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

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

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

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

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




Method Infoallhigh-res
multi-view
indooroutdoorbotani.boulde.bridgedoorexhibi.lectur.living.loungeobserv.old co.statueterrac.
sort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
fmvsm_l_conf0.5_n_a99.71 199.67 199.85 4399.86 2599.61 8699.56 15399.63 4699.48 399.98 1399.83 11198.75 6199.99 499.97 299.96 1799.94 17
fmvsm_l_conf0.5_n99.71 199.67 199.85 4399.84 3899.63 8299.56 15399.63 4699.47 699.98 1399.82 12298.75 6199.99 499.97 299.97 999.94 17
MED-MVS99.70 399.64 499.90 899.88 1399.81 3399.69 6399.87 699.48 399.90 3499.89 4599.30 499.95 7698.83 17599.89 6799.93 22
TestfortrainingZip a99.70 399.63 699.92 199.88 1399.90 299.69 6399.79 1199.48 399.93 2999.89 4598.78 5399.93 10899.32 8699.88 7499.93 22
test_fmvsmconf_n99.70 399.64 499.87 2299.80 6399.66 7199.48 23099.64 4299.45 1399.92 3099.92 1898.62 7799.99 499.96 1399.99 199.96 7
test_fmvsm_n_192099.69 699.66 399.78 7199.84 3899.44 11799.58 13799.69 2299.43 1999.98 1399.91 2698.62 77100.00 199.97 299.95 2299.90 27
APDe-MVScopyleft99.66 799.57 1099.92 199.77 7899.89 699.75 4399.56 8999.02 6299.88 4299.85 8999.18 1199.96 4199.22 10799.92 3899.90 27
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test_fmvsmvis_n_192099.65 899.61 899.77 7499.38 28299.37 12499.58 13799.62 5199.41 2399.87 4899.92 1898.81 49100.00 199.97 299.93 3299.94 17
reproduce_model99.63 999.54 1399.90 899.78 7099.88 1099.56 15399.55 9999.15 3899.90 3499.90 3699.00 2399.97 2999.11 12599.91 4599.86 43
fmvsm_l_conf0.5_n_399.61 1099.51 1899.92 199.84 3899.82 2899.54 17399.66 3299.46 999.98 1399.89 4597.27 13399.99 499.97 299.95 2299.95 11
reproduce-ours99.61 1099.52 1499.90 899.76 8299.88 1099.52 18499.54 10899.13 4199.89 3999.89 4598.96 2699.96 4199.04 13599.90 5699.85 47
our_new_method99.61 1099.52 1499.90 899.76 8299.88 1099.52 18499.54 10899.13 4199.89 3999.89 4598.96 2699.96 4199.04 13599.90 5699.85 47
SED-MVS99.61 1099.52 1499.88 1699.84 3899.90 299.60 11699.48 20799.08 5699.91 3199.81 13799.20 899.96 4198.91 15699.85 9399.79 92
lecture99.60 1499.50 1999.89 1299.89 899.90 299.75 4399.59 7299.06 6199.88 4299.85 8998.41 9399.96 4199.28 9999.84 10199.83 64
DVP-MVS++99.59 1599.50 1999.88 1699.51 23299.88 1099.87 899.51 15898.99 6999.88 4299.81 13799.27 699.96 4198.85 16999.80 12599.81 79
fmvsm_l_conf0.5_n_999.58 1699.47 2499.92 199.85 3199.82 2899.47 24099.63 4699.45 1399.98 1399.89 4597.02 14899.99 499.98 199.96 1799.95 11
TSAR-MVS + MP.99.58 1699.50 1999.81 6099.91 199.66 7199.63 10399.39 28898.91 8399.78 8299.85 8999.36 299.94 9198.84 17299.88 7499.82 72
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EI-MVSNet-UG-set99.58 1699.57 1099.64 10299.78 7099.14 16299.60 11699.45 25399.01 6499.90 3499.83 11198.98 2599.93 10899.59 4599.95 2299.86 43
EI-MVSNet-Vis-set99.58 1699.56 1299.64 10299.78 7099.15 16199.61 11499.45 25399.01 6499.89 3999.82 12299.01 1999.92 12399.56 4999.95 2299.85 47
DVP-MVScopyleft99.57 2099.47 2499.88 1699.85 3199.89 699.57 14599.37 30599.10 4899.81 6999.80 15598.94 3399.96 4198.93 15399.86 8699.81 79
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
ME-MVS99.56 2199.46 2899.86 3499.80 6399.81 3399.37 29199.70 1899.18 3599.83 6499.83 11198.74 6699.93 10898.83 17599.89 6799.83 64
fmvsm_s_conf0.5_n_a99.56 2199.47 2499.85 4399.83 4799.64 8199.52 18499.65 3999.10 4899.98 1399.92 1897.35 12999.96 4199.94 2199.92 3899.95 11
test_fmvsmconf0.1_n99.55 2399.45 3099.86 3499.44 26399.65 7599.50 20599.61 6099.45 1399.87 4899.92 1897.31 13099.97 2999.95 1699.99 199.97 4
fmvsm_s_conf0.5_n_899.54 2499.42 3299.89 1299.83 4799.74 5499.51 19499.62 5199.46 999.99 299.90 3696.60 17299.98 2099.95 1699.95 2299.96 7
fmvsm_s_conf0.5_n_699.54 2499.44 3199.85 4399.51 23299.67 6899.50 20599.64 4299.43 1999.98 1399.78 17997.26 13699.95 7699.95 1699.93 3299.92 25
SteuartSystems-ACMMP99.54 2499.42 3299.87 2299.82 5399.81 3399.59 12799.51 15898.62 11399.79 7799.83 11199.28 599.97 2998.48 22699.90 5699.84 54
Skip Steuart: Steuart Systems R&D Blog.
XVS99.53 2799.42 3299.87 2299.85 3199.83 2299.69 6399.68 2498.98 7299.37 21999.74 20398.81 4999.94 9198.79 18399.86 8699.84 54
MTAPA99.52 2899.39 3999.89 1299.90 499.86 1899.66 8399.47 22998.79 9699.68 12199.81 13798.43 9099.97 2998.88 15999.90 5699.83 64
fmvsm_s_conf0.5_n99.51 2999.40 3799.85 4399.84 3899.65 7599.51 19499.67 2799.13 4199.98 1399.92 1896.60 17299.96 4199.95 1699.96 1799.95 11
HPM-MVS_fast99.51 2999.40 3799.85 4399.91 199.79 4199.76 3899.56 8997.72 26099.76 9299.75 19799.13 1399.92 12399.07 13299.92 3899.85 47
mvsany_test199.50 3199.46 2899.62 10999.61 19199.09 16798.94 42299.48 20799.10 4899.96 2799.91 2698.85 4399.96 4199.72 3299.58 16999.82 72
CS-MVS99.50 3199.48 2299.54 12699.76 8299.42 11999.90 199.55 9998.56 11999.78 8299.70 22098.65 7599.79 24899.65 4199.78 13499.41 267
SPE-MVS-test99.49 3399.48 2299.54 12699.78 7099.30 13999.89 299.58 7798.56 11999.73 9999.69 23198.55 8299.82 23099.69 3499.85 9399.48 246
HFP-MVS99.49 3399.37 4399.86 3499.87 2099.80 3899.66 8399.67 2798.15 17999.68 12199.69 23199.06 1799.96 4198.69 19599.87 7899.84 54
ACMMPR99.49 3399.36 4599.86 3499.87 2099.79 4199.66 8399.67 2798.15 17999.67 12799.69 23198.95 3199.96 4198.69 19599.87 7899.84 54
DeepC-MVS_fast98.69 199.49 3399.39 3999.77 7499.63 17199.59 8999.36 29799.46 24299.07 5899.79 7799.82 12298.85 4399.92 12398.68 19799.87 7899.82 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
region2R99.48 3799.35 4799.87 2299.88 1399.80 3899.65 8999.66 3298.13 18699.66 13299.68 23998.96 2699.96 4198.62 20499.87 7899.84 54
APD-MVS_3200maxsize99.48 3799.35 4799.85 4399.76 8299.83 2299.63 10399.54 10898.36 14299.79 7799.82 12298.86 4299.95 7698.62 20499.81 12099.78 98
DELS-MVS99.48 3799.42 3299.65 9699.72 11199.40 12299.05 39499.66 3299.14 4099.57 16999.80 15598.46 8899.94 9199.57 4899.84 10199.60 199
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
ZNCC-MVS99.47 4099.33 5199.87 2299.87 2099.81 3399.64 9799.67 2798.08 20499.55 17699.64 25898.91 3899.96 4198.72 19099.90 5699.82 72
ACMMP_NAP99.47 4099.34 4999.88 1699.87 2099.86 1899.47 24099.48 20798.05 21299.76 9299.86 8298.82 4899.93 10898.82 18299.91 4599.84 54
MVSMamba_PlusPlus99.46 4299.41 3699.64 10299.68 13599.50 10999.75 4399.50 18298.27 15499.87 4899.92 1898.09 10899.94 9199.65 4199.95 2299.47 252
BridgeMVS99.46 4299.39 3999.67 9199.55 21599.58 9499.74 4899.51 15898.42 13599.87 4899.84 10498.05 11199.91 13599.58 4799.94 3099.52 229
DPE-MVScopyleft99.46 4299.32 5399.91 699.78 7099.88 1099.36 29799.51 15898.73 10399.88 4299.84 10498.72 6899.96 4198.16 26299.87 7899.88 36
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSLP-MVS++99.46 4299.47 2499.44 17799.60 19799.16 15699.41 27299.71 1698.98 7299.45 19299.78 17999.19 1099.54 33099.28 9999.84 10199.63 191
SR-MVS-dyc-post99.45 4699.31 5999.85 4399.76 8299.82 2899.63 10399.52 13398.38 13899.76 9299.82 12298.53 8399.95 7698.61 20799.81 12099.77 100
PGM-MVS99.45 4699.31 5999.86 3499.87 2099.78 4799.58 13799.65 3997.84 24499.71 11499.80 15599.12 1499.97 2998.33 24799.87 7899.83 64
CP-MVS99.45 4699.32 5399.85 4399.83 4799.75 5199.69 6399.52 13398.07 20599.53 17999.63 26498.93 3799.97 2998.74 18799.91 4599.83 64
ACMMPcopyleft99.45 4699.32 5399.82 5799.89 899.67 6899.62 10899.69 2298.12 19499.63 15099.84 10498.73 6799.96 4198.55 22299.83 11399.81 79
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
SMA-MVScopyleft99.44 5099.30 6199.85 4399.73 10799.83 2299.56 15399.47 22997.45 29499.78 8299.82 12299.18 1199.91 13598.79 18399.89 6799.81 79
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
mPP-MVS99.44 5099.30 6199.86 3499.88 1399.79 4199.69 6399.48 20798.12 19499.50 18499.75 19798.78 5399.97 2998.57 21699.89 6799.83 64
EC-MVSNet99.44 5099.39 3999.58 11799.56 21199.49 11099.88 499.58 7798.38 13899.73 9999.69 23198.20 10399.70 29299.64 4399.82 11799.54 223
SR-MVS99.43 5399.29 6599.86 3499.75 9299.83 2299.59 12799.62 5198.21 17099.73 9999.79 17298.68 7199.96 4198.44 23399.77 13799.79 92
MCST-MVS99.43 5399.30 6199.82 5799.79 6899.74 5499.29 32299.40 28598.79 9699.52 18199.62 26998.91 3899.90 14898.64 20199.75 14299.82 72
MSP-MVS99.42 5599.27 7299.88 1699.89 899.80 3899.67 7699.50 18298.70 10799.77 8699.49 31698.21 10299.95 7698.46 23199.77 13799.88 36
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
UA-Net99.42 5599.29 6599.80 6499.62 18099.55 9799.50 20599.70 1898.79 9699.77 8699.96 197.45 12499.96 4198.92 15599.90 5699.89 30
HPM-MVScopyleft99.42 5599.28 6899.83 5699.90 499.72 5699.81 2099.54 10897.59 27599.68 12199.63 26498.91 3899.94 9198.58 21399.91 4599.84 54
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CNVR-MVS99.42 5599.30 6199.78 7199.62 18099.71 5899.26 34299.52 13398.82 9099.39 21599.71 21698.96 2699.85 19098.59 21299.80 12599.77 100
fmvsm_s_conf0.5_n_1099.41 5999.24 7799.92 199.83 4799.84 2099.53 18299.56 8999.45 1399.99 299.92 1894.92 26099.99 499.97 299.97 999.95 11
fmvsm_s_conf0.5_n_999.41 5999.28 6899.81 6099.84 3899.52 10699.48 23099.62 5199.46 999.99 299.92 1895.24 24799.96 4199.97 299.97 999.96 7
SD-MVS99.41 5999.52 1499.05 24199.74 10099.68 6499.46 24499.52 13399.11 4799.88 4299.91 2699.43 197.70 48298.72 19099.93 3299.77 100
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
MVS_111021_LR99.41 5999.33 5199.65 9699.77 7899.51 10898.94 42299.85 898.82 9099.65 14299.74 20398.51 8599.80 24298.83 17599.89 6799.64 186
MVS_111021_HR99.41 5999.32 5399.66 9299.72 11199.47 11498.95 42099.85 898.82 9099.54 17799.73 20998.51 8599.74 26998.91 15699.88 7499.77 100
MM99.40 6499.28 6899.74 8099.67 13899.31 13699.52 18498.87 42699.55 199.74 9799.80 15596.47 18099.98 2099.97 299.97 999.94 17
