UA-Net | | | 96.56 12 | 96.73 23 | 96.36 10 | 98.99 1 | 97.90 7 | 97.79 42 | 95.64 9 | 92.78 60 | 92.54 90 | 96.23 81 | 95.02 142 | 94.31 21 | 98.43 14 | 98.12 11 | 98.89 3 | 98.58 2 |
|
zzz-MVS | | | 96.18 22 | 96.01 43 | 96.38 8 | 98.30 2 | 96.18 51 | 98.51 14 | 94.48 21 | 94.56 28 | 94.81 42 | 91.73 149 | 96.96 87 | 94.30 22 | 98.09 20 | 97.83 15 | 97.91 43 | 96.73 32 |
|
mPP-MVS | | | | | | 98.24 3 | | | | | | | 97.65 71 | | | | | |
|
MP-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 96.13 24 | 95.93 46 | 96.37 9 | 98.19 4 | 97.31 24 | 98.49 15 | 94.53 20 | 91.39 101 | 94.38 47 | 94.32 119 | 96.43 105 | 94.59 17 | 97.75 37 | 97.44 26 | 98.04 38 | 96.88 28 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
ambc | | | | 94.61 77 | | 98.09 5 | 95.14 87 | 91.71 192 | | 94.18 37 | 96.46 14 | 96.26 78 | 96.30 107 | 91.26 76 | 94.70 105 | 92.00 146 | 93.45 180 | 93.67 90 |
|
HPM-MVS++ | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 95.21 50 | 94.89 67 | 95.59 24 | 97.79 6 | 95.39 78 | 97.68 44 | 94.05 29 | 91.91 82 | 94.35 48 | 93.38 131 | 95.07 141 | 92.94 42 | 96.01 76 | 95.88 64 | 96.73 76 | 96.61 36 |
|
DeepC-MVS | | 92.47 4 | 96.44 15 | 96.75 22 | 96.08 17 | 97.57 7 | 97.19 29 | 97.96 35 | 94.28 23 | 95.29 20 | 94.92 37 | 98.31 21 | 96.92 89 | 93.69 29 | 96.81 61 | 96.50 47 | 98.06 37 | 96.27 41 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CP-MVS | | | 96.21 21 | 96.16 41 | 96.27 13 | 97.56 8 | 97.13 32 | 98.43 16 | 94.70 16 | 92.62 63 | 94.13 52 | 92.71 139 | 98.03 57 | 94.54 19 | 98.00 26 | 97.60 20 | 98.23 29 | 97.05 20 |
|
ACMMPR | | | 96.54 13 | 96.71 24 | 96.35 11 | 97.55 9 | 97.63 11 | 98.62 10 | 94.54 17 | 94.45 30 | 94.19 50 | 95.04 106 | 97.35 77 | 94.92 13 | 97.85 31 | 97.50 23 | 98.26 28 | 97.17 14 |
|
PGM-MVS | | | 95.90 35 | 95.72 49 | 96.10 16 | 97.53 10 | 97.45 20 | 98.55 13 | 94.12 28 | 90.25 121 | 93.71 65 | 93.20 133 | 97.18 81 | 94.63 16 | 97.68 38 | 97.34 32 | 98.08 35 | 96.97 22 |
|
SMA-MVS | | | 95.99 30 | 96.48 29 | 95.41 30 | 97.43 11 | 97.36 21 | 97.55 48 | 93.70 38 | 94.05 39 | 93.79 61 | 97.02 67 | 94.53 147 | 92.28 53 | 97.53 41 | 97.19 33 | 97.73 47 | 97.67 7 |
|
DTE-MVSNet | | | 97.16 6 | 97.75 8 | 96.47 6 | 97.40 12 | 97.95 5 | 98.20 28 | 96.89 4 | 95.30 19 | 95.15 28 | 98.66 10 | 98.80 17 | 92.77 47 | 98.97 7 | 98.27 9 | 98.44 22 | 96.28 40 |
|
SixPastTwentyTwo | | | 97.36 4 | 97.73 9 | 96.92 2 | 97.36 13 | 96.15 52 | 98.29 23 | 94.43 22 | 96.50 10 | 96.96 8 | 98.74 7 | 98.74 19 | 96.04 3 | 99.03 5 | 97.74 16 | 98.44 22 | 97.22 12 |
|
train_agg | | | 93.89 77 | 93.46 115 | 94.40 54 | 97.35 14 | 93.78 132 | 97.63 46 | 92.19 59 | 88.12 151 | 90.52 133 | 93.57 130 | 95.78 121 | 92.31 52 | 94.78 104 | 93.46 120 | 96.36 89 | 94.70 71 |
|
X-MVS | | | 95.33 48 | 95.13 63 | 95.57 26 | 97.35 14 | 97.48 16 | 98.43 16 | 94.28 23 | 92.30 72 | 93.28 73 | 86.89 198 | 96.82 94 | 91.87 59 | 97.85 31 | 97.59 21 | 98.19 30 | 96.95 25 |
|
HFP-MVS | | | 96.18 22 | 96.53 28 | 95.77 21 | 97.34 16 | 97.26 26 | 98.16 30 | 94.54 17 | 94.45 30 | 92.52 91 | 95.05 104 | 96.95 88 | 93.89 26 | 97.28 45 | 97.46 24 | 98.19 30 | 97.25 9 |
|
APD-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 95.38 46 | 95.68 50 | 95.03 44 | 97.30 17 | 96.90 35 | 97.83 39 | 93.92 31 | 89.40 137 | 90.35 135 | 95.41 94 | 97.69 70 | 92.97 40 | 97.24 48 | 97.17 35 | 97.83 45 | 95.96 46 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
EPNet | | | 90.17 149 | 89.07 167 | 91.45 128 | 97.25 18 | 90.62 185 | 94.84 125 | 93.54 41 | 80.96 201 | 91.85 107 | 86.98 197 | 85.88 190 | 77.79 202 | 92.30 158 | 92.58 132 | 93.41 181 | 94.20 80 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
ACMM | | 90.06 9 | 96.31 18 | 96.42 31 | 96.19 15 | 97.21 19 | 97.16 31 | 98.71 5 | 93.79 35 | 94.35 34 | 93.81 60 | 92.80 138 | 98.23 43 | 95.11 9 | 98.07 22 | 97.45 25 | 98.51 17 | 96.86 29 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
PEN-MVS | | | 97.16 6 | 97.87 5 | 96.33 12 | 97.20 20 | 97.97 4 | 98.25 25 | 96.86 5 | 95.09 24 | 94.93 36 | 98.66 10 | 99.16 7 | 92.27 54 | 98.98 6 | 98.39 7 | 98.49 18 | 96.83 30 |
|
ACMMP | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 96.12 25 | 96.27 37 | 95.93 19 | 97.20 20 | 97.60 12 | 98.64 8 | 93.74 36 | 92.47 65 | 93.13 80 | 93.23 132 | 98.06 54 | 94.51 20 | 97.99 27 | 97.57 22 | 98.39 26 | 96.99 21 |
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 |
ACMMP_Plus | | | 95.86 37 | 96.18 38 | 95.47 29 | 97.11 22 | 97.26 26 | 98.37 21 | 93.48 42 | 93.49 46 | 93.99 55 | 95.61 87 | 94.11 152 | 92.49 48 | 97.87 30 | 97.44 26 | 97.40 55 | 97.52 8 |
|
PS-CasMVS | | | 97.22 5 | 97.84 6 | 96.50 5 | 97.08 23 | 97.92 6 | 98.17 29 | 97.02 2 | 94.71 26 | 95.32 24 | 98.52 14 | 98.97 11 | 92.91 43 | 99.04 4 | 98.47 5 | 98.49 18 | 97.24 11 |
|
CP-MVSNet | | | 96.97 10 | 97.42 12 | 96.44 7 | 97.06 24 | 97.82 8 | 98.12 31 | 96.98 3 | 93.50 45 | 95.21 26 | 97.98 30 | 98.44 31 | 92.83 46 | 98.93 8 | 98.37 8 | 98.46 21 | 96.91 27 |
|
SteuartSystems-ACMMP | | | 95.96 32 | 96.13 42 | 95.76 22 | 97.06 24 | 97.36 21 | 98.40 20 | 94.24 25 | 91.49 92 | 91.91 106 | 94.50 115 | 96.89 90 | 94.99 11 | 98.01 25 | 97.44 26 | 97.97 41 | 97.25 9 |
Skip Steuart: Steuart Systems R&D Blog. |
PMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 87.16 16 | 95.88 36 | 96.47 30 | 95.19 38 | 97.00 26 | 96.02 55 | 96.70 68 | 91.57 77 | 94.43 32 | 95.33 23 | 97.16 63 | 95.37 131 | 92.39 50 | 98.89 10 | 98.72 3 | 98.17 32 | 94.71 69 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
WR-MVS | | | 97.53 3 | 98.20 3 | 96.76 3 | 96.93 27 | 98.17 1 | 98.60 11 | 96.67 6 | 96.39 12 | 94.46 44 | 99.14 1 | 98.92 12 | 94.57 18 | 99.06 3 | 98.80 2 | 99.32 1 | 96.92 26 |
|
HSP-MVS | | | 95.04 52 | 95.45 57 | 94.57 51 | 96.87 28 | 97.77 10 | 98.71 5 | 93.88 33 | 91.21 106 | 91.48 115 | 95.36 95 | 98.37 37 | 90.73 95 | 94.37 111 | 92.98 127 | 95.77 122 | 98.08 3 |
|
TSAR-MVS + MP. | | | 95.99 30 | 96.57 27 | 95.31 33 | 96.87 28 | 96.50 44 | 98.71 5 | 91.58 76 | 93.25 51 | 92.71 85 | 96.86 69 | 96.57 101 | 93.92 24 | 98.09 20 | 97.91 13 | 98.08 35 | 96.81 31 |
|
XVS | | | | | | 96.86 30 | 97.48 16 | 98.73 3 | | | 93.28 73 | | 96.82 94 | | | | 98.17 32 | |
|
X-MVStestdata | | | | | | 96.86 30 | 97.48 16 | 98.73 3 | | | 93.28 73 | | 96.82 94 | | | | 98.17 32 | |
|
NCCC | | | 93.87 80 | 93.42 116 | 94.40 54 | 96.84 32 | 95.42 74 | 96.47 80 | 92.62 48 | 92.36 70 | 92.05 102 | 83.83 214 | 95.55 124 | 91.84 61 | 95.89 78 | 95.23 79 | 96.56 81 | 95.63 53 |
|
CPTT-MVS | | | 95.00 53 | 94.52 79 | 95.57 26 | 96.84 32 | 96.78 36 | 97.88 37 | 93.67 40 | 92.20 74 | 92.35 97 | 85.87 205 | 97.56 73 | 94.98 12 | 96.96 54 | 96.07 59 | 97.70 50 | 96.18 42 |
|
