Regformer-2 | | | 86.63 32 | 86.53 30 | 86.95 39 | 89.33 109 | 71.24 48 | 88.43 100 | 92.05 64 | 82.50 1 | 86.88 18 | 90.09 101 | 74.45 13 | 95.61 42 | 84.38 27 | 90.63 71 | 94.01 29 |
|
UA-Net | | | 85.08 55 | 84.96 51 | 85.45 62 | 92.07 58 | 68.07 111 | 89.78 61 | 90.86 108 | 82.48 2 | 84.60 43 | 93.20 41 | 69.35 56 | 95.22 57 | 71.39 146 | 90.88 69 | 93.07 69 |
|
Regformer-1 | | | 86.41 36 | 86.33 31 | 86.64 44 | 89.33 109 | 70.93 54 | 88.43 100 | 91.39 95 | 82.14 3 | 86.65 19 | 90.09 101 | 74.39 16 | 95.01 67 | 83.97 33 | 90.63 71 | 93.97 31 |
|
CANet | | | 86.45 33 | 86.10 37 | 87.51 30 | 90.09 85 | 70.94 53 | 89.70 64 | 92.59 46 | 81.78 4 | 81.32 83 | 91.43 75 | 70.34 45 | 97.23 4 | 84.26 29 | 93.36 49 | 94.37 15 |
|
Regformer-4 | | | 85.68 46 | 85.45 44 | 86.35 48 | 88.95 126 | 69.67 74 | 88.29 110 | 91.29 97 | 81.73 5 | 85.36 28 | 90.01 104 | 72.62 30 | 95.35 56 | 83.28 37 | 87.57 106 | 94.03 27 |
|
MVS_0304 | | | 86.37 38 | 85.81 42 | 88.02 9 | 90.13 83 | 72.39 36 | 89.66 66 | 92.75 41 | 81.64 6 | 82.66 70 | 92.04 58 | 64.44 93 | 97.35 2 | 84.76 23 | 94.25 44 | 94.33 18 |
|
NCCC | | | 88.06 9 | 88.01 12 | 88.24 6 | 94.41 15 | 73.62 8 | 91.22 34 | 92.83 38 | 81.50 7 | 85.79 25 | 93.47 37 | 73.02 26 | 97.00 8 | 84.90 19 | 94.94 27 | 94.10 23 |
|
EPNet | | | 83.72 62 | 82.92 68 | 86.14 54 | 84.22 229 | 69.48 79 | 91.05 36 | 85.27 234 | 81.30 8 | 76.83 153 | 91.65 66 | 66.09 80 | 95.56 44 | 76.00 96 | 93.85 47 | 93.38 57 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
Regformer-3 | | | 85.23 52 | 85.07 50 | 85.70 61 | 88.95 126 | 69.01 86 | 88.29 110 | 89.91 145 | 80.95 9 | 85.01 31 | 90.01 104 | 72.45 31 | 94.19 98 | 82.50 46 | 87.57 106 | 93.90 35 |
|
CNVR-MVS | | | 88.93 6 | 89.13 6 | 88.33 4 | 94.77 5 | 73.82 6 | 90.51 43 | 93.00 29 | 80.90 10 | 88.06 13 | 94.06 27 | 76.43 5 | 96.84 9 | 88.48 5 | 95.99 6 | 94.34 17 |
|
3Dnovator+ | | 77.84 4 | 85.48 47 | 84.47 55 | 88.51 3 | 91.08 68 | 73.49 14 | 93.18 4 | 93.78 8 | 80.79 11 | 76.66 157 | 93.37 38 | 60.40 166 | 96.75 14 | 77.20 85 | 93.73 48 | 95.29 1 |
|
TranMVSNet+NR-MVSNet | | | 80.84 106 | 80.31 103 | 82.42 169 | 87.85 161 | 62.33 228 | 87.74 124 | 91.33 96 | 80.55 12 | 77.99 133 | 89.86 107 | 65.23 88 | 92.62 172 | 67.05 182 | 75.24 270 | 92.30 93 |
|
HSP-MVS | | | 89.28 2 | 89.76 2 | 87.85 21 | 94.28 18 | 73.46 15 | 92.90 8 | 92.73 42 | 80.27 13 | 91.35 5 | 94.16 23 | 78.35 3 | 96.77 12 | 89.59 1 | 94.22 45 | 93.33 60 |
|
HPM-MVS++ | | | 89.02 5 | 89.15 5 | 88.63 1 | 95.01 3 | 76.03 1 | 92.38 16 | 92.85 37 | 80.26 14 | 87.78 15 | 94.27 19 | 75.89 8 | 96.81 11 | 87.45 10 | 96.44 2 | 93.05 70 |
|
UniMVSNet_NR-MVSNet | | | 81.88 87 | 81.54 85 | 82.92 149 | 88.46 145 | 63.46 209 | 87.13 150 | 92.37 54 | 80.19 15 | 78.38 118 | 89.14 125 | 71.66 36 | 93.05 159 | 70.05 155 | 76.46 252 | 92.25 95 |
|
SteuartSystems-ACMMP | | | 88.72 7 | 88.86 7 | 88.32 5 | 92.14 57 | 72.96 21 | 93.73 3 | 93.67 9 | 80.19 15 | 88.10 12 | 94.80 7 | 73.76 22 | 97.11 5 | 87.51 9 | 95.82 10 | 94.90 4 |
Skip Steuart: Steuart Systems R&D Blog. |
EI-MVSNet-Vis-set | | | 84.19 57 | 83.81 57 | 85.31 64 | 88.18 152 | 67.85 114 | 87.66 125 | 89.73 149 | 80.05 17 | 82.95 62 | 89.59 114 | 70.74 43 | 94.82 77 | 80.66 59 | 84.72 142 | 93.28 61 |
|
EI-MVSNet-UG-set | | | 83.81 60 | 83.38 60 | 85.09 71 | 87.87 160 | 67.53 118 | 87.44 136 | 89.66 150 | 79.74 18 | 82.23 72 | 89.41 123 | 70.24 47 | 94.74 81 | 79.95 63 | 83.92 149 | 92.99 75 |
|
zzz-MVS | | | 87.53 16 | 87.41 17 | 87.90 17 | 94.18 23 | 74.25 2 | 90.23 51 | 92.02 65 | 79.45 19 | 85.88 22 | 94.80 7 | 68.07 62 | 96.21 30 | 86.69 12 | 95.34 19 | 93.23 62 |
|
MTAPA | | | 87.23 23 | 87.00 23 | 87.90 17 | 94.18 23 | 74.25 2 | 86.58 170 | 92.02 65 | 79.45 19 | 85.88 22 | 94.80 7 | 68.07 62 | 96.21 30 | 86.69 12 | 95.34 19 | 93.23 62 |
|
XVS | | | 87.18 24 | 86.91 26 | 88.00 11 | 94.42 13 | 73.33 17 | 92.78 9 | 92.99 31 | 79.14 21 | 83.67 56 | 94.17 22 | 67.45 69 | 96.60 21 | 83.06 39 | 94.50 36 | 94.07 25 |
|
X-MVStestdata | | | 80.37 126 | 77.83 161 | 88.00 11 | 94.42 13 | 73.33 17 | 92.78 9 | 92.99 31 | 79.14 21 | 83.67 56 | 12.47 365 | 67.45 69 | 96.60 21 | 83.06 39 | 94.50 36 | 94.07 25 |
|
HQP_MVS | | | 83.64 63 | 83.14 63 | 85.14 69 | 90.08 86 | 68.71 97 | 91.25 32 | 92.44 49 | 79.12 23 | 78.92 106 | 91.00 86 | 60.42 164 | 95.38 53 | 78.71 70 | 86.32 128 | 91.33 118 |
|
plane_prior2 | | | | | | | | 91.25 32 | | 79.12 23 | | | | | | | |
|
IS-MVSNet | | | 83.15 70 | 82.81 69 | 84.18 96 | 89.94 89 | 63.30 213 | 91.59 27 | 88.46 195 | 79.04 25 | 79.49 100 | 92.16 56 | 65.10 89 | 94.28 91 | 67.71 172 | 91.86 59 | 94.95 3 |
|
DU-MVS | | | 81.12 102 | 80.52 99 | 82.90 150 | 87.80 170 | 63.46 209 | 87.02 155 | 91.87 76 | 79.01 26 | 78.38 118 | 89.07 127 | 65.02 90 | 93.05 159 | 70.05 155 | 76.46 252 | 92.20 97 |
|
NR-MVSNet | | | 80.23 129 | 79.38 124 | 82.78 162 | 87.80 170 | 63.34 212 | 86.31 178 | 91.09 103 | 79.01 26 | 72.17 232 | 89.07 127 | 67.20 72 | 92.81 170 | 66.08 189 | 75.65 261 | 92.20 97 |
|
DELS-MVS | | | 85.41 50 | 85.30 48 | 85.77 60 | 88.49 142 | 67.93 113 | 85.52 209 | 93.44 15 | 78.70 28 | 83.63 58 | 89.03 129 | 74.57 12 | 95.71 41 | 80.26 62 | 94.04 46 | 93.66 44 |
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 |
WR-MVS | | | 79.49 146 | 79.22 133 | 80.27 216 | 88.79 134 | 58.35 258 | 85.06 216 | 88.61 193 | 78.56 29 | 77.65 138 | 88.34 146 | 63.81 99 | 90.66 236 | 64.98 199 | 77.22 235 | 91.80 109 |
|
plane_prior3 | | | | | | | 68.60 101 | | | 78.44 30 | 78.92 106 | | | | | | |
|
UniMVSNet (Re) | | | 81.60 93 | 81.11 91 | 83.09 136 | 88.38 148 | 64.41 187 | 87.60 126 | 93.02 28 | 78.42 31 | 78.56 111 | 88.16 151 | 69.78 51 | 93.26 147 | 69.58 161 | 76.49 251 | 91.60 110 |
|
SD-MVS | | | 88.06 9 | 88.50 9 | 86.71 43 | 92.60 53 | 72.71 26 | 91.81 26 | 93.19 23 | 77.87 32 | 90.32 7 | 94.00 28 | 74.83 11 | 93.78 120 | 87.63 8 | 94.27 43 | 93.65 49 |
|
CP-MVSNet | | | 78.22 171 | 78.34 151 | 77.84 260 | 87.83 168 | 54.54 308 | 87.94 120 | 91.17 101 | 77.65 33 | 73.48 210 | 88.49 142 | 62.24 136 | 88.43 276 | 62.19 216 | 74.07 278 | 90.55 149 |
|
plane_prior | | | | | | | 68.71 97 | 90.38 48 | | 77.62 34 | | | | | | 86.16 131 | |
|
VDD-MVS | | | 83.01 74 | 82.36 75 | 84.96 74 | 91.02 70 | 66.40 135 | 88.91 84 | 88.11 198 | 77.57 35 | 84.39 47 | 93.29 40 | 52.19 224 | 93.91 111 | 77.05 88 | 88.70 93 | 94.57 10 |
|
MP-MVS | | | 87.71 13 | 87.64 14 | 87.93 16 | 94.36 17 | 73.88 4 | 92.71 13 | 92.65 45 | 77.57 35 | 83.84 53 | 94.40 18 | 72.24 33 | 96.28 28 | 85.65 15 | 95.30 23 | 93.62 51 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
PEN-MVS | | | 77.73 186 | 77.69 167 | 77.84 260 | 87.07 191 | 53.91 312 | 87.91 122 | 91.18 100 | 77.56 37 | 73.14 214 | 88.82 132 | 61.23 150 | 89.17 263 | 59.95 235 | 72.37 291 | 90.43 154 |
|
OPM-MVS | | | 83.50 65 | 82.95 67 | 85.14 69 | 88.79 134 | 70.95 52 | 89.13 80 | 91.52 89 | 77.55 38 | 80.96 90 | 91.75 64 | 60.71 158 | 94.50 87 | 79.67 65 | 86.51 126 | 89.97 179 |
|
DeepPCF-MVS | | 80.84 1 | 88.10 8 | 88.56 8 | 86.73 42 | 92.24 55 | 69.03 84 | 89.57 68 | 93.39 18 | 77.53 39 | 89.79 8 | 94.12 25 | 78.98 2 | 96.58 23 | 85.66 14 | 95.72 11 | 94.58 8 |
|
PS-CasMVS | | | 78.01 179 | 78.09 155 | 77.77 262 | 87.71 175 | 54.39 310 | 88.02 116 | 91.22 98 | 77.50 40 | 73.26 212 | 88.64 137 | 60.73 157 | 88.41 277 | 61.88 220 | 73.88 282 | 90.53 150 |
|
MSLP-MVS++ | | | 85.43 49 | 85.76 43 | 84.45 86 | 91.93 60 | 70.24 62 | 90.71 40 | 92.86 36 | 77.46 41 | 84.22 49 | 92.81 54 | 67.16 73 | 92.94 164 | 80.36 60 | 94.35 41 | 90.16 161 |
|
3Dnovator | | 76.31 5 | 83.38 69 | 82.31 76 | 86.59 46 | 87.94 159 | 72.94 24 | 90.64 41 | 92.14 62 | 77.21 42 | 75.47 184 | 92.83 51 | 58.56 173 | 94.72 82 | 73.24 126 | 92.71 54 | 92.13 100 |
|
WR-MVS_H | | | 78.51 166 | 78.49 145 | 78.56 249 | 88.02 157 | 56.38 292 | 88.43 100 | 92.67 43 | 77.14 43 | 73.89 208 | 87.55 168 | 66.25 78 | 89.24 256 | 58.92 244 | 73.55 285 | 90.06 170 |
|
DeepC-MVS | | 79.81 2 | 87.08 27 | 86.88 27 | 87.69 27 | 91.16 67 | 72.32 39 | 90.31 49 | 93.94 6 | 77.12 44 | 82.82 65 | 94.23 21 | 72.13 34 | 97.09 6 | 84.83 22 | 95.37 18 | 93.65 49 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
FC-MVSNet-test | | | 81.52 94 | 82.02 80 | 80.03 219 | 88.42 147 | 55.97 297 | 87.95 119 | 93.42 17 | 77.10 45 | 77.38 143 | 90.98 88 | 69.96 49 | 91.79 196 | 68.46 170 | 84.50 144 | 92.33 91 |
|
DTE-MVSNet | | | 76.99 205 | 76.80 181 | 77.54 266 | 86.24 200 | 53.06 323 | 87.52 133 | 90.66 112 | 77.08 46 | 72.50 220 | 88.67 136 | 60.48 163 | 89.52 250 | 57.33 260 | 70.74 302 | 90.05 171 |
|
LFMVS | | | 81.82 89 | 81.23 88 | 83.57 117 | 91.89 61 | 63.43 211 | 89.84 57 | 81.85 281 | 77.04 47 | 83.21 59 | 93.10 43 | 52.26 223 | 93.43 143 | 71.98 141 | 89.95 80 | 93.85 37 |
|
UGNet | | | 80.83 108 | 79.59 116 | 84.54 83 | 88.04 156 | 68.09 110 | 89.42 69 | 88.16 197 | 76.95 48 | 76.22 169 | 89.46 119 | 49.30 274 | 93.94 108 | 68.48 169 | 90.31 73 | 91.60 110 |
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 |
FIs | | | 82.07 84 | 82.42 72 | 81.04 204 | 88.80 133 | 58.34 259 | 88.26 112 | 93.49 14 | 76.93 49 | 78.47 114 | 91.04 83 | 69.92 50 | 92.34 182 | 69.87 158 | 84.97 139 | 92.44 89 |
|
GST-MVS | | | 87.42 19 | 87.26 18 | 87.89 20 | 94.12 25 | 72.97 20 | 92.39 15 | 93.43 16 | 76.89 50 | 84.68 40 | 93.99 29 | 70.67 44 | 96.82 10 | 84.18 32 | 95.01 25 | 93.90 35 |
|
mPP-MVS | | | 86.67 31 | 86.32 32 | 87.72 25 | 94.41 15 | 73.55 10 | 92.74 11 | 92.22 58 | 76.87 51 | 82.81 67 | 94.25 20 | 66.44 77 | 96.24 29 | 82.88 43 | 94.28 42 | 93.38 57 |
