PGM-MVS | | | 93.96 30 | 93.72 33 | 94.68 28 | 98.43 13 | 86.22 40 | 95.30 62 | 97.78 1 | 87.45 85 | 93.26 32 | 97.33 12 | 84.62 48 | 99.51 15 | 90.75 61 | 98.57 35 | 98.32 26 |
|
FC-MVSNet-test | | | 90.27 88 | 90.18 80 | 90.53 154 | 93.71 175 | 79.85 177 | 95.77 46 | 97.59 2 | 89.31 40 | 86.27 143 | 94.67 104 | 81.93 73 | 97.01 209 | 84.26 127 | 88.09 204 | 94.71 166 |
|
FIs | | | 90.51 85 | 90.35 76 | 90.99 145 | 93.99 165 | 80.98 149 | 95.73 47 | 97.54 3 | 89.15 44 | 86.72 134 | 94.68 103 | 81.83 74 | 97.24 192 | 85.18 112 | 88.31 201 | 94.76 165 |
|
PHI-MVS | | | 93.89 32 | 93.65 34 | 94.62 31 | 96.84 59 | 86.43 32 | 96.69 21 | 97.49 4 | 85.15 133 | 93.56 30 | 96.28 56 | 85.60 36 | 99.31 29 | 92.45 26 | 98.79 12 | 98.12 43 |
|
UniMVSNet (Re) | | | 89.80 99 | 89.07 101 | 92.01 105 | 93.60 178 | 84.52 64 | 94.78 101 | 97.47 5 | 89.26 41 | 86.44 140 | 92.32 180 | 82.10 68 | 97.39 180 | 84.81 118 | 80.84 279 | 94.12 196 |
|
test_part1 | | | | | | | | | 97.45 6 | | | | 91.93 1 | | | 99.02 3 | 98.67 5 |
|
ESAPD | | | 95.32 3 | 95.38 3 | 95.17 7 | 98.55 5 | 87.22 11 | 95.99 36 | 97.45 6 | 88.25 66 | 96.40 3 | 97.60 5 | 91.93 1 | 99.62 1 | 93.18 19 | 99.02 3 | 98.67 5 |
|
ACMMP | | | 93.24 48 | 92.88 49 | 94.30 42 | 98.09 28 | 85.33 54 | 96.86 17 | 97.45 6 | 88.33 63 | 90.15 84 | 97.03 27 | 81.44 75 | 99.51 15 | 90.85 60 | 95.74 84 | 98.04 49 |
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 |
APDe-MVS | | | 95.46 1 | 95.64 1 | 94.91 13 | 98.26 20 | 86.29 39 | 97.46 2 | 97.40 9 | 89.03 47 | 96.20 5 | 98.10 1 | 89.39 7 | 99.34 24 | 95.88 1 | 99.03 2 | 99.10 1 |
|
CSCG | | | 93.23 49 | 93.05 43 | 93.76 56 | 98.04 30 | 84.07 78 | 96.22 29 | 97.37 10 | 84.15 153 | 90.05 85 | 95.66 80 | 87.77 14 | 99.15 39 | 89.91 66 | 98.27 43 | 98.07 46 |
|
ACMMP_Plus | | | 94.74 11 | 94.56 12 | 95.28 5 | 98.02 31 | 87.70 5 | 95.68 50 | 97.34 11 | 88.28 65 | 95.30 11 | 97.67 4 | 85.90 34 | 99.54 11 | 93.91 10 | 98.95 5 | 98.60 9 |
|
HFP-MVS | | | 94.52 12 | 94.40 13 | 94.86 15 | 98.61 3 | 86.81 17 | 96.94 10 | 97.34 11 | 88.63 56 | 93.65 24 | 97.21 19 | 86.10 30 | 99.49 17 | 92.35 30 | 98.77 15 | 98.30 27 |
|
#test# | | | 94.32 21 | 94.14 22 | 94.86 15 | 98.61 3 | 86.81 17 | 96.43 23 | 97.34 11 | 87.51 84 | 93.65 24 | 97.21 19 | 86.10 30 | 99.49 17 | 91.68 48 | 98.77 15 | 98.30 27 |
|
MSLP-MVS++ | | | 93.72 34 | 94.08 24 | 92.65 83 | 97.31 47 | 83.43 92 | 95.79 45 | 97.33 14 | 90.03 27 | 93.58 28 | 96.96 29 | 84.87 46 | 97.76 140 | 92.19 34 | 98.66 29 | 96.76 95 |
|
VPA-MVSNet | | | 89.62 101 | 88.96 103 | 91.60 124 | 93.86 169 | 82.89 108 | 95.46 58 | 97.33 14 | 87.91 73 | 88.43 100 | 93.31 144 | 74.17 162 | 97.40 177 | 87.32 94 | 82.86 249 | 94.52 180 |
|
ACMMPR | | | 94.43 16 | 94.28 16 | 94.91 13 | 98.63 2 | 86.69 22 | 96.94 10 | 97.32 16 | 88.63 56 | 93.53 31 | 97.26 16 | 85.04 43 | 99.54 11 | 92.35 30 | 98.78 14 | 98.50 12 |
|
SMA-MVS | | | 95.20 5 | 95.10 7 | 95.51 3 | 98.14 25 | 88.26 4 | 96.26 28 | 97.31 17 | 86.04 116 | 97.82 1 | 98.10 1 | 88.43 11 | 99.56 3 | 94.66 4 | 99.13 1 | 98.71 4 |
|
WR-MVS_H | | | 87.80 156 | 87.37 138 | 89.10 231 | 93.23 187 | 78.12 237 | 95.61 55 | 97.30 18 | 87.90 74 | 83.72 219 | 92.01 196 | 79.65 95 | 96.01 260 | 76.36 230 | 80.54 283 | 93.16 250 |
|
SteuartSystems-ACMMP | | | 95.20 5 | 95.32 6 | 94.85 17 | 96.99 56 | 86.33 35 | 97.33 3 | 97.30 18 | 91.38 12 | 95.39 9 | 97.46 10 | 88.98 10 | 99.40 22 | 94.12 8 | 98.89 8 | 98.82 2 |
Skip Steuart: Steuart Systems R&D Blog. |
CP-MVS | | | 94.34 19 | 94.21 20 | 94.74 27 | 98.39 16 | 86.64 26 | 97.60 1 | 97.24 20 | 88.53 60 | 92.73 44 | 97.23 17 | 85.20 41 | 99.32 28 | 92.15 35 | 98.83 11 | 98.25 35 |
|
MVS_111021_HR | | | 93.45 39 | 93.31 38 | 93.84 51 | 96.99 56 | 84.84 57 | 93.24 208 | 97.24 20 | 88.76 53 | 91.60 69 | 95.85 74 | 86.07 32 | 98.66 81 | 91.91 43 | 98.16 46 | 98.03 50 |
|
region2R | | | 94.43 16 | 94.27 17 | 94.92 12 | 98.65 1 | 86.67 24 | 96.92 14 | 97.23 22 | 88.60 58 | 93.58 28 | 97.27 14 | 85.22 40 | 99.54 11 | 92.21 32 | 98.74 19 | 98.56 11 |
|
XVS | | | 94.45 14 | 94.32 14 | 94.85 17 | 98.54 7 | 86.60 27 | 96.93 12 | 97.19 23 | 90.66 22 | 92.85 37 | 97.16 24 | 85.02 44 | 99.49 17 | 91.99 39 | 98.56 36 | 98.47 15 |
|
X-MVStestdata | | | 88.31 137 | 86.13 184 | 94.85 17 | 98.54 7 | 86.60 27 | 96.93 12 | 97.19 23 | 90.66 22 | 92.85 37 | 23.41 353 | 85.02 44 | 99.49 17 | 91.99 39 | 98.56 36 | 98.47 15 |
|
MP-MVS-pluss | | | 94.21 25 | 94.00 27 | 94.85 17 | 98.17 24 | 86.65 25 | 94.82 98 | 97.17 25 | 86.26 111 | 92.83 39 | 97.87 3 | 85.57 37 | 99.56 3 | 94.37 7 | 98.92 7 | 98.34 24 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
DELS-MVS | | | 93.43 41 | 93.25 39 | 93.97 47 | 95.42 107 | 85.04 56 | 93.06 215 | 97.13 26 | 90.74 20 | 91.84 63 | 95.09 93 | 86.32 29 | 99.21 33 | 91.22 53 | 98.45 39 | 97.65 66 |
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 |
MCST-MVS | | | 94.45 14 | 94.20 21 | 95.19 6 | 98.46 12 | 87.50 9 | 95.00 87 | 97.12 27 | 87.13 90 | 92.51 51 | 96.30 55 | 89.24 8 | 99.34 24 | 93.46 13 | 98.62 33 | 98.73 3 |
|
UniMVSNet_NR-MVSNet | | | 89.92 97 | 89.29 96 | 91.81 119 | 93.39 182 | 83.72 84 | 94.43 126 | 97.12 27 | 89.80 31 | 86.46 137 | 93.32 143 | 83.16 56 | 97.23 194 | 84.92 115 | 81.02 275 | 94.49 184 |
|
SD-MVS | | | 94.96 8 | 95.33 5 | 93.88 50 | 97.25 53 | 86.69 22 | 96.19 30 | 97.11 29 | 90.42 24 | 96.95 2 | 97.27 14 | 89.53 5 | 96.91 217 | 94.38 6 | 98.85 9 | 98.03 50 |
|
DeepPCF-MVS | | 89.96 1 | 94.20 26 | 94.77 10 | 92.49 89 | 96.52 67 | 80.00 173 | 94.00 168 | 97.08 30 | 90.05 26 | 95.65 8 | 97.29 13 | 89.66 4 | 98.97 62 | 93.95 9 | 98.71 20 | 98.50 12 |
|
HPM-MVS | | | 94.02 28 | 93.88 28 | 94.43 38 | 98.39 16 | 85.78 50 | 97.25 5 | 97.07 31 | 86.90 101 | 92.62 48 | 96.80 36 | 84.85 47 | 99.17 36 | 92.43 27 | 98.65 31 | 98.33 25 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
3Dnovator | | 86.66 5 | 91.73 64 | 90.82 72 | 94.44 36 | 94.59 142 | 86.37 33 | 97.18 6 | 97.02 32 | 89.20 42 | 84.31 209 | 96.66 42 | 73.74 170 | 99.17 36 | 86.74 101 | 97.96 51 | 97.79 64 |
|
DeepC-MVS | | 88.79 3 | 93.31 43 | 92.99 45 | 94.26 43 | 96.07 86 | 85.83 49 | 94.89 93 | 96.99 33 | 89.02 48 | 89.56 88 | 97.37 11 | 82.51 61 | 99.38 23 | 92.20 33 | 98.30 42 | 97.57 70 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MP-MVS | | | 94.25 22 | 94.07 25 | 94.77 24 | 98.47 11 | 86.31 37 | 96.71 20 | 96.98 34 | 89.04 46 | 91.98 61 | 97.19 21 | 85.43 38 | 99.56 3 | 92.06 38 | 98.79 12 | 98.44 20 |
|
zzz-MVS | | | 94.47 13 | 94.30 15 | 95.00 10 | 98.42 14 | 86.95 13 | 95.06 83 | 96.97 35 | 91.07 14 | 93.14 35 | 97.56 7 | 84.30 50 | 99.56 3 | 93.43 14 | 98.75 17 | 98.47 15 |
|
MTGPA | | | | | | | | | 96.97 35 | | | | | | | | |
|
MTAPA | | | 94.42 18 | 94.22 18 | 95.00 10 | 98.42 14 | 86.95 13 | 94.36 138 | 96.97 35 | 91.07 14 | 93.14 35 | 97.56 7 | 84.30 50 | 99.56 3 | 93.43 14 | 98.75 17 | 98.47 15 |
|
HPM-MVS++ | | | 95.14 7 | 94.91 9 | 95.83 1 | 98.25 21 | 89.65 1 | 95.92 41 | 96.96 38 | 91.75 8 | 94.02 20 | 96.83 33 | 88.12 12 | 99.55 8 | 93.41 16 | 98.94 6 | 98.28 29 |
|
CNVR-MVS | | | 95.40 2 | 95.37 4 | 95.50 4 | 98.11 26 | 88.51 3 | 95.29 64 | 96.96 38 | 92.09 3 | 95.32 10 | 97.08 26 | 89.49 6 | 99.33 27 | 95.10 2 | 98.85 9 | 98.66 7 |
|
APD-MVS | | | 94.24 23 | 94.07 25 | 94.75 26 | 98.06 29 | 86.90 16 | 95.88 42 | 96.94 40 | 85.68 122 | 95.05 12 | 97.18 22 | 87.31 20 | 99.07 45 | 91.90 46 | 98.61 34 | 98.28 29 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
NCCC | | | 94.81 10 | 94.69 11 | 95.17 7 | 97.83 33 | 87.46 10 | 95.66 52 | 96.93 41 | 92.34 2 | 93.94 21 | 96.58 46 | 87.74 15 | 99.44 21 | 92.83 23 | 98.40 40 | 98.62 8 |
|
mPP-MVS | | | 93.99 29 | 93.78 31 | 94.63 30 | 98.50 9 | 85.90 48 | 96.87 16 | 96.91 42 | 88.70 54 | 91.83 65 | 97.17 23 | 83.96 53 | 99.55 8 | 91.44 52 | 98.64 32 | 98.43 21 |
|
DeepC-MVS_fast | | 89.43 2 | 94.04 27 | 93.79 30 | 94.80 23 | 97.48 42 | 86.78 19 | 95.65 54 | 96.89 43 | 89.40 38 | 92.81 40 | 96.97 28 | 85.37 39 | 99.24 32 | 90.87 59 | 98.69 22 | 98.38 23 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
test_prior3 | | | 93.60 37 | 93.53 36 | 93.82 52 | 97.29 49 | 84.49 65 | 94.12 152 | 96.88 44 | 87.67 81 | 92.63 46 | 96.39 53 | 86.62 26 | 98.87 68 | 91.50 50 | 98.67 27 | 98.11 44 |
|
test_prior | | | | | 93.82 52 | 97.29 49 | 84.49 65 | | 96.88 44 | | | | | 98.87 68 | | | 98.11 44 |
|
APD-MVS_3200maxsize | | | 93.78 33 | 93.77 32 | 93.80 55 | 97.92 32 | 84.19 76 | 96.30 26 | 96.87 46 | 86.96 97 | 93.92 22 | 97.47 9 | 83.88 54 | 98.96 65 | 92.71 25 | 97.87 53 | 98.26 34 |
|
PVSNet_BlendedMVS | | | 89.98 93 | 89.70 87 | 90.82 148 | 96.12 79 | 81.25 140 | 93.92 171 | 96.83 47 | 83.49 169 | 89.10 93 | 92.26 185 | 81.04 79 | 98.85 74 | 86.72 104 | 87.86 206 | 92.35 275 |
|
PVSNet_Blended | | | 90.73 79 | 90.32 77 | 91.98 108 | 96.12 79 | 81.25 140 | 92.55 231 | 96.83 47 | 82.04 208 | 89.10 93 | 92.56 173 | 81.04 79 | 98.85 74 | 86.72 104 | 95.91 82 | 95.84 124 |
|
原ACMM1 | | | | | 92.01 105 | 97.34 46 | 81.05 147 | | 96.81 49 | 78.89 251 | 90.45 80 | 95.92 71 | 82.65 60 | 98.84 76 | 80.68 176 | 98.26 44 | 96.14 109 |
|
HPM-MVS_fast | | | 93.40 42 | 93.22 40 | 93.94 49 | 98.36 18 | 84.83 58 | 97.15 7 | 96.80 50 | 85.77 119 | 92.47 52 | 97.13 25 | 82.38 62 | 99.07 45 | 90.51 63 | 98.40 40 | 97.92 58 |
