MCST-MVS | | | 98.18 2 | 97.95 4 | 98.86 1 | 99.85 3 | 96.60 5 | 99.70 10 | 97.98 52 | 97.18 2 | 95.96 61 | 99.33 8 | 92.62 12 | 100.00 1 | 98.99 6 | 99.93 1 | 99.98 2 |
|
NCCC | | | 98.12 3 | 98.11 3 | 98.13 15 | 99.76 6 | 94.46 39 | 99.81 5 | 97.88 57 | 96.54 4 | 98.84 6 | 99.46 5 | 92.55 13 | 99.98 8 | 98.25 22 | 99.93 1 | 99.94 6 |
|
MSLP-MVS++ | | | 97.50 9 | 97.45 10 | 97.63 27 | 99.65 13 | 93.21 58 | 99.70 10 | 98.13 45 | 94.61 16 | 97.78 30 | 99.46 5 | 89.85 40 | 99.81 52 | 97.97 24 | 99.91 3 | 99.88 15 |
|
CNVR-MVS | | | 98.46 1 | 98.38 1 | 98.72 3 | 99.80 4 | 96.19 9 | 99.80 7 | 97.99 51 | 97.05 3 | 99.41 1 | 99.59 2 | 92.89 11 | 100.00 1 | 98.99 6 | 99.90 4 | 99.96 4 |
|
test9_res | | | | | | | | | | | | | | | 98.60 11 | 99.87 5 | 99.90 9 |
|
agg_prior2 | | | | | | | | | | | | | | | 97.84 27 | 99.87 5 | 99.91 8 |
|
HPM-MVS++ | | | 97.72 6 | 97.59 7 | 98.14 14 | 99.53 33 | 94.76 30 | 99.19 53 | 97.75 73 | 95.66 11 | 98.21 15 | 99.29 9 | 91.10 19 | 99.99 4 | 97.68 28 | 99.87 5 | 99.68 47 |
|
MG-MVS | | | 97.24 12 | 96.83 20 | 98.47 9 | 99.79 5 | 95.71 12 | 99.07 71 | 99.06 15 | 94.45 18 | 96.42 56 | 98.70 72 | 88.81 51 | 99.74 60 | 95.35 64 | 99.86 8 | 99.97 3 |
|
HSP-MVS | | | 97.73 5 | 98.15 2 | 96.44 91 | 99.54 27 | 90.14 127 | 99.41 38 | 97.47 116 | 95.46 14 | 98.60 8 | 99.19 18 | 95.71 4 | 99.49 86 | 98.15 23 | 99.85 9 | 99.69 46 |
|
train_agg | | | 97.20 13 | 97.08 14 | 97.57 31 | 99.57 23 | 93.17 59 | 99.38 40 | 97.66 83 | 90.18 93 | 98.39 11 | 99.18 20 | 90.94 27 | 99.66 65 | 98.58 14 | 99.85 9 | 99.88 15 |
|
agg_prior3 | | | 97.09 17 | 96.97 16 | 97.45 34 | 99.56 25 | 92.79 70 | 99.36 44 | 97.67 82 | 89.59 103 | 98.36 13 | 99.16 24 | 90.57 34 | 99.68 62 | 98.58 14 | 99.85 9 | 99.88 15 |
|
test_part1 | | | | | | | | | 97.69 79 | | | | 93.96 6 | | | 99.83 12 | 99.90 9 |
|
ESAPD | | | 97.97 4 | 97.82 6 | 98.43 10 | 99.54 27 | 95.42 14 | 99.43 33 | 97.69 79 | 92.81 44 | 98.13 16 | 99.48 3 | 93.96 6 | 99.97 13 | 99.52 1 | 99.83 12 | 99.90 9 |
|
TSAR-MVS + MP. | | | 97.44 10 | 97.46 9 | 97.39 39 | 99.12 52 | 93.49 56 | 98.52 135 | 97.50 113 | 94.46 17 | 98.99 2 | 98.64 75 | 91.58 16 | 99.08 114 | 98.49 17 | 99.83 12 | 99.60 58 |
|
test_prior3 | | | 97.07 18 | 97.09 13 | 97.01 50 | 99.58 19 | 91.77 81 | 99.57 19 | 97.57 101 | 91.43 70 | 98.12 19 | 98.97 47 | 90.43 36 | 99.49 86 | 98.33 19 | 99.81 15 | 99.79 25 |
|
test_prior2 | | | | | | | | 99.57 19 | | 91.43 70 | 98.12 19 | 98.97 47 | 90.43 36 | | 98.33 19 | 99.81 15 | |
|
APDe-MVS | | | 97.53 7 | 97.47 8 | 97.70 25 | 99.58 19 | 93.63 51 | 99.56 21 | 97.52 108 | 93.59 32 | 98.01 24 | 99.12 31 | 90.80 32 | 99.55 78 | 99.26 4 | 99.79 17 | 99.93 7 |
|
CDPH-MVS | | | 96.56 31 | 96.18 35 | 97.70 25 | 99.59 18 | 93.92 48 | 99.13 68 | 97.44 121 | 89.02 119 | 97.90 28 | 99.22 15 | 88.90 50 | 99.49 86 | 94.63 77 | 99.79 17 | 99.68 47 |
|
agg_prior1 | | | 97.12 15 | 97.03 15 | 97.38 40 | 99.54 27 | 92.66 71 | 99.35 46 | 97.64 88 | 90.38 88 | 97.98 25 | 99.17 22 | 90.84 31 | 99.61 74 | 98.57 16 | 99.78 19 | 99.87 19 |
|
region2R | | | 96.30 41 | 96.17 37 | 96.70 76 | 99.70 7 | 90.31 124 | 99.46 30 | 97.66 83 | 90.55 84 | 97.07 40 | 99.07 35 | 86.85 86 | 99.97 13 | 95.43 62 | 99.74 20 | 99.81 22 |
|
SD-MVS | | | 97.51 8 | 97.40 11 | 97.81 23 | 99.01 58 | 93.79 50 | 99.33 49 | 97.38 128 | 93.73 29 | 98.83 7 | 99.02 41 | 90.87 30 | 99.88 34 | 98.69 10 | 99.74 20 | 99.77 34 |
|
HFP-MVS | | | 96.42 37 | 96.26 34 | 96.90 61 | 99.69 8 | 90.96 111 | 99.47 27 | 97.81 66 | 90.54 85 | 96.88 43 | 99.05 38 | 87.57 68 | 99.96 17 | 95.65 57 | 99.72 22 | 99.78 29 |
|
#test# | | | 96.48 34 | 96.34 32 | 96.90 61 | 99.69 8 | 90.96 111 | 99.53 24 | 97.81 66 | 90.94 78 | 96.88 43 | 99.05 38 | 87.57 68 | 99.96 17 | 95.87 56 | 99.72 22 | 99.78 29 |
|
ACMMPR | | | 96.28 42 | 96.14 40 | 96.73 73 | 99.68 10 | 90.47 122 | 99.47 27 | 97.80 68 | 90.54 85 | 96.83 50 | 99.03 40 | 86.51 92 | 99.95 20 | 95.65 57 | 99.72 22 | 99.75 35 |
|
CP-MVS | | | 96.22 43 | 96.15 39 | 96.42 92 | 99.67 11 | 89.62 142 | 99.70 10 | 97.61 94 | 90.07 99 | 96.00 58 | 99.16 24 | 87.43 72 | 99.92 26 | 96.03 54 | 99.72 22 | 99.70 44 |
|
test12 | | | | | 97.83 22 | 99.33 43 | 94.45 40 | | 97.55 104 | | 97.56 31 | | 88.60 53 | 99.50 85 | | 99.71 26 | 99.55 60 |
|
DeepC-MVS_fast | | 93.52 2 | 97.16 14 | 96.84 19 | 98.13 15 | 99.61 17 | 94.45 40 | 98.85 97 | 97.64 88 | 96.51 6 | 95.88 62 | 99.39 7 | 87.35 78 | 99.99 4 | 96.61 41 | 99.69 27 | 99.96 4 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
APD-MVS | | | 96.95 21 | 96.72 23 | 97.63 27 | 99.51 34 | 93.58 52 | 99.16 58 | 97.44 121 | 90.08 98 | 98.59 9 | 99.07 35 | 89.06 47 | 99.42 94 | 97.92 25 | 99.66 28 | 99.88 15 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
HPM-MVS | | | 95.41 60 | 95.22 56 | 95.99 106 | 99.29 44 | 89.14 148 | 99.17 57 | 97.09 151 | 87.28 172 | 95.40 71 | 98.48 86 | 84.93 111 | 99.38 97 | 95.64 61 | 99.65 29 | 99.47 68 |
|
test222 | | | | | | 98.32 78 | 91.21 100 | 98.08 185 | 97.58 98 | 83.74 229 | 95.87 63 | 99.02 41 | 86.74 87 | | | 99.64 30 | 99.81 22 |
|
mPP-MVS | | | 95.90 50 | 95.75 48 | 96.38 94 | 99.58 19 | 89.41 147 | 99.26 51 | 97.41 125 | 90.66 80 | 94.82 80 | 98.95 52 | 86.15 98 | 99.98 8 | 95.24 67 | 99.64 30 | 99.74 38 |
|
SteuartSystems-ACMMP | | | 97.25 11 | 97.34 12 | 97.01 50 | 97.38 106 | 91.46 90 | 99.75 8 | 97.66 83 | 94.14 21 | 98.13 16 | 99.26 10 | 92.16 14 | 99.66 65 | 97.91 26 | 99.64 30 | 99.90 9 |
Skip Steuart: Steuart Systems R&D Blog. |
HPM-MVS_fast | | | 94.89 66 | 94.62 63 | 95.70 116 | 99.11 53 | 88.44 162 | 99.14 65 | 97.11 147 | 85.82 191 | 95.69 67 | 98.47 87 | 83.46 125 | 99.32 103 | 93.16 97 | 99.63 33 | 99.35 71 |
|
新几何1 | | | | | 97.40 38 | 98.92 63 | 92.51 77 | | 97.77 72 | 85.52 194 | 96.69 54 | 99.06 37 | 88.08 64 | 99.89 33 | 84.88 173 | 99.62 34 | 99.79 25 |
|
原ACMM1 | | | | | 96.18 99 | 99.03 57 | 90.08 130 | | 97.63 92 | 88.98 120 | 97.00 41 | 98.97 47 | 88.14 63 | 99.71 61 | 88.23 144 | 99.62 34 | 98.76 118 |
|
PHI-MVS | | | 96.65 29 | 96.46 28 | 97.21 44 | 99.34 40 | 91.77 81 | 99.70 10 | 98.05 47 | 86.48 185 | 98.05 21 | 99.20 17 | 89.33 45 | 99.96 17 | 98.38 18 | 99.62 34 | 99.90 9 |
|
1121 | | | 95.19 63 | 94.45 65 | 97.42 36 | 98.88 65 | 92.58 75 | 96.22 250 | 97.75 73 | 85.50 196 | 96.86 46 | 99.01 45 | 88.59 55 | 99.90 30 | 87.64 150 | 99.60 37 | 99.79 25 |
|
DELS-MVS | | | 97.12 15 | 96.60 26 | 98.68 5 | 98.03 85 | 96.57 6 | 99.84 3 | 97.84 61 | 96.36 7 | 95.20 75 | 98.24 92 | 88.17 61 | 99.83 47 | 96.11 52 | 99.60 37 | 99.64 52 |
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 |
MP-MVS | | | 96.00 47 | 95.82 45 | 96.54 87 | 99.47 36 | 90.13 129 | 99.36 44 | 97.41 125 | 90.64 83 | 95.49 70 | 98.95 52 | 85.51 104 | 99.98 8 | 96.00 55 | 99.59 39 | 99.52 62 |
|
MVS_111021_HR | | | 96.69 27 | 96.69 24 | 96.72 75 | 98.58 75 | 91.00 110 | 99.14 65 | 99.45 1 | 93.86 26 | 95.15 76 | 98.73 68 | 88.48 56 | 99.76 58 | 97.23 31 | 99.56 40 | 99.40 69 |
|
DeepPCF-MVS | | 93.56 1 | 96.55 32 | 97.84 5 | 92.68 192 | 98.71 71 | 78.11 301 | 99.70 10 | 97.71 78 | 98.18 1 | 97.36 36 | 99.76 1 | 90.37 38 | 99.94 22 | 99.27 3 | 99.54 41 | 99.99 1 |
|
CPTT-MVS | | | 94.60 77 | 94.43 66 | 95.09 135 | 99.66 12 | 86.85 195 | 99.44 31 | 97.47 116 | 83.22 243 | 94.34 87 | 98.96 50 | 82.50 143 | 99.55 78 | 94.81 73 | 99.50 42 | 98.88 106 |
|
MP-MVS-pluss | | | 95.80 53 | 95.30 53 | 97.29 42 | 98.95 62 | 92.66 71 | 98.59 129 | 97.14 144 | 88.95 122 | 93.12 101 | 99.25 11 | 85.62 101 | 99.94 22 | 96.56 43 | 99.48 43 | 99.28 79 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
ACMMP_Plus | | | 96.59 30 | 96.18 35 | 97.81 23 | 98.82 68 | 93.55 53 | 98.88 96 | 97.59 96 | 90.66 80 | 97.98 25 | 99.14 28 | 86.59 89 | 100.00 1 | 96.47 44 | 99.46 44 | 99.89 14 |
|
PGM-MVS | | | 95.85 51 | 95.65 50 | 96.45 90 | 99.50 35 | 89.77 139 | 98.22 172 | 98.90 17 | 89.19 113 | 96.74 52 | 98.95 52 | 85.91 100 | 99.92 26 | 93.94 82 | 99.46 44 | 99.66 50 |
|
testdata | | | | | 95.26 131 | 98.20 80 | 87.28 187 | | 97.60 95 | 85.21 200 | 98.48 10 | 99.15 26 | 88.15 62 | 98.72 127 | 90.29 122 | 99.45 46 | 99.78 29 |
|
XVS | | | 96.47 35 | 96.37 30 | 96.77 69 | 99.62 15 | 90.66 120 | 99.43 33 | 97.58 98 | 92.41 54 | 96.86 46 | 98.96 50 | 87.37 74 | 99.87 37 | 95.65 57 | 99.43 47 | 99.78 29 |
|
X-MVStestdata | | | 90.69 163 | 88.66 176 | 96.77 69 | 99.62 15 | 90.66 120 | 99.43 33 | 97.58 98 | 92.41 54 | 96.86 46 | 29.59 355 | 87.37 74 | 99.87 37 | 95.65 57 | 99.43 47 | 99.78 29 |
|
MVS | | | 93.92 86 | 92.28 107 | 98.83 2 | 95.69 159 | 96.82 3 | 96.22 250 | 98.17 41 | 84.89 208 | 84.34 195 | 98.61 77 | 79.32 166 | 99.83 47 | 93.88 84 | 99.43 47 | 99.86 20 |
|
MPTG | | | 96.21 44 | 95.96 41 | 96.96 58 | 99.29 44 | 91.19 101 | 98.69 112 | 97.45 118 | 92.58 46 | 94.39 85 | 99.24 13 | 86.43 94 | 99.99 4 | 96.22 48 | 99.40 50 | 99.71 42 |
|
MTAPA | | | 96.09 46 | 95.80 47 | 96.96 58 | 99.29 44 | 91.19 101 | 97.23 214 | 97.45 118 | 92.58 46 | 94.39 85 | 99.24 13 | 86.43 94 | 99.99 4 | 96.22 48 | 99.40 50 | 99.71 42 |
|
旧先验1 | | | | | | 98.97 59 | 92.90 68 | | 97.74 75 | | | 99.15 26 | 91.05 20 | | | 99.33 52 | 99.60 58 |
|
