3Dnovator+ | | 77.84 4 | 85.48 45 | 84.47 53 | 88.51 2 | 91.08 65 | 73.49 13 | 93.18 4 | 93.78 7 | 80.79 11 | 76.66 144 | 93.37 36 | 60.40 162 | 96.75 13 | 77.20 80 | 93.73 47 | 95.29 1 |
|
TSAR-MVS + MP. | | | 88.02 11 | 88.11 9 | 87.72 23 | 93.68 27 | 72.13 39 | 91.41 28 | 92.35 50 | 74.62 91 | 88.90 7 | 93.85 29 | 75.75 10 | 96.00 35 | 87.80 5 | 94.63 33 | 95.04 2 |
|
IS-MVSNet | | | 83.15 66 | 82.81 65 | 84.18 91 | 89.94 83 | 63.30 201 | 91.59 26 | 88.46 184 | 79.04 25 | 79.49 90 | 92.16 54 | 65.10 84 | 94.28 86 | 67.71 162 | 91.86 58 | 94.95 3 |
|
SteuartSystems-ACMMP | | | 88.72 6 | 88.86 6 | 88.32 4 | 92.14 54 | 72.96 19 | 93.73 3 | 93.67 8 | 80.19 15 | 88.10 10 | 94.80 6 | 73.76 22 | 97.11 5 | 87.51 8 | 95.82 10 | 94.90 4 |
Skip Steuart: Steuart Systems R&D Blog. |
canonicalmvs | | | 85.91 40 | 85.87 38 | 86.04 54 | 89.84 85 | 69.44 79 | 90.45 45 | 93.00 26 | 76.70 56 | 88.01 12 | 91.23 75 | 73.28 24 | 93.91 105 | 81.50 49 | 88.80 88 | 94.77 5 |
|
alignmvs | | | 85.48 45 | 85.32 45 | 85.96 55 | 89.51 97 | 69.47 77 | 89.74 59 | 92.47 44 | 76.17 67 | 87.73 14 | 91.46 72 | 70.32 44 | 93.78 114 | 81.51 48 | 88.95 85 | 94.63 6 |
|
MP-MVS-pluss | | | 87.67 13 | 87.72 12 | 87.54 27 | 93.64 28 | 72.04 40 | 89.80 57 | 93.50 11 | 75.17 85 | 86.34 18 | 95.29 2 | 70.86 39 | 96.00 35 | 88.78 3 | 96.04 5 | 94.58 7 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
DeepPCF-MVS | | 80.84 1 | 88.10 7 | 88.56 7 | 86.73 40 | 92.24 52 | 69.03 81 | 89.57 64 | 93.39 15 | 77.53 39 | 89.79 6 | 94.12 24 | 78.98 3 | 96.58 22 | 85.66 14 | 95.72 11 | 94.58 7 |
|
VDD-MVS | | | 83.01 70 | 82.36 70 | 84.96 70 | 91.02 66 | 66.40 131 | 88.91 78 | 88.11 187 | 77.57 35 | 84.39 43 | 93.29 38 | 52.19 218 | 93.91 105 | 77.05 83 | 88.70 90 | 94.57 9 |
|
VDDNet | | | 81.52 89 | 80.67 90 | 84.05 95 | 90.44 73 | 64.13 185 | 89.73 60 | 85.91 220 | 71.11 158 | 83.18 56 | 93.48 33 | 50.54 253 | 93.49 129 | 73.40 119 | 88.25 99 | 94.54 10 |
|
APDe-MVS | | | 89.15 3 | 89.63 3 | 87.73 21 | 94.49 10 | 71.69 43 | 93.83 2 | 93.96 4 | 75.70 72 | 91.06 4 | 96.03 1 | 76.84 5 | 97.03 7 | 89.09 2 | 95.65 15 | 94.47 11 |
|
MCST-MVS | | | 87.37 19 | 87.25 17 | 87.73 21 | 94.53 9 | 72.46 33 | 89.82 55 | 93.82 6 | 73.07 127 | 84.86 36 | 92.89 47 | 76.22 7 | 96.33 25 | 84.89 21 | 95.13 24 | 94.40 12 |
|
CANet | | | 86.45 31 | 86.10 35 | 87.51 28 | 90.09 79 | 70.94 51 | 89.70 61 | 92.59 43 | 81.78 4 | 81.32 75 | 91.43 73 | 70.34 43 | 97.23 4 | 84.26 29 | 93.36 48 | 94.37 13 |
|
PHI-MVS | | | 86.43 32 | 86.17 34 | 87.24 31 | 90.88 69 | 70.96 49 | 92.27 17 | 94.07 3 | 72.45 139 | 85.22 27 | 91.90 60 | 69.47 52 | 96.42 24 | 83.28 36 | 95.94 7 | 94.35 14 |
|
CNVR-MVS | | | 88.93 5 | 89.13 5 | 88.33 3 | 94.77 4 | 73.82 6 | 90.51 41 | 93.00 26 | 80.90 10 | 88.06 11 | 94.06 26 | 76.43 6 | 96.84 9 | 88.48 4 | 95.99 6 | 94.34 15 |
|
MVS_0304 | | | 86.37 36 | 85.81 40 | 88.02 8 | 90.13 77 | 72.39 34 | 89.66 62 | 92.75 38 | 81.64 6 | 82.66 65 | 92.04 56 | 64.44 88 | 97.35 3 | 84.76 23 | 94.25 43 | 94.33 16 |
|
HPM-MVS | | | 87.11 23 | 86.98 22 | 87.50 29 | 93.88 24 | 72.16 38 | 92.19 20 | 93.33 16 | 76.07 69 | 83.81 50 | 93.95 28 | 69.77 50 | 96.01 34 | 85.15 16 | 94.66 32 | 94.32 17 |
|
CDPH-MVS | | | 85.76 42 | 85.29 47 | 87.17 33 | 93.49 31 | 71.08 47 | 88.58 92 | 92.42 48 | 68.32 210 | 84.61 38 | 93.48 33 | 72.32 31 | 96.15 32 | 79.00 62 | 95.43 17 | 94.28 18 |
|
DeepC-MVS_fast | | 79.65 3 | 86.91 26 | 86.62 27 | 87.76 20 | 93.52 30 | 72.37 36 | 91.26 29 | 93.04 23 | 76.62 57 | 84.22 45 | 93.36 37 | 71.44 36 | 96.76 12 | 80.82 53 | 95.33 21 | 94.16 19 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
EPP-MVSNet | | | 83.40 64 | 83.02 62 | 84.57 78 | 90.13 77 | 64.47 179 | 92.32 16 | 90.73 102 | 74.45 93 | 79.35 92 | 91.10 76 | 69.05 56 | 95.12 59 | 72.78 124 | 87.22 110 | 94.13 20 |
|
NCCC | | | 88.06 8 | 88.01 11 | 88.24 5 | 94.41 14 | 73.62 7 | 91.22 32 | 92.83 35 | 81.50 7 | 85.79 23 | 93.47 35 | 73.02 26 | 97.00 8 | 84.90 19 | 94.94 26 | 94.10 21 |
|
ACMMP_Plus | | | 88.05 10 | 88.08 10 | 87.94 12 | 93.70 25 | 73.05 18 | 90.86 35 | 93.59 9 | 76.27 66 | 88.14 9 | 95.09 5 | 71.06 38 | 96.67 15 | 87.67 6 | 96.37 4 | 94.09 22 |
|
XVS | | | 87.18 22 | 86.91 24 | 88.00 9 | 94.42 12 | 73.33 16 | 92.78 9 | 92.99 28 | 79.14 21 | 83.67 52 | 94.17 21 | 67.45 66 | 96.60 20 | 83.06 38 | 94.50 35 | 94.07 23 |
|
X-MVStestdata | | | 80.37 118 | 77.83 152 | 88.00 9 | 94.42 12 | 73.33 16 | 92.78 9 | 92.99 28 | 79.14 21 | 83.67 52 | 12.47 351 | 67.45 66 | 96.60 20 | 83.06 38 | 94.50 35 | 94.07 23 |
|
region2R | | | 87.42 18 | 87.20 19 | 88.09 6 | 94.63 6 | 73.55 9 | 93.03 7 | 93.12 22 | 76.73 55 | 84.45 40 | 94.52 9 | 69.09 55 | 96.70 14 | 84.37 28 | 94.83 30 | 94.03 25 |
|
Regformer-4 | | | 85.68 44 | 85.45 42 | 86.35 46 | 88.95 117 | 69.67 72 | 88.29 103 | 91.29 91 | 81.73 5 | 85.36 25 | 90.01 98 | 72.62 29 | 95.35 55 | 83.28 36 | 87.57 103 | 94.03 25 |
|
Regformer-2 | | | 86.63 30 | 86.53 28 | 86.95 37 | 89.33 102 | 71.24 46 | 88.43 94 | 92.05 59 | 82.50 1 | 86.88 16 | 90.09 96 | 74.45 14 | 95.61 41 | 84.38 27 | 90.63 70 | 94.01 27 |
|
ACMMPR | | | 87.44 16 | 87.23 18 | 88.08 7 | 94.64 5 | 73.59 8 | 93.04 5 | 93.20 19 | 76.78 52 | 84.66 37 | 94.52 9 | 68.81 57 | 96.65 16 | 84.53 25 | 94.90 27 | 94.00 28 |
|
Regformer-1 | | | 86.41 34 | 86.33 29 | 86.64 42 | 89.33 102 | 70.93 52 | 88.43 94 | 91.39 89 | 82.14 3 | 86.65 17 | 90.09 96 | 74.39 17 | 95.01 66 | 83.97 32 | 90.63 70 | 93.97 29 |
|
test_prior3 | | | 86.73 27 | 86.86 26 | 86.33 47 | 92.61 48 | 69.59 73 | 88.85 81 | 92.97 31 | 75.41 78 | 84.91 31 | 93.54 31 | 74.28 19 | 95.48 45 | 83.31 34 | 95.86 8 | 93.91 30 |
|
test_prior | | | | | 86.33 47 | 92.61 48 | 69.59 73 | | 92.97 31 | | | | | 95.48 45 | | | 93.91 30 |
|
Regformer-3 | | | 85.23 50 | 85.07 48 | 85.70 57 | 88.95 117 | 69.01 83 | 88.29 103 | 89.91 136 | 80.95 9 | 85.01 28 | 90.01 98 | 72.45 30 | 94.19 92 | 82.50 45 | 87.57 103 | 93.90 32 |
|
LFMVS | | | 81.82 84 | 81.23 83 | 83.57 110 | 91.89 58 | 63.43 199 | 89.84 54 | 81.85 267 | 77.04 47 | 83.21 55 | 93.10 41 | 52.26 217 | 93.43 134 | 71.98 136 | 89.95 78 | 93.85 33 |
|
Effi-MVS+ | | | 83.62 60 | 83.08 60 | 85.24 63 | 88.38 138 | 67.45 115 | 88.89 79 | 89.15 157 | 75.50 77 | 82.27 66 | 88.28 137 | 69.61 51 | 94.45 83 | 77.81 74 | 87.84 101 | 93.84 34 |
|
MVS_Test | | | 83.15 66 | 83.06 61 | 83.41 115 | 86.86 181 | 63.21 204 | 86.11 175 | 92.00 63 | 74.31 94 | 82.87 60 | 89.44 113 | 70.03 46 | 93.21 139 | 77.39 79 | 88.50 97 | 93.81 35 |
|
test_part1 | | | | | | | | | 94.09 1 | | | | 81.79 1 | | | 96.38 2 | 93.74 36 |
|
ESAPD | | | 89.40 1 | 89.87 1 | 87.98 11 | 95.06 1 | 72.65 26 | 92.22 18 | 94.09 1 | 75.63 74 | 91.80 1 | 95.29 2 | 81.79 1 | 97.56 1 | 86.60 12 | 96.38 2 | 93.74 36 |
|
HFP-MVS | | | 87.58 14 | 87.47 15 | 87.94 12 | 94.58 7 | 73.54 11 | 93.04 5 | 93.24 17 | 76.78 52 | 84.91 31 | 94.44 14 | 70.78 40 | 96.61 18 | 84.53 25 | 94.89 28 | 93.66 38 |
|
#test# | | | 87.33 20 | 87.13 20 | 87.94 12 | 94.58 7 | 73.54 11 | 92.34 15 | 93.24 17 | 75.23 82 | 84.91 31 | 94.44 14 | 70.78 40 | 96.61 18 | 83.75 33 | 94.89 28 | 93.66 38 |
|
VNet | | | 82.21 77 | 82.41 68 | 81.62 178 | 90.82 70 | 60.93 225 | 84.47 217 | 89.78 138 | 76.36 64 | 84.07 47 | 91.88 61 | 64.71 87 | 90.26 227 | 70.68 143 | 88.89 86 | 93.66 38 |
|
PGM-MVS | | | 86.68 28 | 86.27 31 | 87.90 16 | 94.22 19 | 73.38 15 | 90.22 50 | 93.04 23 | 75.53 76 | 83.86 48 | 94.42 16 | 67.87 63 | 96.64 17 | 82.70 43 | 94.57 34 | 93.66 38 |
|
DELS-MVS | | | 85.41 48 | 85.30 46 | 85.77 56 | 88.49 133 | 67.93 109 | 85.52 199 | 93.44 13 | 78.70 28 | 83.63 54 | 89.03 119 | 74.57 13 | 95.71 40 | 80.26 58 | 94.04 45 | 93.66 38 |
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 |
SD-MVS | | | 88.06 8 | 88.50 8 | 86.71 41 | 92.60 50 | 72.71 24 | 91.81 25 | 93.19 20 | 77.87 32 | 90.32 5 | 94.00 27 | 74.83 12 | 93.78 114 | 87.63 7 | 94.27 42 | 93.65 43 |
|
DeepC-MVS | | 79.81 2 | 87.08 25 | 86.88 25 | 87.69 25 | 91.16 64 | 72.32 37 | 90.31 47 | 93.94 5 | 77.12 44 | 82.82 61 | 94.23 20 | 72.13 33 | 97.09 6 | 84.83 22 | 95.37 18 | 93.65 43 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MP-MVS | | | 87.71 12 | 87.64 13 | 87.93 15 | 94.36 16 | 73.88 4 | 92.71 13 | 92.65 42 | 77.57 35 | 83.84 49 | 94.40 17 | 72.24 32 | 96.28 27 | 85.65 15 | 95.30 23 | 93.62 45 |
|
HPM-MVS_fast | | | 85.35 49 | 84.95 50 | 86.57 45 | 93.69 26 | 70.58 58 | 92.15 21 | 91.62 80 | 73.89 103 | 82.67 64 | 94.09 25 | 62.60 122 | 95.54 44 | 80.93 51 | 92.93 50 | 93.57 46 |
|
CSCG | | | 86.41 34 | 86.19 33 | 87.07 36 | 92.91 42 | 72.48 32 | 90.81 36 | 93.56 10 | 73.95 99 | 83.16 57 | 91.07 78 | 75.94 8 | 95.19 57 | 79.94 60 | 94.38 39 | 93.55 47 |
|
test12 | | | | | 86.80 39 | 92.63 47 | 70.70 57 | | 91.79 74 | | 82.71 63 | | 71.67 34 | 96.16 31 | | 94.50 35 | 93.54 48 |
|
APD-MVS_3200maxsize | | | 85.97 39 | 85.88 37 | 86.22 50 | 92.69 46 | 69.53 75 | 91.93 23 | 92.99 28 | 73.54 114 | 85.94 19 | 94.51 12 | 65.80 80 | 95.61 41 | 83.04 40 | 92.51 55 | 93.53 49 |
|
mvs_anonymous | | | 79.42 141 | 79.11 126 | 80.34 200 | 84.45 212 | 57.97 250 | 82.59 249 | 87.62 198 | 67.40 221 | 76.17 160 | 88.56 130 | 68.47 58 | 89.59 237 | 70.65 144 | 86.05 124 | 93.47 50 |
