APDe-MVS | | | 95.46 1 | 95.64 1 | 94.91 10 | 98.26 18 | 86.29 35 | 97.46 2 | 97.40 7 | 89.03 45 | 96.20 2 | 98.10 1 | 89.39 5 | 99.34 20 | 95.88 1 | 99.03 1 | 99.10 1 |
|
ACMMP_Plus | | | 94.74 9 | 94.56 10 | 95.28 4 | 98.02 28 | 87.70 4 | 95.68 47 | 97.34 9 | 88.28 63 | 95.30 8 | 97.67 3 | 85.90 31 | 99.54 7 | 93.91 9 | 98.95 2 | 98.60 6 |
|
HPM-MVS++ | | | 95.14 5 | 94.91 7 | 95.83 1 | 98.25 19 | 89.65 1 | 95.92 38 | 96.96 35 | 91.75 7 | 94.02 17 | 96.83 30 | 88.12 9 | 99.55 5 | 93.41 14 | 98.94 3 | 98.28 26 |
|
MP-MVS-pluss | | | 94.21 23 | 94.00 25 | 94.85 14 | 98.17 22 | 86.65 21 | 94.82 86 | 97.17 22 | 86.26 98 | 92.83 36 | 97.87 2 | 85.57 34 | 99.56 1 | 94.37 6 | 98.92 4 | 98.34 20 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
SteuartSystems-ACMMP | | | 95.20 4 | 95.32 5 | 94.85 14 | 96.99 53 | 86.33 31 | 97.33 3 | 97.30 15 | 91.38 11 | 95.39 6 | 97.46 7 | 88.98 8 | 99.40 18 | 94.12 7 | 98.89 5 | 98.82 2 |
Skip Steuart: Steuart Systems R&D Blog. |
SD-MVS | | | 94.96 6 | 95.33 4 | 93.88 47 | 97.25 50 | 86.69 18 | 96.19 29 | 97.11 26 | 90.42 23 | 96.95 1 | 97.27 11 | 89.53 3 | 96.91 202 | 94.38 5 | 98.85 6 | 98.03 46 |
|
CNVR-MVS | | | 95.40 2 | 95.37 3 | 95.50 3 | 98.11 23 | 88.51 3 | 95.29 61 | 96.96 35 | 92.09 3 | 95.32 7 | 97.08 23 | 89.49 4 | 99.33 23 | 95.10 2 | 98.85 6 | 98.66 4 |
|
CP-MVS | | | 94.34 17 | 94.21 18 | 94.74 24 | 98.39 14 | 86.64 22 | 97.60 1 | 97.24 17 | 88.53 58 | 92.73 41 | 97.23 14 | 85.20 38 | 99.32 24 | 92.15 31 | 98.83 8 | 98.25 31 |
|
MP-MVS | | | 94.25 20 | 94.07 23 | 94.77 21 | 98.47 9 | 86.31 33 | 96.71 20 | 96.98 31 | 89.04 44 | 91.98 58 | 97.19 18 | 85.43 35 | 99.56 1 | 92.06 34 | 98.79 9 | 98.44 17 |
|
PHI-MVS | | | 93.89 30 | 93.65 32 | 94.62 28 | 96.84 56 | 86.43 28 | 96.69 21 | 97.49 4 | 85.15 119 | 93.56 27 | 96.28 53 | 85.60 33 | 99.31 25 | 92.45 22 | 98.79 9 | 98.12 39 |
|
ACMMPR | | | 94.43 14 | 94.28 14 | 94.91 10 | 98.63 2 | 86.69 18 | 96.94 10 | 97.32 14 | 88.63 54 | 93.53 28 | 97.26 13 | 85.04 40 | 99.54 7 | 92.35 26 | 98.78 11 | 98.50 9 |
|
HFP-MVS | | | 94.52 10 | 94.40 11 | 94.86 12 | 98.61 3 | 86.81 13 | 96.94 10 | 97.34 9 | 88.63 54 | 93.65 21 | 97.21 16 | 86.10 27 | 99.49 13 | 92.35 26 | 98.77 12 | 98.30 24 |
|
#test# | | | 94.32 19 | 94.14 20 | 94.86 12 | 98.61 3 | 86.81 13 | 96.43 23 | 97.34 9 | 87.51 82 | 93.65 21 | 97.21 16 | 86.10 27 | 99.49 13 | 91.68 44 | 98.77 12 | 98.30 24 |
|
MPTG | | | 94.47 11 | 94.30 13 | 95.00 7 | 98.42 12 | 86.95 9 | 95.06 76 | 96.97 32 | 91.07 13 | 93.14 32 | 97.56 4 | 84.30 47 | 99.56 1 | 93.43 12 | 98.75 14 | 98.47 12 |
|
MTAPA | | | 94.42 16 | 94.22 16 | 95.00 7 | 98.42 12 | 86.95 9 | 94.36 121 | 96.97 32 | 91.07 13 | 93.14 32 | 97.56 4 | 84.30 47 | 99.56 1 | 93.43 12 | 98.75 14 | 98.47 12 |
|
region2R | | | 94.43 14 | 94.27 15 | 94.92 9 | 98.65 1 | 86.67 20 | 96.92 14 | 97.23 19 | 88.60 56 | 93.58 25 | 97.27 11 | 85.22 37 | 99.54 7 | 92.21 28 | 98.74 16 | 98.56 8 |
|
test9_res | | | | | | | | | | | | | | | 91.91 39 | 98.71 17 | 98.07 42 |
|
DeepPCF-MVS | | 89.96 1 | 94.20 24 | 94.77 8 | 92.49 87 | 96.52 64 | 80.00 169 | 94.00 145 | 97.08 27 | 90.05 25 | 95.65 5 | 97.29 10 | 89.66 2 | 98.97 58 | 93.95 8 | 98.71 17 | 98.50 9 |
|
train_agg | | | 93.44 37 | 93.08 39 | 94.52 30 | 97.53 34 | 86.49 26 | 94.07 137 | 96.78 48 | 81.86 199 | 92.77 38 | 96.20 57 | 87.63 14 | 99.12 38 | 92.14 32 | 98.69 19 | 97.94 50 |
|
agg_prior3 | | | 93.27 42 | 92.89 45 | 94.40 37 | 97.49 37 | 86.12 38 | 94.07 137 | 96.73 52 | 81.46 207 | 92.46 50 | 96.05 64 | 86.90 21 | 99.15 35 | 92.14 32 | 98.69 19 | 97.94 50 |
|
DeepC-MVS_fast | | 89.43 2 | 94.04 25 | 93.79 28 | 94.80 20 | 97.48 39 | 86.78 15 | 95.65 51 | 96.89 40 | 89.40 36 | 92.81 37 | 96.97 25 | 85.37 36 | 99.24 28 | 90.87 55 | 98.69 19 | 98.38 19 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
TSAR-MVS + MP. | | | 94.85 7 | 94.94 6 | 94.58 29 | 98.25 19 | 86.33 31 | 96.11 31 | 96.62 62 | 88.14 67 | 96.10 3 | 96.96 26 | 89.09 7 | 98.94 62 | 94.48 4 | 98.68 22 | 98.48 11 |
|
agg_prior2 | | | | | | | | | | | | | | | 90.54 58 | 98.68 22 | 98.27 28 |
|
test_prior3 | | | 93.60 35 | 93.53 34 | 93.82 49 | 97.29 46 | 84.49 61 | 94.12 129 | 96.88 41 | 87.67 79 | 92.63 43 | 96.39 50 | 86.62 23 | 98.87 65 | 91.50 46 | 98.67 24 | 98.11 40 |
|
test_prior2 | | | | | | | | 94.12 129 | | 87.67 79 | 92.63 43 | 96.39 50 | 86.62 23 | | 91.50 46 | 98.67 24 | |
|
MSLP-MVS++ | | | 93.72 32 | 94.08 22 | 92.65 80 | 97.31 44 | 83.43 89 | 95.79 42 | 97.33 12 | 90.03 26 | 93.58 25 | 96.96 26 | 84.87 43 | 97.76 126 | 92.19 30 | 98.66 26 | 96.76 91 |
|
CDPH-MVS | | | 92.83 50 | 92.30 52 | 94.44 32 | 97.79 31 | 86.11 39 | 94.06 140 | 96.66 59 | 80.09 218 | 92.77 38 | 96.63 40 | 86.62 23 | 99.04 46 | 87.40 87 | 98.66 26 | 98.17 34 |
|
HPM-MVS | | | 94.02 26 | 93.88 26 | 94.43 34 | 98.39 14 | 85.78 46 | 97.25 5 | 97.07 28 | 86.90 89 | 92.62 45 | 96.80 33 | 84.85 44 | 99.17 32 | 92.43 23 | 98.65 28 | 98.33 21 |
|
mPP-MVS | | | 93.99 27 | 93.78 29 | 94.63 27 | 98.50 7 | 85.90 44 | 96.87 16 | 96.91 39 | 88.70 52 | 91.83 62 | 97.17 20 | 83.96 50 | 99.55 5 | 91.44 48 | 98.64 29 | 98.43 18 |
|
MCST-MVS | | | 94.45 12 | 94.20 19 | 95.19 5 | 98.46 10 | 87.50 7 | 95.00 77 | 97.12 24 | 87.13 85 | 92.51 48 | 96.30 52 | 89.24 6 | 99.34 20 | 93.46 11 | 98.62 30 | 98.73 3 |
|
APD-MVS | | | 94.24 21 | 94.07 23 | 94.75 23 | 98.06 26 | 86.90 12 | 95.88 39 | 96.94 37 | 85.68 108 | 95.05 9 | 97.18 19 | 87.31 17 | 99.07 41 | 91.90 42 | 98.61 31 | 98.28 26 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
PGM-MVS | | | 93.96 28 | 93.72 31 | 94.68 25 | 98.43 11 | 86.22 36 | 95.30 59 | 97.78 1 | 87.45 83 | 93.26 29 | 97.33 9 | 84.62 45 | 99.51 11 | 90.75 57 | 98.57 32 | 98.32 23 |
|
XVS | | | 94.45 12 | 94.32 12 | 94.85 14 | 98.54 5 | 86.60 23 | 96.93 12 | 97.19 20 | 90.66 21 | 92.85 34 | 97.16 21 | 85.02 41 | 99.49 13 | 91.99 35 | 98.56 33 | 98.47 12 |
|
X-MVStestdata | | | 88.31 135 | 86.13 171 | 94.85 14 | 98.54 5 | 86.60 23 | 96.93 12 | 97.19 20 | 90.66 21 | 92.85 34 | 23.41 329 | 85.02 41 | 99.49 13 | 91.99 35 | 98.56 33 | 98.47 12 |
|
agg_prior1 | | | 93.29 41 | 92.97 43 | 94.26 40 | 97.38 41 | 85.92 41 | 93.92 148 | 96.72 54 | 81.96 193 | 92.16 54 | 96.23 55 | 87.85 10 | 98.97 58 | 91.95 38 | 98.55 35 | 97.90 55 |
|
DELS-MVS | | | 93.43 38 | 93.25 37 | 93.97 44 | 95.42 91 | 85.04 52 | 93.06 192 | 97.13 23 | 90.74 19 | 91.84 60 | 95.09 89 | 86.32 26 | 99.21 29 | 91.22 49 | 98.45 36 | 97.65 62 |
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 |
HPM-MVS_fast | | | 93.40 39 | 93.22 38 | 93.94 46 | 98.36 16 | 84.83 54 | 97.15 7 | 96.80 47 | 85.77 105 | 92.47 49 | 97.13 22 | 82.38 59 | 99.07 41 | 90.51 59 | 98.40 37 | 97.92 54 |
|
NCCC | | | 94.81 8 | 94.69 9 | 95.17 6 | 97.83 30 | 87.46 8 | 95.66 49 | 96.93 38 | 92.34 2 | 93.94 18 | 96.58 43 | 87.74 12 | 99.44 17 | 92.83 19 | 98.40 37 | 98.62 5 |
|
DeepC-MVS | | 88.79 3 | 93.31 40 | 92.99 42 | 94.26 40 | 96.07 75 | 85.83 45 | 94.89 82 | 96.99 30 | 89.02 46 | 89.56 84 | 97.37 8 | 82.51 58 | 99.38 19 | 92.20 29 | 98.30 39 | 97.57 66 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CSCG | | | 93.23 46 | 93.05 40 | 93.76 53 | 98.04 27 | 84.07 74 | 96.22 28 | 97.37 8 | 84.15 139 | 90.05 81 | 95.66 76 | 87.77 11 | 99.15 35 | 89.91 62 | 98.27 40 | 98.07 42 |
|
原ACMM1 | | | | | 92.01 103 | 97.34 43 | 81.05 144 | | 96.81 46 | 78.89 228 | 90.45 75 | 95.92 67 | 82.65 57 | 98.84 73 | 80.68 173 | 98.26 41 | 96.14 105 |
|
MVS_111021_HR | | | 93.45 36 | 93.31 36 | 93.84 48 | 96.99 53 | 84.84 53 | 93.24 185 | 97.24 17 | 88.76 51 | 91.60 66 | 95.85 69 | 86.07 29 | 98.66 78 | 91.91 39 | 98.16 42 | 98.03 46 |
|
test12 | | | | | 94.34 38 | 97.13 51 | 86.15 37 | | 96.29 78 | | 91.04 72 | | 85.08 39 | 99.01 52 | | 98.13 43 | 97.86 56 |
|
新几何1 | | | | | 93.10 64 | 97.30 45 | 84.35 70 | | 95.56 127 | 71.09 294 | 91.26 69 | 96.24 54 | 82.87 56 | 98.86 68 | 79.19 203 | 98.10 44 | 96.07 109 |
|
1121 | | | 90.42 83 | 89.49 87 | 93.20 60 | 97.27 48 | 84.46 64 | 92.63 204 | 95.51 134 | 71.01 295 | 91.20 70 | 96.21 56 | 82.92 55 | 99.05 43 | 80.56 175 | 98.07 45 | 96.10 107 |
|
MVS_0305 | | | 93.26 44 | 92.88 46 | 94.41 35 | 95.67 85 | 85.37 49 | 94.82 86 | 96.55 67 | 91.88 5 | 90.44 77 | 95.74 73 | 79.90 83 | 99.52 10 | 92.90 17 | 98.05 46 | 98.33 21 |
|
HSP-MVS | | | 95.30 3 | 95.48 2 | 94.76 22 | 98.49 8 | 86.52 25 | 96.91 15 | 96.73 52 | 91.73 8 | 96.10 3 | 96.69 36 | 89.90 1 | 99.30 26 | 94.70 3 | 98.04 47 | 98.45 16 |
|
3Dnovator | | 86.66 5 | 91.73 61 | 90.82 69 | 94.44 32 | 94.59 119 | 86.37 29 | 97.18 6 | 97.02 29 | 89.20 40 | 84.31 187 | 96.66 39 | 73.74 166 | 99.17 32 | 86.74 97 | 97.96 48 | 97.79 60 |
