EPNet | | | 91.79 60 | 91.02 67 | 94.10 45 | 90.10 289 | 85.25 54 | 96.03 34 | 92.05 250 | 92.83 1 | 87.39 122 | 95.78 75 | 79.39 95 | 99.01 55 | 88.13 81 | 97.48 59 | 98.05 47 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
NCCC | | | 94.81 9 | 94.69 10 | 95.17 6 | 97.83 32 | 87.46 9 | 95.66 51 | 96.93 40 | 92.34 2 | 93.94 20 | 96.58 45 | 87.74 14 | 99.44 20 | 92.83 22 | 98.40 39 | 98.62 7 |
|
CNVR-MVS | | | 95.40 2 | 95.37 4 | 95.50 3 | 98.11 25 | 88.51 3 | 95.29 63 | 96.96 37 | 92.09 3 | 95.32 9 | 97.08 25 | 89.49 6 | 99.33 26 | 95.10 2 | 98.85 8 | 98.66 6 |
|
UA-Net | | | 92.83 52 | 92.54 52 | 93.68 56 | 96.10 83 | 84.71 59 | 95.66 51 | 96.39 77 | 91.92 4 | 93.22 32 | 96.49 49 | 83.16 55 | 98.87 67 | 84.47 121 | 95.47 88 | 97.45 73 |
|
CANet | | | 93.54 37 | 93.20 40 | 94.55 32 | 95.65 98 | 85.73 50 | 94.94 89 | 96.69 60 | 91.89 5 | 90.69 77 | 95.88 72 | 81.99 71 | 99.54 10 | 93.14 20 | 97.95 51 | 98.39 21 |
|
Regformer-2 | | | 94.33 19 | 94.22 17 | 94.68 27 | 95.54 101 | 86.75 20 | 94.57 116 | 96.70 58 | 91.84 6 | 94.41 12 | 96.56 47 | 87.19 20 | 99.13 40 | 93.50 11 | 97.65 57 | 98.16 38 |
|
MVS_0304 | | | 93.25 46 | 92.62 50 | 95.14 8 | 95.72 96 | 87.58 7 | 94.71 107 | 96.59 67 | 91.78 7 | 91.46 69 | 96.18 63 | 75.45 146 | 99.55 7 | 93.53 10 | 98.19 44 | 98.28 28 |
|
HPM-MVS++ | | | 95.14 6 | 94.91 8 | 95.83 1 | 98.25 21 | 89.65 1 | 95.92 40 | 96.96 37 | 91.75 8 | 94.02 19 | 96.83 32 | 88.12 11 | 99.55 7 | 93.41 15 | 98.94 5 | 98.28 28 |
|
HSP-MVS | | | 95.30 4 | 95.48 2 | 94.76 24 | 98.49 10 | 86.52 28 | 96.91 15 | 96.73 54 | 91.73 9 | 96.10 5 | 96.69 38 | 89.90 3 | 99.30 29 | 94.70 3 | 98.04 49 | 98.45 18 |
|
Regformer-1 | | | 94.22 23 | 94.13 22 | 94.51 34 | 95.54 101 | 86.36 33 | 94.57 116 | 96.44 72 | 91.69 10 | 94.32 14 | 96.56 47 | 87.05 22 | 99.03 50 | 93.35 16 | 97.65 57 | 98.15 39 |
|
Regformer-4 | | | 93.91 30 | 93.81 28 | 94.19 44 | 95.36 107 | 85.47 51 | 94.68 108 | 96.41 75 | 91.60 11 | 93.75 22 | 96.71 36 | 85.95 32 | 99.10 43 | 93.21 17 | 96.65 72 | 98.01 51 |
|
SteuartSystems-ACMMP | | | 95.20 5 | 95.32 6 | 94.85 16 | 96.99 55 | 86.33 34 | 97.33 3 | 97.30 17 | 91.38 12 | 95.39 8 | 97.46 9 | 88.98 10 | 99.40 21 | 94.12 7 | 98.89 7 | 98.82 2 |
Skip Steuart: Steuart Systems R&D Blog. |
Regformer-3 | | | 93.68 34 | 93.64 34 | 93.81 53 | 95.36 107 | 84.61 60 | 94.68 108 | 95.83 113 | 91.27 13 | 93.60 26 | 96.71 36 | 85.75 34 | 98.86 70 | 92.87 21 | 96.65 72 | 97.96 52 |
|
MPTG | | | 94.47 12 | 94.30 14 | 95.00 9 | 98.42 14 | 86.95 12 | 95.06 82 | 96.97 34 | 91.07 14 | 93.14 34 | 97.56 6 | 84.30 49 | 99.56 3 | 93.43 13 | 98.75 16 | 98.47 14 |
|
MTAPA | | | 94.42 17 | 94.22 17 | 95.00 9 | 98.42 14 | 86.95 12 | 94.36 137 | 96.97 34 | 91.07 14 | 93.14 34 | 97.56 6 | 84.30 49 | 99.56 3 | 93.43 13 | 98.75 16 | 98.47 14 |
|
EI-MVSNet-Vis-set | | | 93.01 51 | 92.92 46 | 93.29 59 | 95.01 123 | 83.51 90 | 94.48 119 | 95.77 117 | 90.87 16 | 92.52 49 | 96.67 40 | 84.50 48 | 99.00 58 | 91.99 38 | 94.44 107 | 97.36 74 |
|
3Dnovator+ | | 87.14 4 | 92.42 56 | 91.37 60 | 95.55 2 | 95.63 99 | 88.73 2 | 97.07 8 | 96.77 52 | 90.84 17 | 84.02 212 | 96.62 43 | 75.95 135 | 99.34 23 | 87.77 85 | 97.68 55 | 98.59 9 |
|
HQP_MVS | | | 90.60 83 | 90.19 78 | 91.82 116 | 94.70 137 | 82.73 112 | 95.85 42 | 96.22 86 | 90.81 18 | 86.91 129 | 94.86 97 | 74.23 158 | 98.12 109 | 88.15 79 | 89.99 165 | 94.63 170 |
|
plane_prior2 | | | | | | | | 95.85 42 | | 90.81 18 | | | | | | | |
|
DELS-MVS | | | 93.43 40 | 93.25 38 | 93.97 46 | 95.42 106 | 85.04 55 | 93.06 214 | 97.13 25 | 90.74 20 | 91.84 62 | 95.09 92 | 86.32 28 | 99.21 32 | 91.22 52 | 98.45 38 | 97.65 65 |
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 |
EI-MVSNet-UG-set | | | 92.74 54 | 92.62 50 | 93.12 65 | 94.86 131 | 83.20 96 | 94.40 127 | 95.74 120 | 90.71 21 | 92.05 59 | 96.60 44 | 84.00 51 | 98.99 59 | 91.55 48 | 93.63 116 | 97.17 81 |
|
XVS | | | 94.45 13 | 94.32 13 | 94.85 16 | 98.54 7 | 86.60 26 | 96.93 12 | 97.19 22 | 90.66 22 | 92.85 36 | 97.16 23 | 85.02 43 | 99.49 16 | 91.99 38 | 98.56 35 | 98.47 14 |
|
X-MVStestdata | | | 88.31 136 | 86.13 183 | 94.85 16 | 98.54 7 | 86.60 26 | 96.93 12 | 97.19 22 | 90.66 22 | 92.85 36 | 23.41 351 | 85.02 43 | 99.49 16 | 91.99 38 | 98.56 35 | 98.47 14 |
|
SD-MVS | | | 94.96 7 | 95.33 5 | 93.88 49 | 97.25 52 | 86.69 21 | 96.19 29 | 97.11 28 | 90.42 24 | 96.95 1 | 97.27 13 | 89.53 5 | 96.91 216 | 94.38 5 | 98.85 8 | 98.03 49 |
|
plane_prior3 | | | | | | | 82.75 109 | | | 90.26 25 | 86.91 129 | | | | | | |
|
DeepPCF-MVS | | 89.96 1 | 94.20 25 | 94.77 9 | 92.49 88 | 96.52 66 | 80.00 172 | 94.00 167 | 97.08 29 | 90.05 26 | 95.65 7 | 97.29 12 | 89.66 4 | 98.97 61 | 93.95 8 | 98.71 19 | 98.50 11 |
|
MSLP-MVS++ | | | 93.72 33 | 94.08 23 | 92.65 82 | 97.31 46 | 83.43 91 | 95.79 44 | 97.33 14 | 90.03 27 | 93.58 27 | 96.96 28 | 84.87 45 | 97.76 139 | 92.19 33 | 98.66 28 | 96.76 94 |
|
canonicalmvs | | | 93.27 44 | 92.75 49 | 94.85 16 | 95.70 97 | 87.66 5 | 96.33 25 | 96.41 75 | 90.00 28 | 94.09 17 | 94.60 106 | 82.33 62 | 98.62 84 | 92.40 28 | 92.86 134 | 98.27 31 |
|
Vis-MVSNet | | | 91.75 62 | 91.23 63 | 93.29 59 | 95.32 110 | 83.78 82 | 96.14 30 | 95.98 101 | 89.89 29 | 90.45 79 | 96.58 45 | 75.09 150 | 98.31 101 | 84.75 118 | 96.90 66 | 97.78 64 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
TranMVSNet+NR-MVSNet | | | 88.84 125 | 87.95 128 | 91.49 125 | 92.68 201 | 83.01 103 | 94.92 91 | 96.31 80 | 89.88 30 | 85.53 163 | 93.85 133 | 76.63 120 | 96.96 212 | 81.91 158 | 79.87 293 | 94.50 181 |
|
UniMVSNet_NR-MVSNet | | | 89.92 96 | 89.29 95 | 91.81 118 | 93.39 181 | 83.72 83 | 94.43 125 | 97.12 26 | 89.80 31 | 86.46 136 | 93.32 142 | 83.16 55 | 97.23 193 | 84.92 114 | 81.02 274 | 94.49 183 |
|
alignmvs | | | 93.08 50 | 92.50 53 | 94.81 21 | 95.62 100 | 87.61 6 | 95.99 35 | 96.07 96 | 89.77 32 | 94.12 16 | 94.87 96 | 80.56 80 | 98.66 80 | 92.42 27 | 93.10 129 | 98.15 39 |
|
TSAR-MVS + GP. | | | 93.66 35 | 93.41 36 | 94.41 38 | 96.59 63 | 86.78 18 | 94.40 127 | 93.93 217 | 89.77 32 | 94.21 15 | 95.59 81 | 87.35 18 | 98.61 85 | 92.72 23 | 96.15 80 | 97.83 61 |
|
IS-MVSNet | | | 91.43 67 | 91.09 66 | 92.46 89 | 95.87 93 | 81.38 137 | 96.95 9 | 93.69 222 | 89.72 34 | 89.50 89 | 95.98 68 | 78.57 102 | 97.77 138 | 83.02 139 | 96.50 76 | 98.22 35 |
|
plane_prior | | | | | | | 82.73 112 | 95.21 73 | | 89.66 35 | | | | | | 89.88 168 | |
|
DU-MVS | | | 89.34 114 | 88.50 112 | 91.85 114 | 93.04 192 | 83.72 83 | 94.47 122 | 96.59 67 | 89.50 36 | 86.46 136 | 93.29 145 | 77.25 113 | 97.23 193 | 84.92 114 | 81.02 274 | 94.59 174 |
|
CANet_DTU | | | 90.26 88 | 89.41 92 | 92.81 77 | 93.46 180 | 83.01 103 | 93.48 194 | 94.47 194 | 89.43 37 | 87.76 117 | 94.23 117 | 70.54 211 | 99.03 50 | 84.97 113 | 96.39 78 | 96.38 102 |
|
DeepC-MVS_fast | | 89.43 2 | 94.04 26 | 93.79 29 | 94.80 22 | 97.48 41 | 86.78 18 | 95.65 53 | 96.89 42 | 89.40 38 | 92.81 39 | 96.97 27 | 85.37 38 | 99.24 31 | 90.87 58 | 98.69 21 | 98.38 22 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
UGNet | | | 89.95 94 | 88.95 103 | 92.95 73 | 94.51 144 | 83.31 94 | 95.70 48 | 95.23 164 | 89.37 39 | 87.58 119 | 93.94 126 | 64.00 278 | 98.78 77 | 83.92 131 | 96.31 79 | 96.74 96 |
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 |
FC-MVSNet-test | | | 90.27 87 | 90.18 79 | 90.53 153 | 93.71 174 | 79.85 176 | 95.77 45 | 97.59 2 | 89.31 40 | 86.27 142 | 94.67 103 | 81.93 72 | 97.01 208 | 84.26 126 | 88.09 203 | 94.71 165 |
|
UniMVSNet (Re) | | | 89.80 98 | 89.07 100 | 92.01 104 | 93.60 177 | 84.52 63 | 94.78 100 | 97.47 5 | 89.26 41 | 86.44 139 | 92.32 179 | 82.10 67 | 97.39 179 | 84.81 117 | 80.84 278 | 94.12 195 |
|
3Dnovator | | 86.66 5 | 91.73 63 | 90.82 71 | 94.44 35 | 94.59 141 | 86.37 32 | 97.18 6 | 97.02 31 | 89.20 42 | 84.31 208 | 96.66 41 | 73.74 169 | 99.17 35 | 86.74 100 | 97.96 50 | 97.79 63 |
|
VNet | | | 92.24 57 | 91.91 56 | 93.24 61 | 96.59 63 | 83.43 91 | 94.84 96 | 96.44 72 | 89.19 43 | 94.08 18 | 95.90 71 | 77.85 112 | 98.17 105 | 88.90 72 | 93.38 123 | 98.13 41 |
|
FIs | | | 90.51 84 | 90.35 75 | 90.99 144 | 93.99 164 | 80.98 148 | 95.73 46 | 97.54 3 | 89.15 44 | 86.72 133 | 94.68 102 | 81.83 73 | 97.24 191 | 85.18 111 | 88.31 200 | 94.76 164 |
|
NR-MVSNet | | | 88.58 131 | 87.47 135 | 91.93 110 | 93.04 192 | 84.16 76 | 94.77 101 | 96.25 84 | 89.05 45 | 80.04 268 | 93.29 145 | 79.02 96 | 97.05 206 | 81.71 162 | 80.05 288 | 94.59 174 |
|
MP-MVS | | | 94.25 21 | 94.07 24 | 94.77 23 | 98.47 11 | 86.31 36 | 96.71 20 | 96.98 33 | 89.04 46 | 91.98 60 | 97.19 20 | 85.43 37 | 99.56 3 | 92.06 37 | 98.79 11 | 98.44 19 |
|
APDe-MVS | | | 95.46 1 | 95.64 1 | 94.91 12 | 98.26 20 | 86.29 38 | 97.46 2 | 97.40 9 | 89.03 47 | 96.20 4 | 98.10 1 | 89.39 7 | 99.34 23 | 95.88 1 | 99.03 1 | 99.10 1 |
|
DeepC-MVS | | 88.79 3 | 93.31 42 | 92.99 44 | 94.26 42 | 96.07 85 | 85.83 48 | 94.89 92 | 96.99 32 | 89.02 48 | 89.56 87 | 97.37 10 | 82.51 60 | 99.38 22 | 92.20 32 | 98.30 41 | 97.57 69 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
OPM-MVS | | | 90.12 89 | 89.56 88 | 91.82 116 | 93.14 188 | 83.90 79 | 94.16 150 | 95.74 120 | 88.96 49 | 87.86 108 | 95.43 83 | 72.48 186 | 97.91 134 | 88.10 82 | 90.18 164 | 93.65 228 |
