LCM-MVSNet | | | 99.86 1 | 99.86 1 | 99.87 1 | 99.99 1 | 99.77 1 | 99.77 1 | 99.80 1 | 99.97 1 | 99.97 1 | 99.95 1 | 99.74 1 | 99.98 1 | 99.56 1 | 100.00 1 | 99.85 4 |
|
Anonymous20231211 | | | 99.29 2 | 99.41 2 | 98.91 22 | 99.94 2 | 97.08 37 | 99.47 3 | 99.51 5 | 99.56 2 | 99.83 3 | 99.80 2 | 99.13 3 | 99.90 13 | 97.55 49 | 99.93 21 | 99.75 13 |
|
UA-Net | | | 98.88 7 | 98.76 16 | 99.22 2 | 99.11 77 | 97.89 10 | 99.47 3 | 99.32 8 | 99.08 9 | 97.87 136 | 99.67 3 | 96.47 73 | 99.92 4 | 97.88 34 | 99.98 3 | 99.85 4 |
|
pmmvs6 | | | 99.07 4 | 99.24 4 | 98.56 44 | 99.81 3 | 96.38 56 | 98.87 9 | 99.30 9 | 99.01 15 | 99.63 9 | 99.66 4 | 99.27 2 | 99.68 103 | 97.75 41 | 99.89 33 | 99.62 31 |
|
OurMVSNet-221017-0 | | | 98.61 19 | 98.61 25 | 98.63 41 | 99.77 4 | 96.35 57 | 99.17 6 | 99.05 38 | 98.05 41 | 99.61 11 | 99.52 5 | 93.72 163 | 99.88 19 | 98.72 20 | 99.88 34 | 99.65 24 |
|
ANet_high | | | 98.31 31 | 98.94 8 | 96.41 176 | 99.33 47 | 89.64 213 | 97.92 55 | 99.56 4 | 99.27 5 | 99.66 8 | 99.50 6 | 97.67 25 | 99.83 30 | 97.55 49 | 99.98 3 | 99.77 9 |
|
mvs_tets | | | 98.90 5 | 98.94 8 | 98.75 30 | 99.69 8 | 96.48 54 | 98.54 20 | 99.22 10 | 96.23 110 | 99.71 5 | 99.48 7 | 98.77 7 | 99.93 2 | 98.89 10 | 99.95 13 | 99.84 6 |
|
gg-mvs-nofinetune | | | 88.28 314 | 86.96 318 | 92.23 309 | 92.84 346 | 84.44 311 | 98.19 40 | 74.60 354 | 99.08 9 | 87.01 345 | 99.47 8 | 56.93 352 | 98.23 328 | 78.91 334 | 95.61 321 | 94.01 336 |
|
PS-MVSNAJss | | | 98.53 22 | 98.63 21 | 98.21 68 | 99.68 9 | 94.82 104 | 98.10 44 | 99.21 11 | 96.91 87 | 99.75 4 | 99.45 9 | 95.82 90 | 99.92 4 | 98.80 13 | 99.96 11 | 99.89 1 |
|
test_djsdf | | | 98.73 13 | 98.74 18 | 98.69 37 | 99.63 13 | 96.30 59 | 98.67 12 | 99.02 51 | 96.50 99 | 99.32 21 | 99.44 10 | 97.43 30 | 99.92 4 | 98.73 17 | 99.95 13 | 99.86 3 |
|
v52 | | | 98.85 8 | 99.01 5 | 98.37 55 | 99.61 15 | 95.53 82 | 99.01 7 | 99.04 45 | 98.48 26 | 99.31 22 | 99.41 11 | 96.82 56 | 99.87 21 | 99.44 2 | 99.95 13 | 99.70 19 |
|
V4 | | | 98.85 8 | 99.01 5 | 98.37 55 | 99.61 15 | 95.53 82 | 99.01 7 | 99.04 45 | 98.48 26 | 99.31 22 | 99.41 11 | 96.81 57 | 99.87 21 | 99.44 2 | 99.95 13 | 99.70 19 |
|
wuykxyi23d | | | 98.68 17 | 98.53 26 | 99.13 3 | 99.44 34 | 97.97 7 | 96.85 117 | 99.02 51 | 95.81 126 | 99.88 2 | 99.38 13 | 98.14 14 | 99.69 97 | 98.32 28 | 99.95 13 | 99.73 16 |
|
anonymousdsp | | | 98.72 16 | 98.63 21 | 98.99 10 | 99.62 14 | 97.29 34 | 98.65 15 | 99.19 14 | 95.62 131 | 99.35 20 | 99.37 14 | 97.38 32 | 99.90 13 | 98.59 23 | 99.91 27 | 99.77 9 |
|
jajsoiax | | | 98.77 11 | 98.79 15 | 98.74 32 | 99.66 10 | 96.48 54 | 98.45 25 | 99.12 22 | 95.83 125 | 99.67 7 | 99.37 14 | 98.25 11 | 99.92 4 | 98.77 14 | 99.94 19 | 99.82 7 |
|
K. test v3 | | | 96.44 154 | 96.28 154 | 96.95 145 | 99.41 40 | 91.53 186 | 97.65 71 | 90.31 331 | 98.89 18 | 98.93 43 | 99.36 16 | 84.57 278 | 99.92 4 | 97.81 37 | 99.56 96 | 99.39 93 |
|
LTVRE_ROB | | 96.88 1 | 99.18 3 | 99.34 3 | 98.72 35 | 99.71 7 | 96.99 40 | 99.69 2 | 99.57 3 | 99.02 14 | 99.62 10 | 99.36 16 | 98.53 8 | 99.52 162 | 98.58 24 | 99.95 13 | 99.66 23 |
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 |
v13 | | | 98.02 44 | 98.52 27 | 96.51 169 | 99.02 88 | 90.14 204 | 98.07 46 | 99.09 29 | 98.10 40 | 99.13 32 | 99.35 18 | 94.84 121 | 99.74 59 | 99.12 5 | 99.98 3 | 99.65 24 |
|
SixPastTwentyTwo | | | 97.49 89 | 97.57 80 | 97.26 131 | 99.56 19 | 92.33 166 | 98.28 31 | 96.97 257 | 98.30 33 | 99.45 14 | 99.35 18 | 88.43 253 | 99.89 17 | 98.01 31 | 99.76 50 | 99.54 45 |
|
v12 | | | 97.97 47 | 98.47 28 | 96.46 173 | 98.98 92 | 90.01 208 | 97.97 51 | 99.08 30 | 98.00 43 | 99.11 34 | 99.34 20 | 94.70 124 | 99.73 64 | 99.07 6 | 99.98 3 | 99.64 27 |
|
Gipuma | | | 98.07 41 | 98.31 37 | 97.36 125 | 99.76 5 | 96.28 60 | 98.51 21 | 99.10 25 | 98.76 20 | 96.79 182 | 99.34 20 | 96.61 65 | 98.82 288 | 96.38 83 | 99.50 111 | 96.98 287 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
v11 | | | 97.82 67 | 98.36 34 | 96.17 194 | 98.93 94 | 89.16 230 | 97.79 61 | 99.08 30 | 97.64 60 | 99.19 29 | 99.32 22 | 94.28 143 | 99.72 70 | 99.07 6 | 99.97 8 | 99.63 29 |
|
V9 | | | 97.90 58 | 98.40 32 | 96.40 177 | 98.93 94 | 89.86 210 | 97.86 58 | 99.07 34 | 97.88 47 | 99.05 36 | 99.30 23 | 94.53 135 | 99.72 70 | 99.01 8 | 99.98 3 | 99.63 29 |
|
JIA-IIPM | | | 91.79 280 | 90.69 291 | 95.11 236 | 93.80 337 | 90.98 192 | 94.16 254 | 91.78 317 | 96.38 103 | 90.30 332 | 99.30 23 | 72.02 330 | 98.90 277 | 88.28 273 | 90.17 339 | 95.45 324 |
|
V14 | | | 97.83 64 | 98.33 36 | 96.35 178 | 98.88 100 | 89.72 211 | 97.75 65 | 99.05 38 | 97.74 51 | 99.01 38 | 99.27 25 | 94.35 140 | 99.71 80 | 98.95 9 | 99.97 8 | 99.62 31 |
|
TransMVSNet (Re) | | | 98.38 28 | 98.67 19 | 97.51 108 | 99.51 26 | 93.39 152 | 98.20 39 | 98.87 81 | 98.23 35 | 99.48 12 | 99.27 25 | 98.47 9 | 99.55 155 | 96.52 78 | 99.53 104 | 99.60 34 |
|
v15 | | | 97.77 70 | 98.26 40 | 96.30 183 | 98.81 101 | 89.59 218 | 97.62 74 | 99.04 45 | 97.59 62 | 98.97 42 | 99.24 27 | 94.19 147 | 99.70 88 | 98.88 11 | 99.97 8 | 99.61 33 |
|
Baseline_NR-MVSNet | | | 97.72 73 | 97.79 59 | 97.50 111 | 99.56 19 | 93.29 153 | 95.44 187 | 98.86 83 | 98.20 37 | 98.37 75 | 99.24 27 | 94.69 125 | 99.55 155 | 95.98 97 | 99.79 47 | 99.65 24 |
|
v7n | | | 98.73 13 | 98.99 7 | 97.95 81 | 99.64 12 | 94.20 125 | 98.67 12 | 99.14 20 | 99.08 9 | 99.42 16 | 99.23 29 | 96.53 68 | 99.91 12 | 99.27 4 | 99.93 21 | 99.73 16 |
|
pm-mvs1 | | | 98.47 24 | 98.67 19 | 97.86 85 | 99.52 25 | 94.58 112 | 98.28 31 | 99.00 62 | 97.57 63 | 99.27 26 | 99.22 30 | 98.32 10 | 99.50 174 | 97.09 68 | 99.75 54 | 99.50 50 |
|
TDRefinement | | | 98.90 5 | 98.86 11 | 99.02 8 | 99.54 23 | 98.06 6 | 99.34 5 | 99.44 7 | 98.85 19 | 99.00 40 | 99.20 31 | 97.42 31 | 99.59 143 | 97.21 62 | 99.76 50 | 99.40 90 |
|
GBi-Net | | | 96.99 112 | 96.80 130 | 97.56 103 | 97.96 208 | 93.67 141 | 98.23 34 | 98.66 129 | 95.59 133 | 97.99 114 | 99.19 32 | 89.51 244 | 99.73 64 | 94.60 150 | 99.44 130 | 99.30 110 |
|
test1 | | | 96.99 112 | 96.80 130 | 97.56 103 | 97.96 208 | 93.67 141 | 98.23 34 | 98.66 129 | 95.59 133 | 97.99 114 | 99.19 32 | 89.51 244 | 99.73 64 | 94.60 150 | 99.44 130 | 99.30 110 |
|
FMVSNet1 | | | 97.95 50 | 98.08 46 | 97.56 103 | 99.14 75 | 93.67 141 | 98.23 34 | 98.66 129 | 97.41 78 | 99.00 40 | 99.19 32 | 95.47 104 | 99.73 64 | 95.83 101 | 99.76 50 | 99.30 110 |
|
v17 | | | 97.70 75 | 98.17 42 | 96.28 186 | 98.77 105 | 89.59 218 | 97.62 74 | 99.01 60 | 97.54 65 | 98.72 53 | 99.18 35 | 94.06 151 | 99.68 103 | 98.74 16 | 99.92 24 | 99.58 36 |
|
v16 | | | 97.69 76 | 98.16 43 | 96.29 185 | 98.75 106 | 89.60 216 | 97.62 74 | 99.01 60 | 97.53 67 | 98.69 55 | 99.18 35 | 94.05 152 | 99.68 103 | 98.73 17 | 99.88 34 | 99.58 36 |
|
VDDNet | | | 96.98 115 | 96.84 126 | 97.41 122 | 99.40 41 | 93.26 154 | 97.94 53 | 95.31 284 | 99.26 6 | 98.39 74 | 99.18 35 | 87.85 260 | 99.62 127 | 95.13 133 | 99.09 189 | 99.35 104 |
|
DSMNet-mixed | | | 92.19 269 | 91.83 265 | 93.25 291 | 96.18 302 | 83.68 317 | 96.27 138 | 93.68 297 | 76.97 345 | 92.54 313 | 99.18 35 | 89.20 249 | 98.55 311 | 83.88 316 | 98.60 234 | 97.51 272 |
|
v10 | | | 97.55 85 | 97.97 51 | 96.31 182 | 98.60 128 | 89.64 213 | 97.44 86 | 99.02 51 | 96.60 96 | 98.72 53 | 99.16 39 | 93.48 167 | 99.72 70 | 98.76 15 | 99.92 24 | 99.58 36 |
|
v748 | | | 98.58 20 | 98.89 10 | 97.67 98 | 99.61 15 | 93.53 148 | 98.59 16 | 98.90 75 | 98.97 17 | 99.43 15 | 99.15 40 | 96.53 68 | 99.85 24 | 98.88 11 | 99.91 27 | 99.64 27 |
|
MIMVSNet1 | | | 98.51 23 | 98.45 31 | 98.67 38 | 99.72 6 | 96.71 46 | 98.76 10 | 98.89 77 | 98.49 25 | 99.38 18 | 99.14 41 | 95.44 106 | 99.84 28 | 96.47 81 | 99.80 46 | 99.47 64 |
|
v18 | | | 97.60 82 | 98.06 48 | 96.23 187 | 98.68 120 | 89.46 221 | 97.48 85 | 98.98 67 | 97.33 81 | 98.60 59 | 99.13 42 | 93.86 155 | 99.67 109 | 98.62 21 | 99.87 36 | 99.56 41 |
|
Vis-MVSNet | | | 98.27 32 | 98.34 35 | 98.07 73 | 99.33 47 | 95.21 94 | 98.04 48 | 99.46 6 | 97.32 82 | 97.82 140 | 99.11 43 | 96.75 59 | 99.86 23 | 97.84 36 | 99.36 154 | 99.15 133 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v8 | | | 97.60 82 | 98.06 48 | 96.23 187 | 98.71 113 | 89.44 222 | 97.43 87 | 98.82 99 | 97.29 83 | 98.74 51 | 99.10 44 | 93.86 155 | 99.68 103 | 98.61 22 | 99.94 19 | 99.56 41 |
|
MVS-HIRNet | | | 88.40 313 | 90.20 299 | 82.99 337 | 97.01 281 | 60.04 355 | 93.11 293 | 85.61 349 | 84.45 317 | 88.72 339 | 99.09 45 | 84.72 277 | 98.23 328 | 82.52 321 | 96.59 308 | 90.69 348 |
|
ACMH | | 93.61 9 | 98.44 25 | 98.76 16 | 97.51 108 | 99.43 37 | 93.54 147 | 98.23 34 | 99.05 38 | 97.40 79 | 99.37 19 | 99.08 46 | 98.79 6 | 99.47 181 | 97.74 42 | 99.71 63 | 99.50 50 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
DTE-MVSNet | | | 98.79 10 | 98.86 11 | 98.59 42 | 99.55 21 | 96.12 63 | 98.48 24 | 99.10 25 | 99.36 3 | 99.29 25 | 99.06 47 | 97.27 37 | 99.93 2 | 97.71 43 | 99.91 27 | 99.70 19 |
|
PEN-MVS | | | 98.75 12 | 98.85 13 | 98.44 49 | 99.58 18 | 95.67 76 | 98.45 25 | 99.15 19 | 99.33 4 | 99.30 24 | 99.00 48 | 97.27 37 | 99.92 4 | 97.64 44 | 99.92 24 | 99.75 13 |
|