|
APD-MVS_3200maxsize | | | 93.78 31 | 93.77 30 | 93.80 52 | 97.92 29 | 84.19 72 | 96.30 26 | 96.87 43 | 86.96 87 | 93.92 19 | 97.47 6 | 83.88 51 | 98.96 61 | 92.71 21 | 97.87 49 | 98.26 30 |
|
CPTT-MVS | | | 91.99 56 | 91.80 55 | 92.55 84 | 98.24 21 | 81.98 124 | 96.76 19 | 96.49 68 | 81.89 198 | 90.24 78 | 96.44 49 | 78.59 99 | 98.61 83 | 89.68 63 | 97.85 50 | 97.06 83 |
|
test222 | | | | | | 96.55 63 | 81.70 126 | 92.22 218 | 95.01 169 | 68.36 301 | 90.20 79 | 96.14 61 | 80.26 81 | | | 97.80 51 | 96.05 111 |
|
3Dnovator+ | | 87.14 4 | 92.42 54 | 91.37 58 | 95.55 2 | 95.63 86 | 88.73 2 | 97.07 8 | 96.77 50 | 90.84 16 | 84.02 191 | 96.62 41 | 75.95 133 | 99.34 20 | 87.77 82 | 97.68 52 | 98.59 7 |
|
旧先验1 | | | | | | 96.79 57 | 81.81 125 | | 95.67 119 | | | 96.81 31 | 86.69 22 | | | 97.66 53 | 96.97 85 |
|
Regformer-1 | | | 94.22 22 | 94.13 21 | 94.51 31 | 95.54 88 | 86.36 30 | 94.57 101 | 96.44 69 | 91.69 9 | 94.32 12 | 96.56 45 | 87.05 20 | 99.03 47 | 93.35 15 | 97.65 54 | 98.15 36 |
|
Regformer-2 | | | 94.33 18 | 94.22 16 | 94.68 25 | 95.54 88 | 86.75 17 | 94.57 101 | 96.70 56 | 91.84 6 | 94.41 10 | 96.56 45 | 87.19 18 | 99.13 37 | 93.50 10 | 97.65 54 | 98.16 35 |
|
EPNet | | | 91.79 58 | 91.02 65 | 94.10 43 | 90.10 267 | 85.25 51 | 96.03 34 | 92.05 246 | 92.83 1 | 87.39 107 | 95.78 71 | 79.39 93 | 99.01 52 | 88.13 78 | 97.48 56 | 98.05 44 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
testdata | | | | | 90.49 154 | 96.40 65 | 77.89 227 | | 95.37 150 | 72.51 284 | 93.63 23 | 96.69 36 | 82.08 66 | 97.65 131 | 83.08 135 | 97.39 57 | 95.94 114 |
|
MVS_111021_LR | | | 92.47 53 | 92.29 53 | 92.98 71 | 95.99 78 | 84.43 68 | 93.08 190 | 96.09 91 | 88.20 65 | 91.12 71 | 95.72 75 | 81.33 73 | 97.76 126 | 91.74 43 | 97.37 58 | 96.75 92 |
|
abl_6 | | | 93.18 47 | 93.05 40 | 93.57 56 | 97.52 36 | 84.27 71 | 95.53 54 | 96.67 58 | 87.85 74 | 93.20 31 | 97.22 15 | 80.35 78 | 99.18 31 | 91.91 39 | 97.21 59 | 97.26 72 |
|
MVSFormer | | | 91.68 63 | 91.30 59 | 92.80 76 | 93.86 146 | 83.88 77 | 95.96 36 | 95.90 105 | 84.66 128 | 91.76 63 | 94.91 91 | 77.92 107 | 97.30 170 | 89.64 64 | 97.11 60 | 97.24 73 |
|
lupinMVS | | | 90.92 73 | 90.21 75 | 93.03 68 | 93.86 146 | 83.88 77 | 92.81 199 | 93.86 215 | 79.84 220 | 91.76 63 | 94.29 110 | 77.92 107 | 98.04 114 | 90.48 60 | 97.11 60 | 97.17 78 |
|
MG-MVS | | | 91.77 59 | 91.70 56 | 92.00 105 | 97.08 52 | 80.03 168 | 93.60 169 | 95.18 164 | 87.85 74 | 90.89 73 | 96.47 48 | 82.06 67 | 98.36 93 | 85.07 109 | 97.04 62 | 97.62 63 |
|
jason | | | 90.80 74 | 90.10 78 | 92.90 74 | 93.04 171 | 83.53 87 | 93.08 190 | 94.15 200 | 80.22 216 | 91.41 67 | 94.91 91 | 76.87 113 | 97.93 120 | 90.28 61 | 96.90 63 | 97.24 73 |
jason: jason. |
Vis-MVSNet | | | 91.75 60 | 91.23 61 | 93.29 57 | 95.32 94 | 83.78 79 | 96.14 30 | 95.98 98 | 89.89 28 | 90.45 75 | 96.58 43 | 75.09 147 | 98.31 99 | 84.75 114 | 96.90 63 | 97.78 61 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
114514_t | | | 89.51 103 | 88.50 111 | 92.54 85 | 98.11 23 | 81.99 123 | 95.16 70 | 96.36 76 | 70.19 297 | 85.81 135 | 95.25 84 | 76.70 116 | 98.63 81 | 82.07 152 | 96.86 65 | 97.00 84 |
|
Vis-MVSNet (Re-imp) | | | 89.59 101 | 89.44 89 | 90.03 179 | 95.74 83 | 75.85 249 | 95.61 52 | 90.80 274 | 87.66 81 | 87.83 100 | 95.40 81 | 76.79 115 | 96.46 220 | 78.37 208 | 96.73 66 | 97.80 59 |
|
API-MVS | | | 90.66 77 | 90.07 79 | 92.45 89 | 96.36 67 | 84.57 59 | 96.06 33 | 95.22 163 | 82.39 186 | 89.13 88 | 94.27 113 | 80.32 79 | 98.46 90 | 80.16 184 | 96.71 67 | 94.33 172 |
|
MAR-MVS | | | 90.30 84 | 89.37 90 | 93.07 67 | 96.61 60 | 84.48 63 | 95.68 47 | 95.67 119 | 82.36 187 | 87.85 99 | 92.85 156 | 76.63 118 | 98.80 74 | 80.01 185 | 96.68 68 | 95.91 115 |
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020 |
Regformer-3 | | | 93.68 33 | 93.64 33 | 93.81 51 | 95.36 92 | 84.61 57 | 94.68 96 | 95.83 110 | 91.27 12 | 93.60 24 | 96.71 34 | 85.75 32 | 98.86 68 | 92.87 18 | 96.65 69 | 97.96 49 |
|
Regformer-4 | | | 93.91 29 | 93.81 27 | 94.19 42 | 95.36 92 | 85.47 47 | 94.68 96 | 96.41 72 | 91.60 10 | 93.75 20 | 96.71 34 | 85.95 30 | 99.10 40 | 93.21 16 | 96.65 69 | 98.01 48 |
|
OpenMVS | | 83.78 11 | 88.74 127 | 87.29 138 | 93.08 65 | 92.70 178 | 85.39 48 | 96.57 22 | 96.43 71 | 78.74 233 | 80.85 234 | 96.07 63 | 69.64 217 | 99.01 52 | 78.01 214 | 96.65 69 | 94.83 151 |
|
QAPM | | | 89.51 103 | 88.15 123 | 93.59 55 | 94.92 106 | 84.58 58 | 96.82 18 | 96.70 56 | 78.43 236 | 83.41 205 | 96.19 60 | 73.18 173 | 99.30 26 | 77.11 223 | 96.54 72 | 96.89 89 |
|
IS-MVSNet | | | 91.43 65 | 91.09 64 | 92.46 88 | 95.87 81 | 81.38 135 | 96.95 9 | 93.69 219 | 89.72 33 | 89.50 86 | 95.98 65 | 78.57 100 | 97.77 125 | 83.02 137 | 96.50 73 | 98.22 32 |
|
DP-MVS Recon | | | 91.95 57 | 91.28 60 | 93.96 45 | 98.33 17 | 85.92 41 | 94.66 99 | 96.66 59 | 82.69 184 | 90.03 82 | 95.82 70 | 82.30 61 | 99.03 47 | 84.57 116 | 96.48 74 | 96.91 87 |
|
UGNet | | | 89.95 91 | 88.95 100 | 92.95 72 | 94.51 122 | 83.31 92 | 95.70 46 | 95.23 161 | 89.37 37 | 87.58 104 | 93.94 123 | 64.00 256 | 98.78 75 | 83.92 128 | 96.31 75 | 96.74 93 |
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 |
TSAR-MVS + GP. | | | 93.66 34 | 93.41 35 | 94.41 35 | 96.59 61 | 86.78 15 | 94.40 111 | 93.93 214 | 89.77 31 | 94.21 13 | 95.59 78 | 87.35 16 | 98.61 83 | 92.72 20 | 96.15 76 | 97.83 58 |
|
PVSNet_Blended | | | 90.73 76 | 90.32 74 | 91.98 106 | 96.12 72 | 81.25 137 | 92.55 208 | 96.83 44 | 82.04 192 | 89.10 89 | 92.56 166 | 81.04 75 | 98.85 71 | 86.72 100 | 95.91 77 | 95.84 119 |
|
PS-MVSNAJ | | | 91.18 70 | 90.92 66 | 91.96 107 | 95.26 97 | 82.60 116 | 92.09 223 | 95.70 118 | 86.27 97 | 91.84 60 | 92.46 167 | 79.70 88 | 98.99 56 | 89.08 67 | 95.86 78 | 94.29 173 |
|
ACMMP | | | 93.24 45 | 92.88 46 | 94.30 39 | 98.09 25 | 85.33 50 | 96.86 17 | 97.45 6 | 88.33 61 | 90.15 80 | 97.03 24 | 81.44 71 | 99.51 11 | 90.85 56 | 95.74 79 | 98.04 45 |
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence |
LCM-MVSNet-Re | | | 88.30 136 | 88.32 118 | 88.27 233 | 94.71 114 | 72.41 275 | 93.15 186 | 90.98 269 | 87.77 76 | 79.25 252 | 91.96 186 | 78.35 103 | 95.75 248 | 83.04 136 | 95.62 80 | 96.65 94 |
|
CHOSEN 1792x2688 | | | 88.84 124 | 87.69 130 | 92.30 95 | 96.14 71 | 81.42 134 | 90.01 250 | 95.86 109 | 74.52 269 | 87.41 105 | 93.94 123 | 75.46 143 | 98.36 93 | 80.36 179 | 95.53 81 | 97.12 81 |
|
AdaColmap | | | 89.89 94 | 89.07 97 | 92.37 93 | 97.41 40 | 83.03 99 | 94.42 110 | 95.92 102 | 82.81 180 | 86.34 127 | 94.65 101 | 73.89 162 | 99.02 49 | 80.69 172 | 95.51 82 | 95.05 140 |
|
MVS | | | 87.44 165 | 86.10 173 | 91.44 126 | 92.61 180 | 83.62 84 | 92.63 204 | 95.66 121 | 67.26 305 | 81.47 227 | 92.15 176 | 77.95 106 | 98.22 101 | 79.71 194 | 95.48 83 | 92.47 246 |
|
UA-Net | | | 92.83 50 | 92.54 50 | 93.68 54 | 96.10 74 | 84.71 56 | 95.66 49 | 96.39 74 | 91.92 4 | 93.22 30 | 96.49 47 | 83.16 53 | 98.87 65 | 84.47 117 | 95.47 84 | 97.45 70 |
|
xiu_mvs_v2_base | | | 91.13 71 | 90.89 68 | 91.86 112 | 94.97 104 | 82.42 117 | 92.24 217 | 95.64 124 | 86.11 102 | 91.74 65 | 93.14 149 | 79.67 91 | 98.89 64 | 89.06 68 | 95.46 85 | 94.28 174 |
|
PVSNet_Blended_VisFu | | | 91.38 66 | 90.91 67 | 92.80 76 | 96.39 66 | 83.17 95 | 94.87 84 | 96.66 59 | 83.29 161 | 89.27 87 | 94.46 105 | 80.29 80 | 99.17 32 | 87.57 85 | 95.37 86 | 96.05 111 |
|
PAPM_NR | | | 91.22 69 | 90.78 70 | 92.52 86 | 97.60 33 | 81.46 132 | 94.37 117 | 96.24 82 | 86.39 96 | 87.41 105 | 94.80 98 | 82.06 67 | 98.48 89 | 82.80 141 | 95.37 86 | 97.61 64 |
|
CHOSEN 280x420 | | | 85.15 211 | 83.99 210 | 88.65 216 | 92.47 181 | 78.40 215 | 79.68 314 | 92.76 232 | 74.90 266 | 81.41 229 | 89.59 241 | 69.85 215 | 95.51 255 | 79.92 189 | 95.29 88 | 92.03 256 |
|
TAPA-MVS | | 84.62 6 | 88.16 139 | 87.01 150 | 91.62 121 | 96.64 59 | 80.65 154 | 94.39 113 | 96.21 86 | 76.38 251 | 86.19 130 | 95.44 79 | 79.75 86 | 98.08 111 | 62.75 295 | 95.29 88 | 96.13 106 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
LS3D | | | 87.89 148 | 86.32 168 | 92.59 83 | 96.07 75 | 82.92 104 | 95.23 65 | 94.92 177 | 75.66 258 | 82.89 210 | 95.98 65 | 72.48 183 | 99.21 29 | 68.43 273 | 95.23 90 | 95.64 127 |
|
MVS_test0326 | | | 89.70 98 | 88.72 105 | 92.63 81 | 93.09 168 | 82.94 103 | 93.45 174 | 95.39 147 | 88.13 68 | 86.14 132 | 93.33 139 | 69.86 214 | 98.94 62 | 83.99 127 | 95.11 91 | 96.00 113 |
|
MVS_Test | | | 91.31 67 | 91.11 62 | 91.93 109 | 94.37 127 | 80.14 164 | 93.46 173 | 95.80 111 | 86.46 95 | 91.35 68 | 93.77 132 | 82.21 63 | 98.09 110 | 87.57 85 | 94.95 92 | 97.55 68 |
