APDe-MVS | | | 97.82 1 | 97.73 1 | 98.08 8 | 99.15 25 | 94.82 12 | 98.81 2 | 98.30 22 | 94.76 24 | 98.30 4 | 98.90 1 | 93.77 8 | 99.68 37 | 97.93 1 | 99.69 1 | 99.75 1 |
|
MVS_0304 | | | 96.05 50 | 95.45 52 | 97.85 14 | 97.75 101 | 94.50 15 | 96.87 147 | 97.95 81 | 95.46 6 | 95.60 72 | 98.01 48 | 80.96 191 | 99.83 15 | 97.23 2 | 99.25 46 | 99.23 49 |
|
TSAR-MVS + MP. | | | 97.42 6 | 97.33 6 | 97.69 28 | 99.25 20 | 94.24 23 | 98.07 34 | 97.85 88 | 93.72 47 | 98.57 2 | 98.35 24 | 93.69 9 | 99.40 87 | 97.06 3 | 99.46 25 | 99.44 32 |
|
CNVR-MVS | | | 97.68 2 | 97.44 5 | 98.37 3 | 98.90 32 | 95.86 2 | 97.27 111 | 98.08 50 | 95.81 3 | 97.87 11 | 98.31 33 | 94.26 4 | 99.68 37 | 97.02 4 | 99.49 23 | 99.57 13 |
|
SD-MVS | | | 97.41 7 | 97.53 2 | 97.06 56 | 98.57 51 | 94.46 16 | 97.92 42 | 98.14 40 | 94.82 21 | 99.01 1 | 98.55 9 | 94.18 5 | 97.41 276 | 96.94 5 | 99.64 3 | 99.32 43 |
|
Regformer-4 | | | 96.97 22 | 96.87 16 | 97.25 48 | 98.34 62 | 92.66 66 | 96.96 137 | 98.01 69 | 95.12 13 | 97.14 23 | 98.42 18 | 91.82 34 | 99.61 47 | 96.90 6 | 99.13 56 | 99.50 24 |
|
CANet | | | 96.39 42 | 96.02 44 | 97.50 38 | 97.62 107 | 93.38 49 | 97.02 132 | 97.96 79 | 95.42 8 | 94.86 82 | 97.81 61 | 87.38 89 | 99.82 18 | 96.88 7 | 99.20 51 | 99.29 45 |
|
TSAR-MVS + GP. | | | 96.69 33 | 96.49 32 | 97.27 47 | 98.31 67 | 93.39 48 | 96.79 157 | 96.72 196 | 94.17 36 | 97.44 15 | 97.66 71 | 92.76 13 | 99.33 92 | 96.86 8 | 97.76 95 | 99.08 62 |
|
Regformer-3 | | | 96.85 27 | 96.80 22 | 97.01 57 | 98.34 62 | 92.02 84 | 96.96 137 | 97.76 91 | 95.01 16 | 97.08 28 | 98.42 18 | 91.71 35 | 99.54 67 | 96.80 9 | 99.13 56 | 99.48 28 |
|
Regformer-2 | | | 97.16 13 | 96.99 11 | 97.67 29 | 98.32 65 | 93.84 35 | 96.83 150 | 98.10 47 | 95.24 10 | 97.49 13 | 98.25 39 | 92.57 19 | 99.61 47 | 96.80 9 | 99.29 43 | 99.56 15 |
|
Regformer-1 | | | 97.10 15 | 96.96 13 | 97.54 37 | 98.32 65 | 93.48 46 | 96.83 150 | 97.99 76 | 95.20 12 | 97.46 14 | 98.25 39 | 92.48 22 | 99.58 55 | 96.79 11 | 99.29 43 | 99.55 17 |
|
DeepPCF-MVS | | 93.97 1 | 96.61 36 | 97.09 8 | 95.15 135 | 98.09 80 | 86.63 253 | 96.00 226 | 98.15 38 | 95.43 7 | 97.95 9 | 98.56 7 | 93.40 10 | 99.36 91 | 96.77 12 | 99.48 24 | 99.45 30 |
|
HSP-MVS | | | 97.53 5 | 97.49 4 | 97.63 34 | 99.40 5 | 93.77 40 | 98.53 9 | 97.85 88 | 95.55 5 | 98.56 3 | 97.81 61 | 93.90 6 | 99.65 41 | 96.62 13 | 99.21 50 | 99.48 28 |
|
MSLP-MVS++ | | | 96.94 24 | 97.06 9 | 96.59 68 | 98.72 37 | 91.86 88 | 97.67 65 | 98.49 12 | 94.66 27 | 97.24 18 | 98.41 21 | 92.31 26 | 98.94 126 | 96.61 14 | 99.46 25 | 98.96 71 |
|
MP-MVS-pluss | | | 96.70 32 | 96.27 39 | 97.98 10 | 99.23 23 | 94.71 13 | 96.96 137 | 98.06 57 | 90.67 134 | 95.55 74 | 98.78 2 | 91.07 44 | 99.86 8 | 96.58 15 | 99.55 14 | 99.38 39 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
SteuartSystems-ACMMP | | | 97.62 3 | 97.53 2 | 97.87 13 | 98.39 59 | 94.25 22 | 98.43 16 | 98.27 24 | 95.34 9 | 98.11 5 | 98.56 7 | 94.53 3 | 99.71 29 | 96.57 16 | 99.62 7 | 99.65 3 |
Skip Steuart: Steuart Systems R&D Blog. |
MCST-MVS | | | 97.18 11 | 96.84 18 | 98.20 5 | 99.30 16 | 95.35 5 | 97.12 127 | 98.07 55 | 93.54 53 | 96.08 53 | 97.69 68 | 93.86 7 | 99.71 29 | 96.50 17 | 99.39 34 | 99.55 17 |
|
EI-MVSNet-Vis-set | | | 96.51 38 | 96.47 33 | 96.63 65 | 98.24 71 | 91.20 107 | 96.89 146 | 97.73 94 | 94.74 25 | 96.49 41 | 98.49 13 | 90.88 49 | 99.58 55 | 96.44 18 | 98.32 80 | 99.13 57 |
|
VDD-MVS | | | 93.82 102 | 93.08 106 | 96.02 97 | 97.88 95 | 89.96 142 | 97.72 60 | 95.85 237 | 92.43 85 | 95.86 62 | 98.44 16 | 68.42 308 | 99.39 88 | 96.31 19 | 94.85 147 | 98.71 90 |
|
ACMMP_Plus | | | 97.20 10 | 96.86 17 | 98.23 4 | 99.09 26 | 95.16 8 | 97.60 81 | 98.19 33 | 92.82 78 | 97.93 10 | 98.74 3 | 91.60 38 | 99.86 8 | 96.26 20 | 99.52 17 | 99.67 2 |
|
EI-MVSNet-UG-set | | | 96.34 43 | 96.30 38 | 96.47 76 | 98.20 75 | 90.93 118 | 96.86 148 | 97.72 97 | 94.67 26 | 96.16 50 | 98.46 14 | 90.43 53 | 99.58 55 | 96.23 21 | 97.96 89 | 98.90 78 |
|
xiu_mvs_v1_base_debu | | | 95.01 70 | 94.76 66 | 95.75 107 | 96.58 145 | 91.71 89 | 96.25 210 | 97.35 142 | 92.99 69 | 96.70 31 | 96.63 121 | 82.67 159 | 99.44 82 | 96.22 22 | 97.46 99 | 96.11 193 |
|
xiu_mvs_v1_base | | | 95.01 70 | 94.76 66 | 95.75 107 | 96.58 145 | 91.71 89 | 96.25 210 | 97.35 142 | 92.99 69 | 96.70 31 | 96.63 121 | 82.67 159 | 99.44 82 | 96.22 22 | 97.46 99 | 96.11 193 |
|
xiu_mvs_v1_base_debi | | | 95.01 70 | 94.76 66 | 95.75 107 | 96.58 145 | 91.71 89 | 96.25 210 | 97.35 142 | 92.99 69 | 96.70 31 | 96.63 121 | 82.67 159 | 99.44 82 | 96.22 22 | 97.46 99 | 96.11 193 |
|
alignmvs | | | 95.87 56 | 95.23 59 | 97.78 20 | 97.56 112 | 95.19 7 | 97.86 46 | 97.17 152 | 94.39 32 | 96.47 42 | 96.40 133 | 85.89 104 | 99.20 98 | 96.21 25 | 95.11 145 | 98.95 73 |
|
canonicalmvs | | | 96.02 52 | 95.45 52 | 97.75 24 | 97.59 110 | 95.15 9 | 98.28 22 | 97.60 108 | 94.52 29 | 96.27 47 | 96.12 143 | 87.65 83 | 99.18 101 | 96.20 26 | 94.82 149 | 98.91 77 |
|
MPTG | | | 97.07 17 | 96.77 24 | 97.97 11 | 99.37 10 | 94.42 18 | 97.15 125 | 98.08 50 | 95.07 14 | 96.11 51 | 98.59 5 | 90.88 49 | 99.90 1 | 96.18 27 | 99.50 21 | 99.58 11 |
|
MTAPA | | | 97.08 16 | 96.78 23 | 97.97 11 | 99.37 10 | 94.42 18 | 97.24 113 | 98.08 50 | 95.07 14 | 96.11 51 | 98.59 5 | 90.88 49 | 99.90 1 | 96.18 27 | 99.50 21 | 99.58 11 |
|
APD-MVS_3200maxsize | | | 96.81 28 | 96.71 26 | 97.12 55 | 99.01 30 | 92.31 73 | 97.98 40 | 98.06 57 | 93.11 66 | 97.44 15 | 98.55 9 | 90.93 47 | 99.55 65 | 96.06 29 | 99.25 46 | 99.51 23 |
|
MVS_111021_HR | | | 96.68 35 | 96.58 30 | 96.99 58 | 98.46 53 | 92.31 73 | 96.20 215 | 98.90 2 | 94.30 35 | 95.86 62 | 97.74 66 | 92.33 23 | 99.38 90 | 96.04 30 | 99.42 30 | 99.28 48 |
|
PHI-MVS | | | 96.77 30 | 96.46 34 | 97.71 27 | 98.40 57 | 94.07 29 | 98.21 28 | 98.45 15 | 89.86 152 | 97.11 26 | 98.01 48 | 92.52 21 | 99.69 35 | 96.03 31 | 99.53 16 | 99.36 41 |
|
HPM-MVS++ | | | 97.34 8 | 96.97 12 | 98.47 1 | 99.08 27 | 96.16 1 | 97.55 86 | 97.97 78 | 95.59 4 | 96.61 35 | 97.89 52 | 92.57 19 | 99.84 14 | 95.95 32 | 99.51 19 | 99.40 35 |
|
DELS-MVS | | | 96.61 36 | 96.38 37 | 97.30 44 | 97.79 98 | 93.19 53 | 95.96 227 | 98.18 35 | 95.23 11 | 95.87 61 | 97.65 72 | 91.45 40 | 99.70 34 | 95.87 33 | 99.44 29 | 99.00 69 |
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_LR | | | 96.24 46 | 96.19 43 | 96.39 80 | 98.23 74 | 91.35 102 | 96.24 213 | 98.79 4 | 93.99 39 | 95.80 65 | 97.65 72 | 89.92 60 | 99.24 97 | 95.87 33 | 99.20 51 | 98.58 94 |
|
NCCC | | | 97.30 9 | 97.03 10 | 98.11 7 | 98.77 35 | 95.06 10 | 97.34 105 | 98.04 64 | 95.96 2 | 97.09 27 | 97.88 54 | 93.18 11 | 99.71 29 | 95.84 35 | 99.17 53 | 99.56 15 |
|
VNet | | | 95.89 55 | 95.45 52 | 97.21 52 | 98.07 81 | 92.94 60 | 97.50 89 | 98.15 38 | 93.87 41 | 97.52 12 | 97.61 78 | 85.29 110 | 99.53 70 | 95.81 36 | 95.27 143 | 99.16 53 |
|
XVS | | | 97.18 11 | 96.96 13 | 97.81 17 | 99.38 8 | 94.03 31 | 98.59 7 | 98.20 31 | 94.85 17 | 96.59 37 | 98.29 36 | 91.70 36 | 99.80 20 | 95.66 37 | 99.40 32 | 99.62 7 |
|
X-MVStestdata | | | 91.71 175 | 89.67 232 | 97.81 17 | 99.38 8 | 94.03 31 | 98.59 7 | 98.20 31 | 94.85 17 | 96.59 37 | 32.69 350 | 91.70 36 | 99.80 20 | 95.66 37 | 99.40 32 | 99.62 7 |
|
HFP-MVS | | | 97.14 14 | 96.92 15 | 97.83 15 | 99.42 3 | 94.12 27 | 98.52 10 | 98.32 19 | 93.21 60 | 97.18 20 | 98.29 36 | 92.08 28 | 99.83 15 | 95.63 39 | 99.59 9 | 99.54 19 |
|
ACMMPR | | | 97.07 17 | 96.84 18 | 97.79 19 | 99.44 2 | 93.88 33 | 98.52 10 | 98.31 21 | 93.21 60 | 97.15 22 | 98.33 30 | 91.35 41 | 99.86 8 | 95.63 39 | 99.59 9 | 99.62 7 |
|
HPM-MVS | | | 96.69 33 | 96.45 35 | 97.40 40 | 99.36 12 | 93.11 55 | 98.87 1 | 98.06 57 | 91.17 123 | 96.40 45 | 97.99 50 | 90.99 46 | 99.58 55 | 95.61 41 | 99.61 8 | 99.49 26 |
|
CP-MVS | | | 97.02 20 | 96.81 21 | 97.64 32 | 99.33 14 | 93.54 44 | 98.80 3 | 98.28 23 | 92.99 69 | 96.45 44 | 98.30 35 | 91.90 33 | 99.85 11 | 95.61 41 | 99.68 2 | 99.54 19 |
|
DeepC-MVS | | 93.07 3 | 96.06 49 | 95.66 50 | 97.29 45 | 97.96 87 | 93.17 54 | 97.30 110 | 98.06 57 | 93.92 40 | 93.38 105 | 98.66 4 | 86.83 94 | 99.73 25 | 95.60 43 | 99.22 49 | 98.96 71 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
region2R | | | 97.07 17 | 96.84 18 | 97.77 22 | 99.46 1 | 93.79 37 | 98.52 10 | 98.24 28 | 93.19 63 | 97.14 23 | 98.34 27 | 91.59 39 | 99.87 5 | 95.46 44 | 99.59 9 | 99.64 4 |
|
lupinMVS | | | 94.99 74 | 94.56 72 | 96.29 88 | 96.34 160 | 91.21 105 | 95.83 233 | 96.27 215 | 88.93 183 | 96.22 48 | 96.88 105 | 86.20 101 | 98.85 134 | 95.27 45 | 99.05 62 | 98.82 85 |
|
mPP-MVS | | | 96.86 26 | 96.60 28 | 97.64 32 | 99.40 5 | 93.44 47 | 98.50 13 | 98.09 49 | 93.27 59 | 95.95 60 | 98.33 30 | 91.04 45 | 99.88 3 | 95.20 46 | 99.57 13 | 99.60 10 |
|
test_part3 | | | | | | | | 97.50 89 | | 93.81 45 | | 98.53 11 | | 99.87 5 | 95.19 47 | | |
|
ESAPD | | | 97.57 4 | 97.29 7 | 98.41 2 | 99.28 17 | 95.74 3 | 97.50 89 | 98.26 25 | 93.81 45 | 98.10 6 | 98.53 11 | 95.31 1 | 99.87 5 | 95.19 47 | 99.63 4 | 99.63 5 |
|
DeepC-MVS_fast | | 93.89 2 | 96.93 25 | 96.64 27 | 97.78 20 | 98.64 46 | 94.30 20 | 97.41 97 | 98.04 64 | 94.81 22 | 96.59 37 | 98.37 23 | 91.24 42 | 99.64 46 | 95.16 49 | 99.52 17 | 99.42 34 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
