MG-MVS | | | 78.42 17 | 76.99 30 | 82.73 2 | 93.17 1 | 64.46 1 | 89.93 22 | 88.51 28 | 64.83 65 | 73.52 36 | 88.09 94 | 48.07 43 | 92.19 37 | 62.24 120 | 84.53 36 | 91.53 41 |
|
MCST-MVS | | | 83.01 1 | 83.30 2 | 82.15 5 | 92.84 2 | 57.58 15 | 93.77 1 | 91.10 4 | 75.95 2 | 77.10 18 | 93.09 9 | 54.15 12 | 95.57 3 | 85.80 1 | 85.87 21 | 93.31 6 |
|
MAR-MVS | | | 76.76 38 | 75.60 43 | 80.21 20 | 90.87 3 | 54.68 68 | 89.14 32 | 89.11 16 | 62.95 96 | 70.54 63 | 92.33 17 | 41.05 146 | 94.95 7 | 57.90 154 | 86.55 16 | 91.00 52 |
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 |
DP-MVS Recon | | | 71.99 98 | 70.31 102 | 77.01 90 | 90.65 4 | 53.44 93 | 89.37 29 | 82.97 155 | 56.33 215 | 63.56 131 | 89.47 74 | 34.02 224 | 92.15 40 | 54.05 187 | 72.41 133 | 85.43 160 |
|
CNVR-MVS | | | 81.76 4 | 81.90 4 | 81.33 9 | 90.04 5 | 57.70 13 | 91.71 3 | 88.87 19 | 70.31 14 | 77.64 17 | 93.87 2 | 52.58 19 | 93.91 16 | 84.17 2 | 87.92 11 | 92.39 21 |
|
API-MVS | | | 74.17 67 | 72.07 81 | 80.49 15 | 90.02 6 | 58.55 7 | 87.30 59 | 84.27 116 | 57.51 193 | 65.77 99 | 87.77 100 | 41.61 143 | 95.97 2 | 51.71 206 | 82.63 45 | 86.94 132 |
|
LFMVS | | | 78.52 15 | 77.14 28 | 82.67 3 | 89.58 7 | 58.90 6 | 91.27 11 | 88.05 38 | 63.22 93 | 74.63 28 | 90.83 48 | 41.38 145 | 94.40 11 | 75.42 37 | 79.90 72 | 94.72 1 |
|
NCCC | | | 79.57 13 | 79.23 13 | 80.59 14 | 89.50 8 | 56.99 21 | 91.38 8 | 88.17 37 | 67.71 29 | 73.81 32 | 92.75 13 | 46.88 55 | 93.28 20 | 78.79 16 | 84.07 39 | 91.50 43 |
|
test_part2 | | | | | | 89.33 9 | 55.48 40 | | | | 82.27 2 | | | | | | |
|
v1.0 | | | 34.14 330 | 45.52 314 | 0.00 359 | 89.33 9 | 0.00 373 | 0.00 365 | 88.42 30 | 56.02 218 | 82.27 2 | 93.65 4 | 0.00 374 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
ESAPD | | | 79.82 12 | 79.66 10 | 80.29 17 | 89.27 11 | 55.08 55 | 88.70 37 | 87.92 41 | 55.55 224 | 81.21 5 | 93.69 3 | 56.51 7 | 94.27 13 | 78.36 19 | 85.70 24 | 91.51 42 |
|
CSCG | | | 80.41 8 | 79.72 8 | 82.49 4 | 89.12 12 | 57.67 14 | 89.29 31 | 91.54 2 | 59.19 149 | 71.82 53 | 90.05 64 | 59.72 3 | 96.04 1 | 78.37 18 | 88.40 9 | 93.75 2 |
|
APDe-MVS | | | 78.44 16 | 78.20 17 | 79.19 34 | 88.56 13 | 54.55 72 | 89.76 26 | 87.77 46 | 55.91 219 | 78.56 14 | 92.49 16 | 48.20 42 | 92.65 29 | 79.49 13 | 83.04 43 | 90.39 67 |
|
APD-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 76.15 47 | 75.68 41 | 77.54 76 | 88.52 14 | 53.44 93 | 87.26 62 | 85.03 98 | 53.79 237 | 74.91 26 | 91.68 32 | 43.80 97 | 90.31 74 | 74.36 42 | 81.82 51 | 88.87 101 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
DeepC-MVS_fast | | 67.50 3 | 78.00 24 | 77.63 22 | 79.13 36 | 88.52 14 | 55.12 52 | 89.95 21 | 85.98 75 | 68.31 23 | 71.33 57 | 92.75 13 | 45.52 75 | 90.37 72 | 71.15 61 | 85.14 30 | 91.91 31 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
114514_t | | | 69.87 132 | 67.88 135 | 75.85 115 | 88.38 16 | 52.35 139 | 86.94 67 | 83.68 138 | 53.70 238 | 55.68 223 | 85.60 124 | 30.07 259 | 91.20 54 | 55.84 171 | 71.02 142 | 83.99 178 |
|
WTY-MVS | | | 77.47 31 | 77.52 25 | 77.30 82 | 88.33 17 | 46.25 248 | 88.46 39 | 90.32 6 | 71.40 10 | 72.32 50 | 91.72 30 | 53.44 15 | 92.37 34 | 66.28 90 | 75.42 107 | 93.28 7 |
|
PAPR | | | 75.20 58 | 74.13 57 | 78.41 55 | 88.31 18 | 55.10 54 | 84.31 134 | 85.66 78 | 63.76 84 | 67.55 79 | 90.73 50 | 43.48 108 | 89.40 95 | 66.36 89 | 77.03 92 | 90.73 57 |
|
DP-MVS | | | 59.24 264 | 56.12 271 | 68.63 258 | 88.24 19 | 50.35 179 | 82.51 174 | 64.43 333 | 41.10 319 | 46.70 292 | 78.77 211 | 24.75 293 | 88.57 130 | 22.26 336 | 56.29 264 | 66.96 337 |
|
AdaColmap | ![Method available as binary. binary](img/icon_binary.png) | | 67.86 169 | 65.48 185 | 75.00 130 | 88.15 20 | 54.99 57 | 86.10 80 | 76.63 263 | 49.30 276 | 57.80 196 | 86.65 116 | 29.39 262 | 88.94 117 | 45.10 243 | 70.21 146 | 81.06 232 |
|
0601test | | | 75.85 51 | 74.83 53 | 78.91 40 | 88.08 21 | 51.94 145 | 91.30 9 | 89.28 13 | 57.91 183 | 71.19 59 | 89.20 78 | 42.03 134 | 92.77 25 | 69.41 70 | 75.07 112 | 92.01 29 |
|
Anonymous20240521 | | | 75.85 51 | 74.83 53 | 78.91 40 | 88.08 21 | 51.94 145 | 91.30 9 | 89.28 13 | 57.91 183 | 71.19 59 | 89.20 78 | 42.03 134 | 92.77 25 | 69.41 70 | 75.07 112 | 92.01 29 |
|
Regformer-1 | | | 77.80 27 | 77.44 26 | 78.88 42 | 87.78 23 | 52.44 135 | 87.60 47 | 90.08 8 | 68.86 20 | 72.49 48 | 91.79 27 | 47.69 47 | 94.90 8 | 73.57 48 | 77.05 90 | 89.31 87 |
|
Regformer-2 | | | 77.15 32 | 76.82 31 | 78.14 62 | 87.78 23 | 51.84 149 | 87.60 47 | 89.12 15 | 67.23 33 | 71.93 52 | 91.79 27 | 46.03 70 | 93.53 19 | 72.85 56 | 77.05 90 | 89.05 96 |
|
CANet | | | 80.90 5 | 81.17 6 | 80.09 25 | 87.62 25 | 54.21 77 | 91.60 6 | 86.47 67 | 73.13 5 | 79.89 10 | 93.10 7 | 49.88 38 | 92.98 22 | 84.09 3 | 84.75 34 | 93.08 11 |
|
VNet | | | 77.99 25 | 77.92 20 | 78.19 61 | 87.43 26 | 50.12 185 | 90.93 14 | 91.41 3 | 67.48 32 | 75.12 25 | 90.15 63 | 46.77 56 | 91.00 56 | 73.52 49 | 78.46 80 | 93.44 4 |
|
HPM-MVS++ | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 80.50 7 | 80.71 7 | 79.88 27 | 87.34 27 | 55.20 50 | 89.93 22 | 87.55 51 | 66.04 48 | 79.46 12 | 93.00 12 | 53.10 17 | 91.76 44 | 80.40 11 | 89.56 4 | 92.68 19 |
|
DWT-MVSNet_test | | | 75.47 56 | 73.87 60 | 80.29 17 | 87.33 28 | 57.05 20 | 82.86 169 | 87.96 40 | 72.59 6 | 67.29 81 | 87.79 98 | 51.61 24 | 91.52 47 | 54.75 184 | 72.63 131 | 92.29 23 |
|
Anonymous202405211 | | | 70.11 123 | 67.88 135 | 76.79 97 | 87.20 29 | 47.24 236 | 89.49 28 | 77.38 254 | 54.88 230 | 66.14 92 | 86.84 112 | 20.93 316 | 91.54 46 | 56.45 167 | 71.62 137 | 91.59 37 |
|
test12 | | | | | 79.24 33 | 86.89 30 | 56.08 34 | | 85.16 94 | | 72.27 51 | | 47.15 52 | 91.10 55 | | 85.93 20 | 90.54 62 |
|
DELS-MVS | | | 82.32 3 | 82.50 3 | 81.79 6 | 86.80 31 | 56.89 23 | 92.77 2 | 86.30 72 | 77.83 1 | 77.88 15 | 92.13 19 | 60.24 2 | 94.78 10 | 78.97 15 | 89.61 3 | 93.69 3 |
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 | | | | | 77.77 70 | 86.68 32 | 50.61 171 | 68.67 313 | 88.45 29 | | 68.73 71 | 87.45 105 | 59.15 4 | 90.67 64 | 54.83 181 | 87.67 12 | 92.03 28 |
|
PatchFormer-LS_test | | | 74.17 67 | 72.30 75 | 79.77 28 | 86.61 33 | 57.26 18 | 82.02 179 | 84.80 104 | 71.85 9 | 64.73 111 | 87.52 103 | 50.33 35 | 90.40 71 | 54.23 186 | 68.63 157 | 91.64 35 |
|
CDPH-MVS | | | 76.05 49 | 75.19 48 | 78.62 49 | 86.51 34 | 54.98 58 | 87.32 57 | 84.59 109 | 58.62 169 | 70.75 61 | 90.85 47 | 43.10 116 | 90.63 66 | 70.50 65 | 84.51 37 | 90.24 70 |
|
test_prior3 | | | 77.59 28 | 77.33 27 | 78.39 56 | 86.35 35 | 54.91 61 | 89.04 33 | 85.45 81 | 61.88 109 | 73.55 34 | 91.46 37 | 48.01 45 | 89.70 90 | 74.73 39 | 85.46 25 | 90.55 59 |
|
test_prior | | | | | 78.39 56 | 86.35 35 | 54.91 61 | | 85.45 81 | | | | | 89.70 90 | | | 90.55 59 |
|
gg-mvs-nofinetune | | | 67.43 179 | 64.53 200 | 76.13 107 | 85.95 37 | 47.79 229 | 64.38 321 | 88.28 36 | 39.34 322 | 66.62 86 | 41.27 349 | 58.69 5 | 89.00 108 | 49.64 216 | 86.62 15 | 91.59 37 |
|
PVSNet_BlendedMVS | | | 73.42 78 | 73.30 63 | 73.76 164 | 85.91 38 | 51.83 150 | 86.18 79 | 84.24 119 | 65.40 57 | 69.09 69 | 80.86 197 | 46.70 57 | 88.13 147 | 75.43 35 | 65.92 178 | 81.33 227 |
|
PVSNet_Blended | | | 76.53 42 | 76.54 32 | 76.50 99 | 85.91 38 | 51.83 150 | 88.89 35 | 84.24 119 | 67.82 27 | 69.09 69 | 89.33 77 | 46.70 57 | 88.13 147 | 75.43 35 | 81.48 54 | 89.55 83 |
|
test_8 | | | | | | 85.72 40 | 55.31 45 | 87.60 47 | 83.88 135 | 57.84 186 | 72.84 42 | 90.99 40 | 44.99 79 | 88.34 138 | | | |
|
TEST9 | | | | | | 85.68 41 | 55.42 41 | 87.59 50 | 84.00 130 | 57.72 189 | 72.99 39 | 90.98 41 | 44.87 82 | 88.58 127 | | | |
|
train_agg | | | 76.91 35 | 76.40 35 | 78.45 54 | 85.68 41 | 55.42 41 | 87.59 50 | 84.00 130 | 57.84 186 | 72.99 39 | 90.98 41 | 44.99 79 | 88.58 127 | 78.19 20 | 85.32 28 | 91.34 48 |
|
agg_prior3 | | | 76.73 40 | 76.15 39 | 78.48 52 | 85.66 43 | 55.59 37 | 87.54 54 | 83.95 134 | 57.78 188 | 71.78 54 | 90.81 49 | 44.33 87 | 88.52 132 | 78.19 20 | 85.32 28 | 91.34 48 |
|
MVS_0304 | | | 79.84 11 | 79.71 9 | 80.25 19 | 85.64 44 | 54.62 70 | 90.58 16 | 84.48 111 | 72.51 8 | 79.22 13 | 93.09 9 | 42.01 136 | 93.28 20 | 84.00 4 | 85.84 22 | 92.87 16 |
|
agg_prior1 | | | 76.68 41 | 76.24 38 | 78.00 65 | 85.64 44 | 54.92 59 | 87.55 53 | 83.61 141 | 57.99 181 | 72.53 46 | 91.05 39 | 45.36 76 | 88.10 149 | 77.76 25 | 84.68 35 | 90.99 53 |
|
agg_prior | | | | | | 85.64 44 | 54.92 59 | | 83.61 141 | | 72.53 46 | | | 88.10 149 | | | |
|
PS-MVSNAJ | | | 80.06 9 | 79.52 11 | 81.68 7 | 85.58 47 | 60.97 3 | 91.69 4 | 87.02 56 | 70.62 12 | 80.75 7 | 93.22 6 | 37.77 176 | 92.50 31 | 82.75 6 | 86.25 18 | 91.57 39 |
|
MVSTER | | | 73.25 80 | 72.33 73 | 76.01 112 | 85.54 48 | 53.76 83 | 83.52 149 | 87.16 54 | 67.06 35 | 63.88 126 | 81.66 186 | 52.77 18 | 90.44 68 | 64.66 105 | 64.69 184 | 83.84 185 |
|
Regformer-3 | | | 76.02 50 | 75.47 45 | 77.70 72 | 85.49 49 | 51.47 158 | 85.12 108 | 90.19 7 | 68.52 22 | 69.36 67 | 90.66 51 | 46.45 66 | 94.81 9 | 70.25 67 | 73.16 123 | 86.81 138 |
|
Regformer-4 | | | 75.06 59 | 74.59 55 | 76.47 100 | 85.49 49 | 50.33 180 | 85.12 108 | 88.61 24 | 66.42 38 | 68.48 72 | 90.66 51 | 44.15 92 | 92.68 28 | 69.24 72 | 73.16 123 | 86.39 145 |
|
EPNet | | | 78.36 20 | 78.49 15 | 77.97 67 | 85.49 49 | 52.04 143 | 89.36 30 | 84.07 128 | 73.22 4 | 77.03 19 | 91.72 30 | 49.32 40 | 90.17 81 | 73.46 50 | 82.77 44 | 91.69 34 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
DeepPCF-MVS | | 69.37 1 | 80.65 6 | 81.56 5 | 77.94 69 | 85.46 52 | 49.56 193 | 90.99 13 | 86.66 64 | 70.58 13 | 80.07 9 | 95.30 1 | 56.18 8 | 90.97 58 | 82.57 8 | 86.22 19 | 93.28 7 |
|
SD-MVS | | | 76.18 46 | 74.85 52 | 80.18 21 | 85.39 53 | 56.90 22 | 85.75 88 | 82.45 163 | 56.79 203 | 74.48 29 | 91.81 26 | 43.72 103 | 90.75 63 | 74.61 41 | 78.65 79 | 92.91 13 |
