Anonymous20231211 | | | 99.83 1 | 99.80 1 | 99.86 1 | 99.97 1 | 99.87 1 | 99.90 1 | 99.92 1 | 99.76 1 | 99.82 2 | 99.79 37 | 99.98 1 | 99.63 12 | 99.84 3 | 99.78 3 | 99.94 1 | 99.61 6 |
|
UA-Net | | | 99.30 24 | 99.22 23 | 99.39 46 | 99.94 2 | 99.66 16 | 98.91 122 | 99.86 9 | 97.74 55 | 98.74 123 | 99.00 102 | 99.60 38 | 99.17 71 | 99.50 23 | 99.39 25 | 99.70 23 | 99.64 2 |
|
DTE-MVSNet | | | 99.52 13 | 99.27 19 | 99.82 3 | 99.93 3 | 99.77 4 | 99.79 10 | 99.87 7 | 97.89 45 | 99.70 11 | 99.55 62 | 99.21 78 | 99.77 2 | 99.65 10 | 99.43 23 | 99.90 3 | 99.36 21 |
|
SixPastTwentyTwo | | | 99.70 4 | 99.59 7 | 99.82 3 | 99.93 3 | 99.80 2 | 99.86 3 | 99.87 7 | 98.87 14 | 99.79 5 | 99.85 27 | 99.33 63 | 99.74 7 | 99.85 2 | 99.82 1 | 99.74 22 | 99.63 4 |
|
pmmvs6 | | | 99.74 3 | 99.75 2 | 99.73 15 | 99.92 5 | 99.67 15 | 99.76 14 | 99.84 11 | 99.59 2 | 99.52 27 | 99.87 18 | 99.91 2 | 99.43 39 | 99.87 1 | 99.81 2 | 99.89 6 | 99.52 10 |
|
PS-CasMVS | | | 99.50 14 | 99.23 21 | 99.82 3 | 99.92 5 | 99.75 7 | 99.78 11 | 99.89 2 | 97.30 85 | 99.71 6 | 99.60 53 | 99.23 74 | 99.71 9 | 99.65 10 | 99.55 18 | 99.90 3 | 99.56 8 |
|
PEN-MVS | | | 99.54 11 | 99.30 18 | 99.83 2 | 99.92 5 | 99.76 5 | 99.80 8 | 99.88 4 | 97.60 66 | 99.71 6 | 99.59 55 | 99.52 43 | 99.75 6 | 99.64 12 | 99.51 19 | 99.90 3 | 99.46 17 |
|
WR-MVS | | | 99.61 10 | 99.44 11 | 99.82 3 | 99.92 5 | 99.80 2 | 99.80 8 | 99.89 2 | 98.54 19 | 99.66 15 | 99.78 40 | 99.16 86 | 99.68 10 | 99.70 6 | 99.63 6 | 99.94 1 | 99.49 16 |
|
v52 | | | 99.67 6 | 99.59 7 | 99.76 9 | 99.91 9 | 99.69 11 | 99.85 4 | 99.79 16 | 99.12 9 | 99.68 12 | 99.95 2 | 99.72 14 | 99.77 2 | 99.58 17 | 99.61 11 | 99.54 38 | 99.50 13 |
|
V4 | | | 99.67 6 | 99.60 6 | 99.76 9 | 99.91 9 | 99.69 11 | 99.85 4 | 99.79 16 | 99.13 8 | 99.68 12 | 99.95 2 | 99.72 14 | 99.77 2 | 99.58 17 | 99.61 11 | 99.54 38 | 99.50 13 |
|
CP-MVSNet | | | 99.39 20 | 99.04 29 | 99.80 7 | 99.91 9 | 99.70 10 | 99.75 15 | 99.88 4 | 96.82 107 | 99.68 12 | 99.32 76 | 98.86 120 | 99.68 10 | 99.57 21 | 99.47 21 | 99.89 6 | 99.52 10 |
|
WR-MVS_H | | | 99.48 15 | 99.23 21 | 99.76 9 | 99.91 9 | 99.76 5 | 99.75 15 | 99.88 4 | 97.27 88 | 99.58 20 | 99.56 59 | 99.24 72 | 99.56 18 | 99.60 15 | 99.60 14 | 99.88 8 | 99.58 7 |
|
gm-plane-assit | | | 94.62 208 | 91.39 217 | 98.39 156 | 99.90 13 | 99.47 35 | 99.40 66 | 99.65 43 | 97.44 77 | 99.56 23 | 99.68 44 | 59.40 241 | 94.23 209 | 96.17 196 | 94.77 207 | 97.61 191 | 92.79 216 |
|
v7n | | | 99.68 5 | 99.61 4 | 99.76 9 | 99.89 14 | 99.74 8 | 99.87 2 | 99.82 14 | 99.20 6 | 99.71 6 | 99.96 1 | 99.73 12 | 99.76 5 | 99.58 17 | 99.59 15 | 99.52 43 | 99.46 17 |
|
NR-MVSNet | | | 99.10 39 | 98.68 56 | 99.58 21 | 99.89 14 | 99.23 59 | 99.35 72 | 99.63 47 | 96.58 122 | 99.36 38 | 99.05 96 | 98.67 132 | 99.46 32 | 99.63 13 | 98.73 73 | 99.80 15 | 98.88 68 |
|
TransMVSNet (Re) | | | 99.45 18 | 99.32 16 | 99.61 17 | 99.88 16 | 99.60 18 | 99.75 15 | 99.63 47 | 99.11 10 | 99.28 57 | 99.83 31 | 98.35 140 | 99.27 62 | 99.70 6 | 99.62 10 | 99.84 10 | 99.03 48 |
|
anonymousdsp | | | 99.64 9 | 99.55 9 | 99.74 14 | 99.87 17 | 99.56 22 | 99.82 7 | 99.73 28 | 98.54 19 | 99.71 6 | 99.92 6 | 99.84 7 | 99.61 13 | 99.70 6 | 99.63 6 | 99.69 26 | 99.64 2 |
|
FC-MVSNet-train | | | 99.13 37 | 99.05 28 | 99.21 74 | 99.87 17 | 99.57 21 | 99.67 20 | 99.60 54 | 96.75 114 | 98.28 155 | 99.48 67 | 99.52 43 | 98.10 135 | 99.47 26 | 99.37 27 | 99.76 21 | 99.21 32 |
|
FC-MVSNet-test | | | 99.32 23 | 99.33 14 | 99.31 65 | 99.87 17 | 99.65 17 | 99.63 29 | 99.75 25 | 97.76 50 | 97.29 202 | 99.87 18 | 99.63 33 | 99.52 24 | 99.66 9 | 99.63 6 | 99.77 19 | 99.12 37 |
|
v748 | | | 99.67 6 | 99.61 4 | 99.75 13 | 99.87 17 | 99.68 13 | 99.84 6 | 99.79 16 | 99.14 7 | 99.64 17 | 99.89 12 | 99.88 5 | 99.72 8 | 99.58 17 | 99.57 17 | 99.62 30 | 99.50 13 |
|
LTVRE_ROB | | 98.82 1 | 99.76 2 | 99.75 2 | 99.77 8 | 99.87 17 | 99.71 9 | 99.77 12 | 99.76 22 | 99.52 3 | 99.80 3 | 99.79 37 | 99.91 2 | 99.56 18 | 99.83 4 | 99.75 4 | 99.86 9 | 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 |
EPP-MVSNet | | | 98.61 95 | 98.19 104 | 99.11 87 | 99.86 22 | 99.60 18 | 99.44 63 | 99.53 70 | 97.37 83 | 96.85 210 | 98.69 112 | 93.75 186 | 99.18 68 | 99.22 36 | 99.35 29 | 99.82 13 | 99.32 23 |
|
MIMVSNet1 | | | 99.46 17 | 99.34 13 | 99.60 19 | 99.83 23 | 99.68 13 | 99.74 18 | 99.71 33 | 98.20 27 | 99.41 35 | 99.86 22 | 99.66 27 | 99.41 42 | 99.50 23 | 99.39 25 | 99.50 49 | 99.10 41 |
|
CSCG | | | 99.23 27 | 99.15 25 | 99.32 64 | 99.83 23 | 99.45 36 | 98.97 114 | 99.21 139 | 98.83 15 | 99.04 93 | 99.43 71 | 99.64 31 | 99.26 63 | 98.85 65 | 98.20 103 | 99.62 30 | 99.62 5 |
|
conf0.05thres1000 | | | 97.44 160 | 95.93 181 | 99.20 77 | 99.82 25 | 99.56 22 | 99.41 64 | 99.61 52 | 97.42 79 | 98.01 170 | 94.34 205 | 82.73 220 | 98.68 101 | 99.33 32 | 99.42 24 | 99.67 27 | 98.74 84 |
|
pm-mvs1 | | | 99.47 16 | 99.38 12 | 99.57 22 | 99.82 25 | 99.49 32 | 99.63 29 | 99.65 43 | 98.88 13 | 99.31 47 | 99.85 27 | 99.02 112 | 99.23 65 | 99.60 15 | 99.58 16 | 99.80 15 | 99.22 31 |
|
IS_MVSNet | | | 98.20 125 | 98.00 116 | 98.44 152 | 99.82 25 | 99.48 33 | 99.25 86 | 99.56 58 | 95.58 155 | 93.93 232 | 97.56 149 | 96.52 172 | 98.27 127 | 99.08 46 | 99.20 36 | 99.80 15 | 98.56 100 |
|
tfpnnormal | | | 99.19 31 | 98.90 39 | 99.54 26 | 99.81 28 | 99.55 26 | 99.60 35 | 99.54 66 | 98.53 21 | 99.23 61 | 98.40 120 | 98.23 143 | 99.40 43 | 99.29 33 | 99.36 28 | 99.63 29 | 98.95 61 |
|
TranMVSNet+NR-MVSNet | | | 99.23 27 | 98.91 38 | 99.61 17 | 99.81 28 | 99.45 36 | 99.47 58 | 99.68 37 | 97.28 87 | 99.39 36 | 99.54 63 | 99.08 107 | 99.45 34 | 99.09 43 | 98.84 61 | 99.83 11 | 99.04 46 |
|
Vis-MVSNet (Re-imp) | | | 98.46 111 | 98.23 102 | 98.73 129 | 99.81 28 | 99.29 53 | 98.79 135 | 99.50 76 | 96.20 141 | 96.03 215 | 98.29 125 | 96.98 168 | 98.54 112 | 99.11 40 | 99.08 43 | 99.70 23 | 98.62 93 |
|
ACMH+ | | 97.53 7 | 99.29 25 | 99.20 24 | 99.40 45 | 99.81 28 | 99.22 62 | 99.59 36 | 99.50 76 | 98.64 18 | 98.29 154 | 99.21 85 | 99.69 19 | 99.57 16 | 99.53 22 | 99.33 30 | 99.66 28 | 98.81 74 |
|
ACMH | | 97.81 6 | 99.44 19 | 99.33 14 | 99.56 23 | 99.81 28 | 99.42 38 | 99.73 19 | 99.58 55 | 99.02 11 | 99.10 81 | 99.41 73 | 99.69 19 | 99.60 14 | 99.45 27 | 99.26 35 | 99.55 37 | 99.05 45 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMMPR | | | 99.05 42 | 98.72 51 | 99.44 36 | 99.79 33 | 99.12 80 | 99.35 72 | 99.56 58 | 97.74 55 | 99.21 62 | 97.72 144 | 99.55 41 | 99.29 60 | 98.90 64 | 98.81 63 | 99.41 64 | 99.19 33 |