|
HQP-NCC | | | | | | 94.17 154 | | 94.39 129 | | 88.81 50 | 85.43 172 | | | | | | |
|
ACMP_Plane | | | | | | 94.17 154 | | 94.39 129 | | 88.81 50 | 85.43 172 | | | | | | |
|
HQP-MVS | | | 89.80 98 | 89.28 96 | 91.34 129 | 94.17 154 | 81.56 129 | 94.39 129 | 96.04 99 | 88.81 50 | 85.43 172 | 93.97 125 | 73.83 167 | 97.96 130 | 87.11 97 | 89.77 170 | 94.50 181 |
|
MVS_111021_HR | | | 93.45 38 | 93.31 37 | 93.84 50 | 96.99 55 | 84.84 56 | 93.24 207 | 97.24 19 | 88.76 53 | 91.60 68 | 95.85 73 | 86.07 31 | 98.66 80 | 91.91 42 | 98.16 45 | 98.03 49 |
|
mPP-MVS | | | 93.99 28 | 93.78 30 | 94.63 29 | 98.50 9 | 85.90 47 | 96.87 16 | 96.91 41 | 88.70 54 | 91.83 64 | 97.17 22 | 83.96 52 | 99.55 7 | 91.44 51 | 98.64 31 | 98.43 20 |
|
VPNet | | | 88.20 139 | 87.47 135 | 90.39 167 | 93.56 178 | 79.46 186 | 94.04 163 | 95.54 134 | 88.67 55 | 86.96 127 | 94.58 107 | 69.33 223 | 97.15 197 | 84.05 130 | 80.53 283 | 94.56 177 |
|
HFP-MVS | | | 94.52 11 | 94.40 12 | 94.86 14 | 98.61 3 | 86.81 16 | 96.94 10 | 97.34 11 | 88.63 56 | 93.65 23 | 97.21 18 | 86.10 29 | 99.49 16 | 92.35 29 | 98.77 14 | 98.30 26 |
|
ACMMPR | | | 94.43 15 | 94.28 15 | 94.91 12 | 98.63 2 | 86.69 21 | 96.94 10 | 97.32 16 | 88.63 56 | 93.53 30 | 97.26 15 | 85.04 42 | 99.54 10 | 92.35 29 | 98.78 13 | 98.50 11 |
|
region2R | | | 94.43 15 | 94.27 16 | 94.92 11 | 98.65 1 | 86.67 23 | 96.92 14 | 97.23 21 | 88.60 58 | 93.58 27 | 97.27 13 | 85.22 39 | 99.54 10 | 92.21 31 | 98.74 18 | 98.56 10 |
|
WR-MVS | | | 88.38 133 | 87.67 132 | 90.52 159 | 93.30 184 | 80.18 164 | 93.26 205 | 95.96 103 | 88.57 59 | 85.47 168 | 92.81 166 | 76.12 124 | 96.91 216 | 81.24 165 | 82.29 252 | 94.47 186 |
|
CP-MVS | | | 94.34 18 | 94.21 19 | 94.74 26 | 98.39 16 | 86.64 25 | 97.60 1 | 97.24 19 | 88.53 60 | 92.73 43 | 97.23 16 | 85.20 40 | 99.32 27 | 92.15 34 | 98.83 10 | 98.25 34 |
|
CP-MVSNet | | | 87.63 163 | 87.26 141 | 88.74 235 | 93.12 189 | 76.59 265 | 95.29 63 | 96.58 69 | 88.43 61 | 83.49 225 | 92.98 160 | 75.28 147 | 95.83 266 | 78.97 206 | 81.15 271 | 93.79 214 |
|
VDD-MVS | | | 90.74 77 | 89.92 85 | 93.20 62 | 96.27 71 | 83.02 102 | 95.73 46 | 93.86 218 | 88.42 62 | 92.53 48 | 96.84 31 | 62.09 285 | 98.64 82 | 90.95 57 | 92.62 136 | 97.93 56 |
|
ACMMP | | | 93.24 47 | 92.88 48 | 94.30 41 | 98.09 27 | 85.33 53 | 96.86 17 | 97.45 6 | 88.33 63 | 90.15 83 | 97.03 26 | 81.44 74 | 99.51 14 | 90.85 59 | 95.74 83 | 98.04 48 |
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 |
nrg030 | | | 91.08 74 | 90.39 74 | 93.17 64 | 93.07 190 | 86.91 14 | 96.41 24 | 96.26 82 | 88.30 64 | 88.37 100 | 94.85 99 | 82.19 66 | 97.64 146 | 91.09 53 | 82.95 246 | 94.96 150 |
|
ACMMP_Plus | | | 94.74 10 | 94.56 11 | 95.28 4 | 98.02 30 | 87.70 4 | 95.68 49 | 97.34 11 | 88.28 65 | 95.30 10 | 97.67 3 | 85.90 33 | 99.54 10 | 93.91 9 | 98.95 4 | 98.60 8 |
|
test_part3 | | | | | | | | 95.99 35 | | 88.25 66 | | 97.60 4 | | 99.62 1 | 93.18 18 | | |
|
ESAPD | | | 95.32 3 | 95.38 3 | 95.17 6 | 98.55 5 | 87.22 10 | 95.99 35 | 97.45 6 | 88.25 66 | 96.40 2 | 97.60 4 | 91.93 1 | 99.62 1 | 93.18 18 | 99.02 2 | 98.67 4 |
|
PS-CasMVS | | | 87.32 179 | 86.88 153 | 88.63 238 | 92.99 195 | 76.33 268 | 95.33 58 | 96.61 66 | 88.22 68 | 83.30 228 | 93.07 154 | 73.03 178 | 95.79 269 | 78.36 211 | 81.00 276 | 93.75 220 |
|
MVS_111021_LR | | | 92.47 55 | 92.29 55 | 92.98 72 | 95.99 88 | 84.43 71 | 93.08 212 | 96.09 94 | 88.20 69 | 91.12 74 | 95.72 78 | 81.33 76 | 97.76 139 | 91.74 46 | 97.37 61 | 96.75 95 |
|
TSAR-MVS + MP. | | | 94.85 8 | 94.94 7 | 94.58 31 | 98.25 21 | 86.33 34 | 96.11 31 | 96.62 65 | 88.14 70 | 96.10 5 | 96.96 28 | 89.09 9 | 98.94 65 | 94.48 4 | 98.68 24 | 98.48 13 |
|
PEN-MVS | | | 86.80 192 | 86.27 181 | 88.40 251 | 92.32 206 | 75.71 272 | 95.18 74 | 96.38 78 | 87.97 71 | 82.82 232 | 93.15 150 | 73.39 174 | 95.92 262 | 76.15 233 | 79.03 296 | 93.59 235 |
|
testdata1 | | | | | | | | 92.15 242 | | 87.94 72 | | | | | | | |
|
VPA-MVSNet | | | 89.62 100 | 88.96 102 | 91.60 123 | 93.86 168 | 82.89 107 | 95.46 57 | 97.33 14 | 87.91 73 | 88.43 99 | 93.31 143 | 74.17 161 | 97.40 176 | 87.32 93 | 82.86 248 | 94.52 179 |
|
WR-MVS_H | | | 87.80 155 | 87.37 137 | 89.10 230 | 93.23 186 | 78.12 236 | 95.61 54 | 97.30 17 | 87.90 74 | 83.72 218 | 92.01 195 | 79.65 94 | 96.01 259 | 76.36 229 | 80.54 282 | 93.16 249 |
|
CLD-MVS | | | 89.47 106 | 88.90 105 | 91.18 134 | 94.22 153 | 82.07 124 | 92.13 243 | 96.09 94 | 87.90 74 | 85.37 179 | 92.45 174 | 74.38 156 | 97.56 149 | 87.15 95 | 90.43 158 | 93.93 204 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
abl_6 | | | 93.18 49 | 93.05 42 | 93.57 58 | 97.52 38 | 84.27 74 | 95.53 56 | 96.67 61 | 87.85 76 | 93.20 33 | 97.22 17 | 80.35 81 | 99.18 34 | 91.91 42 | 97.21 62 | 97.26 75 |
|
MG-MVS | | | 91.77 61 | 91.70 58 | 92.00 106 | 97.08 54 | 80.03 171 | 93.60 191 | 95.18 167 | 87.85 76 | 90.89 76 | 96.47 50 | 82.06 69 | 98.36 95 | 85.07 112 | 97.04 65 | 97.62 66 |
|
LCM-MVSNet-Re | | | 88.30 137 | 88.32 119 | 88.27 254 | 94.71 136 | 72.41 297 | 93.15 208 | 90.98 283 | 87.77 78 | 79.25 274 | 91.96 196 | 78.35 105 | 95.75 270 | 83.04 138 | 95.62 84 | 96.65 97 |
|
Effi-MVS+-dtu | | | 88.65 129 | 88.35 116 | 89.54 210 | 93.33 182 | 76.39 266 | 94.47 122 | 94.36 197 | 87.70 79 | 85.43 172 | 89.56 265 | 73.45 172 | 97.26 189 | 85.57 109 | 91.28 143 | 94.97 147 |
|
mvs-test1 | | | 89.45 107 | 89.14 98 | 90.38 169 | 93.33 182 | 77.63 251 | 94.95 88 | 94.36 197 | 87.70 79 | 87.10 126 | 92.81 166 | 73.45 172 | 98.03 127 | 85.57 109 | 93.04 130 | 95.48 134 |
|
test_prior3 | | | 93.60 36 | 93.53 35 | 93.82 51 | 97.29 48 | 84.49 64 | 94.12 151 | 96.88 43 | 87.67 81 | 92.63 45 | 96.39 52 | 86.62 25 | 98.87 67 | 91.50 49 | 98.67 26 | 98.11 43 |
|
test_prior2 | | | | | | | | 94.12 151 | | 87.67 81 | 92.63 45 | 96.39 52 | 86.62 25 | | 91.50 49 | 98.67 26 | |
|
Vis-MVSNet (Re-imp) | | | 89.59 102 | 89.44 91 | 90.03 191 | 95.74 95 | 75.85 271 | 95.61 54 | 90.80 288 | 87.66 83 | 87.83 114 | 95.40 84 | 76.79 117 | 96.46 242 | 78.37 210 | 96.73 69 | 97.80 62 |
|
#test# | | | 94.32 20 | 94.14 21 | 94.86 14 | 98.61 3 | 86.81 16 | 96.43 23 | 97.34 11 | 87.51 84 | 93.65 23 | 97.21 18 | 86.10 29 | 99.49 16 | 91.68 47 | 98.77 14 | 98.30 26 |
|
PGM-MVS | | | 93.96 29 | 93.72 32 | 94.68 27 | 98.43 13 | 86.22 39 | 95.30 61 | 97.78 1 | 87.45 85 | 93.26 31 | 97.33 11 | 84.62 47 | 99.51 14 | 90.75 60 | 98.57 34 | 98.32 25 |
|
DTE-MVSNet | | | 86.11 205 | 85.48 199 | 87.98 261 | 91.65 222 | 74.92 275 | 94.93 90 | 95.75 119 | 87.36 86 | 82.26 237 | 93.04 155 | 72.85 179 | 95.82 267 | 74.04 249 | 77.46 301 | 93.20 247 |
|
tfpn111 | | | 87.63 163 | 86.68 165 | 90.47 163 | 96.12 78 | 78.55 217 | 95.03 83 | 91.58 263 | 87.15 87 | 88.06 104 | 92.29 181 | 68.91 231 | 98.15 108 | 69.88 276 | 91.10 144 | 94.71 165 |
|
conf200view11 | | | 87.65 159 | 86.71 162 | 90.46 165 | 96.12 78 | 78.55 217 | 95.03 83 | 91.58 263 | 87.15 87 | 88.06 104 | 92.29 181 | 68.91 231 | 98.10 115 | 70.13 271 | 91.10 144 | 94.71 165 |
|
thres100view900 | | | 87.63 163 | 86.71 162 | 90.38 169 | 96.12 78 | 78.55 217 | 95.03 83 | 91.58 263 | 87.15 87 | 88.06 104 | 92.29 181 | 68.91 231 | 98.10 115 | 70.13 271 | 91.10 144 | 94.48 184 |
|
MCST-MVS | | | 94.45 13 | 94.20 20 | 95.19 5 | 98.46 12 | 87.50 8 | 95.00 86 | 97.12 26 | 87.13 90 | 92.51 50 | 96.30 54 | 89.24 8 | 99.34 23 | 93.46 12 | 98.62 32 | 98.73 3 |
|
Effi-MVS+ | | | 91.59 66 | 91.11 64 | 93.01 71 | 94.35 152 | 83.39 93 | 94.60 113 | 95.10 169 | 87.10 91 | 90.57 78 | 93.10 153 | 81.43 75 | 98.07 124 | 89.29 69 | 94.48 104 | 97.59 68 |
|
view600 | | | 87.62 166 | 86.65 167 | 90.53 153 | 96.19 73 | 78.52 221 | 95.29 63 | 91.09 275 | 87.08 92 | 87.84 110 | 93.03 156 | 68.86 235 | 98.11 111 | 69.44 278 | 91.02 152 | 94.96 150 |
|
view800 | | | 87.62 166 | 86.65 167 | 90.53 153 | 96.19 73 | 78.52 221 | 95.29 63 | 91.09 275 | 87.08 92 | 87.84 110 | 93.03 156 | 68.86 235 | 98.11 111 | 69.44 278 | 91.02 152 | 94.96 150 |
|
conf0.05thres1000 | | | 87.62 166 | 86.65 167 | 90.53 153 | 96.19 73 | 78.52 221 | 95.29 63 | 91.09 275 | 87.08 92 | 87.84 110 | 93.03 156 | 68.86 235 | 98.11 111 | 69.44 278 | 91.02 152 | 94.96 150 |
|
tfpn | | | 87.62 166 | 86.65 167 | 90.53 153 | 96.19 73 | 78.52 221 | 95.29 63 | 91.09 275 | 87.08 92 | 87.84 110 | 93.03 156 | 68.86 235 | 98.11 111 | 69.44 278 | 91.02 152 | 94.96 150 |
|
thres600view7 | | | 87.65 159 | 86.67 166 | 90.59 150 | 96.08 84 | 78.72 213 | 94.88 94 | 91.58 263 | 87.06 96 | 88.08 103 | 92.30 180 | 68.91 231 | 98.10 115 | 70.05 275 | 91.10 144 | 94.96 150 |
|
APD-MVS_3200maxsize | | | 93.78 32 | 93.77 31 | 93.80 54 | 97.92 31 | 84.19 75 | 96.30 26 | 96.87 45 | 86.96 97 | 93.92 21 | 97.47 8 | 83.88 53 | 98.96 64 | 92.71 24 | 97.87 52 | 98.26 33 |
|
OMC-MVS | | | 91.23 70 | 90.62 73 | 93.08 67 | 96.27 71 | 84.07 77 | 93.52 193 | 95.93 104 | 86.95 98 | 89.51 88 | 96.13 65 | 78.50 103 | 98.35 97 | 85.84 107 | 92.90 133 | 96.83 93 |
|
tfpn200view9 | | | 87.58 172 | 86.64 171 | 90.41 166 | 95.99 88 | 78.64 215 | 94.58 114 | 91.98 254 | 86.94 99 | 88.09 101 | 91.77 201 | 69.18 228 | 98.10 115 | 70.13 271 | 91.10 144 | 94.48 184 |
