thres200 | | | 88.92 111 | 87.65 117 | 92.73 88 | 96.30 86 | 85.62 35 | 97.85 36 | 98.86 1 | 84.38 123 | 84.82 126 | 93.99 152 | 75.12 139 | 98.01 120 | 70.86 238 | 86.67 160 | 94.56 186 |
|
tfpn111 | | | 88.08 132 | 86.70 140 | 92.20 107 | 96.10 91 | 84.90 49 | 97.14 90 | 98.85 2 | 82.69 159 | 83.41 145 | 93.66 158 | 75.43 129 | 97.82 132 | 67.13 263 | 85.88 171 | 93.89 194 |
|
conf200view11 | | | 88.27 130 | 86.95 136 | 92.24 104 | 96.10 91 | 84.90 49 | 97.14 90 | 98.85 2 | 82.69 159 | 83.41 145 | 93.66 158 | 75.43 129 | 97.93 122 | 69.04 248 | 86.24 166 | 93.89 194 |
|
thres100view900 | | | 88.30 128 | 86.95 136 | 92.33 102 | 96.10 91 | 84.90 49 | 97.14 90 | 98.85 2 | 82.69 159 | 83.41 145 | 93.66 158 | 75.43 129 | 97.93 122 | 69.04 248 | 86.24 166 | 94.17 187 |
|
tfpn200view9 | | | 88.48 123 | 87.15 131 | 92.47 97 | 96.21 87 | 85.30 40 | 97.44 66 | 98.85 2 | 83.37 146 | 83.99 137 | 93.82 155 | 75.36 134 | 97.93 122 | 69.04 248 | 86.24 166 | 94.17 187 |
|
view600 | | | 87.45 143 | 85.98 146 | 91.88 119 | 95.90 98 | 84.52 56 | 96.68 128 | 98.85 2 | 81.85 171 | 82.30 158 | 93.39 162 | 75.44 125 | 97.66 138 | 64.02 282 | 85.36 178 | 93.45 203 |
|
view800 | | | 87.45 143 | 85.98 146 | 91.88 119 | 95.90 98 | 84.52 56 | 96.68 128 | 98.85 2 | 81.85 171 | 82.30 158 | 93.39 162 | 75.44 125 | 97.66 138 | 64.02 282 | 85.36 178 | 93.45 203 |
|
conf0.05thres1000 | | | 87.45 143 | 85.98 146 | 91.88 119 | 95.90 98 | 84.52 56 | 96.68 128 | 98.85 2 | 81.85 171 | 82.30 158 | 93.39 162 | 75.44 125 | 97.66 138 | 64.02 282 | 85.36 178 | 93.45 203 |
|
tfpn | | | 87.45 143 | 85.98 146 | 91.88 119 | 95.90 98 | 84.52 56 | 96.68 128 | 98.85 2 | 81.85 171 | 82.30 158 | 93.39 162 | 75.44 125 | 97.66 138 | 64.02 282 | 85.36 178 | 93.45 203 |
|
thres600view7 | | | 88.06 133 | 86.70 140 | 92.15 109 | 96.10 91 | 85.17 44 | 97.14 90 | 98.85 2 | 82.70 158 | 83.41 145 | 93.66 158 | 75.43 129 | 97.82 132 | 67.13 263 | 85.88 171 | 93.45 203 |
|
thres400 | | | 88.42 126 | 87.15 131 | 92.23 105 | 96.21 87 | 85.30 40 | 97.44 66 | 98.85 2 | 83.37 146 | 83.99 137 | 93.82 155 | 75.36 134 | 97.93 122 | 69.04 248 | 86.24 166 | 93.45 203 |
|
MVS_111021_HR | | | 93.41 33 | 93.39 30 | 93.47 61 | 97.34 74 | 82.83 92 | 97.56 58 | 98.27 12 | 89.16 36 | 89.71 81 | 97.14 77 | 79.77 64 | 99.56 42 | 93.65 32 | 97.94 46 | 98.02 65 |
|
sss | | | 90.87 78 | 89.96 81 | 93.60 50 | 94.15 151 | 83.84 71 | 97.14 90 | 98.13 13 | 85.93 80 | 89.68 82 | 96.09 101 | 71.67 163 | 99.30 58 | 87.69 97 | 89.16 137 | 97.66 93 |
|
MG-MVS | | | 94.25 18 | 93.72 25 | 95.85 7 | 99.38 3 | 89.35 6 | 97.98 32 | 98.09 14 | 89.99 29 | 92.34 49 | 96.97 83 | 81.30 50 | 98.99 86 | 88.54 87 | 98.88 14 | 99.20 9 |
|
VNet | | | 92.11 55 | 91.22 63 | 94.79 17 | 96.91 80 | 86.98 22 | 97.91 33 | 97.96 15 | 86.38 72 | 93.65 37 | 95.74 105 | 70.16 178 | 98.95 91 | 93.39 35 | 88.87 141 | 98.43 38 |
|
0601test | | | 91.46 67 | 90.53 71 | 94.24 28 | 97.41 68 | 85.18 42 | 98.08 27 | 97.72 16 | 80.94 184 | 89.85 78 | 96.14 99 | 75.61 118 | 98.81 98 | 90.42 68 | 88.56 146 | 98.74 22 |
|
Anonymous20240521 | | | 91.46 67 | 90.53 71 | 94.24 28 | 97.41 68 | 85.18 42 | 98.08 27 | 97.72 16 | 80.94 184 | 89.85 78 | 96.14 99 | 75.61 118 | 98.81 98 | 90.42 68 | 88.56 146 | 98.74 22 |
|
WTY-MVS | | | 92.65 48 | 91.68 57 | 95.56 9 | 96.00 95 | 88.90 8 | 98.23 22 | 97.65 18 | 88.57 40 | 89.82 80 | 97.22 75 | 79.29 66 | 99.06 82 | 89.57 79 | 88.73 143 | 98.73 26 |
|
EPNet | | | 94.06 23 | 94.15 22 | 93.76 41 | 97.27 76 | 84.35 60 | 98.29 20 | 97.64 19 | 94.57 4 | 95.36 14 | 96.88 86 | 79.96 63 | 99.12 79 | 91.30 57 | 96.11 77 | 97.82 82 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
HY-MVS | | 84.06 6 | 91.63 63 | 90.37 73 | 95.39 12 | 96.12 90 | 88.25 10 | 90.22 294 | 97.58 20 | 88.33 46 | 90.50 72 | 91.96 179 | 79.26 68 | 99.06 82 | 90.29 70 | 89.07 138 | 98.88 17 |
|
PVSNet | | 82.34 9 | 89.02 108 | 87.79 115 | 92.71 89 | 95.49 112 | 81.50 120 | 97.70 50 | 97.29 21 | 87.76 56 | 85.47 121 | 95.12 131 | 56.90 279 | 98.90 95 | 80.33 152 | 94.02 97 | 97.71 90 |
|
PGM-MVS | | | 91.93 58 | 91.80 55 | 92.32 103 | 98.27 42 | 79.74 161 | 95.28 195 | 97.27 22 | 83.83 138 | 90.89 70 | 97.78 49 | 76.12 113 | 99.56 42 | 88.82 85 | 97.93 48 | 97.66 93 |
|
IB-MVS | | 85.34 4 | 88.67 118 | 87.14 133 | 93.26 65 | 93.12 177 | 84.32 61 | 98.76 10 | 97.27 22 | 87.19 65 | 79.36 200 | 90.45 204 | 83.92 30 | 98.53 106 | 84.41 120 | 69.79 279 | 96.93 127 |
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 |
MVS | | | 90.60 82 | 88.64 102 | 96.50 1 | 94.25 149 | 90.53 4 | 93.33 249 | 97.21 24 | 77.59 243 | 78.88 203 | 97.31 70 | 71.52 166 | 99.69 28 | 89.60 78 | 98.03 44 | 99.27 7 |
|
CSCG | | | 92.02 56 | 91.65 58 | 93.12 71 | 98.53 29 | 80.59 138 | 97.47 64 | 97.18 25 | 77.06 252 | 84.64 131 | 97.98 39 | 83.98 29 | 99.52 44 | 90.72 64 | 97.33 61 | 99.23 8 |
|
PHI-MVS | | | 93.59 31 | 93.63 26 | 93.48 58 | 98.05 49 | 81.76 112 | 98.64 13 | 97.13 26 | 82.60 162 | 94.09 34 | 98.49 10 | 80.35 55 | 99.85 10 | 94.74 24 | 98.62 23 | 98.83 18 |
|
CNVR-MVS | | | 96.30 1 | 96.54 1 | 95.55 10 | 99.31 5 | 87.69 17 | 99.06 5 | 97.12 27 | 94.66 3 | 96.79 4 | 98.78 4 | 86.42 14 | 99.95 2 | 97.59 3 | 99.18 3 | 99.00 13 |
|
conf0.01 | | | 85.70 177 | 84.35 173 | 89.77 185 | 94.53 138 | 79.70 164 | 95.17 200 | 97.11 28 | 75.97 255 | 79.44 193 | 95.31 116 | 81.90 43 | 95.73 236 | 56.78 312 | 82.91 202 | 93.89 194 |
|
conf0.002 | | | 85.70 177 | 84.35 173 | 89.77 185 | 94.53 138 | 79.70 164 | 95.17 200 | 97.11 28 | 75.97 255 | 79.44 193 | 95.31 116 | 81.90 43 | 95.73 236 | 56.78 312 | 82.91 202 | 93.89 194 |
|
thresconf0.02 | | | 85.80 170 | 84.35 173 | 90.17 166 | 94.53 138 | 79.70 164 | 95.17 200 | 97.11 28 | 75.97 255 | 79.44 193 | 95.31 116 | 81.90 43 | 95.73 236 | 56.78 312 | 82.91 202 | 95.09 174 |
|
tfpn_n400 | | | 85.80 170 | 84.35 173 | 90.17 166 | 94.53 138 | 79.70 164 | 95.17 200 | 97.11 28 | 75.97 255 | 79.44 193 | 95.31 116 | 81.90 43 | 95.73 236 | 56.78 312 | 82.91 202 | 95.09 174 |
|
tfpnconf | | | 85.80 170 | 84.35 173 | 90.17 166 | 94.53 138 | 79.70 164 | 95.17 200 | 97.11 28 | 75.97 255 | 79.44 193 | 95.31 116 | 81.90 43 | 95.73 236 | 56.78 312 | 82.91 202 | 95.09 174 |
|
tfpnview11 | | | 85.80 170 | 84.35 173 | 90.17 166 | 94.53 138 | 79.70 164 | 95.17 200 | 97.11 28 | 75.97 255 | 79.44 193 | 95.31 116 | 81.90 43 | 95.73 236 | 56.78 312 | 82.91 202 | 95.09 174 |
|
MCST-MVS | | | 96.17 2 | 96.12 4 | 96.32 3 | 99.42 2 | 89.36 5 | 98.94 9 | 97.10 34 | 95.17 2 | 92.11 50 | 98.46 11 | 87.33 10 | 99.97 1 | 97.21 5 | 99.31 1 | 99.63 2 |
|
tfpn1000 | | | 86.43 162 | 85.10 160 | 90.41 159 | 95.56 110 | 80.51 144 | 95.90 176 | 97.09 35 | 75.91 261 | 80.02 190 | 94.82 137 | 84.78 19 | 95.47 254 | 57.36 307 | 84.46 187 | 95.26 173 |
|
tfpn_ndepth | | | 87.25 148 | 86.00 145 | 91.01 147 | 95.86 102 | 81.46 121 | 96.53 135 | 97.09 35 | 77.35 247 | 81.36 174 | 95.07 133 | 84.74 20 | 95.86 225 | 60.88 297 | 85.14 184 | 95.72 161 |
|
VPA-MVSNet | | | 85.32 182 | 83.83 183 | 89.77 185 | 90.25 230 | 82.63 94 | 96.36 151 | 97.07 37 | 83.03 153 | 81.21 177 | 89.02 220 | 61.58 245 | 96.31 200 | 85.02 116 | 70.95 264 | 90.36 222 |
|
Regformer-1 | | | 94.00 25 | 94.04 23 | 93.87 38 | 98.41 35 | 84.29 62 | 97.43 70 | 97.04 38 | 89.50 33 | 92.75 46 | 98.13 24 | 82.60 39 | 99.26 61 | 93.55 33 | 96.99 65 | 98.06 62 |
|
DELS-MVS | | | 94.98 7 | 94.49 14 | 96.44 2 | 96.42 85 | 90.59 3 | 99.21 2 | 97.02 39 | 94.40 5 | 91.46 58 | 97.08 80 | 83.32 34 | 99.69 28 | 92.83 43 | 98.70 21 | 99.04 11 |
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 |
GG-mvs-BLEND | | | | | 93.49 57 | 94.94 128 | 86.26 25 | 81.62 336 | 97.00 40 | | 88.32 99 | 94.30 146 | 91.23 2 | 96.21 205 | 88.49 89 | 97.43 57 | 98.00 70 |
|
Regformer-2 | | | 93.92 26 | 94.01 24 | 93.67 46 | 98.41 35 | 83.75 72 | 97.43 70 | 97.00 40 | 89.43 35 | 92.69 47 | 98.13 24 | 82.48 40 | 99.22 64 | 93.51 34 | 96.99 65 | 98.04 63 |
|
Regformer-3 | | | 93.19 34 | 93.19 33 | 93.19 68 | 98.10 47 | 83.01 89 | 97.08 101 | 96.98 42 | 88.98 37 | 91.35 63 | 97.89 44 | 80.80 52 | 99.23 62 | 92.30 49 | 95.20 87 | 97.32 113 |
|
gg-mvs-nofinetune | | | 85.48 181 | 82.90 197 | 93.24 66 | 94.51 146 | 85.82 31 | 79.22 340 | 96.97 43 | 61.19 336 | 87.33 108 | 53.01 354 | 90.58 3 | 96.07 209 | 86.07 108 | 97.23 62 | 97.81 83 |
|
NCCC | | | 95.63 3 | 95.94 5 | 94.69 20 | 99.21 6 | 85.15 45 | 99.16 3 | 96.96 44 | 94.11 6 | 95.59 13 | 98.64 7 | 85.07 17 | 99.91 3 | 95.61 18 | 99.10 5 | 99.00 13 |
|
FIs | | | 86.73 158 | 86.10 144 | 88.61 203 | 90.05 235 | 80.21 151 | 96.14 164 | 96.95 45 | 85.56 89 | 78.37 207 | 92.30 175 | 76.73 103 | 95.28 262 | 79.51 160 | 79.27 226 | 90.35 223 |
|
PVSNet_0 | | 77.72 15 | 81.70 238 | 78.95 248 | 89.94 179 | 90.77 224 | 76.72 252 | 95.96 170 | 96.95 45 | 85.01 103 | 70.24 280 | 88.53 227 | 52.32 301 | 98.20 117 | 86.68 107 | 44.08 353 | 94.89 179 |
|
HPM-MVS++ | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 95.32 5 | 95.48 7 | 94.85 16 | 98.62 25 | 86.04 27 | 97.81 40 | 96.93 47 | 92.45 11 | 95.69 12 | 98.50 9 | 85.38 16 | 99.85 10 | 94.75 23 | 99.18 3 | 98.65 29 |
|
MSLP-MVS++ | | | 94.28 16 | 94.39 17 | 93.97 35 | 98.30 41 | 84.06 67 | 98.64 13 | 96.93 47 | 90.71 22 | 93.08 42 | 98.70 5 | 79.98 62 | 99.21 66 | 94.12 30 | 99.07 7 | 98.63 30 |
|
Regformer-4 | | | 93.06 37 | 93.12 34 | 92.89 81 | 98.10 47 | 82.20 101 | 97.08 101 | 96.92 49 | 88.87 39 | 91.23 65 | 97.89 44 | 80.57 54 | 99.19 71 | 92.21 51 | 95.20 87 | 97.29 118 |
|
UniMVSNet (Re) | | | 85.31 183 | 84.23 180 | 88.55 204 | 89.75 238 | 80.55 140 | 96.72 123 | 96.89 50 | 85.42 90 | 78.40 206 | 88.93 221 | 75.38 133 | 95.52 251 | 78.58 169 | 68.02 295 | 89.57 238 |
