APDe-MVS | | | 97.82 1 | 97.73 1 | 98.08 9 | 99.15 25 | 94.82 13 | 98.81 2 | 98.30 22 | 94.76 24 | 98.30 5 | 98.90 2 | 93.77 8 | 99.68 38 | 97.93 1 | 99.69 1 | 99.75 1 |
|
MVS_0304 | | | 96.05 51 | 95.45 53 | 97.85 15 | 97.75 103 | 94.50 16 | 96.87 149 | 97.95 82 | 95.46 6 | 95.60 73 | 98.01 49 | 80.96 192 | 99.83 15 | 97.23 2 | 99.25 47 | 99.23 50 |
|
TSAR-MVS + MP. | | | 97.42 6 | 97.33 6 | 97.69 29 | 99.25 20 | 94.24 24 | 98.07 34 | 97.85 89 | 93.72 47 | 98.57 3 | 98.35 25 | 93.69 9 | 99.40 88 | 97.06 3 | 99.46 26 | 99.44 33 |
|
CNVR-MVS | | | 97.68 2 | 97.44 5 | 98.37 3 | 98.90 33 | 95.86 2 | 97.27 113 | 98.08 51 | 95.81 3 | 97.87 12 | 98.31 34 | 94.26 4 | 99.68 38 | 97.02 4 | 99.49 24 | 99.57 14 |
|
SD-MVS | | | 97.41 7 | 97.53 2 | 97.06 57 | 98.57 52 | 94.46 17 | 97.92 43 | 98.14 41 | 94.82 21 | 99.01 2 | 98.55 10 | 94.18 5 | 97.41 278 | 96.94 5 | 99.64 4 | 99.32 44 |
|
Regformer-4 | | | 96.97 23 | 96.87 17 | 97.25 49 | 98.34 63 | 92.66 67 | 96.96 139 | 98.01 70 | 95.12 13 | 97.14 24 | 98.42 19 | 91.82 35 | 99.61 48 | 96.90 6 | 99.13 57 | 99.50 25 |
|
CANet | | | 96.39 43 | 96.02 45 | 97.50 39 | 97.62 109 | 93.38 50 | 97.02 134 | 97.96 80 | 95.42 8 | 94.86 83 | 97.81 62 | 87.38 90 | 99.82 19 | 96.88 7 | 99.20 52 | 99.29 46 |
|
TSAR-MVS + GP. | | | 96.69 34 | 96.49 33 | 97.27 48 | 98.31 68 | 93.39 49 | 96.79 159 | 96.72 197 | 94.17 36 | 97.44 16 | 97.66 72 | 92.76 14 | 99.33 93 | 96.86 8 | 97.76 96 | 99.08 63 |
|
Regformer-3 | | | 96.85 28 | 96.80 23 | 97.01 58 | 98.34 63 | 92.02 85 | 96.96 139 | 97.76 92 | 95.01 16 | 97.08 29 | 98.42 19 | 91.71 36 | 99.54 68 | 96.80 9 | 99.13 57 | 99.48 29 |
|
Regformer-2 | | | 97.16 14 | 96.99 12 | 97.67 30 | 98.32 66 | 93.84 36 | 96.83 152 | 98.10 48 | 95.24 10 | 97.49 14 | 98.25 40 | 92.57 20 | 99.61 48 | 96.80 9 | 99.29 44 | 99.56 16 |
|
Regformer-1 | | | 97.10 16 | 96.96 14 | 97.54 38 | 98.32 66 | 93.48 47 | 96.83 152 | 97.99 77 | 95.20 12 | 97.46 15 | 98.25 40 | 92.48 23 | 99.58 56 | 96.79 11 | 99.29 44 | 99.55 18 |
|
DeepPCF-MVS | | 93.97 1 | 96.61 37 | 97.09 8 | 95.15 136 | 98.09 81 | 86.63 255 | 96.00 228 | 98.15 39 | 95.43 7 | 97.95 10 | 98.56 8 | 93.40 10 | 99.36 92 | 96.77 12 | 99.48 25 | 99.45 31 |
|
SMA-MVS | | | 97.36 8 | 97.06 9 | 98.25 4 | 99.06 29 | 95.30 7 | 97.94 41 | 98.19 33 | 90.66 137 | 99.06 1 | 98.94 1 | 93.33 11 | 99.83 15 | 96.72 13 | 99.68 2 | 99.63 5 |
|
HSP-MVS | | | 97.53 5 | 97.49 4 | 97.63 35 | 99.40 5 | 93.77 41 | 98.53 9 | 97.85 89 | 95.55 5 | 98.56 4 | 97.81 62 | 93.90 6 | 99.65 42 | 96.62 14 | 99.21 51 | 99.48 29 |
|
MSLP-MVS++ | | | 96.94 25 | 97.06 9 | 96.59 69 | 98.72 38 | 91.86 89 | 97.67 67 | 98.49 12 | 94.66 27 | 97.24 19 | 98.41 22 | 92.31 27 | 98.94 128 | 96.61 15 | 99.46 26 | 98.96 72 |
|
MP-MVS-pluss | | | 96.70 33 | 96.27 40 | 97.98 11 | 99.23 23 | 94.71 14 | 96.96 139 | 98.06 58 | 90.67 135 | 95.55 75 | 98.78 3 | 91.07 45 | 99.86 8 | 96.58 16 | 99.55 15 | 99.38 40 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
SteuartSystems-ACMMP | | | 97.62 3 | 97.53 2 | 97.87 14 | 98.39 60 | 94.25 23 | 98.43 16 | 98.27 24 | 95.34 9 | 98.11 6 | 98.56 8 | 94.53 3 | 99.71 30 | 96.57 17 | 99.62 8 | 99.65 3 |
Skip Steuart: Steuart Systems R&D Blog. |
MCST-MVS | | | 97.18 12 | 96.84 19 | 98.20 6 | 99.30 16 | 95.35 5 | 97.12 129 | 98.07 56 | 93.54 53 | 96.08 54 | 97.69 69 | 93.86 7 | 99.71 30 | 96.50 18 | 99.39 35 | 99.55 18 |
|
EI-MVSNet-Vis-set | | | 96.51 39 | 96.47 34 | 96.63 66 | 98.24 72 | 91.20 108 | 96.89 148 | 97.73 95 | 94.74 25 | 96.49 42 | 98.49 14 | 90.88 50 | 99.58 56 | 96.44 19 | 98.32 81 | 99.13 58 |
|
VDD-MVS | | | 93.82 103 | 93.08 107 | 96.02 98 | 97.88 97 | 89.96 143 | 97.72 61 | 95.85 238 | 92.43 85 | 95.86 63 | 98.44 17 | 68.42 310 | 99.39 89 | 96.31 20 | 94.85 148 | 98.71 91 |
|
ACMMP_Plus | | | 97.20 11 | 96.86 18 | 98.23 5 | 99.09 26 | 95.16 9 | 97.60 83 | 98.19 33 | 92.82 78 | 97.93 11 | 98.74 4 | 91.60 39 | 99.86 8 | 96.26 21 | 99.52 18 | 99.67 2 |
|
EI-MVSNet-UG-set | | | 96.34 44 | 96.30 39 | 96.47 77 | 98.20 76 | 90.93 119 | 96.86 150 | 97.72 98 | 94.67 26 | 96.16 51 | 98.46 15 | 90.43 54 | 99.58 56 | 96.23 22 | 97.96 90 | 98.90 79 |
|
xiu_mvs_v1_base_debu | | | 95.01 71 | 94.76 67 | 95.75 108 | 96.58 147 | 91.71 90 | 96.25 212 | 97.35 143 | 92.99 69 | 96.70 32 | 96.63 122 | 82.67 160 | 99.44 83 | 96.22 23 | 97.46 100 | 96.11 195 |
|
xiu_mvs_v1_base | | | 95.01 71 | 94.76 67 | 95.75 108 | 96.58 147 | 91.71 90 | 96.25 212 | 97.35 143 | 92.99 69 | 96.70 32 | 96.63 122 | 82.67 160 | 99.44 83 | 96.22 23 | 97.46 100 | 96.11 195 |
|
xiu_mvs_v1_base_debi | | | 95.01 71 | 94.76 67 | 95.75 108 | 96.58 147 | 91.71 90 | 96.25 212 | 97.35 143 | 92.99 69 | 96.70 32 | 96.63 122 | 82.67 160 | 99.44 83 | 96.22 23 | 97.46 100 | 96.11 195 |
|
alignmvs | | | 95.87 57 | 95.23 60 | 97.78 21 | 97.56 114 | 95.19 8 | 97.86 47 | 97.17 153 | 94.39 32 | 96.47 43 | 96.40 134 | 85.89 105 | 99.20 99 | 96.21 26 | 95.11 146 | 98.95 74 |
|
canonicalmvs | | | 96.02 53 | 95.45 53 | 97.75 25 | 97.59 112 | 95.15 10 | 98.28 22 | 97.60 109 | 94.52 29 | 96.27 48 | 96.12 144 | 87.65 84 | 99.18 102 | 96.20 27 | 94.82 150 | 98.91 78 |
|
zzz-MVS | | | 97.07 18 | 96.77 25 | 97.97 12 | 99.37 10 | 94.42 19 | 97.15 127 | 98.08 51 | 95.07 14 | 96.11 52 | 98.59 6 | 90.88 50 | 99.90 1 | 96.18 28 | 99.50 22 | 99.58 12 |
|
MTAPA | | | 97.08 17 | 96.78 24 | 97.97 12 | 99.37 10 | 94.42 19 | 97.24 115 | 98.08 51 | 95.07 14 | 96.11 52 | 98.59 6 | 90.88 50 | 99.90 1 | 96.18 28 | 99.50 22 | 99.58 12 |
|
APD-MVS_3200maxsize | | | 96.81 29 | 96.71 27 | 97.12 56 | 99.01 31 | 92.31 74 | 97.98 40 | 98.06 58 | 93.11 66 | 97.44 16 | 98.55 10 | 90.93 48 | 99.55 66 | 96.06 30 | 99.25 47 | 99.51 24 |
|
MVS_111021_HR | | | 96.68 36 | 96.58 31 | 96.99 59 | 98.46 54 | 92.31 74 | 96.20 217 | 98.90 2 | 94.30 35 | 95.86 63 | 97.74 67 | 92.33 24 | 99.38 91 | 96.04 31 | 99.42 31 | 99.28 49 |
|
PHI-MVS | | | 96.77 31 | 96.46 35 | 97.71 28 | 98.40 58 | 94.07 30 | 98.21 28 | 98.45 15 | 89.86 154 | 97.11 27 | 98.01 49 | 92.52 22 | 99.69 36 | 96.03 32 | 99.53 17 | 99.36 42 |
|
HPM-MVS++ | | | 97.34 9 | 96.97 13 | 98.47 1 | 99.08 27 | 96.16 1 | 97.55 88 | 97.97 79 | 95.59 4 | 96.61 36 | 97.89 53 | 92.57 20 | 99.84 14 | 95.95 33 | 99.51 20 | 99.40 36 |
|
DELS-MVS | | | 96.61 37 | 96.38 38 | 97.30 45 | 97.79 100 | 93.19 54 | 95.96 229 | 98.18 36 | 95.23 11 | 95.87 62 | 97.65 73 | 91.45 41 | 99.70 35 | 95.87 34 | 99.44 30 | 99.00 70 |
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 47 | 96.19 44 | 96.39 81 | 98.23 75 | 91.35 103 | 96.24 215 | 98.79 4 | 93.99 39 | 95.80 66 | 97.65 73 | 89.92 61 | 99.24 98 | 95.87 34 | 99.20 52 | 98.58 95 |
|
NCCC | | | 97.30 10 | 97.03 11 | 98.11 8 | 98.77 36 | 95.06 11 | 97.34 107 | 98.04 65 | 95.96 2 | 97.09 28 | 97.88 55 | 93.18 12 | 99.71 30 | 95.84 36 | 99.17 54 | 99.56 16 |
|
VNet | | | 95.89 56 | 95.45 53 | 97.21 53 | 98.07 82 | 92.94 61 | 97.50 91 | 98.15 39 | 93.87 41 | 97.52 13 | 97.61 79 | 85.29 111 | 99.53 71 | 95.81 37 | 95.27 144 | 99.16 54 |
|
XVS | | | 97.18 12 | 96.96 14 | 97.81 18 | 99.38 8 | 94.03 32 | 98.59 7 | 98.20 31 | 94.85 17 | 96.59 38 | 98.29 37 | 91.70 37 | 99.80 21 | 95.66 38 | 99.40 33 | 99.62 8 |
|
X-MVStestdata | | | 91.71 177 | 89.67 234 | 97.81 18 | 99.38 8 | 94.03 32 | 98.59 7 | 98.20 31 | 94.85 17 | 96.59 38 | 32.69 352 | 91.70 37 | 99.80 21 | 95.66 38 | 99.40 33 | 99.62 8 |
|
HFP-MVS | | | 97.14 15 | 96.92 16 | 97.83 16 | 99.42 3 | 94.12 28 | 98.52 10 | 98.32 19 | 93.21 60 | 97.18 21 | 98.29 37 | 92.08 29 | 99.83 15 | 95.63 40 | 99.59 10 | 99.54 20 |
|
ACMMPR | | | 97.07 18 | 96.84 19 | 97.79 20 | 99.44 2 | 93.88 34 | 98.52 10 | 98.31 21 | 93.21 60 | 97.15 23 | 98.33 31 | 91.35 42 | 99.86 8 | 95.63 40 | 99.59 10 | 99.62 8 |
|
HPM-MVS | | | 96.69 34 | 96.45 36 | 97.40 41 | 99.36 12 | 93.11 56 | 98.87 1 | 98.06 58 | 91.17 124 | 96.40 46 | 97.99 51 | 90.99 47 | 99.58 56 | 95.61 42 | 99.61 9 | 99.49 27 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
CP-MVS | | | 97.02 21 | 96.81 22 | 97.64 33 | 99.33 14 | 93.54 45 | 98.80 3 | 98.28 23 | 92.99 69 | 96.45 45 | 98.30 36 | 91.90 34 | 99.85 11 | 95.61 42 | 99.68 2 | 99.54 20 |
|
DeepC-MVS | | 93.07 3 | 96.06 50 | 95.66 51 | 97.29 46 | 97.96 88 | 93.17 55 | 97.30 112 | 98.06 58 | 93.92 40 | 93.38 106 | 98.66 5 | 86.83 95 | 99.73 26 | 95.60 44 | 99.22 50 | 98.96 72 |
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 18 | 96.84 19 | 97.77 23 | 99.46 1 | 93.79 38 | 98.52 10 | 98.24 28 | 93.19 63 | 97.14 24 | 98.34 28 | 91.59 40 | 99.87 5 | 95.46 45 | 99.59 10 | 99.64 4 |
|
lupinMVS | | | 94.99 75 | 94.56 73 | 96.29 89 | 96.34 162 | 91.21 106 | 95.83 235 | 96.27 216 | 88.93 185 | 96.22 49 | 96.88 106 | 86.20 102 | 98.85 136 | 95.27 46 | 99.05 63 | 98.82 86 |
|
mPP-MVS | | | 96.86 27 | 96.60 29 | 97.64 33 | 99.40 5 | 93.44 48 | 98.50 13 | 98.09 50 | 93.27 59 | 95.95 61 | 98.33 31 | 91.04 46 | 99.88 3 | 95.20 47 | 99.57 14 | 99.60 11 |
|
test_part3 | | | | | | | | 97.50 91 | | 93.81 45 | | 98.53 12 | | 99.87 5 | 95.19 48 | | |
|
ESAPD | | | 97.57 4 | 97.29 7 | 98.41 2 | 99.28 17 | 95.74 3 | 97.50 91 | 98.26 25 | 93.81 45 | 98.10 7 | 98.53 12 | 95.31 1 | 99.87 5 | 95.19 48 | 99.63 5 | 99.63 5 |
|
DeepC-MVS_fast | | 93.89 2 | 96.93 26 | 96.64 28 | 97.78 21 | 98.64 47 | 94.30 21 | 97.41 99 | 98.04 65 | 94.81 22 | 96.59 38 | 98.37 24 | 91.24 43 | 99.64 47 | 95.16 50 | 99.52 18 | 99.42 35 |