|
thres400 | | | 87.62 166 | 86.64 171 | 90.57 151 | 95.99 88 | 78.64 215 | 94.58 114 | 91.98 254 | 86.94 99 | 88.09 101 | 91.77 201 | 69.18 228 | 98.10 115 | 70.13 271 | 91.10 144 | 94.96 150 |
|
HPM-MVS | | | 94.02 27 | 93.88 27 | 94.43 37 | 98.39 16 | 85.78 49 | 97.25 5 | 97.07 30 | 86.90 101 | 92.62 47 | 96.80 35 | 84.85 46 | 99.17 35 | 92.43 26 | 98.65 30 | 98.33 24 |
|
LFMVS | | | 90.08 90 | 89.13 99 | 92.95 73 | 96.71 60 | 82.32 121 | 96.08 32 | 89.91 304 | 86.79 102 | 92.15 58 | 96.81 33 | 62.60 282 | 98.34 98 | 87.18 94 | 93.90 112 | 98.19 36 |
|
LPG-MVS_test | | | 89.45 107 | 88.90 105 | 91.12 135 | 94.47 145 | 81.49 132 | 95.30 61 | 96.14 90 | 86.73 103 | 85.45 169 | 95.16 89 | 69.89 216 | 98.10 115 | 87.70 86 | 89.23 179 | 93.77 218 |
|
LGP-MVS_train | | | | | 91.12 135 | 94.47 145 | 81.49 132 | | 96.14 90 | 86.73 103 | 85.45 169 | 95.16 89 | 69.89 216 | 98.10 115 | 87.70 86 | 89.23 179 | 93.77 218 |
|
EPNet_dtu | | | 86.49 201 | 85.94 190 | 88.14 259 | 90.24 287 | 72.82 289 | 94.11 153 | 92.20 246 | 86.66 105 | 79.42 273 | 92.36 178 | 73.52 170 | 95.81 268 | 71.26 262 | 93.66 115 | 95.80 126 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
ACMP | | 84.23 8 | 89.01 123 | 88.35 116 | 90.99 144 | 94.73 134 | 81.27 138 | 95.07 80 | 95.89 110 | 86.48 106 | 83.67 220 | 94.30 112 | 69.33 223 | 97.99 129 | 87.10 99 | 88.55 192 | 93.72 222 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
MVS_Test | | | 91.31 69 | 91.11 64 | 91.93 110 | 94.37 149 | 80.14 166 | 93.46 196 | 95.80 115 | 86.46 107 | 91.35 71 | 93.77 135 | 82.21 65 | 98.09 122 | 87.57 88 | 94.95 95 | 97.55 71 |
|
thres200 | | | 87.21 185 | 86.24 182 | 90.12 182 | 95.36 107 | 78.53 220 | 93.26 205 | 92.10 247 | 86.42 108 | 88.00 107 | 91.11 236 | 69.24 227 | 98.00 128 | 69.58 277 | 91.04 151 | 93.83 213 |
|
PAPM_NR | | | 91.22 71 | 90.78 72 | 92.52 87 | 97.60 35 | 81.46 134 | 94.37 133 | 96.24 85 | 86.39 109 | 87.41 120 | 94.80 101 | 82.06 69 | 98.48 91 | 82.80 143 | 95.37 90 | 97.61 67 |
|
PS-MVSNAJ | | | 91.18 72 | 90.92 68 | 91.96 108 | 95.26 113 | 82.60 118 | 92.09 245 | 95.70 122 | 86.27 110 | 91.84 62 | 92.46 173 | 79.70 90 | 98.99 59 | 89.08 70 | 95.86 82 | 94.29 189 |
|
MP-MVS-pluss | | | 94.21 24 | 94.00 26 | 94.85 16 | 98.17 24 | 86.65 24 | 94.82 97 | 97.17 24 | 86.26 111 | 92.83 38 | 97.87 2 | 85.57 36 | 99.56 3 | 94.37 6 | 98.92 6 | 98.34 23 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
PS-MVSNAJss | | | 89.97 93 | 89.62 87 | 91.02 142 | 91.90 212 | 80.85 153 | 95.26 70 | 95.98 101 | 86.26 111 | 86.21 143 | 94.29 113 | 79.70 90 | 97.65 144 | 88.87 73 | 88.10 201 | 94.57 176 |
|
EPP-MVSNet | | | 91.70 64 | 91.56 59 | 92.13 103 | 95.88 91 | 80.50 162 | 97.33 3 | 95.25 160 | 86.15 113 | 89.76 86 | 95.60 80 | 83.42 54 | 98.32 100 | 87.37 92 | 93.25 126 | 97.56 70 |
|
XVG-OURS | | | 89.40 112 | 88.70 108 | 91.52 124 | 94.06 157 | 81.46 134 | 91.27 260 | 96.07 96 | 86.14 114 | 88.89 95 | 95.77 76 | 68.73 240 | 97.26 189 | 87.39 91 | 89.96 167 | 95.83 124 |
|
xiu_mvs_v2_base | | | 91.13 73 | 90.89 70 | 91.86 113 | 94.97 126 | 82.42 119 | 92.24 239 | 95.64 128 | 86.11 115 | 91.74 67 | 93.14 151 | 79.67 93 | 98.89 66 | 89.06 71 | 95.46 89 | 94.28 190 |
|
Fast-Effi-MVS+-dtu | | | 87.44 176 | 86.72 161 | 89.63 208 | 92.04 211 | 77.68 250 | 94.03 164 | 93.94 216 | 85.81 116 | 82.42 235 | 91.32 225 | 70.33 213 | 97.06 205 | 80.33 183 | 90.23 163 | 94.14 194 |
|
XVG-OURS-SEG-HR | | | 89.95 94 | 89.45 90 | 91.47 126 | 94.00 163 | 81.21 142 | 91.87 247 | 96.06 98 | 85.78 117 | 88.55 97 | 95.73 77 | 74.67 154 | 97.27 187 | 88.71 74 | 89.64 172 | 95.91 119 |
|
HPM-MVS_fast | | | 93.40 41 | 93.22 39 | 93.94 48 | 98.36 18 | 84.83 57 | 97.15 7 | 96.80 49 | 85.77 118 | 92.47 51 | 97.13 24 | 82.38 61 | 99.07 44 | 90.51 62 | 98.40 39 | 97.92 57 |
|
EI-MVSNet | | | 89.10 117 | 88.86 107 | 89.80 201 | 91.84 214 | 78.30 231 | 93.70 186 | 95.01 172 | 85.73 119 | 87.15 124 | 95.28 85 | 79.87 87 | 97.21 195 | 83.81 133 | 87.36 209 | 93.88 208 |
|
IterMVS-LS | | | 88.36 135 | 87.91 130 | 89.70 206 | 93.80 171 | 78.29 232 | 93.73 182 | 95.08 171 | 85.73 119 | 84.75 194 | 91.90 199 | 79.88 86 | 96.92 215 | 83.83 132 | 82.51 250 | 93.89 206 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
APD-MVS | | | 94.24 22 | 94.07 24 | 94.75 25 | 98.06 28 | 86.90 15 | 95.88 41 | 96.94 39 | 85.68 121 | 95.05 11 | 97.18 21 | 87.31 19 | 99.07 44 | 91.90 45 | 98.61 33 | 98.28 28 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
K. test v3 | | | 81.59 274 | 80.15 275 | 85.91 293 | 89.89 295 | 69.42 316 | 92.57 229 | 87.71 328 | 85.56 122 | 73.44 311 | 89.71 262 | 55.58 312 | 95.52 276 | 77.17 224 | 69.76 326 | 92.78 262 |
|
SixPastTwentyTwo | | | 83.91 255 | 82.90 254 | 86.92 283 | 90.99 260 | 70.67 309 | 93.48 194 | 91.99 253 | 85.54 123 | 77.62 283 | 92.11 189 | 60.59 296 | 96.87 218 | 76.05 234 | 77.75 299 | 93.20 247 |
|
ITE_SJBPF | | | | | 88.24 256 | 91.88 213 | 77.05 261 | | 92.92 231 | 85.54 123 | 80.13 267 | 93.30 144 | 57.29 309 | 96.20 252 | 72.46 258 | 84.71 229 | 91.49 288 |
|
BH-RMVSNet | | | 88.37 134 | 87.48 134 | 91.02 142 | 95.28 111 | 79.45 188 | 92.89 220 | 93.07 230 | 85.45 125 | 86.91 129 | 94.84 100 | 70.35 212 | 97.76 139 | 73.97 250 | 94.59 101 | 95.85 122 |
|
semantic-postprocess | | | | | 88.18 258 | 91.71 219 | 76.87 263 | | 92.65 239 | 85.40 126 | 81.44 249 | 90.54 247 | 66.21 266 | 95.00 297 | 81.04 167 | 81.05 272 | 92.66 264 |
|
GA-MVS | | | 86.61 197 | 85.27 204 | 90.66 149 | 91.33 241 | 78.71 214 | 90.40 266 | 93.81 221 | 85.34 127 | 85.12 183 | 89.57 264 | 61.25 291 | 97.11 201 | 80.99 170 | 89.59 173 | 96.15 107 |
|
ACMM | | 84.12 9 | 89.14 116 | 88.48 115 | 91.12 135 | 94.65 140 | 81.22 141 | 95.31 59 | 96.12 93 | 85.31 128 | 85.92 147 | 94.34 109 | 70.19 215 | 98.06 125 | 85.65 108 | 88.86 190 | 94.08 199 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
xiu_mvs_v1_base_debu | | | 90.64 80 | 90.05 82 | 92.40 91 | 93.97 165 | 84.46 67 | 93.32 198 | 95.46 141 | 85.17 129 | 92.25 53 | 94.03 119 | 70.59 207 | 98.57 87 | 90.97 54 | 94.67 97 | 94.18 191 |
|
xiu_mvs_v1_base | | | 90.64 80 | 90.05 82 | 92.40 91 | 93.97 165 | 84.46 67 | 93.32 198 | 95.46 141 | 85.17 129 | 92.25 53 | 94.03 119 | 70.59 207 | 98.57 87 | 90.97 54 | 94.67 97 | 94.18 191 |
|
xiu_mvs_v1_base_debi | | | 90.64 80 | 90.05 82 | 92.40 91 | 93.97 165 | 84.46 67 | 93.32 198 | 95.46 141 | 85.17 129 | 92.25 53 | 94.03 119 | 70.59 207 | 98.57 87 | 90.97 54 | 94.67 97 | 94.18 191 |
|
PHI-MVS | | | 93.89 31 | 93.65 33 | 94.62 30 | 96.84 58 | 86.43 31 | 96.69 21 | 97.49 4 | 85.15 132 | 93.56 29 | 96.28 55 | 85.60 35 | 99.31 28 | 92.45 25 | 98.79 11 | 98.12 42 |
|
mvs_tets | | | 88.06 144 | 87.28 140 | 90.38 169 | 90.94 264 | 79.88 174 | 95.22 72 | 95.66 125 | 85.10 133 | 84.21 211 | 93.94 126 | 63.53 280 | 97.40 176 | 88.50 76 | 88.40 199 | 93.87 209 |
|
XVG-ACMP-BASELINE | | | 86.00 209 | 84.84 214 | 89.45 215 | 91.20 251 | 78.00 238 | 91.70 252 | 95.55 132 | 85.05 134 | 82.97 230 | 92.25 185 | 54.49 317 | 97.48 154 | 82.93 140 | 87.45 208 | 92.89 257 |
|
jajsoiax | | | 88.24 138 | 87.50 133 | 90.48 162 | 90.89 268 | 80.14 166 | 95.31 59 | 95.65 127 | 84.97 135 | 84.24 210 | 94.02 122 | 65.31 272 | 97.42 169 | 88.56 75 | 88.52 194 | 93.89 206 |
|
v2v482 | | | 87.84 151 | 87.06 149 | 90.17 175 | 90.99 260 | 79.23 208 | 94.00 167 | 95.13 168 | 84.87 136 | 85.53 163 | 92.07 193 | 74.45 155 | 97.45 158 | 84.71 119 | 81.75 264 | 93.85 212 |
|
v148 | | | 87.04 189 | 86.32 179 | 89.21 226 | 90.94 264 | 77.26 259 | 93.71 185 | 94.43 195 | 84.84 137 | 84.36 206 | 90.80 241 | 76.04 130 | 97.05 206 | 82.12 153 | 79.60 294 | 93.31 244 |
|
v8 | | | 87.50 175 | 86.71 162 | 89.89 196 | 91.37 235 | 79.40 194 | 94.50 118 | 95.38 151 | 84.81 138 | 83.60 222 | 91.33 223 | 76.05 128 | 97.42 169 | 82.84 142 | 80.51 285 | 92.84 259 |
|
BH-untuned | | | 88.60 130 | 88.13 125 | 90.01 193 | 95.24 120 | 78.50 226 | 93.29 203 | 94.15 204 | 84.75 139 | 84.46 200 | 93.40 139 | 75.76 140 | 97.40 176 | 77.59 219 | 94.52 103 | 94.12 195 |
|
OurMVSNet-221017-0 | | | 85.35 227 | 84.64 219 | 87.49 271 | 90.77 271 | 72.59 295 | 94.01 166 | 94.40 196 | 84.72 140 | 79.62 272 | 93.17 149 | 61.91 287 | 96.72 227 | 81.99 156 | 81.16 269 | 93.16 249 |
|
MVSFormer | | | 91.68 65 | 91.30 61 | 92.80 78 | 93.86 168 | 83.88 80 | 95.96 38 | 95.90 108 | 84.66 141 | 91.76 65 | 94.91 94 | 77.92 109 | 97.30 183 | 89.64 67 | 97.11 63 | 97.24 76 |
|
test_djsdf | | | 89.03 121 | 88.64 109 | 90.21 174 | 90.74 273 | 79.28 202 | 95.96 38 | 95.90 108 | 84.66 141 | 85.33 181 | 92.94 161 | 74.02 164 | 97.30 183 | 89.64 67 | 88.53 193 | 94.05 200 |
|
MVSTER | | | 88.84 125 | 88.29 121 | 90.51 160 | 92.95 196 | 80.44 163 | 93.73 182 | 95.01 172 | 84.66 141 | 87.15 124 | 93.12 152 | 72.79 180 | 97.21 195 | 87.86 84 | 87.36 209 | 93.87 209 |
|
v1neww | | | 87.98 145 | 87.25 142 | 90.16 176 | 91.38 233 | 79.41 190 | 94.37 133 | 95.28 156 | 84.48 144 | 85.77 150 | 91.53 213 | 76.12 124 | 97.45 158 | 84.45 123 | 81.89 259 | 93.61 233 |
|
