PS-MVSNAJ | | | 94.17 19 | 93.52 29 | 96.10 4 | 95.65 102 | 92.35 1 | 98.21 23 | 95.79 134 | 92.42 12 | 96.24 6 | 98.18 18 | 71.04 162 | 99.17 74 | 96.77 9 | 97.39 59 | 96.79 126 |
|
xiu_mvs_v2_base | | | 93.92 26 | 93.26 31 | 95.91 6 | 95.07 118 | 92.02 2 | 98.19 24 | 95.68 138 | 92.06 14 | 96.01 10 | 98.14 23 | 70.83 165 | 98.96 88 | 96.74 10 | 96.57 73 | 96.76 129 |
|
DELS-MVS | | | 94.98 7 | 94.49 14 | 96.44 2 | 96.42 80 | 90.59 3 | 99.21 2 | 97.02 37 | 94.40 5 | 91.46 55 | 97.08 79 | 83.32 32 | 99.69 28 | 92.83 44 | 98.70 22 | 99.04 12 |
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 |
MVS | | | 90.60 76 | 88.64 94 | 96.50 1 | 94.25 141 | 90.53 4 | 93.33 236 | 97.21 22 | 77.59 234 | 78.88 191 | 97.31 69 | 71.52 157 | 99.69 28 | 89.60 74 | 98.03 45 | 99.27 6 |
|
MCST-MVS | | | 96.17 2 | 96.12 4 | 96.32 3 | 99.42 2 | 89.36 5 | 98.94 9 | 97.10 32 | 95.17 2 | 92.11 48 | 98.46 11 | 87.33 8 | 99.97 1 | 97.21 6 | 99.31 1 | 99.63 2 |
|
MG-MVS | | | 94.25 18 | 93.72 25 | 95.85 7 | 99.38 3 | 89.35 6 | 97.98 32 | 98.09 14 | 89.99 29 | 92.34 47 | 96.97 82 | 81.30 48 | 98.99 86 | 88.54 83 | 98.88 15 | 99.20 8 |
|
MVS_0304 | | | 93.82 29 | 93.11 35 | 95.95 5 | 96.79 77 | 89.15 7 | 98.56 15 | 95.30 159 | 93.61 9 | 94.82 23 | 98.02 34 | 66.60 197 | 99.88 7 | 96.94 8 | 97.39 59 | 98.81 20 |
|
WTY-MVS | | | 92.65 48 | 91.68 56 | 95.56 9 | 96.00 90 | 88.90 8 | 98.23 22 | 97.65 16 | 88.57 40 | 89.82 73 | 97.22 74 | 79.29 64 | 99.06 82 | 89.57 75 | 88.73 139 | 98.73 25 |
|
canonicalmvs | | | 92.27 53 | 91.22 61 | 95.41 11 | 95.80 99 | 88.31 9 | 97.09 94 | 94.64 190 | 88.49 43 | 92.99 42 | 97.31 69 | 72.68 147 | 98.57 100 | 93.38 38 | 88.58 141 | 99.36 4 |
|
HY-MVS | | 84.06 6 | 91.63 61 | 90.37 69 | 95.39 12 | 96.12 85 | 88.25 10 | 90.22 281 | 97.58 18 | 88.33 46 | 90.50 68 | 91.96 171 | 79.26 66 | 99.06 82 | 90.29 68 | 89.07 135 | 98.88 18 |
|
CANet | | | 94.89 8 | 94.64 12 | 95.63 8 | 97.55 61 | 88.12 11 | 99.06 5 | 96.39 98 | 94.07 7 | 95.34 14 | 97.80 48 | 76.83 97 | 99.87 8 | 97.08 7 | 97.64 52 | 98.89 17 |
|
MVSFormer | | | 91.36 66 | 90.57 68 | 93.73 42 | 93.00 166 | 88.08 12 | 94.80 205 | 94.48 195 | 80.74 184 | 94.90 21 | 97.13 77 | 78.84 71 | 95.10 257 | 83.77 120 | 97.46 54 | 98.02 61 |
|
lupinMVS | | | 93.87 28 | 93.58 28 | 94.75 18 | 93.00 166 | 88.08 12 | 99.15 4 | 95.50 147 | 91.03 19 | 94.90 21 | 97.66 51 | 78.84 71 | 97.56 138 | 94.64 27 | 97.46 54 | 98.62 30 |
|
PAPM | | | 92.87 41 | 92.40 46 | 94.30 25 | 92.25 183 | 87.85 14 | 96.40 143 | 96.38 99 | 91.07 18 | 88.72 88 | 96.90 83 | 82.11 39 | 97.37 148 | 90.05 70 | 97.70 51 | 97.67 86 |
|
alignmvs | | | 92.97 39 | 92.26 49 | 95.12 13 | 95.54 104 | 87.77 15 | 98.67 11 | 96.38 99 | 88.04 50 | 93.01 41 | 97.45 63 | 79.20 68 | 98.60 98 | 93.25 41 | 88.76 138 | 98.99 16 |
|
FMVSNet3 | | | 84.71 181 | 82.71 192 | 90.70 144 | 94.55 128 | 87.71 16 | 95.92 166 | 94.67 186 | 81.73 170 | 75.82 229 | 88.08 222 | 66.99 191 | 94.47 270 | 71.23 222 | 75.38 235 | 89.91 225 |
|
CNVR-MVS | | | 96.30 1 | 96.54 1 | 95.55 10 | 99.31 5 | 87.69 17 | 99.06 5 | 97.12 25 | 94.66 3 | 96.79 4 | 98.78 4 | 86.42 12 | 99.95 2 | 97.59 4 | 99.18 3 | 99.00 14 |
|
xiu_mvs_v1_base_debu | | | 90.54 77 | 89.54 84 | 93.55 50 | 92.31 176 | 87.58 18 | 96.99 100 | 94.87 173 | 87.23 62 | 93.27 36 | 97.56 58 | 57.43 265 | 98.32 108 | 92.72 45 | 93.46 105 | 94.74 173 |
|
xiu_mvs_v1_base | | | 90.54 77 | 89.54 84 | 93.55 50 | 92.31 176 | 87.58 18 | 96.99 100 | 94.87 173 | 87.23 62 | 93.27 36 | 97.56 58 | 57.43 265 | 98.32 108 | 92.72 45 | 93.46 105 | 94.74 173 |
|
xiu_mvs_v1_base_debi | | | 90.54 77 | 89.54 84 | 93.55 50 | 92.31 176 | 87.58 18 | 96.99 100 | 94.87 173 | 87.23 62 | 93.27 36 | 97.56 58 | 57.43 265 | 98.32 108 | 92.72 45 | 93.46 105 | 94.74 173 |
|
jason | | | 92.73 44 | 92.23 50 | 94.21 28 | 90.50 214 | 87.30 21 | 98.65 12 | 95.09 164 | 90.61 23 | 92.76 43 | 97.13 77 | 75.28 130 | 97.30 151 | 93.32 39 | 96.75 72 | 98.02 61 |
jason: jason. |
VNet | | | 92.11 54 | 91.22 61 | 94.79 17 | 96.91 76 | 86.98 22 | 97.91 33 | 97.96 15 | 86.38 71 | 93.65 35 | 95.74 102 | 70.16 170 | 98.95 90 | 93.39 36 | 88.87 137 | 98.43 36 |
|
3Dnovator+ | | 82.88 8 | 89.63 91 | 87.85 103 | 94.99 15 | 94.49 138 | 86.76 23 | 97.84 36 | 95.74 135 | 86.10 74 | 75.47 233 | 96.02 99 | 65.00 214 | 99.51 47 | 82.91 135 | 97.07 64 | 98.72 26 |
|
OpenMVS | | 79.58 14 | 86.09 156 | 83.62 178 | 93.50 54 | 90.95 208 | 86.71 24 | 97.44 63 | 95.83 132 | 75.35 259 | 72.64 252 | 95.72 103 | 57.42 268 | 99.64 34 | 71.41 220 | 95.85 83 | 94.13 180 |
|
GG-mvs-BLEND | | | | | 93.49 55 | 94.94 120 | 86.26 25 | 81.62 324 | 97.00 38 | | 88.32 93 | 94.30 140 | 91.23 2 | 96.21 195 | 88.49 85 | 97.43 57 | 98.00 66 |
|
CANet_DTU | | | 90.98 71 | 90.04 73 | 93.83 37 | 94.76 124 | 86.23 26 | 96.32 147 | 93.12 260 | 93.11 10 | 93.71 34 | 96.82 88 | 63.08 225 | 99.48 49 | 84.29 115 | 95.12 90 | 95.77 151 |
|
HPM-MVS++ | | | 95.32 5 | 95.48 7 | 94.85 16 | 98.62 24 | 86.04 27 | 97.81 39 | 96.93 45 | 92.45 11 | 95.69 11 | 98.50 9 | 85.38 14 | 99.85 10 | 94.75 24 | 99.18 3 | 98.65 28 |
|
cascas | | | 86.50 150 | 84.48 161 | 92.55 90 | 92.64 173 | 85.95 28 | 97.04 99 | 95.07 166 | 75.32 260 | 80.50 170 | 91.02 184 | 54.33 290 | 97.98 117 | 86.79 101 | 87.62 147 | 93.71 189 |
|
QAPM | | | 86.88 143 | 84.51 159 | 93.98 32 | 94.04 147 | 85.89 29 | 97.19 78 | 96.05 121 | 73.62 281 | 75.12 236 | 95.62 107 | 62.02 232 | 99.74 21 | 70.88 226 | 96.06 79 | 96.30 144 |
|
gg-mvs-nofinetune | | | 85.48 172 | 82.90 188 | 93.24 63 | 94.51 137 | 85.82 30 | 79.22 328 | 96.97 41 | 61.19 325 | 87.33 101 | 53.01 342 | 90.58 3 | 96.07 199 | 86.07 103 | 97.23 62 | 97.81 79 |
|
1314 | | | 88.94 101 | 87.20 119 | 94.17 29 | 93.21 161 | 85.73 31 | 93.33 236 | 96.64 70 | 82.89 149 | 75.98 226 | 96.36 95 | 66.83 193 | 99.39 54 | 83.52 129 | 96.02 80 | 97.39 104 |
|
3Dnovator | | 82.32 10 | 89.33 95 | 87.64 108 | 94.42 23 | 93.73 154 | 85.70 32 | 97.73 47 | 96.75 56 | 86.73 70 | 76.21 224 | 95.93 100 | 62.17 229 | 99.68 30 | 81.67 140 | 97.81 50 | 97.88 73 |
|
DeepC-MVS_fast | | 89.06 2 | 94.48 14 | 94.30 20 | 95.02 14 | 98.86 10 | 85.68 33 | 98.06 29 | 96.64 70 | 93.64 8 | 91.74 53 | 98.54 8 | 80.17 58 | 99.90 4 | 92.28 51 | 98.75 19 | 99.49 3 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
thres200 | | | 88.92 102 | 87.65 107 | 92.73 82 | 96.30 81 | 85.62 34 | 97.85 35 | 98.86 1 | 84.38 118 | 84.82 118 | 93.99 145 | 75.12 132 | 98.01 116 | 70.86 227 | 86.67 153 | 94.56 176 |
|
test12 | | | | | 94.25 27 | 98.34 37 | 85.55 35 | | 96.35 101 | | 92.36 46 | | 80.84 49 | 99.22 64 | | 98.31 37 | 97.98 68 |
|
LFMVS | | | 89.27 96 | 87.64 108 | 94.16 31 | 97.16 73 | 85.52 36 | 97.18 79 | 94.66 187 | 79.17 219 | 89.63 77 | 96.57 93 | 55.35 283 | 98.22 112 | 89.52 77 | 89.54 132 | 98.74 23 |
|
FMVSNet2 | | | 82.79 212 | 80.44 221 | 89.83 173 | 92.66 170 | 85.43 37 | 95.42 185 | 94.35 200 | 79.06 221 | 74.46 237 | 87.28 228 | 56.38 278 | 94.31 273 | 69.72 235 | 74.68 239 | 89.76 226 |
|
SMA-MVS | | | 94.64 11 | 94.66 11 | 94.58 21 | 98.02 48 | 85.42 38 | 97.47 61 | 96.74 57 | 85.49 88 | 98.01 1 | 98.70 5 | 82.85 35 | 99.84 12 | 95.79 17 | 98.92 14 | 98.49 35 |
|
nrg030 | | | 86.79 147 | 85.43 144 | 90.87 142 | 88.76 238 | 85.34 39 | 97.06 98 | 94.33 201 | 84.31 120 | 80.45 172 | 91.98 170 | 72.36 149 | 96.36 188 | 88.48 86 | 71.13 252 | 90.93 208 |
|
tfpn200view9 | | | 88.48 113 | 87.15 121 | 92.47 91 | 96.21 82 | 85.30 40 | 97.44 63 | 98.85 2 | 83.37 141 | 83.99 128 | 93.82 148 | 75.36 127 | 97.93 118 | 69.04 237 | 86.24 158 | 94.17 177 |
|
thres400 | | | 88.42 116 | 87.15 121 | 92.23 100 | 96.21 82 | 85.30 40 | 97.44 63 | 98.85 2 | 83.37 141 | 83.99 128 | 93.82 148 | 75.36 127 | 97.93 118 | 69.04 237 | 86.24 158 | 93.45 193 |
|
thres600view7 | | | 88.06 123 | 86.70 130 | 92.15 104 | 96.10 86 | 85.17 42 | 97.14 85 | 98.85 2 | 82.70 152 | 83.41 135 | 93.66 151 | 75.43 122 | 97.82 127 | 67.13 252 | 85.88 163 | 93.45 193 |
|
NCCC | | | 95.63 3 | 95.94 5 | 94.69 20 | 99.21 6 | 85.15 43 | 99.16 3 | 96.96 42 | 94.11 6 | 95.59 12 | 98.64 7 | 85.07 15 | 99.91 3 | 95.61 19 | 99.10 5 | 99.00 14 |
|
test_part2 | | | | | | 98.90 7 | 85.14 44 | | | | 96.07 8 | | | | | | |
|
ESAPD | | | 95.32 5 | 95.52 6 | 94.70 19 | 98.90 7 | 85.14 44 | 98.15 25 | 96.77 53 | 84.95 102 | 96.07 8 | 98.83 2 | 89.33 6 | 99.80 14 | 97.78 2 | 98.95 12 | 99.18 10 |
|
DP-MVS Recon | | | 91.72 59 | 90.85 65 | 94.34 24 | 99.50 1 | 85.00 46 | 98.51 16 | 95.96 125 | 80.57 188 | 88.08 96 | 97.63 56 | 76.84 96 | 99.89 6 | 85.67 105 | 94.88 91 | 98.13 54 |
|
MVS_Test | | | 90.29 83 | 89.18 89 | 93.62 47 | 95.23 112 | 84.93 47 | 94.41 211 | 94.66 187 | 84.31 120 | 90.37 70 | 91.02 184 | 75.13 131 | 97.82 127 | 83.11 133 | 94.42 93 | 98.12 55 |
|
tfpn111 | | | 88.08 122 | 86.70 130 | 92.20 102 | 96.10 86 | 84.90 48 | 97.14 85 | 98.85 2 | 82.69 153 | 83.41 135 | 93.66 151 | 75.43 122 | 97.82 127 | 67.13 252 | 85.88 163 | 93.89 184 |
|
conf200view11 | | | 88.27 120 | 86.95 126 | 92.24 99 | 96.10 86 | 84.90 48 | 97.14 85 | 98.85 2 | 82.69 153 | 83.41 135 | 93.66 151 | 75.43 122 | 97.93 118 | 69.04 237 | 86.24 158 | 93.89 184 |
|