|
VPNet | | | 78.69 164 | 78.66 141 | 78.76 246 | 88.31 150 | 55.72 303 | 84.45 232 | 86.63 220 | 76.79 52 | 78.26 126 | 90.55 94 | 59.30 169 | 89.70 248 | 66.63 184 | 77.05 237 | 90.88 130 |
|
HFP-MVS | | | 87.58 15 | 87.47 16 | 87.94 13 | 94.58 8 | 73.54 12 | 93.04 5 | 93.24 20 | 76.78 53 | 84.91 34 | 94.44 15 | 70.78 41 | 96.61 19 | 84.53 25 | 94.89 29 | 93.66 44 |
|
ACMMPR | | | 87.44 17 | 87.23 20 | 88.08 8 | 94.64 6 | 73.59 9 | 93.04 5 | 93.20 22 | 76.78 53 | 84.66 41 | 94.52 10 | 68.81 60 | 96.65 17 | 84.53 25 | 94.90 28 | 94.00 30 |
|
ACMMP | | | 85.89 43 | 85.39 45 | 87.38 32 | 93.59 32 | 72.63 30 | 92.74 11 | 93.18 24 | 76.78 53 | 80.73 92 | 93.82 32 | 64.33 94 | 96.29 27 | 82.67 45 | 90.69 70 | 93.23 62 |
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 |
region2R | | | 87.42 19 | 87.20 21 | 88.09 7 | 94.63 7 | 73.55 10 | 93.03 7 | 93.12 25 | 76.73 56 | 84.45 44 | 94.52 10 | 69.09 58 | 96.70 15 | 84.37 28 | 94.83 31 | 94.03 27 |
|
canonicalmvs | | | 85.91 42 | 85.87 40 | 86.04 57 | 89.84 91 | 69.44 82 | 90.45 47 | 93.00 29 | 76.70 57 | 88.01 14 | 91.23 77 | 73.28 24 | 93.91 111 | 81.50 52 | 88.80 91 | 94.77 6 |
|
CP-MVS | | | 87.11 25 | 86.92 25 | 87.68 28 | 94.20 22 | 73.86 5 | 93.98 1 | 92.82 40 | 76.62 58 | 83.68 55 | 94.46 14 | 67.93 64 | 95.95 38 | 84.20 31 | 94.39 39 | 93.23 62 |
|
DeepC-MVS_fast | | 79.65 3 | 86.91 28 | 86.62 29 | 87.76 22 | 93.52 33 | 72.37 38 | 91.26 31 | 93.04 26 | 76.62 58 | 84.22 49 | 93.36 39 | 71.44 37 | 96.76 13 | 80.82 57 | 95.33 21 | 94.16 21 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
TSAR-MVS + GP. | | | 85.71 45 | 85.33 46 | 86.84 40 | 91.34 65 | 72.50 33 | 89.07 81 | 87.28 214 | 76.41 60 | 85.80 24 | 90.22 99 | 74.15 21 | 95.37 55 | 81.82 48 | 91.88 58 | 92.65 82 |
|
HQP-NCC | | | | | | 89.33 109 | | 89.17 75 | | 76.41 60 | 77.23 148 | | | | | | |
|
ACMP_Plane | | | | | | 89.33 109 | | 89.17 75 | | 76.41 60 | 77.23 148 | | | | | | |
|
HQP-MVS | | | 82.61 79 | 82.02 80 | 84.37 88 | 89.33 109 | 66.98 128 | 89.17 75 | 92.19 60 | 76.41 60 | 77.23 148 | 90.23 98 | 60.17 167 | 95.11 61 | 77.47 82 | 85.99 133 | 91.03 125 |
|
CANet_DTU | | | 80.61 116 | 79.87 109 | 82.83 156 | 85.60 208 | 63.17 219 | 87.36 137 | 88.65 190 | 76.37 64 | 75.88 176 | 88.44 144 | 53.51 214 | 93.07 158 | 73.30 125 | 89.74 82 | 92.25 95 |
|
VNet | | | 82.21 82 | 82.41 73 | 81.62 191 | 90.82 74 | 60.93 238 | 84.47 229 | 89.78 147 | 76.36 65 | 84.07 51 | 91.88 63 | 64.71 92 | 90.26 239 | 70.68 149 | 88.89 88 | 93.66 44 |
|
Vis-MVSNet | | | 83.46 66 | 82.80 70 | 85.43 63 | 90.25 82 | 68.74 95 | 90.30 50 | 90.13 134 | 76.33 66 | 80.87 91 | 92.89 49 | 61.00 155 | 94.20 97 | 72.45 135 | 90.97 67 | 93.35 59 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
ACMMP_Plus | | | 88.05 11 | 88.08 11 | 87.94 13 | 93.70 28 | 73.05 19 | 90.86 37 | 93.59 11 | 76.27 67 | 88.14 11 | 95.09 6 | 71.06 39 | 96.67 16 | 87.67 7 | 96.37 3 | 94.09 24 |
|
alignmvs | | | 85.48 47 | 85.32 47 | 85.96 59 | 89.51 103 | 69.47 80 | 89.74 62 | 92.47 48 | 76.17 68 | 87.73 16 | 91.46 74 | 70.32 46 | 93.78 120 | 81.51 51 | 88.95 87 | 94.63 7 |
|
MVS_111021_HR | | | 85.14 54 | 84.75 54 | 86.32 51 | 91.65 63 | 72.70 27 | 85.98 187 | 90.33 125 | 76.11 69 | 82.08 73 | 91.61 69 | 71.36 38 | 94.17 100 | 81.02 53 | 92.58 55 | 92.08 101 |
|
HPM-MVS | | | 87.11 25 | 86.98 24 | 87.50 31 | 93.88 27 | 72.16 40 | 92.19 20 | 93.33 19 | 76.07 70 | 83.81 54 | 93.95 30 | 69.77 52 | 96.01 35 | 85.15 16 | 94.66 33 | 94.32 19 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
ESAPD | | | 89.48 1 | 89.98 1 | 88.01 10 | 94.80 4 | 72.69 28 | 91.59 27 | 94.10 1 | 75.90 71 | 92.29 1 | 95.66 3 | 81.67 1 | 97.38 1 | 87.44 11 | 96.34 4 | 93.95 32 |
|
CLD-MVS | | | 82.31 81 | 81.65 84 | 84.29 93 | 88.47 144 | 67.73 117 | 85.81 196 | 92.35 55 | 75.78 72 | 78.33 120 | 86.58 204 | 64.01 97 | 94.35 89 | 76.05 95 | 87.48 111 | 90.79 132 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
testdata1 | | | | | | | | 84.14 240 | | 75.71 73 | | | | | | | |
|
APDe-MVS | | | 89.15 3 | 89.63 3 | 87.73 23 | 94.49 11 | 71.69 45 | 93.83 2 | 93.96 5 | 75.70 74 | 91.06 6 | 96.03 1 | 76.84 4 | 97.03 7 | 89.09 2 | 95.65 15 | 94.47 13 |
|
VPA-MVSNet | | | 80.60 117 | 80.55 98 | 80.76 208 | 88.07 155 | 60.80 241 | 86.86 160 | 91.58 87 | 75.67 75 | 80.24 95 | 89.45 121 | 63.34 101 | 90.25 240 | 70.51 151 | 79.22 219 | 91.23 121 |
|
v1.0 | | | 37.66 338 | 50.21 329 | 0.00 359 | 95.06 1 | 0.00 374 | 0.00 365 | 94.09 2 | 75.63 76 | 91.80 3 | 95.29 4 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
diffmvs1 | | | 82.63 77 | 82.51 71 | 82.96 148 | 83.87 248 | 63.47 208 | 85.19 211 | 89.42 157 | 75.58 77 | 81.38 82 | 89.89 106 | 67.42 71 | 91.69 206 | 81.01 54 | 88.88 89 | 93.71 43 |
|
PGM-MVS | | | 86.68 30 | 86.27 33 | 87.90 17 | 94.22 21 | 73.38 16 | 90.22 52 | 93.04 26 | 75.53 78 | 83.86 52 | 94.42 17 | 67.87 66 | 96.64 18 | 82.70 44 | 94.57 35 | 93.66 44 |
|
Effi-MVS+ | | | 83.62 64 | 83.08 64 | 85.24 67 | 88.38 148 | 67.45 119 | 88.89 85 | 89.15 167 | 75.50 79 | 82.27 71 | 88.28 148 | 69.61 54 | 94.45 88 | 77.81 79 | 87.84 104 | 93.84 39 |
|
test_prior3 | | | 86.73 29 | 86.86 28 | 86.33 49 | 92.61 51 | 69.59 76 | 88.85 87 | 92.97 34 | 75.41 80 | 84.91 34 | 93.54 33 | 74.28 18 | 95.48 46 | 83.31 35 | 95.86 8 | 93.91 33 |
|
test_prior2 | | | | | | | | 88.85 87 | | 75.41 80 | 84.91 34 | 93.54 33 | 74.28 18 | | 83.31 35 | 95.86 8 | |
|
LPG-MVS_test | | | 82.08 83 | 81.27 87 | 84.50 84 | 89.23 117 | 68.76 93 | 90.22 52 | 91.94 72 | 75.37 82 | 76.64 158 | 91.51 71 | 54.29 207 | 94.91 70 | 78.44 72 | 83.78 150 | 89.83 183 |
|
LGP-MVS_train | | | | | 84.50 84 | 89.23 117 | 68.76 93 | | 91.94 72 | 75.37 82 | 76.64 158 | 91.51 71 | 54.29 207 | 94.91 70 | 78.44 72 | 83.78 150 | 89.83 183 |
|
#test# | | | 87.33 22 | 87.13 22 | 87.94 13 | 94.58 8 | 73.54 12 | 92.34 17 | 93.24 20 | 75.23 84 | 84.91 34 | 94.44 15 | 70.78 41 | 96.61 19 | 83.75 34 | 94.89 29 | 93.66 44 |
|
MG-MVS | | | 83.41 67 | 83.45 59 | 83.28 125 | 92.74 48 | 62.28 230 | 88.17 114 | 89.50 154 | 75.22 85 | 81.49 81 | 92.74 55 | 66.75 74 | 95.11 61 | 72.85 128 | 91.58 61 | 92.45 88 |
|
LCM-MVSNet-Re | | | 77.05 204 | 76.94 179 | 77.36 269 | 87.20 189 | 51.60 327 | 80.06 281 | 80.46 293 | 75.20 86 | 67.69 287 | 86.72 191 | 62.48 131 | 88.98 268 | 63.44 206 | 89.25 86 | 91.51 113 |
|
MP-MVS-pluss | | | 87.67 14 | 87.72 13 | 87.54 29 | 93.64 31 | 72.04 42 | 89.80 60 | 93.50 13 | 75.17 87 | 86.34 20 | 95.29 4 | 70.86 40 | 96.00 36 | 88.78 3 | 96.04 5 | 94.58 8 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
Effi-MVS+-dtu | | | 80.03 136 | 78.57 144 | 84.42 87 | 85.13 216 | 68.74 95 | 88.77 90 | 88.10 199 | 74.99 88 | 74.97 201 | 83.49 267 | 57.27 184 | 93.36 144 | 73.53 122 | 80.88 192 | 91.18 122 |
|
mvs-test1 | | | 80.88 104 | 79.40 123 | 85.29 65 | 85.13 216 | 69.75 73 | 89.28 71 | 88.10 199 | 74.99 88 | 76.44 163 | 86.72 191 | 57.27 184 | 94.26 96 | 73.53 122 | 83.18 167 | 91.87 105 |
|
diffmvs | | | 81.48 97 | 81.21 90 | 82.31 172 | 83.28 263 | 62.72 225 | 85.09 215 | 88.63 192 | 74.99 88 | 78.31 121 | 88.81 133 | 65.80 84 | 91.36 218 | 79.03 66 | 86.95 117 | 92.84 78 |
|
OMC-MVS | | | 82.69 76 | 81.97 82 | 84.85 78 | 88.75 136 | 67.42 120 | 87.98 117 | 90.87 107 | 74.92 91 | 79.72 98 | 91.65 66 | 62.19 137 | 93.96 106 | 75.26 108 | 86.42 127 | 93.16 67 |
|
nrg030 | | | 83.88 59 | 83.53 58 | 84.96 74 | 86.77 196 | 69.28 83 | 90.46 46 | 92.67 43 | 74.79 92 | 82.95 62 | 91.33 76 | 72.70 29 | 93.09 157 | 80.79 58 | 79.28 218 | 92.50 87 |
|
SMA-MVS | | | 89.08 4 | 89.23 4 | 88.61 2 | 94.25 19 | 73.73 7 | 92.40 14 | 93.63 10 | 74.77 93 | 92.29 1 | 95.97 2 | 74.28 18 | 97.24 3 | 88.58 4 | 96.91 1 | 94.87 5 |
|
MVS_111021_LR | | | 82.61 79 | 82.11 77 | 84.11 97 | 88.82 131 | 71.58 46 | 85.15 214 | 86.16 227 | 74.69 94 | 80.47 94 | 91.04 83 | 62.29 134 | 90.55 237 | 80.33 61 | 90.08 78 | 90.20 160 |
|
TSAR-MVS + MP. | | | 88.02 12 | 88.11 10 | 87.72 25 | 93.68 30 | 72.13 41 | 91.41 30 | 92.35 55 | 74.62 95 | 88.90 9 | 93.85 31 | 75.75 9 | 96.00 36 | 87.80 6 | 94.63 34 | 95.04 2 |
|
ACMP | | 74.13 6 | 81.51 96 | 80.57 97 | 84.36 89 | 89.42 105 | 68.69 100 | 89.97 56 | 91.50 93 | 74.46 96 | 75.04 200 | 90.41 95 | 53.82 212 | 94.54 84 | 77.56 81 | 82.91 170 | 89.86 182 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
EPP-MVSNet | | | 83.40 68 | 83.02 66 | 84.57 82 | 90.13 83 | 64.47 185 | 92.32 18 | 90.73 109 | 74.45 97 | 79.35 102 | 91.10 80 | 69.05 59 | 95.12 60 | 72.78 129 | 87.22 113 | 94.13 22 |
|
MVS_Test | | | 83.15 70 | 83.06 65 | 83.41 122 | 86.86 193 | 63.21 216 | 86.11 185 | 92.00 68 | 74.31 98 | 82.87 64 | 89.44 122 | 70.03 48 | 93.21 148 | 77.39 84 | 88.50 100 | 93.81 40 |
|
IterMVS-LS | | | 80.06 135 | 79.38 124 | 82.11 174 | 85.89 203 | 63.20 217 | 86.79 163 | 89.34 159 | 74.19 99 | 75.45 186 | 86.72 191 | 66.62 75 | 92.39 179 | 72.58 133 | 76.86 243 | 90.75 134 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
EI-MVSNet | | | 80.52 120 | 79.98 107 | 82.12 173 | 84.28 226 | 63.19 218 | 86.41 175 | 88.95 179 | 74.18 100 | 78.69 108 | 87.54 169 | 66.62 75 | 92.43 177 | 72.57 134 | 80.57 198 | 90.74 135 |
|
Vis-MVSNet (Re-imp) | | | 78.36 169 | 78.45 146 | 78.07 258 | 88.64 138 | 51.78 326 | 86.70 167 | 79.63 302 | 74.14 101 | 75.11 198 | 90.83 89 | 61.29 149 | 89.75 246 | 58.10 253 | 91.60 60 | 92.69 81 |
|
v8 | | | 79.97 138 | 79.02 136 | 82.80 159 | 84.09 239 | 64.50 183 | 87.96 118 | 90.29 128 | 74.13 102 | 75.24 195 | 86.81 188 | 62.88 112 | 93.89 113 | 74.39 113 | 75.40 266 | 90.00 172 |