DeepC-MVS | | 95.41 4 | 97.82 67 | 97.70 65 | 98.16 69 | 98.78 104 | 95.72 73 | 96.23 143 | 99.02 51 | 93.92 197 | 98.62 56 | 98.99 49 | 97.69 23 | 99.62 127 | 96.18 87 | 99.87 36 | 99.15 133 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
VPA-MVSNet | | | 98.27 32 | 98.46 29 | 97.70 94 | 99.06 82 | 93.80 137 | 97.76 64 | 99.00 62 | 98.40 29 | 99.07 35 | 98.98 50 | 96.89 50 | 99.75 54 | 97.19 65 | 99.79 47 | 99.55 44 |
|
lessismore_v0 | | | | | 97.05 140 | 99.36 45 | 92.12 174 | | 84.07 351 | | 98.77 50 | 98.98 50 | 85.36 272 | 99.74 59 | 97.34 59 | 99.37 151 | 99.30 110 |
|
testing_2 | | | 97.43 91 | 97.71 64 | 96.60 162 | 98.91 97 | 90.85 194 | 96.01 153 | 98.54 143 | 94.78 169 | 98.78 48 | 98.96 52 | 96.35 77 | 99.54 157 | 97.25 60 | 99.82 42 | 99.40 90 |
|
PS-CasMVS | | | 98.73 13 | 98.85 13 | 98.39 54 | 99.55 21 | 95.47 84 | 98.49 22 | 99.13 21 | 99.22 7 | 99.22 28 | 98.96 52 | 97.35 33 | 99.92 4 | 97.79 39 | 99.93 21 | 99.79 8 |
|
EU-MVSNet | | | 94.25 226 | 94.47 213 | 93.60 283 | 98.14 191 | 82.60 319 | 97.24 94 | 92.72 310 | 85.08 311 | 98.48 68 | 98.94 54 | 82.59 282 | 98.76 294 | 97.47 56 | 99.53 104 | 99.44 79 |
|
LCM-MVSNet-Re | | | 97.33 100 | 97.33 91 | 97.32 127 | 98.13 194 | 93.79 138 | 96.99 109 | 99.65 2 | 96.74 94 | 99.47 13 | 98.93 55 | 96.91 49 | 99.84 28 | 90.11 246 | 99.06 194 | 98.32 226 |
|
XXY-MVS | | | 97.54 86 | 97.70 65 | 97.07 139 | 99.46 32 | 92.21 170 | 97.22 95 | 99.00 62 | 94.93 164 | 98.58 61 | 98.92 56 | 97.31 35 | 99.41 206 | 94.44 153 | 99.43 139 | 99.59 35 |
|
mvs_anonymous | | | 95.36 192 | 96.07 161 | 93.21 292 | 96.29 297 | 81.56 321 | 94.60 236 | 97.66 228 | 93.30 212 | 96.95 177 | 98.91 57 | 93.03 181 | 99.38 220 | 96.60 75 | 97.30 296 | 98.69 196 |
|
UGNet | | | 96.81 134 | 96.56 141 | 97.58 102 | 96.64 289 | 93.84 136 | 97.75 65 | 97.12 252 | 96.47 102 | 93.62 287 | 98.88 58 | 93.22 176 | 99.53 159 | 95.61 111 | 99.69 67 | 99.36 103 |
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 |
FC-MVSNet-test | | | 98.16 36 | 98.37 33 | 97.56 103 | 99.49 30 | 93.10 156 | 98.35 28 | 99.21 11 | 98.43 28 | 98.89 44 | 98.83 59 | 94.30 142 | 99.81 33 | 97.87 35 | 99.91 27 | 99.77 9 |
|
new-patchmatchnet | | | 95.67 175 | 96.58 139 | 92.94 299 | 97.48 255 | 80.21 326 | 92.96 294 | 98.19 192 | 94.83 167 | 98.82 46 | 98.79 60 | 93.31 174 | 99.51 172 | 95.83 101 | 99.04 195 | 99.12 141 |
|
WR-MVS_H | | | 98.65 18 | 98.62 23 | 98.75 30 | 99.51 26 | 96.61 50 | 98.55 19 | 99.17 15 | 99.05 12 | 99.17 31 | 98.79 60 | 95.47 104 | 99.89 17 | 97.95 32 | 99.91 27 | 99.75 13 |
|
ab-mvs | | | 96.59 147 | 96.59 138 | 96.60 162 | 98.64 121 | 92.21 170 | 98.35 28 | 97.67 226 | 94.45 178 | 96.99 170 | 98.79 60 | 94.96 119 | 99.49 176 | 90.39 243 | 99.07 192 | 98.08 244 |
|
EG-PatchMatch MVS | | | 97.69 76 | 97.79 59 | 97.40 123 | 99.06 82 | 93.52 149 | 95.96 160 | 98.97 69 | 94.55 177 | 98.82 46 | 98.76 63 | 97.31 35 | 99.29 236 | 97.20 64 | 99.44 130 | 99.38 95 |
|
no-one | | | 94.84 210 | 94.76 202 | 95.09 238 | 98.29 159 | 87.49 270 | 91.82 315 | 97.49 238 | 88.21 279 | 97.84 139 | 98.75 64 | 91.51 220 | 99.27 238 | 88.96 263 | 99.99 2 | 98.52 208 |
|
nrg030 | | | 98.54 21 | 98.62 23 | 98.32 60 | 99.22 56 | 95.66 77 | 97.90 56 | 99.08 30 | 98.31 32 | 99.02 37 | 98.74 65 | 97.68 24 | 99.61 133 | 97.77 40 | 99.85 39 | 99.70 19 |
|
VDD-MVS | | | 97.37 96 | 97.25 96 | 97.74 92 | 98.69 119 | 94.50 115 | 97.04 107 | 95.61 282 | 98.59 23 | 98.51 65 | 98.72 66 | 92.54 195 | 99.58 145 | 96.02 94 | 99.49 118 | 99.12 141 |
|
PatchT | | | 93.75 240 | 93.57 236 | 94.29 267 | 95.05 321 | 87.32 275 | 96.05 150 | 92.98 305 | 97.54 65 | 94.25 264 | 98.72 66 | 75.79 310 | 99.24 242 | 95.92 99 | 95.81 315 | 96.32 311 |
|
RPSCF | | | 97.87 61 | 97.51 84 | 98.95 14 | 99.15 67 | 98.43 3 | 97.56 80 | 99.06 36 | 96.19 111 | 98.48 68 | 98.70 68 | 94.72 123 | 99.24 242 | 94.37 158 | 99.33 164 | 99.17 129 |
|
APDe-MVS | | | 98.14 37 | 98.03 50 | 98.47 48 | 98.72 110 | 96.04 66 | 98.07 46 | 99.10 25 | 95.96 119 | 98.59 60 | 98.69 69 | 96.94 48 | 99.81 33 | 96.64 74 | 99.58 91 | 99.57 40 |
|
IterMVS-LS | | | 96.92 122 | 97.29 93 | 95.79 217 | 98.51 142 | 88.13 253 | 95.10 212 | 98.66 129 | 96.99 84 | 98.46 70 | 98.68 70 | 92.55 193 | 99.74 59 | 96.91 72 | 99.79 47 | 99.50 50 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tfpnnormal | | | 97.72 73 | 97.97 51 | 96.94 146 | 99.26 51 | 92.23 169 | 97.83 60 | 98.45 151 | 98.25 34 | 99.13 32 | 98.66 71 | 96.65 63 | 99.69 97 | 93.92 172 | 99.62 79 | 98.91 171 |
|
FIs | | | 97.93 54 | 98.07 47 | 97.48 115 | 99.38 43 | 92.95 158 | 98.03 50 | 99.11 23 | 98.04 42 | 98.62 56 | 98.66 71 | 93.75 162 | 99.78 39 | 97.23 61 | 99.84 40 | 99.73 16 |
|
CP-MVSNet | | | 98.42 26 | 98.46 29 | 98.30 63 | 99.46 32 | 95.22 92 | 98.27 33 | 98.84 87 | 99.05 12 | 99.01 38 | 98.65 73 | 95.37 107 | 99.90 13 | 97.57 48 | 99.91 27 | 99.77 9 |
|
FMVSNet2 | | | 96.72 140 | 96.67 136 | 96.87 151 | 97.96 208 | 91.88 180 | 97.15 96 | 98.06 206 | 95.59 133 | 98.50 67 | 98.62 74 | 89.51 244 | 99.65 115 | 94.99 138 | 99.60 87 | 99.07 151 |
|
PMVS | | 89.60 17 | 96.71 142 | 96.97 119 | 95.95 210 | 99.51 26 | 97.81 13 | 97.42 88 | 97.49 238 | 97.93 45 | 95.95 218 | 98.58 75 | 96.88 52 | 96.91 342 | 89.59 253 | 99.36 154 | 93.12 341 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
CR-MVSNet | | | 93.29 251 | 92.79 249 | 94.78 249 | 95.44 316 | 88.15 251 | 96.18 145 | 97.20 247 | 84.94 313 | 94.10 269 | 98.57 76 | 77.67 297 | 99.39 215 | 95.17 129 | 95.81 315 | 96.81 295 |
|
Patchmtry | | | 95.03 205 | 94.59 209 | 96.33 180 | 94.83 323 | 90.82 196 | 96.38 133 | 97.20 247 | 96.59 97 | 97.49 148 | 98.57 76 | 77.67 297 | 99.38 220 | 92.95 192 | 99.62 79 | 98.80 186 |
|
ambc | | | | | 96.56 168 | 98.23 175 | 91.68 185 | 97.88 57 | 98.13 198 | | 98.42 73 | 98.56 78 | 94.22 146 | 99.04 261 | 94.05 170 | 99.35 157 | 98.95 163 |
|
3Dnovator | | 96.53 2 | 97.61 81 | 97.64 72 | 97.50 111 | 97.74 237 | 93.65 145 | 98.49 22 | 98.88 79 | 96.86 91 | 97.11 165 | 98.55 79 | 95.82 90 | 99.73 64 | 95.94 98 | 99.42 142 | 99.13 136 |
|
semantic-postprocess | | | | | 94.85 247 | 97.68 242 | 85.53 290 | | 97.63 234 | 96.99 84 | 98.36 76 | 98.54 80 | 87.44 262 | 99.75 54 | 97.07 69 | 99.08 190 | 99.27 120 |
|
COLMAP_ROB | | 94.48 6 | 98.25 34 | 98.11 45 | 98.64 40 | 99.21 59 | 97.35 32 | 97.96 52 | 99.16 16 | 98.34 31 | 98.78 48 | 98.52 81 | 97.32 34 | 99.45 190 | 94.08 167 | 99.67 73 | 99.13 136 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
ACMH+ | | 93.58 10 | 98.23 35 | 98.31 37 | 97.98 80 | 99.39 42 | 95.22 92 | 97.55 81 | 99.20 13 | 98.21 36 | 99.25 27 | 98.51 82 | 98.21 12 | 99.40 209 | 94.79 144 | 99.72 59 | 99.32 106 |
|
RPMNet | | | 94.22 227 | 94.03 229 | 94.78 249 | 95.44 316 | 88.15 251 | 96.18 145 | 93.73 294 | 97.43 70 | 94.10 269 | 98.49 83 | 79.40 290 | 99.39 215 | 95.69 104 | 95.81 315 | 96.81 295 |
|
IterMVS | | | 95.42 189 | 95.83 169 | 94.20 268 | 97.52 254 | 83.78 316 | 92.41 306 | 97.47 242 | 95.49 137 | 98.06 109 | 98.49 83 | 87.94 256 | 99.58 145 | 96.02 94 | 99.02 196 | 99.23 123 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
DP-MVS | | | 97.87 61 | 97.89 55 | 97.81 88 | 98.62 126 | 94.82 104 | 97.13 99 | 98.79 101 | 98.98 16 | 98.74 51 | 98.49 83 | 95.80 96 | 99.49 176 | 95.04 137 | 99.44 130 | 99.11 144 |
|
TranMVSNet+NR-MVSNet | | | 98.33 29 | 98.30 39 | 98.43 50 | 99.07 81 | 95.87 69 | 96.73 121 | 99.05 38 | 98.67 21 | 98.84 45 | 98.45 86 | 97.58 27 | 99.88 19 | 96.45 82 | 99.86 38 | 99.54 45 |
|
3Dnovator+ | | 96.13 3 | 97.73 72 | 97.59 78 | 98.15 70 | 98.11 195 | 95.60 78 | 98.04 48 | 98.70 121 | 98.13 38 | 96.93 178 | 98.45 86 | 95.30 111 | 99.62 127 | 95.64 109 | 98.96 200 | 99.24 122 |
|
VPNet | | | 97.26 104 | 97.49 86 | 96.59 164 | 99.47 31 | 90.58 200 | 96.27 138 | 98.53 144 | 97.77 49 | 98.46 70 | 98.41 88 | 94.59 131 | 99.68 103 | 94.61 149 | 99.29 170 | 99.52 48 |
|
test_0402 | | | 97.84 63 | 97.97 51 | 97.47 116 | 99.19 62 | 94.07 128 | 96.71 122 | 98.73 112 | 98.66 22 | 98.56 62 | 98.41 88 | 96.84 55 | 99.69 97 | 94.82 141 | 99.81 43 | 98.64 198 |
|
v1240 | | | 96.74 137 | 97.02 118 | 95.91 213 | 98.18 184 | 88.52 245 | 95.39 195 | 98.88 79 | 93.15 220 | 98.46 70 | 98.40 90 | 92.80 185 | 99.71 80 | 98.45 25 | 99.49 118 | 99.49 58 |
|
v7 | | | 96.93 120 | 97.17 106 | 96.23 187 | 98.59 130 | 89.64 213 | 95.96 160 | 98.66 129 | 94.41 181 | 97.87 136 | 98.38 91 | 93.47 168 | 99.64 118 | 97.93 33 | 99.24 175 | 99.43 83 |
|
ACMMP_Plus | | | 97.89 59 | 97.63 74 | 98.67 38 | 99.35 46 | 96.84 43 | 96.36 134 | 98.79 101 | 95.07 160 | 97.88 131 | 98.35 92 | 97.24 40 | 99.72 70 | 96.05 91 | 99.58 91 | 99.45 71 |
|
v1192 | | | 96.83 132 | 97.06 116 | 96.15 195 | 98.28 162 | 89.29 227 | 95.36 197 | 98.77 105 | 93.73 206 | 98.11 100 | 98.34 93 | 93.02 182 | 99.67 109 | 98.35 26 | 99.58 91 | 99.50 50 |
|
pmmvs-eth3d | | | 96.49 151 | 96.18 156 | 97.42 121 | 98.25 173 | 94.29 120 | 94.77 233 | 98.07 205 | 89.81 265 | 97.97 118 | 98.33 94 | 93.11 177 | 99.08 257 | 95.46 116 | 99.84 40 | 98.89 174 |
|
PM-MVS | | | 97.36 99 | 97.10 112 | 98.14 71 | 98.91 97 | 96.77 45 | 96.20 144 | 98.63 136 | 93.82 204 | 98.54 63 | 98.33 94 | 93.98 153 | 99.05 260 | 95.99 96 | 99.45 129 | 98.61 202 |
|
