gg-mvs-nofinetune | | | 98.40 169 | 98.26 162 | 98.57 184 | 99.83 82 | 98.86 160 | 98.77 188 | 99.97 1 | 99.57 24 | 99.99 1 | 99.99 1 | 93.81 186 | 93.50 206 | 98.91 144 | 98.20 161 | 99.33 160 | 98.52 173 |
|
Gipuma | | | 99.55 51 | 99.23 81 | 99.91 5 | 99.87 47 | 99.52 60 | 99.86 10 | 99.93 24 | 99.87 1 | 99.96 2 | 96.72 197 | 99.55 121 | 99.97 1 | 99.77 27 | 99.46 43 | 99.87 32 | 99.74 29 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
SixPastTwentyTwo | | | 99.89 2 | 99.85 5 | 99.93 1 | 99.97 2 | 99.88 1 | 99.92 2 | 99.97 1 | 99.66 12 | 99.94 3 | 99.94 11 | 99.74 98 | 99.81 7 | 99.97 1 | 99.89 1 | 99.96 3 | 99.89 4 |
|
WR-MVS | | | 99.79 9 | 99.68 13 | 99.91 5 | 99.95 9 | 99.83 2 | 99.87 9 | 99.96 10 | 99.39 41 | 99.93 4 | 99.87 34 | 99.29 142 | 99.77 14 | 99.83 17 | 99.72 15 | 99.97 1 | 99.82 13 |
|
EG-PatchMatch MVS | | | 99.59 40 | 99.49 41 | 99.70 57 | 99.82 87 | 99.26 114 | 99.39 111 | 99.83 75 | 98.99 87 | 99.93 4 | 99.54 81 | 99.92 47 | 99.51 57 | 99.78 25 | 99.50 36 | 99.73 78 | 99.41 111 |
|
MIMVSNet1 | | | 99.79 9 | 99.75 10 | 99.84 19 | 99.89 35 | 99.83 2 | 99.84 14 | 99.89 44 | 99.31 48 | 99.93 4 | 99.92 16 | 99.97 8 | 99.68 29 | 99.89 8 | 99.64 21 | 99.82 49 | 99.66 45 |
|
LTVRE_ROB | | 99.39 1 | 99.90 1 | 99.87 1 | 99.93 1 | 99.97 2 | 99.82 6 | 99.91 3 | 99.92 31 | 99.75 4 | 99.93 4 | 99.89 30 | 100.00 1 | 99.87 2 | 99.93 3 | 99.82 7 | 99.96 3 | 99.90 2 |
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 |
ambc | | | | 98.83 138 | | 99.72 129 | 98.52 181 | 98.84 182 | | 98.96 92 | 99.92 8 | 99.34 108 | 99.74 98 | 99.04 117 | 98.68 169 | 97.57 181 | 99.46 141 | 98.99 161 |
|
TransMVSNet (Re) | | | 99.72 21 | 99.59 27 | 99.88 12 | 99.95 9 | 99.76 15 | 99.88 7 | 99.94 22 | 99.58 23 | 99.92 8 | 99.90 27 | 98.55 160 | 99.65 35 | 99.89 8 | 99.76 12 | 99.95 7 | 99.70 39 |
|
DTE-MVSNet | | | 99.75 15 | 99.61 22 | 99.92 4 | 99.95 9 | 99.81 9 | 99.86 10 | 99.96 10 | 99.18 64 | 99.92 8 | 99.66 64 | 99.45 127 | 99.85 3 | 99.80 22 | 99.56 27 | 99.96 3 | 99.79 21 |
|
Anonymous20231206 | | | 99.48 60 | 99.31 68 | 99.69 59 | 99.79 98 | 99.57 45 | 99.63 60 | 99.79 99 | 98.88 103 | 99.91 11 | 99.72 55 | 99.93 39 | 99.59 43 | 99.24 98 | 98.63 136 | 99.43 148 | 99.18 139 |
|
PVSNet_Blended_VisFu | | | 99.66 28 | 99.64 17 | 99.67 62 | 99.91 29 | 99.71 26 | 99.61 62 | 99.79 99 | 99.41 37 | 99.91 11 | 99.85 38 | 99.61 115 | 99.00 120 | 99.67 39 | 99.42 47 | 99.81 52 | 99.81 16 |
|
pmmvs6 | | | 99.88 4 | 99.87 1 | 99.89 9 | 99.97 2 | 99.76 15 | 99.89 5 | 99.96 10 | 99.82 2 | 99.90 13 | 99.92 16 | 99.95 23 | 99.68 29 | 99.93 3 | 99.88 2 | 99.95 7 | 99.86 9 |
|
DeepC-MVS | | 99.05 5 | 99.74 16 | 99.64 17 | 99.84 19 | 99.90 32 | 99.39 84 | 99.79 26 | 99.81 90 | 99.69 7 | 99.90 13 | 99.87 34 | 99.98 4 | 99.81 7 | 99.62 48 | 99.32 54 | 99.83 46 | 99.65 48 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
v7n | | | 99.89 2 | 99.86 3 | 99.93 1 | 99.97 2 | 99.83 2 | 99.93 1 | 99.96 10 | 99.77 3 | 99.89 15 | 99.99 1 | 99.86 72 | 99.84 5 | 99.89 8 | 99.81 8 | 99.97 1 | 99.88 6 |
|
UniMVSNet_NR-MVSNet | | | 99.41 75 | 99.12 106 | 99.76 40 | 99.86 61 | 99.48 67 | 99.50 92 | 99.81 90 | 98.84 109 | 99.89 15 | 99.45 97 | 98.32 166 | 99.59 43 | 99.22 102 | 98.89 108 | 99.90 21 | 99.63 52 |
|
PEN-MVS | | | 99.77 11 | 99.65 16 | 99.91 5 | 99.95 9 | 99.80 11 | 99.86 10 | 99.97 1 | 99.08 76 | 99.89 15 | 99.69 61 | 99.68 108 | 99.84 5 | 99.81 21 | 99.64 21 | 99.95 7 | 99.81 16 |
|
DU-MVS | | | 99.48 60 | 99.26 75 | 99.75 44 | 99.85 70 | 99.38 87 | 99.50 92 | 99.81 90 | 98.86 106 | 99.89 15 | 99.51 92 | 98.98 150 | 99.59 43 | 99.46 61 | 98.97 99 | 99.87 32 | 99.63 52 |
|
UniMVSNet (Re) | | | 99.50 56 | 99.29 71 | 99.75 44 | 99.86 61 | 99.47 69 | 99.51 88 | 99.82 82 | 98.90 101 | 99.89 15 | 99.64 67 | 99.00 149 | 99.55 50 | 99.32 81 | 99.08 81 | 99.90 21 | 99.59 62 |
|
thisisatest0515 | | | 99.73 18 | 99.67 14 | 99.81 26 | 99.93 22 | 99.74 23 | 99.68 51 | 99.91 36 | 99.59 21 | 99.88 20 | 99.73 51 | 99.81 85 | 99.55 50 | 99.59 49 | 99.53 33 | 99.89 24 | 99.70 39 |
|
tfpnnormal | | | 99.74 16 | 99.63 19 | 99.86 15 | 99.93 22 | 99.75 21 | 99.80 25 | 99.89 44 | 99.31 48 | 99.88 20 | 99.43 98 | 99.66 111 | 99.77 14 | 99.80 22 | 99.71 16 | 99.92 17 | 99.76 26 |
|
test20.03 | | | 99.68 26 | 99.60 25 | 99.76 40 | 99.91 29 | 99.70 29 | 99.68 51 | 99.87 49 | 99.05 82 | 99.88 20 | 99.92 16 | 99.88 64 | 99.50 61 | 99.77 27 | 99.42 47 | 99.75 69 | 99.49 93 |
|
Baseline_NR-MVSNet | | | 99.62 32 | 99.48 42 | 99.78 36 | 99.85 70 | 99.76 15 | 99.59 68 | 99.82 82 | 98.84 109 | 99.88 20 | 99.91 23 | 99.04 148 | 99.61 40 | 99.46 61 | 99.78 10 | 99.94 14 | 99.60 60 |
|
TranMVSNet+NR-MVSNet | | | 99.59 40 | 99.42 52 | 99.80 31 | 99.87 47 | 99.55 47 | 99.64 56 | 99.86 53 | 99.05 82 | 99.88 20 | 99.72 55 | 99.33 140 | 99.64 37 | 99.47 60 | 99.14 68 | 99.91 19 | 99.67 44 |
|
TDRefinement | | | 99.81 7 | 99.76 9 | 99.86 15 | 99.83 82 | 99.53 53 | 99.89 5 | 99.91 36 | 99.73 5 | 99.88 20 | 99.83 43 | 99.96 13 | 99.76 16 | 99.91 6 | 99.81 8 | 99.86 36 | 99.59 62 |
|
UniMVSNet_ETH3D | | | 99.81 7 | 99.79 7 | 99.85 18 | 99.98 1 | 99.76 15 | 99.73 43 | 99.96 10 | 99.68 9 | 99.87 26 | 99.59 76 | 99.91 53 | 99.58 46 | 99.90 7 | 99.85 3 | 99.96 3 | 99.81 16 |
|
pm-mvs1 | | | 99.77 11 | 99.69 12 | 99.86 15 | 99.94 19 | 99.68 30 | 99.84 14 | 99.93 24 | 99.59 21 | 99.87 26 | 99.92 16 | 99.21 145 | 99.65 35 | 99.88 11 | 99.77 11 | 99.93 16 | 99.78 22 |
|
v2v482 | | | 99.56 49 | 99.35 63 | 99.81 26 | 99.87 47 | 99.35 97 | 99.75 35 | 99.85 60 | 99.56 25 | 99.87 26 | 99.95 6 | 99.44 129 | 99.66 33 | 98.91 144 | 98.76 122 | 99.86 36 | 99.45 101 |
|
NR-MVSNet | | | 99.52 53 | 99.29 71 | 99.80 31 | 99.96 7 | 99.38 87 | 99.55 76 | 99.81 90 | 98.86 106 | 99.87 26 | 99.51 92 | 98.81 156 | 99.72 26 | 99.86 14 | 99.04 88 | 99.89 24 | 99.54 82 |
