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.
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mvs5depth99.30 3399.59 1298.44 27799.65 7195.35 36799.82 399.94 399.83 799.42 11299.94 298.13 12599.96 1399.63 3699.96 28100.00 1
test_fmvsmconf0.01_n99.57 1099.63 1099.36 7499.87 1298.13 14998.08 19699.95 299.45 5099.98 299.75 1699.80 199.97 699.82 1299.99 599.99 2
fmvsm_s_conf0.1_n_a99.17 5299.30 4498.80 19599.75 3496.59 30397.97 22699.86 1798.22 20299.88 2199.71 2298.59 6799.84 17799.73 2899.98 1299.98 3
fmvsm_s_conf0.1_n_299.20 5099.38 2898.65 23099.69 6196.08 33097.49 30199.90 1299.53 4199.88 2199.64 3798.51 7699.90 8199.83 1099.98 1299.97 4
mmtdpeth99.30 3399.42 2598.92 17199.58 9496.89 28899.48 1399.92 899.92 298.26 33699.80 1198.33 9699.91 7499.56 4199.95 3999.97 4
fmvsm_s_conf0.1_n99.16 5699.33 3798.64 23299.71 4996.10 32597.87 23999.85 1998.56 17699.90 1499.68 2598.69 5799.85 15899.72 3099.98 1299.97 4
test_fmvs399.12 6999.41 2698.25 29999.76 3095.07 38399.05 6899.94 397.78 24899.82 3499.84 398.56 7399.71 30899.96 199.96 2899.97 4
test_fmvsmconf0.1_n99.49 1599.54 1499.34 8399.78 2498.11 15197.77 25399.90 1299.33 6699.97 399.66 3299.71 399.96 1399.79 1999.99 599.96 8
test_f98.67 16098.87 11198.05 32799.72 4595.59 34898.51 13599.81 3296.30 37299.78 3999.82 596.14 27598.63 50199.82 1299.93 5799.95 9
test_fmvs298.70 14798.97 9897.89 34099.54 12394.05 42098.55 12699.92 896.78 34699.72 4799.78 1396.60 25299.67 34199.91 299.90 8899.94 10
PS-MVSNAJss99.46 1799.49 1699.35 8099.90 498.15 14699.20 4999.65 7699.48 4499.92 899.71 2298.07 12899.96 1399.53 48100.00 199.93 11
test_vis3_rt99.14 6299.17 6099.07 13699.78 2498.38 12298.92 8399.94 397.80 24599.91 1299.67 3097.15 21198.91 49399.76 2399.56 28899.92 12
fmvsm_s_conf0.5_n_299.14 6299.31 4198.63 23699.49 14896.08 33097.38 31599.81 3299.48 4499.84 3099.57 4998.46 8299.89 9799.82 1299.97 2199.91 13
MVStest195.86 41595.60 40896.63 43495.87 52891.70 47697.93 22898.94 33698.03 22599.56 7499.66 3271.83 51898.26 50699.35 5899.24 36699.91 13
fmvsm_s_conf0.5_n_a99.10 7299.20 5898.78 20299.55 11796.59 30397.79 24999.82 3198.21 20499.81 3699.53 6498.46 8299.84 17799.70 3399.97 2199.90 15
fmvsm_s_conf0.5_n_999.17 5299.38 2898.53 26399.51 13495.82 34297.62 27999.78 3699.72 1499.90 1499.48 7598.66 5999.89 9799.85 699.93 5799.89 16
fmvsm_s_conf0.5_n99.09 7399.26 5098.61 24299.55 11796.09 32897.74 26199.81 3298.55 17799.85 2799.55 5698.60 6699.84 17799.69 3599.98 1299.89 16
test_fmvsmconf_n99.44 1999.48 1899.31 9499.64 7798.10 15497.68 26899.84 2399.29 7299.92 899.57 4999.60 599.96 1399.74 2799.98 1299.89 16
test_djsdf99.52 1399.51 1599.53 3899.86 1498.74 9199.39 2099.56 11899.11 10099.70 5199.73 2099.00 2799.97 699.26 6599.98 1299.89 16
fmvsm_s_conf0.5_n_1199.21 4799.34 3598.80 19599.48 15696.56 30897.97 22699.69 5699.63 2899.84 3099.54 6298.21 11599.94 4199.76 2399.95 3999.88 20
mvs_tets99.63 699.67 699.49 5599.88 998.61 10399.34 2399.71 4899.27 7499.90 1499.74 1899.68 499.97 699.55 4399.99 599.88 20
fmvsm_s_conf0.5_n_899.13 6699.26 5098.74 21599.51 13496.44 31697.65 27499.65 7699.66 2399.78 3999.48 7597.92 14299.93 5399.72 3099.95 3999.87 22
fmvsm_s_conf0.5_n_798.83 12299.04 8798.20 30699.30 21094.83 39397.23 33399.36 21598.64 16099.84 3099.43 8898.10 12799.91 7499.56 4199.96 2899.87 22
fmvsm_l_conf0.5_n_399.45 1899.48 1899.34 8399.59 9298.21 14397.82 24499.84 2399.41 5799.92 899.41 9499.51 899.95 2599.84 999.97 2199.87 22
ttmdpeth97.91 27298.02 25497.58 37798.69 36794.10 41998.13 18698.90 34697.95 23197.32 41399.58 4795.95 29298.75 49896.41 33299.22 37099.87 22
jajsoiax99.58 999.61 1199.48 5799.87 1298.61 10399.28 4099.66 7099.09 11099.89 1899.68 2599.53 799.97 699.50 5099.99 599.87 22
EU-MVSNet97.66 30098.50 17395.13 49099.63 8385.84 52498.35 16198.21 41798.23 20099.54 7999.46 8095.02 32499.68 33698.24 14599.87 10099.87 22
fmvsm_s_conf0.5_n_399.22 4699.37 3198.78 20299.46 16296.58 30697.65 27499.72 4699.47 4799.86 2499.50 6898.94 3199.89 9799.75 2699.97 2199.86 28
UA-Net99.47 1699.40 2799.70 299.49 14899.29 2399.80 499.72 4699.82 899.04 20199.81 898.05 13199.96 1398.85 9899.99 599.86 28
fmvsm_l_conf0.5_n_999.32 3299.43 2498.98 15899.59 9297.18 26697.44 31099.83 2699.56 3999.91 1299.34 11599.36 1399.93 5399.83 1099.98 1299.85 30
MM98.22 23797.99 25798.91 17398.66 37796.97 28097.89 23594.44 50699.54 4098.95 22199.14 17993.50 37399.92 6599.80 1799.96 2899.85 30
LCM-MVSNet99.93 199.92 199.94 199.99 199.97 199.90 199.89 1499.98 199.99 199.96 199.77 2100.00 199.81 16100.00 199.85 30
fmvsm_l_conf0.5_n_a99.19 5199.27 4798.94 16599.65 7197.05 27597.80 24899.76 3998.70 15899.78 3999.11 18798.79 4399.95 2599.85 699.96 2899.83 33
fmvsm_l_conf0.5_n99.21 4799.28 4699.02 14999.64 7797.28 25297.82 24499.76 3998.73 15199.82 3499.09 19698.81 3999.95 2599.86 499.96 2899.83 33
mvsany_test398.87 11298.92 10298.74 21599.38 18596.94 28498.58 12399.10 30896.49 36199.96 499.81 898.18 11899.45 44398.97 8999.79 15999.83 33
PDCNetPlus95.22 43894.73 44596.70 43397.85 45591.14 49293.94 50399.97 193.06 47898.95 22198.89 26074.32 51599.14 48195.63 37699.93 5799.82 36
fmvsm_s_conf0.5_n_1099.15 5799.27 4798.78 20299.47 15996.56 30897.75 25999.71 4899.60 3599.74 4699.44 8597.96 13999.95 2599.86 499.94 5199.82 36
SSC-MVS98.71 14298.74 12898.62 23899.72 4596.08 33098.74 9998.64 38999.74 1299.67 5999.24 14494.57 34099.95 2599.11 7799.24 36699.82 36
anonymousdsp99.51 1499.47 2199.62 999.88 999.08 6999.34 2399.69 5698.93 13299.65 6399.72 2198.93 3399.95 2599.11 77100.00 199.82 36
ANet_high99.57 1099.67 699.28 9699.89 698.09 15599.14 5899.93 699.82 899.93 699.81 899.17 2099.94 4199.31 61100.00 199.82 36
MED-MVS99.01 9098.84 11999.52 4499.58 9498.93 7998.68 10999.60 9298.85 14599.53 8399.16 16997.87 14999.83 19596.67 30499.64 25499.81 41
TestfortrainingZip a99.09 7398.92 10299.61 1399.58 9499.17 4398.68 10999.27 26398.85 14599.61 7099.16 16997.14 21299.86 14498.39 13899.57 28499.81 41
fmvsm_s_conf0.5_n_499.01 9099.22 5498.38 28499.31 20695.48 35897.56 29099.73 4598.87 14099.75 4499.27 13198.80 4199.86 14499.80 1799.90 8899.81 41
PS-CasMVS99.40 2599.33 3799.62 999.71 4999.10 6599.29 3699.53 13399.53 4199.46 10199.41 9498.23 11099.95 2598.89 9699.95 3999.81 41
VortexMVS97.98 26898.31 21297.02 41498.88 32691.45 48198.03 20799.47 16698.65 15999.55 7799.47 7891.49 41399.81 22499.32 6099.91 8099.80 45
FC-MVSNet-test99.27 3799.25 5299.34 8399.77 2798.37 12499.30 3599.57 10999.61 3499.40 11799.50 6897.12 21399.85 15899.02 8699.94 5199.80 45
test_cas_vis1_n_192098.33 21998.68 14197.27 40199.69 6192.29 47098.03 20799.85 1997.62 26099.96 499.62 4093.98 36299.74 28999.52 4999.86 10799.79 47
test_vis1_n_192098.40 20598.92 10296.81 42899.74 3790.76 49998.15 18499.91 1098.33 18999.89 1899.55 5695.07 32399.88 11599.76 2399.93 5799.79 47
CP-MVSNet99.21 4799.09 8299.56 2699.65 7198.96 7799.13 5999.34 22799.42 5599.33 13799.26 13797.01 22199.94 4198.74 10799.93 5799.79 47
fmvsm_s_conf0.5_n_599.07 8299.10 8098.99 15499.47 15997.22 25997.40 31299.83 2697.61 26399.85 2799.30 12598.80 4199.95 2599.71 3299.90 8899.78 50
UniMVSNet_ETH3D99.69 299.69 499.69 399.84 1799.34 1999.69 599.58 10199.90 399.86 2499.78 1399.58 699.95 2599.00 8799.95 3999.78 50
CVMVSNet96.25 39797.21 32493.38 51499.10 27180.56 54497.20 33898.19 42096.94 33299.00 20699.02 21189.50 43599.80 23396.36 33699.59 27599.78 50
reproduce_monomvs95.00 44495.25 42894.22 50097.51 48383.34 53597.86 24098.44 40498.51 17899.29 14899.30 12567.68 52699.56 40298.89 9699.81 14099.77 53
Anonymous2023121199.27 3799.27 4799.26 10199.29 21298.18 14499.49 1299.51 14199.70 1599.80 3799.68 2596.84 23099.83 19599.21 7099.91 8099.77 53
PEN-MVS99.41 2499.34 3599.62 999.73 3899.14 5799.29 3699.54 12999.62 3299.56 7499.42 8998.16 12299.96 1398.78 10299.93 5799.77 53
WR-MVS_H99.33 3099.22 5499.65 899.71 4999.24 2999.32 2699.55 12399.46 4999.50 9399.34 11597.30 20099.93 5398.90 9499.93 5799.77 53
LTVRE_ROB98.40 199.67 399.71 299.56 2699.85 1699.11 6499.90 199.78 3699.63 2899.78 3999.67 3099.48 1099.81 22499.30 6299.97 2199.77 53
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
WB-MVS98.52 19198.55 16498.43 27899.65 7195.59 34898.52 13098.77 37299.65 2599.52 8799.00 22694.34 35099.93 5398.65 11498.83 41699.76 58
patch_mono-298.51 19298.63 15198.17 30999.38 18594.78 39597.36 32099.69 5698.16 21498.49 31199.29 12897.06 21699.97 698.29 14499.91 8099.76 58
nrg03099.40 2599.35 3399.54 3199.58 9499.13 6098.98 7699.48 15699.68 1999.46 10199.26 13798.62 6499.73 29699.17 7499.92 7199.76 58
FIs99.14 6299.09 8299.29 9599.70 5798.28 13399.13 5999.52 13999.48 4499.24 16699.41 9496.79 23799.82 20798.69 11299.88 9599.76 58
v7n99.53 1299.57 1399.41 6999.88 998.54 11199.45 1499.61 9099.66 2399.68 5799.66 3298.44 8499.95 2599.73 2899.96 2899.75 62
APDe-MVScopyleft98.99 9498.79 12499.60 1699.21 23899.15 5298.87 8999.48 15697.57 26799.35 13099.24 14497.83 15199.89 9797.88 18199.70 22699.75 62
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DTE-MVSNet99.43 2299.35 3399.66 799.71 4999.30 2199.31 3099.51 14199.64 2699.56 7499.46 8098.23 11099.97 698.78 10299.93 5799.72 64
MSC_two_6792asdad99.32 9198.43 40798.37 12498.86 35799.89 9797.14 25399.60 27199.71 65
No_MVS99.32 9198.43 40798.37 12498.86 35799.89 9797.14 25399.60 27199.71 65
PMMVS298.07 25798.08 24898.04 32899.41 17994.59 40494.59 48299.40 20397.50 27698.82 25598.83 27596.83 23299.84 17797.50 22299.81 14099.71 65
Baseline_NR-MVSNet98.98 9898.86 11599.36 7499.82 1998.55 10897.47 30699.57 10999.37 6099.21 17299.61 4396.76 24099.83 19598.06 16199.83 12699.71 65
XXY-MVS99.14 6299.15 6799.10 12899.76 3097.74 20898.85 9399.62 8798.48 18099.37 12599.49 7498.75 4799.86 14498.20 15099.80 15299.71 65
test_0728_THIRD98.17 21199.08 18999.02 21197.89 14799.88 11597.07 26099.71 21799.70 70
MSP-MVS98.40 20598.00 25699.61 1399.57 10399.25 2898.57 12499.35 22197.55 27199.31 14697.71 42494.61 33999.88 11596.14 35199.19 37899.70 70
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
SSC-MVS3.298.53 18798.79 12497.74 35699.46 16293.62 44696.45 39099.34 22799.33 6698.93 23098.70 30797.90 14399.90 8199.12 7699.92 7199.69 72
NormalMVS98.26 23297.97 26199.15 12199.64 7797.83 19498.28 16799.43 18999.24 7798.80 25998.85 26889.76 43199.94 4198.04 16499.67 24299.68 73
KinetiMVS99.03 8899.02 9099.03 14699.70 5797.48 23098.43 14899.29 25699.70 1599.60 7199.07 19896.13 27799.94 4199.42 5599.87 10099.68 73