|
TEST9 | | | | | | 97.53 37 | 86.49 30 | 94.07 160 | 96.78 51 | 81.61 227 | 92.77 41 | 96.20 60 | 87.71 16 | 99.12 42 | | | |
|
train_agg | | | 93.44 40 | 93.08 42 | 94.52 34 | 97.53 37 | 86.49 30 | 94.07 160 | 96.78 51 | 81.86 222 | 92.77 41 | 96.20 60 | 87.63 17 | 99.12 42 | 92.14 36 | 98.69 22 | 97.94 54 |
|
3Dnovator+ | | 87.14 4 | 92.42 57 | 91.37 61 | 95.55 2 | 95.63 100 | 88.73 2 | 97.07 8 | 96.77 53 | 90.84 17 | 84.02 213 | 96.62 44 | 75.95 136 | 99.34 24 | 87.77 86 | 97.68 56 | 98.59 10 |
|
test_8 | | | | | | 97.49 40 | 86.30 38 | 94.02 166 | 96.76 54 | 81.86 222 | 92.70 45 | 96.20 60 | 87.63 17 | 99.02 54 | | | |
|
HSP-MVS | | | 95.30 4 | 95.48 2 | 94.76 25 | 98.49 10 | 86.52 29 | 96.91 15 | 96.73 55 | 91.73 9 | 96.10 6 | 96.69 39 | 89.90 3 | 99.30 30 | 94.70 3 | 98.04 50 | 98.45 19 |
|
agg_prior3 | | | 93.27 45 | 92.89 48 | 94.40 40 | 97.49 40 | 86.12 42 | 94.07 160 | 96.73 55 | 81.46 230 | 92.46 53 | 96.05 68 | 86.90 24 | 99.15 39 | 92.14 36 | 98.69 22 | 97.94 54 |
|
agg_prior1 | | | 93.29 44 | 92.97 46 | 94.26 43 | 97.38 44 | 85.92 45 | 93.92 171 | 96.72 57 | 81.96 209 | 92.16 57 | 96.23 58 | 87.85 13 | 98.97 62 | 91.95 42 | 98.55 38 | 97.90 59 |
|
agg_prior | | | | | | 97.38 44 | 85.92 45 | | 96.72 57 | | 92.16 57 | | | 98.97 62 | | | |
|
Regformer-2 | | | 94.33 20 | 94.22 18 | 94.68 28 | 95.54 102 | 86.75 21 | 94.57 117 | 96.70 59 | 91.84 6 | 94.41 13 | 96.56 48 | 87.19 21 | 99.13 41 | 93.50 12 | 97.65 58 | 98.16 39 |
|
QAPM | | | 89.51 105 | 88.15 125 | 93.59 58 | 94.92 129 | 84.58 62 | 96.82 18 | 96.70 59 | 78.43 259 | 83.41 227 | 96.19 63 | 73.18 177 | 99.30 30 | 77.11 226 | 96.54 76 | 96.89 93 |
|
CANet | | | 93.54 38 | 93.20 41 | 94.55 33 | 95.65 99 | 85.73 51 | 94.94 90 | 96.69 61 | 91.89 5 | 90.69 78 | 95.88 73 | 81.99 72 | 99.54 11 | 93.14 21 | 97.95 52 | 98.39 22 |
|
abl_6 | | | 93.18 50 | 93.05 43 | 93.57 59 | 97.52 39 | 84.27 75 | 95.53 57 | 96.67 62 | 87.85 76 | 93.20 34 | 97.22 18 | 80.35 82 | 99.18 35 | 91.91 43 | 97.21 63 | 97.26 76 |
|
CDPH-MVS | | | 92.83 53 | 92.30 55 | 94.44 36 | 97.79 34 | 86.11 43 | 94.06 163 | 96.66 63 | 80.09 241 | 92.77 41 | 96.63 43 | 86.62 26 | 99.04 50 | 87.40 91 | 98.66 29 | 98.17 38 |
|
PVSNet_Blended_VisFu | | | 91.38 69 | 90.91 70 | 92.80 79 | 96.39 69 | 83.17 98 | 94.87 96 | 96.66 63 | 83.29 175 | 89.27 91 | 94.46 109 | 80.29 84 | 99.17 36 | 87.57 89 | 95.37 91 | 96.05 117 |
|
DP-MVS Recon | | | 91.95 60 | 91.28 63 | 93.96 48 | 98.33 19 | 85.92 45 | 94.66 112 | 96.66 63 | 82.69 198 | 90.03 86 | 95.82 75 | 82.30 64 | 99.03 51 | 84.57 121 | 96.48 78 | 96.91 91 |
|
TSAR-MVS + MP. | | | 94.85 9 | 94.94 8 | 94.58 32 | 98.25 21 | 86.33 35 | 96.11 32 | 96.62 66 | 88.14 70 | 96.10 6 | 96.96 29 | 89.09 9 | 98.94 66 | 94.48 5 | 98.68 25 | 98.48 14 |
|
PS-CasMVS | | | 87.32 180 | 86.88 154 | 88.63 239 | 92.99 196 | 76.33 269 | 95.33 59 | 96.61 67 | 88.22 68 | 83.30 229 | 93.07 155 | 73.03 179 | 95.79 270 | 78.36 212 | 81.00 277 | 93.75 221 |
|
MVS_0304 | | | 93.25 47 | 92.62 51 | 95.14 9 | 95.72 97 | 87.58 8 | 94.71 108 | 96.59 68 | 91.78 7 | 91.46 70 | 96.18 64 | 75.45 147 | 99.55 8 | 93.53 11 | 98.19 45 | 98.28 29 |
|
DU-MVS | | | 89.34 115 | 88.50 113 | 91.85 115 | 93.04 193 | 83.72 84 | 94.47 123 | 96.59 68 | 89.50 36 | 86.46 137 | 93.29 146 | 77.25 114 | 97.23 194 | 84.92 115 | 81.02 275 | 94.59 175 |
|
CP-MVSNet | | | 87.63 164 | 87.26 142 | 88.74 236 | 93.12 190 | 76.59 266 | 95.29 64 | 96.58 70 | 88.43 61 | 83.49 226 | 92.98 161 | 75.28 148 | 95.83 267 | 78.97 207 | 81.15 272 | 93.79 215 |
|
test11 | | | | | | | | | 96.57 71 | | | | | | | | |
|
CPTT-MVS | | | 91.99 59 | 91.80 58 | 92.55 86 | 98.24 23 | 81.98 127 | 96.76 19 | 96.49 72 | 81.89 214 | 90.24 82 | 96.44 52 | 78.59 102 | 98.61 86 | 89.68 67 | 97.85 54 | 97.06 87 |
|
Regformer-1 | | | 94.22 24 | 94.13 23 | 94.51 35 | 95.54 102 | 86.36 34 | 94.57 117 | 96.44 73 | 91.69 10 | 94.32 15 | 96.56 48 | 87.05 23 | 99.03 51 | 93.35 17 | 97.65 58 | 98.15 40 |
|
VNet | | | 92.24 58 | 91.91 57 | 93.24 62 | 96.59 64 | 83.43 92 | 94.84 97 | 96.44 73 | 89.19 43 | 94.08 19 | 95.90 72 | 77.85 113 | 98.17 106 | 88.90 73 | 93.38 124 | 98.13 42 |
|
OpenMVS | | 83.78 11 | 88.74 129 | 87.29 140 | 93.08 68 | 92.70 201 | 85.39 53 | 96.57 22 | 96.43 75 | 78.74 256 | 80.85 257 | 96.07 67 | 69.64 221 | 99.01 56 | 78.01 217 | 96.65 73 | 94.83 162 |
|
canonicalmvs | | | 93.27 45 | 92.75 50 | 94.85 17 | 95.70 98 | 87.66 6 | 96.33 25 | 96.41 76 | 90.00 28 | 94.09 18 | 94.60 107 | 82.33 63 | 98.62 85 | 92.40 29 | 92.86 135 | 98.27 32 |
|
Regformer-4 | | | 93.91 31 | 93.81 29 | 94.19 45 | 95.36 108 | 85.47 52 | 94.68 109 | 96.41 76 | 91.60 11 | 93.75 23 | 96.71 37 | 85.95 33 | 99.10 44 | 93.21 18 | 96.65 73 | 98.01 52 |
|
UA-Net | | | 92.83 53 | 92.54 53 | 93.68 57 | 96.10 84 | 84.71 60 | 95.66 52 | 96.39 78 | 91.92 4 | 93.22 33 | 96.49 50 | 83.16 56 | 98.87 68 | 84.47 122 | 95.47 89 | 97.45 74 |
|
PEN-MVS | | | 86.80 193 | 86.27 182 | 88.40 252 | 92.32 207 | 75.71 273 | 95.18 75 | 96.38 79 | 87.97 71 | 82.82 233 | 93.15 151 | 73.39 175 | 95.92 263 | 76.15 234 | 79.03 297 | 93.59 236 |
|
114514_t | | | 89.51 105 | 88.50 113 | 92.54 87 | 98.11 26 | 81.99 126 | 95.16 77 | 96.36 80 | 70.19 321 | 85.81 149 | 95.25 88 | 76.70 119 | 98.63 84 | 82.07 155 | 96.86 69 | 97.00 88 |
|
TranMVSNet+NR-MVSNet | | | 88.84 126 | 87.95 129 | 91.49 126 | 92.68 202 | 83.01 104 | 94.92 92 | 96.31 81 | 89.88 30 | 85.53 164 | 93.85 134 | 76.63 121 | 96.96 213 | 81.91 159 | 79.87 294 | 94.50 182 |
|
test12 | | | | | 94.34 41 | 97.13 54 | 86.15 41 | | 96.29 82 | | 91.04 76 | | 85.08 42 | 99.01 56 | | 98.13 47 | 97.86 60 |
|
nrg030 | | | 91.08 75 | 90.39 75 | 93.17 65 | 93.07 191 | 86.91 15 | 96.41 24 | 96.26 83 | 88.30 64 | 88.37 101 | 94.85 100 | 82.19 67 | 97.64 147 | 91.09 54 | 82.95 247 | 94.96 151 |
|
无先验 | | | | | | | | 93.28 205 | 96.26 83 | 73.95 296 | | | | 99.05 47 | 80.56 178 | | 96.59 99 |
|
NR-MVSNet | | | 88.58 132 | 87.47 136 | 91.93 111 | 93.04 193 | 84.16 77 | 94.77 102 | 96.25 85 | 89.05 45 | 80.04 269 | 93.29 146 | 79.02 97 | 97.05 207 | 81.71 163 | 80.05 289 | 94.59 175 |
|
PAPM_NR | | | 91.22 72 | 90.78 73 | 92.52 88 | 97.60 36 | 81.46 135 | 94.37 134 | 96.24 86 | 86.39 109 | 87.41 121 | 94.80 102 | 82.06 70 | 98.48 92 | 82.80 144 | 95.37 91 | 97.61 68 |
|
HQP_MVS | | | 90.60 84 | 90.19 79 | 91.82 117 | 94.70 138 | 82.73 113 | 95.85 43 | 96.22 87 | 90.81 18 | 86.91 130 | 94.86 98 | 74.23 159 | 98.12 110 | 88.15 80 | 89.99 166 | 94.63 171 |
|
plane_prior5 | | | | | | | | | 96.22 87 | | | | | 98.12 110 | 88.15 80 | 89.99 166 | 94.63 171 |
|
PAPR | | | 90.02 92 | 89.27 98 | 92.29 98 | 95.78 95 | 80.95 151 | 92.68 226 | 96.22 87 | 81.91 212 | 86.66 135 | 93.75 138 | 82.23 65 | 98.44 95 | 79.40 205 | 94.79 97 | 97.48 73 |
|
TAPA-MVS | | 84.62 6 | 88.16 141 | 87.01 152 | 91.62 123 | 96.64 62 | 80.65 157 | 94.39 130 | 96.21 90 | 76.38 274 | 86.19 145 | 95.44 83 | 79.75 89 | 98.08 124 | 62.75 318 | 95.29 93 | 96.13 110 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
LPG-MVS_test | | | 89.45 108 | 88.90 106 | 91.12 136 | 94.47 146 | 81.49 133 | 95.30 62 | 96.14 91 | 86.73 103 | 85.45 170 | 95.16 90 | 69.89 217 | 98.10 116 | 87.70 87 | 89.23 180 | 93.77 219 |
|
LGP-MVS_train | | | | | 91.12 136 | 94.47 146 | 81.49 133 | | 96.14 91 | 86.73 103 | 85.45 170 | 95.16 90 | 69.89 217 | 98.10 116 | 87.70 87 | 89.23 180 | 93.77 219 |
|
pcd1.5k->3k | | | 37.02 329 | 38.84 330 | 31.53 342 | 92.33 206 | 0.00 362 | 0.00 353 | 96.13 93 | 0.00 357 | 0.00 358 | 0.00 359 | 72.70 182 | 0.00 360 | 0.00 357 | 88.43 198 | 94.60 174 |
|
ACMM | | 84.12 9 | 89.14 117 | 88.48 116 | 91.12 136 | 94.65 141 | 81.22 142 | 95.31 60 | 96.12 94 | 85.31 129 | 85.92 148 | 94.34 110 | 70.19 216 | 98.06 126 | 85.65 109 | 88.86 191 | 94.08 200 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
MVS_111021_LR | | | 92.47 56 | 92.29 56 | 92.98 73 | 95.99 89 | 84.43 72 | 93.08 213 | 96.09 95 | 88.20 69 | 91.12 75 | 95.72 79 | 81.33 77 | 97.76 140 | 91.74 47 | 97.37 62 | 96.75 96 |
|
CLD-MVS | | | 89.47 107 | 88.90 106 | 91.18 135 | 94.22 154 | 82.07 125 | 92.13 244 | 96.09 95 | 87.90 74 | 85.37 180 | 92.45 175 | 74.38 157 | 97.56 150 | 87.15 96 | 90.43 159 | 93.93 205 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
alignmvs | | | 93.08 51 | 92.50 54 | 94.81 22 | 95.62 101 | 87.61 7 | 95.99 36 | 96.07 97 | 89.77 32 | 94.12 17 | 94.87 97 | 80.56 81 | 98.66 81 | 92.42 28 | 93.10 130 | 98.15 40 |
|
XVG-OURS | | | 89.40 113 | 88.70 109 | 91.52 125 | 94.06 158 | 81.46 135 | 91.27 261 | 96.07 97 | 86.14 114 | 88.89 96 | 95.77 77 | 68.73 241 | 97.26 190 | 87.39 92 | 89.96 168 | 95.83 125 |
|
XVG-OURS-SEG-HR | | | 89.95 95 | 89.45 91 | 91.47 127 | 94.00 164 | 81.21 143 | 91.87 248 | 96.06 99 | 85.78 118 | 88.55 98 | 95.73 78 | 74.67 155 | 97.27 188 | 88.71 75 | 89.64 173 | 95.91 120 |
|
HQP3-MVS | | | | | | | | | 96.04 100 | | | | | | | 89.77 171 | |
|
HQP-MVS | | | 89.80 99 | 89.28 97 | 91.34 130 | 94.17 155 | 81.56 130 | 94.39 130 | 96.04 100 | 88.81 50 | 85.43 173 | 93.97 126 | 73.83 168 | 97.96 131 | 87.11 98 | 89.77 171 | 94.50 182 |
|