|
mPP-MVS | | | 86.67 29 | 86.32 30 | 87.72 23 | 94.41 14 | 73.55 9 | 92.74 11 | 92.22 53 | 76.87 50 | 82.81 62 | 94.25 19 | 66.44 73 | 96.24 28 | 82.88 42 | 94.28 41 | 93.38 51 |
|
EPNet | | | 83.72 58 | 82.92 64 | 86.14 52 | 84.22 216 | 69.48 76 | 91.05 34 | 85.27 224 | 81.30 8 | 76.83 141 | 91.65 64 | 66.09 76 | 95.56 43 | 76.00 91 | 93.85 46 | 93.38 51 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
Vis-MVSNet | | | 83.46 62 | 82.80 66 | 85.43 59 | 90.25 76 | 68.74 91 | 90.30 48 | 90.13 127 | 76.33 65 | 80.87 83 | 92.89 47 | 61.00 151 | 94.20 91 | 72.45 130 | 90.97 66 | 93.35 53 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
HSP-MVS | | | 89.28 2 | 89.76 2 | 87.85 19 | 94.28 17 | 73.46 14 | 92.90 8 | 92.73 39 | 80.27 13 | 91.35 3 | 94.16 22 | 78.35 4 | 96.77 11 | 89.59 1 | 94.22 44 | 93.33 54 |
|
EI-MVSNet-Vis-set | | | 84.19 54 | 83.81 54 | 85.31 60 | 88.18 142 | 67.85 110 | 87.66 118 | 89.73 140 | 80.05 17 | 82.95 58 | 89.59 105 | 70.74 42 | 94.82 74 | 80.66 55 | 84.72 134 | 93.28 55 |
|
MPTG | | | 87.53 15 | 87.41 16 | 87.90 16 | 94.18 21 | 74.25 2 | 90.23 49 | 92.02 60 | 79.45 19 | 85.88 20 | 94.80 6 | 68.07 59 | 96.21 29 | 86.69 10 | 95.34 19 | 93.23 56 |
|
MTAPA | | | 87.23 21 | 87.00 21 | 87.90 16 | 94.18 21 | 74.25 2 | 86.58 162 | 92.02 60 | 79.45 19 | 85.88 20 | 94.80 6 | 68.07 59 | 96.21 29 | 86.69 10 | 95.34 19 | 93.23 56 |
|
CP-MVS | | | 87.11 23 | 86.92 23 | 87.68 26 | 94.20 20 | 73.86 5 | 93.98 1 | 92.82 37 | 76.62 57 | 83.68 51 | 94.46 13 | 67.93 61 | 95.95 37 | 84.20 31 | 94.39 38 | 93.23 56 |
|
ACMMP | | | 85.89 41 | 85.39 43 | 87.38 30 | 93.59 29 | 72.63 28 | 92.74 11 | 93.18 21 | 76.78 52 | 80.73 84 | 93.82 30 | 64.33 89 | 96.29 26 | 82.67 44 | 90.69 69 | 93.23 56 |
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 |
PAPM_NR | | | 83.02 69 | 82.41 68 | 84.82 75 | 92.47 51 | 66.37 132 | 87.93 114 | 91.80 73 | 73.82 108 | 77.32 133 | 90.66 87 | 67.90 62 | 94.90 71 | 70.37 146 | 89.48 82 | 93.19 60 |
|
OMC-MVS | | | 82.69 72 | 81.97 77 | 84.85 74 | 88.75 127 | 67.42 116 | 87.98 110 | 90.87 100 | 74.92 88 | 79.72 88 | 91.65 64 | 62.19 133 | 93.96 100 | 75.26 103 | 86.42 120 | 93.16 61 |
|
PAPR | | | 81.66 87 | 80.89 88 | 83.99 100 | 90.27 75 | 64.00 189 | 86.76 158 | 91.77 77 | 68.84 197 | 77.13 140 | 89.50 106 | 67.63 64 | 94.88 72 | 67.55 164 | 88.52 96 | 93.09 62 |
|
UA-Net | | | 85.08 53 | 84.96 49 | 85.45 58 | 92.07 55 | 68.07 107 | 89.78 58 | 90.86 101 | 82.48 2 | 84.60 39 | 93.20 39 | 69.35 53 | 95.22 56 | 71.39 142 | 90.88 68 | 93.07 63 |
|
HPM-MVS++ | | | 89.02 4 | 89.15 4 | 88.63 1 | 95.01 3 | 76.03 1 | 92.38 14 | 92.85 34 | 80.26 14 | 87.78 13 | 94.27 18 | 75.89 9 | 96.81 10 | 87.45 9 | 96.44 1 | 93.05 64 |
|
train_agg | | | 86.43 32 | 86.20 32 | 87.13 34 | 93.26 35 | 72.96 19 | 88.75 86 | 91.89 69 | 68.69 199 | 85.00 29 | 93.10 41 | 74.43 15 | 95.41 50 | 84.97 17 | 95.71 12 | 93.02 65 |
|
agg_prior3 | | | 86.16 38 | 85.85 39 | 87.10 35 | 93.31 32 | 72.86 23 | 88.77 84 | 91.68 79 | 68.29 211 | 84.26 44 | 92.83 49 | 72.83 27 | 95.42 49 | 84.97 17 | 95.71 12 | 93.02 65 |
|
agg_prior1 | | | 86.22 37 | 86.09 36 | 86.62 43 | 92.85 43 | 71.94 41 | 88.59 91 | 91.78 75 | 68.96 196 | 84.41 41 | 93.18 40 | 74.94 11 | 94.93 67 | 84.75 24 | 95.33 21 | 93.01 67 |
|
EI-MVSNet-UG-set | | | 83.81 56 | 83.38 57 | 85.09 67 | 87.87 149 | 67.53 114 | 87.44 129 | 89.66 141 | 79.74 18 | 82.23 67 | 89.41 114 | 70.24 45 | 94.74 76 | 79.95 59 | 83.92 140 | 92.99 68 |
|
diffmvs | | | 79.51 136 | 78.59 134 | 82.25 160 | 83.31 248 | 62.66 213 | 84.17 227 | 88.11 187 | 67.64 214 | 76.09 161 | 87.47 158 | 64.01 92 | 91.15 211 | 71.71 139 | 84.82 133 | 92.94 69 |
|
test9_res | | | | | | | | | | | | | | | 84.90 19 | 95.70 14 | 92.87 70 |
|
agg_prior2 | | | | | | | | | | | | | | | 82.91 41 | 95.45 16 | 92.70 71 |
|
APD-MVS | | | 87.44 16 | 87.52 14 | 87.19 32 | 94.24 18 | 72.39 34 | 91.86 24 | 92.83 35 | 73.01 128 | 88.58 8 | 94.52 9 | 73.36 23 | 96.49 23 | 84.26 29 | 95.01 25 | 92.70 71 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
Vis-MVSNet (Re-imp) | | | 78.36 158 | 78.45 138 | 78.07 244 | 88.64 129 | 51.78 311 | 86.70 159 | 79.63 288 | 74.14 97 | 75.11 184 | 90.83 85 | 61.29 145 | 89.75 234 | 58.10 241 | 91.60 59 | 92.69 73 |
|
TSAR-MVS + GP. | | | 85.71 43 | 85.33 44 | 86.84 38 | 91.34 62 | 72.50 31 | 89.07 76 | 87.28 204 | 76.41 59 | 85.80 22 | 90.22 94 | 74.15 21 | 95.37 54 | 81.82 47 | 91.88 57 | 92.65 74 |
|
Test4 | | | 77.83 173 | 75.90 190 | 83.62 107 | 80.24 295 | 65.25 154 | 85.27 201 | 90.67 103 | 69.03 194 | 66.48 285 | 83.75 250 | 43.07 294 | 93.00 153 | 75.93 92 | 88.66 91 | 92.62 75 |
|
test_normal | | | 79.81 131 | 78.45 138 | 83.89 104 | 82.70 265 | 65.40 148 | 85.82 185 | 89.48 146 | 69.39 182 | 70.12 244 | 85.66 222 | 57.15 182 | 93.71 123 | 77.08 82 | 88.62 92 | 92.56 76 |
|
nrg030 | | | 83.88 55 | 83.53 55 | 84.96 70 | 86.77 184 | 69.28 80 | 90.46 44 | 92.67 40 | 74.79 89 | 82.95 58 | 91.33 74 | 72.70 28 | 93.09 148 | 80.79 54 | 79.28 207 | 92.50 77 |
|
MG-MVS | | | 83.41 63 | 83.45 56 | 83.28 118 | 92.74 45 | 62.28 218 | 88.17 107 | 89.50 145 | 75.22 83 | 81.49 74 | 92.74 53 | 66.75 70 | 95.11 60 | 72.85 123 | 91.58 60 | 92.45 78 |
|
FIs | | | 82.07 79 | 82.42 67 | 81.04 191 | 88.80 124 | 58.34 244 | 88.26 105 | 93.49 12 | 76.93 49 | 78.47 104 | 91.04 79 | 69.92 48 | 92.34 172 | 69.87 150 | 84.97 130 | 92.44 79 |
|
DI_MVS_plusplus_test | | | 79.89 130 | 78.58 135 | 83.85 105 | 82.89 261 | 65.32 152 | 86.12 174 | 89.55 143 | 69.64 181 | 70.55 235 | 85.82 219 | 57.24 180 | 93.81 112 | 76.85 85 | 88.55 94 | 92.41 80 |
|
FC-MVSNet-test | | | 81.52 89 | 82.02 75 | 80.03 206 | 88.42 137 | 55.97 282 | 87.95 112 | 93.42 14 | 77.10 45 | 77.38 131 | 90.98 84 | 69.96 47 | 91.79 186 | 68.46 160 | 84.50 135 | 92.33 81 |
|
Fast-Effi-MVS+ | | | 80.81 101 | 79.92 100 | 83.47 111 | 88.85 119 | 64.51 173 | 85.53 197 | 89.39 148 | 70.79 162 | 78.49 103 | 85.06 235 | 67.54 65 | 93.58 125 | 67.03 172 | 86.58 117 | 92.32 82 |
|
TranMVSNet+NR-MVSNet | | | 80.84 98 | 80.31 95 | 82.42 157 | 87.85 150 | 62.33 216 | 87.74 117 | 91.33 90 | 80.55 12 | 77.99 122 | 89.86 100 | 65.23 83 | 92.62 162 | 67.05 171 | 75.24 258 | 92.30 83 |
|
ab-mvs | | | 79.51 136 | 78.97 129 | 81.14 189 | 88.46 135 | 60.91 226 | 83.84 233 | 89.24 155 | 70.36 169 | 79.03 94 | 88.87 121 | 63.23 101 | 90.21 229 | 65.12 184 | 82.57 166 | 92.28 84 |
|
CANet_DTU | | | 80.61 108 | 79.87 101 | 82.83 145 | 85.60 196 | 63.17 207 | 87.36 130 | 88.65 180 | 76.37 63 | 75.88 162 | 88.44 133 | 53.51 208 | 93.07 149 | 73.30 120 | 89.74 80 | 92.25 85 |
|
UniMVSNet_NR-MVSNet | | | 81.88 82 | 81.54 80 | 82.92 138 | 88.46 135 | 63.46 197 | 87.13 142 | 92.37 49 | 80.19 15 | 78.38 108 | 89.14 116 | 71.66 35 | 93.05 150 | 70.05 147 | 76.46 240 | 92.25 85 |
|
DU-MVS | | | 81.12 94 | 80.52 93 | 82.90 139 | 87.80 159 | 63.46 197 | 87.02 147 | 91.87 71 | 79.01 26 | 78.38 108 | 89.07 117 | 65.02 85 | 93.05 150 | 70.05 147 | 76.46 240 | 92.20 87 |
|
NR-MVSNet | | | 80.23 121 | 79.38 116 | 82.78 150 | 87.80 159 | 63.34 200 | 86.31 170 | 91.09 97 | 79.01 26 | 72.17 218 | 89.07 117 | 67.20 68 | 92.81 160 | 66.08 178 | 75.65 249 | 92.20 87 |
|
TAPA-MVS | | 73.13 9 | 79.15 145 | 77.94 150 | 82.79 149 | 89.59 92 | 62.99 211 | 88.16 108 | 91.51 85 | 65.77 234 | 77.14 139 | 91.09 77 | 60.91 152 | 93.21 139 | 50.26 277 | 87.05 112 | 92.17 89 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
3Dnovator | | 76.31 5 | 83.38 65 | 82.31 71 | 86.59 44 | 87.94 148 | 72.94 22 | 90.64 39 | 92.14 57 | 77.21 42 | 75.47 170 | 92.83 49 | 58.56 169 | 94.72 77 | 73.24 121 | 92.71 53 | 92.13 90 |
|
MVS_111021_HR | | | 85.14 52 | 84.75 52 | 86.32 49 | 91.65 60 | 72.70 25 | 85.98 177 | 90.33 118 | 76.11 68 | 82.08 68 | 91.61 67 | 71.36 37 | 94.17 94 | 81.02 50 | 92.58 54 | 92.08 91 |
|
MVSFormer | | | 82.85 71 | 82.05 74 | 85.24 63 | 87.35 171 | 70.21 61 | 90.50 42 | 90.38 113 | 68.55 201 | 81.32 75 | 89.47 108 | 61.68 136 | 93.46 130 | 78.98 63 | 90.26 73 | 92.05 92 |
|
jason | | | 81.39 92 | 80.29 96 | 84.70 77 | 86.63 185 | 69.90 68 | 85.95 178 | 86.77 208 | 63.24 255 | 81.07 81 | 89.47 108 | 61.08 150 | 92.15 176 | 78.33 70 | 90.07 77 | 92.05 92 |
jason: jason. |
HyFIR lowres test | | | 77.53 180 | 75.40 199 | 83.94 103 | 89.59 92 | 66.62 128 | 80.36 267 | 88.64 181 | 56.29 309 | 76.45 147 | 85.17 232 | 57.64 175 | 93.28 137 | 61.34 215 | 83.10 159 | 91.91 94 |
|
mvs-test1 | | | 80.88 96 | 79.40 115 | 85.29 61 | 85.13 203 | 69.75 71 | 89.28 67 | 88.10 189 | 74.99 86 | 76.44 150 | 86.72 179 | 57.27 178 | 94.26 90 | 73.53 117 | 83.18 158 | 91.87 95 |
|
XVG-OURS-SEG-HR | | | 80.81 101 | 79.76 104 | 83.96 102 | 85.60 196 | 68.78 88 | 83.54 238 | 90.50 110 | 70.66 166 | 76.71 143 | 91.66 63 | 60.69 155 | 91.26 208 | 76.94 84 | 81.58 175 | 91.83 96 |
|
lupinMVS | | | 81.39 92 | 80.27 97 | 84.76 76 | 87.35 171 | 70.21 61 | 85.55 195 | 86.41 212 | 62.85 261 | 81.32 75 | 88.61 127 | 61.68 136 | 92.24 175 | 78.41 69 | 90.26 73 | 91.83 96 |
|
abl_6 | | | 85.23 50 | 84.95 50 | 86.07 53 | 92.23 53 | 70.48 59 | 90.80 37 | 92.08 58 | 73.51 115 | 85.26 26 | 94.16 22 | 62.75 115 | 95.92 38 | 82.46 46 | 91.30 64 | 91.81 98 |