MP-MVS-pluss | | | 97.69 76 | 97.36 90 | 98.70 36 | 99.50 29 | 96.84 43 | 95.38 196 | 98.99 65 | 92.45 237 | 98.11 100 | 98.31 96 | 97.25 39 | 99.77 47 | 96.60 75 | 99.62 79 | 99.48 61 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
v1144 | | | 96.84 129 | 97.08 114 | 96.13 199 | 98.42 152 | 89.28 228 | 95.41 194 | 98.67 127 | 94.21 190 | 97.97 118 | 98.31 96 | 93.06 178 | 99.65 115 | 98.06 30 | 99.62 79 | 99.45 71 |
|
LFMVS | | | 95.32 194 | 94.88 198 | 96.62 161 | 98.03 199 | 91.47 188 | 97.65 71 | 90.72 326 | 99.11 8 | 97.89 129 | 98.31 96 | 79.20 291 | 99.48 179 | 93.91 173 | 99.12 187 | 98.93 168 |
|
V42 | | | 97.04 110 | 97.16 107 | 96.68 160 | 98.59 130 | 91.05 191 | 96.33 136 | 98.36 165 | 94.60 173 | 97.99 114 | 98.30 99 | 93.32 173 | 99.62 127 | 97.40 58 | 99.53 104 | 99.38 95 |
|
v144192 | | | 96.69 143 | 96.90 124 | 96.03 205 | 98.25 173 | 88.92 236 | 95.49 185 | 98.77 105 | 93.05 222 | 98.09 104 | 98.29 100 | 92.51 197 | 99.70 88 | 98.11 29 | 99.56 96 | 99.47 64 |
|
MVS_Test | | | 96.27 157 | 96.79 132 | 94.73 251 | 96.94 284 | 86.63 284 | 96.18 145 | 98.33 170 | 94.94 162 | 96.07 215 | 98.28 101 | 95.25 112 | 99.26 240 | 97.21 62 | 97.90 262 | 98.30 229 |
|
FMVSNet5 | | | 93.39 249 | 92.35 255 | 96.50 170 | 95.83 309 | 90.81 198 | 97.31 89 | 98.27 180 | 92.74 232 | 96.27 207 | 98.28 101 | 62.23 349 | 99.67 109 | 90.86 226 | 99.36 154 | 99.03 155 |
|
abl_6 | | | 98.42 26 | 98.19 41 | 99.09 4 | 99.16 64 | 98.10 5 | 97.73 69 | 99.11 23 | 97.76 50 | 98.62 56 | 98.27 103 | 97.88 21 | 99.80 37 | 95.67 105 | 99.50 111 | 99.38 95 |
|
v1921920 | | | 96.72 140 | 96.96 121 | 95.99 206 | 98.21 177 | 88.79 242 | 95.42 192 | 98.79 101 | 93.22 214 | 98.19 93 | 98.26 104 | 92.68 188 | 99.70 88 | 98.34 27 | 99.55 100 | 99.49 58 |
|
v2v482 | | | 96.78 136 | 97.06 116 | 95.95 210 | 98.57 133 | 88.77 243 | 95.36 197 | 98.26 182 | 95.18 151 | 97.85 138 | 98.23 105 | 92.58 192 | 99.63 121 | 97.80 38 | 99.69 67 | 99.45 71 |
|
LPG-MVS_test | | | 97.94 52 | 97.67 68 | 98.74 32 | 99.15 67 | 97.02 38 | 97.09 105 | 99.02 51 | 95.15 153 | 98.34 78 | 98.23 105 | 97.91 19 | 99.70 88 | 94.41 155 | 99.73 56 | 99.50 50 |
|
LGP-MVS_train | | | | | 98.74 32 | 99.15 67 | 97.02 38 | | 99.02 51 | 95.15 153 | 98.34 78 | 98.23 105 | 97.91 19 | 99.70 88 | 94.41 155 | 99.73 56 | 99.50 50 |
|
HPM-MVS_fast | | | 98.32 30 | 98.13 44 | 98.88 23 | 99.54 23 | 97.48 27 | 98.35 28 | 99.03 50 | 95.88 122 | 97.88 131 | 98.22 108 | 98.15 13 | 99.74 59 | 96.50 80 | 99.62 79 | 99.42 85 |
|
MIMVSNet | | | 93.42 248 | 92.86 247 | 95.10 237 | 98.17 186 | 88.19 250 | 98.13 43 | 93.69 295 | 92.07 240 | 95.04 241 | 98.21 109 | 80.95 286 | 99.03 264 | 81.42 327 | 98.06 253 | 98.07 246 |
|
v1141 | | | 96.86 126 | 97.14 109 | 96.04 202 | 98.55 135 | 89.06 233 | 95.44 187 | 98.33 170 | 95.14 155 | 97.93 124 | 98.19 110 | 93.36 171 | 99.62 127 | 97.61 45 | 99.69 67 | 99.44 79 |
|
divwei89l23v2f112 | | | 96.86 126 | 97.14 109 | 96.04 202 | 98.54 138 | 89.06 233 | 95.44 187 | 98.33 170 | 95.14 155 | 97.93 124 | 98.19 110 | 93.36 171 | 99.61 133 | 97.61 45 | 99.68 71 | 99.44 79 |
|
EI-MVSNet | | | 96.63 146 | 96.93 122 | 95.74 218 | 97.26 272 | 88.13 253 | 95.29 204 | 97.65 230 | 96.99 84 | 97.94 121 | 98.19 110 | 92.55 193 | 99.58 145 | 96.91 72 | 99.56 96 | 99.50 50 |
|
CVMVSNet | | | 92.33 267 | 92.79 249 | 90.95 319 | 97.26 272 | 75.84 341 | 95.29 204 | 92.33 313 | 81.86 324 | 96.27 207 | 98.19 110 | 81.44 284 | 98.46 315 | 94.23 165 | 98.29 243 | 98.55 207 |
|
v1 | | | 96.86 126 | 97.14 109 | 96.04 202 | 98.55 135 | 89.06 233 | 95.44 187 | 98.33 170 | 95.14 155 | 97.94 121 | 98.18 114 | 93.39 170 | 99.61 133 | 97.61 45 | 99.69 67 | 99.44 79 |
|
PVSNet_Blended_VisFu | | | 95.95 168 | 95.80 170 | 96.42 175 | 99.28 50 | 90.62 199 | 95.31 202 | 99.08 30 | 88.40 276 | 96.97 176 | 98.17 115 | 92.11 205 | 99.78 39 | 93.64 179 | 99.21 177 | 98.86 181 |
|
v1neww | | | 96.97 116 | 97.24 98 | 96.15 195 | 98.70 115 | 89.44 222 | 95.97 156 | 98.33 170 | 95.25 145 | 97.88 131 | 98.15 116 | 93.83 158 | 99.61 133 | 97.50 53 | 99.50 111 | 99.41 87 |
|
v7new | | | 96.97 116 | 97.24 98 | 96.15 195 | 98.70 115 | 89.44 222 | 95.97 156 | 98.33 170 | 95.25 145 | 97.88 131 | 98.15 116 | 93.83 158 | 99.61 133 | 97.50 53 | 99.50 111 | 99.41 87 |
|
v6 | | | 96.97 116 | 97.24 98 | 96.15 195 | 98.71 113 | 89.44 222 | 95.97 156 | 98.33 170 | 95.25 145 | 97.89 129 | 98.15 116 | 93.86 155 | 99.61 133 | 97.51 52 | 99.50 111 | 99.42 85 |
|
EI-MVSNet-UG-set | | | 97.32 101 | 97.40 88 | 97.09 138 | 97.34 268 | 92.01 178 | 95.33 200 | 97.65 230 | 97.74 51 | 98.30 85 | 98.14 119 | 95.04 117 | 99.69 97 | 97.55 49 | 99.52 108 | 99.58 36 |
|
APD-MVS_3200maxsize | | | 98.13 39 | 97.90 54 | 98.79 28 | 98.79 103 | 97.31 33 | 97.55 81 | 98.92 73 | 97.72 55 | 98.25 88 | 98.13 120 | 97.10 43 | 99.75 54 | 95.44 117 | 99.24 175 | 99.32 106 |
|
QAPM | | | 95.88 171 | 95.57 176 | 96.80 152 | 97.90 213 | 91.84 182 | 98.18 41 | 98.73 112 | 88.41 275 | 96.42 195 | 98.13 120 | 94.73 122 | 99.75 54 | 88.72 266 | 98.94 204 | 98.81 185 |
|
ACMM | | 93.33 11 | 98.05 42 | 97.79 59 | 98.85 24 | 99.15 67 | 97.55 23 | 96.68 123 | 98.83 95 | 95.21 148 | 98.36 76 | 98.13 120 | 98.13 16 | 99.62 127 | 96.04 92 | 99.54 102 | 99.39 93 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
EI-MVSNet-Vis-set | | | 97.32 101 | 97.39 89 | 97.11 136 | 97.36 264 | 92.08 176 | 95.34 199 | 97.65 230 | 97.74 51 | 98.29 86 | 98.11 123 | 95.05 115 | 99.68 103 | 97.50 53 | 99.50 111 | 99.56 41 |
|
wuyk23d | | | 93.25 252 | 95.20 185 | 87.40 333 | 96.07 304 | 95.38 85 | 97.04 107 | 94.97 285 | 95.33 142 | 99.70 6 | 98.11 123 | 98.14 14 | 91.94 350 | 77.76 339 | 99.68 71 | 74.89 350 |
|
SD-MVS | | | 97.37 96 | 97.70 65 | 96.35 178 | 98.14 191 | 95.13 95 | 96.54 124 | 98.92 73 | 95.94 120 | 99.19 29 | 98.08 125 | 97.74 22 | 95.06 348 | 95.24 125 | 99.54 102 | 98.87 180 |
|
OPM-MVS | | | 97.54 86 | 97.25 96 | 98.41 51 | 99.11 77 | 96.61 50 | 95.24 208 | 98.46 150 | 94.58 176 | 98.10 103 | 98.07 126 | 97.09 44 | 99.39 215 | 95.16 130 | 99.44 130 | 99.21 124 |
|
AllTest | | | 97.20 108 | 96.92 123 | 98.06 74 | 99.08 79 | 96.16 61 | 97.14 98 | 99.16 16 | 94.35 185 | 97.78 141 | 98.07 126 | 95.84 87 | 99.12 251 | 91.41 212 | 99.42 142 | 98.91 171 |
|
TestCases | | | | | 98.06 74 | 99.08 79 | 96.16 61 | | 99.16 16 | 94.35 185 | 97.78 141 | 98.07 126 | 95.84 87 | 99.12 251 | 91.41 212 | 99.42 142 | 98.91 171 |
|
TSAR-MVS + MP. | | | 97.42 92 | 97.23 102 | 98.00 79 | 99.38 43 | 95.00 98 | 97.63 73 | 98.20 188 | 93.00 223 | 98.16 95 | 98.06 129 | 95.89 85 | 99.72 70 | 95.67 105 | 99.10 188 | 99.28 117 |
|
EPP-MVSNet | | | 96.84 129 | 96.58 139 | 97.65 99 | 99.18 63 | 93.78 139 | 98.68 11 | 96.34 267 | 97.91 46 | 97.30 158 | 98.06 129 | 88.46 252 | 99.85 24 | 93.85 174 | 99.40 149 | 99.32 106 |
|
ACMMP | | | 98.05 42 | 97.75 63 | 98.93 18 | 99.23 55 | 97.60 19 | 98.09 45 | 98.96 70 | 95.75 128 | 97.91 126 | 98.06 129 | 96.89 50 | 99.76 48 | 95.32 122 | 99.57 94 | 99.43 83 |
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 |
XVG-ACMP-BASELINE | | | 97.58 84 | 97.28 95 | 98.49 46 | 99.16 64 | 96.90 42 | 96.39 129 | 98.98 67 | 95.05 161 | 98.06 109 | 98.02 132 | 95.86 86 | 99.56 151 | 94.37 158 | 99.64 77 | 99.00 157 |
|
PVSNet_BlendedMVS | | | 95.02 206 | 94.93 196 | 95.27 232 | 97.79 231 | 87.40 273 | 94.14 256 | 98.68 124 | 88.94 271 | 94.51 259 | 98.01 133 | 93.04 179 | 99.30 233 | 89.77 251 | 99.49 118 | 99.11 144 |
|
OpenMVS | | 94.22 8 | 95.48 184 | 95.20 185 | 96.32 181 | 97.16 277 | 91.96 179 | 97.74 67 | 98.84 87 | 87.26 287 | 94.36 263 | 98.01 133 | 93.95 154 | 99.67 109 | 90.70 234 | 98.75 220 | 97.35 281 |
|
MVSTER | | | 94.21 230 | 93.93 231 | 95.05 240 | 95.83 309 | 86.46 285 | 95.18 210 | 97.65 230 | 92.41 238 | 97.94 121 | 98.00 135 | 72.39 328 | 99.58 145 | 96.36 84 | 99.56 96 | 99.12 141 |
|
IS-MVSNet | | | 96.93 120 | 96.68 135 | 97.70 94 | 99.25 54 | 94.00 131 | 98.57 17 | 96.74 264 | 98.36 30 | 98.14 98 | 97.98 136 | 88.23 254 | 99.71 80 | 93.10 189 | 99.72 59 | 99.38 95 |
|
MPTG | | | 98.01 46 | 97.66 69 | 99.06 5 | 99.44 34 | 97.90 8 | 95.66 176 | 98.73 112 | 97.69 57 | 97.90 127 | 97.96 137 | 95.81 94 | 99.82 31 | 96.13 88 | 99.61 84 | 99.45 71 |
|
MTAPA | | | 98.14 37 | 97.84 57 | 99.06 5 | 99.44 34 | 97.90 8 | 97.25 92 | 98.73 112 | 97.69 57 | 97.90 127 | 97.96 137 | 95.81 94 | 99.82 31 | 96.13 88 | 99.61 84 | 99.45 71 |
|
v148 | | | 96.58 148 | 96.97 119 | 95.42 228 | 98.63 125 | 87.57 268 | 95.09 214 | 97.90 211 | 95.91 121 | 98.24 89 | 97.96 137 | 93.42 169 | 99.39 215 | 96.04 92 | 99.52 108 | 99.29 116 |
|
MDA-MVSNet-bldmvs | | | 95.69 173 | 95.67 173 | 95.74 218 | 98.48 146 | 88.76 244 | 92.84 295 | 97.25 245 | 96.00 117 | 97.59 143 | 97.95 140 | 91.38 223 | 99.46 186 | 93.16 188 | 96.35 311 | 98.99 160 |
|
PGM-MVS | | | 97.88 60 | 97.52 83 | 98.96 13 | 99.20 60 | 97.62 18 | 97.09 105 | 99.06 36 | 95.45 138 | 97.55 144 | 97.94 141 | 97.11 42 | 99.78 39 | 94.77 146 | 99.46 125 | 99.48 61 |
|
LS3D | | | 97.77 70 | 97.50 85 | 98.57 43 | 96.24 298 | 97.58 21 | 98.45 25 | 98.85 84 | 98.58 24 | 97.51 146 | 97.94 141 | 95.74 97 | 99.63 121 | 95.19 127 | 98.97 199 | 98.51 209 |