|
PAPR | | | 90.02 88 | 89.27 94 | 92.29 96 | 95.78 82 | 80.95 148 | 92.68 203 | 96.22 83 | 81.91 196 | 86.66 120 | 93.75 134 | 82.23 62 | 98.44 92 | 79.40 202 | 94.79 93 | 97.48 69 |
|
xiu_mvs_v1_base_debu | | | 90.64 78 | 90.05 80 | 92.40 90 | 93.97 143 | 84.46 64 | 93.32 176 | 95.46 137 | 85.17 116 | 92.25 51 | 94.03 116 | 70.59 204 | 98.57 85 | 90.97 51 | 94.67 94 | 94.18 175 |
|
xiu_mvs_v1_base | | | 90.64 78 | 90.05 80 | 92.40 90 | 93.97 143 | 84.46 64 | 93.32 176 | 95.46 137 | 85.17 116 | 92.25 51 | 94.03 116 | 70.59 204 | 98.57 85 | 90.97 51 | 94.67 94 | 94.18 175 |
|
xiu_mvs_v1_base_debi | | | 90.64 78 | 90.05 80 | 92.40 90 | 93.97 143 | 84.46 64 | 93.32 176 | 95.46 137 | 85.17 116 | 92.25 51 | 94.03 116 | 70.59 204 | 98.57 85 | 90.97 51 | 94.67 94 | 94.18 175 |
|
gg-mvs-nofinetune | | | 81.77 250 | 79.37 260 | 88.99 210 | 90.85 248 | 77.73 233 | 86.29 286 | 79.63 324 | 74.88 267 | 83.19 208 | 69.05 317 | 60.34 275 | 96.11 233 | 75.46 235 | 94.64 97 | 93.11 229 |
|
BH-RMVSNet | | | 88.37 133 | 87.48 133 | 91.02 141 | 95.28 95 | 79.45 185 | 92.89 198 | 93.07 227 | 85.45 112 | 86.91 114 | 94.84 97 | 70.35 208 | 97.76 126 | 73.97 248 | 94.59 98 | 95.85 118 |
|
test_normal | | | 88.13 141 | 86.78 159 | 92.18 99 | 90.55 259 | 81.19 141 | 92.74 201 | 94.64 187 | 83.84 143 | 77.49 262 | 90.51 228 | 68.49 230 | 98.16 104 | 88.22 75 | 94.55 99 | 97.21 76 |
|
BH-untuned | | | 88.60 129 | 88.13 124 | 90.01 181 | 95.24 98 | 78.50 212 | 93.29 182 | 94.15 200 | 84.75 126 | 84.46 179 | 93.40 136 | 75.76 138 | 97.40 163 | 77.59 217 | 94.52 100 | 94.12 179 |
|
Effi-MVS+ | | | 91.59 64 | 91.11 62 | 93.01 69 | 94.35 130 | 83.39 91 | 94.60 100 | 95.10 166 | 87.10 86 | 90.57 74 | 93.10 151 | 81.43 72 | 98.07 112 | 89.29 66 | 94.48 101 | 97.59 65 |
|
Test4 | | | 85.75 202 | 83.72 216 | 91.83 114 | 88.08 290 | 81.03 145 | 92.48 209 | 95.54 130 | 83.38 159 | 73.40 290 | 88.57 254 | 50.99 302 | 97.37 167 | 86.61 102 | 94.47 102 | 97.09 82 |
|
PCF-MVS | | 84.11 10 | 87.74 156 | 86.08 174 | 92.70 79 | 94.02 137 | 84.43 68 | 89.27 261 | 95.87 108 | 73.62 274 | 84.43 181 | 94.33 107 | 78.48 102 | 98.86 68 | 70.27 265 | 94.45 103 | 94.81 152 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
EI-MVSNet-Vis-set | | | 93.01 49 | 92.92 44 | 93.29 57 | 95.01 101 | 83.51 88 | 94.48 104 | 95.77 113 | 90.87 15 | 92.52 47 | 96.67 38 | 84.50 46 | 99.00 55 | 91.99 35 | 94.44 104 | 97.36 71 |
|
DI_MVS_plusplus_test | | | 88.15 140 | 86.82 155 | 92.14 101 | 90.67 254 | 81.07 143 | 93.01 193 | 94.59 188 | 83.83 145 | 77.78 259 | 90.63 223 | 68.51 229 | 98.16 104 | 88.02 80 | 94.37 105 | 97.17 78 |
|
MS-PatchMatch | | | 85.05 213 | 84.16 207 | 87.73 244 | 91.42 208 | 78.51 211 | 91.25 239 | 93.53 220 | 77.50 243 | 80.15 243 | 91.58 196 | 61.99 264 | 95.51 255 | 75.69 233 | 94.35 106 | 89.16 294 |
|
MVS_dtu | | | 89.71 97 | 88.60 109 | 93.01 69 | 93.24 162 | 83.54 86 | 93.30 180 | 94.05 203 | 88.15 66 | 86.91 114 | 94.12 115 | 69.51 219 | 99.02 49 | 83.89 129 | 94.25 107 | 96.48 98 |
|
mvs_anonymous | | | 89.37 112 | 89.32 91 | 89.51 199 | 93.47 157 | 74.22 255 | 91.65 232 | 94.83 182 | 82.91 178 | 85.45 156 | 93.79 131 | 81.23 74 | 96.36 225 | 86.47 103 | 94.09 108 | 97.94 50 |
|
MVP-Stereo | | | 85.97 196 | 84.86 199 | 89.32 203 | 90.92 244 | 82.19 120 | 92.11 222 | 94.19 198 | 78.76 232 | 78.77 254 | 91.63 194 | 68.38 232 | 96.56 213 | 75.01 241 | 93.95 109 | 89.20 293 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
LFMVS | | | 90.08 87 | 89.13 96 | 92.95 72 | 96.71 58 | 82.32 119 | 96.08 32 | 89.91 290 | 86.79 90 | 92.15 56 | 96.81 31 | 62.60 260 | 98.34 96 | 87.18 91 | 93.90 110 | 98.19 33 |
|
PVSNet | | 78.82 18 | 85.55 204 | 84.65 204 | 88.23 236 | 94.72 113 | 71.93 276 | 87.12 282 | 92.75 233 | 78.80 231 | 84.95 171 | 90.53 227 | 64.43 255 | 96.71 208 | 74.74 242 | 93.86 111 | 96.06 110 |
|
CNLPA | | | 89.07 117 | 87.98 126 | 92.34 94 | 96.87 55 | 84.78 55 | 94.08 135 | 93.24 224 | 81.41 208 | 84.46 179 | 95.13 88 | 75.57 141 | 96.62 210 | 77.21 221 | 93.84 112 | 95.61 128 |
|
EPNet_dtu | | | 86.49 189 | 85.94 178 | 88.14 238 | 90.24 265 | 72.82 267 | 94.11 131 | 92.20 243 | 86.66 93 | 79.42 251 | 92.36 172 | 73.52 167 | 95.81 246 | 71.26 260 | 93.66 113 | 95.80 122 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
EI-MVSNet-UG-set | | | 92.74 52 | 92.62 49 | 93.12 63 | 94.86 109 | 83.20 94 | 94.40 111 | 95.74 116 | 90.71 20 | 92.05 57 | 96.60 42 | 84.00 49 | 98.99 56 | 91.55 45 | 93.63 114 | 97.17 78 |
|
Fast-Effi-MVS+ | | | 89.41 109 | 88.64 107 | 91.71 119 | 94.74 111 | 80.81 152 | 93.54 170 | 95.10 166 | 83.11 164 | 86.82 118 | 90.67 222 | 79.74 87 | 97.75 129 | 80.51 177 | 93.55 115 | 96.57 96 |
|
1314 | | | 87.51 163 | 86.57 163 | 90.34 161 | 92.42 182 | 79.74 176 | 92.63 204 | 95.35 152 | 78.35 237 | 80.14 244 | 91.62 195 | 74.05 160 | 97.15 184 | 81.05 164 | 93.53 116 | 94.12 179 |
|
BH-w/o | | | 87.57 162 | 87.05 149 | 89.12 207 | 94.90 108 | 77.90 226 | 92.41 211 | 93.51 221 | 82.89 179 | 83.70 198 | 91.34 203 | 75.75 139 | 97.07 191 | 75.49 234 | 93.49 117 | 92.39 249 |
|
PMMVS | | | 85.71 203 | 84.96 196 | 87.95 241 | 88.90 282 | 77.09 238 | 88.68 269 | 90.06 286 | 72.32 285 | 86.47 121 | 90.76 221 | 72.15 186 | 94.40 279 | 81.78 159 | 93.49 117 | 92.36 250 |
|
PatchMatch-RL | | | 86.77 183 | 85.54 183 | 90.47 156 | 95.88 79 | 82.71 112 | 90.54 243 | 92.31 240 | 79.82 221 | 84.32 186 | 91.57 198 | 68.77 225 | 96.39 223 | 73.16 253 | 93.48 119 | 92.32 252 |
|
PLC | | 84.53 7 | 89.06 119 | 88.03 125 | 92.15 100 | 97.27 48 | 82.69 113 | 94.29 122 | 95.44 143 | 79.71 222 | 84.01 192 | 94.18 114 | 76.68 117 | 98.75 76 | 77.28 220 | 93.41 120 | 95.02 141 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
VNet | | | 92.24 55 | 91.91 54 | 93.24 59 | 96.59 61 | 83.43 89 | 94.84 85 | 96.44 69 | 89.19 41 | 94.08 16 | 95.90 68 | 77.85 110 | 98.17 103 | 88.90 69 | 93.38 121 | 98.13 38 |
|
test-LLR | | | 85.87 197 | 85.41 188 | 87.25 255 | 90.95 240 | 71.67 278 | 89.55 255 | 89.88 291 | 83.41 157 | 84.54 177 | 87.95 263 | 67.25 235 | 95.11 272 | 81.82 157 | 93.37 122 | 94.97 143 |
|
test-mter | | | 84.54 229 | 83.64 220 | 87.25 255 | 90.95 240 | 71.67 278 | 89.55 255 | 89.88 291 | 79.17 225 | 84.54 177 | 87.95 263 | 55.56 291 | 95.11 272 | 81.82 157 | 93.37 122 | 94.97 143 |
|
EPP-MVSNet | | | 91.70 62 | 91.56 57 | 92.13 102 | 95.88 79 | 80.50 160 | 97.33 3 | 95.25 157 | 86.15 100 | 89.76 83 | 95.60 77 | 83.42 52 | 98.32 98 | 87.37 89 | 93.25 124 | 97.56 67 |
|
CDS-MVSNet | | | 89.45 106 | 88.51 110 | 92.29 96 | 93.62 154 | 83.61 85 | 93.01 193 | 94.68 186 | 81.95 194 | 87.82 101 | 93.24 145 | 78.69 97 | 96.99 196 | 80.34 180 | 93.23 125 | 96.28 101 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
PAPM | | | 86.68 184 | 85.39 189 | 90.53 150 | 93.05 170 | 79.33 198 | 89.79 254 | 94.77 185 | 78.82 230 | 81.95 223 | 93.24 145 | 76.81 114 | 97.30 170 | 66.94 279 | 93.16 126 | 94.95 147 |
|
alignmvs | | | 93.08 48 | 92.50 51 | 94.81 19 | 95.62 87 | 87.61 6 | 95.99 35 | 96.07 93 | 89.77 31 | 94.12 14 | 94.87 93 | 80.56 77 | 98.66 78 | 92.42 24 | 93.10 127 | 98.15 36 |
|
mvs-test1 | | | 89.45 106 | 89.14 95 | 90.38 159 | 93.33 159 | 77.63 235 | 94.95 79 | 94.36 193 | 87.70 77 | 87.10 111 | 92.81 160 | 73.45 169 | 98.03 115 | 85.57 106 | 93.04 128 | 95.48 130 |
|
TAMVS | | | 89.21 114 | 88.29 120 | 91.96 107 | 93.71 152 | 82.62 115 | 93.30 180 | 94.19 198 | 82.22 188 | 87.78 102 | 93.94 123 | 78.83 95 | 96.95 200 | 77.70 216 | 92.98 129 | 96.32 100 |
|
diffmvs | | | 89.07 117 | 88.32 118 | 91.34 128 | 93.24 162 | 79.79 174 | 92.29 216 | 94.98 172 | 80.24 215 | 87.38 108 | 92.45 168 | 78.02 105 | 97.33 168 | 83.29 134 | 92.93 130 | 96.91 87 |
|
OMC-MVS | | | 91.23 68 | 90.62 71 | 93.08 65 | 96.27 69 | 84.07 74 | 93.52 171 | 95.93 101 | 86.95 88 | 89.51 85 | 96.13 62 | 78.50 101 | 98.35 95 | 85.84 104 | 92.90 131 | 96.83 90 |
|
canonicalmvs | | | 93.27 42 | 92.75 48 | 94.85 14 | 95.70 84 | 87.66 5 | 96.33 25 | 96.41 72 | 90.00 27 | 94.09 15 | 94.60 103 | 82.33 60 | 98.62 82 | 92.40 25 | 92.86 132 | 98.27 28 |
|
TESTMET0.1,1 | | | 83.74 237 | 82.85 234 | 86.42 269 | 89.96 271 | 71.21 282 | 89.55 255 | 87.88 304 | 77.41 244 | 83.37 206 | 87.31 271 | 56.71 288 | 93.65 286 | 80.62 174 | 92.85 133 | 94.40 171 |
|
VDD-MVS | | | 90.74 75 | 89.92 83 | 93.20 60 | 96.27 69 | 83.02 100 | 95.73 44 | 93.86 215 | 88.42 60 | 92.53 46 | 96.84 29 | 62.09 263 | 98.64 80 | 90.95 54 | 92.62 134 | 97.93 53 |
|