PS-MVSNAJss | | | 89.97 94 | 89.62 88 | 91.02 143 | 91.90 213 | 80.85 154 | 95.26 71 | 95.98 102 | 86.26 111 | 86.21 144 | 94.29 114 | 79.70 91 | 97.65 145 | 88.87 74 | 88.10 202 | 94.57 177 |
|
Vis-MVSNet | | | 91.75 63 | 91.23 64 | 93.29 60 | 95.32 111 | 83.78 83 | 96.14 31 | 95.98 102 | 89.89 29 | 90.45 80 | 96.58 46 | 75.09 151 | 98.31 102 | 84.75 119 | 96.90 67 | 97.78 65 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
WR-MVS | | | 88.38 134 | 87.67 133 | 90.52 160 | 93.30 185 | 80.18 165 | 93.26 206 | 95.96 104 | 88.57 59 | 85.47 169 | 92.81 167 | 76.12 125 | 96.91 217 | 81.24 166 | 82.29 253 | 94.47 187 |
|
OMC-MVS | | | 91.23 71 | 90.62 74 | 93.08 68 | 96.27 72 | 84.07 78 | 93.52 194 | 95.93 105 | 86.95 98 | 89.51 89 | 96.13 66 | 78.50 104 | 98.35 98 | 85.84 108 | 92.90 134 | 96.83 94 |
|
v7n | | | 86.81 192 | 85.76 194 | 89.95 196 | 90.72 275 | 79.25 205 | 95.07 81 | 95.92 106 | 84.45 148 | 82.29 237 | 90.86 241 | 72.60 185 | 97.53 152 | 79.42 204 | 80.52 285 | 93.08 255 |
|
AdaColmap | | | 89.89 98 | 89.07 101 | 92.37 95 | 97.41 43 | 83.03 102 | 94.42 127 | 95.92 106 | 82.81 194 | 86.34 142 | 94.65 105 | 73.89 166 | 99.02 54 | 80.69 175 | 95.51 87 | 95.05 145 |
|
cascas | | | 86.43 203 | 84.98 208 | 90.80 149 | 92.10 211 | 80.92 152 | 90.24 269 | 95.91 108 | 73.10 302 | 83.57 224 | 88.39 281 | 65.15 275 | 97.46 157 | 84.90 117 | 91.43 143 | 94.03 202 |
|
MVSFormer | | | 91.68 66 | 91.30 62 | 92.80 79 | 93.86 169 | 83.88 81 | 95.96 39 | 95.90 109 | 84.66 142 | 91.76 66 | 94.91 95 | 77.92 110 | 97.30 184 | 89.64 68 | 97.11 64 | 97.24 77 |
|
test_djsdf | | | 89.03 122 | 88.64 110 | 90.21 175 | 90.74 274 | 79.28 203 | 95.96 39 | 95.90 109 | 84.66 142 | 85.33 182 | 92.94 162 | 74.02 165 | 97.30 184 | 89.64 68 | 88.53 194 | 94.05 201 |
|
ACMP | | 84.23 8 | 89.01 124 | 88.35 117 | 90.99 145 | 94.73 135 | 81.27 139 | 95.07 81 | 95.89 111 | 86.48 106 | 83.67 221 | 94.30 113 | 69.33 224 | 97.99 130 | 87.10 100 | 88.55 193 | 93.72 223 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
PCF-MVS | | 84.11 10 | 87.74 158 | 86.08 187 | 92.70 82 | 94.02 160 | 84.43 72 | 89.27 284 | 95.87 112 | 73.62 298 | 84.43 203 | 94.33 111 | 78.48 105 | 98.86 71 | 70.27 268 | 94.45 107 | 94.81 163 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
CHOSEN 1792x2688 | | | 88.84 126 | 87.69 132 | 92.30 97 | 96.14 78 | 81.42 137 | 90.01 273 | 95.86 113 | 74.52 293 | 87.41 121 | 93.94 127 | 75.46 146 | 98.36 96 | 80.36 182 | 95.53 86 | 97.12 85 |
|
Regformer-3 | | | 93.68 35 | 93.64 35 | 93.81 54 | 95.36 108 | 84.61 61 | 94.68 109 | 95.83 114 | 91.27 13 | 93.60 27 | 96.71 37 | 85.75 35 | 98.86 71 | 92.87 22 | 96.65 73 | 97.96 53 |
|
tfpnnormal | | | 84.72 247 | 83.23 251 | 89.20 228 | 92.79 200 | 80.05 170 | 94.48 120 | 95.81 115 | 82.38 201 | 81.08 255 | 91.21 231 | 69.01 231 | 96.95 214 | 61.69 320 | 80.59 282 | 90.58 312 |
|
MVS_Test | | | 91.31 70 | 91.11 65 | 91.93 111 | 94.37 150 | 80.14 167 | 93.46 197 | 95.80 116 | 86.46 107 | 91.35 72 | 93.77 136 | 82.21 66 | 98.09 123 | 87.57 89 | 94.95 96 | 97.55 72 |
|
HyFIR lowres test | | | 88.09 144 | 86.81 158 | 91.93 111 | 96.00 88 | 80.63 158 | 90.01 273 | 95.79 117 | 73.42 299 | 87.68 119 | 92.10 191 | 73.86 167 | 97.96 131 | 80.75 174 | 91.70 141 | 97.19 81 |
|
EI-MVSNet-Vis-set | | | 93.01 52 | 92.92 47 | 93.29 60 | 95.01 124 | 83.51 91 | 94.48 120 | 95.77 118 | 90.87 16 | 92.52 50 | 96.67 41 | 84.50 49 | 99.00 59 | 91.99 39 | 94.44 108 | 97.36 75 |
|
cdsmvs_eth3d_5k | | | 22.14 331 | 29.52 332 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 95.76 119 | 0.00 357 | 0.00 358 | 94.29 114 | 75.66 143 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
DTE-MVSNet | | | 86.11 206 | 85.48 200 | 87.98 262 | 91.65 223 | 74.92 276 | 94.93 91 | 95.75 120 | 87.36 86 | 82.26 238 | 93.04 156 | 72.85 180 | 95.82 268 | 74.04 250 | 77.46 303 | 93.20 248 |
|
OPM-MVS | | | 90.12 90 | 89.56 89 | 91.82 117 | 93.14 189 | 83.90 80 | 94.16 151 | 95.74 121 | 88.96 49 | 87.86 109 | 95.43 84 | 72.48 187 | 97.91 135 | 88.10 83 | 90.18 165 | 93.65 229 |
|
EI-MVSNet-UG-set | | | 92.74 55 | 92.62 51 | 93.12 66 | 94.86 132 | 83.20 97 | 94.40 128 | 95.74 121 | 90.71 21 | 92.05 60 | 96.60 45 | 84.00 52 | 98.99 60 | 91.55 49 | 93.63 117 | 97.17 82 |
|
PS-MVSNAJ | | | 91.18 73 | 90.92 69 | 91.96 109 | 95.26 114 | 82.60 119 | 92.09 246 | 95.70 123 | 86.27 110 | 91.84 63 | 92.46 174 | 79.70 91 | 98.99 60 | 89.08 71 | 95.86 83 | 94.29 190 |
|
旧先验1 | | | | | | 96.79 60 | 81.81 128 | | 95.67 124 | | | 96.81 34 | 86.69 25 | | | 97.66 57 | 96.97 89 |
|
MAR-MVS | | | 90.30 87 | 89.37 94 | 93.07 70 | 96.61 63 | 84.48 67 | 95.68 50 | 95.67 124 | 82.36 202 | 87.85 110 | 92.85 163 | 76.63 121 | 98.80 77 | 80.01 188 | 96.68 72 | 95.91 120 |
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 |
mvs_tets | | | 88.06 145 | 87.28 141 | 90.38 170 | 90.94 265 | 79.88 175 | 95.22 73 | 95.66 126 | 85.10 134 | 84.21 212 | 93.94 127 | 63.53 282 | 97.40 177 | 88.50 77 | 88.40 200 | 93.87 210 |
|
MVS | | | 87.44 177 | 86.10 186 | 91.44 128 | 92.61 203 | 83.62 88 | 92.63 227 | 95.66 126 | 67.26 329 | 81.47 249 | 92.15 187 | 77.95 109 | 98.22 104 | 79.71 197 | 95.48 88 | 92.47 270 |
|
jajsoiax | | | 88.24 139 | 87.50 134 | 90.48 163 | 90.89 269 | 80.14 167 | 95.31 60 | 95.65 128 | 84.97 136 | 84.24 211 | 94.02 123 | 65.31 274 | 97.42 170 | 88.56 76 | 88.52 195 | 93.89 207 |
|
xiu_mvs_v2_base | | | 91.13 74 | 90.89 71 | 91.86 114 | 94.97 127 | 82.42 120 | 92.24 240 | 95.64 129 | 86.11 115 | 91.74 68 | 93.14 152 | 79.67 94 | 98.89 67 | 89.06 72 | 95.46 90 | 94.28 191 |
|
ab-mvs | | | 89.41 111 | 88.35 117 | 92.60 84 | 95.15 122 | 82.65 117 | 92.20 242 | 95.60 130 | 83.97 155 | 88.55 98 | 93.70 139 | 74.16 163 | 98.21 105 | 82.46 150 | 89.37 176 | 96.94 90 |
|
testing_2 | | | 83.40 262 | 81.02 267 | 90.56 153 | 85.06 325 | 80.51 162 | 91.37 259 | 95.57 131 | 82.92 191 | 67.06 330 | 85.54 313 | 49.47 329 | 97.24 192 | 86.74 101 | 85.44 223 | 93.93 205 |
|
新几何1 | | | | | 93.10 67 | 97.30 48 | 84.35 74 | | 95.56 132 | 71.09 318 | 91.26 73 | 96.24 57 | 82.87 59 | 98.86 71 | 79.19 206 | 98.10 48 | 96.07 115 |
|
anonymousdsp | | | 87.84 152 | 87.09 147 | 90.12 183 | 89.13 301 | 80.54 161 | 94.67 111 | 95.55 133 | 82.05 206 | 83.82 217 | 92.12 188 | 71.47 197 | 97.15 198 | 87.15 96 | 87.80 207 | 92.67 264 |
|
XVG-ACMP-BASELINE | | | 86.00 210 | 84.84 215 | 89.45 216 | 91.20 252 | 78.00 239 | 91.70 253 | 95.55 133 | 85.05 135 | 82.97 231 | 92.25 186 | 54.49 319 | 97.48 155 | 82.93 141 | 87.45 209 | 92.89 258 |
|
v52 | | | 86.50 200 | 85.53 199 | 89.39 218 | 89.17 300 | 78.99 212 | 94.72 106 | 95.54 135 | 83.59 163 | 82.10 241 | 90.60 247 | 71.59 194 | 97.45 159 | 82.52 146 | 79.99 291 | 91.73 285 |
|
V4 | | | 86.50 200 | 85.54 196 | 89.39 218 | 89.13 301 | 78.99 212 | 94.73 103 | 95.54 135 | 83.59 163 | 82.10 241 | 90.61 246 | 71.60 193 | 97.45 159 | 82.52 146 | 80.01 290 | 91.74 284 |
|
VPNet | | | 88.20 140 | 87.47 136 | 90.39 168 | 93.56 179 | 79.46 187 | 94.04 164 | 95.54 135 | 88.67 55 | 86.96 128 | 94.58 108 | 69.33 224 | 97.15 198 | 84.05 131 | 80.53 284 | 94.56 178 |
|
Test4 | | | 85.75 219 | 83.72 237 | 91.83 116 | 88.08 314 | 81.03 148 | 92.48 232 | 95.54 135 | 83.38 173 | 73.40 314 | 88.57 278 | 50.99 326 | 97.37 181 | 86.61 106 | 94.47 106 | 97.09 86 |
|
1121 | | | 90.42 86 | 89.49 90 | 93.20 63 | 97.27 51 | 84.46 68 | 92.63 227 | 95.51 139 | 71.01 319 | 91.20 74 | 96.21 59 | 82.92 58 | 99.05 47 | 80.56 178 | 98.07 49 | 96.10 113 |
|
v748 | | | 86.27 204 | 85.28 204 | 89.25 226 | 90.26 287 | 77.58 259 | 94.89 93 | 95.50 140 | 84.28 152 | 81.41 251 | 90.46 252 | 72.57 186 | 97.32 183 | 79.81 196 | 78.36 298 | 92.84 260 |
|
v1192 | | | 87.25 183 | 86.33 179 | 90.00 195 | 90.76 273 | 79.04 211 | 93.80 177 | 95.48 141 | 82.57 199 | 85.48 168 | 91.18 233 | 73.38 176 | 97.42 170 | 82.30 152 | 82.06 256 | 93.53 238 |
|
xiu_mvs_v1_base_debu | | | 90.64 81 | 90.05 83 | 92.40 92 | 93.97 166 | 84.46 68 | 93.32 199 | 95.46 142 | 85.17 130 | 92.25 54 | 94.03 120 | 70.59 208 | 98.57 88 | 90.97 55 | 94.67 98 | 94.18 192 |
|
xiu_mvs_v1_base | | | 90.64 81 | 90.05 83 | 92.40 92 | 93.97 166 | 84.46 68 | 93.32 199 | 95.46 142 | 85.17 130 | 92.25 54 | 94.03 120 | 70.59 208 | 98.57 88 | 90.97 55 | 94.67 98 | 94.18 192 |
|
xiu_mvs_v1_base_debi | | | 90.64 81 | 90.05 83 | 92.40 92 | 93.97 166 | 84.46 68 | 93.32 199 | 95.46 142 | 85.17 130 | 92.25 54 | 94.03 120 | 70.59 208 | 98.57 88 | 90.97 55 | 94.67 98 | 94.18 192 |
|
v7 | | | 87.75 157 | 86.96 153 | 90.12 183 | 91.20 252 | 79.50 182 | 94.28 140 | 95.46 142 | 83.45 170 | 85.75 153 | 91.56 213 | 75.13 149 | 97.43 168 | 83.60 135 | 82.18 255 | 93.42 243 |
|
v10 | | | 87.25 183 | 86.38 177 | 89.85 198 | 91.19 254 | 79.50 182 | 94.48 120 | 95.45 146 | 83.79 160 | 83.62 222 | 91.19 232 | 75.13 149 | 97.42 170 | 81.94 158 | 80.60 281 | 92.63 266 |
|
F-COLMAP | | | 87.95 149 | 86.80 159 | 91.40 129 | 96.35 71 | 80.88 153 | 94.73 103 | 95.45 146 | 79.65 246 | 82.04 244 | 94.61 106 | 71.13 199 | 98.50 91 | 76.24 233 | 91.05 151 | 94.80 164 |
|
PLC | | 84.53 7 | 89.06 121 | 88.03 127 | 92.15 102 | 97.27 51 | 82.69 116 | 94.29 139 | 95.44 148 | 79.71 245 | 84.01 214 | 94.18 119 | 76.68 120 | 98.75 79 | 77.28 223 | 93.41 123 | 95.02 146 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