|
WR-MVS | | | 79.49 138 | 79.22 125 | 80.27 203 | 88.79 125 | 58.35 243 | 85.06 205 | 88.61 182 | 78.56 29 | 77.65 127 | 88.34 135 | 63.81 95 | 90.66 224 | 64.98 187 | 77.22 223 | 91.80 99 |
|
UniMVSNet (Re) | | | 81.60 88 | 81.11 85 | 83.09 126 | 88.38 138 | 64.41 181 | 87.60 119 | 93.02 25 | 78.42 31 | 78.56 101 | 88.16 139 | 69.78 49 | 93.26 138 | 69.58 152 | 76.49 239 | 91.60 100 |
|
UGNet | | | 80.83 100 | 79.59 108 | 84.54 79 | 88.04 145 | 68.09 106 | 89.42 65 | 88.16 186 | 76.95 48 | 76.22 156 | 89.46 110 | 49.30 263 | 93.94 102 | 68.48 159 | 90.31 72 | 91.60 100 |
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 |
XVG-OURS | | | 80.41 113 | 79.23 124 | 83.97 101 | 85.64 195 | 69.02 82 | 83.03 248 | 90.39 112 | 71.09 159 | 77.63 128 | 91.49 71 | 54.62 200 | 91.35 206 | 75.71 96 | 83.47 150 | 91.54 102 |
|
LCM-MVSNet-Re | | | 77.05 191 | 76.94 167 | 77.36 255 | 87.20 177 | 51.60 312 | 80.06 269 | 80.46 279 | 75.20 84 | 67.69 273 | 86.72 179 | 62.48 127 | 88.98 256 | 63.44 194 | 89.25 84 | 91.51 103 |
|
DP-MVS Recon | | | 83.11 68 | 82.09 73 | 86.15 51 | 94.44 11 | 70.92 53 | 88.79 83 | 92.20 54 | 70.53 167 | 79.17 93 | 91.03 81 | 64.12 91 | 96.03 33 | 68.39 161 | 90.14 75 | 91.50 104 |
|
pcd1.5k->3k | | | 34.07 324 | 35.26 324 | 30.50 339 | 86.92 180 | 0.00 360 | 0.00 351 | 91.58 82 | 0.00 355 | 0.00 356 | 0.00 357 | 56.23 186 | 0.00 358 | 0.00 355 | 82.60 165 | 91.49 105 |
|
PS-MVSNAJss | | | 82.07 79 | 81.31 81 | 84.34 87 | 86.51 186 | 67.27 120 | 89.27 68 | 91.51 85 | 71.75 149 | 79.37 91 | 90.22 94 | 63.15 103 | 94.27 87 | 77.69 75 | 82.36 168 | 91.49 105 |
|
HQP_MVS | | | 83.64 59 | 83.14 59 | 85.14 65 | 90.08 80 | 68.71 93 | 91.25 30 | 92.44 45 | 79.12 23 | 78.92 96 | 91.00 82 | 60.42 160 | 95.38 52 | 78.71 65 | 86.32 121 | 91.33 107 |
|
plane_prior5 | | | | | | | | | 92.44 45 | | | | | 95.38 52 | 78.71 65 | 86.32 121 | 91.33 107 |
|
GA-MVS | | | 76.87 194 | 75.17 207 | 81.97 165 | 82.75 263 | 62.58 214 | 81.44 261 | 86.35 215 | 72.16 147 | 74.74 189 | 82.89 257 | 46.20 278 | 92.02 179 | 68.85 157 | 81.09 179 | 91.30 109 |
|
VPA-MVSNet | | | 80.60 109 | 80.55 92 | 80.76 195 | 88.07 144 | 60.80 228 | 86.86 152 | 91.58 82 | 75.67 73 | 80.24 86 | 89.45 112 | 63.34 97 | 90.25 228 | 70.51 145 | 79.22 208 | 91.23 110 |
|
Effi-MVS+-dtu | | | 80.03 127 | 78.57 136 | 84.42 83 | 85.13 203 | 68.74 91 | 88.77 84 | 88.10 189 | 74.99 86 | 74.97 187 | 83.49 254 | 57.27 178 | 93.36 135 | 73.53 117 | 80.88 181 | 91.18 111 |
|
v2v482 | | | 80.23 121 | 79.29 122 | 83.05 129 | 83.62 241 | 64.14 184 | 87.04 146 | 89.97 133 | 73.61 111 | 78.18 118 | 87.22 165 | 61.10 149 | 93.82 111 | 76.11 89 | 76.78 237 | 91.18 111 |
|
HQP4-MVS | | | | | | | | | | | 77.24 135 | | | 95.11 60 | | | 91.03 113 |
|
HQP-MVS | | | 82.61 74 | 82.02 75 | 84.37 84 | 89.33 102 | 66.98 124 | 89.17 70 | 92.19 55 | 76.41 59 | 77.23 136 | 90.23 93 | 60.17 163 | 95.11 60 | 77.47 77 | 85.99 125 | 91.03 113 |
|
RPSCF | | | 73.23 247 | 71.46 246 | 78.54 237 | 82.50 269 | 59.85 233 | 82.18 252 | 82.84 253 | 58.96 290 | 71.15 232 | 89.41 114 | 45.48 285 | 84.77 291 | 58.82 234 | 71.83 284 | 91.02 115 |
|
test_djsdf | | | 80.30 119 | 79.32 118 | 83.27 119 | 83.98 233 | 65.37 151 | 90.50 42 | 90.38 113 | 68.55 201 | 76.19 157 | 88.70 123 | 56.44 185 | 93.46 130 | 78.98 63 | 80.14 194 | 90.97 116 |
|
PCF-MVS | | 73.52 7 | 80.38 117 | 78.84 130 | 85.01 69 | 87.71 164 | 68.99 84 | 83.65 235 | 91.46 88 | 63.00 258 | 77.77 126 | 90.28 91 | 66.10 75 | 95.09 64 | 61.40 213 | 88.22 100 | 90.94 117 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
VPNet | | | 78.69 153 | 78.66 132 | 78.76 233 | 88.31 140 | 55.72 288 | 84.45 220 | 86.63 210 | 76.79 51 | 78.26 115 | 90.55 89 | 59.30 165 | 89.70 236 | 66.63 173 | 77.05 225 | 90.88 118 |
|
CPTT-MVS | | | 83.73 57 | 83.33 58 | 84.92 73 | 93.28 34 | 70.86 54 | 92.09 22 | 90.38 113 | 68.75 198 | 79.57 89 | 92.83 49 | 60.60 158 | 93.04 152 | 80.92 52 | 91.56 61 | 90.86 119 |
|
CLD-MVS | | | 82.31 76 | 81.65 79 | 84.29 88 | 88.47 134 | 67.73 113 | 85.81 186 | 92.35 50 | 75.78 70 | 78.33 110 | 86.58 192 | 64.01 92 | 94.35 84 | 76.05 90 | 87.48 108 | 90.79 120 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
v1192 | | | 79.59 135 | 78.43 141 | 83.07 128 | 83.55 243 | 64.52 171 | 86.93 150 | 90.58 107 | 70.83 161 | 77.78 125 | 85.90 215 | 59.15 166 | 93.94 102 | 73.96 112 | 77.19 224 | 90.76 121 |
|
IterMVS-LS | | | 80.06 126 | 79.38 116 | 82.11 162 | 85.89 191 | 63.20 205 | 86.79 155 | 89.34 149 | 74.19 95 | 75.45 172 | 86.72 179 | 66.62 71 | 92.39 169 | 72.58 128 | 76.86 231 | 90.75 122 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
EI-MVSNet | | | 80.52 112 | 79.98 99 | 82.12 161 | 84.28 213 | 63.19 206 | 86.41 167 | 88.95 169 | 74.18 96 | 78.69 98 | 87.54 156 | 66.62 71 | 92.43 167 | 72.57 129 | 80.57 187 | 90.74 123 |
|
v1921920 | | | 79.22 144 | 78.03 148 | 82.80 147 | 83.30 249 | 63.94 191 | 86.80 154 | 90.33 118 | 69.91 175 | 77.48 130 | 85.53 226 | 58.44 170 | 93.75 118 | 73.60 116 | 76.85 232 | 90.71 124 |
|
v1141 | | | 80.19 123 | 79.31 119 | 82.85 142 | 83.84 236 | 64.12 186 | 87.14 139 | 90.08 129 | 73.13 123 | 78.27 112 | 86.39 198 | 62.67 120 | 93.75 118 | 75.40 101 | 76.83 234 | 90.68 125 |
|
divwei89l23v2f112 | | | 80.19 123 | 79.31 119 | 82.85 142 | 83.84 236 | 64.11 188 | 87.13 142 | 90.08 129 | 73.13 123 | 78.27 112 | 86.39 198 | 62.69 118 | 93.75 118 | 75.40 101 | 76.82 235 | 90.68 125 |
|
v1 | | | 80.19 123 | 79.31 119 | 82.85 142 | 83.83 238 | 64.12 186 | 87.14 139 | 90.07 131 | 73.13 123 | 78.27 112 | 86.38 202 | 62.72 117 | 93.75 118 | 75.41 100 | 76.82 235 | 90.68 125 |
|
QAPM | | | 80.88 96 | 79.50 113 | 85.03 68 | 88.01 147 | 68.97 85 | 91.59 26 | 92.00 63 | 66.63 226 | 75.15 183 | 92.16 54 | 57.70 174 | 95.45 47 | 63.52 193 | 88.76 89 | 90.66 128 |
|
v144192 | | | 79.47 139 | 78.37 142 | 82.78 150 | 83.35 246 | 63.96 190 | 86.96 148 | 90.36 116 | 69.99 174 | 77.50 129 | 85.67 221 | 60.66 156 | 93.77 116 | 74.27 109 | 76.58 238 | 90.62 129 |
|
v1240 | | | 78.99 149 | 77.78 154 | 82.64 154 | 83.21 250 | 63.54 194 | 86.62 161 | 90.30 120 | 69.74 180 | 77.33 132 | 85.68 220 | 57.04 183 | 93.76 117 | 73.13 122 | 76.92 229 | 90.62 129 |
|
v1144 | | | 80.03 127 | 79.03 127 | 83.01 131 | 83.78 239 | 64.51 173 | 87.11 144 | 90.57 108 | 71.96 148 | 78.08 121 | 86.20 206 | 61.41 142 | 93.94 102 | 74.93 105 | 77.23 222 | 90.60 131 |
|
testing_2 | | | 75.73 216 | 73.34 224 | 82.89 141 | 77.37 313 | 65.22 155 | 84.10 230 | 90.54 109 | 69.09 190 | 60.46 309 | 81.15 282 | 40.48 307 | 92.84 159 | 76.36 88 | 80.54 189 | 90.60 131 |
|
1112_ss | | | 77.40 189 | 76.43 174 | 80.32 201 | 89.11 116 | 60.41 231 | 83.65 235 | 87.72 197 | 62.13 269 | 73.05 201 | 86.72 179 | 62.58 124 | 89.97 231 | 62.11 207 | 80.80 183 | 90.59 133 |
|
v1neww | | | 80.40 114 | 79.54 109 | 82.98 133 | 84.10 224 | 64.51 173 | 87.57 121 | 90.22 122 | 73.25 120 | 78.47 104 | 86.65 187 | 62.83 111 | 93.86 108 | 75.72 94 | 77.02 226 | 90.58 134 |
|
v7new | | | 80.40 114 | 79.54 109 | 82.98 133 | 84.10 224 | 64.51 173 | 87.57 121 | 90.22 122 | 73.25 120 | 78.47 104 | 86.65 187 | 62.83 111 | 93.86 108 | 75.72 94 | 77.02 226 | 90.58 134 |
|
v6 | | | 80.40 114 | 79.54 109 | 82.98 133 | 84.09 226 | 64.50 177 | 87.57 121 | 90.22 122 | 73.25 120 | 78.47 104 | 86.63 189 | 62.84 110 | 93.86 108 | 75.73 93 | 77.02 226 | 90.58 134 |
|
CP-MVSNet | | | 78.22 159 | 78.34 143 | 77.84 246 | 87.83 157 | 54.54 293 | 87.94 113 | 91.17 95 | 77.65 33 | 73.48 196 | 88.49 131 | 62.24 132 | 88.43 264 | 62.19 204 | 74.07 266 | 90.55 137 |
|
PS-CasMVS | | | 78.01 167 | 78.09 147 | 77.77 248 | 87.71 164 | 54.39 295 | 88.02 109 | 91.22 92 | 77.50 40 | 73.26 198 | 88.64 126 | 60.73 153 | 88.41 265 | 61.88 208 | 73.88 270 | 90.53 138 |
|
v7 | | | 80.24 120 | 79.26 123 | 83.15 123 | 84.07 230 | 64.94 162 | 87.56 124 | 90.67 103 | 72.26 144 | 78.28 111 | 86.51 196 | 61.45 141 | 94.03 99 | 75.14 104 | 77.41 220 | 90.49 139 |
|
CDS-MVSNet | | | 79.07 147 | 77.70 156 | 83.17 122 | 87.60 166 | 68.23 104 | 84.40 223 | 86.20 216 | 67.49 219 | 76.36 151 | 86.54 194 | 61.54 139 | 90.79 222 | 61.86 209 | 87.33 109 | 90.49 139 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
TAMVS | | | 78.89 151 | 77.51 159 | 83.03 130 | 87.80 159 | 67.79 112 | 84.72 210 | 85.05 227 | 67.63 215 | 76.75 142 | 87.70 150 | 62.25 131 | 90.82 221 | 58.53 237 | 87.13 111 | 90.49 139 |
|
PEN-MVS | | | 77.73 174 | 77.69 157 | 77.84 246 | 87.07 179 | 53.91 297 | 87.91 115 | 91.18 94 | 77.56 37 | 73.14 200 | 88.82 122 | 61.23 146 | 89.17 251 | 59.95 223 | 72.37 279 | 90.43 142 |
|
Test_1112_low_res | | | 76.40 204 | 75.44 197 | 79.27 221 | 89.28 108 | 58.09 246 | 81.69 257 | 87.07 206 | 59.53 287 | 72.48 208 | 86.67 185 | 61.30 144 | 89.33 242 | 60.81 219 | 80.15 193 | 90.41 143 |
|
HY-MVS | | 69.67 12 | 77.95 169 | 77.15 164 | 80.36 199 | 87.57 170 | 60.21 232 | 83.37 246 | 87.78 196 | 66.11 230 | 75.37 175 | 87.06 174 | 63.27 99 | 90.48 226 | 61.38 214 | 82.43 167 | 90.40 144 |
|