|
USDC | | | 94.56 222 | 94.57 211 | 94.55 260 | 97.78 235 | 86.43 286 | 92.75 298 | 98.65 135 | 85.96 300 | 96.91 179 | 97.93 143 | 90.82 228 | 98.74 295 | 90.71 233 | 99.59 89 | 98.47 211 |
|
test20.03 | | | 96.58 148 | 96.61 137 | 96.48 172 | 98.49 144 | 91.72 184 | 95.68 175 | 97.69 225 | 96.81 92 | 98.27 87 | 97.92 144 | 94.18 148 | 98.71 298 | 90.78 230 | 99.66 75 | 99.00 157 |
|
FMVSNet3 | | | 95.26 198 | 94.94 194 | 96.22 191 | 96.53 292 | 90.06 205 | 95.99 154 | 97.66 228 | 94.11 194 | 97.99 114 | 97.91 145 | 80.22 289 | 99.63 121 | 94.60 150 | 99.44 130 | 98.96 162 |
|
Regformer-3 | | | 97.25 105 | 97.29 93 | 97.11 136 | 97.35 265 | 92.32 167 | 95.26 206 | 97.62 235 | 97.67 59 | 98.17 94 | 97.89 146 | 95.05 115 | 99.56 151 | 97.16 66 | 99.42 142 | 99.46 66 |
|
Regformer-4 | | | 97.53 88 | 97.47 87 | 97.71 93 | 97.35 265 | 93.91 133 | 95.26 206 | 98.14 197 | 97.97 44 | 98.34 78 | 97.89 146 | 95.49 102 | 99.71 80 | 97.41 57 | 99.42 142 | 99.51 49 |
|
SteuartSystems-ACMMP | | | 98.02 44 | 97.76 62 | 98.79 28 | 99.43 37 | 97.21 36 | 97.15 96 | 98.90 75 | 96.58 98 | 98.08 106 | 97.87 148 | 97.02 47 | 99.76 48 | 95.25 124 | 99.59 89 | 99.40 90 |
Skip Steuart: Steuart Systems R&D Blog. |
DU-MVS | | | 97.79 69 | 97.60 77 | 98.36 58 | 98.73 108 | 95.78 71 | 95.65 178 | 98.87 81 | 97.57 63 | 98.31 83 | 97.83 149 | 94.69 125 | 99.85 24 | 97.02 70 | 99.71 63 | 99.46 66 |
|
NR-MVSNet | | | 97.96 48 | 97.86 56 | 98.26 65 | 98.73 108 | 95.54 80 | 98.14 42 | 98.73 112 | 97.79 48 | 99.42 16 | 97.83 149 | 94.40 139 | 99.78 39 | 95.91 100 | 99.76 50 | 99.46 66 |
|
CHOSEN 1792x2688 | | | 94.10 233 | 93.41 238 | 96.18 193 | 99.16 64 | 90.04 206 | 92.15 309 | 98.68 124 | 79.90 334 | 96.22 210 | 97.83 149 | 87.92 259 | 99.42 195 | 89.18 259 | 99.65 76 | 99.08 149 |
|
TAMVS | | | 95.49 182 | 94.94 194 | 97.16 133 | 98.31 157 | 93.41 151 | 95.07 217 | 96.82 261 | 91.09 254 | 97.51 146 | 97.82 152 | 89.96 238 | 99.42 195 | 88.42 271 | 99.44 130 | 98.64 198 |
|
UniMVSNet (Re) | | | 97.83 64 | 97.65 70 | 98.35 59 | 98.80 102 | 95.86 70 | 95.92 164 | 99.04 45 | 97.51 68 | 98.22 90 | 97.81 153 | 94.68 127 | 99.78 39 | 97.14 67 | 99.75 54 | 99.41 87 |
|
VNet | | | 96.84 129 | 96.83 127 | 96.88 150 | 98.06 197 | 92.02 177 | 96.35 135 | 97.57 237 | 97.70 56 | 97.88 131 | 97.80 154 | 92.40 200 | 99.54 157 | 94.73 148 | 98.96 200 | 99.08 149 |
|
YYNet1 | | | 94.73 213 | 94.84 200 | 94.41 263 | 97.47 259 | 85.09 298 | 90.29 329 | 95.85 277 | 92.52 234 | 97.53 145 | 97.76 155 | 91.97 210 | 99.18 247 | 93.31 183 | 96.86 301 | 98.95 163 |
|
MDA-MVSNet_test_wron | | | 94.73 213 | 94.83 201 | 94.42 262 | 97.48 255 | 85.15 296 | 90.28 330 | 95.87 275 | 92.52 234 | 97.48 151 | 97.76 155 | 91.92 214 | 99.17 249 | 93.32 182 | 96.80 304 | 98.94 165 |
|
TinyColmap | | | 96.00 167 | 96.34 152 | 94.96 242 | 97.90 213 | 87.91 262 | 94.13 257 | 98.49 148 | 94.41 181 | 98.16 95 | 97.76 155 | 96.29 79 | 98.68 303 | 90.52 238 | 99.42 142 | 98.30 229 |
|
Patchmatch-RL test | | | 94.66 217 | 94.49 212 | 95.19 234 | 98.54 138 | 88.91 237 | 92.57 302 | 98.74 111 | 91.46 251 | 98.32 81 | 97.75 158 | 77.31 302 | 98.81 290 | 96.06 90 | 99.61 84 | 97.85 259 |
|
MP-MVS | | | 97.64 79 | 97.18 105 | 99.00 9 | 99.32 49 | 97.77 14 | 97.49 84 | 98.73 112 | 96.27 107 | 95.59 231 | 97.75 158 | 96.30 78 | 99.78 39 | 93.70 178 | 99.48 121 | 99.45 71 |
|
ACMP | | 92.54 13 | 97.47 90 | 97.10 112 | 98.55 45 | 99.04 85 | 96.70 47 | 96.24 142 | 98.89 77 | 93.71 207 | 97.97 118 | 97.75 158 | 97.44 29 | 99.63 121 | 93.22 186 | 99.70 66 | 99.32 106 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
MVP-Stereo | | | 95.69 173 | 95.28 183 | 96.92 147 | 98.15 190 | 93.03 157 | 95.64 180 | 98.20 188 | 90.39 259 | 96.63 186 | 97.73 161 | 91.63 218 | 99.10 255 | 91.84 205 | 97.31 295 | 98.63 200 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
testmv | | | 95.51 180 | 95.33 182 | 96.05 201 | 98.23 175 | 89.51 220 | 93.50 282 | 98.63 136 | 94.25 188 | 98.22 90 | 97.73 161 | 92.51 197 | 99.47 181 | 85.22 307 | 99.72 59 | 99.17 129 |
|
mPP-MVS | | | 97.91 57 | 97.53 82 | 99.04 7 | 99.22 56 | 97.87 11 | 97.74 67 | 98.78 104 | 96.04 115 | 97.10 166 | 97.73 161 | 96.53 68 | 99.78 39 | 95.16 130 | 99.50 111 | 99.46 66 |
|
XVG-OURS | | | 97.12 109 | 96.74 133 | 98.26 65 | 98.99 90 | 97.45 29 | 93.82 270 | 99.05 38 | 95.19 150 | 98.32 81 | 97.70 164 | 95.22 113 | 98.41 317 | 94.27 163 | 98.13 251 | 98.93 168 |
|
UniMVSNet_NR-MVSNet | | | 97.83 64 | 97.65 70 | 98.37 55 | 98.72 110 | 95.78 71 | 95.66 176 | 99.02 51 | 98.11 39 | 98.31 83 | 97.69 165 | 94.65 129 | 99.85 24 | 97.02 70 | 99.71 63 | 99.48 61 |
|
XVS | | | 97.96 48 | 97.63 74 | 98.94 15 | 99.15 67 | 97.66 16 | 97.77 62 | 98.83 95 | 97.42 71 | 96.32 203 | 97.64 166 | 96.49 71 | 99.72 70 | 95.66 107 | 99.37 151 | 99.45 71 |
|
ACMMPR | | | 97.95 50 | 97.62 76 | 98.94 15 | 99.20 60 | 97.56 22 | 97.59 78 | 98.83 95 | 96.05 113 | 97.46 153 | 97.63 167 | 96.77 58 | 99.76 48 | 95.61 111 | 99.46 125 | 99.49 58 |
|
Anonymous20231206 | | | 95.27 197 | 95.06 191 | 95.88 214 | 98.72 110 | 89.37 226 | 95.70 172 | 97.85 214 | 88.00 283 | 96.98 171 | 97.62 168 | 91.95 211 | 99.34 226 | 89.21 258 | 99.53 104 | 98.94 165 |
|
region2R | | | 97.92 55 | 97.59 78 | 98.92 19 | 99.22 56 | 97.55 23 | 97.60 77 | 98.84 87 | 96.00 117 | 97.22 160 | 97.62 168 | 96.87 53 | 99.76 48 | 95.48 115 | 99.43 139 | 99.46 66 |
|
test_part3 | | | | | | | | 95.64 180 | | 94.84 165 | | 97.60 170 | | 99.76 48 | 91.22 218 | | |
|
ESAPD | | | 97.22 107 | 96.82 128 | 98.40 53 | 99.03 86 | 96.07 64 | 95.64 180 | 98.84 87 | 94.84 165 | 98.08 106 | 97.60 170 | 96.69 61 | 99.76 48 | 91.22 218 | 99.44 130 | 99.37 100 |
|
APD-MVS | | | 97.00 111 | 96.53 145 | 98.41 51 | 98.55 135 | 96.31 58 | 96.32 137 | 98.77 105 | 92.96 229 | 97.44 154 | 97.58 172 | 95.84 87 | 99.74 59 | 91.96 200 | 99.35 157 | 99.19 126 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
HFP-MVS | | | 97.94 52 | 97.64 72 | 98.83 25 | 99.15 67 | 97.50 25 | 97.59 78 | 98.84 87 | 96.05 113 | 97.49 148 | 97.54 173 | 97.07 45 | 99.70 88 | 95.61 111 | 99.46 125 | 99.30 110 |
|
#test# | | | 97.62 80 | 97.22 103 | 98.83 25 | 99.15 67 | 97.50 25 | 96.81 119 | 98.84 87 | 94.25 188 | 97.49 148 | 97.54 173 | 97.07 45 | 99.70 88 | 94.37 158 | 99.46 125 | 99.30 110 |
|
UnsupCasMVSNet_eth | | | 95.91 169 | 95.73 172 | 96.44 174 | 98.48 146 | 91.52 187 | 95.31 202 | 98.45 151 | 95.76 127 | 97.48 151 | 97.54 173 | 89.53 243 | 98.69 300 | 94.43 154 | 94.61 327 | 99.13 136 |
|
XVG-OURS-SEG-HR | | | 97.38 95 | 97.07 115 | 98.30 63 | 99.01 89 | 97.41 31 | 94.66 234 | 99.02 51 | 95.20 149 | 98.15 97 | 97.52 176 | 98.83 5 | 98.43 316 | 94.87 139 | 96.41 310 | 99.07 151 |
|
MG-MVS | | | 94.08 235 | 94.00 230 | 94.32 265 | 97.09 279 | 85.89 287 | 93.19 292 | 95.96 273 | 92.52 234 | 94.93 244 | 97.51 177 | 89.54 241 | 98.77 293 | 87.52 289 | 97.71 275 | 98.31 227 |
|
Regformer-1 | | | 97.27 103 | 97.16 107 | 97.61 101 | 97.21 274 | 93.86 135 | 94.85 229 | 98.04 208 | 97.62 61 | 98.03 112 | 97.50 178 | 95.34 108 | 99.63 121 | 96.52 78 | 99.31 166 | 99.35 104 |
|
Regformer-2 | | | 97.41 93 | 97.24 98 | 97.93 82 | 97.21 274 | 94.72 107 | 94.85 229 | 98.27 180 | 97.74 51 | 98.11 100 | 97.50 178 | 95.58 100 | 99.69 97 | 96.57 77 | 99.31 166 | 99.37 100 |
|
HPM-MVS | | | 98.11 40 | 97.83 58 | 98.92 19 | 99.42 39 | 97.46 28 | 98.57 17 | 99.05 38 | 95.43 140 | 97.41 155 | 97.50 178 | 97.98 17 | 99.79 38 | 95.58 114 | 99.57 94 | 99.50 50 |
|
CP-MVS | | | 97.92 55 | 97.56 81 | 98.99 10 | 98.99 90 | 97.82 12 | 97.93 54 | 98.96 70 | 96.11 112 | 96.89 180 | 97.45 181 | 96.85 54 | 99.78 39 | 95.19 127 | 99.63 78 | 99.38 95 |
|
diffmvs | | | 95.00 207 | 95.00 193 | 95.01 241 | 96.53 292 | 87.96 261 | 95.73 169 | 98.32 179 | 90.67 258 | 91.89 319 | 97.43 182 | 92.07 208 | 98.90 277 | 95.44 117 | 96.88 300 | 98.16 242 |
|
N_pmnet | | | 95.18 199 | 94.23 221 | 98.06 74 | 97.85 215 | 96.55 52 | 92.49 304 | 91.63 318 | 89.34 267 | 98.09 104 | 97.41 183 | 90.33 232 | 99.06 259 | 91.58 211 | 99.31 166 | 98.56 205 |
|
tpm | | | 91.08 291 | 90.85 288 | 91.75 312 | 95.33 319 | 78.09 332 | 95.03 222 | 91.27 321 | 88.75 272 | 93.53 291 | 97.40 184 | 71.24 331 | 99.30 233 | 91.25 217 | 93.87 329 | 97.87 258 |
|
MDTV_nov1_ep13 | | | | 91.28 272 | | 94.31 330 | 73.51 345 | 94.80 231 | 93.16 304 | 86.75 295 | 93.45 295 | 97.40 184 | 76.37 306 | 98.55 311 | 88.85 264 | 96.43 309 | |
|
DeepPCF-MVS | | 94.58 5 | 96.90 124 | 96.43 149 | 98.31 62 | 97.48 255 | 97.23 35 | 92.56 303 | 98.60 139 | 92.84 231 | 98.54 63 | 97.40 184 | 96.64 64 | 98.78 292 | 94.40 157 | 99.41 148 | 98.93 168 |
|
MSLP-MVS++ | | | 96.42 156 | 96.71 134 | 95.57 223 | 97.82 221 | 90.56 202 | 95.71 171 | 98.84 87 | 94.72 171 | 96.71 184 | 97.39 187 | 94.91 120 | 98.10 332 | 95.28 123 | 99.02 196 | 98.05 249 |
|
EPNet | | | 93.72 241 | 92.62 253 | 97.03 143 | 87.61 355 | 92.25 168 | 96.27 138 | 91.28 320 | 96.74 94 | 87.65 343 | 97.39 187 | 85.00 275 | 99.64 118 | 92.14 199 | 99.48 121 | 99.20 125 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PMMVS2 | | | 93.66 243 | 94.07 227 | 92.45 306 | 97.57 250 | 80.67 325 | 86.46 342 | 96.00 271 | 93.99 195 | 97.10 166 | 97.38 189 | 89.90 239 | 97.82 335 | 88.76 265 | 99.47 123 | 98.86 181 |