v7new | | | 87.98 145 | 87.25 142 | 90.16 176 | 91.38 233 | 79.41 190 | 94.37 133 | 95.28 156 | 84.48 144 | 85.77 150 | 91.53 213 | 76.12 124 | 97.45 158 | 84.45 123 | 81.89 259 | 93.61 233 |
|
v6 | | | 87.98 145 | 87.25 142 | 90.16 176 | 91.36 236 | 79.39 195 | 94.37 133 | 95.27 159 | 84.48 144 | 85.78 149 | 91.51 215 | 76.15 123 | 97.46 156 | 84.46 122 | 81.88 261 | 93.62 232 |
|
v7n | | | 86.81 191 | 85.76 193 | 89.95 195 | 90.72 274 | 79.25 204 | 95.07 80 | 95.92 105 | 84.45 147 | 82.29 236 | 90.86 240 | 72.60 184 | 97.53 151 | 79.42 203 | 80.52 284 | 93.08 254 |
|
v1141 | | | 87.84 151 | 87.09 146 | 90.11 187 | 91.23 248 | 79.25 204 | 94.08 157 | 95.24 161 | 84.44 148 | 85.69 157 | 91.31 226 | 75.91 136 | 97.44 165 | 84.17 128 | 81.74 265 | 93.63 231 |
|
divwei89l23v2f112 | | | 87.84 151 | 87.09 146 | 90.10 189 | 91.23 248 | 79.24 206 | 94.09 155 | 95.24 161 | 84.44 148 | 85.70 155 | 91.31 226 | 75.91 136 | 97.44 165 | 84.17 128 | 81.73 266 | 93.64 229 |
|
v1 | | | 87.85 150 | 87.10 145 | 90.11 187 | 91.21 250 | 79.24 206 | 94.09 155 | 95.24 161 | 84.44 148 | 85.70 155 | 91.31 226 | 75.96 134 | 97.45 158 | 84.18 127 | 81.73 266 | 93.64 229 |
|
v748 | | | 86.27 203 | 85.28 203 | 89.25 225 | 90.26 286 | 77.58 258 | 94.89 92 | 95.50 139 | 84.28 151 | 81.41 250 | 90.46 251 | 72.57 185 | 97.32 182 | 79.81 195 | 78.36 297 | 92.84 259 |
|
CSCG | | | 93.23 48 | 93.05 42 | 93.76 55 | 98.04 29 | 84.07 77 | 96.22 28 | 97.37 10 | 84.15 152 | 90.05 84 | 95.66 79 | 87.77 13 | 99.15 38 | 89.91 65 | 98.27 42 | 98.07 45 |
|
Baseline_NR-MVSNet | | | 87.07 188 | 86.63 173 | 88.40 251 | 91.44 226 | 77.87 243 | 94.23 142 | 92.57 240 | 84.12 153 | 85.74 154 | 92.08 191 | 77.25 113 | 96.04 256 | 82.29 152 | 79.94 291 | 91.30 292 |
|
ab-mvs | | | 89.41 110 | 88.35 116 | 92.60 83 | 95.15 121 | 82.65 116 | 92.20 241 | 95.60 129 | 83.97 154 | 88.55 97 | 93.70 138 | 74.16 162 | 98.21 104 | 82.46 149 | 89.37 175 | 96.94 89 |
|
FMVSNet3 | | | 87.40 178 | 86.11 184 | 91.30 131 | 93.79 173 | 83.64 86 | 94.20 149 | 94.81 186 | 83.89 155 | 84.37 203 | 91.87 200 | 68.45 246 | 96.56 235 | 78.23 213 | 85.36 223 | 93.70 223 |
|
test_normal | | | 88.13 142 | 86.78 160 | 92.18 100 | 90.55 281 | 81.19 143 | 92.74 223 | 94.64 190 | 83.84 156 | 77.49 284 | 90.51 250 | 68.49 244 | 98.16 106 | 88.22 78 | 94.55 102 | 97.21 79 |
|
pm-mvs1 | | | 86.61 197 | 85.54 195 | 89.82 198 | 91.44 226 | 80.18 164 | 95.28 69 | 94.85 183 | 83.84 156 | 81.66 247 | 92.62 171 | 72.45 188 | 96.48 240 | 79.67 197 | 78.06 298 | 92.82 261 |
|
DI_MVS_plusplus_test | | | 88.15 141 | 86.82 156 | 92.14 102 | 90.67 276 | 81.07 145 | 93.01 215 | 94.59 191 | 83.83 158 | 77.78 281 | 90.63 244 | 68.51 243 | 98.16 106 | 88.02 83 | 94.37 108 | 97.17 81 |
|
v10 | | | 87.25 182 | 86.38 176 | 89.85 197 | 91.19 253 | 79.50 181 | 94.48 119 | 95.45 145 | 83.79 159 | 83.62 221 | 91.19 231 | 75.13 148 | 97.42 169 | 81.94 157 | 80.60 280 | 92.63 265 |
|
testgi | | | 80.94 284 | 80.20 274 | 83.18 308 | 87.96 314 | 66.29 324 | 91.28 259 | 90.70 291 | 83.70 160 | 78.12 278 | 92.84 163 | 51.37 323 | 90.82 327 | 63.34 313 | 82.46 251 | 92.43 270 |
|
V42 | | | 87.68 158 | 86.86 154 | 90.15 180 | 90.58 278 | 80.14 166 | 94.24 141 | 95.28 156 | 83.66 161 | 85.67 158 | 91.33 223 | 74.73 153 | 97.41 174 | 84.43 125 | 81.83 262 | 92.89 257 |
|
v52 | | | 86.50 199 | 85.53 198 | 89.39 217 | 89.17 299 | 78.99 211 | 94.72 105 | 95.54 134 | 83.59 162 | 82.10 240 | 90.60 246 | 71.59 193 | 97.45 158 | 82.52 145 | 79.99 290 | 91.73 284 |
|
V4 | | | 86.50 199 | 85.54 195 | 89.39 217 | 89.13 300 | 78.99 211 | 94.73 102 | 95.54 134 | 83.59 162 | 82.10 240 | 90.61 245 | 71.60 192 | 97.45 158 | 82.52 145 | 80.01 289 | 91.74 283 |
|
GBi-Net | | | 87.26 180 | 85.98 188 | 91.08 138 | 94.01 160 | 83.10 98 | 95.14 77 | 94.94 176 | 83.57 164 | 84.37 203 | 91.64 204 | 66.59 262 | 96.34 248 | 78.23 213 | 85.36 223 | 93.79 214 |
|
test1 | | | 87.26 180 | 85.98 188 | 91.08 138 | 94.01 160 | 83.10 98 | 95.14 77 | 94.94 176 | 83.57 164 | 84.37 203 | 91.64 204 | 66.59 262 | 96.34 248 | 78.23 213 | 85.36 223 | 93.79 214 |
|
FMVSNet2 | | | 87.19 186 | 85.82 192 | 91.30 131 | 94.01 160 | 83.67 85 | 94.79 99 | 94.94 176 | 83.57 164 | 83.88 214 | 92.05 194 | 66.59 262 | 96.51 238 | 77.56 220 | 85.01 227 | 93.73 221 |
|
Patchmatch-test1 | | | 85.81 216 | 84.71 216 | 89.12 228 | 92.15 207 | 76.60 264 | 91.12 263 | 91.69 261 | 83.53 167 | 85.50 166 | 88.56 277 | 66.79 260 | 95.00 297 | 72.69 257 | 90.35 160 | 95.76 127 |
|
PVSNet_BlendedMVS | | | 89.98 92 | 89.70 86 | 90.82 147 | 96.12 78 | 81.25 139 | 93.92 170 | 96.83 46 | 83.49 168 | 89.10 92 | 92.26 184 | 81.04 78 | 98.85 73 | 86.72 103 | 87.86 205 | 92.35 274 |
|
v7 | | | 87.75 156 | 86.96 152 | 90.12 182 | 91.20 251 | 79.50 181 | 94.28 139 | 95.46 141 | 83.45 169 | 85.75 152 | 91.56 212 | 75.13 148 | 97.43 167 | 83.60 134 | 82.18 254 | 93.42 242 |
|
test-LLR | | | 85.87 211 | 85.41 200 | 87.25 276 | 90.95 262 | 71.67 300 | 89.55 277 | 89.88 305 | 83.41 170 | 84.54 198 | 87.95 285 | 67.25 257 | 95.11 294 | 81.82 159 | 93.37 124 | 94.97 147 |
|
test0.0.03 1 | | | 82.41 268 | 81.69 261 | 84.59 302 | 88.23 309 | 72.89 288 | 90.24 268 | 87.83 327 | 83.41 170 | 79.86 269 | 89.78 261 | 67.25 257 | 88.99 330 | 65.18 308 | 83.42 244 | 91.90 281 |
|
Test4 | | | 85.75 218 | 83.72 236 | 91.83 115 | 88.08 312 | 81.03 147 | 92.48 231 | 95.54 134 | 83.38 172 | 73.40 312 | 88.57 276 | 50.99 324 | 97.37 180 | 86.61 105 | 94.47 105 | 97.09 85 |
|
v1144 | | | 87.61 171 | 86.79 159 | 90.06 190 | 91.01 259 | 79.34 198 | 93.95 169 | 95.42 150 | 83.36 173 | 85.66 159 | 91.31 226 | 74.98 152 | 97.42 169 | 83.37 135 | 82.06 255 | 93.42 242 |
|
PVSNet_Blended_VisFu | | | 91.38 68 | 90.91 69 | 92.80 78 | 96.39 68 | 83.17 97 | 94.87 95 | 96.66 62 | 83.29 174 | 89.27 90 | 94.46 108 | 80.29 83 | 99.17 35 | 87.57 88 | 95.37 90 | 96.05 116 |
|
IB-MVS | | 80.51 15 | 85.24 230 | 83.26 249 | 91.19 133 | 92.13 209 | 79.86 175 | 91.75 249 | 91.29 274 | 83.28 175 | 80.66 259 | 88.49 278 | 61.28 290 | 98.46 92 | 80.99 170 | 79.46 295 | 95.25 141 |
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021 |
IterMVS | | | 84.88 239 | 83.98 231 | 87.60 267 | 91.44 226 | 76.03 270 | 90.18 270 | 92.41 242 | 83.24 176 | 81.06 255 | 90.42 252 | 66.60 261 | 94.28 302 | 79.46 199 | 80.98 277 | 92.48 268 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Fast-Effi-MVS+ | | | 89.41 110 | 88.64 109 | 91.71 120 | 94.74 133 | 80.81 154 | 93.54 192 | 95.10 169 | 83.11 177 | 86.82 132 | 90.67 243 | 79.74 89 | 97.75 142 | 80.51 179 | 93.55 117 | 96.57 99 |
|
WTY-MVS | | | 89.60 101 | 88.92 104 | 91.67 121 | 95.47 105 | 81.15 144 | 92.38 235 | 94.78 187 | 83.11 177 | 89.06 94 | 94.32 111 | 78.67 100 | 96.61 234 | 81.57 163 | 90.89 156 | 97.24 76 |
|
V9 | | | 84.77 243 | 83.50 244 | 88.58 242 | 91.33 241 | 79.46 186 | 93.75 180 | 94.00 213 | 83.07 179 | 76.07 297 | 86.43 300 | 75.97 133 | 95.37 286 | 79.91 192 | 70.93 322 | 90.91 301 |
|
v15 | | | 84.79 241 | 83.53 242 | 88.57 245 | 91.30 247 | 79.41 190 | 93.70 186 | 94.01 210 | 83.06 180 | 76.27 291 | 86.42 303 | 76.03 131 | 95.38 285 | 80.01 187 | 71.00 319 | 90.92 300 |
|
v12 | | | 84.74 244 | 83.46 245 | 88.58 242 | 91.32 243 | 79.50 181 | 93.75 180 | 94.01 210 | 83.06 180 | 75.98 299 | 86.41 304 | 75.82 139 | 95.36 288 | 79.87 193 | 70.89 323 | 90.89 303 |
|
V14 | | | 84.79 241 | 83.52 243 | 88.57 245 | 91.32 243 | 79.43 189 | 93.72 184 | 94.01 210 | 83.06 180 | 76.22 292 | 86.43 300 | 76.01 132 | 95.37 286 | 79.96 189 | 70.99 320 | 90.91 301 |
|
v17 | | | 84.93 238 | 83.70 237 | 88.62 239 | 91.36 236 | 79.48 184 | 93.83 173 | 94.03 209 | 83.04 183 | 76.51 290 | 86.57 299 | 76.05 128 | 95.42 283 | 80.31 185 | 71.65 316 | 90.96 297 |
|
v16 | | | 84.96 236 | 83.74 235 | 88.62 239 | 91.40 231 | 79.48 184 | 93.83 173 | 94.04 207 | 83.03 184 | 76.54 289 | 86.59 298 | 76.11 127 | 95.42 283 | 80.33 183 | 71.80 314 | 90.95 299 |
|
v13 | | | 84.72 246 | 83.44 247 | 88.58 242 | 91.31 246 | 79.52 180 | 93.77 178 | 94.00 213 | 83.03 184 | 75.85 300 | 86.38 305 | 75.84 138 | 95.35 289 | 79.83 194 | 70.95 321 | 90.87 304 |
|
LTVRE_ROB | | 82.13 13 | 86.26 204 | 84.90 212 | 90.34 172 | 94.44 148 | 81.50 131 | 92.31 237 | 94.89 181 | 83.03 184 | 79.63 271 | 92.67 169 | 69.69 219 | 97.79 137 | 71.20 263 | 86.26 217 | 91.72 285 |
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 |
v18 | | | 84.97 235 | 83.76 233 | 88.60 241 | 91.36 236 | 79.41 190 | 93.82 175 | 94.04 207 | 83.00 187 | 76.61 288 | 86.60 297 | 76.19 122 | 95.43 282 | 80.39 180 | 71.79 315 | 90.96 297 |
|
UnsupCasMVSNet_eth | | | 80.07 287 | 78.27 289 | 85.46 296 | 85.24 322 | 72.63 294 | 88.45 294 | 94.87 182 | 82.99 188 | 71.64 320 | 88.07 284 | 56.34 311 | 91.75 324 | 73.48 254 | 63.36 337 | 92.01 280 |
|
XXY-MVS | | | 87.65 159 | 86.85 155 | 90.03 191 | 92.14 208 | 80.60 159 | 93.76 179 | 95.23 164 | 82.94 189 | 84.60 196 | 94.02 122 | 74.27 157 | 95.49 280 | 81.04 167 | 83.68 239 | 94.01 203 |
|
testing_2 | | | 83.40 261 | 81.02 266 | 90.56 152 | 85.06 323 | 80.51 161 | 91.37 258 | 95.57 130 | 82.92 190 | 67.06 328 | 85.54 311 | 49.47 327 | 97.24 191 | 86.74 100 | 85.44 222 | 93.93 204 |
|