|
SteuartSystems-ACMMP | | | 98.94 53 | 98.52 69 | 99.43 39 | 99.79 33 | 99.13 78 | 99.33 76 | 99.55 60 | 96.17 142 | 99.04 93 | 97.53 150 | 99.65 30 | 99.46 32 | 99.04 52 | 98.76 69 | 99.44 56 | 99.35 22 |
Skip Steuart: Steuart Systems R&D Blog. |
testgi | | | 98.18 128 | 98.44 83 | 97.89 181 | 99.78 35 | 99.23 59 | 98.78 136 | 99.21 139 | 97.26 90 | 97.41 192 | 97.39 154 | 99.36 61 | 92.85 219 | 98.82 68 | 98.66 80 | 99.31 81 | 98.35 113 |
|
LGP-MVS_train | | | 98.84 73 | 98.33 94 | 99.44 36 | 99.78 35 | 98.98 105 | 99.39 67 | 99.55 60 | 95.41 157 | 98.90 109 | 97.51 151 | 99.68 22 | 99.44 37 | 99.03 53 | 98.81 63 | 99.57 36 | 98.91 65 |
|
zzz-MVS | | | 98.94 53 | 98.57 64 | 99.37 53 | 99.77 37 | 99.15 76 | 99.24 87 | 99.55 60 | 97.38 82 | 99.16 71 | 96.64 174 | 99.69 19 | 99.15 75 | 99.09 43 | 98.92 54 | 99.37 71 | 99.11 38 |
|
XVS | | | | | | 99.77 37 | 99.07 85 | 99.46 60 | | | 98.95 102 | | 99.37 57 | | | | 99.33 77 | |
|
X-MVStestdata | | | | | | 99.77 37 | 99.07 85 | 99.46 60 | | | 98.95 102 | | 99.37 57 | | | | 99.33 77 | |
|
APDe-MVS | | | 99.15 36 | 98.95 32 | 99.39 46 | 99.77 37 | 99.28 54 | 99.52 51 | 99.54 66 | 97.22 93 | 99.06 88 | 99.20 86 | 99.64 31 | 99.05 82 | 99.14 38 | 99.02 52 | 99.39 69 | 99.17 35 |
|
test20.03 | | | 98.84 73 | 98.74 50 | 98.95 107 | 99.77 37 | 99.33 48 | 99.21 92 | 99.46 85 | 97.29 86 | 98.88 114 | 99.65 49 | 99.10 101 | 97.07 167 | 99.11 40 | 98.76 69 | 99.32 80 | 97.98 146 |
|
ACMMP | | | 98.82 80 | 98.33 94 | 99.39 46 | 99.77 37 | 99.14 77 | 99.37 69 | 99.54 66 | 96.47 132 | 99.03 95 | 96.26 183 | 99.52 43 | 99.28 61 | 98.92 62 | 98.80 66 | 99.37 71 | 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 86 | 98.09 110 | 99.39 46 | 99.76 43 | 99.07 85 | 99.30 78 | 99.51 73 | 94.76 173 | 99.18 67 | 96.70 172 | 99.51 46 | 99.20 66 | 98.79 71 | 98.71 76 | 99.39 69 | 99.11 38 |
|
UniMVSNet_NR-MVSNet | | | 98.97 49 | 98.46 73 | 99.56 23 | 99.76 43 | 99.34 46 | 99.29 79 | 99.61 52 | 96.55 126 | 99.55 24 | 99.05 96 | 97.96 154 | 99.36 55 | 98.84 66 | 98.50 90 | 99.81 14 | 98.97 55 |
|
PVSNet_Blended_VisFu | | | 98.98 48 | 98.79 47 | 99.21 74 | 99.76 43 | 99.34 46 | 99.35 72 | 99.35 116 | 97.12 99 | 99.46 32 | 99.56 59 | 98.89 118 | 98.08 138 | 99.05 48 | 98.58 85 | 99.27 87 | 98.98 54 |
|
X-MVS | | | 98.59 98 | 97.99 117 | 99.30 66 | 99.75 46 | 99.07 85 | 99.17 96 | 99.50 76 | 96.62 119 | 98.95 102 | 93.95 206 | 99.37 57 | 99.11 78 | 98.94 59 | 98.86 57 | 99.35 75 | 99.09 42 |
|
MP-MVS | | | 98.78 83 | 98.30 96 | 99.34 62 | 99.75 46 | 98.95 114 | 99.26 84 | 99.46 85 | 95.78 153 | 99.17 68 | 96.98 167 | 99.72 14 | 99.06 81 | 98.84 66 | 98.74 72 | 99.33 77 | 99.11 38 |
|
UniMVSNet (Re) | | | 99.08 41 | 98.69 55 | 99.54 26 | 99.75 46 | 99.33 48 | 99.29 79 | 99.64 46 | 96.75 114 | 99.48 31 | 99.30 78 | 98.69 128 | 99.26 63 | 98.94 59 | 98.76 69 | 99.78 18 | 99.02 51 |
|
mPP-MVS | | | | | | 99.75 46 | | | | | | | 99.49 50 | | | | | |
|
DU-MVS | | | 99.04 43 | 98.59 61 | 99.56 23 | 99.74 50 | 99.23 59 | 99.29 79 | 99.63 47 | 96.58 122 | 99.55 24 | 99.05 96 | 98.68 130 | 99.36 55 | 99.03 53 | 98.60 83 | 99.77 19 | 98.97 55 |
|
Baseline_NR-MVSNet | | | 99.18 34 | 98.87 41 | 99.54 26 | 99.74 50 | 99.56 22 | 99.36 71 | 99.62 51 | 96.53 128 | 99.29 52 | 99.85 27 | 98.64 134 | 99.40 43 | 99.03 53 | 99.63 6 | 99.83 11 | 98.86 69 |
|
ACMP | | 96.54 13 | 98.87 66 | 98.40 88 | 99.41 42 | 99.74 50 | 98.88 125 | 99.29 79 | 99.50 76 | 96.85 103 | 98.96 100 | 97.05 163 | 99.66 27 | 99.43 39 | 98.98 57 | 98.60 83 | 99.52 43 | 98.81 74 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMM | | 96.66 11 | 98.90 61 | 98.44 83 | 99.44 36 | 99.74 50 | 98.95 114 | 99.47 58 | 99.55 60 | 97.66 62 | 99.09 85 | 96.43 179 | 99.41 51 | 99.35 58 | 98.95 58 | 98.67 78 | 99.45 54 | 99.03 48 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
COLMAP_ROB | | 98.29 2 | 99.37 21 | 99.25 20 | 99.51 30 | 99.74 50 | 99.12 80 | 99.56 40 | 99.39 93 | 98.96 12 | 99.17 68 | 99.44 70 | 99.63 33 | 99.58 15 | 99.48 25 | 99.27 33 | 99.60 34 | 98.81 74 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
SMA-MVS | | | 98.94 53 | 98.80 46 | 99.11 87 | 99.73 55 | 99.09 82 | 98.78 136 | 99.18 144 | 96.32 137 | 98.89 112 | 99.19 88 | 99.72 14 | 98.75 97 | 99.09 43 | 98.89 56 | 99.31 81 | 99.27 26 |
|
tfpn | | | 94.97 204 | 91.60 216 | 98.90 110 | 99.73 55 | 99.33 48 | 99.11 103 | 99.51 73 | 95.05 163 | 97.19 207 | 89.03 221 | 62.62 238 | 98.37 120 | 98.53 80 | 98.97 53 | 99.48 52 | 97.70 154 |
|
DeepC-MVS | | 97.88 4 | 99.33 22 | 99.15 25 | 99.53 29 | 99.73 55 | 99.05 89 | 99.49 56 | 99.40 91 | 98.42 22 | 99.55 24 | 99.71 43 | 99.89 4 | 99.49 29 | 99.14 38 | 98.81 63 | 99.54 38 | 99.02 51 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MVS_0304 | | | 98.57 100 | 98.36 91 | 98.82 122 | 99.72 58 | 98.94 118 | 98.92 120 | 99.14 150 | 96.76 112 | 99.33 43 | 98.30 124 | 99.73 12 | 96.74 170 | 98.05 122 | 97.79 129 | 99.08 102 | 98.97 55 |
|
HyFIR lowres test | | | 98.08 130 | 97.16 149 | 99.14 84 | 99.72 58 | 98.91 121 | 99.41 64 | 99.58 55 | 97.93 39 | 98.82 117 | 99.24 80 | 95.81 179 | 98.73 99 | 95.16 211 | 95.13 204 | 98.60 162 | 97.94 147 |
|
1111 | | | 94.22 215 | 92.26 212 | 96.51 216 | 99.71 60 | 98.75 133 | 99.03 107 | 99.83 12 | 95.01 165 | 93.39 234 | 99.54 63 | 60.23 239 | 89.58 229 | 97.90 132 | 97.62 149 | 97.50 194 | 96.75 182 |
|
.test1245 | | | 74.10 231 | 68.09 233 | 81.11 232 | 99.71 60 | 98.75 133 | 99.03 107 | 99.83 12 | 95.01 165 | 93.39 234 | 99.54 63 | 60.23 239 | 89.58 229 | 97.90 132 | 10.38 235 | 5.14 239 | 14.81 235 |
|
view800 | | | 96.48 180 | 94.42 192 | 98.87 114 | 99.70 62 | 99.26 55 | 99.05 106 | 99.45 89 | 94.77 172 | 97.32 199 | 88.21 222 | 83.40 218 | 98.28 126 | 98.37 92 | 99.33 30 | 99.44 56 | 97.58 159 |
|
TDRefinement | | | 99.54 11 | 99.50 10 | 99.60 19 | 99.70 62 | 99.35 45 | 99.77 12 | 99.58 55 | 99.40 5 | 99.28 57 | 99.66 45 | 99.41 51 | 99.55 20 | 99.74 5 | 99.65 5 | 99.70 23 | 99.25 27 |
|
Gipuma | | | 99.22 29 | 98.86 42 | 99.64 16 | 99.70 62 | 99.24 57 | 99.17 96 | 99.63 47 | 99.52 3 | 99.89 1 | 96.54 178 | 99.14 92 | 99.93 1 | 99.42 29 | 99.15 38 | 99.52 43 | 99.04 46 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