|
DeepC-MVS_fast | | 94.34 7 | 96.74 137 | 96.51 147 | 97.44 120 | 97.69 241 | 94.15 126 | 96.02 152 | 98.43 155 | 93.17 219 | 97.30 158 | 97.38 189 | 95.48 103 | 99.28 237 | 93.74 177 | 99.34 159 | 98.88 178 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
WR-MVS | | | 96.90 124 | 96.81 129 | 97.16 133 | 98.56 134 | 92.20 172 | 94.33 242 | 98.12 199 | 97.34 80 | 98.20 92 | 97.33 191 | 92.81 184 | 99.75 54 | 94.79 144 | 99.81 43 | 99.54 45 |
|
ITE_SJBPF | | | | | 97.85 86 | 98.64 121 | 96.66 48 | | 98.51 147 | 95.63 130 | 97.22 160 | 97.30 192 | 95.52 101 | 98.55 311 | 90.97 223 | 98.90 206 | 98.34 225 |
|
Vis-MVSNet (Re-imp) | | | 95.11 201 | 94.85 199 | 95.87 215 | 99.12 76 | 89.17 229 | 97.54 83 | 94.92 286 | 96.50 99 | 96.58 187 | 97.27 193 | 83.64 279 | 99.48 179 | 88.42 271 | 99.67 73 | 98.97 161 |
|
pmmvs4 | | | 94.82 212 | 94.19 224 | 96.70 158 | 97.42 262 | 92.75 161 | 92.09 312 | 96.76 262 | 86.80 294 | 95.73 228 | 97.22 194 | 89.28 247 | 98.89 280 | 93.28 184 | 99.14 182 | 98.46 213 |
|
OMC-MVS | | | 96.48 152 | 96.00 163 | 97.91 83 | 98.30 158 | 96.01 68 | 94.86 228 | 98.60 139 | 91.88 247 | 97.18 162 | 97.21 195 | 96.11 81 | 99.04 261 | 90.49 241 | 99.34 159 | 98.69 196 |
|
LP | | | 93.12 253 | 92.78 251 | 94.14 269 | 94.50 328 | 85.48 291 | 95.73 169 | 95.68 280 | 92.97 228 | 95.05 240 | 97.17 196 | 81.93 283 | 99.40 209 | 93.06 190 | 88.96 342 | 97.55 270 |
|
pmmvs5 | | | 94.63 219 | 94.34 219 | 95.50 226 | 97.63 248 | 88.34 249 | 94.02 260 | 97.13 251 | 87.15 290 | 95.22 237 | 97.15 197 | 87.50 261 | 99.27 238 | 93.99 171 | 99.26 174 | 98.88 178 |
|
CPTT-MVS | | | 96.69 143 | 96.08 160 | 98.49 46 | 98.89 99 | 96.64 49 | 97.25 92 | 98.77 105 | 92.89 230 | 96.01 217 | 97.13 198 | 92.23 202 | 99.67 109 | 92.24 198 | 99.34 159 | 99.17 129 |
|
MS-PatchMatch | | | 94.83 211 | 94.91 197 | 94.57 259 | 96.81 288 | 87.10 279 | 94.23 248 | 97.34 243 | 88.74 273 | 97.14 164 | 97.11 199 | 91.94 212 | 98.23 328 | 92.99 191 | 97.92 260 | 98.37 219 |
|
FPMVS | | | 89.92 303 | 88.63 310 | 93.82 279 | 98.37 154 | 96.94 41 | 91.58 317 | 93.34 302 | 88.00 283 | 90.32 331 | 97.10 200 | 70.87 333 | 91.13 351 | 71.91 347 | 96.16 314 | 93.39 340 |
|
DELS-MVS | | | 96.17 162 | 96.23 155 | 95.99 206 | 97.55 253 | 90.04 206 | 92.38 307 | 98.52 145 | 94.13 193 | 96.55 192 | 97.06 201 | 94.99 118 | 99.58 145 | 95.62 110 | 99.28 171 | 98.37 219 |
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 |
CNVR-MVS | | | 96.92 122 | 96.55 142 | 98.03 78 | 98.00 205 | 95.54 80 | 94.87 227 | 98.17 193 | 94.60 173 | 96.38 197 | 97.05 202 | 95.67 98 | 99.36 224 | 95.12 134 | 99.08 190 | 99.19 126 |
|
旧先验1 | | | | | | 97.80 226 | 93.87 134 | | 97.75 220 | | | 97.04 203 | 93.57 166 | | | 98.68 227 | 98.72 194 |
|
testdata | | | | | 95.70 221 | 98.16 188 | 90.58 200 | | 97.72 223 | 80.38 332 | 95.62 230 | 97.02 204 | 92.06 209 | 98.98 270 | 89.06 262 | 98.52 236 | 97.54 271 |
|
PatchmatchNet | | | 91.98 273 | 91.87 264 | 92.30 308 | 94.60 326 | 79.71 327 | 95.12 211 | 93.59 300 | 89.52 266 | 93.61 288 | 97.02 204 | 77.94 295 | 99.18 247 | 90.84 227 | 94.57 328 | 98.01 255 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
Patchmatch-test | | | 93.60 245 | 93.25 241 | 94.63 254 | 96.14 303 | 87.47 271 | 96.04 151 | 94.50 290 | 93.57 209 | 96.47 193 | 96.97 206 | 76.50 305 | 98.61 306 | 90.67 235 | 98.41 242 | 97.81 262 |
|
CostFormer | | | 89.75 304 | 89.25 303 | 91.26 316 | 94.69 325 | 78.00 335 | 95.32 201 | 91.98 315 | 81.50 327 | 90.55 328 | 96.96 207 | 71.06 332 | 98.89 280 | 88.59 269 | 92.63 334 | 96.87 292 |
|
test_normal | | | 95.51 180 | 95.46 179 | 95.68 222 | 97.97 207 | 89.12 232 | 93.73 273 | 95.86 276 | 91.98 243 | 97.17 163 | 96.94 208 | 91.55 219 | 99.42 195 | 95.21 126 | 98.73 224 | 98.51 209 |
|
DI_MVS_plusplus_test | | | 95.46 186 | 95.43 180 | 95.55 224 | 98.05 198 | 88.84 240 | 94.18 252 | 95.75 278 | 91.92 246 | 97.32 157 | 96.94 208 | 91.44 221 | 99.39 215 | 94.81 142 | 98.48 239 | 98.43 215 |
|
114514_t | | | 93.96 237 | 93.22 242 | 96.19 192 | 99.06 82 | 90.97 193 | 95.99 154 | 98.94 72 | 73.88 348 | 93.43 296 | 96.93 210 | 92.38 201 | 99.37 223 | 89.09 260 | 99.28 171 | 98.25 234 |
|
MVS_0304 | | | 96.22 159 | 95.94 168 | 97.04 141 | 97.07 280 | 92.54 162 | 94.19 251 | 99.04 45 | 95.17 152 | 93.74 282 | 96.92 211 | 91.77 217 | 99.73 64 | 95.76 103 | 99.81 43 | 98.85 183 |
|
Test_1112_low_res | | | 93.53 247 | 92.86 247 | 95.54 225 | 98.60 128 | 88.86 239 | 92.75 298 | 98.69 122 | 82.66 323 | 92.65 310 | 96.92 211 | 84.75 276 | 99.56 151 | 90.94 224 | 97.76 264 | 98.19 239 |
|
tpmrst | | | 90.31 297 | 90.61 293 | 89.41 326 | 94.06 335 | 72.37 348 | 95.06 219 | 93.69 295 | 88.01 282 | 92.32 315 | 96.86 213 | 77.45 299 | 98.82 288 | 91.04 220 | 87.01 345 | 97.04 286 |
|
PHI-MVS | | | 96.96 119 | 96.53 145 | 98.25 67 | 97.48 255 | 96.50 53 | 96.76 120 | 98.85 84 | 93.52 210 | 96.19 212 | 96.85 214 | 95.94 84 | 99.42 195 | 93.79 176 | 99.43 139 | 98.83 184 |
|
patchmatchnet-post | | | | | | | | | | | | 96.84 215 | 77.36 301 | 99.42 195 | | | |
|
ADS-MVSNet2 | | | 91.47 288 | 90.51 294 | 94.36 264 | 95.51 314 | 85.63 288 | 95.05 220 | 95.70 279 | 83.46 320 | 92.69 308 | 96.84 215 | 79.15 292 | 99.41 206 | 85.66 303 | 90.52 337 | 98.04 250 |
|
ADS-MVSNet | | | 90.95 294 | 90.26 297 | 93.04 295 | 95.51 314 | 82.37 320 | 95.05 220 | 93.41 301 | 83.46 320 | 92.69 308 | 96.84 215 | 79.15 292 | 98.70 299 | 85.66 303 | 90.52 337 | 98.04 250 |
|
HY-MVS | | 91.43 15 | 92.58 258 | 91.81 266 | 94.90 245 | 96.49 294 | 88.87 238 | 97.31 89 | 94.62 288 | 85.92 301 | 90.50 330 | 96.84 215 | 85.05 274 | 99.40 209 | 83.77 318 | 95.78 318 | 96.43 310 |
|
tpmp4_e23 | | | 88.46 312 | 87.54 315 | 91.22 317 | 94.56 327 | 78.08 333 | 95.63 183 | 93.17 303 | 79.08 338 | 85.85 346 | 96.80 219 | 65.86 348 | 98.85 287 | 84.10 314 | 92.85 332 | 96.72 299 |
|
UnsupCasMVSNet_bld | | | 94.72 215 | 94.26 220 | 96.08 200 | 98.62 126 | 90.54 203 | 93.38 286 | 98.05 207 | 90.30 260 | 97.02 169 | 96.80 219 | 89.54 241 | 99.16 250 | 88.44 270 | 96.18 313 | 98.56 205 |
|
HQP_MVS | | | 96.66 145 | 96.33 153 | 97.68 97 | 98.70 115 | 94.29 120 | 96.50 126 | 98.75 109 | 96.36 104 | 96.16 213 | 96.77 221 | 91.91 215 | 99.46 186 | 92.59 194 | 99.20 178 | 99.28 117 |
|
plane_prior4 | | | | | | | | | | | | 96.77 221 | | | | | |
|
MVS_111021_HR | | | 96.73 139 | 96.54 144 | 97.27 129 | 98.35 156 | 93.66 144 | 93.42 284 | 98.36 165 | 94.74 170 | 96.58 187 | 96.76 223 | 96.54 67 | 98.99 268 | 94.87 139 | 99.27 173 | 99.15 133 |
|
CANet | | | 95.86 172 | 95.65 174 | 96.49 171 | 96.41 296 | 90.82 196 | 94.36 241 | 98.41 160 | 94.94 162 | 92.62 312 | 96.73 224 | 92.68 188 | 99.71 80 | 95.12 134 | 99.60 87 | 98.94 165 |
|
1121 | | | 94.26 225 | 93.26 240 | 97.27 129 | 98.26 172 | 94.73 106 | 95.86 165 | 97.71 224 | 77.96 342 | 94.53 258 | 96.71 225 | 91.93 213 | 99.40 209 | 87.71 277 | 98.64 230 | 97.69 265 |
|
TSAR-MVS + GP. | | | 96.47 153 | 96.12 157 | 97.49 114 | 97.74 237 | 95.23 89 | 94.15 255 | 96.90 259 | 93.26 213 | 98.04 111 | 96.70 226 | 94.41 138 | 98.89 280 | 94.77 146 | 99.14 182 | 98.37 219 |
|
test222 | | | | | | 98.17 186 | 93.24 155 | 92.74 300 | 97.61 236 | 75.17 346 | 94.65 249 | 96.69 227 | 90.96 227 | | | 98.66 228 | 97.66 266 |
|
Patchmatch-test1 | | | 93.38 250 | 93.59 235 | 92.73 302 | 96.24 298 | 81.40 322 | 93.24 290 | 94.00 293 | 91.58 250 | 94.57 256 | 96.67 228 | 87.94 256 | 99.03 264 | 90.42 242 | 97.66 280 | 97.77 263 |
|
æ–°å‡ ä½•1 | | | | | 97.25 132 | 98.29 159 | 94.70 109 | | 97.73 222 | 77.98 341 | 94.83 246 | 96.67 228 | 92.08 207 | 99.45 190 | 88.17 275 | 98.65 229 | 97.61 268 |
|
MVS_111021_LR | | | 96.82 133 | 96.55 142 | 97.62 100 | 98.27 164 | 95.34 87 | 93.81 271 | 98.33 170 | 94.59 175 | 96.56 189 | 96.63 230 | 96.61 65 | 98.73 296 | 94.80 143 | 99.34 159 | 98.78 189 |
|
CDPH-MVS | | | 95.45 188 | 94.65 205 | 97.84 87 | 98.28 162 | 94.96 100 | 93.73 273 | 98.33 170 | 85.03 312 | 95.44 232 | 96.60 231 | 95.31 110 | 99.44 193 | 90.01 248 | 99.13 184 | 99.11 144 |
|
CMPMVS | | 73.10 23 | 92.74 257 | 91.39 269 | 96.77 154 | 93.57 340 | 94.67 110 | 94.21 250 | 97.67 226 | 80.36 333 | 93.61 288 | 96.60 231 | 82.85 281 | 97.35 339 | 84.86 310 | 98.78 217 | 98.29 231 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
CDS-MVSNet | | | 94.88 209 | 94.12 226 | 97.14 135 | 97.64 247 | 93.57 146 | 93.96 265 | 97.06 254 | 90.05 263 | 96.30 206 | 96.55 233 | 86.10 268 | 99.47 181 | 90.10 247 | 99.31 166 | 98.40 216 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
LF4IMVS | | | 96.07 164 | 95.63 175 | 97.36 125 | 98.19 181 | 95.55 79 | 95.44 187 | 98.82 99 | 92.29 239 | 95.70 229 | 96.55 233 | 92.63 191 | 98.69 300 | 91.75 209 | 99.33 164 | 97.85 259 |
|
HPM-MVS++ | | | 96.99 112 | 96.38 150 | 98.81 27 | 98.64 121 | 97.59 20 | 95.97 156 | 98.20 188 | 95.51 136 | 95.06 239 | 96.53 235 | 94.10 150 | 99.70 88 | 94.29 162 | 99.15 181 | 99.13 136 |
|