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 80 | 94.39 81 | 96.18 94 | 95.52 191 | 90.93 119 | 96.09 221 | 96.52 209 | 89.28 166 | 96.01 59 | 97.32 90 | 84.70 119 | 98.77 143 | 95.15 51 | 98.91 69 | 98.85 83 |
jason: jason. |
#test# | | | 97.02 21 | 96.75 26 | 97.83 16 | 99.42 3 | 94.12 28 | 98.15 29 | 98.32 19 | 92.57 83 | 97.18 21 | 98.29 37 | 92.08 29 | 99.83 15 | 95.12 52 | 99.59 10 | 99.54 20 |
|
abl_6 | | | 96.40 42 | 96.21 42 | 96.98 60 | 98.89 34 | 92.20 79 | 97.89 45 | 98.03 67 | 93.34 58 | 97.22 20 | 98.42 19 | 87.93 80 | 99.72 29 | 95.10 53 | 99.07 62 | 99.02 65 |
|
train_agg | | | 96.30 45 | 95.83 48 | 97.72 26 | 98.70 39 | 94.19 25 | 96.41 194 | 98.02 68 | 88.58 197 | 96.03 55 | 97.56 84 | 92.73 16 | 99.59 53 | 95.04 54 | 99.37 40 | 99.39 37 |
|
agg_prior3 | | | 96.16 49 | 95.67 50 | 97.62 36 | 98.67 41 | 93.88 34 | 96.41 194 | 98.00 72 | 87.93 222 | 95.81 65 | 97.47 88 | 92.33 24 | 99.59 53 | 95.04 54 | 99.37 40 | 99.39 37 |
|
agg_prior1 | | | 96.22 48 | 95.77 49 | 97.56 37 | 98.67 41 | 93.79 38 | 96.28 210 | 98.00 72 | 88.76 194 | 95.68 69 | 97.55 86 | 92.70 18 | 99.57 64 | 95.01 56 | 99.32 42 | 99.32 44 |
|
test_prior3 | | | 96.46 41 | 96.20 43 | 97.23 50 | 98.67 41 | 92.99 58 | 96.35 202 | 98.00 72 | 92.80 79 | 96.03 55 | 97.59 80 | 92.01 31 | 99.41 86 | 95.01 56 | 99.38 36 | 99.29 46 |
|
test_prior2 | | | | | | | | 96.35 202 | | 92.80 79 | 96.03 55 | 97.59 80 | 92.01 31 | | 95.01 56 | 99.38 36 | |
|
nrg030 | | | 94.05 96 | 93.31 104 | 96.27 90 | 95.22 209 | 94.59 15 | 98.34 19 | 97.46 125 | 92.93 76 | 91.21 168 | 96.64 118 | 87.23 92 | 98.22 187 | 94.99 59 | 85.80 261 | 95.98 203 |
|
VDDNet | | | 93.05 127 | 92.07 135 | 96.02 98 | 96.84 138 | 90.39 133 | 98.08 33 | 95.85 238 | 86.22 260 | 95.79 67 | 98.46 15 | 67.59 313 | 99.19 100 | 94.92 60 | 94.85 148 | 98.47 108 |
|
APD-MVS | | | 96.95 24 | 96.60 29 | 98.01 10 | 99.03 30 | 94.93 12 | 97.72 61 | 98.10 48 | 91.50 113 | 98.01 9 | 98.32 33 | 92.33 24 | 99.58 56 | 94.85 61 | 99.51 20 | 99.53 23 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
MP-MVS | | | 96.77 31 | 96.45 36 | 97.72 26 | 99.39 7 | 93.80 37 | 98.41 17 | 98.06 58 | 93.37 55 | 95.54 76 | 98.34 28 | 90.59 53 | 99.88 3 | 94.83 62 | 99.54 16 | 99.49 27 |
|
test9_res | | | | | | | | | | | | | | | 94.81 63 | 99.38 36 | 99.45 31 |
|
PS-MVSNAJ | | | 95.37 62 | 95.33 58 | 95.49 121 | 97.35 122 | 90.66 127 | 95.31 258 | 97.48 120 | 93.85 42 | 96.51 41 | 95.70 168 | 88.65 71 | 99.65 42 | 94.80 64 | 98.27 82 | 96.17 190 |
|
HPM-MVS_fast | | | 96.51 39 | 96.27 40 | 97.22 52 | 99.32 15 | 92.74 64 | 98.74 4 | 98.06 58 | 90.57 145 | 96.77 31 | 98.35 25 | 90.21 57 | 99.53 71 | 94.80 64 | 99.63 5 | 99.38 40 |
|
xiu_mvs_v2_base | | | 95.32 64 | 95.29 59 | 95.40 127 | 97.22 124 | 90.50 130 | 95.44 253 | 97.44 132 | 93.70 49 | 96.46 44 | 96.18 141 | 88.59 74 | 99.53 71 | 94.79 66 | 97.81 93 | 96.17 190 |
|
CSCG | | | 96.05 51 | 95.91 47 | 96.46 79 | 99.24 21 | 90.47 131 | 98.30 21 | 98.57 11 | 89.01 179 | 93.97 98 | 97.57 82 | 92.62 19 | 99.76 24 | 94.66 67 | 99.27 46 | 99.15 56 |
|
ACMMP | | | 96.27 46 | 95.93 46 | 97.28 47 | 99.24 21 | 92.62 68 | 98.25 25 | 98.81 3 | 92.99 69 | 94.56 87 | 98.39 23 | 88.96 66 | 99.85 11 | 94.57 68 | 97.63 97 | 99.36 42 |
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 29 | 96.53 32 | 97.65 31 | 99.35 13 | 93.53 46 | 97.65 70 | 98.98 1 | 92.22 88 | 97.14 24 | 98.44 17 | 91.17 44 | 99.85 11 | 94.35 69 | 99.46 26 | 99.57 14 |
|
LFMVS | | | 93.60 110 | 92.63 121 | 96.52 71 | 98.13 80 | 91.27 105 | 97.94 41 | 93.39 316 | 90.57 145 | 96.29 47 | 98.31 34 | 69.00 306 | 99.16 104 | 94.18 70 | 95.87 136 | 99.12 60 |
|
MVSFormer | | | 95.37 62 | 95.16 62 | 95.99 100 | 96.34 162 | 91.21 106 | 98.22 26 | 97.57 112 | 91.42 117 | 96.22 49 | 97.32 90 | 86.20 102 | 97.92 239 | 94.07 71 | 99.05 63 | 98.85 83 |
|
test_djsdf | | | 93.07 126 | 92.76 114 | 94.00 190 | 93.49 287 | 88.70 193 | 98.22 26 | 97.57 112 | 91.42 117 | 90.08 192 | 95.55 175 | 82.85 157 | 97.92 239 | 94.07 71 | 91.58 206 | 95.40 232 |
|
mvs_anonymous | | | 93.82 103 | 93.74 87 | 94.06 187 | 96.44 159 | 85.41 267 | 95.81 236 | 97.05 169 | 89.85 156 | 90.09 191 | 96.36 136 | 87.44 89 | 97.75 255 | 93.97 73 | 96.69 123 | 99.02 65 |
|
VPA-MVSNet | | | 93.24 121 | 92.48 130 | 95.51 119 | 95.70 187 | 92.39 73 | 97.86 47 | 98.66 9 | 92.30 87 | 92.09 140 | 95.37 184 | 80.49 204 | 98.40 175 | 93.95 74 | 85.86 260 | 95.75 216 |
|
agg_prior2 | | | | | | | | | | | | | | | 93.94 75 | 99.38 36 | 99.50 25 |
|
mvs_tets | | | 92.31 157 | 91.76 144 | 93.94 198 | 93.41 289 | 88.29 200 | 97.63 81 | 97.53 116 | 92.04 102 | 88.76 230 | 96.45 132 | 74.62 279 | 98.09 200 | 93.91 76 | 91.48 208 | 95.45 226 |
|
Effi-MVS+ | | | 94.93 76 | 94.45 79 | 96.36 84 | 96.61 145 | 91.47 99 | 96.41 194 | 97.41 136 | 91.02 129 | 94.50 88 | 95.92 151 | 87.53 87 | 98.78 141 | 93.89 77 | 96.81 118 | 98.84 85 |
|
jajsoiax | | | 92.42 152 | 91.89 142 | 94.03 189 | 93.33 293 | 88.50 197 | 97.73 59 | 97.53 116 | 92.00 104 | 88.85 229 | 96.50 130 | 75.62 272 | 98.11 197 | 93.88 78 | 91.56 207 | 95.48 222 |
|
XVG-OURS-SEG-HR | | | 93.86 102 | 93.55 93 | 94.81 156 | 97.06 132 | 88.53 196 | 95.28 259 | 97.45 129 | 91.68 110 | 94.08 95 | 97.68 70 | 82.41 169 | 98.90 131 | 93.84 79 | 92.47 190 | 96.98 160 |
|
PS-MVSNAJss | | | 93.74 106 | 93.51 96 | 94.44 174 | 93.91 274 | 89.28 183 | 97.75 55 | 97.56 115 | 92.50 84 | 89.94 194 | 96.54 128 | 88.65 71 | 98.18 191 | 93.83 80 | 90.90 217 | 95.86 205 |
|
EPNet | | | 95.20 68 | 94.56 73 | 97.14 55 | 92.80 306 | 92.68 66 | 97.85 49 | 94.87 283 | 96.64 1 | 92.46 129 | 97.80 64 | 86.23 100 | 99.65 42 | 93.72 81 | 98.62 75 | 99.10 62 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
test_normal | | | 92.01 167 | 90.75 190 | 95.80 106 | 93.24 295 | 89.97 141 | 95.93 231 | 96.24 219 | 90.62 140 | 81.63 300 | 93.45 271 | 74.98 276 | 98.89 133 | 93.61 82 | 97.04 114 | 98.55 96 |
|
PVSNet_Blended_VisFu | | | 95.27 65 | 94.91 65 | 96.38 82 | 98.20 76 | 90.86 121 | 97.27 113 | 98.25 27 | 90.21 148 | 94.18 94 | 97.27 92 | 87.48 88 | 99.73 26 | 93.53 83 | 97.77 95 | 98.55 96 |
|
DI_MVS_plusplus_test | | | 92.01 167 | 90.77 188 | 95.73 111 | 93.34 291 | 89.78 148 | 96.14 219 | 96.18 222 | 90.58 144 | 81.80 299 | 93.50 268 | 74.95 277 | 98.90 131 | 93.51 84 | 96.94 115 | 98.51 101 |
|
CPTT-MVS | | | 95.57 60 | 95.19 61 | 96.70 63 | 99.27 19 | 91.48 98 | 98.33 20 | 98.11 46 | 87.79 225 | 95.17 80 | 98.03 47 | 87.09 93 | 99.61 48 | 93.51 84 | 99.42 31 | 99.02 65 |
|
MVSTER | | | 93.20 122 | 92.81 113 | 94.37 177 | 96.56 150 | 89.59 160 | 97.06 131 | 97.12 160 | 91.24 123 | 91.30 157 | 95.96 149 | 82.02 177 | 98.05 213 | 93.48 86 | 90.55 222 | 95.47 224 |
|
PVSNet_BlendedMVS | | | 94.06 95 | 93.92 83 | 94.47 173 | 98.27 69 | 89.46 168 | 96.73 164 | 98.36 16 | 90.17 149 | 94.36 90 | 95.24 190 | 88.02 77 | 99.58 56 | 93.44 87 | 90.72 220 | 94.36 286 |
|
PVSNet_Blended | | | 94.87 79 | 94.56 73 | 95.81 105 | 98.27 69 | 89.46 168 | 95.47 252 | 98.36 16 | 88.84 188 | 94.36 90 | 96.09 147 | 88.02 77 | 99.58 56 | 93.44 87 | 98.18 84 | 98.40 115 |
|
3Dnovator | | 91.36 5 | 95.19 69 | 94.44 80 | 97.44 40 | 96.56 150 | 93.36 52 | 98.65 6 | 98.36 16 | 94.12 37 | 89.25 225 | 98.06 46 | 82.20 174 | 99.77 23 | 93.41 89 | 99.32 42 | 99.18 53 |
|
EPP-MVSNet | | | 95.22 67 | 95.04 64 | 95.76 107 | 97.49 121 | 89.56 161 | 98.67 5 | 97.00 176 | 90.69 134 | 94.24 93 | 97.62 78 | 89.79 62 | 98.81 139 | 93.39 90 | 96.49 127 | 98.92 77 |
|
CHOSEN 280x420 | | | 93.12 124 | 92.72 119 | 94.34 179 | 96.71 144 | 87.27 238 | 90.29 326 | 97.72 98 | 86.61 256 | 91.34 154 | 95.29 187 | 84.29 124 | 98.41 174 | 93.25 91 | 98.94 68 | 97.35 156 |
|
3Dnovator+ | | 91.43 4 | 95.40 61 | 94.48 78 | 98.16 7 | 96.90 136 | 95.34 6 | 98.48 14 | 97.87 86 | 94.65 28 | 88.53 235 | 98.02 48 | 83.69 128 | 99.71 30 | 93.18 92 | 98.96 67 | 99.44 33 |
|
HQP_MVS | | | 93.78 105 | 93.43 100 | 94.82 154 | 96.21 166 | 89.99 138 | 97.74 57 | 97.51 118 | 94.85 17 | 91.34 154 | 96.64 118 | 81.32 188 | 98.60 154 | 93.02 93 | 92.23 193 | 95.86 205 |
|
plane_prior5 | | | | | | | | | 97.51 118 | | | | | 98.60 154 | 93.02 93 | 92.23 193 | 95.86 205 |
|
MVS_Test | | | 94.89 78 | 94.62 71 | 95.68 112 | 96.83 140 | 89.55 162 | 96.70 172 | 97.17 153 | 91.17 124 | 95.60 73 | 96.11 146 | 87.87 81 | 98.76 144 | 93.01 95 | 97.17 111 | 98.72 89 |
|
CLD-MVS | | | 92.98 129 | 92.53 127 | 94.32 180 | 96.12 175 | 89.20 185 | 95.28 259 | 97.47 123 | 92.66 81 | 89.90 195 | 95.62 171 | 80.58 202 | 98.40 175 | 92.73 96 | 92.40 191 | 95.38 234 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
XVG-OURS | | | 93.72 107 | 93.35 103 | 94.80 157 | 97.07 130 | 88.61 194 | 94.79 268 | 97.46 125 | 91.97 105 | 93.99 96 | 97.86 58 | 81.74 183 | 98.88 135 | 92.64 97 | 92.67 189 | 96.92 168 |
|
旧先验2 | | | | | | | | 95.94 230 | | 81.66 304 | 97.34 18 | | | 98.82 138 | 92.26 98 | | |
|
CDPH-MVS | | | 95.97 54 | 95.38 56 | 97.77 23 | 98.93 32 | 94.44 18 | 96.35 202 | 97.88 84 | 86.98 246 | 96.65 35 | 97.89 53 | 91.99 33 | 99.47 79 | 92.26 98 | 99.46 26 | 99.39 37 |
|
