APDe-MVS | | | 95.46 1 | 95.64 1 | 94.91 11 | 98.26 19 | 86.29 37 | 97.46 2 | 97.40 9 | 89.03 47 | 96.20 3 | 98.10 1 | 89.39 6 | 99.34 21 | 95.88 1 | 99.03 1 | 99.10 1 |
|
HSP-MVS | | | 95.30 3 | 95.48 2 | 94.76 23 | 98.49 9 | 86.52 27 | 96.91 15 | 96.73 54 | 91.73 9 | 96.10 4 | 96.69 36 | 89.90 2 | 99.30 27 | 94.70 3 | 98.04 48 | 98.45 17 |
|
CNVR-MVS | | | 95.40 2 | 95.37 3 | 95.50 3 | 98.11 24 | 88.51 3 | 95.29 61 | 96.96 37 | 92.09 3 | 95.32 8 | 97.08 23 | 89.49 5 | 99.33 24 | 95.10 2 | 98.85 7 | 98.66 5 |
|
SD-MVS | | | 94.96 6 | 95.33 4 | 93.88 48 | 97.25 51 | 86.69 20 | 96.19 29 | 97.11 28 | 90.42 24 | 96.95 1 | 97.27 11 | 89.53 4 | 96.91 213 | 94.38 5 | 98.85 7 | 98.03 48 |
|
SteuartSystems-ACMMP | | | 95.20 4 | 95.32 5 | 94.85 15 | 96.99 54 | 86.33 33 | 97.33 3 | 97.30 17 | 91.38 12 | 95.39 7 | 97.46 7 | 88.98 9 | 99.40 19 | 94.12 7 | 98.89 6 | 98.82 2 |
Skip Steuart: Steuart Systems R&D Blog. |
TSAR-MVS + MP. | | | 94.85 7 | 94.94 6 | 94.58 30 | 98.25 20 | 86.33 33 | 96.11 31 | 96.62 65 | 88.14 68 | 96.10 4 | 96.96 26 | 89.09 8 | 98.94 63 | 94.48 4 | 98.68 23 | 98.48 12 |
|
HPM-MVS++ | | | 95.14 5 | 94.91 7 | 95.83 1 | 98.25 20 | 89.65 1 | 95.92 38 | 96.96 37 | 91.75 8 | 94.02 18 | 96.83 30 | 88.12 10 | 99.55 5 | 93.41 15 | 98.94 4 | 98.28 27 |
|
DeepPCF-MVS | | 89.96 1 | 94.20 24 | 94.77 8 | 92.49 87 | 96.52 65 | 80.00 171 | 94.00 162 | 97.08 29 | 90.05 26 | 95.65 6 | 97.29 10 | 89.66 3 | 98.97 59 | 93.95 8 | 98.71 18 | 98.50 10 |
|
NCCC | | | 94.81 8 | 94.69 9 | 95.17 6 | 97.83 31 | 87.46 9 | 95.66 49 | 96.93 40 | 92.34 2 | 93.94 19 | 96.58 43 | 87.74 13 | 99.44 18 | 92.83 20 | 98.40 38 | 98.62 6 |
|
ACMMP_Plus | | | 94.74 9 | 94.56 10 | 95.28 4 | 98.02 29 | 87.70 4 | 95.68 47 | 97.34 11 | 88.28 65 | 95.30 9 | 97.67 3 | 85.90 32 | 99.54 8 | 93.91 9 | 98.95 3 | 98.60 7 |
|
HFP-MVS | | | 94.52 10 | 94.40 11 | 94.86 13 | 98.61 3 | 86.81 15 | 96.94 10 | 97.34 11 | 88.63 56 | 93.65 22 | 97.21 16 | 86.10 28 | 99.49 14 | 92.35 27 | 98.77 13 | 98.30 25 |
|
XVS | | | 94.45 12 | 94.32 12 | 94.85 15 | 98.54 6 | 86.60 25 | 96.93 12 | 97.19 22 | 90.66 22 | 92.85 35 | 97.16 21 | 85.02 42 | 99.49 14 | 91.99 36 | 98.56 34 | 98.47 13 |
|
MPTG | | | 94.47 11 | 94.30 13 | 95.00 8 | 98.42 13 | 86.95 11 | 95.06 80 | 96.97 34 | 91.07 14 | 93.14 33 | 97.56 4 | 84.30 48 | 99.56 1 | 93.43 13 | 98.75 15 | 98.47 13 |
|
ACMMPR | | | 94.43 14 | 94.28 14 | 94.91 11 | 98.63 2 | 86.69 20 | 96.94 10 | 97.32 16 | 88.63 56 | 93.53 29 | 97.26 13 | 85.04 41 | 99.54 8 | 92.35 27 | 98.78 12 | 98.50 10 |
|
region2R | | | 94.43 14 | 94.27 15 | 94.92 10 | 98.65 1 | 86.67 22 | 96.92 14 | 97.23 21 | 88.60 58 | 93.58 26 | 97.27 11 | 85.22 38 | 99.54 8 | 92.21 29 | 98.74 17 | 98.56 9 |
|
MTAPA | | | 94.42 16 | 94.22 16 | 95.00 8 | 98.42 13 | 86.95 11 | 94.36 134 | 96.97 34 | 91.07 14 | 93.14 33 | 97.56 4 | 84.30 48 | 99.56 1 | 93.43 13 | 98.75 15 | 98.47 13 |
|
Regformer-2 | | | 94.33 18 | 94.22 16 | 94.68 26 | 95.54 99 | 86.75 19 | 94.57 113 | 96.70 58 | 91.84 6 | 94.41 11 | 96.56 45 | 87.19 19 | 99.13 38 | 93.50 11 | 97.65 56 | 98.16 37 |
|
CP-MVS | | | 94.34 17 | 94.21 18 | 94.74 25 | 98.39 15 | 86.64 24 | 97.60 1 | 97.24 19 | 88.53 60 | 92.73 42 | 97.23 14 | 85.20 39 | 99.32 25 | 92.15 32 | 98.83 9 | 98.25 33 |
|
MCST-MVS | | | 94.45 12 | 94.20 19 | 95.19 5 | 98.46 11 | 87.50 8 | 95.00 83 | 97.12 26 | 87.13 87 | 92.51 49 | 96.30 52 | 89.24 7 | 99.34 21 | 93.46 12 | 98.62 31 | 98.73 3 |
|
#test# | | | 94.32 19 | 94.14 20 | 94.86 13 | 98.61 3 | 86.81 15 | 96.43 23 | 97.34 11 | 87.51 82 | 93.65 22 | 97.21 16 | 86.10 28 | 99.49 14 | 91.68 45 | 98.77 13 | 98.30 25 |
|
Regformer-1 | | | 94.22 22 | 94.13 21 | 94.51 33 | 95.54 99 | 86.36 32 | 94.57 113 | 96.44 72 | 91.69 10 | 94.32 13 | 96.56 45 | 87.05 21 | 99.03 48 | 93.35 16 | 97.65 56 | 98.15 38 |
|
MSLP-MVS++ | | | 93.72 32 | 94.08 22 | 92.65 81 | 97.31 45 | 83.43 90 | 95.79 42 | 97.33 14 | 90.03 27 | 93.58 26 | 96.96 26 | 84.87 44 | 97.76 136 | 92.19 31 | 98.66 27 | 96.76 93 |
|
MP-MVS | | | 94.25 20 | 94.07 23 | 94.77 22 | 98.47 10 | 86.31 35 | 96.71 20 | 96.98 33 | 89.04 46 | 91.98 59 | 97.19 18 | 85.43 36 | 99.56 1 | 92.06 35 | 98.79 10 | 98.44 18 |
|
APD-MVS | | | 94.24 21 | 94.07 23 | 94.75 24 | 98.06 27 | 86.90 14 | 95.88 39 | 96.94 39 | 85.68 118 | 95.05 10 | 97.18 19 | 87.31 18 | 99.07 42 | 91.90 43 | 98.61 32 | 98.28 27 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
MP-MVS-pluss | | | 94.21 23 | 94.00 25 | 94.85 15 | 98.17 23 | 86.65 23 | 94.82 94 | 97.17 24 | 86.26 108 | 92.83 37 | 97.87 2 | 85.57 35 | 99.56 1 | 94.37 6 | 98.92 5 | 98.34 22 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
HPM-MVS | | | 94.02 26 | 93.88 26 | 94.43 36 | 98.39 15 | 85.78 48 | 97.25 5 | 97.07 30 | 86.90 98 | 92.62 46 | 96.80 33 | 84.85 45 | 99.17 33 | 92.43 24 | 98.65 29 | 98.33 23 |
|
Regformer-4 | | | 93.91 29 | 93.81 27 | 94.19 43 | 95.36 105 | 85.47 50 | 94.68 105 | 96.41 75 | 91.60 11 | 93.75 21 | 96.71 34 | 85.95 31 | 99.10 41 | 93.21 17 | 96.65 71 | 98.01 50 |
|
DeepC-MVS_fast | | 89.43 2 | 94.04 25 | 93.79 28 | 94.80 21 | 97.48 40 | 86.78 17 | 95.65 51 | 96.89 42 | 89.40 38 | 92.81 38 | 96.97 25 | 85.37 37 | 99.24 29 | 90.87 56 | 98.69 20 | 98.38 21 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
mPP-MVS | | | 93.99 27 | 93.78 29 | 94.63 28 | 98.50 8 | 85.90 46 | 96.87 16 | 96.91 41 | 88.70 54 | 91.83 63 | 97.17 20 | 83.96 51 | 99.55 5 | 91.44 49 | 98.64 30 | 98.43 19 |
|
APD-MVS_3200maxsize | | | 93.78 31 | 93.77 30 | 93.80 53 | 97.92 30 | 84.19 74 | 96.30 26 | 96.87 45 | 86.96 94 | 93.92 20 | 97.47 6 | 83.88 52 | 98.96 62 | 92.71 22 | 97.87 51 | 98.26 32 |
|
PGM-MVS | | | 93.96 28 | 93.72 31 | 94.68 26 | 98.43 12 | 86.22 38 | 95.30 59 | 97.78 1 | 87.45 83 | 93.26 30 | 97.33 9 | 84.62 46 | 99.51 12 | 90.75 58 | 98.57 33 | 98.32 24 |
|
PHI-MVS | | | 93.89 30 | 93.65 32 | 94.62 29 | 96.84 57 | 86.43 30 | 96.69 21 | 97.49 4 | 85.15 129 | 93.56 28 | 96.28 53 | 85.60 34 | 99.31 26 | 92.45 23 | 98.79 10 | 98.12 41 |
|
Regformer-3 | | | 93.68 33 | 93.64 33 | 93.81 52 | 95.36 105 | 84.61 59 | 94.68 105 | 95.83 113 | 91.27 13 | 93.60 25 | 96.71 34 | 85.75 33 | 98.86 68 | 92.87 19 | 96.65 71 | 97.96 51 |
|
test_prior3 | | | 93.60 35 | 93.53 34 | 93.82 50 | 97.29 47 | 84.49 63 | 94.12 146 | 96.88 43 | 87.67 79 | 92.63 44 | 96.39 50 | 86.62 24 | 98.87 65 | 91.50 47 | 98.67 25 | 98.11 42 |
|
TSAR-MVS + GP. | | | 93.66 34 | 93.41 35 | 94.41 37 | 96.59 62 | 86.78 17 | 94.40 124 | 93.93 217 | 89.77 32 | 94.21 14 | 95.59 79 | 87.35 17 | 98.61 83 | 92.72 21 | 96.15 79 | 97.83 60 |
|
MVS_111021_HR | | | 93.45 37 | 93.31 36 | 93.84 49 | 96.99 54 | 84.84 55 | 93.24 202 | 97.24 19 | 88.76 53 | 91.60 67 | 95.85 71 | 86.07 30 | 98.66 78 | 91.91 40 | 98.16 44 | 98.03 48 |
|
DELS-MVS | | | 93.43 39 | 93.25 37 | 93.97 45 | 95.42 104 | 85.04 54 | 93.06 209 | 97.13 25 | 90.74 20 | 91.84 61 | 95.09 90 | 86.32 27 | 99.21 30 | 91.22 50 | 98.45 37 | 97.65 64 |
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 |
HPM-MVS_fast | | | 93.40 40 | 93.22 38 | 93.94 47 | 98.36 17 | 84.83 56 | 97.15 7 | 96.80 49 | 85.77 115 | 92.47 50 | 97.13 22 | 82.38 60 | 99.07 42 | 90.51 60 | 98.40 38 | 97.92 56 |
|
CANet | | | 93.54 36 | 93.20 39 | 94.55 31 | 95.65 96 | 85.73 49 | 94.94 86 | 96.69 60 | 91.89 5 | 90.69 76 | 95.88 70 | 81.99 70 | 99.54 8 | 93.14 18 | 97.95 50 | 98.39 20 |
|
train_agg | | | 93.44 38 | 93.08 40 | 94.52 32 | 97.53 35 | 86.49 28 | 94.07 154 | 96.78 50 | 81.86 216 | 92.77 39 | 96.20 57 | 87.63 15 | 99.12 39 | 92.14 33 | 98.69 20 | 97.94 52 |
|
abl_6 | | | 93.18 48 | 93.05 41 | 93.57 57 | 97.52 37 | 84.27 73 | 95.53 54 | 96.67 61 | 87.85 74 | 93.20 32 | 97.22 15 | 80.35 80 | 99.18 32 | 91.91 40 | 97.21 61 | 97.26 74 |
|
CSCG | | | 93.23 47 | 93.05 41 | 93.76 54 | 98.04 28 | 84.07 76 | 96.22 28 | 97.37 10 | 84.15 149 | 90.05 83 | 95.66 77 | 87.77 12 | 99.15 36 | 89.91 63 | 98.27 41 | 98.07 44 |
|
DeepC-MVS | | 88.79 3 | 93.31 41 | 92.99 43 | 94.26 41 | 96.07 83 | 85.83 47 | 94.89 89 | 96.99 32 | 89.02 48 | 89.56 86 | 97.37 8 | 82.51 59 | 99.38 20 | 92.20 30 | 98.30 40 | 97.57 68 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
agg_prior1 | | | 93.29 42 | 92.97 44 | 94.26 41 | 97.38 42 | 85.92 43 | 93.92 165 | 96.72 56 | 81.96 205 | 92.16 55 | 96.23 55 | 87.85 11 | 98.97 59 | 91.95 39 | 98.55 36 | 97.90 57 |
|
EI-MVSNet-Vis-set | | | 93.01 50 | 92.92 45 | 93.29 58 | 95.01 119 | 83.51 89 | 94.48 116 | 95.77 117 | 90.87 16 | 92.52 48 | 96.67 38 | 84.50 47 | 99.00 56 | 91.99 36 | 94.44 106 | 97.36 73 |
|
agg_prior3 | | | 93.27 43 | 92.89 46 | 94.40 38 | 97.49 38 | 86.12 40 | 94.07 154 | 96.73 54 | 81.46 224 | 92.46 51 | 96.05 65 | 86.90 22 | 99.15 36 | 92.14 33 | 98.69 20 | 97.94 52 |
|
ACMMP | | | 93.24 46 | 92.88 47 | 94.30 40 | 98.09 26 | 85.33 52 | 96.86 17 | 97.45 7 | 88.33 63 | 90.15 82 | 97.03 24 | 81.44 73 | 99.51 12 | 90.85 57 | 95.74 82 | 98.04 47 |
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 |
canonicalmvs | | | 93.27 43 | 92.75 48 | 94.85 15 | 95.70 95 | 87.66 5 | 96.33 25 | 96.41 75 | 90.00 28 | 94.09 16 | 94.60 104 | 82.33 61 | 98.62 82 | 92.40 26 | 92.86 133 | 98.27 30 |
