Regformer-1 | | | 75.47 42 | 74.93 42 | 77.09 42 | 80.43 90 | 57.70 69 | 79.50 98 | 82.13 70 | 67.84 1 | 75.73 21 | 80.75 190 | 56.50 17 | 86.07 62 | 71.07 42 | 80.38 82 | 87.50 23 |
|
Regformer-2 | | | 75.63 41 | 74.99 40 | 77.54 36 | 80.43 90 | 58.32 63 | 79.50 98 | 82.92 61 | 67.84 1 | 75.94 18 | 80.75 190 | 55.73 24 | 86.80 44 | 71.44 41 | 80.38 82 | 87.50 23 |
|
CANet | | | 76.46 33 | 75.93 34 | 78.06 31 | 81.29 76 | 57.53 71 | 82.35 55 | 83.31 54 | 67.78 3 | 70.09 77 | 86.34 74 | 54.92 30 | 88.90 14 | 72.68 33 | 84.55 50 | 87.76 16 |
|
UA-Net | | | 73.13 61 | 72.93 59 | 73.76 94 | 83.58 53 | 51.66 149 | 78.75 102 | 77.66 177 | 67.75 4 | 72.61 57 | 89.42 31 | 49.82 75 | 83.29 128 | 53.61 167 | 83.14 56 | 86.32 55 |
|
MVS_0304 | | | 76.73 31 | 76.04 32 | 78.78 21 | 81.32 75 | 58.89 56 | 82.50 53 | 84.07 30 | 67.73 5 | 72.08 63 | 87.28 57 | 49.49 78 | 89.57 6 | 73.52 30 | 86.40 42 | 87.87 11 |
|
CNVR-MVS | | | 79.84 6 | 79.97 6 | 79.45 5 | 87.90 2 | 62.17 20 | 84.37 24 | 85.03 15 | 66.96 6 | 77.58 13 | 90.06 23 | 59.47 10 | 89.13 11 | 78.67 7 | 89.73 6 | 87.03 36 |
|
TranMVSNet+NR-MVSNet | | | 70.36 104 | 70.10 91 | 71.17 174 | 78.64 124 | 42.97 256 | 76.53 157 | 81.16 96 | 66.95 7 | 68.53 112 | 85.42 93 | 51.61 61 | 83.07 134 | 52.32 174 | 69.70 225 | 87.46 25 |
|
Regformer-4 | | | 74.25 52 | 73.48 53 | 76.57 51 | 79.75 102 | 56.54 86 | 78.54 109 | 81.49 84 | 66.93 8 | 73.90 38 | 80.30 200 | 53.84 42 | 85.98 67 | 69.76 45 | 76.84 131 | 87.17 33 |
|
3Dnovator+ | | 66.72 4 | 75.84 40 | 74.57 43 | 79.66 4 | 82.40 62 | 59.92 45 | 85.83 14 | 86.32 7 | 66.92 9 | 67.80 129 | 89.24 35 | 42.03 170 | 89.38 8 | 64.07 91 | 86.50 41 | 89.69 1 |
|
NCCC | | | 78.58 10 | 78.31 11 | 79.39 6 | 87.51 5 | 62.61 16 | 85.20 21 | 84.42 23 | 66.73 10 | 74.67 31 | 89.38 33 | 55.30 27 | 89.18 10 | 74.19 23 | 87.34 30 | 86.38 47 |
|
SteuartSystems-ACMMP | | | 79.48 7 | 79.31 7 | 79.98 1 | 83.01 58 | 62.18 19 | 87.60 2 | 85.83 8 | 66.69 11 | 78.03 12 | 90.98 7 | 54.26 36 | 90.06 3 | 78.42 8 | 89.02 10 | 87.69 17 |
Skip Steuart: Steuart Systems R&D Blog. |
Regformer-3 | | | 73.89 55 | 73.28 57 | 75.71 61 | 79.75 102 | 55.48 107 | 78.54 109 | 79.93 124 | 66.58 12 | 73.62 43 | 80.30 200 | 54.87 31 | 84.54 102 | 69.09 49 | 76.84 131 | 87.10 35 |
|
EPNet | | | 73.09 62 | 72.16 64 | 75.90 56 | 75.95 184 | 56.28 89 | 83.05 40 | 72.39 227 | 66.53 13 | 65.27 160 | 87.00 58 | 50.40 72 | 85.47 78 | 62.48 108 | 86.32 43 | 85.94 64 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
UniMVSNet_NR-MVSNet | | | 71.11 86 | 71.00 80 | 71.44 165 | 79.20 109 | 44.13 245 | 76.02 171 | 82.60 66 | 66.48 14 | 68.20 116 | 84.60 103 | 56.82 15 | 82.82 144 | 54.62 158 | 70.43 202 | 87.36 30 |
|
HSP-MVS | | | 80.69 2 | 81.20 1 | 79.14 10 | 86.21 19 | 62.73 12 | 86.09 11 | 85.03 15 | 65.51 15 | 83.81 1 | 90.51 14 | 63.71 3 | 89.23 9 | 81.51 1 | 88.44 14 | 85.45 87 |
|
HPM-MVS++ | | | 79.88 5 | 80.14 5 | 79.10 13 | 88.17 1 | 64.80 1 | 86.59 4 | 83.70 42 | 65.37 16 | 78.78 10 | 90.64 10 | 58.63 13 | 87.24 34 | 79.00 5 | 90.37 3 | 85.26 99 |
|
NR-MVSNet | | | 69.54 122 | 68.85 114 | 71.59 163 | 78.05 140 | 43.81 249 | 74.20 201 | 80.86 105 | 65.18 17 | 62.76 186 | 84.52 104 | 52.35 56 | 83.59 122 | 50.96 183 | 70.78 195 | 87.37 28 |
|
zzz-MVS | | | 77.61 21 | 77.36 20 | 78.35 26 | 86.08 23 | 63.57 2 | 83.37 37 | 80.97 102 | 65.13 18 | 75.77 19 | 90.88 8 | 48.63 104 | 86.66 48 | 77.23 9 | 88.17 20 | 84.81 112 |
|
MTAPA | | | 76.90 28 | 76.42 29 | 78.35 26 | 86.08 23 | 63.57 2 | 74.92 191 | 80.97 102 | 65.13 18 | 75.77 19 | 90.88 8 | 48.63 104 | 86.66 48 | 77.23 9 | 88.17 20 | 84.81 112 |
|
EI-MVSNet-Vis-set | | | 72.42 70 | 71.59 69 | 74.91 72 | 78.47 128 | 54.02 117 | 77.05 149 | 79.33 147 | 65.03 20 | 71.68 66 | 79.35 224 | 52.75 51 | 84.89 93 | 66.46 66 | 74.23 147 | 85.83 67 |
|
WR-MVS | | | 68.47 146 | 68.47 121 | 68.44 210 | 80.20 96 | 39.84 273 | 73.75 209 | 76.07 196 | 64.68 21 | 68.11 120 | 83.63 120 | 50.39 73 | 79.14 215 | 49.78 189 | 69.66 226 | 86.34 52 |
|
XVS | | | 77.17 25 | 76.56 28 | 79.00 16 | 86.32 17 | 62.62 14 | 85.83 14 | 83.92 34 | 64.55 22 | 72.17 61 | 90.01 25 | 47.95 113 | 88.01 25 | 71.55 39 | 86.74 38 | 86.37 50 |
|
X-MVStestdata | | | 70.21 108 | 67.28 147 | 79.00 16 | 86.32 17 | 62.62 14 | 85.83 14 | 83.92 34 | 64.55 22 | 72.17 61 | 6.49 359 | 47.95 113 | 88.01 25 | 71.55 39 | 86.74 38 | 86.37 50 |
|
HQP_MVS | | | 74.31 50 | 73.73 51 | 76.06 54 | 81.41 73 | 56.31 87 | 84.22 26 | 84.01 32 | 64.52 24 | 69.27 101 | 86.10 78 | 45.26 146 | 87.21 36 | 68.16 52 | 80.58 78 | 84.65 117 |
|
plane_prior2 | | | | | | | | 84.22 26 | | 64.52 24 | | | | | | | |
|
EI-MVSNet-UG-set | | | 71.92 77 | 71.06 79 | 74.52 82 | 77.98 142 | 53.56 123 | 76.62 155 | 79.16 149 | 64.40 26 | 71.18 68 | 78.95 229 | 52.19 57 | 84.66 101 | 65.47 74 | 73.57 156 | 85.32 96 |
|
DU-MVS | | | 70.01 111 | 69.53 100 | 71.44 165 | 78.05 140 | 44.13 245 | 75.01 188 | 81.51 83 | 64.37 27 | 68.20 116 | 84.52 104 | 49.12 99 | 82.82 144 | 54.62 158 | 70.43 202 | 87.37 28 |
|
LFMVS | | | 71.78 79 | 71.59 69 | 72.32 152 | 83.40 54 | 46.38 228 | 79.75 93 | 71.08 232 | 64.18 28 | 72.80 55 | 88.64 45 | 42.58 166 | 83.72 119 | 57.41 142 | 84.49 51 | 86.86 39 |
|
IS-MVSNet | | | 71.57 81 | 71.00 80 | 73.27 121 | 78.86 117 | 45.63 233 | 80.22 85 | 78.69 158 | 64.14 29 | 66.46 145 | 87.36 54 | 49.30 82 | 85.60 72 | 50.26 187 | 83.71 55 | 88.59 3 |
|
plane_prior3 | | | | | | | 56.09 94 | | | 63.92 30 | 69.27 101 | | | | | | |
|
MP-MVS | | | 78.35 13 | 78.26 13 | 78.64 23 | 86.54 13 | 63.47 5 | 86.02 13 | 83.55 45 | 63.89 31 | 73.60 44 | 90.60 11 | 54.85 32 | 86.72 47 | 77.20 11 | 88.06 23 | 85.74 73 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
DELS-MVS | | | 74.76 45 | 74.46 44 | 75.65 63 | 77.84 145 | 52.25 143 | 75.59 175 | 84.17 28 | 63.76 32 | 73.15 50 | 82.79 131 | 59.58 9 | 86.80 44 | 67.24 61 | 86.04 44 | 87.89 9 |
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 |
OPM-MVS | | | 74.73 46 | 74.25 46 | 76.19 53 | 80.81 84 | 59.01 54 | 82.60 50 | 83.64 43 | 63.74 33 | 72.52 58 | 87.49 52 | 47.18 123 | 85.88 70 | 69.47 47 | 80.78 74 | 83.66 154 |
|
UniMVSNet (Re) | | | 70.63 94 | 70.20 88 | 71.89 155 | 78.55 125 | 45.29 235 | 75.94 172 | 82.92 61 | 63.68 34 | 68.16 118 | 83.59 121 | 53.89 41 | 83.49 124 | 53.97 163 | 71.12 193 | 86.89 38 |
|
test_part3 | | | | | | | | 86.37 5 | | 63.49 35 | | 91.40 4 | | 90.90 1 | 75.98 14 | | |
|
ESAPD | | | 80.72 1 | 81.17 2 | 79.38 7 | 87.58 3 | 60.47 38 | 86.37 5 | 86.64 3 | 63.49 35 | 83.42 2 | 91.40 4 | 65.59 1 | 90.90 1 | 75.98 14 | 90.06 4 | 86.78 42 |
|
DeepC-MVS | | 69.38 2 | 78.56 11 | 78.14 14 | 79.83 2 | 83.60 52 | 61.62 25 | 84.17 28 | 86.85 2 | 63.23 37 | 73.84 40 | 90.25 21 | 57.68 14 | 89.96 4 | 74.62 21 | 89.03 9 | 87.89 9 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
VDD-MVS | | | 72.50 67 | 72.09 65 | 73.75 96 | 81.58 69 | 49.69 196 | 77.76 131 | 77.63 178 | 63.21 38 | 73.21 49 | 89.02 38 | 42.14 169 | 83.32 127 | 61.72 123 | 82.50 64 | 88.25 6 |
|
plane_prior | | | | | | | 56.31 87 | 83.58 35 | | 63.19 39 | | | | | | 80.48 81 | |
|
ACMMP | | | 76.02 37 | 75.33 39 | 78.07 30 | 85.20 36 | 61.91 23 | 85.49 20 | 84.44 22 | 63.04 40 | 69.80 89 | 89.74 30 | 45.43 142 | 87.16 38 | 72.01 37 | 82.87 62 | 85.14 101 |
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 |
PEN-MVS | | | 66.60 183 | 66.45 166 | 67.04 220 | 77.11 168 | 36.56 304 | 77.03 150 | 80.42 118 | 62.95 41 | 62.51 194 | 84.03 113 | 46.69 130 | 79.07 216 | 44.22 232 | 63.08 279 | 85.51 82 |
|
APDe-MVS | | | 80.16 3 | 80.59 3 | 78.86 20 | 86.64 11 | 60.02 42 | 88.12 1 | 86.42 6 | 62.94 42 | 82.40 4 | 92.12 1 | 59.64 8 | 89.76 5 | 78.70 6 | 88.32 18 | 86.79 41 |
|
mPP-MVS | | | 76.54 32 | 75.93 34 | 78.34 28 | 86.47 15 | 63.50 4 | 85.74 17 | 82.28 69 | 62.90 43 | 71.77 65 | 90.26 20 | 46.61 131 | 86.55 55 | 71.71 38 | 85.66 46 | 84.97 108 |
|
ACMMP_Plus | | | 78.77 9 | 78.78 9 | 78.74 22 | 85.44 32 | 61.04 32 | 83.84 32 | 85.16 13 | 62.88 44 | 78.10 11 | 91.26 6 | 52.51 52 | 88.39 16 | 79.34 4 | 90.52 2 | 86.78 42 |
|
DeepPCF-MVS | | 69.58 1 | 79.03 8 | 79.00 8 | 79.13 11 | 84.92 44 | 60.32 40 | 83.03 41 | 85.33 12 | 62.86 45 | 80.17 6 | 90.03 24 | 61.76 4 | 88.95 13 | 74.21 22 | 88.67 13 | 88.12 7 |
|
HFP-MVS | | | 78.01 16 | 77.65 16 | 79.10 13 | 86.71 8 | 62.81 10 | 86.29 8 | 84.32 25 | 62.82 46 | 73.96 35 | 90.50 15 | 53.20 49 | 88.35 17 | 74.02 24 | 87.05 31 | 86.13 59 |
|
ACMMPR | | | 77.71 18 | 77.23 21 | 79.16 8 | 86.75 7 | 62.93 9 | 86.29 8 | 84.24 27 | 62.82 46 | 73.55 45 | 90.56 13 | 49.80 76 | 88.24 20 | 74.02 24 | 87.03 33 | 86.32 55 |
|
region2R | | | 77.67 20 | 77.18 22 | 79.15 9 | 86.76 6 | 62.95 8 | 86.29 8 | 84.16 29 | 62.81 48 | 73.30 48 | 90.58 12 | 49.90 74 | 88.21 21 | 73.78 26 | 87.03 33 | 86.29 57 |
|
HPM-MVS | | | 77.28 23 | 76.85 24 | 78.54 24 | 85.00 39 | 60.81 35 | 82.91 44 | 85.08 14 | 62.57 49 | 73.09 51 | 89.97 26 | 50.90 70 | 87.48 32 | 75.30 16 | 86.85 36 | 87.33 31 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
DTE-MVSNet | | | 65.58 194 | 65.34 182 | 66.31 227 | 76.06 183 | 34.79 313 | 76.43 159 | 79.38 145 | 62.55 50 | 61.66 212 | 83.83 116 | 45.60 138 | 79.15 214 | 41.64 256 | 60.88 294 | 85.00 106 |
|
SMA-MVS | | | 80.16 3 | 80.31 4 | 79.72 3 | 86.49 14 | 61.95 22 | 86.33 7 | 85.75 10 | 62.49 51 | 82.20 5 | 92.00 2 | 56.53 16 | 89.54 7 | 79.51 3 | 91.48 1 | 87.68 18 |
|
