UA-Net | | | 99.30 23 | 99.22 22 | 99.39 46 | 99.94 1 | 99.66 16 | 98.91 127 | 99.86 8 | 97.74 54 | 98.74 125 | 99.00 102 | 99.60 38 | 99.17 70 | 99.50 23 | 99.39 25 | 99.70 23 | 99.64 2 |
|
DTE-MVSNet | | | 99.52 12 | 99.27 18 | 99.82 2 | 99.93 2 | 99.77 3 | 99.79 9 | 99.87 6 | 97.89 44 | 99.70 10 | 99.55 61 | 99.21 78 | 99.77 2 | 99.65 9 | 99.43 22 | 99.90 2 | 99.36 21 |
|
SixPastTwentyTwo | | | 99.70 3 | 99.59 6 | 99.82 2 | 99.93 2 | 99.80 1 | 99.86 2 | 99.87 6 | 98.87 13 | 99.79 4 | 99.85 27 | 99.33 63 | 99.74 7 | 99.85 2 | 99.82 1 | 99.74 21 | 99.63 4 |
|
pmmvs6 | | | 99.74 2 | 99.75 1 | 99.73 14 | 99.92 4 | 99.67 14 | 99.76 13 | 99.84 10 | 99.59 1 | 99.52 26 | 99.87 18 | 99.91 1 | 99.43 38 | 99.87 1 | 99.81 2 | 99.89 5 | 99.52 9 |
|
PS-CasMVS | | | 99.50 13 | 99.23 20 | 99.82 2 | 99.92 4 | 99.75 6 | 99.78 10 | 99.89 1 | 97.30 88 | 99.71 5 | 99.60 52 | 99.23 74 | 99.71 9 | 99.65 9 | 99.55 17 | 99.90 2 | 99.56 7 |
|
PEN-MVS | | | 99.54 10 | 99.30 17 | 99.83 1 | 99.92 4 | 99.76 4 | 99.80 7 | 99.88 3 | 97.60 66 | 99.71 5 | 99.59 54 | 99.52 43 | 99.75 6 | 99.64 11 | 99.51 18 | 99.90 2 | 99.46 17 |
|
WR-MVS | | | 99.61 9 | 99.44 10 | 99.82 2 | 99.92 4 | 99.80 1 | 99.80 7 | 99.89 1 | 98.54 18 | 99.66 14 | 99.78 39 | 99.16 86 | 99.68 10 | 99.70 5 | 99.63 5 | 99.94 1 | 99.49 15 |
|
v52 | | | 99.67 5 | 99.59 6 | 99.76 8 | 99.91 8 | 99.69 10 | 99.85 3 | 99.79 15 | 99.12 8 | 99.68 11 | 99.95 2 | 99.72 13 | 99.77 2 | 99.58 16 | 99.61 10 | 99.54 40 | 99.50 12 |
|
V4 | | | 99.67 5 | 99.60 5 | 99.76 8 | 99.91 8 | 99.69 10 | 99.85 3 | 99.79 15 | 99.13 7 | 99.68 11 | 99.95 2 | 99.72 13 | 99.77 2 | 99.58 16 | 99.61 10 | 99.54 40 | 99.50 12 |
|
CP-MVSNet | | | 99.39 19 | 99.04 28 | 99.80 6 | 99.91 8 | 99.70 9 | 99.75 14 | 99.88 3 | 96.82 111 | 99.68 11 | 99.32 75 | 98.86 122 | 99.68 10 | 99.57 20 | 99.47 20 | 99.89 5 | 99.52 9 |
|
WR-MVS_H | | | 99.48 14 | 99.23 20 | 99.76 8 | 99.91 8 | 99.76 4 | 99.75 14 | 99.88 3 | 97.27 91 | 99.58 19 | 99.56 58 | 99.24 72 | 99.56 17 | 99.60 14 | 99.60 13 | 99.88 7 | 99.58 6 |
|
gm-plane-assit | | | 94.62 215 | 91.39 225 | 98.39 163 | 99.90 12 | 99.47 35 | 99.40 65 | 99.65 43 | 97.44 78 | 99.56 22 | 99.68 43 | 59.40 250 | 94.23 218 | 96.17 205 | 94.77 216 | 97.61 201 | 92.79 224 |
|
v7n | | | 99.68 4 | 99.61 3 | 99.76 8 | 99.89 13 | 99.74 7 | 99.87 1 | 99.82 13 | 99.20 5 | 99.71 5 | 99.96 1 | 99.73 11 | 99.76 5 | 99.58 16 | 99.59 14 | 99.52 45 | 99.46 17 |
|
NR-MVSNet | | | 99.10 39 | 98.68 58 | 99.58 20 | 99.89 13 | 99.23 63 | 99.35 73 | 99.63 47 | 96.58 126 | 99.36 37 | 99.05 96 | 98.67 136 | 99.46 31 | 99.63 12 | 98.73 77 | 99.80 14 | 98.88 74 |
|
TransMVSNet (Re) | | | 99.45 17 | 99.32 15 | 99.61 16 | 99.88 15 | 99.60 18 | 99.75 14 | 99.63 47 | 99.11 9 | 99.28 57 | 99.83 31 | 98.35 146 | 99.27 61 | 99.70 5 | 99.62 9 | 99.84 9 | 99.03 51 |
|
anonymousdsp | | | 99.64 8 | 99.55 8 | 99.74 13 | 99.87 16 | 99.56 22 | 99.82 6 | 99.73 27 | 98.54 18 | 99.71 5 | 99.92 6 | 99.84 6 | 99.61 12 | 99.70 5 | 99.63 5 | 99.69 26 | 99.64 2 |
|
FC-MVSNet-train | | | 99.13 37 | 99.05 27 | 99.21 74 | 99.87 16 | 99.57 21 | 99.67 19 | 99.60 55 | 96.75 118 | 98.28 160 | 99.48 66 | 99.52 43 | 98.10 136 | 99.47 26 | 99.37 27 | 99.76 20 | 99.21 32 |
|
FC-MVSNet-test | | | 99.32 22 | 99.33 13 | 99.31 65 | 99.87 16 | 99.65 17 | 99.63 28 | 99.75 24 | 97.76 49 | 97.29 209 | 99.87 18 | 99.63 33 | 99.52 23 | 99.66 8 | 99.63 5 | 99.77 18 | 99.12 37 |
|
v748 | | | 99.67 5 | 99.61 3 | 99.75 12 | 99.87 16 | 99.68 12 | 99.84 5 | 99.79 15 | 99.14 6 | 99.64 16 | 99.89 12 | 99.88 4 | 99.72 8 | 99.58 16 | 99.57 16 | 99.62 31 | 99.50 12 |
|
LTVRE_ROB | | 98.82 1 | 99.76 1 | 99.75 1 | 99.77 7 | 99.87 16 | 99.71 8 | 99.77 11 | 99.76 21 | 99.52 2 | 99.80 2 | 99.79 37 | 99.91 1 | 99.56 17 | 99.83 3 | 99.75 3 | 99.86 8 | 99.75 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 |
Anonymous20240521 | | | 98.86 71 | 98.57 66 | 99.19 78 | 99.86 21 | 99.67 14 | 99.39 66 | 99.71 32 | 97.53 71 | 98.69 128 | 95.85 197 | 98.48 143 | 97.75 152 | 99.57 20 | 99.41 24 | 99.72 22 | 99.48 16 |
|
EPP-MVSNet | | | 98.61 98 | 98.19 107 | 99.11 88 | 99.86 21 | 99.60 18 | 99.44 62 | 99.53 72 | 97.37 86 | 96.85 218 | 98.69 112 | 93.75 193 | 99.18 67 | 99.22 38 | 99.35 29 | 99.82 12 | 99.32 23 |
|
MIMVSNet1 | | | 99.46 16 | 99.34 12 | 99.60 18 | 99.83 23 | 99.68 12 | 99.74 17 | 99.71 32 | 98.20 26 | 99.41 34 | 99.86 22 | 99.66 26 | 99.41 41 | 99.50 23 | 99.39 25 | 99.50 51 | 99.10 42 |
|
CSCG | | | 99.23 26 | 99.15 24 | 99.32 64 | 99.83 23 | 99.45 36 | 98.97 119 | 99.21 145 | 98.83 14 | 99.04 94 | 99.43 70 | 99.64 31 | 99.26 62 | 98.85 69 | 98.20 109 | 99.62 31 | 99.62 5 |
|
conf0.05thres1000 | | | 97.44 166 | 95.93 188 | 99.20 77 | 99.82 25 | 99.56 22 | 99.41 63 | 99.61 53 | 97.42 81 | 98.01 177 | 94.34 214 | 82.73 229 | 98.68 100 | 99.33 34 | 99.42 23 | 99.67 27 | 98.74 91 |
|
Anonymous20231211 | | | 98.89 62 | 98.79 46 | 98.99 107 | 99.82 25 | 99.41 40 | 99.18 98 | 99.31 131 | 96.92 105 | 98.54 138 | 98.58 118 | 98.84 125 | 97.46 160 | 99.45 27 | 99.29 33 | 99.65 29 | 99.08 44 |
|
pm-mvs1 | | | 99.47 15 | 99.38 11 | 99.57 21 | 99.82 25 | 99.49 32 | 99.63 28 | 99.65 43 | 98.88 12 | 99.31 47 | 99.85 27 | 99.02 112 | 99.23 64 | 99.60 14 | 99.58 15 | 99.80 14 | 99.22 31 |
|
IS_MVSNet | | | 98.20 128 | 98.00 119 | 98.44 159 | 99.82 25 | 99.48 33 | 99.25 87 | 99.56 59 | 95.58 162 | 93.93 240 | 97.56 155 | 96.52 180 | 98.27 128 | 99.08 47 | 99.20 38 | 99.80 14 | 98.56 109 |
|
tfpnnormal | | | 99.19 30 | 98.90 39 | 99.54 25 | 99.81 29 | 99.55 26 | 99.60 34 | 99.54 68 | 98.53 20 | 99.23 61 | 98.40 123 | 98.23 149 | 99.40 42 | 99.29 35 | 99.36 28 | 99.63 30 | 98.95 65 |
|
TranMVSNet+NR-MVSNet | | | 99.23 26 | 98.91 38 | 99.61 16 | 99.81 29 | 99.45 36 | 99.47 57 | 99.68 37 | 97.28 90 | 99.39 35 | 99.54 62 | 99.08 107 | 99.45 33 | 99.09 45 | 98.84 64 | 99.83 10 | 99.04 49 |
|
Vis-MVSNet (Re-imp) | | | 98.46 113 | 98.23 105 | 98.73 133 | 99.81 29 | 99.29 56 | 98.79 141 | 99.50 79 | 96.20 145 | 96.03 223 | 98.29 128 | 96.98 176 | 98.54 112 | 99.11 42 | 99.08 46 | 99.70 23 | 98.62 100 |
|
ACMH+ | | 97.53 7 | 99.29 24 | 99.20 23 | 99.40 45 | 99.81 29 | 99.22 66 | 99.59 35 | 99.50 79 | 98.64 17 | 98.29 159 | 99.21 86 | 99.69 18 | 99.57 15 | 99.53 22 | 99.33 30 | 99.66 28 | 98.81 81 |
|
ACMH | | 97.81 6 | 99.44 18 | 99.33 13 | 99.56 22 | 99.81 29 | 99.42 39 | 99.73 18 | 99.58 56 | 99.02 10 | 99.10 82 | 99.41 72 | 99.69 18 | 99.60 13 | 99.45 27 | 99.26 37 | 99.55 39 | 99.05 48 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
Anonymous202405211 | | | | 98.44 86 | | 99.79 34 | 99.32 54 | 99.05 109 | 99.34 123 | 96.59 125 | | 97.95 146 | 97.68 165 | 97.16 173 | 99.36 31 | 99.28 34 | 99.61 35 | 98.90 71 |