|
MVS_0304 | | | 93.25 45 | 92.62 49 | 95.14 7 | 95.72 94 | 87.58 7 | 94.71 104 | 96.59 67 | 91.78 7 | 91.46 68 | 96.18 61 | 75.45 145 | 99.55 5 | 93.53 10 | 98.19 43 | 98.28 27 |
|
EI-MVSNet-UG-set | | | 92.74 53 | 92.62 49 | 93.12 64 | 94.86 127 | 83.20 95 | 94.40 124 | 95.74 120 | 90.71 21 | 92.05 58 | 96.60 42 | 84.00 50 | 98.99 57 | 91.55 46 | 93.63 115 | 97.17 80 |
|
UA-Net | | | 92.83 51 | 92.54 51 | 93.68 55 | 96.10 81 | 84.71 58 | 95.66 49 | 96.39 77 | 91.92 4 | 93.22 31 | 96.49 47 | 83.16 54 | 98.87 65 | 84.47 119 | 95.47 87 | 97.45 72 |
|
alignmvs | | | 93.08 49 | 92.50 52 | 94.81 20 | 95.62 98 | 87.61 6 | 95.99 35 | 96.07 96 | 89.77 32 | 94.12 15 | 94.87 94 | 80.56 79 | 98.66 78 | 92.42 25 | 93.10 128 | 98.15 38 |
|
CDPH-MVS | | | 92.83 51 | 92.30 53 | 94.44 34 | 97.79 32 | 86.11 41 | 94.06 157 | 96.66 62 | 80.09 235 | 92.77 39 | 96.63 40 | 86.62 24 | 99.04 47 | 87.40 88 | 98.66 27 | 98.17 36 |
|
MVS_111021_LR | | | 92.47 54 | 92.29 54 | 92.98 71 | 95.99 86 | 84.43 70 | 93.08 207 | 96.09 94 | 88.20 67 | 91.12 73 | 95.72 76 | 81.33 75 | 97.76 136 | 91.74 44 | 97.37 60 | 96.75 94 |
|
VNet | | | 92.24 56 | 91.91 55 | 93.24 60 | 96.59 62 | 83.43 90 | 94.84 93 | 96.44 72 | 89.19 43 | 94.08 17 | 95.90 69 | 77.85 111 | 98.17 103 | 88.90 70 | 93.38 122 | 98.13 40 |
|
CPTT-MVS | | | 91.99 57 | 91.80 56 | 92.55 84 | 98.24 22 | 81.98 125 | 96.76 19 | 96.49 71 | 81.89 210 | 90.24 80 | 96.44 49 | 78.59 100 | 98.61 83 | 89.68 64 | 97.85 52 | 97.06 85 |
|
MG-MVS | | | 91.77 60 | 91.70 57 | 92.00 105 | 97.08 53 | 80.03 170 | 93.60 186 | 95.18 167 | 87.85 74 | 90.89 75 | 96.47 48 | 82.06 68 | 98.36 93 | 85.07 110 | 97.04 64 | 97.62 65 |
|
EPP-MVSNet | | | 91.70 63 | 91.56 58 | 92.13 102 | 95.88 89 | 80.50 161 | 97.33 3 | 95.25 160 | 86.15 110 | 89.76 85 | 95.60 78 | 83.42 53 | 98.32 98 | 87.37 90 | 93.25 125 | 97.56 69 |
|
3Dnovator+ | | 87.14 4 | 92.42 55 | 91.37 59 | 95.55 2 | 95.63 97 | 88.73 2 | 97.07 8 | 96.77 52 | 90.84 17 | 84.02 208 | 96.62 41 | 75.95 134 | 99.34 21 | 87.77 83 | 97.68 54 | 98.59 8 |
|
MVSFormer | | | 91.68 64 | 91.30 60 | 92.80 77 | 93.86 164 | 83.88 79 | 95.96 36 | 95.90 108 | 84.66 138 | 91.76 64 | 94.91 92 | 77.92 108 | 97.30 180 | 89.64 65 | 97.11 62 | 97.24 75 |
|
DP-MVS Recon | | | 91.95 58 | 91.28 61 | 93.96 46 | 98.33 18 | 85.92 43 | 94.66 108 | 96.66 62 | 82.69 194 | 90.03 84 | 95.82 72 | 82.30 62 | 99.03 48 | 84.57 118 | 96.48 76 | 96.91 89 |
|
Vis-MVSNet | | | 91.75 61 | 91.23 62 | 93.29 58 | 95.32 108 | 83.78 81 | 96.14 30 | 95.98 101 | 89.89 29 | 90.45 78 | 96.58 43 | 75.09 149 | 98.31 99 | 84.75 116 | 96.90 65 | 97.78 63 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
Effi-MVS+ | | | 91.59 65 | 91.11 63 | 93.01 70 | 94.35 148 | 83.39 92 | 94.60 110 | 95.10 169 | 87.10 88 | 90.57 77 | 93.10 151 | 81.43 74 | 98.07 121 | 89.29 67 | 94.48 103 | 97.59 67 |
|
MVS_Test | | | 91.31 68 | 91.11 63 | 91.93 109 | 94.37 145 | 80.14 165 | 93.46 191 | 95.80 115 | 86.46 104 | 91.35 70 | 93.77 133 | 82.21 64 | 98.09 119 | 87.57 86 | 94.95 94 | 97.55 70 |
|
IS-MVSNet | | | 91.43 66 | 91.09 65 | 92.46 88 | 95.87 91 | 81.38 136 | 96.95 9 | 93.69 222 | 89.72 34 | 89.50 88 | 95.98 66 | 78.57 101 | 97.77 135 | 83.02 137 | 96.50 75 | 98.22 34 |
|
EPNet | | | 91.79 59 | 91.02 66 | 94.10 44 | 90.10 285 | 85.25 53 | 96.03 34 | 92.05 250 | 92.83 1 | 87.39 120 | 95.78 73 | 79.39 94 | 99.01 53 | 88.13 79 | 97.48 58 | 98.05 46 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PS-MVSNAJ | | | 91.18 71 | 90.92 67 | 91.96 107 | 95.26 111 | 82.60 117 | 92.09 240 | 95.70 122 | 86.27 107 | 91.84 61 | 92.46 171 | 79.70 89 | 98.99 57 | 89.08 68 | 95.86 81 | 94.29 183 |
|
PVSNet_Blended_VisFu | | | 91.38 67 | 90.91 68 | 92.80 77 | 96.39 67 | 83.17 96 | 94.87 92 | 96.66 62 | 83.29 171 | 89.27 89 | 94.46 106 | 80.29 82 | 99.17 33 | 87.57 86 | 95.37 89 | 96.05 113 |
|
xiu_mvs_v2_base | | | 91.13 72 | 90.89 69 | 91.86 112 | 94.97 122 | 82.42 118 | 92.24 234 | 95.64 128 | 86.11 112 | 91.74 66 | 93.14 149 | 79.67 92 | 98.89 64 | 89.06 69 | 95.46 88 | 94.28 184 |
|
3Dnovator | | 86.66 5 | 91.73 62 | 90.82 70 | 94.44 34 | 94.59 137 | 86.37 31 | 97.18 6 | 97.02 31 | 89.20 42 | 84.31 204 | 96.66 39 | 73.74 168 | 99.17 33 | 86.74 98 | 97.96 49 | 97.79 62 |
|
PAPM_NR | | | 91.22 70 | 90.78 71 | 92.52 86 | 97.60 34 | 81.46 133 | 94.37 130 | 96.24 85 | 86.39 106 | 87.41 118 | 94.80 99 | 82.06 68 | 98.48 89 | 82.80 141 | 95.37 89 | 97.61 66 |
|
OMC-MVS | | | 91.23 69 | 90.62 72 | 93.08 66 | 96.27 70 | 84.07 76 | 93.52 188 | 95.93 104 | 86.95 95 | 89.51 87 | 96.13 63 | 78.50 102 | 98.35 95 | 85.84 105 | 92.90 132 | 96.83 92 |
|
nrg030 | | | 91.08 73 | 90.39 73 | 93.17 63 | 93.07 186 | 86.91 13 | 96.41 24 | 96.26 82 | 88.30 64 | 88.37 99 | 94.85 97 | 82.19 65 | 97.64 143 | 91.09 51 | 82.95 242 | 94.96 147 |
|
FIs | | | 90.51 83 | 90.35 74 | 90.99 143 | 93.99 160 | 80.98 147 | 95.73 44 | 97.54 3 | 89.15 44 | 86.72 131 | 94.68 100 | 81.83 72 | 97.24 188 | 85.18 109 | 88.31 196 | 94.76 161 |
|
PVSNet_Blended | | | 90.73 77 | 90.32 75 | 91.98 106 | 96.12 77 | 81.25 138 | 92.55 225 | 96.83 46 | 82.04 204 | 89.10 91 | 92.56 170 | 81.04 77 | 98.85 71 | 86.72 101 | 95.91 80 | 95.84 120 |
|
lupinMVS | | | 90.92 74 | 90.21 76 | 93.03 69 | 93.86 164 | 83.88 79 | 92.81 216 | 93.86 218 | 79.84 237 | 91.76 64 | 94.29 111 | 77.92 108 | 98.04 123 | 90.48 61 | 97.11 62 | 97.17 80 |
|
HQP_MVS | | | 90.60 82 | 90.19 77 | 91.82 115 | 94.70 133 | 82.73 111 | 95.85 40 | 96.22 86 | 90.81 18 | 86.91 127 | 94.86 95 | 74.23 157 | 98.12 106 | 88.15 77 | 89.99 163 | 94.63 164 |
|
FC-MVSNet-test | | | 90.27 86 | 90.18 78 | 90.53 152 | 93.71 170 | 79.85 175 | 95.77 43 | 97.59 2 | 89.31 40 | 86.27 140 | 94.67 101 | 81.93 71 | 97.01 205 | 84.26 124 | 88.09 199 | 94.71 162 |
|
jason | | | 90.80 75 | 90.10 79 | 92.90 74 | 93.04 188 | 83.53 88 | 93.08 207 | 94.15 204 | 80.22 233 | 91.41 69 | 94.91 92 | 76.87 114 | 97.93 130 | 90.28 62 | 96.90 65 | 97.24 75 |
jason: jason. |
API-MVS | | | 90.66 78 | 90.07 80 | 92.45 89 | 96.36 68 | 84.57 61 | 96.06 33 | 95.22 166 | 82.39 196 | 89.13 90 | 94.27 114 | 80.32 81 | 98.46 90 | 80.16 184 | 96.71 69 | 94.33 182 |
|
xiu_mvs_v1_base_debu | | | 90.64 79 | 90.05 81 | 92.40 90 | 93.97 161 | 84.46 66 | 93.32 193 | 95.46 141 | 85.17 126 | 92.25 52 | 94.03 117 | 70.59 206 | 98.57 85 | 90.97 52 | 94.67 96 | 94.18 185 |
|
xiu_mvs_v1_base | | | 90.64 79 | 90.05 81 | 92.40 90 | 93.97 161 | 84.46 66 | 93.32 193 | 95.46 141 | 85.17 126 | 92.25 52 | 94.03 117 | 70.59 206 | 98.57 85 | 90.97 52 | 94.67 96 | 94.18 185 |
|
xiu_mvs_v1_base_debi | | | 90.64 79 | 90.05 81 | 92.40 90 | 93.97 161 | 84.46 66 | 93.32 193 | 95.46 141 | 85.17 126 | 92.25 52 | 94.03 117 | 70.59 206 | 98.57 85 | 90.97 52 | 94.67 96 | 94.18 185 |
|
VDD-MVS | | | 90.74 76 | 89.92 84 | 93.20 61 | 96.27 70 | 83.02 101 | 95.73 44 | 93.86 218 | 88.42 62 | 92.53 47 | 96.84 29 | 62.09 281 | 98.64 80 | 90.95 55 | 92.62 135 | 97.93 55 |
|
PVSNet_BlendedMVS | | | 89.98 91 | 89.70 85 | 90.82 146 | 96.12 77 | 81.25 138 | 93.92 165 | 96.83 46 | 83.49 165 | 89.10 91 | 92.26 181 | 81.04 77 | 98.85 71 | 86.72 101 | 87.86 201 | 92.35 268 |
|
PS-MVSNAJss | | | 89.97 92 | 89.62 86 | 91.02 141 | 91.90 208 | 80.85 152 | 95.26 68 | 95.98 101 | 86.26 108 | 86.21 141 | 94.29 111 | 79.70 89 | 97.65 141 | 88.87 71 | 88.10 197 | 94.57 170 |
|
OPM-MVS | | | 90.12 88 | 89.56 87 | 91.82 115 | 93.14 184 | 83.90 78 | 94.16 145 | 95.74 120 | 88.96 49 | 87.86 106 | 95.43 81 | 72.48 185 | 97.91 131 | 88.10 80 | 90.18 162 | 93.65 222 |
|
1121 | | | 90.42 84 | 89.49 88 | 93.20 61 | 97.27 49 | 84.46 66 | 92.63 221 | 95.51 138 | 71.01 312 | 91.20 72 | 96.21 56 | 82.92 56 | 99.05 44 | 80.56 175 | 98.07 47 | 96.10 109 |
|
XVG-OURS-SEG-HR | | | 89.95 93 | 89.45 89 | 91.47 125 | 94.00 159 | 81.21 141 | 91.87 242 | 96.06 98 | 85.78 114 | 88.55 96 | 95.73 75 | 74.67 153 | 97.27 184 | 88.71 72 | 89.64 170 | 95.91 116 |
|
Vis-MVSNet (Re-imp) | | | 89.59 101 | 89.44 90 | 90.03 189 | 95.74 93 | 75.85 267 | 95.61 52 | 90.80 287 | 87.66 81 | 87.83 112 | 95.40 82 | 76.79 116 | 96.46 237 | 78.37 208 | 96.73 68 | 97.80 61 |
|
CANet_DTU | | | 90.26 87 | 89.41 91 | 92.81 76 | 93.46 176 | 83.01 102 | 93.48 189 | 94.47 194 | 89.43 37 | 87.76 115 | 94.23 115 | 70.54 210 | 99.03 48 | 84.97 111 | 96.39 77 | 96.38 101 |
|
MAR-MVS | | | 90.30 85 | 89.37 92 | 93.07 68 | 96.61 61 | 84.48 65 | 95.68 47 | 95.67 123 | 82.36 198 | 87.85 107 | 92.85 160 | 76.63 119 | 98.80 74 | 80.01 185 | 96.68 70 | 95.91 116 |
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 |
mvs_anonymous | | | 89.37 112 | 89.32 93 | 89.51 209 | 93.47 175 | 74.22 273 | 91.65 249 | 94.83 185 | 82.91 188 | 85.45 167 | 93.79 132 | 81.23 76 | 96.36 242 | 86.47 104 | 94.09 109 | 97.94 52 |
|
UniMVSNet_NR-MVSNet | | | 89.92 95 | 89.29 94 | 91.81 117 | 93.39 177 | 83.72 82 | 94.43 122 | 97.12 26 | 89.80 31 | 86.46 134 | 93.32 140 | 83.16 54 | 97.23 190 | 84.92 112 | 81.02 270 | 94.49 177 |
|
HQP-MVS | | | 89.80 97 | 89.28 95 | 91.34 128 | 94.17 150 | 81.56 128 | 94.39 126 | 96.04 99 | 88.81 50 | 85.43 170 | 93.97 123 | 73.83 166 | 97.96 127 | 87.11 95 | 89.77 168 | 94.50 175 |