|
FC-MVSNet-test | | | 85.96 166 | 85.39 154 | 87.66 227 | 89.38 247 | 78.02 226 | 95.65 187 | 96.87 51 | 85.12 99 | 77.34 219 | 91.94 181 | 76.28 111 | 94.74 276 | 77.09 187 | 78.82 229 | 90.21 226 |
|
EI-MVSNet-Vis-set | | | 91.84 60 | 91.77 56 | 92.04 114 | 97.60 60 | 81.17 125 | 96.61 132 | 96.87 51 | 88.20 48 | 89.19 90 | 97.55 61 | 78.69 78 | 99.14 77 | 90.29 70 | 90.94 130 | 95.80 158 |
|
EI-MVSNet-UG-set | | | 91.35 71 | 91.22 63 | 91.73 127 | 97.39 70 | 80.68 136 | 96.47 139 | 96.83 53 | 87.92 52 | 88.30 101 | 97.36 69 | 77.84 88 | 99.13 78 | 89.43 82 | 89.45 136 | 95.37 169 |
|
无先验 | | | | | | | | 96.87 115 | 96.78 54 | 77.39 245 | | | | 99.52 44 | 79.95 157 | | 98.43 38 |
|
SMA-MVS | | | 94.70 11 | 94.68 11 | 94.76 18 | 98.02 50 | 85.94 29 | 97.47 64 | 96.77 55 | 85.32 92 | 97.92 1 | 98.70 5 | 83.09 37 | 99.84 12 | 95.79 16 | 99.08 6 | 98.49 37 |
|
test_part1 | | | | | 0.00 359 | | 0.00 374 | 0.00 365 | 96.77 55 | | | | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
v1.0 | | | 39.63 337 | 52.84 330 | 0.00 359 | 98.90 7 | 0.00 374 | 0.00 365 | 96.77 55 | 84.95 105 | 96.07 9 | 98.83 3 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
MVS_111021_LR | | | 91.60 65 | 91.64 59 | 91.47 134 | 95.74 105 | 78.79 201 | 96.15 163 | 96.77 55 | 88.49 43 | 88.64 95 | 97.07 81 | 72.33 159 | 99.19 71 | 93.13 41 | 96.48 74 | 96.43 144 |
|
3Dnovator | | 82.32 10 | 89.33 103 | 87.64 118 | 94.42 23 | 93.73 165 | 85.70 33 | 97.73 49 | 96.75 59 | 86.73 70 | 76.21 236 | 95.93 103 | 62.17 237 | 99.68 30 | 81.67 146 | 97.81 49 | 97.88 77 |
|
ESAPD | | | 95.32 5 | 95.55 6 | 94.64 21 | 98.79 12 | 84.87 52 | 97.77 43 | 96.74 60 | 86.11 75 | 96.54 6 | 98.89 2 | 88.39 9 | 99.74 20 | 97.67 2 | 99.05 8 | 99.31 5 |
|
PVSNet_BlendedMVS | | | 90.05 92 | 89.96 81 | 90.33 161 | 97.47 64 | 83.86 69 | 98.02 31 | 96.73 61 | 87.98 51 | 89.53 86 | 89.61 215 | 76.42 107 | 99.57 40 | 94.29 28 | 79.59 222 | 87.57 285 |
|
PVSNet_Blended | | | 93.13 35 | 92.98 37 | 93.57 51 | 97.47 64 | 83.86 69 | 99.32 1 | 96.73 61 | 91.02 20 | 89.53 86 | 96.21 98 | 76.42 107 | 99.57 40 | 94.29 28 | 95.81 84 | 97.29 118 |
|
ACMMP | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 90.39 87 | 89.97 80 | 91.64 129 | 97.58 62 | 78.21 222 | 96.78 120 | 96.72 63 | 84.73 110 | 84.72 129 | 97.23 74 | 71.22 168 | 99.63 35 | 88.37 92 | 92.41 116 | 97.08 124 |
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 |
新几何1 | | | | | 93.12 71 | 97.44 66 | 81.60 119 | | 96.71 64 | 74.54 284 | 91.22 66 | 97.57 57 | 79.13 71 | 99.51 47 | 77.40 183 | 98.46 27 | 98.26 49 |
|
HFP-MVS | | | 92.89 40 | 92.86 39 | 92.98 78 | 98.71 14 | 81.12 126 | 97.58 56 | 96.70 65 | 85.20 97 | 91.75 53 | 97.97 41 | 78.47 79 | 99.71 24 | 90.95 60 | 98.41 30 | 98.12 59 |
|
#test# | | | 92.99 38 | 92.99 36 | 92.98 78 | 98.71 14 | 81.12 126 | 97.77 43 | 96.70 65 | 85.75 83 | 91.75 53 | 97.97 41 | 78.47 79 | 99.71 24 | 91.36 56 | 98.41 30 | 98.12 59 |
|
ACMMPR | | | 92.69 46 | 92.67 43 | 92.75 87 | 98.66 19 | 80.57 139 | 97.58 56 | 96.69 67 | 85.20 97 | 91.57 56 | 97.92 43 | 77.01 98 | 99.67 32 | 90.95 60 | 98.41 30 | 98.00 70 |
|
DeepPCF-MVS | | 89.82 1 | 94.61 12 | 96.17 3 | 89.91 180 | 97.09 79 | 70.21 305 | 98.99 8 | 96.69 67 | 95.57 1 | 95.08 19 | 99.23 1 | 86.40 15 | 99.87 8 | 97.84 1 | 98.66 22 | 99.65 1 |
|
thisisatest0530 | | | 89.65 98 | 89.02 97 | 91.53 132 | 93.46 170 | 80.78 134 | 96.52 136 | 96.67 69 | 81.69 177 | 83.79 142 | 94.90 136 | 88.85 7 | 97.68 137 | 77.80 172 | 87.49 156 | 96.14 152 |
|
tttt0517 | | | 88.57 122 | 88.19 108 | 89.71 188 | 93.00 178 | 75.99 259 | 95.67 185 | 96.67 69 | 80.78 190 | 81.82 172 | 94.40 144 | 88.97 6 | 97.58 146 | 76.05 198 | 86.31 163 | 95.57 165 |
|
thisisatest0515 | | | 90.95 77 | 90.26 74 | 93.01 77 | 94.03 158 | 84.27 64 | 97.91 33 | 96.67 69 | 83.18 149 | 86.87 113 | 95.51 112 | 88.66 8 | 97.85 131 | 80.46 151 | 89.01 139 | 96.92 129 |
|
1121 | | | 90.66 80 | 89.82 86 | 93.16 70 | 97.39 70 | 81.71 116 | 93.33 249 | 96.66 72 | 74.45 285 | 91.38 59 | 97.55 61 | 79.27 67 | 99.52 44 | 79.95 157 | 98.43 29 | 98.26 49 |
|
ACMMP_Plus | | | 93.46 32 | 93.23 32 | 94.17 31 | 97.16 77 | 84.28 63 | 96.82 117 | 96.65 73 | 86.24 73 | 94.27 29 | 97.99 37 | 77.94 86 | 99.83 13 | 93.39 35 | 98.57 24 | 98.39 40 |
|
TEST9 | | | | | | 98.64 22 | 83.71 73 | 97.82 38 | 96.65 73 | 84.29 127 | 95.16 16 | 98.09 29 | 84.39 23 | 99.36 56 | | | |
|
train_agg | | | 94.28 16 | 94.45 15 | 93.74 42 | 98.64 22 | 83.71 73 | 97.82 38 | 96.65 73 | 84.50 118 | 95.16 16 | 98.09 29 | 84.33 24 | 99.36 56 | 95.91 14 | 98.96 12 | 98.16 54 |
|
agg_prior3 | | | 94.10 21 | 94.29 21 | 93.53 55 | 98.62 25 | 83.03 88 | 97.80 42 | 96.64 76 | 84.28 128 | 95.01 20 | 98.03 33 | 83.40 33 | 99.41 53 | 95.91 14 | 98.96 12 | 98.16 54 |
|
1314 | | | 88.94 110 | 87.20 129 | 94.17 31 | 93.21 173 | 85.73 32 | 93.33 249 | 96.64 76 | 82.89 155 | 75.98 238 | 96.36 96 | 66.83 201 | 99.39 54 | 83.52 135 | 96.02 80 | 97.39 111 |
|
DeepC-MVS_fast | | 89.06 2 | 94.48 14 | 94.30 20 | 95.02 14 | 98.86 10 | 85.68 34 | 98.06 29 | 96.64 76 | 93.64 8 | 91.74 55 | 98.54 8 | 80.17 60 | 99.90 4 | 92.28 50 | 98.75 18 | 99.49 3 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
test_8 | | | | | | 98.63 24 | 83.64 76 | 97.81 40 | 96.63 79 | 84.50 118 | 95.10 18 | 98.11 28 | 84.33 24 | 99.23 62 | | | |
|
原ACMM1 | | | | | 91.22 141 | 97.77 57 | 78.10 225 | | 96.61 80 | 81.05 183 | 91.28 64 | 97.42 67 | 77.92 87 | 98.98 87 | 79.85 159 | 98.51 25 | 96.59 140 |
|
MAR-MVS | | | 90.63 81 | 90.22 76 | 91.86 123 | 98.47 34 | 78.20 223 | 97.18 84 | 96.61 80 | 83.87 137 | 88.18 102 | 98.18 18 | 68.71 183 | 99.75 18 | 83.66 131 | 97.15 63 | 97.63 96 |
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 |
SteuartSystems-ACMMP | | | 94.13 20 | 94.44 16 | 93.20 67 | 95.41 115 | 81.35 123 | 99.02 7 | 96.59 82 | 89.50 33 | 94.18 33 | 98.36 13 | 83.68 32 | 99.45 51 | 94.77 22 | 98.45 28 | 98.81 19 |
Skip Steuart: Steuart Systems R&D Blog. |
TESTMET0.1,1 | | | 89.83 94 | 89.34 93 | 91.31 135 | 92.54 187 | 80.19 152 | 97.11 95 | 96.57 83 | 86.15 74 | 86.85 114 | 91.83 183 | 79.32 65 | 96.95 178 | 81.30 147 | 92.35 117 | 96.77 135 |
|
agg_prior1 | | | 94.10 21 | 94.31 19 | 93.48 58 | 98.59 27 | 83.13 84 | 97.77 43 | 96.56 84 | 84.38 123 | 94.19 31 | 98.13 24 | 84.66 21 | 99.16 75 | 95.74 17 | 98.74 19 | 98.15 56 |
|
agg_prior | | | | | | 98.59 27 | 83.13 84 | | 96.56 84 | | 94.19 31 | | | 99.16 75 | | | |
|
DWT-MVSNet_test | | | 90.52 86 | 89.80 87 | 92.70 90 | 95.73 107 | 82.20 101 | 93.69 238 | 96.55 86 | 88.34 45 | 87.04 112 | 95.34 115 | 86.53 12 | 97.55 148 | 76.32 195 | 88.66 144 | 98.34 41 |
|
旧先验1 | | | | | | 97.39 70 | 79.58 173 | | 96.54 87 | | | 98.08 32 | 84.00 28 | | | 97.42 58 | 97.62 97 |
|
WR-MVS_H | | | 81.02 245 | 80.09 236 | 83.79 291 | 88.08 260 | 71.26 299 | 94.46 220 | 96.54 87 | 80.08 211 | 72.81 263 | 86.82 252 | 70.36 176 | 92.65 303 | 64.18 280 | 67.50 301 | 87.46 289 |
|
region2R | | | 92.72 45 | 92.70 42 | 92.79 85 | 98.68 16 | 80.53 142 | 97.53 60 | 96.51 89 | 85.22 95 | 91.94 51 | 97.98 39 | 77.26 94 | 99.67 32 | 90.83 63 | 98.37 34 | 98.18 51 |
|
EPP-MVSNet | | | 89.76 96 | 89.72 88 | 89.87 181 | 93.78 161 | 76.02 258 | 97.22 79 | 96.51 89 | 79.35 224 | 85.11 123 | 95.01 135 | 84.82 18 | 97.10 173 | 87.46 100 | 88.21 150 | 96.50 142 |
|
test11 | | | | | | | | | 96.50 91 | | | | | | | | |
|
EPNet_dtu | | | 87.65 140 | 87.89 112 | 86.93 242 | 94.57 136 | 71.37 296 | 96.72 123 | 96.50 91 | 88.56 41 | 87.12 110 | 95.02 134 | 75.91 116 | 94.01 290 | 66.62 267 | 90.00 134 | 95.42 168 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
testdata | | | | | 90.13 170 | 95.92 97 | 74.17 272 | | 96.49 93 | 73.49 292 | 94.82 24 | 97.99 37 | 78.80 76 | 97.93 122 | 83.53 134 | 97.52 53 | 98.29 46 |
|
HSP-MVS | | | 95.55 4 | 96.51 2 | 92.66 91 | 98.31 40 | 80.10 154 | 97.42 72 | 96.46 94 | 92.20 13 | 97.11 3 | 98.29 14 | 93.46 1 | 99.10 80 | 96.01 12 | 99.30 2 | 98.77 21 |
|
test222 | | | | | | 96.15 89 | 78.41 213 | 95.87 178 | 96.46 94 | 71.97 302 | 89.66 83 | 97.45 63 | 76.33 110 | | | 98.24 38 | 98.30 45 |
|
XVS | | | 92.69 46 | 92.71 40 | 92.63 94 | 98.52 30 | 80.29 147 | 97.37 75 | 96.44 96 | 87.04 67 | 91.38 59 | 97.83 47 | 77.24 96 | 99.59 38 | 90.46 66 | 98.07 42 | 98.02 65 |
|
X-MVStestdata | | | 86.26 164 | 84.14 181 | 92.63 94 | 98.52 30 | 80.29 147 | 97.37 75 | 96.44 96 | 87.04 67 | 91.38 59 | 20.73 367 | 77.24 96 | 99.59 38 | 90.46 66 | 98.07 42 | 98.02 65 |
|
TSAR-MVS + MP. | | | 94.79 10 | 95.17 8 | 93.64 47 | 97.66 58 | 84.10 66 | 95.85 180 | 96.42 98 | 91.26 17 | 97.49 2 | 96.80 90 | 86.50 13 | 98.49 108 | 95.54 19 | 99.03 9 | 98.33 42 |
|
APDe-MVS | | | 94.56 13 | 94.75 10 | 93.96 36 | 98.84 11 | 83.40 81 | 98.04 30 | 96.41 99 | 85.79 82 | 95.00 21 | 98.28 15 | 84.32 27 | 99.18 73 | 97.35 4 | 98.77 17 | 99.28 6 |
|
UniMVSNet_NR-MVSNet | | | 85.49 180 | 84.59 167 | 88.21 213 | 89.44 246 | 79.36 174 | 96.71 125 | 96.41 99 | 85.22 95 | 78.11 209 | 90.98 197 | 76.97 99 | 95.14 266 | 79.14 165 | 68.30 292 | 90.12 229 |
|
test_prior3 | | | 94.03 24 | 94.34 18 | 93.09 73 | 98.68 16 | 81.91 106 | 98.37 18 | 96.40 101 | 86.08 77 | 94.57 27 | 98.02 34 | 83.14 35 | 99.06 82 | 95.05 20 | 98.79 15 | 98.29 46 |
|
test_prior | | | | | 93.09 73 | 98.68 16 | 81.91 106 | | 96.40 101 | | | | | 99.06 82 | | | 98.29 46 |
|
CP-MVS | | | 92.54 51 | 92.60 45 | 92.34 101 | 98.50 32 | 79.90 157 | 98.40 17 | 96.40 101 | 84.75 109 | 90.48 73 | 98.09 29 | 77.40 93 | 99.21 66 | 91.15 59 | 98.23 39 | 97.92 76 |
|