|
USDC | | | 99.29 105 | 98.98 124 | 99.65 67 | 99.72 129 | 98.87 159 | 99.47 101 | 99.66 145 | 99.35 45 | 99.87 26 | 99.58 77 | 99.87 71 | 99.51 57 | 98.85 155 | 97.93 173 | 99.65 94 | 98.38 176 |
|
ACMH | | 99.11 4 | 99.72 21 | 99.63 19 | 99.84 19 | 99.87 47 | 99.59 42 | 99.83 17 | 99.88 48 | 99.46 34 | 99.87 26 | 99.66 64 | 99.95 23 | 99.76 16 | 99.73 32 | 99.47 41 | 99.84 42 | 99.52 90 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
new-patchmatchnet | | | 98.49 165 | 97.60 174 | 99.53 92 | 99.90 32 | 99.55 47 | 99.77 28 | 99.48 171 | 99.67 10 | 99.86 32 | 99.98 3 | 99.98 4 | 99.50 61 | 96.90 196 | 91.52 199 | 98.67 183 | 95.62 200 |
|
v144192 | | | 99.58 43 | 99.39 57 | 99.80 31 | 99.87 47 | 99.44 73 | 99.77 28 | 99.84 69 | 99.64 15 | 99.86 32 | 99.93 14 | 99.35 137 | 99.72 26 | 98.92 141 | 98.82 116 | 99.74 74 | 99.66 45 |
|
v1921920 | | | 99.59 40 | 99.40 56 | 99.82 22 | 99.88 41 | 99.45 71 | 99.81 23 | 99.83 75 | 99.65 13 | 99.86 32 | 99.95 6 | 99.29 142 | 99.75 18 | 98.98 135 | 98.86 112 | 99.78 60 | 99.59 62 |
|
v1240 | | | 99.58 43 | 99.38 60 | 99.82 22 | 99.89 35 | 99.49 66 | 99.82 21 | 99.83 75 | 99.63 17 | 99.86 32 | 99.96 4 | 98.92 154 | 99.75 18 | 99.15 118 | 98.96 101 | 99.76 66 | 99.56 76 |
|
PS-CasMVS | | | 99.73 18 | 99.59 27 | 99.90 8 | 99.95 9 | 99.80 11 | 99.85 13 | 99.97 1 | 98.95 93 | 99.86 32 | 99.73 51 | 99.36 134 | 99.81 7 | 99.83 17 | 99.67 18 | 99.95 7 | 99.83 12 |
|
CLD-MVS | | | 99.30 101 | 99.01 121 | 99.63 73 | 99.75 117 | 98.89 158 | 99.35 119 | 99.60 151 | 98.53 139 | 99.86 32 | 99.57 78 | 99.94 33 | 99.52 56 | 98.96 136 | 98.10 167 | 99.70 86 | 99.08 150 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
pmmvs-eth3d | | | 99.61 33 | 99.48 42 | 99.75 44 | 99.87 47 | 99.30 107 | 99.75 35 | 99.89 44 | 99.23 56 | 99.85 38 | 99.88 33 | 99.97 8 | 99.49 65 | 99.46 61 | 99.01 92 | 99.68 88 | 99.52 90 |
|
v1192 | | | 99.60 38 | 99.41 53 | 99.82 22 | 99.89 35 | 99.43 76 | 99.81 23 | 99.84 69 | 99.63 17 | 99.85 38 | 99.95 6 | 99.35 137 | 99.72 26 | 99.01 129 | 98.90 107 | 99.82 49 | 99.58 70 |
|
MDA-MVSNet-bldmvs | | | 99.11 125 | 99.11 108 | 99.12 155 | 99.91 29 | 99.38 87 | 99.77 28 | 98.72 199 | 99.31 48 | 99.85 38 | 99.43 98 | 98.26 167 | 99.48 68 | 99.85 15 | 98.47 145 | 96.99 196 | 99.08 150 |
|
Vis-MVSNet | | | 99.76 13 | 99.78 8 | 99.75 44 | 99.92 25 | 99.77 14 | 99.83 17 | 99.85 60 | 99.43 35 | 99.85 38 | 99.84 40 | 100.00 1 | 99.13 109 | 99.83 17 | 99.66 19 | 99.90 21 | 99.90 2 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
gm-plane-assit | | | 96.82 194 | 94.84 201 | 99.13 153 | 99.95 9 | 99.78 13 | 99.69 50 | 99.92 31 | 99.19 62 | 99.84 42 | 99.92 16 | 72.93 216 | 96.44 194 | 98.21 182 | 97.01 187 | 98.92 178 | 96.87 197 |
|
v1144 | | | 99.61 33 | 99.43 49 | 99.82 22 | 99.88 41 | 99.41 81 | 99.76 31 | 99.86 53 | 99.64 15 | 99.84 42 | 99.95 6 | 99.49 125 | 99.74 21 | 99.00 131 | 98.93 104 | 99.84 42 | 99.58 70 |
|
CP-MVSNet | | | 99.68 26 | 99.51 37 | 99.89 9 | 99.95 9 | 99.76 15 | 99.83 17 | 99.96 10 | 98.83 111 | 99.84 42 | 99.65 66 | 99.09 147 | 99.80 10 | 99.78 25 | 99.62 25 | 99.95 7 | 99.82 13 |
|
WR-MVS_H | | | 99.73 18 | 99.61 22 | 99.88 12 | 99.95 9 | 99.82 6 | 99.83 17 | 99.96 10 | 99.01 85 | 99.84 42 | 99.71 58 | 99.41 133 | 99.74 21 | 99.77 27 | 99.70 17 | 99.95 7 | 99.82 13 |
|
MDTV_nov1_ep13_2view | | | 98.73 157 | 98.31 161 | 99.22 143 | 99.75 117 | 99.24 122 | 99.75 35 | 99.93 24 | 99.31 48 | 99.84 42 | 99.86 37 | 99.81 85 | 99.31 88 | 97.40 194 | 94.77 193 | 96.73 198 | 97.81 189 |
|
anonymousdsp | | | 99.87 5 | 99.86 3 | 99.88 12 | 99.95 9 | 99.75 21 | 99.90 4 | 99.96 10 | 99.69 7 | 99.83 47 | 99.96 4 | 99.99 3 | 99.74 21 | 99.95 2 | 99.83 4 | 99.91 19 | 99.88 6 |
|
v148 | | | 99.58 43 | 99.43 49 | 99.76 40 | 99.87 47 | 99.40 83 | 99.76 31 | 99.85 60 | 99.48 32 | 99.83 47 | 99.82 45 | 99.83 82 | 99.51 57 | 99.20 108 | 98.82 116 | 99.75 69 | 99.45 101 |
|
IterMVS-SCA-FT | | | 99.15 122 | 98.96 126 | 99.38 118 | 99.87 47 | 99.54 50 | 99.53 81 | 99.79 99 | 98.94 95 | 99.82 49 | 99.92 16 | 97.65 172 | 98.82 133 | 98.95 138 | 98.26 157 | 98.45 186 | 99.47 99 |
|
TinyColmap | | | 99.21 112 | 98.89 133 | 99.59 78 | 99.61 152 | 98.61 177 | 99.47 101 | 99.67 142 | 99.02 84 | 99.82 49 | 99.15 126 | 99.74 98 | 99.35 78 | 99.17 116 | 98.33 155 | 99.63 106 | 98.22 182 |
|
COLMAP_ROB | | 99.18 2 | 99.70 23 | 99.60 25 | 99.81 26 | 99.84 76 | 99.37 91 | 99.76 31 | 99.84 69 | 99.54 29 | 99.82 49 | 99.64 67 | 99.95 23 | 99.75 18 | 99.79 24 | 99.56 27 | 99.83 46 | 99.37 121 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
APDe-MVS | | | 99.60 38 | 99.48 42 | 99.73 52 | 99.85 70 | 99.51 64 | 99.75 35 | 99.85 60 | 99.17 65 | 99.81 52 | 99.56 79 | 99.94 33 | 99.44 72 | 99.42 70 | 99.22 58 | 99.67 90 | 99.54 82 |
|
ETV-MVS | | | 99.58 43 | 99.50 40 | 99.67 62 | 99.82 87 | 99.57 45 | 99.60 66 | 99.79 99 | 98.87 104 | 99.80 53 | 99.53 87 | 99.62 114 | 99.18 97 | 99.74 30 | 99.54 31 | 99.87 32 | 99.62 55 |
|
SteuartSystems-ACMMP | | | 99.47 63 | 99.22 84 | 99.76 40 | 99.88 41 | 99.36 93 | 99.65 55 | 99.84 69 | 98.47 141 | 99.80 53 | 98.68 156 | 99.96 13 | 99.68 29 | 99.37 73 | 99.06 83 | 99.72 81 | 99.66 45 |
Skip Steuart: Steuart Systems R&D Blog. |
ACMH+ | | 98.94 6 | 99.69 25 | 99.59 27 | 99.81 26 | 99.88 41 | 99.41 81 | 99.75 35 | 99.86 53 | 99.43 35 | 99.80 53 | 99.54 81 | 99.97 8 | 99.73 24 | 99.82 20 | 99.52 35 | 99.85 39 | 99.43 107 |
|
3Dnovator | | 99.16 3 | 99.42 73 | 99.22 84 | 99.65 67 | 99.78 102 | 99.13 139 | 99.50 92 | 99.85 60 | 99.40 39 | 99.80 53 | 98.59 161 | 99.79 93 | 99.30 89 | 99.20 108 | 99.06 83 | 99.71 84 | 99.35 124 |
|