|
PAPR | | | 90.02 90 | 89.27 96 | 92.29 96 | 95.78 92 | 80.95 149 | 92.68 220 | 96.22 86 | 81.91 208 | 86.66 132 | 93.75 135 | 82.23 63 | 98.44 92 | 79.40 202 | 94.79 95 | 97.48 71 |
|
mvs-test1 | | | 89.45 106 | 89.14 97 | 90.38 167 | 93.33 178 | 77.63 249 | 94.95 85 | 94.36 197 | 87.70 77 | 87.10 124 | 92.81 164 | 73.45 171 | 98.03 124 | 85.57 107 | 93.04 129 | 95.48 131 |
|
LFMVS | | | 90.08 89 | 89.13 98 | 92.95 72 | 96.71 59 | 82.32 120 | 96.08 32 | 89.91 303 | 86.79 99 | 92.15 57 | 96.81 31 | 62.60 278 | 98.34 96 | 87.18 92 | 93.90 111 | 98.19 35 |
|
UniMVSNet (Re) | | | 89.80 97 | 89.07 99 | 92.01 103 | 93.60 173 | 84.52 62 | 94.78 97 | 97.47 5 | 89.26 41 | 86.44 137 | 92.32 177 | 82.10 66 | 97.39 176 | 84.81 115 | 80.84 274 | 94.12 189 |
|
AdaColmap | | | 89.89 96 | 89.07 99 | 92.37 93 | 97.41 41 | 83.03 100 | 94.42 123 | 95.92 105 | 82.81 190 | 86.34 139 | 94.65 102 | 73.89 164 | 99.02 51 | 80.69 172 | 95.51 85 | 95.05 141 |
|
VPA-MVSNet | | | 89.62 99 | 88.96 101 | 91.60 122 | 93.86 164 | 82.89 106 | 95.46 55 | 97.33 14 | 87.91 71 | 88.43 98 | 93.31 141 | 74.17 160 | 97.40 173 | 87.32 91 | 82.86 244 | 94.52 173 |
|
UGNet | | | 89.95 93 | 88.95 102 | 92.95 72 | 94.51 140 | 83.31 93 | 95.70 46 | 95.23 164 | 89.37 39 | 87.58 117 | 93.94 124 | 64.00 274 | 98.78 75 | 83.92 129 | 96.31 78 | 96.74 95 |
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 |
WTY-MVS | | | 89.60 100 | 88.92 103 | 91.67 120 | 95.47 103 | 81.15 143 | 92.38 230 | 94.78 187 | 83.11 174 | 89.06 93 | 94.32 109 | 78.67 99 | 96.61 229 | 81.57 161 | 90.89 154 | 97.24 75 |
|
LPG-MVS_test | | | 89.45 106 | 88.90 104 | 91.12 134 | 94.47 141 | 81.49 131 | 95.30 59 | 96.14 90 | 86.73 100 | 85.45 167 | 95.16 87 | 69.89 215 | 98.10 112 | 87.70 84 | 89.23 177 | 93.77 212 |
|
CLD-MVS | | | 89.47 105 | 88.90 104 | 91.18 133 | 94.22 149 | 82.07 123 | 92.13 238 | 96.09 94 | 87.90 72 | 85.37 177 | 92.45 172 | 74.38 155 | 97.56 146 | 87.15 93 | 90.43 156 | 93.93 198 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
EI-MVSNet | | | 89.10 116 | 88.86 106 | 89.80 199 | 91.84 210 | 78.30 229 | 93.70 181 | 95.01 172 | 85.73 116 | 87.15 122 | 95.28 83 | 79.87 86 | 97.21 192 | 83.81 131 | 87.36 205 | 93.88 202 |
|
XVG-OURS | | | 89.40 111 | 88.70 107 | 91.52 123 | 94.06 153 | 81.46 133 | 91.27 255 | 96.07 96 | 86.14 111 | 88.89 94 | 95.77 74 | 68.73 238 | 97.26 186 | 87.39 89 | 89.96 165 | 95.83 121 |
|
Fast-Effi-MVS+ | | | 89.41 109 | 88.64 108 | 91.71 119 | 94.74 129 | 80.81 153 | 93.54 187 | 95.10 169 | 83.11 174 | 86.82 130 | 90.67 238 | 79.74 88 | 97.75 139 | 80.51 177 | 93.55 116 | 96.57 98 |
|
test_djsdf | | | 89.03 120 | 88.64 108 | 90.21 172 | 90.74 269 | 79.28 201 | 95.96 36 | 95.90 108 | 84.66 138 | 85.33 179 | 92.94 159 | 74.02 163 | 97.30 180 | 89.64 65 | 88.53 189 | 94.05 194 |
|
CDS-MVSNet | | | 89.45 106 | 88.51 110 | 92.29 96 | 93.62 172 | 83.61 87 | 93.01 210 | 94.68 189 | 81.95 206 | 87.82 113 | 93.24 145 | 78.69 98 | 96.99 206 | 80.34 180 | 93.23 126 | 96.28 103 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
DU-MVS | | | 89.34 113 | 88.50 111 | 91.85 113 | 93.04 188 | 83.72 82 | 94.47 119 | 96.59 67 | 89.50 36 | 86.46 134 | 93.29 143 | 77.25 112 | 97.23 190 | 84.92 112 | 81.02 270 | 94.59 168 |
|
114514_t | | | 89.51 103 | 88.50 111 | 92.54 85 | 98.11 24 | 81.99 124 | 95.16 74 | 96.36 79 | 70.19 314 | 85.81 146 | 95.25 85 | 76.70 117 | 98.63 81 | 82.07 152 | 96.86 67 | 97.00 86 |
|
VDDNet | | | 89.56 102 | 88.49 113 | 92.76 79 | 95.07 118 | 82.09 122 | 96.30 26 | 93.19 228 | 81.05 229 | 91.88 60 | 96.86 28 | 61.16 290 | 98.33 97 | 88.43 75 | 92.49 136 | 97.84 59 |
|
ACMM | | 84.12 9 | 89.14 115 | 88.48 114 | 91.12 134 | 94.65 136 | 81.22 140 | 95.31 57 | 96.12 93 | 85.31 125 | 85.92 145 | 94.34 107 | 70.19 214 | 98.06 122 | 85.65 106 | 88.86 186 | 94.08 193 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
Effi-MVS+-dtu | | | 88.65 128 | 88.35 115 | 89.54 206 | 93.33 178 | 76.39 262 | 94.47 119 | 94.36 197 | 87.70 77 | 85.43 170 | 89.56 260 | 73.45 171 | 97.26 186 | 85.57 107 | 91.28 142 | 94.97 144 |
|
ab-mvs | | | 89.41 109 | 88.35 115 | 92.60 82 | 95.15 117 | 82.65 115 | 92.20 236 | 95.60 129 | 83.97 151 | 88.55 96 | 93.70 136 | 74.16 161 | 98.21 102 | 82.46 147 | 89.37 173 | 96.94 88 |
|
ACMP | | 84.23 8 | 89.01 122 | 88.35 115 | 90.99 143 | 94.73 130 | 81.27 137 | 95.07 78 | 95.89 110 | 86.48 103 | 83.67 216 | 94.30 110 | 69.33 222 | 97.99 126 | 87.10 97 | 88.55 188 | 93.72 216 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
LCM-MVSNet-Re | | | 88.30 136 | 88.32 118 | 88.27 250 | 94.71 132 | 72.41 293 | 93.15 203 | 90.98 282 | 87.77 76 | 79.25 270 | 91.96 193 | 78.35 104 | 95.75 265 | 83.04 136 | 95.62 83 | 96.65 96 |
|
diffmvs | | | 89.07 117 | 88.32 118 | 91.34 128 | 93.24 181 | 79.79 176 | 92.29 233 | 94.98 175 | 80.24 232 | 87.38 121 | 92.45 172 | 78.02 106 | 97.33 178 | 83.29 134 | 92.93 131 | 96.91 89 |
|
MVSTER | | | 88.84 124 | 88.29 120 | 90.51 159 | 92.95 192 | 80.44 162 | 93.73 177 | 95.01 172 | 84.66 138 | 87.15 122 | 93.12 150 | 72.79 179 | 97.21 192 | 87.86 82 | 87.36 205 | 93.87 203 |
|
TAMVS | | | 89.21 114 | 88.29 120 | 91.96 107 | 93.71 170 | 82.62 116 | 93.30 197 | 94.19 202 | 82.22 199 | 87.78 114 | 93.94 124 | 78.83 96 | 96.95 210 | 77.70 216 | 92.98 130 | 96.32 102 |
|
sss | | | 88.93 123 | 88.26 122 | 90.94 145 | 94.05 154 | 80.78 154 | 91.71 246 | 95.38 151 | 81.55 222 | 88.63 95 | 93.91 128 | 75.04 150 | 95.47 276 | 82.47 146 | 91.61 140 | 96.57 98 |
|
QAPM | | | 89.51 103 | 88.15 123 | 93.59 56 | 94.92 124 | 84.58 60 | 96.82 18 | 96.70 58 | 78.43 253 | 83.41 222 | 96.19 60 | 73.18 175 | 99.30 27 | 77.11 223 | 96.54 74 | 96.89 91 |
|
BH-untuned | | | 88.60 129 | 88.13 124 | 90.01 191 | 95.24 116 | 78.50 224 | 93.29 198 | 94.15 204 | 84.75 136 | 84.46 196 | 93.40 137 | 75.76 139 | 97.40 173 | 77.59 217 | 94.52 102 | 94.12 189 |
|
PLC | | 84.53 7 | 89.06 119 | 88.03 125 | 92.15 100 | 97.27 49 | 82.69 114 | 94.29 135 | 95.44 147 | 79.71 239 | 84.01 209 | 94.18 116 | 76.68 118 | 98.75 76 | 77.28 220 | 93.41 121 | 95.02 142 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
CNLPA | | | 89.07 117 | 87.98 126 | 92.34 94 | 96.87 56 | 84.78 57 | 94.08 152 | 93.24 227 | 81.41 225 | 84.46 196 | 95.13 89 | 75.57 142 | 96.62 227 | 77.21 221 | 93.84 113 | 95.61 129 |
|
TranMVSNet+NR-MVSNet | | | 88.84 124 | 87.95 127 | 91.49 124 | 92.68 197 | 83.01 102 | 94.92 88 | 96.31 80 | 89.88 30 | 85.53 161 | 93.85 131 | 76.63 119 | 96.96 209 | 81.91 156 | 79.87 289 | 94.50 175 |
|
HY-MVS | | 83.01 12 | 89.03 120 | 87.94 128 | 92.29 96 | 94.86 127 | 82.77 107 | 92.08 241 | 94.49 193 | 81.52 223 | 86.93 126 | 92.79 166 | 78.32 105 | 98.23 100 | 79.93 188 | 90.55 155 | 95.88 118 |
|
IterMVS-LS | | | 88.36 134 | 87.91 129 | 89.70 202 | 93.80 167 | 78.29 230 | 93.73 177 | 95.08 171 | 85.73 116 | 84.75 190 | 91.90 196 | 79.88 85 | 96.92 212 | 83.83 130 | 82.51 246 | 93.89 200 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CHOSEN 1792x2688 | | | 88.84 124 | 87.69 130 | 92.30 95 | 96.14 76 | 81.42 135 | 90.01 267 | 95.86 112 | 74.52 286 | 87.41 118 | 93.94 124 | 75.46 144 | 98.36 93 | 80.36 179 | 95.53 84 | 97.12 83 |
|
WR-MVS | | | 88.38 132 | 87.67 131 | 90.52 158 | 93.30 180 | 80.18 163 | 93.26 200 | 95.96 103 | 88.57 59 | 85.47 166 | 92.81 164 | 76.12 123 | 96.91 213 | 81.24 163 | 82.29 248 | 94.47 180 |
|
jajsoiax | | | 88.24 137 | 87.50 132 | 90.48 161 | 90.89 264 | 80.14 165 | 95.31 57 | 95.65 127 | 84.97 132 | 84.24 206 | 94.02 120 | 65.31 268 | 97.42 166 | 88.56 73 | 88.52 190 | 93.89 200 |
|
BH-RMVSNet | | | 88.37 133 | 87.48 133 | 91.02 141 | 95.28 109 | 79.45 187 | 92.89 215 | 93.07 230 | 85.45 122 | 86.91 127 | 94.84 98 | 70.35 211 | 97.76 136 | 73.97 248 | 94.59 100 | 95.85 119 |
|
VPNet | | | 88.20 138 | 87.47 134 | 90.39 165 | 93.56 174 | 79.46 185 | 94.04 158 | 95.54 134 | 88.67 55 | 86.96 125 | 94.58 105 | 69.33 222 | 97.15 194 | 84.05 128 | 80.53 279 | 94.56 171 |
|
NR-MVSNet | | | 88.58 130 | 87.47 134 | 91.93 109 | 93.04 188 | 84.16 75 | 94.77 98 | 96.25 84 | 89.05 45 | 80.04 264 | 93.29 143 | 79.02 95 | 97.05 203 | 81.71 160 | 80.05 284 | 94.59 168 |
|
WR-MVS_H | | | 87.80 154 | 87.37 136 | 89.10 226 | 93.23 182 | 78.12 234 | 95.61 52 | 97.30 17 | 87.90 72 | 83.72 214 | 92.01 192 | 79.65 93 | 96.01 254 | 76.36 227 | 80.54 278 | 93.16 243 |
|
1112_ss | | | 88.42 131 | 87.33 137 | 91.72 118 | 94.92 124 | 80.98 147 | 92.97 213 | 94.54 192 | 78.16 258 | 83.82 212 | 93.88 129 | 78.78 97 | 97.91 131 | 79.45 198 | 89.41 172 | 96.26 104 |
|
OpenMVS | | 83.78 11 | 88.74 127 | 87.29 138 | 93.08 66 | 92.70 196 | 85.39 51 | 96.57 22 | 96.43 74 | 78.74 250 | 80.85 252 | 96.07 64 | 69.64 219 | 99.01 53 | 78.01 214 | 96.65 71 | 94.83 158 |
|
mvs_tets | | | 88.06 143 | 87.28 139 | 90.38 167 | 90.94 260 | 79.88 173 | 95.22 70 | 95.66 125 | 85.10 130 | 84.21 207 | 93.94 124 | 63.53 276 | 97.40 173 | 88.50 74 | 88.40 195 | 93.87 203 |
|
CP-MVSNet | | | 87.63 162 | 87.26 140 | 88.74 231 | 93.12 185 | 76.59 261 | 95.29 61 | 96.58 69 | 88.43 61 | 83.49 221 | 92.98 158 | 75.28 146 | 95.83 261 | 78.97 204 | 81.15 267 | 93.79 208 |