EPMVS | | | 89.26 307 | 88.55 311 | 91.39 314 | 92.36 348 | 79.11 329 | 95.65 178 | 79.86 352 | 88.60 274 | 93.12 302 | 96.53 235 | 70.73 334 | 98.10 332 | 90.75 231 | 89.32 341 | 96.98 287 |
|
HyFIR lowres test | | | 93.72 241 | 92.65 252 | 96.91 149 | 98.93 94 | 91.81 183 | 91.23 323 | 98.52 145 | 82.69 322 | 96.46 194 | 96.52 237 | 80.38 288 | 99.90 13 | 90.36 244 | 98.79 216 | 99.03 155 |
|
BH-RMVSNet | | | 94.56 222 | 94.44 217 | 94.91 243 | 97.57 250 | 87.44 272 | 93.78 272 | 96.26 268 | 93.69 208 | 96.41 196 | 96.50 238 | 92.10 206 | 99.00 267 | 85.96 299 | 97.71 275 | 98.31 227 |
|
HSP-MVS | | | 97.37 96 | 96.85 125 | 98.92 19 | 99.26 51 | 97.70 15 | 97.66 70 | 98.23 184 | 95.65 129 | 98.51 65 | 96.46 239 | 92.15 203 | 99.81 33 | 95.14 132 | 98.58 235 | 99.26 121 |
|
1111 | | | 88.78 309 | 89.39 302 | 86.96 334 | 98.53 140 | 62.84 353 | 91.49 318 | 97.48 240 | 94.45 178 | 96.56 189 | 96.45 240 | 43.83 359 | 98.87 284 | 86.33 297 | 99.40 149 | 99.18 128 |
|
.test1245 | | | 73.49 327 | 79.27 328 | 56.15 340 | 98.53 140 | 62.84 353 | 91.49 318 | 97.48 240 | 94.45 178 | 96.56 189 | 96.45 240 | 43.83 359 | 98.87 284 | 86.33 297 | 8.32 354 | 6.75 354 |
|
原ACMM1 | | | | | 96.58 165 | 98.16 188 | 92.12 174 | | 98.15 196 | 85.90 302 | 93.49 292 | 96.43 242 | 92.47 199 | 99.38 220 | 87.66 280 | 98.62 231 | 98.23 235 |
|
tpm2 | | | 88.47 311 | 87.69 314 | 90.79 320 | 94.98 322 | 77.34 337 | 95.09 214 | 91.83 316 | 77.51 344 | 89.40 336 | 96.41 243 | 67.83 346 | 98.73 296 | 83.58 320 | 92.60 335 | 96.29 312 |
|
OpenMVS_ROB | | 91.80 14 | 93.64 244 | 93.05 243 | 95.42 228 | 97.31 271 | 91.21 190 | 95.08 216 | 96.68 266 | 81.56 326 | 96.88 181 | 96.41 243 | 90.44 231 | 99.25 241 | 85.39 306 | 97.67 279 | 95.80 318 |
|
F-COLMAP | | | 95.30 195 | 94.38 218 | 98.05 77 | 98.64 121 | 96.04 66 | 95.61 184 | 98.66 129 | 89.00 270 | 93.22 301 | 96.40 245 | 92.90 183 | 99.35 225 | 87.45 290 | 97.53 286 | 98.77 190 |
|
NCCC | | | 96.52 150 | 95.99 164 | 98.10 72 | 97.81 222 | 95.68 75 | 95.00 223 | 98.20 188 | 95.39 141 | 95.40 234 | 96.36 246 | 93.81 160 | 99.45 190 | 93.55 181 | 98.42 241 | 99.17 129 |
|
new_pmnet | | | 92.34 266 | 91.69 267 | 94.32 265 | 96.23 300 | 89.16 230 | 92.27 308 | 92.88 307 | 84.39 318 | 95.29 235 | 96.35 247 | 85.66 270 | 96.74 345 | 84.53 312 | 97.56 284 | 97.05 285 |
|
Test4 | | | 95.39 190 | 95.24 184 | 95.82 216 | 98.07 196 | 89.60 216 | 94.40 240 | 98.49 148 | 91.39 252 | 97.40 156 | 96.32 248 | 87.32 264 | 99.41 206 | 95.09 136 | 98.71 226 | 98.44 214 |
|
tpmvs | | | 90.79 296 | 90.87 287 | 90.57 322 | 92.75 347 | 76.30 339 | 95.79 168 | 93.64 298 | 91.04 255 | 91.91 318 | 96.26 249 | 77.19 303 | 98.86 286 | 89.38 256 | 89.85 340 | 96.56 304 |
|
test_prior3 | | | 95.91 169 | 95.39 181 | 97.46 117 | 97.79 231 | 94.26 123 | 93.33 288 | 98.42 158 | 94.21 190 | 94.02 273 | 96.25 250 | 93.64 164 | 99.34 226 | 91.90 201 | 98.96 200 | 98.79 187 |
|
test_prior2 | | | | | | | | 93.33 288 | | 94.21 190 | 94.02 273 | 96.25 250 | 93.64 164 | | 91.90 201 | 98.96 200 | |
|
testgi | | | 96.07 164 | 96.50 148 | 94.80 248 | 99.26 51 | 87.69 267 | 95.96 160 | 98.58 142 | 95.08 159 | 98.02 113 | 96.25 250 | 97.92 18 | 97.60 338 | 88.68 268 | 98.74 221 | 99.11 144 |
|
DP-MVS Recon | | | 95.55 179 | 95.13 187 | 96.80 152 | 98.51 142 | 93.99 132 | 94.60 236 | 98.69 122 | 90.20 261 | 95.78 225 | 96.21 253 | 92.73 187 | 98.98 270 | 90.58 237 | 98.86 213 | 97.42 274 |
|
MVSFormer | | | 96.14 163 | 96.36 151 | 95.49 227 | 97.68 242 | 87.81 265 | 98.67 12 | 99.02 51 | 96.50 99 | 94.48 261 | 96.15 254 | 86.90 265 | 99.92 4 | 98.73 17 | 99.13 184 | 98.74 192 |
|
jason | | | 94.39 224 | 94.04 228 | 95.41 230 | 98.29 159 | 87.85 264 | 92.74 300 | 96.75 263 | 85.38 310 | 95.29 235 | 96.15 254 | 88.21 255 | 99.65 115 | 94.24 164 | 99.34 159 | 98.74 192 |
jason: jason. |
dp | | | 88.08 315 | 88.05 313 | 88.16 332 | 92.85 345 | 68.81 350 | 94.17 253 | 92.88 307 | 85.47 306 | 91.38 323 | 96.14 256 | 68.87 343 | 98.81 290 | 86.88 294 | 83.80 349 | 96.87 292 |
|
MCST-MVS | | | 96.24 158 | 95.80 170 | 97.56 103 | 98.75 106 | 94.13 127 | 94.66 234 | 98.17 193 | 90.17 262 | 96.21 211 | 96.10 257 | 95.14 114 | 99.43 194 | 94.13 166 | 98.85 215 | 99.13 136 |
|
TEST9 | | | | | | 97.84 219 | 95.23 89 | 93.62 277 | 98.39 161 | 86.81 293 | 93.78 279 | 95.99 258 | 94.68 127 | 99.52 162 | | | |
|
train_agg | | | 95.46 186 | 94.66 204 | 97.88 84 | 97.84 219 | 95.23 89 | 93.62 277 | 98.39 161 | 87.04 291 | 93.78 279 | 95.99 258 | 94.58 132 | 99.52 162 | 91.76 207 | 98.90 206 | 98.89 174 |
|
MSDG | | | 95.33 193 | 95.13 187 | 95.94 212 | 97.40 263 | 91.85 181 | 91.02 324 | 98.37 164 | 95.30 143 | 96.31 205 | 95.99 258 | 94.51 136 | 98.38 321 | 89.59 253 | 97.65 281 | 97.60 269 |
|
agg_prior1 | | | 95.39 190 | 94.60 208 | 97.75 91 | 97.80 226 | 94.96 100 | 93.39 285 | 98.36 165 | 87.20 289 | 93.49 292 | 95.97 261 | 94.65 129 | 99.53 159 | 91.69 210 | 98.86 213 | 98.77 190 |
|
test_8 | | | | | | 97.81 222 | 95.07 97 | 93.54 280 | 98.38 163 | 87.04 291 | 93.71 283 | 95.96 262 | 94.58 132 | 99.52 162 | | | |
|
CSCG | | | 97.40 94 | 97.30 92 | 97.69 96 | 98.95 93 | 94.83 103 | 97.28 91 | 98.99 65 | 96.35 106 | 98.13 99 | 95.95 263 | 95.99 83 | 99.66 114 | 94.36 161 | 99.73 56 | 98.59 203 |
|
TAPA-MVS | | 93.32 12 | 94.93 208 | 94.23 221 | 97.04 141 | 98.18 184 | 94.51 113 | 95.22 209 | 98.73 112 | 81.22 329 | 96.25 209 | 95.95 263 | 93.80 161 | 98.98 270 | 89.89 249 | 98.87 211 | 97.62 267 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
agg_prior3 | | | 95.30 195 | 94.46 216 | 97.80 89 | 97.80 226 | 95.00 98 | 93.63 276 | 98.34 169 | 86.33 297 | 93.40 299 | 95.84 265 | 94.15 149 | 99.50 174 | 91.76 207 | 98.90 206 | 98.89 174 |
|
sss | | | 94.22 227 | 93.72 233 | 95.74 218 | 97.71 240 | 89.95 209 | 93.84 269 | 96.98 256 | 88.38 278 | 93.75 281 | 95.74 266 | 87.94 256 | 98.89 280 | 91.02 221 | 98.10 252 | 98.37 219 |
|
CNLPA | | | 95.04 204 | 94.47 213 | 96.75 155 | 97.81 222 | 95.25 88 | 94.12 258 | 97.89 212 | 94.41 181 | 94.57 256 | 95.69 267 | 90.30 235 | 98.35 324 | 86.72 296 | 98.76 219 | 96.64 301 |
|
PCF-MVS | | 89.43 18 | 92.12 271 | 90.64 292 | 96.57 167 | 97.80 226 | 93.48 150 | 89.88 335 | 98.45 151 | 74.46 347 | 96.04 216 | 95.68 268 | 90.71 229 | 99.31 231 | 73.73 343 | 99.01 198 | 96.91 291 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
BH-untuned | | | 94.69 216 | 94.75 203 | 94.52 261 | 97.95 212 | 87.53 269 | 94.07 259 | 97.01 255 | 93.99 195 | 97.10 166 | 95.65 269 | 92.65 190 | 98.95 275 | 87.60 287 | 96.74 305 | 97.09 283 |
|
CANet_DTU | | | 94.65 218 | 94.21 223 | 95.96 208 | 95.90 307 | 89.68 212 | 93.92 266 | 97.83 217 | 93.19 215 | 90.12 333 | 95.64 270 | 88.52 251 | 99.57 150 | 93.27 185 | 99.47 123 | 98.62 201 |
|
PatchMatch-RL | | | 94.61 220 | 93.81 232 | 97.02 144 | 98.19 181 | 95.72 73 | 93.66 275 | 97.23 246 | 88.17 280 | 94.94 243 | 95.62 271 | 91.43 222 | 98.57 308 | 87.36 291 | 97.68 278 | 96.76 297 |
|
tpm cat1 | | | 88.01 316 | 87.33 316 | 90.05 325 | 94.48 329 | 76.28 340 | 94.47 239 | 94.35 292 | 73.84 349 | 89.26 337 | 95.61 272 | 73.64 317 | 98.30 326 | 84.13 313 | 86.20 346 | 95.57 323 |
|
Effi-MVS+-dtu | | | 96.81 134 | 96.09 159 | 98.99 10 | 96.90 286 | 98.69 2 | 96.42 128 | 98.09 201 | 95.86 123 | 95.15 238 | 95.54 273 | 94.26 144 | 99.81 33 | 94.06 168 | 98.51 238 | 98.47 211 |
|
AdaColmap | | | 95.11 201 | 94.62 207 | 96.58 165 | 97.33 269 | 94.45 116 | 94.92 225 | 98.08 203 | 93.15 220 | 93.98 276 | 95.53 274 | 94.34 141 | 99.10 255 | 85.69 302 | 98.61 232 | 96.20 313 |
|
WTY-MVS | | | 93.55 246 | 93.00 245 | 95.19 234 | 97.81 222 | 87.86 263 | 93.89 267 | 96.00 271 | 89.02 269 | 94.07 271 | 95.44 275 | 86.27 267 | 99.33 229 | 87.69 279 | 96.82 302 | 98.39 218 |
|
test1235678 | | | 92.95 254 | 92.40 254 | 94.61 255 | 96.95 283 | 86.87 281 | 90.75 326 | 97.75 220 | 91.00 256 | 96.33 199 | 95.38 276 | 85.21 273 | 98.92 276 | 79.00 333 | 99.20 178 | 98.03 252 |
|
PLC | | 91.02 16 | 94.05 236 | 92.90 246 | 97.51 108 | 98.00 205 | 95.12 96 | 94.25 246 | 98.25 183 | 86.17 298 | 91.48 322 | 95.25 277 | 91.01 226 | 99.19 246 | 85.02 309 | 96.69 306 | 98.22 236 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
pmmvs3 | | | 90.00 300 | 88.90 309 | 93.32 288 | 94.20 334 | 85.34 292 | 91.25 322 | 92.56 312 | 78.59 339 | 93.82 278 | 95.17 278 | 67.36 347 | 98.69 300 | 89.08 261 | 98.03 254 | 95.92 314 |
|
NP-MVS | | | | | | 98.14 191 | 93.72 140 | | | | | 95.08 279 | | | | | |
|
HQP-MVS | | | 95.17 200 | 94.58 210 | 96.92 147 | 97.85 215 | 92.47 164 | 94.26 243 | 98.43 155 | 93.18 216 | 92.86 305 | 95.08 279 | 90.33 232 | 99.23 244 | 90.51 239 | 98.74 221 | 99.05 154 |
|
cdsmvs_eth3d_5k | | | 24.22 330 | 32.30 331 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 98.10 200 | 0.00 355 | 0.00 356 | 95.06 281 | 97.54 28 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
lupinMVS | | | 93.77 239 | 93.28 239 | 95.24 233 | 97.68 242 | 87.81 265 | 92.12 310 | 96.05 270 | 84.52 315 | 94.48 261 | 95.06 281 | 86.90 265 | 99.63 121 | 93.62 180 | 99.13 184 | 98.27 232 |
|