CANet | | | 94.89 8 | 94.64 12 | 95.63 8 | 97.55 63 | 88.12 11 | 99.06 5 | 96.39 104 | 94.07 7 | 95.34 15 | 97.80 48 | 76.83 101 | 99.87 8 | 97.08 6 | 97.64 52 | 98.89 16 |
|
GST-MVS | | | 92.43 53 | 92.22 51 | 93.04 76 | 98.17 44 | 81.64 118 | 97.40 74 | 96.38 105 | 84.71 111 | 90.90 69 | 97.40 68 | 77.55 91 | 99.76 14 | 89.75 77 | 97.74 50 | 97.72 88 |
|
alignmvs | | | 92.97 39 | 92.26 49 | 95.12 13 | 95.54 111 | 87.77 15 | 98.67 11 | 96.38 105 | 88.04 50 | 93.01 43 | 97.45 63 | 79.20 70 | 98.60 102 | 93.25 40 | 88.76 142 | 98.99 15 |
|
PAPM | | | 92.87 41 | 92.40 46 | 94.30 25 | 92.25 196 | 87.85 14 | 96.40 150 | 96.38 105 | 91.07 18 | 88.72 94 | 96.90 84 | 82.11 41 | 97.37 158 | 90.05 72 | 97.70 51 | 97.67 92 |
|
test12 | | | | | 94.25 27 | 98.34 38 | 85.55 36 | | 96.35 108 | | 92.36 48 | | 80.84 51 | 99.22 64 | | 98.31 36 | 97.98 72 |
|
PatchFormer-LS_test | | | 90.14 91 | 89.30 94 | 92.65 93 | 95.43 113 | 82.46 96 | 93.46 245 | 96.35 108 | 88.56 41 | 84.82 126 | 95.22 122 | 84.63 22 | 97.55 148 | 78.40 171 | 86.81 159 | 97.94 75 |
|
zzz-MVS | | | 92.74 42 | 92.71 40 | 92.86 82 | 97.90 52 | 80.85 132 | 96.47 139 | 96.33 110 | 87.92 52 | 90.20 75 | 98.18 18 | 76.71 105 | 99.76 14 | 92.57 47 | 98.09 40 | 97.96 73 |
|
MTGPA | ![Method available as binary. binary](img/icon_binary.png) | | | | | | | | 96.33 110 | | | | | | | | |
|
MTAPA | | | 92.45 52 | 92.31 48 | 92.86 82 | 97.90 52 | 80.85 132 | 92.88 263 | 96.33 110 | 87.92 52 | 90.20 75 | 98.18 18 | 76.71 105 | 99.76 14 | 92.57 47 | 98.09 40 | 97.96 73 |
|
EPMVS | | | 87.47 142 | 85.90 151 | 92.18 108 | 95.41 115 | 82.26 100 | 87.00 319 | 96.28 113 | 85.88 81 | 84.23 134 | 85.57 275 | 75.07 140 | 96.26 202 | 71.14 236 | 92.50 114 | 98.03 64 |
|
CDPH-MVS | | | 93.12 36 | 92.91 38 | 93.74 42 | 98.65 21 | 83.88 68 | 97.67 52 | 96.26 114 | 83.00 154 | 93.22 41 | 98.24 16 | 81.31 49 | 99.21 66 | 89.12 83 | 98.74 19 | 98.14 57 |
|
WR-MVS | | | 84.32 196 | 82.96 195 | 88.41 206 | 89.38 247 | 80.32 146 | 96.59 133 | 96.25 115 | 83.97 134 | 76.63 228 | 90.36 207 | 67.53 187 | 94.86 274 | 75.82 201 | 70.09 274 | 90.06 233 |
|
UGNet | | | 87.73 139 | 86.55 142 | 91.27 138 | 95.16 122 | 79.11 185 | 96.35 152 | 96.23 116 | 88.14 49 | 87.83 105 | 90.48 203 | 50.65 304 | 99.09 81 | 80.13 156 | 94.03 96 | 95.60 164 |
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 |
tfpnnormal | | | 78.14 268 | 75.42 274 | 86.31 249 | 88.33 258 | 79.24 178 | 94.41 222 | 96.22 117 | 73.51 291 | 69.81 281 | 85.52 277 | 55.43 291 | 95.75 232 | 47.65 341 | 67.86 297 | 83.95 322 |
|
MP-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 92.61 49 | 92.67 43 | 92.42 99 | 98.13 46 | 79.73 162 | 97.33 77 | 96.20 118 | 85.63 85 | 90.53 71 | 97.66 51 | 78.14 84 | 99.70 27 | 92.12 52 | 98.30 37 | 97.85 80 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
PAPR | | | 92.74 42 | 92.17 52 | 94.45 22 | 98.89 9 | 84.87 52 | 97.20 82 | 96.20 118 | 87.73 57 | 88.40 97 | 98.12 27 | 78.71 77 | 99.76 14 | 87.99 95 | 96.28 75 | 98.74 22 |
|
SD-MVS | | | 94.84 9 | 95.02 9 | 94.29 26 | 97.87 56 | 84.61 55 | 97.76 47 | 96.19 120 | 89.59 32 | 96.66 5 | 98.17 22 | 84.33 24 | 99.60 37 | 96.09 11 | 98.50 26 | 98.66 28 |
|
CHOSEN 280x420 | | | 91.71 62 | 91.85 53 | 91.29 137 | 94.94 128 | 82.69 93 | 87.89 312 | 96.17 121 | 85.94 79 | 87.27 109 | 94.31 145 | 90.27 4 | 95.65 243 | 94.04 31 | 95.86 82 | 95.53 166 |
|
CHOSEN 1792x2688 | | | 91.07 74 | 90.21 77 | 93.64 47 | 95.18 121 | 83.53 77 | 96.26 160 | 96.13 122 | 88.92 38 | 84.90 125 | 93.10 170 | 72.86 155 | 99.62 36 | 88.86 84 | 95.67 85 | 97.79 85 |
|
PAPM_NR | | | 91.46 67 | 90.82 68 | 93.37 62 | 98.50 32 | 81.81 111 | 95.03 210 | 96.13 122 | 84.65 114 | 86.10 119 | 97.65 55 | 79.24 69 | 99.75 18 | 83.20 137 | 96.88 70 | 98.56 34 |
|
CostFormer | | | 89.08 106 | 88.39 106 | 91.15 142 | 93.13 176 | 79.15 184 | 88.61 307 | 96.11 124 | 83.14 150 | 89.58 85 | 86.93 247 | 83.83 31 | 96.87 183 | 88.22 93 | 85.92 170 | 97.42 109 |
|
mPP-MVS | | | 91.88 59 | 91.82 54 | 92.07 112 | 98.38 37 | 78.63 204 | 97.29 78 | 96.09 125 | 85.12 99 | 88.45 96 | 97.66 51 | 75.53 121 | 99.68 30 | 89.83 74 | 98.02 45 | 97.88 77 |
|
APD-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 93.61 30 | 93.59 27 | 93.69 45 | 98.76 13 | 83.26 82 | 97.21 80 | 96.09 125 | 82.41 164 | 94.65 26 | 98.21 17 | 81.96 42 | 98.81 98 | 94.65 25 | 98.36 35 | 99.01 12 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
MDTV_nov1_ep13 | | | | 83.69 184 | | 94.09 154 | 81.01 128 | 86.78 321 | 96.09 125 | 83.81 139 | 84.75 128 | 84.32 290 | 74.44 146 | 96.54 192 | 63.88 286 | 85.07 185 | |
|
QAPM | | | 86.88 152 | 84.51 168 | 93.98 34 | 94.04 156 | 85.89 30 | 97.19 83 | 96.05 128 | 73.62 290 | 75.12 248 | 95.62 110 | 62.02 240 | 99.74 20 | 70.88 237 | 96.06 79 | 96.30 151 |
|
MP-MVS-pluss | | | 92.58 50 | 92.35 47 | 93.29 63 | 97.30 75 | 82.53 95 | 96.44 144 | 96.04 129 | 84.68 113 | 89.12 91 | 98.37 12 | 77.48 92 | 99.74 20 | 93.31 39 | 98.38 33 | 97.59 99 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
tpm2 | | | 87.35 147 | 86.26 143 | 90.62 155 | 92.93 181 | 78.67 202 | 88.06 311 | 95.99 130 | 79.33 225 | 87.40 106 | 86.43 263 | 80.28 57 | 96.40 196 | 80.23 154 | 85.73 175 | 96.79 133 |
|
DeepC-MVS | | 86.58 3 | 91.53 66 | 91.06 66 | 92.94 80 | 94.52 144 | 81.89 108 | 95.95 171 | 95.98 131 | 90.76 21 | 83.76 143 | 96.76 91 | 73.24 152 | 99.71 24 | 91.67 54 | 96.96 67 | 97.22 122 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
test-LLR | | | 88.48 123 | 87.98 111 | 89.98 176 | 92.26 194 | 77.23 244 | 97.11 95 | 95.96 132 | 83.76 140 | 86.30 117 | 91.38 187 | 72.30 160 | 96.78 188 | 80.82 149 | 91.92 123 | 95.94 155 |
|
test-mter | | | 88.95 109 | 88.60 103 | 89.98 176 | 92.26 194 | 77.23 244 | 97.11 95 | 95.96 132 | 85.32 92 | 86.30 117 | 91.38 187 | 76.37 109 | 96.78 188 | 80.82 149 | 91.92 123 | 95.94 155 |
|
DP-MVS Recon | | | 91.72 61 | 90.85 67 | 94.34 24 | 99.50 1 | 85.00 47 | 98.51 16 | 95.96 132 | 80.57 198 | 88.08 103 | 97.63 56 | 76.84 100 | 99.89 6 | 85.67 110 | 94.88 91 | 98.13 58 |
|
cdsmvs_eth3d_5k | | | 21.43 344 | 28.57 345 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 95.93 135 | 0.00 369 | 0.00 371 | 97.66 51 | 63.57 228 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
tpmp4_e23 | | | 86.46 160 | 84.95 163 | 90.98 148 | 93.74 164 | 78.60 206 | 88.13 310 | 95.90 136 | 79.65 220 | 85.42 122 | 85.67 270 | 80.08 61 | 97.06 174 | 71.71 228 | 84.26 190 | 97.28 120 |
|
TAMVS | | | 88.48 123 | 87.79 115 | 90.56 156 | 91.09 219 | 79.18 182 | 96.45 142 | 95.88 137 | 83.64 143 | 83.12 151 | 93.33 166 | 75.94 115 | 95.74 235 | 82.40 142 | 88.27 149 | 96.75 137 |
|
PVSNet_Blended_VisFu | | | 91.24 72 | 90.77 69 | 92.66 91 | 95.09 123 | 82.40 97 | 97.77 43 | 95.87 138 | 88.26 47 | 86.39 115 | 93.94 153 | 76.77 102 | 99.27 59 | 88.80 86 | 94.00 99 | 96.31 150 |
|
OpenMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 79.58 14 | 86.09 165 | 83.62 187 | 93.50 56 | 90.95 221 | 86.71 24 | 97.44 66 | 95.83 139 | 75.35 268 | 72.64 264 | 95.72 106 | 57.42 277 | 99.64 34 | 71.41 231 | 95.85 83 | 94.13 190 |
|
CDS-MVSNet | | | 89.50 100 | 88.96 99 | 91.14 143 | 91.94 209 | 80.93 130 | 97.09 99 | 95.81 140 | 84.26 129 | 84.72 129 | 94.20 148 | 80.31 56 | 95.64 244 | 83.37 136 | 88.96 140 | 96.85 132 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
PS-MVSNAJ | | | 94.17 19 | 93.52 29 | 96.10 4 | 95.65 108 | 92.35 1 | 98.21 23 | 95.79 141 | 92.42 12 | 96.24 7 | 98.18 18 | 71.04 171 | 99.17 74 | 96.77 8 | 97.39 59 | 96.79 133 |
|
3Dnovator+ | | 82.88 8 | 89.63 99 | 87.85 113 | 94.99 15 | 94.49 147 | 86.76 23 | 97.84 37 | 95.74 142 | 86.10 76 | 75.47 245 | 96.02 102 | 65.00 222 | 99.51 47 | 82.91 141 | 97.07 64 | 98.72 27 |
|
HPM-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 91.62 64 | 91.53 60 | 91.89 118 | 97.88 55 | 79.22 181 | 96.99 105 | 95.73 143 | 82.07 168 | 89.50 88 | 97.19 76 | 75.59 120 | 98.93 94 | 90.91 62 | 97.94 46 | 97.54 100 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
ab-mvs | | | 87.08 149 | 84.94 164 | 93.48 58 | 93.34 172 | 83.67 75 | 88.82 304 | 95.70 144 | 81.18 181 | 84.55 132 | 90.14 211 | 62.72 234 | 98.94 93 | 85.49 112 | 82.54 213 | 97.85 80 |
|
xiu_mvs_v2_base | | | 93.92 26 | 93.26 31 | 95.91 6 | 95.07 125 | 92.02 2 | 98.19 24 | 95.68 145 | 92.06 14 | 96.01 11 | 98.14 23 | 70.83 174 | 98.96 88 | 96.74 9 | 96.57 73 | 96.76 136 |
|
CP-MVSNet | | | 81.01 246 | 80.08 237 | 83.79 291 | 87.91 262 | 70.51 302 | 94.29 229 | 95.65 146 | 80.83 189 | 72.54 266 | 88.84 222 | 63.71 227 | 92.32 306 | 68.58 257 | 68.36 291 | 88.55 263 |
|
PatchmatchNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 86.83 154 | 85.12 159 | 91.95 116 | 94.12 153 | 82.27 99 | 86.55 323 | 95.64 147 | 84.59 116 | 82.98 153 | 84.99 285 | 77.26 94 | 95.96 220 | 68.61 256 | 91.34 128 | 97.64 95 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
API-MVS | | | 90.18 90 | 88.97 98 | 93.80 40 | 98.66 19 | 82.95 91 | 97.50 63 | 95.63 148 | 75.16 272 | 86.31 116 | 97.69 50 | 72.49 157 | 99.90 4 | 81.26 148 | 96.07 78 | 98.56 34 |
|
AdaColmap | ![Method available as binary. binary](img/icon_binary.png) | | 88.81 114 | 87.61 121 | 92.39 100 | 99.33 4 | 79.95 155 | 96.70 127 | 95.58 149 | 77.51 244 | 83.05 152 | 96.69 93 | 61.90 244 | 99.72 23 | 84.29 121 | 93.47 106 | 97.50 105 |
|
Patchmatch-test1 | | | 84.89 188 | 82.76 200 | 91.27 138 | 92.30 192 | 81.86 109 | 92.88 263 | 95.56 150 | 84.85 107 | 82.52 154 | 85.19 280 | 58.04 268 | 94.21 286 | 65.93 273 | 87.58 155 | 97.74 87 |
|
dp | | | 84.30 197 | 82.31 205 | 90.28 162 | 94.24 150 | 77.97 227 | 86.57 322 | 95.53 151 | 79.94 215 | 80.75 180 | 85.16 282 | 71.49 167 | 96.39 197 | 63.73 287 | 83.36 196 | 96.48 143 |