CP-MVSNet | | | 66.49 186 | 66.41 169 | 66.72 222 | 77.67 149 | 36.33 306 | 76.83 154 | 79.52 140 | 62.45 52 | 62.54 192 | 83.47 125 | 46.32 132 | 78.37 227 | 45.47 227 | 63.43 276 | 85.45 87 |
|
CP-MVS | | | 77.12 26 | 76.68 26 | 78.43 25 | 86.05 25 | 63.18 7 | 87.55 3 | 83.45 48 | 62.44 53 | 72.68 56 | 90.50 15 | 48.18 111 | 87.34 33 | 73.59 29 | 85.71 45 | 84.76 116 |
|
PS-CasMVS | | | 66.42 187 | 66.32 172 | 66.70 224 | 77.60 156 | 36.30 308 | 76.94 152 | 79.61 129 | 62.36 54 | 62.43 201 | 83.66 119 | 45.69 136 | 78.37 227 | 45.35 229 | 63.26 277 | 85.42 90 |
|
3Dnovator | | 64.47 5 | 72.49 68 | 71.39 73 | 75.79 57 | 77.70 147 | 58.99 55 | 80.66 81 | 83.15 58 | 62.24 55 | 65.46 157 | 86.59 68 | 42.38 168 | 85.52 76 | 59.59 136 | 84.72 49 | 82.85 173 |
|
MP-MVS-pluss | | | 78.35 13 | 78.46 10 | 78.03 32 | 84.96 40 | 59.52 47 | 82.93 43 | 85.39 11 | 62.15 56 | 76.41 17 | 91.51 3 | 52.47 54 | 86.78 46 | 80.66 2 | 89.64 8 | 87.80 14 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
HQP-NCC | | | | | | 80.66 85 | | 82.31 57 | | 62.10 57 | 67.85 124 | | | | | | |
|
ACMP_Plane | | | | | | 80.66 85 | | 82.31 57 | | 62.10 57 | 67.85 124 | | | | | | |
|
HQP-MVS | | | 73.45 58 | 72.80 60 | 75.40 67 | 80.66 85 | 54.94 110 | 82.31 57 | 83.90 36 | 62.10 57 | 67.85 124 | 85.54 91 | 45.46 140 | 86.93 42 | 67.04 63 | 80.35 84 | 84.32 124 |
|
VPNet | | | 67.52 164 | 68.11 128 | 65.74 243 | 79.18 110 | 36.80 302 | 72.17 230 | 72.83 225 | 62.04 60 | 67.79 130 | 85.83 84 | 48.88 103 | 76.60 248 | 51.30 181 | 72.97 169 | 83.81 145 |
|
WR-MVS_H | | | 67.02 174 | 66.92 156 | 67.33 219 | 77.95 143 | 37.75 293 | 77.57 137 | 82.11 72 | 62.03 61 | 62.65 189 | 82.48 139 | 50.57 71 | 79.46 203 | 42.91 246 | 64.01 271 | 84.79 114 |
|
DeepC-MVS_fast | | 68.24 3 | 77.25 24 | 76.63 27 | 79.12 12 | 86.15 21 | 60.86 34 | 84.71 22 | 84.85 19 | 61.98 62 | 73.06 52 | 88.88 41 | 53.72 43 | 89.06 12 | 68.27 51 | 88.04 24 | 87.42 27 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
PGM-MVS | | | 76.77 30 | 76.06 31 | 78.88 19 | 86.14 22 | 62.73 12 | 82.55 51 | 83.74 41 | 61.71 63 | 72.45 60 | 90.34 18 | 48.48 108 | 88.13 22 | 72.32 34 | 86.85 36 | 85.78 68 |
|
Effi-MVS+ | | | 73.31 60 | 72.54 62 | 75.62 64 | 77.87 144 | 53.64 121 | 79.62 96 | 79.61 129 | 61.63 64 | 72.02 64 | 82.61 136 | 56.44 18 | 85.97 68 | 63.99 94 | 79.07 105 | 87.25 32 |
|
#test# | | | 77.83 17 | 77.41 19 | 79.10 13 | 86.71 8 | 62.81 10 | 85.69 18 | 84.32 25 | 61.61 65 | 73.96 35 | 90.50 15 | 53.20 49 | 88.35 17 | 73.68 27 | 87.05 31 | 86.13 59 |
|
MG-MVS | | | 73.96 54 | 73.89 49 | 74.16 85 | 85.65 28 | 49.69 196 | 81.59 70 | 81.29 91 | 61.45 66 | 71.05 69 | 88.11 48 | 51.77 59 | 87.73 29 | 61.05 126 | 83.09 57 | 85.05 105 |
|
LPG-MVS_test | | | 72.74 65 | 71.74 68 | 75.76 58 | 80.22 94 | 57.51 72 | 82.55 51 | 83.40 50 | 61.32 67 | 66.67 143 | 87.33 55 | 39.15 203 | 86.59 51 | 67.70 57 | 77.30 126 | 83.19 164 |
|
LGP-MVS_train | | | | | 75.76 58 | 80.22 94 | 57.51 72 | | 83.40 50 | 61.32 67 | 66.67 143 | 87.33 55 | 39.15 203 | 86.59 51 | 67.70 57 | 77.30 126 | 83.19 164 |
|
CLD-MVS | | | 73.33 59 | 72.68 61 | 75.29 70 | 78.82 118 | 53.33 127 | 78.23 115 | 84.79 20 | 61.30 69 | 70.41 73 | 81.04 177 | 52.41 55 | 87.12 39 | 64.61 81 | 82.49 65 | 85.41 94 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
MVS_111021_HR | | | 74.02 53 | 73.46 55 | 75.69 62 | 83.01 58 | 60.63 37 | 77.29 145 | 78.40 169 | 61.18 70 | 70.58 71 | 85.97 81 | 54.18 38 | 84.00 115 | 67.52 60 | 82.98 60 | 82.45 179 |
|
FIs | | | 70.82 89 | 71.43 71 | 68.98 203 | 78.33 131 | 38.14 290 | 76.96 151 | 83.59 44 | 61.02 71 | 67.33 136 | 86.73 61 | 55.07 28 | 81.64 169 | 54.61 160 | 79.22 102 | 87.14 34 |
|
FC-MVSNet-test | | | 69.80 115 | 70.58 85 | 67.46 216 | 77.61 155 | 34.73 315 | 76.05 169 | 83.19 57 | 60.84 72 | 65.88 154 | 86.46 71 | 54.52 35 | 80.76 188 | 52.52 173 | 78.12 116 | 86.91 37 |
|
v8 | | | 70.33 106 | 69.28 109 | 73.49 108 | 73.15 235 | 50.22 178 | 78.62 106 | 80.78 106 | 60.79 73 | 66.45 146 | 82.11 150 | 49.35 80 | 84.98 86 | 63.58 100 | 68.71 233 | 85.28 97 |
|
CSCG | | | 76.92 27 | 76.75 25 | 77.41 38 | 83.96 51 | 59.60 46 | 82.95 42 | 86.50 5 | 60.78 74 | 75.27 22 | 84.83 97 | 60.76 5 | 86.56 54 | 67.86 56 | 87.87 28 | 86.06 62 |
|
Vis-MVSNet | | | 72.18 73 | 71.37 74 | 74.61 80 | 81.29 76 | 55.41 108 | 80.90 77 | 78.28 171 | 60.73 75 | 69.23 104 | 88.09 49 | 44.36 155 | 82.65 153 | 57.68 140 | 81.75 70 | 85.77 70 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
APD-MVS | | | 78.02 15 | 78.04 15 | 77.98 33 | 86.44 16 | 60.81 35 | 85.52 19 | 84.36 24 | 60.61 76 | 79.05 9 | 90.30 19 | 55.54 26 | 88.32 19 | 73.48 31 | 87.03 33 | 84.83 111 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
ACMP | | 63.53 6 | 72.30 71 | 71.20 78 | 75.59 66 | 80.28 92 | 57.54 70 | 82.74 47 | 82.84 65 | 60.58 77 | 65.24 162 | 86.18 76 | 39.25 201 | 86.03 65 | 66.95 65 | 76.79 133 | 83.22 162 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
testdata1 | | | | | | | | 72.65 219 | | 60.50 78 | | | | | | | |
|
UGNet | | | 68.81 135 | 67.39 142 | 73.06 125 | 78.33 131 | 54.47 115 | 79.77 92 | 75.40 203 | 60.45 79 | 63.22 181 | 84.40 107 | 32.71 278 | 80.91 183 | 51.71 179 | 80.56 80 | 83.81 145 |
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 |
EPP-MVSNet | | | 72.16 75 | 71.31 76 | 74.71 75 | 78.68 123 | 49.70 194 | 82.10 61 | 81.65 80 | 60.40 80 | 65.94 152 | 85.84 83 | 51.74 60 | 86.37 60 | 55.93 147 | 79.55 97 | 88.07 8 |
|
test_prior3 | | | 76.89 29 | 76.96 23 | 76.69 47 | 84.20 49 | 57.27 74 | 81.75 64 | 84.88 17 | 60.37 81 | 75.01 23 | 89.06 36 | 56.22 21 | 86.43 58 | 72.19 35 | 88.96 11 | 86.38 47 |
|
test_prior2 | | | | | | | | 81.75 64 | | 60.37 81 | 75.01 23 | 89.06 36 | 56.22 21 | | 72.19 35 | 88.96 11 | |
|
SD-MVS | | | 77.70 19 | 77.62 17 | 77.93 34 | 84.47 47 | 61.88 24 | 84.55 23 | 83.87 38 | 60.37 81 | 79.89 7 | 89.38 33 | 54.97 29 | 85.58 74 | 76.12 13 | 84.94 48 | 86.33 53 |
|
VNet | | | 69.68 118 | 70.19 89 | 68.16 211 | 79.73 105 | 41.63 266 | 70.53 251 | 77.38 183 | 60.37 81 | 70.69 70 | 86.63 66 | 51.08 66 | 77.09 243 | 53.61 167 | 81.69 72 | 85.75 72 |
|
diffmvs | | | 70.36 104 | 69.99 92 | 71.46 164 | 70.48 278 | 48.19 211 | 74.59 198 | 76.30 192 | 60.36 85 | 67.75 132 | 83.81 118 | 51.22 64 | 79.77 198 | 67.92 55 | 77.50 122 | 86.42 46 |
|
canonicalmvs | | | 74.67 47 | 74.98 41 | 73.71 98 | 78.94 116 | 50.56 168 | 80.23 84 | 83.87 38 | 60.30 86 | 77.15 14 | 86.56 70 | 59.65 7 | 82.00 164 | 66.01 69 | 82.12 66 | 88.58 4 |
|
v7n | | | 69.01 133 | 67.36 144 | 73.98 87 | 72.51 254 | 52.65 135 | 78.54 109 | 81.30 90 | 60.26 87 | 62.67 188 | 81.62 162 | 43.61 160 | 84.49 103 | 57.01 143 | 68.70 234 | 84.79 114 |
|
HPM-MVS_fast | | | 74.30 51 | 73.46 55 | 76.80 46 | 84.45 48 | 59.04 53 | 83.65 34 | 81.05 97 | 60.15 88 | 70.43 72 | 89.84 28 | 41.09 188 | 85.59 73 | 67.61 59 | 82.90 61 | 85.77 70 |
|
VPA-MVSNet | | | 69.02 132 | 69.47 106 | 67.69 215 | 77.42 158 | 41.00 270 | 74.04 203 | 79.68 127 | 60.06 89 | 69.26 103 | 84.81 98 | 51.06 67 | 77.58 237 | 54.44 161 | 74.43 145 | 84.48 122 |
|
v13 | | | 68.29 150 | 66.84 157 | 72.63 136 | 73.50 223 | 50.83 159 | 78.25 114 | 79.58 136 | 60.05 90 | 60.76 226 | 77.68 248 | 49.11 102 | 82.77 146 | 62.17 115 | 60.45 302 | 84.30 126 |
|
v10 | | | 70.21 108 | 69.02 112 | 73.81 91 | 73.51 222 | 50.92 156 | 78.74 103 | 81.39 87 | 60.05 90 | 66.39 147 | 81.83 157 | 47.58 117 | 85.41 81 | 62.80 105 | 68.86 232 | 85.09 104 |
|
v12 | | | 68.28 151 | 66.83 159 | 72.60 138 | 73.43 225 | 50.74 161 | 78.18 116 | 79.59 134 | 60.01 92 | 60.89 225 | 77.66 249 | 49.12 99 | 82.77 146 | 62.18 113 | 60.46 301 | 84.29 127 |
|
V9 | | | 68.27 152 | 66.84 157 | 72.56 139 | 73.39 228 | 50.63 164 | 78.10 121 | 79.60 131 | 59.94 93 | 61.05 223 | 77.62 250 | 49.18 95 | 82.77 146 | 62.17 115 | 60.48 300 | 84.27 128 |
|
v2v482 | | | 70.50 98 | 69.45 107 | 73.66 100 | 72.62 252 | 50.03 187 | 77.58 136 | 80.51 116 | 59.90 94 | 69.52 95 | 82.14 149 | 47.53 119 | 84.88 95 | 65.07 77 | 70.17 214 | 86.09 61 |
|
Baseline_NR-MVSNet | | | 67.05 173 | 67.56 136 | 65.50 249 | 75.65 186 | 37.70 294 | 75.42 178 | 74.65 213 | 59.90 94 | 68.14 119 | 83.15 130 | 49.12 99 | 77.20 241 | 52.23 175 | 69.78 222 | 81.60 189 |
|
API-MVS | | | 72.17 74 | 71.41 72 | 74.45 83 | 81.95 66 | 57.22 76 | 84.03 30 | 80.38 119 | 59.89 96 | 68.40 113 | 82.33 142 | 49.64 77 | 87.83 28 | 51.87 176 | 84.16 54 | 78.30 239 |
|
v11 | | | 68.15 160 | 66.73 162 | 72.42 149 | 73.43 225 | 50.28 176 | 77.94 127 | 79.65 128 | 59.88 97 | 61.11 222 | 77.55 254 | 48.25 110 | 82.75 151 | 61.88 122 | 60.85 295 | 84.23 131 |
|
Effi-MVS+-dtu | | | 69.64 120 | 67.53 138 | 75.95 55 | 76.10 181 | 62.29 18 | 80.20 86 | 76.06 197 | 59.83 98 | 65.26 161 | 77.09 259 | 41.56 179 | 84.02 114 | 60.60 128 | 71.09 194 | 81.53 190 |
|
mvs-test1 | | | 70.44 102 | 68.19 126 | 77.18 40 | 76.10 181 | 63.22 6 | 80.59 82 | 76.06 197 | 59.83 98 | 66.32 148 | 79.87 207 | 41.56 179 | 85.53 75 | 60.60 128 | 72.77 171 | 82.80 174 |
|
V14 | | | 68.25 154 | 66.82 160 | 72.52 142 | 73.33 229 | 50.53 169 | 78.02 124 | 79.60 131 | 59.83 98 | 61.16 221 | 77.57 253 | 49.19 94 | 82.77 146 | 62.18 113 | 60.50 299 | 84.26 129 |
|
v15 | | | 68.22 157 | 66.81 161 | 72.47 147 | 73.25 230 | 50.40 172 | 77.92 128 | 79.60 131 | 59.77 101 | 61.28 219 | 77.52 255 | 49.25 91 | 82.77 146 | 62.16 117 | 60.51 298 | 84.24 130 |
|