v144192 | | | 87.19 187 | 86.35 178 | 89.74 203 | 90.64 278 | 78.24 235 | 93.92 171 | 95.43 149 | 81.93 211 | 85.51 166 | 91.05 239 | 74.21 161 | 97.45 159 | 82.86 142 | 81.56 269 | 93.53 238 |
|
v1921920 | | | 86.97 191 | 86.06 188 | 89.69 208 | 90.53 283 | 78.11 238 | 93.80 177 | 95.43 149 | 81.90 213 | 85.33 182 | 91.05 239 | 72.66 183 | 97.41 175 | 82.05 156 | 81.80 264 | 93.53 238 |
|
v1144 | | | 87.61 172 | 86.79 160 | 90.06 191 | 91.01 260 | 79.34 199 | 93.95 170 | 95.42 151 | 83.36 174 | 85.66 160 | 91.31 227 | 74.98 153 | 97.42 170 | 83.37 136 | 82.06 256 | 93.42 243 |
|
v8 | | | 87.50 176 | 86.71 163 | 89.89 197 | 91.37 236 | 79.40 195 | 94.50 119 | 95.38 152 | 84.81 139 | 83.60 223 | 91.33 224 | 76.05 129 | 97.42 170 | 82.84 143 | 80.51 286 | 92.84 260 |
|
sss | | | 88.93 125 | 88.26 124 | 90.94 147 | 94.05 159 | 80.78 156 | 91.71 252 | 95.38 152 | 81.55 228 | 88.63 97 | 93.91 131 | 75.04 152 | 95.47 282 | 82.47 149 | 91.61 142 | 96.57 100 |
|
v1240 | | | 86.78 194 | 85.85 192 | 89.56 210 | 90.45 284 | 77.79 246 | 93.61 191 | 95.37 154 | 81.65 224 | 85.43 173 | 91.15 235 | 71.50 196 | 97.43 168 | 81.47 165 | 82.05 258 | 93.47 242 |
|
testdata | | | | | 90.49 162 | 96.40 68 | 77.89 243 | | 95.37 154 | 72.51 308 | 93.63 26 | 96.69 39 | 82.08 69 | 97.65 145 | 83.08 138 | 97.39 61 | 95.94 119 |
|
1314 | | | 87.51 175 | 86.57 175 | 90.34 173 | 92.42 205 | 79.74 180 | 92.63 227 | 95.35 156 | 78.35 260 | 80.14 267 | 91.62 209 | 74.05 164 | 97.15 198 | 81.05 167 | 93.53 119 | 94.12 196 |
|
v1neww | | | 87.98 146 | 87.25 143 | 90.16 177 | 91.38 234 | 79.41 191 | 94.37 134 | 95.28 157 | 84.48 145 | 85.77 151 | 91.53 214 | 76.12 125 | 97.45 159 | 84.45 124 | 81.89 260 | 93.61 234 |
|
v7new | | | 87.98 146 | 87.25 143 | 90.16 177 | 91.38 234 | 79.41 191 | 94.37 134 | 95.28 157 | 84.48 145 | 85.77 151 | 91.53 214 | 76.12 125 | 97.45 159 | 84.45 124 | 81.89 260 | 93.61 234 |
|
V42 | | | 87.68 159 | 86.86 155 | 90.15 181 | 90.58 279 | 80.14 167 | 94.24 142 | 95.28 157 | 83.66 162 | 85.67 159 | 91.33 224 | 74.73 154 | 97.41 175 | 84.43 126 | 81.83 263 | 92.89 258 |
|
v6 | | | 87.98 146 | 87.25 143 | 90.16 177 | 91.36 237 | 79.39 196 | 94.37 134 | 95.27 160 | 84.48 145 | 85.78 150 | 91.51 216 | 76.15 124 | 97.46 157 | 84.46 123 | 81.88 262 | 93.62 233 |
|
EPP-MVSNet | | | 91.70 65 | 91.56 60 | 92.13 104 | 95.88 92 | 80.50 163 | 97.33 3 | 95.25 161 | 86.15 113 | 89.76 87 | 95.60 81 | 83.42 55 | 98.32 101 | 87.37 93 | 93.25 127 | 97.56 71 |
|
v1141 | | | 87.84 152 | 87.09 147 | 90.11 188 | 91.23 249 | 79.25 205 | 94.08 158 | 95.24 162 | 84.44 149 | 85.69 158 | 91.31 227 | 75.91 137 | 97.44 166 | 84.17 129 | 81.74 266 | 93.63 232 |
|
divwei89l23v2f112 | | | 87.84 152 | 87.09 147 | 90.10 190 | 91.23 249 | 79.24 207 | 94.09 156 | 95.24 162 | 84.44 149 | 85.70 156 | 91.31 227 | 75.91 137 | 97.44 166 | 84.17 129 | 81.73 267 | 93.64 230 |
|
v1 | | | 87.85 151 | 87.10 146 | 90.11 188 | 91.21 251 | 79.24 207 | 94.09 156 | 95.24 162 | 84.44 149 | 85.70 156 | 91.31 227 | 75.96 135 | 97.45 159 | 84.18 128 | 81.73 267 | 93.64 230 |
|
UGNet | | | 89.95 95 | 88.95 104 | 92.95 74 | 94.51 145 | 83.31 95 | 95.70 49 | 95.23 165 | 89.37 39 | 87.58 120 | 93.94 127 | 64.00 280 | 98.78 78 | 83.92 132 | 96.31 80 | 96.74 97 |
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 |
XXY-MVS | | | 87.65 160 | 86.85 156 | 90.03 192 | 92.14 209 | 80.60 160 | 93.76 180 | 95.23 165 | 82.94 190 | 84.60 197 | 94.02 123 | 74.27 158 | 95.49 281 | 81.04 168 | 83.68 240 | 94.01 204 |
|
API-MVS | | | 90.66 80 | 90.07 82 | 92.45 91 | 96.36 70 | 84.57 63 | 96.06 34 | 95.22 167 | 82.39 200 | 89.13 92 | 94.27 117 | 80.32 83 | 98.46 93 | 80.16 187 | 96.71 71 | 94.33 189 |
|
MG-MVS | | | 91.77 62 | 91.70 59 | 92.00 107 | 97.08 55 | 80.03 172 | 93.60 192 | 95.18 168 | 87.85 76 | 90.89 77 | 96.47 51 | 82.06 70 | 98.36 96 | 85.07 113 | 97.04 66 | 97.62 67 |
|
v2v482 | | | 87.84 152 | 87.06 150 | 90.17 176 | 90.99 261 | 79.23 209 | 94.00 168 | 95.13 169 | 84.87 137 | 85.53 164 | 92.07 194 | 74.45 156 | 97.45 159 | 84.71 120 | 81.75 265 | 93.85 213 |
|
Effi-MVS+ | | | 91.59 67 | 91.11 65 | 93.01 72 | 94.35 153 | 83.39 94 | 94.60 114 | 95.10 170 | 87.10 91 | 90.57 79 | 93.10 154 | 81.43 76 | 98.07 125 | 89.29 70 | 94.48 105 | 97.59 69 |
|
Fast-Effi-MVS+ | | | 89.41 111 | 88.64 110 | 91.71 121 | 94.74 134 | 80.81 155 | 93.54 193 | 95.10 170 | 83.11 178 | 86.82 133 | 90.67 244 | 79.74 90 | 97.75 143 | 80.51 180 | 93.55 118 | 96.57 100 |
|
IterMVS-LS | | | 88.36 136 | 87.91 131 | 89.70 207 | 93.80 172 | 78.29 233 | 93.73 183 | 95.08 172 | 85.73 120 | 84.75 195 | 91.90 200 | 79.88 87 | 96.92 216 | 83.83 133 | 82.51 251 | 93.89 207 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
test222 | | | | | | 96.55 66 | 81.70 129 | 92.22 241 | 95.01 173 | 68.36 325 | 90.20 83 | 96.14 65 | 80.26 85 | | | 97.80 55 | 96.05 117 |
|
EI-MVSNet | | | 89.10 118 | 88.86 108 | 89.80 202 | 91.84 215 | 78.30 232 | 93.70 187 | 95.01 173 | 85.73 120 | 87.15 125 | 95.28 86 | 79.87 88 | 97.21 196 | 83.81 134 | 87.36 210 | 93.88 209 |
|
MVSTER | | | 88.84 126 | 88.29 122 | 90.51 161 | 92.95 197 | 80.44 164 | 93.73 183 | 95.01 173 | 84.66 142 | 87.15 125 | 93.12 153 | 72.79 181 | 97.21 196 | 87.86 85 | 87.36 210 | 93.87 210 |
|
diffmvs | | | 89.07 119 | 88.32 120 | 91.34 130 | 93.24 186 | 79.79 178 | 92.29 239 | 94.98 176 | 80.24 238 | 87.38 124 | 92.45 175 | 78.02 108 | 97.33 182 | 83.29 137 | 92.93 133 | 96.91 91 |
|
GBi-Net | | | 87.26 181 | 85.98 189 | 91.08 139 | 94.01 161 | 83.10 99 | 95.14 78 | 94.94 177 | 83.57 165 | 84.37 204 | 91.64 205 | 66.59 263 | 96.34 249 | 78.23 214 | 85.36 224 | 93.79 215 |
|
test1 | | | 87.26 181 | 85.98 189 | 91.08 139 | 94.01 161 | 83.10 99 | 95.14 78 | 94.94 177 | 83.57 165 | 84.37 204 | 91.64 205 | 66.59 263 | 96.34 249 | 78.23 214 | 85.36 224 | 93.79 215 |
|
FMVSNet2 | | | 87.19 187 | 85.82 193 | 91.30 132 | 94.01 161 | 83.67 86 | 94.79 100 | 94.94 177 | 83.57 165 | 83.88 215 | 92.05 195 | 66.59 263 | 96.51 239 | 77.56 221 | 85.01 228 | 93.73 222 |
|
FMVSNet1 | | | 85.85 213 | 84.11 229 | 91.08 139 | 92.81 199 | 83.10 99 | 95.14 78 | 94.94 177 | 81.64 225 | 82.68 234 | 91.64 205 | 59.01 306 | 96.34 249 | 75.37 239 | 83.78 237 | 93.79 215 |
|
LS3D | | | 87.89 150 | 86.32 180 | 92.59 85 | 96.07 86 | 82.92 107 | 95.23 72 | 94.92 181 | 75.66 281 | 82.89 232 | 95.98 69 | 72.48 187 | 99.21 33 | 68.43 288 | 95.23 95 | 95.64 132 |
|
LTVRE_ROB | | 82.13 13 | 86.26 205 | 84.90 213 | 90.34 173 | 94.44 149 | 81.50 132 | 92.31 238 | 94.89 182 | 83.03 185 | 79.63 272 | 92.67 170 | 69.69 220 | 97.79 138 | 71.20 264 | 86.26 218 | 91.72 286 |
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 |
UnsupCasMVSNet_eth | | | 80.07 289 | 78.27 291 | 85.46 298 | 85.24 324 | 72.63 296 | 88.45 296 | 94.87 183 | 82.99 189 | 71.64 322 | 88.07 286 | 56.34 313 | 91.75 326 | 73.48 255 | 63.36 339 | 92.01 281 |
|
pm-mvs1 | | | 86.61 198 | 85.54 196 | 89.82 199 | 91.44 227 | 80.18 165 | 95.28 70 | 94.85 184 | 83.84 157 | 81.66 248 | 92.62 172 | 72.45 189 | 96.48 241 | 79.67 198 | 78.06 299 | 92.82 262 |
|
ACMH | | 80.38 17 | 85.36 227 | 83.68 239 | 90.39 168 | 94.45 148 | 80.63 158 | 94.73 103 | 94.85 184 | 82.09 205 | 77.24 287 | 92.65 171 | 60.01 302 | 97.58 148 | 72.25 260 | 84.87 229 | 92.96 256 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
mvs_anonymous | | | 89.37 114 | 89.32 95 | 89.51 214 | 93.47 180 | 74.22 279 | 91.65 255 | 94.83 186 | 82.91 192 | 85.45 170 | 93.79 135 | 81.23 78 | 96.36 248 | 86.47 107 | 94.09 111 | 97.94 54 |
|
FMVSNet3 | | | 87.40 179 | 86.11 185 | 91.30 132 | 93.79 174 | 83.64 87 | 94.20 150 | 94.81 187 | 83.89 156 | 84.37 204 | 91.87 201 | 68.45 247 | 96.56 236 | 78.23 214 | 85.36 224 | 93.70 224 |
|
WTY-MVS | | | 89.60 102 | 88.92 105 | 91.67 122 | 95.47 106 | 81.15 145 | 92.38 236 | 94.78 188 | 83.11 178 | 89.06 95 | 94.32 112 | 78.67 101 | 96.61 235 | 81.57 164 | 90.89 157 | 97.24 77 |
|
PAPM | | | 86.68 197 | 85.39 202 | 90.53 154 | 93.05 192 | 79.33 202 | 89.79 277 | 94.77 189 | 78.82 253 | 81.95 245 | 93.24 148 | 76.81 117 | 97.30 184 | 66.94 295 | 93.16 129 | 94.95 158 |
|
CDS-MVSNet | | | 89.45 108 | 88.51 112 | 92.29 98 | 93.62 177 | 83.61 89 | 93.01 216 | 94.68 190 | 81.95 210 | 87.82 116 | 93.24 148 | 78.69 100 | 96.99 210 | 80.34 183 | 93.23 128 | 96.28 105 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
test_normal | | | 88.13 143 | 86.78 161 | 92.18 101 | 90.55 282 | 81.19 144 | 92.74 224 | 94.64 191 | 83.84 157 | 77.49 286 | 90.51 251 | 68.49 245 | 98.16 107 | 88.22 79 | 94.55 103 | 97.21 80 |
|
DI_MVS_plusplus_test | | | 88.15 142 | 86.82 157 | 92.14 103 | 90.67 277 | 81.07 146 | 93.01 216 | 94.59 192 | 83.83 159 | 77.78 282 | 90.63 245 | 68.51 244 | 98.16 107 | 88.02 84 | 94.37 109 | 97.17 82 |
|
1112_ss | | | 88.42 133 | 87.33 139 | 91.72 120 | 94.92 129 | 80.98 149 | 92.97 219 | 94.54 193 | 78.16 264 | 83.82 217 | 93.88 132 | 78.78 99 | 97.91 135 | 79.45 201 | 89.41 175 | 96.26 106 |
|
HY-MVS | | 83.01 12 | 89.03 122 | 87.94 130 | 92.29 98 | 94.86 132 | 82.77 109 | 92.08 247 | 94.49 194 | 81.52 229 | 86.93 129 | 92.79 169 | 78.32 107 | 98.23 103 | 79.93 191 | 90.55 158 | 95.88 122 |
