.40 9198.42 34199.33 12599.46 19399.21 30091.18 33199.82 25798.35 15591.26 37499.32 243
DSMNet-mixed99.48 5899.65 2598.95 26699.71 11497.27 31899.50 7799.82 4199.59 8899.41 20999.85 4299.62 16100.00 199.53 3299.89 9999.59 137
thisisatest053097.45 30096.95 31198.94 26799.68 13597.73 30699.09 18594.19 37398.61 21899.56 16499.30 27984.30 36999.93 7598.27 16299.54 26599.16 275
mvs_anonymous99.28 11199.39 7198.94 26799.19 29997.81 30399.02 19799.55 19099.78 4299.85 4399.80 6098.24 17799.86 20199.57 2799.50 27299.15 277
MG-MVS98.52 24598.39 23998.94 26799.15 30497.39 31698.18 29199.21 30398.89 19099.23 24499.63 15997.37 24199.74 30094.22 35099.61 24599.69 57
GA-MVS97.99 28397.68 29398.93 27099.52 19798.04 29497.19 35599.05 31598.32 25498.81 29698.97 33489.89 34999.41 36798.33 15799.05 31699.34 239
cl____98.54 24398.41 23798.92 27199.03 32397.80 30497.46 34599.59 16898.90 18799.60 14799.46 24293.85 30399.78 28497.97 18999.89 9999.17 273
DIV-MVS_self_test98.54 24398.42 23698.92 27199.03 32397.80 30497.46 34599.59 16898.90 18799.60 14799.46 24293.87 30299.78 28497.97 18999.89 9999.18 271
ET-MVSNet_ETH3D96.78 31596.07 32498.91 27399.26 28797.92 30197.70 33396.05 36697.96 27792.37 37598.43 36387.06 35699.90 13798.27 16297.56 36398.91 316
xiu_mvs_v1_base_debu99.23 12299.34 8198.91 27399.59 15898.23 28098.47 27099.66 12099.61 8099.68 11298.94 33899.39 2699.97 1999.18 8399.55 25998.51 338
xiu_mvs_v1_base99.23 12299.34 8198.91 27399.59 15898.23 28098.47 27099.66 12099.61 8099.68 11298.94 33899.39 2699.97 1999.18 8399.55 25998.51 338
xiu_mvs_v1_base_debi99.23 12299.34 8198.91 27399.59 15898.23 28098.47 27099.66 12099.61 8099.68 11298.94 33899.39 2699.97 1999.18 8399.55 25998.51 338
MSLP-MVS++99.05 17299.09 13998.91 27399.21 29498.36 27698.82 23299.47 23298.85 19398.90 28699.56 20598.78 10699.09 37098.57 14399.68 21999.26 252
pmmvs398.08 27897.80 28798.91 27399.41 24297.69 30897.87 32699.66 12095.87 34099.50 18599.51 22390.35 34499.97 1998.55 14499.47 27799.08 293
tttt051797.62 29597.20 30498.90 27999.76 8797.40 31599.48 8194.36 37199.06 17099.70 10799.49 23184.55 36899.94 6198.73 13499.65 23399.36 234
OpenMVS_ROBcopyleft97.31 1797.36 30496.84 31598.89 28099.29 28099.45 14198.87 22299.48 22886.54 37199.44 19599.74 9197.34 24299.86 20191.61 36199.28 30397.37 366
MDA-MVSNet_test_wron98.95 19498.99 17198.85 28199.64 14597.16 32198.23 28999.33 27298.93 18399.56 16499.66 14297.39 23999.83 24798.29 16099.88 10799.55 154
PMVScopyleft92.94 2198.82 21098.81 19998.85 28199.84 3597.99 29599.20 14799.47 23299.71 5099.42 20199.82 5598.09 19199.47 36493.88 35699.85 12799.07 298
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
YYNet198.95 19498.99 17198.84 28399.64 14597.14 32298.22 29099.32 27498.92 18599.59 15099.66 14297.40 23799.83 24798.27 16299.90 8999.55 154
new_pmnet98.88 20398.89 18998.84 28399.70 12297.62 30998.15 29499.50 22197.98 27399.62 13999.54 21498.15 18899.94 6197.55 23099.84 13298.95 312
CR-MVSNet98.35 26498.20 25798.83 28599.05 32098.12 28799.30 11699.67 11697.39 30699.16 25799.79 6691.87 32599.91 11798.78 13098.77 33198.44 343