|
v1neww | | | 87.98 144 | 87.25 141 | 90.16 174 | 91.38 229 | 79.41 189 | 94.37 130 | 95.28 156 | 84.48 141 | 85.77 148 | 91.53 210 | 76.12 123 | 97.45 155 | 84.45 121 | 81.89 255 | 93.61 227 |
|
v7new | | | 87.98 144 | 87.25 141 | 90.16 174 | 91.38 229 | 79.41 189 | 94.37 130 | 95.28 156 | 84.48 141 | 85.77 148 | 91.53 210 | 76.12 123 | 97.45 155 | 84.45 121 | 81.89 255 | 93.61 227 |
|
v6 | | | 87.98 144 | 87.25 141 | 90.16 174 | 91.36 232 | 79.39 194 | 94.37 130 | 95.27 159 | 84.48 141 | 85.78 147 | 91.51 212 | 76.15 122 | 97.46 153 | 84.46 120 | 81.88 257 | 93.62 226 |
|
v1 | | | 87.85 149 | 87.10 144 | 90.11 185 | 91.21 246 | 79.24 205 | 94.09 150 | 95.24 161 | 84.44 145 | 85.70 153 | 91.31 221 | 75.96 133 | 97.45 155 | 84.18 125 | 81.73 262 | 93.64 223 |
|
anonymousdsp | | | 87.84 150 | 87.09 145 | 90.12 180 | 89.13 296 | 80.54 159 | 94.67 107 | 95.55 132 | 82.05 202 | 83.82 212 | 92.12 184 | 71.47 195 | 97.15 194 | 87.15 93 | 87.80 202 | 92.67 257 |
|
v1141 | | | 87.84 150 | 87.09 145 | 90.11 185 | 91.23 244 | 79.25 203 | 94.08 152 | 95.24 161 | 84.44 145 | 85.69 155 | 91.31 221 | 75.91 135 | 97.44 162 | 84.17 126 | 81.74 261 | 93.63 225 |
|
divwei89l23v2f112 | | | 87.84 150 | 87.09 145 | 90.10 187 | 91.23 244 | 79.24 205 | 94.09 150 | 95.24 161 | 84.44 145 | 85.70 153 | 91.31 221 | 75.91 135 | 97.44 162 | 84.17 126 | 81.73 262 | 93.64 223 |
|
v2v482 | | | 87.84 150 | 87.06 148 | 90.17 173 | 90.99 256 | 79.23 207 | 94.00 162 | 95.13 168 | 84.87 133 | 85.53 161 | 92.07 190 | 74.45 154 | 97.45 155 | 84.71 117 | 81.75 260 | 93.85 206 |
|
BH-w/o | | | 87.57 171 | 87.05 149 | 89.12 224 | 94.90 126 | 77.90 239 | 92.41 228 | 93.51 224 | 82.89 189 | 83.70 215 | 91.34 217 | 75.75 140 | 97.07 201 | 75.49 234 | 93.49 118 | 92.39 266 |
|
TAPA-MVS | | 84.62 6 | 88.16 139 | 87.01 150 | 91.62 121 | 96.64 60 | 80.65 155 | 94.39 126 | 96.21 89 | 76.38 268 | 86.19 142 | 95.44 80 | 79.75 87 | 98.08 120 | 62.75 311 | 95.29 91 | 96.13 108 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
v7 | | | 87.75 155 | 86.96 151 | 90.12 180 | 91.20 247 | 79.50 180 | 94.28 136 | 95.46 141 | 83.45 166 | 85.75 150 | 91.56 209 | 75.13 147 | 97.43 164 | 83.60 132 | 82.18 250 | 93.42 236 |
|
PS-CasMVS | | | 87.32 177 | 86.88 152 | 88.63 234 | 92.99 191 | 76.33 264 | 95.33 56 | 96.61 66 | 88.22 66 | 83.30 224 | 93.07 152 | 73.03 177 | 95.79 264 | 78.36 209 | 81.00 272 | 93.75 214 |
|
V42 | | | 87.68 157 | 86.86 153 | 90.15 178 | 90.58 274 | 80.14 165 | 94.24 138 | 95.28 156 | 83.66 158 | 85.67 156 | 91.33 218 | 74.73 152 | 97.41 171 | 84.43 123 | 81.83 258 | 92.89 251 |
|
XXY-MVS | | | 87.65 158 | 86.85 154 | 90.03 189 | 92.14 204 | 80.60 158 | 93.76 174 | 95.23 164 | 82.94 186 | 84.60 192 | 94.02 120 | 74.27 156 | 95.49 275 | 81.04 165 | 83.68 235 | 94.01 197 |
|
DI_MVS_plusplus_test | | | 88.15 140 | 86.82 155 | 92.14 101 | 90.67 272 | 81.07 144 | 93.01 210 | 94.59 191 | 83.83 155 | 77.78 277 | 90.63 239 | 68.51 241 | 98.16 104 | 88.02 81 | 94.37 107 | 97.17 80 |
|
HyFIR lowres test | | | 88.09 142 | 86.81 156 | 91.93 109 | 96.00 85 | 80.63 156 | 90.01 267 | 95.79 116 | 73.42 292 | 87.68 116 | 92.10 187 | 73.86 165 | 97.96 127 | 80.75 171 | 91.70 139 | 97.19 79 |
|
F-COLMAP | | | 87.95 147 | 86.80 157 | 91.40 127 | 96.35 69 | 80.88 151 | 94.73 99 | 95.45 145 | 79.65 240 | 82.04 239 | 94.61 103 | 71.13 197 | 98.50 88 | 76.24 230 | 91.05 148 | 94.80 160 |
|
v1144 | | | 87.61 169 | 86.79 158 | 90.06 188 | 91.01 255 | 79.34 197 | 93.95 164 | 95.42 150 | 83.36 170 | 85.66 157 | 91.31 221 | 74.98 151 | 97.42 166 | 83.37 133 | 82.06 251 | 93.42 236 |
|
test_normal | | | 88.13 141 | 86.78 159 | 92.18 99 | 90.55 277 | 81.19 142 | 92.74 218 | 94.64 190 | 83.84 153 | 77.49 280 | 90.51 245 | 68.49 242 | 98.16 104 | 88.22 76 | 94.55 101 | 97.21 78 |
|
Fast-Effi-MVS+-dtu | | | 87.44 174 | 86.72 160 | 89.63 204 | 92.04 207 | 77.68 248 | 94.03 159 | 93.94 216 | 85.81 113 | 82.42 231 | 91.32 220 | 70.33 212 | 97.06 202 | 80.33 181 | 90.23 161 | 94.14 188 |
|
conf200view11 | | | 87.65 158 | 86.71 161 | 90.46 163 | 96.12 77 | 78.55 216 | 95.03 81 | 91.58 263 | 87.15 85 | 88.06 103 | 92.29 179 | 68.91 230 | 98.10 112 | 70.13 269 | 91.10 143 | 94.71 162 |
|
thres100view900 | | | 87.63 162 | 86.71 161 | 90.38 167 | 96.12 77 | 78.55 216 | 95.03 81 | 91.58 263 | 87.15 85 | 88.06 103 | 92.29 179 | 68.91 230 | 98.10 112 | 70.13 269 | 91.10 143 | 94.48 178 |
|
v8 | | | 87.50 173 | 86.71 161 | 89.89 194 | 91.37 231 | 79.40 193 | 94.50 115 | 95.38 151 | 84.81 135 | 83.60 218 | 91.33 218 | 76.05 127 | 97.42 166 | 82.84 140 | 80.51 281 | 92.84 253 |
|
thres600view7 | | | 87.65 158 | 86.67 164 | 90.59 149 | 96.08 82 | 78.72 212 | 94.88 91 | 91.58 263 | 87.06 93 | 88.08 102 | 92.30 178 | 68.91 230 | 98.10 112 | 70.05 273 | 91.10 143 | 94.96 147 |
|
view600 | | | 87.62 164 | 86.65 165 | 90.53 152 | 96.19 72 | 78.52 219 | 95.29 61 | 91.09 274 | 87.08 89 | 87.84 108 | 93.03 154 | 68.86 233 | 98.11 108 | 69.44 275 | 91.02 150 | 94.96 147 |
|
view800 | | | 87.62 164 | 86.65 165 | 90.53 152 | 96.19 72 | 78.52 219 | 95.29 61 | 91.09 274 | 87.08 89 | 87.84 108 | 93.03 154 | 68.86 233 | 98.11 108 | 69.44 275 | 91.02 150 | 94.96 147 |
|
conf0.05thres1000 | | | 87.62 164 | 86.65 165 | 90.53 152 | 96.19 72 | 78.52 219 | 95.29 61 | 91.09 274 | 87.08 89 | 87.84 108 | 93.03 154 | 68.86 233 | 98.11 108 | 69.44 275 | 91.02 150 | 94.96 147 |
|
tfpn | | | 87.62 164 | 86.65 165 | 90.53 152 | 96.19 72 | 78.52 219 | 95.29 61 | 91.09 274 | 87.08 89 | 87.84 108 | 93.03 154 | 68.86 233 | 98.11 108 | 69.44 275 | 91.02 150 | 94.96 147 |
|
tfpn200view9 | | | 87.58 170 | 86.64 169 | 90.41 164 | 95.99 86 | 78.64 214 | 94.58 111 | 91.98 254 | 86.94 96 | 88.09 100 | 91.77 198 | 69.18 227 | 98.10 112 | 70.13 269 | 91.10 143 | 94.48 178 |
|
thres400 | | | 87.62 164 | 86.64 169 | 90.57 150 | 95.99 86 | 78.64 214 | 94.58 111 | 91.98 254 | 86.94 96 | 88.09 100 | 91.77 198 | 69.18 227 | 98.10 112 | 70.13 269 | 91.10 143 | 94.96 147 |
|
Baseline_NR-MVSNet | | | 87.07 186 | 86.63 171 | 88.40 247 | 91.44 222 | 77.87 241 | 94.23 139 | 92.57 240 | 84.12 150 | 85.74 152 | 92.08 188 | 77.25 112 | 96.04 251 | 82.29 150 | 79.94 287 | 91.30 286 |
|
1314 | | | 87.51 172 | 86.57 172 | 90.34 170 | 92.42 200 | 79.74 178 | 92.63 221 | 95.35 155 | 78.35 254 | 80.14 262 | 91.62 205 | 74.05 162 | 97.15 194 | 81.05 164 | 93.53 117 | 94.12 189 |
|
Test_1112_low_res | | | 87.65 158 | 86.51 173 | 91.08 137 | 94.94 123 | 79.28 201 | 91.77 243 | 94.30 200 | 76.04 273 | 83.51 220 | 92.37 175 | 77.86 110 | 97.73 140 | 78.69 207 | 89.13 183 | 96.22 105 |
|
v10 | | | 87.25 180 | 86.38 174 | 89.85 195 | 91.19 249 | 79.50 180 | 94.48 116 | 95.45 145 | 83.79 156 | 83.62 217 | 91.19 226 | 75.13 147 | 97.42 166 | 81.94 155 | 80.60 276 | 92.63 259 |
|
v144192 | | | 87.19 184 | 86.35 175 | 89.74 200 | 90.64 273 | 78.24 232 | 93.92 165 | 95.43 148 | 81.93 207 | 85.51 163 | 91.05 233 | 74.21 159 | 97.45 155 | 82.86 139 | 81.56 264 | 93.53 231 |
|
v1192 | | | 87.25 180 | 86.33 176 | 90.00 192 | 90.76 268 | 79.04 209 | 93.80 171 | 95.48 140 | 82.57 195 | 85.48 165 | 91.18 227 | 73.38 174 | 97.42 166 | 82.30 149 | 82.06 251 | 93.53 231 |
|
v148 | | | 87.04 187 | 86.32 177 | 89.21 222 | 90.94 260 | 77.26 255 | 93.71 180 | 94.43 195 | 84.84 134 | 84.36 202 | 90.80 236 | 76.04 129 | 97.05 203 | 82.12 151 | 79.60 290 | 93.31 238 |
|
LS3D | | | 87.89 148 | 86.32 177 | 92.59 83 | 96.07 83 | 82.92 105 | 95.23 69 | 94.92 180 | 75.66 275 | 82.89 227 | 95.98 66 | 72.48 185 | 99.21 30 | 68.43 284 | 95.23 93 | 95.64 128 |
|
PEN-MVS | | | 86.80 190 | 86.27 179 | 88.40 247 | 92.32 202 | 75.71 268 | 95.18 72 | 96.38 78 | 87.97 69 | 82.82 228 | 93.15 148 | 73.39 173 | 95.92 257 | 76.15 231 | 79.03 292 | 93.59 229 |
|
thres200 | | | 87.21 183 | 86.24 180 | 90.12 180 | 95.36 105 | 78.53 218 | 93.26 200 | 92.10 247 | 86.42 105 | 88.00 105 | 91.11 231 | 69.24 226 | 98.00 125 | 69.58 274 | 91.04 149 | 93.83 207 |
|
X-MVStestdata | | | 88.31 135 | 86.13 181 | 94.85 15 | 98.54 6 | 86.60 25 | 96.93 12 | 97.19 22 | 90.66 22 | 92.85 35 | 23.41 346 | 85.02 42 | 99.49 14 | 91.99 36 | 98.56 34 | 98.47 13 |
|
FMVSNet3 | | | 87.40 176 | 86.11 182 | 91.30 130 | 93.79 169 | 83.64 85 | 94.20 144 | 94.81 186 | 83.89 152 | 84.37 199 | 91.87 197 | 68.45 244 | 96.56 230 | 78.23 211 | 85.36 219 | 93.70 217 |
|
MVS | | | 87.44 174 | 86.10 183 | 91.44 126 | 92.61 198 | 83.62 86 | 92.63 221 | 95.66 125 | 67.26 322 | 81.47 244 | 92.15 183 | 77.95 107 | 98.22 101 | 79.71 194 | 95.48 86 | 92.47 263 |
|
PCF-MVS | | 84.11 10 | 87.74 156 | 86.08 184 | 92.70 80 | 94.02 155 | 84.43 70 | 89.27 278 | 95.87 111 | 73.62 291 | 84.43 198 | 94.33 108 | 78.48 103 | 98.86 68 | 70.27 265 | 94.45 105 | 94.81 159 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
v1921920 | | | 86.97 188 | 86.06 185 | 89.69 203 | 90.53 278 | 78.11 235 | 93.80 171 | 95.43 148 | 81.90 209 | 85.33 179 | 91.05 233 | 72.66 181 | 97.41 171 | 82.05 153 | 81.80 259 | 93.53 231 |
|