GST-MVS99.40 6499.24 7799.85 4399.86 2599.79 4199.60 11699.67 2797.97 22899.63 15099.68 23998.52 8499.95 7698.38 24099.86 8699.81 79
HPM-MVS++copyleft99.39 6699.23 8199.87 2299.75 9299.84 2099.43 26199.51 15898.68 11099.27 24999.53 30298.64 7699.96 4198.44 23399.80 12599.79 92
SF-MVS99.38 6799.24 7799.79 6899.79 6899.68 6499.57 14599.54 10897.82 25099.71 11499.80 15598.95 3199.93 10898.19 25899.84 10199.74 118
fmvsm_s_conf0.5_n_599.37 6899.21 8399.86 3499.80 6399.68 6499.42 26899.61 6099.37 2699.97 2599.86 8294.96 25599.99 499.97 299.93 3299.92 25
fmvsm_s_conf0.5_n_399.37 6899.20 8599.87 2299.75 9299.70 6099.48 23099.66 3299.45 1399.99 299.93 1094.64 28899.97 2999.94 2199.97 999.95 11
fmvsm_s_conf0.1_n_299.37 6899.22 8299.81 6099.77 7899.75 5199.46 24499.60 6799.47 699.98 1399.94 694.98 25499.95 7699.97 299.79 13299.73 128
MP-MVS-pluss99.37 6899.20 8599.88 1699.90 499.87 1799.30 31799.52 13397.18 32099.60 16299.79 17298.79 5299.95 7698.83 17599.91 4599.83 64
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
fmvsm_s_conf0.5_n_499.36 7299.24 7799.73 8399.78 7099.53 10299.49 22299.60 6799.42 2299.99 299.86 8295.15 25099.95 7699.95 1699.89 6799.73 128
TSAR-MVS + GP.99.36 7299.36 4599.36 19299.67 13898.61 25699.07 38799.33 32799.00 6799.82 6899.81 13799.06 1799.84 19999.09 13099.42 18199.65 179
PVSNet_Blended_VisFu99.36 7299.28 6899.61 11099.86 2599.07 17299.47 24099.93 297.66 26999.71 11499.86 8297.73 11999.96 4199.47 6699.82 11799.79 92
fmvsm_s_conf0.5_n_799.34 7599.29 6599.48 16399.70 12298.63 25299.42 26899.63 4699.46 999.98 1399.88 5895.59 23099.96 4199.97 299.98 499.85 47
NCCC99.34 7599.19 8799.79 6899.61 19199.65 7599.30 31799.48 20798.86 8599.21 26499.63 26498.72 6899.90 14898.25 25499.63 16499.80 88
MP-MVScopyleft99.33 7799.15 9299.87 2299.88 1399.82 2899.66 8399.46 24298.09 20099.48 18899.74 20398.29 9999.96 4197.93 28499.87 7899.82 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_s_conf0.5_n_1199.32 7899.16 9199.80 6499.83 4799.70 6099.57 14599.56 8999.45 1399.99 299.93 1094.18 31199.99 499.96 1399.98 499.73 128
fmvsm_s_conf0.5_n_299.32 7899.13 9499.89 1299.80 6399.77 4899.44 25599.58 7799.47 699.99 299.93 1094.04 31699.96 4199.96 1399.93 3299.93 22
PS-MVSNAJ99.32 7899.32 5399.30 20899.57 20798.94 20098.97 41699.46 24298.92 8299.71 11499.24 38799.01 1999.98 2099.35 7899.66 15998.97 322
CSCG99.32 7899.32 5399.32 20199.85 3198.29 28299.71 5899.66 3298.11 19699.41 20899.80 15598.37 9699.96 4198.99 14199.96 1799.72 138
PHI-MVS99.30 8299.17 9099.70 8799.56 21199.52 10699.58 13799.80 1097.12 32699.62 15499.73 20998.58 7999.90 14898.61 20799.91 4599.68 162
DeepC-MVS98.35 299.30 8299.19 8799.64 10299.82 5399.23 14999.62 10899.55 9998.94 7999.63 15099.95 395.82 21899.94 9199.37 7799.97 999.73 128
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.1_n99.29 8499.10 9899.86 3499.70 12299.65 7599.53 18299.62 5198.74 10299.99 299.95 394.53 29699.94 9199.89 2599.96 1799.97 4
xiu_mvs_v1_base_debu99.29 8499.27 7299.34 19599.63 17198.97 18699.12 37799.51 15898.86 8599.84 5599.47 32698.18 10499.99 499.50 5799.31 19199.08 304
xiu_mvs_v1_base99.29 8499.27 7299.34 19599.63 17198.97 18699.12 37799.51 15898.86 8599.84 5599.47 32698.18 10499.99 499.50 5799.31 19199.08 304
xiu_mvs_v1_base_debi99.29 8499.27 7299.34 19599.63 17198.97 18699.12 37799.51 15898.86 8599.84 5599.47 32698.18 10499.99 499.50 5799.31 19199.08 304
NormalMVS99.27 8899.19 8799.52 14199.89 898.83 23099.65 8999.52 13399.10 4899.84 5599.76 19295.80 22099.99 499.30 9199.84 10199.74 118
APD-MVScopyleft99.27 8899.08 10499.84 5599.75 9299.79 4199.50 20599.50 18297.16 32299.77 8699.82 12298.78 5399.94 9197.56 32699.86 8699.80 88
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
LS3D99.27 8899.12 9699.74 8099.18 33799.75 5199.56 15399.57 8498.45 13199.49 18799.85 8997.77 11899.94 9198.33 24799.84 10199.52 229
fmvsm_s_conf0.1_n_a99.26 9199.06 10999.85 4399.52 22999.62 8399.54 17399.62 5198.69 10899.99 299.96 194.47 29899.94 9199.88 2699.92 3899.98 2
patch_mono-299.26 9199.62 798.16 36899.81 5794.59 45199.52 18499.64 4299.33 2999.73 9999.90 3699.00 2399.99 499.69 3499.98 499.89 30
ETV-MVS99.26 9199.21 8399.40 18599.46 25699.30 13999.56 15399.52 13398.52 12399.44 19799.27 38398.41 9399.86 18299.10 12899.59 16899.04 312
xiu_mvs_v2_base99.26 9199.25 7699.29 21199.53 22398.91 20799.02 40299.45 25398.80 9599.71 11499.26 38598.94 3399.98 2099.34 8399.23 20098.98 320
CANet99.25 9599.14 9399.59 11499.41 27199.16 15699.35 30299.57 8498.82 9099.51 18399.61 27396.46 18199.95 7699.59 4599.98 499.65 179
3Dnovator97.25 999.24 9699.05 11199.81 6099.12 35399.66 7199.84 1299.74 1399.09 5598.92 32199.90 3695.94 21199.98 2098.95 14999.92 3899.79 92
LuminaMVS99.23 9799.10 9899.61 11099.35 28999.31 13699.46 24499.13 38498.61 11499.86 5299.89 4596.41 18699.91 13599.67 3799.51 17499.63 191
dcpmvs_299.23 9799.58 998.16 36899.83 4794.68 44899.76 3899.52 13399.07 5899.98 1399.88 5898.56 8199.93 10899.67 3799.98 499.87 41
test_fmvsmconf0.01_n99.22 9999.03 11699.79 6898.42 45199.48 11299.55 16899.51 15899.39 2499.78 8299.93 1094.80 26999.95 7699.93 2399.95 2299.94 17
diffmvs_AUTHOR99.19 10099.10 9899.48 16399.64 16698.85 22599.32 31199.48 20798.50 12599.81 6999.81 13796.82 16099.88 16899.40 7299.12 21899.71 149
CHOSEN 1792x268899.19 10099.10 9899.45 17299.89 898.52 26699.39 28499.94 198.73 10399.11 28499.89 4595.50 23399.94 9199.50 5799.97 999.89 30
F-COLMAP99.19 10099.04 11399.64 10299.78 7099.27 14499.42 26899.54 10897.29 31199.41 20899.59 27898.42 9299.93 10898.19 25899.69 15399.73 128
E3new99.18 10399.08 10499.48 16399.63 17198.94 20099.46 24499.50 18298.06 20999.72 10499.84 10497.27 13399.84 19999.10 12899.13 21399.67 167
viewcassd2359sk1199.18 10399.08 10499.49 15999.65 16198.95 19699.48 23099.51 15898.10 19999.72 10499.87 7397.13 13999.84 19999.13 12299.14 21099.69 156
viewmanbaseed2359cas99.18 10399.07 10899.50 15299.62 18099.01 18099.50 20599.52 13398.25 16299.68 12199.82 12296.93 15399.80 24299.15 12199.11 22099.70 153
EIA-MVS99.18 10399.09 10399.45 17299.49 24699.18 15399.67 7699.53 12497.66 26999.40 21399.44 33398.10 10799.81 23598.94 15099.62 16599.35 277
3Dnovator+97.12 1399.18 10398.97 14499.82 5799.17 34599.68 6499.81 2099.51 15899.20 3498.72 35199.89 4595.68 22799.97 2998.86 16799.86 8699.81 79
MVSFormer99.17 10899.12 9699.29 21199.51 23298.94 20099.88 499.46 24297.55 28199.80 7499.65 25297.39 12599.28 37399.03 13799.85 9399.65 179
sss99.17 10899.05 11199.53 13499.62 18098.97 18699.36 29799.62 5197.83 24599.67 12799.65 25297.37 12899.95 7699.19 11199.19 20499.68 162
SSM_040499.16 11099.06 10999.44 17799.65 16198.96 19099.49 22299.50 18298.14 18399.62 15499.85 8996.85 15599.85 19099.19 11199.26 19699.52 229
guyue99.16 11099.04 11399.52 14199.69 12898.92 20699.59 12798.81 43498.73 10399.90 3499.87 7395.34 24099.88 16899.66 4099.81 12099.74 118
test_cas_vis1_n_192099.16 11099.01 13399.61 11099.81 5798.86 22499.65 8999.64 4299.39 2499.97 2599.94 693.20 34099.98 2099.55 5099.91 4599.99 1
DP-MVS99.16 11098.95 15299.78 7199.77 7899.53 10299.41 27299.50 18297.03 33899.04 30199.88 5897.39 12599.92 12398.66 19999.90 5699.87 41
E6new99.15 11499.03 11699.50 15299.66 15098.90 21299.60 11699.53 12498.13 18699.72 10499.91 2696.31 19099.84 19999.30 9199.10 22799.76 107
E699.15 11499.03 11699.50 15299.66 15098.90 21299.60 11699.53 12498.13 18699.72 10499.91 2696.31 19099.84 19999.30 9199.10 22799.76 107
E299.15 11499.03 11699.49 15999.65 16198.93 20599.49 22299.52 13398.14 18399.72 10499.88 5896.57 17699.84 19999.17 11799.13 21399.72 138
E399.15 11499.03 11699.49 15999.62 18098.91 20799.49 22299.52 13398.13 18699.72 10499.88 5896.61 17199.84 19999.17 11799.13 21399.72 138
SymmetryMVS99.15 11499.02 12699.52 14199.72 11198.83 23099.65 8999.34 31999.10 4899.84 5599.76 19295.80 22099.99 499.30 9198.72 26699.73 128
MGCNet99.15 11498.96 14899.73 8398.92 39199.37 12499.37 29196.92 49399.51 299.66 13299.78 17996.69 16799.97 2999.84 2899.97 999.84 54
casdiffmvs_mvgpermissive99.15 11499.02 12699.55 12599.66 15099.09 16799.64 9799.56 8998.26 15799.45 19299.87 7396.03 20599.81 23599.54 5199.15 20999.73 128
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline99.15 11499.02 12699.53 13499.66 15099.14 16299.72 5499.48 20798.35 14399.42 20399.84 10496.07 20299.79 24899.51 5699.14 21099.67 167
E5new99.14 12299.02 12699.50 15299.69 12898.91 20799.60 11699.53 12498.13 18699.72 10499.91 2696.26 19599.84 19999.30 9199.10 22799.76 107
E599.14 12299.02 12699.50 15299.69 12898.91 20799.60 11699.53 12498.13 18699.72 10499.91 2696.26 19599.84 19999.30 9199.10 22799.76 107
diffmvspermissive99.14 12299.02 12699.51 14699.61 19198.96 19099.28 32899.49 19598.46 12999.72 10499.71 21696.50 17999.88 16899.31 8899.11 22099.67 167
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CNLPA99.14 12298.99 13999.59 11499.58 20199.41 12199.16 36799.44 26298.45 13199.19 27199.49 31698.08 10999.89 16397.73 30899.75 14299.48 246
hybridcas99.13 12699.00 13799.51 14699.70 12299.04 17699.65 8999.52 13398.20 17299.75 9699.88 5895.78 22299.78 25599.41 7099.16 20599.71 149
E499.13 12699.01 13399.49 15999.68 13598.90 21299.52 18499.52 13398.13 18699.71 11499.90 3696.32 18899.84 19999.21 10999.11 22099.75 113
SSM_040799.13 12699.03 11699.43 18099.62 18098.88 21799.51 19499.50 18298.14 18399.37 21999.85 8996.85 15599.83 22199.19 11199.25 19799.60 199
CDPH-MVS99.13 12698.91 16099.80 6499.75 9299.71 5899.15 37099.41 27896.60 37299.60 16299.55 29398.83 4799.90 14897.48 33599.83 11399.78 98
casdiffmvspermissive99.13 12698.98 14299.56 12399.65 16199.16 15699.56 15399.50 18298.33 14699.41 20899.86 8295.92 21299.83 22199.45 6899.16 20599.70 153
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
jason99.13 12699.03 11699.45 17299.46 25698.87 22199.12 37799.26 36198.03 22199.79 7799.65 25297.02 14899.85 19099.02 13999.90 5699.65 179
jason: jason.