Fast-Effi-MVS+ | | | 98.42 112 | 97.79 124 | 99.15 81 | 99.69 65 | 98.66 141 | 98.94 117 | 99.68 37 | 94.49 177 | 99.05 90 | 98.06 136 | 98.86 120 | 98.48 115 | 98.18 111 | 97.78 130 | 99.05 108 | 98.54 101 |
|
v148 | | | 98.77 84 | 98.45 77 | 99.15 81 | 99.68 66 | 98.94 118 | 99.49 56 | 99.31 126 | 97.95 38 | 98.91 108 | 99.65 49 | 99.62 35 | 99.18 68 | 97.99 125 | 97.64 148 | 98.33 175 | 97.38 166 |
|
v13 | | | 99.22 29 | 98.99 31 | 99.49 31 | 99.68 66 | 99.58 20 | 99.67 20 | 99.77 21 | 98.10 29 | 99.36 38 | 99.88 13 | 99.37 57 | 99.54 22 | 98.50 82 | 98.51 89 | 98.92 121 | 99.03 48 |
|
CP-MVS | | | 98.86 70 | 98.43 85 | 99.36 55 | 99.68 66 | 98.97 112 | 99.19 95 | 99.46 85 | 96.60 121 | 99.20 63 | 97.11 162 | 99.51 46 | 99.15 75 | 98.92 62 | 98.82 62 | 99.45 54 | 99.08 43 |
|
HSP-MVS | | | 98.50 106 | 98.05 113 | 99.03 97 | 99.67 69 | 99.33 48 | 99.51 52 | 99.26 131 | 95.28 159 | 98.51 136 | 98.19 129 | 99.74 11 | 98.29 125 | 97.69 152 | 96.70 179 | 98.96 114 | 99.41 20 |
|
ACMMP_Plus | | | 98.94 53 | 98.72 51 | 99.21 74 | 99.67 69 | 99.08 84 | 99.26 84 | 99.39 93 | 96.84 104 | 98.88 114 | 98.22 127 | 99.68 22 | 98.82 92 | 99.06 47 | 98.90 55 | 99.25 89 | 99.25 27 |
|
HFP-MVS | | | 98.97 49 | 98.70 53 | 99.29 69 | 99.67 69 | 98.98 105 | 99.13 100 | 99.53 70 | 97.76 50 | 98.90 109 | 98.07 134 | 99.50 48 | 99.14 77 | 98.64 77 | 98.78 67 | 99.37 71 | 99.18 34 |
|
v12 | | | 99.19 31 | 98.95 32 | 99.48 32 | 99.67 69 | 99.56 22 | 99.66 22 | 99.76 22 | 98.06 31 | 99.33 43 | 99.88 13 | 99.34 62 | 99.53 23 | 98.42 89 | 98.43 94 | 98.91 124 | 98.97 55 |
|
tfpn_n400 | | | 97.59 152 | 96.36 170 | 99.01 101 | 99.66 73 | 99.19 68 | 99.21 92 | 99.55 60 | 97.62 63 | 97.77 177 | 94.60 200 | 87.78 199 | 98.27 127 | 98.44 85 | 98.72 74 | 99.62 30 | 98.21 128 |
|
tfpnconf | | | 97.59 152 | 96.36 170 | 99.01 101 | 99.66 73 | 99.19 68 | 99.21 92 | 99.55 60 | 97.62 63 | 97.77 177 | 94.60 200 | 87.78 199 | 98.27 127 | 98.44 85 | 98.72 74 | 99.62 30 | 98.21 128 |
|
gg-mvs-nofinetune | | | 96.77 176 | 96.52 166 | 97.06 199 | 99.66 73 | 97.82 187 | 97.54 212 | 99.86 9 | 98.69 17 | 98.61 127 | 99.94 4 | 89.62 194 | 88.37 233 | 97.55 163 | 96.67 181 | 98.30 176 | 95.35 199 |
|
v11 | | | 99.19 31 | 98.95 32 | 99.47 33 | 99.66 73 | 99.54 28 | 99.65 23 | 99.73 28 | 98.06 31 | 99.38 37 | 99.92 6 | 99.40 54 | 99.55 20 | 98.29 102 | 98.50 90 | 98.88 129 | 98.92 64 |
|
V9 | | | 99.16 35 | 98.90 39 | 99.46 34 | 99.66 73 | 99.54 28 | 99.65 23 | 99.75 25 | 98.01 34 | 99.31 47 | 99.87 18 | 99.31 66 | 99.51 25 | 98.34 96 | 98.34 97 | 98.90 126 | 98.91 65 |
|
thres600view7 | | | 96.35 185 | 94.27 194 | 98.79 125 | 99.66 73 | 99.18 70 | 98.94 117 | 99.38 100 | 94.37 185 | 97.21 204 | 87.19 225 | 84.10 217 | 98.10 135 | 98.16 112 | 99.47 21 | 99.42 61 | 97.43 163 |
|
view600 | | | 96.39 184 | 94.30 193 | 98.82 122 | 99.65 79 | 99.16 75 | 98.98 113 | 99.36 111 | 94.46 179 | 97.39 195 | 87.28 223 | 84.16 216 | 98.16 134 | 98.16 112 | 99.48 20 | 99.40 66 | 97.42 164 |
|
CANet | | | 98.47 109 | 98.30 96 | 98.67 137 | 99.65 79 | 98.87 126 | 98.82 133 | 99.01 164 | 96.14 143 | 99.29 52 | 98.86 107 | 99.01 113 | 96.54 174 | 98.36 94 | 98.08 108 | 98.72 153 | 98.80 78 |
|
v1141 | | | 98.87 66 | 98.45 77 | 99.36 55 | 99.65 79 | 99.04 93 | 99.56 40 | 99.38 100 | 97.83 46 | 99.29 52 | 99.86 22 | 99.16 86 | 99.40 43 | 97.68 153 | 97.78 130 | 98.86 134 | 97.82 150 |
|
divwei89l23v2f112 | | | 98.87 66 | 98.45 77 | 99.36 55 | 99.65 79 | 99.04 93 | 99.56 40 | 99.38 100 | 97.83 46 | 99.29 52 | 99.86 22 | 99.15 90 | 99.40 43 | 97.68 153 | 97.78 130 | 98.86 134 | 97.82 150 |
|
V14 | | | 99.13 37 | 98.85 44 | 99.45 35 | 99.65 79 | 99.52 30 | 99.63 29 | 99.74 27 | 97.97 36 | 99.30 50 | 99.87 18 | 99.27 70 | 99.49 29 | 98.23 108 | 98.24 100 | 98.88 129 | 98.83 70 |
|
v2v482 | | | 98.85 72 | 98.40 88 | 99.38 51 | 99.65 79 | 98.98 105 | 99.55 43 | 99.39 93 | 97.92 40 | 99.35 41 | 99.85 27 | 99.14 92 | 99.39 53 | 97.50 165 | 97.78 130 | 98.98 113 | 97.60 157 |
|
v1 | | | 98.87 66 | 98.45 77 | 99.36 55 | 99.65 79 | 99.04 93 | 99.55 43 | 99.38 100 | 97.83 46 | 99.30 50 | 99.86 22 | 99.17 83 | 99.40 43 | 97.68 153 | 97.77 137 | 98.86 134 | 97.82 150 |
|
Vis-MVSNet | | | 99.25 26 | 99.32 16 | 99.17 79 | 99.65 79 | 99.55 26 | 99.63 29 | 99.33 119 | 98.16 28 | 99.29 52 | 99.65 49 | 99.77 8 | 97.56 153 | 99.44 28 | 99.14 39 | 99.58 35 | 99.51 12 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
tfpn1000 | | | 97.10 169 | 95.97 179 | 98.41 154 | 99.64 87 | 99.30 52 | 98.89 126 | 99.49 80 | 96.49 129 | 95.97 217 | 95.31 193 | 85.62 213 | 96.92 169 | 97.86 136 | 99.13 41 | 99.53 42 | 98.11 137 |
|
v15 | | | 99.09 40 | 98.79 47 | 99.43 39 | 99.64 87 | 99.50 31 | 99.61 33 | 99.73 28 | 97.92 40 | 99.28 57 | 99.86 22 | 99.24 72 | 99.47 31 | 98.12 119 | 98.14 105 | 98.87 131 | 98.76 81 |
|
APD-MVS | | | 98.47 109 | 97.97 118 | 99.05 95 | 99.64 87 | 98.91 121 | 98.94 117 | 99.45 89 | 94.40 183 | 98.77 119 | 97.26 156 | 99.41 51 | 98.21 132 | 98.67 75 | 98.57 87 | 99.31 81 | 98.57 97 |
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 87 | 98.90 124 | 98.81 134 | 99.27 130 | 96.55 126 | 99.71 6 | 99.31 77 | 99.66 27 | 99.17 71 | 99.28 35 | 99.11 42 | 99.10 97 | 98.57 97 |
|
LS3D | | | 98.79 82 | 98.52 69 | 99.12 85 | 99.64 87 | 99.09 82 | 99.24 87 | 99.46 85 | 97.75 53 | 98.93 106 | 97.47 152 | 98.23 143 | 97.98 141 | 99.36 30 | 99.30 32 | 99.46 53 | 98.42 109 |
|
tfpnview11 | | | 97.49 157 | 96.22 174 | 98.97 105 | 99.63 92 | 99.24 57 | 99.12 102 | 99.54 66 | 96.76 112 | 97.77 177 | 94.60 200 | 87.78 199 | 98.25 130 | 97.93 129 | 99.14 39 | 99.52 43 | 98.08 140 |
|
IterMVS-LS | | | 98.23 122 | 97.66 129 | 98.90 110 | 99.63 92 | 99.38 43 | 99.07 105 | 99.48 81 | 97.75 53 | 98.81 118 | 99.37 75 | 94.57 184 | 97.88 145 | 96.54 192 | 97.04 173 | 98.53 167 | 98.97 55 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Effi-MVS+ | | | 98.11 129 | 97.29 141 | 99.06 92 | 99.62 94 | 98.55 150 | 98.16 186 | 99.80 15 | 94.64 174 | 99.15 74 | 96.59 175 | 97.43 161 | 98.44 116 | 97.46 167 | 97.90 119 | 99.17 94 | 98.45 106 |
|
v1144 | | | 98.94 53 | 98.53 67 | 99.42 41 | 99.62 94 | 99.03 98 | 99.58 37 | 99.36 111 | 97.99 35 | 99.49 30 | 99.91 11 | 99.20 80 | 99.51 25 | 97.61 159 | 97.85 127 | 98.95 116 | 98.10 138 |
|