v17 | | | 68.37 148 | 67.00 154 | 72.48 143 | 73.22 231 | 50.31 174 | 78.10 121 | 79.58 136 | 59.71 102 | 61.67 211 | 77.60 251 | 49.31 81 | 82.89 139 | 62.37 110 | 61.48 291 | 84.23 131 |
|
v16 | | | 68.38 147 | 67.01 153 | 72.47 147 | 73.22 231 | 50.29 175 | 78.10 121 | 79.59 134 | 59.71 102 | 61.72 210 | 77.60 251 | 49.28 87 | 82.89 139 | 62.36 111 | 61.54 288 | 84.23 131 |
|
v1neww | | | 70.66 91 | 69.70 95 | 73.53 105 | 73.15 235 | 50.22 178 | 78.11 118 | 80.68 107 | 59.65 104 | 69.83 86 | 81.67 159 | 49.29 84 | 84.96 88 | 64.55 82 | 70.38 205 | 85.42 90 |
|
v7new | | | 70.66 91 | 69.70 95 | 73.53 105 | 73.15 235 | 50.22 178 | 78.11 118 | 80.68 107 | 59.65 104 | 69.83 86 | 81.67 159 | 49.29 84 | 84.96 88 | 64.55 82 | 70.38 205 | 85.42 90 |
|
v6 | | | 70.66 91 | 69.70 95 | 73.53 105 | 73.14 238 | 50.21 181 | 78.11 118 | 80.67 109 | 59.65 104 | 69.82 88 | 81.65 161 | 49.29 84 | 84.96 88 | 64.55 82 | 70.39 204 | 85.42 90 |
|
CANet_DTU | | | 68.18 158 | 67.71 135 | 69.59 195 | 74.83 198 | 46.24 229 | 78.66 105 | 76.85 189 | 59.60 107 | 63.45 180 | 82.09 151 | 35.25 249 | 77.41 239 | 59.88 133 | 78.76 110 | 85.14 101 |
|
EI-MVSNet | | | 69.27 128 | 68.44 123 | 71.73 159 | 74.47 205 | 49.39 200 | 75.20 184 | 78.45 166 | 59.60 107 | 69.16 105 | 76.51 269 | 51.29 62 | 82.50 155 | 59.86 135 | 71.45 191 | 83.30 160 |
|
IterMVS-LS | | | 69.22 131 | 68.48 120 | 71.43 167 | 74.44 207 | 49.40 199 | 76.23 164 | 77.55 179 | 59.60 107 | 65.85 155 | 81.59 165 | 51.28 63 | 81.58 172 | 59.87 134 | 69.90 220 | 83.30 160 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
VDDNet | | | 71.81 78 | 71.33 75 | 73.26 122 | 82.80 60 | 47.60 219 | 78.74 103 | 75.27 204 | 59.59 110 | 72.94 53 | 89.40 32 | 41.51 182 | 83.91 116 | 58.75 137 | 82.99 59 | 88.26 5 |
|
v18 | | | 68.33 149 | 66.96 155 | 72.42 149 | 73.13 239 | 50.16 183 | 77.97 126 | 79.57 138 | 59.57 111 | 61.80 208 | 77.50 256 | 49.30 82 | 82.90 138 | 62.31 112 | 61.50 289 | 84.20 137 |
|
v1141 | | | 70.50 98 | 69.53 100 | 73.41 114 | 72.92 245 | 50.00 188 | 77.69 132 | 80.60 111 | 59.50 112 | 69.60 92 | 81.43 168 | 49.24 93 | 84.77 96 | 64.48 86 | 70.30 211 | 85.46 86 |
|
divwei89l23v2f112 | | | 70.50 98 | 69.53 100 | 73.41 114 | 72.91 246 | 50.00 188 | 77.69 132 | 80.59 112 | 59.50 112 | 69.60 92 | 81.43 168 | 49.26 89 | 84.77 96 | 64.48 86 | 70.31 210 | 85.47 84 |
|
v1 | | | 70.50 98 | 69.53 100 | 73.42 113 | 72.91 246 | 50.00 188 | 77.69 132 | 80.59 112 | 59.50 112 | 69.59 94 | 81.42 170 | 49.26 89 | 84.77 96 | 64.49 85 | 70.30 211 | 85.47 84 |
|
alignmvs | | | 73.86 56 | 73.99 48 | 73.45 110 | 78.20 134 | 50.50 170 | 78.57 107 | 82.43 67 | 59.40 115 | 76.57 15 | 86.71 63 | 56.42 19 | 81.23 177 | 65.84 71 | 81.79 68 | 88.62 2 |
|
MVS_Test | | | 72.45 69 | 72.46 63 | 72.42 149 | 74.88 196 | 48.50 208 | 76.28 162 | 83.14 59 | 59.40 115 | 72.46 59 | 84.68 99 | 55.66 25 | 81.12 178 | 65.98 70 | 79.66 94 | 87.63 20 |
|
TSAR-MVS + MP. | | | 78.44 12 | 78.28 12 | 78.90 18 | 84.96 40 | 61.41 28 | 84.03 30 | 83.82 40 | 59.34 117 | 79.37 8 | 89.76 29 | 59.84 6 | 87.62 31 | 76.69 12 | 86.74 38 | 87.68 18 |
|
MSLP-MVS++ | | | 73.77 57 | 73.47 54 | 74.66 77 | 83.02 57 | 59.29 51 | 82.30 60 | 81.88 75 | 59.34 117 | 71.59 67 | 86.83 59 | 45.94 135 | 83.65 121 | 65.09 76 | 85.22 47 | 81.06 206 |
|
PAPM_NR | | | 72.63 66 | 71.80 67 | 75.13 71 | 81.72 68 | 53.42 126 | 79.91 90 | 83.28 56 | 59.14 119 | 66.31 149 | 85.90 82 | 51.86 58 | 86.06 63 | 57.45 141 | 80.62 76 | 85.91 65 |
|
v7 | | | 70.57 95 | 69.48 105 | 73.85 89 | 73.50 223 | 50.92 156 | 78.27 113 | 81.43 85 | 58.93 120 | 69.61 91 | 81.49 167 | 47.56 118 | 85.43 80 | 63.94 95 | 70.62 197 | 85.21 100 |
|
v148 | | | 68.24 156 | 67.19 151 | 71.40 168 | 70.43 280 | 47.77 217 | 75.76 174 | 77.03 187 | 58.91 121 | 67.36 135 | 80.10 204 | 48.60 107 | 81.89 165 | 60.01 132 | 66.52 254 | 84.53 120 |
|
TransMVSNet (Re) | | | 64.72 203 | 64.33 191 | 65.87 242 | 75.22 193 | 38.56 287 | 74.66 196 | 75.08 211 | 58.90 122 | 61.79 209 | 82.63 135 | 51.18 65 | 78.07 232 | 43.63 239 | 55.87 315 | 80.99 211 |
|
abl_6 | | | 74.34 49 | 73.50 52 | 76.86 44 | 82.43 61 | 60.16 41 | 83.48 36 | 81.86 76 | 58.81 123 | 73.95 37 | 89.86 27 | 41.87 173 | 86.62 50 | 67.98 54 | 81.23 73 | 83.80 148 |
|
Anonymous202405211 | | | 66.84 178 | 65.99 174 | 69.40 199 | 80.19 97 | 42.21 260 | 71.11 245 | 71.31 231 | 58.80 124 | 67.90 122 | 86.39 73 | 29.83 296 | 79.65 200 | 49.60 195 | 78.78 109 | 86.33 53 |
|
LCM-MVSNet-Re | | | 61.88 237 | 61.35 229 | 63.46 261 | 74.58 203 | 31.48 335 | 61.42 306 | 58.14 316 | 58.71 125 | 53.02 302 | 79.55 220 | 43.07 164 | 76.80 246 | 45.69 221 | 77.96 118 | 82.11 184 |
|
v1144 | | | 70.42 103 | 69.31 108 | 73.76 94 | 73.22 231 | 50.64 163 | 77.83 129 | 81.43 85 | 58.58 126 | 69.40 99 | 81.16 174 | 47.53 119 | 85.29 83 | 64.01 93 | 70.64 196 | 85.34 95 |
|
v748 | | | 67.26 167 | 65.67 178 | 72.02 154 | 69.90 288 | 49.77 193 | 76.24 163 | 79.57 138 | 58.58 126 | 60.49 229 | 80.38 195 | 44.47 154 | 82.17 162 | 56.16 146 | 65.26 262 | 84.12 139 |
|
TSAR-MVS + GP. | | | 74.90 44 | 74.15 47 | 77.17 41 | 82.00 65 | 58.77 58 | 81.80 63 | 78.57 160 | 58.58 126 | 74.32 33 | 84.51 106 | 55.94 23 | 87.22 35 | 67.11 62 | 84.48 52 | 85.52 81 |
|
BH-RMVSNet | | | 68.81 135 | 67.42 141 | 72.97 126 | 80.11 99 | 52.53 138 | 74.26 200 | 76.29 193 | 58.48 129 | 68.38 114 | 84.20 109 | 42.59 165 | 83.83 118 | 46.53 212 | 75.91 137 | 82.56 175 |
|
APD-MVS_3200maxsize | | | 74.96 43 | 74.39 45 | 76.67 49 | 82.20 63 | 58.24 64 | 83.67 33 | 83.29 55 | 58.41 130 | 73.71 42 | 90.14 22 | 45.62 137 | 85.99 66 | 69.64 46 | 82.85 63 | 85.78 68 |
|
OMC-MVS | | | 71.40 85 | 70.60 84 | 73.78 92 | 76.60 176 | 53.15 129 | 79.74 94 | 79.78 125 | 58.37 131 | 68.75 108 | 86.45 72 | 45.43 142 | 80.60 189 | 62.58 106 | 77.73 119 | 87.58 22 |
|
nrg030 | | | 72.96 63 | 73.01 58 | 72.84 131 | 75.41 191 | 50.24 177 | 80.02 87 | 82.89 64 | 58.36 132 | 74.44 32 | 86.73 61 | 58.90 12 | 80.83 184 | 65.84 71 | 74.46 144 | 87.44 26 |
|
K. test v3 | | | 60.47 244 | 57.11 262 | 70.56 183 | 73.74 221 | 48.22 210 | 75.10 187 | 62.55 300 | 58.27 133 | 53.62 298 | 76.31 271 | 27.81 307 | 81.59 171 | 47.42 205 | 39.18 344 | 81.88 187 |
|
MVS_111021_LR | | | 69.50 123 | 68.78 116 | 71.65 161 | 78.38 129 | 59.33 50 | 74.82 193 | 70.11 238 | 58.08 134 | 67.83 128 | 84.68 99 | 41.96 171 | 76.34 251 | 65.62 73 | 77.54 120 | 79.30 233 |
|
0601test | | | 69.69 117 | 69.13 110 | 71.36 169 | 78.37 130 | 45.74 232 | 74.71 195 | 80.20 122 | 57.91 135 | 70.01 82 | 83.83 116 | 42.44 167 | 82.87 141 | 54.97 155 | 79.72 93 | 85.48 83 |
|
v52 | | | 67.09 171 | 65.16 186 | 72.87 129 | 66.77 308 | 51.60 150 | 73.69 210 | 79.45 143 | 57.88 136 | 62.46 195 | 78.57 235 | 40.95 190 | 83.34 125 | 61.99 120 | 64.70 267 | 83.68 151 |
|
V4 | | | 67.09 171 | 65.16 186 | 72.87 129 | 66.76 309 | 51.60 150 | 73.69 210 | 79.45 143 | 57.88 136 | 62.45 196 | 78.58 234 | 40.96 189 | 83.34 125 | 61.99 120 | 64.71 265 | 83.68 151 |
|
Fast-Effi-MVS+-dtu | | | 67.37 165 | 65.33 183 | 73.48 109 | 72.94 244 | 57.78 68 | 77.47 140 | 76.88 188 | 57.60 138 | 61.97 205 | 76.85 263 | 39.31 200 | 80.49 190 | 54.72 157 | 70.28 213 | 82.17 183 |
|
v1192 | | | 69.97 113 | 68.68 117 | 73.85 89 | 73.19 234 | 50.94 154 | 77.68 135 | 81.36 88 | 57.51 139 | 68.95 107 | 80.85 186 | 45.28 145 | 85.33 82 | 62.97 104 | 70.37 207 | 85.27 98 |
|
ACMH+ | | 57.40 11 | 66.12 189 | 64.06 192 | 72.30 153 | 77.79 146 | 52.83 133 | 80.39 83 | 78.03 172 | 57.30 140 | 57.47 266 | 82.55 138 | 27.68 308 | 84.17 108 | 45.54 224 | 69.78 222 | 79.90 224 |
|
BH-untuned | | | 68.27 152 | 67.29 146 | 71.21 172 | 79.74 104 | 53.22 128 | 76.06 168 | 77.46 182 | 57.19 141 | 66.10 150 | 81.61 163 | 45.37 144 | 83.50 123 | 45.42 228 | 76.68 135 | 76.91 260 |
|
tfpn111 | | | 63.33 215 | 62.34 215 | 66.30 228 | 77.31 159 | 38.66 284 | 72.65 219 | 69.11 250 | 57.07 142 | 62.45 196 | 81.03 178 | 37.01 227 | 79.23 207 | 31.38 308 | 73.09 167 | 81.03 207 |
|
conf200view11 | | | 63.38 214 | 62.41 213 | 66.29 230 | 77.31 159 | 38.66 284 | 72.65 219 | 69.11 250 | 57.07 142 | 62.45 196 | 81.03 178 | 37.01 227 | 79.17 210 | 31.84 299 | 73.25 162 | 81.03 207 |
|
thres100view900 | | | 63.28 218 | 62.41 213 | 65.89 241 | 77.31 159 | 38.66 284 | 72.65 219 | 69.11 250 | 57.07 142 | 62.45 196 | 81.03 178 | 37.01 227 | 79.17 210 | 31.84 299 | 73.25 162 | 79.83 225 |
|
DP-MVS Recon | | | 72.15 76 | 70.73 83 | 76.40 52 | 86.57 12 | 57.99 66 | 81.15 76 | 82.96 60 | 57.03 145 | 66.78 142 | 85.56 89 | 44.50 152 | 88.11 23 | 51.77 178 | 80.23 87 | 83.10 168 |
|
thres600view7 | | | 63.30 217 | 62.27 216 | 66.41 226 | 77.18 167 | 38.87 281 | 72.35 227 | 69.11 250 | 56.98 146 | 62.37 202 | 80.96 182 | 37.01 227 | 79.00 220 | 31.43 307 | 73.05 168 | 81.36 194 |
|
V42 | | | 68.65 139 | 67.35 145 | 72.56 139 | 68.93 294 | 50.18 182 | 72.90 217 | 79.47 141 | 56.92 147 | 69.45 98 | 80.26 202 | 46.29 133 | 82.99 135 | 64.07 91 | 67.82 246 | 84.53 120 |
|
MCST-MVS | | | 77.48 22 | 77.45 18 | 77.54 36 | 86.67 10 | 58.36 62 | 83.22 39 | 86.93 1 | 56.91 148 | 74.91 27 | 88.19 47 | 59.15 11 | 87.68 30 | 73.67 28 | 87.45 29 | 86.57 45 |
|
view600 | | | 62.77 224 | 61.84 221 | 65.55 245 | 77.28 162 | 36.87 298 | 72.15 231 | 67.78 258 | 56.79 149 | 61.46 215 | 81.92 152 | 36.88 231 | 78.42 223 | 29.86 315 | 72.46 176 | 81.36 194 |
|
view800 | | | 62.77 224 | 61.84 221 | 65.55 245 | 77.28 162 | 36.87 298 | 72.15 231 | 67.78 258 | 56.79 149 | 61.46 215 | 81.92 152 | 36.88 231 | 78.42 223 | 29.86 315 | 72.46 176 | 81.36 194 |