lupinMVS99.13 12699.01 13399.46 17199.51 23298.94 20099.05 39499.16 38097.86 23899.80 7499.56 29097.39 12599.86 18298.94 15099.85 9399.58 214
EPP-MVSNet99.13 12698.99 13999.53 13499.65 16199.06 17399.81 2099.33 32797.43 29899.60 16299.88 5897.14 13899.84 19999.13 12298.94 24599.69 156
MG-MVS99.13 12699.02 12699.45 17299.57 20798.63 25299.07 38799.34 31998.99 6999.61 15999.82 12297.98 11399.87 17597.00 37299.80 12599.85 47
KinetiMVS99.12 13598.92 15799.70 8799.67 13899.40 12299.67 7699.63 4698.73 10399.94 2899.81 13794.54 29499.96 4198.40 23899.93 3299.74 118
BP-MVS199.12 13598.94 15499.65 9699.51 23299.30 13999.67 7698.92 41398.48 12799.84 5599.69 23194.96 25599.92 12399.62 4499.79 13299.71 149
CHOSEN 280x42099.12 13599.13 9499.08 23799.66 15097.89 30998.43 47599.71 1698.88 8499.62 15499.76 19296.63 17099.70 29299.46 6799.99 199.66 172
DP-MVS Recon99.12 13598.95 15299.65 9699.74 10099.70 6099.27 33399.57 8496.40 38899.42 20399.68 23998.75 6199.80 24297.98 28199.72 14899.44 262
Vis-MVSNetpermissive99.12 13598.97 14499.56 12399.78 7099.10 16699.68 7399.66 3298.49 12699.86 5299.87 7394.77 27499.84 19999.19 11199.41 18299.74 118
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAMVS99.12 13599.08 10499.24 22199.46 25698.55 26099.51 19499.46 24298.09 20099.45 19299.82 12298.34 9799.51 33298.70 19298.93 24699.67 167
hybrid99.11 14199.01 13399.41 18399.64 16698.76 24099.35 30299.52 13398.31 15099.80 7499.84 10496.16 19999.79 24899.40 7299.06 23599.68 162
viewdifsd2359ckpt0799.11 14199.00 13799.43 18099.63 17198.73 24299.45 24899.54 10898.33 14699.62 15499.81 13796.17 19899.87 17599.27 10299.14 21099.69 156
SDMVSNet99.11 14198.90 16299.75 7799.81 5799.59 8999.81 2099.65 3998.78 9999.64 14799.88 5894.56 29199.93 10899.67 3798.26 29699.72 138
VNet99.11 14198.90 16299.73 8399.52 22999.56 9599.41 27299.39 28899.01 6499.74 9799.78 17995.56 23199.92 12399.52 5598.18 30499.72 138
CPTT-MVS99.11 14198.90 16299.74 8099.80 6399.46 11599.59 12799.49 19597.03 33899.63 15099.69 23197.27 13399.96 4197.82 29599.84 10199.81 79
HyFIR lowres test99.11 14198.92 15799.65 9699.90 499.37 12499.02 40299.91 397.67 26899.59 16599.75 19795.90 21499.73 27599.53 5399.02 24199.86 43
MVS_Test99.10 14798.97 14499.48 16399.49 24699.14 16299.67 7699.34 31997.31 30999.58 16699.76 19297.65 12199.82 23098.87 16299.07 23499.46 257
AstraMVS99.09 14899.03 11699.25 21899.66 15098.13 29199.57 14598.24 47098.82 9099.91 3199.88 5895.81 21999.90 14899.72 3299.67 15899.74 118
CDS-MVSNet99.09 14899.03 11699.25 21899.42 26698.73 24299.45 24899.46 24298.11 19699.46 19199.77 18898.01 11299.37 35698.70 19298.92 24899.66 172
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
viewmacassd2359aftdt99.08 15098.94 15499.50 15299.66 15098.96 19099.51 19499.54 10898.27 15499.42 20399.89 4595.88 21699.80 24299.20 11099.11 22099.76 107
mamba_040899.08 15098.96 14899.44 17799.62 18098.88 21799.25 34499.47 22998.05 21299.37 21999.81 13796.85 15599.85 19098.98 14299.25 19799.60 199
GDP-MVS99.08 15098.89 16699.64 10299.53 22399.34 12899.64 9799.48 20798.32 14899.77 8699.66 25095.14 25199.93 10898.97 14799.50 17699.64 186
PVSNet_Blended99.08 15098.97 14499.42 18299.76 8298.79 23698.78 44399.91 396.74 35799.67 12799.49 31697.53 12299.88 16898.98 14299.85 9399.60 199
OMC-MVS99.08 15099.04 11399.20 22599.67 13898.22 28699.28 32899.52 13398.07 20599.66 13299.81 13797.79 11799.78 25597.79 29999.81 12099.60 199
viewdifsd2359ckpt1399.06 15598.93 15699.45 17299.63 17198.96 19099.50 20599.51 15897.83 24599.28 24399.80 15596.68 16999.71 28599.05 13499.12 21899.68 162
SSM_0407299.06 15598.96 14899.35 19499.62 18098.88 21799.25 34499.47 22998.05 21299.37 21999.81 13796.85 15599.58 32498.98 14299.25 19799.60 199
mvsmamba99.06 15598.96 14899.36 19299.47 25498.64 25199.70 5999.05 39697.61 27499.65 14299.83 11196.54 17799.92 12399.19 11199.62 16599.51 238
WTY-MVS99.06 15598.88 16999.61 11099.62 18099.16 15699.37 29199.56 8998.04 21999.53 17999.62 26996.84 15999.94 9198.85 16998.49 28199.72 138
IS-MVSNet99.05 15998.87 17099.57 12199.73 10799.32 13299.75 4399.20 37598.02 22499.56 17099.86 8296.54 17799.67 30198.09 26999.13 21399.73 128
PAPM_NR99.04 16098.84 17899.66 9299.74 10099.44 11799.39 28499.38 29697.70 26499.28 24399.28 38098.34 9799.85 19096.96 37699.45 17999.69 156
API-MVS99.04 16099.03 11699.06 23999.40 27699.31 13699.55 16899.56 8998.54 12199.33 23399.39 34998.76 5899.78 25596.98 37499.78 13498.07 455
mvs_anonymous99.03 16298.99 13999.16 22999.38 28298.52 26699.51 19499.38 29697.79 25199.38 21799.81 13797.30 13199.45 33899.35 7898.99 24399.51 238
sasdasda99.02 16398.86 17399.51 14699.42 26699.32 13299.80 2599.48 20798.63 11199.31 23598.81 43397.09 14399.75 26699.27 10297.90 31599.47 252
train_agg99.02 16398.77 18699.77 7499.67 13899.65 7599.05 39499.41 27896.28 39298.95 31799.49 31698.76 5899.91 13597.63 31799.72 14899.75 113
canonicalmvs99.02 16398.86 17399.51 14699.42 26699.32 13299.80 2599.48 20798.63 11199.31 23598.81 43397.09 14399.75 26699.27 10297.90 31599.47 252
balanced_ft_v199.02 16398.98 14299.15 23399.39 27998.12 29399.79 3199.51 15898.20 17299.66 13299.87 7394.84 26599.93 10899.69 3499.84 10199.41 267
PLCcopyleft97.94 499.02 16398.85 17699.53 13499.66 15099.01 18099.24 34999.52 13396.85 35099.27 24999.48 32398.25 10199.91 13597.76 30499.62 16599.65 179
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
viewdifsd2359ckpt0999.01 16898.87 17099.40 18599.62 18098.79 23699.44 25599.51 15897.76 25599.35 22899.69 23196.42 18599.75 26698.97 14799.11 22099.66 172
viewmambaseed2359dif99.01 16898.90 16299.32 20199.58 20198.51 26899.33 30899.54 10897.85 24199.44 19799.85 8996.01 20699.79 24899.41 7099.13 21399.67 167
MGCFI-Net99.01 16898.85 17699.50 15299.42 26699.26 14599.82 1699.48 20798.60 11699.28 24398.81 43397.04 14799.76 26399.29 9797.87 31899.47 252
AdaColmapbinary99.01 16898.80 18199.66 9299.56 21199.54 9999.18 36599.70 1898.18 17799.35 22899.63 26496.32 18899.90 14897.48 33599.77 13799.55 221
1112_ss98.98 17298.77 18699.59 11499.68 13599.02 17899.25 34499.48 20797.23 31799.13 28099.58 28296.93 15399.90 14898.87 16298.78 26399.84 54
MSDG98.98 17298.80 18199.53 13499.76 8299.19 15198.75 44699.55 9997.25 31499.47 18999.77 18897.82 11699.87 17596.93 37999.90 5699.54 223
casdiffseed41469214798.97 17498.78 18599.53 13499.66 15099.16 15699.61 11499.52 13398.01 22599.21 26499.88 5894.82 26699.70 29299.29 9799.04 23899.74 118
CANet_DTU98.97 17498.87 17099.25 21899.33 29598.42 27999.08 38699.30 34699.16 3799.43 20099.75 19795.27 24399.97 2998.56 21999.95 2299.36 276
DPM-MVS98.95 17698.71 19499.66 9299.63 17199.55 9798.64 45899.10 38797.93 23199.42 20399.55 29398.67 7399.80 24295.80 41399.68 15699.61 196
114514_t98.93 17798.67 19899.72 8699.85 3199.53 10299.62 10899.59 7292.65 46399.71 11499.78 17998.06 11099.90 14898.84 17299.91 4599.74 118
PS-MVSNAJss98.92 17898.92 15798.90 26698.78 41298.53 26299.78 3399.54 10898.07 20599.00 30899.76 19299.01 1999.37 35699.13 12297.23 35898.81 331
RRT-MVS98.91 17998.75 18899.39 19099.46 25698.61 25699.76 3899.50 18298.06 20999.81 6999.88 5893.91 32399.94 9199.11 12599.27 19499.61 196
Test_1112_low_res98.89 18098.66 20199.57 12199.69 12898.95 19699.03 39999.47 22996.98 34099.15 27899.23 38896.77 16499.89 16398.83 17598.78 26399.86 43
Elysia98.88 18198.65 20399.58 11799.58 20199.34 12899.65 8999.52 13398.26 15799.83 6499.87 7393.37 33499.90 14897.81 29799.91 4599.49 243
StellarMVS98.88 18198.65 20399.58 11799.58 20199.34 12899.65 8999.52 13398.26 15799.83 6499.87 7393.37 33499.90 14897.81 29799.91 4599.49 243
test_fmvs198.88 18198.79 18499.16 22999.69 12897.61 32499.55 16899.49 19599.32 3099.98 1399.91 2691.41 39099.96 4199.82 2999.92 3899.90 27
AllTest98.87 18498.72 19299.31 20399.86 2598.48 27399.56 15399.61 6097.85 24199.36 22599.85 8995.95 20999.85 19096.66 39299.83 11399.59 210
UGNet98.87 18498.69 19699.40 18599.22 32898.72 24499.44 25599.68 2499.24 3399.18 27599.42 33792.74 35099.96 4199.34 8399.94 3099.53 228
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
Vis-MVSNet (Re-imp)98.87 18498.72 19299.31 20399.71 11798.88 21799.80 2599.44 26297.91 23399.36 22599.78 17995.49 23499.43 34797.91 28599.11 22099.62 194
IMVS_040798.86 18798.91 16098.72 30199.55 21596.93 36499.50 20599.44 26298.05 21299.66 13299.80 15597.13 13999.65 30998.15 26498.92 24899.60 199
IMVS_040398.86 18798.89 16698.78 29699.55 21596.93 36499.58 13799.44 26298.05 21299.68 12199.80 15596.81 16199.80 24298.15 26498.92 24899.60 199
test_yl98.86 18798.63 20699.54 12699.49 24699.18 15399.50 20599.07 39398.22 16899.61 15999.51 31095.37 23899.84 19998.60 21098.33 28899.59 210
DCV-MVSNet98.86 18798.63 20699.54 12699.49 24699.18 15399.50 20599.07 39398.22 16899.61 15999.51 31095.37 23899.84 19998.60 21098.33 28899.59 210
EPNet98.86 18798.71 19499.30 20897.20 47898.18 28799.62 10898.91 41899.28 3298.63 37099.81 13795.96 20899.99 499.24 10699.72 14899.73 128
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS98.86 18798.80 18199.03 24399.76 8298.79 23699.28 32899.91 397.42 30099.67 12799.37 35597.53 12299.88 16898.98 14297.29 35698.42 433
ab-mvs98.86 18798.63 20699.54 12699.64 16699.19 15199.44 25599.54 10897.77 25499.30 23999.81 13794.20 30899.93 10899.17 11798.82 26099.49 243
MAR-MVS98.86 18798.63 20699.54 12699.37 28599.66 7199.45 24899.54 10896.61 36999.01 30499.40 34597.09 14399.86 18297.68 31699.53 17399.10 299
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
COLMAP_ROBcopyleft97.56 698.86 18798.75 18899.17 22899.88 1398.53 26299.34 30699.59 7297.55 28198.70 35899.89 4595.83 21799.90 14898.10 26899.90 5699.08 304
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
GeoE98.85 19698.62 21199.53 13499.61 19199.08 17099.80 2599.51 15897.10 33099.31 23599.78 17995.23 24899.77 25998.21 25699.03 23999.75 113
HY-MVS97.30 798.85 19698.64 20599.47 16999.42 26699.08 17099.62 10899.36 30797.39 30399.28 24399.68 23996.44 18399.92 12398.37 24298.22 29999.40 270
PVSNet96.02 1798.85 19698.84 17898.89 27099.73 10797.28 33498.32 48199.60 6797.86 23899.50 18499.57 28796.75 16599.86 18298.56 21999.70 15299.54 223
PatchMatch-RL98.84 19998.62 21199.52 14199.71 11799.28 14299.06 39199.77 1297.74 25999.50 18499.53 30295.41 23699.84 19997.17 36599.64 16299.44 262
Effi-MVS+98.81 20098.59 21799.48 16399.46 25699.12 16598.08 49299.50 18297.50 28999.38 21799.41 34196.37 18799.81 23599.11 12598.54 27899.51 238