DeepC-MVS_fast | | 91.38 6 | 94.73 57 | 94.98 64 | 94.44 52 | 96.83 34 | 96.12 53 | 96.69 70 | 92.17 60 | 92.98 56 | 93.72 64 | 94.14 121 | 95.45 129 | 90.49 105 | 95.73 82 | 95.30 76 | 96.71 77 | 95.13 63 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
v1.0 | | | 88.18 179 | 82.50 209 | 94.80 50 | 96.76 35 | 97.29 25 | 97.74 43 | 94.15 27 | 91.69 85 | 90.01 140 | 96.65 73 | 97.29 78 | 92.45 49 | 97.41 43 | 97.18 34 | 97.67 52 | 0.00 246 |
|
WR-MVS_H | | | 97.06 9 | 97.78 7 | 96.23 14 | 96.74 36 | 98.04 3 | 98.25 25 | 97.32 1 | 94.40 33 | 93.71 65 | 98.55 13 | 98.89 13 | 92.97 40 | 98.91 9 | 98.45 6 | 98.38 27 | 97.19 13 |
|
SD-MVS | | | 95.77 40 | 96.17 39 | 95.30 34 | 96.72 37 | 96.19 50 | 97.01 53 | 93.04 45 | 94.03 40 | 92.71 85 | 96.45 76 | 96.78 98 | 93.91 25 | 96.79 62 | 95.89 63 | 98.42 24 | 97.09 18 |
|
LGP-MVS_train | | | 96.10 26 | 96.29 35 | 95.87 20 | 96.72 37 | 97.35 23 | 98.43 16 | 93.83 34 | 90.81 118 | 92.67 89 | 95.05 104 | 98.86 15 | 95.01 10 | 98.11 19 | 97.37 31 | 98.52 16 | 96.50 37 |
|
OPM-MVS | | | 95.96 32 | 96.59 26 | 95.23 36 | 96.67 39 | 96.52 43 | 97.86 38 | 93.28 43 | 95.27 22 | 93.46 70 | 96.26 78 | 98.85 16 | 92.89 44 | 97.09 50 | 96.37 50 | 97.22 64 | 95.78 51 |
|
APDe-MVS | | | 96.23 20 | 97.22 16 | 95.08 42 | 96.66 40 | 97.56 14 | 98.63 9 | 93.69 39 | 94.62 27 | 89.80 143 | 97.73 42 | 98.13 51 | 93.84 27 | 97.79 35 | 97.63 18 | 97.87 44 | 97.08 19 |
|
test20.03 | | | 88.20 177 | 91.26 148 | 84.63 208 | 96.64 41 | 89.39 189 | 90.73 204 | 89.97 112 | 91.07 110 | 72.02 229 | 94.98 108 | 95.45 129 | 69.35 222 | 92.70 144 | 91.19 166 | 89.06 202 | 84.02 193 |
|
ACMP | | 89.62 11 | 95.96 32 | 96.28 36 | 95.59 24 | 96.58 42 | 97.23 28 | 98.26 24 | 93.22 44 | 92.33 71 | 92.31 98 | 94.29 120 | 98.73 20 | 94.68 15 | 98.04 23 | 97.14 37 | 98.47 20 | 96.17 43 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
CNVR-MVS | | | 94.24 68 | 94.47 81 | 93.96 69 | 96.56 43 | 95.67 66 | 96.43 83 | 91.95 67 | 92.08 77 | 91.28 120 | 90.51 161 | 95.35 132 | 91.20 77 | 96.34 73 | 95.50 73 | 96.34 94 | 95.88 48 |
|
TSAR-MVS + GP. | | | 94.25 67 | 94.81 70 | 93.60 81 | 96.52 44 | 95.80 63 | 94.37 134 | 92.47 52 | 90.89 114 | 88.92 148 | 95.34 96 | 94.38 149 | 92.85 45 | 96.36 72 | 95.62 70 | 96.47 84 | 95.28 60 |
|
LS3D | | | 95.83 39 | 96.35 33 | 95.22 37 | 96.47 45 | 97.49 15 | 97.99 32 | 92.35 54 | 94.92 25 | 94.58 43 | 94.88 110 | 95.11 140 | 91.52 68 | 98.48 13 | 98.05 12 | 98.42 24 | 95.49 55 |
|
DU-MVS | | | 95.51 42 | 95.68 50 | 95.33 32 | 96.45 46 | 96.44 46 | 96.61 75 | 95.32 10 | 89.97 127 | 93.78 62 | 97.46 56 | 98.07 53 | 91.19 78 | 97.03 51 | 96.53 45 | 98.61 13 | 94.22 78 |
|
Baseline_NR-MVSNet | | | 94.85 54 | 95.35 59 | 94.26 56 | 96.45 46 | 93.86 131 | 96.70 68 | 94.54 17 | 90.07 125 | 90.17 139 | 98.77 6 | 97.89 63 | 90.64 99 | 97.03 51 | 96.16 55 | 97.04 72 | 93.67 90 |
|
LTVRE_ROB | | 95.06 1 | 97.73 1 | 98.39 1 | 96.95 1 | 96.33 48 | 96.94 33 | 98.30 22 | 94.90 14 | 98.61 1 | 97.73 3 | 97.97 31 | 98.57 27 | 95.74 7 | 99.24 1 | 98.70 4 | 98.72 7 | 98.70 1 |
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 |
CDPH-MVS | | | 93.96 72 | 93.86 95 | 94.08 61 | 96.31 49 | 95.84 61 | 96.92 57 | 91.85 70 | 87.21 165 | 91.25 122 | 92.83 136 | 96.06 116 | 91.05 87 | 95.57 83 | 94.81 90 | 97.12 65 | 94.72 68 |
|
PVSNet_Blended_VisFu | | | 93.60 87 | 93.41 117 | 93.83 74 | 96.31 49 | 95.65 67 | 95.71 108 | 90.58 102 | 88.08 154 | 93.17 78 | 95.29 98 | 92.20 166 | 90.72 96 | 94.69 106 | 93.41 123 | 96.51 83 | 94.54 74 |
|
TranMVSNet+NR-MVSNet | | | 95.72 41 | 96.42 31 | 94.91 47 | 96.21 51 | 96.77 37 | 96.90 60 | 94.99 12 | 92.62 63 | 91.92 105 | 98.51 15 | 98.63 24 | 90.82 94 | 97.27 46 | 96.83 41 | 98.63 12 | 94.31 77 |
|
3Dnovator+ | | 92.82 3 | 95.22 49 | 95.16 61 | 95.29 35 | 96.17 52 | 96.55 39 | 97.64 45 | 94.02 30 | 94.16 38 | 94.29 49 | 92.09 145 | 93.71 157 | 91.90 57 | 96.68 64 | 96.51 46 | 97.70 50 | 96.40 38 |
|
UniMVSNet (Re) | | | 95.46 43 | 95.86 47 | 95.00 45 | 96.09 53 | 96.60 38 | 96.68 72 | 94.99 12 | 90.36 120 | 92.13 101 | 97.64 50 | 98.13 51 | 91.38 71 | 96.90 56 | 96.74 42 | 98.73 6 | 94.63 72 |
|
MIMVSNet1 | | | 92.52 123 | 94.88 68 | 89.77 152 | 96.09 53 | 91.99 170 | 96.92 57 | 89.68 120 | 95.92 16 | 84.55 179 | 96.64 74 | 98.21 46 | 78.44 198 | 96.08 75 | 95.10 81 | 92.91 189 | 90.22 149 |
|
TSAR-MVS + ACMM | | | 95.17 51 | 95.95 44 | 94.26 56 | 96.07 55 | 96.46 45 | 95.67 110 | 94.21 26 | 93.84 42 | 90.99 126 | 97.18 62 | 95.24 139 | 93.55 31 | 96.60 67 | 95.61 71 | 95.06 141 | 96.69 34 |
|
MVS_0304 | | | 93.92 74 | 93.81 100 | 94.05 62 | 96.06 56 | 96.00 56 | 96.43 83 | 92.76 47 | 85.99 175 | 94.43 46 | 94.04 124 | 97.08 83 | 88.12 127 | 94.65 107 | 94.20 108 | 96.47 84 | 94.71 69 |
|
CSCG | | | 96.07 27 | 97.15 18 | 94.81 48 | 96.06 56 | 97.58 13 | 96.52 78 | 90.98 92 | 96.51 9 | 93.60 68 | 97.13 64 | 98.55 29 | 93.01 38 | 97.17 49 | 95.36 75 | 98.68 9 | 97.78 4 |
|
COLMAP_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 93.74 2 | 97.09 8 | 97.98 4 | 96.05 18 | 95.97 58 | 97.78 9 | 98.56 12 | 91.72 73 | 97.53 6 | 96.01 17 | 98.14 25 | 98.76 18 | 95.28 8 | 98.76 11 | 98.23 10 | 98.77 5 | 96.67 35 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
AdaColmap | ![Method available as binary. binary](img/icon_binary.png) | | 92.41 126 | 91.49 146 | 93.48 83 | 95.96 59 | 95.02 92 | 95.37 118 | 91.73 72 | 87.97 157 | 91.28 120 | 82.82 219 | 91.04 172 | 90.62 101 | 95.82 80 | 95.07 82 | 95.95 115 | 92.67 116 |
|
Gipuma | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 95.86 37 | 96.17 39 | 95.50 28 | 95.92 60 | 94.59 109 | 94.77 127 | 92.50 50 | 97.82 5 | 97.90 2 | 95.56 90 | 97.88 66 | 94.71 14 | 98.02 24 | 94.81 90 | 97.23 63 | 94.48 76 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
CANet | | | 93.07 110 | 93.05 126 | 93.10 103 | 95.90 61 | 95.41 76 | 95.88 102 | 91.94 68 | 84.77 183 | 93.36 71 | 94.05 123 | 95.25 138 | 86.25 139 | 94.33 112 | 93.94 110 | 95.30 129 | 93.58 93 |
|
TDRefinement | | | 97.59 2 | 98.32 2 | 96.73 4 | 95.90 61 | 98.10 2 | 99.08 2 | 93.92 31 | 98.24 3 | 96.44 15 | 98.12 26 | 97.86 68 | 96.06 2 | 99.24 1 | 98.93 1 | 99.00 2 | 97.77 5 |
|
EG-PatchMatch MVS | | | 94.81 55 | 95.53 53 | 93.97 67 | 95.89 63 | 94.62 106 | 95.55 116 | 88.18 157 | 92.77 61 | 94.88 39 | 97.04 66 | 98.61 25 | 93.31 33 | 96.89 57 | 95.19 80 | 95.99 114 | 93.56 94 |
|
ESAPD | | | 96.00 29 | 96.80 21 | 95.06 43 | 95.87 64 | 97.47 19 | 98.25 25 | 93.73 37 | 92.38 68 | 91.57 114 | 97.55 53 | 97.97 60 | 92.98 39 | 97.49 42 | 97.61 19 | 97.96 42 | 97.16 15 |
|
gm-plane-assit | | | 86.15 192 | 82.51 208 | 90.40 141 | 95.81 65 | 92.29 163 | 97.99 32 | 84.66 203 | 92.15 76 | 93.15 79 | 97.84 37 | 44.65 247 | 78.60 194 | 88.02 206 | 85.95 202 | 92.20 192 | 76.69 222 |