PAPM_NR | | | 95.43 58 | 95.05 59 | 96.57 86 | 99.42 39 | 90.14 127 | 98.58 130 | 97.51 110 | 90.65 82 | 92.44 108 | 98.90 57 | 87.77 67 | 99.90 30 | 90.88 117 | 99.32 53 | 99.68 47 |
|
PAPM | | | 96.35 38 | 95.94 42 | 97.58 29 | 94.10 197 | 95.25 16 | 98.93 85 | 98.17 41 | 94.26 19 | 93.94 93 | 98.72 70 | 89.68 43 | 97.88 157 | 96.36 47 | 99.29 54 | 99.62 56 |
|
APD-MVS_3200maxsize | | | 95.64 57 | 95.65 50 | 95.62 117 | 99.24 48 | 87.80 171 | 98.42 149 | 97.22 138 | 88.93 124 | 96.64 55 | 98.98 46 | 85.49 105 | 99.36 99 | 96.68 40 | 99.27 55 | 99.70 44 |
|
Regformer-1 | | | 96.97 20 | 96.80 21 | 97.47 33 | 99.46 37 | 93.11 61 | 98.89 94 | 97.94 53 | 92.89 41 | 96.90 42 | 99.02 41 | 89.78 41 | 99.53 80 | 97.06 32 | 99.26 56 | 99.75 35 |
|
Regformer-2 | | | 96.94 23 | 96.78 22 | 97.42 36 | 99.46 37 | 92.97 66 | 98.89 94 | 97.93 54 | 92.86 43 | 96.88 43 | 99.02 41 | 89.74 42 | 99.53 80 | 97.03 33 | 99.26 56 | 99.75 35 |
|
3Dnovator | | 87.35 11 | 93.17 112 | 91.77 122 | 97.37 41 | 95.41 166 | 93.07 63 | 98.82 100 | 97.85 60 | 91.53 67 | 82.56 219 | 97.58 109 | 71.97 235 | 99.82 50 | 91.01 115 | 99.23 58 | 99.22 84 |
|
PS-MVSNAJ | | | 96.87 24 | 96.40 29 | 98.29 11 | 97.35 107 | 97.29 1 | 99.03 76 | 97.11 147 | 95.83 9 | 98.97 3 | 99.14 28 | 82.48 145 | 99.60 76 | 98.60 11 | 99.08 59 | 98.00 153 |
|
MVS_111021_LR | | | 95.78 54 | 95.94 42 | 95.28 130 | 98.19 82 | 87.69 172 | 98.80 102 | 99.26 13 | 93.39 34 | 95.04 78 | 98.69 73 | 84.09 119 | 99.76 58 | 96.96 38 | 99.06 60 | 98.38 138 |
|
PAPR | | | 96.35 38 | 95.82 45 | 97.94 20 | 99.63 14 | 94.19 46 | 99.42 37 | 97.55 104 | 92.43 50 | 93.82 97 | 99.12 31 | 87.30 79 | 99.91 28 | 94.02 81 | 99.06 60 | 99.74 38 |
|
114514_t | | | 94.06 85 | 93.05 93 | 97.06 48 | 99.08 55 | 92.26 79 | 98.97 83 | 97.01 159 | 82.58 254 | 92.57 106 | 98.22 93 | 80.68 160 | 99.30 104 | 89.34 134 | 99.02 62 | 99.63 54 |
|
API-MVS | | | 94.78 69 | 94.18 70 | 96.59 85 | 99.21 49 | 90.06 133 | 98.80 102 | 97.78 71 | 83.59 233 | 93.85 95 | 99.21 16 | 83.79 121 | 99.97 13 | 92.37 105 | 99.00 63 | 99.74 38 |
|
MVSFormer | | | 94.71 73 | 94.08 73 | 96.61 84 | 95.05 181 | 94.87 22 | 97.77 198 | 96.17 200 | 86.84 179 | 98.04 22 | 98.52 81 | 85.52 102 | 95.99 260 | 89.83 125 | 98.97 64 | 98.96 98 |
|
lupinMVS | | | 96.32 40 | 95.94 42 | 97.44 35 | 95.05 181 | 94.87 22 | 99.86 2 | 96.50 179 | 93.82 27 | 98.04 22 | 98.77 64 | 85.52 102 | 98.09 146 | 96.98 37 | 98.97 64 | 99.37 70 |
|
3Dnovator+ | | 87.72 8 | 93.43 100 | 91.84 120 | 98.17 13 | 95.73 158 | 95.08 20 | 98.92 87 | 97.04 155 | 91.42 72 | 81.48 237 | 97.60 108 | 74.60 197 | 99.79 55 | 90.84 118 | 98.97 64 | 99.64 52 |
|
GG-mvs-BLEND | | | | | 96.98 56 | 96.53 136 | 94.81 29 | 87.20 322 | 97.74 75 | | 93.91 94 | 96.40 159 | 96.56 2 | 96.94 208 | 95.08 68 | 98.95 67 | 99.20 85 |
|
gg-mvs-nofinetune | | | 90.00 173 | 87.71 189 | 96.89 65 | 96.15 148 | 94.69 33 | 85.15 328 | 97.74 75 | 68.32 327 | 92.97 105 | 60.16 341 | 96.10 3 | 96.84 210 | 93.89 83 | 98.87 68 | 99.14 87 |
|
MAR-MVS | | | 94.43 79 | 94.09 72 | 95.45 126 | 99.10 54 | 87.47 178 | 98.39 155 | 97.79 70 | 88.37 140 | 94.02 92 | 99.17 22 | 78.64 174 | 99.91 28 | 92.48 104 | 98.85 69 | 98.96 98 |
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 |
Regformer-3 | | | 96.50 33 | 96.36 31 | 96.91 60 | 99.34 40 | 91.72 84 | 98.71 108 | 97.90 56 | 92.48 49 | 96.00 58 | 98.95 52 | 88.60 53 | 99.52 83 | 96.44 45 | 98.83 70 | 99.49 65 |
|
Regformer-4 | | | 96.45 36 | 96.33 33 | 96.81 68 | 99.34 40 | 91.44 91 | 98.71 108 | 97.88 57 | 92.43 50 | 95.97 60 | 98.95 52 | 88.42 57 | 99.51 84 | 96.40 46 | 98.83 70 | 99.49 65 |
|
CSCG | | | 94.87 67 | 94.71 62 | 95.36 127 | 99.54 27 | 86.49 205 | 99.34 48 | 98.15 43 | 82.71 252 | 90.15 142 | 99.25 11 | 89.48 44 | 99.86 42 | 94.97 71 | 98.82 72 | 99.72 41 |
|
CHOSEN 280x420 | | | 96.80 26 | 96.85 18 | 96.66 79 | 97.85 87 | 94.42 42 | 94.76 280 | 98.36 26 | 92.50 48 | 95.62 69 | 97.52 110 | 97.92 1 | 97.38 194 | 98.31 21 | 98.80 73 | 98.20 148 |
|
CANet | | | 97.00 19 | 96.49 27 | 98.55 6 | 98.86 67 | 96.10 10 | 99.83 4 | 97.52 108 | 95.90 8 | 97.21 37 | 98.90 57 | 82.66 142 | 99.93 24 | 98.71 9 | 98.80 73 | 99.63 54 |
|
MVP-Stereo | | | 86.61 225 | 85.83 214 | 88.93 264 | 88.70 294 | 83.85 253 | 96.07 257 | 94.41 279 | 82.15 260 | 75.64 280 | 91.96 227 | 67.65 266 | 96.45 231 | 77.20 246 | 98.72 75 | 86.51 315 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
MVS_0304 | | | 96.12 45 | 95.26 55 | 98.69 4 | 98.44 77 | 96.54 7 | 99.70 10 | 96.89 164 | 95.76 10 | 97.53 32 | 99.12 31 | 72.42 230 | 99.93 24 | 98.75 8 | 98.69 76 | 99.61 57 |
|
QAPM | | | 91.41 149 | 89.49 161 | 97.17 46 | 95.66 161 | 93.42 57 | 98.60 127 | 97.51 110 | 80.92 273 | 81.39 238 | 97.41 115 | 72.89 227 | 99.87 37 | 82.33 199 | 98.68 77 | 98.21 147 |
|
1314 | | | 93.44 99 | 91.98 118 | 97.84 21 | 95.24 168 | 94.38 43 | 96.22 250 | 97.92 55 | 90.18 93 | 82.28 224 | 97.71 105 | 77.63 179 | 99.80 54 | 91.94 110 | 98.67 78 | 99.34 73 |
|
abl_6 | | | 94.63 76 | 94.48 64 | 95.09 135 | 98.61 74 | 86.96 192 | 98.06 187 | 96.97 161 | 89.31 109 | 95.86 64 | 98.56 79 | 79.82 162 | 99.64 71 | 94.53 79 | 98.65 79 | 98.66 123 |
|
DeepC-MVS | | 91.02 4 | 94.56 78 | 93.92 81 | 96.46 89 | 97.16 113 | 90.76 116 | 98.39 155 | 97.11 147 | 93.92 22 | 88.66 160 | 98.33 90 | 78.14 176 | 99.85 44 | 95.02 69 | 98.57 80 | 98.78 116 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
OpenMVS | | 85.28 14 | 90.75 161 | 88.84 172 | 96.48 88 | 93.58 214 | 93.51 55 | 98.80 102 | 97.41 125 | 82.59 253 | 78.62 261 | 97.49 112 | 68.00 263 | 99.82 50 | 84.52 177 | 98.55 81 | 96.11 196 |
|
jason | | | 95.40 61 | 94.86 61 | 97.03 49 | 92.91 226 | 94.23 45 | 99.70 10 | 96.30 190 | 93.56 33 | 96.73 53 | 98.52 81 | 81.46 156 | 97.91 154 | 96.08 53 | 98.47 82 | 98.96 98 |
jason: jason. |
MS-PatchMatch | | | 86.75 221 | 85.92 210 | 89.22 258 | 91.97 236 | 82.47 267 | 96.91 223 | 96.14 202 | 83.74 229 | 77.73 269 | 93.53 204 | 58.19 304 | 97.37 196 | 76.75 251 | 98.35 83 | 87.84 302 |
|
DP-MVS Recon | | | 95.85 51 | 95.15 57 | 97.95 19 | 99.87 2 | 94.38 43 | 99.60 17 | 97.48 115 | 86.58 183 | 94.42 84 | 99.13 30 | 87.36 77 | 99.98 8 | 93.64 89 | 98.33 84 | 99.48 67 |
|
xiu_mvs_v2_base | | | 96.66 28 | 96.17 37 | 98.11 17 | 97.11 115 | 96.96 2 | 99.01 79 | 97.04 155 | 95.51 13 | 98.86 5 | 99.11 34 | 82.19 151 | 99.36 99 | 98.59 13 | 98.14 85 | 98.00 153 |
|
BH-w/o | | | 92.32 128 | 91.79 121 | 93.91 169 | 96.85 122 | 86.18 217 | 99.11 69 | 95.74 226 | 88.13 147 | 84.81 190 | 97.00 138 | 77.26 181 | 97.91 154 | 89.16 139 | 98.03 86 | 97.64 161 |
|
TAPA-MVS | | 87.50 9 | 90.35 164 | 89.05 168 | 94.25 159 | 98.48 76 | 85.17 241 | 98.42 149 | 96.58 175 | 82.44 258 | 87.24 176 | 98.53 80 | 82.77 141 | 98.84 119 | 59.09 326 | 97.88 87 | 98.72 119 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
CHOSEN 1792x2688 | | | 94.35 81 | 93.82 83 | 95.95 109 | 97.40 105 | 88.74 156 | 98.41 151 | 98.27 28 | 92.18 59 | 91.43 120 | 96.40 159 | 78.88 168 | 99.81 52 | 93.59 90 | 97.81 88 | 99.30 76 |
|
BH-untuned | | | 91.46 148 | 90.84 145 | 93.33 178 | 96.51 138 | 84.83 244 | 98.84 99 | 95.50 245 | 86.44 187 | 83.50 200 | 96.70 148 | 75.49 189 | 97.77 165 | 86.78 160 | 97.81 88 | 97.40 167 |
|
Vis-MVSNet | | | 92.64 124 | 91.85 119 | 95.03 140 | 95.12 177 | 88.23 163 | 98.48 142 | 96.81 165 | 91.61 66 | 92.16 112 | 97.22 126 | 71.58 240 | 98.00 153 | 85.85 168 | 97.81 88 | 98.88 106 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
EPNet | | | 96.82 25 | 96.68 25 | 97.25 43 | 98.65 72 | 93.10 62 | 99.48 26 | 98.76 18 | 96.54 4 | 97.84 29 | 98.22 93 | 87.49 71 | 99.66 65 | 95.35 64 | 97.78 91 | 99.00 93 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PVSNet_Blended | | | 95.94 49 | 95.66 49 | 96.75 71 | 98.77 69 | 91.61 87 | 99.88 1 | 98.04 48 | 93.64 31 | 94.21 89 | 97.76 103 | 83.50 123 | 99.87 37 | 97.41 29 | 97.75 92 | 98.79 113 |
|
PLC | | 91.07 3 | 94.23 83 | 94.01 74 | 94.87 142 | 99.17 50 | 87.49 177 | 99.25 52 | 96.55 177 | 88.43 138 | 91.26 123 | 98.21 95 | 85.92 99 | 99.86 42 | 89.77 128 | 97.57 93 | 97.24 171 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
LS3D | | | 90.19 168 | 88.72 174 | 94.59 149 | 98.97 59 | 86.33 212 | 96.90 224 | 96.60 171 | 74.96 307 | 84.06 198 | 98.74 67 | 75.78 187 | 99.83 47 | 74.93 270 | 97.57 93 | 97.62 164 |
|
AdaColmap | | | 93.82 89 | 93.06 92 | 96.10 104 | 99.88 1 | 89.07 149 | 98.33 157 | 97.55 104 | 86.81 181 | 90.39 139 | 98.65 74 | 75.09 190 | 99.98 8 | 93.32 95 | 97.53 95 | 99.26 81 |
|
BH-RMVSNet | | | 91.25 152 | 89.99 158 | 95.03 140 | 96.75 132 | 88.55 159 | 98.65 119 | 94.95 266 | 87.74 159 | 87.74 170 | 97.80 101 | 68.27 260 | 98.14 144 | 80.53 222 | 97.49 96 | 98.41 134 |
|
CANet_DTU | | | 94.31 82 | 93.35 87 | 97.20 45 | 97.03 118 | 94.71 32 | 98.62 123 | 95.54 242 | 95.61 12 | 97.21 37 | 98.47 87 | 71.88 236 | 99.84 45 | 88.38 143 | 97.46 97 | 97.04 177 |
|
PatchMatch-RL | | | 91.47 147 | 90.54 153 | 94.26 158 | 98.20 80 | 86.36 211 | 96.94 222 | 97.14 144 | 87.75 158 | 88.98 157 | 95.75 168 | 71.80 238 | 99.40 96 | 80.92 214 | 97.39 98 | 97.02 178 |
|