|
MVS_111021_HR | | | 76.39 44 | 75.38 47 | 79.42 31 | 85.33 54 | 56.47 28 | 88.15 40 | 84.97 99 | 65.15 64 | 66.06 94 | 89.88 67 | 43.79 98 | 92.16 38 | 75.03 38 | 80.03 70 | 89.64 82 |
|
PHI-MVS | | | 77.49 30 | 77.00 29 | 78.95 39 | 85.33 54 | 50.69 170 | 88.57 38 | 88.59 26 | 58.14 178 | 73.60 33 | 93.31 5 | 43.14 112 | 93.79 17 | 73.81 45 | 88.53 8 | 92.37 22 |
|
TSAR-MVS + GP. | | | 77.82 26 | 77.59 23 | 78.49 51 | 85.25 56 | 50.27 184 | 90.02 19 | 90.57 5 | 56.58 208 | 74.26 30 | 91.60 33 | 54.26 10 | 92.16 38 | 75.87 32 | 79.91 71 | 93.05 12 |
|
FMVSNet3 | | | 68.84 151 | 67.40 151 | 73.19 174 | 85.05 57 | 48.53 215 | 85.71 91 | 85.36 85 | 60.90 123 | 57.58 201 | 79.15 208 | 42.16 129 | 86.77 183 | 47.25 231 | 63.40 193 | 84.27 173 |
|
xiu_mvs_v2_base | | | 79.86 10 | 79.31 12 | 81.53 8 | 85.03 58 | 60.73 4 | 91.65 5 | 86.86 59 | 70.30 15 | 80.77 6 | 93.07 11 | 37.63 181 | 92.28 36 | 82.73 7 | 85.71 23 | 91.57 39 |
|
EPMVS | | | 68.45 161 | 65.44 188 | 77.47 79 | 84.91 59 | 56.17 32 | 71.89 298 | 81.91 173 | 61.72 112 | 60.85 155 | 72.49 278 | 36.21 203 | 87.06 174 | 47.32 230 | 71.62 137 | 89.17 93 |
|
原ACMM1 | | | | | 76.13 107 | 84.89 60 | 54.59 71 | | 85.26 90 | 51.98 260 | 66.70 84 | 87.07 110 | 40.15 156 | 89.70 90 | 51.23 208 | 85.06 32 | 84.10 174 |
|
thres200 | | | 68.71 158 | 67.27 154 | 73.02 175 | 84.73 61 | 46.76 239 | 85.03 119 | 87.73 47 | 62.34 103 | 59.87 162 | 83.45 156 | 43.15 111 | 88.32 141 | 31.25 294 | 67.91 162 | 83.98 180 |
|
HY-MVS | | 67.03 5 | 73.90 71 | 73.14 64 | 76.18 106 | 84.70 62 | 47.36 232 | 75.56 268 | 86.36 71 | 66.27 41 | 70.66 62 | 83.91 141 | 51.05 29 | 89.31 96 | 67.10 84 | 72.61 132 | 91.88 32 |
|
MVS | | | 76.91 35 | 75.48 44 | 81.23 10 | 84.56 63 | 55.21 49 | 80.23 220 | 91.64 1 | 58.65 168 | 65.37 102 | 91.48 36 | 45.72 73 | 95.05 6 | 72.11 58 | 89.52 5 | 93.44 4 |
|
SMA-MVS | | | 79.10 14 | 78.76 14 | 80.12 23 | 84.42 64 | 55.87 35 | 87.58 52 | 86.76 61 | 61.48 117 | 80.26 8 | 93.10 7 | 46.53 59 | 92.41 33 | 79.97 12 | 88.77 6 | 92.08 26 |
|
PVSNet | | 62.49 8 | 69.27 146 | 67.81 138 | 73.64 166 | 84.41 65 | 51.85 148 | 84.63 130 | 77.80 244 | 66.42 38 | 59.80 164 | 84.95 131 | 22.14 310 | 80.44 275 | 55.03 179 | 75.11 111 | 88.62 107 |
|
canonicalmvs | | | 78.17 22 | 77.86 21 | 79.12 37 | 84.30 66 | 54.22 76 | 87.71 45 | 84.57 110 | 67.70 30 | 77.70 16 | 92.11 22 | 50.90 31 | 89.95 84 | 78.18 23 | 77.54 87 | 93.20 9 |
|
HFP-MVS | | | 74.37 64 | 73.13 66 | 78.10 63 | 84.30 66 | 53.68 84 | 85.58 93 | 84.36 113 | 56.82 201 | 65.78 97 | 90.56 53 | 40.70 150 | 90.90 59 | 69.18 73 | 80.88 56 | 89.71 78 |
|
#test# | | | 74.86 62 | 73.78 61 | 78.10 63 | 84.30 66 | 53.68 84 | 86.95 66 | 84.36 113 | 59.00 160 | 65.78 97 | 90.56 53 | 40.70 150 | 90.90 59 | 71.48 59 | 80.88 56 | 89.71 78 |
|
VDD-MVS | | | 76.08 48 | 74.97 50 | 79.44 30 | 84.27 69 | 53.33 103 | 91.13 12 | 85.88 76 | 65.33 60 | 72.37 49 | 89.34 75 | 32.52 239 | 92.76 27 | 77.90 24 | 75.96 101 | 92.22 24 |
|
BH-RMVSNet | | | 70.08 125 | 68.01 132 | 76.27 102 | 84.21 70 | 51.22 166 | 87.29 60 | 79.33 217 | 58.96 162 | 63.63 130 | 86.77 113 | 33.29 232 | 90.30 77 | 44.63 245 | 73.96 118 | 87.30 130 |
|
MVS_Test | | | 75.85 51 | 74.93 51 | 78.62 49 | 84.08 71 | 55.20 50 | 83.99 143 | 85.17 93 | 68.07 25 | 73.38 37 | 82.76 167 | 50.44 33 | 89.00 108 | 65.90 92 | 80.61 60 | 91.64 35 |
|
tfpn200view9 | | | 67.57 175 | 66.13 172 | 71.89 203 | 84.05 72 | 45.07 258 | 83.40 157 | 87.71 49 | 60.79 124 | 57.79 197 | 82.76 167 | 43.53 106 | 87.80 156 | 28.80 300 | 66.36 171 | 82.78 203 |
|
thres400 | | | 67.40 180 | 66.13 172 | 71.19 217 | 84.05 72 | 45.07 258 | 83.40 157 | 87.71 49 | 60.79 124 | 57.79 197 | 82.76 167 | 43.53 106 | 87.80 156 | 28.80 300 | 66.36 171 | 80.71 238 |
|
tpmvs | | | 62.45 247 | 59.42 249 | 71.53 212 | 83.93 74 | 54.32 73 | 70.03 307 | 77.61 249 | 51.91 261 | 53.48 238 | 68.29 304 | 37.91 174 | 86.66 187 | 33.36 284 | 58.27 246 | 73.62 317 |
|
ACMMPR | | | 73.76 73 | 72.61 68 | 77.24 87 | 83.92 75 | 52.96 127 | 85.58 93 | 84.29 115 | 56.82 201 | 65.12 104 | 90.45 55 | 37.24 191 | 90.18 80 | 69.18 73 | 80.84 58 | 88.58 108 |
|
region2R | | | 73.75 74 | 72.55 70 | 77.33 81 | 83.90 76 | 52.98 126 | 85.54 96 | 84.09 121 | 56.83 200 | 65.10 105 | 90.45 55 | 37.34 189 | 90.24 78 | 68.89 75 | 80.83 59 | 88.77 104 |
|
Anonymous20240529 | | | 69.71 133 | 67.28 153 | 77.00 91 | 83.78 77 | 50.36 178 | 88.87 36 | 85.10 97 | 47.22 285 | 64.03 122 | 83.37 158 | 27.93 271 | 92.10 41 | 57.78 156 | 67.44 164 | 88.53 110 |
|
PMMVS | | | 72.98 82 | 72.05 82 | 75.78 117 | 83.57 78 | 48.60 212 | 84.08 138 | 82.85 157 | 61.62 113 | 68.24 75 | 90.33 58 | 28.35 266 | 87.78 160 | 72.71 57 | 76.69 95 | 90.95 54 |
|
alignmvs | | | 78.08 23 | 77.98 19 | 78.39 56 | 83.53 79 | 53.22 112 | 89.77 25 | 85.45 81 | 66.11 43 | 76.59 22 | 91.99 25 | 54.07 13 | 89.05 101 | 77.34 27 | 77.00 93 | 92.89 15 |
|
casdiffmvs | | | 77.54 29 | 76.52 33 | 80.60 13 | 83.43 80 | 58.01 10 | 85.16 103 | 86.39 70 | 65.71 53 | 76.20 23 | 83.87 142 | 50.75 32 | 91.33 51 | 77.37 26 | 79.79 73 | 92.46 20 |
|
XVS | | | 72.92 83 | 71.62 86 | 76.81 94 | 83.41 81 | 52.48 133 | 84.88 123 | 83.20 151 | 58.03 179 | 63.91 124 | 89.63 72 | 35.50 213 | 89.78 87 | 65.50 94 | 80.50 62 | 88.16 113 |
|
X-MVStestdata | | | 65.85 206 | 62.20 215 | 76.81 94 | 83.41 81 | 52.48 133 | 84.88 123 | 83.20 151 | 58.03 179 | 63.91 124 | 4.82 367 | 35.50 213 | 89.78 87 | 65.50 94 | 80.50 62 | 88.16 113 |
|
thres600view7 | | | 66.46 197 | 65.12 193 | 70.47 229 | 83.41 81 | 43.80 270 | 82.15 178 | 87.78 42 | 59.37 143 | 56.02 218 | 82.21 178 | 43.73 99 | 86.90 179 | 26.51 313 | 64.94 180 | 80.71 238 |
|
3Dnovator+ | | 62.71 7 | 72.29 94 | 70.50 98 | 77.65 74 | 83.40 84 | 51.29 164 | 87.32 57 | 86.40 69 | 59.01 159 | 58.49 189 | 88.32 89 | 32.40 240 | 91.27 53 | 57.04 162 | 82.15 50 | 90.38 68 |
|
GST-MVS | | | 74.87 61 | 73.90 59 | 77.77 70 | 83.30 85 | 53.45 92 | 85.75 88 | 85.29 88 | 59.22 148 | 66.50 89 | 89.85 68 | 40.94 147 | 90.76 62 | 70.94 63 | 83.35 42 | 89.10 95 |
|
tfpn111 | | | 66.40 199 | 64.99 195 | 70.63 227 | 83.29 86 | 43.15 274 | 81.67 190 | 87.78 42 | 59.04 156 | 55.92 219 | 82.18 179 | 43.73 99 | 86.83 182 | 26.34 315 | 64.92 181 | 81.89 214 |
|
conf200view11 | | | 66.80 193 | 65.42 189 | 70.95 222 | 83.29 86 | 43.15 274 | 81.67 190 | 87.78 42 | 59.04 156 | 55.92 219 | 82.18 179 | 43.73 99 | 87.80 156 | 28.80 300 | 66.36 171 | 81.89 214 |
|
thres100view900 | | | 66.87 191 | 65.42 189 | 71.24 215 | 83.29 86 | 43.15 274 | 81.67 190 | 87.78 42 | 59.04 156 | 55.92 219 | 82.18 179 | 43.73 99 | 87.80 156 | 28.80 300 | 66.36 171 | 82.78 203 |
|
gm-plane-assit | | | | | | 83.24 89 | 54.21 77 | | | 70.91 11 | | 88.23 92 | | 95.25 5 | 66.37 88 | | |
|
tpmrst | | | 71.04 111 | 69.77 111 | 74.86 138 | 83.19 90 | 55.86 36 | 75.64 267 | 78.73 225 | 67.88 26 | 64.99 109 | 73.73 265 | 49.96 37 | 79.56 286 | 65.92 91 | 67.85 163 | 89.14 94 |
|
casdiffmvs1 | | | 78.37 19 | 77.57 24 | 80.75 12 | 83.13 91 | 58.05 8 | 84.81 125 | 86.65 65 | 63.81 81 | 79.75 11 | 84.50 135 | 53.76 14 | 91.28 52 | 80.50 10 | 81.23 55 | 92.91 13 |
|
新几何1 | | | | | 73.30 173 | 83.10 92 | 53.48 89 | | 71.43 311 | 45.55 296 | 66.14 92 | 87.17 108 | 33.88 228 | 80.54 273 | 48.50 222 | 80.33 65 | 85.88 151 |
|
PatchmatchNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 67.07 188 | 63.63 206 | 77.40 80 | 83.10 92 | 58.03 9 | 72.11 295 | 77.77 246 | 58.85 165 | 59.37 172 | 70.83 289 | 37.84 175 | 84.93 226 | 42.96 252 | 69.83 149 | 89.26 88 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
CHOSEN 1792x2688 | | | 76.24 45 | 74.03 58 | 82.88 1 | 83.09 94 | 62.84 2 | 85.73 90 | 85.39 84 | 69.79 16 | 64.87 110 | 83.49 155 | 41.52 144 | 93.69 18 | 70.55 64 | 81.82 51 | 92.12 25 |
|
1121 | | | 68.79 155 | 66.77 161 | 74.82 139 | 83.08 95 | 53.46 90 | 80.23 220 | 71.53 310 | 45.47 298 | 66.31 91 | 87.19 107 | 34.02 224 | 85.13 219 | 52.78 197 | 80.36 64 | 85.87 152 |
|
Anonymous20231211 | | | 66.08 204 | 63.67 205 | 73.31 172 | 83.07 96 | 48.75 208 | 86.01 83 | 84.67 108 | 45.27 299 | 56.54 213 | 76.67 240 | 28.06 270 | 88.95 115 | 52.78 197 | 59.95 226 | 82.23 212 |
|
IB-MVS | | 68.87 2 | 74.01 70 | 72.03 83 | 79.94 26 | 83.04 97 | 55.50 39 | 90.24 18 | 88.65 22 | 67.14 34 | 61.38 146 | 81.74 185 | 53.21 16 | 94.28 12 | 60.45 134 | 62.41 212 | 90.03 76 |
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 |
MVSFormer | | | 73.53 77 | 72.19 79 | 77.57 75 | 83.02 98 | 55.24 47 | 81.63 194 | 81.44 179 | 50.28 270 | 76.67 20 | 90.91 45 | 44.82 83 | 86.11 199 | 60.83 129 | 80.09 67 | 91.36 46 |
|
lupinMVS | | | 78.38 18 | 78.11 18 | 79.19 34 | 83.02 98 | 55.24 47 | 91.57 7 | 84.82 102 | 69.12 19 | 76.67 20 | 92.02 23 | 44.82 83 | 90.23 79 | 80.83 9 | 80.09 67 | 92.08 26 |
|
HSP-MVS | | | 82.45 2 | 83.62 1 | 78.96 38 | 82.99 100 | 52.71 130 | 85.04 118 | 89.99 10 | 66.08 45 | 86.77 1 | 92.75 13 | 72.05 1 | 91.46 49 | 83.35 5 | 93.53 1 | 92.72 18 |
|
PGM-MVS | | | 72.60 88 | 71.20 94 | 76.80 96 | 82.95 101 | 52.82 129 | 83.07 164 | 82.14 164 | 56.51 213 | 63.18 133 | 89.81 69 | 35.68 212 | 89.76 89 | 67.30 83 | 80.19 66 | 87.83 120 |
|
TR-MVS | | | 69.71 133 | 67.85 137 | 75.27 126 | 82.94 102 | 48.48 218 | 87.40 56 | 80.86 192 | 57.15 197 | 64.61 113 | 87.08 109 | 32.67 238 | 89.64 93 | 46.38 237 | 71.55 139 | 87.68 124 |
|
CP-MVS | | | 72.59 90 | 71.46 89 | 76.00 113 | 82.93 103 | 52.32 140 | 86.93 68 | 82.48 162 | 55.15 227 | 63.65 129 | 90.44 57 | 35.03 217 | 88.53 131 | 68.69 76 | 77.83 84 | 87.15 131 |
|
MP-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 74.99 60 | 74.33 56 | 76.95 92 | 82.89 104 | 53.05 122 | 85.63 92 | 83.50 144 | 57.86 185 | 67.25 82 | 90.24 59 | 43.38 109 | 88.85 120 | 76.03 31 | 82.23 49 | 88.96 99 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