|
conf0.05thres1000 | | | 62.77 224 | 61.84 221 | 65.55 245 | 77.28 162 | 36.87 298 | 72.15 231 | 67.78 258 | 56.79 149 | 61.46 215 | 81.92 152 | 36.88 231 | 78.42 223 | 29.86 315 | 72.46 176 | 81.36 194 |
|
tfpn | | | 62.77 224 | 61.84 221 | 65.55 245 | 77.28 162 | 36.87 298 | 72.15 231 | 67.78 258 | 56.79 149 | 61.46 215 | 81.92 152 | 36.88 231 | 78.42 223 | 29.86 315 | 72.46 176 | 81.36 194 |
|
GA-MVS | | | 65.53 195 | 63.70 199 | 71.02 178 | 70.87 275 | 48.10 212 | 70.48 252 | 74.40 215 | 56.69 153 | 64.70 171 | 76.77 264 | 33.66 265 | 81.10 179 | 55.42 154 | 70.32 209 | 83.87 144 |
|
v144192 | | | 69.71 116 | 68.51 119 | 73.33 118 | 73.10 240 | 50.13 185 | 77.54 139 | 80.64 110 | 56.65 154 | 68.57 111 | 80.55 193 | 46.87 129 | 84.96 88 | 62.98 103 | 69.66 226 | 84.89 110 |
|
tfpn200view9 | | | 63.18 220 | 62.18 218 | 66.21 232 | 76.85 173 | 39.62 275 | 71.96 237 | 69.44 246 | 56.63 155 | 62.61 190 | 79.83 208 | 37.18 222 | 79.17 210 | 31.84 299 | 73.25 162 | 79.83 225 |
|
thres400 | | | 63.31 216 | 62.18 218 | 66.72 222 | 76.85 173 | 39.62 275 | 71.96 237 | 69.44 246 | 56.63 155 | 62.61 190 | 79.83 208 | 37.18 222 | 79.17 210 | 31.84 299 | 73.25 162 | 81.36 194 |
|
GBi-Net | | | 67.21 168 | 66.55 164 | 69.19 200 | 77.63 150 | 43.33 252 | 77.31 142 | 77.83 174 | 56.62 157 | 65.04 165 | 82.70 132 | 41.85 174 | 80.33 192 | 47.18 207 | 72.76 172 | 83.92 141 |
|
test1 | | | 67.21 168 | 66.55 164 | 69.19 200 | 77.63 150 | 43.33 252 | 77.31 142 | 77.83 174 | 56.62 157 | 65.04 165 | 82.70 132 | 41.85 174 | 80.33 192 | 47.18 207 | 72.76 172 | 83.92 141 |
|
FMVSNet2 | | | 66.93 176 | 66.31 173 | 68.79 206 | 77.63 150 | 42.98 255 | 76.11 166 | 77.47 180 | 56.62 157 | 65.22 164 | 82.17 147 | 41.85 174 | 80.18 195 | 47.05 210 | 72.72 175 | 83.20 163 |
|
v1921920 | | | 69.47 124 | 68.17 127 | 73.36 117 | 73.06 241 | 50.10 186 | 77.39 141 | 80.56 114 | 56.58 160 | 68.59 109 | 80.37 196 | 44.72 148 | 84.98 86 | 62.47 109 | 69.82 221 | 85.00 106 |
|
Anonymous20240521 | | | 64.54 208 | 64.00 194 | 66.15 233 | 74.87 197 | 34.97 312 | 74.13 202 | 79.35 146 | 56.54 161 | 58.65 250 | 83.31 126 | 37.31 221 | 80.04 197 | 41.39 257 | 70.44 201 | 83.45 157 |
|
FMVSNet1 | | | 66.70 181 | 65.87 176 | 69.19 200 | 77.49 157 | 43.33 252 | 77.31 142 | 77.83 174 | 56.45 162 | 64.60 172 | 82.70 132 | 38.08 215 | 80.33 192 | 46.08 217 | 72.31 183 | 83.92 141 |
|
v1240 | | | 69.24 130 | 67.91 130 | 73.25 123 | 73.02 243 | 49.82 191 | 77.21 146 | 80.54 115 | 56.43 163 | 68.34 115 | 80.51 194 | 43.33 163 | 84.99 84 | 62.03 119 | 69.77 224 | 84.95 109 |
|
CDPH-MVS | | | 76.31 34 | 75.67 37 | 78.22 29 | 85.35 35 | 59.14 52 | 81.31 74 | 84.02 31 | 56.32 164 | 74.05 34 | 88.98 39 | 53.34 47 | 87.92 27 | 69.23 48 | 88.42 15 | 87.59 21 |
|
Vis-MVSNet (Re-imp) | | | 63.69 212 | 63.88 196 | 63.14 266 | 74.75 200 | 31.04 336 | 71.16 244 | 63.64 292 | 56.32 164 | 59.80 236 | 84.99 95 | 44.51 151 | 75.46 255 | 39.12 266 | 80.62 76 | 82.92 170 |
|
AdaColmap | | | 69.99 112 | 68.66 118 | 73.97 88 | 84.94 42 | 57.83 67 | 82.63 49 | 78.71 157 | 56.28 166 | 64.34 173 | 84.14 110 | 41.57 178 | 87.06 41 | 46.45 213 | 78.88 106 | 77.02 256 |
|
PS-MVSNAJss | | | 72.24 72 | 71.21 77 | 75.31 69 | 78.50 126 | 55.93 98 | 81.63 67 | 82.12 71 | 56.24 167 | 70.02 81 | 85.68 88 | 47.05 124 | 84.34 106 | 65.27 75 | 74.41 146 | 85.67 75 |
|
Fast-Effi-MVS+ | | | 70.28 107 | 69.12 111 | 73.73 97 | 78.50 126 | 51.50 152 | 75.01 188 | 79.46 142 | 56.16 168 | 68.59 109 | 79.55 220 | 53.97 39 | 84.05 110 | 53.34 169 | 77.53 121 | 85.65 77 |
|
agg_prior1 | | | 75.94 38 | 76.01 33 | 75.72 60 | 85.04 37 | 59.96 43 | 81.44 72 | 81.04 98 | 56.14 169 | 74.68 29 | 88.90 40 | 53.91 40 | 84.04 111 | 75.01 20 | 87.92 27 | 83.16 167 |
|
PHI-MVS | | | 75.87 39 | 75.36 38 | 77.41 38 | 80.62 88 | 55.91 99 | 84.28 25 | 85.78 9 | 56.08 170 | 73.41 47 | 86.58 69 | 50.94 69 | 88.54 15 | 70.79 43 | 89.71 7 | 87.79 15 |
|
train_agg | | | 76.27 35 | 76.15 30 | 76.64 50 | 85.58 30 | 61.59 26 | 81.62 68 | 81.26 92 | 55.86 171 | 74.93 25 | 88.81 42 | 53.70 44 | 84.68 99 | 75.24 18 | 88.33 16 | 83.65 155 |
|
test_8 | | | | | | 85.40 33 | 60.96 33 | 81.54 71 | 81.18 95 | 55.86 171 | 74.81 28 | 88.80 44 | 53.70 44 | 84.45 104 | | | |
|
FMVSNet3 | | | 66.32 188 | 65.61 179 | 68.46 209 | 76.48 179 | 42.34 259 | 74.98 190 | 77.15 186 | 55.83 173 | 65.04 165 | 81.16 174 | 39.91 193 | 80.14 196 | 47.18 207 | 72.76 172 | 82.90 172 |
|
PAPR | | | 71.72 80 | 70.82 82 | 74.41 84 | 81.20 80 | 51.17 153 | 79.55 97 | 83.33 53 | 55.81 174 | 66.93 141 | 84.61 102 | 50.95 68 | 86.06 63 | 55.79 150 | 79.20 103 | 86.00 63 |
|
ACMH | | 55.70 15 | 65.20 200 | 63.57 201 | 70.07 189 | 78.07 139 | 52.01 148 | 79.48 100 | 79.69 126 | 55.75 175 | 56.59 272 | 80.98 181 | 27.12 312 | 80.94 181 | 42.90 247 | 71.58 189 | 77.25 254 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
IB-MVS | | 56.42 12 | 65.40 198 | 62.73 210 | 73.40 116 | 74.89 195 | 52.78 134 | 73.09 215 | 75.13 207 | 55.69 176 | 58.48 254 | 73.73 290 | 32.86 273 | 86.32 61 | 50.63 184 | 70.11 215 | 81.10 205 |
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 |
TEST9 | | | | | | 85.58 30 | 61.59 26 | 81.62 68 | 81.26 92 | 55.65 177 | 74.93 25 | 88.81 42 | 53.70 44 | 84.68 99 | | | |
|
thres200 | | | 62.20 232 | 61.16 232 | 65.34 251 | 75.38 192 | 39.99 272 | 69.60 260 | 69.29 248 | 55.64 178 | 61.87 207 | 76.99 260 | 37.07 226 | 78.96 221 | 31.28 309 | 73.28 161 | 77.06 255 |
|
pm-mvs1 | | | 65.24 199 | 64.97 189 | 66.04 237 | 72.38 255 | 39.40 278 | 72.62 223 | 75.63 200 | 55.53 179 | 62.35 203 | 83.18 129 | 47.45 121 | 76.47 249 | 49.06 198 | 66.54 253 | 82.24 181 |
|
agg_prior3 | | | 76.13 36 | 75.89 36 | 76.85 45 | 85.76 26 | 62.02 21 | 81.65 66 | 81.01 100 | 55.51 180 | 73.73 41 | 88.60 46 | 53.23 48 | 84.90 92 | 75.24 18 | 88.33 16 | 83.65 155 |
|
ACMM | | 61.98 7 | 70.80 90 | 69.73 94 | 74.02 86 | 80.59 89 | 58.59 60 | 82.68 48 | 82.02 74 | 55.46 181 | 67.18 138 | 84.39 108 | 38.51 208 | 83.17 131 | 60.65 127 | 76.10 136 | 80.30 219 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
Anonymous20240529 | | | 69.91 114 | 69.02 112 | 72.56 139 | 80.19 97 | 47.65 218 | 77.56 138 | 80.99 101 | 55.45 182 | 69.88 84 | 86.76 60 | 39.24 202 | 82.18 161 | 54.04 162 | 77.10 128 | 87.85 12 |
|
CPTT-MVS | | | 72.78 64 | 72.08 66 | 74.87 74 | 84.88 45 | 61.41 28 | 84.15 29 | 77.86 173 | 55.27 183 | 67.51 134 | 88.08 50 | 41.93 172 | 81.85 166 | 69.04 50 | 80.01 88 | 81.35 200 |
|
XVG-OURS | | | 68.76 138 | 67.37 143 | 72.90 128 | 74.32 209 | 57.22 76 | 70.09 257 | 78.81 155 | 55.24 184 | 67.79 130 | 85.81 86 | 36.54 238 | 78.28 229 | 62.04 118 | 75.74 138 | 83.19 164 |
|
tfpnnormal | | | 62.47 229 | 61.63 227 | 64.99 254 | 74.81 199 | 39.01 280 | 71.22 242 | 73.72 220 | 55.22 185 | 60.21 230 | 80.09 205 | 41.26 187 | 76.98 245 | 30.02 314 | 68.09 243 | 78.97 237 |
|
EPNet_dtu | | | 61.90 235 | 61.97 220 | 61.68 274 | 72.89 248 | 39.78 274 | 75.85 173 | 65.62 273 | 55.09 186 | 54.56 288 | 79.36 223 | 37.59 218 | 67.02 294 | 39.80 265 | 76.95 129 | 78.25 240 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PVSNet_Blended_VisFu | | | 71.45 84 | 70.39 86 | 74.65 78 | 82.01 64 | 58.82 57 | 79.93 89 | 80.35 121 | 55.09 186 | 65.82 156 | 82.16 148 | 49.17 96 | 82.64 154 | 60.34 130 | 78.62 113 | 82.50 178 |
|
PS-MVSNAJ | | | 70.51 97 | 69.70 95 | 72.93 127 | 81.52 70 | 55.79 100 | 74.92 191 | 79.00 152 | 55.04 188 | 69.88 84 | 78.66 231 | 47.05 124 | 82.19 160 | 61.61 124 | 79.58 95 | 80.83 213 |
|
xiu_mvs_v2_base | | | 70.52 96 | 69.75 93 | 72.84 131 | 81.21 79 | 55.63 104 | 75.11 186 | 78.92 153 | 54.92 189 | 69.96 83 | 79.68 213 | 47.00 128 | 82.09 163 | 61.60 125 | 79.37 98 | 80.81 214 |
|
MAR-MVS | | | 71.51 82 | 70.15 90 | 75.60 65 | 81.84 67 | 59.39 49 | 81.38 73 | 82.90 63 | 54.90 190 | 68.08 121 | 78.70 230 | 47.73 115 | 85.51 77 | 51.68 180 | 84.17 53 | 81.88 187 |
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-OURS-SEG-HR | | | 68.81 135 | 67.47 140 | 72.82 133 | 74.40 208 | 56.87 84 | 70.59 250 | 79.04 151 | 54.77 191 | 66.99 140 | 86.01 80 | 39.57 198 | 78.21 230 | 62.54 107 | 73.33 160 | 83.37 159 |
|
Anonymous20231211 | | | 69.28 127 | 68.47 121 | 71.73 159 | 80.28 92 | 47.18 223 | 79.98 88 | 82.37 68 | 54.61 192 | 67.24 137 | 84.01 114 | 39.43 199 | 82.41 158 | 55.45 153 | 72.83 170 | 85.62 78 |
|
SixPastTwentyTwo | | | 61.65 239 | 58.80 251 | 70.20 188 | 75.80 185 | 47.22 222 | 75.59 175 | 69.68 241 | 54.61 192 | 54.11 293 | 79.26 225 | 27.07 313 | 82.96 136 | 43.27 241 | 49.79 332 | 80.41 218 |
|
test_0402 | | | 63.25 219 | 61.01 233 | 69.96 190 | 80.00 100 | 54.37 116 | 76.86 153 | 72.02 229 | 54.58 194 | 58.71 248 | 80.79 188 | 35.00 250 | 84.36 105 | 26.41 335 | 64.71 265 | 71.15 317 |
|
DI_MVS_plusplus_test | | | 69.35 126 | 68.03 129 | 73.30 120 | 71.11 273 | 50.14 184 | 75.49 177 | 79.16 149 | 54.57 195 | 62.45 196 | 80.76 189 | 44.67 150 | 84.20 107 | 64.23 89 | 79.81 91 | 85.54 80 |
|
BH-w/o | | | 66.85 177 | 65.83 177 | 69.90 192 | 79.29 107 | 52.46 140 | 74.66 196 | 76.65 190 | 54.51 196 | 64.85 169 | 78.12 237 | 45.59 139 | 82.95 137 | 43.26 242 | 75.54 140 | 74.27 286 |
|
testing_2 | | | 66.02 190 | 63.77 198 | 72.76 134 | 66.03 314 | 50.48 171 | 72.93 216 | 80.36 120 | 54.41 197 | 54.25 292 | 76.76 265 | 30.89 288 | 83.16 132 | 64.19 90 | 74.08 149 | 84.65 117 |
|