FIs | | | 94.09 94 | 93.70 88 | 95.27 129 | 95.70 187 | 92.03 84 | 98.10 31 | 98.68 7 | 93.36 57 | 90.39 178 | 96.70 113 | 87.63 85 | 97.94 235 | 92.25 100 | 90.50 224 | 95.84 208 |
|
LPG-MVS_test | | | 92.94 131 | 92.56 124 | 94.10 185 | 96.16 171 | 88.26 202 | 97.65 70 | 97.46 125 | 91.29 120 | 90.12 188 | 97.16 97 | 79.05 225 | 98.73 146 | 92.25 100 | 91.89 201 | 95.31 238 |
|
LGP-MVS_train | | | | | 94.10 185 | 96.16 171 | 88.26 202 | | 97.46 125 | 91.29 120 | 90.12 188 | 97.16 97 | 79.05 225 | 98.73 146 | 92.25 100 | 91.89 201 | 95.31 238 |
|
cascas | | | 91.20 209 | 90.08 217 | 94.58 171 | 94.97 221 | 89.16 187 | 93.65 292 | 97.59 111 | 79.90 316 | 89.40 217 | 92.92 278 | 75.36 273 | 98.36 178 | 92.14 103 | 94.75 152 | 96.23 187 |
|
OPM-MVS | | | 93.28 120 | 92.76 114 | 94.82 154 | 94.63 237 | 90.77 125 | 96.65 177 | 97.18 151 | 93.72 47 | 91.68 147 | 97.26 93 | 79.33 222 | 98.63 151 | 92.13 104 | 92.28 192 | 95.07 251 |
|
BP-MVS | | | | | | | | | | | | | | | 92.13 104 | | |
|
HQP-MVS | | | 93.19 123 | 92.74 118 | 94.54 172 | 95.86 180 | 89.33 178 | 96.65 177 | 97.39 137 | 93.55 50 | 90.14 182 | 95.87 153 | 80.95 193 | 98.50 163 | 92.13 104 | 92.10 198 | 95.78 212 |
|
DP-MVS Recon | | | 95.68 58 | 95.12 63 | 97.37 42 | 99.19 24 | 94.19 25 | 97.03 132 | 98.08 51 | 88.35 211 | 95.09 81 | 97.65 73 | 89.97 60 | 99.48 78 | 92.08 107 | 98.59 76 | 98.44 112 |
|
testing_2 | | | 87.33 281 | 85.03 288 | 94.22 181 | 87.77 332 | 89.32 180 | 94.97 266 | 97.11 162 | 89.22 168 | 71.64 334 | 88.73 320 | 55.16 339 | 97.94 235 | 91.95 108 | 88.73 241 | 95.41 228 |
|
Test4 | | | 89.48 251 | 87.50 261 | 95.44 126 | 90.76 320 | 89.72 149 | 95.78 239 | 97.09 163 | 90.28 147 | 77.67 325 | 91.74 299 | 55.42 338 | 98.08 201 | 91.92 109 | 96.83 117 | 98.52 99 |
|
VPNet | | | 92.23 162 | 91.31 167 | 94.99 144 | 95.56 190 | 90.96 117 | 97.22 120 | 97.86 88 | 92.96 75 | 90.96 170 | 96.62 125 | 75.06 275 | 98.20 188 | 91.90 110 | 83.65 294 | 95.80 211 |
|
sss | | | 94.51 84 | 93.80 86 | 96.64 64 | 97.07 130 | 91.97 87 | 96.32 206 | 98.06 58 | 88.94 184 | 94.50 88 | 96.78 108 | 84.60 120 | 99.27 96 | 91.90 110 | 96.02 132 | 98.68 93 |
|
anonymousdsp | | | 92.16 164 | 91.55 158 | 93.97 193 | 92.58 310 | 89.55 162 | 97.51 90 | 97.42 135 | 89.42 164 | 88.40 236 | 94.84 204 | 80.66 201 | 97.88 244 | 91.87 112 | 91.28 212 | 94.48 282 |
|
ACMP | | 89.59 10 | 92.62 141 | 92.14 134 | 94.05 188 | 96.40 160 | 88.20 208 | 97.36 106 | 97.25 150 | 91.52 112 | 88.30 239 | 96.64 118 | 78.46 243 | 98.72 148 | 91.86 113 | 91.48 208 | 95.23 245 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
HyFIR lowres test | | | 93.66 108 | 92.92 111 | 95.87 103 | 98.24 72 | 89.88 145 | 94.58 271 | 98.49 12 | 85.06 273 | 93.78 99 | 95.78 162 | 82.86 156 | 98.67 149 | 91.77 114 | 95.71 140 | 99.07 64 |
|
UGNet | | | 94.04 97 | 93.28 105 | 96.31 86 | 96.85 137 | 91.19 109 | 97.88 46 | 97.68 103 | 94.40 31 | 93.00 121 | 96.18 141 | 73.39 289 | 99.61 48 | 91.72 115 | 98.46 78 | 98.13 124 |
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 | | | 93.37 117 | 92.67 120 | 95.47 124 | 95.34 199 | 92.83 62 | 97.17 125 | 98.58 10 | 92.98 74 | 90.13 186 | 95.80 158 | 88.37 76 | 97.85 245 | 91.71 116 | 83.93 288 | 95.73 218 |
|
DU-MVS | | | 92.90 133 | 92.04 136 | 95.49 121 | 94.95 223 | 92.83 62 | 97.16 126 | 98.24 28 | 93.02 68 | 90.13 186 | 95.71 166 | 83.47 130 | 97.85 245 | 91.71 116 | 83.93 288 | 95.78 212 |
|
Effi-MVS+-dtu | | | 93.08 125 | 93.21 106 | 92.68 253 | 96.02 177 | 83.25 289 | 97.14 128 | 96.72 197 | 93.85 42 | 91.20 169 | 93.44 272 | 83.08 141 | 98.30 184 | 91.69 118 | 95.73 139 | 96.50 182 |
|
mvs-test1 | | | 93.63 109 | 93.69 89 | 93.46 227 | 96.02 177 | 84.61 277 | 97.24 115 | 96.72 197 | 93.85 42 | 92.30 135 | 95.76 163 | 83.08 141 | 98.89 133 | 91.69 118 | 96.54 126 | 96.87 170 |
|
UniMVSNet (Re) | | | 93.31 119 | 92.55 125 | 95.61 114 | 95.39 196 | 93.34 53 | 97.39 103 | 98.71 5 | 93.14 65 | 90.10 190 | 94.83 206 | 87.71 82 | 98.03 218 | 91.67 120 | 83.99 287 | 95.46 225 |
|
LCM-MVSNet-Re | | | 92.50 146 | 92.52 128 | 92.44 256 | 96.82 141 | 81.89 297 | 96.92 146 | 93.71 311 | 92.41 86 | 84.30 284 | 94.60 216 | 85.08 114 | 97.03 291 | 91.51 121 | 97.36 106 | 98.40 115 |
|
FC-MVSNet-test | | | 93.94 100 | 93.57 92 | 95.04 142 | 95.48 193 | 91.45 101 | 98.12 30 | 98.71 5 | 93.37 55 | 90.23 181 | 96.70 113 | 87.66 83 | 97.85 245 | 91.49 122 | 90.39 225 | 95.83 209 |
|
PMMVS | | | 92.86 135 | 92.34 132 | 94.42 176 | 94.92 225 | 86.73 251 | 94.53 273 | 96.38 212 | 84.78 278 | 94.27 92 | 95.12 195 | 83.13 137 | 98.40 175 | 91.47 123 | 96.49 127 | 98.12 125 |
|
Vis-MVSNet | | | 95.23 66 | 94.81 66 | 96.51 74 | 97.18 126 | 91.58 97 | 98.26 24 | 98.12 43 | 94.38 33 | 94.90 82 | 98.15 42 | 82.28 171 | 98.92 129 | 91.45 124 | 98.58 77 | 99.01 69 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
CHOSEN 1792x2688 | | | 94.15 90 | 93.51 96 | 96.06 96 | 98.27 69 | 89.38 174 | 95.18 264 | 98.48 14 | 85.60 266 | 93.76 100 | 97.11 100 | 83.15 135 | 99.61 48 | 91.33 125 | 98.72 73 | 99.19 52 |
|
OMC-MVS | | | 95.09 70 | 94.70 70 | 96.25 92 | 98.46 54 | 91.28 104 | 96.43 192 | 97.57 112 | 92.04 102 | 94.77 85 | 97.96 52 | 87.01 94 | 99.09 118 | 91.31 126 | 96.77 119 | 98.36 119 |
|
MG-MVS | | | 95.61 59 | 95.38 56 | 96.31 86 | 98.42 57 | 90.53 129 | 96.04 224 | 97.48 120 | 93.47 54 | 95.67 72 | 98.10 43 | 89.17 64 | 99.25 97 | 91.27 127 | 98.77 71 | 99.13 58 |
|
ACMM | | 89.79 8 | 92.96 130 | 92.50 129 | 94.35 178 | 96.30 164 | 88.71 192 | 97.58 86 | 97.36 142 | 91.40 119 | 90.53 174 | 96.65 117 | 79.77 215 | 98.75 145 | 91.24 128 | 91.64 204 | 95.59 221 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
WTY-MVS | | | 94.71 82 | 94.02 82 | 96.79 62 | 97.71 105 | 92.05 83 | 96.59 185 | 97.35 143 | 90.61 142 | 94.64 86 | 96.93 104 | 86.41 99 | 99.39 89 | 91.20 129 | 94.71 154 | 98.94 75 |
|
CANet_DTU | | | 94.37 85 | 93.65 91 | 96.55 70 | 96.46 158 | 92.13 81 | 96.21 216 | 96.67 204 | 94.38 33 | 93.53 103 | 97.03 103 | 79.34 221 | 99.71 30 | 90.76 130 | 98.45 79 | 97.82 139 |
|
ab-mvs | | | 93.57 112 | 92.55 125 | 96.64 64 | 97.28 123 | 91.96 88 | 95.40 254 | 97.45 129 | 89.81 158 | 93.22 114 | 96.28 138 | 79.62 218 | 99.46 80 | 90.74 131 | 93.11 184 | 98.50 103 |
|
CostFormer | | | 91.18 212 | 90.70 192 | 92.62 254 | 94.84 229 | 81.76 298 | 94.09 284 | 94.43 295 | 84.15 283 | 92.72 128 | 93.77 259 | 79.43 220 | 98.20 188 | 90.70 132 | 92.18 196 | 97.90 133 |
|
tpmrst | | | 91.44 199 | 91.32 166 | 91.79 278 | 95.15 213 | 79.20 319 | 93.42 295 | 95.37 255 | 88.55 199 | 93.49 104 | 93.67 262 | 82.49 166 | 98.27 185 | 90.41 133 | 89.34 234 | 97.90 133 |
|
UA-Net | | | 95.95 55 | 95.53 52 | 97.20 54 | 97.67 106 | 92.98 60 | 97.65 70 | 98.13 42 | 94.81 22 | 96.61 36 | 98.35 25 | 88.87 67 | 99.51 75 | 90.36 134 | 97.35 107 | 99.11 61 |
|
IS-MVSNet | | | 94.90 77 | 94.52 76 | 96.05 97 | 97.67 106 | 90.56 128 | 98.44 15 | 96.22 220 | 93.21 60 | 93.99 96 | 97.74 67 | 85.55 109 | 98.45 167 | 89.98 135 | 97.86 91 | 99.14 57 |
|
EI-MVSNet | | | 93.03 128 | 92.88 112 | 93.48 225 | 95.77 185 | 86.98 247 | 96.44 190 | 97.12 160 | 90.66 137 | 91.30 157 | 97.64 76 | 86.56 97 | 98.05 213 | 89.91 136 | 90.55 222 | 95.41 228 |
|
IterMVS-LS | | | 92.29 159 | 91.94 141 | 93.34 232 | 96.25 165 | 86.97 248 | 96.57 188 | 97.05 169 | 90.67 135 | 89.50 216 | 94.80 208 | 86.59 96 | 97.64 263 | 89.91 136 | 86.11 259 | 95.40 232 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CDS-MVSNet | | | 94.14 92 | 93.54 94 | 95.93 101 | 96.18 169 | 91.46 100 | 96.33 205 | 97.04 172 | 88.97 183 | 93.56 101 | 96.51 129 | 87.55 86 | 97.89 243 | 89.80 138 | 95.95 134 | 98.44 112 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
WR-MVS | | | 92.34 155 | 91.53 159 | 94.77 160 | 95.13 215 | 90.83 122 | 96.40 198 | 97.98 78 | 91.88 106 | 89.29 222 | 95.54 176 | 82.50 165 | 97.80 250 | 89.79 139 | 85.27 267 | 95.69 219 |
|
NR-MVSNet | | | 92.34 155 | 91.27 169 | 95.53 118 | 94.95 223 | 93.05 57 | 97.39 103 | 98.07 56 | 92.65 82 | 84.46 282 | 95.71 166 | 85.00 115 | 97.77 254 | 89.71 140 | 83.52 295 | 95.78 212 |
|
testdata | | | | | 95.46 125 | 98.18 79 | 88.90 191 | | 97.66 104 | 82.73 297 | 97.03 30 | 98.07 45 | 90.06 58 | 98.85 136 | 89.67 141 | 98.98 66 | 98.64 94 |
|
Baseline_NR-MVSNet | | | 91.20 209 | 90.62 200 | 92.95 244 | 93.83 277 | 88.03 221 | 97.01 136 | 95.12 269 | 88.42 208 | 89.70 207 | 95.13 194 | 83.47 130 | 97.44 275 | 89.66 142 | 83.24 297 | 93.37 301 |
|
PatchFormer-LS_test | | | 91.68 187 | 91.18 174 | 93.19 239 | 95.24 208 | 83.63 287 | 95.53 249 | 95.44 252 | 89.82 157 | 91.37 152 | 92.58 284 | 80.85 200 | 98.52 161 | 89.65 143 | 90.16 227 | 97.42 155 |
|
XXY-MVS | | | 92.16 164 | 91.23 171 | 94.95 149 | 94.75 233 | 90.94 118 | 97.47 97 | 97.43 134 | 89.14 176 | 88.90 227 | 96.43 133 | 79.71 216 | 98.24 186 | 89.56 144 | 87.68 248 | 95.67 220 |
|
diffmvs | | | 93.43 116 | 92.75 116 | 95.48 123 | 96.47 157 | 89.61 158 | 96.09 221 | 97.14 157 | 85.97 263 | 93.09 119 | 95.35 185 | 84.87 117 | 98.55 159 | 89.51 145 | 96.26 131 | 98.28 121 |
|
XVG-ACMP-BASELINE | | | 90.93 218 | 90.21 215 | 93.09 240 | 94.31 248 | 85.89 260 | 95.33 256 | 97.26 148 | 91.06 128 | 89.38 218 | 95.44 183 | 68.61 308 | 98.60 154 | 89.46 146 | 91.05 215 | 94.79 273 |
|