mvs_anonymous | | | 89.37 113 | 89.32 94 | 89.51 213 | 93.47 179 | 74.22 277 | 91.65 254 | 94.83 185 | 82.91 191 | 85.45 169 | 93.79 134 | 81.23 77 | 96.36 247 | 86.47 106 | 94.09 110 | 97.94 53 |
|
BH-w/o | | | 87.57 173 | 87.05 150 | 89.12 228 | 94.90 130 | 77.90 241 | 92.41 233 | 93.51 224 | 82.89 192 | 83.70 219 | 91.34 222 | 75.75 141 | 97.07 204 | 75.49 236 | 93.49 119 | 92.39 272 |
|
v11 | | | 84.67 249 | 83.41 248 | 88.44 250 | 91.32 243 | 79.13 209 | 93.69 189 | 93.99 215 | 82.81 193 | 76.20 293 | 86.24 307 | 75.48 144 | 95.35 289 | 79.53 198 | 71.48 318 | 90.85 305 |
|
AdaColmap | | | 89.89 97 | 89.07 100 | 92.37 94 | 97.41 42 | 83.03 101 | 94.42 126 | 95.92 105 | 82.81 193 | 86.34 141 | 94.65 104 | 73.89 165 | 99.02 53 | 80.69 174 | 95.51 86 | 95.05 144 |
|
TransMVSNet (Re) | | | 84.43 251 | 83.06 252 | 88.54 247 | 91.72 218 | 78.44 227 | 95.18 74 | 92.82 234 | 82.73 195 | 79.67 270 | 92.12 187 | 73.49 171 | 95.96 261 | 71.10 266 | 68.73 330 | 91.21 293 |
|
PatchFormer-LS_test | | | 86.02 208 | 85.13 205 | 88.70 236 | 91.52 223 | 74.12 280 | 91.19 262 | 92.09 248 | 82.71 196 | 84.30 209 | 87.24 294 | 70.87 202 | 96.98 210 | 81.04 167 | 85.17 226 | 95.00 146 |
|
DP-MVS Recon | | | 91.95 59 | 91.28 62 | 93.96 47 | 98.33 19 | 85.92 44 | 94.66 111 | 96.66 62 | 82.69 197 | 90.03 85 | 95.82 74 | 82.30 63 | 99.03 50 | 84.57 120 | 96.48 77 | 96.91 90 |
|
v1192 | | | 87.25 182 | 86.33 178 | 90.00 194 | 90.76 272 | 79.04 210 | 93.80 176 | 95.48 140 | 82.57 198 | 85.48 167 | 91.18 232 | 73.38 175 | 97.42 169 | 82.30 151 | 82.06 255 | 93.53 237 |
|
API-MVS | | | 90.66 79 | 90.07 81 | 92.45 90 | 96.36 69 | 84.57 62 | 96.06 33 | 95.22 166 | 82.39 199 | 89.13 91 | 94.27 116 | 80.32 82 | 98.46 92 | 80.16 186 | 96.71 70 | 94.33 188 |
|
tfpnnormal | | | 84.72 246 | 83.23 250 | 89.20 227 | 92.79 199 | 80.05 169 | 94.48 119 | 95.81 114 | 82.38 200 | 81.08 254 | 91.21 230 | 69.01 230 | 96.95 213 | 61.69 318 | 80.59 281 | 90.58 310 |
|
MAR-MVS | | | 90.30 86 | 89.37 93 | 93.07 69 | 96.61 62 | 84.48 66 | 95.68 49 | 95.67 123 | 82.36 201 | 87.85 109 | 92.85 162 | 76.63 120 | 98.80 76 | 80.01 187 | 96.68 71 | 95.91 119 |
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 |
TAMVS | | | 89.21 115 | 88.29 121 | 91.96 108 | 93.71 174 | 82.62 117 | 93.30 202 | 94.19 202 | 82.22 202 | 87.78 116 | 93.94 126 | 78.83 97 | 96.95 213 | 77.70 218 | 92.98 131 | 96.32 103 |
|
ACMH+ | | 81.04 14 | 85.05 233 | 83.46 245 | 89.82 198 | 94.66 139 | 79.37 196 | 94.44 124 | 94.12 206 | 82.19 203 | 78.04 279 | 92.82 165 | 58.23 306 | 97.54 150 | 73.77 252 | 82.90 247 | 92.54 266 |
|
ACMH | | 80.38 17 | 85.36 226 | 83.68 238 | 90.39 167 | 94.45 147 | 80.63 157 | 94.73 102 | 94.85 183 | 82.09 204 | 77.24 285 | 92.65 170 | 60.01 300 | 97.58 147 | 72.25 259 | 84.87 228 | 92.96 255 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
tfpn1000 | | | 86.06 207 | 84.92 211 | 89.49 214 | 95.54 101 | 77.79 245 | 94.72 105 | 89.07 319 | 82.05 205 | 85.36 180 | 91.94 197 | 68.32 254 | 96.65 230 | 67.04 293 | 90.24 162 | 94.02 202 |
|
anonymousdsp | | | 87.84 151 | 87.09 146 | 90.12 182 | 89.13 300 | 80.54 160 | 94.67 110 | 95.55 132 | 82.05 205 | 83.82 216 | 92.12 187 | 71.47 196 | 97.15 197 | 87.15 95 | 87.80 206 | 92.67 263 |
|
PVSNet_Blended | | | 90.73 78 | 90.32 76 | 91.98 107 | 96.12 78 | 81.25 139 | 92.55 230 | 96.83 46 | 82.04 207 | 89.10 92 | 92.56 172 | 81.04 78 | 98.85 73 | 86.72 103 | 95.91 81 | 95.84 123 |
|
agg_prior1 | | | 93.29 43 | 92.97 45 | 94.26 42 | 97.38 43 | 85.92 44 | 93.92 170 | 96.72 56 | 81.96 208 | 92.16 56 | 96.23 57 | 87.85 12 | 98.97 61 | 91.95 41 | 98.55 37 | 97.90 58 |
|
CDS-MVSNet | | | 89.45 107 | 88.51 111 | 92.29 97 | 93.62 176 | 83.61 88 | 93.01 215 | 94.68 189 | 81.95 209 | 87.82 115 | 93.24 147 | 78.69 99 | 96.99 209 | 80.34 182 | 93.23 127 | 96.28 104 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
v144192 | | | 87.19 186 | 86.35 177 | 89.74 202 | 90.64 277 | 78.24 234 | 93.92 170 | 95.43 148 | 81.93 210 | 85.51 165 | 91.05 238 | 74.21 160 | 97.45 158 | 82.86 141 | 81.56 268 | 93.53 237 |
|
PAPR | | | 90.02 91 | 89.27 97 | 92.29 97 | 95.78 94 | 80.95 150 | 92.68 225 | 96.22 86 | 81.91 211 | 86.66 134 | 93.75 137 | 82.23 64 | 98.44 94 | 79.40 204 | 94.79 96 | 97.48 72 |
|
v1921920 | | | 86.97 190 | 86.06 187 | 89.69 207 | 90.53 282 | 78.11 237 | 93.80 176 | 95.43 148 | 81.90 212 | 85.33 181 | 91.05 238 | 72.66 182 | 97.41 174 | 82.05 155 | 81.80 263 | 93.53 237 |
|
conf0.01 | | | 85.83 214 | 84.54 220 | 89.71 204 | 95.26 113 | 77.63 251 | 94.21 143 | 89.33 312 | 81.89 213 | 84.94 187 | 91.51 215 | 68.43 247 | 96.80 221 | 66.05 299 | 89.23 179 | 94.71 165 |
|
conf0.002 | | | 85.83 214 | 84.54 220 | 89.71 204 | 95.26 113 | 77.63 251 | 94.21 143 | 89.33 312 | 81.89 213 | 84.94 187 | 91.51 215 | 68.43 247 | 96.80 221 | 66.05 299 | 89.23 179 | 94.71 165 |
|
thresconf0.02 | | | 85.75 218 | 84.54 220 | 89.38 219 | 95.26 113 | 77.63 251 | 94.21 143 | 89.33 312 | 81.89 213 | 84.94 187 | 91.51 215 | 68.43 247 | 96.80 221 | 66.05 299 | 89.23 179 | 93.70 223 |
|
tfpn_n400 | | | 85.75 218 | 84.54 220 | 89.38 219 | 95.26 113 | 77.63 251 | 94.21 143 | 89.33 312 | 81.89 213 | 84.94 187 | 91.51 215 | 68.43 247 | 96.80 221 | 66.05 299 | 89.23 179 | 93.70 223 |
|
tfpnconf | | | 85.75 218 | 84.54 220 | 89.38 219 | 95.26 113 | 77.63 251 | 94.21 143 | 89.33 312 | 81.89 213 | 84.94 187 | 91.51 215 | 68.43 247 | 96.80 221 | 66.05 299 | 89.23 179 | 93.70 223 |
|
tfpnview11 | | | 85.75 218 | 84.54 220 | 89.38 219 | 95.26 113 | 77.63 251 | 94.21 143 | 89.33 312 | 81.89 213 | 84.94 187 | 91.51 215 | 68.43 247 | 96.80 221 | 66.05 299 | 89.23 179 | 93.70 223 |
|
CPTT-MVS | | | 91.99 58 | 91.80 57 | 92.55 85 | 98.24 23 | 81.98 126 | 96.76 19 | 96.49 71 | 81.89 213 | 90.24 81 | 96.44 51 | 78.59 101 | 98.61 85 | 89.68 66 | 97.85 53 | 97.06 86 |
|
tfpn_ndepth | | | 86.10 206 | 84.98 207 | 89.43 216 | 95.52 104 | 78.29 232 | 94.62 112 | 89.60 310 | 81.88 220 | 85.43 172 | 90.54 247 | 68.47 245 | 96.85 220 | 68.46 286 | 90.34 161 | 93.15 251 |
|
train_agg | | | 93.44 39 | 93.08 41 | 94.52 33 | 97.53 36 | 86.49 29 | 94.07 159 | 96.78 50 | 81.86 221 | 92.77 40 | 96.20 59 | 87.63 16 | 99.12 41 | 92.14 35 | 98.69 21 | 97.94 53 |
|
test_8 | | | | | | 97.49 39 | 86.30 37 | 94.02 165 | 96.76 53 | 81.86 221 | 92.70 44 | 96.20 59 | 87.63 16 | 99.02 53 | | | |
|
v1240 | | | 86.78 193 | 85.85 191 | 89.56 209 | 90.45 283 | 77.79 245 | 93.61 190 | 95.37 153 | 81.65 223 | 85.43 172 | 91.15 234 | 71.50 195 | 97.43 167 | 81.47 164 | 82.05 257 | 93.47 241 |
|
FMVSNet1 | | | 85.85 212 | 84.11 228 | 91.08 138 | 92.81 198 | 83.10 98 | 95.14 77 | 94.94 176 | 81.64 224 | 82.68 233 | 91.64 204 | 59.01 304 | 96.34 248 | 75.37 238 | 83.78 236 | 93.79 214 |
|
PatchmatchNet | | | 85.85 212 | 84.70 217 | 89.29 224 | 91.76 217 | 75.54 273 | 88.49 293 | 91.30 273 | 81.63 225 | 85.05 184 | 88.70 274 | 71.71 190 | 96.24 251 | 74.61 246 | 89.05 188 | 96.08 113 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
TEST9 | | | | | | 97.53 36 | 86.49 29 | 94.07 159 | 96.78 50 | 81.61 226 | 92.77 40 | 96.20 59 | 87.71 15 | 99.12 41 | | | |
|
sss | | | 88.93 124 | 88.26 123 | 90.94 146 | 94.05 158 | 80.78 155 | 91.71 251 | 95.38 151 | 81.55 227 | 88.63 96 | 93.91 130 | 75.04 151 | 95.47 281 | 82.47 148 | 91.61 141 | 96.57 99 |
|
HY-MVS | | 83.01 12 | 89.03 121 | 87.94 129 | 92.29 97 | 94.86 131 | 82.77 108 | 92.08 246 | 94.49 193 | 81.52 228 | 86.93 128 | 92.79 168 | 78.32 106 | 98.23 102 | 79.93 190 | 90.55 157 | 95.88 121 |
|
agg_prior3 | | | 93.27 44 | 92.89 47 | 94.40 39 | 97.49 39 | 86.12 41 | 94.07 159 | 96.73 54 | 81.46 229 | 92.46 52 | 96.05 67 | 86.90 23 | 99.15 38 | 92.14 35 | 98.69 21 | 97.94 53 |
|
CNLPA | | | 89.07 118 | 87.98 127 | 92.34 95 | 96.87 57 | 84.78 58 | 94.08 157 | 93.24 227 | 81.41 230 | 84.46 200 | 95.13 91 | 75.57 143 | 96.62 232 | 77.21 223 | 93.84 114 | 95.61 132 |
|
EPMVS | | | 83.90 256 | 82.70 257 | 87.51 269 | 90.23 288 | 72.67 292 | 88.62 292 | 81.96 342 | 81.37 231 | 85.01 185 | 88.34 280 | 66.31 265 | 94.45 300 | 75.30 239 | 87.12 212 | 95.43 136 |
|
test20.03 | | | 79.95 288 | 79.08 285 | 82.55 311 | 85.79 320 | 67.74 321 | 91.09 264 | 91.08 279 | 81.23 232 | 74.48 307 | 89.96 259 | 61.63 288 | 90.15 328 | 60.08 322 | 76.38 303 | 89.76 312 |
|
TR-MVS | | | 86.78 193 | 85.76 193 | 89.82 198 | 94.37 149 | 78.41 228 | 92.47 232 | 92.83 233 | 81.11 233 | 86.36 140 | 92.40 176 | 68.73 240 | 97.48 154 | 73.75 253 | 89.85 169 | 93.57 236 |
|
VDDNet | | | 89.56 103 | 88.49 114 | 92.76 80 | 95.07 122 | 82.09 123 | 96.30 26 | 93.19 228 | 81.05 234 | 91.88 61 | 96.86 30 | 61.16 294 | 98.33 99 | 88.43 77 | 92.49 137 | 97.84 60 |
|
tpm | | | 84.73 245 | 84.02 229 | 86.87 286 | 90.33 284 | 68.90 317 | 89.06 287 | 89.94 303 | 80.85 235 | 85.75 152 | 89.86 260 | 68.54 242 | 95.97 260 | 77.76 217 | 84.05 235 | 95.75 128 |
|
DWT-MVSNet_test | | | 84.95 237 | 83.68 238 | 88.77 233 | 91.43 229 | 73.75 283 | 91.74 250 | 90.98 283 | 80.66 236 | 83.84 215 | 87.36 292 | 62.44 283 | 97.11 201 | 78.84 208 | 85.81 219 | 95.46 135 |
|