|
CSCG | | | 86.41 36 | 86.19 35 | 87.07 38 | 92.91 45 | 72.48 34 | 90.81 38 | 93.56 12 | 73.95 103 | 83.16 61 | 91.07 82 | 75.94 7 | 95.19 58 | 79.94 64 | 94.38 40 | 93.55 53 |
|
tfpn111 | | | 76.54 211 | 75.51 208 | 79.61 228 | 89.52 100 | 56.99 279 | 85.83 192 | 83.23 259 | 73.94 104 | 76.32 165 | 87.12 181 | 51.89 231 | 92.06 188 | 48.04 305 | 83.73 156 | 89.78 186 |
|
conf200view11 | | | 76.55 210 | 75.55 206 | 79.57 231 | 89.52 100 | 56.99 279 | 85.83 192 | 83.23 259 | 73.94 104 | 76.32 165 | 87.12 181 | 51.89 231 | 91.95 190 | 48.33 298 | 83.75 152 | 89.78 186 |
|
thres100view900 | | | 76.50 213 | 75.55 206 | 79.33 233 | 89.52 100 | 56.99 279 | 85.83 192 | 83.23 259 | 73.94 104 | 76.32 165 | 87.12 181 | 51.89 231 | 91.95 190 | 48.33 298 | 83.75 152 | 89.07 199 |
|
HPM-MVS_fast | | | 85.35 51 | 84.95 52 | 86.57 47 | 93.69 29 | 70.58 60 | 92.15 22 | 91.62 85 | 73.89 107 | 82.67 69 | 94.09 26 | 62.60 126 | 95.54 45 | 80.93 55 | 92.93 51 | 93.57 52 |
|
view600 | | | 76.20 220 | 75.21 216 | 79.16 238 | 89.64 93 | 55.82 298 | 85.74 197 | 82.06 276 | 73.88 108 | 75.74 179 | 87.85 158 | 51.84 235 | 91.66 208 | 46.75 309 | 83.42 160 | 90.00 172 |
|
view800 | | | 76.20 220 | 75.21 216 | 79.16 238 | 89.64 93 | 55.82 298 | 85.74 197 | 82.06 276 | 73.88 108 | 75.74 179 | 87.85 158 | 51.84 235 | 91.66 208 | 46.75 309 | 83.42 160 | 90.00 172 |
|
conf0.05thres1000 | | | 76.20 220 | 75.21 216 | 79.16 238 | 89.64 93 | 55.82 298 | 85.74 197 | 82.06 276 | 73.88 108 | 75.74 179 | 87.85 158 | 51.84 235 | 91.66 208 | 46.75 309 | 83.42 160 | 90.00 172 |
|
tfpn | | | 76.20 220 | 75.21 216 | 79.16 238 | 89.64 93 | 55.82 298 | 85.74 197 | 82.06 276 | 73.88 108 | 75.74 179 | 87.85 158 | 51.84 235 | 91.66 208 | 46.75 309 | 83.42 160 | 90.00 172 |
|
PAPM_NR | | | 83.02 73 | 82.41 73 | 84.82 79 | 92.47 54 | 66.37 136 | 87.93 121 | 91.80 78 | 73.82 112 | 77.32 145 | 90.66 92 | 67.90 65 | 94.90 72 | 70.37 152 | 89.48 84 | 93.19 66 |
|
thres600view7 | | | 76.50 213 | 75.44 209 | 79.68 225 | 89.40 106 | 57.16 276 | 85.53 207 | 83.23 259 | 73.79 113 | 76.26 168 | 87.09 184 | 51.89 231 | 91.89 194 | 48.05 304 | 83.72 157 | 90.00 172 |
|
v7n | | | 78.97 160 | 77.58 169 | 83.14 134 | 83.45 259 | 65.51 150 | 88.32 108 | 91.21 99 | 73.69 114 | 72.41 229 | 86.32 215 | 57.93 177 | 93.81 118 | 69.18 164 | 75.65 261 | 90.11 164 |
|
v2v482 | | | 80.23 129 | 79.29 130 | 83.05 139 | 83.62 255 | 64.14 191 | 87.04 154 | 89.97 140 | 73.61 115 | 78.18 129 | 87.22 177 | 61.10 153 | 93.82 117 | 76.11 94 | 76.78 249 | 91.18 122 |
|
Baseline_NR-MVSNet | | | 78.15 175 | 78.33 152 | 77.61 264 | 85.79 204 | 56.21 295 | 86.78 164 | 85.76 231 | 73.60 116 | 77.93 134 | 87.57 167 | 65.02 90 | 88.99 267 | 67.14 181 | 75.33 267 | 87.63 249 |
|
BH-RMVSNet | | | 79.61 143 | 78.44 148 | 83.14 134 | 89.38 108 | 65.93 142 | 84.95 218 | 87.15 215 | 73.56 117 | 78.19 128 | 89.79 108 | 56.67 190 | 93.36 144 | 59.53 240 | 86.74 121 | 90.13 163 |
|
APD-MVS_3200maxsize | | | 85.97 41 | 85.88 39 | 86.22 52 | 92.69 49 | 69.53 78 | 91.93 24 | 92.99 31 | 73.54 118 | 85.94 21 | 94.51 13 | 65.80 84 | 95.61 42 | 83.04 41 | 92.51 56 | 93.53 55 |
|
abl_6 | | | 85.23 52 | 84.95 52 | 86.07 55 | 92.23 56 | 70.48 61 | 90.80 39 | 92.08 63 | 73.51 119 | 85.26 29 | 94.16 23 | 62.75 119 | 95.92 39 | 82.46 47 | 91.30 65 | 91.81 108 |
|
v748 | | | 77.97 180 | 76.65 184 | 81.92 180 | 82.29 285 | 63.28 214 | 87.53 132 | 90.35 124 | 73.50 120 | 70.76 248 | 85.55 237 | 58.28 175 | 92.81 170 | 68.81 168 | 72.76 290 | 89.67 192 |
|
tfpn200view9 | | | 76.42 216 | 75.37 213 | 79.55 232 | 89.13 121 | 57.65 271 | 85.17 212 | 83.60 251 | 73.41 121 | 76.45 160 | 86.39 210 | 52.12 225 | 91.95 190 | 48.33 298 | 83.75 152 | 89.07 199 |
|
thres400 | | | 76.50 213 | 75.37 213 | 79.86 221 | 89.13 121 | 57.65 271 | 85.17 212 | 83.60 251 | 73.41 121 | 76.45 160 | 86.39 210 | 52.12 225 | 91.95 190 | 48.33 298 | 83.75 152 | 90.00 172 |
|
v148 | | | 78.72 163 | 77.80 162 | 81.47 195 | 82.73 278 | 61.96 233 | 86.30 179 | 88.08 201 | 73.26 123 | 76.18 171 | 85.47 240 | 62.46 132 | 92.36 181 | 71.92 143 | 73.82 283 | 90.09 166 |
|
v1neww | | | 80.40 122 | 79.54 117 | 82.98 143 | 84.10 237 | 64.51 179 | 87.57 128 | 90.22 129 | 73.25 124 | 78.47 114 | 86.65 199 | 62.83 115 | 93.86 114 | 75.72 99 | 77.02 238 | 90.58 146 |
|
v7new | | | 80.40 122 | 79.54 117 | 82.98 143 | 84.10 237 | 64.51 179 | 87.57 128 | 90.22 129 | 73.25 124 | 78.47 114 | 86.65 199 | 62.83 115 | 93.86 114 | 75.72 99 | 77.02 238 | 90.58 146 |
|
v6 | | | 80.40 122 | 79.54 117 | 82.98 143 | 84.09 239 | 64.50 183 | 87.57 128 | 90.22 129 | 73.25 124 | 78.47 114 | 86.63 201 | 62.84 114 | 93.86 114 | 75.73 98 | 77.02 238 | 90.58 146 |
|
v1141 | | | 80.19 131 | 79.31 127 | 82.85 153 | 83.84 250 | 64.12 193 | 87.14 147 | 90.08 136 | 73.13 127 | 78.27 123 | 86.39 210 | 62.67 124 | 93.75 124 | 75.40 106 | 76.83 246 | 90.68 137 |
|
divwei89l23v2f112 | | | 80.19 131 | 79.31 127 | 82.85 153 | 83.84 250 | 64.11 195 | 87.13 150 | 90.08 136 | 73.13 127 | 78.27 123 | 86.39 210 | 62.69 122 | 93.75 124 | 75.40 106 | 76.82 247 | 90.68 137 |
|
v1 | | | 80.19 131 | 79.31 127 | 82.85 153 | 83.83 252 | 64.12 193 | 87.14 147 | 90.07 138 | 73.13 127 | 78.27 123 | 86.38 214 | 62.72 121 | 93.75 124 | 75.41 105 | 76.82 247 | 90.68 137 |
|
v10 | | | 79.74 142 | 78.67 140 | 82.97 147 | 84.06 244 | 64.95 166 | 87.88 123 | 90.62 113 | 73.11 130 | 75.11 198 | 86.56 205 | 61.46 144 | 94.05 104 | 73.68 118 | 75.55 263 | 89.90 180 |
|
MCST-MVS | | | 87.37 21 | 87.25 19 | 87.73 23 | 94.53 10 | 72.46 35 | 89.82 58 | 93.82 7 | 73.07 131 | 84.86 39 | 92.89 49 | 76.22 6 | 96.33 26 | 84.89 21 | 95.13 24 | 94.40 14 |
|
APD-MVS | | | 87.44 17 | 87.52 15 | 87.19 34 | 94.24 20 | 72.39 36 | 91.86 25 | 92.83 38 | 73.01 132 | 88.58 10 | 94.52 10 | 73.36 23 | 96.49 24 | 84.26 29 | 95.01 25 | 92.70 79 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
v13 | | | 77.50 197 | 76.07 200 | 81.77 182 | 84.23 228 | 65.07 164 | 87.34 139 | 88.91 184 | 72.92 133 | 68.35 283 | 81.97 285 | 62.53 130 | 91.69 206 | 72.20 140 | 66.22 325 | 88.56 231 |
|
v12 | | | 77.51 195 | 76.09 199 | 81.76 184 | 84.22 229 | 64.99 165 | 87.30 142 | 88.93 183 | 72.92 133 | 68.48 282 | 81.97 285 | 62.54 129 | 91.70 205 | 72.24 139 | 66.21 326 | 88.58 229 |
|
K. test v3 | | | 71.19 273 | 68.51 279 | 79.21 236 | 83.04 271 | 57.78 270 | 84.35 236 | 76.91 323 | 72.90 135 | 62.99 319 | 82.86 271 | 39.27 325 | 91.09 229 | 61.65 223 | 52.66 348 | 88.75 218 |
|
V9 | | | 77.52 193 | 76.11 198 | 81.73 185 | 84.19 233 | 64.89 168 | 87.26 144 | 88.94 182 | 72.87 136 | 68.65 278 | 81.96 287 | 62.65 125 | 91.72 202 | 72.27 138 | 66.24 324 | 88.60 226 |
|
V14 | | | 77.52 193 | 76.12 195 | 81.70 186 | 84.15 234 | 64.77 171 | 87.21 146 | 88.95 179 | 72.80 137 | 68.79 275 | 81.94 288 | 62.69 122 | 91.72 202 | 72.31 137 | 66.27 323 | 88.60 226 |
|
v15 | | | 77.51 195 | 76.12 195 | 81.66 189 | 84.09 239 | 64.65 174 | 87.14 147 | 88.96 178 | 72.76 138 | 68.90 274 | 81.91 289 | 62.74 120 | 91.73 200 | 72.32 136 | 66.29 322 | 88.61 225 |
|
v11 | | | 77.45 198 | 76.06 201 | 81.59 193 | 84.22 229 | 64.52 177 | 87.11 152 | 89.02 170 | 72.76 138 | 68.76 276 | 81.90 290 | 62.09 138 | 91.71 204 | 71.98 141 | 66.73 317 | 88.56 231 |
|
v17 | | | 77.68 188 | 76.35 192 | 81.69 187 | 84.15 234 | 64.65 174 | 87.33 140 | 88.99 174 | 72.70 140 | 69.25 273 | 82.07 281 | 62.82 117 | 91.79 196 | 72.69 132 | 67.15 316 | 88.63 222 |
|
v16 | | | 77.69 187 | 76.36 191 | 81.68 188 | 84.15 234 | 64.63 176 | 87.33 140 | 88.99 174 | 72.69 141 | 69.31 272 | 82.08 280 | 62.80 118 | 91.79 196 | 72.70 131 | 67.23 314 | 88.63 222 |
|
v18 | | | 77.67 190 | 76.35 192 | 81.64 190 | 84.09 239 | 64.47 185 | 87.27 143 | 89.01 172 | 72.59 142 | 69.39 269 | 82.04 282 | 62.85 113 | 91.80 195 | 72.72 130 | 67.20 315 | 88.63 222 |
|
Fast-Effi-MVS+-dtu | | | 78.02 178 | 76.49 185 | 82.62 167 | 83.16 268 | 66.96 130 | 86.94 157 | 87.45 213 | 72.45 143 | 71.49 243 | 84.17 257 | 54.79 203 | 91.58 214 | 67.61 173 | 80.31 202 | 89.30 197 |
|
PHI-MVS | | | 86.43 34 | 86.17 36 | 87.24 33 | 90.88 73 | 70.96 51 | 92.27 19 | 94.07 4 | 72.45 143 | 85.22 30 | 91.90 62 | 69.47 55 | 96.42 25 | 83.28 37 | 95.94 7 | 94.35 16 |
|
thres200 | | | 75.55 231 | 74.47 227 | 78.82 245 | 87.78 173 | 57.85 268 | 83.07 258 | 83.51 254 | 72.44 145 | 75.84 177 | 84.42 256 | 52.08 227 | 91.75 199 | 47.41 307 | 83.64 158 | 86.86 268 |
|
0601test | | | 81.17 100 | 80.47 100 | 83.24 128 | 89.13 121 | 63.62 201 | 86.21 181 | 89.95 141 | 72.43 146 | 81.78 78 | 89.61 112 | 57.50 181 | 93.58 132 | 70.75 147 | 86.90 118 | 92.52 85 |
|
Anonymous20240521 | | | 81.17 100 | 80.47 100 | 83.24 128 | 89.13 121 | 63.62 201 | 86.21 181 | 89.95 141 | 72.43 146 | 81.78 78 | 89.61 112 | 57.50 181 | 93.58 132 | 70.75 147 | 86.90 118 | 92.52 85 |
|
v52 | | | 77.94 183 | 76.37 188 | 82.67 164 | 79.39 321 | 65.52 148 | 86.43 173 | 89.94 143 | 72.28 148 | 72.15 234 | 84.94 250 | 55.70 195 | 93.44 141 | 73.64 119 | 72.84 289 | 89.06 201 |
|
V4 | | | 77.95 181 | 76.37 188 | 82.67 164 | 79.40 320 | 65.52 148 | 86.43 173 | 89.94 143 | 72.28 148 | 72.14 235 | 84.95 249 | 55.72 194 | 93.44 141 | 73.64 119 | 72.86 288 | 89.05 205 |
|
v7 | | | 80.24 128 | 79.26 131 | 83.15 133 | 84.07 243 | 64.94 167 | 87.56 131 | 90.67 110 | 72.26 150 | 78.28 122 | 86.51 208 | 61.45 145 | 94.03 105 | 75.14 109 | 77.41 232 | 90.49 151 |
|