|
ACMMPR | | | 99.05 42 | 98.72 52 | 99.44 35 | 99.79 34 | 99.12 84 | 99.35 73 | 99.56 59 | 97.74 54 | 99.21 62 | 97.72 150 | 99.55 41 | 99.29 59 | 98.90 67 | 98.81 67 | 99.41 66 | 99.19 33 |
|
SteuartSystems-ACMMP | | | 98.94 53 | 98.52 72 | 99.43 38 | 99.79 34 | 99.13 82 | 99.33 77 | 99.55 62 | 96.17 147 | 99.04 94 | 97.53 156 | 99.65 30 | 99.46 31 | 99.04 53 | 98.76 73 | 99.44 58 | 99.35 22 |
Skip Steuart: Steuart Systems R&D Blog. |
testgi | | | 98.18 131 | 98.44 86 | 97.89 189 | 99.78 37 | 99.23 63 | 98.78 142 | 99.21 145 | 97.26 93 | 97.41 199 | 97.39 161 | 99.36 61 | 92.85 228 | 98.82 72 | 98.66 84 | 99.31 84 | 98.35 122 |
|
LGP-MVS_train | | | 98.84 75 | 98.33 97 | 99.44 35 | 99.78 37 | 98.98 110 | 99.39 66 | 99.55 62 | 95.41 164 | 98.90 110 | 97.51 157 | 99.68 21 | 99.44 36 | 99.03 54 | 98.81 67 | 99.57 38 | 98.91 69 |
|
zzz-MVS | | | 98.94 53 | 98.57 66 | 99.37 53 | 99.77 39 | 99.15 80 | 99.24 88 | 99.55 62 | 97.38 84 | 99.16 71 | 96.64 182 | 99.69 18 | 99.15 74 | 99.09 45 | 98.92 58 | 99.37 73 | 99.11 38 |
|
XVS | | | | | | 99.77 39 | 99.07 89 | 99.46 59 | | | 98.95 103 | | 99.37 57 | | | | 99.33 80 | |
|
X-MVStestdata | | | | | | 99.77 39 | 99.07 89 | 99.46 59 | | | 98.95 103 | | 99.37 57 | | | | 99.33 80 | |
|
APDe-MVS | | | 99.15 36 | 98.95 31 | 99.39 46 | 99.77 39 | 99.28 57 | 99.52 50 | 99.54 68 | 97.22 96 | 99.06 89 | 99.20 87 | 99.64 31 | 99.05 82 | 99.14 40 | 99.02 55 | 99.39 71 | 99.17 35 |
|
test20.03 | | | 98.84 75 | 98.74 50 | 98.95 111 | 99.77 39 | 99.33 50 | 99.21 93 | 99.46 88 | 97.29 89 | 98.88 114 | 99.65 48 | 99.10 101 | 97.07 176 | 99.11 42 | 98.76 73 | 99.32 83 | 97.98 154 |
|
ACMMP | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 98.82 83 | 98.33 97 | 99.39 46 | 99.77 39 | 99.14 81 | 99.37 69 | 99.54 68 | 96.47 136 | 99.03 96 | 96.26 191 | 99.52 43 | 99.28 60 | 98.92 65 | 98.80 70 | 99.37 73 | 99.16 36 |
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 |
PGM-MVS | | | 98.69 89 | 98.09 113 | 99.39 46 | 99.76 45 | 99.07 89 | 99.30 79 | 99.51 76 | 94.76 180 | 99.18 67 | 96.70 180 | 99.51 46 | 99.20 65 | 98.79 75 | 98.71 80 | 99.39 71 | 99.11 38 |
|
UniMVSNet_NR-MVSNet | | | 98.97 49 | 98.46 76 | 99.56 22 | 99.76 45 | 99.34 48 | 99.29 80 | 99.61 53 | 96.55 130 | 99.55 23 | 99.05 96 | 97.96 160 | 99.36 54 | 98.84 70 | 98.50 96 | 99.81 13 | 98.97 59 |
|
PVSNet_Blended_VisFu | | | 98.98 48 | 98.79 46 | 99.21 74 | 99.76 45 | 99.34 48 | 99.35 73 | 99.35 119 | 97.12 102 | 99.46 31 | 99.56 58 | 98.89 120 | 98.08 139 | 99.05 49 | 98.58 89 | 99.27 89 | 98.98 58 |
|
thisisatest0515 | | | 99.16 34 | 98.94 35 | 99.41 41 | 99.75 48 | 99.43 38 | 99.36 71 | 99.63 47 | 97.68 60 | 99.35 40 | 99.31 76 | 98.90 119 | 99.09 78 | 98.95 59 | 99.20 38 | 99.27 89 | 99.11 38 |
|
X-MVS | | | 98.59 100 | 97.99 120 | 99.30 66 | 99.75 48 | 99.07 89 | 99.17 99 | 99.50 79 | 96.62 122 | 98.95 103 | 93.95 215 | 99.37 57 | 99.11 77 | 98.94 61 | 98.86 60 | 99.35 78 | 99.09 43 |
|
MP-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 98.78 86 | 98.30 99 | 99.34 62 | 99.75 48 | 98.95 119 | 99.26 85 | 99.46 88 | 95.78 159 | 99.17 68 | 96.98 175 | 99.72 13 | 99.06 81 | 98.84 70 | 98.74 76 | 99.33 80 | 99.11 38 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
UniMVSNet (Re) | | | 99.08 41 | 98.69 56 | 99.54 25 | 99.75 48 | 99.33 50 | 99.29 80 | 99.64 46 | 96.75 118 | 99.48 30 | 99.30 78 | 98.69 132 | 99.26 62 | 98.94 61 | 98.76 73 | 99.78 17 | 99.02 54 |
|
mPP-MVS | | | | | | 99.75 48 | | | | | | | 99.49 50 | | | | | |
|
DU-MVS | | | 99.04 43 | 98.59 63 | 99.56 22 | 99.74 53 | 99.23 63 | 99.29 80 | 99.63 47 | 96.58 126 | 99.55 23 | 99.05 96 | 98.68 134 | 99.36 54 | 99.03 54 | 98.60 87 | 99.77 18 | 98.97 59 |
|
Baseline_NR-MVSNet | | | 99.18 33 | 98.87 41 | 99.54 25 | 99.74 53 | 99.56 22 | 99.36 71 | 99.62 52 | 96.53 132 | 99.29 52 | 99.85 27 | 98.64 138 | 99.40 42 | 99.03 54 | 99.63 5 | 99.83 10 | 98.86 75 |
|
ACMP | | 96.54 13 | 98.87 66 | 98.40 91 | 99.41 41 | 99.74 53 | 98.88 131 | 99.29 80 | 99.50 79 | 96.85 107 | 98.96 101 | 97.05 171 | 99.66 26 | 99.43 38 | 98.98 58 | 98.60 87 | 99.52 45 | 98.81 81 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMM | | 96.66 11 | 98.90 60 | 98.44 86 | 99.44 35 | 99.74 53 | 98.95 119 | 99.47 57 | 99.55 62 | 97.66 62 | 99.09 86 | 96.43 187 | 99.41 51 | 99.35 57 | 98.95 59 | 98.67 82 | 99.45 56 | 99.03 51 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
COLMAP_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 98.29 2 | 99.37 20 | 99.25 19 | 99.51 29 | 99.74 53 | 99.12 84 | 99.56 39 | 99.39 96 | 98.96 11 | 99.17 68 | 99.44 69 | 99.63 33 | 99.58 14 | 99.48 25 | 99.27 35 | 99.60 36 | 98.81 81 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
tfpn | | | 94.97 211 | 91.60 224 | 98.90 114 | 99.73 58 | 99.33 50 | 99.11 106 | 99.51 76 | 95.05 170 | 97.19 214 | 89.03 230 | 62.62 247 | 98.37 120 | 98.53 85 | 98.97 56 | 99.48 54 | 97.70 162 |
|
DeepC-MVS | | 97.88 4 | 99.33 21 | 99.15 24 | 99.53 28 | 99.73 58 | 99.05 93 | 99.49 55 | 99.40 94 | 98.42 21 | 99.55 23 | 99.71 42 | 99.89 3 | 99.49 28 | 99.14 40 | 98.81 67 | 99.54 40 | 99.02 54 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
SMA-MVS | | | 98.87 66 | 98.73 51 | 99.04 97 | 99.72 60 | 99.05 93 | 98.64 155 | 99.17 154 | 96.31 141 | 98.80 120 | 99.07 94 | 99.70 17 | 98.67 101 | 98.93 64 | 98.82 65 | 99.23 95 | 99.23 30 |
|
MVS_0304 | | | 98.57 102 | 98.36 94 | 98.82 126 | 99.72 60 | 98.94 123 | 98.92 125 | 99.14 159 | 96.76 116 | 99.33 43 | 98.30 127 | 99.73 11 | 96.74 179 | 98.05 128 | 97.79 135 | 99.08 107 | 98.97 59 |
|
HyFIR lowres test | | | 98.08 134 | 97.16 156 | 99.14 85 | 99.72 60 | 98.91 127 | 99.41 63 | 99.58 56 | 97.93 38 | 98.82 118 | 99.24 81 | 95.81 187 | 98.73 98 | 95.16 221 | 95.13 213 | 98.60 170 | 97.94 155 |
|
1111 | | | 94.22 222 | 92.26 220 | 96.51 224 | 99.71 63 | 98.75 141 | 99.03 111 | 99.83 11 | 95.01 172 | 93.39 242 | 99.54 62 | 60.23 248 | 89.58 238 | 97.90 138 | 97.62 155 | 97.50 204 | 96.75 190 |
|
.test1245 | | | 74.10 239 | 68.09 242 | 81.11 240 | 99.71 63 | 98.75 141 | 99.03 111 | 99.83 11 | 95.01 172 | 93.39 242 | 99.54 62 | 60.23 248 | 89.58 238 | 97.90 138 | 10.38 244 | 5.14 248 | 14.81 243 |
|
view800 | | | 96.48 187 | 94.42 200 | 98.87 118 | 99.70 65 | 99.26 58 | 99.05 109 | 99.45 92 | 94.77 179 | 97.32 206 | 88.21 231 | 83.40 227 | 98.28 127 | 98.37 98 | 99.33 30 | 99.44 58 | 97.58 167 |
|
TDRefinement | | | 99.54 10 | 99.50 9 | 99.60 18 | 99.70 65 | 99.35 47 | 99.77 11 | 99.58 56 | 99.40 4 | 99.28 57 | 99.66 44 | 99.41 51 | 99.55 19 | 99.74 4 | 99.65 4 | 99.70 23 | 99.25 26 |
|