LGP-MVS_train | | | 99.46 67 | 99.18 94 | 99.78 36 | 99.87 47 | 99.25 117 | 99.71 49 | 99.87 49 | 98.02 171 | 99.79 57 | 98.90 143 | 99.96 13 | 99.66 33 | 99.49 56 | 99.17 64 | 99.79 59 | 99.49 93 |
|
PVSNet_BlendedMVS | | | 99.20 114 | 99.17 98 | 99.23 140 | 99.69 134 | 99.33 100 | 99.04 158 | 99.13 192 | 98.41 149 | 99.79 57 | 99.33 109 | 99.36 134 | 98.10 157 | 99.29 87 | 98.87 110 | 99.65 94 | 99.56 76 |
|
PVSNet_Blended | | | 99.20 114 | 99.17 98 | 99.23 140 | 99.69 134 | 99.33 100 | 99.04 158 | 99.13 192 | 98.41 149 | 99.79 57 | 99.33 109 | 99.36 134 | 98.10 157 | 99.29 87 | 98.87 110 | 99.65 94 | 99.56 76 |
|
ACMP | | 98.32 13 | 99.44 69 | 99.18 94 | 99.75 44 | 99.83 82 | 99.18 130 | 99.64 56 | 99.83 75 | 98.81 113 | 99.79 57 | 98.42 172 | 99.96 13 | 99.64 37 | 99.46 61 | 98.98 98 | 99.74 74 | 99.44 104 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
PHI-MVS | | | 99.33 93 | 99.19 91 | 99.49 101 | 99.69 134 | 99.25 117 | 99.27 134 | 99.59 154 | 98.44 145 | 99.78 61 | 99.15 126 | 99.92 47 | 98.95 126 | 99.39 71 | 99.04 88 | 99.64 103 | 99.18 139 |
|
FC-MVSNet-test | | | 99.84 6 | 99.80 6 | 99.89 9 | 99.96 7 | 99.83 2 | 99.84 14 | 99.95 21 | 99.37 42 | 99.77 62 | 99.95 6 | 99.96 13 | 99.85 3 | 99.93 3 | 99.83 4 | 99.95 7 | 99.72 34 |
|
v8 | | | 99.61 33 | 99.45 47 | 99.79 35 | 99.80 94 | 99.59 42 | 99.73 43 | 99.93 24 | 99.48 32 | 99.77 62 | 99.90 27 | 99.48 126 | 99.67 32 | 99.11 122 | 98.89 108 | 99.84 42 | 99.73 31 |
|
DeepC-MVS_fast | | 98.69 9 | 99.32 95 | 99.13 104 | 99.53 92 | 99.63 147 | 98.78 164 | 99.53 81 | 99.33 187 | 99.08 76 | 99.77 62 | 99.18 124 | 99.89 59 | 99.29 90 | 99.00 131 | 98.70 129 | 99.65 94 | 99.30 129 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
v10 | | | 99.65 29 | 99.51 37 | 99.81 26 | 99.83 82 | 99.61 39 | 99.75 35 | 99.94 22 | 99.56 25 | 99.76 65 | 99.94 11 | 99.60 117 | 99.73 24 | 99.11 122 | 99.01 92 | 99.85 39 | 99.74 29 |
|
pmmvs3 | | | 98.85 149 | 98.60 149 | 99.13 153 | 99.66 142 | 98.72 170 | 99.37 115 | 99.06 195 | 98.44 145 | 99.76 65 | 99.74 49 | 99.55 121 | 99.15 105 | 99.04 127 | 96.00 192 | 97.80 191 | 98.72 170 |
|
OpenMVS | | 98.82 8 | 99.17 117 | 98.85 137 | 99.53 92 | 99.75 117 | 99.06 146 | 99.36 116 | 99.82 82 | 98.28 157 | 99.76 65 | 98.47 167 | 99.61 115 | 98.91 128 | 98.80 163 | 98.70 129 | 99.60 116 | 99.04 157 |
|
CS-MVS | | | 99.38 84 | 99.19 91 | 99.59 78 | 99.86 61 | 99.65 33 | 99.28 132 | 99.77 111 | 97.97 174 | 99.75 68 | 98.42 172 | 99.70 106 | 99.03 118 | 99.57 51 | 99.42 47 | 99.87 32 | 99.61 58 |
|
MVS_0304 | | | 99.36 86 | 99.35 63 | 99.37 124 | 99.85 70 | 99.36 93 | 99.39 111 | 99.56 158 | 99.36 44 | 99.75 68 | 99.23 119 | 99.90 56 | 97.97 166 | 99.00 131 | 98.83 115 | 99.69 87 | 99.77 23 |
|
V42 | | | 99.57 48 | 99.41 53 | 99.75 44 | 99.84 76 | 99.37 91 | 99.73 43 | 99.83 75 | 99.41 37 | 99.75 68 | 99.89 30 | 99.42 131 | 99.60 42 | 99.15 118 | 98.96 101 | 99.76 66 | 99.65 48 |
|
EU-MVSNet | | | 99.76 13 | 99.74 11 | 99.78 36 | 99.82 87 | 99.81 9 | 99.88 7 | 99.87 49 | 99.31 48 | 99.75 68 | 99.91 23 | 99.76 97 | 99.78 12 | 99.84 16 | 99.74 14 | 99.56 127 | 99.81 16 |
|
PGM-MVS | | | 99.32 95 | 98.99 122 | 99.71 54 | 99.86 61 | 99.31 105 | 99.59 68 | 99.86 53 | 97.51 186 | 99.75 68 | 98.23 176 | 99.94 33 | 99.53 53 | 99.29 87 | 99.08 81 | 99.65 94 | 99.54 82 |
|
PM-MVS | | | 99.49 59 | 99.43 49 | 99.57 84 | 99.76 113 | 99.34 99 | 99.53 81 | 99.77 111 | 98.93 97 | 99.75 68 | 99.46 96 | 99.83 82 | 99.11 111 | 99.72 33 | 99.29 56 | 99.49 138 | 99.46 100 |
|
IterMVS | | | 99.08 128 | 98.90 132 | 99.29 132 | 99.87 47 | 99.53 53 | 99.52 85 | 99.77 111 | 98.94 95 | 99.75 68 | 99.91 23 | 97.52 176 | 98.72 140 | 98.86 153 | 98.14 164 | 98.09 189 | 99.43 107 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CANet | | | 99.36 86 | 99.39 57 | 99.34 129 | 99.80 94 | 99.35 97 | 99.41 110 | 99.47 172 | 99.20 59 | 99.74 75 | 99.54 81 | 99.68 108 | 98.05 161 | 99.23 100 | 98.97 99 | 99.57 124 | 99.73 31 |
|
CANet_DTU | | | 99.03 134 | 99.18 94 | 98.87 172 | 99.58 163 | 99.03 147 | 99.18 143 | 99.41 179 | 98.65 125 | 99.74 75 | 99.55 80 | 99.71 103 | 96.13 196 | 99.19 111 | 98.92 105 | 99.17 171 | 99.18 139 |
|
TSAR-MVS + GP. | | | 99.33 93 | 99.17 98 | 99.51 98 | 99.71 132 | 99.00 150 | 98.84 182 | 99.71 130 | 98.23 160 | 99.74 75 | 99.53 87 | 99.90 56 | 99.35 78 | 99.38 72 | 98.85 113 | 99.72 81 | 99.31 127 |
|
EPNet_dtu | | | 98.09 173 | 98.25 164 | 97.91 199 | 99.58 163 | 98.02 201 | 98.19 206 | 99.67 142 | 97.94 176 | 99.74 75 | 99.07 136 | 98.71 158 | 93.40 207 | 97.50 191 | 97.09 185 | 96.89 197 | 99.44 104 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CHOSEN 1792x2688 | | | 99.65 29 | 99.55 31 | 99.77 39 | 99.93 22 | 99.60 40 | 99.79 26 | 99.92 31 | 99.73 5 | 99.74 75 | 99.93 14 | 99.98 4 | 99.80 10 | 98.83 161 | 99.01 92 | 99.45 143 | 99.76 26 |
|
CP-MVS | | | 99.41 75 | 99.20 89 | 99.65 67 | 99.80 94 | 99.23 124 | 99.44 105 | 99.75 125 | 98.60 134 | 99.74 75 | 98.66 157 | 99.93 39 | 99.48 68 | 99.33 80 | 99.16 65 | 99.73 78 | 99.48 96 |
|
pmmvs5 | | | 99.58 43 | 99.47 45 | 99.70 57 | 99.84 76 | 99.50 65 | 99.58 72 | 99.80 97 | 98.98 90 | 99.73 81 | 99.92 16 | 99.81 85 | 99.49 65 | 99.28 92 | 99.05 86 | 99.77 64 | 99.73 31 |
|
pmmvs4 | | | 99.34 91 | 99.15 101 | 99.57 84 | 99.77 109 | 98.90 155 | 99.51 88 | 99.77 111 | 99.07 78 | 99.73 81 | 99.72 55 | 99.84 80 | 99.07 113 | 98.85 155 | 98.39 151 | 99.55 130 | 99.27 132 |
|
ACMMPR | | | 99.51 54 | 99.32 67 | 99.72 53 | 99.87 47 | 99.33 100 | 99.61 62 | 99.85 60 | 99.19 62 | 99.73 81 | 98.73 153 | 99.95 23 | 99.61 40 | 99.35 76 | 99.14 68 | 99.66 92 | 99.58 70 |
|
DELS-MVS | | | 99.42 73 | 99.53 35 | 99.29 132 | 99.52 167 | 99.43 76 | 99.42 107 | 99.28 189 | 99.16 69 | 99.72 84 | 99.82 45 | 99.97 8 | 98.17 153 | 99.56 52 | 99.16 65 | 99.65 94 | 99.59 62 |