3Dnovator+ | | 97.85 5 | 98.61 95 | 98.14 106 | 99.15 81 | 99.62 94 | 98.37 161 | 99.10 104 | 99.51 73 | 98.04 33 | 98.98 97 | 96.07 187 | 98.75 126 | 98.55 110 | 98.51 81 | 98.40 95 | 99.17 94 | 98.82 72 |
|
v1192 | | | 98.91 59 | 98.48 72 | 99.41 42 | 99.61 97 | 99.03 98 | 99.64 26 | 99.25 134 | 97.91 42 | 99.58 20 | 99.92 6 | 99.07 109 | 99.45 34 | 97.55 163 | 97.68 144 | 98.93 118 | 98.23 125 |
|
v1240 | | | 98.86 70 | 98.41 86 | 99.38 51 | 99.59 98 | 99.05 89 | 99.65 23 | 99.14 150 | 97.68 61 | 99.66 15 | 99.93 5 | 98.72 127 | 99.45 34 | 97.38 174 | 97.72 142 | 98.79 147 | 98.35 113 |
|
3Dnovator | | 98.16 3 | 98.65 90 | 98.35 92 | 99.00 103 | 99.59 98 | 98.70 137 | 98.90 125 | 99.36 111 | 97.97 36 | 99.09 85 | 96.55 177 | 99.09 105 | 97.97 142 | 98.70 74 | 98.65 81 | 99.12 96 | 98.81 74 |
|
v1921920 | | | 98.89 63 | 98.46 73 | 99.39 46 | 99.58 100 | 99.04 93 | 99.64 26 | 99.17 146 | 97.91 42 | 99.64 17 | 99.92 6 | 98.99 116 | 99.44 37 | 97.44 170 | 97.57 154 | 98.84 138 | 98.35 113 |
|
thres400 | | | 96.22 190 | 94.08 197 | 98.72 130 | 99.58 100 | 99.05 89 | 98.83 130 | 99.22 137 | 94.01 192 | 97.40 193 | 86.34 231 | 84.91 215 | 97.93 143 | 97.85 139 | 99.08 43 | 99.37 71 | 97.28 169 |
|
CDPH-MVS | | | 97.99 131 | 97.23 145 | 98.87 114 | 99.58 100 | 98.29 163 | 98.83 130 | 99.20 142 | 93.76 195 | 98.11 163 | 96.11 185 | 99.16 86 | 98.23 131 | 97.80 144 | 97.22 169 | 99.29 85 | 98.28 119 |
|
UGNet | | | 98.52 105 | 99.00 30 | 97.96 180 | 99.58 100 | 99.26 55 | 99.27 83 | 99.40 91 | 98.07 30 | 98.28 155 | 98.76 110 | 99.71 18 | 92.24 223 | 98.94 59 | 98.85 59 | 99.00 112 | 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 94 | 98.40 88 | 98.89 112 | 99.57 104 | 98.80 128 | 98.63 150 | 99.35 116 | 96.82 107 | 98.60 128 | 98.85 109 | 99.08 107 | 98.09 137 | 98.31 100 | 98.21 101 | 99.08 102 | 98.72 85 |
|
ESAPD | | | 98.60 97 | 98.41 86 | 98.83 119 | 99.56 105 | 99.21 63 | 98.66 149 | 99.47 82 | 95.22 160 | 98.35 149 | 98.48 118 | 99.67 26 | 97.84 148 | 98.80 70 | 98.57 87 | 99.10 97 | 98.93 63 |
|
v144192 | | | 98.88 65 | 98.46 73 | 99.37 53 | 99.56 105 | 99.03 98 | 99.61 33 | 99.26 131 | 97.79 49 | 99.58 20 | 99.88 13 | 99.11 100 | 99.43 39 | 97.38 174 | 97.61 150 | 98.80 145 | 98.43 108 |
|
new-patchmatchnet | | | 97.26 164 | 96.12 176 | 98.58 145 | 99.55 107 | 98.63 143 | 99.14 99 | 97.04 222 | 98.80 16 | 99.19 65 | 99.92 6 | 99.19 81 | 98.92 86 | 95.51 206 | 87.04 222 | 97.66 190 | 93.73 211 |
|
DI_MVS_plusplus_trai | | | 97.57 155 | 96.55 165 | 98.77 126 | 99.55 107 | 98.76 131 | 99.22 90 | 99.00 165 | 97.08 100 | 97.95 173 | 97.78 143 | 91.35 193 | 98.02 140 | 96.20 195 | 96.81 178 | 98.87 131 | 97.87 149 |
|
CHOSEN 1792x2688 | | | 98.31 117 | 98.02 115 | 98.66 139 | 99.55 107 | 98.57 149 | 99.38 68 | 99.25 134 | 98.42 22 | 98.48 142 | 99.58 57 | 99.85 6 | 98.31 124 | 95.75 202 | 95.71 195 | 96.96 202 | 98.27 121 |
|
v1neww | | | 98.84 73 | 98.45 77 | 99.29 69 | 99.54 110 | 98.98 105 | 99.54 47 | 99.37 108 | 97.48 73 | 99.10 81 | 99.80 35 | 99.12 96 | 99.40 43 | 97.85 139 | 97.89 121 | 98.81 140 | 98.04 141 |
|
v7new | | | 98.84 73 | 98.45 77 | 99.29 69 | 99.54 110 | 98.98 105 | 99.54 47 | 99.37 108 | 97.48 73 | 99.10 81 | 99.80 35 | 99.12 96 | 99.40 43 | 97.85 139 | 97.89 121 | 98.81 140 | 98.04 141 |
|
v17 | | | 98.96 51 | 98.63 58 | 99.35 60 | 99.54 110 | 99.41 39 | 99.55 43 | 99.70 34 | 97.40 80 | 99.10 81 | 99.79 37 | 99.10 101 | 99.40 43 | 97.96 126 | 97.99 113 | 98.80 145 | 98.77 80 |
|
v8 | | | 98.94 53 | 98.60 60 | 99.35 60 | 99.54 110 | 99.39 41 | 99.55 43 | 99.67 40 | 97.48 73 | 99.13 76 | 99.81 32 | 99.10 101 | 99.39 53 | 97.86 136 | 97.89 121 | 98.81 140 | 98.66 91 |
|
v6 | | | 98.84 73 | 98.46 73 | 99.30 66 | 99.54 110 | 98.98 105 | 99.54 47 | 99.37 108 | 97.49 72 | 99.11 80 | 99.81 32 | 99.13 95 | 99.40 43 | 97.86 136 | 97.89 121 | 98.81 140 | 98.04 141 |
|
CPTT-MVS | | | 98.28 118 | 97.51 136 | 99.16 80 | 99.54 110 | 98.78 130 | 98.96 115 | 99.36 111 | 96.30 138 | 98.89 112 | 93.10 211 | 99.30 67 | 99.20 66 | 98.35 95 | 97.96 118 | 99.03 110 | 98.82 72 |
|
FMVSNet1 | | | 98.90 61 | 99.10 27 | 98.67 137 | 99.54 110 | 99.48 33 | 99.22 90 | 99.66 41 | 98.39 25 | 97.50 190 | 99.66 45 | 99.04 110 | 96.58 173 | 99.05 48 | 99.03 49 | 99.52 43 | 99.08 43 |
|
HPM-MVS++ | | | 98.56 103 | 98.08 111 | 99.11 87 | 99.53 117 | 98.61 145 | 99.02 111 | 99.32 124 | 96.29 139 | 99.06 88 | 97.23 157 | 99.50 48 | 98.77 95 | 98.15 115 | 97.90 119 | 98.96 114 | 98.90 67 |
|
v16 | | | 98.95 52 | 98.62 59 | 99.34 62 | 99.53 117 | 99.41 39 | 99.54 47 | 99.70 34 | 97.34 84 | 99.07 87 | 99.76 41 | 99.10 101 | 99.40 43 | 97.96 126 | 98.00 112 | 98.79 147 | 98.76 81 |
|
v7 | | | 98.91 59 | 98.53 67 | 99.36 55 | 99.53 117 | 98.99 104 | 99.57 38 | 99.36 111 | 97.58 69 | 99.32 45 | 99.88 13 | 99.23 74 | 99.50 27 | 97.77 147 | 97.98 115 | 98.91 124 | 98.26 122 |
|
MCST-MVS | | | 98.25 121 | 97.57 134 | 99.06 92 | 99.53 117 | 98.24 169 | 98.63 150 | 99.17 146 | 95.88 150 | 98.58 130 | 96.11 185 | 99.09 105 | 99.18 68 | 97.58 162 | 97.31 165 | 99.25 89 | 98.75 83 |
|
CLD-MVS | | | 98.48 108 | 98.15 105 | 98.86 117 | 99.53 117 | 98.35 162 | 98.55 161 | 97.83 214 | 96.02 147 | 98.97 98 | 99.08 93 | 99.75 9 | 99.03 83 | 98.10 121 | 97.33 164 | 99.28 86 | 98.44 107 |
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 73 | 98.59 61 | 99.12 85 | 99.52 122 | 98.50 155 | 99.13 100 | 99.22 137 | 97.76 50 | 98.76 120 | 98.70 111 | 99.61 36 | 98.90 87 | 98.67 75 | 98.37 96 | 99.19 93 | 98.57 97 |
|
v10 | | | 99.01 45 | 98.66 57 | 99.41 42 | 99.52 122 | 99.39 41 | 99.57 38 | 99.66 41 | 97.59 67 | 99.32 45 | 99.88 13 | 99.23 74 | 99.50 27 | 97.77 147 | 97.98 115 | 98.92 121 | 98.78 79 |
|
V42 | | | 98.81 81 | 98.49 71 | 99.18 78 | 99.52 122 | 98.92 120 | 99.50 55 | 99.29 127 | 97.43 78 | 98.97 98 | 99.81 32 | 99.00 115 | 99.30 59 | 97.93 129 | 98.01 111 | 98.51 170 | 98.34 117 |
|
Anonymous20231206 | | | 98.50 106 | 98.03 114 | 99.05 95 | 99.50 125 | 99.01 102 | 99.15 98 | 99.26 131 | 96.38 134 | 99.12 78 | 99.50 66 | 99.12 96 | 98.60 105 | 97.68 153 | 97.24 168 | 98.66 156 | 97.30 168 |
|
v18 | | | 98.89 63 | 98.54 65 | 99.30 66 | 99.50 125 | 99.37 44 | 99.51 52 | 99.68 37 | 97.25 92 | 99.00 96 | 99.76 41 | 99.04 110 | 99.36 55 | 97.81 143 | 97.86 126 | 98.77 150 | 98.68 90 |
|