PatchT98.45 25498.32 24898.83 28598.94 33198.29 27899.24 13798.82 32499.84 3099.08 26899.76 8391.37 32899.94 6198.82 12599.00 32098.26 349
RPMNet98.60 23398.53 22798.83 28599.05 32098.12 28799.30 11699.62 14299.86 2299.16 25799.74 9192.53 31899.92 9598.75 13298.77 33198.44 343
miper_lstm_enhance98.65 22898.60 21598.82 28899.20 29797.33 31797.78 32999.66 12099.01 17299.59 15099.50 22694.62 29699.85 21998.12 17799.90 8999.26 252
FPMVS96.32 32595.50 33398.79 28999.60 15498.17 28598.46 27598.80 32597.16 31796.28 36699.63 15982.19 37099.09 37088.45 36898.89 32799.10 287
xiu_mvs_v2_base99.02 17899.11 13098.77 29099.37 25398.09 29198.13 29799.51 21799.47 10299.42 20198.54 36099.38 3099.97 1998.83 12399.33 29898.24 350
PS-MVSNAJ99.00 18499.08 14198.76 29199.37 25398.10 29098.00 31299.51 21799.47 10299.41 20998.50 36299.28 4299.97 1998.83 12399.34 29698.20 354
test0.0.03 197.37 30396.91 31498.74 29297.72 37297.57 31097.60 33797.36 36098.00 27099.21 25098.02 36990.04 34799.79 28198.37 15295.89 37298.86 320
c3_l98.72 22298.71 20698.72 29399.12 30997.22 32097.68 33499.56 18498.90 18799.54 17199.48 23496.37 27499.73 30397.88 19599.88 10799.21 262
EU-MVSNet99.39 8499.62 2998.72 29399.88 2596.44 33499.56 7299.85 2899.90 899.90 2399.85 4298.09 19199.83 24799.58 2699.95 5499.90 4
new-patchmatchnet99.35 9499.57 4398.71 29599.82 4696.62 33298.55 26199.75 7799.50 9599.88 3399.87 3399.31 3899.88 16899.43 43100.00 199.62 113
thisisatest051596.98 31196.42 31898.66 29699.42 24197.47 31297.27 35294.30 37297.24 31299.15 25998.86 34685.01 36699.87 18197.10 26299.39 28898.63 329
eth_miper_zixun_eth98.68 22698.71 20698.60 29799.10 31596.84 32997.52 34399.54 19698.94 18099.58 15499.48 23496.25 27799.76 29498.01 18599.93 7599.21 262
miper_ehance_all_eth98.59 23698.59 21798.59 29898.98 32997.07 32397.49 34499.52 21398.50 22999.52 17899.37 26096.41 27299.71 30997.86 19999.62 23899.00 308
BH-untuned98.22 27398.09 26798.58 29999.38 25097.24 31998.55 26198.98 31997.81 28699.20 25598.76 35197.01 25699.65 34494.83 34298.33 34798.86 320
IterMVS-SCA-FT99.00 18499.16 11698.51 30099.75 9895.90 34298.07 30599.84 3499.84 3099.89 2799.73 9596.01 28299.99 599.33 61100.00 199.63 102
JIA-IIPM98.06 27997.92 28298.50 30198.59 35797.02 32498.80 23698.51 33799.88 1997.89 34899.87 3391.89 32499.90 13798.16 17597.68 36298.59 332
Patchmatch-test98.10 27797.98 27498.48 30299.27 28596.48 33399.40 9199.07 31298.81 19899.23 24499.57 20290.11 34699.87 18196.69 28399.64 23599.09 290
baseline296.83 31496.28 32098.46 30399.09 31796.91 32798.83 22893.87 37497.23 31396.23 36998.36 36488.12 35399.90 13796.68 28498.14 35498.57 335
IterMVS98.97 18899.16 11698.42 30499.74 10495.64 34598.06 30799.83 3699.83 3399.85 4399.74 9196.10 28199.99 599.27 72100.00 199.63 102
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cl2297.56 29897.28 30098.40 30598.37 36396.75 33097.24 35499.37 26597.31 31099.41 20999.22 29887.30 35499.37 36897.70 21799.62 23899.08 293