thres100view900 | | | 88.30 118 | 86.95 126 | 92.33 97 | 96.10 86 | 84.90 48 | 97.14 85 | 98.85 2 | 82.69 153 | 83.41 135 | 93.66 151 | 75.43 122 | 97.93 118 | 69.04 237 | 86.24 158 | 94.17 177 |
|
PAPR | | | 92.74 42 | 92.17 51 | 94.45 22 | 98.89 9 | 84.87 51 | 97.20 77 | 96.20 111 | 87.73 57 | 88.40 91 | 98.12 27 | 78.71 74 | 99.76 16 | 87.99 91 | 96.28 75 | 98.74 23 |
|
MVSTER | | | 89.25 97 | 88.92 92 | 90.24 153 | 95.98 91 | 84.66 52 | 96.79 113 | 95.36 155 | 87.19 65 | 80.33 174 | 90.61 191 | 90.02 5 | 95.97 206 | 85.38 108 | 78.64 222 | 90.09 221 |
|
SD-MVS | | | 94.84 9 | 95.02 9 | 94.29 26 | 97.87 54 | 84.61 53 | 97.76 45 | 96.19 113 | 89.59 32 | 96.66 5 | 98.17 22 | 84.33 22 | 99.60 37 | 96.09 12 | 98.50 27 | 98.66 27 |
|
view600 | | | 87.45 134 | 85.98 137 | 91.88 113 | 95.90 93 | 84.52 54 | 96.68 122 | 98.85 2 | 81.85 165 | 82.30 148 | 93.39 155 | 75.44 118 | 97.66 131 | 64.02 271 | 85.36 170 | 93.45 193 |
|
view800 | | | 87.45 134 | 85.98 137 | 91.88 113 | 95.90 93 | 84.52 54 | 96.68 122 | 98.85 2 | 81.85 165 | 82.30 148 | 93.39 155 | 75.44 118 | 97.66 131 | 64.02 271 | 85.36 170 | 93.45 193 |
|
conf0.05thres1000 | | | 87.45 134 | 85.98 137 | 91.88 113 | 95.90 93 | 84.52 54 | 96.68 122 | 98.85 2 | 81.85 165 | 82.30 148 | 93.39 155 | 75.44 118 | 97.66 131 | 64.02 271 | 85.36 170 | 93.45 193 |
|
tfpn | | | 87.45 134 | 85.98 137 | 91.88 113 | 95.90 93 | 84.52 54 | 96.68 122 | 98.85 2 | 81.85 165 | 82.30 148 | 93.39 155 | 75.44 118 | 97.66 131 | 64.02 271 | 85.36 170 | 93.45 193 |
|
EPNet | | | 94.06 23 | 94.15 22 | 93.76 39 | 97.27 72 | 84.35 58 | 98.29 20 | 97.64 17 | 94.57 4 | 95.36 13 | 96.88 85 | 79.96 61 | 99.12 79 | 91.30 57 | 96.11 77 | 97.82 78 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
IB-MVS | | 85.34 4 | 88.67 109 | 87.14 123 | 93.26 62 | 93.12 165 | 84.32 59 | 98.76 10 | 97.27 20 | 87.19 65 | 79.36 188 | 90.45 193 | 83.92 28 | 98.53 102 | 84.41 114 | 69.79 269 | 96.93 121 |
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 |
Regformer-1 | | | 94.00 25 | 94.04 23 | 93.87 36 | 98.41 34 | 84.29 60 | 97.43 66 | 97.04 36 | 89.50 33 | 92.75 44 | 98.13 24 | 82.60 37 | 99.26 61 | 93.55 34 | 96.99 65 | 98.06 58 |
|
ACMMP_Plus | | | 93.46 32 | 93.23 32 | 94.17 29 | 97.16 73 | 84.28 61 | 96.82 112 | 96.65 67 | 86.24 72 | 94.27 28 | 97.99 37 | 77.94 83 | 99.83 13 | 93.39 36 | 98.57 25 | 98.39 38 |
|
TSAR-MVS + MP. | | | 94.79 10 | 95.17 8 | 93.64 45 | 97.66 56 | 84.10 62 | 95.85 173 | 96.42 92 | 91.26 17 | 97.49 2 | 96.80 89 | 86.50 11 | 98.49 104 | 95.54 20 | 99.03 7 | 98.33 40 |
|
MSLP-MVS++ | | | 94.28 16 | 94.39 17 | 93.97 33 | 98.30 40 | 84.06 63 | 98.64 13 | 96.93 45 | 90.71 22 | 93.08 40 | 98.70 5 | 79.98 60 | 99.21 66 | 94.12 31 | 99.07 6 | 98.63 29 |
|
CDPH-MVS | | | 93.12 36 | 92.91 38 | 93.74 40 | 98.65 20 | 83.88 64 | 97.67 50 | 96.26 107 | 83.00 148 | 93.22 39 | 98.24 16 | 81.31 47 | 99.21 66 | 89.12 79 | 98.74 20 | 98.14 53 |
|
PVSNet_BlendedMVS | | | 90.05 86 | 89.96 75 | 90.33 151 | 97.47 62 | 83.86 65 | 98.02 31 | 96.73 58 | 87.98 51 | 89.53 79 | 89.61 204 | 76.42 102 | 99.57 40 | 94.29 29 | 79.59 213 | 87.57 274 |
|
PVSNet_Blended | | | 93.13 35 | 92.98 37 | 93.57 49 | 97.47 62 | 83.86 65 | 99.32 1 | 96.73 58 | 91.02 20 | 89.53 79 | 96.21 97 | 76.42 102 | 99.57 40 | 94.29 29 | 95.81 84 | 97.29 111 |
|
sss | | | 90.87 72 | 89.96 75 | 93.60 48 | 94.15 143 | 83.84 67 | 97.14 85 | 98.13 13 | 85.93 78 | 89.68 75 | 96.09 98 | 71.67 154 | 99.30 58 | 87.69 93 | 89.16 134 | 97.66 87 |
|
Regformer-2 | | | 93.92 26 | 94.01 24 | 93.67 44 | 98.41 34 | 83.75 68 | 97.43 66 | 97.00 38 | 89.43 35 | 92.69 45 | 98.13 24 | 82.48 38 | 99.22 64 | 93.51 35 | 96.99 65 | 98.04 59 |
|
TEST9 | | | | | | 98.64 21 | 83.71 69 | 97.82 37 | 96.65 67 | 84.29 122 | 95.16 15 | 98.09 29 | 84.39 21 | 99.36 56 | | | |
|
train_agg | | | 94.28 16 | 94.45 15 | 93.74 40 | 98.64 21 | 83.71 69 | 97.82 37 | 96.65 67 | 84.50 114 | 95.16 15 | 98.09 29 | 84.33 22 | 99.36 56 | 95.91 15 | 98.96 10 | 98.16 50 |
|
ab-mvs | | | 87.08 140 | 84.94 155 | 93.48 56 | 93.34 160 | 83.67 71 | 88.82 291 | 95.70 137 | 81.18 174 | 84.55 124 | 90.14 199 | 62.72 226 | 98.94 92 | 85.49 107 | 82.54 204 | 97.85 76 |
|
test_8 | | | | | | 98.63 23 | 83.64 72 | 97.81 39 | 96.63 73 | 84.50 114 | 95.10 17 | 98.11 28 | 84.33 22 | 99.23 62 | | | |
|
CHOSEN 1792x2688 | | | 91.07 70 | 90.21 71 | 93.64 45 | 95.18 114 | 83.53 73 | 96.26 153 | 96.13 115 | 88.92 38 | 84.90 117 | 93.10 163 | 72.86 146 | 99.62 36 | 88.86 80 | 95.67 85 | 97.79 80 |
|
Effi-MVS+ | | | 90.70 73 | 89.90 78 | 93.09 69 | 93.61 155 | 83.48 74 | 95.20 189 | 92.79 264 | 83.22 143 | 91.82 50 | 95.70 104 | 71.82 153 | 97.48 145 | 91.25 58 | 93.67 102 | 98.32 41 |
|
VPNet | | | 84.69 182 | 82.92 187 | 90.01 164 | 89.01 236 | 83.45 75 | 96.71 119 | 95.46 149 | 85.71 82 | 79.65 180 | 92.18 169 | 56.66 274 | 96.01 205 | 83.05 134 | 67.84 286 | 90.56 210 |
|
APDe-MVS | | | 94.56 13 | 94.75 10 | 93.96 34 | 98.84 11 | 83.40 76 | 98.04 30 | 96.41 93 | 85.79 80 | 95.00 20 | 98.28 15 | 84.32 25 | 99.18 73 | 97.35 5 | 98.77 18 | 99.28 5 |
|
APD-MVS | | | 93.61 30 | 93.59 27 | 93.69 43 | 98.76 12 | 83.26 77 | 97.21 75 | 96.09 118 | 82.41 158 | 94.65 25 | 98.21 17 | 81.96 40 | 98.81 96 | 94.65 26 | 98.36 36 | 99.01 13 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
DI_MVS_plusplus_test | | | 85.92 158 | 83.61 179 | 92.86 76 | 86.43 268 | 83.20 78 | 95.57 181 | 95.46 149 | 85.10 100 | 65.99 284 | 86.84 240 | 56.70 272 | 97.89 125 | 88.10 90 | 92.33 115 | 97.48 100 |
|
agg_prior1 | | | 94.10 21 | 94.31 19 | 93.48 56 | 98.59 26 | 83.13 79 | 97.77 42 | 96.56 78 | 84.38 118 | 94.19 29 | 98.13 24 | 84.66 19 | 99.16 75 | 95.74 18 | 98.74 20 | 98.15 52 |
|
agg_prior | | | | | | 98.59 26 | 83.13 79 | | 96.56 78 | | 94.19 29 | | | 99.16 75 | | | |
|
PCF-MVS | | 84.09 5 | 86.77 148 | 85.00 153 | 92.08 105 | 92.06 190 | 83.07 81 | 92.14 265 | 94.47 197 | 79.63 211 | 76.90 214 | 94.78 133 | 71.15 160 | 99.20 70 | 72.87 208 | 91.05 126 | 93.98 182 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
TSAR-MVS + GP. | | | 94.35 15 | 94.50 13 | 93.89 35 | 97.38 69 | 83.04 82 | 98.10 28 | 95.29 160 | 91.57 15 | 93.81 33 | 97.45 63 | 86.64 9 | 99.43 52 | 96.28 11 | 94.01 97 | 99.20 8 |
|
agg_prior3 | | | 94.10 21 | 94.29 21 | 93.53 53 | 98.62 24 | 83.03 83 | 97.80 41 | 96.64 70 | 84.28 123 | 95.01 19 | 98.03 33 | 83.40 31 | 99.41 53 | 95.91 15 | 98.96 10 | 98.16 50 |
|
test_normal | | | 85.83 160 | 83.51 181 | 92.78 80 | 86.33 273 | 83.01 84 | 95.56 183 | 95.46 149 | 85.11 99 | 65.73 286 | 86.63 245 | 56.62 275 | 97.86 126 | 87.87 92 | 92.29 116 | 97.47 101 |
|
Regformer-3 | | | 93.19 34 | 93.19 33 | 93.19 65 | 98.10 45 | 83.01 84 | 97.08 96 | 96.98 40 | 88.98 37 | 91.35 60 | 97.89 44 | 80.80 50 | 99.23 62 | 92.30 50 | 95.20 87 | 97.32 106 |
|
API-MVS | | | 90.18 84 | 88.97 90 | 93.80 38 | 98.66 18 | 82.95 86 | 97.50 60 | 95.63 141 | 75.16 263 | 86.31 108 | 97.69 50 | 72.49 148 | 99.90 4 | 81.26 142 | 96.07 78 | 98.56 32 |
|
MVS_111021_HR | | | 93.41 33 | 93.39 30 | 93.47 59 | 97.34 70 | 82.83 87 | 97.56 56 | 98.27 12 | 89.16 36 | 89.71 74 | 97.14 76 | 79.77 62 | 99.56 42 | 93.65 33 | 97.94 47 | 98.02 61 |
|
diffmvs | | | 87.96 127 | 86.47 133 | 92.42 93 | 94.26 140 | 82.70 88 | 92.79 253 | 94.03 220 | 77.94 229 | 88.99 86 | 89.98 201 | 70.72 166 | 97.56 138 | 77.75 164 | 91.80 122 | 96.98 118 |
|
CHOSEN 280x420 | | | 91.71 60 | 91.85 52 | 91.29 128 | 94.94 120 | 82.69 89 | 87.89 299 | 96.17 114 | 85.94 77 | 87.27 102 | 94.31 139 | 90.27 4 | 95.65 232 | 94.04 32 | 95.86 82 | 95.53 157 |
|
VPA-MVSNet | | | 85.32 173 | 83.83 174 | 89.77 175 | 90.25 217 | 82.63 90 | 96.36 144 | 97.07 35 | 83.03 147 | 81.21 165 | 89.02 209 | 61.58 237 | 96.31 190 | 85.02 111 | 70.95 254 | 90.36 212 |
|
MP-MVS-pluss | | | 92.58 50 | 92.35 47 | 93.29 61 | 97.30 71 | 82.53 91 | 96.44 137 | 96.04 122 | 84.68 109 | 89.12 84 | 98.37 12 | 77.48 88 | 99.74 21 | 93.31 40 | 98.38 34 | 97.59 93 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
PatchFormer-LS_test | | | 90.14 85 | 89.30 88 | 92.65 87 | 95.43 106 | 82.46 92 | 93.46 232 | 96.35 101 | 88.56 41 | 84.82 118 | 95.22 118 | 84.63 20 | 97.55 140 | 78.40 162 | 86.81 152 | 97.94 71 |
|
PVSNet_Blended_VisFu | | | 91.24 68 | 90.77 67 | 92.66 85 | 95.09 116 | 82.40 93 | 97.77 42 | 95.87 131 | 88.26 47 | 86.39 107 | 93.94 146 | 76.77 98 | 99.27 59 | 88.80 82 | 94.00 98 | 96.31 143 |
|
test_prior4 | | | | | | | 82.34 94 | 97.75 46 | | | | | | | | | |
|
PatchmatchNet | | | 86.83 145 | 85.12 150 | 91.95 110 | 94.12 144 | 82.27 95 | 86.55 311 | 95.64 140 | 84.59 112 | 82.98 143 | 84.99 273 | 77.26 90 | 95.96 210 | 68.61 245 | 91.34 125 | 97.64 89 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
EPMVS | | | 87.47 133 | 85.90 142 | 92.18 103 | 95.41 108 | 82.26 96 | 87.00 307 | 96.28 106 | 85.88 79 | 84.23 126 | 85.57 263 | 75.07 133 | 96.26 192 | 71.14 225 | 92.50 111 | 98.03 60 |
|
Regformer-4 | | | 93.06 37 | 93.12 34 | 92.89 75 | 98.10 45 | 82.20 97 | 97.08 96 | 96.92 47 | 88.87 39 | 91.23 62 | 97.89 44 | 80.57 52 | 99.19 71 | 92.21 52 | 95.20 87 | 97.29 111 |
|
DWT-MVSNet_test | | | 90.52 80 | 89.80 81 | 92.70 84 | 95.73 101 | 82.20 97 | 93.69 227 | 96.55 80 | 88.34 45 | 87.04 105 | 95.34 111 | 86.53 10 | 97.55 140 | 76.32 186 | 88.66 140 | 98.34 39 |