OpenMVS | | 97.26 9 | 97.88 137 | 97.17 148 | 98.70 133 | 99.50 125 | 98.55 150 | 98.34 177 | 99.11 155 | 93.92 193 | 98.90 109 | 95.04 196 | 98.23 143 | 97.38 161 | 98.11 120 | 98.12 106 | 98.95 116 | 98.23 125 |
|
pmmvs-eth3d | | | 98.68 87 | 98.14 106 | 99.29 69 | 99.49 128 | 98.45 158 | 99.45 62 | 99.38 100 | 97.21 94 | 99.50 29 | 99.65 49 | 99.21 78 | 99.16 73 | 97.11 182 | 97.56 155 | 98.79 147 | 97.82 150 |
|
PHI-MVS | | | 98.57 100 | 98.20 103 | 99.00 103 | 99.48 129 | 98.91 121 | 98.68 142 | 99.17 146 | 94.97 168 | 99.27 60 | 98.33 122 | 99.33 63 | 98.05 139 | 98.82 68 | 98.62 82 | 99.34 76 | 98.38 111 |
|
pmmvs5 | | | 98.37 114 | 97.81 123 | 99.03 97 | 99.46 130 | 98.97 112 | 99.03 107 | 98.96 168 | 95.85 151 | 99.05 90 | 99.45 69 | 98.66 133 | 98.79 94 | 96.02 199 | 97.52 156 | 98.87 131 | 98.21 128 |
|
ambc | | | | 97.89 121 | | 99.45 131 | 97.88 185 | 97.78 199 | | 97.27 88 | 99.80 3 | 98.99 103 | 98.48 138 | 98.55 110 | 97.80 144 | 96.68 180 | 98.54 166 | 98.10 138 |
|
canonicalmvs | | | 98.34 116 | 97.92 120 | 98.83 119 | 99.45 131 | 99.21 63 | 98.37 173 | 99.53 70 | 97.06 101 | 97.74 181 | 96.95 169 | 95.05 182 | 98.36 121 | 98.77 72 | 98.85 59 | 99.51 48 | 99.53 9 |
|
IterMVS | | | 97.40 161 | 96.67 159 | 98.25 163 | 99.45 131 | 98.66 141 | 98.87 128 | 98.73 179 | 96.40 133 | 98.94 105 | 99.56 59 | 95.26 181 | 97.58 152 | 95.38 207 | 94.70 208 | 95.90 211 | 96.72 183 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
thres200 | | | 96.23 189 | 94.13 195 | 98.69 135 | 99.44 134 | 99.18 70 | 98.58 159 | 99.38 100 | 93.52 198 | 97.35 197 | 86.33 232 | 85.83 212 | 97.93 143 | 98.16 112 | 98.78 67 | 99.42 61 | 97.10 178 |
|
PCF-MVS | | 95.58 16 | 97.60 150 | 96.67 159 | 98.69 135 | 99.44 134 | 98.23 170 | 98.37 173 | 98.81 175 | 93.01 205 | 98.22 157 | 97.97 140 | 99.59 39 | 98.20 133 | 95.72 204 | 95.08 205 | 99.08 102 | 97.09 180 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
train_agg | | | 97.99 131 | 97.26 142 | 98.83 119 | 99.43 136 | 98.22 171 | 98.91 122 | 99.07 158 | 94.43 181 | 97.96 172 | 96.42 180 | 99.30 67 | 98.81 93 | 97.39 172 | 96.62 182 | 98.82 139 | 98.47 103 |
|
N_pmnet | | | 96.68 178 | 95.70 186 | 97.84 183 | 99.42 137 | 98.00 180 | 99.35 72 | 98.21 202 | 98.40 24 | 98.13 162 | 99.42 72 | 99.30 67 | 97.44 160 | 94.00 221 | 88.79 219 | 94.47 217 | 91.96 219 |
|
USDC | | | 98.26 120 | 97.57 134 | 99.06 92 | 99.42 137 | 97.98 183 | 98.83 130 | 98.85 172 | 97.57 70 | 99.59 19 | 99.15 89 | 98.59 135 | 98.99 84 | 97.42 171 | 96.08 194 | 98.69 155 | 96.23 190 |
|
NCCC | | | 97.84 139 | 96.96 155 | 98.87 114 | 99.39 139 | 98.27 166 | 98.46 166 | 99.02 163 | 96.78 110 | 98.73 124 | 91.12 217 | 98.91 117 | 98.57 108 | 97.83 142 | 97.49 158 | 99.04 109 | 98.33 118 |
|
tfpn111 | | | 96.48 180 | 94.67 191 | 98.59 143 | 99.37 140 | 99.18 70 | 98.68 142 | 99.39 93 | 92.02 214 | 97.21 204 | 90.63 218 | 86.34 207 | 97.45 155 | 98.15 115 | 99.08 43 | 99.43 58 | 97.28 169 |
|
conf200view11 | | | 96.16 193 | 94.08 197 | 98.59 143 | 99.37 140 | 99.18 70 | 98.68 142 | 99.39 93 | 92.02 214 | 97.21 204 | 86.53 228 | 86.34 207 | 97.45 155 | 98.15 115 | 99.08 43 | 99.43 58 | 97.28 169 |
|
thres100view900 | | | 95.74 197 | 93.66 205 | 98.17 169 | 99.37 140 | 98.59 146 | 98.10 187 | 98.33 198 | 92.02 214 | 97.30 200 | 86.53 228 | 86.34 207 | 96.69 171 | 96.77 188 | 98.47 92 | 99.24 91 | 96.89 181 |
|
tfpn200view9 | | | 96.17 191 | 94.08 197 | 98.60 142 | 99.37 140 | 99.18 70 | 98.68 142 | 99.39 93 | 92.02 214 | 97.30 200 | 86.53 228 | 86.34 207 | 97.45 155 | 98.15 115 | 99.08 43 | 99.43 58 | 97.28 169 |
|
testmv | | | 97.48 159 | 96.83 158 | 98.24 166 | 99.37 140 | 97.79 190 | 98.59 157 | 99.07 158 | 92.40 208 | 97.59 185 | 99.24 80 | 98.11 147 | 97.66 150 | 97.64 157 | 97.11 171 | 97.17 198 | 95.54 198 |
|
test1235678 | | | 97.49 157 | 96.84 157 | 98.24 166 | 99.37 140 | 97.79 190 | 98.59 157 | 99.07 158 | 92.41 207 | 97.59 185 | 99.24 80 | 98.15 146 | 97.66 150 | 97.64 157 | 97.12 170 | 97.17 198 | 95.55 197 |
|
RPSCF | | | 98.84 73 | 98.81 45 | 98.89 112 | 99.37 140 | 98.95 114 | 98.51 163 | 98.85 172 | 97.73 57 | 98.33 151 | 98.97 104 | 99.14 92 | 98.95 85 | 99.18 37 | 98.68 77 | 99.31 81 | 98.99 53 |
|
MDA-MVSNet-bldmvs | | | 97.75 141 | 97.26 142 | 98.33 159 | 99.35 147 | 98.45 158 | 99.32 77 | 97.21 220 | 97.90 44 | 99.05 90 | 99.01 101 | 96.86 170 | 99.08 79 | 99.36 30 | 92.97 214 | 95.97 210 | 96.25 189 |
|
CNVR-MVS | | | 98.22 124 | 97.76 125 | 98.76 127 | 99.33 148 | 98.26 167 | 98.48 164 | 98.88 171 | 96.22 140 | 98.47 144 | 95.79 189 | 99.33 63 | 98.35 122 | 98.37 92 | 97.99 113 | 99.03 110 | 98.38 111 |
|
DeepC-MVS_fast | | 97.38 8 | 98.65 90 | 98.34 93 | 99.02 100 | 99.33 148 | 98.29 163 | 98.99 112 | 98.71 181 | 97.40 80 | 99.31 47 | 98.20 128 | 99.40 54 | 98.54 112 | 98.33 99 | 98.18 104 | 99.23 92 | 98.58 95 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MSDG | | | 98.20 125 | 97.88 122 | 98.56 147 | 99.33 148 | 97.74 193 | 98.27 181 | 98.10 205 | 97.20 96 | 98.06 165 | 98.59 116 | 99.16 86 | 98.76 96 | 98.39 91 | 97.71 143 | 98.86 134 | 96.38 187 |
|
thresconf0.02 | | | 95.49 199 | 92.74 210 | 98.70 133 | 99.32 151 | 98.70 137 | 98.87 128 | 99.21 139 | 95.95 148 | 97.57 187 | 90.63 218 | 73.55 232 | 97.86 147 | 96.09 198 | 97.03 174 | 99.40 66 | 97.22 174 |
|
EPNet | | | 96.44 183 | 96.08 177 | 96.86 206 | 99.32 151 | 97.15 202 | 97.69 206 | 99.32 124 | 93.67 196 | 98.11 163 | 95.64 191 | 93.44 188 | 89.07 231 | 96.86 186 | 96.83 177 | 97.67 189 | 98.97 55 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MVS_111021_HR | | | 98.58 99 | 98.26 99 | 98.96 106 | 99.32 151 | 98.81 127 | 98.48 164 | 98.99 166 | 96.81 109 | 99.16 71 | 98.07 134 | 99.23 74 | 98.89 89 | 98.43 88 | 98.27 99 | 98.90 126 | 98.24 124 |
|
PVSNet_BlendedMVS | | | 97.93 135 | 97.66 129 | 98.25 163 | 99.30 154 | 98.67 139 | 98.31 178 | 97.95 209 | 94.30 186 | 98.75 121 | 97.63 146 | 98.76 124 | 96.30 181 | 98.29 102 | 97.78 130 | 98.93 118 | 98.18 132 |
|
PVSNet_Blended | | | 97.93 135 | 97.66 129 | 98.25 163 | 99.30 154 | 98.67 139 | 98.31 178 | 97.95 209 | 94.30 186 | 98.75 121 | 97.63 146 | 98.76 124 | 96.30 181 | 98.29 102 | 97.78 130 | 98.93 118 | 98.18 132 |
|
HQP-MVS | | | 97.58 154 | 96.65 163 | 98.66 139 | 99.30 154 | 97.99 181 | 97.88 197 | 98.65 184 | 94.58 175 | 98.66 125 | 94.65 199 | 99.15 90 | 98.59 106 | 96.10 197 | 95.59 197 | 98.90 126 | 98.50 102 |