Gipuma | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 99.22 28 | 98.86 42 | 99.64 15 | 99.70 65 | 99.24 61 | 99.17 99 | 99.63 47 | 99.52 2 | 99.89 1 | 96.54 186 | 99.14 92 | 99.93 1 | 99.42 30 | 99.15 41 | 99.52 45 | 99.04 49 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
Fast-Effi-MVS+ | | | 98.42 115 | 97.79 129 | 99.15 82 | 99.69 68 | 98.66 149 | 98.94 122 | 99.68 37 | 94.49 185 | 99.05 91 | 98.06 140 | 98.86 122 | 98.48 115 | 98.18 117 | 97.78 136 | 99.05 114 | 98.54 110 |
|
v148 | | | 98.77 87 | 98.45 80 | 99.15 82 | 99.68 69 | 98.94 123 | 99.49 55 | 99.31 131 | 97.95 37 | 98.91 109 | 99.65 48 | 99.62 35 | 99.18 67 | 97.99 131 | 97.64 154 | 98.33 184 | 97.38 174 |
|
v13 | | | 99.22 28 | 98.99 30 | 99.49 30 | 99.68 69 | 99.58 20 | 99.67 19 | 99.77 20 | 98.10 28 | 99.36 37 | 99.88 13 | 99.37 57 | 99.54 21 | 98.50 87 | 98.51 95 | 98.92 128 | 99.03 51 |
|
CP-MVS | | | 98.86 71 | 98.43 89 | 99.36 55 | 99.68 69 | 98.97 117 | 99.19 96 | 99.46 88 | 96.60 124 | 99.20 63 | 97.11 170 | 99.51 46 | 99.15 74 | 98.92 65 | 98.82 65 | 99.45 56 | 99.08 44 |
|
HSP-MVS | | | 98.50 108 | 98.05 116 | 99.03 98 | 99.67 72 | 99.33 50 | 99.51 51 | 99.26 137 | 95.28 166 | 98.51 141 | 98.19 132 | 99.74 10 | 98.29 126 | 97.69 158 | 96.70 186 | 98.96 121 | 99.41 20 |
|
ACMMP_NAP | | | 98.94 53 | 98.72 52 | 99.21 74 | 99.67 72 | 99.08 87 | 99.26 85 | 99.39 96 | 96.84 108 | 98.88 114 | 98.22 130 | 99.68 21 | 98.82 92 | 99.06 48 | 98.90 59 | 99.25 92 | 99.25 26 |
|
HFP-MVS | | | 98.97 49 | 98.70 54 | 99.29 69 | 99.67 72 | 98.98 110 | 99.13 103 | 99.53 72 | 97.76 49 | 98.90 110 | 98.07 138 | 99.50 48 | 99.14 76 | 98.64 81 | 98.78 71 | 99.37 73 | 99.18 34 |
|
v12 | | | 99.19 30 | 98.95 31 | 99.48 31 | 99.67 72 | 99.56 22 | 99.66 21 | 99.76 21 | 98.06 30 | 99.33 43 | 99.88 13 | 99.34 62 | 99.53 22 | 98.42 95 | 98.43 100 | 98.91 131 | 98.97 59 |
|
tfpn_n400 | | | 97.59 158 | 96.36 176 | 99.01 102 | 99.66 76 | 99.19 72 | 99.21 93 | 99.55 62 | 97.62 63 | 97.77 184 | 94.60 209 | 87.78 206 | 98.27 128 | 98.44 90 | 98.72 78 | 99.62 31 | 98.21 136 |
|
tfpnconf | | | 97.59 158 | 96.36 176 | 99.01 102 | 99.66 76 | 99.19 72 | 99.21 93 | 99.55 62 | 97.62 63 | 97.77 184 | 94.60 209 | 87.78 206 | 98.27 128 | 98.44 90 | 98.72 78 | 99.62 31 | 98.21 136 |
|
gg-mvs-nofinetune | | | 96.77 183 | 96.52 172 | 97.06 207 | 99.66 76 | 97.82 197 | 97.54 222 | 99.86 8 | 98.69 16 | 98.61 131 | 99.94 4 | 89.62 201 | 88.37 242 | 97.55 169 | 96.67 188 | 98.30 185 | 95.35 207 |
|
v11 | | | 99.19 30 | 98.95 31 | 99.47 32 | 99.66 76 | 99.54 28 | 99.65 22 | 99.73 27 | 98.06 30 | 99.38 36 | 99.92 6 | 99.40 54 | 99.55 19 | 98.29 108 | 98.50 96 | 98.88 136 | 98.92 68 |
|
V9 | | | 99.16 34 | 98.90 39 | 99.46 33 | 99.66 76 | 99.54 28 | 99.65 22 | 99.75 24 | 98.01 33 | 99.31 47 | 99.87 18 | 99.31 66 | 99.51 24 | 98.34 102 | 98.34 103 | 98.90 133 | 98.91 69 |
|
thres600view7 | | | 96.35 192 | 94.27 202 | 98.79 129 | 99.66 76 | 99.18 74 | 98.94 122 | 99.38 103 | 94.37 193 | 97.21 211 | 87.19 234 | 84.10 226 | 98.10 136 | 98.16 118 | 99.47 20 | 99.42 63 | 97.43 171 |
|
view600 | | | 96.39 191 | 94.30 201 | 98.82 126 | 99.65 82 | 99.16 79 | 98.98 117 | 99.36 114 | 94.46 187 | 97.39 202 | 87.28 232 | 84.16 225 | 98.16 135 | 98.16 118 | 99.48 19 | 99.40 68 | 97.42 172 |
|
CANet | | | 98.47 111 | 98.30 99 | 98.67 142 | 99.65 82 | 98.87 132 | 98.82 138 | 99.01 173 | 96.14 148 | 99.29 52 | 98.86 107 | 99.01 113 | 96.54 183 | 98.36 100 | 98.08 114 | 98.72 161 | 98.80 85 |
|
v1141 | | | 98.87 66 | 98.45 80 | 99.36 55 | 99.65 82 | 99.04 98 | 99.56 39 | 99.38 103 | 97.83 45 | 99.29 52 | 99.86 22 | 99.16 86 | 99.40 42 | 97.68 159 | 97.78 136 | 98.86 141 | 97.82 158 |
|
divwei89l23v2f112 | | | 98.87 66 | 98.45 80 | 99.36 55 | 99.65 82 | 99.04 98 | 99.56 39 | 99.38 103 | 97.83 45 | 99.29 52 | 99.86 22 | 99.15 90 | 99.40 42 | 97.68 159 | 97.78 136 | 98.86 141 | 97.82 158 |
|
V14 | | | 99.13 37 | 98.85 44 | 99.45 34 | 99.65 82 | 99.52 30 | 99.63 28 | 99.74 26 | 97.97 35 | 99.30 50 | 99.87 18 | 99.27 70 | 99.49 28 | 98.23 114 | 98.24 106 | 98.88 136 | 98.83 76 |
|
v2v482 | | | 98.85 74 | 98.40 91 | 99.38 51 | 99.65 82 | 98.98 110 | 99.55 42 | 99.39 96 | 97.92 39 | 99.35 40 | 99.85 27 | 99.14 92 | 99.39 52 | 97.50 171 | 97.78 136 | 98.98 120 | 97.60 165 |
|
v1 | | | 98.87 66 | 98.45 80 | 99.36 55 | 99.65 82 | 99.04 98 | 99.55 42 | 99.38 103 | 97.83 45 | 99.30 50 | 99.86 22 | 99.17 83 | 99.40 42 | 97.68 159 | 97.77 143 | 98.86 141 | 97.82 158 |
|
Vis-MVSNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 99.25 25 | 99.32 15 | 99.17 80 | 99.65 82 | 99.55 26 | 99.63 28 | 99.33 124 | 98.16 27 | 99.29 52 | 99.65 48 | 99.77 7 | 97.56 158 | 99.44 29 | 99.14 42 | 99.58 37 | 99.51 11 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
tfpn1000 | | | 97.10 176 | 95.97 185 | 98.41 161 | 99.64 90 | 99.30 55 | 98.89 131 | 99.49 83 | 96.49 133 | 95.97 225 | 95.31 202 | 85.62 220 | 96.92 178 | 97.86 142 | 99.13 44 | 99.53 44 | 98.11 145 |
|
v15 | | | 99.09 40 | 98.79 46 | 99.43 38 | 99.64 90 | 99.50 31 | 99.61 32 | 99.73 27 | 97.92 39 | 99.28 57 | 99.86 22 | 99.24 72 | 99.47 30 | 98.12 125 | 98.14 111 | 98.87 138 | 98.76 88 |
|
APD-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 98.47 111 | 97.97 121 | 99.05 95 | 99.64 90 | 98.91 127 | 98.94 122 | 99.45 92 | 94.40 191 | 98.77 121 | 97.26 164 | 99.41 51 | 98.21 133 | 98.67 79 | 98.57 91 | 99.31 84 | 98.57 106 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
EG-PatchMatch MVS | | | 99.01 45 | 98.77 49 | 99.28 73 | 99.64 90 | 98.90 130 | 98.81 139 | 99.27 136 | 96.55 130 | 99.71 5 | 99.31 76 | 99.66 26 | 99.17 70 | 99.28 37 | 99.11 45 | 99.10 101 | 98.57 106 |
|
LS3D | | | 98.79 85 | 98.52 72 | 99.12 86 | 99.64 90 | 99.09 86 | 99.24 88 | 99.46 88 | 97.75 52 | 98.93 107 | 97.47 158 | 98.23 149 | 97.98 142 | 99.36 31 | 99.30 32 | 99.46 55 | 98.42 118 |
|
tfpnview11 | | | 97.49 163 | 96.22 180 | 98.97 109 | 99.63 95 | 99.24 61 | 99.12 105 | 99.54 68 | 96.76 116 | 97.77 184 | 94.60 209 | 87.78 206 | 98.25 131 | 97.93 135 | 99.14 42 | 99.52 45 | 98.08 148 |
|
IterMVS-LS | | | 98.23 125 | 97.66 134 | 98.90 114 | 99.63 95 | 99.38 45 | 99.07 108 | 99.48 84 | 97.75 52 | 98.81 119 | 99.37 74 | 94.57 192 | 97.88 146 | 96.54 199 | 97.04 179 | 98.53 175 | 98.97 59 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Effi-MVS+ | | | 98.11 132 | 97.29 147 | 99.06 92 | 99.62 97 | 98.55 158 | 98.16 195 | 99.80 14 | 94.64 181 | 99.15 74 | 96.59 183 | 97.43 169 | 98.44 116 | 97.46 173 | 97.90 125 | 99.17 98 | 98.45 115 |
|
v1144 | | | 98.94 53 | 98.53 70 | 99.42 40 | 99.62 97 | 99.03 103 | 99.58 36 | 99.36 114 | 97.99 34 | 99.49 29 | 99.91 11 | 99.20 80 | 99.51 24 | 97.61 165 | 97.85 133 | 98.95 123 | 98.10 146 |