jason | | | 94.84 79 | 94.39 80 | 96.18 93 | 95.52 189 | 90.93 118 | 96.09 219 | 96.52 208 | 89.28 164 | 96.01 58 | 97.32 89 | 84.70 118 | 98.77 141 | 95.15 50 | 98.91 68 | 98.85 82 |
jason: jason. |
#test# | | | 97.02 20 | 96.75 25 | 97.83 15 | 99.42 3 | 94.12 27 | 98.15 29 | 98.32 19 | 92.57 83 | 97.18 20 | 98.29 36 | 92.08 28 | 99.83 15 | 95.12 51 | 99.59 9 | 99.54 19 |
|
abl_6 | | | 96.40 41 | 96.21 41 | 96.98 59 | 98.89 33 | 92.20 78 | 97.89 44 | 98.03 66 | 93.34 58 | 97.22 19 | 98.42 18 | 87.93 79 | 99.72 28 | 95.10 52 | 99.07 61 | 99.02 64 |
|
train_agg | | | 96.30 44 | 95.83 47 | 97.72 25 | 98.70 38 | 94.19 24 | 96.41 192 | 98.02 67 | 88.58 195 | 96.03 54 | 97.56 83 | 92.73 15 | 99.59 52 | 95.04 53 | 99.37 39 | 99.39 36 |
|
agg_prior3 | | | 96.16 48 | 95.67 49 | 97.62 35 | 98.67 40 | 93.88 33 | 96.41 192 | 98.00 71 | 87.93 220 | 95.81 64 | 97.47 87 | 92.33 23 | 99.59 52 | 95.04 53 | 99.37 39 | 99.39 36 |
|
agg_prior1 | | | 96.22 47 | 95.77 48 | 97.56 36 | 98.67 40 | 93.79 37 | 96.28 208 | 98.00 71 | 88.76 192 | 95.68 68 | 97.55 85 | 92.70 17 | 99.57 63 | 95.01 55 | 99.32 41 | 99.32 43 |
|
test_prior3 | | | 96.46 40 | 96.20 42 | 97.23 49 | 98.67 40 | 92.99 57 | 96.35 200 | 98.00 71 | 92.80 79 | 96.03 54 | 97.59 79 | 92.01 30 | 99.41 85 | 95.01 55 | 99.38 35 | 99.29 45 |
|
test_prior2 | | | | | | | | 96.35 200 | | 92.80 79 | 96.03 54 | 97.59 79 | 92.01 30 | | 95.01 55 | 99.38 35 | |
|
nrg030 | | | 94.05 95 | 93.31 103 | 96.27 89 | 95.22 207 | 94.59 14 | 98.34 19 | 97.46 124 | 92.93 76 | 91.21 166 | 96.64 117 | 87.23 91 | 98.22 185 | 94.99 58 | 85.80 259 | 95.98 201 |
|
VDDNet | | | 93.05 126 | 92.07 134 | 96.02 97 | 96.84 136 | 90.39 132 | 98.08 33 | 95.85 237 | 86.22 258 | 95.79 66 | 98.46 14 | 67.59 311 | 99.19 99 | 94.92 59 | 94.85 147 | 98.47 107 |
|
APD-MVS | | | 96.95 23 | 96.60 28 | 98.01 9 | 99.03 29 | 94.93 11 | 97.72 60 | 98.10 47 | 91.50 112 | 98.01 8 | 98.32 32 | 92.33 23 | 99.58 55 | 94.85 60 | 99.51 19 | 99.53 22 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
MP-MVS | | | 96.77 30 | 96.45 35 | 97.72 25 | 99.39 7 | 93.80 36 | 98.41 17 | 98.06 57 | 93.37 55 | 95.54 75 | 98.34 27 | 90.59 52 | 99.88 3 | 94.83 61 | 99.54 15 | 99.49 26 |
|
test9_res | | | | | | | | | | | | | | | 94.81 62 | 99.38 35 | 99.45 30 |
|
PS-MVSNAJ | | | 95.37 61 | 95.33 57 | 95.49 120 | 97.35 120 | 90.66 126 | 95.31 256 | 97.48 119 | 93.85 42 | 96.51 40 | 95.70 167 | 88.65 70 | 99.65 41 | 94.80 63 | 98.27 81 | 96.17 188 |
|
HPM-MVS_fast | | | 96.51 38 | 96.27 39 | 97.22 51 | 99.32 15 | 92.74 63 | 98.74 4 | 98.06 57 | 90.57 143 | 96.77 30 | 98.35 24 | 90.21 56 | 99.53 70 | 94.80 63 | 99.63 4 | 99.38 39 |
|
xiu_mvs_v2_base | | | 95.32 63 | 95.29 58 | 95.40 126 | 97.22 122 | 90.50 129 | 95.44 251 | 97.44 131 | 93.70 49 | 96.46 43 | 96.18 140 | 88.59 73 | 99.53 70 | 94.79 65 | 97.81 92 | 96.17 188 |
|
CSCG | | | 96.05 50 | 95.91 46 | 96.46 78 | 99.24 21 | 90.47 130 | 98.30 21 | 98.57 11 | 89.01 177 | 93.97 97 | 97.57 81 | 92.62 18 | 99.76 23 | 94.66 66 | 99.27 45 | 99.15 55 |
|
ACMMP | | | 96.27 45 | 95.93 45 | 97.28 46 | 99.24 21 | 92.62 67 | 98.25 25 | 98.81 3 | 92.99 69 | 94.56 86 | 98.39 22 | 88.96 65 | 99.85 11 | 94.57 67 | 97.63 96 | 99.36 41 |
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 |
PGM-MVS | | | 96.81 28 | 96.53 31 | 97.65 30 | 99.35 13 | 93.53 45 | 97.65 68 | 98.98 1 | 92.22 88 | 97.14 23 | 98.44 16 | 91.17 43 | 99.85 11 | 94.35 68 | 99.46 25 | 99.57 13 |
|
LFMVS | | | 93.60 109 | 92.63 120 | 96.52 70 | 98.13 79 | 91.27 104 | 97.94 41 | 93.39 314 | 90.57 143 | 96.29 46 | 98.31 33 | 69.00 304 | 99.16 103 | 94.18 69 | 95.87 135 | 99.12 59 |
|
MVSFormer | | | 95.37 61 | 95.16 61 | 95.99 99 | 96.34 160 | 91.21 105 | 98.22 26 | 97.57 111 | 91.42 116 | 96.22 48 | 97.32 89 | 86.20 101 | 97.92 237 | 94.07 70 | 99.05 62 | 98.85 82 |
|
test_djsdf | | | 93.07 125 | 92.76 113 | 94.00 188 | 93.49 285 | 88.70 191 | 98.22 26 | 97.57 111 | 91.42 116 | 90.08 190 | 95.55 174 | 82.85 156 | 97.92 237 | 94.07 70 | 91.58 204 | 95.40 230 |
|
mvs_anonymous | | | 93.82 102 | 93.74 86 | 94.06 185 | 96.44 157 | 85.41 265 | 95.81 234 | 97.05 168 | 89.85 154 | 90.09 189 | 96.36 135 | 87.44 88 | 97.75 253 | 93.97 72 | 96.69 122 | 99.02 64 |
|
VPA-MVSNet | | | 93.24 120 | 92.48 129 | 95.51 118 | 95.70 185 | 92.39 72 | 97.86 46 | 98.66 9 | 92.30 87 | 92.09 138 | 95.37 183 | 80.49 203 | 98.40 173 | 93.95 73 | 85.86 258 | 95.75 214 |
|
agg_prior2 | | | | | | | | | | | | | | | 93.94 74 | 99.38 35 | 99.50 24 |
|
mvs_tets | | | 92.31 155 | 91.76 143 | 93.94 196 | 93.41 287 | 88.29 198 | 97.63 79 | 97.53 115 | 92.04 101 | 88.76 228 | 96.45 131 | 74.62 277 | 98.09 198 | 93.91 75 | 91.48 206 | 95.45 224 |
|
Effi-MVS+ | | | 94.93 75 | 94.45 78 | 96.36 83 | 96.61 143 | 91.47 98 | 96.41 192 | 97.41 135 | 91.02 128 | 94.50 87 | 95.92 150 | 87.53 86 | 98.78 139 | 93.89 76 | 96.81 117 | 98.84 84 |
|
jajsoiax | | | 92.42 150 | 91.89 141 | 94.03 187 | 93.33 291 | 88.50 195 | 97.73 58 | 97.53 115 | 92.00 103 | 88.85 227 | 96.50 129 | 75.62 270 | 98.11 195 | 93.88 77 | 91.56 205 | 95.48 220 |
|
XVG-OURS-SEG-HR | | | 93.86 101 | 93.55 92 | 94.81 154 | 97.06 130 | 88.53 194 | 95.28 257 | 97.45 128 | 91.68 109 | 94.08 94 | 97.68 69 | 82.41 168 | 98.90 129 | 93.84 78 | 92.47 188 | 96.98 159 |
|
PS-MVSNAJss | | | 93.74 105 | 93.51 95 | 94.44 172 | 93.91 272 | 89.28 181 | 97.75 54 | 97.56 114 | 92.50 84 | 89.94 192 | 96.54 127 | 88.65 70 | 98.18 189 | 93.83 79 | 90.90 215 | 95.86 203 |
|
EPNet | | | 95.20 67 | 94.56 72 | 97.14 54 | 92.80 304 | 92.68 65 | 97.85 48 | 94.87 282 | 96.64 1 | 92.46 127 | 97.80 63 | 86.23 99 | 99.65 41 | 93.72 80 | 98.62 74 | 99.10 61 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
test_normal | | | 92.01 165 | 90.75 188 | 95.80 105 | 93.24 293 | 89.97 140 | 95.93 229 | 96.24 218 | 90.62 138 | 81.63 298 | 93.45 269 | 74.98 274 | 98.89 131 | 93.61 81 | 97.04 113 | 98.55 95 |
|
PVSNet_Blended_VisFu | | | 95.27 64 | 94.91 64 | 96.38 81 | 98.20 75 | 90.86 120 | 97.27 111 | 98.25 27 | 90.21 146 | 94.18 93 | 97.27 91 | 87.48 87 | 99.73 25 | 93.53 82 | 97.77 94 | 98.55 95 |
|
DI_MVS_plusplus_test | | | 92.01 165 | 90.77 186 | 95.73 110 | 93.34 289 | 89.78 147 | 96.14 217 | 96.18 221 | 90.58 142 | 81.80 297 | 93.50 266 | 74.95 275 | 98.90 129 | 93.51 83 | 96.94 114 | 98.51 100 |
|
CPTT-MVS | | | 95.57 59 | 95.19 60 | 96.70 62 | 99.27 19 | 91.48 97 | 98.33 20 | 98.11 45 | 87.79 223 | 95.17 79 | 98.03 46 | 87.09 92 | 99.61 47 | 93.51 83 | 99.42 30 | 99.02 64 |
|
MVSTER | | | 93.20 121 | 92.81 112 | 94.37 175 | 96.56 148 | 89.59 159 | 97.06 129 | 97.12 159 | 91.24 122 | 91.30 155 | 95.96 148 | 82.02 176 | 98.05 211 | 93.48 85 | 90.55 220 | 95.47 222 |
|
PVSNet_BlendedMVS | | | 94.06 94 | 93.92 82 | 94.47 171 | 98.27 68 | 89.46 167 | 96.73 162 | 98.36 16 | 90.17 147 | 94.36 89 | 95.24 189 | 88.02 76 | 99.58 55 | 93.44 86 | 90.72 218 | 94.36 284 |
|
PVSNet_Blended | | | 94.87 78 | 94.56 72 | 95.81 104 | 98.27 68 | 89.46 167 | 95.47 250 | 98.36 16 | 88.84 186 | 94.36 89 | 96.09 146 | 88.02 76 | 99.58 55 | 93.44 86 | 98.18 83 | 98.40 114 |
|
3Dnovator | | 91.36 5 | 95.19 68 | 94.44 79 | 97.44 39 | 96.56 148 | 93.36 51 | 98.65 6 | 98.36 16 | 94.12 37 | 89.25 223 | 98.06 45 | 82.20 173 | 99.77 22 | 93.41 88 | 99.32 41 | 99.18 52 |
|
EPP-MVSNet | | | 95.22 66 | 95.04 63 | 95.76 106 | 97.49 119 | 89.56 160 | 98.67 5 | 97.00 175 | 90.69 133 | 94.24 92 | 97.62 77 | 89.79 61 | 98.81 137 | 93.39 89 | 96.49 126 | 98.92 76 |
|
CHOSEN 280x420 | | | 93.12 123 | 92.72 118 | 94.34 177 | 96.71 142 | 87.27 236 | 90.29 324 | 97.72 97 | 86.61 254 | 91.34 152 | 95.29 186 | 84.29 123 | 98.41 172 | 93.25 90 | 98.94 67 | 97.35 155 |
|
3Dnovator+ | | 91.43 4 | 95.40 60 | 94.48 77 | 98.16 6 | 96.90 134 | 95.34 6 | 98.48 14 | 97.87 85 | 94.65 28 | 88.53 233 | 98.02 47 | 83.69 127 | 99.71 29 | 93.18 91 | 98.96 66 | 99.44 32 |
|
HQP_MVS | | | 93.78 104 | 93.43 99 | 94.82 152 | 96.21 164 | 89.99 137 | 97.74 56 | 97.51 117 | 94.85 17 | 91.34 152 | 96.64 117 | 81.32 187 | 98.60 152 | 93.02 92 | 92.23 191 | 95.86 203 |
|
plane_prior5 | | | | | | | | | 97.51 117 | | | | | 98.60 152 | 93.02 92 | 92.23 191 | 95.86 203 |
|
MVS_Test | | | 94.89 77 | 94.62 70 | 95.68 111 | 96.83 138 | 89.55 161 | 96.70 170 | 97.17 152 | 91.17 123 | 95.60 72 | 96.11 145 | 87.87 80 | 98.76 142 | 93.01 94 | 97.17 110 | 98.72 88 |
|
CLD-MVS | | | 92.98 128 | 92.53 126 | 94.32 178 | 96.12 173 | 89.20 183 | 95.28 257 | 97.47 122 | 92.66 81 | 89.90 193 | 95.62 170 | 80.58 201 | 98.40 173 | 92.73 95 | 92.40 189 | 95.38 232 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
XVG-OURS | | | 93.72 106 | 93.35 102 | 94.80 155 | 97.07 128 | 88.61 192 | 94.79 266 | 97.46 124 | 91.97 104 | 93.99 95 | 97.86 57 | 81.74 182 | 98.88 133 | 92.64 96 | 92.67 187 | 96.92 167 |
|
旧先验2 | | | | | | | | 95.94 228 | | 81.66 302 | 97.34 17 | | | 98.82 136 | 92.26 97 | | |
|
CDPH-MVS | | | 95.97 53 | 95.38 55 | 97.77 22 | 98.93 31 | 94.44 17 | 96.35 200 | 97.88 83 | 86.98 244 | 96.65 34 | 97.89 52 | 91.99 32 | 99.47 78 | 92.26 97 | 99.46 25 | 99.39 36 |
|
FIs | | | 94.09 93 | 93.70 87 | 95.27 128 | 95.70 185 | 92.03 83 | 98.10 31 | 98.68 7 | 93.36 57 | 90.39 176 | 96.70 112 | 87.63 84 | 97.94 233 | 92.25 99 | 90.50 222 | 95.84 206 |