LTVRE_ROB | | 55.42 16 | 63.15 221 | 61.23 231 | 68.92 204 | 76.57 177 | 47.80 215 | 59.92 312 | 76.39 191 | 54.35 198 | 58.67 249 | 82.46 140 | 29.44 299 | 81.49 173 | 42.12 250 | 71.14 192 | 77.46 248 |
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 |
test_normal | | | 69.26 129 | 67.90 131 | 73.32 119 | 70.84 276 | 50.38 173 | 75.30 180 | 79.17 148 | 54.23 199 | 62.00 204 | 80.61 192 | 44.69 149 | 83.89 117 | 64.33 88 | 79.95 90 | 85.69 74 |
|
Test4 | | | 67.77 163 | 65.97 175 | 73.19 124 | 68.64 295 | 50.58 165 | 74.80 194 | 80.48 117 | 54.13 200 | 59.11 244 | 79.07 228 | 33.89 263 | 83.12 133 | 63.61 99 | 79.98 89 | 85.87 66 |
|
ab-mvs | | | 66.65 182 | 66.42 168 | 67.37 217 | 76.17 180 | 41.73 264 | 70.41 254 | 76.14 195 | 53.99 201 | 65.98 151 | 83.51 123 | 49.48 79 | 76.24 252 | 48.60 201 | 73.46 158 | 84.14 138 |
|
casdiffmvs | | | 74.55 48 | 73.78 50 | 76.87 43 | 79.00 114 | 56.18 92 | 82.36 54 | 84.45 21 | 53.88 202 | 73.46 46 | 85.76 87 | 56.38 20 | 86.59 51 | 70.70 44 | 78.04 117 | 87.83 13 |
|
XVG-ACMP-BASELINE | | | 64.36 209 | 62.23 217 | 70.74 181 | 72.35 256 | 52.45 141 | 70.80 249 | 78.45 166 | 53.84 203 | 59.87 234 | 81.10 176 | 16.24 338 | 79.32 206 | 55.64 152 | 71.76 187 | 80.47 216 |
|
PVSNet_BlendedMVS | | | 68.56 144 | 67.72 133 | 71.07 177 | 77.03 170 | 50.57 166 | 74.50 199 | 81.52 81 | 53.66 204 | 64.22 177 | 79.72 212 | 49.13 97 | 82.87 141 | 55.82 148 | 73.92 151 | 79.77 228 |
|
EG-PatchMatch MVS | | | 64.71 204 | 62.87 207 | 70.22 186 | 77.68 148 | 53.48 124 | 77.99 125 | 78.82 154 | 53.37 205 | 56.03 275 | 77.41 258 | 24.75 326 | 84.04 111 | 46.37 214 | 73.42 159 | 73.14 297 |
|
DP-MVS | | | 65.68 192 | 63.66 200 | 71.75 158 | 84.93 43 | 56.87 84 | 80.74 80 | 73.16 224 | 53.06 206 | 59.09 245 | 82.35 141 | 36.79 236 | 85.94 69 | 32.82 295 | 69.96 219 | 72.45 304 |
|
TR-MVS | | | 66.59 185 | 65.07 188 | 71.17 174 | 79.18 110 | 49.63 198 | 73.48 212 | 75.20 206 | 52.95 207 | 67.90 122 | 80.33 199 | 39.81 195 | 83.68 120 | 43.20 243 | 73.56 157 | 80.20 220 |
|
QAPM | | | 70.05 110 | 68.81 115 | 73.78 92 | 76.54 178 | 53.43 125 | 83.23 38 | 83.48 46 | 52.89 208 | 65.90 153 | 86.29 75 | 41.55 181 | 86.49 57 | 51.01 182 | 78.40 115 | 81.42 192 |
|
OpenMVS | | 61.03 9 | 68.85 134 | 67.56 136 | 72.70 135 | 74.26 210 | 53.99 118 | 81.21 75 | 81.34 89 | 52.70 209 | 62.75 187 | 85.55 90 | 38.86 206 | 84.14 109 | 48.41 203 | 83.01 58 | 79.97 223 |
|
pmmvs6 | | | 63.69 212 | 62.82 209 | 66.27 231 | 70.63 277 | 39.27 279 | 73.13 214 | 75.47 202 | 52.69 210 | 59.75 237 | 82.30 143 | 39.71 196 | 77.03 244 | 47.40 206 | 64.35 270 | 82.53 176 |
|
IterMVS | | | 62.79 223 | 61.27 230 | 67.35 218 | 69.37 292 | 52.04 147 | 71.17 243 | 68.24 257 | 52.63 211 | 59.82 235 | 76.91 262 | 37.32 220 | 72.36 272 | 52.80 172 | 63.19 278 | 77.66 246 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
mvs_tets | | | 68.18 158 | 66.36 170 | 73.63 103 | 75.61 188 | 55.35 109 | 80.77 79 | 78.56 161 | 52.48 212 | 64.27 176 | 84.10 112 | 27.45 310 | 81.84 167 | 63.45 102 | 70.56 200 | 83.69 150 |
|
jajsoiax | | | 68.25 154 | 66.45 166 | 73.66 100 | 75.62 187 | 55.49 106 | 80.82 78 | 78.51 163 | 52.33 213 | 64.33 174 | 84.11 111 | 28.28 304 | 81.81 168 | 63.48 101 | 70.62 197 | 83.67 153 |
|
TAMVS | | | 66.78 180 | 65.27 184 | 71.33 171 | 79.16 112 | 53.67 120 | 73.84 208 | 69.59 243 | 52.32 214 | 65.28 159 | 81.72 158 | 44.49 153 | 77.40 240 | 42.32 249 | 78.66 112 | 82.92 170 |
|
CDS-MVSNet | | | 66.80 179 | 65.37 181 | 71.10 176 | 78.98 115 | 53.13 131 | 73.27 213 | 71.07 233 | 52.15 215 | 64.72 170 | 80.23 203 | 43.56 161 | 77.10 242 | 45.48 226 | 78.88 106 | 83.05 169 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
PatchFormer-LS_test | | | 62.20 232 | 60.59 235 | 67.04 220 | 72.18 259 | 46.82 226 | 70.36 255 | 68.62 255 | 51.92 216 | 59.19 243 | 70.23 308 | 36.86 235 | 75.07 264 | 50.23 188 | 65.68 259 | 79.23 234 |
|
PVSNet_Blended | | | 68.59 140 | 67.72 133 | 71.19 173 | 77.03 170 | 50.57 166 | 72.51 225 | 81.52 81 | 51.91 217 | 64.22 177 | 77.77 245 | 49.13 97 | 82.87 141 | 55.82 148 | 79.58 95 | 80.14 222 |
|
conf0.01 | | | 59.97 246 | 58.81 244 | 63.42 262 | 74.15 212 | 33.83 321 | 68.32 268 | 64.22 283 | 51.79 218 | 58.04 257 | 79.57 214 | 35.41 242 | 75.41 256 | 29.57 321 | 68.26 236 | 81.03 207 |
|
conf0.002 | | | 59.97 246 | 58.81 244 | 63.42 262 | 74.15 212 | 33.83 321 | 68.32 268 | 64.22 283 | 51.79 218 | 58.04 257 | 79.57 214 | 35.41 242 | 75.41 256 | 29.57 321 | 68.26 236 | 81.03 207 |
|
thresconf0.02 | | | 59.40 254 | 58.81 244 | 61.17 280 | 74.15 212 | 33.83 321 | 68.32 268 | 64.22 283 | 51.79 218 | 58.04 257 | 79.57 214 | 35.41 242 | 75.41 256 | 29.57 321 | 68.26 236 | 74.25 287 |
|
tfpn_n400 | | | 59.40 254 | 58.81 244 | 61.17 280 | 74.15 212 | 33.83 321 | 68.32 268 | 64.22 283 | 51.79 218 | 58.04 257 | 79.57 214 | 35.41 242 | 75.41 256 | 29.57 321 | 68.26 236 | 74.25 287 |
|
tfpnconf | | | 59.40 254 | 58.81 244 | 61.17 280 | 74.15 212 | 33.83 321 | 68.32 268 | 64.22 283 | 51.79 218 | 58.04 257 | 79.57 214 | 35.41 242 | 75.41 256 | 29.57 321 | 68.26 236 | 74.25 287 |
|
tfpnview11 | | | 59.40 254 | 58.81 244 | 61.17 280 | 74.15 212 | 33.83 321 | 68.32 268 | 64.22 283 | 51.79 218 | 58.04 257 | 79.57 214 | 35.41 242 | 75.41 256 | 29.57 321 | 68.26 236 | 74.25 287 |
|
mvs_anonymous | | | 68.03 161 | 67.51 139 | 69.59 195 | 72.08 260 | 44.57 242 | 71.99 236 | 75.23 205 | 51.67 224 | 67.06 139 | 82.57 137 | 54.68 33 | 77.94 233 | 56.56 144 | 75.71 139 | 86.26 58 |
|
xiu_mvs_v1_base_debu | | | 68.58 141 | 67.28 147 | 72.48 143 | 78.19 135 | 57.19 78 | 75.28 181 | 75.09 208 | 51.61 225 | 70.04 78 | 81.41 171 | 32.79 274 | 79.02 217 | 63.81 96 | 77.31 123 | 81.22 202 |
|
xiu_mvs_v1_base | | | 68.58 141 | 67.28 147 | 72.48 143 | 78.19 135 | 57.19 78 | 75.28 181 | 75.09 208 | 51.61 225 | 70.04 78 | 81.41 171 | 32.79 274 | 79.02 217 | 63.81 96 | 77.31 123 | 81.22 202 |
|
xiu_mvs_v1_base_debi | | | 68.58 141 | 67.28 147 | 72.48 143 | 78.19 135 | 57.19 78 | 75.28 181 | 75.09 208 | 51.61 225 | 70.04 78 | 81.41 171 | 32.79 274 | 79.02 217 | 63.81 96 | 77.31 123 | 81.22 202 |
|
MVSTER | | | 67.16 170 | 65.58 180 | 71.88 156 | 70.37 282 | 49.70 194 | 70.25 256 | 78.45 166 | 51.52 228 | 69.16 105 | 80.37 196 | 38.45 209 | 82.50 155 | 60.19 131 | 71.46 190 | 83.44 158 |
|
CNLPA | | | 65.43 196 | 64.02 193 | 69.68 193 | 78.73 122 | 58.07 65 | 77.82 130 | 70.71 235 | 51.49 229 | 61.57 214 | 83.58 122 | 38.23 213 | 70.82 278 | 43.90 236 | 70.10 216 | 80.16 221 |
|
原ACMM1 | | | | | 74.69 76 | 85.39 34 | 59.40 48 | | 83.42 49 | 51.47 230 | 70.27 76 | 86.61 67 | 48.61 106 | 86.51 56 | 53.85 164 | 87.96 25 | 78.16 241 |
|
MSDG | | | 61.81 238 | 59.23 241 | 69.55 198 | 72.64 251 | 52.63 136 | 70.45 253 | 75.81 199 | 51.38 231 | 53.70 296 | 76.11 272 | 29.52 297 | 81.08 180 | 37.70 272 | 65.79 258 | 74.93 278 |
|
test20.03 | | | 53.87 289 | 54.02 287 | 53.41 314 | 61.47 330 | 28.11 342 | 61.30 307 | 59.21 312 | 51.34 232 | 52.09 304 | 77.43 257 | 33.29 269 | 58.55 323 | 29.76 319 | 60.27 303 | 73.58 295 |
|
MVSFormer | | | 71.50 83 | 70.38 87 | 74.88 73 | 78.76 120 | 57.15 81 | 82.79 45 | 78.48 164 | 51.26 233 | 69.49 96 | 83.22 127 | 43.99 158 | 83.24 129 | 66.06 67 | 79.37 98 | 84.23 131 |
|
test_djsdf | | | 69.45 125 | 67.74 132 | 74.58 81 | 74.57 204 | 54.92 112 | 82.79 45 | 78.48 164 | 51.26 233 | 65.41 158 | 83.49 124 | 38.37 210 | 83.24 129 | 66.06 67 | 69.25 229 | 85.56 79 |
|
tfpn1000 | | | 59.24 259 | 58.70 252 | 60.86 285 | 73.75 220 | 33.99 319 | 68.86 266 | 63.98 290 | 51.25 235 | 57.29 268 | 79.51 222 | 34.58 253 | 75.26 262 | 29.08 328 | 69.99 217 | 73.32 296 |
|
PAPM | | | 67.92 162 | 66.69 163 | 71.63 162 | 78.09 138 | 49.02 203 | 77.09 148 | 81.24 94 | 51.04 236 | 60.91 224 | 83.98 115 | 47.71 116 | 84.99 84 | 40.81 259 | 79.32 101 | 80.90 212 |
|
tfpn_ndepth | | | 59.57 253 | 59.02 243 | 61.23 279 | 73.81 219 | 35.60 310 | 69.40 263 | 65.59 274 | 50.96 237 | 57.96 263 | 77.72 246 | 34.81 251 | 75.91 254 | 30.36 312 | 70.57 199 | 72.18 310 |
|
gg-mvs-nofinetune | | | 57.86 268 | 56.43 269 | 62.18 272 | 72.62 252 | 35.35 311 | 66.57 279 | 56.33 325 | 50.65 238 | 57.64 265 | 57.10 341 | 30.65 289 | 76.36 250 | 37.38 274 | 78.88 106 | 74.82 280 |
|
TAPA-MVS | | 59.36 10 | 66.60 183 | 65.20 185 | 70.81 179 | 76.63 175 | 48.75 206 | 76.52 158 | 80.04 123 | 50.64 239 | 65.24 162 | 84.93 96 | 39.15 203 | 78.54 222 | 36.77 277 | 76.88 130 | 85.14 101 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
MVP-Stereo | | | 65.41 197 | 63.80 197 | 70.22 186 | 77.62 154 | 55.53 105 | 76.30 161 | 78.53 162 | 50.59 240 | 56.47 273 | 78.65 232 | 39.84 194 | 82.68 152 | 44.10 235 | 72.12 185 | 72.44 305 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
PCF-MVS | | 61.88 8 | 70.95 87 | 69.49 104 | 75.35 68 | 77.63 150 | 55.71 101 | 76.04 170 | 81.81 78 | 50.30 241 | 69.66 90 | 85.40 94 | 52.51 52 | 84.89 93 | 51.82 177 | 80.24 86 | 85.45 87 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
DWT-MVSNet_test | | | 61.90 235 | 59.93 239 | 67.83 213 | 71.98 263 | 46.09 230 | 71.03 247 | 69.71 239 | 50.09 242 | 58.51 253 | 70.62 305 | 30.21 293 | 77.63 236 | 49.28 196 | 67.91 244 | 79.78 227 |
|
test-LLR | | | 58.15 266 | 58.13 258 | 58.22 293 | 68.57 296 | 44.80 238 | 65.46 287 | 57.92 317 | 50.08 243 | 55.44 279 | 69.82 311 | 32.62 279 | 57.44 326 | 49.66 193 | 73.62 154 | 72.41 306 |
|