|
HyFIR lowres test | | | 89.36 102 | 88.60 103 | 91.63 130 | 94.91 130 | 80.76 135 | 95.60 188 | 95.53 151 | 82.56 163 | 84.03 136 | 91.24 190 | 78.03 85 | 96.81 186 | 87.07 103 | 88.41 148 | 97.32 113 |
|
APD-MVS_3200maxsize | | | 91.23 73 | 91.35 62 | 90.89 150 | 97.89 54 | 76.35 255 | 96.30 158 | 95.52 153 | 79.82 217 | 91.03 68 | 97.88 46 | 74.70 144 | 98.54 105 | 92.11 53 | 96.89 69 | 97.77 86 |
|
lupinMVS | | | 93.87 28 | 93.58 28 | 94.75 19 | 93.00 178 | 88.08 12 | 99.15 4 | 95.50 154 | 91.03 19 | 94.90 22 | 97.66 51 | 78.84 74 | 97.56 147 | 94.64 26 | 97.46 54 | 98.62 31 |
|
casdiffmvs1 | | | 91.94 57 | 91.49 61 | 93.28 64 | 95.02 127 | 83.53 77 | 95.37 194 | 95.49 155 | 86.52 71 | 94.24 30 | 91.65 184 | 79.04 72 | 97.74 136 | 91.67 54 | 94.45 93 | 98.57 32 |
|
HPM-MVS_fast | | | 90.38 88 | 90.17 78 | 91.03 145 | 97.61 59 | 77.35 242 | 97.15 89 | 95.48 156 | 79.51 222 | 88.79 93 | 96.90 84 | 71.64 165 | 98.81 98 | 87.01 104 | 97.44 56 | 96.94 126 |
|
test_normal | | | 85.83 169 | 83.51 190 | 92.78 86 | 86.33 286 | 83.01 89 | 95.56 191 | 95.46 157 | 85.11 101 | 65.73 299 | 86.63 256 | 56.62 284 | 97.86 130 | 87.87 96 | 92.29 119 | 97.47 108 |
|
VPNet | | | 84.69 191 | 82.92 196 | 90.01 174 | 89.01 249 | 83.45 80 | 96.71 125 | 95.46 157 | 85.71 84 | 79.65 192 | 92.18 177 | 56.66 283 | 96.01 215 | 83.05 140 | 67.84 298 | 90.56 220 |
|
DI_MVS_plusplus_test | | | 85.92 167 | 83.61 188 | 92.86 82 | 86.43 281 | 83.20 83 | 95.57 189 | 95.46 157 | 85.10 102 | 65.99 297 | 86.84 251 | 56.70 281 | 97.89 129 | 88.10 94 | 92.33 118 | 97.48 107 |
|
114514_t | | | 88.79 116 | 87.57 122 | 92.45 98 | 98.21 43 | 81.74 113 | 96.99 105 | 95.45 160 | 75.16 272 | 82.48 155 | 95.69 108 | 68.59 184 | 98.50 107 | 80.33 152 | 95.18 89 | 97.10 123 |
|
JIA-IIPM | | | 79.00 261 | 77.20 257 | 84.40 283 | 89.74 240 | 64.06 327 | 75.30 348 | 95.44 161 | 62.15 331 | 81.90 170 | 59.08 352 | 78.92 73 | 95.59 248 | 66.51 270 | 85.78 174 | 93.54 200 |
|
DU-MVS | | | 84.57 193 | 83.33 193 | 88.28 211 | 88.76 251 | 79.36 174 | 96.43 148 | 95.41 162 | 85.42 90 | 78.11 209 | 90.82 198 | 67.61 185 | 95.14 266 | 79.14 165 | 68.30 292 | 90.33 224 |
|
EI-MVSNet | | | 85.80 170 | 85.20 157 | 87.59 229 | 91.55 213 | 77.41 240 | 95.13 206 | 95.36 163 | 80.43 202 | 80.33 186 | 94.71 139 | 73.72 150 | 95.97 216 | 76.96 190 | 78.64 231 | 89.39 240 |
|
MVSTER | | | 89.25 105 | 88.92 100 | 90.24 163 | 95.98 96 | 84.66 54 | 96.79 119 | 95.36 163 | 87.19 65 | 80.33 186 | 90.61 202 | 90.02 5 | 95.97 216 | 85.38 113 | 78.64 231 | 90.09 231 |
|
CPTT-MVS | | | 89.72 97 | 89.87 85 | 89.29 191 | 98.33 39 | 73.30 276 | 97.70 50 | 95.35 165 | 75.68 264 | 87.40 106 | 97.44 66 | 70.43 175 | 98.25 115 | 89.56 80 | 96.90 68 | 96.33 149 |
|
tpmvs | | | 83.04 218 | 80.77 227 | 89.84 182 | 95.43 113 | 77.96 228 | 85.59 328 | 95.32 166 | 75.31 270 | 76.27 235 | 83.70 299 | 73.89 148 | 97.41 155 | 59.53 299 | 81.93 214 | 94.14 189 |
|
MVS_0304 | | | 93.82 29 | 93.11 35 | 95.95 5 | 96.79 81 | 89.15 7 | 98.56 15 | 95.30 167 | 93.61 9 | 94.82 24 | 98.02 34 | 66.60 205 | 99.88 7 | 96.94 7 | 97.39 59 | 98.81 19 |
|
PS-CasMVS | | | 80.27 250 | 79.18 245 | 83.52 297 | 87.56 265 | 69.88 307 | 94.08 231 | 95.29 168 | 80.27 207 | 72.08 267 | 88.51 228 | 59.22 258 | 92.23 308 | 67.49 261 | 68.15 294 | 88.45 267 |
|
TSAR-MVS + GP. | | | 94.35 15 | 94.50 13 | 93.89 37 | 97.38 73 | 83.04 87 | 98.10 26 | 95.29 168 | 91.57 15 | 93.81 35 | 97.45 63 | 86.64 11 | 99.43 52 | 96.28 10 | 94.01 98 | 99.20 9 |
|
tpmrst | | | 88.36 127 | 87.38 127 | 91.31 135 | 94.36 148 | 79.92 156 | 87.32 315 | 95.26 170 | 85.32 92 | 88.34 98 | 86.13 268 | 80.60 53 | 96.70 190 | 83.78 125 | 85.34 183 | 97.30 116 |
|
NR-MVSNet | | | 83.35 213 | 81.52 219 | 88.84 198 | 88.76 251 | 81.31 124 | 94.45 221 | 95.16 171 | 84.65 114 | 67.81 288 | 90.82 198 | 70.36 176 | 94.87 273 | 74.75 209 | 66.89 305 | 90.33 224 |
|
jason | | | 92.73 44 | 92.23 50 | 94.21 30 | 90.50 227 | 87.30 21 | 98.65 12 | 95.09 172 | 90.61 23 | 92.76 45 | 97.13 78 | 75.28 137 | 97.30 161 | 93.32 38 | 96.75 72 | 98.02 65 |
jason: jason. |
tpm cat1 | | | 83.63 204 | 81.38 221 | 90.39 160 | 93.53 169 | 78.19 224 | 85.56 329 | 95.09 172 | 70.78 309 | 78.51 205 | 83.28 303 | 74.80 143 | 97.03 175 | 66.77 266 | 84.05 191 | 95.95 154 |
|
cascas | | | 86.50 159 | 84.48 170 | 92.55 96 | 92.64 186 | 85.95 28 | 97.04 104 | 95.07 174 | 75.32 269 | 80.50 182 | 91.02 194 | 54.33 299 | 97.98 121 | 86.79 105 | 87.62 153 | 93.71 199 |
|
abl_6 | | | 89.80 95 | 89.71 89 | 90.07 171 | 96.53 84 | 75.52 262 | 94.48 219 | 95.04 175 | 81.12 182 | 89.22 89 | 97.00 82 | 68.83 182 | 98.96 88 | 89.86 73 | 95.27 86 | 95.73 160 |
|
CVMVSNet | | | 84.83 189 | 85.57 152 | 82.63 304 | 91.55 213 | 60.38 336 | 95.13 206 | 95.03 176 | 80.60 197 | 82.10 168 | 94.71 139 | 66.40 207 | 90.19 333 | 74.30 213 | 90.32 133 | 97.31 115 |
|
test0.0.03 1 | | | 82.79 223 | 82.48 203 | 83.74 293 | 86.81 269 | 72.22 283 | 96.52 136 | 95.03 176 | 83.76 140 | 73.00 260 | 93.20 167 | 72.30 160 | 88.88 336 | 64.15 281 | 77.52 239 | 90.12 229 |
|
casdiffmvs | | | 90.98 75 | 90.24 75 | 93.19 68 | 94.60 135 | 84.15 65 | 95.01 211 | 94.98 178 | 84.98 104 | 91.53 57 | 91.14 192 | 76.72 104 | 97.62 143 | 89.78 76 | 93.42 110 | 97.81 83 |
|
PMMVS | | | 89.46 101 | 89.92 83 | 88.06 214 | 94.64 133 | 69.57 311 | 96.22 161 | 94.95 179 | 87.27 61 | 91.37 62 | 96.54 95 | 65.88 210 | 97.39 157 | 88.54 87 | 93.89 100 | 97.23 121 |
|
Anonymous20240529 | | | 83.15 216 | 80.60 230 | 90.80 152 | 95.74 105 | 78.27 217 | 96.81 118 | 94.92 180 | 60.10 341 | 81.89 171 | 92.54 174 | 45.82 322 | 98.82 97 | 79.25 164 | 78.32 235 | 95.31 171 |
|
mvs_anonymous | | | 88.68 117 | 87.62 120 | 91.86 123 | 94.80 131 | 81.69 117 | 93.53 244 | 94.92 180 | 82.03 169 | 78.87 204 | 90.43 205 | 75.77 117 | 95.34 259 | 85.04 115 | 93.16 111 | 98.55 36 |
|
CLD-MVS | | | 87.97 136 | 87.48 124 | 89.44 189 | 92.16 199 | 80.54 141 | 98.14 25 | 94.92 180 | 91.41 16 | 79.43 199 | 95.40 114 | 62.34 236 | 97.27 164 | 90.60 65 | 82.90 208 | 90.50 221 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
xiu_mvs_v1_base_debu | | | 90.54 83 | 89.54 90 | 93.55 52 | 92.31 189 | 87.58 18 | 96.99 105 | 94.87 183 | 87.23 62 | 93.27 38 | 97.56 58 | 57.43 274 | 98.32 112 | 92.72 44 | 93.46 107 | 94.74 183 |
|
xiu_mvs_v1_base | | | 90.54 83 | 89.54 90 | 93.55 52 | 92.31 189 | 87.58 18 | 96.99 105 | 94.87 183 | 87.23 62 | 93.27 38 | 97.56 58 | 57.43 274 | 98.32 112 | 92.72 44 | 93.46 107 | 94.74 183 |
|
xiu_mvs_v1_base_debi | | | 90.54 83 | 89.54 90 | 93.55 52 | 92.31 189 | 87.58 18 | 96.99 105 | 94.87 183 | 87.23 62 | 93.27 38 | 97.56 58 | 57.43 274 | 98.32 112 | 92.72 44 | 93.46 107 | 94.74 183 |
|
GA-MVS | | | 85.79 175 | 84.04 182 | 91.02 146 | 89.47 245 | 80.27 149 | 96.90 114 | 94.84 186 | 85.57 86 | 80.88 178 | 89.08 218 | 56.56 285 | 96.47 195 | 77.72 178 | 85.35 182 | 96.34 147 |
|
TranMVSNet+NR-MVSNet | | | 83.24 215 | 81.71 216 | 87.83 222 | 87.71 263 | 78.81 200 | 96.13 166 | 94.82 187 | 84.52 117 | 76.18 237 | 90.78 200 | 64.07 226 | 94.60 279 | 74.60 211 | 66.59 309 | 90.09 231 |
|
HQP3-MVS | | | | | | | | | 94.80 188 | | | | | | | 83.01 199 | |
|
HQP-MVS | | | 87.91 138 | 87.55 123 | 88.98 196 | 92.08 200 | 78.48 209 | 97.63 53 | 94.80 188 | 90.52 24 | 82.30 158 | 94.56 141 | 65.40 218 | 97.32 159 | 87.67 98 | 83.01 199 | 91.13 214 |
|
TAPA-MVS | | 81.61 12 | 85.02 185 | 83.67 185 | 89.06 193 | 96.79 81 | 73.27 278 | 95.92 173 | 94.79 190 | 74.81 279 | 80.47 183 | 96.83 88 | 71.07 170 | 98.19 118 | 49.82 337 | 92.57 113 | 95.71 162 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PEN-MVS | | | 79.47 256 | 78.26 251 | 83.08 300 | 86.36 285 | 68.58 314 | 93.85 235 | 94.77 191 | 79.76 218 | 71.37 269 | 88.55 225 | 59.79 249 | 92.46 304 | 64.50 279 | 65.40 310 | 88.19 273 |
|
pcd1.5k->3k | | | 34.11 340 | 35.46 340 | 30.05 355 | 86.70 270 | 0.00 374 | 0.00 365 | 94.74 192 | 0.00 369 | 0.00 371 | 0.00 371 | 58.13 266 | 0.00 371 | 0.00 368 | 79.56 224 | 90.14 227 |
|
HQP_MVS | | | 87.50 141 | 87.09 134 | 88.74 201 | 91.86 210 | 77.96 228 | 97.18 84 | 94.69 193 | 89.89 30 | 81.33 175 | 94.15 149 | 64.77 223 | 97.30 161 | 87.08 101 | 82.82 209 | 90.96 216 |
|
plane_prior5 | | | | | | | | | 94.69 193 | | | | | 97.30 161 | 87.08 101 | 82.82 209 | 90.96 216 |
|
tpm | | | 85.55 179 | 84.47 171 | 88.80 200 | 90.19 232 | 75.39 264 | 88.79 305 | 94.69 193 | 84.83 108 | 83.96 139 | 85.21 279 | 78.22 83 | 94.68 278 | 76.32 195 | 78.02 237 | 96.34 147 |
|
FMVSNet3 | | | 84.71 190 | 82.71 201 | 90.70 154 | 94.55 137 | 87.71 16 | 95.92 173 | 94.67 196 | 81.73 176 | 75.82 241 | 88.08 233 | 66.99 199 | 94.47 281 | 71.23 233 | 75.38 245 | 89.91 235 |
|
UA-Net | | | 88.92 111 | 88.48 105 | 90.24 163 | 94.06 155 | 77.18 246 | 93.04 260 | 94.66 197 | 87.39 60 | 91.09 67 | 93.89 154 | 74.92 141 | 98.18 119 | 75.83 200 | 91.43 127 | 95.35 170 |
|
LFMVS | | | 89.27 104 | 87.64 118 | 94.16 33 | 97.16 77 | 85.52 37 | 97.18 84 | 94.66 197 | 79.17 229 | 89.63 84 | 96.57 94 | 55.35 292 | 98.22 116 | 89.52 81 | 89.54 135 | 98.74 22 |
|
MVS_Test | | | 90.29 89 | 89.18 96 | 93.62 49 | 95.23 119 | 84.93 48 | 94.41 222 | 94.66 197 | 84.31 125 | 90.37 74 | 91.02 194 | 75.13 138 | 97.82 132 | 83.11 139 | 94.42 94 | 98.12 59 |
|
canonicalmvs | | | 92.27 54 | 91.22 63 | 95.41 11 | 95.80 104 | 88.31 9 | 97.09 99 | 94.64 200 | 88.49 43 | 92.99 44 | 97.31 70 | 72.68 156 | 98.57 104 | 93.38 37 | 88.58 145 | 99.36 4 |
|
VDDNet | | | 86.44 161 | 84.51 168 | 92.22 106 | 91.56 212 | 81.83 110 | 97.10 98 | 94.64 200 | 69.50 314 | 87.84 104 | 95.19 125 | 48.01 314 | 97.92 128 | 89.82 75 | 86.92 157 | 96.89 130 |