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 |
QAPM | | | 99.41 75 | 99.21 88 | 99.64 72 | 99.78 102 | 99.16 132 | 99.51 88 | 99.85 60 | 99.20 59 | 99.72 84 | 99.43 98 | 99.81 85 | 99.25 93 | 98.87 150 | 98.71 128 | 99.71 84 | 99.30 129 |
|
ACMMP | | | 99.36 86 | 99.06 113 | 99.71 54 | 99.86 61 | 99.36 93 | 99.63 60 | 99.85 60 | 98.33 154 | 99.72 84 | 97.73 186 | 99.94 33 | 99.53 53 | 99.37 73 | 99.13 75 | 99.65 94 | 99.56 76 |
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 |
3Dnovator+ | | 98.92 7 | 99.31 99 | 99.03 117 | 99.63 73 | 99.77 109 | 98.90 155 | 99.52 85 | 99.81 90 | 99.37 42 | 99.72 84 | 98.03 181 | 99.73 101 | 99.32 85 | 98.99 134 | 98.81 119 | 99.67 90 | 99.36 122 |
|
MSDG | | | 99.32 95 | 99.09 111 | 99.58 81 | 99.75 117 | 98.74 168 | 99.36 116 | 99.54 161 | 99.14 72 | 99.72 84 | 99.24 117 | 99.89 59 | 99.51 57 | 99.30 84 | 98.76 122 | 99.62 112 | 98.54 172 |
|
IterMVS-LS | | | 99.16 120 | 98.82 141 | 99.57 84 | 99.87 47 | 99.71 26 | 99.58 72 | 99.92 31 | 99.24 55 | 99.71 89 | 99.73 51 | 95.79 181 | 98.91 128 | 98.82 162 | 98.66 133 | 99.43 148 | 99.77 23 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
ACMM | | 98.37 12 | 99.47 63 | 99.23 81 | 99.74 50 | 99.86 61 | 99.19 129 | 99.68 51 | 99.86 53 | 99.16 69 | 99.71 89 | 98.52 165 | 99.95 23 | 99.62 39 | 99.35 76 | 99.02 90 | 99.74 74 | 99.42 110 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
N_pmnet | | | 98.64 160 | 98.23 167 | 99.11 158 | 99.78 102 | 99.25 117 | 99.75 35 | 99.39 183 | 99.65 13 | 99.70 91 | 99.78 48 | 99.89 59 | 98.81 135 | 97.60 189 | 94.28 194 | 97.24 195 | 97.15 196 |
|
SCA | | | 97.25 193 | 96.05 196 | 98.64 182 | 99.36 191 | 99.02 148 | 99.27 134 | 99.96 10 | 98.25 158 | 99.69 92 | 98.71 155 | 94.66 185 | 97.95 167 | 93.95 201 | 92.35 197 | 95.64 202 | 95.40 202 |
|
XVS | | | | | | 99.86 61 | 99.30 107 | 99.72 47 | | | 99.69 92 | | 99.93 39 | | | | 99.60 116 | |
|
X-MVStestdata | | | | | | 99.86 61 | 99.30 107 | 99.72 47 | | | 99.69 92 | | 99.93 39 | | | | 99.60 116 | |
|
X-MVS | | | 99.30 101 | 98.99 122 | 99.66 65 | 99.85 70 | 99.30 107 | 99.49 99 | 99.82 82 | 98.32 155 | 99.69 92 | 97.31 194 | 99.93 39 | 99.50 61 | 99.37 73 | 99.16 65 | 99.60 116 | 99.53 85 |
|
tpm | | | 96.56 198 | 94.68 202 | 98.74 178 | 99.12 201 | 97.90 204 | 98.79 187 | 99.93 24 | 96.79 204 | 99.69 92 | 99.19 123 | 81.48 214 | 97.56 173 | 95.46 199 | 93.97 195 | 97.37 194 | 97.99 185 |
|
CSCG | | | 99.61 33 | 99.52 36 | 99.71 54 | 99.89 35 | 99.62 37 | 99.52 85 | 99.76 120 | 99.61 19 | 99.69 92 | 99.73 51 | 99.96 13 | 99.57 48 | 99.27 95 | 98.62 137 | 99.81 52 | 99.85 11 |
|
PMVS | | 94.32 17 | 99.27 106 | 99.55 31 | 98.94 167 | 99.60 156 | 99.43 76 | 99.39 111 | 99.54 161 | 98.99 87 | 99.69 92 | 99.60 74 | 99.81 85 | 95.68 198 | 99.88 11 | 99.83 4 | 99.73 78 | 99.31 127 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
DCV-MVSNet | | | 99.43 70 | 99.23 81 | 99.67 62 | 99.92 25 | 99.76 15 | 99.64 56 | 99.93 24 | 99.06 80 | 99.68 99 | 97.77 184 | 98.97 151 | 98.97 123 | 99.72 33 | 99.54 31 | 99.88 26 | 99.81 16 |
|
MP-MVS | | | 99.35 89 | 99.09 111 | 99.65 67 | 99.84 76 | 99.22 125 | 99.59 68 | 99.78 105 | 98.13 163 | 99.67 100 | 98.44 169 | 99.93 39 | 99.43 74 | 99.31 83 | 99.09 80 | 99.60 116 | 99.49 93 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
MVS-HIRNet | | | 98.45 167 | 98.25 164 | 98.69 179 | 99.12 201 | 97.81 207 | 98.55 196 | 99.85 60 | 98.58 136 | 99.67 100 | 99.61 71 | 99.86 72 | 97.46 175 | 97.95 188 | 96.37 190 | 97.49 193 | 97.56 193 |
|
CPTT-MVS | | | 99.21 112 | 98.89 133 | 99.58 81 | 99.72 129 | 99.12 142 | 99.30 129 | 99.76 120 | 98.62 130 | 99.66 102 | 97.51 189 | 99.89 59 | 99.48 68 | 99.01 129 | 98.64 135 | 99.58 123 | 99.40 116 |
|
MVS_111021_HR | | | 99.30 101 | 99.14 102 | 99.48 102 | 99.58 163 | 99.25 117 | 99.27 134 | 99.61 149 | 98.74 118 | 99.66 102 | 99.02 140 | 99.84 80 | 99.33 82 | 99.20 108 | 98.76 122 | 99.44 145 | 99.18 139 |
|
EIA-MVS | | | 99.23 109 | 99.03 117 | 99.47 103 | 99.83 82 | 99.64 35 | 99.16 145 | 99.81 90 | 97.11 193 | 99.65 104 | 98.44 169 | 99.78 96 | 98.61 145 | 99.46 61 | 99.22 58 | 99.75 69 | 99.59 62 |
|
PatchmatchNet | | | 96.81 195 | 95.41 198 | 98.43 189 | 99.43 184 | 98.30 191 | 99.23 139 | 99.93 24 | 98.19 161 | 99.64 105 | 98.81 149 | 93.50 187 | 97.43 176 | 92.89 203 | 90.78 202 | 94.94 207 | 95.41 201 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
LS3D | | | 99.39 81 | 99.28 73 | 99.52 96 | 99.77 109 | 99.39 84 | 99.55 76 | 99.82 82 | 98.93 97 | 99.64 105 | 98.52 165 | 99.67 110 | 98.58 146 | 99.74 30 | 99.63 23 | 99.75 69 | 99.06 153 |
|
UGNet | | | 99.40 79 | 99.61 22 | 99.16 151 | 99.88 41 | 99.64 35 | 99.61 62 | 99.77 111 | 99.31 48 | 99.63 107 | 99.33 109 | 99.93 39 | 96.46 193 | 99.63 45 | 99.53 33 | 99.63 106 | 99.89 4 |
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 |
Anonymous20231211 | | | 99.47 63 | 99.39 57 | 99.57 84 | 99.89 35 | 99.60 40 | 99.50 92 | 99.69 133 | 98.91 100 | 99.62 108 | 99.17 125 | 99.35 137 | 98.86 132 | 99.63 45 | 99.46 43 | 99.84 42 | 99.62 55 |
|
MTAPA | | | | | | | | | | | 99.62 108 | | 99.95 23 | | | | | |
|
casdiffmvs | | | 99.61 33 | 99.55 31 | 99.68 60 | 99.89 35 | 99.53 53 | 99.64 56 | 99.68 139 | 99.51 30 | 99.62 108 | 99.90 27 | 99.96 13 | 99.37 76 | 99.28 92 | 99.25 57 | 99.88 26 | 99.44 104 |
|
Fast-Effi-MVS+ | | | 99.39 81 | 99.18 94 | 99.63 73 | 99.86 61 | 99.28 112 | 99.45 104 | 99.91 36 | 98.47 141 | 99.61 111 | 99.50 94 | 99.57 119 | 99.17 98 | 99.24 98 | 98.66 133 | 99.78 60 | 99.59 62 |
|
abl_6 | | | | | 99.21 145 | 99.49 176 | 98.62 176 | 98.90 177 | 99.44 177 | 97.08 194 | 99.61 111 | 97.19 195 | 99.73 101 | 98.35 151 | | | 99.45 143 | 98.84 163 |