|
conf0.01 | | | 94.53 211 | 91.09 219 | 98.53 150 | 99.29 157 | 99.05 89 | 98.68 142 | 99.35 116 | 92.02 214 | 97.04 208 | 84.45 234 | 68.52 234 | 97.45 155 | 97.79 146 | 99.08 43 | 99.41 64 | 96.70 184 |
|
TinyColmap | | | 98.27 119 | 97.62 133 | 99.03 97 | 99.29 157 | 97.79 190 | 98.92 120 | 98.95 169 | 97.48 73 | 99.52 27 | 98.65 114 | 97.86 156 | 98.90 87 | 98.34 96 | 97.27 166 | 98.64 159 | 95.97 193 |
|
TSAR-MVS + GP. | | | 98.54 104 | 98.29 98 | 98.82 122 | 99.28 159 | 98.59 146 | 97.73 202 | 99.24 136 | 95.93 149 | 98.59 129 | 99.07 95 | 99.17 83 | 98.86 90 | 98.44 85 | 98.10 107 | 99.26 88 | 98.72 85 |
|
MVS_Test | | | 97.69 145 | 97.15 150 | 98.33 159 | 99.27 160 | 98.43 160 | 98.25 182 | 99.29 127 | 95.00 167 | 97.39 195 | 98.86 107 | 98.00 152 | 97.14 165 | 95.38 207 | 96.22 188 | 98.62 160 | 98.15 136 |
|
conf0.002 | | | 93.97 216 | 90.06 223 | 98.52 151 | 99.26 161 | 99.02 101 | 98.68 142 | 99.33 119 | 92.02 214 | 97.01 209 | 83.82 235 | 63.41 237 | 97.45 155 | 97.73 150 | 97.98 115 | 99.40 66 | 96.47 186 |
|
PM-MVS | | | 98.57 100 | 98.24 101 | 98.95 107 | 99.26 161 | 98.59 146 | 99.03 107 | 98.74 178 | 96.84 104 | 99.44 34 | 99.13 90 | 98.31 142 | 98.75 97 | 98.03 123 | 98.21 101 | 98.48 171 | 98.58 95 |
|
PMVS | | 92.51 17 | 98.66 89 | 98.86 42 | 98.43 153 | 99.26 161 | 98.98 105 | 98.60 156 | 98.59 188 | 97.73 57 | 99.45 33 | 99.38 74 | 98.54 137 | 95.24 194 | 99.62 14 | 99.61 11 | 99.42 61 | 98.17 134 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
Effi-MVS+-dtu | | | 97.78 140 | 97.37 139 | 98.26 162 | 99.25 164 | 98.50 155 | 97.89 196 | 99.19 143 | 94.51 176 | 98.16 160 | 95.93 188 | 98.80 123 | 95.97 185 | 98.27 107 | 97.38 161 | 99.10 97 | 98.23 125 |
|
AdaColmap | | | 97.57 155 | 96.57 164 | 98.74 128 | 99.25 164 | 98.01 179 | 98.36 176 | 98.98 167 | 94.44 180 | 98.47 144 | 92.44 215 | 97.91 155 | 98.62 104 | 98.19 110 | 97.74 139 | 98.73 152 | 97.28 169 |
|
DELS-MVS | | | 98.63 93 | 98.70 53 | 98.55 148 | 99.24 166 | 99.04 93 | 98.96 115 | 98.52 191 | 96.83 106 | 98.38 147 | 99.58 57 | 99.68 22 | 97.06 168 | 98.74 73 | 98.44 93 | 99.10 97 | 98.59 94 |
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 32 | 99.11 87 | 99.23 167 | 98.79 129 | 99.51 52 | 98.73 179 | 97.50 71 | 98.56 131 | 99.03 99 | 99.59 39 | 99.16 73 | 99.29 33 | 99.17 37 | 99.50 49 | 99.24 30 |
|
pmmvs4 | | | 97.87 138 | 97.02 153 | 98.86 117 | 99.20 168 | 97.68 195 | 98.89 126 | 99.03 162 | 96.57 124 | 99.12 78 | 99.03 99 | 97.26 165 | 98.42 118 | 95.16 211 | 96.34 186 | 98.53 167 | 97.10 178 |
|
test-LLR | | | 94.79 206 | 93.71 203 | 96.06 220 | 99.20 168 | 96.16 212 | 96.31 227 | 98.50 192 | 89.98 231 | 94.08 230 | 97.01 164 | 86.43 205 | 92.20 224 | 96.76 189 | 95.31 200 | 96.05 208 | 94.31 207 |
|
test0.0.03 1 | | | 95.81 196 | 95.77 185 | 95.85 224 | 99.20 168 | 98.15 174 | 97.49 216 | 98.50 192 | 92.24 209 | 92.74 237 | 96.82 171 | 92.70 190 | 88.60 232 | 97.31 178 | 97.01 176 | 98.57 165 | 96.19 191 |
|
CANet_DTU | | | 97.65 148 | 97.50 137 | 97.82 185 | 99.19 171 | 98.08 176 | 98.41 169 | 98.67 183 | 94.40 183 | 99.16 71 | 98.32 123 | 98.69 128 | 93.96 212 | 97.87 135 | 97.61 150 | 97.51 193 | 97.56 160 |
|
EPNet_dtu | | | 96.31 186 | 95.96 180 | 96.72 210 | 99.18 172 | 95.39 226 | 97.03 223 | 99.13 154 | 93.02 204 | 99.35 41 | 97.23 157 | 97.07 167 | 90.70 228 | 95.74 203 | 95.08 205 | 94.94 214 | 98.16 135 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
tfpn_ndepth | | | 96.69 177 | 95.49 188 | 98.09 174 | 99.17 173 | 99.13 78 | 98.61 155 | 99.38 100 | 94.90 171 | 95.85 219 | 92.85 213 | 88.19 198 | 96.07 184 | 97.28 179 | 98.67 78 | 99.49 51 | 97.44 162 |
|
TSAR-MVS + ACMM | | | 98.64 92 | 98.58 63 | 98.72 130 | 99.17 173 | 98.63 143 | 98.69 141 | 99.10 157 | 97.69 60 | 98.30 153 | 99.12 92 | 99.38 56 | 98.70 100 | 98.45 84 | 97.51 157 | 98.35 174 | 99.25 27 |
|
GA-MVS | | | 96.84 174 | 95.86 183 | 97.98 178 | 99.16 175 | 98.29 163 | 97.91 194 | 98.64 186 | 95.14 162 | 97.71 183 | 98.04 138 | 88.90 196 | 96.50 175 | 96.41 193 | 96.61 183 | 97.97 187 | 97.60 157 |
|
SD-MVS | | | 98.73 85 | 98.54 65 | 98.95 107 | 99.14 176 | 98.76 131 | 98.46 166 | 99.14 150 | 97.71 59 | 98.56 131 | 98.06 136 | 99.61 36 | 98.85 91 | 98.56 79 | 97.74 139 | 99.54 38 | 99.32 23 |
|
EU-MVSNet | | | 98.68 87 | 98.94 36 | 98.37 158 | 99.14 176 | 98.74 135 | 99.64 26 | 98.20 204 | 98.21 26 | 99.17 68 | 99.66 45 | 99.18 82 | 99.08 79 | 99.11 40 | 98.86 57 | 95.00 213 | 98.83 70 |
|
testus | | | 96.13 194 | 95.13 189 | 97.28 194 | 99.13 178 | 97.00 203 | 96.84 225 | 97.89 213 | 90.48 230 | 97.40 193 | 93.60 208 | 96.47 173 | 95.39 192 | 96.21 194 | 96.19 190 | 97.05 200 | 95.99 192 |
|
MDTV_nov1_ep13_2view | | | 97.12 167 | 96.19 175 | 98.22 168 | 99.13 178 | 98.05 177 | 99.24 87 | 99.47 82 | 97.61 65 | 99.15 74 | 99.59 55 | 99.01 113 | 98.40 119 | 94.87 213 | 90.14 217 | 93.91 218 | 94.04 210 |
|
PLC | | 95.63 15 | 97.73 144 | 97.01 154 | 98.57 146 | 99.10 180 | 97.80 189 | 97.72 203 | 98.77 177 | 96.34 135 | 98.38 147 | 93.46 210 | 98.06 149 | 98.66 102 | 97.90 132 | 97.65 147 | 98.77 150 | 97.90 148 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
CHOSEN 280x420 | | | 96.80 175 | 96.30 172 | 97.39 191 | 99.09 181 | 96.52 206 | 98.76 138 | 99.29 127 | 93.88 194 | 97.65 184 | 98.34 121 | 93.66 187 | 96.29 183 | 98.28 105 | 97.73 141 | 93.27 222 | 95.70 195 |
|
IB-MVS | | 95.85 14 | 95.87 195 | 94.88 190 | 97.02 202 | 99.09 181 | 98.25 168 | 97.16 219 | 97.38 218 | 91.97 221 | 97.77 177 | 83.61 236 | 97.29 164 | 92.03 226 | 97.16 181 | 97.66 145 | 98.66 156 | 98.20 131 |
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 | | | 97.75 141 | 97.68 128 | 97.83 184 | 99.08 183 | 98.20 172 | 98.68 142 | 98.61 187 | 95.63 154 | 97.80 176 | 99.24 80 | 96.93 169 | 94.09 210 | 97.96 126 | 97.82 128 | 98.71 154 | 97.99 144 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
MVS_111021_LR | | | 98.39 113 | 98.11 108 | 98.71 132 | 99.08 183 | 98.54 153 | 98.23 184 | 98.56 190 | 96.57 124 | 99.13 76 | 98.41 119 | 98.86 120 | 98.65 103 | 98.23 108 | 97.87 125 | 98.65 158 | 98.28 119 |
|
TAMVS | | | 96.95 172 | 96.94 156 | 96.97 205 | 99.07 185 | 97.67 196 | 97.98 192 | 97.12 221 | 95.04 164 | 95.41 224 | 99.27 79 | 95.57 180 | 94.09 210 | 97.32 176 | 97.11 171 | 98.16 183 | 96.59 185 |