alignmvs98.81 20098.56 22099.58 11799.43 26499.42 11999.51 19498.96 40898.61 11499.35 22898.92 42894.78 27199.77 25999.35 7898.11 30999.54 223
DeepPCF-MVS98.18 398.81 20099.37 4397.12 43599.60 19791.75 47798.61 46099.44 26299.35 2799.83 6499.85 8998.70 7099.81 23599.02 13999.91 4599.81 79
PMMVS98.80 20398.62 21199.34 19599.27 31398.70 24598.76 44599.31 34197.34 30699.21 26499.07 40497.20 13799.82 23098.56 21998.87 25599.52 229
icg_test_0407_298.79 20498.86 17398.57 31799.55 21596.93 36499.07 38799.44 26298.05 21299.66 13299.80 15597.13 13999.18 39998.15 26498.92 24899.60 199
viewdifsd2359ckpt1198.78 20598.74 19098.89 27099.67 13897.04 35399.50 20599.58 7798.26 15799.56 17099.90 3694.36 30199.87 17599.49 6198.32 29299.77 100
viewmsd2359difaftdt98.78 20598.74 19098.90 26699.67 13897.04 35399.50 20599.58 7798.26 15799.56 17099.90 3694.36 30199.87 17599.49 6198.32 29299.77 100
Effi-MVS+-dtu98.78 20598.89 16698.47 33599.33 29596.91 36999.57 14599.30 34698.47 12899.41 20898.99 41896.78 16399.74 26998.73 18999.38 18398.74 346
FIs98.78 20598.63 20699.23 22399.18 33799.54 9999.83 1599.59 7298.28 15298.79 34599.81 13796.75 16599.37 35699.08 13196.38 37698.78 334
Fast-Effi-MVS+-dtu98.77 20998.83 18098.60 31299.41 27196.99 35999.52 18499.49 19598.11 19699.24 25699.34 36596.96 15299.79 24897.95 28399.45 17999.02 315
sd_testset98.75 21098.57 21899.29 21199.81 5798.26 28499.56 15399.62 5198.78 9999.64 14799.88 5892.02 37299.88 16899.54 5198.26 29699.72 138
FA-MVS(test-final)98.75 21098.53 22299.41 18399.55 21599.05 17599.80 2599.01 40296.59 37499.58 16699.59 27895.39 23799.90 14897.78 30099.49 17799.28 285
FC-MVSNet-test98.75 21098.62 21199.15 23399.08 36499.45 11699.86 1199.60 6798.23 16798.70 35899.82 12296.80 16299.22 39199.07 13296.38 37698.79 332
XVG-OURS98.73 21398.68 19798.88 27599.70 12297.73 31698.92 42499.55 9998.52 12399.45 19299.84 10495.27 24399.91 13598.08 27398.84 25899.00 316
Fast-Effi-MVS+98.70 21498.43 22799.51 14699.51 23299.28 14299.52 18499.47 22996.11 40899.01 30499.34 36596.20 19799.84 19997.88 28798.82 26099.39 271
XVG-OURS-SEG-HR98.69 21598.62 21198.89 27099.71 11797.74 31599.12 37799.54 10898.44 13499.42 20399.71 21694.20 30899.92 12398.54 22398.90 25499.00 316
131498.68 21698.54 22199.11 23698.89 39598.65 24999.27 33399.49 19596.89 34897.99 41799.56 29097.72 12099.83 22197.74 30799.27 19498.84 330
VortexMVS98.67 21798.66 20198.68 30799.62 18097.96 30399.59 12799.41 27898.13 18699.31 23599.70 22095.48 23599.27 37699.40 7297.32 35598.79 332
EI-MVSNet98.67 21798.67 19898.68 30799.35 28997.97 30199.50 20599.38 29696.93 34799.20 26899.83 11197.87 11499.36 36098.38 24097.56 33498.71 350
test_djsdf98.67 21798.57 21898.98 24998.70 42698.91 20799.88 499.46 24297.55 28199.22 26199.88 5895.73 22599.28 37399.03 13797.62 32998.75 342
QAPM98.67 21798.30 23799.80 6499.20 33199.67 6899.77 3599.72 1494.74 43698.73 35099.90 3695.78 22299.98 2096.96 37699.88 7499.76 107
nrg03098.64 22198.42 22899.28 21599.05 37199.69 6399.81 2099.46 24298.04 21999.01 30499.82 12296.69 16799.38 35399.34 8394.59 42198.78 334
test_vis1_n_192098.63 22298.40 23099.31 20399.86 2597.94 30899.67 7699.62 5199.43 1999.99 299.91 2687.29 445100.00 199.92 2499.92 3899.98 2
PAPR98.63 22298.34 23399.51 14699.40 27699.03 17798.80 44199.36 30796.33 38999.00 30899.12 40298.46 8899.84 19995.23 42899.37 19099.66 172
CVMVSNet98.57 22498.67 19898.30 35599.35 28995.59 42099.50 20599.55 9998.60 11699.39 21599.83 11194.48 29799.45 33898.75 18698.56 27699.85 47
IMVS_040498.53 22598.52 22398.55 32399.55 21596.93 36499.20 36199.44 26298.05 21298.96 31599.80 15594.66 28699.13 40798.15 26498.92 24899.60 199
MVSTER98.49 22698.32 23599.00 24799.35 28999.02 17899.54 17399.38 29697.41 30199.20 26899.73 20993.86 32599.36 36098.87 16297.56 33498.62 394
FE-MVS98.48 22798.17 24299.40 18599.54 22298.96 19099.68 7398.81 43495.54 41999.62 15499.70 22093.82 32699.93 10897.35 34899.46 17899.32 282
OpenMVScopyleft96.50 1698.47 22898.12 24999.52 14199.04 37399.53 10299.82 1699.72 1494.56 43998.08 41299.88 5894.73 27999.98 2097.47 33799.76 14099.06 310
IterMVS-LS98.46 22998.42 22898.58 31699.59 19998.00 29999.37 29199.43 27396.94 34699.07 29399.59 27897.87 11499.03 42698.32 24995.62 39998.71 350
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp98.44 23098.28 23898.94 25698.50 44798.96 19099.77 3599.50 18297.07 33298.87 33099.77 18894.76 27599.28 37398.66 19997.60 33098.57 415
jajsoiax98.43 23198.28 23898.88 27598.60 44098.43 27799.82 1699.53 12498.19 17498.63 37099.80 15593.22 33999.44 34399.22 10797.50 34198.77 338
tttt051798.42 23298.14 24699.28 21599.66 15098.38 28099.74 4896.85 49497.68 26699.79 7799.74 20391.39 39199.89 16398.83 17599.56 17099.57 217
BH-untuned98.42 23298.36 23198.59 31399.49 24696.70 37799.27 33399.13 38497.24 31698.80 34399.38 35295.75 22499.74 26997.07 37099.16 20599.33 281
test_fmvs1_n98.41 23498.14 24699.21 22499.82 5397.71 32099.74 4899.49 19599.32 3099.99 299.95 385.32 46499.97 2999.82 2999.84 10199.96 7
D2MVS98.41 23498.50 22498.15 37199.26 31696.62 38399.40 28099.61 6097.71 26198.98 31199.36 35896.04 20499.67 30198.70 19297.41 35198.15 451
BH-RMVSNet98.41 23498.08 25599.40 18599.41 27198.83 23099.30 31798.77 44097.70 26498.94 31999.65 25292.91 34699.74 26996.52 39699.55 17299.64 186
mvs_tets98.40 23798.23 24098.91 26498.67 43198.51 26899.66 8399.53 12498.19 17498.65 36799.81 13792.75 34899.44 34399.31 8897.48 34598.77 338
MonoMVSNet98.38 23898.47 22698.12 37398.59 44296.19 40099.72 5498.79 43897.89 23599.44 19799.52 30696.13 20098.90 45398.64 20197.54 33699.28 285
XXY-MVS98.38 23898.09 25499.24 22199.26 31699.32 13299.56 15399.55 9997.45 29498.71 35299.83 11193.23 33799.63 31998.88 15996.32 37898.76 340
ACMM97.58 598.37 24098.34 23398.48 33099.41 27197.10 34499.56 15399.45 25398.53 12299.04 30199.85 8993.00 34299.71 28598.74 18797.45 34698.64 385
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thisisatest053098.35 24198.03 26199.31 20399.63 17198.56 25999.54 17396.75 49697.53 28599.73 9999.65 25291.25 39599.89 16398.62 20499.56 17099.48 246
tpmrst98.33 24298.48 22597.90 39299.16 34794.78 44499.31 31599.11 38697.27 31299.45 19299.59 27895.33 24199.84 19998.48 22698.61 27099.09 303
baseline198.31 24397.95 27099.38 19199.50 24498.74 24199.59 12798.93 41098.41 13699.14 27999.60 27694.59 28999.79 24898.48 22693.29 44499.61 196
PatchmatchNetpermissive98.31 24398.36 23198.19 36699.16 34795.32 43299.27 33398.92 41397.37 30499.37 21999.58 28294.90 26299.70 29297.43 34399.21 20199.54 223
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous20240521198.30 24597.98 26699.26 21799.57 20798.16 28899.41 27298.55 46196.03 41399.19 27199.74 20391.87 37599.92 12399.16 12098.29 29599.70 153
VPA-MVSNet98.29 24697.95 27099.30 20899.16 34799.54 9999.50 20599.58 7798.27 15499.35 22899.37 35592.53 36099.65 30999.35 7894.46 42298.72 348
UniMVSNet (Re)98.29 24698.00 26499.13 23599.00 37899.36 12799.49 22299.51 15897.95 22998.97 31399.13 39996.30 19299.38 35398.36 24493.34 44398.66 381
HQP_MVS98.27 24898.22 24198.44 34199.29 30896.97 36199.39 28499.47 22998.97 7699.11 28499.61 27392.71 35399.69 29897.78 30097.63 32798.67 372
UniMVSNet_NR-MVSNet98.22 24997.97 26798.96 25298.92 39198.98 18399.48 23099.53 12497.76 25598.71 35299.46 33096.43 18499.22 39198.57 21692.87 45498.69 359
LPG-MVS_test98.22 24998.13 24898.49 32899.33 29597.05 35099.58 13799.55 9997.46 29199.24 25699.83 11192.58 35899.72 27998.09 26997.51 33998.68 364
RPSCF98.22 24998.62 21196.99 43899.82 5391.58 47899.72 5499.44 26296.61 36999.66 13299.89 4595.92 21299.82 23097.46 33899.10 22799.57 217
ADS-MVSNet98.20 25298.08 25598.56 32199.33 29596.48 38899.23 35299.15 38196.24 39699.10 28799.67 24594.11 31399.71 28596.81 38499.05 23699.48 246
OPM-MVS98.19 25398.10 25198.45 33898.88 39697.07 34899.28 32899.38 29698.57 11899.22 26199.81 13792.12 37099.66 30498.08 27397.54 33698.61 403
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
SCA98.19 25398.16 24398.27 36199.30 30495.55 42199.07 38798.97 40697.57 27899.43 20099.57 28792.72 35199.74 26997.58 32199.20 20399.52 229
miper_ehance_all_eth98.18 25598.10 25198.41 34499.23 32497.72 31798.72 45099.31 34196.60 37298.88 32799.29 37897.29 13299.13 40797.60 31995.99 38798.38 438
CR-MVSNet98.17 25697.93 27398.87 27999.18 33798.49 27199.22 35699.33 32796.96 34299.56 17099.38 35294.33 30499.00 43594.83 43598.58 27399.14 296
miper_enhance_ethall98.16 25798.08 25598.41 34498.96 38797.72 31798.45 47499.32 33796.95 34498.97 31399.17 39497.06 14699.22 39197.86 29095.99 38798.29 442
CLD-MVS98.16 25798.10 25198.33 35199.29 30896.82 37498.75 44699.44 26297.83 24599.13 28099.55 29392.92 34499.67 30198.32 24997.69 32598.48 425
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thisisatest051598.14 25997.79 28799.19 22699.50 24498.50 27098.61 46096.82 49596.95 34499.54 17799.43 33591.66 38499.86 18298.08 27399.51 17499.22 293
pmmvs498.13 26097.90 27598.81 29198.61 43898.87 22198.99 41099.21 37496.44 38499.06 29899.58 28295.90 21499.11 41397.18 36496.11 38398.46 430
WR-MVS_H98.13 26097.87 28098.90 26699.02 37598.84 22799.70 5999.59 7297.27 31298.40 39099.19 39395.53 23299.23 38498.34 24693.78 43998.61 403
c3_l98.12 26298.04 26098.38 34899.30 30497.69 32198.81 44099.33 32796.67 36298.83 33899.34 36597.11 14298.99 43797.58 32195.34 40698.48 425
ACMH97.28 898.10 26397.99 26598.44 34199.41 27196.96 36399.60 11699.56 8998.09 20098.15 41099.91 2690.87 40299.70 29298.88 15997.45 34698.67 372
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
usedtu_dtu_shiyan198.09 26497.82 28498.89 27098.70 42698.90 21298.57 46399.47 22996.78 35498.87 33099.05 40894.75 27699.23 38497.45 34096.74 36698.53 419
FE-MVSNET398.09 26497.82 28498.89 27098.70 42698.90 21298.57 46399.47 22996.78 35498.87 33099.05 40894.75 27699.23 38497.45 34096.74 36698.53 419
Anonymous2024052998.09 26497.68 30499.34 19599.66 15098.44 27699.40 28099.43 27393.67 44799.22 26199.89 4590.23 41099.93 10899.26 10598.33 28899.66 172
CP-MVSNet98.09 26497.78 29099.01 24598.97 38699.24 14899.67 7699.46 24297.25 31498.48 38499.64 25893.79 32799.06 42298.63 20394.10 43398.74 346
dmvs_re98.08 26898.16 24397.85 39899.55 21594.67 44999.70 5998.92 41398.15 17999.06 29899.35 36193.67 33199.25 38197.77 30397.25 35799.64 186