BH-untuned | | | 79.47 147 | 78.60 142 | 82.05 175 | 89.19 119 | 65.91 143 | 86.07 186 | 88.52 194 | 72.18 151 | 75.42 187 | 87.69 164 | 61.15 152 | 93.54 136 | 60.38 232 | 86.83 120 | 86.70 272 |
|
TransMVSNet (Re) | | | 75.39 234 | 74.56 225 | 77.86 259 | 85.50 210 | 57.10 278 | 86.78 164 | 86.09 229 | 72.17 152 | 71.53 242 | 87.34 172 | 63.01 111 | 89.31 255 | 56.84 263 | 61.83 334 | 87.17 261 |
|
GA-MVS | | | 76.87 207 | 75.17 220 | 81.97 178 | 82.75 277 | 62.58 226 | 81.44 273 | 86.35 225 | 72.16 153 | 74.74 203 | 82.89 270 | 46.20 291 | 92.02 189 | 68.85 167 | 81.09 190 | 91.30 120 |
|
v1144 | | | 80.03 136 | 79.03 135 | 83.01 141 | 83.78 253 | 64.51 179 | 87.11 152 | 90.57 115 | 71.96 154 | 78.08 132 | 86.20 218 | 61.41 146 | 93.94 108 | 74.93 110 | 77.23 234 | 90.60 143 |
|
PS-MVSNAJss | | | 82.07 84 | 81.31 86 | 84.34 91 | 86.51 198 | 67.27 124 | 89.27 72 | 91.51 90 | 71.75 155 | 79.37 101 | 90.22 99 | 63.15 107 | 94.27 92 | 77.69 80 | 82.36 179 | 91.49 115 |
|
EPNet_dtu | | | 75.46 232 | 74.86 221 | 77.23 272 | 82.57 282 | 54.60 307 | 86.89 159 | 83.09 264 | 71.64 156 | 66.25 301 | 85.86 229 | 55.99 193 | 88.04 281 | 54.92 270 | 86.55 125 | 89.05 205 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
GBi-Net | | | 78.40 167 | 77.40 171 | 81.40 197 | 87.60 178 | 63.01 220 | 88.39 104 | 89.28 161 | 71.63 157 | 75.34 190 | 87.28 173 | 54.80 200 | 91.11 224 | 62.72 210 | 79.57 213 | 90.09 166 |
|
test1 | | | 78.40 167 | 77.40 171 | 81.40 197 | 87.60 178 | 63.01 220 | 88.39 104 | 89.28 161 | 71.63 157 | 75.34 190 | 87.28 173 | 54.80 200 | 91.11 224 | 62.72 210 | 79.57 213 | 90.09 166 |
|
FMVSNet2 | | | 78.20 173 | 77.21 174 | 81.20 200 | 87.60 178 | 62.89 224 | 87.47 135 | 89.02 170 | 71.63 157 | 75.29 194 | 87.28 173 | 54.80 200 | 91.10 227 | 62.38 214 | 79.38 216 | 89.61 193 |
|
V42 | | | 79.38 152 | 78.24 154 | 82.83 156 | 81.10 301 | 65.50 151 | 85.55 205 | 89.82 146 | 71.57 160 | 78.21 127 | 86.12 219 | 60.66 160 | 93.18 152 | 75.64 102 | 75.46 265 | 89.81 185 |
|
API-MVS | | | 81.99 86 | 81.23 88 | 84.26 94 | 90.94 71 | 70.18 68 | 91.10 35 | 89.32 160 | 71.51 161 | 78.66 110 | 88.28 148 | 65.26 87 | 95.10 64 | 64.74 201 | 91.23 66 | 87.51 252 |
|
tttt0517 | | | 79.40 150 | 77.91 159 | 83.90 110 | 88.10 154 | 63.84 199 | 88.37 107 | 84.05 246 | 71.45 162 | 76.78 154 | 89.12 126 | 49.93 269 | 94.89 73 | 70.18 154 | 83.18 167 | 92.96 76 |
|
pm-mvs1 | | | 77.25 203 | 76.68 183 | 78.93 243 | 84.22 229 | 58.62 256 | 86.41 175 | 88.36 196 | 71.37 163 | 73.31 211 | 88.01 156 | 61.22 151 | 89.15 264 | 64.24 203 | 73.01 287 | 89.03 207 |
|
FMVSNet3 | | | 77.88 184 | 76.85 180 | 80.97 205 | 86.84 194 | 62.36 227 | 86.52 172 | 88.77 186 | 71.13 164 | 75.34 190 | 86.66 198 | 54.07 210 | 91.10 227 | 62.72 210 | 79.57 213 | 89.45 195 |
|
VDDNet | | | 81.52 94 | 80.67 96 | 84.05 101 | 90.44 79 | 64.13 192 | 89.73 63 | 85.91 230 | 71.11 165 | 83.18 60 | 93.48 35 | 50.54 260 | 93.49 138 | 73.40 124 | 88.25 102 | 94.54 12 |
|
XVG-OURS | | | 80.41 121 | 79.23 132 | 83.97 107 | 85.64 207 | 69.02 85 | 83.03 259 | 90.39 119 | 71.09 166 | 77.63 139 | 91.49 73 | 54.62 206 | 91.35 219 | 75.71 101 | 83.47 159 | 91.54 112 |
|
SixPastTwentyTwo | | | 73.37 256 | 71.26 263 | 79.70 224 | 85.08 218 | 57.89 267 | 85.57 201 | 83.56 253 | 71.03 167 | 65.66 303 | 85.88 228 | 42.10 314 | 92.57 174 | 59.11 243 | 63.34 331 | 88.65 221 |
|
v1192 | | | 79.59 144 | 78.43 149 | 83.07 138 | 83.55 257 | 64.52 177 | 86.93 158 | 90.58 114 | 70.83 168 | 77.78 136 | 85.90 227 | 59.15 170 | 93.94 108 | 73.96 117 | 77.19 236 | 90.76 133 |
|
Fast-Effi-MVS+ | | | 80.81 109 | 79.92 108 | 83.47 118 | 88.85 128 | 64.51 179 | 85.53 207 | 89.39 158 | 70.79 169 | 78.49 113 | 85.06 247 | 67.54 68 | 93.58 132 | 67.03 183 | 86.58 124 | 92.32 92 |
|
PS-MVSNAJ | | | 81.69 90 | 81.02 93 | 83.70 113 | 89.51 103 | 68.21 109 | 84.28 238 | 90.09 135 | 70.79 169 | 81.26 87 | 85.62 236 | 63.15 107 | 94.29 90 | 75.62 103 | 88.87 90 | 88.59 228 |
|
LTVRE_ROB | | 69.57 13 | 76.25 219 | 74.54 226 | 81.41 196 | 88.60 139 | 64.38 188 | 79.24 289 | 89.12 168 | 70.76 171 | 69.79 266 | 87.86 157 | 49.09 276 | 93.20 150 | 56.21 266 | 80.16 203 | 86.65 273 |
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 |
xiu_mvs_v2_base | | | 81.69 90 | 81.05 92 | 83.60 115 | 89.15 120 | 68.03 112 | 84.46 231 | 90.02 139 | 70.67 172 | 81.30 86 | 86.53 207 | 63.17 106 | 94.19 98 | 75.60 104 | 88.54 98 | 88.57 230 |
|
XVG-OURS-SEG-HR | | | 80.81 109 | 79.76 112 | 83.96 108 | 85.60 208 | 68.78 91 | 83.54 249 | 90.50 117 | 70.66 173 | 76.71 156 | 91.66 65 | 60.69 159 | 91.26 221 | 76.94 89 | 81.58 186 | 91.83 106 |
|
Anonymous202405211 | | | 78.25 170 | 77.01 177 | 81.99 177 | 91.03 69 | 60.67 242 | 84.77 221 | 83.90 248 | 70.65 174 | 80.00 96 | 91.20 78 | 41.08 319 | 91.43 216 | 65.21 195 | 85.26 137 | 93.85 37 |
|
DP-MVS Recon | | | 83.11 72 | 82.09 78 | 86.15 53 | 94.44 12 | 70.92 55 | 88.79 89 | 92.20 59 | 70.53 175 | 79.17 103 | 91.03 85 | 64.12 96 | 96.03 34 | 68.39 171 | 90.14 77 | 91.50 114 |
|
FMVSNet1 | | | 77.44 199 | 76.12 195 | 81.40 197 | 86.81 195 | 63.01 220 | 88.39 104 | 89.28 161 | 70.49 176 | 74.39 206 | 87.28 173 | 49.06 277 | 91.11 224 | 60.91 229 | 78.52 221 | 90.09 166 |
|
ab-mvs | | | 79.51 145 | 78.97 137 | 81.14 202 | 88.46 145 | 60.91 239 | 83.84 244 | 89.24 165 | 70.36 177 | 79.03 104 | 88.87 131 | 63.23 105 | 90.21 241 | 65.12 196 | 82.57 177 | 92.28 94 |
|
tfpnnormal | | | 74.39 238 | 73.16 238 | 78.08 257 | 86.10 202 | 58.05 262 | 84.65 226 | 87.53 210 | 70.32 178 | 71.22 245 | 85.63 235 | 54.97 199 | 89.86 244 | 43.03 333 | 75.02 271 | 86.32 280 |
|
ACMM | | 73.20 8 | 80.78 114 | 79.84 110 | 83.58 116 | 89.31 114 | 68.37 104 | 89.99 55 | 91.60 86 | 70.28 179 | 77.25 146 | 89.66 110 | 53.37 215 | 93.53 137 | 74.24 115 | 82.85 171 | 88.85 214 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMH+ | | 68.96 14 | 76.01 226 | 74.01 231 | 82.03 176 | 88.60 139 | 65.31 157 | 88.86 86 | 87.55 209 | 70.25 180 | 67.75 286 | 87.47 171 | 41.27 317 | 93.19 151 | 58.37 250 | 75.94 257 | 87.60 250 |
|
IB-MVS | | 68.01 15 | 75.85 228 | 73.36 236 | 83.31 124 | 84.76 220 | 66.03 139 | 83.38 250 | 85.06 236 | 70.21 181 | 69.40 268 | 81.05 296 | 45.76 295 | 94.66 83 | 65.10 197 | 75.49 264 | 89.25 198 |
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 |
thisisatest0530 | | | 79.40 150 | 77.76 165 | 84.31 92 | 87.69 177 | 65.10 163 | 87.36 137 | 84.26 244 | 70.04 182 | 77.42 142 | 88.26 150 | 49.94 267 | 94.79 79 | 70.20 153 | 84.70 143 | 93.03 71 |
|
v144192 | | | 79.47 147 | 78.37 150 | 82.78 162 | 83.35 260 | 63.96 197 | 86.96 156 | 90.36 123 | 69.99 183 | 77.50 140 | 85.67 233 | 60.66 160 | 93.77 122 | 74.27 114 | 76.58 250 | 90.62 141 |
|
v1921920 | | | 79.22 154 | 78.03 156 | 82.80 159 | 83.30 262 | 63.94 198 | 86.80 162 | 90.33 125 | 69.91 184 | 77.48 141 | 85.53 238 | 58.44 174 | 93.75 124 | 73.60 121 | 76.85 244 | 90.71 136 |
|
ACMH | | 67.68 16 | 75.89 227 | 73.93 232 | 81.77 182 | 88.71 137 | 66.61 133 | 88.62 96 | 89.01 172 | 69.81 185 | 66.78 296 | 86.70 196 | 41.95 316 | 91.51 215 | 55.64 267 | 78.14 226 | 87.17 261 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
MAR-MVS | | | 81.84 88 | 80.70 95 | 85.27 66 | 91.32 66 | 71.53 47 | 89.82 58 | 90.92 105 | 69.77 186 | 78.50 112 | 86.21 217 | 62.36 133 | 94.52 86 | 65.36 194 | 92.05 57 | 89.77 190 |
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 |
XVG-ACMP-BASELINE | | | 76.11 225 | 74.27 230 | 81.62 191 | 83.20 265 | 64.67 173 | 83.60 248 | 89.75 148 | 69.75 187 | 71.85 238 | 87.09 184 | 32.78 340 | 92.11 187 | 69.99 157 | 80.43 201 | 88.09 240 |
|
BH-w/o | | | 78.21 172 | 77.33 173 | 80.84 206 | 88.81 132 | 65.13 162 | 84.87 219 | 87.85 205 | 69.75 187 | 74.52 205 | 84.74 254 | 61.34 147 | 93.11 156 | 58.24 252 | 85.84 135 | 84.27 305 |
|
v1240 | | | 78.99 159 | 77.78 163 | 82.64 166 | 83.21 264 | 63.54 205 | 86.62 169 | 90.30 127 | 69.74 189 | 77.33 144 | 85.68 232 | 57.04 189 | 93.76 123 | 73.13 127 | 76.92 241 | 90.62 141 |
|
DI_MVS_plusplus_test | | | 79.89 139 | 78.58 143 | 83.85 112 | 82.89 275 | 65.32 156 | 86.12 184 | 89.55 152 | 69.64 190 | 70.55 249 | 85.82 231 | 57.24 186 | 93.81 118 | 76.85 90 | 88.55 97 | 92.41 90 |
|
test_normal | | | 79.81 140 | 78.45 146 | 83.89 111 | 82.70 279 | 65.40 152 | 85.82 195 | 89.48 155 | 69.39 191 | 70.12 258 | 85.66 234 | 57.15 188 | 93.71 130 | 77.08 87 | 88.62 95 | 92.56 84 |
|
PVSNet_Blended_VisFu | | | 82.62 78 | 81.83 83 | 84.96 74 | 90.80 75 | 69.76 72 | 88.74 94 | 91.70 83 | 69.39 191 | 78.96 105 | 88.46 143 | 65.47 86 | 94.87 75 | 74.42 112 | 88.57 96 | 90.24 159 |
|
mvs_tets | | | 79.13 156 | 77.77 164 | 83.22 130 | 84.70 221 | 66.37 136 | 89.17 75 | 90.19 132 | 69.38 193 | 75.40 188 | 89.46 119 | 44.17 302 | 93.15 153 | 76.78 92 | 80.70 196 | 90.14 162 |
|
PVSNet_BlendedMVS | | | 80.60 117 | 80.02 106 | 82.36 171 | 88.85 128 | 65.40 152 | 86.16 183 | 92.00 68 | 69.34 194 | 78.11 130 | 86.09 220 | 66.02 82 | 94.27 92 | 71.52 144 | 82.06 180 | 87.39 254 |
|
AdaColmap | | | 80.58 119 | 79.42 122 | 84.06 100 | 93.09 43 | 68.91 89 | 89.36 70 | 88.97 177 | 69.27 195 | 75.70 183 | 89.69 109 | 57.20 187 | 95.77 40 | 63.06 209 | 88.41 101 | 87.50 253 |
|
ITE_SJBPF | | | | | 78.22 255 | 81.77 290 | 60.57 243 | | 83.30 257 | 69.25 196 | 67.54 288 | 87.20 178 | 36.33 336 | 87.28 286 | 54.34 272 | 74.62 275 | 86.80 269 |
|
jajsoiax | | | 79.29 153 | 77.96 157 | 83.27 126 | 84.68 222 | 66.57 134 | 89.25 74 | 90.16 133 | 69.20 197 | 75.46 185 | 89.49 116 | 45.75 296 | 93.13 155 | 76.84 91 | 80.80 194 | 90.11 164 |
