SD-MVS | | | 98.64 11 | 98.68 3 | 98.53 74 | 99.33 44 | 98.36 23 | 98.90 72 | 98.85 53 | 97.28 21 | 99.72 1 | 99.39 7 | 96.63 9 | 97.60 294 | 98.17 23 | 99.85 2 | 99.64 55 |
|
TSAR-MVS + MP. | | | 98.78 3 | 98.62 4 | 99.24 26 | 99.69 17 | 98.28 29 | 99.14 43 | 98.66 107 | 96.84 43 | 99.56 2 | 99.31 21 | 96.34 12 | 99.70 93 | 98.32 20 | 99.73 36 | 99.73 29 |
|
HSP-MVS | | | 98.70 5 | 98.52 8 | 99.24 26 | 99.75 3 | 98.23 30 | 99.26 17 | 98.58 120 | 97.52 7 | 99.41 3 | 98.78 87 | 96.00 25 | 99.79 71 | 97.79 38 | 99.59 54 | 99.69 37 |
|
APDe-MVS | | | 99.02 1 | 98.84 1 | 99.55 2 | 99.57 25 | 98.96 4 | 99.39 5 | 98.93 36 | 97.38 17 | 99.41 3 | 99.54 1 | 96.66 7 | 99.84 44 | 98.86 2 | 99.85 2 | 99.87 1 |
|
SteuartSystems-ACMMP | | | 98.90 2 | 98.75 2 | 99.36 13 | 99.22 73 | 98.43 18 | 99.10 50 | 98.87 49 | 97.38 17 | 99.35 5 | 99.40 6 | 97.78 1 | 99.87 37 | 97.77 39 | 99.85 2 | 99.78 7 |
Skip Steuart: Steuart Systems R&D Blog. |
test_part2 | | | | | | 99.63 21 | 99.18 1 | | | | 99.27 6 | | | | | | |
|
ESAPD | | | 98.70 5 | 98.39 15 | 99.62 1 | 99.63 21 | 99.18 1 | 98.55 150 | 98.84 54 | 96.40 57 | 99.27 6 | 99.31 21 | 97.38 2 | 99.93 9 | 96.37 95 | 99.78 14 | 99.76 20 |
|
abl_6 | | | 98.30 41 | 98.03 39 | 99.13 39 | 99.56 26 | 97.76 53 | 99.13 46 | 98.82 58 | 96.14 63 | 99.26 8 | 99.37 12 | 93.33 78 | 99.93 9 | 96.96 67 | 99.67 41 | 99.69 37 |
|
DeepPCF-MVS | | 96.37 2 | 97.93 49 | 98.48 13 | 96.30 229 | 99.00 88 | 89.54 291 | 97.43 262 | 98.87 49 | 98.16 2 | 99.26 8 | 99.38 11 | 96.12 19 | 99.64 102 | 98.30 21 | 99.77 19 | 99.72 32 |
|
APD-MVS | | | 98.35 36 | 98.00 41 | 99.42 10 | 99.51 28 | 98.72 9 | 98.80 100 | 98.82 58 | 94.52 132 | 99.23 10 | 99.25 30 | 95.54 39 | 99.80 59 | 96.52 89 | 99.77 19 | 99.74 27 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
Regformer-2 | | | 98.69 8 | 98.52 8 | 99.19 29 | 99.35 39 | 98.01 43 | 98.37 172 | 98.81 61 | 97.48 11 | 99.21 11 | 99.21 34 | 96.13 18 | 99.80 59 | 98.40 18 | 99.73 36 | 99.75 22 |
|
Regformer-1 | | | 98.66 9 | 98.51 10 | 99.12 41 | 99.35 39 | 97.81 52 | 98.37 172 | 98.76 75 | 97.49 10 | 99.20 12 | 99.21 34 | 96.08 21 | 99.79 71 | 98.42 16 | 99.73 36 | 99.75 22 |
|
APD-MVS_3200maxsize | | | 98.53 26 | 98.33 25 | 99.15 38 | 99.50 29 | 97.92 47 | 99.15 42 | 98.81 61 | 96.24 60 | 99.20 12 | 99.37 12 | 95.30 45 | 99.80 59 | 97.73 41 | 99.67 41 | 99.72 32 |
|
ACMMP_Plus | | | 98.61 14 | 98.30 26 | 99.55 2 | 99.62 23 | 98.95 5 | 98.82 91 | 98.81 61 | 95.80 74 | 99.16 14 | 99.47 4 | 95.37 42 | 99.92 15 | 97.89 32 | 99.75 31 | 99.79 4 |
|
Regformer-4 | | | 98.64 11 | 98.53 7 | 98.99 48 | 99.43 37 | 97.37 65 | 98.40 170 | 98.79 69 | 97.46 12 | 99.09 15 | 99.31 21 | 95.86 33 | 99.80 59 | 98.64 4 | 99.76 25 | 99.79 4 |
|
Regformer-3 | | | 98.59 17 | 98.50 11 | 98.86 58 | 99.43 37 | 97.05 76 | 98.40 170 | 98.68 97 | 97.43 13 | 99.06 16 | 99.31 21 | 95.80 34 | 99.77 81 | 98.62 6 | 99.76 25 | 99.78 7 |
|
PGM-MVS | | | 98.49 28 | 98.23 33 | 99.27 24 | 99.72 11 | 98.08 40 | 98.99 62 | 99.49 5 | 95.43 88 | 99.03 17 | 99.32 20 | 95.56 37 | 99.94 3 | 96.80 79 | 99.77 19 | 99.78 7 |
|
VNet | | | 97.79 55 | 97.40 62 | 98.96 52 | 98.88 105 | 97.55 59 | 98.63 137 | 98.93 36 | 96.74 46 | 99.02 18 | 98.84 82 | 90.33 129 | 99.83 45 | 98.53 10 | 96.66 158 | 99.50 73 |
|
xiu_mvs_v1_base_debu | | | 97.60 62 | 97.56 52 | 97.72 120 | 98.35 134 | 95.98 113 | 97.86 234 | 98.51 132 | 97.13 34 | 99.01 19 | 98.40 119 | 91.56 108 | 99.80 59 | 98.53 10 | 98.68 104 | 97.37 197 |
|
xiu_mvs_v1_base | | | 97.60 62 | 97.56 52 | 97.72 120 | 98.35 134 | 95.98 113 | 97.86 234 | 98.51 132 | 97.13 34 | 99.01 19 | 98.40 119 | 91.56 108 | 99.80 59 | 98.53 10 | 98.68 104 | 97.37 197 |
|
xiu_mvs_v1_base_debi | | | 97.60 62 | 97.56 52 | 97.72 120 | 98.35 134 | 95.98 113 | 97.86 234 | 98.51 132 | 97.13 34 | 99.01 19 | 98.40 119 | 91.56 108 | 99.80 59 | 98.53 10 | 98.68 104 | 97.37 197 |
|
TSAR-MVS + GP. | | | 98.38 33 | 98.24 32 | 98.81 59 | 99.22 73 | 97.25 71 | 98.11 206 | 98.29 167 | 97.19 30 | 98.99 22 | 99.02 60 | 96.22 13 | 99.67 98 | 98.52 14 | 98.56 112 | 99.51 71 |
|
HFP-MVS | | | 98.63 13 | 98.40 14 | 99.32 17 | 99.72 11 | 98.29 27 | 99.23 22 | 98.96 31 | 96.10 67 | 98.94 23 | 99.17 41 | 96.06 22 | 99.92 15 | 97.62 45 | 99.78 14 | 99.75 22 |
|
region2R | | | 98.61 14 | 98.38 17 | 99.29 19 | 99.74 7 | 98.16 36 | 99.23 22 | 98.93 36 | 96.15 62 | 98.94 23 | 99.17 41 | 95.91 30 | 99.94 3 | 97.55 50 | 99.79 10 | 99.78 7 |
|
#test# | | | 98.54 25 | 98.27 28 | 99.32 17 | 99.72 11 | 98.29 27 | 98.98 65 | 98.96 31 | 95.65 80 | 98.94 23 | 99.17 41 | 96.06 22 | 99.92 15 | 97.21 60 | 99.78 14 | 99.75 22 |
|
HPM-MVS_fast | | | 98.38 33 | 98.13 36 | 99.12 41 | 99.75 3 | 97.86 48 | 99.44 4 | 98.82 58 | 94.46 136 | 98.94 23 | 99.20 37 | 95.16 50 | 99.74 87 | 97.58 47 | 99.85 2 | 99.77 14 |
|
ACMMPR | | | 98.59 17 | 98.36 19 | 99.29 19 | 99.74 7 | 98.15 37 | 99.23 22 | 98.95 33 | 96.10 67 | 98.93 27 | 99.19 40 | 95.70 35 | 99.94 3 | 97.62 45 | 99.79 10 | 99.78 7 |
|
DeepC-MVS_fast | | 96.70 1 | 98.55 23 | 98.34 22 | 99.18 33 | 99.25 66 | 98.04 41 | 98.50 160 | 98.78 71 | 97.72 4 | 98.92 28 | 99.28 27 | 95.27 46 | 99.82 50 | 97.55 50 | 99.77 19 | 99.69 37 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
EI-MVSNet-Vis-set | | | 98.47 29 | 98.39 15 | 98.69 63 | 99.46 34 | 96.49 98 | 98.30 183 | 98.69 94 | 97.21 28 | 98.84 29 | 99.36 16 | 95.41 41 | 99.78 76 | 98.62 6 | 99.65 45 | 99.80 3 |
|
MSLP-MVS++ | | | 98.56 22 | 98.57 5 | 98.55 72 | 99.26 65 | 96.80 85 | 98.71 122 | 99.05 23 | 97.28 21 | 98.84 29 | 99.28 27 | 96.47 11 | 99.40 133 | 98.52 14 | 99.70 39 | 99.47 79 |
|
PHI-MVS | | | 98.34 37 | 98.06 38 | 99.18 33 | 99.15 80 | 98.12 39 | 99.04 58 | 99.09 19 | 93.32 188 | 98.83 31 | 99.10 50 | 96.54 10 | 99.83 45 | 97.70 43 | 99.76 25 | 99.59 63 |
|
MVSFormer | | | 97.57 65 | 97.49 57 | 97.84 113 | 98.07 154 | 95.76 138 | 99.47 2 | 98.40 152 | 94.98 115 | 98.79 32 | 98.83 83 | 92.34 88 | 98.41 251 | 96.91 69 | 99.59 54 | 99.34 90 |
|
lupinMVS | | | 97.44 72 | 97.22 69 | 98.12 99 | 98.07 154 | 95.76 138 | 97.68 248 | 97.76 227 | 94.50 133 | 98.79 32 | 98.61 102 | 92.34 88 | 99.30 139 | 97.58 47 | 99.59 54 | 99.31 93 |
|
CDPH-MVS | | | 97.94 48 | 97.49 57 | 99.28 21 | 99.47 33 | 98.44 16 | 97.91 226 | 98.67 104 | 92.57 212 | 98.77 34 | 98.85 81 | 95.93 29 | 99.72 88 | 95.56 120 | 99.69 40 | 99.68 43 |
|
CNVR-MVS | | | 98.78 3 | 98.56 6 | 99.45 9 | 99.32 47 | 98.87 7 | 98.47 163 | 98.81 61 | 97.72 4 | 98.76 35 | 99.16 44 | 97.05 4 | 99.78 76 | 98.06 25 | 99.66 44 | 99.69 37 |
|
EI-MVSNet-UG-set | | | 98.41 31 | 98.34 22 | 98.61 68 | 99.45 35 | 96.32 105 | 98.28 185 | 98.68 97 | 97.17 31 | 98.74 36 | 99.37 12 | 95.25 47 | 99.79 71 | 98.57 8 | 99.54 66 | 99.73 29 |
|
UA-Net | | | 97.96 46 | 97.62 49 | 98.98 50 | 98.86 107 | 97.47 62 | 98.89 76 | 99.08 20 | 96.67 49 | 98.72 37 | 99.54 1 | 93.15 81 | 99.81 52 | 94.87 136 | 98.83 100 | 99.65 52 |
|
旧先验2 | | | | | | | | 97.57 256 | | 91.30 256 | 98.67 38 | | | 99.80 59 | 95.70 117 | | |
|
PS-MVSNAJ | | | 97.73 56 | 97.77 45 | 97.62 130 | 98.68 121 | 95.58 143 | 97.34 271 | 98.51 132 | 97.29 20 | 98.66 39 | 97.88 163 | 94.51 62 | 99.90 27 | 97.87 33 | 99.17 88 | 97.39 195 |
|
xiu_mvs_v2_base | | | 97.66 61 | 97.70 48 | 97.56 138 | 98.61 127 | 95.46 149 | 97.44 260 | 98.46 142 | 97.15 32 | 98.65 40 | 98.15 143 | 94.33 68 | 99.80 59 | 97.84 36 | 98.66 108 | 97.41 193 |
|
LFMVS | | | 95.86 130 | 94.98 152 | 98.47 79 | 98.87 106 | 96.32 105 | 98.84 88 | 96.02 312 | 93.40 185 | 98.62 41 | 99.20 37 | 74.99 319 | 99.63 105 | 97.72 42 | 97.20 150 | 99.46 83 |
|
HPM-MVS | | | 98.36 35 | 98.10 37 | 99.13 39 | 99.74 7 | 97.82 51 | 99.53 1 | 98.80 68 | 94.63 129 | 98.61 42 | 98.97 67 | 95.13 51 | 99.77 81 | 97.65 44 | 99.83 7 | 99.79 4 |
|
testdata | | | | | 98.26 90 | 99.20 76 | 95.36 152 | | 98.68 97 | 91.89 236 | 98.60 43 | 99.10 50 | 94.44 67 | 99.82 50 | 94.27 153 | 99.44 76 | 99.58 65 |
|
CP-MVS | | | 98.57 21 | 98.36 19 | 99.19 29 | 99.66 19 | 97.86 48 | 99.34 11 | 98.87 49 | 95.96 70 | 98.60 43 | 99.13 46 | 96.05 24 | 99.94 3 | 97.77 39 | 99.86 1 | 99.77 14 |
|
jason | | | 97.32 80 | 97.08 74 | 98.06 105 | 97.45 193 | 95.59 142 | 97.87 233 | 97.91 223 | 94.79 122 | 98.55 45 | 98.83 83 | 91.12 116 | 99.23 145 | 97.58 47 | 99.60 51 | 99.34 90 |
jason: jason. |
MCST-MVS | | | 98.65 10 | 98.37 18 | 99.48 6 | 99.60 24 | 98.87 7 | 98.41 169 | 98.68 97 | 97.04 38 | 98.52 46 | 98.80 86 | 96.78 6 | 99.83 45 | 97.93 28 | 99.61 50 | 99.74 27 |
|
XVS | | | 98.70 5 | 98.49 12 | 99.34 14 | 99.70 15 | 98.35 24 | 99.29 14 | 98.88 47 | 97.40 14 | 98.46 47 | 99.20 37 | 95.90 31 | 99.89 29 | 97.85 34 | 99.74 34 | 99.78 7 |
|
X-MVStestdata | | | 94.06 241 | 92.30 259 | 99.34 14 | 99.70 15 | 98.35 24 | 99.29 14 | 98.88 47 | 97.40 14 | 98.46 47 | 43.50 349 | 95.90 31 | 99.89 29 | 97.85 34 | 99.74 34 | 99.78 7 |
|
MG-MVS | | | 97.81 54 | 97.60 50 | 98.44 81 | 99.12 82 | 95.97 117 | 97.75 243 | 98.78 71 | 96.89 42 | 98.46 47 | 99.22 33 | 93.90 75 | 99.68 97 | 94.81 139 | 99.52 68 | 99.67 48 |
|
NCCC | | | 98.61 14 | 98.35 21 | 99.38 11 | 99.28 62 | 98.61 12 | 98.45 164 | 98.76 75 | 97.82 3 | 98.45 50 | 98.93 75 | 96.65 8 | 99.83 45 | 97.38 57 | 99.41 78 | 99.71 34 |