dcpmvs_298.78 13399.11 7497.78 34999.56 11193.67 44399.06 6699.86 1799.50 4399.66 6099.26 13797.21 20899.99 298.00 16999.91 8099.68 73
test_0728_SECOND99.60 1699.50 14099.23 3098.02 21099.32 23599.88 11596.99 26799.63 26099.68 73
OurMVSNet-221017-099.37 2899.31 4199.53 3899.91 398.98 7199.63 799.58 10199.44 5299.78 3999.76 1596.39 26299.92 6599.44 5499.92 7199.68 73
fmvsm_s_conf0.5_n_699.08 7999.21 5798.69 22399.36 19296.51 31097.62 27999.68 6398.43 18299.85 2799.10 19099.12 2399.88 11599.77 2299.92 7199.67 78
CHOSEN 1792x268897.49 31297.14 32998.54 26199.68 6496.09 32896.50 38799.62 8791.58 49698.84 25098.97 23592.36 39699.88 11596.76 29199.95 3999.67 78
reproduce_model99.15 5798.97 9899.67 499.33 20399.44 998.15 18499.47 16699.12 9999.52 8799.32 12398.31 9799.90 8197.78 19199.73 19999.66 80
IU-MVS99.49 14899.15 5298.87 35292.97 47999.41 11496.76 29199.62 26499.66 80
test_241102_TWO99.30 24898.03 22599.26 15699.02 21197.51 18599.88 11596.91 27499.60 27199.66 80
DPE-MVScopyleft98.59 17498.26 22299.57 2199.27 21899.15 5297.01 34899.39 20597.67 25699.44 10798.99 22897.53 18299.89 9795.40 38499.68 23699.66 80
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TransMVSNet (Re)99.44 1999.47 2199.36 7499.80 2198.58 10699.27 4299.57 10999.39 5899.75 4499.62 4099.17 2099.83 19599.06 8299.62 26499.66 80
EI-MVSNet-UG-set98.69 15198.71 13598.62 23899.10 27196.37 31897.23 33398.87 35299.20 8499.19 17498.99 22897.30 20099.85 15898.77 10599.79 15999.65 85
Elysia99.15 5799.14 6899.18 11399.63 8397.92 18398.50 13799.43 18999.67 2099.70 5199.13 18196.66 24799.98 499.54 4499.96 2899.64 86
StellarMVS99.15 5799.14 6899.18 11399.63 8397.92 18398.50 13799.43 18999.67 2099.70 5199.13 18196.66 24799.98 499.54 4499.96 2899.64 86
pmmvs699.67 399.70 399.60 1699.90 499.27 2699.53 999.76 3999.64 2699.84 3099.83 499.50 999.87 13599.36 5799.92 7199.64 86
EI-MVSNet-Vis-set98.68 15798.70 13898.63 23699.09 27496.40 31797.23 33398.86 35799.20 8499.18 17998.97 23597.29 20299.85 15898.72 10999.78 16499.64 86
ACMH96.65 799.25 4099.24 5399.26 10199.72 4598.38 12299.07 6599.55 12398.30 19399.65 6399.45 8499.22 1799.76 27098.44 13199.77 17299.64 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DP-MVS98.93 10498.81 12399.28 9699.21 23898.45 11798.46 14599.33 23399.63 2899.48 9699.15 17597.23 20699.75 28297.17 24999.66 25099.63 91
reproduce-ours99.09 7398.90 10599.67 499.27 21899.49 598.00 21499.42 19599.05 11799.48 9699.27 13198.29 9999.89 9797.61 21099.71 21799.62 92
our_new_method99.09 7398.90 10599.67 499.27 21899.49 598.00 21499.42 19599.05 11799.48 9699.27 13198.29 9999.89 9797.61 21099.71 21799.62 92
test_fmvs1_n98.09 25598.28 21697.52 38699.68 6493.47 44898.63 11699.93 695.41 42099.68 5799.64 3791.88 40799.48 43399.82 1299.87 10099.62 92
test111196.49 38196.82 35295.52 48199.42 17687.08 52199.22 4687.14 53999.11 10099.46 10199.58 4788.69 43999.86 14498.80 10099.95 3999.62 92
VPA-MVSNet99.30 3399.30 4499.28 9699.49 14898.36 12799.00 7399.45 17599.63 2899.52 8799.44 8598.25 10799.88 11599.09 7999.84 11499.62 92
LPG-MVS_test98.71 14298.46 18399.47 6199.57 10398.97 7398.23 17399.48 15696.60 35599.10 18799.06 19998.71 5199.83 19595.58 38099.78 16499.62 92
LGP-MVS_train99.47 6199.57 10398.97 7399.48 15696.60 35599.10 18799.06 19998.71 5199.83 19595.58 38099.78 16499.62 92
Test_1112_low_res96.99 35996.55 37598.31 29399.35 19795.47 36195.84 43699.53 13391.51 49896.80 44398.48 35091.36 41599.83 19596.58 31399.53 29999.62 92
tt0320-xc99.64 599.68 599.50 5499.72 4598.98 7199.51 1099.85 1999.86 699.88 2199.82 599.02 2699.90 8199.54 4499.95 3999.61 100
v1098.97 9999.11 7498.55 25699.44 16996.21 32498.90 8499.55 12398.73 15199.48 9699.60 4596.63 25199.83 19599.70 3399.99 599.61 100
sc_t199.62 799.66 899.53 3899.82 1999.09 6899.50 1199.63 8199.88 499.86 2499.80 1199.03 2499.89 9799.48 5299.93 5799.60 102
test_vis1_n98.31 22498.50 17397.73 35999.76 3094.17 41598.68 10999.91 1096.31 37099.79 3899.57 4992.85 38999.42 44999.79 1999.84 11499.60 102
v899.01 9099.16 6298.57 24999.47 15996.31 32198.90 8499.47 16699.03 12199.52 8799.57 4996.93 22699.81 22499.60 3799.98 1299.60 102
EI-MVSNet98.40 20598.51 17098.04 32899.10 27194.73 39897.20 33898.87 35298.97 12799.06 19199.02 21196.00 28499.80 23398.58 11999.82 13399.60 102
SixPastTwentyTwo98.75 13898.62 15399.16 11899.83 1897.96 17899.28 4098.20 41899.37 6099.70 5199.65 3692.65 39399.93 5399.04 8499.84 11499.60 102
IterMVS-LS98.55 18298.70 13898.09 31999.48 15694.73 39897.22 33799.39 20598.97 12799.38 12199.31 12496.00 28499.93 5398.58 11999.97 2199.60 102
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test97.19 34296.60 37398.96 16299.62 8797.28 25295.17 46199.50 14694.21 45499.01 20598.32 37186.61 45499.99 297.10 25899.84 11499.60 102
lecture99.25 4099.12 7199.62 999.64 7799.40 1198.89 8899.51 14199.19 8999.37 12599.25 14298.36 9099.88 11598.23 14799.67 24299.59 109
tt032099.61 899.65 999.48 5799.71 4998.94 7899.54 899.83 2699.87 599.89 1899.82 598.75 4799.90 8199.54 4499.95 3999.59 109
ACMMP_NAP98.75 13898.48 17999.57 2199.58 9499.29 2397.82 24499.25 27196.94 33298.78 26199.12 18598.02 13299.84 17797.13 25699.67 24299.59 109
VPNet98.87 11298.83 12099.01 15199.70 5797.62 22098.43 14899.35 22199.47 4799.28 15099.05 20696.72 24499.82 20798.09 15899.36 34099.59 109
WR-MVS98.40 20598.19 23399.03 14699.00 30197.65 21796.85 36098.94 33698.57 17398.89 23798.50 34795.60 30499.85 15897.54 21899.85 10999.59 109
HPM-MVScopyleft98.79 13198.53 16899.59 2099.65 7199.29 2399.16 5599.43 18996.74 34898.61 29198.38 36198.62 6499.87 13596.47 32799.67 24299.59 109
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EG-PatchMatch MVS98.99 9499.01 9298.94 16599.50 14097.47 23198.04 20599.59 9898.15 21999.40 11799.36 11098.58 7299.76 27098.78 10299.68 23699.59 109
Vis-MVSNetpermissive99.34 2999.36 3299.27 9999.73 3898.26 13599.17 5499.78 3699.11 10099.27 15299.48 7598.82 3899.95 2598.94 9199.93 5799.59 109
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MED-MVS test99.45 6499.58 9498.93 7998.68 10999.60 9296.46 36499.53 8398.77 28899.83 19596.67 30499.64 25499.58 117
ME-MVS98.61 17098.33 21099.44 6599.24 23098.93 7997.45 30899.06 31498.14 22099.06 19198.77 28896.97 22499.82 20796.67 30499.64 25499.58 117
MP-MVS-pluss98.57 17798.23 22799.60 1699.69 6199.35 1697.16 34399.38 20794.87 43598.97 21598.99 22898.01 13399.88 11597.29 24099.70 22699.58 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
region2R98.69 15198.40 19199.54 3199.53 12799.17 4398.52 13099.31 24097.46 28498.44 31898.51 34397.83 15199.88 11596.46 32899.58 28099.58 117
ACMMPR98.70 14798.42 18999.54 3199.52 13199.14 5798.52 13099.31 24097.47 27998.56 30298.54 33897.75 15999.88 11596.57 31599.59 27599.58 117
PGM-MVS98.66 16198.37 19999.55 2899.53 12799.18 4298.23 17399.49 15497.01 32998.69 27598.88 26298.00 13499.89 9795.87 36499.59 27599.58 117
SteuartSystems-ACMMP98.79 13198.54 16699.54 3199.73 3899.16 4898.23 17399.31 24097.92 23598.90 23498.90 25498.00 13499.88 11596.15 35099.72 20899.58 117
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SDMVSNet99.23 4599.32 3998.96 16299.68 6497.35 24098.84 9599.48 15699.69 1799.63 6699.68 2599.03 2499.96 1397.97 17499.92 7199.57 124
sd_testset99.28 3699.31 4199.19 11299.68 6498.06 16599.41 1799.30 24899.69 1799.63 6699.68 2599.25 1699.96 1397.25 24399.92 7199.57 124
TranMVSNet+NR-MVSNet99.17 5299.07 8599.46 6399.37 19198.87 8498.39 15799.42 19599.42 5599.36 12899.06 19998.38 8999.95 2598.34 14199.90 8899.57 124
mPP-MVS98.64 16498.34 20599.54 3199.54 12399.17 4398.63 11699.24 27697.47 27998.09 35098.68 31197.62 17099.89 9796.22 34599.62 26499.57 124
PVSNet_Blended_VisFu98.17 24898.15 24098.22 30599.73 3895.15 37997.36 32099.68 6394.45 44998.99 21099.27 13196.87 22999.94 4197.13 25699.91 8099.57 124
1112_ss97.29 33396.86 34898.58 24699.34 20296.32 32096.75 36799.58 10193.14 47596.89 43797.48 44192.11 40499.86 14496.91 27499.54 29599.57 124
MTAPA98.88 11198.64 14999.61 1399.67 6899.36 1598.43 14899.20 28298.83 14998.89 23798.90 25496.98 22399.92 6597.16 25099.70 22699.56 130
XVS98.72 14198.45 18499.53 3899.46 16299.21 3298.65 11499.34 22798.62 16597.54 39598.63 32597.50 18699.83 19596.79 28799.53 29999.56 130
pm-mvs199.44 1999.48 1899.33 8999.80 2198.63 10099.29 3699.63 8199.30 7199.65 6399.60 4599.16 2299.82 20799.07 8099.83 12699.56 130
X-MVStestdata94.32 45292.59 47499.53 3899.46 16299.21 3298.65 11499.34 22798.62 16597.54 39545.85 54197.50 18699.83 19596.79 28799.53 29999.56 130
HPM-MVS_fast99.01 9098.82 12199.57 2199.71 4999.35 1699.00 7399.50 14697.33 29798.94 22998.86 26598.75 4799.82 20797.53 21999.71 21799.56 130
K. test v398.00 26497.66 29399.03 14699.79 2397.56 22399.19 5392.47 52299.62 3299.52 8799.66 3289.61 43399.96 1399.25 6799.81 14099.56 130
CP-MVS98.70 14798.42 18999.52 4499.36 19299.12 6298.72 10499.36 21597.54 27398.30 33098.40 35897.86 15099.89 9796.53 32499.72 20899.56 130
viewmacassd2359aftdt98.86 11698.87 11198.83 18899.53 12797.32 24597.70 26699.64 7898.22 20299.25 16499.27 13198.40 8699.61 38197.98 17399.87 10099.55 137
FE-MVSNET98.59 17498.50 17398.87 17799.58 9497.30 24698.08 19699.74 4496.94 33298.97 21599.10 19096.94 22599.74 28997.33 23699.86 10799.55 137
ZNCC-MVS98.68 15798.40 19199.54 3199.57 10399.21 3298.46 14599.29 25697.28 30498.11 34898.39 35998.00 13499.87 13596.86 28499.64 25499.55 137
v119298.60 17298.66 14698.41 28099.27 21895.88 33897.52 29699.36 21597.41 28999.33 13799.20 15696.37 26599.82 20799.57 3999.92 7199.55 137
v124098.55 18298.62 15398.32 29199.22 23695.58 35097.51 29899.45 17597.16 32099.45 10699.24 14496.12 27999.85 15899.60 3799.88 9599.55 137
UGNet98.53 18798.45 18498.79 19997.94 45096.96 28299.08 6298.54 39899.10 10796.82 44299.47 7896.55 25499.84 17798.56 12499.94 5199.55 137
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
usedtu_dtu_shiyan298.99 9498.86 11599.39 7299.73 3898.71 9799.05 6899.47 16699.16 9499.49 9499.12 18596.34 26799.93 5398.05 16399.36 34099.54 143
E5new99.05 8399.11 7498.85 18099.60 8897.30 24698.42 15199.63 8198.73 15199.26 15699.39 10098.71 5199.70 31598.43 13399.84 11499.54 143
E6new99.05 8399.11 7498.85 18099.60 8897.30 24698.42 15199.63 8198.73 15199.26 15699.39 10098.71 5199.70 31598.43 13399.84 11499.54 143
E699.05 8399.11 7498.85 18099.60 8897.30 24698.42 15199.63 8198.73 15199.26 15699.39 10098.71 5199.70 31598.43 13399.84 11499.54 143