AdaColmap | | | 94.34 86 | 93.68 90 | 96.31 86 | 98.59 49 | 91.68 93 | 96.59 185 | 97.81 91 | 89.87 153 | 92.15 138 | 97.06 102 | 83.62 129 | 99.54 68 | 89.34 147 | 98.07 87 | 97.70 143 |
|
TranMVSNet+NR-MVSNet | | | 92.50 146 | 91.63 153 | 95.14 137 | 94.76 232 | 92.07 82 | 97.53 89 | 98.11 46 | 92.90 77 | 89.56 213 | 96.12 144 | 83.16 134 | 97.60 266 | 89.30 148 | 83.20 298 | 95.75 216 |
|
1314 | | | 92.81 138 | 92.03 137 | 95.14 137 | 95.33 202 | 89.52 165 | 96.04 224 | 97.44 132 | 87.72 228 | 86.25 271 | 95.33 186 | 83.84 126 | 98.79 140 | 89.26 149 | 97.05 113 | 97.11 158 |
|
v2v482 | | | 91.59 191 | 90.85 185 | 93.80 202 | 93.87 276 | 88.17 210 | 96.94 145 | 96.88 191 | 89.54 160 | 89.53 214 | 94.90 199 | 81.70 184 | 98.02 221 | 89.25 150 | 85.04 275 | 95.20 246 |
|
114514_t | | | 93.95 99 | 93.06 108 | 96.63 66 | 99.07 28 | 91.61 94 | 97.46 98 | 97.96 80 | 77.99 324 | 93.00 121 | 97.57 82 | 86.14 104 | 99.33 93 | 89.22 151 | 99.15 55 | 98.94 75 |
|
PAPM_NR | | | 95.01 71 | 94.59 72 | 96.26 91 | 98.89 34 | 90.68 126 | 97.24 115 | 97.73 95 | 91.80 107 | 92.93 126 | 96.62 125 | 89.13 65 | 99.14 107 | 89.21 152 | 97.78 94 | 98.97 71 |
|
IB-MVS | | 87.33 17 | 89.91 244 | 88.28 255 | 94.79 159 | 95.26 207 | 87.70 233 | 95.12 265 | 93.95 309 | 89.35 165 | 87.03 264 | 92.49 285 | 70.74 300 | 99.19 100 | 89.18 153 | 81.37 307 | 97.49 153 |
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 | 93.87 101 | 92.95 110 | 96.63 66 | 97.10 129 | 92.49 72 | 95.64 244 | 96.64 205 | 89.05 178 | 93.00 121 | 95.79 161 | 85.77 108 | 99.45 82 | 89.16 154 | 94.35 155 | 97.96 130 |
|
v6 | | | 91.69 182 | 91.00 178 | 93.75 207 | 94.14 256 | 88.12 215 | 97.20 121 | 96.98 177 | 89.19 169 | 89.90 195 | 94.42 226 | 83.04 145 | 98.07 205 | 89.07 155 | 85.10 270 | 95.07 251 |
|
v1neww | | | 91.70 180 | 91.01 176 | 93.75 207 | 94.19 251 | 88.14 213 | 97.20 121 | 96.98 177 | 89.18 171 | 89.87 198 | 94.44 224 | 83.10 139 | 98.06 210 | 89.06 156 | 85.09 271 | 95.06 254 |
|
v7new | | | 91.70 180 | 91.01 176 | 93.75 207 | 94.19 251 | 88.14 213 | 97.20 121 | 96.98 177 | 89.18 171 | 89.87 198 | 94.44 224 | 83.10 139 | 98.06 210 | 89.06 156 | 85.09 271 | 95.06 254 |
|
V42 | | | 91.58 192 | 90.87 183 | 93.73 210 | 94.05 268 | 88.50 197 | 97.32 110 | 96.97 180 | 88.80 193 | 89.71 206 | 94.33 231 | 82.54 164 | 98.05 213 | 89.01 158 | 85.07 273 | 94.64 279 |
|
OurMVSNet-221017-0 | | | 90.51 233 | 90.19 216 | 91.44 286 | 93.41 289 | 81.25 301 | 96.98 138 | 96.28 215 | 91.68 110 | 86.55 269 | 96.30 137 | 74.20 282 | 97.98 226 | 88.96 159 | 87.40 253 | 95.09 248 |
|
API-MVS | | | 94.84 80 | 94.49 77 | 95.90 102 | 97.90 96 | 92.00 86 | 97.80 52 | 97.48 120 | 89.19 169 | 94.81 84 | 96.71 111 | 88.84 68 | 99.17 103 | 88.91 160 | 98.76 72 | 96.53 180 |
|
divwei89l23v2f112 | | | 91.61 188 | 90.89 180 | 93.78 204 | 94.01 269 | 88.22 206 | 96.96 139 | 96.96 181 | 89.17 173 | 89.75 204 | 94.28 242 | 83.02 147 | 98.03 218 | 88.86 161 | 84.98 278 | 95.08 249 |
|
v1141 | | | 91.61 188 | 90.89 180 | 93.78 204 | 94.01 269 | 88.24 204 | 96.96 139 | 96.96 181 | 89.17 173 | 89.75 204 | 94.29 240 | 82.99 149 | 98.03 218 | 88.85 162 | 85.00 276 | 95.07 251 |
|
v1 | | | 91.61 188 | 90.89 180 | 93.78 204 | 94.01 269 | 88.21 207 | 96.96 139 | 96.96 181 | 89.17 173 | 89.78 203 | 94.29 240 | 82.97 151 | 98.05 213 | 88.85 162 | 84.99 277 | 95.08 249 |
|
test-LLR | | | 91.42 200 | 91.19 173 | 92.12 268 | 94.59 238 | 80.66 304 | 94.29 278 | 92.98 324 | 91.11 126 | 90.76 172 | 92.37 287 | 79.02 227 | 98.07 205 | 88.81 164 | 96.74 120 | 97.63 144 |
|
test-mter | | | 90.19 240 | 89.54 237 | 92.12 268 | 94.59 238 | 80.66 304 | 94.29 278 | 92.98 324 | 87.68 229 | 90.76 172 | 92.37 287 | 67.67 312 | 98.07 205 | 88.81 164 | 96.74 120 | 97.63 144 |
|
TAMVS | | | 94.01 98 | 93.46 98 | 95.64 113 | 96.16 171 | 90.45 132 | 96.71 169 | 96.89 190 | 89.27 167 | 93.46 105 | 96.92 105 | 87.29 91 | 97.94 235 | 88.70 166 | 95.74 138 | 98.53 98 |
|
Patchmatch-RL test | | | 87.38 280 | 86.24 279 | 90.81 294 | 88.74 328 | 78.40 322 | 88.12 336 | 93.17 317 | 87.11 241 | 82.17 295 | 89.29 317 | 81.95 179 | 95.60 318 | 88.64 167 | 77.02 317 | 98.41 114 |
|
TESTMET0.1,1 | | | 90.06 242 | 89.42 239 | 91.97 272 | 94.41 245 | 80.62 306 | 94.29 278 | 91.97 333 | 87.28 238 | 90.44 177 | 92.47 286 | 68.79 307 | 97.67 260 | 88.50 168 | 96.60 125 | 97.61 148 |
|
Vis-MVSNet (Re-imp) | | | 94.15 90 | 93.88 84 | 94.95 149 | 97.61 110 | 87.92 227 | 98.10 31 | 95.80 241 | 92.22 88 | 93.02 120 | 97.45 89 | 84.53 122 | 97.91 242 | 88.24 169 | 97.97 89 | 99.02 65 |
|
DWT-MVSNet_test | | | 90.76 222 | 89.89 225 | 93.38 230 | 95.04 219 | 83.70 285 | 95.85 234 | 94.30 301 | 88.19 216 | 90.46 176 | 92.80 279 | 73.61 287 | 98.50 163 | 88.16 170 | 90.58 221 | 97.95 131 |
|
1112_ss | | | 93.37 117 | 92.42 131 | 96.21 93 | 97.05 133 | 90.99 115 | 96.31 207 | 96.72 197 | 86.87 252 | 89.83 200 | 96.69 115 | 86.51 98 | 99.14 107 | 88.12 171 | 93.67 172 | 98.50 103 |
|
CVMVSNet | | | 91.23 208 | 91.75 145 | 89.67 307 | 95.77 185 | 74.69 327 | 96.44 190 | 94.88 280 | 85.81 264 | 92.18 137 | 97.64 76 | 79.07 224 | 95.58 319 | 88.06 172 | 95.86 137 | 98.74 87 |
|
v7 | | | 91.47 198 | 90.73 191 | 93.68 215 | 94.13 257 | 88.16 211 | 97.09 130 | 97.05 169 | 88.38 209 | 89.80 201 | 94.52 217 | 82.21 173 | 98.01 222 | 88.00 173 | 85.42 264 | 94.87 263 |
|
MAR-MVS | | | 94.22 88 | 93.46 98 | 96.51 74 | 98.00 83 | 92.19 80 | 97.67 67 | 97.47 123 | 88.13 220 | 93.00 121 | 95.84 155 | 84.86 118 | 99.51 75 | 87.99 174 | 98.17 85 | 97.83 138 |
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 82 | 98.59 49 | 91.09 114 | | 97.89 83 | 87.41 234 | 95.22 79 | 97.68 70 | 90.25 55 | 99.54 68 | 87.95 175 | 99.12 60 | 98.49 105 |
|
CP-MVSNet | | | 91.89 172 | 91.24 170 | 93.82 201 | 95.05 218 | 88.57 195 | 97.82 51 | 98.19 33 | 91.70 109 | 88.21 242 | 95.76 163 | 81.96 178 | 97.52 270 | 87.86 176 | 84.65 281 | 95.37 235 |
|
v148 | | | 90.99 216 | 90.38 206 | 92.81 248 | 93.83 277 | 85.80 261 | 96.78 161 | 96.68 202 | 89.45 163 | 88.75 231 | 93.93 254 | 82.96 153 | 97.82 249 | 87.83 177 | 83.25 296 | 94.80 271 |
|
v1144 | | | 91.37 203 | 90.60 201 | 93.68 215 | 93.89 275 | 88.23 205 | 96.84 151 | 97.03 174 | 88.37 210 | 89.69 208 | 94.39 227 | 82.04 176 | 97.98 226 | 87.80 178 | 85.37 265 | 94.84 265 |
|
gm-plane-assit | | | | | | 93.22 297 | 78.89 321 | | | 84.82 277 | | 93.52 267 | | 98.64 150 | 87.72 179 | | |
|
pmmvs4 | | | 90.93 218 | 89.85 227 | 94.17 183 | 93.34 291 | 90.79 124 | 94.60 270 | 96.02 226 | 84.62 279 | 87.45 253 | 95.15 192 | 81.88 181 | 97.45 274 | 87.70 180 | 87.87 247 | 94.27 290 |
|
Test_1112_low_res | | | 92.84 137 | 91.84 143 | 95.85 104 | 97.04 134 | 89.97 141 | 95.53 249 | 96.64 205 | 85.38 267 | 89.65 210 | 95.18 191 | 85.86 106 | 99.10 115 | 87.70 180 | 93.58 177 | 98.49 105 |
|
æ— å…ˆéªŒ | | | | | | | | 95.79 237 | 97.87 86 | 83.87 288 | | | | 99.65 42 | 87.68 182 | | 98.89 81 |
|
1121 | | | 94.71 82 | 93.83 85 | 97.34 43 | 98.57 52 | 93.64 43 | 96.04 224 | 97.73 95 | 81.56 308 | 95.68 69 | 97.85 59 | 90.23 56 | 99.65 42 | 87.68 182 | 99.12 60 | 98.73 88 |
|
Fast-Effi-MVS+ | | | 93.46 114 | 92.75 116 | 95.59 115 | 96.77 142 | 90.03 135 | 96.81 156 | 97.13 159 | 88.19 216 | 91.30 157 | 94.27 244 | 86.21 101 | 98.63 151 | 87.66 184 | 96.46 129 | 98.12 125 |
|
CNLPA | | | 94.28 87 | 93.53 95 | 96.52 71 | 98.38 61 | 92.55 70 | 96.59 185 | 96.88 191 | 90.13 150 | 91.91 142 | 97.24 94 | 85.21 112 | 99.09 118 | 87.64 185 | 97.83 92 | 97.92 132 |
|
v8 | | | 91.29 207 | 90.53 203 | 93.57 222 | 94.15 255 | 88.12 215 | 97.34 107 | 97.06 168 | 88.99 180 | 88.32 238 | 94.26 246 | 83.08 141 | 98.01 222 | 87.62 186 | 83.92 290 | 94.57 280 |
|
pmmvs5 | | | 89.86 247 | 88.87 247 | 92.82 245 | 92.86 304 | 86.23 258 | 96.26 211 | 95.39 253 | 84.24 282 | 87.12 261 | 94.51 218 | 74.27 281 | 97.36 282 | 87.61 187 | 87.57 249 | 94.86 264 |
|
Fast-Effi-MVS+-dtu | | | 92.29 159 | 91.99 139 | 93.21 238 | 95.27 204 | 85.52 266 | 97.03 132 | 96.63 207 | 92.09 96 | 89.11 226 | 95.14 193 | 80.33 208 | 98.08 201 | 87.54 188 | 94.74 153 | 96.03 202 |
|
OpenMVS | | 89.19 12 | 92.86 135 | 91.68 148 | 96.40 80 | 95.34 199 | 92.73 65 | 98.27 23 | 98.12 43 | 84.86 276 | 85.78 274 | 97.75 66 | 78.89 239 | 99.74 25 | 87.50 189 | 98.65 74 | 96.73 173 |
|
v52 | | | 90.70 228 | 90.00 221 | 92.82 245 | 93.24 295 | 87.03 245 | 97.60 83 | 97.14 157 | 88.21 214 | 87.69 249 | 93.94 253 | 80.91 196 | 98.07 205 | 87.39 190 | 83.87 292 | 93.36 302 |
|
V4 | | | 90.71 227 | 90.00 221 | 92.82 245 | 93.21 298 | 87.03 245 | 97.59 85 | 97.16 156 | 88.21 214 | 87.69 249 | 93.92 255 | 80.93 195 | 98.06 210 | 87.39 190 | 83.90 291 | 93.39 300 |
|
semantic-postprocess | | | | | 91.82 276 | 95.52 191 | 84.20 280 | | 96.15 223 | 90.61 142 | 87.39 256 | 94.27 244 | 75.63 271 | 96.44 298 | 87.34 192 | 86.88 256 | 94.82 269 |
|
PLC | | 91.00 6 | 94.11 93 | 93.43 100 | 96.13 95 | 98.58 51 | 91.15 113 | 96.69 174 | 97.39 137 | 87.29 237 | 91.37 152 | 96.71 111 | 88.39 75 | 99.52 74 | 87.33 193 | 97.13 112 | 97.73 141 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
tpm | | | 90.25 237 | 89.74 233 | 91.76 281 | 93.92 273 | 79.73 315 | 93.98 285 | 93.54 315 | 88.28 212 | 91.99 141 | 93.25 275 | 77.51 262 | 97.44 275 | 87.30 194 | 87.94 246 | 98.12 125 |