|
abl_6 | | | | | 98.38 157 | 99.03 186 | 98.04 178 | 98.08 189 | 98.65 184 | 93.23 201 | 98.56 131 | 94.58 203 | 98.57 136 | 97.17 164 | | | 98.81 140 | 97.42 164 |
|
MIMVSNet | | | 97.24 165 | 97.15 150 | 97.36 193 | 99.03 186 | 98.52 154 | 98.55 161 | 99.73 28 | 94.94 170 | 94.94 229 | 97.98 139 | 97.37 163 | 93.66 214 | 97.60 160 | 97.34 163 | 98.23 180 | 96.29 188 |
|
diffmvs | | | 97.29 163 | 96.67 159 | 98.01 177 | 99.00 188 | 97.82 187 | 98.37 173 | 99.18 144 | 96.73 116 | 97.74 181 | 99.08 93 | 94.26 185 | 96.50 175 | 94.86 215 | 95.67 196 | 97.29 196 | 98.25 123 |
|
Fast-Effi-MVS+-dtu | | | 96.99 170 | 96.46 167 | 97.61 189 | 98.98 189 | 97.89 184 | 97.54 212 | 99.76 22 | 93.43 199 | 96.55 213 | 94.93 197 | 98.06 149 | 94.32 208 | 96.93 185 | 96.50 185 | 98.53 167 | 97.47 161 |
|
no-one | | | 99.01 45 | 98.94 36 | 99.09 91 | 98.97 190 | 98.55 150 | 99.37 69 | 99.04 161 | 97.59 67 | 99.36 38 | 99.66 45 | 99.75 9 | 99.57 16 | 98.47 83 | 99.27 33 | 98.21 181 | 99.30 25 |
|
MAR-MVS | | | 97.12 167 | 96.28 173 | 98.11 173 | 98.94 191 | 97.22 200 | 97.65 207 | 99.38 100 | 90.93 229 | 98.15 161 | 95.17 194 | 97.13 166 | 96.48 177 | 97.71 151 | 97.40 160 | 98.06 184 | 98.40 110 |
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++ | | | 97.99 131 | 97.64 132 | 98.40 155 | 98.91 192 | 98.47 157 | 97.12 221 | 98.78 176 | 96.49 129 | 98.48 142 | 93.57 209 | 99.12 96 | 98.51 114 | 98.31 100 | 98.58 85 | 98.58 164 | 98.95 61 |
|
ADS-MVSNet | | | 94.41 214 | 92.13 214 | 97.07 198 | 98.86 193 | 96.60 205 | 98.38 172 | 98.47 195 | 96.13 145 | 98.02 167 | 96.98 167 | 87.50 203 | 95.87 187 | 89.89 226 | 87.58 221 | 92.79 226 | 90.27 225 |
|
OMC-MVS | | | 98.35 115 | 98.10 109 | 98.64 141 | 98.85 194 | 97.99 181 | 98.56 160 | 98.21 202 | 97.26 90 | 98.87 116 | 98.54 117 | 99.27 70 | 98.43 117 | 98.34 96 | 97.66 145 | 98.92 121 | 97.65 156 |
|
MVSTER | | | 95.38 201 | 93.99 201 | 97.01 203 | 98.83 195 | 98.95 114 | 96.62 226 | 99.14 150 | 92.17 211 | 97.44 191 | 97.29 155 | 77.88 227 | 91.63 227 | 97.45 168 | 96.18 191 | 98.41 173 | 97.99 144 |
|
TAPA-MVS | | 96.65 12 | 98.23 122 | 97.96 119 | 98.55 148 | 98.81 196 | 98.16 173 | 98.40 170 | 97.94 211 | 96.68 117 | 98.49 140 | 98.61 115 | 98.89 118 | 98.57 108 | 97.45 168 | 97.59 152 | 99.09 101 | 98.35 113 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
CostFormer | | | 92.75 221 | 89.49 224 | 96.55 214 | 98.78 197 | 95.83 223 | 97.55 211 | 98.59 188 | 91.83 222 | 97.34 198 | 96.31 182 | 78.53 226 | 94.50 204 | 86.14 231 | 84.92 227 | 92.54 227 | 92.84 215 |
|
PatchmatchNet | | | 93.88 218 | 91.08 220 | 97.14 197 | 98.75 198 | 96.01 218 | 98.25 182 | 99.39 93 | 94.95 169 | 98.96 100 | 96.32 181 | 85.35 214 | 95.50 191 | 88.89 228 | 85.89 226 | 91.99 230 | 90.15 226 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
GBi-Net | | | 97.69 145 | 97.75 126 | 97.62 187 | 98.71 199 | 99.21 63 | 98.62 152 | 99.33 119 | 94.09 189 | 95.60 221 | 98.17 131 | 95.97 176 | 94.39 205 | 99.05 48 | 99.03 49 | 99.08 102 | 98.70 87 |
|
test1 | | | 97.69 145 | 97.75 126 | 97.62 187 | 98.71 199 | 99.21 63 | 98.62 152 | 99.33 119 | 94.09 189 | 95.60 221 | 98.17 131 | 95.97 176 | 94.39 205 | 99.05 48 | 99.03 49 | 99.08 102 | 98.70 87 |
|
FMVSNet2 | | | 97.94 134 | 98.08 111 | 97.77 186 | 98.71 199 | 99.21 63 | 98.62 152 | 99.47 82 | 96.62 119 | 96.37 214 | 99.20 86 | 97.70 158 | 94.39 205 | 97.39 172 | 97.75 138 | 99.08 102 | 98.70 87 |
|
PatchMatch-RL | | | 97.24 165 | 96.45 168 | 98.17 169 | 98.70 202 | 97.57 197 | 97.31 217 | 98.48 194 | 94.42 182 | 98.39 146 | 95.74 190 | 96.35 175 | 97.88 145 | 97.75 149 | 97.48 159 | 98.24 179 | 95.87 194 |
|
LP | | | 95.33 203 | 93.45 206 | 97.54 190 | 98.68 203 | 97.40 198 | 98.73 139 | 98.41 196 | 96.33 136 | 98.92 107 | 97.84 142 | 88.30 197 | 95.92 186 | 92.98 222 | 89.38 218 | 94.56 216 | 91.90 220 |
|
MS-PatchMatch | | | 97.60 150 | 97.22 146 | 98.04 176 | 98.67 204 | 97.18 201 | 97.91 194 | 98.28 199 | 95.82 152 | 98.34 150 | 97.66 145 | 98.38 139 | 97.77 149 | 97.10 183 | 97.25 167 | 97.27 197 | 97.18 176 |
|
PatchT | | | 95.49 199 | 93.29 207 | 98.06 175 | 98.65 205 | 96.20 211 | 98.91 122 | 99.73 28 | 92.00 220 | 98.50 137 | 96.67 173 | 83.25 219 | 96.34 179 | 94.40 217 | 95.50 198 | 96.21 206 | 95.04 202 |
|
CR-MVSNet | | | 95.38 201 | 93.01 208 | 98.16 171 | 98.63 206 | 95.85 221 | 97.64 208 | 99.78 19 | 91.27 225 | 98.50 137 | 96.84 170 | 82.16 221 | 96.34 179 | 94.40 217 | 95.50 198 | 98.05 185 | 95.04 202 |
|
EPMVS | | | 93.67 219 | 90.82 221 | 96.99 204 | 98.62 207 | 96.39 210 | 98.40 170 | 99.11 155 | 95.54 156 | 97.87 175 | 97.14 160 | 81.27 225 | 94.97 198 | 88.54 230 | 86.80 223 | 92.95 224 | 90.06 227 |
|
CVMVSNet | | | 97.38 162 | 97.39 138 | 97.37 192 | 98.58 208 | 97.72 194 | 98.70 140 | 97.42 217 | 97.21 94 | 95.95 218 | 99.46 68 | 93.31 189 | 97.38 161 | 97.60 160 | 97.78 130 | 96.18 207 | 98.66 91 |
|
CNLPA | | | 97.75 141 | 97.26 142 | 98.32 161 | 98.58 208 | 97.86 186 | 97.80 198 | 98.09 206 | 96.49 129 | 98.49 140 | 96.15 184 | 98.08 148 | 98.35 122 | 98.00 124 | 97.03 174 | 98.61 161 | 97.21 175 |
|
E-PMN | | | 92.28 226 | 90.12 222 | 94.79 228 | 98.56 210 | 90.90 234 | 95.16 232 | 93.68 232 | 95.36 158 | 95.10 228 | 96.56 176 | 89.05 195 | 95.24 194 | 95.21 210 | 81.84 232 | 90.98 232 | 81.94 233 |
|
RPMNet | | | 94.72 207 | 92.01 215 | 97.88 182 | 98.56 210 | 95.85 221 | 97.78 199 | 99.70 34 | 91.27 225 | 98.33 151 | 93.69 207 | 81.88 222 | 94.91 199 | 92.60 224 | 94.34 210 | 98.01 186 | 94.46 206 |
|
MDTV_nov1_ep13 | | | 94.47 212 | 92.15 213 | 97.17 196 | 98.54 212 | 96.42 209 | 98.10 187 | 98.89 170 | 94.49 177 | 98.02 167 | 97.41 153 | 86.49 204 | 95.56 190 | 90.85 225 | 87.95 220 | 93.91 218 | 91.45 223 |
|
test2356 | | | 92.46 222 | 88.72 229 | 96.82 207 | 98.48 213 | 95.34 227 | 96.22 230 | 98.09 206 | 87.46 236 | 96.01 216 | 92.82 214 | 64.42 235 | 95.10 196 | 94.08 219 | 94.05 211 | 97.02 201 | 92.87 214 |
|
TSAR-MVS + COLMAP | | | 97.62 149 | 97.31 140 | 97.98 178 | 98.47 214 | 97.39 199 | 98.29 180 | 98.25 200 | 96.68 117 | 97.54 189 | 98.87 106 | 98.04 151 | 97.08 166 | 96.78 187 | 96.26 187 | 98.26 178 | 97.12 177 |
|
tpmp4_e23 | | | 92.43 224 | 88.82 227 | 96.64 213 | 98.46 215 | 95.17 228 | 97.61 210 | 98.85 172 | 92.42 206 | 98.18 158 | 93.03 212 | 74.92 230 | 93.80 213 | 88.91 227 | 84.60 228 | 92.95 224 | 92.66 217 |