|
semantic-postprocess | | | | | 80.11 218 | 82.69 280 | 64.85 169 | | 83.47 255 | 69.16 198 | 70.49 252 | 84.15 258 | 50.83 251 | 88.15 279 | 69.23 163 | 72.14 294 | 87.34 256 |
|
testing_2 | | | 75.73 229 | 73.34 237 | 82.89 152 | 77.37 329 | 65.22 159 | 84.10 241 | 90.54 116 | 69.09 199 | 60.46 325 | 81.15 295 | 40.48 321 | 92.84 169 | 76.36 93 | 80.54 200 | 90.60 143 |
|
xiu_mvs_v1_base_debu | | | 80.80 111 | 79.72 113 | 84.03 103 | 87.35 183 | 70.19 65 | 85.56 202 | 88.77 186 | 69.06 200 | 81.83 74 | 88.16 151 | 50.91 247 | 92.85 166 | 78.29 76 | 87.56 108 | 89.06 201 |
|
xiu_mvs_v1_base | | | 80.80 111 | 79.72 113 | 84.03 103 | 87.35 183 | 70.19 65 | 85.56 202 | 88.77 186 | 69.06 200 | 81.83 74 | 88.16 151 | 50.91 247 | 92.85 166 | 78.29 76 | 87.56 108 | 89.06 201 |
|
xiu_mvs_v1_base_debi | | | 80.80 111 | 79.72 113 | 84.03 103 | 87.35 183 | 70.19 65 | 85.56 202 | 88.77 186 | 69.06 200 | 81.83 74 | 88.16 151 | 50.91 247 | 92.85 166 | 78.29 76 | 87.56 108 | 89.06 201 |
|
Test4 | | | 77.83 185 | 75.90 202 | 83.62 114 | 80.24 309 | 65.25 158 | 85.27 210 | 90.67 110 | 69.03 203 | 66.48 299 | 83.75 263 | 43.07 307 | 93.00 163 | 75.93 97 | 88.66 94 | 92.62 83 |
|
MVSTER | | | 79.01 158 | 77.88 160 | 82.38 170 | 83.07 269 | 64.80 170 | 84.08 242 | 88.95 179 | 69.01 204 | 78.69 108 | 87.17 180 | 54.70 204 | 92.43 177 | 74.69 111 | 80.57 198 | 89.89 181 |
|
agg_prior1 | | | 86.22 39 | 86.09 38 | 86.62 45 | 92.85 46 | 71.94 43 | 88.59 97 | 91.78 80 | 68.96 205 | 84.41 45 | 93.18 42 | 74.94 10 | 94.93 68 | 84.75 24 | 95.33 21 | 93.01 74 |
|
PAPR | | | 81.66 92 | 80.89 94 | 83.99 106 | 90.27 81 | 64.00 196 | 86.76 166 | 91.77 82 | 68.84 206 | 77.13 152 | 89.50 115 | 67.63 67 | 94.88 74 | 67.55 174 | 88.52 99 | 93.09 68 |
|
CPTT-MVS | | | 83.73 61 | 83.33 61 | 84.92 77 | 93.28 37 | 70.86 56 | 92.09 23 | 90.38 120 | 68.75 207 | 79.57 99 | 92.83 51 | 60.60 162 | 93.04 161 | 80.92 56 | 91.56 62 | 90.86 131 |
|
train_agg | | | 86.43 34 | 86.20 34 | 87.13 36 | 93.26 38 | 72.96 21 | 88.75 92 | 91.89 74 | 68.69 208 | 85.00 32 | 93.10 43 | 74.43 14 | 95.41 51 | 84.97 17 | 95.71 12 | 93.02 72 |
|
test_8 | | | | | | 93.13 40 | 72.57 32 | 88.68 95 | 91.84 77 | 68.69 208 | 84.87 38 | 93.10 43 | 74.43 14 | 95.16 59 | | | |
|
MVSFormer | | | 82.85 75 | 82.05 79 | 85.24 67 | 87.35 183 | 70.21 63 | 90.50 44 | 90.38 120 | 68.55 210 | 81.32 83 | 89.47 117 | 61.68 140 | 93.46 139 | 78.98 68 | 90.26 74 | 92.05 102 |
|
test_djsdf | | | 80.30 127 | 79.32 126 | 83.27 126 | 83.98 246 | 65.37 155 | 90.50 44 | 90.38 120 | 68.55 210 | 76.19 170 | 88.70 134 | 56.44 191 | 93.46 139 | 78.98 68 | 80.14 205 | 90.97 128 |
|
TEST9 | | | | | | 93.26 38 | 72.96 21 | 88.75 92 | 91.89 74 | 68.44 212 | 85.00 32 | 93.10 43 | 74.36 17 | 95.41 51 | | | |
|
conf0.01 | | | 73.67 246 | 72.42 246 | 77.42 267 | 87.85 161 | 53.28 317 | 83.38 250 | 79.08 305 | 68.40 213 | 72.45 223 | 86.08 221 | 50.60 253 | 89.19 257 | 44.25 324 | 79.66 207 | 89.78 186 |
|
conf0.002 | | | 73.67 246 | 72.42 246 | 77.42 267 | 87.85 161 | 53.28 317 | 83.38 250 | 79.08 305 | 68.40 213 | 72.45 223 | 86.08 221 | 50.60 253 | 89.19 257 | 44.25 324 | 79.66 207 | 89.78 186 |
|
thresconf0.02 | | | 73.39 252 | 72.42 246 | 76.31 278 | 87.85 161 | 53.28 317 | 83.38 250 | 79.08 305 | 68.40 213 | 72.45 223 | 86.08 221 | 50.60 253 | 89.19 257 | 44.25 324 | 79.66 207 | 86.48 275 |
|
tfpn_n400 | | | 73.39 252 | 72.42 246 | 76.31 278 | 87.85 161 | 53.28 317 | 83.38 250 | 79.08 305 | 68.40 213 | 72.45 223 | 86.08 221 | 50.60 253 | 89.19 257 | 44.25 324 | 79.66 207 | 86.48 275 |
|
tfpnconf | | | 73.39 252 | 72.42 246 | 76.31 278 | 87.85 161 | 53.28 317 | 83.38 250 | 79.08 305 | 68.40 213 | 72.45 223 | 86.08 221 | 50.60 253 | 89.19 257 | 44.25 324 | 79.66 207 | 86.48 275 |
|
tfpnview11 | | | 73.39 252 | 72.42 246 | 76.31 278 | 87.85 161 | 53.28 317 | 83.38 250 | 79.08 305 | 68.40 213 | 72.45 223 | 86.08 221 | 50.60 253 | 89.19 257 | 44.25 324 | 79.66 207 | 86.48 275 |
|
CDPH-MVS | | | 85.76 44 | 85.29 49 | 87.17 35 | 93.49 34 | 71.08 49 | 88.58 98 | 92.42 52 | 68.32 219 | 84.61 42 | 93.48 35 | 72.32 32 | 96.15 33 | 79.00 67 | 95.43 17 | 94.28 20 |
|
agg_prior3 | | | 86.16 40 | 85.85 41 | 87.10 37 | 93.31 35 | 72.86 25 | 88.77 90 | 91.68 84 | 68.29 220 | 84.26 48 | 92.83 51 | 72.83 28 | 95.42 50 | 84.97 17 | 95.71 12 | 93.02 72 |
|
casdiffmvs | | | 83.96 58 | 83.25 62 | 86.07 55 | 88.48 143 | 69.60 75 | 89.26 73 | 92.40 53 | 68.07 221 | 82.82 65 | 90.03 103 | 69.77 52 | 94.86 76 | 81.79 49 | 86.64 123 | 93.75 42 |
|
IterMVS | | | 74.29 239 | 72.94 240 | 78.35 254 | 81.53 293 | 63.49 207 | 81.58 271 | 82.49 269 | 68.06 222 | 69.99 261 | 83.69 265 | 51.66 241 | 85.54 297 | 65.85 191 | 71.64 297 | 86.01 288 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tfpn1000 | | | 73.44 251 | 72.49 244 | 76.29 282 | 87.81 169 | 53.69 314 | 84.05 243 | 78.81 312 | 67.99 223 | 72.09 236 | 86.27 216 | 49.95 266 | 89.04 266 | 44.09 330 | 81.38 187 | 86.15 283 |
|
TAMVS | | | 78.89 162 | 77.51 170 | 83.03 140 | 87.80 170 | 67.79 116 | 84.72 222 | 85.05 237 | 67.63 224 | 76.75 155 | 87.70 163 | 62.25 135 | 90.82 233 | 58.53 249 | 87.13 114 | 90.49 151 |
|
PVSNet_Blended | | | 80.98 103 | 80.34 102 | 82.90 150 | 88.85 128 | 65.40 152 | 84.43 233 | 92.00 68 | 67.62 225 | 78.11 130 | 85.05 248 | 66.02 82 | 94.27 92 | 71.52 144 | 89.50 83 | 89.01 208 |
|
tfpn_ndepth | | | 73.70 244 | 72.75 241 | 76.52 276 | 87.78 173 | 54.92 306 | 84.32 237 | 80.28 297 | 67.57 226 | 72.50 220 | 84.82 251 | 50.12 263 | 89.44 253 | 45.73 319 | 81.66 185 | 85.20 295 |
|
TR-MVS | | | 77.44 199 | 76.18 194 | 81.20 200 | 88.24 151 | 63.24 215 | 84.61 227 | 86.40 223 | 67.55 227 | 77.81 135 | 86.48 209 | 54.10 209 | 93.15 153 | 57.75 256 | 82.72 174 | 87.20 260 |
|
CDS-MVSNet | | | 79.07 157 | 77.70 166 | 83.17 132 | 87.60 178 | 68.23 108 | 84.40 235 | 86.20 226 | 67.49 228 | 76.36 164 | 86.54 206 | 61.54 143 | 90.79 234 | 61.86 221 | 87.33 112 | 90.49 151 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
PatchFormer-LS_test | | | 74.50 237 | 73.05 239 | 78.86 244 | 82.95 273 | 59.55 251 | 81.65 270 | 82.30 272 | 67.44 229 | 71.62 241 | 78.15 318 | 52.34 221 | 88.92 272 | 65.05 198 | 75.90 258 | 88.12 239 |
|
mvs_anonymous | | | 79.42 149 | 79.11 134 | 80.34 213 | 84.45 225 | 57.97 265 | 82.59 260 | 87.62 208 | 67.40 230 | 76.17 173 | 88.56 141 | 68.47 61 | 89.59 249 | 70.65 150 | 86.05 132 | 93.47 56 |
|
OpenMVS | | 72.83 10 | 79.77 141 | 78.33 152 | 84.09 99 | 85.17 213 | 69.91 69 | 90.57 42 | 90.97 104 | 66.70 231 | 72.17 232 | 91.91 61 | 54.70 204 | 93.96 106 | 61.81 222 | 90.95 68 | 88.41 236 |
|
test-LLR | | | 72.94 264 | 72.43 245 | 74.48 298 | 81.35 297 | 58.04 263 | 78.38 297 | 77.46 319 | 66.66 232 | 69.95 262 | 79.00 313 | 48.06 280 | 79.24 323 | 66.13 186 | 84.83 140 | 86.15 283 |
|
test20.03 | | | 67.45 297 | 66.95 296 | 68.94 322 | 75.48 338 | 44.84 344 | 77.50 304 | 77.67 318 | 66.66 232 | 63.01 318 | 83.80 262 | 47.02 285 | 78.40 327 | 42.53 335 | 68.86 310 | 83.58 312 |
|
test0.0.03 1 | | | 68.00 295 | 67.69 292 | 68.90 323 | 77.55 327 | 47.43 340 | 75.70 314 | 72.95 342 | 66.66 232 | 66.56 297 | 82.29 277 | 48.06 280 | 75.87 338 | 44.97 323 | 74.51 276 | 83.41 313 |
|
QAPM | | | 80.88 104 | 79.50 121 | 85.03 72 | 88.01 158 | 68.97 88 | 91.59 27 | 92.00 68 | 66.63 235 | 75.15 197 | 92.16 56 | 57.70 178 | 95.45 48 | 63.52 205 | 88.76 92 | 90.66 140 |
|
XXY-MVS | | | 75.41 233 | 75.56 205 | 74.96 294 | 83.59 256 | 57.82 269 | 80.59 278 | 83.87 249 | 66.54 236 | 74.93 202 | 88.31 147 | 63.24 104 | 80.09 321 | 62.16 217 | 76.85 244 | 86.97 266 |
|
casdiffmvs1 | | | 84.76 56 | 84.33 56 | 86.04 57 | 89.40 106 | 68.78 91 | 89.67 65 | 92.54 47 | 66.43 237 | 85.41 26 | 90.75 90 | 72.88 27 | 94.76 80 | 81.64 50 | 90.24 76 | 94.57 10 |
|
OurMVSNet-221017-0 | | | 74.26 240 | 72.42 246 | 79.80 223 | 83.76 254 | 59.59 248 | 85.92 190 | 86.64 219 | 66.39 238 | 66.96 294 | 87.58 166 | 39.46 324 | 91.60 213 | 65.76 192 | 69.27 306 | 88.22 237 |
|
Patchmatch-test1 | | | 73.49 249 | 71.85 256 | 78.41 253 | 84.05 245 | 62.17 231 | 79.96 283 | 79.29 304 | 66.30 239 | 72.38 230 | 79.58 309 | 51.95 230 | 85.08 301 | 55.46 268 | 77.67 229 | 87.99 241 |
|
testgi | | | 66.67 302 | 66.53 298 | 67.08 328 | 75.62 336 | 41.69 351 | 75.93 310 | 76.50 324 | 66.11 240 | 65.20 309 | 86.59 203 | 35.72 338 | 74.71 342 | 43.71 331 | 73.38 286 | 84.84 301 |
|
HY-MVS | | 69.67 12 | 77.95 181 | 77.15 175 | 80.36 212 | 87.57 182 | 60.21 246 | 83.37 257 | 87.78 206 | 66.11 240 | 75.37 189 | 87.06 186 | 63.27 103 | 90.48 238 | 61.38 226 | 82.43 178 | 90.40 156 |
|
EG-PatchMatch MVS | | | 74.04 241 | 71.82 257 | 80.71 209 | 84.92 219 | 67.42 120 | 85.86 191 | 88.08 201 | 66.04 242 | 64.22 313 | 83.85 260 | 35.10 339 | 92.56 175 | 57.44 258 | 80.83 193 | 82.16 324 |
|
CNLPA | | | 78.08 176 | 76.79 182 | 81.97 178 | 90.40 80 | 71.07 50 | 87.59 127 | 84.55 240 | 66.03 243 | 72.38 230 | 89.64 111 | 57.56 180 | 86.04 294 | 59.61 238 | 83.35 164 | 88.79 217 |
|
Anonymous20240529 | | | 80.19 131 | 78.89 138 | 84.10 98 | 90.60 76 | 64.75 172 | 88.95 83 | 90.90 106 | 65.97 244 | 80.59 93 | 91.17 79 | 49.97 265 | 93.73 129 | 69.16 165 | 82.70 175 | 93.81 40 |
|
TAPA-MVS | | 73.13 9 | 79.15 155 | 77.94 158 | 82.79 161 | 89.59 98 | 62.99 223 | 88.16 115 | 91.51 90 | 65.77 245 | 77.14 151 | 91.09 81 | 60.91 156 | 93.21 148 | 50.26 290 | 87.05 115 | 92.17 99 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