|
PatchT | | | 79.75 252 | 76.85 263 | 88.42 205 | 89.55 243 | 75.49 263 | 77.37 346 | 94.61 202 | 63.07 326 | 82.46 156 | 73.32 342 | 75.52 122 | 93.41 299 | 51.36 331 | 84.43 188 | 96.36 145 |
|
MS-PatchMatch | | | 83.05 217 | 81.82 212 | 86.72 245 | 89.64 241 | 79.10 186 | 94.88 214 | 94.59 203 | 79.70 219 | 70.67 275 | 89.65 214 | 50.43 306 | 96.82 185 | 70.82 240 | 95.99 81 | 84.25 318 |
|
OMC-MVS | | | 88.80 115 | 88.16 109 | 90.72 153 | 95.30 118 | 77.92 231 | 94.81 215 | 94.51 204 | 86.80 69 | 84.97 124 | 96.85 87 | 67.53 187 | 98.60 102 | 85.08 114 | 87.62 153 | 95.63 163 |
|
MVSFormer | | | 91.36 70 | 90.57 70 | 93.73 44 | 93.00 178 | 88.08 12 | 94.80 216 | 94.48 205 | 80.74 194 | 94.90 22 | 97.13 78 | 78.84 74 | 95.10 268 | 83.77 126 | 97.46 54 | 98.02 65 |
|
test_djsdf | | | 83.00 220 | 82.45 204 | 84.64 275 | 84.07 319 | 69.78 308 | 94.80 216 | 94.48 205 | 80.74 194 | 75.41 246 | 87.70 236 | 61.32 246 | 95.10 268 | 83.77 126 | 79.76 219 | 89.04 249 |
|
PCF-MVS | | 84.09 5 | 86.77 157 | 85.00 162 | 92.08 111 | 92.06 203 | 83.07 86 | 92.14 278 | 94.47 207 | 79.63 221 | 76.90 226 | 94.78 138 | 71.15 169 | 99.20 70 | 72.87 219 | 91.05 129 | 93.98 192 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
VDD-MVS | | | 88.28 129 | 87.02 135 | 92.06 113 | 95.09 123 | 80.18 153 | 97.55 59 | 94.45 208 | 83.09 151 | 89.10 92 | 95.92 104 | 47.97 315 | 98.49 108 | 93.08 42 | 86.91 158 | 97.52 103 |
|
PLC | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 83.97 7 | 88.00 135 | 87.38 127 | 89.83 183 | 98.02 50 | 76.46 253 | 97.16 88 | 94.43 209 | 79.26 228 | 81.98 169 | 96.28 97 | 69.36 180 | 99.27 59 | 77.71 179 | 92.25 120 | 93.77 198 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
diffmvs | | | 89.05 107 | 88.22 107 | 91.55 131 | 93.88 160 | 79.73 162 | 93.18 259 | 94.40 210 | 84.43 121 | 88.32 99 | 90.40 206 | 72.91 154 | 97.41 155 | 84.71 118 | 91.74 125 | 97.51 104 |
|
FMVSNet2 | | | 82.79 223 | 80.44 232 | 89.83 183 | 92.66 183 | 85.43 38 | 95.42 193 | 94.35 211 | 79.06 231 | 74.46 249 | 87.28 239 | 56.38 287 | 94.31 284 | 69.72 246 | 74.68 249 | 89.76 236 |
|
nrg030 | | | 86.79 156 | 85.43 153 | 90.87 151 | 88.76 251 | 85.34 39 | 97.06 103 | 94.33 212 | 84.31 125 | 80.45 184 | 91.98 178 | 72.36 158 | 96.36 198 | 88.48 90 | 71.13 262 | 90.93 218 |
|
ACMM | | 80.70 13 | 83.72 203 | 82.85 198 | 86.31 249 | 91.19 218 | 72.12 286 | 95.88 177 | 94.29 213 | 80.44 200 | 77.02 224 | 91.96 179 | 55.24 293 | 97.14 172 | 79.30 163 | 80.38 218 | 89.67 237 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
XXY-MVS | | | 83.84 201 | 82.00 206 | 89.35 190 | 87.13 267 | 81.38 122 | 95.72 183 | 94.26 214 | 80.15 210 | 75.92 240 | 90.63 201 | 61.96 243 | 96.52 193 | 78.98 167 | 73.28 256 | 90.14 227 |
|
diffmvs1 | | | 89.92 93 | 89.20 95 | 92.09 110 | 94.14 152 | 80.52 143 | 93.69 238 | 94.25 215 | 84.71 111 | 89.91 77 | 91.00 196 | 74.90 142 | 97.55 148 | 86.71 106 | 93.48 105 | 98.18 51 |
|
testing_2 | | | 76.96 286 | 73.18 296 | 88.30 210 | 75.87 346 | 79.64 171 | 89.92 296 | 94.21 216 | 80.16 209 | 51.23 345 | 75.94 335 | 33.94 346 | 95.81 228 | 82.28 143 | 75.12 248 | 89.46 239 |
|
OPM-MVS | | | 85.84 168 | 85.10 160 | 88.06 214 | 88.34 257 | 77.83 234 | 95.72 183 | 94.20 217 | 87.89 55 | 80.45 184 | 94.05 151 | 58.57 261 | 97.26 165 | 83.88 124 | 82.76 211 | 89.09 247 |
|
Test4 | | | 82.30 232 | 79.15 247 | 91.78 126 | 81.84 325 | 81.74 113 | 94.04 232 | 94.20 217 | 84.86 106 | 59.75 331 | 83.88 294 | 37.14 341 | 96.28 201 | 84.60 119 | 92.00 122 | 97.30 116 |
|
Vis-MVSNet (Re-imp) | | | 88.88 113 | 88.87 101 | 88.91 197 | 93.89 159 | 74.43 270 | 96.93 113 | 94.19 219 | 84.39 122 | 83.22 150 | 95.67 109 | 78.24 82 | 94.70 277 | 78.88 168 | 94.40 95 | 97.61 98 |
|
Anonymous20231211 | | | 79.72 253 | 77.19 258 | 87.33 235 | 95.59 109 | 77.16 247 | 95.18 199 | 94.18 220 | 59.31 343 | 72.57 265 | 86.20 267 | 47.89 316 | 95.66 242 | 74.53 212 | 69.24 286 | 89.18 245 |
|
v1neww | | | 83.45 207 | 81.95 207 | 87.95 219 | 86.66 271 | 79.04 189 | 96.32 154 | 94.17 221 | 80.76 191 | 77.56 212 | 87.25 242 | 67.02 197 | 96.08 207 | 77.73 175 | 70.07 275 | 88.74 261 |
|
v7new | | | 83.45 207 | 81.95 207 | 87.95 219 | 86.66 271 | 79.04 189 | 96.32 154 | 94.17 221 | 80.76 191 | 77.56 212 | 87.25 242 | 67.02 197 | 96.08 207 | 77.73 175 | 70.07 275 | 88.74 261 |
|
v6 | | | 83.45 207 | 81.94 209 | 87.95 219 | 86.62 275 | 79.03 192 | 96.32 154 | 94.17 221 | 80.76 191 | 77.57 211 | 87.23 244 | 67.03 196 | 96.09 206 | 77.73 175 | 70.06 277 | 88.75 259 |
|
PS-MVSNAJss | | | 84.91 187 | 84.30 179 | 86.74 243 | 85.89 300 | 74.40 271 | 94.95 212 | 94.16 224 | 83.93 135 | 76.45 231 | 90.11 212 | 71.04 171 | 95.77 230 | 83.16 138 | 79.02 228 | 90.06 233 |
|
v1 | | | 83.37 210 | 81.82 212 | 88.01 216 | 86.58 279 | 79.24 178 | 96.45 142 | 94.13 225 | 80.88 186 | 77.48 216 | 86.88 248 | 67.15 192 | 96.04 210 | 77.15 184 | 69.67 283 | 88.76 257 |
|
LPG-MVS_test | | | 84.20 198 | 83.49 191 | 86.33 246 | 90.88 222 | 73.06 279 | 95.28 195 | 94.13 225 | 82.20 166 | 76.31 232 | 93.20 167 | 54.83 297 | 96.95 178 | 83.72 128 | 80.83 216 | 88.98 251 |
|
LGP-MVS_train | | | | | 86.33 246 | 90.88 222 | 73.06 279 | | 94.13 225 | 82.20 166 | 76.31 232 | 93.20 167 | 54.83 297 | 96.95 178 | 83.72 128 | 80.83 216 | 88.98 251 |
|
v1141 | | | 83.36 211 | 81.81 214 | 88.01 216 | 86.61 277 | 79.26 177 | 96.44 144 | 94.12 228 | 80.88 186 | 77.48 216 | 86.87 249 | 67.08 194 | 96.03 211 | 77.14 185 | 69.69 282 | 88.75 259 |
|
divwei89l23v2f112 | | | 83.36 211 | 81.81 214 | 88.01 216 | 86.60 278 | 79.23 180 | 96.44 144 | 94.12 228 | 80.88 186 | 77.49 214 | 86.87 249 | 67.08 194 | 96.03 211 | 77.14 185 | 69.67 283 | 88.76 257 |
|
V42 | | | 83.04 218 | 81.53 218 | 87.57 231 | 86.27 290 | 79.09 187 | 95.87 178 | 94.11 230 | 80.35 204 | 77.22 222 | 86.79 254 | 65.32 220 | 96.02 214 | 77.74 174 | 70.14 270 | 87.61 284 |
|
XVG-OURS-SEG-HR | | | 85.74 176 | 85.16 158 | 87.49 233 | 90.22 231 | 71.45 295 | 91.29 288 | 94.09 231 | 81.37 179 | 83.90 141 | 95.22 122 | 60.30 248 | 97.53 153 | 85.58 111 | 84.42 189 | 93.50 201 |
|
XVG-OURS | | | 85.18 184 | 84.38 172 | 87.59 229 | 90.42 229 | 71.73 292 | 91.06 291 | 94.07 232 | 82.00 170 | 83.29 149 | 95.08 132 | 56.42 286 | 97.55 148 | 83.70 130 | 83.42 195 | 93.49 202 |
|
v2v482 | | | 83.46 206 | 81.86 211 | 88.25 212 | 86.19 292 | 79.65 170 | 96.34 153 | 94.02 233 | 81.56 178 | 77.32 220 | 88.23 230 | 65.62 213 | 96.03 211 | 77.77 173 | 69.72 281 | 89.09 247 |
|
v7 | | | 82.99 221 | 81.41 220 | 87.73 225 | 86.41 282 | 78.86 197 | 96.10 167 | 93.98 234 | 79.88 216 | 77.49 214 | 87.11 246 | 65.44 216 | 95.97 216 | 75.69 203 | 70.59 268 | 88.36 269 |
|
jajsoiax | | | 82.12 234 | 81.15 224 | 85.03 263 | 84.19 317 | 70.70 301 | 94.22 230 | 93.95 235 | 83.07 152 | 73.48 254 | 89.75 213 | 49.66 309 | 95.37 258 | 82.24 144 | 79.76 219 | 89.02 250 |
|
v1144 | | | 82.90 222 | 81.27 223 | 87.78 224 | 86.29 288 | 79.07 188 | 96.14 164 | 93.93 236 | 80.05 212 | 77.38 218 | 86.80 253 | 65.50 214 | 95.93 222 | 75.21 206 | 70.13 271 | 88.33 271 |
|
RPMNet | | | 79.32 258 | 75.75 270 | 90.06 172 | 90.16 233 | 79.75 159 | 79.02 342 | 93.92 237 | 58.43 345 | 82.27 166 | 72.55 343 | 73.03 153 | 93.67 296 | 46.10 343 | 86.25 164 | 96.75 137 |
|
UnsupCasMVSNet_eth | | | 73.25 304 | 70.57 306 | 81.30 311 | 77.53 339 | 66.33 321 | 87.24 316 | 93.89 238 | 80.38 203 | 57.90 338 | 81.59 309 | 42.91 330 | 90.56 330 | 65.18 277 | 48.51 348 | 87.01 294 |
|
v7n | | | 79.32 258 | 77.34 256 | 85.28 260 | 84.05 320 | 72.89 282 | 93.38 247 | 93.87 239 | 75.02 275 | 70.68 274 | 84.37 289 | 59.58 252 | 95.62 246 | 67.60 260 | 67.50 301 | 87.32 291 |
|
Vis-MVSNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 88.67 118 | 87.82 114 | 91.24 140 | 92.68 182 | 78.82 198 | 96.95 111 | 93.85 240 | 87.55 58 | 87.07 111 | 95.13 130 | 63.43 231 | 97.21 166 | 77.58 181 | 96.15 76 | 97.70 91 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v148 | | | 82.41 230 | 80.89 225 | 86.99 241 | 86.18 293 | 76.81 250 | 96.27 159 | 93.82 241 | 80.49 199 | 75.28 247 | 86.11 269 | 67.32 190 | 95.75 232 | 75.48 204 | 67.03 304 | 88.42 268 |
|
BH-w/o | | | 88.24 131 | 87.47 125 | 90.54 157 | 95.03 126 | 78.54 207 | 97.41 73 | 93.82 241 | 84.08 131 | 78.23 208 | 94.51 143 | 69.34 181 | 97.21 166 | 80.21 155 | 94.58 92 | 95.87 157 |
|
TR-MVS | | | 86.30 163 | 84.93 165 | 90.42 158 | 94.63 134 | 77.58 237 | 96.57 134 | 93.82 241 | 80.30 205 | 82.42 157 | 95.16 127 | 58.74 260 | 97.55 148 | 74.88 208 | 87.82 152 | 96.13 153 |
|
v1192 | | | 82.31 231 | 80.55 231 | 87.60 228 | 85.94 298 | 78.47 212 | 95.85 180 | 93.80 244 | 79.33 225 | 76.97 225 | 86.51 258 | 63.33 232 | 95.87 224 | 73.11 218 | 70.13 271 | 88.46 266 |
|
ACMP | | 81.66 11 | 84.00 199 | 83.22 194 | 86.33 246 | 91.53 215 | 72.95 281 | 95.91 175 | 93.79 245 | 83.70 142 | 73.79 252 | 92.22 176 | 54.31 300 | 96.89 182 | 83.98 123 | 79.74 221 | 89.16 246 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
v144192 | | | 82.43 227 | 80.73 228 | 87.54 232 | 85.81 301 | 78.22 219 | 95.98 169 | 93.78 246 | 79.09 230 | 77.11 223 | 86.49 259 | 64.66 225 | 95.91 223 | 74.20 214 | 69.42 285 | 88.49 264 |
|
mvs_tets | | | 81.74 237 | 80.71 229 | 84.84 267 | 84.22 316 | 70.29 304 | 93.91 234 | 93.78 246 | 82.77 157 | 73.37 255 | 89.46 216 | 47.36 319 | 95.31 261 | 81.99 145 | 79.55 225 | 88.92 255 |
|
F-COLMAP | | | 84.50 194 | 83.44 192 | 87.67 226 | 95.22 120 | 72.22 283 | 95.95 171 | 93.78 246 | 75.74 262 | 76.30 234 | 95.18 126 | 59.50 253 | 98.45 110 | 72.67 221 | 86.59 162 | 92.35 211 |