UGNet | | | 91.91 142 | 90.85 144 | 95.10 134 | 97.06 117 | 88.69 157 | 98.01 189 | 98.24 30 | 92.41 54 | 92.39 109 | 93.61 201 | 60.52 300 | 99.68 62 | 88.14 145 | 97.25 99 | 96.92 183 |
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 |
PVSNet | | 87.13 12 | 93.69 92 | 92.83 97 | 96.28 97 | 97.99 86 | 90.22 126 | 99.38 40 | 98.93 16 | 91.42 72 | 93.66 98 | 97.68 106 | 71.29 242 | 99.64 71 | 87.94 147 | 97.20 100 | 98.98 96 |
|
EI-MVSNet-Vis-set | | | 95.76 56 | 95.63 52 | 96.17 101 | 99.14 51 | 90.33 123 | 98.49 141 | 97.82 63 | 91.92 61 | 94.75 81 | 98.88 59 | 87.06 82 | 99.48 91 | 95.40 63 | 97.17 101 | 98.70 121 |
|
CNLPA | | | 93.64 96 | 92.74 98 | 96.36 95 | 98.96 61 | 90.01 135 | 99.19 53 | 95.89 221 | 86.22 188 | 89.40 155 | 98.85 60 | 80.66 161 | 99.84 45 | 88.57 142 | 96.92 102 | 99.24 82 |
|
xiu_mvs_v1_base_debu | | | 94.73 70 | 93.98 75 | 96.99 53 | 95.19 171 | 95.24 17 | 98.62 123 | 96.50 179 | 92.99 37 | 97.52 33 | 98.83 61 | 72.37 231 | 99.15 108 | 97.03 33 | 96.74 103 | 96.58 191 |
|
xiu_mvs_v1_base | | | 94.73 70 | 93.98 75 | 96.99 53 | 95.19 171 | 95.24 17 | 98.62 123 | 96.50 179 | 92.99 37 | 97.52 33 | 98.83 61 | 72.37 231 | 99.15 108 | 97.03 33 | 96.74 103 | 96.58 191 |
|
xiu_mvs_v1_base_debi | | | 94.73 70 | 93.98 75 | 96.99 53 | 95.19 171 | 95.24 17 | 98.62 123 | 96.50 179 | 92.99 37 | 97.52 33 | 98.83 61 | 72.37 231 | 99.15 108 | 97.03 33 | 96.74 103 | 96.58 191 |
|
MVS_Test | | | 93.67 95 | 92.67 100 | 96.69 77 | 96.72 133 | 92.66 71 | 97.22 215 | 96.03 205 | 87.69 162 | 95.12 77 | 94.03 188 | 81.55 154 | 98.28 141 | 89.17 138 | 96.46 106 | 99.14 87 |
|
EI-MVSNet-UG-set | | | 95.43 58 | 95.29 54 | 95.86 112 | 99.07 56 | 89.87 136 | 98.43 148 | 97.80 68 | 91.78 64 | 94.11 91 | 98.77 64 | 86.25 97 | 99.48 91 | 94.95 72 | 96.45 107 | 98.22 146 |
|
TSAR-MVS + GP. | | | 96.95 21 | 96.91 17 | 97.07 47 | 98.88 65 | 91.62 86 | 99.58 18 | 96.54 178 | 95.09 15 | 96.84 49 | 98.63 76 | 91.16 17 | 99.77 57 | 99.04 5 | 96.42 108 | 99.81 22 |
|
PVSNet_Blended_VisFu | | | 94.67 74 | 94.11 71 | 96.34 96 | 97.14 114 | 91.10 106 | 99.32 50 | 97.43 123 | 92.10 60 | 91.53 118 | 96.38 162 | 83.29 129 | 99.68 62 | 93.42 94 | 96.37 109 | 98.25 145 |
|
Vis-MVSNet (Re-imp) | | | 93.26 109 | 93.00 95 | 94.06 164 | 96.14 149 | 86.71 201 | 98.68 115 | 96.70 168 | 88.30 142 | 89.71 150 | 97.64 107 | 85.43 108 | 96.39 235 | 88.06 146 | 96.32 110 | 99.08 90 |
|
EPMVS | | | 92.59 126 | 91.59 126 | 95.59 119 | 97.22 111 | 90.03 134 | 91.78 308 | 98.04 48 | 90.42 87 | 91.66 114 | 90.65 255 | 86.49 93 | 97.46 185 | 81.78 208 | 96.31 111 | 99.28 79 |
|
PMMVS | | | 93.62 97 | 93.90 82 | 92.79 188 | 96.79 131 | 81.40 274 | 98.85 97 | 96.81 165 | 91.25 75 | 96.82 51 | 98.15 97 | 77.02 182 | 98.13 145 | 93.15 98 | 96.30 112 | 98.83 110 |
|
TESTMET0.1,1 | | | 93.82 89 | 93.26 89 | 95.49 125 | 95.21 170 | 90.25 125 | 99.15 62 | 97.54 107 | 89.18 115 | 91.79 113 | 94.87 179 | 89.13 46 | 97.63 176 | 86.21 161 | 96.29 113 | 98.60 124 |
|
test-LLR | | | 93.11 113 | 92.68 99 | 94.40 154 | 94.94 185 | 87.27 188 | 99.15 62 | 97.25 134 | 90.21 91 | 91.57 115 | 94.04 186 | 84.89 112 | 97.58 179 | 85.94 164 | 96.13 114 | 98.36 141 |
|
test-mter | | | 93.27 108 | 92.89 96 | 94.40 154 | 94.94 185 | 87.27 188 | 99.15 62 | 97.25 134 | 88.95 122 | 91.57 115 | 94.04 186 | 88.03 65 | 97.58 179 | 85.94 164 | 96.13 114 | 98.36 141 |
|
Effi-MVS+ | | | 93.87 88 | 93.15 91 | 96.02 105 | 95.79 155 | 90.76 116 | 96.70 232 | 95.78 224 | 86.98 176 | 95.71 66 | 97.17 130 | 79.58 163 | 98.01 152 | 94.57 78 | 96.09 116 | 99.31 75 |
|
mvs_anonymous | | | 92.50 127 | 91.65 124 | 95.06 138 | 96.60 135 | 89.64 141 | 97.06 220 | 96.44 183 | 86.64 182 | 84.14 196 | 93.93 192 | 82.49 144 | 96.17 254 | 91.47 111 | 96.08 117 | 99.35 71 |
|
IS-MVSNet | | | 93.00 114 | 92.51 103 | 94.49 151 | 96.14 149 | 87.36 185 | 98.31 160 | 95.70 230 | 88.58 131 | 90.17 141 | 97.50 111 | 83.02 138 | 97.22 197 | 87.06 153 | 96.07 118 | 98.90 105 |
|
PatchmatchNet | | | 92.05 141 | 91.04 134 | 95.06 138 | 96.17 147 | 89.04 150 | 91.26 312 | 97.26 133 | 89.56 106 | 90.64 132 | 90.56 261 | 88.35 59 | 97.11 201 | 79.53 225 | 96.07 118 | 99.03 92 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
F-COLMAP | | | 92.07 137 | 91.75 123 | 93.02 184 | 98.16 83 | 82.89 263 | 98.79 105 | 95.97 208 | 86.54 184 | 87.92 169 | 97.80 101 | 78.69 173 | 99.65 69 | 85.97 163 | 95.93 120 | 96.53 194 |
|
mvs-test1 | | | 91.57 145 | 92.20 110 | 89.70 249 | 95.15 175 | 74.34 310 | 99.51 25 | 95.40 253 | 91.92 61 | 91.02 126 | 97.25 123 | 74.27 207 | 98.08 149 | 89.45 130 | 95.83 121 | 96.67 184 |
|
ACMMP | | | 94.67 74 | 94.30 67 | 95.79 113 | 99.25 47 | 88.13 165 | 98.41 151 | 98.67 23 | 90.38 88 | 91.43 120 | 98.72 70 | 82.22 150 | 99.95 20 | 93.83 86 | 95.76 122 | 99.29 77 |
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 |
LCM-MVSNet-Re | | | 88.59 196 | 88.61 177 | 88.51 271 | 95.53 163 | 72.68 317 | 96.85 225 | 88.43 340 | 88.45 135 | 73.14 290 | 90.63 256 | 75.82 186 | 94.38 294 | 92.95 99 | 95.71 123 | 98.48 132 |
|
diffmvs | | | 92.07 137 | 90.77 149 | 95.97 108 | 96.41 140 | 91.32 99 | 96.46 239 | 95.98 206 | 81.73 265 | 94.33 88 | 93.36 206 | 78.72 172 | 98.20 142 | 84.28 178 | 95.66 124 | 98.41 134 |
|
PCF-MVS | | 89.78 5 | 91.26 150 | 89.63 160 | 96.16 102 | 95.44 165 | 91.58 89 | 95.29 276 | 96.10 203 | 85.07 204 | 82.75 215 | 97.45 113 | 78.28 175 | 99.78 56 | 80.60 221 | 95.65 125 | 97.12 172 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
DI_MVS_plusplus_test | | | 89.41 181 | 87.24 196 | 95.92 111 | 89.06 289 | 90.75 118 | 98.18 177 | 96.63 169 | 89.29 111 | 70.54 299 | 90.31 266 | 63.50 289 | 98.40 137 | 92.25 107 | 95.44 126 | 98.60 124 |
|
test_normal | | | 89.37 182 | 87.18 198 | 95.93 110 | 88.94 291 | 90.83 114 | 98.24 170 | 96.62 170 | 89.31 109 | 70.38 301 | 90.20 273 | 63.50 289 | 98.37 138 | 92.06 109 | 95.41 127 | 98.59 127 |
|
Fast-Effi-MVS+ | | | 91.72 144 | 90.79 148 | 94.49 151 | 95.89 153 | 87.40 182 | 99.54 23 | 95.70 230 | 85.01 206 | 89.28 156 | 95.68 169 | 77.75 178 | 97.57 183 | 83.22 190 | 95.06 128 | 98.51 130 |
|
Test4 | | | 85.71 242 | 82.59 258 | 95.07 137 | 84.45 319 | 89.84 138 | 97.20 216 | 95.73 227 | 89.19 113 | 64.59 324 | 87.58 295 | 40.59 338 | 96.77 213 | 88.95 141 | 95.01 129 | 98.60 124 |
|
EPNet_dtu | | | 92.28 129 | 92.15 112 | 92.70 191 | 97.29 109 | 84.84 243 | 98.64 121 | 97.82 63 | 92.91 40 | 93.02 104 | 97.02 137 | 85.48 107 | 95.70 270 | 72.25 296 | 94.89 130 | 97.55 166 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
UA-Net | | | 93.30 106 | 92.62 101 | 95.34 128 | 96.27 143 | 88.53 161 | 95.88 264 | 96.97 161 | 90.90 79 | 95.37 72 | 97.07 135 | 82.38 148 | 99.10 113 | 83.91 186 | 94.86 131 | 98.38 138 |
|
MVS-HIRNet | | | 79.01 292 | 75.13 299 | 90.66 229 | 93.82 210 | 81.69 272 | 85.16 327 | 93.75 288 | 54.54 339 | 74.17 286 | 59.15 343 | 57.46 306 | 96.58 216 | 63.74 315 | 94.38 132 | 93.72 204 |
|
Patchmatch-test1 | | | 90.10 170 | 88.61 177 | 94.57 150 | 94.95 184 | 88.83 152 | 96.26 246 | 97.21 139 | 90.06 100 | 90.03 143 | 90.68 251 | 66.61 274 | 95.83 267 | 77.31 243 | 94.36 133 | 99.05 91 |
|
OMC-MVS | | | 93.90 87 | 93.62 85 | 94.73 146 | 98.63 73 | 87.00 191 | 98.04 188 | 96.56 176 | 92.19 58 | 92.46 107 | 98.73 68 | 79.49 165 | 99.14 111 | 92.16 108 | 94.34 134 | 98.03 152 |
|
DP-MVS | | | 88.75 194 | 86.56 202 | 95.34 128 | 98.92 63 | 87.45 179 | 97.64 202 | 93.52 292 | 70.55 318 | 81.49 236 | 97.25 123 | 74.43 204 | 99.88 34 | 71.14 300 | 94.09 135 | 98.67 122 |
|
sss | | | 94.85 68 | 93.94 80 | 97.58 29 | 96.43 139 | 94.09 47 | 98.93 85 | 99.16 14 | 89.50 107 | 95.27 73 | 97.85 99 | 81.50 155 | 99.65 69 | 92.79 103 | 94.02 136 | 98.99 95 |
|
EPP-MVSNet | | | 93.75 91 | 93.67 84 | 94.01 166 | 95.86 154 | 85.70 233 | 98.67 117 | 97.66 83 | 84.46 213 | 91.36 122 | 97.18 128 | 91.16 17 | 97.79 163 | 92.93 100 | 93.75 137 | 98.53 129 |
|
DWT-MVSNet_test | | | 94.36 80 | 93.95 79 | 95.62 117 | 96.99 119 | 89.47 145 | 96.62 235 | 97.38 128 | 90.96 77 | 93.07 103 | 97.27 122 | 93.73 8 | 98.09 146 | 85.86 167 | 93.65 138 | 99.29 77 |
|
CVMVSNet | | | 90.30 165 | 90.91 143 | 88.46 272 | 94.32 194 | 73.58 314 | 97.61 203 | 97.59 96 | 90.16 96 | 88.43 161 | 97.10 133 | 76.83 183 | 92.86 304 | 82.64 197 | 93.54 139 | 98.93 103 |
|
JIA-IIPM | | | 85.97 234 | 84.85 232 | 89.33 257 | 93.23 223 | 73.68 313 | 85.05 329 | 97.13 146 | 69.62 323 | 91.56 117 | 68.03 339 | 88.03 65 | 96.96 206 | 77.89 241 | 93.12 140 | 97.34 169 |
|
Effi-MVS+-dtu | | | 89.97 174 | 90.68 151 | 87.81 285 | 95.15 175 | 71.98 319 | 97.87 195 | 95.40 253 | 91.92 61 | 87.57 171 | 91.44 233 | 74.27 207 | 96.84 210 | 89.45 130 | 93.10 141 | 94.60 201 |
|
HY-MVS | | 88.56 7 | 95.29 62 | 94.23 69 | 98.48 8 | 97.72 90 | 96.41 8 | 94.03 288 | 98.74 19 | 92.42 53 | 95.65 68 | 94.76 181 | 86.52 91 | 99.49 86 | 95.29 66 | 92.97 142 | 99.53 61 |
|
LFMVS | | | 92.23 131 | 90.84 145 | 96.42 92 | 98.24 79 | 91.08 108 | 98.24 170 | 96.22 197 | 83.39 241 | 94.74 82 | 98.31 91 | 61.12 299 | 98.85 118 | 94.45 80 | 92.82 143 | 99.32 74 |
|