mvs_anonymous | | | 72.29 94 | 70.74 96 | 76.94 93 | 82.85 105 | 54.72 66 | 78.43 246 | 81.54 178 | 63.77 83 | 61.69 145 | 79.32 205 | 51.11 28 | 85.31 216 | 62.15 122 | 75.79 104 | 90.79 56 |
|
3Dnovator | | 64.70 6 | 74.46 63 | 72.48 71 | 80.41 16 | 82.84 106 | 55.40 44 | 83.08 163 | 88.61 24 | 67.61 31 | 59.85 163 | 88.66 85 | 34.57 220 | 93.97 14 | 58.42 147 | 88.70 7 | 91.85 33 |
|
BH-w/o | | | 70.02 127 | 68.51 126 | 74.56 144 | 82.77 107 | 50.39 177 | 86.60 72 | 78.14 236 | 59.77 135 | 59.65 166 | 85.57 125 | 39.27 165 | 87.30 170 | 49.86 214 | 74.94 114 | 85.99 148 |
|
Fast-Effi-MVS+ | | | 72.73 86 | 71.15 95 | 77.48 78 | 82.75 108 | 54.76 63 | 86.77 70 | 80.64 195 | 63.05 95 | 65.93 95 | 84.01 139 | 44.42 86 | 89.03 105 | 56.45 167 | 76.36 100 | 88.64 106 |
|
GBi-Net | | | 67.09 186 | 65.47 186 | 71.96 197 | 82.71 109 | 46.36 244 | 83.52 149 | 83.31 145 | 58.55 170 | 57.58 201 | 76.23 246 | 36.72 199 | 86.20 195 | 47.25 231 | 63.40 193 | 83.32 192 |
|
test1 | | | 67.09 186 | 65.47 186 | 71.96 197 | 82.71 109 | 46.36 244 | 83.52 149 | 83.31 145 | 58.55 170 | 57.58 201 | 76.23 246 | 36.72 199 | 86.20 195 | 47.25 231 | 63.40 193 | 83.32 192 |
|
FMVSNet2 | | | 67.57 175 | 65.79 177 | 72.90 177 | 82.71 109 | 47.97 228 | 85.15 104 | 84.93 100 | 58.55 170 | 56.71 211 | 78.26 218 | 36.72 199 | 86.67 186 | 46.15 239 | 62.94 206 | 84.07 175 |
|
mPP-MVS | | | 71.79 102 | 70.38 101 | 76.04 111 | 82.65 112 | 52.06 142 | 84.45 131 | 81.78 175 | 55.59 223 | 62.05 143 | 89.68 71 | 33.48 230 | 88.28 144 | 65.45 99 | 78.24 82 | 87.77 122 |
|
CANet_DTU | | | 73.71 75 | 73.14 64 | 75.40 123 | 82.61 113 | 50.05 186 | 84.67 129 | 79.36 215 | 69.72 17 | 75.39 24 | 90.03 65 | 29.41 261 | 85.93 208 | 67.99 79 | 79.11 77 | 90.22 71 |
|
EI-MVSNet-Vis-set | | | 73.19 81 | 72.60 69 | 74.99 131 | 82.56 114 | 49.80 189 | 82.55 173 | 89.00 18 | 66.17 42 | 65.89 96 | 88.98 80 | 43.83 96 | 92.29 35 | 65.38 103 | 69.01 153 | 82.87 202 |
|
dp | | | 64.41 215 | 61.58 228 | 72.90 177 | 82.40 115 | 54.09 79 | 72.53 289 | 76.59 264 | 60.39 130 | 55.68 223 | 70.39 292 | 35.18 216 | 76.90 305 | 39.34 261 | 61.71 216 | 87.73 123 |
|
MS-PatchMatch | | | 72.34 92 | 71.26 92 | 75.61 118 | 82.38 116 | 55.55 38 | 88.00 41 | 89.95 11 | 65.38 58 | 56.51 215 | 80.74 199 | 32.28 242 | 92.89 23 | 57.95 153 | 88.10 10 | 78.39 270 |
|
CostFormer | | | 73.89 72 | 72.30 75 | 78.66 47 | 82.36 117 | 56.58 24 | 75.56 268 | 85.30 87 | 66.06 46 | 70.50 64 | 76.88 237 | 57.02 6 | 89.06 99 | 68.27 78 | 68.74 155 | 90.33 69 |
|
QAPM | | | 71.88 99 | 69.33 117 | 79.52 29 | 82.20 118 | 54.30 74 | 86.30 77 | 88.77 21 | 56.61 207 | 59.72 165 | 87.48 104 | 33.90 227 | 95.36 4 | 47.48 229 | 81.49 53 | 88.90 100 |
|
view600 | | | 64.79 209 | 63.45 207 | 68.82 251 | 82.13 119 | 40.75 295 | 79.41 236 | 88.29 32 | 56.54 209 | 53.26 239 | 81.30 190 | 44.26 88 | 85.01 222 | 22.97 325 | 62.85 207 | 80.71 238 |
|
view800 | | | 64.79 209 | 63.45 207 | 68.82 251 | 82.13 119 | 40.75 295 | 79.41 236 | 88.29 32 | 56.54 209 | 53.26 239 | 81.30 190 | 44.26 88 | 85.01 222 | 22.97 325 | 62.85 207 | 80.71 238 |
|
conf0.05thres1000 | | | 64.79 209 | 63.45 207 | 68.82 251 | 82.13 119 | 40.75 295 | 79.41 236 | 88.29 32 | 56.54 209 | 53.26 239 | 81.30 190 | 44.26 88 | 85.01 222 | 22.97 325 | 62.85 207 | 80.71 238 |
|
tfpn | | | 64.79 209 | 63.45 207 | 68.82 251 | 82.13 119 | 40.75 295 | 79.41 236 | 88.29 32 | 56.54 209 | 53.26 239 | 81.30 190 | 44.26 88 | 85.01 222 | 22.97 325 | 62.85 207 | 80.71 238 |
|
HPM-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 72.60 88 | 71.50 88 | 75.89 114 | 82.02 123 | 51.42 160 | 80.70 213 | 83.05 153 | 56.12 217 | 64.03 122 | 89.53 73 | 37.55 183 | 88.37 136 | 70.48 66 | 80.04 69 | 87.88 119 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
TESTMET0.1,1 | | | 72.86 84 | 72.33 73 | 74.46 145 | 81.98 124 | 50.77 168 | 85.13 105 | 85.47 80 | 66.09 44 | 67.30 80 | 83.69 147 | 37.27 190 | 83.57 247 | 65.06 104 | 78.97 78 | 89.05 96 |
|
tpmp4_e23 | | | 70.01 128 | 67.13 156 | 78.65 48 | 81.93 125 | 57.90 12 | 73.99 279 | 81.35 181 | 60.61 128 | 65.28 103 | 73.78 264 | 52.48 20 | 88.60 126 | 48.40 224 | 66.35 175 | 89.44 85 |
|
ACMMP_Plus | | | 76.43 43 | 75.66 42 | 78.73 45 | 81.92 126 | 54.67 69 | 84.06 140 | 85.35 86 | 61.10 120 | 72.99 39 | 91.50 35 | 40.25 153 | 91.00 56 | 76.84 29 | 86.98 13 | 90.51 63 |
|
Effi-MVS+ | | | 75.24 57 | 73.61 62 | 80.16 22 | 81.92 126 | 57.42 16 | 85.21 101 | 76.71 261 | 60.68 127 | 73.32 38 | 89.34 75 | 47.30 50 | 91.63 45 | 68.28 77 | 79.72 74 | 91.42 44 |
|
tfpn_ndepth | | | 64.50 214 | 63.34 211 | 67.99 261 | 81.84 128 | 38.30 306 | 79.26 241 | 83.57 143 | 53.69 239 | 52.86 244 | 84.51 134 | 46.96 54 | 84.79 227 | 24.28 320 | 63.09 204 | 80.87 235 |
|
PLC | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 52.38 18 | 60.89 255 | 58.97 255 | 66.68 275 | 81.77 129 | 45.70 253 | 78.96 243 | 74.04 291 | 43.66 310 | 47.63 279 | 83.19 162 | 23.52 301 | 77.78 300 | 37.47 264 | 60.46 221 | 76.55 295 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
MDTV_nov1_ep13 | | | | 61.56 230 | | 81.68 130 | 55.12 52 | 72.41 291 | 78.18 234 | 59.19 149 | 58.85 182 | 69.29 296 | 34.69 219 | 86.16 198 | 36.76 273 | 62.96 205 | |
|
thisisatest0515 | | | 73.64 76 | 72.20 78 | 77.97 67 | 81.63 131 | 53.01 125 | 86.69 71 | 88.81 20 | 62.53 101 | 64.06 121 | 85.65 123 | 52.15 23 | 92.50 31 | 58.43 145 | 69.84 148 | 88.39 112 |
|
BH-untuned | | | 68.28 165 | 66.40 165 | 73.91 158 | 81.62 132 | 50.01 187 | 85.56 95 | 77.39 253 | 57.63 191 | 57.47 206 | 83.69 147 | 36.36 202 | 87.08 173 | 44.81 244 | 73.08 128 | 84.65 168 |
|
EI-MVSNet-UG-set | | | 72.37 91 | 71.73 84 | 74.29 150 | 81.60 133 | 49.29 198 | 81.85 185 | 88.64 23 | 65.29 62 | 65.05 106 | 88.29 90 | 43.18 110 | 91.83 43 | 63.74 107 | 67.97 161 | 81.75 220 |
|
sss | | | 70.49 119 | 70.13 107 | 71.58 211 | 81.59 134 | 39.02 302 | 80.78 212 | 84.71 107 | 59.34 144 | 66.61 87 | 88.09 94 | 37.17 192 | 85.52 212 | 61.82 124 | 71.02 142 | 90.20 72 |
|
APD-MVS_3200maxsize | | | 69.62 137 | 68.23 130 | 73.80 163 | 81.58 135 | 48.22 224 | 81.91 183 | 79.50 211 | 48.21 280 | 64.24 120 | 89.75 70 | 31.91 248 | 87.55 165 | 63.08 110 | 73.85 120 | 85.64 156 |
|
旧先验1 | | | | | | 81.57 136 | 47.48 231 | | 71.83 305 | | | 88.66 85 | 36.94 195 | | | 78.34 81 | 88.67 105 |
|
zzz-MVS | | | 74.15 69 | 73.11 67 | 77.27 84 | 81.54 137 | 53.57 87 | 84.02 142 | 81.31 182 | 59.41 141 | 68.39 73 | 90.96 43 | 36.07 205 | 89.01 106 | 73.80 46 | 82.45 47 | 89.23 89 |
|
MTAPA | | | 72.73 86 | 71.22 93 | 77.27 84 | 81.54 137 | 53.57 87 | 67.06 316 | 81.31 182 | 59.41 141 | 68.39 73 | 90.96 43 | 36.07 205 | 89.01 106 | 73.80 46 | 82.45 47 | 89.23 89 |
|
PAPM_NR | | | 71.80 101 | 69.98 109 | 77.26 86 | 81.54 137 | 53.34 101 | 78.60 245 | 85.25 91 | 53.46 240 | 60.53 159 | 88.66 85 | 45.69 74 | 89.24 97 | 56.49 164 | 79.62 75 | 89.19 92 |
|
ACMMP | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 70.81 115 | 69.29 118 | 75.39 124 | 81.52 140 | 51.92 147 | 83.43 155 | 83.03 154 | 56.67 206 | 58.80 183 | 88.91 82 | 31.92 247 | 88.58 127 | 65.89 93 | 73.39 122 | 85.67 154 |
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 |
MSLP-MVS++ | | | 74.21 66 | 72.25 77 | 80.11 24 | 81.45 141 | 56.47 28 | 86.32 76 | 79.65 209 | 58.19 177 | 66.36 90 | 92.29 18 | 36.11 204 | 90.66 65 | 67.39 82 | 82.49 46 | 93.18 10 |
|
tpm cat1 | | | 66.28 200 | 62.78 212 | 76.77 98 | 81.40 142 | 57.14 19 | 70.03 307 | 77.19 256 | 53.00 245 | 58.76 184 | 70.73 291 | 46.17 67 | 86.73 185 | 43.27 250 | 64.46 186 | 86.44 143 |
|
MP-MVS-pluss | | | 75.54 55 | 75.03 49 | 77.04 88 | 81.37 143 | 52.65 132 | 84.34 133 | 84.46 112 | 61.16 118 | 69.14 68 | 91.76 29 | 39.98 160 | 88.99 110 | 78.19 20 | 84.89 33 | 89.48 84 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
TSAR-MVS + MP. | | | 78.31 21 | 78.26 16 | 78.48 52 | 81.33 144 | 56.31 31 | 81.59 197 | 86.41 68 | 69.61 18 | 81.72 4 | 88.16 93 | 55.09 9 | 88.04 151 | 74.12 44 | 86.31 17 | 91.09 50 |
|
PVSNet_Blended_VisFu | | | 73.40 79 | 72.44 72 | 76.30 101 | 81.32 145 | 54.70 67 | 85.81 84 | 78.82 222 | 63.70 85 | 64.53 114 | 85.38 127 | 47.11 53 | 87.38 169 | 67.75 81 | 77.55 86 | 86.81 138 |
|
LS3D | | | 56.40 285 | 53.82 282 | 64.12 294 | 81.12 146 | 45.69 254 | 73.42 282 | 66.14 328 | 35.30 340 | 43.24 309 | 79.88 202 | 22.18 309 | 79.62 285 | 19.10 347 | 64.00 189 | 67.05 336 |
|
SteuartSystems-ACMMP | | | 77.08 33 | 76.33 36 | 79.34 32 | 80.98 147 | 55.31 45 | 89.76 26 | 86.91 58 | 62.94 97 | 71.65 55 | 91.56 34 | 42.33 126 | 92.56 30 | 77.14 28 | 83.69 41 | 90.15 74 |
Skip Steuart: Steuart Systems R&D Blog. |
CPTT-MVS | | | 67.15 185 | 65.84 176 | 71.07 219 | 80.96 148 | 50.32 181 | 81.94 182 | 74.10 289 | 46.18 294 | 57.91 194 | 87.64 102 | 29.57 260 | 81.31 267 | 64.10 106 | 70.18 147 | 81.56 222 |
|
Vis-MVSNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 70.61 118 | 69.34 116 | 74.42 147 | 80.95 149 | 48.49 217 | 86.03 82 | 77.51 251 | 58.74 167 | 65.55 101 | 87.78 99 | 34.37 221 | 85.95 207 | 52.53 202 | 80.61 60 | 88.80 102 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
ab-mvs | | | 70.65 117 | 69.11 120 | 75.29 125 | 80.87 150 | 46.23 249 | 73.48 281 | 85.24 92 | 59.99 134 | 66.65 85 | 80.94 196 | 43.13 113 | 88.69 122 | 63.58 108 | 68.07 159 | 90.95 54 |
|
tpm2 | | | 70.82 114 | 68.44 127 | 77.98 66 | 80.78 151 | 56.11 33 | 74.21 278 | 81.28 185 | 60.24 132 | 68.04 76 | 75.27 256 | 52.26 22 | 88.50 133 | 55.82 172 | 68.03 160 | 89.33 86 |
|