diffmvs | | | 89.07 118 | 88.32 119 | 91.34 129 | 93.24 185 | 79.79 177 | 92.29 238 | 94.98 175 | 80.24 237 | 87.38 123 | 92.45 174 | 78.02 107 | 97.33 181 | 83.29 136 | 92.93 132 | 96.91 90 |
|
jason | | | 90.80 76 | 90.10 80 | 92.90 75 | 93.04 192 | 83.53 89 | 93.08 212 | 94.15 204 | 80.22 238 | 91.41 70 | 94.91 94 | 76.87 115 | 97.93 133 | 90.28 64 | 96.90 66 | 97.24 76 |
jason: jason. |
tpmrst | | | 85.35 227 | 84.99 206 | 86.43 289 | 90.88 269 | 67.88 320 | 88.71 290 | 91.43 271 | 80.13 239 | 86.08 146 | 88.80 272 | 73.05 177 | 96.02 258 | 82.48 147 | 83.40 245 | 95.40 137 |
|
CDPH-MVS | | | 92.83 52 | 92.30 54 | 94.44 35 | 97.79 33 | 86.11 42 | 94.06 162 | 96.66 62 | 80.09 240 | 92.77 40 | 96.63 42 | 86.62 25 | 99.04 49 | 87.40 90 | 98.66 28 | 98.17 37 |
|
PM-MVS | | | 78.11 296 | 76.12 298 | 84.09 307 | 83.54 328 | 70.08 313 | 88.97 288 | 85.27 336 | 79.93 241 | 74.73 305 | 86.43 300 | 34.70 342 | 93.48 310 | 79.43 202 | 72.06 313 | 88.72 322 |
|
lupinMVS | | | 90.92 75 | 90.21 77 | 93.03 70 | 93.86 168 | 83.88 80 | 92.81 221 | 93.86 218 | 79.84 242 | 91.76 65 | 94.29 113 | 77.92 109 | 98.04 126 | 90.48 63 | 97.11 63 | 97.17 81 |
|
PatchMatch-RL | | | 86.77 195 | 85.54 195 | 90.47 163 | 95.88 91 | 82.71 114 | 90.54 265 | 92.31 243 | 79.82 243 | 84.32 207 | 91.57 211 | 68.77 239 | 96.39 245 | 73.16 255 | 93.48 121 | 92.32 275 |
|
PLC | | 84.53 7 | 89.06 120 | 88.03 126 | 92.15 101 | 97.27 50 | 82.69 115 | 94.29 138 | 95.44 147 | 79.71 244 | 84.01 213 | 94.18 118 | 76.68 119 | 98.75 78 | 77.28 222 | 93.41 122 | 95.02 145 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
F-COLMAP | | | 87.95 148 | 86.80 158 | 91.40 128 | 96.35 70 | 80.88 152 | 94.73 102 | 95.45 145 | 79.65 245 | 82.04 243 | 94.61 105 | 71.13 198 | 98.50 90 | 76.24 232 | 91.05 150 | 94.80 163 |
|
MIMVSNet | | | 82.59 267 | 80.53 270 | 88.76 234 | 91.51 224 | 78.32 230 | 86.57 307 | 90.13 298 | 79.32 246 | 80.70 258 | 88.69 275 | 52.98 321 | 93.07 317 | 66.03 305 | 88.86 190 | 94.90 158 |
|
test-mter | | | 84.54 250 | 83.64 240 | 87.25 276 | 90.95 262 | 71.67 300 | 89.55 277 | 89.88 305 | 79.17 247 | 84.54 198 | 87.95 285 | 55.56 313 | 95.11 294 | 81.82 159 | 93.37 124 | 94.97 147 |
|
MDA-MVSNet-bldmvs | | | 78.85 295 | 76.31 296 | 86.46 288 | 89.76 296 | 73.88 282 | 88.79 289 | 90.42 292 | 79.16 248 | 59.18 336 | 88.33 281 | 60.20 298 | 94.04 304 | 62.00 317 | 68.96 328 | 91.48 289 |
|
tpmvs | | | 83.35 262 | 82.07 259 | 87.20 280 | 91.07 258 | 71.00 307 | 88.31 295 | 91.70 260 | 78.91 249 | 80.49 262 | 87.18 295 | 69.30 226 | 97.08 203 | 68.12 291 | 83.56 241 | 93.51 240 |
|
原ACMM1 | | | | | 92.01 104 | 97.34 45 | 81.05 146 | | 96.81 48 | 78.89 250 | 90.45 79 | 95.92 70 | 82.65 59 | 98.84 75 | 80.68 175 | 98.26 43 | 96.14 108 |
|
MSDG | | | 84.86 240 | 83.09 251 | 90.14 181 | 93.80 171 | 80.05 169 | 89.18 286 | 93.09 229 | 78.89 250 | 78.19 277 | 91.91 198 | 65.86 271 | 97.27 187 | 68.47 285 | 88.45 196 | 93.11 252 |
|
PAPM | | | 86.68 196 | 85.39 201 | 90.53 153 | 93.05 191 | 79.33 201 | 89.79 276 | 94.77 188 | 78.82 252 | 81.95 244 | 93.24 147 | 76.81 116 | 97.30 183 | 66.94 294 | 93.16 128 | 94.95 157 |
|
PVSNet | | 78.82 18 | 85.55 224 | 84.65 218 | 88.23 257 | 94.72 135 | 71.93 298 | 87.12 304 | 92.75 236 | 78.80 253 | 84.95 186 | 90.53 249 | 64.43 277 | 96.71 229 | 74.74 244 | 93.86 113 | 96.06 115 |
|
MVP-Stereo | | | 85.97 210 | 84.86 213 | 89.32 223 | 90.92 266 | 82.19 122 | 92.11 244 | 94.19 202 | 78.76 254 | 78.77 276 | 91.63 207 | 68.38 253 | 96.56 235 | 75.01 243 | 93.95 111 | 89.20 317 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
OpenMVS | | 83.78 11 | 88.74 128 | 87.29 139 | 93.08 67 | 92.70 200 | 85.39 52 | 96.57 22 | 96.43 74 | 78.74 255 | 80.85 256 | 96.07 66 | 69.64 220 | 99.01 55 | 78.01 216 | 96.65 72 | 94.83 161 |
|
MDTV_nov1_ep13 | | | | 83.56 241 | | 91.69 221 | 69.93 314 | 87.75 300 | 91.54 268 | 78.60 256 | 84.86 193 | 88.90 270 | 69.54 221 | 96.03 257 | 70.25 268 | 88.93 189 | |
|
Patchmatch-RL test | | | 81.67 272 | 79.96 276 | 86.81 287 | 85.42 321 | 71.23 303 | 82.17 331 | 87.50 331 | 78.47 257 | 77.19 286 | 82.50 322 | 70.81 204 | 93.48 310 | 82.66 144 | 72.89 311 | 95.71 129 |
|
QAPM | | | 89.51 104 | 88.15 124 | 93.59 57 | 94.92 128 | 84.58 61 | 96.82 18 | 96.70 58 | 78.43 258 | 83.41 226 | 96.19 62 | 73.18 176 | 99.30 29 | 77.11 225 | 96.54 75 | 96.89 92 |
|
1314 | | | 87.51 174 | 86.57 174 | 90.34 172 | 92.42 204 | 79.74 179 | 92.63 226 | 95.35 155 | 78.35 259 | 80.14 266 | 91.62 208 | 74.05 163 | 97.15 197 | 81.05 166 | 93.53 118 | 94.12 195 |
|
CR-MVSNet | | | 85.35 227 | 83.76 233 | 90.12 182 | 90.58 278 | 79.34 198 | 85.24 316 | 91.96 256 | 78.27 260 | 85.55 161 | 87.87 288 | 71.03 200 | 95.61 272 | 73.96 251 | 89.36 176 | 95.40 137 |
|
USDC | | | 82.76 264 | 81.26 265 | 87.26 275 | 91.17 254 | 74.55 276 | 89.27 283 | 93.39 226 | 78.26 261 | 75.30 302 | 92.08 191 | 54.43 318 | 96.63 231 | 71.64 260 | 85.79 221 | 90.61 307 |
|
new-patchmatchnet | | | 76.41 299 | 75.17 299 | 80.13 314 | 82.65 332 | 59.61 334 | 87.66 301 | 91.08 279 | 78.23 262 | 69.85 322 | 83.22 319 | 54.76 316 | 91.63 326 | 64.14 312 | 64.89 333 | 89.16 318 |
|
1112_ss | | | 88.42 132 | 87.33 138 | 91.72 119 | 94.92 128 | 80.98 148 | 92.97 218 | 94.54 192 | 78.16 263 | 83.82 216 | 93.88 131 | 78.78 98 | 97.91 134 | 79.45 200 | 89.41 174 | 96.26 105 |
|
MIMVSNet1 | | | 79.38 292 | 77.28 292 | 85.69 294 | 86.35 319 | 73.67 284 | 91.61 255 | 92.75 236 | 78.11 264 | 72.64 316 | 88.12 283 | 48.16 329 | 91.97 323 | 60.32 321 | 77.49 300 | 91.43 290 |
|
MS-PatchMatch | | | 85.05 233 | 84.16 227 | 87.73 265 | 91.42 230 | 78.51 225 | 91.25 261 | 93.53 223 | 77.50 265 | 80.15 265 | 91.58 209 | 61.99 286 | 95.51 277 | 75.69 235 | 94.35 109 | 89.16 318 |
|
AllTest | | | 83.42 259 | 81.39 263 | 89.52 211 | 95.01 123 | 77.79 245 | 93.12 209 | 90.89 286 | 77.41 266 | 76.12 295 | 93.34 140 | 54.08 319 | 97.51 152 | 68.31 288 | 84.27 233 | 93.26 245 |
|
TestCases | | | | | 89.52 211 | 95.01 123 | 77.79 245 | | 90.89 286 | 77.41 266 | 76.12 295 | 93.34 140 | 54.08 319 | 97.51 152 | 68.31 288 | 84.27 233 | 93.26 245 |
|
TESTMET0.1,1 | | | 83.74 258 | 82.85 255 | 86.42 290 | 89.96 293 | 71.21 304 | 89.55 277 | 87.88 326 | 77.41 266 | 83.37 227 | 87.31 293 | 56.71 310 | 93.65 308 | 80.62 176 | 92.85 135 | 94.40 187 |
|
gm-plane-assit | | | | | | 89.60 298 | 68.00 319 | | | 77.28 269 | | 88.99 269 | | 97.57 148 | 79.44 201 | | |
|
EG-PatchMatch MVS | | | 82.37 269 | 80.34 271 | 88.46 249 | 90.27 285 | 79.35 197 | 92.80 222 | 94.33 199 | 77.14 270 | 73.26 313 | 90.18 255 | 47.47 331 | 96.72 227 | 70.25 268 | 87.32 211 | 89.30 315 |
|
FMVSNet5 | | | 81.52 276 | 79.60 280 | 87.27 274 | 91.17 254 | 77.95 239 | 91.49 256 | 92.26 245 | 76.87 271 | 76.16 294 | 87.91 287 | 51.67 322 | 92.34 319 | 67.74 292 | 81.16 269 | 91.52 287 |
|
TDRefinement | | | 79.81 289 | 77.34 291 | 87.22 279 | 79.24 338 | 75.48 274 | 93.12 209 | 92.03 251 | 76.45 272 | 75.01 303 | 91.58 209 | 49.19 328 | 96.44 243 | 70.22 270 | 69.18 327 | 89.75 313 |
|
LF4IMVS | | | 80.37 286 | 79.07 286 | 84.27 306 | 86.64 318 | 69.87 315 | 89.39 282 | 91.05 281 | 76.38 273 | 74.97 304 | 90.00 257 | 47.85 330 | 94.25 303 | 74.55 247 | 80.82 279 | 88.69 323 |
|
TAPA-MVS | | 84.62 6 | 88.16 140 | 87.01 151 | 91.62 122 | 96.64 61 | 80.65 156 | 94.39 129 | 96.21 89 | 76.38 273 | 86.19 144 | 95.44 82 | 79.75 88 | 98.08 123 | 62.75 316 | 95.29 92 | 96.13 109 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
dp | | | 81.47 277 | 80.23 273 | 85.17 299 | 89.92 294 | 65.49 327 | 86.74 305 | 90.10 299 | 76.30 275 | 81.10 253 | 87.12 296 | 62.81 281 | 95.92 262 | 68.13 290 | 79.88 292 | 94.09 198 |
|
CostFormer | | | 85.77 217 | 84.94 210 | 88.26 255 | 91.16 256 | 72.58 296 | 89.47 281 | 91.04 282 | 76.26 276 | 86.45 138 | 89.97 258 | 70.74 205 | 96.86 219 | 82.35 150 | 87.07 214 | 95.34 140 |
|
RPSCF | | | 85.07 232 | 84.27 226 | 87.48 272 | 92.91 197 | 70.62 310 | 91.69 253 | 92.46 241 | 76.20 277 | 82.67 234 | 95.22 88 | 63.94 279 | 97.29 186 | 77.51 221 | 85.80 220 | 94.53 178 |
|
Test_1112_low_res | | | 87.65 159 | 86.51 175 | 91.08 138 | 94.94 127 | 79.28 202 | 91.77 248 | 94.30 200 | 76.04 278 | 83.51 224 | 92.37 177 | 77.86 111 | 97.73 143 | 78.69 209 | 89.13 187 | 96.22 106 |
|
pmmvs4 | | | 85.43 225 | 83.86 232 | 90.16 176 | 90.02 292 | 82.97 105 | 90.27 267 | 92.67 238 | 75.93 279 | 80.73 257 | 91.74 203 | 71.05 199 | 95.73 271 | 78.85 207 | 83.46 243 | 91.78 282 |
|
LS3D | | | 87.89 149 | 86.32 179 | 92.59 84 | 96.07 85 | 82.92 106 | 95.23 71 | 94.92 180 | 75.66 280 | 82.89 231 | 95.98 68 | 72.48 186 | 99.21 32 | 68.43 287 | 95.23 94 | 95.64 131 |
|
pmmvs5 | | | 84.21 252 | 82.84 256 | 88.34 253 | 88.95 303 | 76.94 262 | 92.41 233 | 91.91 258 | 75.63 281 | 80.28 263 | 91.18 232 | 64.59 276 | 95.57 274 | 77.09 226 | 83.47 242 | 92.53 267 |
|
pmmvs-eth3d | | | 80.97 283 | 78.72 288 | 87.74 264 | 84.99 324 | 79.97 173 | 90.11 271 | 91.65 262 | 75.36 282 | 73.51 310 | 86.03 308 | 59.45 302 | 93.96 305 | 75.17 240 | 72.21 312 | 89.29 316 |
|
test_0402 | | | 81.30 280 | 79.17 284 | 87.67 266 | 93.19 187 | 78.17 235 | 92.98 217 | 91.71 259 | 75.25 283 | 76.02 298 | 90.31 253 | 59.23 303 | 96.37 246 | 50.22 334 | 83.63 240 | 88.47 328 |