HyFIR lowres test | | | 93.68 94 | 93.29 88 | 94.87 142 | 97.57 99 | 88.04 167 | 98.18 177 | 98.47 24 | 87.57 164 | 91.24 124 | 95.05 177 | 85.49 105 | 97.46 185 | 93.22 96 | 92.82 143 | 99.10 89 |
|
CDS-MVSNet | | | 93.47 98 | 93.04 94 | 94.76 144 | 94.75 189 | 89.45 146 | 98.82 100 | 97.03 157 | 87.91 154 | 90.97 127 | 96.48 157 | 89.06 47 | 96.36 237 | 89.50 129 | 92.81 145 | 98.49 131 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
WTY-MVS | | | 95.97 48 | 95.11 58 | 98.54 7 | 97.62 93 | 96.65 4 | 99.44 31 | 98.74 19 | 92.25 57 | 95.21 74 | 98.46 89 | 86.56 90 | 99.46 93 | 95.00 70 | 92.69 146 | 99.50 64 |
|
MSDG | | | 88.29 200 | 86.37 204 | 94.04 165 | 96.90 121 | 86.15 219 | 96.52 237 | 94.36 280 | 77.89 300 | 79.22 257 | 96.95 141 | 69.72 249 | 99.59 77 | 73.20 289 | 92.58 147 | 96.37 195 |
|
TR-MVS | | | 90.77 160 | 89.44 162 | 94.76 144 | 96.31 142 | 88.02 168 | 97.92 191 | 95.96 210 | 85.52 194 | 88.22 162 | 97.23 125 | 66.80 272 | 98.09 146 | 84.58 176 | 92.38 148 | 98.17 149 |
|
MDTV_nov1_ep13 | | | | 90.47 155 | | 96.14 149 | 88.55 159 | 91.34 311 | 97.51 110 | 89.58 104 | 92.24 110 | 90.50 263 | 86.99 85 | 97.61 178 | 77.64 242 | 92.34 149 | |
|
TAMVS | | | 92.62 125 | 92.09 115 | 94.20 160 | 94.10 197 | 87.68 173 | 98.41 151 | 96.97 161 | 87.53 165 | 89.74 148 | 96.04 166 | 84.77 115 | 96.49 226 | 88.97 140 | 92.31 150 | 98.42 133 |
|
ADS-MVSNet2 | | | 87.62 205 | 86.88 200 | 89.86 245 | 96.21 145 | 79.14 290 | 87.15 323 | 92.99 298 | 83.01 247 | 89.91 145 | 87.27 299 | 78.87 169 | 92.80 308 | 74.20 277 | 92.27 151 | 97.64 161 |
|
ADS-MVSNet | | | 88.99 185 | 87.30 194 | 94.07 163 | 96.21 145 | 87.56 176 | 87.15 323 | 96.78 167 | 83.01 247 | 89.91 145 | 87.27 299 | 78.87 169 | 97.01 205 | 74.20 277 | 92.27 151 | 97.64 161 |
|
PatchFormer-LS_test | | | 94.08 84 | 93.60 86 | 95.53 124 | 96.92 120 | 89.57 143 | 96.51 238 | 97.34 132 | 91.29 74 | 92.22 111 | 97.18 128 | 91.66 15 | 98.02 151 | 87.05 154 | 92.21 153 | 99.00 93 |
|
cascas | | | 90.93 158 | 89.33 165 | 95.76 115 | 95.69 159 | 93.03 65 | 98.99 82 | 96.59 172 | 80.49 275 | 86.79 182 | 94.45 184 | 65.23 282 | 98.60 136 | 93.52 91 | 92.18 154 | 95.66 198 |
|
CR-MVSNet | | | 88.83 190 | 87.38 193 | 93.16 181 | 93.47 216 | 86.24 214 | 84.97 330 | 94.20 283 | 88.92 125 | 90.76 130 | 86.88 303 | 84.43 116 | 94.82 290 | 70.64 301 | 92.17 155 | 98.41 134 |
|
RPMNet | | | 84.62 249 | 81.78 262 | 93.16 181 | 93.47 216 | 86.24 214 | 84.97 330 | 96.28 194 | 64.85 333 | 90.76 130 | 78.80 332 | 80.95 159 | 94.82 290 | 53.76 331 | 92.17 155 | 98.41 134 |
|
DSMNet-mixed | | | 81.60 275 | 81.43 266 | 82.10 311 | 84.36 320 | 60.79 332 | 93.63 293 | 86.74 342 | 79.00 283 | 79.32 256 | 87.15 301 | 63.87 287 | 89.78 329 | 66.89 309 | 91.92 157 | 95.73 197 |
|
VNet | | | 95.08 64 | 94.26 68 | 97.55 32 | 98.07 84 | 93.88 49 | 98.68 115 | 98.73 21 | 90.33 90 | 97.16 39 | 97.43 114 | 79.19 167 | 99.53 80 | 96.91 39 | 91.85 158 | 99.24 82 |
|
tpmrst | | | 92.78 116 | 92.16 111 | 94.65 148 | 96.27 143 | 87.45 179 | 91.83 307 | 97.10 150 | 89.10 118 | 94.68 83 | 90.69 249 | 88.22 60 | 97.73 172 | 89.78 127 | 91.80 159 | 98.77 117 |
|
alignmvs | | | 95.77 55 | 95.00 60 | 98.06 18 | 97.35 107 | 95.68 13 | 99.71 9 | 97.50 113 | 91.50 68 | 96.16 57 | 98.61 77 | 86.28 96 | 99.00 116 | 96.19 50 | 91.74 160 | 99.51 63 |
|
CostFormer | | | 92.89 115 | 92.48 104 | 94.12 162 | 94.99 183 | 85.89 227 | 92.89 298 | 97.00 160 | 86.98 176 | 95.00 79 | 90.78 244 | 90.05 39 | 97.51 184 | 92.92 101 | 91.73 161 | 98.96 98 |
|
Fast-Effi-MVS+-dtu | | | 88.84 189 | 88.59 180 | 89.58 252 | 93.44 219 | 78.18 299 | 98.65 119 | 94.62 274 | 88.46 134 | 84.12 197 | 95.37 175 | 68.91 255 | 96.52 224 | 82.06 202 | 91.70 162 | 94.06 202 |
|
PatchT | | | 85.44 243 | 83.19 247 | 92.22 197 | 93.13 225 | 83.00 259 | 83.80 336 | 96.37 185 | 70.62 317 | 90.55 133 | 79.63 330 | 84.81 114 | 94.87 288 | 58.18 328 | 91.59 163 | 98.79 113 |
|
tpm2 | | | 91.77 143 | 91.09 133 | 93.82 172 | 94.83 187 | 85.56 236 | 92.51 301 | 97.16 143 | 84.00 220 | 93.83 96 | 90.66 254 | 87.54 70 | 97.17 199 | 87.73 149 | 91.55 164 | 98.72 119 |
|
tpm cat1 | | | 88.89 187 | 87.27 195 | 93.76 173 | 95.79 155 | 85.32 237 | 90.76 316 | 97.09 151 | 76.14 304 | 85.72 185 | 88.59 289 | 82.92 139 | 98.04 150 | 76.96 247 | 91.43 165 | 97.90 159 |
|
canonicalmvs | | | 95.02 65 | 93.96 78 | 98.20 12 | 97.53 100 | 95.92 11 | 98.71 108 | 96.19 199 | 91.78 64 | 95.86 64 | 98.49 85 | 79.53 164 | 99.03 115 | 96.12 51 | 91.42 166 | 99.66 50 |
|
Patchmatch-test | | | 86.25 231 | 84.06 243 | 92.82 187 | 94.42 192 | 82.88 264 | 82.88 338 | 94.23 282 | 71.58 314 | 79.39 255 | 90.62 257 | 89.00 49 | 96.42 232 | 63.03 316 | 91.37 167 | 99.16 86 |
|
dp | | | 90.16 169 | 88.83 173 | 94.14 161 | 96.38 141 | 86.42 207 | 91.57 309 | 97.06 154 | 84.76 210 | 88.81 158 | 90.19 274 | 84.29 118 | 97.43 187 | 75.05 269 | 91.35 168 | 98.56 128 |
|
VDDNet | | | 90.08 172 | 88.54 182 | 94.69 147 | 94.41 193 | 87.68 173 | 98.21 175 | 96.40 184 | 76.21 303 | 93.33 100 | 97.75 104 | 54.93 315 | 98.77 121 | 94.71 76 | 90.96 169 | 97.61 165 |
|
thres200 | | | 93.69 92 | 92.59 102 | 96.97 57 | 97.76 88 | 94.74 31 | 99.35 46 | 99.36 2 | 89.23 112 | 91.21 125 | 96.97 140 | 83.42 126 | 98.77 121 | 85.08 171 | 90.96 169 | 97.39 168 |
|
conf200view11 | | | 93.32 105 | 92.15 112 | 96.84 66 | 97.62 93 | 94.84 24 | 99.06 73 | 99.36 2 | 87.96 150 | 90.47 135 | 96.78 144 | 83.29 129 | 98.75 123 | 84.11 182 | 90.69 171 | 96.94 179 |
|
thres100view900 | | | 93.34 104 | 92.15 112 | 96.90 61 | 97.62 93 | 94.84 24 | 99.06 73 | 99.36 2 | 87.96 150 | 90.47 135 | 96.78 144 | 83.29 129 | 98.75 123 | 84.11 182 | 90.69 171 | 97.12 172 |
|
tfpn200view9 | | | 93.43 100 | 92.27 108 | 96.90 61 | 97.68 91 | 94.84 24 | 99.18 55 | 99.36 2 | 88.45 135 | 90.79 128 | 96.90 142 | 83.31 127 | 98.75 123 | 84.11 182 | 90.69 171 | 97.12 172 |
|
thres400 | | | 93.39 102 | 92.27 108 | 96.73 73 | 97.68 91 | 94.84 24 | 99.18 55 | 99.36 2 | 88.45 135 | 90.79 128 | 96.90 142 | 83.31 127 | 98.75 123 | 84.11 182 | 90.69 171 | 96.61 185 |
|
VDD-MVS | | | 91.24 153 | 90.18 156 | 94.45 153 | 97.08 116 | 85.84 231 | 98.40 154 | 96.10 203 | 86.99 174 | 93.36 99 | 98.16 96 | 54.27 317 | 99.20 105 | 96.59 42 | 90.63 175 | 98.31 144 |
|
tpmp4_e23 | | | 91.05 155 | 90.07 157 | 93.97 168 | 95.77 157 | 85.30 238 | 92.64 299 | 97.09 151 | 84.42 215 | 91.53 118 | 90.31 266 | 87.38 73 | 97.82 161 | 80.86 216 | 90.62 176 | 98.79 113 |
|
tfpn_ndepth | | | 93.28 107 | 92.32 105 | 96.16 102 | 97.74 89 | 92.86 69 | 99.01 79 | 98.19 39 | 85.50 196 | 89.84 147 | 97.12 132 | 93.57 9 | 97.58 179 | 79.39 228 | 90.50 177 | 98.04 151 |
|
tfpn111 | | | 93.20 110 | 92.00 116 | 96.83 67 | 97.62 93 | 94.84 24 | 99.06 73 | 99.36 2 | 87.96 150 | 90.47 135 | 96.78 144 | 83.29 129 | 98.71 128 | 82.93 194 | 90.47 178 | 96.94 179 |
|
thres600view7 | | | 93.18 111 | 92.00 116 | 96.75 71 | 97.62 93 | 94.92 21 | 99.07 71 | 99.36 2 | 87.96 150 | 90.47 135 | 96.78 144 | 83.29 129 | 98.71 128 | 82.93 194 | 90.47 178 | 96.61 185 |
|
tfpn1000 | | | 92.67 123 | 91.64 125 | 95.78 114 | 97.61 98 | 92.34 78 | 98.69 112 | 98.18 40 | 84.15 218 | 88.80 159 | 96.99 139 | 93.56 10 | 97.21 198 | 76.56 253 | 90.19 180 | 97.77 160 |
|
view600 | | | 92.78 116 | 91.50 128 | 96.63 80 | 97.51 101 | 94.66 34 | 98.91 88 | 99.36 2 | 87.31 168 | 89.64 151 | 96.59 151 | 83.26 134 | 98.63 132 | 80.76 217 | 90.15 181 | 96.61 185 |
|
view800 | | | 92.78 116 | 91.50 128 | 96.63 80 | 97.51 101 | 94.66 34 | 98.91 88 | 99.36 2 | 87.31 168 | 89.64 151 | 96.59 151 | 83.26 134 | 98.63 132 | 80.76 217 | 90.15 181 | 96.61 185 |
|
conf0.05thres1000 | | | 92.78 116 | 91.50 128 | 96.63 80 | 97.51 101 | 94.66 34 | 98.91 88 | 99.36 2 | 87.31 168 | 89.64 151 | 96.59 151 | 83.26 134 | 98.63 132 | 80.76 217 | 90.15 181 | 96.61 185 |
|
tfpn | | | 92.78 116 | 91.50 128 | 96.63 80 | 97.51 101 | 94.66 34 | 98.91 88 | 99.36 2 | 87.31 168 | 89.64 151 | 96.59 151 | 83.26 134 | 98.63 132 | 80.76 217 | 90.15 181 | 96.61 185 |
|
GA-MVS | | | 90.10 170 | 88.69 175 | 94.33 156 | 92.44 230 | 87.97 169 | 99.08 70 | 96.26 195 | 89.65 102 | 86.92 179 | 93.11 213 | 68.09 261 | 96.96 206 | 82.54 198 | 90.15 181 | 98.05 150 |
|
tpmvs | | | 89.16 183 | 87.76 187 | 93.35 177 | 97.19 112 | 84.75 245 | 90.58 318 | 97.36 130 | 81.99 261 | 84.56 192 | 89.31 284 | 83.98 120 | 98.17 143 | 74.85 272 | 90.00 186 | 97.12 172 |
|
1112_ss | | | 92.71 121 | 91.55 127 | 96.20 98 | 95.56 162 | 91.12 104 | 98.48 142 | 94.69 272 | 88.29 143 | 86.89 180 | 98.50 83 | 87.02 83 | 98.66 130 | 84.75 174 | 89.77 187 | 98.81 111 |
|
conf0.01 | | | 92.06 139 | 90.99 135 | 95.24 132 | 96.84 123 | 91.39 92 | 98.31 160 | 98.20 32 | 83.57 234 | 88.08 163 | 97.34 116 | 91.05 20 | 97.40 188 | 75.80 259 | 89.74 188 | 96.94 179 |
|
conf0.002 | | | 92.06 139 | 90.99 135 | 95.24 132 | 96.84 123 | 91.39 92 | 98.31 160 | 98.20 32 | 83.57 234 | 88.08 163 | 97.34 116 | 91.05 20 | 97.40 188 | 75.80 259 | 89.74 188 | 96.94 179 |
|
thresconf0.02 | | | 92.14 132 | 90.99 135 | 95.58 120 | 96.84 123 | 91.39 92 | 98.31 160 | 98.20 32 | 83.57 234 | 88.08 163 | 97.34 116 | 91.05 20 | 97.40 188 | 75.80 259 | 89.74 188 | 97.94 155 |
|