|
GA-MVS | | | 91.38 202 | 90.31 207 | 94.59 167 | 94.65 236 | 87.62 234 | 94.34 276 | 96.19 221 | 90.73 133 | 90.35 179 | 93.83 256 | 71.84 292 | 97.96 233 | 87.22 195 | 93.61 175 | 98.21 122 |
|
BH-untuned | | | 92.94 131 | 92.62 122 | 93.92 199 | 97.22 124 | 86.16 259 | 96.40 198 | 96.25 218 | 90.06 151 | 89.79 202 | 96.17 143 | 83.19 133 | 98.35 179 | 87.19 196 | 97.27 109 | 97.24 157 |
|
v144192 | | | 91.06 214 | 90.28 209 | 93.39 229 | 93.66 282 | 87.23 241 | 96.83 152 | 97.07 166 | 87.43 233 | 89.69 208 | 94.28 242 | 81.48 185 | 98.00 225 | 87.18 197 | 84.92 279 | 94.93 261 |
|
RPSCF | | | 90.75 224 | 90.86 184 | 90.42 301 | 96.84 138 | 76.29 325 | 95.61 246 | 96.34 213 | 83.89 286 | 91.38 151 | 97.87 56 | 76.45 265 | 98.78 141 | 87.16 198 | 92.23 193 | 96.20 188 |
|
PS-CasMVS | | | 91.55 194 | 90.84 187 | 93.69 214 | 94.96 222 | 88.28 201 | 97.84 50 | 98.24 28 | 91.46 115 | 88.04 244 | 95.80 158 | 79.67 217 | 97.48 272 | 87.02 199 | 84.54 283 | 95.31 238 |
|
pm-mvs1 | | | 90.72 226 | 89.65 236 | 93.96 194 | 94.29 249 | 89.63 157 | 97.79 53 | 96.82 194 | 89.07 177 | 86.12 273 | 95.48 182 | 78.61 241 | 97.78 252 | 86.97 200 | 81.67 305 | 94.46 283 |
|
IterMVS | | | 90.15 241 | 89.67 234 | 91.61 283 | 95.48 193 | 83.72 283 | 94.33 277 | 96.12 224 | 89.99 152 | 87.31 259 | 94.15 248 | 75.78 270 | 96.27 301 | 86.97 200 | 86.89 255 | 94.83 267 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
F-COLMAP | | | 93.58 111 | 92.98 109 | 95.37 128 | 98.40 58 | 88.98 189 | 97.18 124 | 97.29 147 | 87.75 227 | 90.49 175 | 97.10 101 | 85.21 112 | 99.50 77 | 86.70 202 | 96.72 122 | 97.63 144 |
|
PVSNet | | 86.66 18 | 92.24 161 | 91.74 147 | 93.73 210 | 97.77 102 | 83.69 286 | 92.88 305 | 96.72 197 | 87.91 223 | 93.00 121 | 94.86 203 | 78.51 242 | 99.05 124 | 86.53 203 | 97.45 104 | 98.47 108 |
|
v1192 | | | 91.07 213 | 90.23 213 | 93.58 221 | 93.70 280 | 87.82 230 | 96.73 164 | 97.07 166 | 87.77 226 | 89.58 211 | 94.32 232 | 80.90 199 | 97.97 229 | 86.52 204 | 85.48 262 | 94.95 257 |
|
æ–°å‡ ä½•1 | | | | | 97.32 44 | 98.60 48 | 93.59 44 | | 97.75 93 | 81.58 306 | 95.75 68 | 97.85 59 | 90.04 59 | 99.67 40 | 86.50 205 | 99.13 57 | 98.69 92 |
|
v10 | | | 91.04 215 | 90.23 213 | 93.49 224 | 94.12 259 | 88.16 211 | 97.32 110 | 97.08 165 | 88.26 213 | 88.29 240 | 94.22 247 | 82.17 175 | 97.97 229 | 86.45 206 | 84.12 286 | 94.33 287 |
|
v1921920 | | | 90.85 220 | 90.03 220 | 93.29 234 | 93.55 283 | 86.96 249 | 96.74 163 | 97.04 172 | 87.36 235 | 89.52 215 | 94.34 230 | 80.23 210 | 97.97 229 | 86.27 207 | 85.21 268 | 94.94 259 |
|
MDTV_nov1_ep13_2view | | | | | | | 70.35 335 | 93.10 303 | | 83.88 287 | 93.55 102 | | 82.47 168 | | 86.25 208 | | 98.38 118 |
|
test_post1 | | | | | | | | 92.81 307 | | | | 16.58 356 | 80.53 203 | 97.68 259 | 86.20 209 | | |
|
PAPR | | | 94.18 89 | 93.42 102 | 96.48 76 | 97.64 108 | 91.42 102 | 95.55 247 | 97.71 101 | 88.99 180 | 92.34 134 | 95.82 157 | 89.19 63 | 99.11 109 | 86.14 210 | 97.38 105 | 98.90 79 |
|
GBi-Net | | | 91.35 204 | 90.27 210 | 94.59 167 | 96.51 153 | 91.18 110 | 97.50 91 | 96.93 186 | 88.82 190 | 89.35 219 | 94.51 218 | 73.87 283 | 97.29 285 | 86.12 211 | 88.82 237 | 95.31 238 |
|
test1 | | | 91.35 204 | 90.27 210 | 94.59 167 | 96.51 153 | 91.18 110 | 97.50 91 | 96.93 186 | 88.82 190 | 89.35 219 | 94.51 218 | 73.87 283 | 97.29 285 | 86.12 211 | 88.82 237 | 95.31 238 |
|
FMVSNet3 | | | 91.78 174 | 90.69 193 | 95.03 143 | 96.53 152 | 92.27 76 | 97.02 134 | 96.93 186 | 89.79 159 | 89.35 219 | 94.65 214 | 77.01 263 | 97.47 273 | 86.12 211 | 88.82 237 | 95.35 236 |
|
EPMVS | | | 90.70 228 | 89.81 229 | 93.37 231 | 94.73 234 | 84.21 279 | 93.67 291 | 88.02 343 | 89.50 162 | 92.38 132 | 93.49 269 | 77.82 260 | 97.78 252 | 86.03 214 | 92.68 188 | 98.11 128 |
|
MVS | | | 91.71 177 | 90.44 204 | 95.51 119 | 95.20 211 | 91.59 96 | 96.04 224 | 97.45 129 | 73.44 336 | 87.36 257 | 95.60 172 | 85.42 110 | 99.10 115 | 85.97 215 | 97.46 100 | 95.83 209 |
|
testdata2 | | | | | | | | | | | | | | 99.67 40 | 85.96 216 | | |
|
K. test v3 | | | 87.64 279 | 86.75 277 | 90.32 302 | 93.02 303 | 79.48 317 | 96.61 182 | 92.08 332 | 90.66 137 | 80.25 319 | 94.09 249 | 67.21 316 | 96.65 297 | 85.96 216 | 80.83 310 | 94.83 267 |
|
WR-MVS_H | | | 92.00 169 | 91.35 164 | 93.95 195 | 95.09 217 | 89.47 166 | 98.04 35 | 98.68 7 | 91.46 115 | 88.34 237 | 94.68 212 | 85.86 106 | 97.56 267 | 85.77 218 | 84.24 285 | 94.82 269 |
|
gg-mvs-nofinetune | | | 87.82 277 | 85.61 284 | 94.44 174 | 94.46 242 | 89.27 184 | 91.21 321 | 84.61 349 | 80.88 311 | 89.89 197 | 74.98 341 | 71.50 294 | 97.53 269 | 85.75 219 | 97.21 110 | 96.51 181 |
|
v748 | | | 90.34 235 | 89.54 237 | 92.75 250 | 93.25 294 | 85.71 263 | 97.61 82 | 97.17 153 | 88.54 200 | 87.20 260 | 93.54 266 | 81.02 191 | 98.01 222 | 85.73 220 | 81.80 303 | 94.52 281 |
|
tpm2 | | | 89.96 243 | 89.21 242 | 92.23 262 | 94.91 227 | 81.25 301 | 93.78 288 | 94.42 296 | 80.62 314 | 91.56 148 | 93.44 272 | 76.44 266 | 97.94 235 | 85.60 221 | 92.08 200 | 97.49 153 |
|
v1240 | | | 90.70 228 | 89.85 227 | 93.23 236 | 93.51 286 | 86.80 250 | 96.61 182 | 97.02 175 | 87.16 240 | 89.58 211 | 94.31 233 | 79.55 219 | 97.98 226 | 85.52 222 | 85.44 263 | 94.90 262 |
|
PEN-MVS | | | 91.20 209 | 90.44 204 | 93.48 225 | 94.49 241 | 87.91 229 | 97.76 54 | 98.18 36 | 91.29 120 | 87.78 247 | 95.74 165 | 80.35 207 | 97.33 283 | 85.46 223 | 82.96 299 | 95.19 247 |
|
QAPM | | | 93.45 115 | 92.27 133 | 96.98 60 | 96.77 142 | 92.62 68 | 98.39 18 | 98.12 43 | 84.50 281 | 88.27 241 | 97.77 65 | 82.39 170 | 99.81 20 | 85.40 224 | 98.81 70 | 98.51 101 |
|
EU-MVSNet | | | 88.72 260 | 88.90 246 | 88.20 310 | 93.15 301 | 74.21 328 | 96.63 181 | 94.22 304 | 85.18 270 | 87.32 258 | 95.97 148 | 76.16 267 | 94.98 324 | 85.27 225 | 86.17 257 | 95.41 228 |
|
BH-w/o | | | 92.14 166 | 91.75 145 | 93.31 233 | 96.99 135 | 85.73 262 | 95.67 241 | 95.69 243 | 88.73 195 | 89.26 224 | 94.82 207 | 82.97 151 | 98.07 205 | 85.26 226 | 96.32 130 | 96.13 194 |
|
FMVSNet2 | | | 91.31 206 | 90.08 217 | 94.99 144 | 96.51 153 | 92.21 77 | 97.41 99 | 96.95 184 | 88.82 190 | 88.62 232 | 94.75 210 | 73.87 283 | 97.42 277 | 85.20 227 | 88.55 243 | 95.35 236 |
|
PM-MVS | | | 83.48 300 | 81.86 303 | 88.31 309 | 87.83 331 | 77.59 323 | 93.43 294 | 91.75 334 | 86.91 249 | 80.63 309 | 89.91 305 | 44.42 345 | 95.84 314 | 85.17 228 | 76.73 319 | 91.50 330 |
|
LF4IMVS | | | 87.94 276 | 87.25 268 | 89.98 305 | 92.38 312 | 80.05 314 | 94.38 275 | 95.25 263 | 87.59 231 | 84.34 283 | 94.74 211 | 64.31 323 | 97.66 262 | 84.83 229 | 87.45 250 | 92.23 323 |
|
PatchmatchNet | | | 91.91 171 | 91.35 164 | 93.59 219 | 95.38 197 | 84.11 281 | 93.15 301 | 95.39 253 | 89.54 160 | 92.10 139 | 93.68 261 | 82.82 158 | 98.13 194 | 84.81 230 | 95.32 143 | 98.52 99 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
pmmvs6 | | | 87.81 278 | 86.19 280 | 92.69 252 | 91.32 317 | 86.30 257 | 97.34 107 | 96.41 211 | 80.59 315 | 84.05 289 | 94.37 229 | 67.37 315 | 97.67 260 | 84.75 231 | 79.51 313 | 94.09 292 |
|
v18 | | | 88.71 261 | 87.52 260 | 92.27 258 | 94.16 254 | 88.11 217 | 96.82 155 | 95.96 227 | 87.03 242 | 80.76 306 | 89.81 307 | 83.15 135 | 96.22 302 | 84.69 232 | 75.31 324 | 92.49 312 |
|
v7n | | | 90.76 222 | 89.86 226 | 93.45 228 | 93.54 284 | 87.60 235 | 97.70 66 | 97.37 140 | 88.85 187 | 87.65 251 | 94.08 250 | 81.08 190 | 98.10 198 | 84.68 233 | 83.79 293 | 94.66 278 |
|
SixPastTwentyTwo | | | 89.15 255 | 88.54 252 | 90.98 290 | 93.49 287 | 80.28 311 | 96.70 172 | 94.70 284 | 90.78 131 | 84.15 287 | 95.57 173 | 71.78 293 | 97.71 258 | 84.63 234 | 85.07 273 | 94.94 259 |
|
v17 | | | 88.67 263 | 87.47 263 | 92.26 260 | 94.13 257 | 88.09 219 | 96.81 156 | 95.95 228 | 87.02 243 | 80.72 307 | 89.75 309 | 83.11 138 | 96.20 303 | 84.61 235 | 75.15 326 | 92.49 312 |
|
v16 | | | 88.69 262 | 87.50 261 | 92.26 260 | 94.19 251 | 88.11 217 | 96.81 156 | 95.95 228 | 87.01 244 | 80.71 308 | 89.80 308 | 83.08 141 | 96.20 303 | 84.61 235 | 75.34 323 | 92.48 314 |
|
TDRefinement | | | 86.53 286 | 84.76 291 | 91.85 275 | 82.23 342 | 84.25 278 | 96.38 200 | 95.35 256 | 84.97 275 | 84.09 288 | 94.94 196 | 65.76 321 | 98.34 181 | 84.60 237 | 74.52 331 | 92.97 303 |
|
ACMH | | 87.59 16 | 90.53 232 | 89.42 239 | 93.87 200 | 96.21 166 | 87.92 227 | 97.24 115 | 96.94 185 | 88.45 201 | 83.91 290 | 96.27 139 | 71.92 291 | 98.62 153 | 84.43 238 | 89.43 233 | 95.05 256 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMH+ | | 87.92 14 | 90.20 239 | 89.18 243 | 93.25 235 | 96.48 156 | 86.45 256 | 96.99 137 | 96.68 202 | 88.83 189 | 84.79 281 | 96.22 140 | 70.16 304 | 98.53 160 | 84.42 239 | 88.04 245 | 94.77 275 |
|
v15 | | | 88.53 265 | 87.31 265 | 92.20 263 | 94.09 263 | 88.05 220 | 96.72 167 | 95.90 232 | 87.01 244 | 80.53 311 | 89.60 313 | 83.02 147 | 96.13 305 | 84.29 240 | 74.64 327 | 92.41 318 |
|
V14 | | | 88.52 266 | 87.30 266 | 92.17 265 | 94.12 259 | 87.99 222 | 96.72 167 | 95.91 231 | 86.98 246 | 80.50 312 | 89.63 310 | 83.03 146 | 96.12 307 | 84.23 241 | 74.60 329 | 92.40 319 |
|
V9 | | | 88.49 269 | 87.26 267 | 92.18 264 | 94.12 259 | 87.97 225 | 96.73 164 | 95.90 232 | 86.95 248 | 80.40 314 | 89.61 311 | 82.98 150 | 96.13 305 | 84.14 242 | 74.55 330 | 92.44 316 |