|
GBi-Net | | | 82.42 217 | 80.43 222 | 88.39 196 | 92.66 170 | 81.95 99 | 94.30 215 | 93.38 250 | 79.06 221 | 75.82 229 | 85.66 259 | 56.38 278 | 93.84 281 | 71.23 222 | 75.38 235 | 89.38 232 |
|
test1 | | | 82.42 217 | 80.43 222 | 88.39 196 | 92.66 170 | 81.95 99 | 94.30 215 | 93.38 250 | 79.06 221 | 75.82 229 | 85.66 259 | 56.38 278 | 93.84 281 | 71.23 222 | 75.38 235 | 89.38 232 |
|
FMVSNet1 | | | 79.50 243 | 76.54 254 | 88.39 196 | 88.47 243 | 81.95 99 | 94.30 215 | 93.38 250 | 73.14 284 | 72.04 255 | 85.66 259 | 43.86 313 | 93.84 281 | 65.48 264 | 72.53 249 | 89.38 232 |
|
test_prior3 | | | 94.03 24 | 94.34 18 | 93.09 69 | 98.68 15 | 81.91 102 | 98.37 18 | 96.40 95 | 86.08 75 | 94.57 26 | 98.02 34 | 83.14 33 | 99.06 82 | 95.05 21 | 98.79 16 | 98.29 44 |
|
test_prior | | | | | 93.09 69 | 98.68 15 | 81.91 102 | | 96.40 95 | | | | | 99.06 82 | | | 98.29 44 |
|
DeepC-MVS | | 86.58 3 | 91.53 64 | 91.06 64 | 92.94 74 | 94.52 135 | 81.89 104 | 95.95 164 | 95.98 124 | 90.76 21 | 83.76 133 | 96.76 90 | 73.24 144 | 99.71 24 | 91.67 55 | 96.96 67 | 97.22 115 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
Patchmatch-test1 | | | 84.89 179 | 82.76 191 | 91.27 129 | 92.30 179 | 81.86 105 | 92.88 249 | 95.56 143 | 84.85 105 | 82.52 144 | 85.19 268 | 58.04 259 | 94.21 275 | 65.93 262 | 87.58 149 | 97.74 82 |
|
VDDNet | | | 86.44 152 | 84.51 159 | 92.22 101 | 91.56 199 | 81.83 106 | 97.10 93 | 94.64 190 | 69.50 303 | 87.84 97 | 95.19 121 | 48.01 305 | 97.92 124 | 89.82 73 | 86.92 150 | 96.89 123 |
|
PAPM_NR | | | 91.46 65 | 90.82 66 | 93.37 60 | 98.50 31 | 81.81 107 | 95.03 200 | 96.13 115 | 84.65 110 | 86.10 111 | 97.65 55 | 79.24 67 | 99.75 19 | 83.20 131 | 96.88 70 | 98.56 32 |
|
PHI-MVS | | | 93.59 31 | 93.63 26 | 93.48 56 | 98.05 47 | 81.76 108 | 98.64 13 | 97.13 24 | 82.60 156 | 94.09 32 | 98.49 10 | 80.35 53 | 99.85 10 | 94.74 25 | 98.62 24 | 98.83 19 |
|
Test4 | | | 82.30 221 | 79.15 236 | 91.78 119 | 81.84 311 | 81.74 109 | 94.04 221 | 94.20 205 | 84.86 104 | 59.75 317 | 83.88 281 | 37.14 329 | 96.28 191 | 84.60 113 | 92.00 119 | 97.30 109 |
|
114514_t | | | 88.79 107 | 87.57 112 | 92.45 92 | 98.21 42 | 81.74 109 | 96.99 100 | 95.45 152 | 75.16 263 | 82.48 145 | 95.69 105 | 68.59 176 | 98.50 103 | 80.33 145 | 95.18 89 | 97.10 116 |
|
MDTV_nov1_ep13_2view | | | | | | | 81.74 109 | 86.80 308 | | 80.65 186 | 85.65 112 | | 74.26 139 | | 76.52 184 | | 96.98 118 |
|
1121 | | | 90.66 74 | 89.82 80 | 93.16 66 | 97.39 66 | 81.71 112 | 93.33 236 | 96.66 66 | 74.45 276 | 91.38 56 | 97.55 61 | 79.27 65 | 99.52 44 | 79.95 150 | 98.43 30 | 98.26 47 |
|
mvs_anonymous | | | 88.68 108 | 87.62 110 | 91.86 117 | 94.80 123 | 81.69 113 | 93.53 231 | 94.92 171 | 82.03 163 | 78.87 192 | 90.43 194 | 75.77 112 | 95.34 248 | 85.04 110 | 93.16 108 | 98.55 34 |
|
新几何1 | | | | | 93.12 67 | 97.44 64 | 81.60 114 | | 96.71 61 | 74.54 275 | 91.22 63 | 97.57 57 | 79.13 69 | 99.51 47 | 77.40 174 | 98.46 28 | 98.26 47 |
|
PVSNet | | 82.34 9 | 89.02 99 | 87.79 105 | 92.71 83 | 95.49 105 | 81.50 115 | 97.70 48 | 97.29 19 | 87.76 56 | 85.47 113 | 95.12 127 | 56.90 270 | 98.90 94 | 80.33 145 | 94.02 96 | 97.71 84 |
|
tfpn_ndepth | | | 87.25 139 | 86.00 136 | 91.01 138 | 95.86 97 | 81.46 116 | 96.53 129 | 97.09 33 | 77.35 238 | 81.36 162 | 95.07 129 | 84.74 18 | 95.86 215 | 60.88 286 | 85.14 176 | 95.72 153 |
|
XXY-MVS | | | 83.84 191 | 82.00 197 | 89.35 179 | 87.13 254 | 81.38 117 | 95.72 176 | 94.26 203 | 80.15 200 | 75.92 228 | 90.63 190 | 61.96 235 | 96.52 183 | 78.98 158 | 73.28 246 | 90.14 217 |
|
SteuartSystems-ACMMP | | | 94.13 20 | 94.44 16 | 93.20 64 | 95.41 108 | 81.35 118 | 99.02 7 | 96.59 76 | 89.50 33 | 94.18 31 | 98.36 13 | 83.68 30 | 99.45 51 | 94.77 23 | 98.45 29 | 98.81 20 |
Skip Steuart: Steuart Systems R&D Blog. |
NR-MVSNet | | | 83.35 203 | 81.52 209 | 88.84 187 | 88.76 238 | 81.31 119 | 94.45 210 | 95.16 163 | 84.65 110 | 67.81 275 | 90.82 187 | 70.36 168 | 94.87 262 | 74.75 199 | 66.89 293 | 90.33 214 |
|
EI-MVSNet-Vis-set | | | 91.84 58 | 91.77 55 | 92.04 108 | 97.60 58 | 81.17 120 | 96.61 126 | 96.87 49 | 88.20 48 | 89.19 83 | 97.55 61 | 78.69 75 | 99.14 77 | 90.29 68 | 90.94 127 | 95.80 150 |
|
HFP-MVS | | | 92.89 40 | 92.86 39 | 92.98 72 | 98.71 13 | 81.12 121 | 97.58 54 | 96.70 62 | 85.20 95 | 91.75 51 | 97.97 41 | 78.47 76 | 99.71 24 | 90.95 60 | 98.41 31 | 98.12 55 |
|
#test# | | | 92.99 38 | 92.99 36 | 92.98 72 | 98.71 13 | 81.12 121 | 97.77 42 | 96.70 62 | 85.75 81 | 91.75 51 | 97.97 41 | 78.47 76 | 99.71 24 | 91.36 56 | 98.41 31 | 98.12 55 |
|
MDTV_nov1_ep13 | | | | 83.69 175 | | 94.09 145 | 81.01 123 | 86.78 309 | 96.09 118 | 83.81 134 | 84.75 120 | 84.32 277 | 74.44 138 | 96.54 182 | 63.88 275 | 85.07 177 | |
|
1112_ss | | | 88.60 112 | 87.47 115 | 92.00 109 | 93.21 161 | 80.97 124 | 96.47 132 | 92.46 267 | 83.64 138 | 80.86 167 | 97.30 71 | 80.24 56 | 97.62 136 | 77.60 171 | 85.49 168 | 97.40 103 |
|
CDS-MVSNet | | | 89.50 92 | 88.96 91 | 91.14 134 | 91.94 196 | 80.93 125 | 97.09 94 | 95.81 133 | 84.26 124 | 84.72 121 | 94.20 141 | 80.31 54 | 95.64 233 | 83.37 130 | 88.96 136 | 96.85 125 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
Test_1112_low_res | | | 88.03 124 | 86.73 129 | 91.94 111 | 93.15 163 | 80.88 126 | 96.44 137 | 92.41 268 | 83.59 140 | 80.74 169 | 91.16 182 | 80.18 57 | 97.59 137 | 77.48 173 | 85.40 169 | 97.36 105 |
|
zzz-MVS | | | 92.74 42 | 92.71 40 | 92.86 76 | 97.90 50 | 80.85 127 | 96.47 132 | 96.33 103 | 87.92 52 | 90.20 71 | 98.18 18 | 76.71 100 | 99.76 16 | 92.57 48 | 98.09 41 | 97.96 69 |
|
MTAPA | | | 92.45 52 | 92.31 48 | 92.86 76 | 97.90 50 | 80.85 127 | 92.88 249 | 96.33 103 | 87.92 52 | 90.20 71 | 98.18 18 | 76.71 100 | 99.76 16 | 92.57 48 | 98.09 41 | 97.96 69 |
|
HyFIR lowres test | | | 89.36 94 | 88.60 95 | 91.63 123 | 94.91 122 | 80.76 129 | 95.60 180 | 95.53 144 | 82.56 157 | 84.03 127 | 91.24 181 | 78.03 82 | 96.81 176 | 87.07 99 | 88.41 142 | 97.32 106 |
|
EI-MVSNet-UG-set | | | 91.35 67 | 91.22 61 | 91.73 120 | 97.39 66 | 80.68 130 | 96.47 132 | 96.83 51 | 87.92 52 | 88.30 94 | 97.36 68 | 77.84 85 | 99.13 78 | 89.43 78 | 89.45 133 | 95.37 160 |
|
MIMVSNet | | | 79.18 248 | 75.99 257 | 88.72 191 | 87.37 253 | 80.66 131 | 79.96 325 | 91.82 274 | 77.38 237 | 74.33 238 | 81.87 295 | 41.78 321 | 90.74 317 | 66.36 261 | 83.10 190 | 94.76 172 |
|
CSCG | | | 92.02 55 | 91.65 57 | 93.12 67 | 98.53 28 | 80.59 132 | 97.47 61 | 97.18 23 | 77.06 243 | 84.64 123 | 97.98 39 | 83.98 27 | 99.52 44 | 90.72 64 | 97.33 61 | 99.23 7 |
|
ACMMPR | | | 92.69 46 | 92.67 43 | 92.75 81 | 98.66 18 | 80.57 133 | 97.58 54 | 96.69 64 | 85.20 95 | 91.57 54 | 97.92 43 | 77.01 94 | 99.67 32 | 90.95 60 | 98.41 31 | 98.00 66 |
|
UniMVSNet (Re) | | | 85.31 174 | 84.23 171 | 88.55 193 | 89.75 225 | 80.55 134 | 96.72 117 | 96.89 48 | 85.42 89 | 78.40 194 | 88.93 210 | 75.38 126 | 95.52 240 | 78.58 160 | 68.02 284 | 89.57 228 |
|
CLD-MVS | | | 87.97 126 | 87.48 114 | 89.44 178 | 92.16 186 | 80.54 135 | 98.14 27 | 94.92 171 | 91.41 16 | 79.43 187 | 95.40 110 | 62.34 228 | 97.27 154 | 90.60 65 | 82.90 200 | 90.50 211 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
region2R | | | 92.72 45 | 92.70 42 | 92.79 79 | 98.68 15 | 80.53 136 | 97.53 58 | 96.51 83 | 85.22 93 | 91.94 49 | 97.98 39 | 77.26 90 | 99.67 32 | 90.83 63 | 98.37 35 | 98.18 49 |
|
tfpn1000 | | | 86.43 153 | 85.10 151 | 90.41 149 | 95.56 103 | 80.51 137 | 95.90 169 | 97.09 33 | 75.91 252 | 80.02 178 | 94.82 132 | 84.78 17 | 95.47 243 | 57.36 295 | 84.46 179 | 95.26 163 |
|
pmmvs4 | | | 82.54 215 | 80.79 216 | 87.79 212 | 86.11 282 | 80.49 138 | 93.55 230 | 93.18 257 | 77.29 239 | 73.35 244 | 89.40 206 | 65.26 213 | 95.05 260 | 75.32 195 | 73.61 242 | 87.83 268 |
|
WR-MVS | | | 84.32 186 | 82.96 186 | 88.41 195 | 89.38 234 | 80.32 139 | 96.59 127 | 96.25 108 | 83.97 129 | 76.63 216 | 90.36 195 | 67.53 179 | 94.86 263 | 75.82 191 | 70.09 264 | 90.06 223 |
|
XVS | | | 92.69 46 | 92.71 40 | 92.63 88 | 98.52 29 | 80.29 140 | 97.37 70 | 96.44 90 | 87.04 67 | 91.38 56 | 97.83 47 | 77.24 92 | 99.59 38 | 90.46 66 | 98.07 43 | 98.02 61 |
|
X-MVStestdata | | | 86.26 155 | 84.14 172 | 92.63 88 | 98.52 29 | 80.29 140 | 97.37 70 | 96.44 90 | 87.04 67 | 91.38 56 | 20.73 355 | 77.24 92 | 99.59 38 | 90.46 66 | 98.07 43 | 98.02 61 |
|
GA-MVS | | | 85.79 166 | 84.04 173 | 91.02 137 | 89.47 232 | 80.27 142 | 96.90 109 | 94.84 176 | 85.57 84 | 80.88 166 | 89.08 207 | 56.56 276 | 96.47 185 | 77.72 169 | 85.35 174 | 96.34 140 |
|
BH-RMVSNet | | | 86.84 144 | 85.28 147 | 91.49 124 | 95.35 110 | 80.26 143 | 96.95 106 | 92.21 269 | 82.86 150 | 81.77 161 | 95.46 109 | 59.34 248 | 97.64 135 | 69.79 234 | 93.81 101 | 96.57 134 |
|
FIs | | | 86.73 149 | 86.10 135 | 88.61 192 | 90.05 222 | 80.21 144 | 96.14 157 | 96.95 43 | 85.56 87 | 78.37 195 | 92.30 167 | 76.73 99 | 95.28 251 | 79.51 153 | 79.27 217 | 90.35 213 |
|
TESTMET0.1,1 | | | 89.83 87 | 89.34 87 | 91.31 126 | 92.54 174 | 80.19 145 | 97.11 90 | 96.57 77 | 86.15 73 | 86.85 106 | 91.83 175 | 79.32 63 | 96.95 168 | 81.30 141 | 92.35 114 | 96.77 128 |
|
VDD-MVS | | | 88.28 119 | 87.02 125 | 92.06 107 | 95.09 116 | 80.18 146 | 97.55 57 | 94.45 198 | 83.09 145 | 89.10 85 | 95.92 101 | 47.97 306 | 98.49 104 | 93.08 43 | 86.91 151 | 97.52 97 |