tfpn_n400 | | | 92.14 132 | 90.99 135 | 95.58 120 | 96.84 123 | 91.39 92 | 98.31 160 | 98.20 32 | 83.57 234 | 88.08 163 | 97.34 116 | 91.05 20 | 97.40 188 | 75.80 259 | 89.74 188 | 97.94 155 |
|
tfpnconf | | | 92.14 132 | 90.99 135 | 95.58 120 | 96.84 123 | 91.39 92 | 98.31 160 | 98.20 32 | 83.57 234 | 88.08 163 | 97.34 116 | 91.05 20 | 97.40 188 | 75.80 259 | 89.74 188 | 97.94 155 |
|
tfpnview11 | | | 92.14 132 | 90.99 135 | 95.58 120 | 96.84 123 | 91.39 92 | 98.31 160 | 98.20 32 | 83.57 234 | 88.08 163 | 97.34 116 | 91.05 20 | 97.40 188 | 75.80 259 | 89.74 188 | 97.94 155 |
|
Test_1112_low_res | | | 92.27 130 | 90.97 141 | 96.18 99 | 95.53 163 | 91.10 106 | 98.47 144 | 94.66 273 | 88.28 144 | 86.83 181 | 93.50 205 | 87.00 84 | 98.65 131 | 84.69 175 | 89.74 188 | 98.80 112 |
|
XVG-OURS-SEG-HR | | | 90.95 157 | 90.66 152 | 91.83 203 | 95.18 174 | 81.14 280 | 95.92 261 | 95.92 215 | 88.40 139 | 90.33 140 | 97.85 99 | 70.66 245 | 99.38 97 | 92.83 102 | 88.83 195 | 94.98 199 |
|
COLMAP_ROB | | 82.69 18 | 84.54 252 | 82.82 252 | 89.70 249 | 96.72 133 | 78.85 293 | 95.89 262 | 92.83 307 | 71.55 315 | 77.54 272 | 95.89 167 | 59.40 303 | 99.14 111 | 67.26 307 | 88.26 196 | 91.11 251 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
MIMVSNet | | | 84.48 253 | 81.83 261 | 92.42 195 | 91.73 242 | 87.36 185 | 85.52 326 | 94.42 278 | 81.40 268 | 81.91 232 | 87.58 295 | 51.92 322 | 92.81 307 | 73.84 282 | 88.15 197 | 97.08 176 |
|
ab-mvs | | | 91.05 155 | 89.17 166 | 96.69 77 | 95.96 152 | 91.72 84 | 92.62 300 | 97.23 137 | 85.61 193 | 89.74 148 | 93.89 194 | 68.55 258 | 99.42 94 | 91.09 113 | 87.84 198 | 98.92 104 |
|
XVG-OURS | | | 90.83 159 | 90.49 154 | 91.86 202 | 95.23 169 | 81.25 278 | 95.79 269 | 95.92 215 | 88.96 121 | 90.02 144 | 98.03 98 | 71.60 239 | 99.35 101 | 91.06 114 | 87.78 199 | 94.98 199 |
|
AllTest | | | 84.97 246 | 83.12 248 | 90.52 232 | 96.82 129 | 78.84 294 | 95.89 262 | 92.17 314 | 77.96 297 | 75.94 277 | 95.50 171 | 55.48 312 | 99.18 106 | 71.15 298 | 87.14 200 | 93.55 205 |
|
TestCases | | | | | 90.52 232 | 96.82 129 | 78.84 294 | | 92.17 314 | 77.96 297 | 75.94 277 | 95.50 171 | 55.48 312 | 99.18 106 | 71.15 298 | 87.14 200 | 93.55 205 |
|
test2356 | | | 80.96 282 | 81.77 263 | 78.52 319 | 81.02 327 | 62.33 330 | 98.22 172 | 94.49 275 | 79.38 282 | 74.56 283 | 90.34 265 | 70.65 246 | 85.10 338 | 60.83 320 | 86.42 202 | 88.14 299 |
|
HQP3-MVS | | | | | | | | | 96.37 185 | | | | | | | 86.29 203 | |
|
HQP-MVS | | | 91.50 146 | 91.23 132 | 92.29 196 | 93.95 201 | 86.39 209 | 99.16 58 | 96.37 185 | 93.92 22 | 87.57 171 | 96.67 149 | 73.34 220 | 97.77 165 | 93.82 87 | 86.29 203 | 92.72 208 |
|
plane_prior | | | | | | | 86.07 222 | 99.14 65 | | 93.81 28 | | | | | | 86.26 205 | |
|
HQP_MVS | | | 91.26 150 | 90.95 142 | 92.16 198 | 93.84 208 | 86.07 222 | 99.02 77 | 96.30 190 | 93.38 35 | 86.99 177 | 96.52 155 | 72.92 225 | 97.75 170 | 93.46 92 | 86.17 206 | 92.67 210 |
|
plane_prior5 | | | | | | | | | 96.30 190 | | | | | 97.75 170 | 93.46 92 | 86.17 206 | 92.67 210 |
|
OPM-MVS | | | 89.76 176 | 89.15 167 | 91.57 213 | 90.53 255 | 85.58 235 | 98.11 182 | 95.93 214 | 92.88 42 | 86.05 183 | 96.47 158 | 67.06 271 | 97.87 158 | 89.29 137 | 86.08 208 | 91.26 248 |
|
RPSCF | | | 85.33 244 | 85.55 221 | 84.67 305 | 94.63 191 | 62.28 331 | 93.73 291 | 93.76 287 | 74.38 310 | 85.23 189 | 97.06 136 | 64.09 285 | 98.31 139 | 80.98 212 | 86.08 208 | 93.41 207 |
|
CLD-MVS | | | 91.06 154 | 90.71 150 | 92.10 199 | 94.05 200 | 86.10 220 | 99.55 22 | 96.29 193 | 94.16 20 | 84.70 191 | 97.17 130 | 69.62 250 | 97.82 161 | 94.74 75 | 86.08 208 | 92.39 213 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
test0.0.03 1 | | | 88.96 186 | 88.61 177 | 90.03 243 | 91.09 249 | 84.43 247 | 98.97 83 | 97.02 158 | 90.21 91 | 80.29 244 | 96.31 163 | 84.89 112 | 91.93 324 | 72.98 292 | 85.70 211 | 93.73 203 |
|
LPG-MVS_test | | | 88.86 188 | 88.47 183 | 90.06 241 | 93.35 221 | 80.95 282 | 98.22 172 | 95.94 212 | 87.73 160 | 83.17 205 | 96.11 164 | 66.28 276 | 97.77 165 | 90.19 123 | 85.19 212 | 91.46 242 |
|
LGP-MVS_train | | | | | 90.06 241 | 93.35 221 | 80.95 282 | | 95.94 212 | 87.73 160 | 83.17 205 | 96.11 164 | 66.28 276 | 97.77 165 | 90.19 123 | 85.19 212 | 91.46 242 |
|
testpf | | | 80.59 285 | 80.13 272 | 81.97 313 | 94.25 196 | 71.65 320 | 60.37 348 | 95.46 249 | 70.99 316 | 76.97 273 | 87.74 293 | 73.58 217 | 91.67 325 | 76.86 249 | 84.97 214 | 82.60 336 |
|
ACMM | | 86.95 13 | 88.77 193 | 88.22 186 | 90.43 234 | 93.61 213 | 81.34 276 | 98.50 139 | 95.92 215 | 87.88 155 | 83.85 199 | 95.20 176 | 67.20 269 | 97.89 156 | 86.90 158 | 84.90 215 | 92.06 228 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CMPMVS | | 58.40 21 | 80.48 286 | 80.11 274 | 81.59 315 | 85.10 317 | 59.56 334 | 94.14 287 | 95.95 211 | 68.54 326 | 60.71 328 | 93.31 207 | 55.35 314 | 97.87 158 | 83.06 193 | 84.85 216 | 87.33 308 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
ACMP | | 87.39 10 | 88.71 195 | 88.24 185 | 90.12 240 | 93.91 206 | 81.06 281 | 98.50 139 | 95.67 232 | 89.43 108 | 80.37 243 | 95.55 170 | 65.67 279 | 97.83 160 | 90.55 121 | 84.51 217 | 91.47 241 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
test_djsdf | | | 88.26 201 | 87.73 188 | 89.84 246 | 88.05 301 | 82.21 268 | 97.77 198 | 96.17 200 | 86.84 179 | 82.41 223 | 91.95 228 | 72.07 234 | 95.99 260 | 89.83 125 | 84.50 218 | 91.32 246 |
|
jajsoiax | | | 87.35 207 | 86.51 203 | 89.87 244 | 87.75 306 | 81.74 271 | 97.03 221 | 95.98 206 | 88.47 132 | 80.15 246 | 93.80 196 | 61.47 296 | 96.36 237 | 89.44 132 | 84.47 219 | 91.50 240 |
|
mvs_tets | | | 87.09 216 | 86.22 206 | 89.71 248 | 87.87 302 | 81.39 275 | 96.73 231 | 95.90 219 | 88.19 146 | 79.99 247 | 93.61 201 | 59.96 302 | 96.31 247 | 89.40 133 | 84.34 220 | 91.43 244 |
|
anonymousdsp | | | 86.69 222 | 85.75 217 | 89.53 253 | 86.46 315 | 82.94 260 | 96.39 241 | 95.71 229 | 83.97 221 | 79.63 252 | 90.70 247 | 68.85 256 | 95.94 263 | 86.01 162 | 84.02 221 | 89.72 290 |
|
XVG-ACMP-BASELINE | | | 85.86 236 | 84.95 230 | 88.57 269 | 89.90 267 | 77.12 304 | 94.30 284 | 95.60 241 | 87.40 167 | 82.12 227 | 92.99 216 | 53.42 320 | 97.66 174 | 85.02 172 | 83.83 222 | 90.92 261 |
|
ACMMP++ | | | | | | | | | | | | | | | | 83.83 222 | |
|
pcd1.5k->3k | | | 35.91 328 | 37.64 328 | 30.74 341 | 89.49 284 | 0.00 360 | 0.00 351 | 96.36 188 | 0.00 355 | 0.00 356 | 0.00 357 | 69.17 254 | 0.00 358 | 0.00 355 | 83.71 224 | 92.21 222 |
|
EG-PatchMatch MVS | | | 79.92 288 | 77.59 288 | 86.90 292 | 87.06 313 | 77.90 303 | 96.20 254 | 94.06 285 | 74.61 308 | 66.53 322 | 88.76 288 | 40.40 339 | 96.20 253 | 67.02 308 | 83.66 225 | 86.61 313 |
|
PVSNet_BlendedMVS | | | 93.36 103 | 93.20 90 | 93.84 171 | 98.77 69 | 91.61 87 | 99.47 27 | 98.04 48 | 91.44 69 | 94.21 89 | 92.63 220 | 83.50 123 | 99.87 37 | 97.41 29 | 83.37 226 | 90.05 284 |
|
PS-MVSNAJss | | | 89.54 179 | 89.05 168 | 91.00 223 | 88.77 292 | 84.36 248 | 97.39 206 | 95.97 208 | 88.47 132 | 81.88 233 | 93.80 196 | 82.48 145 | 96.50 225 | 89.34 134 | 83.34 227 | 92.15 224 |
|
testus | | | 77.11 301 | 76.95 294 | 77.58 320 | 80.02 330 | 58.93 336 | 97.78 196 | 90.48 330 | 79.68 280 | 72.84 294 | 90.61 259 | 37.72 341 | 86.57 337 | 60.28 324 | 83.18 228 | 87.23 310 |
|
EI-MVSNet | | | 89.87 175 | 89.38 164 | 91.36 218 | 94.32 194 | 85.87 228 | 97.61 203 | 96.59 172 | 85.10 202 | 85.51 187 | 97.10 133 | 81.30 158 | 96.56 218 | 83.85 188 | 83.03 229 | 91.64 235 |
|
MVSTER | | | 92.71 121 | 92.32 105 | 93.86 170 | 97.29 109 | 92.95 67 | 99.01 79 | 96.59 172 | 90.09 97 | 85.51 187 | 94.00 190 | 94.61 5 | 96.56 218 | 90.77 120 | 83.03 229 | 92.08 227 |
|
FIs | | | 90.70 162 | 89.87 159 | 93.18 180 | 92.29 231 | 91.12 104 | 98.17 180 | 98.25 29 | 89.11 117 | 83.44 201 | 94.82 180 | 82.26 149 | 96.17 254 | 87.76 148 | 82.76 231 | 92.25 218 |
|
tpm | | | 89.67 177 | 88.95 170 | 91.82 204 | 92.54 229 | 81.43 273 | 92.95 297 | 95.92 215 | 87.81 156 | 90.50 134 | 89.44 281 | 84.99 110 | 95.65 271 | 83.67 189 | 82.71 232 | 98.38 138 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 82.64 233 | |
|
FC-MVSNet-test | | | 90.22 167 | 89.40 163 | 92.67 193 | 91.78 241 | 89.86 137 | 97.89 192 | 98.22 31 | 88.81 127 | 82.96 210 | 94.66 182 | 81.90 152 | 95.96 262 | 85.89 166 | 82.52 234 | 92.20 223 |
|
ITE_SJBPF | | | | | 87.93 283 | 92.26 232 | 76.44 305 | | 93.47 293 | 87.67 163 | 79.95 248 | 95.49 173 | 56.50 309 | 97.38 194 | 75.24 268 | 82.33 235 | 89.98 286 |
|
OpenMVS_ROB | | 73.86 20 | 77.99 298 | 75.06 300 | 86.77 293 | 83.81 323 | 77.94 302 | 96.38 242 | 91.53 324 | 67.54 329 | 68.38 306 | 87.13 302 | 43.94 332 | 96.08 258 | 55.03 330 | 81.83 236 | 86.29 319 |
|
LTVRE_ROB | | 81.71 19 | 84.59 251 | 82.72 256 | 90.18 238 | 92.89 227 | 83.18 258 | 93.15 296 | 94.74 269 | 78.99 284 | 75.14 282 | 92.69 218 | 65.64 280 | 97.63 176 | 69.46 302 | 81.82 237 | 89.74 289 |
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 |
USDC | | | 84.74 247 | 82.93 249 | 90.16 239 | 91.73 242 | 83.54 255 | 95.00 278 | 93.30 294 | 88.77 128 | 73.19 289 | 93.30 208 | 53.62 319 | 97.65 175 | 75.88 258 | 81.54 238 | 89.30 293 |
|