CHOSEN 1792x2688 | | | 77.63 179 | 75.69 191 | 83.44 112 | 89.98 82 | 68.58 98 | 78.70 283 | 87.50 201 | 56.38 308 | 75.80 164 | 86.84 175 | 58.67 168 | 91.40 205 | 61.58 212 | 85.75 128 | 90.34 145 |
|
114514_t | | | 80.68 107 | 79.51 112 | 84.20 90 | 94.09 23 | 67.27 120 | 89.64 63 | 91.11 96 | 58.75 293 | 74.08 193 | 90.72 86 | 58.10 172 | 95.04 65 | 69.70 151 | 89.42 83 | 90.30 146 |
|
PVSNet_Blended_VisFu | | | 82.62 73 | 81.83 78 | 84.96 70 | 90.80 71 | 69.76 70 | 88.74 88 | 91.70 78 | 69.39 182 | 78.96 95 | 88.46 132 | 65.47 81 | 94.87 73 | 74.42 107 | 88.57 93 | 90.24 147 |
|
MVS_111021_LR | | | 82.61 74 | 82.11 72 | 84.11 92 | 88.82 122 | 71.58 44 | 85.15 204 | 86.16 217 | 74.69 90 | 80.47 85 | 91.04 79 | 62.29 130 | 90.55 225 | 80.33 57 | 90.08 76 | 90.20 148 |
|
MSLP-MVS++ | | | 85.43 47 | 85.76 41 | 84.45 82 | 91.93 57 | 70.24 60 | 90.71 38 | 92.86 33 | 77.46 41 | 84.22 45 | 92.81 52 | 67.16 69 | 92.94 154 | 80.36 56 | 94.35 40 | 90.16 149 |
|
mvs_tets | | | 79.13 146 | 77.77 155 | 83.22 121 | 84.70 208 | 66.37 132 | 89.17 70 | 90.19 125 | 69.38 184 | 75.40 174 | 89.46 110 | 44.17 289 | 93.15 144 | 76.78 87 | 80.70 185 | 90.14 150 |
|
BH-RMVSNet | | | 79.61 134 | 78.44 140 | 83.14 124 | 89.38 101 | 65.93 138 | 84.95 207 | 87.15 205 | 73.56 113 | 78.19 117 | 89.79 101 | 56.67 184 | 93.36 135 | 59.53 228 | 86.74 115 | 90.13 151 |
|
v7n | | | 78.97 150 | 77.58 158 | 83.14 124 | 83.45 245 | 65.51 146 | 88.32 101 | 91.21 93 | 73.69 110 | 72.41 215 | 86.32 203 | 57.93 173 | 93.81 112 | 69.18 155 | 75.65 249 | 90.11 152 |
|
jajsoiax | | | 79.29 143 | 77.96 149 | 83.27 119 | 84.68 209 | 66.57 130 | 89.25 69 | 90.16 126 | 69.20 188 | 75.46 171 | 89.49 107 | 45.75 283 | 93.13 146 | 76.84 86 | 80.80 183 | 90.11 152 |
|
v148 | | | 78.72 152 | 77.80 153 | 81.47 182 | 82.73 264 | 61.96 221 | 86.30 171 | 88.08 191 | 73.26 119 | 76.18 158 | 85.47 228 | 62.46 128 | 92.36 171 | 71.92 138 | 73.82 271 | 90.09 154 |
|
GBi-Net | | | 78.40 156 | 77.40 160 | 81.40 184 | 87.60 166 | 63.01 208 | 88.39 98 | 89.28 151 | 71.63 151 | 75.34 176 | 87.28 161 | 54.80 194 | 91.11 212 | 62.72 198 | 79.57 202 | 90.09 154 |
|
test1 | | | 78.40 156 | 77.40 160 | 81.40 184 | 87.60 166 | 63.01 208 | 88.39 98 | 89.28 151 | 71.63 151 | 75.34 176 | 87.28 161 | 54.80 194 | 91.11 212 | 62.72 198 | 79.57 202 | 90.09 154 |
|
FMVSNet1 | | | 77.44 187 | 76.12 183 | 81.40 184 | 86.81 183 | 63.01 208 | 88.39 98 | 89.28 151 | 70.49 168 | 74.39 192 | 87.28 161 | 49.06 266 | 91.11 212 | 60.91 217 | 78.52 210 | 90.09 154 |
|
WR-MVS_H | | | 78.51 155 | 78.49 137 | 78.56 236 | 88.02 146 | 56.38 277 | 88.43 94 | 92.67 40 | 77.14 43 | 73.89 194 | 87.55 155 | 66.25 74 | 89.24 244 | 58.92 232 | 73.55 273 | 90.06 158 |
|
DTE-MVSNet | | | 76.99 192 | 76.80 169 | 77.54 252 | 86.24 188 | 53.06 308 | 87.52 126 | 90.66 105 | 77.08 46 | 72.50 206 | 88.67 125 | 60.48 159 | 89.52 238 | 57.33 248 | 70.74 290 | 90.05 159 |
|
view600 | | | 76.20 207 | 75.21 203 | 79.16 225 | 89.64 87 | 55.82 283 | 85.74 187 | 82.06 262 | 73.88 104 | 75.74 165 | 87.85 145 | 51.84 229 | 91.66 197 | 46.75 295 | 83.42 151 | 90.00 160 |
|
view800 | | | 76.20 207 | 75.21 203 | 79.16 225 | 89.64 87 | 55.82 283 | 85.74 187 | 82.06 262 | 73.88 104 | 75.74 165 | 87.85 145 | 51.84 229 | 91.66 197 | 46.75 295 | 83.42 151 | 90.00 160 |
|
conf0.05thres1000 | | | 76.20 207 | 75.21 203 | 79.16 225 | 89.64 87 | 55.82 283 | 85.74 187 | 82.06 262 | 73.88 104 | 75.74 165 | 87.85 145 | 51.84 229 | 91.66 197 | 46.75 295 | 83.42 151 | 90.00 160 |
|
tfpn | | | 76.20 207 | 75.21 203 | 79.16 225 | 89.64 87 | 55.82 283 | 85.74 187 | 82.06 262 | 73.88 104 | 75.74 165 | 87.85 145 | 51.84 229 | 91.66 197 | 46.75 295 | 83.42 151 | 90.00 160 |
|
v8 | | | 79.97 129 | 79.02 128 | 82.80 147 | 84.09 226 | 64.50 177 | 87.96 111 | 90.29 121 | 74.13 98 | 75.24 181 | 86.81 176 | 62.88 108 | 93.89 107 | 74.39 108 | 75.40 254 | 90.00 160 |
|
thres600view7 | | | 76.50 200 | 75.44 197 | 79.68 212 | 89.40 100 | 57.16 261 | 85.53 197 | 83.23 245 | 73.79 109 | 76.26 155 | 87.09 172 | 51.89 225 | 91.89 184 | 48.05 290 | 83.72 148 | 90.00 160 |
|
thres400 | | | 76.50 200 | 75.37 200 | 79.86 208 | 89.13 114 | 57.65 256 | 85.17 202 | 83.60 238 | 73.41 117 | 76.45 147 | 86.39 198 | 52.12 219 | 91.95 180 | 48.33 284 | 83.75 143 | 90.00 160 |
|
OPM-MVS | | | 83.50 61 | 82.95 63 | 85.14 65 | 88.79 125 | 70.95 50 | 89.13 75 | 91.52 84 | 77.55 38 | 80.96 82 | 91.75 62 | 60.71 154 | 94.50 82 | 79.67 61 | 86.51 119 | 89.97 167 |
|
v10 | | | 79.74 133 | 78.67 131 | 82.97 137 | 84.06 231 | 64.95 161 | 87.88 116 | 90.62 106 | 73.11 126 | 75.11 184 | 86.56 193 | 61.46 140 | 94.05 98 | 73.68 113 | 75.55 251 | 89.90 168 |
|
MVSTER | | | 79.01 148 | 77.88 151 | 82.38 158 | 83.07 255 | 64.80 165 | 84.08 231 | 88.95 169 | 69.01 195 | 78.69 98 | 87.17 168 | 54.70 198 | 92.43 167 | 74.69 106 | 80.57 187 | 89.89 169 |
|
ACMP | | 74.13 6 | 81.51 91 | 80.57 91 | 84.36 85 | 89.42 99 | 68.69 96 | 89.97 53 | 91.50 87 | 74.46 92 | 75.04 186 | 90.41 90 | 53.82 206 | 94.54 79 | 77.56 76 | 82.91 160 | 89.86 170 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
LPG-MVS_test | | | 82.08 78 | 81.27 82 | 84.50 80 | 89.23 110 | 68.76 89 | 90.22 50 | 91.94 67 | 75.37 80 | 76.64 145 | 91.51 69 | 54.29 201 | 94.91 69 | 78.44 67 | 83.78 141 | 89.83 171 |
|
LGP-MVS_train | | | | | 84.50 80 | 89.23 110 | 68.76 89 | | 91.94 67 | 75.37 80 | 76.64 145 | 91.51 69 | 54.29 201 | 94.91 69 | 78.44 67 | 83.78 141 | 89.83 171 |
|
V42 | | | 79.38 142 | 78.24 146 | 82.83 145 | 81.10 287 | 65.50 147 | 85.55 195 | 89.82 137 | 71.57 154 | 78.21 116 | 86.12 207 | 60.66 156 | 93.18 143 | 75.64 97 | 75.46 253 | 89.81 173 |
|
tfpn111 | | | 76.54 198 | 75.51 196 | 79.61 215 | 89.52 94 | 56.99 264 | 85.83 182 | 83.23 245 | 73.94 100 | 76.32 152 | 87.12 169 | 51.89 225 | 92.06 178 | 48.04 291 | 83.73 147 | 89.78 174 |
|
conf0.01 | | | 73.67 233 | 72.42 233 | 77.42 253 | 87.85 150 | 53.28 302 | 83.38 239 | 79.08 291 | 68.40 204 | 72.45 209 | 86.08 209 | 50.60 247 | 89.19 245 | 44.25 310 | 79.66 196 | 89.78 174 |
|
conf0.002 | | | 73.67 233 | 72.42 233 | 77.42 253 | 87.85 150 | 53.28 302 | 83.38 239 | 79.08 291 | 68.40 204 | 72.45 209 | 86.08 209 | 50.60 247 | 89.19 245 | 44.25 310 | 79.66 196 | 89.78 174 |
|
conf200view11 | | | 76.55 197 | 75.55 194 | 79.57 218 | 89.52 94 | 56.99 264 | 85.83 182 | 83.23 245 | 73.94 100 | 76.32 152 | 87.12 169 | 51.89 225 | 91.95 180 | 48.33 284 | 83.75 143 | 89.78 174 |
|
MAR-MVS | | | 81.84 83 | 80.70 89 | 85.27 62 | 91.32 63 | 71.53 45 | 89.82 55 | 90.92 99 | 69.77 177 | 78.50 102 | 86.21 205 | 62.36 129 | 94.52 81 | 65.36 183 | 92.05 56 | 89.77 178 |
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 |
tpmp4_e23 | | | 73.45 237 | 71.17 251 | 80.31 202 | 83.55 243 | 59.56 236 | 81.88 253 | 82.33 257 | 57.94 298 | 70.51 237 | 81.62 278 | 51.19 239 | 91.63 201 | 53.96 262 | 77.51 219 | 89.75 179 |
|
v748 | | | 77.97 168 | 76.65 172 | 81.92 167 | 82.29 271 | 63.28 202 | 87.53 125 | 90.35 117 | 73.50 116 | 70.76 234 | 85.55 225 | 58.28 171 | 92.81 160 | 68.81 158 | 72.76 278 | 89.67 180 |
|
anonymousdsp | | | 78.60 154 | 77.15 164 | 82.98 133 | 80.51 293 | 67.08 122 | 87.24 137 | 89.53 144 | 65.66 236 | 75.16 182 | 87.19 167 | 52.52 211 | 92.25 174 | 77.17 81 | 79.34 206 | 89.61 181 |
|
FMVSNet2 | | | 78.20 161 | 77.21 163 | 81.20 187 | 87.60 166 | 62.89 212 | 87.47 128 | 89.02 160 | 71.63 151 | 75.29 180 | 87.28 161 | 54.80 194 | 91.10 215 | 62.38 202 | 79.38 205 | 89.61 181 |
|
FMVSNet3 | | | 77.88 172 | 76.85 168 | 80.97 192 | 86.84 182 | 62.36 215 | 86.52 164 | 88.77 176 | 71.13 157 | 75.34 176 | 86.66 186 | 54.07 204 | 91.10 215 | 62.72 198 | 79.57 202 | 89.45 183 |
|
cascas | | | 76.72 196 | 74.64 210 | 82.99 132 | 85.78 193 | 65.88 140 | 82.33 251 | 89.21 156 | 60.85 277 | 72.74 203 | 81.02 284 | 47.28 272 | 93.75 118 | 67.48 165 | 85.02 129 | 89.34 184 |
|
Fast-Effi-MVS+-dtu | | | 78.02 166 | 76.49 173 | 82.62 155 | 83.16 254 | 66.96 126 | 86.94 149 | 87.45 203 | 72.45 139 | 71.49 229 | 84.17 245 | 54.79 197 | 91.58 203 | 67.61 163 | 80.31 191 | 89.30 185 |
|
IB-MVS | | 68.01 15 | 75.85 215 | 73.36 223 | 83.31 117 | 84.76 207 | 66.03 135 | 83.38 239 | 85.06 226 | 70.21 173 | 69.40 254 | 81.05 283 | 45.76 282 | 94.66 78 | 65.10 185 | 75.49 252 | 89.25 186 |
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 |
thres100view900 | | | 76.50 200 | 75.55 194 | 79.33 220 | 89.52 94 | 56.99 264 | 85.83 182 | 83.23 245 | 73.94 100 | 76.32 152 | 87.12 169 | 51.89 225 | 91.95 180 | 48.33 284 | 83.75 143 | 89.07 187 |
|
tfpn200view9 | | | 76.42 203 | 75.37 200 | 79.55 219 | 89.13 114 | 57.65 256 | 85.17 202 | 83.60 238 | 73.41 117 | 76.45 147 | 86.39 198 | 52.12 219 | 91.95 180 | 48.33 284 | 83.75 143 | 89.07 187 |
|
xiu_mvs_v1_base_debu | | | 80.80 103 | 79.72 105 | 84.03 97 | 87.35 171 | 70.19 63 | 85.56 192 | 88.77 176 | 69.06 191 | 81.83 69 | 88.16 139 | 50.91 241 | 92.85 156 | 78.29 71 | 87.56 105 | 89.06 189 |
|
xiu_mvs_v1_base | | | 80.80 103 | 79.72 105 | 84.03 97 | 87.35 171 | 70.19 63 | 85.56 192 | 88.77 176 | 69.06 191 | 81.83 69 | 88.16 139 | 50.91 241 | 92.85 156 | 78.29 71 | 87.56 105 | 89.06 189 |
|