GBi-Net | | | 87.26 178 | 85.98 186 | 91.08 137 | 94.01 156 | 83.10 97 | 95.14 75 | 94.94 176 | 83.57 161 | 84.37 199 | 91.64 201 | 66.59 258 | 96.34 243 | 78.23 211 | 85.36 219 | 93.79 208 |
|
test1 | | | 87.26 178 | 85.98 186 | 91.08 137 | 94.01 156 | 83.10 97 | 95.14 75 | 94.94 176 | 83.57 161 | 84.37 199 | 91.64 201 | 66.59 258 | 96.34 243 | 78.23 211 | 85.36 219 | 93.79 208 |
|
EPNet_dtu | | | 86.49 199 | 85.94 188 | 88.14 255 | 90.24 283 | 72.82 285 | 94.11 148 | 92.20 246 | 86.66 102 | 79.42 269 | 92.36 176 | 73.52 169 | 95.81 263 | 71.26 260 | 93.66 114 | 95.80 123 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
v1240 | | | 86.78 191 | 85.85 189 | 89.56 205 | 90.45 279 | 77.79 243 | 93.61 185 | 95.37 153 | 81.65 218 | 85.43 170 | 91.15 229 | 71.50 194 | 97.43 164 | 81.47 162 | 82.05 253 | 93.47 235 |
|
FMVSNet2 | | | 87.19 184 | 85.82 190 | 91.30 130 | 94.01 156 | 83.67 84 | 94.79 96 | 94.94 176 | 83.57 161 | 83.88 210 | 92.05 191 | 66.59 258 | 96.51 233 | 77.56 218 | 85.01 223 | 93.73 215 |
|
v7n | | | 86.81 189 | 85.76 191 | 89.95 193 | 90.72 270 | 79.25 203 | 95.07 78 | 95.92 105 | 84.45 144 | 82.29 232 | 90.86 235 | 72.60 183 | 97.53 148 | 79.42 201 | 80.52 280 | 93.08 248 |
|
TR-MVS | | | 86.78 191 | 85.76 191 | 89.82 196 | 94.37 145 | 78.41 226 | 92.47 227 | 92.83 233 | 81.11 228 | 86.36 138 | 92.40 174 | 68.73 238 | 97.48 151 | 73.75 251 | 89.85 167 | 93.57 230 |
|
pm-mvs1 | | | 86.61 195 | 85.54 193 | 89.82 196 | 91.44 222 | 80.18 163 | 95.28 67 | 94.85 183 | 83.84 153 | 81.66 243 | 92.62 169 | 72.45 187 | 96.48 235 | 79.67 195 | 78.06 294 | 92.82 255 |
|
V4 | | | 86.50 197 | 85.54 193 | 89.39 213 | 89.13 296 | 78.99 210 | 94.73 99 | 95.54 134 | 83.59 159 | 82.10 236 | 90.61 240 | 71.60 191 | 97.45 155 | 82.52 143 | 80.01 285 | 91.74 277 |
|
PatchMatch-RL | | | 86.77 193 | 85.54 193 | 90.47 162 | 95.88 89 | 82.71 113 | 90.54 260 | 92.31 243 | 79.82 238 | 84.32 203 | 91.57 208 | 68.77 237 | 96.39 240 | 73.16 253 | 93.48 120 | 92.32 269 |
|
v52 | | | 86.50 197 | 85.53 196 | 89.39 213 | 89.17 295 | 78.99 210 | 94.72 102 | 95.54 134 | 83.59 159 | 82.10 236 | 90.60 241 | 71.59 192 | 97.45 155 | 82.52 143 | 79.99 286 | 91.73 278 |
|
DTE-MVSNet | | | 86.11 203 | 85.48 197 | 87.98 257 | 91.65 218 | 74.92 271 | 94.93 87 | 95.75 119 | 87.36 84 | 82.26 233 | 93.04 153 | 72.85 178 | 95.82 262 | 74.04 247 | 77.46 297 | 93.20 241 |
|
test-LLR | | | 85.87 209 | 85.41 198 | 87.25 272 | 90.95 258 | 71.67 296 | 89.55 272 | 89.88 304 | 83.41 167 | 84.54 194 | 87.95 280 | 67.25 253 | 95.11 289 | 81.82 157 | 93.37 123 | 94.97 144 |
|
PAPM | | | 86.68 194 | 85.39 199 | 90.53 152 | 93.05 187 | 79.33 200 | 89.79 271 | 94.77 188 | 78.82 247 | 81.95 240 | 93.24 145 | 76.81 115 | 97.30 180 | 66.94 291 | 93.16 127 | 94.95 154 |
|
DP-MVS | | | 87.25 180 | 85.36 200 | 92.90 74 | 97.65 33 | 83.24 94 | 94.81 95 | 92.00 252 | 74.99 281 | 81.92 241 | 95.00 91 | 72.66 181 | 99.05 44 | 66.92 293 | 92.33 137 | 96.40 100 |
|
v748 | | | 86.27 201 | 85.28 201 | 89.25 221 | 90.26 282 | 77.58 254 | 94.89 89 | 95.50 139 | 84.28 148 | 81.41 246 | 90.46 246 | 72.57 184 | 97.32 179 | 79.81 193 | 78.36 293 | 92.84 253 |
|
GA-MVS | | | 86.61 195 | 85.27 202 | 90.66 148 | 91.33 237 | 78.71 213 | 90.40 261 | 93.81 221 | 85.34 124 | 85.12 181 | 89.57 259 | 61.25 287 | 97.11 198 | 80.99 168 | 89.59 171 | 96.15 106 |
|
PatchFormer-LS_test | | | 86.02 206 | 85.13 203 | 88.70 232 | 91.52 219 | 74.12 276 | 91.19 257 | 92.09 248 | 82.71 193 | 84.30 205 | 87.24 289 | 70.87 201 | 96.98 207 | 81.04 165 | 85.17 222 | 95.00 143 |
|
tpmrst | | | 85.35 223 | 84.99 204 | 86.43 285 | 90.88 265 | 67.88 316 | 88.71 285 | 91.43 270 | 80.13 234 | 86.08 144 | 88.80 267 | 73.05 176 | 96.02 253 | 82.48 145 | 83.40 241 | 95.40 134 |
|
tfpn_ndepth | | | 86.10 204 | 84.98 205 | 89.43 212 | 95.52 102 | 78.29 230 | 94.62 109 | 89.60 309 | 81.88 215 | 85.43 170 | 90.54 242 | 68.47 243 | 96.85 217 | 68.46 283 | 90.34 159 | 93.15 245 |
|
cascas | | | 86.43 200 | 84.98 205 | 90.80 147 | 92.10 206 | 80.92 150 | 90.24 263 | 95.91 107 | 73.10 295 | 83.57 219 | 88.39 274 | 65.15 269 | 97.46 153 | 84.90 114 | 91.43 141 | 94.03 195 |
|
PMMVS | | | 85.71 219 | 84.96 207 | 87.95 258 | 88.90 300 | 77.09 256 | 88.68 286 | 90.06 299 | 72.32 302 | 86.47 133 | 90.76 237 | 72.15 188 | 94.40 296 | 81.78 159 | 93.49 118 | 92.36 267 |
|
CostFormer | | | 85.77 213 | 84.94 208 | 88.26 251 | 91.16 252 | 72.58 292 | 89.47 276 | 91.04 281 | 76.26 271 | 86.45 136 | 89.97 253 | 70.74 204 | 96.86 216 | 82.35 148 | 87.07 210 | 95.34 137 |
|
tfpn1000 | | | 86.06 205 | 84.92 209 | 89.49 210 | 95.54 99 | 77.79 243 | 94.72 102 | 89.07 316 | 82.05 202 | 85.36 178 | 91.94 194 | 68.32 250 | 96.65 225 | 67.04 290 | 90.24 160 | 94.02 196 |
|
LTVRE_ROB | | 82.13 13 | 86.26 202 | 84.90 210 | 90.34 170 | 94.44 144 | 81.50 130 | 92.31 232 | 94.89 181 | 83.03 181 | 79.63 267 | 92.67 167 | 69.69 218 | 97.79 134 | 71.20 261 | 86.26 213 | 91.72 279 |
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 |
MVP-Stereo | | | 85.97 208 | 84.86 211 | 89.32 219 | 90.92 262 | 82.19 121 | 92.11 239 | 94.19 202 | 78.76 249 | 78.77 272 | 91.63 204 | 68.38 249 | 96.56 230 | 75.01 241 | 93.95 110 | 89.20 311 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
XVG-ACMP-BASELINE | | | 86.00 207 | 84.84 212 | 89.45 211 | 91.20 247 | 78.00 236 | 91.70 247 | 95.55 132 | 85.05 131 | 82.97 226 | 92.25 182 | 54.49 313 | 97.48 151 | 82.93 138 | 87.45 204 | 92.89 251 |
|
CVMVSNet | | | 84.69 244 | 84.79 213 | 84.37 300 | 91.84 210 | 64.92 324 | 93.70 181 | 91.47 269 | 66.19 324 | 86.16 143 | 95.28 83 | 67.18 255 | 93.33 307 | 80.89 170 | 90.42 157 | 94.88 156 |
|
Patchmatch-test1 | | | 85.81 212 | 84.71 214 | 89.12 224 | 92.15 203 | 76.60 260 | 91.12 258 | 91.69 261 | 83.53 164 | 85.50 164 | 88.56 272 | 66.79 256 | 95.00 292 | 72.69 255 | 90.35 158 | 95.76 124 |
|
PatchmatchNet | | | 85.85 210 | 84.70 215 | 89.29 220 | 91.76 213 | 75.54 269 | 88.49 288 | 91.30 272 | 81.63 220 | 85.05 182 | 88.70 269 | 71.71 189 | 96.24 246 | 74.61 244 | 89.05 184 | 96.08 110 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
PVSNet | | 78.82 18 | 85.55 220 | 84.65 216 | 88.23 253 | 94.72 131 | 71.93 294 | 87.12 299 | 92.75 236 | 78.80 248 | 84.95 184 | 90.53 244 | 64.43 273 | 96.71 224 | 74.74 242 | 93.86 112 | 96.06 112 |
|
OurMVSNet-221017-0 | | | 85.35 223 | 84.64 217 | 87.49 267 | 90.77 267 | 72.59 291 | 94.01 161 | 94.40 196 | 84.72 137 | 79.62 268 | 93.17 147 | 61.91 283 | 96.72 222 | 81.99 154 | 81.16 265 | 93.16 243 |
|
thresconf0.02 | | | 85.75 214 | 84.54 218 | 89.38 215 | 95.26 111 | 77.63 249 | 94.21 140 | 89.33 311 | 81.89 210 | 84.94 185 | 91.51 212 | 68.43 245 | 96.80 218 | 66.05 296 | 89.23 177 | 93.70 217 |
|
tfpn_n400 | | | 85.75 214 | 84.54 218 | 89.38 215 | 95.26 111 | 77.63 249 | 94.21 140 | 89.33 311 | 81.89 210 | 84.94 185 | 91.51 212 | 68.43 245 | 96.80 218 | 66.05 296 | 89.23 177 | 93.70 217 |
|
tfpnconf | | | 85.75 214 | 84.54 218 | 89.38 215 | 95.26 111 | 77.63 249 | 94.21 140 | 89.33 311 | 81.89 210 | 84.94 185 | 91.51 212 | 68.43 245 | 96.80 218 | 66.05 296 | 89.23 177 | 93.70 217 |
|
tfpnview11 | | | 85.75 214 | 84.54 218 | 89.38 215 | 95.26 111 | 77.63 249 | 94.21 140 | 89.33 311 | 81.89 210 | 84.94 185 | 91.51 212 | 68.43 245 | 96.80 218 | 66.05 296 | 89.23 177 | 93.70 217 |
|
RPSCF | | | 85.07 228 | 84.27 222 | 87.48 268 | 92.91 193 | 70.62 306 | 91.69 248 | 92.46 241 | 76.20 272 | 82.67 230 | 95.22 86 | 63.94 275 | 97.29 183 | 77.51 219 | 85.80 216 | 94.53 172 |
|
MS-PatchMatch | | | 85.05 229 | 84.16 223 | 87.73 261 | 91.42 226 | 78.51 223 | 91.25 256 | 93.53 223 | 77.50 260 | 80.15 261 | 91.58 206 | 61.99 282 | 95.51 272 | 75.69 233 | 94.35 108 | 89.16 312 |
|
FMVSNet1 | | | 85.85 210 | 84.11 224 | 91.08 137 | 92.81 194 | 83.10 97 | 95.14 75 | 94.94 176 | 81.64 219 | 82.68 229 | 91.64 201 | 59.01 300 | 96.34 243 | 75.37 236 | 83.78 232 | 93.79 208 |
|
tpm | | | 84.73 241 | 84.02 225 | 86.87 282 | 90.33 280 | 68.90 313 | 89.06 282 | 89.94 302 | 80.85 230 | 85.75 150 | 89.86 255 | 68.54 240 | 95.97 255 | 77.76 215 | 84.05 231 | 95.75 125 |
|
CHOSEN 280x420 | | | 85.15 227 | 83.99 226 | 88.65 233 | 92.47 199 | 78.40 227 | 79.68 331 | 92.76 235 | 74.90 283 | 81.41 246 | 89.59 258 | 69.85 217 | 95.51 272 | 79.92 189 | 95.29 91 | 92.03 273 |
|
IterMVS | | | 84.88 235 | 83.98 227 | 87.60 263 | 91.44 222 | 76.03 266 | 90.18 265 | 92.41 242 | 83.24 173 | 81.06 251 | 90.42 247 | 66.60 257 | 94.28 297 | 79.46 197 | 80.98 273 | 92.48 262 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
pmmvs4 | | | 85.43 221 | 83.86 228 | 90.16 174 | 90.02 288 | 82.97 104 | 90.27 262 | 92.67 238 | 75.93 274 | 80.73 253 | 91.74 200 | 71.05 198 | 95.73 266 | 78.85 205 | 83.46 239 | 91.78 276 |
|
v18 | | | 84.97 231 | 83.76 229 | 88.60 237 | 91.36 232 | 79.41 189 | 93.82 170 | 94.04 207 | 83.00 184 | 76.61 284 | 86.60 292 | 76.19 121 | 95.43 277 | 80.39 178 | 71.79 311 | 90.96 291 |
|
CR-MVSNet | | | 85.35 223 | 83.76 229 | 90.12 180 | 90.58 274 | 79.34 197 | 85.24 311 | 91.96 256 | 78.27 255 | 85.55 159 | 87.87 283 | 71.03 199 | 95.61 267 | 73.96 249 | 89.36 174 | 95.40 134 |