DU-MVS98.08 26897.79 28798.96 25298.87 39998.98 18399.41 27299.45 25397.87 23798.71 35299.50 31394.82 26699.22 39198.57 21692.87 45498.68 364
v2v48298.06 27097.77 29298.92 26098.90 39498.82 23399.57 14599.36 30796.65 36499.19 27199.35 36194.20 30899.25 38197.72 31094.97 41498.69 359
V4298.06 27097.79 28798.86 28298.98 38498.84 22799.69 6399.34 31996.53 37699.30 23999.37 35594.67 28499.32 36897.57 32594.66 41998.42 433
test-LLR98.06 27097.90 27598.55 32398.79 40997.10 34498.67 45397.75 47997.34 30698.61 37498.85 43094.45 29999.45 33897.25 35699.38 18399.10 299
WR-MVS98.06 27097.73 29999.06 23998.86 40299.25 14799.19 36399.35 31497.30 31098.66 36199.43 33593.94 32099.21 39698.58 21394.28 42898.71 350
ACMP97.20 1198.06 27097.94 27298.45 33899.37 28597.01 35799.44 25599.49 19597.54 28498.45 38799.79 17291.95 37499.72 27997.91 28597.49 34498.62 394
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
eth_miper_zixun_eth98.05 27597.96 26898.33 35199.26 31697.38 33198.56 46799.31 34196.65 36498.88 32799.52 30696.58 17499.12 41297.39 34595.53 40398.47 427
test111198.04 27698.11 25097.83 40499.74 10093.82 46099.58 13795.40 50599.12 4699.65 14299.93 1090.73 40399.84 19999.43 6999.38 18399.82 72
ECVR-MVScopyleft98.04 27698.05 25998.00 38299.74 10094.37 45599.59 12794.98 50699.13 4199.66 13299.93 1090.67 40499.84 19999.40 7299.38 18399.80 88
EPNet_dtu98.03 27897.96 26898.23 36498.27 45495.54 42399.23 35298.75 44199.02 6297.82 42699.71 21696.11 20199.48 33393.04 45899.65 16199.69 156
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FMVSNet398.03 27897.76 29698.84 28699.39 27998.98 18399.40 28099.38 29696.67 36299.07 29399.28 38092.93 34398.98 43897.10 36696.65 36998.56 416
ADS-MVSNet298.02 28098.07 25897.87 39499.33 29595.19 43599.23 35299.08 39096.24 39699.10 28799.67 24594.11 31398.93 45096.81 38499.05 23699.48 246
HQP-MVS98.02 28097.90 27598.37 34999.19 33496.83 37298.98 41399.39 28898.24 16498.66 36199.40 34592.47 36299.64 31397.19 36297.58 33298.64 385
LTVRE_ROB97.16 1298.02 28097.90 27598.40 34699.23 32496.80 37599.70 5999.60 6797.12 32698.18 40899.70 22091.73 38099.72 27998.39 23997.45 34698.68 364
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
cl____98.01 28397.84 28398.55 32399.25 32097.97 30198.71 45199.34 31996.47 38398.59 37799.54 29895.65 22899.21 39697.21 35895.77 39398.46 430
DIV-MVS_self_test98.01 28397.85 28298.48 33099.24 32297.95 30698.71 45199.35 31496.50 37798.60 37699.54 29895.72 22699.03 42697.21 35895.77 39398.46 430
miper_lstm_enhance98.00 28597.91 27498.28 36099.34 29497.43 32998.88 42999.36 30796.48 38198.80 34399.55 29395.98 20798.91 45197.27 35495.50 40498.51 423
BH-w/o98.00 28597.89 27998.32 35399.35 28996.20 39999.01 40798.90 42096.42 38698.38 39199.00 41695.26 24599.72 27996.06 40698.61 27099.03 313
v114497.98 28797.69 30398.85 28598.87 39998.66 24899.54 17399.35 31496.27 39499.23 26099.35 36194.67 28499.23 38496.73 38795.16 41098.68 364
EU-MVSNet97.98 28798.03 26197.81 40798.72 42396.65 38299.66 8399.66 3298.09 20098.35 39699.82 12295.25 24698.01 47497.41 34495.30 40798.78 334
tpmvs97.98 28798.02 26397.84 40199.04 37394.73 44599.31 31599.20 37596.10 41298.76 34899.42 33794.94 25799.81 23596.97 37598.45 28298.97 322
tt080597.97 29097.77 29298.57 31799.59 19996.61 38499.45 24899.08 39098.21 17098.88 32799.80 15588.66 42999.70 29298.58 21397.72 32499.39 271
NR-MVSNet97.97 29097.61 31399.02 24498.87 39999.26 14599.47 24099.42 27597.63 27197.08 44599.50 31395.07 25399.13 40797.86 29093.59 44098.68 364
v897.95 29297.63 31198.93 25898.95 38898.81 23599.80 2599.41 27896.03 41399.10 28799.42 33794.92 26099.30 37196.94 37894.08 43498.66 381
Patchmatch-test97.93 29397.65 30798.77 29799.18 33797.07 34899.03 39999.14 38396.16 40398.74 34999.57 28794.56 29199.72 27993.36 45399.11 22099.52 229
PS-CasMVS97.93 29397.59 31598.95 25498.99 38199.06 17399.68 7399.52 13397.13 32498.31 39899.68 23992.44 36699.05 42398.51 22494.08 43498.75 342
TranMVSNet+NR-MVSNet97.93 29397.66 30698.76 29898.78 41298.62 25499.65 8999.49 19597.76 25598.49 38399.60 27694.23 30798.97 44598.00 28092.90 45298.70 355
test_vis1_n97.92 29697.44 33799.34 19599.53 22398.08 29599.74 4899.49 19599.15 38100.00 199.94 679.51 48699.98 2099.88 2699.76 14099.97 4
v14419297.92 29697.60 31498.87 27998.83 40698.65 24999.55 16899.34 31996.20 39999.32 23499.40 34594.36 30199.26 37996.37 40395.03 41398.70 355
ACMH+97.24 1097.92 29697.78 29098.32 35399.46 25696.68 38199.56 15399.54 10898.41 13697.79 42899.87 7390.18 41299.66 30498.05 27797.18 36198.62 394
LFMVS97.90 29997.35 34999.54 12699.52 22999.01 18099.39 28498.24 47097.10 33099.65 14299.79 17284.79 46799.91 13599.28 9998.38 28599.69 156
reproduce_monomvs97.89 30097.87 28097.96 38799.51 23295.45 42799.60 11699.25 36499.17 3698.85 33799.49 31689.29 42199.64 31399.35 7896.31 37998.78 334
Anonymous2023121197.88 30197.54 31998.90 26699.71 11798.53 26299.48 23099.57 8494.16 44298.81 34199.68 23993.23 33799.42 34998.84 17294.42 42598.76 340
OurMVSNet-221017-097.88 30197.77 29298.19 36698.71 42596.53 38699.88 499.00 40397.79 25198.78 34699.94 691.68 38199.35 36397.21 35896.99 36598.69 359
v7n97.87 30397.52 32198.92 26098.76 41998.58 25899.84 1299.46 24296.20 39998.91 32299.70 22094.89 26399.44 34396.03 40793.89 43798.75 342
baseline297.87 30397.55 31698.82 28899.18 33798.02 29899.41 27296.58 50096.97 34196.51 45299.17 39493.43 33299.57 32597.71 31199.03 23998.86 328
thres600view797.86 30597.51 32398.92 26099.72 11197.95 30699.59 12798.74 44497.94 23099.27 24998.62 44191.75 37899.86 18293.73 44898.19 30398.96 324
UBG97.85 30697.48 32698.95 25499.25 32097.64 32299.24 34998.74 44497.90 23498.64 36898.20 45988.65 43099.81 23598.27 25298.40 28399.42 264
cl2297.85 30697.64 31098.48 33099.09 36197.87 31098.60 46299.33 32797.11 32998.87 33099.22 38992.38 36799.17 40198.21 25695.99 38798.42 433
v1097.85 30697.52 32198.86 28298.99 38198.67 24799.75 4399.41 27895.70 41798.98 31199.41 34194.75 27699.23 38496.01 40994.63 42098.67 372
GA-MVS97.85 30697.47 32999.00 24799.38 28297.99 30098.57 46399.15 38197.04 33798.90 32499.30 37689.83 41599.38 35396.70 38998.33 28899.62 194
testing3-297.84 31097.70 30298.24 36399.53 22395.37 43199.55 16898.67 45598.46 12999.27 24999.34 36586.58 45299.83 22199.32 8698.63 26999.52 229
tfpnnormal97.84 31097.47 32998.98 24999.20 33199.22 15099.64 9799.61 6096.32 39098.27 40299.70 22093.35 33699.44 34395.69 41695.40 40598.27 443
VPNet97.84 31097.44 33799.01 24599.21 32998.94 20099.48 23099.57 8498.38 13899.28 24399.73 20988.89 42499.39 35199.19 11193.27 44598.71 350
LCM-MVSNet-Re97.83 31398.15 24596.87 44499.30 30492.25 47599.59 12798.26 46897.43 29896.20 45699.13 39996.27 19398.73 46098.17 26198.99 24399.64 186
XVG-ACMP-BASELINE97.83 31397.71 30198.20 36599.11 35596.33 39399.41 27299.52 13398.06 20999.05 30099.50 31389.64 41899.73 27597.73 30897.38 35398.53 419
IterMVS97.83 31397.77 29298.02 37999.58 20196.27 39699.02 40299.48 20797.22 31898.71 35299.70 22092.75 34899.13 40797.46 33896.00 38698.67 372
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT97.82 31697.75 29798.06 37699.57 20796.36 39299.02 40299.49 19597.18 32098.71 35299.72 21392.72 35199.14 40497.44 34295.86 39298.67 372
EPMVS97.82 31697.65 30798.35 35098.88 39695.98 40399.49 22294.71 51097.57 27899.26 25499.48 32392.46 36599.71 28597.87 28999.08 23399.35 277
MVP-Stereo97.81 31897.75 29797.99 38397.53 47096.60 38598.96 41798.85 42997.22 31897.23 43999.36 35895.28 24299.46 33695.51 42099.78 13497.92 468
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v119297.81 31897.44 33798.91 26498.88 39698.68 24699.51 19499.34 31996.18 40199.20 26899.34 36594.03 31799.36 36095.32 42695.18 40998.69 359
ttmdpeth97.80 32097.63 31198.29 35698.77 41797.38 33199.64 9799.36 30798.78 9996.30 45599.58 28292.34 36999.39 35198.36 24495.58 40098.10 453
v192192097.80 32097.45 33298.84 28698.80 40898.53 26299.52 18499.34 31996.15 40599.24 25699.47 32693.98 31999.29 37295.40 42495.13 41198.69 359
v14897.79 32297.55 31698.50 32798.74 42097.72 31799.54 17399.33 32796.26 39598.90 32499.51 31094.68 28399.14 40497.83 29493.15 44998.63 392
thres40097.77 32397.38 34598.92 26099.69 12897.96 30399.50 20598.73 45097.83 24599.17 27698.45 44891.67 38299.83 22193.22 45598.18 30498.96 324
thres100view90097.76 32497.45 33298.69 30699.72 11197.86 31299.59 12798.74 44497.93 23199.26 25498.62 44191.75 37899.83 22193.22 45598.18 30498.37 439
PEN-MVS97.76 32497.44 33798.72 30198.77 41798.54 26199.78 3399.51 15897.06 33498.29 40199.64 25892.63 35798.89 45498.09 26993.16 44898.72 348
Baseline_NR-MVSNet97.76 32497.45 33298.68 30799.09 36198.29 28299.41 27298.85 42995.65 41898.63 37099.67 24594.82 26699.10 41698.07 27692.89 45398.64 385
TR-MVS97.76 32497.41 34398.82 28899.06 36797.87 31098.87 43198.56 45996.63 36898.68 36099.22 38992.49 36199.65 30995.40 42497.79 32298.95 326
Patchmtry97.75 32897.40 34498.81 29199.10 35898.87 22199.11 38399.33 32794.83 43498.81 34199.38 35294.33 30499.02 43096.10 40595.57 40198.53 419
dp97.75 32897.80 28697.59 42299.10 35893.71 46399.32 31198.88 42496.48 38199.08 29299.55 29392.67 35699.82 23096.52 39698.58 27399.24 291
WBMVS97.74 33097.50 32498.46 33699.24 32297.43 32999.21 35899.42 27597.45 29498.96 31599.41 34188.83 42599.23 38498.94 15096.02 38498.71 350
TAPA-MVS97.07 1597.74 33097.34 35298.94 25699.70 12297.53 32599.25 34499.51 15891.90 47299.30 23999.63 26498.78 5399.64 31388.09 48499.87 7899.65 179
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VDD-MVS97.73 33297.35 34998.88 27599.47 25497.12 34399.34 30698.85 42998.19 17499.67 12799.85 8982.98 47599.92 12399.49 6198.32 29299.60 199
MIMVSNet97.73 33297.45 33298.57 31799.45 26297.50 32799.02 40298.98 40596.11 40899.41 20899.14 39890.28 40698.74 45995.74 41498.93 24699.47 252
tfpn200view997.72 33497.38 34598.72 30199.69 12897.96 30399.50 20598.73 45097.83 24599.17 27698.45 44891.67 38299.83 22193.22 45598.18 30498.37 439
CostFormer97.72 33497.73 29997.71 41499.15 35194.02 45999.54 17399.02 40194.67 43799.04 30199.35 36192.35 36899.77 25998.50 22597.94 31499.34 280
FMVSNet297.72 33497.36 34798.80 29399.51 23298.84 22799.45 24899.42 27596.49 37898.86 33699.29 37890.26 40798.98 43896.44 39896.56 37298.58 413
test0.0.03 197.71 33797.42 34298.56 32198.41 45297.82 31398.78 44398.63 45797.34 30698.05 41698.98 42094.45 29998.98 43895.04 43197.15 36298.89 327