|
COLMAP_ROB | | 80.39 16 | 83.96 254 | 82.04 260 | 89.74 202 | 95.28 111 | 79.75 178 | 94.25 140 | 92.28 244 | 75.17 284 | 78.02 280 | 93.77 135 | 58.60 305 | 97.84 136 | 65.06 309 | 85.92 218 | 91.63 286 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
TinyColmap | | | 79.76 290 | 77.69 290 | 85.97 292 | 91.71 219 | 73.12 286 | 89.55 277 | 90.36 294 | 75.03 285 | 72.03 318 | 90.19 254 | 46.22 333 | 96.19 253 | 63.11 314 | 81.03 273 | 88.59 324 |
|
DP-MVS | | | 87.25 182 | 85.36 202 | 92.90 75 | 97.65 34 | 83.24 95 | 94.81 98 | 92.00 252 | 74.99 286 | 81.92 245 | 95.00 93 | 72.66 182 | 99.05 46 | 66.92 296 | 92.33 138 | 96.40 101 |
|
PatchT | | | 82.68 266 | 81.27 264 | 86.89 285 | 90.09 290 | 70.94 308 | 84.06 323 | 90.15 297 | 74.91 287 | 85.63 160 | 83.57 317 | 69.37 222 | 94.87 299 | 65.19 307 | 88.50 195 | 94.84 160 |
|
CHOSEN 280x420 | | | 85.15 231 | 83.99 230 | 88.65 237 | 92.47 203 | 78.40 229 | 79.68 336 | 92.76 235 | 74.90 288 | 81.41 250 | 89.59 263 | 69.85 218 | 95.51 277 | 79.92 191 | 95.29 92 | 92.03 279 |
|
gg-mvs-nofinetune | | | 81.77 271 | 79.37 281 | 88.99 231 | 90.85 270 | 77.73 249 | 86.29 308 | 79.63 346 | 74.88 289 | 83.19 229 | 69.05 339 | 60.34 297 | 96.11 255 | 75.46 237 | 94.64 100 | 93.11 252 |
|
pmmvs6 | | | 83.42 259 | 81.60 262 | 88.87 232 | 88.01 313 | 77.87 243 | 94.96 87 | 94.24 201 | 74.67 290 | 78.80 275 | 91.09 237 | 60.17 299 | 96.49 239 | 77.06 227 | 75.40 306 | 92.23 277 |
|
CHOSEN 1792x2688 | | | 88.84 125 | 87.69 131 | 92.30 96 | 96.14 77 | 81.42 136 | 90.01 272 | 95.86 112 | 74.52 291 | 87.41 120 | 93.94 126 | 75.46 145 | 98.36 95 | 80.36 181 | 95.53 85 | 97.12 84 |
|
MDA-MVSNet_test_wron | | | 79.21 294 | 77.19 294 | 85.29 297 | 88.22 310 | 72.77 291 | 85.87 311 | 90.06 300 | 74.34 292 | 62.62 335 | 87.56 291 | 66.14 268 | 91.99 322 | 66.90 297 | 73.01 309 | 91.10 296 |
|
YYNet1 | | | 79.22 293 | 77.20 293 | 85.28 298 | 88.20 311 | 72.66 293 | 85.87 311 | 90.05 302 | 74.33 293 | 62.70 334 | 87.61 290 | 66.09 269 | 92.03 321 | 66.94 294 | 72.97 310 | 91.15 294 |
|
无先验 | | | | | | | | 93.28 204 | 96.26 82 | 73.95 294 | | | | 99.05 46 | 80.56 177 | | 96.59 98 |
|
Anonymous20231206 | | | 81.03 282 | 79.77 278 | 84.82 301 | 87.85 316 | 70.26 312 | 91.42 257 | 92.08 249 | 73.67 295 | 77.75 282 | 89.25 267 | 62.43 284 | 93.08 316 | 61.50 319 | 82.00 258 | 91.12 295 |
|
PCF-MVS | | 84.11 10 | 87.74 157 | 86.08 186 | 92.70 81 | 94.02 159 | 84.43 71 | 89.27 283 | 95.87 111 | 73.62 296 | 84.43 202 | 94.33 110 | 78.48 104 | 98.86 70 | 70.27 267 | 94.45 106 | 94.81 162 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
HyFIR lowres test | | | 88.09 143 | 86.81 157 | 91.93 110 | 96.00 87 | 80.63 157 | 90.01 272 | 95.79 116 | 73.42 297 | 87.68 118 | 92.10 190 | 73.86 166 | 97.96 130 | 80.75 173 | 91.70 140 | 97.19 80 |
|
MDTV_nov1_ep13_2view | | | | | | | 55.91 342 | 87.62 302 | | 73.32 298 | 84.59 197 | | 70.33 213 | | 74.65 245 | | 95.50 133 |
|
JIA-IIPM | | | 81.04 281 | 78.98 287 | 87.25 276 | 88.64 305 | 73.48 285 | 81.75 332 | 89.61 309 | 73.19 299 | 82.05 242 | 73.71 336 | 66.07 270 | 95.87 265 | 71.18 265 | 84.60 230 | 92.41 271 |
|
cascas | | | 86.43 202 | 84.98 207 | 90.80 148 | 92.10 210 | 80.92 151 | 90.24 268 | 95.91 107 | 73.10 300 | 83.57 223 | 88.39 279 | 65.15 273 | 97.46 156 | 84.90 116 | 91.43 142 | 94.03 201 |
|
ANet_high | | | 58.88 318 | 54.22 321 | 72.86 325 | 56.50 354 | 56.67 339 | 80.75 334 | 86.00 333 | 73.09 301 | 37.39 345 | 64.63 343 | 22.17 350 | 79.49 348 | 43.51 343 | 23.96 349 | 82.43 337 |
|
tpmp4_e23 | | | 83.87 257 | 82.33 258 | 88.48 248 | 91.46 225 | 72.82 289 | 89.82 275 | 91.57 267 | 73.02 302 | 81.86 246 | 89.05 268 | 66.20 267 | 96.97 211 | 71.57 261 | 86.39 216 | 95.66 130 |
|
ADS-MVSNet2 | | | 81.66 273 | 79.71 279 | 87.50 270 | 91.35 239 | 74.19 278 | 83.33 327 | 88.48 323 | 72.90 303 | 82.24 238 | 85.77 309 | 64.98 274 | 93.20 314 | 64.57 310 | 83.74 237 | 95.12 142 |
|
ADS-MVSNet | | | 81.56 275 | 79.78 277 | 86.90 284 | 91.35 239 | 71.82 299 | 83.33 327 | 89.16 318 | 72.90 303 | 82.24 238 | 85.77 309 | 64.98 274 | 93.76 306 | 64.57 310 | 83.74 237 | 95.12 142 |
|
PVSNet_0 | | 73.20 20 | 77.22 297 | 74.83 300 | 84.37 304 | 90.70 275 | 71.10 305 | 83.09 329 | 89.67 308 | 72.81 305 | 73.93 309 | 83.13 320 | 60.79 295 | 93.70 307 | 68.54 284 | 50.84 342 | 88.30 329 |
|
testdata | | | | | 90.49 161 | 96.40 67 | 77.89 242 | | 95.37 153 | 72.51 306 | 93.63 25 | 96.69 38 | 82.08 68 | 97.65 144 | 83.08 137 | 97.39 60 | 95.94 118 |
|
PMMVS | | | 85.71 223 | 84.96 209 | 87.95 262 | 88.90 304 | 77.09 260 | 88.68 291 | 90.06 300 | 72.32 307 | 86.47 135 | 90.76 242 | 72.15 189 | 94.40 301 | 81.78 161 | 93.49 119 | 92.36 273 |
|
testus | | | 74.41 303 | 73.35 301 | 77.59 320 | 82.49 333 | 57.08 337 | 86.02 309 | 90.21 296 | 72.28 308 | 72.89 315 | 84.32 314 | 37.08 340 | 86.96 336 | 52.24 330 | 82.65 249 | 88.73 321 |
|
Patchmtry | | | 82.71 265 | 80.93 268 | 88.06 260 | 90.05 291 | 76.37 267 | 84.74 318 | 91.96 256 | 72.28 308 | 81.32 252 | 87.87 288 | 71.03 200 | 95.50 279 | 68.97 283 | 80.15 287 | 92.32 275 |
|
tpm2 | | | 84.08 253 | 82.94 253 | 87.48 272 | 91.39 232 | 71.27 302 | 89.23 285 | 90.37 293 | 71.95 310 | 84.64 195 | 89.33 266 | 67.30 256 | 96.55 237 | 75.17 240 | 87.09 213 | 94.63 170 |
|
UnsupCasMVSNet_bld | | | 76.23 300 | 73.27 302 | 85.09 300 | 83.79 327 | 72.92 287 | 85.65 315 | 93.47 225 | 71.52 311 | 68.84 324 | 79.08 332 | 49.77 325 | 93.21 313 | 66.81 298 | 60.52 339 | 89.13 320 |
|
RPMNet | | | 83.18 263 | 80.87 269 | 90.12 182 | 90.58 278 | 79.34 198 | 85.24 316 | 90.78 289 | 71.44 312 | 85.55 161 | 82.97 321 | 70.87 202 | 95.61 272 | 61.01 320 | 89.36 176 | 95.40 137 |
|
test2356 | | | 74.50 302 | 73.27 302 | 78.20 316 | 80.81 334 | 59.84 332 | 83.76 326 | 88.33 325 | 71.43 313 | 72.37 317 | 81.84 325 | 45.60 334 | 86.26 338 | 50.97 332 | 84.32 231 | 88.50 325 |
|
旧先验2 | | | | | | | | 93.36 197 | | 71.25 314 | 94.37 13 | | | 97.13 200 | 86.74 100 | | |
|
testpf | | | 71.41 309 | 72.11 306 | 69.30 329 | 84.53 325 | 59.79 333 | 62.74 346 | 83.14 339 | 71.11 315 | 68.83 325 | 81.57 327 | 46.70 332 | 84.83 343 | 74.51 248 | 75.86 305 | 63.30 343 |
|
新几何1 | | | | | 93.10 66 | 97.30 47 | 84.35 73 | | 95.56 131 | 71.09 316 | 91.26 72 | 96.24 56 | 82.87 58 | 98.86 70 | 79.19 205 | 98.10 47 | 96.07 114 |
|
1121 | | | 90.42 85 | 89.49 89 | 93.20 62 | 97.27 50 | 84.46 67 | 92.63 226 | 95.51 138 | 71.01 317 | 91.20 73 | 96.21 58 | 82.92 57 | 99.05 46 | 80.56 177 | 98.07 48 | 96.10 112 |
|
Patchmatch-test | | | 81.37 278 | 79.30 282 | 87.58 268 | 90.92 266 | 74.16 279 | 80.99 333 | 87.68 329 | 70.52 318 | 76.63 287 | 88.81 271 | 71.21 197 | 92.76 318 | 60.01 324 | 86.93 215 | 95.83 124 |
|
114514_t | | | 89.51 104 | 88.50 112 | 92.54 86 | 98.11 25 | 81.99 125 | 95.16 76 | 96.36 79 | 70.19 319 | 85.81 148 | 95.25 87 | 76.70 118 | 98.63 83 | 82.07 154 | 96.86 68 | 97.00 87 |
|
test1235678 | | | 72.22 306 | 70.31 307 | 77.93 319 | 78.04 339 | 58.04 336 | 85.76 313 | 89.80 307 | 70.15 320 | 63.43 333 | 80.20 330 | 42.24 337 | 87.24 335 | 48.68 336 | 74.50 307 | 88.50 325 |
|
N_pmnet | | | 68.89 311 | 68.44 312 | 70.23 327 | 89.07 302 | 28.79 356 | 88.06 296 | 19.50 357 | 69.47 321 | 71.86 319 | 84.93 312 | 61.24 292 | 91.75 324 | 54.70 328 | 77.15 302 | 90.15 311 |
|
OpenMVS_ROB | | 74.94 19 | 79.51 291 | 77.03 295 | 86.93 282 | 87.00 317 | 76.23 269 | 92.33 236 | 90.74 290 | 68.93 322 | 74.52 306 | 88.23 282 | 49.58 326 | 96.62 232 | 57.64 326 | 84.29 232 | 87.94 330 |
|
test222 | | | | | | 96.55 65 | 81.70 128 | 92.22 240 | 95.01 172 | 68.36 323 | 90.20 82 | 96.14 64 | 80.26 84 | | | 97.80 54 | 96.05 116 |
|
LP | | | 75.51 301 | 72.15 305 | 85.61 295 | 87.86 315 | 73.93 281 | 80.20 335 | 88.43 324 | 67.39 324 | 70.05 321 | 80.56 329 | 58.18 307 | 93.18 315 | 46.28 340 | 70.36 325 | 89.71 314 |
|
1111 | | | 70.54 310 | 69.71 309 | 73.04 324 | 79.30 336 | 44.83 349 | 84.23 321 | 88.96 320 | 67.33 325 | 65.42 330 | 82.28 323 | 41.11 338 | 88.11 333 | 47.12 338 | 71.60 317 | 86.19 332 |
|
.test1245 | | | 57.63 320 | 61.79 317 | 45.14 338 | 79.30 336 | 44.83 349 | 84.23 321 | 88.96 320 | 67.33 325 | 65.42 330 | 82.28 323 | 41.11 338 | 88.11 333 | 47.12 338 | 0.39 353 | 2.46 354 |
|
MVS | | | 87.44 176 | 86.10 185 | 91.44 127 | 92.61 202 | 83.62 87 | 92.63 226 | 95.66 125 | 67.26 327 | 81.47 248 | 92.15 186 | 77.95 108 | 98.22 103 | 79.71 196 | 95.48 87 | 92.47 269 |
|
tpm cat1 | | | 81.96 270 | 80.27 272 | 87.01 281 | 91.09 257 | 71.02 306 | 87.38 303 | 91.53 269 | 66.25 328 | 80.17 264 | 86.35 306 | 68.22 255 | 96.15 254 | 69.16 282 | 82.29 252 | 93.86 211 |
|
CVMVSNet | | | 84.69 248 | 84.79 215 | 84.37 304 | 91.84 214 | 64.92 328 | 93.70 186 | 91.47 270 | 66.19 329 | 86.16 145 | 95.28 85 | 67.18 259 | 93.33 312 | 80.89 172 | 90.42 159 | 94.88 159 |
|
testmv | | | 65.49 313 | 62.66 314 | 73.96 323 | 68.78 345 | 53.14 344 | 84.70 319 | 88.56 322 | 65.94 330 | 52.35 339 | 74.65 335 | 25.02 348 | 85.14 341 | 43.54 342 | 60.40 340 | 83.60 333 |