|
HSP-MVS | | | 95.55 4 | 96.51 2 | 92.66 85 | 98.31 39 | 80.10 147 | 97.42 68 | 96.46 88 | 92.20 13 | 97.11 3 | 98.29 14 | 93.46 1 | 99.10 80 | 96.01 13 | 99.30 2 | 98.77 22 |
|
AdaColmap | | | 88.81 105 | 87.61 111 | 92.39 95 | 99.33 4 | 79.95 148 | 96.70 121 | 95.58 142 | 77.51 235 | 83.05 142 | 96.69 92 | 61.90 236 | 99.72 23 | 84.29 115 | 93.47 104 | 97.50 98 |
|
tpmrst | | | 88.36 117 | 87.38 117 | 91.31 126 | 94.36 139 | 79.92 149 | 87.32 303 | 95.26 162 | 85.32 91 | 88.34 92 | 86.13 256 | 80.60 51 | 96.70 180 | 83.78 119 | 85.34 175 | 97.30 109 |
|
CP-MVS | | | 92.54 51 | 92.60 45 | 92.34 96 | 98.50 31 | 79.90 150 | 98.40 17 | 96.40 95 | 84.75 107 | 90.48 69 | 98.09 29 | 77.40 89 | 99.21 66 | 91.15 59 | 98.23 40 | 97.92 72 |
|
ADS-MVSNet | | | 81.26 232 | 78.36 238 | 89.96 168 | 93.78 150 | 79.78 151 | 79.48 326 | 93.60 242 | 73.09 285 | 80.14 176 | 79.99 304 | 62.15 230 | 95.24 253 | 59.49 289 | 83.52 185 | 94.85 170 |
|
CR-MVSNet | | | 83.53 195 | 81.36 212 | 90.06 162 | 90.16 220 | 79.75 152 | 79.02 330 | 91.12 282 | 84.24 125 | 82.27 156 | 80.35 302 | 75.45 116 | 93.67 285 | 63.37 279 | 86.25 156 | 96.75 130 |
|
RPMNet | | | 79.32 246 | 75.75 258 | 90.06 162 | 90.16 220 | 79.75 152 | 79.02 330 | 93.92 225 | 58.43 332 | 82.27 156 | 72.55 330 | 73.03 145 | 93.67 285 | 46.10 331 | 86.25 156 | 96.75 130 |
|
PGM-MVS | | | 91.93 56 | 91.80 54 | 92.32 98 | 98.27 41 | 79.74 154 | 95.28 186 | 97.27 20 | 83.83 133 | 90.89 66 | 97.78 49 | 76.12 108 | 99.56 42 | 88.82 81 | 97.93 49 | 97.66 87 |
|
MP-MVS | | | 92.61 49 | 92.67 43 | 92.42 93 | 98.13 44 | 79.73 155 | 97.33 72 | 96.20 111 | 85.63 83 | 90.53 67 | 97.66 51 | 78.14 81 | 99.70 27 | 92.12 53 | 98.30 38 | 97.85 76 |
|
conf0.01 | | | 85.70 168 | 84.35 164 | 89.77 175 | 94.53 129 | 79.70 156 | 95.17 190 | 97.11 26 | 75.97 246 | 79.44 181 | 95.31 112 | 81.90 41 | 95.73 226 | 56.78 300 | 82.91 194 | 93.89 184 |
|
conf0.002 | | | 85.70 168 | 84.35 164 | 89.77 175 | 94.53 129 | 79.70 156 | 95.17 190 | 97.11 26 | 75.97 246 | 79.44 181 | 95.31 112 | 81.90 41 | 95.73 226 | 56.78 300 | 82.91 194 | 93.89 184 |
|
thresconf0.02 | | | 85.80 161 | 84.35 164 | 90.17 156 | 94.53 129 | 79.70 156 | 95.17 190 | 97.11 26 | 75.97 246 | 79.44 181 | 95.31 112 | 81.90 41 | 95.73 226 | 56.78 300 | 82.91 194 | 95.09 164 |
|
tfpn_n400 | | | 85.80 161 | 84.35 164 | 90.17 156 | 94.53 129 | 79.70 156 | 95.17 190 | 97.11 26 | 75.97 246 | 79.44 181 | 95.31 112 | 81.90 41 | 95.73 226 | 56.78 300 | 82.91 194 | 95.09 164 |
|
tfpnconf | | | 85.80 161 | 84.35 164 | 90.17 156 | 94.53 129 | 79.70 156 | 95.17 190 | 97.11 26 | 75.97 246 | 79.44 181 | 95.31 112 | 81.90 41 | 95.73 226 | 56.78 300 | 82.91 194 | 95.09 164 |
|
tfpnview11 | | | 85.80 161 | 84.35 164 | 90.17 156 | 94.53 129 | 79.70 156 | 95.17 190 | 97.11 26 | 75.97 246 | 79.44 181 | 95.31 112 | 81.90 41 | 95.73 226 | 56.78 300 | 82.91 194 | 95.09 164 |
|
v2v482 | | | 83.46 196 | 81.86 201 | 88.25 201 | 86.19 279 | 79.65 162 | 96.34 146 | 94.02 221 | 81.56 171 | 77.32 208 | 88.23 219 | 65.62 205 | 96.03 201 | 77.77 163 | 69.72 271 | 89.09 236 |
|
gm-plane-assit | | | | | | 92.27 180 | 79.64 163 | | | 84.47 116 | | 95.15 124 | | 97.93 118 | 85.81 104 | | |
|
testing_2 | | | 76.96 273 | 73.18 283 | 88.30 199 | 75.87 332 | 79.64 163 | 89.92 283 | 94.21 204 | 80.16 199 | 51.23 331 | 75.94 322 | 33.94 334 | 95.81 218 | 82.28 137 | 75.12 238 | 89.46 229 |
|
旧先验1 | | | | | | 97.39 66 | 79.58 165 | | 96.54 81 | | | 98.08 32 | 84.00 26 | | | 97.42 58 | 97.62 91 |
|
UniMVSNet_NR-MVSNet | | | 85.49 171 | 84.59 158 | 88.21 202 | 89.44 233 | 79.36 166 | 96.71 119 | 96.41 93 | 85.22 93 | 78.11 197 | 90.98 186 | 76.97 95 | 95.14 255 | 79.14 156 | 68.30 281 | 90.12 219 |
|
DU-MVS | | | 84.57 184 | 83.33 184 | 88.28 200 | 88.76 238 | 79.36 166 | 96.43 141 | 95.41 154 | 85.42 89 | 78.11 197 | 90.82 187 | 67.61 177 | 95.14 255 | 79.14 156 | 68.30 281 | 90.33 214 |
|
CNLPA | | | 86.96 141 | 85.37 146 | 91.72 121 | 97.59 59 | 79.34 168 | 97.21 75 | 91.05 285 | 74.22 277 | 78.90 190 | 96.75 91 | 67.21 183 | 98.95 90 | 74.68 200 | 90.77 128 | 96.88 124 |
|
v1141 | | | 83.36 201 | 81.81 204 | 88.01 205 | 86.61 264 | 79.26 169 | 96.44 137 | 94.12 215 | 80.88 177 | 77.48 204 | 86.87 238 | 67.08 186 | 96.03 201 | 77.14 176 | 69.69 272 | 88.75 248 |
|
tfpnnormal | | | 78.14 256 | 75.42 262 | 86.31 237 | 88.33 245 | 79.24 170 | 94.41 211 | 96.22 110 | 73.51 282 | 69.81 268 | 85.52 265 | 55.43 282 | 95.75 222 | 47.65 329 | 67.86 285 | 83.95 310 |
|
v1 | | | 83.37 200 | 81.82 202 | 88.01 205 | 86.58 266 | 79.24 170 | 96.45 135 | 94.13 212 | 80.88 177 | 77.48 204 | 86.88 237 | 67.15 184 | 96.04 200 | 77.15 175 | 69.67 273 | 88.76 246 |
|
divwei89l23v2f112 | | | 83.36 201 | 81.81 204 | 88.01 205 | 86.60 265 | 79.23 172 | 96.44 137 | 94.12 215 | 80.88 177 | 77.49 202 | 86.87 238 | 67.08 186 | 96.03 201 | 77.14 176 | 69.67 273 | 88.76 246 |
|
HPM-MVS | | | 91.62 62 | 91.53 59 | 91.89 112 | 97.88 53 | 79.22 173 | 96.99 100 | 95.73 136 | 82.07 162 | 89.50 81 | 97.19 75 | 75.59 113 | 98.93 93 | 90.91 62 | 97.94 47 | 97.54 94 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
TAMVS | | | 88.48 113 | 87.79 105 | 90.56 146 | 91.09 206 | 79.18 174 | 96.45 135 | 95.88 130 | 83.64 138 | 83.12 141 | 93.33 159 | 75.94 110 | 95.74 225 | 82.40 136 | 88.27 143 | 96.75 130 |
|
Fast-Effi-MVS+ | | | 87.93 128 | 86.94 128 | 90.92 140 | 94.04 147 | 79.16 175 | 98.26 21 | 93.72 237 | 81.29 173 | 83.94 131 | 92.90 164 | 69.83 171 | 96.68 181 | 76.70 182 | 91.74 123 | 96.93 121 |
|
CostFormer | | | 89.08 98 | 88.39 98 | 91.15 133 | 93.13 164 | 79.15 176 | 88.61 294 | 96.11 117 | 83.14 144 | 89.58 78 | 86.93 236 | 83.83 29 | 96.87 173 | 88.22 89 | 85.92 162 | 97.42 102 |
|
UGNet | | | 87.73 130 | 86.55 132 | 91.27 129 | 95.16 115 | 79.11 177 | 96.35 145 | 96.23 109 | 88.14 49 | 87.83 98 | 90.48 192 | 50.65 295 | 99.09 81 | 80.13 149 | 94.03 95 | 95.60 156 |
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 |
MS-PatchMatch | | | 83.05 206 | 81.82 202 | 86.72 233 | 89.64 228 | 79.10 178 | 94.88 203 | 94.59 193 | 79.70 209 | 70.67 262 | 89.65 203 | 50.43 297 | 96.82 175 | 70.82 229 | 95.99 81 | 84.25 306 |
|
V42 | | | 83.04 207 | 81.53 208 | 87.57 220 | 86.27 277 | 79.09 179 | 95.87 171 | 94.11 217 | 80.35 194 | 77.22 210 | 86.79 243 | 65.32 212 | 96.02 204 | 77.74 165 | 70.14 260 | 87.61 273 |
|
v1144 | | | 82.90 211 | 81.27 213 | 87.78 213 | 86.29 275 | 79.07 180 | 96.14 157 | 93.93 224 | 80.05 202 | 77.38 206 | 86.80 242 | 65.50 206 | 95.93 212 | 75.21 196 | 70.13 261 | 88.33 260 |
|
v1neww | | | 83.45 197 | 81.95 198 | 87.95 208 | 86.66 258 | 79.04 181 | 96.32 147 | 94.17 208 | 80.76 181 | 77.56 200 | 87.25 231 | 67.02 189 | 96.08 197 | 77.73 166 | 70.07 265 | 88.74 250 |
|
v7new | | | 83.45 197 | 81.95 198 | 87.95 208 | 86.66 258 | 79.04 181 | 96.32 147 | 94.17 208 | 80.76 181 | 77.56 200 | 87.25 231 | 67.02 189 | 96.08 197 | 77.73 166 | 70.07 265 | 88.74 250 |
|
v8 | | | 81.88 225 | 80.06 228 | 87.32 224 | 86.63 261 | 79.04 181 | 94.41 211 | 93.65 240 | 78.77 225 | 73.19 247 | 85.57 263 | 66.87 192 | 95.81 218 | 73.84 206 | 67.61 288 | 87.11 281 |
|
v6 | | | 83.45 197 | 81.94 200 | 87.95 208 | 86.62 262 | 79.03 184 | 96.32 147 | 94.17 208 | 80.76 181 | 77.57 199 | 87.23 233 | 67.03 188 | 96.09 196 | 77.73 166 | 70.06 267 | 88.75 248 |
|
v18 | | | 77.96 259 | 75.61 260 | 84.98 251 | 86.66 258 | 79.01 185 | 93.02 247 | 90.94 287 | 75.69 254 | 63.19 296 | 77.62 311 | 67.11 185 | 92.07 299 | 70.05 231 | 56.24 318 | 83.87 311 |
|
v17 | | | 77.79 261 | 75.41 263 | 84.94 252 | 86.53 267 | 78.94 186 | 92.83 252 | 90.88 289 | 75.51 256 | 62.97 301 | 77.50 313 | 66.69 195 | 92.03 301 | 69.80 233 | 56.01 320 | 83.83 312 |
|
v16 | | | 77.84 260 | 75.47 261 | 84.93 253 | 86.62 262 | 78.93 187 | 92.84 251 | 90.89 288 | 75.50 257 | 63.03 300 | 77.54 312 | 66.82 194 | 92.04 300 | 69.82 232 | 56.22 319 | 83.82 313 |
|
v10 | | | 81.43 230 | 79.53 233 | 87.11 227 | 86.38 270 | 78.87 188 | 94.31 214 | 93.43 247 | 77.88 231 | 73.24 246 | 85.26 266 | 65.44 208 | 95.75 222 | 72.14 213 | 67.71 287 | 86.72 286 |
|
v7 | | | 82.99 210 | 81.41 210 | 87.73 214 | 86.41 269 | 78.86 189 | 96.10 160 | 93.98 222 | 79.88 206 | 77.49 202 | 87.11 235 | 65.44 208 | 95.97 206 | 75.69 193 | 70.59 258 | 88.36 258 |
|
v15 | | | 77.52 263 | 75.09 264 | 84.82 255 | 86.37 271 | 78.82 190 | 92.58 255 | 90.78 291 | 75.47 258 | 62.53 303 | 77.17 314 | 66.58 198 | 91.92 302 | 69.18 236 | 55.16 322 | 83.73 314 |
|
Vis-MVSNet | | | 88.67 109 | 87.82 104 | 91.24 131 | 92.68 169 | 78.82 190 | 96.95 106 | 93.85 228 | 87.55 58 | 87.07 104 | 95.13 126 | 63.43 223 | 97.21 156 | 77.58 172 | 96.15 76 | 97.70 85 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
TranMVSNet+NR-MVSNet | | | 83.24 205 | 81.71 206 | 87.83 211 | 87.71 250 | 78.81 192 | 96.13 159 | 94.82 177 | 84.52 113 | 76.18 225 | 90.78 189 | 64.07 218 | 94.60 268 | 74.60 201 | 66.59 297 | 90.09 221 |
|
MVS_111021_LR | | | 91.60 63 | 91.64 58 | 91.47 125 | 95.74 100 | 78.79 193 | 96.15 156 | 96.77 53 | 88.49 43 | 88.64 89 | 97.07 80 | 72.33 150 | 99.19 71 | 93.13 42 | 96.48 74 | 96.43 137 |
|