CHOSEN 280x42098.41 25798.41 23798.40 30599.34 26795.89 34396.94 36199.44 24198.80 20099.25 24099.52 21993.51 30999.98 898.94 11799.98 2499.32 243
API-MVS98.38 26098.39 23998.35 30798.83 34299.26 18599.14 16899.18 30598.59 21998.66 31098.78 35098.61 12999.57 35694.14 35199.56 25596.21 370
PVSNet97.47 1598.42 25698.44 23498.35 30799.46 22996.26 33696.70 36499.34 27197.68 29099.00 27599.13 30797.40 23799.72 30597.59 22999.68 21999.08 293
miper_enhance_ethall98.03 28097.94 28098.32 30998.27 36596.43 33596.95 36099.41 24896.37 33499.43 19998.96 33694.74 29499.69 31797.71 21499.62 23898.83 323
TR-MVS97.44 30197.15 30698.32 30998.53 35997.46 31398.47 27097.91 35196.85 32598.21 33498.51 36196.42 27099.51 36292.16 36097.29 36497.98 359
PAPM95.61 33894.71 34098.31 31199.12 30996.63 33196.66 36598.46 34090.77 36796.25 36798.68 35493.01 31399.69 31781.60 37597.86 36198.62 330
MVEpermissive92.54 2296.66 31996.11 32398.31 31199.68 13597.55 31197.94 32195.60 36899.37 11990.68 37698.70 35396.56 26498.61 37486.94 37499.55 25998.77 326
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
131498.00 28297.90 28598.27 31398.90 33397.45 31499.30 11699.06 31494.98 35297.21 36299.12 31198.43 15699.67 33395.58 33098.56 34297.71 362
ppachtmachnet_test98.89 20299.12 12798.20 31499.66 14195.24 34997.63 33599.68 11199.08 16499.78 7199.62 16898.65 12599.88 16898.02 18299.96 4599.48 195
SD-MVS99.01 18299.30 9298.15 31599.50 20899.40 15598.94 21699.61 14999.22 14499.75 8499.82 5599.54 2295.51 37797.48 23599.87 11699.54 162
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
our_test_398.85 20799.09 13998.13 31699.66 14194.90 35297.72 33199.58 17799.07 16699.64 12799.62 16898.19 18599.93 7598.41 15099.95 5499.55 154
ADS-MVSNet297.78 28797.66 29598.12 31799.14 30595.36 34799.22 14498.75 32796.97 32198.25 33199.64 14990.90 33699.94 6196.51 29499.56 25599.08 293
DeepMVS_CXcopyleft97.98 31899.69 12696.95 32599.26 28975.51 37395.74 37198.28 36696.47 26899.62 34891.23 36397.89 35997.38 365
gg-mvs-nofinetune95.87 33495.17 33897.97 31998.19 36796.95 32599.69 3589.23 38099.89 1496.24 36899.94 1481.19 37199.51 36293.99 35598.20 35097.44 364
thres600view796.60 32096.16 32297.93 32099.63 14796.09 34099.18 15397.57 35598.77 20498.72 30697.32 37787.04 35799.72 30588.57 36798.62 34097.98 359
thres40096.40 32295.89 32697.92 32199.58 16396.11 33899.00 20197.54 35898.43 23498.52 32096.98 38086.85 35999.67 33387.62 37098.51 34497.98 359
ADS-MVSNet97.72 29397.67 29497.86 32299.14 30594.65 35399.22 14498.86 32196.97 32198.25 33199.64 14990.90 33699.84 23696.51 29499.56 25599.08 293
IB-MVS95.41 2095.30 33994.46 34397.84 32398.76 35295.33 34897.33 35096.07 36596.02 33895.37 37397.41 37676.17 38199.96 3797.54 23195.44 37398.22 351
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
CVMVSNet98.61 23198.88 19097.80 32499.58 16393.60 35999.26 12999.64 13799.66 6899.72 9999.67 13893.26 31099.93 7599.30 6699.81 15999.87 9
BH-w/o97.20 30697.01 30997.76 32599.08 31895.69 34498.03 30998.52 33695.76 34397.96 34598.02 36995.62 28799.47 36492.82 35997.25 36598.12 356