|
UniMVSNet_NR-MVSNet | | | 95.34 47 | 95.51 54 | 95.14 39 | 95.80 66 | 96.55 39 | 96.61 75 | 94.79 15 | 90.04 126 | 93.78 62 | 97.51 54 | 97.25 79 | 91.19 78 | 96.68 64 | 96.31 54 | 98.65 11 | 94.22 78 |
|
HQP-MVS | | | 92.87 115 | 92.49 133 | 93.31 90 | 95.75 67 | 95.01 93 | 95.64 111 | 91.06 90 | 88.54 148 | 91.62 113 | 88.16 184 | 96.25 109 | 89.47 112 | 92.26 159 | 91.81 148 | 96.34 94 | 95.40 56 |
|
RPSCF | | | 95.46 43 | 96.95 20 | 93.73 80 | 95.72 68 | 95.94 58 | 95.58 114 | 88.08 161 | 95.31 18 | 91.34 118 | 96.26 78 | 98.04 56 | 93.63 30 | 98.28 16 | 97.67 17 | 98.01 39 | 97.13 16 |
|
EPNet_dtu | | | 87.40 188 | 86.27 194 | 88.72 169 | 95.68 69 | 83.37 215 | 92.09 184 | 90.08 106 | 78.11 227 | 91.29 119 | 86.33 201 | 89.74 178 | 75.39 214 | 89.07 194 | 87.89 195 | 87.81 207 | 89.38 160 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
testgi | | | 86.49 190 | 90.31 155 | 82.03 213 | 95.63 70 | 88.18 193 | 93.47 154 | 84.89 201 | 93.23 53 | 69.54 236 | 87.16 195 | 97.96 61 | 60.66 231 | 91.90 169 | 89.90 176 | 87.99 205 | 83.84 194 |
|
v7n | | | 96.49 14 | 97.20 17 | 95.65 23 | 95.57 71 | 96.04 54 | 97.93 36 | 92.49 51 | 96.40 11 | 97.13 7 | 98.99 4 | 99.41 3 | 93.79 28 | 97.84 33 | 96.15 56 | 97.00 73 | 95.60 54 |
|
MSLP-MVS++ | | | 93.91 75 | 94.30 88 | 93.45 84 | 95.51 72 | 95.83 62 | 93.12 167 | 91.93 69 | 91.45 97 | 91.40 117 | 87.42 193 | 96.12 115 | 93.27 34 | 96.57 68 | 96.40 49 | 95.49 126 | 96.29 39 |
|
CLD-MVS | | | 92.81 117 | 94.32 86 | 91.05 132 | 95.39 73 | 95.31 80 | 95.82 104 | 81.44 220 | 89.40 137 | 91.94 104 | 95.86 84 | 97.36 76 | 85.83 141 | 95.35 87 | 94.59 102 | 95.85 119 | 92.34 127 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
MCST-MVS | | | 93.60 87 | 93.40 119 | 93.83 74 | 95.30 74 | 95.40 77 | 96.49 79 | 90.87 94 | 90.08 124 | 91.72 111 | 90.28 165 | 95.99 118 | 91.69 64 | 93.94 125 | 92.99 126 | 96.93 75 | 95.13 63 |
|
ACMH | | 90.17 8 | 96.61 11 | 97.69 10 | 95.35 31 | 95.29 75 | 96.94 33 | 98.43 16 | 92.05 65 | 98.04 4 | 95.38 22 | 98.07 28 | 99.25 6 | 93.23 36 | 98.35 15 | 97.16 36 | 97.72 48 | 96.00 45 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
FC-MVSNet-train | | | 92.75 119 | 95.40 58 | 89.66 156 | 95.21 76 | 94.82 100 | 97.00 54 | 89.40 132 | 91.13 108 | 81.71 197 | 97.72 43 | 96.43 105 | 77.57 205 | 96.89 57 | 96.72 43 | 97.05 70 | 94.09 81 |
|
ACMH+ | | 89.90 10 | 96.27 19 | 97.52 11 | 94.81 48 | 95.19 77 | 97.18 30 | 97.97 34 | 92.52 49 | 96.72 8 | 90.50 134 | 97.31 59 | 99.11 8 | 94.10 23 | 98.67 12 | 97.90 14 | 98.56 15 | 95.79 50 |
|
EPP-MVSNet | | | 93.63 86 | 93.95 92 | 93.26 94 | 95.15 78 | 96.54 42 | 96.18 99 | 91.97 66 | 91.74 84 | 85.76 169 | 94.95 109 | 84.27 194 | 91.60 67 | 97.61 40 | 97.38 30 | 98.87 4 | 95.18 62 |
|
Effi-MVS+-dtu | | | 92.32 130 | 91.66 143 | 93.09 104 | 95.13 79 | 94.73 103 | 94.57 132 | 92.14 61 | 81.74 198 | 90.33 136 | 88.13 185 | 95.91 119 | 89.24 113 | 94.23 121 | 93.65 119 | 97.12 65 | 93.23 100 |
|
MVS_111021_HR | | | 93.82 82 | 94.26 90 | 93.31 90 | 95.01 80 | 93.97 128 | 95.73 107 | 89.75 118 | 92.06 78 | 92.49 92 | 94.01 126 | 96.05 117 | 90.61 103 | 95.95 77 | 94.78 93 | 96.28 99 | 93.04 106 |
|
FC-MVSNet-test | | | 91.49 139 | 94.43 82 | 88.07 183 | 94.97 81 | 90.53 186 | 95.42 117 | 91.18 87 | 93.24 52 | 72.94 227 | 98.37 17 | 93.86 155 | 78.78 192 | 97.82 34 | 96.13 58 | 95.13 136 | 91.05 143 |
|
PHI-MVS | | | 94.65 58 | 94.84 69 | 94.44 52 | 94.95 82 | 96.55 39 | 96.46 81 | 91.10 89 | 88.96 141 | 96.00 18 | 94.55 114 | 95.32 134 | 90.67 97 | 96.97 53 | 96.69 44 | 97.44 54 | 94.84 65 |
|
casdiffmvs1 | | | 93.02 112 | 93.07 125 | 92.96 108 | 94.93 83 | 95.42 74 | 96.24 97 | 90.96 93 | 91.68 87 | 92.69 88 | 93.74 128 | 96.88 91 | 87.86 128 | 90.19 184 | 89.56 182 | 95.09 140 | 94.03 83 |
|
IS_MVSNet | | | 92.76 118 | 93.25 123 | 92.19 118 | 94.91 84 | 95.56 68 | 95.86 103 | 92.12 62 | 88.10 152 | 82.71 191 | 93.15 134 | 88.30 183 | 88.86 116 | 97.29 44 | 96.95 39 | 98.66 10 | 93.38 96 |
|
pmmvs6 | | | 94.58 59 | 97.30 15 | 91.40 129 | 94.84 85 | 94.61 107 | 93.40 157 | 92.43 53 | 98.51 2 | 85.61 173 | 98.73 9 | 99.53 2 | 84.40 149 | 97.88 29 | 97.03 38 | 97.72 48 | 94.79 67 |
|
PLC | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 87.27 15 | 93.08 109 | 92.92 127 | 93.26 94 | 94.67 86 | 95.03 90 | 94.38 133 | 90.10 105 | 91.69 85 | 92.14 100 | 87.24 194 | 93.91 154 | 91.61 66 | 95.05 98 | 94.73 99 | 96.67 79 | 92.80 111 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
PCF-MVS | | 87.46 14 | 92.44 125 | 91.80 141 | 93.19 98 | 94.66 87 | 95.80 63 | 96.37 93 | 90.19 104 | 87.57 159 | 92.23 99 | 89.26 175 | 93.97 153 | 89.24 113 | 91.32 174 | 90.82 170 | 96.46 86 | 93.86 87 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
Vis-MVSNet (Re-imp) | | | 90.68 144 | 92.18 136 | 88.92 167 | 94.63 88 | 92.75 152 | 92.91 171 | 91.20 86 | 89.21 140 | 75.01 222 | 93.96 127 | 89.07 182 | 82.72 163 | 95.88 79 | 95.30 76 | 97.08 69 | 89.08 164 |
|
TSAR-MVS + COLMAP | | | 93.06 111 | 93.65 105 | 92.36 113 | 94.62 89 | 94.28 115 | 95.36 119 | 89.46 131 | 92.18 75 | 91.64 112 | 95.55 91 | 95.27 137 | 88.60 120 | 93.24 136 | 92.50 133 | 94.46 168 | 92.55 121 |
|
CNLPA | | | 93.14 108 | 93.67 104 | 92.53 112 | 94.62 89 | 94.73 103 | 95.00 123 | 86.57 187 | 92.85 59 | 92.43 94 | 90.94 154 | 94.67 145 | 90.35 107 | 95.41 85 | 93.70 115 | 96.23 104 | 93.37 97 |
|
OMC-MVS | | | 94.74 56 | 95.46 56 | 93.91 72 | 94.62 89 | 96.26 49 | 96.64 74 | 89.36 134 | 94.20 36 | 94.15 51 | 94.02 125 | 97.73 69 | 91.34 73 | 96.15 74 | 95.04 84 | 97.37 57 | 94.80 66 |
|
conf0.05thres1000 | | | 91.24 141 | 91.85 140 | 90.53 138 | 94.59 92 | 94.56 111 | 94.33 139 | 89.52 128 | 93.67 44 | 83.77 184 | 91.04 152 | 79.10 211 | 83.98 150 | 96.66 66 | 95.56 72 | 96.98 74 | 92.36 125 |
|
CDS-MVSNet | | | 88.41 171 | 89.79 158 | 86.79 194 | 94.55 93 | 90.82 182 | 92.50 180 | 89.85 116 | 83.26 192 | 80.52 203 | 91.05 151 | 89.93 177 | 69.11 223 | 93.17 139 | 92.71 131 | 94.21 173 | 87.63 178 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
Anonymous202405211 | | | | 94.63 75 | | 94.51 94 | 94.96 97 | 93.94 146 | 91.35 82 | 90.82 116 | | 95.60 89 | 95.85 120 | 81.74 176 | 96.47 69 | 95.84 65 | 97.39 56 | 92.85 109 |
|
v748 | | | 96.05 28 | 97.00 19 | 94.95 46 | 94.41 95 | 94.77 102 | 96.72 67 | 91.03 91 | 96.12 15 | 96.71 11 | 98.74 7 | 99.59 1 | 93.55 31 | 97.97 28 | 95.96 60 | 97.28 60 | 95.84 49 |
|
TransMVSNet (Re) | | | 93.55 91 | 96.32 34 | 90.32 143 | 94.38 96 | 94.05 123 | 93.30 164 | 89.53 127 | 97.15 7 | 85.12 175 | 98.83 5 | 97.89 63 | 82.21 167 | 96.75 63 | 96.14 57 | 97.35 58 | 93.46 95 |