VDDNet | | | 89.56 102 | 88.49 113 | 92.76 78 | 95.07 100 | 82.09 121 | 96.30 26 | 93.19 225 | 81.05 212 | 91.88 59 | 96.86 28 | 61.16 272 | 98.33 97 | 88.43 74 | 92.49 135 | 97.84 57 |
|
DP-MVS | | | 87.25 171 | 85.36 190 | 92.90 74 | 97.65 32 | 83.24 93 | 94.81 88 | 92.00 248 | 74.99 264 | 81.92 224 | 95.00 90 | 72.66 179 | 99.05 43 | 66.92 281 | 92.33 136 | 96.40 99 |
|
GG-mvs-BLEND | | | | | 87.94 242 | 89.73 275 | 77.91 225 | 87.80 276 | 78.23 326 | | 80.58 238 | 83.86 293 | 59.88 279 | 95.33 269 | 71.20 261 | 92.22 137 | 90.60 286 |
|
HyFIR lowres test | | | 88.09 142 | 86.81 156 | 91.93 109 | 96.00 77 | 80.63 155 | 90.01 250 | 95.79 112 | 73.42 275 | 87.68 103 | 92.10 180 | 73.86 163 | 97.96 117 | 80.75 171 | 91.70 138 | 97.19 77 |
|
sss | | | 88.93 123 | 88.26 122 | 90.94 145 | 94.05 136 | 80.78 153 | 91.71 229 | 95.38 148 | 81.55 205 | 88.63 93 | 93.91 127 | 75.04 148 | 95.47 259 | 82.47 146 | 91.61 139 | 96.57 96 |
|
cascas | | | 86.43 190 | 84.98 195 | 90.80 147 | 92.10 188 | 80.92 149 | 90.24 246 | 95.91 104 | 73.10 278 | 83.57 202 | 88.39 257 | 65.15 251 | 97.46 143 | 84.90 112 | 91.43 140 | 94.03 185 |
|
Effi-MVS+-dtu | | | 88.65 128 | 88.35 115 | 89.54 196 | 93.33 159 | 76.39 244 | 94.47 106 | 94.36 193 | 87.70 77 | 85.43 159 | 89.56 243 | 73.45 169 | 97.26 176 | 85.57 106 | 91.28 141 | 94.97 143 |
|
F-COLMAP | | | 87.95 147 | 86.80 157 | 91.40 127 | 96.35 68 | 80.88 150 | 94.73 92 | 95.45 141 | 79.65 223 | 82.04 222 | 94.61 102 | 71.13 195 | 98.50 88 | 76.24 230 | 91.05 142 | 94.80 153 |
|
WTY-MVS | | | 89.60 100 | 88.92 101 | 91.67 120 | 95.47 90 | 81.15 142 | 92.38 213 | 94.78 184 | 83.11 164 | 89.06 91 | 94.32 108 | 78.67 98 | 96.61 212 | 81.57 161 | 90.89 143 | 97.24 73 |
|
HY-MVS | | 83.01 12 | 89.03 120 | 87.94 128 | 92.29 96 | 94.86 109 | 82.77 106 | 92.08 224 | 94.49 190 | 81.52 206 | 86.93 113 | 92.79 162 | 78.32 104 | 98.23 100 | 79.93 188 | 90.55 144 | 95.88 117 |
|
CLD-MVS | | | 89.47 105 | 88.90 102 | 91.18 133 | 94.22 131 | 82.07 122 | 92.13 221 | 96.09 91 | 87.90 72 | 85.37 165 | 92.45 168 | 74.38 153 | 97.56 136 | 87.15 92 | 90.43 145 | 93.93 187 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
CVMVSNet | | | 84.69 227 | 84.79 201 | 84.37 283 | 91.84 192 | 64.92 306 | 93.70 164 | 91.47 260 | 66.19 307 | 86.16 131 | 95.28 82 | 67.18 237 | 93.33 290 | 80.89 170 | 90.42 146 | 94.88 149 |
|
Patchmatch-test1 | | | 85.81 200 | 84.71 202 | 89.12 207 | 92.15 185 | 76.60 242 | 91.12 241 | 91.69 255 | 83.53 154 | 85.50 153 | 88.56 255 | 66.79 238 | 95.00 275 | 72.69 255 | 90.35 147 | 95.76 123 |
|
Fast-Effi-MVS+-dtu | | | 87.44 165 | 86.72 160 | 89.63 194 | 92.04 189 | 77.68 234 | 94.03 142 | 93.94 213 | 85.81 103 | 82.42 214 | 91.32 206 | 70.33 209 | 97.06 192 | 80.33 181 | 90.23 148 | 94.14 178 |
|
OPM-MVS | | | 90.12 86 | 89.56 86 | 91.82 115 | 93.14 166 | 83.90 76 | 94.16 128 | 95.74 116 | 88.96 47 | 87.86 98 | 95.43 80 | 72.48 183 | 97.91 121 | 88.10 79 | 90.18 149 | 93.65 206 |
|
HQP_MVS | | | 90.60 81 | 90.19 76 | 91.82 115 | 94.70 115 | 82.73 110 | 95.85 40 | 96.22 83 | 90.81 17 | 86.91 114 | 94.86 94 | 74.23 155 | 98.12 106 | 88.15 76 | 89.99 150 | 94.63 156 |
|
plane_prior5 | | | | | | | | | 96.22 83 | | | | | 98.12 106 | 88.15 76 | 89.99 150 | 94.63 156 |
|
XVG-OURS | | | 89.40 111 | 88.70 106 | 91.52 123 | 94.06 135 | 81.46 132 | 91.27 238 | 96.07 93 | 86.14 101 | 88.89 92 | 95.77 72 | 68.73 226 | 97.26 176 | 87.39 88 | 89.96 152 | 95.83 120 |
|
plane_prior | | | | | | | 82.73 110 | 95.21 67 | | 89.66 34 | | | | | | 89.88 153 | |
|
TR-MVS | | | 86.78 181 | 85.76 181 | 89.82 186 | 94.37 127 | 78.41 214 | 92.47 210 | 92.83 230 | 81.11 211 | 86.36 126 | 92.40 170 | 68.73 226 | 97.48 141 | 73.75 251 | 89.85 154 | 93.57 214 |
|
HQP3-MVS | | | | | | | | | 96.04 96 | | | | | | | 89.77 155 | |
|
HQP-MVS | | | 89.80 95 | 89.28 93 | 91.34 128 | 94.17 132 | 81.56 127 | 94.39 113 | 96.04 96 | 88.81 48 | 85.43 159 | 93.97 122 | 73.83 164 | 97.96 117 | 87.11 94 | 89.77 155 | 94.50 167 |
|
XVG-OURS-SEG-HR | | | 89.95 91 | 89.45 88 | 91.47 125 | 94.00 141 | 81.21 140 | 91.87 225 | 96.06 95 | 85.78 104 | 88.55 94 | 95.73 74 | 74.67 151 | 97.27 174 | 88.71 71 | 89.64 157 | 95.91 115 |
|
GA-MVS | | | 86.61 185 | 85.27 192 | 90.66 148 | 91.33 219 | 78.71 210 | 90.40 244 | 93.81 218 | 85.34 114 | 85.12 168 | 89.57 242 | 61.25 269 | 97.11 188 | 80.99 168 | 89.59 158 | 96.15 104 |
|
1112_ss | | | 88.42 131 | 87.33 137 | 91.72 118 | 94.92 106 | 80.98 146 | 92.97 196 | 94.54 189 | 78.16 241 | 83.82 195 | 93.88 128 | 78.78 96 | 97.91 121 | 79.45 198 | 89.41 159 | 96.26 102 |
|
ab-mvs | | | 89.41 109 | 88.35 115 | 92.60 82 | 95.15 99 | 82.65 114 | 92.20 219 | 95.60 125 | 83.97 141 | 88.55 94 | 93.70 135 | 74.16 159 | 98.21 102 | 82.46 147 | 89.37 160 | 96.94 86 |
|
CR-MVSNet | | | 85.35 207 | 83.76 213 | 90.12 171 | 90.58 256 | 79.34 195 | 85.24 294 | 91.96 250 | 78.27 238 | 85.55 148 | 87.87 266 | 71.03 197 | 95.61 250 | 73.96 249 | 89.36 161 | 95.40 133 |
|
RPMNet | | | 83.18 242 | 80.87 248 | 90.12 171 | 90.58 256 | 79.34 195 | 85.24 294 | 90.78 275 | 71.44 290 | 85.55 148 | 82.97 299 | 70.87 199 | 95.61 250 | 61.01 298 | 89.36 161 | 95.40 133 |
|
DSMNet-mixed | | | 76.94 277 | 76.29 276 | 78.89 294 | 83.10 307 | 56.11 319 | 87.78 277 | 79.77 323 | 60.65 315 | 75.64 279 | 88.71 251 | 61.56 267 | 88.34 310 | 60.07 301 | 89.29 163 | 92.21 255 |
|
LPG-MVS_test | | | 89.45 106 | 88.90 102 | 91.12 134 | 94.47 123 | 81.49 130 | 95.30 59 | 96.14 87 | 86.73 91 | 85.45 156 | 95.16 86 | 69.89 212 | 98.10 108 | 87.70 83 | 89.23 164 | 93.77 200 |
|
LGP-MVS_train | | | | | 91.12 134 | 94.47 123 | 81.49 130 | | 96.14 87 | 86.73 91 | 85.45 156 | 95.16 86 | 69.89 212 | 98.10 108 | 87.70 83 | 89.23 164 | 93.77 200 |
|
Test_1112_low_res | | | 87.65 158 | 86.51 164 | 91.08 137 | 94.94 105 | 79.28 199 | 91.77 226 | 94.30 196 | 76.04 256 | 83.51 203 | 92.37 171 | 77.86 109 | 97.73 130 | 78.69 207 | 89.13 166 | 96.22 103 |
|
PatchmatchNet | | | 85.85 198 | 84.70 203 | 89.29 204 | 91.76 195 | 75.54 251 | 88.49 271 | 91.30 263 | 81.63 203 | 85.05 169 | 88.70 252 | 71.71 187 | 96.24 229 | 74.61 244 | 89.05 167 | 96.08 108 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
MDTV_nov1_ep13 | | | | 83.56 221 | | 91.69 199 | 69.93 292 | 87.75 278 | 91.54 258 | 78.60 234 | 84.86 172 | 88.90 248 | 69.54 218 | 96.03 235 | 70.25 266 | 88.93 168 | |
|
MIMVSNet | | | 82.59 246 | 80.53 249 | 88.76 213 | 91.51 202 | 78.32 216 | 86.57 285 | 90.13 284 | 79.32 224 | 80.70 236 | 88.69 253 | 52.98 299 | 93.07 295 | 66.03 284 | 88.86 169 | 94.90 148 |
|
ACMM | | 84.12 9 | 89.14 115 | 88.48 114 | 91.12 134 | 94.65 118 | 81.22 139 | 95.31 57 | 96.12 90 | 85.31 115 | 85.92 134 | 94.34 106 | 70.19 211 | 98.06 113 | 85.65 105 | 88.86 169 | 94.08 183 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMP | | 84.23 8 | 89.01 122 | 88.35 115 | 90.99 143 | 94.73 112 | 81.27 136 | 95.07 74 | 95.89 107 | 86.48 94 | 83.67 199 | 94.30 109 | 69.33 221 | 97.99 116 | 87.10 96 | 88.55 171 | 93.72 204 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
test_djsdf | | | 89.03 120 | 88.64 107 | 90.21 163 | 90.74 251 | 79.28 199 | 95.96 36 | 95.90 105 | 84.66 128 | 85.33 166 | 92.94 155 | 74.02 161 | 97.30 170 | 89.64 64 | 88.53 172 | 94.05 184 |
|
jajsoiax | | | 88.24 137 | 87.50 132 | 90.48 155 | 90.89 246 | 80.14 164 | 95.31 57 | 95.65 123 | 84.97 122 | 84.24 189 | 94.02 119 | 65.31 250 | 97.42 156 | 88.56 72 | 88.52 173 | 93.89 189 |
|
PatchT | | | 82.68 245 | 81.27 243 | 86.89 264 | 90.09 268 | 70.94 286 | 84.06 301 | 90.15 283 | 74.91 265 | 85.63 147 | 83.57 295 | 69.37 220 | 94.87 277 | 65.19 286 | 88.50 174 | 94.84 150 |
|
MSDG | | | 84.86 220 | 83.09 230 | 90.14 170 | 93.80 149 | 80.05 167 | 89.18 264 | 93.09 226 | 78.89 228 | 78.19 255 | 91.91 187 | 65.86 249 | 97.27 174 | 68.47 272 | 88.45 175 | 93.11 229 |
|
pcd1.5k->3k | | | 37.02 306 | 38.84 307 | 31.53 319 | 92.33 183 | 0.00 338 | 0.00 329 | 96.13 89 | 0.00 333 | 0.00 334 | 0.00 335 | 72.70 178 | 0.00 336 | 0.00 333 | 88.43 176 | 94.60 159 |
|
MVS-HIRNet | | | 73.70 283 | 72.20 283 | 78.18 297 | 91.81 194 | 56.42 318 | 82.94 308 | 82.58 318 | 55.24 317 | 68.88 301 | 66.48 318 | 55.32 293 | 95.13 271 | 58.12 303 | 88.42 177 | 83.01 311 |
|
mvs_tets | | | 88.06 143 | 87.28 139 | 90.38 159 | 90.94 242 | 79.88 171 | 95.22 66 | 95.66 121 | 85.10 120 | 84.21 190 | 93.94 123 | 63.53 258 | 97.40 163 | 88.50 73 | 88.40 178 | 93.87 192 |
|
FIs | | | 90.51 82 | 90.35 73 | 90.99 143 | 93.99 142 | 80.98 146 | 95.73 44 | 97.54 3 | 89.15 42 | 86.72 119 | 94.68 99 | 81.83 70 | 97.24 178 | 85.18 108 | 88.31 179 | 94.76 154 |
|