h-mvs3397.70 33897.28 36198.97 25199.70 12297.27 33599.36 29799.45 25398.94 7999.66 13299.64 25894.93 25899.99 499.48 6484.36 48999.65 179
myMVS_eth3d2897.69 33997.34 35298.73 29999.27 31397.52 32699.33 30898.78 43998.03 22198.82 34098.49 44686.64 45199.46 33698.44 23398.24 29899.23 292
v124097.69 33997.32 35698.79 29498.85 40398.43 27799.48 23099.36 30796.11 40899.27 24999.36 35893.76 32999.24 38394.46 43895.23 40898.70 355
cascas97.69 33997.43 34198.48 33098.60 44097.30 33398.18 48799.39 28892.96 45998.41 38998.78 43793.77 32899.27 37698.16 26298.61 27098.86 328
pm-mvs197.68 34297.28 36198.88 27599.06 36798.62 25499.50 20599.45 25396.32 39097.87 42499.79 17292.47 36299.35 36397.54 32893.54 44198.67 372
GBi-Net97.68 34297.48 32698.29 35699.51 23297.26 33799.43 26199.48 20796.49 37899.07 29399.32 37390.26 40798.98 43897.10 36696.65 36998.62 394
test197.68 34297.48 32698.29 35699.51 23297.26 33799.43 26199.48 20796.49 37899.07 29399.32 37390.26 40798.98 43897.10 36696.65 36998.62 394
tpm97.67 34597.55 31698.03 37799.02 37595.01 44099.43 26198.54 46296.44 38499.12 28299.34 36591.83 37799.60 32297.75 30696.46 37499.48 246
PCF-MVS97.08 1497.66 34697.06 37499.47 16999.61 19199.09 16798.04 49399.25 36491.24 47698.51 38199.70 22094.55 29399.91 13592.76 46399.85 9399.42 264
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WB-MVSnew97.65 34797.65 30797.63 41798.78 41297.62 32399.13 37498.33 46697.36 30599.07 29398.94 42495.64 22999.15 40292.95 45998.68 26896.12 499
our_test_397.65 34797.68 30497.55 42398.62 43694.97 44198.84 43699.30 34696.83 35398.19 40799.34 36597.01 15099.02 43095.00 43296.01 38598.64 385
testgi97.65 34797.50 32498.13 37299.36 28896.45 38999.42 26899.48 20797.76 25597.87 42499.45 33291.09 39998.81 45694.53 43798.52 27999.13 298
thres20097.61 35097.28 36198.62 31199.64 16698.03 29799.26 34298.74 44497.68 26699.09 29098.32 45491.66 38499.81 23592.88 46098.22 29998.03 458
PAPM97.59 35197.09 37399.07 23899.06 36798.26 28498.30 48299.10 38794.88 43298.08 41299.34 36596.27 19399.64 31389.87 47698.92 24899.31 283
UWE-MVS97.58 35297.29 36098.48 33099.09 36196.25 39799.01 40796.61 49997.86 23899.19 27199.01 41588.72 42699.90 14897.38 34698.69 26799.28 285
SD_040397.55 35397.53 32097.62 41899.61 19193.64 46699.72 5499.44 26298.03 22198.62 37399.39 34996.06 20399.57 32587.88 48699.01 24299.66 172
VDDNet97.55 35397.02 37599.16 22999.49 24698.12 29399.38 28999.30 34695.35 42199.68 12199.90 3682.62 47799.93 10899.31 8898.13 30899.42 264
TESTMET0.1,197.55 35397.27 36498.40 34698.93 38996.53 38698.67 45397.61 48496.96 34298.64 36899.28 38088.63 43299.45 33897.30 35299.38 18399.21 294
pmmvs597.52 35697.30 35898.16 36898.57 44396.73 37699.27 33398.90 42096.14 40698.37 39299.53 30291.54 38799.14 40497.51 33295.87 39198.63 392
LF4IMVS97.52 35697.46 33197.70 41598.98 38495.55 42199.29 32298.82 43298.07 20598.66 36199.64 25889.97 41399.61 32197.01 37196.68 36897.94 466
DTE-MVSNet97.51 35897.19 36798.46 33698.63 43598.13 29199.84 1299.48 20796.68 36197.97 41999.67 24592.92 34498.56 46396.88 38392.60 45898.70 355
testing1197.50 35997.10 37298.71 30499.20 33196.91 36999.29 32298.82 43297.89 23598.21 40698.40 45085.63 46099.83 22198.45 23298.04 31199.37 275
ETVMVS97.50 35996.90 37999.29 21199.23 32498.78 23999.32 31198.90 42097.52 28798.56 37898.09 46684.72 46899.69 29897.86 29097.88 31799.39 271
hse-mvs297.50 35997.14 36998.59 31399.49 24697.05 35099.28 32899.22 37098.94 7999.66 13299.42 33794.93 25899.65 30999.48 6483.80 49399.08 304
SixPastTwentyTwo97.50 35997.33 35598.03 37798.65 43396.23 39899.77 3598.68 45397.14 32397.90 42299.93 1090.45 40599.18 39997.00 37296.43 37598.67 372
JIA-IIPM97.50 35997.02 37598.93 25898.73 42197.80 31499.30 31798.97 40691.73 47398.91 32294.86 50195.10 25299.71 28597.58 32197.98 31299.28 285
ppachtmachnet_test97.49 36497.45 33297.61 42198.62 43695.24 43398.80 44199.46 24296.11 40898.22 40599.62 26996.45 18298.97 44593.77 44695.97 39098.61 403
test-mter97.49 36497.13 37198.55 32398.79 40997.10 34498.67 45397.75 47996.65 36498.61 37498.85 43088.23 43699.45 33897.25 35699.38 18399.10 299
testing9197.44 36697.02 37598.71 30499.18 33796.89 37199.19 36399.04 39797.78 25398.31 39898.29 45585.41 46399.85 19098.01 27997.95 31399.39 271
tpm297.44 36697.34 35297.74 41399.15 35194.36 45699.45 24898.94 40993.45 45398.90 32499.44 33391.35 39299.59 32397.31 34998.07 31099.29 284
tpm cat197.39 36897.36 34797.50 42599.17 34593.73 46299.43 26199.31 34191.27 47598.71 35299.08 40394.31 30699.77 25996.41 40198.50 28099.00 316
UWE-MVS-2897.36 36997.24 36597.75 41198.84 40594.44 45399.24 34997.58 48697.98 22799.00 30899.00 41691.35 39299.53 33193.75 44798.39 28499.27 289
testing9997.36 36996.94 37898.63 31099.18 33796.70 37799.30 31798.93 41097.71 26198.23 40398.26 45784.92 46699.84 19998.04 27897.85 32099.35 277
SSC-MVS3.297.34 37197.15 36897.93 38999.02 37595.76 41599.48 23099.58 7797.62 27399.09 29099.53 30287.95 43999.27 37696.42 39995.66 39898.75 342
USDC97.34 37197.20 36697.75 41199.07 36595.20 43498.51 47099.04 39797.99 22698.31 39899.86 8289.02 42299.55 32995.67 41897.36 35498.49 424
UniMVSNet_ETH3D97.32 37396.81 38198.87 27999.40 27697.46 32899.51 19499.53 12495.86 41698.54 38099.77 18882.44 47899.66 30498.68 19797.52 33899.50 242
testing397.28 37496.76 38398.82 28899.37 28598.07 29699.45 24899.36 30797.56 28097.89 42398.95 42383.70 47298.82 45596.03 40798.56 27699.58 214
MVS97.28 37496.55 38799.48 16398.78 41298.95 19699.27 33399.39 28883.53 49698.08 41299.54 29896.97 15199.87 17594.23 44299.16 20599.63 191
test_fmvs297.25 37697.30 35897.09 43699.43 26493.31 46999.73 5298.87 42698.83 8999.28 24399.80 15584.45 46999.66 30497.88 28797.45 34698.30 441
DSMNet-mixed97.25 37697.35 34996.95 44197.84 46493.61 46799.57 14596.63 49896.13 40798.87 33098.61 44394.59 28997.70 48295.08 43098.86 25699.55 221
MS-PatchMatch97.24 37897.32 35696.99 43898.45 45093.51 46898.82 43999.32 33797.41 30198.13 41199.30 37688.99 42399.56 32795.68 41799.80 12597.90 470
testing22297.16 37996.50 38899.16 22999.16 34798.47 27599.27 33398.66 45697.71 26198.23 40398.15 46182.28 48099.84 19997.36 34797.66 32699.18 295
TransMVSNet (Re)97.15 38096.58 38698.86 28299.12 35398.85 22599.49 22298.91 41895.48 42097.16 44399.80 15593.38 33399.11 41394.16 44491.73 46298.62 394
TinyColmap97.12 38196.89 38097.83 40499.07 36595.52 42498.57 46398.74 44497.58 27797.81 42799.79 17288.16 43799.56 32795.10 42997.21 35998.39 437
K. test v397.10 38296.79 38298.01 38098.72 42396.33 39399.87 897.05 49197.59 27596.16 45799.80 15588.71 42799.04 42496.69 39096.55 37398.65 383
Syy-MVS97.09 38397.14 36996.95 44199.00 37892.73 47399.29 32299.39 28897.06 33497.41 43398.15 46193.92 32298.68 46191.71 46998.34 28699.45 260
PatchT97.03 38496.44 39098.79 29498.99 38198.34 28199.16 36799.07 39392.13 47099.52 18197.31 48794.54 29498.98 43888.54 48298.73 26599.03 313
mmtdpeth96.95 38596.71 38497.67 41699.33 29594.90 44399.89 299.28 35298.15 17999.72 10498.57 44486.56 45399.90 14899.82 2989.02 48098.20 448
myMVS_eth3d96.89 38696.37 39198.43 34399.00 37897.16 34199.29 32299.39 28897.06 33497.41 43398.15 46183.46 47498.68 46195.27 42798.34 28699.45 260
AUN-MVS96.88 38796.31 39398.59 31399.48 25397.04 35399.27 33399.22 37097.44 29798.51 38199.41 34191.97 37399.66 30497.71 31183.83 49299.07 309
FMVSNet196.84 38896.36 39298.29 35699.32 30297.26 33799.43 26199.48 20795.11 42598.55 37999.32 37383.95 47198.98 43895.81 41296.26 38098.62 394
test250696.81 38996.65 38597.29 43199.74 10092.21 47699.60 11685.06 52799.13 4199.77 8699.93 1087.82 44399.85 19099.38 7699.38 18399.80 88
RPMNet96.72 39095.90 40399.19 22699.18 33798.49 27199.22 35699.52 13388.72 48699.56 17097.38 48394.08 31599.95 7686.87 49398.58 27399.14 296
mvs5depth96.66 39196.22 39597.97 38597.00 48396.28 39598.66 45699.03 40096.61 36996.93 44999.79 17287.20 44699.47 33496.65 39494.13 43198.16 450
test_040296.64 39296.24 39497.85 39898.85 40396.43 39099.44 25599.26 36193.52 45096.98 44799.52 30688.52 43399.20 39892.58 46697.50 34197.93 467
X-MVStestdata96.55 39395.45 41299.87 2299.85 3199.83 2299.69 6399.68 2498.98 7299.37 21964.01 53598.81 4999.94 9198.79 18399.86 8699.84 54
pmmvs696.53 39496.09 39997.82 40698.69 42995.47 42599.37 29199.47 22993.46 45297.41 43399.78 17987.06 45099.33 36696.92 38192.70 45698.65 383
ET-MVSNet_ETH3D96.49 39595.64 40999.05 24199.53 22398.82 23398.84 43697.51 48797.63 27184.77 50499.21 39292.09 37198.91 45198.98 14292.21 46099.41 267
UnsupCasMVSNet_eth96.44 39696.12 39797.40 42898.65 43395.65 41899.36 29799.51 15897.13 32496.04 45998.99 41888.40 43498.17 47096.71 38890.27 47398.40 436
FMVSNet596.43 39796.19 39697.15 43299.11 35595.89 41099.32 31199.52 13394.47 44198.34 39799.07 40487.54 44497.07 48892.61 46595.72 39698.47 427
new_pmnet96.38 39896.03 40097.41 42798.13 45995.16 43799.05 39499.20 37593.94 44397.39 43698.79 43691.61 38699.04 42490.43 47495.77 39398.05 457
Anonymous2023120696.22 39996.03 40096.79 44697.31 47694.14 45899.63 10399.08 39096.17 40297.04 44699.06 40693.94 32097.76 48086.96 49295.06 41298.47 427
IB-MVS95.67 1896.22 39995.44 41398.57 31799.21 32996.70 37798.65 45797.74 48196.71 35997.27 43898.54 44586.03 45799.92 12398.47 22986.30 48699.10 299
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
Anonymous2024052196.20 40195.89 40497.13 43497.72 46994.96 44299.79 3199.29 35093.01 45797.20 44299.03 41289.69 41798.36 46791.16 47296.13 38298.07 455
gg-mvs-nofinetune96.17 40295.32 41498.73 29998.79 40998.14 29099.38 28994.09 51291.07 47898.07 41591.04 51489.62 41999.35 36396.75 38699.09 23298.68 364
test20.0396.12 40395.96 40296.63 44797.44 47195.45 42799.51 19499.38 29696.55 37596.16 45799.25 38693.76 32996.17 49787.35 48994.22 42998.27 443
PVSNet_094.43 1996.09 40495.47 41197.94 38899.31 30394.34 45797.81 49799.70 1897.12 32697.46 43298.75 43889.71 41699.79 24897.69 31581.69 50199.68 162
MVStest196.08 40595.48 41097.89 39398.93 38996.70 37799.56 15399.35 31492.69 46291.81 48999.46 33089.90 41498.96 44795.00 43292.61 45798.00 462
EG-PatchMatch MVS95.97 40695.69 40796.81 44597.78 46692.79 47299.16 36798.93 41096.16 40394.08 47599.22 38982.72 47699.47 33495.67 41897.50 34198.17 449
APD_test195.87 40796.49 38994.00 46499.53 22384.01 49499.54 17399.32 33795.91 41597.99 41799.85 8985.49 46299.88 16891.96 46798.84 25898.12 452