|
MVS_Test | | | 97.28 81 | 97.00 77 | 98.13 98 | 98.33 138 | 95.97 117 | 98.74 116 | 98.07 213 | 94.27 139 | 98.44 51 | 98.07 148 | 92.48 87 | 99.26 142 | 96.43 92 | 98.19 127 | 99.16 113 |
|
MVS_111021_LR | | | 98.34 37 | 98.23 33 | 98.67 65 | 99.27 63 | 96.90 82 | 97.95 221 | 99.58 3 | 97.14 33 | 98.44 51 | 99.01 64 | 95.03 53 | 99.62 107 | 97.91 29 | 99.75 31 | 99.50 73 |
|
VDDNet | | | 95.36 166 | 94.53 179 | 97.86 112 | 98.10 153 | 95.13 161 | 98.85 85 | 97.75 228 | 90.46 267 | 98.36 53 | 99.39 7 | 73.27 326 | 99.64 102 | 97.98 27 | 96.58 161 | 98.81 140 |
|
mPP-MVS | | | 98.51 27 | 98.26 29 | 99.25 25 | 99.75 3 | 98.04 41 | 99.28 16 | 98.81 61 | 96.24 60 | 98.35 54 | 99.23 31 | 95.46 40 | 99.94 3 | 97.42 55 | 99.81 8 | 99.77 14 |
|
DELS-MVS | | | 98.40 32 | 98.20 35 | 98.99 48 | 99.00 88 | 97.66 54 | 97.75 243 | 98.89 44 | 97.71 6 | 98.33 55 | 98.97 67 | 94.97 54 | 99.88 36 | 98.42 16 | 99.76 25 | 99.42 87 |
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 |
MVS_111021_HR | | | 98.47 29 | 98.34 22 | 98.88 57 | 99.22 73 | 97.32 66 | 97.91 226 | 99.58 3 | 97.20 29 | 98.33 55 | 99.00 65 | 95.99 26 | 99.64 102 | 98.05 26 | 99.76 25 | 99.69 37 |
|
HPM-MVS++ | | | 98.58 19 | 98.25 30 | 99.55 2 | 99.50 29 | 99.08 3 | 98.72 121 | 98.66 107 | 97.51 8 | 98.15 57 | 98.83 83 | 95.70 35 | 99.92 15 | 97.53 52 | 99.67 41 | 99.66 50 |
|
新几何1 | | | | | 99.16 36 | 99.34 41 | 98.01 43 | | 98.69 94 | 90.06 276 | 98.13 58 | 98.95 73 | 94.60 60 | 99.89 29 | 91.97 215 | 99.47 71 | 99.59 63 |
|
API-MVS | | | 97.41 75 | 97.25 66 | 97.91 110 | 98.70 118 | 96.80 85 | 98.82 91 | 98.69 94 | 94.53 131 | 98.11 59 | 98.28 133 | 94.50 65 | 99.57 115 | 94.12 157 | 99.49 69 | 97.37 197 |
|
CPTT-MVS | | | 97.72 57 | 97.32 64 | 98.92 54 | 99.64 20 | 97.10 75 | 99.12 48 | 98.81 61 | 92.34 226 | 98.09 60 | 99.08 56 | 93.01 82 | 99.92 15 | 96.06 101 | 99.77 19 | 99.75 22 |
|
test12 | | | | | 99.18 33 | 99.16 78 | 98.19 34 | | 98.53 128 | | 98.07 61 | | 95.13 51 | 99.72 88 | | 99.56 63 | 99.63 57 |
|
test222 | | | | | | 99.23 72 | 97.17 74 | 97.40 263 | 98.66 107 | 88.68 300 | 98.05 62 | 98.96 71 | 94.14 71 | | | 99.53 67 | 99.61 58 |
|
DP-MVS Recon | | | 97.86 52 | 97.46 59 | 99.06 46 | 99.53 27 | 98.35 24 | 98.33 176 | 98.89 44 | 92.62 209 | 98.05 62 | 98.94 74 | 95.34 44 | 99.65 100 | 96.04 102 | 99.42 77 | 99.19 108 |
|
Vis-MVSNet | | | 97.42 74 | 97.11 72 | 98.34 87 | 98.66 122 | 96.23 108 | 99.22 28 | 99.00 26 | 96.63 51 | 98.04 64 | 99.21 34 | 88.05 187 | 99.35 138 | 96.01 104 | 99.21 86 | 99.45 85 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
MVS_0304 | | | 97.70 58 | 97.25 66 | 99.07 44 | 98.90 97 | 97.83 50 | 98.20 191 | 98.74 79 | 97.51 8 | 98.03 65 | 99.06 58 | 86.12 225 | 99.93 9 | 99.02 1 | 99.64 47 | 99.44 86 |
|
MPTG | | | 98.55 23 | 98.25 30 | 99.46 7 | 99.76 1 | 98.64 10 | 98.55 150 | 98.74 79 | 97.27 25 | 98.02 66 | 99.39 7 | 94.81 56 | 99.96 1 | 97.91 29 | 99.79 10 | 99.77 14 |
|
MTAPA | | | 98.58 19 | 98.29 27 | 99.46 7 | 99.76 1 | 98.64 10 | 98.90 72 | 98.74 79 | 97.27 25 | 98.02 66 | 99.39 7 | 94.81 56 | 99.96 1 | 97.91 29 | 99.79 10 | 99.77 14 |
|
1121 | | | 97.37 78 | 96.77 88 | 99.16 36 | 99.34 41 | 97.99 46 | 98.19 195 | 98.68 97 | 90.14 274 | 98.01 68 | 98.97 67 | 94.80 58 | 99.87 37 | 93.36 174 | 99.46 74 | 99.61 58 |
|
sss | | | 97.39 76 | 96.98 78 | 98.61 68 | 98.60 128 | 96.61 93 | 98.22 189 | 98.93 36 | 93.97 150 | 98.01 68 | 98.48 114 | 91.98 101 | 99.85 42 | 96.45 91 | 98.15 128 | 99.39 88 |
|
alignmvs | | | 97.56 66 | 97.07 75 | 99.01 47 | 98.66 122 | 98.37 22 | 98.83 89 | 98.06 215 | 96.74 46 | 98.00 70 | 97.65 184 | 90.80 123 | 99.48 131 | 98.37 19 | 96.56 162 | 99.19 108 |
|
OMC-MVS | | | 97.55 67 | 97.34 63 | 98.20 93 | 99.33 44 | 95.92 130 | 98.28 185 | 98.59 115 | 95.52 85 | 97.97 71 | 99.10 50 | 93.28 80 | 99.49 127 | 95.09 134 | 98.88 96 | 99.19 108 |
|
VDD-MVS | | | 95.82 132 | 95.23 142 | 97.61 135 | 98.84 110 | 93.98 226 | 98.68 130 | 97.40 261 | 95.02 114 | 97.95 72 | 99.34 19 | 74.37 324 | 99.78 76 | 98.64 4 | 96.80 156 | 99.08 122 |
|
PVSNet_BlendedMVS | | | 96.73 101 | 96.60 94 | 97.12 162 | 99.25 66 | 95.35 154 | 98.26 187 | 99.26 8 | 94.28 138 | 97.94 73 | 97.46 195 | 92.74 85 | 99.81 52 | 96.88 74 | 93.32 230 | 96.20 287 |
|
PVSNet_Blended | | | 97.38 77 | 97.12 71 | 98.14 96 | 99.25 66 | 95.35 154 | 97.28 275 | 99.26 8 | 93.13 194 | 97.94 73 | 98.21 140 | 92.74 85 | 99.81 52 | 96.88 74 | 99.40 80 | 99.27 100 |
|
MP-MVS | | | 98.33 39 | 98.01 40 | 99.28 21 | 99.75 3 | 98.18 35 | 99.22 28 | 98.79 69 | 96.13 64 | 97.92 75 | 99.23 31 | 94.54 61 | 99.94 3 | 96.74 81 | 99.78 14 | 99.73 29 |
|
MDTV_nov1_ep13_2view | | | | | | | 84.26 322 | 96.89 293 | | 90.97 264 | 97.90 76 | | 89.89 134 | | 93.91 161 | | 99.18 112 |
|
test_prior3 | | | 98.22 43 | 97.90 44 | 99.19 29 | 99.31 49 | 98.22 32 | 97.80 239 | 98.84 54 | 96.12 65 | 97.89 77 | 98.69 94 | 95.96 27 | 99.70 93 | 96.89 71 | 99.60 51 | 99.65 52 |
|
test_prior2 | | | | | | | | 97.80 239 | | 96.12 65 | 97.89 77 | 98.69 94 | 95.96 27 | | 96.89 71 | 99.60 51 | |
|
原ACMM1 | | | | | 98.65 66 | 99.32 47 | 96.62 91 | | 98.67 104 | 93.27 191 | 97.81 79 | 98.97 67 | 95.18 49 | 99.83 45 | 93.84 163 | 99.46 74 | 99.50 73 |
|
114514_t | | | 96.93 94 | 96.27 105 | 98.92 54 | 99.50 29 | 97.63 56 | 98.85 85 | 98.90 42 | 84.80 321 | 97.77 80 | 99.11 48 | 92.84 83 | 99.66 99 | 94.85 137 | 99.77 19 | 99.47 79 |
|
PMMVS | | | 96.60 104 | 96.33 103 | 97.41 148 | 97.90 165 | 93.93 227 | 97.35 270 | 98.41 150 | 92.84 205 | 97.76 81 | 97.45 197 | 91.10 118 | 99.20 152 | 96.26 97 | 97.91 134 | 99.11 118 |
|
PVSNet | | 91.96 18 | 96.35 114 | 96.15 109 | 96.96 171 | 99.17 77 | 92.05 259 | 96.08 310 | 98.68 97 | 93.69 168 | 97.75 82 | 97.80 173 | 88.86 155 | 99.69 96 | 94.26 154 | 99.01 91 | 99.15 114 |
|
TEST9 | | | | | | 99.31 49 | 98.50 14 | 97.92 223 | 98.73 84 | 92.63 208 | 97.74 83 | 98.68 96 | 96.20 14 | 99.80 59 | | | |
|
train_agg | | | 97.97 45 | 97.52 55 | 99.33 16 | 99.31 49 | 98.50 14 | 97.92 223 | 98.73 84 | 92.98 199 | 97.74 83 | 98.68 96 | 96.20 14 | 99.80 59 | 96.59 85 | 99.57 57 | 99.68 43 |
|
CANet | | | 98.05 44 | 97.76 46 | 98.90 56 | 98.73 115 | 97.27 68 | 98.35 174 | 98.78 71 | 97.37 19 | 97.72 85 | 98.96 71 | 91.53 112 | 99.92 15 | 98.79 3 | 99.65 45 | 99.51 71 |
|
test_8 | | | | | | 99.29 57 | 98.44 16 | 97.89 231 | 98.72 86 | 92.98 199 | 97.70 86 | 98.66 99 | 96.20 14 | 99.80 59 | | | |
|
MP-MVS-pluss | | | 98.31 40 | 97.92 43 | 99.49 5 | 99.72 11 | 98.88 6 | 98.43 167 | 98.78 71 | 94.10 142 | 97.69 87 | 99.42 5 | 95.25 47 | 99.92 15 | 98.09 24 | 99.80 9 | 99.67 48 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
canonicalmvs | | | 97.67 60 | 97.23 68 | 98.98 50 | 98.70 118 | 98.38 19 | 99.34 11 | 98.39 154 | 96.76 45 | 97.67 88 | 97.40 199 | 92.26 91 | 99.49 127 | 98.28 22 | 96.28 179 | 99.08 122 |
|
PVSNet_Blended_VisFu | | | 97.70 58 | 97.46 59 | 98.44 81 | 99.27 63 | 95.91 132 | 98.63 137 | 99.16 17 | 94.48 135 | 97.67 88 | 98.88 79 | 92.80 84 | 99.91 24 | 97.11 62 | 99.12 89 | 99.50 73 |
|
WTY-MVS | | | 97.37 78 | 96.92 80 | 98.72 62 | 98.86 107 | 96.89 84 | 98.31 181 | 98.71 91 | 95.26 102 | 97.67 88 | 98.56 108 | 92.21 94 | 99.78 76 | 95.89 106 | 96.85 155 | 99.48 78 |
|
Effi-MVS+ | | | 97.12 88 | 96.69 90 | 98.39 85 | 98.19 147 | 96.72 89 | 97.37 267 | 98.43 149 | 93.71 165 | 97.65 91 | 98.02 151 | 92.20 95 | 99.25 143 | 96.87 77 | 97.79 140 | 99.19 108 |
|
HyFIR lowres test | | | 96.90 96 | 96.49 99 | 98.14 96 | 99.33 44 | 95.56 145 | 97.38 265 | 99.65 2 | 92.34 226 | 97.61 92 | 98.20 141 | 89.29 140 | 99.10 166 | 96.97 65 | 97.60 146 | 99.77 14 |
|
ACMMP | | | 98.23 42 | 97.95 42 | 99.09 43 | 99.74 7 | 97.62 57 | 99.03 59 | 99.41 6 | 95.98 69 | 97.60 93 | 99.36 16 | 94.45 66 | 99.93 9 | 97.14 61 | 98.85 99 | 99.70 36 |
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence |
agg_prior3 | | | 97.87 51 | 97.42 61 | 99.23 28 | 99.29 57 | 98.23 30 | 97.92 223 | 98.72 86 | 92.38 225 | 97.59 94 | 98.64 101 | 96.09 20 | 99.79 71 | 96.59 85 | 99.57 57 | 99.68 43 |
|
agg_prior1 | | | 97.95 47 | 97.51 56 | 99.28 21 | 99.30 54 | 98.38 19 | 97.81 238 | 98.72 86 | 93.16 193 | 97.57 95 | 98.66 99 | 96.14 17 | 99.81 52 | 96.63 84 | 99.56 63 | 99.66 50 |
|
agg_prior | | | | | | 99.30 54 | 98.38 19 | | 98.72 86 | | 97.57 95 | | | 99.81 52 | | | |
|
tpmrst | | | 95.63 141 | 95.69 126 | 95.44 260 | 97.54 185 | 88.54 307 | 96.97 285 | 97.56 235 | 93.50 177 | 97.52 97 | 96.93 249 | 89.49 135 | 99.16 154 | 95.25 131 | 96.42 167 | 98.64 151 |
|
MDTV_nov1_ep13 | | | | 95.40 130 | | 97.48 188 | 88.34 309 | 96.85 295 | 97.29 270 | 93.74 162 | 97.48 98 | 97.26 209 | 89.18 143 | 99.05 170 | 91.92 217 | 97.43 148 | |
|
EPMVS | | | 94.99 182 | 94.48 180 | 96.52 212 | 97.22 206 | 91.75 265 | 97.23 277 | 91.66 344 | 94.11 141 | 97.28 99 | 96.81 258 | 85.70 238 | 98.84 198 | 93.04 184 | 97.28 149 | 98.97 130 |
|
IS-MVSNet | | | 97.22 83 | 96.88 81 | 98.25 91 | 98.85 109 | 96.36 103 | 99.19 34 | 97.97 220 | 95.39 90 | 97.23 100 | 98.99 66 | 91.11 117 | 98.93 187 | 94.60 143 | 98.59 110 | 99.47 79 |
|
EPP-MVSNet | | | 97.46 68 | 97.28 65 | 97.99 107 | 98.64 124 | 95.38 151 | 99.33 13 | 98.31 162 | 93.61 174 | 97.19 101 | 99.07 57 | 94.05 72 | 99.23 145 | 96.89 71 | 98.43 119 | 99.37 89 |