|
Fast-Effi-MVS+ | | | 87.93 137 | 86.94 138 | 90.92 149 | 94.04 156 | 79.16 183 | 98.26 21 | 93.72 249 | 81.29 180 | 83.94 140 | 92.90 171 | 69.83 179 | 96.68 191 | 76.70 191 | 91.74 125 | 96.93 127 |
|
v1921920 | | | 82.02 235 | 80.23 235 | 87.41 234 | 85.62 302 | 77.92 231 | 95.79 182 | 93.69 250 | 78.86 234 | 76.67 227 | 86.44 261 | 62.50 235 | 95.83 227 | 72.69 220 | 69.77 280 | 88.47 265 |
|
DTE-MVSNet | | | 78.37 266 | 77.06 259 | 82.32 308 | 85.22 309 | 67.17 319 | 93.40 246 | 93.66 251 | 78.71 236 | 70.53 276 | 88.29 229 | 59.06 259 | 92.23 308 | 61.38 295 | 63.28 318 | 87.56 286 |
|
v8 | | | 81.88 236 | 80.06 239 | 87.32 236 | 86.63 274 | 79.04 189 | 94.41 222 | 93.65 252 | 78.77 235 | 73.19 259 | 85.57 275 | 66.87 200 | 95.81 228 | 73.84 217 | 67.61 300 | 87.11 292 |
|
v748 | | | 78.69 264 | 76.79 264 | 84.39 284 | 83.40 323 | 70.78 300 | 93.25 255 | 93.62 253 | 74.96 276 | 69.40 283 | 83.74 296 | 59.40 254 | 95.39 256 | 68.74 254 | 64.59 312 | 86.99 295 |
|
ADS-MVSNet | | | 81.26 243 | 78.36 249 | 89.96 178 | 93.78 161 | 79.78 158 | 79.48 338 | 93.60 254 | 73.09 295 | 80.14 188 | 79.99 317 | 62.15 238 | 95.24 264 | 59.49 300 | 83.52 193 | 94.85 180 |
|
PatchMatch-RL | | | 85.00 186 | 83.66 186 | 89.02 195 | 95.86 102 | 74.55 269 | 92.49 270 | 93.60 254 | 79.30 227 | 79.29 201 | 91.47 185 | 58.53 262 | 98.45 110 | 70.22 241 | 92.17 121 | 94.07 191 |
|
anonymousdsp | | | 80.98 247 | 79.97 240 | 84.01 286 | 81.73 326 | 70.44 303 | 92.49 270 | 93.58 256 | 77.10 251 | 72.98 261 | 86.31 265 | 57.58 273 | 94.90 272 | 79.32 162 | 78.63 233 | 86.69 298 |
|
v1240 | | | 81.70 238 | 79.83 242 | 87.30 238 | 85.50 303 | 77.70 236 | 95.48 192 | 93.44 257 | 78.46 238 | 76.53 230 | 86.44 261 | 60.85 247 | 95.84 226 | 71.59 230 | 70.17 269 | 88.35 270 |
|
V4 | | | 78.70 262 | 76.95 260 | 83.95 287 | 81.66 328 | 71.34 297 | 91.94 280 | 93.44 257 | 74.69 282 | 70.35 279 | 83.73 297 | 58.07 267 | 95.50 252 | 71.84 225 | 66.86 306 | 85.02 312 |
|
v52 | | | 78.70 262 | 76.95 260 | 83.95 287 | 81.71 327 | 71.34 297 | 91.93 281 | 93.43 259 | 74.69 282 | 70.36 277 | 83.71 298 | 58.04 268 | 95.50 252 | 71.84 225 | 66.82 307 | 85.00 313 |
|
v10 | | | 81.43 241 | 79.53 244 | 87.11 239 | 86.38 283 | 78.87 196 | 94.31 225 | 93.43 259 | 77.88 240 | 73.24 258 | 85.26 278 | 65.44 216 | 95.75 232 | 72.14 224 | 67.71 299 | 86.72 297 |
|
IterMVS-LS | | | 83.93 200 | 82.80 199 | 87.31 237 | 91.46 216 | 77.39 241 | 95.66 186 | 93.43 259 | 80.44 200 | 75.51 244 | 87.26 241 | 73.72 150 | 95.16 265 | 76.99 188 | 70.72 266 | 89.39 240 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
GBi-Net | | | 82.42 228 | 80.43 233 | 88.39 207 | 92.66 183 | 81.95 103 | 94.30 226 | 93.38 262 | 79.06 231 | 75.82 241 | 85.66 271 | 56.38 287 | 93.84 292 | 71.23 233 | 75.38 245 | 89.38 242 |
|
test1 | | | 82.42 228 | 80.43 233 | 88.39 207 | 92.66 183 | 81.95 103 | 94.30 226 | 93.38 262 | 79.06 231 | 75.82 241 | 85.66 271 | 56.38 287 | 93.84 292 | 71.23 233 | 75.38 245 | 89.38 242 |
|
FMVSNet1 | | | 79.50 255 | 76.54 266 | 88.39 207 | 88.47 256 | 81.95 103 | 94.30 226 | 93.38 262 | 73.14 294 | 72.04 268 | 85.66 271 | 43.86 324 | 93.84 292 | 65.48 275 | 72.53 259 | 89.38 242 |
|
BH-untuned | | | 86.95 151 | 85.94 150 | 89.99 175 | 94.52 144 | 77.46 239 | 96.78 120 | 93.37 265 | 81.80 175 | 76.62 229 | 93.81 157 | 66.64 204 | 97.02 176 | 76.06 197 | 93.88 101 | 95.48 167 |
|
Effi-MVS+-dtu | | | 84.61 192 | 84.90 166 | 83.72 294 | 91.96 206 | 63.14 330 | 94.95 212 | 93.34 266 | 85.57 86 | 79.79 191 | 87.12 245 | 61.99 241 | 95.61 247 | 83.55 132 | 85.83 173 | 92.41 210 |
|
mvs-test1 | | | 86.83 154 | 87.17 130 | 85.81 254 | 91.96 206 | 65.24 323 | 97.90 35 | 93.34 266 | 85.57 86 | 84.51 133 | 95.14 129 | 61.99 241 | 97.19 168 | 83.55 132 | 90.55 132 | 95.00 178 |
|
CMPMVS | ![Method available as binary. binary](img/icon_binary.png) | 54.94 21 | 75.71 294 | 74.56 287 | 79.17 321 | 79.69 334 | 55.98 343 | 89.59 297 | 93.30 268 | 60.28 339 | 53.85 343 | 89.07 219 | 47.68 318 | 96.33 199 | 76.55 192 | 81.02 215 | 85.22 310 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
pmmvs4 | | | 82.54 226 | 80.79 226 | 87.79 223 | 86.11 295 | 80.49 145 | 93.55 243 | 93.18 269 | 77.29 248 | 73.35 256 | 89.40 217 | 65.26 221 | 95.05 271 | 75.32 205 | 73.61 252 | 87.83 279 |
|
XVG-ACMP-BASELINE | | | 79.38 257 | 77.90 253 | 83.81 290 | 84.98 311 | 67.14 320 | 89.03 303 | 93.18 269 | 80.26 208 | 72.87 262 | 88.15 232 | 38.55 338 | 96.26 202 | 76.05 198 | 78.05 236 | 88.02 276 |
|
testpf | | | 70.88 313 | 70.47 307 | 72.08 334 | 88.92 250 | 59.57 339 | 48.62 362 | 93.15 271 | 63.05 327 | 63.07 312 | 79.51 320 | 58.33 264 | 86.63 342 | 66.93 265 | 72.69 258 | 70.05 353 |
|
CANet_DTU | | | 90.98 75 | 90.04 79 | 93.83 39 | 94.76 132 | 86.23 26 | 96.32 154 | 93.12 272 | 93.11 10 | 93.71 36 | 96.82 89 | 63.08 233 | 99.48 49 | 84.29 121 | 95.12 90 | 95.77 159 |
|
IS-MVSNet | | | 88.67 118 | 88.16 109 | 90.20 165 | 93.61 166 | 76.86 249 | 96.77 122 | 93.07 273 | 84.02 133 | 83.62 144 | 95.60 111 | 74.69 145 | 96.24 204 | 78.43 170 | 93.66 104 | 97.49 106 |
|
UnsupCasMVSNet_bld | | | 68.60 319 | 64.50 320 | 80.92 314 | 74.63 347 | 67.80 316 | 83.97 331 | 92.94 274 | 65.12 324 | 54.63 342 | 68.23 349 | 35.97 342 | 92.17 310 | 60.13 298 | 44.83 351 | 82.78 335 |
|
MVP-Stereo | | | 82.65 225 | 81.67 217 | 85.59 257 | 86.10 296 | 78.29 216 | 93.33 249 | 92.82 275 | 77.75 241 | 69.17 286 | 87.98 234 | 59.28 257 | 95.76 231 | 71.77 227 | 96.88 70 | 82.73 336 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
Effi-MVS+ | | | 90.70 79 | 89.90 84 | 93.09 73 | 93.61 166 | 83.48 79 | 95.20 198 | 92.79 276 | 83.22 148 | 91.82 52 | 95.70 107 | 71.82 162 | 97.48 154 | 91.25 58 | 93.67 103 | 98.32 43 |
|
EU-MVSNet | | | 76.92 288 | 76.95 260 | 76.83 325 | 84.10 318 | 54.73 346 | 91.77 284 | 92.71 277 | 72.74 298 | 69.57 282 | 88.69 223 | 58.03 270 | 87.43 340 | 64.91 278 | 70.00 278 | 88.33 271 |
|
pm-mvs1 | | | 80.05 251 | 78.02 252 | 86.15 251 | 85.42 304 | 75.81 260 | 95.11 208 | 92.69 278 | 77.13 249 | 70.36 277 | 87.43 238 | 58.44 263 | 95.27 263 | 71.36 232 | 64.25 314 | 87.36 290 |
|
1112_ss | | | 88.60 121 | 87.47 125 | 92.00 115 | 93.21 173 | 80.97 129 | 96.47 139 | 92.46 279 | 83.64 143 | 80.86 179 | 97.30 72 | 80.24 58 | 97.62 143 | 77.60 180 | 85.49 176 | 97.40 110 |
|
Test_1112_low_res | | | 88.03 134 | 86.73 139 | 91.94 117 | 93.15 175 | 80.88 131 | 96.44 144 | 92.41 280 | 83.59 145 | 80.74 181 | 91.16 191 | 80.18 59 | 97.59 145 | 77.48 182 | 85.40 177 | 97.36 112 |
|
BH-RMVSNet | | | 86.84 153 | 85.28 156 | 91.49 133 | 95.35 117 | 80.26 150 | 96.95 111 | 92.21 281 | 82.86 156 | 81.77 173 | 95.46 113 | 59.34 256 | 97.64 142 | 69.79 245 | 93.81 102 | 96.57 141 |
|
test2356 | | | 74.41 299 | 74.53 288 | 74.07 332 | 76.13 345 | 54.45 347 | 94.74 218 | 92.08 282 | 71.96 303 | 65.51 301 | 83.05 305 | 56.96 278 | 83.71 350 | 52.74 328 | 77.58 238 | 84.06 320 |
|
LS3D | | | 82.22 233 | 79.94 241 | 89.06 193 | 97.43 67 | 74.06 274 | 93.20 257 | 92.05 283 | 61.90 332 | 73.33 257 | 95.21 124 | 59.35 255 | 99.21 66 | 54.54 323 | 92.48 115 | 93.90 193 |
|
EG-PatchMatch MVS | | | 74.92 296 | 72.02 300 | 83.62 295 | 83.76 322 | 73.28 277 | 93.62 241 | 92.04 284 | 68.57 316 | 58.88 333 | 83.80 295 | 31.87 350 | 95.57 250 | 56.97 310 | 78.67 230 | 82.00 342 |
|
IterMVS | | | 80.67 248 | 79.16 246 | 85.20 261 | 89.79 237 | 76.08 257 | 92.97 262 | 91.86 285 | 80.28 206 | 71.20 271 | 85.14 283 | 57.93 272 | 91.34 324 | 72.52 222 | 70.74 265 | 88.18 274 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MIMVSNet | | | 79.18 260 | 75.99 269 | 88.72 202 | 87.37 266 | 80.66 137 | 79.96 337 | 91.82 286 | 77.38 246 | 74.33 250 | 81.87 308 | 41.78 332 | 90.74 329 | 66.36 272 | 83.10 198 | 94.76 182 |
|
semantic-postprocess | | | | | 84.73 271 | 89.63 242 | 74.66 267 | | 91.81 287 | 80.05 212 | 71.06 273 | 85.18 281 | 57.98 271 | 91.40 323 | 72.48 223 | 70.70 267 | 88.12 275 |
|
our_test_3 | | | 77.90 272 | 75.37 276 | 85.48 259 | 85.39 305 | 76.74 251 | 93.63 240 | 91.67 288 | 73.39 293 | 65.72 300 | 84.65 288 | 58.20 265 | 93.13 301 | 57.82 306 | 67.87 296 | 86.57 299 |
|
pmmvs5 | | | 81.34 242 | 79.54 243 | 86.73 244 | 85.02 310 | 76.91 248 | 96.22 161 | 91.65 289 | 77.65 242 | 73.55 253 | 88.61 224 | 55.70 290 | 94.43 282 | 74.12 215 | 73.35 255 | 88.86 256 |
|
ACMH | | 75.40 17 | 77.99 269 | 74.96 279 | 87.10 240 | 90.67 225 | 76.41 254 | 93.19 258 | 91.64 290 | 72.47 301 | 63.44 309 | 87.61 237 | 43.34 327 | 97.16 169 | 58.34 304 | 73.94 251 | 87.72 280 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
Fast-Effi-MVS+-dtu | | | 83.33 214 | 82.60 202 | 85.50 258 | 89.55 243 | 69.38 312 | 96.09 168 | 91.38 291 | 82.30 165 | 75.96 239 | 91.41 186 | 56.71 280 | 95.58 249 | 75.13 207 | 84.90 186 | 91.54 212 |
|
YYNet1 | | | 73.53 303 | 70.43 308 | 82.85 302 | 84.52 314 | 71.73 292 | 91.69 286 | 91.37 292 | 67.63 317 | 46.79 349 | 81.21 311 | 55.04 295 | 90.43 331 | 55.93 319 | 59.70 325 | 86.38 301 |
|
ppachtmachnet_test | | | 77.19 283 | 74.22 291 | 86.13 252 | 85.39 305 | 78.22 219 | 93.98 233 | 91.36 293 | 71.74 305 | 67.11 291 | 84.87 286 | 56.67 282 | 93.37 300 | 52.21 329 | 64.59 312 | 86.80 296 |
|
Anonymous202405211 | | | 84.41 195 | 81.93 210 | 91.85 125 | 96.78 83 | 78.41 213 | 97.44 66 | 91.34 294 | 70.29 311 | 84.06 135 | 94.26 147 | 41.09 335 | 98.96 88 | 79.46 161 | 82.65 212 | 98.17 53 |
|
MDA-MVSNet_test_wron | | | 73.54 302 | 70.43 308 | 82.86 301 | 84.55 312 | 71.85 288 | 91.74 285 | 91.32 295 | 67.63 317 | 46.73 350 | 81.09 312 | 55.11 294 | 90.42 332 | 55.91 320 | 59.76 324 | 86.31 302 |