|
CANet_DTU | | | 90.26 89 | 89.41 93 | 92.81 78 | 93.46 181 | 83.01 104 | 93.48 195 | 94.47 195 | 89.43 37 | 87.76 118 | 94.23 118 | 70.54 212 | 99.03 51 | 84.97 114 | 96.39 79 | 96.38 103 |
|
v148 | | | 87.04 190 | 86.32 180 | 89.21 227 | 90.94 265 | 77.26 260 | 93.71 186 | 94.43 196 | 84.84 138 | 84.36 207 | 90.80 242 | 76.04 131 | 97.05 207 | 82.12 154 | 79.60 295 | 93.31 245 |
|
OurMVSNet-221017-0 | | | 85.35 228 | 84.64 220 | 87.49 272 | 90.77 272 | 72.59 297 | 94.01 167 | 94.40 197 | 84.72 141 | 79.62 273 | 93.17 150 | 61.91 289 | 96.72 228 | 81.99 157 | 81.16 270 | 93.16 250 |
|
Effi-MVS+-dtu | | | 88.65 130 | 88.35 117 | 89.54 211 | 93.33 183 | 76.39 267 | 94.47 123 | 94.36 198 | 87.70 79 | 85.43 173 | 89.56 266 | 73.45 173 | 97.26 190 | 85.57 110 | 91.28 144 | 94.97 148 |
|
mvs-test1 | | | 89.45 108 | 89.14 99 | 90.38 170 | 93.33 183 | 77.63 252 | 94.95 89 | 94.36 198 | 87.70 79 | 87.10 127 | 92.81 167 | 73.45 173 | 98.03 128 | 85.57 110 | 93.04 131 | 95.48 135 |
|
EG-PatchMatch MVS | | | 82.37 270 | 80.34 272 | 88.46 250 | 90.27 286 | 79.35 198 | 92.80 223 | 94.33 200 | 77.14 271 | 73.26 315 | 90.18 256 | 47.47 333 | 96.72 228 | 70.25 269 | 87.32 212 | 89.30 317 |
|
Test_1112_low_res | | | 87.65 160 | 86.51 176 | 91.08 139 | 94.94 128 | 79.28 203 | 91.77 249 | 94.30 201 | 76.04 279 | 83.51 225 | 92.37 178 | 77.86 112 | 97.73 144 | 78.69 210 | 89.13 188 | 96.22 107 |
|
pmmvs6 | | | 83.42 260 | 81.60 263 | 88.87 233 | 88.01 315 | 77.87 244 | 94.96 88 | 94.24 202 | 74.67 292 | 78.80 276 | 91.09 238 | 60.17 301 | 96.49 240 | 77.06 228 | 75.40 308 | 92.23 278 |
|
MVP-Stereo | | | 85.97 211 | 84.86 214 | 89.32 224 | 90.92 267 | 82.19 123 | 92.11 245 | 94.19 203 | 78.76 255 | 78.77 277 | 91.63 208 | 68.38 254 | 96.56 236 | 75.01 244 | 93.95 112 | 89.20 319 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
TAMVS | | | 89.21 116 | 88.29 122 | 91.96 109 | 93.71 175 | 82.62 118 | 93.30 203 | 94.19 203 | 82.22 203 | 87.78 117 | 93.94 127 | 78.83 98 | 96.95 214 | 77.70 219 | 92.98 132 | 96.32 104 |
|
jason | | | 90.80 77 | 90.10 81 | 92.90 76 | 93.04 193 | 83.53 90 | 93.08 213 | 94.15 205 | 80.22 239 | 91.41 71 | 94.91 95 | 76.87 116 | 97.93 134 | 90.28 65 | 96.90 67 | 97.24 77 |
jason: jason. |
BH-untuned | | | 88.60 131 | 88.13 126 | 90.01 194 | 95.24 121 | 78.50 227 | 93.29 204 | 94.15 205 | 84.75 140 | 84.46 201 | 93.40 140 | 75.76 141 | 97.40 177 | 77.59 220 | 94.52 104 | 94.12 196 |
|
ACMH+ | | 81.04 14 | 85.05 234 | 83.46 246 | 89.82 199 | 94.66 140 | 79.37 197 | 94.44 125 | 94.12 207 | 82.19 204 | 78.04 280 | 92.82 166 | 58.23 308 | 97.54 151 | 73.77 253 | 82.90 248 | 92.54 267 |
|
v18 | | | 84.97 236 | 83.76 234 | 88.60 242 | 91.36 237 | 79.41 191 | 93.82 176 | 94.04 208 | 83.00 188 | 76.61 290 | 86.60 299 | 76.19 123 | 95.43 283 | 80.39 181 | 71.79 317 | 90.96 299 |
|
v16 | | | 84.96 237 | 83.74 236 | 88.62 240 | 91.40 232 | 79.48 185 | 93.83 174 | 94.04 208 | 83.03 185 | 76.54 291 | 86.59 300 | 76.11 128 | 95.42 284 | 80.33 184 | 71.80 316 | 90.95 301 |
|
v17 | | | 84.93 239 | 83.70 238 | 88.62 240 | 91.36 237 | 79.48 185 | 93.83 174 | 94.03 210 | 83.04 184 | 76.51 292 | 86.57 301 | 76.05 129 | 95.42 284 | 80.31 186 | 71.65 318 | 90.96 299 |
|
v15 | | | 84.79 242 | 83.53 243 | 88.57 246 | 91.30 248 | 79.41 191 | 93.70 187 | 94.01 211 | 83.06 181 | 76.27 293 | 86.42 305 | 76.03 132 | 95.38 286 | 80.01 188 | 71.00 321 | 90.92 302 |
|
v12 | | | 84.74 245 | 83.46 246 | 88.58 243 | 91.32 244 | 79.50 182 | 93.75 181 | 94.01 211 | 83.06 181 | 75.98 301 | 86.41 306 | 75.82 140 | 95.36 290 | 79.87 194 | 70.89 325 | 90.89 305 |
|
V14 | | | 84.79 242 | 83.52 244 | 88.57 246 | 91.32 244 | 79.43 190 | 93.72 185 | 94.01 211 | 83.06 181 | 76.22 294 | 86.43 302 | 76.01 133 | 95.37 287 | 79.96 190 | 70.99 322 | 90.91 303 |
|
v13 | | | 84.72 247 | 83.44 248 | 88.58 243 | 91.31 247 | 79.52 181 | 93.77 179 | 94.00 214 | 83.03 185 | 75.85 302 | 86.38 307 | 75.84 139 | 95.35 291 | 79.83 195 | 70.95 323 | 90.87 306 |
|
V9 | | | 84.77 244 | 83.50 245 | 88.58 243 | 91.33 242 | 79.46 187 | 93.75 181 | 94.00 214 | 83.07 180 | 76.07 299 | 86.43 302 | 75.97 134 | 95.37 287 | 79.91 193 | 70.93 324 | 90.91 303 |
|
v11 | | | 84.67 250 | 83.41 249 | 88.44 251 | 91.32 244 | 79.13 210 | 93.69 190 | 93.99 216 | 82.81 194 | 76.20 295 | 86.24 309 | 75.48 145 | 95.35 291 | 79.53 199 | 71.48 320 | 90.85 307 |
|
Fast-Effi-MVS+-dtu | | | 87.44 177 | 86.72 162 | 89.63 209 | 92.04 212 | 77.68 251 | 94.03 165 | 93.94 217 | 85.81 117 | 82.42 236 | 91.32 226 | 70.33 214 | 97.06 206 | 80.33 184 | 90.23 164 | 94.14 195 |
|
TSAR-MVS + GP. | | | 93.66 36 | 93.41 37 | 94.41 39 | 96.59 64 | 86.78 19 | 94.40 128 | 93.93 218 | 89.77 32 | 94.21 16 | 95.59 82 | 87.35 19 | 98.61 86 | 92.72 24 | 96.15 81 | 97.83 62 |
|
VDD-MVS | | | 90.74 78 | 89.92 86 | 93.20 63 | 96.27 72 | 83.02 103 | 95.73 47 | 93.86 219 | 88.42 62 | 92.53 49 | 96.84 32 | 62.09 287 | 98.64 83 | 90.95 58 | 92.62 137 | 97.93 57 |
|
lupinMVS | | | 90.92 76 | 90.21 78 | 93.03 71 | 93.86 169 | 83.88 81 | 92.81 222 | 93.86 219 | 79.84 243 | 91.76 66 | 94.29 114 | 77.92 110 | 98.04 127 | 90.48 64 | 97.11 64 | 97.17 82 |
|
CMPMVS | | 59.16 21 | 80.52 287 | 79.20 285 | 84.48 305 | 83.98 328 | 67.63 324 | 89.95 275 | 93.84 221 | 64.79 334 | 66.81 331 | 91.14 236 | 57.93 310 | 95.17 294 | 76.25 232 | 88.10 202 | 90.65 308 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
GA-MVS | | | 86.61 198 | 85.27 205 | 90.66 150 | 91.33 242 | 78.71 215 | 90.40 267 | 93.81 222 | 85.34 128 | 85.12 184 | 89.57 265 | 61.25 293 | 97.11 202 | 80.99 171 | 89.59 174 | 96.15 108 |
|
IS-MVSNet | | | 91.43 68 | 91.09 67 | 92.46 90 | 95.87 94 | 81.38 138 | 96.95 9 | 93.69 223 | 89.72 34 | 89.50 90 | 95.98 69 | 78.57 103 | 97.77 139 | 83.02 140 | 96.50 77 | 98.22 36 |
|
MS-PatchMatch | | | 85.05 234 | 84.16 228 | 87.73 266 | 91.42 231 | 78.51 226 | 91.25 262 | 93.53 224 | 77.50 266 | 80.15 266 | 91.58 210 | 61.99 288 | 95.51 278 | 75.69 236 | 94.35 110 | 89.16 320 |
|
BH-w/o | | | 87.57 174 | 87.05 151 | 89.12 229 | 94.90 131 | 77.90 242 | 92.41 234 | 93.51 225 | 82.89 193 | 83.70 220 | 91.34 223 | 75.75 142 | 97.07 205 | 75.49 237 | 93.49 120 | 92.39 273 |
|
UnsupCasMVSNet_bld | | | 76.23 302 | 73.27 304 | 85.09 302 | 83.79 329 | 72.92 289 | 85.65 317 | 93.47 226 | 71.52 313 | 68.84 326 | 79.08 334 | 49.77 327 | 93.21 315 | 66.81 299 | 60.52 341 | 89.13 322 |
|
USDC | | | 82.76 265 | 81.26 266 | 87.26 276 | 91.17 255 | 74.55 277 | 89.27 284 | 93.39 227 | 78.26 262 | 75.30 304 | 92.08 192 | 54.43 320 | 96.63 232 | 71.64 261 | 85.79 222 | 90.61 309 |
|
CNLPA | | | 89.07 119 | 87.98 128 | 92.34 96 | 96.87 58 | 84.78 59 | 94.08 158 | 93.24 228 | 81.41 231 | 84.46 201 | 95.13 92 | 75.57 144 | 96.62 233 | 77.21 224 | 93.84 115 | 95.61 133 |
|
VDDNet | | | 89.56 104 | 88.49 115 | 92.76 81 | 95.07 123 | 82.09 124 | 96.30 26 | 93.19 229 | 81.05 235 | 91.88 62 | 96.86 31 | 61.16 296 | 98.33 100 | 88.43 78 | 92.49 138 | 97.84 61 |
|
MSDG | | | 84.86 241 | 83.09 252 | 90.14 182 | 93.80 172 | 80.05 170 | 89.18 287 | 93.09 230 | 78.89 251 | 78.19 278 | 91.91 199 | 65.86 273 | 97.27 188 | 68.47 286 | 88.45 197 | 93.11 253 |
|
BH-RMVSNet | | | 88.37 135 | 87.48 135 | 91.02 143 | 95.28 112 | 79.45 189 | 92.89 221 | 93.07 231 | 85.45 126 | 86.91 130 | 94.84 101 | 70.35 213 | 97.76 140 | 73.97 251 | 94.59 102 | 95.85 123 |
|
ITE_SJBPF | | | | | 88.24 257 | 91.88 214 | 77.05 262 | | 92.92 232 | 85.54 124 | 80.13 268 | 93.30 145 | 57.29 311 | 96.20 253 | 72.46 259 | 84.71 230 | 91.49 290 |
|
ambc | | | | | 83.06 311 | 79.99 337 | 63.51 332 | 77.47 341 | 92.86 233 | | 74.34 310 | 84.45 315 | 28.74 346 | 95.06 298 | 73.06 257 | 68.89 331 | 90.61 309 |
|
TR-MVS | | | 86.78 194 | 85.76 194 | 89.82 199 | 94.37 150 | 78.41 229 | 92.47 233 | 92.83 234 | 81.11 234 | 86.36 141 | 92.40 177 | 68.73 241 | 97.48 155 | 73.75 254 | 89.85 170 | 93.57 237 |
|
TransMVSNet (Re) | | | 84.43 252 | 83.06 253 | 88.54 248 | 91.72 219 | 78.44 228 | 95.18 75 | 92.82 235 | 82.73 196 | 79.67 271 | 92.12 188 | 73.49 172 | 95.96 262 | 71.10 267 | 68.73 332 | 91.21 295 |
|
CHOSEN 280x420 | | | 85.15 232 | 83.99 231 | 88.65 238 | 92.47 204 | 78.40 230 | 79.68 338 | 92.76 236 | 74.90 290 | 81.41 251 | 89.59 264 | 69.85 219 | 95.51 278 | 79.92 192 | 95.29 93 | 92.03 280 |
|
MIMVSNet1 | | | 79.38 294 | 77.28 294 | 85.69 296 | 86.35 321 | 73.67 286 | 91.61 256 | 92.75 237 | 78.11 265 | 72.64 318 | 88.12 285 | 48.16 331 | 91.97 325 | 60.32 323 | 77.49 302 | 91.43 292 |
|
PVSNet | | 78.82 18 | 85.55 225 | 84.65 219 | 88.23 258 | 94.72 136 | 71.93 300 | 87.12 306 | 92.75 237 | 78.80 254 | 84.95 187 | 90.53 250 | 64.43 279 | 96.71 230 | 74.74 245 | 93.86 114 | 96.06 116 |
|
pmmvs4 | | | 85.43 226 | 83.86 233 | 90.16 177 | 90.02 293 | 82.97 106 | 90.27 268 | 92.67 239 | 75.93 280 | 80.73 258 | 91.74 204 | 71.05 200 | 95.73 272 | 78.85 208 | 83.46 244 | 91.78 283 |
|
semantic-postprocess | | | | | 88.18 259 | 91.71 220 | 76.87 264 | | 92.65 240 | 85.40 127 | 81.44 250 | 90.54 248 | 66.21 267 | 95.00 299 | 81.04 168 | 81.05 273 | 92.66 265 |
|