|
CANet_DTU | | | 96.96 93 | 96.55 96 | 98.21 92 | 98.17 151 | 96.07 112 | 97.98 218 | 98.21 178 | 97.24 27 | 97.13 102 | 98.93 75 | 86.88 214 | 99.91 24 | 95.00 135 | 99.37 82 | 98.66 149 |
|
CHOSEN 1792x2688 | | | 97.12 88 | 96.80 83 | 98.08 102 | 99.30 54 | 94.56 210 | 98.05 211 | 99.71 1 | 93.57 175 | 97.09 103 | 98.91 78 | 88.17 182 | 99.89 29 | 96.87 77 | 99.56 63 | 99.81 2 |
|
PatchT | | | 93.06 260 | 91.97 262 | 96.35 225 | 96.69 237 | 92.67 252 | 94.48 331 | 97.08 278 | 86.62 309 | 97.08 104 | 92.23 331 | 87.94 189 | 97.90 285 | 78.89 325 | 96.69 157 | 98.49 157 |
|
PatchmatchNet | | | 95.71 137 | 95.52 129 | 96.29 230 | 97.58 182 | 90.72 278 | 96.84 296 | 97.52 241 | 94.06 144 | 97.08 104 | 96.96 242 | 89.24 142 | 98.90 192 | 92.03 213 | 98.37 120 | 99.26 101 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
MAR-MVS | | | 96.91 95 | 96.40 101 | 98.45 80 | 98.69 120 | 96.90 82 | 98.66 135 | 98.68 97 | 92.40 224 | 97.07 106 | 97.96 156 | 91.54 111 | 99.75 85 | 93.68 167 | 98.92 94 | 98.69 146 |
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 |
PAPM_NR | | | 97.46 68 | 97.11 72 | 98.50 76 | 99.50 29 | 96.41 101 | 98.63 137 | 98.60 114 | 95.18 106 | 97.06 107 | 98.06 149 | 94.26 70 | 99.57 115 | 93.80 165 | 98.87 98 | 99.52 68 |
|
TAMVS | | | 97.02 91 | 96.79 85 | 97.70 124 | 98.06 156 | 95.31 156 | 98.52 155 | 98.31 162 | 93.95 151 | 97.05 108 | 98.61 102 | 93.49 77 | 98.52 227 | 95.33 126 | 97.81 139 | 99.29 98 |
|
CSCG | | | 97.85 53 | 97.74 47 | 98.20 93 | 99.67 18 | 95.16 159 | 99.22 28 | 99.32 7 | 93.04 196 | 97.02 109 | 98.92 77 | 95.36 43 | 99.91 24 | 97.43 54 | 99.64 47 | 99.52 68 |
|
tfpn_ndepth | | | 95.53 149 | 94.90 161 | 97.39 153 | 98.96 94 | 95.88 135 | 99.05 56 | 95.27 326 | 93.80 159 | 96.95 110 | 96.93 249 | 85.53 240 | 99.40 133 | 91.54 226 | 96.10 187 | 96.89 224 |
|
CDS-MVSNet | | | 96.99 92 | 96.69 90 | 97.90 111 | 98.05 157 | 95.98 113 | 98.20 191 | 98.33 161 | 93.67 172 | 96.95 110 | 98.49 113 | 93.54 76 | 98.42 244 | 95.24 132 | 97.74 143 | 99.31 93 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
XVG-OURS-SEG-HR | | | 96.51 109 | 96.34 102 | 97.02 167 | 98.77 113 | 93.76 232 | 97.79 241 | 98.50 137 | 95.45 87 | 96.94 112 | 99.09 54 | 87.87 193 | 99.55 123 | 96.76 80 | 95.83 196 | 97.74 184 |
|
CR-MVSNet | | | 94.76 197 | 94.15 197 | 96.59 202 | 97.00 218 | 93.43 240 | 94.96 324 | 97.56 235 | 92.46 213 | 96.93 113 | 96.24 277 | 88.15 183 | 97.88 289 | 87.38 296 | 96.65 159 | 98.46 158 |
|
RPMNet | | | 92.52 264 | 91.17 267 | 96.59 202 | 97.00 218 | 93.43 240 | 94.96 324 | 97.26 273 | 82.27 328 | 96.93 113 | 92.12 332 | 86.98 212 | 97.88 289 | 76.32 330 | 96.65 159 | 98.46 158 |
|
Patchmatch-test1 | | | 95.32 170 | 94.97 154 | 96.35 225 | 97.67 175 | 91.29 271 | 97.33 272 | 97.60 233 | 94.68 124 | 96.92 115 | 96.95 243 | 83.97 272 | 98.50 230 | 91.33 231 | 98.32 123 | 99.25 102 |
|
PatchMatch-RL | | | 96.59 106 | 96.03 113 | 98.27 89 | 99.31 49 | 96.51 97 | 97.91 226 | 99.06 21 | 93.72 164 | 96.92 115 | 98.06 149 | 88.50 177 | 99.65 100 | 91.77 221 | 99.00 92 | 98.66 149 |
|
DeepC-MVS | | 95.98 3 | 97.88 50 | 97.58 51 | 98.77 60 | 99.25 66 | 96.93 80 | 98.83 89 | 98.75 78 | 96.96 41 | 96.89 117 | 99.50 3 | 90.46 126 | 99.87 37 | 97.84 36 | 99.76 25 | 99.52 68 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
diffmvs | | | 96.32 116 | 95.74 119 | 98.07 104 | 98.26 141 | 96.14 110 | 98.53 154 | 98.23 176 | 90.10 275 | 96.88 118 | 97.73 176 | 90.16 132 | 99.15 155 | 93.90 162 | 97.85 138 | 98.91 136 |
|
tfpn1000 | | | 95.72 135 | 95.11 146 | 97.58 136 | 99.00 88 | 95.73 140 | 99.24 20 | 95.49 325 | 94.08 143 | 96.87 119 | 97.45 197 | 85.81 236 | 99.30 139 | 91.78 220 | 96.22 184 | 97.71 187 |
|
XVG-OURS | | | 96.55 108 | 96.41 100 | 96.99 168 | 98.75 114 | 93.76 232 | 97.50 259 | 98.52 130 | 95.67 78 | 96.83 120 | 99.30 26 | 88.95 152 | 99.53 124 | 95.88 107 | 96.26 180 | 97.69 188 |
|
AdaColmap | | | 97.15 87 | 96.70 89 | 98.48 78 | 99.16 78 | 96.69 90 | 98.01 215 | 98.89 44 | 94.44 137 | 96.83 120 | 98.68 96 | 90.69 124 | 99.76 83 | 94.36 149 | 99.29 85 | 98.98 129 |
|
CostFormer | | | 94.95 186 | 94.73 171 | 95.60 253 | 97.28 202 | 89.06 298 | 97.53 257 | 96.89 296 | 89.66 289 | 96.82 122 | 96.72 261 | 86.05 232 | 98.95 186 | 95.53 121 | 96.13 186 | 98.79 141 |
|
UGNet | | | 96.78 100 | 96.30 104 | 98.19 95 | 98.24 142 | 95.89 134 | 98.88 78 | 98.93 36 | 97.39 16 | 96.81 123 | 97.84 167 | 82.60 281 | 99.90 27 | 96.53 88 | 99.49 69 | 98.79 141 |
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 |
CNLPA | | | 97.45 71 | 97.03 76 | 98.73 61 | 99.05 83 | 97.44 64 | 98.07 210 | 98.53 128 | 95.32 100 | 96.80 124 | 98.53 109 | 93.32 79 | 99.72 88 | 94.31 152 | 99.31 84 | 99.02 125 |
|
CHOSEN 280x420 | | | 97.18 85 | 97.18 70 | 97.20 156 | 98.81 111 | 93.27 243 | 95.78 318 | 99.15 18 | 95.25 103 | 96.79 125 | 98.11 146 | 92.29 90 | 99.07 169 | 98.56 9 | 99.85 2 | 99.25 102 |
|
HY-MVS | | 93.96 8 | 96.82 99 | 96.23 108 | 98.57 70 | 98.46 133 | 97.00 77 | 98.14 201 | 98.21 178 | 93.95 151 | 96.72 126 | 97.99 155 | 91.58 107 | 99.76 83 | 94.51 147 | 96.54 163 | 98.95 134 |
|
PAPR | | | 96.84 98 | 96.24 107 | 98.65 66 | 98.72 117 | 96.92 81 | 97.36 269 | 98.57 121 | 93.33 187 | 96.67 127 | 97.57 191 | 94.30 69 | 99.56 117 | 91.05 236 | 98.59 110 | 99.47 79 |
|
thres600view7 | | | 95.49 154 | 94.77 169 | 97.67 127 | 98.98 91 | 95.02 164 | 98.85 85 | 96.90 293 | 95.38 91 | 96.63 128 | 96.90 251 | 84.29 263 | 99.59 109 | 88.65 284 | 96.33 173 | 98.40 161 |
|
conf200view11 | | | 95.40 162 | 94.70 172 | 97.50 144 | 98.98 91 | 94.92 172 | 98.87 79 | 96.90 293 | 95.38 91 | 96.61 129 | 96.88 254 | 84.29 263 | 99.56 117 | 88.11 290 | 96.29 175 | 98.02 176 |
|
thres100view900 | | | 95.38 163 | 94.70 172 | 97.41 148 | 98.98 91 | 94.92 172 | 98.87 79 | 96.90 293 | 95.38 91 | 96.61 129 | 96.88 254 | 84.29 263 | 99.56 117 | 88.11 290 | 96.29 175 | 97.76 182 |
|
Vis-MVSNet (Re-imp) | | | 96.87 97 | 96.55 96 | 97.83 114 | 98.73 115 | 95.46 149 | 99.20 32 | 98.30 165 | 94.96 117 | 96.60 131 | 98.87 80 | 90.05 133 | 98.59 216 | 93.67 168 | 98.60 109 | 99.46 83 |
|
CVMVSNet | | | 95.43 158 | 96.04 112 | 93.57 300 | 97.93 163 | 83.62 323 | 98.12 204 | 98.59 115 | 95.68 77 | 96.56 132 | 99.02 60 | 87.51 203 | 97.51 297 | 93.56 171 | 97.44 147 | 99.60 61 |
|
RPSCF | | | 94.87 190 | 95.40 130 | 93.26 304 | 98.89 104 | 82.06 329 | 98.33 176 | 98.06 215 | 90.30 271 | 96.56 132 | 99.26 29 | 87.09 209 | 99.49 127 | 93.82 164 | 96.32 174 | 98.24 171 |
|
tfpn200view9 | | | 95.32 170 | 94.62 175 | 97.43 147 | 98.94 95 | 94.98 168 | 98.68 130 | 96.93 291 | 95.33 98 | 96.55 134 | 96.53 268 | 84.23 267 | 99.56 117 | 88.11 290 | 96.29 175 | 97.76 182 |
|
thres400 | | | 95.38 163 | 94.62 175 | 97.65 129 | 98.94 95 | 94.98 168 | 98.68 130 | 96.93 291 | 95.33 98 | 96.55 134 | 96.53 268 | 84.23 267 | 99.56 117 | 88.11 290 | 96.29 175 | 98.40 161 |
|
thres200 | | | 95.25 172 | 94.57 177 | 97.28 154 | 98.81 111 | 94.92 172 | 98.20 191 | 97.11 277 | 95.24 105 | 96.54 136 | 96.22 281 | 84.58 255 | 99.53 124 | 87.93 294 | 96.50 165 | 97.39 195 |
|
ab-mvs | | | 96.42 112 | 95.71 124 | 98.55 72 | 98.63 125 | 96.75 88 | 97.88 232 | 98.74 79 | 93.84 156 | 96.54 136 | 98.18 142 | 85.34 245 | 99.75 85 | 95.93 105 | 96.35 172 | 99.15 114 |
|
view600 | | | 95.60 144 | 94.93 156 | 97.62 130 | 99.05 83 | 94.85 177 | 99.09 51 | 97.01 285 | 95.36 94 | 96.52 138 | 97.37 200 | 84.55 256 | 99.59 109 | 89.07 275 | 96.39 168 | 98.40 161 |
|
view800 | | | 95.60 144 | 94.93 156 | 97.62 130 | 99.05 83 | 94.85 177 | 99.09 51 | 97.01 285 | 95.36 94 | 96.52 138 | 97.37 200 | 84.55 256 | 99.59 109 | 89.07 275 | 96.39 168 | 98.40 161 |
|
conf0.05thres1000 | | | 95.60 144 | 94.93 156 | 97.62 130 | 99.05 83 | 94.85 177 | 99.09 51 | 97.01 285 | 95.36 94 | 96.52 138 | 97.37 200 | 84.55 256 | 99.59 109 | 89.07 275 | 96.39 168 | 98.40 161 |
|
tfpn | | | 95.60 144 | 94.93 156 | 97.62 130 | 99.05 83 | 94.85 177 | 99.09 51 | 97.01 285 | 95.36 94 | 96.52 138 | 97.37 200 | 84.55 256 | 99.59 109 | 89.07 275 | 96.39 168 | 98.40 161 |
|
mvs-test1 | | | 96.60 104 | 96.68 92 | 96.37 223 | 97.89 166 | 91.81 262 | 98.56 148 | 98.10 208 | 96.57 52 | 96.52 138 | 97.94 158 | 90.81 121 | 99.45 132 | 95.72 113 | 98.01 131 | 97.86 181 |
|
conf0.002 | | | 95.56 148 | 94.84 164 | 97.72 120 | 98.90 97 | 95.93 124 | 99.17 35 | 95.70 318 | 93.42 180 | 96.50 143 | 97.16 214 | 86.12 225 | 99.22 147 | 90.51 244 | 96.06 188 | 98.02 176 |
|
thresconf0.02 | | | 95.50 150 | 94.84 164 | 97.51 140 | 98.90 97 | 95.93 124 | 99.17 35 | 95.70 318 | 93.42 180 | 96.50 143 | 97.16 214 | 86.12 225 | 99.22 147 | 90.51 244 | 96.06 188 | 97.37 197 |
|
tfpn_n400 | | | 95.50 150 | 94.84 164 | 97.51 140 | 98.90 97 | 95.93 124 | 99.17 35 | 95.70 318 | 93.42 180 | 96.50 143 | 97.16 214 | 86.12 225 | 99.22 147 | 90.51 244 | 96.06 188 | 97.37 197 |
|
tfpnconf | | | 95.50 150 | 94.84 164 | 97.51 140 | 98.90 97 | 95.93 124 | 99.17 35 | 95.70 318 | 93.42 180 | 96.50 143 | 97.16 214 | 86.12 225 | 99.22 147 | 90.51 244 | 96.06 188 | 97.37 197 |
|