PS-MVSNAJss | | | 89.97 90 | 89.62 85 | 91.02 141 | 91.90 190 | 80.85 151 | 95.26 64 | 95.98 98 | 86.26 98 | 86.21 129 | 94.29 110 | 79.70 88 | 97.65 131 | 88.87 70 | 88.10 180 | 94.57 162 |
|
CMPMVS | | 59.16 21 | 80.52 264 | 79.20 262 | 84.48 282 | 83.98 304 | 67.63 300 | 89.95 252 | 93.84 217 | 64.79 310 | 66.81 307 | 91.14 215 | 57.93 286 | 95.17 270 | 76.25 229 | 88.10 180 | 90.65 283 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
FC-MVSNet-test | | | 90.27 85 | 90.18 77 | 90.53 150 | 93.71 152 | 79.85 173 | 95.77 43 | 97.59 2 | 89.31 38 | 86.27 128 | 94.67 100 | 81.93 69 | 97.01 195 | 84.26 122 | 88.09 182 | 94.71 155 |
|
ACMMP++ | | | | | | | | | | | | | | | | 88.01 183 | |
|
PVSNet_BlendedMVS | | | 89.98 89 | 89.70 84 | 90.82 146 | 96.12 72 | 81.25 137 | 93.92 148 | 96.83 44 | 83.49 155 | 89.10 89 | 92.26 174 | 81.04 75 | 98.85 71 | 86.72 100 | 87.86 184 | 92.35 251 |
|
anonymousdsp | | | 87.84 150 | 87.09 145 | 90.12 171 | 89.13 278 | 80.54 158 | 94.67 98 | 95.55 128 | 82.05 191 | 83.82 195 | 92.12 177 | 71.47 193 | 97.15 184 | 87.15 92 | 87.80 185 | 92.67 240 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 87.47 186 | |
|
XVG-ACMP-BASELINE | | | 86.00 195 | 84.84 200 | 89.45 200 | 91.20 229 | 78.00 223 | 91.70 230 | 95.55 128 | 85.05 121 | 82.97 209 | 92.25 175 | 54.49 295 | 97.48 141 | 82.93 138 | 87.45 187 | 92.89 234 |
|
EI-MVSNet | | | 89.10 116 | 88.86 104 | 89.80 189 | 91.84 192 | 78.30 217 | 93.70 164 | 95.01 169 | 85.73 106 | 87.15 109 | 95.28 82 | 79.87 85 | 97.21 182 | 83.81 131 | 87.36 188 | 93.88 191 |
|
MVSTER | | | 88.84 124 | 88.29 120 | 90.51 153 | 92.95 175 | 80.44 161 | 93.73 160 | 95.01 169 | 84.66 128 | 87.15 109 | 93.12 150 | 72.79 177 | 97.21 182 | 87.86 81 | 87.36 188 | 93.87 192 |
|
EG-PatchMatch MVS | | | 82.37 248 | 80.34 250 | 88.46 228 | 90.27 263 | 79.35 194 | 92.80 200 | 94.33 195 | 77.14 248 | 73.26 291 | 90.18 233 | 47.47 309 | 96.72 206 | 70.25 266 | 87.32 190 | 89.30 291 |
|
EPMVS | | | 83.90 235 | 82.70 236 | 87.51 248 | 90.23 266 | 72.67 270 | 88.62 270 | 81.96 320 | 81.37 209 | 85.01 170 | 88.34 258 | 66.31 243 | 94.45 278 | 75.30 237 | 87.12 191 | 95.43 132 |
|
tpm2 | | | 84.08 232 | 82.94 232 | 87.48 251 | 91.39 210 | 71.27 280 | 89.23 263 | 90.37 279 | 71.95 288 | 84.64 174 | 89.33 244 | 67.30 234 | 96.55 215 | 75.17 238 | 87.09 192 | 94.63 156 |
|
CostFormer | | | 85.77 201 | 84.94 197 | 88.26 234 | 91.16 234 | 72.58 274 | 89.47 259 | 91.04 268 | 76.26 254 | 86.45 124 | 89.97 236 | 70.74 202 | 96.86 205 | 82.35 148 | 87.07 193 | 95.34 136 |
|
Patchmatch-test | | | 81.37 257 | 79.30 261 | 87.58 247 | 90.92 244 | 74.16 257 | 80.99 311 | 87.68 307 | 70.52 296 | 76.63 265 | 88.81 249 | 71.21 194 | 92.76 296 | 60.01 302 | 86.93 194 | 95.83 120 |
|
tpmp4_e23 | | | 83.87 236 | 82.33 237 | 88.48 227 | 91.46 203 | 72.82 267 | 89.82 253 | 91.57 257 | 73.02 280 | 81.86 225 | 89.05 246 | 66.20 245 | 96.97 198 | 71.57 259 | 86.39 195 | 95.66 126 |
|
LTVRE_ROB | | 82.13 13 | 86.26 192 | 84.90 198 | 90.34 161 | 94.44 126 | 81.50 129 | 92.31 215 | 94.89 178 | 83.03 171 | 79.63 249 | 92.67 163 | 69.69 216 | 97.79 124 | 71.20 261 | 86.26 196 | 91.72 262 |
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 |
COLMAP_ROB | | 80.39 16 | 83.96 233 | 82.04 239 | 89.74 190 | 95.28 95 | 79.75 175 | 94.25 124 | 92.28 241 | 75.17 262 | 78.02 258 | 93.77 132 | 58.60 283 | 97.84 123 | 65.06 288 | 85.92 197 | 91.63 263 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
DWT-MVSNet_test | | | 84.95 217 | 83.68 218 | 88.77 212 | 91.43 207 | 73.75 261 | 91.74 228 | 90.98 269 | 80.66 214 | 83.84 194 | 87.36 270 | 62.44 261 | 97.11 188 | 78.84 206 | 85.81 198 | 95.46 131 |
|
RPSCF | | | 85.07 212 | 84.27 206 | 87.48 251 | 92.91 176 | 70.62 288 | 91.69 231 | 92.46 238 | 76.20 255 | 82.67 213 | 95.22 85 | 63.94 257 | 97.29 173 | 77.51 219 | 85.80 199 | 94.53 164 |
|
USDC | | | 82.76 243 | 81.26 244 | 87.26 254 | 91.17 232 | 74.55 254 | 89.27 261 | 93.39 223 | 78.26 239 | 75.30 280 | 92.08 181 | 54.43 296 | 96.63 209 | 71.64 258 | 85.79 200 | 90.61 284 |
|
testing_2 | | | 83.40 240 | 81.02 245 | 90.56 149 | 85.06 301 | 80.51 159 | 91.37 236 | 95.57 126 | 82.92 177 | 67.06 306 | 85.54 289 | 49.47 305 | 97.24 178 | 86.74 97 | 85.44 201 | 93.93 187 |
|
GBi-Net | | | 87.26 169 | 85.98 176 | 91.08 137 | 94.01 138 | 83.10 96 | 95.14 71 | 94.94 173 | 83.57 151 | 84.37 182 | 91.64 191 | 66.59 240 | 96.34 226 | 78.23 211 | 85.36 202 | 93.79 196 |
|
test1 | | | 87.26 169 | 85.98 176 | 91.08 137 | 94.01 138 | 83.10 96 | 95.14 71 | 94.94 173 | 83.57 151 | 84.37 182 | 91.64 191 | 66.59 240 | 96.34 226 | 78.23 211 | 85.36 202 | 93.79 196 |
|
FMVSNet3 | | | 87.40 167 | 86.11 172 | 91.30 130 | 93.79 151 | 83.64 83 | 94.20 127 | 94.81 183 | 83.89 142 | 84.37 182 | 91.87 189 | 68.45 231 | 96.56 213 | 78.23 211 | 85.36 202 | 93.70 205 |
|
PatchFormer-LS_test | | | 86.02 194 | 85.13 193 | 88.70 215 | 91.52 201 | 74.12 258 | 91.19 240 | 92.09 244 | 82.71 183 | 84.30 188 | 87.24 272 | 70.87 199 | 96.98 197 | 81.04 165 | 85.17 205 | 95.00 142 |
|
FMVSNet2 | | | 87.19 174 | 85.82 180 | 91.30 130 | 94.01 138 | 83.67 82 | 94.79 89 | 94.94 173 | 83.57 151 | 83.88 193 | 92.05 184 | 66.59 240 | 96.51 216 | 77.56 218 | 85.01 206 | 93.73 203 |
|
ACMH | | 80.38 17 | 85.36 206 | 83.68 218 | 90.39 157 | 94.45 125 | 80.63 155 | 94.73 92 | 94.85 180 | 82.09 190 | 77.24 263 | 92.65 164 | 60.01 278 | 97.58 134 | 72.25 257 | 84.87 207 | 92.96 232 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ITE_SJBPF | | | | | 88.24 235 | 91.88 191 | 77.05 239 | | 92.92 228 | 85.54 110 | 80.13 245 | 93.30 142 | 57.29 287 | 96.20 230 | 72.46 256 | 84.71 208 | 91.49 265 |
|
JIA-IIPM | | | 81.04 260 | 78.98 266 | 87.25 255 | 88.64 283 | 73.48 263 | 81.75 310 | 89.61 295 | 73.19 277 | 82.05 221 | 73.71 314 | 66.07 248 | 95.87 243 | 71.18 263 | 84.60 209 | 92.41 248 |
|
test2356 | | | 74.50 281 | 73.27 281 | 78.20 295 | 80.81 312 | 59.84 310 | 83.76 304 | 88.33 303 | 71.43 291 | 72.37 295 | 81.84 303 | 45.60 312 | 86.26 316 | 50.97 310 | 84.32 210 | 88.50 301 |
|
OpenMVS_ROB | | 74.94 19 | 79.51 270 | 77.03 274 | 86.93 261 | 87.00 295 | 76.23 247 | 92.33 214 | 90.74 276 | 68.93 300 | 74.52 284 | 88.23 260 | 49.58 304 | 96.62 210 | 57.64 304 | 84.29 211 | 87.94 306 |
|
AllTest | | | 83.42 238 | 81.39 242 | 89.52 197 | 95.01 101 | 77.79 230 | 93.12 187 | 90.89 272 | 77.41 244 | 76.12 273 | 93.34 137 | 54.08 297 | 97.51 139 | 68.31 274 | 84.27 212 | 93.26 223 |
|
TestCases | | | | | 89.52 197 | 95.01 101 | 77.79 230 | | 90.89 272 | 77.41 244 | 76.12 273 | 93.34 137 | 54.08 297 | 97.51 139 | 68.31 274 | 84.27 212 | 93.26 223 |
|
tpm | | | 84.73 225 | 84.02 209 | 86.87 265 | 90.33 262 | 68.90 295 | 89.06 265 | 89.94 289 | 80.85 213 | 85.75 139 | 89.86 238 | 68.54 228 | 95.97 238 | 77.76 215 | 84.05 214 | 95.75 124 |
|
FMVSNet1 | | | 85.85 198 | 84.11 208 | 91.08 137 | 92.81 177 | 83.10 96 | 95.14 71 | 94.94 173 | 81.64 202 | 82.68 212 | 91.64 191 | 59.01 282 | 96.34 226 | 75.37 236 | 83.78 215 | 93.79 196 |
|
ADS-MVSNet2 | | | 81.66 252 | 79.71 258 | 87.50 249 | 91.35 217 | 74.19 256 | 83.33 305 | 88.48 301 | 72.90 281 | 82.24 217 | 85.77 287 | 64.98 252 | 93.20 292 | 64.57 289 | 83.74 216 | 95.12 138 |
|
ADS-MVSNet | | | 81.56 254 | 79.78 256 | 86.90 263 | 91.35 217 | 71.82 277 | 83.33 305 | 89.16 297 | 72.90 281 | 82.24 217 | 85.77 287 | 64.98 252 | 93.76 284 | 64.57 289 | 83.74 216 | 95.12 138 |
|
XXY-MVS | | | 87.65 158 | 86.85 154 | 90.03 179 | 92.14 186 | 80.60 157 | 93.76 157 | 95.23 161 | 82.94 176 | 84.60 175 | 94.02 119 | 74.27 154 | 95.49 258 | 81.04 165 | 83.68 218 | 94.01 186 |
|
test_0402 | | | 81.30 259 | 79.17 263 | 87.67 245 | 93.19 165 | 78.17 220 | 92.98 195 | 91.71 253 | 75.25 261 | 76.02 276 | 90.31 231 | 59.23 281 | 96.37 224 | 50.22 312 | 83.63 219 | 88.47 304 |
|
tpmvs | | | 83.35 241 | 82.07 238 | 87.20 259 | 91.07 236 | 71.00 285 | 88.31 273 | 91.70 254 | 78.91 227 | 80.49 240 | 87.18 273 | 69.30 224 | 97.08 190 | 68.12 277 | 83.56 220 | 93.51 218 |
|
pmmvs5 | | | 84.21 231 | 82.84 235 | 88.34 232 | 88.95 281 | 76.94 240 | 92.41 211 | 91.91 252 | 75.63 259 | 80.28 241 | 91.18 212 | 64.59 254 | 95.57 252 | 77.09 224 | 83.47 221 | 92.53 244 |
|
pmmvs4 | | | 85.43 205 | 83.86 212 | 90.16 165 | 90.02 270 | 82.97 102 | 90.27 245 | 92.67 235 | 75.93 257 | 80.73 235 | 91.74 190 | 71.05 196 | 95.73 249 | 78.85 205 | 83.46 222 | 91.78 259 |
|
test0.0.03 1 | | | 82.41 247 | 81.69 240 | 84.59 281 | 88.23 287 | 72.89 266 | 90.24 246 | 87.83 305 | 83.41 157 | 79.86 247 | 89.78 239 | 67.25 235 | 88.99 308 | 65.18 287 | 83.42 223 | 91.90 258 |