xiu_mvs_v1_base_debi | | | 80.80 103 | 79.72 105 | 84.03 97 | 87.35 171 | 70.19 63 | 85.56 192 | 88.77 176 | 69.06 191 | 81.83 69 | 88.16 139 | 50.91 241 | 92.85 156 | 78.29 71 | 87.56 105 | 89.06 189 |
|
v52 | | | 77.94 171 | 76.37 176 | 82.67 152 | 79.39 305 | 65.52 144 | 86.43 165 | 89.94 134 | 72.28 142 | 72.15 220 | 84.94 238 | 55.70 189 | 93.44 132 | 73.64 114 | 72.84 277 | 89.06 189 |
|
V4 | | | 77.95 169 | 76.37 176 | 82.67 152 | 79.40 304 | 65.52 144 | 86.43 165 | 89.94 134 | 72.28 142 | 72.14 221 | 84.95 237 | 55.72 188 | 93.44 132 | 73.64 114 | 72.86 276 | 89.05 193 |
|
EPNet_dtu | | | 75.46 219 | 74.86 208 | 77.23 258 | 82.57 268 | 54.60 292 | 86.89 151 | 83.09 250 | 71.64 150 | 66.25 287 | 85.86 217 | 55.99 187 | 88.04 269 | 54.92 258 | 86.55 118 | 89.05 193 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
pm-mvs1 | | | 77.25 190 | 76.68 171 | 78.93 230 | 84.22 216 | 58.62 241 | 86.41 167 | 88.36 185 | 71.37 156 | 73.31 197 | 88.01 143 | 61.22 147 | 89.15 252 | 64.24 191 | 73.01 275 | 89.03 195 |
|
PVSNet_Blended | | | 80.98 95 | 80.34 94 | 82.90 139 | 88.85 119 | 65.40 148 | 84.43 221 | 92.00 63 | 67.62 216 | 78.11 119 | 85.05 236 | 66.02 78 | 94.27 87 | 71.52 140 | 89.50 81 | 89.01 196 |
|
PAPM | | | 77.68 176 | 76.40 175 | 81.51 181 | 87.29 176 | 61.85 222 | 83.78 234 | 89.59 142 | 64.74 243 | 71.23 230 | 88.70 123 | 62.59 123 | 93.66 124 | 52.66 268 | 87.03 113 | 89.01 196 |
|
WTY-MVS | | | 75.65 217 | 75.68 192 | 75.57 275 | 86.40 187 | 56.82 268 | 77.92 289 | 82.40 256 | 65.10 240 | 76.18 158 | 87.72 149 | 63.13 106 | 80.90 304 | 60.31 221 | 81.96 170 | 89.00 198 |
|
æ— å…ˆéªŒ | | | | | | | | 87.48 127 | 88.98 166 | 60.00 283 | | | | 94.12 95 | 67.28 167 | | 88.97 199 |
|
GSMVS | | | | | | | | | | | | | | | | | 88.96 200 |
|
sam_mvs1 | | | | | | | | | | | | | 51.32 237 | | | | 88.96 200 |
|
ACMM | | 73.20 8 | 80.78 106 | 79.84 102 | 83.58 109 | 89.31 107 | 68.37 100 | 89.99 52 | 91.60 81 | 70.28 171 | 77.25 134 | 89.66 103 | 53.37 209 | 93.53 128 | 74.24 110 | 82.85 161 | 88.85 202 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
pmmvs6 | | | 74.69 223 | 73.39 222 | 78.61 235 | 81.38 282 | 57.48 259 | 86.64 160 | 87.95 193 | 64.99 242 | 70.18 241 | 86.61 190 | 50.43 254 | 89.52 238 | 62.12 206 | 70.18 292 | 88.83 203 |
|
原ACMM1 | | | | | 84.35 86 | 93.01 41 | 68.79 87 | | 92.44 45 | 63.96 253 | 81.09 80 | 91.57 68 | 66.06 77 | 95.45 47 | 67.19 169 | 94.82 31 | 88.81 204 |
|
CNLPA | | | 78.08 164 | 76.79 170 | 81.97 165 | 90.40 74 | 71.07 48 | 87.59 120 | 84.55 230 | 66.03 233 | 72.38 216 | 89.64 104 | 57.56 176 | 86.04 282 | 59.61 226 | 83.35 155 | 88.79 205 |
|
K. test v3 | | | 71.19 260 | 68.51 266 | 79.21 223 | 83.04 257 | 57.78 255 | 84.35 224 | 76.91 308 | 72.90 131 | 62.99 304 | 82.86 258 | 39.27 311 | 91.09 217 | 61.65 211 | 52.66 335 | 88.75 206 |
|
旧先验1 | | | | | | 91.96 56 | 65.79 142 | | 86.37 214 | | | 93.08 45 | 69.31 54 | | | 92.74 52 | 88.74 207 |
|
PatchmatchNet | | | 73.12 248 | 71.33 248 | 78.49 239 | 83.18 252 | 60.85 227 | 79.63 273 | 78.57 299 | 64.13 249 | 71.73 225 | 79.81 295 | 51.20 238 | 85.97 283 | 57.40 247 | 76.36 242 | 88.66 208 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
SixPastTwentyTwo | | | 73.37 243 | 71.26 250 | 79.70 211 | 85.08 205 | 57.89 252 | 85.57 191 | 83.56 240 | 71.03 160 | 65.66 289 | 85.88 216 | 42.10 301 | 92.57 164 | 59.11 231 | 63.34 317 | 88.65 209 |
|
v18 | | | 77.67 178 | 76.35 180 | 81.64 177 | 84.09 226 | 64.47 179 | 87.27 135 | 89.01 162 | 72.59 138 | 69.39 255 | 82.04 269 | 62.85 109 | 91.80 185 | 72.72 125 | 67.20 302 | 88.63 210 |
|
v17 | | | 77.68 176 | 76.35 180 | 81.69 174 | 84.15 221 | 64.65 168 | 87.33 132 | 88.99 164 | 72.70 136 | 69.25 259 | 82.07 268 | 62.82 113 | 91.79 186 | 72.69 127 | 67.15 303 | 88.63 210 |
|
v16 | | | 77.69 175 | 76.36 179 | 81.68 175 | 84.15 221 | 64.63 170 | 87.33 132 | 88.99 164 | 72.69 137 | 69.31 258 | 82.08 267 | 62.80 114 | 91.79 186 | 72.70 126 | 67.23 301 | 88.63 210 |
|
v15 | | | 77.51 183 | 76.12 183 | 81.66 176 | 84.09 226 | 64.65 168 | 87.14 139 | 88.96 168 | 72.76 134 | 68.90 260 | 81.91 276 | 62.74 116 | 91.73 190 | 72.32 131 | 66.29 308 | 88.61 213 |
|
V14 | | | 77.52 181 | 76.12 183 | 81.70 173 | 84.15 221 | 64.77 166 | 87.21 138 | 88.95 169 | 72.80 133 | 68.79 261 | 81.94 275 | 62.69 118 | 91.72 192 | 72.31 132 | 66.27 309 | 88.60 214 |
|
V9 | | | 77.52 181 | 76.11 186 | 81.73 172 | 84.19 220 | 64.89 163 | 87.26 136 | 88.94 172 | 72.87 132 | 68.65 264 | 81.96 274 | 62.65 121 | 91.72 192 | 72.27 133 | 66.24 310 | 88.60 214 |
|
PS-MVSNAJ | | | 81.69 85 | 81.02 87 | 83.70 106 | 89.51 97 | 68.21 105 | 84.28 226 | 90.09 128 | 70.79 162 | 81.26 79 | 85.62 224 | 63.15 103 | 94.29 85 | 75.62 98 | 88.87 87 | 88.59 216 |
|
v12 | | | 77.51 183 | 76.09 187 | 81.76 171 | 84.22 216 | 64.99 160 | 87.30 134 | 88.93 173 | 72.92 129 | 68.48 268 | 81.97 272 | 62.54 125 | 91.70 195 | 72.24 134 | 66.21 312 | 88.58 217 |
|
xiu_mvs_v2_base | | | 81.69 85 | 81.05 86 | 83.60 108 | 89.15 113 | 68.03 108 | 84.46 219 | 90.02 132 | 70.67 165 | 81.30 78 | 86.53 195 | 63.17 102 | 94.19 92 | 75.60 99 | 88.54 95 | 88.57 218 |
|
v13 | | | 77.50 185 | 76.07 188 | 81.77 169 | 84.23 215 | 65.07 159 | 87.34 131 | 88.91 174 | 72.92 129 | 68.35 269 | 81.97 272 | 62.53 126 | 91.69 196 | 72.20 135 | 66.22 311 | 88.56 219 |
|
v11 | | | 77.45 186 | 76.06 189 | 81.59 180 | 84.22 216 | 64.52 171 | 87.11 144 | 89.02 160 | 72.76 134 | 68.76 262 | 81.90 277 | 62.09 134 | 91.71 194 | 71.98 136 | 66.73 304 | 88.56 219 |
|
DWT-MVSNet_test | | | 73.70 231 | 71.86 242 | 79.21 223 | 82.91 260 | 58.94 239 | 82.34 250 | 82.17 259 | 65.21 238 | 71.05 233 | 78.31 301 | 44.21 288 | 90.17 230 | 63.29 196 | 77.28 221 | 88.53 221 |
|
CostFormer | | | 75.24 222 | 73.90 220 | 79.27 221 | 82.65 267 | 58.27 245 | 80.80 262 | 82.73 254 | 61.57 272 | 75.33 179 | 83.13 256 | 55.52 190 | 91.07 218 | 64.98 187 | 78.34 214 | 88.45 222 |
|
lessismore_v0 | | | | | 78.97 229 | 81.01 288 | 57.15 262 | | 65.99 342 | | 61.16 307 | 82.82 259 | 39.12 312 | 91.34 207 | 59.67 225 | 46.92 339 | 88.43 223 |
|
OpenMVS | | 72.83 10 | 79.77 132 | 78.33 144 | 84.09 93 | 85.17 200 | 69.91 67 | 90.57 40 | 90.97 98 | 66.70 222 | 72.17 218 | 91.91 59 | 54.70 198 | 93.96 100 | 61.81 210 | 90.95 67 | 88.41 224 |
|
OurMVSNet-221017-0 | | | 74.26 227 | 72.42 233 | 79.80 210 | 83.76 240 | 59.59 234 | 85.92 180 | 86.64 209 | 66.39 228 | 66.96 280 | 87.58 153 | 39.46 310 | 91.60 202 | 65.76 181 | 69.27 294 | 88.22 225 |
|
LS3D | | | 76.95 193 | 74.82 209 | 83.37 116 | 90.45 72 | 67.36 119 | 89.15 74 | 86.94 207 | 61.87 271 | 69.52 253 | 90.61 88 | 51.71 234 | 94.53 80 | 46.38 302 | 86.71 116 | 88.21 226 |
|
PatchFormer-LS_test | | | 74.50 224 | 73.05 226 | 78.86 231 | 82.95 259 | 59.55 237 | 81.65 258 | 82.30 258 | 67.44 220 | 71.62 227 | 78.15 303 | 52.34 215 | 88.92 260 | 65.05 186 | 75.90 246 | 88.12 227 |
|
XVG-ACMP-BASELINE | | | 76.11 212 | 74.27 217 | 81.62 178 | 83.20 251 | 64.67 167 | 83.60 237 | 89.75 139 | 69.75 178 | 71.85 224 | 87.09 172 | 32.78 326 | 92.11 177 | 69.99 149 | 80.43 190 | 88.09 228 |
|
Patchmatch-test1 | | | 73.49 236 | 71.85 243 | 78.41 240 | 84.05 232 | 62.17 219 | 79.96 271 | 79.29 290 | 66.30 229 | 72.38 216 | 79.58 296 | 51.95 224 | 85.08 289 | 55.46 256 | 77.67 218 | 87.99 229 |
|
tpm2 | | | 73.26 246 | 71.46 246 | 78.63 234 | 83.34 247 | 56.71 271 | 80.65 265 | 80.40 280 | 56.63 307 | 73.55 195 | 82.02 270 | 51.80 233 | 91.24 209 | 56.35 253 | 78.42 213 | 87.95 230 |
|
MDTV_nov1_ep13_2view | | | | | | | 37.79 342 | 75.16 302 | | 55.10 312 | 66.53 284 | | 49.34 262 | | 53.98 261 | | 87.94 231 |
|
Patchmatch-test | | | 64.82 294 | 63.24 292 | 69.57 306 | 79.42 303 | 49.82 323 | 63.49 336 | 69.05 338 | 51.98 326 | 59.95 312 | 80.13 291 | 50.91 241 | 70.98 339 | 40.66 324 | 73.57 272 | 87.90 232 |
|
PLC | | 70.83 11 | 78.05 165 | 76.37 176 | 83.08 127 | 91.88 59 | 67.80 111 | 88.19 106 | 89.46 147 | 64.33 248 | 69.87 250 | 88.38 134 | 53.66 207 | 93.58 125 | 58.86 233 | 82.73 163 | 87.86 233 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
tpm | | | 72.37 255 | 71.71 245 | 74.35 285 | 82.19 272 | 52.00 309 | 79.22 278 | 77.29 306 | 64.56 245 | 72.95 202 | 83.68 253 | 51.35 236 | 83.26 298 | 58.33 239 | 75.80 247 | 87.81 234 |
|
Patchmatch-RL test | | | 70.24 269 | 67.78 278 | 77.61 250 | 77.43 312 | 59.57 235 | 71.16 312 | 70.33 331 | 62.94 260 | 68.65 264 | 72.77 323 | 50.62 246 | 85.49 286 | 69.58 152 | 66.58 306 | 87.77 235 |
|
F-COLMAP | | | 76.38 205 | 74.33 216 | 82.50 156 | 89.28 108 | 66.95 127 | 88.41 97 | 89.03 159 | 64.05 250 | 66.83 281 | 88.61 127 | 46.78 275 | 92.89 155 | 57.48 245 | 78.55 209 | 87.67 236 |
|
Baseline_NR-MVSNet | | | 78.15 163 | 78.33 144 | 77.61 250 | 85.79 192 | 56.21 280 | 86.78 156 | 85.76 221 | 73.60 112 | 77.93 123 | 87.57 154 | 65.02 85 | 88.99 255 | 67.14 170 | 75.33 255 | 87.63 237 |
|
ACMH+ | | 68.96 14 | 76.01 213 | 74.01 218 | 82.03 164 | 88.60 130 | 65.31 153 | 88.86 80 | 87.55 199 | 70.25 172 | 67.75 272 | 87.47 158 | 41.27 304 | 93.19 142 | 58.37 238 | 75.94 245 | 87.60 238 |
|