V14 | | | 77.43 265 | 74.99 265 | 84.75 256 | 86.32 274 | 78.67 194 | 92.44 259 | 90.77 292 | 75.28 262 | 62.42 304 | 77.13 315 | 66.40 199 | 91.88 303 | 69.01 241 | 55.14 323 | 83.70 315 |
|
tpm2 | | | 87.35 138 | 86.26 134 | 90.62 145 | 92.93 168 | 78.67 194 | 88.06 298 | 95.99 123 | 79.33 215 | 87.40 99 | 86.43 252 | 80.28 55 | 96.40 186 | 80.23 147 | 85.73 167 | 96.79 126 |
|
mPP-MVS | | | 91.88 57 | 91.82 53 | 92.07 106 | 98.38 36 | 78.63 196 | 97.29 73 | 96.09 118 | 85.12 97 | 88.45 90 | 97.66 51 | 75.53 114 | 99.68 30 | 89.83 72 | 98.02 46 | 97.88 73 |
|
v11 | | | 77.21 268 | 74.72 270 | 84.68 260 | 86.29 275 | 78.62 197 | 92.30 263 | 90.63 297 | 74.86 269 | 62.32 305 | 76.73 320 | 65.49 207 | 91.83 305 | 68.17 248 | 55.72 321 | 83.59 318 |
|
tpmp4_e23 | | | 86.46 151 | 84.95 154 | 90.98 139 | 93.74 153 | 78.60 198 | 88.13 297 | 95.90 129 | 79.65 210 | 85.42 114 | 85.67 258 | 80.08 59 | 97.06 164 | 71.71 217 | 84.26 182 | 97.28 113 |
|
BH-w/o | | | 88.24 121 | 87.47 115 | 90.54 147 | 95.03 119 | 78.54 199 | 97.41 69 | 93.82 229 | 84.08 126 | 78.23 196 | 94.51 138 | 69.34 173 | 97.21 156 | 80.21 148 | 94.58 92 | 95.87 149 |
|
V9 | | | 77.32 267 | 74.87 268 | 84.69 259 | 86.26 278 | 78.52 200 | 92.33 262 | 90.72 293 | 75.11 265 | 62.21 306 | 77.08 317 | 66.19 201 | 91.87 304 | 68.84 242 | 55.06 325 | 83.69 316 |
|
HQP5-MVS | | | | | | | 78.48 201 | | | | | | | | | | |
|
DP-MVS | | | 81.47 229 | 78.28 239 | 91.04 135 | 98.14 43 | 78.48 201 | 95.09 199 | 86.97 321 | 61.14 326 | 71.12 259 | 92.78 166 | 59.59 243 | 99.38 55 | 53.11 315 | 86.61 154 | 95.27 162 |
|
HQP-MVS | | | 87.91 129 | 87.55 113 | 88.98 185 | 92.08 187 | 78.48 201 | 97.63 51 | 94.80 178 | 90.52 24 | 82.30 148 | 94.56 136 | 65.40 210 | 97.32 149 | 87.67 94 | 83.01 191 | 91.13 204 |
|
v1192 | | | 82.31 220 | 80.55 220 | 87.60 217 | 85.94 285 | 78.47 204 | 95.85 173 | 93.80 232 | 79.33 215 | 76.97 213 | 86.51 247 | 63.33 224 | 95.87 214 | 73.11 207 | 70.13 261 | 88.46 255 |
|
v12 | | | 77.20 269 | 74.73 269 | 84.63 263 | 86.15 281 | 78.41 205 | 92.17 264 | 90.71 294 | 74.92 268 | 62.05 308 | 77.00 318 | 65.83 203 | 91.83 305 | 68.69 244 | 55.01 326 | 83.64 317 |
|
test222 | | | | | | 96.15 84 | 78.41 205 | 95.87 171 | 96.46 88 | 71.97 292 | 89.66 76 | 97.45 63 | 76.33 105 | | | 98.24 39 | 98.30 43 |
|
MVP-Stereo | | | 82.65 214 | 81.67 207 | 85.59 245 | 86.10 283 | 78.29 207 | 93.33 236 | 92.82 263 | 77.75 232 | 69.17 273 | 87.98 223 | 59.28 249 | 95.76 221 | 71.77 216 | 96.88 70 | 82.73 324 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
v13 | | | 77.11 272 | 74.63 272 | 84.55 265 | 86.08 284 | 78.27 208 | 92.06 266 | 90.68 296 | 74.73 271 | 61.86 311 | 76.93 319 | 65.73 204 | 91.81 308 | 68.55 247 | 55.07 324 | 83.59 318 |
|
ppachtmachnet_test | | | 77.19 270 | 74.22 278 | 86.13 240 | 85.39 292 | 78.22 209 | 93.98 222 | 91.36 280 | 71.74 295 | 67.11 278 | 84.87 274 | 56.67 273 | 93.37 289 | 52.21 317 | 64.59 300 | 86.80 285 |
|
v144192 | | | 82.43 216 | 80.73 218 | 87.54 221 | 85.81 288 | 78.22 209 | 95.98 162 | 93.78 234 | 79.09 220 | 77.11 211 | 86.49 248 | 64.66 217 | 95.91 213 | 74.20 203 | 69.42 275 | 88.49 253 |
|
NP-MVS | | | | | | 92.04 191 | 78.22 209 | | | | | 94.56 136 | | | | | |
|
ACMMP | | | 90.39 81 | 89.97 74 | 91.64 122 | 97.58 60 | 78.21 212 | 96.78 114 | 96.72 60 | 84.73 108 | 84.72 121 | 97.23 73 | 71.22 159 | 99.63 35 | 88.37 88 | 92.41 113 | 97.08 117 |
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 |
MAR-MVS | | | 90.63 75 | 90.22 70 | 91.86 117 | 98.47 33 | 78.20 213 | 97.18 79 | 96.61 74 | 83.87 132 | 88.18 95 | 98.18 18 | 68.71 175 | 99.75 19 | 83.66 125 | 97.15 63 | 97.63 90 |
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 |
tpm cat1 | | | 83.63 194 | 81.38 211 | 90.39 150 | 93.53 158 | 78.19 214 | 85.56 317 | 95.09 164 | 70.78 299 | 78.51 193 | 83.28 290 | 74.80 135 | 97.03 165 | 66.77 255 | 84.05 183 | 95.95 146 |
|
原ACMM1 | | | | | 91.22 132 | 97.77 55 | 78.10 215 | | 96.61 74 | 81.05 176 | 91.28 61 | 97.42 67 | 77.92 84 | 98.98 87 | 79.85 152 | 98.51 26 | 96.59 133 |
|
FC-MVSNet-test | | | 85.96 157 | 85.39 145 | 87.66 216 | 89.38 234 | 78.02 216 | 95.65 179 | 96.87 49 | 85.12 97 | 77.34 207 | 91.94 173 | 76.28 106 | 94.74 265 | 77.09 178 | 78.82 220 | 90.21 216 |
|
dp | | | 84.30 187 | 82.31 196 | 90.28 152 | 94.24 142 | 77.97 217 | 86.57 310 | 95.53 144 | 79.94 205 | 80.75 168 | 85.16 270 | 71.49 158 | 96.39 187 | 63.73 276 | 83.36 188 | 96.48 136 |
|
tpmvs | | | 83.04 207 | 80.77 217 | 89.84 172 | 95.43 106 | 77.96 218 | 85.59 316 | 95.32 158 | 75.31 261 | 76.27 223 | 83.70 286 | 73.89 140 | 97.41 146 | 59.53 288 | 81.93 205 | 94.14 179 |
|
HQP_MVS | | | 87.50 132 | 87.09 124 | 88.74 190 | 91.86 197 | 77.96 218 | 97.18 79 | 94.69 183 | 89.89 30 | 81.33 163 | 94.15 142 | 64.77 215 | 97.30 151 | 87.08 97 | 82.82 201 | 90.96 206 |
|
plane_prior | | | | | | | 77.96 218 | 97.52 59 | | 90.36 27 | | | | | | 82.96 193 | |
|
v1921920 | | | 82.02 224 | 80.23 224 | 87.41 223 | 85.62 289 | 77.92 221 | 95.79 175 | 93.69 238 | 78.86 224 | 76.67 215 | 86.44 250 | 62.50 227 | 95.83 217 | 72.69 209 | 69.77 270 | 88.47 254 |
|
plane_prior6 | | | | | | 91.98 192 | 77.92 221 | | | | | | 64.77 215 | | | | |
|
OMC-MVS | | | 88.80 106 | 88.16 99 | 90.72 143 | 95.30 111 | 77.92 221 | 94.81 204 | 94.51 194 | 86.80 69 | 84.97 116 | 96.85 86 | 67.53 179 | 98.60 98 | 85.08 109 | 87.62 147 | 95.63 155 |
|
OPM-MVS | | | 85.84 159 | 85.10 151 | 88.06 203 | 88.34 244 | 77.83 224 | 95.72 176 | 94.20 205 | 87.89 55 | 80.45 172 | 94.05 144 | 58.57 253 | 97.26 155 | 83.88 118 | 82.76 203 | 89.09 236 |
|
plane_prior3 | | | | | | | 77.75 225 | | | 90.17 28 | 81.33 163 | | | | | | |
|
v1240 | | | 81.70 227 | 79.83 231 | 87.30 226 | 85.50 290 | 77.70 226 | 95.48 184 | 93.44 245 | 78.46 228 | 76.53 218 | 86.44 250 | 60.85 239 | 95.84 216 | 71.59 219 | 70.17 259 | 88.35 259 |
|
TR-MVS | | | 86.30 154 | 84.93 156 | 90.42 148 | 94.63 126 | 77.58 227 | 96.57 128 | 93.82 229 | 80.30 195 | 82.42 147 | 95.16 123 | 58.74 252 | 97.55 140 | 74.88 198 | 87.82 146 | 96.13 145 |
|
plane_prior7 | | | | | | 91.86 197 | 77.55 228 | | | | | | | | | | |
|
BH-untuned | | | 86.95 142 | 85.94 141 | 89.99 165 | 94.52 135 | 77.46 229 | 96.78 114 | 93.37 253 | 81.80 169 | 76.62 217 | 93.81 150 | 66.64 196 | 97.02 166 | 76.06 188 | 93.88 100 | 95.48 158 |
|
EI-MVSNet | | | 85.80 161 | 85.20 148 | 87.59 218 | 91.55 200 | 77.41 230 | 95.13 196 | 95.36 155 | 80.43 192 | 80.33 174 | 94.71 134 | 73.72 142 | 95.97 206 | 76.96 181 | 78.64 222 | 89.39 230 |
|
IterMVS-LS | | | 83.93 190 | 82.80 190 | 87.31 225 | 91.46 203 | 77.39 231 | 95.66 178 | 93.43 247 | 80.44 190 | 75.51 232 | 87.26 230 | 73.72 142 | 95.16 254 | 76.99 179 | 70.72 256 | 89.39 230 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
HPM-MVS_fast | | | 90.38 82 | 90.17 72 | 91.03 136 | 97.61 57 | 77.35 232 | 97.15 84 | 95.48 148 | 79.51 212 | 88.79 87 | 96.90 83 | 71.64 156 | 98.81 96 | 87.01 100 | 97.44 56 | 96.94 120 |
|
MSDG | | | 80.62 238 | 77.77 243 | 89.14 181 | 93.43 159 | 77.24 233 | 91.89 269 | 90.18 299 | 69.86 302 | 68.02 274 | 91.94 173 | 52.21 293 | 98.84 95 | 59.32 291 | 83.12 189 | 91.35 203 |
|
test-LLR | | | 88.48 113 | 87.98 101 | 89.98 166 | 92.26 181 | 77.23 234 | 97.11 90 | 95.96 125 | 83.76 135 | 86.30 109 | 91.38 178 | 72.30 151 | 96.78 178 | 80.82 143 | 91.92 120 | 95.94 147 |
|
test-mter | | | 88.95 100 | 88.60 95 | 89.98 166 | 92.26 181 | 77.23 234 | 97.11 90 | 95.96 125 | 85.32 91 | 86.30 109 | 91.38 178 | 76.37 104 | 96.78 178 | 80.82 143 | 91.92 120 | 95.94 147 |
|
UA-Net | | | 88.92 102 | 88.48 97 | 90.24 153 | 94.06 146 | 77.18 236 | 93.04 246 | 94.66 187 | 87.39 60 | 91.09 64 | 93.89 147 | 74.92 134 | 98.18 115 | 75.83 190 | 91.43 124 | 95.35 161 |
|
pmmvs5 | | | 81.34 231 | 79.54 232 | 86.73 232 | 85.02 296 | 76.91 237 | 96.22 154 | 91.65 276 | 77.65 233 | 73.55 241 | 88.61 213 | 55.70 281 | 94.43 271 | 74.12 204 | 73.35 245 | 88.86 245 |
|
IS-MVSNet | | | 88.67 109 | 88.16 99 | 90.20 155 | 93.61 155 | 76.86 238 | 96.77 116 | 93.07 261 | 84.02 128 | 83.62 134 | 95.60 108 | 74.69 137 | 96.24 194 | 78.43 161 | 93.66 103 | 97.49 99 |
|
v148 | | | 82.41 219 | 80.89 215 | 86.99 229 | 86.18 280 | 76.81 239 | 96.27 152 | 93.82 229 | 80.49 189 | 75.28 235 | 86.11 257 | 67.32 182 | 95.75 222 | 75.48 194 | 67.03 292 | 88.42 257 |
|
PVSNet_0 | | 77.72 15 | 81.70 227 | 78.95 237 | 89.94 169 | 90.77 211 | 76.72 240 | 95.96 163 | 96.95 43 | 85.01 101 | 70.24 267 | 88.53 216 | 52.32 292 | 98.20 113 | 86.68 102 | 44.08 342 | 94.89 169 |
|
PLC | | 83.97 7 | 88.00 125 | 87.38 117 | 89.83 173 | 98.02 48 | 76.46 241 | 97.16 83 | 94.43 199 | 79.26 218 | 81.98 159 | 96.28 96 | 69.36 172 | 99.27 59 | 77.71 170 | 92.25 117 | 93.77 188 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
ACMH | | 75.40 17 | 77.99 257 | 74.96 266 | 87.10 228 | 90.67 212 | 76.41 242 | 93.19 245 | 91.64 277 | 72.47 291 | 63.44 295 | 87.61 226 | 43.34 316 | 97.16 159 | 58.34 293 | 73.94 241 | 87.72 269 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
APD-MVS_3200maxsize | | | 91.23 69 | 91.35 60 | 90.89 141 | 97.89 52 | 76.35 243 | 96.30 151 | 95.52 146 | 79.82 207 | 91.03 65 | 97.88 46 | 74.70 136 | 98.54 101 | 92.11 54 | 96.89 69 | 97.77 81 |