|
LPG-MVS_test | | | 92.94 130 | 92.56 123 | 94.10 183 | 96.16 169 | 88.26 200 | 97.65 68 | 97.46 124 | 91.29 119 | 90.12 186 | 97.16 96 | 79.05 224 | 98.73 144 | 92.25 99 | 91.89 199 | 95.31 236 |
|
LGP-MVS_train | | | | | 94.10 183 | 96.16 169 | 88.26 200 | | 97.46 124 | 91.29 119 | 90.12 186 | 97.16 96 | 79.05 224 | 98.73 144 | 92.25 99 | 91.89 199 | 95.31 236 |
|
cascas | | | 91.20 207 | 90.08 215 | 94.58 169 | 94.97 219 | 89.16 185 | 93.65 290 | 97.59 110 | 79.90 314 | 89.40 215 | 92.92 276 | 75.36 271 | 98.36 176 | 92.14 102 | 94.75 151 | 96.23 185 |
|
OPM-MVS | | | 93.28 119 | 92.76 113 | 94.82 152 | 94.63 235 | 90.77 124 | 96.65 175 | 97.18 150 | 93.72 47 | 91.68 145 | 97.26 92 | 79.33 221 | 98.63 149 | 92.13 103 | 92.28 190 | 95.07 249 |
|
BP-MVS | | | | | | | | | | | | | | | 92.13 103 | | |
|
HQP-MVS | | | 93.19 122 | 92.74 117 | 94.54 170 | 95.86 178 | 89.33 176 | 96.65 175 | 97.39 136 | 93.55 50 | 90.14 180 | 95.87 152 | 80.95 192 | 98.50 161 | 92.13 103 | 92.10 196 | 95.78 210 |
|
DP-MVS Recon | | | 95.68 57 | 95.12 62 | 97.37 41 | 99.19 24 | 94.19 24 | 97.03 130 | 98.08 50 | 88.35 209 | 95.09 80 | 97.65 72 | 89.97 59 | 99.48 77 | 92.08 106 | 98.59 75 | 98.44 111 |
|
testing_2 | | | 87.33 279 | 85.03 286 | 94.22 179 | 87.77 330 | 89.32 178 | 94.97 264 | 97.11 161 | 89.22 166 | 71.64 332 | 88.73 318 | 55.16 337 | 97.94 233 | 91.95 107 | 88.73 239 | 95.41 226 |
|
Test4 | | | 89.48 249 | 87.50 259 | 95.44 125 | 90.76 318 | 89.72 148 | 95.78 237 | 97.09 162 | 90.28 145 | 77.67 323 | 91.74 297 | 55.42 336 | 98.08 199 | 91.92 108 | 96.83 116 | 98.52 98 |
|
VPNet | | | 92.23 160 | 91.31 165 | 94.99 142 | 95.56 188 | 90.96 116 | 97.22 118 | 97.86 87 | 92.96 75 | 90.96 168 | 96.62 124 | 75.06 273 | 98.20 186 | 91.90 109 | 83.65 292 | 95.80 209 |
|
sss | | | 94.51 83 | 93.80 85 | 96.64 63 | 97.07 128 | 91.97 86 | 96.32 204 | 98.06 57 | 88.94 182 | 94.50 87 | 96.78 107 | 84.60 119 | 99.27 95 | 91.90 109 | 96.02 131 | 98.68 92 |
|
anonymousdsp | | | 92.16 162 | 91.55 156 | 93.97 191 | 92.58 308 | 89.55 161 | 97.51 88 | 97.42 134 | 89.42 162 | 88.40 234 | 94.84 202 | 80.66 200 | 97.88 242 | 91.87 111 | 91.28 210 | 94.48 280 |
|
ACMP | | 89.59 10 | 92.62 140 | 92.14 133 | 94.05 186 | 96.40 158 | 88.20 206 | 97.36 104 | 97.25 149 | 91.52 111 | 88.30 237 | 96.64 117 | 78.46 242 | 98.72 146 | 91.86 112 | 91.48 206 | 95.23 243 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
HyFIR lowres test | | | 93.66 107 | 92.92 110 | 95.87 102 | 98.24 71 | 89.88 144 | 94.58 269 | 98.49 12 | 85.06 271 | 93.78 98 | 95.78 161 | 82.86 155 | 98.67 147 | 91.77 113 | 95.71 139 | 99.07 63 |
|
UGNet | | | 94.04 96 | 93.28 104 | 96.31 85 | 96.85 135 | 91.19 108 | 97.88 45 | 97.68 102 | 94.40 31 | 93.00 119 | 96.18 140 | 73.39 287 | 99.61 47 | 91.72 114 | 98.46 77 | 98.13 123 |
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 |
UniMVSNet_NR-MVSNet | | | 93.37 116 | 92.67 119 | 95.47 123 | 95.34 197 | 92.83 61 | 97.17 123 | 98.58 10 | 92.98 74 | 90.13 184 | 95.80 157 | 88.37 75 | 97.85 243 | 91.71 115 | 83.93 286 | 95.73 216 |
|
DU-MVS | | | 92.90 132 | 92.04 135 | 95.49 120 | 94.95 221 | 92.83 61 | 97.16 124 | 98.24 28 | 93.02 68 | 90.13 184 | 95.71 165 | 83.47 129 | 97.85 243 | 91.71 115 | 83.93 286 | 95.78 210 |
|
Effi-MVS+-dtu | | | 93.08 124 | 93.21 105 | 92.68 251 | 96.02 175 | 83.25 287 | 97.14 126 | 96.72 196 | 93.85 42 | 91.20 167 | 93.44 270 | 83.08 140 | 98.30 182 | 91.69 117 | 95.73 138 | 96.50 180 |
|
mvs-test1 | | | 93.63 108 | 93.69 88 | 93.46 225 | 96.02 175 | 84.61 275 | 97.24 113 | 96.72 196 | 93.85 42 | 92.30 133 | 95.76 162 | 83.08 140 | 98.89 131 | 91.69 117 | 96.54 125 | 96.87 169 |
|
UniMVSNet (Re) | | | 93.31 118 | 92.55 124 | 95.61 113 | 95.39 194 | 93.34 52 | 97.39 101 | 98.71 5 | 93.14 65 | 90.10 188 | 94.83 204 | 87.71 81 | 98.03 216 | 91.67 119 | 83.99 285 | 95.46 223 |
|
LCM-MVSNet-Re | | | 92.50 145 | 92.52 127 | 92.44 254 | 96.82 139 | 81.89 295 | 96.92 144 | 93.71 309 | 92.41 86 | 84.30 282 | 94.60 214 | 85.08 113 | 97.03 289 | 91.51 120 | 97.36 105 | 98.40 114 |
|
FC-MVSNet-test | | | 93.94 99 | 93.57 91 | 95.04 140 | 95.48 191 | 91.45 100 | 98.12 30 | 98.71 5 | 93.37 55 | 90.23 179 | 96.70 112 | 87.66 82 | 97.85 243 | 91.49 121 | 90.39 223 | 95.83 207 |
|
PMMVS | | | 92.86 134 | 92.34 131 | 94.42 174 | 94.92 223 | 86.73 249 | 94.53 271 | 96.38 211 | 84.78 276 | 94.27 91 | 95.12 194 | 83.13 136 | 98.40 173 | 91.47 122 | 96.49 126 | 98.12 124 |
|
Vis-MVSNet | | | 95.23 65 | 94.81 65 | 96.51 73 | 97.18 124 | 91.58 96 | 98.26 24 | 98.12 42 | 94.38 33 | 94.90 81 | 98.15 41 | 82.28 170 | 98.92 127 | 91.45 123 | 98.58 76 | 99.01 68 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
CHOSEN 1792x2688 | | | 94.15 89 | 93.51 95 | 96.06 95 | 98.27 68 | 89.38 173 | 95.18 262 | 98.48 14 | 85.60 264 | 93.76 99 | 97.11 99 | 83.15 134 | 99.61 47 | 91.33 124 | 98.72 72 | 99.19 51 |
|
OMC-MVS | | | 95.09 69 | 94.70 69 | 96.25 91 | 98.46 53 | 91.28 103 | 96.43 190 | 97.57 111 | 92.04 101 | 94.77 84 | 97.96 51 | 87.01 93 | 99.09 116 | 91.31 125 | 96.77 118 | 98.36 118 |
|
MG-MVS | | | 95.61 58 | 95.38 55 | 96.31 85 | 98.42 56 | 90.53 128 | 96.04 222 | 97.48 119 | 93.47 54 | 95.67 71 | 98.10 42 | 89.17 63 | 99.25 96 | 91.27 126 | 98.77 70 | 99.13 57 |
|
ACMM | | 89.79 8 | 92.96 129 | 92.50 128 | 94.35 176 | 96.30 162 | 88.71 190 | 97.58 84 | 97.36 141 | 91.40 118 | 90.53 172 | 96.65 116 | 79.77 214 | 98.75 143 | 91.24 127 | 91.64 202 | 95.59 219 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
WTY-MVS | | | 94.71 81 | 94.02 81 | 96.79 61 | 97.71 103 | 92.05 82 | 96.59 183 | 97.35 142 | 90.61 140 | 94.64 85 | 96.93 103 | 86.41 98 | 99.39 88 | 91.20 128 | 94.71 153 | 98.94 74 |
|
CANet_DTU | | | 94.37 84 | 93.65 90 | 96.55 69 | 96.46 156 | 92.13 80 | 96.21 214 | 96.67 203 | 94.38 33 | 93.53 102 | 97.03 102 | 79.34 220 | 99.71 29 | 90.76 129 | 98.45 78 | 97.82 138 |
|
ab-mvs | | | 93.57 111 | 92.55 124 | 96.64 63 | 97.28 121 | 91.96 87 | 95.40 252 | 97.45 128 | 89.81 156 | 93.22 112 | 96.28 137 | 79.62 217 | 99.46 79 | 90.74 130 | 93.11 182 | 98.50 102 |
|
CostFormer | | | 91.18 210 | 90.70 190 | 92.62 252 | 94.84 227 | 81.76 296 | 94.09 282 | 94.43 293 | 84.15 281 | 92.72 126 | 93.77 257 | 79.43 219 | 98.20 186 | 90.70 131 | 92.18 194 | 97.90 132 |
|
tpmrst | | | 91.44 197 | 91.32 164 | 91.79 276 | 95.15 211 | 79.20 317 | 93.42 293 | 95.37 254 | 88.55 197 | 93.49 103 | 93.67 260 | 82.49 165 | 98.27 183 | 90.41 132 | 89.34 232 | 97.90 132 |
|
UA-Net | | | 95.95 54 | 95.53 51 | 97.20 53 | 97.67 104 | 92.98 59 | 97.65 68 | 98.13 41 | 94.81 22 | 96.61 35 | 98.35 24 | 88.87 66 | 99.51 74 | 90.36 133 | 97.35 106 | 99.11 60 |
|
IS-MVSNet | | | 94.90 76 | 94.52 75 | 96.05 96 | 97.67 104 | 90.56 127 | 98.44 15 | 96.22 219 | 93.21 60 | 93.99 95 | 97.74 66 | 85.55 108 | 98.45 165 | 89.98 134 | 97.86 90 | 99.14 56 |
|
EI-MVSNet | | | 93.03 127 | 92.88 111 | 93.48 223 | 95.77 183 | 86.98 245 | 96.44 188 | 97.12 159 | 90.66 136 | 91.30 155 | 97.64 75 | 86.56 96 | 98.05 211 | 89.91 135 | 90.55 220 | 95.41 226 |
|
IterMVS-LS | | | 92.29 157 | 91.94 140 | 93.34 230 | 96.25 163 | 86.97 246 | 96.57 186 | 97.05 168 | 90.67 134 | 89.50 214 | 94.80 206 | 86.59 95 | 97.64 261 | 89.91 135 | 86.11 257 | 95.40 230 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CDS-MVSNet | | | 94.14 91 | 93.54 93 | 95.93 100 | 96.18 167 | 91.46 99 | 96.33 203 | 97.04 171 | 88.97 181 | 93.56 100 | 96.51 128 | 87.55 85 | 97.89 241 | 89.80 137 | 95.95 133 | 98.44 111 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
WR-MVS | | | 92.34 153 | 91.53 157 | 94.77 158 | 95.13 213 | 90.83 121 | 96.40 196 | 97.98 77 | 91.88 105 | 89.29 220 | 95.54 175 | 82.50 164 | 97.80 248 | 89.79 138 | 85.27 265 | 95.69 217 |
|
NR-MVSNet | | | 92.34 153 | 91.27 167 | 95.53 117 | 94.95 221 | 93.05 56 | 97.39 101 | 98.07 55 | 92.65 82 | 84.46 280 | 95.71 165 | 85.00 114 | 97.77 252 | 89.71 139 | 83.52 293 | 95.78 210 |
|
testdata | | | | | 95.46 124 | 98.18 78 | 88.90 189 | | 97.66 103 | 82.73 295 | 97.03 29 | 98.07 44 | 90.06 57 | 98.85 134 | 89.67 140 | 98.98 65 | 98.64 93 |
|
Baseline_NR-MVSNet | | | 91.20 207 | 90.62 198 | 92.95 242 | 93.83 275 | 88.03 219 | 97.01 134 | 95.12 268 | 88.42 206 | 89.70 205 | 95.13 193 | 83.47 129 | 97.44 273 | 89.66 141 | 83.24 295 | 93.37 299 |
|
PatchFormer-LS_test | | | 91.68 185 | 91.18 172 | 93.19 237 | 95.24 206 | 83.63 285 | 95.53 247 | 95.44 251 | 89.82 155 | 91.37 150 | 92.58 282 | 80.85 199 | 98.52 159 | 89.65 142 | 90.16 225 | 97.42 154 |
|
XXY-MVS | | | 92.16 162 | 91.23 169 | 94.95 147 | 94.75 231 | 90.94 117 | 97.47 95 | 97.43 133 | 89.14 174 | 88.90 225 | 96.43 132 | 79.71 215 | 98.24 184 | 89.56 143 | 87.68 246 | 95.67 218 |
|
diffmvs | | | 93.43 115 | 92.75 115 | 95.48 122 | 96.47 155 | 89.61 157 | 96.09 219 | 97.14 156 | 85.97 261 | 93.09 117 | 95.35 184 | 84.87 116 | 98.55 157 | 89.51 144 | 96.26 130 | 98.28 120 |
|
XVG-ACMP-BASELINE | | | 90.93 216 | 90.21 213 | 93.09 238 | 94.31 246 | 85.89 258 | 95.33 254 | 97.26 147 | 91.06 127 | 89.38 216 | 95.44 182 | 68.61 306 | 98.60 152 | 89.46 145 | 91.05 213 | 94.79 271 |
|