|
test12356 | | | 64.99 314 | 63.78 313 | 68.61 331 | 72.69 342 | 39.14 352 | 78.46 337 | 87.61 330 | 64.91 331 | 55.77 337 | 77.48 333 | 28.10 345 | 85.59 340 | 44.69 341 | 64.35 334 | 81.12 338 |
|
CMPMVS | | 59.16 21 | 80.52 285 | 79.20 283 | 84.48 303 | 83.98 326 | 67.63 322 | 89.95 274 | 93.84 220 | 64.79 332 | 66.81 329 | 91.14 235 | 57.93 308 | 95.17 292 | 76.25 231 | 88.10 201 | 90.65 306 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
EU-MVSNet | | | 81.32 279 | 80.95 267 | 82.42 312 | 88.50 307 | 63.67 329 | 93.32 198 | 91.33 272 | 64.02 333 | 80.57 261 | 92.83 164 | 61.21 293 | 92.27 320 | 76.34 230 | 80.38 286 | 91.32 291 |
|
no-one | | | 61.56 316 | 56.58 318 | 76.49 322 | 67.80 348 | 62.76 331 | 78.13 338 | 86.11 332 | 63.16 334 | 43.24 343 | 64.70 342 | 26.12 347 | 88.95 331 | 50.84 333 | 29.15 345 | 77.77 340 |
|
new_pmnet | | | 72.15 307 | 70.13 308 | 78.20 316 | 82.95 331 | 65.68 325 | 83.91 324 | 82.40 341 | 62.94 335 | 64.47 332 | 79.82 331 | 42.85 336 | 86.26 338 | 57.41 327 | 74.44 308 | 82.65 336 |
|
Anonymous20231211 | | | 72.97 305 | 69.63 310 | 83.00 310 | 83.05 330 | 66.91 323 | 92.69 224 | 89.45 311 | 61.06 336 | 67.50 327 | 83.46 318 | 34.34 343 | 93.61 309 | 51.11 331 | 63.97 335 | 88.48 327 |
|
DSMNet-mixed | | | 76.94 298 | 76.29 297 | 78.89 315 | 83.10 329 | 56.11 341 | 87.78 299 | 79.77 345 | 60.65 337 | 75.64 301 | 88.71 273 | 61.56 289 | 88.34 332 | 60.07 323 | 89.29 178 | 92.21 278 |
|
pmmvs3 | | | 71.81 308 | 68.71 311 | 81.11 313 | 75.86 340 | 70.42 311 | 86.74 305 | 83.66 338 | 58.95 338 | 68.64 326 | 80.89 328 | 36.93 341 | 89.52 329 | 63.10 315 | 63.59 336 | 83.39 334 |
|
MVS-HIRNet | | | 73.70 304 | 72.20 304 | 78.18 318 | 91.81 216 | 56.42 340 | 82.94 330 | 82.58 340 | 55.24 339 | 68.88 323 | 66.48 340 | 55.32 315 | 95.13 293 | 58.12 325 | 88.42 198 | 83.01 335 |
|
PMMVS2 | | | 59.60 317 | 56.40 319 | 69.21 330 | 68.83 344 | 46.58 347 | 73.02 344 | 77.48 349 | 55.07 340 | 49.21 341 | 72.95 338 | 17.43 354 | 80.04 346 | 49.32 335 | 44.33 343 | 80.99 339 |
|
FPMVS | | | 64.63 315 | 62.55 315 | 70.88 326 | 70.80 343 | 56.71 338 | 84.42 320 | 84.42 337 | 51.78 341 | 49.57 340 | 81.61 326 | 23.49 349 | 81.48 345 | 40.61 345 | 76.25 304 | 74.46 342 |
|
LCM-MVSNet | | | 66.00 312 | 62.16 316 | 77.51 321 | 64.51 350 | 58.29 335 | 83.87 325 | 90.90 285 | 48.17 342 | 54.69 338 | 73.31 337 | 16.83 355 | 86.75 337 | 65.47 306 | 61.67 338 | 87.48 331 |
|
DeepMVS_CX | | | | | 56.31 336 | 74.23 341 | 51.81 345 | | 56.67 355 | 44.85 343 | 48.54 342 | 75.16 334 | 27.87 346 | 58.74 353 | 40.92 344 | 52.22 341 | 58.39 347 |
|
PNet_i23d | | | 50.48 323 | 47.18 323 | 60.36 334 | 68.59 346 | 44.56 351 | 72.75 345 | 72.61 350 | 43.92 344 | 33.91 347 | 60.19 345 | 6.16 356 | 73.52 349 | 38.50 346 | 28.04 346 | 63.01 344 |
|
Gipuma | | | 57.99 319 | 54.91 320 | 67.24 332 | 88.51 306 | 65.59 326 | 52.21 349 | 90.33 295 | 43.58 345 | 42.84 344 | 51.18 347 | 20.29 352 | 85.07 342 | 34.77 347 | 70.45 324 | 51.05 348 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PMVS | | 47.18 22 | 52.22 321 | 48.46 322 | 63.48 333 | 45.72 355 | 46.20 348 | 73.41 342 | 78.31 347 | 41.03 346 | 30.06 348 | 65.68 341 | 6.05 357 | 83.43 344 | 30.04 348 | 65.86 331 | 60.80 345 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
wuykxyi23d | | | 50.55 322 | 44.13 324 | 69.81 328 | 56.77 352 | 54.58 343 | 73.22 343 | 80.78 343 | 39.79 347 | 22.08 352 | 46.69 349 | 4.03 359 | 79.71 347 | 47.65 337 | 26.13 347 | 75.14 341 |
|
E-PMN | | | 43.23 325 | 42.29 325 | 46.03 337 | 65.58 349 | 37.41 353 | 73.51 341 | 64.62 351 | 33.99 348 | 28.47 350 | 47.87 348 | 19.90 353 | 67.91 350 | 22.23 350 | 24.45 348 | 32.77 349 |
|
EMVS | | | 42.07 326 | 41.12 326 | 44.92 339 | 63.45 351 | 35.56 355 | 73.65 340 | 63.48 352 | 33.05 349 | 26.88 351 | 45.45 350 | 21.27 351 | 67.14 351 | 19.80 351 | 23.02 350 | 32.06 350 |
|
MVE | | 39.65 23 | 43.39 324 | 38.59 329 | 57.77 335 | 56.52 353 | 48.77 346 | 55.38 348 | 58.64 354 | 29.33 350 | 28.96 349 | 52.65 346 | 4.68 358 | 64.62 352 | 28.11 349 | 33.07 344 | 59.93 346 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
wuyk23d | | | 21.27 330 | 20.48 331 | 23.63 342 | 68.59 346 | 36.41 354 | 49.57 350 | 6.85 358 | 9.37 351 | 7.89 353 | 4.46 356 | 4.03 359 | 31.37 354 | 17.47 352 | 16.07 352 | 3.12 352 |
|
tmp_tt | | | 35.64 328 | 39.24 327 | 24.84 341 | 14.87 356 | 23.90 357 | 62.71 347 | 51.51 356 | 6.58 352 | 36.66 346 | 62.08 344 | 44.37 335 | 30.34 355 | 52.40 329 | 22.00 351 | 20.27 351 |
|
testmvs | | | 8.92 331 | 11.52 332 | 1.12 344 | 1.06 357 | 0.46 359 | 86.02 309 | 0.65 359 | 0.62 353 | 2.74 354 | 9.52 354 | 0.31 362 | 0.45 357 | 2.38 353 | 0.39 353 | 2.46 354 |
|
test123 | | | 8.76 332 | 11.22 333 | 1.39 343 | 0.85 358 | 0.97 358 | 85.76 313 | 0.35 360 | 0.54 354 | 2.45 355 | 8.14 355 | 0.60 361 | 0.48 356 | 2.16 354 | 0.17 355 | 2.71 353 |
|
cdsmvs_eth3d_5k | | | 22.14 329 | 29.52 330 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 95.76 118 | 0.00 355 | 0.00 356 | 94.29 113 | 75.66 142 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
pcd_1.5k_mvsjas | | | 6.64 334 | 8.86 335 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 79.70 90 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
pcd1.5k->3k | | | 37.02 327 | 38.84 328 | 31.53 340 | 92.33 205 | 0.00 360 | 0.00 351 | 96.13 92 | 0.00 355 | 0.00 356 | 0.00 357 | 72.70 181 | 0.00 358 | 0.00 355 | 88.43 197 | 94.60 173 |
|
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 | | | 7.82 333 | 10.43 334 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 93.88 131 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
uanet | | | 0.00 335 | 0.00 336 | 0.00 345 | 0.00 359 | 0.00 360 | 0.00 351 | 0.00 361 | 0.00 355 | 0.00 356 | 0.00 357 | 0.00 363 | 0.00 358 | 0.00 355 | 0.00 356 | 0.00 356 |
|
GSMVS | | | | | | | | | | | | | | | | | 96.12 110 |
|
test_part2 | | | | | | 98.55 5 | 87.22 10 | | | | 96.40 2 | | | | | | |
|
test_part1 | | | | | | | | | 97.45 6 | | | | 91.93 1 | | | 99.02 2 | 98.67 4 |
|
sam_mvs1 | | | | | | | | | | | | | 71.70 191 | | | | 96.12 110 |
|
sam_mvs | | | | | | | | | | | | | 70.60 206 | | | | |
|
ambc | | | | | 83.06 309 | 79.99 335 | 63.51 330 | 77.47 339 | 92.86 232 | | 74.34 308 | 84.45 313 | 28.74 344 | 95.06 296 | 73.06 256 | 68.89 329 | 90.61 307 |
|
MTGPA | | | | | | | | | 96.97 34 | | | | | | | | |
|
test_post1 | | | | | | | | 88.00 297 | | | | 9.81 353 | 69.31 225 | 95.53 275 | 76.65 228 | | |
|
test_post | | | | | | | | | | | | 10.29 352 | 70.57 210 | 95.91 264 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 83.76 316 | 71.53 194 | 96.48 240 | | | |
|
GG-mvs-BLEND | | | | | 87.94 263 | 89.73 297 | 77.91 240 | 87.80 298 | 78.23 348 | | 80.58 260 | 83.86 315 | 59.88 301 | 95.33 291 | 71.20 263 | 92.22 139 | 90.60 309 |
|
MTMP | | | | | | | | | 60.64 353 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 91.91 42 | 98.71 19 | 98.07 45 |
|
agg_prior2 | | | | | | | | | | | | | | | 90.54 61 | 98.68 24 | 98.27 31 |
|
agg_prior | | | | | | 97.38 43 | 85.92 44 | | 96.72 56 | | 92.16 56 | | | 98.97 61 | | | |
|
test_prior4 | | | | | | | 85.96 43 | 94.11 153 | | | | | | | | | |
|
test_prior | | | | | 93.82 51 | 97.29 48 | 84.49 64 | | 96.88 43 | | | | | 98.87 67 | | | 98.11 43 |
|
新几何2 | | | | | | | | 93.11 211 | | | | | | | | | |
|
旧先验1 | | | | | | 96.79 59 | 81.81 127 | | 95.67 123 | | | 96.81 33 | 86.69 24 | | | 97.66 56 | 96.97 88 |
|
原ACMM2 | | | | | | | | 92.94 219 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 98.75 78 | 78.30 212 | | |
|
segment_acmp | | | | | | | | | | | | | 87.16 21 | | | | |
|
test12 | | | | | 94.34 40 | 97.13 53 | 86.15 40 | | 96.29 81 | | 91.04 75 | | 85.08 41 | 99.01 55 | | 98.13 46 | 97.86 59 |
|
plane_prior7 | | | | | | 94.70 137 | 82.74 111 | | | | | | | | | | |
|
plane_prior6 | | | | | | 94.52 143 | 82.75 109 | | | | | | 74.23 158 | | | | |
|
plane_prior5 | | | | | | | | | 96.22 86 | | | | | 98.12 109 | 88.15 79 | 89.99 165 | 94.63 170 |
|
plane_prior4 | | | | | | | | | | | | 94.86 97 | | | | | |
|
plane_prior1 | | | | | | 94.59 141 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 361 | | | | | | | | |
|
nn | | | | | | | | | 0.00 361 | | | | | | | | |
|
door-mid | | | | | | | | | 85.49 334 | | | | | | | | |
|
lessismore_v0 | | | | | 86.04 291 | 88.46 308 | 68.78 318 | | 80.59 344 | | 73.01 314 | 90.11 256 | 55.39 314 | 96.43 244 | 75.06 242 | 65.06 332 | 92.90 256 |
|
test11 | | | | | | | | | 96.57 70 | | | | | | | | |
|
door | | | | | | | | | 85.33 335 | | | | | | | | |
|
HQP5-MVS | | | | | | | 81.56 129 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 87.11 97 | | |
|
HQP4-MVS | | | | | | | | | | | 85.43 172 | | | 97.96 130 | | | 94.51 180 |
|
HQP3-MVS | | | | | | | | | 96.04 99 | | | | | | | 89.77 170 | |
|
HQP2-MVS | | | | | | | | | | | | | 73.83 167 | | | | |
|
NP-MVS | | | | | | 94.37 149 | 82.42 119 | | | | | 93.98 124 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 87.47 207 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 88.01 204 | |
|
Test By Simon | | | | | | | | | | | | | 80.02 85 | | | | |
|