Baseline_NR-MVSNet | | | 87.07 189 | 86.63 174 | 88.40 252 | 91.44 227 | 77.87 244 | 94.23 143 | 92.57 241 | 84.12 154 | 85.74 155 | 92.08 192 | 77.25 114 | 96.04 257 | 82.29 153 | 79.94 292 | 91.30 294 |
|
RPSCF | | | 85.07 233 | 84.27 227 | 87.48 273 | 92.91 198 | 70.62 312 | 91.69 254 | 92.46 242 | 76.20 278 | 82.67 235 | 95.22 89 | 63.94 281 | 97.29 187 | 77.51 222 | 85.80 221 | 94.53 179 |
|
IterMVS | | | 84.88 240 | 83.98 232 | 87.60 268 | 91.44 227 | 76.03 271 | 90.18 271 | 92.41 243 | 83.24 177 | 81.06 256 | 90.42 253 | 66.60 262 | 94.28 304 | 79.46 200 | 80.98 278 | 92.48 269 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PatchMatch-RL | | | 86.77 196 | 85.54 196 | 90.47 164 | 95.88 92 | 82.71 115 | 90.54 266 | 92.31 244 | 79.82 244 | 84.32 208 | 91.57 212 | 68.77 240 | 96.39 246 | 73.16 256 | 93.48 122 | 92.32 276 |
|
COLMAP_ROB | | 80.39 16 | 83.96 255 | 82.04 261 | 89.74 203 | 95.28 112 | 79.75 179 | 94.25 141 | 92.28 245 | 75.17 286 | 78.02 281 | 93.77 136 | 58.60 307 | 97.84 137 | 65.06 311 | 85.92 219 | 91.63 288 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
FMVSNet5 | | | 81.52 278 | 79.60 282 | 87.27 275 | 91.17 255 | 77.95 240 | 91.49 257 | 92.26 246 | 76.87 272 | 76.16 296 | 87.91 289 | 51.67 324 | 92.34 321 | 67.74 293 | 81.16 270 | 91.52 289 |
|
EPNet_dtu | | | 86.49 202 | 85.94 191 | 88.14 260 | 90.24 288 | 72.82 291 | 94.11 154 | 92.20 247 | 86.66 105 | 79.42 274 | 92.36 179 | 73.52 171 | 95.81 269 | 71.26 263 | 93.66 116 | 95.80 127 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
ppachtmachnet_test | | | 81.84 272 | 80.07 277 | 87.15 282 | 88.46 309 | 74.43 278 | 89.04 289 | 92.16 248 | 75.33 284 | 77.75 283 | 88.99 270 | 66.20 268 | 95.37 287 | 65.12 310 | 77.60 301 | 91.65 287 |
|
thres200 | | | 87.21 186 | 86.24 183 | 90.12 183 | 95.36 108 | 78.53 221 | 93.26 206 | 92.10 249 | 86.42 108 | 88.00 108 | 91.11 237 | 69.24 228 | 98.00 129 | 69.58 278 | 91.04 152 | 93.83 214 |
|
PatchFormer-LS_test | | | 86.02 209 | 85.13 206 | 88.70 237 | 91.52 224 | 74.12 282 | 91.19 263 | 92.09 250 | 82.71 197 | 84.30 210 | 87.24 296 | 70.87 203 | 96.98 211 | 81.04 168 | 85.17 227 | 95.00 147 |
|
Anonymous20231206 | | | 81.03 284 | 79.77 280 | 84.82 303 | 87.85 318 | 70.26 314 | 91.42 258 | 92.08 251 | 73.67 297 | 77.75 283 | 89.25 268 | 62.43 286 | 93.08 318 | 61.50 321 | 82.00 259 | 91.12 297 |
|
EPNet | | | 91.79 61 | 91.02 68 | 94.10 46 | 90.10 290 | 85.25 55 | 96.03 35 | 92.05 252 | 92.83 1 | 87.39 123 | 95.78 76 | 79.39 96 | 99.01 56 | 88.13 82 | 97.48 60 | 98.05 48 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
TDRefinement | | | 79.81 291 | 77.34 293 | 87.22 280 | 79.24 340 | 75.48 275 | 93.12 210 | 92.03 253 | 76.45 273 | 75.01 305 | 91.58 210 | 49.19 330 | 96.44 244 | 70.22 271 | 69.18 329 | 89.75 315 |
|
DP-MVS | | | 87.25 183 | 85.36 203 | 92.90 76 | 97.65 35 | 83.24 96 | 94.81 99 | 92.00 254 | 74.99 288 | 81.92 246 | 95.00 94 | 72.66 183 | 99.05 47 | 66.92 297 | 92.33 139 | 96.40 102 |
|
SixPastTwentyTwo | | | 83.91 256 | 82.90 255 | 86.92 285 | 90.99 261 | 70.67 311 | 93.48 195 | 91.99 255 | 85.54 124 | 77.62 285 | 92.11 190 | 60.59 298 | 96.87 219 | 76.05 235 | 77.75 300 | 93.20 248 |
|
tfpn200view9 | | | 87.58 173 | 86.64 172 | 90.41 167 | 95.99 89 | 78.64 216 | 94.58 115 | 91.98 256 | 86.94 99 | 88.09 102 | 91.77 202 | 69.18 229 | 98.10 116 | 70.13 272 | 91.10 145 | 94.48 185 |
|
thres400 | | | 87.62 167 | 86.64 172 | 90.57 152 | 95.99 89 | 78.64 216 | 94.58 115 | 91.98 256 | 86.94 99 | 88.09 102 | 91.77 202 | 69.18 229 | 98.10 116 | 70.13 272 | 91.10 145 | 94.96 151 |
|
CR-MVSNet | | | 85.35 228 | 83.76 234 | 90.12 183 | 90.58 279 | 79.34 199 | 85.24 318 | 91.96 258 | 78.27 261 | 85.55 162 | 87.87 290 | 71.03 201 | 95.61 273 | 73.96 252 | 89.36 177 | 95.40 138 |
|
Patchmtry | | | 82.71 266 | 80.93 269 | 88.06 261 | 90.05 292 | 76.37 268 | 84.74 320 | 91.96 258 | 72.28 310 | 81.32 253 | 87.87 290 | 71.03 201 | 95.50 280 | 68.97 284 | 80.15 288 | 92.32 276 |
|
pmmvs5 | | | 84.21 253 | 82.84 257 | 88.34 254 | 88.95 304 | 76.94 263 | 92.41 234 | 91.91 260 | 75.63 282 | 80.28 264 | 91.18 233 | 64.59 278 | 95.57 275 | 77.09 227 | 83.47 243 | 92.53 268 |
|
test_0402 | | | 81.30 282 | 79.17 286 | 87.67 267 | 93.19 188 | 78.17 236 | 92.98 218 | 91.71 261 | 75.25 285 | 76.02 300 | 90.31 254 | 59.23 305 | 96.37 247 | 50.22 336 | 83.63 241 | 88.47 330 |
|
tpmvs | | | 83.35 263 | 82.07 260 | 87.20 281 | 91.07 259 | 71.00 309 | 88.31 297 | 91.70 262 | 78.91 250 | 80.49 263 | 87.18 297 | 69.30 227 | 97.08 204 | 68.12 292 | 83.56 242 | 93.51 241 |
|
Patchmatch-test1 | | | 85.81 217 | 84.71 217 | 89.12 229 | 92.15 208 | 76.60 265 | 91.12 264 | 91.69 263 | 83.53 168 | 85.50 167 | 88.56 279 | 66.79 261 | 95.00 299 | 72.69 258 | 90.35 161 | 95.76 128 |
|
pmmvs-eth3d | | | 80.97 285 | 78.72 290 | 87.74 265 | 84.99 326 | 79.97 174 | 90.11 272 | 91.65 264 | 75.36 283 | 73.51 312 | 86.03 310 | 59.45 304 | 93.96 307 | 75.17 241 | 72.21 314 | 89.29 318 |
|
tfpn111 | | | 87.63 164 | 86.68 166 | 90.47 164 | 96.12 79 | 78.55 218 | 95.03 84 | 91.58 265 | 87.15 87 | 88.06 105 | 92.29 182 | 68.91 232 | 98.15 109 | 69.88 277 | 91.10 145 | 94.71 166 |
|
conf200view11 | | | 87.65 160 | 86.71 163 | 90.46 166 | 96.12 79 | 78.55 218 | 95.03 84 | 91.58 265 | 87.15 87 | 88.06 105 | 92.29 182 | 68.91 232 | 98.10 116 | 70.13 272 | 91.10 145 | 94.71 166 |
|
thres100view900 | | | 87.63 164 | 86.71 163 | 90.38 170 | 96.12 79 | 78.55 218 | 95.03 84 | 91.58 265 | 87.15 87 | 88.06 105 | 92.29 182 | 68.91 232 | 98.10 116 | 70.13 272 | 91.10 145 | 94.48 185 |
|
thres600view7 | | | 87.65 160 | 86.67 167 | 90.59 151 | 96.08 85 | 78.72 214 | 94.88 95 | 91.58 265 | 87.06 96 | 88.08 104 | 92.30 181 | 68.91 232 | 98.10 116 | 70.05 276 | 91.10 145 | 94.96 151 |
|
tpmp4_e23 | | | 83.87 258 | 82.33 259 | 88.48 249 | 91.46 226 | 72.82 291 | 89.82 276 | 91.57 269 | 73.02 304 | 81.86 247 | 89.05 269 | 66.20 268 | 96.97 212 | 71.57 262 | 86.39 217 | 95.66 131 |
|
MDTV_nov1_ep13 | | | | 83.56 242 | | 91.69 222 | 69.93 316 | 87.75 302 | 91.54 270 | 78.60 257 | 84.86 194 | 88.90 272 | 69.54 222 | 96.03 258 | 70.25 269 | 88.93 190 | |
|
tpm cat1 | | | 81.96 271 | 80.27 273 | 87.01 283 | 91.09 258 | 71.02 308 | 87.38 305 | 91.53 271 | 66.25 330 | 80.17 265 | 86.35 308 | 68.22 256 | 96.15 255 | 69.16 283 | 82.29 253 | 93.86 212 |
|
CVMVSNet | | | 84.69 249 | 84.79 216 | 84.37 306 | 91.84 215 | 64.92 330 | 93.70 187 | 91.47 272 | 66.19 331 | 86.16 146 | 95.28 86 | 67.18 260 | 93.33 314 | 80.89 173 | 90.42 160 | 94.88 160 |
|
tpmrst | | | 85.35 228 | 84.99 207 | 86.43 291 | 90.88 270 | 67.88 322 | 88.71 292 | 91.43 273 | 80.13 240 | 86.08 147 | 88.80 274 | 73.05 178 | 96.02 259 | 82.48 148 | 83.40 246 | 95.40 138 |
|
EU-MVSNet | | | 81.32 281 | 80.95 268 | 82.42 314 | 88.50 308 | 63.67 331 | 93.32 199 | 91.33 274 | 64.02 335 | 80.57 262 | 92.83 165 | 61.21 295 | 92.27 322 | 76.34 231 | 80.38 287 | 91.32 293 |
|
PatchmatchNet | | | 85.85 213 | 84.70 218 | 89.29 225 | 91.76 218 | 75.54 274 | 88.49 295 | 91.30 275 | 81.63 226 | 85.05 185 | 88.70 276 | 71.71 191 | 96.24 252 | 74.61 247 | 89.05 189 | 96.08 114 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
IB-MVS | | 80.51 15 | 85.24 231 | 83.26 250 | 91.19 134 | 92.13 210 | 79.86 176 | 91.75 250 | 91.29 276 | 83.28 176 | 80.66 260 | 88.49 280 | 61.28 292 | 98.46 93 | 80.99 171 | 79.46 296 | 95.25 142 |
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 |
view600 | | | 87.62 167 | 86.65 168 | 90.53 154 | 96.19 74 | 78.52 222 | 95.29 64 | 91.09 277 | 87.08 92 | 87.84 111 | 93.03 157 | 68.86 236 | 98.11 112 | 69.44 279 | 91.02 153 | 94.96 151 |
|
view800 | | | 87.62 167 | 86.65 168 | 90.53 154 | 96.19 74 | 78.52 222 | 95.29 64 | 91.09 277 | 87.08 92 | 87.84 111 | 93.03 157 | 68.86 236 | 98.11 112 | 69.44 279 | 91.02 153 | 94.96 151 |
|
conf0.05thres1000 | | | 87.62 167 | 86.65 168 | 90.53 154 | 96.19 74 | 78.52 222 | 95.29 64 | 91.09 277 | 87.08 92 | 87.84 111 | 93.03 157 | 68.86 236 | 98.11 112 | 69.44 279 | 91.02 153 | 94.96 151 |
|
tfpn | | | 87.62 167 | 86.65 168 | 90.53 154 | 96.19 74 | 78.52 222 | 95.29 64 | 91.09 277 | 87.08 92 | 87.84 111 | 93.03 157 | 68.86 236 | 98.11 112 | 69.44 279 | 91.02 153 | 94.96 151 |
|
new-patchmatchnet | | | 76.41 301 | 75.17 301 | 80.13 316 | 82.65 334 | 59.61 336 | 87.66 303 | 91.08 281 | 78.23 263 | 69.85 324 | 83.22 321 | 54.76 318 | 91.63 328 | 64.14 314 | 64.89 335 | 89.16 320 |
|
test20.03 | | | 79.95 290 | 79.08 287 | 82.55 313 | 85.79 322 | 67.74 323 | 91.09 265 | 91.08 281 | 81.23 233 | 74.48 309 | 89.96 260 | 61.63 290 | 90.15 330 | 60.08 324 | 76.38 305 | 89.76 314 |
|
LF4IMVS | | | 80.37 288 | 79.07 288 | 84.27 308 | 86.64 320 | 69.87 317 | 89.39 283 | 91.05 283 | 76.38 274 | 74.97 306 | 90.00 258 | 47.85 332 | 94.25 305 | 74.55 248 | 80.82 280 | 88.69 325 |
|
CostFormer | | | 85.77 218 | 84.94 211 | 88.26 256 | 91.16 257 | 72.58 298 | 89.47 282 | 91.04 284 | 76.26 277 | 86.45 139 | 89.97 259 | 70.74 206 | 96.86 220 | 82.35 151 | 87.07 215 | 95.34 141 |
|
LCM-MVSNet-Re | | | 88.30 138 | 88.32 120 | 88.27 255 | 94.71 137 | 72.41 299 | 93.15 209 | 90.98 285 | 87.77 78 | 79.25 275 | 91.96 197 | 78.35 106 | 95.75 271 | 83.04 139 | 95.62 85 | 96.65 98 |