AdaColmap | | | 94.34 85 | 93.68 89 | 96.31 85 | 98.59 48 | 91.68 92 | 96.59 183 | 97.81 90 | 89.87 151 | 92.15 136 | 97.06 101 | 83.62 128 | 99.54 67 | 89.34 146 | 98.07 86 | 97.70 142 |
|
TranMVSNet+NR-MVSNet | | | 92.50 145 | 91.63 152 | 95.14 136 | 94.76 230 | 92.07 81 | 97.53 87 | 98.11 45 | 92.90 77 | 89.56 211 | 96.12 143 | 83.16 133 | 97.60 264 | 89.30 147 | 83.20 296 | 95.75 214 |
|
1314 | | | 92.81 137 | 92.03 136 | 95.14 136 | 95.33 200 | 89.52 164 | 96.04 222 | 97.44 131 | 87.72 226 | 86.25 269 | 95.33 185 | 83.84 125 | 98.79 138 | 89.26 148 | 97.05 112 | 97.11 157 |
|
v2v482 | | | 91.59 189 | 90.85 183 | 93.80 200 | 93.87 274 | 88.17 208 | 96.94 143 | 96.88 190 | 89.54 158 | 89.53 212 | 94.90 198 | 81.70 183 | 98.02 219 | 89.25 149 | 85.04 273 | 95.20 244 |
|
114514_t | | | 93.95 98 | 93.06 107 | 96.63 65 | 99.07 28 | 91.61 93 | 97.46 96 | 97.96 79 | 77.99 322 | 93.00 119 | 97.57 81 | 86.14 103 | 99.33 92 | 89.22 150 | 99.15 54 | 98.94 74 |
|
PAPM_NR | | | 95.01 70 | 94.59 71 | 96.26 90 | 98.89 33 | 90.68 125 | 97.24 113 | 97.73 94 | 91.80 106 | 92.93 124 | 96.62 124 | 89.13 64 | 99.14 106 | 89.21 151 | 97.78 93 | 98.97 70 |
|
IB-MVS | | 87.33 17 | 89.91 242 | 88.28 253 | 94.79 157 | 95.26 205 | 87.70 231 | 95.12 263 | 93.95 307 | 89.35 163 | 87.03 262 | 92.49 283 | 70.74 298 | 99.19 99 | 89.18 152 | 81.37 305 | 97.49 152 |
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 |
HY-MVS | | 89.66 9 | 93.87 100 | 92.95 109 | 96.63 65 | 97.10 127 | 92.49 71 | 95.64 242 | 96.64 204 | 89.05 176 | 93.00 119 | 95.79 160 | 85.77 107 | 99.45 81 | 89.16 153 | 94.35 154 | 97.96 129 |
|
v6 | | | 91.69 180 | 91.00 176 | 93.75 205 | 94.14 254 | 88.12 213 | 97.20 119 | 96.98 176 | 89.19 167 | 89.90 193 | 94.42 224 | 83.04 144 | 98.07 203 | 89.07 154 | 85.10 268 | 95.07 249 |
|
v1neww | | | 91.70 178 | 91.01 174 | 93.75 205 | 94.19 249 | 88.14 211 | 97.20 119 | 96.98 176 | 89.18 169 | 89.87 196 | 94.44 222 | 83.10 138 | 98.06 208 | 89.06 155 | 85.09 269 | 95.06 252 |
|
v7new | | | 91.70 178 | 91.01 174 | 93.75 205 | 94.19 249 | 88.14 211 | 97.20 119 | 96.98 176 | 89.18 169 | 89.87 196 | 94.44 222 | 83.10 138 | 98.06 208 | 89.06 155 | 85.09 269 | 95.06 252 |
|
V42 | | | 91.58 190 | 90.87 181 | 93.73 208 | 94.05 266 | 88.50 195 | 97.32 108 | 96.97 179 | 88.80 191 | 89.71 204 | 94.33 229 | 82.54 163 | 98.05 211 | 89.01 157 | 85.07 271 | 94.64 277 |
|
OurMVSNet-221017-0 | | | 90.51 231 | 90.19 214 | 91.44 284 | 93.41 287 | 81.25 299 | 96.98 136 | 96.28 214 | 91.68 109 | 86.55 267 | 96.30 136 | 74.20 280 | 97.98 224 | 88.96 158 | 87.40 251 | 95.09 246 |
|
API-MVS | | | 94.84 79 | 94.49 76 | 95.90 101 | 97.90 94 | 92.00 85 | 97.80 51 | 97.48 119 | 89.19 167 | 94.81 83 | 96.71 110 | 88.84 67 | 99.17 102 | 88.91 159 | 98.76 71 | 96.53 178 |
|
divwei89l23v2f112 | | | 91.61 186 | 90.89 178 | 93.78 202 | 94.01 267 | 88.22 204 | 96.96 137 | 96.96 180 | 89.17 171 | 89.75 202 | 94.28 240 | 83.02 146 | 98.03 216 | 88.86 160 | 84.98 276 | 95.08 247 |
|
v1141 | | | 91.61 186 | 90.89 178 | 93.78 202 | 94.01 267 | 88.24 202 | 96.96 137 | 96.96 180 | 89.17 171 | 89.75 202 | 94.29 238 | 82.99 148 | 98.03 216 | 88.85 161 | 85.00 274 | 95.07 249 |
|
v1 | | | 91.61 186 | 90.89 178 | 93.78 202 | 94.01 267 | 88.21 205 | 96.96 137 | 96.96 180 | 89.17 171 | 89.78 201 | 94.29 238 | 82.97 150 | 98.05 211 | 88.85 161 | 84.99 275 | 95.08 247 |
|
test-LLR | | | 91.42 198 | 91.19 171 | 92.12 266 | 94.59 236 | 80.66 302 | 94.29 276 | 92.98 322 | 91.11 125 | 90.76 170 | 92.37 285 | 79.02 226 | 98.07 203 | 88.81 163 | 96.74 119 | 97.63 143 |
|
test-mter | | | 90.19 238 | 89.54 235 | 92.12 266 | 94.59 236 | 80.66 302 | 94.29 276 | 92.98 322 | 87.68 227 | 90.76 170 | 92.37 285 | 67.67 310 | 98.07 203 | 88.81 163 | 96.74 119 | 97.63 143 |
|
TAMVS | | | 94.01 97 | 93.46 97 | 95.64 112 | 96.16 169 | 90.45 131 | 96.71 167 | 96.89 189 | 89.27 165 | 93.46 104 | 96.92 104 | 87.29 90 | 97.94 233 | 88.70 165 | 95.74 137 | 98.53 97 |
|
Patchmatch-RL test | | | 87.38 278 | 86.24 277 | 90.81 292 | 88.74 326 | 78.40 320 | 88.12 334 | 93.17 315 | 87.11 239 | 82.17 293 | 89.29 315 | 81.95 178 | 95.60 316 | 88.64 166 | 77.02 315 | 98.41 113 |
|
TESTMET0.1,1 | | | 90.06 240 | 89.42 237 | 91.97 270 | 94.41 243 | 80.62 304 | 94.29 276 | 91.97 331 | 87.28 236 | 90.44 175 | 92.47 284 | 68.79 305 | 97.67 258 | 88.50 167 | 96.60 124 | 97.61 147 |
|
Vis-MVSNet (Re-imp) | | | 94.15 89 | 93.88 83 | 94.95 147 | 97.61 108 | 87.92 225 | 98.10 31 | 95.80 240 | 92.22 88 | 93.02 118 | 97.45 88 | 84.53 121 | 97.91 240 | 88.24 168 | 97.97 88 | 99.02 64 |
|
DWT-MVSNet_test | | | 90.76 220 | 89.89 223 | 93.38 228 | 95.04 217 | 83.70 283 | 95.85 232 | 94.30 299 | 88.19 214 | 90.46 174 | 92.80 277 | 73.61 285 | 98.50 161 | 88.16 169 | 90.58 219 | 97.95 130 |
|
1112_ss | | | 93.37 116 | 92.42 130 | 96.21 92 | 97.05 131 | 90.99 114 | 96.31 205 | 96.72 196 | 86.87 250 | 89.83 198 | 96.69 114 | 86.51 97 | 99.14 106 | 88.12 170 | 93.67 170 | 98.50 102 |
|
CVMVSNet | | | 91.23 206 | 91.75 144 | 89.67 305 | 95.77 183 | 74.69 325 | 96.44 188 | 94.88 279 | 85.81 262 | 92.18 135 | 97.64 75 | 79.07 223 | 95.58 317 | 88.06 171 | 95.86 136 | 98.74 86 |
|
v7 | | | 91.47 196 | 90.73 189 | 93.68 213 | 94.13 255 | 88.16 209 | 97.09 128 | 97.05 168 | 88.38 207 | 89.80 199 | 94.52 215 | 82.21 172 | 98.01 220 | 88.00 172 | 85.42 262 | 94.87 261 |
|
MAR-MVS | | | 94.22 87 | 93.46 97 | 96.51 73 | 98.00 82 | 92.19 79 | 97.67 65 | 97.47 122 | 88.13 218 | 93.00 119 | 95.84 154 | 84.86 117 | 99.51 74 | 87.99 173 | 98.17 84 | 97.83 137 |
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 |
原ACMM1 | | | | | 96.38 81 | 98.59 48 | 91.09 113 | | 97.89 82 | 87.41 232 | 95.22 78 | 97.68 69 | 90.25 54 | 99.54 67 | 87.95 174 | 99.12 59 | 98.49 104 |
|
CP-MVSNet | | | 91.89 170 | 91.24 168 | 93.82 199 | 95.05 216 | 88.57 193 | 97.82 50 | 98.19 33 | 91.70 108 | 88.21 240 | 95.76 162 | 81.96 177 | 97.52 268 | 87.86 175 | 84.65 279 | 95.37 233 |
|
v148 | | | 90.99 214 | 90.38 204 | 92.81 246 | 93.83 275 | 85.80 259 | 96.78 159 | 96.68 201 | 89.45 161 | 88.75 229 | 93.93 252 | 82.96 152 | 97.82 247 | 87.83 176 | 83.25 294 | 94.80 269 |
|
v1144 | | | 91.37 201 | 90.60 199 | 93.68 213 | 93.89 273 | 88.23 203 | 96.84 149 | 97.03 173 | 88.37 208 | 89.69 206 | 94.39 225 | 82.04 175 | 97.98 224 | 87.80 177 | 85.37 263 | 94.84 263 |
|
gm-plane-assit | | | | | | 93.22 295 | 78.89 319 | | | 84.82 275 | | 93.52 265 | | 98.64 148 | 87.72 178 | | |
|
pmmvs4 | | | 90.93 216 | 89.85 225 | 94.17 181 | 93.34 289 | 90.79 123 | 94.60 268 | 96.02 225 | 84.62 277 | 87.45 251 | 95.15 191 | 81.88 180 | 97.45 272 | 87.70 179 | 87.87 245 | 94.27 288 |
|
Test_1112_low_res | | | 92.84 136 | 91.84 142 | 95.85 103 | 97.04 132 | 89.97 140 | 95.53 247 | 96.64 204 | 85.38 265 | 89.65 208 | 95.18 190 | 85.86 105 | 99.10 113 | 87.70 179 | 93.58 175 | 98.49 104 |
|
æ— å…ˆéªŒ | | | | | | | | 95.79 235 | 97.87 85 | 83.87 286 | | | | 99.65 41 | 87.68 181 | | 98.89 80 |
|
1121 | | | 94.71 81 | 93.83 84 | 97.34 42 | 98.57 51 | 93.64 42 | 96.04 222 | 97.73 94 | 81.56 306 | 95.68 68 | 97.85 58 | 90.23 55 | 99.65 41 | 87.68 181 | 99.12 59 | 98.73 87 |
|
Fast-Effi-MVS+ | | | 93.46 113 | 92.75 115 | 95.59 114 | 96.77 140 | 90.03 134 | 96.81 154 | 97.13 158 | 88.19 214 | 91.30 155 | 94.27 242 | 86.21 100 | 98.63 149 | 87.66 183 | 96.46 128 | 98.12 124 |
|
CNLPA | | | 94.28 86 | 93.53 94 | 96.52 70 | 98.38 60 | 92.55 69 | 96.59 183 | 96.88 190 | 90.13 148 | 91.91 140 | 97.24 93 | 85.21 111 | 99.09 116 | 87.64 184 | 97.83 91 | 97.92 131 |
|
v8 | | | 91.29 205 | 90.53 201 | 93.57 220 | 94.15 253 | 88.12 213 | 97.34 105 | 97.06 167 | 88.99 178 | 88.32 236 | 94.26 244 | 83.08 140 | 98.01 220 | 87.62 185 | 83.92 288 | 94.57 278 |
|
pmmvs5 | | | 89.86 245 | 88.87 245 | 92.82 243 | 92.86 302 | 86.23 256 | 96.26 209 | 95.39 252 | 84.24 280 | 87.12 259 | 94.51 216 | 74.27 279 | 97.36 280 | 87.61 186 | 87.57 247 | 94.86 262 |
|
Fast-Effi-MVS+-dtu | | | 92.29 157 | 91.99 138 | 93.21 236 | 95.27 202 | 85.52 264 | 97.03 130 | 96.63 206 | 92.09 95 | 89.11 224 | 95.14 192 | 80.33 207 | 98.08 199 | 87.54 187 | 94.74 152 | 96.03 200 |
|
OpenMVS | | 89.19 12 | 92.86 134 | 91.68 147 | 96.40 79 | 95.34 197 | 92.73 64 | 98.27 23 | 98.12 42 | 84.86 274 | 85.78 272 | 97.75 65 | 78.89 238 | 99.74 24 | 87.50 188 | 98.65 73 | 96.73 172 |
|
v52 | | | 90.70 226 | 90.00 219 | 92.82 243 | 93.24 293 | 87.03 243 | 97.60 81 | 97.14 156 | 88.21 212 | 87.69 247 | 93.94 251 | 80.91 195 | 98.07 203 | 87.39 189 | 83.87 290 | 93.36 300 |
|
V4 | | | 90.71 225 | 90.00 219 | 92.82 243 | 93.21 296 | 87.03 243 | 97.59 83 | 97.16 155 | 88.21 212 | 87.69 247 | 93.92 253 | 80.93 194 | 98.06 208 | 87.39 189 | 83.90 289 | 93.39 298 |
|
semantic-postprocess | | | | | 91.82 274 | 95.52 189 | 84.20 278 | | 96.15 222 | 90.61 140 | 87.39 254 | 94.27 242 | 75.63 269 | 96.44 296 | 87.34 191 | 86.88 254 | 94.82 267 |
|
PLC | | 91.00 6 | 94.11 92 | 93.43 99 | 96.13 94 | 98.58 50 | 91.15 112 | 96.69 172 | 97.39 136 | 87.29 235 | 91.37 150 | 96.71 110 | 88.39 74 | 99.52 73 | 87.33 192 | 97.13 111 | 97.73 140 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
tpm | | | 90.25 235 | 89.74 231 | 91.76 279 | 93.92 271 | 79.73 313 | 93.98 283 | 93.54 313 | 88.28 210 | 91.99 139 | 93.25 273 | 77.51 260 | 97.44 273 | 87.30 193 | 87.94 244 | 98.12 124 |