Patchmatch-RL test95.84 40895.81 40695.95 45595.61 49990.57 48398.24 48398.39 46495.10 42795.20 46498.67 44094.78 27197.77 47996.28 40490.02 47499.51 238
test_vis1_rt95.81 40995.65 40896.32 45199.67 13891.35 47999.49 22296.74 49798.25 16295.24 46298.10 46574.96 48899.90 14899.53 5398.85 25797.70 476
sc_t195.75 41095.05 41797.87 39498.83 40694.61 45099.21 35899.45 25387.45 48897.97 41999.85 8981.19 48399.43 34798.27 25293.20 44799.57 217
MVS-HIRNet95.75 41095.16 41597.51 42499.30 30493.69 46498.88 42995.78 50285.09 49598.78 34692.65 51091.29 39499.37 35694.85 43499.85 9399.46 257
tt032095.71 41295.07 41697.62 41899.05 37195.02 43999.25 34499.52 13386.81 48997.97 41999.72 21383.58 47399.15 40296.38 40293.35 44298.68 364
blended_shiyan895.56 41394.79 42097.87 39496.60 48795.90 40998.85 43299.27 35992.19 46598.47 38597.94 47091.43 38999.11 41397.26 35581.09 50498.60 406
blended_shiyan695.54 41494.78 42197.84 40196.60 48795.89 41098.85 43299.28 35292.17 46998.43 38897.95 46991.44 38899.02 43097.30 35280.97 50598.60 406
MIMVSNet195.51 41595.04 41896.92 44397.38 47395.60 41999.52 18499.50 18293.65 44896.97 44899.17 39485.28 46596.56 49488.36 48395.55 40298.60 406
MDA-MVSNet_test_wron95.45 41694.60 42598.01 38098.16 45897.21 34099.11 38399.24 36793.49 45180.73 51398.98 42093.02 34198.18 46994.22 44394.45 42498.64 385
wanda-best-256-51295.43 41794.66 42397.77 40996.45 48995.68 41698.48 47199.28 35292.18 46798.36 39397.68 47591.20 39699.03 42697.31 34980.97 50598.60 406
FE-blended-shiyan795.43 41794.66 42397.77 40996.45 48995.68 41698.48 47199.28 35292.18 46798.36 39397.68 47591.20 39699.03 42697.31 34980.97 50598.60 406
TDRefinement95.42 41994.57 42897.97 38589.83 52996.11 40299.48 23098.75 44196.74 35796.68 45199.88 5888.65 43099.71 28598.37 24282.74 49998.09 454
gbinet_0.2-2-1-0.0295.40 42094.58 42797.85 39896.11 49495.97 40498.56 46799.26 36192.12 47198.47 38597.49 48190.23 41099.00 43597.71 31181.25 50298.58 413
YYNet195.36 42194.51 42997.92 39097.89 46297.10 34499.10 38599.23 36893.26 45580.77 51299.04 41192.81 34798.02 47394.30 43994.18 43098.64 385
pmmvs-eth3d95.34 42294.73 42297.15 43295.53 50195.94 40699.35 30299.10 38795.13 42393.55 47997.54 48088.15 43897.91 47694.58 43689.69 47897.61 478
tt0320-xc95.31 42394.59 42697.45 42698.92 39194.73 44599.20 36199.31 34186.74 49097.23 43999.72 21381.14 48498.95 44897.08 36991.98 46198.67 372
blend_shiyan495.25 42494.39 43197.84 40196.70 48695.92 40798.84 43699.28 35292.21 46498.16 40997.84 47287.10 44999.07 41997.53 32981.87 50098.54 417
0.4-1-1-0.195.23 42594.22 43398.26 36297.39 47295.86 41297.59 50197.62 48293.85 44594.97 46997.03 48987.20 44699.87 17598.47 22983.84 49199.05 311
FE-MVSNET295.10 42694.44 43097.08 43795.08 50595.97 40499.51 19499.37 30595.02 42994.10 47497.57 47886.18 45697.66 48493.28 45489.86 47697.61 478
usedtu_blend_shiyan595.04 42794.10 43497.86 39796.45 48995.92 40799.29 32299.22 37086.17 49398.36 39397.68 47591.20 39699.07 41997.53 32980.97 50598.60 406
dmvs_testset95.02 42896.12 39791.72 47499.10 35880.43 50799.58 13797.87 47897.47 29095.22 46398.82 43293.99 31895.18 50288.09 48494.91 41799.56 220
KD-MVS_self_test95.00 42994.34 43296.96 44097.07 48295.39 43099.56 15399.44 26295.11 42597.13 44497.32 48691.86 37697.27 48790.35 47581.23 50398.23 447
MDA-MVSNet-bldmvs94.96 43093.98 43797.92 39098.24 45597.27 33599.15 37099.33 32793.80 44680.09 51499.03 41288.31 43597.86 47893.49 45194.36 42698.62 394
N_pmnet94.95 43195.83 40592.31 47298.47 44879.33 51199.12 37792.81 51893.87 44497.68 42999.13 39993.87 32499.01 43391.38 47196.19 38198.59 412
0.4-1-1-0.294.94 43293.92 43997.99 38396.84 48595.13 43896.64 50797.62 48293.45 45394.92 47096.56 49387.14 44899.86 18298.43 23683.69 49598.98 320
0.3-1-1-0.01594.79 43393.69 44598.10 37496.99 48495.46 42697.02 50597.61 48493.53 44994.03 47696.54 49485.60 46199.86 18298.43 23683.45 49698.99 319
KD-MVS_2432*160094.62 43493.72 44297.31 42997.19 47995.82 41398.34 47899.20 37595.00 43097.57 43098.35 45287.95 43998.10 47192.87 46177.00 51498.01 459
miper_refine_blended94.62 43493.72 44297.31 42997.19 47995.82 41398.34 47899.20 37595.00 43097.57 43098.35 45287.95 43998.10 47192.87 46177.00 51498.01 459
CL-MVSNet_self_test94.49 43693.97 43896.08 45396.16 49393.67 46598.33 48099.38 29695.13 42397.33 43798.15 46192.69 35596.57 49388.67 48179.87 51297.99 463
new-patchmatchnet94.48 43794.08 43695.67 45795.08 50592.41 47499.18 36599.28 35294.55 44093.49 48097.37 48487.86 44297.01 49091.57 47088.36 48197.61 478
OpenMVS_ROBcopyleft92.34 2094.38 43893.70 44496.41 45097.38 47393.17 47099.06 39198.75 44186.58 49194.84 47198.26 45781.53 48199.32 36889.01 48097.87 31896.76 490
RoMa-SfM94.36 43993.86 44095.88 45698.61 43890.62 48298.85 43299.04 39791.63 47494.14 47399.49 31677.16 48799.09 41892.66 46493.13 45097.91 469
CMPMVSbinary69.68 2394.13 44094.90 41991.84 47397.24 47780.01 50898.52 46999.48 20789.01 48491.99 48899.67 24585.67 45999.13 40795.44 42297.03 36496.39 496
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs394.09 44193.25 44896.60 44894.76 50894.49 45298.92 42498.18 47489.66 48096.48 45398.06 46786.28 45597.33 48689.68 47787.20 48597.97 465
FE-MVSNET94.07 44293.36 44796.22 45294.05 51294.71 44799.56 15398.36 46593.15 45693.76 47897.55 47986.47 45496.49 49587.48 48789.83 47797.48 483
mvsany_test393.77 44393.45 44694.74 46295.78 49788.01 48899.64 9798.25 46998.28 15294.31 47297.97 46868.89 50398.51 46597.50 33390.37 47197.71 473
UnsupCasMVSNet_bld93.53 44492.51 45096.58 44997.38 47393.82 46098.24 48399.48 20791.10 47793.10 48196.66 49274.89 49098.37 46694.03 44587.71 48497.56 481
dongtai93.26 44592.93 44994.25 46399.39 27985.68 49297.68 49993.27 51492.87 46096.85 45099.39 34982.33 47997.48 48576.78 50697.80 32199.58 214
LoFTR93.25 44692.33 45295.99 45497.91 46090.83 48099.06 39198.56 45992.19 46590.24 49398.18 46072.97 49199.26 37989.37 47892.52 45997.89 471
DKM93.17 44792.50 45195.21 46098.53 44690.26 48498.74 44998.90 42093.00 45892.61 48499.06 40670.06 50097.74 48191.92 46889.65 47997.62 477
WB-MVS93.10 44894.10 43490.12 48595.51 50381.88 50099.73 5299.27 35995.05 42893.09 48298.91 42994.70 28291.89 51376.62 50794.02 43696.58 494
PM-MVS92.96 44992.23 45395.14 46195.61 49989.98 48699.37 29198.21 47294.80 43595.04 46897.69 47465.06 50697.90 47794.30 43989.98 47597.54 482
SSC-MVS92.73 45093.73 44189.72 48895.02 50781.38 50299.76 3899.23 36894.87 43392.80 48398.93 42594.71 28191.37 51574.49 51293.80 43896.42 495
test_fmvs392.10 45191.77 45493.08 47096.19 49286.25 48999.82 1698.62 45896.65 36495.19 46596.90 49055.05 51595.93 49996.63 39590.92 47097.06 489
MatchFormer91.94 45290.72 45795.58 45897.82 46589.79 48798.92 42498.87 42688.24 48788.03 49897.92 47170.39 49899.23 38485.21 49891.12 46697.72 472
test_f91.90 45391.26 45693.84 46695.52 50285.92 49099.69 6398.53 46395.31 42293.87 47796.37 49655.33 51498.27 46895.70 41590.98 46997.32 485
usedtu_dtu_shiyan291.34 45489.96 46395.47 45993.61 51690.81 48199.15 37098.68 45386.37 49295.19 46598.27 45672.64 49397.05 48985.40 49780.32 51098.54 417
test_method91.10 45591.36 45590.31 48295.85 49673.72 52094.89 50999.25 36468.39 51195.82 46099.02 41480.50 48598.95 44893.64 44994.89 41898.25 445
Gipumacopyleft90.99 45690.15 46193.51 46798.73 42190.12 48593.98 51499.45 25379.32 49992.28 48594.91 50069.61 50197.98 47587.42 48895.67 39792.45 507
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
kuosan90.92 45790.11 46293.34 46898.78 41285.59 49398.15 49093.16 51689.37 48392.07 48798.38 45181.48 48295.19 50162.54 51897.04 36399.25 290
SP-DiffGlue90.78 45890.71 45890.98 47895.45 50481.30 50397.92 49697.30 48975.18 50292.09 48695.93 49774.93 48994.89 50593.46 45294.12 43296.74 492
testf190.42 45990.68 45989.65 48997.78 46673.97 51899.13 37498.81 43489.62 48191.80 49098.93 42562.23 51098.80 45786.61 49491.17 46496.19 497
APD_test290.42 45990.68 45989.65 48997.78 46673.97 51899.13 37498.81 43489.62 48191.80 49098.93 42562.23 51098.80 45786.61 49491.17 46496.19 497
ELoFTR89.95 46188.65 46693.85 46595.93 49585.85 49198.64 45898.31 46790.34 47985.03 50397.76 47360.28 51299.01 43387.27 49084.26 49096.71 493
SP-LightGlue89.28 46288.68 46491.06 47798.21 45780.90 50598.19 48696.96 49272.38 50589.60 49694.43 50372.44 49495.06 50382.91 50093.03 45197.22 486
SP-SuperGlue89.23 46388.68 46490.88 47998.23 45680.60 50698.16 48897.30 48973.08 50489.64 49594.62 50271.80 49694.91 50482.11 50293.22 44697.14 488
SP-NN88.62 46488.17 46789.96 48697.89 46278.51 51297.19 50396.09 50171.28 50788.29 49794.00 50671.98 49593.65 50982.37 50194.46 42297.71 473
SP-MNN88.33 46587.78 46889.95 48798.28 45377.92 51398.01 49495.69 50470.61 50986.18 50194.36 50471.09 49794.76 50681.51 50394.32 42797.17 487
ALIKED-NN88.27 46687.61 46990.24 48398.46 44979.97 50997.04 50494.61 51175.25 50186.99 49996.90 49072.78 49295.78 50075.45 51091.01 46894.97 502
ALIKED-LG88.17 46787.32 47090.75 48098.67 43181.68 50198.16 48894.72 50978.63 50086.08 50297.07 48870.16 49996.62 49271.97 51490.37 47193.95 504
test_vis3_rt87.04 46885.81 47290.73 48193.99 51381.96 49999.76 3890.23 52292.81 46181.35 51191.56 51240.06 53099.07 41994.27 44188.23 48291.15 510
ALIKED-MNN86.97 46985.90 47190.16 48499.06 36779.59 51097.93 49594.82 50772.37 50684.41 50595.46 49868.55 50496.43 49672.40 51388.11 48394.47 503
PMMVS286.87 47085.37 47491.35 47690.21 52683.80 49698.89 42897.45 48883.13 49891.67 49295.03 49948.49 52494.70 50785.86 49677.62 51395.54 500
LCM-MVSNet86.80 47185.22 47591.53 47587.81 53280.96 50498.23 48598.99 40471.05 50890.13 49496.51 49548.45 52596.88 49190.51 47385.30 48896.76 490
FPMVS84.93 47285.65 47382.75 49886.77 53363.39 52598.35 47798.92 41374.11 50383.39 50898.98 42050.85 51892.40 51284.54 49994.97 41492.46 506
PDCNetPlus84.77 47383.24 47689.36 49194.33 51183.93 49598.13 49176.80 53283.26 49786.31 50097.33 48562.90 50892.65 51087.20 49162.90 51891.50 509
XFeat-NN82.84 47483.12 47782.00 50094.35 51067.14 52493.32 51989.27 52362.21 51784.06 50693.50 50869.15 50289.40 51678.92 50483.33 49789.46 513
EGC-MVSNET82.80 47577.86 48297.62 41897.91 46096.12 40199.33 30899.28 3528.40 53625.05 53799.27 38384.11 47099.33 36689.20 47998.22 29997.42 484
tmp_tt82.80 47581.52 47986.66 49366.61 53968.44 52392.79 52297.92 47668.96 51080.04 51599.85 8985.77 45896.15 49897.86 29043.89 52795.39 501