|
tfpn | | | 87.65 185 | 85.66 197 | 89.96 149 | 94.36 97 | 93.94 129 | 93.85 149 | 89.02 139 | 88.71 147 | 82.78 189 | 83.79 215 | 53.79 242 | 83.43 157 | 95.35 87 | 94.54 103 | 96.35 93 | 89.51 159 |
|
Effi-MVS+ | | | 92.93 113 | 92.16 138 | 93.83 74 | 94.29 98 | 93.53 142 | 95.04 122 | 92.98 46 | 85.27 180 | 94.46 44 | 90.24 166 | 95.34 133 | 89.99 109 | 93.72 127 | 94.23 107 | 96.22 105 | 92.79 112 |
|
MAR-MVS | | | 91.86 136 | 91.14 149 | 92.71 109 | 94.29 98 | 94.24 116 | 94.91 124 | 91.82 71 | 81.66 199 | 93.32 72 | 84.51 212 | 93.42 160 | 86.86 135 | 95.16 96 | 94.44 105 | 95.05 142 | 94.53 75 |
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 |
NR-MVSNet | | | 94.55 61 | 95.66 52 | 93.25 96 | 94.26 100 | 96.44 46 | 96.69 70 | 95.32 10 | 89.97 127 | 91.79 110 | 97.46 56 | 98.39 36 | 82.85 160 | 96.87 59 | 96.48 48 | 98.57 14 | 93.98 85 |
|
1111 | | | 76.85 232 | 78.03 229 | 75.46 232 | 94.16 101 | 78.29 229 | 86.40 230 | 89.12 136 | 87.23 163 | 61.26 240 | 95.15 101 | 44.14 248 | 51.46 240 | 86.04 216 | 81.00 216 | 70.40 243 | 74.37 228 |
|
.test1245 | | | 60.07 239 | 56.75 242 | 63.93 239 | 94.16 101 | 78.29 229 | 86.40 230 | 89.12 136 | 87.23 163 | 61.26 240 | 95.15 101 | 44.14 248 | 51.46 240 | 86.04 216 | 2.51 244 | 1.21 248 | 3.92 244 |
|
thisisatest0515 | | | 93.79 83 | 94.41 83 | 93.06 105 | 94.14 103 | 92.50 158 | 95.56 115 | 88.55 151 | 91.61 90 | 92.45 93 | 96.84 70 | 95.71 122 | 90.62 101 | 94.58 108 | 95.07 82 | 97.05 70 | 94.58 73 |
|
TAPA-MVS | | 88.94 13 | 93.78 84 | 94.31 87 | 93.18 99 | 94.14 103 | 95.99 57 | 95.74 106 | 86.98 181 | 93.43 48 | 93.88 59 | 90.16 167 | 96.88 91 | 91.05 87 | 94.33 112 | 93.95 109 | 97.28 60 | 95.40 56 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
pm-mvs1 | | | 93.27 101 | 95.94 45 | 90.16 144 | 94.13 105 | 93.66 134 | 92.61 178 | 89.91 115 | 95.73 17 | 84.28 182 | 98.51 15 | 98.29 39 | 82.80 161 | 96.44 70 | 95.76 66 | 97.25 62 | 93.21 101 |
|
DeepPCF-MVS | | 90.68 7 | 94.56 60 | 94.92 66 | 94.15 58 | 94.11 106 | 95.71 65 | 97.03 52 | 90.65 99 | 93.39 50 | 94.08 53 | 95.29 98 | 94.15 151 | 93.21 37 | 95.22 94 | 94.92 88 | 95.82 121 | 95.75 52 |
|
Anonymous20240521 | | | 93.49 93 | 95.15 62 | 91.55 125 | 94.05 107 | 95.92 59 | 95.15 120 | 91.21 85 | 92.76 62 | 87.01 164 | 89.71 170 | 97.16 82 | 83.90 153 | 97.65 39 | 96.87 40 | 97.99 40 | 95.95 47 |
|
view800 | | | 89.42 158 | 89.11 166 | 89.78 151 | 94.00 108 | 93.71 133 | 93.96 145 | 88.47 155 | 88.10 152 | 82.91 187 | 82.61 220 | 79.85 209 | 83.10 159 | 94.92 101 | 95.38 74 | 96.26 103 | 89.19 161 |
|
Anonymous20231206 | | | 87.45 187 | 89.66 161 | 84.87 205 | 94.00 108 | 87.73 199 | 91.36 196 | 86.41 190 | 88.89 144 | 75.03 221 | 92.59 140 | 96.82 94 | 72.48 220 | 89.72 189 | 88.06 194 | 89.93 199 | 83.81 195 |
|
test-LLR | | | 80.62 217 | 77.20 234 | 84.62 209 | 93.99 110 | 75.11 236 | 87.04 224 | 87.32 176 | 70.11 240 | 78.59 216 | 83.17 217 | 71.60 222 | 73.88 218 | 82.32 226 | 79.20 222 | 86.91 211 | 78.87 218 |
|
test0.0.03 1 | | | 81.51 213 | 83.30 207 | 79.42 221 | 93.99 110 | 86.50 203 | 85.93 234 | 87.32 176 | 78.16 225 | 61.62 239 | 80.78 224 | 81.78 202 | 59.87 232 | 88.40 202 | 87.27 198 | 87.78 209 | 80.19 210 |
|
tfpn_n400 | | | 89.03 163 | 89.39 163 | 88.61 171 | 93.98 112 | 92.33 161 | 91.83 187 | 88.97 141 | 92.97 57 | 78.90 209 | 84.93 208 | 78.24 213 | 81.77 174 | 95.00 99 | 93.67 116 | 96.22 105 | 88.59 169 |
|
tfpnconf | | | 89.03 163 | 89.39 163 | 88.61 171 | 93.98 112 | 92.33 161 | 91.83 187 | 88.97 141 | 92.97 57 | 78.90 209 | 84.93 208 | 78.24 213 | 81.77 174 | 95.00 99 | 93.67 116 | 96.22 105 | 88.59 169 |
|
v1240 | | | 93.89 77 | 93.72 103 | 94.09 59 | 93.98 112 | 94.31 113 | 97.12 49 | 89.37 133 | 90.74 119 | 96.92 9 | 98.05 29 | 97.89 63 | 92.15 55 | 91.53 172 | 91.60 154 | 94.99 144 | 91.93 134 |
|
tfpnnormal | | | 92.45 124 | 94.77 72 | 89.74 153 | 93.95 115 | 93.44 144 | 93.25 165 | 88.49 154 | 95.27 22 | 83.20 186 | 96.51 75 | 96.23 110 | 83.17 158 | 95.47 84 | 94.52 104 | 96.38 88 | 91.97 133 |
|
v11 | | | 94.32 66 | 94.62 76 | 93.97 67 | 93.95 115 | 95.31 80 | 96.83 63 | 91.30 84 | 91.95 80 | 95.51 20 | 98.32 20 | 98.61 25 | 91.44 70 | 92.83 142 | 92.23 138 | 94.77 153 | 93.08 105 |
|
tfpn1000 | | | 88.13 180 | 88.68 174 | 87.49 188 | 93.94 117 | 92.64 155 | 91.50 195 | 88.70 150 | 90.12 123 | 74.35 224 | 86.74 200 | 75.27 219 | 80.14 183 | 94.16 122 | 94.66 100 | 96.33 96 | 87.16 183 |
|
canonicalmvs | | | 93.38 98 | 94.36 85 | 92.24 117 | 93.94 117 | 96.41 48 | 94.18 142 | 90.47 103 | 93.07 55 | 88.47 154 | 88.66 179 | 93.78 156 | 88.80 117 | 95.74 81 | 95.75 67 | 97.57 53 | 97.13 16 |
|
tfpnview11 | | | 88.74 168 | 88.95 168 | 88.50 173 | 93.91 119 | 92.43 160 | 91.70 193 | 88.90 146 | 90.93 113 | 78.90 209 | 84.93 208 | 78.24 213 | 81.71 177 | 94.32 114 | 94.60 101 | 95.86 117 | 87.23 182 |
|
v1192 | | | 93.98 71 | 93.94 93 | 94.01 64 | 93.91 119 | 94.63 105 | 97.00 54 | 89.75 118 | 91.01 111 | 96.50 12 | 97.93 32 | 98.26 41 | 91.74 62 | 92.06 161 | 92.05 143 | 95.18 135 | 91.66 140 |
|
MVS_111021_LR | | | 93.15 107 | 93.65 105 | 92.56 111 | 93.89 121 | 92.28 164 | 95.09 121 | 86.92 183 | 91.26 105 | 92.99 83 | 94.46 117 | 96.22 111 | 90.64 99 | 95.11 97 | 93.45 121 | 95.85 119 | 92.74 115 |
|
TinyColmap | | | 93.17 106 | 93.33 122 | 93.00 107 | 93.84 122 | 92.76 151 | 94.75 129 | 88.90 146 | 93.97 41 | 97.48 4 | 95.28 100 | 95.29 135 | 88.37 122 | 95.31 92 | 91.58 155 | 94.65 159 | 89.10 163 |
|
v52 | | | 96.35 16 | 97.40 13 | 95.12 40 | 93.83 123 | 95.54 69 | 97.82 40 | 88.95 144 | 96.27 13 | 97.22 5 | 99.11 2 | 99.40 4 | 95.80 5 | 98.16 18 | 96.37 50 | 97.10 67 | 96.96 23 |
|
V4 | | | 96.35 16 | 97.40 13 | 95.12 40 | 93.83 123 | 95.54 69 | 97.82 40 | 88.95 144 | 96.27 13 | 97.21 6 | 99.10 3 | 99.40 4 | 95.79 6 | 98.17 17 | 96.37 50 | 97.10 67 | 96.96 23 |
|
Fast-Effi-MVS+ | | | 92.93 113 | 92.64 132 | 93.27 93 | 93.81 125 | 93.88 130 | 95.90 101 | 90.61 100 | 83.98 188 | 92.71 85 | 92.81 137 | 96.22 111 | 90.67 97 | 94.90 103 | 93.92 111 | 95.92 116 | 92.77 113 |
|
v13 | | | 94.54 62 | 94.93 65 | 94.09 59 | 93.81 125 | 95.44 73 | 96.99 56 | 91.67 74 | 92.43 67 | 95.20 27 | 98.33 18 | 98.73 20 | 91.87 59 | 93.67 130 | 92.26 136 | 95.00 143 | 93.63 92 |
|
Anonymous20231211 | | | 93.19 105 | 95.50 55 | 90.49 140 | 93.77 127 | 95.29 83 | 94.36 138 | 90.04 110 | 91.44 98 | 84.59 178 | 96.72 72 | 97.65 71 | 82.45 166 | 97.25 47 | 96.32 53 | 97.74 46 | 93.79 88 |
|
abl_6 | | | | | 91.88 122 | 93.76 128 | 94.98 95 | 95.64 111 | 88.97 141 | 86.20 173 | 90.00 141 | 86.31 202 | 94.50 148 | 87.31 130 | | | 95.60 124 | 92.48 123 |
|