|
GA-MVS | | | 91.38 200 | 90.31 205 | 94.59 165 | 94.65 234 | 87.62 232 | 94.34 274 | 96.19 220 | 90.73 132 | 90.35 177 | 93.83 254 | 71.84 290 | 97.96 231 | 87.22 194 | 93.61 173 | 98.21 121 |
|
BH-untuned | | | 92.94 130 | 92.62 121 | 93.92 197 | 97.22 122 | 86.16 257 | 96.40 196 | 96.25 217 | 90.06 149 | 89.79 200 | 96.17 142 | 83.19 132 | 98.35 177 | 87.19 195 | 97.27 108 | 97.24 156 |
|
v144192 | | | 91.06 212 | 90.28 207 | 93.39 227 | 93.66 280 | 87.23 239 | 96.83 150 | 97.07 165 | 87.43 231 | 89.69 206 | 94.28 240 | 81.48 184 | 98.00 223 | 87.18 196 | 84.92 277 | 94.93 259 |
|
RPSCF | | | 90.75 222 | 90.86 182 | 90.42 299 | 96.84 136 | 76.29 323 | 95.61 244 | 96.34 212 | 83.89 284 | 91.38 149 | 97.87 55 | 76.45 263 | 98.78 139 | 87.16 197 | 92.23 191 | 96.20 186 |
|
PS-CasMVS | | | 91.55 192 | 90.84 185 | 93.69 212 | 94.96 220 | 88.28 199 | 97.84 49 | 98.24 28 | 91.46 114 | 88.04 242 | 95.80 157 | 79.67 216 | 97.48 270 | 87.02 198 | 84.54 281 | 95.31 236 |
|
pm-mvs1 | | | 90.72 224 | 89.65 234 | 93.96 192 | 94.29 247 | 89.63 156 | 97.79 52 | 96.82 193 | 89.07 175 | 86.12 271 | 95.48 181 | 78.61 240 | 97.78 250 | 86.97 199 | 81.67 303 | 94.46 281 |
|
IterMVS | | | 90.15 239 | 89.67 232 | 91.61 281 | 95.48 191 | 83.72 281 | 94.33 275 | 96.12 223 | 89.99 150 | 87.31 257 | 94.15 246 | 75.78 268 | 96.27 299 | 86.97 199 | 86.89 253 | 94.83 265 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
F-COLMAP | | | 93.58 110 | 92.98 108 | 95.37 127 | 98.40 57 | 88.98 187 | 97.18 122 | 97.29 146 | 87.75 225 | 90.49 173 | 97.10 100 | 85.21 111 | 99.50 76 | 86.70 201 | 96.72 121 | 97.63 143 |
|
PVSNet | | 86.66 18 | 92.24 159 | 91.74 146 | 93.73 208 | 97.77 100 | 83.69 284 | 92.88 303 | 96.72 196 | 87.91 221 | 93.00 119 | 94.86 201 | 78.51 241 | 99.05 122 | 86.53 202 | 97.45 103 | 98.47 107 |
|
v1192 | | | 91.07 211 | 90.23 211 | 93.58 219 | 93.70 278 | 87.82 228 | 96.73 162 | 97.07 165 | 87.77 224 | 89.58 209 | 94.32 230 | 80.90 198 | 97.97 227 | 86.52 203 | 85.48 260 | 94.95 255 |
|
æ–°å‡ ä½•1 | | | | | 97.32 43 | 98.60 47 | 93.59 43 | | 97.75 92 | 81.58 304 | 95.75 67 | 97.85 58 | 90.04 58 | 99.67 39 | 86.50 204 | 99.13 56 | 98.69 91 |
|
v10 | | | 91.04 213 | 90.23 211 | 93.49 222 | 94.12 257 | 88.16 209 | 97.32 108 | 97.08 164 | 88.26 211 | 88.29 238 | 94.22 245 | 82.17 174 | 97.97 227 | 86.45 205 | 84.12 284 | 94.33 285 |
|
v1921920 | | | 90.85 218 | 90.03 218 | 93.29 232 | 93.55 281 | 86.96 247 | 96.74 161 | 97.04 171 | 87.36 233 | 89.52 213 | 94.34 228 | 80.23 209 | 97.97 227 | 86.27 206 | 85.21 266 | 94.94 257 |
|
MDTV_nov1_ep13_2view | | | | | | | 70.35 333 | 93.10 301 | | 83.88 285 | 93.55 101 | | 82.47 167 | | 86.25 207 | | 98.38 117 |
|
test_post1 | | | | | | | | 92.81 305 | | | | 16.58 354 | 80.53 202 | 97.68 257 | 86.20 208 | | |
|
PAPR | | | 94.18 88 | 93.42 101 | 96.48 75 | 97.64 106 | 91.42 101 | 95.55 245 | 97.71 100 | 88.99 178 | 92.34 132 | 95.82 156 | 89.19 62 | 99.11 108 | 86.14 209 | 97.38 104 | 98.90 78 |
|
GBi-Net | | | 91.35 202 | 90.27 208 | 94.59 165 | 96.51 151 | 91.18 109 | 97.50 89 | 96.93 185 | 88.82 188 | 89.35 217 | 94.51 216 | 73.87 281 | 97.29 283 | 86.12 210 | 88.82 235 | 95.31 236 |
|
test1 | | | 91.35 202 | 90.27 208 | 94.59 165 | 96.51 151 | 91.18 109 | 97.50 89 | 96.93 185 | 88.82 188 | 89.35 217 | 94.51 216 | 73.87 281 | 97.29 283 | 86.12 210 | 88.82 235 | 95.31 236 |
|
FMVSNet3 | | | 91.78 172 | 90.69 191 | 95.03 141 | 96.53 150 | 92.27 75 | 97.02 132 | 96.93 185 | 89.79 157 | 89.35 217 | 94.65 212 | 77.01 261 | 97.47 271 | 86.12 210 | 88.82 235 | 95.35 234 |
|
EPMVS | | | 90.70 226 | 89.81 227 | 93.37 229 | 94.73 232 | 84.21 277 | 93.67 289 | 88.02 341 | 89.50 160 | 92.38 130 | 93.49 267 | 77.82 258 | 97.78 250 | 86.03 213 | 92.68 186 | 98.11 127 |
|
MVS | | | 91.71 175 | 90.44 202 | 95.51 118 | 95.20 209 | 91.59 95 | 96.04 222 | 97.45 128 | 73.44 334 | 87.36 255 | 95.60 171 | 85.42 109 | 99.10 113 | 85.97 214 | 97.46 99 | 95.83 207 |
|
testdata2 | | | | | | | | | | | | | | 99.67 39 | 85.96 215 | | |
|
K. test v3 | | | 87.64 277 | 86.75 275 | 90.32 300 | 93.02 301 | 79.48 315 | 96.61 180 | 92.08 330 | 90.66 136 | 80.25 317 | 94.09 247 | 67.21 314 | 96.65 295 | 85.96 215 | 80.83 308 | 94.83 265 |
|
WR-MVS_H | | | 92.00 167 | 91.35 162 | 93.95 193 | 95.09 215 | 89.47 165 | 98.04 35 | 98.68 7 | 91.46 114 | 88.34 235 | 94.68 210 | 85.86 105 | 97.56 265 | 85.77 217 | 84.24 283 | 94.82 267 |
|
gg-mvs-nofinetune | | | 87.82 275 | 85.61 282 | 94.44 172 | 94.46 240 | 89.27 182 | 91.21 319 | 84.61 347 | 80.88 309 | 89.89 195 | 74.98 339 | 71.50 292 | 97.53 267 | 85.75 218 | 97.21 109 | 96.51 179 |
|
v748 | | | 90.34 233 | 89.54 235 | 92.75 248 | 93.25 292 | 85.71 261 | 97.61 80 | 97.17 152 | 88.54 198 | 87.20 258 | 93.54 264 | 81.02 190 | 98.01 220 | 85.73 219 | 81.80 301 | 94.52 279 |
|
tpm2 | | | 89.96 241 | 89.21 240 | 92.23 260 | 94.91 225 | 81.25 299 | 93.78 286 | 94.42 294 | 80.62 312 | 91.56 146 | 93.44 270 | 76.44 264 | 97.94 233 | 85.60 220 | 92.08 198 | 97.49 152 |
|
v1240 | | | 90.70 226 | 89.85 225 | 93.23 234 | 93.51 284 | 86.80 248 | 96.61 180 | 97.02 174 | 87.16 238 | 89.58 209 | 94.31 231 | 79.55 218 | 97.98 224 | 85.52 221 | 85.44 261 | 94.90 260 |
|
PEN-MVS | | | 91.20 207 | 90.44 202 | 93.48 223 | 94.49 239 | 87.91 227 | 97.76 53 | 98.18 35 | 91.29 119 | 87.78 245 | 95.74 164 | 80.35 206 | 97.33 281 | 85.46 222 | 82.96 297 | 95.19 245 |
|
QAPM | | | 93.45 114 | 92.27 132 | 96.98 59 | 96.77 140 | 92.62 67 | 98.39 18 | 98.12 42 | 84.50 279 | 88.27 239 | 97.77 64 | 82.39 169 | 99.81 19 | 85.40 223 | 98.81 69 | 98.51 100 |
|
EU-MVSNet | | | 88.72 258 | 88.90 244 | 88.20 308 | 93.15 299 | 74.21 326 | 96.63 179 | 94.22 302 | 85.18 268 | 87.32 256 | 95.97 147 | 76.16 265 | 94.98 322 | 85.27 224 | 86.17 255 | 95.41 226 |
|
BH-w/o | | | 92.14 164 | 91.75 144 | 93.31 231 | 96.99 133 | 85.73 260 | 95.67 239 | 95.69 242 | 88.73 193 | 89.26 222 | 94.82 205 | 82.97 150 | 98.07 203 | 85.26 225 | 96.32 129 | 96.13 192 |
|
FMVSNet2 | | | 91.31 204 | 90.08 215 | 94.99 142 | 96.51 151 | 92.21 76 | 97.41 97 | 96.95 183 | 88.82 188 | 88.62 230 | 94.75 208 | 73.87 281 | 97.42 275 | 85.20 226 | 88.55 241 | 95.35 234 |
|
PM-MVS | | | 83.48 298 | 81.86 301 | 88.31 307 | 87.83 329 | 77.59 321 | 93.43 292 | 91.75 332 | 86.91 247 | 80.63 307 | 89.91 303 | 44.42 343 | 95.84 312 | 85.17 227 | 76.73 317 | 91.50 328 |
|
LF4IMVS | | | 87.94 274 | 87.25 266 | 89.98 303 | 92.38 310 | 80.05 312 | 94.38 273 | 95.25 262 | 87.59 229 | 84.34 281 | 94.74 209 | 64.31 321 | 97.66 260 | 84.83 228 | 87.45 248 | 92.23 321 |
|
PatchmatchNet | | | 91.91 169 | 91.35 162 | 93.59 217 | 95.38 195 | 84.11 279 | 93.15 299 | 95.39 252 | 89.54 158 | 92.10 137 | 93.68 259 | 82.82 157 | 98.13 192 | 84.81 229 | 95.32 142 | 98.52 98 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
pmmvs6 | | | 87.81 276 | 86.19 278 | 92.69 250 | 91.32 315 | 86.30 255 | 97.34 105 | 96.41 210 | 80.59 313 | 84.05 287 | 94.37 227 | 67.37 313 | 97.67 258 | 84.75 230 | 79.51 311 | 94.09 290 |
|
v18 | | | 88.71 259 | 87.52 258 | 92.27 256 | 94.16 252 | 88.11 215 | 96.82 153 | 95.96 226 | 87.03 240 | 80.76 304 | 89.81 305 | 83.15 134 | 96.22 300 | 84.69 231 | 75.31 322 | 92.49 310 |
|
v7n | | | 90.76 220 | 89.86 224 | 93.45 226 | 93.54 282 | 87.60 233 | 97.70 64 | 97.37 139 | 88.85 185 | 87.65 249 | 94.08 248 | 81.08 189 | 98.10 196 | 84.68 232 | 83.79 291 | 94.66 276 |
|
SixPastTwentyTwo | | | 89.15 253 | 88.54 250 | 90.98 288 | 93.49 285 | 80.28 309 | 96.70 170 | 94.70 283 | 90.78 130 | 84.15 285 | 95.57 172 | 71.78 291 | 97.71 256 | 84.63 233 | 85.07 271 | 94.94 257 |
|
v17 | | | 88.67 261 | 87.47 261 | 92.26 258 | 94.13 255 | 88.09 217 | 96.81 154 | 95.95 227 | 87.02 241 | 80.72 305 | 89.75 307 | 83.11 137 | 96.20 301 | 84.61 234 | 75.15 324 | 92.49 310 |
|
v16 | | | 88.69 260 | 87.50 259 | 92.26 258 | 94.19 249 | 88.11 215 | 96.81 154 | 95.95 227 | 87.01 242 | 80.71 306 | 89.80 306 | 83.08 140 | 96.20 301 | 84.61 234 | 75.34 321 | 92.48 312 |
|
TDRefinement | | | 86.53 284 | 84.76 289 | 91.85 273 | 82.23 340 | 84.25 276 | 96.38 198 | 95.35 255 | 84.97 273 | 84.09 286 | 94.94 195 | 65.76 319 | 98.34 179 | 84.60 236 | 74.52 329 | 92.97 301 |
|
ACMH | | 87.59 16 | 90.53 230 | 89.42 237 | 93.87 198 | 96.21 164 | 87.92 225 | 97.24 113 | 96.94 184 | 88.45 199 | 83.91 288 | 96.27 138 | 71.92 289 | 98.62 151 | 84.43 237 | 89.43 231 | 95.05 254 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMH+ | | 87.92 14 | 90.20 237 | 89.18 241 | 93.25 233 | 96.48 154 | 86.45 254 | 96.99 135 | 96.68 201 | 88.83 187 | 84.79 279 | 96.22 139 | 70.16 302 | 98.53 158 | 84.42 238 | 88.04 243 | 94.77 273 |
|
v15 | | | 88.53 263 | 87.31 263 | 92.20 261 | 94.09 261 | 88.05 218 | 96.72 165 | 95.90 231 | 87.01 242 | 80.53 309 | 89.60 311 | 83.02 146 | 96.13 303 | 84.29 239 | 74.64 325 | 92.41 316 |
|
V14 | | | 88.52 264 | 87.30 264 | 92.17 263 | 94.12 257 | 87.99 220 | 96.72 165 | 95.91 230 | 86.98 244 | 80.50 310 | 89.63 308 | 83.03 145 | 96.12 305 | 84.23 240 | 74.60 327 | 92.40 317 |
|