|
MVS_111021_LR | | | 99.25 107 | 99.13 104 | 99.39 114 | 99.50 175 | 99.14 135 | 99.23 139 | 99.50 169 | 98.67 123 | 99.61 111 | 99.12 130 | 99.81 85 | 99.16 101 | 99.28 92 | 98.67 132 | 99.35 158 | 99.21 138 |
|
Effi-MVS+ | | | 99.20 114 | 98.93 128 | 99.50 100 | 99.79 98 | 99.26 114 | 98.82 185 | 99.96 10 | 98.37 153 | 99.60 114 | 99.12 130 | 98.36 164 | 99.05 116 | 98.93 139 | 98.82 116 | 99.78 60 | 99.68 41 |
|
TAPA-MVS | | 98.54 10 | 99.30 101 | 99.24 80 | 99.36 128 | 99.44 182 | 98.77 166 | 99.00 165 | 99.41 179 | 99.23 56 | 99.60 114 | 99.50 94 | 99.86 72 | 99.15 105 | 99.29 87 | 98.95 103 | 99.56 127 | 99.08 150 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
zzz-MVS | | | 99.51 54 | 99.36 61 | 99.68 60 | 99.88 41 | 99.38 87 | 99.53 81 | 99.84 69 | 99.11 75 | 99.59 116 | 98.93 142 | 99.95 23 | 99.58 46 | 99.44 68 | 99.21 60 | 99.65 94 | 99.52 90 |
|
MCST-MVS | | | 99.17 117 | 98.82 141 | 99.57 84 | 99.75 117 | 98.70 172 | 99.25 138 | 99.69 133 | 98.62 130 | 99.59 116 | 98.54 163 | 99.79 93 | 99.53 53 | 98.48 177 | 98.15 163 | 99.64 103 | 99.43 107 |
|
PLC | | 97.83 16 | 98.88 147 | 98.52 155 | 99.30 131 | 99.45 180 | 98.60 178 | 98.65 191 | 99.49 170 | 98.66 124 | 99.59 116 | 96.33 198 | 99.59 118 | 99.17 98 | 98.87 150 | 98.53 142 | 99.46 141 | 99.05 155 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
PCF-MVS | | 97.86 15 | 98.95 141 | 98.53 153 | 99.44 109 | 99.70 133 | 98.80 163 | 98.96 168 | 99.69 133 | 98.65 125 | 99.59 116 | 99.33 109 | 99.94 33 | 99.12 110 | 98.01 187 | 97.11 184 | 99.59 122 | 97.83 188 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
HFP-MVS | | | 99.46 67 | 99.30 69 | 99.65 67 | 99.82 87 | 99.25 117 | 99.50 92 | 99.82 82 | 99.23 56 | 99.58 120 | 98.86 144 | 99.94 33 | 99.56 49 | 99.14 120 | 99.12 78 | 99.63 106 | 99.56 76 |
|
OMC-MVS | | | 99.11 125 | 98.95 127 | 99.29 132 | 99.37 188 | 98.57 179 | 99.19 142 | 99.20 191 | 98.87 104 | 99.58 120 | 99.13 128 | 99.88 64 | 99.00 120 | 99.19 111 | 98.46 146 | 99.43 148 | 98.57 171 |
|
ADS-MVSNet | | | 97.29 192 | 96.17 195 | 98.59 183 | 99.59 160 | 98.70 172 | 99.32 122 | 99.86 53 | 98.47 141 | 99.56 122 | 99.08 134 | 98.16 168 | 97.34 177 | 92.92 202 | 91.17 200 | 95.91 201 | 94.72 204 |
|
HyFIR lowres test | | | 99.50 56 | 99.26 75 | 99.80 31 | 99.95 9 | 99.62 37 | 99.76 31 | 99.97 1 | 99.67 10 | 99.56 122 | 99.94 11 | 98.40 163 | 99.78 12 | 98.84 160 | 98.59 140 | 99.76 66 | 99.72 34 |
|
CNLPA | | | 98.82 150 | 98.52 155 | 99.18 149 | 99.21 199 | 98.50 183 | 98.73 189 | 99.34 186 | 98.73 120 | 99.56 122 | 97.55 188 | 99.42 131 | 99.06 115 | 98.93 139 | 98.10 167 | 99.21 170 | 98.38 176 |
|
OPM-MVS | | | 99.39 81 | 99.22 84 | 99.59 78 | 99.76 113 | 98.82 161 | 99.51 88 | 99.79 99 | 99.17 65 | 99.53 125 | 99.31 113 | 99.95 23 | 99.35 78 | 99.22 102 | 98.79 121 | 99.60 116 | 99.27 132 |
|
MTMP | | | | | | | | | | | 99.53 125 | | 99.92 47 | | | | | |
|
FC-MVSNet-train | | | 99.70 23 | 99.67 14 | 99.74 50 | 99.94 19 | 99.71 26 | 99.82 21 | 99.91 36 | 99.14 72 | 99.53 125 | 99.70 59 | 99.88 64 | 99.33 82 | 99.88 11 | 99.61 26 | 99.94 14 | 99.77 23 |
|
testgi | | | 99.43 70 | 99.47 45 | 99.38 118 | 99.90 32 | 99.67 32 | 99.30 129 | 99.73 128 | 98.64 129 | 99.53 125 | 99.52 90 | 99.90 56 | 98.08 159 | 99.65 43 | 99.40 51 | 99.75 69 | 99.55 81 |
|
NCCC | | | 98.88 147 | 98.42 159 | 99.42 110 | 99.62 148 | 98.81 162 | 99.10 153 | 99.54 161 | 98.76 115 | 99.53 125 | 95.97 200 | 99.80 91 | 99.16 101 | 98.49 176 | 98.06 170 | 99.55 130 | 99.05 155 |
|
HQP-MVS | | | 98.70 159 | 98.19 168 | 99.28 136 | 99.61 152 | 98.52 181 | 98.71 190 | 99.35 184 | 97.97 174 | 99.53 125 | 97.38 192 | 99.85 78 | 99.14 107 | 97.53 190 | 96.85 188 | 99.36 156 | 99.26 135 |
|
UA-Net | | | 99.64 31 | 99.62 21 | 99.66 65 | 99.97 2 | 99.82 6 | 99.14 148 | 99.96 10 | 98.95 93 | 99.52 131 | 99.38 106 | 99.86 72 | 99.55 50 | 99.72 33 | 99.66 19 | 99.80 56 | 99.94 1 |
|
HPM-MVS++ | | | 99.23 109 | 98.98 124 | 99.53 92 | 99.75 117 | 99.02 148 | 99.44 105 | 99.77 111 | 98.65 125 | 99.52 131 | 98.72 154 | 99.92 47 | 99.33 82 | 98.77 166 | 98.40 150 | 99.40 152 | 99.36 122 |
|
DWT-MVSNet_training | | | 94.92 204 | 92.14 207 | 98.15 194 | 99.37 188 | 98.43 186 | 98.99 166 | 98.51 201 | 96.76 205 | 99.52 131 | 97.35 193 | 77.20 215 | 97.08 182 | 89.76 208 | 90.38 204 | 95.43 204 | 95.13 203 |
|
EPMVS | | | 96.76 196 | 95.30 200 | 98.46 188 | 99.42 185 | 98.47 184 | 99.32 122 | 99.91 36 | 98.42 148 | 99.51 134 | 99.07 136 | 92.81 193 | 97.12 180 | 92.39 204 | 91.71 198 | 95.51 203 | 94.20 206 |
|
DeepMVS_CX | | | | | | | 96.39 210 | 97.15 212 | 88.89 209 | 97.94 176 | 99.51 134 | 95.71 201 | 97.88 170 | 98.19 152 | 98.92 141 | | 97.73 192 | 97.75 191 |
|
MS-PatchMatch | | | 98.94 142 | 98.71 146 | 99.21 145 | 99.52 167 | 98.22 196 | 98.97 167 | 99.53 166 | 98.76 115 | 99.50 136 | 98.59 161 | 99.56 120 | 98.68 141 | 98.63 171 | 98.45 148 | 99.05 174 | 98.73 168 |
|
diffmvs | | | 99.38 84 | 99.33 65 | 99.45 107 | 99.87 47 | 99.39 84 | 99.28 132 | 99.58 157 | 99.55 27 | 99.50 136 | 99.85 38 | 99.85 78 | 98.94 127 | 98.58 173 | 98.68 131 | 99.51 135 | 99.39 118 |
|
baseline | | | 99.24 108 | 99.30 69 | 99.17 150 | 99.78 102 | 99.14 135 | 99.10 153 | 99.69 133 | 98.97 91 | 99.49 138 | 99.84 40 | 99.88 64 | 97.99 165 | 98.85 155 | 98.73 126 | 98.98 177 | 99.72 34 |
|
RPMNet | | | 97.70 183 | 96.54 188 | 99.06 162 | 99.57 166 | 98.23 193 | 98.95 171 | 99.97 1 | 96.89 201 | 99.49 138 | 99.13 128 | 89.63 202 | 97.09 181 | 96.68 197 | 97.02 186 | 99.26 165 | 98.19 183 |
|
ACMMP_NAP | | | 99.47 63 | 99.33 65 | 99.63 73 | 99.85 70 | 99.28 112 | 99.56 75 | 99.83 75 | 98.75 117 | 99.48 140 | 99.03 139 | 99.95 23 | 99.47 71 | 99.48 57 | 99.19 61 | 99.57 124 | 99.59 62 |