|
3Dnovator+ | | 97.85 5 | 98.61 98 | 98.14 109 | 99.15 82 | 99.62 97 | 98.37 171 | 99.10 107 | 99.51 76 | 98.04 32 | 98.98 98 | 96.07 195 | 98.75 129 | 98.55 110 | 98.51 86 | 98.40 101 | 99.17 98 | 98.82 79 |
|
v1192 | | | 98.91 58 | 98.48 75 | 99.41 41 | 99.61 100 | 99.03 103 | 99.64 25 | 99.25 140 | 97.91 41 | 99.58 19 | 99.92 6 | 99.07 109 | 99.45 33 | 97.55 169 | 97.68 150 | 98.93 125 | 98.23 133 |
|
v1240 | | | 98.86 71 | 98.41 90 | 99.38 51 | 99.59 101 | 99.05 93 | 99.65 22 | 99.14 159 | 97.68 60 | 99.66 14 | 99.93 5 | 98.72 130 | 99.45 33 | 97.38 180 | 97.72 148 | 98.79 154 | 98.35 122 |
|
3Dnovator | | 98.16 3 | 98.65 93 | 98.35 95 | 99.00 104 | 99.59 101 | 98.70 145 | 98.90 130 | 99.36 114 | 97.97 35 | 99.09 86 | 96.55 185 | 99.09 105 | 97.97 143 | 98.70 78 | 98.65 85 | 99.12 100 | 98.81 81 |
|
v1921920 | | | 98.89 62 | 98.46 76 | 99.39 46 | 99.58 103 | 99.04 98 | 99.64 25 | 99.17 154 | 97.91 41 | 99.64 16 | 99.92 6 | 98.99 116 | 99.44 36 | 97.44 176 | 97.57 160 | 98.84 145 | 98.35 122 |
|
thres400 | | | 96.22 197 | 94.08 205 | 98.72 135 | 99.58 103 | 99.05 93 | 98.83 135 | 99.22 143 | 94.01 201 | 97.40 200 | 86.34 240 | 84.91 223 | 97.93 144 | 97.85 145 | 99.08 46 | 99.37 73 | 97.28 177 |
|
CDPH-MVS | | | 97.99 135 | 97.23 151 | 98.87 118 | 99.58 103 | 98.29 173 | 98.83 135 | 99.20 149 | 93.76 204 | 98.11 170 | 96.11 193 | 99.16 86 | 98.23 132 | 97.80 150 | 97.22 175 | 99.29 87 | 98.28 128 |
|
UGNet | | | 98.52 107 | 99.00 29 | 97.96 188 | 99.58 103 | 99.26 58 | 99.27 84 | 99.40 94 | 98.07 29 | 98.28 160 | 98.76 110 | 99.71 16 | 92.24 232 | 98.94 61 | 98.85 62 | 99.00 119 | 99.43 19 |
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 |
QAPM | | | 98.62 97 | 98.40 91 | 98.89 116 | 99.57 107 | 98.80 135 | 98.63 156 | 99.35 119 | 96.82 111 | 98.60 132 | 98.85 109 | 99.08 107 | 98.09 138 | 98.31 106 | 98.21 107 | 99.08 107 | 98.72 92 |
|
v1.0 | | | 90.99 237 | 84.28 240 | 98.83 123 | 99.56 108 | 99.21 67 | 98.66 154 | 99.47 85 | 95.22 167 | 98.35 154 | 98.48 121 | 99.67 25 | 97.84 150 | 98.80 74 | 98.57 91 | 99.10 101 | 0.00 246 |
|
v144192 | | | 98.88 65 | 98.46 76 | 99.37 53 | 99.56 108 | 99.03 103 | 99.61 32 | 99.26 137 | 97.79 48 | 99.58 19 | 99.88 13 | 99.11 100 | 99.43 38 | 97.38 180 | 97.61 156 | 98.80 152 | 98.43 117 |
|
new-patchmatchnet | | | 97.26 169 | 96.12 182 | 98.58 152 | 99.55 110 | 98.63 151 | 99.14 102 | 97.04 231 | 98.80 15 | 99.19 65 | 99.92 6 | 99.19 81 | 98.92 86 | 95.51 215 | 87.04 231 | 97.66 200 | 93.73 219 |
|
DI_MVS_plusplus_trai | | | 97.57 161 | 96.55 171 | 98.77 130 | 99.55 110 | 98.76 139 | 99.22 91 | 99.00 174 | 97.08 103 | 97.95 180 | 97.78 149 | 91.35 200 | 98.02 141 | 96.20 203 | 96.81 184 | 98.87 138 | 97.87 157 |
|
CHOSEN 1792x2688 | | | 98.31 120 | 98.02 118 | 98.66 144 | 99.55 110 | 98.57 157 | 99.38 68 | 99.25 140 | 98.42 21 | 98.48 147 | 99.58 56 | 99.85 5 | 98.31 124 | 95.75 211 | 95.71 205 | 96.96 211 | 98.27 130 |
|
v1neww | | | 98.84 75 | 98.45 80 | 99.29 69 | 99.54 113 | 98.98 110 | 99.54 46 | 99.37 111 | 97.48 74 | 99.10 82 | 99.80 35 | 99.12 96 | 99.40 42 | 97.85 145 | 97.89 127 | 98.81 147 | 98.04 149 |
|
v7new | | | 98.84 75 | 98.45 80 | 99.29 69 | 99.54 113 | 98.98 110 | 99.54 46 | 99.37 111 | 97.48 74 | 99.10 82 | 99.80 35 | 99.12 96 | 99.40 42 | 97.85 145 | 97.89 127 | 98.81 147 | 98.04 149 |
|
v17 | | | 98.96 51 | 98.63 60 | 99.35 60 | 99.54 113 | 99.41 40 | 99.55 42 | 99.70 34 | 97.40 82 | 99.10 82 | 99.79 37 | 99.10 101 | 99.40 42 | 97.96 132 | 97.99 119 | 98.80 152 | 98.77 87 |
|
v8 | | | 98.94 53 | 98.60 62 | 99.35 60 | 99.54 113 | 99.39 43 | 99.55 42 | 99.67 40 | 97.48 74 | 99.13 77 | 99.81 32 | 99.10 101 | 99.39 52 | 97.86 142 | 97.89 127 | 98.81 147 | 98.66 98 |
|
v6 | | | 98.84 75 | 98.46 76 | 99.30 66 | 99.54 113 | 98.98 110 | 99.54 46 | 99.37 111 | 97.49 73 | 99.11 81 | 99.81 32 | 99.13 95 | 99.40 42 | 97.86 142 | 97.89 127 | 98.81 147 | 98.04 149 |
|
CPTT-MVS | | | 98.28 121 | 97.51 141 | 99.16 81 | 99.54 113 | 98.78 138 | 98.96 120 | 99.36 114 | 96.30 142 | 98.89 113 | 93.10 220 | 99.30 67 | 99.20 65 | 98.35 101 | 97.96 124 | 99.03 117 | 98.82 79 |
|
FMVSNet1 | | | 98.90 60 | 99.10 26 | 98.67 142 | 99.54 113 | 99.48 33 | 99.22 91 | 99.66 41 | 98.39 24 | 97.50 196 | 99.66 44 | 99.04 110 | 96.58 182 | 99.05 49 | 99.03 52 | 99.52 45 | 99.08 44 |
|
HPM-MVS++ | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 98.56 105 | 98.08 114 | 99.11 88 | 99.53 120 | 98.61 153 | 99.02 115 | 99.32 129 | 96.29 143 | 99.06 89 | 97.23 165 | 99.50 48 | 98.77 95 | 98.15 121 | 97.90 125 | 98.96 121 | 98.90 71 |
|
v16 | | | 98.95 52 | 98.62 61 | 99.34 62 | 99.53 120 | 99.41 40 | 99.54 46 | 99.70 34 | 97.34 87 | 99.07 88 | 99.76 40 | 99.10 101 | 99.40 42 | 97.96 132 | 98.00 118 | 98.79 154 | 98.76 88 |
|
v7 | | | 98.91 58 | 98.53 70 | 99.36 55 | 99.53 120 | 98.99 109 | 99.57 37 | 99.36 114 | 97.58 69 | 99.32 45 | 99.88 13 | 99.23 74 | 99.50 26 | 97.77 153 | 97.98 121 | 98.91 131 | 98.26 131 |
|
MCST-MVS | | | 98.25 124 | 97.57 139 | 99.06 92 | 99.53 120 | 98.24 179 | 98.63 156 | 99.17 154 | 95.88 155 | 98.58 134 | 96.11 193 | 99.09 105 | 99.18 67 | 97.58 168 | 97.31 171 | 99.25 92 | 98.75 90 |
|
CLD-MVS | | | 98.48 110 | 98.15 108 | 98.86 121 | 99.53 120 | 98.35 172 | 98.55 167 | 97.83 223 | 96.02 152 | 98.97 99 | 99.08 93 | 99.75 8 | 99.03 83 | 98.10 127 | 97.33 170 | 99.28 88 | 98.44 116 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
OPM-MVS | | | 98.84 75 | 98.59 63 | 99.12 86 | 99.52 125 | 98.50 164 | 99.13 103 | 99.22 143 | 97.76 49 | 98.76 122 | 98.70 111 | 99.61 36 | 98.90 87 | 98.67 79 | 98.37 102 | 99.19 97 | 98.57 106 |
|
v10 | | | 99.01 45 | 98.66 59 | 99.41 41 | 99.52 125 | 99.39 43 | 99.57 37 | 99.66 41 | 97.59 67 | 99.32 45 | 99.88 13 | 99.23 74 | 99.50 26 | 97.77 153 | 97.98 121 | 98.92 128 | 98.78 86 |
|
V42 | | | 98.81 84 | 98.49 74 | 99.18 79 | 99.52 125 | 98.92 125 | 99.50 54 | 99.29 133 | 97.43 80 | 98.97 99 | 99.81 32 | 99.00 115 | 99.30 58 | 97.93 135 | 98.01 117 | 98.51 178 | 98.34 126 |
|
Anonymous20231206 | | | 98.50 108 | 98.03 117 | 99.05 95 | 99.50 128 | 99.01 107 | 99.15 101 | 99.26 137 | 96.38 138 | 99.12 79 | 99.50 65 | 99.12 96 | 98.60 105 | 97.68 159 | 97.24 174 | 98.66 164 | 97.30 176 |
|
v18 | | | 98.89 62 | 98.54 68 | 99.30 66 | 99.50 128 | 99.37 46 | 99.51 51 | 99.68 37 | 97.25 95 | 99.00 97 | 99.76 40 | 99.04 110 | 99.36 54 | 97.81 149 | 97.86 132 | 98.77 157 | 98.68 97 |
|
OpenMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 97.26 9 | 97.88 142 | 97.17 154 | 98.70 138 | 99.50 128 | 98.55 158 | 98.34 184 | 99.11 164 | 93.92 202 | 98.90 110 | 95.04 205 | 98.23 149 | 97.38 167 | 98.11 126 | 98.12 112 | 98.95 123 | 98.23 133 |