V9 | | | 88.49 267 | 87.26 265 | 92.18 262 | 94.12 257 | 87.97 223 | 96.73 162 | 95.90 231 | 86.95 246 | 80.40 312 | 89.61 309 | 82.98 149 | 96.13 303 | 84.14 241 | 74.55 328 | 92.44 314 |
|
MS-PatchMatch | | | 90.27 234 | 89.77 228 | 91.78 277 | 94.33 245 | 84.72 274 | 95.55 245 | 96.73 195 | 86.17 259 | 86.36 268 | 95.28 188 | 71.28 294 | 97.80 248 | 84.09 242 | 98.14 85 | 92.81 305 |
|
v12 | | | 88.46 268 | 87.23 268 | 92.17 263 | 94.10 260 | 87.99 220 | 96.71 167 | 95.90 231 | 86.91 247 | 80.34 314 | 89.58 312 | 82.92 153 | 96.11 307 | 84.09 242 | 74.50 330 | 92.42 315 |
|
v13 | | | 88.45 269 | 87.22 269 | 92.16 265 | 94.08 263 | 87.95 224 | 96.71 167 | 95.90 231 | 86.86 251 | 80.27 316 | 89.55 313 | 82.92 153 | 96.12 305 | 84.02 244 | 74.63 326 | 92.40 317 |
|
PatchMatch-RL | | | 92.90 132 | 92.02 137 | 95.56 115 | 98.19 77 | 90.80 122 | 95.27 259 | 97.18 150 | 87.96 219 | 91.86 142 | 95.68 168 | 80.44 204 | 98.99 124 | 84.01 245 | 97.54 98 | 96.89 168 |
|
lessismore_v0 | | | | | 90.45 298 | 91.96 313 | 79.09 318 | | 87.19 344 | | 80.32 315 | 94.39 225 | 66.31 316 | 97.55 266 | 84.00 246 | 76.84 316 | 94.70 274 |
|
CMPMVS | | 62.92 21 | 85.62 292 | 84.92 287 | 87.74 310 | 89.14 325 | 73.12 329 | 94.17 279 | 96.80 194 | 73.98 332 | 73.65 328 | 94.93 196 | 66.36 315 | 97.61 263 | 83.95 247 | 91.28 210 | 92.48 312 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
MVP-Stereo | | | 90.74 223 | 90.08 215 | 92.71 249 | 93.19 298 | 88.20 206 | 95.86 231 | 96.27 215 | 86.07 260 | 84.86 278 | 94.76 207 | 77.84 257 | 97.75 253 | 83.88 248 | 98.01 87 | 92.17 323 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
LS3D | | | 93.57 111 | 92.61 122 | 96.47 76 | 97.59 110 | 91.61 93 | 97.67 65 | 97.72 97 | 85.17 269 | 90.29 178 | 98.34 27 | 84.60 119 | 99.73 25 | 83.85 249 | 98.27 81 | 98.06 128 |
|
v11 | | | 88.41 270 | 87.19 272 | 92.08 268 | 94.08 263 | 87.77 229 | 96.75 160 | 95.85 237 | 86.74 252 | 80.50 310 | 89.50 314 | 82.49 165 | 96.08 308 | 83.55 250 | 75.20 323 | 92.38 319 |
|
Patchmatch-test1 | | | 91.54 193 | 90.85 183 | 93.59 217 | 95.59 187 | 84.95 271 | 94.72 267 | 95.58 247 | 90.82 129 | 92.25 134 | 93.58 263 | 75.80 267 | 97.41 276 | 83.35 251 | 95.98 132 | 98.40 114 |
|
testpf | | | 80.97 305 | 81.40 303 | 79.65 324 | 91.53 314 | 72.43 330 | 73.47 346 | 89.55 339 | 78.63 319 | 80.81 302 | 89.06 316 | 61.36 326 | 91.36 336 | 83.34 252 | 84.89 278 | 75.15 343 |
|
DTE-MVSNet | | | 90.56 229 | 89.75 230 | 93.01 240 | 93.95 270 | 87.25 237 | 97.64 72 | 97.65 105 | 90.74 131 | 87.12 259 | 95.68 168 | 79.97 212 | 97.00 292 | 83.33 253 | 81.66 304 | 94.78 272 |
|
BH-RMVSNet | | | 92.72 139 | 91.97 139 | 94.97 145 | 97.16 125 | 87.99 220 | 96.15 216 | 95.60 245 | 90.62 138 | 91.87 141 | 97.15 98 | 78.41 243 | 98.57 155 | 83.16 254 | 97.60 97 | 98.36 118 |
|
pmmvs-eth3d | | | 86.22 287 | 84.45 290 | 91.53 282 | 88.34 327 | 87.25 237 | 94.47 272 | 95.01 272 | 83.47 290 | 79.51 320 | 89.61 309 | 69.75 303 | 95.71 314 | 83.13 255 | 76.73 317 | 91.64 325 |
|
FMVSNet1 | | | 89.88 244 | 88.31 252 | 94.59 165 | 95.41 193 | 91.18 109 | 97.50 89 | 96.93 185 | 86.62 253 | 87.41 253 | 94.51 216 | 65.94 318 | 97.29 283 | 83.04 256 | 87.43 249 | 95.31 236 |
|
tfpn_ndepth | | | 91.88 171 | 90.96 177 | 94.62 164 | 97.73 102 | 89.93 143 | 97.75 54 | 92.92 324 | 88.93 183 | 91.73 143 | 93.80 256 | 78.91 231 | 98.49 164 | 83.02 257 | 93.86 169 | 95.45 224 |
|
MDTV_nov1_ep13 | | | | 90.76 187 | | 95.22 207 | 80.33 307 | 93.03 302 | 95.28 259 | 88.14 217 | 92.84 125 | 93.83 254 | 81.34 186 | 98.08 199 | 82.86 258 | 94.34 155 | |
|
TR-MVS | | | 91.48 195 | 90.59 200 | 94.16 182 | 96.40 158 | 87.33 234 | 95.67 239 | 95.34 258 | 87.68 227 | 91.46 148 | 95.52 176 | 76.77 262 | 98.35 177 | 82.85 259 | 93.61 173 | 96.79 171 |
|
JIA-IIPM | | | 88.26 272 | 87.04 273 | 91.91 271 | 93.52 283 | 81.42 298 | 89.38 330 | 94.38 295 | 80.84 310 | 90.93 169 | 80.74 336 | 79.22 222 | 97.92 237 | 82.76 260 | 91.62 203 | 96.38 184 |
|
PVSNet_0 | | 82.17 19 | 85.46 293 | 83.64 294 | 90.92 290 | 95.27 202 | 79.49 314 | 90.55 323 | 95.60 245 | 83.76 287 | 83.00 291 | 89.95 302 | 71.09 295 | 97.97 227 | 82.75 261 | 60.79 341 | 95.31 236 |
|
ambc | | | | | 86.56 315 | 83.60 337 | 70.00 335 | 85.69 338 | 94.97 275 | | 80.60 308 | 88.45 320 | 37.42 345 | 96.84 294 | 82.69 262 | 75.44 320 | 92.86 302 |
|
USDC | | | 88.94 254 | 87.83 256 | 92.27 256 | 94.66 233 | 84.96 270 | 93.86 285 | 95.90 231 | 87.34 234 | 83.40 290 | 95.56 173 | 67.43 312 | 98.19 188 | 82.64 263 | 89.67 230 | 93.66 294 |
|
tpmp4_e23 | | | 89.58 248 | 88.59 248 | 92.54 253 | 95.16 210 | 81.53 297 | 94.11 281 | 95.09 269 | 81.66 302 | 88.60 231 | 93.44 270 | 75.11 272 | 98.33 180 | 82.45 264 | 91.72 201 | 97.75 139 |
|
tfpn1000 | | | 91.99 168 | 91.05 173 | 94.80 155 | 97.78 99 | 89.66 155 | 97.91 43 | 92.90 325 | 88.99 178 | 91.73 143 | 94.84 202 | 78.99 230 | 98.33 180 | 82.41 265 | 93.91 168 | 96.40 183 |
|
ITE_SJBPF | | | | | 92.43 255 | 95.34 197 | 85.37 266 | | 95.92 229 | 91.47 113 | 87.75 246 | 96.39 134 | 71.00 296 | 97.96 231 | 82.36 266 | 89.86 229 | 93.97 291 |
|
UnsupCasMVSNet_eth | | | 85.99 289 | 84.45 290 | 90.62 296 | 89.97 321 | 82.40 292 | 93.62 291 | 97.37 139 | 89.86 152 | 78.59 322 | 92.37 285 | 65.25 320 | 95.35 320 | 82.27 267 | 70.75 334 | 94.10 289 |
|
GG-mvs-BLEND | | | | | 93.62 215 | 93.69 279 | 89.20 183 | 92.39 311 | 83.33 348 | | 87.98 244 | 89.84 304 | 71.00 296 | 96.87 293 | 82.08 268 | 95.40 141 | 94.80 269 |
|
view600 | | | 92.55 141 | 91.68 147 | 95.18 130 | 97.98 83 | 89.44 169 | 98.00 36 | 94.57 288 | 92.09 95 | 93.17 113 | 95.52 176 | 78.14 249 | 99.11 108 | 81.61 269 | 94.04 162 | 96.98 159 |
|
view800 | | | 92.55 141 | 91.68 147 | 95.18 130 | 97.98 83 | 89.44 169 | 98.00 36 | 94.57 288 | 92.09 95 | 93.17 113 | 95.52 176 | 78.14 249 | 99.11 108 | 81.61 269 | 94.04 162 | 96.98 159 |
|
conf0.05thres1000 | | | 92.55 141 | 91.68 147 | 95.18 130 | 97.98 83 | 89.44 169 | 98.00 36 | 94.57 288 | 92.09 95 | 93.17 113 | 95.52 176 | 78.14 249 | 99.11 108 | 81.61 269 | 94.04 162 | 96.98 159 |
|
tfpn | | | 92.55 141 | 91.68 147 | 95.18 130 | 97.98 83 | 89.44 169 | 98.00 36 | 94.57 288 | 92.09 95 | 93.17 113 | 95.52 176 | 78.14 249 | 99.11 108 | 81.61 269 | 94.04 162 | 96.98 159 |
|
thres600view7 | | | 92.49 147 | 91.60 153 | 95.18 130 | 97.91 93 | 89.47 165 | 97.65 68 | 94.66 284 | 92.18 94 | 93.33 106 | 94.91 197 | 78.06 253 | 99.10 113 | 81.61 269 | 94.06 161 | 96.98 159 |
|
LTVRE_ROB | | 88.41 13 | 90.99 214 | 89.92 222 | 94.19 180 | 96.18 167 | 89.55 161 | 96.31 205 | 97.09 162 | 87.88 222 | 85.67 273 | 95.91 151 | 78.79 239 | 98.57 155 | 81.50 274 | 89.98 226 | 94.44 282 |
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 |
tpmvs | | | 89.83 246 | 89.15 242 | 91.89 272 | 94.92 223 | 80.30 308 | 93.11 300 | 95.46 250 | 86.28 256 | 88.08 241 | 92.65 279 | 80.44 204 | 98.52 159 | 81.47 275 | 89.92 228 | 96.84 170 |
|
conf200view11 | | | 92.45 148 | 91.58 154 | 95.05 139 | 97.92 91 | 89.37 174 | 97.71 62 | 94.66 284 | 92.20 90 | 93.31 107 | 94.90 198 | 78.06 253 | 99.08 118 | 81.40 276 | 94.08 157 | 96.70 174 |
|
thres100view900 | | | 92.43 149 | 91.58 154 | 94.98 144 | 97.92 91 | 89.37 174 | 97.71 62 | 94.66 284 | 92.20 90 | 93.31 107 | 94.90 198 | 78.06 253 | 99.08 118 | 81.40 276 | 94.08 157 | 96.48 181 |
|
tfpn200view9 | | | 92.38 152 | 91.52 158 | 94.95 147 | 97.85 96 | 89.29 179 | 97.41 97 | 94.88 279 | 92.19 92 | 93.27 110 | 94.46 220 | 78.17 246 | 99.08 118 | 81.40 276 | 94.08 157 | 96.48 181 |
|
thres400 | | | 92.42 150 | 91.52 158 | 95.12 138 | 97.85 96 | 89.29 179 | 97.41 97 | 94.88 279 | 92.19 92 | 93.27 110 | 94.46 220 | 78.17 246 | 99.08 118 | 81.40 276 | 94.08 157 | 96.98 159 |
|
DP-MVS | | | 92.76 138 | 91.51 160 | 96.52 70 | 98.77 35 | 90.99 114 | 97.38 103 | 96.08 224 | 82.38 297 | 89.29 220 | 97.87 55 | 83.77 126 | 99.69 35 | 81.37 280 | 96.69 122 | 98.89 80 |
|
conf0.01 | | | 91.74 173 | 90.67 192 | 94.94 150 | 97.55 113 | 89.68 149 | 97.64 72 | 93.14 316 | 88.43 200 | 91.24 160 | 94.30 232 | 78.91 231 | 98.45 165 | 81.28 281 | 93.57 176 | 96.70 174 |
|
conf0.002 | | | 91.74 173 | 90.67 192 | 94.94 150 | 97.55 113 | 89.68 149 | 97.64 72 | 93.14 316 | 88.43 200 | 91.24 160 | 94.30 232 | 78.91 231 | 98.45 165 | 81.28 281 | 93.57 176 | 96.70 174 |
|
thresconf0.02 | | | 91.69 180 | 90.67 192 | 94.75 159 | 97.55 113 | 89.68 149 | 97.64 72 | 93.14 316 | 88.43 200 | 91.24 160 | 94.30 232 | 78.91 231 | 98.45 165 | 81.28 281 | 93.57 176 | 96.11 193 |
|
tfpn_n400 | | | 91.69 180 | 90.67 192 | 94.75 159 | 97.55 113 | 89.68 149 | 97.64 72 | 93.14 316 | 88.43 200 | 91.24 160 | 94.30 232 | 78.91 231 | 98.45 165 | 81.28 281 | 93.57 176 | 96.11 193 |
|
tfpnconf | | | 91.69 180 | 90.67 192 | 94.75 159 | 97.55 113 | 89.68 149 | 97.64 72 | 93.14 316 | 88.43 200 | 91.24 160 | 94.30 232 | 78.91 231 | 98.45 165 | 81.28 281 | 93.57 176 | 96.11 193 |
|
tfpnview11 | | | 91.69 180 | 90.67 192 | 94.75 159 | 97.55 113 | 89.68 149 | 97.64 72 | 93.14 316 | 88.43 200 | 91.24 160 | 94.30 232 | 78.91 231 | 98.45 165 | 81.28 281 | 93.57 176 | 96.11 193 |
|
thres200 | | | 92.23 160 | 91.39 161 | 94.75 159 | 97.61 108 | 89.03 186 | 96.60 182 | 95.09 269 | 92.08 100 | 93.28 109 | 94.00 249 | 78.39 244 | 99.04 123 | 81.26 287 | 94.18 156 | 96.19 187 |