v12 | | | 94.44 63 | 94.79 71 | 94.02 63 | 93.75 129 | 95.37 79 | 96.92 57 | 91.61 75 | 92.21 73 | 95.10 29 | 98.27 22 | 98.69 22 | 91.73 63 | 93.49 132 | 92.15 141 | 94.97 147 | 93.37 97 |
|
v1921920 | | | 93.90 76 | 93.82 98 | 94.00 65 | 93.74 130 | 94.31 113 | 97.12 49 | 89.33 135 | 91.13 108 | 96.77 10 | 97.90 33 | 98.06 54 | 91.95 56 | 91.93 168 | 91.54 156 | 95.10 138 | 91.85 135 |
|
v1144 | | | 93.83 81 | 93.87 94 | 93.78 77 | 93.72 131 | 94.57 110 | 96.85 61 | 89.98 111 | 91.31 103 | 95.90 19 | 97.89 34 | 98.40 35 | 91.13 82 | 92.01 164 | 92.01 145 | 95.10 138 | 90.94 144 |
|
thres600view7 | | | 89.14 161 | 88.83 169 | 89.51 159 | 93.71 132 | 93.55 140 | 93.93 147 | 88.02 162 | 87.30 162 | 82.40 192 | 81.18 223 | 80.63 207 | 82.69 164 | 94.27 116 | 95.90 62 | 96.27 101 | 88.94 165 |
|
V9 | | | 94.33 65 | 94.66 74 | 93.94 70 | 93.69 133 | 95.31 80 | 96.84 62 | 91.53 78 | 92.04 79 | 95.00 33 | 98.22 23 | 98.64 23 | 91.62 65 | 93.29 135 | 92.05 143 | 94.93 148 | 93.10 103 |
|
casdiffmvs | | | 91.65 137 | 91.03 150 | 92.36 113 | 93.69 133 | 94.95 98 | 95.60 113 | 91.36 81 | 85.32 178 | 91.43 116 | 91.77 146 | 95.47 128 | 87.29 131 | 88.58 200 | 88.39 193 | 94.81 152 | 92.75 114 |
|
V14 | | | 94.21 69 | 94.52 79 | 93.85 73 | 93.62 135 | 95.25 84 | 96.76 66 | 91.42 79 | 91.83 83 | 94.91 38 | 98.15 24 | 98.57 27 | 91.49 69 | 93.06 140 | 91.93 147 | 94.90 149 | 92.82 110 |
|
view600 | | | 89.09 162 | 88.78 172 | 89.46 160 | 93.59 136 | 93.33 146 | 93.92 148 | 87.76 167 | 87.40 160 | 82.79 188 | 81.29 222 | 80.71 206 | 82.59 165 | 94.28 115 | 95.72 68 | 96.12 111 | 88.70 168 |
|
v15 | | | 94.09 70 | 94.37 84 | 93.77 78 | 93.56 137 | 95.18 85 | 96.68 72 | 91.34 83 | 91.64 88 | 94.83 41 | 98.09 27 | 98.51 30 | 91.37 72 | 92.84 141 | 91.80 149 | 94.85 150 | 92.53 122 |
|
tttt0517 | | | 89.64 154 | 88.05 180 | 91.49 126 | 93.52 138 | 91.65 173 | 93.67 150 | 87.53 170 | 82.77 195 | 89.39 145 | 90.37 164 | 70.05 226 | 88.21 124 | 93.71 129 | 93.79 113 | 96.63 80 | 94.04 82 |
|
v144192 | | | 93.89 77 | 93.85 96 | 93.94 70 | 93.50 139 | 94.33 112 | 97.12 49 | 89.49 129 | 90.89 114 | 96.49 13 | 97.78 41 | 98.27 40 | 91.89 58 | 92.17 160 | 91.70 151 | 95.19 134 | 91.78 138 |
|
v1141 | | | 93.47 95 | 93.56 110 | 93.36 87 | 93.48 140 | 94.17 121 | 96.42 86 | 89.62 121 | 91.44 98 | 94.99 35 | 97.81 39 | 98.42 33 | 90.94 92 | 92.00 165 | 91.38 163 | 94.74 156 | 89.69 157 |
|
divwei89l23v2f112 | | | 93.47 95 | 93.56 110 | 93.37 85 | 93.48 140 | 94.17 121 | 96.42 86 | 89.62 121 | 91.46 95 | 95.00 33 | 97.81 39 | 98.42 33 | 90.94 92 | 92.00 165 | 91.38 163 | 94.75 154 | 89.70 155 |
|
v1 | | | 93.48 94 | 93.57 109 | 93.37 85 | 93.48 140 | 94.18 120 | 96.41 88 | 89.61 123 | 91.46 95 | 95.03 30 | 97.82 38 | 98.43 32 | 90.95 91 | 92.00 165 | 91.37 165 | 94.75 154 | 89.70 155 |
|
v2v482 | | | 93.42 97 | 93.49 114 | 93.32 89 | 93.44 143 | 94.05 123 | 96.36 95 | 89.76 117 | 91.41 100 | 95.24 25 | 97.63 51 | 98.34 38 | 90.44 106 | 91.65 170 | 91.76 150 | 94.69 157 | 89.62 158 |
|
v7 | | | 93.65 85 | 93.73 102 | 93.57 82 | 93.38 144 | 94.60 108 | 96.83 63 | 89.92 114 | 89.69 134 | 95.02 31 | 97.89 34 | 98.24 42 | 91.27 74 | 92.38 152 | 92.18 139 | 94.99 144 | 91.12 142 |
|
v10 | | | 93.96 72 | 94.12 91 | 93.77 78 | 93.37 145 | 95.45 72 | 96.83 63 | 91.13 88 | 89.70 133 | 95.02 31 | 97.88 36 | 98.23 43 | 91.27 74 | 92.39 151 | 92.18 139 | 94.99 144 | 93.00 107 |
|
Fast-Effi-MVS+-dtu | | | 89.57 156 | 88.42 177 | 90.92 134 | 93.35 146 | 91.57 174 | 93.01 169 | 95.71 8 | 78.94 222 | 87.65 159 | 84.68 211 | 93.14 163 | 82.00 169 | 90.84 178 | 91.01 168 | 93.78 178 | 88.77 167 |
|
new-patchmatchnet | | | 84.45 200 | 88.75 173 | 79.43 220 | 93.28 147 | 81.87 221 | 81.68 239 | 83.48 210 | 94.47 29 | 71.53 230 | 98.33 18 | 97.88 66 | 58.61 234 | 90.35 181 | 77.33 226 | 87.99 205 | 81.05 206 |
|
IterMVS-LS | | | 92.10 133 | 92.33 134 | 91.82 123 | 93.18 148 | 93.66 134 | 92.80 176 | 92.27 55 | 90.82 116 | 90.59 132 | 97.19 61 | 90.97 173 | 87.76 129 | 89.60 190 | 90.94 169 | 94.34 171 | 93.16 102 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
thisisatest0530 | | | 89.54 157 | 87.99 182 | 91.35 130 | 93.17 149 | 91.31 176 | 93.45 155 | 87.53 170 | 82.96 193 | 89.17 147 | 90.45 162 | 70.32 225 | 88.21 124 | 93.37 134 | 93.79 113 | 96.54 82 | 93.71 89 |
|
thres400 | | | 88.54 170 | 88.15 179 | 88.98 164 | 93.17 149 | 92.84 150 | 93.56 153 | 86.93 182 | 86.45 170 | 82.37 193 | 79.96 225 | 81.46 204 | 81.83 172 | 93.21 138 | 94.76 94 | 96.04 112 | 88.39 174 |
|
v17 | | | 93.60 87 | 93.85 96 | 93.30 92 | 93.15 151 | 94.99 94 | 96.46 81 | 90.81 95 | 89.58 136 | 93.61 67 | 97.66 49 | 98.15 50 | 91.19 78 | 92.60 148 | 91.61 153 | 94.61 164 | 92.37 124 |
|
v1neww | | | 93.27 101 | 93.40 119 | 93.12 100 | 93.13 152 | 94.20 117 | 96.39 89 | 89.56 124 | 89.87 131 | 93.95 56 | 97.71 45 | 98.21 46 | 91.09 84 | 92.36 153 | 91.49 157 | 94.62 162 | 89.96 151 |
|
v7new | | | 93.27 101 | 93.40 119 | 93.12 100 | 93.13 152 | 94.20 117 | 96.39 89 | 89.56 124 | 89.87 131 | 93.95 56 | 97.71 45 | 98.21 46 | 91.09 84 | 92.36 153 | 91.49 157 | 94.62 162 | 89.96 151 |
|
v8 | | | 93.60 87 | 93.82 98 | 93.34 88 | 93.13 152 | 95.06 89 | 96.39 89 | 90.75 97 | 89.90 129 | 94.03 54 | 97.70 47 | 98.21 46 | 91.08 86 | 92.36 153 | 91.47 161 | 94.63 160 | 92.07 130 |
|
v6 | | | 93.27 101 | 93.41 117 | 93.12 100 | 93.13 152 | 94.20 117 | 96.39 89 | 89.55 126 | 89.89 130 | 93.93 58 | 97.72 43 | 98.22 45 | 91.10 83 | 92.36 153 | 91.49 157 | 94.63 160 | 89.95 153 |
|
CANet_DTU | | | 88.95 166 | 89.51 162 | 88.29 180 | 93.12 156 | 91.22 178 | 93.61 152 | 83.47 211 | 80.07 213 | 90.71 131 | 89.19 176 | 93.68 158 | 76.27 213 | 91.44 173 | 91.17 167 | 92.59 190 | 89.83 154 |
|
v16 | | | 93.53 92 | 93.80 101 | 93.20 97 | 93.10 157 | 94.98 95 | 96.43 83 | 90.81 95 | 89.39 139 | 93.12 81 | 97.63 51 | 98.01 58 | 91.19 78 | 92.60 148 | 91.65 152 | 94.58 166 | 92.36 125 |
|
gg-mvs-nofinetune | | | 88.32 172 | 88.81 170 | 87.75 186 | 93.07 158 | 89.37 190 | 89.06 217 | 95.94 7 | 95.29 20 | 87.15 161 | 97.38 58 | 76.38 217 | 68.05 226 | 91.04 176 | 89.10 188 | 93.24 184 | 83.10 200 |
|
3Dnovator | | 91.81 5 | 93.36 99 | 94.27 89 | 92.29 116 | 92.99 159 | 95.03 90 | 95.76 105 | 87.79 166 | 93.82 43 | 92.38 96 | 92.19 144 | 93.37 161 | 88.14 126 | 95.26 93 | 94.85 89 | 96.69 78 | 95.40 56 |
|
v18 | | | 93.33 100 | 93.59 108 | 93.04 106 | 92.94 160 | 94.87 99 | 96.31 96 | 90.59 101 | 88.96 141 | 92.89 84 | 97.51 54 | 97.90 62 | 91.01 90 | 92.33 157 | 91.48 160 | 94.50 167 | 92.05 131 |
|
USDC | | | 92.17 132 | 92.17 137 | 92.18 119 | 92.93 161 | 92.22 165 | 93.66 151 | 87.41 174 | 93.49 46 | 97.99 1 | 94.10 122 | 96.68 99 | 86.46 137 | 92.04 163 | 89.18 186 | 94.61 164 | 87.47 179 |