XFeat-MNN82.40 47782.10 47883.31 49693.04 51868.49 52295.39 50890.86 52060.29 51881.56 51094.09 50566.79 50591.70 51476.62 50780.26 51189.74 512
E-PMN80.61 47879.88 48082.81 49790.75 52476.38 51697.69 49895.76 50366.44 51383.52 50792.25 51162.54 50987.16 52468.53 51661.40 51984.89 516
EMVS80.02 47979.22 48182.43 49991.19 52376.40 51597.55 50292.49 51966.36 51583.01 50991.27 51364.63 50785.79 52765.82 51760.65 52085.08 515
GLUNet-SfM78.99 48076.32 48486.99 49289.16 53173.30 52193.36 51890.45 52166.38 51474.95 52093.30 50952.29 51794.61 50875.35 51151.65 52593.07 505
ANet_high77.30 48174.86 48884.62 49575.88 53777.61 51497.63 50093.15 51788.81 48564.27 52389.29 52536.51 53383.93 52875.89 50952.31 52392.33 508
SIFT-NN76.99 48277.37 48375.84 50297.10 48162.39 52694.15 51387.21 52559.41 51979.90 51690.73 51654.60 51688.56 51947.22 52086.03 48776.57 518
MVEpermissive76.82 2176.91 48374.31 48984.70 49485.38 53676.05 51796.88 50693.17 51567.39 51271.28 52189.01 52721.66 54087.69 52271.74 51572.29 51690.35 511
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVScopyleft70.75 2275.98 48474.97 48779.01 50170.98 53855.18 53793.37 51798.21 47265.08 51661.78 52693.83 50721.74 53992.53 51178.59 50591.12 46689.34 514
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
SIFT-MNN75.73 48575.71 48575.77 50395.65 49860.92 52894.36 51187.62 52458.67 52075.90 51890.94 51549.64 52289.04 51844.85 52583.80 49377.35 517
SIFT-NN-NCMNet75.53 48675.57 48675.42 50493.93 51461.35 52794.41 51086.44 52658.51 52176.23 51790.44 51850.56 51989.34 51746.60 52183.04 49875.58 520
SIFT-NN-CMatch72.61 48771.92 49074.68 50592.79 51960.24 53093.28 52081.57 53058.24 52375.18 51990.26 52049.66 52187.35 52346.02 52260.26 52176.45 519
SIFT-NCM-Cal71.65 48870.76 49274.34 50694.61 50960.18 53194.16 51281.72 52957.21 52555.36 52989.56 52442.48 52688.45 52041.31 53080.41 50974.39 522
SIFT-NN-UMatch71.65 48870.86 49174.00 50790.69 52560.53 52993.59 51581.89 52858.42 52260.99 52789.71 52350.18 52087.89 52145.77 52366.55 51773.57 524
SIFT-NN-PointCN70.32 49069.71 49372.13 51090.01 52758.29 53593.45 51676.20 53356.66 52870.25 52289.20 52648.94 52383.41 52945.45 52457.26 52274.70 521
SIFT-ConvMatch69.43 49168.09 49473.45 50893.86 51560.02 53292.57 52377.69 53157.58 52462.69 52490.53 51742.14 52786.65 52643.98 52651.72 52473.67 523
SIFT-UMatch68.14 49266.40 49573.38 50992.20 52259.42 53392.84 52176.01 53456.87 52658.37 52890.35 51941.97 52887.16 52442.64 52746.35 52673.55 525
SIFT-CM-Cal66.94 49365.48 49671.33 51193.05 51758.77 53491.46 52670.45 53656.64 52961.97 52589.98 52140.72 52983.32 53042.57 52842.47 52871.90 526
SIFT-UM-Cal64.60 49462.65 49770.42 51292.22 52158.07 53692.29 52466.92 53756.70 52750.16 53189.97 52237.90 53182.95 53142.33 52935.40 53170.24 528
SIFT-PointCN62.71 49561.56 49866.18 51389.53 53050.88 53891.81 52572.35 53553.65 53050.49 53086.32 52933.30 53476.23 53335.91 53440.66 52971.43 527
SIFT-PCN-Cal61.29 49660.21 49964.54 51489.88 52850.56 53991.21 52765.73 53853.15 53148.59 53287.20 52836.60 53276.52 53237.37 53332.17 53266.54 529
SIFT-NCMNet55.02 49753.54 50059.46 51586.55 53447.35 54187.85 52846.22 53951.77 53244.11 53383.50 53027.88 53768.75 53432.81 53521.14 53562.27 530
wuyk23d40.18 49841.29 50336.84 51686.18 53549.12 54079.73 52922.81 54127.64 53325.46 53628.45 53621.98 53848.89 53555.80 51923.56 53412.51 533
testmvs39.17 49943.78 50125.37 51836.04 54116.84 54398.36 47626.56 54020.06 53438.51 53567.32 53129.64 53615.30 53737.59 53139.90 53043.98 532
test12339.01 50042.50 50228.53 51739.17 54020.91 54298.75 44619.17 54219.83 53538.57 53466.67 53233.16 53515.42 53637.50 53229.66 53349.26 531
cdsmvs_eth3d_5k24.64 50132.85 5040.00 5190.00 5420.00 5440.00 53099.51 1580.00 5370.00 53899.56 29096.58 1740.00 5380.00 5360.00 5360.00 534
ab-mvs-re8.30 50211.06 5050.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 53899.58 2820.00 5410.00 5380.00 5360.00 5360.00 534
pcd_1.5k_mvsjas8.27 50311.03 5060.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.27 53899.01 190.00 5380.00 5360.00 5360.00 534
test_blank0.13 5040.17 5070.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5381.57 5370.00 5410.00 5380.00 5360.00 5360.00 534
mmdepth0.02 5050.03 5080.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.27 5380.00 5410.00 5380.00 5360.00 5360.00 534
monomultidepth0.02 5050.03 5080.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.27 5380.00 5410.00 5380.00 5360.00 5360.00 534
uanet_test0.02 5050.03 5080.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.27 5380.00 5410.00 5380.00 5360.00 5360.00 534
DCPMVS0.02 5050.03 5080.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.27 5380.00 5410.00 5380.00 5360.00 5360.00 534
sosnet-low-res0.02 5050.03 5080.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.27 5380.00 5410.00 5380.00 5360.00 5360.00 534
sosnet0.02 5050.03 5080.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.27 5380.00 5410.00 5380.00 5360.00 5360.00 534
uncertanet0.02 5050.03 5080.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.27 5380.00 5410.00 5380.00 5360.00 5360.00 534
Regformer0.02 5050.03 5080.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.27 5380.00 5410.00 5380.00 5360.00 5360.00 534
uanet0.02 5050.03 5080.00 5190.00 5420.00 5440.00 5300.00 5430.00 5370.00 5380.27 5380.00 5410.00 5380.00 5360.00 5360.00 534
MED-MVS test99.87 2299.88 1399.81 3399.69 6399.87 699.34 2899.90 3499.83 11199.95 7698.83 17599.89 6799.83 64
TestfortrainingZip99.69 8999.58 20199.62 8399.69 6399.38 29698.98 7299.84 5599.75 19798.84 4599.78 25599.21 20199.66 172
WAC-MVS97.16 34195.47 421
FOURS199.91 199.93 199.87 899.56 8999.10 4899.81 69
MSC_two_6792asdad99.87 2299.51 23299.76 4999.33 32799.96 4198.87 16299.84 10199.89 30
PC_three_145298.18 17799.84 5599.70 22099.31 398.52 46498.30 25199.80 12599.81 79
No_MVS99.87 2299.51 23299.76 4999.33 32799.96 4198.87 16299.84 10199.89 30
test_one_060199.81 5799.88 1099.49 19598.97 7699.65 14299.81 13799.09 15
eth-test20.00 542
eth-test0.00 542
ZD-MVS99.71 11799.79 4199.61 6096.84 35199.56 17099.54 29898.58 7999.96 4196.93 37999.75 142
RE-MVS-def99.34 4999.76 8299.82 2899.63 10399.52 13398.38 13899.76 9299.82 12298.75 6198.61 20799.81 12099.77 100
IU-MVS99.84 3899.88 1099.32 33798.30 15199.84 5598.86 16799.85 9399.89 30
OPU-MVS99.64 10299.56 21199.72 5699.60 11699.70 22099.27 699.42 34998.24 25599.80 12599.79 92
test_241102_TWO99.48 20799.08 5699.88 4299.81 13798.94 3399.96 4198.91 15699.84 10199.88 36
test_241102_ONE99.84 3899.90 299.48 20799.07 5899.91 3199.74 20399.20 899.76 263
9.1499.10 9899.72 11199.40 28099.51 15897.53 28599.64 14799.78 17998.84 4599.91 13597.63 31799.82 117
save fliter99.76 8299.59 8999.14 37399.40 28599.00 67
test_0728_THIRD98.99 6999.81 6999.80 15599.09 1599.96 4198.85 16999.90 5699.88 36
test_0728_SECOND99.91 699.84 3899.89 699.57 14599.51 15899.96 4198.93 15399.86 8699.88 36
test072699.85 3199.89 699.62 10899.50 18299.10 4899.86 5299.82 12298.94 33
GSMVS99.52 229
test_part299.81 5799.83 2299.77 86
sam_mvs194.86 26499.52 229
sam_mvs94.72 280
ambc93.06 47192.68 52082.36 49798.47 47398.73 45095.09 46797.41 48255.55 51399.10 41696.42 39991.32 46397.71 473
MTGPAbinary99.47 229
test_post199.23 35265.14 53494.18 31199.71 28597.58 321
test_post65.99 53394.65 28799.73 275
patchmatchnet-post98.70 43994.79 27099.74 269
GG-mvs-BLEND98.45 33898.55 44498.16 28899.43 26193.68 51397.23 43998.46 44789.30 42099.22 39195.43 42398.22 29997.98 464
MTMP99.54 17398.88 424
gm-plane-assit98.54 44592.96 47194.65 43899.15 39799.64 31397.56 326
test9_res97.49 33499.72 14899.75 113
TEST999.67 13899.65 7599.05 39499.41 27896.22 39898.95 31799.49 31698.77 5799.91 135
test_899.67 13899.61 8699.03 39999.41 27896.28 39298.93 32099.48 32398.76 5899.91 135
agg_prior297.21 35899.73 14799.75 113
agg_prior99.67 13899.62 8399.40 28598.87 33099.91 135
TestCases99.31 20399.86 2598.48 27399.61 6097.85 24199.36 22599.85 8995.95 20999.85 19096.66 39299.83 11399.59 210
test_prior499.56 9598.99 410
test_prior298.96 41798.34 14499.01 30499.52 30698.68 7197.96 28299.74 145
test_prior99.68 9099.67 13899.48 11299.56 8999.83 22199.74 118
旧先验298.96 41796.70 36099.47 18999.94 9198.19 258
新几何299.01 407
新几何199.75 7799.75 9299.59 8999.54 10896.76 35699.29 24299.64 25898.43 9099.94 9196.92 38199.66 15999.72 138
旧先验199.74 10099.59 8999.54 10899.69 23198.47 8799.68 15699.73 128
无先验98.99 41099.51 15896.89 34899.93 10897.53 32999.72 138
原ACMM298.95 420
原ACMM199.65 9699.73 10799.33 13199.47 22997.46 29199.12 28299.66 25098.67 7399.91 13597.70 31499.69 15399.71 149
test22299.75 9299.49 11098.91 42799.49 19596.42 38699.34 23299.65 25298.28 10099.69 15399.72 138
testdata299.95 7696.67 391
segment_acmp98.96 26
testdata99.54 12699.75 9298.95 19699.51 15897.07 33299.43 20099.70 22098.87 4199.94 9197.76 30499.64 16299.72 138
testdata198.85 43298.32 148
test1299.75 7799.64 16699.61 8699.29 35099.21 26498.38 9599.89 16399.74 14599.74 118
plane_prior799.29 30897.03 356
plane_prior699.27 31396.98 36092.71 353
plane_prior599.47 22999.69 29897.78 30097.63 32798.67 372
plane_prior499.61 273
plane_prior397.00 35898.69 10899.11 284
plane_prior299.39 28498.97 76
plane_prior199.26 316
plane_prior96.97 36199.21 35898.45 13197.60 330
n20.00 543
nn0.00 543
door-mid98.05 475
lessismore_v097.79 40898.69 42995.44 42994.75 50895.71 46199.87 7388.69 42899.32 36895.89 41094.93 41698.62 394
LGP-MVS_train98.49 32899.33 29597.05 35099.55 9997.46 29199.24 25699.83 11192.58 35899.72 27998.09 26997.51 33998.68 364
test1199.35 314
door97.92 476
HQP5-MVS96.83 372
HQP-NCC99.19 33498.98 41398.24 16498.66 361
ACMP_Plane99.19 33498.98 41398.24 16498.66 361
BP-MVS97.19 362
HQP4-MVS98.66 36199.64 31398.64 385
HQP3-MVS99.39 28897.58 332
HQP2-MVS92.47 362
NP-MVS99.23 32496.92 36899.40 345
MDTV_nov1_ep13_2view95.18 43699.35 30296.84 35199.58 16695.19 24997.82 29599.46 257
MDTV_nov1_ep1398.32 23599.11 35594.44 45399.27 33398.74 44497.51 28899.40 21399.62 26994.78 27199.76 26397.59 32098.81 262
ACMMP++_ref97.19 360
ACMMP++97.43 350
Test By Simon98.75 61
ITE_SJBPF98.08 37599.29 30896.37 39198.92 41398.34 14498.83 33899.75 19791.09 39999.62 32095.82 41197.40 35298.25 445
DeepMVS_CXcopyleft93.34 46899.29 30882.27 49899.22 37085.15 49496.33 45499.05 40890.97 40199.73 27593.57 45097.77 32398.01 459