|
CR-MVSNet | | | 90.82 219 | 89.77 228 | 93.95 193 | 94.45 241 | 87.19 240 | 90.23 325 | 95.68 243 | 86.89 249 | 92.40 128 | 92.36 288 | 80.91 195 | 97.05 287 | 81.09 288 | 93.95 166 | 97.60 148 |
|
MSDG | | | 91.42 198 | 90.24 210 | 94.96 146 | 97.15 126 | 88.91 188 | 93.69 288 | 96.32 213 | 85.72 263 | 86.93 264 | 96.47 130 | 80.24 208 | 98.98 125 | 80.57 289 | 95.05 146 | 96.98 159 |
|
dp | | | 88.90 256 | 88.26 254 | 90.81 292 | 94.58 238 | 76.62 322 | 92.85 304 | 94.93 277 | 85.12 270 | 90.07 191 | 93.07 274 | 75.81 266 | 98.12 194 | 80.53 290 | 87.42 250 | 97.71 141 |
|
tpm cat1 | | | 88.36 271 | 87.21 270 | 91.81 275 | 95.13 213 | 80.55 305 | 92.58 307 | 95.70 241 | 74.97 330 | 87.45 251 | 91.96 293 | 78.01 256 | 98.17 190 | 80.39 291 | 88.74 238 | 96.72 173 |
|
AllTest | | | 90.23 236 | 88.98 243 | 93.98 189 | 97.94 89 | 86.64 250 | 96.51 187 | 95.54 248 | 85.38 265 | 85.49 275 | 96.77 108 | 70.28 300 | 99.15 104 | 80.02 292 | 92.87 183 | 96.15 190 |
|
TestCases | | | | | 93.98 189 | 97.94 89 | 86.64 250 | | 95.54 248 | 85.38 265 | 85.49 275 | 96.77 108 | 70.28 300 | 99.15 104 | 80.02 292 | 92.87 183 | 96.15 190 |
|
ADS-MVSNet2 | | | 89.45 250 | 88.59 248 | 92.03 269 | 95.86 178 | 82.26 293 | 90.93 320 | 94.32 298 | 83.23 292 | 91.28 158 | 91.81 295 | 79.01 228 | 95.99 309 | 79.52 294 | 91.39 208 | 97.84 135 |
|
ADS-MVSNet | | | 89.89 243 | 88.68 247 | 93.53 221 | 95.86 178 | 84.89 272 | 90.93 320 | 95.07 271 | 83.23 292 | 91.28 158 | 91.81 295 | 79.01 228 | 97.85 243 | 79.52 294 | 91.39 208 | 97.84 135 |
|
EPNet_dtu | | | 91.71 175 | 91.28 166 | 92.99 241 | 93.76 277 | 83.71 282 | 96.69 172 | 95.28 259 | 93.15 64 | 87.02 263 | 95.95 149 | 83.37 131 | 97.38 279 | 79.46 296 | 96.84 115 | 97.88 134 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
TransMVSNet (Re) | | | 88.94 254 | 87.56 257 | 93.08 239 | 94.35 244 | 88.45 197 | 97.73 58 | 95.23 263 | 87.47 230 | 84.26 283 | 95.29 186 | 79.86 213 | 97.33 281 | 79.44 297 | 74.44 331 | 93.45 297 |
|
EG-PatchMatch MVS | | | 87.02 282 | 85.44 283 | 91.76 279 | 92.67 306 | 85.00 269 | 96.08 221 | 96.45 209 | 83.41 291 | 79.52 319 | 93.49 267 | 57.10 332 | 97.72 255 | 79.34 298 | 90.87 216 | 92.56 308 |
|
Patchmtry | | | 88.64 262 | 87.25 266 | 92.78 247 | 94.09 261 | 86.64 250 | 89.82 328 | 95.68 243 | 80.81 311 | 87.63 250 | 92.36 288 | 80.91 195 | 97.03 289 | 78.86 299 | 85.12 267 | 94.67 275 |
|
FMVSNet5 | | | 87.29 280 | 85.79 281 | 91.78 277 | 94.80 229 | 87.28 235 | 95.49 249 | 95.28 259 | 84.09 282 | 83.85 289 | 91.82 294 | 62.95 323 | 94.17 325 | 78.48 300 | 85.34 264 | 93.91 292 |
|
COLMAP_ROB | | 87.81 15 | 90.40 232 | 89.28 239 | 93.79 201 | 97.95 88 | 87.13 242 | 96.92 144 | 95.89 236 | 82.83 294 | 86.88 266 | 97.18 95 | 73.77 284 | 99.29 94 | 78.44 301 | 93.62 172 | 94.95 255 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
test0.0.03 1 | | | 89.37 252 | 88.70 246 | 91.41 285 | 92.47 309 | 85.63 262 | 95.22 261 | 92.70 327 | 91.11 125 | 86.91 265 | 93.65 261 | 79.02 226 | 93.19 330 | 78.00 302 | 89.18 233 | 95.41 226 |
|
MIMVSNet | | | 88.50 266 | 86.76 274 | 93.72 210 | 94.84 227 | 87.77 229 | 91.39 315 | 94.05 304 | 86.41 255 | 87.99 243 | 92.59 281 | 63.27 322 | 95.82 313 | 77.44 303 | 92.84 185 | 97.57 150 |
|
MDA-MVSNet_test_wron | | | 85.87 290 | 84.23 292 | 90.80 294 | 92.38 310 | 82.57 289 | 93.17 297 | 95.15 266 | 82.15 298 | 67.65 334 | 92.33 291 | 78.20 245 | 95.51 318 | 77.33 304 | 79.74 309 | 94.31 287 |
|
YYNet1 | | | 85.87 290 | 84.23 292 | 90.78 295 | 92.38 310 | 82.46 291 | 93.17 297 | 95.14 267 | 82.12 299 | 67.69 333 | 92.36 288 | 78.16 248 | 95.50 319 | 77.31 305 | 79.73 310 | 94.39 283 |
|
UnsupCasMVSNet_bld | | | 82.13 304 | 79.46 306 | 90.14 302 | 88.00 328 | 82.47 290 | 90.89 322 | 96.62 207 | 78.94 318 | 75.61 325 | 84.40 334 | 56.63 333 | 96.31 298 | 77.30 306 | 66.77 340 | 91.63 326 |
|
PCF-MVS | | 89.48 11 | 91.56 191 | 89.95 221 | 96.36 83 | 96.60 144 | 92.52 70 | 92.51 308 | 97.26 147 | 79.41 315 | 88.90 225 | 96.56 126 | 84.04 124 | 99.55 65 | 77.01 307 | 97.30 107 | 97.01 158 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
testgi | | | 87.97 273 | 87.21 270 | 90.24 301 | 92.86 302 | 80.76 301 | 96.67 174 | 94.97 275 | 91.74 107 | 85.52 274 | 95.83 155 | 62.66 324 | 94.47 324 | 76.25 308 | 88.36 242 | 95.48 220 |
|
TinyColmap | | | 86.82 283 | 85.35 285 | 91.21 286 | 94.91 225 | 82.99 288 | 93.94 284 | 94.02 306 | 83.58 288 | 81.56 299 | 94.68 210 | 62.34 325 | 98.13 192 | 75.78 309 | 87.35 252 | 92.52 309 |
|
PAPM | | | 91.52 194 | 90.30 206 | 95.20 129 | 95.30 201 | 89.83 145 | 93.38 294 | 96.85 192 | 86.26 257 | 88.59 232 | 95.80 157 | 84.88 115 | 98.15 191 | 75.67 310 | 95.93 134 | 97.63 143 |
|
tfpnnormal | | | 89.70 247 | 88.40 251 | 93.60 216 | 95.15 211 | 90.10 133 | 97.56 85 | 98.16 37 | 87.28 236 | 86.16 270 | 94.63 213 | 77.57 259 | 98.05 211 | 74.48 311 | 84.59 280 | 92.65 306 |
|
DSMNet-mixed | | | 86.34 286 | 86.12 280 | 87.00 313 | 89.88 322 | 70.43 331 | 94.93 265 | 90.08 338 | 77.97 323 | 85.42 277 | 92.78 278 | 74.44 278 | 93.96 326 | 74.43 312 | 95.14 144 | 96.62 177 |
|
Patchmatch-test | | | 89.42 251 | 87.99 255 | 93.70 211 | 95.27 202 | 85.11 267 | 88.98 331 | 94.37 296 | 81.11 307 | 87.10 261 | 93.69 258 | 82.28 170 | 97.50 269 | 74.37 313 | 94.76 150 | 98.48 106 |
|
LCM-MVSNet | | | 72.55 312 | 69.39 315 | 82.03 320 | 70.81 350 | 65.42 341 | 90.12 327 | 94.36 297 | 55.02 342 | 65.88 337 | 81.72 335 | 24.16 354 | 89.96 339 | 74.32 314 | 68.10 338 | 90.71 331 |
|
new-patchmatchnet | | | 83.18 299 | 81.87 300 | 87.11 312 | 86.88 332 | 75.99 324 | 93.70 287 | 95.18 265 | 85.02 272 | 77.30 324 | 88.40 321 | 65.99 317 | 93.88 327 | 74.19 315 | 70.18 335 | 91.47 329 |
|
MDA-MVSNet-bldmvs | | | 85.00 294 | 82.95 296 | 91.17 287 | 93.13 300 | 83.33 286 | 94.56 270 | 95.00 273 | 84.57 278 | 65.13 338 | 92.65 279 | 70.45 299 | 95.85 311 | 73.57 316 | 77.49 314 | 94.33 285 |
|
pmmvs3 | | | 79.97 306 | 77.50 310 | 87.39 311 | 82.80 338 | 79.38 316 | 92.70 306 | 90.75 336 | 70.69 336 | 78.66 321 | 87.47 330 | 51.34 340 | 93.40 328 | 73.39 317 | 69.65 336 | 89.38 333 |
|
PatchT | | | 88.87 257 | 87.42 262 | 93.22 235 | 94.08 263 | 85.10 268 | 89.51 329 | 94.64 287 | 81.92 300 | 92.36 131 | 88.15 324 | 80.05 211 | 97.01 291 | 72.43 318 | 93.65 171 | 97.54 151 |
|
Anonymous20231206 | | | 87.09 281 | 86.14 279 | 89.93 304 | 91.22 316 | 80.35 306 | 96.11 218 | 95.35 255 | 83.57 289 | 84.16 284 | 93.02 275 | 73.54 286 | 95.61 315 | 72.16 319 | 86.14 256 | 93.84 293 |
|
MVS-HIRNet | | | 82.47 303 | 81.21 304 | 86.26 316 | 95.38 195 | 69.21 336 | 88.96 332 | 89.49 340 | 66.28 338 | 80.79 303 | 74.08 341 | 68.48 307 | 97.39 278 | 71.93 320 | 95.47 140 | 92.18 322 |
|
new_pmnet | | | 82.89 300 | 81.12 305 | 88.18 309 | 89.63 323 | 80.18 310 | 91.77 314 | 92.57 328 | 76.79 326 | 75.56 326 | 88.23 323 | 61.22 327 | 94.48 323 | 71.43 321 | 82.92 298 | 89.87 332 |
|
TAPA-MVS | | 90.10 7 | 92.30 156 | 91.22 170 | 95.56 115 | 98.33 64 | 89.60 158 | 96.79 157 | 97.65 105 | 81.83 301 | 91.52 147 | 97.23 94 | 87.94 78 | 98.91 128 | 71.31 322 | 98.37 79 | 98.17 122 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
test20.03 | | | 86.14 288 | 85.40 284 | 88.35 306 | 90.12 319 | 80.06 311 | 95.90 230 | 95.20 264 | 88.59 194 | 81.29 300 | 93.62 262 | 71.43 293 | 92.65 331 | 71.26 323 | 81.17 306 | 92.34 320 |
|
tmp_tt | | | 51.94 326 | 53.82 323 | 46.29 339 | 33.73 355 | 45.30 355 | 78.32 345 | 67.24 355 | 18.02 350 | 50.93 344 | 87.05 331 | 52.99 339 | 53.11 353 | 70.76 324 | 25.29 350 | 40.46 350 |
|
MIMVSNet1 | | | 84.93 295 | 83.05 295 | 90.56 297 | 89.56 324 | 84.84 273 | 95.40 252 | 95.35 255 | 83.91 283 | 80.38 313 | 92.21 292 | 57.23 331 | 93.34 329 | 70.69 325 | 82.75 300 | 93.50 295 |
|
RPMNet | | | 88.52 264 | 86.72 276 | 93.95 193 | 94.45 241 | 87.19 240 | 90.23 325 | 94.99 274 | 77.87 324 | 92.40 128 | 87.55 329 | 80.17 210 | 97.05 287 | 68.84 326 | 93.95 166 | 97.60 148 |
|
N_pmnet | | | 78.73 308 | 78.71 307 | 78.79 326 | 92.80 304 | 46.50 353 | 94.14 280 | 43.71 356 | 78.61 320 | 80.83 301 | 91.66 298 | 74.94 276 | 96.36 297 | 67.24 327 | 84.45 282 | 93.50 295 |
|
OpenMVS_ROB | | 81.14 20 | 84.42 296 | 82.28 297 | 90.83 291 | 90.06 320 | 84.05 280 | 95.73 238 | 94.04 305 | 73.89 333 | 80.17 318 | 91.53 299 | 59.15 329 | 97.64 261 | 66.92 328 | 89.05 234 | 90.80 330 |
|
testus | | | 82.63 302 | 82.15 298 | 84.07 318 | 87.31 331 | 67.67 337 | 93.18 295 | 94.29 300 | 82.47 296 | 82.14 294 | 90.69 300 | 53.01 338 | 91.94 334 | 66.30 329 | 89.96 227 | 92.62 307 |
|
test2356 | | | 82.77 301 | 82.14 299 | 84.65 317 | 85.77 334 | 70.36 332 | 91.22 318 | 93.69 312 | 81.58 304 | 81.82 296 | 89.00 317 | 60.63 328 | 90.77 337 | 64.74 330 | 90.80 217 | 92.82 303 |
|
PMMVS2 | | | 70.19 315 | 66.92 317 | 80.01 323 | 76.35 342 | 65.67 340 | 86.22 337 | 87.58 343 | 64.83 340 | 62.38 339 | 80.29 338 | 26.78 352 | 88.49 343 | 63.79 331 | 54.07 342 | 85.88 337 |
|
test_0402 | | | 86.46 285 | 84.79 288 | 91.45 283 | 95.02 218 | 85.55 263 | 96.29 207 | 94.89 278 | 80.90 308 | 82.21 292 | 93.97 250 | 68.21 309 | 97.29 283 | 62.98 332 | 88.68 240 | 91.51 327 |