tfpnview11 | | | 95.50 150 | 94.84 164 | 97.51 140 | 98.90 97 | 95.93 124 | 99.17 35 | 95.70 318 | 93.42 180 | 96.50 143 | 97.16 214 | 86.12 225 | 99.22 147 | 90.51 244 | 96.06 188 | 97.37 197 |
|
ADS-MVSNet2 | | | 94.58 213 | 94.40 186 | 95.11 274 | 98.00 158 | 88.74 302 | 96.04 311 | 97.30 269 | 90.15 272 | 96.47 148 | 96.64 265 | 87.89 191 | 97.56 296 | 90.08 254 | 97.06 151 | 99.02 125 |
|
ADS-MVSNet | | | 95.00 181 | 94.45 184 | 96.63 197 | 98.00 158 | 91.91 261 | 96.04 311 | 97.74 229 | 90.15 272 | 96.47 148 | 96.64 265 | 87.89 191 | 98.96 182 | 90.08 254 | 97.06 151 | 99.02 125 |
|
Effi-MVS+-dtu | | | 96.29 117 | 96.56 95 | 95.51 254 | 97.89 166 | 90.22 285 | 98.80 100 | 98.10 208 | 96.57 52 | 96.45 150 | 96.66 263 | 90.81 121 | 98.91 189 | 95.72 113 | 97.99 132 | 97.40 194 |
|
PLC | | 95.07 4 | 97.20 84 | 96.78 86 | 98.44 81 | 99.29 57 | 96.31 107 | 98.14 201 | 98.76 75 | 92.41 223 | 96.39 151 | 98.31 132 | 94.92 55 | 99.78 76 | 94.06 158 | 98.77 103 | 99.23 104 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
tpm | | | 94.13 237 | 93.80 219 | 95.12 273 | 96.50 245 | 87.91 313 | 97.44 260 | 95.89 317 | 92.62 209 | 96.37 152 | 96.30 276 | 84.13 270 | 98.30 264 | 93.24 177 | 91.66 250 | 99.14 116 |
|
TAPA-MVS | | 93.98 7 | 95.35 167 | 94.56 178 | 97.74 119 | 99.13 81 | 94.83 188 | 98.33 176 | 98.64 112 | 86.62 309 | 96.29 153 | 98.61 102 | 94.00 74 | 99.29 141 | 80.00 321 | 99.41 78 | 99.09 119 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
tpm2 | | | 94.19 231 | 93.76 224 | 95.46 258 | 97.23 205 | 89.04 299 | 97.31 274 | 96.85 299 | 87.08 308 | 96.21 154 | 96.79 259 | 83.75 277 | 98.74 206 | 92.43 205 | 96.23 182 | 98.59 153 |
|
F-COLMAP | | | 97.09 90 | 96.80 83 | 97.97 108 | 99.45 35 | 94.95 171 | 98.55 150 | 98.62 113 | 93.02 197 | 96.17 155 | 98.58 107 | 94.01 73 | 99.81 52 | 93.95 160 | 98.90 95 | 99.14 116 |
|
PatchFormer-LS_test | | | 95.47 155 | 95.27 141 | 96.08 238 | 97.59 181 | 90.66 279 | 98.10 208 | 97.34 265 | 93.98 149 | 96.08 156 | 96.15 283 | 87.65 201 | 99.12 159 | 95.27 130 | 95.24 200 | 98.44 160 |
|
JIA-IIPM | | | 93.35 252 | 92.49 256 | 95.92 241 | 96.48 247 | 90.65 280 | 95.01 323 | 96.96 289 | 85.93 315 | 96.08 156 | 87.33 336 | 87.70 199 | 98.78 205 | 91.35 230 | 95.58 198 | 98.34 168 |
|
BH-RMVSNet | | | 95.92 128 | 95.32 138 | 97.69 125 | 98.32 139 | 94.64 202 | 98.19 195 | 97.45 256 | 94.56 130 | 96.03 158 | 98.61 102 | 85.02 248 | 99.12 159 | 90.68 240 | 99.06 90 | 99.30 96 |
|
dp | | | 94.15 236 | 93.90 214 | 94.90 278 | 97.31 201 | 86.82 319 | 96.97 285 | 97.19 276 | 91.22 261 | 96.02 159 | 96.61 267 | 85.51 241 | 99.02 177 | 90.00 258 | 94.30 203 | 98.85 137 |
|
EPNet | | | 97.28 81 | 96.87 82 | 98.51 75 | 94.98 309 | 96.14 110 | 98.90 72 | 97.02 283 | 98.28 1 | 95.99 160 | 99.11 48 | 91.36 113 | 99.89 29 | 96.98 64 | 99.19 87 | 99.50 73 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
LS3D | | | 97.16 86 | 96.66 93 | 98.68 64 | 98.53 132 | 97.19 73 | 98.93 70 | 98.90 42 | 92.83 206 | 95.99 160 | 99.37 12 | 92.12 97 | 99.87 37 | 93.67 168 | 99.57 57 | 98.97 130 |
|
TR-MVS | | | 94.94 188 | 94.20 194 | 97.17 159 | 97.75 172 | 94.14 223 | 97.59 254 | 97.02 283 | 92.28 230 | 95.75 162 | 97.64 186 | 83.88 274 | 98.96 182 | 89.77 260 | 96.15 185 | 98.40 161 |
|
VPA-MVSNet | | | 95.75 134 | 95.11 146 | 97.69 125 | 97.24 204 | 97.27 68 | 98.94 69 | 99.23 12 | 95.13 108 | 95.51 163 | 97.32 206 | 85.73 237 | 98.91 189 | 97.33 58 | 89.55 266 | 96.89 224 |
|
HQP_MVS | | | 96.14 122 | 95.90 116 | 96.85 177 | 97.42 194 | 94.60 208 | 98.80 100 | 98.56 122 | 97.28 21 | 95.34 164 | 98.28 133 | 87.09 209 | 99.03 175 | 96.07 99 | 94.27 204 | 96.92 216 |
|
plane_prior3 | | | | | | | 94.61 206 | | | 97.02 39 | 95.34 164 | | | | | | |
|
DWT-MVSNet_test | | | 94.82 194 | 94.36 187 | 96.20 233 | 97.35 199 | 90.79 276 | 98.34 175 | 96.57 307 | 92.91 202 | 95.33 166 | 96.44 273 | 82.00 283 | 99.12 159 | 94.52 146 | 95.78 197 | 98.70 145 |
|
Fast-Effi-MVS+ | | | 96.28 119 | 95.70 125 | 98.03 106 | 98.29 140 | 95.97 117 | 98.58 143 | 98.25 173 | 91.74 240 | 95.29 167 | 97.23 211 | 91.03 120 | 99.15 155 | 92.90 191 | 97.96 133 | 98.97 130 |
|
EI-MVSNet | | | 95.96 125 | 95.83 118 | 96.36 224 | 97.93 163 | 93.70 236 | 98.12 204 | 98.27 168 | 93.70 167 | 95.07 168 | 99.02 60 | 92.23 93 | 98.54 220 | 94.68 140 | 93.46 225 | 96.84 230 |
|
MVSTER | | | 96.06 123 | 95.72 121 | 97.08 165 | 98.23 143 | 95.93 124 | 98.73 119 | 98.27 168 | 94.86 121 | 95.07 168 | 98.09 147 | 88.21 181 | 98.54 220 | 96.59 85 | 93.46 225 | 96.79 234 |
|
OPM-MVS | | | 95.69 139 | 95.33 137 | 96.76 181 | 96.16 278 | 94.63 203 | 98.43 167 | 98.39 154 | 96.64 50 | 95.02 170 | 98.78 87 | 85.15 247 | 99.05 170 | 95.21 133 | 94.20 207 | 96.60 264 |
|
Fast-Effi-MVS+-dtu | | | 95.87 129 | 95.85 117 | 95.91 242 | 97.74 173 | 91.74 266 | 98.69 126 | 98.15 193 | 95.56 83 | 94.92 171 | 97.68 183 | 88.98 150 | 98.79 204 | 93.19 179 | 97.78 141 | 97.20 205 |
|
TESTMET0.1,1 | | | 94.18 233 | 93.69 228 | 95.63 252 | 96.92 223 | 89.12 297 | 96.91 289 | 94.78 332 | 93.17 192 | 94.88 172 | 96.45 272 | 78.52 303 | 98.92 188 | 93.09 181 | 98.50 114 | 98.85 137 |
|
VPNet | | | 94.99 182 | 94.19 195 | 97.40 150 | 97.16 212 | 96.57 94 | 98.71 122 | 98.97 29 | 95.67 78 | 94.84 173 | 98.24 139 | 80.36 296 | 98.67 210 | 96.46 90 | 87.32 297 | 96.96 213 |
|
1112_ss | | | 96.63 103 | 96.00 114 | 98.50 76 | 98.56 129 | 96.37 102 | 98.18 199 | 98.10 208 | 92.92 201 | 94.84 173 | 98.43 117 | 92.14 96 | 99.58 114 | 94.35 150 | 96.51 164 | 99.56 67 |
|
test-LLR | | | 95.10 179 | 94.87 162 | 95.80 247 | 96.77 231 | 89.70 289 | 96.91 289 | 95.21 327 | 95.11 109 | 94.83 175 | 95.72 294 | 87.71 197 | 98.97 179 | 93.06 182 | 98.50 114 | 98.72 143 |
|
test-mter | | | 94.08 239 | 93.51 239 | 95.80 247 | 96.77 231 | 89.70 289 | 96.91 289 | 95.21 327 | 92.89 203 | 94.83 175 | 95.72 294 | 77.69 307 | 98.97 179 | 93.06 182 | 98.50 114 | 98.72 143 |
|
Test_1112_low_res | | | 96.34 115 | 95.66 128 | 98.36 86 | 98.56 129 | 95.94 121 | 97.71 245 | 98.07 213 | 92.10 232 | 94.79 177 | 97.29 208 | 91.75 104 | 99.56 117 | 94.17 155 | 96.50 165 | 99.58 65 |
|
GA-MVS | | | 94.81 195 | 94.03 205 | 97.14 160 | 97.15 213 | 93.86 229 | 96.76 298 | 97.58 234 | 94.00 147 | 94.76 178 | 97.04 234 | 80.91 289 | 98.48 231 | 91.79 219 | 96.25 181 | 99.09 119 |
|
BH-untuned | | | 95.95 126 | 95.72 121 | 96.65 194 | 98.55 131 | 92.26 256 | 98.23 188 | 97.79 226 | 93.73 163 | 94.62 179 | 98.01 153 | 88.97 151 | 99.00 178 | 93.04 184 | 98.51 113 | 98.68 147 |
|
test_djsdf | | | 96.00 124 | 95.69 126 | 96.93 174 | 95.72 295 | 95.49 148 | 99.47 2 | 98.40 152 | 94.98 115 | 94.58 180 | 97.86 164 | 89.16 144 | 98.41 251 | 96.91 69 | 94.12 212 | 96.88 226 |
|
cascas | | | 94.63 209 | 93.86 216 | 96.93 174 | 96.91 225 | 94.27 220 | 96.00 314 | 98.51 132 | 85.55 317 | 94.54 181 | 96.23 279 | 84.20 269 | 98.87 195 | 95.80 111 | 96.98 154 | 97.66 189 |
|
DP-MVS | | | 96.59 106 | 95.93 115 | 98.57 70 | 99.34 41 | 96.19 109 | 98.70 125 | 98.39 154 | 89.45 293 | 94.52 182 | 99.35 18 | 91.85 103 | 99.85 42 | 92.89 193 | 98.88 96 | 99.68 43 |
|
gg-mvs-nofinetune | | | 92.21 267 | 90.58 281 | 97.13 161 | 96.75 234 | 95.09 162 | 95.85 316 | 89.40 347 | 85.43 318 | 94.50 183 | 81.98 340 | 80.80 292 | 98.40 257 | 92.16 207 | 98.33 122 | 97.88 180 |
|
mvs_anonymous | | | 96.70 102 | 96.53 98 | 97.18 158 | 98.19 147 | 93.78 231 | 98.31 181 | 98.19 182 | 94.01 146 | 94.47 184 | 98.27 136 | 92.08 99 | 98.46 236 | 97.39 56 | 97.91 134 | 99.31 93 |
|
v1neww | | | 94.83 191 | 94.22 191 | 96.68 189 | 96.39 251 | 94.85 177 | 98.87 79 | 98.11 203 | 92.45 218 | 94.45 185 | 97.06 229 | 88.82 160 | 98.54 220 | 92.93 188 | 88.91 276 | 96.65 257 |
|
v7new | | | 94.83 191 | 94.22 191 | 96.68 189 | 96.39 251 | 94.85 177 | 98.87 79 | 98.11 203 | 92.45 218 | 94.45 185 | 97.06 229 | 88.82 160 | 98.54 220 | 92.93 188 | 88.91 276 | 96.65 257 |
|
HQP-NCC | | | | | | 97.20 208 | | 98.05 211 | | 96.43 54 | 94.45 185 | | | | | | |
|
ACMP_Plane | | | | | | 97.20 208 | | 98.05 211 | | 96.43 54 | 94.45 185 | | | | | | |
|
HQP4-MVS | | | | | | | | | | | 94.45 185 | | | 98.96 182 | | | 96.87 227 |
|
HQP-MVS | | | 95.72 135 | 95.40 130 | 96.69 186 | 97.20 208 | 94.25 221 | 98.05 211 | 98.46 142 | 96.43 54 | 94.45 185 | 97.73 176 | 86.75 215 | 98.96 182 | 95.30 127 | 94.18 208 | 96.86 229 |
|
v6 | | | 94.83 191 | 94.21 193 | 96.69 186 | 96.36 255 | 94.85 177 | 98.87 79 | 98.11 203 | 92.46 213 | 94.44 191 | 97.05 233 | 88.76 166 | 98.57 218 | 92.95 187 | 88.92 275 | 96.65 257 |
|
MSDG | | | 95.93 127 | 95.30 140 | 97.83 114 | 98.90 97 | 95.36 152 | 96.83 297 | 98.37 157 | 91.32 255 | 94.43 192 | 98.73 93 | 90.27 130 | 99.60 108 | 90.05 256 | 98.82 101 | 98.52 155 |
|
nrg030 | | | 96.28 119 | 95.72 121 | 97.96 109 | 96.90 226 | 98.15 37 | 99.39 5 | 98.31 162 | 95.47 86 | 94.42 193 | 98.35 125 | 92.09 98 | 98.69 207 | 97.50 53 | 89.05 272 | 97.04 209 |
|
CLD-MVS | | | 95.62 142 | 95.34 135 | 96.46 219 | 97.52 187 | 93.75 234 | 97.27 276 | 98.46 142 | 95.53 84 | 94.42 193 | 98.00 154 | 86.21 223 | 98.97 179 | 96.25 98 | 94.37 202 | 96.66 255 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