test0.0.03 1 | | | 53.32 292 | 53.59 289 | 52.50 318 | 62.81 326 | 29.45 339 | 59.51 313 | 54.11 336 | 50.08 243 | 54.40 290 | 74.31 287 | 32.62 279 | 55.92 337 | 30.50 311 | 63.95 272 | 72.15 312 |
|
COLMAP_ROB | | 52.97 17 | 61.27 242 | 58.81 244 | 68.64 207 | 74.63 202 | 52.51 139 | 78.42 112 | 73.30 222 | 49.92 245 | 50.96 308 | 81.51 166 | 23.06 329 | 79.40 204 | 31.63 304 | 65.85 256 | 74.01 293 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
tpmvs | | | 58.47 262 | 56.95 265 | 63.03 268 | 70.20 283 | 41.21 267 | 67.90 277 | 67.23 265 | 49.62 246 | 54.73 286 | 70.84 303 | 34.14 259 | 76.24 252 | 36.64 281 | 61.29 292 | 71.64 313 |
|
HY-MVS | | 56.14 13 | 64.55 207 | 63.89 195 | 66.55 225 | 74.73 201 | 41.02 268 | 69.96 258 | 74.43 214 | 49.29 247 | 61.66 212 | 80.92 183 | 47.43 122 | 76.68 247 | 44.91 231 | 71.69 188 | 81.94 185 |
|
MIMVSNet1 | | | 55.17 284 | 54.31 284 | 57.77 297 | 70.03 285 | 32.01 333 | 65.68 285 | 64.81 278 | 49.19 248 | 46.75 322 | 76.00 273 | 25.53 322 | 64.04 305 | 28.65 329 | 62.13 285 | 77.26 253 |
|
Patchmatch-test1 | | | 59.75 250 | 58.00 259 | 64.98 255 | 74.14 218 | 48.06 213 | 63.35 298 | 63.23 295 | 49.13 249 | 59.33 242 | 71.46 299 | 37.45 219 | 69.59 283 | 41.39 257 | 62.57 282 | 77.30 250 |
|
testgi | | | 51.90 296 | 52.37 293 | 50.51 323 | 60.39 337 | 23.55 353 | 58.42 316 | 58.15 315 | 49.03 250 | 51.83 305 | 79.21 226 | 22.39 330 | 55.59 338 | 29.24 327 | 62.64 281 | 72.40 308 |
|
MIMVSNet | | | 57.35 270 | 57.07 263 | 58.22 293 | 74.21 211 | 37.18 295 | 62.46 301 | 60.88 308 | 48.88 251 | 55.29 282 | 75.99 275 | 31.68 286 | 62.04 312 | 31.87 298 | 72.35 181 | 75.43 272 |
|
gm-plane-assit | | | | | | 71.40 271 | 41.72 265 | | | 48.85 252 | | 73.31 292 | | 82.48 157 | 48.90 199 | | |
|
cascas | | | 65.98 191 | 63.42 202 | 73.64 102 | 77.26 166 | 52.58 137 | 72.26 229 | 77.21 185 | 48.56 253 | 61.21 220 | 74.60 285 | 32.57 282 | 85.82 71 | 50.38 186 | 76.75 134 | 82.52 177 |
|
PLC | | 56.13 14 | 65.09 201 | 63.21 204 | 70.72 182 | 81.04 82 | 54.87 113 | 78.57 107 | 77.47 180 | 48.51 254 | 55.71 276 | 81.89 156 | 33.71 264 | 79.71 199 | 41.66 254 | 70.37 207 | 77.58 247 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
LS3D | | | 64.71 204 | 62.50 212 | 71.34 170 | 79.72 106 | 55.71 101 | 79.82 91 | 74.72 212 | 48.50 255 | 56.62 271 | 84.62 101 | 33.59 266 | 82.34 159 | 29.65 320 | 75.23 141 | 75.97 264 |
|
anonymousdsp | | | 67.00 175 | 64.82 190 | 73.57 104 | 70.09 284 | 56.13 93 | 76.35 160 | 77.35 184 | 48.43 256 | 64.99 168 | 80.84 187 | 33.01 271 | 80.34 191 | 64.66 79 | 67.64 249 | 84.23 131 |
|
无先验 | | | | | | | | 79.66 95 | 74.30 216 | 48.40 257 | | | | 80.78 186 | 53.62 165 | | 79.03 236 |
|
114514_t | | | 70.83 88 | 69.56 99 | 74.64 79 | 86.21 19 | 54.63 114 | 82.34 56 | 81.81 78 | 48.22 258 | 63.01 184 | 85.83 84 | 40.92 191 | 87.10 40 | 57.91 139 | 79.79 92 | 82.18 182 |
|
tpm | | | 57.34 271 | 58.16 256 | 54.86 308 | 71.80 266 | 34.77 314 | 67.47 278 | 56.04 328 | 48.20 259 | 60.10 231 | 76.92 261 | 37.17 224 | 53.41 343 | 40.76 260 | 65.01 263 | 76.40 263 |
|
MDA-MVSNet-bldmvs | | | 53.87 289 | 50.81 296 | 63.05 267 | 66.25 311 | 48.58 207 | 56.93 321 | 63.82 291 | 48.09 260 | 41.22 337 | 70.48 307 | 30.34 291 | 68.00 290 | 34.24 290 | 45.92 339 | 72.57 302 |
|
XXY-MVS | | | 60.68 243 | 61.67 226 | 57.70 298 | 70.43 280 | 38.45 288 | 64.19 296 | 66.47 268 | 48.05 261 | 63.22 181 | 80.86 185 | 49.28 87 | 60.47 316 | 45.25 230 | 67.28 251 | 74.19 291 |
|
F-COLMAP | | | 63.05 222 | 60.87 234 | 69.58 197 | 76.99 172 | 53.63 122 | 78.12 117 | 76.16 194 | 47.97 262 | 52.41 303 | 81.61 163 | 27.87 306 | 78.11 231 | 40.07 262 | 66.66 252 | 77.00 257 |
|
Patchmatch-RL test | | | 58.16 265 | 55.49 275 | 66.15 233 | 67.92 301 | 48.89 205 | 60.66 311 | 51.07 341 | 47.86 263 | 59.36 239 | 62.71 334 | 34.02 261 | 72.27 274 | 56.41 145 | 59.40 305 | 77.30 250 |
|
ANet_high | | | 41.38 319 | 37.47 323 | 53.11 315 | 39.73 358 | 24.45 352 | 56.94 320 | 69.69 240 | 47.65 264 | 26.04 349 | 52.32 344 | 12.44 345 | 62.38 311 | 21.80 343 | 10.61 358 | 72.49 303 |
|
CostFormer | | | 64.04 210 | 62.51 211 | 68.61 208 | 71.88 264 | 45.77 231 | 71.30 241 | 70.60 236 | 47.55 265 | 64.31 175 | 76.61 267 | 41.63 177 | 79.62 202 | 49.74 191 | 69.00 230 | 80.42 217 |
|
PatchmatchNet | | | 59.84 249 | 58.24 255 | 64.65 257 | 73.05 242 | 46.70 227 | 69.42 262 | 62.18 302 | 47.55 265 | 58.88 247 | 71.96 297 | 34.49 256 | 69.16 285 | 42.99 245 | 63.60 274 | 78.07 242 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
ITE_SJBPF | | | | | 62.09 273 | 66.16 312 | 44.55 243 | | 64.32 282 | 47.36 267 | 55.31 281 | 80.34 198 | 19.27 334 | 62.68 310 | 36.29 285 | 62.39 284 | 79.04 235 |
|
OurMVSNet-221017-0 | | | 61.37 241 | 58.63 254 | 69.61 194 | 72.05 261 | 48.06 213 | 73.93 207 | 72.51 226 | 47.23 268 | 54.74 285 | 80.92 183 | 21.49 333 | 81.24 176 | 48.57 202 | 56.22 314 | 79.53 230 |
|
tpmrst | | | 58.24 264 | 58.70 252 | 56.84 300 | 66.97 305 | 34.32 317 | 69.57 261 | 61.14 307 | 47.17 269 | 58.58 252 | 71.60 298 | 41.28 186 | 60.41 317 | 49.20 197 | 62.84 280 | 75.78 268 |
|
PVSNet | | 50.76 19 | 58.40 263 | 57.39 261 | 61.42 276 | 75.53 189 | 44.04 247 | 61.43 305 | 63.45 293 | 47.04 270 | 56.91 269 | 73.61 291 | 27.00 314 | 64.76 303 | 39.12 266 | 72.40 180 | 75.47 271 |
|
FMVSNet5 | | | 55.86 280 | 54.93 278 | 58.66 292 | 71.05 274 | 36.35 305 | 64.18 297 | 62.48 301 | 46.76 271 | 50.66 312 | 74.73 284 | 25.80 320 | 64.04 305 | 33.11 294 | 65.57 260 | 75.59 270 |
|
jason | | | 69.65 119 | 68.39 124 | 73.43 112 | 78.27 133 | 56.88 83 | 77.12 147 | 73.71 221 | 46.53 272 | 69.34 100 | 83.22 127 | 43.37 162 | 79.18 209 | 64.77 78 | 79.20 103 | 84.23 131 |
jason: jason. |
MS-PatchMatch | | | 62.42 230 | 61.46 228 | 65.31 252 | 75.21 194 | 52.10 144 | 72.05 235 | 74.05 218 | 46.41 273 | 57.42 267 | 74.36 286 | 34.35 258 | 77.57 238 | 45.62 223 | 73.67 153 | 66.26 328 |
|
1112_ss | | | 64.00 211 | 63.36 203 | 65.93 240 | 79.28 108 | 42.58 258 | 71.35 240 | 72.36 228 | 46.41 273 | 60.55 228 | 77.89 242 | 46.27 134 | 73.28 269 | 46.18 215 | 69.97 218 | 81.92 186 |
|
lupinMVS | | | 69.57 121 | 68.28 125 | 73.44 111 | 78.76 120 | 57.15 81 | 76.57 156 | 73.29 223 | 46.19 275 | 69.49 96 | 82.18 145 | 43.99 158 | 79.23 207 | 64.66 79 | 79.37 98 | 83.93 140 |
|
testdata | | | | | 64.66 256 | 81.52 70 | 52.93 132 | | 65.29 276 | 46.09 276 | 73.88 39 | 87.46 53 | 38.08 215 | 66.26 298 | 53.31 170 | 78.48 114 | 74.78 281 |
|
UnsupCasMVSNet_eth | | | 53.16 294 | 52.47 292 | 55.23 305 | 59.45 341 | 33.39 329 | 59.43 314 | 69.13 249 | 45.98 277 | 50.35 314 | 72.32 295 | 29.30 300 | 58.26 324 | 42.02 252 | 44.30 341 | 74.05 292 |
|
AllTest | | | 57.08 273 | 54.65 280 | 64.39 258 | 71.44 268 | 49.03 201 | 69.92 259 | 67.30 263 | 45.97 278 | 47.16 319 | 79.77 210 | 17.47 335 | 67.56 291 | 33.65 292 | 59.16 306 | 76.57 261 |
|
TestCases | | | | | 64.39 258 | 71.44 268 | 49.03 201 | | 67.30 263 | 45.97 278 | 47.16 319 | 79.77 210 | 17.47 335 | 67.56 291 | 33.65 292 | 59.16 306 | 76.57 261 |
|
WTY-MVS | | | 59.75 250 | 60.39 236 | 57.85 296 | 72.32 257 | 37.83 292 | 61.05 310 | 64.18 289 | 45.95 280 | 61.91 206 | 79.11 227 | 47.01 127 | 60.88 315 | 42.50 248 | 69.49 228 | 74.83 279 |
|
testpf | | | 44.11 315 | 45.40 310 | 40.26 337 | 60.52 336 | 27.34 344 | 33.26 354 | 54.33 335 | 45.87 281 | 41.08 338 | 60.26 337 | 16.46 337 | 59.14 321 | 46.09 216 | 50.68 331 | 34.31 353 |
|
semantic-postprocess | | | | | 65.40 250 | 71.99 262 | 50.80 160 | | 69.63 242 | 45.71 282 | 60.61 227 | 77.93 240 | 36.56 237 | 65.99 300 | 55.67 151 | 63.50 275 | 79.42 231 |
|
旧先验2 | | | | | | | | 76.08 167 | | 45.32 283 | 76.55 16 | | | 65.56 302 | 58.75 137 | | |
|
OpenMVS_ROB | | 52.78 18 | 60.03 245 | 58.14 257 | 65.69 244 | 70.47 279 | 44.82 237 | 75.33 179 | 70.86 234 | 45.04 284 | 56.06 274 | 76.00 273 | 26.89 315 | 79.65 200 | 35.36 287 | 67.29 250 | 72.60 301 |
|
tpmp4_e23 | | | 62.71 228 | 60.13 237 | 70.45 185 | 73.40 227 | 48.39 209 | 72.82 218 | 69.49 245 | 44.88 285 | 59.91 233 | 74.99 281 | 37.79 217 | 81.47 174 | 40.22 261 | 67.71 248 | 81.48 191 |
|
TinyColmap | | | 54.14 286 | 51.72 294 | 61.40 277 | 66.84 307 | 41.97 261 | 66.52 280 | 68.51 256 | 44.81 286 | 42.69 336 | 75.77 276 | 11.66 348 | 72.94 270 | 31.96 297 | 56.77 312 | 69.27 324 |
|
MDTV_nov1_ep13 | | | | 57.00 264 | | 72.73 250 | 38.26 289 | 65.02 293 | 64.73 280 | 44.74 287 | 55.46 278 | 72.48 294 | 32.61 281 | 70.47 281 | 37.47 273 | 67.75 247 | |
|
新几何1 | | | | | 70.76 180 | 85.66 27 | 61.13 31 | | 66.43 270 | 44.68 288 | 70.29 74 | 86.64 65 | 41.29 185 | 75.23 263 | 49.72 192 | 81.75 70 | 75.93 266 |
|
1121 | | | 68.53 145 | 67.16 152 | 72.63 136 | 85.64 29 | 61.14 30 | 73.95 204 | 66.46 269 | 44.61 289 | 70.28 75 | 86.68 64 | 41.42 183 | 80.78 186 | 53.62 165 | 81.79 68 | 75.97 264 |
|
Patchmtry | | | 57.16 272 | 56.47 268 | 59.23 287 | 69.17 293 | 34.58 316 | 62.98 299 | 63.15 296 | 44.53 290 | 56.83 270 | 74.84 282 | 35.83 240 | 68.71 287 | 40.03 263 | 60.91 293 | 74.39 285 |
|
ppachtmachnet_test | | | 58.06 267 | 55.38 276 | 66.10 236 | 69.51 290 | 48.99 204 | 68.01 276 | 66.13 271 | 44.50 291 | 54.05 294 | 70.74 304 | 32.09 285 | 72.34 273 | 36.68 280 | 56.71 313 | 76.99 259 |
|
PatchT | | | 53.17 293 | 53.44 290 | 52.33 319 | 68.29 300 | 25.34 350 | 58.21 317 | 54.41 334 | 44.46 292 | 54.56 288 | 69.05 314 | 33.32 268 | 60.94 314 | 36.93 276 | 61.76 287 | 70.73 319 |
|