tpm97.15 30796.95 31197.75 32698.91 33294.24 35599.32 10997.96 34997.71 28998.29 32899.32 27586.72 36399.92 9598.10 18096.24 37199.09 290
test-LLR97.15 30796.95 31197.74 32798.18 36895.02 35097.38 34796.10 36398.00 27097.81 35298.58 35590.04 34799.91 11797.69 22398.78 32998.31 346
test-mter96.23 32895.73 33197.74 32798.18 36895.02 35097.38 34796.10 36397.90 27997.81 35298.58 35579.12 37899.91 11797.69 22398.78 32998.31 346
RRT_test8_iter0597.35 30597.25 30297.63 32998.81 34693.13 36199.26 12999.89 1699.51 9499.83 5199.68 13279.03 37999.88 16899.53 3299.72 20599.89 8
tfpn200view996.30 32695.89 32697.53 33099.58 16396.11 33899.00 20197.54 35898.43 23498.52 32096.98 38086.85 35999.67 33387.62 37098.51 34496.81 368
cascas96.99 31096.82 31697.48 33197.57 37595.64 34596.43 36699.56 18491.75 36497.13 36497.61 37495.58 28898.63 37396.68 28499.11 31398.18 355
thres100view90096.39 32396.03 32597.47 33299.63 14795.93 34199.18 15397.57 35598.75 20898.70 30897.31 37887.04 35799.67 33387.62 37098.51 34496.81 368
PVSNet_095.53 1995.85 33595.31 33797.47 33298.78 35093.48 36095.72 36899.40 25596.18 33797.37 35897.73 37295.73 28599.58 35595.49 33181.40 37599.36 234
TESTMET0.1,196.24 32795.84 32997.41 33498.24 36693.84 35897.38 34795.84 36798.43 23497.81 35298.56 35879.77 37599.89 15397.77 20698.77 33198.52 337
GG-mvs-BLEND97.36 33597.59 37396.87 32899.70 2988.49 38194.64 37497.26 37980.66 37399.12 36991.50 36296.50 37096.08 372
SCA98.11 27698.36 24297.36 33599.20 29792.99 36298.17 29398.49 33998.24 25899.10 26799.57 20296.01 28299.94 6196.86 27399.62 23899.14 281
thres20096.09 32995.68 33297.33 33799.48 21996.22 33798.53 26597.57 35598.06 26998.37 32796.73 38286.84 36199.61 35286.99 37398.57 34196.16 371
KD-MVS_2432*160095.89 33295.41 33597.31 33894.96 37793.89 35697.09 35799.22 29997.23 31398.88 28799.04 32179.23 37699.54 35796.24 30796.81 36698.50 341
miper_refine_blended95.89 33295.41 33597.31 33894.96 37793.89 35697.09 35799.22 29997.23 31398.88 28799.04 32179.23 37699.54 35796.24 30796.81 36698.50 341
PatchmatchNetpermissive97.65 29497.80 28797.18 34098.82 34592.49 36499.17 15898.39 34398.12 26498.79 29999.58 19490.71 34099.89 15397.23 25499.41 28699.16 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS96.53 32196.32 31997.17 34198.18 36892.97 36399.39 9389.95 37998.21 26098.61 31399.59 19286.69 36499.72 30596.99 26699.23 31198.81 324
EPNet_dtu97.62 29597.79 28997.11 34296.67 37692.31 36598.51 26798.04 34799.24 13895.77 37099.47 23993.78 30599.66 33798.98 10899.62 23899.37 231
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ECVR-MVScopyleft97.73 29098.04 26996.78 34399.59 15890.81 37599.72 2490.43 37899.89 1499.86 4199.86 3993.60 30899.89 15399.46 4099.99 1299.65 88
tmp_tt95.75 33695.42 33496.76 34489.90 38194.42 35498.86 22397.87 35278.01 37299.30 23699.69 12197.70 21995.89 37699.29 6998.14 35499.95 1
MVS-HIRNet97.86 28498.22 25596.76 34499.28 28391.53 37198.38 27892.60 37599.13 15999.31 23199.96 1297.18 25199.68 32898.34 15699.83 14299.07 298
tpm296.35 32496.22 32196.73 34698.88 33991.75 36999.21 14698.51 33793.27 36197.89 34899.21 30084.83 36799.70 31196.04 31398.18 35398.75 327