|
tttt0517 | | | 97.18 173 | 95.92 189 | 98.65 147 | 99.49 131 | 98.92 125 | 98.29 187 | 99.20 149 | 94.37 193 | 98.17 165 | 97.37 162 | 84.72 224 | 97.68 153 | 98.55 84 | 98.56 93 | 99.10 101 | 98.95 65 |
|
pmmvs-eth3d | | | 98.68 90 | 98.14 109 | 99.29 69 | 99.49 131 | 98.45 167 | 99.45 61 | 99.38 103 | 97.21 97 | 99.50 28 | 99.65 48 | 99.21 78 | 99.16 72 | 97.11 188 | 97.56 161 | 98.79 154 | 97.82 158 |
|
casdiffmvs1 | | | 98.43 114 | 97.95 123 | 98.98 108 | 99.49 131 | 99.08 87 | 98.80 140 | 99.56 59 | 97.38 84 | 99.14 76 | 98.62 115 | 98.51 142 | 97.85 149 | 96.20 203 | 96.80 185 | 99.04 115 | 99.08 44 |
|
PHI-MVS | | | 98.57 102 | 98.20 106 | 99.00 104 | 99.48 134 | 98.91 127 | 98.68 147 | 99.17 154 | 94.97 175 | 99.27 60 | 98.33 125 | 99.33 63 | 98.05 140 | 98.82 72 | 98.62 86 | 99.34 79 | 98.38 120 |
|
thisisatest0530 | | | 97.20 172 | 95.95 187 | 98.66 144 | 99.46 135 | 98.84 133 | 98.29 187 | 99.20 149 | 94.51 183 | 98.25 162 | 97.42 159 | 85.03 222 | 97.68 153 | 98.43 93 | 98.56 93 | 99.08 107 | 98.89 73 |
|
pmmvs5 | | | 98.37 117 | 97.81 128 | 99.03 98 | 99.46 135 | 98.97 117 | 99.03 111 | 98.96 177 | 95.85 156 | 99.05 91 | 99.45 68 | 98.66 137 | 98.79 94 | 96.02 208 | 97.52 162 | 98.87 138 | 98.21 136 |
|
ambc | | | | 97.89 126 | | 99.45 137 | 97.88 195 | 97.78 209 | | 97.27 91 | 99.80 2 | 98.99 103 | 98.48 143 | 98.55 110 | 97.80 150 | 96.68 187 | 98.54 174 | 98.10 146 |
|
canonicalmvs | | | 98.34 119 | 97.92 125 | 98.83 123 | 99.45 137 | 99.21 67 | 98.37 181 | 99.53 72 | 97.06 104 | 97.74 188 | 96.95 177 | 95.05 190 | 98.36 121 | 98.77 76 | 98.85 62 | 99.51 50 | 99.53 8 |
|
IterMVS | | | 97.40 167 | 96.67 166 | 98.25 170 | 99.45 137 | 98.66 149 | 98.87 133 | 98.73 188 | 96.40 137 | 98.94 106 | 99.56 58 | 95.26 189 | 97.58 157 | 95.38 216 | 94.70 217 | 95.90 220 | 96.72 191 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
thres200 | | | 96.23 196 | 94.13 203 | 98.69 140 | 99.44 140 | 99.18 74 | 98.58 165 | 99.38 103 | 93.52 207 | 97.35 204 | 86.33 241 | 85.83 219 | 97.93 144 | 98.16 118 | 98.78 71 | 99.42 63 | 97.10 186 |
|
PCF-MVS | | 95.58 16 | 97.60 156 | 96.67 166 | 98.69 140 | 99.44 140 | 98.23 180 | 98.37 181 | 98.81 184 | 93.01 214 | 98.22 163 | 97.97 144 | 99.59 39 | 98.20 134 | 95.72 213 | 95.08 214 | 99.08 107 | 97.09 188 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
train_agg | | | 97.99 135 | 97.26 148 | 98.83 123 | 99.43 142 | 98.22 181 | 98.91 127 | 99.07 167 | 94.43 189 | 97.96 179 | 96.42 188 | 99.30 67 | 98.81 93 | 97.39 178 | 96.62 190 | 98.82 146 | 98.47 112 |
|
N_pmnet | | | 96.68 185 | 95.70 194 | 97.84 191 | 99.42 143 | 98.00 190 | 99.35 73 | 98.21 211 | 98.40 23 | 98.13 169 | 99.42 71 | 99.30 67 | 97.44 166 | 94.00 230 | 88.79 228 | 94.47 226 | 91.96 227 |
|
USDC | | | 98.26 123 | 97.57 139 | 99.06 92 | 99.42 143 | 97.98 193 | 98.83 135 | 98.85 181 | 97.57 70 | 99.59 18 | 99.15 89 | 98.59 139 | 98.99 84 | 97.42 177 | 96.08 203 | 98.69 163 | 96.23 198 |
|
casdiffmvs | | | 97.89 141 | 97.17 154 | 98.73 133 | 99.41 145 | 98.79 136 | 98.49 170 | 99.52 75 | 95.60 161 | 98.88 114 | 98.09 137 | 97.63 167 | 97.33 170 | 95.28 218 | 96.20 198 | 98.77 157 | 98.60 102 |
|
NCCC | | | 97.84 144 | 96.96 162 | 98.87 118 | 99.39 146 | 98.27 176 | 98.46 173 | 99.02 172 | 96.78 114 | 98.73 127 | 91.12 226 | 98.91 118 | 98.57 108 | 97.83 148 | 97.49 164 | 99.04 115 | 98.33 127 |
|
tfpn111 | | | 96.48 187 | 94.67 199 | 98.59 150 | 99.37 147 | 99.18 74 | 98.68 147 | 99.39 96 | 92.02 223 | 97.21 211 | 90.63 227 | 86.34 214 | 97.45 161 | 98.15 121 | 99.08 46 | 99.43 60 | 97.28 177 |
|
conf200view11 | | | 96.16 200 | 94.08 205 | 98.59 150 | 99.37 147 | 99.18 74 | 98.68 147 | 99.39 96 | 92.02 223 | 97.21 211 | 86.53 237 | 86.34 214 | 97.45 161 | 98.15 121 | 99.08 46 | 99.43 60 | 97.28 177 |
|
thres100view900 | | | 95.74 204 | 93.66 213 | 98.17 177 | 99.37 147 | 98.59 154 | 98.10 196 | 98.33 207 | 92.02 223 | 97.30 207 | 86.53 237 | 86.34 214 | 96.69 180 | 96.77 194 | 98.47 98 | 99.24 94 | 96.89 189 |
|
tfpn200view9 | | | 96.17 198 | 94.08 205 | 98.60 149 | 99.37 147 | 99.18 74 | 98.68 147 | 99.39 96 | 92.02 223 | 97.30 207 | 86.53 237 | 86.34 214 | 97.45 161 | 98.15 121 | 99.08 46 | 99.43 60 | 97.28 177 |
|
testmv | | | 97.48 165 | 96.83 165 | 98.24 174 | 99.37 147 | 97.79 199 | 98.59 163 | 99.07 167 | 92.40 217 | 97.59 191 | 99.24 81 | 98.11 153 | 97.66 155 | 97.64 163 | 97.11 177 | 97.17 207 | 95.54 206 |
|
test1235678 | | | 97.49 163 | 96.84 164 | 98.24 174 | 99.37 147 | 97.79 199 | 98.59 163 | 99.07 167 | 92.41 216 | 97.59 191 | 99.24 81 | 98.15 152 | 97.66 155 | 97.64 163 | 97.12 176 | 97.17 207 | 95.55 205 |
|
RPSCF | | | 98.84 75 | 98.81 45 | 98.89 116 | 99.37 147 | 98.95 119 | 98.51 169 | 98.85 181 | 97.73 56 | 98.33 156 | 98.97 104 | 99.14 92 | 98.95 85 | 99.18 39 | 98.68 81 | 99.31 84 | 98.99 57 |
|
MDA-MVSNet-bldmvs | | | 97.75 146 | 97.26 148 | 98.33 166 | 99.35 154 | 98.45 167 | 99.32 78 | 97.21 229 | 97.90 43 | 99.05 91 | 99.01 101 | 96.86 178 | 99.08 79 | 99.36 31 | 92.97 223 | 95.97 219 | 96.25 197 |
|
CNVR-MVS | | | 98.22 127 | 97.76 130 | 98.76 131 | 99.33 155 | 98.26 177 | 98.48 171 | 98.88 180 | 96.22 144 | 98.47 149 | 95.79 198 | 99.33 63 | 98.35 122 | 98.37 98 | 97.99 119 | 99.03 117 | 98.38 120 |
|
DeepC-MVS_fast | | 97.38 8 | 98.65 93 | 98.34 96 | 99.02 101 | 99.33 155 | 98.29 173 | 98.99 116 | 98.71 190 | 97.40 82 | 99.31 47 | 98.20 131 | 99.40 54 | 98.54 112 | 98.33 105 | 98.18 110 | 99.23 95 | 98.58 104 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MSDG | | | 98.20 128 | 97.88 127 | 98.56 154 | 99.33 155 | 97.74 202 | 98.27 190 | 98.10 214 | 97.20 99 | 98.06 172 | 98.59 117 | 99.16 86 | 98.76 96 | 98.39 97 | 97.71 149 | 98.86 141 | 96.38 195 |
|
thresconf0.02 | | | 95.49 206 | 92.74 218 | 98.70 138 | 99.32 158 | 98.70 145 | 98.87 133 | 99.21 145 | 95.95 153 | 97.57 193 | 90.63 227 | 73.55 241 | 97.86 148 | 96.09 207 | 97.03 180 | 99.40 68 | 97.22 182 |
|
EPNet | | | 96.44 190 | 96.08 183 | 96.86 214 | 99.32 158 | 97.15 212 | 97.69 216 | 99.32 129 | 93.67 205 | 98.11 170 | 95.64 200 | 93.44 195 | 89.07 240 | 96.86 192 | 96.83 183 | 97.67 199 | 98.97 59 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MVS_111021_HR | | | 98.58 101 | 98.26 102 | 98.96 110 | 99.32 158 | 98.81 134 | 98.48 171 | 98.99 175 | 96.81 113 | 99.16 71 | 98.07 138 | 99.23 74 | 98.89 89 | 98.43 93 | 98.27 105 | 98.90 133 | 98.24 132 |
|
PVSNet_BlendedMVS | | | 97.93 139 | 97.66 134 | 98.25 170 | 99.30 161 | 98.67 147 | 98.31 185 | 97.95 218 | 94.30 195 | 98.75 123 | 97.63 152 | 98.76 127 | 96.30 190 | 98.29 108 | 97.78 136 | 98.93 125 | 98.18 140 |