|
EMVS | | | 91.84 227 | 89.39 226 | 94.70 229 | 98.44 216 | 90.84 235 | 95.27 231 | 93.53 233 | 95.18 161 | 95.26 226 | 95.62 192 | 87.59 202 | 94.77 201 | 94.87 213 | 80.72 233 | 90.95 233 | 80.88 234 |
|
tpmrst | | | 92.45 223 | 89.48 225 | 95.92 222 | 98.43 217 | 95.03 229 | 97.14 220 | 97.92 212 | 94.16 188 | 97.56 188 | 97.86 141 | 81.63 224 | 93.56 215 | 85.89 233 | 82.86 229 | 90.91 234 | 88.95 232 |
|
tpm | | | 93.89 217 | 91.21 218 | 97.03 201 | 98.36 218 | 96.07 216 | 97.53 215 | 99.65 43 | 92.24 209 | 98.64 126 | 97.23 157 | 74.67 231 | 94.64 203 | 92.68 223 | 90.73 216 | 93.37 221 | 94.82 205 |
|
tpm cat1 | | | 91.52 228 | 87.70 230 | 95.97 221 | 98.33 219 | 94.98 230 | 97.06 222 | 98.03 208 | 92.11 213 | 98.03 166 | 94.77 198 | 77.19 228 | 92.71 220 | 83.56 234 | 82.24 231 | 91.67 231 | 89.04 231 |
|
PMMVS2 | | | 96.29 188 | 97.05 152 | 95.40 225 | 98.32 220 | 96.16 212 | 98.18 185 | 97.46 216 | 97.20 96 | 84.51 239 | 99.60 53 | 98.68 130 | 96.37 178 | 98.59 78 | 97.38 161 | 97.58 192 | 91.76 221 |
|
DWT-MVSNet_training | | | 91.07 229 | 86.55 231 | 96.35 217 | 98.28 221 | 95.82 224 | 98.00 190 | 95.03 229 | 91.24 227 | 97.99 171 | 90.35 220 | 63.43 236 | 95.25 193 | 86.06 232 | 86.62 224 | 93.55 220 | 92.30 218 |
|
test12356 | | | 95.71 198 | 95.55 187 | 95.89 223 | 98.27 222 | 96.48 207 | 96.90 224 | 97.35 219 | 92.13 212 | 95.64 220 | 99.13 90 | 97.97 153 | 92.34 222 | 96.94 184 | 96.55 184 | 94.87 215 | 89.61 228 |
|
new_pmnet | | | 96.59 179 | 96.40 169 | 96.81 208 | 98.24 223 | 95.46 225 | 97.71 205 | 94.75 230 | 96.92 102 | 96.80 212 | 99.23 84 | 97.81 157 | 96.69 171 | 96.58 191 | 95.16 203 | 96.69 203 | 93.64 212 |
|
dps | | | 92.35 225 | 88.78 228 | 96.52 215 | 98.21 224 | 95.94 220 | 97.78 199 | 98.38 197 | 89.88 233 | 96.81 211 | 95.07 195 | 75.31 229 | 94.70 202 | 88.62 229 | 86.21 225 | 93.21 223 | 90.41 224 |
|
FMVSNet5 | | | 94.57 210 | 92.77 209 | 96.67 212 | 97.88 225 | 98.72 136 | 97.54 212 | 98.70 182 | 88.64 235 | 95.11 227 | 86.90 226 | 81.77 223 | 93.27 216 | 97.92 131 | 98.07 109 | 97.50 194 | 97.34 167 |
|
TESTMET0.1,1 | | | 94.44 213 | 93.71 203 | 95.30 227 | 97.84 226 | 96.16 212 | 96.31 227 | 95.32 228 | 89.98 231 | 94.08 230 | 97.01 164 | 86.43 205 | 92.20 224 | 96.76 189 | 95.31 200 | 96.05 208 | 94.31 207 |
|
FPMVS | | | 96.97 171 | 97.20 147 | 96.70 211 | 97.75 227 | 96.11 215 | 97.72 203 | 95.47 226 | 97.13 98 | 98.02 167 | 97.57 148 | 96.67 171 | 92.97 218 | 99.00 56 | 98.34 97 | 98.28 177 | 95.58 196 |
|
test-mter | | | 94.62 208 | 94.02 200 | 95.32 226 | 97.72 228 | 96.75 204 | 96.23 229 | 95.67 225 | 89.83 234 | 93.23 236 | 96.99 166 | 85.94 211 | 92.66 221 | 97.32 176 | 96.11 193 | 96.44 204 | 95.22 201 |
|
pmmvs3 | | | 96.30 187 | 95.87 182 | 96.80 209 | 97.66 229 | 96.48 207 | 97.93 193 | 93.80 231 | 93.40 200 | 98.54 134 | 98.27 126 | 97.50 160 | 97.37 163 | 97.49 166 | 93.11 213 | 95.52 212 | 94.85 204 |
|
FMVSNet3 | | | 96.85 173 | 96.67 159 | 97.06 199 | 97.56 230 | 99.01 102 | 97.99 191 | 99.33 119 | 94.09 189 | 95.60 221 | 98.17 131 | 95.97 176 | 93.26 217 | 94.76 216 | 96.22 188 | 98.59 163 | 98.46 104 |
|
CMPMVS | | 74.71 19 | 96.17 191 | 96.06 178 | 96.30 218 | 97.41 231 | 94.52 231 | 94.83 233 | 95.46 227 | 91.57 223 | 97.26 203 | 94.45 204 | 98.33 141 | 94.98 197 | 98.28 105 | 97.59 152 | 97.86 188 | 97.68 155 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PMMVS | | | 96.47 182 | 95.81 184 | 97.23 195 | 97.38 232 | 95.96 219 | 97.31 217 | 96.91 223 | 93.21 202 | 97.93 174 | 97.14 160 | 97.64 159 | 95.70 188 | 95.24 209 | 96.18 191 | 98.17 182 | 95.33 200 |
|
MVS-HIRNet | | | 94.86 205 | 93.83 202 | 96.07 219 | 97.07 233 | 94.00 232 | 94.31 234 | 99.17 146 | 91.23 228 | 98.17 159 | 98.69 112 | 97.43 161 | 95.66 189 | 94.05 220 | 91.92 215 | 92.04 229 | 89.46 229 |
|
DeepPCF-MVS | | 96.68 10 | 98.20 125 | 98.26 99 | 98.12 172 | 97.03 234 | 98.11 175 | 98.44 168 | 97.70 215 | 96.77 111 | 98.52 135 | 98.91 105 | 99.17 83 | 98.58 107 | 98.41 90 | 98.02 110 | 98.46 172 | 98.46 104 |
|
testpf | | | 87.81 230 | 83.90 232 | 92.37 230 | 96.76 235 | 88.65 236 | 93.04 236 | 98.24 201 | 85.20 237 | 95.28 225 | 86.82 227 | 72.43 233 | 82.35 234 | 82.62 235 | 82.30 230 | 88.55 235 | 89.29 230 |
|
MVE | | 82.47 18 | 93.12 220 | 94.09 196 | 91.99 231 | 90.79 236 | 82.50 238 | 93.93 235 | 96.30 224 | 96.06 146 | 88.81 238 | 98.19 129 | 96.38 174 | 97.56 153 | 97.24 180 | 95.18 202 | 84.58 236 | 93.07 213 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
tmp_tt | | | | | 65.28 233 | 82.24 237 | 71.50 239 | 70.81 239 | 23.21 235 | 96.14 143 | 81.70 240 | 85.98 233 | 92.44 191 | 49.84 235 | 95.81 201 | 94.36 209 | 83.86 237 | |
|
testmvs | | | 9.73 233 | 13.38 234 | 5.48 236 | 3.62 238 | 4.12 240 | 6.40 241 | 3.19 237 | 14.92 238 | 7.68 243 | 22.10 237 | 13.89 243 | 6.83 236 | 13.47 236 | 10.38 235 | 5.14 239 | 14.81 235 |
|
test123 | | | 9.37 234 | 12.26 235 | 6.00 235 | 3.32 239 | 4.06 241 | 6.39 242 | 3.41 236 | 13.20 239 | 10.48 242 | 16.43 238 | 16.22 242 | 6.76 237 | 11.37 237 | 10.40 234 | 5.62 238 | 14.10 237 |
|
GG-mvs-BLEND | | | 65.66 232 | 92.62 211 | 34.20 234 | 1.45 240 | 93.75 233 | 85.40 238 | 1.64 238 | 91.37 224 | 17.21 241 | 87.25 224 | 94.78 183 | 3.25 238 | 95.64 205 | 93.80 212 | 96.27 205 | 91.74 222 |
|
sosnet-low-res | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 241 | 0.00 242 | 0.00 243 | 0.00 239 | 0.00 240 | 0.00 244 | 0.00 239 | 0.00 244 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 241 | 0.00 238 |
|
sosnet | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 241 | 0.00 242 | 0.00 243 | 0.00 239 | 0.00 240 | 0.00 244 | 0.00 239 | 0.00 244 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 241 | 0.00 238 |
|
MTAPA | | | | | | | | | | | 99.19 65 | | 99.68 22 | | | | | |
|
MTMP | | | | | | | | | | | 99.20 63 | | 99.54 42 | | | | | |
|
Patchmatch-RL test | | | | | | | | 32.47 240 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 93.07 203 | | | | | | | | |
|
Patchmtry | | | | | | | 96.05 217 | 97.64 208 | 99.78 19 | | 98.50 137 | | | | | | | |
|
DeepMVS_CX | | | | | | | 87.86 237 | 92.27 237 | 61.98 234 | 93.64 197 | 93.62 233 | 91.17 216 | 91.67 192 | 94.90 200 | 95.99 200 | | 92.48 228 | 94.18 209 |
|