1112_ss | | | 70.05 126 | 69.37 115 | 72.10 190 | 80.77 152 | 42.78 279 | 85.12 108 | 76.75 260 | 59.69 136 | 61.19 148 | 92.12 20 | 47.48 48 | 83.84 243 | 53.04 193 | 68.21 158 | 89.66 80 |
|
DeepC-MVS | | 67.15 4 | 76.90 37 | 76.27 37 | 78.80 44 | 80.70 153 | 55.02 56 | 86.39 74 | 86.71 62 | 66.96 36 | 67.91 77 | 89.97 66 | 48.03 44 | 91.41 50 | 75.60 34 | 84.14 38 | 89.96 77 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CLD-MVS | | | 75.60 54 | 75.39 46 | 76.24 103 | 80.69 154 | 52.40 136 | 90.69 15 | 86.20 74 | 74.40 3 | 65.01 108 | 88.93 81 | 42.05 133 | 90.58 67 | 76.57 30 | 73.96 118 | 85.73 153 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
HPM-MVS_fast | | | 67.86 169 | 66.28 168 | 72.61 183 | 80.67 155 | 48.34 222 | 81.18 203 | 75.95 273 | 50.81 269 | 59.55 170 | 88.05 96 | 27.86 272 | 85.98 204 | 58.83 143 | 73.58 121 | 83.51 189 |
|
conf0.01 | | | 63.04 234 | 61.74 219 | 66.95 271 | 80.60 156 | 35.92 314 | 76.01 260 | 84.09 121 | 52.62 248 | 50.87 260 | 83.60 149 | 46.49 60 | 83.04 252 | 22.59 330 | 58.77 239 | 81.89 214 |
|
conf0.002 | | | 63.04 234 | 61.74 219 | 66.95 271 | 80.60 156 | 35.92 314 | 76.01 260 | 84.09 121 | 52.62 248 | 50.87 260 | 83.60 149 | 46.49 60 | 83.04 252 | 22.59 330 | 58.77 239 | 81.89 214 |
|
thresconf0.02 | | | 62.84 237 | 61.74 219 | 66.14 278 | 80.60 156 | 35.92 314 | 76.01 260 | 84.09 121 | 52.62 248 | 50.87 260 | 83.60 149 | 46.49 60 | 83.04 252 | 22.59 330 | 58.77 239 | 79.44 253 |
|
tfpn_n400 | | | 62.84 237 | 61.74 219 | 66.14 278 | 80.60 156 | 35.92 314 | 76.01 260 | 84.09 121 | 52.62 248 | 50.87 260 | 83.60 149 | 46.49 60 | 83.04 252 | 22.59 330 | 58.77 239 | 79.44 253 |
|
tfpnconf | | | 62.84 237 | 61.74 219 | 66.14 278 | 80.60 156 | 35.92 314 | 76.01 260 | 84.09 121 | 52.62 248 | 50.87 260 | 83.60 149 | 46.49 60 | 83.04 252 | 22.59 330 | 58.77 239 | 79.44 253 |
|
tfpnview11 | | | 62.84 237 | 61.74 219 | 66.14 278 | 80.60 156 | 35.92 314 | 76.01 260 | 84.09 121 | 52.62 248 | 50.87 260 | 83.60 149 | 46.49 60 | 83.04 252 | 22.59 330 | 58.77 239 | 79.44 253 |
|
ADS-MVSNet2 | | | 55.21 292 | 51.44 295 | 66.51 276 | 80.60 156 | 49.56 193 | 55.03 340 | 65.44 330 | 44.72 302 | 51.00 256 | 61.19 329 | 22.83 302 | 75.41 310 | 28.54 305 | 53.63 281 | 74.57 309 |
|
ADS-MVSNet | | | 56.17 286 | 51.95 294 | 68.84 249 | 80.60 156 | 53.07 121 | 55.03 340 | 70.02 320 | 44.72 302 | 51.00 256 | 61.19 329 | 22.83 302 | 78.88 288 | 28.54 305 | 53.63 281 | 74.57 309 |
|
UGNet | | | 68.71 158 | 67.11 157 | 73.50 170 | 80.55 164 | 47.61 230 | 84.08 138 | 78.51 230 | 59.45 139 | 65.68 100 | 82.73 170 | 23.78 296 | 85.08 221 | 52.80 196 | 76.40 96 | 87.80 121 |
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 |
tfpn1000 | | | 62.79 243 | 61.74 219 | 65.95 283 | 80.50 165 | 35.93 313 | 76.53 259 | 83.99 132 | 51.24 266 | 49.82 268 | 83.44 157 | 47.32 49 | 83.02 258 | 21.84 337 | 60.99 218 | 78.89 258 |
|
PAPM | | | 76.76 38 | 76.07 40 | 78.81 43 | 80.20 166 | 59.11 5 | 86.86 69 | 86.23 73 | 68.60 21 | 70.18 65 | 88.84 84 | 51.57 25 | 87.16 172 | 65.48 96 | 86.68 14 | 90.15 74 |
|
tpm | | | 68.36 162 | 67.48 150 | 70.97 221 | 79.93 167 | 51.34 162 | 76.58 257 | 78.75 224 | 67.73 28 | 63.54 132 | 74.86 258 | 48.33 41 | 72.36 328 | 53.93 188 | 63.71 191 | 89.21 91 |
|
thisisatest0530 | | | 70.47 121 | 68.56 125 | 76.20 105 | 79.78 168 | 51.52 157 | 83.49 154 | 88.58 27 | 57.62 192 | 58.60 185 | 82.79 166 | 51.03 30 | 91.48 48 | 52.84 195 | 62.36 214 | 85.59 158 |
|
jason | | | 77.01 34 | 76.45 34 | 78.69 46 | 79.69 169 | 54.74 64 | 90.56 17 | 83.99 132 | 68.26 24 | 74.10 31 | 90.91 45 | 42.14 130 | 89.99 83 | 79.30 14 | 79.12 76 | 91.36 46 |
jason: jason. |
VDDNet | | | 74.37 64 | 72.13 80 | 81.09 11 | 79.58 170 | 56.52 27 | 90.02 19 | 86.70 63 | 52.61 254 | 71.23 58 | 87.20 106 | 31.75 249 | 93.96 15 | 74.30 43 | 75.77 105 | 92.79 17 |
|
test222 | | | | | | 79.36 171 | 50.97 167 | 77.99 249 | 67.84 323 | 42.54 316 | 62.84 137 | 86.53 117 | 30.26 257 | | | 76.91 94 | 85.23 161 |
|
cascas | | | 69.01 150 | 66.13 172 | 77.66 73 | 79.36 171 | 55.41 43 | 86.99 64 | 83.75 137 | 56.69 205 | 58.92 179 | 81.35 189 | 24.31 294 | 92.10 41 | 53.23 190 | 70.61 144 | 85.46 159 |
|
1314 | | | 71.11 110 | 69.41 114 | 76.22 104 | 79.32 173 | 50.49 175 | 80.23 220 | 85.14 96 | 59.44 140 | 58.93 178 | 88.89 83 | 33.83 229 | 89.60 94 | 61.49 125 | 77.42 89 | 88.57 109 |
|
LCM-MVSNet-Re | | | 58.82 269 | 56.54 266 | 65.68 284 | 79.31 174 | 29.09 341 | 61.39 331 | 45.79 353 | 60.73 126 | 37.65 326 | 72.47 279 | 31.42 251 | 81.08 268 | 49.66 215 | 70.41 145 | 86.87 133 |
|
CNLPA | | | 60.59 257 | 58.44 256 | 67.05 270 | 79.21 175 | 47.26 235 | 79.75 230 | 64.34 334 | 42.46 317 | 51.90 251 | 83.94 140 | 27.79 274 | 75.41 310 | 37.12 267 | 59.49 234 | 78.47 264 |
|
EPP-MVSNet | | | 71.14 108 | 70.07 108 | 74.33 149 | 79.18 176 | 46.52 242 | 83.81 145 | 86.49 66 | 56.32 216 | 57.95 193 | 84.90 132 | 54.23 11 | 89.14 98 | 58.14 151 | 69.65 150 | 87.33 128 |
|
diffmvs1 | | | 72.81 85 | 71.73 84 | 76.06 109 | 79.03 177 | 51.59 153 | 79.01 242 | 76.46 266 | 65.23 63 | 69.81 66 | 83.07 163 | 46.12 68 | 86.94 178 | 70.95 62 | 75.87 103 | 89.66 80 |
|
HQP-NCC | | | | | | 79.02 178 | | 88.00 41 | | 65.45 54 | 64.48 115 | | | | | | |
|
ACMP_Plane | | | | | | 79.02 178 | | 88.00 41 | | 65.45 54 | 64.48 115 | | | | | | |
|
HQP-MVS | | | 72.34 92 | 71.44 90 | 75.03 129 | 79.02 178 | 51.56 154 | 88.00 41 | 83.68 138 | 65.45 54 | 64.48 115 | 85.13 128 | 37.35 187 | 88.62 124 | 66.70 86 | 73.12 125 | 84.91 166 |
|
diffmvs | | | 71.81 100 | 70.47 99 | 75.80 116 | 78.97 181 | 51.56 154 | 78.25 247 | 76.39 268 | 65.75 51 | 67.61 78 | 81.37 188 | 44.14 93 | 86.84 181 | 67.81 80 | 73.97 117 | 89.00 98 |
|
UA-Net | | | 67.32 181 | 66.23 169 | 70.59 228 | 78.85 182 | 41.23 292 | 73.60 280 | 75.45 279 | 61.54 114 | 66.61 87 | 84.53 133 | 38.73 169 | 86.57 192 | 42.48 255 | 74.24 115 | 83.98 180 |
|
NP-MVS | | | | | | 78.76 183 | 50.43 176 | | | | | 85.12 129 | | | | | |
|
VPA-MVSNet | | | 71.12 109 | 70.66 97 | 72.49 187 | 78.75 184 | 44.43 264 | 87.64 46 | 90.02 9 | 63.97 78 | 65.02 107 | 81.58 187 | 42.14 130 | 87.42 168 | 63.42 109 | 63.38 196 | 85.63 157 |
|
Test_1112_low_res | | | 67.18 184 | 66.23 169 | 70.02 242 | 78.75 184 | 41.02 293 | 83.43 155 | 73.69 295 | 57.29 195 | 58.45 190 | 82.39 176 | 45.30 77 | 80.88 270 | 50.50 211 | 66.26 176 | 88.16 113 |
|
test-LLR | | | 69.65 136 | 69.01 121 | 71.60 209 | 78.67 186 | 48.17 225 | 85.13 105 | 79.72 206 | 59.18 151 | 63.13 134 | 82.58 172 | 36.91 196 | 80.24 278 | 60.56 132 | 75.17 109 | 86.39 145 |
|
test-mter | | | 68.36 162 | 67.29 152 | 71.60 209 | 78.67 186 | 48.17 225 | 85.13 105 | 79.72 206 | 53.38 241 | 63.13 134 | 82.58 172 | 27.23 277 | 80.24 278 | 60.56 132 | 75.17 109 | 86.39 145 |
|
EPNet_dtu | | | 66.25 201 | 66.71 163 | 64.87 292 | 78.66 188 | 34.12 323 | 82.80 170 | 75.51 277 | 61.75 111 | 64.47 118 | 86.90 111 | 37.06 193 | 72.46 327 | 43.65 249 | 69.63 151 | 88.02 118 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
VPNet | | | 72.07 97 | 71.42 91 | 74.04 155 | 78.64 189 | 47.17 237 | 89.91 24 | 87.97 39 | 72.56 7 | 64.66 112 | 85.04 130 | 41.83 139 | 88.33 140 | 61.17 127 | 60.97 219 | 86.62 140 |
|
Patchmatch-test1 | | | 63.23 231 | 59.16 252 | 75.43 122 | 78.58 190 | 57.92 11 | 61.61 329 | 77.53 250 | 56.71 204 | 57.75 199 | 70.98 288 | 31.97 246 | 78.19 291 | 40.97 258 | 56.36 262 | 90.18 73 |
|
abl_6 | | | 68.03 167 | 66.15 171 | 73.66 165 | 78.54 191 | 48.48 218 | 79.77 228 | 78.04 240 | 47.39 284 | 63.70 128 | 88.25 91 | 28.21 267 | 89.06 99 | 60.17 139 | 71.25 140 | 83.45 190 |
|
IS-MVSNet | | | 68.80 154 | 67.55 148 | 72.54 185 | 78.50 192 | 43.43 272 | 81.03 206 | 79.35 216 | 59.12 155 | 57.27 209 | 86.71 114 | 46.05 69 | 87.70 162 | 44.32 246 | 75.60 106 | 86.49 142 |
|
TAPA-MVS | | 56.12 14 | 61.82 251 | 60.18 245 | 66.71 273 | 78.48 193 | 37.97 308 | 75.19 273 | 76.41 267 | 46.82 289 | 57.04 210 | 86.52 118 | 27.67 275 | 77.03 303 | 26.50 314 | 67.02 167 | 85.14 162 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
plane_prior6 | | | | | | 78.42 194 | 49.39 196 | | | | | | 36.04 208 | | | | |
|
OpenMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 61.00 11 | 69.99 130 | 67.55 148 | 77.30 82 | 78.37 195 | 54.07 81 | 84.36 132 | 85.76 77 | 57.22 196 | 56.71 211 | 87.67 101 | 30.79 254 | 92.83 24 | 43.04 251 | 84.06 40 | 85.01 164 |
|
plane_prior1 | | | | | | 78.31 196 | | | | | | | | | | | |
|
tttt0517 | | | 68.33 164 | 66.29 167 | 74.46 145 | 78.08 197 | 49.06 200 | 80.88 210 | 89.08 17 | 54.40 235 | 54.75 226 | 80.77 198 | 51.31 27 | 90.33 73 | 49.35 218 | 58.01 252 | 83.99 178 |
|
HQP_MVS | | | 70.96 113 | 69.91 110 | 74.12 153 | 77.95 198 | 49.57 191 | 85.76 86 | 82.59 160 | 63.60 88 | 62.15 140 | 83.28 160 | 36.04 208 | 88.30 142 | 65.46 97 | 72.34 134 | 84.49 169 |
|
plane_prior7 | | | | | | 77.95 198 | 48.46 220 | | | | | | | | | | |
|
FIs | | | 70.00 129 | 70.24 106 | 69.30 245 | 77.93 200 | 38.55 304 | 83.99 143 | 87.72 48 | 66.86 37 | 57.66 200 | 84.17 138 | 52.28 21 | 85.31 216 | 52.72 201 | 68.80 154 | 84.02 176 |
|
PatchMatch-RL | | | 56.66 281 | 53.75 283 | 65.37 289 | 77.91 201 | 45.28 256 | 69.78 309 | 60.38 340 | 41.35 318 | 47.57 280 | 73.73 265 | 16.83 333 | 76.91 304 | 36.99 270 | 59.21 236 | 73.92 315 |
|
XXY-MVS | | | 70.18 122 | 69.28 119 | 72.89 179 | 77.64 202 | 42.88 278 | 85.06 117 | 87.50 52 | 62.58 100 | 62.66 138 | 82.34 177 | 43.64 105 | 89.83 86 | 58.42 147 | 63.70 192 | 85.96 150 |
|
testdata | | | | | 67.08 269 | 77.59 203 | 45.46 255 | | 69.20 322 | 44.47 304 | 71.50 56 | 88.34 88 | 31.21 252 | 70.76 334 | 52.20 203 | 75.88 102 | 85.03 163 |
|