|
PVSNet_Blended | | | 97.93 139 | 97.66 134 | 98.25 170 | 99.30 161 | 98.67 147 | 98.31 185 | 97.95 218 | 94.30 195 | 98.75 123 | 97.63 152 | 98.76 127 | 96.30 190 | 98.29 108 | 97.78 136 | 98.93 125 | 98.18 140 |
|
HQP-MVS | | | 97.58 160 | 96.65 169 | 98.66 144 | 99.30 161 | 97.99 191 | 97.88 207 | 98.65 193 | 94.58 182 | 98.66 129 | 94.65 208 | 99.15 90 | 98.59 106 | 96.10 206 | 95.59 206 | 98.90 133 | 98.50 111 |
|
our_test_3 | | | | | | 99.29 164 | 97.72 203 | 98.98 117 | | | | | | | | | | |
|
conf0.01 | | | 94.53 218 | 91.09 227 | 98.53 157 | 99.29 164 | 99.05 93 | 98.68 147 | 99.35 119 | 92.02 223 | 97.04 215 | 84.45 243 | 68.52 243 | 97.45 161 | 97.79 152 | 99.08 46 | 99.41 66 | 96.70 192 |
|
TinyColmap | | | 98.27 122 | 97.62 138 | 99.03 98 | 99.29 164 | 97.79 199 | 98.92 125 | 98.95 178 | 97.48 74 | 99.52 26 | 98.65 114 | 97.86 162 | 98.90 87 | 98.34 102 | 97.27 172 | 98.64 167 | 95.97 201 |
|
TSAR-MVS + GP. | | | 98.54 106 | 98.29 101 | 98.82 126 | 99.28 167 | 98.59 154 | 97.73 212 | 99.24 142 | 95.93 154 | 98.59 133 | 99.07 94 | 99.17 83 | 98.86 90 | 98.44 90 | 98.10 113 | 99.26 91 | 98.72 92 |
|
MVS_Test | | | 97.69 151 | 97.15 157 | 98.33 166 | 99.27 168 | 98.43 169 | 98.25 191 | 99.29 133 | 95.00 174 | 97.39 202 | 98.86 107 | 98.00 158 | 97.14 174 | 95.38 216 | 96.22 196 | 98.62 168 | 98.15 144 |
|
conf0.002 | | | 93.97 223 | 90.06 231 | 98.52 158 | 99.26 169 | 99.02 106 | 98.68 147 | 99.33 124 | 92.02 223 | 97.01 217 | 83.82 244 | 63.41 246 | 97.45 161 | 97.73 156 | 97.98 121 | 99.40 68 | 96.47 194 |
|
PM-MVS | | | 98.57 102 | 98.24 104 | 98.95 111 | 99.26 169 | 98.59 154 | 99.03 111 | 98.74 187 | 96.84 108 | 99.44 33 | 99.13 90 | 98.31 148 | 98.75 97 | 98.03 129 | 98.21 107 | 98.48 179 | 98.58 104 |
|
PMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 92.51 17 | 98.66 92 | 98.86 42 | 98.43 160 | 99.26 169 | 98.98 110 | 98.60 162 | 98.59 197 | 97.73 56 | 99.45 32 | 99.38 73 | 98.54 141 | 95.24 203 | 99.62 13 | 99.61 10 | 99.42 63 | 98.17 142 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
Effi-MVS+-dtu | | | 97.78 145 | 97.37 145 | 98.26 169 | 99.25 172 | 98.50 164 | 97.89 206 | 99.19 152 | 94.51 183 | 98.16 167 | 95.93 196 | 98.80 126 | 95.97 194 | 98.27 113 | 97.38 167 | 99.10 101 | 98.23 133 |
|
AdaColmap | ![Method available as binary. binary](img/icon_binary.png) | | 97.57 161 | 96.57 170 | 98.74 132 | 99.25 172 | 98.01 189 | 98.36 183 | 98.98 176 | 94.44 188 | 98.47 149 | 92.44 224 | 97.91 161 | 98.62 104 | 98.19 116 | 97.74 145 | 98.73 160 | 97.28 177 |
|
DELS-MVS | | | 98.63 96 | 98.70 54 | 98.55 155 | 99.24 174 | 99.04 98 | 98.96 120 | 98.52 200 | 96.83 110 | 98.38 152 | 99.58 56 | 99.68 21 | 97.06 177 | 98.74 77 | 98.44 99 | 99.10 101 | 98.59 103 |
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 |
TSAR-MVS + MP. | | | 99.02 44 | 98.95 31 | 99.11 88 | 99.23 175 | 98.79 136 | 99.51 51 | 98.73 188 | 97.50 72 | 98.56 135 | 99.03 99 | 99.59 39 | 99.16 72 | 99.29 35 | 99.17 40 | 99.50 51 | 99.24 29 |
|
diffmvs1 | | | 98.09 133 | 97.95 123 | 98.25 170 | 99.23 175 | 98.55 158 | 98.39 179 | 99.18 153 | 97.44 78 | 97.04 215 | 98.58 118 | 98.96 117 | 97.32 171 | 96.66 197 | 96.63 189 | 98.34 183 | 98.83 76 |
|
pmmvs4 | | | 97.87 143 | 97.02 160 | 98.86 121 | 99.20 177 | 97.68 205 | 98.89 131 | 99.03 171 | 96.57 128 | 99.12 79 | 99.03 99 | 97.26 173 | 98.42 118 | 95.16 221 | 96.34 194 | 98.53 175 | 97.10 186 |
|
test-LLR | | | 94.79 213 | 93.71 211 | 96.06 228 | 99.20 177 | 96.16 222 | 96.31 237 | 98.50 201 | 89.98 240 | 94.08 238 | 97.01 172 | 86.43 212 | 92.20 233 | 96.76 195 | 95.31 209 | 96.05 217 | 94.31 215 |
|
test0.0.03 1 | | | 95.81 203 | 95.77 193 | 95.85 232 | 99.20 177 | 98.15 184 | 97.49 226 | 98.50 201 | 92.24 218 | 92.74 245 | 96.82 179 | 92.70 197 | 88.60 241 | 97.31 184 | 97.01 182 | 98.57 173 | 96.19 199 |
|
CANet_DTU | | | 97.65 154 | 97.50 142 | 97.82 193 | 99.19 180 | 98.08 186 | 98.41 176 | 98.67 192 | 94.40 191 | 99.16 71 | 98.32 126 | 98.69 132 | 93.96 221 | 97.87 141 | 97.61 156 | 97.51 203 | 97.56 168 |
|
EPNet_dtu | | | 96.31 193 | 95.96 186 | 96.72 218 | 99.18 181 | 95.39 236 | 97.03 233 | 99.13 163 | 93.02 213 | 99.35 40 | 97.23 165 | 97.07 175 | 90.70 237 | 95.74 212 | 95.08 214 | 94.94 223 | 98.16 143 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
tfpn_ndepth | | | 96.69 184 | 95.49 196 | 98.09 182 | 99.17 182 | 99.13 82 | 98.61 161 | 99.38 103 | 94.90 178 | 95.85 227 | 92.85 222 | 88.19 205 | 96.07 193 | 97.28 185 | 98.67 82 | 99.49 53 | 97.44 170 |
|
TSAR-MVS + ACMM | | | 98.64 95 | 98.58 65 | 98.72 135 | 99.17 182 | 98.63 151 | 98.69 146 | 99.10 166 | 97.69 59 | 98.30 158 | 99.12 92 | 99.38 56 | 98.70 99 | 98.45 89 | 97.51 163 | 98.35 182 | 99.25 26 |
|
GA-MVS | | | 96.84 181 | 95.86 191 | 97.98 186 | 99.16 184 | 98.29 173 | 97.91 204 | 98.64 195 | 95.14 169 | 97.71 189 | 98.04 142 | 88.90 203 | 96.50 184 | 96.41 200 | 96.61 191 | 97.97 197 | 97.60 165 |
|
diffmvs | | | 97.72 150 | 97.44 143 | 98.04 184 | 99.15 185 | 98.43 169 | 97.93 202 | 99.21 145 | 96.18 146 | 97.46 197 | 97.96 145 | 98.71 131 | 96.41 186 | 96.34 201 | 95.84 204 | 98.10 193 | 98.62 100 |
|
SD-MVS | | | 98.73 88 | 98.54 68 | 98.95 111 | 99.14 186 | 98.76 139 | 98.46 173 | 99.14 159 | 97.71 58 | 98.56 135 | 98.06 140 | 99.61 36 | 98.85 91 | 98.56 83 | 97.74 145 | 99.54 40 | 99.32 23 |
|
EU-MVSNet | | | 98.68 90 | 98.94 35 | 98.37 165 | 99.14 186 | 98.74 143 | 99.64 25 | 98.20 213 | 98.21 25 | 99.17 68 | 99.66 44 | 99.18 82 | 99.08 79 | 99.11 42 | 98.86 60 | 95.00 222 | 98.83 76 |
|
testus | | | 96.13 201 | 95.13 197 | 97.28 202 | 99.13 188 | 97.00 213 | 96.84 235 | 97.89 222 | 90.48 239 | 97.40 200 | 93.60 217 | 96.47 181 | 95.39 201 | 96.21 202 | 96.19 199 | 97.05 209 | 95.99 200 |
|
MDTV_nov1_ep13_2view | | | 97.12 174 | 96.19 181 | 98.22 176 | 99.13 188 | 98.05 187 | 99.24 88 | 99.47 85 | 97.61 65 | 99.15 74 | 99.59 54 | 99.01 113 | 98.40 119 | 94.87 223 | 90.14 226 | 93.91 227 | 94.04 218 |
|
PLC | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 95.63 15 | 97.73 149 | 97.01 161 | 98.57 153 | 99.10 190 | 97.80 198 | 97.72 213 | 98.77 186 | 96.34 139 | 98.38 152 | 93.46 219 | 98.06 155 | 98.66 102 | 97.90 138 | 97.65 153 | 98.77 157 | 97.90 156 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
CHOSEN 280x420 | | | 96.80 182 | 96.30 178 | 97.39 199 | 99.09 191 | 96.52 216 | 98.76 143 | 99.29 133 | 93.88 203 | 97.65 190 | 98.34 124 | 93.66 194 | 96.29 192 | 98.28 111 | 97.73 147 | 93.27 231 | 95.70 203 |
|