tpmrst97.73 29098.07 26896.73 34698.71 35492.00 36699.10 18198.86 32198.52 22798.92 28399.54 21491.90 32399.82 25798.02 18299.03 31898.37 345
DWT-MVSNet_test96.03 33195.80 33096.71 34898.50 36091.93 36799.25 13697.87 35295.99 33996.81 36597.61 37481.02 37299.66 33797.20 25797.98 35798.54 336
tpmvs97.39 30297.69 29296.52 34998.41 36191.76 36899.30 11698.94 32097.74 28797.85 35199.55 21292.40 32199.73 30396.25 30698.73 33798.06 357
test111197.74 28998.16 26396.49 35099.60 15489.86 37999.71 2891.21 37699.89 1499.88 3399.87 3393.73 30699.90 13799.56 2899.99 1299.70 53
CostFormer96.71 31896.79 31796.46 35198.90 33390.71 37699.41 9098.68 32994.69 35898.14 33999.34 27386.32 36599.80 27897.60 22898.07 35698.88 318
E-PMN97.14 30997.43 29796.27 35298.79 34891.62 37095.54 36999.01 31899.44 10998.88 28799.12 31192.78 31599.68 32894.30 34999.03 31897.50 363
dp96.86 31397.07 30796.24 35398.68 35690.30 37899.19 15298.38 34497.35 30898.23 33399.59 19287.23 35599.82 25796.27 30598.73 33798.59 332
tpm cat196.78 31596.98 31096.16 35498.85 34090.59 37799.08 18899.32 27492.37 36397.73 35799.46 24291.15 33299.69 31796.07 31298.80 32898.21 352
EMVS96.96 31297.28 30095.99 35598.76 35291.03 37395.26 37098.61 33399.34 12298.92 28398.88 34593.79 30499.66 33792.87 35899.05 31697.30 367
test250694.73 34094.59 34295.15 35699.59 15885.90 38199.75 1674.01 38299.89 1499.71 10499.86 3979.00 38099.90 13799.52 3499.99 1299.65 88
wuyk23d97.58 29799.13 12392.93 35799.69 12699.49 12999.52 7599.77 6597.97 27499.96 899.79 6699.84 399.94 6195.85 32299.82 15179.36 373
test_method91.72 34192.32 34489.91 35893.49 38070.18 38290.28 37199.56 18461.71 37595.39 37299.52 21993.90 30199.94 6198.76 13198.27 34999.62 113
test12329.31 34333.05 34818.08 35925.93 38312.24 38397.53 34110.93 38411.78 37724.21 37850.08 38721.04 3828.60 37823.51 37632.43 37733.39 374
testmvs28.94 34433.33 34615.79 36026.03 3829.81 38496.77 36315.67 38311.55 37823.87 37950.74 38619.03 3838.53 37923.21 37733.07 37629.03 375
test_blank8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
uanet_test8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
DCPMVS8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
cdsmvs_eth3d_5k24.88 34533.17 3470.00 3610.00 3840.00 3850.00 37299.62 1420.00 3790.00 38099.13 30799.82 40.00 3800.00 3780.00 3780.00 376
pcd_1.5k_mvsjas16.61 34622.14 3490.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 199.28 420.00 3800.00 3780.00 3780.00 376
sosnet-low-res8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
sosnet8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
uncertanet8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
Regformer8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
ab-mvs-re8.26 35511.02 3580.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 38099.16 3050.00 3840.00 3800.00 3780.00 3780.00 376
uanet8.33 34711.11 3500.00 3610.00 3840.00 3850.00 3720.00 3850.00 3790.00 380100.00 10.00 3840.00 3800.00 3780.00 3780.00 376