EPMVS | | | 53.96 287 | 53.69 288 | 54.79 309 | 66.12 313 | 31.96 334 | 62.34 303 | 49.05 344 | 44.42 293 | 55.54 277 | 71.33 301 | 30.22 292 | 56.70 330 | 41.65 255 | 62.54 283 | 75.71 269 |
|
pmmvs4 | | | 61.48 240 | 59.39 240 | 67.76 214 | 71.57 267 | 53.86 119 | 71.42 239 | 65.34 275 | 44.20 294 | 59.46 238 | 77.92 241 | 35.90 239 | 74.71 266 | 43.87 237 | 64.87 264 | 74.71 282 |
|
dp | | | 51.89 297 | 51.60 295 | 52.77 317 | 68.44 299 | 32.45 331 | 62.36 302 | 54.57 333 | 44.16 295 | 49.31 315 | 67.91 316 | 28.87 303 | 56.61 331 | 33.89 291 | 54.89 318 | 69.24 325 |
|
PatchMatch-RL | | | 56.25 278 | 54.55 281 | 61.32 278 | 77.06 169 | 56.07 95 | 65.57 286 | 54.10 337 | 44.13 296 | 53.49 301 | 71.27 302 | 25.20 323 | 66.78 295 | 36.52 283 | 63.66 273 | 61.12 337 |
|
our_test_3 | | | 56.49 274 | 54.42 282 | 62.68 270 | 69.51 290 | 45.48 234 | 66.08 283 | 61.49 306 | 44.11 297 | 50.73 311 | 69.60 313 | 33.05 270 | 68.15 289 | 38.38 269 | 56.86 311 | 74.40 284 |
|
USDC | | | 56.35 277 | 54.24 285 | 62.69 269 | 64.74 319 | 40.31 271 | 65.05 292 | 73.83 219 | 43.93 298 | 47.58 317 | 77.71 247 | 15.36 340 | 75.05 265 | 38.19 271 | 61.81 286 | 72.70 300 |
|
PM-MVS | | | 52.33 295 | 50.19 298 | 58.75 291 | 62.10 328 | 45.14 236 | 65.75 284 | 40.38 354 | 43.60 299 | 53.52 299 | 72.65 293 | 9.16 354 | 65.87 301 | 50.41 185 | 54.18 321 | 65.24 330 |
|
pmmvs-eth3d | | | 58.81 260 | 56.31 270 | 66.30 228 | 67.61 302 | 52.42 142 | 72.30 228 | 64.76 279 | 43.55 300 | 54.94 284 | 74.19 288 | 28.95 301 | 72.60 271 | 43.31 240 | 57.21 310 | 73.88 294 |
|
new-patchmatchnet | | | 47.56 307 | 47.73 305 | 47.06 328 | 58.81 342 | 9.37 361 | 48.78 340 | 59.21 312 | 43.28 301 | 44.22 329 | 68.66 315 | 25.67 321 | 57.20 329 | 31.57 306 | 49.35 335 | 74.62 283 |
|
Test_1112_low_res | | | 62.32 231 | 61.77 225 | 64.00 260 | 79.08 113 | 39.53 277 | 68.17 274 | 70.17 237 | 43.25 302 | 59.03 246 | 79.90 206 | 44.08 156 | 71.24 277 | 43.79 238 | 68.42 235 | 81.25 201 |
|
RPMNet | | | 58.70 261 | 56.29 271 | 65.96 238 | 69.96 286 | 52.07 145 | 65.31 290 | 62.15 303 | 43.20 303 | 59.36 239 | 70.15 310 | 35.37 248 | 70.75 279 | 36.42 284 | 64.65 268 | 75.06 274 |
|
tpm2 | | | 62.07 234 | 60.10 238 | 67.99 212 | 72.79 249 | 43.86 248 | 71.05 246 | 66.85 267 | 43.14 304 | 62.77 185 | 75.39 279 | 38.32 211 | 80.80 185 | 41.69 253 | 68.88 231 | 79.32 232 |
|
JIA-IIPM | | | 51.56 298 | 47.68 306 | 63.21 265 | 64.61 320 | 50.73 162 | 47.71 341 | 58.77 314 | 42.90 305 | 48.46 316 | 51.72 345 | 24.97 324 | 70.24 282 | 36.06 286 | 53.89 322 | 68.64 326 |
|
1314 | | | 64.61 206 | 63.21 204 | 68.80 205 | 71.87 265 | 47.46 220 | 73.95 204 | 78.39 170 | 42.88 306 | 59.97 232 | 76.60 268 | 38.11 214 | 79.39 205 | 54.84 156 | 72.32 182 | 79.55 229 |
|
HyFIR lowres test | | | 65.67 193 | 63.01 206 | 73.67 99 | 79.97 101 | 55.65 103 | 69.07 265 | 75.52 201 | 42.68 307 | 63.53 179 | 77.95 239 | 40.43 192 | 81.64 169 | 46.01 218 | 71.91 186 | 83.73 149 |
|
CR-MVSNet | | | 59.91 248 | 57.90 260 | 65.96 238 | 69.96 286 | 52.07 145 | 65.31 290 | 63.15 296 | 42.48 308 | 59.36 239 | 74.84 282 | 35.83 240 | 70.75 279 | 45.50 225 | 64.65 268 | 75.06 274 |
|
test222 | | | | | | 83.14 55 | 58.68 59 | 72.57 224 | 63.45 293 | 41.78 309 | 67.56 133 | 86.12 77 | 37.13 225 | | | 78.73 111 | 74.98 277 |
|
TDRefinement | | | 53.44 291 | 50.72 297 | 61.60 275 | 64.31 322 | 46.96 224 | 70.89 248 | 65.27 277 | 41.78 309 | 44.61 328 | 77.98 238 | 11.52 349 | 66.36 297 | 28.57 330 | 51.59 327 | 71.49 314 |
|
sss | | | 56.17 279 | 56.57 267 | 54.96 307 | 66.93 306 | 36.32 307 | 57.94 318 | 61.69 305 | 41.67 311 | 58.64 251 | 75.32 280 | 38.72 207 | 56.25 335 | 42.04 251 | 66.19 255 | 72.31 309 |
|
PVSNet_0 | | 43.31 20 | 47.46 308 | 45.64 308 | 52.92 316 | 67.60 303 | 44.65 240 | 54.06 327 | 54.64 332 | 41.59 312 | 46.15 323 | 58.75 340 | 30.99 287 | 58.66 322 | 32.18 296 | 24.81 350 | 55.46 344 |
|
MVS | | | 67.37 165 | 66.33 171 | 70.51 184 | 75.46 190 | 50.94 154 | 73.95 204 | 81.85 77 | 41.57 313 | 62.54 192 | 78.57 235 | 47.98 112 | 85.47 78 | 52.97 171 | 82.05 67 | 75.14 273 |
|
Anonymous20231206 | | | 55.10 285 | 55.30 277 | 54.48 310 | 69.81 289 | 33.94 320 | 62.91 300 | 62.13 304 | 41.08 314 | 55.18 283 | 75.65 277 | 32.75 277 | 56.59 332 | 30.32 313 | 67.86 245 | 72.91 298 |
|
MDA-MVSNet_test_wron | | | 50.71 301 | 48.95 300 | 56.00 304 | 61.17 332 | 41.84 262 | 51.90 335 | 56.45 323 | 40.96 315 | 44.79 327 | 67.84 317 | 30.04 295 | 55.07 342 | 36.71 279 | 50.69 330 | 71.11 318 |
|
YYNet1 | | | 50.73 300 | 48.96 299 | 56.03 303 | 61.10 333 | 41.78 263 | 51.94 334 | 56.44 324 | 40.94 316 | 44.84 326 | 67.80 318 | 30.08 294 | 55.08 341 | 36.77 277 | 50.71 329 | 71.22 315 |
|
1111 | | | 44.40 314 | 45.00 311 | 42.61 335 | 57.55 344 | 17.33 358 | 53.82 330 | 57.05 321 | 40.78 317 | 44.11 330 | 66.57 322 | 13.37 343 | 45.77 350 | 22.15 340 | 49.58 333 | 64.73 332 |
|
.test1245 | | | 34.88 326 | 39.49 321 | 21.04 347 | 57.55 344 | 17.33 358 | 53.82 330 | 57.05 321 | 40.78 317 | 44.11 330 | 66.57 322 | 13.37 343 | 45.77 350 | 22.15 340 | 0.00 361 | 0.03 362 |
|
CHOSEN 1792x2688 | | | 65.08 202 | 62.84 208 | 71.82 157 | 81.49 72 | 56.26 90 | 66.32 282 | 74.20 217 | 40.53 319 | 63.16 183 | 78.65 232 | 41.30 184 | 77.80 235 | 45.80 220 | 74.09 148 | 81.40 193 |
|
pmmvs5 | | | 56.47 275 | 55.68 274 | 58.86 290 | 61.41 331 | 36.71 303 | 66.37 281 | 62.75 299 | 40.38 320 | 53.70 296 | 76.62 266 | 34.56 254 | 67.05 293 | 40.02 264 | 65.27 261 | 72.83 299 |
|
MDTV_nov1_ep13_2view | | | | | | | 25.89 347 | 61.22 308 | | 40.10 321 | 51.10 307 | | 32.97 272 | | 38.49 268 | | 78.61 238 |
|
tpm cat1 | | | 59.25 258 | 56.95 265 | 66.15 233 | 72.19 258 | 46.96 224 | 68.09 275 | 65.76 272 | 40.03 322 | 57.81 264 | 70.56 306 | 38.32 211 | 74.51 267 | 38.26 270 | 61.50 289 | 77.00 257 |
|
test-mter | | | 56.42 276 | 55.82 273 | 58.22 293 | 68.57 296 | 44.80 238 | 65.46 287 | 57.92 317 | 39.94 323 | 55.44 279 | 69.82 311 | 21.92 332 | 57.44 326 | 49.66 193 | 73.62 154 | 72.41 306 |
|
UnsupCasMVSNet_bld | | | 50.07 302 | 48.87 301 | 53.66 312 | 60.97 335 | 33.67 327 | 57.62 319 | 64.56 281 | 39.47 324 | 47.38 318 | 64.02 330 | 27.47 309 | 59.32 320 | 34.69 289 | 43.68 342 | 67.98 327 |
|
TESTMET0.1,1 | | | 55.28 283 | 54.90 279 | 56.42 301 | 66.56 310 | 43.67 250 | 65.46 287 | 56.27 326 | 39.18 325 | 53.83 295 | 67.44 319 | 24.21 327 | 55.46 340 | 48.04 204 | 73.11 166 | 70.13 320 |
|
test1235678 | | | 45.66 309 | 44.46 314 | 49.26 324 | 59.88 339 | 28.68 341 | 56.36 323 | 55.54 331 | 39.12 326 | 40.89 339 | 63.40 332 | 14.41 342 | 57.32 328 | 21.05 344 | 49.47 334 | 61.78 335 |
|
LP | | | 48.51 304 | 45.51 309 | 57.52 299 | 62.86 325 | 44.53 244 | 52.38 333 | 59.84 310 | 38.11 327 | 42.81 335 | 61.02 335 | 23.23 328 | 63.02 308 | 24.10 338 | 45.24 340 | 65.02 331 |
|
test2356 | | | 45.61 310 | 44.66 312 | 48.47 327 | 60.15 338 | 28.08 343 | 52.44 332 | 52.83 340 | 38.01 328 | 46.13 324 | 60.98 336 | 15.08 341 | 55.54 339 | 20.43 347 | 55.85 316 | 61.78 335 |
|
testmv | | | 42.25 317 | 40.11 320 | 48.66 325 | 53.23 346 | 27.02 345 | 56.62 322 | 55.74 330 | 37.25 329 | 33.10 345 | 59.52 339 | 7.78 356 | 56.58 333 | 19.61 348 | 38.13 346 | 62.40 334 |
|
ADS-MVSNet2 | | | 51.33 299 | 48.76 302 | 59.07 289 | 66.02 315 | 44.60 241 | 50.90 336 | 59.76 311 | 36.90 330 | 50.74 309 | 66.18 325 | 26.38 316 | 63.11 307 | 27.17 331 | 54.76 319 | 69.50 322 |
|
ADS-MVSNet | | | 48.48 305 | 47.77 304 | 50.63 322 | 66.02 315 | 29.92 338 | 50.90 336 | 50.87 343 | 36.90 330 | 50.74 309 | 66.18 325 | 26.38 316 | 52.47 345 | 27.17 331 | 54.76 319 | 69.50 322 |
|
RPSCF | | | 55.80 281 | 54.22 286 | 60.53 286 | 65.13 318 | 42.91 257 | 64.30 295 | 57.62 319 | 36.84 332 | 58.05 256 | 82.28 144 | 28.01 305 | 56.24 336 | 37.14 275 | 58.61 308 | 82.44 180 |
|
testus | | | 44.59 313 | 43.87 315 | 46.76 329 | 59.85 340 | 24.65 351 | 53.86 328 | 55.82 329 | 36.26 333 | 43.97 332 | 63.42 331 | 8.39 355 | 53.14 344 | 20.70 346 | 52.52 325 | 62.51 333 |
|
Patchmatch-test | | | 49.08 303 | 48.28 303 | 51.50 321 | 64.40 321 | 30.85 337 | 45.68 344 | 48.46 347 | 35.60 334 | 46.10 325 | 72.10 296 | 34.47 257 | 46.37 349 | 27.08 333 | 60.65 297 | 77.27 252 |
|
CHOSEN 280x420 | | | 47.83 306 | 46.36 307 | 52.24 320 | 67.37 304 | 49.78 192 | 38.91 352 | 43.11 353 | 35.00 335 | 43.27 334 | 63.30 333 | 28.95 301 | 49.19 348 | 36.53 282 | 60.80 296 | 57.76 342 |
|
N_pmnet | | | 39.35 322 | 40.28 319 | 36.54 339 | 63.76 323 | 1.62 365 | 49.37 339 | 0.76 366 | 34.62 336 | 43.61 333 | 66.38 324 | 26.25 318 | 42.57 355 | 26.02 337 | 51.77 326 | 65.44 329 |
|
no-one | | | 40.85 320 | 36.09 324 | 55.14 306 | 48.55 351 | 38.72 283 | 42.15 350 | 62.92 298 | 34.60 337 | 23.55 350 | 49.74 349 | 12.21 346 | 66.16 299 | 26.27 336 | 24.84 349 | 60.54 338 |
|
PMMVS | | | 53.96 287 | 53.26 291 | 56.04 302 | 62.60 327 | 50.92 156 | 61.17 309 | 56.09 327 | 32.81 338 | 53.51 300 | 66.84 321 | 34.04 260 | 59.93 319 | 44.14 234 | 68.18 242 | 57.27 343 |
|
test12356 | | | 36.16 325 | 35.94 325 | 36.83 338 | 50.82 350 | 8.52 362 | 44.84 347 | 53.49 339 | 32.72 339 | 30.11 347 | 55.08 342 | 7.11 358 | 49.47 347 | 16.60 350 | 32.68 348 | 52.50 345 |
|
CMPMVS | | 42.80 21 | 57.81 269 | 55.97 272 | 63.32 264 | 60.98 334 | 47.38 221 | 64.66 294 | 69.50 244 | 32.06 340 | 46.83 321 | 77.80 244 | 29.50 298 | 71.36 276 | 48.68 200 | 73.75 152 | 71.21 316 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