|
FMVSNet5 | | | 76.46 277 | 74.16 279 | 83.35 286 | 90.05 222 | 76.17 244 | 89.58 285 | 89.85 301 | 71.39 298 | 65.29 289 | 80.42 301 | 50.61 296 | 87.70 328 | 61.05 285 | 69.24 276 | 86.18 292 |
|
IterMVS | | | 80.67 237 | 79.16 235 | 85.20 248 | 89.79 224 | 76.08 245 | 92.97 248 | 91.86 273 | 80.28 196 | 71.20 258 | 85.14 271 | 57.93 263 | 91.34 312 | 72.52 211 | 70.74 255 | 88.18 263 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
EPP-MVSNet | | | 89.76 89 | 89.72 82 | 89.87 171 | 93.78 150 | 76.02 246 | 97.22 74 | 96.51 83 | 79.35 214 | 85.11 115 | 95.01 131 | 84.82 16 | 97.10 163 | 87.46 96 | 88.21 144 | 96.50 135 |
|
pm-mvs1 | | | 80.05 240 | 78.02 241 | 86.15 239 | 85.42 291 | 75.81 247 | 95.11 198 | 92.69 266 | 77.13 240 | 70.36 264 | 87.43 227 | 58.44 255 | 95.27 252 | 71.36 221 | 64.25 302 | 87.36 279 |
|
Patchmtry | | | 77.36 266 | 74.59 273 | 85.67 244 | 89.75 225 | 75.75 248 | 77.85 333 | 91.12 282 | 60.28 328 | 71.23 257 | 80.35 302 | 75.45 116 | 93.56 287 | 57.94 294 | 67.34 291 | 87.68 271 |
|
abl_6 | | | 89.80 88 | 89.71 83 | 90.07 161 | 96.53 79 | 75.52 249 | 94.48 208 | 95.04 167 | 81.12 175 | 89.22 82 | 97.00 81 | 68.83 174 | 98.96 88 | 89.86 71 | 95.27 86 | 95.73 152 |
|
PatchT | | | 79.75 241 | 76.85 251 | 88.42 194 | 89.55 230 | 75.49 250 | 77.37 334 | 94.61 192 | 63.07 315 | 82.46 146 | 73.32 329 | 75.52 115 | 93.41 288 | 51.36 319 | 84.43 180 | 96.36 138 |
|
tpm | | | 85.55 170 | 84.47 162 | 88.80 189 | 90.19 219 | 75.39 251 | 88.79 292 | 94.69 183 | 84.83 106 | 83.96 130 | 85.21 267 | 78.22 80 | 94.68 267 | 76.32 186 | 78.02 227 | 96.34 140 |
|
TransMVSNet (Re) | | | 76.94 274 | 74.38 276 | 84.62 264 | 85.92 286 | 75.25 252 | 95.28 186 | 89.18 307 | 73.88 280 | 67.22 276 | 86.46 249 | 59.64 242 | 94.10 277 | 59.24 292 | 52.57 332 | 84.50 305 |
|
Baseline_NR-MVSNet | | | 81.22 233 | 80.07 227 | 84.68 260 | 85.32 294 | 75.12 253 | 96.48 131 | 88.80 310 | 76.24 245 | 77.28 209 | 86.40 253 | 67.61 177 | 94.39 272 | 75.73 192 | 66.73 296 | 84.54 304 |
|
semantic-postprocess | | | | | 84.73 258 | 89.63 229 | 74.66 254 | | 91.81 275 | 80.05 202 | 71.06 260 | 85.18 269 | 57.98 262 | 91.40 311 | 72.48 212 | 70.70 257 | 88.12 264 |
|
USDC | | | 78.65 253 | 76.25 255 | 85.85 241 | 87.58 251 | 74.60 255 | 89.58 285 | 90.58 298 | 84.05 127 | 63.13 297 | 88.23 219 | 40.69 325 | 96.86 174 | 66.57 258 | 75.81 233 | 86.09 294 |
|
PatchMatch-RL | | | 85.00 177 | 83.66 177 | 89.02 184 | 95.86 97 | 74.55 256 | 92.49 257 | 93.60 242 | 79.30 217 | 79.29 189 | 91.47 176 | 58.53 254 | 98.45 106 | 70.22 230 | 92.17 118 | 94.07 181 |
|
Vis-MVSNet (Re-imp) | | | 88.88 104 | 88.87 93 | 88.91 186 | 93.89 149 | 74.43 257 | 96.93 108 | 94.19 207 | 84.39 117 | 83.22 140 | 95.67 106 | 78.24 79 | 94.70 266 | 78.88 159 | 94.40 94 | 97.61 92 |
|
PS-MVSNAJss | | | 84.91 178 | 84.30 170 | 86.74 231 | 85.89 287 | 74.40 258 | 94.95 201 | 94.16 211 | 83.93 130 | 76.45 219 | 90.11 200 | 71.04 162 | 95.77 220 | 83.16 132 | 79.02 219 | 90.06 223 |
|
testdata | | | | | 90.13 160 | 95.92 92 | 74.17 259 | | 96.49 87 | 73.49 283 | 94.82 23 | 97.99 37 | 78.80 73 | 97.93 118 | 83.53 128 | 97.52 53 | 98.29 44 |
|
Patchmatch-test | | | 78.25 255 | 74.72 270 | 88.83 188 | 91.20 204 | 74.10 260 | 73.91 340 | 88.70 313 | 59.89 330 | 66.82 279 | 85.12 272 | 78.38 78 | 94.54 269 | 48.84 327 | 79.58 214 | 97.86 75 |
|
LS3D | | | 82.22 222 | 79.94 230 | 89.06 182 | 97.43 65 | 74.06 261 | 93.20 244 | 92.05 271 | 61.90 321 | 73.33 245 | 95.21 120 | 59.35 247 | 99.21 66 | 54.54 311 | 92.48 112 | 93.90 183 |
|
pmmvs-eth3d | | | 73.59 288 | 70.66 292 | 82.38 293 | 76.40 329 | 73.38 262 | 89.39 289 | 89.43 304 | 72.69 289 | 60.34 316 | 77.79 310 | 46.43 311 | 91.26 314 | 66.42 260 | 57.06 315 | 82.51 325 |
|
CPTT-MVS | | | 89.72 90 | 89.87 79 | 89.29 180 | 98.33 38 | 73.30 263 | 97.70 48 | 95.35 157 | 75.68 255 | 87.40 99 | 97.44 66 | 70.43 167 | 98.25 111 | 89.56 76 | 96.90 68 | 96.33 142 |
|
EG-PatchMatch MVS | | | 74.92 283 | 72.02 287 | 83.62 282 | 83.76 308 | 73.28 264 | 93.62 228 | 92.04 272 | 68.57 305 | 58.88 319 | 83.80 282 | 31.87 338 | 95.57 239 | 56.97 298 | 78.67 221 | 82.00 330 |
|
TAPA-MVS | | 81.61 12 | 85.02 176 | 83.67 176 | 89.06 182 | 96.79 77 | 73.27 265 | 95.92 166 | 94.79 180 | 74.81 270 | 80.47 171 | 96.83 87 | 71.07 161 | 98.19 114 | 49.82 325 | 92.57 110 | 95.71 154 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
LPG-MVS_test | | | 84.20 188 | 83.49 182 | 86.33 234 | 90.88 209 | 73.06 266 | 95.28 186 | 94.13 212 | 82.20 160 | 76.31 220 | 93.20 160 | 54.83 288 | 96.95 168 | 83.72 122 | 80.83 207 | 88.98 240 |
|
LGP-MVS_train | | | | | 86.33 234 | 90.88 209 | 73.06 266 | | 94.13 212 | 82.20 160 | 76.31 220 | 93.20 160 | 54.83 288 | 96.95 168 | 83.72 122 | 80.83 207 | 88.98 240 |
|
ACMP | | 81.66 11 | 84.00 189 | 83.22 185 | 86.33 234 | 91.53 202 | 72.95 268 | 95.91 168 | 93.79 233 | 83.70 137 | 73.79 240 | 92.22 168 | 54.31 291 | 96.89 172 | 83.98 117 | 79.74 212 | 89.16 235 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
v7n | | | 79.32 246 | 77.34 245 | 85.28 247 | 84.05 306 | 72.89 269 | 93.38 234 | 93.87 227 | 75.02 266 | 70.68 261 | 84.37 276 | 59.58 244 | 95.62 235 | 67.60 249 | 67.50 289 | 87.32 280 |
|
test0.0.03 1 | | | 82.79 212 | 82.48 194 | 83.74 280 | 86.81 256 | 72.22 270 | 96.52 130 | 95.03 168 | 83.76 135 | 73.00 248 | 93.20 160 | 72.30 151 | 88.88 324 | 64.15 270 | 77.52 229 | 90.12 219 |
|
F-COLMAP | | | 84.50 185 | 83.44 183 | 87.67 215 | 95.22 113 | 72.22 270 | 95.95 164 | 93.78 234 | 75.74 253 | 76.30 222 | 95.18 122 | 59.50 245 | 98.45 106 | 72.67 210 | 86.59 155 | 92.35 201 |
|
ADS-MVSNet2 | | | 79.57 242 | 77.53 244 | 85.71 243 | 93.78 150 | 72.13 272 | 79.48 326 | 86.11 326 | 73.09 285 | 80.14 176 | 79.99 304 | 62.15 230 | 90.14 322 | 59.49 289 | 83.52 185 | 94.85 170 |
|
ACMM | | 80.70 13 | 83.72 193 | 82.85 189 | 86.31 237 | 91.19 205 | 72.12 273 | 95.88 170 | 94.29 202 | 80.44 190 | 77.02 212 | 91.96 171 | 55.24 284 | 97.14 162 | 79.30 155 | 80.38 209 | 89.67 227 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
LTVRE_ROB | | 73.68 18 | 77.99 257 | 75.74 259 | 84.74 257 | 90.45 215 | 72.02 274 | 86.41 312 | 91.12 282 | 72.57 290 | 66.63 280 | 87.27 229 | 54.95 287 | 96.98 167 | 56.29 306 | 75.98 231 | 85.21 299 |
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 |
MDA-MVSNet_test_wron | | | 73.54 289 | 70.43 295 | 82.86 288 | 84.55 298 | 71.85 275 | 91.74 272 | 91.32 281 | 67.63 306 | 46.73 337 | 81.09 299 | 55.11 285 | 90.42 320 | 55.91 308 | 59.76 312 | 86.31 290 |
|
OpenMVS_ROB | | 68.52 20 | 73.02 293 | 69.57 297 | 83.37 285 | 80.54 318 | 71.82 276 | 93.60 229 | 88.22 314 | 62.37 319 | 61.98 309 | 83.15 291 | 35.31 333 | 95.47 243 | 45.08 332 | 75.88 232 | 82.82 322 |
|
test_0402 | | | 72.68 294 | 69.54 298 | 82.09 296 | 88.67 241 | 71.81 277 | 92.72 254 | 86.77 323 | 61.52 323 | 62.21 306 | 83.91 280 | 43.22 317 | 93.76 284 | 34.60 342 | 72.23 250 | 80.72 332 |
|
LP | | | 68.54 307 | 63.92 309 | 82.39 292 | 87.93 248 | 71.79 278 | 72.37 343 | 86.01 328 | 55.89 335 | 58.33 322 | 71.46 334 | 49.58 301 | 90.10 323 | 32.25 344 | 61.48 309 | 85.27 297 |
|
YYNet1 | | | 73.53 290 | 70.43 295 | 82.85 289 | 84.52 300 | 71.73 279 | 91.69 273 | 91.37 279 | 67.63 306 | 46.79 336 | 81.21 298 | 55.04 286 | 90.43 319 | 55.93 307 | 59.70 313 | 86.38 289 |
|
XVG-OURS | | | 85.18 175 | 84.38 163 | 87.59 218 | 90.42 216 | 71.73 279 | 91.06 278 | 94.07 219 | 82.00 164 | 83.29 139 | 95.08 128 | 56.42 277 | 97.55 140 | 83.70 124 | 83.42 187 | 93.49 192 |
|
ACMH+ | | 76.62 16 | 77.47 264 | 74.94 267 | 85.05 249 | 91.07 207 | 71.58 281 | 93.26 241 | 90.01 300 | 71.80 294 | 64.76 290 | 88.55 214 | 41.62 322 | 96.48 184 | 62.35 282 | 71.00 253 | 87.09 282 |
|
XVG-OURS-SEG-HR | | | 85.74 167 | 85.16 149 | 87.49 222 | 90.22 218 | 71.45 282 | 91.29 275 | 94.09 218 | 81.37 172 | 83.90 132 | 95.22 118 | 60.30 240 | 97.53 144 | 85.58 106 | 84.42 181 | 93.50 191 |
|
EPNet_dtu | | | 87.65 131 | 87.89 102 | 86.93 230 | 94.57 127 | 71.37 283 | 96.72 117 | 96.50 85 | 88.56 41 | 87.12 103 | 95.02 130 | 75.91 111 | 94.01 279 | 66.62 256 | 90.00 131 | 95.42 159 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
v52 | | | 78.70 250 | 76.95 248 | 83.95 274 | 81.71 313 | 71.34 284 | 91.93 268 | 93.43 247 | 74.69 273 | 70.36 264 | 83.71 285 | 58.04 259 | 95.50 241 | 71.84 214 | 66.82 295 | 85.00 301 |
|
V4 | | | 78.70 250 | 76.95 248 | 83.95 274 | 81.66 314 | 71.34 284 | 91.94 267 | 93.44 245 | 74.69 273 | 70.35 266 | 83.73 284 | 58.07 258 | 95.50 241 | 71.84 214 | 66.86 294 | 85.02 300 |
|
WR-MVS_H | | | 81.02 234 | 80.09 225 | 83.79 278 | 88.08 247 | 71.26 286 | 94.46 209 | 96.54 81 | 80.08 201 | 72.81 251 | 86.82 241 | 70.36 168 | 92.65 291 | 64.18 269 | 67.50 289 | 87.46 278 |
|
v748 | | | 78.69 252 | 76.79 252 | 84.39 271 | 83.40 309 | 70.78 287 | 93.25 242 | 93.62 241 | 74.96 267 | 69.40 270 | 83.74 283 | 59.40 246 | 95.39 245 | 68.74 243 | 64.59 300 | 86.99 284 |
|
jajsoiax | | | 82.12 223 | 81.15 214 | 85.03 250 | 84.19 303 | 70.70 288 | 94.22 219 | 93.95 223 | 83.07 146 | 73.48 242 | 89.75 202 | 49.66 300 | 95.37 247 | 82.24 138 | 79.76 210 | 89.02 239 |