E599.05 8399.11 7498.85 18099.60 8897.30 24698.42 15199.63 8198.73 15199.26 15699.39 10098.71 5199.70 31598.43 13399.84 11499.54 143
AstraMVS98.16 25098.07 25098.41 28099.51 13495.86 33998.00 21495.14 50098.97 12799.43 10899.24 14493.25 37699.84 17799.21 7099.87 10099.54 143
WBMVS95.18 43994.78 44196.37 44397.68 47089.74 50895.80 43798.73 38197.54 27398.30 33098.44 35470.06 52099.82 20796.62 31099.87 10099.54 143
test250692.39 48691.89 48893.89 50699.38 18582.28 54099.32 2666.03 54799.08 11498.77 26499.57 4966.26 53099.84 17798.71 11099.95 3999.54 143
ECVR-MVScopyleft96.42 38796.61 37195.85 47099.38 18588.18 51699.22 4686.00 54199.08 11499.36 12899.57 4988.47 44499.82 20798.52 12799.95 3999.54 143
v14419298.54 18598.57 16298.45 27599.21 23895.98 33397.63 27899.36 21597.15 32299.32 14399.18 16395.84 29699.84 17799.50 5099.91 8099.54 143
v192192098.54 18598.60 15898.38 28499.20 24295.76 34697.56 29099.36 21597.23 31499.38 12199.17 16796.02 28299.84 17799.57 3999.90 8899.54 143
MP-MVScopyleft98.46 19798.09 24599.54 3199.57 10399.22 3198.50 13799.19 28697.61 26397.58 39198.66 31797.40 19499.88 11594.72 40199.60 27199.54 143
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MIMVSNet199.38 2799.32 3999.55 2899.86 1499.19 4199.41 1799.59 9899.59 3699.71 4999.57 4997.12 21399.90 8199.21 7099.87 10099.54 143
ACMMPcopyleft98.75 13898.50 17399.52 4499.56 11199.16 4898.87 8999.37 21197.16 32098.82 25599.01 22297.71 16199.87 13596.29 34299.69 23099.54 143
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-MVScopyleft98.40 20598.03 25399.51 4999.16 25799.21 3298.05 20399.22 27994.16 45698.98 21199.10 19097.52 18499.79 24696.45 32999.64 25499.53 157
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
HFP-MVS98.71 14298.44 18699.51 4999.49 14899.16 4898.52 13099.31 24097.47 27998.58 29898.50 34797.97 13899.85 15896.57 31599.59 27599.53 157
UniMVSNet_NR-MVSNet98.86 11698.68 14199.40 7199.17 25598.74 9197.68 26899.40 20399.14 9899.06 19198.59 33396.71 24599.93 5398.57 12199.77 17299.53 157
E498.87 11298.88 10898.81 19299.52 13197.23 25697.62 27999.61 9098.58 17199.18 17999.33 11898.29 9999.69 32597.99 17299.83 12699.52 160
GST-MVS98.61 17098.30 21399.52 4499.51 13499.20 3898.26 17199.25 27197.44 28798.67 27998.39 35997.68 16299.85 15896.00 35699.51 30599.52 160
MGCNet97.44 31797.01 33798.72 21996.42 51996.74 29897.20 33891.97 52998.46 18198.30 33098.79 28492.74 39199.91 7499.30 6299.94 5199.52 160
TDRefinement99.42 2399.38 2899.55 2899.76 3099.33 2099.68 699.71 4899.38 5999.53 8399.61 4398.64 6199.80 23398.24 14599.84 11499.52 160
FE-MVSNET299.15 5799.22 5498.94 16599.70 5797.49 22798.62 11899.67 6998.85 14599.34 13499.54 6298.47 7799.81 22498.93 9299.91 8099.51 164
v114498.60 17298.66 14698.41 28099.36 19295.90 33797.58 28899.34 22797.51 27599.27 15299.15 17596.34 26799.80 23399.47 5399.93 5799.51 164
v2v48298.56 17898.62 15398.37 28799.42 17695.81 34397.58 28899.16 29797.90 23799.28 15099.01 22295.98 28999.79 24699.33 5999.90 8899.51 164
CPTT-MVS97.84 28797.36 31399.27 9999.31 20698.46 11698.29 16699.27 26394.90 43497.83 37498.37 36294.90 32699.84 17793.85 42999.54 29599.51 164
casdiffseed41469214799.09 7399.12 7199.01 15199.55 11797.91 18598.30 16599.68 6399.04 11999.19 17499.37 10498.98 2899.61 38198.13 15499.83 12699.50 168
viewdifsd2359ckpt1198.84 11999.04 8798.24 30199.56 11195.51 35397.38 31599.70 5399.16 9499.57 7299.40 9798.26 10599.71 30898.55 12599.82 13399.50 168
viewmsd2359difaftdt98.84 11999.04 8798.24 30199.56 11195.51 35397.38 31599.70 5399.16 9499.57 7299.40 9798.26 10599.71 30898.55 12599.82 13399.50 168
LuminaMVS98.39 21198.20 22998.98 15899.50 14097.49 22797.78 25097.69 43398.75 15099.49 9499.25 14292.30 39999.94 4199.14 7599.88 9599.50 168
DU-MVS98.82 12598.63 15199.39 7299.16 25798.74 9197.54 29499.25 27198.84 14899.06 19198.76 29396.76 24099.93 5398.57 12199.77 17299.50 168
NR-MVSNet98.95 10298.82 12199.36 7499.16 25798.72 9699.22 4699.20 28299.10 10799.72 4798.76 29396.38 26499.86 14498.00 16999.82 13399.50 168
casdiffmvs_mvgpermissive99.12 6999.16 6298.99 15499.43 17497.73 21098.00 21499.62 8799.22 8099.55 7799.22 15298.93 3399.75 28298.66 11399.81 14099.50 168
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ACMH+96.62 999.08 7999.00 9499.33 8999.71 4998.83 8698.60 12199.58 10199.11 10099.53 8399.18 16398.81 3999.67 34196.71 29999.77 17299.50 168
SymmetryMVS98.05 25997.71 28899.09 13299.29 21297.83 19498.28 16797.64 43899.24 7798.80 25998.85 26889.76 43199.94 4198.04 16499.50 31399.49 176
DVP-MVS++98.90 10898.70 13899.51 4998.43 40799.15 5299.43 1599.32 23598.17 21199.26 15699.02 21198.18 11899.88 11597.07 26099.45 32199.49 176
PC_three_145293.27 47299.40 11798.54 33898.22 11397.00 52595.17 38999.45 32199.49 176
GeoE99.05 8398.99 9699.25 10499.44 16998.35 12898.73 10399.56 11898.42 18398.91 23398.81 28198.94 3199.91 7498.35 14099.73 19999.49 176
h-mvs3397.77 29197.33 31699.10 12899.21 23897.84 19398.35 16198.57 39599.11 10098.58 29899.02 21188.65 44299.96 1398.11 15696.34 50799.49 176
IterMVS-SCA-FT97.85 28698.18 23596.87 42499.27 21891.16 49195.53 44699.25 27199.10 10799.41 11499.35 11193.10 38299.96 1398.65 11499.94 5199.49 176
new-patchmatchnet98.35 21498.74 12897.18 40599.24 23092.23 47296.42 39499.48 15698.30 19399.69 5599.53 6497.44 19299.82 20798.84 9999.77 17299.49 176
APD-MVScopyleft98.10 25297.67 29099.42 6799.11 26998.93 7997.76 25699.28 26094.97 43298.72 27198.77 28897.04 21799.85 15893.79 43099.54 29599.49 176
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EPP-MVSNet98.30 22598.04 25299.07 13699.56 11197.83 19499.29 3698.07 42499.03 12198.59 29699.13 18192.16 40199.90 8196.87 28299.68 23699.49 176
DeepC-MVS97.60 498.97 9998.93 10199.10 12899.35 19797.98 17498.01 21399.46 17197.56 26999.54 7999.50 6898.97 2999.84 17798.06 16199.92 7199.49 176
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMM96.08 1298.91 10698.73 13099.48 5799.55 11799.14 5798.07 20099.37 21197.62 26099.04 20198.96 23998.84 3799.79 24697.43 23099.65 25299.49 176
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
guyue98.01 26397.93 26798.26 29799.45 16795.48 35898.08 19696.24 48098.89 13899.34 13499.14 17991.32 41699.82 20799.07 8099.83 12699.48 187
DVP-MVScopyleft98.77 13698.52 16999.52 4499.50 14099.21 3298.02 21098.84 36197.97 22999.08 18999.02 21197.61 17299.88 11596.99 26799.63 26099.48 187
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
SR-MVS98.71 14298.43 18799.57 2199.18 25399.35 1698.36 16099.29 25698.29 19698.88 24198.85 26897.53 18299.87 13596.14 35199.31 35299.48 187
TSAR-MVS + MP.98.63 16698.49 17899.06 14299.64 7797.90 18798.51 13598.94 33696.96 33099.24 16698.89 26097.83 15199.81 22496.88 28199.49 31699.48 187
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
VDDNet98.21 24097.95 26299.01 15199.58 9497.74 20899.01 7197.29 44999.67 2098.97 21599.50 6890.45 42599.80 23397.88 18199.20 37599.48 187
IterMVS97.73 29398.11 24496.57 43699.24 23090.28 50295.52 44899.21 28098.86 14299.33 13799.33 11893.11 38199.94 4198.49 12899.94 5199.48 187
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IS-MVSNet98.19 24397.90 27199.08 13499.57 10397.97 17599.31 3098.32 41199.01 12398.98 21199.03 21091.59 40999.79 24695.49 38299.80 15299.48 187
ACMP95.32 1598.41 20298.09 24599.36 7499.51 13498.79 8997.68 26899.38 20795.76 40298.81 25798.82 27898.36 9099.82 20794.75 39899.77 17299.48 187
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cashybrid299.12 6999.12 7199.09 13299.53 12798.08 15998.34 16399.66 7099.35 6499.35 13099.23 15098.39 8899.72 30698.46 12999.81 14099.47 195
MCST-MVS98.00 26497.63 29799.10 12899.24 23098.17 14596.89 35998.73 38195.66 40497.92 36497.70 42697.17 21099.66 35496.18 34999.23 36999.47 195
3Dnovator+97.89 398.69 15198.51 17099.24 10698.81 34198.40 12099.02 7099.19 28698.99 12498.07 35299.28 12997.11 21599.84 17796.84 28599.32 35099.47 195
hybridcas99.08 7999.13 7098.92 17199.54 12397.61 22198.22 17799.66 7099.27 7499.40 11799.24 14498.47 7799.70 31598.59 11899.80 15299.46 198
diffmvs_AUTHOR98.50 19398.59 16098.23 30499.35 19795.48 35896.61 37999.60 9298.37 18498.90 23499.00 22697.37 19699.76 27098.22 14899.85 10999.46 198
HPM-MVS++copyleft98.10 25297.64 29599.48 5799.09 27499.13 6097.52 29698.75 37897.46 28496.90 43697.83 41796.01 28399.84 17795.82 36899.35 34399.46 198
V4298.78 13398.78 12698.76 20999.44 16997.04 27698.27 17099.19 28697.87 23999.25 16499.16 16996.84 23099.78 25899.21 7099.84 11499.46 198
APD-MVS_3200maxsize98.84 11998.61 15799.53 3899.19 24599.27 2698.49 14099.33 23398.64 16099.03 20498.98 23397.89 14799.85 15896.54 32399.42 33299.46 198
UniMVSNet (Re)98.87 11298.71 13599.35 8099.24 23098.73 9497.73 26399.38 20798.93 13299.12 18398.73 29696.77 23899.86 14498.63 11699.80 15299.46 198
SR-MVS-dyc-post98.81 12798.55 16499.57 2199.20 24299.38 1298.48 14399.30 24898.64 16098.95 22198.96 23997.49 18999.86 14496.56 31999.39 33699.45 204
RE-MVS-def98.58 16199.20 24299.38 1298.48 14399.30 24898.64 16098.95 22198.96 23997.75 15996.56 31999.39 33699.45 204
HQP_MVS97.99 26797.67 29098.93 16899.19 24597.65 21797.77 25399.27 26398.20 20897.79 37797.98 40594.90 32699.70 31594.42 41099.51 30599.45 204
plane_prior599.27 26399.70 31594.42 41099.51 30599.45 204
lessismore_v098.97 16099.73 3897.53 22686.71 54099.37 12599.52 6789.93 42899.92 6598.99 8899.72 20899.44 208
TAMVS98.24 23698.05 25198.80 19599.07 27897.18 26697.88 23698.81 36696.66 35499.17 18299.21 15494.81 33299.77 26496.96 27299.88 9599.44 208
DeepPCF-MVS96.93 598.32 22098.01 25599.23 10898.39 41298.97 7395.03 46599.18 29096.88 33899.33 13798.78 28698.16 12299.28 47196.74 29499.62 26499.44 208
3Dnovator98.27 298.81 12798.73 13099.05 14398.76 34897.81 20299.25 4399.30 24898.57 17398.55 30499.33 11897.95 14099.90 8197.16 25099.67 24299.44 208
E298.70 14798.68 14198.73 21799.40 18197.10 27397.48 30299.57 10998.09 22299.00 20699.20 15697.90 14399.67 34197.73 20199.77 17299.43 212
E398.69 15198.68 14198.73 21799.40 18197.10 27397.48 30299.57 10998.09 22299.00 20699.20 15697.90 14399.67 34197.73 20199.77 17299.43 212
MVSFormer98.26 23298.43 18797.77 35098.88 32693.89 43699.39 2099.56 11899.11 10098.16 34298.13 39093.81 36699.97 699.26 6599.57 28499.43 212
jason97.45 31697.35 31497.76 35399.24 23093.93 43295.86 43398.42 40794.24 45398.50 31098.13 39094.82 33099.91 7497.22 24599.73 19999.43 212
jason: jason.