|
Anonymous20231211 | | | 78.22 310 | 75.30 311 | 86.99 314 | 86.14 333 | 74.16 327 | 95.62 243 | 93.88 308 | 66.43 337 | 74.44 327 | 87.86 326 | 41.39 344 | 95.11 321 | 62.49 333 | 69.46 337 | 91.71 324 |
|
DeepMVS_CX | | | | | 74.68 331 | 90.84 317 | 64.34 342 | | 81.61 351 | 65.34 339 | 67.47 336 | 88.01 325 | 48.60 341 | 80.13 348 | 62.33 334 | 73.68 333 | 79.58 341 |
|
test1235678 | | | 79.82 307 | 78.53 308 | 83.69 319 | 82.55 339 | 67.55 338 | 92.50 309 | 94.13 303 | 79.28 316 | 72.10 331 | 86.45 332 | 57.27 330 | 90.68 338 | 61.60 335 | 80.90 307 | 92.82 303 |
|
no-one | | | 68.12 316 | 63.78 319 | 81.13 321 | 74.01 345 | 70.22 334 | 87.61 336 | 90.71 337 | 72.63 335 | 53.13 343 | 71.89 342 | 30.29 348 | 91.45 335 | 61.53 336 | 32.21 346 | 81.72 340 |
|
LP | | | 84.13 297 | 81.85 302 | 90.97 289 | 93.20 297 | 82.12 294 | 87.68 335 | 94.27 301 | 76.80 325 | 81.93 295 | 88.52 319 | 72.97 288 | 95.95 310 | 59.53 337 | 81.73 302 | 94.84 263 |
|
test12356 | | | 74.97 311 | 74.13 312 | 77.49 327 | 78.81 341 | 56.23 349 | 88.53 333 | 92.75 326 | 75.14 327 | 67.50 335 | 85.07 333 | 44.88 342 | 89.96 339 | 58.71 338 | 75.75 319 | 86.26 335 |
|
1111 | | | 78.29 309 | 77.55 309 | 80.50 322 | 83.89 335 | 59.98 345 | 91.89 312 | 93.71 309 | 75.06 328 | 73.60 329 | 87.67 327 | 55.66 334 | 92.60 332 | 58.54 339 | 77.92 313 | 88.93 334 |
|
.test1245 | | | 65.38 318 | 69.22 316 | 53.86 338 | 83.89 335 | 59.98 345 | 91.89 312 | 93.71 309 | 75.06 328 | 73.60 329 | 87.67 327 | 55.66 334 | 92.60 332 | 58.54 339 | 2.96 352 | 9.00 352 |
|
wuykxyi23d | | | 56.92 322 | 51.11 326 | 74.38 332 | 62.30 352 | 61.47 344 | 80.09 343 | 84.87 346 | 49.62 345 | 30.80 351 | 57.20 349 | 7.03 357 | 82.94 346 | 55.69 341 | 32.36 345 | 78.72 342 |
|
testmv | | | 72.22 313 | 70.02 313 | 78.82 325 | 73.06 348 | 61.75 343 | 91.24 317 | 92.31 329 | 74.45 331 | 61.06 340 | 80.51 337 | 34.21 346 | 88.63 342 | 55.31 342 | 68.07 339 | 86.06 336 |
|
FPMVS | | | 71.27 314 | 69.85 314 | 75.50 329 | 74.64 343 | 59.03 347 | 91.30 316 | 91.50 333 | 58.80 341 | 57.92 341 | 88.28 322 | 29.98 350 | 85.53 345 | 53.43 343 | 82.84 299 | 81.95 339 |
|
ANet_high | | | 63.94 319 | 59.58 320 | 77.02 328 | 61.24 353 | 66.06 339 | 85.66 339 | 87.93 342 | 78.53 321 | 42.94 345 | 71.04 343 | 25.42 353 | 80.71 347 | 52.60 344 | 30.83 348 | 84.28 338 |
|
PNet_i23d | | | 59.01 320 | 55.87 321 | 68.44 333 | 73.98 346 | 51.37 350 | 81.36 342 | 82.41 349 | 52.37 344 | 42.49 347 | 70.39 344 | 11.39 355 | 79.99 349 | 49.77 345 | 38.71 344 | 73.97 344 |
|
Gipuma | | | 67.86 317 | 65.41 318 | 75.18 330 | 92.66 307 | 73.45 328 | 66.50 348 | 94.52 292 | 53.33 343 | 57.80 342 | 66.07 345 | 30.81 347 | 89.20 341 | 48.15 346 | 78.88 312 | 62.90 347 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PMVS | | 53.92 22 | 58.58 321 | 55.40 322 | 68.12 334 | 51.00 354 | 48.64 351 | 78.86 344 | 87.10 345 | 46.77 346 | 35.84 350 | 74.28 340 | 8.76 356 | 86.34 344 | 42.07 347 | 73.91 332 | 69.38 345 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MVE | | 50.73 23 | 53.25 324 | 48.81 327 | 66.58 335 | 65.34 351 | 57.50 348 | 72.49 347 | 70.94 354 | 40.15 349 | 39.28 349 | 63.51 346 | 6.89 359 | 73.48 352 | 38.29 348 | 42.38 343 | 68.76 346 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
E-PMN | | | 53.28 323 | 52.56 324 | 55.43 336 | 74.43 344 | 47.13 352 | 83.63 341 | 76.30 352 | 42.23 347 | 42.59 346 | 62.22 347 | 28.57 351 | 74.40 350 | 31.53 349 | 31.51 347 | 44.78 348 |
|
EMVS | | | 52.08 325 | 51.31 325 | 54.39 337 | 72.62 349 | 45.39 354 | 83.84 340 | 75.51 353 | 41.13 348 | 40.77 348 | 59.65 348 | 30.08 349 | 73.60 351 | 28.31 350 | 29.90 349 | 44.18 349 |
|
wuyk23d | | | 25.11 328 | 24.57 330 | 26.74 341 | 73.98 346 | 39.89 356 | 57.88 349 | 9.80 357 | 12.27 351 | 10.39 352 | 6.97 355 | 7.03 357 | 36.44 354 | 25.43 351 | 17.39 351 | 3.89 354 |
|
testmvs | | | 13.36 330 | 16.33 331 | 4.48 343 | 5.04 356 | 2.26 358 | 93.18 295 | 3.28 358 | 2.70 352 | 8.24 353 | 21.66 351 | 2.29 361 | 2.19 355 | 7.58 352 | 2.96 352 | 9.00 352 |
|
test123 | | | 13.04 331 | 15.66 332 | 5.18 342 | 4.51 357 | 3.45 357 | 92.50 309 | 1.81 359 | 2.50 353 | 7.58 354 | 20.15 352 | 3.67 360 | 2.18 356 | 7.13 353 | 1.07 354 | 9.90 351 |
|
cdsmvs_eth3d_5k | | | 23.24 329 | 30.99 329 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 97.63 107 | 0.00 354 | 0.00 355 | 96.88 105 | 84.38 122 | 0.00 357 | 0.00 354 | 0.00 355 | 0.00 355 |
|
pcd_1.5k_mvsjas | | | 7.39 333 | 9.85 334 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 0.00 360 | 0.00 354 | 0.00 355 | 0.00 356 | 88.65 70 | 0.00 357 | 0.00 354 | 0.00 355 | 0.00 355 |
|
pcd1.5k->3k | | | 38.37 327 | 40.51 328 | 31.96 340 | 94.29 247 | 0.00 359 | 0.00 350 | 97.69 101 | 0.00 354 | 0.00 355 | 0.00 356 | 81.45 185 | 0.00 357 | 0.00 354 | 91.11 212 | 95.89 202 |
|
sosnet-low-res | | | 0.00 334 | 0.00 335 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 0.00 360 | 0.00 354 | 0.00 355 | 0.00 356 | 0.00 362 | 0.00 357 | 0.00 354 | 0.00 355 | 0.00 355 |
|
sosnet | | | 0.00 334 | 0.00 335 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 0.00 360 | 0.00 354 | 0.00 355 | 0.00 356 | 0.00 362 | 0.00 357 | 0.00 354 | 0.00 355 | 0.00 355 |
|
uncertanet | | | 0.00 334 | 0.00 335 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 0.00 360 | 0.00 354 | 0.00 355 | 0.00 356 | 0.00 362 | 0.00 357 | 0.00 354 | 0.00 355 | 0.00 355 |
|
Regformer | | | 0.00 334 | 0.00 335 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 0.00 360 | 0.00 354 | 0.00 355 | 0.00 356 | 0.00 362 | 0.00 357 | 0.00 354 | 0.00 355 | 0.00 355 |
|
ab-mvs-re | | | 8.06 332 | 10.74 333 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 0.00 360 | 0.00 354 | 0.00 355 | 96.69 114 | 0.00 362 | 0.00 357 | 0.00 354 | 0.00 355 | 0.00 355 |
|
uanet | | | 0.00 334 | 0.00 335 | 0.00 344 | 0.00 358 | 0.00 359 | 0.00 350 | 0.00 360 | 0.00 354 | 0.00 355 | 0.00 356 | 0.00 362 | 0.00 357 | 0.00 354 | 0.00 355 | 0.00 355 |
|
GSMVS | | | | | | | | | | | | | | | | | 98.45 109 |
|
test_part2 | | | | | | 99.28 17 | 95.74 3 | | | | 98.10 6 | | | | | | |
|
test_part1 | | | | | | | | | 98.26 25 | | | | 95.31 1 | | | 99.63 4 | 99.63 5 |
|
sam_mvs1 | | | | | | | | | | | | | 82.76 158 | | | | 98.45 109 |
|
sam_mvs | | | | | | | | | | | | | 81.94 179 | | | | |
|
MTGPA | | | | | | | | | 98.08 50 | | | | | | | | |
|
test_post | | | | | | | | | | | | 17.58 353 | 81.76 181 | 98.08 199 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 90.45 301 | 82.65 162 | 98.10 196 | | | |
|
MTMP | | | | | | | | | 82.03 350 | | | | | | | | |
|
TEST9 | | | | | | 98.70 38 | 94.19 24 | 96.41 192 | 98.02 67 | 88.17 216 | 96.03 54 | 97.56 83 | 92.74 14 | 99.59 52 | | | |
|
test_8 | | | | | | 98.67 40 | 94.06 30 | 96.37 199 | 98.01 69 | 88.58 195 | 95.98 59 | 97.55 85 | 92.73 15 | 99.58 55 | | | |
|
agg_prior | | | | | | 98.67 40 | 93.79 37 | | 98.00 71 | | 95.68 68 | | | 99.57 63 | | | |
|
test_prior4 | | | | | | | 93.66 41 | 96.42 191 | | | | | | | | | |
|
test_prior | | | | | 97.23 49 | 98.67 40 | 92.99 57 | | 98.00 71 | | | | | 99.41 85 | | | 99.29 45 |
|
æ–°å‡ ä½•2 | | | | | | | | 95.79 235 | | | | | | | | | |
|
旧先验1 | | | | | | 98.38 60 | 93.38 49 | | 97.75 92 | | | 98.09 43 | 92.30 27 | | | 99.01 64 | 99.16 53 |
|
原ACMM2 | | | | | | | | 95.67 239 | | | | | | | | | |
|
test222 | | | | | | 98.24 71 | 92.21 76 | 95.33 254 | 97.60 108 | 79.22 317 | 95.25 77 | 97.84 60 | 88.80 68 | | | 99.15 54 | 98.72 88 |
|
segment_acmp | | | | | | | | | | | | | 92.89 12 | | | | |
|
testdata1 | | | | | | | | 95.26 260 | | 93.10 67 | | | | | | | |
|
test12 | | | | | 97.65 30 | 98.46 53 | 94.26 21 | | 97.66 103 | | 95.52 76 | | 90.89 48 | 99.46 79 | | 99.25 46 | 99.22 50 |
|
plane_prior7 | | | | | | 96.21 164 | 89.98 139 | | | | | | | | | | |
|
plane_prior6 | | | | | | 96.10 174 | 90.00 135 | | | | | | 81.32 187 | | | | |
|
plane_prior4 | | | | | | | | | | | | 96.64 117 | | | | | |
|
plane_prior3 | | | | | | | 90.00 135 | | | 94.46 30 | 91.34 152 | | | | | | |
|
plane_prior2 | | | | | | | | 97.74 56 | | 94.85 17 | | | | | | | |
|
plane_prior1 | | | | | | 96.14 172 | | | | | | | | | | | |
|
plane_prior | | | | | | | 89.99 137 | 97.24 113 | | 94.06 38 | | | | | | 92.16 195 | |
|
n2 | | | | | | | | | 0.00 360 | | | | | | | | |
|
nn | | | | | | | | | 0.00 360 | | | | | | | | |
|
door-mid | | | | | | | | | 91.06 335 | | | | | | | | |
|
test11 | | | | | | | | | 97.88 83 | | | | | | | | |
|
door | | | | | | | | | 91.13 334 | | | | | | | | |
|
HQP5-MVS | | | | | | | 89.33 176 | | | | | | | | | | |
|
HQP-NCC | | | | | | 95.86 178 | | 96.65 175 | | 93.55 50 | 90.14 180 | | | | | | |
|
ACMP_Plane | | | | | | 95.86 178 | | 96.65 175 | | 93.55 50 | 90.14 180 | | | | | | |
|
HQP4-MVS | | | | | | | | | | | 90.14 180 | | | 98.50 161 | | | 95.78 210 |
|
HQP3-MVS | | | | | | | | | 97.39 136 | | | | | | | 92.10 196 | |
|
HQP2-MVS | | | | | | | | | | | | | 80.95 192 | | | | |
|
NP-MVS | | | | | | 95.99 177 | 89.81 146 | | | | | 95.87 152 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 90.30 224 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 91.02 214 | |
|
Test By Simon | | | | | | | | | | | | | 88.73 69 | | | | |
|