|
CR-MVSNet | | | 83.53 205 | 81.36 222 | 90.06 172 | 90.16 233 | 79.75 159 | 79.02 342 | 91.12 296 | 84.24 130 | 82.27 166 | 80.35 315 | 75.45 123 | 93.67 296 | 63.37 290 | 86.25 164 | 96.75 137 |
|
Patchmtry | | | 77.36 279 | 74.59 286 | 85.67 256 | 89.75 238 | 75.75 261 | 77.85 345 | 91.12 296 | 60.28 339 | 71.23 270 | 80.35 315 | 75.45 123 | 93.56 298 | 57.94 305 | 67.34 303 | 87.68 282 |
|
LTVRE_ROB | | 73.68 18 | 77.99 269 | 75.74 271 | 84.74 270 | 90.45 228 | 72.02 287 | 86.41 324 | 91.12 296 | 72.57 300 | 66.63 293 | 87.27 240 | 54.95 296 | 96.98 177 | 56.29 318 | 75.98 241 | 85.21 311 |
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 |
OurMVSNet-221017-0 | | | 77.18 284 | 76.06 268 | 80.55 315 | 83.78 321 | 60.00 337 | 90.35 293 | 91.05 299 | 77.01 253 | 66.62 294 | 87.92 235 | 47.73 317 | 94.03 289 | 71.63 229 | 68.44 290 | 87.62 283 |
|
CNLPA | | | 86.96 150 | 85.37 155 | 91.72 128 | 97.59 61 | 79.34 176 | 97.21 80 | 91.05 299 | 74.22 286 | 78.90 202 | 96.75 92 | 67.21 191 | 98.95 91 | 74.68 210 | 90.77 131 | 96.88 131 |
|
v18 | | | 77.96 271 | 75.61 272 | 84.98 264 | 86.66 271 | 79.01 193 | 93.02 261 | 90.94 301 | 75.69 263 | 63.19 310 | 77.62 324 | 67.11 193 | 92.07 311 | 70.05 242 | 56.24 330 | 83.87 323 |
|
v16 | | | 77.84 273 | 75.47 273 | 84.93 266 | 86.62 275 | 78.93 195 | 92.84 265 | 90.89 302 | 75.50 266 | 63.03 314 | 77.54 325 | 66.82 202 | 92.04 312 | 69.82 243 | 56.22 331 | 83.82 325 |
|
pmmvs6 | | | 74.65 298 | 71.67 301 | 83.60 296 | 79.13 336 | 69.94 306 | 93.31 253 | 90.88 303 | 61.05 338 | 65.83 298 | 84.15 292 | 43.43 326 | 94.83 275 | 66.62 267 | 60.63 322 | 86.02 307 |
|
v17 | | | 77.79 274 | 75.41 275 | 84.94 265 | 86.53 280 | 78.94 194 | 92.83 266 | 90.88 303 | 75.51 265 | 62.97 315 | 77.50 326 | 66.69 203 | 92.03 313 | 69.80 244 | 56.01 332 | 83.83 324 |
|
v15 | | | 77.52 276 | 75.09 277 | 84.82 268 | 86.37 284 | 78.82 198 | 92.58 268 | 90.78 305 | 75.47 267 | 62.53 317 | 77.17 327 | 66.58 206 | 91.92 314 | 69.18 247 | 55.16 334 | 83.73 326 |
|
V14 | | | 77.43 278 | 74.99 278 | 84.75 269 | 86.32 287 | 78.67 202 | 92.44 272 | 90.77 306 | 75.28 271 | 62.42 318 | 77.13 328 | 66.40 207 | 91.88 315 | 69.01 252 | 55.14 335 | 83.70 327 |
|
V9 | | | 77.32 280 | 74.87 281 | 84.69 272 | 86.26 291 | 78.52 208 | 92.33 275 | 90.72 307 | 75.11 274 | 62.21 320 | 77.08 330 | 66.19 209 | 91.87 316 | 68.84 253 | 55.06 337 | 83.69 328 |
|
Anonymous20231206 | | | 75.29 295 | 73.64 294 | 80.22 316 | 80.75 329 | 63.38 329 | 93.36 248 | 90.71 308 | 73.09 295 | 67.12 290 | 83.70 299 | 50.33 307 | 90.85 328 | 53.63 326 | 70.10 273 | 86.44 300 |
|
v12 | | | 77.20 282 | 74.73 282 | 84.63 276 | 86.15 294 | 78.41 213 | 92.17 277 | 90.71 308 | 74.92 277 | 62.05 322 | 77.00 331 | 65.83 211 | 91.83 317 | 68.69 255 | 55.01 338 | 83.64 329 |
|
v13 | | | 77.11 285 | 74.63 285 | 84.55 278 | 86.08 297 | 78.27 217 | 92.06 279 | 90.68 310 | 74.73 280 | 61.86 325 | 76.93 332 | 65.73 212 | 91.81 320 | 68.55 258 | 55.07 336 | 83.59 330 |
|
v11 | | | 77.21 281 | 74.72 283 | 84.68 273 | 86.29 288 | 78.62 205 | 92.30 276 | 90.63 311 | 74.86 278 | 62.32 319 | 76.73 333 | 65.49 215 | 91.83 317 | 68.17 259 | 55.72 333 | 83.59 330 |
|
USDC | | | 78.65 265 | 76.25 267 | 85.85 253 | 87.58 264 | 74.60 268 | 89.58 298 | 90.58 312 | 84.05 132 | 63.13 311 | 88.23 230 | 40.69 337 | 96.86 184 | 66.57 269 | 75.81 243 | 86.09 306 |
|
MSDG | | | 80.62 249 | 77.77 254 | 89.14 192 | 93.43 171 | 77.24 243 | 91.89 282 | 90.18 313 | 69.86 313 | 68.02 287 | 91.94 181 | 52.21 302 | 98.84 96 | 59.32 302 | 83.12 197 | 91.35 213 |
|
ACMH+ | | 76.62 16 | 77.47 277 | 74.94 280 | 85.05 262 | 91.07 220 | 71.58 294 | 93.26 254 | 90.01 314 | 71.80 304 | 64.76 304 | 88.55 225 | 41.62 333 | 96.48 194 | 62.35 293 | 71.00 263 | 87.09 293 |
|
FMVSNet5 | | | 76.46 290 | 74.16 292 | 83.35 299 | 90.05 235 | 76.17 256 | 89.58 298 | 89.85 315 | 71.39 308 | 65.29 303 | 80.42 314 | 50.61 305 | 87.70 339 | 61.05 296 | 69.24 286 | 86.18 304 |
|
ambc | | | | | 76.02 328 | 68.11 353 | 51.43 349 | 64.97 357 | 89.59 316 | | 60.49 329 | 74.49 336 | 17.17 359 | 92.46 304 | 61.50 294 | 52.85 343 | 84.17 319 |
|
ITE_SJBPF | | | | | 82.38 306 | 87.00 268 | 65.59 322 | | 89.55 317 | 79.99 214 | 69.37 284 | 91.30 189 | 41.60 334 | 95.33 260 | 62.86 292 | 74.63 250 | 86.24 303 |
|
pmmvs-eth3d | | | 73.59 301 | 70.66 305 | 82.38 306 | 76.40 343 | 73.38 275 | 89.39 302 | 89.43 318 | 72.69 299 | 60.34 330 | 77.79 323 | 46.43 321 | 91.26 326 | 66.42 271 | 57.06 327 | 82.51 337 |
|
test20.03 | | | 72.36 309 | 71.15 303 | 75.98 329 | 77.79 338 | 59.16 340 | 92.40 273 | 89.35 319 | 74.09 287 | 61.50 326 | 84.32 290 | 48.09 313 | 85.54 348 | 50.63 335 | 62.15 320 | 83.24 332 |
|
SixPastTwentyTwo | | | 76.04 291 | 74.32 290 | 81.22 312 | 84.54 313 | 61.43 335 | 91.16 289 | 89.30 320 | 77.89 239 | 64.04 306 | 86.31 265 | 48.23 312 | 94.29 285 | 63.54 289 | 63.84 316 | 87.93 278 |
|
TransMVSNet (Re) | | | 76.94 287 | 74.38 289 | 84.62 277 | 85.92 299 | 75.25 265 | 95.28 195 | 89.18 321 | 73.88 289 | 67.22 289 | 86.46 260 | 59.64 250 | 94.10 288 | 59.24 303 | 52.57 344 | 84.50 317 |
|
MIMVSNet1 | | | 69.44 315 | 66.65 317 | 77.84 322 | 76.48 342 | 62.84 331 | 87.42 314 | 88.97 322 | 66.96 322 | 57.75 339 | 79.72 319 | 32.77 349 | 85.83 345 | 46.32 342 | 63.42 317 | 84.85 315 |
|
K. test v3 | | | 73.62 300 | 71.59 302 | 79.69 318 | 82.98 324 | 59.85 338 | 90.85 292 | 88.83 323 | 77.13 249 | 58.90 332 | 82.11 306 | 43.62 325 | 91.72 321 | 65.83 274 | 54.10 341 | 87.50 288 |
|
Baseline_NR-MVSNet | | | 81.22 244 | 80.07 238 | 84.68 273 | 85.32 308 | 75.12 266 | 96.48 138 | 88.80 324 | 76.24 254 | 77.28 221 | 86.40 264 | 67.61 185 | 94.39 283 | 75.73 202 | 66.73 308 | 84.54 316 |
|
MDA-MVSNet-bldmvs | | | 71.45 311 | 67.94 314 | 81.98 310 | 85.33 307 | 68.50 315 | 92.35 274 | 88.76 325 | 70.40 310 | 42.99 351 | 81.96 307 | 46.57 320 | 91.31 325 | 48.75 340 | 54.39 340 | 86.11 305 |
|
new-patchmatchnet | | | 68.85 318 | 65.93 318 | 77.61 323 | 73.57 350 | 63.94 328 | 90.11 295 | 88.73 326 | 71.62 306 | 55.08 341 | 73.60 338 | 40.84 336 | 87.22 341 | 51.35 332 | 48.49 349 | 81.67 343 |
|
Patchmatch-test | | | 78.25 267 | 74.72 283 | 88.83 199 | 91.20 217 | 74.10 273 | 73.91 352 | 88.70 327 | 59.89 342 | 66.82 292 | 85.12 284 | 78.38 81 | 94.54 280 | 48.84 339 | 79.58 223 | 97.86 79 |
|
OpenMVS_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 68.52 20 | 73.02 306 | 69.57 310 | 83.37 298 | 80.54 332 | 71.82 289 | 93.60 242 | 88.22 328 | 62.37 330 | 61.98 323 | 83.15 304 | 35.31 345 | 95.47 254 | 45.08 344 | 75.88 242 | 82.82 334 |
|
test1235678 | | | 64.50 324 | 62.19 324 | 71.42 335 | 66.82 355 | 48.00 352 | 89.44 300 | 87.90 329 | 62.82 329 | 49.12 348 | 71.31 347 | 30.14 353 | 82.19 352 | 41.88 347 | 60.32 323 | 84.06 320 |
|
RPSCF | | | 77.73 275 | 76.63 265 | 81.06 313 | 88.66 255 | 55.76 345 | 87.77 313 | 87.88 330 | 64.82 325 | 74.14 251 | 92.79 172 | 49.22 311 | 96.81 186 | 67.47 262 | 76.88 240 | 90.62 219 |
|
MVS-HIRNet | | | 71.36 312 | 67.00 315 | 84.46 282 | 90.58 226 | 69.74 309 | 79.15 341 | 87.74 331 | 46.09 354 | 61.96 324 | 50.50 355 | 45.14 323 | 95.64 244 | 53.74 325 | 88.11 151 | 88.00 277 |
|
testus | | | 70.06 314 | 69.09 312 | 72.98 333 | 74.54 348 | 51.28 351 | 93.78 236 | 87.34 332 | 71.49 307 | 62.69 316 | 83.46 301 | 24.44 355 | 84.77 349 | 51.22 333 | 72.85 257 | 82.90 333 |
|
1111 | | | 65.60 323 | 64.33 321 | 69.41 336 | 68.26 351 | 45.11 356 | 87.06 317 | 87.32 333 | 54.99 349 | 51.20 346 | 73.45 339 | 63.57 228 | 85.70 346 | 36.53 351 | 56.59 329 | 77.42 347 |
|
.test1245 | | | 54.61 328 | 58.07 327 | 44.24 351 | 68.26 351 | 45.11 356 | 87.06 317 | 87.32 333 | 54.99 349 | 51.20 346 | 73.45 339 | 63.57 228 | 85.70 346 | 36.53 351 | 0.21 366 | 1.91 366 |
|
DP-MVS | | | 81.47 240 | 78.28 250 | 91.04 144 | 98.14 45 | 78.48 209 | 95.09 209 | 86.97 335 | 61.14 337 | 71.12 272 | 92.78 173 | 59.59 251 | 99.38 55 | 53.11 327 | 86.61 161 | 95.27 172 |
|
COLMAP_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 73.24 19 | 75.74 293 | 73.00 298 | 83.94 289 | 92.38 188 | 69.08 313 | 91.85 283 | 86.93 336 | 61.48 335 | 65.32 302 | 90.27 208 | 42.27 331 | 96.93 181 | 50.91 334 | 75.63 244 | 85.80 308 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
test_0402 | | | 72.68 307 | 69.54 311 | 82.09 309 | 88.67 254 | 71.81 290 | 92.72 267 | 86.77 337 | 61.52 334 | 62.21 320 | 83.91 293 | 43.22 328 | 93.76 295 | 34.60 353 | 72.23 260 | 80.72 344 |
|
testgi | | | 74.88 297 | 73.40 295 | 79.32 320 | 80.13 333 | 61.75 333 | 93.21 256 | 86.64 338 | 79.49 223 | 66.56 295 | 91.06 193 | 35.51 344 | 88.67 337 | 56.79 311 | 71.25 261 | 87.56 286 |
|
TDRefinement | | | 69.20 317 | 65.78 319 | 79.48 319 | 66.04 356 | 62.21 332 | 88.21 309 | 86.12 339 | 62.92 328 | 61.03 328 | 85.61 274 | 33.23 347 | 94.16 287 | 55.82 321 | 53.02 342 | 82.08 341 |
|
ADS-MVSNet2 | | | 79.57 254 | 77.53 255 | 85.71 255 | 93.78 161 | 72.13 285 | 79.48 338 | 86.11 340 | 73.09 295 | 80.14 188 | 79.99 317 | 62.15 238 | 90.14 334 | 59.49 300 | 83.52 193 | 94.85 180 |
|
LF4IMVS | | | 72.36 309 | 70.82 304 | 76.95 324 | 79.18 335 | 56.33 342 | 86.12 325 | 86.11 340 | 69.30 315 | 63.06 313 | 86.66 255 | 33.03 348 | 92.25 307 | 65.33 276 | 68.64 289 | 82.28 340 |
|
LP | | | 68.54 320 | 63.92 322 | 82.39 305 | 87.93 261 | 71.79 291 | 72.37 355 | 86.01 342 | 55.89 348 | 58.33 336 | 71.46 346 | 49.58 310 | 90.10 335 | 32.25 355 | 61.48 321 | 85.27 309 |
|
TinyColmap | | | 72.41 308 | 68.99 313 | 82.68 303 | 88.11 259 | 69.59 310 | 88.41 308 | 85.20 343 | 65.55 323 | 57.91 337 | 84.82 287 | 30.80 352 | 95.94 221 | 51.38 330 | 68.70 288 | 82.49 339 |