FOURS199.83 3999.89 899.74 1899.71 9799.69 5899.63 131
PC_three_145297.56 29499.68 11299.41 24999.09 6397.09 37596.66 28699.60 24899.62 113
test_one_060199.63 14799.76 5299.55 19099.23 14099.31 23199.61 17798.59 131
eth-test20.00 384
eth-test0.00 384
ZD-MVS99.43 23699.61 10899.43 24596.38 33399.11 26599.07 31697.86 21099.92 9594.04 35399.49 274
RE-MVS-def99.13 12399.54 18699.74 6299.26 12999.62 14299.16 15399.52 17899.64 14998.57 13497.27 24899.61 24599.54 162
IU-MVS99.69 12699.77 4599.22 29997.50 30099.69 11097.75 21099.70 21199.77 37
test_241102_TWO99.54 19699.13 15999.76 7899.63 15998.32 17299.92 9597.85 20199.69 21499.75 44
test_241102_ONE99.69 12699.82 2999.54 19699.12 16299.82 5399.49 23198.91 8799.52 361
9.1498.64 21299.45 23298.81 23399.60 16197.52 29999.28 23799.56 20598.53 14399.83 24795.36 33699.64 235
save fliter99.53 19299.25 18998.29 28499.38 26499.07 166
test_0728_THIRD99.18 14799.62 13999.61 17798.58 13399.91 11797.72 21299.80 16499.77 37
test072699.69 12699.80 3799.24 13799.57 17999.16 15399.73 9799.65 14798.35 167
GSMVS99.14 281
test_part299.62 15199.67 8799.55 169
sam_mvs190.81 33999.14 281
sam_mvs90.52 343
MTGPAbinary99.53 205
test_post199.14 16851.63 38589.54 35099.82 25796.86 273
test_post52.41 38490.25 34599.86 201
patchmatchnet-post99.62 16890.58 34199.94 61
MTMP99.09 18598.59 335
gm-plane-assit97.59 37389.02 38093.47 36098.30 36599.84 23696.38 301
test9_res95.10 33999.44 28099.50 185
TEST999.35 25799.35 17098.11 30099.41 24894.83 35797.92 34698.99 32898.02 19799.85 219
test_899.34 26799.31 17698.08 30499.40 25594.90 35397.87 35098.97 33498.02 19799.84 236
agg_prior294.58 34799.46 27999.50 185
agg_prior99.35 25799.36 16699.39 25897.76 35599.85 219
test_prior499.19 20598.00 312
test_prior297.95 31997.87 28198.05 34199.05 31897.90 20695.99 31699.49 274
旧先验297.94 32195.33 34898.94 27999.88 16896.75 280
新几何298.04 308
旧先验199.49 21399.29 17999.26 28999.39 25797.67 22499.36 29499.46 204
无先验98.01 31099.23 29695.83 34199.85 21995.79 32599.44 211
原ACMM297.92 323
test22299.51 20299.08 22097.83 32899.29 28395.21 35098.68 30999.31 27797.28 24499.38 28999.43 217
testdata299.89 15395.99 316
segment_acmp98.37 165
testdata197.72 33197.86 284
plane_prior799.58 16399.38 160
plane_prior699.47 22499.26 18597.24 245
plane_prior599.54 19699.82 25795.84 32399.78 17599.60 128
plane_prior499.25 291
plane_prior399.31 17698.36 24399.14 261
plane_prior298.80 23698.94 180
plane_prior199.51 202
plane_prior99.24 19498.42 27697.87 28199.71 209
n20.00 385
nn0.00 385
door-mid99.83 36
test1199.29 283
door99.77 65
HQP5-MVS98.94 232
HQP-NCC99.31 27497.98 31597.45 30298.15 335
ACMP_Plane99.31 27497.98 31597.45 30298.15 335
BP-MVS94.73 343
HQP4-MVS98.15 33599.70 31199.53 167
HQP3-MVS99.37 26599.67 226
HQP2-MVS96.67 262
NP-MVS99.40 24599.13 21098.83 347
MDTV_nov1_ep13_2view91.44 37299.14 16897.37 30799.21 25091.78 32796.75 28099.03 302
MDTV_nov1_ep1397.73 29198.70 35590.83 37499.15 16698.02 34898.51 22898.82 29599.61 17790.98 33499.66 33796.89 27298.92 324
ACMMP++_ref99.94 67
ACMMP++99.79 169
Test By Simon98.41 159