MSDG | | | 73.36 258 | 70.99 265 | 80.49 210 | 84.51 224 | 65.80 145 | 80.71 276 | 86.13 228 | 65.70 246 | 65.46 304 | 83.74 264 | 44.60 299 | 90.91 232 | 51.13 285 | 76.89 242 | 84.74 302 |
|
anonymousdsp | | | 78.60 165 | 77.15 175 | 82.98 143 | 80.51 307 | 67.08 126 | 87.24 145 | 89.53 153 | 65.66 247 | 75.16 196 | 87.19 179 | 52.52 217 | 92.25 184 | 77.17 86 | 79.34 217 | 89.61 193 |
|
test_0402 | | | 72.79 265 | 70.44 268 | 79.84 222 | 88.13 153 | 65.99 141 | 85.93 189 | 84.29 242 | 65.57 248 | 67.40 291 | 85.49 239 | 46.92 286 | 92.61 173 | 35.88 343 | 74.38 277 | 80.94 328 |
|
DWT-MVSNet_test | | | 73.70 244 | 71.86 255 | 79.21 236 | 82.91 274 | 58.94 253 | 82.34 261 | 82.17 273 | 65.21 249 | 71.05 247 | 78.31 315 | 44.21 301 | 90.17 242 | 63.29 208 | 77.28 233 | 88.53 233 |
|
UnsupCasMVSNet_eth | | | 67.33 298 | 65.99 299 | 71.37 312 | 73.48 341 | 51.47 329 | 75.16 316 | 85.19 235 | 65.20 250 | 60.78 324 | 80.93 300 | 42.35 311 | 77.20 333 | 57.12 261 | 53.69 347 | 85.44 293 |
|
WTY-MVS | | | 75.65 230 | 75.68 204 | 75.57 289 | 86.40 199 | 56.82 283 | 77.92 303 | 82.40 270 | 65.10 251 | 76.18 171 | 87.72 162 | 63.13 110 | 80.90 317 | 60.31 233 | 81.96 181 | 89.00 210 |
|
thisisatest0515 | | | 77.33 202 | 75.38 212 | 83.18 131 | 85.27 212 | 63.80 200 | 82.11 264 | 83.27 258 | 65.06 252 | 75.91 175 | 83.84 261 | 49.54 271 | 94.27 92 | 67.24 179 | 86.19 130 | 91.48 117 |
|
MVP-Stereo | | | 76.12 224 | 74.46 228 | 81.13 203 | 85.37 211 | 69.79 71 | 84.42 234 | 87.95 203 | 65.03 253 | 67.46 289 | 85.33 242 | 53.28 216 | 91.73 200 | 58.01 254 | 83.27 165 | 81.85 325 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
Anonymous20231211 | | | 78.97 160 | 77.69 167 | 82.81 158 | 90.54 77 | 64.29 189 | 90.11 54 | 91.51 90 | 65.01 254 | 76.16 174 | 88.13 155 | 50.56 259 | 93.03 162 | 69.68 160 | 77.56 230 | 91.11 124 |
|
pmmvs6 | | | 74.69 236 | 73.39 235 | 78.61 248 | 81.38 296 | 57.48 274 | 86.64 168 | 87.95 203 | 64.99 255 | 70.18 255 | 86.61 202 | 50.43 261 | 89.52 250 | 62.12 218 | 70.18 304 | 88.83 215 |
|
PAPM | | | 77.68 188 | 76.40 187 | 81.51 194 | 87.29 188 | 61.85 234 | 83.78 245 | 89.59 151 | 64.74 256 | 71.23 244 | 88.70 134 | 62.59 127 | 93.66 131 | 52.66 280 | 87.03 116 | 89.01 208 |
|
MIMVSNet | | | 70.69 277 | 69.30 273 | 74.88 295 | 84.52 223 | 56.35 293 | 75.87 313 | 79.42 303 | 64.59 257 | 67.76 285 | 82.41 275 | 41.10 318 | 81.54 316 | 46.64 315 | 81.34 188 | 86.75 271 |
|
tpm | | | 72.37 268 | 71.71 258 | 74.35 300 | 82.19 286 | 52.00 324 | 79.22 290 | 77.29 321 | 64.56 258 | 72.95 216 | 83.68 266 | 51.35 242 | 83.26 311 | 58.33 251 | 75.80 259 | 87.81 246 |
|
MDA-MVSNet-bldmvs | | | 66.68 301 | 63.66 305 | 75.75 286 | 79.28 322 | 60.56 244 | 73.92 322 | 78.35 314 | 64.43 259 | 50.13 350 | 79.87 307 | 44.02 303 | 83.67 307 | 46.10 317 | 56.86 342 | 83.03 319 |
|
MIMVSNet1 | | | 68.58 292 | 66.78 297 | 73.98 303 | 80.07 311 | 51.82 325 | 80.77 275 | 84.37 241 | 64.40 260 | 59.75 329 | 82.16 279 | 36.47 335 | 83.63 308 | 42.73 334 | 70.33 303 | 86.48 275 |
|
PLC | | 70.83 11 | 78.05 177 | 76.37 188 | 83.08 137 | 91.88 62 | 67.80 115 | 88.19 113 | 89.46 156 | 64.33 261 | 69.87 264 | 88.38 145 | 53.66 213 | 93.58 132 | 58.86 245 | 82.73 173 | 87.86 245 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
PatchmatchNet | | | 73.12 261 | 71.33 261 | 78.49 252 | 83.18 266 | 60.85 240 | 79.63 285 | 78.57 313 | 64.13 262 | 71.73 239 | 79.81 308 | 51.20 244 | 85.97 295 | 57.40 259 | 76.36 254 | 88.66 220 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
tpmvs | | | 71.09 274 | 69.29 274 | 76.49 277 | 82.04 287 | 56.04 296 | 78.92 294 | 81.37 285 | 64.05 263 | 67.18 293 | 78.28 316 | 49.74 270 | 89.77 245 | 49.67 293 | 72.37 291 | 83.67 311 |
|
F-COLMAP | | | 76.38 218 | 74.33 229 | 82.50 168 | 89.28 115 | 66.95 131 | 88.41 103 | 89.03 169 | 64.05 263 | 66.83 295 | 88.61 138 | 46.78 287 | 92.89 165 | 57.48 257 | 78.55 220 | 87.67 248 |
|
DP-MVS | | | 76.78 208 | 74.57 224 | 83.42 120 | 93.29 36 | 69.46 81 | 88.55 99 | 83.70 250 | 63.98 265 | 70.20 254 | 88.89 130 | 54.01 211 | 94.80 78 | 46.66 313 | 81.88 183 | 86.01 288 |
|
原ACMM1 | | | | | 84.35 90 | 93.01 44 | 68.79 90 | | 92.44 49 | 63.96 266 | 81.09 88 | 91.57 70 | 66.06 81 | 95.45 48 | 67.19 180 | 94.82 32 | 88.81 216 |
|
PM-MVS | | | 66.41 304 | 64.14 304 | 73.20 305 | 73.92 339 | 56.45 289 | 78.97 293 | 64.96 360 | 63.88 267 | 64.72 310 | 80.24 303 | 19.84 355 | 83.44 309 | 66.24 185 | 64.52 330 | 79.71 333 |
|
jason | | | 81.39 98 | 80.29 104 | 84.70 81 | 86.63 197 | 69.90 70 | 85.95 188 | 86.77 218 | 63.24 268 | 81.07 89 | 89.47 117 | 61.08 154 | 92.15 186 | 78.33 75 | 90.07 79 | 92.05 102 |
jason: jason. |
gg-mvs-nofinetune | | | 69.95 285 | 67.96 286 | 75.94 285 | 83.07 269 | 54.51 309 | 77.23 306 | 70.29 347 | 63.11 269 | 70.32 253 | 62.33 349 | 43.62 304 | 88.69 274 | 53.88 275 | 87.76 105 | 84.62 304 |
|
tpmrst | | | 72.39 266 | 72.13 253 | 73.18 306 | 80.54 306 | 49.91 336 | 79.91 284 | 79.08 305 | 63.11 269 | 71.69 240 | 79.95 305 | 55.32 197 | 82.77 312 | 65.66 193 | 73.89 281 | 86.87 267 |
|
PCF-MVS | | 73.52 7 | 80.38 125 | 78.84 139 | 85.01 73 | 87.71 175 | 68.99 87 | 83.65 246 | 91.46 94 | 63.00 271 | 77.77 137 | 90.28 96 | 66.10 79 | 95.09 65 | 61.40 225 | 88.22 103 | 90.94 129 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
COLMAP_ROB | | 66.92 17 | 73.01 262 | 70.41 269 | 80.81 207 | 87.13 190 | 65.63 147 | 88.30 109 | 84.19 245 | 62.96 272 | 63.80 316 | 87.69 164 | 38.04 330 | 92.56 175 | 46.66 313 | 74.91 272 | 84.24 306 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
Patchmatch-RL test | | | 70.24 282 | 67.78 291 | 77.61 264 | 77.43 328 | 59.57 249 | 71.16 326 | 70.33 346 | 62.94 273 | 68.65 278 | 72.77 337 | 50.62 252 | 85.49 298 | 69.58 161 | 66.58 320 | 87.77 247 |
|
lupinMVS | | | 81.39 98 | 80.27 105 | 84.76 80 | 87.35 183 | 70.21 63 | 85.55 205 | 86.41 222 | 62.85 274 | 81.32 83 | 88.61 138 | 61.68 140 | 92.24 185 | 78.41 74 | 90.26 74 | 91.83 106 |
|
EPMVS | | | 69.02 290 | 68.16 283 | 71.59 310 | 79.61 317 | 49.80 338 | 77.40 305 | 66.93 356 | 62.82 275 | 70.01 259 | 79.05 311 | 45.79 294 | 77.86 331 | 56.58 264 | 75.26 269 | 87.13 263 |
|
PatchMatch-RL | | | 72.38 267 | 70.90 266 | 76.80 275 | 88.60 139 | 67.38 122 | 79.53 286 | 76.17 325 | 62.75 276 | 69.36 270 | 82.00 284 | 45.51 297 | 84.89 302 | 53.62 276 | 80.58 197 | 78.12 336 |
|
gm-plane-assit | | | | | | 81.40 295 | 53.83 313 | | | 62.72 277 | | 80.94 299 | | 92.39 179 | 63.40 207 | | |
|
FMVSNet5 | | | 69.50 287 | 67.96 286 | 74.15 302 | 82.97 272 | 55.35 304 | 80.01 282 | 82.12 275 | 62.56 278 | 63.02 317 | 81.53 292 | 36.92 334 | 81.92 314 | 48.42 297 | 74.06 279 | 85.17 298 |
|
sss | | | 73.60 248 | 73.64 234 | 73.51 304 | 82.80 276 | 55.01 305 | 76.12 309 | 81.69 282 | 62.47 279 | 74.68 204 | 85.85 230 | 57.32 183 | 78.11 329 | 60.86 230 | 80.93 191 | 87.39 254 |
|
AllTest | | | 70.96 275 | 68.09 285 | 79.58 229 | 85.15 214 | 63.62 201 | 84.58 228 | 79.83 300 | 62.31 280 | 60.32 326 | 86.73 189 | 32.02 341 | 88.96 270 | 50.28 288 | 71.57 298 | 86.15 283 |
|
TestCases | | | | | 79.58 229 | 85.15 214 | 63.62 201 | | 79.83 300 | 62.31 280 | 60.32 326 | 86.73 189 | 32.02 341 | 88.96 270 | 50.28 288 | 71.57 298 | 86.15 283 |
|
1112_ss | | | 77.40 201 | 76.43 186 | 80.32 214 | 89.11 125 | 60.41 245 | 83.65 246 | 87.72 207 | 62.13 282 | 73.05 215 | 86.72 191 | 62.58 128 | 89.97 243 | 62.11 219 | 80.80 194 | 90.59 145 |
|
PVSNet | | 64.34 18 | 72.08 269 | 70.87 267 | 75.69 287 | 86.21 201 | 56.44 290 | 74.37 321 | 80.73 289 | 62.06 283 | 70.17 256 | 82.23 278 | 42.86 309 | 83.31 310 | 54.77 271 | 84.45 146 | 87.32 257 |
|
LS3D | | | 76.95 206 | 74.82 222 | 83.37 123 | 90.45 78 | 67.36 123 | 89.15 79 | 86.94 217 | 61.87 284 | 69.52 267 | 90.61 93 | 51.71 240 | 94.53 85 | 46.38 316 | 86.71 122 | 88.21 238 |
|
CostFormer | | | 75.24 235 | 73.90 233 | 79.27 234 | 82.65 281 | 58.27 260 | 80.80 274 | 82.73 268 | 61.57 285 | 75.33 193 | 83.13 269 | 55.52 196 | 91.07 230 | 64.98 199 | 78.34 225 | 88.45 234 |
|
new-patchmatchnet | | | 61.73 313 | 61.73 313 | 61.70 336 | 72.74 345 | 24.50 367 | 69.16 336 | 78.03 316 | 61.40 286 | 56.72 338 | 75.53 330 | 38.42 328 | 76.48 336 | 45.95 318 | 57.67 341 | 84.13 308 |
|
ANet_high | | | 50.57 330 | 46.10 333 | 63.99 331 | 48.67 366 | 39.13 353 | 70.99 329 | 80.85 287 | 61.39 287 | 31.18 358 | 57.70 354 | 17.02 359 | 73.65 347 | 31.22 352 | 15.89 363 | 79.18 334 |
|
MS-PatchMatch | | | 73.83 243 | 72.67 242 | 77.30 271 | 83.87 248 | 66.02 140 | 81.82 266 | 84.66 239 | 61.37 288 | 68.61 280 | 82.82 272 | 47.29 283 | 88.21 278 | 59.27 241 | 84.32 147 | 77.68 338 |
|
USDC | | | 70.33 281 | 68.37 280 | 76.21 284 | 80.60 305 | 56.23 294 | 79.19 291 | 86.49 221 | 60.89 289 | 61.29 322 | 85.47 240 | 31.78 343 | 89.47 252 | 53.37 277 | 76.21 255 | 82.94 321 |
|
cascas | | | 76.72 209 | 74.64 223 | 82.99 142 | 85.78 205 | 65.88 144 | 82.33 262 | 89.21 166 | 60.85 290 | 72.74 217 | 81.02 297 | 47.28 284 | 93.75 124 | 67.48 175 | 85.02 138 | 89.34 196 |
|
MDTV_nov1_ep13 | | | | 69.97 272 | | 83.18 266 | 53.48 315 | 77.10 307 | 80.18 299 | 60.45 291 | 69.33 271 | 80.44 301 | 48.89 278 | 86.90 287 | 51.60 283 | 78.51 222 | |
|
TinyColmap | | | 67.30 299 | 64.81 301 | 74.76 297 | 81.92 289 | 56.68 287 | 80.29 280 | 81.49 284 | 60.33 292 | 56.27 340 | 83.22 268 | 24.77 349 | 87.66 284 | 45.52 320 | 69.47 305 | 79.95 332 |
|
test-mter | | | 71.41 272 | 70.39 270 | 74.48 298 | 81.35 297 | 58.04 263 | 78.38 297 | 77.46 319 | 60.32 293 | 69.95 262 | 79.00 313 | 36.08 337 | 79.24 323 | 66.13 186 | 84.83 140 | 86.15 283 |