1112_ss | | | 94.12 232 | 93.42 237 | 96.23 187 | 98.59 130 | 90.85 194 | 94.24 247 | 98.85 84 | 85.49 305 | 92.97 303 | 94.94 283 | 86.01 269 | 99.64 118 | 91.78 206 | 97.92 260 | 98.20 238 |
|
ab-mvs-re | | | 7.91 334 | 10.55 335 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 94.94 283 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
Fast-Effi-MVS+-dtu | | | 96.44 154 | 96.12 157 | 97.39 124 | 97.18 276 | 94.39 117 | 95.46 186 | 98.73 112 | 96.03 116 | 94.72 247 | 94.92 285 | 96.28 80 | 99.69 97 | 93.81 175 | 97.98 255 | 98.09 243 |
|
EPNet_dtu | | | 91.39 289 | 90.75 290 | 93.31 289 | 90.48 353 | 82.61 318 | 94.80 231 | 92.88 307 | 93.39 211 | 81.74 351 | 94.90 286 | 81.36 285 | 99.11 254 | 88.28 273 | 98.87 211 | 98.21 237 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
Effi-MVS+ | | | 96.19 161 | 96.01 162 | 96.71 157 | 97.43 261 | 92.19 173 | 96.12 148 | 99.10 25 | 95.45 138 | 93.33 300 | 94.71 287 | 97.23 41 | 99.56 151 | 93.21 187 | 97.54 285 | 98.37 219 |
|
GA-MVS | | | 92.83 256 | 92.15 258 | 94.87 246 | 96.97 282 | 87.27 276 | 90.03 331 | 96.12 269 | 91.83 248 | 94.05 272 | 94.57 288 | 76.01 309 | 98.97 274 | 92.46 196 | 97.34 294 | 98.36 224 |
|
xiu_mvs_v1_base_debu | | | 95.62 176 | 95.96 165 | 94.60 256 | 98.01 202 | 88.42 246 | 93.99 262 | 98.21 185 | 92.98 224 | 95.91 219 | 94.53 289 | 96.39 74 | 99.72 70 | 95.43 119 | 98.19 248 | 95.64 320 |
|
xiu_mvs_v1_base | | | 95.62 176 | 95.96 165 | 94.60 256 | 98.01 202 | 88.42 246 | 93.99 262 | 98.21 185 | 92.98 224 | 95.91 219 | 94.53 289 | 96.39 74 | 99.72 70 | 95.43 119 | 98.19 248 | 95.64 320 |
|
xiu_mvs_v1_base_debi | | | 95.62 176 | 95.96 165 | 94.60 256 | 98.01 202 | 88.42 246 | 93.99 262 | 98.21 185 | 92.98 224 | 95.91 219 | 94.53 289 | 96.39 74 | 99.72 70 | 95.43 119 | 98.19 248 | 95.64 320 |
|
view600 | | | 92.56 259 | 92.11 259 | 93.91 274 | 98.45 148 | 84.76 303 | 97.10 101 | 90.23 332 | 97.42 71 | 96.98 171 | 94.48 292 | 73.62 318 | 99.60 139 | 82.49 322 | 98.28 244 | 97.36 275 |
|
view800 | | | 92.56 259 | 92.11 259 | 93.91 274 | 98.45 148 | 84.76 303 | 97.10 101 | 90.23 332 | 97.42 71 | 96.98 171 | 94.48 292 | 73.62 318 | 99.60 139 | 82.49 322 | 98.28 244 | 97.36 275 |
|
conf0.05thres1000 | | | 92.56 259 | 92.11 259 | 93.91 274 | 98.45 148 | 84.76 303 | 97.10 101 | 90.23 332 | 97.42 71 | 96.98 171 | 94.48 292 | 73.62 318 | 99.60 139 | 82.49 322 | 98.28 244 | 97.36 275 |
|
tfpn | | | 92.56 259 | 92.11 259 | 93.91 274 | 98.45 148 | 84.76 303 | 97.10 101 | 90.23 332 | 97.42 71 | 96.98 171 | 94.48 292 | 73.62 318 | 99.60 139 | 82.49 322 | 98.28 244 | 97.36 275 |
|
PVSNet_Blended | | | 93.96 237 | 93.65 234 | 94.91 243 | 97.79 231 | 87.40 273 | 91.43 320 | 98.68 124 | 84.50 316 | 94.51 259 | 94.48 292 | 93.04 179 | 99.30 233 | 89.77 251 | 98.61 232 | 98.02 254 |
|
PAPM_NR | | | 94.61 220 | 94.17 225 | 95.96 208 | 98.36 155 | 91.23 189 | 95.93 163 | 97.95 209 | 92.98 224 | 93.42 297 | 94.43 297 | 90.53 230 | 98.38 321 | 87.60 287 | 96.29 312 | 98.27 232 |
|
API-MVS | | | 95.09 203 | 95.01 192 | 95.31 231 | 96.61 290 | 94.02 130 | 96.83 118 | 97.18 249 | 95.60 132 | 95.79 224 | 94.33 298 | 94.54 134 | 98.37 323 | 85.70 301 | 98.52 236 | 93.52 338 |
|
mvs-test1 | | | 96.20 160 | 95.50 178 | 98.32 60 | 96.90 286 | 98.16 4 | 95.07 217 | 98.09 201 | 95.86 123 | 93.63 286 | 94.32 299 | 94.26 144 | 99.71 80 | 94.06 168 | 97.27 297 | 97.07 284 |
|
alignmvs | | | 96.01 166 | 95.52 177 | 97.50 111 | 97.77 236 | 94.71 108 | 96.07 149 | 96.84 260 | 97.48 69 | 96.78 183 | 94.28 300 | 85.50 271 | 99.40 209 | 96.22 86 | 98.73 224 | 98.40 216 |
|
CLD-MVS | | | 95.47 185 | 95.07 189 | 96.69 159 | 98.27 164 | 92.53 163 | 91.36 321 | 98.67 127 | 91.22 253 | 95.78 225 | 94.12 301 | 95.65 99 | 98.98 270 | 90.81 228 | 99.72 59 | 98.57 204 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
TR-MVS | | | 92.54 263 | 92.20 257 | 93.57 284 | 96.49 294 | 86.66 283 | 93.51 281 | 94.73 287 | 89.96 264 | 94.95 242 | 93.87 302 | 90.24 237 | 98.61 306 | 81.18 328 | 94.88 324 | 95.45 324 |
|
canonicalmvs | | | 97.23 106 | 97.21 104 | 97.30 128 | 97.65 246 | 94.39 117 | 97.84 59 | 99.05 38 | 97.42 71 | 96.68 185 | 93.85 303 | 97.63 26 | 99.33 229 | 96.29 85 | 98.47 240 | 98.18 241 |
|
xiu_mvs_v2_base | | | 94.22 227 | 94.63 206 | 92.99 298 | 97.32 270 | 84.84 301 | 92.12 310 | 97.84 215 | 91.96 244 | 94.17 266 | 93.43 304 | 96.07 82 | 99.71 80 | 91.27 215 | 97.48 288 | 94.42 330 |
|
test12356 | | | 87.98 317 | 88.41 312 | 86.69 335 | 95.84 308 | 63.49 352 | 87.15 341 | 97.32 244 | 87.21 288 | 91.78 321 | 93.36 305 | 70.66 335 | 98.39 319 | 74.70 342 | 97.64 282 | 98.19 239 |
|
CHOSEN 280x420 | | | 89.98 301 | 89.19 307 | 92.37 307 | 95.60 313 | 81.13 323 | 86.22 343 | 97.09 253 | 81.44 328 | 87.44 344 | 93.15 306 | 73.99 313 | 99.47 181 | 88.69 267 | 99.07 192 | 96.52 305 |
|
thres600view7 | | | 92.03 272 | 91.43 268 | 93.82 279 | 98.19 181 | 84.61 307 | 96.27 138 | 90.39 327 | 96.81 92 | 96.37 198 | 93.11 307 | 73.44 324 | 99.49 176 | 80.32 329 | 97.95 256 | 97.36 275 |
|
E-PMN | | | 89.52 306 | 89.78 301 | 88.73 328 | 93.14 342 | 77.61 336 | 83.26 347 | 92.02 314 | 94.82 168 | 93.71 283 | 93.11 307 | 75.31 311 | 96.81 343 | 85.81 300 | 96.81 303 | 91.77 345 |
|
tfpn111 | | | 91.92 274 | 91.39 269 | 93.49 286 | 98.21 177 | 84.50 308 | 96.39 129 | 90.39 327 | 96.87 88 | 96.33 199 | 93.08 309 | 73.44 324 | 99.51 172 | 79.87 330 | 97.94 259 | 96.46 306 |
|
conf200view11 | | | 91.81 279 | 91.26 274 | 93.46 287 | 98.21 177 | 84.50 308 | 96.39 129 | 90.39 327 | 96.87 88 | 96.33 199 | 93.08 309 | 73.44 324 | 99.42 195 | 78.85 335 | 97.74 265 | 96.46 306 |
|
thres100view900 | | | 91.76 281 | 91.26 274 | 93.26 290 | 98.21 177 | 84.50 308 | 96.39 129 | 90.39 327 | 96.87 88 | 96.33 199 | 93.08 309 | 73.44 324 | 99.42 195 | 78.85 335 | 97.74 265 | 95.85 316 |
|
1314 | | | 92.38 265 | 92.30 256 | 92.64 304 | 95.42 318 | 85.15 296 | 95.86 165 | 96.97 257 | 85.40 309 | 90.62 326 | 93.06 312 | 91.12 225 | 97.80 336 | 86.74 295 | 95.49 323 | 94.97 328 |
|
PAPM | | | 87.64 320 | 85.84 323 | 93.04 295 | 96.54 291 | 84.99 299 | 88.42 339 | 95.57 283 | 79.52 335 | 83.82 348 | 93.05 313 | 80.57 287 | 98.41 317 | 62.29 351 | 92.79 333 | 95.71 319 |
|
Fast-Effi-MVS+ | | | 95.49 182 | 95.07 189 | 96.75 155 | 97.67 245 | 92.82 159 | 94.22 249 | 98.60 139 | 91.61 249 | 93.42 297 | 92.90 314 | 96.73 60 | 99.70 88 | 92.60 193 | 97.89 263 | 97.74 264 |
|
PNet_i23d | | | 83.82 325 | 83.39 325 | 85.10 336 | 96.07 304 | 65.16 351 | 81.87 349 | 94.37 291 | 90.87 257 | 93.92 277 | 92.89 315 | 52.80 357 | 96.44 347 | 77.52 341 | 70.22 351 | 93.70 337 |
|
tfpn1000 | | | 91.88 278 | 91.20 276 | 93.89 278 | 97.96 208 | 87.13 278 | 97.13 99 | 88.16 347 | 94.41 181 | 94.87 245 | 92.77 316 | 68.34 344 | 99.47 181 | 89.24 257 | 97.95 256 | 95.06 326 |
|
MVS | | | 90.02 299 | 89.20 306 | 92.47 305 | 94.71 324 | 86.90 280 | 95.86 165 | 96.74 264 | 64.72 350 | 90.62 326 | 92.77 316 | 92.54 195 | 98.39 319 | 79.30 332 | 95.56 322 | 92.12 343 |
|
BH-w/o | | | 92.14 270 | 91.94 263 | 92.73 302 | 97.13 278 | 85.30 293 | 92.46 305 | 95.64 281 | 89.33 268 | 94.21 265 | 92.74 318 | 89.60 240 | 98.24 327 | 81.68 326 | 94.66 326 | 94.66 329 |
|
PAPR | | | 92.22 268 | 91.27 273 | 95.07 239 | 95.73 312 | 88.81 241 | 91.97 313 | 97.87 213 | 85.80 303 | 90.91 324 | 92.73 319 | 91.16 224 | 98.33 325 | 79.48 331 | 95.76 319 | 98.08 244 |
|
MAR-MVS | | | 94.21 230 | 93.03 244 | 97.76 90 | 96.94 284 | 97.44 30 | 96.97 116 | 97.15 250 | 87.89 285 | 92.00 317 | 92.73 319 | 92.14 204 | 99.12 251 | 83.92 315 | 97.51 287 | 96.73 298 |
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 |
PS-MVSNAJ | | | 94.10 233 | 94.47 213 | 93.00 297 | 97.35 265 | 84.88 300 | 91.86 314 | 97.84 215 | 91.96 244 | 94.17 266 | 92.50 321 | 95.82 90 | 99.71 80 | 91.27 215 | 97.48 288 | 94.40 331 |
|
PMMVS | | | 92.39 264 | 91.08 277 | 96.30 183 | 93.12 343 | 92.81 160 | 90.58 328 | 95.96 273 | 79.17 337 | 91.85 320 | 92.27 322 | 90.29 236 | 98.66 305 | 89.85 250 | 96.68 307 | 97.43 273 |
|
PVSNet | | 86.72 19 | 91.10 290 | 90.97 286 | 91.49 313 | 97.56 252 | 78.04 334 | 87.17 340 | 94.60 289 | 84.65 314 | 92.34 314 | 92.20 323 | 87.37 263 | 98.47 314 | 85.17 308 | 97.69 277 | 97.96 256 |
|
tfpn200view9 | | | 91.55 287 | 91.00 278 | 93.21 292 | 98.02 200 | 84.35 312 | 95.70 172 | 90.79 324 | 96.26 108 | 95.90 222 | 92.13 324 | 73.62 318 | 99.42 195 | 78.85 335 | 97.74 265 | 95.85 316 |
|
thres400 | | | 91.68 286 | 91.00 278 | 93.71 281 | 98.02 200 | 84.35 312 | 95.70 172 | 90.79 324 | 96.26 108 | 95.90 222 | 92.13 324 | 73.62 318 | 99.42 195 | 78.85 335 | 97.74 265 | 97.36 275 |
|
MVE | | 73.61 22 | 86.48 322 | 85.92 322 | 88.18 331 | 96.23 300 | 85.28 294 | 81.78 350 | 75.79 353 | 86.01 299 | 82.53 350 | 91.88 326 | 92.74 186 | 87.47 353 | 71.42 348 | 94.86 325 | 91.78 344 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
EMVS | | | 89.06 308 | 89.22 304 | 88.61 329 | 93.00 344 | 77.34 337 | 82.91 348 | 90.92 323 | 94.64 172 | 92.63 311 | 91.81 327 | 76.30 307 | 97.02 341 | 83.83 317 | 96.90 299 | 91.48 346 |
|
conf0.01 | | | 91.90 275 | 90.98 280 | 94.67 252 | 98.27 164 | 88.03 255 | 96.98 110 | 88.58 340 | 93.90 198 | 94.64 250 | 91.45 328 | 69.62 337 | 99.52 162 | 87.62 281 | 97.74 265 | 96.46 306 |