|
APD-MVS | | | 99.17 117 | 98.92 129 | 99.46 105 | 99.78 102 | 99.24 122 | 99.34 120 | 99.78 105 | 97.79 180 | 99.48 140 | 98.25 175 | 99.88 64 | 98.77 136 | 99.18 114 | 98.92 105 | 99.63 106 | 99.18 139 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
new_pmnet | | | 98.91 145 | 98.89 133 | 98.94 167 | 99.51 173 | 98.27 192 | 99.15 146 | 98.66 200 | 99.17 65 | 99.48 140 | 99.79 47 | 99.80 91 | 98.49 148 | 99.23 100 | 98.20 161 | 98.34 187 | 97.74 192 |
|
RPSCF | | | 99.48 60 | 99.45 47 | 99.52 96 | 99.73 127 | 99.33 100 | 99.13 149 | 99.77 111 | 99.33 46 | 99.47 143 | 99.39 105 | 99.92 47 | 99.36 77 | 99.63 45 | 99.13 75 | 99.63 106 | 99.41 111 |
|
DI_MVS_plusplus_trai | | | 98.74 155 | 98.08 172 | 99.51 98 | 99.79 98 | 99.29 111 | 99.61 62 | 99.60 151 | 99.20 59 | 99.46 144 | 99.09 133 | 92.93 189 | 98.97 123 | 98.27 181 | 98.35 153 | 99.65 94 | 99.45 101 |
|
tpm cat1 | | | 95.52 203 | 93.49 204 | 97.88 200 | 99.28 197 | 97.87 206 | 98.65 191 | 99.77 111 | 97.27 190 | 99.46 144 | 98.04 180 | 90.99 198 | 95.46 199 | 88.57 209 | 88.14 208 | 94.64 210 | 93.54 208 |
|
IB-MVS | | 98.10 14 | 97.76 181 | 97.40 179 | 98.18 192 | 99.62 148 | 99.11 143 | 98.24 204 | 98.35 204 | 96.56 206 | 99.44 146 | 91.28 210 | 98.96 153 | 93.84 204 | 98.09 183 | 98.62 137 | 99.56 127 | 99.18 139 |
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 |
AdaColmap | | | 98.93 143 | 98.53 153 | 99.39 114 | 99.52 167 | 98.65 175 | 99.11 152 | 99.59 154 | 98.08 167 | 99.44 146 | 97.46 191 | 99.45 127 | 99.24 94 | 98.92 141 | 98.44 149 | 99.44 145 | 98.73 168 |
|
CR-MVSNet | | | 97.91 175 | 96.80 185 | 99.22 143 | 99.60 156 | 98.23 193 | 98.91 174 | 99.97 1 | 96.89 201 | 99.43 148 | 99.10 132 | 89.24 204 | 98.15 154 | 98.04 184 | 97.78 174 | 99.26 165 | 98.30 179 |
|
Patchmtry | | | | | | | 98.19 198 | 98.91 174 | 99.97 1 | | 99.43 148 | | | | | | | |
|
PatchT | | | 98.11 171 | 97.12 182 | 99.26 138 | 99.65 146 | 98.34 190 | 99.57 74 | 99.97 1 | 97.48 187 | 99.43 148 | 99.04 138 | 90.84 199 | 98.15 154 | 98.04 184 | 97.78 174 | 98.82 180 | 98.30 179 |
|
thisisatest0530 | | | 98.78 153 | 98.26 162 | 99.39 114 | 99.78 102 | 99.43 76 | 99.07 155 | 99.64 147 | 98.44 145 | 99.42 151 | 99.22 120 | 92.68 195 | 98.63 143 | 99.30 84 | 99.14 68 | 99.80 56 | 99.60 60 |
|
CostFormer | | | 95.61 201 | 93.35 206 | 98.24 191 | 99.48 177 | 98.03 200 | 98.65 191 | 99.83 75 | 96.93 199 | 99.42 151 | 98.83 146 | 83.65 213 | 97.08 182 | 90.39 207 | 89.54 206 | 94.94 207 | 96.11 199 |
|
DPE-MVS | | | 99.41 75 | 99.36 61 | 99.47 103 | 99.66 142 | 99.48 67 | 99.46 103 | 99.75 125 | 98.65 125 | 99.41 153 | 99.67 62 | 99.95 23 | 98.82 133 | 99.21 105 | 99.14 68 | 99.72 81 | 99.40 116 |
|
MVSTER | | | 97.55 189 | 96.75 186 | 98.48 187 | 99.46 179 | 99.54 50 | 98.24 204 | 99.77 111 | 97.56 185 | 99.41 153 | 99.31 113 | 84.86 211 | 94.66 203 | 98.86 153 | 97.75 176 | 99.34 159 | 99.38 119 |
|
CDS-MVSNet | | | 99.15 122 | 99.10 109 | 99.21 145 | 99.59 160 | 99.22 125 | 99.48 100 | 99.47 172 | 98.89 102 | 99.41 153 | 99.84 40 | 98.11 169 | 97.76 169 | 99.26 97 | 99.01 92 | 99.57 124 | 99.38 119 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
FMVSNet1 | | | 99.50 56 | 99.57 30 | 99.42 110 | 99.67 141 | 99.65 33 | 99.60 66 | 99.91 36 | 99.40 39 | 99.39 156 | 99.83 43 | 99.27 144 | 98.14 156 | 99.68 36 | 99.50 36 | 99.81 52 | 99.68 41 |
|
tttt0517 | | | 98.77 154 | 98.25 164 | 99.38 118 | 99.79 98 | 99.46 70 | 99.07 155 | 99.64 147 | 98.40 152 | 99.38 157 | 99.21 122 | 92.54 196 | 98.63 143 | 99.34 79 | 99.14 68 | 99.80 56 | 99.62 55 |
|
MSLP-MVS++ | | | 98.92 144 | 98.73 145 | 99.14 152 | 99.44 182 | 99.00 150 | 98.36 201 | 99.35 184 | 98.82 112 | 99.38 157 | 96.06 199 | 99.79 93 | 99.07 113 | 98.88 149 | 99.05 86 | 99.27 164 | 99.53 85 |
|
MVS_Test | | | 99.09 127 | 98.92 129 | 99.29 132 | 99.61 152 | 99.07 145 | 99.04 158 | 99.81 90 | 98.58 136 | 99.37 159 | 99.74 49 | 98.87 155 | 98.41 150 | 98.61 172 | 98.01 171 | 99.50 137 | 99.57 75 |
|
CDPH-MVS | | | 99.05 132 | 98.63 148 | 99.54 91 | 99.75 117 | 98.78 164 | 99.59 68 | 99.68 139 | 97.79 180 | 99.37 159 | 98.20 177 | 99.86 72 | 99.14 107 | 98.58 173 | 98.01 171 | 99.68 88 | 99.16 145 |
|
CNVR-MVS | | | 99.08 128 | 98.83 138 | 99.37 124 | 99.61 152 | 98.74 168 | 99.15 146 | 99.54 161 | 98.59 135 | 99.37 159 | 98.15 178 | 99.88 64 | 99.08 112 | 98.91 144 | 98.46 146 | 99.48 139 | 99.06 153 |
|
TSAR-MVS + MP. | | | 99.56 49 | 99.54 34 | 99.58 81 | 99.69 134 | 99.14 135 | 99.73 43 | 99.45 174 | 99.50 31 | 99.35 162 | 99.60 74 | 99.93 39 | 99.50 61 | 99.56 52 | 99.37 52 | 99.77 64 | 99.64 51 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
SD-MVS | | | 99.35 89 | 99.26 75 | 99.46 105 | 99.66 142 | 99.15 134 | 98.92 173 | 99.67 142 | 99.55 27 | 99.35 162 | 98.83 146 | 99.91 53 | 99.35 78 | 99.19 111 | 98.53 142 | 99.78 60 | 99.68 41 |
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024 |
GA-MVS | | | 98.59 163 | 98.15 169 | 99.09 159 | 99.59 160 | 99.13 139 | 98.84 182 | 99.52 168 | 98.61 133 | 99.35 162 | 99.67 62 | 93.03 188 | 97.73 171 | 98.90 148 | 98.26 157 | 99.51 135 | 99.48 96 |
|
SMA-MVS | | | 99.43 70 | 99.41 53 | 99.45 107 | 99.82 87 | 99.31 105 | 99.02 163 | 99.59 154 | 99.06 80 | 99.34 165 | 99.53 87 | 99.96 13 | 99.38 75 | 99.29 87 | 99.13 75 | 99.53 132 | 99.59 62 |
|
PatchMatch-RL | | | 98.80 152 | 98.52 155 | 99.12 155 | 99.38 187 | 98.70 172 | 98.56 194 | 99.55 160 | 97.81 179 | 99.34 165 | 97.57 187 | 99.31 141 | 98.67 142 | 99.27 95 | 98.62 137 | 99.22 169 | 98.35 178 |
|
MAR-MVS | | | 98.54 164 | 98.15 169 | 98.98 164 | 99.37 188 | 98.09 199 | 98.56 194 | 99.65 146 | 96.11 209 | 99.27 167 | 97.16 196 | 99.50 124 | 98.03 163 | 98.87 150 | 98.23 159 | 99.01 175 | 99.13 148 |