LPG-MVS_test | | | 95.62 142 | 95.34 135 | 96.47 216 | 97.46 190 | 93.54 237 | 98.99 62 | 98.54 125 | 94.67 125 | 94.36 195 | 98.77 89 | 85.39 242 | 99.11 163 | 95.71 115 | 94.15 210 | 96.76 237 |
|
LGP-MVS_train | | | | | 96.47 216 | 97.46 190 | 93.54 237 | | 98.54 125 | 94.67 125 | 94.36 195 | 98.77 89 | 85.39 242 | 99.11 163 | 95.71 115 | 94.15 210 | 96.76 237 |
|
v144192 | | | 94.39 222 | 93.70 227 | 96.48 215 | 96.06 281 | 94.35 217 | 98.58 143 | 98.16 192 | 91.45 246 | 94.33 197 | 97.02 236 | 87.50 205 | 98.45 238 | 91.08 233 | 89.11 271 | 96.63 260 |
|
v1 | | | 94.75 199 | 94.11 202 | 96.69 186 | 96.27 270 | 94.87 175 | 98.69 126 | 98.12 198 | 92.43 221 | 94.32 198 | 96.94 245 | 88.71 169 | 98.54 220 | 92.66 197 | 88.84 281 | 96.67 252 |
|
v1141 | | | 94.75 199 | 94.11 202 | 96.67 192 | 96.27 270 | 94.86 176 | 98.69 126 | 98.12 198 | 92.43 221 | 94.31 199 | 96.94 245 | 88.78 165 | 98.48 231 | 92.63 198 | 88.85 280 | 96.67 252 |
|
V42 | | | 94.78 196 | 94.14 198 | 96.70 185 | 96.33 262 | 95.22 158 | 98.97 66 | 98.09 211 | 92.32 228 | 94.31 199 | 97.06 229 | 88.39 178 | 98.55 219 | 92.90 191 | 88.87 278 | 96.34 284 |
|
v7 | | | 94.69 203 | 94.04 204 | 96.62 199 | 96.41 250 | 94.79 196 | 98.78 107 | 98.13 196 | 91.89 236 | 94.30 201 | 97.16 214 | 88.13 185 | 98.45 238 | 91.96 216 | 89.65 263 | 96.61 262 |
|
divwei89l23v2f112 | | | 94.76 197 | 94.12 201 | 96.67 192 | 96.28 268 | 94.85 177 | 98.69 126 | 98.12 198 | 92.44 220 | 94.29 202 | 96.94 245 | 88.85 157 | 98.48 231 | 92.67 196 | 88.79 282 | 96.67 252 |
|
ACMM | | 93.85 9 | 95.69 139 | 95.38 134 | 96.61 200 | 97.61 179 | 93.84 230 | 98.91 71 | 98.44 146 | 95.25 103 | 94.28 203 | 98.47 115 | 86.04 234 | 99.12 159 | 95.50 122 | 93.95 217 | 96.87 227 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
IterMVS-LS | | | 95.46 156 | 95.21 143 | 96.22 232 | 98.12 152 | 93.72 235 | 98.32 180 | 98.13 196 | 93.71 165 | 94.26 204 | 97.31 207 | 92.24 92 | 98.10 273 | 94.63 141 | 90.12 258 | 96.84 230 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
v1921920 | | | 94.20 230 | 93.47 241 | 96.40 222 | 95.98 284 | 94.08 224 | 98.52 155 | 98.15 193 | 91.33 254 | 94.25 205 | 97.20 213 | 86.41 220 | 98.42 244 | 90.04 257 | 89.39 269 | 96.69 251 |
|
BH-w/o | | | 95.38 163 | 95.08 148 | 96.26 231 | 98.34 137 | 91.79 263 | 97.70 246 | 97.43 258 | 92.87 204 | 94.24 206 | 97.22 212 | 88.66 170 | 98.84 198 | 91.55 225 | 97.70 144 | 98.16 173 |
|
XVG-ACMP-BASELINE | | | 94.54 215 | 94.14 198 | 95.75 250 | 96.55 242 | 91.65 267 | 98.11 206 | 98.44 146 | 94.96 117 | 94.22 207 | 97.90 161 | 79.18 302 | 99.11 163 | 94.05 159 | 93.85 218 | 96.48 279 |
|
v1144 | | | 94.59 212 | 93.92 212 | 96.60 201 | 96.21 272 | 94.78 198 | 98.59 141 | 98.14 195 | 91.86 239 | 94.21 208 | 97.02 236 | 87.97 188 | 98.41 251 | 91.72 222 | 89.57 264 | 96.61 262 |
|
v1192 | | | 94.32 224 | 93.58 234 | 96.53 211 | 96.10 279 | 94.45 212 | 98.50 160 | 98.17 190 | 91.54 244 | 94.19 209 | 97.06 229 | 86.95 213 | 98.43 243 | 90.14 252 | 89.57 264 | 96.70 246 |
|
PAPM | | | 94.95 186 | 94.00 207 | 97.78 118 | 97.04 217 | 95.65 141 | 96.03 313 | 98.25 173 | 91.23 260 | 94.19 209 | 97.80 173 | 91.27 115 | 98.86 197 | 82.61 316 | 97.61 145 | 98.84 139 |
|
tpmp4_e23 | | | 93.91 245 | 93.42 244 | 95.38 266 | 97.62 178 | 88.59 306 | 97.52 258 | 97.34 265 | 87.94 304 | 94.17 211 | 96.79 259 | 82.91 279 | 99.05 170 | 90.62 242 | 95.91 194 | 98.50 156 |
|
Patchmatch-test | | | 94.42 220 | 93.68 229 | 96.63 197 | 97.60 180 | 91.76 264 | 94.83 328 | 97.49 253 | 89.45 293 | 94.14 212 | 97.10 222 | 88.99 147 | 98.83 200 | 85.37 311 | 98.13 129 | 99.29 98 |
|
v1240 | | | 94.06 241 | 93.29 245 | 96.34 227 | 96.03 283 | 93.90 228 | 98.44 165 | 98.17 190 | 91.18 262 | 94.13 213 | 97.01 238 | 86.05 232 | 98.42 244 | 89.13 274 | 89.50 267 | 96.70 246 |
|
GBi-Net | | | 94.49 216 | 93.80 219 | 96.56 207 | 98.21 144 | 95.00 165 | 98.82 91 | 98.18 185 | 92.46 213 | 94.09 214 | 97.07 226 | 81.16 286 | 97.95 282 | 92.08 209 | 92.14 241 | 96.72 242 |
|
test1 | | | 94.49 216 | 93.80 219 | 96.56 207 | 98.21 144 | 95.00 165 | 98.82 91 | 98.18 185 | 92.46 213 | 94.09 214 | 97.07 226 | 81.16 286 | 97.95 282 | 92.08 209 | 92.14 241 | 96.72 242 |
|
FMVSNet3 | | | 94.97 185 | 94.26 190 | 97.11 163 | 98.18 149 | 96.62 91 | 98.56 148 | 98.26 172 | 93.67 172 | 94.09 214 | 97.10 222 | 84.25 266 | 98.01 279 | 92.08 209 | 92.14 241 | 96.70 246 |
|
MIMVSNet | | | 93.26 256 | 92.21 260 | 96.41 221 | 97.73 174 | 93.13 248 | 95.65 319 | 97.03 282 | 91.27 259 | 94.04 217 | 96.06 285 | 75.33 317 | 97.19 302 | 86.56 301 | 96.23 182 | 98.92 135 |
|
FIs | | | 96.51 109 | 96.12 110 | 97.67 127 | 97.13 214 | 97.54 60 | 99.36 8 | 99.22 14 | 95.89 71 | 94.03 218 | 98.35 125 | 91.98 101 | 98.44 241 | 96.40 93 | 92.76 237 | 97.01 210 |
|
v2v482 | | | 94.69 203 | 94.03 205 | 96.65 194 | 96.17 275 | 94.79 196 | 98.67 133 | 98.08 212 | 92.72 207 | 94.00 219 | 97.16 214 | 87.69 200 | 98.45 238 | 92.91 190 | 88.87 278 | 96.72 242 |
|
FC-MVSNet-test | | | 96.42 112 | 96.05 111 | 97.53 139 | 96.95 221 | 97.27 68 | 99.36 8 | 99.23 12 | 95.83 73 | 93.93 220 | 98.37 123 | 92.00 100 | 98.32 260 | 96.02 103 | 92.72 238 | 97.00 211 |
|
UniMVSNet (Re) | | | 95.78 133 | 95.19 144 | 97.58 136 | 96.99 220 | 97.47 62 | 98.79 105 | 99.18 16 | 95.60 81 | 93.92 221 | 97.04 234 | 91.68 105 | 98.48 231 | 95.80 111 | 87.66 294 | 96.79 234 |
|
UniMVSNet_NR-MVSNet | | | 95.71 137 | 95.15 145 | 97.40 150 | 96.84 229 | 96.97 78 | 98.74 116 | 99.24 10 | 95.16 107 | 93.88 222 | 97.72 179 | 91.68 105 | 98.31 262 | 95.81 109 | 87.25 299 | 96.92 216 |
|
DU-MVS | | | 95.42 159 | 94.76 170 | 97.40 150 | 96.53 243 | 96.97 78 | 98.66 135 | 98.99 28 | 95.43 88 | 93.88 222 | 97.69 180 | 88.57 172 | 98.31 262 | 95.81 109 | 87.25 299 | 96.92 216 |
|
Baseline_NR-MVSNet | | | 94.35 223 | 93.81 218 | 95.96 240 | 96.20 273 | 94.05 225 | 98.61 140 | 96.67 304 | 91.44 247 | 93.85 224 | 97.60 188 | 88.57 172 | 98.14 271 | 94.39 148 | 86.93 302 | 95.68 300 |
|
PS-MVSNAJss | | | 96.43 111 | 96.26 106 | 96.92 176 | 95.84 291 | 95.08 163 | 99.16 41 | 98.50 137 | 95.87 72 | 93.84 225 | 98.34 129 | 94.51 62 | 98.61 213 | 96.88 74 | 93.45 227 | 97.06 207 |
|
tpmvs | | | 94.60 210 | 94.36 187 | 95.33 269 | 97.46 190 | 88.60 305 | 96.88 294 | 97.68 230 | 91.29 257 | 93.80 226 | 96.42 274 | 88.58 171 | 99.24 144 | 91.06 234 | 96.04 193 | 98.17 172 |
|
3Dnovator | | 94.51 5 | 97.46 68 | 96.93 79 | 99.07 44 | 97.78 171 | 97.64 55 | 99.35 10 | 99.06 21 | 97.02 39 | 93.75 227 | 99.16 44 | 89.25 141 | 99.92 15 | 97.22 59 | 99.75 31 | 99.64 55 |
|
ITE_SJBPF | | | | | 95.44 260 | 97.42 194 | 91.32 270 | | 97.50 247 | 95.09 112 | 93.59 228 | 98.35 125 | 81.70 285 | 98.88 194 | 89.71 263 | 93.39 229 | 96.12 289 |
|
TranMVSNet+NR-MVSNet | | | 95.14 178 | 94.48 180 | 97.11 163 | 96.45 248 | 96.36 103 | 99.03 59 | 99.03 24 | 95.04 113 | 93.58 229 | 97.93 159 | 88.27 180 | 98.03 278 | 94.13 156 | 86.90 304 | 96.95 215 |
|
COLMAP_ROB | | 93.27 12 | 95.33 169 | 94.87 162 | 96.71 183 | 99.29 57 | 93.24 245 | 98.58 143 | 98.11 203 | 89.92 281 | 93.57 230 | 99.10 50 | 86.37 221 | 99.79 71 | 90.78 238 | 98.10 130 | 97.09 206 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
tpm cat1 | | | 93.36 251 | 92.80 251 | 95.07 275 | 97.58 182 | 87.97 312 | 96.76 298 | 97.86 224 | 82.17 329 | 93.53 231 | 96.04 286 | 86.13 224 | 99.13 158 | 89.24 272 | 95.87 195 | 98.10 174 |
|
AllTest | | | 95.24 173 | 94.65 174 | 96.99 168 | 99.25 66 | 93.21 246 | 98.59 141 | 98.18 185 | 91.36 251 | 93.52 232 | 98.77 89 | 84.67 253 | 99.72 88 | 89.70 264 | 97.87 136 | 98.02 176 |
|
TestCases | | | | | 96.99 168 | 99.25 66 | 93.21 246 | | 98.18 185 | 91.36 251 | 93.52 232 | 98.77 89 | 84.67 253 | 99.72 88 | 89.70 264 | 97.87 136 | 98.02 176 |
|
FMVSNet2 | | | 94.47 218 | 93.61 232 | 97.04 166 | 98.21 144 | 96.43 100 | 98.79 105 | 98.27 168 | 92.46 213 | 93.50 234 | 97.09 224 | 81.16 286 | 98.00 280 | 91.09 232 | 91.93 245 | 96.70 246 |
|
v148 | | | 94.29 226 | 93.76 224 | 95.91 242 | 96.10 279 | 92.93 250 | 98.58 143 | 97.97 220 | 92.59 211 | 93.47 235 | 96.95 243 | 88.53 175 | 98.32 260 | 92.56 200 | 87.06 301 | 96.49 278 |
|
pmmvs4 | | | 94.69 203 | 93.99 209 | 96.81 179 | 95.74 293 | 95.94 121 | 97.40 263 | 97.67 231 | 90.42 269 | 93.37 236 | 97.59 189 | 89.08 146 | 98.20 269 | 92.97 186 | 91.67 249 | 96.30 286 |
|
PCF-MVS | | 93.45 11 | 94.68 206 | 93.43 242 | 98.42 84 | 98.62 126 | 96.77 87 | 95.48 320 | 98.20 181 | 84.63 322 | 93.34 237 | 98.32 131 | 88.55 174 | 99.81 52 | 84.80 312 | 98.96 93 | 98.68 147 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
XXY-MVS | | | 95.20 176 | 94.45 184 | 97.46 145 | 96.75 234 | 96.56 95 | 98.86 84 | 98.65 111 | 93.30 190 | 93.27 238 | 98.27 136 | 84.85 252 | 98.87 195 | 94.82 138 | 91.26 254 | 96.96 213 |
|
jajsoiax | | | 95.45 157 | 95.03 149 | 96.73 182 | 95.42 304 | 94.63 203 | 99.14 43 | 98.52 130 | 95.74 75 | 93.22 239 | 98.36 124 | 83.87 275 | 98.65 211 | 96.95 68 | 94.04 213 | 96.91 221 |
|
mvs_tets | | | 95.41 161 | 95.00 150 | 96.65 194 | 95.58 299 | 94.42 213 | 99.00 61 | 98.55 124 | 95.73 76 | 93.21 240 | 98.38 122 | 83.45 278 | 98.63 212 | 97.09 63 | 94.00 215 | 96.91 221 |
|