|
1314 | | | 76.53 212 | 75.30 215 | 80.21 217 | 83.93 247 | 62.32 229 | 84.66 223 | 88.81 185 | 60.23 294 | 70.16 257 | 84.07 259 | 55.30 198 | 90.73 235 | 67.37 176 | 83.21 166 | 87.59 251 |
|
PatchT | | | 68.46 294 | 67.85 288 | 70.29 318 | 80.70 304 | 43.93 346 | 72.47 324 | 74.88 331 | 60.15 295 | 70.55 249 | 76.57 326 | 49.94 267 | 81.59 315 | 50.58 286 | 74.83 273 | 85.34 294 |
|
无先验 | | | | | | | | 87.48 134 | 88.98 176 | 60.00 296 | | | | 94.12 101 | 67.28 177 | | 88.97 211 |
|
CR-MVSNet | | | 73.37 256 | 71.27 262 | 79.67 226 | 81.32 299 | 65.19 160 | 75.92 311 | 80.30 295 | 59.92 297 | 72.73 218 | 81.19 293 | 52.50 218 | 86.69 288 | 59.84 236 | 77.71 227 | 87.11 264 |
|
TDRefinement | | | 67.49 296 | 64.34 303 | 76.92 273 | 73.47 342 | 61.07 237 | 84.86 220 | 82.98 265 | 59.77 298 | 58.30 332 | 85.13 245 | 26.06 347 | 87.89 282 | 47.92 306 | 60.59 339 | 81.81 326 |
|
dp | | | 66.80 300 | 65.43 300 | 70.90 317 | 79.74 316 | 48.82 339 | 75.12 318 | 74.77 333 | 59.61 299 | 64.08 314 | 77.23 323 | 42.89 308 | 80.72 318 | 48.86 296 | 66.58 320 | 83.16 316 |
|
our_test_3 | | | 69.14 289 | 67.00 295 | 75.57 289 | 79.80 314 | 58.80 254 | 77.96 302 | 77.81 317 | 59.55 300 | 62.90 320 | 78.25 317 | 47.43 282 | 83.97 305 | 51.71 282 | 67.58 313 | 83.93 310 |
|
Test_1112_low_res | | | 76.40 217 | 75.44 209 | 79.27 234 | 89.28 115 | 58.09 261 | 81.69 269 | 87.07 216 | 59.53 301 | 72.48 222 | 86.67 197 | 61.30 148 | 89.33 254 | 60.81 231 | 80.15 204 | 90.41 155 |
|
pmmvs4 | | | 74.03 242 | 71.91 254 | 80.39 211 | 81.96 288 | 68.32 105 | 81.45 272 | 82.14 274 | 59.32 302 | 69.87 264 | 85.13 245 | 52.40 220 | 88.13 280 | 60.21 234 | 74.74 274 | 84.73 303 |
|
testdata | | | | | 79.97 220 | 90.90 72 | 64.21 190 | | 84.71 238 | 59.27 303 | 85.40 27 | 92.91 48 | 62.02 139 | 89.08 265 | 68.95 166 | 91.37 64 | 86.63 274 |
|
ppachtmachnet_test | | | 70.04 284 | 67.34 294 | 78.14 256 | 79.80 314 | 61.13 236 | 79.19 291 | 80.59 290 | 59.16 304 | 65.27 306 | 79.29 310 | 46.75 288 | 87.29 285 | 49.33 294 | 66.72 318 | 86.00 290 |
|
RPSCF | | | 73.23 260 | 71.46 259 | 78.54 250 | 82.50 283 | 59.85 247 | 82.18 263 | 82.84 267 | 58.96 305 | 71.15 246 | 89.41 123 | 45.48 298 | 84.77 303 | 58.82 246 | 71.83 296 | 91.02 127 |
|
pmmvs-eth3d | | | 70.50 280 | 67.83 289 | 78.52 251 | 77.37 329 | 66.18 138 | 81.82 266 | 81.51 283 | 58.90 306 | 63.90 315 | 80.42 302 | 42.69 310 | 86.28 293 | 58.56 248 | 65.30 328 | 83.11 317 |
|
OpenMVS_ROB | | 64.09 19 | 70.56 279 | 68.19 282 | 77.65 263 | 80.26 308 | 59.41 252 | 85.01 217 | 82.96 266 | 58.76 307 | 65.43 305 | 82.33 276 | 37.63 333 | 91.23 223 | 45.34 322 | 76.03 256 | 82.32 322 |
|
114514_t | | | 80.68 115 | 79.51 120 | 84.20 95 | 94.09 26 | 67.27 124 | 89.64 67 | 91.11 102 | 58.75 308 | 74.08 207 | 90.72 91 | 58.10 176 | 95.04 66 | 69.70 159 | 89.42 85 | 90.30 158 |
|
Patchmtry | | | 70.74 276 | 69.16 275 | 75.49 291 | 80.72 303 | 54.07 311 | 74.94 320 | 80.30 295 | 58.34 309 | 70.01 259 | 81.19 293 | 52.50 218 | 86.54 290 | 53.37 277 | 71.09 300 | 85.87 291 |
|
旧先验2 | | | | | | | | 86.56 171 | | 58.10 310 | 87.04 17 | | | 88.98 268 | 74.07 116 | | |
|
testpf | | | 56.51 323 | 57.58 320 | 53.30 343 | 71.99 347 | 41.19 352 | 46.89 360 | 69.32 352 | 58.06 311 | 52.87 347 | 69.45 345 | 27.99 345 | 72.73 348 | 59.59 239 | 62.07 333 | 45.98 358 |
|
JIA-IIPM | | | 66.32 305 | 62.82 311 | 76.82 274 | 77.09 331 | 61.72 235 | 65.34 347 | 75.38 327 | 58.04 312 | 64.51 311 | 62.32 350 | 42.05 315 | 86.51 291 | 51.45 284 | 69.22 307 | 82.21 323 |
|
tpmp4_e23 | | | 73.45 250 | 71.17 264 | 80.31 215 | 83.55 257 | 59.56 250 | 81.88 265 | 82.33 271 | 57.94 313 | 70.51 251 | 81.62 291 | 51.19 245 | 91.63 212 | 53.96 274 | 77.51 231 | 89.75 191 |
|
pmmvs5 | | | 71.55 271 | 70.20 271 | 75.61 288 | 77.83 326 | 56.39 291 | 81.74 268 | 80.89 286 | 57.76 314 | 67.46 289 | 84.49 255 | 49.26 275 | 85.32 300 | 57.08 262 | 75.29 268 | 85.11 299 |
|
TESTMET0.1,1 | | | 69.89 286 | 69.00 276 | 72.55 307 | 79.27 323 | 56.85 282 | 78.38 297 | 74.71 335 | 57.64 315 | 68.09 284 | 77.19 324 | 37.75 331 | 76.70 334 | 63.92 204 | 84.09 148 | 84.10 309 |
|
RPMNet | | | 71.62 270 | 68.94 277 | 79.67 226 | 81.32 299 | 65.19 160 | 75.92 311 | 78.30 315 | 57.60 316 | 72.73 218 | 76.45 327 | 52.30 222 | 86.69 288 | 48.14 303 | 77.71 227 | 87.11 264 |
|
新几何1 | | | | | 83.42 120 | 93.13 40 | 70.71 58 | | 85.48 232 | 57.43 317 | 81.80 77 | 91.98 60 | 63.28 102 | 92.27 183 | 64.60 202 | 92.99 50 | 87.27 258 |
|
1121 | | | 80.84 106 | 79.77 111 | 84.05 101 | 93.11 42 | 70.78 57 | 84.66 223 | 85.42 233 | 57.37 318 | 81.76 80 | 92.02 59 | 63.41 100 | 94.12 101 | 67.28 177 | 92.93 51 | 87.26 259 |
|
YYNet1 | | | 65.03 307 | 62.91 309 | 71.38 311 | 75.85 334 | 56.60 288 | 69.12 337 | 74.66 337 | 57.28 319 | 54.12 342 | 77.87 320 | 45.85 293 | 74.48 343 | 49.95 291 | 61.52 336 | 83.05 318 |
|
MDA-MVSNet_test_wron | | | 65.03 307 | 62.92 308 | 71.37 312 | 75.93 333 | 56.73 284 | 69.09 338 | 74.73 334 | 57.28 319 | 54.03 343 | 77.89 319 | 45.88 292 | 74.39 344 | 49.89 292 | 61.55 335 | 82.99 320 |
|
Anonymous20231206 | | | 68.60 291 | 67.80 290 | 71.02 316 | 80.23 310 | 50.75 333 | 78.30 300 | 80.47 292 | 56.79 321 | 66.11 302 | 82.63 274 | 46.35 289 | 78.95 325 | 43.62 332 | 75.70 260 | 83.36 314 |
|
tpm2 | | | 73.26 259 | 71.46 259 | 78.63 247 | 83.34 261 | 56.71 286 | 80.65 277 | 80.40 294 | 56.63 322 | 73.55 209 | 82.02 283 | 51.80 239 | 91.24 222 | 56.35 265 | 78.42 224 | 87.95 242 |
|
CHOSEN 1792x2688 | | | 77.63 191 | 75.69 203 | 83.44 119 | 89.98 88 | 68.58 102 | 78.70 296 | 87.50 211 | 56.38 323 | 75.80 178 | 86.84 187 | 58.67 172 | 91.40 217 | 61.58 224 | 85.75 136 | 90.34 157 |
|
HyFIR lowres test | | | 77.53 192 | 75.40 211 | 83.94 109 | 89.59 98 | 66.62 132 | 80.36 279 | 88.64 191 | 56.29 324 | 76.45 160 | 85.17 244 | 57.64 179 | 93.28 146 | 61.34 227 | 83.10 169 | 91.91 104 |
|
PVSNet_0 | | 57.27 20 | 61.67 314 | 59.27 315 | 68.85 324 | 79.61 317 | 57.44 275 | 68.01 341 | 73.44 341 | 55.93 325 | 58.54 331 | 70.41 342 | 44.58 300 | 77.55 332 | 47.01 308 | 35.91 354 | 71.55 348 |
|
UnsupCasMVSNet_bld | | | 63.70 312 | 61.53 314 | 70.21 319 | 73.69 340 | 51.39 330 | 72.82 323 | 81.89 280 | 55.63 326 | 57.81 333 | 71.80 339 | 38.67 327 | 78.61 326 | 49.26 295 | 52.21 349 | 80.63 329 |
|
MDTV_nov1_ep13_2view | | | | | | | 37.79 356 | 75.16 316 | | 55.10 327 | 66.53 298 | | 49.34 273 | | 53.98 273 | | 87.94 243 |
|
MVS | | | 78.19 174 | 76.99 178 | 81.78 181 | 85.66 206 | 66.99 127 | 84.66 223 | 90.47 118 | 55.08 328 | 72.02 237 | 85.27 243 | 63.83 98 | 94.11 103 | 66.10 188 | 89.80 81 | 84.24 306 |
|
test222 | | | | | | 91.50 64 | 68.26 107 | 84.16 239 | 83.20 263 | 54.63 329 | 79.74 97 | 91.63 68 | 58.97 171 | | | 91.42 63 | 86.77 270 |
|
test1235678 | | | 58.74 319 | 56.89 322 | 64.30 330 | 69.70 350 | 41.87 350 | 71.05 327 | 74.87 332 | 54.06 330 | 50.63 349 | 71.53 340 | 25.30 348 | 74.10 345 | 31.80 351 | 63.10 332 | 76.93 342 |
|
1111 | | | 57.11 322 | 56.82 323 | 57.97 340 | 69.10 351 | 28.28 362 | 68.90 339 | 74.54 338 | 54.01 331 | 53.71 344 | 74.51 332 | 23.09 351 | 67.90 357 | 32.28 348 | 61.26 337 | 77.73 337 |
|
.test1245 | | | 45.55 332 | 50.02 330 | 32.14 352 | 69.10 351 | 28.28 362 | 68.90 339 | 74.54 338 | 54.01 331 | 53.71 344 | 74.51 332 | 23.09 351 | 67.90 357 | 32.28 348 | 0.02 366 | 0.25 367 |
|
test2356 | | | 59.50 316 | 58.08 316 | 63.74 332 | 71.23 348 | 41.88 349 | 67.59 342 | 72.42 344 | 53.72 333 | 57.65 334 | 70.74 341 | 26.31 346 | 72.40 349 | 32.03 350 | 71.06 301 | 76.93 342 |
|
CHOSEN 280x420 | | | 66.51 303 | 64.71 302 | 71.90 309 | 81.45 294 | 63.52 206 | 57.98 355 | 68.95 354 | 53.57 334 | 62.59 321 | 76.70 325 | 46.22 290 | 75.29 341 | 55.25 269 | 79.68 206 | 76.88 344 |
|
ADS-MVSNet2 | | | 66.20 306 | 63.33 306 | 74.82 296 | 79.92 312 | 58.75 255 | 67.55 343 | 75.19 329 | 53.37 335 | 65.25 307 | 75.86 328 | 42.32 312 | 80.53 319 | 41.57 336 | 68.91 308 | 85.18 296 |
|
ADS-MVSNet | | | 64.36 310 | 62.88 310 | 68.78 325 | 79.92 312 | 47.17 341 | 67.55 343 | 71.18 345 | 53.37 335 | 65.25 307 | 75.86 328 | 42.32 312 | 73.99 346 | 41.57 336 | 68.91 308 | 85.18 296 |
|
testus | | | 59.00 318 | 57.91 317 | 62.25 335 | 72.25 346 | 39.09 354 | 69.74 331 | 75.02 330 | 53.04 337 | 57.21 336 | 73.72 335 | 18.76 357 | 70.33 353 | 32.86 346 | 68.57 311 | 77.35 339 |
|
LF4IMVS | | | 64.02 311 | 62.19 312 | 69.50 321 | 70.90 349 | 53.29 316 | 76.13 308 | 77.18 322 | 52.65 338 | 58.59 330 | 80.98 298 | 23.55 350 | 76.52 335 | 53.06 279 | 66.66 319 | 78.68 335 |
|
tpm cat1 | | | 70.57 278 | 68.31 281 | 77.35 270 | 82.41 284 | 57.95 266 | 78.08 301 | 80.22 298 | 52.04 339 | 68.54 281 | 77.66 322 | 52.00 229 | 87.84 283 | 51.77 281 | 72.07 295 | 86.25 281 |
|
Patchmatch-test | | | 64.82 309 | 63.24 307 | 69.57 320 | 79.42 319 | 49.82 337 | 63.49 350 | 69.05 353 | 51.98 340 | 59.95 328 | 80.13 304 | 50.91 247 | 70.98 352 | 40.66 338 | 73.57 284 | 87.90 244 |
|
LP | | | 61.36 315 | 57.78 318 | 72.09 308 | 75.54 337 | 58.53 257 | 67.16 345 | 75.22 328 | 51.90 341 | 54.13 341 | 69.97 343 | 37.73 332 | 80.45 320 | 32.74 347 | 55.63 344 | 77.29 340 |
|
N_pmnet | | | 52.79 327 | 53.26 325 | 51.40 346 | 78.99 324 | 7.68 371 | 69.52 333 | 3.89 372 | 51.63 342 | 57.01 337 | 74.98 331 | 40.83 320 | 65.96 359 | 37.78 341 | 64.67 329 | 80.56 331 |
|
testmv | | | 53.85 325 | 51.03 327 | 62.31 334 | 61.46 358 | 38.88 355 | 70.95 330 | 74.69 336 | 51.11 343 | 41.26 352 | 66.85 346 | 14.28 361 | 72.13 350 | 29.19 353 | 49.51 351 | 75.93 345 |