|
QAPM | | | 92.57 122 | 93.51 112 | 91.47 127 | 92.91 162 | 94.82 100 | 93.01 169 | 87.51 172 | 91.49 92 | 91.21 123 | 92.24 142 | 91.70 168 | 88.74 118 | 94.54 109 | 94.39 106 | 95.41 127 | 95.37 59 |
|
v148 | | | 92.38 127 | 92.78 130 | 91.91 121 | 92.86 163 | 92.13 168 | 94.84 125 | 87.03 180 | 91.47 94 | 93.07 82 | 96.92 68 | 98.89 13 | 90.10 108 | 92.05 162 | 89.69 179 | 93.56 179 | 88.27 176 |
|
DELS-MVS | | | 92.33 129 | 93.61 107 | 90.83 135 | 92.84 164 | 95.13 88 | 94.76 128 | 87.22 179 | 87.78 158 | 88.42 156 | 95.78 86 | 95.28 136 | 85.71 142 | 94.44 110 | 93.91 112 | 96.01 113 | 92.97 108 |
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 |
OpenMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 89.22 12 | 91.09 142 | 91.42 147 | 90.71 136 | 92.79 165 | 93.61 139 | 92.74 177 | 85.47 196 | 86.10 174 | 90.73 127 | 85.71 206 | 93.07 164 | 86.69 136 | 94.07 124 | 93.34 124 | 95.86 117 | 94.02 84 |
|
pmmvs-eth3d | | | 92.34 128 | 92.33 134 | 92.34 115 | 92.67 166 | 90.67 183 | 96.37 93 | 89.06 138 | 90.98 112 | 93.60 68 | 97.13 64 | 97.02 85 | 88.29 123 | 90.20 183 | 91.42 162 | 94.07 174 | 88.89 166 |
|
MSDG | | | 92.09 134 | 92.84 129 | 91.22 131 | 92.55 167 | 92.97 148 | 93.42 156 | 85.43 197 | 90.24 122 | 91.83 108 | 94.70 111 | 94.59 146 | 88.48 121 | 94.91 102 | 93.31 125 | 95.59 125 | 89.15 162 |
|
FPMVS | | | 90.81 143 | 91.60 144 | 89.88 150 | 92.52 168 | 88.18 193 | 93.31 163 | 83.62 208 | 91.59 91 | 88.45 155 | 88.96 177 | 89.73 179 | 86.96 133 | 96.42 71 | 95.69 69 | 94.43 169 | 90.65 145 |
|
thres200 | | | 88.29 174 | 87.88 183 | 88.76 168 | 92.50 169 | 93.55 140 | 92.47 181 | 88.02 162 | 84.80 181 | 81.44 198 | 79.28 227 | 82.20 200 | 81.83 172 | 94.27 116 | 93.67 116 | 96.27 101 | 87.40 180 |
|
DI_MVS_plusplus_trai | | | 90.68 144 | 90.40 154 | 91.00 133 | 92.43 170 | 92.61 156 | 94.17 143 | 88.98 140 | 88.32 150 | 88.76 152 | 93.67 129 | 87.58 185 | 86.44 138 | 89.74 188 | 90.33 172 | 95.24 133 | 90.56 148 |
|
PVSNet_BlendedMVS | | | 90.09 150 | 90.12 156 | 90.05 147 | 92.40 171 | 92.74 153 | 91.74 189 | 85.89 192 | 80.54 208 | 90.30 137 | 88.54 180 | 95.51 125 | 84.69 147 | 92.64 146 | 90.25 174 | 95.28 131 | 90.61 146 |
|
PVSNet_Blended | | | 90.09 150 | 90.12 156 | 90.05 147 | 92.40 171 | 92.74 153 | 91.74 189 | 85.89 192 | 80.54 208 | 90.30 137 | 88.54 180 | 95.51 125 | 84.69 147 | 92.64 146 | 90.25 174 | 95.28 131 | 90.61 146 |
|
FMVSNet1 | | | 92.86 116 | 95.26 60 | 90.06 146 | 92.40 171 | 95.16 86 | 94.37 134 | 92.22 56 | 93.18 54 | 82.16 196 | 96.76 71 | 97.48 75 | 81.85 171 | 95.32 89 | 94.98 85 | 97.34 59 | 93.93 86 |
|
GA-MVS | | | 88.76 167 | 88.04 181 | 89.59 157 | 92.32 174 | 91.46 175 | 92.28 183 | 86.62 185 | 83.82 190 | 89.84 142 | 92.51 141 | 81.94 201 | 83.53 156 | 89.41 192 | 89.27 185 | 92.95 188 | 87.90 177 |
|
tfpn111 | | | 87.59 186 | 86.89 189 | 88.41 175 | 92.28 175 | 93.64 136 | 93.36 158 | 88.12 158 | 80.90 202 | 80.71 201 | 73.93 237 | 82.25 196 | 79.65 187 | 94.27 116 | 94.76 94 | 96.36 89 | 88.48 171 |
|
conf200view11 | | | 87.93 182 | 87.51 186 | 88.41 175 | 92.28 175 | 93.64 136 | 93.36 158 | 88.12 158 | 80.90 202 | 80.71 201 | 78.25 228 | 82.25 196 | 79.65 187 | 94.27 116 | 94.76 94 | 96.36 89 | 88.48 171 |
|
thres100view900 | | | 86.46 191 | 86.00 196 | 86.99 192 | 92.28 175 | 91.03 179 | 91.09 198 | 84.49 204 | 80.90 202 | 80.89 199 | 78.25 228 | 82.25 196 | 77.57 205 | 90.17 185 | 92.84 129 | 95.63 123 | 86.57 187 |
|
tfpn200view9 | | | 87.94 181 | 87.51 186 | 88.44 174 | 92.28 175 | 93.63 138 | 93.35 162 | 88.11 160 | 80.90 202 | 80.89 199 | 78.25 228 | 82.25 196 | 79.65 187 | 94.27 116 | 94.76 94 | 96.36 89 | 88.48 171 |
|
thresconf0.02 | | | 84.34 201 | 82.02 211 | 87.06 190 | 92.23 179 | 90.93 180 | 91.05 199 | 86.43 189 | 88.83 146 | 77.65 219 | 73.93 237 | 55.81 241 | 79.68 186 | 90.62 180 | 90.28 173 | 95.30 129 | 83.73 196 |
|
PatchMatch-RL | | | 89.59 155 | 88.80 171 | 90.51 139 | 92.20 180 | 88.00 196 | 91.72 191 | 86.64 184 | 84.75 184 | 88.25 157 | 87.10 196 | 90.66 175 | 89.85 111 | 93.23 137 | 92.28 135 | 94.41 170 | 85.60 192 |
|
MVS_Test | | | 90.19 148 | 90.58 151 | 89.74 153 | 92.12 181 | 91.74 172 | 92.51 179 | 88.54 152 | 82.80 194 | 87.50 160 | 94.62 112 | 95.02 142 | 83.97 151 | 88.69 197 | 89.32 184 | 93.79 177 | 91.85 135 |
|
conf0.01 | | | 85.72 194 | 83.49 205 | 88.32 178 | 92.11 182 | 93.35 145 | 93.36 158 | 88.02 162 | 80.90 202 | 80.51 204 | 74.83 235 | 59.86 240 | 79.65 187 | 93.80 126 | 94.76 94 | 96.29 98 | 86.94 184 |
|
PM-MVS | | | 92.65 121 | 93.20 124 | 92.00 120 | 92.11 182 | 90.16 187 | 95.99 100 | 84.81 202 | 91.31 103 | 92.41 95 | 95.87 83 | 96.64 100 | 92.35 51 | 93.65 131 | 92.91 128 | 94.34 171 | 91.85 135 |
|
conf0.002 | | | 84.82 197 | 81.84 212 | 88.30 179 | 92.05 184 | 93.28 147 | 93.36 158 | 88.00 165 | 80.90 202 | 80.48 205 | 73.43 239 | 52.48 245 | 79.65 187 | 93.72 127 | 92.82 130 | 96.28 99 | 86.22 188 |
|
IB-MVS | | 86.01 17 | 88.24 175 | 87.63 185 | 88.94 166 | 92.03 185 | 91.77 171 | 92.40 182 | 85.58 195 | 78.24 224 | 84.85 176 | 71.99 240 | 93.45 159 | 83.96 152 | 93.48 133 | 92.33 134 | 94.84 151 | 92.15 129 |
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 |
our_test_3 | | | | | | 91.78 186 | 88.87 192 | 94.37 134 | | | | | | | | | | |
|
N_pmnet | | | 79.33 219 | 84.22 201 | 73.62 235 | 91.72 187 | 73.72 240 | 86.11 232 | 76.36 227 | 92.38 68 | 53.38 243 | 95.54 93 | 95.62 123 | 59.14 233 | 84.23 221 | 74.84 234 | 75.03 240 | 73.25 232 |
|
MIMVSNet | | | 84.76 199 | 86.75 190 | 82.44 212 | 91.71 188 | 85.95 204 | 89.74 213 | 89.49 129 | 85.28 179 | 69.69 235 | 87.93 187 | 90.88 174 | 64.85 228 | 88.26 203 | 87.74 196 | 89.18 201 | 81.24 204 |
|
pmmvs4 | | | 89.95 152 | 89.32 165 | 90.69 137 | 91.60 189 | 89.17 191 | 94.37 134 | 87.63 169 | 88.07 155 | 91.02 125 | 94.50 115 | 90.50 176 | 86.13 140 | 86.33 213 | 89.40 183 | 93.39 182 | 87.29 181 |
|
V42 | | | 92.67 120 | 93.50 113 | 91.71 124 | 91.41 190 | 92.96 149 | 95.71 108 | 85.00 199 | 89.67 135 | 93.22 76 | 97.67 48 | 98.01 58 | 91.02 89 | 92.65 145 | 92.12 142 | 93.86 176 | 91.42 141 |
|
MDTV_nov1_ep13_2view | | | 88.22 176 | 87.85 184 | 88.65 170 | 91.40 191 | 86.75 202 | 94.07 144 | 84.97 200 | 88.86 145 | 93.20 77 | 96.11 82 | 96.21 113 | 83.70 154 | 87.29 210 | 80.29 219 | 84.56 218 | 79.46 215 |
|
tfpn_ndepth | | | 85.89 193 | 86.40 193 | 85.30 203 | 91.31 192 | 92.47 159 | 90.78 202 | 87.75 168 | 84.79 182 | 71.04 231 | 76.95 232 | 78.80 212 | 74.52 217 | 92.72 143 | 93.43 122 | 96.39 87 | 85.65 191 |
|
HyFIR lowres test | | | 88.19 178 | 86.56 192 | 90.09 145 | 91.24 193 | 92.17 167 | 94.30 140 | 88.79 148 | 84.06 186 | 85.45 174 | 89.52 173 | 85.64 192 | 88.64 119 | 85.40 220 | 87.28 197 | 92.14 193 | 81.87 203 |