|
v16 | | | 84.96 232 | 83.74 231 | 88.62 235 | 91.40 227 | 79.48 183 | 93.83 168 | 94.04 207 | 83.03 181 | 76.54 285 | 86.59 293 | 76.11 126 | 95.42 278 | 80.33 181 | 71.80 310 | 90.95 293 |
|
Test4 | | | 85.75 214 | 83.72 232 | 91.83 114 | 88.08 308 | 81.03 146 | 92.48 226 | 95.54 134 | 83.38 169 | 73.40 308 | 88.57 271 | 50.99 320 | 97.37 177 | 86.61 103 | 94.47 104 | 97.09 84 |
|
v17 | | | 84.93 234 | 83.70 233 | 88.62 235 | 91.36 232 | 79.48 183 | 93.83 168 | 94.03 209 | 83.04 180 | 76.51 286 | 86.57 294 | 76.05 127 | 95.42 278 | 80.31 183 | 71.65 312 | 90.96 291 |
|
DWT-MVSNet_test | | | 84.95 233 | 83.68 234 | 88.77 229 | 91.43 225 | 73.75 279 | 91.74 245 | 90.98 282 | 80.66 231 | 83.84 211 | 87.36 287 | 62.44 279 | 97.11 198 | 78.84 206 | 85.81 215 | 95.46 132 |
|
ACMH | | 80.38 17 | 85.36 222 | 83.68 234 | 90.39 165 | 94.45 143 | 80.63 156 | 94.73 99 | 94.85 183 | 82.09 201 | 77.24 281 | 92.65 168 | 60.01 296 | 97.58 144 | 72.25 257 | 84.87 224 | 92.96 249 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
test-mter | | | 84.54 246 | 83.64 236 | 87.25 272 | 90.95 258 | 71.67 296 | 89.55 272 | 89.88 304 | 79.17 242 | 84.54 194 | 87.95 280 | 55.56 309 | 95.11 289 | 81.82 157 | 93.37 123 | 94.97 144 |
|
MDTV_nov1_ep13 | | | | 83.56 237 | | 91.69 217 | 69.93 310 | 87.75 295 | 91.54 267 | 78.60 251 | 84.86 189 | 88.90 265 | 69.54 220 | 96.03 252 | 70.25 266 | 88.93 185 | |
|
v15 | | | 84.79 237 | 83.53 238 | 88.57 241 | 91.30 243 | 79.41 189 | 93.70 181 | 94.01 210 | 83.06 177 | 76.27 287 | 86.42 298 | 76.03 130 | 95.38 280 | 80.01 185 | 71.00 315 | 90.92 294 |
|
V14 | | | 84.79 237 | 83.52 239 | 88.57 241 | 91.32 239 | 79.43 188 | 93.72 179 | 94.01 210 | 83.06 177 | 76.22 288 | 86.43 295 | 76.01 131 | 95.37 281 | 79.96 187 | 70.99 316 | 90.91 295 |
|
V9 | | | 84.77 239 | 83.50 240 | 88.58 238 | 91.33 237 | 79.46 185 | 93.75 175 | 94.00 213 | 83.07 176 | 76.07 293 | 86.43 295 | 75.97 132 | 95.37 281 | 79.91 190 | 70.93 318 | 90.91 295 |
|
v12 | | | 84.74 240 | 83.46 241 | 88.58 238 | 91.32 239 | 79.50 180 | 93.75 175 | 94.01 210 | 83.06 177 | 75.98 295 | 86.41 299 | 75.82 138 | 95.36 283 | 79.87 191 | 70.89 319 | 90.89 297 |
|
ACMH+ | | 81.04 14 | 85.05 229 | 83.46 241 | 89.82 196 | 94.66 135 | 79.37 195 | 94.44 121 | 94.12 206 | 82.19 200 | 78.04 275 | 92.82 163 | 58.23 302 | 97.54 147 | 73.77 250 | 82.90 243 | 92.54 260 |
|
v13 | | | 84.72 242 | 83.44 243 | 88.58 238 | 91.31 242 | 79.52 179 | 93.77 173 | 94.00 213 | 83.03 181 | 75.85 296 | 86.38 300 | 75.84 137 | 95.35 284 | 79.83 192 | 70.95 317 | 90.87 298 |
|
v11 | | | 84.67 245 | 83.41 244 | 88.44 246 | 91.32 239 | 79.13 208 | 93.69 184 | 93.99 215 | 82.81 190 | 76.20 289 | 86.24 302 | 75.48 143 | 95.35 284 | 79.53 196 | 71.48 314 | 90.85 299 |
|
IB-MVS | | 80.51 15 | 85.24 226 | 83.26 245 | 91.19 132 | 92.13 205 | 79.86 174 | 91.75 244 | 91.29 273 | 83.28 172 | 80.66 255 | 88.49 273 | 61.28 286 | 98.46 90 | 80.99 168 | 79.46 291 | 95.25 138 |
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 |
tfpnnormal | | | 84.72 242 | 83.23 246 | 89.20 223 | 92.79 195 | 80.05 168 | 94.48 116 | 95.81 114 | 82.38 197 | 81.08 250 | 91.21 225 | 69.01 229 | 96.95 210 | 61.69 313 | 80.59 277 | 90.58 304 |
|
MSDG | | | 84.86 236 | 83.09 247 | 90.14 179 | 93.80 167 | 80.05 168 | 89.18 281 | 93.09 229 | 78.89 245 | 78.19 273 | 91.91 195 | 65.86 267 | 97.27 184 | 68.47 282 | 88.45 192 | 93.11 246 |
|
TransMVSNet (Re) | | | 84.43 247 | 83.06 248 | 88.54 243 | 91.72 214 | 78.44 225 | 95.18 72 | 92.82 234 | 82.73 192 | 79.67 266 | 92.12 184 | 73.49 170 | 95.96 256 | 71.10 264 | 68.73 326 | 91.21 287 |
|
tpm2 | | | 84.08 249 | 82.94 249 | 87.48 268 | 91.39 228 | 71.27 298 | 89.23 280 | 90.37 292 | 71.95 305 | 84.64 191 | 89.33 261 | 67.30 252 | 96.55 232 | 75.17 238 | 87.09 209 | 94.63 164 |
|
SixPastTwentyTwo | | | 83.91 251 | 82.90 250 | 86.92 279 | 90.99 256 | 70.67 305 | 93.48 189 | 91.99 253 | 85.54 120 | 77.62 279 | 92.11 186 | 60.59 292 | 96.87 215 | 76.05 232 | 77.75 295 | 93.20 241 |
|
TESTMET0.1,1 | | | 83.74 254 | 82.85 251 | 86.42 286 | 89.96 289 | 71.21 300 | 89.55 272 | 87.88 323 | 77.41 261 | 83.37 223 | 87.31 288 | 56.71 306 | 93.65 303 | 80.62 174 | 92.85 134 | 94.40 181 |
|
pmmvs5 | | | 84.21 248 | 82.84 252 | 88.34 249 | 88.95 299 | 76.94 258 | 92.41 228 | 91.91 258 | 75.63 276 | 80.28 259 | 91.18 227 | 64.59 272 | 95.57 269 | 77.09 224 | 83.47 238 | 92.53 261 |
|
EPMVS | | | 83.90 252 | 82.70 253 | 87.51 265 | 90.23 284 | 72.67 288 | 88.62 287 | 81.96 339 | 81.37 226 | 85.01 183 | 88.34 275 | 66.31 261 | 94.45 295 | 75.30 237 | 87.12 208 | 95.43 133 |
|
tpmp4_e23 | | | 83.87 253 | 82.33 254 | 88.48 244 | 91.46 221 | 72.82 285 | 89.82 270 | 91.57 266 | 73.02 297 | 81.86 242 | 89.05 263 | 66.20 263 | 96.97 208 | 71.57 259 | 86.39 212 | 95.66 127 |
|
tpmvs | | | 83.35 258 | 82.07 255 | 87.20 276 | 91.07 254 | 71.00 303 | 88.31 290 | 91.70 260 | 78.91 244 | 80.49 258 | 87.18 290 | 69.30 225 | 97.08 200 | 68.12 288 | 83.56 237 | 93.51 234 |
|
COLMAP_ROB | | 80.39 16 | 83.96 250 | 82.04 256 | 89.74 200 | 95.28 109 | 79.75 177 | 94.25 137 | 92.28 244 | 75.17 279 | 78.02 276 | 93.77 133 | 58.60 301 | 97.84 133 | 65.06 304 | 85.92 214 | 91.63 280 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
test0.0.03 1 | | | 82.41 264 | 81.69 257 | 84.59 298 | 88.23 305 | 72.89 284 | 90.24 263 | 87.83 324 | 83.41 167 | 79.86 265 | 89.78 256 | 67.25 253 | 88.99 325 | 65.18 303 | 83.42 240 | 91.90 275 |
|
pmmvs6 | | | 83.42 255 | 81.60 258 | 88.87 228 | 88.01 309 | 77.87 241 | 94.96 84 | 94.24 201 | 74.67 285 | 78.80 271 | 91.09 232 | 60.17 295 | 96.49 234 | 77.06 225 | 75.40 302 | 92.23 271 |
|
AllTest | | | 83.42 255 | 81.39 259 | 89.52 207 | 95.01 119 | 77.79 243 | 93.12 204 | 90.89 285 | 77.41 261 | 76.12 291 | 93.34 138 | 54.08 315 | 97.51 149 | 68.31 285 | 84.27 229 | 93.26 239 |
|
PatchT | | | 82.68 262 | 81.27 260 | 86.89 281 | 90.09 286 | 70.94 304 | 84.06 318 | 90.15 296 | 74.91 282 | 85.63 158 | 83.57 312 | 69.37 221 | 94.87 294 | 65.19 302 | 88.50 191 | 94.84 157 |
|
USDC | | | 82.76 260 | 81.26 261 | 87.26 271 | 91.17 250 | 74.55 272 | 89.27 278 | 93.39 226 | 78.26 256 | 75.30 298 | 92.08 188 | 54.43 314 | 96.63 226 | 71.64 258 | 85.79 217 | 90.61 301 |
|
testing_2 | | | 83.40 257 | 81.02 262 | 90.56 151 | 85.06 319 | 80.51 160 | 91.37 253 | 95.57 130 | 82.92 187 | 67.06 324 | 85.54 306 | 49.47 323 | 97.24 188 | 86.74 98 | 85.44 218 | 93.93 198 |
|
EU-MVSNet | | | 81.32 275 | 80.95 263 | 82.42 308 | 88.50 303 | 63.67 325 | 93.32 193 | 91.33 271 | 64.02 328 | 80.57 257 | 92.83 162 | 61.21 289 | 92.27 315 | 76.34 228 | 80.38 282 | 91.32 285 |
|
Patchmtry | | | 82.71 261 | 80.93 264 | 88.06 256 | 90.05 287 | 76.37 263 | 84.74 313 | 91.96 256 | 72.28 303 | 81.32 248 | 87.87 283 | 71.03 199 | 95.50 274 | 68.97 280 | 80.15 283 | 92.32 269 |
|
RPMNet | | | 83.18 259 | 80.87 265 | 90.12 180 | 90.58 274 | 79.34 197 | 85.24 311 | 90.78 288 | 71.44 307 | 85.55 159 | 82.97 316 | 70.87 201 | 95.61 267 | 61.01 315 | 89.36 174 | 95.40 134 |
|
MIMVSNet | | | 82.59 263 | 80.53 266 | 88.76 230 | 91.51 220 | 78.32 228 | 86.57 302 | 90.13 297 | 79.32 241 | 80.70 254 | 88.69 270 | 52.98 317 | 93.07 312 | 66.03 300 | 88.86 186 | 94.90 155 |
|
EG-PatchMatch MVS | | | 82.37 265 | 80.34 267 | 88.46 245 | 90.27 281 | 79.35 196 | 92.80 217 | 94.33 199 | 77.14 265 | 73.26 309 | 90.18 250 | 47.47 327 | 96.72 222 | 70.25 266 | 87.32 207 | 89.30 309 |
|
tpm cat1 | | | 81.96 266 | 80.27 268 | 87.01 277 | 91.09 253 | 71.02 302 | 87.38 298 | 91.53 268 | 66.25 323 | 80.17 260 | 86.35 301 | 68.22 251 | 96.15 249 | 69.16 279 | 82.29 248 | 93.86 205 |
|
dp | | | 81.47 273 | 80.23 269 | 85.17 295 | 89.92 290 | 65.49 323 | 86.74 300 | 90.10 298 | 76.30 270 | 81.10 249 | 87.12 291 | 62.81 277 | 95.92 257 | 68.13 287 | 79.88 288 | 94.09 192 |
|
testgi | | | 80.94 280 | 80.20 270 | 83.18 304 | 87.96 310 | 66.29 320 | 91.28 254 | 90.70 290 | 83.70 157 | 78.12 274 | 92.84 161 | 51.37 319 | 90.82 322 | 63.34 308 | 82.46 247 | 92.43 264 |
|
K. test v3 | | | 81.59 270 | 80.15 271 | 85.91 289 | 89.89 291 | 69.42 312 | 92.57 224 | 87.71 325 | 85.56 119 | 73.44 307 | 89.71 257 | 55.58 308 | 95.52 271 | 77.17 222 | 69.76 322 | 92.78 256 |
|
Patchmatch-RL test | | | 81.67 268 | 79.96 272 | 86.81 283 | 85.42 317 | 71.23 299 | 82.17 326 | 87.50 328 | 78.47 252 | 77.19 282 | 82.50 317 | 70.81 203 | 93.48 305 | 82.66 142 | 72.89 307 | 95.71 126 |
|
ADS-MVSNet | | | 81.56 271 | 79.78 273 | 86.90 280 | 91.35 235 | 71.82 295 | 83.33 322 | 89.16 315 | 72.90 298 | 82.24 234 | 85.77 304 | 64.98 270 | 93.76 301 | 64.57 305 | 83.74 233 | 95.12 139 |
|
Anonymous20231206 | | | 81.03 278 | 79.77 274 | 84.82 297 | 87.85 312 | 70.26 308 | 91.42 252 | 92.08 249 | 73.67 290 | 77.75 278 | 89.25 262 | 62.43 280 | 93.08 311 | 61.50 314 | 82.00 254 | 91.12 289 |
|
ADS-MVSNet2 | | | 81.66 269 | 79.71 275 | 87.50 266 | 91.35 235 | 74.19 274 | 83.33 322 | 88.48 320 | 72.90 298 | 82.24 234 | 85.77 304 | 64.98 270 | 93.20 309 | 64.57 305 | 83.74 233 | 95.12 139 |
|