|
PMMVS | | | 69.34 288 | 68.67 278 | 71.35 314 | 75.67 335 | 62.03 232 | 75.17 315 | 73.46 340 | 50.00 344 | 68.68 277 | 79.05 311 | 52.07 228 | 78.13 328 | 61.16 228 | 82.77 172 | 73.90 346 |
|
no-one | | | 51.08 328 | 45.79 334 | 66.95 329 | 57.92 361 | 50.49 335 | 59.63 354 | 76.04 326 | 48.04 345 | 31.85 356 | 56.10 356 | 19.12 356 | 80.08 322 | 36.89 342 | 26.52 356 | 70.29 349 |
|
CMPMVS | | 51.72 21 | 70.19 283 | 68.16 283 | 76.28 283 | 73.15 344 | 57.55 273 | 79.47 287 | 83.92 247 | 48.02 346 | 56.48 339 | 84.81 252 | 43.13 306 | 86.42 292 | 62.67 213 | 81.81 184 | 84.89 300 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
test12356 | | | 49.28 331 | 48.51 332 | 51.59 345 | 62.06 357 | 19.11 368 | 60.40 352 | 72.45 343 | 47.60 347 | 40.64 354 | 65.68 347 | 13.84 362 | 68.72 355 | 27.29 355 | 46.67 353 | 66.94 351 |
|
CVMVSNet | | | 72.99 263 | 72.58 243 | 74.25 301 | 84.28 226 | 50.85 332 | 86.41 175 | 83.45 256 | 44.56 348 | 73.23 213 | 87.54 169 | 49.38 272 | 85.70 296 | 65.90 190 | 78.44 223 | 86.19 282 |
|
EU-MVSNet | | | 68.53 293 | 67.61 293 | 71.31 315 | 78.51 325 | 47.01 342 | 84.47 229 | 84.27 243 | 42.27 349 | 66.44 300 | 84.79 253 | 40.44 322 | 83.76 306 | 58.76 247 | 68.54 312 | 83.17 315 |
|
FPMVS | | | 53.68 326 | 51.64 326 | 59.81 338 | 65.08 355 | 51.03 331 | 69.48 334 | 69.58 350 | 41.46 350 | 40.67 353 | 72.32 338 | 16.46 360 | 70.00 354 | 24.24 358 | 65.42 327 | 58.40 355 |
|
pmmvs3 | | | 57.79 320 | 54.26 324 | 68.37 326 | 64.02 356 | 56.72 285 | 75.12 318 | 65.17 358 | 40.20 351 | 52.93 346 | 69.86 344 | 20.36 354 | 75.48 340 | 45.45 321 | 55.25 346 | 72.90 347 |
|
new_pmnet | | | 50.91 329 | 50.29 328 | 52.78 344 | 68.58 353 | 34.94 360 | 63.71 349 | 56.63 362 | 39.73 352 | 44.95 351 | 65.47 348 | 21.93 353 | 58.48 361 | 34.98 344 | 56.62 343 | 64.92 352 |
|
MVS-HIRNet | | | 59.14 317 | 57.67 319 | 63.57 333 | 81.65 291 | 43.50 347 | 71.73 325 | 65.06 359 | 39.59 353 | 51.43 348 | 57.73 353 | 38.34 329 | 82.58 313 | 39.53 339 | 73.95 280 | 64.62 353 |
|
PMMVS2 | | | 40.82 335 | 38.86 337 | 46.69 348 | 53.84 362 | 16.45 369 | 48.61 359 | 49.92 365 | 37.49 354 | 31.67 357 | 60.97 352 | 8.14 368 | 56.42 362 | 28.42 354 | 30.72 355 | 67.19 350 |
|
LCM-MVSNet | | | 54.25 324 | 49.68 331 | 67.97 327 | 53.73 363 | 45.28 343 | 66.85 346 | 80.78 288 | 35.96 355 | 39.45 355 | 62.23 351 | 8.70 367 | 78.06 330 | 48.24 302 | 51.20 350 | 80.57 330 |
|
PMVS | | 37.38 22 | 44.16 334 | 40.28 336 | 55.82 341 | 40.82 369 | 42.54 348 | 65.12 348 | 63.99 361 | 34.43 356 | 24.48 360 | 57.12 355 | 3.92 369 | 76.17 337 | 17.10 361 | 55.52 345 | 48.75 356 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
Gipuma | | | 45.18 333 | 41.86 335 | 55.16 342 | 77.03 332 | 51.52 328 | 32.50 363 | 80.52 291 | 32.46 357 | 27.12 359 | 35.02 360 | 9.52 366 | 75.50 339 | 22.31 359 | 60.21 340 | 38.45 360 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PNet_i23d | | | 38.26 337 | 35.42 338 | 46.79 347 | 58.74 359 | 35.48 358 | 59.65 353 | 51.25 364 | 32.45 358 | 23.44 363 | 47.53 358 | 2.04 371 | 58.96 360 | 25.60 357 | 18.09 361 | 45.92 359 |
|
DSMNet-mixed | | | 57.77 321 | 56.90 321 | 60.38 337 | 67.70 354 | 35.61 357 | 69.18 335 | 53.97 363 | 32.30 359 | 57.49 335 | 79.88 306 | 40.39 323 | 68.57 356 | 38.78 340 | 72.37 291 | 76.97 341 |
|
E-PMN | | | 31.77 340 | 30.64 341 | 35.15 350 | 52.87 364 | 27.67 364 | 57.09 357 | 47.86 366 | 24.64 360 | 16.40 365 | 33.05 362 | 11.23 364 | 54.90 363 | 14.46 363 | 18.15 360 | 22.87 362 |
|
wuykxyi23d | | | 39.76 336 | 33.18 340 | 59.51 339 | 46.98 367 | 44.01 345 | 57.70 356 | 67.74 355 | 24.13 361 | 13.98 367 | 34.33 361 | 1.27 372 | 71.33 351 | 34.23 345 | 18.23 359 | 63.18 354 |
|
EMVS | | | 30.81 341 | 29.65 342 | 34.27 351 | 50.96 365 | 25.95 366 | 56.58 358 | 46.80 367 | 24.01 362 | 15.53 366 | 30.68 363 | 12.47 363 | 54.43 364 | 12.81 364 | 17.05 362 | 22.43 363 |
|
MVE | | 26.22 23 | 30.37 342 | 25.89 344 | 43.81 349 | 44.55 368 | 35.46 359 | 28.87 364 | 39.07 368 | 18.20 363 | 18.58 364 | 40.18 359 | 2.68 370 | 47.37 365 | 17.07 362 | 23.78 358 | 48.60 357 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
DeepMVS_CX | | | | | 27.40 354 | 40.17 370 | 26.90 365 | | 24.59 371 | 17.44 364 | 23.95 361 | 48.61 357 | 9.77 365 | 26.48 366 | 18.06 360 | 24.47 357 | 28.83 361 |
|
wuyk23d | | | 16.82 345 | 15.94 346 | 19.46 355 | 58.74 359 | 31.45 361 | 39.22 361 | 3.74 373 | 6.84 365 | 6.04 368 | 2.70 368 | 1.27 372 | 24.29 367 | 10.54 365 | 14.40 365 | 2.63 365 |
|
tmp_tt | | | 18.61 344 | 21.40 345 | 10.23 356 | 4.82 371 | 10.11 370 | 34.70 362 | 30.74 370 | 1.48 366 | 23.91 362 | 26.07 364 | 28.42 344 | 13.41 368 | 27.12 356 | 15.35 364 | 7.17 364 |
|
testmvs | | | 6.04 348 | 8.02 349 | 0.10 358 | 0.08 372 | 0.03 373 | 69.74 331 | 0.04 374 | 0.05 367 | 0.31 369 | 1.68 369 | 0.02 375 | 0.04 369 | 0.24 366 | 0.02 366 | 0.25 367 |
|
test123 | | | 6.12 347 | 8.11 348 | 0.14 357 | 0.06 373 | 0.09 372 | 71.05 327 | 0.03 375 | 0.04 368 | 0.25 370 | 1.30 370 | 0.05 374 | 0.03 370 | 0.21 367 | 0.01 368 | 0.29 366 |
|
cdsmvs_eth3d_5k | | | 19.96 343 | 26.61 343 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 89.26 164 | 0.00 369 | 0.00 371 | 88.61 138 | 61.62 142 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
pcd_1.5k_mvsjas | | | 5.26 349 | 7.02 350 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 63.15 107 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
pcd1.5k->3k | | | 34.07 339 | 35.26 339 | 30.50 353 | 86.92 192 | 0.00 374 | 0.00 365 | 91.58 87 | 0.00 369 | 0.00 371 | 0.00 371 | 56.23 192 | 0.00 371 | 0.00 368 | 82.60 176 | 91.49 115 |
|
sosnet-low-res | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
sosnet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
uncertanet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
Regformer | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
ab-mvs-re | | | 7.23 346 | 9.64 347 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 86.72 191 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
uanet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
GSMVS | | | | | | | | | | | | | | | | | 88.96 212 |
|
test_part2 | | | | | | 95.06 1 | 72.65 29 | | | | 91.80 3 | | | | | | |
|
test_part1 | | | | | 0.00 359 | | 0.00 374 | 0.00 365 | 94.09 2 | | | | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
sam_mvs1 | | | | | | | | | | | | | 51.32 243 | | | | 88.96 212 |
|
sam_mvs | | | | | | | | | | | | | 50.01 264 | | | | |
|
ambc | | | | | 75.24 293 | 73.16 343 | 50.51 334 | 63.05 351 | 87.47 212 | | 64.28 312 | 77.81 321 | 17.80 358 | 89.73 247 | 57.88 255 | 60.64 338 | 85.49 292 |
|
MTGPA | | | | | | | | | 92.02 65 | | | | | | | | |
|
test_post1 | | | | | | | | 78.90 295 | | | | 5.43 367 | 48.81 279 | 85.44 299 | 59.25 242 | | |
|
test_post | | | | | | | | | | | | 5.46 366 | 50.36 262 | 84.24 304 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 74.00 334 | 51.12 246 | 88.60 275 | | | |
|
GG-mvs-BLEND | | | | | 75.38 292 | 81.59 292 | 55.80 302 | 79.32 288 | 69.63 349 | | 67.19 292 | 73.67 336 | 43.24 305 | 88.90 273 | 50.41 287 | 84.50 144 | 81.45 327 |
|
MTMP | | | | | | | | 92.18 21 | 32.83 369 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 84.90 19 | 95.70 14 | 92.87 77 |
|
agg_prior2 | | | | | | | | | | | | | | | 82.91 42 | 95.45 16 | 92.70 79 |
|
agg_prior | | | | | | 92.85 46 | 71.94 43 | | 91.78 80 | | 84.41 45 | | | 94.93 68 | | | |
|
test_prior4 | | | | | | | 72.60 31 | 89.01 82 | | | | | | | | | |
|
test_prior | | | | | 86.33 49 | 92.61 51 | 69.59 76 | | 92.97 34 | | | | | 95.48 46 | | | 93.91 33 |
|
新几何2 | | | | | | | | 86.29 180 | | | | | | | | | |
|
旧先验1 | | | | | | 91.96 59 | 65.79 146 | | 86.37 224 | | | 93.08 47 | 69.31 57 | | | 92.74 53 | 88.74 219 |
|
原ACMM2 | | | | | | | | 86.86 160 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 91.01 231 | 62.37 215 | | |
|
segment_acmp | | | | | | | | | | | | | 73.08 25 | | | | |
|
test12 | | | | | 86.80 41 | 92.63 50 | 70.70 59 | | 91.79 79 | | 82.71 68 | | 71.67 35 | 96.16 32 | | 94.50 36 | 93.54 54 |
|
plane_prior7 | | | | | | 90.08 86 | 68.51 103 | | | | | | | | | | |
|
plane_prior6 | | | | | | 89.84 91 | 68.70 99 | | | | | | 60.42 164 | | | | |
|
plane_prior5 | | | | | | | | | 92.44 49 | | | | | 95.38 53 | 78.71 70 | 86.32 128 | 91.33 118 |
|
plane_prior4 | | | | | | | | | | | | 91.00 86 | | | | | |
|
plane_prior1 | | | | | | 89.90 90 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 376 | | | | | | | | |
|
nn | | | | | | | | | 0.00 376 | | | | | | | | |
|
door-mid | | | | | | | | | 69.98 348 | | | | | | | | |
|
lessismore_v0 | | | | | 78.97 242 | 81.01 302 | 57.15 277 | | 65.99 357 | | 61.16 323 | 82.82 272 | 39.12 326 | 91.34 220 | 59.67 237 | 46.92 352 | 88.43 235 |
|
test11 | | | | | | | | | 92.23 57 | | | | | | | | |
|
door | | | | | | | | | 69.44 351 | | | | | | | | |
|
HQP5-MVS | | | | | | | 66.98 128 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.47 82 | | |
|
HQP4-MVS | | | | | | | | | | | 77.24 147 | | | 95.11 61 | | | 91.03 125 |
|
HQP3-MVS | | | | | | | | | 92.19 60 | | | | | | | 85.99 133 | |
|
HQP2-MVS | | | | | | | | | | | | | 60.17 167 | | | | |
|
NP-MVS | | | | | | 89.62 97 | 68.32 105 | | | | | 90.24 97 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 81.95 182 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 81.25 189 | |
|
Test By Simon | | | | | | | | | | | | | 64.33 94 | | | | |
|