|
DWT-MVSNet_test | | | 84.95 238 | 83.68 239 | 88.77 234 | 91.43 230 | 73.75 285 | 91.74 251 | 90.98 285 | 80.66 237 | 83.84 216 | 87.36 294 | 62.44 285 | 97.11 202 | 78.84 209 | 85.81 220 | 95.46 136 |
|
LCM-MVSNet | | | 66.00 314 | 62.16 318 | 77.51 323 | 64.51 352 | 58.29 337 | 83.87 327 | 90.90 287 | 48.17 344 | 54.69 340 | 73.31 339 | 16.83 357 | 86.75 339 | 65.47 307 | 61.67 340 | 87.48 333 |
|
AllTest | | | 83.42 260 | 81.39 264 | 89.52 212 | 95.01 124 | 77.79 246 | 93.12 210 | 90.89 288 | 77.41 267 | 76.12 297 | 93.34 141 | 54.08 321 | 97.51 153 | 68.31 289 | 84.27 234 | 93.26 246 |
|
TestCases | | | | | 89.52 212 | 95.01 124 | 77.79 246 | | 90.89 288 | 77.41 267 | 76.12 297 | 93.34 141 | 54.08 321 | 97.51 153 | 68.31 289 | 84.27 234 | 93.26 246 |
|
Vis-MVSNet (Re-imp) | | | 89.59 103 | 89.44 92 | 90.03 192 | 95.74 96 | 75.85 272 | 95.61 55 | 90.80 290 | 87.66 83 | 87.83 115 | 95.40 85 | 76.79 118 | 96.46 243 | 78.37 211 | 96.73 70 | 97.80 63 |
|
RPMNet | | | 83.18 264 | 80.87 270 | 90.12 183 | 90.58 279 | 79.34 199 | 85.24 318 | 90.78 291 | 71.44 314 | 85.55 162 | 82.97 323 | 70.87 203 | 95.61 273 | 61.01 322 | 89.36 177 | 95.40 138 |
|
OpenMVS_ROB | | 74.94 19 | 79.51 293 | 77.03 297 | 86.93 284 | 87.00 319 | 76.23 270 | 92.33 237 | 90.74 292 | 68.93 324 | 74.52 308 | 88.23 284 | 49.58 328 | 96.62 233 | 57.64 328 | 84.29 233 | 87.94 332 |
|
testgi | | | 80.94 286 | 80.20 275 | 83.18 310 | 87.96 316 | 66.29 326 | 91.28 260 | 90.70 293 | 83.70 161 | 78.12 279 | 92.84 164 | 51.37 325 | 90.82 329 | 63.34 315 | 82.46 252 | 92.43 271 |
|
MDA-MVSNet-bldmvs | | | 78.85 297 | 76.31 298 | 86.46 290 | 89.76 297 | 73.88 284 | 88.79 291 | 90.42 294 | 79.16 249 | 59.18 338 | 88.33 283 | 60.20 300 | 94.04 306 | 62.00 319 | 68.96 330 | 91.48 291 |
|
tpm2 | | | 84.08 254 | 82.94 254 | 87.48 273 | 91.39 233 | 71.27 304 | 89.23 286 | 90.37 295 | 71.95 312 | 84.64 196 | 89.33 267 | 67.30 257 | 96.55 238 | 75.17 241 | 87.09 214 | 94.63 171 |
|
TinyColmap | | | 79.76 292 | 77.69 292 | 85.97 294 | 91.71 220 | 73.12 288 | 89.55 278 | 90.36 296 | 75.03 287 | 72.03 320 | 90.19 255 | 46.22 335 | 96.19 254 | 63.11 316 | 81.03 274 | 88.59 326 |
|
Gipuma | | | 57.99 321 | 54.91 322 | 67.24 334 | 88.51 307 | 65.59 328 | 52.21 351 | 90.33 297 | 43.58 347 | 42.84 346 | 51.18 349 | 20.29 354 | 85.07 344 | 34.77 349 | 70.45 326 | 51.05 350 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
testus | | | 74.41 305 | 73.35 303 | 77.59 322 | 82.49 335 | 57.08 339 | 86.02 311 | 90.21 298 | 72.28 310 | 72.89 317 | 84.32 316 | 37.08 342 | 86.96 338 | 52.24 332 | 82.65 250 | 88.73 323 |
|
PatchT | | | 82.68 267 | 81.27 265 | 86.89 287 | 90.09 291 | 70.94 310 | 84.06 325 | 90.15 299 | 74.91 289 | 85.63 161 | 83.57 319 | 69.37 223 | 94.87 301 | 65.19 308 | 88.50 196 | 94.84 161 |
|
MIMVSNet | | | 82.59 268 | 80.53 271 | 88.76 235 | 91.51 225 | 78.32 231 | 86.57 309 | 90.13 300 | 79.32 247 | 80.70 259 | 88.69 277 | 52.98 323 | 93.07 319 | 66.03 306 | 88.86 191 | 94.90 159 |
|
dp | | | 81.47 279 | 80.23 274 | 85.17 301 | 89.92 295 | 65.49 329 | 86.74 307 | 90.10 301 | 76.30 276 | 81.10 254 | 87.12 298 | 62.81 283 | 95.92 263 | 68.13 291 | 79.88 293 | 94.09 199 |
|
MDA-MVSNet_test_wron | | | 79.21 296 | 77.19 296 | 85.29 299 | 88.22 312 | 72.77 293 | 85.87 313 | 90.06 302 | 74.34 294 | 62.62 337 | 87.56 293 | 66.14 270 | 91.99 324 | 66.90 298 | 73.01 311 | 91.10 298 |
|
PMMVS | | | 85.71 224 | 84.96 210 | 87.95 263 | 88.90 305 | 77.09 261 | 88.68 293 | 90.06 302 | 72.32 309 | 86.47 136 | 90.76 243 | 72.15 190 | 94.40 303 | 81.78 162 | 93.49 120 | 92.36 274 |
|
YYNet1 | | | 79.22 295 | 77.20 295 | 85.28 300 | 88.20 313 | 72.66 295 | 85.87 313 | 90.05 304 | 74.33 295 | 62.70 336 | 87.61 292 | 66.09 271 | 92.03 323 | 66.94 295 | 72.97 312 | 91.15 296 |
|
tpm | | | 84.73 246 | 84.02 230 | 86.87 288 | 90.33 285 | 68.90 319 | 89.06 288 | 89.94 305 | 80.85 236 | 85.75 153 | 89.86 261 | 68.54 243 | 95.97 261 | 77.76 218 | 84.05 236 | 95.75 129 |
|
LFMVS | | | 90.08 91 | 89.13 100 | 92.95 74 | 96.71 61 | 82.32 122 | 96.08 33 | 89.91 306 | 86.79 102 | 92.15 59 | 96.81 34 | 62.60 284 | 98.34 99 | 87.18 95 | 93.90 113 | 98.19 37 |
|
test-LLR | | | 85.87 212 | 85.41 201 | 87.25 277 | 90.95 263 | 71.67 302 | 89.55 278 | 89.88 307 | 83.41 171 | 84.54 199 | 87.95 287 | 67.25 258 | 95.11 296 | 81.82 160 | 93.37 125 | 94.97 148 |
|
test-mter | | | 84.54 251 | 83.64 241 | 87.25 277 | 90.95 263 | 71.67 302 | 89.55 278 | 89.88 307 | 79.17 248 | 84.54 199 | 87.95 287 | 55.56 315 | 95.11 296 | 81.82 160 | 93.37 125 | 94.97 148 |
|
test1235678 | | | 72.22 308 | 70.31 309 | 77.93 321 | 78.04 341 | 58.04 338 | 85.76 315 | 89.80 309 | 70.15 322 | 63.43 335 | 80.20 332 | 42.24 339 | 87.24 337 | 48.68 338 | 74.50 309 | 88.50 327 |
|
PVSNet_0 | | 73.20 20 | 77.22 299 | 74.83 302 | 84.37 306 | 90.70 276 | 71.10 307 | 83.09 331 | 89.67 310 | 72.81 307 | 73.93 311 | 83.13 322 | 60.79 297 | 93.70 309 | 68.54 285 | 50.84 344 | 88.30 331 |
|
JIA-IIPM | | | 81.04 283 | 78.98 289 | 87.25 277 | 88.64 306 | 73.48 287 | 81.75 334 | 89.61 311 | 73.19 301 | 82.05 243 | 73.71 338 | 66.07 272 | 95.87 266 | 71.18 266 | 84.60 231 | 92.41 272 |
|
tfpn_ndepth | | | 86.10 207 | 84.98 208 | 89.43 217 | 95.52 105 | 78.29 233 | 94.62 113 | 89.60 312 | 81.88 221 | 85.43 173 | 90.54 248 | 68.47 246 | 96.85 221 | 68.46 287 | 90.34 162 | 93.15 252 |
|
Anonymous20231211 | | | 72.97 307 | 69.63 312 | 83.00 312 | 83.05 332 | 66.91 325 | 92.69 225 | 89.45 313 | 61.06 338 | 67.50 329 | 83.46 320 | 34.34 345 | 93.61 311 | 51.11 333 | 63.97 337 | 88.48 329 |
|
conf0.01 | | | 85.83 215 | 84.54 221 | 89.71 205 | 95.26 114 | 77.63 252 | 94.21 144 | 89.33 314 | 81.89 214 | 84.94 188 | 91.51 216 | 68.43 248 | 96.80 222 | 66.05 300 | 89.23 180 | 94.71 166 |
|
conf0.002 | | | 85.83 215 | 84.54 221 | 89.71 205 | 95.26 114 | 77.63 252 | 94.21 144 | 89.33 314 | 81.89 214 | 84.94 188 | 91.51 216 | 68.43 248 | 96.80 222 | 66.05 300 | 89.23 180 | 94.71 166 |
|
thresconf0.02 | | | 85.75 219 | 84.54 221 | 89.38 220 | 95.26 114 | 77.63 252 | 94.21 144 | 89.33 314 | 81.89 214 | 84.94 188 | 91.51 216 | 68.43 248 | 96.80 222 | 66.05 300 | 89.23 180 | 93.70 224 |
|
tfpn_n400 | | | 85.75 219 | 84.54 221 | 89.38 220 | 95.26 114 | 77.63 252 | 94.21 144 | 89.33 314 | 81.89 214 | 84.94 188 | 91.51 216 | 68.43 248 | 96.80 222 | 66.05 300 | 89.23 180 | 93.70 224 |
|
tfpnconf | | | 85.75 219 | 84.54 221 | 89.38 220 | 95.26 114 | 77.63 252 | 94.21 144 | 89.33 314 | 81.89 214 | 84.94 188 | 91.51 216 | 68.43 248 | 96.80 222 | 66.05 300 | 89.23 180 | 93.70 224 |
|
tfpnview11 | | | 85.75 219 | 84.54 221 | 89.38 220 | 95.26 114 | 77.63 252 | 94.21 144 | 89.33 314 | 81.89 214 | 84.94 188 | 91.51 216 | 68.43 248 | 96.80 222 | 66.05 300 | 89.23 180 | 93.70 224 |
|
ADS-MVSNet | | | 81.56 277 | 79.78 279 | 86.90 286 | 91.35 240 | 71.82 301 | 83.33 329 | 89.16 320 | 72.90 305 | 82.24 239 | 85.77 311 | 64.98 276 | 93.76 308 | 64.57 312 | 83.74 238 | 95.12 143 |
|
tfpn1000 | | | 86.06 208 | 84.92 212 | 89.49 215 | 95.54 102 | 77.79 246 | 94.72 106 | 89.07 321 | 82.05 206 | 85.36 181 | 91.94 198 | 68.32 255 | 96.65 231 | 67.04 294 | 90.24 163 | 94.02 203 |
|
1111 | | | 70.54 312 | 69.71 311 | 73.04 326 | 79.30 338 | 44.83 351 | 84.23 323 | 88.96 322 | 67.33 327 | 65.42 332 | 82.28 325 | 41.11 340 | 88.11 335 | 47.12 340 | 71.60 319 | 86.19 334 |
|
.test1245 | | | 57.63 322 | 61.79 319 | 45.14 340 | 79.30 338 | 44.83 351 | 84.23 323 | 88.96 322 | 67.33 327 | 65.42 332 | 82.28 325 | 41.11 340 | 88.11 335 | 47.12 340 | 0.39 355 | 2.46 356 |
|
testmv | | | 65.49 315 | 62.66 316 | 73.96 325 | 68.78 347 | 53.14 346 | 84.70 321 | 88.56 324 | 65.94 332 | 52.35 341 | 74.65 337 | 25.02 350 | 85.14 343 | 43.54 344 | 60.40 342 | 83.60 335 |
|
ADS-MVSNet2 | | | 81.66 275 | 79.71 281 | 87.50 271 | 91.35 240 | 74.19 280 | 83.33 329 | 88.48 325 | 72.90 305 | 82.24 239 | 85.77 311 | 64.98 276 | 93.20 316 | 64.57 312 | 83.74 238 | 95.12 143 |
|
LP | | | 75.51 303 | 72.15 307 | 85.61 297 | 87.86 317 | 73.93 283 | 80.20 337 | 88.43 326 | 67.39 326 | 70.05 323 | 80.56 331 | 58.18 309 | 93.18 317 | 46.28 342 | 70.36 327 | 89.71 316 |
|
test2356 | | | 74.50 304 | 73.27 304 | 78.20 318 | 80.81 336 | 59.84 334 | 83.76 328 | 88.33 327 | 71.43 315 | 72.37 319 | 81.84 327 | 45.60 336 | 86.26 340 | 50.97 334 | 84.32 232 | 88.50 327 |
|
TESTMET0.1,1 | | | 83.74 259 | 82.85 256 | 86.42 292 | 89.96 294 | 71.21 306 | 89.55 278 | 87.88 328 | 77.41 267 | 83.37 228 | 87.31 295 | 56.71 312 | 93.65 310 | 80.62 177 | 92.85 136 | 94.40 188 |
|
test0.0.03 1 | | | 82.41 269 | 81.69 262 | 84.59 304 | 88.23 311 | 72.89 290 | 90.24 269 | 87.83 329 | 83.41 171 | 79.86 270 | 89.78 262 | 67.25 258 | 88.99 332 | 65.18 309 | 83.42 245 | 91.90 282 |
|
K. test v3 | | | 81.59 276 | 80.15 276 | 85.91 295 | 89.89 296 | 69.42 318 | 92.57 230 | 87.71 330 | 85.56 123 | 73.44 313 | 89.71 263 | 55.58 314 | 95.52 277 | 77.17 225 | 69.76 328 | 92.78 263 |
|
Patchmatch-test | | | 81.37 280 | 79.30 284 | 87.58 269 | 90.92 267 | 74.16 281 | 80.99 335 | 87.68 331 | 70.52 320 | 76.63 289 | 88.81 273 | 71.21 198 | 92.76 320 | 60.01 326 | 86.93 216 | 95.83 125 |
|