NCCC97.86 28197.47 30899.05 14398.61 38298.07 16296.98 35198.90 34697.63 25997.04 42697.93 41095.99 28899.66 35495.31 38598.82 41899.43 212
Anonymous2024052198.69 15198.87 11198.16 31299.77 2795.11 38299.08 6299.44 18399.34 6599.33 13799.55 5694.10 36199.94 4199.25 6799.96 2899.42 217
MVS_111021_HR98.25 23598.08 24898.75 21199.09 27497.46 23395.97 42499.27 26397.60 26597.99 36098.25 37998.15 12499.38 45596.87 28299.57 28499.42 217
COLMAP_ROBcopyleft96.50 1098.99 9498.85 11899.41 6999.58 9499.10 6598.74 9999.56 11899.09 11099.33 13799.19 15998.40 8699.72 30695.98 35899.76 18899.42 217
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SED-MVS98.91 10698.72 13299.49 5599.49 14899.17 4398.10 19399.31 24098.03 22599.66 6099.02 21198.36 9099.88 11596.91 27499.62 26499.41 220
OPU-MVS98.82 19098.59 38798.30 13298.10 19398.52 34298.18 11898.75 49894.62 40299.48 31799.41 220
our_test_397.39 32297.73 28596.34 44498.70 36289.78 50794.61 48198.97 33596.50 36099.04 20198.85 26895.98 28999.84 17797.26 24299.67 24299.41 220
casdiffmvspermissive98.95 10299.00 9498.81 19299.38 18597.33 24297.82 24499.57 10999.17 9399.35 13099.17 16798.35 9499.69 32598.46 12999.73 19999.41 220
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
YYNet197.60 30397.67 29097.39 39799.04 28793.04 45595.27 45798.38 41097.25 30898.92 23298.95 24395.48 31099.73 29696.99 26798.74 42299.41 220
MDA-MVSNet_test_wron97.60 30397.66 29397.41 39699.04 28793.09 45195.27 45798.42 40797.26 30798.88 24198.95 24395.43 31299.73 29697.02 26398.72 42499.41 220
GBi-Net98.65 16298.47 18199.17 11598.90 32098.24 13799.20 4999.44 18398.59 16898.95 22199.55 5694.14 35799.86 14497.77 19399.69 23099.41 220
test198.65 16298.47 18199.17 11598.90 32098.24 13799.20 4999.44 18398.59 16898.95 22199.55 5694.14 35799.86 14497.77 19399.69 23099.41 220
FMVSNet199.17 5299.17 6099.17 11599.55 11798.24 13799.20 4999.44 18399.21 8299.43 10899.55 5697.82 15499.86 14498.42 13799.89 9499.41 220
test_fmvs197.72 29497.94 26597.07 41398.66 37792.39 46797.68 26899.81 3295.20 42799.54 7999.44 8591.56 41199.41 45099.78 2199.77 17299.40 229
viewdifsd2359ckpt0798.71 14298.86 11598.26 29799.43 17495.65 34797.20 33899.66 7099.20 8499.29 14899.01 22298.29 9999.73 29697.92 17799.75 19299.39 230
viewmanbaseed2359cas98.58 17698.54 16698.70 22199.28 21597.13 27297.47 30699.55 12397.55 27198.96 22098.92 24897.77 15799.59 39097.59 21399.77 17299.39 230
KD-MVS_self_test99.25 4099.18 5999.44 6599.63 8399.06 7098.69 10899.54 12999.31 6999.62 6999.53 6497.36 19799.86 14499.24 6999.71 21799.39 230
v14898.45 19998.60 15898.00 33199.44 16994.98 38597.44 31099.06 31498.30 19399.32 14398.97 23596.65 24999.62 37398.37 13999.85 10999.39 230
test20.0398.78 13398.77 12798.78 20299.46 16297.20 26297.78 25099.24 27699.04 11999.41 11498.90 25497.65 16599.76 27097.70 20399.79 15999.39 230
CDPH-MVS97.26 33496.66 36699.07 13699.00 30198.15 14696.03 42199.01 32991.21 50297.79 37797.85 41596.89 22899.69 32592.75 46399.38 33999.39 230
EPNet96.14 40195.44 41798.25 29990.76 54495.50 35797.92 23194.65 50398.97 12792.98 52098.85 26889.12 43799.87 13595.99 35799.68 23699.39 230
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS98.17 24897.87 27399.07 13698.67 37298.24 13797.01 34898.93 33997.25 30897.62 38798.34 36697.27 20399.57 39996.42 33199.33 34799.39 230
DeepC-MVS_fast96.85 698.30 22598.15 24098.75 21198.61 38297.23 25697.76 25699.09 31097.31 30198.75 26798.66 31797.56 17799.64 36696.10 35599.55 29399.39 230
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
dtuplus98.32 22098.39 19498.10 31799.15 26195.29 37196.68 37199.51 14197.32 29999.18 17999.15 17597.61 17299.62 37397.19 24799.74 19599.38 239
SF-MVS98.53 18798.27 21999.32 9199.31 20698.75 9098.19 17899.41 19996.77 34798.83 25298.90 25497.80 15599.82 20795.68 37499.52 30299.38 239
test9_res93.28 44699.15 38399.38 239
hybridnocas0798.32 22098.37 19998.17 30999.14 26395.51 35396.67 37399.56 11897.85 24198.75 26798.95 24396.65 24999.63 36998.00 16999.78 16499.37 242
BP-MVS197.40 32196.97 33998.71 22099.07 27896.81 29398.34 16397.18 45398.58 17198.17 33998.61 33084.01 48299.94 4198.97 8999.78 16499.37 242
OPM-MVS98.56 17898.32 21199.25 10499.41 17998.73 9497.13 34599.18 29097.10 32398.75 26798.92 24898.18 11899.65 36196.68 30399.56 28899.37 242
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
agg_prior292.50 47099.16 38199.37 242
AllTest98.44 20098.20 22999.16 11899.50 14098.55 10898.25 17299.58 10196.80 34498.88 24199.06 19997.65 16599.57 39994.45 40899.61 26999.37 242
TestCases99.16 11899.50 14098.55 10899.58 10196.80 34498.88 24199.06 19997.65 16599.57 39994.45 40899.61 26999.37 242
MDA-MVSNet-bldmvs97.94 27197.91 27098.06 32599.44 16994.96 38696.63 37799.15 30298.35 18698.83 25299.11 18794.31 35299.85 15896.60 31298.72 42499.37 242
MVSTER96.86 36496.55 37597.79 34897.91 45294.21 41397.56 29098.87 35297.49 27899.06 19199.05 20680.72 49599.80 23398.44 13199.82 13399.37 242
dtuonlycased97.70 29698.19 23396.24 44999.75 3489.51 50994.69 47799.64 7898.23 20099.46 10198.57 33598.25 10799.85 15895.65 37599.44 32899.36 250
viewcassd2359sk1198.55 18298.51 17098.67 22699.29 21296.99 27997.39 31399.54 12997.73 25198.81 25799.08 19797.55 17899.66 35497.52 22199.67 24299.36 250
pmmvs597.64 30197.49 30598.08 32299.14 26395.12 38196.70 37099.05 31893.77 46698.62 28998.83 27593.23 37799.75 28298.33 14399.76 18899.36 250
Anonymous2023120698.21 24098.21 22898.20 30699.51 13495.43 36398.13 18699.32 23596.16 37998.93 23098.82 27896.00 28499.83 19597.32 23899.73 19999.36 250
train_agg97.10 34796.45 38299.07 13698.71 35898.08 15995.96 42699.03 32391.64 49495.85 47797.53 43596.47 25799.76 27093.67 43399.16 38199.36 250
PVSNet_BlendedMVS97.55 30897.53 30297.60 37598.92 31693.77 44096.64 37699.43 18994.49 44497.62 38799.18 16396.82 23399.67 34194.73 39999.93 5799.36 250
hybrid98.22 23798.27 21998.08 32299.13 26695.24 37396.61 37999.53 13397.43 28898.46 31598.97 23596.75 24399.65 36197.84 18699.69 23099.35 256
Anonymous2024052998.93 10498.87 11199.12 12499.19 24598.22 14299.01 7198.99 33299.25 7699.54 7999.37 10497.04 21799.80 23397.89 17899.52 30299.35 256
F-COLMAP97.30 33196.68 36299.14 12299.19 24598.39 12197.27 33299.30 24892.93 48096.62 45298.00 40395.73 29999.68 33692.62 46698.46 44499.35 256
viewdifsd2359ckpt1398.39 21198.29 21598.70 22199.26 22797.19 26397.51 29899.48 15696.94 33298.58 29898.82 27897.47 19199.55 40697.21 24699.33 34799.34 259
ppachtmachnet_test97.50 30997.74 28296.78 43098.70 36291.23 49094.55 48399.05 31896.36 36799.21 17298.79 28496.39 26299.78 25896.74 29499.82 13399.34 259
VDD-MVS98.56 17898.39 19499.07 13699.13 26698.07 16298.59 12297.01 45899.59 3699.11 18499.27 13194.82 33099.79 24698.34 14199.63 26099.34 259
testgi98.32 22098.39 19498.13 31499.57 10395.54 35197.78 25099.49 15497.37 29499.19 17497.65 42898.96 3099.49 42996.50 32698.99 40499.34 259
diffmvspermissive98.22 23798.24 22698.17 30999.00 30195.44 36296.38 39699.58 10197.79 24798.53 30798.50 34796.76 24099.74 28997.95 17699.64 25499.34 259
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UnsupCasMVSNet_eth97.89 27597.60 29998.75 21199.31 20697.17 26897.62 27999.35 22198.72 15798.76 26698.68 31192.57 39499.74 28997.76 19795.60 52199.34 259
dtuonly96.49 38197.28 31794.10 50298.80 34483.27 53693.66 50999.48 15695.10 42897.87 36998.30 37395.61 30399.68 33696.98 27099.75 19299.33 265
viewmambaseed2359dif98.19 24398.26 22297.99 33399.02 29795.03 38496.59 38299.53 13396.21 37499.00 20698.99 22897.62 17099.61 38197.62 20999.72 20899.33 265
baseline98.96 10199.02 9098.76 20999.38 18597.26 25498.49 14099.50 14698.86 14299.19 17499.06 19998.23 11099.69 32598.71 11099.76 18899.33 265
MG-MVS96.77 36896.61 37197.26 40298.31 41793.06 45295.93 42998.12 42396.45 36597.92 36498.73 29693.77 36899.39 45391.19 49399.04 39599.33 265
DKM98.18 24597.95 26298.85 18099.35 19798.31 13196.68 37199.69 5696.90 33798.61 29198.77 28894.41 34598.93 49197.32 23899.84 11499.32 269
HQP4-MVS95.56 48399.54 41299.32 269
CDS-MVSNet97.69 29797.35 31498.69 22398.73 35297.02 27896.92 35898.75 37895.89 39298.59 29698.67 31392.08 40599.74 28996.72 29799.81 14099.32 269
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP-MVS97.00 35896.49 37898.55 25698.67 37296.79 29496.29 40399.04 32196.05 38295.55 48496.84 46293.84 36499.54 41292.82 45999.26 36499.32 269
RPSCF98.62 16998.36 20199.42 6799.65 7199.42 1098.55 12699.57 10997.72 25398.90 23499.26 13796.12 27999.52 41895.72 37199.71 21799.32 269
E3new98.41 20298.34 20598.62 23899.19 24596.90 28797.32 32399.50 14697.40 29198.63 28598.92 24897.21 20899.65 36197.34 23499.52 30299.31 274
MVP-Stereo98.08 25697.92 26898.57 24998.96 30896.79 29497.90 23499.18 29096.41 36698.46 31598.95 24395.93 29399.60 38596.51 32598.98 40799.31 274
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS98.40 20598.68 14197.54 38498.96 30897.99 17197.88 23699.36 21598.20 20899.63 6699.04 20898.76 4695.33 53696.56 31999.74 19599.31 274
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
VNet98.42 20198.30 21398.79 19998.79 34797.29 25198.23 17398.66 38699.31 6998.85 24898.80 28294.80 33399.78 25898.13 15499.13 38699.31 274
test_prior98.95 16498.69 36797.95 17999.03 32399.59 39099.30 278
USDC97.41 32097.40 30997.44 39498.94 31093.67 44395.17 46199.53 13394.03 46298.97 21599.10 19095.29 31599.34 46095.84 36799.73 19999.30 278
viewdifsd2359ckpt0998.13 25197.92 26898.77 20799.18 25397.35 24097.29 32799.53 13395.81 39998.09 35098.47 35196.34 26799.66 35497.02 26399.51 30599.29 280
test_fmvsm_n_192099.33 3099.45 2398.99 15499.57 10397.73 21097.93 22899.83 2699.22 8099.93 699.30 12599.42 1199.96 1399.85 699.99 599.29 280
FMVSNet298.49 19498.40 19198.75 21198.90 32097.14 27198.61 12099.13 30498.59 16899.19 17499.28 12994.14 35799.82 20797.97 17499.80 15299.29 280
RoMa-SfM98.46 19798.27 21999.02 14999.35 19798.32 13097.56 29099.70 5395.88 39399.38 12198.65 31996.41 26099.46 44097.78 19199.71 21799.28 283
gbinet_0.2-2-1-0.0295.44 43194.55 44698.14 31395.99 52795.34 36994.71 47398.29 41396.00 38796.05 47490.50 53684.99 47199.79 24697.33 23697.07 49899.28 283
XVG-OURS-SEG-HR98.49 19498.28 21699.14 12299.49 14898.83 8696.54 38399.48 15697.32 29999.11 18498.61 33099.33 1599.30 46796.23 34498.38 44699.28 283