CVMVSNet | | | 59.63 252 | 59.14 242 | 61.08 284 | 74.47 205 | 38.84 282 | 75.20 184 | 68.74 254 | 31.15 341 | 58.24 255 | 76.51 269 | 32.39 283 | 68.58 288 | 49.77 190 | 65.84 257 | 75.81 267 |
|
FPMVS | | | 42.18 318 | 41.11 318 | 45.39 330 | 58.03 343 | 41.01 269 | 49.50 338 | 53.81 338 | 30.07 342 | 33.71 344 | 64.03 328 | 11.69 347 | 52.08 346 | 14.01 353 | 55.11 317 | 43.09 350 |
|
EU-MVSNet | | | 55.61 282 | 54.41 283 | 59.19 288 | 65.41 317 | 33.42 328 | 72.44 226 | 71.91 230 | 28.81 343 | 51.27 306 | 73.87 289 | 24.76 325 | 69.08 286 | 43.04 244 | 58.20 309 | 75.06 274 |
|
LF4IMVS | | | 42.95 316 | 42.26 316 | 45.04 331 | 48.30 352 | 32.50 330 | 54.80 325 | 48.49 346 | 28.03 344 | 40.51 341 | 70.16 309 | 9.24 353 | 43.89 353 | 31.63 304 | 49.18 336 | 58.72 340 |
|
MVS-HIRNet | | | 45.52 311 | 44.48 313 | 48.65 326 | 68.49 298 | 34.05 318 | 59.41 315 | 44.50 352 | 27.03 345 | 37.96 343 | 50.47 348 | 26.16 319 | 64.10 304 | 26.74 334 | 59.52 304 | 47.82 347 |
|
PMVS | | 28.69 22 | 36.22 324 | 33.29 328 | 45.02 332 | 36.82 360 | 35.98 309 | 54.68 326 | 48.74 345 | 26.31 346 | 21.02 351 | 51.61 346 | 2.88 364 | 60.10 318 | 9.99 357 | 47.58 337 | 38.99 352 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
pmmvs3 | | | 44.92 312 | 41.95 317 | 53.86 311 | 52.58 348 | 43.55 251 | 62.11 304 | 46.90 351 | 26.05 347 | 40.63 340 | 60.19 338 | 11.08 351 | 57.91 325 | 31.83 303 | 46.15 338 | 60.11 339 |
|
PMMVS2 | | | 27.40 332 | 25.91 332 | 31.87 343 | 39.46 359 | 6.57 363 | 31.17 355 | 28.52 360 | 23.96 348 | 20.45 352 | 48.94 350 | 4.20 361 | 37.94 358 | 16.51 351 | 19.97 351 | 51.09 346 |
|
Gipuma | | | 34.77 327 | 31.91 329 | 43.33 334 | 62.05 329 | 37.87 291 | 20.39 357 | 67.03 266 | 23.23 349 | 18.41 353 | 25.84 354 | 4.24 360 | 62.73 309 | 14.71 352 | 51.32 328 | 29.38 355 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
new_pmnet | | | 34.13 328 | 34.29 327 | 33.64 340 | 52.63 347 | 18.23 357 | 44.43 348 | 33.90 357 | 22.81 350 | 30.89 346 | 53.18 343 | 10.48 352 | 35.72 359 | 20.77 345 | 39.51 343 | 46.98 348 |
|
LCM-MVSNet | | | 40.30 321 | 35.88 326 | 53.57 313 | 42.24 355 | 29.15 340 | 45.21 346 | 60.53 309 | 22.23 351 | 28.02 348 | 50.98 347 | 3.72 362 | 61.78 313 | 31.22 310 | 38.76 345 | 69.78 321 |
|
PNet_i23d | | | 27.88 331 | 25.99 331 | 33.55 341 | 47.54 353 | 25.89 347 | 47.24 343 | 32.91 358 | 21.44 352 | 15.90 354 | 38.09 351 | 0.85 366 | 42.76 354 | 16.90 349 | 13.03 356 | 32.00 354 |
|
E-PMN | | | 23.77 333 | 22.73 334 | 26.90 345 | 42.02 356 | 20.67 354 | 42.66 349 | 35.70 355 | 17.43 353 | 10.28 358 | 25.05 355 | 6.42 359 | 42.39 356 | 10.28 356 | 14.71 353 | 17.63 356 |
|
EMVS | | | 22.97 334 | 21.84 336 | 26.36 346 | 40.20 357 | 19.53 356 | 41.95 351 | 34.64 356 | 17.09 354 | 9.73 359 | 22.83 357 | 7.29 357 | 42.22 357 | 9.18 358 | 13.66 355 | 17.32 357 |
|
DSMNet-mixed | | | 39.30 323 | 38.72 322 | 41.03 336 | 51.22 349 | 19.66 355 | 45.53 345 | 31.35 359 | 15.83 355 | 39.80 342 | 67.42 320 | 22.19 331 | 45.13 352 | 22.43 339 | 52.69 324 | 58.31 341 |
|
wuykxyi23d | | | 28.12 330 | 22.54 335 | 44.87 333 | 34.97 361 | 32.11 332 | 37.96 353 | 47.31 349 | 13.32 356 | 9.29 360 | 23.72 356 | 0.45 367 | 56.58 333 | 21.85 342 | 13.98 354 | 45.93 349 |
|
MVE | | 17.77 23 | 21.41 335 | 17.77 337 | 32.34 342 | 34.34 362 | 25.44 349 | 16.11 358 | 24.11 361 | 11.19 357 | 13.22 356 | 31.92 352 | 1.58 365 | 30.95 360 | 10.47 355 | 17.03 352 | 40.62 351 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
DeepMVS_CX | | | | | 12.03 349 | 17.97 363 | 10.91 360 | | 10.60 364 | 7.46 358 | 11.07 357 | 28.36 353 | 3.28 363 | 11.29 362 | 8.01 359 | 9.74 360 | 13.89 358 |
|
wuyk23d | | | 13.32 337 | 12.52 338 | 15.71 348 | 47.54 353 | 26.27 346 | 31.06 356 | 1.98 365 | 4.93 359 | 5.18 361 | 1.94 362 | 0.45 367 | 18.54 361 | 6.81 360 | 12.83 357 | 2.33 360 |
|
tmp_tt | | | 9.43 338 | 11.14 339 | 4.30 350 | 2.38 364 | 4.40 364 | 13.62 359 | 16.08 363 | 0.39 360 | 15.89 355 | 13.06 358 | 15.80 339 | 5.54 363 | 12.63 354 | 10.46 359 | 2.95 359 |
|
testmvs | | | 4.52 341 | 6.03 342 | 0.01 352 | 0.01 365 | 0.00 367 | 53.86 328 | 0.00 367 | 0.01 361 | 0.04 362 | 0.27 363 | 0.00 370 | 0.00 364 | 0.04 361 | 0.00 361 | 0.03 362 |
|
test123 | | | 4.73 340 | 6.30 341 | 0.02 351 | 0.01 365 | 0.01 366 | 56.36 323 | 0.00 367 | 0.01 361 | 0.04 362 | 0.21 364 | 0.01 369 | 0.00 364 | 0.03 362 | 0.00 361 | 0.04 361 |
|
cdsmvs_eth3d_5k | | | 17.50 336 | 23.34 333 | 0.00 353 | 0.00 367 | 0.00 367 | 0.00 360 | 78.63 159 | 0.00 363 | 0.00 364 | 82.18 145 | 49.25 91 | 0.00 364 | 0.00 363 | 0.00 361 | 0.00 364 |
|
pcd_1.5k_mvsjas | | | 3.92 342 | 5.23 343 | 0.00 353 | 0.00 367 | 0.00 367 | 0.00 360 | 0.00 367 | 0.00 363 | 0.00 364 | 0.00 365 | 47.05 124 | 0.00 364 | 0.00 363 | 0.00 361 | 0.00 364 |
|
pcd1.5k->3k | | | 30.06 329 | 30.56 330 | 28.55 344 | 78.81 119 | 0.00 367 | 0.00 360 | 82.07 73 | 0.00 363 | 0.00 364 | 0.00 365 | 39.61 197 | 0.00 364 | 0.00 363 | 74.56 143 | 85.66 76 |
|
sosnet-low-res | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 367 | 0.00 360 | 0.00 367 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 370 | 0.00 364 | 0.00 363 | 0.00 361 | 0.00 364 |
|
sosnet | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 367 | 0.00 360 | 0.00 367 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 370 | 0.00 364 | 0.00 363 | 0.00 361 | 0.00 364 |
|
uncertanet | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 367 | 0.00 360 | 0.00 367 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 370 | 0.00 364 | 0.00 363 | 0.00 361 | 0.00 364 |
|
Regformer | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 367 | 0.00 360 | 0.00 367 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 370 | 0.00 364 | 0.00 363 | 0.00 361 | 0.00 364 |
|
ab-mvs-re | | | 6.49 339 | 8.65 340 | 0.00 353 | 0.00 367 | 0.00 367 | 0.00 360 | 0.00 367 | 0.00 363 | 0.00 364 | 77.89 242 | 0.00 370 | 0.00 364 | 0.00 363 | 0.00 361 | 0.00 364 |
|
uanet | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 367 | 0.00 360 | 0.00 367 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 370 | 0.00 364 | 0.00 363 | 0.00 361 | 0.00 364 |
|
GSMVS | | | | | | | | | | | | | | | | | 78.05 243 |
|
test_part2 | | | | | | 87.58 3 | 60.47 38 | | | | 83.42 2 | | | | | | |
|
test_part1 | | | | | | | | | 86.64 3 | | | | 65.59 1 | | | 90.06 4 | 86.78 42 |
|
sam_mvs1 | | | | | | | | | | | | | 34.74 252 | | | | 78.05 243 |
|
sam_mvs | | | | | | | | | | | | | 33.43 267 | | | | |
|
ambc | | | | | 65.13 253 | 63.72 324 | 37.07 296 | 47.66 342 | 78.78 156 | | 54.37 291 | 71.42 300 | 11.24 350 | 80.94 181 | 45.64 222 | 53.85 323 | 77.38 249 |
|
MTGPA | | | | | | | | | 80.97 102 | | | | | | | | |
|
test_post1 | | | | | | | | 68.67 267 | | | | 3.64 360 | 32.39 283 | 69.49 284 | 44.17 233 | | |
|
test_post | | | | | | | | | | | | 3.55 361 | 33.90 262 | 66.52 296 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 64.03 328 | 34.50 255 | 74.27 268 | | | |
|
GG-mvs-BLEND | | | | | 62.34 271 | 71.36 272 | 37.04 297 | 69.20 264 | 57.33 320 | | 54.73 286 | 65.48 327 | 30.37 290 | 77.82 234 | 34.82 288 | 74.93 142 | 72.17 311 |
|
MTMP | | | | | | | | 86.03 12 | 17.08 362 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 75.28 17 | 88.31 19 | 83.81 145 |
|
agg_prior2 | | | | | | | | | | | | | | | 73.09 32 | 87.93 26 | 84.33 123 |
|
agg_prior | | | | | | 85.04 37 | 59.96 43 | | 81.04 98 | | 74.68 29 | | | 84.04 111 | | | |
|
test_prior4 | | | | | | | 62.51 17 | 82.08 62 | | | | | | | | | |
|
test_prior | | | | | 76.69 47 | 84.20 49 | 57.27 74 | | 84.88 17 | | | | | 86.43 58 | | | 86.38 47 |
|
新几何2 | | | | | | | | 76.12 165 | | | | | | | | | |
|
旧先验1 | | | | | | 83.04 56 | 53.15 129 | | 67.52 262 | | | 87.85 51 | 44.08 156 | | | 80.76 75 | 78.03 245 |
|
原ACMM2 | | | | | | | | 79.02 101 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 72.18 275 | 46.95 211 | | |
|
segment_acmp | | | | | | | | | | | | | 54.23 37 | | | | |
|
test12 | | | | | 77.76 35 | 84.52 46 | 58.41 61 | | 83.36 52 | | 72.93 54 | | 54.61 34 | 88.05 24 | | 88.12 22 | 86.81 40 |
|
plane_prior7 | | | | | | 81.41 73 | 55.96 97 | | | | | | | | | | |
|
plane_prior6 | | | | | | 81.20 80 | 56.24 91 | | | | | | 45.26 146 | | | | |
|
plane_prior5 | | | | | | | | | 84.01 32 | | | | | 87.21 36 | 68.16 52 | 80.58 78 | 84.65 117 |
|
plane_prior4 | | | | | | | | | | | | 86.10 78 | | | | | |
|
plane_prior1 | | | | | | 81.27 78 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 367 | | | | | | | | |
|
nn | | | | | | | | | 0.00 367 | | | | | | | | |
|
door-mid | | | | | | | | | 47.19 350 | | | | | | | | |
|
lessismore_v0 | | | | | 69.91 191 | 71.42 270 | 47.80 215 | | 50.90 342 | | 50.39 313 | 75.56 278 | 27.43 311 | 81.33 175 | 45.91 219 | 34.10 347 | 80.59 215 |
|
test11 | | | | | | | | | 83.47 47 | | | | | | | | |
|
door | | | | | | | | | 47.60 348 | | | | | | | | |
|
HQP5-MVS | | | | | | | 54.94 110 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 67.04 63 | | |
|
HQP4-MVS | | | | | | | | | | | 67.85 124 | | | 86.93 42 | | | 84.32 124 |
|
HQP3-MVS | | | | | | | | | 83.90 36 | | | | | | | 80.35 84 | |
|
HQP2-MVS | | | | | | | | | | | | | 45.46 140 | | | | |
|
NP-MVS | | | | | | 80.98 83 | 56.05 96 | | | | | 85.54 91 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 74.07 150 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 72.16 184 | |
|
Test By Simon | | | | | | | | | | | | | 48.33 109 | | | | |
|