CDS-MVSNet | | | 70.48 120 | 69.43 113 | 73.64 166 | 77.56 204 | 48.83 207 | 83.51 153 | 77.45 252 | 63.27 92 | 62.33 139 | 85.54 126 | 43.85 95 | 83.29 250 | 57.38 161 | 74.00 116 | 88.79 103 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
Vis-MVSNet (Re-imp) | | | 65.52 207 | 65.63 182 | 65.17 290 | 77.49 205 | 30.54 336 | 75.49 271 | 77.73 248 | 59.34 144 | 52.26 248 | 86.69 115 | 49.38 39 | 80.53 274 | 37.07 269 | 75.28 108 | 84.42 171 |
|
PVSNet_0 | | 57.04 13 | 61.19 254 | 57.24 262 | 73.02 175 | 77.45 206 | 50.31 182 | 79.43 234 | 77.36 255 | 63.96 79 | 47.51 283 | 72.45 280 | 25.03 292 | 83.78 245 | 52.76 200 | 19.22 354 | 84.96 165 |
|
FMVSNet1 | | | 64.57 213 | 62.11 216 | 71.96 197 | 77.32 207 | 46.36 244 | 83.52 149 | 83.31 145 | 52.43 258 | 54.42 229 | 76.23 246 | 27.80 273 | 86.20 195 | 42.59 254 | 61.34 217 | 83.32 192 |
|
MVS_111021_LR | | | 69.07 147 | 67.91 133 | 72.54 185 | 77.27 208 | 49.56 193 | 79.77 228 | 73.96 292 | 59.33 146 | 60.73 157 | 87.82 97 | 30.19 258 | 81.53 265 | 69.94 68 | 72.19 136 | 86.53 141 |
|
xiu_mvs_v1_base_debu | | | 71.60 103 | 70.29 103 | 75.55 119 | 77.26 209 | 53.15 115 | 85.34 97 | 79.37 212 | 55.83 220 | 72.54 43 | 90.19 60 | 22.38 306 | 86.66 187 | 73.28 52 | 76.39 97 | 86.85 135 |
|
xiu_mvs_v1_base | | | 71.60 103 | 70.29 103 | 75.55 119 | 77.26 209 | 53.15 115 | 85.34 97 | 79.37 212 | 55.83 220 | 72.54 43 | 90.19 60 | 22.38 306 | 86.66 187 | 73.28 52 | 76.39 97 | 86.85 135 |
|
xiu_mvs_v1_base_debi | | | 71.60 103 | 70.29 103 | 75.55 119 | 77.26 209 | 53.15 115 | 85.34 97 | 79.37 212 | 55.83 220 | 72.54 43 | 90.19 60 | 22.38 306 | 86.66 187 | 73.28 52 | 76.39 97 | 86.85 135 |
|
FMVSNet5 | | | 58.61 271 | 56.45 267 | 65.10 291 | 77.20 212 | 39.74 300 | 74.77 274 | 77.12 258 | 50.27 272 | 43.28 308 | 67.71 311 | 26.15 284 | 76.90 305 | 36.78 272 | 54.78 276 | 78.65 262 |
|
PCF-MVS | | 61.03 10 | 70.10 124 | 68.40 128 | 75.22 128 | 77.15 213 | 51.99 144 | 79.30 240 | 82.12 168 | 56.47 214 | 61.88 144 | 86.48 119 | 43.98 94 | 87.24 171 | 55.37 176 | 72.79 130 | 86.43 144 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
HyFIR lowres test | | | 69.94 131 | 67.58 146 | 77.04 88 | 77.11 214 | 57.29 17 | 81.49 200 | 79.11 220 | 58.27 175 | 58.86 181 | 80.41 200 | 42.33 126 | 86.96 177 | 61.91 123 | 68.68 156 | 86.87 133 |
|
test_0402 | | | 56.45 284 | 53.03 286 | 66.69 274 | 76.78 215 | 50.31 182 | 81.76 187 | 69.61 321 | 42.79 315 | 43.88 303 | 72.13 283 | 22.82 304 | 86.46 193 | 16.57 352 | 50.94 293 | 63.31 345 |
|
ACMH | | 53.70 16 | 59.78 261 | 55.94 273 | 71.28 214 | 76.59 216 | 48.35 221 | 80.15 223 | 76.11 269 | 49.74 274 | 41.91 313 | 73.45 272 | 16.50 336 | 90.31 74 | 31.42 292 | 57.63 258 | 75.17 305 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
TAMVS | | | 69.51 139 | 68.16 131 | 73.56 169 | 76.30 217 | 48.71 209 | 82.57 172 | 77.17 257 | 62.10 106 | 61.32 147 | 84.23 137 | 41.90 137 | 83.46 248 | 54.80 183 | 73.09 127 | 88.50 111 |
|
tfpnnormal | | | 61.47 252 | 59.09 253 | 68.62 259 | 76.29 218 | 41.69 287 | 81.14 205 | 85.16 94 | 54.48 234 | 51.32 253 | 73.63 269 | 32.32 241 | 86.89 180 | 21.78 339 | 55.71 271 | 77.29 289 |
|
FC-MVSNet-test | | | 67.49 177 | 67.91 133 | 66.21 277 | 76.06 219 | 33.06 328 | 80.82 211 | 87.18 53 | 64.44 70 | 54.81 225 | 82.87 164 | 50.40 34 | 82.60 259 | 48.05 226 | 66.55 169 | 82.98 200 |
|
MVS-HIRNet | | | 49.01 309 | 44.71 315 | 61.92 306 | 76.06 219 | 46.61 241 | 63.23 324 | 54.90 346 | 24.77 350 | 33.56 340 | 36.60 352 | 21.28 314 | 75.88 308 | 29.49 297 | 62.54 211 | 63.26 346 |
|
MVP-Stereo | | | 70.97 112 | 70.44 100 | 72.59 184 | 76.03 221 | 51.36 161 | 85.02 120 | 86.99 57 | 60.31 131 | 56.53 214 | 78.92 210 | 40.11 157 | 90.00 82 | 60.00 140 | 90.01 2 | 76.41 296 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
nrg030 | | | 72.27 96 | 71.56 87 | 74.42 147 | 75.93 222 | 50.60 172 | 86.97 65 | 83.21 150 | 62.75 99 | 67.15 83 | 84.38 136 | 50.07 36 | 86.66 187 | 71.19 60 | 62.37 213 | 85.99 148 |
|
WR-MVS | | | 67.58 174 | 66.76 162 | 70.04 241 | 75.92 223 | 45.06 261 | 86.23 78 | 85.28 89 | 64.31 71 | 58.50 188 | 81.00 194 | 44.80 85 | 82.00 264 | 49.21 219 | 55.57 272 | 83.06 198 |
|
MIMVSNet | | | 63.12 233 | 60.29 244 | 71.61 208 | 75.92 223 | 46.65 240 | 65.15 317 | 81.94 170 | 59.14 153 | 54.65 227 | 69.47 295 | 25.74 286 | 80.63 272 | 41.03 257 | 69.56 152 | 87.55 125 |
|
UniMVSNet_NR-MVSNet | | | 68.82 152 | 68.29 129 | 70.40 232 | 75.71 225 | 42.59 281 | 84.23 135 | 86.78 60 | 66.31 40 | 58.51 186 | 82.45 174 | 51.57 25 | 84.64 230 | 53.11 191 | 55.96 267 | 83.96 182 |
|
OPM-MVS | | | 70.75 116 | 69.58 112 | 74.26 151 | 75.55 226 | 51.34 162 | 86.05 81 | 83.29 148 | 61.94 108 | 62.95 136 | 85.77 122 | 34.15 223 | 88.44 134 | 65.44 100 | 71.07 141 | 82.99 199 |
|
Effi-MVS+-dtu | | | 66.24 202 | 64.96 197 | 70.08 237 | 75.17 227 | 49.64 190 | 82.01 180 | 74.48 285 | 62.15 104 | 57.83 195 | 76.08 251 | 30.59 255 | 83.79 244 | 65.40 101 | 60.93 220 | 76.81 291 |
|
mvs-test1 | | | 69.04 148 | 67.57 147 | 73.44 171 | 75.17 227 | 51.68 152 | 86.57 73 | 74.48 285 | 62.15 104 | 62.07 142 | 85.79 121 | 30.59 255 | 87.48 166 | 65.40 101 | 65.94 177 | 81.18 231 |
|
GA-MVS | | | 69.04 148 | 66.70 164 | 76.06 109 | 75.11 229 | 52.36 138 | 83.12 162 | 80.23 199 | 63.32 91 | 60.65 158 | 79.22 207 | 30.98 253 | 88.37 136 | 61.25 126 | 66.41 170 | 87.46 126 |
|
IterMVS-LS | | | 66.63 194 | 65.36 191 | 70.42 231 | 75.10 230 | 48.90 205 | 81.45 201 | 76.69 262 | 61.05 121 | 55.71 222 | 77.10 236 | 45.86 72 | 83.65 246 | 57.44 159 | 57.88 256 | 78.70 260 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
EI-MVSNet | | | 69.70 135 | 68.70 124 | 72.68 182 | 75.00 231 | 48.90 205 | 79.54 232 | 87.16 54 | 61.05 121 | 63.88 126 | 83.74 145 | 45.87 71 | 90.44 68 | 57.42 160 | 64.68 185 | 78.70 260 |
|
CVMVSNet | | | 60.85 256 | 60.44 243 | 62.07 303 | 75.00 231 | 32.73 330 | 79.54 232 | 73.49 298 | 36.98 330 | 56.28 217 | 83.74 145 | 29.28 263 | 69.53 337 | 46.48 236 | 63.23 199 | 83.94 183 |
|
ACMP | | 61.11 9 | 66.24 202 | 64.33 201 | 72.00 196 | 74.89 233 | 49.12 199 | 83.18 161 | 79.83 204 | 55.41 226 | 52.29 246 | 82.68 171 | 25.83 285 | 86.10 201 | 60.89 128 | 63.94 190 | 80.78 236 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
MSDG | | | 59.44 263 | 55.14 277 | 72.32 188 | 74.69 234 | 50.71 169 | 74.39 277 | 73.58 296 | 44.44 305 | 43.40 307 | 77.52 228 | 19.45 321 | 90.87 61 | 31.31 293 | 57.49 259 | 75.38 304 |
|
ACMH+ | | 54.58 15 | 58.55 273 | 55.24 275 | 68.50 260 | 74.68 235 | 45.80 252 | 80.27 217 | 70.21 319 | 47.15 286 | 42.77 310 | 75.48 255 | 16.73 335 | 85.98 204 | 35.10 280 | 54.78 276 | 73.72 316 |
|
UniMVSNet (Re) | | | 67.71 172 | 66.80 159 | 70.45 230 | 74.44 236 | 42.93 277 | 82.42 175 | 84.90 101 | 63.69 86 | 59.63 167 | 80.99 195 | 47.18 51 | 85.23 218 | 51.17 209 | 56.75 261 | 83.19 197 |
|
LPG-MVS_test | | | 66.44 198 | 64.58 199 | 72.02 194 | 74.42 237 | 48.60 212 | 83.07 164 | 80.64 195 | 54.69 232 | 53.75 235 | 83.83 143 | 25.73 287 | 86.98 175 | 60.33 137 | 64.71 182 | 80.48 244 |
|
LGP-MVS_train | | | | | 72.02 194 | 74.42 237 | 48.60 212 | | 80.64 195 | 54.69 232 | 53.75 235 | 83.83 143 | 25.73 287 | 86.98 175 | 60.33 137 | 64.71 182 | 80.48 244 |
|
Baseline_NR-MVSNet | | | 65.49 208 | 64.27 202 | 69.13 246 | 74.37 239 | 41.65 288 | 83.39 159 | 78.85 221 | 59.56 137 | 59.62 168 | 76.88 237 | 40.75 148 | 87.44 167 | 49.99 213 | 55.05 273 | 78.28 277 |
|
ACMM | | 58.35 12 | 64.35 217 | 62.01 217 | 71.38 213 | 74.21 240 | 48.51 216 | 82.25 177 | 79.66 208 | 47.61 282 | 54.54 228 | 80.11 201 | 25.26 289 | 86.00 203 | 51.26 207 | 63.16 201 | 79.64 252 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CHOSEN 280x420 | | | 57.53 279 | 56.38 270 | 60.97 312 | 74.01 241 | 48.10 227 | 46.30 347 | 54.31 347 | 48.18 281 | 50.88 259 | 77.43 231 | 38.37 172 | 59.16 349 | 54.83 181 | 63.14 202 | 75.66 302 |
|
TransMVSNet (Re) | | | 62.82 241 | 60.76 238 | 69.02 247 | 73.98 242 | 41.61 289 | 86.36 75 | 79.30 218 | 56.90 198 | 52.53 245 | 76.44 242 | 41.85 138 | 87.60 164 | 38.83 262 | 40.61 330 | 77.86 283 |
|
CR-MVSNet | | | 62.47 246 | 59.04 254 | 72.77 180 | 73.97 243 | 56.57 25 | 60.52 332 | 71.72 306 | 60.04 133 | 57.49 204 | 65.86 318 | 38.94 166 | 80.31 276 | 42.86 253 | 59.93 227 | 81.42 224 |
|
RPMNet | | | 58.49 275 | 53.74 284 | 72.77 180 | 73.97 243 | 56.57 25 | 60.52 332 | 72.39 302 | 35.72 335 | 57.49 204 | 58.87 337 | 37.73 179 | 80.31 276 | 27.01 312 | 59.93 227 | 81.42 224 |
|
pcd1.5k->3k | | | 27.74 334 | 27.68 334 | 27.93 351 | 73.75 245 | 0.00 373 | 0.00 365 | 85.50 79 | 0.00 367 | 0.00 371 | 0.00 371 | 26.52 281 | 0.00 369 | 0.00 368 | 63.37 197 | 83.79 186 |
|
TranMVSNet+NR-MVSNet | | | 66.94 190 | 65.61 183 | 70.93 223 | 73.45 246 | 43.38 273 | 83.02 166 | 84.25 117 | 65.31 61 | 58.33 192 | 81.90 184 | 39.92 161 | 85.52 212 | 49.43 217 | 54.89 275 | 83.89 184 |
|
Patchmatch-test | | | 53.33 301 | 48.17 305 | 68.81 255 | 73.31 247 | 42.38 285 | 42.98 351 | 58.23 342 | 32.53 342 | 38.79 325 | 70.77 290 | 39.66 162 | 73.51 321 | 25.18 317 | 52.06 288 | 90.55 59 |
|
EG-PatchMatch MVS | | | 62.40 248 | 59.59 247 | 70.81 224 | 73.29 248 | 49.05 201 | 85.81 84 | 84.78 105 | 51.85 263 | 44.19 301 | 73.48 271 | 15.52 339 | 89.85 85 | 40.16 259 | 67.24 165 | 73.54 318 |
|
DU-MVS | | | 66.84 192 | 65.74 179 | 70.16 235 | 73.27 249 | 42.59 281 | 81.50 198 | 82.92 156 | 63.53 90 | 58.51 186 | 82.11 182 | 40.75 148 | 84.64 230 | 53.11 191 | 55.96 267 | 83.24 195 |
|
NR-MVSNet | | | 67.25 182 | 65.99 175 | 71.04 220 | 73.27 249 | 43.91 268 | 85.32 100 | 84.75 106 | 66.05 47 | 53.65 237 | 82.11 182 | 45.05 78 | 85.97 206 | 47.55 228 | 56.18 265 | 83.24 195 |
|