|
MS-PatchMatch | | | 90.27 236 | 89.77 230 | 91.78 279 | 94.33 247 | 84.72 276 | 95.55 247 | 96.73 196 | 86.17 261 | 86.36 270 | 95.28 189 | 71.28 296 | 97.80 250 | 84.09 243 | 98.14 86 | 92.81 307 |
|
v12 | | | 88.46 270 | 87.23 270 | 92.17 265 | 94.10 262 | 87.99 222 | 96.71 169 | 95.90 232 | 86.91 249 | 80.34 316 | 89.58 314 | 82.92 154 | 96.11 309 | 84.09 243 | 74.50 332 | 92.42 317 |
|
v13 | | | 88.45 271 | 87.22 271 | 92.16 267 | 94.08 265 | 87.95 226 | 96.71 169 | 95.90 232 | 86.86 253 | 80.27 318 | 89.55 315 | 82.92 154 | 96.12 307 | 84.02 245 | 74.63 328 | 92.40 319 |
|
PatchMatch-RL | | | 92.90 133 | 92.02 138 | 95.56 116 | 98.19 78 | 90.80 123 | 95.27 261 | 97.18 151 | 87.96 221 | 91.86 144 | 95.68 169 | 80.44 205 | 98.99 126 | 84.01 246 | 97.54 99 | 96.89 169 |
|
lessismore_v0 | | | | | 90.45 300 | 91.96 315 | 79.09 320 | | 87.19 346 | | 80.32 317 | 94.39 227 | 66.31 318 | 97.55 268 | 84.00 247 | 76.84 318 | 94.70 276 |
|
CMPMVS | | 62.92 21 | 85.62 294 | 84.92 289 | 87.74 312 | 89.14 327 | 73.12 331 | 94.17 281 | 96.80 195 | 73.98 334 | 73.65 330 | 94.93 197 | 66.36 317 | 97.61 265 | 83.95 248 | 91.28 212 | 92.48 314 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
MVP-Stereo | | | 90.74 225 | 90.08 217 | 92.71 251 | 93.19 300 | 88.20 208 | 95.86 233 | 96.27 216 | 86.07 262 | 84.86 280 | 94.76 209 | 77.84 259 | 97.75 255 | 83.88 249 | 98.01 88 | 92.17 325 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
LS3D | | | 93.57 112 | 92.61 123 | 96.47 77 | 97.59 112 | 91.61 94 | 97.67 67 | 97.72 98 | 85.17 271 | 90.29 180 | 98.34 28 | 84.60 120 | 99.73 26 | 83.85 250 | 98.27 82 | 98.06 129 |
|
v11 | | | 88.41 272 | 87.19 274 | 92.08 270 | 94.08 265 | 87.77 231 | 96.75 162 | 95.85 238 | 86.74 254 | 80.50 312 | 89.50 316 | 82.49 166 | 96.08 310 | 83.55 251 | 75.20 325 | 92.38 321 |
|
Patchmatch-test1 | | | 91.54 195 | 90.85 185 | 93.59 219 | 95.59 189 | 84.95 273 | 94.72 269 | 95.58 248 | 90.82 130 | 92.25 136 | 93.58 265 | 75.80 269 | 97.41 278 | 83.35 252 | 95.98 133 | 98.40 115 |
|
testpf | | | 80.97 307 | 81.40 305 | 79.65 326 | 91.53 316 | 72.43 332 | 73.47 348 | 89.55 341 | 78.63 321 | 80.81 304 | 89.06 318 | 61.36 328 | 91.36 338 | 83.34 253 | 84.89 280 | 75.15 345 |
|
DTE-MVSNet | | | 90.56 231 | 89.75 232 | 93.01 242 | 93.95 272 | 87.25 239 | 97.64 74 | 97.65 106 | 90.74 132 | 87.12 261 | 95.68 169 | 79.97 213 | 97.00 294 | 83.33 254 | 81.66 306 | 94.78 274 |
|
BH-RMVSNet | | | 92.72 140 | 91.97 140 | 94.97 147 | 97.16 127 | 87.99 222 | 96.15 218 | 95.60 246 | 90.62 140 | 91.87 143 | 97.15 99 | 78.41 244 | 98.57 157 | 83.16 255 | 97.60 98 | 98.36 119 |
|
pmmvs-eth3d | | | 86.22 289 | 84.45 292 | 91.53 284 | 88.34 329 | 87.25 239 | 94.47 274 | 95.01 273 | 83.47 292 | 79.51 322 | 89.61 311 | 69.75 305 | 95.71 316 | 83.13 256 | 76.73 319 | 91.64 327 |
|
FMVSNet1 | | | 89.88 246 | 88.31 254 | 94.59 167 | 95.41 195 | 91.18 110 | 97.50 91 | 96.93 186 | 86.62 255 | 87.41 255 | 94.51 218 | 65.94 320 | 97.29 285 | 83.04 257 | 87.43 251 | 95.31 238 |
|
tfpn_ndepth | | | 91.88 173 | 90.96 179 | 94.62 166 | 97.73 104 | 89.93 144 | 97.75 55 | 92.92 326 | 88.93 185 | 91.73 145 | 93.80 258 | 78.91 232 | 98.49 166 | 83.02 258 | 93.86 171 | 95.45 226 |
|
MDTV_nov1_ep13 | | | | 90.76 189 | | 95.22 209 | 80.33 309 | 93.03 304 | 95.28 260 | 88.14 219 | 92.84 127 | 93.83 256 | 81.34 187 | 98.08 201 | 82.86 259 | 94.34 156 | |
|
TR-MVS | | | 91.48 197 | 90.59 202 | 94.16 184 | 96.40 160 | 87.33 236 | 95.67 241 | 95.34 259 | 87.68 229 | 91.46 150 | 95.52 177 | 76.77 264 | 98.35 179 | 82.85 260 | 93.61 175 | 96.79 172 |
|
JIA-IIPM | | | 88.26 274 | 87.04 275 | 91.91 273 | 93.52 285 | 81.42 300 | 89.38 332 | 94.38 297 | 80.84 312 | 90.93 171 | 80.74 338 | 79.22 223 | 97.92 239 | 82.76 261 | 91.62 205 | 96.38 186 |
|
PVSNet_0 | | 82.17 19 | 85.46 295 | 83.64 296 | 90.92 292 | 95.27 204 | 79.49 316 | 90.55 325 | 95.60 246 | 83.76 289 | 83.00 293 | 89.95 304 | 71.09 297 | 97.97 229 | 82.75 262 | 60.79 343 | 95.31 238 |
|
ambc | | | | | 86.56 317 | 83.60 339 | 70.00 337 | 85.69 340 | 94.97 276 | | 80.60 310 | 88.45 322 | 37.42 347 | 96.84 296 | 82.69 263 | 75.44 322 | 92.86 304 |
|
USDC | | | 88.94 256 | 87.83 258 | 92.27 258 | 94.66 235 | 84.96 272 | 93.86 287 | 95.90 232 | 87.34 236 | 83.40 292 | 95.56 174 | 67.43 314 | 98.19 190 | 82.64 264 | 89.67 232 | 93.66 296 |
|
tpmp4_e23 | | | 89.58 250 | 88.59 250 | 92.54 255 | 95.16 212 | 81.53 299 | 94.11 283 | 95.09 270 | 81.66 304 | 88.60 233 | 93.44 272 | 75.11 274 | 98.33 182 | 82.45 265 | 91.72 203 | 97.75 140 |
|
tfpn1000 | | | 91.99 170 | 91.05 175 | 94.80 157 | 97.78 101 | 89.66 156 | 97.91 44 | 92.90 327 | 88.99 180 | 91.73 145 | 94.84 204 | 78.99 231 | 98.33 182 | 82.41 266 | 93.91 170 | 96.40 185 |
|
ITE_SJBPF | | | | | 92.43 257 | 95.34 199 | 85.37 268 | | 95.92 230 | 91.47 114 | 87.75 248 | 96.39 135 | 71.00 298 | 97.96 233 | 82.36 267 | 89.86 231 | 93.97 293 |
|
UnsupCasMVSNet_eth | | | 85.99 291 | 84.45 292 | 90.62 298 | 89.97 323 | 82.40 294 | 93.62 293 | 97.37 140 | 89.86 154 | 78.59 324 | 92.37 287 | 65.25 322 | 95.35 322 | 82.27 268 | 70.75 336 | 94.10 291 |
|
GG-mvs-BLEND | | | | | 93.62 217 | 93.69 281 | 89.20 185 | 92.39 313 | 83.33 350 | | 87.98 246 | 89.84 306 | 71.00 298 | 96.87 295 | 82.08 269 | 95.40 142 | 94.80 271 |
|
view600 | | | 92.55 142 | 91.68 148 | 95.18 131 | 97.98 84 | 89.44 170 | 98.00 36 | 94.57 290 | 92.09 96 | 93.17 115 | 95.52 177 | 78.14 250 | 99.11 109 | 81.61 270 | 94.04 164 | 96.98 160 |
|
view800 | | | 92.55 142 | 91.68 148 | 95.18 131 | 97.98 84 | 89.44 170 | 98.00 36 | 94.57 290 | 92.09 96 | 93.17 115 | 95.52 177 | 78.14 250 | 99.11 109 | 81.61 270 | 94.04 164 | 96.98 160 |
|
conf0.05thres1000 | | | 92.55 142 | 91.68 148 | 95.18 131 | 97.98 84 | 89.44 170 | 98.00 36 | 94.57 290 | 92.09 96 | 93.17 115 | 95.52 177 | 78.14 250 | 99.11 109 | 81.61 270 | 94.04 164 | 96.98 160 |
|
tfpn | | | 92.55 142 | 91.68 148 | 95.18 131 | 97.98 84 | 89.44 170 | 98.00 36 | 94.57 290 | 92.09 96 | 93.17 115 | 95.52 177 | 78.14 250 | 99.11 109 | 81.61 270 | 94.04 164 | 96.98 160 |
|
thres600view7 | | | 92.49 148 | 91.60 154 | 95.18 131 | 97.91 95 | 89.47 166 | 97.65 70 | 94.66 285 | 92.18 95 | 93.33 107 | 94.91 198 | 78.06 254 | 99.10 115 | 81.61 270 | 94.06 162 | 96.98 160 |
|
LTVRE_ROB | | 88.41 13 | 90.99 216 | 89.92 224 | 94.19 182 | 96.18 169 | 89.55 162 | 96.31 207 | 97.09 163 | 87.88 224 | 85.67 275 | 95.91 152 | 78.79 240 | 98.57 157 | 81.50 275 | 89.98 228 | 94.44 284 |
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 | | | 89.83 248 | 89.15 244 | 91.89 274 | 94.92 225 | 80.30 310 | 93.11 302 | 95.46 251 | 86.28 258 | 88.08 243 | 92.65 281 | 80.44 205 | 98.52 161 | 81.47 276 | 89.92 230 | 96.84 171 |
|
conf200view11 | | | 92.45 149 | 91.58 155 | 95.05 141 | 97.92 92 | 89.37 175 | 97.71 63 | 94.66 285 | 92.20 90 | 93.31 108 | 94.90 199 | 78.06 254 | 99.08 120 | 81.40 277 | 94.08 158 | 96.70 175 |
|
thres100view900 | | | 92.43 151 | 91.58 155 | 94.98 146 | 97.92 92 | 89.37 175 | 97.71 63 | 94.66 285 | 92.20 90 | 93.31 108 | 94.90 199 | 78.06 254 | 99.08 120 | 81.40 277 | 94.08 158 | 96.48 183 |
|
tfpn200view9 | | | 92.38 154 | 91.52 160 | 94.95 149 | 97.85 98 | 89.29 181 | 97.41 99 | 94.88 280 | 92.19 93 | 93.27 112 | 94.46 222 | 78.17 247 | 99.08 120 | 81.40 277 | 94.08 158 | 96.48 183 |
|
thres400 | | | 92.42 152 | 91.52 160 | 95.12 139 | 97.85 98 | 89.29 181 | 97.41 99 | 94.88 280 | 92.19 93 | 93.27 112 | 94.46 222 | 78.17 247 | 99.08 120 | 81.40 277 | 94.08 158 | 96.98 160 |
|
tfpn111 | | | 92.45 149 | 91.58 155 | 95.06 140 | 97.92 92 | 89.37 175 | 97.71 63 | 94.66 285 | 92.20 90 | 93.31 108 | 94.90 199 | 78.06 254 | 99.11 109 | 81.37 281 | 94.06 162 | 96.70 175 |
|
DP-MVS | | | 92.76 139 | 91.51 162 | 96.52 71 | 98.77 36 | 90.99 115 | 97.38 105 | 96.08 225 | 82.38 299 | 89.29 222 | 97.87 56 | 83.77 127 | 99.69 36 | 81.37 281 | 96.69 123 | 98.89 81 |
|
conf0.01 | | | 91.74 175 | 90.67 194 | 94.94 152 | 97.55 115 | 89.68 150 | 97.64 74 | 93.14 318 | 88.43 202 | 91.24 162 | 94.30 234 | 78.91 232 | 98.45 167 | 81.28 283 | 93.57 178 | 96.70 175 |
|
conf0.002 | | | 91.74 175 | 90.67 194 | 94.94 152 | 97.55 115 | 89.68 150 | 97.64 74 | 93.14 318 | 88.43 202 | 91.24 162 | 94.30 234 | 78.91 232 | 98.45 167 | 81.28 283 | 93.57 178 | 96.70 175 |
|
thresconf0.02 | | | 91.69 182 | 90.67 194 | 94.75 161 | 97.55 115 | 89.68 150 | 97.64 74 | 93.14 318 | 88.43 202 | 91.24 162 | 94.30 234 | 78.91 232 | 98.45 167 | 81.28 283 | 93.57 178 | 96.11 195 |
|
tfpn_n400 | | | 91.69 182 | 90.67 194 | 94.75 161 | 97.55 115 | 89.68 150 | 97.64 74 | 93.14 318 | 88.43 202 | 91.24 162 | 94.30 234 | 78.91 232 | 98.45 167 | 81.28 283 | 93.57 178 | 96.11 195 |
|
tfpnconf | | | 91.69 182 | 90.67 194 | 94.75 161 | 97.55 115 | 89.68 150 | 97.64 74 | 93.14 318 | 88.43 202 | 91.24 162 | 94.30 234 | 78.91 232 | 98.45 167 | 81.28 283 | 93.57 178 | 96.11 195 |
|
tfpnview11 | | | 91.69 182 | 90.67 194 | 94.75 161 | 97.55 115 | 89.68 150 | 97.64 74 | 93.14 318 | 88.43 202 | 91.24 162 | 94.30 234 | 78.91 232 | 98.45 167 | 81.28 283 | 93.57 178 | 96.11 195 |
|
thres200 | | | 92.23 162 | 91.39 163 | 94.75 161 | 97.61 110 | 89.03 188 | 96.60 184 | 95.09 270 | 92.08 101 | 93.28 111 | 94.00 251 | 78.39 245 | 99.04 125 | 81.26 289 | 94.18 157 | 96.19 189 |