mamba_040898.80 12998.88 10898.55 25699.27 21896.50 31198.00 21499.60 9298.93 13299.22 16998.84 27398.59 6799.89 9797.74 19999.72 20899.27 286
SSM_0407298.80 12998.88 10898.56 25499.27 21896.50 31198.00 21499.60 9298.93 13299.22 16998.84 27398.59 6799.90 8197.74 19999.72 20899.27 286
SSM_040798.86 11698.96 10098.55 25699.27 21896.50 31198.04 20599.66 7099.09 11099.22 16999.02 21198.79 4399.87 13597.87 18399.72 20899.27 286
test1298.93 16898.58 38997.83 19498.66 38696.53 45795.51 30899.69 32599.13 38699.27 286
DSMNet-mixed97.42 31997.60 29996.87 42499.15 26191.46 48098.54 12899.12 30592.87 48397.58 39199.63 3996.21 27399.90 8195.74 37099.54 29599.27 286
N_pmnet97.63 30297.17 32598.99 15499.27 21897.86 19195.98 42393.41 51995.25 42499.47 10098.90 25495.63 30299.85 15896.91 27499.73 19999.27 286
ambc98.24 30198.82 33895.97 33598.62 11899.00 33199.27 15299.21 15496.99 22299.50 42596.55 32299.50 31399.26 292
DenseAffine98.10 25297.86 27498.84 18699.32 20497.93 18296.62 37899.76 3996.68 35398.65 28298.72 29894.46 34399.33 46296.76 29199.75 19299.25 293
LFMVS97.20 34196.72 35998.64 23298.72 35496.95 28398.93 8294.14 51499.74 1298.78 26199.01 22284.45 47799.73 29697.44 22999.27 36099.25 293
FMVSNet596.01 40695.20 43298.41 28097.53 47896.10 32598.74 9999.50 14697.22 31798.03 35799.04 20869.80 52199.88 11597.27 24199.71 21799.25 293
BH-RMVSNet96.83 36596.58 37497.58 37798.47 40094.05 42096.67 37397.36 44396.70 35297.87 36997.98 40595.14 32199.44 44590.47 50398.58 43999.25 293
testf199.25 4099.16 6299.51 4999.89 699.63 398.71 10699.69 5698.90 13699.43 10899.35 11198.86 3599.67 34197.81 18899.81 14099.24 297
APD_test299.25 4099.16 6299.51 4999.89 699.63 398.71 10699.69 5698.90 13699.43 10899.35 11198.86 3599.67 34197.81 18899.81 14099.24 297
SSM_040498.90 10899.01 9298.57 24999.42 17696.59 30398.13 18699.66 7099.09 11099.30 14799.02 21198.79 4399.89 9797.87 18399.80 15299.23 299
旧先验198.82 33897.45 23498.76 37498.34 36695.50 30999.01 40199.23 299
test22298.92 31696.93 28595.54 44598.78 37185.72 52996.86 44098.11 39394.43 34499.10 39199.23 299
XVG-ACMP-BASELINE98.56 17898.34 20599.22 10999.54 12398.59 10597.71 26499.46 17197.25 30898.98 21198.99 22897.54 18099.84 17795.88 36199.74 19599.23 299
FMVSNet397.50 30997.24 32198.29 29598.08 44395.83 34197.86 24098.91 34597.89 23898.95 22198.95 24387.06 45199.81 22497.77 19399.69 23099.23 299
icg_test_0407_298.20 24298.38 19797.65 36899.03 29094.03 42395.78 43899.45 17598.16 21499.06 19198.71 30098.27 10399.68 33697.50 22299.45 32199.22 304
IMVS_040798.39 21198.64 14997.66 36699.03 29094.03 42398.10 19399.45 17598.16 21499.06 19198.71 30098.27 10399.71 30897.50 22299.45 32199.22 304
IMVS_040498.07 25798.20 22997.69 36199.03 29094.03 42396.67 37399.45 17598.16 21498.03 35798.71 30096.80 23699.82 20797.50 22299.45 32199.22 304
IMVS_040398.34 21598.56 16397.66 36699.03 29094.03 42397.98 22299.45 17598.16 21498.89 23798.71 30097.90 14399.74 28997.50 22299.45 32199.22 304
无先验95.74 44098.74 38089.38 51599.73 29692.38 47399.22 304
blended_shiyan895.98 40995.33 42397.94 33697.05 49994.87 39295.34 45598.59 39296.17 37597.09 42292.39 52787.62 45099.76 27097.65 20696.05 51899.20 309
tttt051795.64 42394.98 43697.64 37199.36 19293.81 43898.72 10490.47 53398.08 22498.67 27998.34 36673.88 51699.92 6597.77 19399.51 30599.20 309
pmmvs-eth3d98.47 19698.34 20598.86 17999.30 21097.76 20697.16 34399.28 26095.54 41199.42 11299.19 15997.27 20399.63 36997.89 17899.97 2199.20 309
MS-PatchMatch97.68 29897.75 28197.45 39398.23 42993.78 43997.29 32798.84 36196.10 38198.64 28498.65 31996.04 28199.36 45696.84 28599.14 38499.20 309
新几何198.91 17398.94 31097.76 20698.76 37487.58 52696.75 44598.10 39494.80 33399.78 25892.73 46499.00 40299.20 309
PHI-MVS98.29 22897.95 26299.34 8398.44 40599.16 4898.12 19099.38 20796.01 38698.06 35398.43 35597.80 15599.67 34195.69 37399.58 28099.20 309
blended_shiyan695.99 40895.33 42397.95 33597.06 49794.89 39095.34 45598.58 39396.17 37597.06 42492.41 52687.64 44999.76 27097.64 20796.09 51299.19 315
GDP-MVS97.50 30997.11 33298.67 22699.02 29796.85 29198.16 18399.71 4898.32 19198.52 30998.54 33883.39 48699.95 2598.79 10199.56 28899.19 315
Anonymous20240521197.90 27397.50 30499.08 13498.90 32098.25 13698.53 12996.16 48198.87 14099.11 18498.86 26590.40 42699.78 25897.36 23399.31 35299.19 315
CANet97.87 28097.76 28098.19 30897.75 46195.51 35396.76 36699.05 31897.74 25096.93 43098.21 38495.59 30599.89 9797.86 18599.93 5799.19 315
XVG-OURS98.53 18798.34 20599.11 12699.50 14098.82 8895.97 42499.50 14697.30 30299.05 19998.98 23399.35 1499.32 46495.72 37199.68 23699.18 319
WTY-MVS96.67 37196.27 39097.87 34398.81 34194.61 40396.77 36597.92 42894.94 43397.12 41997.74 42391.11 41899.82 20793.89 42698.15 46099.18 319
Vis-MVSNet (Re-imp)97.46 31497.16 32698.34 29099.55 11796.10 32598.94 8198.44 40498.32 19198.16 34298.62 32888.76 43899.73 29693.88 42799.79 15999.18 319
TinyColmap97.89 27597.98 25897.60 37598.86 32994.35 40996.21 40899.44 18397.45 28699.06 19198.88 26297.99 13799.28 47194.38 41499.58 28099.18 319
wanda-best-256-51295.48 42994.74 44397.68 36296.53 51394.12 41794.17 49598.57 39595.84 39596.71 44691.16 53286.05 46199.76 27097.57 21496.09 51299.17 323
FE-blended-shiyan795.48 42994.74 44397.68 36296.53 51394.12 41794.17 49598.57 39595.84 39596.71 44691.16 53286.05 46199.76 27097.57 21496.09 51299.17 323
usedtu_blend_shiyan596.20 40095.62 40697.94 33696.53 51394.93 38798.83 9699.59 9898.89 13896.71 44691.16 53286.05 46199.73 29696.70 30096.09 51299.17 323
testdata98.09 31998.93 31295.40 36498.80 36890.08 51197.45 40598.37 36295.26 31699.70 31593.58 43798.95 41099.17 323
lupinMVS97.06 35296.86 34897.65 36898.88 32693.89 43695.48 44997.97 42693.53 46998.16 34297.58 43293.81 36699.91 7496.77 29099.57 28499.17 323
Patchmtry97.35 32696.97 33998.50 27097.31 49096.47 31498.18 17998.92 34398.95 13198.78 26199.37 10485.44 46999.85 15895.96 35999.83 12699.17 323
usedtu_dtu_shiyan197.37 32397.13 33098.11 31599.03 29095.40 36494.47 48598.99 33296.87 33997.97 36197.81 41892.12 40299.75 28297.49 22799.43 33099.16 329
FE-MVSNET397.37 32397.13 33098.11 31599.03 29095.40 36494.47 48598.99 33296.87 33997.97 36197.81 41892.12 40299.75 28297.49 22799.43 33099.16 329
SD_040396.28 39495.83 39897.64 37198.72 35494.30 41098.87 8998.77 37297.80 24596.53 45798.02 40297.34 19899.47 43676.93 53699.48 31799.16 329
RRT-MVS97.88 27897.98 25897.61 37498.15 43693.77 44098.97 7799.64 7899.16 9498.69 27599.42 8991.60 40899.89 9797.63 20898.52 44399.16 329
sss97.21 34096.93 34198.06 32598.83 33595.22 37796.75 36798.48 40394.49 44497.27 41497.90 41192.77 39099.80 23396.57 31599.32 35099.16 329
CSCG98.68 15798.50 17399.20 11099.45 16798.63 10098.56 12599.57 10997.87 23998.85 24898.04 40097.66 16499.84 17796.72 29799.81 14099.13 334
MVS_111021_LR98.30 22598.12 24398.83 18899.16 25798.03 16796.09 41899.30 24897.58 26698.10 34998.24 38198.25 10799.34 46096.69 30299.65 25299.12 335
miper_lstm_enhance97.18 34397.16 32697.25 40398.16 43592.85 45895.15 46399.31 24097.25 30898.74 27098.78 28690.07 42799.78 25897.19 24799.80 15299.11 336
testing393.51 46892.09 48197.75 35498.60 38494.40 40797.32 32395.26 49997.56 26996.79 44495.50 49353.57 54599.77 26495.26 38798.97 40899.08 337
原ACMM198.35 28998.90 32096.25 32298.83 36592.48 48796.07 47298.10 39495.39 31399.71 30892.61 46798.99 40499.08 337
QAPM97.31 32996.81 35498.82 19098.80 34497.49 22799.06 6699.19 28690.22 50997.69 38399.16 16996.91 22799.90 8190.89 49999.41 33399.07 339
PAPM_NR96.82 36796.32 38698.30 29499.07 27896.69 30197.48 30298.76 37495.81 39996.61 45396.47 47294.12 36099.17 47890.82 50197.78 47499.06 340
eth_miper_zixun_eth97.23 33897.25 32097.17 40798.00 44792.77 46094.71 47399.18 29097.27 30698.56 30298.74 29591.89 40699.69 32597.06 26299.81 14099.05 341
D2MVS97.84 28797.84 27697.83 34599.14 26394.74 39796.94 35498.88 35095.84 39598.89 23798.96 23994.40 34799.69 32597.55 21699.95 3999.05 341
c3_l97.36 32597.37 31297.31 39898.09 44293.25 45095.01 46699.16 29797.05 32598.77 26498.72 29892.88 38799.64 36696.93 27399.76 18899.05 341
PLCcopyleft94.65 1696.51 37895.73 40298.85 18098.75 35097.91 18596.42 39499.06 31490.94 50695.59 48197.38 44894.41 34599.59 39090.93 49798.04 46999.05 341
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tfpnnormal98.90 10898.90 10598.91 17399.67 6897.82 19999.00 7399.44 18399.45 5099.51 9299.24 14498.20 11799.86 14495.92 36099.69 23099.04 345
CANet_DTU97.26 33497.06 33497.84 34497.57 47394.65 40296.19 41098.79 36997.23 31495.14 49498.24 38193.22 37899.84 17797.34 23499.84 11499.04 345
PM-MVS98.82 12598.72 13299.12 12499.64 7798.54 11197.98 22299.68 6397.62 26099.34 13499.18 16397.54 18099.77 26497.79 19099.74 19599.04 345
TestfortrainingZip98.97 16098.30 41898.43 11998.68 10998.26 41497.76 24998.86 24798.16 38995.15 32099.47 43697.55 47999.02 348
TSAR-MVS + GP.98.18 24597.98 25898.77 20798.71 35897.88 18996.32 40198.66 38696.33 36899.23 16898.51 34397.48 19099.40 45197.16 25099.46 31999.02 348
DIV-MVS_self_test97.02 35596.84 35097.58 37797.82 45894.03 42394.66 47899.16 29797.04 32698.63 28598.71 30088.69 43999.69 32597.00 26599.81 14099.01 350
GA-MVS95.86 41595.32 42597.49 38998.60 38494.15 41693.83 50697.93 42795.49 41396.68 44997.42 44683.21 48799.30 46796.22 34598.55 44199.01 350
OMC-MVS97.88 27897.49 30599.04 14598.89 32598.63 10096.94 35499.25 27195.02 43098.53 30798.51 34397.27 20399.47 43693.50 44199.51 30599.01 350
cl____97.02 35596.83 35197.58 37797.82 45894.04 42294.66 47899.16 29797.04 32698.63 28598.71 30088.68 44199.69 32597.00 26599.81 14099.00 353
pmmvs497.58 30697.28 31798.51 26698.84 33396.93 28595.40 45398.52 40193.60 46898.61 29198.65 31995.10 32299.60 38596.97 27199.79 15998.99 354
blend_shiyan492.09 49290.16 49997.88 34196.78 50794.93 38795.24 45998.58 39396.22 37396.07 47291.42 53163.46 54099.73 29696.70 30076.98 54098.98 355
EPNet_dtu94.93 44594.78 44195.38 48693.58 53587.68 51896.78 36495.69 49597.35 29689.14 53598.09 39688.15 44799.49 42994.95 39599.30 35698.98 355
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t96.50 38095.77 40098.69 22399.48 15697.43 23797.84 24399.55 12381.42 53596.51 46198.58 33495.53 30699.67 34193.41 44499.58 28098.98 355