ACMH | | 83.09 17 | 84.60 250 | 82.61 257 | 90.57 230 | 93.18 224 | 82.94 260 | 96.27 245 | 94.92 267 | 81.01 271 | 72.61 296 | 93.61 201 | 56.54 308 | 97.79 163 | 74.31 275 | 81.07 239 | 90.99 259 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
GBi-Net | | | 86.67 223 | 84.96 228 | 91.80 205 | 95.11 178 | 88.81 153 | 96.77 227 | 95.25 260 | 82.94 249 | 82.12 227 | 90.25 268 | 62.89 291 | 94.97 285 | 79.04 230 | 80.24 240 | 91.62 237 |
|
test1 | | | 86.67 223 | 84.96 228 | 91.80 205 | 95.11 178 | 88.81 153 | 96.77 227 | 95.25 260 | 82.94 249 | 82.12 227 | 90.25 268 | 62.89 291 | 94.97 285 | 79.04 230 | 80.24 240 | 91.62 237 |
|
FMVSNet3 | | | 88.81 192 | 87.08 199 | 93.99 167 | 96.52 137 | 94.59 38 | 98.08 185 | 96.20 198 | 85.85 190 | 82.12 227 | 91.60 232 | 74.05 212 | 95.40 278 | 79.04 230 | 80.24 240 | 91.99 230 |
|
testgi | | | 82.29 266 | 81.00 270 | 86.17 296 | 87.24 311 | 74.84 309 | 97.39 206 | 91.62 322 | 88.63 129 | 75.85 279 | 95.42 174 | 46.07 331 | 91.55 326 | 66.87 310 | 79.94 243 | 92.12 225 |
|
testing_2 | | | 80.92 283 | 77.24 291 | 91.98 201 | 78.88 333 | 87.83 170 | 93.96 289 | 95.72 228 | 84.27 217 | 56.20 334 | 80.42 325 | 38.64 340 | 96.40 234 | 87.20 152 | 79.85 244 | 91.72 233 |
|
test_0402 | | | 78.81 294 | 76.33 296 | 86.26 295 | 91.18 248 | 78.44 298 | 95.88 264 | 91.34 325 | 68.55 325 | 70.51 300 | 89.91 276 | 52.65 321 | 94.99 284 | 47.14 336 | 79.78 245 | 85.34 329 |
|
FMVSNet2 | | | 86.90 219 | 84.79 234 | 93.24 179 | 95.11 178 | 92.54 76 | 97.67 201 | 95.86 223 | 82.94 249 | 80.55 241 | 91.17 236 | 62.89 291 | 95.29 280 | 77.23 244 | 79.71 246 | 91.90 231 |
|
pmmvs4 | | | 87.58 206 | 86.17 207 | 91.80 205 | 89.58 281 | 88.92 151 | 97.25 212 | 95.28 259 | 82.54 255 | 80.49 242 | 93.17 212 | 75.62 188 | 96.05 259 | 82.75 196 | 78.90 247 | 90.42 276 |
|
ACMH+ | | 83.78 15 | 84.21 255 | 82.56 259 | 89.15 260 | 93.73 212 | 79.16 289 | 96.43 240 | 94.28 281 | 81.09 270 | 74.00 287 | 94.03 188 | 54.58 316 | 97.67 173 | 76.10 256 | 78.81 248 | 90.63 273 |
|
XXY-MVS | | | 87.75 202 | 86.02 208 | 92.95 186 | 90.46 256 | 89.70 140 | 97.71 200 | 95.90 219 | 84.02 219 | 80.95 239 | 94.05 185 | 67.51 267 | 97.10 203 | 85.16 170 | 78.41 249 | 92.04 229 |
|
pmmvs5 | | | 85.87 235 | 84.40 241 | 90.30 237 | 88.53 296 | 84.23 249 | 98.60 127 | 93.71 289 | 81.53 267 | 80.29 244 | 92.02 224 | 64.51 284 | 95.52 274 | 82.04 203 | 78.34 250 | 91.15 250 |
|
LF4IMVS | | | 81.94 270 | 81.17 269 | 84.25 306 | 87.23 312 | 68.87 327 | 93.35 295 | 91.93 319 | 83.35 242 | 75.40 281 | 93.00 215 | 49.25 328 | 96.65 215 | 78.88 233 | 78.11 251 | 87.22 311 |
|
TinyColmap | | | 80.42 287 | 77.94 287 | 87.85 284 | 92.09 235 | 78.58 296 | 93.74 290 | 89.94 334 | 74.99 306 | 69.77 302 | 91.78 229 | 46.09 330 | 97.58 179 | 65.17 314 | 77.89 252 | 87.38 307 |
|
FMVSNet1 | | | 83.94 261 | 81.32 268 | 91.80 205 | 91.94 238 | 88.81 153 | 96.77 227 | 95.25 260 | 77.98 295 | 78.25 268 | 90.25 268 | 50.37 326 | 94.97 285 | 73.27 288 | 77.81 253 | 91.62 237 |
|
OurMVSNet-221017-0 | | | 84.13 260 | 83.59 246 | 85.77 299 | 87.81 303 | 70.24 323 | 94.89 279 | 93.65 291 | 86.08 189 | 76.53 274 | 93.28 209 | 61.41 297 | 96.14 256 | 80.95 213 | 77.69 254 | 90.93 260 |
|
IterMVS | | | 85.81 238 | 84.67 236 | 89.22 258 | 93.51 215 | 83.67 254 | 96.32 244 | 94.80 268 | 85.09 203 | 78.69 259 | 90.17 275 | 66.57 275 | 93.17 300 | 79.48 227 | 77.42 255 | 90.81 263 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
semantic-postprocess | | | | | 89.00 263 | 93.46 218 | 82.90 262 | | 94.70 271 | 85.02 205 | 78.62 261 | 90.35 264 | 66.63 273 | 93.33 299 | 79.38 229 | 77.36 256 | 90.76 267 |
|
EU-MVSNet | | | 84.19 257 | 84.42 240 | 83.52 308 | 88.64 295 | 67.37 328 | 96.04 258 | 95.76 225 | 85.29 199 | 78.44 266 | 93.18 211 | 70.67 244 | 91.48 327 | 75.79 265 | 75.98 257 | 91.70 234 |
|
Anonymous20231206 | | | 80.76 284 | 79.42 280 | 84.79 304 | 84.78 318 | 72.98 315 | 96.53 236 | 92.97 299 | 79.56 281 | 74.33 284 | 88.83 287 | 61.27 298 | 92.15 321 | 60.59 322 | 75.92 258 | 89.24 295 |
|
IterMVS-LS | | | 88.34 198 | 87.44 192 | 91.04 222 | 94.10 197 | 85.85 230 | 98.10 183 | 95.48 247 | 85.12 201 | 82.03 231 | 91.21 235 | 81.35 157 | 95.63 272 | 83.86 187 | 75.73 259 | 91.63 236 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
LP | | | 77.80 300 | 74.39 302 | 88.01 281 | 91.93 239 | 79.02 292 | 80.88 340 | 92.90 304 | 65.43 331 | 72.00 297 | 81.29 322 | 65.78 278 | 92.73 313 | 43.76 341 | 75.58 260 | 92.27 217 |
|
VPA-MVSNet | | | 89.10 184 | 87.66 190 | 93.45 176 | 92.56 228 | 91.02 109 | 97.97 190 | 98.32 27 | 86.92 178 | 86.03 184 | 92.01 225 | 68.84 257 | 97.10 203 | 90.92 116 | 75.34 261 | 92.23 220 |
|
nrg030 | | | 90.23 166 | 88.87 171 | 94.32 157 | 91.53 244 | 93.54 54 | 98.79 105 | 95.89 221 | 88.12 148 | 84.55 193 | 94.61 183 | 78.80 171 | 96.88 209 | 92.35 106 | 75.21 262 | 92.53 212 |
|
v7 | | | 86.91 218 | 85.45 223 | 91.29 219 | 90.06 259 | 86.73 199 | 98.26 168 | 95.49 246 | 83.08 246 | 82.95 211 | 90.96 241 | 73.37 218 | 96.42 232 | 79.90 224 | 74.97 263 | 90.71 270 |
|
v1192 | | | 86.32 230 | 84.71 235 | 91.17 220 | 89.53 283 | 86.40 208 | 98.13 181 | 95.44 251 | 82.52 256 | 82.42 222 | 90.62 257 | 71.58 240 | 96.33 244 | 77.23 244 | 74.88 264 | 90.79 265 |
|
v1240 | | | 85.77 240 | 84.11 242 | 90.73 228 | 89.26 288 | 85.15 242 | 97.88 194 | 95.23 264 | 81.89 264 | 82.16 226 | 90.55 262 | 69.60 251 | 96.31 247 | 75.59 267 | 74.87 265 | 90.72 269 |
|
FMVSNet5 | | | 82.29 266 | 80.54 271 | 87.52 287 | 93.79 211 | 84.01 251 | 93.73 291 | 92.47 311 | 76.92 302 | 74.27 285 | 86.15 307 | 63.69 288 | 89.24 330 | 69.07 303 | 74.79 266 | 89.29 294 |
|
v1144 | | | 86.83 220 | 85.31 225 | 91.40 217 | 89.75 274 | 87.21 190 | 98.31 160 | 95.45 250 | 83.22 243 | 82.70 217 | 90.78 244 | 73.36 219 | 96.36 237 | 79.49 226 | 74.69 267 | 90.63 273 |
|
v1921920 | | | 86.02 233 | 84.44 239 | 90.77 227 | 89.32 287 | 85.20 239 | 98.10 183 | 95.35 257 | 82.19 259 | 82.25 225 | 90.71 246 | 70.73 243 | 96.30 250 | 76.85 250 | 74.49 268 | 90.80 264 |
|
WR-MVS | | | 88.54 197 | 87.22 197 | 92.52 194 | 91.93 239 | 89.50 144 | 98.56 131 | 97.84 61 | 86.99 174 | 81.87 234 | 93.81 195 | 74.25 209 | 95.92 266 | 85.29 169 | 74.43 269 | 92.12 225 |
|
v6 | | | 87.27 211 | 85.86 213 | 91.50 214 | 89.97 264 | 86.84 197 | 98.45 145 | 95.67 232 | 83.85 225 | 83.11 207 | 90.97 240 | 74.46 202 | 96.58 216 | 81.97 204 | 74.34 270 | 91.09 252 |
|
v1neww | | | 87.29 209 | 85.88 211 | 91.50 214 | 90.07 257 | 86.87 193 | 98.45 145 | 95.66 235 | 83.84 226 | 83.07 208 | 90.99 238 | 74.58 199 | 96.56 218 | 81.96 205 | 74.33 271 | 91.07 255 |
|
v7new | | | 87.29 209 | 85.88 211 | 91.50 214 | 90.07 257 | 86.87 193 | 98.45 145 | 95.66 235 | 83.84 226 | 83.07 208 | 90.99 238 | 74.58 199 | 96.56 218 | 81.96 205 | 74.33 271 | 91.07 255 |
|
Patchmtry | | | 83.61 264 | 81.64 264 | 89.50 254 | 93.36 220 | 82.84 265 | 84.10 333 | 94.20 283 | 69.47 324 | 79.57 253 | 86.88 303 | 84.43 116 | 94.78 292 | 68.48 305 | 74.30 273 | 90.88 262 |
|
V42 | | | 87.00 217 | 85.68 220 | 90.98 224 | 89.91 265 | 86.08 221 | 98.32 159 | 95.61 240 | 83.67 232 | 82.72 216 | 90.67 252 | 74.00 213 | 96.53 222 | 81.94 207 | 74.28 274 | 90.32 278 |
|
SixPastTwentyTwo | | | 82.63 265 | 81.58 265 | 85.79 298 | 88.12 300 | 71.01 322 | 95.17 277 | 92.54 310 | 84.33 216 | 72.93 293 | 92.08 222 | 60.41 301 | 95.61 273 | 74.47 274 | 74.15 275 | 90.75 268 |
|
v2v482 | | | 87.27 211 | 85.76 215 | 91.78 209 | 89.59 280 | 87.58 175 | 98.56 131 | 95.54 242 | 84.53 212 | 82.51 220 | 91.78 229 | 73.11 224 | 96.47 229 | 82.07 201 | 74.14 276 | 91.30 247 |
|
v1141 | | | 87.23 213 | 85.75 217 | 91.67 210 | 89.88 269 | 87.43 181 | 98.52 135 | 95.62 238 | 83.91 222 | 82.83 214 | 90.69 249 | 74.70 192 | 96.49 226 | 81.53 211 | 74.08 277 | 91.07 255 |
|
v1 | | | 87.23 213 | 85.76 215 | 91.66 211 | 89.88 269 | 87.37 184 | 98.54 133 | 95.64 237 | 83.91 222 | 82.88 212 | 90.70 247 | 74.64 193 | 96.53 222 | 81.54 210 | 74.08 277 | 91.08 253 |
|
divwei89l23v2f112 | | | 87.23 213 | 85.75 217 | 91.66 211 | 89.88 269 | 87.40 182 | 98.53 134 | 95.62 238 | 83.91 222 | 82.84 213 | 90.67 252 | 74.75 191 | 96.49 226 | 81.55 209 | 74.05 279 | 91.08 253 |
|
v144192 | | | 86.40 228 | 84.89 231 | 90.91 225 | 89.48 285 | 85.59 234 | 98.21 175 | 95.43 252 | 82.45 257 | 82.62 218 | 90.58 260 | 72.79 228 | 96.36 237 | 78.45 236 | 74.04 280 | 90.79 265 |
|
tfpnnormal | | | 83.65 263 | 81.35 267 | 90.56 231 | 91.37 247 | 88.06 166 | 97.29 210 | 97.87 59 | 78.51 289 | 76.20 275 | 90.91 242 | 64.78 283 | 96.47 229 | 61.71 319 | 73.50 281 | 87.13 312 |
|
N_pmnet | | | 70.19 310 | 69.87 309 | 71.12 325 | 88.24 298 | 30.63 356 | 95.85 267 | 28.70 357 | 70.18 321 | 68.73 304 | 86.55 305 | 64.04 286 | 93.81 296 | 53.12 332 | 73.46 282 | 88.94 297 |
|
CP-MVSNet | | | 86.54 226 | 85.45 223 | 89.79 247 | 91.02 251 | 82.78 266 | 97.38 208 | 97.56 103 | 85.37 198 | 79.53 254 | 93.03 214 | 71.86 237 | 95.25 281 | 79.92 223 | 73.43 283 | 91.34 245 |
|
PS-CasMVS | | | 85.81 238 | 84.58 237 | 89.49 255 | 90.77 253 | 82.11 269 | 97.20 216 | 97.36 130 | 84.83 209 | 79.12 258 | 92.84 217 | 67.42 268 | 95.16 283 | 78.39 237 | 73.25 284 | 91.21 249 |