|
CP-MVSNet | | | 81.01 235 | 80.08 226 | 83.79 278 | 87.91 249 | 70.51 289 | 94.29 218 | 95.65 139 | 80.83 180 | 72.54 253 | 88.84 211 | 63.71 219 | 92.32 294 | 68.58 246 | 68.36 280 | 88.55 252 |
|
anonymousdsp | | | 80.98 236 | 79.97 229 | 84.01 273 | 81.73 312 | 70.44 290 | 92.49 257 | 93.58 244 | 77.10 242 | 72.98 249 | 86.31 254 | 57.58 264 | 94.90 261 | 79.32 154 | 78.63 224 | 86.69 287 |
|
mvs_tets | | | 81.74 226 | 80.71 219 | 84.84 254 | 84.22 302 | 70.29 291 | 93.91 223 | 93.78 234 | 82.77 151 | 73.37 243 | 89.46 205 | 47.36 309 | 95.31 250 | 81.99 139 | 79.55 216 | 88.92 244 |
|
DeepPCF-MVS | | 89.82 1 | 94.61 12 | 96.17 3 | 89.91 170 | 97.09 75 | 70.21 292 | 98.99 8 | 96.69 64 | 95.57 1 | 95.08 18 | 99.23 1 | 86.40 13 | 99.87 8 | 97.84 1 | 98.66 23 | 99.65 1 |
|
pmmvs6 | | | 74.65 285 | 71.67 288 | 83.60 283 | 79.13 322 | 69.94 293 | 93.31 240 | 90.88 289 | 61.05 327 | 65.83 285 | 84.15 279 | 43.43 315 | 94.83 264 | 66.62 256 | 60.63 310 | 86.02 295 |
|
PS-CasMVS | | | 80.27 239 | 79.18 234 | 83.52 284 | 87.56 252 | 69.88 294 | 94.08 220 | 95.29 160 | 80.27 197 | 72.08 254 | 88.51 217 | 59.22 250 | 92.23 296 | 67.49 250 | 68.15 283 | 88.45 256 |
|
test_djsdf | | | 83.00 209 | 82.45 195 | 84.64 262 | 84.07 305 | 69.78 295 | 94.80 205 | 94.48 195 | 80.74 184 | 75.41 234 | 87.70 225 | 61.32 238 | 95.10 257 | 83.77 120 | 79.76 210 | 89.04 238 |
|
MVS-HIRNet | | | 71.36 299 | 67.00 302 | 84.46 269 | 90.58 213 | 69.74 296 | 79.15 329 | 87.74 317 | 46.09 341 | 61.96 310 | 50.50 343 | 45.14 312 | 95.64 233 | 53.74 313 | 88.11 145 | 88.00 266 |
|
TinyColmap | | | 72.41 295 | 68.99 300 | 82.68 290 | 88.11 246 | 69.59 297 | 88.41 295 | 85.20 329 | 65.55 312 | 57.91 323 | 84.82 275 | 30.80 340 | 95.94 211 | 51.38 318 | 68.70 277 | 82.49 327 |
|
PMMVS | | | 89.46 93 | 89.92 77 | 88.06 203 | 94.64 125 | 69.57 298 | 96.22 154 | 94.95 170 | 87.27 61 | 91.37 59 | 96.54 94 | 65.88 202 | 97.39 147 | 88.54 83 | 93.89 99 | 97.23 114 |
|
Fast-Effi-MVS+-dtu | | | 83.33 204 | 82.60 193 | 85.50 246 | 89.55 230 | 69.38 299 | 96.09 161 | 91.38 278 | 82.30 159 | 75.96 227 | 91.41 177 | 56.71 271 | 95.58 238 | 75.13 197 | 84.90 178 | 91.54 202 |
|
COLMAP_ROB | | 73.24 19 | 75.74 280 | 73.00 285 | 83.94 276 | 92.38 175 | 69.08 300 | 91.85 270 | 86.93 322 | 61.48 324 | 65.32 288 | 90.27 196 | 42.27 320 | 96.93 171 | 50.91 322 | 75.63 234 | 85.80 296 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
PEN-MVS | | | 79.47 244 | 78.26 240 | 83.08 287 | 86.36 272 | 68.58 301 | 93.85 224 | 94.77 181 | 79.76 208 | 71.37 256 | 88.55 214 | 59.79 241 | 92.46 292 | 64.50 268 | 65.40 298 | 88.19 262 |
|
MDA-MVSNet-bldmvs | | | 71.45 298 | 67.94 301 | 81.98 297 | 85.33 293 | 68.50 302 | 92.35 261 | 88.76 311 | 70.40 300 | 42.99 338 | 81.96 294 | 46.57 310 | 91.31 313 | 48.75 328 | 54.39 328 | 86.11 293 |
|
UnsupCasMVSNet_bld | | | 68.60 306 | 64.50 307 | 80.92 301 | 74.63 333 | 67.80 303 | 83.97 319 | 92.94 262 | 65.12 313 | 54.63 328 | 68.23 337 | 35.97 330 | 92.17 298 | 60.13 287 | 44.83 340 | 82.78 323 |
|
AllTest | | | 75.92 279 | 73.06 284 | 84.47 267 | 92.18 184 | 67.29 304 | 91.07 277 | 84.43 332 | 67.63 306 | 63.48 293 | 90.18 197 | 38.20 327 | 97.16 159 | 57.04 296 | 73.37 243 | 88.97 242 |
|
TestCases | | | | | 84.47 267 | 92.18 184 | 67.29 304 | | 84.43 332 | 67.63 306 | 63.48 293 | 90.18 197 | 38.20 327 | 97.16 159 | 57.04 296 | 73.37 243 | 88.97 242 |
|
DTE-MVSNet | | | 78.37 254 | 77.06 247 | 82.32 295 | 85.22 295 | 67.17 306 | 93.40 233 | 93.66 239 | 78.71 226 | 70.53 263 | 88.29 218 | 59.06 251 | 92.23 296 | 61.38 284 | 63.28 306 | 87.56 275 |
|
XVG-ACMP-BASELINE | | | 79.38 245 | 77.90 242 | 83.81 277 | 84.98 297 | 67.14 307 | 89.03 290 | 93.18 257 | 80.26 198 | 72.87 250 | 88.15 221 | 38.55 326 | 96.26 192 | 76.05 189 | 78.05 226 | 88.02 265 |
|
UnsupCasMVSNet_eth | | | 73.25 291 | 70.57 293 | 81.30 298 | 77.53 325 | 66.33 308 | 87.24 304 | 93.89 226 | 80.38 193 | 57.90 324 | 81.59 296 | 42.91 319 | 90.56 318 | 65.18 266 | 48.51 336 | 87.01 283 |
|
ITE_SJBPF | | | | | 82.38 293 | 87.00 255 | 65.59 309 | | 89.55 303 | 79.99 204 | 69.37 271 | 91.30 180 | 41.60 323 | 95.33 249 | 62.86 281 | 74.63 240 | 86.24 291 |
|
mvs-test1 | | | 86.83 145 | 87.17 120 | 85.81 242 | 91.96 193 | 65.24 310 | 97.90 34 | 93.34 254 | 85.57 84 | 84.51 125 | 95.14 125 | 61.99 233 | 97.19 158 | 83.55 126 | 90.55 129 | 95.00 168 |
|
pmmvs3 | | | 65.75 309 | 62.18 312 | 76.45 314 | 67.12 341 | 64.54 311 | 88.68 293 | 85.05 330 | 54.77 339 | 57.54 326 | 73.79 324 | 29.40 342 | 86.21 333 | 55.49 310 | 47.77 338 | 78.62 334 |
|
Patchmatch-RL test | | | 76.65 276 | 74.01 280 | 84.55 265 | 77.37 327 | 64.23 312 | 78.49 332 | 82.84 340 | 78.48 227 | 64.63 291 | 73.40 328 | 76.05 109 | 91.70 310 | 76.99 179 | 57.84 314 | 97.72 83 |
|
LCM-MVSNet-Re | | | 83.75 192 | 83.54 180 | 84.39 271 | 93.54 157 | 64.14 313 | 92.51 256 | 84.03 335 | 83.90 131 | 66.14 283 | 86.59 246 | 67.36 181 | 92.68 290 | 84.89 112 | 92.87 109 | 96.35 139 |
|
JIA-IIPM | | | 79.00 249 | 77.20 246 | 84.40 270 | 89.74 227 | 64.06 314 | 75.30 336 | 95.44 153 | 62.15 320 | 81.90 160 | 59.08 340 | 78.92 70 | 95.59 237 | 66.51 259 | 85.78 166 | 93.54 190 |
|
new-patchmatchnet | | | 68.85 305 | 65.93 305 | 77.61 310 | 73.57 336 | 63.94 315 | 90.11 282 | 88.73 312 | 71.62 296 | 55.08 327 | 73.60 325 | 40.84 324 | 87.22 330 | 51.35 320 | 48.49 337 | 81.67 331 |
|
Anonymous20231206 | | | 75.29 282 | 73.64 281 | 80.22 303 | 80.75 315 | 63.38 316 | 93.36 235 | 90.71 294 | 73.09 285 | 67.12 277 | 83.70 286 | 50.33 298 | 90.85 316 | 53.63 314 | 70.10 263 | 86.44 288 |
|
Effi-MVS+-dtu | | | 84.61 183 | 84.90 157 | 83.72 281 | 91.96 193 | 63.14 317 | 94.95 201 | 93.34 254 | 85.57 84 | 79.79 179 | 87.12 234 | 61.99 233 | 95.61 236 | 83.55 126 | 85.83 165 | 92.41 200 |
|
MIMVSNet1 | | | 69.44 302 | 66.65 304 | 77.84 309 | 76.48 328 | 62.84 318 | 87.42 302 | 88.97 308 | 66.96 311 | 57.75 325 | 79.72 306 | 32.77 337 | 85.83 334 | 46.32 330 | 63.42 305 | 84.85 303 |
|
TDRefinement | | | 69.20 304 | 65.78 306 | 79.48 306 | 66.04 343 | 62.21 319 | 88.21 296 | 86.12 325 | 62.92 317 | 61.03 314 | 85.61 262 | 33.23 335 | 94.16 276 | 55.82 309 | 53.02 330 | 82.08 329 |
|
testgi | | | 74.88 284 | 73.40 282 | 79.32 307 | 80.13 319 | 61.75 320 | 93.21 243 | 86.64 324 | 79.49 213 | 66.56 282 | 91.06 183 | 35.51 332 | 88.67 325 | 56.79 299 | 71.25 251 | 87.56 275 |
|
new_pmnet | | | 66.18 308 | 63.18 310 | 75.18 318 | 76.27 330 | 61.74 321 | 83.79 320 | 84.66 331 | 56.64 334 | 51.57 330 | 71.85 332 | 31.29 339 | 87.93 327 | 49.98 324 | 62.55 307 | 75.86 337 |
|
SixPastTwentyTwo | | | 76.04 278 | 74.32 277 | 81.22 299 | 84.54 299 | 61.43 322 | 91.16 276 | 89.30 306 | 77.89 230 | 64.04 292 | 86.31 254 | 48.23 303 | 94.29 274 | 63.54 278 | 63.84 304 | 87.93 267 |
|
CVMVSNet | | | 84.83 180 | 85.57 143 | 82.63 291 | 91.55 200 | 60.38 323 | 95.13 196 | 95.03 168 | 80.60 187 | 82.10 158 | 94.71 134 | 66.40 199 | 90.19 321 | 74.30 202 | 90.32 130 | 97.31 108 |
|
OurMVSNet-221017-0 | | | 77.18 271 | 76.06 256 | 80.55 302 | 83.78 307 | 60.00 324 | 90.35 280 | 91.05 285 | 77.01 244 | 66.62 281 | 87.92 224 | 47.73 307 | 94.03 278 | 71.63 218 | 68.44 279 | 87.62 272 |
|
K. test v3 | | | 73.62 287 | 71.59 289 | 79.69 305 | 82.98 310 | 59.85 325 | 90.85 279 | 88.83 309 | 77.13 240 | 58.90 318 | 82.11 293 | 43.62 314 | 91.72 309 | 65.83 263 | 54.10 329 | 87.50 277 |
|
testpf | | | 70.88 300 | 70.47 294 | 72.08 322 | 88.92 237 | 59.57 326 | 48.62 350 | 93.15 259 | 63.05 316 | 63.07 298 | 79.51 307 | 58.33 256 | 86.63 331 | 66.93 254 | 72.69 248 | 70.05 342 |
|
test20.03 | | | 72.36 296 | 71.15 290 | 75.98 316 | 77.79 324 | 59.16 327 | 92.40 260 | 89.35 305 | 74.09 278 | 61.50 312 | 84.32 277 | 48.09 304 | 85.54 337 | 50.63 323 | 62.15 308 | 83.24 320 |
|
lessismore_v0 | | | | | 79.98 304 | 80.59 317 | 58.34 328 | | 80.87 343 | | 58.49 320 | 83.46 288 | 43.10 318 | 93.89 280 | 63.11 280 | 48.68 335 | 87.72 269 |
|
LF4IMVS | | | 72.36 296 | 70.82 291 | 76.95 311 | 79.18 321 | 56.33 329 | 86.12 313 | 86.11 326 | 69.30 304 | 63.06 299 | 86.66 244 | 33.03 336 | 92.25 295 | 65.33 265 | 68.64 278 | 82.28 328 |
|
CMPMVS | | 54.94 21 | 75.71 281 | 74.56 274 | 79.17 308 | 79.69 320 | 55.98 330 | 89.59 284 | 93.30 256 | 60.28 328 | 53.85 329 | 89.07 208 | 47.68 308 | 96.33 189 | 76.55 183 | 81.02 206 | 85.22 298 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PM-MVS | | | 69.32 303 | 66.93 303 | 76.49 313 | 73.60 335 | 55.84 331 | 85.91 314 | 79.32 347 | 74.72 272 | 61.09 313 | 78.18 309 | 21.76 344 | 91.10 315 | 70.86 227 | 56.90 316 | 82.51 325 |
|
RPSCF | | | 77.73 262 | 76.63 253 | 81.06 300 | 88.66 242 | 55.76 332 | 87.77 300 | 87.88 316 | 64.82 314 | 74.14 239 | 92.79 165 | 49.22 302 | 96.81 176 | 67.47 251 | 76.88 230 | 90.62 209 |
|
EU-MVSNet | | | 76.92 275 | 76.95 248 | 76.83 312 | 84.10 304 | 54.73 333 | 91.77 271 | 92.71 265 | 72.74 288 | 69.57 269 | 88.69 212 | 58.03 261 | 87.43 329 | 64.91 267 | 70.00 268 | 88.33 260 |
|
test2356 | | | 74.41 286 | 74.53 275 | 74.07 319 | 76.13 331 | 54.45 334 | 94.74 207 | 92.08 270 | 71.96 293 | 65.51 287 | 83.05 292 | 56.96 269 | 83.71 339 | 52.74 316 | 77.58 228 | 84.06 308 |