|
GBi-Net | | | 89.35 159 | 90.58 151 | 87.91 184 | 91.22 194 | 94.05 123 | 92.88 173 | 90.05 107 | 79.40 214 | 78.60 213 | 90.58 158 | 87.05 186 | 78.54 195 | 95.32 89 | 94.98 85 | 96.17 108 | 92.67 116 |
|
test1 | | | 89.35 159 | 90.58 151 | 87.91 184 | 91.22 194 | 94.05 123 | 92.88 173 | 90.05 107 | 79.40 214 | 78.60 213 | 90.58 158 | 87.05 186 | 78.54 195 | 95.32 89 | 94.98 85 | 96.17 108 | 92.67 116 |
|
FMVSNet2 | | | 90.28 147 | 92.04 139 | 88.23 181 | 91.22 194 | 94.05 123 | 92.88 173 | 90.69 98 | 86.53 169 | 79.89 207 | 94.38 118 | 92.73 165 | 78.54 195 | 91.64 171 | 92.26 136 | 96.17 108 | 92.67 116 |
|
diffmvs1 | | | 91.31 140 | 92.90 128 | 89.45 161 | 91.20 197 | 92.51 157 | 92.91 171 | 88.54 152 | 91.21 106 | 85.63 172 | 95.02 107 | 97.50 74 | 85.37 143 | 90.92 177 | 89.89 177 | 93.08 187 | 93.27 99 |
|
tpm | | | 81.58 212 | 78.84 221 | 84.79 207 | 91.11 198 | 79.50 225 | 89.79 212 | 83.75 206 | 79.30 218 | 92.05 102 | 90.98 153 | 64.78 235 | 74.54 215 | 80.50 231 | 76.67 228 | 77.49 235 | 80.15 211 |
|
UGNet | | | 92.31 131 | 94.70 73 | 89.53 158 | 90.99 199 | 95.53 71 | 96.19 98 | 92.10 64 | 91.35 102 | 85.76 169 | 95.31 97 | 95.48 127 | 76.84 209 | 95.22 94 | 94.79 92 | 95.32 128 | 95.19 61 |
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022 |
Vis-MVSNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 94.39 64 | 95.85 48 | 92.68 110 | 90.91 200 | 95.88 60 | 97.62 47 | 91.41 80 | 91.95 80 | 89.20 146 | 97.29 60 | 96.26 108 | 90.60 104 | 96.95 55 | 95.91 61 | 96.32 97 | 96.71 33 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
diffmvs | | | 90.35 146 | 91.54 145 | 88.96 165 | 90.90 201 | 92.20 166 | 91.93 185 | 88.45 156 | 86.34 172 | 86.36 167 | 93.13 135 | 96.99 86 | 83.54 155 | 90.32 182 | 88.69 192 | 92.59 190 | 93.09 104 |
|
IterMVS | | | 88.32 172 | 88.25 178 | 88.41 175 | 90.83 202 | 91.24 177 | 93.07 168 | 81.69 217 | 86.77 167 | 88.55 153 | 95.61 87 | 86.91 189 | 87.01 132 | 87.38 208 | 83.77 208 | 89.29 200 | 86.06 189 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MS-PatchMatch | | | 87.72 184 | 88.62 176 | 86.66 196 | 90.81 203 | 88.18 193 | 90.92 200 | 82.25 214 | 85.86 176 | 80.40 206 | 90.14 168 | 89.29 181 | 84.93 144 | 89.39 193 | 89.12 187 | 90.67 196 | 88.34 175 |
|
TAMVS | | | 82.96 205 | 86.15 195 | 79.24 223 | 90.57 204 | 83.12 218 | 87.29 223 | 75.12 231 | 84.06 186 | 65.81 238 | 92.22 143 | 88.27 184 | 69.11 223 | 88.72 195 | 87.26 199 | 87.56 210 | 79.38 216 |
|
CR-MVSNet | | | 85.32 196 | 81.58 213 | 89.69 155 | 90.36 205 | 84.79 209 | 86.72 228 | 92.22 56 | 75.38 232 | 90.73 127 | 90.41 163 | 67.88 230 | 84.86 145 | 83.76 222 | 85.74 203 | 93.24 184 | 83.14 198 |
|
CHOSEN 1792x2688 | | | 86.64 189 | 86.62 191 | 86.65 197 | 90.33 206 | 87.86 198 | 93.19 166 | 83.30 212 | 83.95 189 | 82.32 194 | 87.93 187 | 89.34 180 | 86.92 134 | 85.64 219 | 84.95 205 | 83.85 224 | 86.68 186 |
|
tpmp4_e23 | | | 82.16 208 | 78.26 226 | 86.70 195 | 89.92 207 | 84.82 208 | 91.17 197 | 89.95 113 | 81.21 200 | 87.10 162 | 81.91 221 | 64.01 236 | 77.88 201 | 79.89 233 | 74.99 233 | 84.18 222 | 81.00 207 |
|
PatchmatchNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 82.44 206 | 78.69 223 | 86.83 193 | 89.81 208 | 81.55 222 | 90.78 202 | 87.27 178 | 82.39 197 | 88.85 149 | 88.31 183 | 70.96 224 | 81.90 170 | 78.58 235 | 74.33 235 | 82.35 230 | 74.69 226 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
RPMNet | | | 83.42 204 | 78.40 224 | 89.28 162 | 89.79 209 | 84.79 209 | 90.64 205 | 92.11 63 | 75.38 232 | 87.10 162 | 79.80 226 | 61.99 239 | 82.79 162 | 81.88 228 | 82.07 213 | 93.23 186 | 82.87 201 |
|
FMVSNet3 | | | 87.90 183 | 88.63 175 | 87.04 191 | 89.78 210 | 93.46 143 | 91.62 194 | 90.05 107 | 79.40 214 | 78.60 213 | 90.58 158 | 87.05 186 | 77.07 208 | 88.03 205 | 89.86 178 | 95.12 137 | 92.04 132 |
|
tpm cat1 | | | 80.03 218 | 75.93 237 | 84.81 206 | 89.31 211 | 83.26 217 | 88.86 218 | 86.55 188 | 79.24 219 | 86.10 168 | 84.22 213 | 63.62 237 | 77.37 207 | 73.43 240 | 70.88 238 | 80.67 231 | 76.87 221 |
|
pmmvs5 | | | 88.63 169 | 89.70 160 | 87.39 189 | 89.24 212 | 90.64 184 | 91.87 186 | 82.13 215 | 83.34 191 | 87.86 158 | 94.58 113 | 96.15 114 | 79.87 184 | 87.33 209 | 89.07 189 | 93.39 182 | 86.76 185 |
|
MVSTER | | | 84.79 198 | 83.79 203 | 85.96 199 | 89.14 213 | 89.80 188 | 89.39 215 | 82.99 213 | 74.16 236 | 82.78 189 | 85.97 204 | 66.81 232 | 76.84 209 | 90.77 179 | 88.83 191 | 94.66 158 | 90.19 150 |
|
CostFormer | | | 82.15 209 | 79.54 219 | 85.20 204 | 88.92 214 | 85.70 205 | 90.87 201 | 86.26 191 | 79.19 220 | 83.87 183 | 87.89 189 | 69.20 228 | 76.62 211 | 77.50 238 | 75.28 231 | 84.69 217 | 82.02 202 |
|
MDTV_nov1_ep13 | | | 82.33 207 | 79.66 218 | 85.45 201 | 88.83 215 | 83.88 213 | 90.09 210 | 81.98 216 | 79.07 221 | 88.82 150 | 88.70 178 | 73.77 220 | 78.41 199 | 80.29 232 | 76.08 229 | 84.56 218 | 75.83 223 |
|
DWT-MVSNet_training | | | 79.22 222 | 73.99 239 | 85.33 202 | 88.57 216 | 84.41 211 | 90.56 206 | 80.96 221 | 73.90 237 | 85.72 171 | 75.62 233 | 50.09 246 | 81.30 178 | 76.91 239 | 77.02 227 | 84.88 216 | 79.97 213 |
|
tpmrst | | | 78.81 225 | 76.18 236 | 81.87 214 | 88.56 217 | 77.45 231 | 86.74 227 | 81.52 218 | 80.08 212 | 83.48 185 | 90.84 157 | 66.88 231 | 74.54 215 | 73.04 241 | 71.02 237 | 76.38 237 | 73.95 231 |
|
anonymousdsp | | | 95.45 45 | 96.70 25 | 93.99 66 | 88.43 218 | 92.05 169 | 99.18 1 | 85.42 198 | 94.29 35 | 96.10 16 | 98.63 12 | 99.08 10 | 96.11 1 | 97.77 36 | 97.41 29 | 98.70 8 | 97.69 6 |
|
EU-MVSNet | | | 91.63 138 | 92.73 131 | 90.35 142 | 88.36 219 | 87.89 197 | 96.53 77 | 81.51 219 | 92.45 66 | 91.82 109 | 96.44 77 | 97.05 84 | 93.26 35 | 94.10 123 | 88.94 190 | 90.61 197 | 92.24 128 |
|
E-PMN | | | 77.81 229 | 77.88 231 | 77.73 229 | 88.26 220 | 70.48 243 | 80.19 241 | 71.20 233 | 86.66 168 | 72.89 228 | 88.09 186 | 81.74 203 | 78.75 193 | 90.02 187 | 68.30 239 | 75.10 239 | 59.85 241 |
|
EMVS | | | 77.65 230 | 77.49 233 | 77.83 227 | 87.75 221 | 71.02 242 | 81.13 240 | 70.54 234 | 86.38 171 | 74.52 223 | 89.38 174 | 80.19 208 | 78.22 200 | 89.48 191 | 67.13 240 | 74.83 241 | 58.84 242 |
|
testmv | | | 81.49 215 | 84.76 199 | 77.67 230 | 87.67 222 | 80.25 224 | 90.12 208 | 77.62 224 | 80.34 211 | 69.71 233 | 90.92 156 | 96.47 103 | 56.57 236 | 88.58 200 | 84.92 207 | 84.33 221 | 71.86 236 |
|
test1235678 | | | 81.50 214 | 84.78 198 | 77.67 230 | 87.67 222 | 80.27 223 | 90.12 208 | 77.62 224 | 80.36 210 | 69.71 233 | 90.93 155 | 96.51 102 | 56.57 236 | 88.60 199 | 84.93 206 | 84.34 220 | 71.87 235 |
|