PVSNet_Blended96.88 36296.68 36297.47 39298.92 31693.77 44094.71 47399.43 18990.98 50597.62 38797.36 45096.82 23399.67 34194.73 39999.56 28898.98 355
ArgMatch-SfM97.96 27097.72 28698.66 22899.02 29797.33 24296.49 38899.52 13995.46 41598.71 27498.29 37696.14 27599.69 32596.30 34099.56 28898.97 359
APD_test198.83 12298.66 14699.34 8399.78 2499.47 898.42 15199.45 17598.28 19898.98 21199.19 15997.76 15899.58 39796.57 31599.55 29398.97 359
PAPR95.29 43594.47 44797.75 35497.50 48495.14 38094.89 47098.71 38391.39 50095.35 49195.48 49594.57 34099.14 48184.95 52397.37 48998.97 359
EGC-MVSNET85.24 50380.54 50699.34 8399.77 2799.20 3899.08 6299.29 25612.08 54320.84 54499.42 8997.55 17899.85 15897.08 25999.72 20898.96 362
thisisatest053095.27 43694.45 44897.74 35699.19 24594.37 40897.86 24090.20 53497.17 31998.22 33797.65 42873.53 51799.90 8196.90 27999.35 34398.95 363
mvs_anonymous97.83 28998.16 23996.87 42498.18 43291.89 47497.31 32598.90 34697.37 29498.83 25299.46 8096.28 27099.79 24698.90 9498.16 45998.95 363
baseline195.96 41295.44 41797.52 38698.51 39893.99 43098.39 15796.09 48598.21 20498.40 32697.76 42286.88 45299.63 36995.42 38389.27 53498.95 363
CLD-MVS97.49 31297.16 32698.48 27299.07 27897.03 27794.71 47399.21 28094.46 44698.06 35397.16 45697.57 17699.48 43394.46 40799.78 16498.95 363
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MSLP-MVS++98.02 26198.14 24297.64 37198.58 38995.19 37897.48 30299.23 27897.47 27997.90 36698.62 32897.04 21798.81 49697.55 21699.41 33398.94 367
DELS-MVS98.27 23098.20 22998.48 27298.86 32996.70 30095.60 44499.20 28297.73 25198.45 31798.71 30097.50 18699.82 20798.21 14999.59 27598.93 368
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
cl2295.79 41895.39 42096.98 41796.77 50892.79 45994.40 48898.53 39994.59 44397.89 36798.17 38782.82 49199.24 47396.37 33499.03 39698.92 369
LS3D98.63 16698.38 19799.36 7497.25 49199.38 1299.12 6199.32 23599.21 8298.44 31898.88 26297.31 19999.80 23396.58 31399.34 34598.92 369
CMPMVSbinary75.91 2396.29 39395.44 41798.84 18696.25 52298.69 9897.02 34799.12 30588.90 51897.83 37498.86 26589.51 43498.90 49491.92 47799.51 30598.92 369
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LCM-MVSNet-Re98.64 16498.48 17999.11 12698.85 33298.51 11398.49 14099.83 2698.37 18499.69 5599.46 8098.21 11599.92 6594.13 42099.30 35698.91 372
mvsmamba97.57 30797.26 31998.51 26698.69 36796.73 29998.74 9997.25 45097.03 32897.88 36899.23 15090.95 41999.87 13596.61 31199.00 40298.91 372
DPM-MVS96.32 39195.59 41098.51 26698.76 34897.21 26194.54 48498.26 41491.94 49396.37 46597.25 45493.06 38499.43 44791.42 48898.74 42298.89 374
test_yl96.69 36996.29 38897.90 33898.28 42195.24 37397.29 32797.36 44398.21 20498.17 33997.86 41386.27 45699.55 40694.87 39698.32 44898.89 374
DCV-MVSNet96.69 36996.29 38897.90 33898.28 42195.24 37397.29 32797.36 44398.21 20498.17 33997.86 41386.27 45699.55 40694.87 39698.32 44898.89 374
SPE-MVS-test99.13 6699.09 8299.26 10199.13 26698.97 7399.31 3099.88 1599.44 5298.16 34298.51 34398.64 6199.93 5398.91 9399.85 10998.88 377
UnsupCasMVSNet_bld97.30 33196.92 34398.45 27599.28 21596.78 29796.20 40999.27 26395.42 41798.28 33498.30 37393.16 37999.71 30894.99 39297.37 48998.87 378
Effi-MVS+98.02 26197.82 27798.62 23898.53 39697.19 26397.33 32299.68 6397.30 30296.68 44997.46 44498.56 7399.80 23396.63 30998.20 45598.86 379
test_040298.76 13798.71 13598.93 16899.56 11198.14 14898.45 14799.34 22799.28 7398.95 22198.91 25198.34 9599.79 24695.63 37699.91 8098.86 379
PMatch-SfM97.89 27597.64 29598.66 22899.26 22797.44 23696.08 41999.51 14196.72 34998.47 31499.13 18193.62 37299.70 31597.14 25398.80 41998.83 381
PatchmatchNetpermissive95.58 42595.67 40595.30 48997.34 48887.32 52097.65 27496.65 47295.30 42197.07 42398.69 30984.77 47499.75 28294.97 39498.64 43398.83 381
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing3-293.78 46493.91 45593.39 51398.82 33881.72 54297.76 25695.28 49898.60 16796.54 45696.66 46765.85 53399.62 37396.65 30898.99 40498.82 383
test_vis1_rt97.75 29297.72 28697.83 34598.81 34196.35 31997.30 32699.69 5694.61 44297.87 36998.05 39996.26 27198.32 50598.74 10798.18 45698.82 383
CL-MVSNet_self_test97.44 31797.22 32398.08 32298.57 39195.78 34594.30 49198.79 36996.58 35798.60 29498.19 38694.74 33699.64 36696.41 33298.84 41598.82 383
miper_ehance_all_eth97.06 35297.03 33597.16 40997.83 45793.06 45294.66 47899.09 31095.99 38898.69 27598.45 35392.73 39299.61 38196.79 28799.03 39698.82 383
MIMVSNet96.62 37496.25 39197.71 36099.04 28794.66 40199.16 5596.92 46697.23 31497.87 36999.10 19086.11 46099.65 36191.65 48399.21 37398.82 383
hse-mvs297.46 31497.07 33398.64 23298.73 35297.33 24297.45 30897.64 43899.11 10098.58 29897.98 40588.65 44299.79 24698.11 15697.39 48898.81 388
GSMVS98.81 388
sam_mvs184.74 47598.81 388
SCA96.41 38896.66 36695.67 47698.24 42688.35 51495.85 43596.88 46796.11 38097.67 38498.67 31393.10 38299.85 15894.16 41699.22 37098.81 388
Patchmatch-RL test97.26 33497.02 33697.99 33399.52 13195.53 35296.13 41599.71 4897.47 27999.27 15299.16 16984.30 48099.62 37397.89 17899.77 17298.81 388
AUN-MVS96.24 39995.45 41698.60 24498.70 36297.22 25997.38 31597.65 43695.95 39095.53 48897.96 40982.11 49499.79 24696.31 33897.44 48598.80 393
ITE_SJBPF98.87 17799.22 23698.48 11599.35 22197.50 27698.28 33498.60 33297.64 16899.35 45993.86 42899.27 36098.79 394
tpm94.67 44794.34 45295.66 47797.68 47088.42 51397.88 23694.90 50194.46 44696.03 47698.56 33778.66 50699.79 24695.88 36195.01 52498.78 395
Patchmatch-test96.55 37796.34 38597.17 40798.35 41493.06 45298.40 15697.79 42997.33 29798.41 32198.67 31383.68 48599.69 32595.16 39099.31 35298.77 396
EC-MVSNet99.09 7399.05 8699.20 11099.28 21598.93 7999.24 4499.84 2399.08 11498.12 34798.37 36298.72 5099.90 8199.05 8399.77 17298.77 396
PMMVS96.51 37895.98 39498.09 31997.53 47895.84 34094.92 46898.84 36191.58 49696.05 47495.58 49095.68 30199.66 35495.59 37998.09 46398.76 398
test_method79.78 50479.50 50780.62 52280.21 54745.76 55070.82 53898.41 40931.08 54280.89 54297.71 42484.85 47397.37 52191.51 48780.03 53898.75 399
ab-mvs98.41 20298.36 20198.59 24599.19 24597.23 25699.32 2698.81 36697.66 25798.62 28999.40 9796.82 23399.80 23395.88 36199.51 30598.75 399
ELoFTR97.81 29097.74 28298.04 32899.39 18395.79 34497.28 33199.58 10194.13 45799.38 12199.37 10493.31 37599.60 38597.23 24499.96 2898.74 401
CHOSEN 280x42095.51 42895.47 41495.65 47898.25 42488.27 51593.25 51798.88 35093.53 46994.65 50397.15 45786.17 45899.93 5397.41 23199.93 5798.73 402
test_fmvsmvis_n_192099.26 3999.49 1698.54 26199.66 7096.97 28098.00 21499.85 1999.24 7799.92 899.50 6899.39 1299.95 2599.89 399.98 1298.71 403
MVS_Test98.18 24598.36 20197.67 36498.48 39994.73 39898.18 17999.02 32697.69 25498.04 35699.11 18797.22 20799.56 40298.57 12198.90 41498.71 403
PVSNet93.40 1795.67 42195.70 40395.57 47998.83 33588.57 51292.50 52297.72 43192.69 48596.49 46496.44 47393.72 36999.43 44793.61 43499.28 35998.71 403
alignmvs97.35 32696.88 34798.78 20298.54 39498.09 15597.71 26497.69 43399.20 8497.59 39095.90 48488.12 44899.55 40698.18 15198.96 40998.70 406
ADS-MVSNet295.43 43294.98 43696.76 43198.14 43791.74 47597.92 23197.76 43090.23 50796.51 46198.91 25185.61 46699.85 15892.88 45796.90 49998.69 407
ADS-MVSNet95.24 43794.93 43996.18 45498.14 43790.10 50497.92 23197.32 44890.23 50796.51 46198.91 25185.61 46699.74 28992.88 45796.90 49998.69 407
MDTV_nov1_ep13_2view74.92 54697.69 26790.06 51297.75 38085.78 46593.52 43998.69 407
LoFTR97.97 26997.79 27898.53 26398.80 34497.47 23197.01 34899.55 12395.55 40999.46 10199.22 15294.22 35599.44 44596.45 32999.82 13398.68 410
MSDG97.71 29597.52 30398.28 29698.91 31996.82 29294.42 48799.37 21197.65 25898.37 32798.29 37697.40 19499.33 46294.09 42199.22 37098.68 410
mvsany_test197.60 30397.54 30197.77 35097.72 46295.35 36795.36 45497.13 45694.13 45799.71 4999.33 11897.93 14199.30 46797.60 21298.94 41198.67 412
CS-MVS99.13 6699.10 8099.24 10699.06 28399.15 5299.36 2299.88 1599.36 6398.21 33898.46 35298.68 5899.93 5399.03 8599.85 10998.64 413
Syy-MVS96.04 40495.56 41297.49 38997.10 49594.48 40596.18 41296.58 47495.65 40594.77 50092.29 52991.27 41799.36 45698.17 15398.05 46798.63 414
myMVS_eth3d91.92 49490.45 49596.30 44597.10 49590.90 49596.18 41296.58 47495.65 40594.77 50092.29 52953.88 54499.36 45689.59 50998.05 46798.63 414
BridgeMVS98.63 16698.72 13298.38 28498.66 37796.68 30298.90 8499.42 19598.99 12498.97 21599.19 15995.81 29799.85 15898.77 10599.77 17298.60 416
miper_enhance_ethall96.01 40695.74 40196.81 42896.41 52092.27 47193.69 50898.89 34991.14 50398.30 33097.35 45190.58 42499.58 39796.31 33899.03 39698.60 416
Effi-MVS+-dtu98.26 23297.90 27199.35 8098.02 44699.49 598.02 21099.16 29798.29 19697.64 38597.99 40496.44 25999.95 2596.66 30798.93 41298.60 416
new_pmnet96.99 35996.76 35697.67 36498.72 35494.89 39095.95 42898.20 41892.62 48698.55 30498.54 33894.88 32999.52 41893.96 42499.44 32898.59 419
MVSMamba_PlusPlus98.83 12298.98 9798.36 28899.32 20496.58 30698.90 8499.41 19999.75 1098.72 27199.50 6896.17 27499.94 4199.27 6499.78 16498.57 420
testing9193.32 47292.27 47896.47 43997.54 47691.25 48896.17 41496.76 47097.18 31893.65 51893.50 51865.11 53599.63 36993.04 45297.45 48498.53 421
EIA-MVS98.00 26497.74 28298.80 19598.72 35498.09 15598.05 20399.60 9297.39 29296.63 45195.55 49197.68 16299.80 23396.73 29699.27 36098.52 422
PatchMatch-RL97.24 33796.78 35598.61 24299.03 29097.83 19496.36 39899.06 31493.49 47197.36 41297.78 42095.75 29899.49 42993.44 44398.77 42098.52 422
sasdasda98.34 21598.26 22298.58 24698.46 40297.82 19998.96 7899.46 17199.19 8997.46 40295.46 49698.59 6799.46 44098.08 15998.71 42698.46 424
ET-MVSNet_ETH3D94.30 45493.21 46697.58 37798.14 43794.47 40694.78 47293.24 52194.72 43989.56 53395.87 48578.57 50899.81 22496.91 27497.11 49798.46 424
canonicalmvs98.34 21598.26 22298.58 24698.46 40297.82 19998.96 7899.46 17199.19 8997.46 40295.46 49698.59 6799.46 44098.08 15998.71 42698.46 424
UBG93.25 47492.32 47696.04 46297.72 46290.16 50395.92 43195.91 48996.03 38593.95 51593.04 52369.60 52299.52 41890.72 50297.98 47198.45 427