PS-MVSNAJss | | | 68.78 156 | 67.17 155 | 73.62 168 | 73.01 251 | 48.33 223 | 84.95 121 | 84.81 103 | 59.30 147 | 58.91 180 | 79.84 203 | 37.77 176 | 88.86 119 | 62.83 112 | 63.12 203 | 83.67 188 |
|
v18 | | | 64.36 216 | 61.80 218 | 72.05 191 | 72.97 252 | 53.31 104 | 81.16 204 | 77.76 247 | 59.14 153 | 48.50 273 | 68.97 299 | 42.91 118 | 84.38 232 | 56.62 163 | 48.17 297 | 78.47 264 |
|
OMC-MVS | | | 65.97 205 | 65.06 194 | 68.71 257 | 72.97 252 | 42.58 283 | 78.61 244 | 75.35 280 | 54.72 231 | 59.31 173 | 86.25 120 | 33.30 231 | 77.88 297 | 57.99 152 | 67.05 166 | 85.66 155 |
|
PatchT | | | 56.60 282 | 52.97 287 | 67.48 265 | 72.94 254 | 46.16 250 | 57.30 338 | 73.78 294 | 38.77 324 | 54.37 230 | 57.26 340 | 37.52 184 | 78.06 295 | 32.02 289 | 52.79 285 | 78.23 280 |
|
v16 | | | 64.25 218 | 61.66 227 | 72.03 192 | 72.91 255 | 53.28 110 | 80.93 207 | 77.81 243 | 58.86 164 | 48.30 274 | 68.80 302 | 42.70 123 | 84.37 233 | 56.44 169 | 48.14 298 | 78.44 267 |
|
v1neww | | | 69.43 143 | 67.62 143 | 74.89 135 | 72.90 256 | 53.31 104 | 85.12 108 | 81.11 186 | 64.29 72 | 61.00 149 | 78.53 213 | 42.88 120 | 88.98 111 | 62.66 114 | 60.06 223 | 82.37 209 |
|
v7new | | | 69.43 143 | 67.62 143 | 74.89 135 | 72.90 256 | 53.31 104 | 85.12 108 | 81.11 186 | 64.29 72 | 61.00 149 | 78.53 213 | 42.88 120 | 88.98 111 | 62.66 114 | 60.06 223 | 82.37 209 |
|
v17 | | | 64.19 219 | 61.58 228 | 72.03 192 | 72.89 258 | 53.28 110 | 80.91 208 | 77.80 244 | 58.87 163 | 48.22 275 | 68.77 303 | 42.69 124 | 84.37 233 | 56.43 170 | 47.66 301 | 78.43 268 |
|
v8 | | | 67.25 182 | 64.99 195 | 74.04 155 | 72.89 258 | 53.31 104 | 82.37 176 | 80.11 201 | 61.54 114 | 54.29 231 | 76.02 252 | 42.89 119 | 88.41 135 | 58.43 145 | 56.36 262 | 80.39 246 |
|
v6 | | | 69.43 143 | 67.61 145 | 74.88 137 | 72.87 260 | 53.30 108 | 85.12 108 | 81.10 188 | 64.29 72 | 60.99 151 | 78.52 215 | 42.88 120 | 88.98 111 | 62.67 113 | 60.06 223 | 82.37 209 |
|
DI_MVS_plusplus_test | | | 71.30 107 | 68.98 122 | 78.26 60 | 72.76 261 | 54.08 80 | 81.72 189 | 83.22 149 | 65.75 51 | 51.94 250 | 78.47 217 | 36.01 210 | 90.31 74 | 73.33 51 | 77.60 85 | 90.40 66 |
|
F-COLMAP | | | 55.96 289 | 53.65 285 | 62.87 301 | 72.76 261 | 42.77 280 | 74.70 276 | 70.37 317 | 40.03 320 | 41.11 317 | 79.36 204 | 17.77 329 | 73.70 320 | 32.80 288 | 53.96 280 | 72.15 324 |
|
v1141 | | | 69.50 140 | 67.67 139 | 74.98 132 | 72.73 263 | 53.41 96 | 85.08 114 | 82.14 164 | 64.79 67 | 60.88 152 | 78.19 220 | 43.09 117 | 89.04 102 | 62.51 116 | 59.61 230 | 82.47 207 |
|
divwei89l23v2f112 | | | 69.50 140 | 67.67 139 | 74.98 132 | 72.72 264 | 53.41 96 | 85.08 114 | 82.14 164 | 64.79 67 | 60.88 152 | 78.19 220 | 43.11 114 | 89.04 102 | 62.51 116 | 59.62 229 | 82.48 206 |
|
v1 | | | 69.49 142 | 67.67 139 | 74.98 132 | 72.69 265 | 53.41 96 | 85.08 114 | 82.13 167 | 64.80 66 | 60.87 154 | 78.19 220 | 43.11 114 | 89.04 102 | 62.51 116 | 59.61 230 | 82.49 205 |
|
IterMVS | | | 63.77 223 | 61.67 226 | 70.08 237 | 72.68 266 | 51.24 165 | 80.44 215 | 75.51 277 | 60.51 129 | 51.41 252 | 73.70 268 | 32.08 245 | 78.91 287 | 54.30 185 | 54.35 278 | 80.08 249 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
v11 | | | 63.44 228 | 60.66 240 | 71.79 207 | 72.61 267 | 53.02 124 | 79.80 227 | 78.08 239 | 58.30 174 | 47.27 287 | 67.91 308 | 40.67 152 | 84.14 240 | 54.93 180 | 46.39 311 | 78.23 280 |
|
v15 | | | 63.83 222 | 61.13 233 | 71.93 200 | 72.60 268 | 53.21 113 | 80.44 215 | 78.22 232 | 58.80 166 | 47.57 280 | 68.22 305 | 42.50 125 | 84.18 235 | 55.82 172 | 46.02 313 | 78.39 270 |
|
v7 | | | 68.76 157 | 66.79 160 | 74.68 142 | 72.60 268 | 53.37 99 | 84.72 127 | 80.88 191 | 63.80 82 | 60.43 160 | 78.21 219 | 40.05 159 | 88.89 118 | 60.34 136 | 60.07 222 | 81.77 219 |
|
v10 | | | 66.61 195 | 64.20 203 | 73.83 162 | 72.59 270 | 53.37 99 | 81.88 184 | 79.91 203 | 61.11 119 | 54.09 233 | 75.60 254 | 40.06 158 | 88.26 145 | 56.47 165 | 56.10 266 | 79.86 251 |
|
V14 | | | 63.72 224 | 60.99 235 | 71.91 202 | 72.58 271 | 53.18 114 | 80.24 219 | 78.19 233 | 58.53 173 | 47.35 286 | 68.10 306 | 42.28 128 | 84.18 235 | 55.68 174 | 45.97 314 | 78.36 273 |
|
Patchmtry | | | 56.56 283 | 52.95 288 | 67.42 266 | 72.53 272 | 50.59 173 | 59.05 334 | 71.72 306 | 37.86 328 | 46.92 289 | 65.86 318 | 38.94 166 | 80.06 281 | 36.94 271 | 46.72 310 | 71.60 327 |
|
V9 | | | 63.60 225 | 60.84 236 | 71.87 204 | 72.51 273 | 53.12 118 | 80.04 224 | 78.15 235 | 58.25 176 | 47.14 288 | 67.98 307 | 42.08 132 | 84.18 235 | 55.47 175 | 45.92 316 | 78.32 274 |
|
v12 | | | 63.47 227 | 60.68 239 | 71.85 205 | 72.45 274 | 53.08 120 | 79.83 226 | 78.13 237 | 57.95 182 | 46.89 290 | 67.87 309 | 41.81 140 | 84.17 238 | 55.30 177 | 45.87 317 | 78.29 276 |
|
Fast-Effi-MVS+-dtu | | | 66.53 196 | 64.10 204 | 73.84 161 | 72.41 275 | 52.30 141 | 84.73 126 | 75.66 276 | 59.51 138 | 56.34 216 | 79.11 209 | 28.11 269 | 85.85 209 | 57.74 157 | 63.29 198 | 83.35 191 |
|
v13 | | | 63.36 229 | 60.54 242 | 71.82 206 | 72.41 275 | 53.03 123 | 79.64 231 | 78.10 238 | 57.66 190 | 46.67 293 | 67.75 310 | 41.68 141 | 84.17 238 | 55.11 178 | 45.82 318 | 78.25 279 |
|
v1144 | | | 68.81 153 | 66.82 158 | 74.80 141 | 72.34 277 | 53.46 90 | 84.68 128 | 81.77 176 | 64.25 75 | 60.28 161 | 77.91 224 | 40.23 154 | 88.95 115 | 60.37 135 | 59.52 232 | 81.97 213 |
|
v2v482 | | | 69.55 138 | 67.64 142 | 75.26 127 | 72.32 278 | 53.83 82 | 84.93 122 | 81.94 170 | 65.37 59 | 60.80 156 | 79.25 206 | 41.62 142 | 88.98 111 | 63.03 111 | 59.51 233 | 82.98 200 |
|
test_normal | | | 71.31 106 | 68.95 123 | 78.39 56 | 72.30 279 | 54.25 75 | 81.67 190 | 84.05 129 | 65.94 50 | 51.31 254 | 78.09 223 | 36.06 207 | 90.43 70 | 73.00 55 | 78.09 83 | 90.50 64 |
|
test0.0.03 1 | | | 62.54 244 | 62.44 214 | 62.86 302 | 72.28 280 | 29.51 338 | 82.93 167 | 78.78 223 | 59.18 151 | 53.07 243 | 82.41 175 | 36.91 196 | 77.39 301 | 37.45 265 | 58.96 237 | 81.66 221 |
|
LP | | | 47.05 316 | 42.23 321 | 61.53 309 | 72.04 281 | 49.37 197 | 49.48 344 | 65.50 329 | 34.57 341 | 34.29 337 | 52.30 345 | 17.73 330 | 75.32 312 | 17.56 350 | 36.57 337 | 59.91 347 |
|
v1192 | | | 67.96 168 | 65.74 179 | 74.63 143 | 71.79 282 | 53.43 95 | 84.06 140 | 80.99 190 | 63.19 94 | 59.56 169 | 77.46 230 | 37.50 186 | 88.65 123 | 58.20 150 | 58.93 238 | 81.79 218 |
|
v148 | | | 68.24 166 | 66.35 166 | 73.88 159 | 71.76 283 | 51.47 158 | 84.23 135 | 81.90 174 | 63.69 86 | 58.94 177 | 76.44 242 | 43.72 103 | 87.78 160 | 60.63 131 | 55.86 269 | 82.39 208 |
|
v144192 | | | 67.86 169 | 65.76 178 | 74.16 152 | 71.68 284 | 53.09 119 | 84.14 137 | 80.83 193 | 62.85 98 | 59.21 175 | 77.28 233 | 39.30 164 | 88.00 152 | 58.67 144 | 57.88 256 | 81.40 226 |
|
pm-mvs1 | | | 64.12 220 | 62.56 213 | 68.78 256 | 71.68 284 | 38.87 303 | 82.89 168 | 81.57 177 | 55.54 225 | 53.89 234 | 77.82 225 | 37.73 179 | 86.74 184 | 48.46 223 | 53.49 284 | 80.72 237 |
|
MDA-MVSNet-bldmvs | | | 51.56 306 | 47.75 309 | 63.00 300 | 71.60 286 | 47.32 234 | 69.70 310 | 72.12 304 | 43.81 309 | 27.65 350 | 63.38 324 | 21.97 311 | 75.96 307 | 27.30 311 | 32.19 346 | 65.70 340 |
|
v1921920 | | | 67.45 178 | 65.23 192 | 74.10 154 | 71.51 287 | 52.90 128 | 83.75 147 | 80.44 198 | 62.48 102 | 59.12 176 | 77.13 234 | 36.98 194 | 87.90 153 | 57.53 158 | 58.14 250 | 81.49 223 |
|
our_test_3 | | | 59.11 266 | 55.08 278 | 71.18 218 | 71.42 288 | 53.29 109 | 81.96 181 | 74.52 284 | 48.32 279 | 42.08 311 | 69.28 297 | 28.14 268 | 82.15 261 | 34.35 282 | 45.68 319 | 78.11 282 |
|
ppachtmachnet_test | | | 58.56 272 | 54.34 279 | 71.24 215 | 71.42 288 | 54.74 64 | 81.84 186 | 72.27 303 | 49.02 278 | 45.86 300 | 68.99 298 | 26.27 282 | 83.30 249 | 30.12 296 | 43.23 326 | 75.69 301 |
|
v1240 | | | 66.99 189 | 64.68 198 | 73.93 157 | 71.38 290 | 52.66 131 | 83.39 159 | 79.98 202 | 61.97 107 | 58.44 191 | 77.11 235 | 35.25 215 | 87.81 155 | 56.46 166 | 58.15 248 | 81.33 227 |
|
JIA-IIPM | | | 52.33 305 | 47.77 308 | 66.03 282 | 71.20 291 | 46.92 238 | 40.00 355 | 76.48 265 | 37.10 329 | 46.73 291 | 37.02 351 | 32.96 234 | 77.88 297 | 35.97 274 | 52.45 287 | 73.29 321 |
|
OpenMVS_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 53.19 17 | 59.20 265 | 56.00 272 | 68.83 250 | 71.13 292 | 44.30 265 | 83.64 148 | 75.02 282 | 46.42 293 | 46.48 296 | 73.03 274 | 18.69 325 | 88.14 146 | 27.74 309 | 61.80 215 | 74.05 314 |
|
SixPastTwentyTwo | | | 54.37 293 | 50.10 299 | 67.21 267 | 70.70 293 | 41.46 290 | 74.73 275 | 64.69 332 | 47.56 283 | 39.12 323 | 69.49 294 | 18.49 327 | 84.69 229 | 31.87 290 | 34.20 344 | 75.48 303 |
|
V42 | | | 67.66 173 | 65.60 184 | 73.86 160 | 70.69 294 | 53.63 86 | 81.50 198 | 78.61 228 | 63.85 80 | 59.49 171 | 77.49 229 | 37.98 173 | 87.65 163 | 62.33 119 | 58.43 245 | 80.29 247 |
|
semantic-postprocess | | | | | 60.08 314 | 70.68 295 | 45.07 258 | | 74.25 288 | 43.54 311 | 50.02 267 | 73.73 265 | 32.22 243 | 56.74 350 | 51.06 210 | 53.60 283 | 78.42 269 |
|
pmmvs4 | | | 63.34 230 | 61.07 234 | 70.16 235 | 70.14 296 | 50.53 174 | 79.97 225 | 71.41 312 | 55.08 228 | 54.12 232 | 78.58 212 | 32.79 237 | 82.09 263 | 50.33 212 | 57.22 260 | 77.86 283 |
|
MDA-MVSNet_test_wron | | | 53.82 299 | 49.95 301 | 65.43 287 | 70.13 297 | 49.05 201 | 72.30 292 | 71.65 309 | 44.23 307 | 31.85 345 | 63.13 325 | 23.68 300 | 74.01 316 | 33.25 286 | 39.35 333 | 73.23 322 |
|
YYNet1 | | | 53.82 299 | 49.96 300 | 65.41 288 | 70.09 298 | 48.95 203 | 72.30 292 | 71.66 308 | 44.25 306 | 31.89 344 | 63.07 326 | 23.73 297 | 73.95 317 | 33.26 285 | 39.40 332 | 73.34 320 |
|
LTVRE_ROB | | 45.45 19 | 52.73 302 | 49.74 302 | 61.69 307 | 69.78 299 | 34.99 320 | 44.52 349 | 67.60 325 | 43.11 314 | 43.79 304 | 74.03 262 | 18.54 326 | 81.45 266 | 28.39 307 | 57.94 253 | 68.62 334 |
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 |