dps | | | 81.42 216 | 77.88 231 | 85.56 200 | 87.67 222 | 85.17 207 | 88.37 221 | 87.46 173 | 74.37 235 | 84.55 179 | 86.80 199 | 62.18 238 | 80.20 182 | 81.13 230 | 77.52 225 | 85.10 215 | 77.98 220 |
|
FMVSNet5 | | | 79.08 224 | 78.83 222 | 79.38 222 | 87.52 225 | 86.78 201 | 87.64 222 | 78.15 223 | 69.54 242 | 70.64 232 | 65.97 244 | 65.44 234 | 63.87 229 | 90.17 185 | 90.46 171 | 88.48 204 | 83.45 197 |
|
CVMVSNet | | | 88.97 165 | 89.73 159 | 88.10 182 | 87.33 226 | 85.22 206 | 94.68 130 | 78.68 222 | 88.94 143 | 86.98 165 | 95.55 91 | 85.71 191 | 89.87 110 | 91.19 175 | 89.69 179 | 91.05 195 | 91.78 138 |
|
EPMVS | | | 79.26 220 | 78.20 228 | 80.49 216 | 87.04 227 | 78.86 227 | 86.08 233 | 83.51 209 | 82.63 196 | 73.94 225 | 89.59 171 | 68.67 229 | 72.03 221 | 78.17 236 | 75.08 232 | 80.37 232 | 74.37 228 |
|
testpf | | | 72.68 237 | 66.81 241 | 79.53 219 | 86.52 228 | 73.89 239 | 83.56 236 | 88.74 149 | 58.70 245 | 79.68 208 | 71.31 241 | 53.64 243 | 62.23 230 | 68.68 242 | 66.64 241 | 76.46 236 | 74.82 225 |
|
testus | | | 78.20 228 | 81.50 214 | 74.36 234 | 85.59 229 | 79.36 226 | 86.99 226 | 65.76 235 | 76.01 230 | 73.00 226 | 77.98 231 | 93.35 162 | 51.30 242 | 86.33 213 | 82.79 211 | 83.50 226 | 74.68 227 |
|
no-one | | | 92.05 135 | 94.57 78 | 89.12 163 | 85.55 230 | 87.65 200 | 94.21 141 | 77.34 226 | 93.43 48 | 89.64 144 | 95.11 103 | 99.11 8 | 95.86 4 | 95.38 86 | 95.24 78 | 92.08 194 | 96.11 44 |
|
CMPMVS | ![Method available as binary. binary](img/icon_binary.png) | 66.55 18 | 85.55 195 | 87.46 188 | 83.32 211 | 84.99 231 | 81.97 220 | 79.19 242 | 75.93 228 | 79.32 217 | 88.82 150 | 85.09 207 | 91.07 171 | 82.12 168 | 92.56 150 | 89.63 181 | 88.84 203 | 92.56 120 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
test2356 | | | 72.95 236 | 71.24 240 | 74.95 233 | 84.89 232 | 75.49 235 | 82.67 238 | 75.38 229 | 68.02 243 | 68.65 237 | 74.40 236 | 52.81 244 | 55.61 239 | 81.50 229 | 79.80 220 | 82.50 228 | 66.70 239 |
|
test-mter | | | 78.71 226 | 78.35 225 | 79.12 225 | 84.03 233 | 76.58 232 | 88.51 220 | 59.06 240 | 71.06 238 | 78.87 212 | 83.73 216 | 71.83 221 | 76.44 212 | 83.41 225 | 80.61 217 | 87.79 208 | 81.24 204 |
|
new_pmnet | | | 76.65 233 | 83.52 204 | 68.63 237 | 82.60 234 | 72.08 241 | 76.76 244 | 64.17 236 | 84.41 185 | 49.73 245 | 91.77 146 | 91.53 169 | 56.16 238 | 86.59 211 | 83.26 210 | 82.37 229 | 75.02 224 |
|
TESTMET0.1,1 | | | 77.47 231 | 77.20 234 | 77.78 228 | 81.94 235 | 75.11 236 | 87.04 224 | 58.33 242 | 70.11 240 | 78.59 216 | 83.17 217 | 71.60 222 | 73.88 218 | 82.32 226 | 79.20 222 | 86.91 211 | 78.87 218 |
|
ADS-MVSNet | | | 79.11 223 | 79.38 220 | 78.80 226 | 81.90 236 | 75.59 234 | 84.36 235 | 83.69 207 | 87.31 161 | 76.76 220 | 87.58 191 | 76.90 216 | 68.55 225 | 78.70 234 | 75.56 230 | 77.53 234 | 74.07 230 |
|
MVS-HIRNet | | | 78.28 227 | 75.28 238 | 81.79 215 | 80.33 237 | 69.38 244 | 76.83 243 | 86.59 186 | 70.76 239 | 86.66 166 | 89.57 172 | 81.04 205 | 77.74 203 | 77.81 237 | 71.65 236 | 82.62 227 | 66.73 238 |
|
CHOSEN 280x420 | | | 79.24 221 | 78.26 226 | 80.38 217 | 79.60 238 | 68.80 245 | 89.32 216 | 75.38 229 | 77.25 228 | 78.02 218 | 75.57 234 | 76.17 218 | 81.19 179 | 88.61 198 | 81.39 215 | 78.79 233 | 80.03 212 |
|
LP | | | 84.09 202 | 84.31 200 | 83.85 210 | 79.40 239 | 84.34 212 | 90.26 207 | 84.02 205 | 87.99 156 | 84.66 177 | 91.61 150 | 79.13 210 | 80.58 181 | 85.90 218 | 81.59 214 | 84.16 223 | 79.59 214 |
|
pmmvs3 | | | 81.69 211 | 83.83 202 | 79.19 224 | 78.33 240 | 78.57 228 | 89.53 214 | 58.71 241 | 78.88 223 | 84.34 181 | 88.36 182 | 91.96 167 | 77.69 204 | 87.48 207 | 82.42 212 | 86.54 213 | 79.18 217 |
|
PatchT | | | 83.44 203 | 81.10 215 | 86.18 198 | 77.92 241 | 82.58 219 | 89.87 211 | 87.39 175 | 75.88 231 | 90.73 127 | 89.86 169 | 66.71 233 | 84.86 145 | 83.76 222 | 85.74 203 | 86.33 214 | 83.14 198 |
|
test12356 | | | 75.40 234 | 80.89 216 | 69.01 236 | 77.43 242 | 75.75 233 | 83.03 237 | 61.48 238 | 78.13 226 | 59.08 242 | 87.69 190 | 94.95 144 | 57.37 235 | 88.18 204 | 80.59 218 | 75.65 238 | 60.93 240 |
|
PMMVS2 | | | 69.86 238 | 82.14 210 | 55.52 240 | 75.19 243 | 63.08 246 | 75.52 245 | 60.97 239 | 88.50 149 | 25.11 248 | 91.77 146 | 96.44 104 | 25.43 243 | 88.70 196 | 79.34 221 | 70.93 242 | 67.17 237 |
|
MDA-MVSNet-bldmvs | | | 89.75 153 | 91.67 142 | 87.50 187 | 74.25 244 | 90.88 181 | 94.68 130 | 85.89 192 | 91.64 88 | 91.03 124 | 95.86 84 | 94.35 150 | 89.10 115 | 96.87 59 | 86.37 201 | 90.04 198 | 85.72 190 |
|
PMMVS | | | 81.93 210 | 83.48 206 | 80.12 218 | 72.35 245 | 75.05 238 | 88.54 219 | 64.01 237 | 77.02 229 | 82.22 195 | 87.51 192 | 91.12 170 | 79.70 185 | 86.59 211 | 86.64 200 | 93.88 175 | 80.41 208 |
|
MVE | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | 60.41 19 | 73.21 235 | 80.84 217 | 64.30 238 | 56.34 246 | 57.24 247 | 75.28 246 | 72.76 232 | 87.14 166 | 41.39 246 | 86.31 202 | 85.30 193 | 80.66 180 | 86.17 215 | 83.36 209 | 59.35 244 | 80.38 209 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
tmp_tt | | | | | 28.44 241 | 36.05 247 | 15.86 249 | 21.29 249 | 6.40 244 | 54.52 246 | 51.96 244 | 50.37 245 | 38.68 250 | 9.55 244 | 61.75 244 | 59.66 242 | 45.36 246 | |
|
testmvs | | | 2.38 241 | 3.35 243 | 1.26 244 | 0.83 248 | 0.96 251 | 1.53 251 | 0.83 245 | 3.59 247 | 1.63 251 | 6.03 246 | 2.93 251 | 1.55 246 | 3.49 245 | 2.51 244 | 1.21 248 | 3.92 244 |
|
test123 | | | 2.16 242 | 2.82 244 | 1.41 243 | 0.62 249 | 1.18 250 | 1.53 251 | 0.82 246 | 2.78 248 | 2.27 250 | 4.18 247 | 1.98 252 | 1.64 245 | 2.58 246 | 3.01 243 | 1.56 247 | 4.00 243 |
|
GG-mvs-BLEND | | | 54.28 240 | 77.89 230 | 26.72 242 | 0.37 250 | 83.31 216 | 70.04 247 | 0.39 247 | 74.71 234 | 5.36 249 | 68.78 242 | 83.06 195 | 0.62 247 | 83.73 224 | 78.99 224 | 83.55 225 | 72.68 234 |
|
sosnet-low-res | | | 0.00 243 | 0.00 245 | 0.00 245 | 0.00 251 | 0.00 252 | 0.00 253 | 0.00 248 | 0.00 249 | 0.00 252 | 0.00 248 | 0.00 253 | 0.00 248 | 0.00 247 | 0.00 246 | 0.00 250 | 0.00 246 |
|
sosnet | | | 0.00 243 | 0.00 245 | 0.00 245 | 0.00 251 | 0.00 252 | 0.00 253 | 0.00 248 | 0.00 249 | 0.00 252 | 0.00 248 | 0.00 253 | 0.00 248 | 0.00 247 | 0.00 246 | 0.00 250 | 0.00 246 |
|
MTAPA | | | | | | | | | | | 94.88 39 | | 96.88 91 | | | | | |
|
MTMP | | | | | | | | | | | 95.43 21 | | 97.25 79 | | | | | |
|
Patchmatch-RL test | | | | | | | | 8.96 250 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 85.48 177 | | | | | | | | |
|
Patchmtry | | | | | | | 83.74 214 | 86.72 228 | 92.22 56 | | 90.73 127 | | | | | | | |
|
DeepMVS_CX | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | | | | | 47.68 248 | 53.20 248 | 19.21 243 | 63.24 244 | 26.96 247 | 66.50 243 | 69.82 227 | 66.91 227 | 64.27 243 | | 54.91 245 | 72.72 233 |
|