FMVSNet5 | | | 81.52 272 | 79.60 276 | 87.27 270 | 91.17 250 | 77.95 237 | 91.49 251 | 92.26 245 | 76.87 266 | 76.16 290 | 87.91 282 | 51.67 318 | 92.34 314 | 67.74 289 | 81.16 265 | 91.52 281 |
|
gg-mvs-nofinetune | | | 81.77 267 | 79.37 277 | 88.99 227 | 90.85 266 | 77.73 247 | 86.29 303 | 79.63 343 | 74.88 284 | 83.19 225 | 69.05 334 | 60.34 293 | 96.11 250 | 75.46 235 | 94.64 99 | 93.11 246 |
|
Patchmatch-test | | | 81.37 274 | 79.30 278 | 87.58 264 | 90.92 262 | 74.16 275 | 80.99 328 | 87.68 326 | 70.52 313 | 76.63 283 | 88.81 266 | 71.21 196 | 92.76 313 | 60.01 319 | 86.93 211 | 95.83 121 |
|
CMPMVS | | 59.16 21 | 80.52 281 | 79.20 279 | 84.48 299 | 83.98 322 | 67.63 318 | 89.95 269 | 93.84 220 | 64.79 327 | 66.81 325 | 91.14 230 | 57.93 304 | 95.17 287 | 76.25 229 | 88.10 197 | 90.65 300 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
test_0402 | | | 81.30 276 | 79.17 280 | 87.67 262 | 93.19 183 | 78.17 233 | 92.98 212 | 91.71 259 | 75.25 278 | 76.02 294 | 90.31 248 | 59.23 299 | 96.37 241 | 50.22 329 | 83.63 236 | 88.47 322 |
|
test20.03 | | | 79.95 284 | 79.08 281 | 82.55 307 | 85.79 316 | 67.74 317 | 91.09 259 | 91.08 278 | 81.23 227 | 74.48 303 | 89.96 254 | 61.63 284 | 90.15 323 | 60.08 317 | 76.38 299 | 89.76 306 |
|
LF4IMVS | | | 80.37 282 | 79.07 282 | 84.27 302 | 86.64 314 | 69.87 311 | 89.39 277 | 91.05 280 | 76.38 268 | 74.97 300 | 90.00 252 | 47.85 326 | 94.25 298 | 74.55 245 | 80.82 275 | 88.69 317 |
|
JIA-IIPM | | | 81.04 277 | 78.98 283 | 87.25 272 | 88.64 301 | 73.48 281 | 81.75 327 | 89.61 308 | 73.19 294 | 82.05 238 | 73.71 331 | 66.07 266 | 95.87 260 | 71.18 263 | 84.60 226 | 92.41 265 |
|
pmmvs-eth3d | | | 80.97 279 | 78.72 284 | 87.74 260 | 84.99 320 | 79.97 172 | 90.11 266 | 91.65 262 | 75.36 277 | 73.51 306 | 86.03 303 | 59.45 298 | 93.96 300 | 75.17 238 | 72.21 308 | 89.29 310 |
|
UnsupCasMVSNet_eth | | | 80.07 283 | 78.27 285 | 85.46 292 | 85.24 318 | 72.63 290 | 88.45 289 | 94.87 182 | 82.99 185 | 71.64 316 | 88.07 279 | 56.34 307 | 91.75 319 | 73.48 252 | 63.36 333 | 92.01 274 |
|
TinyColmap | | | 79.76 286 | 77.69 286 | 85.97 288 | 91.71 215 | 73.12 282 | 89.55 272 | 90.36 293 | 75.03 280 | 72.03 314 | 90.19 249 | 46.22 329 | 96.19 248 | 63.11 309 | 81.03 269 | 88.59 318 |
|
TDRefinement | | | 79.81 285 | 77.34 287 | 87.22 275 | 79.24 334 | 75.48 270 | 93.12 204 | 92.03 251 | 76.45 267 | 75.01 299 | 91.58 206 | 49.19 324 | 96.44 238 | 70.22 268 | 69.18 323 | 89.75 307 |
|
MIMVSNet1 | | | 79.38 288 | 77.28 288 | 85.69 290 | 86.35 315 | 73.67 280 | 91.61 250 | 92.75 236 | 78.11 259 | 72.64 312 | 88.12 278 | 48.16 325 | 91.97 318 | 60.32 316 | 77.49 296 | 91.43 284 |
|
YYNet1 | | | 79.22 289 | 77.20 289 | 85.28 294 | 88.20 307 | 72.66 289 | 85.87 306 | 90.05 301 | 74.33 288 | 62.70 330 | 87.61 285 | 66.09 265 | 92.03 316 | 66.94 291 | 72.97 306 | 91.15 288 |
|
MDA-MVSNet_test_wron | | | 79.21 290 | 77.19 290 | 85.29 293 | 88.22 306 | 72.77 287 | 85.87 306 | 90.06 299 | 74.34 287 | 62.62 331 | 87.56 286 | 66.14 264 | 91.99 317 | 66.90 294 | 73.01 305 | 91.10 290 |
|
OpenMVS_ROB | | 74.94 19 | 79.51 287 | 77.03 291 | 86.93 278 | 87.00 313 | 76.23 265 | 92.33 231 | 90.74 289 | 68.93 317 | 74.52 302 | 88.23 277 | 49.58 322 | 96.62 227 | 57.64 321 | 84.29 228 | 87.94 324 |
|
MDA-MVSNet-bldmvs | | | 78.85 291 | 76.31 292 | 86.46 284 | 89.76 292 | 73.88 278 | 88.79 284 | 90.42 291 | 79.16 243 | 59.18 332 | 88.33 276 | 60.20 294 | 94.04 299 | 62.00 312 | 68.96 324 | 91.48 283 |
|
DSMNet-mixed | | | 76.94 294 | 76.29 293 | 78.89 311 | 83.10 325 | 56.11 337 | 87.78 294 | 79.77 342 | 60.65 332 | 75.64 297 | 88.71 268 | 61.56 285 | 88.34 327 | 60.07 318 | 89.29 176 | 92.21 272 |
|
PM-MVS | | | 78.11 292 | 76.12 294 | 84.09 303 | 83.54 324 | 70.08 309 | 88.97 283 | 85.27 333 | 79.93 236 | 74.73 301 | 86.43 295 | 34.70 338 | 93.48 305 | 79.43 200 | 72.06 309 | 88.72 316 |
|
new-patchmatchnet | | | 76.41 295 | 75.17 295 | 80.13 310 | 82.65 328 | 59.61 330 | 87.66 296 | 91.08 278 | 78.23 257 | 69.85 318 | 83.22 314 | 54.76 312 | 91.63 321 | 64.14 307 | 64.89 329 | 89.16 312 |
|
PVSNet_0 | | 73.20 20 | 77.22 293 | 74.83 296 | 84.37 300 | 90.70 271 | 71.10 301 | 83.09 324 | 89.67 307 | 72.81 300 | 73.93 305 | 83.13 315 | 60.79 291 | 93.70 302 | 68.54 281 | 50.84 338 | 88.30 323 |
|
testus | | | 74.41 299 | 73.35 297 | 77.59 316 | 82.49 329 | 57.08 333 | 86.02 304 | 90.21 295 | 72.28 303 | 72.89 311 | 84.32 309 | 37.08 336 | 86.96 331 | 52.24 325 | 82.65 245 | 88.73 315 |
|
test2356 | | | 74.50 298 | 73.27 298 | 78.20 312 | 80.81 330 | 59.84 328 | 83.76 321 | 88.33 322 | 71.43 308 | 72.37 313 | 81.84 320 | 45.60 330 | 86.26 333 | 50.97 327 | 84.32 227 | 88.50 319 |
|
UnsupCasMVSNet_bld | | | 76.23 296 | 73.27 298 | 85.09 296 | 83.79 323 | 72.92 283 | 85.65 310 | 93.47 225 | 71.52 306 | 68.84 320 | 79.08 327 | 49.77 321 | 93.21 308 | 66.81 295 | 60.52 335 | 89.13 314 |
|
MVS-HIRNet | | | 73.70 300 | 72.20 300 | 78.18 314 | 91.81 212 | 56.42 336 | 82.94 325 | 82.58 337 | 55.24 334 | 68.88 319 | 66.48 335 | 55.32 311 | 95.13 288 | 58.12 320 | 88.42 194 | 83.01 329 |
|
LP | | | 75.51 297 | 72.15 301 | 85.61 291 | 87.86 311 | 73.93 277 | 80.20 330 | 88.43 321 | 67.39 319 | 70.05 317 | 80.56 324 | 58.18 303 | 93.18 310 | 46.28 335 | 70.36 321 | 89.71 308 |
|
testpf | | | 71.41 305 | 72.11 302 | 69.30 325 | 84.53 321 | 59.79 329 | 62.74 341 | 83.14 336 | 71.11 310 | 68.83 321 | 81.57 322 | 46.70 328 | 84.83 338 | 74.51 246 | 75.86 301 | 63.30 337 |
|
test1235678 | | | 72.22 302 | 70.31 303 | 77.93 315 | 78.04 335 | 58.04 332 | 85.76 308 | 89.80 306 | 70.15 315 | 63.43 329 | 80.20 325 | 42.24 333 | 87.24 330 | 48.68 331 | 74.50 303 | 88.50 319 |
|
new_pmnet | | | 72.15 303 | 70.13 304 | 78.20 312 | 82.95 327 | 65.68 321 | 83.91 319 | 82.40 338 | 62.94 330 | 64.47 328 | 79.82 326 | 42.85 332 | 86.26 333 | 57.41 322 | 74.44 304 | 82.65 330 |
|
1111 | | | 70.54 306 | 69.71 305 | 73.04 320 | 79.30 332 | 44.83 345 | 84.23 316 | 88.96 317 | 67.33 320 | 65.42 326 | 82.28 318 | 41.11 334 | 88.11 328 | 47.12 333 | 71.60 313 | 86.19 326 |
|
Anonymous20231211 | | | 72.97 301 | 69.63 306 | 83.00 306 | 83.05 326 | 66.91 319 | 92.69 219 | 89.45 310 | 61.06 331 | 67.50 323 | 83.46 313 | 34.34 339 | 93.61 304 | 51.11 326 | 63.97 331 | 88.48 321 |
|
pmmvs3 | | | 71.81 304 | 68.71 307 | 81.11 309 | 75.86 336 | 70.42 307 | 86.74 300 | 83.66 335 | 58.95 333 | 68.64 322 | 80.89 323 | 36.93 337 | 89.52 324 | 63.10 310 | 63.59 332 | 83.39 328 |
|
N_pmnet | | | 68.89 307 | 68.44 308 | 70.23 323 | 89.07 298 | 28.79 352 | 88.06 291 | 19.50 354 | 69.47 316 | 71.86 315 | 84.93 307 | 61.24 288 | 91.75 319 | 54.70 323 | 77.15 298 | 90.15 305 |
|
test12356 | | | 64.99 310 | 63.78 309 | 68.61 327 | 72.69 338 | 39.14 348 | 78.46 332 | 87.61 327 | 64.91 326 | 55.77 333 | 77.48 328 | 28.10 341 | 85.59 335 | 44.69 336 | 64.35 330 | 81.12 332 |
|
testmv | | | 65.49 309 | 62.66 310 | 73.96 319 | 68.78 341 | 53.14 340 | 84.70 314 | 88.56 319 | 65.94 325 | 52.35 335 | 74.65 330 | 25.02 344 | 85.14 336 | 43.54 337 | 60.40 336 | 83.60 327 |
|
FPMVS | | | 64.63 311 | 62.55 311 | 70.88 322 | 70.80 339 | 56.71 334 | 84.42 315 | 84.42 334 | 51.78 336 | 49.57 336 | 81.61 321 | 23.49 345 | 81.48 340 | 40.61 340 | 76.25 300 | 74.46 336 |
|
LCM-MVSNet | | | 66.00 308 | 62.16 312 | 77.51 317 | 64.51 346 | 58.29 331 | 83.87 320 | 90.90 284 | 48.17 337 | 54.69 334 | 73.31 332 | 16.83 351 | 86.75 332 | 65.47 301 | 61.67 334 | 87.48 325 |
|
.test1245 | | | 57.63 316 | 61.79 313 | 45.14 334 | 79.30 332 | 44.83 345 | 84.23 316 | 88.96 317 | 67.33 320 | 65.42 326 | 82.28 318 | 41.11 334 | 88.11 328 | 47.12 333 | 0.39 349 | 2.46 348 |
|
no-one | | | 61.56 312 | 56.58 314 | 76.49 318 | 67.80 344 | 62.76 327 | 78.13 333 | 86.11 329 | 63.16 329 | 43.24 339 | 64.70 337 | 26.12 343 | 88.95 326 | 50.84 328 | 29.15 341 | 77.77 334 |
|
PMMVS2 | | | 59.60 313 | 56.40 315 | 69.21 326 | 68.83 340 | 46.58 343 | 73.02 339 | 77.48 346 | 55.07 335 | 49.21 337 | 72.95 333 | 17.43 350 | 80.04 341 | 49.32 330 | 44.33 339 | 80.99 333 |
|
Gipuma | | | 57.99 315 | 54.91 316 | 67.24 328 | 88.51 302 | 65.59 322 | 52.21 344 | 90.33 294 | 43.58 340 | 42.84 340 | 51.18 342 | 20.29 348 | 85.07 337 | 34.77 342 | 70.45 320 | 51.05 342 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
ANet_high | | | 58.88 314 | 54.22 317 | 72.86 321 | 56.50 350 | 56.67 335 | 80.75 329 | 86.00 330 | 73.09 296 | 37.39 341 | 64.63 338 | 22.17 346 | 79.49 343 | 43.51 338 | 23.96 345 | 82.43 331 |
|
PMVS | | 47.18 22 | 52.22 317 | 48.46 318 | 63.48 329 | 45.72 351 | 46.20 344 | 73.41 337 | 78.31 344 | 41.03 341 | 30.06 344 | 65.68 336 | 6.05 353 | 83.43 339 | 30.04 343 | 65.86 327 | 60.80 339 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
PNet_i23d | | | 50.48 319 | 47.18 319 | 60.36 330 | 68.59 342 | 44.56 347 | 72.75 340 | 72.61 347 | 43.92 339 | 33.91 343 | 60.19 340 | 6.16 352 | 73.52 344 | 38.50 341 | 28.04 342 | 63.01 338 |
|
wuykxyi23d | | | 50.55 318 | 44.13 320 | 69.81 324 | 56.77 348 | 54.58 339 | 73.22 338 | 80.78 340 | 39.79 342 | 22.08 348 | 46.69 344 | 4.03 355 | 79.71 342 | 47.65 332 | 26.13 343 | 75.14 335 |