|
Anonymous20231211 | | | 61.03 313 | 56.76 315 | 73.82 320 | 71.24 337 | 53.47 335 | 87.60 301 | 81.65 342 | 44.19 342 | 51.08 334 | 71.82 333 | 20.79 345 | 88.46 326 | 35.45 341 | 47.07 339 | 79.52 333 |
|
no-one | | | 51.12 319 | 45.81 321 | 67.03 325 | 53.16 351 | 52.22 336 | 75.21 337 | 80.40 344 | 54.89 338 | 28.26 346 | 48.50 345 | 15.54 349 | 82.81 340 | 39.29 337 | 17.06 348 | 66.07 345 |
|
ambc | | | | | 76.02 315 | 68.11 340 | 51.43 337 | 64.97 345 | 89.59 302 | | 60.49 315 | 74.49 323 | 17.17 348 | 92.46 292 | 61.50 283 | 52.85 331 | 84.17 307 |
|
Gipuma | | | 45.11 322 | 42.05 322 | 54.30 335 | 80.69 316 | 51.30 338 | 35.80 351 | 83.81 337 | 28.13 347 | 27.94 347 | 34.53 349 | 11.41 355 | 76.70 347 | 21.45 349 | 54.65 327 | 34.90 351 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
testus | | | 70.06 301 | 69.09 299 | 72.98 321 | 74.54 334 | 51.28 339 | 93.78 225 | 87.34 318 | 71.49 297 | 62.69 302 | 83.46 288 | 24.44 343 | 84.77 338 | 51.22 321 | 72.85 247 | 82.90 321 |
|
test1235678 | | | 64.50 311 | 62.19 311 | 71.42 323 | 66.82 342 | 48.00 340 | 89.44 287 | 87.90 315 | 62.82 318 | 49.12 335 | 71.31 335 | 30.14 341 | 82.19 341 | 41.88 335 | 60.32 311 | 84.06 308 |
|
DSMNet-mixed | | | 73.13 292 | 72.45 286 | 75.19 317 | 77.51 326 | 46.82 341 | 85.09 318 | 82.01 341 | 67.61 310 | 69.27 272 | 81.33 297 | 50.89 294 | 86.28 332 | 54.54 311 | 83.80 184 | 92.46 199 |
|
PMMVS2 | | | 50.90 320 | 46.31 320 | 64.67 328 | 55.53 346 | 46.67 342 | 77.30 335 | 71.02 349 | 40.89 343 | 34.16 344 | 59.32 339 | 9.83 356 | 76.14 348 | 40.09 336 | 28.63 345 | 71.21 340 |
|
ANet_high | | | 46.22 321 | 41.28 324 | 61.04 333 | 39.91 356 | 46.25 343 | 70.59 344 | 76.18 348 | 58.87 331 | 23.09 348 | 48.00 346 | 12.58 353 | 66.54 351 | 28.65 347 | 13.62 351 | 70.35 341 |
|
1111 | | | 65.60 310 | 64.33 308 | 69.41 324 | 68.26 338 | 45.11 344 | 87.06 305 | 87.32 319 | 54.99 336 | 51.20 332 | 73.45 326 | 63.57 220 | 85.70 335 | 36.53 339 | 56.59 317 | 77.42 336 |
|
.test1245 | | | 54.61 316 | 58.07 314 | 44.24 339 | 68.26 338 | 45.11 344 | 87.06 305 | 87.32 319 | 54.99 336 | 51.20 332 | 73.45 326 | 63.57 220 | 85.70 335 | 36.53 339 | 0.21 355 | 1.91 355 |
|
DeepMVS_CX | | | | | 64.06 330 | 78.53 323 | 43.26 346 | | 68.11 352 | 69.94 301 | 38.55 340 | 76.14 321 | 18.53 347 | 79.34 343 | 43.72 333 | 41.62 344 | 69.57 343 |
|
LCM-MVSNet | | | 52.52 318 | 48.24 319 | 65.35 326 | 47.63 353 | 41.45 347 | 72.55 342 | 83.62 338 | 31.75 345 | 37.66 341 | 57.92 341 | 9.19 357 | 76.76 346 | 49.26 326 | 44.60 341 | 77.84 335 |
|
testmv | | | 54.58 317 | 51.53 318 | 63.74 331 | 53.58 349 | 40.82 348 | 83.26 321 | 83.92 336 | 54.07 340 | 36.35 342 | 61.26 338 | 14.80 350 | 77.07 344 | 33.00 343 | 43.53 343 | 73.33 339 |
|
FPMVS | | | 55.09 315 | 52.93 317 | 61.57 332 | 55.98 345 | 40.51 349 | 83.11 322 | 83.41 339 | 37.61 344 | 34.95 343 | 71.95 331 | 14.40 351 | 76.95 345 | 29.81 346 | 65.16 299 | 67.25 344 |
|
test12356 | | | 58.24 314 | 56.06 316 | 64.77 327 | 60.65 344 | 39.42 350 | 82.78 323 | 84.34 334 | 57.47 333 | 42.65 339 | 69.10 336 | 19.21 346 | 81.18 342 | 38.97 338 | 49.40 333 | 73.69 338 |
|
wuykxyi23d | | | 37.75 326 | 31.85 329 | 55.46 334 | 40.00 355 | 38.01 351 | 59.81 347 | 69.47 350 | 25.46 349 | 12.42 354 | 30.55 353 | 2.06 361 | 67.08 350 | 31.81 345 | 15.03 349 | 61.29 346 |
|
MVE | | 35.65 22 | 33.85 328 | 29.49 331 | 46.92 338 | 41.86 354 | 36.28 352 | 50.45 349 | 56.52 356 | 18.75 352 | 18.28 350 | 37.84 348 | 2.41 360 | 58.41 353 | 18.71 350 | 20.62 346 | 46.06 349 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
PNet_i23d | | | 41.20 324 | 38.13 325 | 50.41 336 | 55.23 347 | 36.24 353 | 73.80 341 | 65.45 354 | 29.75 346 | 21.36 349 | 47.05 347 | 3.43 358 | 63.23 352 | 28.17 348 | 18.92 347 | 51.76 347 |
|
PMVS | | 34.80 23 | 39.19 325 | 35.53 326 | 50.18 337 | 29.72 357 | 30.30 354 | 59.60 348 | 66.20 353 | 26.06 348 | 17.91 351 | 49.53 344 | 3.12 359 | 74.09 349 | 18.19 351 | 49.40 333 | 46.14 348 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
tmp_tt | | | 41.54 323 | 41.93 323 | 40.38 340 | 20.10 358 | 26.84 355 | 61.93 346 | 59.09 355 | 14.81 353 | 28.51 345 | 80.58 300 | 35.53 331 | 48.33 356 | 63.70 277 | 13.11 352 | 45.96 350 |
|
E-PMN | | | 32.70 329 | 32.39 328 | 33.65 341 | 53.35 350 | 25.70 356 | 74.07 339 | 53.33 357 | 21.08 350 | 17.17 352 | 33.63 351 | 11.85 354 | 54.84 354 | 12.98 352 | 14.04 350 | 20.42 352 |
|
EMVS | | | 31.70 330 | 31.45 330 | 32.48 342 | 50.72 352 | 23.95 357 | 74.78 338 | 52.30 358 | 20.36 351 | 16.08 353 | 31.48 352 | 12.80 352 | 53.60 355 | 11.39 353 | 13.10 353 | 19.88 353 |
|
wuyk23d | | | 14.10 332 | 13.89 333 | 14.72 344 | 55.23 347 | 22.91 358 | 33.83 352 | 3.56 360 | 4.94 354 | 4.11 355 | 2.28 358 | 2.06 361 | 19.66 357 | 10.23 354 | 8.74 354 | 1.59 357 |
|
N_pmnet | | | 61.30 312 | 60.20 313 | 64.60 329 | 84.32 301 | 17.00 359 | 91.67 274 | 10.98 359 | 61.77 322 | 58.45 321 | 78.55 308 | 49.89 299 | 91.83 305 | 42.27 334 | 63.94 303 | 84.97 302 |
|
test123 | | | 9.07 334 | 11.73 335 | 1.11 345 | 0.50 360 | 0.77 360 | 89.44 287 | 0.20 362 | 0.34 356 | 2.15 357 | 10.72 357 | 0.34 363 | 0.32 358 | 1.79 356 | 0.08 357 | 2.23 354 |
|
testmvs | | | 9.92 333 | 12.94 334 | 0.84 346 | 0.65 359 | 0.29 361 | 93.78 225 | 0.39 361 | 0.42 355 | 2.85 356 | 15.84 356 | 0.17 364 | 0.30 359 | 2.18 355 | 0.21 355 | 1.91 355 |
|
cdsmvs_eth3d_5k | | | 21.43 331 | 28.57 332 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 95.93 128 | 0.00 357 | 0.00 358 | 97.66 51 | 63.57 220 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
pcd_1.5k_mvsjas | | | 5.92 336 | 7.89 337 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 71.04 162 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
pcd1.5k->3k | | | 34.11 327 | 35.46 327 | 30.05 343 | 86.70 257 | 0.00 362 | 0.00 353 | 94.74 182 | 0.00 357 | 0.00 358 | 0.00 359 | 58.13 257 | 0.00 360 | 0.00 357 | 79.56 215 | 90.14 217 |
|
sosnet-low-res | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
sosnet | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
uncertanet | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
Regformer | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
ab-mvs-re | | | 8.11 335 | 10.81 336 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 97.30 71 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
uanet | | | 0.00 337 | 0.00 338 | 0.00 347 | 0.00 361 | 0.00 362 | 0.00 353 | 0.00 363 | 0.00 357 | 0.00 358 | 0.00 359 | 0.00 365 | 0.00 360 | 0.00 357 | 0.00 358 | 0.00 358 |
|
GSMVS | | | | | | | | | | | | | | | | | 97.54 94 |
|
test_part3 | | | | | | | | 98.15 25 | | 84.95 102 | | 98.83 2 | | 99.80 14 | 97.78 2 | | |
|
test_part1 | | | | | | | | | 96.77 53 | | | | 89.33 6 | | | 98.95 12 | 99.18 10 |
|
sam_mvs1 | | | | | | | | | | | | | 77.59 87 | | | | 97.54 94 |
|
sam_mvs | | | | | | | | | | | | | 75.35 129 | | | | |
|
MTGPA | | | | | | | | | 96.33 103 | | | | | | | | |
|
test_post1 | | | | | | | | 85.88 315 | | | | 30.24 354 | 73.77 141 | 95.07 259 | 73.89 205 | | |
|
test_post | | | | | | | | | | | | 33.80 350 | 76.17 107 | 95.97 206 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 77.09 316 | 77.78 86 | 95.39 245 | | | |
|
MTMP | | | | | | | | | 68.16 351 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 96.00 14 | 99.03 7 | 98.31 42 |
|
agg_prior2 | | | | | | | | | | | | | | | 94.30 28 | 99.00 9 | 98.57 31 |
|
test_prior2 | | | | | | | | 98.37 18 | | 86.08 75 | 94.57 26 | 98.02 34 | 83.14 33 | | 95.05 21 | 98.79 16 | |
|
旧先验2 | | | | | | | | 96.97 105 | | 74.06 279 | 96.10 7 | | | 97.76 130 | 88.38 87 | | |
|
新几何2 | | | | | | | | 96.42 142 | | | | | | | | | |
|
无先验 | | | | | | | | 96.87 110 | 96.78 52 | 77.39 236 | | | | 99.52 44 | 79.95 150 | | 98.43 36 |
|
原ACMM2 | | | | | | | | 96.84 111 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 99.48 49 | 76.45 185 | | |
|
segment_acmp | | | | | | | | | | | | | 82.69 36 | | | | |
|
testdata1 | | | | | | | | 95.57 181 | | 87.44 59 | | | | | | | |
|
plane_prior5 | | | | | | | | | 94.69 183 | | | | | 97.30 151 | 87.08 97 | 82.82 201 | 90.96 206 |
|
plane_prior4 | | | | | | | | | | | | 94.15 142 | | | | | |
|
plane_prior2 | | | | | | | | 97.18 79 | | 89.89 30 | | | | | | | |
|
plane_prior1 | | | | | | 91.95 195 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 363 | | | | | | | | |
|
nn | | | | | | | | | 0.00 363 | | | | | | | | |
|
door-mid | | | | | | | | | 79.75 346 | | | | | | | | |
|
test11 | | | | | | | | | 96.50 85 | | | | | | | | |
|
door | | | | | | | | | 80.13 345 | | | | | | | | |
|
HQP-NCC | | | | | | 92.08 187 | | 97.63 51 | | 90.52 24 | 82.30 148 | | | | | | |
|
ACMP_Plane | | | | | | 92.08 187 | | 97.63 51 | | 90.52 24 | 82.30 148 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 87.67 94 | | |
|
HQP4-MVS | | | | | | | | | | | 82.30 148 | | | 97.32 149 | | | 91.13 204 |
|
HQP3-MVS | | | | | | | | | 94.80 178 | | | | | | | 83.01 191 | |
|
HQP2-MVS | | | | | | | | | | | | | 65.40 210 | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 78.45 225 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 79.05 218 | |
|
Test By Simon | | | | | | | | | | | | | 71.65 155 | | | | |
|