IB-MVS | | 95.85 14 | 95.87 202 | 94.88 198 | 97.02 210 | 99.09 191 | 98.25 178 | 97.16 229 | 97.38 227 | 91.97 230 | 97.77 184 | 83.61 245 | 97.29 172 | 92.03 235 | 97.16 187 | 97.66 151 | 98.66 164 | 98.20 139 |
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 |
CDS-MVSNet | | | 97.75 146 | 97.68 133 | 97.83 192 | 99.08 193 | 98.20 182 | 98.68 147 | 98.61 196 | 95.63 160 | 97.80 183 | 99.24 81 | 96.93 177 | 94.09 219 | 97.96 132 | 97.82 134 | 98.71 162 | 97.99 152 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
MVS_111021_LR | | | 98.39 116 | 98.11 111 | 98.71 137 | 99.08 193 | 98.54 162 | 98.23 193 | 98.56 199 | 96.57 128 | 99.13 77 | 98.41 122 | 98.86 122 | 98.65 103 | 98.23 114 | 97.87 131 | 98.65 166 | 98.28 128 |
|
TAMVS | | | 96.95 179 | 96.94 163 | 96.97 213 | 99.07 195 | 97.67 206 | 97.98 201 | 97.12 230 | 95.04 171 | 95.41 232 | 99.27 79 | 95.57 188 | 94.09 219 | 97.32 182 | 97.11 177 | 98.16 192 | 96.59 193 |
|
ESAPD | | | 98.84 75 | 98.69 56 | 99.00 104 | 99.05 196 | 99.26 58 | 99.19 96 | 99.35 119 | 95.85 156 | 98.74 125 | 99.27 79 | 99.66 26 | 98.30 125 | 98.90 67 | 98.93 57 | 99.37 73 | 99.00 56 |
|
abl_6 | | | | | 98.38 164 | 99.03 197 | 98.04 188 | 98.08 198 | 98.65 193 | 93.23 210 | 98.56 135 | 94.58 212 | 98.57 140 | 97.17 172 | | | 98.81 147 | 97.42 172 |
|
MIMVSNet | | | 97.24 170 | 97.15 157 | 97.36 201 | 99.03 197 | 98.52 163 | 98.55 167 | 99.73 27 | 94.94 177 | 94.94 237 | 97.98 143 | 97.37 171 | 93.66 223 | 97.60 166 | 97.34 169 | 98.23 189 | 96.29 196 |
|
Fast-Effi-MVS+-dtu | | | 96.99 177 | 96.46 173 | 97.61 197 | 98.98 199 | 97.89 194 | 97.54 222 | 99.76 21 | 93.43 208 | 96.55 221 | 94.93 206 | 98.06 155 | 94.32 217 | 96.93 191 | 96.50 193 | 98.53 175 | 97.47 169 |
|
no-one | | | 99.01 45 | 98.94 35 | 99.09 91 | 98.97 200 | 98.55 158 | 99.37 69 | 99.04 170 | 97.59 67 | 99.36 37 | 99.66 44 | 99.75 8 | 99.57 15 | 98.47 88 | 99.27 35 | 98.21 190 | 99.30 25 |
|
MAR-MVS | | | 97.12 174 | 96.28 179 | 98.11 181 | 98.94 201 | 97.22 210 | 97.65 217 | 99.38 103 | 90.93 238 | 98.15 168 | 95.17 203 | 97.13 174 | 96.48 185 | 97.71 157 | 97.40 166 | 98.06 194 | 98.40 119 |
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 |
MSLP-MVS++ | | | 97.99 135 | 97.64 137 | 98.40 162 | 98.91 202 | 98.47 166 | 97.12 231 | 98.78 185 | 96.49 133 | 98.48 147 | 93.57 218 | 99.12 96 | 98.51 114 | 98.31 106 | 98.58 89 | 98.58 172 | 98.95 65 |
|
ADS-MVSNet | | | 94.41 221 | 92.13 222 | 97.07 206 | 98.86 203 | 96.60 215 | 98.38 180 | 98.47 204 | 96.13 150 | 98.02 174 | 96.98 175 | 87.50 210 | 95.87 196 | 89.89 235 | 87.58 230 | 92.79 235 | 90.27 233 |
|
OMC-MVS | | | 98.35 118 | 98.10 112 | 98.64 148 | 98.85 204 | 97.99 191 | 98.56 166 | 98.21 211 | 97.26 93 | 98.87 117 | 98.54 120 | 99.27 70 | 98.43 117 | 98.34 102 | 97.66 151 | 98.92 128 | 97.65 164 |
|
MVSTER | | | 95.38 208 | 93.99 209 | 97.01 211 | 98.83 205 | 98.95 119 | 96.62 236 | 99.14 159 | 92.17 220 | 97.44 198 | 97.29 163 | 77.88 236 | 91.63 236 | 97.45 174 | 96.18 200 | 98.41 181 | 97.99 152 |
|
TAPA-MVS | | 96.65 12 | 98.23 125 | 97.96 122 | 98.55 155 | 98.81 206 | 98.16 183 | 98.40 177 | 97.94 220 | 96.68 120 | 98.49 145 | 98.61 116 | 98.89 120 | 98.57 108 | 97.45 174 | 97.59 158 | 99.09 106 | 98.35 122 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
CostFormer | | | 92.75 228 | 89.49 232 | 96.55 222 | 98.78 207 | 95.83 233 | 97.55 221 | 98.59 197 | 91.83 231 | 97.34 205 | 96.31 190 | 78.53 235 | 94.50 213 | 86.14 240 | 84.92 236 | 92.54 236 | 92.84 223 |
|
PatchmatchNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 93.88 225 | 91.08 228 | 97.14 205 | 98.75 208 | 96.01 228 | 98.25 191 | 99.39 96 | 94.95 176 | 98.96 101 | 96.32 189 | 85.35 221 | 95.50 200 | 88.89 237 | 85.89 235 | 91.99 239 | 90.15 234 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
GBi-Net | | | 97.69 151 | 97.75 131 | 97.62 195 | 98.71 209 | 99.21 67 | 98.62 158 | 99.33 124 | 94.09 198 | 95.60 229 | 98.17 134 | 95.97 184 | 94.39 214 | 99.05 49 | 99.03 52 | 99.08 107 | 98.70 94 |
|
test1 | | | 97.69 151 | 97.75 131 | 97.62 195 | 98.71 209 | 99.21 67 | 98.62 158 | 99.33 124 | 94.09 198 | 95.60 229 | 98.17 134 | 95.97 184 | 94.39 214 | 99.05 49 | 99.03 52 | 99.08 107 | 98.70 94 |
|
FMVSNet2 | | | 97.94 138 | 98.08 114 | 97.77 194 | 98.71 209 | 99.21 67 | 98.62 158 | 99.47 85 | 96.62 122 | 96.37 222 | 99.20 87 | 97.70 164 | 94.39 214 | 97.39 178 | 97.75 144 | 99.08 107 | 98.70 94 |
|
PatchMatch-RL | | | 97.24 170 | 96.45 174 | 98.17 177 | 98.70 212 | 97.57 207 | 97.31 227 | 98.48 203 | 94.42 190 | 98.39 151 | 95.74 199 | 96.35 183 | 97.88 146 | 97.75 155 | 97.48 165 | 98.24 188 | 95.87 202 |
|
LP | | | 95.33 210 | 93.45 214 | 97.54 198 | 98.68 213 | 97.40 208 | 98.73 144 | 98.41 205 | 96.33 140 | 98.92 108 | 97.84 148 | 88.30 204 | 95.92 195 | 92.98 231 | 89.38 227 | 94.56 225 | 91.90 228 |
|
MS-PatchMatch | | | 97.60 156 | 97.22 152 | 98.04 184 | 98.67 214 | 97.18 211 | 97.91 204 | 98.28 208 | 95.82 158 | 98.34 155 | 97.66 151 | 98.38 145 | 97.77 151 | 97.10 189 | 97.25 173 | 97.27 206 | 97.18 184 |
|
PatchT | | | 95.49 206 | 93.29 215 | 98.06 183 | 98.65 215 | 96.20 221 | 98.91 127 | 99.73 27 | 92.00 229 | 98.50 142 | 96.67 181 | 83.25 228 | 96.34 188 | 94.40 226 | 95.50 207 | 96.21 215 | 95.04 210 |
|
CR-MVSNet | | | 95.38 208 | 93.01 216 | 98.16 179 | 98.63 216 | 95.85 231 | 97.64 218 | 99.78 18 | 91.27 234 | 98.50 142 | 96.84 178 | 82.16 230 | 96.34 188 | 94.40 226 | 95.50 207 | 98.05 195 | 95.04 210 |
|
EPMVS | | | 93.67 226 | 90.82 229 | 96.99 212 | 98.62 217 | 96.39 220 | 98.40 177 | 99.11 164 | 95.54 163 | 97.87 182 | 97.14 168 | 81.27 234 | 94.97 207 | 88.54 239 | 86.80 232 | 92.95 233 | 90.06 235 |
|
CVMVSNet | | | 97.38 168 | 97.39 144 | 97.37 200 | 98.58 218 | 97.72 203 | 98.70 145 | 97.42 226 | 97.21 97 | 95.95 226 | 99.46 67 | 93.31 196 | 97.38 167 | 97.60 166 | 97.78 136 | 96.18 216 | 98.66 98 |
|
CNLPA | | | 97.75 146 | 97.26 148 | 98.32 168 | 98.58 218 | 97.86 196 | 97.80 208 | 98.09 215 | 96.49 133 | 98.49 145 | 96.15 192 | 98.08 154 | 98.35 122 | 98.00 130 | 97.03 180 | 98.61 169 | 97.21 183 |
|
E-PMN | | | 92.28 233 | 90.12 230 | 94.79 236 | 98.56 220 | 90.90 244 | 95.16 242 | 93.68 241 | 95.36 165 | 95.10 236 | 96.56 184 | 89.05 202 | 95.24 203 | 95.21 220 | 81.84 241 | 90.98 241 | 81.94 241 |
|
RPMNet | | | 94.72 214 | 92.01 223 | 97.88 190 | 98.56 220 | 95.85 231 | 97.78 209 | 99.70 34 | 91.27 234 | 98.33 156 | 93.69 216 | 81.88 231 | 94.91 208 | 92.60 233 | 94.34 219 | 98.01 196 | 94.46 214 |
|
MDTV_nov1_ep13 | | | 94.47 219 | 92.15 221 | 97.17 204 | 98.54 222 | 96.42 219 | 98.10 196 | 98.89 179 | 94.49 185 | 98.02 174 | 97.41 160 | 86.49 211 | 95.56 199 | 90.85 234 | 87.95 229 | 93.91 227 | 91.45 231 |
|