|
CR-MVSNet | | | 90.82 221 | 89.77 230 | 93.95 195 | 94.45 243 | 87.19 242 | 90.23 327 | 95.68 244 | 86.89 251 | 92.40 130 | 92.36 290 | 80.91 196 | 97.05 289 | 81.09 290 | 93.95 168 | 97.60 149 |
|
MSDG | | | 91.42 200 | 90.24 212 | 94.96 148 | 97.15 128 | 88.91 190 | 93.69 290 | 96.32 214 | 85.72 265 | 86.93 266 | 96.47 131 | 80.24 209 | 98.98 127 | 80.57 291 | 95.05 147 | 96.98 160 |
|
dp | | | 88.90 258 | 88.26 256 | 90.81 294 | 94.58 240 | 76.62 324 | 92.85 306 | 94.93 278 | 85.12 272 | 90.07 193 | 93.07 276 | 75.81 268 | 98.12 196 | 80.53 292 | 87.42 252 | 97.71 142 |
|
tpm cat1 | | | 88.36 273 | 87.21 272 | 91.81 277 | 95.13 215 | 80.55 307 | 92.58 309 | 95.70 242 | 74.97 332 | 87.45 253 | 91.96 295 | 78.01 258 | 98.17 192 | 80.39 293 | 88.74 240 | 96.72 174 |
|
AllTest | | | 90.23 238 | 88.98 245 | 93.98 191 | 97.94 90 | 86.64 252 | 96.51 189 | 95.54 249 | 85.38 267 | 85.49 277 | 96.77 109 | 70.28 302 | 99.15 105 | 80.02 294 | 92.87 185 | 96.15 192 |
|
TestCases | | | | | 93.98 191 | 97.94 90 | 86.64 252 | | 95.54 249 | 85.38 267 | 85.49 277 | 96.77 109 | 70.28 302 | 99.15 105 | 80.02 294 | 92.87 185 | 96.15 192 |
|
ADS-MVSNet2 | | | 89.45 252 | 88.59 250 | 92.03 271 | 95.86 180 | 82.26 295 | 90.93 322 | 94.32 300 | 83.23 294 | 91.28 160 | 91.81 297 | 79.01 229 | 95.99 311 | 79.52 296 | 91.39 210 | 97.84 136 |
|
ADS-MVSNet | | | 89.89 245 | 88.68 249 | 93.53 223 | 95.86 180 | 84.89 274 | 90.93 322 | 95.07 272 | 83.23 294 | 91.28 160 | 91.81 297 | 79.01 229 | 97.85 245 | 79.52 296 | 91.39 210 | 97.84 136 |
|
EPNet_dtu | | | 91.71 177 | 91.28 168 | 92.99 243 | 93.76 279 | 83.71 284 | 96.69 174 | 95.28 260 | 93.15 64 | 87.02 265 | 95.95 150 | 83.37 132 | 97.38 281 | 79.46 298 | 96.84 116 | 97.88 135 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
TransMVSNet (Re) | | | 88.94 256 | 87.56 259 | 93.08 241 | 94.35 246 | 88.45 199 | 97.73 59 | 95.23 264 | 87.47 232 | 84.26 285 | 95.29 187 | 79.86 214 | 97.33 283 | 79.44 299 | 74.44 333 | 93.45 299 |
|
EG-PatchMatch MVS | | | 87.02 284 | 85.44 285 | 91.76 281 | 92.67 308 | 85.00 271 | 96.08 223 | 96.45 210 | 83.41 293 | 79.52 321 | 93.49 269 | 57.10 334 | 97.72 257 | 79.34 300 | 90.87 218 | 92.56 310 |
|
Patchmtry | | | 88.64 264 | 87.25 268 | 92.78 249 | 94.09 263 | 86.64 252 | 89.82 330 | 95.68 244 | 80.81 313 | 87.63 252 | 92.36 290 | 80.91 196 | 97.03 291 | 78.86 301 | 85.12 269 | 94.67 277 |
|
FMVSNet5 | | | 87.29 282 | 85.79 283 | 91.78 279 | 94.80 231 | 87.28 237 | 95.49 251 | 95.28 260 | 84.09 284 | 83.85 291 | 91.82 296 | 62.95 325 | 94.17 327 | 78.48 302 | 85.34 266 | 93.91 294 |
|
COLMAP_ROB | | 87.81 15 | 90.40 234 | 89.28 241 | 93.79 203 | 97.95 89 | 87.13 244 | 96.92 146 | 95.89 237 | 82.83 296 | 86.88 268 | 97.18 96 | 73.77 286 | 99.29 95 | 78.44 303 | 93.62 174 | 94.95 257 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
test0.0.03 1 | | | 89.37 254 | 88.70 248 | 91.41 287 | 92.47 311 | 85.63 264 | 95.22 263 | 92.70 329 | 91.11 126 | 86.91 267 | 93.65 263 | 79.02 227 | 93.19 332 | 78.00 304 | 89.18 235 | 95.41 228 |
|
MIMVSNet | | | 88.50 268 | 86.76 276 | 93.72 212 | 94.84 229 | 87.77 231 | 91.39 317 | 94.05 306 | 86.41 257 | 87.99 245 | 92.59 283 | 63.27 324 | 95.82 315 | 77.44 305 | 92.84 187 | 97.57 151 |
|
MDA-MVSNet_test_wron | | | 85.87 292 | 84.23 294 | 90.80 296 | 92.38 312 | 82.57 291 | 93.17 299 | 95.15 267 | 82.15 300 | 67.65 336 | 92.33 293 | 78.20 246 | 95.51 320 | 77.33 306 | 79.74 311 | 94.31 289 |
|
YYNet1 | | | 85.87 292 | 84.23 294 | 90.78 297 | 92.38 312 | 82.46 293 | 93.17 299 | 95.14 268 | 82.12 301 | 67.69 335 | 92.36 290 | 78.16 249 | 95.50 321 | 77.31 307 | 79.73 312 | 94.39 285 |
|
UnsupCasMVSNet_bld | | | 82.13 306 | 79.46 308 | 90.14 304 | 88.00 330 | 82.47 292 | 90.89 324 | 96.62 208 | 78.94 320 | 75.61 327 | 84.40 336 | 56.63 335 | 96.31 300 | 77.30 308 | 66.77 342 | 91.63 328 |
|
PCF-MVS | | 89.48 11 | 91.56 193 | 89.95 223 | 96.36 84 | 96.60 146 | 92.52 71 | 92.51 310 | 97.26 148 | 79.41 317 | 88.90 227 | 96.56 127 | 84.04 125 | 99.55 66 | 77.01 309 | 97.30 108 | 97.01 159 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
testgi | | | 87.97 275 | 87.21 272 | 90.24 303 | 92.86 304 | 80.76 303 | 96.67 176 | 94.97 276 | 91.74 108 | 85.52 276 | 95.83 156 | 62.66 326 | 94.47 326 | 76.25 310 | 88.36 244 | 95.48 222 |
|
TinyColmap | | | 86.82 285 | 85.35 287 | 91.21 288 | 94.91 227 | 82.99 290 | 93.94 286 | 94.02 308 | 83.58 290 | 81.56 301 | 94.68 212 | 62.34 327 | 98.13 194 | 75.78 311 | 87.35 254 | 92.52 311 |
|
PAPM | | | 91.52 196 | 90.30 208 | 95.20 130 | 95.30 203 | 89.83 146 | 93.38 296 | 96.85 193 | 86.26 259 | 88.59 234 | 95.80 158 | 84.88 116 | 98.15 193 | 75.67 312 | 95.93 135 | 97.63 144 |
|
tfpnnormal | | | 89.70 249 | 88.40 253 | 93.60 218 | 95.15 213 | 90.10 134 | 97.56 87 | 98.16 38 | 87.28 238 | 86.16 272 | 94.63 215 | 77.57 261 | 98.05 213 | 74.48 313 | 84.59 282 | 92.65 308 |
|
DSMNet-mixed | | | 86.34 288 | 86.12 282 | 87.00 315 | 89.88 324 | 70.43 333 | 94.93 267 | 90.08 340 | 77.97 325 | 85.42 279 | 92.78 280 | 74.44 280 | 93.96 328 | 74.43 314 | 95.14 145 | 96.62 179 |
|
Patchmatch-test | | | 89.42 253 | 87.99 257 | 93.70 213 | 95.27 204 | 85.11 269 | 88.98 333 | 94.37 298 | 81.11 309 | 87.10 263 | 93.69 260 | 82.28 171 | 97.50 271 | 74.37 315 | 94.76 151 | 98.48 107 |
|
LCM-MVSNet | | | 72.55 314 | 69.39 317 | 82.03 322 | 70.81 352 | 65.42 343 | 90.12 329 | 94.36 299 | 55.02 344 | 65.88 339 | 81.72 337 | 24.16 356 | 89.96 341 | 74.32 316 | 68.10 340 | 90.71 333 |
|
new-patchmatchnet | | | 83.18 301 | 81.87 302 | 87.11 314 | 86.88 334 | 75.99 326 | 93.70 289 | 95.18 266 | 85.02 274 | 77.30 326 | 88.40 323 | 65.99 319 | 93.88 329 | 74.19 317 | 70.18 337 | 91.47 331 |
|
MDA-MVSNet-bldmvs | | | 85.00 296 | 82.95 298 | 91.17 289 | 93.13 302 | 83.33 288 | 94.56 272 | 95.00 274 | 84.57 280 | 65.13 340 | 92.65 281 | 70.45 301 | 95.85 313 | 73.57 318 | 77.49 316 | 94.33 287 |
|
pmmvs3 | | | 79.97 308 | 77.50 312 | 87.39 313 | 82.80 340 | 79.38 318 | 92.70 308 | 90.75 338 | 70.69 338 | 78.66 323 | 87.47 332 | 51.34 342 | 93.40 330 | 73.39 319 | 69.65 338 | 89.38 335 |
|
PatchT | | | 88.87 259 | 87.42 264 | 93.22 237 | 94.08 265 | 85.10 270 | 89.51 331 | 94.64 289 | 81.92 302 | 92.36 133 | 88.15 326 | 80.05 212 | 97.01 293 | 72.43 320 | 93.65 173 | 97.54 152 |
|
Anonymous20231206 | | | 87.09 283 | 86.14 281 | 89.93 306 | 91.22 318 | 80.35 308 | 96.11 220 | 95.35 256 | 83.57 291 | 84.16 286 | 93.02 277 | 73.54 288 | 95.61 317 | 72.16 321 | 86.14 258 | 93.84 295 |
|
MVS-HIRNet | | | 82.47 305 | 81.21 306 | 86.26 318 | 95.38 197 | 69.21 338 | 88.96 334 | 89.49 342 | 66.28 340 | 80.79 305 | 74.08 343 | 68.48 309 | 97.39 280 | 71.93 322 | 95.47 141 | 92.18 324 |
|
new_pmnet | | | 82.89 302 | 81.12 307 | 88.18 311 | 89.63 325 | 80.18 312 | 91.77 316 | 92.57 330 | 76.79 328 | 75.56 328 | 88.23 325 | 61.22 329 | 94.48 325 | 71.43 323 | 82.92 300 | 89.87 334 |
|
TAPA-MVS | | 90.10 7 | 92.30 158 | 91.22 172 | 95.56 116 | 98.33 65 | 89.60 159 | 96.79 159 | 97.65 106 | 81.83 303 | 91.52 149 | 97.23 95 | 87.94 79 | 98.91 130 | 71.31 324 | 98.37 80 | 98.17 123 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
test20.03 | | | 86.14 290 | 85.40 286 | 88.35 308 | 90.12 321 | 80.06 313 | 95.90 232 | 95.20 265 | 88.59 196 | 81.29 302 | 93.62 264 | 71.43 295 | 92.65 333 | 71.26 325 | 81.17 308 | 92.34 322 |
|
tmp_tt | | | 51.94 328 | 53.82 325 | 46.29 341 | 33.73 357 | 45.30 357 | 78.32 347 | 67.24 357 | 18.02 352 | 50.93 346 | 87.05 333 | 52.99 341 | 53.11 355 | 70.76 326 | 25.29 352 | 40.46 352 |
|
MIMVSNet1 | | | 84.93 297 | 83.05 297 | 90.56 299 | 89.56 326 | 84.84 275 | 95.40 254 | 95.35 256 | 83.91 285 | 80.38 315 | 92.21 294 | 57.23 333 | 93.34 331 | 70.69 327 | 82.75 302 | 93.50 297 |
|
RPMNet | | | 88.52 266 | 86.72 278 | 93.95 195 | 94.45 243 | 87.19 242 | 90.23 327 | 94.99 275 | 77.87 326 | 92.40 130 | 87.55 331 | 80.17 211 | 97.05 289 | 68.84 328 | 93.95 168 | 97.60 149 |
|
N_pmnet | | | 78.73 310 | 78.71 309 | 78.79 328 | 92.80 306 | 46.50 355 | 94.14 282 | 43.71 358 | 78.61 322 | 80.83 303 | 91.66 300 | 74.94 278 | 96.36 299 | 67.24 329 | 84.45 284 | 93.50 297 |
|
OpenMVS_ROB | | 81.14 20 | 84.42 298 | 82.28 299 | 90.83 293 | 90.06 322 | 84.05 282 | 95.73 240 | 94.04 307 | 73.89 335 | 80.17 320 | 91.53 301 | 59.15 331 | 97.64 263 | 66.92 330 | 89.05 236 | 90.80 332 |
|
testus | | | 82.63 304 | 82.15 300 | 84.07 320 | 87.31 333 | 67.67 339 | 93.18 297 | 94.29 302 | 82.47 298 | 82.14 296 | 90.69 302 | 53.01 340 | 91.94 336 | 66.30 331 | 89.96 229 | 92.62 309 |
|
test2356 | | | 82.77 303 | 82.14 301 | 84.65 319 | 85.77 336 | 70.36 334 | 91.22 320 | 93.69 314 | 81.58 306 | 81.82 298 | 89.00 319 | 60.63 330 | 90.77 339 | 64.74 332 | 90.80 219 | 92.82 305 |
|
PMMVS2 | | | 70.19 317 | 66.92 319 | 80.01 325 | 76.35 344 | 65.67 342 | 86.22 339 | 87.58 345 | 64.83 342 | 62.38 341 | 80.29 340 | 26.78 354 | 88.49 345 | 63.79 333 | 54.07 344 | 85.88 339 |
|
test_0402 | | | 86.46 287 | 84.79 290 | 91.45 285 | 95.02 220 | 85.55 265 | 96.29 209 | 94.89 279 | 80.90 310 | 82.21 294 | 93.97 252 | 68.21 311 | 97.29 285 | 62.98 334 | 88.68 242 | 91.51 329 |