|
WR-MVS_H | | | 86.53 227 | 85.49 222 | 89.66 251 | 91.04 250 | 83.31 257 | 97.53 205 | 98.20 32 | 84.95 207 | 79.64 251 | 90.90 243 | 78.01 177 | 95.33 279 | 76.29 255 | 72.81 285 | 90.35 277 |
|
FPMVS | | | 61.57 313 | 60.32 315 | 65.34 330 | 60.14 347 | 42.44 350 | 91.02 314 | 89.72 335 | 44.15 342 | 42.63 342 | 80.93 323 | 19.02 348 | 80.59 345 | 42.50 342 | 72.76 286 | 73.00 341 |
|
v10 | | | 85.73 241 | 84.01 244 | 90.87 226 | 90.03 260 | 86.73 199 | 97.20 216 | 95.22 265 | 81.25 269 | 79.85 250 | 89.75 278 | 73.30 223 | 96.28 251 | 76.87 248 | 72.64 287 | 89.61 291 |
|
UniMVSNet (Re) | | | 89.50 180 | 88.32 184 | 93.03 183 | 92.21 233 | 90.96 111 | 98.90 93 | 98.39 25 | 89.13 116 | 83.22 202 | 92.03 223 | 81.69 153 | 96.34 243 | 86.79 159 | 72.53 288 | 91.81 232 |
|
UniMVSNet_NR-MVSNet | | | 89.60 178 | 88.55 181 | 92.75 190 | 92.17 234 | 90.07 131 | 98.74 107 | 98.15 43 | 88.37 140 | 83.21 203 | 93.98 191 | 82.86 140 | 95.93 264 | 86.95 156 | 72.47 289 | 92.25 218 |
|
DU-MVS | | | 88.83 190 | 87.51 191 | 92.79 188 | 91.46 245 | 90.07 131 | 98.71 108 | 97.62 93 | 88.87 126 | 83.21 203 | 93.68 198 | 74.63 195 | 95.93 264 | 86.95 156 | 72.47 289 | 92.36 214 |
|
v8 | | | 86.11 232 | 84.45 238 | 91.10 221 | 89.99 263 | 86.85 195 | 97.24 213 | 95.36 255 | 81.99 261 | 79.89 249 | 89.86 277 | 74.53 201 | 96.39 235 | 78.83 234 | 72.32 291 | 90.05 284 |
|
V4 | | | 84.20 256 | 82.92 250 | 88.02 280 | 87.59 309 | 79.91 287 | 96.21 253 | 95.36 255 | 79.88 278 | 78.51 264 | 89.00 286 | 69.52 252 | 96.32 245 | 77.96 239 | 72.29 292 | 87.83 304 |
|
VPNet | | | 88.30 199 | 86.57 201 | 93.49 175 | 91.95 237 | 91.35 98 | 98.18 177 | 97.20 140 | 88.61 130 | 84.52 194 | 94.89 178 | 62.21 294 | 96.76 214 | 89.34 134 | 72.26 293 | 92.36 214 |
|
v52 | | | 84.19 257 | 82.92 250 | 88.01 281 | 87.64 308 | 79.92 286 | 96.23 248 | 95.32 258 | 79.87 279 | 78.51 264 | 89.05 285 | 69.50 253 | 96.32 245 | 77.95 240 | 72.24 294 | 87.79 305 |
|
v7n | | | 84.42 254 | 82.75 255 | 89.43 256 | 88.15 299 | 81.86 270 | 96.75 230 | 95.67 232 | 80.53 274 | 78.38 267 | 89.43 282 | 69.89 247 | 96.35 242 | 73.83 283 | 72.13 295 | 90.07 283 |
|
new_pmnet | | | 76.02 302 | 73.71 303 | 82.95 309 | 83.88 322 | 72.85 316 | 91.26 312 | 92.26 313 | 70.44 319 | 62.60 326 | 81.37 321 | 47.64 329 | 92.32 319 | 61.85 318 | 72.10 296 | 83.68 333 |
|
IB-MVS | | 89.43 6 | 92.12 136 | 90.83 147 | 95.98 107 | 95.40 167 | 90.78 115 | 99.81 5 | 98.06 46 | 91.23 76 | 85.63 186 | 93.66 200 | 90.63 33 | 98.78 120 | 91.22 112 | 71.85 297 | 98.36 141 |
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 |
NR-MVSNet | | | 87.74 204 | 86.00 209 | 92.96 185 | 91.46 245 | 90.68 119 | 96.65 234 | 97.42 124 | 88.02 149 | 73.42 288 | 93.68 198 | 77.31 180 | 95.83 267 | 84.26 179 | 71.82 298 | 92.36 214 |
|
v148 | | | 86.38 229 | 85.06 227 | 90.37 236 | 89.47 286 | 84.10 250 | 98.52 135 | 95.48 247 | 83.80 228 | 80.93 240 | 90.22 271 | 74.60 197 | 96.31 247 | 80.92 214 | 71.55 299 | 90.69 271 |
|
Baseline_NR-MVSNet | | | 85.83 237 | 84.82 233 | 88.87 265 | 88.73 293 | 83.34 256 | 98.63 122 | 91.66 321 | 80.41 276 | 82.44 221 | 91.35 234 | 74.63 195 | 95.42 277 | 84.13 181 | 71.39 300 | 87.84 302 |
|
TranMVSNet+NR-MVSNet | | | 87.75 202 | 86.31 205 | 92.07 200 | 90.81 252 | 88.56 158 | 98.33 157 | 97.18 141 | 87.76 157 | 81.87 234 | 93.90 193 | 72.45 229 | 95.43 276 | 83.13 192 | 71.30 301 | 92.23 220 |
|
PEN-MVS | | | 85.21 245 | 83.93 245 | 89.07 262 | 89.89 268 | 81.31 277 | 97.09 219 | 97.24 136 | 84.45 214 | 78.66 260 | 92.68 219 | 68.44 259 | 94.87 288 | 75.98 257 | 70.92 302 | 91.04 258 |
|
MIMVSNet1 | | | 75.92 303 | 73.30 304 | 83.81 307 | 81.29 326 | 75.57 307 | 92.26 304 | 92.05 317 | 73.09 313 | 67.48 319 | 86.18 306 | 40.87 337 | 87.64 333 | 55.78 329 | 70.68 303 | 88.21 298 |
|
v748 | | | 83.84 262 | 82.31 260 | 88.41 274 | 87.65 307 | 79.10 291 | 96.66 233 | 95.51 244 | 80.09 277 | 77.65 270 | 88.53 290 | 69.81 248 | 96.23 252 | 75.67 266 | 69.25 304 | 89.91 287 |
|
pm-mvs1 | | | 84.68 248 | 82.78 254 | 90.40 235 | 89.58 281 | 85.18 240 | 97.31 209 | 94.73 270 | 81.93 263 | 76.05 276 | 92.01 225 | 65.48 281 | 96.11 257 | 78.75 235 | 69.14 305 | 89.91 287 |
|
DTE-MVSNet | | | 84.14 259 | 82.80 253 | 88.14 279 | 88.95 290 | 79.87 288 | 96.81 226 | 96.24 196 | 83.50 240 | 77.60 271 | 92.52 221 | 67.89 265 | 94.24 295 | 72.64 295 | 69.05 306 | 90.32 278 |
|
test20.03 | | | 78.51 296 | 77.48 289 | 81.62 314 | 83.07 324 | 71.03 321 | 96.11 256 | 92.83 307 | 81.66 266 | 69.31 303 | 89.68 279 | 57.53 305 | 87.29 334 | 58.65 327 | 68.47 307 | 86.53 314 |
|
test1235678 | | | 71.07 309 | 69.53 311 | 75.71 322 | 71.87 340 | 55.27 342 | 94.32 282 | 90.76 328 | 70.23 320 | 57.61 333 | 79.06 331 | 43.13 333 | 83.72 340 | 50.48 333 | 68.30 308 | 88.14 299 |
|
K. test v3 | | | 81.04 281 | 79.77 275 | 84.83 303 | 87.41 310 | 70.23 324 | 95.60 273 | 93.93 286 | 83.70 231 | 67.51 318 | 89.35 283 | 55.76 310 | 93.58 298 | 76.67 252 | 68.03 309 | 90.67 272 |
|
MDA-MVSNet_test_wron | | | 79.65 290 | 77.05 292 | 87.45 288 | 87.79 305 | 80.13 284 | 96.25 247 | 94.44 276 | 73.87 311 | 51.80 337 | 87.47 298 | 68.04 262 | 92.12 322 | 66.02 311 | 67.79 310 | 90.09 281 |
|
YYNet1 | | | 79.64 291 | 77.04 293 | 87.43 289 | 87.80 304 | 79.98 285 | 96.23 248 | 94.44 276 | 73.83 312 | 51.83 336 | 87.53 297 | 67.96 264 | 92.07 323 | 66.00 312 | 67.75 311 | 90.23 280 |
|
pmmvs6 | | | 79.90 289 | 77.31 290 | 87.67 286 | 84.17 321 | 78.13 300 | 95.86 266 | 93.68 290 | 67.94 328 | 72.67 295 | 89.62 280 | 50.98 325 | 95.75 269 | 74.80 273 | 66.04 312 | 89.14 296 |
|
Gipuma | | | 54.77 319 | 52.22 320 | 62.40 332 | 86.50 314 | 59.37 335 | 50.20 349 | 90.35 331 | 36.52 345 | 41.20 343 | 49.49 347 | 18.33 350 | 81.29 343 | 32.10 347 | 65.34 313 | 46.54 349 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
DeepMVS_CX | | | | | 76.08 321 | 90.74 254 | 51.65 344 | | 90.84 327 | 86.47 186 | 57.89 332 | 87.98 291 | 35.88 342 | 92.60 315 | 65.77 313 | 65.06 314 | 83.97 332 |
|
MDA-MVSNet-bldmvs | | | 77.82 299 | 74.75 301 | 87.03 291 | 88.33 297 | 78.52 297 | 96.34 243 | 92.85 306 | 75.57 305 | 48.87 339 | 87.89 292 | 57.32 307 | 92.49 318 | 60.79 321 | 64.80 315 | 90.08 282 |
|
1111 | | | 72.28 308 | 71.36 307 | 75.02 323 | 73.04 337 | 57.38 338 | 92.30 302 | 90.22 332 | 62.27 335 | 59.46 329 | 80.36 326 | 76.23 184 | 87.07 335 | 44.29 339 | 64.08 316 | 80.59 337 |
|
Patchmatch-RL test | | | 81.90 271 | 80.13 272 | 87.23 290 | 80.71 328 | 70.12 325 | 84.07 334 | 88.19 341 | 83.16 245 | 70.57 298 | 82.18 311 | 87.18 80 | 92.59 316 | 82.28 200 | 62.78 317 | 98.98 96 |
|
lessismore_v0 | | | | | 85.08 301 | 85.59 316 | 69.28 326 | | 90.56 329 | | 67.68 315 | 90.21 272 | 54.21 318 | 95.46 275 | 73.88 281 | 62.64 318 | 90.50 275 |
|
PM-MVS | | | 74.88 304 | 72.85 305 | 80.98 316 | 78.98 332 | 64.75 329 | 90.81 315 | 85.77 344 | 80.95 272 | 68.23 310 | 82.81 309 | 29.08 344 | 92.84 305 | 76.54 254 | 62.46 319 | 85.36 328 |
|
pmmvs-eth3d | | | 78.71 295 | 76.16 297 | 86.38 294 | 80.25 329 | 81.19 279 | 94.17 286 | 92.13 316 | 77.97 296 | 66.90 321 | 82.31 310 | 55.76 310 | 92.56 317 | 73.63 285 | 62.31 320 | 85.38 327 |
|
test12356 | | | 66.36 312 | 65.12 312 | 70.08 328 | 66.92 342 | 50.46 345 | 89.96 319 | 88.58 339 | 66.00 330 | 53.38 335 | 78.13 334 | 32.89 343 | 82.87 341 | 48.36 335 | 61.87 321 | 76.92 338 |
|
ambc | | | | | 79.60 317 | 72.76 339 | 56.61 340 | 76.20 342 | 92.01 318 | | 68.25 309 | 80.23 328 | 23.34 346 | 94.73 293 | 73.78 284 | 60.81 322 | 87.48 306 |
|
v16 | | | 81.90 271 | 79.65 277 | 88.65 267 | 90.02 262 | 86.66 202 | 96.01 259 | 93.07 297 | 78.53 288 | 68.27 307 | 82.05 313 | 74.39 205 | 92.96 302 | 74.02 280 | 60.48 323 | 86.33 318 |
|
v18 | | | 82.00 268 | 79.76 276 | 88.72 266 | 90.03 260 | 86.81 198 | 96.17 255 | 93.12 295 | 78.70 286 | 68.39 305 | 82.10 312 | 74.64 193 | 93.00 301 | 74.21 276 | 60.45 324 | 86.35 316 |
|
v11 | | | 81.38 278 | 79.03 285 | 88.41 274 | 89.68 277 | 86.43 206 | 95.74 270 | 92.82 309 | 78.03 294 | 67.74 313 | 81.45 320 | 73.33 222 | 92.69 314 | 72.23 297 | 60.27 325 | 86.11 325 |
|
v17 | | | 81.87 273 | 79.61 278 | 88.64 268 | 89.91 265 | 86.64 203 | 96.01 259 | 93.08 296 | 78.54 287 | 68.27 307 | 81.96 314 | 74.44 203 | 92.95 303 | 74.03 279 | 60.22 326 | 86.34 317 |
|
v15 | | | 81.62 274 | 79.32 281 | 88.52 270 | 89.80 272 | 86.56 204 | 95.83 268 | 92.96 300 | 78.50 290 | 67.88 311 | 81.68 316 | 74.22 210 | 92.82 306 | 73.46 286 | 59.55 327 | 86.18 321 |
|
v13 | | | 81.30 280 | 78.99 286 | 88.25 278 | 89.61 279 | 85.87 228 | 95.39 275 | 92.90 304 | 77.93 299 | 67.45 320 | 81.52 319 | 73.66 216 | 92.75 311 | 72.91 293 | 59.53 328 | 86.14 324 |
|
V14 | | | 81.55 276 | 79.26 282 | 88.42 273 | 89.80 272 | 86.33 212 | 95.72 271 | 92.96 300 | 78.35 291 | 67.82 312 | 81.70 315 | 74.13 211 | 92.78 310 | 73.32 287 | 59.50 329 | 86.16 323 |
|
TDRefinement | | | 78.01 297 | 75.31 298 | 86.10 297 | 70.06 341 | 73.84 312 | 93.59 294 | 91.58 323 | 74.51 309 | 73.08 291 | 91.04 237 | 49.63 327 | 97.12 200 | 74.88 271 | 59.47 330 | 87.33 308 |