test2356 | | | 92.46 229 | 88.72 237 | 96.82 215 | 98.48 223 | 95.34 237 | 96.22 240 | 98.09 215 | 87.46 245 | 96.01 224 | 92.82 223 | 64.42 244 | 95.10 205 | 94.08 228 | 94.05 220 | 97.02 210 | 92.87 222 |
|
TSAR-MVS + COLMAP | | | 97.62 155 | 97.31 146 | 97.98 186 | 98.47 224 | 97.39 209 | 98.29 187 | 98.25 209 | 96.68 120 | 97.54 195 | 98.87 106 | 98.04 157 | 97.08 175 | 96.78 193 | 96.26 195 | 98.26 187 | 97.12 185 |
|
tpmp4_e23 | | | 92.43 231 | 88.82 235 | 96.64 221 | 98.46 225 | 95.17 238 | 97.61 220 | 98.85 181 | 92.42 215 | 98.18 164 | 93.03 221 | 74.92 239 | 93.80 222 | 88.91 236 | 84.60 237 | 92.95 233 | 92.66 225 |
|
EMVS | | | 91.84 234 | 89.39 234 | 94.70 237 | 98.44 226 | 90.84 245 | 95.27 241 | 93.53 242 | 95.18 168 | 95.26 234 | 95.62 201 | 87.59 209 | 94.77 210 | 94.87 223 | 80.72 242 | 90.95 242 | 80.88 242 |
|
tpmrst | | | 92.45 230 | 89.48 233 | 95.92 230 | 98.43 227 | 95.03 239 | 97.14 230 | 97.92 221 | 94.16 197 | 97.56 194 | 97.86 147 | 81.63 233 | 93.56 224 | 85.89 242 | 82.86 238 | 90.91 243 | 88.95 240 |
|
tpm | | | 93.89 224 | 91.21 226 | 97.03 209 | 98.36 228 | 96.07 226 | 97.53 225 | 99.65 43 | 92.24 218 | 98.64 130 | 97.23 165 | 74.67 240 | 94.64 212 | 92.68 232 | 90.73 225 | 93.37 230 | 94.82 213 |
|
tpm cat1 | | | 91.52 235 | 87.70 238 | 95.97 229 | 98.33 229 | 94.98 240 | 97.06 232 | 98.03 217 | 92.11 222 | 98.03 173 | 94.77 207 | 77.19 237 | 92.71 229 | 83.56 243 | 82.24 240 | 91.67 240 | 89.04 239 |
|
PMMVS2 | | | 96.29 195 | 97.05 159 | 95.40 233 | 98.32 230 | 96.16 222 | 98.18 194 | 97.46 225 | 97.20 99 | 84.51 247 | 99.60 52 | 98.68 134 | 96.37 187 | 98.59 82 | 97.38 167 | 97.58 202 | 91.76 229 |
|
DWT-MVSNet_training | | | 91.07 236 | 86.55 239 | 96.35 225 | 98.28 231 | 95.82 234 | 98.00 199 | 95.03 238 | 91.24 236 | 97.99 178 | 90.35 229 | 63.43 245 | 95.25 202 | 86.06 241 | 86.62 233 | 93.55 229 | 92.30 226 |
|
test12356 | | | 95.71 205 | 95.55 195 | 95.89 231 | 98.27 232 | 96.48 217 | 96.90 234 | 97.35 228 | 92.13 221 | 95.64 228 | 99.13 90 | 97.97 159 | 92.34 231 | 96.94 190 | 96.55 192 | 94.87 224 | 89.61 236 |
|
new_pmnet | | | 96.59 186 | 96.40 175 | 96.81 216 | 98.24 233 | 95.46 235 | 97.71 215 | 94.75 239 | 96.92 105 | 96.80 220 | 99.23 85 | 97.81 163 | 96.69 180 | 96.58 198 | 95.16 212 | 96.69 212 | 93.64 220 |
|
dps | | | 92.35 232 | 88.78 236 | 96.52 223 | 98.21 234 | 95.94 230 | 97.78 209 | 98.38 206 | 89.88 242 | 96.81 219 | 95.07 204 | 75.31 238 | 94.70 211 | 88.62 238 | 86.21 234 | 93.21 232 | 90.41 232 |
|
FMVSNet5 | | | 94.57 217 | 92.77 217 | 96.67 220 | 97.88 235 | 98.72 144 | 97.54 222 | 98.70 191 | 88.64 244 | 95.11 235 | 86.90 235 | 81.77 232 | 93.27 225 | 97.92 137 | 98.07 115 | 97.50 204 | 97.34 175 |
|
TESTMET0.1,1 | | | 94.44 220 | 93.71 211 | 95.30 235 | 97.84 236 | 96.16 222 | 96.31 237 | 95.32 237 | 89.98 240 | 94.08 238 | 97.01 172 | 86.43 212 | 92.20 233 | 96.76 195 | 95.31 209 | 96.05 217 | 94.31 215 |
|
FPMVS | | | 96.97 178 | 97.20 153 | 96.70 219 | 97.75 237 | 96.11 225 | 97.72 213 | 95.47 235 | 97.13 101 | 98.02 174 | 97.57 154 | 96.67 179 | 92.97 227 | 99.00 57 | 98.34 103 | 98.28 186 | 95.58 204 |
|
test-mter | | | 94.62 215 | 94.02 208 | 95.32 234 | 97.72 238 | 96.75 214 | 96.23 239 | 95.67 234 | 89.83 243 | 93.23 244 | 96.99 174 | 85.94 218 | 92.66 230 | 97.32 182 | 96.11 202 | 96.44 213 | 95.22 209 |
|
pmmvs3 | | | 96.30 194 | 95.87 190 | 96.80 217 | 97.66 239 | 96.48 217 | 97.93 202 | 93.80 240 | 93.40 209 | 98.54 138 | 98.27 129 | 97.50 168 | 97.37 169 | 97.49 172 | 93.11 222 | 95.52 221 | 94.85 212 |
|
FMVSNet3 | | | 96.85 180 | 96.67 166 | 97.06 207 | 97.56 240 | 99.01 107 | 97.99 200 | 99.33 124 | 94.09 198 | 95.60 229 | 98.17 134 | 95.97 184 | 93.26 226 | 94.76 225 | 96.22 196 | 98.59 171 | 98.46 113 |
|
CMPMVS | ![Method available as binary. binary](img/icon_binary.png) | 74.71 19 | 96.17 198 | 96.06 184 | 96.30 226 | 97.41 241 | 94.52 241 | 94.83 243 | 95.46 236 | 91.57 232 | 97.26 210 | 94.45 213 | 98.33 147 | 94.98 206 | 98.28 111 | 97.59 158 | 97.86 198 | 97.68 163 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PMMVS | | | 96.47 189 | 95.81 192 | 97.23 203 | 97.38 242 | 95.96 229 | 97.31 227 | 96.91 232 | 93.21 211 | 97.93 181 | 97.14 168 | 97.64 166 | 95.70 197 | 95.24 219 | 96.18 200 | 98.17 191 | 95.33 208 |
|
MVS-HIRNet | | | 94.86 212 | 93.83 210 | 96.07 227 | 97.07 243 | 94.00 242 | 94.31 244 | 99.17 154 | 91.23 237 | 98.17 165 | 98.69 112 | 97.43 169 | 95.66 198 | 94.05 229 | 91.92 224 | 92.04 238 | 89.46 237 |
|
DeepPCF-MVS | | 96.68 10 | 98.20 128 | 98.26 102 | 98.12 180 | 97.03 244 | 98.11 185 | 98.44 175 | 97.70 224 | 96.77 115 | 98.52 140 | 98.91 105 | 99.17 83 | 98.58 107 | 98.41 96 | 98.02 116 | 98.46 180 | 98.46 113 |
|
testpf | | | 87.81 238 | 83.90 241 | 92.37 238 | 96.76 245 | 88.65 246 | 93.04 246 | 98.24 210 | 85.20 246 | 95.28 233 | 86.82 236 | 72.43 242 | 82.35 243 | 82.62 244 | 82.30 239 | 88.55 244 | 89.29 238 |
|
MVE | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | 82.47 18 | 93.12 227 | 94.09 204 | 91.99 239 | 90.79 246 | 82.50 248 | 93.93 245 | 96.30 233 | 96.06 151 | 88.81 246 | 98.19 132 | 96.38 182 | 97.56 158 | 97.24 186 | 95.18 211 | 84.58 245 | 93.07 221 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
tmp_tt | | | | | 65.28 241 | 82.24 247 | 71.50 249 | 70.81 249 | 23.21 244 | 96.14 148 | 81.70 248 | 85.98 242 | 92.44 198 | 49.84 244 | 95.81 210 | 94.36 218 | 83.86 246 | |
|
testmvs | | | 9.73 241 | 13.38 243 | 5.48 244 | 3.62 248 | 4.12 250 | 6.40 251 | 3.19 246 | 14.92 247 | 7.68 251 | 22.10 246 | 13.89 252 | 6.83 245 | 13.47 245 | 10.38 244 | 5.14 248 | 14.81 243 |
|
test123 | | | 9.37 242 | 12.26 244 | 6.00 243 | 3.32 249 | 4.06 251 | 6.39 252 | 3.41 245 | 13.20 248 | 10.48 250 | 16.43 247 | 16.22 251 | 6.76 246 | 11.37 246 | 10.40 243 | 5.62 247 | 14.10 245 |
|
GG-mvs-BLEND | | | 65.66 240 | 92.62 219 | 34.20 242 | 1.45 250 | 93.75 243 | 85.40 248 | 1.64 247 | 91.37 233 | 17.21 249 | 87.25 233 | 94.78 191 | 3.25 247 | 95.64 214 | 93.80 221 | 96.27 214 | 91.74 230 |
|
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 | | | | | | | | | | | 99.19 65 | | 99.68 21 | | | | | |
|
MTMP | | | | | | | | | | | 99.20 63 | | 99.54 42 | | | | | |
|
Patchmatch-RL test | | | | | | | | 32.47 250 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 93.07 212 | | | | | | | | |
|
Patchmtry | | | | | | | 96.05 227 | 97.64 218 | 99.78 18 | | 98.50 142 | | | | | | | |
|
DeepMVS_CX | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | | | | | 87.86 247 | 92.27 247 | 61.98 243 | 93.64 206 | 93.62 241 | 91.17 225 | 91.67 199 | 94.90 209 | 95.99 209 | | 92.48 237 | 94.18 217 |
|