1314 | | | 76.53 199 | 75.30 202 | 80.21 204 | 83.93 234 | 62.32 217 | 84.66 211 | 88.81 175 | 60.23 281 | 70.16 243 | 84.07 247 | 55.30 192 | 90.73 223 | 67.37 166 | 83.21 157 | 87.59 239 |
|
API-MVS | | | 81.99 81 | 81.23 83 | 84.26 89 | 90.94 67 | 70.18 66 | 91.10 33 | 89.32 150 | 71.51 155 | 78.66 100 | 88.28 137 | 65.26 82 | 95.10 63 | 64.74 189 | 91.23 65 | 87.51 240 |
|
AdaColmap | | | 80.58 111 | 79.42 114 | 84.06 94 | 93.09 40 | 68.91 86 | 89.36 66 | 88.97 167 | 69.27 186 | 75.70 169 | 89.69 102 | 57.20 181 | 95.77 39 | 63.06 197 | 88.41 98 | 87.50 241 |
|
PVSNet_BlendedMVS | | | 80.60 109 | 80.02 98 | 82.36 159 | 88.85 119 | 65.40 148 | 86.16 173 | 92.00 63 | 69.34 185 | 78.11 119 | 86.09 208 | 66.02 78 | 94.27 87 | 71.52 140 | 82.06 169 | 87.39 242 |
|
sss | | | 73.60 235 | 73.64 221 | 73.51 290 | 82.80 262 | 55.01 290 | 76.12 295 | 81.69 268 | 62.47 266 | 74.68 190 | 85.85 218 | 57.32 177 | 78.11 316 | 60.86 218 | 80.93 180 | 87.39 242 |
|
semantic-postprocess | | | | | 80.11 205 | 82.69 266 | 64.85 164 | | 83.47 242 | 69.16 189 | 70.49 238 | 84.15 246 | 50.83 245 | 88.15 267 | 69.23 154 | 72.14 282 | 87.34 244 |
|
PVSNet | | 64.34 18 | 72.08 256 | 70.87 254 | 75.69 273 | 86.21 189 | 56.44 275 | 74.37 307 | 80.73 276 | 62.06 270 | 70.17 242 | 82.23 265 | 42.86 296 | 83.31 297 | 54.77 259 | 84.45 137 | 87.32 245 |
|
æ–°å‡ ä½•1 | | | | | 83.42 113 | 93.13 37 | 70.71 56 | | 85.48 222 | 57.43 302 | 81.80 72 | 91.98 58 | 63.28 98 | 92.27 173 | 64.60 190 | 92.99 49 | 87.27 246 |
|
1121 | | | 80.84 98 | 79.77 103 | 84.05 95 | 93.11 39 | 70.78 55 | 84.66 211 | 85.42 223 | 57.37 303 | 81.76 73 | 92.02 57 | 63.41 96 | 94.12 95 | 67.28 167 | 92.93 50 | 87.26 247 |
|
TR-MVS | | | 77.44 187 | 76.18 182 | 81.20 187 | 88.24 141 | 63.24 203 | 84.61 215 | 86.40 213 | 67.55 218 | 77.81 124 | 86.48 197 | 54.10 203 | 93.15 144 | 57.75 244 | 82.72 164 | 87.20 248 |
|
TransMVSNet (Re) | | | 75.39 221 | 74.56 212 | 77.86 245 | 85.50 198 | 57.10 263 | 86.78 156 | 86.09 219 | 72.17 146 | 71.53 228 | 87.34 160 | 63.01 107 | 89.31 243 | 56.84 251 | 61.83 320 | 87.17 249 |
|
ACMH | | 67.68 16 | 75.89 214 | 73.93 219 | 81.77 169 | 88.71 128 | 66.61 129 | 88.62 90 | 89.01 162 | 69.81 176 | 66.78 282 | 86.70 184 | 41.95 303 | 91.51 204 | 55.64 255 | 78.14 215 | 87.17 249 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
EPMVS | | | 69.02 275 | 68.16 270 | 71.59 296 | 79.61 301 | 49.80 324 | 77.40 291 | 66.93 341 | 62.82 262 | 70.01 245 | 79.05 297 | 45.79 281 | 77.86 318 | 56.58 252 | 75.26 257 | 87.13 251 |
|
CR-MVSNet | | | 73.37 243 | 71.27 249 | 79.67 213 | 81.32 285 | 65.19 156 | 75.92 297 | 80.30 281 | 59.92 284 | 72.73 204 | 81.19 280 | 52.50 212 | 86.69 276 | 59.84 224 | 77.71 216 | 87.11 252 |
|
RPMNet | | | 71.62 257 | 68.94 264 | 79.67 213 | 81.32 285 | 65.19 156 | 75.92 297 | 78.30 301 | 57.60 301 | 72.73 204 | 76.45 313 | 52.30 216 | 86.69 276 | 48.14 289 | 77.71 216 | 87.11 252 |
|
XXY-MVS | | | 75.41 220 | 75.56 193 | 74.96 279 | 83.59 242 | 57.82 254 | 80.59 266 | 83.87 236 | 66.54 227 | 74.93 188 | 88.31 136 | 63.24 100 | 80.09 308 | 62.16 205 | 76.85 232 | 86.97 254 |
|
tpmrst | | | 72.39 253 | 72.13 240 | 73.18 292 | 80.54 292 | 49.91 322 | 79.91 272 | 79.08 291 | 63.11 256 | 71.69 226 | 79.95 292 | 55.32 191 | 82.77 299 | 65.66 182 | 73.89 269 | 86.87 255 |
|
thres200 | | | 75.55 218 | 74.47 214 | 78.82 232 | 87.78 162 | 57.85 253 | 83.07 247 | 83.51 241 | 72.44 141 | 75.84 163 | 84.42 244 | 52.08 221 | 91.75 189 | 47.41 293 | 83.64 149 | 86.86 256 |
|
ITE_SJBPF | | | | | 78.22 242 | 81.77 276 | 60.57 229 | | 83.30 244 | 69.25 187 | 67.54 274 | 87.20 166 | 36.33 322 | 87.28 274 | 54.34 260 | 74.62 263 | 86.80 257 |
|
test222 | | | | | | 91.50 61 | 68.26 103 | 84.16 228 | 83.20 249 | 54.63 314 | 79.74 87 | 91.63 66 | 58.97 167 | | | 91.42 62 | 86.77 258 |
|
MIMVSNet | | | 70.69 264 | 69.30 260 | 74.88 280 | 84.52 210 | 56.35 278 | 75.87 299 | 79.42 289 | 64.59 244 | 67.76 271 | 82.41 262 | 41.10 305 | 81.54 303 | 46.64 301 | 81.34 177 | 86.75 259 |
|
BH-untuned | | | 79.47 139 | 78.60 133 | 82.05 163 | 89.19 112 | 65.91 139 | 86.07 176 | 88.52 183 | 72.18 145 | 75.42 173 | 87.69 151 | 61.15 148 | 93.54 127 | 60.38 220 | 86.83 114 | 86.70 260 |
|
LTVRE_ROB | | 69.57 13 | 76.25 206 | 74.54 213 | 81.41 183 | 88.60 130 | 64.38 182 | 79.24 277 | 89.12 158 | 70.76 164 | 69.79 252 | 87.86 144 | 49.09 265 | 93.20 141 | 56.21 254 | 80.16 192 | 86.65 261 |
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 |
testdata | | | | | 79.97 207 | 90.90 68 | 64.21 183 | | 84.71 228 | 59.27 289 | 85.40 24 | 92.91 46 | 62.02 135 | 89.08 253 | 68.95 156 | 91.37 63 | 86.63 262 |
|
thresconf0.02 | | | 73.39 239 | 72.42 233 | 76.31 264 | 87.85 150 | 53.28 302 | 83.38 239 | 79.08 291 | 68.40 204 | 72.45 209 | 86.08 209 | 50.60 247 | 89.19 245 | 44.25 310 | 79.66 196 | 86.48 263 |
|
tfpn_n400 | | | 73.39 239 | 72.42 233 | 76.31 264 | 87.85 150 | 53.28 302 | 83.38 239 | 79.08 291 | 68.40 204 | 72.45 209 | 86.08 209 | 50.60 247 | 89.19 245 | 44.25 310 | 79.66 196 | 86.48 263 |
|
tfpnconf | | | 73.39 239 | 72.42 233 | 76.31 264 | 87.85 150 | 53.28 302 | 83.38 239 | 79.08 291 | 68.40 204 | 72.45 209 | 86.08 209 | 50.60 247 | 89.19 245 | 44.25 310 | 79.66 196 | 86.48 263 |
|
tfpnview11 | | | 73.39 239 | 72.42 233 | 76.31 264 | 87.85 150 | 53.28 302 | 83.38 239 | 79.08 291 | 68.40 204 | 72.45 209 | 86.08 209 | 50.60 247 | 89.19 245 | 44.25 310 | 79.66 196 | 86.48 263 |
|
MIMVSNet1 | | | 68.58 277 | 66.78 282 | 73.98 288 | 80.07 297 | 51.82 310 | 80.77 263 | 84.37 231 | 64.40 247 | 59.75 313 | 82.16 266 | 36.47 321 | 83.63 295 | 42.73 320 | 70.33 291 | 86.48 263 |
|
tfpnnormal | | | 74.39 225 | 73.16 225 | 78.08 243 | 86.10 190 | 58.05 247 | 84.65 214 | 87.53 200 | 70.32 170 | 71.22 231 | 85.63 223 | 54.97 193 | 89.86 232 | 43.03 319 | 75.02 259 | 86.32 268 |
|
tpm cat1 | | | 70.57 265 | 68.31 268 | 77.35 256 | 82.41 270 | 57.95 251 | 78.08 288 | 80.22 284 | 52.04 325 | 68.54 267 | 77.66 308 | 52.00 223 | 87.84 271 | 51.77 269 | 72.07 283 | 86.25 269 |
|
CVMVSNet | | | 72.99 250 | 72.58 230 | 74.25 286 | 84.28 213 | 50.85 318 | 86.41 167 | 83.45 243 | 44.56 334 | 73.23 199 | 87.54 156 | 49.38 261 | 85.70 284 | 65.90 179 | 78.44 212 | 86.19 270 |
|
tfpn1000 | | | 73.44 238 | 72.49 231 | 76.29 268 | 87.81 158 | 53.69 299 | 84.05 232 | 78.81 298 | 67.99 213 | 72.09 222 | 86.27 204 | 49.95 258 | 89.04 254 | 44.09 316 | 81.38 176 | 86.15 271 |
|
AllTest | | | 70.96 262 | 68.09 272 | 79.58 216 | 85.15 201 | 63.62 192 | 84.58 216 | 79.83 286 | 62.31 267 | 60.32 310 | 86.73 177 | 32.02 327 | 88.96 258 | 50.28 275 | 71.57 286 | 86.15 271 |
|
TestCases | | | | | 79.58 216 | 85.15 201 | 63.62 192 | | 79.83 286 | 62.31 267 | 60.32 310 | 86.73 177 | 32.02 327 | 88.96 258 | 50.28 275 | 71.57 286 | 86.15 271 |
|
test-LLR | | | 72.94 251 | 72.43 232 | 74.48 283 | 81.35 283 | 58.04 248 | 78.38 284 | 77.46 304 | 66.66 223 | 69.95 248 | 79.00 299 | 48.06 269 | 79.24 310 | 66.13 175 | 84.83 131 | 86.15 271 |
|
test-mter | | | 71.41 259 | 70.39 257 | 74.48 283 | 81.35 283 | 58.04 248 | 78.38 284 | 77.46 304 | 60.32 280 | 69.95 248 | 79.00 299 | 36.08 323 | 79.24 310 | 66.13 175 | 84.83 131 | 86.15 271 |
|
IterMVS | | | 74.29 226 | 72.94 227 | 78.35 241 | 81.53 279 | 63.49 196 | 81.58 259 | 82.49 255 | 68.06 212 | 69.99 247 | 83.69 252 | 51.66 235 | 85.54 285 | 65.85 180 | 71.64 285 | 86.01 276 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
DP-MVS | | | 76.78 195 | 74.57 211 | 83.42 113 | 93.29 33 | 69.46 78 | 88.55 93 | 83.70 237 | 63.98 252 | 70.20 240 | 88.89 120 | 54.01 205 | 94.80 75 | 46.66 299 | 81.88 172 | 86.01 276 |
|
Patchmtry | | | 70.74 263 | 69.16 262 | 75.49 276 | 80.72 289 | 54.07 296 | 74.94 306 | 80.30 281 | 58.34 294 | 70.01 245 | 81.19 280 | 52.50 212 | 86.54 278 | 53.37 265 | 71.09 288 | 85.87 278 |
|
ambc | | | | | 75.24 278 | 73.16 328 | 50.51 320 | 63.05 337 | 87.47 202 | | 64.28 297 | 77.81 307 | 17.80 345 | 89.73 235 | 57.88 243 | 60.64 324 | 85.49 279 |
|
UnsupCasMVSNet_eth | | | 67.33 283 | 65.99 284 | 71.37 298 | 73.48 326 | 51.47 314 | 75.16 302 | 85.19 225 | 65.20 239 | 60.78 308 | 80.93 287 | 42.35 298 | 77.20 320 | 57.12 249 | 53.69 334 | 85.44 280 |
|
PatchT | | | 68.46 279 | 67.85 275 | 70.29 304 | 80.70 290 | 43.93 332 | 72.47 310 | 74.88 316 | 60.15 282 | 70.55 235 | 76.57 312 | 49.94 259 | 81.59 302 | 50.58 273 | 74.83 261 | 85.34 281 |
|
tfpn_ndepth | | | 73.70 231 | 72.75 228 | 76.52 262 | 87.78 162 | 54.92 291 | 84.32 225 | 80.28 283 | 67.57 217 | 72.50 206 | 84.82 239 | 50.12 256 | 89.44 241 | 45.73 305 | 81.66 174 | 85.20 282 |
|
ADS-MVSNet2 | | | 66.20 291 | 63.33 291 | 74.82 281 | 79.92 298 | 58.75 240 | 67.55 329 | 75.19 314 | 53.37 321 | 65.25 292 | 75.86 314 | 42.32 299 | 80.53 306 | 41.57 322 | 68.91 296 | 85.18 283 |
|
ADS-MVSNet | | | 64.36 296 | 62.88 295 | 68.78 311 | 79.92 298 | 47.17 327 | 67.55 329 | 71.18 330 | 53.37 321 | 65.25 292 | 75.86 314 | 42.32 299 | 73.99 333 | 41.57 322 | 68.91 296 | 85.18 283 |
|
FMVSNet5 | | | 69.50 273 | 67.96 273 | 74.15 287 | 82.97 258 | 55.35 289 | 80.01 270 | 82.12 261 | 62.56 265 | 63.02 302 | 81.53 279 | 36.92 320 | 81.92 301 | 48.42 283 | 74.06 267 | 85.17 285 |