|
TransMVSNet (Re) | | | 81.97 269 | 79.61 278 | 89.08 261 | 89.70 276 | 84.01 251 | 97.26 211 | 91.85 320 | 78.84 285 | 73.07 292 | 91.62 231 | 67.17 270 | 95.21 282 | 67.50 306 | 59.46 331 | 88.02 301 |
|
V9 | | | 81.46 277 | 79.15 283 | 88.39 276 | 89.75 274 | 86.17 218 | 95.62 272 | 92.92 302 | 78.22 292 | 67.65 316 | 81.64 317 | 73.95 214 | 92.80 308 | 73.15 290 | 59.43 332 | 86.21 320 |
|
v12 | | | 81.37 279 | 79.05 284 | 88.33 277 | 89.68 277 | 86.05 224 | 95.48 274 | 92.92 302 | 78.08 293 | 67.55 317 | 81.58 318 | 73.75 215 | 92.75 311 | 73.05 291 | 59.37 333 | 86.18 321 |
|
PMVS | | 41.42 23 | 45.67 323 | 42.50 324 | 55.17 336 | 34.28 355 | 32.37 354 | 66.24 346 | 78.71 350 | 30.72 347 | 22.04 351 | 59.59 342 | 4.59 357 | 77.85 347 | 27.49 348 | 58.84 334 | 55.29 347 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
UnsupCasMVSNet_eth | | | 78.90 293 | 76.67 295 | 85.58 300 | 82.81 325 | 74.94 308 | 91.98 306 | 96.31 189 | 84.64 211 | 65.84 323 | 87.71 294 | 51.33 323 | 92.23 320 | 72.89 294 | 56.50 335 | 89.56 292 |
|
PVSNet_0 | | 83.28 16 | 87.31 208 | 85.16 226 | 93.74 174 | 94.78 188 | 84.59 246 | 98.91 88 | 98.69 22 | 89.81 101 | 78.59 263 | 93.23 210 | 61.95 295 | 99.34 102 | 94.75 74 | 55.72 336 | 97.30 170 |
|
new-patchmatchnet | | | 74.80 305 | 72.40 306 | 81.99 312 | 78.36 334 | 72.20 318 | 94.44 281 | 92.36 312 | 77.06 301 | 63.47 325 | 79.98 329 | 51.04 324 | 88.85 331 | 60.53 323 | 54.35 337 | 84.92 330 |
|
pmmvs3 | | | 72.86 307 | 69.76 310 | 82.17 310 | 73.86 336 | 74.19 311 | 94.20 285 | 89.01 337 | 64.23 334 | 67.72 314 | 80.91 324 | 41.48 335 | 88.65 332 | 62.40 317 | 54.02 338 | 83.68 333 |
|
Anonymous20231211 | | | 67.10 311 | 63.29 314 | 78.54 318 | 75.68 335 | 60.00 333 | 92.05 305 | 88.86 338 | 49.84 340 | 59.35 331 | 78.48 333 | 26.15 345 | 90.76 328 | 45.96 338 | 53.24 339 | 84.88 331 |
|
testmv | | | 60.41 315 | 57.98 316 | 67.69 329 | 58.16 350 | 47.14 347 | 89.09 320 | 86.74 342 | 61.52 338 | 44.30 341 | 68.44 337 | 20.98 347 | 79.92 346 | 40.94 343 | 51.67 340 | 76.01 339 |
|
LCM-MVSNet | | | 60.07 316 | 56.37 317 | 71.18 324 | 54.81 351 | 48.67 346 | 82.17 339 | 89.48 336 | 37.95 343 | 49.13 338 | 69.12 336 | 13.75 355 | 81.76 342 | 59.28 325 | 51.63 341 | 83.10 335 |
|
UnsupCasMVSNet_bld | | | 73.85 306 | 70.14 308 | 84.99 302 | 79.44 331 | 75.73 306 | 88.53 321 | 95.24 263 | 70.12 322 | 61.94 327 | 74.81 335 | 41.41 336 | 93.62 297 | 68.65 304 | 51.13 342 | 85.62 326 |
|
PMMVS2 | | | 58.97 317 | 55.07 318 | 70.69 327 | 62.72 343 | 55.37 341 | 85.97 325 | 80.52 348 | 49.48 341 | 45.94 340 | 68.31 338 | 15.73 353 | 80.78 344 | 49.79 334 | 37.12 343 | 75.91 340 |
|
MVE | | 44.00 22 | 41.70 325 | 37.64 328 | 53.90 337 | 49.46 352 | 43.37 349 | 65.09 347 | 66.66 354 | 26.19 350 | 25.77 350 | 48.53 348 | 3.58 360 | 63.35 352 | 26.15 349 | 27.28 344 | 54.97 348 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
PNet_i23d | | | 48.05 322 | 44.98 323 | 57.28 334 | 60.15 345 | 42.39 351 | 80.85 341 | 73.14 353 | 36.78 344 | 27.46 347 | 56.66 344 | 6.38 356 | 68.34 349 | 36.65 345 | 26.72 345 | 61.10 345 |
|
wuykxyi23d | | | 43.53 324 | 37.95 327 | 60.27 333 | 45.36 353 | 44.79 348 | 68.27 345 | 74.26 352 | 33.48 346 | 18.21 353 | 40.16 354 | 3.64 358 | 71.01 348 | 38.85 344 | 19.31 346 | 65.02 344 |
|
no-one | | | 56.69 318 | 51.89 321 | 71.08 326 | 59.35 349 | 58.65 337 | 83.78 337 | 84.81 347 | 61.73 337 | 36.46 345 | 56.52 345 | 18.15 351 | 84.78 339 | 47.03 337 | 19.19 347 | 69.81 343 |
|
E-PMN | | | 41.02 326 | 40.93 325 | 41.29 339 | 61.97 344 | 33.83 353 | 84.00 335 | 65.17 355 | 27.17 348 | 27.56 346 | 46.72 349 | 17.63 352 | 60.41 353 | 19.32 350 | 18.82 348 | 29.61 350 |
|
ANet_high | | | 50.71 321 | 46.17 322 | 64.33 331 | 44.27 354 | 52.30 343 | 76.13 343 | 78.73 349 | 64.95 332 | 27.37 348 | 55.23 346 | 14.61 354 | 67.74 350 | 36.01 346 | 18.23 349 | 72.95 342 |
|
EMVS | | | 39.96 327 | 39.88 326 | 40.18 340 | 59.57 348 | 32.12 355 | 84.79 332 | 64.57 356 | 26.27 349 | 26.14 349 | 44.18 352 | 18.73 349 | 59.29 354 | 17.03 351 | 17.67 350 | 29.12 351 |
|
tmp_tt | | | 53.66 320 | 52.86 319 | 56.05 335 | 32.75 356 | 41.97 352 | 73.42 344 | 76.12 351 | 21.91 351 | 39.68 344 | 96.39 161 | 42.59 334 | 65.10 351 | 78.00 238 | 14.92 351 | 61.08 346 |
|
wuyk23d | | | 16.71 331 | 16.73 333 | 16.65 342 | 60.15 345 | 25.22 357 | 41.24 350 | 5.17 358 | 6.56 352 | 5.48 355 | 3.61 356 | 3.64 358 | 22.72 355 | 15.20 352 | 9.52 352 | 1.99 355 |
|
.test1245 | | | 61.50 314 | 64.44 313 | 52.65 338 | 73.04 337 | 57.38 338 | 92.30 302 | 90.22 332 | 62.27 335 | 59.46 329 | 80.36 326 | 76.23 184 | 87.07 335 | 44.29 339 | 1.80 353 | 13.50 353 |
|
testmvs | | | 18.81 330 | 23.05 331 | 6.10 344 | 4.48 357 | 2.29 359 | 97.78 196 | 3.00 359 | 3.27 353 | 18.60 352 | 62.71 340 | 1.53 362 | 2.49 357 | 14.26 353 | 1.80 353 | 13.50 353 |
|
test123 | | | 16.58 332 | 19.47 332 | 7.91 343 | 3.59 358 | 5.37 358 | 94.32 282 | 1.39 360 | 2.49 354 | 13.98 354 | 44.60 351 | 2.91 361 | 2.65 356 | 11.35 354 | 0.57 355 | 15.70 352 |
|
cdsmvs_eth3d_5k | | | 22.52 329 | 30.03 330 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 97.17 142 | 0.00 355 | 0.00 356 | 98.77 64 | 74.35 206 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
pcd_1.5k_mvsjas | | | 6.87 334 | 9.16 335 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 82.48 145 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
sosnet-low-res | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
sosnet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
uncertanet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
Regformer | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
ab-mvs-re | | | 8.21 333 | 10.94 334 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 98.50 83 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
uanet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
GSMVS | | | | | | | | | | | | | | | | | 98.84 108 |
|
test_part3 | | | | | | | | 99.43 33 | | 92.81 44 | | 99.48 3 | | 99.97 13 | 99.52 1 | | |
|
test_part2 | | | | | | 99.54 27 | 95.42 14 | | | | 98.13 16 | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 88.39 58 | | | | 98.84 108 |
|
sam_mvs | | | | | | | | | | | | | 87.08 81 | | | | |
|
MTGPA | | | | | | | | | 97.45 118 | | | | | | | | |
|
test_post1 | | | | | | | | 90.74 317 | | | | 41.37 353 | 85.38 109 | 96.36 237 | 83.16 191 | | |
|
test_post | | | | | | | | | | | | 46.00 350 | 87.37 74 | 97.11 201 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 84.86 308 | 88.73 52 | 96.81 212 | | | |
|
MTMP | | | | | | | | | 91.09 326 | | | | | | | | |
|
gm-plane-assit | | | | | | 94.69 190 | 88.14 164 | | | 88.22 145 | | 97.20 127 | | 98.29 140 | 90.79 119 | | |
|
TEST9 | | | | | | 99.57 23 | 93.17 59 | 99.38 40 | 97.66 83 | 89.57 105 | 98.39 11 | 99.18 20 | 90.88 29 | 99.66 65 | | | |
|
test_8 | | | | | | 99.55 26 | 93.07 63 | 99.37 43 | 97.64 88 | 90.18 93 | 98.36 13 | 99.19 18 | 90.94 27 | 99.64 71 | | | |
|
agg_prior | | | | | | 99.54 27 | 92.66 71 | | 97.64 88 | | 97.98 25 | | | 99.61 74 | | | |
|
test_prior4 | | | | | | | 92.00 80 | 99.41 38 | | | | | | | | | |
|
test_prior | | | | | 97.01 50 | 99.58 19 | 91.77 81 | | 97.57 101 | | | | | 99.49 86 | | | 99.79 25 |
|
旧先验2 | | | | | | | | 98.67 117 | | 85.75 192 | 98.96 4 | | | 98.97 117 | 93.84 85 | | |
|
新几何2 | | | | | | | | 98.26 168 | | | | | | | | | |
|
无先验 | | | | | | | | 98.52 135 | 97.82 63 | 87.20 173 | | | | 99.90 30 | 87.64 150 | | 99.85 21 |
|
原ACMM2 | | | | | | | | 98.69 112 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 99.88 34 | 84.16 180 | | |
|
segment_acmp | | | | | | | | | | | | | 90.56 35 | | | | |
|
testdata1 | | | | | | | | 97.89 192 | | 92.43 50 | | | | | | | |
|
plane_prior7 | | | | | | 93.84 208 | 85.73 232 | | | | | | | | | | |
|
plane_prior6 | | | | | | 93.92 205 | 86.02 225 | | | | | | 72.92 225 | | | | |
|
plane_prior4 | | | | | | | | | | | | 96.52 155 | | | | | |
|
plane_prior3 | | | | | | | 85.91 226 | | | 93.65 30 | 86.99 177 | | | | | | |
|
plane_prior2 | | | | | | | | 99.02 77 | | 93.38 35 | | | | | | | |
|
plane_prior1 | | | | | | 93.90 207 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 361 | | | | | | | | |
|
nn | | | | | | | | | 0.00 361 | | | | | | | | |
|
door-mid | | | | | | | | | 84.90 346 | | | | | | | | |
|
test11 | | | | | | | | | 97.68 81 | | | | | | | | |
|
door | | | | | | | | | 85.30 345 | | | | | | | | |
|
HQP5-MVS | | | | | | | 86.39 209 | | | | | | | | | | |
|
HQP-NCC | | | | | | 93.95 201 | | 99.16 58 | | 93.92 22 | 87.57 171 | | | | | | |
|
ACMP_Plane | | | | | | 93.95 201 | | 99.16 58 | | 93.92 22 | 87.57 171 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 93.82 87 | | |
|
HQP4-MVS | | | | | | | | | | | 87.57 171 | | | 97.77 165 | | | 92.72 208 |
|
HQP2-MVS | | | | | | | | | | | | | 73.34 220 | | | | |
|
NP-MVS | | | | | | 93.94 204 | 86.22 216 | | | | | 96.67 149 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 91.17 103 | 91.38 310 | | 87.45 166 | 93.08 102 | | 86.67 88 | | 87.02 155 | | 98.95 102 |
|
Test By Simon | | | | | | | | | | | | | 83.62 122 | | | | |
|