|
Anonymous20231211 | | | 78.22 312 | 75.30 313 | 86.99 316 | 86.14 335 | 74.16 329 | 95.62 245 | 93.88 310 | 66.43 339 | 74.44 329 | 87.86 328 | 41.39 346 | 95.11 323 | 62.49 335 | 69.46 339 | 91.71 326 |
|
DeepMVS_CX | | | | | 74.68 333 | 90.84 319 | 64.34 344 | | 81.61 353 | 65.34 341 | 67.47 338 | 88.01 327 | 48.60 343 | 80.13 350 | 62.33 336 | 73.68 335 | 79.58 343 |
|
test1235678 | | | 79.82 309 | 78.53 310 | 83.69 321 | 82.55 341 | 67.55 340 | 92.50 311 | 94.13 305 | 79.28 318 | 72.10 333 | 86.45 334 | 57.27 332 | 90.68 340 | 61.60 337 | 80.90 309 | 92.82 305 |
|
no-one | | | 68.12 318 | 63.78 321 | 81.13 323 | 74.01 347 | 70.22 336 | 87.61 338 | 90.71 339 | 72.63 337 | 53.13 345 | 71.89 344 | 30.29 350 | 91.45 337 | 61.53 338 | 32.21 348 | 81.72 342 |
|
LP | | | 84.13 299 | 81.85 304 | 90.97 291 | 93.20 299 | 82.12 296 | 87.68 337 | 94.27 303 | 76.80 327 | 81.93 297 | 88.52 321 | 72.97 290 | 95.95 312 | 59.53 339 | 81.73 304 | 94.84 265 |
|
test12356 | | | 74.97 313 | 74.13 314 | 77.49 329 | 78.81 343 | 56.23 351 | 88.53 335 | 92.75 328 | 75.14 329 | 67.50 337 | 85.07 335 | 44.88 344 | 89.96 341 | 58.71 340 | 75.75 321 | 86.26 337 |
|
1111 | | | 78.29 311 | 77.55 311 | 80.50 324 | 83.89 337 | 59.98 347 | 91.89 314 | 93.71 311 | 75.06 330 | 73.60 331 | 87.67 329 | 55.66 336 | 92.60 334 | 58.54 341 | 77.92 315 | 88.93 336 |
|
.test1245 | | | 65.38 320 | 69.22 318 | 53.86 340 | 83.89 337 | 59.98 347 | 91.89 314 | 93.71 311 | 75.06 330 | 73.60 331 | 87.67 329 | 55.66 336 | 92.60 334 | 58.54 341 | 2.96 354 | 9.00 354 |
|
wuykxyi23d | | | 56.92 324 | 51.11 328 | 74.38 334 | 62.30 354 | 61.47 346 | 80.09 345 | 84.87 348 | 49.62 347 | 30.80 353 | 57.20 351 | 7.03 359 | 82.94 348 | 55.69 343 | 32.36 347 | 78.72 344 |
|
testmv | | | 72.22 315 | 70.02 315 | 78.82 327 | 73.06 350 | 61.75 345 | 91.24 319 | 92.31 331 | 74.45 333 | 61.06 342 | 80.51 339 | 34.21 348 | 88.63 344 | 55.31 344 | 68.07 341 | 86.06 338 |
|
FPMVS | | | 71.27 316 | 69.85 316 | 75.50 331 | 74.64 345 | 59.03 349 | 91.30 318 | 91.50 335 | 58.80 343 | 57.92 343 | 88.28 324 | 29.98 352 | 85.53 347 | 53.43 345 | 82.84 301 | 81.95 341 |
|
ANet_high | | | 63.94 321 | 59.58 322 | 77.02 330 | 61.24 355 | 66.06 341 | 85.66 341 | 87.93 344 | 78.53 323 | 42.94 347 | 71.04 345 | 25.42 355 | 80.71 349 | 52.60 346 | 30.83 350 | 84.28 340 |
|
PNet_i23d | | | 59.01 322 | 55.87 323 | 68.44 335 | 73.98 348 | 51.37 352 | 81.36 344 | 82.41 351 | 52.37 346 | 42.49 349 | 70.39 346 | 11.39 357 | 79.99 351 | 49.77 347 | 38.71 346 | 73.97 346 |
|
Gipuma | | | 67.86 319 | 65.41 320 | 75.18 332 | 92.66 309 | 73.45 330 | 66.50 350 | 94.52 294 | 53.33 345 | 57.80 344 | 66.07 347 | 30.81 349 | 89.20 343 | 48.15 348 | 78.88 314 | 62.90 349 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PMVS | | 53.92 22 | 58.58 323 | 55.40 324 | 68.12 336 | 51.00 356 | 48.64 353 | 78.86 346 | 87.10 347 | 46.77 348 | 35.84 352 | 74.28 342 | 8.76 358 | 86.34 346 | 42.07 349 | 73.91 334 | 69.38 347 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MVE | | 50.73 23 | 53.25 326 | 48.81 329 | 66.58 337 | 65.34 353 | 57.50 350 | 72.49 349 | 70.94 356 | 40.15 351 | 39.28 351 | 63.51 348 | 6.89 361 | 73.48 354 | 38.29 350 | 42.38 345 | 68.76 348 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
E-PMN | | | 53.28 325 | 52.56 326 | 55.43 338 | 74.43 346 | 47.13 354 | 83.63 343 | 76.30 354 | 42.23 349 | 42.59 348 | 62.22 349 | 28.57 353 | 74.40 352 | 31.53 351 | 31.51 349 | 44.78 350 |
|
EMVS | | | 52.08 327 | 51.31 327 | 54.39 339 | 72.62 351 | 45.39 356 | 83.84 342 | 75.51 355 | 41.13 350 | 40.77 350 | 59.65 350 | 30.08 351 | 73.60 353 | 28.31 352 | 29.90 351 | 44.18 351 |
|
wuyk23d | | | 25.11 330 | 24.57 332 | 26.74 343 | 73.98 348 | 39.89 358 | 57.88 351 | 9.80 359 | 12.27 353 | 10.39 354 | 6.97 357 | 7.03 359 | 36.44 356 | 25.43 353 | 17.39 353 | 3.89 356 |
|
testmvs | | | 13.36 332 | 16.33 333 | 4.48 345 | 5.04 358 | 2.26 360 | 93.18 297 | 3.28 360 | 2.70 354 | 8.24 355 | 21.66 353 | 2.29 363 | 2.19 357 | 7.58 354 | 2.96 354 | 9.00 354 |
|
test123 | | | 13.04 333 | 15.66 334 | 5.18 344 | 4.51 359 | 3.45 359 | 92.50 311 | 1.81 361 | 2.50 355 | 7.58 356 | 20.15 354 | 3.67 362 | 2.18 358 | 7.13 355 | 1.07 356 | 9.90 353 |
|
cdsmvs_eth3d_5k | | | 23.24 331 | 30.99 331 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 97.63 108 | 0.00 356 | 0.00 357 | 96.88 106 | 84.38 123 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
pcd_1.5k_mvsjas | | | 7.39 335 | 9.85 336 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 0.00 362 | 0.00 356 | 0.00 357 | 0.00 358 | 88.65 71 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
pcd1.5k->3k | | | 38.37 329 | 40.51 330 | 31.96 342 | 94.29 249 | 0.00 361 | 0.00 352 | 97.69 102 | 0.00 356 | 0.00 357 | 0.00 358 | 81.45 186 | 0.00 359 | 0.00 356 | 91.11 214 | 95.89 204 |
|
sosnet-low-res | | | 0.00 336 | 0.00 337 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 0.00 362 | 0.00 356 | 0.00 357 | 0.00 358 | 0.00 364 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
sosnet | | | 0.00 336 | 0.00 337 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 0.00 362 | 0.00 356 | 0.00 357 | 0.00 358 | 0.00 364 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
uncertanet | | | 0.00 336 | 0.00 337 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 0.00 362 | 0.00 356 | 0.00 357 | 0.00 358 | 0.00 364 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
Regformer | | | 0.00 336 | 0.00 337 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 0.00 362 | 0.00 356 | 0.00 357 | 0.00 358 | 0.00 364 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
ab-mvs-re | | | 8.06 334 | 10.74 335 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 0.00 362 | 0.00 356 | 0.00 357 | 96.69 115 | 0.00 364 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
uanet | | | 0.00 336 | 0.00 337 | 0.00 346 | 0.00 360 | 0.00 361 | 0.00 352 | 0.00 362 | 0.00 356 | 0.00 357 | 0.00 358 | 0.00 364 | 0.00 359 | 0.00 356 | 0.00 357 | 0.00 357 |
|
GSMVS | | | | | | | | | | | | | | | | | 98.45 110 |
|
test_part2 | | | | | | 99.28 17 | 95.74 3 | | | | 98.10 7 | | | | | | |
|
test_part1 | | | | | | | | | 98.26 25 | | | | 95.31 1 | | | 99.63 5 | 99.63 5 |
|
sam_mvs1 | | | | | | | | | | | | | 82.76 159 | | | | 98.45 110 |
|
sam_mvs | | | | | | | | | | | | | 81.94 180 | | | | |
|
MTGPA | | | | | | | | | 98.08 51 | | | | | | | | |
|
test_post | | | | | | | | | | | | 17.58 355 | 81.76 182 | 98.08 201 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 90.45 303 | 82.65 163 | 98.10 198 | | | |
|
MTMP | | | | | | | | | 82.03 352 | | | | | | | | |
|
TEST9 | | | | | | 98.70 39 | 94.19 25 | 96.41 194 | 98.02 68 | 88.17 218 | 96.03 55 | 97.56 84 | 92.74 15 | 99.59 53 | | | |
|
test_8 | | | | | | 98.67 41 | 94.06 31 | 96.37 201 | 98.01 70 | 88.58 197 | 95.98 60 | 97.55 86 | 92.73 16 | 99.58 56 | | | |
|
agg_prior | | | | | | 98.67 41 | 93.79 38 | | 98.00 72 | | 95.68 69 | | | 99.57 64 | | | |
|
test_prior4 | | | | | | | 93.66 42 | 96.42 193 | | | | | | | | | |
|
test_prior | | | | | 97.23 50 | 98.67 41 | 92.99 58 | | 98.00 72 | | | | | 99.41 86 | | | 99.29 46 |
|
æ–°å‡ ä½•2 | | | | | | | | 95.79 237 | | | | | | | | | |
|
旧先验1 | | | | | | 98.38 61 | 93.38 50 | | 97.75 93 | | | 98.09 44 | 92.30 28 | | | 99.01 65 | 99.16 54 |
|
原ACMM2 | | | | | | | | 95.67 241 | | | | | | | | | |
|
test222 | | | | | | 98.24 72 | 92.21 77 | 95.33 256 | 97.60 109 | 79.22 319 | 95.25 78 | 97.84 61 | 88.80 69 | | | 99.15 55 | 98.72 89 |
|
segment_acmp | | | | | | | | | | | | | 92.89 13 | | | | |
|
testdata1 | | | | | | | | 95.26 262 | | 93.10 67 | | | | | | | |
|
test12 | | | | | 97.65 31 | 98.46 54 | 94.26 22 | | 97.66 104 | | 95.52 77 | | 90.89 49 | 99.46 80 | | 99.25 47 | 99.22 51 |
|
plane_prior7 | | | | | | 96.21 166 | 89.98 140 | | | | | | | | | | |
|
plane_prior6 | | | | | | 96.10 176 | 90.00 136 | | | | | | 81.32 188 | | | | |
|
plane_prior4 | | | | | | | | | | | | 96.64 118 | | | | | |
|
plane_prior3 | | | | | | | 90.00 136 | | | 94.46 30 | 91.34 154 | | | | | | |
|
plane_prior2 | | | | | | | | 97.74 57 | | 94.85 17 | | | | | | | |
|
plane_prior1 | | | | | | 96.14 174 | | | | | | | | | | | |
|
plane_prior | | | | | | | 89.99 138 | 97.24 115 | | 94.06 38 | | | | | | 92.16 197 | |
|
n2 | | | | | | | | | 0.00 362 | | | | | | | | |
|
nn | | | | | | | | | 0.00 362 | | | | | | | | |
|
door-mid | | | | | | | | | 91.06 337 | | | | | | | | |
|
test11 | | | | | | | | | 97.88 84 | | | | | | | | |
|
door | | | | | | | | | 91.13 336 | | | | | | | | |
|
HQP5-MVS | | | | | | | 89.33 178 | | | | | | | | | | |
|
HQP-NCC | | | | | | 95.86 180 | | 96.65 177 | | 93.55 50 | 90.14 182 | | | | | | |
|
ACMP_Plane | | | | | | 95.86 180 | | 96.65 177 | | 93.55 50 | 90.14 182 | | | | | | |
|
HQP4-MVS | | | | | | | | | | | 90.14 182 | | | 98.50 163 | | | 95.78 212 |
|
HQP3-MVS | | | | | | | | | 97.39 137 | | | | | | | 92.10 198 | |
|
HQP2-MVS | | | | | | | | | | | | | 80.95 193 | | | | |
|
NP-MVS | | | | | | 95.99 179 | 89.81 147 | | | | | 95.87 153 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 90.30 226 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 91.02 216 | |
|
Test By Simon | | | | | | | | | | | | | 88.73 70 | | | | |
|