|
pmmvs5 | | | 71.55 258 | 70.20 258 | 75.61 274 | 77.83 310 | 56.39 276 | 81.74 256 | 80.89 273 | 57.76 299 | 67.46 275 | 84.49 243 | 49.26 264 | 85.32 288 | 57.08 250 | 75.29 256 | 85.11 286 |
|
CMPMVS | | 51.72 21 | 70.19 270 | 68.16 270 | 76.28 269 | 73.15 329 | 57.55 258 | 79.47 275 | 83.92 235 | 48.02 332 | 56.48 324 | 84.81 240 | 43.13 293 | 86.42 280 | 62.67 201 | 81.81 173 | 84.89 287 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
testgi | | | 66.67 287 | 66.53 283 | 67.08 314 | 75.62 321 | 41.69 337 | 75.93 296 | 76.50 309 | 66.11 230 | 65.20 294 | 86.59 191 | 35.72 324 | 74.71 329 | 43.71 317 | 73.38 274 | 84.84 288 |
|
MSDG | | | 73.36 245 | 70.99 252 | 80.49 197 | 84.51 211 | 65.80 141 | 80.71 264 | 86.13 218 | 65.70 235 | 65.46 290 | 83.74 251 | 44.60 286 | 90.91 220 | 51.13 272 | 76.89 230 | 84.74 289 |
|
pmmvs4 | | | 74.03 229 | 71.91 241 | 80.39 198 | 81.96 274 | 68.32 101 | 81.45 260 | 82.14 260 | 59.32 288 | 69.87 250 | 85.13 233 | 52.40 214 | 88.13 268 | 60.21 222 | 74.74 262 | 84.73 290 |
|
gg-mvs-nofinetune | | | 69.95 271 | 67.96 273 | 75.94 271 | 83.07 255 | 54.51 294 | 77.23 292 | 70.29 332 | 63.11 256 | 70.32 239 | 62.33 335 | 43.62 291 | 88.69 262 | 53.88 263 | 87.76 102 | 84.62 291 |
|
BH-w/o | | | 78.21 160 | 77.33 162 | 80.84 193 | 88.81 123 | 65.13 158 | 84.87 208 | 87.85 195 | 69.75 178 | 74.52 191 | 84.74 242 | 61.34 143 | 93.11 147 | 58.24 240 | 85.84 127 | 84.27 292 |
|
MVS | | | 78.19 162 | 76.99 166 | 81.78 168 | 85.66 194 | 66.99 123 | 84.66 211 | 90.47 111 | 55.08 313 | 72.02 223 | 85.27 231 | 63.83 94 | 94.11 97 | 66.10 177 | 89.80 79 | 84.24 293 |
|
COLMAP_ROB | | 66.92 17 | 73.01 249 | 70.41 256 | 80.81 194 | 87.13 178 | 65.63 143 | 88.30 102 | 84.19 234 | 62.96 259 | 63.80 301 | 87.69 151 | 38.04 316 | 92.56 165 | 46.66 299 | 74.91 260 | 84.24 293 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
new-patchmatchnet | | | 61.73 299 | 61.73 299 | 61.70 322 | 72.74 330 | 24.50 353 | 69.16 322 | 78.03 302 | 61.40 273 | 56.72 323 | 75.53 316 | 38.42 314 | 76.48 323 | 45.95 304 | 57.67 327 | 84.13 295 |
|
TESTMET0.1,1 | | | 69.89 272 | 69.00 263 | 72.55 293 | 79.27 307 | 56.85 267 | 78.38 284 | 74.71 320 | 57.64 300 | 68.09 270 | 77.19 310 | 37.75 317 | 76.70 321 | 63.92 192 | 84.09 139 | 84.10 296 |
|
tpmvs | | | 71.09 261 | 69.29 261 | 76.49 263 | 82.04 273 | 56.04 281 | 78.92 281 | 81.37 272 | 64.05 250 | 67.18 279 | 78.28 302 | 49.74 260 | 89.77 233 | 49.67 280 | 72.37 279 | 83.67 297 |
|
test20.03 | | | 67.45 282 | 66.95 281 | 68.94 308 | 75.48 323 | 44.84 330 | 77.50 290 | 77.67 303 | 66.66 223 | 63.01 303 | 83.80 249 | 47.02 273 | 78.40 314 | 42.53 321 | 68.86 298 | 83.58 298 |
|
test0.0.03 1 | | | 68.00 280 | 67.69 279 | 68.90 309 | 77.55 311 | 47.43 326 | 75.70 300 | 72.95 327 | 66.66 223 | 66.56 283 | 82.29 264 | 48.06 269 | 75.87 325 | 44.97 309 | 74.51 264 | 83.41 299 |
|
Anonymous20231206 | | | 68.60 276 | 67.80 277 | 71.02 302 | 80.23 296 | 50.75 319 | 78.30 287 | 80.47 278 | 56.79 306 | 66.11 288 | 82.63 261 | 46.35 276 | 78.95 312 | 43.62 318 | 75.70 248 | 83.36 300 |
|
EU-MVSNet | | | 68.53 278 | 67.61 280 | 71.31 301 | 78.51 309 | 47.01 328 | 84.47 217 | 84.27 233 | 42.27 335 | 66.44 286 | 84.79 241 | 40.44 308 | 83.76 293 | 58.76 235 | 68.54 300 | 83.17 301 |
|
dp | | | 66.80 285 | 65.43 285 | 70.90 303 | 79.74 300 | 48.82 325 | 75.12 304 | 74.77 318 | 59.61 286 | 64.08 299 | 77.23 309 | 42.89 295 | 80.72 305 | 48.86 282 | 66.58 306 | 83.16 302 |
|
pmmvs-eth3d | | | 70.50 267 | 67.83 276 | 78.52 238 | 77.37 313 | 66.18 134 | 81.82 254 | 81.51 270 | 58.90 291 | 63.90 300 | 80.42 289 | 42.69 297 | 86.28 281 | 58.56 236 | 65.30 314 | 83.11 303 |
|
YYNet1 | | | 65.03 292 | 62.91 294 | 71.38 297 | 75.85 319 | 56.60 273 | 69.12 323 | 74.66 322 | 57.28 304 | 54.12 327 | 77.87 306 | 45.85 280 | 74.48 330 | 49.95 278 | 61.52 322 | 83.05 304 |
|
MDA-MVSNet-bldmvs | | | 66.68 286 | 63.66 290 | 75.75 272 | 79.28 306 | 60.56 230 | 73.92 308 | 78.35 300 | 64.43 246 | 50.13 335 | 79.87 294 | 44.02 290 | 83.67 294 | 46.10 303 | 56.86 329 | 83.03 305 |
|
Anonymous20231211 | | | 64.82 294 | 61.79 298 | 73.91 289 | 77.11 315 | 50.92 317 | 85.29 200 | 81.53 269 | 54.19 315 | 57.98 317 | 78.03 304 | 26.90 332 | 87.83 272 | 37.92 327 | 57.12 328 | 82.99 306 |
|
MDA-MVSNet_test_wron | | | 65.03 292 | 62.92 293 | 71.37 298 | 75.93 318 | 56.73 269 | 69.09 324 | 74.73 319 | 57.28 304 | 54.03 328 | 77.89 305 | 45.88 279 | 74.39 331 | 49.89 279 | 61.55 321 | 82.99 306 |
|
USDC | | | 70.33 268 | 68.37 267 | 76.21 270 | 80.60 291 | 56.23 279 | 79.19 279 | 86.49 211 | 60.89 276 | 61.29 306 | 85.47 228 | 31.78 329 | 89.47 240 | 53.37 265 | 76.21 243 | 82.94 308 |
|
OpenMVS_ROB | | 64.09 19 | 70.56 266 | 68.19 269 | 77.65 249 | 80.26 294 | 59.41 238 | 85.01 206 | 82.96 252 | 58.76 292 | 65.43 291 | 82.33 263 | 37.63 319 | 91.23 210 | 45.34 308 | 76.03 244 | 82.32 309 |
|
JIA-IIPM | | | 66.32 290 | 62.82 296 | 76.82 260 | 77.09 316 | 61.72 223 | 65.34 333 | 75.38 312 | 58.04 297 | 64.51 296 | 62.32 336 | 42.05 302 | 86.51 279 | 51.45 271 | 69.22 295 | 82.21 310 |
|
EG-PatchMatch MVS | | | 74.04 228 | 71.82 244 | 80.71 196 | 84.92 206 | 67.42 116 | 85.86 181 | 88.08 191 | 66.04 232 | 64.22 298 | 83.85 248 | 35.10 325 | 92.56 165 | 57.44 246 | 80.83 182 | 82.16 311 |
|
MVP-Stereo | | | 76.12 211 | 74.46 215 | 81.13 190 | 85.37 199 | 69.79 69 | 84.42 222 | 87.95 193 | 65.03 241 | 67.46 275 | 85.33 230 | 53.28 210 | 91.73 190 | 58.01 242 | 83.27 156 | 81.85 312 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
TDRefinement | | | 67.49 281 | 64.34 288 | 76.92 259 | 73.47 327 | 61.07 224 | 84.86 209 | 82.98 251 | 59.77 285 | 58.30 316 | 85.13 233 | 26.06 334 | 87.89 270 | 47.92 292 | 60.59 325 | 81.81 313 |
|
GG-mvs-BLEND | | | | | 75.38 277 | 81.59 278 | 55.80 287 | 79.32 276 | 69.63 334 | | 67.19 278 | 73.67 322 | 43.24 292 | 88.90 261 | 50.41 274 | 84.50 135 | 81.45 314 |
|
test_0402 | | | 72.79 252 | 70.44 255 | 79.84 209 | 88.13 143 | 65.99 137 | 85.93 179 | 84.29 232 | 65.57 237 | 67.40 277 | 85.49 227 | 46.92 274 | 92.61 163 | 35.88 330 | 74.38 265 | 80.94 315 |
|
UnsupCasMVSNet_bld | | | 63.70 298 | 61.53 300 | 70.21 305 | 73.69 325 | 51.39 315 | 72.82 309 | 81.89 266 | 55.63 311 | 57.81 318 | 71.80 325 | 38.67 313 | 78.61 313 | 49.26 281 | 52.21 336 | 80.63 316 |
|
LCM-MVSNet | | | 54.25 310 | 49.68 316 | 67.97 313 | 53.73 348 | 45.28 329 | 66.85 332 | 80.78 275 | 35.96 341 | 39.45 340 | 62.23 337 | 8.70 354 | 78.06 317 | 48.24 288 | 51.20 337 | 80.57 317 |
|
N_pmnet | | | 52.79 313 | 53.26 311 | 51.40 332 | 78.99 308 | 7.68 357 | 69.52 319 | 3.89 357 | 51.63 328 | 57.01 322 | 74.98 317 | 40.83 306 | 65.96 346 | 37.78 328 | 64.67 315 | 80.56 318 |
|
TinyColmap | | | 67.30 284 | 64.81 286 | 74.76 282 | 81.92 275 | 56.68 272 | 80.29 268 | 81.49 271 | 60.33 279 | 56.27 325 | 83.22 255 | 24.77 336 | 87.66 273 | 45.52 306 | 69.47 293 | 79.95 319 |
|
PM-MVS | | | 66.41 289 | 64.14 289 | 73.20 291 | 73.92 324 | 56.45 274 | 78.97 280 | 64.96 345 | 63.88 254 | 64.72 295 | 80.24 290 | 19.84 342 | 83.44 296 | 66.24 174 | 64.52 316 | 79.71 320 |
|
ANet_high | | | 50.57 316 | 46.10 318 | 63.99 317 | 48.67 351 | 39.13 339 | 70.99 315 | 80.85 274 | 61.39 274 | 31.18 343 | 57.70 340 | 17.02 346 | 73.65 334 | 31.22 339 | 15.89 350 | 79.18 321 |
|
LF4IMVS | | | 64.02 297 | 62.19 297 | 69.50 307 | 70.90 334 | 53.29 301 | 76.13 294 | 77.18 307 | 52.65 324 | 58.59 314 | 80.98 285 | 23.55 337 | 76.52 322 | 53.06 267 | 66.66 305 | 78.68 322 |
|
PatchMatch-RL | | | 72.38 254 | 70.90 253 | 76.80 261 | 88.60 130 | 67.38 118 | 79.53 274 | 76.17 310 | 62.75 263 | 69.36 256 | 82.00 271 | 45.51 284 | 84.89 290 | 53.62 264 | 80.58 186 | 78.12 323 |
|
1111 | | | 57.11 308 | 56.82 309 | 57.97 326 | 69.10 336 | 28.28 348 | 68.90 325 | 74.54 323 | 54.01 317 | 53.71 329 | 74.51 318 | 23.09 338 | 67.90 344 | 32.28 335 | 61.26 323 | 77.73 324 |
|
MS-PatchMatch | | | 73.83 230 | 72.67 229 | 77.30 257 | 83.87 235 | 66.02 136 | 81.82 254 | 84.66 229 | 61.37 275 | 68.61 266 | 82.82 259 | 47.29 271 | 88.21 266 | 59.27 229 | 84.32 138 | 77.68 325 |
|
testus | | | 59.00 304 | 57.91 303 | 62.25 321 | 72.25 331 | 39.09 340 | 69.74 317 | 75.02 315 | 53.04 323 | 57.21 321 | 73.72 321 | 18.76 344 | 70.33 340 | 32.86 333 | 68.57 299 | 77.35 326 |
|
LP | | | 61.36 301 | 57.78 304 | 72.09 294 | 75.54 322 | 58.53 242 | 67.16 331 | 75.22 313 | 51.90 327 | 54.13 326 | 69.97 329 | 37.73 318 | 80.45 307 | 32.74 334 | 55.63 331 | 77.29 327 |
|
DSMNet-mixed | | | 57.77 307 | 56.90 307 | 60.38 323 | 67.70 339 | 35.61 343 | 69.18 321 | 53.97 348 | 32.30 345 | 57.49 320 | 79.88 293 | 40.39 309 | 68.57 343 | 38.78 326 | 72.37 279 | 76.97 328 |
|
test2356 | | | 59.50 302 | 58.08 302 | 63.74 318 | 71.23 333 | 41.88 335 | 67.59 328 | 72.42 329 | 53.72 319 | 57.65 319 | 70.74 327 | 26.31 333 | 72.40 336 | 32.03 337 | 71.06 289 | 76.93 329 |
|
test1235678 | | | 58.74 305 | 56.89 308 | 64.30 316 | 69.70 335 | 41.87 336 | 71.05 313 | 74.87 317 | 54.06 316 | 50.63 334 | 71.53 326 | 25.30 335 | 74.10 332 | 31.80 338 | 63.10 318 | 76.93 329 |