|
tpmrst | | | 85.35 207 | 84.99 194 | 86.43 268 | 90.88 247 | 67.88 298 | 88.71 268 | 91.43 261 | 80.13 217 | 86.08 133 | 88.80 250 | 73.05 174 | 96.02 236 | 82.48 145 | 83.40 224 | 95.40 133 |
|
nrg030 | | | 91.08 72 | 90.39 72 | 93.17 62 | 93.07 169 | 86.91 11 | 96.41 24 | 96.26 79 | 88.30 62 | 88.37 97 | 94.85 96 | 82.19 64 | 97.64 133 | 91.09 50 | 82.95 225 | 94.96 146 |
|
ACMH+ | | 81.04 14 | 85.05 213 | 83.46 225 | 89.82 186 | 94.66 117 | 79.37 193 | 94.44 108 | 94.12 202 | 82.19 189 | 78.04 257 | 92.82 159 | 58.23 284 | 97.54 137 | 73.77 250 | 82.90 226 | 92.54 243 |
|
VPA-MVSNet | | | 89.62 99 | 88.96 99 | 91.60 122 | 93.86 146 | 82.89 105 | 95.46 55 | 97.33 12 | 87.91 71 | 88.43 96 | 93.31 141 | 74.17 158 | 97.40 163 | 87.32 90 | 82.86 227 | 94.52 165 |
|
testus | | | 74.41 282 | 73.35 280 | 77.59 299 | 82.49 311 | 57.08 315 | 86.02 287 | 90.21 282 | 72.28 286 | 72.89 293 | 84.32 292 | 37.08 318 | 86.96 314 | 52.24 308 | 82.65 228 | 88.73 297 |
|
IterMVS-LS | | | 88.36 134 | 87.91 129 | 89.70 192 | 93.80 149 | 78.29 218 | 93.73 160 | 95.08 168 | 85.73 106 | 84.75 173 | 91.90 188 | 79.88 84 | 96.92 201 | 83.83 130 | 82.51 229 | 93.89 189 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
testgi | | | 80.94 263 | 80.20 253 | 83.18 287 | 87.96 292 | 66.29 302 | 91.28 237 | 90.70 277 | 83.70 147 | 78.12 256 | 92.84 157 | 51.37 301 | 90.82 305 | 63.34 292 | 82.46 230 | 92.43 247 |
|
WR-MVS | | | 88.38 132 | 87.67 131 | 90.52 152 | 93.30 161 | 80.18 162 | 93.26 184 | 95.96 100 | 88.57 57 | 85.47 155 | 92.81 160 | 76.12 122 | 96.91 202 | 81.24 163 | 82.29 231 | 94.47 170 |
|
tpm cat1 | | | 81.96 249 | 80.27 251 | 87.01 260 | 91.09 235 | 71.02 284 | 87.38 281 | 91.53 259 | 66.25 306 | 80.17 242 | 86.35 284 | 68.22 233 | 96.15 232 | 69.16 269 | 82.29 231 | 93.86 194 |
|
v7 | | | 87.75 155 | 86.96 151 | 90.12 171 | 91.20 229 | 79.50 178 | 94.28 123 | 95.46 137 | 83.45 156 | 85.75 139 | 91.56 199 | 75.13 145 | 97.43 154 | 83.60 132 | 82.18 233 | 93.42 220 |
|
v1192 | | | 87.25 171 | 86.33 167 | 90.00 182 | 90.76 250 | 79.04 207 | 93.80 154 | 95.48 136 | 82.57 185 | 85.48 154 | 91.18 212 | 73.38 172 | 97.42 156 | 82.30 149 | 82.06 234 | 93.53 215 |
|
v1144 | | | 87.61 161 | 86.79 158 | 90.06 178 | 91.01 237 | 79.34 195 | 93.95 147 | 95.42 146 | 83.36 160 | 85.66 146 | 91.31 207 | 74.98 149 | 97.42 156 | 83.37 133 | 82.06 234 | 93.42 220 |
|
v1240 | | | 86.78 181 | 85.85 179 | 89.56 195 | 90.45 261 | 77.79 230 | 93.61 168 | 95.37 150 | 81.65 201 | 85.43 159 | 91.15 214 | 71.50 192 | 97.43 154 | 81.47 162 | 82.05 236 | 93.47 219 |
|
Anonymous20231206 | | | 81.03 261 | 79.77 257 | 84.82 280 | 87.85 294 | 70.26 290 | 91.42 235 | 92.08 245 | 73.67 273 | 77.75 260 | 89.25 245 | 62.43 262 | 93.08 294 | 61.50 297 | 82.00 237 | 91.12 272 |
|
v1neww | | | 87.98 144 | 87.25 141 | 90.16 165 | 91.38 211 | 79.41 187 | 94.37 117 | 95.28 153 | 84.48 131 | 85.77 137 | 91.53 200 | 76.12 122 | 97.45 145 | 84.45 119 | 81.89 238 | 93.61 211 |
|
v7new | | | 87.98 144 | 87.25 141 | 90.16 165 | 91.38 211 | 79.41 187 | 94.37 117 | 95.28 153 | 84.48 131 | 85.77 137 | 91.53 200 | 76.12 122 | 97.45 145 | 84.45 119 | 81.89 238 | 93.61 211 |
|
v6 | | | 87.98 144 | 87.25 141 | 90.16 165 | 91.36 214 | 79.39 192 | 94.37 117 | 95.27 156 | 84.48 131 | 85.78 136 | 91.51 202 | 76.15 121 | 97.46 143 | 84.46 118 | 81.88 240 | 93.62 210 |
|
V42 | | | 87.68 157 | 86.86 153 | 90.15 169 | 90.58 256 | 80.14 164 | 94.24 125 | 95.28 153 | 83.66 148 | 85.67 145 | 91.33 204 | 74.73 150 | 97.41 161 | 84.43 121 | 81.83 241 | 92.89 234 |
|
v1921920 | | | 86.97 178 | 86.06 175 | 89.69 193 | 90.53 260 | 78.11 222 | 93.80 154 | 95.43 144 | 81.90 197 | 85.33 166 | 91.05 217 | 72.66 179 | 97.41 161 | 82.05 153 | 81.80 242 | 93.53 215 |
|
v2v482 | | | 87.84 150 | 87.06 148 | 90.17 164 | 90.99 238 | 79.23 205 | 94.00 145 | 95.13 165 | 84.87 123 | 85.53 150 | 92.07 183 | 74.45 152 | 97.45 145 | 84.71 115 | 81.75 243 | 93.85 195 |
|
v1141 | | | 87.84 150 | 87.09 145 | 90.11 175 | 91.23 226 | 79.25 201 | 94.08 135 | 95.24 158 | 84.44 135 | 85.69 144 | 91.31 207 | 75.91 134 | 97.44 152 | 84.17 124 | 81.74 244 | 93.63 209 |
|
divwei89l23v2f112 | | | 87.84 150 | 87.09 145 | 90.10 177 | 91.23 226 | 79.24 203 | 94.09 133 | 95.24 158 | 84.44 135 | 85.70 142 | 91.31 207 | 75.91 134 | 97.44 152 | 84.17 124 | 81.73 245 | 93.64 207 |
|
v1 | | | 87.85 149 | 87.10 144 | 90.11 175 | 91.21 228 | 79.24 203 | 94.09 133 | 95.24 158 | 84.44 135 | 85.70 142 | 91.31 207 | 75.96 132 | 97.45 145 | 84.18 123 | 81.73 245 | 93.64 207 |
|
v144192 | | | 87.19 174 | 86.35 166 | 89.74 190 | 90.64 255 | 78.24 219 | 93.92 148 | 95.43 144 | 81.93 195 | 85.51 152 | 91.05 217 | 74.21 157 | 97.45 145 | 82.86 139 | 81.56 247 | 93.53 215 |
|
OurMVSNet-221017-0 | | | 85.35 207 | 84.64 205 | 87.49 250 | 90.77 249 | 72.59 273 | 94.01 144 | 94.40 192 | 84.72 127 | 79.62 250 | 93.17 147 | 61.91 265 | 96.72 206 | 81.99 154 | 81.16 248 | 93.16 227 |
|
FMVSNet5 | | | 81.52 255 | 79.60 259 | 87.27 253 | 91.17 232 | 77.95 224 | 91.49 234 | 92.26 242 | 76.87 249 | 76.16 272 | 87.91 265 | 51.67 300 | 92.34 297 | 67.74 278 | 81.16 248 | 91.52 264 |
|
CP-MVSNet | | | 87.63 160 | 87.26 140 | 88.74 214 | 93.12 167 | 76.59 243 | 95.29 61 | 96.58 65 | 88.43 59 | 83.49 204 | 92.98 154 | 75.28 144 | 95.83 244 | 78.97 204 | 81.15 250 | 93.79 196 |
|
semantic-postprocess | | | | | 88.18 237 | 91.71 197 | 76.87 241 | | 92.65 236 | 85.40 113 | 81.44 228 | 90.54 226 | 66.21 244 | 95.00 275 | 81.04 165 | 81.05 251 | 92.66 241 |
|
TinyColmap | | | 79.76 269 | 77.69 269 | 85.97 271 | 91.71 197 | 73.12 264 | 89.55 255 | 90.36 280 | 75.03 263 | 72.03 296 | 90.19 232 | 46.22 311 | 96.19 231 | 63.11 293 | 81.03 252 | 88.59 300 |
|
UniMVSNet_NR-MVSNet | | | 89.92 93 | 89.29 92 | 91.81 117 | 93.39 158 | 83.72 80 | 94.43 109 | 97.12 24 | 89.80 30 | 86.46 122 | 93.32 140 | 83.16 53 | 97.23 180 | 84.92 110 | 81.02 253 | 94.49 169 |
|
DU-MVS | | | 89.34 113 | 88.50 111 | 91.85 113 | 93.04 171 | 83.72 80 | 94.47 106 | 96.59 64 | 89.50 35 | 86.46 122 | 93.29 143 | 77.25 111 | 97.23 180 | 84.92 110 | 81.02 253 | 94.59 160 |
|
PS-CasMVS | | | 87.32 168 | 86.88 152 | 88.63 217 | 92.99 174 | 76.33 246 | 95.33 56 | 96.61 63 | 88.22 64 | 83.30 207 | 93.07 152 | 73.03 175 | 95.79 247 | 78.36 209 | 81.00 255 | 93.75 202 |
|
IterMVS | | | 84.88 219 | 83.98 211 | 87.60 246 | 91.44 204 | 76.03 248 | 90.18 248 | 92.41 239 | 83.24 163 | 81.06 233 | 90.42 230 | 66.60 239 | 94.28 280 | 79.46 197 | 80.98 256 | 92.48 245 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
UniMVSNet (Re) | | | 89.80 95 | 89.07 97 | 92.01 103 | 93.60 155 | 84.52 60 | 94.78 90 | 97.47 5 | 89.26 39 | 86.44 125 | 92.32 173 | 82.10 65 | 97.39 166 | 84.81 113 | 80.84 257 | 94.12 179 |
|
LF4IMVS | | | 80.37 265 | 79.07 265 | 84.27 285 | 86.64 296 | 69.87 293 | 89.39 260 | 91.05 267 | 76.38 251 | 74.97 282 | 90.00 235 | 47.85 308 | 94.25 281 | 74.55 245 | 80.82 258 | 88.69 299 |
|
v10 | | | 87.25 171 | 86.38 165 | 89.85 185 | 91.19 231 | 79.50 178 | 94.48 104 | 95.45 141 | 83.79 146 | 83.62 200 | 91.19 211 | 75.13 145 | 97.42 156 | 81.94 155 | 80.60 259 | 92.63 242 |
|
WR-MVS_H | | | 87.80 154 | 87.37 136 | 89.10 209 | 93.23 164 | 78.12 221 | 95.61 52 | 97.30 15 | 87.90 72 | 83.72 197 | 92.01 185 | 79.65 92 | 96.01 237 | 76.36 227 | 80.54 260 | 93.16 227 |
|
VPNet | | | 88.20 138 | 87.47 134 | 90.39 157 | 93.56 156 | 79.46 183 | 94.04 141 | 95.54 130 | 88.67 53 | 86.96 112 | 94.58 104 | 69.33 221 | 97.15 184 | 84.05 126 | 80.53 261 | 94.56 163 |
|
v7n | | | 86.81 179 | 85.76 181 | 89.95 183 | 90.72 252 | 79.25 201 | 95.07 74 | 95.92 102 | 84.45 134 | 82.29 215 | 90.86 219 | 72.60 181 | 97.53 138 | 79.42 201 | 80.52 262 | 93.08 231 |
|
v8 | | | 87.50 164 | 86.71 161 | 89.89 184 | 91.37 213 | 79.40 191 | 94.50 103 | 95.38 148 | 84.81 125 | 83.60 201 | 91.33 204 | 76.05 126 | 97.42 156 | 82.84 140 | 80.51 263 | 92.84 236 |
|
EU-MVSNet | | | 81.32 258 | 80.95 246 | 82.42 291 | 88.50 285 | 63.67 307 | 93.32 176 | 91.33 262 | 64.02 311 | 80.57 239 | 92.83 158 | 61.21 271 | 92.27 298 | 76.34 228 | 80.38 264 | 91.32 268 |
|
Patchmtry | | | 82.71 244 | 80.93 247 | 88.06 239 | 90.05 269 | 76.37 245 | 84.74 296 | 91.96 250 | 72.28 286 | 81.32 231 | 87.87 266 | 71.03 197 | 95.50 257 | 68.97 270 | 80.15 265 | 92.32 252 |
|
NR-MVSNet | | | 88.58 130 | 87.47 134 | 91.93 109 | 93.04 171 | 84.16 73 | 94.77 91 | 96.25 81 | 89.05 43 | 80.04 246 | 93.29 143 | 79.02 94 | 97.05 193 | 81.71 160 | 80.05 266 | 94.59 160 |
|