tt080598.69 15198.62 15398.90 17699.75 3499.30 2199.15 5796.97 46198.86 14298.87 24697.62 43198.63 6398.96 48999.41 5698.29 45298.45 427
TAPA-MVS96.21 1196.63 37395.95 39698.65 23098.93 31298.09 15596.93 35699.28 26083.58 53298.13 34697.78 42096.13 27799.40 45193.52 43999.29 35898.45 427
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MGCFI-Net98.34 21598.28 21698.51 26698.47 40097.59 22298.96 7899.48 15699.18 9297.40 40895.50 49398.66 5999.50 42598.18 15198.71 42698.44 430
BH-untuned96.83 36596.75 35897.08 41198.74 35193.33 44996.71 36998.26 41496.72 34998.44 31897.37 44995.20 31799.47 43691.89 47897.43 48698.44 430
WB-MVSnew95.73 42095.57 41196.23 45196.70 51090.70 50096.07 42093.86 51695.60 40797.04 42695.45 50096.00 28499.55 40691.04 49498.31 45098.43 432
pmmvs395.03 44294.40 45096.93 42097.70 46792.53 46495.08 46497.71 43288.57 52197.71 38198.08 39779.39 50299.82 20796.19 34799.11 39098.43 432
DP-MVS Recon97.33 32896.92 34398.57 24999.09 27497.99 17196.79 36299.35 22193.18 47497.71 38198.07 39895.00 32599.31 46593.97 42399.13 38698.42 434
testing9993.04 47891.98 48696.23 45197.53 47890.70 50096.35 39995.94 48896.87 33993.41 51993.43 52063.84 53799.59 39093.24 44897.19 49498.40 435
ETVMVS92.60 48491.08 49397.18 40597.70 46793.65 44596.54 38395.70 49396.51 35894.68 50292.39 52761.80 54199.50 42586.97 51697.41 48798.40 435
Fast-Effi-MVS+-dtu98.27 23098.09 24598.81 19298.43 40798.11 15197.61 28499.50 14698.64 16097.39 41097.52 43898.12 12699.95 2596.90 27998.71 42698.38 437
LF4IMVS97.90 27397.69 28998.52 26599.17 25597.66 21597.19 34299.47 16696.31 37097.85 37398.20 38596.71 24599.52 41894.62 40299.72 20898.38 437
testing1193.08 47792.02 48396.26 44897.56 47490.83 49796.32 40195.70 49396.47 36392.66 52393.73 51564.36 53699.59 39093.77 43197.57 47898.37 439
Fast-Effi-MVS+97.67 29997.38 31198.57 24998.71 35897.43 23797.23 33399.45 17594.82 43796.13 46996.51 46998.52 7599.91 7496.19 34798.83 41698.37 439
test0.0.03 194.51 44993.69 45996.99 41696.05 52493.61 44794.97 46793.49 51896.17 37597.57 39394.88 50782.30 49299.01 48893.60 43694.17 52898.37 439
UWE-MVS92.38 48791.76 49094.21 50197.16 49384.65 52995.42 45288.45 53795.96 38996.17 46895.84 48766.36 52999.71 30891.87 47998.64 43398.28 442
FE-MVS95.66 42294.95 43897.77 35098.53 39695.28 37299.40 1996.09 48593.11 47697.96 36399.26 13779.10 50499.77 26492.40 47298.71 42698.27 443
baseline293.73 46592.83 47296.42 44197.70 46791.28 48796.84 36189.77 53593.96 46592.44 52595.93 48379.14 50399.77 26492.94 45496.76 50398.21 444
thisisatest051594.12 45993.16 46796.97 41898.60 38492.90 45793.77 50790.61 53294.10 45996.91 43395.87 48574.99 51499.80 23394.52 40599.12 38998.20 445
EPMVS93.72 46693.27 46595.09 49296.04 52587.76 51798.13 18685.01 54294.69 44096.92 43198.64 32378.47 51099.31 46595.04 39196.46 50698.20 445
balanced_ft_v198.28 22998.35 20498.10 31798.08 44396.23 32399.23 4599.26 26998.34 18797.46 40299.42 8995.38 31499.88 11598.60 11799.34 34598.17 447
dp93.47 46993.59 46193.13 51696.64 51181.62 54397.66 27296.42 47892.80 48496.11 47098.64 32378.55 50999.59 39093.31 44592.18 53398.16 448
CNLPA97.17 34496.71 36098.55 25698.56 39298.05 16696.33 40098.93 33996.91 33697.06 42497.39 44794.38 34899.45 44391.66 48299.18 38098.14 449
dmvs_re95.98 40995.39 42097.74 35698.86 32997.45 23498.37 15995.69 49597.95 23196.56 45595.95 48290.70 42397.68 51688.32 51296.13 51198.11 450
HY-MVS95.94 1395.90 41495.35 42297.55 38397.95 44994.79 39498.81 9896.94 46492.28 49095.17 49398.57 33589.90 42999.75 28291.20 49297.33 49398.10 451
CostFormer93.97 46193.78 45894.51 49797.53 47885.83 52597.98 22295.96 48789.29 51694.99 49798.63 32578.63 50799.62 37394.54 40496.50 50598.09 452
FA-MVS(test-final)96.99 35996.82 35297.50 38898.70 36294.78 39599.34 2396.99 45995.07 42998.48 31399.33 11888.41 44599.65 36196.13 35398.92 41398.07 453
AdaColmapbinary97.14 34696.71 36098.46 27498.34 41597.80 20396.95 35398.93 33995.58 40896.92 43197.66 42795.87 29599.53 41490.97 49699.14 38498.04 454
KD-MVS_2432*160092.87 48291.99 48495.51 48291.37 54089.27 51094.07 49898.14 42195.42 41797.25 41596.44 47367.86 52499.24 47391.28 49096.08 51698.02 455
miper_refine_blended92.87 48291.99 48495.51 48291.37 54089.27 51094.07 49898.14 42195.42 41797.25 41596.44 47367.86 52499.24 47391.28 49096.08 51698.02 455
TESTMET0.1,192.19 49191.77 48993.46 51096.48 51882.80 53994.05 50091.52 53194.45 44994.00 51394.88 50766.65 52899.56 40295.78 36998.11 46298.02 455
testing22291.96 49390.37 49696.72 43297.47 48592.59 46296.11 41794.76 50296.83 34392.90 52192.87 52457.92 54399.55 40686.93 51797.52 48098.00 458
PCF-MVS92.86 1894.36 45193.00 47098.42 27998.70 36297.56 22393.16 51999.11 30779.59 53697.55 39497.43 44592.19 40099.73 29679.85 53399.45 32197.97 459
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-2890.22 49789.28 50093.02 51794.50 53482.87 53896.52 38687.51 53895.21 42692.36 52696.04 47971.57 51998.25 50772.04 53897.77 47597.94 460
myMVS_eth3d2892.92 48192.31 47794.77 49397.84 45687.59 51996.19 41096.11 48397.08 32494.27 50693.49 51966.07 53298.78 49791.78 48097.93 47397.92 461
OpenMVScopyleft96.65 797.09 34996.68 36298.32 29198.32 41697.16 26998.86 9299.37 21189.48 51496.29 46799.15 17596.56 25399.90 8192.90 45699.20 37597.89 462
Gipumacopyleft99.03 8899.16 6298.64 23299.94 298.51 11399.32 2699.75 4399.58 3898.60 29499.62 4098.22 11399.51 42497.70 20399.73 19997.89 462
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PVSNet_089.98 2191.15 49690.30 49893.70 50897.72 46284.34 53390.24 52997.42 44190.20 51093.79 51693.09 52290.90 42198.89 49586.57 52072.76 54197.87 464
test-LLR93.90 46293.85 45694.04 50396.53 51384.62 53094.05 50092.39 52396.17 37594.12 50995.07 50182.30 49299.67 34195.87 36498.18 45697.82 465
test-mter92.33 48991.76 49094.04 50396.53 51384.62 53094.05 50092.39 52394.00 46494.12 50995.07 50165.63 53499.67 34195.87 36498.18 45697.82 465
tpm293.09 47692.58 47594.62 49697.56 47486.53 52297.66 27295.79 49286.15 52894.07 51198.23 38375.95 51299.53 41490.91 49896.86 50297.81 467
CR-MVSNet96.28 39495.95 39697.28 40097.71 46594.22 41198.11 19198.92 34392.31 48996.91 43399.37 10485.44 46999.81 22497.39 23297.36 49197.81 467
RPMNet97.02 35596.93 34197.30 39997.71 46594.22 41198.11 19199.30 24899.37 6096.91 43399.34 11586.72 45399.87 13597.53 21997.36 49197.81 467
tpmrst95.07 44195.46 41593.91 50597.11 49484.36 53297.62 27996.96 46294.98 43196.35 46698.80 28285.46 46899.59 39095.60 37896.23 50997.79 470
ALIKED-LG97.10 34796.63 36898.50 27097.96 44898.68 9997.75 25999.68 6395.86 39498.36 32998.33 37091.58 41099.04 48390.87 50099.31 35297.77 471
PAPM91.88 49590.34 49796.51 43798.06 44592.56 46392.44 52397.17 45486.35 52790.38 53296.01 48086.61 45499.21 47670.65 53995.43 52297.75 472
SP-LightGlue97.22 33997.01 33797.88 34197.33 48997.19 26396.38 39699.08 31297.28 30496.53 45797.50 43992.36 39698.70 50097.84 18698.76 42197.74 473
FPMVS93.44 47092.23 47997.08 41199.25 22997.86 19195.61 44397.16 45592.90 48293.76 51798.65 31975.94 51395.66 53479.30 53497.49 48297.73 474
MAR-MVS96.47 38495.70 40398.79 19997.92 45199.12 6298.28 16798.60 39192.16 49195.54 48796.17 47894.77 33599.52 41889.62 50798.23 45397.72 475
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
ETV-MVS98.03 26097.86 27498.56 25498.69 36798.07 16297.51 29899.50 14698.10 22197.50 39995.51 49298.41 8599.88 11596.27 34399.24 36697.71 476
thres600view794.45 45093.83 45796.29 44699.06 28391.53 47997.99 22194.24 51298.34 18797.44 40695.01 50379.84 49899.67 34184.33 52498.23 45397.66 477
thres40094.14 45893.44 46296.24 44998.93 31291.44 48297.60 28594.29 50997.94 23397.10 42094.31 51379.67 50099.62 37383.05 52798.08 46497.66 477
IB-MVS91.63 1992.24 49090.90 49496.27 44797.22 49291.24 48994.36 49093.33 52092.37 48892.24 52794.58 51266.20 53199.89 9793.16 45094.63 52697.66 477
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
tpmvs95.02 44395.25 42894.33 49896.39 52185.87 52398.08 19696.83 46995.46 41595.51 48998.69 30985.91 46499.53 41494.16 41696.23 50997.58 480
cascas94.79 44694.33 45396.15 45996.02 52692.36 46992.34 52499.26 26985.34 53095.08 49694.96 50692.96 38698.53 50394.41 41398.59 43897.56 481
MatchFormer97.07 35196.92 34397.49 38998.44 40595.92 33696.79 36299.14 30393.08 47799.32 14399.10 19093.89 36399.03 48492.78 46299.78 16497.52 482
PatchT96.65 37296.35 38497.54 38497.40 48695.32 37097.98 22296.64 47399.33 6696.89 43799.42 8984.32 47999.81 22497.69 20597.49 48297.48 483
TR-MVS95.55 42695.12 43496.86 42797.54 47693.94 43196.49 38896.53 47694.36 45297.03 42896.61 46894.26 35499.16 47986.91 51896.31 50897.47 484
SP-SuperGlue97.31 32997.23 32297.57 38296.96 50197.24 25596.26 40798.76 37497.68 25596.88 43997.85 41594.32 35198.01 51097.76 19798.57 44097.45 485
dmvs_testset92.94 48092.21 48095.13 49098.59 38790.99 49497.65 27492.09 52596.95 33194.00 51393.55 51792.34 39896.97 52672.20 53792.52 53197.43 486
MonoMVSNet96.25 39796.53 37795.39 48596.57 51291.01 49398.82 9797.68 43598.57 17398.03 35799.37 10490.92 42097.78 51594.99 39293.88 52997.38 487
JIA-IIPM95.52 42795.03 43597.00 41596.85 50594.03 42396.93 35695.82 49099.20 8494.63 50499.71 2283.09 48899.60 38594.42 41094.64 52597.36 488
SP-MNN96.46 38596.24 39297.10 41096.71 50995.98 33396.00 42297.33 44795.82 39894.93 49897.10 46193.70 37098.01 51096.30 34098.30 45197.30 489
MASt3R-SfM96.02 40595.82 39996.60 43597.03 50094.90 38994.26 49398.53 39988.40 52398.41 32198.67 31392.39 39597.62 51895.31 38599.41 33397.29 490
ALIKED-MNN95.97 41195.30 42698.00 33197.66 47298.12 15096.98 35199.41 19991.11 50494.04 51297.30 45291.56 41198.61 50289.99 50599.63 26097.28 491
BH-w/o95.13 44094.89 44095.86 46998.20 43091.31 48595.65 44297.37 44293.64 46796.52 46095.70 48993.04 38599.02 48688.10 51395.82 51997.24 492
tpm cat193.29 47393.13 46993.75 50797.39 48784.74 52897.39 31397.65 43683.39 53394.16 50898.41 35782.86 49099.39 45391.56 48695.35 52397.14 493
SP-NN94.67 44794.44 44995.36 48795.12 53195.23 37694.27 49296.10 48494.46 44690.91 53095.76 48891.47 41493.87 53895.23 38896.62 50497.00 494
SP-DiffGlue96.87 36396.76 35697.21 40495.17 53096.88 29096.12 41698.93 33996.51 35898.37 32797.55 43493.65 37197.83 51396.11 35498.45 44596.92 495