testpf | | | 45.92 318 | 45.81 313 | 46.27 337 | 69.56 300 | 27.86 344 | 23.18 360 | 73.91 293 | 44.10 308 | 36.99 327 | 57.16 341 | 20.56 317 | 71.77 329 | 42.17 256 | 44.64 322 | 39.18 355 |
|
pmmvs5 | | | 62.80 242 | 61.18 232 | 67.66 264 | 69.53 301 | 42.37 286 | 82.65 171 | 75.19 281 | 54.30 236 | 52.03 249 | 78.51 216 | 31.64 250 | 80.67 271 | 48.60 221 | 58.15 248 | 79.95 250 |
|
WR-MVS_H | | | 58.91 268 | 58.04 258 | 61.54 308 | 69.07 302 | 33.83 325 | 76.91 254 | 81.99 169 | 51.40 265 | 48.17 276 | 74.67 259 | 40.23 154 | 74.15 314 | 31.78 291 | 48.10 299 | 76.64 293 |
|
test_djsdf | | | 63.84 221 | 61.56 230 | 70.70 225 | 68.78 303 | 44.69 262 | 81.63 194 | 81.44 179 | 50.28 270 | 52.27 247 | 76.26 245 | 26.72 279 | 86.11 199 | 60.83 129 | 55.84 270 | 81.29 230 |
|
Anonymous20231206 | | | 59.08 267 | 57.59 260 | 63.55 297 | 68.77 304 | 32.14 333 | 80.26 218 | 79.78 205 | 50.00 273 | 49.39 269 | 72.39 281 | 26.64 280 | 78.36 290 | 33.12 287 | 57.94 253 | 80.14 248 |
|
K. test v3 | | | 54.04 296 | 49.42 303 | 67.92 263 | 68.55 305 | 42.57 284 | 75.51 270 | 63.07 336 | 52.07 259 | 39.21 322 | 64.59 322 | 19.34 322 | 82.21 260 | 37.11 268 | 25.31 350 | 78.97 257 |
|
CP-MVSNet | | | 58.54 274 | 57.57 261 | 61.46 310 | 68.50 306 | 33.96 324 | 76.90 255 | 78.60 229 | 51.67 264 | 47.83 277 | 76.60 241 | 34.99 218 | 72.79 325 | 35.45 276 | 47.58 302 | 77.64 287 |
|
N_pmnet | | | 41.25 321 | 39.77 324 | 45.66 339 | 68.50 306 | 0.82 371 | 72.51 290 | 0.38 373 | 35.61 336 | 35.26 333 | 61.51 328 | 20.07 320 | 67.74 340 | 23.51 323 | 40.63 329 | 68.42 335 |
|
jajsoiax | | | 63.21 232 | 60.84 236 | 70.32 233 | 68.33 308 | 44.45 263 | 81.23 202 | 81.05 189 | 53.37 242 | 50.96 258 | 77.81 226 | 17.49 331 | 85.49 214 | 59.31 141 | 58.05 251 | 81.02 233 |
|
PS-CasMVS | | | 58.12 277 | 57.03 265 | 61.37 311 | 68.24 309 | 33.80 326 | 76.73 256 | 78.01 241 | 51.20 267 | 47.54 282 | 76.20 249 | 32.85 235 | 72.76 326 | 35.17 278 | 47.37 304 | 77.55 288 |
|
mvs_tets | | | 62.96 236 | 60.55 241 | 70.19 234 | 68.22 310 | 44.24 267 | 80.90 209 | 80.74 194 | 52.99 246 | 50.82 266 | 77.56 227 | 16.74 334 | 85.44 215 | 59.04 142 | 57.94 253 | 80.89 234 |
|
COLMAP_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 43.60 20 | 50.90 307 | 48.05 306 | 59.47 315 | 67.81 311 | 40.57 299 | 71.25 300 | 62.72 338 | 36.49 334 | 36.19 329 | 73.51 270 | 13.48 341 | 73.92 318 | 20.71 343 | 50.26 294 | 63.92 343 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
Test4 | | | 68.64 160 | 65.68 181 | 77.53 77 | 67.78 312 | 53.34 101 | 79.42 235 | 82.84 158 | 65.96 49 | 46.54 295 | 76.15 250 | 25.16 290 | 88.83 121 | 69.74 69 | 77.53 88 | 90.43 65 |
|
PEN-MVS | | | 58.35 276 | 57.15 263 | 61.94 305 | 67.55 313 | 34.39 322 | 77.01 253 | 78.35 231 | 51.87 262 | 47.72 278 | 76.73 239 | 33.91 226 | 73.75 319 | 34.03 283 | 47.17 306 | 77.68 285 |
|
v7n | | | 62.50 245 | 59.27 251 | 72.20 189 | 67.25 314 | 49.83 188 | 77.87 250 | 80.12 200 | 52.50 257 | 48.80 272 | 73.07 273 | 32.10 244 | 87.90 153 | 46.83 234 | 54.92 274 | 78.86 259 |
|
v748 | | | 61.35 253 | 58.24 257 | 70.69 226 | 66.28 315 | 47.35 233 | 76.58 257 | 79.17 219 | 53.09 244 | 46.37 297 | 71.50 286 | 33.18 233 | 86.33 194 | 46.78 235 | 51.19 292 | 78.39 270 |
|
pmmvs6 | | | 59.64 262 | 57.15 263 | 67.09 268 | 66.01 316 | 36.86 312 | 80.50 214 | 78.64 226 | 45.05 301 | 49.05 271 | 73.94 263 | 27.28 276 | 86.10 201 | 43.96 248 | 49.94 295 | 78.31 275 |
|
DTE-MVSNet | | | 57.03 280 | 55.73 274 | 60.95 313 | 65.94 317 | 32.57 331 | 75.71 266 | 77.09 259 | 51.16 268 | 46.65 294 | 76.34 244 | 32.84 236 | 73.22 323 | 30.94 295 | 44.87 321 | 77.06 290 |
|
TinyColmap | | | 48.15 312 | 44.49 317 | 59.13 317 | 65.73 318 | 38.04 307 | 63.34 323 | 62.86 337 | 38.78 323 | 29.48 348 | 67.23 315 | 6.46 355 | 73.30 322 | 24.59 319 | 41.90 328 | 66.04 338 |
|
XVG-OURS | | | 61.88 250 | 59.34 250 | 69.49 243 | 65.37 319 | 46.27 247 | 64.80 320 | 73.49 298 | 47.04 287 | 57.41 208 | 82.85 165 | 25.15 291 | 78.18 292 | 53.00 194 | 64.98 179 | 84.01 177 |
|
XVG-OURS-SEG-HR | | | 62.02 249 | 59.54 248 | 69.46 244 | 65.30 320 | 45.88 251 | 65.06 318 | 73.57 297 | 46.45 292 | 57.42 207 | 83.35 159 | 26.95 278 | 78.09 294 | 53.77 189 | 64.03 188 | 84.42 171 |
|
OurMVSNet-221017-0 | | | 52.39 304 | 48.73 304 | 63.35 299 | 65.21 321 | 38.42 305 | 68.54 314 | 64.95 331 | 38.19 325 | 39.57 321 | 71.43 287 | 13.23 342 | 79.92 282 | 37.16 266 | 40.32 331 | 71.72 326 |
|
AllTest | | | 47.32 314 | 44.66 316 | 55.32 326 | 65.08 322 | 37.50 310 | 62.96 326 | 54.25 348 | 35.45 338 | 33.42 341 | 72.82 275 | 9.98 345 | 59.33 347 | 24.13 321 | 43.84 324 | 69.13 332 |
|
TestCases | | | | | 55.32 326 | 65.08 322 | 37.50 310 | | 54.25 348 | 35.45 338 | 33.42 341 | 72.82 275 | 9.98 345 | 59.33 347 | 24.13 321 | 43.84 324 | 69.13 332 |
|
lessismore_v0 | | | | | 67.98 262 | 64.76 324 | 41.25 291 | | 45.75 354 | | 36.03 330 | 65.63 320 | 19.29 323 | 84.11 241 | 35.67 275 | 21.24 353 | 78.59 263 |
|
UnsupCasMVSNet_eth | | | 57.56 278 | 55.15 276 | 64.79 293 | 64.57 325 | 33.12 327 | 73.17 285 | 83.87 136 | 58.98 161 | 41.75 314 | 70.03 293 | 22.54 305 | 79.92 282 | 46.12 240 | 35.31 339 | 81.32 229 |
|
USDC | | | 54.36 294 | 51.23 296 | 63.76 296 | 64.29 326 | 37.71 309 | 62.84 327 | 73.48 300 | 56.85 199 | 35.47 332 | 71.94 285 | 9.23 348 | 78.43 289 | 38.43 263 | 48.57 296 | 75.13 306 |
|
Patchmatch-RL test | | | 58.72 270 | 54.32 280 | 71.92 201 | 63.91 327 | 44.25 266 | 61.73 328 | 55.19 345 | 57.38 194 | 49.31 270 | 54.24 343 | 37.60 182 | 80.89 269 | 62.19 121 | 47.28 305 | 90.63 58 |
|
anonymousdsp | | | 60.46 258 | 57.65 259 | 68.88 248 | 63.63 328 | 45.09 257 | 72.93 287 | 78.63 227 | 46.52 291 | 51.12 255 | 72.80 277 | 21.46 313 | 83.07 251 | 57.79 155 | 53.97 279 | 78.47 264 |
|
UnsupCasMVSNet_bld | | | 53.86 298 | 50.53 298 | 63.84 295 | 63.52 329 | 34.75 321 | 71.38 299 | 81.92 172 | 46.53 290 | 38.95 324 | 57.93 338 | 20.55 318 | 80.20 280 | 39.91 260 | 34.09 345 | 76.57 294 |
|
test20.03 | | | 55.22 291 | 54.07 281 | 58.68 318 | 63.14 330 | 25.00 347 | 77.69 251 | 74.78 283 | 52.64 247 | 43.43 306 | 72.39 281 | 26.21 283 | 74.76 313 | 29.31 298 | 47.05 308 | 76.28 297 |
|
testgi | | | 54.25 295 | 52.57 292 | 59.29 316 | 62.76 331 | 21.65 353 | 72.21 294 | 70.47 315 | 53.25 243 | 41.94 312 | 77.33 232 | 14.28 340 | 77.95 296 | 29.18 299 | 51.72 289 | 78.28 277 |
|
EU-MVSNet | | | 52.63 303 | 50.72 297 | 58.37 320 | 62.69 332 | 28.13 343 | 72.60 288 | 75.97 272 | 30.94 345 | 40.76 319 | 72.11 284 | 20.16 319 | 70.80 333 | 35.11 279 | 46.11 312 | 76.19 298 |
|
XVG-ACMP-BASELINE | | | 56.03 287 | 52.85 289 | 65.58 285 | 61.91 333 | 40.95 294 | 63.36 322 | 72.43 301 | 45.20 300 | 46.02 298 | 74.09 261 | 9.20 349 | 78.12 293 | 45.13 242 | 58.27 246 | 77.66 286 |
|
testing_2 | | | 63.60 225 | 59.86 246 | 74.82 139 | 61.87 334 | 52.39 137 | 73.06 286 | 82.76 159 | 61.49 116 | 39.96 320 | 67.39 313 | 21.06 315 | 88.34 138 | 67.07 85 | 64.10 187 | 83.72 187 |
|
test2356 | | | 53.94 297 | 52.37 293 | 58.64 319 | 61.58 335 | 27.53 346 | 78.20 248 | 74.33 287 | 46.92 288 | 44.01 302 | 66.04 317 | 18.91 324 | 74.11 315 | 28.80 300 | 52.55 286 | 74.28 311 |
|
MIMVSNet1 | | | 50.35 308 | 47.81 307 | 57.96 321 | 61.53 336 | 27.80 345 | 67.40 315 | 74.06 290 | 43.25 313 | 33.31 343 | 65.38 321 | 16.03 337 | 71.34 332 | 21.80 338 | 47.55 303 | 74.75 307 |
|
v52 | | | 59.82 259 | 56.41 268 | 70.06 239 | 61.49 337 | 48.67 210 | 69.46 311 | 75.80 274 | 52.55 255 | 47.49 284 | 68.82 301 | 28.60 264 | 85.70 210 | 52.13 204 | 51.34 291 | 75.80 299 |
|
V4 | | | 59.82 259 | 56.41 268 | 70.05 240 | 61.49 337 | 48.67 210 | 69.46 311 | 75.79 275 | 52.55 255 | 47.49 284 | 68.83 300 | 28.60 264 | 85.70 210 | 52.13 204 | 51.35 290 | 75.80 299 |
|
pmmvs-eth3d | | | 55.97 288 | 52.78 290 | 65.54 286 | 61.02 339 | 46.44 243 | 75.36 272 | 67.72 324 | 49.61 275 | 43.65 305 | 67.58 312 | 21.63 312 | 77.04 302 | 44.11 247 | 44.33 323 | 73.15 323 |
|
CMPMVS | ![Method available as binary. binary](img/icon_binary.png) | 40.41 21 | 55.34 290 | 52.64 291 | 63.46 298 | 60.88 340 | 43.84 269 | 61.58 330 | 71.06 313 | 30.43 346 | 36.33 328 | 74.63 260 | 24.14 295 | 75.44 309 | 48.05 226 | 66.62 168 | 71.12 330 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
Gipuma | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 27.47 335 | 24.26 336 | 37.12 344 | 60.55 341 | 29.17 340 | 11.68 363 | 60.00 341 | 14.18 357 | 10.52 361 | 15.12 363 | 2.20 366 | 63.01 345 | 8.39 359 | 35.65 338 | 19.18 360 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
ITE_SJBPF | | | | | 51.84 331 | 58.03 342 | 31.94 334 | | 53.57 350 | 36.67 333 | 41.32 316 | 75.23 257 | 11.17 344 | 51.57 354 | 25.81 316 | 48.04 300 | 72.02 325 |
|
new-patchmatchnet | | | 48.21 311 | 46.55 310 | 53.18 329 | 57.73 343 | 18.19 362 | 70.24 305 | 71.02 314 | 45.70 295 | 33.70 339 | 60.23 331 | 18.00 328 | 69.86 336 | 27.97 308 | 34.35 342 | 71.49 329 |
|
RPSCF | | | 45.77 319 | 44.13 318 | 50.68 332 | 57.67 344 | 29.66 337 | 54.92 342 | 45.25 355 | 26.69 349 | 45.92 299 | 75.92 253 | 17.43 332 | 45.70 359 | 27.44 310 | 45.95 315 | 76.67 292 |
|
testus | | | 48.97 310 | 46.53 311 | 56.31 325 | 57.39 345 | 24.08 349 | 73.40 283 | 70.45 316 | 43.37 312 | 35.52 331 | 63.95 323 | 4.77 360 | 71.36 331 | 24.88 318 | 45.02 320 | 73.50 319 |
|
1111 | | | 48.00 313 | 46.30 312 | 53.08 330 | 55.68 346 | 20.86 356 | 70.41 303 | 76.03 270 | 36.88 331 | 34.86 334 | 59.55 335 | 23.72 298 | 68.13 338 | 20.82 341 | 38.76 335 | 70.25 331 |
|
.test1245 | | | 38.91 324 | 41.99 322 | 29.67 349 | 55.68 346 | 20.86 356 | 70.41 303 | 76.03 270 | 36.88 331 | 34.86 334 | 59.55 335 | 23.72 298 | 68.13 338 | 20.82 341 | 0.00 366 | 0.02 366 |