V4 | | | 86.50 187 | 85.54 183 | 89.39 201 | 89.13 278 | 78.99 208 | 94.73 92 | 95.54 130 | 83.59 149 | 82.10 219 | 90.61 224 | 71.60 189 | 97.45 145 | 82.52 143 | 80.01 267 | 91.74 260 |
|
v52 | | | 86.50 187 | 85.53 186 | 89.39 201 | 89.17 277 | 78.99 208 | 94.72 95 | 95.54 130 | 83.59 149 | 82.10 219 | 90.60 225 | 71.59 190 | 97.45 145 | 82.52 143 | 79.99 268 | 91.73 261 |
|
Baseline_NR-MVSNet | | | 87.07 176 | 86.63 162 | 88.40 230 | 91.44 204 | 77.87 228 | 94.23 126 | 92.57 237 | 84.12 140 | 85.74 141 | 92.08 181 | 77.25 111 | 96.04 234 | 82.29 150 | 79.94 269 | 91.30 269 |
|
dp | | | 81.47 256 | 80.23 252 | 85.17 278 | 89.92 272 | 65.49 305 | 86.74 283 | 90.10 285 | 76.30 253 | 81.10 232 | 87.12 274 | 62.81 259 | 95.92 240 | 68.13 276 | 79.88 270 | 94.09 182 |
|
TranMVSNet+NR-MVSNet | | | 88.84 124 | 87.95 127 | 91.49 124 | 92.68 179 | 83.01 101 | 94.92 81 | 96.31 77 | 89.88 29 | 85.53 150 | 93.85 130 | 76.63 118 | 96.96 199 | 81.91 156 | 79.87 271 | 94.50 167 |
|
v148 | | | 87.04 177 | 86.32 168 | 89.21 206 | 90.94 242 | 77.26 237 | 93.71 163 | 94.43 191 | 84.84 124 | 84.36 185 | 90.80 220 | 76.04 128 | 97.05 193 | 82.12 151 | 79.60 272 | 93.31 222 |
|
IB-MVS | | 80.51 15 | 85.24 210 | 83.26 229 | 91.19 132 | 92.13 187 | 79.86 172 | 91.75 227 | 91.29 264 | 83.28 162 | 80.66 237 | 88.49 256 | 61.28 268 | 98.46 90 | 80.99 168 | 79.46 273 | 95.25 137 |
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 |
PEN-MVS | | | 86.80 180 | 86.27 170 | 88.40 230 | 92.32 184 | 75.71 250 | 95.18 68 | 96.38 75 | 87.97 69 | 82.82 211 | 93.15 148 | 73.39 171 | 95.92 240 | 76.15 231 | 79.03 274 | 93.59 213 |
|
v748 | | | 86.27 191 | 85.28 191 | 89.25 205 | 90.26 264 | 77.58 236 | 94.89 82 | 95.50 135 | 84.28 138 | 81.41 229 | 90.46 229 | 72.57 182 | 97.32 169 | 79.81 193 | 78.36 275 | 92.84 236 |
|
pm-mvs1 | | | 86.61 185 | 85.54 183 | 89.82 186 | 91.44 204 | 80.18 162 | 95.28 63 | 94.85 180 | 83.84 143 | 81.66 226 | 92.62 165 | 72.45 185 | 96.48 218 | 79.67 195 | 78.06 276 | 92.82 238 |
|
SixPastTwentyTwo | | | 83.91 234 | 82.90 233 | 86.92 262 | 90.99 238 | 70.67 287 | 93.48 172 | 91.99 249 | 85.54 110 | 77.62 261 | 92.11 179 | 60.59 274 | 96.87 204 | 76.05 232 | 77.75 277 | 93.20 225 |
|
MIMVSNet1 | | | 79.38 271 | 77.28 271 | 85.69 273 | 86.35 297 | 73.67 262 | 91.61 233 | 92.75 233 | 78.11 242 | 72.64 294 | 88.12 261 | 48.16 307 | 91.97 301 | 60.32 299 | 77.49 278 | 91.43 267 |
|
DTE-MVSNet | | | 86.11 193 | 85.48 187 | 87.98 240 | 91.65 200 | 74.92 253 | 94.93 80 | 95.75 115 | 87.36 84 | 82.26 216 | 93.04 153 | 72.85 176 | 95.82 245 | 74.04 247 | 77.46 279 | 93.20 225 |
|
N_pmnet | | | 68.89 290 | 68.44 291 | 70.23 306 | 89.07 280 | 28.79 334 | 88.06 274 | 19.50 335 | 69.47 299 | 71.86 297 | 84.93 290 | 61.24 270 | 91.75 302 | 54.70 306 | 77.15 280 | 90.15 287 |
|
test20.03 | | | 79.95 267 | 79.08 264 | 82.55 290 | 85.79 298 | 67.74 299 | 91.09 242 | 91.08 265 | 81.23 210 | 74.48 285 | 89.96 237 | 61.63 266 | 90.15 306 | 60.08 300 | 76.38 281 | 89.76 288 |
|
FPMVS | | | 64.63 294 | 62.55 294 | 70.88 305 | 70.80 321 | 56.71 316 | 84.42 298 | 84.42 315 | 51.78 319 | 49.57 318 | 81.61 304 | 23.49 327 | 81.48 323 | 40.61 323 | 76.25 282 | 74.46 318 |
|
testpf | | | 71.41 288 | 72.11 285 | 69.30 308 | 84.53 303 | 59.79 311 | 62.74 324 | 83.14 317 | 71.11 293 | 68.83 303 | 81.57 305 | 46.70 310 | 84.83 321 | 74.51 246 | 75.86 283 | 63.30 319 |
|
pmmvs6 | | | 83.42 238 | 81.60 241 | 88.87 211 | 88.01 291 | 77.87 228 | 94.96 78 | 94.24 197 | 74.67 268 | 78.80 253 | 91.09 216 | 60.17 277 | 96.49 217 | 77.06 225 | 75.40 284 | 92.23 254 |
|
test1235678 | | | 72.22 285 | 70.31 286 | 77.93 298 | 78.04 317 | 58.04 314 | 85.76 291 | 89.80 293 | 70.15 298 | 63.43 311 | 80.20 308 | 42.24 315 | 87.24 313 | 48.68 314 | 74.50 285 | 88.50 301 |
|
new_pmnet | | | 72.15 286 | 70.13 287 | 78.20 295 | 82.95 309 | 65.68 303 | 83.91 302 | 82.40 319 | 62.94 313 | 64.47 310 | 79.82 309 | 42.85 314 | 86.26 316 | 57.41 305 | 74.44 286 | 82.65 312 |
|
MDA-MVSNet_test_wron | | | 79.21 273 | 77.19 273 | 85.29 276 | 88.22 288 | 72.77 269 | 85.87 289 | 90.06 286 | 74.34 270 | 62.62 313 | 87.56 269 | 66.14 246 | 91.99 300 | 66.90 282 | 73.01 287 | 91.10 273 |
|
YYNet1 | | | 79.22 272 | 77.20 272 | 85.28 277 | 88.20 289 | 72.66 271 | 85.87 289 | 90.05 288 | 74.33 271 | 62.70 312 | 87.61 268 | 66.09 247 | 92.03 299 | 66.94 279 | 72.97 288 | 91.15 271 |
|
Patchmatch-RL test | | | 81.67 251 | 79.96 255 | 86.81 266 | 85.42 299 | 71.23 281 | 82.17 309 | 87.50 309 | 78.47 235 | 77.19 264 | 82.50 300 | 70.81 201 | 93.48 288 | 82.66 142 | 72.89 289 | 95.71 125 |
|
pmmvs-eth3d | | | 80.97 262 | 78.72 267 | 87.74 243 | 84.99 302 | 79.97 170 | 90.11 249 | 91.65 256 | 75.36 260 | 73.51 288 | 86.03 286 | 59.45 280 | 93.96 283 | 75.17 238 | 72.21 290 | 89.29 292 |
|
PM-MVS | | | 78.11 275 | 76.12 277 | 84.09 286 | 83.54 306 | 70.08 291 | 88.97 266 | 85.27 314 | 79.93 219 | 74.73 283 | 86.43 278 | 34.70 320 | 93.48 288 | 79.43 200 | 72.06 291 | 88.72 298 |
|
v16 | | | 84.96 216 | 83.74 215 | 88.62 218 | 91.40 209 | 79.48 181 | 93.83 151 | 94.04 204 | 83.03 171 | 76.54 267 | 86.59 276 | 76.11 125 | 95.42 261 | 80.33 181 | 71.80 292 | 90.95 276 |
|
v18 | | | 84.97 215 | 83.76 213 | 88.60 220 | 91.36 214 | 79.41 187 | 93.82 153 | 94.04 204 | 83.00 174 | 76.61 266 | 86.60 275 | 76.19 120 | 95.43 260 | 80.39 178 | 71.79 293 | 90.96 274 |
|
v17 | | | 84.93 218 | 83.70 217 | 88.62 218 | 91.36 214 | 79.48 181 | 93.83 151 | 94.03 206 | 83.04 170 | 76.51 268 | 86.57 277 | 76.05 126 | 95.42 261 | 80.31 183 | 71.65 294 | 90.96 274 |
|
1111 | | | 70.54 289 | 69.71 288 | 73.04 303 | 79.30 314 | 44.83 327 | 84.23 299 | 88.96 298 | 67.33 303 | 65.42 308 | 82.28 301 | 41.11 316 | 88.11 311 | 47.12 316 | 71.60 295 | 86.19 308 |
|
v11 | | | 84.67 228 | 83.41 228 | 88.44 229 | 91.32 221 | 79.13 206 | 93.69 167 | 93.99 212 | 82.81 180 | 76.20 271 | 86.24 285 | 75.48 142 | 95.35 267 | 79.53 196 | 71.48 296 | 90.85 282 |
|
v15 | | | 84.79 221 | 83.53 222 | 88.57 224 | 91.30 225 | 79.41 187 | 93.70 164 | 94.01 207 | 83.06 167 | 76.27 269 | 86.42 281 | 76.03 129 | 95.38 263 | 80.01 185 | 71.00 297 | 90.92 277 |
|
V14 | | | 84.79 221 | 83.52 223 | 88.57 224 | 91.32 221 | 79.43 186 | 93.72 162 | 94.01 207 | 83.06 167 | 76.22 270 | 86.43 278 | 76.01 130 | 95.37 264 | 79.96 187 | 70.99 298 | 90.91 278 |
|
v13 | | | 84.72 226 | 83.44 227 | 88.58 221 | 91.31 224 | 79.52 177 | 93.77 156 | 94.00 210 | 83.03 171 | 75.85 278 | 86.38 283 | 75.84 136 | 95.35 267 | 79.83 192 | 70.95 299 | 90.87 281 |
|
V9 | | | 84.77 223 | 83.50 224 | 88.58 221 | 91.33 219 | 79.46 183 | 93.75 158 | 94.00 210 | 83.07 166 | 76.07 275 | 86.43 278 | 75.97 131 | 95.37 264 | 79.91 190 | 70.93 300 | 90.91 278 |
|
v12 | | | 84.74 224 | 83.46 225 | 88.58 221 | 91.32 221 | 79.50 178 | 93.75 158 | 94.01 207 | 83.06 167 | 75.98 277 | 86.41 282 | 75.82 137 | 95.36 266 | 79.87 191 | 70.89 301 | 90.89 280 |
|
Gipuma | | | 57.99 298 | 54.91 299 | 67.24 311 | 88.51 284 | 65.59 304 | 52.21 327 | 90.33 281 | 43.58 323 | 42.84 322 | 51.18 325 | 20.29 330 | 85.07 320 | 34.77 325 | 70.45 302 | 51.05 324 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
LP | | | 75.51 280 | 72.15 284 | 85.61 274 | 87.86 293 | 73.93 259 | 80.20 313 | 88.43 302 | 67.39 302 | 70.05 299 | 80.56 307 | 58.18 285 | 93.18 293 | 46.28 318 | 70.36 303 | 89.71 290 |
|
K. test v3 | | | 81.59 253 | 80.15 254 | 85.91 272 | 89.89 273 | 69.42 294 | 92.57 207 | 87.71 306 | 85.56 109 | 73.44 289 | 89.71 240 | 55.58 290 | 95.52 254 | 77.17 222 | 69.76 304 | 92.78 239 |
|
TDRefinement | | | 79.81 268 | 77.34 270 | 87.22 258 | 79.24 316 | 75.48 252 | 93.12 187 | 92.03 247 | 76.45 250 | 75.01 281 | 91.58 196 | 49.19 306 | 96.44 221 | 70.22 268 | 69.18 305 | 89.75 289 |
|
MDA-MVSNet-bldmvs | | | 78.85 274 | 76.31 275 | 86.46 267 | 89.76 274 | 73.88 260 | 88.79 267 | 90.42 278 | 79.16 226 | 59.18 314 | 88.33 259 | 60.20 276 | 94.04 282 | 62.00 296 | 68.96 306 | 91.48 266 |
|
ambc | | | | | 83.06 288 | 79.99 313 | 63.51 308 | 77.47 317 | 92.86 229 | | 74.34 286 | 84.45 291 | 28.74 322 | 95.06 274 | 73.06 254 | 68.89 307 | 90.61 284 |
|
TransMVSNet (Re) | | | 84.43 230 | 83.06 231 | 88.54 226 | 91.72 196 | 78.44 213 | 95.18 68 | 92.82 231 | 82.73 182 | 79.67 248 | 92.12 177 | 73.49 168 | 95.96 239 | 71.10 264 | 68.73 308 | 91.21 270 |
|
PMVS | | 47.18 22 | 52.22 300 | 48.46 301 | 63.48 312 | 45.72 333 | 46.20 326 | 73.41 320 | 78.31 325 | 41.03 324 | 30.06 326 | 65.68 319 | 6.05 335 | 83.43 322 | 30.04 326 | 65.86 309 | 60.80 321 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