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 |
CMPMVS | | 76.62 19 | 98.64 160 | 98.60 149 | 98.68 180 | 99.33 193 | 97.07 208 | 98.11 209 | 98.50 202 | 97.69 183 | 99.26 168 | 98.35 174 | 99.66 111 | 97.62 172 | 99.43 69 | 99.02 90 | 99.24 167 | 99.01 158 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
ET-MVSNet_ETH3D | | | 97.44 190 | 96.29 193 | 98.78 176 | 97.93 212 | 98.95 154 | 98.91 174 | 99.09 194 | 98.00 172 | 99.24 169 | 98.83 146 | 84.62 212 | 98.02 164 | 97.43 193 | 97.38 183 | 99.48 139 | 98.84 163 |
|
MDTV_nov1_ep13 | | | 97.41 191 | 96.26 194 | 98.76 177 | 99.47 178 | 98.43 186 | 99.26 137 | 99.82 82 | 98.06 169 | 99.23 170 | 99.22 120 | 92.86 192 | 98.05 161 | 95.33 200 | 93.66 196 | 96.73 198 | 96.26 198 |
|
FPMVS | | | 98.48 166 | 98.83 138 | 98.07 197 | 99.09 203 | 97.98 202 | 99.07 155 | 98.04 208 | 98.99 87 | 99.22 171 | 98.85 145 | 99.43 130 | 93.79 205 | 99.66 41 | 99.11 79 | 99.24 167 | 97.76 190 |
|
DVP-MVS | | | 99.53 52 | 99.51 37 | 99.55 89 | 99.82 87 | 99.58 44 | 99.54 80 | 99.78 105 | 99.28 54 | 99.21 172 | 99.70 59 | 99.97 8 | 99.32 85 | 99.32 81 | 99.14 68 | 99.64 103 | 99.58 70 |
|
TSAR-MVS + ACMM | | | 99.31 99 | 99.26 75 | 99.37 124 | 99.66 142 | 98.97 153 | 99.20 141 | 99.56 158 | 99.33 46 | 99.19 173 | 99.54 81 | 99.91 53 | 99.32 85 | 99.12 121 | 98.34 154 | 99.29 162 | 99.65 48 |
|
tpmrst | | | 96.18 200 | 94.47 203 | 98.18 192 | 99.52 167 | 97.89 205 | 98.96 168 | 99.79 99 | 98.07 168 | 99.16 174 | 99.30 116 | 92.69 194 | 96.69 190 | 90.76 206 | 88.85 207 | 94.96 206 | 93.69 207 |
|
Vis-MVSNet (Re-imp) | | | 99.40 79 | 99.28 73 | 99.55 89 | 99.92 25 | 99.68 30 | 99.31 124 | 99.87 49 | 98.69 122 | 99.16 174 | 99.08 134 | 98.64 159 | 99.20 96 | 99.65 43 | 99.46 43 | 99.83 46 | 99.72 34 |
|
PMMVS | | | 98.71 158 | 98.55 152 | 98.90 171 | 99.28 197 | 98.45 185 | 98.53 197 | 99.45 174 | 97.67 184 | 99.15 176 | 98.76 150 | 99.54 123 | 97.79 168 | 98.77 166 | 98.23 159 | 99.16 172 | 98.46 175 |
|
Fast-Effi-MVS+-dtu | | | 98.82 150 | 98.80 143 | 98.84 175 | 99.51 173 | 98.90 155 | 98.96 168 | 99.91 36 | 98.29 156 | 99.11 177 | 98.47 167 | 99.63 113 | 96.03 197 | 99.21 105 | 98.12 165 | 99.52 133 | 99.01 158 |
|
Effi-MVS+-dtu | | | 99.01 135 | 99.05 114 | 98.98 164 | 99.60 156 | 99.13 139 | 99.03 162 | 99.61 149 | 98.52 140 | 99.01 178 | 98.53 164 | 99.83 82 | 96.95 186 | 99.48 57 | 98.59 140 | 99.66 92 | 99.25 136 |
|
canonicalmvs | | | 99.00 136 | 98.68 147 | 99.37 124 | 99.68 140 | 99.42 80 | 98.94 172 | 99.89 44 | 99.00 86 | 98.99 179 | 98.43 171 | 95.69 182 | 98.96 125 | 99.18 114 | 99.18 62 | 99.74 74 | 99.88 6 |
|
train_agg | | | 98.89 146 | 98.48 158 | 99.38 118 | 99.69 134 | 98.76 167 | 99.31 124 | 99.60 151 | 97.71 182 | 98.98 180 | 97.89 182 | 99.89 59 | 99.29 90 | 98.32 178 | 97.59 180 | 99.42 151 | 99.16 145 |
|
TAMVS | | | 99.05 132 | 99.02 120 | 99.08 160 | 99.69 134 | 99.22 125 | 99.33 121 | 99.32 188 | 99.16 69 | 98.97 181 | 99.87 34 | 97.36 177 | 97.76 169 | 99.21 105 | 99.00 96 | 99.44 145 | 99.33 125 |
|
DeepPCF-MVS | | 98.38 11 | 99.16 120 | 99.20 89 | 99.12 155 | 99.20 200 | 98.71 171 | 98.85 181 | 99.06 195 | 99.17 65 | 98.96 182 | 99.61 71 | 99.86 72 | 99.29 90 | 99.17 116 | 98.72 127 | 99.36 156 | 99.15 147 |
|
EPP-MVSNet | | | 99.34 91 | 99.10 109 | 99.62 77 | 99.94 19 | 99.74 23 | 99.66 54 | 99.80 97 | 99.07 78 | 98.93 183 | 99.61 71 | 96.13 180 | 99.49 65 | 99.67 39 | 99.63 23 | 99.92 17 | 99.86 9 |
|
DPM-MVS | | | 98.10 172 | 97.32 180 | 99.01 163 | 99.52 167 | 97.92 203 | 98.47 199 | 99.45 174 | 98.25 158 | 98.91 184 | 93.99 204 | 99.69 107 | 98.73 139 | 96.29 198 | 96.32 191 | 99.00 176 | 98.77 167 |
|
EMVS | | | 96.47 199 | 95.38 199 | 97.74 204 | 99.42 185 | 95.37 212 | 98.07 210 | 98.27 207 | 97.85 178 | 98.90 185 | 97.48 190 | 98.73 157 | 97.20 178 | 97.21 195 | 90.39 203 | 94.59 211 | 90.65 210 |
|
FMVSNet2 | | | 99.07 130 | 99.19 91 | 98.93 169 | 99.02 205 | 99.53 53 | 99.31 124 | 99.84 69 | 98.86 106 | 98.88 186 | 99.64 67 | 98.44 162 | 96.92 187 | 99.35 76 | 99.00 96 | 99.61 113 | 99.53 85 |
|
baseline1 | | | 98.39 170 | 97.59 175 | 99.31 130 | 99.78 102 | 99.45 71 | 99.13 149 | 99.53 166 | 98.06 169 | 98.87 187 | 98.63 159 | 90.04 201 | 98.76 137 | 98.85 155 | 98.84 114 | 99.81 52 | 99.28 131 |
|
dps | | | 95.59 202 | 93.46 205 | 98.08 195 | 99.33 193 | 98.22 196 | 98.87 179 | 99.70 131 | 96.17 208 | 98.87 187 | 97.75 185 | 86.85 210 | 96.60 191 | 91.24 205 | 89.62 205 | 95.10 205 | 94.34 205 |
|
EPNet | | | 98.06 174 | 98.11 171 | 98.00 198 | 99.60 156 | 98.99 152 | 98.38 200 | 99.68 139 | 98.18 162 | 98.85 189 | 97.89 182 | 95.60 183 | 92.72 208 | 98.30 179 | 98.10 167 | 98.76 181 | 99.72 34 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MSP-MVS | | | 99.32 95 | 99.26 75 | 99.38 118 | 99.76 113 | 99.54 50 | 99.42 107 | 99.72 129 | 98.92 99 | 98.84 190 | 98.96 141 | 99.96 13 | 98.91 128 | 98.72 168 | 99.14 68 | 99.63 106 | 99.58 70 |
|
baseline2 | | | 97.87 176 | 97.18 181 | 98.67 181 | 99.34 192 | 99.17 131 | 98.48 198 | 98.82 198 | 97.08 194 | 98.83 191 | 98.75 151 | 89.47 203 | 97.03 185 | 98.67 170 | 98.27 156 | 99.52 133 | 98.83 165 |
|
TSAR-MVS + COLMAP | | | 98.74 155 | 98.58 151 | 98.93 169 | 99.29 196 | 98.23 193 | 99.04 158 | 99.24 190 | 98.79 114 | 98.80 192 | 99.37 107 | 99.71 103 | 98.06 160 | 98.02 186 | 97.46 182 | 99.16 172 | 98.48 174 |
|
MIMVSNet | | | 99.00 136 | 99.03 117 | 98.97 166 | 99.32 195 | 99.32 104 | 99.39 111 | 99.91 36 | 98.41 149 | 98.76 193 | 99.24 117 | 99.17 146 | 97.13 179 | 99.30 84 | 98.80 120 | 99.29 162 | 99.01 158 |
|