anonymousdsp | | | 95.42 159 | 94.91 160 | 96.94 173 | 95.10 308 | 95.90 133 | 99.14 43 | 98.41 150 | 93.75 160 | 93.16 241 | 97.46 195 | 87.50 205 | 98.41 251 | 95.63 119 | 94.03 214 | 96.50 277 |
|
v8 | | | 94.47 218 | 93.77 222 | 96.57 206 | 96.36 255 | 94.83 188 | 99.05 56 | 98.19 182 | 91.92 235 | 93.16 241 | 96.97 241 | 88.82 160 | 98.48 231 | 91.69 223 | 87.79 292 | 96.39 281 |
|
WR-MVS | | | 95.15 177 | 94.46 182 | 97.22 155 | 96.67 239 | 96.45 99 | 98.21 190 | 98.81 61 | 94.15 140 | 93.16 241 | 97.69 180 | 87.51 203 | 98.30 264 | 95.29 129 | 88.62 283 | 96.90 223 |
|
EPNet_dtu | | | 95.21 175 | 94.95 155 | 95.99 239 | 96.17 275 | 90.45 283 | 98.16 200 | 97.27 272 | 96.77 44 | 93.14 244 | 98.33 130 | 90.34 128 | 98.42 244 | 85.57 308 | 98.81 102 | 99.09 119 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
QAPM | | | 96.29 117 | 95.40 130 | 98.96 52 | 97.85 168 | 97.60 58 | 99.23 22 | 98.93 36 | 89.76 285 | 93.11 245 | 99.02 60 | 89.11 145 | 99.93 9 | 91.99 214 | 99.62 49 | 99.34 90 |
|
GG-mvs-BLEND | | | | | 96.59 202 | 96.34 258 | 94.98 168 | 96.51 308 | 88.58 348 | | 93.10 246 | 94.34 308 | 80.34 297 | 98.05 277 | 89.53 267 | 96.99 153 | 96.74 239 |
|
v10 | | | 94.29 226 | 93.55 235 | 96.51 213 | 96.39 251 | 94.80 193 | 98.99 62 | 98.19 182 | 91.35 253 | 93.02 247 | 96.99 239 | 88.09 186 | 98.41 251 | 90.50 249 | 88.41 285 | 96.33 285 |
|
3Dnovator+ | | 94.38 6 | 97.43 73 | 96.78 86 | 99.38 11 | 97.83 169 | 98.52 13 | 99.37 7 | 98.71 91 | 97.09 37 | 92.99 248 | 99.13 46 | 89.36 138 | 99.89 29 | 96.97 65 | 99.57 57 | 99.71 34 |
|
Patchmtry | | | 93.22 257 | 92.35 258 | 95.84 245 | 96.77 231 | 93.09 249 | 94.66 330 | 97.56 235 | 87.37 307 | 92.90 249 | 96.24 277 | 88.15 183 | 97.90 285 | 87.37 297 | 90.10 259 | 96.53 273 |
|
v52 | | | 94.18 233 | 93.52 237 | 96.13 236 | 95.95 286 | 94.29 219 | 99.23 22 | 98.21 178 | 91.42 248 | 92.84 250 | 96.89 252 | 87.85 194 | 98.53 226 | 91.51 227 | 87.81 290 | 95.57 303 |
|
V4 | | | 94.18 233 | 93.52 237 | 96.13 236 | 95.89 288 | 94.31 218 | 99.23 22 | 98.22 177 | 91.42 248 | 92.82 251 | 96.89 252 | 87.93 190 | 98.52 227 | 91.51 227 | 87.81 290 | 95.58 302 |
|
v7n | | | 94.19 231 | 93.43 242 | 96.47 216 | 95.90 287 | 94.38 216 | 99.26 17 | 98.34 160 | 91.99 234 | 92.76 252 | 97.13 221 | 88.31 179 | 98.52 227 | 89.48 269 | 87.70 293 | 96.52 274 |
|
MVS | | | 94.67 207 | 93.54 236 | 98.08 102 | 96.88 227 | 96.56 95 | 98.19 195 | 98.50 137 | 78.05 335 | 92.69 253 | 98.02 151 | 91.07 119 | 99.63 105 | 90.09 253 | 98.36 121 | 98.04 175 |
|
DSMNet-mixed | | | 92.52 264 | 92.58 255 | 92.33 308 | 94.15 317 | 82.65 327 | 98.30 183 | 94.26 337 | 89.08 298 | 92.65 254 | 95.73 292 | 85.01 249 | 95.76 326 | 86.24 303 | 97.76 142 | 98.59 153 |
|
EU-MVSNet | | | 93.66 248 | 94.14 198 | 92.25 309 | 95.96 285 | 83.38 324 | 98.52 155 | 98.12 198 | 94.69 123 | 92.61 255 | 98.13 145 | 87.36 207 | 96.39 323 | 91.82 218 | 90.00 260 | 96.98 212 |
|
semantic-postprocess | | | | | 94.85 280 | 97.98 162 | 90.56 282 | | 98.11 203 | 93.75 160 | 92.58 256 | 97.48 194 | 83.91 273 | 97.41 299 | 92.48 204 | 91.30 252 | 96.58 266 |
|
pmmvs5 | | | 93.65 250 | 92.97 249 | 95.68 251 | 95.49 302 | 92.37 255 | 98.20 191 | 97.28 271 | 89.66 289 | 92.58 256 | 97.26 209 | 82.14 282 | 98.09 275 | 93.18 180 | 90.95 255 | 96.58 266 |
|
WR-MVS_H | | | 95.05 180 | 94.46 182 | 96.81 179 | 96.86 228 | 95.82 137 | 99.24 20 | 99.24 10 | 93.87 155 | 92.53 258 | 96.84 257 | 90.37 127 | 98.24 268 | 93.24 177 | 87.93 289 | 96.38 282 |
|
ACMP | | 93.49 10 | 95.34 168 | 94.98 152 | 96.43 220 | 97.67 175 | 93.48 239 | 98.73 119 | 98.44 146 | 94.94 120 | 92.53 258 | 98.53 109 | 84.50 261 | 99.14 157 | 95.48 123 | 94.00 215 | 96.66 255 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
test0.0.03 1 | | | 94.08 239 | 93.51 239 | 95.80 247 | 95.53 301 | 92.89 251 | 97.38 265 | 95.97 314 | 95.11 109 | 92.51 260 | 96.66 263 | 87.71 197 | 96.94 305 | 87.03 299 | 93.67 220 | 97.57 190 |
|
IB-MVS | | 91.98 17 | 93.27 255 | 91.97 262 | 97.19 157 | 97.47 189 | 93.41 242 | 97.09 283 | 95.99 313 | 93.32 188 | 92.47 261 | 95.73 292 | 78.06 305 | 99.53 124 | 94.59 144 | 82.98 316 | 98.62 152 |
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 |
IterMVS | | | 94.09 238 | 93.85 217 | 94.80 283 | 97.99 160 | 90.35 284 | 97.18 280 | 98.12 198 | 93.68 170 | 92.46 262 | 97.34 204 | 84.05 271 | 97.41 299 | 92.51 203 | 91.33 251 | 96.62 261 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CP-MVSNet | | | 94.94 188 | 94.30 189 | 96.83 178 | 96.72 236 | 95.56 145 | 99.11 49 | 98.95 33 | 93.89 153 | 92.42 263 | 97.90 161 | 87.19 208 | 98.12 272 | 94.32 151 | 88.21 286 | 96.82 233 |
|
PS-CasMVS | | | 94.67 207 | 93.99 209 | 96.71 183 | 96.68 238 | 95.26 157 | 99.13 46 | 99.03 24 | 93.68 170 | 92.33 264 | 97.95 157 | 85.35 244 | 98.10 273 | 93.59 170 | 88.16 288 | 96.79 234 |
|
FMVSNet1 | | | 93.19 258 | 92.07 261 | 96.56 207 | 97.54 185 | 95.00 165 | 98.82 91 | 98.18 185 | 90.38 270 | 92.27 265 | 97.07 226 | 73.68 325 | 97.95 282 | 89.36 271 | 91.30 252 | 96.72 242 |
|
PEN-MVS | | | 94.42 220 | 93.73 226 | 96.49 214 | 96.28 268 | 94.84 186 | 99.17 35 | 99.00 26 | 93.51 176 | 92.23 266 | 97.83 170 | 86.10 231 | 97.90 285 | 92.55 201 | 86.92 303 | 96.74 239 |
|
v748 | | | 93.75 247 | 93.06 247 | 95.82 246 | 95.73 294 | 92.64 253 | 99.25 19 | 98.24 175 | 91.60 243 | 92.22 267 | 96.52 270 | 87.60 202 | 98.46 236 | 90.64 241 | 85.72 311 | 96.36 283 |
|
OurMVSNet-221017-0 | | | 94.21 229 | 94.00 207 | 94.85 280 | 95.60 298 | 89.22 296 | 98.89 76 | 97.43 258 | 95.29 101 | 92.18 268 | 98.52 112 | 82.86 280 | 98.59 216 | 93.46 172 | 91.76 248 | 96.74 239 |
|
MS-PatchMatch | | | 93.84 246 | 93.63 230 | 94.46 292 | 96.18 274 | 89.45 292 | 97.76 242 | 98.27 168 | 92.23 231 | 92.13 269 | 97.49 193 | 79.50 299 | 98.69 207 | 89.75 262 | 99.38 81 | 95.25 305 |
|
1314 | | | 96.25 121 | 95.73 120 | 97.79 117 | 97.13 214 | 95.55 147 | 98.19 195 | 98.59 115 | 93.47 178 | 92.03 270 | 97.82 171 | 91.33 114 | 99.49 127 | 94.62 142 | 98.44 117 | 98.32 170 |
|
DTE-MVSNet | | | 93.98 243 | 93.26 246 | 96.14 235 | 96.06 281 | 94.39 215 | 99.20 32 | 98.86 52 | 93.06 195 | 91.78 271 | 97.81 172 | 85.87 235 | 97.58 295 | 90.53 243 | 86.17 308 | 96.46 280 |
|
LF4IMVS | | | 93.14 259 | 92.79 252 | 94.20 295 | 95.88 289 | 88.67 304 | 97.66 250 | 97.07 279 | 93.81 158 | 91.71 272 | 97.65 184 | 77.96 306 | 98.81 202 | 91.47 229 | 91.92 246 | 95.12 306 |
|
testgi | | | 93.06 260 | 92.45 257 | 94.88 279 | 96.43 249 | 89.90 286 | 98.75 112 | 97.54 240 | 95.60 81 | 91.63 273 | 97.91 160 | 74.46 323 | 97.02 304 | 86.10 304 | 93.67 220 | 97.72 186 |
|
tfpnnormal | | | 93.66 248 | 92.70 254 | 96.55 210 | 96.94 222 | 95.94 121 | 98.97 66 | 99.19 15 | 91.04 263 | 91.38 274 | 97.34 204 | 84.94 250 | 98.61 213 | 85.45 310 | 89.02 274 | 95.11 307 |
|
LTVRE_ROB | | 92.95 15 | 94.60 210 | 93.90 214 | 96.68 189 | 97.41 197 | 94.42 213 | 98.52 155 | 98.59 115 | 91.69 241 | 91.21 275 | 98.35 125 | 84.87 251 | 99.04 174 | 91.06 234 | 93.44 228 | 96.60 264 |
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 |
OpenMVS | | 93.04 13 | 95.83 131 | 95.00 150 | 98.32 88 | 97.18 211 | 97.32 66 | 99.21 31 | 98.97 29 | 89.96 278 | 91.14 276 | 99.05 59 | 86.64 217 | 99.92 15 | 93.38 173 | 99.47 71 | 97.73 185 |
|
pm-mvs1 | | | 93.94 244 | 93.06 247 | 96.59 202 | 96.49 246 | 95.16 159 | 98.95 68 | 98.03 219 | 92.32 228 | 91.08 277 | 97.84 167 | 84.54 260 | 98.41 251 | 92.16 207 | 86.13 310 | 96.19 288 |
|
LP | | | 91.12 285 | 89.99 287 | 94.53 288 | 96.35 257 | 88.70 303 | 93.86 335 | 97.35 264 | 84.88 320 | 90.98 278 | 94.77 303 | 84.40 262 | 97.43 298 | 75.41 333 | 91.89 247 | 97.47 191 |
|
MVS-HIRNet | | | 89.46 297 | 88.40 299 | 92.64 306 | 97.58 182 | 82.15 328 | 94.16 334 | 93.05 343 | 75.73 337 | 90.90 279 | 82.52 339 | 79.42 300 | 98.33 259 | 83.53 314 | 98.68 104 | 97.43 192 |
|
FMVSNet5 | | | 91.81 278 | 90.92 272 | 94.49 289 | 97.21 207 | 92.09 258 | 98.00 217 | 97.55 239 | 89.31 296 | 90.86 280 | 95.61 297 | 74.48 322 | 95.32 328 | 85.57 308 | 89.70 262 | 96.07 291 |
|
USDC | | | 93.33 254 | 92.71 253 | 95.21 270 | 96.83 230 | 90.83 275 | 96.91 289 | 97.50 247 | 93.84 156 | 90.72 281 | 98.14 144 | 77.69 307 | 98.82 201 | 89.51 268 | 93.21 234 | 95.97 293 |
|
MVP-Stereo | | | 94.28 228 | 93.92 212 | 95.35 268 | 94.95 310 | 92.60 254 | 97.97 219 | 97.65 232 | 91.61 242 | 90.68 282 | 97.09 224 | 86.32 222 | 98.42 244 | 89.70 264 | 99.34 83 | 95.02 310 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
ACMH+ | | 92.99 14 | 94.30 225 | 93.77 222 | 95.88 244 | 97.81 170 | 92.04 260 | 98.71 122 | 98.37 157 | 93.99 148 | 90.60 283 | 98.47 115 | 80.86 291 | 99.05 170 | 92.75 195 | 92.40 240 | 96.55 271 |
|
testpf | | | 88.74 300 | 89.09 293 | 87.69 318 | 95.78 292 | 83.16 326 | 84.05 346 | 94.13 340 | 85.22 319 | 90.30 284 | 94.39 307 | 74.92 320 | 95.80 325 | 89.77 260 | 93.28 233 | 84.10 342 |
|
Anonymous20231206 | | | 91.66 280 | 91.10 268 | 93.33 302 | 94.02 319 | 87.35 316 | 98.58 143 | 97.26 273 | 90.48 266 | 90.16 285 | 96.31 275 | 83.83 276 | 96.53 321 | 79.36 323 | 89.90 261 | 96.12 289 |
|