lessismore_v0 | | | | | 86.04 270 | 88.46 286 | 68.78 296 | | 80.59 322 | | 73.01 292 | 90.11 234 | 55.39 292 | 96.43 222 | 75.06 240 | 65.06 310 | 92.90 233 |
|
new-patchmatchnet | | | 76.41 278 | 75.17 278 | 80.13 293 | 82.65 310 | 59.61 312 | 87.66 279 | 91.08 265 | 78.23 240 | 69.85 300 | 83.22 297 | 54.76 294 | 91.63 304 | 64.14 291 | 64.89 311 | 89.16 294 |
|
test12356 | | | 64.99 293 | 63.78 292 | 68.61 310 | 72.69 320 | 39.14 330 | 78.46 315 | 87.61 308 | 64.91 309 | 55.77 315 | 77.48 311 | 28.10 323 | 85.59 318 | 44.69 319 | 64.35 312 | 81.12 314 |
|
Anonymous20231211 | | | 72.97 284 | 69.63 289 | 83.00 289 | 83.05 308 | 66.91 301 | 92.69 202 | 89.45 296 | 61.06 314 | 67.50 305 | 83.46 296 | 34.34 321 | 93.61 287 | 51.11 309 | 63.97 313 | 88.48 303 |
|
pmmvs3 | | | 71.81 287 | 68.71 290 | 81.11 292 | 75.86 318 | 70.42 289 | 86.74 283 | 83.66 316 | 58.95 316 | 68.64 304 | 80.89 306 | 36.93 319 | 89.52 307 | 63.10 294 | 63.59 314 | 83.39 310 |
|
UnsupCasMVSNet_eth | | | 80.07 266 | 78.27 268 | 85.46 275 | 85.24 300 | 72.63 272 | 88.45 272 | 94.87 179 | 82.99 175 | 71.64 298 | 88.07 262 | 56.34 289 | 91.75 302 | 73.48 252 | 63.36 315 | 92.01 257 |
|
LCM-MVSNet | | | 66.00 291 | 62.16 295 | 77.51 300 | 64.51 328 | 58.29 313 | 83.87 303 | 90.90 271 | 48.17 320 | 54.69 316 | 73.31 315 | 16.83 333 | 86.75 315 | 65.47 285 | 61.67 316 | 87.48 307 |
|
UnsupCasMVSNet_bld | | | 76.23 279 | 73.27 281 | 85.09 279 | 83.79 305 | 72.92 265 | 85.65 293 | 93.47 222 | 71.52 289 | 68.84 302 | 79.08 310 | 49.77 303 | 93.21 291 | 66.81 283 | 60.52 317 | 89.13 296 |
|
testmv | | | 65.49 292 | 62.66 293 | 73.96 302 | 68.78 323 | 53.14 322 | 84.70 297 | 88.56 300 | 65.94 308 | 52.35 317 | 74.65 313 | 25.02 326 | 85.14 319 | 43.54 320 | 60.40 318 | 83.60 309 |
|
DeepMVS_CX | | | | | 56.31 315 | 74.23 319 | 51.81 323 | | 56.67 333 | 44.85 321 | 48.54 320 | 75.16 312 | 27.87 324 | 58.74 331 | 40.92 322 | 52.22 319 | 58.39 323 |
|
PVSNet_0 | | 73.20 20 | 77.22 276 | 74.83 279 | 84.37 283 | 90.70 253 | 71.10 283 | 83.09 307 | 89.67 294 | 72.81 283 | 73.93 287 | 83.13 298 | 60.79 273 | 93.70 285 | 68.54 271 | 50.84 320 | 88.30 305 |
|
PMMVS2 | | | 59.60 296 | 56.40 298 | 69.21 309 | 68.83 322 | 46.58 325 | 73.02 322 | 77.48 327 | 55.07 318 | 49.21 319 | 72.95 316 | 17.43 332 | 80.04 324 | 49.32 313 | 44.33 321 | 80.99 315 |
|
MVE | | 39.65 23 | 43.39 303 | 38.59 308 | 57.77 314 | 56.52 331 | 48.77 324 | 55.38 326 | 58.64 332 | 29.33 328 | 28.96 327 | 52.65 324 | 4.68 336 | 64.62 330 | 28.11 327 | 33.07 322 | 59.93 322 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
no-one | | | 61.56 295 | 56.58 297 | 76.49 301 | 67.80 326 | 62.76 309 | 78.13 316 | 86.11 310 | 63.16 312 | 43.24 321 | 64.70 320 | 26.12 325 | 88.95 309 | 50.84 311 | 29.15 323 | 77.77 316 |
|
PNet_i23d | | | 50.48 302 | 47.18 302 | 60.36 313 | 68.59 324 | 44.56 329 | 72.75 323 | 72.61 328 | 43.92 322 | 33.91 325 | 60.19 323 | 6.16 334 | 73.52 327 | 38.50 324 | 28.04 324 | 63.01 320 |
|
wuykxyi23d | | | 50.55 301 | 44.13 303 | 69.81 307 | 56.77 330 | 54.58 321 | 73.22 321 | 80.78 321 | 39.79 325 | 22.08 330 | 46.69 327 | 4.03 337 | 79.71 325 | 47.65 315 | 26.13 325 | 75.14 317 |
|
E-PMN | | | 43.23 304 | 42.29 304 | 46.03 316 | 65.58 327 | 37.41 331 | 73.51 319 | 64.62 329 | 33.99 326 | 28.47 328 | 47.87 326 | 19.90 331 | 67.91 328 | 22.23 328 | 24.45 326 | 32.77 325 |
|
ANet_high | | | 58.88 297 | 54.22 300 | 72.86 304 | 56.50 332 | 56.67 317 | 80.75 312 | 86.00 311 | 73.09 279 | 37.39 323 | 64.63 321 | 22.17 328 | 79.49 326 | 43.51 321 | 23.96 327 | 82.43 313 |
|
EMVS | | | 42.07 305 | 41.12 305 | 44.92 318 | 63.45 329 | 35.56 333 | 73.65 318 | 63.48 330 | 33.05 327 | 26.88 329 | 45.45 328 | 21.27 329 | 67.14 329 | 19.80 329 | 23.02 328 | 32.06 326 |
|
tmp_tt | | | 35.64 307 | 39.24 306 | 24.84 320 | 14.87 334 | 23.90 335 | 62.71 325 | 51.51 334 | 6.58 330 | 36.66 324 | 62.08 322 | 44.37 313 | 30.34 333 | 52.40 307 | 22.00 329 | 20.27 327 |
|
wuyk23d | | | 21.27 309 | 20.48 310 | 23.63 321 | 68.59 324 | 36.41 332 | 49.57 328 | 6.85 336 | 9.37 329 | 7.89 331 | 4.46 334 | 4.03 337 | 31.37 332 | 17.47 330 | 16.07 330 | 3.12 328 |
|
.test1245 | | | 57.63 299 | 61.79 296 | 45.14 317 | 79.30 314 | 44.83 327 | 84.23 299 | 88.96 298 | 67.33 303 | 65.42 308 | 82.28 301 | 41.11 316 | 88.11 311 | 47.12 316 | 0.39 331 | 2.46 330 |
|
testmvs | | | 8.92 310 | 11.52 311 | 1.12 323 | 1.06 335 | 0.46 337 | 86.02 287 | 0.65 337 | 0.62 331 | 2.74 332 | 9.52 332 | 0.31 340 | 0.45 335 | 2.38 331 | 0.39 331 | 2.46 330 |
|
test123 | | | 8.76 311 | 11.22 312 | 1.39 322 | 0.85 336 | 0.97 336 | 85.76 291 | 0.35 338 | 0.54 332 | 2.45 333 | 8.14 333 | 0.60 339 | 0.48 334 | 2.16 332 | 0.17 333 | 2.71 329 |
|
cdsmvs_eth3d_5k | | | 22.14 308 | 29.52 309 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 95.76 114 | 0.00 333 | 0.00 334 | 94.29 110 | 75.66 140 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
pcd_1.5k_mvsjas | | | 6.64 313 | 8.86 314 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 79.70 88 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
sosnet-low-res | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
sosnet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
uncertanet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
Regformer | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
ab-mvs-re | | | 7.82 312 | 10.43 313 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 93.88 128 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
uanet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
sam_mvs1 | | | | | | | | | | | | | 71.70 188 | | | | |
|
sam_mvs | | | | | | | | | | | | | 70.60 203 | | | | |
|
MTGPA | | | | | | | | | 96.97 32 | | | | | | | | |
|
test_post1 | | | | | | | | 88.00 275 | | | | 9.81 331 | 69.31 223 | 95.53 253 | 76.65 226 | | |
|
test_post | | | | | | | | | | | | 10.29 330 | 70.57 207 | 95.91 242 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 83.76 294 | 71.53 191 | 96.48 218 | | | |
|
MTMP | | | | | | | | | 60.64 331 | | | | | | | | |
|
gm-plane-assit | | | | | | 89.60 276 | 68.00 297 | | | 77.28 247 | | 88.99 247 | | 97.57 135 | 79.44 199 | | |
|
TEST9 | | | | | | 97.53 34 | 86.49 26 | 94.07 137 | 96.78 48 | 81.61 204 | 92.77 38 | 96.20 57 | 87.71 13 | 99.12 38 | | | |
|
test_8 | | | | | | 97.49 37 | 86.30 34 | 94.02 143 | 96.76 51 | 81.86 199 | 92.70 42 | 96.20 57 | 87.63 14 | 99.02 49 | | | |
|
agg_prior | | | | | | 97.38 41 | 85.92 41 | | 96.72 54 | | 92.16 54 | | | 98.97 58 | | | |
|
test_prior4 | | | | | | | 85.96 40 | 94.11 131 | | | | | | | | | |
|
test_prior | | | | | 93.82 49 | 97.29 46 | 84.49 61 | | 96.88 41 | | | | | 98.87 65 | | | 98.11 40 |
|
旧先验2 | | | | | | | | 93.36 175 | | 71.25 292 | 94.37 11 | | | 97.13 187 | 86.74 97 | | |
|
新几何2 | | | | | | | | 93.11 189 | | | | | | | | | |
|
无先验 | | | | | | | | 93.28 183 | 96.26 79 | 73.95 272 | | | | 99.05 43 | 80.56 175 | | 96.59 95 |
|
原ACMM2 | | | | | | | | 92.94 197 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 98.75 76 | 78.30 210 | | |
|
segment_acmp | | | | | | | | | | | | | 87.16 19 | | | | |
|
testdata1 | | | | | | | | 92.15 220 | | 87.94 70 | | | | | | | |
|
plane_prior7 | | | | | | 94.70 115 | 82.74 109 | | | | | | | | | | |
|
plane_prior6 | | | | | | 94.52 121 | 82.75 107 | | | | | | 74.23 155 | | | | |
|
plane_prior4 | | | | | | | | | | | | 94.86 94 | | | | | |
|
plane_prior3 | | | | | | | 82.75 107 | | | 90.26 24 | 86.91 114 | | | | | | |
|
plane_prior2 | | | | | | | | 95.85 40 | | 90.81 17 | | | | | | | |
|
plane_prior1 | | | | | | 94.59 119 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 339 | | | | | | | | |
|
nn | | | | | | | | | 0.00 339 | | | | | | | | |
|
door-mid | | | | | | | | | 85.49 312 | | | | | | | | |
|
test11 | | | | | | | | | 96.57 66 | | | | | | | | |
|
door | | | | | | | | | 85.33 313 | | | | | | | | |
|
HQP5-MVS | | | | | | | 81.56 127 | | | | | | | | | | |
|
HQP-NCC | | | | | | 94.17 132 | | 94.39 113 | | 88.81 48 | 85.43 159 | | | | | | |
|
ACMP_Plane | | | | | | 94.17 132 | | 94.39 113 | | 88.81 48 | 85.43 159 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 87.11 94 | | |
|
HQP4-MVS | | | | | | | | | | | 85.43 159 | | | 97.96 117 | | | 94.51 166 |
|
HQP2-MVS | | | | | | | | | | | | | 73.83 164 | | | | |
|
NP-MVS | | | | | | 94.37 127 | 82.42 117 | | | | | 93.98 121 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 55.91 320 | 87.62 280 | | 73.32 276 | 84.59 176 | | 70.33 209 | | 74.65 243 | | 95.50 129 |
|
Test By Simon | | | | | | | | | | | | | 80.02 82 | | | | |
|