xiu_mvs_v1_base_debu97.86 28198.17 23696.92 42198.98 30593.91 43396.45 39099.17 29497.85 24198.41 32197.14 45898.47 7799.92 6598.02 16699.05 39296.92 495
xiu_mvs_v1_base97.86 28198.17 23696.92 42198.98 30593.91 43396.45 39099.17 29497.85 24198.41 32197.14 45898.47 7799.92 6598.02 16699.05 39296.92 495
xiu_mvs_v1_base_debi97.86 28198.17 23696.92 42198.98 30593.91 43396.45 39099.17 29497.85 24198.41 32197.14 45898.47 7799.92 6598.02 16699.05 39296.92 495
PMVScopyleft91.26 2097.86 28197.94 26597.65 36899.71 4997.94 18198.52 13098.68 38498.99 12497.52 39799.35 11197.41 19398.18 50891.59 48599.67 24296.82 499
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
0.4-1-1-0.188.42 49985.91 50295.94 46593.08 53691.54 47890.99 52892.04 52789.96 51384.83 53983.25 53863.75 53899.52 41893.25 44782.07 53596.75 500
131495.74 41995.60 40896.17 45597.53 47892.75 46198.07 20098.31 41291.22 50194.25 50796.68 46695.53 30699.03 48491.64 48497.18 49596.74 501
MVS-HIRNet94.32 45295.62 40690.42 52198.46 40275.36 54596.29 40389.13 53695.25 42495.38 49099.75 1692.88 38799.19 47794.07 42299.39 33696.72 502
OpenMVS_ROBcopyleft95.38 1495.84 41795.18 43397.81 34798.41 41197.15 27097.37 31998.62 39083.86 53198.65 28298.37 36294.29 35399.68 33688.41 51198.62 43796.60 503
ALIKED-NN94.29 45593.41 46496.94 41996.18 52397.66 21594.90 46998.68 38488.85 51990.43 53196.81 46489.82 43096.59 53186.67 51998.33 44796.58 504
0.3-1-1-0.01587.27 50184.50 50595.57 47991.70 53990.77 49889.41 53492.04 52788.98 51782.46 54181.35 53960.36 54299.50 42592.96 45381.23 53796.45 505
0.4-1-1-0.287.49 50084.89 50395.31 48891.33 54290.08 50588.47 53592.07 52688.70 52084.06 54081.08 54063.62 53999.49 42992.93 45581.71 53696.37 506
thres100view90094.19 45693.67 46095.75 47399.06 28391.35 48498.03 20794.24 51298.33 18997.40 40894.98 50579.84 49899.62 37383.05 52798.08 46496.29 507
tfpn200view994.03 46093.44 46295.78 47298.93 31291.44 48297.60 28594.29 50997.94 23397.10 42094.31 51379.67 50099.62 37383.05 52798.08 46496.29 507
MVS93.19 47592.09 48196.50 43896.91 50394.03 42398.07 20098.06 42568.01 53994.56 50596.48 47195.96 29199.30 46783.84 52596.89 50196.17 509
gg-mvs-nofinetune92.37 48891.20 49295.85 47095.80 52992.38 46899.31 3081.84 54499.75 1091.83 52899.74 1868.29 52399.02 48687.15 51597.12 49696.16 510
xiu_mvs_v2_base97.16 34597.49 30596.17 45598.54 39492.46 46595.45 45098.84 36197.25 30897.48 40196.49 47098.31 9799.90 8196.34 33798.68 43196.15 511
PS-MVSNAJ97.08 35097.39 31096.16 45798.56 39292.46 46595.24 45998.85 36097.25 30897.49 40095.99 48198.07 12899.90 8196.37 33498.67 43296.12 512
E-PMN94.17 45794.37 45193.58 50996.86 50485.71 52690.11 53197.07 45798.17 21197.82 37697.19 45584.62 47698.94 49089.77 50697.68 47796.09 513
EMVS93.83 46394.02 45493.23 51596.83 50684.96 52789.77 53296.32 47997.92 23597.43 40796.36 47686.17 45898.93 49187.68 51497.73 47695.81 514
MVEpermissive83.40 2292.50 48591.92 48794.25 49998.83 33591.64 47792.71 52083.52 54395.92 39186.46 53895.46 49695.20 31795.40 53580.51 53298.64 43395.73 515
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres20093.72 46693.14 46895.46 48498.66 37791.29 48696.61 37994.63 50497.39 29296.83 44193.71 51679.88 49799.56 40282.40 53098.13 46195.54 516
GLUNet-SfM86.26 50284.68 50491.01 52080.58 54683.56 53478.04 53793.59 51776.70 53795.29 49294.72 51077.51 51194.26 53766.39 54099.33 34795.20 517
API-MVS97.04 35496.91 34697.42 39597.88 45398.23 14198.18 17998.50 40297.57 26797.39 41096.75 46596.77 23899.15 48090.16 50499.02 39994.88 518
GG-mvs-BLEND94.76 49494.54 53392.13 47399.31 3080.47 54588.73 53691.01 53567.59 52798.16 50982.30 53194.53 52793.98 519
SIFT-PointCN96.45 38696.47 37996.39 44298.13 44097.54 22593.31 51697.23 45294.67 44198.68 27898.32 37194.64 33897.81 51493.50 44199.77 17293.83 520
XFeat-MNN93.41 47192.98 47194.68 49592.63 53792.92 45689.72 53395.81 49192.10 49297.23 41796.29 47784.95 47297.31 52389.60 50898.54 44293.81 521
SIFT-ConvMatch96.57 37596.62 36996.43 44098.20 43098.27 13493.88 50496.88 46795.29 42298.88 24198.25 37995.18 31997.43 52093.22 44999.83 12693.59 522
SIFT-NCM-Cal96.56 37696.68 36296.20 45398.27 42398.44 11894.40 48896.67 47195.29 42297.63 38698.17 38796.40 26196.59 53193.61 43499.66 25093.57 523
SIFT-MNN95.92 41395.97 39595.74 47598.18 43298.00 16994.17 49596.99 45995.74 40397.16 41897.90 41190.71 42295.79 53393.71 43299.21 37393.44 524
SIFT-NN-PointCN96.06 40296.11 39395.91 46797.88 45397.73 21093.49 51297.51 44093.22 47396.57 45498.26 37896.23 27296.60 53092.54 46999.27 36093.40 525
DeepMVS_CXcopyleft93.44 51298.24 42694.21 41394.34 50864.28 54091.34 52994.87 50989.45 43692.77 53977.54 53593.14 53093.35 526
SIFT-NN-CMatch95.63 42495.48 41396.08 46198.24 42698.00 16992.71 52094.29 50994.20 45595.85 47797.26 45395.72 30097.01 52491.99 47699.02 39993.23 527
SIFT-NN92.96 47992.79 47393.46 51096.92 50296.45 31591.89 52694.39 50792.91 48192.54 52495.46 49688.26 44690.71 54185.22 52297.52 48093.22 528
SIFT-PCN-Cal96.34 38996.46 38196.01 46498.17 43496.89 28893.48 51397.35 44694.84 43699.35 13098.30 37394.70 33797.92 51292.03 47599.88 9593.21 529
SIFT-UM-Cal96.49 38196.62 36996.12 46098.13 44097.89 18893.35 51598.44 40495.48 41498.63 28598.34 36695.45 31197.45 51992.22 47499.50 31393.02 530
SIFT-CM-Cal96.28 39496.31 38796.16 45798.39 41298.11 15193.46 51496.47 47794.81 43898.49 31198.43 35594.48 34297.34 52292.60 46899.70 22693.02 530
SIFT-UMatch96.33 39096.47 37995.89 46898.29 41997.95 17993.84 50597.24 45195.78 40198.72 27198.04 40093.45 37496.81 52793.14 45199.73 19992.91 532
SIFT-NN-NCMNet95.39 43395.22 43095.92 46698.29 41998.34 12993.58 51194.60 50594.07 46194.84 49997.53 43594.37 34996.62 52991.01 49598.64 43392.80 533
SIFT-NCMNet96.30 39296.40 38396.03 46397.80 46097.68 21492.34 52496.94 46495.55 40998.84 25098.63 32594.17 35697.63 51793.57 43899.71 21792.77 534
SIFT-NN-UMatch95.38 43495.26 42795.75 47398.25 42497.78 20493.24 51895.66 49794.01 46395.10 49597.47 44393.12 38096.78 52892.42 47198.04 46992.69 535
XFeat-NN89.63 49889.13 50191.14 51990.93 54390.02 50684.90 53694.05 51588.10 52492.89 52293.33 52178.74 50590.89 54083.46 52695.72 52092.52 536
tmp_tt78.77 50578.73 50878.90 52358.45 54874.76 54794.20 49478.26 54639.16 54186.71 53792.82 52580.50 49675.19 54386.16 52192.29 53286.74 537
dongtai76.24 50675.95 50977.12 52492.39 53867.91 54890.16 53059.44 54982.04 53489.42 53494.67 51149.68 54681.74 54248.06 54177.66 53981.72 538
kuosan69.30 50768.95 51070.34 52587.68 54565.00 54991.11 52759.90 54869.02 53874.46 54388.89 53748.58 54768.03 54428.61 54272.33 54277.99 539
wuyk23d96.06 40297.62 29891.38 51898.65 38198.57 10798.85 9396.95 46396.86 34299.90 1499.16 16999.18 1998.40 50489.23 51099.77 17277.18 540
test12317.04 51020.11 5137.82 52610.25 5504.91 55194.80 4714.47 5514.93 54410.00 54624.28 5439.69 5483.64 54510.14 54312.43 54414.92 541
testmvs17.12 50920.53 5126.87 52712.05 5494.20 55293.62 5106.73 5504.62 54510.41 54524.33 5428.28 5493.56 5469.69 54415.07 54312.86 542
mmdepth0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
monomultidepth0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
test_blank0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
uanet_test0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
DCPMVS0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
cdsmvs_eth3d_5k24.66 50832.88 5110.00 5280.00 5510.00 5530.00 53999.10 3080.00 5460.00 54797.58 43299.21 180.00 5470.00 5450.00 5450.00 543
pcd_1.5k_mvsjas8.17 51110.90 5140.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 54698.07 1280.00 5470.00 5450.00 5450.00 543
sosnet-low-res0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
sosnet0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
uncertanet0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
Regformer0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
ab-mvs-re8.12 51210.83 5150.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 54797.48 4410.00 5500.00 5470.00 5450.00 5450.00 543
uanet0.00 5130.00 5160.00 5280.00 5510.00 5530.00 5390.00 5520.00 5460.00 5470.00 5460.00 5500.00 5470.00 5450.00 5450.00 543
WAC-MVS90.90 49591.37 489
FOURS199.73 3899.67 299.43 1599.54 12999.43 5499.26 156
test_one_060199.39 18399.20 3899.31 24098.49 17998.66 28199.02 21197.64 168
eth-test20.00 551
eth-test0.00 551
ZD-MVS99.01 30098.84 8599.07 31394.10 45998.05 35598.12 39296.36 26699.86 14492.70 46599.19 378
test_241102_ONE99.49 14899.17 4399.31 24097.98 22899.66 6098.90 25498.36 9099.48 433
9.1497.78 27999.07 27897.53 29599.32 23595.53 41298.54 30698.70 30797.58 17599.76 27094.32 41599.46 319
save fliter99.11 26997.97 17596.53 38599.02 32698.24 199
test072699.50 14099.21 3298.17 18299.35 22197.97 22999.26 15699.06 19997.61 172
test_part299.36 19299.10 6599.05 199
sam_mvs84.29 481
MTGPAbinary99.20 282
test_post197.59 28720.48 54583.07 48999.66 35494.16 416
test_post21.25 54483.86 48499.70 315
patchmatchnet-post98.77 28884.37 47899.85 158
MTMP97.93 22891.91 530
gm-plane-assit94.83 53281.97 54188.07 52594.99 50499.60 38591.76 481
TEST998.71 35898.08 15995.96 42699.03 32391.40 49995.85 47797.53 43596.52 25599.76 270
test_898.67 37298.01 16895.91 43299.02 32691.64 49495.79 48097.50 43996.47 25799.76 270
agg_prior98.68 37197.99 17199.01 32995.59 48199.77 264
test_prior497.97 17595.86 433
test_prior295.74 44096.48 36296.11 47097.63 43095.92 29494.16 41699.20 375
旧先验295.76 43988.56 52297.52 39799.66 35494.48 406
新几何295.93 429
原ACMM295.53 446
testdata299.79 24692.80 461
segment_acmp97.02 220
testdata195.44 45196.32 369
plane_prior799.19 24597.87 190
plane_prior698.99 30497.70 21394.90 326
plane_prior497.98 405
plane_prior397.78 20497.41 28997.79 377
plane_prior297.77 25398.20 208
plane_prior199.05 286
plane_prior97.65 21797.07 34696.72 34999.36 340
n20.00 552
nn0.00 552
door-mid99.57 109
test1198.87 352
door99.41 199
HQP5-MVS96.79 294
HQP-NCC98.67 37296.29 40396.05 38295.55 484
ACMP_Plane98.67 37296.29 40396.05 38295.55 484
BP-MVS92.82 459
HQP3-MVS99.04 32199.26 364
HQP2-MVS93.84 364
NP-MVS98.84 33397.39 23996.84 462
MDTV_nov1_ep1395.22 43097.06 49783.20 53797.74 26196.16 48194.37 45196.99 42998.83 27583.95 48399.53 41493.90 42597.95 472
ACMMP++_ref99.77 172
ACMMP++99.68 236
Test By Simon96.52 255