test12356 | | | 64.99 316 | 63.78 315 | 68.61 333 | 72.69 344 | 39.14 354 | 78.46 339 | 87.61 332 | 64.91 333 | 55.77 339 | 77.48 335 | 28.10 347 | 85.59 342 | 44.69 343 | 64.35 336 | 81.12 340 |
|
Patchmatch-RL test | | | 81.67 274 | 79.96 278 | 86.81 289 | 85.42 323 | 71.23 305 | 82.17 333 | 87.50 333 | 78.47 258 | 77.19 288 | 82.50 324 | 70.81 205 | 93.48 312 | 82.66 145 | 72.89 313 | 95.71 130 |
|
no-one | | | 61.56 318 | 56.58 320 | 76.49 324 | 67.80 350 | 62.76 333 | 78.13 340 | 86.11 334 | 63.16 336 | 43.24 345 | 64.70 344 | 26.12 349 | 88.95 333 | 50.84 335 | 29.15 347 | 77.77 342 |
|
ANet_high | | | 58.88 320 | 54.22 323 | 72.86 327 | 56.50 356 | 56.67 341 | 80.75 336 | 86.00 335 | 73.09 303 | 37.39 347 | 64.63 345 | 22.17 352 | 79.49 350 | 43.51 345 | 23.96 351 | 82.43 339 |
|
door-mid | | | | | | | | | 85.49 336 | | | | | | | | |
|
door | | | | | | | | | 85.33 337 | | | | | | | | |
|
PM-MVS | | | 78.11 298 | 76.12 300 | 84.09 309 | 83.54 330 | 70.08 315 | 88.97 290 | 85.27 338 | 79.93 242 | 74.73 307 | 86.43 302 | 34.70 344 | 93.48 312 | 79.43 203 | 72.06 315 | 88.72 324 |
|
FPMVS | | | 64.63 317 | 62.55 317 | 70.88 328 | 70.80 345 | 56.71 340 | 84.42 322 | 84.42 339 | 51.78 343 | 49.57 342 | 81.61 328 | 23.49 351 | 81.48 347 | 40.61 347 | 76.25 306 | 74.46 344 |
|
pmmvs3 | | | 71.81 310 | 68.71 313 | 81.11 315 | 75.86 342 | 70.42 313 | 86.74 307 | 83.66 340 | 58.95 340 | 68.64 328 | 80.89 330 | 36.93 343 | 89.52 331 | 63.10 317 | 63.59 338 | 83.39 336 |
|
testpf | | | 71.41 311 | 72.11 308 | 69.30 331 | 84.53 327 | 59.79 335 | 62.74 348 | 83.14 341 | 71.11 317 | 68.83 327 | 81.57 329 | 46.70 334 | 84.83 345 | 74.51 249 | 75.86 307 | 63.30 345 |
|
MVS-HIRNet | | | 73.70 306 | 72.20 306 | 78.18 320 | 91.81 217 | 56.42 342 | 82.94 332 | 82.58 342 | 55.24 341 | 68.88 325 | 66.48 342 | 55.32 317 | 95.13 295 | 58.12 327 | 88.42 199 | 83.01 337 |
|
new_pmnet | | | 72.15 309 | 70.13 310 | 78.20 318 | 82.95 333 | 65.68 327 | 83.91 326 | 82.40 343 | 62.94 337 | 64.47 334 | 79.82 333 | 42.85 338 | 86.26 340 | 57.41 329 | 74.44 310 | 82.65 338 |
|
EPMVS | | | 83.90 257 | 82.70 258 | 87.51 270 | 90.23 289 | 72.67 294 | 88.62 294 | 81.96 344 | 81.37 232 | 85.01 186 | 88.34 282 | 66.31 266 | 94.45 302 | 75.30 240 | 87.12 213 | 95.43 137 |
|
wuykxyi23d | | | 50.55 324 | 44.13 326 | 69.81 330 | 56.77 354 | 54.58 345 | 73.22 345 | 80.78 345 | 39.79 349 | 22.08 354 | 46.69 351 | 4.03 361 | 79.71 349 | 47.65 339 | 26.13 349 | 75.14 343 |
|
lessismore_v0 | | | | | 86.04 293 | 88.46 309 | 68.78 320 | | 80.59 346 | | 73.01 316 | 90.11 257 | 55.39 316 | 96.43 245 | 75.06 243 | 65.06 334 | 92.90 257 |
|
DSMNet-mixed | | | 76.94 300 | 76.29 299 | 78.89 317 | 83.10 331 | 56.11 343 | 87.78 301 | 79.77 347 | 60.65 339 | 75.64 303 | 88.71 275 | 61.56 291 | 88.34 334 | 60.07 325 | 89.29 179 | 92.21 279 |
|
gg-mvs-nofinetune | | | 81.77 273 | 79.37 283 | 88.99 232 | 90.85 271 | 77.73 250 | 86.29 310 | 79.63 348 | 74.88 291 | 83.19 230 | 69.05 341 | 60.34 299 | 96.11 256 | 75.46 238 | 94.64 101 | 93.11 253 |
|
PMVS | | 47.18 22 | 52.22 323 | 48.46 324 | 63.48 335 | 45.72 357 | 46.20 350 | 73.41 344 | 78.31 349 | 41.03 348 | 30.06 350 | 65.68 343 | 6.05 359 | 83.43 346 | 30.04 350 | 65.86 333 | 60.80 347 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
GG-mvs-BLEND | | | | | 87.94 264 | 89.73 298 | 77.91 241 | 87.80 300 | 78.23 350 | | 80.58 261 | 83.86 317 | 59.88 303 | 95.33 293 | 71.20 264 | 92.22 140 | 90.60 311 |
|
PMMVS2 | | | 59.60 319 | 56.40 321 | 69.21 332 | 68.83 346 | 46.58 349 | 73.02 346 | 77.48 351 | 55.07 342 | 49.21 343 | 72.95 340 | 17.43 356 | 80.04 348 | 49.32 337 | 44.33 345 | 80.99 341 |
|
PNet_i23d | | | 50.48 325 | 47.18 325 | 60.36 336 | 68.59 348 | 44.56 353 | 72.75 347 | 72.61 352 | 43.92 346 | 33.91 349 | 60.19 347 | 6.16 358 | 73.52 351 | 38.50 348 | 28.04 348 | 63.01 346 |
|
E-PMN | | | 43.23 327 | 42.29 327 | 46.03 339 | 65.58 351 | 37.41 355 | 73.51 343 | 64.62 353 | 33.99 350 | 28.47 352 | 47.87 350 | 19.90 355 | 67.91 352 | 22.23 352 | 24.45 350 | 32.77 351 |
|
EMVS | | | 42.07 328 | 41.12 328 | 44.92 341 | 63.45 353 | 35.56 357 | 73.65 342 | 63.48 354 | 33.05 351 | 26.88 353 | 45.45 352 | 21.27 353 | 67.14 353 | 19.80 353 | 23.02 352 | 32.06 352 |
|
MTMP | | | | | | | | | 60.64 355 | | | | | | | | |
|
MVE | | 39.65 23 | 43.39 326 | 38.59 331 | 57.77 337 | 56.52 355 | 48.77 348 | 55.38 350 | 58.64 356 | 29.33 352 | 28.96 351 | 52.65 348 | 4.68 360 | 64.62 354 | 28.11 351 | 33.07 346 | 59.93 348 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
DeepMVS_CX | | | | | 56.31 338 | 74.23 343 | 51.81 347 | | 56.67 357 | 44.85 345 | 48.54 344 | 75.16 336 | 27.87 348 | 58.74 355 | 40.92 346 | 52.22 343 | 58.39 349 |
|
tmp_tt | | | 35.64 330 | 39.24 329 | 24.84 343 | 14.87 358 | 23.90 359 | 62.71 349 | 51.51 358 | 6.58 354 | 36.66 348 | 62.08 346 | 44.37 337 | 30.34 357 | 52.40 331 | 22.00 353 | 20.27 353 |
|
N_pmnet | | | 68.89 313 | 68.44 314 | 70.23 329 | 89.07 303 | 28.79 358 | 88.06 298 | 19.50 359 | 69.47 323 | 71.86 321 | 84.93 314 | 61.24 294 | 91.75 326 | 54.70 330 | 77.15 304 | 90.15 313 |
|
wuyk23d | | | 21.27 332 | 20.48 333 | 23.63 344 | 68.59 348 | 36.41 356 | 49.57 352 | 6.85 360 | 9.37 353 | 7.89 355 | 4.46 358 | 4.03 361 | 31.37 356 | 17.47 354 | 16.07 354 | 3.12 354 |
|
testmvs | | | 8.92 333 | 11.52 334 | 1.12 346 | 1.06 359 | 0.46 361 | 86.02 311 | 0.65 361 | 0.62 355 | 2.74 356 | 9.52 356 | 0.31 364 | 0.45 359 | 2.38 355 | 0.39 355 | 2.46 356 |
|
test123 | | | 8.76 334 | 11.22 335 | 1.39 345 | 0.85 360 | 0.97 360 | 85.76 315 | 0.35 362 | 0.54 356 | 2.45 357 | 8.14 357 | 0.60 363 | 0.48 358 | 2.16 356 | 0.17 357 | 2.71 355 |
|
pcd_1.5k_mvsjas | | | 6.64 336 | 8.86 337 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 79.70 91 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
sosnet-low-res | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
sosnet | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
uncertanet | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
Regformer | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
n2 | | | | | | | | | 0.00 363 | | | | | | | | |
|
nn | | | | | | | | | 0.00 363 | | | | | | | | |
|
ab-mvs-re | | | 7.82 335 | 10.43 336 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 93.88 132 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
uanet | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
GSMVS | | | | | | | | | | | | | | | | | 96.12 111 |
|
test_part3 | | | | | | | | 95.99 36 | | 88.25 66 | | 97.60 5 | | 99.62 1 | 93.18 19 | | |
|
test_part2 | | | | | | 98.55 5 | 87.22 11 | | | | 96.40 3 | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 71.70 192 | | | | 96.12 111 |
|
sam_mvs | | | | | | | | | | | | | 70.60 207 | | | | |
|
test_post1 | | | | | | | | 88.00 299 | | | | 9.81 355 | 69.31 226 | 95.53 276 | 76.65 229 | | |
|
test_post | | | | | | | | | | | | 10.29 354 | 70.57 211 | 95.91 265 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 83.76 318 | 71.53 195 | 96.48 241 | | | |
|
gm-plane-assit | | | | | | 89.60 299 | 68.00 321 | | | 77.28 270 | | 88.99 270 | | 97.57 149 | 79.44 202 | | |
|
test9_res | | | | | | | | | | | | | | | 91.91 43 | 98.71 20 | 98.07 46 |
|
agg_prior2 | | | | | | | | | | | | | | | 90.54 62 | 98.68 25 | 98.27 32 |
|
test_prior4 | | | | | | | 85.96 44 | 94.11 154 | | | | | | | | | |
|
test_prior2 | | | | | | | | 94.12 152 | | 87.67 81 | 92.63 46 | 96.39 53 | 86.62 26 | | 91.50 50 | 98.67 27 | |
|
旧先验2 | | | | | | | | 93.36 198 | | 71.25 316 | 94.37 14 | | | 97.13 201 | 86.74 101 | | |
|
新几何2 | | | | | | | | 93.11 212 | | | | | | | | | |
|
原ACMM2 | | | | | | | | 92.94 220 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 98.75 79 | 78.30 213 | | |
|
segment_acmp | | | | | | | | | | | | | 87.16 22 | | | | |
|
testdata1 | | | | | | | | 92.15 243 | | 87.94 72 | | | | | | | |
|
plane_prior7 | | | | | | 94.70 138 | 82.74 112 | | | | | | | | | | |
|
plane_prior6 | | | | | | 94.52 144 | 82.75 110 | | | | | | 74.23 159 | | | | |
|
plane_prior4 | | | | | | | | | | | | 94.86 98 | | | | | |
|
plane_prior3 | | | | | | | 82.75 110 | | | 90.26 25 | 86.91 130 | | | | | | |
|
plane_prior2 | | | | | | | | 95.85 43 | | 90.81 18 | | | | | | | |
|
plane_prior1 | | | | | | 94.59 142 | | | | | | | | | | | |
|
plane_prior | | | | | | | 82.73 113 | 95.21 74 | | 89.66 35 | | | | | | 89.88 169 | |
|
HQP5-MVS | | | | | | | 81.56 130 | | | | | | | | | | |
|
HQP-NCC | | | | | | 94.17 155 | | 94.39 130 | | 88.81 50 | 85.43 173 | | | | | | |
|
ACMP_Plane | | | | | | 94.17 155 | | 94.39 130 | | 88.81 50 | 85.43 173 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 87.11 98 | | |
|
HQP4-MVS | | | | | | | | | | | 85.43 173 | | | 97.96 131 | | | 94.51 181 |
|
HQP2-MVS | | | | | | | | | | | | | 73.83 168 | | | | |
|
NP-MVS | | | | | | 94.37 150 | 82.42 120 | | | | | 93.98 125 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 55.91 344 | 87.62 304 | | 73.32 300 | 84.59 198 | | 70.33 214 | | 74.65 246 | | 95.50 134 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 87.47 208 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 88.01 205 | |
|
Test By Simon | | | | | | | | | | | | | 80.02 86 | | | | |
|