|
conf0.002 | | | 91.90 275 | 90.98 280 | 94.67 252 | 98.27 164 | 88.03 255 | 96.98 110 | 88.58 340 | 93.90 198 | 94.64 250 | 91.45 328 | 69.62 337 | 99.52 162 | 87.62 281 | 97.74 265 | 96.46 306 |
|
thresconf0.02 | | | 91.72 282 | 90.98 280 | 93.97 270 | 98.27 164 | 88.03 255 | 96.98 110 | 88.58 340 | 93.90 198 | 94.64 250 | 91.45 328 | 69.62 337 | 99.52 162 | 87.62 281 | 97.74 265 | 94.35 332 |
|
tfpn_n400 | | | 91.72 282 | 90.98 280 | 93.97 270 | 98.27 164 | 88.03 255 | 96.98 110 | 88.58 340 | 93.90 198 | 94.64 250 | 91.45 328 | 69.62 337 | 99.52 162 | 87.62 281 | 97.74 265 | 94.35 332 |
|
tfpnconf | | | 91.72 282 | 90.98 280 | 93.97 270 | 98.27 164 | 88.03 255 | 96.98 110 | 88.58 340 | 93.90 198 | 94.64 250 | 91.45 328 | 69.62 337 | 99.52 162 | 87.62 281 | 97.74 265 | 94.35 332 |
|
tfpnview11 | | | 91.72 282 | 90.98 280 | 93.97 270 | 98.27 164 | 88.03 255 | 96.98 110 | 88.58 340 | 93.90 198 | 94.64 250 | 91.45 328 | 69.62 337 | 99.52 162 | 87.62 281 | 97.74 265 | 94.35 332 |
|
cascas | | | 91.89 277 | 91.35 271 | 93.51 285 | 94.27 331 | 85.60 289 | 88.86 338 | 98.61 138 | 79.32 336 | 92.16 316 | 91.44 334 | 89.22 248 | 98.12 331 | 90.80 229 | 97.47 290 | 96.82 294 |
|
IB-MVS | | 85.98 20 | 88.63 310 | 86.95 319 | 93.68 282 | 95.12 320 | 84.82 302 | 90.85 325 | 90.17 336 | 87.55 286 | 88.48 340 | 91.34 335 | 58.01 351 | 99.59 143 | 87.24 292 | 93.80 330 | 96.63 303 |
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 |
thres200 | | | 91.00 292 | 90.42 296 | 92.77 301 | 97.47 259 | 83.98 315 | 94.01 261 | 91.18 322 | 95.12 158 | 95.44 232 | 91.21 336 | 73.93 314 | 99.31 231 | 77.76 339 | 97.63 283 | 95.01 327 |
|
test0.0.03 1 | | | 90.11 298 | 89.21 305 | 92.83 300 | 93.89 336 | 86.87 281 | 91.74 316 | 88.74 339 | 92.02 241 | 94.71 248 | 91.14 337 | 73.92 315 | 94.48 349 | 83.75 319 | 92.94 331 | 97.16 282 |
|
test-LLR | | | 89.97 302 | 89.90 300 | 90.16 323 | 94.24 332 | 74.98 342 | 89.89 332 | 89.06 337 | 92.02 241 | 89.97 334 | 90.77 338 | 73.92 315 | 98.57 308 | 91.88 203 | 97.36 292 | 96.92 289 |
|
test-mter | | | 87.92 318 | 87.17 317 | 90.16 323 | 94.24 332 | 74.98 342 | 89.89 332 | 89.06 337 | 86.44 296 | 89.97 334 | 90.77 338 | 54.96 355 | 98.57 308 | 91.88 203 | 97.36 292 | 96.92 289 |
|
testus | | | 90.90 295 | 90.51 294 | 92.06 310 | 96.07 304 | 79.45 328 | 88.99 336 | 98.44 154 | 85.46 307 | 94.15 268 | 90.77 338 | 89.12 250 | 98.01 334 | 73.66 344 | 97.95 256 | 98.71 195 |
|
tfpn_ndepth | | | 90.98 293 | 90.24 298 | 93.20 294 | 97.72 239 | 87.18 277 | 96.52 125 | 88.20 346 | 92.63 233 | 93.69 285 | 90.70 341 | 68.22 345 | 99.42 195 | 86.98 293 | 97.47 290 | 93.00 342 |
|
testpf | | | 82.70 326 | 84.35 324 | 77.74 338 | 88.97 354 | 73.23 346 | 93.85 268 | 84.33 350 | 88.10 281 | 85.06 347 | 90.42 342 | 52.62 358 | 91.05 352 | 91.00 222 | 84.82 348 | 68.93 351 |
|
TESTMET0.1,1 | | | 87.20 321 | 86.57 321 | 89.07 327 | 93.62 338 | 72.84 347 | 89.89 332 | 87.01 348 | 85.46 307 | 89.12 338 | 90.20 343 | 56.00 354 | 97.72 337 | 90.91 225 | 96.92 298 | 96.64 301 |
|
gm-plane-assit | | | | | | 91.79 349 | 71.40 349 | | | 81.67 325 | | 90.11 344 | | 98.99 268 | 84.86 310 | | |
|
DWT-MVSNet_test | | | 87.92 318 | 86.77 320 | 91.39 314 | 93.18 341 | 78.62 330 | 95.10 212 | 91.42 319 | 85.58 304 | 88.00 341 | 88.73 345 | 60.60 350 | 98.90 277 | 90.60 236 | 87.70 344 | 96.65 300 |
|
PatchFormer-LS_test | | | 89.62 305 | 89.12 308 | 91.11 318 | 93.62 338 | 78.42 331 | 94.57 238 | 93.62 299 | 88.39 277 | 90.54 329 | 88.40 346 | 72.33 329 | 99.03 264 | 92.41 197 | 88.20 343 | 95.89 315 |
|
DeepMVS_CX | | | | | 77.17 339 | 90.94 352 | 85.28 294 | | 74.08 356 | 52.51 351 | 80.87 352 | 88.03 347 | 75.25 312 | 70.63 354 | 59.23 352 | 84.94 347 | 75.62 349 |
|
test2356 | | | 85.45 323 | 83.26 326 | 92.01 311 | 91.12 350 | 80.76 324 | 85.16 344 | 92.90 306 | 83.90 319 | 90.63 325 | 87.71 348 | 53.10 356 | 97.24 340 | 69.20 349 | 95.65 320 | 98.03 252 |
|
PVSNet_0 | | 81.89 21 | 84.49 324 | 83.21 327 | 88.34 330 | 95.76 311 | 74.97 344 | 83.49 346 | 92.70 311 | 78.47 340 | 87.94 342 | 86.90 349 | 83.38 280 | 96.63 346 | 73.44 345 | 66.86 352 | 93.40 339 |
|
GG-mvs-BLEND | | | | | 90.60 321 | 91.00 351 | 84.21 314 | 98.23 34 | 72.63 357 | | 82.76 349 | 84.11 350 | 56.14 353 | 96.79 344 | 72.20 346 | 92.09 336 | 90.78 347 |
|
tmp_tt | | | 57.23 328 | 62.50 329 | 41.44 341 | 34.77 356 | 49.21 357 | 83.93 345 | 60.22 358 | 15.31 352 | 71.11 353 | 79.37 351 | 70.09 336 | 44.86 355 | 64.76 350 | 82.93 350 | 30.25 352 |
|
X-MVStestdata | | | 92.86 255 | 90.83 289 | 98.94 15 | 99.15 67 | 97.66 16 | 97.77 62 | 98.83 95 | 97.42 71 | 96.32 203 | 36.50 352 | 96.49 71 | 99.72 70 | 95.66 107 | 99.37 151 | 99.45 71 |
|
testmvs | | | 12.33 332 | 15.23 333 | 3.64 344 | 5.77 358 | 2.23 359 | 88.99 336 | 3.62 359 | 2.30 354 | 5.29 354 | 13.09 353 | 4.52 362 | 1.95 356 | 5.16 354 | 8.32 354 | 6.75 354 |
|
test123 | | | 12.59 331 | 15.49 332 | 3.87 343 | 6.07 357 | 2.55 358 | 90.75 326 | 2.59 360 | 2.52 353 | 5.20 355 | 13.02 354 | 4.96 361 | 1.85 357 | 5.20 353 | 9.09 353 | 7.23 353 |
|
test_post | | | | | | | | | | | | 10.87 355 | 76.83 304 | 99.07 258 | | | |
|
test_post1 | | | | | | | | 94.98 224 | | | | 10.37 356 | 76.21 308 | 99.04 261 | 89.47 255 | | |
|
pcd_1.5k_mvsjas | | | 7.98 333 | 10.65 334 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 95.82 90 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
pcd1.5k->3k | | | 41.47 329 | 44.19 330 | 33.29 342 | 99.65 11 | 0.00 360 | 0.00 351 | 99.07 34 | 0.00 355 | 0.00 356 | 0.00 357 | 99.04 4 | 0.00 358 | 0.00 355 | 99.96 11 | 99.87 2 |
|
sosnet-low-res | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
sosnet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
uncertanet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
Regformer | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
uanet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
GSMVS | | | | | | | | | | | | | | | | | 98.06 247 |
|
test_part2 | | | | | | 99.03 86 | 96.07 64 | | | | 98.08 106 | | | | | | |
|
test_part1 | | | | | | | | | 98.84 87 | | | | 96.69 61 | | | 99.44 130 | 99.37 100 |
|
sam_mvs1 | | | | | | | | | | | | | 77.80 296 | | | | 98.06 247 |
|
sam_mvs | | | | | | | | | | | | | 77.38 300 | | | | |
|
MTGPA | | | | | | | | | 98.73 112 | | | | | | | | |
|
MTMP | | | | | | | | | 74.60 354 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 91.29 214 | 98.89 210 | 99.00 157 |
|
agg_prior2 | | | | | | | | | | | | | | | 90.34 245 | 98.90 206 | 99.10 148 |
|
agg_prior | | | | | | 97.80 226 | 94.96 100 | | 98.36 165 | | 93.49 292 | | | 99.53 159 | | | |
|
test_prior4 | | | | | | | 95.38 85 | 93.61 279 | | | | | | | | | |
|
test_prior | | | | | 97.46 117 | 97.79 231 | 94.26 123 | | 98.42 158 | | | | | 99.34 226 | | | 98.79 187 |
|
旧先验2 | | | | | | | | 93.35 287 | | 77.95 343 | 95.77 227 | | | 98.67 304 | 90.74 232 | | |
|
æ–°å‡ ä½•2 | | | | | | | | 93.43 283 | | | | | | | | | |
|
æ— å…ˆéªŒ | | | | | | | | 93.20 291 | 97.91 210 | 80.78 330 | | | | 99.40 209 | 87.71 277 | | 97.94 257 |
|
原ACMM2 | | | | | | | | 92.82 296 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 99.46 186 | 87.84 276 | | |
|
segment_acmp | | | | | | | | | | | | | 95.34 108 | | | | |
|
testdata1 | | | | | | | | 92.77 297 | | 93.78 205 | | | | | | | |
|
test12 | | | | | 97.46 117 | 97.61 249 | 94.07 128 | | 97.78 219 | | 93.57 290 | | 93.31 174 | 99.42 195 | | 98.78 217 | 98.89 174 |
|
plane_prior7 | | | | | | 98.70 115 | 94.67 110 | | | | | | | | | | |
|
plane_prior6 | | | | | | 98.38 153 | 94.37 119 | | | | | | 91.91 215 | | | | |
|
plane_prior5 | | | | | | | | | 98.75 109 | | | | | 99.46 186 | 92.59 194 | 99.20 178 | 99.28 117 |
|
plane_prior3 | | | | | | | 94.51 113 | | | 95.29 144 | 96.16 213 | | | | | | |
|
plane_prior2 | | | | | | | | 96.50 126 | | 96.36 104 | | | | | | | |
|
plane_prior1 | | | | | | 98.49 144 | | | | | | | | | | | |
|
plane_prior | | | | | | | 94.29 120 | 95.42 192 | | 94.31 187 | | | | | | 98.93 205 | |
|
n2 | | | | | | | | | 0.00 361 | | | | | | | | |
|
nn | | | | | | | | | 0.00 361 | | | | | | | | |
|
door-mid | | | | | | | | | 98.17 193 | | | | | | | | |
|
test11 | | | | | | | | | 98.08 203 | | | | | | | | |
|
door | | | | | | | | | 97.81 218 | | | | | | | | |
|
HQP5-MVS | | | | | | | 92.47 164 | | | | | | | | | | |
|
HQP-NCC | | | | | | 97.85 215 | | 94.26 243 | | 93.18 216 | 92.86 305 | | | | | | |
|
ACMP_Plane | | | | | | 97.85 215 | | 94.26 243 | | 93.18 216 | 92.86 305 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 90.51 239 | | |
|
HQP4-MVS | | | | | | | | | | | 92.87 304 | | | 99.23 244 | | | 99.06 153 |
|
HQP3-MVS | | | | | | | | | 98.43 155 | | | | | | | 98.74 221 | |
|
HQP2-MVS | | | | | | | | | | | | | 90.33 232 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 57.28 356 | 94.89 226 | | 80.59 331 | 94.02 273 | | 78.66 294 | | 85.50 305 | | 97.82 261 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 99.52 108 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 99.55 100 | |
|
Test By Simon | | | | | | | | | | | | | 94.51 136 | | | | |
|