|
ambc | | | | | 62.06 304 | 53.98 348 | 29.38 339 | 35.08 357 | 79.65 209 | | 41.37 315 | 59.96 332 | 6.27 356 | 82.15 261 | 35.34 277 | 38.22 336 | 74.65 308 |
|
test1235678 | | | 47.09 315 | 43.82 319 | 56.91 323 | 53.18 349 | 24.90 348 | 71.93 296 | 70.31 318 | 39.54 321 | 31.44 346 | 56.59 342 | 9.50 347 | 71.55 330 | 22.63 329 | 39.24 334 | 74.28 311 |
|
PM-MVS | | | 46.92 317 | 43.76 320 | 56.41 324 | 52.18 350 | 32.26 332 | 63.21 325 | 38.18 359 | 37.99 327 | 40.78 318 | 66.20 316 | 5.09 358 | 65.42 343 | 48.19 225 | 41.99 327 | 71.54 328 |
|
TDRefinement | | | 40.91 322 | 38.37 325 | 48.55 335 | 50.45 351 | 33.03 329 | 58.98 335 | 50.97 351 | 28.50 347 | 29.89 347 | 67.39 313 | 6.21 357 | 54.51 351 | 17.67 349 | 35.25 340 | 58.11 348 |
|
new_pmnet | | | 33.56 331 | 31.89 331 | 38.59 342 | 49.01 352 | 20.42 358 | 51.01 343 | 37.92 360 | 20.58 351 | 23.45 351 | 46.79 348 | 6.66 354 | 49.28 356 | 20.00 346 | 31.57 348 | 46.09 354 |
|
pmmvs3 | | | 45.53 320 | 41.55 323 | 57.44 322 | 48.97 353 | 39.68 301 | 70.06 306 | 57.66 343 | 28.32 348 | 34.06 338 | 57.29 339 | 8.50 350 | 66.85 342 | 34.86 281 | 34.26 343 | 65.80 339 |
|
DSMNet-mixed | | | 38.35 325 | 35.36 327 | 47.33 336 | 48.11 354 | 14.91 364 | 37.87 356 | 36.60 361 | 19.18 354 | 34.37 336 | 59.56 334 | 15.53 338 | 53.01 353 | 20.14 345 | 46.89 309 | 74.07 313 |
|
FPMVS | | | 35.40 328 | 33.67 329 | 40.57 341 | 46.34 355 | 28.74 342 | 41.05 353 | 57.05 344 | 20.37 353 | 22.27 353 | 53.38 344 | 6.87 353 | 44.94 360 | 8.62 358 | 47.11 307 | 48.01 353 |
|
test12356 | | | 37.84 326 | 35.07 328 | 46.18 338 | 45.03 356 | 8.02 369 | 57.70 337 | 62.67 339 | 31.83 344 | 22.78 352 | 50.25 346 | 4.46 361 | 66.95 341 | 17.25 351 | 23.62 352 | 63.57 344 |
|
testmv | | | 39.64 323 | 36.01 326 | 50.55 333 | 42.18 357 | 21.56 354 | 64.81 319 | 66.88 327 | 32.22 343 | 22.25 354 | 47.47 347 | 4.33 362 | 64.81 344 | 17.71 348 | 26.22 349 | 65.29 341 |
|
LF4IMVS | | | 33.04 332 | 32.55 330 | 34.52 346 | 40.96 358 | 22.03 352 | 44.45 350 | 35.62 362 | 20.42 352 | 28.12 349 | 62.35 327 | 5.03 359 | 31.88 365 | 21.61 340 | 34.42 341 | 49.63 352 |
|
PNet_i23d | | | 25.11 337 | 23.09 338 | 31.17 348 | 40.18 359 | 21.30 355 | 57.99 336 | 33.28 364 | 13.77 358 | 9.94 362 | 30.29 358 | 0.45 371 | 43.74 361 | 13.61 356 | 8.28 358 | 28.46 358 |
|
wuyk23d | | | 9.11 345 | 8.77 347 | 10.15 355 | 40.18 359 | 16.76 363 | 20.28 361 | 1.01 372 | 2.58 365 | 2.66 368 | 0.98 368 | 0.23 372 | 12.49 367 | 4.08 365 | 6.90 363 | 1.19 365 |
|
no-one | | | 37.21 327 | 31.48 332 | 54.40 328 | 39.62 361 | 31.91 335 | 45.68 348 | 67.42 326 | 35.54 337 | 14.59 357 | 35.91 354 | 7.35 351 | 73.20 324 | 22.98 324 | 14.23 355 | 58.09 349 |
|
PMMVS2 | | | 26.71 336 | 22.98 339 | 37.87 343 | 36.89 362 | 8.51 368 | 42.51 352 | 29.32 367 | 19.09 355 | 13.01 358 | 37.54 350 | 2.23 365 | 53.11 352 | 14.54 353 | 11.71 356 | 51.99 351 |
|
E-PMN | | | 19.16 340 | 18.40 341 | 21.44 352 | 36.19 363 | 13.63 365 | 47.59 345 | 30.89 365 | 10.73 360 | 5.91 365 | 16.59 361 | 3.66 364 | 39.77 362 | 5.95 362 | 8.14 359 | 10.92 362 |
|
EMVS | | | 18.42 341 | 17.66 342 | 20.71 353 | 34.13 364 | 12.64 366 | 46.94 346 | 29.94 366 | 10.46 362 | 5.58 366 | 14.93 364 | 4.23 363 | 38.83 363 | 5.24 364 | 7.51 362 | 10.67 363 |
|
ANet_high | | | 34.39 329 | 29.59 333 | 48.78 334 | 30.34 365 | 22.28 351 | 55.53 339 | 63.79 335 | 38.11 326 | 15.47 356 | 36.56 353 | 6.94 352 | 59.98 346 | 13.93 354 | 5.64 365 | 64.08 342 |
|
MVE | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | 16.60 23 | 17.34 343 | 13.39 344 | 29.16 350 | 28.43 366 | 19.72 360 | 13.73 362 | 23.63 368 | 7.23 364 | 7.96 363 | 21.41 359 | 0.80 370 | 36.08 364 | 6.97 360 | 10.39 357 | 31.69 357 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
LCM-MVSNet | | | 28.07 333 | 23.85 337 | 40.71 340 | 27.46 367 | 18.93 361 | 30.82 358 | 46.19 352 | 12.76 359 | 16.40 355 | 34.70 356 | 1.90 367 | 48.69 357 | 20.25 344 | 24.22 351 | 54.51 350 |
|
PMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 19.57 22 | 25.07 338 | 22.43 340 | 32.99 347 | 23.12 368 | 22.98 350 | 40.98 354 | 35.19 363 | 15.99 356 | 11.95 360 | 35.87 355 | 1.47 369 | 49.29 355 | 5.41 363 | 31.90 347 | 26.70 359 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
wuykxyi23d | | | 19.94 339 | 14.87 343 | 35.13 345 | 22.47 369 | 19.80 359 | 25.80 359 | 38.64 358 | 7.61 363 | 4.88 367 | 13.58 366 | 0.23 372 | 48.42 358 | 13.11 357 | 7.53 360 | 37.18 356 |
|
DeepMVS_CX | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | | | 13.10 354 | 21.34 370 | 8.99 367 | | 10.02 371 | 10.59 361 | 7.53 364 | 30.55 357 | 1.82 368 | 14.55 366 | 6.83 361 | 7.52 361 | 15.75 361 |
|
tmp_tt | | | 9.44 344 | 10.68 345 | 5.73 356 | 2.49 371 | 4.21 370 | 10.48 364 | 18.04 369 | 0.34 366 | 12.59 359 | 20.49 360 | 11.39 343 | 7.03 368 | 13.84 355 | 6.46 364 | 5.95 364 |
|
testmvs | | | 6.14 347 | 8.18 348 | 0.01 357 | 0.01 372 | 0.00 373 | 73.40 283 | 0.00 374 | 0.00 367 | 0.02 369 | 0.15 369 | 0.00 374 | 0.00 369 | 0.02 366 | 0.00 366 | 0.02 366 |
|
cdsmvs_eth3d_5k | | | 18.33 342 | 24.44 335 | 0.00 359 | 0.00 373 | 0.00 373 | 0.00 365 | 89.40 12 | 0.00 367 | 0.00 371 | 92.02 23 | 38.55 170 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
pcd_1.5k_mvsjas | | | 3.15 349 | 4.20 350 | 0.00 359 | 0.00 373 | 0.00 373 | 0.00 365 | 0.00 374 | 0.00 367 | 0.00 371 | 0.00 371 | 37.77 176 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
sosnet-low-res | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 373 | 0.00 373 | 0.00 365 | 0.00 374 | 0.00 367 | 0.00 371 | 0.00 371 | 0.00 374 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
sosnet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 373 | 0.00 373 | 0.00 365 | 0.00 374 | 0.00 367 | 0.00 371 | 0.00 371 | 0.00 374 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
uncertanet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 373 | 0.00 373 | 0.00 365 | 0.00 374 | 0.00 367 | 0.00 371 | 0.00 371 | 0.00 374 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
Regformer | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 373 | 0.00 373 | 0.00 365 | 0.00 374 | 0.00 367 | 0.00 371 | 0.00 371 | 0.00 374 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
test123 | | | 6.01 348 | 8.01 349 | 0.01 357 | 0.00 373 | 0.01 372 | 71.93 296 | 0.00 374 | 0.00 367 | 0.02 369 | 0.11 370 | 0.00 374 | 0.00 369 | 0.02 366 | 0.00 366 | 0.02 366 |
|
ab-mvs-re | | | 7.68 346 | 10.24 346 | 0.00 359 | 0.00 373 | 0.00 373 | 0.00 365 | 0.00 374 | 0.00 367 | 0.00 371 | 92.12 20 | 0.00 374 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
uanet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 373 | 0.00 373 | 0.00 365 | 0.00 374 | 0.00 367 | 0.00 371 | 0.00 371 | 0.00 374 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
GSMVS | | | | | | | | | | | | | | | | | 88.13 116 |
|
test_part1 | | | | | 0.00 359 | | 0.00 373 | 0.00 365 | 88.42 30 | | | | 0.00 374 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
sam_mvs1 | | | | | | | | | | | | | 38.86 168 | | | | 88.13 116 |
|
sam_mvs | | | | | | | | | | | | | 35.99 211 | | | | |
|
MTGPA | ![Method available as binary. binary](img/icon_binary.png) | | | | | | | | 81.31 182 | | | | | | | | |
|
test_post1 | | | | | | | | 70.84 302 | | | | 14.72 365 | 34.33 222 | 83.86 242 | 48.80 220 | | |
|
test_post | | | | | | | | | | | | 16.22 362 | 37.52 184 | 84.72 228 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 59.74 333 | 38.41 171 | 79.91 284 | | | |
|
MTMP | | | | | | | | 87.27 61 | 15.34 370 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 78.72 17 | 85.44 27 | 91.39 45 |
|
agg_prior2 | | | | | | | | | | | | | | | 75.65 33 | 85.11 31 | 91.01 51 |
|
test_prior4 | | | | | | | 56.39 30 | 87.15 63 | | | | | | | | | |
|
test_prior2 | | | | | | | | 89.04 33 | | 61.88 109 | 73.55 34 | 91.46 37 | 48.01 45 | | 74.73 39 | 85.46 25 | |
|
旧先验2 | | | | | | | | 81.73 188 | | 45.53 297 | 74.66 27 | | | 70.48 335 | 58.31 149 | | |
|
新几何2 | | | | | | | | 81.61 196 | | | | | | | | | |
|
无先验 | | | | | | | | 85.19 102 | 78.00 242 | 49.08 277 | | | | 85.13 219 | 52.78 197 | | 87.45 127 |
|
原ACMM2 | | | | | | | | 83.77 146 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 77.81 299 | 45.64 241 | | |
|
segment_acmp | | | | | | | | | | | | | 44.97 81 | | | | |
|
testdata1 | | | | | | | | 77.55 252 | | 64.14 76 | | | | | | | |
|
plane_prior5 | | | | | | | | | 82.59 160 | | | | | 88.30 142 | 65.46 97 | 72.34 134 | 84.49 169 |
|
plane_prior4 | | | | | | | | | | | | 83.28 160 | | | | | |
|
plane_prior3 | | | | | | | 48.95 203 | | | 64.01 77 | 62.15 140 | | | | | | |
|
plane_prior2 | | | | | | | | 85.76 86 | | 63.60 88 | | | | | | | |
|
plane_prior | | | | | | | 49.57 191 | 87.43 55 | | 64.57 69 | | | | | | 72.84 129 | |
|
n2 | | | | | | | | | 0.00 374 | | | | | | | | |
|
nn | | | | | | | | | 0.00 374 | | | | | | | | |
|
door-mid | | | | | | | | | 41.31 357 | | | | | | | | |
|
test11 | | | | | | | | | 84.25 117 | | | | | | | | |
|
door | | | | | | | | | 43.27 356 | | | | | | | | |
|
HQP5-MVS | | | | | | | 51.56 154 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 66.70 86 | | |
|
HQP4-MVS | | | | | | | | | | | 64.47 118 | | | 88.61 125 | | | 84.91 166 |
|
HQP3-MVS | | | | | | | | | 83.68 138 | | | | | | | 73.12 125 | |
|
HQP2-MVS | | | | | | | | | | | | | 37.35 187 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 43.62 271 | 71.13 301 | | 54.95 229 | 59.29 174 | | 36.76 198 | | 46.33 238 | | 87.32 129 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 63.20 200 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 59.38 235 | |
|
Test By Simon | | | | | | | | | | | | | 39.38 163 | | | | |
|