SixPastTwentyTwo | | | 93.34 253 | 92.86 250 | 94.75 284 | 95.67 296 | 89.41 294 | 98.75 112 | 96.67 304 | 93.89 153 | 90.15 286 | 98.25 138 | 80.87 290 | 98.27 267 | 90.90 237 | 90.64 256 | 96.57 268 |
|
PVSNet_0 | | 88.72 19 | 91.28 283 | 90.03 286 | 95.00 276 | 97.99 160 | 87.29 317 | 94.84 327 | 98.50 137 | 92.06 233 | 89.86 287 | 95.19 298 | 79.81 298 | 99.39 135 | 92.27 206 | 69.79 340 | 98.33 169 |
|
ACMH | | 92.88 16 | 94.55 214 | 93.95 211 | 96.34 227 | 97.63 177 | 93.26 244 | 98.81 97 | 98.49 141 | 93.43 179 | 89.74 288 | 98.53 109 | 81.91 284 | 99.08 168 | 93.69 166 | 93.30 231 | 96.70 246 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
pmmvs6 | | | 91.77 279 | 90.63 280 | 95.17 272 | 94.69 315 | 91.24 272 | 98.67 133 | 97.92 222 | 86.14 312 | 89.62 289 | 97.56 192 | 75.79 316 | 98.34 258 | 90.75 239 | 84.56 315 | 95.94 294 |
|
TinyColmap | | | 92.31 266 | 91.53 265 | 94.65 286 | 96.92 223 | 89.75 288 | 96.92 287 | 96.68 303 | 90.45 268 | 89.62 289 | 97.85 166 | 76.06 315 | 98.81 202 | 86.74 300 | 92.51 239 | 95.41 304 |
|
TransMVSNet (Re) | | | 92.67 262 | 91.51 266 | 96.15 234 | 96.58 241 | 94.65 201 | 98.90 72 | 96.73 300 | 90.86 265 | 89.46 291 | 97.86 164 | 85.62 239 | 98.09 275 | 86.45 302 | 81.12 321 | 95.71 299 |
|
testus | | | 88.91 299 | 89.08 294 | 88.40 317 | 91.39 326 | 76.05 335 | 96.56 304 | 96.48 308 | 89.38 295 | 89.39 292 | 95.17 300 | 70.94 329 | 93.56 335 | 77.04 329 | 95.41 199 | 95.61 301 |
|
NR-MVSNet | | | 94.98 184 | 94.16 196 | 97.44 146 | 96.53 243 | 97.22 72 | 98.74 116 | 98.95 33 | 94.96 117 | 89.25 293 | 97.69 180 | 89.32 139 | 98.18 270 | 94.59 144 | 87.40 296 | 96.92 216 |
|
test2356 | | | 88.68 301 | 88.61 297 | 88.87 316 | 89.90 332 | 78.23 332 | 95.11 322 | 96.66 306 | 88.66 301 | 89.06 294 | 94.33 309 | 73.14 327 | 92.56 339 | 75.56 332 | 95.11 201 | 95.81 297 |
|
LCM-MVSNet-Re | | | 95.22 174 | 95.32 138 | 94.91 277 | 98.18 149 | 87.85 314 | 98.75 112 | 95.66 323 | 95.11 109 | 88.96 295 | 96.85 256 | 90.26 131 | 97.65 292 | 95.65 118 | 98.44 117 | 99.22 105 |
|
TDRefinement | | | 91.06 286 | 89.68 289 | 95.21 270 | 85.35 339 | 91.49 268 | 98.51 159 | 97.07 279 | 91.47 245 | 88.83 296 | 97.84 167 | 77.31 311 | 99.09 167 | 92.79 194 | 77.98 333 | 95.04 309 |
|
N_pmnet | | | 87.12 305 | 87.77 302 | 85.17 325 | 95.46 303 | 61.92 348 | 97.37 267 | 70.66 355 | 85.83 316 | 88.73 297 | 96.04 286 | 85.33 246 | 97.76 291 | 80.02 320 | 90.48 257 | 95.84 295 |
|
test_0402 | | | 91.32 282 | 90.27 284 | 94.48 290 | 96.60 240 | 91.12 273 | 98.50 160 | 97.22 275 | 86.10 313 | 88.30 298 | 96.98 240 | 77.65 309 | 97.99 281 | 78.13 327 | 92.94 236 | 94.34 323 |
|
test20.03 | | | 90.89 288 | 90.38 282 | 92.43 307 | 93.48 320 | 88.14 311 | 98.33 176 | 97.56 235 | 93.40 185 | 87.96 299 | 96.71 262 | 80.69 293 | 94.13 332 | 79.15 324 | 86.17 308 | 95.01 311 |
|
MIMVSNet1 | | | 89.67 296 | 88.28 301 | 93.82 298 | 92.81 324 | 91.08 274 | 98.01 215 | 97.45 256 | 87.95 303 | 87.90 300 | 95.87 290 | 67.63 335 | 94.56 331 | 78.73 326 | 88.18 287 | 95.83 296 |
|
Patchmatch-RL test | | | 91.49 281 | 90.85 273 | 93.41 301 | 91.37 327 | 84.40 321 | 92.81 336 | 95.93 316 | 91.87 238 | 87.25 301 | 94.87 302 | 88.99 147 | 96.53 321 | 92.54 202 | 82.00 318 | 99.30 96 |
|
pmmvs3 | | | 86.67 306 | 84.86 308 | 92.11 310 | 88.16 334 | 87.19 318 | 96.63 301 | 94.75 333 | 79.88 333 | 87.22 302 | 92.75 326 | 66.56 336 | 95.20 329 | 81.24 319 | 76.56 336 | 93.96 329 |
|
K. test v3 | | | 92.55 263 | 91.91 264 | 94.48 290 | 95.64 297 | 89.24 295 | 99.07 55 | 94.88 331 | 94.04 145 | 86.78 303 | 97.59 189 | 77.64 310 | 97.64 293 | 92.08 209 | 89.43 268 | 96.57 268 |
|
lessismore_v0 | | | | | 94.45 293 | 94.93 311 | 88.44 308 | | 91.03 345 | | 86.77 304 | 97.64 186 | 76.23 314 | 98.42 244 | 90.31 251 | 85.64 312 | 96.51 276 |
|
ambc | | | | | 89.49 315 | 86.66 338 | 75.78 336 | 92.66 337 | 96.72 301 | | 86.55 305 | 92.50 328 | 46.01 345 | 97.90 285 | 90.32 250 | 82.09 317 | 94.80 312 |
|
PM-MVS | | | 87.77 303 | 86.55 305 | 91.40 312 | 91.03 329 | 83.36 325 | 96.92 287 | 95.18 329 | 91.28 258 | 86.48 306 | 93.42 311 | 53.27 342 | 96.74 315 | 89.43 270 | 81.97 319 | 94.11 326 |
|
DI_MVS_plusplus_test | | | 94.74 201 | 93.62 231 | 98.09 101 | 95.34 305 | 95.92 130 | 98.09 209 | 97.34 265 | 94.66 127 | 85.89 307 | 95.91 288 | 80.49 295 | 99.38 136 | 96.66 83 | 98.22 125 | 98.97 130 |
|
OpenMVS_ROB | | 86.42 20 | 89.00 298 | 87.43 304 | 93.69 299 | 93.08 322 | 89.42 293 | 97.91 226 | 96.89 296 | 78.58 334 | 85.86 308 | 94.69 304 | 69.48 331 | 98.29 266 | 77.13 328 | 93.29 232 | 93.36 332 |
|
UnsupCasMVSNet_eth | | | 90.99 287 | 89.92 288 | 94.19 296 | 94.08 318 | 89.83 287 | 97.13 282 | 98.67 104 | 93.69 168 | 85.83 309 | 96.19 282 | 75.15 318 | 96.74 315 | 89.14 273 | 79.41 327 | 96.00 292 |
|
test_normal | | | 94.72 202 | 93.59 233 | 98.11 100 | 95.30 306 | 95.95 120 | 97.91 226 | 97.39 263 | 94.64 128 | 85.70 310 | 95.88 289 | 80.52 294 | 99.36 137 | 96.69 82 | 98.30 124 | 99.01 128 |
|
new_pmnet | | | 90.06 293 | 89.00 296 | 93.22 305 | 94.18 316 | 88.32 310 | 96.42 309 | 96.89 296 | 86.19 311 | 85.67 311 | 93.62 310 | 77.18 312 | 97.10 303 | 81.61 318 | 89.29 270 | 94.23 324 |
|
v17 | | | 92.08 270 | 90.94 270 | 95.48 257 | 96.34 258 | 94.83 188 | 98.81 97 | 97.52 241 | 89.95 279 | 85.32 312 | 93.24 314 | 88.91 153 | 96.91 307 | 88.76 281 | 79.63 326 | 94.71 315 |
|
EG-PatchMatch MVS | | | 91.13 284 | 90.12 285 | 94.17 297 | 94.73 314 | 89.00 300 | 98.13 203 | 97.81 225 | 89.22 297 | 85.32 312 | 96.46 271 | 67.71 334 | 98.42 244 | 87.89 295 | 93.82 219 | 95.08 308 |
|
v18 | | | 92.10 269 | 90.97 269 | 95.50 255 | 96.34 258 | 94.85 177 | 98.82 91 | 97.52 241 | 89.99 277 | 85.31 314 | 93.26 313 | 88.90 154 | 96.92 306 | 88.82 280 | 79.77 325 | 94.73 313 |
|
v16 | | | 92.08 270 | 90.94 270 | 95.49 256 | 96.38 254 | 94.84 186 | 98.81 97 | 97.51 244 | 89.94 280 | 85.25 315 | 93.28 312 | 88.86 155 | 96.91 307 | 88.70 282 | 79.78 324 | 94.72 314 |
|
v15 | | | 91.94 272 | 90.77 274 | 95.43 262 | 96.31 266 | 94.83 188 | 98.77 108 | 97.50 247 | 89.92 281 | 85.13 316 | 93.08 317 | 88.76 166 | 96.86 309 | 88.40 285 | 79.10 328 | 94.61 319 |
|
v11 | | | 91.85 277 | 90.68 279 | 95.36 267 | 96.34 258 | 94.74 200 | 98.80 100 | 97.43 258 | 89.60 291 | 85.09 317 | 93.03 319 | 88.53 175 | 96.75 314 | 87.37 297 | 79.96 323 | 94.58 321 |
|
V14 | | | 91.93 273 | 90.76 275 | 95.42 265 | 96.33 262 | 94.81 192 | 98.77 108 | 97.51 244 | 89.86 283 | 85.09 317 | 93.13 315 | 88.80 164 | 96.83 311 | 88.32 286 | 79.06 330 | 94.60 320 |
|
V9 | | | 91.91 274 | 90.73 276 | 95.45 259 | 96.32 265 | 94.80 193 | 98.77 108 | 97.50 247 | 89.81 284 | 85.03 319 | 93.08 317 | 88.76 166 | 96.86 309 | 88.24 287 | 79.03 331 | 94.69 316 |
|
v12 | | | 91.89 275 | 90.70 277 | 95.43 262 | 96.31 266 | 94.80 193 | 98.76 111 | 97.50 247 | 89.76 285 | 84.95 320 | 93.00 320 | 88.82 160 | 96.82 313 | 88.23 288 | 79.00 332 | 94.68 318 |
|
v13 | | | 91.88 276 | 90.69 278 | 95.43 262 | 96.33 262 | 94.78 198 | 98.75 112 | 97.50 247 | 89.68 288 | 84.93 321 | 92.98 321 | 88.84 158 | 96.83 311 | 88.14 289 | 79.09 329 | 94.69 316 |
|
pmmvs-eth3d | | | 90.36 292 | 89.05 295 | 94.32 294 | 91.10 328 | 92.12 257 | 97.63 253 | 96.95 290 | 88.86 299 | 84.91 322 | 93.13 315 | 78.32 304 | 96.74 315 | 88.70 282 | 81.81 320 | 94.09 327 |
|
DeepMVS_CX | | | | | 86.78 321 | 97.09 216 | 72.30 341 | | 95.17 330 | 75.92 336 | 84.34 323 | 95.19 298 | 70.58 330 | 95.35 327 | 79.98 322 | 89.04 273 | 92.68 333 |
|
new-patchmatchnet | | | 88.50 302 | 87.45 303 | 91.67 311 | 90.31 330 | 85.89 320 | 97.16 281 | 97.33 268 | 89.47 292 | 83.63 324 | 92.77 325 | 76.38 313 | 95.06 330 | 82.70 315 | 77.29 334 | 94.06 328 |
|
UnsupCasMVSNet_bld | | | 87.17 304 | 85.12 307 | 93.31 303 | 91.94 325 | 88.77 301 | 94.92 326 | 98.30 165 | 84.30 323 | 82.30 325 | 90.04 333 | 63.96 339 | 97.25 301 | 85.85 307 | 74.47 339 | 93.93 330 |
|
Test4 | | | 92.21 267 | 90.34 283 | 97.82 116 | 92.83 323 | 95.87 136 | 97.94 222 | 98.05 218 | 94.50 133 | 82.12 326 | 94.48 305 | 59.54 341 | 98.54 220 | 95.39 125 | 98.22 125 | 99.06 124 |
|
Anonymous20231211 | | | 83.69 309 | 81.50 311 | 90.26 313 | 89.23 333 | 80.10 331 | 97.97 219 | 97.06 281 | 72.79 339 | 82.05 327 | 92.57 327 | 50.28 343 | 96.32 324 | 76.15 331 | 75.38 337 | 94.37 322 |
|
CMPMVS | | 66.06 21 | 89.70 295 | 89.67 290 | 89.78 314 | 93.19 321 | 76.56 334 | 97.00 284 | 98.35 159 | 80.97 331 | 81.57 328 | 97.75 175 | 74.75 321 | 98.61 213 | 89.85 259 | 93.63 222 | 94.17 325 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
test1235678 | | | 86.26 307 | 85.81 306 | 87.62 319 | 86.97 337 | 75.00 339 | 96.55 306 | 96.32 311 | 86.08 314 | 81.32 329 | 92.98 321 | 73.10 328 | 92.05 340 | 71.64 336 | 87.32 297 | 95.81 297 |
|
1111 | | | 84.94 308 | 84.30 309 | 86.86 320 | 87.59 335 | 75.10 337 | 96.63 301 | 96.43 309 | 82.53 326 | 80.75 330 | 92.91 323 | 68.94 332 | 93.79 333 | 68.24 339 | 84.66 314 | 91.70 334 |
|
.test1245 | | | 73.05 317 | 76.31 315 | 63.27 338 | 87.59 335 | 75.10 337 | 96.63 301 | 96.43 309 | 82.53 326 | 80.75 330 | 92.91 323 | 68.94 332 | 93.79 333 | 68.24 339 | 12.72 351 | 20.91 351 |
|