|
CHOSEN 280x420 | | | 66.51 288 | 64.71 287 | 71.90 295 | 81.45 280 | 63.52 195 | 57.98 341 | 68.95 339 | 53.57 320 | 62.59 305 | 76.70 311 | 46.22 277 | 75.29 328 | 55.25 257 | 79.68 195 | 76.88 331 |
|
testmv | | | 53.85 311 | 51.03 313 | 62.31 320 | 61.46 343 | 38.88 341 | 70.95 316 | 74.69 321 | 51.11 329 | 41.26 337 | 66.85 332 | 14.28 348 | 72.13 337 | 29.19 340 | 49.51 338 | 75.93 332 |
|
PMMVS | | | 69.34 274 | 68.67 265 | 71.35 300 | 75.67 320 | 62.03 220 | 75.17 301 | 73.46 325 | 50.00 330 | 68.68 263 | 79.05 297 | 52.07 222 | 78.13 315 | 61.16 216 | 82.77 162 | 73.90 333 |
|
pmmvs3 | | | 57.79 306 | 54.26 310 | 68.37 312 | 64.02 341 | 56.72 270 | 75.12 304 | 65.17 343 | 40.20 337 | 52.93 331 | 69.86 330 | 20.36 341 | 75.48 327 | 45.45 307 | 55.25 333 | 72.90 334 |
|
PVSNet_0 | | 57.27 20 | 61.67 300 | 59.27 301 | 68.85 310 | 79.61 301 | 57.44 260 | 68.01 327 | 73.44 326 | 55.93 310 | 58.54 315 | 70.41 328 | 44.58 287 | 77.55 319 | 47.01 294 | 35.91 341 | 71.55 335 |
|
no-one | | | 51.08 314 | 45.79 319 | 66.95 315 | 57.92 346 | 50.49 321 | 59.63 340 | 76.04 311 | 48.04 331 | 31.85 341 | 56.10 342 | 19.12 343 | 80.08 309 | 36.89 329 | 26.52 343 | 70.29 336 |
|
PMMVS2 | | | 40.82 321 | 38.86 322 | 46.69 334 | 53.84 347 | 16.45 355 | 48.61 345 | 49.92 350 | 37.49 340 | 31.67 342 | 60.97 338 | 8.14 355 | 56.42 349 | 28.42 341 | 30.72 342 | 67.19 337 |
|
test12356 | | | 49.28 317 | 48.51 317 | 51.59 331 | 62.06 342 | 19.11 354 | 60.40 338 | 72.45 328 | 47.60 333 | 40.64 339 | 65.68 333 | 13.84 349 | 68.72 342 | 27.29 342 | 46.67 340 | 66.94 338 |
|
new_pmnet | | | 50.91 315 | 50.29 314 | 52.78 330 | 68.58 338 | 34.94 346 | 63.71 335 | 56.63 347 | 39.73 338 | 44.95 336 | 65.47 334 | 21.93 340 | 58.48 348 | 34.98 331 | 56.62 330 | 64.92 339 |
|
MVS-HIRNet | | | 59.14 303 | 57.67 305 | 63.57 319 | 81.65 277 | 43.50 333 | 71.73 311 | 65.06 344 | 39.59 339 | 51.43 333 | 57.73 339 | 38.34 315 | 82.58 300 | 39.53 325 | 73.95 268 | 64.62 340 |
|
wuykxyi23d | | | 39.76 322 | 33.18 325 | 59.51 325 | 46.98 352 | 44.01 331 | 57.70 342 | 67.74 340 | 24.13 347 | 13.98 352 | 34.33 347 | 1.27 359 | 71.33 338 | 34.23 332 | 18.23 346 | 63.18 341 |
|
FPMVS | | | 53.68 312 | 51.64 312 | 59.81 324 | 65.08 340 | 51.03 316 | 69.48 320 | 69.58 335 | 41.46 336 | 40.67 338 | 72.32 324 | 16.46 347 | 70.00 341 | 24.24 345 | 65.42 313 | 58.40 342 |
|
PMVS | | 37.38 22 | 44.16 320 | 40.28 321 | 55.82 327 | 40.82 354 | 42.54 334 | 65.12 334 | 63.99 346 | 34.43 342 | 24.48 345 | 57.12 341 | 3.92 356 | 76.17 324 | 17.10 348 | 55.52 332 | 48.75 343 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MVE | | 26.22 23 | 30.37 327 | 25.89 329 | 43.81 335 | 44.55 353 | 35.46 345 | 28.87 350 | 39.07 353 | 18.20 349 | 18.58 349 | 40.18 345 | 2.68 357 | 47.37 352 | 17.07 349 | 23.78 345 | 48.60 344 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testpf | | | 56.51 309 | 57.58 306 | 53.30 329 | 71.99 332 | 41.19 338 | 46.89 346 | 69.32 337 | 58.06 296 | 52.87 332 | 69.45 331 | 27.99 331 | 72.73 335 | 59.59 227 | 62.07 319 | 45.98 345 |
|
PNet_i23d | | | 38.26 323 | 35.42 323 | 46.79 333 | 58.74 344 | 35.48 344 | 59.65 339 | 51.25 349 | 32.45 344 | 23.44 348 | 47.53 344 | 2.04 358 | 58.96 347 | 25.60 344 | 18.09 348 | 45.92 346 |
|
Gipuma | | | 45.18 319 | 41.86 320 | 55.16 328 | 77.03 317 | 51.52 313 | 32.50 349 | 80.52 277 | 32.46 343 | 27.12 344 | 35.02 346 | 9.52 353 | 75.50 326 | 22.31 346 | 60.21 326 | 38.45 347 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
DeepMVS_CX | | | | | 27.40 340 | 40.17 355 | 26.90 351 | | 24.59 356 | 17.44 350 | 23.95 346 | 48.61 343 | 9.77 352 | 26.48 353 | 18.06 347 | 24.47 344 | 28.83 348 |
|
E-PMN | | | 31.77 325 | 30.64 326 | 35.15 336 | 52.87 349 | 27.67 350 | 57.09 343 | 47.86 351 | 24.64 346 | 16.40 350 | 33.05 348 | 11.23 351 | 54.90 350 | 14.46 350 | 18.15 347 | 22.87 349 |
|
EMVS | | | 30.81 326 | 29.65 327 | 34.27 337 | 50.96 350 | 25.95 352 | 56.58 344 | 46.80 352 | 24.01 348 | 15.53 351 | 30.68 349 | 12.47 350 | 54.43 351 | 12.81 351 | 17.05 349 | 22.43 350 |
|
tmp_tt | | | 18.61 329 | 21.40 330 | 10.23 342 | 4.82 356 | 10.11 356 | 34.70 348 | 30.74 355 | 1.48 352 | 23.91 347 | 26.07 350 | 28.42 330 | 13.41 355 | 27.12 343 | 15.35 351 | 7.17 351 |
|
wuyk23d | | | 16.82 330 | 15.94 331 | 19.46 341 | 58.74 344 | 31.45 347 | 39.22 347 | 3.74 358 | 6.84 351 | 6.04 353 | 2.70 354 | 1.27 359 | 24.29 354 | 10.54 352 | 14.40 352 | 2.63 352 |
|
test123 | | | 6.12 332 | 8.11 333 | 0.14 343 | 0.06 358 | 0.09 358 | 71.05 313 | 0.03 360 | 0.04 354 | 0.25 355 | 1.30 356 | 0.05 361 | 0.03 357 | 0.21 354 | 0.01 355 | 0.29 353 |
|
.test1245 | | | 45.55 318 | 50.02 315 | 32.14 338 | 69.10 336 | 28.28 348 | 68.90 325 | 74.54 323 | 54.01 317 | 53.71 329 | 74.51 318 | 23.09 338 | 67.90 344 | 32.28 335 | 0.02 353 | 0.25 354 |
|
testmvs | | | 6.04 333 | 8.02 334 | 0.10 344 | 0.08 357 | 0.03 359 | 69.74 317 | 0.04 359 | 0.05 353 | 0.31 354 | 1.68 355 | 0.02 362 | 0.04 356 | 0.24 353 | 0.02 353 | 0.25 354 |
|
cdsmvs_eth3d_5k | | | 19.96 328 | 26.61 328 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 89.26 154 | 0.00 355 | 0.00 356 | 88.61 127 | 61.62 138 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
pcd_1.5k_mvsjas | | | 5.26 334 | 7.02 335 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 63.15 103 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
sosnet-low-res | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
sosnet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
uncertanet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
Regformer | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
ab-mvs-re | | | 7.23 331 | 9.64 332 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 86.72 179 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
uanet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
test_part3 | | | | | | | | 92.22 18 | | 75.63 74 | | 95.29 2 | | 97.56 1 | 86.60 12 | | |
|
test_part2 | | | | | | 95.06 1 | 72.65 26 | | | | 91.80 1 | | | | | | |
|
sam_mvs | | | | | | | | | | | | | 50.01 257 | | | | |
|
MTGPA | | | | | | | | | 92.02 60 | | | | | | | | |
|
test_post1 | | | | | | | | 78.90 282 | | | | 5.43 353 | 48.81 268 | 85.44 287 | 59.25 230 | | |
|
test_post | | | | | | | | | | | | 5.46 352 | 50.36 255 | 84.24 292 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 74.00 320 | 51.12 240 | 88.60 263 | | | |
|
MTMP | | | | | | | | | 32.83 354 | | | | | | | | |
|
gm-plane-assit | | | | | | 81.40 281 | 53.83 298 | | | 62.72 264 | | 80.94 286 | | 92.39 169 | 63.40 195 | | |
|
TEST9 | | | | | | 93.26 35 | 72.96 19 | 88.75 86 | 91.89 69 | 68.44 203 | 85.00 29 | 93.10 41 | 74.36 18 | 95.41 50 | | | |
|
test_8 | | | | | | 93.13 37 | 72.57 30 | 88.68 89 | 91.84 72 | 68.69 199 | 84.87 35 | 93.10 41 | 74.43 15 | 95.16 58 | | | |
|
agg_prior | | | | | | 92.85 43 | 71.94 41 | | 91.78 75 | | 84.41 41 | | | 94.93 67 | | | |
|
test_prior4 | | | | | | | 72.60 29 | 89.01 77 | | | | | | | | | |
|
test_prior2 | | | | | | | | 88.85 81 | | 75.41 78 | 84.91 31 | 93.54 31 | 74.28 19 | | 83.31 34 | 95.86 8 | |
|
旧先验2 | | | | | | | | 86.56 163 | | 58.10 295 | 87.04 15 | | | 88.98 256 | 74.07 111 | | |
|
æ–°å‡ ä½•2 | | | | | | | | 86.29 172 | | | | | | | | | |
|
原ACMM2 | | | | | | | | 86.86 152 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 91.01 219 | 62.37 203 | | |
|
segment_acmp | | | | | | | | | | | | | 73.08 25 | | | | |
|
testdata1 | | | | | | | | 84.14 229 | | 75.71 71 | | | | | | | |
|
plane_prior7 | | | | | | 90.08 80 | 68.51 99 | | | | | | | | | | |
|
plane_prior6 | | | | | | 89.84 85 | 68.70 95 | | | | | | 60.42 160 | | | | |
|
plane_prior4 | | | | | | | | | | | | 91.00 82 | | | | | |
|
plane_prior3 | | | | | | | 68.60 97 | | | 78.44 30 | 78.92 96 | | | | | | |
|
plane_prior2 | | | | | | | | 91.25 30 | | 79.12 23 | | | | | | | |
|
plane_prior1 | | | | | | 89.90 84 | | | | | | | | | | | |
|
plane_prior | | | | | | | 68.71 93 | 90.38 46 | | 77.62 34 | | | | | | 86.16 123 | |
|
n2 | | | | | | | | | 0.00 361 | | | | | | | | |
|
nn | | | | | | | | | 0.00 361 | | | | | | | | |
|
door-mid | | | | | | | | | 69.98 333 | | | | | | | | |
|
test11 | | | | | | | | | 92.23 52 | | | | | | | | |
|
door | | | | | | | | | 69.44 336 | | | | | | | | |
|
HQP5-MVS | | | | | | | 66.98 124 | | | | | | | | | | |
|
HQP-NCC | | | | | | 89.33 102 | | 89.17 70 | | 76.41 59 | 77.23 136 | | | | | | |
|
ACMP_Plane | | | | | | 89.33 102 | | 89.17 70 | | 76.41 59 | 77.23 136 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.47 77 | | |
|
HQP3-MVS | | | | | | | | | 92.19 55 | | | | | | | 85.99 125 | |
|
HQP2-MVS | | | | | | | | | | | | | 60.17 163 | | | | |
|
NP-MVS | | | | | | 89.62 91 | 68.32 101 | | | | | 90.24 92 | | | | | |
|
MDTV_nov1_ep13 | | | | 69.97 259 | | 83.18 252 | 53.48 300 | 77.10 293 | 80.18 285 | 60.45 278 | 69.33 257 | 80.44 288 | 48.89 267 | 86.90 275 | 51.60 270 | 78.51 211 | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 81.95 171 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 81.25 178 | |
|
Test By Simon | | | | | | | | | | | | | 64.33 89 | | | | |
|