E-PMN | | | 96.72 197 | 95.78 197 | 97.81 201 | 99.45 180 | 95.46 211 | 98.14 208 | 98.33 206 | 97.99 173 | 98.73 194 | 98.09 179 | 98.97 151 | 97.54 174 | 97.45 192 | 91.09 201 | 94.70 209 | 91.40 209 |
|
thres100view900 | | | 97.69 184 | 96.37 192 | 99.23 140 | 99.74 125 | 99.21 128 | 98.81 186 | 99.43 178 | 96.10 210 | 98.70 195 | 92.99 206 | 89.10 205 | 98.88 131 | 98.58 173 | 99.31 55 | 99.82 49 | 99.27 132 |
|
tfpn200view9 | | | 97.85 179 | 96.54 188 | 99.38 118 | 99.74 125 | 99.52 60 | 99.17 144 | 99.76 120 | 96.10 210 | 98.70 195 | 92.99 206 | 89.10 205 | 99.00 120 | 99.11 122 | 99.56 27 | 99.88 26 | 99.41 111 |
|
CHOSEN 280x420 | | | 98.99 138 | 98.91 131 | 99.07 161 | 99.77 109 | 99.26 114 | 99.55 76 | 99.92 31 | 98.62 130 | 98.67 197 | 99.62 70 | 97.20 178 | 98.44 149 | 99.50 55 | 99.18 62 | 98.08 190 | 98.99 161 |
|
thres200 | | | 97.87 176 | 96.56 187 | 99.39 114 | 99.76 113 | 99.52 60 | 99.13 149 | 99.76 120 | 96.88 203 | 98.66 198 | 92.87 208 | 88.77 207 | 99.16 101 | 99.11 122 | 99.42 47 | 99.88 26 | 99.33 125 |
|
thres400 | | | 97.82 180 | 96.47 191 | 99.40 113 | 99.81 93 | 99.44 73 | 99.29 131 | 99.69 133 | 97.15 191 | 98.57 199 | 92.82 209 | 87.96 208 | 99.16 101 | 98.96 136 | 99.55 30 | 99.86 36 | 99.41 111 |
|
GBi-Net | | | 98.96 139 | 99.05 114 | 98.85 173 | 99.02 205 | 99.53 53 | 99.31 124 | 99.78 105 | 98.13 163 | 98.48 200 | 99.43 98 | 97.58 173 | 96.92 187 | 99.68 36 | 99.50 36 | 99.61 113 | 99.53 85 |
|
test1 | | | 98.96 139 | 99.05 114 | 98.85 173 | 99.02 205 | 99.53 53 | 99.31 124 | 99.78 105 | 98.13 163 | 98.48 200 | 99.43 98 | 97.58 173 | 96.92 187 | 99.68 36 | 99.50 36 | 99.61 113 | 99.53 85 |
|
FMVSNet3 | | | 98.63 162 | 98.75 144 | 98.49 186 | 98.10 211 | 99.44 73 | 99.02 163 | 99.78 105 | 98.13 163 | 98.48 200 | 99.43 98 | 97.58 173 | 96.16 195 | 98.85 155 | 98.39 151 | 99.40 152 | 99.41 111 |
|
FMVSNet5 | | | 97.69 184 | 96.98 183 | 98.53 185 | 98.53 209 | 99.36 93 | 98.90 177 | 99.54 161 | 96.38 207 | 98.44 203 | 95.38 202 | 90.08 200 | 97.05 184 | 99.46 61 | 99.06 83 | 98.73 182 | 99.12 149 |
|
PMMVS2 | | | 99.23 109 | 99.22 84 | 99.24 139 | 99.80 94 | 99.14 135 | 99.50 92 | 99.82 82 | 99.12 74 | 98.41 204 | 99.91 23 | 99.98 4 | 98.51 147 | 99.48 57 | 98.76 122 | 99.38 154 | 98.14 184 |
|
thres600view7 | | | 97.86 178 | 96.53 190 | 99.41 112 | 99.84 76 | 99.52 60 | 99.36 116 | 99.76 120 | 97.32 189 | 98.38 205 | 93.24 205 | 87.25 209 | 99.23 95 | 99.11 122 | 99.75 13 | 99.88 26 | 99.48 96 |
|
CVMVSNet | | | 99.06 131 | 98.88 136 | 99.28 136 | 99.52 167 | 99.53 53 | 99.42 107 | 99.69 133 | 98.74 118 | 98.27 206 | 99.89 30 | 95.48 184 | 99.44 72 | 99.46 61 | 99.33 53 | 99.32 161 | 99.75 28 |
|
MVE | | 91.08 18 | 97.68 186 | 97.65 173 | 97.71 205 | 98.46 210 | 91.62 214 | 97.92 211 | 98.86 197 | 98.73 120 | 97.99 207 | 98.64 158 | 99.96 13 | 99.17 98 | 99.59 49 | 97.75 176 | 93.87 212 | 97.27 194 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
test-LLR | | | 97.74 182 | 97.46 177 | 98.08 195 | 99.62 148 | 98.37 188 | 98.26 202 | 99.41 179 | 97.03 196 | 97.38 208 | 99.54 81 | 92.89 190 | 95.12 201 | 98.78 164 | 97.68 178 | 98.65 184 | 97.90 186 |
|
TESTMET0.1,1 | | | 97.62 188 | 97.46 177 | 97.81 201 | 99.07 204 | 98.37 188 | 98.26 202 | 98.35 204 | 97.03 196 | 97.38 208 | 99.54 81 | 92.89 190 | 95.12 201 | 98.78 164 | 97.68 178 | 98.65 184 | 97.90 186 |
|
IS_MVSNet | | | 99.15 122 | 99.12 106 | 99.19 148 | 99.92 25 | 99.73 25 | 99.55 76 | 99.86 53 | 98.45 144 | 96.91 210 | 98.74 152 | 98.33 165 | 99.02 119 | 99.54 54 | 99.47 41 | 99.88 26 | 99.61 58 |
|
test-mter | | | 97.65 187 | 97.57 176 | 97.75 203 | 98.90 208 | 98.56 180 | 98.15 207 | 98.45 203 | 96.92 200 | 96.84 211 | 99.52 90 | 92.53 197 | 95.24 200 | 99.04 127 | 98.12 165 | 98.90 179 | 98.29 181 |
|
test0.0.03 1 | | | 98.41 168 | 98.41 160 | 98.40 190 | 99.62 148 | 99.16 132 | 98.87 179 | 99.41 179 | 97.15 191 | 96.60 212 | 99.31 113 | 97.00 179 | 96.55 192 | 98.91 144 | 98.51 144 | 99.37 155 | 98.82 166 |
|
tmp_tt | | | | | 88.14 206 | 96.68 213 | 91.91 213 | 93.70 213 | 61.38 210 | 99.61 19 | 90.51 213 | 99.40 104 | 99.71 103 | 90.32 209 | 99.22 102 | 99.44 46 | 96.25 200 | |
|
GG-mvs-BLEND | | | 70.44 205 | 96.91 184 | 39.57 207 | 3.32 216 | 96.51 209 | 91.01 214 | 4.05 213 | 97.03 196 | 33.20 214 | 94.67 203 | 97.75 171 | 7.59 212 | 98.28 180 | 96.85 188 | 98.24 188 | 97.26 195 |
|
testmvs | | | 22.33 206 | 29.66 208 | 13.79 208 | 8.97 214 | 10.35 215 | 15.53 217 | 8.09 212 | 32.51 212 | 19.87 215 | 45.18 211 | 30.56 218 | 17.05 211 | 29.96 210 | 24.74 209 | 13.21 213 | 34.30 211 |
|
test123 | | | 21.52 207 | 28.47 209 | 13.42 209 | 7.29 215 | 10.12 216 | 15.70 216 | 8.31 211 | 31.54 213 | 19.34 216 | 36.33 212 | 37.40 217 | 17.14 210 | 27.45 211 | 23.17 210 | 12.73 214 | 33.30 212 |
|
sosnet-low-res | | | 0.00 208 | 0.00 210 | 0.00 210 | 0.00 217 | 0.00 217 | 0.00 218 | 0.00 214 | 0.00 214 | 0.00 217 | 0.00 213 | 0.00 219 | 0.00 213 | 0.00 212 | 0.00 211 | 0.00 215 | 0.00 213 |
|
sosnet | | | 0.00 208 | 0.00 210 | 0.00 210 | 0.00 217 | 0.00 217 | 0.00 218 | 0.00 214 | 0.00 214 | 0.00 217 | 0.00 213 | 0.00 219 | 0.00 213 | 0.00 212 | 0.00 211 | 0.00 215 | 0.00 213 |
|
SR-MVS | | | | | | 99.73 127 | | | 99.74 127 | | | | 99.88 64 | | | | | |
|
Anonymous202405211 | | | | 99.14 102 | | 99.87 47 | 99.55 47 | 99.50 92 | 99.70 131 | 98.55 138 | | 98.61 160 | 98.46 161 | 98.76 137 | 99.66 41 | 99.50 36 | 99.85 39 | 99.63 52 |
|
our_test_3 | | | | | | 99.75 117 | 99.11 143 | 99.74 42 | | | | | | | | | | |
|
test_part1 | | | | | | | | | | | | | | | | | | 99.23 137 |
|
Patchmatch-RL test | | | | | | | | 65.75 215 | | | | | | | | | | |
|
mPP-MVS | | | | | | 99.84 76 | | | | | | | 99.92 47 | | | | | |
|
NP-MVS | | | | | | | | | | 97.37 188 | | | | | | | | |
|