test12356 | | | 83.47 310 | 83.37 310 | 83.78 326 | 84.43 340 | 70.09 344 | 95.12 321 | 95.60 324 | 82.98 324 | 78.89 332 | 92.43 330 | 64.99 337 | 91.41 342 | 70.36 337 | 85.55 313 | 89.82 336 |
|
testing_2 | | | 90.61 291 | 88.50 298 | 96.95 172 | 90.08 331 | 95.57 144 | 97.69 247 | 98.06 215 | 93.02 197 | 76.55 333 | 92.48 329 | 61.18 340 | 98.44 241 | 95.45 124 | 91.98 244 | 96.84 230 |
|
LCM-MVSNet | | | 78.70 312 | 76.24 316 | 86.08 322 | 77.26 349 | 71.99 342 | 94.34 332 | 96.72 301 | 61.62 343 | 76.53 334 | 89.33 334 | 33.91 352 | 92.78 338 | 81.85 317 | 74.60 338 | 93.46 331 |
|
PMMVS2 | | | 77.95 314 | 75.44 317 | 85.46 323 | 82.54 341 | 74.95 340 | 94.23 333 | 93.08 342 | 72.80 338 | 74.68 335 | 87.38 335 | 36.36 350 | 91.56 341 | 73.95 334 | 63.94 341 | 89.87 335 |
|
Gipuma | | | 78.40 313 | 76.75 314 | 83.38 327 | 95.54 300 | 80.43 330 | 79.42 347 | 97.40 261 | 64.67 341 | 73.46 336 | 80.82 342 | 45.65 346 | 93.14 337 | 66.32 341 | 87.43 295 | 76.56 347 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
YYNet1 | | | 90.70 290 | 89.39 291 | 94.62 287 | 94.79 313 | 90.65 280 | 97.20 278 | 97.46 254 | 87.54 306 | 72.54 337 | 95.74 291 | 86.51 218 | 96.66 319 | 86.00 305 | 86.76 306 | 96.54 272 |
|
MDA-MVSNet_test_wron | | | 90.71 289 | 89.38 292 | 94.68 285 | 94.83 312 | 90.78 277 | 97.19 279 | 97.46 254 | 87.60 305 | 72.41 338 | 95.72 294 | 86.51 218 | 96.71 318 | 85.92 306 | 86.80 305 | 96.56 270 |
|
MDA-MVSNet-bldmvs | | | 89.97 294 | 88.35 300 | 94.83 282 | 95.21 307 | 91.34 269 | 97.64 251 | 97.51 244 | 88.36 302 | 71.17 339 | 96.13 284 | 79.22 301 | 96.63 320 | 83.65 313 | 86.27 307 | 96.52 274 |
|
testmv | | | 78.74 311 | 77.35 312 | 82.89 328 | 78.16 348 | 69.30 345 | 95.87 315 | 94.65 334 | 81.11 330 | 70.98 340 | 87.11 337 | 46.31 344 | 90.42 343 | 65.28 342 | 76.72 335 | 88.95 337 |
|
FPMVS | | | 77.62 315 | 77.14 313 | 79.05 330 | 79.25 345 | 60.97 349 | 95.79 317 | 95.94 315 | 65.96 340 | 67.93 341 | 94.40 306 | 37.73 349 | 88.88 345 | 68.83 338 | 88.46 284 | 87.29 338 |
|
no-one | | | 74.41 316 | 70.76 318 | 85.35 324 | 79.88 344 | 76.83 333 | 94.68 329 | 94.22 338 | 80.33 332 | 63.81 342 | 79.73 343 | 35.45 351 | 93.36 336 | 71.78 335 | 36.99 348 | 85.86 341 |
|
tmp_tt | | | 68.90 319 | 66.97 319 | 74.68 334 | 50.78 354 | 59.95 350 | 87.13 342 | 83.47 353 | 38.80 349 | 62.21 343 | 96.23 279 | 64.70 338 | 76.91 352 | 88.91 279 | 30.49 349 | 87.19 339 |
|
E-PMN | | | 64.94 322 | 64.25 322 | 67.02 336 | 82.28 342 | 59.36 352 | 91.83 339 | 85.63 351 | 52.69 346 | 60.22 344 | 77.28 345 | 41.06 348 | 80.12 350 | 46.15 348 | 41.14 345 | 61.57 349 |
|
EMVS | | | 64.07 323 | 63.26 324 | 66.53 337 | 81.73 343 | 58.81 353 | 91.85 338 | 84.75 352 | 51.93 348 | 59.09 345 | 75.13 346 | 43.32 347 | 79.09 351 | 42.03 349 | 39.47 346 | 61.69 348 |
|
PNet_i23d | | | 67.70 320 | 65.07 321 | 75.60 332 | 78.61 346 | 59.61 351 | 89.14 341 | 88.24 349 | 61.83 342 | 52.37 346 | 80.89 341 | 18.91 354 | 84.91 347 | 62.70 344 | 52.93 343 | 82.28 343 |
|
MVE | | 62.14 22 | 63.28 325 | 59.38 325 | 74.99 333 | 74.33 350 | 65.47 347 | 85.55 344 | 80.50 354 | 52.02 347 | 51.10 347 | 75.00 347 | 10.91 359 | 80.50 349 | 51.60 347 | 53.40 342 | 78.99 345 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
ANet_high | | | 69.08 318 | 65.37 320 | 80.22 329 | 65.99 352 | 71.96 343 | 90.91 340 | 90.09 346 | 82.62 325 | 49.93 348 | 78.39 344 | 29.36 353 | 81.75 348 | 62.49 345 | 38.52 347 | 86.95 340 |
|
PMVS | | 61.03 23 | 65.95 321 | 63.57 323 | 73.09 335 | 57.90 353 | 51.22 354 | 85.05 345 | 93.93 341 | 54.45 345 | 44.32 349 | 83.57 338 | 13.22 355 | 89.15 344 | 58.68 346 | 81.00 322 | 78.91 346 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
wuykxyi23d | | | 63.73 324 | 58.86 326 | 78.35 331 | 67.62 351 | 67.90 346 | 86.56 343 | 87.81 350 | 58.26 344 | 42.49 350 | 70.28 348 | 11.55 357 | 85.05 346 | 63.66 343 | 41.50 344 | 82.11 344 |
|
testmvs | | | 21.48 329 | 24.95 330 | 11.09 342 | 14.89 355 | 6.47 357 | 96.56 304 | 9.87 357 | 7.55 351 | 17.93 351 | 39.02 350 | 9.43 360 | 5.90 355 | 16.56 352 | 12.72 351 | 20.91 351 |
|
test123 | | | 20.95 330 | 23.72 331 | 12.64 341 | 13.54 356 | 8.19 356 | 96.55 306 | 6.13 358 | 7.48 352 | 16.74 352 | 37.98 351 | 12.97 356 | 6.05 354 | 16.69 351 | 5.43 353 | 23.68 350 |
|
wuyk23d | | | 30.17 327 | 30.18 329 | 30.16 340 | 78.61 346 | 43.29 355 | 66.79 348 | 14.21 356 | 17.31 350 | 14.82 353 | 11.93 354 | 11.55 357 | 41.43 353 | 37.08 350 | 19.30 350 | 5.76 353 |
|
cdsmvs_eth3d_5k | | | 23.98 328 | 31.98 328 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 98.59 115 | 0.00 353 | 0.00 354 | 98.61 102 | 90.60 125 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
pcd_1.5k_mvsjas | | | 7.88 332 | 10.50 333 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 0.00 359 | 0.00 353 | 0.00 354 | 0.00 355 | 94.51 62 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
pcd1.5k->3k | | | 39.42 326 | 41.78 327 | 32.35 339 | 96.17 275 | 0.00 358 | 0.00 349 | 98.54 125 | 0.00 353 | 0.00 354 | 0.00 355 | 87.78 196 | 0.00 356 | 0.00 353 | 93.56 224 | 97.06 207 |
|
sosnet-low-res | | | 0.00 333 | 0.00 334 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 0.00 359 | 0.00 353 | 0.00 354 | 0.00 355 | 0.00 361 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
sosnet | | | 0.00 333 | 0.00 334 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 0.00 359 | 0.00 353 | 0.00 354 | 0.00 355 | 0.00 361 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
uncertanet | | | 0.00 333 | 0.00 334 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 0.00 359 | 0.00 353 | 0.00 354 | 0.00 355 | 0.00 361 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
Regformer | | | 0.00 333 | 0.00 334 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 0.00 359 | 0.00 353 | 0.00 354 | 0.00 355 | 0.00 361 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
ab-mvs-re | | | 8.20 331 | 10.94 332 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 0.00 359 | 0.00 353 | 0.00 354 | 98.43 117 | 0.00 361 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
uanet | | | 0.00 333 | 0.00 334 | 0.00 343 | 0.00 357 | 0.00 358 | 0.00 349 | 0.00 359 | 0.00 353 | 0.00 354 | 0.00 355 | 0.00 361 | 0.00 356 | 0.00 353 | 0.00 354 | 0.00 354 |
|
GSMVS | | | | | | | | | | | | | | | | | 99.20 106 |
|
test_part3 | | | | | | | | 98.55 150 | | 96.40 57 | | 99.31 21 | | 99.93 9 | 96.37 95 | | |
|
test_part1 | | | | | | | | | 98.84 54 | | | | 97.38 2 | | | 99.78 14 | 99.76 20 |
|
sam_mvs1 | | | | | | | | | | | | | 89.45 136 | | | | 99.20 106 |
|
sam_mvs | | | | | | | | | | | | | 88.99 147 | | | | |
|
MTGPA | | | | | | | | | 98.74 79 | | | | | | | | |
|
test_post1 | | | | | | | | 96.68 300 | | | | 30.43 353 | 87.85 194 | 98.69 207 | 92.59 199 | | |
|
test_post | | | | | | | | | | | | 31.83 352 | 88.83 159 | 98.91 189 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 95.10 301 | 89.42 137 | 98.89 193 | | | |
|
MTMP | | | | | | | | | 94.14 339 | | | | | | | | |
|
gm-plane-assit | | | | | | 95.88 289 | 87.47 315 | | | 89.74 287 | | 96.94 245 | | 99.19 153 | 93.32 176 | | |
|
test9_res | | | | | | | | | | | | | | | 96.39 94 | 99.57 57 | 99.69 37 |
|
agg_prior2 | | | | | | | | | | | | | | | 95.87 108 | 99.57 57 | 99.68 43 |
|
test_prior4 | | | | | | | 98.01 43 | 97.86 234 | | | | | | | | | |
|
test_prior | | | | | 99.19 29 | 99.31 49 | 98.22 32 | | 98.84 54 | | | | | 99.70 93 | | | 99.65 52 |
|
新几何2 | | | | | | | | 97.64 251 | | | | | | | | | |
|
旧先验1 | | | | | | 99.29 57 | 97.48 61 | | 98.70 93 | | | 99.09 54 | 95.56 37 | | | 99.47 71 | 99.61 58 |
|
无先验 | | | | | | | | 97.58 255 | 98.72 86 | 91.38 250 | | | | 99.87 37 | 93.36 174 | | 99.60 61 |
|
原ACMM2 | | | | | | | | 97.67 249 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 99.89 29 | 91.65 224 | | |
|
segment_acmp | | | | | | | | | | | | | 96.85 5 | | | | |
|
testdata1 | | | | | | | | 97.32 273 | | 96.34 59 | | | | | | | |
|
plane_prior7 | | | | | | 97.42 194 | 94.63 203 | | | | | | | | | | |
|
plane_prior6 | | | | | | 97.35 199 | 94.61 206 | | | | | | 87.09 209 | | | | |
|
plane_prior5 | | | | | | | | | 98.56 122 | | | | | 99.03 175 | 96.07 99 | 94.27 204 | 96.92 216 |
|
plane_prior4 | | | | | | | | | | | | 98.28 133 | | | | | |
|
plane_prior2 | | | | | | | | 98.80 100 | | 97.28 21 | | | | | | | |
|
plane_prior1 | | | | | | 97.37 198 | | | | | | | | | | | |
|
plane_prior | | | | | | | 94.60 208 | 98.44 165 | | 96.74 46 | | | | | | 94.22 206 | |
|
n2 | | | | | | | | | 0.00 359 | | | | | | | | |
|
nn | | | | | | | | | 0.00 359 | | | | | | | | |
|
door-mid | | | | | | | | | 94.37 336 | | | | | | | | |
|
test11 | | | | | | | | | 98.66 107 | | | | | | | | |
|
door | | | | | | | | | 94.64 335 | | | | | | | | |
|
HQP5-MVS | | | | | | | 94.25 221 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 95.30 127 | | |
|
HQP3-MVS | | | | | | | | | 98.46 142 | | | | | | | 94.18 208 | |
|
HQP2-MVS | | | | | | | | | | | | | 86.75 215 | | | | |
|
NP-MVS | | | | | | 97.28 202 | 94.51 211 | | | | | 97.73 176 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 92.97 235 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 93.61 223 | |
|
Test By Simon | | | | | | | | | | | | | 94.64 59 | | | | |
|