|
pmmvs3 | | | 65.75 322 | 62.18 325 | 76.45 327 | 67.12 354 | 64.54 324 | 88.68 306 | 85.05 344 | 54.77 352 | 57.54 340 | 73.79 337 | 29.40 354 | 86.21 344 | 55.49 322 | 47.77 350 | 78.62 345 |
|
new_pmnet | | | 66.18 321 | 63.18 323 | 75.18 331 | 76.27 344 | 61.74 334 | 83.79 332 | 84.66 345 | 56.64 347 | 51.57 344 | 71.85 345 | 31.29 351 | 87.93 338 | 49.98 336 | 62.55 319 | 75.86 348 |
|
AllTest | | | 75.92 292 | 73.06 297 | 84.47 280 | 92.18 197 | 67.29 317 | 91.07 290 | 84.43 346 | 67.63 317 | 63.48 307 | 90.18 209 | 38.20 339 | 97.16 169 | 57.04 308 | 73.37 253 | 88.97 253 |
|
TestCases | | | | | 84.47 280 | 92.18 197 | 67.29 317 | | 84.43 346 | 67.63 317 | 63.48 307 | 90.18 209 | 38.20 339 | 97.16 169 | 57.04 308 | 73.37 253 | 88.97 253 |
|
test12356 | | | 58.24 326 | 56.06 328 | 64.77 339 | 60.65 357 | 39.42 362 | 82.78 335 | 84.34 348 | 57.47 346 | 42.65 352 | 69.10 348 | 19.21 357 | 81.18 353 | 38.97 350 | 49.40 345 | 73.69 349 |
|
LCM-MVSNet-Re | | | 83.75 202 | 83.54 189 | 84.39 284 | 93.54 168 | 64.14 326 | 92.51 269 | 84.03 349 | 83.90 136 | 66.14 296 | 86.59 257 | 67.36 189 | 92.68 302 | 84.89 117 | 92.87 112 | 96.35 146 |
|
testmv | | | 54.58 329 | 51.53 331 | 63.74 343 | 53.58 362 | 40.82 360 | 83.26 333 | 83.92 350 | 54.07 353 | 36.35 355 | 61.26 350 | 14.80 361 | 77.07 355 | 33.00 354 | 43.53 354 | 73.33 350 |
|
Gipuma | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 45.11 334 | 42.05 335 | 54.30 347 | 80.69 330 | 51.30 350 | 35.80 363 | 83.81 351 | 28.13 359 | 27.94 360 | 34.53 361 | 11.41 366 | 76.70 358 | 21.45 360 | 54.65 339 | 34.90 362 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
LCM-MVSNet | | | 52.52 330 | 48.24 332 | 65.35 338 | 47.63 366 | 41.45 359 | 72.55 354 | 83.62 352 | 31.75 357 | 37.66 354 | 57.92 353 | 9.19 368 | 76.76 357 | 49.26 338 | 44.60 352 | 77.84 346 |
|
FPMVS | | | 55.09 327 | 52.93 329 | 61.57 344 | 55.98 358 | 40.51 361 | 83.11 334 | 83.41 353 | 37.61 356 | 34.95 356 | 71.95 344 | 14.40 362 | 76.95 356 | 29.81 357 | 65.16 311 | 67.25 355 |
|
Patchmatch-RL test | | | 76.65 289 | 74.01 293 | 84.55 278 | 77.37 341 | 64.23 325 | 78.49 344 | 82.84 354 | 78.48 237 | 64.63 305 | 73.40 341 | 76.05 114 | 91.70 322 | 76.99 188 | 57.84 326 | 97.72 88 |
|
DSMNet-mixed | | | 73.13 305 | 72.45 299 | 75.19 330 | 77.51 340 | 46.82 353 | 85.09 330 | 82.01 355 | 67.61 321 | 69.27 285 | 81.33 310 | 50.89 303 | 86.28 343 | 54.54 323 | 83.80 192 | 92.46 209 |
|
lessismore_v0 | | | | | 79.98 317 | 80.59 331 | 58.34 341 | | 80.87 356 | | 58.49 334 | 83.46 301 | 43.10 329 | 93.89 291 | 63.11 291 | 48.68 347 | 87.72 280 |
|
no-one | | | 51.12 331 | 45.81 334 | 67.03 337 | 53.16 364 | 52.22 348 | 75.21 349 | 80.40 357 | 54.89 351 | 28.26 359 | 48.50 357 | 15.54 360 | 82.81 351 | 39.29 349 | 17.06 359 | 66.07 356 |
|
door | | | | | | | | | 80.13 358 | | | | | | | | |
|
door-mid | | | | | | | | | 79.75 359 | | | | | | | | |
|
PM-MVS | | | 69.32 316 | 66.93 316 | 76.49 326 | 73.60 349 | 55.84 344 | 85.91 326 | 79.32 360 | 74.72 281 | 61.09 327 | 78.18 322 | 21.76 356 | 91.10 327 | 70.86 238 | 56.90 328 | 82.51 337 |
|
ANet_high | | | 46.22 333 | 41.28 337 | 61.04 345 | 39.91 369 | 46.25 355 | 70.59 356 | 76.18 361 | 58.87 344 | 23.09 361 | 48.00 358 | 12.58 364 | 66.54 362 | 28.65 358 | 13.62 362 | 70.35 352 |
|
PMMVS2 | | | 50.90 332 | 46.31 333 | 64.67 340 | 55.53 359 | 46.67 354 | 77.30 347 | 71.02 362 | 40.89 355 | 34.16 357 | 59.32 351 | 9.83 367 | 76.14 359 | 40.09 348 | 28.63 356 | 71.21 351 |
|
wuykxyi23d | | | 37.75 339 | 31.85 342 | 55.46 346 | 40.00 368 | 38.01 363 | 59.81 359 | 69.47 363 | 25.46 361 | 12.42 367 | 30.55 365 | 2.06 372 | 67.08 361 | 31.81 356 | 15.03 360 | 61.29 357 |
|
MTMP | | | | | | | | 97.53 60 | 68.16 364 | | | | | | | | |
|
DeepMVS_CX | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | | | 64.06 342 | 78.53 337 | 43.26 358 | | 68.11 365 | 69.94 312 | 38.55 353 | 76.14 334 | 18.53 358 | 79.34 354 | 43.72 345 | 41.62 355 | 69.57 354 |
|
PMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 34.80 23 | 39.19 338 | 35.53 339 | 50.18 349 | 29.72 370 | 30.30 366 | 59.60 360 | 66.20 366 | 26.06 360 | 17.91 364 | 49.53 356 | 3.12 370 | 74.09 360 | 18.19 362 | 49.40 345 | 46.14 359 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
PNet_i23d | | | 41.20 336 | 38.13 338 | 50.41 348 | 55.23 360 | 36.24 365 | 73.80 353 | 65.45 367 | 29.75 358 | 21.36 362 | 47.05 359 | 3.43 369 | 63.23 363 | 28.17 359 | 18.92 358 | 51.76 358 |
|
tmp_tt | | | 41.54 335 | 41.93 336 | 40.38 352 | 20.10 371 | 26.84 367 | 61.93 358 | 59.09 368 | 14.81 365 | 28.51 358 | 80.58 313 | 35.53 343 | 48.33 367 | 63.70 288 | 13.11 363 | 45.96 361 |
|
MVE | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | 35.65 22 | 33.85 341 | 29.49 344 | 46.92 350 | 41.86 367 | 36.28 364 | 50.45 361 | 56.52 369 | 18.75 364 | 18.28 363 | 37.84 360 | 2.41 371 | 58.41 364 | 18.71 361 | 20.62 357 | 46.06 360 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
E-PMN | | | 32.70 342 | 32.39 341 | 33.65 353 | 53.35 363 | 25.70 368 | 74.07 351 | 53.33 370 | 21.08 362 | 17.17 365 | 33.63 363 | 11.85 365 | 54.84 365 | 12.98 363 | 14.04 361 | 20.42 363 |
|
EMVS | | | 31.70 343 | 31.45 343 | 32.48 354 | 50.72 365 | 23.95 369 | 74.78 350 | 52.30 371 | 20.36 363 | 16.08 366 | 31.48 364 | 12.80 363 | 53.60 366 | 11.39 364 | 13.10 364 | 19.88 364 |
|
N_pmnet | | | 61.30 325 | 60.20 326 | 64.60 341 | 84.32 315 | 17.00 371 | 91.67 287 | 10.98 372 | 61.77 333 | 58.45 335 | 78.55 321 | 49.89 308 | 91.83 317 | 42.27 346 | 63.94 315 | 84.97 314 |
|
wuyk23d | | | 14.10 345 | 13.89 346 | 14.72 356 | 55.23 360 | 22.91 370 | 33.83 364 | 3.56 373 | 4.94 366 | 4.11 368 | 2.28 370 | 2.06 372 | 19.66 368 | 10.23 365 | 8.74 365 | 1.59 368 |
|
testmvs | | | 9.92 346 | 12.94 347 | 0.84 358 | 0.65 372 | 0.29 373 | 93.78 236 | 0.39 374 | 0.42 367 | 2.85 369 | 15.84 368 | 0.17 375 | 0.30 370 | 2.18 366 | 0.21 366 | 1.91 366 |
|
test123 | | | 9.07 347 | 11.73 348 | 1.11 357 | 0.50 373 | 0.77 372 | 89.44 300 | 0.20 375 | 0.34 368 | 2.15 370 | 10.72 369 | 0.34 374 | 0.32 369 | 1.79 367 | 0.08 368 | 2.23 365 |
|
pcd_1.5k_mvsjas | | | 5.92 349 | 7.89 350 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 71.04 171 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
sosnet-low-res | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
sosnet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
uncertanet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
Regformer | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
n2 | | | | | | | | | 0.00 376 | | | | | | | | |
|
nn | | | | | | | | | 0.00 376 | | | | | | | | |
|
ab-mvs-re | | | 8.11 348 | 10.81 349 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 97.30 72 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
uanet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
GSMVS | | | | | | | | | | | | | | | | | 97.54 100 |
|
test_part2 | | | | | | 98.90 7 | 85.14 46 | | | | 96.07 9 | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 77.59 90 | | | | 97.54 100 |
|
sam_mvs | | | | | | | | | | | | | 75.35 136 | | | | |
|
test_post1 | | | | | | | | 85.88 327 | | | | 30.24 366 | 73.77 149 | 95.07 270 | 73.89 216 | | |
|
test_post | | | | | | | | | | | | 33.80 362 | 76.17 112 | 95.97 216 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 77.09 329 | 77.78 89 | 95.39 256 | | | |
|
gm-plane-assit | | | | | | 92.27 193 | 79.64 171 | | | 84.47 120 | | 95.15 128 | | 97.93 122 | 85.81 109 | | |
|
test9_res | | | | | | | | | | | | | | | 96.00 13 | 99.03 9 | 98.31 44 |
|
agg_prior2 | | | | | | | | | | | | | | | 94.30 27 | 99.00 11 | 98.57 32 |
|
test_prior4 | | | | | | | 82.34 98 | 97.75 48 | | | | | | | | | |
|
test_prior2 | | | | | | | | 98.37 18 | | 86.08 77 | 94.57 27 | 98.02 34 | 83.14 35 | | 95.05 20 | 98.79 15 | |
|
旧先验2 | | | | | | | | 96.97 110 | | 74.06 288 | 96.10 8 | | | 97.76 135 | 88.38 91 | | |
|
新几何2 | | | | | | | | 96.42 149 | | | | | | | | | |
|
原ACMM2 | | | | | | | | 96.84 116 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 99.48 49 | 76.45 194 | | |
|
segment_acmp | | | | | | | | | | | | | 82.69 38 | | | | |
|
testdata1 | | | | | | | | 95.57 189 | | 87.44 59 | | | | | | | |
|
plane_prior7 | | | | | | 91.86 210 | 77.55 238 | | | | | | | | | | |
|
plane_prior6 | | | | | | 91.98 205 | 77.92 231 | | | | | | 64.77 223 | | | | |
|
plane_prior4 | | | | | | | | | | | | 94.15 149 | | | | | |
|
plane_prior3 | | | | | | | 77.75 235 | | | 90.17 28 | 81.33 175 | | | | | | |
|
plane_prior2 | | | | | | | | 97.18 84 | | 89.89 30 | | | | | | | |
|
plane_prior1 | | | | | | 91.95 208 | | | | | | | | | | | |
|
plane_prior | | | | | | | 77.96 228 | 97.52 62 | | 90.36 27 | | | | | | 82.96 201 | |
|
HQP5-MVS | | | | | | | 78.48 209 | | | | | | | | | | |
|
HQP-NCC | | | | | | 92.08 200 | | 97.63 53 | | 90.52 24 | 82.30 158 | | | | | | |
|
ACMP_Plane | | | | | | 92.08 200 | | 97.63 53 | | 90.52 24 | 82.30 158 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 87.67 98 | | |
|
HQP4-MVS | | | | | | | | | | | 82.30 158 | | | 97.32 159 | | | 91.13 214 |
|
HQP2-MVS | | | | | | | | | | | | | 65.40 218 | | | | |
|
NP-MVS | | | | | | 92.04 204 | 78.22 219 | | | | | 94.56 141 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 81.74 113 | 86.80 320 | | 80.65 196 | 85.65 120 | | 74.26 147 | | 76.52 193 | | 96.98 125 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 78.45 234 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 79.05 227 | |
|
Test By Simon | | | | | | | | | | | | | 71.65 164 | | | | |
|