|
E-PMN | | | 43.23 321 | 42.29 321 | 46.03 333 | 65.58 345 | 37.41 349 | 73.51 336 | 64.62 348 | 33.99 343 | 28.47 346 | 47.87 343 | 19.90 349 | 67.91 345 | 22.23 345 | 24.45 344 | 32.77 343 |
|
EMVS | | | 42.07 322 | 41.12 322 | 44.92 335 | 63.45 347 | 35.56 351 | 73.65 335 | 63.48 349 | 33.05 344 | 26.88 347 | 45.45 345 | 21.27 347 | 67.14 346 | 19.80 346 | 23.02 346 | 32.06 344 |
|
tmp_tt | | | 35.64 324 | 39.24 323 | 24.84 337 | 14.87 352 | 23.90 353 | 62.71 342 | 51.51 353 | 6.58 347 | 36.66 342 | 62.08 339 | 44.37 331 | 30.34 350 | 52.40 324 | 22.00 347 | 20.27 345 |
|
pcd1.5k->3k | | | 37.02 323 | 38.84 324 | 31.53 336 | 92.33 201 | 0.00 356 | 0.00 346 | 96.13 92 | 0.00 350 | 0.00 352 | 0.00 352 | 72.70 180 | 0.00 353 | 0.00 350 | 88.43 193 | 94.60 167 |
|
MVE | | 39.65 23 | 43.39 320 | 38.59 325 | 57.77 331 | 56.52 349 | 48.77 342 | 55.38 343 | 58.64 351 | 29.33 345 | 28.96 345 | 52.65 341 | 4.68 354 | 64.62 347 | 28.11 344 | 33.07 340 | 59.93 340 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
cdsmvs_eth3d_5k | | | 22.14 325 | 29.52 326 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 95.76 118 | 0.00 350 | 0.00 352 | 94.29 111 | 75.66 141 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
wuyk23d | | | 21.27 326 | 20.48 327 | 23.63 338 | 68.59 342 | 36.41 350 | 49.57 345 | 6.85 355 | 9.37 346 | 7.89 349 | 4.46 351 | 4.03 355 | 31.37 349 | 17.47 347 | 16.07 348 | 3.12 346 |
|
testmvs | | | 8.92 327 | 11.52 328 | 1.12 340 | 1.06 353 | 0.46 355 | 86.02 304 | 0.65 356 | 0.62 348 | 2.74 350 | 9.52 349 | 0.31 358 | 0.45 352 | 2.38 348 | 0.39 349 | 2.46 348 |
|
test123 | | | 8.76 328 | 11.22 329 | 1.39 339 | 0.85 354 | 0.97 354 | 85.76 308 | 0.35 357 | 0.54 349 | 2.45 351 | 8.14 350 | 0.60 357 | 0.48 351 | 2.16 349 | 0.17 351 | 2.71 347 |
|
ab-mvs-re | | | 7.82 329 | 10.43 330 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 93.88 129 | 0.00 359 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
pcd_1.5k_mvsjas | | | 6.64 330 | 8.86 331 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 0.00 352 | 79.70 89 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
sosnet-low-res | | | 0.00 331 | 0.00 332 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 0.00 352 | 0.00 359 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
sosnet | | | 0.00 331 | 0.00 332 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 0.00 352 | 0.00 359 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
uncertanet | | | 0.00 331 | 0.00 332 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 0.00 352 | 0.00 359 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
Regformer | | | 0.00 331 | 0.00 332 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 0.00 352 | 0.00 359 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
uanet | | | 0.00 331 | 0.00 332 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 0.00 352 | 0.00 359 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
ESAPD_part2 | | | | | | 98.55 5 | 87.22 10 | | | | 96.40 2 | | | | | | |
|
ESAPD_part1 | | | | | | | | | 97.45 7 | | | | 91.93 1 | | | 99.02 2 | 98.67 4 |
|
ESAPD | | | | | | | | | 97.46 6 | | | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 71.70 190 | | | | |
|
sam_mvs | | | | | | | | | | | | | 70.60 205 | | | | |
|
semantic-postprocess | | | | | 88.18 254 | 91.71 215 | 76.87 259 | | 92.65 239 | 85.40 123 | 81.44 245 | 90.54 242 | 66.21 262 | 95.00 292 | 81.04 165 | 81.05 268 | 92.66 258 |
|
ambc | | | | | 83.06 305 | 79.99 331 | 63.51 326 | 77.47 334 | 92.86 232 | | 74.34 304 | 84.45 308 | 28.74 340 | 95.06 291 | 73.06 254 | 68.89 325 | 90.61 301 |
|
MTGPA | | | | | | | | | 96.97 34 | | | | | | | | |
|
test_post1 | | | | | | | | 88.00 292 | | | | 9.81 348 | 69.31 224 | 95.53 270 | 76.65 226 | | |
|
test_post | | | | | | | | | | | | 10.29 347 | 70.57 209 | 95.91 259 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 83.76 311 | 71.53 193 | 96.48 235 | | | |
|
GG-mvs-BLEND | | | | | 87.94 259 | 89.73 293 | 77.91 238 | 87.80 293 | 78.23 345 | | 80.58 256 | 83.86 310 | 59.88 297 | 95.33 286 | 71.20 261 | 92.22 138 | 90.60 303 |
|
MTMP | | | | | | | | | 60.64 350 | | | | | | | | |
|
gm-plane-assit | | | | | | 89.60 294 | 68.00 315 | | | 77.28 264 | | 88.99 264 | | 97.57 145 | 79.44 199 | | |
|
test9_res | | | | | | | | | | | | | | | 91.91 40 | 98.71 18 | 98.07 44 |
|
TEST9 | | | | | | 97.53 35 | 86.49 28 | 94.07 154 | 96.78 50 | 81.61 221 | 92.77 39 | 96.20 57 | 87.71 14 | 99.12 39 | | | |
|
test_8 | | | | | | 97.49 38 | 86.30 36 | 94.02 160 | 96.76 53 | 81.86 216 | 92.70 43 | 96.20 57 | 87.63 15 | 99.02 51 | | | |
|
agg_prior2 | | | | | | | | | | | | | | | 90.54 59 | 98.68 23 | 98.27 30 |
|
agg_prior | | | | | | 97.38 42 | 85.92 43 | | 96.72 56 | | 92.16 55 | | | 98.97 59 | | | |
|
TestCases | | | | | 89.52 207 | 95.01 119 | 77.79 243 | | 90.89 285 | 77.41 261 | 76.12 291 | 93.34 138 | 54.08 315 | 97.51 149 | 68.31 285 | 84.27 229 | 93.26 239 |
|
test_prior4 | | | | | | | 85.96 42 | 94.11 148 | | | | | | | | | |
|
test_prior2 | | | | | | | | 94.12 146 | | 87.67 79 | 92.63 44 | 96.39 50 | 86.62 24 | | 91.50 47 | 98.67 25 | |
|
test_prior | | | | | 93.82 50 | 97.29 47 | 84.49 63 | | 96.88 43 | | | | | 98.87 65 | | | 98.11 42 |
|
旧先验2 | | | | | | | | 93.36 192 | | 71.25 309 | 94.37 12 | | | 97.13 197 | 86.74 98 | | |
|
æ–°å‡ ä½•2 | | | | | | | | 93.11 206 | | | | | | | | | |
|
æ–°å‡ ä½•1 | | | | | 93.10 65 | 97.30 46 | 84.35 72 | | 95.56 131 | 71.09 311 | 91.26 71 | 96.24 54 | 82.87 57 | 98.86 68 | 79.19 203 | 98.10 46 | 96.07 111 |
|
旧先验1 | | | | | | 96.79 58 | 81.81 126 | | 95.67 123 | | | 96.81 31 | 86.69 23 | | | 97.66 55 | 96.97 87 |
|
æ— å…ˆéªŒ | | | | | | | | 93.28 199 | 96.26 82 | 73.95 289 | | | | 99.05 44 | 80.56 175 | | 96.59 97 |
|
原ACMM2 | | | | | | | | 92.94 214 | | | | | | | | | |
|
原ACMM1 | | | | | 92.01 103 | 97.34 44 | 81.05 145 | | 96.81 48 | 78.89 245 | 90.45 78 | 95.92 68 | 82.65 58 | 98.84 73 | 80.68 173 | 98.26 42 | 96.14 107 |
|
test222 | | | | | | 96.55 64 | 81.70 127 | 92.22 235 | 95.01 172 | 68.36 318 | 90.20 81 | 96.14 62 | 80.26 83 | | | 97.80 53 | 96.05 113 |
|
testdata2 | | | | | | | | | | | | | | 98.75 76 | 78.30 210 | | |
|
segment_acmp | | | | | | | | | | | | | 87.16 20 | | | | |
|
testdata | | | | | 90.49 160 | 96.40 66 | 77.89 240 | | 95.37 153 | 72.51 301 | 93.63 24 | 96.69 36 | 82.08 67 | 97.65 141 | 83.08 135 | 97.39 59 | 95.94 115 |
|
testdata1 | | | | | | | | 92.15 237 | | 87.94 70 | | | | | | | |
|
test12 | | | | | 94.34 39 | 97.13 52 | 86.15 39 | | 96.29 81 | | 91.04 74 | | 85.08 40 | 99.01 53 | | 98.13 45 | 97.86 58 |
|
plane_prior7 | | | | | | 94.70 133 | 82.74 110 | | | | | | | | | | |
|
plane_prior6 | | | | | | 94.52 139 | 82.75 108 | | | | | | 74.23 157 | | | | |
|
plane_prior5 | | | | | | | | | 96.22 86 | | | | | 98.12 106 | 88.15 77 | 89.99 163 | 94.63 164 |
|
plane_prior4 | | | | | | | | | | | | 94.86 95 | | | | | |
|
plane_prior3 | | | | | | | 82.75 108 | | | 90.26 25 | 86.91 127 | | | | | | |
|
plane_prior2 | | | | | | | | 95.85 40 | | 90.81 18 | | | | | | | |
|
plane_prior1 | | | | | | 94.59 137 | | | | | | | | | | | |
|
plane_prior | | | | | | | 82.73 111 | 95.21 71 | | 89.66 35 | | | | | | 89.88 166 | |
|
n2 | | | | | | | | | 0.00 358 | | | | | | | | |
|
nn | | | | | | | | | 0.00 358 | | | | | | | | |
|
door-mid | | | | | | | | | 85.49 331 | | | | | | | | |
|
lessismore_v0 | | | | | 86.04 287 | 88.46 304 | 68.78 314 | | 80.59 341 | | 73.01 310 | 90.11 251 | 55.39 310 | 96.43 239 | 75.06 240 | 65.06 328 | 92.90 250 |
|
LGP-MVS_train | | | | | 91.12 134 | 94.47 141 | 81.49 131 | | 96.14 90 | 86.73 100 | 85.45 167 | 95.16 87 | 69.89 215 | 98.10 112 | 87.70 84 | 89.23 177 | 93.77 212 |
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test11 | | | | | | | | | 96.57 70 | | | | | | | | |
|
door | | | | | | | | | 85.33 332 | | | | | | | | |
|
HQP5-MVS | | | | | | | 81.56 128 | | | | | | | | | | |
|
HQP-NCC | | | | | | 94.17 150 | | 94.39 126 | | 88.81 50 | 85.43 170 | | | | | | |
|
ACMP_Plane | | | | | | 94.17 150 | | 94.39 126 | | 88.81 50 | 85.43 170 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 87.11 95 | | |
|
HQP4-MVS | | | | | | | | | | | 85.43 170 | | | 97.96 127 | | | 94.51 174 |
|
HQP3-MVS | | | | | | | | | 96.04 99 | | | | | | | 89.77 168 | |
|
HQP2-MVS | | | | | | | | | | | | | 73.83 166 | | | | |
|
NP-MVS | | | | | | 94.37 145 | 82.42 118 | | | | | 93.98 122 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 55.91 338 | 87.62 297 | | 73.32 293 | 84.59 193 | | 70.33 212 | | 74.65 243 | | 95.50 130 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 87.47 203 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 88.01 200 | |
|
Test By Simon | | | | | | | | | | | | | 80.02 84 | | | | |
|
ITE_SJBPF | | | | | 88.24 252 | 91.88 209 | 77.05 257 | | 92.92 231 | 85.54 120 | 80.13 263 | 93.30 142 | 57.29 305 | 96.20 247 | 72.46 256 | 84.71 225 | 91.49 282 |
|
DeepMVS_CX | | | | | 56.31 332 | 74.23 337 | 51.81 341 | | 56.67 352 | 44.85 338 | 48.54 338 | 75.16 329 | 27.87 342 | 58.74 348 | 40.92 339 | 52.22 337 | 58.39 341 |
|